Repository: AIGC-Audio/AudioGPT Branch: main Commit: a674543c537b Files: 362 Total size: 14.2 MB Directory structure: gitextract_mez1vc95/ ├── .gitignore ├── LICENSE ├── NeuralSeq/ │ ├── LICENSE │ ├── README.md │ ├── configs/ │ │ ├── config_base.yaml │ │ ├── singing/ │ │ │ ├── base.yaml │ │ │ └── fs2.yaml │ │ └── tts/ │ │ ├── base.yaml │ │ ├── base_zh.yaml │ │ ├── emotion/ │ │ │ ├── base_text2mel.yaml │ │ │ └── pre_align.py │ │ ├── fs2.yaml │ │ ├── hifigan.yaml │ │ ├── libritts/ │ │ │ ├── base_text2mel.yaml │ │ │ ├── fs2.yaml │ │ │ ├── pre_align.py │ │ │ └── pwg.yaml │ │ ├── lj/ │ │ │ ├── base_mel2wav.yaml │ │ │ ├── base_text2mel.yaml │ │ │ ├── fs2.yaml │ │ │ ├── hifigan.yaml │ │ │ └── pwg.yaml │ │ └── pwg.yaml │ ├── data_gen/ │ │ └── tts/ │ │ ├── base_binarizer.py │ │ ├── base_binarizer_emotion.py │ │ ├── base_preprocess.py │ │ ├── binarizer_zh.py │ │ ├── data_gen_utils.py │ │ ├── emotion/ │ │ │ ├── audio.py │ │ │ ├── inference.py │ │ │ ├── model.py │ │ │ ├── params_data.py │ │ │ ├── params_model.py │ │ │ └── test_emotion.py │ │ ├── txt_processors/ │ │ │ ├── __init__.py │ │ │ ├── base_text_processor.py │ │ │ ├── en.py │ │ │ ├── zh.py │ │ │ └── zh_g2pM.py │ │ └── wav_processors/ │ │ ├── __init__.py │ │ ├── base_processor.py │ │ └── common_processors.py │ ├── egs/ │ │ ├── datasets/ │ │ │ └── audio/ │ │ │ ├── emotion/ │ │ │ │ ├── base_text2mel.yaml │ │ │ │ └── pre_align.py │ │ │ ├── libritts/ │ │ │ │ ├── base_text2mel.yaml │ │ │ │ ├── fs2.yaml │ │ │ │ ├── pre_align.py │ │ │ │ └── pwg.yaml │ │ │ ├── lj/ │ │ │ │ ├── base_mel2wav.yaml │ │ │ │ ├── preprocess.py │ │ │ │ └── pwg.yaml │ │ │ └── vctk/ │ │ │ ├── base_mel2wav.yaml │ │ │ ├── fs2.yaml │ │ │ ├── pre_align.py │ │ │ └── pwg.yaml │ │ └── egs_bases/ │ │ ├── config_base.yaml │ │ ├── svs/ │ │ │ ├── base.yaml │ │ │ ├── lj_ds_beta6.yaml │ │ │ ├── midi/ │ │ │ │ ├── cascade/ │ │ │ │ │ └── opencs/ │ │ │ │ │ ├── aux_rel.yaml │ │ │ │ │ ├── ds60_rel.yaml │ │ │ │ │ └── opencpop_statis.yaml │ │ │ │ ├── e2e/ │ │ │ │ │ ├── opencpop/ │ │ │ │ │ │ ├── ds1000-10dil.yaml │ │ │ │ │ │ ├── ds1000.yaml │ │ │ │ │ │ └── ds100_adj_rel.yaml │ │ │ │ │ └── popcs/ │ │ │ │ │ └── ds100_adj_rel.yaml │ │ │ │ └── pe.yaml │ │ │ ├── popcs_ds_beta6.yaml │ │ │ ├── popcs_ds_beta6_offline.yaml │ │ │ └── popcs_fs2.yaml │ │ └── tts/ │ │ ├── base.yaml │ │ ├── base_zh.yaml │ │ ├── fs2.yaml │ │ ├── fs2_adv.yaml │ │ ├── ps.yaml │ │ ├── ps_flow.yaml │ │ ├── ps_flow_small.yaml │ │ └── vocoder/ │ │ ├── base.yaml │ │ ├── hifigan.yaml │ │ └── pwg.yaml │ ├── gitattributes │ ├── inference/ │ │ ├── svs/ │ │ │ ├── base_svs_infer.py │ │ │ ├── ds_cascade.py │ │ │ ├── ds_e2e.py │ │ │ └── opencpop/ │ │ │ ├── cpop_pinyin2ph.txt │ │ │ └── map.py │ │ └── tts/ │ │ ├── GenerSpeech.py │ │ ├── PortaSpeech.py │ │ └── base_tts_infer.py │ ├── modules/ │ │ ├── GenerSpeech/ │ │ │ ├── config/ │ │ │ │ └── generspeech.yaml │ │ │ ├── model/ │ │ │ │ ├── generspeech.py │ │ │ │ ├── glow_modules.py │ │ │ │ ├── mixstyle.py │ │ │ │ ├── prosody_util.py │ │ │ │ └── wavenet.py │ │ │ └── task/ │ │ │ ├── dataset.py │ │ │ └── generspeech.py │ │ ├── __init__.py │ │ ├── commons/ │ │ │ ├── align_ops.py │ │ │ ├── common_layers.py │ │ │ ├── conv.py │ │ │ ├── espnet_positional_embedding.py │ │ │ ├── normalizing_flow/ │ │ │ │ ├── glow_modules.py │ │ │ │ ├── res_flow.py │ │ │ │ └── utils.py │ │ │ ├── rel_transformer.py │ │ │ ├── ssim.py │ │ │ ├── transformer.py │ │ │ └── wavenet.py │ │ ├── diff/ │ │ │ ├── candidate_decoder.py │ │ │ ├── diffusion.py │ │ │ ├── net.py │ │ │ └── shallow_diffusion_tts.py │ │ ├── diffsinger_midi/ │ │ │ └── fs2.py │ │ ├── fastspeech/ │ │ │ ├── fs2.py │ │ │ ├── pe.py │ │ │ └── tts_modules.py │ │ ├── hifigan/ │ │ │ ├── hifigan.py │ │ │ └── mel_utils.py │ │ ├── parallel_wavegan/ │ │ │ ├── __init__.py │ │ │ ├── layers/ │ │ │ │ ├── __init__.py │ │ │ │ ├── causal_conv.py │ │ │ │ ├── pqmf.py │ │ │ │ ├── residual_block.py │ │ │ │ ├── residual_stack.py │ │ │ │ ├── tf_layers.py │ │ │ │ └── upsample.py │ │ │ ├── losses/ │ │ │ │ ├── __init__.py │ │ │ │ └── stft_loss.py │ │ │ ├── models/ │ │ │ │ ├── __init__.py │ │ │ │ ├── melgan.py │ │ │ │ ├── parallel_wavegan.py │ │ │ │ └── source.py │ │ │ ├── optimizers/ │ │ │ │ ├── __init__.py │ │ │ │ └── radam.py │ │ │ ├── stft_loss.py │ │ │ └── utils/ │ │ │ ├── __init__.py │ │ │ └── utils.py │ │ └── syntaspeech/ │ │ ├── multi_window_disc.py │ │ ├── syntactic_graph_buider.py │ │ ├── syntactic_graph_encoder.py │ │ └── syntaspeech.py │ ├── tasks/ │ │ ├── base_task.py │ │ ├── run.py │ │ ├── svs/ │ │ │ ├── __init__.py │ │ │ ├── diffsinger_task.py │ │ │ ├── diffspeech_task.py │ │ │ └── task.py │ │ ├── tts/ │ │ │ ├── dataset_utils.py │ │ │ ├── fs2.py │ │ │ ├── fs2_adv.py │ │ │ ├── fs2_utils.py │ │ │ ├── pe.py │ │ │ ├── ps.py │ │ │ ├── ps_adv.py │ │ │ ├── ps_flow.py │ │ │ ├── synta.py │ │ │ ├── tts.py │ │ │ ├── tts_base.py │ │ │ └── tts_utils.py │ │ └── vocoder/ │ │ ├── dataset_utils.py │ │ └── vocoder_base.py │ ├── utils/ │ │ ├── __init__.py │ │ ├── audio.py │ │ ├── ckpt_utils.py │ │ ├── cwt.py │ │ ├── dtw.py │ │ ├── hparams.py │ │ ├── indexed_datasets.py │ │ ├── multiprocess_utils.py │ │ ├── os_utils.py │ │ ├── pitch_utils.py │ │ ├── pl_utils.py │ │ ├── plot.py │ │ ├── text_encoder.py │ │ ├── text_norm.py │ │ ├── training_utils.py │ │ └── tts_utils.py │ └── vocoders/ │ ├── __init__.py │ ├── base_vocoder.py │ ├── hifigan.py │ ├── pwg.py │ └── vocoder_utils.py ├── README.md ├── assets/ │ └── README.md ├── audio-chatgpt.py ├── audio_detection/ │ ├── __init__.py │ ├── audio_infer/ │ │ ├── __init__.py │ │ ├── metadata/ │ │ │ ├── black_list/ │ │ │ │ ├── groundtruth_weak_label_evaluation_set.csv │ │ │ │ └── groundtruth_weak_label_testing_set.csv │ │ │ └── class_labels_indices.csv │ │ ├── pytorch/ │ │ │ ├── evaluate.py │ │ │ ├── finetune_template.py │ │ │ ├── inference.py │ │ │ ├── losses.py │ │ │ ├── main.py │ │ │ ├── models.py │ │ │ └── pytorch_utils.py │ │ └── utils/ │ │ ├── config.py │ │ ├── crash.py │ │ ├── create_black_list.py │ │ ├── create_indexes.py │ │ ├── data_generator.py │ │ ├── dataset.py │ │ ├── plot_for_paper.py │ │ ├── plot_statistics.py │ │ └── utilities.py │ └── target_sound_detection/ │ └── src/ │ ├── models.py │ └── utils.py ├── audio_to_text/ │ ├── __init__.py │ ├── captioning/ │ │ ├── __init__.py │ │ ├── models/ │ │ │ ├── __init__.py │ │ │ ├── base_model.py │ │ │ ├── decoder.py │ │ │ ├── encoder.py │ │ │ ├── transformer_model.py │ │ │ └── utils.py │ │ └── utils/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── bert/ │ │ │ ├── create_sent_embedding.py │ │ │ └── create_word_embedding.py │ │ ├── build_vocab.py │ │ ├── build_vocab_ltp.py │ │ ├── build_vocab_spacy.py │ │ ├── eval_round_robin.py │ │ ├── fasttext/ │ │ │ └── create_word_embedding.py │ │ ├── lr_scheduler.py │ │ ├── model_eval_diff.py │ │ ├── predict_nn.py │ │ ├── remove_optimizer.py │ │ ├── report_results.py │ │ ├── tokenize_caption.py │ │ ├── train_util.py │ │ └── word2vec/ │ │ └── create_word_embedding.py │ └── inference_waveform.py ├── download.sh ├── mono2binaural/ │ └── src/ │ ├── models.py │ ├── utils.py │ └── warping.py ├── requirements.txt ├── run.md ├── sound_extraction/ │ ├── model/ │ │ ├── LASSNet.py │ │ ├── film.py │ │ ├── modules.py │ │ ├── resunet_film.py │ │ └── text_encoder.py │ └── utils/ │ ├── create_mixtures.py │ ├── stft.py │ └── wav_io.py └── text_to_audio/ └── Make_An_Audio/ ├── configs/ │ ├── img_to_audio/ │ │ └── img2audio_args.yaml │ ├── inpaint/ │ │ └── txt2audio_args.yaml │ └── text_to_audio/ │ ├── clap_args.yaml │ ├── hifigan_args.yaml │ └── txt2audio_args.yaml ├── ldm/ │ ├── data/ │ │ └── extract_mel_spectrogram.py │ ├── lr_scheduler.py │ ├── models/ │ │ ├── autoencoder.py │ │ ├── autoencoder_multi.py │ │ └── diffusion/ │ │ ├── __init__.py │ │ ├── classifier.py │ │ ├── ddim.py │ │ ├── ddpm.py │ │ ├── ddpm_audio.py │ │ ├── ddpm_audio_inpaint.py │ │ └── plms.py │ ├── modules/ │ │ ├── attention.py │ │ ├── diffusionmodules/ │ │ │ ├── __init__.py │ │ │ ├── custom_openaimodel.py │ │ │ ├── model.py │ │ │ ├── openaimodel.py │ │ │ └── util.py │ │ ├── discriminator/ │ │ │ ├── model.py │ │ │ └── multi_window_disc.py │ │ ├── distributions/ │ │ │ ├── __init__.py │ │ │ └── distributions.py │ │ ├── ema.py │ │ ├── encoders/ │ │ │ ├── CLAP/ │ │ │ │ ├── CLAPWrapper.py │ │ │ │ ├── __init__.py │ │ │ │ ├── audio.py │ │ │ │ ├── clap.py │ │ │ │ ├── config.yml │ │ │ │ └── utils.py │ │ │ ├── __init__.py │ │ │ ├── modules.py │ │ │ └── open_clap/ │ │ │ ├── __init__.py │ │ │ ├── bert.py │ │ │ ├── factory.py │ │ │ ├── feature_fusion.py │ │ │ ├── htsat.py │ │ │ ├── linear_probe.py │ │ │ ├── loss.py │ │ │ ├── model.py │ │ │ ├── model_configs/ │ │ │ │ ├── HTSAT-base.json │ │ │ │ ├── HTSAT-large.json │ │ │ │ ├── HTSAT-tiny-win-1536.json │ │ │ │ ├── HTSAT-tiny.json │ │ │ │ ├── PANN-10.json │ │ │ │ ├── PANN-14-fmax-18k.json │ │ │ │ ├── PANN-14-fmax-8k-20s.json │ │ │ │ ├── PANN-14-tiny-transformer.json │ │ │ │ ├── PANN-14-win-1536.json │ │ │ │ ├── PANN-14.json │ │ │ │ ├── PANN-6.json │ │ │ │ ├── RN101-quickgelu.json │ │ │ │ ├── RN101.json │ │ │ │ ├── RN50-quickgelu.json │ │ │ │ ├── RN50.json │ │ │ │ ├── RN50x16.json │ │ │ │ ├── RN50x4.json │ │ │ │ ├── ViT-B-16.json │ │ │ │ ├── ViT-B-32-quickgelu.json │ │ │ │ ├── ViT-B-32.json │ │ │ │ └── ViT-L-14.json │ │ │ ├── openai.py │ │ │ ├── pann_model.py │ │ │ ├── pretrained.py │ │ │ ├── timm_model.py │ │ │ ├── tokenizer.py │ │ │ ├── transform.py │ │ │ ├── utils.py │ │ │ └── version.py │ │ ├── image_degradation/ │ │ │ ├── __init__.py │ │ │ ├── bsrgan.py │ │ │ ├── bsrgan_light.py │ │ │ └── utils_image.py │ │ ├── losses_audio/ │ │ │ ├── __init__.py │ │ │ ├── contperceptual.py │ │ │ ├── contperceptual_dis.py │ │ │ ├── lpaps.py │ │ │ ├── vggishish/ │ │ │ │ ├── config/ │ │ │ │ │ ├── melception.yaml │ │ │ │ │ └── vggish.yaml │ │ │ │ ├── data/ │ │ │ │ │ ├── train_means_stds_melspec_10s_22050hz.txt │ │ │ │ │ ├── vggsound.csv │ │ │ │ │ ├── vggsound_test.txt │ │ │ │ │ ├── vggsound_train.txt │ │ │ │ │ └── vggsound_valid.txt │ │ │ │ ├── dataset.py │ │ │ │ ├── logger.py │ │ │ │ ├── loss.py │ │ │ │ ├── metrics.py │ │ │ │ ├── model.py │ │ │ │ ├── predict.py │ │ │ │ ├── train_melception.py │ │ │ │ ├── train_vggishish.py │ │ │ │ └── transforms.py │ │ │ └── vqperceptual.py │ │ └── x_transformer.py │ └── util.py ├── useful_ckpts/ │ └── CLAP/ │ └── config.yml ├── vocoder/ │ ├── bigvgan/ │ │ ├── __init__.py │ │ ├── activations.py │ │ ├── alias_free_torch/ │ │ │ ├── __init__.py │ │ │ ├── act.py │ │ │ ├── filter.py │ │ │ └── resample.py │ │ └── models.py │ ├── hifigan/ │ │ └── modules.py │ └── logs/ │ └── hifi_0127/ │ └── args.yml └── wav_evaluation/ └── models/ ├── CLAPWrapper.py ├── __init__.py ├── audio.py ├── clap.py └── utils.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # JetBrains PyCharm IDE .idea/ .github/ .circleci/ # Byte-compiled / optimized / DLL files *__pycache__/ __pycache__/ *.py[cod] *$py.class # C extensions *.so # macOS dir files .DS_Store # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # Checkpoints checkpoints # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv venv/ ENV/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # Generated files /fairseq/temporal_convolution_tbc /fairseq/modules/*_layer/*_forward.cu /fairseq/modules/*_layer/*_backward.cu /fairseq/version.py # data data-bin/ # reranking /examples/reranking/rerank_data # Cython-generated C++ source files /fairseq/data/data_utils_fast.cpp /fairseq/data/token_block_utils_fast.cpp # VSCODE .vscode/ftp-sync.json .vscode/settings.json # Experimental Folder experimental/* # Weights and Biases logs wandb/ # Hydra artifacts nohup.out multirun outputs ================================================ FILE: LICENSE ================================================ ================================================ FILE: NeuralSeq/LICENSE ================================================ MIT License Copyright (c) 2021 Jinglin Liu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: NeuralSeq/README.md ================================================ In this directory, we support FastSpeech, GenerSpeech, SyntaSpeech, DiffSinger ================================================ FILE: NeuralSeq/configs/config_base.yaml ================================================ # task binary_data_dir: '' work_dir: '' # experiment directory. infer: false # infer seed: 1234 debug: false save_codes: - configs - modules - tasks - utils - usr ############# # dataset ############# ds_workers: 1 test_num: 100 valid_num: 100 endless_ds: false sort_by_len: true ######### # train and eval ######### load_ckpt: '' save_ckpt: true save_best: false num_ckpt_keep: 3 clip_grad_norm: 0 accumulate_grad_batches: 1 log_interval: 100 num_sanity_val_steps: 5 # steps of validation at the beginning check_val_every_n_epoch: 10 val_check_interval: 2000 max_epochs: 1000 max_updates: 160000 max_tokens: 31250 max_sentences: 100000 max_eval_tokens: -1 max_eval_sentences: -1 test_input_dir: '' ================================================ FILE: NeuralSeq/configs/singing/base.yaml ================================================ base_config: - configs/tts/base.yaml - configs/tts/base_zh.yaml datasets: [] test_prefixes: [] test_num: 0 valid_num: 0 pre_align_cls: data_gen.singing.pre_align.SingingPreAlign binarizer_cls: data_gen.singing.binarize.SingingBinarizer pre_align_args: use_tone: false # for ZH forced_align: mfa use_sox: true hop_size: 128 # Hop size. fft_size: 512 # FFT size. win_size: 512 # FFT size. max_frames: 8000 fmin: 50 # Minimum freq in mel basis calculation. fmax: 11025 # Maximum frequency in mel basis calculation. pitch_type: frame hidden_size: 256 mel_loss: "ssim:0.5|l1:0.5" lambda_f0: 0.0 lambda_uv: 0.0 lambda_energy: 0.0 lambda_ph_dur: 0.0 lambda_sent_dur: 0.0 lambda_word_dur: 0.0 predictor_grad: 0.0 use_spk_embed: true use_spk_id: false max_tokens: 20000 max_updates: 400000 num_spk: 100 save_f0: true use_gt_dur: true use_gt_f0: true ================================================ FILE: NeuralSeq/configs/singing/fs2.yaml ================================================ base_config: - configs/tts/fs2.yaml - configs/singing/base.yaml ================================================ FILE: NeuralSeq/configs/tts/base.yaml ================================================ # task base_config: configs/config_base.yaml task_cls: '' ############# # dataset ############# raw_data_dir: '' processed_data_dir: '' binary_data_dir: '' dict_dir: '' pre_align_cls: '' binarizer_cls: data_gen.tts.base_binarizer.BaseBinarizer pre_align_args: use_tone: true # for ZH forced_align: mfa use_sox: false txt_processor: en allow_no_txt: false denoise: false binarization_args: shuffle: false with_txt: true with_wav: false with_align: true with_spk_embed: true with_f0: true with_f0cwt: true loud_norm: false endless_ds: true reset_phone_dict: true test_num: 100 valid_num: 100 max_frames: 1550 max_input_tokens: 1550 audio_num_mel_bins: 80 audio_sample_rate: 22050 hop_size: 256 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) win_size: 1024 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) fmin: 80 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) fmax: 7600 # To be increased/reduced depending on data. fft_size: 1024 # Extra window size is filled with 0 paddings to match this parameter min_level_db: -100 num_spk: 1 mel_vmin: -6 mel_vmax: 1.5 ds_workers: 4 ######### # model ######### dropout: 0.1 enc_layers: 4 dec_layers: 4 hidden_size: 384 num_heads: 2 prenet_dropout: 0.5 prenet_hidden_size: 256 stop_token_weight: 5.0 enc_ffn_kernel_size: 9 dec_ffn_kernel_size: 9 ffn_act: gelu ffn_padding: 'SAME' ########### # optimization ########### lr: 2.0 warmup_updates: 8000 optimizer_adam_beta1: 0.9 optimizer_adam_beta2: 0.98 weight_decay: 0 clip_grad_norm: 1 ########### # train and eval ########### max_tokens: 30000 max_sentences: 100000 max_eval_sentences: 1 max_eval_tokens: 60000 train_set_name: 'train' valid_set_name: 'valid' test_set_name: 'test' vocoder: pwg vocoder_ckpt: '' profile_infer: false out_wav_norm: false save_gt: false save_f0: false gen_dir_name: '' use_denoise: false ================================================ FILE: NeuralSeq/configs/tts/base_zh.yaml ================================================ pre_align_args: txt_processor: zh_g2pM binarizer_cls: data_gen.tts.binarizer_zh.ZhBinarizer ================================================ FILE: NeuralSeq/configs/tts/emotion/base_text2mel.yaml ================================================ raw_data_dir: 'data/raw/ESD' processed_data_dir: 'data/processed/emotion' binary_data_dir: 'data/binary/emotion' pre_align_cls: configs.tts.emotion.pre_align.EmoPreAlign audio_sample_rate: 16000 binarization_args: shuffle: true binarizer_cls: data_gen.tts.base_binarizer_emotion.EmotionBinarizer use_spk_id: true test_num: 200 num_spk: 10 pitch_type: frame min_frames: 128 num_test_samples: 30 mel_loss: "ssim:0.5|l1:0.5" vocoder_ckpt: '' use_emotion: true ================================================ FILE: NeuralSeq/configs/tts/emotion/pre_align.py ================================================ import os from data_gen.tts.base_preprocess import BasePreprocessor import glob import re class EmoPreAlign(BasePreprocessor): def meta_data(self): spks = ['0012', '0011', '0013', '0014', '0015', '0016', '0017', '0018', '0019', '0020'] pattern = re.compile('[\t\n ]+') for spk in spks: for line in open(f"{self.raw_data_dir}/{spk}/{spk}.txt", 'r'): # 打开文件 line = re.sub(pattern, ' ', line) if line == ' ': continue split_ = line.split(' ') txt = ' '.join(split_[1: -2]) item_name = split_[0] emotion = split_[-2] wav_fn = f'{self.raw_data_dir}/{spk}/{emotion}/{item_name}.wav' yield item_name, wav_fn, txt, spk, emotion if __name__ == "__main__": EmoPreAlign().process() ================================================ FILE: NeuralSeq/configs/tts/fs2.yaml ================================================ base_config: configs/tts/base.yaml task_cls: tasks.tts.fs2.FastSpeech2Task # model hidden_size: 256 dropout: 0.1 encoder_type: fft # fft|tacotron|tacotron2|conformer encoder_K: 8 # for tacotron encoder decoder_type: fft # fft|rnn|conv|conformer use_pos_embed: true # duration predictor_hidden: -1 predictor_kernel: 5 predictor_layers: 2 dur_predictor_kernel: 3 dur_predictor_layers: 2 predictor_dropout: 0.5 # pitch and energy use_pitch_embed: true pitch_type: ph # frame|ph|cwt use_uv: true cwt_hidden_size: 128 cwt_layers: 2 cwt_loss: l1 cwt_add_f0_loss: false cwt_std_scale: 0.8 pitch_ar: false #pitch_embed_type: 0q pitch_loss: 'l1' # l1|l2|ssim pitch_norm: log use_energy_embed: false # reference encoder and speaker embedding use_spk_id: false use_split_spk_id: false use_spk_embed: false use_var_enc: false lambda_commit: 0.25 ref_norm_layer: bn pitch_enc_hidden_stride_kernel: - 0,2,5 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size - 0,2,5 - 0,2,5 dur_enc_hidden_stride_kernel: - 0,2,3 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size - 0,2,3 - 0,1,3 # mel mel_loss: l1:0.5|ssim:0.5 # l1|l2|gdl|ssim or l1:0.5|ssim:0.5 # loss lambda lambda_f0: 1.0 lambda_uv: 1.0 lambda_energy: 0.1 lambda_ph_dur: 1.0 lambda_sent_dur: 1.0 lambda_word_dur: 1.0 predictor_grad: 0.1 # train and eval pretrain_fs_ckpt: '' warmup_updates: 2000 max_tokens: 32000 max_sentences: 100000 max_eval_sentences: 1 max_updates: 120000 num_valid_plots: 5 num_test_samples: 0 test_ids: [] use_gt_dur: false use_gt_f0: false # exp dur_loss: mse # huber|mol norm_type: gn ================================================ FILE: NeuralSeq/configs/tts/hifigan.yaml ================================================ base_config: configs/tts/pwg.yaml task_cls: tasks.vocoder.hifigan.HifiGanTask resblock: "1" adam_b1: 0.8 adam_b2: 0.99 upsample_rates: [ 8,8,2,2 ] upsample_kernel_sizes: [ 16,16,4,4 ] upsample_initial_channel: 128 resblock_kernel_sizes: [ 3,7,11 ] resblock_dilation_sizes: [ [ 1,3,5 ], [ 1,3,5 ], [ 1,3,5 ] ] lambda_mel: 45.0 max_samples: 8192 max_sentences: 16 generator_params: lr: 0.0002 # Generator's learning rate. aux_context_window: 0 # Context window size for auxiliary feature. discriminator_optimizer_params: lr: 0.0002 # Discriminator's learning rate. ================================================ FILE: NeuralSeq/configs/tts/libritts/base_text2mel.yaml ================================================ raw_data_dir: 'data/raw/LibriTTS' processed_data_dir: 'data/processed/libritts' binary_data_dir: 'data/binary/libritts' pre_align_cls: configs.tts.libritts.pre_align.LibrittsPreAlign binarization_args: shuffle: true use_spk_id: true test_num: 200 num_spk: 2320 pitch_type: frame min_frames: 128 num_test_samples: 30 mel_loss: "ssim:0.5|l1:0.5" vocoder_ckpt: '' ================================================ FILE: NeuralSeq/configs/tts/libritts/fs2.yaml ================================================ base_config: - configs/tts/fs2.yaml - ./base_text2mel.yaml ================================================ FILE: NeuralSeq/configs/tts/libritts/pre_align.py ================================================ import os from data_gen.tts.base_preprocess import BasePreprocessor import glob class LibrittsPreAlign(BasePreprocessor): def meta_data(self): wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*.wav')) for wav_fn in wav_fns: item_name = os.path.basename(wav_fn)[:-4] txt_fn = f'{wav_fn[:-4]}.normalized.txt' with open(txt_fn, 'r') as f: txt = f.readlines() f.close() spk = item_name.split("_")[0] # Example: # # 'item_name': '103_1241_000000_000001' # 'wav_fn': 'LibriTTS/train-clean-100/103/1241/103_1241_000000_000001.wav' # 'txt': 'matthew Cuthbert is surprised' # 'spk_name': '103' yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt[0], 'spk_name': spk} if __name__ == "__main__": LibrittsPreAlign().process() ================================================ FILE: NeuralSeq/configs/tts/libritts/pwg.yaml ================================================ base_config: egs/egs_bases/tts/vocoder/pwg.yaml raw_data_dir: 'data/raw/LibriTTS' processed_data_dir: 'data/processed/libritts' binary_data_dir: 'data/binary/libritts_wav' generator_params: kernel_size: 5 num_spk: 400 max_samples: 20480 ================================================ FILE: NeuralSeq/configs/tts/lj/base_mel2wav.yaml ================================================ raw_data_dir: 'data/raw/LJSpeech-1.1' processed_data_dir: 'data/processed/ljspeech' binary_data_dir: 'data/binary/ljspeech_wav' ================================================ FILE: NeuralSeq/configs/tts/lj/base_text2mel.yaml ================================================ raw_data_dir: 'data/raw/LJSpeech-1.1' processed_data_dir: 'data/processed/ljspeech' binary_data_dir: 'data/binary/ljspeech' pre_align_cls: data_gen.tts.lj.pre_align.LJPreAlign pitch_type: cwt mel_loss: l1 num_test_samples: 20 test_ids: [ 68, 70, 74, 87, 110, 172, 190, 215, 231, 294, 316, 324, 402, 422, 485, 500, 505, 508, 509, 519 ] use_energy_embed: false test_num: 523 valid_num: 348 ================================================ FILE: NeuralSeq/configs/tts/lj/fs2.yaml ================================================ base_config: - configs/tts/fs2.yaml - configs/tts/lj/base_text2mel.yaml ================================================ FILE: NeuralSeq/configs/tts/lj/hifigan.yaml ================================================ base_config: - configs/tts/hifigan.yaml - configs/tts/lj/base_mel2wav.yaml ================================================ FILE: NeuralSeq/configs/tts/lj/pwg.yaml ================================================ base_config: - configs/tts/pwg.yaml - configs/tts/lj/base_mel2wav.yaml ================================================ FILE: NeuralSeq/configs/tts/pwg.yaml ================================================ base_config: configs/tts/base.yaml task_cls: tasks.vocoder.pwg.PwgTask binarization_args: with_wav: true with_spk_embed: false with_align: false test_input_dir: '' ########### # train and eval ########### max_samples: 25600 max_sentences: 5 max_eval_sentences: 1 max_updates: 1000000 val_check_interval: 2000 ########################################################### # FEATURE EXTRACTION SETTING # ########################################################### sampling_rate: 22050 # Sampling rate. fft_size: 1024 # FFT size. hop_size: 256 # Hop size. win_length: null # Window length. # If set to null, it will be the same as fft_size. window: "hann" # Window function. num_mels: 80 # Number of mel basis. fmin: 80 # Minimum freq in mel basis calculation. fmax: 7600 # Maximum frequency in mel basis calculation. format: "hdf5" # Feature file format. "npy" or "hdf5" is supported. ########################################################### # GENERATOR NETWORK ARCHITECTURE SETTING # ########################################################### generator_params: in_channels: 1 # Number of input channels. out_channels: 1 # Number of output channels. kernel_size: 3 # Kernel size of dilated convolution. layers: 30 # Number of residual block layers. stacks: 3 # Number of stacks i.e., dilation cycles. residual_channels: 64 # Number of channels in residual conv. gate_channels: 128 # Number of channels in gated conv. skip_channels: 64 # Number of channels in skip conv. aux_channels: 80 # Number of channels for auxiliary feature conv. # Must be the same as num_mels. aux_context_window: 2 # Context window size for auxiliary feature. # If set to 2, previous 2 and future 2 frames will be considered. dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. use_weight_norm: true # Whether to use weight norm. # If set to true, it will be applied to all of the conv layers. upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture. upsample_params: # Upsampling network parameters. upsample_scales: [4, 4, 4, 4] # Upsampling scales. Prodcut of these must be the same as hop size. use_pitch_embed: false ########################################################### # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # ########################################################### discriminator_params: in_channels: 1 # Number of input channels. out_channels: 1 # Number of output channels. kernel_size: 3 # Number of output channels. layers: 10 # Number of conv layers. conv_channels: 64 # Number of chnn layers. bias: true # Whether to use bias parameter in conv. use_weight_norm: true # Whether to use weight norm. # If set to true, it will be applied to all of the conv layers. nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv. nonlinear_activation_params: # Nonlinear function parameters negative_slope: 0.2 # Alpha in LeakyReLU. ########################################################### # STFT LOSS SETTING # ########################################################### stft_loss_params: fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. window: "hann_window" # Window function for STFT-based loss use_mel_loss: false ########################################################### # ADVERSARIAL LOSS SETTING # ########################################################### lambda_adv: 4.0 # Loss balancing coefficient. ########################################################### # OPTIMIZER & SCHEDULER SETTING # ########################################################### generator_optimizer_params: lr: 0.0001 # Generator's learning rate. eps: 1.0e-6 # Generator's epsilon. weight_decay: 0.0 # Generator's weight decay coefficient. generator_scheduler_params: step_size: 200000 # Generator's scheduler step size. gamma: 0.5 # Generator's scheduler gamma. # At each step size, lr will be multiplied by this parameter. generator_grad_norm: 10 # Generator's gradient norm. discriminator_optimizer_params: lr: 0.00005 # Discriminator's learning rate. eps: 1.0e-6 # Discriminator's epsilon. weight_decay: 0.0 # Discriminator's weight decay coefficient. discriminator_scheduler_params: step_size: 200000 # Discriminator's scheduler step size. gamma: 0.5 # Discriminator's scheduler gamma. # At each step size, lr will be multiplied by this parameter. discriminator_grad_norm: 1 # Discriminator's gradient norm. disc_start_steps: 40000 # Number of steps to start to train discriminator. ================================================ FILE: NeuralSeq/data_gen/tts/base_binarizer.py ================================================ import os os.environ["OMP_NUM_THREADS"] = "1" from utils.multiprocess_utils import chunked_multiprocess_run import random import traceback import json from resemblyzer import VoiceEncoder from tqdm import tqdm from data_gen.tts.data_gen_utils import get_mel2ph, get_pitch, build_phone_encoder from utils.hparams import set_hparams, hparams import numpy as np from utils.indexed_datasets import IndexedDatasetBuilder from vocoders.base_vocoder import VOCODERS import pandas as pd class BinarizationError(Exception): pass class BaseBinarizer: def __init__(self, processed_data_dir=None): if processed_data_dir is None: processed_data_dir = hparams['processed_data_dir'] self.processed_data_dirs = processed_data_dir.split(",") self.binarization_args = hparams['binarization_args'] self.pre_align_args = hparams['pre_align_args'] self.forced_align = self.pre_align_args['forced_align'] tg_dir = None if self.forced_align == 'mfa': tg_dir = 'mfa_outputs' if self.forced_align == 'kaldi': tg_dir = 'kaldi_outputs' self.item2txt = {} self.item2ph = {} self.item2wavfn = {} self.item2tgfn = {} self.item2spk = {} for ds_id, processed_data_dir in enumerate(self.processed_data_dirs): self.meta_df = pd.read_csv(f"{processed_data_dir}/metadata_phone.csv", dtype=str) for r_idx, r in self.meta_df.iterrows(): item_name = raw_item_name = r['item_name'] if len(self.processed_data_dirs) > 1: item_name = f'ds{ds_id}_{item_name}' self.item2txt[item_name] = r['txt'] self.item2ph[item_name] = r['ph'] self.item2wavfn[item_name] = os.path.join(hparams['raw_data_dir'], 'wavs', os.path.basename(r['wav_fn']).split('_')[1]) self.item2spk[item_name] = r.get('spk', 'SPK1') if len(self.processed_data_dirs) > 1: self.item2spk[item_name] = f"ds{ds_id}_{self.item2spk[item_name]}" if tg_dir is not None: self.item2tgfn[item_name] = f"{processed_data_dir}/{tg_dir}/{raw_item_name}.TextGrid" self.item_names = sorted(list(self.item2txt.keys())) if self.binarization_args['shuffle']: random.seed(1234) random.shuffle(self.item_names) @property def train_item_names(self): return self.item_names[hparams['test_num']+hparams['valid_num']:] @property def valid_item_names(self): return self.item_names[0: hparams['test_num']+hparams['valid_num']] # @property def test_item_names(self): return self.item_names[0: hparams['test_num']] # Audios for MOS testing are in 'test_ids' def build_spk_map(self): spk_map = set() for item_name in self.item_names: spk_name = self.item2spk[item_name] spk_map.add(spk_name) spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))} assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map) return spk_map def item_name2spk_id(self, item_name): return self.spk_map[self.item2spk[item_name]] def _phone_encoder(self): ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json" ph_set = [] if hparams['reset_phone_dict'] or not os.path.exists(ph_set_fn): for processed_data_dir in self.processed_data_dirs: ph_set += [x.split(' ')[0] for x in open(f'{processed_data_dir}/dict.txt').readlines()] ph_set = sorted(set(ph_set)) json.dump(ph_set, open(ph_set_fn, 'w')) else: ph_set = json.load(open(ph_set_fn, 'r')) print("| phone set: ", ph_set) return build_phone_encoder(hparams['binary_data_dir']) def meta_data(self, prefix): if prefix == 'valid': item_names = self.valid_item_names elif prefix == 'test': item_names = self.test_item_names else: item_names = self.train_item_names for item_name in item_names: ph = self.item2ph[item_name] txt = self.item2txt[item_name] tg_fn = self.item2tgfn.get(item_name) wav_fn = self.item2wavfn[item_name] spk_id = self.item_name2spk_id(item_name) yield item_name, ph, txt, tg_fn, wav_fn, spk_id def process(self): os.makedirs(hparams['binary_data_dir'], exist_ok=True) self.spk_map = self.build_spk_map() print("| spk_map: ", self.spk_map) spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json" json.dump(self.spk_map, open(spk_map_fn, 'w')) self.phone_encoder = self._phone_encoder() self.process_data('valid') self.process_data('test') self.process_data('train') def process_data(self, prefix): data_dir = hparams['binary_data_dir'] args = [] builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}') lengths = [] f0s = [] total_sec = 0 if self.binarization_args['with_spk_embed']: voice_encoder = VoiceEncoder().cuda() meta_data = list(self.meta_data(prefix)) for m in meta_data: args.append(list(m) + [self.phone_encoder, self.binarization_args]) num_workers = int(os.getenv('N_PROC', os.cpu_count() // 3)) for f_id, (_, item) in enumerate( zip(tqdm(meta_data), chunked_multiprocess_run(self.process_item, args, num_workers=num_workers))): if item is None: continue item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \ if self.binarization_args['with_spk_embed'] else None if not self.binarization_args['with_wav'] and 'wav' in item: print("del wav") del item['wav'] builder.add_item(item) lengths.append(item['len']) total_sec += item['sec'] if item.get('f0') is not None: f0s.append(item['f0']) builder.finalize() np.save(f'{data_dir}/{prefix}_lengths.npy', lengths) if len(f0s) > 0: f0s = np.concatenate(f0s, 0) f0s = f0s[f0s != 0] np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()]) print(f"| {prefix} total duration: {total_sec:.3f}s") @classmethod def process_item(cls, item_name, ph, txt, tg_fn, wav_fn, spk_id, encoder, binarization_args): if hparams['vocoder'] in VOCODERS: wav, mel = VOCODERS[hparams['vocoder']].wav2spec(wav_fn) else: wav, mel = VOCODERS[hparams['vocoder'].split('.')[-1]].wav2spec(wav_fn) res = { 'item_name': item_name, 'txt': txt, 'ph': ph, 'mel': mel, 'wav': wav, 'wav_fn': wav_fn, 'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0], 'spk_id': spk_id } try: if binarization_args['with_f0']: cls.get_pitch(wav, mel, res) if binarization_args['with_f0cwt']: cls.get_f0cwt(res['f0'], res) if binarization_args['with_txt']: try: phone_encoded = res['phone'] = encoder.encode(ph) except: traceback.print_exc() raise BinarizationError(f"Empty phoneme") if binarization_args['with_align']: cls.get_align(tg_fn, ph, mel, phone_encoded, res) except BinarizationError as e: print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {wav_fn}") return None return res @staticmethod def get_align(tg_fn, ph, mel, phone_encoded, res): if tg_fn is not None and os.path.exists(tg_fn): mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams) else: raise BinarizationError(f"Align not found") if mel2ph.max() - 1 >= len(phone_encoded): raise BinarizationError( f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}") res['mel2ph'] = mel2ph res['dur'] = dur @staticmethod def get_pitch(wav, mel, res): f0, pitch_coarse = get_pitch(wav, mel, hparams) if sum(f0) == 0: raise BinarizationError("Empty f0") res['f0'] = f0 res['pitch'] = pitch_coarse @staticmethod def get_f0cwt(f0, res): from utils.cwt import get_cont_lf0, get_lf0_cwt uv, cont_lf0_lpf = get_cont_lf0(f0) logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf) cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm) if np.any(np.isnan(Wavelet_lf0)): raise BinarizationError("NaN CWT") res['cwt_spec'] = Wavelet_lf0 res['cwt_scales'] = scales res['f0_mean'] = logf0s_mean_org res['f0_std'] = logf0s_std_org if __name__ == "__main__": set_hparams() BaseBinarizer().process() ================================================ FILE: NeuralSeq/data_gen/tts/base_binarizer_emotion.py ================================================ import os os.environ["OMP_NUM_THREADS"] = "1" import torch from collections import Counter from utils.text_encoder import TokenTextEncoder from data_gen.tts.emotion import inference as EmotionEncoder from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance from data_gen.tts.emotion.inference import preprocess_wav from utils.multiprocess_utils import chunked_multiprocess_run import random import traceback import json from resemblyzer import VoiceEncoder from tqdm import tqdm from data_gen.tts.data_gen_utils import get_mel2ph, get_pitch, build_phone_encoder, is_sil_phoneme from utils.hparams import hparams, set_hparams import numpy as np from utils.indexed_datasets import IndexedDatasetBuilder from vocoders.base_vocoder import get_vocoder_cls import pandas as pd class BinarizationError(Exception): pass class EmotionBinarizer: def __init__(self, processed_data_dir=None): if processed_data_dir is None: processed_data_dir = hparams['processed_data_dir'] self.processed_data_dirs = processed_data_dir.split(",") self.binarization_args = hparams['binarization_args'] self.pre_align_args = hparams['pre_align_args'] self.item2txt = {} self.item2ph = {} self.item2wavfn = {} self.item2tgfn = {} self.item2spk = {} self.item2emo = {} def load_meta_data(self): for ds_id, processed_data_dir in enumerate(self.processed_data_dirs): self.meta_df = pd.read_csv(f"{processed_data_dir}/metadata_phone.csv", dtype=str) for r_idx, r in tqdm(self.meta_df.iterrows(), desc='Loading meta data.'): item_name = raw_item_name = r['item_name'] if len(self.processed_data_dirs) > 1: item_name = f'ds{ds_id}_{item_name}' self.item2txt[item_name] = r['txt'] self.item2ph[item_name] = r['ph'] self.item2wavfn[item_name] = r['wav_fn'] self.item2spk[item_name] = r.get('spk_name', 'SPK1') \ if self.binarization_args['with_spk_id'] else 'SPK1' if len(self.processed_data_dirs) > 1: self.item2spk[item_name] = f"ds{ds_id}_{self.item2spk[item_name]}" self.item2tgfn[item_name] = f"{processed_data_dir}/mfa_outputs/{raw_item_name}.TextGrid" self.item2emo[item_name] = r.get('others', '"Neutral"') self.item_names = sorted(list(self.item2txt.keys())) if self.binarization_args['shuffle']: random.seed(1234) random.shuffle(self.item_names) @property def train_item_names(self): return self.item_names[hparams['test_num']:] @property def valid_item_names(self): return self.item_names[:hparams['test_num']] @property def test_item_names(self): return self.valid_item_names def build_spk_map(self): spk_map = set() for item_name in self.item_names: spk_name = self.item2spk[item_name] spk_map.add(spk_name) spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))} print("| #Spk: ", len(spk_map)) assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map) return spk_map def build_emo_map(self): emo_map = set() for item_name in self.item_names: emo_name = self.item2emo[item_name] emo_map.add(emo_name) emo_map = {x: i for i, x in enumerate(sorted(list(emo_map)))} print("| #Emo: ", len(emo_map)) return emo_map def item_name2spk_id(self, item_name): return self.spk_map[self.item2spk[item_name]] def item_name2emo_id(self, item_name): return self.emo_map[self.item2emo[item_name]] def _phone_encoder(self): ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json" ph_set = [] if self.binarization_args['reset_phone_dict'] or not os.path.exists(ph_set_fn): for ph_sent in self.item2ph.values(): ph_set += ph_sent.split(' ') ph_set = sorted(set(ph_set)) json.dump(ph_set, open(ph_set_fn, 'w')) print("| Build phone set: ", ph_set) else: ph_set = json.load(open(ph_set_fn, 'r')) print("| Load phone set: ", ph_set) return build_phone_encoder(hparams['binary_data_dir']) def _word_encoder(self): fn = f"{hparams['binary_data_dir']}/word_set.json" word_set = [] if self.binarization_args['reset_word_dict']: for word_sent in self.item2txt.values(): word_set += [x for x in word_sent.split(' ') if x != ''] word_set = Counter(word_set) total_words = sum(word_set.values()) word_set = word_set.most_common(hparams['word_size']) num_unk_words = total_words - sum([x[1] for x in word_set]) word_set = [x[0] for x in word_set] json.dump(word_set, open(fn, 'w')) print(f"| Build word set. Size: {len(word_set)}, #total words: {total_words}," f" #unk_words: {num_unk_words}, word_set[:10]:, {word_set[:10]}.") else: word_set = json.load(open(fn, 'r')) print("| Load word set. Size: ", len(word_set), word_set[:10]) return TokenTextEncoder(None, vocab_list=word_set, replace_oov='') def meta_data(self, prefix): if prefix == 'valid': item_names = self.valid_item_names elif prefix == 'test': item_names = self.test_item_names else: item_names = self.train_item_names for item_name in item_names: ph = self.item2ph[item_name] txt = self.item2txt[item_name] tg_fn = self.item2tgfn.get(item_name) wav_fn = self.item2wavfn[item_name] spk_id = self.item_name2spk_id(item_name) emotion = self.item_name2emo_id(item_name) yield item_name, ph, txt, tg_fn, wav_fn, spk_id, emotion def process(self): self.load_meta_data() os.makedirs(hparams['binary_data_dir'], exist_ok=True) self.spk_map = self.build_spk_map() print("| spk_map: ", self.spk_map) spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json" json.dump(self.spk_map, open(spk_map_fn, 'w')) self.emo_map = self.build_emo_map() print("| emo_map: ", self.emo_map) emo_map_fn = f"{hparams['binary_data_dir']}/emo_map.json" json.dump(self.emo_map, open(emo_map_fn, 'w')) self.phone_encoder = self._phone_encoder() self.word_encoder = None EmotionEncoder.load_model(hparams['emotion_encoder_path']) if self.binarization_args['with_word']: self.word_encoder = self._word_encoder() self.process_data('valid') self.process_data('test') self.process_data('train') def process_data(self, prefix): data_dir = hparams['binary_data_dir'] args = [] builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}') ph_lengths = [] mel_lengths = [] f0s = [] total_sec = 0 if self.binarization_args['with_spk_embed']: voice_encoder = VoiceEncoder().cuda() meta_data = list(self.meta_data(prefix)) for m in meta_data: args.append(list(m) + [(self.phone_encoder, self.word_encoder), self.binarization_args]) num_workers = self.num_workers for f_id, (_, item) in enumerate( zip(tqdm(meta_data), chunked_multiprocess_run(self.process_item, args, num_workers=num_workers))): if item is None: continue item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \ if self.binarization_args['with_spk_embed'] else None processed_wav = preprocess_wav(item['wav_fn']) item['emo_embed'] = Embed_utterance(processed_wav) if not self.binarization_args['with_wav'] and 'wav' in item: del item['wav'] builder.add_item(item) mel_lengths.append(item['len']) if 'ph_len' in item: ph_lengths.append(item['ph_len']) total_sec += item['sec'] if item.get('f0') is not None: f0s.append(item['f0']) builder.finalize() np.save(f'{data_dir}/{prefix}_lengths.npy', mel_lengths) if len(ph_lengths) > 0: np.save(f'{data_dir}/{prefix}_ph_lengths.npy', ph_lengths) if len(f0s) > 0: f0s = np.concatenate(f0s, 0) f0s = f0s[f0s != 0] np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()]) print(f"| {prefix} total duration: {total_sec:.3f}s") @classmethod def process_item(cls, item_name, ph, txt, tg_fn, wav_fn, spk_id, emotion, encoder, binarization_args): res = {'item_name': item_name, 'txt': txt, 'ph': ph, 'wav_fn': wav_fn, 'spk_id': spk_id, 'emotion': emotion} if binarization_args['with_linear']: wav, mel, linear_stft = get_vocoder_cls(hparams).wav2spec(wav_fn) # , return_linear=True res['linear'] = linear_stft else: wav, mel = get_vocoder_cls(hparams).wav2spec(wav_fn) wav = wav.astype(np.float16) res.update({'mel': mel, 'wav': wav, 'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]}) try: if binarization_args['with_f0']: cls.get_pitch(res) if binarization_args['with_f0cwt']: cls.get_f0cwt(res) if binarization_args['with_txt']: ph_encoder, word_encoder = encoder try: res['phone'] = ph_encoder.encode(ph) res['ph_len'] = len(res['phone']) except: traceback.print_exc() raise BinarizationError(f"Empty phoneme") if binarization_args['with_align']: cls.get_align(tg_fn, res) if binarization_args['trim_eos_bos']: bos_dur = res['dur'][0] eos_dur = res['dur'][-1] res['mel'] = mel[bos_dur:-eos_dur] res['f0'] = res['f0'][bos_dur:-eos_dur] res['pitch'] = res['pitch'][bos_dur:-eos_dur] res['mel2ph'] = res['mel2ph'][bos_dur:-eos_dur] res['wav'] = wav[bos_dur * hparams['hop_size']:-eos_dur * hparams['hop_size']] res['dur'] = res['dur'][1:-1] res['len'] = res['mel'].shape[0] if binarization_args['with_word']: cls.get_word(res, word_encoder) except BinarizationError as e: print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {wav_fn}") return None except Exception as e: traceback.print_exc() print(f"| Skip item. item_name: {item_name}, wav_fn: {wav_fn}") return None return res @staticmethod def get_align(tg_fn, res): ph = res['ph'] mel = res['mel'] phone_encoded = res['phone'] if tg_fn is not None and os.path.exists(tg_fn): mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams) else: raise BinarizationError(f"Align not found") if mel2ph.max() - 1 >= len(phone_encoded): raise BinarizationError( f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}") res['mel2ph'] = mel2ph res['dur'] = dur @staticmethod def get_pitch(res): wav, mel = res['wav'], res['mel'] f0, pitch_coarse = get_pitch(wav, mel, hparams) if sum(f0) == 0: raise BinarizationError("Empty f0") res['f0'] = f0 res['pitch'] = pitch_coarse @staticmethod def get_f0cwt(res): from utils.cwt import get_cont_lf0, get_lf0_cwt f0 = res['f0'] uv, cont_lf0_lpf = get_cont_lf0(f0) logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf) cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm) if np.any(np.isnan(Wavelet_lf0)): raise BinarizationError("NaN CWT") res['cwt_spec'] = Wavelet_lf0 res['cwt_scales'] = scales res['f0_mean'] = logf0s_mean_org res['f0_std'] = logf0s_std_org @staticmethod def get_word(res, word_encoder): ph_split = res['ph'].split(" ") # ph side mapping to word ph_words = [] # ['', 'N_AW1_', ',', 'AE1_Z_|', 'AO1_L_|', 'B_UH1_K_S_|', 'N_AA1_T_|', ....] ph2word = np.zeros([len(ph_split)], dtype=int) last_ph_idx_for_word = [] # [2, 11, ...] for i, ph in enumerate(ph_split): if ph == '|': last_ph_idx_for_word.append(i) elif not ph[0].isalnum(): if ph not in ['']: last_ph_idx_for_word.append(i - 1) last_ph_idx_for_word.append(i) start_ph_idx_for_word = [0] + [i + 1 for i in last_ph_idx_for_word[:-1]] for i, (s_w, e_w) in enumerate(zip(start_ph_idx_for_word, last_ph_idx_for_word)): ph_words.append(ph_split[s_w:e_w + 1]) ph2word[s_w:e_w + 1] = i ph2word = ph2word.tolist() ph_words = ["_".join(w) for w in ph_words] # mel side mapping to word mel2word = [] dur_word = [0 for _ in range(len(ph_words))] for i, m2p in enumerate(res['mel2ph']): word_idx = ph2word[m2p - 1] mel2word.append(ph2word[m2p - 1]) dur_word[word_idx] += 1 ph2word = [x + 1 for x in ph2word] # 0预留给padding mel2word = [x + 1 for x in mel2word] # 0预留给padding res['ph_words'] = ph_words # [T_word] res['ph2word'] = ph2word # [T_ph] res['mel2word'] = mel2word # [T_mel] res['dur_word'] = dur_word # [T_word] words = [x for x in res['txt'].split(" ") if x != ''] while len(words) > 0 and is_sil_phoneme(words[0]): words = words[1:] while len(words) > 0 and is_sil_phoneme(words[-1]): words = words[:-1] words = [''] + words + [''] word_tokens = word_encoder.encode(" ".join(words)) res['words'] = words res['word_tokens'] = word_tokens assert len(words) == len(ph_words), [words, ph_words] @property def num_workers(self): return int(os.getenv('N_PROC', hparams.get('N_PROC', os.cpu_count()))) if __name__ == "__main__": set_hparams() EmotionBinarizer().process() ================================================ FILE: NeuralSeq/data_gen/tts/base_preprocess.py ================================================ import json import os import random import re import traceback from collections import Counter from functools import partial import pandas as pd import librosa from tqdm import tqdm from data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls from data_gen.tts.wav_processors.base_processor import get_wav_processor_cls from utils.hparams import hparams from utils.multiprocess_utils import multiprocess_run_tqdm from utils.os_utils import link_file, move_file, remove_file from data_gen.tts.data_gen_utils import is_sil_phoneme, build_token_encoder class BasePreprocessor: def __init__(self): self.preprocess_args = hparams['preprocess_args'] txt_processor = self.preprocess_args['txt_processor'] self.txt_processor = get_txt_processor_cls(txt_processor) self.raw_data_dir = hparams['raw_data_dir'] self.processed_dir = hparams['processed_data_dir'] self.spk_map_fn = f"{self.processed_dir}/spk_map.json" def meta_data(self): """ :return: {'item_name': Str, 'wav_fn': Str, 'txt': Str, 'spk_name': Str, 'txt_loader': None or Func} """ raise NotImplementedError def process(self): processed_dir = self.processed_dir wav_processed_tmp_dir = f'{processed_dir}/processed_tmp' remove_file(wav_processed_tmp_dir) os.makedirs(wav_processed_tmp_dir, exist_ok=True) wav_processed_dir = f'{processed_dir}/{self.wav_processed_dirname}' remove_file(wav_processed_dir) os.makedirs(wav_processed_dir, exist_ok=True) meta_data = list(tqdm(self.meta_data(), desc='Load meta data')) item_names = [d['item_name'] for d in meta_data] assert len(item_names) == len(set(item_names)), 'Key `item_name` should be Unique.' # preprocess data phone_list = [] word_list = [] spk_names = set() process_item = partial(self.preprocess_first_pass, txt_processor=self.txt_processor, wav_processed_dir=wav_processed_dir, wav_processed_tmp=wav_processed_tmp_dir, preprocess_args=self.preprocess_args) items = [] args = [{ 'item_name': item_raw['item_name'], 'txt_raw': item_raw['txt'], 'wav_fn': item_raw['wav_fn'], 'txt_loader': item_raw.get('txt_loader'), 'others': item_raw.get('others', None) } for item_raw in meta_data] for item_, (item_id, item) in zip(meta_data, multiprocess_run_tqdm(process_item, args, desc='Preprocess')): if item is not None: item_.update(item) item = item_ if 'txt_loader' in item: del item['txt_loader'] item['id'] = item_id item['spk_name'] = item.get('spk_name', '') item['others'] = item.get('others', None) phone_list += item['ph'].split(" ") word_list += item['word'].split(" ") spk_names.add(item['spk_name']) items.append(item) # add encoded tokens ph_encoder, word_encoder = self._phone_encoder(phone_list), self._word_encoder(word_list) spk_map = self.build_spk_map(spk_names) args = [{ 'ph': item['ph'], 'word': item['word'], 'spk_name': item['spk_name'], 'word_encoder': word_encoder, 'ph_encoder': ph_encoder, 'spk_map': spk_map } for item in items] for idx, item_new_kv in multiprocess_run_tqdm(self.preprocess_second_pass, args, desc='Add encoded tokens'): items[idx].update(item_new_kv) # build mfa data if self.preprocess_args['use_mfa']: mfa_dict = set() mfa_input_dir = f'{processed_dir}/mfa_inputs' remove_file(mfa_input_dir) # group MFA inputs for better parallelism mfa_groups = [i // self.preprocess_args['nsample_per_mfa_group'] for i in range(len(items))] if self.preprocess_args['mfa_group_shuffle']: random.seed(hparams['seed']) random.shuffle(mfa_groups) args = [{ 'item': item, 'mfa_input_dir': mfa_input_dir, 'mfa_group': mfa_group, 'wav_processed_tmp': wav_processed_tmp_dir, 'preprocess_args': self.preprocess_args } for item, mfa_group in zip(items, mfa_groups)] for i, (ph_gb_word_nosil, new_wav_align_fn) in multiprocess_run_tqdm( self.build_mfa_inputs, args, desc='Build MFA data'): items[i]['wav_align_fn'] = new_wav_align_fn for w in ph_gb_word_nosil.split(" "): mfa_dict.add(f"{w} {w.replace('_', ' ')}") mfa_dict = sorted(mfa_dict) with open(f'{processed_dir}/mfa_dict.txt', 'w') as f: f.writelines([f'{l}\n' for l in mfa_dict]) with open(f"{processed_dir}/{self.meta_csv_filename}.json", 'w') as f: f.write(re.sub(r'\n\s+([\d+\]])', r'\1', json.dumps(items, ensure_ascii=False, sort_keys=False, indent=1))) remove_file(wav_processed_tmp_dir) @classmethod def preprocess_first_pass(cls, item_name, txt_raw, txt_processor, wav_fn, wav_processed_dir, wav_processed_tmp, preprocess_args, txt_loader=None, others=None): try: if txt_loader is not None: txt_raw = txt_loader(txt_raw) ph, txt, word, ph2word, ph_gb_word = cls.txt_to_ph(txt_processor, txt_raw, preprocess_args) wav_fn, wav_align_fn = cls.process_wav( item_name, wav_fn, hparams['processed_data_dir'], wav_processed_tmp, preprocess_args) # wav for binarization ext = os.path.splitext(wav_fn)[1] os.makedirs(wav_processed_dir, exist_ok=True) new_wav_fn = f"{wav_processed_dir}/{item_name}{ext}" move_link_func = move_file if os.path.dirname(wav_fn) == wav_processed_tmp else link_file move_link_func(wav_fn, new_wav_fn) return { 'txt': txt, 'txt_raw': txt_raw, 'ph': ph, 'word': word, 'ph2word': ph2word, 'ph_gb_word': ph_gb_word, 'wav_fn': new_wav_fn, 'wav_align_fn': wav_align_fn, 'others': others } except: traceback.print_exc() print(f"| Error is caught. item_name: {item_name}.") return None @staticmethod def txt_to_ph(txt_processor, txt_raw, preprocess_args): txt_struct, txt = txt_processor.process(txt_raw, preprocess_args) ph = [p for w in txt_struct for p in w[1]] ph_gb_word = ["_".join(w[1]) for w in txt_struct] words = [w[0] for w in txt_struct] # word_id=0 is reserved for padding ph2word = [w_id + 1 for w_id, w in enumerate(txt_struct) for _ in range(len(w[1]))] return " ".join(ph), txt, " ".join(words), ph2word, " ".join(ph_gb_word) @staticmethod def process_wav(item_name, wav_fn, processed_dir, wav_processed_tmp, preprocess_args): processors = [get_wav_processor_cls(v) for v in preprocess_args['wav_processors']] processors = [k() for k in processors if k is not None] if len(processors) >= 1: sr_file = librosa.core.get_samplerate(wav_fn) output_fn_for_align = None ext = os.path.splitext(wav_fn)[1] input_fn = f"{wav_processed_tmp}/{item_name}{ext}" link_file(wav_fn, input_fn) for p in processors: outputs = p.process(input_fn, sr_file, wav_processed_tmp, processed_dir, item_name, preprocess_args) if len(outputs) == 3: input_fn, sr, output_fn_for_align = outputs else: input_fn, sr = outputs if output_fn_for_align is None: return input_fn, input_fn else: return input_fn, output_fn_for_align else: return wav_fn, wav_fn def _phone_encoder(self, ph_set): ph_set_fn = f"{self.processed_dir}/phone_set.json" if self.preprocess_args['reset_phone_dict'] or not os.path.exists(ph_set_fn): ph_set = sorted(set(ph_set)) json.dump(ph_set, open(ph_set_fn, 'w'), ensure_ascii=False) print("| Build phone set: ", ph_set) else: ph_set = json.load(open(ph_set_fn, 'r')) print("| Load phone set: ", ph_set) return build_token_encoder(ph_set_fn) def _word_encoder(self, word_set): word_set_fn = f"{self.processed_dir}/word_set.json" if self.preprocess_args['reset_word_dict']: word_set = Counter(word_set) total_words = sum(word_set.values()) word_set = word_set.most_common(hparams['word_dict_size']) num_unk_words = total_words - sum([x[1] for x in word_set]) word_set = ['', ''] + [x[0] for x in word_set] word_set = sorted(set(word_set)) json.dump(word_set, open(word_set_fn, 'w'), ensure_ascii=False) print(f"| Build word set. Size: {len(word_set)}, #total words: {total_words}," f" #unk_words: {num_unk_words}, word_set[:10]:, {word_set[:10]}.") else: word_set = json.load(open(word_set_fn, 'r')) print("| Load word set. Size: ", len(word_set), word_set[:10]) return build_token_encoder(word_set_fn) @classmethod def preprocess_second_pass(cls, word, ph, spk_name, word_encoder, ph_encoder, spk_map): word_token = word_encoder.encode(word) ph_token = ph_encoder.encode(ph) spk_id = spk_map[spk_name] return {'word_token': word_token, 'ph_token': ph_token, 'spk_id': spk_id} def build_spk_map(self, spk_names): spk_map = {x: i for i, x in enumerate(sorted(list(spk_names)))} assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map) print(f"| Number of spks: {len(spk_map)}, spk_map: {spk_map}") json.dump(spk_map, open(self.spk_map_fn, 'w'), ensure_ascii=False) return spk_map @classmethod def build_mfa_inputs(cls, item, mfa_input_dir, mfa_group, wav_processed_tmp, preprocess_args): item_name = item['item_name'] wav_align_fn = item['wav_align_fn'] ph_gb_word = item['ph_gb_word'] ext = os.path.splitext(wav_align_fn)[1] mfa_input_group_dir = f'{mfa_input_dir}/{mfa_group}' os.makedirs(mfa_input_group_dir, exist_ok=True) new_wav_align_fn = f"{mfa_input_group_dir}/{item_name}{ext}" move_link_func = move_file if os.path.dirname(wav_align_fn) == wav_processed_tmp else link_file move_link_func(wav_align_fn, new_wav_align_fn) ph_gb_word_nosil = " ".join(["_".join([p for p in w.split("_") if not is_sil_phoneme(p)]) for w in ph_gb_word.split(" ") if not is_sil_phoneme(w)]) with open(f'{mfa_input_group_dir}/{item_name}.lab', 'w') as f_txt: f_txt.write(ph_gb_word_nosil) return ph_gb_word_nosil, new_wav_align_fn def load_spk_map(self, base_dir): spk_map_fn = f"{base_dir}/spk_map.json" spk_map = json.load(open(spk_map_fn, 'r')) return spk_map def load_dict(self, base_dir): ph_encoder = build_token_encoder(f'{base_dir}/phone_set.json') word_encoder = build_token_encoder(f'{base_dir}/word_set.json') return ph_encoder, word_encoder @property def meta_csv_filename(self): return 'metadata' @property def wav_processed_dirname(self): return 'wav_processed' ================================================ FILE: NeuralSeq/data_gen/tts/binarizer_zh.py ================================================ import os os.environ["OMP_NUM_THREADS"] = "1" from data_gen.tts.txt_processors.zh_g2pM import ALL_SHENMU from data_gen.tts.base_binarizer import BaseBinarizer, BinarizationError from data_gen.tts.data_gen_utils import get_mel2ph from utils.hparams import set_hparams, hparams import numpy as np class ZhBinarizer(BaseBinarizer): @staticmethod def get_align(tg_fn, ph, mel, phone_encoded, res): if tg_fn is not None and os.path.exists(tg_fn): _, dur = get_mel2ph(tg_fn, ph, mel, hparams) else: raise BinarizationError(f"Align not found") ph_list = ph.split(" ") assert len(dur) == len(ph_list) mel2ph = [] # 分隔符的时长分配给韵母 dur_cumsum = np.pad(np.cumsum(dur), [1, 0], mode='constant', constant_values=0) for i in range(len(dur)): p = ph_list[i] if p[0] != '<' and not p[0].isalpha(): uv_ = res['f0'][dur_cumsum[i]:dur_cumsum[i + 1]] == 0 j = 0 while j < len(uv_) and not uv_[j]: j += 1 dur[i - 1] += j dur[i] -= j if dur[i] < 100: dur[i - 1] += dur[i] dur[i] = 0 # 声母和韵母等长 for i in range(len(dur)): p = ph_list[i] if p in ALL_SHENMU: p_next = ph_list[i + 1] if not (dur[i] > 0 and p_next[0].isalpha() and p_next not in ALL_SHENMU): print(f"assert dur[i] > 0 and p_next[0].isalpha() and p_next not in ALL_SHENMU, " f"dur[i]: {dur[i]}, p: {p}, p_next: {p_next}.") continue total = dur[i + 1] + dur[i] dur[i] = total // 2 dur[i + 1] = total - dur[i] for i in range(len(dur)): mel2ph += [i + 1] * dur[i] mel2ph = np.array(mel2ph) if mel2ph.max() - 1 >= len(phone_encoded): raise BinarizationError(f"| Align does not match: {(mel2ph.max() - 1, len(phone_encoded))}") res['mel2ph'] = mel2ph res['dur'] = dur if __name__ == "__main__": set_hparams() ZhBinarizer().process() ================================================ FILE: NeuralSeq/data_gen/tts/data_gen_utils.py ================================================ import warnings warnings.filterwarnings("ignore") import parselmouth import os import torch from skimage.transform import resize from utils.text_encoder import TokenTextEncoder from utils.pitch_utils import f0_to_coarse import struct import webrtcvad from scipy.ndimage.morphology import binary_dilation import librosa import numpy as np from utils import audio import pyloudnorm as pyln import re import json from collections import OrderedDict PUNCS = '!,.?;:' int16_max = (2 ** 15) - 1 def trim_long_silences(path, sr=None, return_raw_wav=False, norm=True, vad_max_silence_length=12): """ Ensures that segments without voice in the waveform remain no longer than a threshold determined by the VAD parameters in params.py. :param wav: the raw waveform as a numpy array of floats :param vad_max_silence_length: Maximum number of consecutive silent frames a segment can have. :return: the same waveform with silences trimmed away (length <= original wav length) """ ## Voice Activation Detection # Window size of the VAD. Must be either 10, 20 or 30 milliseconds. # This sets the granularity of the VAD. Should not need to be changed. sampling_rate = 16000 wav_raw, sr = librosa.core.load(path, sr=sr) if norm: meter = pyln.Meter(sr) # create BS.1770 meter loudness = meter.integrated_loudness(wav_raw) wav_raw = pyln.normalize.loudness(wav_raw, loudness, -20.0) if np.abs(wav_raw).max() > 1.0: wav_raw = wav_raw / np.abs(wav_raw).max() wav = librosa.resample(wav_raw, sr, sampling_rate, res_type='kaiser_best') vad_window_length = 30 # In milliseconds # Number of frames to average together when performing the moving average smoothing. # The larger this value, the larger the VAD variations must be to not get smoothed out. vad_moving_average_width = 8 # Compute the voice detection window size samples_per_window = (vad_window_length * sampling_rate) // 1000 # Trim the end of the audio to have a multiple of the window size wav = wav[:len(wav) - (len(wav) % samples_per_window)] # Convert the float waveform to 16-bit mono PCM pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16)) # Perform voice activation detection voice_flags = [] vad = webrtcvad.Vad(mode=3) for window_start in range(0, len(wav), samples_per_window): window_end = window_start + samples_per_window voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2], sample_rate=sampling_rate)) voice_flags = np.array(voice_flags) # Smooth the voice detection with a moving average def moving_average(array, width): array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2))) ret = np.cumsum(array_padded, dtype=float) ret[width:] = ret[width:] - ret[:-width] return ret[width - 1:] / width audio_mask = moving_average(voice_flags, vad_moving_average_width) audio_mask = np.round(audio_mask).astype(np.bool) # Dilate the voiced regions audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1)) audio_mask = np.repeat(audio_mask, samples_per_window) audio_mask = resize(audio_mask, (len(wav_raw),)) > 0 if return_raw_wav: return wav_raw, audio_mask, sr return wav_raw[audio_mask], audio_mask, sr def process_utterance(wav_path, fft_size=1024, hop_size=256, win_length=1024, window="hann", num_mels=80, fmin=80, fmax=7600, eps=1e-6, sample_rate=22050, loud_norm=False, min_level_db=-100, return_linear=False, trim_long_sil=False, vocoder='pwg'): if isinstance(wav_path, str): if trim_long_sil: wav, _, _ = trim_long_silences(wav_path, sample_rate) else: wav, _ = librosa.core.load(wav_path, sr=sample_rate) else: wav = wav_path if loud_norm: meter = pyln.Meter(sample_rate) # create BS.1770 meter loudness = meter.integrated_loudness(wav) wav = pyln.normalize.loudness(wav, loudness, -22.0) if np.abs(wav).max() > 1: wav = wav / np.abs(wav).max() # get amplitude spectrogram x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size, win_length=win_length, window=window, pad_mode="constant") spc = np.abs(x_stft) # (n_bins, T) # get mel basis fmin = 0 if fmin == -1 else fmin fmax = sample_rate / 2 if fmax == -1 else fmax mel_basis = librosa.filters.mel(sample_rate, fft_size, num_mels, fmin, fmax) mel = mel_basis @ spc if vocoder == 'pwg': mel = np.log10(np.maximum(eps, mel)) # (n_mel_bins, T) else: assert False, f'"{vocoder}" is not in ["pwg"].' l_pad, r_pad = audio.librosa_pad_lr(wav, fft_size, hop_size, 1) wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0) wav = wav[:mel.shape[1] * hop_size] if not return_linear: return wav, mel else: spc = audio.amp_to_db(spc) spc = audio.normalize(spc, {'min_level_db': min_level_db}) return wav, mel, spc def get_pitch(wav_data, mel, hparams): """ :param wav_data: [T] :param mel: [T, 80] :param hparams: :return: """ time_step = hparams['hop_size'] / hparams['audio_sample_rate'] * 1000 f0_min = 80 f0_max = 750 if hparams['hop_size'] == 128: pad_size = 4 elif hparams['hop_size'] == 256: pad_size = 2 else: assert False f0 = parselmouth.Sound(wav_data, hparams['audio_sample_rate']).to_pitch_ac( time_step=time_step / 1000, voicing_threshold=0.6, pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency'] lpad = pad_size * 2 rpad = len(mel) - len(f0) - lpad f0 = np.pad(f0, [[lpad, rpad]], mode='constant') # mel and f0 are extracted by 2 different libraries. we should force them to have the same length. # Attention: we find that new version of some libraries could cause ``rpad'' to be a negetive value... # Just to be sure, we recommend users to set up the same environments as them in requirements_auto.txt (by Anaconda) delta_l = len(mel) - len(f0) assert np.abs(delta_l) <= 8 if delta_l > 0: f0 = np.concatenate([f0, [f0[-1]] * delta_l], 0) f0 = f0[:len(mel)] pitch_coarse = f0_to_coarse(f0) return f0, pitch_coarse def remove_empty_lines(text): """remove empty lines""" assert (len(text) > 0) assert (isinstance(text, list)) text = [t.strip() for t in text] if "" in text: text.remove("") return text class TextGrid(object): def __init__(self, text): text = remove_empty_lines(text) self.text = text self.line_count = 0 self._get_type() self._get_time_intval() self._get_size() self.tier_list = [] self._get_item_list() def _extract_pattern(self, pattern, inc): """ Parameters ---------- pattern : regex to extract pattern inc : increment of line count after extraction Returns ------- group : extracted info """ try: group = re.match(pattern, self.text[self.line_count]).group(1) self.line_count += inc except AttributeError: raise ValueError("File format error at line %d:%s" % (self.line_count, self.text[self.line_count])) return group def _get_type(self): self.file_type = self._extract_pattern(r"File type = \"(.*)\"", 2) def _get_time_intval(self): self.xmin = self._extract_pattern(r"xmin = (.*)", 1) self.xmax = self._extract_pattern(r"xmax = (.*)", 2) def _get_size(self): self.size = int(self._extract_pattern(r"size = (.*)", 2)) def _get_item_list(self): """Only supports IntervalTier currently""" for itemIdx in range(1, self.size + 1): tier = OrderedDict() item_list = [] tier_idx = self._extract_pattern(r"item \[(.*)\]:", 1) tier_class = self._extract_pattern(r"class = \"(.*)\"", 1) if tier_class != "IntervalTier": raise NotImplementedError("Only IntervalTier class is supported currently") tier_name = self._extract_pattern(r"name = \"(.*)\"", 1) tier_xmin = self._extract_pattern(r"xmin = (.*)", 1) tier_xmax = self._extract_pattern(r"xmax = (.*)", 1) tier_size = self._extract_pattern(r"intervals: size = (.*)", 1) for i in range(int(tier_size)): item = OrderedDict() item["idx"] = self._extract_pattern(r"intervals \[(.*)\]", 1) item["xmin"] = self._extract_pattern(r"xmin = (.*)", 1) item["xmax"] = self._extract_pattern(r"xmax = (.*)", 1) item["text"] = self._extract_pattern(r"text = \"(.*)\"", 1) item_list.append(item) tier["idx"] = tier_idx tier["class"] = tier_class tier["name"] = tier_name tier["xmin"] = tier_xmin tier["xmax"] = tier_xmax tier["size"] = tier_size tier["items"] = item_list self.tier_list.append(tier) def toJson(self): _json = OrderedDict() _json["file_type"] = self.file_type _json["xmin"] = self.xmin _json["xmax"] = self.xmax _json["size"] = self.size _json["tiers"] = self.tier_list return json.dumps(_json, ensure_ascii=False, indent=2) def get_mel2ph(tg_fn, ph, mel, hparams): ph_list = ph.split(" ") with open(tg_fn, "r") as f: tg = f.readlines() tg = remove_empty_lines(tg) tg = TextGrid(tg) tg = json.loads(tg.toJson()) split = np.ones(len(ph_list) + 1, np.float) * -1 tg_idx = 0 ph_idx = 0 tg_align = [x for x in tg['tiers'][-1]['items']] tg_align_ = [] for x in tg_align: x['xmin'] = float(x['xmin']) x['xmax'] = float(x['xmax']) if x['text'] in ['sil', 'sp', '', 'SIL', 'PUNC']: x['text'] = '' if len(tg_align_) > 0 and tg_align_[-1]['text'] == '': tg_align_[-1]['xmax'] = x['xmax'] continue tg_align_.append(x) tg_align = tg_align_ tg_len = len([x for x in tg_align if x['text'] != '']) ph_len = len([x for x in ph_list if not is_sil_phoneme(x)]) assert tg_len == ph_len, (tg_len, ph_len, tg_align, ph_list, tg_fn) while tg_idx < len(tg_align) or ph_idx < len(ph_list): if tg_idx == len(tg_align) and is_sil_phoneme(ph_list[ph_idx]): split[ph_idx] = 1e8 ph_idx += 1 continue x = tg_align[tg_idx] if x['text'] == '' and ph_idx == len(ph_list): tg_idx += 1 continue assert ph_idx < len(ph_list), (tg_len, ph_len, tg_align, ph_list, tg_fn) ph = ph_list[ph_idx] if x['text'] == '' and not is_sil_phoneme(ph): assert False, (ph_list, tg_align) if x['text'] != '' and is_sil_phoneme(ph): ph_idx += 1 else: assert (x['text'] == '' and is_sil_phoneme(ph)) \ or x['text'].lower() == ph.lower() \ or x['text'].lower() == 'sil', (x['text'], ph) split[ph_idx] = x['xmin'] if ph_idx > 0 and split[ph_idx - 1] == -1 and is_sil_phoneme(ph_list[ph_idx - 1]): split[ph_idx - 1] = split[ph_idx] ph_idx += 1 tg_idx += 1 assert tg_idx == len(tg_align), (tg_idx, [x['text'] for x in tg_align]) assert ph_idx >= len(ph_list) - 1, (ph_idx, ph_list, len(ph_list), [x['text'] for x in tg_align], tg_fn) mel2ph = np.zeros([mel.shape[0]], np.int) split[0] = 0 split[-1] = 1e8 for i in range(len(split) - 1): assert split[i] != -1 and split[i] <= split[i + 1], (split[:-1],) split = [int(s * hparams['audio_sample_rate'] / hparams['hop_size'] + 0.5) for s in split] for ph_idx in range(len(ph_list)): mel2ph[split[ph_idx]:split[ph_idx + 1]] = ph_idx + 1 mel2ph_torch = torch.from_numpy(mel2ph) T_t = len(ph_list) dur = mel2ph_torch.new_zeros([T_t + 1]).scatter_add(0, mel2ph_torch, torch.ones_like(mel2ph_torch)) dur = dur[1:].numpy() return mel2ph, dur def build_phone_encoder(data_dir): phone_list_file = os.path.join(data_dir, 'phone_set.json') phone_list = json.load(open(phone_list_file)) return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') def build_word_encoder(data_dir): word_list_file = os.path.join(data_dir, 'word_set.json') word_list = json.load(open(word_list_file)) return TokenTextEncoder(None, vocab_list=word_list, replace_oov=',') def is_sil_phoneme(p): return not p[0].isalpha() def build_token_encoder(token_list_file): token_list = json.load(open(token_list_file)) return TokenTextEncoder(None, vocab_list=token_list, replace_oov='') ================================================ FILE: NeuralSeq/data_gen/tts/emotion/audio.py ================================================ from scipy.ndimage.morphology import binary_dilation from data_gen.tts.emotion.params_data import * from pathlib import Path from typing import Optional, Union import numpy as np import webrtcvad import librosa import struct int16_max = (2 ** 15) - 1 def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray], source_sr: Optional[int] = None): """ Applies the preprocessing operations used in training the Speaker Encoder to a waveform either on disk or in memory. The waveform will be resampled to match the data hyperparameters. :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not just .wav), either the waveform as a numpy array of floats. :param source_sr: if passing an audio waveform, the sampling rate of the waveform before preprocessing. After preprocessing, the waveform's sampling rate will match the data hyperparameters. If passing a filepath, the sampling rate will be automatically detected and this argument will be ignored. """ # Load the wav from disk if needed if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path): wav, source_sr = librosa.load(str(fpath_or_wav), sr=None) else: wav = fpath_or_wav # Resample the wav if needed if source_sr is not None and source_sr != sampling_rate: wav = librosa.resample(wav, source_sr, sampling_rate) # Apply the preprocessing: normalize volume and shorten long silences wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True) wav = trim_long_silences(wav) return wav def wav_to_mel_spectrogram(wav): """ Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform. Note: this not a log-mel spectrogram. """ frames = librosa.feature.melspectrogram( wav, sampling_rate, n_fft=int(sampling_rate * mel_window_length / 1000), hop_length=int(sampling_rate * mel_window_step / 1000), n_mels=mel_n_channels ) return frames.astype(np.float32).T def trim_long_silences(wav): """ Ensures that segments without voice in the waveform remain no longer than a threshold determined by the VAD parameters in params.py. :param wav: the raw waveform as a numpy array of floats :return: the same waveform with silences trimmed away (length <= original wav length) """ # Compute the voice detection window size samples_per_window = (vad_window_length * sampling_rate) // 1000 # Trim the end of the audio to have a multiple of the window size wav = wav[:len(wav) - (len(wav) % samples_per_window)] # Convert the float waveform to 16-bit mono PCM pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16)) # Perform voice activation detection voice_flags = [] vad = webrtcvad.Vad(mode=3) for window_start in range(0, len(wav), samples_per_window): window_end = window_start + samples_per_window voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2], sample_rate=sampling_rate)) voice_flags = np.array(voice_flags) # Smooth the voice detection with a moving average def moving_average(array, width): array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2))) ret = np.cumsum(array_padded, dtype=float) ret[width:] = ret[width:] - ret[:-width] return ret[width - 1:] / width audio_mask = moving_average(voice_flags, vad_moving_average_width) audio_mask = np.round(audio_mask).astype(np.bool) # Dilate the voiced regions audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1)) audio_mask = np.repeat(audio_mask, samples_per_window) return wav[audio_mask == True] def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False): if increase_only and decrease_only: raise ValueError("Both increase only and decrease only are set") dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2)) if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only): return wav return wav * (10 ** (dBFS_change / 20)) ================================================ FILE: NeuralSeq/data_gen/tts/emotion/inference.py ================================================ from data_gen.tts.emotion.params_data import * from data_gen.tts.emotion.model import EmotionEncoder from data_gen.tts.emotion.audio import preprocess_wav # We want to expose this function from here from matplotlib import cm from data_gen.tts.emotion import audio from pathlib import Path import matplotlib.pyplot as plt import numpy as np import torch _model = None # type: EmotionEncoder _device = None # type: torch.device def load_model(weights_fpath: Path, device=None): """ Loads the model in memory. If this function is not explicitely called, it will be run on the first call to embed_frames() with the default weights file. :param weights_fpath: the path to saved model weights. :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The model will be loaded and will run on this device. Outputs will however always be on the cpu. If None, will default to your GPU if it"s available, otherwise your CPU. """ # TODO: I think the slow loading of the encoder might have something to do with the device it # was saved on. Worth investigating. global _model, _device if device is None: _device = torch.device("cuda" if torch.cuda.is_available() else "cpu") elif isinstance(device, str): _device = torch.device(device) _model = EmotionEncoder(_device, torch.device("cpu")) checkpoint = torch.load(weights_fpath) _model.load_state_dict(checkpoint["model_state"]) _model.eval() print("Loaded encoder trained to step %d" % (checkpoint["step"])) def is_loaded(): return _model is not None def embed_frames_batch(frames_batch): """ Computes embeddings for a batch of mel spectrogram. :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape (batch_size, n_frames, n_channels) :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size) """ if _model is None: raise Exception("Model was not loaded. Call load_model() before inference.") frames = torch.from_numpy(frames_batch).to(_device) embed = _model.inference(frames).detach().cpu().numpy() return embed def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames, min_pad_coverage=0.75, overlap=0.5): """ Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain partial utterances of each. Both the waveform and the mel spectrogram slices are returned, so as to make each partial utterance waveform correspond to its spectrogram. This function assumes that the mel spectrogram parameters used are those defined in params_data.py. The returned ranges may be indexing further than the length of the waveform. It is recommended that you pad the waveform with zeros up to wave_slices[-1].stop. :param n_samples: the number of samples in the waveform :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial utterance :param min_pad_coverage: when reaching the last partial utterance, it may or may not have enough frames. If at least of are present, then the last partial utterance will be considered, as if we padded the audio. Otherwise, it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial utterance, this parameter is ignored so that the function always returns at least 1 slice. :param overlap: by how much the partial utterance should overlap. If set to 0, the partial utterances are entirely disjoint. :return: the waveform slices and mel spectrogram slices as lists of array slices. Index respectively the waveform and the mel spectrogram with these slices to obtain the partial utterances. """ assert 0 <= overlap < 1 assert 0 < min_pad_coverage <= 1 samples_per_frame = int((sampling_rate * mel_window_step / 1000)) n_frames = int(np.ceil((n_samples + 1) / samples_per_frame)) frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1) # Compute the slices wav_slices, mel_slices = [], [] steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1) for i in range(0, steps, frame_step): mel_range = np.array([i, i + partial_utterance_n_frames]) wav_range = mel_range * samples_per_frame mel_slices.append(slice(*mel_range)) wav_slices.append(slice(*wav_range)) # Evaluate whether extra padding is warranted or not last_wav_range = wav_slices[-1] coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start) if coverage < min_pad_coverage and len(mel_slices) > 1: mel_slices = mel_slices[:-1] wav_slices = wav_slices[:-1] return wav_slices, mel_slices def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs): """ Computes an embedding for a single utterance. # TODO: handle multiple wavs to benefit from batching on GPU :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32 :param using_partials: if True, then the utterance is split in partial utterances of frames and the utterance embedding is computed from their normalized average. If False, the utterance is instead computed from feeding the entire spectogram to the network. :param return_partials: if True, the partial embeddings will also be returned along with the wav slices that correspond to the partial embeddings. :param kwargs: additional arguments to compute_partial_splits() :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If is True, the partial utterances as a numpy array of float32 of shape (n_partials, model_embedding_size) and the wav partials as a list of slices will also be returned. If is simultaneously set to False, both these values will be None instead. """ # Process the entire utterance if not using partials if not using_partials: frames = audio.wav_to_mel_spectrogram(wav) embed = embed_frames_batch(frames[None, ...])[0] if return_partials: return embed, None, None return embed # Compute where to split the utterance into partials and pad if necessary wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs) max_wave_length = wave_slices[-1].stop if max_wave_length >= len(wav): wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant") # Split the utterance into partials frames = audio.wav_to_mel_spectrogram(wav) frames_batch = np.array([frames[s] for s in mel_slices]) partial_embeds = embed_frames_batch(frames_batch) # Compute the utterance embedding from the partial embeddings raw_embed = np.mean(partial_embeds, axis=0) embed = raw_embed / np.linalg.norm(raw_embed, 2) if return_partials: return embed, partial_embeds, wave_slices return embed def embed_speaker(wavs, **kwargs): raise NotImplemented() def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)): if ax is None: ax = plt.gca() if shape is None: height = int(np.sqrt(len(embed))) shape = (height, -1) embed = embed.reshape(shape) cmap = cm.get_cmap() mappable = ax.imshow(embed, cmap=cmap) cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04) cbar.set_clim(*color_range) ax.set_xticks([]), ax.set_yticks([]) ax.set_title(title) ================================================ FILE: NeuralSeq/data_gen/tts/emotion/model.py ================================================ from data_gen.tts.emotion.params_model import * from data_gen.tts.emotion.params_data import * from torch.nn.utils import clip_grad_norm_ from scipy.optimize import brentq from torch import nn import numpy as np import torch class EmotionEncoder(nn.Module): def __init__(self, device, loss_device): super().__init__() self.loss_device = loss_device # Network defition self.lstm = nn.LSTM(input_size=mel_n_channels, hidden_size=model_hidden_size, num_layers=model_num_layers, batch_first=True).to(device) self.linear = nn.Linear(in_features=model_hidden_size, out_features=model_embedding_size).to(device) self.relu = torch.nn.ReLU().to(device) # Cosine similarity scaling (with fixed initial parameter values) self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device) self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device) # Loss self.loss_fn = nn.CrossEntropyLoss().to(loss_device) def do_gradient_ops(self): # Gradient scale self.similarity_weight.grad *= 0.01 self.similarity_bias.grad *= 0.01 # Gradient clipping clip_grad_norm_(self.parameters(), 3, norm_type=2) def forward(self, utterances, hidden_init=None): """ Computes the embeddings of a batch of utterance spectrograms. :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape (batch_size, n_frames, n_channels) :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, batch_size, hidden_size). Will default to a tensor of zeros if None. :return: the embeddings as a tensor of shape (batch_size, embedding_size) """ # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state # and the final cell state. out, (hidden, cell) = self.lstm(utterances, hidden_init) # We take only the hidden state of the last layer embeds_raw = self.relu(self.linear(hidden[-1])) # L2-normalize it embeds = embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) return embeds def inference(self, utterances, hidden_init=None): """ Computes the embeddings of a batch of utterance spectrograms. :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape (batch_size, n_frames, n_channels) :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, batch_size, hidden_size). Will default to a tensor of zeros if None. :return: the embeddings as a tensor of shape (batch_size, embedding_size) """ # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state # and the final cell state. out, (hidden, cell) = self.lstm(utterances, hidden_init) return hidden[-1] ================================================ FILE: NeuralSeq/data_gen/tts/emotion/params_data.py ================================================ ## Mel-filterbank mel_window_length = 25 # In milliseconds mel_window_step = 10 # In milliseconds mel_n_channels = 40 ## Audio sampling_rate = 16000 # Number of spectrogram frames in a partial utterance partials_n_frames = 160 # 1600 ms # Number of spectrogram frames at inference inference_n_frames = 80 # 800 ms ## Voice Activation Detection # Window size of the VAD. Must be either 10, 20 or 30 milliseconds. # This sets the granularity of the VAD. Should not need to be changed. vad_window_length = 30 # In milliseconds # Number of frames to average together when performing the moving average smoothing. # The larger this value, the larger the VAD variations must be to not get smoothed out. vad_moving_average_width = 8 # Maximum number of consecutive silent frames a segment can have. vad_max_silence_length = 6 ## Audio volume normalization audio_norm_target_dBFS = -30 ================================================ FILE: NeuralSeq/data_gen/tts/emotion/params_model.py ================================================ ## Model parameters model_hidden_size = 256 model_embedding_size = 256 model_num_layers = 3 ## Training parameters learning_rate_init = 1e-4 speakers_per_batch = 6 utterances_per_speaker = 20 ================================================ FILE: NeuralSeq/data_gen/tts/emotion/test_emotion.py ================================================ #!/usr/bin/env python3 -u # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ Run inference for pre-processed data with a trained model. """ import logging import math import numpy, math, pdb, sys, random import time, os, itertools, shutil, importlib import argparse import os import sys import glob from sklearn import metrics import soundfile as sf #import sentencepiece as spm import torch import inference as encoder import torch.nn as nn import torch.nn.functional as F from pathlib import Path logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) from resemblyzer import VoiceEncoder, preprocess_wav def tuneThresholdfromScore(scores, labels, target_fa, target_fr=None): fpr, tpr, thresholds = metrics.roc_curve(labels, scores, pos_label=1) fnr = 1 - tpr fnr = fnr * 100 fpr = fpr * 100 tunedThreshold = []; if target_fr: for tfr in target_fr: idx = numpy.nanargmin(numpy.absolute((tfr - fnr))) tunedThreshold.append([thresholds[idx], fpr[idx], fnr[idx]]); for tfa in target_fa: idx = numpy.nanargmin(numpy.absolute((tfa - fpr))) # numpy.where(fpr<=tfa)[0][-1] tunedThreshold.append([thresholds[idx], fpr[idx], fnr[idx]]); idxE = numpy.nanargmin(numpy.absolute((fnr - fpr))) eer = max(fpr[idxE], fnr[idxE]) return (tunedThreshold, eer, fpr, fnr); def loadWAV(filename, max_frames, evalmode=True, num_eval=10): # Maximum audio length max_audio = max_frames * 160 + 240 # Read wav file and convert to torch tensor audio,sample_rate = sf.read(filename) feats_v0 = torch.from_numpy(audio).float() audiosize = audio.shape[0] if audiosize <= max_audio: shortage = math.floor((max_audio - audiosize + 1) / 2) audio = numpy.pad(audio, (shortage, shortage), 'constant', constant_values=0) audiosize = audio.shape[0] if evalmode: startframe = numpy.linspace(0, audiosize - max_audio, num=num_eval) else: startframe = numpy.array([numpy.int64(random.random() * (audiosize - max_audio))]) feats = [] if evalmode and max_frames == 0: feats.append(audio) else: for asf in startframe: feats.append(audio[int(asf):int(asf) + max_audio]) feat = numpy.stack(feats, axis=0) feat = torch.FloatTensor(feat) return feat; def evaluateFromList(listfilename, print_interval=100, test_path='', multi=False): lines = [] files = [] feats = {} tstart = time.time() ## Read all lines with open(listfilename) as listfile: while True: line = listfile.readline(); if (not line): break; data = line.split(); ## Append random label if missing if len(data) == 2: data = [random.randint(0,1)] + data files.append(data[1]) files.append(data[2]) lines.append(line) setfiles = list(set(files)) setfiles.sort() ## Save all features to file for idx, file in enumerate(setfiles): # preprocessed_wav = encoder.preprocess_wav(os.path.join(test_path,file)) # embed = encoder.embed_utterance(preprocessed_wav) processed_wav = preprocess_wav(os.path.join(test_path,file)) embed = voice_encoder.embed_utterance(processed_wav) torch.cuda.empty_cache() ref_feat = torch.from_numpy(embed).unsqueeze(0) feats[file] = ref_feat telapsed = time.time() - tstart if idx % print_interval == 0: sys.stdout.write("\rReading %d of %d: %.2f Hz, embedding size %d"%(idx,len(setfiles),idx/telapsed,ref_feat.size()[1])); print('') all_scores = []; all_labels = []; all_trials = []; tstart = time.time() ## Read files and compute all scores for idx, line in enumerate(lines): data = line.split(); ## Append random label if missing if len(data) == 2: data = [random.randint(0,1)] + data ref_feat = feats[data[1]] com_feat = feats[data[2]] ref_feat = ref_feat.cuda() com_feat = com_feat.cuda() # normalize feats ref_feat = F.normalize(ref_feat, p=2, dim=1) com_feat = F.normalize(com_feat, p=2, dim=1) dist = F.pairwise_distance(ref_feat.unsqueeze(-1), com_feat.unsqueeze(-1)).detach().cpu().numpy(); score = -1 * numpy.mean(dist); all_scores.append(score); all_labels.append(int(data[0])); all_trials.append(data[1]+" "+data[2]) if idx % print_interval == 0: telapsed = time.time() - tstart sys.stdout.write("\rComputing %d of %d: %.2f Hz"%(idx,len(lines),idx/telapsed)); sys.stdout.flush(); print('\n') return (all_scores, all_labels, all_trials); if __name__ == '__main__': parser = argparse.ArgumentParser("baseline") parser.add_argument("--data_root", type=str, help="", required=True) parser.add_argument("--list", type=str, help="", required=True) parser.add_argument("--model_dir", type=str, help="model parameters for AudioEncoder", required=True) args = parser.parse_args() # Load the models one by one. print("Preparing the encoder...") # encoder.load_model(Path(args.model_dir)) print("Insert the wav file name...") voice_encoder = VoiceEncoder().cuda() sc, lab, trials = evaluateFromList(args.list, print_interval=100, test_path=args.data_root) result = tuneThresholdfromScore(sc, lab, [1, 0.1]); print('EER %2.4f'%result[1]) ================================================ FILE: NeuralSeq/data_gen/tts/txt_processors/__init__.py ================================================ from . import en ================================================ FILE: NeuralSeq/data_gen/tts/txt_processors/base_text_processor.py ================================================ from data_gen.tts.data_gen_utils import is_sil_phoneme REGISTERED_TEXT_PROCESSORS = {} def register_txt_processors(name): def _f(cls): REGISTERED_TEXT_PROCESSORS[name] = cls return cls return _f def get_txt_processor_cls(name): return REGISTERED_TEXT_PROCESSORS.get(name, None) class BaseTxtProcessor: @staticmethod def sp_phonemes(): return ['|'] @classmethod def process(cls, txt, preprocess_args): raise NotImplementedError @classmethod def postprocess(cls, txt_struct, preprocess_args): # remove sil phoneme in head and tail while len(txt_struct) > 0 and is_sil_phoneme(txt_struct[0][0]): txt_struct = txt_struct[1:] while len(txt_struct) > 0 and is_sil_phoneme(txt_struct[-1][0]): txt_struct = txt_struct[:-1] if preprocess_args['with_phsep']: txt_struct = cls.add_bdr(txt_struct) if preprocess_args['add_eos_bos']: txt_struct = [["", [""]]] + txt_struct + [["", [""]]] return txt_struct @classmethod def add_bdr(cls, txt_struct): txt_struct_ = [] for i, ts in enumerate(txt_struct): txt_struct_.append(ts) if i != len(txt_struct) - 1 and \ not is_sil_phoneme(txt_struct[i][0]) and not is_sil_phoneme(txt_struct[i + 1][0]): txt_struct_.append(['|', ['|']]) return txt_struct_ ================================================ FILE: NeuralSeq/data_gen/tts/txt_processors/en.py ================================================ import re import unicodedata from g2p_en import G2p from g2p_en.expand import normalize_numbers from nltk import pos_tag from nltk.tokenize import TweetTokenizer from data_gen.tts.txt_processors.base_text_processor import BaseTxtProcessor, register_txt_processors from data_gen.tts.data_gen_utils import is_sil_phoneme, PUNCS class EnG2p(G2p): word_tokenize = TweetTokenizer().tokenize def __call__(self, text): # preprocessing words = EnG2p.word_tokenize(text) tokens = pos_tag(words) # tuples of (word, tag) # steps prons = [] for word, pos in tokens: if re.search("[a-z]", word) is None: pron = [word] elif word in self.homograph2features: # Check homograph pron1, pron2, pos1 = self.homograph2features[word] if pos.startswith(pos1): pron = pron1 else: pron = pron2 elif word in self.cmu: # lookup CMU dict pron = self.cmu[word][0] else: # predict for oov pron = self.predict(word) prons.extend(pron) prons.extend([" "]) return prons[:-1] @register_txt_processors('en') class TxtProcessor(BaseTxtProcessor): g2p = EnG2p() @staticmethod def preprocess_text(text): text = normalize_numbers(text) text = ''.join(char for char in unicodedata.normalize('NFD', text) if unicodedata.category(char) != 'Mn') # Strip accents text = text.lower() text = re.sub("[\'\"()]+", "", text) text = re.sub("[-]+", " ", text) text = re.sub(f"[^ a-z{PUNCS}]", "", text) text = re.sub(f" ?([{PUNCS}]) ?", r"\1", text) # !! -> ! text = re.sub(f"([{PUNCS}])+", r"\1", text) # !! -> ! text = text.replace("i.e.", "that is") text = text.replace("i.e.", "that is") text = text.replace("etc.", "etc") text = re.sub(f"([{PUNCS}])", r" \1 ", text) text = re.sub(rf"\s+", r" ", text) return text @classmethod def process(cls, txt, preprocess_args): txt = cls.preprocess_text(txt).strip() phs = cls.g2p(txt) txt_struct = [[w, []] for w in txt.split(" ")] i_word = 0 for p in phs: if p == ' ': i_word += 1 else: txt_struct[i_word][1].append(p) txt_struct = cls.postprocess(txt_struct, preprocess_args) return txt_struct, txt ================================================ FILE: NeuralSeq/data_gen/tts/txt_processors/zh.py ================================================ import re import jieba from pypinyin import pinyin, Style from data_gen.tts.data_gen_utils import PUNCS from data_gen.tts.txt_processors.base_text_processor import BaseTxtProcessor from utils.text_norm import NSWNormalizer class TxtProcessor(BaseTxtProcessor): table = {ord(f): ord(t) for f, t in zip( u'：，。！？【】（）％＃＠＆１２３４５６７８９０', u':,.!?[]()%#@&1234567890')} @staticmethod def preprocess_text(text): text = text.translate(TxtProcessor.table) text = NSWNormalizer(text).normalize(remove_punc=False) text = re.sub("[\'\"()]+", "", text) text = re.sub("[-]+", " ", text) text = re.sub(f"[^ A-Za-z\u4e00-\u9fff{PUNCS}]", "", text) text = re.sub(f"([{PUNCS}])+", r"\1", text) # !! -> ! text = re.sub(f"([{PUNCS}])", r" \1 ", text) text = re.sub(rf"\s+", r"", text) text = re.sub(rf"[A-Za-z]+", r"$", text) return text @classmethod def process(cls, txt, pre_align_args): txt = cls.preprocess_text(txt) shengmu = pinyin(txt, style=Style.INITIALS) # https://blog.csdn.net/zhoulei124/article/details/89055403 yunmu_finals = pinyin(txt, style=Style.FINALS) yunmu_tone3 = pinyin(txt, style=Style.FINALS_TONE3) yunmu = [[t[0] + '5'] if t[0] == f[0] else t for f, t in zip(yunmu_finals, yunmu_tone3)] \ if pre_align_args['use_tone'] else yunmu_finals assert len(shengmu) == len(yunmu) phs = ["|"] for a, b, c in zip(shengmu, yunmu, yunmu_finals): if a[0] == c[0]: phs += [a[0], "|"] else: phs += [a[0], b[0], "|"] return phs, txt ================================================ FILE: NeuralSeq/data_gen/tts/txt_processors/zh_g2pM.py ================================================ import re import jieba from pypinyin import pinyin, Style from data_gen.tts.data_gen_utils import PUNCS from data_gen.tts.txt_processors import zh from g2pM import G2pM ALL_SHENMU = ['zh', 'ch', 'sh', 'b', 'p', 'm', 'f', 'd', 't', 'n', 'l', 'g', 'k', 'h', 'j', 'q', 'x', 'r', 'z', 'c', 's', 'y', 'w'] ALL_YUNMU = ['a', 'ai', 'an', 'ang', 'ao', 'e', 'ei', 'en', 'eng', 'er', 'i', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'iu', 'ng', 'o', 'ong', 'ou', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn'] class TxtProcessor(zh.TxtProcessor): model = G2pM() @staticmethod def sp_phonemes(): return ['|', '#'] @classmethod def process(cls, txt, pre_align_args): txt = cls.preprocess_text(txt) ph_list = cls.model(txt, tone=pre_align_args['use_tone'], char_split=True) seg_list = '#'.join(jieba.cut(txt)) assert len(ph_list) == len([s for s in seg_list if s != '#']), (ph_list, seg_list) # 加入词边界'#' ph_list_ = [] seg_idx = 0 for p in ph_list: p = p.replace("u:", "v") if seg_list[seg_idx] == '#': ph_list_.append('#') seg_idx += 1 else: ph_list_.append("|") seg_idx += 1 if re.findall('[\u4e00-\u9fff]', p): if pre_align_args['use_tone']: p = pinyin(p, style=Style.TONE3, strict=True)[0][0] if p[-1] not in ['1', '2', '3', '4', '5']: p = p + '5' else: p = pinyin(p, style=Style.NORMAL, strict=True)[0][0] finished = False if len([c.isalpha() for c in p]) > 1: for shenmu in ALL_SHENMU: if p.startswith(shenmu) and not p.lstrip(shenmu).isnumeric(): ph_list_ += [shenmu, p.lstrip(shenmu)] finished = True break if not finished: ph_list_.append(p) ph_list = ph_list_ # 去除静音符号周围的词边界标记 [..., '#', ',', '#', ...] sil_phonemes = list(PUNCS) + TxtProcessor.sp_phonemes() ph_list_ = [] for i in range(0, len(ph_list), 1): if ph_list[i] != '#' or (ph_list[i - 1] not in sil_phonemes and ph_list[i + 1] not in sil_phonemes): ph_list_.append(ph_list[i]) ph_list = ph_list_ return ph_list, txt if __name__ == '__main__': phs, txt = TxtProcessor.process('他来到了，网易杭研大厦', {'use_tone': True}) print(phs) ================================================ FILE: NeuralSeq/data_gen/tts/wav_processors/__init__.py ================================================ from . import base_processor from . import common_processors ================================================ FILE: NeuralSeq/data_gen/tts/wav_processors/base_processor.py ================================================ REGISTERED_WAV_PROCESSORS = {} def register_wav_processors(name): def _f(cls): REGISTERED_WAV_PROCESSORS[name] = cls return cls return _f def get_wav_processor_cls(name): return REGISTERED_WAV_PROCESSORS.get(name, None) class BaseWavProcessor: @property def name(self): raise NotImplementedError def output_fn(self, input_fn): return f'{input_fn[:-4]}_{self.name}.wav' def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): raise NotImplementedError ================================================ FILE: NeuralSeq/data_gen/tts/wav_processors/common_processors.py ================================================ import os import subprocess import librosa import numpy as np from data_gen.tts.wav_processors.base_processor import BaseWavProcessor, register_wav_processors from data_gen.tts.data_gen_utils import trim_long_silences from utils.audio import save_wav, rnnoise from utils.hparams import hparams @register_wav_processors(name='sox_to_wav') class ConvertToWavProcessor(BaseWavProcessor): @property def name(self): return 'ToWav' def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): if input_fn[-4:] == '.wav': return input_fn, sr else: output_fn = self.output_fn(input_fn) subprocess.check_call(f'sox -v 0.95 "{input_fn}" -t wav "{output_fn}"', shell=True) return output_fn, sr @register_wav_processors(name='sox_resample') class ResampleProcessor(BaseWavProcessor): @property def name(self): return 'Resample' def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): output_fn = self.output_fn(input_fn) sr_file = librosa.core.get_samplerate(input_fn) if sr != sr_file: subprocess.check_call(f'sox -v 0.95 "{input_fn}" -r{sr} "{output_fn}"', shell=True) y, _ = librosa.core.load(input_fn, sr=sr) y, _ = librosa.effects.trim(y) save_wav(y, output_fn, sr) return output_fn, sr else: return input_fn, sr @register_wav_processors(name='trim_sil') class TrimSILProcessor(BaseWavProcessor): @property def name(self): return 'TrimSIL' def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): output_fn = self.output_fn(input_fn) y, _ = librosa.core.load(input_fn, sr=sr) y, _ = librosa.effects.trim(y) save_wav(y, output_fn, sr) return output_fn @register_wav_processors(name='trim_all_sil') class TrimAllSILProcessor(BaseWavProcessor): @property def name(self): return 'TrimSIL' def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): output_fn = self.output_fn(input_fn) y, audio_mask, _ = trim_long_silences( input_fn, vad_max_silence_length=preprocess_args.get('vad_max_silence_length', 12)) save_wav(y, output_fn, sr) if preprocess_args['save_sil_mask']: os.makedirs(f'{processed_dir}/sil_mask', exist_ok=True) np.save(f'{processed_dir}/sil_mask/{item_name}.npy', audio_mask) return output_fn, sr @register_wav_processors(name='denoise') class DenoiseProcessor(BaseWavProcessor): @property def name(self): return 'Denoise' def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): output_fn = self.output_fn(input_fn) rnnoise(input_fn, output_fn, out_sample_rate=sr) return output_fn, sr ================================================ FILE: NeuralSeq/egs/datasets/audio/emotion/base_text2mel.yaml ================================================ raw_data_dir: 'data/raw/ESD' processed_data_dir: 'data/processed/emotion' binary_data_dir: 'data/binary/emotion' pre_align_cls: egs.datasets.audio.emotion.pre_align.EmoPreAlign audio_sample_rate: 16000 binarization_args: shuffle: true binarizer_cls: data_gen.tts.base_binarizer_emotion.EmotionBinarizer use_spk_id: true test_num: 200 num_spk: 10 pitch_type: frame min_frames: 128 num_test_samples: 30 mel_loss: "ssim:0.5|l1:0.5" vocoder_ckpt: '' use_emotion: true ================================================ FILE: NeuralSeq/egs/datasets/audio/emotion/pre_align.py ================================================ import os from data_gen.tts.base_preprocess import BasePreprocessor import glob import re class EmoPreAlign(BasePreprocessor): def meta_data(self): spks = ['0012', '0011', '0013', '0014', '0015', '0016', '0017', '0018', '0019', '0020'] pattern = re.compile('[\t\n ]+') for spk in spks: for line in open(f"{self.raw_data_dir}/{spk}/{spk}.txt", 'r'): # 打开文件 line = re.sub(pattern, ' ', line) if line == ' ': continue split_ = line.split(' ') txt = ' '.join(split_[1: -2]) item_name = split_[0] emotion = split_[-2] wav_fn = f'{self.raw_data_dir}/{spk}/{emotion}/{item_name}.wav' yield item_name, wav_fn, txt, spk, emotion if __name__ == "__main__": EmoPreAlign().process() ================================================ FILE: NeuralSeq/egs/datasets/audio/libritts/base_text2mel.yaml ================================================ raw_data_dir: 'data/raw/LibriTTS' processed_data_dir: 'data/processed/libritts' binary_data_dir: 'data/binary/libritts' pre_align_cls: egs.datasets.audio.libritts.pre_align.LibrittsPreAlign binarization_args: shuffle: true use_spk_id: true test_num: 200 num_spk: 2320 pitch_type: frame min_frames: 128 num_test_samples: 30 mel_loss: "ssim:0.5|l1:0.5" vocoder_ckpt: '' ================================================ FILE: NeuralSeq/egs/datasets/audio/libritts/fs2.yaml ================================================ base_config: - egs/egs_bases/tts/fs2.yaml - ./base_text2mel.yaml ================================================ FILE: NeuralSeq/egs/datasets/audio/libritts/pre_align.py ================================================ import os from data_gen.tts.base_preprocess import BasePreprocessor import glob class LibrittsPreAlign(BasePreprocessor): def meta_data(self): wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*.wav')) for wav_fn in wav_fns: item_name = os.path.basename(wav_fn)[:-4] txt_fn = f'{wav_fn[:-4]}.normalized.txt' with open(txt_fn, 'r') as f: txt = f.readlines() f.close() spk = item_name.split("_")[0] # Example: # # 'item_name': '103_1241_000000_000001' # 'wav_fn': 'LibriTTS/train-clean-100/103/1241/103_1241_000000_000001.wav' # 'txt': 'matthew Cuthbert is surprised' # 'spk_name': '103' yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt[0], 'spk_name': spk} if __name__ == "__main__": LibrittsPreAlign().process() ================================================ FILE: NeuralSeq/egs/datasets/audio/libritts/pwg.yaml ================================================ base_config: egs/egs_bases/tts/vocoder/pwg.yaml raw_data_dir: 'data/raw/LibriTTS' processed_data_dir: 'data/processed/libritts' binary_data_dir: 'data/binary/libritts_wav' generator_params: kernel_size: 5 num_spk: 400 max_samples: 20480 ================================================ FILE: NeuralSeq/egs/datasets/audio/lj/base_mel2wav.yaml ================================================ raw_data_dir: 'data/raw/LJSpeech-1.1' processed_data_dir: 'data/processed/ljspeech' binary_data_dir: 'data/binary/ljspeech_wav' binarization_args: with_spk_embed: false ================================================ FILE: NeuralSeq/egs/datasets/audio/lj/preprocess.py ================================================ from data_gen.tts.base_preprocess import BasePreprocessor class LJPreprocess(BasePreprocessor): def meta_data(self): for l in open(f'{self.raw_data_dir}/metadata.csv').readlines(): item_name, _, txt = l.strip().split("|") wav_fn = f"{self.raw_data_dir}/wavs/{item_name}.wav" yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt} ================================================ FILE: NeuralSeq/egs/datasets/audio/lj/pwg.yaml ================================================ base_config: - egs/egs_bases/tts/vocoder/pwg.yaml - ./base_mel2wav.yaml ================================================ FILE: NeuralSeq/egs/datasets/audio/vctk/base_mel2wav.yaml ================================================ raw_data_dir: 'data/raw/VCTK-Corpus' processed_data_dir: 'data/processed/vctk' binary_data_dir: 'data/binary/vctk_wav' ================================================ FILE: NeuralSeq/egs/datasets/audio/vctk/fs2.yaml ================================================ base_config: - egs/egs_bases/tts/fs2.yaml raw_data_dir: 'data/raw/VCTK-Corpus' processed_data_dir: 'data/processed/vctk' binary_data_dir: 'data/binary/vctk' pre_align_cls: egs.datasets.audio.vctk.pre_align.VCTKPreAlign use_spk_id: true test_num: 200 num_spk: 400 binarization_args: shuffle: true trim_eos_bos: true ================================================ FILE: NeuralSeq/egs/datasets/audio/vctk/pre_align.py ================================================ import os from data_gen.tts.base_pre_align import BasePreAlign import glob class VCTKPreAlign(BasePreAlign): def meta_data(self): wav_fns = glob.glob(f'{self.raw_data_dir}/wav48/*/*.wav') for wav_fn in wav_fns: item_name = os.path.basename(wav_fn)[:-4] spk = item_name.split("_")[0] txt_fn = wav_fn.split("/") txt_fn[-1] = f'{item_name}.txt' txt_fn[-3] = f'txt' txt_fn = "/".join(txt_fn) if os.path.exists(txt_fn) and os.path.exists(wav_fn): yield item_name, wav_fn, (self.load_txt, txt_fn), spk if __name__ == "__main__": VCTKPreAlign().process() ================================================ FILE: NeuralSeq/egs/datasets/audio/vctk/pwg.yaml ================================================ base_config: - egs/egs_bases/tts/vocoder/pwg.yaml - ./base_mel2wav.yaml num_spk: 400 max_samples: 20480 ================================================ FILE: NeuralSeq/egs/egs_bases/config_base.yaml ================================================ # task binary_data_dir: '' work_dir: '' # experiment directory. infer: false # inference amp: false seed: 1234 debug: false save_codes: [] # - configs # - modules # - tasks # - utils # - usr ############# # dataset ############# ds_workers: 1 test_num: 100 endless_ds: false sort_by_len: true ######### # train and eval ######### print_nan_grads: false load_ckpt: '' save_best: true num_ckpt_keep: 3 clip_grad_norm: 0 accumulate_grad_batches: 1 tb_log_interval: 100 num_sanity_val_steps: 5 # steps of validation at the beginning check_val_every_n_epoch: 10 val_check_interval: 2000 valid_monitor_key: 'val_loss' valid_monitor_mode: 'min' max_epochs: 1000 max_updates: 1000000 max_tokens: 31250 max_sentences: 100000 max_valid_tokens: -1 max_valid_sentences: -1 test_input_dir: '' resume_from_checkpoint: 0 rename_tmux: true ================================================ FILE: NeuralSeq/egs/egs_bases/svs/base.yaml ================================================ task_cls: tasks.svs.task.DiffFsTask pitch_type: frame timesteps: 100 dilation_cycle_length: 1 residual_layers: 20 residual_channels: 256 lr: 0.001 decay_steps: 50000 keep_bins: 80 spec_min: [ ] spec_max: [ ] content_cond_steps: [ ] # [ 0, 10000 ] spk_cond_steps: [ ] # [ 0, 10000 ] # train and eval fs2_ckpt: '' max_updates: 400000 # max_updates: 200000 use_gt_dur: true use_gt_f0: true gen_tgt_spk_id: -1 max_sentences: 48 num_sanity_val_steps: 1 num_valid_plots: 1 ================================================ FILE: NeuralSeq/egs/egs_bases/svs/lj_ds_beta6.yaml ================================================ base_config: - configs/tts/lj/fs2.yaml - ./base.yaml # spec_min and spec_max are calculated on the training set. spec_min: [ -4.7574, -4.6783, -4.6431, -4.5832, -4.5390, -4.6771, -4.8089, -4.7672, -4.5784, -4.7755, -4.7150, -4.8919, -4.8271, -4.7389, -4.6047, -4.7759, -4.6799, -4.8201, -4.7823, -4.8262, -4.7857, -4.7545, -4.9358, -4.9733, -5.1134, -5.1395, -4.9016, -4.8434, -5.0189, -4.8460, -5.0529, -4.9510, -5.0217, -5.0049, -5.1831, -5.1445, -5.1015, -5.0281, -4.9887, -4.9916, -4.9785, -4.9071, -4.9488, -5.0342, -4.9332, -5.0650, -4.8924, -5.0875, -5.0483, -5.0848, -5.1809, -5.0677, -5.0015, -5.0792, -5.0636, -5.2413, -5.1421, -5.1710, -5.3256, -5.0511, -5.1186, -5.0057, -5.0446, -5.1173, -5.0325, -5.1085, -5.0053, -5.0755, -5.1176, -5.1004, -5.2153, -5.2757, -5.3025, -5.2867, -5.2918, -5.3328, -5.2731, -5.2985, -5.2400, -5.2211 ] spec_max: [ -0.5982, -0.0778, 0.1205, 0.2747, 0.4657, 0.5123, 0.5684, 0.7093, 0.6461, 0.6420, 0.7316, 0.7715, 0.7681, 0.8349, 0.7815, 0.7591, 0.7910, 0.7433, 0.7352, 0.6869, 0.6854, 0.6623, 0.5353, 0.6492, 0.6909, 0.6106, 0.5761, 0.5936, 0.5638, 0.4054, 0.4545, 0.3589, 0.3037, 0.3380, 0.1599, 0.2433, 0.2741, 0.2130, 0.1569, 0.1911, 0.2324, 0.1586, 0.1221, 0.0341, -0.0558, 0.0553, -0.1153, -0.0933, -0.1171, -0.0050, -0.1519, -0.1629, -0.0522, -0.0739, -0.2069, -0.2405, -0.1244, -0.2116, -0.1361, -0.1575, -0.1442, 0.0513, -0.1567, -0.2000, 0.0086, -0.0698, 0.1385, 0.0941, 0.1864, 0.1225, 0.2176, 0.2566, 0.1670, 0.1007, 0.1444, 0.0888, 0.1998, 0.2414, 0.2932, 0.3047 ] task_cls: tasks.svs.diffspeech_task.DiffSpeechTask vocoder: vocoders.hifigan.HifiGAN vocoder_ckpt: checkpoints/0414_hifi_lj_1 num_valid_plots: 10 use_gt_dur: false use_gt_f0: false pitch_type: cwt pitch_extractor: 'parselmouth' max_updates: 160000 lr: 0.001 timesteps: 100 K_step: 71 diff_loss_type: l1 diff_decoder_type: 'wavenet' schedule_type: 'linear' max_beta: 0.06 fs2_ckpt: checkpoints/fs2_lj_1/model_ckpt_steps_150000.ckpt save_gt: true ================================================ FILE: NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/aux_rel.yaml ================================================ base_config: - configs/singing/fs2.yaml - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml audio_sample_rate: 24000 hop_size: 128 # Hop size. fft_size: 512 # FFT size. win_size: 512 # FFT size. fmin: 30 fmax: 12000 min_level_db: -120 binarization_args: with_wav: true with_spk_embed: false with_align: true raw_data_dir: 'data/raw/opencpop/segments' processed_data_dir: 'xxx' binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer binary_data_dir: 'data/binary/opencpop-midi-dp' use_midi: true # for midi exp use_gt_f0: false # for midi exp use_gt_dur: false # for further midi exp lambda_f0: 1.0 lambda_uv: 1.0 #lambda_energy: 0.1 lambda_ph_dur: 1.0 lambda_sent_dur: 1.0 lambda_word_dur: 1.0 predictor_grad: 0.1 pe_enable: false pe_ckpt: '' num_spk: 1 test_prefixes: [ '2044', '2086', '2092', '2093', '2100', ] task_cls: tasks.svs.diffsinger_task.AuxDecoderMIDITask #vocoder: tasks.svs.singingvocoder.highgan.HighGAN #vocoder_ckpt: checkpoints/h_2_model/checkpoint-530000steps.pkl vocoder: vocoders.hifigan.HifiGAN vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128 use_nsf: true # config for experiments max_frames: 5000 max_tokens: 40000 predictor_layers: 5 rel_pos: true dur_predictor_layers: 5 # * use_spk_embed: false num_valid_plots: 10 max_updates: 160000 save_gt: true ================================================ FILE: NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/ds60_rel.yaml ================================================ base_config: - egs/egs_bases/svs/popcs_ds_beta6.yaml - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer binary_data_dir: 'data/binary/opencpop-midi-dp' #switch_midi2f0_step: 174000 use_midi: true # for midi exp use_gt_f0: false # for midi exp use_gt_dur: false # for further midi exp lambda_f0: 1.0 lambda_uv: 1.0 #lambda_energy: 0.1 lambda_ph_dur: 1.0 lambda_sent_dur: 1.0 lambda_word_dur: 1.0 predictor_grad: 0.1 pe_enable: false pe_ckpt: '' fs2_ckpt: 'checkpoints/0302_opencpop_fs_midi/model_ckpt_steps_160000.ckpt' # #num_valid_plots: 0 task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask K_step: 60 max_tokens: 36000 predictor_layers: 5 dilation_cycle_length: 4 # * rel_pos: true dur_predictor_layers: 5 # * max_updates: 160000 gaussian_start: false mask_uv_prob: 0.15 ================================================ FILE: NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml ================================================ spec_min: [-6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6.] spec_max: [-7.9453e-01, -8.1116e-01, -6.1631e-01, -3.0679e-01, -1.3863e-01, -5.0652e-02, -1.1563e-01, -1.0679e-01, -9.1068e-02, -6.2174e-02, -7.5302e-02, -7.2217e-02, -6.3815e-02, -7.3299e-02, 7.3610e-03, -7.2508e-02, -5.0234e-02, -1.6534e-01, -2.6928e-01, -2.0782e-01, -2.0823e-01, -1.1702e-01, -7.0128e-02, -6.5868e-02, -1.2675e-02, 1.5121e-03, -8.9902e-02, -2.1392e-01, -2.3789e-01, -2.8922e-01, -3.0405e-01, -2.3029e-01, -2.2088e-01, -2.1542e-01, -2.9367e-01, -3.0137e-01, -3.8281e-01, -4.3590e-01, -2.8681e-01, -4.6855e-01, -5.7485e-01, -4.7022e-01, -5.4266e-01, -4.4848e-01, -6.4120e-01, -6.8700e-01, -6.4860e-01, -7.6436e-01, -4.9971e-01, -7.1068e-01, -6.9724e-01, -6.1487e-01, -5.5843e-01, -6.9773e-01, -5.7502e-01, -7.0919e-01, -8.2431e-01, -8.4213e-01, -9.0431e-01, -8.2840e-01, -7.7945e-01, -8.2758e-01, -8.7699e-01, -1.0532e+00, -1.0766e+00, -1.1198e+00, -1.0185e+00, -9.8983e-01, -1.0001e+00, -1.0756e+00, -1.0024e+00, -1.0304e+00, -1.0579e+00, -1.0188e+00, -1.0500e+00, -1.0842e+00, -1.0923e+00, -1.1223e+00, -1.2381e+00, -1.6467e+00] mel_vmin: -6. #-6. mel_vmax: 1.5 wav2spec_eps: 1e-6 raw_data_dir: 'data/raw/opencpop/segments' processed_data_dir: 'xxx' binary_data_dir: 'data/binary/opencpop-midi-dp' datasets: [ 'opencpop', ] test_prefixes: [ '2044', '2086', '2092', '2093', '2100', ] ================================================ FILE: NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000-10dil.yaml ================================================ base_config: - egs/egs_bases/svs/popcs_ds_beta6.yaml - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer binary_data_dir: 'data/binary/opencpop-midi-dp' #switch_midi2f0_step: 174000 use_midi: true # for midi exp use_gt_dur: false # for further midi exp lambda_ph_dur: 1.0 lambda_sent_dur: 1.0 lambda_word_dur: 1.0 predictor_grad: 0.1 dur_predictor_layers: 5 # * fs2_ckpt: '' # #num_valid_plots: 0 task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask timesteps: 1000 K_step: 1000 max_beta: 0.02 max_tokens: 36000 max_updates: 320000 gaussian_start: True use_pitch_embed: false use_gt_f0: false # for midi exp lambda_f0: 0. lambda_uv: 0. dilation_cycle_length: 10 # * rel_pos: true predictor_layers: 5 pe_enable: true pe_ckpt: 'checkpoints/0102_xiaoma_pe' ================================================ FILE: NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml ================================================ base_config: - egs/egs_bases/svs/popcs_ds_beta6.yaml - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer binary_data_dir: 'data/binary/opencpop-midi-dp' #switch_midi2f0_step: 174000 use_midi: true # for midi exp use_gt_dur: false # for further midi exp lambda_ph_dur: 1.0 lambda_sent_dur: 1.0 lambda_word_dur: 1.0 predictor_grad: 0.1 dur_predictor_layers: 5 # * fs2_ckpt: '' # #num_valid_plots: 0 task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask # for diffusion schedule timesteps: 1000 K_step: 1000 max_beta: 0.02 max_tokens: 36000 max_updates: 320000 gaussian_start: True pndm_speedup: 10 use_pitch_embed: false use_gt_f0: false # for midi exp lambda_f0: 0. lambda_uv: 0. dilation_cycle_length: 4 # * rel_pos: true predictor_layers: 5 pe_enable: true pe_ckpt: 'checkpoints/0102_xiaoma_pe' ================================================ FILE: NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds100_adj_rel.yaml ================================================ base_config: - egs/egs_bases/svs/popcs_ds_beta6.yaml - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer binary_data_dir: 'data/binary/opencpop-midi-dp' #switch_midi2f0_step: 174000 use_midi: true # for midi exp use_gt_dur: false # for further midi exp lambda_ph_dur: 1.0 lambda_sent_dur: 1.0 lambda_word_dur: 1.0 predictor_grad: 0.1 dur_predictor_layers: 5 # * fs2_ckpt: '' # #num_valid_plots: 0 task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask K_step: 100 max_tokens: 36000 max_updates: 160000 gaussian_start: True use_pitch_embed: false use_gt_f0: false # for midi exp lambda_f0: 0. lambda_uv: 0. dilation_cycle_length: 4 # * rel_pos: true predictor_layers: 5 pe_enable: true pe_ckpt: 'checkpoints/0102_xiaoma_pe' ================================================ FILE: NeuralSeq/egs/egs_bases/svs/midi/e2e/popcs/ds100_adj_rel.yaml ================================================ base_config: - egs/egs_bases/svs/popcs_ds_beta6.yaml - egs/egs_bases/svs/midi/cascade/popcs/popcs_statis.yaml binarizer_cls: data_gen.singing.binarize.MidiSingingBinarizer binary_data_dir: 'data/binary/popcs-midi-dp' #switch_midi2f0_step: 174000 use_midi: true # for midi exp use_gt_dur: false # for further midi exp lambda_ph_dur: 1.0 lambda_sent_dur: 1.0 lambda_word_dur: 1.0 predictor_grad: 0.1 dur_predictor_layers: 5 # * fs2_ckpt: '' # #num_valid_plots: 0 task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask K_step: 100 max_tokens: 40000 max_updates: 160000 gaussian_start: True use_pitch_embed: false use_gt_f0: false # for midi exp lambda_f0: 0. lambda_uv: 0. dilation_cycle_length: 4 # * rel_pos: true predictor_layers: 5 pe_enable: true pe_ckpt: 'checkpoints/0102_xiaoma_pe' ================================================ FILE: NeuralSeq/egs/egs_bases/svs/midi/pe.yaml ================================================ base_config: - configs/tts/lj/fs2.yaml max_frames: 8000 audio_sample_rate: 24000 hop_size: 128 # Hop size. fft_size: 512 # FFT size. win_size: 512 # FFT size. fmin: 30 fmax: 12000 min_level_db: -120 binary_data_dir: 'xxx' pitch_type: frame task_cls: tasks.tts.pe.PitchExtractionTask pitch_extractor_conv_layers: 2 # config for experiments max_tokens: 20000 use_spk_embed: false num_valid_plots: 10 max_updates: 60000 ================================================ FILE: NeuralSeq/egs/egs_bases/svs/popcs_ds_beta6.yaml ================================================ base_config: - configs/tts/fs2.yaml - configs/singing/base.yaml - ./base.yaml audio_sample_rate: 24000 hop_size: 128 # Hop size. fft_size: 512 # FFT size. win_size: 512 # FFT size. fmin: 30 fmax: 12000 min_level_db: -120 binarization_args: with_wav: true with_spk_embed: false with_align: true raw_data_dir: 'data/raw/popcs' processed_data_dir: 'data/processed/popcs' binary_data_dir: 'data/binary/popcs-pmf0' num_spk: 1 datasets: [ 'popcs', ] test_prefixes: [ 'popcs-说散就散', 'popcs-隐形的翅膀', ] spec_min: [-6.8276, -7.0270, -6.8142, -7.1429, -7.6669, -7.6000, -7.1148, -6.9640, -6.8414, -6.6596, -6.6880, -6.7439, -6.7986, -7.4940, -7.7845, -7.6586, -6.9288, -6.7639, -6.9118, -6.8246, -6.7183, -7.1769, -6.9794, -7.4513, -7.3422, -7.5623, -6.9610, -6.8158, -6.9595, -6.8403, -6.5688, -6.6356, -7.0209, -6.5002, -6.7819, -6.5232, -6.6927, -6.5701, -6.5531, -6.7069, -6.6462, -6.4523, -6.5954, -6.4264, -6.4487, -6.7070, -6.4025, -6.3042, -6.4008, -6.3857, -6.3903, -6.3094, -6.2491, -6.3518, -6.3566, -6.4168, -6.2481, -6.3624, -6.2858, -6.2575, -6.3638, -6.4520, -6.1835, -6.2754, -6.1253, -6.1645, -6.0638, -6.1262, -6.0710, -6.1039, -6.4428, -6.1363, -6.1054, -6.1252, -6.1797, -6.0235, -6.0758, -5.9453, -6.0213, -6.0446] spec_max: [ 0.2645, 0.0583, -0.2344, -0.0184, 0.1227, 0.1533, 0.1103, 0.1212, 0.2421, 0.1809, 0.2134, 0.3161, 0.3301, 0.3289, 0.2667, 0.2421, 0.2581, 0.2600, 0.1394, 0.1907, 0.1082, 0.1474, 0.1680, 0.2550, 0.1057, 0.0826, 0.0423, 0.1203, -0.0701, -0.0056, 0.0477, -0.0639, -0.0272, -0.0728, -0.1648, -0.0855, -0.2652, -0.1998, -0.1547, -0.2167, -0.4181, -0.5463, -0.4161, -0.4733, -0.6518, -0.5387, -0.4290, -0.4191, -0.4151, -0.3042, -0.3810, -0.4160, -0.4496, -0.2847, -0.4676, -0.4658, -0.4931, -0.4885, -0.5547, -0.5481, -0.6948, -0.7968, -0.8455, -0.8392, -0.8770, -0.9520, -0.8749, -0.7297, -0.8374, -0.8667, -0.7157, -0.9035, -0.9219, -0.8801, -0.9298, -0.9009, -0.9604, -1.0537, -1.0781, -1.3766] task_cls: tasks.svs.diffsinger_task.DiffSingerTask #vocoder: tasks.svs.singingvocoder.highgan.HighGAN #vocoder_ckpt: checkpoints/h_2_model/checkpoint-530000steps.pkl vocoder: vocoders.hifigan.HifiGAN vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128 pitch_extractor: 'parselmouth' # config for experiments use_spk_embed: false num_valid_plots: 10 max_updates: 160000 lr: 0.001 timesteps: 100 K_step: 51 diff_loss_type: l1 diff_decoder_type: 'wavenet' schedule_type: 'linear' max_beta: 0.06 fs2_ckpt: '' use_nsf: true ================================================ FILE: NeuralSeq/egs/egs_bases/svs/popcs_ds_beta6_offline.yaml ================================================ base_config: - ./popcs_ds_beta6.yaml fs2_ckpt: checkpoints/popcs_fs2_pmf0_1230/model_ckpt_steps_160000.ckpt # to be infer num_valid_plots: 0 task_cls: tasks.svs.diffsinger_task.DiffSingerOfflineTask # tmp: #pe_enable: true #pe_ckpt: '' vocoder: vocoders.hifigan.HifiGAN vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128 ================================================ FILE: NeuralSeq/egs/egs_bases/svs/popcs_fs2.yaml ================================================ base_config: - configs/singing/fs2.yaml audio_sample_rate: 24000 hop_size: 128 # Hop size. fft_size: 512 # FFT size. win_size: 512 # FFT size. fmin: 30 fmax: 12000 min_level_db: -120 binarization_args: with_wav: true with_spk_embed: false with_align: true raw_data_dir: 'data/raw/popcs' processed_data_dir: 'data/processed/popcs' binary_data_dir: 'data/binary/popcs-pmf0' num_spk: 1 datasets: [ 'popcs', ] test_prefixes: [ 'popcs-说散就散', 'popcs-隐形的翅膀', ] task_cls: tasks.tts.fs2.FastSpeech2Task #vocoder: tasks.svs.singingvocoder.highgan.HighGAN #vocoder_ckpt: checkpoints/h_2_model/checkpoint-530000steps.pkl vocoder: vocoders.hifigan.HifiGAN vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128 use_nsf: true # config for experiments max_tokens: 18000 use_spk_embed: false num_valid_plots: 10 max_updates: 160000 save_gt: true # tmp: #pe_enable: true #pe_ckpt: '' ================================================ FILE: NeuralSeq/egs/egs_bases/tts/base.yaml ================================================ # task base_config: ../config_base.yaml task_cls: '' ############# # dataset ############# raw_data_dir: '' processed_data_dir: '' binary_data_dir: '' dict_dir: '' pre_align_cls: '' binarizer_cls: data_gen.tts.base_binarizer.BaseBinarizer pre_align_args: txt_processor: en use_tone: true # for ZH sox_resample: false sox_to_wav: false allow_no_txt: false trim_sil: false denoise: false binarization_args: shuffle: false with_txt: true with_wav: false with_align: true with_spk_embed: false with_spk_id: true with_f0: true with_f0cwt: false with_linear: false with_word: true trim_sil: false trim_eos_bos: false reset_phone_dict: true reset_word_dict: true word_size: 30000 pitch_extractor: parselmouth loud_norm: false endless_ds: true test_num: 100 min_frames: 0 max_frames: 1548 frames_multiple: 1 max_input_tokens: 1550 audio_num_mel_bins: 80 audio_sample_rate: 22050 hop_size: 256 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) win_size: 1024 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) fmin: 80 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) fmax: 7600 # To be increased/reduced depending on data. fft_size: 1024 # Extra window size is filled with 0 paddings to match this parameter min_level_db: -100 ref_level_db: 20 griffin_lim_iters: 60 num_spk: 1 mel_vmin: -6 mel_vmax: 1.5 ds_workers: 1 ######### # model ######### dropout: 0.1 enc_layers: 4 dec_layers: 4 hidden_size: 256 num_heads: 2 enc_ffn_kernel_size: 9 dec_ffn_kernel_size: 9 ffn_act: gelu ffn_padding: 'SAME' use_spk_id: false use_split_spk_id: false use_spk_embed: false ########### # optimization ########### lr: 2.0 scheduler: rsqrt # rsqrt|none warmup_updates: 8000 optimizer_adam_beta1: 0.9 optimizer_adam_beta2: 0.98 weight_decay: 0 clip_grad_norm: 1 clip_grad_value: 0 ########### # train and eval ########### max_tokens: 30000 max_sentences: 100000 max_valid_sentences: 1 max_valid_tokens: 60000 valid_infer_interval: 10000 train_set_name: 'train' train_sets: '' valid_set_name: 'valid' test_set_name: 'test' num_test_samples: 0 num_valid_plots: 10 test_ids: [ ] vocoder_denoise_c: 0.0 profile_infer: false out_wav_norm: false save_gt: true save_f0: false gen_dir_name: '' ================================================ FILE: NeuralSeq/egs/egs_bases/tts/base_zh.yaml ================================================ base_config: ./base.yaml preprocess_args: txt_processor: zh use_tone: true word_size: 3000 ================================================ FILE: NeuralSeq/egs/egs_bases/tts/fs2.yaml ================================================ base_config: ./base.yaml task_cls: tasks.tts.fs2.FastSpeech2Task # model hidden_size: 256 dropout: 0.1 encoder_type: fft # rel_fft|fft|tacotron|tacotron2|conformer decoder_type: fft # fft|rnn|conv|conformer|wn # rnn enc/dec encoder_K: 8 decoder_rnn_dim: 0 # for rnn decoder, 0 -> hidden_size * 2 # fft enc/dec use_pos_embed: true dec_num_heads: 2 dec_layers: 4 ffn_hidden_size: 1024 enc_ffn_kernel_size: 9 dec_ffn_kernel_size: 9 # conv enc/dec enc_dec_norm: ln conv_use_pos: false layers_in_block: 2 enc_dilations: [ 1, 1, 1, 1 ] enc_kernel_size: 5 dec_dilations: [ 1, 1, 1, 1 ] # for conv decoder dec_kernel_size: 5 dur_loss: mse # huber|mol # duration predictor_hidden: -1 predictor_kernel: 5 predictor_layers: 2 dur_predictor_kernel: 3 dur_predictor_layers: 2 predictor_dropout: 0.5 # pitch and energy pitch_norm: standard # standard|log use_pitch_embed: true pitch_type: frame # frame|ph|cwt use_uv: true cwt_hidden_size: 128 cwt_layers: 2 cwt_loss: l1 cwt_add_f0_loss: false cwt_std_scale: 0.8 pitch_ar: false pitch_embed_type: 0 pitch_loss: 'l1' # l1|l2|ssim pitch_ssim_win: 11 use_energy_embed: false # reference encoder and speaker embedding use_ref_enc: false use_var_enc: false lambda_commit: 0.25 var_enc_vq_codes: 64 ref_norm_layer: bn dec_inp_add_noise: false sil_add_noise: false ref_hidden_stride_kernel: - 0,3,5 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size - 0,3,5 - 0,2,5 - 0,2,5 - 0,2,5 pitch_enc_hidden_stride_kernel: - 0,2,5 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size - 0,2,5 - 0,2,5 dur_enc_hidden_stride_kernel: - 0,2,3 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size - 0,2,3 - 0,1,3 # mel mel_loss: l1:0.5|ssim:0.5 # l1|l2|gdl|ssim or l1:0.5|ssim:0.5 # loss lambda lambda_f0: 1.0 lambda_uv: 1.0 lambda_energy: 0.1 lambda_ph_dur: 0.1 lambda_sent_dur: 1.0 lambda_word_dur: 1.0 predictor_grad: 0.1 # train and eval pretrain_fs_ckpt: '' warmup_updates: 2000 max_tokens: 32000 max_sentences: 100000 max_valid_sentences: 1 max_updates: 120000 use_gt_dur: false use_gt_f0: false ds_workers: 2 lr: 1.0 ================================================ FILE: NeuralSeq/egs/egs_bases/tts/fs2_adv.yaml ================================================ base_config: ./fs2.yaml task_cls: tasks.tts.fs2_adv.FastSpeech2AdvTask disc_win_num: 3 disc_interval: 1 disc_reduction: stack # stack|sum|none disc_start_steps: 0 rerun_gen: false disc_norm: in mel_disc_hidden_size: 128 # mel decoder mel_gan: true lambda_mel_adv: 0.1 mel_hidden_size: 256 # others dropout: 0.05 pitch_embed_type: 0 enc_ffn_kernel_size: 9 dec_ffn_kernel_size: 9 use_cond_disc: false optimizer_adam_beta1: 0.5 optimizer_adam_beta2: 0.999 generator_grad_norm: 5.0 # Generator's gradient norm. disc_hidden_size: 128 disc_lr: 0.0001 # Discriminator's learning rate. discriminator_optimizer_params: eps: 1.0e-6 # Discriminator's epsilon. weight_decay: 0.0 # Discriminator's weight decay coefficient. discriminator_scheduler_params: step_size: 60000 # Discriminator's scheduler step size. gamma: 0.5 # D5iscriminator's scheduler gamma. # At each step size, lr will be multiplied by this parameter. discriminator_grad_norm: 1 # Discriminator's gradient norm. max_updates: 400000 max_tokens: 30000 max_sentences: 80 val_check_interval: 2000 gen_dir_name: '' num_ckpt_keep: 2 save_best: false ================================================ FILE: NeuralSeq/egs/egs_bases/tts/ps.yaml ================================================ base_config: ./fs2.yaml ########################### # models ########################### # encoders hidden_size: 192 ffn_hidden_size: 768 enc_ffn_kernel_size: 5 enc_layers: 4 dur_level: word encoder_type: rel_fft use_word_encoder: true # mix ling encoder word_enc_layers: 4 word_encoder_type: rel_fft use_pitch_embed: false enc_prenet: true enc_pre_ln: true text_encoder_postnet: true dropout: 0.0 add_word_pos: true # dur predictor dur_predictor_layers: 3 dur_predictor_kernel: 5 predictor_dropout: 0.2 ## fvae use_fvae: true latent_size: 16 fvae_encoder_type: conv fvae_decoder_type: conv fvae_enc_dec_hidden: 192 fvae_kernel_size: 5 fvae_enc_n_layers: 8 fvae_dec_n_layers: 4 fvae_strides: 4 fvae_noise_scale: 1.0 # prior flow use_prior_flow: true prior_flow_hidden: 64 prior_flow_kernel_size: 3 prior_flow_n_blocks: 4 ########################### # training and inference ########################### lambda_kl: 1.0 kl_min: 0.0 lambda_sent_dur: 0.0 kl_start_steps: 10000 posterior_start_steps: 0 frames_multiple: 4 num_valid_plots: 10 lr: 0.0002 warmup_updates: 8000 max_tokens: 40000 valid_infer_interval: 10000 max_sentences: 80 max_updates: 480000 ================================================ FILE: NeuralSeq/egs/egs_bases/tts/ps_flow.yaml ================================================ base_config: ./ps2.yaml task_cls: tasks.tts.ps_flow.PortaSpeechFlowTask use_post_flow: true detach_postflow_input: true post_flow_lr: 0.001 post_glow_hidden: 192 post_glow_kernel_size: 3 post_glow_n_blocks: 12 post_glow_n_block_layers: 3 post_share_cond_layers: false share_wn_layers: 4 use_cond_proj: false use_latent_cond: false use_txt_cond: true sigmoid_scale: false post_glow_training_start: 160000 noise_scale: 0.8 infer_post_glow: true two_stage: true ================================================ FILE: NeuralSeq/egs/egs_bases/tts/ps_flow_small.yaml ================================================ base_config: ./ps_flow.yaml ########################### # models ########################### # encoders hidden_size: 128 ffn_hidden_size: 512 enc_ffn_kernel_size: 3 enc_layers: 3 word_enc_layers: 3 # dur predictor dur_predictor_layers: 3 dur_predictor_kernel: 5 predictor_dropout: 0.2 ## fvae use_fvae: true latent_size: 16 fvae_encoder_type: wn fvae_decoder_type: wn fvae_enc_dec_hidden: 128 fvae_kernel_size: 3 fvae_enc_n_layers: 8 fvae_dec_n_layers: 3 fvae_strides: 4 fvae_noise_scale: 1.0 # prior flow use_prior_flow: true prior_flow_hidden: 32 prior_flow_kernel_size: 3 prior_flow_n_blocks: 3 # post flow post_glow_hidden: 128 post_glow_kernel_size: 3 post_glow_n_blocks: 8 post_glow_n_block_layers: 3 share_wn_layers: 4 noise_scale: 0.6 ================================================ FILE: NeuralSeq/egs/egs_bases/tts/vocoder/base.yaml ================================================ base_config: ../base.yaml binarization_args: with_wav: true with_spk_embed: false with_align: false with_word: false with_txt: false ########### # train and eval ########### max_samples: 25600 max_sentences: 5 max_valid_sentences: 1 max_updates: 1000000 val_check_interval: 2000 ########################################################### # FEATURE EXTRACTION SETTING # ########################################################### fft_size: 1024 # FFT size. hop_size: 256 # Hop size. win_length: null # Window length. # If set to null, it will be the same as fft_size. window: "hann" # Window function. num_mels: 80 # Number of mel basis. fmin: 80 # Minimum freq in mel basis calculation. fmax: 7600 # Maximum frequency in mel basis calculation. aux_context_window: 0 # Context window size for auxiliary feature. use_pitch_embed: false generator_grad_norm: 10 # Generator's gradient norm. discriminator_grad_norm: 1 # Discriminator's gradient norm. disc_start_steps: 40000 # Number of steps to start to train discriminator. ================================================ FILE: NeuralSeq/egs/egs_bases/tts/vocoder/hifigan.yaml ================================================ base_config: ./base.yaml task_cls: tasks.vocoder.hifigan.HifiGanTask resblock: "1" adam_b1: 0.8 adam_b2: 0.99 upsample_rates: [ 8,8,2,2 ] upsample_kernel_sizes: [ 16,16,4,4 ] upsample_initial_channel: 512 resblock_kernel_sizes: [ 3,7,11 ] resblock_dilation_sizes: [ [ 1,3,5 ], [ 1,3,5 ], [ 1,3,5 ] ] use_pitch_embed: false use_fm_loss: false use_ms_stft: false lambda_mel: 5.0 lambda_mel_adv: 1.0 lambda_cdisc: 4.0 lambda_adv: 1.0 lr: 0.0002 # Generator's learning rate. generator_scheduler_params: step_size: 600 gamma: 0.999 discriminator_scheduler_params: step_size: 600 gamma: 0.999 max_updates: 3000000 ================================================ FILE: NeuralSeq/egs/egs_bases/tts/vocoder/pwg.yaml ================================================ base_config: ./base.yaml task_cls: tasks.vocoder.pwg.PwgTask aux_context_window: 2 # Context window size for auxiliary feature. use_pitch_embed: false ########################################################### # GENERATOR NETWORK ARCHITECTURE SETTING # ########################################################### generator_params: in_channels: 1 # Number of input channels. out_channels: 1 # Number of output channels. kernel_size: 3 # Kernel size of dilated convolution. layers: 30 # Number of residual block layers. stacks: 3 # Number of stacks i.e., dilation cycles. residual_channels: 64 # Number of channels in residual conv. gate_channels: 128 # Number of channels in gated conv. skip_channels: 64 # Number of channels in skip conv. aux_channels: 80 # Number of channels for auxiliary feature conv. # Must be the same as num_mels. # If set to 2, previous 2 and future 2 frames will be considered. dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. use_weight_norm: true # Whether to use weight norm. # If set to true, it will be applied to all of the conv layers. upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture. upsample_params: # Upsampling network parameters. upsample_scales: [4, 4, 4, 4] # Upsampling scales. Prodcut of these must be the same as hop size. use_pitch_embed: false use_nsf: false ########################################################### # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # ########################################################### discriminator_params: in_channels: 1 # Number of input channels. out_channels: 1 # Number of output channels. kernel_size: 3 # Number of output channels. layers: 10 # Number of conv layers. conv_channels: 64 # Number of chnn layers. bias: true # Whether to use bias parameter in conv. use_weight_norm: true # Whether to use weight norm. # If set to true, it will be applied to all of the conv layers. nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv. nonlinear_activation_params: # Nonlinear function parameters negative_slope: 0.2 # Alpha in LeakyReLU. rerun_gen: true ########################################################### # STFT LOSS SETTING # ########################################################### stft_loss_params: fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. window: "hann_window" # Window function for STFT-based loss use_mel_loss: false ########################################################### # ADVERSARIAL LOSS SETTING # ########################################################### lambda_adv: 4.0 # Loss balancing coefficient. ########################################################### # OPTIMIZER & SCHEDULER SETTING # ########################################################### generator_optimizer_params: lr: 0.0001 # Generator's learning rate. eps: 1.0e-6 # Generator's epsilon. weight_decay: 0.0 # Generator's weight decay coefficient. generator_scheduler_params: step_size: 200000 # Generator's scheduler step size. gamma: 0.5 # Generator's scheduler gamma. # At each step size, lr will be multiplied by this parameter. generator_grad_norm: 10 # Generator's gradient norm. discriminator_optimizer_params: lr: 0.00005 # Discriminator's learning rate. eps: 1.0e-6 # Discriminator's epsilon. weight_decay: 0.0 # Discriminator's weight decay coefficient. discriminator_scheduler_params: step_size: 200000 # Discriminator's scheduler step size. gamma: 0.5 # Discriminator's scheduler gamma. # At each step size, lr will be multiplied by this parameter. discriminator_grad_norm: 1 # Discriminator's gradient norm. disc_start_steps: 40000 # Number of steps to start to train discriminator. ================================================ FILE: NeuralSeq/gitattributes ================================================ *.7z filter=lfs diff=lfs merge=lfs -text *.arrow filter=lfs diff=lfs merge=lfs -text *.bin filter=lfs diff=lfs merge=lfs -text *.bz2 filter=lfs diff=lfs merge=lfs -text *.ftz filter=lfs diff=lfs merge=lfs -text *.gz filter=lfs diff=lfs merge=lfs -text *.h5 filter=lfs diff=lfs merge=lfs -text *.joblib filter=lfs diff=lfs merge=lfs -text *.lfs.* filter=lfs diff=lfs merge=lfs -text *.model filter=lfs diff=lfs merge=lfs -text *.msgpack filter=lfs diff=lfs merge=lfs -text *.npy filter=lfs diff=lfs merge=lfs -text *.npz filter=lfs diff=lfs merge=lfs -text *.onnx filter=lfs diff=lfs merge=lfs -text *.ot filter=lfs diff=lfs merge=lfs -text *.parquet filter=lfs diff=lfs merge=lfs -text *.pickle filter=lfs diff=lfs merge=lfs -text *.pkl filter=lfs diff=lfs merge=lfs -text *.pb filter=lfs diff=lfs merge=lfs -text *.pt filter=lfs diff=lfs merge=lfs -text *.pth filter=lfs diff=lfs merge=lfs -text *.rar filter=lfs diff=lfs merge=lfs -text saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.tar.* filter=lfs diff=lfs merge=lfs -text *.tflite filter=lfs diff=lfs merge=lfs -text *.tgz filter=lfs diff=lfs merge=lfs -text *.wasm filter=lfs diff=lfs merge=lfs -text *.xz filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zstandard filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text model_ckpt_steps* filter=lfs diff=lfs merge=lfs -text checkpoints/0831_opencpop_ds1000 filter=lfs diff=lfs merge=lfs -text ================================================ FILE: NeuralSeq/inference/svs/base_svs_infer.py ================================================ import os import torch import numpy as np from modules.hifigan.hifigan import HifiGanGenerator from vocoders.hifigan import HifiGAN from inference.svs.opencpop.map import cpop_pinyin2ph_func from utils import load_ckpt from utils.hparams import set_hparams, hparams from utils.text_encoder import TokenTextEncoder from pypinyin import pinyin, lazy_pinyin, Style import librosa import glob import re class BaseSVSInfer: def __init__(self, hparams, device=None): if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' self.hparams = hparams self.device = device phone_list = ["AP", "SP", "a", "ai", "an", "ang", "ao", "b", "c", "ch", "d", "e", "ei", "en", "eng", "er", "f", "g", "h", "i", "ia", "ian", "iang", "iao", "ie", "in", "ing", "iong", "iu", "j", "k", "l", "m", "n", "o", "ong", "ou", "p", "q", "r", "s", "sh", "t", "u", "ua", "uai", "uan", "uang", "ui", "un", "uo", "v", "van", "ve", "vn", "w", "x", "y", "z", "zh"] self.ph_encoder = TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') self.pinyin2phs = cpop_pinyin2ph_func() self.spk_map = {'opencpop': 0} self.model = self.build_model() self.model.eval() self.model.to(self.device) self.vocoder = self.build_vocoder() self.vocoder.eval() self.vocoder.to(self.device) def build_model(self): raise NotImplementedError def forward_model(self, inp): raise NotImplementedError def build_vocoder(self): base_dir = hparams['vocoder_ckpt'] config_path = f'{base_dir}/config.yaml' ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1] print('| load HifiGAN: ', ckpt) ckpt_dict = torch.load(ckpt, map_location="cpu") config = set_hparams(config_path, global_hparams=False) state = ckpt_dict["state_dict"]["model_gen"] vocoder = HifiGanGenerator(config) vocoder.load_state_dict(state, strict=True) vocoder.remove_weight_norm() vocoder = vocoder.eval().to(self.device) return vocoder def run_vocoder(self, c, **kwargs): c = c.transpose(2, 1) # [B, 80, T] f0 = kwargs.get('f0') # [B, T] if f0 is not None and hparams.get('use_nsf'): # f0 = torch.FloatTensor(f0).to(self.device) y = self.vocoder(c, f0).view(-1) else: y = self.vocoder(c).view(-1) # [T] return y[None] def preprocess_word_level_input(self, inp): # Pypinyin can't solve polyphonic words text_raw = inp['text'].replace('最长', '最常').replace('长睫毛', '常睫毛') \ .replace('那么长', '那么常').replace('多长', '多常') \ .replace('很长', '很常') # We hope someone could provide a better g2p module for us by opening pull requests. # lyric pinyins = lazy_pinyin(text_raw, strict=False) ph_per_word_lst = [self.pinyin2phs[pinyin.strip()] for pinyin in pinyins if pinyin.strip() in self.pinyin2phs] # Note note_per_word_lst = [x.strip() for x in inp['notes'].split('|') if x.strip() != ''] mididur_per_word_lst = [x.strip() for x in inp['notes_duration'].split('|') if x.strip() != ''] if len(note_per_word_lst) == len(ph_per_word_lst) == len(mididur_per_word_lst): print('Pass word-notes check.') else: print('The number of words does\'t match the number of notes\' windows. ', 'You should split the note(s) for each word by | mark.') print(ph_per_word_lst, note_per_word_lst, mididur_per_word_lst) print(len(ph_per_word_lst), len(note_per_word_lst), len(mididur_per_word_lst)) return None note_lst = [] ph_lst = [] midi_dur_lst = [] is_slur = [] for idx, ph_per_word in enumerate(ph_per_word_lst): # for phs in one word: # single ph like ['ai'] or multiple phs like ['n', 'i'] ph_in_this_word = ph_per_word.split() # for notes in one word: # single note like ['D4'] or multiple notes like ['D4', 'E4'] which means a 'slur' here. note_in_this_word = note_per_word_lst[idx].split() midi_dur_in_this_word = mididur_per_word_lst[idx].split() # process for the model input # Step 1. # Deal with note of 'not slur' case or the first note of 'slur' case # j ie # F#4/Gb4 F#4/Gb4 # 0 0 for ph in ph_in_this_word: ph_lst.append(ph) note_lst.append(note_in_this_word[0]) midi_dur_lst.append(midi_dur_in_this_word[0]) is_slur.append(0) # step 2. # Deal with the 2nd, 3rd... notes of 'slur' case # j ie ie # F#4/Gb4 F#4/Gb4 C#4/Db4 # 0 0 1 if len(note_in_this_word) > 1: # is_slur = True, we should repeat the YUNMU to match the 2nd, 3rd... notes. for idx in range(1, len(note_in_this_word)): ph_lst.append(ph_in_this_word[-1]) note_lst.append(note_in_this_word[idx]) midi_dur_lst.append(midi_dur_in_this_word[idx]) is_slur.append(1) ph_seq = ' '.join(ph_lst) if len(ph_lst) == len(note_lst) == len(midi_dur_lst): print(len(ph_lst), len(note_lst), len(midi_dur_lst)) print('Pass word-notes check.') else: print('The number of words does\'t match the number of notes\' windows. ', 'You should split the note(s) for each word by | mark.') return None return ph_seq, note_lst, midi_dur_lst, is_slur def preprocess_phoneme_level_input(self, inp): ph_seq = inp['ph_seq'] note_lst = inp['note_seq'].split() midi_dur_lst = inp['note_dur_seq'].split() is_slur = [float(x) for x in inp['is_slur_seq'].split()] print(len(note_lst), len(ph_seq.split()), len(midi_dur_lst)) if len(note_lst) == len(ph_seq.split()) == len(midi_dur_lst): print('Pass word-notes check.') else: print('The number of words does\'t match the number of notes\' windows. ', 'You should split the note(s) for each word by | mark.') return None return ph_seq, note_lst, midi_dur_lst, is_slur def preprocess_input(self, inp, input_type='word'): """ :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)} :return: """ item_name = inp.get('item_name', '') spk_name = inp.get('spk_name', 'opencpop') # single spk spk_id = self.spk_map[spk_name] # get ph seq, note lst, midi dur lst, is slur lst. if input_type == 'word': ret = self.preprocess_word_level_input(inp) elif input_type == 'phoneme': # like transcriptions.txt in Opencpop dataset. ret = self.preprocess_phoneme_level_input(inp) else: print('Invalid input type.') return None if ret: ph_seq, note_lst, midi_dur_lst, is_slur = ret else: print('==========> Preprocess_word_level or phone_level input wrong.') return None # convert note lst to midi id; convert note dur lst to midi duration try: midis = [librosa.note_to_midi(x.split("/")[0]) if x != 'rest' else 0 for x in note_lst] midi_dur_lst = [float(x) for x in midi_dur_lst] except Exception as e: print(e) print('Invalid Input Type.') return None ph_token = self.ph_encoder.encode(ph_seq) item = {'item_name': item_name, 'text': inp['text'], 'ph': ph_seq, 'spk_id': spk_id, 'ph_token': ph_token, 'pitch_midi': np.asarray(midis), 'midi_dur': np.asarray(midi_dur_lst), 'is_slur': np.asarray(is_slur), } item['ph_len'] = len(item['ph_token']) return item def input_to_batch(self, item): item_names = [item['item_name']] text = [item['text']] ph = [item['ph']] txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device) txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) spk_ids = torch.LongTensor(item['spk_id'])[None, :].to(self.device) pitch_midi = torch.LongTensor(item['pitch_midi'])[None, :hparams['max_frames']].to(self.device) midi_dur = torch.FloatTensor(item['midi_dur'])[None, :hparams['max_frames']].to(self.device) is_slur = torch.LongTensor(item['is_slur'])[None, :hparams['max_frames']].to(self.device) batch = { 'item_name': item_names, 'text': text, 'ph': ph, 'txt_tokens': txt_tokens, 'txt_lengths': txt_lengths, 'spk_ids': spk_ids, 'pitch_midi': pitch_midi, 'midi_dur': midi_dur, 'is_slur': is_slur } return batch def postprocess_output(self, output): return output def infer_once(self, inp): inp = self.preprocess_input(inp, input_type=inp['input_type'] if inp.get('input_type') else 'word') output = self.forward_model(inp) output = self.postprocess_output(output) return output @classmethod def example_run(cls, inp): from utils.audio import save_wav set_hparams(print_hparams=False) infer_ins = cls(hparams) out = infer_ins.infer_once(inp) os.makedirs('infer_out', exist_ok=True) save_wav(out, f'infer_out/example_out.wav', hparams['audio_sample_rate']) # if __name__ == '__main__': # debug # a = BaseSVSInfer(hparams) # a.preprocess_input({'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP', # 'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest', # 'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590' # }) # b = { # 'text': '小酒窝长睫毛AP是你最美的记号', # 'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4', # 'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340' # } # c = { # 'text': '小酒窝长睫毛AP是你最美的记号', # 'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao', # 'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4', # 'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340', # 'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0' # } # input like Opencpop dataset. # a.preprocess_input(b) # a.preprocess_input(c, input_type='phoneme') ================================================ FILE: NeuralSeq/inference/svs/ds_cascade.py ================================================ import torch from inference.svs.base_svs_infer import BaseSVSInfer from utils import load_ckpt from utils.hparams import hparams from modulesmodules.diff.shallow_diffusion_tts import GaussianDiffusion from tasks.svs.diffsinger_task import DIFF_DECODERS class DiffSingerCascadeInfer(BaseSVSInfer): def build_model(self): model = GaussianDiffusion( phone_encoder=self.ph_encoder, out_dims=hparams['audio_num_mel_bins'], denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) model.eval() load_ckpt(model, hparams['work_dir'], 'model') return model def forward_model(self, inp): sample = self.input_to_batch(inp) txt_tokens = sample['txt_tokens'] # [B, T_t] spk_id = sample.get('spk_ids') with torch.no_grad(): output = self.model(txt_tokens, spk_id=spk_id, ref_mels=None, infer=True, pitch_midi=sample['pitch_midi'], midi_dur=sample['midi_dur'], is_slur=sample['is_slur']) mel_out = output['mel_out'] # [B, T,80] f0_pred = output['f0_denorm'] wav_out = self.run_vocoder(mel_out, f0=f0_pred) wav_out = wav_out.cpu().numpy() return wav_out[0] if __name__ == '__main__': inp = { 'text': '小酒窝长睫毛AP是你最美的记号', 'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4', 'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340', 'input_type': 'word' } # user input: Chinese characters c = { 'text': '小酒窝长睫毛AP是你最美的记号', 'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao', 'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4', 'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340', 'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0', 'input_type': 'phoneme' } # input like Opencpop dataset. DiffSingerCascadeInfer.example_run(inp) # # CUDA_VISIBLE_DEVICES=1 python inference/svs/ds_cascade.py --config egs/egs_bases/svs/midi/cascade/opencs/ds60_rel.yaml --exp_name 0303_opencpop_ds58_midi ================================================ FILE: NeuralSeq/inference/svs/ds_e2e.py ================================================ import torch # from inference.tts.fs import FastSpeechInfer # from modules.tts.fs2_orig import FastSpeech2Orig from inference.svs.base_svs_infer import BaseSVSInfer from utils import load_ckpt from utils.hparams import hparams from modules.diff.shallow_diffusion_tts import GaussianDiffusion from tasks.svs.diffsinger_task import DIFF_DECODERS from modules.fastspeech.pe import PitchExtractor import utils class DiffSingerE2EInfer(BaseSVSInfer): def build_model(self): model = GaussianDiffusion( phone_encoder=self.ph_encoder, out_dims=hparams['audio_num_mel_bins'], denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) model.eval() load_ckpt(model, hparams['work_dir'], 'model') if hparams.get('pe_enable') is not None and hparams['pe_enable']: self.pe = PitchExtractor().to(self.device) utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True) self.pe.eval() return model def forward_model(self, inp): sample = self.input_to_batch(inp) txt_tokens = sample['txt_tokens'] # [B, T_t] spk_id = sample.get('spk_ids') with torch.no_grad(): output = self.model(txt_tokens, spk_id=spk_id, ref_mels=None, infer=True, pitch_midi=sample['pitch_midi'], midi_dur=sample['midi_dur'], is_slur=sample['is_slur']) mel_out = output['mel_out'] # [B, T,80] if hparams.get('pe_enable') is not None and hparams['pe_enable']: f0_pred = self.pe(mel_out)['f0_denorm_pred'] # pe predict from Pred mel else: f0_pred = output['f0_denorm'] wav_out = self.run_vocoder(mel_out, f0=f0_pred) wav_out = wav_out.cpu().numpy() return wav_out[0] if __name__ == '__main__': inp = { 'text': '小酒窝长睫毛AP是你最美的记号', 'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4', 'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340', 'input_type': 'word' } # user input: Chinese characters inp = { 'text': '小酒窝长睫毛AP是你最美的记号', 'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao', 'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4', 'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340', 'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0', 'input_type': 'phoneme' } # input like Opencpop dataset. DiffSingerE2EInfer.example_run(inp) # CUDA_VISIBLE_DEVICES=3 python inference/svs/ds_e2e.py --config egs/egs_bases/svs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name 0228_opencpop_ds100_rel ================================================ FILE: NeuralSeq/inference/svs/opencpop/cpop_pinyin2ph.txt ================================================ | a | a | | ai | ai | | an | an | | ang | ang | | ao | ao | | ba | b a | | bai | b ai | | ban | b an | | bang | b ang | | bao | b ao | | bei | b ei | | ben | b en | | beng | b eng | | bi | b i | | bian | b ian | | biao | b iao | | bie | b ie | | bin | b in | | bing | b ing | | bo | b o | | bu | b u | | ca | c a | | cai | c ai | | can | c an | | cang | c ang | | cao | c ao | | ce | c e | | cei | c ei | | cen | c en | | ceng | c eng | | cha | ch a | | chai | ch ai | | chan | ch an | | chang | ch ang | | chao | ch ao | | che | ch e | | chen | ch en | | cheng | ch eng | | chi | ch i | | chong | ch ong | | chou | ch ou | | chu | ch u | | chua | ch ua | | chuai | ch uai | | chuan | ch uan | | chuang | ch uang | | chui | ch ui | | chun | ch un | | chuo | ch uo | | ci | c i | | cong | c ong | | cou | c ou | | cu | c u | | cuan | c uan | | cui | c ui | | cun | c un | | cuo | c uo | | da | d a | | dai | d ai | | dan | d an | | dang | d ang | | dao | d ao | | de | d e | | dei | d ei | | den | d en | | deng | d eng | | di | d i | | dia | d ia | | dian | d ian | | diao | d iao | | die | d ie | | ding | d ing | | diu | d iu | | dong | d ong | | dou | d ou | | du | d u | | duan | d uan | | dui | d ui | | dun | d un | | duo | d uo | | e | e | | ei | ei | | en | en | | eng | eng | | er | er | | fa | f a | | fan | f an | | fang | f ang | | fei | f ei | | fen | f en | | feng | f eng | | fo | f o | | fou | f ou | | fu | f u | | ga | g a | | gai | g ai | | gan | g an | | gang | g ang | | gao | g ao | | ge | g e | | gei | g ei | | gen | g en | | geng | g eng | | gong | g ong | | gou | g ou | | gu | g u | | gua | g ua | | guai | g uai | | guan | g uan | | guang | g uang | | gui | g ui | | gun | g un | | guo | g uo | | ha | h a | | hai | h ai | | han | h an | | hang | h ang | | hao | h ao | | he | h e | | hei | h ei | | hen | h en | | heng | h eng | | hm | h m | | hng | h ng | | hong | h ong | | hou | h ou | | hu | h u | | hua | h ua | | huai | h uai | | huan | h uan | | huang | h uang | | hui | h ui | | hun | h un | | huo | h uo | | ji | j i | | jia | j ia | | jian | j ian | | jiang | j iang | | jiao | j iao | | jie | j ie | | jin | j in | | jing | j ing | | jiong | j iong | | jiu | j iu | | ju | j v | | juan | j van | | jue | j ve | | jun | j vn | | ka | k a | | kai | k ai | | kan | k an | | kang | k ang | | kao | k ao | | ke | k e | | kei | k ei | | ken | k en | | keng | k eng | | kong | k ong | | kou | k ou | | ku | k u | | kua | k ua | | kuai | k uai | | kuan | k uan | | kuang | k uang | | kui | k ui | | kun | k un | | kuo | k uo | | la | l a | | lai | l ai | | lan | l an | | lang | l ang | | lao | l ao | | le | l e | | lei | l ei | | leng | l eng | | li | l i | | lia | l ia | | lian | l ian | | liang | l iang | | liao | l iao | | lie | l ie | | lin | l in | | ling | l ing | | liu | l iu | | lo | l o | | long | l ong | | lou | l ou | | lu | l u | | luan | l uan | | lun | l un | | luo | l uo | | lv | l v | | lve | l ve | | m | m | | ma | m a | | mai | m ai | | man | m an | | mang | m ang | | mao | m ao | | me | m e | | mei | m ei | | men | m en | | meng | m eng | | mi | m i | | mian | m ian | | miao | m iao | | mie | m ie | | min | m in | | ming | m ing | | miu | m iu | | mo | m o | | mou | m ou | | mu | m u | | n | n | | na | n a | | nai | n ai | | nan | n an | | nang | n ang | | nao | n ao | | ne | n e | | nei | n ei | | nen | n en | | neng | n eng | | ng | n g | | ni | n i | | nian | n ian | | niang | n iang | | niao | n iao | | nie | n ie | | nin | n in | | ning | n ing | | niu | n iu | | nong | n ong | | nou | n ou | | nu | n u | | nuan | n uan | | nun | n un | | nuo | n uo | | nv | n v | | nve | n ve | | o | o | | ou | ou | | pa | p a | | pai | p ai | | pan | p an | | pang | p ang | | pao | p ao | | pei | p ei | | pen | p en | | peng | p eng | | pi | p i | | pian | p ian | | piao | p iao | | pie | p ie | | pin | p in | | ping | p ing | | po | p o | | pou | p ou | | pu | p u | | qi | q i | | qia | q ia | | qian | q ian | | qiang | q iang | | qiao | q iao | | qie | q ie | | qin | q in | | qing | q ing | | qiong | q iong | | qiu | q iu | | qu | q v | | quan | q van | | que | q ve | | qun | q vn | | ran | r an | | rang | r ang | | rao | r ao | | re | r e | | ren | r en | | reng | r eng | | ri | r i | | rong | r ong | | rou | r ou | | ru | r u | | rua | r ua | | ruan | r uan | | rui | r ui | | run | r un | | ruo | r uo | | sa | s a | | sai | s ai | | san | s an | | sang | s ang | | sao | s ao | | se | s e | | sen | s en | | seng | s eng | | sha | sh a | | shai | sh ai | | shan | sh an | | shang | sh ang | | shao | sh ao | | she | sh e | | shei | sh ei | | shen | sh en | | sheng | sh eng | | shi | sh i | | shou | sh ou | | shu | sh u | | shua | sh ua | | shuai | sh uai | | shuan | sh uan | | shuang | sh uang | | shui | sh ui | | shun | sh un | | shuo | sh uo | | si | s i | | song | s ong | | sou | s ou | | su | s u | | suan | s uan | | sui | s ui | | sun | s un | | suo | s uo | | ta | t a | | tai | t ai | | tan | t an | | tang | t ang | | tao | t ao | | te | t e | | tei | t ei | | teng | t eng | | ti | t i | | tian | t ian | | tiao | t iao | | tie | t ie | | ting | t ing | | tong | t ong | | tou | t ou | | tu | t u | | tuan | t uan | | tui | t ui | | tun | t un | | tuo | t uo | | wa | w a | | wai | w ai | | wan | w an | | wang | w ang | | wei | w ei | | wen | w en | | weng | w eng | | wo | w o | | wu | w u | | xi | x i | | xia | x ia | | xian | x ian | | xiang | x iang | | xiao | x iao | | xie | x ie | | xin | x in | | xing | x ing | | xiong | x iong | | xiu | x iu | | xu | x v | | xuan | x van | | xue | x ve | | xun | x vn | | ya | y a | | yan | y an | | yang | y ang | | yao | y ao | | ye | y e | | yi | y i | | yin | y in | | ying | y ing | | yo | y o | | yong | y ong | | you | y ou | | yu | y v | | yuan | y van | | yue | y ve | | yun | y vn | | za | z a | | zai | z ai | | zan | z an | | zang | z ang | | zao | z ao | | ze | z e | | zei | z ei | | zen | z en | | zeng | z eng | | zha | zh a | | zhai | zh ai | | zhan | zh an | | zhang | zh ang | | zhao | zh ao | | zhe | zh e | | zhei | zh ei | | zhen | zh en | | zheng | zh eng | | zhi | zh i | | zhong | zh ong | | zhou | zh ou | | zhu | zh u | | zhua | zh ua | | zhuai | zh uai | | zhuan | zh uan | | zhuang | zh uang | | zhui | zh ui | | zhun | zh un | | zhuo | zh uo | | zi | z i | | zong | z ong | | zou | z ou | | zu | z u | | zuan | z uan | | zui | z ui | | zun | z un | | zuo | z uo | ================================================ FILE: NeuralSeq/inference/svs/opencpop/map.py ================================================ def cpop_pinyin2ph_func(): # In the README file of opencpop dataset, they defined a "pinyin to phoneme mapping table" pinyin2phs = {'AP': 'AP', 'SP': 'SP'} with open('NeuralSeq/inference/svs/opencpop/cpop_pinyin2ph.txt') as rf: for line in rf.readlines(): elements = [x.strip() for x in line.split('|') if x.strip() != ''] pinyin2phs[elements[0]] = elements[1] return pinyin2phs ================================================ FILE: NeuralSeq/inference/tts/GenerSpeech.py ================================================ import torch import os import importlib from inference.tts.base_tts_infer import BaseTTSInfer from utils.ckpt_utils import load_ckpt, get_last_checkpoint from modules.GenerSpeech.model.generspeech import GenerSpeech from data_gen.tts.emotion import inference as EmotionEncoder from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance from data_gen.tts.emotion.inference import preprocess_wav from data_gen.tts.data_gen_utils import is_sil_phoneme from resemblyzer import VoiceEncoder from utils import audio class GenerSpeechInfer(BaseTTSInfer): def build_model(self): model = GenerSpeech(self.ph_encoder) model.eval() load_ckpt(model, self.hparams['work_dir'], 'model') return model def preprocess_input(self, inp): """ :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)} :return: """ # processed text preprocessor, preprocess_args = self.preprocessor, self.preprocess_args text_raw = inp['text'] item_name = inp.get('item_name', '') ph, txt, word, ph2word, ph_gb_word = preprocessor.txt_to_ph(preprocessor.txt_processor, text_raw, preprocess_args) ph_token = self.ph_encoder.encode(ph) # processed ref audio ref_audio = inp['ref_audio'] processed_ref_audio = 'example/temp.wav' voice_encoder = VoiceEncoder().cuda() encoder = [self.ph_encoder, self.word_encoder] EmotionEncoder.load_model(self.hparams['emotion_encoder_path']) binarizer_cls = self.hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') pkg = ".".join(binarizer_cls.split(".")[:-1]) cls_name = binarizer_cls.split(".")[-1] binarizer_cls = getattr(importlib.import_module(pkg), cls_name) ref_audio_raw, ref_text_raw = self.asr(ref_audio) # prepare text ph_ref, txt_ref, word_ref, ph2word_ref, ph_gb_word_ref = preprocessor.txt_to_ph(preprocessor.txt_processor, ref_text_raw, preprocess_args) ph_gb_word_nosil = ["_".join([p for p in w.split("_") if not is_sil_phoneme(p)]) for w in ph_gb_word_ref.split(" ") if not is_sil_phoneme(w)] phs_for_align = ['SIL'] + ph_gb_word_nosil + ['SIL'] phs_for_align = " ".join(phs_for_align) # prepare files for alignment os.system('rm -r example/; mkdir example/') audio.save_wav(ref_audio_raw, processed_ref_audio, self.hparams['audio_sample_rate']) with open(f'example/temp.lab', 'w') as f_txt: f_txt.write(phs_for_align) os.system(f'mfa align example/ {self.hparams["binary_data_dir"]}/mfa_dict.txt {self.hparams["binary_data_dir"]}/mfa_model.zip example/textgrid/ --clean') item2tgfn = 'example/textgrid/temp.TextGrid' # prepare textgrid alignment item = binarizer_cls.process_item(item_name, ph_ref, txt_ref, item2tgfn, processed_ref_audio, 0, 0, encoder, self.hparams['binarization_args']) item['emo_embed'] = Embed_utterance(preprocess_wav(item['wav_fn'])) item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) item.update({ 'ref_ph': item['ph'], 'ph': ph, 'ph_token': ph_token, 'text': txt }) return item def input_to_batch(self, item): item_names = [item['item_name']] text = [item['text']] ph = [item['ph']] txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device) txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) mels = torch.FloatTensor(item['mel'])[None, :].to(self.device) f0 = torch.FloatTensor(item['f0'])[None, :].to(self.device) # uv = torch.FloatTensor(item['uv']).to(self.device) mel2ph = torch.LongTensor(item['mel2ph'])[None, :].to(self.device) spk_embed = torch.FloatTensor(item['spk_embed'])[None, :].to(self.device) emo_embed = torch.FloatTensor(item['emo_embed'])[None, :].to(self.device) ph2word = torch.LongTensor(item['ph2word'])[None, :].to(self.device) mel2word = torch.LongTensor(item['mel2word'])[None, :].to(self.device) word_tokens = torch.LongTensor(item['word_tokens'])[None, :].to(self.device) batch = { 'item_name': item_names, 'text': text, 'ph': ph, 'mels': mels, 'f0': f0, 'txt_tokens': txt_tokens, 'txt_lengths': txt_lengths, 'spk_embed': spk_embed, 'emo_embed': emo_embed, 'mel2ph': mel2ph, 'ph2word': ph2word, 'mel2word': mel2word, 'word_tokens': word_tokens, } return batch def forward_model(self, inp): sample = self.input_to_batch(inp) txt_tokens = sample['txt_tokens'] # [B, T_t] with torch.no_grad(): output = self.model(txt_tokens, ref_mel2ph=sample['mel2ph'], ref_mel2word=sample['mel2word'], ref_mels=sample['mels'], spk_embed=sample['spk_embed'], emo_embed=sample['emo_embed'], global_steps=300000, infer=True) mel_out = output['mel_out'] wav_out = self.run_vocoder(mel_out) wav_out = wav_out.squeeze().cpu().numpy() return wav_out if __name__ == '__main__': inp = { 'text': 'here we go', 'ref_audio': 'assets/0011_001570.wav' } GenerSpeechInfer.example_run(inp) ================================================ FILE: NeuralSeq/inference/tts/PortaSpeech.py ================================================ import torch from inference.tts.base_tts_infer import BaseTTSInfer from utils.ckpt_utils import load_ckpt from modules.portaspeech.portaspeech import PortaSpeech class TTSInference(BaseTTSInfer): def __init__(self, hparams, device=None): super().__init__(hparams, device) print("Initializing TTS model to %s" % device) self.spk_map = self.preprocessor.load_spk_map(self.data_dir) print("TTS loaded!") def build_model(self): model = PortaSpeech(self.ph_encoder, self.word_encoder) load_ckpt(model, self.hparams['work_dir'], 'model') with torch.no_grad(): model.store_inverse_all() return model def forward_model(self, inp): sample = self.input_to_batch(inp) with torch.no_grad(): output = self.model( sample['txt_tokens'], sample['word_tokens'], ph2word=sample['ph2word'], word_len=sample['word_lengths'].max(), infer=True, forward_post_glow=True, spk_id=sample.get('spk_ids') ) mel_out = output['mel_out'] wav_out = self.run_vocoder(mel_out) wav_out = wav_out.cpu().numpy() return wav_out[0] def preprocess_input(self, inp): """ :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)} :return: """ preprocessor, preprocess_args = self.preprocessor, self.preprocess_args text_raw = inp['text'] item_name = inp.get('item_name', '') spk_name = inp.get('spk_name', '') ph, txt, word, ph2word, ph_gb_word = preprocessor.txt_to_ph( preprocessor.txt_processor, text_raw, preprocess_args) word_token = self.word_encoder.encode(word) ph_token = self.ph_encoder.encode(ph) spk_id = self.spk_map[spk_name] item = {'item_name': item_name, 'text': txt, 'ph': ph, 'spk_id': spk_id, 'ph_token': ph_token, 'word_token': word_token, 'ph2word': ph2word, 'ph_words':ph_gb_word, 'words': word} item['ph_len'] = len(item['ph_token']) return item def input_to_batch(self, item): item_names = [item['item_name']] text = [item['text']] ph = [item['ph']] txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device) txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) word_tokens = torch.LongTensor(item['word_token'])[None, :].to(self.device) word_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) ph2word = torch.LongTensor(item['ph2word'])[None, :].to(self.device) spk_ids = torch.LongTensor(item['spk_id'])[None, :].to(self.device) batch = { 'item_name': item_names, 'text': text, 'ph': ph, 'txt_tokens': txt_tokens, 'txt_lengths': txt_lengths, 'word_tokens': word_tokens, 'word_lengths': word_lengths, 'ph2word': ph2word, 'spk_ids': spk_ids, } return batch def postprocess_output(self, output): return output ================================================ FILE: NeuralSeq/inference/tts/base_tts_infer.py ================================================ from tasks.tts.dataset_utils import FastSpeechWordDataset from tasks.tts.tts_utils import load_data_preprocessor from vocoders.hifigan import HifiGanGenerator import os import librosa import soundfile as sf from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from string import punctuation import torch from utils.ckpt_utils import load_ckpt from utils.hparams import set_hparams from utils.hparams import hparams as hp class BaseTTSInfer: def __init__(self, hparams, device=None): if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' self.hparams = hparams self.device = device self.data_dir = hparams['binary_data_dir'] self.preprocessor, self.preprocess_args = load_data_preprocessor() self.ph_encoder, self.word_encoder = self.preprocessor.load_dict(self.data_dir) self.ds_cls = FastSpeechWordDataset self.model = self.build_model() self.model.eval() self.model.to(self.device) self.vocoder = self.build_vocoder() self.vocoder.eval() self.vocoder.to(self.device) self.asr_processor, self.asr_model = self.build_asr() def build_model(self): raise NotImplementedError def forward_model(self, inp): raise NotImplementedError def build_asr(self): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") # facebook/wav2vec2-base-960h wav2vec2-large-960h-lv60-self model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to(self.device) return processor, model def build_vocoder(self): base_dir = self.hparams['vocoder_ckpt'] config_path = f'{base_dir}/config.yaml' config = set_hparams(config_path, global_hparams=False) vocoder = HifiGanGenerator(config) load_ckpt(vocoder, base_dir, 'model_gen') return vocoder def run_vocoder(self, c): c = c.transpose(2, 1) y = self.vocoder(c)[:, 0] return y def preprocess_input(self, inp): raise NotImplementedError def input_to_batch(self, item): raise NotImplementedError def postprocess_output(self, output): return output def infer_once(self, inp): inp = self.preprocess_input(inp) output = self.forward_model(inp) output = self.postprocess_output(output) return output @classmethod def example_run(cls, inp): from utils.audio import save_wav #set_hparams(print_hparams=False) infer_ins = cls(hp) out = infer_ins.infer_once(inp) os.makedirs('infer_out', exist_ok=True) save_wav(out, f'infer_out/{hp["text"]}.wav', hp['audio_sample_rate']) print(f'Save at infer_out/{hp["text"]}.wav.') def asr(self, file): sample_rate = self.hparams['audio_sample_rate'] audio_input, source_sample_rate = sf.read(file) # Resample the wav if needed if sample_rate is not None and source_sample_rate != sample_rate: audio_input = librosa.resample(audio_input, source_sample_rate, sample_rate) # pad input values and return pt tensor input_values = self.asr_processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # retrieve logits & take argmax logits = self.asr_model(input_values.cuda()).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = self.asr_processor.decode(predicted_ids[0]) transcription = transcription.rstrip(punctuation) return audio_input, transcription ================================================ FILE: NeuralSeq/modules/GenerSpeech/config/generspeech.yaml ================================================ base_config: - egs/egs_bases/tts/fs2.yaml - egs/datasets/audio/emotion/base_text2mel.yaml task_cls: modules.GenerSpeech.task.generspeech.GenerSpeechTask # emotion encoder emotion_encoder_path: checkpoints/Emotion_encoder.pt # set the emotion encoder path # vocoder vocoder: hifigan vocoder_ckpt: checkpoints/trainset_hifigan # dataset raw_data_dir: 'data/raw/training_set' processed_data_dir: 'data/processed/training_set' binary_data_dir: 'data/binary/training_set' test_input_dir: '' # process binarizer_cls: data_gen.tts.base_binarizer_emotion.EmotionBinarizer audio_sample_rate: 16000 hop_size: 256 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) win_size: 1024 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) fmin: 80 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) fmax: 7600 # To be increased/reduced depending on data. fft_size: 1024 # Extra window size is filled with 0 paddings to match this parameter min_level_db: -100 ref_level_db: 20 binarization_args: reset_phone_dict: true reset_word_dict: true shuffle: true trim_eos_bos: false trim_sil: false with_align: true with_f0: true with_f0cwt: false with_linear: false with_spk_embed: true with_spk_id: true with_txt: true with_wav: true with_word: true preprocess_cls: egs.datasets.audio.libritts.pre_align.LibrittsPreAlign preprocess_args: nsample_per_mfa_group: 1000 # text process txt_processor: en use_mfa: true with_phsep: true reset_phone_dict: true reset_word_dict: true add_eos_bos: true # mfa mfa_group_shuffle: false mfa_offset: 0.02 # wav processors wav_processors: [] save_sil_mask: true vad_max_silence_length: 12 # data word_dict_size: 10000 num_spk: 500 use_spk_embed: true use_spk_id: false use_word: true use_emotion: true use_gt_dur: false ref_audio: '' text: '' # training num_sanity_val_steps: -1 max_updates: 300000 max_sentences: 100000 num_test_samples: 72 ## glow post_glow_hidden: 128 post_glow_kernel_size: 3 post_glow_n_blocks: 8 post_glow_n_block_layers: 3 share_wn_layers: 4 sigmoid_scale: false post_share_cond_layers: false use_txt_cond: true use_latent_cond: true noise_scale: 0.8 # prosody extractor lambda_commit: 0.25 vq_start: 20500 vae_dropout: 0.0 nVQ: 128 forcing: 20000 crop: false predictor_grad: 1.0 ================================================ FILE: NeuralSeq/modules/GenerSpeech/model/generspeech.py ================================================ import torch from modules.GenerSpeech.model.glow_modules import Glow from modules.fastspeech.tts_modules import PitchPredictor import random from modules.GenerSpeech.model.prosody_util import ProsodyAligner, LocalStyleAdaptor from utils.pitch_utils import f0_to_coarse, denorm_f0 from modules.commons.common_layers import * import torch.distributions as dist from utils.hparams import hparams from modules.GenerSpeech.model.mixstyle import MixStyle from modules.fastspeech.fs2 import FastSpeech2 import json from modules.fastspeech.tts_modules import DEFAULT_MAX_SOURCE_POSITIONS, DEFAULT_MAX_TARGET_POSITIONS class GenerSpeech(FastSpeech2): ''' GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech https://arxiv.org/abs/2205.07211 ''' def __init__(self, dictionary, out_dims=None): super().__init__(dictionary, out_dims) # Mixstyle self.norm = MixStyle(p=0.5, alpha=0.1, eps=1e-6, hidden_size=self.hidden_size) # emotion embedding self.emo_embed_proj = Linear(256, self.hidden_size, bias=True) # build prosody extractor ## frame level self.prosody_extractor_utter = LocalStyleAdaptor(self.hidden_size, hparams['nVQ'], self.padding_idx) self.l1_utter = nn.Linear(self.hidden_size * 2, self.hidden_size) self.align_utter = ProsodyAligner(num_layers=2) ## phoneme level self.prosody_extractor_ph = LocalStyleAdaptor(self.hidden_size, hparams['nVQ'], self.padding_idx) self.l1_ph = nn.Linear(self.hidden_size * 2, self.hidden_size) self.align_ph = ProsodyAligner(num_layers=2) ## word level self.prosody_extractor_word = LocalStyleAdaptor(self.hidden_size, hparams['nVQ'], self.padding_idx) self.l1_word = nn.Linear(self.hidden_size * 2, self.hidden_size) self.align_word = ProsodyAligner(num_layers=2) self.pitch_inpainter_predictor = PitchPredictor( self.hidden_size, n_chans=self.hidden_size, n_layers=3, dropout_rate=0.1, odim=2, padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel']) # build attention layer self.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS self.embed_positions = SinusoidalPositionalEmbedding( self.hidden_size, self.padding_idx, init_size=self.max_source_positions + self.padding_idx + 1, ) # build post flow cond_hs = 80 if hparams.get('use_txt_cond', True): cond_hs = cond_hs + hparams['hidden_size'] cond_hs = cond_hs + hparams['hidden_size'] * 3 # for emo, spk embedding and prosody embedding self.post_flow = Glow( 80, hparams['post_glow_hidden'], hparams['post_glow_kernel_size'], 1, hparams['post_glow_n_blocks'], hparams['post_glow_n_block_layers'], n_split=4, n_sqz=2, gin_channels=cond_hs, share_cond_layers=hparams['post_share_cond_layers'], share_wn_layers=hparams['share_wn_layers'], sigmoid_scale=hparams['sigmoid_scale'] ) self.prior_dist = dist.Normal(0, 1) def forward(self, txt_tokens, mel2ph=None, ref_mel2ph=None, ref_mel2word=None, spk_embed=None, emo_embed=None, ref_mels=None, f0=None, uv=None, skip_decoder=False, global_steps=0, infer=False, **kwargs): ret = {} encoder_out = self.encoder(txt_tokens) # [B, T, C] src_nonpadding = (txt_tokens > 0).float()[:, :, None] # add spk/emo embed spk_embed = self.spk_embed_proj(spk_embed)[:, None, :] emo_embed = self.emo_embed_proj(emo_embed)[:, None, :] # add dur dur_inp = (encoder_out + spk_embed + emo_embed) * src_nonpadding mel2ph = self.add_dur(dur_inp, mel2ph, txt_tokens, ret) tgt_nonpadding = (mel2ph > 0).float()[:, :, None] decoder_inp = self.expand_states(encoder_out, mel2ph) decoder_inp = self.norm(decoder_inp, spk_embed + emo_embed) # add prosody VQ ret['ref_mel2ph'] = ref_mel2ph ret['ref_mel2word'] = ref_mel2word prosody_utter_mel = self.get_prosody_utter(decoder_inp, ref_mels, ret, infer, global_steps) prosody_ph_mel = self.get_prosody_ph(decoder_inp, ref_mels, ret, infer, global_steps) prosody_word_mel = self.get_prosody_word(decoder_inp, ref_mels, ret, infer, global_steps) # add pitch embed pitch_inp_domain_agnostic = decoder_inp * tgt_nonpadding pitch_inp_domain_specific = (decoder_inp + spk_embed + emo_embed + prosody_utter_mel + prosody_ph_mel + prosody_word_mel) * tgt_nonpadding predicted_pitch = self.inpaint_pitch(pitch_inp_domain_agnostic, pitch_inp_domain_specific, f0, uv, mel2ph, ret) # decode decoder_inp = decoder_inp + spk_embed + emo_embed + predicted_pitch + prosody_utter_mel + prosody_ph_mel + prosody_word_mel ret['decoder_inp'] = decoder_inp = decoder_inp * tgt_nonpadding if skip_decoder: return ret ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs) # postflow is_training = self.training ret['x_mask'] = tgt_nonpadding ret['spk_embed'] = spk_embed ret['emo_embed'] = emo_embed ret['ref_prosody'] = prosody_utter_mel + prosody_ph_mel + prosody_word_mel self.run_post_glow(ref_mels, infer, is_training, ret) return ret def get_prosody_ph(self, encoder_out, ref_mels, ret, infer=False, global_steps=0): # get VQ prosody if global_steps > hparams['vq_start'] or infer: prosody_embedding, loss, ppl = self.prosody_extractor_ph(ref_mels, ret['ref_mel2ph'], no_vq=False) ret['vq_loss_ph'] = loss ret['ppl_ph'] = ppl else: prosody_embedding = self.prosody_extractor_ph(ref_mels, ret['ref_mel2ph'], no_vq=True) # add positional embedding positions = self.embed_positions(prosody_embedding[:, :, 0]) prosody_embedding = self.l1_ph(torch.cat([prosody_embedding, positions], dim=-1)) # style-to-content attention src_key_padding_mask = encoder_out[:, :, 0].eq(self.padding_idx).data prosody_key_padding_mask = prosody_embedding[:, :, 0].eq(self.padding_idx).data if global_steps < hparams['forcing']: output, guided_loss, attn_emo = self.align_ph(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), src_key_padding_mask, prosody_key_padding_mask, forcing=True) else: output, guided_loss, attn_emo = self.align_ph(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), src_key_padding_mask, prosody_key_padding_mask, forcing=False) ret['gloss_ph'] = guided_loss ret['attn_ph'] = attn_emo return output.transpose(0, 1) def get_prosody_word(self, encoder_out, ref_mels, ret, infer=False, global_steps=0): # get VQ prosody if global_steps > hparams['vq_start'] or infer: prosody_embedding, loss, ppl = self.prosody_extractor_word(ref_mels, ret['ref_mel2word'], no_vq=False) ret['vq_loss_word'] = loss ret['ppl_word'] = ppl else: prosody_embedding = self.prosody_extractor_word(ref_mels, ret['ref_mel2word'], no_vq=True) # add positional embedding positions = self.embed_positions(prosody_embedding[:, :, 0]) prosody_embedding = self.l1_word(torch.cat([prosody_embedding, positions], dim=-1)) # style-to-content attention src_key_padding_mask = encoder_out[:, :, 0].eq(self.padding_idx).data prosody_key_padding_mask = prosody_embedding[:, :, 0].eq(self.padding_idx).data if global_steps < hparams['forcing']: output, guided_loss, attn_emo = self.align_word(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), src_key_padding_mask, prosody_key_padding_mask, forcing=True) else: output, guided_loss, attn_emo = self.align_word(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), src_key_padding_mask, prosody_key_padding_mask, forcing=False) ret['gloss_word'] = guided_loss ret['attn_word'] = attn_emo return output.transpose(0, 1) def get_prosody_utter(self, encoder_out, ref_mels, ret, infer=False, global_steps=0): # get VQ prosody if global_steps > hparams['vq_start'] or infer: prosody_embedding, loss, ppl = self.prosody_extractor_utter(ref_mels, no_vq=False) ret['vq_loss_utter'] = loss ret['ppl_utter'] = ppl else: prosody_embedding = self.prosody_extractor_utter(ref_mels, no_vq=True) # add positional embedding positions = self.embed_positions(prosody_embedding[:, :, 0]) prosody_embedding = self.l1_utter(torch.cat([prosody_embedding, positions], dim=-1)) # style-to-content attention src_key_padding_mask = encoder_out[:, :, 0].eq(self.padding_idx).data prosody_key_padding_mask = prosody_embedding[:, :, 0].eq(self.padding_idx).data if global_steps < hparams['forcing']: output, guided_loss, attn_emo = self.align_utter(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), src_key_padding_mask, prosody_key_padding_mask, forcing=True) else: output, guided_loss, attn_emo = self.align_utter(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), src_key_padding_mask, prosody_key_padding_mask, forcing=False) ret['gloss_utter'] = guided_loss ret['attn_utter'] = attn_emo return output.transpose(0, 1) def inpaint_pitch(self, pitch_inp_domain_agnostic, pitch_inp_domain_specific, f0, uv, mel2ph, ret): if hparams['pitch_type'] == 'frame': pitch_padding = mel2ph == 0 if hparams['predictor_grad'] != 1: pitch_inp_domain_agnostic = pitch_inp_domain_agnostic.detach() + hparams['predictor_grad'] * (pitch_inp_domain_agnostic - pitch_inp_domain_agnostic.detach()) pitch_inp_domain_specific = pitch_inp_domain_specific.detach() + hparams['predictor_grad'] * (pitch_inp_domain_specific - pitch_inp_domain_specific.detach()) pitch_domain_agnostic = self.pitch_predictor(pitch_inp_domain_agnostic) pitch_domain_specific = self.pitch_inpainter_predictor(pitch_inp_domain_specific) pitch_pred = pitch_domain_agnostic + pitch_domain_specific ret['pitch_pred'] = pitch_pred use_uv = hparams['pitch_type'] == 'frame' and hparams['use_uv'] if f0 is None: f0 = pitch_pred[:, :, 0] # [B, T] if use_uv: uv = pitch_pred[:, :, 1] > 0 # [B, T] f0_denorm = denorm_f0(f0, uv if use_uv else None, hparams, pitch_padding=pitch_padding) pitch = f0_to_coarse(f0_denorm) # start from 0 [B, T_txt] ret['f0_denorm'] = f0_denorm ret['f0_denorm_pred'] = denorm_f0(pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None, hparams, pitch_padding=pitch_padding) if hparams['pitch_type'] == 'ph': pitch = torch.gather(F.pad(pitch, [1, 0]), 1, mel2ph) ret['f0_denorm'] = torch.gather(F.pad(ret['f0_denorm'], [1, 0]), 1, mel2ph) ret['f0_denorm_pred'] = torch.gather(F.pad(ret['f0_denorm_pred'], [1, 0]), 1, mel2ph) pitch_embed = self.pitch_embed(pitch) return pitch_embed def run_post_glow(self, tgt_mels, infer, is_training, ret): x_recon = ret['mel_out'].transpose(1, 2) g = x_recon B, _, T = g.shape if hparams.get('use_txt_cond', True): g = torch.cat([g, ret['decoder_inp'].transpose(1, 2)], 1) g_spk_embed = ret['spk_embed'].repeat(1, T, 1).transpose(1, 2) g_emo_embed = ret['emo_embed'].repeat(1, T, 1).transpose(1, 2) l_ref_prosody = ret['ref_prosody'].transpose(1, 2) g = torch.cat([g, g_spk_embed, g_emo_embed, l_ref_prosody], dim=1) prior_dist = self.prior_dist if not infer: if is_training: self.train() x_mask = ret['x_mask'].transpose(1, 2) y_lengths = x_mask.sum(-1) g = g.detach() tgt_mels = tgt_mels.transpose(1, 2) z_postflow, ldj = self.post_flow(tgt_mels, x_mask, g=g) ldj = ldj / y_lengths / 80 ret['z_pf'], ret['ldj_pf'] = z_postflow, ldj ret['postflow'] = -prior_dist.log_prob(z_postflow).mean() - ldj.mean() else: x_mask = torch.ones_like(x_recon[:, :1, :]) z_post = prior_dist.sample(x_recon.shape).to(g.device) * hparams['noise_scale'] x_recon_, _ = self.post_flow(z_post, x_mask, g, reverse=True) x_recon = x_recon_ ret['mel_out'] = x_recon.transpose(1, 2) ================================================ FILE: NeuralSeq/modules/GenerSpeech/model/glow_modules.py ================================================ import scipy from torch.nn import functional as F import torch from torch import nn import numpy as np from modules.commons.common_layers import Permute from modules.fastspeech.tts_modules import FFTBlocks from modules.GenerSpeech.model.wavenet import fused_add_tanh_sigmoid_multiply, WN class LayerNorm(nn.Module): def __init__(self, channels, eps=1e-4): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = nn.Parameter(torch.zeros(channels)) def forward(self, x): n_dims = len(x.shape) mean = torch.mean(x, 1, keepdim=True) variance = torch.mean((x - mean) ** 2, 1, keepdim=True) x = (x - mean) * torch.rsqrt(variance + self.eps) shape = [1, -1] + [1] * (n_dims - 2) x = x * self.gamma.view(*shape) + self.beta.view(*shape) return x class ConvReluNorm(nn.Module): def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.out_channels = out_channels self.kernel_size = kernel_size self.n_layers = n_layers self.p_dropout = p_dropout assert n_layers > 1, "Number of layers should be larger than 0." self.conv_layers = nn.ModuleList() self.norm_layers = nn.ModuleList() self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.relu_drop = nn.Sequential( nn.ReLU(), nn.Dropout(p_dropout)) for _ in range(n_layers - 1): self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.proj.weight.data.zero_() self.proj.bias.data.zero_() def forward(self, x, x_mask): x_org = x for i in range(self.n_layers): x = self.conv_layers[i](x * x_mask) x = self.norm_layers[i](x) x = self.relu_drop(x) x = x_org + self.proj(x) return x * x_mask class ActNorm(nn.Module): # glow中的线性变换层 def __init__(self, channels, ddi=False, **kwargs): super().__init__() self.channels = channels self.initialized = not ddi self.logs = nn.Parameter(torch.zeros(1, channels, 1)) self.bias = nn.Parameter(torch.zeros(1, channels, 1)) def forward(self, x, x_mask=None, reverse=False, **kwargs): if x_mask is None: x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype) x_len = torch.sum(x_mask, [1, 2]) if not self.initialized: self.initialize(x, x_mask) self.initialized = True if reverse: z = (x - self.bias) * torch.exp(-self.logs) * x_mask logdet = torch.sum(-self.logs) * x_len else: z = (self.bias + torch.exp(self.logs) * x) * x_mask logdet = torch.sum(self.logs) * x_len # [b] return z, logdet def store_inverse(self): pass def set_ddi(self, ddi): self.initialized = not ddi def initialize(self, x, x_mask): with torch.no_grad(): denom = torch.sum(x_mask, [0, 2]) m = torch.sum(x * x_mask, [0, 2]) / denom m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom v = m_sq - (m ** 2) logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) self.bias.data.copy_(bias_init) self.logs.data.copy_(logs_init) class InvConvNear(nn.Module): # 可逆卷积 def __init__(self, channels, n_split=4, no_jacobian=False, lu=True, n_sqz=2, **kwargs): super().__init__() assert (n_split % 2 == 0) self.channels = channels self.n_split = n_split self.n_sqz = n_sqz self.no_jacobian = no_jacobian w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0] if torch.det(w_init) < 0: w_init[:, 0] = -1 * w_init[:, 0] self.lu = lu if lu: # LU decomposition can slightly speed up the inverse np_p, np_l, np_u = scipy.linalg.lu(w_init) np_s = np.diag(np_u) np_sign_s = np.sign(np_s) np_log_s = np.log(np.abs(np_s)) np_u = np.triu(np_u, k=1) l_mask = np.tril(np.ones(w_init.shape, dtype=float), -1) eye = np.eye(*w_init.shape, dtype=float) self.register_buffer('p', torch.Tensor(np_p.astype(float))) self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) self.l = nn.Parameter(torch.Tensor(np_l.astype(float)), requires_grad=True) self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)), requires_grad=True) self.u = nn.Parameter(torch.Tensor(np_u.astype(float)), requires_grad=True) self.register_buffer('l_mask', torch.Tensor(l_mask)) self.register_buffer('eye', torch.Tensor(eye)) else: self.weight = nn.Parameter(w_init) def forward(self, x, x_mask=None, reverse=False, **kwargs): b, c, t = x.size() assert (c % self.n_split == 0) if x_mask is None: x_mask = 1 x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t else: x_len = torch.sum(x_mask, [1, 2]) x = x.view(b, self.n_sqz, c // self.n_split, self.n_split // self.n_sqz, t) x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t) if self.lu: self.weight, log_s = self._get_weight() logdet = log_s.sum() logdet = logdet * (c / self.n_split) * x_len else: logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b] if reverse: if hasattr(self, "weight_inv"): weight = self.weight_inv else: weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) logdet = -logdet else: weight = self.weight if self.no_jacobian: logdet = 0 weight = weight.view(self.n_split, self.n_split, 1, 1) z = F.conv2d(x, weight) z = z.view(b, self.n_sqz, self.n_split // self.n_sqz, c // self.n_split, t) z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask return z, logdet def _get_weight(self): l, log_s, u = self.l, self.log_s, self.u l = l * self.l_mask + self.eye u = u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(log_s)) weight = torch.matmul(self.p, torch.matmul(l, u)) return weight, log_s def store_inverse(self): weight, _ = self._get_weight() self.weight_inv = torch.inverse(weight.float()).to(next(self.parameters()).device) class InvConv(nn.Module): def __init__(self, channels, no_jacobian=False, lu=True, **kwargs): super().__init__() w_shape = [channels, channels] w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(float) LU_decomposed = lu if not LU_decomposed: # Sample a random orthogonal matrix: self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init))) else: np_p, np_l, np_u = scipy.linalg.lu(w_init) np_s = np.diag(np_u) np_sign_s = np.sign(np_s) np_log_s = np.log(np.abs(np_s)) np_u = np.triu(np_u, k=1) l_mask = np.tril(np.ones(w_shape, dtype=float), -1) eye = np.eye(*w_shape, dtype=float) self.register_buffer('p', torch.Tensor(np_p.astype(float))) self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) self.l = nn.Parameter(torch.Tensor(np_l.astype(float))) self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float))) self.u = nn.Parameter(torch.Tensor(np_u.astype(float))) self.l_mask = torch.Tensor(l_mask) self.eye = torch.Tensor(eye) self.w_shape = w_shape self.LU = LU_decomposed self.weight = None def get_weight(self, device, reverse): w_shape = self.w_shape self.p = self.p.to(device) self.sign_s = self.sign_s.to(device) self.l_mask = self.l_mask.to(device) self.eye = self.eye.to(device) l = self.l * self.l_mask + self.eye u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s)) dlogdet = self.log_s.sum() if not reverse: w = torch.matmul(self.p, torch.matmul(l, u)) else: l = torch.inverse(l.double()).float() u = torch.inverse(u.double()).float() w = torch.matmul(u, torch.matmul(l, self.p.inverse())) return w.view(w_shape[0], w_shape[1], 1), dlogdet def forward(self, x, x_mask=None, reverse=False, **kwargs): """ log-det = log|abs(|W|)| * pixels """ b, c, t = x.size() if x_mask is None: x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t else: x_len = torch.sum(x_mask, [1, 2]) logdet = 0 if not reverse: weight, dlogdet = self.get_weight(x.device, reverse) z = F.conv1d(x, weight) if logdet is not None: logdet = logdet + dlogdet * x_len return z, logdet else: if self.weight is None: weight, dlogdet = self.get_weight(x.device, reverse) else: weight, dlogdet = self.weight, self.dlogdet z = F.conv1d(x, weight) if logdet is not None: logdet = logdet - dlogdet * x_len return z, logdet def store_inverse(self): self.weight, self.dlogdet = self.get_weight('cuda', reverse=True) class Flip(nn.Module): def forward(self, x, *args, reverse=False, **kwargs): x = torch.flip(x, [1]) logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) return x, logdet def store_inverse(self): pass class CouplingBlock(nn.Module): # 仿射耦合层 def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0, sigmoid_scale=False, share_cond_layers=False, wn=None): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.gin_channels = gin_channels self.p_dropout = p_dropout self.sigmoid_scale = sigmoid_scale start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) start = torch.nn.utils.weight_norm(start) self.start = start # Initializing last layer to 0 makes the affine coupling layers # do nothing at first. This helps with training stability end = torch.nn.Conv1d(hidden_channels, in_channels, 1) end.weight.data.zero_() end.bias.data.zero_() self.end = end self.wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, p_dropout, share_cond_layers) if wn is not None: self.wn.in_layers = wn.in_layers self.wn.res_skip_layers = wn.res_skip_layers def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): if x_mask is None: x_mask = 1 x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] x = self.start(x_0) * x_mask x = self.wn(x, x_mask, g) out = self.end(x) z_0 = x_0 m = out[:, :self.in_channels // 2, :] logs = out[:, self.in_channels // 2:, :] if self.sigmoid_scale: logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) if reverse: z_1 = (x_1 - m) * torch.exp(-logs) * x_mask logdet = torch.sum(-logs * x_mask, [1, 2]) else: z_1 = (m + torch.exp(logs) * x_1) * x_mask logdet = torch.sum(logs * x_mask, [1, 2]) z = torch.cat([z_0, z_1], 1) return z, logdet def store_inverse(self): self.wn.remove_weight_norm() class GlowFFTBlocks(FFTBlocks): def __init__(self, hidden_size=128, gin_channels=256, num_layers=2, ffn_kernel_size=5, dropout=None, num_heads=4, use_pos_embed=True, use_last_norm=True, norm='ln', use_pos_embed_alpha=True): super().__init__(hidden_size, num_layers, ffn_kernel_size, dropout, num_heads, use_pos_embed, use_last_norm, norm, use_pos_embed_alpha) self.inp_proj = nn.Conv1d(hidden_size + gin_channels, hidden_size, 1) def forward(self, x, x_mask=None, g=None): """ :param x: [B, C_x, T] :param x_mask: [B, 1, T] :param g: [B, C_g, T] :return: [B, C_x, T] """ if g is not None: x = self.inp_proj(torch.cat([x, g], 1)) x = x.transpose(1, 2) x = super(GlowFFTBlocks, self).forward(x, x_mask[:, 0] == 0) x = x.transpose(1, 2) return x class TransformerCouplingBlock(nn.Module): def __init__(self, in_channels, hidden_channels, n_layers, gin_channels=0, p_dropout=0, sigmoid_scale=False): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.n_layers = n_layers self.gin_channels = gin_channels self.p_dropout = p_dropout self.sigmoid_scale = sigmoid_scale start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) self.start = start # Initializing last layer to 0 makes the affine coupling layers # do nothing at first. This helps with training stability end = torch.nn.Conv1d(hidden_channels, in_channels, 1) end.weight.data.zero_() end.bias.data.zero_() self.end = end self.fft_blocks = GlowFFTBlocks( hidden_size=hidden_channels, ffn_kernel_size=3, gin_channels=gin_channels, num_layers=n_layers) def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): if x_mask is None: x_mask = 1 x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] x = self.start(x_0) * x_mask x = self.fft_blocks(x, x_mask, g) out = self.end(x) z_0 = x_0 m = out[:, :self.in_channels // 2, :] logs = out[:, self.in_channels // 2:, :] if self.sigmoid_scale: logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) if reverse: z_1 = (x_1 - m) * torch.exp(-logs) * x_mask logdet = torch.sum(-logs * x_mask, [1, 2]) else: z_1 = (m + torch.exp(logs) * x_1) * x_mask logdet = torch.sum(logs * x_mask, [1, 2]) z = torch.cat([z_0, z_1], 1) return z, logdet def store_inverse(self): pass class FreqFFTCouplingBlock(nn.Module): def __init__(self, in_channels, hidden_channels, n_layers, gin_channels=0, p_dropout=0, sigmoid_scale=False): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.n_layers = n_layers self.gin_channels = gin_channels self.p_dropout = p_dropout self.sigmoid_scale = sigmoid_scale hs = hidden_channels stride = 8 self.start = torch.nn.Conv2d(3, hs, kernel_size=stride * 2, stride=stride, padding=stride // 2) end = nn.ConvTranspose2d(hs, 2, kernel_size=stride, stride=stride) end.weight.data.zero_() end.bias.data.zero_() self.end = nn.Sequential( nn.Conv2d(hs * 3, hs, 3, 1, 1), nn.ReLU(), nn.GroupNorm(4, hs), nn.Conv2d(hs, hs, 3, 1, 1), end ) self.fft_v = FFTBlocks(hidden_size=hs, ffn_kernel_size=1, num_layers=n_layers) self.fft_h = nn.Sequential( nn.Conv1d(hs, hs, 3, 1, 1), nn.ReLU(), nn.Conv1d(hs, hs, 3, 1, 1), ) self.fft_g = nn.Sequential( nn.Conv1d( gin_channels - 160, hs, kernel_size=stride * 2, stride=stride, padding=stride // 2), Permute(0, 2, 1), FFTBlocks(hidden_size=hs, ffn_kernel_size=1, num_layers=n_layers), Permute(0, 2, 1), ) def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): g_, _ = unsqueeze(g) g_mel = g_[:, :80] g_txt = g_[:, 80:] g_mel, _ = squeeze(g_mel) g_txt, _ = squeeze(g_txt) # [B, C, T] if x_mask is None: x_mask = 1 x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] x = torch.stack([x_0, g_mel[:, :80], g_mel[:, 80:]], 1) x = self.start(x) # [B, C, N_bins, T] B, C, N_bins, T = x.shape x_v = self.fft_v(x.permute(0, 3, 2, 1).reshape(B * T, N_bins, C)) x_v = x_v.reshape(B, T, N_bins, -1).permute(0, 3, 2, 1) # x_v = x x_h = self.fft_h(x.permute(0, 2, 1, 3).reshape(B * N_bins, C, T)) x_h = x_h.reshape(B, N_bins, -1, T).permute(0, 2, 1, 3) # x_h = x x_g = self.fft_g(g_txt)[:, :, None, :].repeat(1, 1, 10, 1) x = torch.cat([x_v, x_h, x_g], 1) out = self.end(x) z_0 = x_0 m = out[:, 0] logs = out[:, 1] if self.sigmoid_scale: logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) if reverse: z_1 = (x_1 - m) * torch.exp(-logs) * x_mask logdet = torch.sum(-logs * x_mask, [1, 2]) else: z_1 = (m + torch.exp(logs) * x_1) * x_mask logdet = torch.sum(logs * x_mask, [1, 2]) z = torch.cat([z_0, z_1], 1) return z, logdet def store_inverse(self): pass class Glow(nn.Module): def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_blocks, n_layers, p_dropout=0., n_split=4, n_sqz=2, sigmoid_scale=False, gin_channels=0, inv_conv_type='near', share_cond_layers=False, share_wn_layers=0, ): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_blocks = n_blocks self.n_layers = n_layers self.p_dropout = p_dropout self.n_split = n_split self.n_sqz = n_sqz self.sigmoid_scale = sigmoid_scale self.gin_channels = gin_channels self.share_cond_layers = share_cond_layers if gin_channels != 0 and share_cond_layers: cond_layer = torch.nn.Conv1d(gin_channels * n_sqz, 2 * hidden_channels * n_layers, 1) self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') wn = None self.flows = nn.ModuleList() for b in range(n_blocks): self.flows.append(ActNorm(channels=in_channels * n_sqz)) if inv_conv_type == 'near': self.flows.append(InvConvNear(channels=in_channels * n_sqz, n_split=n_split, n_sqz=n_sqz)) if inv_conv_type == 'invconv': self.flows.append(InvConv(channels=in_channels * n_sqz)) if share_wn_layers > 0: if b % share_wn_layers == 0: wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels * n_sqz, p_dropout, share_cond_layers) self.flows.append( CouplingBlock( in_channels * n_sqz, hidden_channels, kernel_size=kernel_size, dilation_rate=dilation_rate, n_layers=n_layers, gin_channels=gin_channels * n_sqz, p_dropout=p_dropout, sigmoid_scale=sigmoid_scale, share_cond_layers=share_cond_layers, wn=wn )) def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False): logdet_tot = 0 if not reverse: flows = self.flows else: flows = reversed(self.flows) if return_hiddens: hs = [] if self.n_sqz > 1: x, x_mask_ = squeeze(x, x_mask, self.n_sqz) if g is not None: g, _ = squeeze(g, x_mask, self.n_sqz) x_mask = x_mask_ if self.share_cond_layers and g is not None: g = self.cond_layer(g) for f in flows: x, logdet = f(x, x_mask, g=g, reverse=reverse) if return_hiddens: hs.append(x) logdet_tot += logdet if self.n_sqz > 1: x, x_mask = unsqueeze(x, x_mask, self.n_sqz) if return_hiddens: return x, logdet_tot, hs return x, logdet_tot def store_inverse(self): def remove_weight_norm(m): try: nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(remove_weight_norm) for f in self.flows: f.store_inverse() class GlowV2(nn.Module): def __init__(self, in_channels=256, hidden_channels=256, kernel_size=3, dilation_rate=1, n_blocks=8, n_layers=4, p_dropout=0., n_split=4, n_split_blocks=3, sigmoid_scale=False, gin_channels=0, share_cond_layers=True): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_blocks = n_blocks self.n_layers = n_layers self.p_dropout = p_dropout self.n_split = n_split self.n_split_blocks = n_split_blocks self.sigmoid_scale = sigmoid_scale self.gin_channels = gin_channels self.cond_layers = nn.ModuleList() self.share_cond_layers = share_cond_layers self.flows = nn.ModuleList() in_channels = in_channels * 2 for l in range(n_split_blocks): blocks = nn.ModuleList() self.flows.append(blocks) gin_channels = gin_channels * 2 if gin_channels != 0 and share_cond_layers: cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1) self.cond_layers.append(torch.nn.utils.weight_norm(cond_layer, name='weight')) for b in range(n_blocks): blocks.append(ActNorm(channels=in_channels)) blocks.append(InvConvNear(channels=in_channels, n_split=n_split)) blocks.append(CouplingBlock( in_channels, hidden_channels, kernel_size=kernel_size, dilation_rate=dilation_rate, n_layers=n_layers, gin_channels=gin_channels, p_dropout=p_dropout, sigmoid_scale=sigmoid_scale, share_cond_layers=share_cond_layers)) def forward(self, x=None, x_mask=None, g=None, reverse=False, concat_zs=True, noise_scale=0.66, return_hiddens=False): logdet_tot = 0 if not reverse: flows = self.flows assert x_mask is not None zs = [] if return_hiddens: hs = [] for i, blocks in enumerate(flows): x, x_mask = squeeze(x, x_mask) g_ = None if g is not None: g, _ = squeeze(g) if self.share_cond_layers: g_ = self.cond_layers[i](g) else: g_ = g for layer in blocks: x, logdet = layer(x, x_mask=x_mask, g=g_, reverse=reverse) if return_hiddens: hs.append(x) logdet_tot += logdet if i == self.n_split_blocks - 1: zs.append(x) else: x, z = torch.chunk(x, 2, 1) zs.append(z) if concat_zs: zs = [z.reshape(x.shape[0], -1) for z in zs] zs = torch.cat(zs, 1) # [B, C*T] if return_hiddens: return zs, logdet_tot, hs return zs, logdet_tot else: flows = reversed(self.flows) if x is not None: assert isinstance(x, list) zs = x else: B, _, T = g.shape zs = self.get_prior(B, T, g.device, noise_scale) zs_ori = zs if g is not None: g_, g = g, [] for i in range(len(self.flows)): g_, _ = squeeze(g_) g.append(self.cond_layers[i](g_) if self.share_cond_layers else g_) else: g = [None for _ in range(len(self.flows))] if x_mask is not None: x_masks = [] for i in range(len(self.flows)): x_mask, _ = squeeze(x_mask) x_masks.append(x_mask) else: x_masks = [None for _ in range(len(self.flows))] x_masks = x_masks[::-1] g = g[::-1] zs = zs[::-1] x = None for i, blocks in enumerate(flows): x = zs[i] if x is None else torch.cat([x, zs[i]], 1) for layer in reversed(blocks): x, logdet = layer(x, x_masks=x_masks[i], g=g[i], reverse=reverse) logdet_tot += logdet x, _ = unsqueeze(x) return x, logdet_tot, zs_ori def store_inverse(self): for f in self.modules(): if hasattr(f, 'store_inverse') and f != self: f.store_inverse() def remove_weight_norm(m): try: nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(remove_weight_norm) def get_prior(self, B, T, device, noise_scale=0.66): C = 80 zs = [] for i in range(len(self.flows)): C, T = C, T // 2 if i == self.n_split_blocks - 1: zs.append(torch.randn(B, C * 2, T).to(device) * noise_scale) else: zs.append(torch.randn(B, C, T).to(device) * noise_scale) return zs def squeeze(x, x_mask=None, n_sqz=2): b, c, t = x.size() t = (t // n_sqz) * n_sqz x = x[:, :, :t] x_sqz = x.view(b, c, t // n_sqz, n_sqz) x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz) if x_mask is not None: x_mask = x_mask[:, :, n_sqz - 1::n_sqz] else: x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype) return x_sqz * x_mask, x_mask def unsqueeze(x, x_mask=None, n_sqz=2): b, c, t = x.size() x_unsqz = x.view(b, n_sqz, c // n_sqz, t) x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz) if x_mask is not None: x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz) else: x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype) return x_unsqz * x_mask, x_mask ================================================ FILE: NeuralSeq/modules/GenerSpeech/model/mixstyle.py ================================================ from modules.commons.common_layers import * import random class MixStyle(nn.Module): """MixStyle. Reference: Zhou et al. Domain Generalization with MixStyle. ICLR 2021. """ def __init__(self, p=0.5, alpha=0.1, eps=1e-6, hidden_size=256): """ Args: p (float): probability of using MixStyle. alpha (float): parameter of the Beta distribution. eps (float): scaling parameter to avoid numerical issues. mix (str): how to mix. """ super().__init__() self.p = p self.beta = torch.distributions.Beta(alpha, alpha) self.eps = eps self.alpha = alpha self._activated = True self.hidden_size = hidden_size self.affine_layer = LinearNorm( hidden_size, 2 * hidden_size, # For both b (bias) g (gain) ) def __repr__(self): return f'MixStyle(p={self.p}, alpha={self.alpha}, eps={self.eps})' def set_activation_status(self, status=True): self._activated = status def forward(self, x, spk_embed): if not self.training or not self._activated: return x if random.random() > self.p: return x B = x.size(0) mu, sig = torch.mean(x, dim=-1, keepdim=True), torch.std(x, dim=-1, keepdim=True) x_normed = (x - mu) / (sig + 1e-6) # [B, T, H_m] lmda = self.beta.sample((B, 1, 1)) lmda = lmda.to(x.device) # Get Bias and Gain mu1, sig1 = torch.split(self.affine_layer(spk_embed), self.hidden_size, dim=-1) # [B, 1, 2 * H_m] --> 2 * [B, 1, H_m] # MixStyle perm = torch.randperm(B) mu2, sig2 = mu1[perm], sig1[perm] mu_mix = mu1*lmda + mu2 * (1-lmda) sig_mix = sig1*lmda + sig2 * (1-lmda) # Perform Scailing and Shifting return sig_mix * x_normed + mu_mix # [B, T, H_m] ================================================ FILE: NeuralSeq/modules/GenerSpeech/model/prosody_util.py ================================================ from torch import nn import copy import torch from utils.hparams import hparams from modules.GenerSpeech.model.wavenet import WN import math from modules.fastspeech.tts_modules import LayerNorm import torch.nn.functional as F from utils.tts_utils import group_hidden_by_segs, sequence_mask from scipy.cluster.vq import kmeans2 from torch.nn import functional as F class VQEmbeddingEMA(nn.Module): def __init__(self, n_embeddings, embedding_dim, commitment_cost=0.25, decay=0.999, epsilon=1e-5, print_vq_prob=False): super(VQEmbeddingEMA, self).__init__() self.commitment_cost = commitment_cost self.n_embeddings = n_embeddings self.decay = decay self.epsilon = epsilon self.print_vq_prob = print_vq_prob self.register_buffer('data_initialized', torch.zeros(1)) init_bound = 1 / 512 embedding = torch.Tensor(n_embeddings, embedding_dim) embedding.uniform_(-init_bound, init_bound) self.register_buffer("embedding", embedding) self.register_buffer("ema_count", torch.zeros(n_embeddings)) self.register_buffer("ema_weight", self.embedding.clone()) def encode(self, x): B, T, _ = x.shape M, D = self.embedding.size() x_flat = x.detach().reshape(-1, D) distances = torch.addmm(torch.sum(self.embedding ** 2, dim=1) + torch.sum(x_flat ** 2, dim=1, keepdim=True), x_flat, self.embedding.t(), alpha=-2.0, beta=1.0) # [B*T_mel, N_vq] indices = torch.argmin(distances.float(), dim=-1) # [B*T_mel] quantized = F.embedding(indices, self.embedding) quantized = quantized.view_as(x) return x_flat, quantized, indices def forward(self, x): """ :param x: [B, T, D] :return: [B, T, D] """ B, T, _ = x.shape M, D = self.embedding.size() if self.training and self.data_initialized.item() == 0: print('| running kmeans in VQVAE') # data driven initialization for the embeddings x_flat = x.detach().reshape(-1, D) rp = torch.randperm(x_flat.size(0)) kd = kmeans2(x_flat[rp].data.cpu().numpy(), self.n_embeddings, minit='points') self.embedding.copy_(torch.from_numpy(kd[0])) x_flat, quantized, indices = self.encode(x) encodings = F.one_hot(indices, M).float() self.ema_weight.copy_(torch.matmul(encodings.t(), x_flat)) self.ema_count.copy_(torch.sum(encodings, dim=0)) x_flat, quantized, indices = self.encode(x) encodings = F.one_hot(indices, M).float() indices = indices.reshape(B, T) if self.training and self.data_initialized.item() != 0: self.ema_count = self.decay * self.ema_count + (1 - self.decay) * torch.sum(encodings, dim=0) n = torch.sum(self.ema_count) self.ema_count = (self.ema_count + self.epsilon) / (n + M * self.epsilon) * n dw = torch.matmul(encodings.t(), x_flat) self.ema_weight = self.decay * self.ema_weight + (1 - self.decay) * dw self.embedding = self.ema_weight / self.ema_count.unsqueeze(-1) self.data_initialized.fill_(1) e_latent_loss = F.mse_loss(x, quantized.detach(), reduction='none') nonpadding = (x.abs().sum(-1) > 0).float() e_latent_loss = (e_latent_loss.mean(-1) * nonpadding).sum() / nonpadding.sum() loss = self.commitment_cost * e_latent_loss quantized = x + (quantized - x).detach() avg_probs = torch.mean(encodings, dim=0) perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) if self.print_vq_prob: print("| VQ code avg_probs: ", avg_probs) return quantized, loss, indices, perplexity class CrossAttenLayer(nn.Module): def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1): super(CrossAttenLayer, self).__init__() self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) self.linear1 = nn.Linear(d_model, dim_feedforward) self.dropout1 = nn.Dropout(dropout) self.norm1 = nn.LayerNorm(d_model) self.linear2 = nn.Linear(dim_feedforward, d_model) self.dropout2 = nn.Dropout(dropout) self.norm2 = nn.LayerNorm(d_model) self.activation = nn.ReLU() def forward(self, src, local_emotion, emotion_key_padding_mask=None, forcing=False): # src: (Tph, B, 256) local_emotion: (Temo, B, 256) emotion_key_padding_mask: (B, Temo) if forcing: maxlength = src.shape[0] k = local_emotion.shape[0] / src.shape[0] lengths1 = torch.ceil(torch.tensor([i for i in range(maxlength)]).to(src.device) * k) + 1 lengths2 = torch.floor(torch.tensor([i for i in range(maxlength)]).to(src.device) * k) - 1 mask1 = sequence_mask(lengths1, local_emotion.shape[0]) mask2 = sequence_mask(lengths2, local_emotion.shape[0]) mask = mask1.float() - mask2.float() attn_emo = mask.repeat(src.shape[1], 1, 1) # (B, Tph, Temo) src2 = torch.matmul(local_emotion.permute(1, 2, 0), attn_emo.float().transpose(1, 2)).permute(2, 0, 1) else: src2, attn_emo = self.multihead_attn(src, local_emotion, local_emotion, key_padding_mask=emotion_key_padding_mask) src = src + self.dropout1(src2) src = self.norm1(src) src2 = self.linear2(self.activation(self.linear1(src))) src = src + self.dropout2(src2) src = self.norm2(src) return src, attn_emo class ProsodyAligner(nn.Module): def __init__(self, num_layers, guided_sigma=0.3, guided_layers=None, norm=None): super(ProsodyAligner, self).__init__() self.layers = nn.ModuleList([CrossAttenLayer(d_model=hparams['hidden_size'], nhead=2) for _ in range(num_layers)]) self.num_layers = num_layers self.norm = norm self.guided_sigma = guided_sigma self.guided_layers = guided_layers if guided_layers is not None else num_layers def forward(self, src, local_emotion, src_key_padding_mask=None, emotion_key_padding_mask=None, forcing=False): output = src guided_loss = 0 attn_emo_list = [] for i, mod in enumerate(self.layers): # output: (Tph, B, 256), global_emotion: (1, B, 256), local_emotion: (Temo, B, 256) mask: None, src_key_padding_mask: (B, Tph), # emotion_key_padding_mask: (B, Temo) output, attn_emo = mod(output, local_emotion, emotion_key_padding_mask=emotion_key_padding_mask, forcing=forcing) attn_emo_list.append(attn_emo.unsqueeze(1)) # attn_emo: (B, Tph, Temo) attn: (B, Tph, Tph) if i < self.guided_layers and src_key_padding_mask is not None: s_length = (~src_key_padding_mask).float().sum(-1) # B emo_length = (~emotion_key_padding_mask).float().sum(-1) attn_w_emo = _make_guided_attention_mask(src_key_padding_mask.size(-1), s_length, emotion_key_padding_mask.size(-1), emo_length, self.guided_sigma) g_loss_emo = attn_emo * attn_w_emo # N, L, S non_padding_mask = (~src_key_padding_mask).unsqueeze(-1) & (~emotion_key_padding_mask).unsqueeze(1) guided_loss = g_loss_emo[non_padding_mask].mean() + guided_loss if self.norm is not None: output = self.norm(output) return output, guided_loss, attn_emo_list def _make_guided_attention_mask(ilen, rilen, olen, rolen, sigma): grid_x, grid_y = torch.meshgrid(torch.arange(ilen, device=rilen.device), torch.arange(olen, device=rolen.device)) grid_x = grid_x.unsqueeze(0).expand(rilen.size(0), -1, -1) grid_y = grid_y.unsqueeze(0).expand(rolen.size(0), -1, -1) rilen = rilen.unsqueeze(1).unsqueeze(1) rolen = rolen.unsqueeze(1).unsqueeze(1) return 1.0 - torch.exp( -((grid_y.float() / rolen - grid_x.float() / rilen) ** 2) / (2 * (sigma ** 2)) ) class LocalStyleAdaptor(nn.Module): def __init__(self, hidden_size, num_vq_codes=64, padding_idx=0): super(LocalStyleAdaptor, self).__init__() self.encoder = ConvBlocks(80, hidden_size, [1] * 5, 5, dropout=hparams['vae_dropout']) self.n_embed = num_vq_codes self.vqvae = VQEmbeddingEMA(self.n_embed, hidden_size, commitment_cost=hparams['lambda_commit']) self.wavenet = WN(hidden_channels=80, gin_channels=80, kernel_size=3, dilation_rate=1, n_layers=4) self.padding_idx = padding_idx self.hidden_size = hidden_size def forward(self, ref_mels, mel2ph=None, no_vq=False): """ :param ref_mels: [B, T, 80] :return: [B, 1, H] """ padding_mask = ref_mels[:, :, 0].eq(self.padding_idx).data ref_mels = self.wavenet(ref_mels.transpose(1, 2), x_mask=(~padding_mask).unsqueeze(1).repeat([1, 80, 1])).transpose(1, 2) if mel2ph is not None: ref_ph, _ = group_hidden_by_segs(ref_mels, mel2ph, torch.max(mel2ph)) else: ref_ph = ref_mels prosody = self.encoder(ref_ph) if no_vq: return prosody z, vq_loss, vq_tokens, ppl = self.vqvae(prosody) vq_loss = vq_loss.mean() return z, vq_loss, ppl class LambdaLayer(nn.Module): def __init__(self, lambd): super(LambdaLayer, self).__init__() self.lambd = lambd def forward(self, x): return self.lambd(x) class Conv1d(nn.Conv1d): """A wrapper around nn.Conv1d, that works on (batch, time, channels)""" def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, dilation=1, groups=1, bias=True, padding=0): super(Conv1d, self).__init__(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, groups=groups, bias=bias, padding=padding) def forward(self, x): return super().forward(x.transpose(2, 1)).transpose(2, 1) def init_weights_func(m): classname = m.__class__.__name__ if classname.find("Conv1d") != -1: torch.nn.init.xavier_uniform_(m.weight) class ResidualBlock(nn.Module): """Implements conv->PReLU->norm n-times""" def __init__(self, channels, kernel_size, dilation, n=2, norm_type='bn', dropout=0.0, c_multiple=2, ln_eps=1e-12): super(ResidualBlock, self).__init__() if norm_type == 'bn': norm_builder = lambda: nn.BatchNorm1d(channels) elif norm_type == 'in': norm_builder = lambda: nn.InstanceNorm1d(channels, affine=True) elif norm_type == 'gn': norm_builder = lambda: nn.GroupNorm(8, channels) elif norm_type == 'ln': norm_builder = lambda: LayerNorm(channels, dim=1, eps=ln_eps) else: norm_builder = lambda: nn.Identity() self.blocks = [ nn.Sequential( norm_builder(), nn.Conv1d(channels, c_multiple * channels, kernel_size, dilation=dilation, padding=(dilation * (kernel_size - 1)) // 2), LambdaLayer(lambda x: x * kernel_size ** -0.5), nn.GELU(), nn.Conv1d(c_multiple * channels, channels, 1, dilation=dilation), ) for i in range(n) ] self.blocks = nn.ModuleList(self.blocks) self.dropout = dropout def forward(self, x): nonpadding = (x.abs().sum(1) > 0).float()[:, None, :] for b in self.blocks: x_ = b(x) if self.dropout > 0 and self.training: x_ = F.dropout(x_, self.dropout, training=self.training) x = x + x_ x = x * nonpadding return x class Pad(nn.ZeroPad2d): def __init__(self, kernel_size, dilation): pad_total = dilation * (kernel_size - 1) begin = pad_total // 2 end = pad_total - begin super(Pad, self).__init__((begin, end, begin, end)) class ZeroTemporalPad(nn.ZeroPad2d): """Pad sequences to equal lentgh in the temporal dimension""" def __init__(self, kernel_size, dilation, causal=False): total_pad = (dilation * (kernel_size - 1)) if causal: super(ZeroTemporalPad, self).__init__((total_pad, 0)) else: begin = total_pad // 2 end = total_pad - begin super(ZeroTemporalPad, self).__init__((begin, end)) class ConvBlocks(nn.Module): """Decodes the expanded phoneme encoding into spectrograms""" def __init__(self, channels, out_dims, dilations, kernel_size, norm_type='ln', layers_in_block=2, c_multiple=2, dropout=0.0, ln_eps=1e-5, init_weights=True): super(ConvBlocks, self).__init__() self.res_blocks = nn.Sequential( *[ResidualBlock(channels, kernel_size, d, n=layers_in_block, norm_type=norm_type, c_multiple=c_multiple, dropout=dropout, ln_eps=ln_eps) for d in dilations], ) if norm_type == 'bn': norm = nn.BatchNorm1d(channels) elif norm_type == 'in': norm = nn.InstanceNorm1d(channels, affine=True) elif norm_type == 'gn': norm = nn.GroupNorm(8, channels) elif norm_type == 'ln': norm = LayerNorm(channels, dim=1, eps=ln_eps) self.last_norm = norm self.post_net1 = nn.Conv1d(channels, out_dims, kernel_size=3, padding=1) if init_weights: self.apply(init_weights_func) def forward(self, x): """ :param x: [B, T, H] :return: [B, T, H] """ x = x.transpose(1, 2) nonpadding = (x.abs().sum(1) > 0).float()[:, None, :] x = self.res_blocks(x) * nonpadding x = self.last_norm(x) * nonpadding x = self.post_net1(x) * nonpadding return x.transpose(1, 2) class TextConvEncoder(ConvBlocks): def __init__(self, embed_tokens, channels, out_dims, dilations, kernel_size, norm_type='ln', layers_in_block=2, c_multiple=2, dropout=0.0, ln_eps=1e-5, init_weights=True): super().__init__(channels, out_dims, dilations, kernel_size, norm_type, layers_in_block, c_multiple, dropout, ln_eps, init_weights) self.embed_tokens = embed_tokens self.embed_scale = math.sqrt(channels) def forward(self, txt_tokens): """ :param txt_tokens: [B, T] :return: { 'encoder_out': [B x T x C] } """ x = self.embed_scale * self.embed_tokens(txt_tokens) return super().forward(x) class ConditionalConvBlocks(ConvBlocks): def __init__(self, channels, g_channels, out_dims, dilations, kernel_size, norm_type='ln', layers_in_block=2, c_multiple=2, dropout=0.0, ln_eps=1e-5, init_weights=True, is_BTC=True): super().__init__(channels, out_dims, dilations, kernel_size, norm_type, layers_in_block, c_multiple, dropout, ln_eps, init_weights) self.g_prenet = nn.Conv1d(g_channels, channels, 3, padding=1) self.is_BTC = is_BTC if init_weights: self.g_prenet.apply(init_weights_func) def forward(self, x, g, x_mask): if self.is_BTC: x = x.transpose(1, 2) g = g.transpose(1, 2) x_mask = x_mask.transpose(1, 2) x = x + self.g_prenet(g) x = x * x_mask if not self.is_BTC: x = x.transpose(1, 2) x = super(ConditionalConvBlocks, self).forward(x) # input needs to be BTC if not self.is_BTC: x = x.transpose(1, 2) return x ================================================ FILE: NeuralSeq/modules/GenerSpeech/model/wavenet.py ================================================ from modules.commons.common_layers import * # @torch.jit.script def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): n_channels_int = n_channels[0] in_act = input_a + input_b t_act = torch.tanh(in_act[:, :n_channels_int, :]) s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) acts = t_act * s_act return acts class WN(torch.nn.Module): def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0, share_cond_layers=False): super(WN, self).__init__() assert (kernel_size % 2 == 1) assert (hidden_channels % 2 == 0) self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.gin_channels = gin_channels self.p_dropout = p_dropout self.share_cond_layers = share_cond_layers self.in_layers = torch.nn.ModuleList() self.res_skip_layers = torch.nn.ModuleList() self.drop = nn.Dropout(p_dropout) if gin_channels != 0 and not share_cond_layers: cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1) self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') for i in range(n_layers): dilation = dilation_rate ** i padding = int((kernel_size * dilation - dilation) / 2) in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding) in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') self.in_layers.append(in_layer) # last one is not necessary if i < n_layers - 1: res_skip_channels = 2 * hidden_channels else: res_skip_channels = hidden_channels res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') self.res_skip_layers.append(res_skip_layer) def forward(self, x, x_mask=None, g=None, **kwargs): output = torch.zeros_like(x) n_channels_tensor = torch.IntTensor([self.hidden_channels]) if g is not None and not self.share_cond_layers: g = self.cond_layer(g) for i in range(self.n_layers): x_in = self.in_layers[i](x) x_in = self.drop(x_in) if g is not None: cond_offset = i * 2 * self.hidden_channels g_l = g[:, cond_offset:cond_offset + 2 * self.hidden_channels, :] else: g_l = torch.zeros_like(x_in) acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) res_skip_acts = self.res_skip_layers[i](acts) if i < self.n_layers - 1: x = (x + res_skip_acts[:, :self.hidden_channels, :]) * x_mask output = output + res_skip_acts[:, self.hidden_channels:, :] else: output = output + res_skip_acts return output * x_mask def remove_weight_norm(self): def remove_weight_norm(m): try: nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(remove_weight_norm) ================================================ FILE: NeuralSeq/modules/GenerSpeech/task/dataset.py ================================================ import matplotlib matplotlib.use('Agg') from tasks.base_task import data_loader from tasks.tts.fs2 import FastSpeech2Task from tasks.tts.dataset_utils import FastSpeechDataset, BaseTTSDataset import glob import importlib from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse from inference.base_tts_infer import load_data_preprocessor from data_gen.tts.emotion import inference as EmotionEncoder from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance from data_gen.tts.emotion.inference import preprocess_wav from tqdm import tqdm from utils.hparams import hparams from data_gen.tts.data_gen_utils import build_phone_encoder, build_word_encoder import random import torch import torch.optim import torch.nn.functional as F import torch.utils.data from utils.indexed_datasets import IndexedDataset from resemblyzer import VoiceEncoder import torch.distributions import numpy as np import utils import os class GenerSpeech_dataset(BaseTTSDataset): def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): super().__init__(prefix, shuffle, test_items, test_sizes, data_dir) self.f0_mean, self.f0_std = hparams.get('f0_mean', None), hparams.get('f0_std', None) if prefix == 'valid': indexed_ds = IndexedDataset(f'{self.data_dir}/train') sizes = np.load(f'{self.data_dir}/train_lengths.npy') index = [i for i in range(len(indexed_ds))] random.shuffle(index) index = index[:300] self.sizes = sizes[index] self.indexed_ds = [] for i in index: self.indexed_ds.append(indexed_ds[i]) self.avail_idxs = list(range(len(self.sizes))) if hparams['min_frames'] > 0: self.avail_idxs = [x for x in self.avail_idxs if self.sizes[x] >= hparams['min_frames']] self.sizes = [self.sizes[i] for i in self.avail_idxs] if prefix == 'test' and hparams['test_input_dir'] != '': self.preprocessor, self.preprocess_args = load_data_preprocessor() self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir']) self.avail_idxs = [i for i, _ in enumerate(self.sizes)] def load_test_inputs(self, test_input_dir): inp_wav_paths = sorted(glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3')) binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') pkg = ".".join(binarizer_cls.split(".")[:-1]) cls_name = binarizer_cls.split(".")[-1] binarizer_cls = getattr(importlib.import_module(pkg), cls_name) phone_encoder = build_phone_encoder(hparams['binary_data_dir']) word_encoder = build_word_encoder(hparams['binary_data_dir']) voice_encoder = VoiceEncoder().cuda() encoder = [phone_encoder, word_encoder] sizes = [] items = [] EmotionEncoder.load_model(hparams['emotion_encoder_path']) preprocessor, preprocess_args = self.preprocessor, self.preprocess_args for wav_fn in tqdm(inp_wav_paths): item_name = wav_fn[len(test_input_dir) + 1:].replace("/", "_") spk_id = emotion = 0 item2tgfn = wav_fn.replace('.wav', '.TextGrid') # prepare textgrid alignment txtpath = wav_fn.replace('.wav', '.txt') # prepare text with open(txtpath, 'r') as f: text_raw = f.readlines() f.close() ph, txt = preprocessor.txt_to_ph(preprocessor.txt_processor, text_raw[0], preprocess_args) item = binarizer_cls.process_item(item_name, ph, txt, item2tgfn, wav_fn, spk_id, emotion, encoder, hparams['binarization_args']) item['emo_embed'] = Embed_utterance(preprocess_wav(item['wav_fn'])) item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) items.append(item) sizes.append(item['len']) return items, sizes def _get_item(self, index): if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: index = self.avail_idxs[index] if self.indexed_ds is None: self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') return self.indexed_ds[index] def __getitem__(self, index): hparams = self.hparams item = self._get_item(index) assert len(item['mel']) == self.sizes[index], (len(item['mel']), self.sizes[index]) max_frames = hparams['max_frames'] spec = torch.Tensor(item['mel'])[:max_frames] max_frames = spec.shape[0] // hparams['frames_multiple'] * hparams['frames_multiple'] spec = spec[:max_frames] phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) sample = { "id": index, "item_name": item['item_name'], "text": item['txt'], "txt_token": phone, "mel": spec, "mel_nonpadding": spec.abs().sum(-1) > 0, } spec = sample['mel'] T = spec.shape[0] sample['mel2ph'] = mel2ph = torch.LongTensor(item['mel2ph'])[:T] if 'mel2ph' in item else None if hparams['use_pitch_embed']: assert 'f0' in item if hparams.get('normalize_pitch', False): f0 = item["f0"] if len(f0 > 0) > 0 and f0[f0 > 0].std() > 0: f0[f0 > 0] = (f0[f0 > 0] - f0[f0 > 0].mean()) / f0[f0 > 0].std() * hparams['f0_std'] + \ hparams['f0_mean'] f0[f0 > 0] = f0[f0 > 0].clip(min=60, max=500) pitch = f0_to_coarse(f0) pitch = torch.LongTensor(pitch[:max_frames]) else: pitch = torch.LongTensor(item.get("pitch"))[:max_frames] if "pitch" in item else None f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) uv = torch.FloatTensor(uv) f0 = torch.FloatTensor(f0) else: f0 = uv = torch.zeros_like(mel2ph) pitch = None sample["f0"], sample["uv"], sample["pitch"] = f0, uv, pitch sample["spk_embed"] = torch.Tensor(item['spk_embed']) sample["emotion"] = item['emotion'] sample["emo_embed"] = torch.Tensor(item['emo_embed']) if hparams.get('use_word', False): sample["ph_words"] = item["ph_words"] sample["word_tokens"] = torch.LongTensor(item["word_tokens"]) sample["mel2word"] = torch.LongTensor(item.get("mel2word"))[:max_frames] sample["ph2word"] = torch.LongTensor(item['ph2word'][:hparams['max_input_tokens']]) return sample def collater(self, samples): if len(samples) == 0: return {} hparams = self.hparams id = torch.LongTensor([s['id'] for s in samples]) item_names = [s['item_name'] for s in samples] text = [s['text'] for s in samples] txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0) mels = utils.collate_2d([s['mel'] for s in samples], 0.0) txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples]) mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) batch = { 'id': id, 'item_name': item_names, 'nsamples': len(samples), 'text': text, 'txt_tokens': txt_tokens, 'txt_lengths': txt_lengths, 'mels': mels, 'mel_lengths': mel_lengths, } f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) pitch = utils.collate_1d([s['pitch'] for s in samples]) if samples[0]['pitch'] is not None else None uv = utils.collate_1d([s['uv'] for s in samples]) mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) if samples[0]['mel2ph'] is not None else None batch.update({ 'mel2ph': mel2ph, 'pitch': pitch, 'f0': f0, 'uv': uv, }) spk_embed = torch.stack([s['spk_embed'] for s in samples]) batch['spk_embed'] = spk_embed emo_embed = torch.stack([s['emo_embed'] for s in samples]) batch['emo_embed'] = emo_embed if hparams.get('use_word', False): ph_words = [s['ph_words'] for s in samples] batch['ph_words'] = ph_words word_tokens = utils.collate_1d([s['word_tokens'] for s in samples], 0) batch['word_tokens'] = word_tokens mel2word = utils.collate_1d([s['mel2word'] for s in samples], 0) batch['mel2word'] = mel2word ph2word = utils.collate_1d([s['ph2word'] for s in samples], 0) batch['ph2word'] = ph2word return batch ================================================ FILE: NeuralSeq/modules/GenerSpeech/task/generspeech.py ================================================ import matplotlib matplotlib.use('Agg') from data_gen.tts.data_gen_utils import get_pitch from modules.fastspeech.tts_modules import mel2ph_to_dur import matplotlib.pyplot as plt from utils import audio from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse from vocoders.base_vocoder import get_vocoder_cls import json from utils.plot import spec_to_figure from utils.hparams import hparams import torch import torch.optim import torch.nn.functional as F import torch.utils.data from modules.GenerSpeech.task.dataset import GenerSpeech_dataset from modules.GenerSpeech.model.generspeech import GenerSpeech import torch.distributions import numpy as np from utils.tts_utils import select_attn import utils import os from tasks.tts.fs2 import FastSpeech2Task class GenerSpeechTask(FastSpeech2Task): def __init__(self): super(GenerSpeechTask, self).__init__() self.dataset_cls = GenerSpeech_dataset def build_tts_model(self): self.model = GenerSpeech(self.phone_encoder) def build_model(self): self.build_tts_model() if hparams['load_ckpt'] != '': self.load_ckpt(hparams['load_ckpt'], strict=False) utils.num_params(self.model) return self.model def run_model(self, model, sample, return_output=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] mel2ph = sample['mel2ph'] # [B, T_s] mel2word = sample['mel2word'] f0 = sample['f0'] # [B, T_s] uv = sample['uv'] # [B, T_s] 0/1 spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') emo_embed = sample.get('emo_embed') output = model(txt_tokens, mel2ph=mel2ph, ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed, emo_embed=emo_embed, ref_mels=target, f0=f0, uv=uv, tgt_mels=target, global_steps=self.global_step, infer=False) losses = {} losses['postflow'] = output['postflow'] if self.global_step > hparams['forcing']: losses['gloss'] = (output['gloss_utter'] + output['gloss_ph'] + output['gloss_word']) / 3 if self.global_step > hparams['vq_start']: losses['vq_loss'] = (output['vq_loss_utter'] + output['vq_loss_ph'] + output['vq_loss_word']) / 3 losses['ppl_utter'] = output['ppl_utter'] losses['ppl_ph'] = output['ppl_ph'] losses['ppl_word'] = output['ppl_word'] self.add_mel_loss(output['mel_out'], target, losses) self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) output['select_attn'] = select_attn(output['attn_ph']) if not return_output: return losses else: return losses, output def validation_step(self, sample, batch_idx): outputs = {} outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] encdec_attn = model_out['select_attn'] mel_out = self.model.out2mel(model_out['mel_out']) outputs = utils.tensors_to_scalars(outputs) if self.global_step % hparams['valid_infer_interval'] == 0 \ and batch_idx < hparams['num_valid_plots']: vmin = hparams['mel_vmin'] vmax = hparams['mel_vmax'] self.plot_mel(batch_idx, sample['mels'], mel_out) self.plot_dur(batch_idx, sample, model_out) if hparams['use_pitch_embed']: self.plot_pitch(batch_idx, sample, model_out) if self.vocoder is None: self.vocoder = get_vocoder_cls(hparams)() if self.global_step > 0: spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') emo_embed = sample.get('emo_embed') ref_mels = sample['mels'] mel2ph = sample['mel2ph'] # [B, T_s] mel2word = sample['mel2word'] # with gt duration model_out = self.model(sample['txt_tokens'], mel2ph=mel2ph, ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed, emo_embed=emo_embed, ref_mels=ref_mels, global_steps=self.global_step, infer=True) wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu()) self.logger.add_audio(f'wav_gtdur_{batch_idx}', wav_pred, self.global_step, hparams['audio_sample_rate']) self.logger.add_figure(f'ali_{batch_idx}', spec_to_figure(encdec_attn[0]), self.global_step) self.logger.add_figure( f'mel_gtdur_{batch_idx}', spec_to_figure(model_out['mel_out'][0], vmin, vmax), self.global_step) # with pred duration model_out = self.model(sample['txt_tokens'], ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed, emo_embed=emo_embed, ref_mels=ref_mels, global_steps=self.global_step, infer=True) self.logger.add_figure( f'mel_{batch_idx}', spec_to_figure(model_out['mel_out'][0], vmin, vmax), self.global_step) wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu()) self.logger.add_audio(f'wav_{batch_idx}', wav_pred, self.global_step, hparams['audio_sample_rate']) # gt wav if self.global_step <= hparams['valid_infer_interval']: mel_gt = sample['mels'][0].cpu() wav_gt = self.vocoder.spec2wav(mel_gt) self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, 22050) return outputs ############ # infer ############ def test_step(self, sample, batch_idx): spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') emo_embed = sample.get('emo_embed') txt_tokens = sample['txt_tokens'] mel2ph, uv, f0 = None, None, None ref_mel2word = sample['mel2word'] ref_mel2ph = sample['mel2ph'] ref_mels = sample['mels'] if hparams['use_gt_dur']: mel2ph = sample['mel2ph'] if hparams['use_gt_f0']: f0 = sample['f0'] uv = sample['uv'] global_steps = 200000 run_model = lambda: self.model( txt_tokens, spk_embed=spk_embed, emo_embed=emo_embed, mel2ph=mel2ph, ref_mel2ph=ref_mel2ph, ref_mel2word=ref_mel2word, f0=f0, uv=uv, ref_mels=ref_mels, global_steps=global_steps, infer=True) outputs = run_model() sample['outputs'] = self.model.out2mel(outputs['mel_out']) sample['mel2ph_pred'] = outputs['mel2ph'] if hparams['use_pitch_embed']: sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) if hparams['pitch_type'] == 'ph': sample['f0'] = torch.gather(F.pad(sample['f0'], [1, 0]), 1, sample['mel2ph']) sample['f0_pred'] = outputs.get('f0_denorm') return self.after_infer(sample) def after_infer(self, predictions, sil_start_frame=0): predictions = utils.unpack_dict_to_list(predictions) assert len(predictions) == 1, 'Only support batch_size=1 in inference.' prediction = predictions[0] prediction = utils.tensors_to_np(prediction) item_name = prediction.get('item_name') text = prediction.get('text') ph_tokens = prediction.get('txt_tokens') mel_gt = prediction["mels"] mel2ph_gt = prediction.get("mel2ph") mel2ph_gt = mel2ph_gt if mel2ph_gt is not None else None mel_pred = prediction["outputs"] mel2ph_pred = prediction.get("mel2ph_pred") f0_gt = prediction.get("f0") f0_pred = prediction.get("f0_pred") str_phs = None if self.phone_encoder is not None and 'txt_tokens' in prediction: str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True) if 'encdec_attn' in prediction: encdec_attn = prediction['encdec_attn'] # (1, Tph, Tmel) encdec_attn = encdec_attn[encdec_attn.max(-1).sum(-1).argmax(-1)] txt_lengths = prediction.get('txt_lengths') encdec_attn = encdec_attn.T[:, :txt_lengths] else: encdec_attn = None wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred) wav_pred[:sil_start_frame * hparams['hop_size']] = 0 gen_dir = self.gen_dir base_fn = f'[{self.results_id:06d}][{item_name}][%s]' # if text is not None: # base_fn += text.replace(":", "%3A")[:80] base_fn = base_fn.replace(' ', '_') if not hparams['profile_infer']: os.makedirs(gen_dir, exist_ok=True) os.makedirs(f'{gen_dir}/wavs', exist_ok=True) os.makedirs(f'{gen_dir}/plot', exist_ok=True) if hparams.get('save_mel_npy', False): os.makedirs(f'{gen_dir}/npy', exist_ok=True) if 'encdec_attn' in prediction: os.makedirs(f'{gen_dir}/attn_plot', exist_ok=True) self.saving_results_futures.append( self.saving_result_pool.apply_async(self.save_result, args=[ wav_pred, mel_pred, base_fn % 'TTS', gen_dir, str_phs, mel2ph_pred, encdec_attn])) if mel_gt is not None and hparams['save_gt']: wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) self.saving_results_futures.append( self.saving_result_pool.apply_async(self.save_result, args=[ wav_gt, mel_gt, base_fn % 'Ref', gen_dir, str_phs, mel2ph_gt])) if hparams['save_f0']: import matplotlib.pyplot as plt f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) fig = plt.figure() plt.plot(f0_pred_, label=r'$\hat{f_0}$') plt.plot(f0_gt_, label=r'$f_0$') plt.legend() plt.tight_layout() plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png') plt.close(fig) print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") self.results_id += 1 return { 'item_name': item_name, 'text': text, 'ph_tokens': self.phone_encoder.decode(ph_tokens.tolist()), 'wav_fn_pred': base_fn % 'TTS', 'wav_fn_gt': base_fn % 'Ref', } @staticmethod def save_result(wav_out, mel, base_fn, gen_dir, str_phs=None, mel2ph=None, alignment=None): audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'], norm=hparams['out_wav_norm']) fig = plt.figure(figsize=(14, 10)) spec_vmin = hparams['mel_vmin'] spec_vmax = hparams['mel_vmax'] heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax) fig.colorbar(heatmap) f0, _ = get_pitch(wav_out, mel, hparams) f0 = f0 / 10 * (f0 > 0) plt.plot(f0, c='white', linewidth=1, alpha=0.6) if mel2ph is not None and str_phs is not None: decoded_txt = str_phs.split(" ") dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy() dur = [0] + list(np.cumsum(dur)) for i in range(len(dur) - 1): shift = (i % 20) + 1 plt.text(dur[i], shift, decoded_txt[i]) plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black') plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black', alpha=1, linewidth=1) plt.tight_layout() plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png') plt.close(fig) if hparams.get('save_mel_npy', False): np.save(f'{gen_dir}/npy/{base_fn}', mel) if alignment is not None: fig, ax = plt.subplots(figsize=(12, 16)) im = ax.imshow(alignment, aspect='auto', origin='lower', interpolation='none') ax.set_xticks(np.arange(0, alignment.shape[1], 5)) ax.set_yticks(np.arange(0, alignment.shape[0], 10)) ax.set_ylabel("$S_p$ index") ax.set_xlabel("$H_c$ index") fig.colorbar(im, ax=ax) fig.savefig(f'{gen_dir}/attn_plot/{base_fn}_attn.png', format='png') plt.close(fig) ================================================ FILE: NeuralSeq/modules/__init__.py ================================================ ================================================ FILE: NeuralSeq/modules/commons/align_ops.py ================================================ import torch import torch.nn.functional as F def build_word_mask(x2word, y2word): return (x2word[:, :, None] == y2word[:, None, :]).long() def mel2ph_to_mel2word(mel2ph, ph2word): mel2word = (ph2word - 1).gather(1, (mel2ph - 1).clamp(min=0)) + 1 mel2word = mel2word * (mel2ph > 0).long() return mel2word def clip_mel2token_to_multiple(mel2token, frames_multiple): max_frames = mel2token.shape[1] // frames_multiple * frames_multiple mel2token = mel2token[:, :max_frames] return mel2token def expand_states(h, mel2token): h = F.pad(h, [0, 0, 1, 0]) mel2token_ = mel2token[..., None].repeat([1, 1, h.shape[-1]]) h = torch.gather(h, 1, mel2token_) # [B, T, H] return h ================================================ FILE: NeuralSeq/modules/commons/common_layers.py ================================================ import math import torch from torch import nn from torch.nn import Parameter import torch.onnx.operators import torch.nn.functional as F import utils class Reshape(nn.Module): def __init__(self, *args): super(Reshape, self).__init__() self.shape = args def forward(self, x): return x.view(self.shape) class Permute(nn.Module): def __init__(self, *args): super(Permute, self).__init__() self.args = args def forward(self, x): return x.permute(self.args) class LinearNorm(torch.nn.Module): def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): super(LinearNorm, self).__init__() self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) torch.nn.init.xavier_uniform_( self.linear_layer.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) def forward(self, x): return self.linear_layer(x) class ConvNorm(torch.nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=None, dilation=1, bias=True, w_init_gain='linear'): super(ConvNorm, self).__init__() if padding is None: assert (kernel_size % 2 == 1) padding = int(dilation * (kernel_size - 1) / 2) self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias) torch.nn.init.xavier_uniform_( self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) def forward(self, signal): conv_signal = self.conv(signal) return conv_signal def Embedding(num_embeddings, embedding_dim, padding_idx=None): m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) if padding_idx is not None: nn.init.constant_(m.weight[padding_idx], 0) return m def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): if not export and torch.cuda.is_available(): try: from apex.normalization import FusedLayerNorm return FusedLayerNorm(normalized_shape, eps, elementwise_affine) except ImportError: pass return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) def Linear(in_features, out_features, bias=True): m = nn.Linear(in_features, out_features, bias) nn.init.xavier_uniform_(m.weight) if bias: nn.init.constant_(m.bias, 0.) return m class SinusoidalPositionalEmbedding(nn.Module): """This module produces sinusoidal positional embeddings of any length. Padding symbols are ignored. """ def __init__(self, embedding_dim, padding_idx, init_size=1024): super().__init__() self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.weights = SinusoidalPositionalEmbedding.get_embedding( init_size, embedding_dim, padding_idx, ) self.register_buffer('_float_tensor', torch.FloatTensor(1)) @staticmethod def get_embedding(num_embeddings, embedding_dim, padding_idx=None): """Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) if embedding_dim % 2 == 1: # zero pad emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) if padding_idx is not None: emb[padding_idx, :] = 0 return emb def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs): """Input is expected to be of size [bsz x seqlen].""" bsz, seq_len = input.shape[:2] max_pos = self.padding_idx + 1 + seq_len if self.weights is None or max_pos > self.weights.size(0): # recompute/expand embeddings if needed self.weights = SinusoidalPositionalEmbedding.get_embedding( max_pos, self.embedding_dim, self.padding_idx, ) self.weights = self.weights.to(self._float_tensor) if incremental_state is not None: # positions is the same for every token when decoding a single step pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) positions = utils.make_positions(input, self.padding_idx) if positions is None else positions return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() def max_positions(self): """Maximum number of supported positions.""" return int(1e5) # an arbitrary large number class ConvTBC(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding=0): super(ConvTBC, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.padding = padding self.weight = torch.nn.Parameter(torch.Tensor( self.kernel_size, in_channels, out_channels)) self.bias = torch.nn.Parameter(torch.Tensor(out_channels)) def forward(self, input): return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding) class MultiheadAttention(nn.Module): def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, self_attention=False, encoder_decoder_attention=False): super().__init__() self.embed_dim = embed_dim self.kdim = kdim if kdim is not None else embed_dim self.vdim = vdim if vdim is not None else embed_dim self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" self.scaling = self.head_dim ** -0.5 self.self_attention = self_attention self.encoder_decoder_attention = encoder_decoder_attention assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \ 'value to be of the same size' if self.qkv_same_dim: self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim)) else: self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim)) self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim)) self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim)) if bias: self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim)) else: self.register_parameter('in_proj_bias', None) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) if add_bias_kv: self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) else: self.bias_k = self.bias_v = None self.add_zero_attn = add_zero_attn self.reset_parameters() self.enable_torch_version = False if hasattr(F, "multi_head_attention_forward"): self.enable_torch_version = True else: self.enable_torch_version = False self.last_attn_probs = None def reset_parameters(self): if self.qkv_same_dim: nn.init.xavier_uniform_(self.in_proj_weight) else: nn.init.xavier_uniform_(self.k_proj_weight) nn.init.xavier_uniform_(self.v_proj_weight) nn.init.xavier_uniform_(self.q_proj_weight) nn.init.xavier_uniform_(self.out_proj.weight) if self.in_proj_bias is not None: nn.init.constant_(self.in_proj_bias, 0.) nn.init.constant_(self.out_proj.bias, 0.) if self.bias_k is not None: nn.init.xavier_normal_(self.bias_k) if self.bias_v is not None: nn.init.xavier_normal_(self.bias_v) def forward( self, query, key, value, key_padding_mask=None, incremental_state=None, need_weights=True, static_kv=False, attn_mask=None, before_softmax=False, need_head_weights=False, enc_dec_attn_constraint_mask=None, reset_attn_weight=None ): """Input shape: Time x Batch x Channel Args: key_padding_mask (ByteTensor, optional): mask to exclude keys that are pads, of shape `(batch, src_len)`, where padding elements are indicated by 1s. need_weights (bool, optional): return the attention weights, averaged over heads (default: False). attn_mask (ByteTensor, optional): typically used to implement causal attention, where the mask prevents the attention from looking forward in time (default: None). before_softmax (bool, optional): return the raw attention weights and values before the attention softmax. need_head_weights (bool, optional): return the attention weights for each head. Implies *need_weights*. Default: return the average attention weights over all heads. """ if need_head_weights: need_weights = True tgt_len, bsz, embed_dim = query.size() assert embed_dim == self.embed_dim assert list(query.size()) == [tgt_len, bsz, embed_dim] if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None: if self.qkv_same_dim: return F.multi_head_attention_forward(query, key, value, self.embed_dim, self.num_heads, self.in_proj_weight, self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, self.training, key_padding_mask, need_weights, attn_mask) else: return F.multi_head_attention_forward(query, key, value, self.embed_dim, self.num_heads, torch.empty([0]), self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, self.training, key_padding_mask, need_weights, attn_mask, use_separate_proj_weight=True, q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight, v_proj_weight=self.v_proj_weight) if incremental_state is not None: print('Not implemented error.') exit() else: saved_state = None if self.self_attention: # self-attention q, k, v = self.in_proj_qkv(query) elif self.encoder_decoder_attention: # encoder-decoder attention q = self.in_proj_q(query) if key is None: assert value is None k = v = None else: k = self.in_proj_k(key) v = self.in_proj_v(key) else: q = self.in_proj_q(query) k = self.in_proj_k(key) v = self.in_proj_v(value) q *= self.scaling if self.bias_k is not None: assert self.bias_v is not None k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1) q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) if k is not None: k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) if v is not None: v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) if saved_state is not None: print('Not implemented error.') exit() src_len = k.size(1) # This is part of a workaround to get around fork/join parallelism # not supporting Optional types. if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]): key_padding_mask = None if key_padding_mask is not None: assert key_padding_mask.size(0) == bsz assert key_padding_mask.size(1) == src_len if self.add_zero_attn: src_len += 1 k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1) attn_weights = torch.bmm(q, k.transpose(1, 2)) attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] if attn_mask is not None: if len(attn_mask.shape) == 2: attn_mask = attn_mask.unsqueeze(0) elif len(attn_mask.shape) == 3: attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape( bsz * self.num_heads, tgt_len, src_len) attn_weights = attn_weights + attn_mask if enc_dec_attn_constraint_mask is not None: # bs x head x L_kv attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.masked_fill( enc_dec_attn_constraint_mask.unsqueeze(2).bool(), -1e9, ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if key_padding_mask is not None: # don't attend to padding symbols attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.masked_fill( key_padding_mask.unsqueeze(1).unsqueeze(2), -1e9, ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) if before_softmax: return attn_weights, v attn_weights_float = utils.softmax(attn_weights, dim=-1) attn_weights = attn_weights_float.type_as(attn_weights) attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training) if reset_attn_weight is not None: if reset_attn_weight: self.last_attn_probs = attn_probs.detach() else: assert self.last_attn_probs is not None attn_probs = self.last_attn_probs attn = torch.bmm(attn_probs, v) assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) attn = self.out_proj(attn) if need_weights: attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0) if not need_head_weights: # average attention weights over heads attn_weights = attn_weights.mean(dim=0) else: attn_weights = None return attn, (attn_weights, attn_logits) def in_proj_qkv(self, query): return self._in_proj(query).chunk(3, dim=-1) def in_proj_q(self, query): if self.qkv_same_dim: return self._in_proj(query, end=self.embed_dim) else: bias = self.in_proj_bias if bias is not None: bias = bias[:self.embed_dim] return F.linear(query, self.q_proj_weight, bias) def in_proj_k(self, key): if self.qkv_same_dim: return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim) else: weight = self.k_proj_weight bias = self.in_proj_bias if bias is not None: bias = bias[self.embed_dim:2 * self.embed_dim] return F.linear(key, weight, bias) def in_proj_v(self, value): if self.qkv_same_dim: return self._in_proj(value, start=2 * self.embed_dim) else: weight = self.v_proj_weight bias = self.in_proj_bias if bias is not None: bias = bias[2 * self.embed_dim:] return F.linear(value, weight, bias) def _in_proj(self, input, start=0, end=None): weight = self.in_proj_weight bias = self.in_proj_bias weight = weight[start:end, :] if bias is not None: bias = bias[start:end] return F.linear(input, weight, bias) def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): return attn_weights class Swish(torch.autograd.Function): @staticmethod def forward(ctx, i): result = i * torch.sigmoid(i) ctx.save_for_backward(i) return result @staticmethod def backward(ctx, grad_output): i = ctx.saved_variables[0] sigmoid_i = torch.sigmoid(i) return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i))) class CustomSwish(nn.Module): def forward(self, input_tensor): return Swish.apply(input_tensor) class TransformerFFNLayer(nn.Module): def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'): super().__init__() self.kernel_size = kernel_size self.dropout = dropout self.act = act if padding == 'SAME': self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2) elif padding == 'LEFT': self.ffn_1 = nn.Sequential( nn.ConstantPad1d((kernel_size - 1, 0), 0.0), nn.Conv1d(hidden_size, filter_size, kernel_size) ) self.ffn_2 = Linear(filter_size, hidden_size) if self.act == 'swish': self.swish_fn = CustomSwish() def forward(self, x, incremental_state=None): # x: T x B x C if incremental_state is not None: assert incremental_state is None, 'Nar-generation does not allow this.' exit(1) x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1) x = x * self.kernel_size ** -0.5 if incremental_state is not None: x = x[-1:] if self.act == 'gelu': x = F.gelu(x) if self.act == 'relu': x = F.relu(x) if self.act == 'swish': x = self.swish_fn(x) x = F.dropout(x, self.dropout, training=self.training) x = self.ffn_2(x) return x class BatchNorm1dTBC(nn.Module): def __init__(self, c): super(BatchNorm1dTBC, self).__init__() self.bn = nn.BatchNorm1d(c) def forward(self, x): """ :param x: [T, B, C] :return: [T, B, C] """ x = x.permute(1, 2, 0) # [B, C, T] x = self.bn(x) # [B, C, T] x = x.permute(2, 0, 1) # [T, B, C] return x class EncSALayer(nn.Module): def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, padding='SAME', norm='ln', act='gelu'): super().__init__() self.c = c self.dropout = dropout self.num_heads = num_heads if num_heads > 0: if norm == 'ln': self.layer_norm1 = LayerNorm(c) elif norm == 'bn': self.layer_norm1 = BatchNorm1dTBC(c) self.self_attn = MultiheadAttention( self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False, ) if norm == 'ln': self.layer_norm2 = LayerNorm(c) elif norm == 'bn': self.layer_norm2 = BatchNorm1dTBC(c) self.ffn = TransformerFFNLayer( c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act) def forward(self, x, encoder_padding_mask=None, **kwargs): layer_norm_training = kwargs.get('layer_norm_training', None) if layer_norm_training is not None: self.layer_norm1.training = layer_norm_training self.layer_norm2.training = layer_norm_training if self.num_heads > 0: residual = x x = self.layer_norm1(x) x, _, = self.self_attn( query=x, key=x, value=x, key_padding_mask=encoder_padding_mask ) x = F.dropout(x, self.dropout, training=self.training) x = residual + x x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] residual = x x = self.layer_norm2(x) x = self.ffn(x) x = F.dropout(x, self.dropout, training=self.training) x = residual + x x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] return x class DecSALayer(nn.Module): def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act='gelu'): super().__init__() self.c = c self.dropout = dropout self.layer_norm1 = LayerNorm(c) self.self_attn = MultiheadAttention( c, num_heads, self_attention=True, dropout=attention_dropout, bias=False ) self.layer_norm2 = LayerNorm(c) self.encoder_attn = MultiheadAttention( c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False, ) self.layer_norm3 = LayerNorm(c) self.ffn = TransformerFFNLayer( c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act) def forward( self, x, encoder_out=None, encoder_padding_mask=None, incremental_state=None, self_attn_mask=None, self_attn_padding_mask=None, attn_out=None, reset_attn_weight=None, **kwargs, ): layer_norm_training = kwargs.get('layer_norm_training', None) if layer_norm_training is not None: self.layer_norm1.training = layer_norm_training self.layer_norm2.training = layer_norm_training self.layer_norm3.training = layer_norm_training residual = x x = self.layer_norm1(x) x, _ = self.self_attn( query=x, key=x, value=x, key_padding_mask=self_attn_padding_mask, incremental_state=incremental_state, attn_mask=self_attn_mask ) x = F.dropout(x, self.dropout, training=self.training) x = residual + x residual = x x = self.layer_norm2(x) if encoder_out is not None: x, attn = self.encoder_attn( query=x, key=encoder_out, value=encoder_out, key_padding_mask=encoder_padding_mask, incremental_state=incremental_state, static_kv=True, enc_dec_attn_constraint_mask=None, #utils.get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'), reset_attn_weight=reset_attn_weight ) attn_logits = attn[1] else: assert attn_out is not None x = self.encoder_attn.in_proj_v(attn_out.transpose(0, 1)) attn_logits = None x = F.dropout(x, self.dropout, training=self.training) x = residual + x residual = x x = self.layer_norm3(x) x = self.ffn(x, incremental_state=incremental_state) x = F.dropout(x, self.dropout, training=self.training) x = residual + x # if len(attn_logits.size()) > 3: # indices = attn_logits.softmax(-1).max(-1).values.sum(-1).argmax(-1) # attn_logits = attn_logits.gather(1, # indices[:, None, None, None].repeat(1, 1, attn_logits.size(-2), attn_logits.size(-1))).squeeze(1) return x, attn_logits ================================================ FILE: NeuralSeq/modules/commons/conv.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F from modules.commons.common_layers import Embedding from modules.fastspeech.tts_modules import LayerNorm class LambdaLayer(nn.Module): def __init__(self, lambd): super(LambdaLayer, self).__init__() self.lambd = lambd def forward(self, x): return self.lambd(x) def init_weights_func(m): classname = m.__class__.__name__ if classname.find("Conv1d") != -1: torch.nn.init.xavier_uniform_(m.weight) class ResidualBlock(nn.Module): """Implements conv->PReLU->norm n-times""" def __init__(self, channels, kernel_size, dilation, n=2, norm_type='bn', dropout=0.0, c_multiple=2, ln_eps=1e-12): super(ResidualBlock, self).__init__() if norm_type == 'bn': norm_builder = lambda: nn.BatchNorm1d(channels) elif norm_type == 'in': norm_builder = lambda: nn.InstanceNorm1d(channels, affine=True) elif norm_type == 'gn': norm_builder = lambda: nn.GroupNorm(8, channels) elif norm_type == 'ln': norm_builder = lambda: LayerNorm(channels, dim=1, eps=ln_eps) else: norm_builder = lambda: nn.Identity() self.blocks = [ nn.Sequential( norm_builder(), nn.Conv1d(channels, c_multiple * channels, kernel_size, dilation=dilation, padding=(dilation * (kernel_size - 1)) // 2), LambdaLayer(lambda x: x * kernel_size ** -0.5), nn.GELU(), nn.Conv1d(c_multiple * channels, channels, 1, dilation=dilation), ) for i in range(n) ] self.blocks = nn.ModuleList(self.blocks) self.dropout = dropout def forward(self, x): nonpadding = (x.abs().sum(1) > 0).float()[:, None, :] for b in self.blocks: x_ = b(x) if self.dropout > 0 and self.training: x_ = F.dropout(x_, self.dropout, training=self.training) x = x + x_ x = x * nonpadding return x class ConvBlocks(nn.Module): """Decodes the expanded phoneme encoding into spectrograms""" def __init__(self, hidden_size, out_dims, dilations, kernel_size, norm_type='ln', layers_in_block=2, c_multiple=2, dropout=0.0, ln_eps=1e-5, init_weights=True, is_BTC=True, num_layers=None, post_net_kernel=3): super(ConvBlocks, self).__init__() self.is_BTC = is_BTC if num_layers is not None: dilations = [1] * num_layers self.res_blocks = nn.Sequential( *[ResidualBlock(hidden_size, kernel_size, d, n=layers_in_block, norm_type=norm_type, c_multiple=c_multiple, dropout=dropout, ln_eps=ln_eps) for d in dilations], ) if norm_type == 'bn': norm = nn.BatchNorm1d(hidden_size) elif norm_type == 'in': norm = nn.InstanceNorm1d(hidden_size, affine=True) elif norm_type == 'gn': norm = nn.GroupNorm(8, hidden_size) elif norm_type == 'ln': norm = LayerNorm(hidden_size, dim=1, eps=ln_eps) self.last_norm = norm self.post_net1 = nn.Conv1d(hidden_size, out_dims, kernel_size=post_net_kernel, padding=post_net_kernel // 2) if init_weights: self.apply(init_weights_func) def forward(self, x, nonpadding=None): """ :param x: [B, T, H] :return: [B, T, H] """ if self.is_BTC: x = x.transpose(1, 2) if nonpadding is None: nonpadding = (x.abs().sum(1) > 0).float()[:, None, :] elif self.is_BTC: nonpadding = nonpadding.transpose(1, 2) x = self.res_blocks(x) * nonpadding x = self.last_norm(x) * nonpadding x = self.post_net1(x) * nonpadding if self.is_BTC: x = x.transpose(1, 2) return x class TextConvEncoder(ConvBlocks): def __init__(self, dict_size, hidden_size, out_dims, dilations, kernel_size, norm_type='ln', layers_in_block=2, c_multiple=2, dropout=0.0, ln_eps=1e-5, init_weights=True, num_layers=None, post_net_kernel=3): super().__init__(hidden_size, out_dims, dilations, kernel_size, norm_type, layers_in_block, c_multiple, dropout, ln_eps, init_weights, num_layers=num_layers, post_net_kernel=post_net_kernel) self.embed_tokens = Embedding(dict_size, hidden_size, 0) self.embed_scale = math.sqrt(hidden_size) def forward(self, txt_tokens): """ :param txt_tokens: [B, T] :return: { 'encoder_out': [B x T x C] } """ x = self.embed_scale * self.embed_tokens(txt_tokens) return super().forward(x) class ConditionalConvBlocks(ConvBlocks): def __init__(self, hidden_size, c_cond, c_out, dilations, kernel_size, norm_type='ln', layers_in_block=2, c_multiple=2, dropout=0.0, ln_eps=1e-5, init_weights=True, is_BTC=True, num_layers=None): super().__init__(hidden_size, c_out, dilations, kernel_size, norm_type, layers_in_block, c_multiple, dropout, ln_eps, init_weights, is_BTC=False, num_layers=num_layers) self.g_prenet = nn.Conv1d(c_cond, hidden_size, 3, padding=1) self.is_BTC_ = is_BTC if init_weights: self.g_prenet.apply(init_weights_func) def forward(self, x, cond, nonpadding=None): if self.is_BTC_: x = x.transpose(1, 2) cond = cond.transpose(1, 2) if nonpadding is not None: nonpadding = nonpadding.transpose(1, 2) if nonpadding is None: nonpadding = x.abs().sum(1)[:, None] x = x + self.g_prenet(cond) x = x * nonpadding x = super(ConditionalConvBlocks, self).forward(x) # input needs to be BTC if self.is_BTC_: x = x.transpose(1, 2) return x ================================================ FILE: NeuralSeq/modules/commons/espnet_positional_embedding.py ================================================ import math import torch class PositionalEncoding(torch.nn.Module): """Positional encoding. Args: d_model (int): Embedding dimension. dropout_rate (float): Dropout rate. max_len (int): Maximum input length. reverse (bool): Whether to reverse the input position. """ def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): """Construct an PositionalEncoding object.""" super(PositionalEncoding, self).__init__() self.d_model = d_model self.reverse = reverse self.xscale = math.sqrt(self.d_model) self.dropout = torch.nn.Dropout(p=dropout_rate) self.pe = None self.extend_pe(torch.tensor(0.0).expand(1, max_len)) def extend_pe(self, x): """Reset the positional encodings.""" if self.pe is not None: if self.pe.size(1) >= x.size(1): if self.pe.dtype != x.dtype or self.pe.device != x.device: self.pe = self.pe.to(dtype=x.dtype, device=x.device) return pe = torch.zeros(x.size(1), self.d_model) if self.reverse: position = torch.arange( x.size(1) - 1, -1, -1.0, dtype=torch.float32 ).unsqueeze(1) else: position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) div_term = torch.exp( torch.arange(0, self.d_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.d_model) ) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) self.pe = pe.to(device=x.device, dtype=x.dtype) def forward(self, x: torch.Tensor): """Add positional encoding. Args: x (torch.Tensor): Input tensor (batch, time, `*`). Returns: torch.Tensor: Encoded tensor (batch, time, `*`). """ self.extend_pe(x) x = x * self.xscale + self.pe[:, : x.size(1)] return self.dropout(x) class ScaledPositionalEncoding(PositionalEncoding): """Scaled positional encoding module. See Sec. 3.2 https://arxiv.org/abs/1809.08895 Args: d_model (int): Embedding dimension. dropout_rate (float): Dropout rate. max_len (int): Maximum input length. """ def __init__(self, d_model, dropout_rate, max_len=5000): """Initialize class.""" super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) self.alpha = torch.nn.Parameter(torch.tensor(1.0)) def reset_parameters(self): """Reset parameters.""" self.alpha.data = torch.tensor(1.0) def forward(self, x): """Add positional encoding. Args: x (torch.Tensor): Input tensor (batch, time, `*`). Returns: torch.Tensor: Encoded tensor (batch, time, `*`). """ self.extend_pe(x) x = x + self.alpha * self.pe[:, : x.size(1)] return self.dropout(x) class RelPositionalEncoding(PositionalEncoding): """Relative positional encoding module. See : Appendix B in https://arxiv.org/abs/1901.02860 Args: d_model (int): Embedding dimension. dropout_rate (float): Dropout rate. max_len (int): Maximum input length. """ def __init__(self, d_model, dropout_rate, max_len=5000): """Initialize class.""" super().__init__(d_model, dropout_rate, max_len, reverse=True) def forward(self, x): """Compute positional encoding. Args: x (torch.Tensor): Input tensor (batch, time, `*`). Returns: torch.Tensor: Encoded tensor (batch, time, `*`). torch.Tensor: Positional embedding tensor (1, time, `*`). """ self.extend_pe(x) x = x * self.xscale pos_emb = self.pe[:, : x.size(1)] return self.dropout(x) + self.dropout(pos_emb) ================================================ FILE: NeuralSeq/modules/commons/normalizing_flow/glow_modules.py ================================================ import scipy from torch.nn import functional as F import torch from torch import nn import numpy as np from modules.commons.wavenet import WN from modules.glow import utils class ActNorm(nn.Module): def __init__(self, channels, ddi=False, **kwargs): super().__init__() self.channels = channels self.initialized = not ddi self.logs = nn.Parameter(torch.zeros(1, channels, 1)) self.bias = nn.Parameter(torch.zeros(1, channels, 1)) def forward(self, x, x_mask=None, reverse=False, **kwargs): if x_mask is None: x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype) x_len = torch.sum(x_mask, [1, 2]) if not self.initialized: self.initialize(x, x_mask) self.initialized = True if reverse: z = (x - self.bias) * torch.exp(-self.logs) * x_mask logdet = torch.sum(-self.logs) * x_len else: z = (self.bias + torch.exp(self.logs) * x) * x_mask logdet = torch.sum(self.logs) * x_len # [b] return z, logdet def store_inverse(self): pass def set_ddi(self, ddi): self.initialized = not ddi def initialize(self, x, x_mask): with torch.no_grad(): denom = torch.sum(x_mask, [0, 2]) m = torch.sum(x * x_mask, [0, 2]) / denom m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom v = m_sq - (m ** 2) logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) self.bias.data.copy_(bias_init) self.logs.data.copy_(logs_init) class InvConvNear(nn.Module): def __init__(self, channels, n_split=4, no_jacobian=False, lu=True, n_sqz=2, **kwargs): super().__init__() assert (n_split % 2 == 0) self.channels = channels self.n_split = n_split self.n_sqz = n_sqz self.no_jacobian = no_jacobian w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0] if torch.det(w_init) < 0: w_init[:, 0] = -1 * w_init[:, 0] self.lu = lu if lu: # LU decomposition can slightly speed up the inverse np_p, np_l, np_u = scipy.linalg.lu(w_init) np_s = np.diag(np_u) np_sign_s = np.sign(np_s) np_log_s = np.log(np.abs(np_s)) np_u = np.triu(np_u, k=1) l_mask = np.tril(np.ones(w_init.shape, dtype=float), -1) eye = np.eye(*w_init.shape, dtype=float) self.register_buffer('p', torch.Tensor(np_p.astype(float))) self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) self.l = nn.Parameter(torch.Tensor(np_l.astype(float)), requires_grad=True) self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)), requires_grad=True) self.u = nn.Parameter(torch.Tensor(np_u.astype(float)), requires_grad=True) self.register_buffer('l_mask', torch.Tensor(l_mask)) self.register_buffer('eye', torch.Tensor(eye)) else: self.weight = nn.Parameter(w_init) def forward(self, x, x_mask=None, reverse=False, **kwargs): b, c, t = x.size() assert (c % self.n_split == 0) if x_mask is None: x_mask = 1 x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t else: x_len = torch.sum(x_mask, [1, 2]) x = x.view(b, self.n_sqz, c // self.n_split, self.n_split // self.n_sqz, t) x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t) if self.lu: self.weight, log_s = self._get_weight() logdet = log_s.sum() logdet = logdet * (c / self.n_split) * x_len else: logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b] if reverse: if hasattr(self, "weight_inv"): weight = self.weight_inv else: weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) logdet = -logdet else: weight = self.weight if self.no_jacobian: logdet = 0 weight = weight.view(self.n_split, self.n_split, 1, 1) z = F.conv2d(x, weight) z = z.view(b, self.n_sqz, self.n_split // self.n_sqz, c // self.n_split, t) z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask return z, logdet def _get_weight(self): l, log_s, u = self.l, self.log_s, self.u l = l * self.l_mask + self.eye u = u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(log_s)) weight = torch.matmul(self.p, torch.matmul(l, u)) return weight, log_s def store_inverse(self): weight, _ = self._get_weight() self.weight_inv = torch.inverse(weight.float()).to(next(self.parameters()).device) class InvConv(nn.Module): def __init__(self, channels, no_jacobian=False, lu=True, **kwargs): super().__init__() w_shape = [channels, channels] w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(float) LU_decomposed = lu if not LU_decomposed: # Sample a random orthogonal matrix: self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init))) else: np_p, np_l, np_u = scipy.linalg.lu(w_init) np_s = np.diag(np_u) np_sign_s = np.sign(np_s) np_log_s = np.log(np.abs(np_s)) np_u = np.triu(np_u, k=1) l_mask = np.tril(np.ones(w_shape, dtype=float), -1) eye = np.eye(*w_shape, dtype=float) self.register_buffer('p', torch.Tensor(np_p.astype(float))) self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) self.l = nn.Parameter(torch.Tensor(np_l.astype(float))) self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float))) self.u = nn.Parameter(torch.Tensor(np_u.astype(float))) self.l_mask = torch.Tensor(l_mask) self.eye = torch.Tensor(eye) self.w_shape = w_shape self.LU = LU_decomposed self.weight = None def get_weight(self, device, reverse): w_shape = self.w_shape self.p = self.p.to(device) self.sign_s = self.sign_s.to(device) self.l_mask = self.l_mask.to(device) self.eye = self.eye.to(device) l = self.l * self.l_mask + self.eye u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s)) dlogdet = self.log_s.sum() if not reverse: w = torch.matmul(self.p, torch.matmul(l, u)) else: l = torch.inverse(l.double()).float() u = torch.inverse(u.double()).float() w = torch.matmul(u, torch.matmul(l, self.p.inverse())) return w.view(w_shape[0], w_shape[1], 1), dlogdet def forward(self, x, x_mask=None, reverse=False, **kwargs): """ log-det = log|abs(|W|)| * pixels """ b, c, t = x.size() if x_mask is None: x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t else: x_len = torch.sum(x_mask, [1, 2]) logdet = 0 if not reverse: weight, dlogdet = self.get_weight(x.device, reverse) z = F.conv1d(x, weight) if logdet is not None: logdet = logdet + dlogdet * x_len return z, logdet else: if self.weight is None: weight, dlogdet = self.get_weight(x.device, reverse) else: weight, dlogdet = self.weight, self.dlogdet z = F.conv1d(x, weight) if logdet is not None: logdet = logdet - dlogdet * x_len return z, logdet def store_inverse(self): self.weight, self.dlogdet = self.get_weight('cuda', reverse=True) class CouplingBlock(nn.Module): def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0, sigmoid_scale=False, wn=None): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.gin_channels = gin_channels self.p_dropout = p_dropout self.sigmoid_scale = sigmoid_scale start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) start = torch.nn.utils.weight_norm(start) self.start = start # Initializing last layer to 0 makes the affine coupling layers # do nothing at first. This helps with training stability end = torch.nn.Conv1d(hidden_channels, in_channels, 1) end.weight.data.zero_() end.bias.data.zero_() self.end = end self.wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, p_dropout) if wn is not None: self.wn.in_layers = wn.in_layers self.wn.res_skip_layers = wn.res_skip_layers def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): if x_mask is None: x_mask = 1 x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] x = self.start(x_0) * x_mask x = self.wn(x, x_mask, g) out = self.end(x) z_0 = x_0 m = out[:, :self.in_channels // 2, :] logs = out[:, self.in_channels // 2:, :] if self.sigmoid_scale: logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) if reverse: z_1 = (x_1 - m) * torch.exp(-logs) * x_mask logdet = torch.sum(-logs * x_mask, [1, 2]) else: z_1 = (m + torch.exp(logs) * x_1) * x_mask logdet = torch.sum(logs * x_mask, [1, 2]) z = torch.cat([z_0, z_1], 1) return z, logdet def store_inverse(self): self.wn.remove_weight_norm() class Glow(nn.Module): def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_blocks, n_layers, p_dropout=0., n_split=4, n_sqz=2, sigmoid_scale=False, gin_channels=0, inv_conv_type='near', share_cond_layers=False, share_wn_layers=0, ): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_blocks = n_blocks self.n_layers = n_layers self.p_dropout = p_dropout self.n_split = n_split self.n_sqz = n_sqz self.sigmoid_scale = sigmoid_scale self.gin_channels = gin_channels self.share_cond_layers = share_cond_layers if gin_channels != 0 and share_cond_layers: cond_layer = torch.nn.Conv1d(gin_channels * n_sqz, 2 * hidden_channels * n_layers, 1) self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') wn = None self.flows = nn.ModuleList() for b in range(n_blocks): self.flows.append(ActNorm(channels=in_channels * n_sqz)) if inv_conv_type == 'near': self.flows.append(InvConvNear(channels=in_channels * n_sqz, n_split=n_split, n_sqz=n_sqz)) if inv_conv_type == 'invconv': self.flows.append(InvConv(channels=in_channels * n_sqz)) if share_wn_layers > 0: if b % share_wn_layers == 0: wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels * n_sqz, p_dropout, share_cond_layers) self.flows.append( CouplingBlock( in_channels * n_sqz, hidden_channels, kernel_size=kernel_size, dilation_rate=dilation_rate, n_layers=n_layers, gin_channels=gin_channels * n_sqz, p_dropout=p_dropout, sigmoid_scale=sigmoid_scale, wn=wn )) def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False): logdet_tot = 0 if not reverse: flows = self.flows else: flows = reversed(self.flows) if return_hiddens: hs = [] if self.n_sqz > 1: x, x_mask_ = utils.squeeze(x, x_mask, self.n_sqz) if g is not None: g, _ = utils.squeeze(g, x_mask, self.n_sqz) x_mask = x_mask_ if self.share_cond_layers and g is not None: g = self.cond_layer(g) for f in flows: x, logdet = f(x, x_mask, g=g, reverse=reverse) if return_hiddens: hs.append(x) logdet_tot += logdet if self.n_sqz > 1: x, x_mask = utils.unsqueeze(x, x_mask, self.n_sqz) if return_hiddens: return x, logdet_tot, hs return x, logdet_tot def store_inverse(self): def remove_weight_norm(m): try: nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(remove_weight_norm) for f in self.flows: f.store_inverse() ================================================ FILE: NeuralSeq/modules/commons/normalizing_flow/res_flow.py ================================================ import torch from torch import nn from modules.commons.conv import ConditionalConvBlocks from modules.commons.wavenet import WN class FlipLayer(nn.Module): def forward(self, x, nonpadding, cond=None, reverse=False): x = torch.flip(x, [1]) return x class CouplingLayer(nn.Module): def __init__(self, c_in, hidden_size, kernel_size, n_layers, p_dropout=0, c_in_g=0, nn_type='wn'): super().__init__() self.channels = c_in self.hidden_size = hidden_size self.kernel_size = kernel_size self.n_layers = n_layers self.c_half = c_in // 2 self.pre = nn.Conv1d(self.c_half, hidden_size, 1) if nn_type == 'wn': self.enc = WN(hidden_size, kernel_size, 1, n_layers, p_dropout=p_dropout, c_cond=c_in_g) elif nn_type == 'conv': self.enc = ConditionalConvBlocks( hidden_size, c_in_g, hidden_size, None, kernel_size, layers_in_block=1, is_BTC=False, num_layers=n_layers) self.post = nn.Conv1d(hidden_size, self.c_half, 1) def forward(self, x, nonpadding, cond=None, reverse=False): x0, x1 = x[:, :self.c_half], x[:, self.c_half:] x_ = self.pre(x0) * nonpadding x_ = self.enc(x_, nonpadding=nonpadding, cond=cond) m = self.post(x_) x1 = m + x1 if not reverse else x1 - m x = torch.cat([x0, x1], 1) return x * nonpadding class ResFlow(nn.Module): def __init__(self, c_in, hidden_size, kernel_size, n_flow_layers, n_flow_steps=4, c_cond=0, nn_type='wn'): super().__init__() self.flows = nn.ModuleList() for i in range(n_flow_steps): self.flows.append( CouplingLayer(c_in, hidden_size, kernel_size, n_flow_layers, c_in_g=c_cond, nn_type=nn_type)) self.flows.append(FlipLayer()) def forward(self, x, nonpadding, cond=None, reverse=False): for flow in (self.flows if not reverse else reversed(self.flows)): x = flow(x, nonpadding, cond=cond, reverse=reverse) return x ================================================ FILE: NeuralSeq/modules/commons/normalizing_flow/utils.py ================================================ import torch def squeeze(x, x_mask=None, n_sqz=2): b, c, t = x.size() t = (t // n_sqz) * n_sqz x = x[:, :, :t] x_sqz = x.view(b, c, t // n_sqz, n_sqz) x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz) if x_mask is not None: x_mask = x_mask[:, :, n_sqz - 1::n_sqz] else: x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype) return x_sqz * x_mask, x_mask def unsqueeze(x, x_mask=None, n_sqz=2): b, c, t = x.size() x_unsqz = x.view(b, n_sqz, c // n_sqz, t) x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz) if x_mask is not None: x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz) else: x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype) return x_unsqz * x_mask, x_mask ================================================ FILE: NeuralSeq/modules/commons/rel_transformer.py ================================================ import math import torch from torch import nn from torch.nn import functional as F from utils.hparams import hparams from modules.commons.common_layers import Embedding from utils.tts_utils import group_hidden_by_segs, expand_word2ph import transformers def convert_pad_shape(pad_shape): l = pad_shape[::-1] pad_shape = [item for sublist in l for item in sublist] return pad_shape def shift_1d(x): x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] return x def sequence_mask(length, max_length=None): if max_length is None: max_length = length.max() x = torch.arange(max_length, dtype=length.dtype, device=length.device) return x.unsqueeze(0) < length.unsqueeze(1) class Encoder(nn.Module): def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=None, block_length=None, pre_ln=False, **kwargs): super().__init__() self.hidden_channels = hidden_channels self.filter_channels = filter_channels self.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout self.window_size = window_size self.block_length = block_length self.pre_ln = pre_ln self.drop = nn.Dropout(p_dropout) self.attn_layers = nn.ModuleList() self.norm_layers_1 = nn.ModuleList() self.ffn_layers = nn.ModuleList() self.norm_layers_2 = nn.ModuleList() for i in range(self.n_layers): self.attn_layers.append( MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size, p_dropout=p_dropout, block_length=block_length)) self.norm_layers_1.append(LayerNorm(hidden_channels)) self.ffn_layers.append( FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) self.norm_layers_2.append(LayerNorm(hidden_channels)) if pre_ln: self.last_ln = LayerNorm(hidden_channels) def forward(self, x, x_mask): attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) for i in range(self.n_layers): x = x * x_mask x_ = x if self.pre_ln: x = self.norm_layers_1[i](x) y = self.attn_layers[i](x, x, attn_mask) y = self.drop(y) x = x_ + y if not self.pre_ln: x = self.norm_layers_1[i](x) x_ = x if self.pre_ln: x = self.norm_layers_2[i](x) y = self.ffn_layers[i](x, x_mask) y = self.drop(y) x = x_ + y if not self.pre_ln: x = self.norm_layers_2[i](x) if self.pre_ln: x = self.last_ln(x) x = x * x_mask return x class MultiHeadAttention(nn.Module): def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0., block_length=None, proximal_bias=False, proximal_init=False): super().__init__() assert channels % n_heads == 0 self.channels = channels self.out_channels = out_channels self.n_heads = n_heads self.window_size = window_size self.heads_share = heads_share self.block_length = block_length self.proximal_bias = proximal_bias self.p_dropout = p_dropout self.attn = None self.k_channels = channels // n_heads self.conv_q = nn.Conv1d(channels, channels, 1) self.conv_k = nn.Conv1d(channels, channels, 1) self.conv_v = nn.Conv1d(channels, channels, 1) if window_size is not None: n_heads_rel = 1 if heads_share else n_heads rel_stddev = self.k_channels ** -0.5 self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.conv_o = nn.Conv1d(channels, out_channels, 1) self.drop = nn.Dropout(p_dropout) nn.init.xavier_uniform_(self.conv_q.weight) nn.init.xavier_uniform_(self.conv_k.weight) if proximal_init: self.conv_k.weight.data.copy_(self.conv_q.weight.data) self.conv_k.bias.data.copy_(self.conv_q.bias.data) nn.init.xavier_uniform_(self.conv_v.weight) def forward(self, x, c, attn_mask=None): q = self.conv_q(x) k = self.conv_k(c) v = self.conv_v(c) x, self.attn = self.attention(q, k, v, mask=attn_mask) x = self.conv_o(x) return x def attention(self, query, key, value, mask=None): # reshape [b, d, t] -> [b, n_h, t, d_k] b, d, t_s, t_t = (*key.size(), query.size(2)) query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) if self.window_size is not None: assert t_s == t_t, "Relative attention is only available for self-attention." key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) rel_logits = self._relative_position_to_absolute_position(rel_logits) scores_local = rel_logits / math.sqrt(self.k_channels) scores = scores + scores_local if self.proximal_bias: assert t_s == t_t, "Proximal bias is only available for self-attention." scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) if mask is not None: scores = scores.masked_fill(mask == 0, -1e4) if self.block_length is not None: block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) scores = scores * block_mask + -1e4 * (1 - block_mask) p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] p_attn = self.drop(p_attn) output = torch.matmul(p_attn, value) if self.window_size is not None: relative_weights = self._absolute_position_to_relative_position(p_attn) value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] return output, p_attn def _matmul_with_relative_values(self, x, y): """ x: [b, h, l, m] y: [h or 1, m, d] ret: [b, h, l, d] """ ret = torch.matmul(x, y.unsqueeze(0)) return ret def _matmul_with_relative_keys(self, x, y): """ x: [b, h, l, d] y: [h or 1, m, d] ret: [b, h, l, m] """ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) return ret def _get_relative_embeddings(self, relative_embeddings, length): max_relative_position = 2 * self.window_size + 1 # Pad first before slice to avoid using cond ops. pad_length = max(length - (self.window_size + 1), 0) slice_start_position = max((self.window_size + 1) - length, 0) slice_end_position = slice_start_position + 2 * length - 1 if pad_length > 0: padded_relative_embeddings = F.pad( relative_embeddings, convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) else: padded_relative_embeddings = relative_embeddings used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] return used_relative_embeddings def _relative_position_to_absolute_position(self, x): """ x: [b, h, l, 2*l-1] ret: [b, h, l, l] """ batch, heads, length, _ = x.size() # Concat columns of pad to shift from relative to absolute indexing. x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) # Concat extra elements so to add up to shape (len+1, 2*len-1). x_flat = x.view([batch, heads, length * 2 * length]) x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) # Reshape and slice out the padded elements. x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:] return x_final def _absolute_position_to_relative_position(self, x): """ x: [b, h, l, l] ret: [b, h, l, 2*l-1] """ batch, heads, length, _ = x.size() # padd along column x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) # add 0's in the beginning that will skew the elements after reshape x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] return x_final def _attention_bias_proximal(self, length): """Bias for self-attention to encourage attention to close positions. Args: length: an integer scalar. Returns: a Tensor with shape [1, 1, length, length] """ r = torch.arange(length, dtype=torch.float32) diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) class FFN(nn.Module): def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.filter_channels = filter_channels self.kernel_size = kernel_size self.p_dropout = p_dropout self.activation = activation self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1) self.drop = nn.Dropout(p_dropout) def forward(self, x, x_mask): x = self.conv_1(x * x_mask) if self.activation == "gelu": x = x * torch.sigmoid(1.702 * x) else: x = torch.relu(x) x = self.drop(x) x = self.conv_2(x * x_mask) return x * x_mask class LayerNorm(nn.Module): def __init__(self, channels, eps=1e-4): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = nn.Parameter(torch.zeros(channels)) def forward(self, x): n_dims = len(x.shape) mean = torch.mean(x, 1, keepdim=True) variance = torch.mean((x - mean) ** 2, 1, keepdim=True) x = (x - mean) * torch.rsqrt(variance + self.eps) shape = [1, -1] + [1] * (n_dims - 2) x = x * self.gamma.view(*shape) + self.beta.view(*shape) return x class ConvReluNorm(nn.Module): def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.out_channels = out_channels self.kernel_size = kernel_size self.n_layers = n_layers self.p_dropout = p_dropout assert n_layers > 1, "Number of layers should be larger than 0." self.conv_layers = nn.ModuleList() self.norm_layers = nn.ModuleList() self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.relu_drop = nn.Sequential( nn.ReLU(), nn.Dropout(p_dropout)) for _ in range(n_layers - 1): self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.proj.weight.data.zero_() self.proj.bias.data.zero_() def forward(self, x, x_mask): x_org = x for i in range(self.n_layers): x = self.conv_layers[i](x * x_mask) x = self.norm_layers[i](x) x = self.relu_drop(x) x = x_org + self.proj(x) return x * x_mask class RelTransformerEncoder(nn.Module): def __init__(self, n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout=0.0, window_size=4, block_length=None, prenet=True, pre_ln=True, ): super().__init__() self.n_vocab = n_vocab self.out_channels = out_channels self.hidden_channels = hidden_channels self.filter_channels = filter_channels self.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout self.window_size = window_size self.block_length = block_length self.prenet = prenet if n_vocab > 0: self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) if prenet: self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels, kernel_size=5, n_layers=3, p_dropout=0) self.encoder = Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, window_size=window_size, block_length=block_length, pre_ln=pre_ln, ) def forward(self, x, x_mask=None): if self.n_vocab > 0: x_lengths = (x > 0).long().sum(-1) x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] else: x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) x = torch.transpose(x, 1, -1) # [b, h, t] x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) if self.prenet: x = self.pre(x, x_mask) x = self.encoder(x, x_mask) return x.transpose(1, 2) class Pooler(nn.Module): """ Parameter-free poolers to get the sentence embedding 'cls': [CLS] representation with BERT/RoBERTa's MLP pooler. 'cls_before_pooler': [CLS] representation without the original MLP pooler. 'avg': average of the last layers' hidden states at each token. 'avg_top2': average of the last two layers. 'avg_first_last': average of the first and the last layers. """ def __init__(self, pooler_type): super().__init__() self.pooler_type = pooler_type assert self.pooler_type in ["cls", "cls_before_pooler", "avg", "avg_top2", "avg_first_last"], "unrecognized pooling type %s" % self.pooler_type def forward(self, attention_mask, outputs): last_hidden = outputs.last_hidden_state pooler_output = outputs.pooler_output hidden_states = outputs.hidden_states if self.pooler_type in ['cls_before_pooler', 'cls']: return last_hidden[:, 0] elif self.pooler_type == "avg": return ((last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)) elif self.pooler_type == "avg_first_last": first_hidden = hidden_states[0] last_hidden = hidden_states[-1] pooled_result = ((first_hidden + last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) return pooled_result elif self.pooler_type == "avg_top2": second_last_hidden = hidden_states[-2] last_hidden = hidden_states[-1] pooled_result = ((last_hidden + second_last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) return pooled_result else: raise NotImplementedError class Similarity(nn.Module): """ Dot product or cosine similarity """ def __init__(self, temp): super().__init__() self.temp = temp self.cos = nn.CosineSimilarity(dim=-1) self.record = None self.pos_avg = 0.0 self.neg_avg = 0.0 def forward(self, x, y): sim = self.cos(x, y) self.record = sim.detach() # [64,64] min_size = min(self.record.shape[0], self.record.shape[1]) # 64 num_item = self.record.shape[0] * self.record.shape[1] # 4096 self.pos_avg = self.record.diag().sum() / min_size if num_item - min_size == 0: self.neg_avg = (self.record.sum() - self.record.diag().sum()) / 1 return sim / self.temp if torch.any(torch.isnan(self.record)).item() is True: print("we got self.record has nan when compute neg_avg") if torch.any(torch.isnan(self.record.diag())).item() is True: print("we got self.record.diag() has nan when compute neg_avg") self.neg_avg = (self.record.sum() - self.record.diag().sum()) / (num_item - min_size) return sim / self.temp class BertPredictionHeadTransform(nn.Module): def __init__(self, hidden_size): super().__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.transform_act_fn = F.gelu self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class BertLMPredictionHead(nn.Module): def __init__(self, hid_dim, out_dim): super().__init__() self.transform = BertPredictionHeadTransform(hid_dim) self.decoder = nn.Linear(hid_dim, out_dim, bias=False) self.bias = nn.Parameter(torch.zeros(out_dim)) self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states # V2_2 # change add to concat. # now support finetune BERT # grad_bert=0.1 & trainable_block_idx=0 class BERTRelTransformerEncoder(nn.Module): def __init__(self, n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout=0.0, window_size=4, block_length=None, prenet=True, pre_ln=True, ): super().__init__() self.n_vocab = n_vocab self.out_channels = out_channels self.hidden_channels = hidden_channels self.filter_channels = filter_channels self.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout self.window_size = window_size self.block_length = block_length self.prenet = prenet if n_vocab > 0: self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) if prenet: self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels, kernel_size=5, n_layers=3, p_dropout=0) self.encoder1 = Encoder( hidden_channels, filter_channels, n_heads, n_layers//2, kernel_size, p_dropout, window_size=window_size, block_length=block_length, pre_ln=pre_ln, ) self.encoder2 = Encoder( hidden_channels, filter_channels, n_heads, n_layers - n_layers//2, kernel_size, p_dropout, window_size=window_size, block_length=block_length, pre_ln=pre_ln, ) if hparams['ds_name'] in ['ljspeech', 'libritts', 'librispeech']: model_name = 'bert-base-uncased' elif hparams['ds_name'] in ['biaobei', 'wenetspeech']: model_name = 'bert-base-chinese' else: raise NotImplementedError() self.tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) config = transformers.AutoConfig.from_pretrained(model_name) if hparams.get("load_bert_from_pretrained", True): print("Load BERT from pretrained model ...") self.bert = transformers.AutoModel.from_pretrained(model_name,config=config) trainable_start_block = hparams.get("bert_trainable_start_block", 0) else: print("Initialize BERT from scratch!") self.bert = transformers.BertModel(config=config) trainable_start_block = 0 for k, v in self.bert.named_parameters(): if 'embeddings' in k: v.requires_grad = False elif 'encoder.layer' in k: block_idx = int(k.split(".")[2]) if block_idx < trainable_start_block: v.requires_grad = False else: v.requires_grad = True elif 'cls' in k: v.requires_grad = True else: print("Unhandled key: {}, set to requires_grad...".format(k)) v.requires_grad = True self.bert_combine = nn.Sequential(*[ nn.Conv1d(768 + hidden_channels, hidden_channels, 3, 1, 1), nn.ReLU(), ]) self.pooler = Pooler("avg") self.sim = Similarity(temp=0.05) def forward(self, x, x_mask=None, bert_feats=None, ph2word=None, **kwargs): if self.n_vocab > 0: x_lengths = (x > 0).long().sum(-1) x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] else: x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) x = torch.transpose(x, 1, -1) # [b, h, t] x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) if self.prenet: x = self.pre(x, x_mask) x = self.encoder1(x, x_mask) bert_outputs = self.bert(bert_feats['bert_input_ids'], attention_mask=bert_feats['bert_attention_mask'], token_type_ids=bert_feats['bert_token_type_ids'], output_hidden_states=True) bert_num_blocks = hparams.get("bert_num_blocks", 12) # total 1+12blocks in bert bert_embedding = bert_outputs['hidden_states'][bert_num_blocks] # bert_embedding = bert_outputs['last_hidden_state'] grad_bert = hparams.get("grad_bert", 0.1) bert_embedding = bert_embedding.detach() * (1-grad_bert) + bert_embedding * grad_bert bert_word_embedding, _ = group_hidden_by_segs(bert_embedding, bert_feats['bert_token2word'], bert_feats['bert_token2word'].max().item()) bert_ph_embedding = expand_word2ph(bert_word_embedding, ph2word) bert_ph_embedding = bert_ph_embedding.transpose(1,2) x = torch.cat([x, bert_ph_embedding], dim=1) x = self.bert_combine(x) x = self.encoder2(x, x_mask) return x.transpose(1, 2) ================================================ FILE: NeuralSeq/modules/commons/ssim.py ================================================ # ''' # https://github.com/One-sixth/ms_ssim_pytorch/blob/master/ssim.py # ''' # # import torch # import torch.jit # import torch.nn.functional as F # # # @torch.jit.script # def create_window(window_size: int, sigma: float, channel: int): # ''' # Create 1-D gauss kernel # :param window_size: the size of gauss kernel # :param sigma: sigma of normal distribution # :param channel: input channel # :return: 1D kernel # ''' # coords = torch.arange(window_size, dtype=torch.float) # coords -= window_size // 2 # # g = torch.exp(-(coords ** 2) / (2 * sigma ** 2)) # g /= g.sum() # # g = g.reshape(1, 1, 1, -1).repeat(channel, 1, 1, 1) # return g # # # @torch.jit.script # def _gaussian_filter(x, window_1d, use_padding: bool): # ''' # Blur input with 1-D kernel # :param x: batch of tensors to be blured # :param window_1d: 1-D gauss kernel # :param use_padding: padding image before conv # :return: blured tensors # ''' # C = x.shape[1] # padding = 0 # if use_padding: # window_size = window_1d.shape[3] # padding = window_size // 2 # out = F.conv2d(x, window_1d, stride=1, padding=(0, padding), groups=C) # out = F.conv2d(out, window_1d.transpose(2, 3), stride=1, padding=(padding, 0), groups=C) # return out # # # @torch.jit.script # def ssim(X, Y, window, data_range: float, use_padding: bool = False): # ''' # Calculate ssim index for X and Y # :param X: images [B, C, H, N_bins] # :param Y: images [B, C, H, N_bins] # :param window: 1-D gauss kernel # :param data_range: value range of input images. (usually 1.0 or 255) # :param use_padding: padding image before conv # :return: # ''' # # K1 = 0.01 # K2 = 0.03 # compensation = 1.0 # # C1 = (K1 * data_range) ** 2 # C2 = (K2 * data_range) ** 2 # # mu1 = _gaussian_filter(X, window, use_padding) # mu2 = _gaussian_filter(Y, window, use_padding) # sigma1_sq = _gaussian_filter(X * X, window, use_padding) # sigma2_sq = _gaussian_filter(Y * Y, window, use_padding) # sigma12 = _gaussian_filter(X * Y, window, use_padding) # # mu1_sq = mu1.pow(2) # mu2_sq = mu2.pow(2) # mu1_mu2 = mu1 * mu2 # # sigma1_sq = compensation * (sigma1_sq - mu1_sq) # sigma2_sq = compensation * (sigma2_sq - mu2_sq) # sigma12 = compensation * (sigma12 - mu1_mu2) # # cs_map = (2 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2) # # Fixed the issue that the negative value of cs_map caused ms_ssim to output Nan. # cs_map = cs_map.clamp_min(0.) # ssim_map = ((2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)) * cs_map # # ssim_val = ssim_map.mean(dim=(1, 2, 3)) # reduce along CHW # cs = cs_map.mean(dim=(1, 2, 3)) # # return ssim_val, cs # # # @torch.jit.script # def ms_ssim(X, Y, window, data_range: float, weights, use_padding: bool = False, eps: float = 1e-8): # ''' # interface of ms-ssim # :param X: a batch of images, (N,C,H,W) # :param Y: a batch of images, (N,C,H,W) # :param window: 1-D gauss kernel # :param data_range: value range of input images. (usually 1.0 or 255) # :param weights: weights for different levels # :param use_padding: padding image before conv # :param eps: use for avoid grad nan. # :return: # ''' # levels = weights.shape[0] # cs_vals = [] # ssim_vals = [] # for _ in range(levels): # ssim_val, cs = ssim(X, Y, window=window, data_range=data_range, use_padding=use_padding) # # Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf. # ssim_val = ssim_val.clamp_min(eps) # cs = cs.clamp_min(eps) # cs_vals.append(cs) # # ssim_vals.append(ssim_val) # padding = (X.shape[2] % 2, X.shape[3] % 2) # X = F.avg_pool2d(X, kernel_size=2, stride=2, padding=padding) # Y = F.avg_pool2d(Y, kernel_size=2, stride=2, padding=padding) # # cs_vals = torch.stack(cs_vals, dim=0) # ms_ssim_val = torch.prod((cs_vals[:-1] ** weights[:-1].unsqueeze(1)) * (ssim_vals[-1] ** weights[-1]), dim=0) # return ms_ssim_val # # # class SSIM(torch.jit.ScriptModule): # __constants__ = ['data_range', 'use_padding'] # # def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False): # ''' # :param window_size: the size of gauss kernel # :param window_sigma: sigma of normal distribution # :param data_range: value range of input images. (usually 1.0 or 255) # :param channel: input channels (default: 3) # :param use_padding: padding image before conv # ''' # super().__init__() # assert window_size % 2 == 1, 'Window size must be odd.' # window = create_window(window_size, window_sigma, channel) # self.register_buffer('window', window) # self.data_range = data_range # self.use_padding = use_padding # # @torch.jit.script_method # def forward(self, X, Y): # r = ssim(X, Y, window=self.window, data_range=self.data_range, use_padding=self.use_padding) # return r[0] # # # class MS_SSIM(torch.jit.ScriptModule): # __constants__ = ['data_range', 'use_padding', 'eps'] # # def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False, weights=None, # levels=None, eps=1e-8): # ''' # class for ms-ssim # :param window_size: the size of gauss kernel # :param window_sigma: sigma of normal distribution # :param data_range: value range of input images. (usually 1.0 or 255) # :param channel: input channels # :param use_padding: padding image before conv # :param weights: weights for different levels. (default [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]) # :param levels: number of downsampling # :param eps: Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf. # ''' # super().__init__() # assert window_size % 2 == 1, 'Window size must be odd.' # self.data_range = data_range # self.use_padding = use_padding # self.eps = eps # # window = create_window(window_size, window_sigma, channel) # self.register_buffer('window', window) # # if weights is None: # weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333] # weights = torch.tensor(weights, dtype=torch.float) # # if levels is not None: # weights = weights[:levels] # weights = weights / weights.sum() # # self.register_buffer('weights', weights) # # @torch.jit.script_method # def forward(self, X, Y): # return ms_ssim(X, Y, window=self.window, data_range=self.data_range, weights=self.weights, # use_padding=self.use_padding, eps=self.eps) # # # if __name__ == '__main__': # print('Simple Test') # im = torch.randint(0, 255, (5, 3, 256, 256), dtype=torch.float, device='cuda') # img1 = im / 255 # img2 = img1 * 0.5 # # losser = SSIM(data_range=1.).cuda() # loss = losser(img1, img2).mean() # # losser2 = MS_SSIM(data_range=1.).cuda() # loss2 = losser2(img1, img2).mean() # # print(loss.item()) # print(loss2.item()) # # if __name__ == '__main__': # print('Training Test') # import cv2 # import torch.optim # import numpy as np # import imageio # import time # # out_test_video = False # # 最好不要直接输出gif图，会非常大，最好先输出mkv文件后用ffmpeg转换到GIF # video_use_gif = False # # im = cv2.imread('test_img1.jpg', 1) # t_im = torch.from_numpy(im).cuda().permute(2, 0, 1).float()[None] / 255. # # if out_test_video: # if video_use_gif: # fps = 0.5 # out_wh = (im.shape[1] // 2, im.shape[0] // 2) # suffix = '.gif' # else: # fps = 5 # out_wh = (im.shape[1], im.shape[0]) # suffix = '.mkv' # video_last_time = time.perf_counter() # video = imageio.get_writer('ssim_test' + suffix, fps=fps) # # # 测试ssim # print('Training SSIM') # rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255. # rand_im.requires_grad = True # optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8) # losser = SSIM(data_range=1., channel=t_im.shape[1]).cuda() # ssim_score = 0 # while ssim_score < 0.999: # optim.zero_grad() # loss = losser(rand_im, t_im) # (-loss).sum().backward() # ssim_score = loss.item() # optim.step() # r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0] # r_im = cv2.putText(r_im, 'ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) # # if out_test_video: # if time.perf_counter() - video_last_time > 1. / fps: # video_last_time = time.perf_counter() # out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB) # out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA) # if isinstance(out_frame, cv2.UMat): # out_frame = out_frame.get() # video.append_data(out_frame) # # cv2.imshow('ssim', r_im) # cv2.setWindowTitle('ssim', 'ssim %f' % ssim_score) # cv2.waitKey(1) # # if out_test_video: # video.close() # # # 测试ms_ssim # if out_test_video: # if video_use_gif: # fps = 0.5 # out_wh = (im.shape[1] // 2, im.shape[0] // 2) # suffix = '.gif' # else: # fps = 5 # out_wh = (im.shape[1], im.shape[0]) # suffix = '.mkv' # video_last_time = time.perf_counter() # video = imageio.get_writer('ms_ssim_test' + suffix, fps=fps) # # print('Training MS_SSIM') # rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255. # rand_im.requires_grad = True # optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8) # losser = MS_SSIM(data_range=1., channel=t_im.shape[1]).cuda() # ssim_score = 0 # while ssim_score < 0.999: # optim.zero_grad() # loss = losser(rand_im, t_im) # (-loss).sum().backward() # ssim_score = loss.item() # optim.step() # r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0] # r_im = cv2.putText(r_im, 'ms_ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) # # if out_test_video: # if time.perf_counter() - video_last_time > 1. / fps: # video_last_time = time.perf_counter() # out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB) # out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA) # if isinstance(out_frame, cv2.UMat): # out_frame = out_frame.get() # video.append_data(out_frame) # # cv2.imshow('ms_ssim', r_im) # cv2.setWindowTitle('ms_ssim', 'ms_ssim %f' % ssim_score) # cv2.waitKey(1) # # if out_test_video: # video.close() """ Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim """ import torch import torch.nn.functional as F from torch.autograd import Variable import numpy as np from math import exp def gaussian(window_size, sigma): gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) return gauss / gauss.sum() def create_window(window_size, channel): _1D_window = gaussian(window_size, 1.5).unsqueeze(1) _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) return window def _ssim(img1, img2, window, window_size, channel, size_average=True): mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) mu1_sq = mu1.pow(2) mu2_sq = mu2.pow(2) mu1_mu2 = mu1 * mu2 sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 C1 = 0.01 ** 2 C2 = 0.03 ** 2 ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) if size_average: return ssim_map.mean() else: return ssim_map.mean(1) class SSIM(torch.nn.Module): def __init__(self, window_size=11, size_average=True): super(SSIM, self).__init__() self.window_size = window_size self.size_average = size_average self.channel = 1 self.window = create_window(window_size, self.channel) def forward(self, img1, img2): (_, channel, _, _) = img1.size() if channel == self.channel and self.window.data.type() == img1.data.type(): window = self.window else: window = create_window(self.window_size, channel) if img1.is_cuda: window = window.cuda(img1.get_device()) window = window.type_as(img1) self.window = window self.channel = channel return _ssim(img1, img2, window, self.window_size, channel, self.size_average) window = None def ssim(img1, img2, window_size=11, size_average=True): (_, channel, _, _) = img1.size() global window if window is None: window = create_window(window_size, channel) if img1.is_cuda: window = window.cuda(img1.get_device()) window = window.type_as(img1) return _ssim(img1, img2, window, window_size, channel, size_average) ================================================ FILE: NeuralSeq/modules/commons/transformer.py ================================================ import math import torch from torch import nn from torch.nn import Parameter, Linear from modules.commons.common_layers import LayerNorm, Embedding from utils.tts_utils import get_incremental_state, set_incremental_state, softmax, make_positions import torch.nn.functional as F DEFAULT_MAX_SOURCE_POSITIONS = 2000 DEFAULT_MAX_TARGET_POSITIONS = 2000 class SinusoidalPositionalEmbedding(nn.Module): """This module produces sinusoidal positional embeddings of any length. Padding symbols are ignored. """ def __init__(self, embedding_dim, padding_idx, init_size=1024): super().__init__() self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.weights = SinusoidalPositionalEmbedding.get_embedding( init_size, embedding_dim, padding_idx, ) self.register_buffer('_float_tensor', torch.FloatTensor(1)) @staticmethod def get_embedding(num_embeddings, embedding_dim, padding_idx=None): """Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) if embedding_dim % 2 == 1: # zero pad emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) if padding_idx is not None: emb[padding_idx, :] = 0 return emb def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs): """Input is expected to be of size [bsz x seqlen].""" bsz, seq_len = input.shape[:2] max_pos = self.padding_idx + 1 + seq_len if self.weights is None or max_pos > self.weights.size(0): # recompute/expand embeddings if needed self.weights = SinusoidalPositionalEmbedding.get_embedding( max_pos, self.embedding_dim, self.padding_idx, ) self.weights = self.weights.to(self._float_tensor) if incremental_state is not None: # positions is the same for every token when decoding a single step pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) positions = make_positions(input, self.padding_idx) if positions is None else positions return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() def max_positions(self): """Maximum number of supported positions.""" return int(1e5) # an arbitrary large number class TransformerFFNLayer(nn.Module): def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'): super().__init__() self.kernel_size = kernel_size self.dropout = dropout self.act = act if padding == 'SAME': self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2) elif padding == 'LEFT': self.ffn_1 = nn.Sequential( nn.ConstantPad1d((kernel_size - 1, 0), 0.0), nn.Conv1d(hidden_size, filter_size, kernel_size) ) self.ffn_2 = Linear(filter_size, hidden_size) def forward(self, x, incremental_state=None): # x: T x B x C if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_input' in saved_state: prev_input = saved_state['prev_input'] x = torch.cat((prev_input, x), dim=0) x = x[-self.kernel_size:] saved_state['prev_input'] = x self._set_input_buffer(incremental_state, saved_state) x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1) x = x * self.kernel_size ** -0.5 if incremental_state is not None: x = x[-1:] if self.act == 'gelu': x = F.gelu(x) if self.act == 'relu': x = F.relu(x) x = F.dropout(x, self.dropout, training=self.training) x = self.ffn_2(x) return x def _get_input_buffer(self, incremental_state): return get_incremental_state( self, incremental_state, 'f', ) or {} def _set_input_buffer(self, incremental_state, buffer): set_incremental_state( self, incremental_state, 'f', buffer, ) def clear_buffer(self, incremental_state): if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_input' in saved_state: del saved_state['prev_input'] self._set_input_buffer(incremental_state, saved_state) class MultiheadAttention(nn.Module): def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, self_attention=False, encoder_decoder_attention=False): super().__init__() self.embed_dim = embed_dim self.kdim = kdim if kdim is not None else embed_dim self.vdim = vdim if vdim is not None else embed_dim self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" self.scaling = self.head_dim ** -0.5 self.self_attention = self_attention self.encoder_decoder_attention = encoder_decoder_attention assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \ 'value to be of the same size' if self.qkv_same_dim: self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim)) else: self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim)) self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim)) self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim)) if bias: self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim)) else: self.register_parameter('in_proj_bias', None) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) if add_bias_kv: self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) else: self.bias_k = self.bias_v = None self.add_zero_attn = add_zero_attn self.reset_parameters() self.enable_torch_version = False if hasattr(F, "multi_head_attention_forward"): self.enable_torch_version = True else: self.enable_torch_version = False self.last_attn_probs = None def reset_parameters(self): if self.qkv_same_dim: nn.init.xavier_uniform_(self.in_proj_weight) else: nn.init.xavier_uniform_(self.k_proj_weight) nn.init.xavier_uniform_(self.v_proj_weight) nn.init.xavier_uniform_(self.q_proj_weight) nn.init.xavier_uniform_(self.out_proj.weight) if self.in_proj_bias is not None: nn.init.constant_(self.in_proj_bias, 0.) nn.init.constant_(self.out_proj.bias, 0.) if self.bias_k is not None: nn.init.xavier_normal_(self.bias_k) if self.bias_v is not None: nn.init.xavier_normal_(self.bias_v) def forward( self, query, key, value, key_padding_mask=None, incremental_state=None, need_weights=True, static_kv=False, attn_mask=None, before_softmax=False, need_head_weights=False, enc_dec_attn_constraint_mask=None, reset_attn_weight=None ): """Input shape: Time x Batch x Channel Args: key_padding_mask (ByteTensor, optional): mask to exclude keys that are pads, of shape `(batch, src_len)`, where padding elements are indicated by 1s. need_weights (bool, optional): return the attention weights, averaged over heads (default: False). attn_mask (ByteTensor, optional): typically used to implement causal attention, where the mask prevents the attention from looking forward in time (default: None). before_softmax (bool, optional): return the raw attention weights and values before the attention softmax. need_head_weights (bool, optional): return the attention weights for each head. Implies *need_weights*. Default: return the average attention weights over all heads. """ if need_head_weights: need_weights = True tgt_len, bsz, embed_dim = query.size() assert embed_dim == self.embed_dim assert list(query.size()) == [tgt_len, bsz, embed_dim] if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None: if self.qkv_same_dim: return F.multi_head_attention_forward(query, key, value, self.embed_dim, self.num_heads, self.in_proj_weight, self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, self.training, key_padding_mask, need_weights, attn_mask) else: return F.multi_head_attention_forward(query, key, value, self.embed_dim, self.num_heads, torch.empty([0]), self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, self.training, key_padding_mask, need_weights, attn_mask, use_separate_proj_weight=True, q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight, v_proj_weight=self.v_proj_weight) if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_key' in saved_state: # previous time steps are cached - no need to recompute # key and value if they are static if static_kv: assert self.encoder_decoder_attention and not self.self_attention key = value = None else: saved_state = None if self.self_attention: # self-attention q, k, v = self.in_proj_qkv(query) elif self.encoder_decoder_attention: # encoder-decoder attention q = self.in_proj_q(query) if key is None: assert value is None k = v = None else: k = self.in_proj_k(key) v = self.in_proj_v(key) else: q = self.in_proj_q(query) k = self.in_proj_k(key) v = self.in_proj_v(value) q *= self.scaling if self.bias_k is not None: assert self.bias_v is not None k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1) q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) if k is not None: k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) if v is not None: v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) if saved_state is not None: # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) if 'prev_key' in saved_state: prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim) if static_kv: k = prev_key else: k = torch.cat((prev_key, k), dim=1) if 'prev_value' in saved_state: prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim) if static_kv: v = prev_value else: v = torch.cat((prev_value, v), dim=1) if 'prev_key_padding_mask' in saved_state and saved_state['prev_key_padding_mask'] is not None: prev_key_padding_mask = saved_state['prev_key_padding_mask'] if static_kv: key_padding_mask = prev_key_padding_mask else: key_padding_mask = torch.cat((prev_key_padding_mask, key_padding_mask), dim=1) saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim) saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim) saved_state['prev_key_padding_mask'] = key_padding_mask self._set_input_buffer(incremental_state, saved_state) src_len = k.size(1) # This is part of a workaround to get around fork/join parallelism # not supporting Optional types. if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]): key_padding_mask = None if key_padding_mask is not None: assert key_padding_mask.size(0) == bsz assert key_padding_mask.size(1) == src_len if self.add_zero_attn: src_len += 1 k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1) attn_weights = torch.bmm(q, k.transpose(1, 2)) attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] if attn_mask is not None: if len(attn_mask.shape) == 2: attn_mask = attn_mask.unsqueeze(0) elif len(attn_mask.shape) == 3: attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape( bsz * self.num_heads, tgt_len, src_len) attn_weights = attn_weights + attn_mask if enc_dec_attn_constraint_mask is not None: # bs x head x L_kv attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.masked_fill( enc_dec_attn_constraint_mask.unsqueeze(2).bool(), -1e8, ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if key_padding_mask is not None: # don't attend to padding symbols attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.masked_fill( key_padding_mask.unsqueeze(1).unsqueeze(2), -1e8, ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) if before_softmax: return attn_weights, v attn_weights_float = softmax(attn_weights, dim=-1) attn_weights = attn_weights_float.type_as(attn_weights) attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training) if reset_attn_weight is not None: if reset_attn_weight: self.last_attn_probs = attn_probs.detach() else: assert self.last_attn_probs is not None attn_probs = self.last_attn_probs attn = torch.bmm(attn_probs, v) assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) attn = self.out_proj(attn) if need_weights: attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0) if not need_head_weights: # average attention weights over heads attn_weights = attn_weights.mean(dim=0) else: attn_weights = None return attn, (attn_weights, attn_logits) def in_proj_qkv(self, query): return self._in_proj(query).chunk(3, dim=-1) def in_proj_q(self, query): if self.qkv_same_dim: return self._in_proj(query, end=self.embed_dim) else: bias = self.in_proj_bias if bias is not None: bias = bias[:self.embed_dim] return F.linear(query, self.q_proj_weight, bias) def in_proj_k(self, key): if self.qkv_same_dim: return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim) else: weight = self.k_proj_weight bias = self.in_proj_bias if bias is not None: bias = bias[self.embed_dim:2 * self.embed_dim] return F.linear(key, weight, bias) def in_proj_v(self, value): if self.qkv_same_dim: return self._in_proj(value, start=2 * self.embed_dim) else: weight = self.v_proj_weight bias = self.in_proj_bias if bias is not None: bias = bias[2 * self.embed_dim:] return F.linear(value, weight, bias) def _in_proj(self, input, start=0, end=None): weight = self.in_proj_weight bias = self.in_proj_bias weight = weight[start:end, :] if bias is not None: bias = bias[start:end] return F.linear(input, weight, bias) def _get_input_buffer(self, incremental_state): return get_incremental_state( self, incremental_state, 'attn_state', ) or {} def _set_input_buffer(self, incremental_state, buffer): set_incremental_state( self, incremental_state, 'attn_state', buffer, ) def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): return attn_weights def clear_buffer(self, incremental_state=None): if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_key' in saved_state: del saved_state['prev_key'] if 'prev_value' in saved_state: del saved_state['prev_value'] self._set_input_buffer(incremental_state, saved_state) class EncSALayer(nn.Module): def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, padding='SAME', act='gelu'): super().__init__() self.c = c self.dropout = dropout self.num_heads = num_heads if num_heads > 0: self.layer_norm1 = LayerNorm(c) self.self_attn = MultiheadAttention( self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False) self.layer_norm2 = LayerNorm(c) self.ffn = TransformerFFNLayer( c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act) def forward(self, x, encoder_padding_mask=None, **kwargs): layer_norm_training = kwargs.get('layer_norm_training', None) if layer_norm_training is not None: self.layer_norm1.training = layer_norm_training self.layer_norm2.training = layer_norm_training if self.num_heads > 0: residual = x x = self.layer_norm1(x) x, _, = self.self_attn( query=x, key=x, value=x, key_padding_mask=encoder_padding_mask ) x = F.dropout(x, self.dropout, training=self.training) x = residual + x x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] residual = x x = self.layer_norm2(x) x = self.ffn(x) x = F.dropout(x, self.dropout, training=self.training) x = residual + x x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] return x class DecSALayer(nn.Module): def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act='gelu'): super().__init__() self.c = c self.dropout = dropout self.layer_norm1 = LayerNorm(c) self.self_attn = MultiheadAttention( c, num_heads, self_attention=True, dropout=attention_dropout, bias=False ) self.layer_norm2 = LayerNorm(c) self.encoder_attn = MultiheadAttention( c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False, ) self.layer_norm3 = LayerNorm(c) self.ffn = TransformerFFNLayer( c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act) def forward( self, x, encoder_out=None, encoder_padding_mask=None, incremental_state=None, self_attn_mask=None, self_attn_padding_mask=None, attn_out=None, reset_attn_weight=None, **kwargs, ): layer_norm_training = kwargs.get('layer_norm_training', None) if layer_norm_training is not None: self.layer_norm1.training = layer_norm_training self.layer_norm2.training = layer_norm_training self.layer_norm3.training = layer_norm_training residual = x x = self.layer_norm1(x) x, _ = self.self_attn( query=x, key=x, value=x, key_padding_mask=self_attn_padding_mask, incremental_state=incremental_state, attn_mask=self_attn_mask ) x = F.dropout(x, self.dropout, training=self.training) x = residual + x attn_logits = None if encoder_out is not None or attn_out is not None: residual = x x = self.layer_norm2(x) if encoder_out is not None: x, attn = self.encoder_attn( query=x, key=encoder_out, value=encoder_out, key_padding_mask=encoder_padding_mask, incremental_state=incremental_state, static_kv=True, enc_dec_attn_constraint_mask=get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'), reset_attn_weight=reset_attn_weight ) attn_logits = attn[1] elif attn_out is not None: x = self.encoder_attn.in_proj_v(attn_out) if encoder_out is not None or attn_out is not None: x = F.dropout(x, self.dropout, training=self.training) x = residual + x residual = x x = self.layer_norm3(x) x = self.ffn(x, incremental_state=incremental_state) x = F.dropout(x, self.dropout, training=self.training) x = residual + x return x, attn_logits def clear_buffer(self, input, encoder_out=None, encoder_padding_mask=None, incremental_state=None): self.encoder_attn.clear_buffer(incremental_state) self.ffn.clear_buffer(incremental_state) def set_buffer(self, name, tensor, incremental_state): return set_incremental_state(self, incremental_state, name, tensor) class TransformerEncoderLayer(nn.Module): def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2): super().__init__() self.hidden_size = hidden_size self.dropout = dropout self.num_heads = num_heads self.op = EncSALayer( hidden_size, num_heads, dropout=dropout, attention_dropout=0.0, relu_dropout=dropout, kernel_size=kernel_size) def forward(self, x, **kwargs): return self.op(x, **kwargs) class TransformerDecoderLayer(nn.Module): def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2): super().__init__() self.hidden_size = hidden_size self.dropout = dropout self.num_heads = num_heads self.op = DecSALayer( hidden_size, num_heads, dropout=dropout, attention_dropout=0.0, relu_dropout=dropout, kernel_size=kernel_size) def forward(self, x, **kwargs): return self.op(x, **kwargs) def clear_buffer(self, *args): return self.op.clear_buffer(*args) def set_buffer(self, *args): return self.op.set_buffer(*args) class FFTBlocks(nn.Module): def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=0.0, num_heads=2, use_pos_embed=True, use_last_norm=True, use_pos_embed_alpha=True): super().__init__() self.num_layers = num_layers embed_dim = self.hidden_size = hidden_size self.dropout = dropout self.use_pos_embed = use_pos_embed self.use_last_norm = use_last_norm if use_pos_embed: self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS self.padding_idx = 0 self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1 self.embed_positions = SinusoidalPositionalEmbedding( embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, ) self.layers = nn.ModuleList([]) self.layers.extend([ TransformerEncoderLayer(self.hidden_size, self.dropout, kernel_size=ffn_kernel_size, num_heads=num_heads) for _ in range(self.num_layers) ]) if self.use_last_norm: self.layer_norm = nn.LayerNorm(embed_dim) else: self.layer_norm = None def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False): """ :param x: [B, T, C] :param padding_mask: [B, T] :return: [B, T, C] or [L, B, T, C] """ padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1] if self.use_pos_embed: positions = self.pos_embed_alpha * self.embed_positions(x[..., 0]) x = x + positions x = F.dropout(x, p=self.dropout, training=self.training) # B x T x C -> T x B x C x = x.transpose(0, 1) * nonpadding_mask_TB hiddens = [] for layer in self.layers: x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB hiddens.append(x) if self.use_last_norm: x = self.layer_norm(x) * nonpadding_mask_TB if return_hiddens: x = torch.stack(hiddens, 0) # [L, T, B, C] x = x.transpose(1, 2) # [L, B, T, C] else: x = x.transpose(0, 1) # [B, T, C] return x class FastSpeechEncoder(FFTBlocks): def __init__(self, dict_size, hidden_size=256, num_layers=4, kernel_size=9, num_heads=2, dropout=0.0): super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads, use_pos_embed=False, dropout=dropout) # use_pos_embed_alpha for compatibility self.embed_tokens = Embedding(dict_size, hidden_size, 0) self.embed_scale = math.sqrt(hidden_size) self.padding_idx = 0 self.embed_positions = SinusoidalPositionalEmbedding( hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, ) def forward(self, txt_tokens, attn_mask=None): """ :param txt_tokens: [B, T] :return: { 'encoder_out': [B x T x C] } """ encoder_padding_mask = txt_tokens.eq(self.padding_idx).data x = self.forward_embedding(txt_tokens) # [B, T, H] if self.num_layers > 0: x = super(FastSpeechEncoder, self).forward(x, encoder_padding_mask, attn_mask=attn_mask) return x def forward_embedding(self, txt_tokens): # embed tokens and positions x = self.embed_scale * self.embed_tokens(txt_tokens) if self.use_pos_embed: positions = self.embed_positions(txt_tokens) x = x + positions x = F.dropout(x, p=self.dropout, training=self.training) return x class FastSpeechDecoder(FFTBlocks): def __init__(self, hidden_size=256, num_layers=4, kernel_size=9, num_heads=2): super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads) ================================================ FILE: NeuralSeq/modules/commons/wavenet.py ================================================ import torch from torch import nn def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): n_channels_int = n_channels[0] in_act = input_a + input_b t_act = torch.tanh(in_act[:, :n_channels_int, :]) s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) acts = t_act * s_act return acts class WN(torch.nn.Module): def __init__(self, hidden_size, kernel_size, dilation_rate, n_layers, c_cond=0, p_dropout=0, share_cond_layers=False, is_BTC=False): super(WN, self).__init__() assert (kernel_size % 2 == 1) assert (hidden_size % 2 == 0) self.is_BTC = is_BTC self.hidden_size = hidden_size self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.gin_channels = c_cond self.p_dropout = p_dropout self.share_cond_layers = share_cond_layers self.in_layers = torch.nn.ModuleList() self.res_skip_layers = torch.nn.ModuleList() self.drop = nn.Dropout(p_dropout) if c_cond != 0 and not share_cond_layers: cond_layer = torch.nn.Conv1d(c_cond, 2 * hidden_size * n_layers, 1) self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') for i in range(n_layers): dilation = dilation_rate ** i padding = int((kernel_size * dilation - dilation) / 2) in_layer = torch.nn.Conv1d(hidden_size, 2 * hidden_size, kernel_size, dilation=dilation, padding=padding) in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') self.in_layers.append(in_layer) # last one is not necessary if i < n_layers - 1: res_skip_channels = 2 * hidden_size else: res_skip_channels = hidden_size res_skip_layer = torch.nn.Conv1d(hidden_size, res_skip_channels, 1) res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') self.res_skip_layers.append(res_skip_layer) def forward(self, x, nonpadding=None, cond=None): if self.is_BTC: x = x.transpose(1, 2) cond = cond.transpose(1, 2) if cond is not None else None nonpadding = nonpadding.transpose(1, 2) if nonpadding is not None else None if nonpadding is None: nonpadding = 1 output = torch.zeros_like(x) n_channels_tensor = torch.IntTensor([self.hidden_size]) if cond is not None and not self.share_cond_layers: cond = self.cond_layer(cond) for i in range(self.n_layers): x_in = self.in_layers[i](x) x_in = self.drop(x_in) if cond is not None: cond_offset = i * 2 * self.hidden_size cond_l = cond[:, cond_offset:cond_offset + 2 * self.hidden_size, :] else: cond_l = torch.zeros_like(x_in) acts = fused_add_tanh_sigmoid_multiply(x_in, cond_l, n_channels_tensor) res_skip_acts = self.res_skip_layers[i](acts) if i < self.n_layers - 1: x = (x + res_skip_acts[:, :self.hidden_size, :]) * nonpadding output = output + res_skip_acts[:, self.hidden_size:, :] else: output = output + res_skip_acts output = output * nonpadding if self.is_BTC: output = output.transpose(1, 2) return output def remove_weight_norm(self): def remove_weight_norm(m): try: nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(remove_weight_norm) ================================================ FILE: NeuralSeq/modules/diff/candidate_decoder.py ================================================ from modules.fastspeech.tts_modules import FastspeechDecoder # from modules.fastspeech.fast_tacotron import DecoderRNN # from modules.fastspeech.speedy_speech.speedy_speech import ConvBlocks # from modules.fastspeech.conformer.conformer import ConformerDecoder import torch from torch.nn import functional as F import torch.nn as nn import math from utils.hparams import hparams from .diffusion import Mish Linear = nn.Linear class SinusoidalPosEmb(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, x): device = x.device half_dim = self.dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, device=device) * -emb) emb = x[:, None] * emb[None, :] emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb def Conv1d(*args, **kwargs): layer = nn.Conv1d(*args, **kwargs) nn.init.kaiming_normal_(layer.weight) return layer class FFT(FastspeechDecoder): def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None): super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads) dim = hparams['residual_channels'] self.input_projection = Conv1d(hparams['audio_num_mel_bins'], dim, 1) self.diffusion_embedding = SinusoidalPosEmb(dim) self.mlp = nn.Sequential( nn.Linear(dim, dim * 4), Mish(), nn.Linear(dim * 4, dim) ) self.get_mel_out = Linear(hparams['hidden_size'], 80, bias=True) self.get_decode_inp = Linear(hparams['hidden_size'] + dim + dim, hparams['hidden_size']) # hs + dim + 80 -> hs def forward(self, spec, diffusion_step, cond, padding_mask=None, attn_mask=None, return_hiddens=False): """ :param spec: [B, 1, 80, T] :param diffusion_step: [B, 1] :param cond: [B, M, T] :return: """ x = spec[:, 0] x = self.input_projection(x).permute([0, 2, 1]) # [B, T, residual_channel] diffusion_step = self.diffusion_embedding(diffusion_step) diffusion_step = self.mlp(diffusion_step) # [B, dim] cond = cond.permute([0, 2, 1]) # [B, T, M] seq_len = cond.shape[1] # [T_mel] time_embed = diffusion_step[:, None, :] # [B, 1, dim] time_embed = time_embed.repeat([1, seq_len, 1]) # # [B, T, dim] decoder_inp = torch.cat([x, cond, time_embed], dim=-1) # [B, T, dim + H + dim] decoder_inp = self.get_decode_inp(decoder_inp) # [B, T, H] x = decoder_inp ''' Required x: [B, T, C] :return: [B, T, C] or [L, B, T, C] ''' padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1] if self.use_pos_embed: positions = self.pos_embed_alpha * self.embed_positions(x[..., 0]) x = x + positions x = F.dropout(x, p=self.dropout, training=self.training) # B x T x C -> T x B x C x = x.transpose(0, 1) * nonpadding_mask_TB hiddens = [] for layer in self.layers: x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB hiddens.append(x) if self.use_last_norm: x = self.layer_norm(x) * nonpadding_mask_TB if return_hiddens: x = torch.stack(hiddens, 0) # [L, T, B, C] x = x.transpose(1, 2) # [L, B, T, C] else: x = x.transpose(0, 1) # [B, T, C] x = self.get_mel_out(x).permute([0, 2, 1]) # [B, 80, T] return x[:, None, :, :] ================================================ FILE: NeuralSeq/modules/diff/diffusion.py ================================================ import math import random from functools import partial from inspect import isfunction from pathlib import Path import numpy as np import torch import torch.nn.functional as F from torch import nn from tqdm import tqdm from einops import rearrange from modules.fastspeech.fs2 import FastSpeech2 from modules.diffsinger_midi.fs2 import FastSpeech2MIDI from utils.hparams import hparams def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def cycle(dl): while True: for data in dl: yield data def num_to_groups(num, divisor): groups = num // divisor remainder = num % divisor arr = [divisor] * groups if remainder > 0: arr.append(remainder) return arr class Residual(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn def forward(self, x, *args, **kwargs): return self.fn(x, *args, **kwargs) + x class SinusoidalPosEmb(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, x): device = x.device half_dim = self.dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, device=device) * -emb) emb = x[:, None] * emb[None, :] emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb class Mish(nn.Module): def forward(self, x): return x * torch.tanh(F.softplus(x)) class Upsample(nn.Module): def __init__(self, dim): super().__init__() self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1) def forward(self, x): return self.conv(x) class Downsample(nn.Module): def __init__(self, dim): super().__init__() self.conv = nn.Conv2d(dim, dim, 3, 2, 1) def forward(self, x): return self.conv(x) class Rezero(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn self.g = nn.Parameter(torch.zeros(1)) def forward(self, x): return self.fn(x) * self.g # building block modules class Block(nn.Module): def __init__(self, dim, dim_out, groups=8): super().__init__() self.block = nn.Sequential( nn.Conv2d(dim, dim_out, 3, padding=1), nn.GroupNorm(groups, dim_out), Mish() ) def forward(self, x): return self.block(x) class ResnetBlock(nn.Module): def __init__(self, dim, dim_out, *, time_emb_dim, groups=8): super().__init__() self.mlp = nn.Sequential( Mish(), nn.Linear(time_emb_dim, dim_out) ) self.block1 = Block(dim, dim_out) self.block2 = Block(dim_out, dim_out) self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity() def forward(self, x, time_emb): h = self.block1(x) h += self.mlp(time_emb)[:, :, None, None] h = self.block2(h) return h + self.res_conv(x) class LinearAttention(nn.Module): def __init__(self, dim, heads=4, dim_head=32): super().__init__() self.heads = heads hidden_dim = dim_head * heads self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) self.to_out = nn.Conv2d(hidden_dim, dim, 1) def forward(self, x): b, c, h, w = x.shape qkv = self.to_qkv(x) q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads=self.heads, qkv=3) k = k.softmax(dim=-1) context = torch.einsum('bhdn,bhen->bhde', k, v) out = torch.einsum('bhde,bhdn->bhen', context, q) out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) return self.to_out(out) # gaussian diffusion trainer class def extract(a, t, x_shape): b, *_ = t.shape out = a.gather(-1, t) return out.reshape(b, *((1,) * (len(x_shape) - 1))) def noise_like(shape, device, repeat=False): repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: torch.randn(shape, device=device) return repeat_noise() if repeat else noise() def cosine_beta_schedule(timesteps, s=0.008): """ cosine schedule as proposed in https://openreview.net/forum?id=-NEXDKk8gZ """ steps = timesteps + 1 x = np.linspace(0, steps, steps) alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 alphas_cumprod = alphas_cumprod / alphas_cumprod[0] betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) return np.clip(betas, a_min=0, a_max=0.999) class GaussianDiffusion(nn.Module): def __init__(self, phone_encoder, out_dims, denoise_fn, timesteps=1000, loss_type='l1', betas=None, spec_min=None, spec_max=None): super().__init__() self.denoise_fn = denoise_fn if hparams.get('use_midi') is not None and hparams['use_midi']: self.fs2 = FastSpeech2MIDI(phone_encoder, out_dims) else: self.fs2 = FastSpeech2(phone_encoder, out_dims) self.fs2.decoder = None self.mel_bins = out_dims if exists(betas): betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas else: betas = cosine_beta_schedule(timesteps) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.loss_type = loss_type to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_torch(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']]) self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']]) def q_mean_variance(self, x_start, t): mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start variance = extract(1. - self.alphas_cumprod, t, x_start.shape) log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) return mean, variance, log_variance def predict_start_from_noise(self, x_t, t, noise): return ( extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise ) def q_posterior(self, x_start, x_t, t): posterior_mean = ( extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t ) posterior_variance = extract(self.posterior_variance, t, x_t.shape) posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) return posterior_mean, posterior_variance, posterior_log_variance_clipped def p_mean_variance(self, x, t, cond, clip_denoised: bool): noise_pred = self.denoise_fn(x, t, cond=cond) x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) if clip_denoised: x_recon.clamp_(-1., 1.) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): b, *_, device = *x.shape, x.device model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised) noise = noise_like(x.shape, device, repeat_noise) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return ( extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise ) def p_losses(self, x_start, t, cond, noise=None, nonpadding=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) x_recon = self.denoise_fn(x_noisy, t, cond) if self.loss_type == 'l1': if nonpadding is not None: loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean() else: # print('are you sure w/o nonpadding?') loss = (noise - x_recon).abs().mean() elif self.loss_type == 'l2': loss = F.mse_loss(noise, x_recon) else: raise NotImplementedError() return loss def forward(self, txt_tokens, mel2ph=None, spk_embed=None, ref_mels=None, f0=None, uv=None, energy=None, infer=False): b, *_, device = *txt_tokens.shape, txt_tokens.device ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, skip_decoder=True, infer=infer) cond = ret['decoder_inp'].transpose(1, 2) if not infer: t = torch.randint(0, self.num_timesteps, (b,), device=device).long() x = ref_mels x = self.norm_spec(x) x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] nonpadding = (mel2ph != 0).float() ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding) else: t = self.num_timesteps shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) x = torch.randn(shape, device=device) for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) x = x[:, 0].transpose(1, 2) ret['mel_out'] = self.denorm_spec(x) return ret def norm_spec(self, x): return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 def denorm_spec(self, x): return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph): return self.fs2.cwt2f0_norm(cwt_spec, mean, std, mel2ph) def out2mel(self, x): return x ================================================ FILE: NeuralSeq/modules/diff/net.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F from math import sqrt from .diffusion import Mish from utils.hparams import hparams Linear = nn.Linear ConvTranspose2d = nn.ConvTranspose2d class AttrDict(dict): def __init__(self, *args, **kwargs): super(AttrDict, self).__init__(*args, **kwargs) self.__dict__ = self def override(self, attrs): if isinstance(attrs, dict): self.__dict__.update(**attrs) elif isinstance(attrs, (list, tuple, set)): for attr in attrs: self.override(attr) elif attrs is not None: raise NotImplementedError return self class SinusoidalPosEmb(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, x): device = x.device half_dim = self.dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, device=device) * -emb) emb = x[:, None] * emb[None, :] emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb def Conv1d(*args, **kwargs): layer = nn.Conv1d(*args, **kwargs) nn.init.kaiming_normal_(layer.weight) return layer @torch.jit.script def silu(x): return x * torch.sigmoid(x) class ResidualBlock(nn.Module): def __init__(self, encoder_hidden, residual_channels, dilation): super().__init__() self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation) self.diffusion_projection = Linear(residual_channels, residual_channels) self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1) self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1) def forward(self, x, conditioner, diffusion_step): diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) conditioner = self.conditioner_projection(conditioner) y = x + diffusion_step y = self.dilated_conv(y) + conditioner gate, filter = torch.chunk(y, 2, dim=1) y = torch.sigmoid(gate) * torch.tanh(filter) y = self.output_projection(y) residual, skip = torch.chunk(y, 2, dim=1) return (x + residual) / sqrt(2.0), skip class DiffNet(nn.Module): def __init__(self, in_dims=80): super().__init__() self.params = params = AttrDict( # Model params encoder_hidden=hparams['hidden_size'], residual_layers=hparams['residual_layers'], residual_channels=hparams['residual_channels'], dilation_cycle_length=hparams['dilation_cycle_length'], ) self.input_projection = Conv1d(in_dims, params.residual_channels, 1) self.diffusion_embedding = SinusoidalPosEmb(params.residual_channels) dim = params.residual_channels self.mlp = nn.Sequential( nn.Linear(dim, dim * 4), Mish(), nn.Linear(dim * 4, dim) ) self.residual_layers = nn.ModuleList([ ResidualBlock(params.encoder_hidden, params.residual_channels, 2 ** (i % params.dilation_cycle_length)) for i in range(params.residual_layers) ]) self.skip_projection = Conv1d(params.residual_channels, params.residual_channels, 1) self.output_projection = Conv1d(params.residual_channels, in_dims, 1) nn.init.zeros_(self.output_projection.weight) def forward(self, spec, diffusion_step, cond): """ :param spec: [B, 1, M, T] :param diffusion_step: [B, 1] :param cond: [B, M, T] :return: """ x = spec[:, 0] x = self.input_projection(x) # x [B, residual_channel, T] x = F.relu(x) diffusion_step = self.diffusion_embedding(diffusion_step) diffusion_step = self.mlp(diffusion_step) skip = [] for layer_id, layer in enumerate(self.residual_layers): x, skip_connection = layer(x, cond, diffusion_step) skip.append(skip_connection) x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers)) x = self.skip_projection(x) x = F.relu(x) x = self.output_projection(x) # [B, 80, T] return x[:, None, :, :] ================================================ FILE: NeuralSeq/modules/diff/shallow_diffusion_tts.py ================================================ import math import random from collections import deque from functools import partial from inspect import isfunction from pathlib import Path import numpy as np import torch import torch.nn.functional as F from torch import nn from tqdm import tqdm from einops import rearrange from modules.fastspeech.fs2 import FastSpeech2 from modules.diffsinger_midi.fs2 import FastSpeech2MIDI from utils.hparams import hparams def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d # gaussian diffusion trainer class def extract(a, t, x_shape): b, *_ = t.shape out = a.gather(-1, t) return out.reshape(b, *((1,) * (len(x_shape) - 1))) def noise_like(shape, device, repeat=False): repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: torch.randn(shape, device=device) return repeat_noise() if repeat else noise() def linear_beta_schedule(timesteps, max_beta=hparams.get('max_beta', 0.01)): """ linear schedule """ betas = np.linspace(1e-4, max_beta, timesteps) return betas def cosine_beta_schedule(timesteps, s=0.008): """ cosine schedule as proposed in https://openreview.net/forum?id=-NEXDKk8gZ """ steps = timesteps + 1 x = np.linspace(0, steps, steps) alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 alphas_cumprod = alphas_cumprod / alphas_cumprod[0] betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) return np.clip(betas, a_min=0, a_max=0.999) beta_schedule = { "cosine": cosine_beta_schedule, "linear": linear_beta_schedule, } class GaussianDiffusion(nn.Module): def __init__(self, phone_encoder, out_dims, denoise_fn, timesteps=1000, K_step=1000, loss_type=hparams.get('diff_loss_type', 'l1'), betas=None, spec_min=None, spec_max=None): super().__init__() self.denoise_fn = denoise_fn if hparams.get('use_midi') is not None and hparams['use_midi']: self.fs2 = FastSpeech2MIDI(phone_encoder, out_dims) else: self.fs2 = FastSpeech2(phone_encoder, out_dims) self.mel_bins = out_dims if exists(betas): betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas else: if 'schedule_type' in hparams.keys(): betas = beta_schedule[hparams['schedule_type']](timesteps) else: betas = cosine_beta_schedule(timesteps) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.K_step = K_step self.loss_type = loss_type self.noise_list = deque(maxlen=4) to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_torch(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']]) self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']]) def q_mean_variance(self, x_start, t): mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start variance = extract(1. - self.alphas_cumprod, t, x_start.shape) log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) return mean, variance, log_variance def predict_start_from_noise(self, x_t, t, noise): return ( extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise ) def q_posterior(self, x_start, x_t, t): posterior_mean = ( extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t ) posterior_variance = extract(self.posterior_variance, t, x_t.shape) posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) return posterior_mean, posterior_variance, posterior_log_variance_clipped def p_mean_variance(self, x, t, cond, clip_denoised: bool): noise_pred = self.denoise_fn(x, t, cond=cond) x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) if clip_denoised: x_recon.clamp_(-1., 1.) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): b, *_, device = *x.shape, x.device model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised) noise = noise_like(x.shape, device, repeat_noise) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False): """ Use the PLMS method from [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778). """ def get_x_pred(x, noise_t, t): a_t = extract(self.alphas_cumprod, t, x.shape) if t[0] < interval: a_prev = torch.ones_like(a_t) else: a_prev = extract(self.alphas_cumprod, torch.max(t-interval, torch.zeros_like(t)), x.shape) a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) x_pred = x + x_delta return x_pred noise_list = self.noise_list noise_pred = self.denoise_fn(x, t, cond=cond) if len(noise_list) == 0: x_pred = get_x_pred(x, noise_pred, t) noise_pred_prev = self.denoise_fn(x_pred, max(t-interval, 0), cond=cond) noise_pred_prime = (noise_pred + noise_pred_prev) / 2 elif len(noise_list) == 1: noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2 elif len(noise_list) == 2: noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12 elif len(noise_list) >= 3: noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24 x_prev = get_x_pred(x, noise_pred_prime, t) noise_list.append(noise_pred) return x_prev def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return ( extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise ) def p_losses(self, x_start, t, cond, noise=None, nonpadding=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) x_recon = self.denoise_fn(x_noisy, t, cond) if self.loss_type == 'l1': if nonpadding is not None: loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean() else: # print('are you sure w/o nonpadding?') loss = (noise - x_recon).abs().mean() elif self.loss_type == 'l2': loss = F.mse_loss(noise, x_recon) else: raise NotImplementedError() return loss def forward(self, txt_tokens, mel2ph=None, spk_embed=None, ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs): b, *_, device = *txt_tokens.shape, txt_tokens.device ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, skip_decoder=(not infer), infer=infer, **kwargs) cond = ret['decoder_inp'].transpose(1, 2) if not infer: t = torch.randint(0, self.K_step, (b,), device=device).long() x = ref_mels x = self.norm_spec(x) x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] ret['diff_loss'] = self.p_losses(x, t, cond) # nonpadding = (mel2ph != 0).float() # ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding) else: ret['fs2_mel'] = ret['mel_out'] fs2_mels = ret['mel_out'] t = self.K_step fs2_mels = self.norm_spec(fs2_mels) fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :] x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long()) if hparams.get('gaussian_start') is not None and hparams['gaussian_start']: print('===> gaussion start.') shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) x = torch.randn(shape, device=device) if hparams.get('pndm_speedup'): print('===> pndm speed:', hparams['pndm_speedup']) self.noise_list = deque(maxlen=4) iteration_interval = hparams['pndm_speedup'] for i in tqdm(reversed(range(0, t, iteration_interval)), desc='sample time step', total=t // iteration_interval): x = self.p_sample_plms(x, torch.full((b,), i, device=device, dtype=torch.long), iteration_interval, cond) else: for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) x = x[:, 0].transpose(1, 2) if mel2ph is not None: # for singing ret['mel_out'] = self.denorm_spec(x) * ((mel2ph > 0).float()[:, :, None]) else: ret['mel_out'] = self.denorm_spec(x) return ret def norm_spec(self, x): return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 def denorm_spec(self, x): return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph): return self.fs2.cwt2f0_norm(cwt_spec, mean, std, mel2ph) def out2mel(self, x): return x class OfflineGaussianDiffusion(GaussianDiffusion): def forward(self, txt_tokens, mel2ph=None, spk_embed=None, ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs): b, *_, device = *txt_tokens.shape, txt_tokens.device ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, skip_decoder=True, infer=True, **kwargs) cond = ret['decoder_inp'].transpose(1, 2) fs2_mels = ref_mels[1] ref_mels = ref_mels[0] if not infer: t = torch.randint(0, self.K_step, (b,), device=device).long() x = ref_mels x = self.norm_spec(x) x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] ret['diff_loss'] = self.p_losses(x, t, cond) else: t = self.K_step fs2_mels = self.norm_spec(fs2_mels) fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :] x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long()) if hparams.get('gaussian_start') is not None and hparams['gaussian_start']: print('===> gaussion start.') shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) x = torch.randn(shape, device=device) for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) x = x[:, 0].transpose(1, 2) ret['mel_out'] = self.denorm_spec(x) return ret ================================================ FILE: NeuralSeq/modules/diffsinger_midi/fs2.py ================================================ from modules.commons.common_layers import * from modules.commons.common_layers import Embedding from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \ EnergyPredictor, FastspeechEncoder from utils.cwt import cwt2f0 from utils.hparams import hparams from utils.pitch_utils import f0_to_coarse, denorm_f0, norm_f0 from modules.fastspeech.fs2 import FastSpeech2 class FastspeechMIDIEncoder(FastspeechEncoder): def forward_embedding(self, txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding): # embed tokens and positions x = self.embed_scale * self.embed_tokens(txt_tokens) x = x + midi_embedding + midi_dur_embedding + slur_embedding if hparams['use_pos_embed']: if hparams.get('rel_pos') is not None and hparams['rel_pos']: x = self.embed_positions(x) else: positions = self.embed_positions(txt_tokens) x = x + positions x = F.dropout(x, p=self.dropout, training=self.training) return x def forward(self, txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding): """ :param txt_tokens: [B, T] :return: { 'encoder_out': [T x B x C] } """ encoder_padding_mask = txt_tokens.eq(self.padding_idx).data x = self.forward_embedding(txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding) # [B, T, H] x = super(FastspeechEncoder, self).forward(x, encoder_padding_mask) return x FS_ENCODERS = { 'fft': lambda hp, embed_tokens, d: FastspeechMIDIEncoder( embed_tokens, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'], num_heads=hp['num_heads']), } class FastSpeech2MIDI(FastSpeech2): def __init__(self, dictionary, out_dims=None): super().__init__(dictionary, out_dims) del self.encoder self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams, self.encoder_embed_tokens, self.dictionary) self.midi_embed = Embedding(300, self.hidden_size, self.padding_idx) self.midi_dur_layer = Linear(1, self.hidden_size) self.is_slur_embed = Embedding(2, self.hidden_size) def forward(self, txt_tokens, mel2ph=None, spk_embed=None, ref_mels=None, f0=None, uv=None, energy=None, skip_decoder=False, spk_embed_dur_id=None, spk_embed_f0_id=None, infer=False, **kwargs): ret = {} midi_embedding = self.midi_embed(kwargs['pitch_midi']) midi_dur_embedding, slur_embedding = 0, 0 if kwargs.get('midi_dur') is not None: midi_dur_embedding = self.midi_dur_layer(kwargs['midi_dur'][:, :, None]) # [B, T, 1] -> [B, T, H] if kwargs.get('is_slur') is not None: slur_embedding = self.is_slur_embed(kwargs['is_slur']) encoder_out = self.encoder(txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding) # [B, T, C] src_nonpadding = (txt_tokens > 0).float()[:, :, None] # add ref style embed # Not implemented # variance encoder var_embed = 0 # encoder_out_dur denotes encoder outputs for duration predictor # in speech adaptation, duration predictor use old speaker embedding if hparams['use_spk_embed']: spk_embed_dur = spk_embed_f0 = spk_embed = self.spk_embed_proj(spk_embed)[:, None, :] elif hparams['use_spk_id']: spk_embed_id = spk_embed if spk_embed_dur_id is None: spk_embed_dur_id = spk_embed_id if spk_embed_f0_id is None: spk_embed_f0_id = spk_embed_id spk_embed = self.spk_embed_proj(spk_embed_id)[:, None, :] spk_embed_dur = spk_embed_f0 = spk_embed if hparams['use_split_spk_id']: spk_embed_dur = self.spk_embed_dur(spk_embed_dur_id)[:, None, :] spk_embed_f0 = self.spk_embed_f0(spk_embed_f0_id)[:, None, :] else: spk_embed_dur = spk_embed_f0 = spk_embed = 0 # add dur dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding mel2ph = self.add_dur(dur_inp, mel2ph, txt_tokens, ret) decoder_inp = F.pad(encoder_out, [0, 0, 1, 0]) mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]]) decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H] tgt_nonpadding = (mel2ph > 0).float()[:, :, None] # add pitch and energy embed pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding if hparams['use_pitch_embed']: pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding decoder_inp = decoder_inp + self.add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph) if hparams['use_energy_embed']: decoder_inp = decoder_inp + self.add_energy(pitch_inp, energy, ret) ret['decoder_inp'] = decoder_inp = (decoder_inp + spk_embed) * tgt_nonpadding if skip_decoder: return ret ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs) return ret ================================================ FILE: NeuralSeq/modules/fastspeech/fs2.py ================================================ from utils.hparams import hparams from modules.commons.common_layers import * from modules.commons.common_layers import Embedding from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \ EnergyPredictor, FastspeechEncoder from utils.cwt import cwt2f0 from utils.pitch_utils import f0_to_coarse, denorm_f0, norm_f0 import torch.nn as nn from modules.commons.rel_transformer import RelTransformerEncoder, BERTRelTransformerEncoder FS_ENCODERS = { 'fft': lambda hp, embed_tokens, d: FastspeechEncoder( embed_tokens, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'], num_heads=hp['num_heads']), } FS_DECODERS = { 'fft': lambda hp: FastspeechDecoder( hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']), } class FastSpeech2(nn.Module): def __init__(self, dictionary, out_dims=None): super().__init__() self.dictionary = dictionary self.padding_idx = dictionary.pad() self.enc_layers = hparams['enc_layers'] self.dec_layers = hparams['dec_layers'] self.hidden_size = hparams['hidden_size'] self.encoder_embed_tokens = self.build_embedding(self.dictionary, self.hidden_size) if hparams.get("use_bert", False): self.ph_encoder = BERTRelTransformerEncoder(len(self.dictionary), hparams['hidden_size'], hparams['hidden_size'], hparams['ffn_hidden_size'], hparams['num_heads'], hparams['enc_layers'], hparams['enc_ffn_kernel_size'], hparams['dropout'], prenet=hparams['enc_prenet'], pre_ln=hparams['enc_pre_ln']) else: self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams, self.encoder_embed_tokens, self.dictionary) self.decoder = FS_DECODERS[hparams['decoder_type']](hparams) self.out_dims = hparams['audio_num_mel_bins'] if out_dims is None else out_dims self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True) if hparams['use_spk_id']: self.spk_embed_proj = Embedding(hparams['num_spk'] + 1, self.hidden_size) if hparams['use_split_spk_id']: self.spk_embed_f0 = Embedding(hparams['num_spk'] + 1, self.hidden_size) self.spk_embed_dur = Embedding(hparams['num_spk'] + 1, self.hidden_size) elif hparams['use_spk_embed']: self.spk_embed_proj = Linear(256, self.hidden_size, bias=True) predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size self.dur_predictor = DurationPredictor( self.hidden_size, n_chans=predictor_hidden, n_layers=hparams['dur_predictor_layers'], dropout_rate=hparams['predictor_dropout'], kernel_size=hparams['dur_predictor_kernel']) self.length_regulator = LengthRegulator() if hparams['use_pitch_embed']: self.pitch_embed = Embedding(300, self.hidden_size, self.padding_idx) self.pitch_predictor = PitchPredictor( self.hidden_size, n_chans=predictor_hidden, n_layers=hparams['predictor_layers'], dropout_rate=hparams['predictor_dropout'], odim=2 if hparams['pitch_type'] == 'frame' else 1, kernel_size=hparams['predictor_kernel']) if hparams.get('use_energy_embed', False): self.energy_embed = Embedding(256, self.hidden_size, self.padding_idx) self.energy_predictor = EnergyPredictor( self.hidden_size, n_chans=predictor_hidden, n_layers=hparams['predictor_layers'], dropout_rate=hparams['predictor_dropout'], odim=1, kernel_size=hparams['predictor_kernel']) def build_embedding(self, dictionary, embed_dim): num_embeddings = len(dictionary) emb = Embedding(num_embeddings, embed_dim, self.padding_idx) return emb def forward(self, txt_tokens, mel2ph=None, spk_embed=None, ref_mels=None, f0=None, uv=None, energy=None, skip_decoder=False, spk_embed_dur_id=None, spk_embed_f0_id=None, infer=False, **kwargs): ret = {} if hparams.get("use_bert", False): encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=kwargs['ph2word'], ret=ret) else: encoder_out = self.encoder(txt_tokens) # [B, T, C] src_nonpadding = (txt_tokens > 0).float()[:, :, None] # add ref style embed # Not implemented # variance encoder var_embed = 0 # encoder_out_dur denotes encoder outputs for duration predictor # in speech adaptation, duration predictor use old speaker embedding if hparams['use_spk_embed']: spk_embed_dur = spk_embed_f0 = spk_embed = self.spk_embed_proj(spk_embed)[:, None, :] elif hparams['use_spk_id']: spk_embed_id = spk_embed if spk_embed_dur_id is None: spk_embed_dur_id = spk_embed_id if spk_embed_f0_id is None: spk_embed_f0_id = spk_embed_id spk_embed = self.spk_embed_proj(spk_embed_id)[:, None, :] spk_embed_dur = spk_embed_f0 = spk_embed if hparams['use_split_spk_id']: spk_embed_dur = self.spk_embed_dur(spk_embed_dur_id)[:, None, :] spk_embed_f0 = self.spk_embed_f0(spk_embed_f0_id)[:, None, :] else: spk_embed_dur = spk_embed_f0 = spk_embed = 0 # add dur dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding mel2ph = self.add_dur(dur_inp, mel2ph, txt_tokens, ret) decoder_inp = F.pad(encoder_out, [0, 0, 1, 0]) mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]]) decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H] tgt_nonpadding = (mel2ph > 0).float()[:, :, None] # add pitch and energy embed pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding if hparams['use_pitch_embed']: pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding decoder_inp = decoder_inp + self.add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph) if hparams.get('use_energy_embed', False): decoder_inp = decoder_inp + self.add_energy(pitch_inp, energy, ret) ret['decoder_inp'] = decoder_inp = (decoder_inp + spk_embed) * tgt_nonpadding if skip_decoder: return ret ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs) return ret def add_dur(self, dur_input, mel2ph, txt_tokens, ret): """ :param dur_input: [B, T_txt, H] :param mel2ph: [B, T_mel] :param txt_tokens: [B, T_txt] :param ret: :return: """ src_padding = txt_tokens == 0 dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach()) if mel2ph is None: dur, xs = self.dur_predictor.inference(dur_input, src_padding) ret['dur'] = xs ret['dur_choice'] = dur mel2ph = self.length_regulator(dur, src_padding).detach() # from modules.fastspeech.fake_modules import FakeLengthRegulator # fake_lr = FakeLengthRegulator() # fake_mel2ph = fake_lr(dur, (1 - src_padding.long()).sum(-1))[..., 0].detach() # print(mel2ph == fake_mel2ph) else: ret['dur'] = self.dur_predictor(dur_input, src_padding) ret['mel2ph'] = mel2ph return mel2ph def add_energy(self, decoder_inp, energy, ret): decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach()) ret['energy_pred'] = energy_pred = self.energy_predictor(decoder_inp)[:, :, 0] if energy is None: energy = energy_pred energy = torch.clamp(energy * 256 // 4, max=255).long() energy_embed = self.energy_embed(energy) return energy_embed def add_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None): if hparams['pitch_type'] == 'ph': pitch_pred_inp = encoder_out.detach() + hparams['predictor_grad'] * (encoder_out - encoder_out.detach()) pitch_padding = encoder_out.sum().abs() == 0 ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp) if f0 is None: f0 = pitch_pred[:, :, 0] ret['f0_denorm'] = f0_denorm = denorm_f0(f0, None, hparams, pitch_padding=pitch_padding) pitch = f0_to_coarse(f0_denorm) # start from 0 [B, T_txt] pitch = F.pad(pitch, [1, 0]) pitch = torch.gather(pitch, 1, mel2ph) # [B, T_mel] pitch_embed = self.pitch_embed(pitch) return pitch_embed decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach()) pitch_padding = mel2ph == 0 if hparams['pitch_type'] == 'cwt': pitch_padding = None ret['cwt'] = cwt_out = self.cwt_predictor(decoder_inp) stats_out = self.cwt_stats_layers(encoder_out[:, 0, :]) # [B, 2] mean = ret['f0_mean'] = stats_out[:, 0] std = ret['f0_std'] = stats_out[:, 1] cwt_spec = cwt_out[:, :, :10] if f0 is None: std = std * hparams['cwt_std_scale'] f0 = self.cwt2f0_norm(cwt_spec, mean, std, mel2ph) if hparams['use_uv']: assert cwt_out.shape[-1] == 11 uv = cwt_out[:, :, -1] > 0 elif hparams['pitch_ar']: ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp, f0 if self.training else None) if f0 is None: f0 = pitch_pred[:, :, 0] else: ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp) if f0 is None: f0 = pitch_pred[:, :, 0] if hparams['use_uv'] and uv is None: uv = pitch_pred[:, :, 1] > 0 ret['f0_denorm'] = f0_denorm = denorm_f0(f0, uv, hparams, pitch_padding=pitch_padding) if pitch_padding is not None: f0[pitch_padding] = 0 pitch = f0_to_coarse(f0_denorm) # start from 0 pitch_embed = self.pitch_embed(pitch) return pitch_embed def run_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs): x = decoder_inp # [B, T, H] x = self.decoder(x) x = self.mel_out(x) return x * tgt_nonpadding def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph): f0 = cwt2f0(cwt_spec, mean, std, hparams['cwt_scales']) f0 = torch.cat( [f0] + [f0[:, -1:]] * (mel2ph.shape[1] - f0.shape[1]), 1) f0_norm = norm_f0(f0, None, hparams) return f0_norm def out2mel(self, out): return out @staticmethod def mel_norm(x): return (x + 5.5) / (6.3 / 2) - 1 @staticmethod def mel_denorm(x): return (x + 1) * (6.3 / 2) - 5.5 def expand_states(self, h, mel2ph): h = F.pad(h, [0, 0, 1, 0]) mel2ph_ = mel2ph[..., None].repeat([1, 1, h.shape[-1]]) h = torch.gather(h, 1, mel2ph_) # [B, T, H] return h ================================================ FILE: NeuralSeq/modules/fastspeech/pe.py ================================================ from modules.commons.common_layers import * from utils.hparams import hparams from modules.fastspeech.tts_modules import PitchPredictor from utils.pitch_utils import denorm_f0 class Prenet(nn.Module): def __init__(self, in_dim=80, out_dim=256, kernel=5, n_layers=3, strides=None): super(Prenet, self).__init__() padding = kernel // 2 self.layers = [] self.strides = strides if strides is not None else [1] * n_layers for l in range(n_layers): self.layers.append(nn.Sequential( nn.Conv1d(in_dim, out_dim, kernel_size=kernel, padding=padding, stride=self.strides[l]), nn.ReLU(), nn.BatchNorm1d(out_dim) )) in_dim = out_dim self.layers = nn.ModuleList(self.layers) self.out_proj = nn.Linear(out_dim, out_dim) def forward(self, x): """ :param x: [B, T, 80] :return: [L, B, T, H], [B, T, H] """ padding_mask = x.abs().sum(-1).eq(0).data # [B, T] nonpadding_mask_TB = 1 - padding_mask.float()[:, None, :] # [B, 1, T] x = x.transpose(1, 2) hiddens = [] for i, l in enumerate(self.layers): nonpadding_mask_TB = nonpadding_mask_TB[:, :, ::self.strides[i]] x = l(x) * nonpadding_mask_TB hiddens.append(x) hiddens = torch.stack(hiddens, 0) # [L, B, H, T] hiddens = hiddens.transpose(2, 3) # [L, B, T, H] x = self.out_proj(x.transpose(1, 2)) # [B, T, H] x = x * nonpadding_mask_TB.transpose(1, 2) return hiddens, x class ConvBlock(nn.Module): def __init__(self, idim=80, n_chans=256, kernel_size=3, stride=1, norm='gn', dropout=0): super().__init__() self.conv = ConvNorm(idim, n_chans, kernel_size, stride=stride) self.norm = norm if self.norm == 'bn': self.norm = nn.BatchNorm1d(n_chans) elif self.norm == 'in': self.norm = nn.InstanceNorm1d(n_chans, affine=True) elif self.norm == 'gn': self.norm = nn.GroupNorm(n_chans // 16, n_chans) elif self.norm == 'ln': self.norm = LayerNorm(n_chans // 16, n_chans) elif self.norm == 'wn': self.conv = torch.nn.utils.weight_norm(self.conv.conv) self.dropout = nn.Dropout(dropout) self.relu = nn.ReLU() def forward(self, x): """ :param x: [B, C, T] :return: [B, C, T] """ x = self.conv(x) if not isinstance(self.norm, str): if self.norm == 'none': pass elif self.norm == 'ln': x = self.norm(x.transpose(1, 2)).transpose(1, 2) else: x = self.norm(x) x = self.relu(x) x = self.dropout(x) return x class ConvStacks(nn.Module): def __init__(self, idim=80, n_layers=5, n_chans=256, odim=32, kernel_size=5, norm='gn', dropout=0, strides=None, res=True): super().__init__() self.conv = torch.nn.ModuleList() self.kernel_size = kernel_size self.res = res self.in_proj = Linear(idim, n_chans) if strides is None: strides = [1] * n_layers else: assert len(strides) == n_layers for idx in range(n_layers): self.conv.append(ConvBlock( n_chans, n_chans, kernel_size, stride=strides[idx], norm=norm, dropout=dropout)) self.out_proj = Linear(n_chans, odim) def forward(self, x, return_hiddens=False): """ :param x: [B, T, H] :return: [B, T, H] """ x = self.in_proj(x) x = x.transpose(1, -1) # (B, idim, Tmax) hiddens = [] for f in self.conv: x_ = f(x) x = x + x_ if self.res else x_ # (B, C, Tmax) hiddens.append(x) x = x.transpose(1, -1) x = self.out_proj(x) # (B, Tmax, H) if return_hiddens: hiddens = torch.stack(hiddens, 1) # [B, L, C, T] return x, hiddens return x class PitchExtractor(nn.Module): def __init__(self, n_mel_bins=80, conv_layers=2): super().__init__() self.hidden_size = hparams['hidden_size'] self.predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size self.conv_layers = conv_layers self.mel_prenet = Prenet(n_mel_bins, self.hidden_size, strides=[1, 1, 1]) if self.conv_layers > 0: self.mel_encoder = ConvStacks( idim=self.hidden_size, n_chans=self.hidden_size, odim=self.hidden_size, n_layers=self.conv_layers) self.pitch_predictor = PitchPredictor( self.hidden_size, n_chans=self.predictor_hidden, n_layers=5, dropout_rate=0.1, odim=2, padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel']) def forward(self, mel_input=None): ret = {} mel_hidden = self.mel_prenet(mel_input)[1] if self.conv_layers > 0: mel_hidden = self.mel_encoder(mel_hidden) ret['pitch_pred'] = pitch_pred = self.pitch_predictor(mel_hidden) pitch_padding = mel_input.abs().sum(-1) == 0 use_uv = hparams['pitch_type'] == 'frame' and hparams['use_uv'] ret['f0_denorm_pred'] = denorm_f0( pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None, hparams, pitch_padding=pitch_padding) return ret ================================================ FILE: NeuralSeq/modules/fastspeech/tts_modules.py ================================================ import logging import math import torch import torch.nn as nn from torch.nn import functional as F from modules.commons.espnet_positional_embedding import RelPositionalEncoding from modules.commons.common_layers import SinusoidalPositionalEmbedding, Linear, EncSALayer, DecSALayer, BatchNorm1dTBC from utils.hparams import hparams DEFAULT_MAX_SOURCE_POSITIONS = 2000 DEFAULT_MAX_TARGET_POSITIONS = 2000 class TransformerEncoderLayer(nn.Module): def __init__(self, hidden_size, dropout, kernel_size=None, num_heads=2, norm='ln'): super().__init__() self.hidden_size = hidden_size self.dropout = dropout self.num_heads = num_heads self.op = EncSALayer( hidden_size, num_heads, dropout=dropout, attention_dropout=0.0, relu_dropout=dropout, kernel_size=kernel_size if kernel_size is not None else hparams['enc_ffn_kernel_size'], padding=hparams['ffn_padding'], norm=norm, act=hparams['ffn_act']) def forward(self, x, **kwargs): return self.op(x, **kwargs) ###################### # fastspeech modules ###################### class LayerNorm(torch.nn.LayerNorm): """Layer normalization module. :param int nout: output dim size :param int dim: dimension to be normalized """ def __init__(self, nout, dim=-1, eps=1e-5): """Construct an LayerNorm object.""" super(LayerNorm, self).__init__(nout, eps=eps) self.dim = dim def forward(self, x): """Apply layer normalization. :param torch.Tensor x: input tensor :return: layer normalized tensor :rtype torch.Tensor """ if self.dim == -1: return super(LayerNorm, self).forward(x) return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1) class DurationPredictor(torch.nn.Module): """Duration predictor module. This is a module of duration predictor described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_. The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder. .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`: https://arxiv.org/pdf/1905.09263.pdf Note: The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`, the outputs are calculated in log domain but in `inference`, those are calculated in linear domain. """ def __init__(self, idim, odims = 1, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0, padding='SAME'): """Initilize duration predictor module. Args: idim (int): Input dimension. n_layers (int, optional): Number of convolutional layers. n_chans (int, optional): Number of channels of convolutional layers. kernel_size (int, optional): Kernel size of convolutional layers. dropout_rate (float, optional): Dropout rate. offset (float, optional): Offset value to avoid nan in log domain. """ super(DurationPredictor, self).__init__() self.offset = offset self.conv = torch.nn.ModuleList() self.kernel_size = kernel_size self.padding = padding for idx in range(n_layers): in_chans = idim if idx == 0 else n_chans self.conv += [torch.nn.Sequential( torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2) if padding == 'SAME' else (kernel_size - 1, 0), 0), torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0), torch.nn.ReLU(), LayerNorm(n_chans, dim=1), torch.nn.Dropout(dropout_rate) )] self.linear = torch.nn.Linear(n_chans, odims) def _forward(self, xs, x_masks=None, is_inference=False): xs = xs.transpose(1, -1) # (B, idim, Tmax) for f in self.conv: xs = f(xs) # (B, C, Tmax) if x_masks is not None: xs = xs * (1 - x_masks.float())[:, None, :] xs = self.linear(xs.transpose(1, -1)) # [B, T, C] xs = xs * (1 - x_masks.float())[:, :, None] # (B, T, C) if is_inference: return self.out2dur(xs), xs else: if hparams['dur_loss'] in ['mse']: xs = xs.squeeze(-1) # (B, Tmax) return xs def out2dur(self, xs): if hparams['dur_loss'] in ['mse']: # NOTE: calculate in log domain xs = xs.squeeze(-1) # (B, Tmax) dur = torch.clamp(torch.round(xs.exp() - self.offset), min=0).long() # avoid negative value elif hparams['dur_loss'] == 'mog': return NotImplementedError elif hparams['dur_loss'] == 'crf': dur = torch.LongTensor(self.crf.decode(xs)).cuda() return dur def forward(self, xs, x_masks=None): """Calculate forward propagation. Args: xs (Tensor): Batch of input sequences (B, Tmax, idim). x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax). Returns: Tensor: Batch of predicted durations in log domain (B, Tmax). """ return self._forward(xs, x_masks, False) def inference(self, xs, x_masks=None): """Inference duration. Args: xs (Tensor): Batch of input sequences (B, Tmax, idim). x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax). Returns: LongTensor: Batch of predicted durations in linear domain (B, Tmax). """ return self._forward(xs, x_masks, True) class SyntaDurationPredictor(torch.nn.Module): def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0): super(SyntaDurationPredictor, self).__init__() from modules.syntaspeech.syntactic_graph_encoder import GraphAuxEnc self.graph_encoder = GraphAuxEnc(in_dim=idim, hid_dim=idim, out_dim=idim) self.offset = offset self.conv = torch.nn.ModuleList() self.kernel_size = kernel_size for idx in range(n_layers): in_chans = idim if idx == 0 else n_chans self.conv += [torch.nn.Sequential( torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2), torch.nn.ReLU(), LayerNorm(n_chans, dim=1), torch.nn.Dropout(dropout_rate) )] self.linear = nn.Sequential(torch.nn.Linear(n_chans, 1), nn.Softplus()) def forward(self, x, x_padding=None, ph2word=None, graph_lst=None, etypes_lst=None): x = x.transpose(1, -1) # (B, idim, Tmax) assert ph2word is not None and graph_lst is not None and etypes_lst is not None x_graph = self.graph_encoder(graph_lst, x, ph2word, etypes_lst) x = x + x_graph * 1. for f in self.conv: x = f(x) # (B, C, Tmax) if x_padding is not None: x = x * (1 - x_padding.float())[:, None, :] x = self.linear(x.transpose(1, -1)) # [B, T, C] x = x * (1 - x_padding.float())[:, :, None] # (B, T, C) x = x[..., 0] # (B, Tmax) return x class LengthRegulator(torch.nn.Module): def __init__(self, pad_value=0.0): super(LengthRegulator, self).__init__() self.pad_value = pad_value def forward(self, dur, dur_padding=None, alpha=1.0): """ Example (no batch dim version): 1. dur = [2,2,3] 2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4] 3. token_mask = [[1,1,0,0,0,0,0], [0,0,1,1,0,0,0], [0,0,0,0,1,1,1]] 4. token_idx * token_mask = [[1,1,0,0,0,0,0], [0,0,2,2,0,0,0], [0,0,0,0,3,3,3]] 5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3] :param dur: Batch of durations of each frame (B, T_txt) :param dur_padding: Batch of padding of each frame (B, T_txt) :param alpha: duration rescale coefficient :return: mel2ph (B, T_speech) """ assert alpha > 0 dur = torch.round(dur.float() * alpha).long() if dur_padding is not None: dur = dur * (1 - dur_padding.long()) token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device) dur_cumsum = torch.cumsum(dur, 1) dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0) pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device) token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None]) mel2ph = (token_idx * token_mask.long()).sum(1) return mel2ph class PitchPredictor(torch.nn.Module): def __init__(self, idim, n_layers=5, n_chans=384, odim=2, kernel_size=5, dropout_rate=0.1, padding='SAME'): """Initilize pitch predictor module. Args: idim (int): Input dimension. n_layers (int, optional): Number of convolutional layers. n_chans (int, optional): Number of channels of convolutional layers. kernel_size (int, optional): Kernel size of convolutional layers. dropout_rate (float, optional): Dropout rate. """ super(PitchPredictor, self).__init__() self.conv = torch.nn.ModuleList() self.kernel_size = kernel_size self.padding = padding for idx in range(n_layers): in_chans = idim if idx == 0 else n_chans self.conv += [torch.nn.Sequential( torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2) if padding == 'SAME' else (kernel_size - 1, 0), 0), torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0), torch.nn.ReLU(), LayerNorm(n_chans, dim=1), torch.nn.Dropout(dropout_rate) )] self.linear = torch.nn.Linear(n_chans, odim) self.embed_positions = SinusoidalPositionalEmbedding(idim, 0, init_size=4096) self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) def forward(self, xs): """ :param xs: [B, T, H] :return: [B, T, H] """ positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0]) xs = xs + positions xs = xs.transpose(1, -1) # (B, idim, Tmax) for f in self.conv: xs = f(xs) # (B, C, Tmax) # NOTE: calculate in log domain xs = self.linear(xs.transpose(1, -1)) # (B, Tmax, H) return xs class EnergyPredictor(PitchPredictor): pass def mel2ph_to_dur(mel2ph, T_txt, max_dur=None): B, _ = mel2ph.shape dur = mel2ph.new_zeros(B, T_txt + 1).scatter_add(1, mel2ph, torch.ones_like(mel2ph)) dur = dur[:, 1:] if max_dur is not None: dur = dur.clamp(max=max_dur) return dur class FFTBlocks(nn.Module): def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=None, num_heads=2, use_pos_embed=True, use_last_norm=True, norm='ln', use_pos_embed_alpha=True): super().__init__() self.num_layers = num_layers embed_dim = self.hidden_size = hidden_size self.dropout = dropout if dropout is not None else hparams['dropout'] self.use_pos_embed = use_pos_embed self.use_last_norm = use_last_norm if use_pos_embed: self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS self.padding_idx = 0 self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1 self.embed_positions = SinusoidalPositionalEmbedding( embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, ) self.layers = nn.ModuleList([]) self.layers.extend([ TransformerEncoderLayer(self.hidden_size, self.dropout, kernel_size=ffn_kernel_size, num_heads=num_heads) for _ in range(self.num_layers) ]) if self.use_last_norm: if norm == 'ln': self.layer_norm = nn.LayerNorm(embed_dim) elif norm == 'bn': self.layer_norm = BatchNorm1dTBC(embed_dim) else: self.layer_norm = None def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False): """ :param x: [B, T, C] :param padding_mask: [B, T] :return: [B, T, C] or [L, B, T, C] """ padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1] if self.use_pos_embed: positions = self.pos_embed_alpha * self.embed_positions(x[..., 0]) x = x + positions x = F.dropout(x, p=self.dropout, training=self.training) # B x T x C -> T x B x C x = x.transpose(0, 1) * nonpadding_mask_TB hiddens = [] for layer in self.layers: x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB hiddens.append(x) if self.use_last_norm: x = self.layer_norm(x) * nonpadding_mask_TB if return_hiddens: x = torch.stack(hiddens, 0) # [L, T, B, C] x = x.transpose(1, 2) # [L, B, T, C] else: x = x.transpose(0, 1) # [B, T, C] return x class FastspeechEncoder(FFTBlocks): def __init__(self, embed_tokens, hidden_size=None, num_layers=None, kernel_size=None, num_heads=2): hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size kernel_size = hparams['enc_ffn_kernel_size'] if kernel_size is None else kernel_size num_layers = hparams['dec_layers'] if num_layers is None else num_layers super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads, use_pos_embed=False) # use_pos_embed_alpha for compatibility self.embed_tokens = embed_tokens self.embed_scale = math.sqrt(hidden_size) self.padding_idx = 0 if hparams.get('rel_pos') is not None and hparams['rel_pos']: self.embed_positions = RelPositionalEncoding(hidden_size, dropout_rate=0.0) else: self.embed_positions = SinusoidalPositionalEmbedding( hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, ) def forward(self, txt_tokens): """ :param txt_tokens: [B, T] :return: { 'encoder_out': [T x B x C] } """ encoder_padding_mask = txt_tokens.eq(self.padding_idx).data x = self.forward_embedding(txt_tokens) # [B, T, H] x = super(FastspeechEncoder, self).forward(x, encoder_padding_mask) return x def forward_embedding(self, txt_tokens): # embed tokens and positions x = self.embed_scale * self.embed_tokens(txt_tokens) if hparams['use_pos_embed']: if hparams.get('rel_pos') is not None and hparams['rel_pos']: x = self.embed_positions(x) else: positions = self.embed_positions(txt_tokens) x = x + positions x = F.dropout(x, p=self.dropout, training=self.training) return x class FastspeechDecoder(FFTBlocks): def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None): num_heads = hparams['num_heads'] if num_heads is None else num_heads hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size kernel_size = hparams['dec_ffn_kernel_size'] if kernel_size is None else kernel_size num_layers = hparams['dec_layers'] if num_layers is None else num_layers super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads) ================================================ FILE: NeuralSeq/modules/hifigan/hifigan.py ================================================ import torch import torch.nn.functional as F import torch.nn as nn from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork from modules.parallel_wavegan.models.source import SourceModuleHnNSF import numpy as np LRELU_SLOPE = 0.1 def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(mean, std) def apply_weight_norm(m): classname = m.__class__.__name__ if classname.find("Conv") != -1: weight_norm(m) def get_padding(kernel_size, dilation=1): return int((kernel_size * dilation - dilation) / 2) class ResBlock1(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): super(ResBlock1, self).__init__() self.h = h self.convs1 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2]))) ]) self.convs1.apply(init_weights) self.convs2 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))) ]) self.convs2.apply(init_weights) def forward(self, x): for c1, c2 in zip(self.convs1, self.convs2): xt = F.leaky_relu(x, LRELU_SLOPE) xt = c1(xt) xt = F.leaky_relu(xt, LRELU_SLOPE) xt = c2(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs1: remove_weight_norm(l) for l in self.convs2: remove_weight_norm(l) class ResBlock2(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): super(ResBlock2, self).__init__() self.h = h self.convs = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))) ]) self.convs.apply(init_weights) def forward(self, x): for c in self.convs: xt = F.leaky_relu(x, LRELU_SLOPE) xt = c(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs: remove_weight_norm(l) class Conv1d1x1(Conv1d): """1x1 Conv1d with customized initialization.""" def __init__(self, in_channels, out_channels, bias): """Initialize 1x1 Conv1d module.""" super(Conv1d1x1, self).__init__(in_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=bias) class HifiGanGenerator(torch.nn.Module): def __init__(self, h, c_out=1): super(HifiGanGenerator, self).__init__() self.h = h self.num_kernels = len(h['resblock_kernel_sizes']) self.num_upsamples = len(h['upsample_rates']) if h['use_pitch_embed']: self.harmonic_num = 8 self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h['upsample_rates'])) self.m_source = SourceModuleHnNSF( sampling_rate=h['audio_sample_rate'], harmonic_num=self.harmonic_num) self.noise_convs = nn.ModuleList() self.conv_pre = weight_norm(Conv1d(80, h['upsample_initial_channel'], 7, 1, padding=3)) resblock = ResBlock1 if h['resblock'] == '1' else ResBlock2 self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(h['upsample_rates'], h['upsample_kernel_sizes'])): c_cur = h['upsample_initial_channel'] // (2 ** (i + 1)) self.ups.append(weight_norm( ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2))) if h['use_pitch_embed']: if i + 1 < len(h['upsample_rates']): stride_f0 = np.prod(h['upsample_rates'][i + 1:]) self.noise_convs.append(Conv1d( 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2)) else: self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = h['upsample_initial_channel'] // (2 ** (i + 1)) for j, (k, d) in enumerate(zip(h['resblock_kernel_sizes'], h['resblock_dilation_sizes'])): self.resblocks.append(resblock(h, ch, k, d)) self.conv_post = weight_norm(Conv1d(ch, c_out, 7, 1, padding=3)) self.ups.apply(init_weights) self.conv_post.apply(init_weights) def forward(self, x, f0=None): if f0 is not None: # harmonic-source signal, noise-source signal, uv flag f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) har_source, noi_source, uv = self.m_source(f0) har_source = har_source.transpose(1, 2) x = self.conv_pre(x) for i in range(self.num_upsamples): x = F.leaky_relu(x, LRELU_SLOPE) x = self.ups[i](x) if f0 is not None: x_source = self.noise_convs[i](har_source) x = x + x_source xs = None for j in range(self.num_kernels): if xs is None: xs = self.resblocks[i * self.num_kernels + j](x) else: xs += self.resblocks[i * self.num_kernels + j](x) x = xs / self.num_kernels x = F.leaky_relu(x) x = self.conv_post(x) x = torch.tanh(x) return x def remove_weight_norm(self): print('Removing weight norm...') for l in self.ups: remove_weight_norm(l) for l in self.resblocks: l.remove_weight_norm() remove_weight_norm(self.conv_pre) remove_weight_norm(self.conv_post) class DiscriminatorP(torch.nn.Module): def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, use_cond=False, c_in=1): super(DiscriminatorP, self).__init__() self.use_cond = use_cond if use_cond: from utils.hparams import hparams t = hparams['hop_size'] self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2) c_in = 2 self.period = period norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv2d(c_in, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), ]) self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) def forward(self, x, mel): fmap = [] if self.use_cond: x_mel = self.cond_net(mel) x = torch.cat([x_mel, x], 1) # 1d to 2d b, c, t = x.shape if t % self.period != 0: # pad first n_pad = self.period - (t % self.period) x = F.pad(x, (0, n_pad), "reflect") t = t + n_pad x = x.view(b, c, t // self.period, self.period) for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap class MultiPeriodDiscriminator(torch.nn.Module): def __init__(self, use_cond=False, c_in=1): super(MultiPeriodDiscriminator, self).__init__() self.discriminators = nn.ModuleList([ DiscriminatorP(2, use_cond=use_cond, c_in=c_in), DiscriminatorP(3, use_cond=use_cond, c_in=c_in), DiscriminatorP(5, use_cond=use_cond, c_in=c_in), DiscriminatorP(7, use_cond=use_cond, c_in=c_in), DiscriminatorP(11, use_cond=use_cond, c_in=c_in), ]) def forward(self, y, y_hat, mel=None): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): y_d_r, fmap_r = d(y, mel) y_d_g, fmap_g = d(y_hat, mel) y_d_rs.append(y_d_r) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs class DiscriminatorS(torch.nn.Module): def __init__(self, use_spectral_norm=False, use_cond=False, upsample_rates=None, c_in=1): super(DiscriminatorS, self).__init__() self.use_cond = use_cond if use_cond: t = np.prod(upsample_rates) self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2) c_in = 2 norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv1d(c_in, 128, 15, 1, padding=7)), norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), ]) self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) def forward(self, x, mel): if self.use_cond: x_mel = self.cond_net(mel) x = torch.cat([x_mel, x], 1) fmap = [] for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap class MultiScaleDiscriminator(torch.nn.Module): def __init__(self, use_cond=False, c_in=1): super(MultiScaleDiscriminator, self).__init__() from utils.hparams import hparams self.discriminators = nn.ModuleList([ DiscriminatorS(use_spectral_norm=True, use_cond=use_cond, upsample_rates=[4, 4, hparams['hop_size'] // 16], c_in=c_in), DiscriminatorS(use_cond=use_cond, upsample_rates=[4, 4, hparams['hop_size'] // 32], c_in=c_in), DiscriminatorS(use_cond=use_cond, upsample_rates=[4, 4, hparams['hop_size'] // 64], c_in=c_in), ]) self.meanpools = nn.ModuleList([ AvgPool1d(4, 2, padding=1), AvgPool1d(4, 2, padding=1) ]) def forward(self, y, y_hat, mel=None): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): if i != 0: y = self.meanpools[i - 1](y) y_hat = self.meanpools[i - 1](y_hat) y_d_r, fmap_r = d(y, mel) y_d_g, fmap_g = d(y_hat, mel) y_d_rs.append(y_d_r) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs def feature_loss(fmap_r, fmap_g): loss = 0 for dr, dg in zip(fmap_r, fmap_g): for rl, gl in zip(dr, dg): loss += torch.mean(torch.abs(rl - gl)) return loss * 2 def discriminator_loss(disc_real_outputs, disc_generated_outputs): r_losses = 0 g_losses = 0 for dr, dg in zip(disc_real_outputs, disc_generated_outputs): r_loss = torch.mean((1 - dr) ** 2) g_loss = torch.mean(dg ** 2) r_losses += r_loss g_losses += g_loss r_losses = r_losses / len(disc_real_outputs) g_losses = g_losses / len(disc_real_outputs) return r_losses, g_losses def cond_discriminator_loss(outputs): loss = 0 for dg in outputs: g_loss = torch.mean(dg ** 2) loss += g_loss loss = loss / len(outputs) return loss def generator_loss(disc_outputs): loss = 0 for dg in disc_outputs: l = torch.mean((1 - dg) ** 2) loss += l loss = loss / len(disc_outputs) return loss ================================================ FILE: NeuralSeq/modules/hifigan/mel_utils.py ================================================ import numpy as np import torch import torch.utils.data from librosa.filters import mel as librosa_mel_fn from scipy.io.wavfile import read MAX_WAV_VALUE = 32768.0 def load_wav(full_path): sampling_rate, data = read(full_path) return data, sampling_rate def dynamic_range_compression(x, C=1, clip_val=1e-5): return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) def dynamic_range_decompression(x, C=1): return np.exp(x) / C def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): return torch.log(torch.clamp(x, min=clip_val) * C) def dynamic_range_decompression_torch(x, C=1): return torch.exp(x) / C def spectral_normalize_torch(magnitudes): output = dynamic_range_compression_torch(magnitudes) return output def spectral_de_normalize_torch(magnitudes): output = dynamic_range_decompression_torch(magnitudes) return output mel_basis = {} hann_window = {} def mel_spectrogram(y, hparams, center=False, complex=False): # hop_size: 512 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) # win_size: 2048 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) # fmin: 55 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) # fmax: 10000 # To be increased/reduced depending on data. # fft_size: 2048 # Extra window size is filled with 0 paddings to match this parameter # n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, n_fft = hparams['fft_size'] num_mels = hparams['audio_num_mel_bins'] sampling_rate = hparams['audio_sample_rate'] hop_size = hparams['hop_size'] win_size = hparams['win_size'] fmin = hparams['fmin'] fmax = hparams['fmax'] y = y.clamp(min=-1., max=1.) global mel_basis, hann_window if fmax not in mel_basis: mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) mel_basis[str(fmax) + '_' + str(y.device)] = torch.from_numpy(mel).float().to(y.device) hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode='reflect') y = y.squeeze(1) spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], center=center, pad_mode='reflect', normalized=False, onesided=True) if not complex: spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) spec = torch.matmul(mel_basis[str(fmax) + '_' + str(y.device)], spec) spec = spectral_normalize_torch(spec) else: B, C, T, _ = spec.shape spec = spec.transpose(1, 2) # [B, T, n_fft, 2] return spec ================================================ FILE: NeuralSeq/modules/parallel_wavegan/__init__.py ================================================ ================================================ FILE: NeuralSeq/modules/parallel_wavegan/layers/__init__.py ================================================ from .causal_conv import * # NOQA from .pqmf import * # NOQA from .residual_block import * # NOQA from modules.parallel_wavegan.layers.residual_stack import * # NOQA from .upsample import * # NOQA ================================================ FILE: NeuralSeq/modules/parallel_wavegan/layers/causal_conv.py ================================================ # -*- coding: utf-8 -*- # Copyright 2020 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) """Causal convolusion layer modules.""" import torch class CausalConv1d(torch.nn.Module): """CausalConv1d module with customized initialization.""" def __init__(self, in_channels, out_channels, kernel_size, dilation=1, bias=True, pad="ConstantPad1d", pad_params={"value": 0.0}): """Initialize CausalConv1d module.""" super(CausalConv1d, self).__init__() self.pad = getattr(torch.nn, pad)((kernel_size - 1) * dilation, **pad_params) self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size, dilation=dilation, bias=bias) def forward(self, x): """Calculate forward propagation. Args: x (Tensor): Input tensor (B, in_channels, T). Returns: Tensor: Output tensor (B, out_channels, T). """ return self.conv(self.pad(x))[:, :, :x.size(2)] class CausalConvTranspose1d(torch.nn.Module): """CausalConvTranspose1d module with customized initialization.""" def __init__(self, in_channels, out_channels, kernel_size, stride, bias=True): """Initialize CausalConvTranspose1d module.""" super(CausalConvTranspose1d, self).__init__() self.deconv = torch.nn.ConvTranspose1d( in_channels, out_channels, kernel_size, stride, bias=bias) self.stride = stride def forward(self, x): """Calculate forward propagation. Args: x (Tensor): Input tensor (B, in_channels, T_in). Returns: Tensor: Output tensor (B, out_channels, T_out). """ return self.deconv(x)[:, :, :-self.stride] ================================================ FILE: NeuralSeq/modules/parallel_wavegan/layers/pqmf.py ================================================ # -*- coding: utf-8 -*- # Copyright 2020 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) """Pseudo QMF modules.""" import numpy as np import torch import torch.nn.functional as F from scipy.signal import kaiser def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0): """Design prototype filter for PQMF. This method is based on `A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`_. Args: taps (int): The number of filter taps. cutoff_ratio (float): Cut-off frequency ratio. beta (float): Beta coefficient for kaiser window. Returns: ndarray: Impluse response of prototype filter (taps + 1,). .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`: https://ieeexplore.ieee.org/abstract/document/681427 """ # check the arguments are valid assert taps % 2 == 0, "The number of taps mush be even number." assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0." # make initial filter omega_c = np.pi * cutoff_ratio with np.errstate(invalid='ignore'): h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \ / (np.pi * (np.arange(taps + 1) - 0.5 * taps)) h_i[taps // 2] = np.cos(0) * cutoff_ratio # fix nan due to indeterminate form # apply kaiser window w = kaiser(taps + 1, beta) h = h_i * w return h class PQMF(torch.nn.Module): """PQMF module. This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_. .. _`Near-perfect-reconstruction pseudo-QMF banks`: https://ieeexplore.ieee.org/document/258122 """ def __init__(self, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0): """Initilize PQMF module. Args: subbands (int): The number of subbands. taps (int): The number of filter taps. cutoff_ratio (float): Cut-off frequency ratio. beta (float): Beta coefficient for kaiser window. """ super(PQMF, self).__init__() # define filter coefficient h_proto = design_prototype_filter(taps, cutoff_ratio, beta) h_analysis = np.zeros((subbands, len(h_proto))) h_synthesis = np.zeros((subbands, len(h_proto))) for k in range(subbands): h_analysis[k] = 2 * h_proto * np.cos( (2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) + (-1) ** k * np.pi / 4) h_synthesis[k] = 2 * h_proto * np.cos( (2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) - (-1) ** k * np.pi / 4) # convert to tensor analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1) synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0) # register coefficients as beffer self.register_buffer("analysis_filter", analysis_filter) self.register_buffer("synthesis_filter", synthesis_filter) # filter for downsampling & upsampling updown_filter = torch.zeros((subbands, subbands, subbands)).float() for k in range(subbands): updown_filter[k, k, 0] = 1.0 self.register_buffer("updown_filter", updown_filter) self.subbands = subbands # keep padding info self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0) def analysis(self, x): """Analysis with PQMF. Args: x (Tensor): Input tensor (B, 1, T). Returns: Tensor: Output tensor (B, subbands, T // subbands). """ x = F.conv1d(self.pad_fn(x), self.analysis_filter) return F.conv1d(x, self.updown_filter, stride=self.subbands) def synthesis(self, x): """Synthesis with PQMF. Args: x (Tensor): Input tensor (B, subbands, T // subbands). Returns: Tensor: Output tensor (B, 1, T). """ x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands) return F.conv1d(self.pad_fn(x), self.synthesis_filter) ================================================ FILE: NeuralSeq/modules/parallel_wavegan/layers/residual_block.py ================================================ # -*- coding: utf-8 -*- """Residual block module in WaveNet. This code is modified from https://github.com/r9y9/wavenet_vocoder. """ import math import torch import torch.nn.functional as F class Conv1d(torch.nn.Conv1d): """Conv1d module with customized initialization.""" def __init__(self, *args, **kwargs): """Initialize Conv1d module.""" super(Conv1d, self).__init__(*args, **kwargs) def reset_parameters(self): """Reset parameters.""" torch.nn.init.kaiming_normal_(self.weight, nonlinearity="relu") if self.bias is not None: torch.nn.init.constant_(self.bias, 0.0) class Conv1d1x1(Conv1d): """1x1 Conv1d with customized initialization.""" def __init__(self, in_channels, out_channels, bias): """Initialize 1x1 Conv1d module.""" super(Conv1d1x1, self).__init__(in_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=bias) class ResidualBlock(torch.nn.Module): """Residual block module in WaveNet.""" def __init__(self, kernel_size=3, residual_channels=64, gate_channels=128, skip_channels=64, aux_channels=80, dropout=0.0, dilation=1, bias=True, use_causal_conv=False ): """Initialize ResidualBlock module. Args: kernel_size (int): Kernel size of dilation convolution layer. residual_channels (int): Number of channels for residual connection. skip_channels (int): Number of channels for skip connection. aux_channels (int): Local conditioning channels i.e. auxiliary input dimension. dropout (float): Dropout probability. dilation (int): Dilation factor. bias (bool): Whether to add bias parameter in convolution layers. use_causal_conv (bool): Whether to use use_causal_conv or non-use_causal_conv convolution. """ super(ResidualBlock, self).__init__() self.dropout = dropout # no future time stamps available if use_causal_conv: padding = (kernel_size - 1) * dilation else: assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." padding = (kernel_size - 1) // 2 * dilation self.use_causal_conv = use_causal_conv # dilation conv self.conv = Conv1d(residual_channels, gate_channels, kernel_size, padding=padding, dilation=dilation, bias=bias) # local conditioning if aux_channels > 0: self.conv1x1_aux = Conv1d1x1(aux_channels, gate_channels, bias=False) else: self.conv1x1_aux = None # conv output is split into two groups gate_out_channels = gate_channels // 2 self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, bias=bias) self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_channels, bias=bias) def forward(self, x, c): """Calculate forward propagation. Args: x (Tensor): Input tensor (B, residual_channels, T). c (Tensor): Local conditioning auxiliary tensor (B, aux_channels, T). Returns: Tensor: Output tensor for residual connection (B, residual_channels, T). Tensor: Output tensor for skip connection (B, skip_channels, T). """ residual = x x = F.dropout(x, p=self.dropout, training=self.training) x = self.conv(x) # remove future time steps if use_causal_conv conv x = x[:, :, :residual.size(-1)] if self.use_causal_conv else x # split into two part for gated activation splitdim = 1 xa, xb = x.split(x.size(splitdim) // 2, dim=splitdim) # local conditioning if c is not None: assert self.conv1x1_aux is not None c = self.conv1x1_aux(c) ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim) xa, xb = xa + ca, xb + cb x = torch.tanh(xa) * torch.sigmoid(xb) # for skip connection s = self.conv1x1_skip(x) # for residual connection x = (self.conv1x1_out(x) + residual) * math.sqrt(0.5) return x, s ================================================ FILE: NeuralSeq/modules/parallel_wavegan/layers/residual_stack.py ================================================ # -*- coding: utf-8 -*- # Copyright 2020 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) """Residual stack module in MelGAN.""" import torch from . import CausalConv1d class ResidualStack(torch.nn.Module): """Residual stack module introduced in MelGAN.""" def __init__(self, kernel_size=3, channels=32, dilation=1, bias=True, nonlinear_activation="LeakyReLU", nonlinear_activation_params={"negative_slope": 0.2}, pad="ReflectionPad1d", pad_params={}, use_causal_conv=False, ): """Initialize ResidualStack module. Args: kernel_size (int): Kernel size of dilation convolution layer. channels (int): Number of channels of convolution layers. dilation (int): Dilation factor. bias (bool): Whether to add bias parameter in convolution layers. nonlinear_activation (str): Activation function module name. nonlinear_activation_params (dict): Hyperparameters for activation function. pad (str): Padding function module name before dilated convolution layer. pad_params (dict): Hyperparameters for padding function. use_causal_conv (bool): Whether to use causal convolution. """ super(ResidualStack, self).__init__() # defile residual stack part if not use_causal_conv: assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." self.stack = torch.nn.Sequential( getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), getattr(torch.nn, pad)((kernel_size - 1) // 2 * dilation, **pad_params), torch.nn.Conv1d(channels, channels, kernel_size, dilation=dilation, bias=bias), getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), torch.nn.Conv1d(channels, channels, 1, bias=bias), ) else: self.stack = torch.nn.Sequential( getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), CausalConv1d(channels, channels, kernel_size, dilation=dilation, bias=bias, pad=pad, pad_params=pad_params), getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), torch.nn.Conv1d(channels, channels, 1, bias=bias), ) # defile extra layer for skip connection self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias) def forward(self, c): """Calculate forward propagation. Args: c (Tensor): Input tensor (B, channels, T). Returns: Tensor: Output tensor (B, chennels, T). """ return self.stack(c) + self.skip_layer(c) ================================================ FILE: NeuralSeq/modules/parallel_wavegan/layers/tf_layers.py ================================================ # -*- coding: utf-8 -*- # Copyright 2020 MINH ANH (@dathudeptrai) # MIT License (https://opensource.org/licenses/MIT) """Tensorflow Layer modules complatible with pytorch.""" import tensorflow as tf class TFReflectionPad1d(tf.keras.layers.Layer): """Tensorflow ReflectionPad1d module.""" def __init__(self, padding_size): """Initialize TFReflectionPad1d module. Args: padding_size (int): Padding size. """ super(TFReflectionPad1d, self).__init__() self.padding_size = padding_size @tf.function def call(self, x): """Calculate forward propagation. Args: x (Tensor): Input tensor (B, T, 1, C). Returns: Tensor: Padded tensor (B, T + 2 * padding_size, 1, C). """ return tf.pad(x, [[0, 0], [self.padding_size, self.padding_size], [0, 0], [0, 0]], "REFLECT") class TFConvTranspose1d(tf.keras.layers.Layer): """Tensorflow ConvTranspose1d module.""" def __init__(self, channels, kernel_size, stride, padding): """Initialize TFConvTranspose1d( module. Args: channels (int): Number of channels. kernel_size (int): kernel size. strides (int): Stride width. padding (str): Padding type ("same" or "valid"). """ super(TFConvTranspose1d, self).__init__() self.conv1d_transpose = tf.keras.layers.Conv2DTranspose( filters=channels, kernel_size=(kernel_size, 1), strides=(stride, 1), padding=padding, ) @tf.function def call(self, x): """Calculate forward propagation. Args: x (Tensor): Input tensor (B, T, 1, C). Returns: Tensors: Output tensor (B, T', 1, C'). """ x = self.conv1d_transpose(x) return x class TFResidualStack(tf.keras.layers.Layer): """Tensorflow ResidualStack module.""" def __init__(self, kernel_size, channels, dilation, bias, nonlinear_activation, nonlinear_activation_params, padding, ): """Initialize TFResidualStack module. Args: kernel_size (int): Kernel size. channles (int): Number of channels. dilation (int): Dilation ine. bias (bool): Whether to add bias parameter in convolution layers. nonlinear_activation (str): Activation function module name. nonlinear_activation_params (dict): Hyperparameters for activation function. padding (str): Padding type ("same" or "valid"). """ super(TFResidualStack, self).__init__() self.block = [ getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params), TFReflectionPad1d(dilation), tf.keras.layers.Conv2D( filters=channels, kernel_size=(kernel_size, 1), dilation_rate=(dilation, 1), use_bias=bias, padding="valid", ), getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params), tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias) ] self.shortcut = tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias) @tf.function def call(self, x): """Calculate forward propagation. Args: x (Tensor): Input tensor (B, T, 1, C). Returns: Tensor: Output tensor (B, T, 1, C). """ _x = tf.identity(x) for i, layer in enumerate(self.block): _x = layer(_x) shortcut = self.shortcut(x) return shortcut + _x ================================================ FILE: NeuralSeq/modules/parallel_wavegan/layers/upsample.py ================================================ # -*- coding: utf-8 -*- """Upsampling module. This code is modified from https://github.com/r9y9/wavenet_vocoder. """ import numpy as np import torch import torch.nn.functional as F from . import Conv1d class Stretch2d(torch.nn.Module): """Stretch2d module.""" def __init__(self, x_scale, y_scale, mode="nearest"): """Initialize Stretch2d module. Args: x_scale (int): X scaling factor (Time axis in spectrogram). y_scale (int): Y scaling factor (Frequency axis in spectrogram). mode (str): Interpolation mode. """ super(Stretch2d, self).__init__() self.x_scale = x_scale self.y_scale = y_scale self.mode = mode def forward(self, x): """Calculate forward propagation. Args: x (Tensor): Input tensor (B, C, F, T). Returns: Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale), """ return F.interpolate( x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode) class Conv2d(torch.nn.Conv2d): """Conv2d module with customized initialization.""" def __init__(self, *args, **kwargs): """Initialize Conv2d module.""" super(Conv2d, self).__init__(*args, **kwargs) def reset_parameters(self): """Reset parameters.""" self.weight.data.fill_(1. / np.prod(self.kernel_size)) if self.bias is not None: torch.nn.init.constant_(self.bias, 0.0) class UpsampleNetwork(torch.nn.Module): """Upsampling network module.""" def __init__(self, upsample_scales, nonlinear_activation=None, nonlinear_activation_params={}, interpolate_mode="nearest", freq_axis_kernel_size=1, use_causal_conv=False, ): """Initialize upsampling network module. Args: upsample_scales (list): List of upsampling scales. nonlinear_activation (str): Activation function name. nonlinear_activation_params (dict): Arguments for specified activation function. interpolate_mode (str): Interpolation mode. freq_axis_kernel_size (int): Kernel size in the direction of frequency axis. """ super(UpsampleNetwork, self).__init__() self.use_causal_conv = use_causal_conv self.up_layers = torch.nn.ModuleList() for scale in upsample_scales: # interpolation layer stretch = Stretch2d(scale, 1, interpolate_mode) self.up_layers += [stretch] # conv layer assert (freq_axis_kernel_size - 1) % 2 == 0, "Not support even number freq axis kernel size." freq_axis_padding = (freq_axis_kernel_size - 1) // 2 kernel_size = (freq_axis_kernel_size, scale * 2 + 1) if use_causal_conv: padding = (freq_axis_padding, scale * 2) else: padding = (freq_axis_padding, scale) conv = Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False) self.up_layers += [conv] # nonlinear if nonlinear_activation is not None: nonlinear = getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params) self.up_layers += [nonlinear] def forward(self, c): """Calculate forward propagation. Args: c : Input tensor (B, C, T). Returns: Tensor: Upsampled tensor (B, C, T'), where T' = T * prod(upsample_scales). """ c = c.unsqueeze(1) # (B, 1, C, T) for f in self.up_layers: if self.use_causal_conv and isinstance(f, Conv2d): c = f(c)[..., :c.size(-1)] else: c = f(c) return c.squeeze(1) # (B, C, T') class ConvInUpsampleNetwork(torch.nn.Module): """Convolution + upsampling network module.""" def __init__(self, upsample_scales, nonlinear_activation=None, nonlinear_activation_params={}, interpolate_mode="nearest", freq_axis_kernel_size=1, aux_channels=80, aux_context_window=0, use_causal_conv=False ): """Initialize convolution + upsampling network module. Args: upsample_scales (list): List of upsampling scales. nonlinear_activation (str): Activation function name. nonlinear_activation_params (dict): Arguments for specified activation function. mode (str): Interpolation mode. freq_axis_kernel_size (int): Kernel size in the direction of frequency axis. aux_channels (int): Number of channels of pre-convolutional layer. aux_context_window (int): Context window size of the pre-convolutional layer. use_causal_conv (bool): Whether to use causal structure. """ super(ConvInUpsampleNetwork, self).__init__() self.aux_context_window = aux_context_window self.use_causal_conv = use_causal_conv and aux_context_window > 0 # To capture wide-context information in conditional features kernel_size = aux_context_window + 1 if use_causal_conv else 2 * aux_context_window + 1 # NOTE(kan-bayashi): Here do not use padding because the input is already padded self.conv_in = Conv1d(aux_channels, aux_channels, kernel_size=kernel_size, bias=False) self.upsample = UpsampleNetwork( upsample_scales=upsample_scales, nonlinear_activation=nonlinear_activation, nonlinear_activation_params=nonlinear_activation_params, interpolate_mode=interpolate_mode, freq_axis_kernel_size=freq_axis_kernel_size, use_causal_conv=use_causal_conv, ) def forward(self, c): """Calculate forward propagation. Args: c : Input tensor (B, C, T'). Returns: Tensor: Upsampled tensor (B, C, T), where T = (T' - aux_context_window * 2) * prod(upsample_scales). Note: The length of inputs considers the context window size. """ c_ = self.conv_in(c) c = c_[:, :, :-self.aux_context_window] if self.use_causal_conv else c_ return self.upsample(c) ================================================ FILE: NeuralSeq/modules/parallel_wavegan/losses/__init__.py ================================================ from .stft_loss import * # NOQA ================================================ FILE: NeuralSeq/modules/parallel_wavegan/losses/stft_loss.py ================================================ # -*- coding: utf-8 -*- # Copyright 2019 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) """STFT-based Loss modules.""" import torch import torch.nn.functional as F def stft(x, fft_size, hop_size, win_length, window): """Perform STFT and convert to magnitude spectrogram. Args: x (Tensor): Input signal tensor (B, T). fft_size (int): FFT size. hop_size (int): Hop size. win_length (int): Window length. window (str): Window function type. Returns: Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). """ x_stft = torch.stft(x, fft_size, hop_size, win_length, window) real = x_stft[..., 0] imag = x_stft[..., 1] # NOTE(kan-bayashi): clamp is needed to avoid nan or inf return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1) class SpectralConvergengeLoss(torch.nn.Module): """Spectral convergence loss module.""" def __init__(self): """Initilize spectral convergence loss module.""" super(SpectralConvergengeLoss, self).__init__() def forward(self, x_mag, y_mag): """Calculate forward propagation. Args: x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). Returns: Tensor: Spectral convergence loss value. """ return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro") class LogSTFTMagnitudeLoss(torch.nn.Module): """Log STFT magnitude loss module.""" def __init__(self): """Initilize los STFT magnitude loss module.""" super(LogSTFTMagnitudeLoss, self).__init__() def forward(self, x_mag, y_mag): """Calculate forward propagation. Args: x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). Returns: Tensor: Log STFT magnitude loss value. """ return F.l1_loss(torch.log(y_mag), torch.log(x_mag)) class STFTLoss(torch.nn.Module): """STFT loss module.""" def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"): """Initialize STFT loss module.""" super(STFTLoss, self).__init__() self.fft_size = fft_size self.shift_size = shift_size self.win_length = win_length self.window = getattr(torch, window)(win_length) self.spectral_convergenge_loss = SpectralConvergengeLoss() self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() def forward(self, x, y): """Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T). Returns: Tensor: Spectral convergence loss value. Tensor: Log STFT magnitude loss value. """ x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) return sc_loss, mag_loss class MultiResolutionSTFTLoss(torch.nn.Module): """Multi resolution STFT loss module.""" def __init__(self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window="hann_window"): """Initialize Multi resolution STFT loss module. Args: fft_sizes (list): List of FFT sizes. hop_sizes (list): List of hop sizes. win_lengths (list): List of window lengths. window (str): Window function type. """ super(MultiResolutionSTFTLoss, self).__init__() assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) self.stft_losses = torch.nn.ModuleList() for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): self.stft_losses += [STFTLoss(fs, ss, wl, window)] def forward(self, x, y): """Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T). Returns: Tensor: Multi resolution spectral convergence loss value. Tensor: Multi resolution log STFT magnitude loss value. """ sc_loss = 0.0 mag_loss = 0.0 for f in self.stft_losses: sc_l, mag_l = f(x, y) sc_loss += sc_l mag_loss += mag_l sc_loss /= len(self.stft_losses) mag_loss /= len(self.stft_losses) return sc_loss, mag_loss ================================================ FILE: NeuralSeq/modules/parallel_wavegan/models/__init__.py ================================================ from .melgan import * # NOQA from .parallel_wavegan import * # NOQA ================================================ FILE: NeuralSeq/modules/parallel_wavegan/models/melgan.py ================================================ # -*- coding: utf-8 -*- # Copyright 2020 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) """MelGAN Modules.""" import logging import numpy as np import torch from modules.parallel_wavegan.layers import CausalConv1d from modules.parallel_wavegan.layers import CausalConvTranspose1d from modules.parallel_wavegan.layers import ResidualStack class MelGANGenerator(torch.nn.Module): """MelGAN generator module.""" def __init__(self, in_channels=80, out_channels=1, kernel_size=7, channels=512, bias=True, upsample_scales=[8, 8, 2, 2], stack_kernel_size=3, stacks=3, nonlinear_activation="LeakyReLU", nonlinear_activation_params={"negative_slope": 0.2}, pad="ReflectionPad1d", pad_params={}, use_final_nonlinear_activation=True, use_weight_norm=True, use_causal_conv=False, ): """Initialize MelGANGenerator module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. kernel_size (int): Kernel size of initial and final conv layer. channels (int): Initial number of channels for conv layer. bias (bool): Whether to add bias parameter in convolution layers. upsample_scales (list): List of upsampling scales. stack_kernel_size (int): Kernel size of dilated conv layers in residual stack. stacks (int): Number of stacks in a single residual stack. nonlinear_activation (str): Activation function module name. nonlinear_activation_params (dict): Hyperparameters for activation function. pad (str): Padding function module name before dilated convolution layer. pad_params (dict): Hyperparameters for padding function. use_final_nonlinear_activation (torch.nn.Module): Activation function for the final layer. use_weight_norm (bool): Whether to use weight norm. If set to true, it will be applied to all of the conv layers. use_causal_conv (bool): Whether to use causal convolution. """ super(MelGANGenerator, self).__init__() # check hyper parameters is valid assert channels >= np.prod(upsample_scales) assert channels % (2 ** len(upsample_scales)) == 0 if not use_causal_conv: assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." # add initial layer layers = [] if not use_causal_conv: layers += [ getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params), torch.nn.Conv1d(in_channels, channels, kernel_size, bias=bias), ] else: layers += [ CausalConv1d(in_channels, channels, kernel_size, bias=bias, pad=pad, pad_params=pad_params), ] for i, upsample_scale in enumerate(upsample_scales): # add upsampling layer layers += [getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)] if not use_causal_conv: layers += [ torch.nn.ConvTranspose1d( channels // (2 ** i), channels // (2 ** (i + 1)), upsample_scale * 2, stride=upsample_scale, padding=upsample_scale // 2 + upsample_scale % 2, output_padding=upsample_scale % 2, bias=bias, ) ] else: layers += [ CausalConvTranspose1d( channels // (2 ** i), channels // (2 ** (i + 1)), upsample_scale * 2, stride=upsample_scale, bias=bias, ) ] # add residual stack for j in range(stacks): layers += [ ResidualStack( kernel_size=stack_kernel_size, channels=channels // (2 ** (i + 1)), dilation=stack_kernel_size ** j, bias=bias, nonlinear_activation=nonlinear_activation, nonlinear_activation_params=nonlinear_activation_params, pad=pad, pad_params=pad_params, use_causal_conv=use_causal_conv, ) ] # add final layer layers += [getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)] if not use_causal_conv: layers += [ getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params), torch.nn.Conv1d(channels // (2 ** (i + 1)), out_channels, kernel_size, bias=bias), ] else: layers += [ CausalConv1d(channels // (2 ** (i + 1)), out_channels, kernel_size, bias=bias, pad=pad, pad_params=pad_params), ] if use_final_nonlinear_activation: layers += [torch.nn.Tanh()] # define the model as a single function self.melgan = torch.nn.Sequential(*layers) # apply weight norm if use_weight_norm: self.apply_weight_norm() # reset parameters self.reset_parameters() def forward(self, c): """Calculate forward propagation. Args: c (Tensor): Input tensor (B, channels, T). Returns: Tensor: Output tensor (B, 1, T ** prod(upsample_scales)). """ return self.melgan(c) def remove_weight_norm(self): """Remove weight normalization module from all of the layers.""" def _remove_weight_norm(m): try: logging.debug(f"Weight norm is removed from {m}.") torch.nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(_remove_weight_norm) def apply_weight_norm(self): """Apply weight normalization module from all of the layers.""" def _apply_weight_norm(m): if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): torch.nn.utils.weight_norm(m) logging.debug(f"Weight norm is applied to {m}.") self.apply(_apply_weight_norm) def reset_parameters(self): """Reset parameters. This initialization follows official implementation manner. https://github.com/descriptinc/melgan-neurips/blob/master/spec2wav/modules.py """ def _reset_parameters(m): if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): m.weight.data.normal_(0.0, 0.02) logging.debug(f"Reset parameters in {m}.") self.apply(_reset_parameters) class MelGANDiscriminator(torch.nn.Module): """MelGAN discriminator module.""" def __init__(self, in_channels=1, out_channels=1, kernel_sizes=[5, 3], channels=16, max_downsample_channels=1024, bias=True, downsample_scales=[4, 4, 4, 4], nonlinear_activation="LeakyReLU", nonlinear_activation_params={"negative_slope": 0.2}, pad="ReflectionPad1d", pad_params={}, ): """Initilize MelGAN discriminator module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. kernel_sizes (list): List of two kernel sizes. The prod will be used for the first conv layer, and the first and the second kernel sizes will be used for the last two layers. For example if kernel_sizes = [5, 3], the first layer kernel size will be 5 * 3 = 15, the last two layers' kernel size will be 5 and 3, respectively. channels (int): Initial number of channels for conv layer. max_downsample_channels (int): Maximum number of channels for downsampling layers. bias (bool): Whether to add bias parameter in convolution layers. downsample_scales (list): List of downsampling scales. nonlinear_activation (str): Activation function module name. nonlinear_activation_params (dict): Hyperparameters for activation function. pad (str): Padding function module name before dilated convolution layer. pad_params (dict): Hyperparameters for padding function. """ super(MelGANDiscriminator, self).__init__() self.layers = torch.nn.ModuleList() # check kernel size is valid assert len(kernel_sizes) == 2 assert kernel_sizes[0] % 2 == 1 assert kernel_sizes[1] % 2 == 1 # add first layer self.layers += [ torch.nn.Sequential( getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params), torch.nn.Conv1d(in_channels, channels, np.prod(kernel_sizes), bias=bias), getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), ) ] # add downsample layers in_chs = channels for downsample_scale in downsample_scales: out_chs = min(in_chs * downsample_scale, max_downsample_channels) self.layers += [ torch.nn.Sequential( torch.nn.Conv1d( in_chs, out_chs, kernel_size=downsample_scale * 10 + 1, stride=downsample_scale, padding=downsample_scale * 5, groups=in_chs // 4, bias=bias, ), getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), ) ] in_chs = out_chs # add final layers out_chs = min(in_chs * 2, max_downsample_channels) self.layers += [ torch.nn.Sequential( torch.nn.Conv1d( in_chs, out_chs, kernel_sizes[0], padding=(kernel_sizes[0] - 1) // 2, bias=bias, ), getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), ) ] self.layers += [ torch.nn.Conv1d( out_chs, out_channels, kernel_sizes[1], padding=(kernel_sizes[1] - 1) // 2, bias=bias, ), ] def forward(self, x): """Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: List: List of output tensors of each layer. """ outs = [] for f in self.layers: x = f(x) outs += [x] return outs class MelGANMultiScaleDiscriminator(torch.nn.Module): """MelGAN multi-scale discriminator module.""" def __init__(self, in_channels=1, out_channels=1, scales=3, downsample_pooling="AvgPool1d", # follow the official implementation setting downsample_pooling_params={ "kernel_size": 4, "stride": 2, "padding": 1, "count_include_pad": False, }, kernel_sizes=[5, 3], channels=16, max_downsample_channels=1024, bias=True, downsample_scales=[4, 4, 4, 4], nonlinear_activation="LeakyReLU", nonlinear_activation_params={"negative_slope": 0.2}, pad="ReflectionPad1d", pad_params={}, use_weight_norm=True, ): """Initilize MelGAN multi-scale discriminator module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. downsample_pooling (str): Pooling module name for downsampling of the inputs. downsample_pooling_params (dict): Parameters for the above pooling module. kernel_sizes (list): List of two kernel sizes. The sum will be used for the first conv layer, and the first and the second kernel sizes will be used for the last two layers. channels (int): Initial number of channels for conv layer. max_downsample_channels (int): Maximum number of channels for downsampling layers. bias (bool): Whether to add bias parameter in convolution layers. downsample_scales (list): List of downsampling scales. nonlinear_activation (str): Activation function module name. nonlinear_activation_params (dict): Hyperparameters for activation function. pad (str): Padding function module name before dilated convolution layer. pad_params (dict): Hyperparameters for padding function. use_causal_conv (bool): Whether to use causal convolution. """ super(MelGANMultiScaleDiscriminator, self).__init__() self.discriminators = torch.nn.ModuleList() # add discriminators for _ in range(scales): self.discriminators += [ MelGANDiscriminator( in_channels=in_channels, out_channels=out_channels, kernel_sizes=kernel_sizes, channels=channels, max_downsample_channels=max_downsample_channels, bias=bias, downsample_scales=downsample_scales, nonlinear_activation=nonlinear_activation, nonlinear_activation_params=nonlinear_activation_params, pad=pad, pad_params=pad_params, ) ] self.pooling = getattr(torch.nn, downsample_pooling)(**downsample_pooling_params) # apply weight norm if use_weight_norm: self.apply_weight_norm() # reset parameters self.reset_parameters() def forward(self, x): """Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: List: List of list of each discriminator outputs, which consists of each layer output tensors. """ outs = [] for f in self.discriminators: outs += [f(x)] x = self.pooling(x) return outs def remove_weight_norm(self): """Remove weight normalization module from all of the layers.""" def _remove_weight_norm(m): try: logging.debug(f"Weight norm is removed from {m}.") torch.nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(_remove_weight_norm) def apply_weight_norm(self): """Apply weight normalization module from all of the layers.""" def _apply_weight_norm(m): if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): torch.nn.utils.weight_norm(m) logging.debug(f"Weight norm is applied to {m}.") self.apply(_apply_weight_norm) def reset_parameters(self): """Reset parameters. This initialization follows official implementation manner. https://github.com/descriptinc/melgan-neurips/blob/master/spec2wav/modules.py """ def _reset_parameters(m): if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): m.weight.data.normal_(0.0, 0.02) logging.debug(f"Reset parameters in {m}.") self.apply(_reset_parameters) ================================================ FILE: NeuralSeq/modules/parallel_wavegan/models/parallel_wavegan.py ================================================ # -*- coding: utf-8 -*- # Copyright 2019 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) """Parallel WaveGAN Modules.""" import logging import math import torch from torch import nn from modules.parallel_wavegan.layers import Conv1d from modules.parallel_wavegan.layers import Conv1d1x1 from modules.parallel_wavegan.layers import ResidualBlock from modules.parallel_wavegan.layers import upsample from modules.parallel_wavegan import models class ParallelWaveGANGenerator(torch.nn.Module): """Parallel WaveGAN Generator module.""" def __init__(self, in_channels=1, out_channels=1, kernel_size=3, layers=30, stacks=3, residual_channels=64, gate_channels=128, skip_channels=64, aux_channels=80, aux_context_window=2, dropout=0.0, bias=True, use_weight_norm=True, use_causal_conv=False, upsample_conditional_features=True, upsample_net="ConvInUpsampleNetwork", upsample_params={"upsample_scales": [4, 4, 4, 4]}, use_pitch_embed=False, ): """Initialize Parallel WaveGAN Generator module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. kernel_size (int): Kernel size of dilated convolution. layers (int): Number of residual block layers. stacks (int): Number of stacks i.e., dilation cycles. residual_channels (int): Number of channels in residual conv. gate_channels (int): Number of channels in gated conv. skip_channels (int): Number of channels in skip conv. aux_channels (int): Number of channels for auxiliary feature conv. aux_context_window (int): Context window size for auxiliary feature. dropout (float): Dropout rate. 0.0 means no dropout applied. bias (bool): Whether to use bias parameter in conv layer. use_weight_norm (bool): Whether to use weight norm. If set to true, it will be applied to all of the conv layers. use_causal_conv (bool): Whether to use causal structure. upsample_conditional_features (bool): Whether to use upsampling network. upsample_net (str): Upsampling network architecture. upsample_params (dict): Upsampling network parameters. """ super(ParallelWaveGANGenerator, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.aux_channels = aux_channels self.layers = layers self.stacks = stacks self.kernel_size = kernel_size # check the number of layers and stacks assert layers % stacks == 0 layers_per_stack = layers // stacks # define first convolution self.first_conv = Conv1d1x1(in_channels, residual_channels, bias=True) # define conv + upsampling network if upsample_conditional_features: upsample_params.update({ "use_causal_conv": use_causal_conv, }) if upsample_net == "MelGANGenerator": assert aux_context_window == 0 upsample_params.update({ "use_weight_norm": False, # not to apply twice "use_final_nonlinear_activation": False, }) self.upsample_net = getattr(models, upsample_net)(**upsample_params) else: if upsample_net == "ConvInUpsampleNetwork": upsample_params.update({ "aux_channels": aux_channels, "aux_context_window": aux_context_window, }) self.upsample_net = getattr(upsample, upsample_net)(**upsample_params) else: self.upsample_net = None # define residual blocks self.conv_layers = torch.nn.ModuleList() for layer in range(layers): dilation = 2 ** (layer % layers_per_stack) conv = ResidualBlock( kernel_size=kernel_size, residual_channels=residual_channels, gate_channels=gate_channels, skip_channels=skip_channels, aux_channels=aux_channels, dilation=dilation, dropout=dropout, bias=bias, use_causal_conv=use_causal_conv, ) self.conv_layers += [conv] # define output layers self.last_conv_layers = torch.nn.ModuleList([ torch.nn.ReLU(inplace=True), Conv1d1x1(skip_channels, skip_channels, bias=True), torch.nn.ReLU(inplace=True), Conv1d1x1(skip_channels, out_channels, bias=True), ]) self.use_pitch_embed = use_pitch_embed if use_pitch_embed: self.pitch_embed = nn.Embedding(300, aux_channels, 0) self.c_proj = nn.Linear(2 * aux_channels, aux_channels) # apply weight norm if use_weight_norm: self.apply_weight_norm() def forward(self, x, c=None, pitch=None, **kwargs): """Calculate forward propagation. Args: x (Tensor): Input noise signal (B, C_in, T). c (Tensor): Local conditioning auxiliary features (B, C ,T'). pitch (Tensor): Local conditioning pitch (B, T'). Returns: Tensor: Output tensor (B, C_out, T) """ # perform upsampling if c is not None and self.upsample_net is not None: if self.use_pitch_embed: p = self.pitch_embed(pitch) c = self.c_proj(torch.cat([c.transpose(1, 2), p], -1)).transpose(1, 2) c = self.upsample_net(c) assert c.size(-1) == x.size(-1), (c.size(-1), x.size(-1)) # encode to hidden representation x = self.first_conv(x) skips = 0 for f in self.conv_layers: x, h = f(x, c) skips += h skips *= math.sqrt(1.0 / len(self.conv_layers)) # apply final layers x = skips for f in self.last_conv_layers: x = f(x) return x def remove_weight_norm(self): """Remove weight normalization module from all of the layers.""" def _remove_weight_norm(m): try: logging.debug(f"Weight norm is removed from {m}.") torch.nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(_remove_weight_norm) def apply_weight_norm(self): """Apply weight normalization module from all of the layers.""" def _apply_weight_norm(m): if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d): torch.nn.utils.weight_norm(m) logging.debug(f"Weight norm is applied to {m}.") self.apply(_apply_weight_norm) @staticmethod def _get_receptive_field_size(layers, stacks, kernel_size, dilation=lambda x: 2 ** x): assert layers % stacks == 0 layers_per_cycle = layers // stacks dilations = [dilation(i % layers_per_cycle) for i in range(layers)] return (kernel_size - 1) * sum(dilations) + 1 @property def receptive_field_size(self): """Return receptive field size.""" return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size) class ParallelWaveGANDiscriminator(torch.nn.Module): """Parallel WaveGAN Discriminator module.""" def __init__(self, in_channels=1, out_channels=1, kernel_size=3, layers=10, conv_channels=64, dilation_factor=1, nonlinear_activation="LeakyReLU", nonlinear_activation_params={"negative_slope": 0.2}, bias=True, use_weight_norm=True, ): """Initialize Parallel WaveGAN Discriminator module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. kernel_size (int): Number of output channels. layers (int): Number of conv layers. conv_channels (int): Number of chnn layers. dilation_factor (int): Dilation factor. For example, if dilation_factor = 2, the dilation will be 2, 4, 8, ..., and so on. nonlinear_activation (str): Nonlinear function after each conv. nonlinear_activation_params (dict): Nonlinear function parameters bias (bool): Whether to use bias parameter in conv. use_weight_norm (bool) Whether to use weight norm. If set to true, it will be applied to all of the conv layers. """ super(ParallelWaveGANDiscriminator, self).__init__() assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." assert dilation_factor > 0, "Dilation factor must be > 0." self.conv_layers = torch.nn.ModuleList() conv_in_channels = in_channels for i in range(layers - 1): if i == 0: dilation = 1 else: dilation = i if dilation_factor == 1 else dilation_factor ** i conv_in_channels = conv_channels padding = (kernel_size - 1) // 2 * dilation conv_layer = [ Conv1d(conv_in_channels, conv_channels, kernel_size=kernel_size, padding=padding, dilation=dilation, bias=bias), getattr(torch.nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params) ] self.conv_layers += conv_layer padding = (kernel_size - 1) // 2 last_conv_layer = Conv1d( conv_in_channels, out_channels, kernel_size=kernel_size, padding=padding, bias=bias) self.conv_layers += [last_conv_layer] # apply weight norm if use_weight_norm: self.apply_weight_norm() def forward(self, x): """Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: Tensor: Output tensor (B, 1, T) """ for f in self.conv_layers: x = f(x) return x def apply_weight_norm(self): """Apply weight normalization module from all of the layers.""" def _apply_weight_norm(m): if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d): torch.nn.utils.weight_norm(m) logging.debug(f"Weight norm is applied to {m}.") self.apply(_apply_weight_norm) def remove_weight_norm(self): """Remove weight normalization module from all of the layers.""" def _remove_weight_norm(m): try: logging.debug(f"Weight norm is removed from {m}.") torch.nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(_remove_weight_norm) class ResidualParallelWaveGANDiscriminator(torch.nn.Module): """Parallel WaveGAN Discriminator module.""" def __init__(self, in_channels=1, out_channels=1, kernel_size=3, layers=30, stacks=3, residual_channels=64, gate_channels=128, skip_channels=64, dropout=0.0, bias=True, use_weight_norm=True, use_causal_conv=False, nonlinear_activation="LeakyReLU", nonlinear_activation_params={"negative_slope": 0.2}, ): """Initialize Parallel WaveGAN Discriminator module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. kernel_size (int): Kernel size of dilated convolution. layers (int): Number of residual block layers. stacks (int): Number of stacks i.e., dilation cycles. residual_channels (int): Number of channels in residual conv. gate_channels (int): Number of channels in gated conv. skip_channels (int): Number of channels in skip conv. dropout (float): Dropout rate. 0.0 means no dropout applied. bias (bool): Whether to use bias parameter in conv. use_weight_norm (bool): Whether to use weight norm. If set to true, it will be applied to all of the conv layers. use_causal_conv (bool): Whether to use causal structure. nonlinear_activation_params (dict): Nonlinear function parameters """ super(ResidualParallelWaveGANDiscriminator, self).__init__() assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." self.in_channels = in_channels self.out_channels = out_channels self.layers = layers self.stacks = stacks self.kernel_size = kernel_size # check the number of layers and stacks assert layers % stacks == 0 layers_per_stack = layers // stacks # define first convolution self.first_conv = torch.nn.Sequential( Conv1d1x1(in_channels, residual_channels, bias=True), getattr(torch.nn, nonlinear_activation)( inplace=True, **nonlinear_activation_params), ) # define residual blocks self.conv_layers = torch.nn.ModuleList() for layer in range(layers): dilation = 2 ** (layer % layers_per_stack) conv = ResidualBlock( kernel_size=kernel_size, residual_channels=residual_channels, gate_channels=gate_channels, skip_channels=skip_channels, aux_channels=-1, dilation=dilation, dropout=dropout, bias=bias, use_causal_conv=use_causal_conv, ) self.conv_layers += [conv] # define output layers self.last_conv_layers = torch.nn.ModuleList([ getattr(torch.nn, nonlinear_activation)( inplace=True, **nonlinear_activation_params), Conv1d1x1(skip_channels, skip_channels, bias=True), getattr(torch.nn, nonlinear_activation)( inplace=True, **nonlinear_activation_params), Conv1d1x1(skip_channels, out_channels, bias=True), ]) # apply weight norm if use_weight_norm: self.apply_weight_norm() def forward(self, x): """Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: Tensor: Output tensor (B, 1, T) """ x = self.first_conv(x) skips = 0 for f in self.conv_layers: x, h = f(x, None) skips += h skips *= math.sqrt(1.0 / len(self.conv_layers)) # apply final layers x = skips for f in self.last_conv_layers: x = f(x) return x def apply_weight_norm(self): """Apply weight normalization module from all of the layers.""" def _apply_weight_norm(m): if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d): torch.nn.utils.weight_norm(m) logging.debug(f"Weight norm is applied to {m}.") self.apply(_apply_weight_norm) def remove_weight_norm(self): """Remove weight normalization module from all of the layers.""" def _remove_weight_norm(m): try: logging.debug(f"Weight norm is removed from {m}.") torch.nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(_remove_weight_norm) ================================================ FILE: NeuralSeq/modules/parallel_wavegan/models/source.py ================================================ import torch import numpy as np import sys import torch.nn.functional as torch_nn_func class SineGen(torch.nn.Module): """ Definition of sine generator SineGen(samp_rate, harmonic_num = 0, sine_amp = 0.1, noise_std = 0.003, voiced_threshold = 0, flag_for_pulse=False) samp_rate: sampling rate in Hz harmonic_num: number of harmonic overtones (default 0) sine_amp: amplitude of sine-wavefrom (default 0.1) noise_std: std of Gaussian noise (default 0.003) voiced_thoreshold: F0 threshold for U/V classification (default 0) flag_for_pulse: this SinGen is used inside PulseGen (default False) Note: when flag_for_pulse is True, the first time step of a voiced segment is always sin(np.pi) or cos(0) """ def __init__(self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=0.003, voiced_threshold=0, flag_for_pulse=False): super(SineGen, self).__init__() self.sine_amp = sine_amp self.noise_std = noise_std self.harmonic_num = harmonic_num self.dim = self.harmonic_num + 1 self.sampling_rate = samp_rate self.voiced_threshold = voiced_threshold self.flag_for_pulse = flag_for_pulse def _f02uv(self, f0): # generate uv signal uv = torch.ones_like(f0) uv = uv * (f0 > self.voiced_threshold) return uv def _f02sine(self, f0_values): """ f0_values: (batchsize, length, dim) where dim indicates fundamental tone and overtones """ # convert to F0 in rad. The interger part n can be ignored # because 2 * np.pi * n doesn't affect phase rad_values = (f0_values / self.sampling_rate) % 1 # initial phase noise (no noise for fundamental component) rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ device=f0_values.device) rand_ini[:, 0] = 0 rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) if not self.flag_for_pulse: # for normal case # To prevent torch.cumsum numerical overflow, # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. # Buffer tmp_over_one_idx indicates the time step to add -1. # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi tmp_over_one = torch.cumsum(rad_values, 1) % 1 tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 cumsum_shift = torch.zeros_like(rad_values) cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi) else: # If necessary, make sure that the first time step of every # voiced segments is sin(pi) or cos(0) # This is used for pulse-train generation # identify the last time step in unvoiced segments uv = self._f02uv(f0_values) uv_1 = torch.roll(uv, shifts=-1, dims=1) uv_1[:, -1, :] = 1 u_loc = (uv < 1) * (uv_1 > 0) # get the instantanouse phase tmp_cumsum = torch.cumsum(rad_values, dim=1) # different batch needs to be processed differently for idx in range(f0_values.shape[0]): temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] # stores the accumulation of i.phase within # each voiced segments tmp_cumsum[idx, :, :] = 0 tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum # rad_values - tmp_cumsum: remove the accumulation of i.phase # within the previous voiced segment. i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) # get the sines sines = torch.cos(i_phase * 2 * np.pi) return sines def forward(self, f0): """ sine_tensor, uv = forward(f0) input F0: tensor(batchsize=1, length, dim=1) f0 for unvoiced steps should be 0 output sine_tensor: tensor(batchsize=1, length, dim) output uv: tensor(batchsize=1, length, 1) """ with torch.no_grad(): f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) # fundamental component f0_buf[:, :, 0] = f0[:, :, 0] for idx in np.arange(self.harmonic_num): # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2) # generate sine waveforms sine_waves = self._f02sine(f0_buf) * self.sine_amp # generate uv signal # uv = torch.ones(f0.shape) # uv = uv * (f0 > self.voiced_threshold) uv = self._f02uv(f0) # noise: for unvoiced should be similar to sine_amp # std = self.sine_amp/3 -> max value ~ self.sine_amp # . for voiced regions is self.noise_std noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 noise = noise_amp * torch.randn_like(sine_waves) # first: set the unvoiced part to 0 by uv # then: additive noise sine_waves = sine_waves * uv + noise return sine_waves, uv, noise class PulseGen(torch.nn.Module): """ Definition of Pulse train generator There are many ways to implement pulse generator. Here, PulseGen is based on SinGen. For a perfect """ def __init__(self, samp_rate, pulse_amp = 0.1, noise_std = 0.003, voiced_threshold = 0): super(PulseGen, self).__init__() self.pulse_amp = pulse_amp self.sampling_rate = samp_rate self.voiced_threshold = voiced_threshold self.noise_std = noise_std self.l_sinegen = SineGen(self.sampling_rate, harmonic_num=0, \ sine_amp=self.pulse_amp, noise_std=0, \ voiced_threshold=self.voiced_threshold, \ flag_for_pulse=True) def forward(self, f0): """ Pulse train generator pulse_train, uv = forward(f0) input F0: tensor(batchsize=1, length, dim=1) f0 for unvoiced steps should be 0 output pulse_train: tensor(batchsize=1, length, dim) output uv: tensor(batchsize=1, length, 1) Note: self.l_sine doesn't make sure that the initial phase of a voiced segment is np.pi, the first pulse in a voiced segment may not be at the first time step within a voiced segment """ with torch.no_grad(): sine_wav, uv, noise = self.l_sinegen(f0) # sine without additive noise pure_sine = sine_wav - noise # step t corresponds to a pulse if # sine[t] > sine[t+1] & sine[t] > sine[t-1] # & sine[t-1], sine[t+1], and sine[t] are voiced # or # sine[t] is voiced, sine[t-1] is unvoiced # we use torch.roll to simulate sine[t+1] and sine[t-1] sine_1 = torch.roll(pure_sine, shifts=1, dims=1) uv_1 = torch.roll(uv, shifts=1, dims=1) uv_1[:, 0, :] = 0 sine_2 = torch.roll(pure_sine, shifts=-1, dims=1) uv_2 = torch.roll(uv, shifts=-1, dims=1) uv_2[:, -1, :] = 0 loc = (pure_sine > sine_1) * (pure_sine > sine_2) \ * (uv_1 > 0) * (uv_2 > 0) * (uv > 0) \ + (uv_1 < 1) * (uv > 0) # pulse train without noise pulse_train = pure_sine * loc # additive noise to pulse train # note that noise from sinegen is zero in voiced regions pulse_noise = torch.randn_like(pure_sine) * self.noise_std # with additive noise on pulse, and unvoiced regions pulse_train += pulse_noise * loc + pulse_noise * (1 - uv) return pulse_train, sine_wav, uv, pulse_noise class SignalsConv1d(torch.nn.Module): """ Filtering input signal with time invariant filter Note: FIRFilter conducted filtering given fixed FIR weight SignalsConv1d convolves two signals Note: this is based on torch.nn.functional.conv1d """ def __init__(self): super(SignalsConv1d, self).__init__() def forward(self, signal, system_ir): """ output = forward(signal, system_ir) signal: (batchsize, length1, dim) system_ir: (length2, dim) output: (batchsize, length1, dim) """ if signal.shape[-1] != system_ir.shape[-1]: print("Error: SignalsConv1d expects shape:") print("signal (batchsize, length1, dim)") print("system_id (batchsize, length2, dim)") print("But received signal: {:s}".format(str(signal.shape))) print(" system_ir: {:s}".format(str(system_ir.shape))) sys.exit(1) padding_length = system_ir.shape[0] - 1 groups = signal.shape[-1] # pad signal on the left signal_pad = torch_nn_func.pad(signal.permute(0, 2, 1), \ (padding_length, 0)) # prepare system impulse response as (dim, 1, length2) # also flip the impulse response ir = torch.flip(system_ir.unsqueeze(1).permute(2, 1, 0), \ dims=[2]) # convolute output = torch_nn_func.conv1d(signal_pad, ir, groups=groups) return output.permute(0, 2, 1) class CyclicNoiseGen_v1(torch.nn.Module): """ CyclicnoiseGen_v1 Cyclic noise with a single parameter of beta. Pytorch v1 implementation assumes f_t is also fixed """ def __init__(self, samp_rate, noise_std=0.003, voiced_threshold=0): super(CyclicNoiseGen_v1, self).__init__() self.samp_rate = samp_rate self.noise_std = noise_std self.voiced_threshold = voiced_threshold self.l_pulse = PulseGen(samp_rate, pulse_amp=1.0, noise_std=noise_std, voiced_threshold=voiced_threshold) self.l_conv = SignalsConv1d() def noise_decay(self, beta, f0mean): """ decayed_noise = noise_decay(beta, f0mean) decayed_noise = n[t]exp(-t * f_mean / beta / samp_rate) beta: (dim=1) or (batchsize=1, 1, dim=1) f0mean (batchsize=1, 1, dim=1) decayed_noise (batchsize=1, length, dim=1) """ with torch.no_grad(): # exp(-1.0 n / T) < 0.01 => n > -log(0.01)*T = 4.60*T # truncate the noise when decayed by -40 dB length = 4.6 * self.samp_rate / f0mean length = length.int() time_idx = torch.arange(0, length, device=beta.device) time_idx = time_idx.unsqueeze(0).unsqueeze(2) time_idx = time_idx.repeat(beta.shape[0], 1, beta.shape[2]) noise = torch.randn(time_idx.shape, device=beta.device) # due to Pytorch implementation, use f0_mean as the f0 factor decay = torch.exp(-time_idx * f0mean / beta / self.samp_rate) return noise * self.noise_std * decay def forward(self, f0s, beta): """ Producde cyclic-noise """ # pulse train pulse_train, sine_wav, uv, noise = self.l_pulse(f0s) pure_pulse = pulse_train - noise # decayed_noise (length, dim=1) if (uv < 1).all(): # all unvoiced cyc_noise = torch.zeros_like(sine_wav) else: f0mean = f0s[uv > 0].mean() decayed_noise = self.noise_decay(beta, f0mean)[0, :, :] # convolute cyc_noise = self.l_conv(pure_pulse, decayed_noise) # add noise in invoiced segments cyc_noise = cyc_noise + noise * (1.0 - uv) return cyc_noise, pulse_train, sine_wav, uv, noise class SineGen(torch.nn.Module): """ Definition of sine generator SineGen(samp_rate, harmonic_num = 0, sine_amp = 0.1, noise_std = 0.003, voiced_threshold = 0, flag_for_pulse=False) samp_rate: sampling rate in Hz harmonic_num: number of harmonic overtones (default 0) sine_amp: amplitude of sine-wavefrom (default 0.1) noise_std: std of Gaussian noise (default 0.003) voiced_thoreshold: F0 threshold for U/V classification (default 0) flag_for_pulse: this SinGen is used inside PulseGen (default False) Note: when flag_for_pulse is True, the first time step of a voiced segment is always sin(np.pi) or cos(0) """ def __init__(self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=0.003, voiced_threshold=0, flag_for_pulse=False): super(SineGen, self).__init__() self.sine_amp = sine_amp self.noise_std = noise_std self.harmonic_num = harmonic_num self.dim = self.harmonic_num + 1 self.sampling_rate = samp_rate self.voiced_threshold = voiced_threshold self.flag_for_pulse = flag_for_pulse def _f02uv(self, f0): # generate uv signal uv = torch.ones_like(f0) uv = uv * (f0 > self.voiced_threshold) return uv def _f02sine(self, f0_values): """ f0_values: (batchsize, length, dim) where dim indicates fundamental tone and overtones """ # convert to F0 in rad. The interger part n can be ignored # because 2 * np.pi * n doesn't affect phase rad_values = (f0_values / self.sampling_rate) % 1 # initial phase noise (no noise for fundamental component) rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ device=f0_values.device) rand_ini[:, 0] = 0 rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) if not self.flag_for_pulse: # for normal case # To prevent torch.cumsum numerical overflow, # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. # Buffer tmp_over_one_idx indicates the time step to add -1. # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi tmp_over_one = torch.cumsum(rad_values, 1) % 1 tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 cumsum_shift = torch.zeros_like(rad_values) cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi) else: # If necessary, make sure that the first time step of every # voiced segments is sin(pi) or cos(0) # This is used for pulse-train generation # identify the last time step in unvoiced segments uv = self._f02uv(f0_values) uv_1 = torch.roll(uv, shifts=-1, dims=1) uv_1[:, -1, :] = 1 u_loc = (uv < 1) * (uv_1 > 0) # get the instantanouse phase tmp_cumsum = torch.cumsum(rad_values, dim=1) # different batch needs to be processed differently for idx in range(f0_values.shape[0]): temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] # stores the accumulation of i.phase within # each voiced segments tmp_cumsum[idx, :, :] = 0 tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum # rad_values - tmp_cumsum: remove the accumulation of i.phase # within the previous voiced segment. i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) # get the sines sines = torch.cos(i_phase * 2 * np.pi) return sines def forward(self, f0): """ sine_tensor, uv = forward(f0) input F0: tensor(batchsize=1, length, dim=1) f0 for unvoiced steps should be 0 output sine_tensor: tensor(batchsize=1, length, dim) output uv: tensor(batchsize=1, length, 1) """ with torch.no_grad(): f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, \ device=f0.device) # fundamental component f0_buf[:, :, 0] = f0[:, :, 0] for idx in np.arange(self.harmonic_num): # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2) # generate sine waveforms sine_waves = self._f02sine(f0_buf) * self.sine_amp # generate uv signal # uv = torch.ones(f0.shape) # uv = uv * (f0 > self.voiced_threshold) uv = self._f02uv(f0) # noise: for unvoiced should be similar to sine_amp # std = self.sine_amp/3 -> max value ~ self.sine_amp # . for voiced regions is self.noise_std noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 noise = noise_amp * torch.randn_like(sine_waves) # first: set the unvoiced part to 0 by uv # then: additive noise sine_waves = sine_waves * uv + noise return sine_waves, uv, noise class SourceModuleCycNoise_v1(torch.nn.Module): """ SourceModuleCycNoise_v1 SourceModule(sampling_rate, noise_std=0.003, voiced_threshod=0) sampling_rate: sampling_rate in Hz noise_std: std of Gaussian noise (default: 0.003) voiced_threshold: threshold to set U/V given F0 (default: 0) cyc, noise, uv = SourceModuleCycNoise_v1(F0_upsampled, beta) F0_upsampled (batchsize, length, 1) beta (1) cyc (batchsize, length, 1) noise (batchsize, length, 1) uv (batchsize, length, 1) """ def __init__(self, sampling_rate, noise_std=0.003, voiced_threshod=0): super(SourceModuleCycNoise_v1, self).__init__() self.sampling_rate = sampling_rate self.noise_std = noise_std self.l_cyc_gen = CyclicNoiseGen_v1(sampling_rate, noise_std, voiced_threshod) def forward(self, f0_upsamped, beta): """ cyc, noise, uv = SourceModuleCycNoise_v1(F0, beta) F0_upsampled (batchsize, length, 1) beta (1) cyc (batchsize, length, 1) noise (batchsize, length, 1) uv (batchsize, length, 1) """ # source for harmonic branch cyc, pulse, sine, uv, add_noi = self.l_cyc_gen(f0_upsamped, beta) # source for noise branch, in the same shape as uv noise = torch.randn_like(uv) * self.noise_std / 3 return cyc, noise, uv class SourceModuleHnNSF(torch.nn.Module): """ SourceModule for hn-nsf SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, add_noise_std=0.003, voiced_threshod=0) sampling_rate: sampling_rate in Hz harmonic_num: number of harmonic above F0 (default: 0) sine_amp: amplitude of sine source signal (default: 0.1) add_noise_std: std of additive Gaussian noise (default: 0.003) note that amplitude of noise in unvoiced is decided by sine_amp voiced_threshold: threhold to set U/V given F0 (default: 0) Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) F0_sampled (batchsize, length, 1) Sine_source (batchsize, length, 1) noise_source (batchsize, length 1) uv (batchsize, length, 1) """ def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, add_noise_std=0.003, voiced_threshod=0): super(SourceModuleHnNSF, self).__init__() self.sine_amp = sine_amp self.noise_std = add_noise_std # to produce sine waveforms self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod) # to merge source harmonics into a single excitation self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) self.l_tanh = torch.nn.Tanh() def forward(self, x): """ Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) F0_sampled (batchsize, length, 1) Sine_source (batchsize, length, 1) noise_source (batchsize, length 1) """ # source for harmonic branch sine_wavs, uv, _ = self.l_sin_gen(x) sine_merge = self.l_tanh(self.l_linear(sine_wavs)) # source for noise branch, in the same shape as uv noise = torch.randn_like(uv) * self.sine_amp / 3 return sine_merge, noise, uv if __name__ == '__main__': source = SourceModuleCycNoise_v1(24000) x = torch.randn(16, 25600, 1) ================================================ FILE: NeuralSeq/modules/parallel_wavegan/optimizers/__init__.py ================================================ from torch.optim import * # NOQA from .radam import * # NOQA ================================================ FILE: NeuralSeq/modules/parallel_wavegan/optimizers/radam.py ================================================ # -*- coding: utf-8 -*- """RAdam optimizer. This code is drived from https://github.com/LiyuanLucasLiu/RAdam. """ import math import torch from torch.optim.optimizer import Optimizer class RAdam(Optimizer): """Rectified Adam optimizer.""" def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): """Initilize RAdam optimizer.""" defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) self.buffer = [[None, None, None] for ind in range(10)] super(RAdam, self).__init__(params, defaults) def __setstate__(self, state): """Set state.""" super(RAdam, self).__setstate__(state) def step(self, closure=None): """Run one step.""" loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad.data.float() if grad.is_sparse: raise RuntimeError('RAdam does not support sparse gradients') p_data_fp32 = p.data.float() state = self.state[p] if len(state) == 0: state['step'] = 0 state['exp_avg'] = torch.zeros_like(p_data_fp32) state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) else: state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] beta1, beta2 = group['betas'] exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) exp_avg.mul_(beta1).add_(1 - beta1, grad) state['step'] += 1 buffered = self.buffer[int(state['step'] % 10)] if state['step'] == buffered[0]: N_sma, step_size = buffered[1], buffered[2] else: buffered[0] = state['step'] beta2_t = beta2 ** state['step'] N_sma_max = 2 / (1 - beta2) - 1 N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) buffered[1] = N_sma # more conservative since it's an approximated value if N_sma >= 5: step_size = math.sqrt( (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step']) # NOQA else: step_size = 1.0 / (1 - beta1 ** state['step']) buffered[2] = step_size if group['weight_decay'] != 0: p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) # more conservative since it's an approximated value if N_sma >= 5: denom = exp_avg_sq.sqrt().add_(group['eps']) p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom) else: p_data_fp32.add_(-step_size * group['lr'], exp_avg) p.data.copy_(p_data_fp32) return loss ================================================ FILE: NeuralSeq/modules/parallel_wavegan/stft_loss.py ================================================ # -*- coding: utf-8 -*- # Copyright 2019 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) """STFT-based Loss modules.""" import librosa import torch from modules.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft class STFTLoss(torch.nn.Module): """STFT loss module.""" def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_mel_loss=False): """Initialize STFT loss module.""" super(STFTLoss, self).__init__() self.fft_size = fft_size self.shift_size = shift_size self.win_length = win_length self.window = getattr(torch, window)(win_length) self.spectral_convergenge_loss = SpectralConvergengeLoss() self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() self.use_mel_loss = use_mel_loss self.mel_basis = None def forward(self, x, y): """Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T). Returns: Tensor: Spectral convergence loss value. Tensor: Log STFT magnitude loss value. """ x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) if self.use_mel_loss: if self.mel_basis is None: self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T x_mag = x_mag @ self.mel_basis y_mag = y_mag @ self.mel_basis sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) return sc_loss, mag_loss class MultiResolutionSTFTLoss(torch.nn.Module): """Multi resolution STFT loss module.""" def __init__(self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window="hann_window", use_mel_loss=False): """Initialize Multi resolution STFT loss module. Args: fft_sizes (list): List of FFT sizes. hop_sizes (list): List of hop sizes. win_lengths (list): List of window lengths. window (str): Window function type. """ super(MultiResolutionSTFTLoss, self).__init__() assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) self.stft_losses = torch.nn.ModuleList() for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): self.stft_losses += [STFTLoss(fs, ss, wl, window, use_mel_loss)] def forward(self, x, y): """Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T). Returns: Tensor: Multi resolution spectral convergence loss value. Tensor: Multi resolution log STFT magnitude loss value. """ sc_loss = 0.0 mag_loss = 0.0 for f in self.stft_losses: sc_l, mag_l = f(x, y) sc_loss += sc_l mag_loss += mag_l sc_loss /= len(self.stft_losses) mag_loss /= len(self.stft_losses) return sc_loss, mag_loss ================================================ FILE: NeuralSeq/modules/parallel_wavegan/utils/__init__.py ================================================ from .utils import * # NOQA ================================================ FILE: NeuralSeq/modules/parallel_wavegan/utils/utils.py ================================================ # -*- coding: utf-8 -*- # Copyright 2019 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) """Utility functions.""" import fnmatch import logging import os import sys import h5py import numpy as np def find_files(root_dir, query="*.wav", include_root_dir=True): """Find files recursively. Args: root_dir (str): Root root_dir to find. query (str): Query to find. include_root_dir (bool): If False, root_dir name is not included. Returns: list: List of found filenames. """ files = [] for root, dirnames, filenames in os.walk(root_dir, followlinks=True): for filename in fnmatch.filter(filenames, query): files.append(os.path.join(root, filename)) if not include_root_dir: files = [file_.replace(root_dir + "/", "") for file_ in files] return files def read_hdf5(hdf5_name, hdf5_path): """Read hdf5 dataset. Args: hdf5_name (str): Filename of hdf5 file. hdf5_path (str): Dataset name in hdf5 file. Return: any: Dataset values. """ if not os.path.exists(hdf5_name): logging.error(f"There is no such a hdf5 file ({hdf5_name}).") sys.exit(1) hdf5_file = h5py.File(hdf5_name, "r") if hdf5_path not in hdf5_file: logging.error(f"There is no such a data in hdf5 file. ({hdf5_path})") sys.exit(1) hdf5_data = hdf5_file[hdf5_path][()] hdf5_file.close() return hdf5_data def write_hdf5(hdf5_name, hdf5_path, write_data, is_overwrite=True): """Write dataset to hdf5. Args: hdf5_name (str): Hdf5 dataset filename. hdf5_path (str): Dataset path in hdf5. write_data (ndarray): Data to write. is_overwrite (bool): Whether to overwrite dataset. """ # convert to numpy array write_data = np.array(write_data) # check folder existence folder_name, _ = os.path.split(hdf5_name) if not os.path.exists(folder_name) and len(folder_name) != 0: os.makedirs(folder_name) # check hdf5 existence if os.path.exists(hdf5_name): # if already exists, open with r+ mode hdf5_file = h5py.File(hdf5_name, "r+") # check dataset existence if hdf5_path in hdf5_file: if is_overwrite: logging.warning("Dataset in hdf5 file already exists. " "recreate dataset in hdf5.") hdf5_file.__delitem__(hdf5_path) else: logging.error("Dataset in hdf5 file already exists. " "if you want to overwrite, please set is_overwrite = True.") hdf5_file.close() sys.exit(1) else: # if not exists, open with w mode hdf5_file = h5py.File(hdf5_name, "w") # write data to hdf5 hdf5_file.create_dataset(hdf5_path, data=write_data) hdf5_file.flush() hdf5_file.close() class HDF5ScpLoader(object): """Loader class for a fests.scp file of hdf5 file. Examples: key1 /some/path/a.h5:feats key2 /some/path/b.h5:feats key3 /some/path/c.h5:feats key4 /some/path/d.h5:feats ... >>> loader = HDF5ScpLoader("hdf5.scp") >>> array = loader["key1"] key1 /some/path/a.h5 key2 /some/path/b.h5 key3 /some/path/c.h5 key4 /some/path/d.h5 ... >>> loader = HDF5ScpLoader("hdf5.scp", "feats") >>> array = loader["key1"] """ def __init__(self, feats_scp, default_hdf5_path="feats"): """Initialize HDF5 scp loader. Args: feats_scp (str): Kaldi-style feats.scp file with hdf5 format. default_hdf5_path (str): Path in hdf5 file. If the scp contain the info, not used. """ self.default_hdf5_path = default_hdf5_path with open(feats_scp) as f: lines = [line.replace("\n", "") for line in f.readlines()] self.data = {} for line in lines: key, value = line.split() self.data[key] = value def get_path(self, key): """Get hdf5 file path for a given key.""" return self.data[key] def __getitem__(self, key): """Get ndarray for a given key.""" p = self.data[key] if ":" in p: return read_hdf5(*p.split(":")) else: return read_hdf5(p, self.default_hdf5_path) def __len__(self): """Return the length of the scp file.""" return len(self.data) def __iter__(self): """Return the iterator of the scp file.""" return iter(self.data) def keys(self): """Return the keys of the scp file.""" return self.data.keys() ================================================ FILE: NeuralSeq/modules/syntaspeech/multi_window_disc.py ================================================ import numpy as np import torch import torch.nn as nn class SingleWindowDisc(nn.Module): def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128): super().__init__() padding = (kernel[0] // 2, kernel[1] // 2) self.model = nn.ModuleList([ nn.Sequential(*[ nn.Conv2d(c_in, hidden_size, kernel, (2, 2), padding), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25), nn.BatchNorm2d(hidden_size, 0.8) ]), nn.Sequential(*[ nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25), nn.BatchNorm2d(hidden_size, 0.8) ]), nn.Sequential(*[ nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25), ]), ]) ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3) self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1) def forward(self, x): """ :param x: [B, C, T, n_bins] :return: validity: [B, 1], h: List of hiddens """ h = [] for l in self.model: x = l(x) h.append(x) x = x.view(x.shape[0], -1) validity = self.adv_layer(x) # [B, 1] return validity, h class MultiWindowDiscriminator(nn.Module): def __init__(self, time_lengths, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128): super(MultiWindowDiscriminator, self).__init__() self.win_lengths = time_lengths self.discriminators = nn.ModuleList() for time_length in time_lengths: self.discriminators += [SingleWindowDisc(time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size)] def forward(self, x, x_len, start_frames_wins=None): ''' Args: x (tensor): input mel, (B, c_in, T, n_bins). x_length (tensor): len of per mel. (B,). Returns: tensor : (B). ''' validity = [] if start_frames_wins is None: start_frames_wins = [None] * len(self.discriminators) h = [] for i, start_frames in zip(range(len(self.discriminators)), start_frames_wins): x_clip, start_frames = self.clip(x, x_len, self.win_lengths[i], start_frames) # (B, win_length, C) start_frames_wins[i] = start_frames if x_clip is None: continue x_clip, h_ = self.discriminators[i](x_clip) h += h_ validity.append(x_clip) if len(validity) != len(self.discriminators): return None, start_frames_wins, h validity = sum(validity) # [B] return validity, start_frames_wins, h def clip(self, x, x_len, win_length, start_frames=None): '''Ramdom clip x to win_length. Args: x (tensor) : (B, c_in, T, n_bins). cond (tensor) : (B, T, H). x_len (tensor) : (B,). win_length (int): target clip length Returns: (tensor) : (B, c_in, win_length, n_bins). ''' T_start = 0 T_end = x_len.max() - win_length if T_end < 0: return None, None, start_frames T_end = T_end.item() if start_frames is None: start_frame = np.random.randint(low=T_start, high=T_end + 1) start_frames = [start_frame] * x.size(0) else: start_frame = start_frames[0] x_batch = x[:, :, start_frame: start_frame + win_length] return x_batch, start_frames class Discriminator(nn.Module): def __init__(self, time_lengths=[32, 64, 128], freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128): super(Discriminator, self).__init__() self.time_lengths = time_lengths self.discriminator = MultiWindowDiscriminator( freq_length=freq_length, time_lengths=time_lengths, kernel=kernel, c_in=c_in, hidden_size=hidden_size ) def forward(self, x, start_frames_wins=None): """ :param x: [B, T, 80] :param return_y_only: :return: """ if len(x.shape) == 3: x = x[:, None, :, :] # [B,1,T,80] x_len = x.sum([1, -1]).ne(0).int().sum([-1]) ret = {'y_c': None, 'y': None} ret['y'], start_frames_wins, ret['h'] = self.discriminator( x, x_len, start_frames_wins=start_frames_wins) ret['start_frames_wins'] = start_frames_wins return ret ================================================ FILE: NeuralSeq/modules/syntaspeech/syntactic_graph_buider.py ================================================ from copy import deepcopy import torch import dgl import stanza import networkx as nx class Sentence2GraphParser: def __init__(self, language='zh', use_gpu=False, download=False): self.language = language if download: self.stanza_parser = stanza.Pipeline(lang=language, use_gpu=use_gpu) else: self.stanza_parser = stanza.Pipeline(lang=language, use_gpu=use_gpu, download_method=None) def parse(self, clean_sentence=None, words=None, ph_words=None): if self.language == 'zh': assert words is not None and ph_words is not None ret = self._parse_zh(words, ph_words) elif self.language == 'en': assert clean_sentence is not None ret = self._parse_en(clean_sentence) else: raise NotImplementedError return ret def _parse_zh(self, words, ph_words, enable_backward_edge=True, enable_recur_edge=True, enable_inter_sentence_edge=True, sequential_edge=False): """ words: , each character in chinese is one item ph_words: , each character in chinese is one item, represented by the phoneme Example: text1 = '宝马配挂跛骡鞍,貂蝉怨枕董翁榻.' words = ['', '宝', '马', '配', '挂', '跛', '骡', '鞍', ',' , '貂', '蝉', '怨', '枕', '董', '翁', '榻', ''] ph_words = ['', 'b_ao3_|', 'm_a3_#', 'p_ei4_|', 'g_ua4_#', 'b_o3_#', 'l_uo2_|', 'an1', ',', 'd_iao1_|', 'ch_an2_#', 'van4_#', 'zh_en3_#', 'd_ong3_|', 'ueng1_#', 't_a4', ''] """ words, ph_words = words[1:-1], ph_words[1:-1] # delete and for i, p_w in enumerate(ph_words): if p_w == ',': # change english ',' into chinese # we found it necessary in stanza's dependency parsing words[i], ph_words[i] = '，', '，' tmp_words = deepcopy(words) num_added_space = 0 for i, p_w in enumerate(ph_words): if p_w.endswith("#"): # add a blank after the p_w with '#', to separate words tmp_words.insert(num_added_space + i + 1, " ") num_added_space += 1 if p_w in ['，', ',']: # add one blank before and after ', ', respectively tmp_words.insert(num_added_space + i + 1, " ") # insert behind ',' first tmp_words.insert(num_added_space + i, " ") # insert before num_added_space += 2 clean_text = ''.join(tmp_words).strip() parser_out = self.stanza_parser(clean_text) idx_to_word = {i + 1: w for i, w in enumerate(words)} vocab_nodes = {} vocab_idx_offset = 0 for sentence in parser_out.sentences: num_nodes_in_current_sentence = 0 for vocab_node in sentence.words: num_nodes_in_current_sentence += 1 vocab_idx = vocab_node.id + vocab_idx_offset vocab_text = vocab_node.text.replace(" ", "") # delete blank in vocab vocab_nodes[vocab_idx] = vocab_text vocab_idx_offset += num_nodes_in_current_sentence # start vocab-to-word alignment vocab_to_word = {} current_word_idx = 1 for vocab_i in vocab_nodes.keys(): vocab_to_word[vocab_i] = [] for w_in_vocab_i in vocab_nodes[vocab_i]: if w_in_vocab_i != idx_to_word[current_word_idx]: raise ValueError("Word Mismatch!") vocab_to_word[vocab_i].append(current_word_idx) # add a path (vocab_node_idx, word_global_idx) current_word_idx += 1 # then we compute the vocab-level edges if len(parser_out.sentences) > 5: print("Detect more than 5 input sentence! pls check whether the sentence is too long!") vocab_level_source_id, vocab_level_dest_id = [], [] vocab_level_edge_types = [] sentences_heads = [] vocab_id_offset = 0 # get forward edges for s in parser_out.sentences: for w in s.words: w_idx = w.id + vocab_id_offset # it starts from 1, just same as binarizer w_dest_idx = w.head + vocab_id_offset if w.head == 0: sentences_heads.append(w_idx) continue vocab_level_source_id.append(w_idx) vocab_level_dest_id.append(w_dest_idx) vocab_id_offset += len(s.words) vocab_level_edge_types += [0] * len(vocab_level_source_id) num_vocab = vocab_id_offset # optional: get backward edges if enable_backward_edge: back_source, back_dest = deepcopy(vocab_level_dest_id), deepcopy(vocab_level_source_id) vocab_level_source_id += back_source vocab_level_dest_id += back_dest vocab_level_edge_types += [1] * len(back_source) # optional: get inter-sentence edges if num_sentences > 1 inter_sentence_source, inter_sentence_dest = [], [] if enable_inter_sentence_edge and len(sentences_heads) > 1: def get_full_graph_edges(nodes): tmp_edges = [] for i, node_i in enumerate(nodes): for j, node_j in enumerate(nodes): if i == j: continue tmp_edges.append((node_i, node_j)) return tmp_edges tmp_edges = get_full_graph_edges(sentences_heads) for (source, dest) in tmp_edges: inter_sentence_source.append(source) inter_sentence_dest.append(dest) vocab_level_source_id += inter_sentence_source vocab_level_dest_id += inter_sentence_dest vocab_level_edge_types += [3] * len(inter_sentence_source) if sequential_edge: seq_source, seq_dest = list(range(1, num_vocab)) + list(range(num_vocab, 0, -1)), \ list(range(2, num_vocab + 1)) + list(range(num_vocab - 1, -1, -1)) vocab_level_source_id += seq_source vocab_level_dest_id += seq_dest vocab_level_edge_types += [4] * (num_vocab - 1) + [5] * (num_vocab - 1) # Then, we use the vocab-level edges and the vocab-to-word path, to construct the word-level graph num_word = len(words) source_id, dest_id, edge_types = [], [], [] for (vocab_start, vocab_end, vocab_edge_type) in zip(vocab_level_source_id, vocab_level_dest_id, vocab_level_edge_types): # connect the first word in the vocab word_start = min(vocab_to_word[vocab_start]) word_end = min(vocab_to_word[vocab_end]) source_id.append(word_start) dest_id.append(word_end) edge_types.append(vocab_edge_type) # sequential connection in words for word_indices_in_v in vocab_to_word.values(): for i, word_idx in enumerate(word_indices_in_v): if i + 1 < len(word_indices_in_v): source_id.append(word_idx) dest_id.append(word_idx + 1) edge_types.append(4) if i - 1 >= 0: source_id.append(word_idx) dest_id.append(word_idx - 1) edge_types.append(5) # optional: get recurrent edges if enable_recur_edge: recur_source, recur_dest = list(range(1, num_word + 1)), list(range(1, num_word + 1)) source_id += recur_source dest_id += recur_dest edge_types += [2] * len(recur_source) # add and source_id += [0, num_word + 1, 1, num_word] dest_id += [1, num_word, 0, num_word + 1] edge_types += [4, 4, 5, 5] # 4 represents sequentially forward, 5 is sequential backward edges = (torch.LongTensor(source_id), torch.LongTensor(dest_id)) dgl_graph = dgl.graph(edges) assert dgl_graph.num_edges() == len(edge_types) return dgl_graph, torch.LongTensor(edge_types) def _parse_en(self, clean_sentence, enable_backward_edge=True, enable_recur_edge=True, enable_inter_sentence_edge=True, sequential_edge=False, consider_bos_for_index=True): """ clean_sentence: , each word or punctuation should be separated by one blank. """ edge_types = [] # required for gated graph neural network clean_sentence = clean_sentence.strip() if clean_sentence.endswith((" .", " ,", " ;", " :", " ?", " !")): clean_sentence = clean_sentence[:-2] if clean_sentence.startswith(". "): clean_sentence = clean_sentence[2:] parser_out = self.stanza_parser(clean_sentence) if len(parser_out.sentences) > 5: print("Detect more than 5 input sentence! pls check whether the sentence is too long!") print(clean_sentence) source_id, dest_id = [], [] sentences_heads = [] word_id_offset = 0 # get forward edges for s in parser_out.sentences: for w in s.words: w_idx = w.id + word_id_offset # it starts from 1, just same as binarizer w_dest_idx = w.head + word_id_offset if w.head == 0: sentences_heads.append(w_idx) continue source_id.append(w_idx) dest_id.append(w_dest_idx) word_id_offset += len(s.words) num_word = word_id_offset edge_types += [0] * len(source_id) # optional: get backward edges if enable_backward_edge: back_source, back_dest = deepcopy(dest_id), deepcopy(source_id) source_id += back_source dest_id += back_dest edge_types += [1] * len(back_source) # optional: get recurrent edges if enable_recur_edge: recur_source, recur_dest = list(range(1, num_word + 1)), list(range(1, num_word + 1)) source_id += recur_source dest_id += recur_dest edge_types += [2] * len(recur_source) # optional: get inter-sentence edges if num_sentences > 1 inter_sentence_source, inter_sentence_dest = [], [] if enable_inter_sentence_edge and len(sentences_heads) > 1: def get_full_graph_edges(nodes): tmp_edges = [] for i, node_i in enumerate(nodes): for j, node_j in enumerate(nodes): if i == j: continue tmp_edges.append((node_i, node_j)) return tmp_edges tmp_edges = get_full_graph_edges(sentences_heads) for (source, dest) in tmp_edges: inter_sentence_source.append(source) inter_sentence_dest.append(dest) source_id += inter_sentence_source dest_id += inter_sentence_dest edge_types += [3] * len(inter_sentence_source) # add and source_id += [0, num_word + 1, 1, num_word] dest_id += [1, num_word, 0, num_word + 1] edge_types += [4, 4, 5, 5] # 4 represents sequentially forward, 5 is sequential backward # optional: sequential edge if sequential_edge: seq_source, seq_dest = list(range(1, num_word)) + list(range(num_word, 0, -1)), \ list(range(2, num_word + 1)) + list(range(num_word - 1, -1, -1)) source_id += seq_source dest_id += seq_dest edge_types += [4] * (num_word - 1) + [5] * (num_word - 1) if consider_bos_for_index: edges = (torch.LongTensor(source_id), torch.LongTensor(dest_id)) else: edges = (torch.LongTensor(source_id) - 1, torch.LongTensor(dest_id) - 1) dgl_graph = dgl.graph(edges) assert dgl_graph.num_edges() == len(edge_types) return dgl_graph, torch.LongTensor(edge_types) def plot_dgl_sentence_graph(dgl_graph, labels): """ labels = {idx: word for idx,word in enumerate(sentence.split(" ")) } """ import matplotlib.pyplot as plt nx_graph = dgl_graph.to_networkx() pos = nx.random_layout(nx_graph) nx.draw(nx_graph, pos, with_labels=False) nx.draw_networkx_labels(nx_graph, pos, labels) plt.show() if __name__ == '__main__': # Unit Test for Chinese Graph Builder parser = Sentence2GraphParser("zh") text1 = '宝马配挂跛骡鞍,貂蝉怨枕董翁榻.' words = ['', '宝', '马', '配', '挂', '跛', '骡', '鞍', ',', '貂', '蝉', '怨', '枕', '董', '翁', '榻', ''] ph_words = ['', 'b_ao3_|', 'm_a3_#', 'p_ei4_|', 'g_ua4_#', 'b_o3_#', 'l_uo2_|', 'an1', ',', 'd_iao1_|', 'ch_an2_#', 'van4_#', 'zh_en3_#', 'd_ong3_|', 'ueng1_#', 't_a4', ''] graph1, etypes1 = parser.parse(text1, words, ph_words) plot_dgl_sentence_graph(graph1, {i: w for i, w in enumerate(ph_words)}) # Unit Test for English Graph Builder parser = Sentence2GraphParser("en") text2 = "I love you . You love me . Mixue ice-scream and tea ." graph2, etypes2 = parser.parse(text2) plot_dgl_sentence_graph(graph2, {i: w for i, w in enumerate((" " + text2 + " ").split(" "))}) ================================================ FILE: NeuralSeq/modules/syntaspeech/syntactic_graph_encoder.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import dgl from dgl.nn.pytorch import GatedGraphConv def sequence_mask(lengths, maxlen, dtype=torch.bool): if maxlen is None: maxlen = lengths.max() mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths).t() mask.type(dtype) return mask def group_hidden_by_segs(h, seg_ids, max_len): """ :param h: [B, T, H] :param seg_ids: [B, T] :return: h_ph: [B, T_ph, H] """ B, T, H = h.shape h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h) all_ones = h.new_ones(h.shape[:2]) cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous() h_gby_segs = h_gby_segs[:, 1:] cnt_gby_segs = cnt_gby_segs[:, 1:] h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1) # assert h_gby_segs.shape[-1] == 192 return h_gby_segs class GraphAuxEnc(nn.Module): def __init__(self, in_dim, hid_dim, out_dim, n_iterations=5, n_edge_types=6): super(GraphAuxEnc, self).__init__() self.in_dim = in_dim self.hid_dim = hid_dim self.out_dim = out_dim self.skip_connect = True self.dropout_after_gae = False self.ggc_1 = GatedGraphConv(in_feats=in_dim, out_feats=hid_dim , n_steps=n_iterations, n_etypes=n_edge_types) self.ggc_2 = GatedGraphConv(in_feats=hid_dim, out_feats=out_dim , n_steps=n_iterations, n_etypes=n_edge_types) self.dropout = nn.Dropout(p=0.5) @staticmethod def ph_encoding_to_word_encoding(ph_encoding, ph2word, word_len): """ ph_encoding: [batch, t_p, hid] ph2word: tensor [batch, t_w] word_len: tensor [batch] """ word_encoding_for_graph, batch_word_encoding, has_word_row_idx = GraphAuxEnc._process_ph_to_word_encoding( ph_encoding, ph2word, word_len) # [batch, t_w, hid] return batch_word_encoding, word_encoding_for_graph def pad_word_encoding_to_phoneme(self, word_encoding, ph2word, t_p): return self._postprocess_word2ph(word_encoding, ph2word, t_p) @staticmethod def _process_ph_to_word_encoding(ph_encoding, ph2word, word_len=None): """ ph_encoding: [batch, t_p, hid] ph2word: tensor [batch, t_w] word_len: tensor [batch] """ word_len = word_len.reshape([-1,]) max_len = max(word_len) num_nodes = sum(word_len) batch_word_encoding = group_hidden_by_segs(ph_encoding, ph2word, max_len) bs, t_p, hid = batch_word_encoding.shape has_word_mask = sequence_mask(word_len, max_len) # [batch, t_p, 1] word_encoding = batch_word_encoding.reshape([bs * t_p, hid]) has_word_row_idx = has_word_mask.reshape([-1]) word_encoding = word_encoding[has_word_row_idx] assert word_encoding.shape[0] == num_nodes return word_encoding, batch_word_encoding, has_word_row_idx @staticmethod def _postprocess_word2ph(word_encoding, ph2word, t_p): word_encoding = F.pad(word_encoding,[0,0,1,0]) ph2word_ = ph2word[:, :, None].repeat([1, 1, word_encoding.shape[-1]]) out = torch.gather(word_encoding, 1, ph2word_) # [B, T, H] return out @staticmethod def _repeat_one_sequence(x, d, T): """Repeat each frame according to duration.""" if d.sum() == 0: d = d.fill_(1) hid = x.shape[-1] expanded_lst = [x_.repeat(int(d_), 1) for x_, d_ in zip(x, d) if d_ != 0] expanded = torch.cat(expanded_lst, dim=0) if T > expanded.shape[0]: expanded = torch.cat([expanded, torch.zeros([T - expanded.shape[0], hid]).to(expanded.device)], dim=0) return expanded def word_forward(self, graph_lst, word_encoding, etypes_lst): """ word encoding in, word encoding out. """ batched_graph = dgl.batch(graph_lst) inp = word_encoding batched_etypes = torch.cat(etypes_lst) # [num_edges_in_batch, 1] assert batched_graph.num_nodes() == inp.shape[0] gcc1_out = self.ggc_1(batched_graph, inp, batched_etypes) if self.dropout_after_gae: gcc1_out = self.dropout(gcc1_out) gcc2_out = self.ggc_2(batched_graph, gcc1_out, batched_etypes) # [num_nodes_in_batch, hin] if self.dropout_after_gae: gcc2_out = self.ggc_2(batched_graph, gcc2_out, batched_etypes) if self.skip_connect: assert self.in_dim == self.hid_dim and self.hid_dim == self.out_dim gcc2_out = inp + gcc1_out + gcc2_out word_len = torch.tensor([g.num_nodes() for g in graph_lst]).reshape([-1]) max_len = max(word_len) has_word_mask = sequence_mask(word_len, max_len) # [batch, t_p, 1] has_word_row_idx = has_word_mask.reshape([-1]) bs = len(graph_lst) t_w = max([g.num_nodes() for g in graph_lst]) hid = word_encoding.shape[-1] output = torch.zeros([bs * t_w, hid]).to(gcc2_out.device) output[has_word_row_idx] = gcc2_out output = output.reshape([bs, t_w, hid]) word_level_output = output return torch.transpose(word_level_output, 1, 2) def forward(self, graph_lst, ph_encoding, ph2word, etypes_lst, return_word_encoding=False): """ graph_lst: [list of dgl_graph] ph_encoding: [batch, hid, t_p] ph2word: [list of list[1,2,2,2,3,3,3]] etypes_lst: [list of etypes]; etypes: torch.LongTensor """ t_p = ph_encoding.shape[-1] ph_encoding = ph_encoding.transpose(1,2) # [batch, t_p, hid] word_len = torch.tensor([g.num_nodes() for g in graph_lst]).reshape([-1]) batched_graph = dgl.batch(graph_lst) inp, batched_word_encoding, has_word_row_idx = self._process_ph_to_word_encoding(ph_encoding, ph2word, word_len=word_len) # [num_nodes_in_batch, in_dim] bs, t_w, hid = batched_word_encoding.shape batched_etypes = torch.cat(etypes_lst) # [num_edges_in_batch, 1] gcc1_out = self.ggc_1(batched_graph, inp, batched_etypes) gcc2_out = self.ggc_2(batched_graph, gcc1_out, batched_etypes) # [num_nodes_in_batch, hin] # skip connection gcc2_out = inp + gcc1_out + gcc2_out # [n_nodes, hid] output = torch.zeros([bs * t_w, hid]).to(gcc2_out.device) output[has_word_row_idx] = gcc2_out output = output.reshape([bs, t_w, hid]) word_level_output = output output = self._postprocess_word2ph(word_level_output, ph2word, t_p) # [batch, t_p, hid] output = torch.transpose(output, 1, 2) if return_word_encoding: return output, torch.transpose(word_level_output, 1, 2) else: return output if __name__ == '__main__': # Unit Test for batching graphs from modules.syntaspeech.syntactic_graph_buider import Sentence2GraphParser, plot_dgl_sentence_graph parser = Sentence2GraphParser("en") # Unit Test for English Graph Builder text1 = "To be or not to be , that 's a question ." text2 = "I love you . You love me . Mixue ice-scream and tea ." graph1, etypes1 = parser.parse(text1) graph2, etypes2 = parser.parse(text2) batched_text = " " + text1 + " " + " " + " " + text2 + " " batched_nodes = [graph1.num_nodes(), graph2.num_nodes()] plot_dgl_sentence_graph(dgl.batch([graph1, graph2]), {i: w for i, w in enumerate(batched_text.split(" "))}) etypes_lst = [etypes1, etypes2] # Unit Test for Graph Encoder forward in_feats = 4 out_feats = 4 enc = GraphAuxEnc(in_dim=in_feats, hid_dim=in_feats, out_dim=out_feats) ph2word = torch.tensor([ [1, 2, 3, 3, 3, 4, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 0], [1, 2, 3, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] ]) inp = torch.randn([2, in_feats, 17]) # [N_sentence, feat, ph_length] graph_lst = [graph1, graph2] out = enc(graph_lst, inp, ph2word, etypes_lst) print(out.shape) # [N_sentence, feat, ph_length] ================================================ FILE: NeuralSeq/modules/syntaspeech/syntaspeech.py ================================================ import math import torch from torch import nn from torch.nn import Linear from utils.hparams import hparams from modules.commons.conv import ConvBlocks, ConditionalConvBlocks from modules.commons.common_layers import Embedding from modules.commons.rel_transformer import RelTransformerEncoder from modules.commons.transformer import MultiheadAttention, FFTBlocks from modules.commons.align_ops import clip_mel2token_to_multiple, build_word_mask, expand_states, mel2ph_to_mel2word from modules.tts.fastspeech import FS_DECODERS, FastSpeech from modules.portaspeech.fvae import SyntaFVAE, FVAE from utils.nn.seq_utils import group_hidden_by_segs from modules.fastspeech.tts_modules import SyntaDurationPredictor class SinusoidalPosEmb(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, x): """ :param x: [B, T] :return: [B, T, H] """ device = x.device half_dim = self.dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, device=device) * -emb) emb = x[:, :, None] * emb[None, :] emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb class SyntaSpeech(FastSpeech): def __init__(self, ph_dict_size, word_dict_size, out_dims=None): super().__init__(ph_dict_size, out_dims) # build linguistic encoder if hparams['num_spk'] > 1: self.spk_embed_proj = Embedding(hparams['num_spk'], self.hidden_size) if hparams['use_word_encoder']: self.word_encoder = RelTransformerEncoder( word_dict_size, self.hidden_size, self.hidden_size, self.hidden_size, 2, hparams['word_enc_layers'], hparams['enc_ffn_kernel_size']) if hparams['dur_level'] == 'word': if hparams['word_encoder_type'] == 'rel_fft': self.ph2word_encoder = RelTransformerEncoder( 0, self.hidden_size, self.hidden_size, self.hidden_size, 2, hparams['word_enc_layers'], hparams['enc_ffn_kernel_size']) if hparams['word_encoder_type'] == 'fft': self.ph2word_encoder = FFTBlocks( self.hidden_size, hparams['word_enc_layers'], 1, num_heads=hparams['num_heads']) self.sin_pos = SinusoidalPosEmb(self.hidden_size) self.enc_pos_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) self.dec_query_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) self.dec_res_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) self.attn = MultiheadAttention(self.hidden_size, 1, encoder_decoder_attention=True, bias=False) self.attn.enable_torch_version = False if hparams['text_encoder_postnet']: self.text_encoder_postnet = ConvBlocks( self.hidden_size, self.hidden_size, [1] * 3, 5, layers_in_block=2) else: self.sin_pos = SinusoidalPosEmb(self.hidden_size) predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size self.dur_predictor = SyntaDurationPredictor( self.hidden_size, n_chans=predictor_hidden, n_layers=hparams['dur_predictor_layers'], dropout_rate=hparams['predictor_dropout'], kernel_size=hparams['dur_predictor_kernel']) # build VAE decoder if hparams['use_fvae']: del self.decoder del self.mel_out if hparams.get("use_gae_in_prior", True): self.fvae = SyntaFVAE( c_in_out=self.out_dims, hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'], kernel_size=hparams['fvae_kernel_size'], enc_n_layers=hparams['fvae_enc_n_layers'], dec_n_layers=hparams['fvae_dec_n_layers'], c_cond=self.hidden_size, use_prior_flow=hparams['use_prior_flow'], flow_hidden=hparams['prior_flow_hidden'], flow_kernel_size=hparams['prior_flow_kernel_size'], flow_n_steps=hparams['prior_flow_n_blocks'], strides=[hparams['fvae_strides']], encoder_type=hparams['fvae_encoder_type'], decoder_type=hparams['fvae_decoder_type'], ) else: self.fvae = FVAE( c_in_out=self.out_dims, hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'], kernel_size=hparams['fvae_kernel_size'], enc_n_layers=hparams['fvae_enc_n_layers'], dec_n_layers=hparams['fvae_dec_n_layers'], c_cond=self.hidden_size, use_prior_flow=hparams['use_prior_flow'], flow_hidden=hparams['prior_flow_hidden'], flow_kernel_size=hparams['prior_flow_kernel_size'], flow_n_steps=hparams['prior_flow_n_blocks'], strides=[hparams['fvae_strides']], encoder_type=hparams['fvae_encoder_type'], decoder_type=hparams['fvae_decoder_type'], ) else: self.decoder = FS_DECODERS[hparams['decoder_type']](hparams) self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True) if hparams['use_pitch_embed']: self.pitch_embed = Embedding(300, self.hidden_size, 0) if hparams['add_word_pos']: self.word_pos_proj = Linear(self.hidden_size, self.hidden_size) def build_embedding(self, dictionary, embed_dim): num_embeddings = len(dictionary) emb = Embedding(num_embeddings, embed_dim, self.padding_idx) return emb def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None, spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None, global_step=None, graph_lst=None, etypes_lst=None, *args, **kwargs): if hparams['use_spk_embed']: spk_embed = spk_embed elif hparams['use_spk_id']: spk_embed = self.spk_embed_proj(spk_id)[:, None, :] else: spk_embed = 0 ret = {} style_embed = self.forward_style_embed(spk_embed, spk_id) # speaker embedding, [B, 1, C] x, tgt_nonpadding = self.run_text_encoder( txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, graph_lst=graph_lst, etypes_lst=etypes_lst, **kwargs) x = x + style_embed # it maybe necessary to achieve multi-speaker x = x * tgt_nonpadding ret['nonpadding'] = tgt_nonpadding if hparams['use_pitch_embed']: x = x + self.pitch_embed(pitch) ret['decoder_inp'] = x if infer and (mel2ph is None or mel2word is None): mel2word = ret['mel2word'] ret['mel_out_fvae'] = ret['mel_out'] = self.run_decoder(x, tgt_nonpadding, ret, infer, tgt_mels, global_step, mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst) return ret def run_text_encoder(self, txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, graph_lst, etypes_lst, **kwargs): word2word = torch.arange(word_len)[None, :].to(ph2word.device) + 1 # [B, T_mel, T_word] src_nonpadding = (txt_tokens > 0).float()[:, :, None] use_bert = hparams.get("use_bert") is True if use_bert: ph_encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst, cl_feats=kwargs['cl_feats'], ret=ret) * src_nonpadding + style_embed else: ph_encoder_out = self.encoder(txt_tokens) * src_nonpadding + style_embed if hparams['use_word_encoder']: word_encoder_out = self.word_encoder(word_tokens) + style_embed ph_encoder_out = ph_encoder_out + expand_states(word_encoder_out, ph2word) dur_input = ph_encoder_out * src_nonpadding if hparams['dur_level'] == 'word': word_encoder_out = 0 h_ph_gb_word = group_hidden_by_segs(ph_encoder_out, ph2word, word_len)[0] word_encoder_out = word_encoder_out + self.ph2word_encoder(h_ph_gb_word) if hparams['use_word_encoder']: word_encoder_out = word_encoder_out + self.word_encoder(word_tokens) mel2word = self.forward_dur(dur_input, mel2word, ret, ph2word=ph2word, word_len=word_len, graph_lst=graph_lst, etypes_lst=etypes_lst) mel2word = clip_mel2token_to_multiple(mel2word, hparams['frames_multiple']) ret['mel2word'] = mel2word tgt_nonpadding = (mel2word > 0).float()[:, :, None] enc_pos = self.get_pos_embed(word2word, ph2word) # [B, T_ph, H] dec_pos = self.get_pos_embed(word2word, mel2word) # [B, T_mel, H] dec_word_mask = build_word_mask(mel2word, ph2word) # [B, T_mel, T_ph] x, weight = self.attention(ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask) if hparams['add_word_pos']: x = x + self.word_pos_proj(dec_pos) ret['attn'] = weight else: mel2ph = self.forward_dur(dur_input, mel2ph, ret) mel2ph = clip_mel2token_to_multiple(mel2ph, hparams['frames_multiple']) mel2word = mel2ph_to_mel2word(mel2ph, ph2word) x = expand_states(ph_encoder_out, mel2ph) if hparams['add_word_pos']: dec_pos = self.get_pos_embed(word2word, mel2word) # [B, T_mel, H] x = x + self.word_pos_proj(dec_pos) tgt_nonpadding = (mel2ph > 0).float()[:, :, None] if hparams['use_word_encoder']: x = x + expand_states(word_encoder_out, mel2word) return x, tgt_nonpadding def attention(self, ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask): ph_kv = self.enc_pos_proj(torch.cat([ph_encoder_out, enc_pos], -1)) word_enc_out_expend = expand_states(word_encoder_out, mel2word) word_enc_out_expend = torch.cat([word_enc_out_expend, dec_pos], -1) if hparams['text_encoder_postnet']: word_enc_out_expend = self.dec_res_proj(word_enc_out_expend) word_enc_out_expend = self.text_encoder_postnet(word_enc_out_expend) dec_q = x_res = word_enc_out_expend else: dec_q = self.dec_query_proj(word_enc_out_expend) x_res = self.dec_res_proj(word_enc_out_expend) ph_kv, dec_q = ph_kv.transpose(0, 1), dec_q.transpose(0, 1) x, (weight, _) = self.attn(dec_q, ph_kv, ph_kv, attn_mask=(1 - dec_word_mask) * -1e9) x = x.transpose(0, 1) x = x + x_res return x, weight def run_decoder(self, x, tgt_nonpadding, ret, infer, tgt_mels=None, global_step=0, mel2word=None, ph2word=None, graph_lst=None, etypes_lst=None): if not hparams['use_fvae']: x = self.decoder(x) x = self.mel_out(x) ret['kl'] = 0 return x * tgt_nonpadding else: # x is the phoneme encoding x = x.transpose(1, 2) # [B, H, T] tgt_nonpadding_BHT = tgt_nonpadding.transpose(1, 2) # [B, H, T] if infer: z = self.fvae(cond=x, infer=True, mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst) else: tgt_mels = tgt_mels.transpose(1, 2) # [B, 80, T] z, ret['kl'], ret['z_p'], ret['m_q'], ret['logs_q'] = self.fvae( tgt_mels, tgt_nonpadding_BHT, cond=x, mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst) if global_step < hparams['posterior_start_steps']: z = torch.randn_like(z) x_recon = self.fvae.decoder(z, nonpadding=tgt_nonpadding_BHT, cond=x).transpose(1, 2) ret['pre_mel_out'] = x_recon return x_recon def forward_dur(self, dur_input, mel2word, ret, **kwargs): """ :param dur_input: [B, T_txt, H] :param mel2ph: [B, T_mel] :param txt_tokens: [B, T_txt] :param ret: :return: """ word_len = kwargs['word_len'] ph2word = kwargs['ph2word'] graph_lst = kwargs['graph_lst'] etypes_lst = kwargs['etypes_lst'] src_padding = dur_input.data.abs().sum(-1) == 0 dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach()) dur = self.dur_predictor(dur_input, src_padding, ph2word, graph_lst, etypes_lst) B, T_ph = ph2word.shape dur = torch.zeros([B, word_len.max() + 1]).to(ph2word.device).scatter_add(1, ph2word, dur) dur = dur[:, 1:] ret['dur'] = dur if mel2word is None: mel2word = self.length_regulator(dur).detach() return mel2word def get_pos_embed(self, word2word, x2word): x_pos = build_word_mask(word2word, x2word).float() # [B, T_word, T_ph] x_pos = (x_pos.cumsum(-1) / x_pos.sum(-1).clamp(min=1)[..., None] * x_pos).sum(1) x_pos = self.sin_pos(x_pos.float()) # [B, T_ph, H] return x_pos def store_inverse_all(self): def remove_weight_norm(m): try: if hasattr(m, 'store_inverse'): m.store_inverse() nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(remove_weight_norm) ================================================ FILE: NeuralSeq/tasks/base_task.py ================================================ import glob import re import subprocess from datetime import datetime import matplotlib matplotlib.use('Agg') from utils.hparams import hparams, set_hparams import random import sys import numpy as np import torch.distributed as dist from pytorch_lightning.loggers import TensorBoardLogger from utils.pl_utils import LatestModelCheckpoint, BaseTrainer, data_loader, DDP from torch import nn import torch.utils.data import utils import logging import os torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system')) log_format = '%(asctime)s %(message)s' logging.basicConfig(stream=sys.stdout, level=logging.INFO, format=log_format, datefmt='%m/%d %I:%M:%S %p') class BaseDataset(torch.utils.data.Dataset): def __init__(self, shuffle): super().__init__() self.hparams = hparams self.shuffle = shuffle self.sort_by_len = hparams['sort_by_len'] self.sizes = None @property def _sizes(self): return self.sizes def __getitem__(self, index): raise NotImplementedError def collater(self, samples): raise NotImplementedError def __len__(self): return len(self._sizes) def num_tokens(self, index): return self.size(index) def size(self, index): """Return an example's size as a float or tuple. This value is used when filtering a dataset with ``--max-positions``.""" size = min(self._sizes[index], hparams['max_frames']) return size def ordered_indices(self): """Return an ordered list of indices. Batches will be constructed based on this order.""" if self.shuffle: indices = np.random.permutation(len(self)) if self.sort_by_len: indices = indices[np.argsort(np.array(self._sizes)[indices], kind='mergesort')] # 先random, 然后稳定排序, 保证排序后同长度的数据顺序是依照random permutation的 (被其随机打乱). else: indices = np.arange(len(self)) return indices @property def num_workers(self): return int(os.getenv('NUM_WORKERS', hparams['ds_workers'])) class BaseTask(nn.Module): def __init__(self, *args, **kwargs): # dataset configs super(BaseTask, self).__init__(*args, **kwargs) self.current_epoch = 0 self.global_step = 0 self.loaded_optimizer_states_dict = {} self.trainer = None self.logger = None self.on_gpu = False self.use_dp = False self.use_ddp = False self.example_input_array = None self.max_tokens = hparams['max_tokens'] self.max_sentences = hparams['max_sentences'] self.max_eval_tokens = hparams['max_eval_tokens'] if self.max_eval_tokens == -1: hparams['max_eval_tokens'] = self.max_eval_tokens = self.max_tokens self.max_eval_sentences = hparams['max_eval_sentences'] if self.max_eval_sentences == -1: hparams['max_eval_sentences'] = self.max_eval_sentences = self.max_sentences self.model = None self.training_losses_meter = None ########### # Training, validation and testing ########### def build_model(self): raise NotImplementedError def load_ckpt(self, ckpt_base_dir, current_model_name=None, model_name='model', force=True, strict=True): # This function is updated on 2021.12.13 if current_model_name is None: current_model_name = model_name utils.load_ckpt(self.__getattr__(current_model_name), ckpt_base_dir, current_model_name, force, strict) def on_epoch_start(self): self.training_losses_meter = {'total_loss': utils.AvgrageMeter()} def _training_step(self, sample, batch_idx, optimizer_idx): """ :param sample: :param batch_idx: :return: total loss: torch.Tensor, loss_log: dict """ raise NotImplementedError def training_step(self, sample, batch_idx, optimizer_idx=-1): loss_ret = self._training_step(sample, batch_idx, optimizer_idx) self.opt_idx = optimizer_idx if loss_ret is None: return {'loss': None} total_loss, log_outputs = loss_ret log_outputs = utils.tensors_to_scalars(log_outputs) for k, v in log_outputs.items(): if k not in self.training_losses_meter: self.training_losses_meter[k] = utils.AvgrageMeter() if not np.isnan(v): self.training_losses_meter[k].update(v) self.training_losses_meter['total_loss'].update(total_loss.item()) try: log_outputs['lr'] = self.scheduler.get_lr() if isinstance(log_outputs['lr'], list): log_outputs['lr'] = log_outputs['lr'][0] except: pass # log_outputs['all_loss'] = total_loss.item() progress_bar_log = log_outputs tb_log = {f'tr/{k}': v for k, v in log_outputs.items()} return { 'loss': total_loss, 'progress_bar': progress_bar_log, 'log': tb_log } def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx): optimizer.step() optimizer.zero_grad() if self.scheduler is not None: self.scheduler.step(self.global_step // hparams['accumulate_grad_batches']) def on_epoch_end(self): loss_outputs = {k: round(v.avg, 4) for k, v in self.training_losses_meter.items()} print(f"\n==============\n " f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}" f"\n==============\n") def validation_step(self, sample, batch_idx): """ :param sample: :param batch_idx: :return: output: dict """ raise NotImplementedError def _validation_end(self, outputs): """ :param outputs: :return: loss_output: dict """ raise NotImplementedError def validation_end(self, outputs): loss_output = self._validation_end(outputs) print(f"\n==============\n " f"valid results: {loss_output}" f"\n==============\n") return { 'log': {f'val/{k}': v for k, v in loss_output.items()}, 'val_loss': loss_output['total_loss'] } def build_scheduler(self, optimizer): raise NotImplementedError def build_optimizer(self, model): raise NotImplementedError def configure_optimizers(self): optm = self.build_optimizer(self.model) self.scheduler = self.build_scheduler(optm) return [optm] def test_start(self): pass def test_step(self, sample, batch_idx): return self.validation_step(sample, batch_idx) def test_end(self, outputs): return self.validation_end(outputs) ########### # Running configuration ########### @classmethod def start(cls): set_hparams() os.environ['MASTER_PORT'] = str(random.randint(15000, 30000)) random.seed(hparams['seed']) np.random.seed(hparams['seed']) task = cls() work_dir = hparams['work_dir'] trainer = BaseTrainer(checkpoint_callback=LatestModelCheckpoint( filepath=work_dir, verbose=True, monitor='val_loss', mode='min', num_ckpt_keep=hparams['num_ckpt_keep'], save_best=hparams['save_best'], period=1 if hparams['save_ckpt'] else 100000 ), logger=TensorBoardLogger( save_dir=work_dir, name='lightning_logs', version='lastest' ), gradient_clip_val=hparams['clip_grad_norm'], val_check_interval=hparams['val_check_interval'], row_log_interval=hparams['log_interval'], max_updates=hparams['max_updates'], num_sanity_val_steps=hparams['num_sanity_val_steps'] if not hparams[ 'validate'] else 10000, accumulate_grad_batches=hparams['accumulate_grad_batches']) if not hparams['infer']: # train t = datetime.now().strftime('%Y%m%d%H%M%S') code_dir = f'{work_dir}/codes/{t}' subprocess.check_call(f'mkdir -p "{code_dir}"', shell=True) for c in hparams['save_codes']: subprocess.check_call(f'cp -r "{c}" "{code_dir}/"', shell=True) print(f"| Copied codes to {code_dir}.") trainer.checkpoint_callback.task = task trainer.fit(task) else: trainer.test(task) def configure_ddp(self, model, device_ids): model = DDP( model, device_ids=device_ids, find_unused_parameters=True ) if dist.get_rank() != 0 and not hparams['debug']: sys.stdout = open(os.devnull, "w") sys.stderr = open(os.devnull, "w") random.seed(hparams['seed']) np.random.seed(hparams['seed']) return model def training_end(self, *args, **kwargs): return None def init_ddp_connection(self, proc_rank, world_size): set_hparams(print_hparams=False) # guarantees unique ports across jobs from same grid search default_port = 12910 # if user gave a port number, use that one instead try: default_port = os.environ['MASTER_PORT'] except Exception: os.environ['MASTER_PORT'] = str(default_port) # figure out the root node addr root_node = '127.0.0.2' root_node = self.trainer.resolve_root_node_address(root_node) os.environ['MASTER_ADDR'] = root_node dist.init_process_group('nccl', rank=proc_rank, world_size=world_size) @data_loader def train_dataloader(self): return None @data_loader def test_dataloader(self): return None @data_loader def val_dataloader(self): return None def on_load_checkpoint(self, checkpoint): pass def on_save_checkpoint(self, checkpoint): pass def on_sanity_check_start(self): pass def on_train_start(self): pass def on_train_end(self): pass def on_batch_start(self, batch): pass def on_batch_end(self): pass def on_pre_performance_check(self): pass def on_post_performance_check(self): pass def on_before_zero_grad(self, optimizer): pass def on_after_backward(self): pass def backward(self, loss, optimizer): loss.backward() def grad_norm(self, norm_type): results = {} total_norm = 0 for name, p in self.named_parameters(): if p.requires_grad: try: param_norm = p.grad.data.norm(norm_type) total_norm += param_norm ** norm_type norm = param_norm ** (1 / norm_type) grad = round(norm.data.cpu().numpy().flatten()[0], 3) results['grad_{}_norm_{}'.format(norm_type, name)] = grad except Exception: # this param had no grad pass total_norm = total_norm ** (1. / norm_type) grad = round(total_norm.data.cpu().numpy().flatten()[0], 3) results['grad_{}_norm_total'.format(norm_type)] = grad return results ================================================ FILE: NeuralSeq/tasks/run.py ================================================ import importlib from utils.hparams import set_hparams, hparams def run_task(): assert hparams['task_cls'] != '' pkg = ".".join(hparams["task_cls"].split(".")[:-1]) cls_name = hparams["task_cls"].split(".")[-1] task_cls = getattr(importlib.import_module(pkg), cls_name) task_cls.start() if __name__ == '__main__': set_hparams() run_task() ================================================ FILE: NeuralSeq/tasks/svs/__init__.py ================================================ ================================================ FILE: NeuralSeq/tasks/svs/diffsinger_task.py ================================================ import torch import utils from utils.hparams import hparams from modules.diff.net import DiffNet from modules.diff.shallow_diffusion_tts import GaussianDiffusion, OfflineGaussianDiffusion from tasks.svs.diffspeech_task import DiffSpeechTask from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder from modules.fastspeech.pe import PitchExtractor from modules.fastspeech.fs2 import FastSpeech2 from modules.diffsinger_midi.fs2 import FastSpeech2MIDI from modules.fastspeech.tts_modules import mel2ph_to_dur from modules.diff.candidate_decoder import FFT from utils.pitch_utils import denorm_f0 from tasks.tts.fs2_utils import FastSpeechDataset from tasks.tts.fs2 import FastSpeech2Task import numpy as np import os import torch.nn.functional as F DIFF_DECODERS = { 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), 'fft': lambda hp: FFT( hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']), } class DiffSingerTask(DiffSpeechTask): def __init__(self): super(DiffSingerTask, self).__init__() self.dataset_cls = FastSpeechDataset self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() if hparams.get('pe_enable') is not None and hparams['pe_enable']: self.pe = PitchExtractor().cuda() utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True) self.pe.eval() def build_tts_model(self): # import torch # from tqdm import tqdm # v_min = torch.ones([80]) * 100 # v_max = torch.ones([80]) * -100 # for i, ds in enumerate(tqdm(self.dataset_cls('train'))): # v_max = torch.max(torch.max(ds['mel'].reshape(-1, 80), 0)[0], v_max) # v_min = torch.min(torch.min(ds['mel'].reshape(-1, 80), 0)[0], v_min) # if i % 100 == 0: # print(i, v_min, v_max) # print('final', v_min, v_max) mel_bins = hparams['audio_num_mel_bins'] self.model = GaussianDiffusion( phone_encoder=self.phone_encoder, out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) if hparams['fs2_ckpt'] != '': utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) # self.model.fs2.decoder = None for k, v in self.model.fs2.named_parameters(): v.requires_grad = False def validation_step(self, sample, batch_idx): outputs = {} txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] energy = sample['energy'] # fs2_mel = sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') mel2ph = sample['mel2ph'] f0 = sample['f0'] uv = sample['uv'] outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: model_out = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True) if hparams.get('pe_enable') is not None and hparams['pe_enable']: gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel else: gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) pred_f0 = model_out.get('f0_denorm') self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}') return outputs class ShallowDiffusionOfflineDataset(FastSpeechDataset): def __getitem__(self, index): sample = super(ShallowDiffusionOfflineDataset, self).__getitem__(index) item = self._get_item(index) if self.prefix != 'train' and hparams['fs2_ckpt'] != '': fs2_ckpt = os.path.dirname(hparams['fs2_ckpt']) item_name = item['item_name'] fs2_mel = torch.Tensor(np.load(f'{fs2_ckpt}/P_mels_npy/{item_name}.npy')) # ~M generated by FFT-singer. sample['fs2_mel'] = fs2_mel return sample def collater(self, samples): batch = super(ShallowDiffusionOfflineDataset, self).collater(samples) if self.prefix != 'train' and hparams['fs2_ckpt'] != '': batch['fs2_mels'] = utils.collate_2d([s['fs2_mel'] for s in samples], 0.0) return batch class DiffSingerOfflineTask(DiffSingerTask): def __init__(self): super(DiffSingerOfflineTask, self).__init__() self.dataset_cls = ShallowDiffusionOfflineDataset def build_tts_model(self): mel_bins = hparams['audio_num_mel_bins'] self.model = OfflineGaussianDiffusion( phone_encoder=self.phone_encoder, out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) # if hparams['fs2_ckpt'] != '': # utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) # self.model.fs2.decoder = None def run_model(self, model, sample, return_output=False, infer=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] mel2ph = sample['mel2ph'] # [B, T_s] f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] fs2_mel = None #sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=[target, fs2_mel], f0=f0, uv=uv, energy=energy, infer=infer) losses = {} if 'diff_loss' in output: losses['mel'] = output['diff_loss'] # self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) # if hparams['use_pitch_embed']: # self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def validation_step(self, sample, batch_idx): outputs = {} txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] energy = sample['energy'] # fs2_mel = sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') mel2ph = sample['mel2ph'] f0 = sample['f0'] uv = sample['uv'] outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: fs2_mel = sample['fs2_mels'] model_out = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=[None, fs2_mel], infer=True) if hparams.get('pe_enable') is not None and hparams['pe_enable']: gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel else: gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) pred_f0 = model_out.get('f0_denorm') self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') self.plot_mel(batch_idx, sample['mels'], fs2_mel, name=f'fs2mel_{batch_idx}') return outputs def test_step(self, sample, batch_idx): spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') txt_tokens = sample['txt_tokens'] energy = sample['energy'] if hparams['profile_infer']: pass else: mel2ph, uv, f0 = None, None, None if hparams['use_gt_dur']: mel2ph = sample['mel2ph'] if hparams['use_gt_f0']: f0 = sample['f0'] uv = sample['uv'] fs2_mel = sample['fs2_mels'] outputs = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=[None, fs2_mel], energy=energy, infer=True) sample['outputs'] = self.model.out2mel(outputs['mel_out']) sample['mel2ph_pred'] = outputs['mel2ph'] if hparams.get('pe_enable') is not None and hparams['pe_enable']: sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred'] # pe predict from Pred mel else: sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) sample['f0_pred'] = outputs.get('f0_denorm') return self.after_infer(sample) class MIDIDataset(FastSpeechDataset): def __getitem__(self, index): sample = super(MIDIDataset, self).__getitem__(index) item = self._get_item(index) sample['f0_midi'] = torch.FloatTensor(item['f0_midi']) sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']] return sample def collater(self, samples): batch = super(MIDIDataset, self).collater(samples) batch['f0_midi'] = utils.collate_1d([s['f0_midi'] for s in samples], 0.0) batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0) # print((batch['pitch_midi'] == f0_to_coarse(batch['f0_midi'])).all()) return batch class OpencpopDataset(FastSpeechDataset): def __getitem__(self, index): sample = super(OpencpopDataset, self).__getitem__(index) item = self._get_item(index) sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']] sample['midi_dur'] = torch.FloatTensor(item['midi_dur'])[:hparams['max_frames']] sample['is_slur'] = torch.LongTensor(item['is_slur'])[:hparams['max_frames']] sample['word_boundary'] = torch.LongTensor(item['word_boundary'])[:hparams['max_frames']] return sample def collater(self, samples): batch = super(OpencpopDataset, self).collater(samples) batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0) batch['midi_dur'] = utils.collate_1d([s['midi_dur'] for s in samples], 0) batch['is_slur'] = utils.collate_1d([s['is_slur'] for s in samples], 0) batch['word_boundary'] = utils.collate_1d([s['word_boundary'] for s in samples], 0) return batch class DiffSingerMIDITask(DiffSingerTask): def __init__(self): super(DiffSingerMIDITask, self).__init__() # self.dataset_cls = MIDIDataset self.dataset_cls = OpencpopDataset def run_model(self, model, sample, return_output=False, infer=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s] mel2ph = sample['mel2ph'] if hparams.get('switch_midi2f0_step') is not None and self.global_step > hparams['switch_midi2f0_step']: f0 = None uv = None else: f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer, pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) losses = {} if 'diff_loss' in output: losses['mel'] = output['diff_loss'] self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def validation_step(self, sample, batch_idx): outputs = {} txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] energy = sample['energy'] # fs2_mel = sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') mel2ph = sample['mel2ph'] outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: model_out = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=None, uv=None, energy=energy, ref_mels=None, infer=True, pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) if hparams.get('pe_enable') is not None and hparams['pe_enable']: gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel else: gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) pred_f0 = model_out.get('f0_denorm') self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}') if hparams['use_pitch_embed']: self.plot_pitch(batch_idx, sample, model_out) return outputs def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None): """ :param dur_pred: [B, T], float, log scale :param mel2ph: [B, T] :param txt_tokens: [B, T] :param losses: :return: """ B, T = txt_tokens.shape nonpadding = (txt_tokens != 0).float() dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding is_sil = torch.zeros_like(txt_tokens).bool() for p in self.sil_ph: is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) is_sil = is_sil.float() # [B, T_txt] # phone duration loss if hparams['dur_loss'] == 'mse': losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() dur_pred = (dur_pred.exp() - 1).clamp(min=0) else: raise NotImplementedError # use linear scale for sent and word duration if hparams['lambda_word_dur'] > 0: idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1] # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur) # midi_dur can be implied by add gt-ph_dur word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred) word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt) wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') word_nonpadding = (word_dur_g > 0).float() wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] if hparams['lambda_sent_dur'] > 0: sent_dur_p = dur_pred.sum(-1) sent_dur_g = dur_gt.sum(-1) sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] class AuxDecoderMIDITask(FastSpeech2Task): def __init__(self): super().__init__() # self.dataset_cls = MIDIDataset self.dataset_cls = OpencpopDataset def build_tts_model(self): if hparams.get('use_midi') is not None and hparams['use_midi']: self.model = FastSpeech2MIDI(self.phone_encoder) else: self.model = FastSpeech2(self.phone_encoder) def run_model(self, model, sample, return_output=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] mel2ph = sample['mel2ph'] # [B, T_s] f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False, pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) losses = {} self.add_mel_loss(output['mel_out'], target, losses) self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None): """ :param dur_pred: [B, T], float, log scale :param mel2ph: [B, T] :param txt_tokens: [B, T] :param losses: :return: """ B, T = txt_tokens.shape nonpadding = (txt_tokens != 0).float() dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding is_sil = torch.zeros_like(txt_tokens).bool() for p in self.sil_ph: is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) is_sil = is_sil.float() # [B, T_txt] # phone duration loss if hparams['dur_loss'] == 'mse': losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() dur_pred = (dur_pred.exp() - 1).clamp(min=0) else: raise NotImplementedError # use linear scale for sent and word duration if hparams['lambda_word_dur'] > 0: idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1] # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur) # midi_dur can be implied by add gt-ph_dur word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred) word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt) wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') word_nonpadding = (word_dur_g > 0).float() wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] if hparams['lambda_sent_dur'] > 0: sent_dur_p = dur_pred.sum(-1) sent_dur_g = dur_gt.sum(-1) sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] def validation_step(self, sample, batch_idx): outputs = {} outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] mel_out = self.model.out2mel(model_out['mel_out']) outputs = utils.tensors_to_scalars(outputs) # if sample['mels'].shape[0] == 1: # self.add_laplace_var(mel_out, sample['mels'], outputs) if batch_idx < hparams['num_valid_plots']: self.plot_mel(batch_idx, sample['mels'], mel_out) self.plot_dur(batch_idx, sample, model_out) if hparams['use_pitch_embed']: self.plot_pitch(batch_idx, sample, model_out) return outputs ================================================ FILE: NeuralSeq/tasks/svs/diffspeech_task.py ================================================ import torch import utils from utils.hparams import hparams from modules.diff.net import DiffNet from modules.diff.shallow_diffusion_tts import GaussianDiffusion from tasks.svs.task import DiffFsTask from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder from utils.pitch_utils import denorm_f0 from tasks.tts.fs2_utils import FastSpeechDataset DIFF_DECODERS = { 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), } class DiffSpeechTask(DiffFsTask): def __init__(self): super(DiffSpeechTask, self).__init__() self.dataset_cls = FastSpeechDataset self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() def build_tts_model(self): mel_bins = hparams['audio_num_mel_bins'] self.model = GaussianDiffusion( phone_encoder=self.phone_encoder, out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) if hparams['fs2_ckpt'] != '': utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) # self.model.fs2.decoder = None for k, v in self.model.fs2.named_parameters(): if not 'predictor' in k: v.requires_grad = False def build_optimizer(self, model): self.optimizer = optimizer = torch.optim.AdamW( filter(lambda p: p.requires_grad, model.parameters()), lr=hparams['lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) return optimizer def run_model(self, model, sample, return_output=False, infer=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s] mel2ph = sample['mel2ph'] f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] # fs2_mel = sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer) losses = {} if 'diff_loss' in output: losses['mel'] = output['diff_loss'] self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def validation_step(self, sample, batch_idx): outputs = {} txt_tokens = sample['txt_tokens'] # [B, T_t] energy = sample['energy'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') mel2ph = sample['mel2ph'] f0 = sample['f0'] uv = sample['uv'] outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: # model_out = self.model( # txt_tokens, spk_embed=spk_embed, mel2ph=None, f0=None, uv=None, energy=None, ref_mels=None, infer=True) # self.plot_mel(batch_idx, model_out['mel_out'], model_out['fs2_mel'], name=f'diffspeech_vs_fs2_{batch_idx}') model_out = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True) gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=model_out.get('f0_denorm')) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out']) return outputs ############ # validation plots ############ def plot_wav(self, batch_idx, gt_wav, wav_out, is_mel=False, gt_f0=None, f0=None, name=None): gt_wav = gt_wav[0].cpu().numpy() wav_out = wav_out[0].cpu().numpy() gt_f0 = gt_f0[0].cpu().numpy() f0 = f0[0].cpu().numpy() if is_mel: gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0) wav_out = self.vocoder.spec2wav(wav_out, f0=f0) self.logger.experiment.add_audio(f'gt_{batch_idx}', gt_wav, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step) self.logger.experiment.add_audio(f'wav_{batch_idx}', wav_out, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step) ================================================ FILE: NeuralSeq/tasks/svs/task.py ================================================ import torch import utils from modules.diff.diffusion import GaussianDiffusion from modules.diff.net import DiffNet from tasks.tts.fs2 import FastSpeech2Task from utils.hparams import hparams DIFF_DECODERS = { 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), } class DiffFsTask(FastSpeech2Task): def build_tts_model(self): mel_bins = hparams['audio_num_mel_bins'] self.model = GaussianDiffusion( phone_encoder=self.phone_encoder, out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) def run_model(self, model, sample, return_output=False, infer=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] mel2ph = sample['mel2ph'] # [B, T_s] f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer) losses = {} if 'diff_loss' in output: losses['mel'] = output['diff_loss'] self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def _training_step(self, sample, batch_idx, _): log_outputs = self.run_model(self.model, sample) total_loss = sum([v for v in log_outputs.values() if isinstance(v, torch.Tensor) and v.requires_grad]) log_outputs['batch_size'] = sample['txt_tokens'].size()[0] log_outputs['lr'] = self.scheduler.get_lr()[0] return total_loss, log_outputs def validation_step(self, sample, batch_idx): outputs = {} outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: _, model_out = self.run_model(self.model, sample, return_output=True, infer=True) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out']) return outputs def build_scheduler(self, optimizer): return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5) def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx): if optimizer is None: return optimizer.step() optimizer.zero_grad() if self.scheduler is not None: self.scheduler.step(self.global_step // hparams['accumulate_grad_batches']) ================================================ FILE: NeuralSeq/tasks/tts/dataset_utils.py ================================================ from utils.cwt import get_lf0_cwt import torch.optim import torch.utils.data import importlib from utils.indexed_datasets import IndexedDataset from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse import numpy as np from tasks.base_task import BaseDataset import torch import torch.optim import torch.utils.data import utils import torch.distributions from utils.hparams import hparams from resemblyzer import VoiceEncoder import json from data_gen.tts.data_gen_utils import build_phone_encoder class BaseTTSDataset(BaseDataset): def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): super().__init__(shuffle) self.data_dir = hparams['binary_data_dir'] if data_dir is None else data_dir self.prefix = prefix self.hparams = hparams self.indexed_ds = None self.ext_mel2ph = None def load_size(): self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') if prefix == 'test': if test_items is not None: self.indexed_ds, self.sizes = test_items, test_sizes else: load_size() if hparams['num_test_samples'] > 0: self.avail_idxs = [x for x in range(hparams['num_test_samples']) \ if x < len(self.sizes)] if len(hparams['test_ids']) > 0: self.avail_idxs = hparams['test_ids'] + self.avail_idxs else: self.avail_idxs = list(range(len(self.sizes))) else: load_size() self.avail_idxs = list(range(len(self.sizes))) if hparams['min_frames'] > 0: self.avail_idxs = [ x for x in self.avail_idxs if self.sizes[x] >= hparams['min_frames']] self.sizes = [self.sizes[i] for i in self.avail_idxs] def _get_item(self, index): if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: index = self.avail_idxs[index] if self.indexed_ds is None: self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') return self.indexed_ds[index] def __getitem__(self, index): hparams = self.hparams item = self._get_item(index) assert len(item['mel']) == self.sizes[index], (len(item['mel']), self.sizes[index]) max_frames = hparams['max_frames'] spec = torch.Tensor(item['mel'])[:max_frames] max_frames = spec.shape[0] // hparams['frames_multiple'] * hparams['frames_multiple'] spec = spec[:max_frames] phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) sample = { "id": index, "item_name": item['item_name'], "text": item['txt'], "txt_token": phone, "mel": spec, "mel_nonpadding": spec.abs().sum(-1) > 0, } if hparams['use_spk_embed']: sample["spk_embed"] = torch.Tensor(item['spk_embed']) if hparams['use_spk_id']: sample["spk_id"] = int(item['spk_id']) return sample def collater(self, samples): if len(samples) == 0: return {} hparams = self.hparams id = torch.LongTensor([s['id'] for s in samples]) item_names = [s['item_name'] for s in samples] text = [s['text'] for s in samples] txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0) mels = utils.collate_2d([s['mel'] for s in samples], 0.0) txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples]) mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) batch = { 'id': id, 'item_name': item_names, 'nsamples': len(samples), 'text': text, 'txt_tokens': txt_tokens, 'txt_lengths': txt_lengths, 'mels': mels, 'mel_lengths': mel_lengths, } if hparams['use_spk_embed']: spk_embed = torch.stack([s['spk_embed'] for s in samples]) batch['spk_embed'] = spk_embed if hparams['use_spk_id']: spk_ids = torch.LongTensor([s['spk_id'] for s in samples]) batch['spk_ids'] = spk_ids return batch class FastSpeechDataset(BaseTTSDataset): def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): super().__init__(prefix, shuffle, test_items, test_sizes, data_dir) self.f0_mean, self.f0_std = hparams.get('f0_mean', None), hparams.get('f0_std', None) if prefix == 'test' and hparams['test_input_dir'] != '': self.data_dir = hparams['test_input_dir'] self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') self.indexed_ds = sorted(self.indexed_ds, key=lambda item: item['item_name']) items = {} for i in range(len(self.indexed_ds)): speaker = self.indexed_ds[i]['item_name'].split('_')[0] if speaker not in items.keys(): items[speaker] = [i] else: items[speaker].append(i) sort_item = sorted(items.values(), key=lambda item_pre_speaker: len(item_pre_speaker), reverse=True) self.avail_idxs = [n for a in sort_item for n in a][:hparams['num_test_samples']] self.indexed_ds, self.sizes = self.load_test_inputs() self.avail_idxs = [i for i in range(hparams['num_test_samples'])] if hparams['pitch_type'] == 'cwt': _, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10)) def __getitem__(self, index): sample = super(FastSpeechDataset, self).__getitem__(index) item = self._get_item(index) hparams = self.hparams max_frames = hparams['max_frames'] spec = sample['mel'] T = spec.shape[0] phone = sample['txt_token'] sample['energy'] = (spec.exp() ** 2).sum(-1).sqrt() sample['mel2ph'] = mel2ph = torch.LongTensor(item['mel2ph'])[:T] if 'mel2ph' in item else None if hparams['use_pitch_embed']: assert 'f0' in item if hparams.get('normalize_pitch', False): f0 = item["f0"] if len(f0 > 0) > 0 and f0[f0 > 0].std() > 0: f0[f0 > 0] = (f0[f0 > 0] - f0[f0 > 0].mean()) / f0[f0 > 0].std() * hparams['f0_std'] + \ hparams['f0_mean'] f0[f0 > 0] = f0[f0 > 0].clip(min=60, max=500) pitch = f0_to_coarse(f0) pitch = torch.LongTensor(pitch[:max_frames]) else: pitch = torch.LongTensor(item.get("pitch"))[:max_frames] if "pitch" in item else None f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) uv = torch.FloatTensor(uv) f0 = torch.FloatTensor(f0) if hparams['pitch_type'] == 'cwt': cwt_spec = torch.Tensor(item['cwt_spec'])[:max_frames] f0_mean = item.get('f0_mean', item.get('cwt_mean')) f0_std = item.get('f0_std', item.get('cwt_std')) sample.update({"cwt_spec": cwt_spec, "f0_mean": f0_mean, "f0_std": f0_std}) elif hparams['pitch_type'] == 'ph': if "f0_ph" in item: f0 = torch.FloatTensor(item['f0_ph']) else: f0 = denorm_f0(f0, None, hparams) f0_phlevel_sum = torch.zeros_like(phone).float().scatter_add(0, mel2ph - 1, f0) f0_phlevel_num = torch.zeros_like(phone).float().scatter_add( 0, mel2ph - 1, torch.ones_like(f0)).clamp_min(1) f0_ph = f0_phlevel_sum / f0_phlevel_num f0, uv = norm_interp_f0(f0_ph, hparams) else: f0 = uv = torch.zeros_like(mel2ph) pitch = None sample["f0"], sample["uv"], sample["pitch"] = f0, uv, pitch if hparams['use_spk_embed']: sample["spk_embed"] = torch.Tensor(item['spk_embed']) if hparams['use_spk_id']: sample["spk_id"] = item['spk_id'] return sample def collater(self, samples): if len(samples) == 0: return {} hparams = self.hparams batch = super(FastSpeechDataset, self).collater(samples) f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) pitch = utils.collate_1d([s['pitch'] for s in samples]) if samples[0]['pitch'] is not None else None uv = utils.collate_1d([s['uv'] for s in samples]) energy = utils.collate_1d([s['energy'] for s in samples], 0.0) mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \ if samples[0]['mel2ph'] is not None else None batch.update({ 'mel2ph': mel2ph, 'energy': energy, 'pitch': pitch, 'f0': f0, 'uv': uv, }) if hparams['pitch_type'] == 'cwt': cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples]) f0_mean = torch.Tensor([s['f0_mean'] for s in samples]) f0_std = torch.Tensor([s['f0_std'] for s in samples]) batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std}) return batch def load_test_inputs(self): binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') pkg = ".".join(binarizer_cls.split(".")[:-1]) cls_name = binarizer_cls.split(".")[-1] binarizer_cls = getattr(importlib.import_module(pkg), cls_name) ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json" ph_set = json.load(open(ph_set_fn, 'r')) print("| phone set: ", ph_set) phone_encoder = build_phone_encoder(hparams['binary_data_dir']) word_encoder = None voice_encoder = VoiceEncoder().cuda() encoder = [phone_encoder, word_encoder] sizes = [] items = [] for i in range(len(self.avail_idxs)): item = self._get_item(i) item2tgfn = f"{hparams['test_input_dir'].replace('binary', 'processed')}/mfa_outputs/{item['item_name']}.TextGrid" item = binarizer_cls.process_item(item['item_name'], item['ph'], item['txt'], item2tgfn, item['wav_fn'], item['spk_id'], encoder, hparams['binarization_args']) item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \ if hparams['binarization_args']['with_spk_embed'] else None # 判断是否保存embedding文件 items.append(item) sizes.append(item['len']) return items, sizes class FastSpeechWordDataset(FastSpeechDataset): def __getitem__(self, index): sample = super(FastSpeechWordDataset, self).__getitem__(index) item = self._get_item(index) max_frames = hparams['max_frames'] sample["ph_words"] = item["ph_words"] sample["word_tokens"] = torch.LongTensor(item["word_tokens"]) sample["mel2word"] = torch.LongTensor(item.get("mel2word"))[:max_frames] sample["ph2word"] = torch.LongTensor(item['ph2word'][:hparams['max_input_tokens']]) return sample def collater(self, samples): batch = super(FastSpeechWordDataset, self).collater(samples) ph_words = [s['ph_words'] for s in samples] batch['ph_words'] = ph_words word_tokens = utils.collate_1d([s['word_tokens'] for s in samples], 0) batch['word_tokens'] = word_tokens mel2word = utils.collate_1d([s['mel2word'] for s in samples], 0) batch['mel2word'] = mel2word ph2word = utils.collate_1d([s['ph2word'] for s in samples], 0) batch['ph2word'] = ph2word return batch ================================================ FILE: NeuralSeq/tasks/tts/fs2.py ================================================ import matplotlib matplotlib.use('Agg') from utils import audio import matplotlib.pyplot as plt from data_gen.tts.data_gen_utils import get_pitch from tasks.tts.fs2_utils import FastSpeechDataset from utils.cwt import cwt2f0 from utils.pl_utils import data_loader import os from multiprocessing.pool import Pool from tqdm import tqdm from modules.fastspeech.tts_modules import mel2ph_to_dur from utils.hparams import hparams from utils.plot import spec_to_figure, dur_to_figure, f0_to_figure from utils.pitch_utils import denorm_f0 from modules.fastspeech.fs2 import FastSpeech2 from tasks.tts.tts import TtsTask import torch import torch.optim import torch.utils.data import torch.nn.functional as F import utils import torch.distributions import numpy as np from modules.commons.ssim import ssim class FastSpeech2Task(TtsTask): def __init__(self): super(FastSpeech2Task, self).__init__() self.dataset_cls = FastSpeechDataset self.mse_loss_fn = torch.nn.MSELoss() mel_losses = hparams['mel_loss'].split("|") self.loss_and_lambda = {} for i, l in enumerate(mel_losses): if l == '': continue if ':' in l: l, lbd = l.split(":") lbd = float(lbd) else: lbd = 1.0 self.loss_and_lambda[l] = lbd print("| Mel losses:", self.loss_and_lambda) self.sil_ph = self.phone_encoder.sil_phonemes() @data_loader def train_dataloader(self): train_dataset = self.dataset_cls(hparams['train_set_name'], shuffle=True) return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences, endless=hparams['endless_ds']) @data_loader def val_dataloader(self): valid_dataset = self.dataset_cls(hparams['valid_set_name'], shuffle=False) return self.build_dataloader(valid_dataset, False, self.max_eval_tokens, self.max_eval_sentences) @data_loader def test_dataloader(self): test_dataset = self.dataset_cls(hparams['test_set_name'], shuffle=False) return self.build_dataloader(test_dataset, False, self.max_eval_tokens, self.max_eval_sentences, batch_by_size=False) def build_tts_model(self): self.model = FastSpeech2(self.phone_encoder) def build_model(self): self.build_tts_model() if hparams['load_ckpt'] != '': self.load_ckpt(hparams['load_ckpt'], strict=True) utils.print_arch(self.model) return self.model def _training_step(self, sample, batch_idx, _): loss_output = self.run_model(self.model, sample) total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad]) loss_output['batch_size'] = sample['txt_tokens'].size()[0] return total_loss, loss_output def validation_step(self, sample, batch_idx): outputs = {} outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] mel_out = self.model.out2mel(model_out['mel_out']) outputs = utils.tensors_to_scalars(outputs) # if sample['mels'].shape[0] == 1: # self.add_laplace_var(mel_out, sample['mels'], outputs) if batch_idx < hparams['num_valid_plots']: self.plot_mel(batch_idx, sample['mels'], mel_out) self.plot_dur(batch_idx, sample, model_out) if hparams['use_pitch_embed']: self.plot_pitch(batch_idx, sample, model_out) return outputs def _validation_end(self, outputs): all_losses_meter = { 'total_loss': utils.AvgrageMeter(), } for output in outputs: n = output['nsamples'] for k, v in output['losses'].items(): if k not in all_losses_meter: all_losses_meter[k] = utils.AvgrageMeter() all_losses_meter[k].update(v, n) all_losses_meter['total_loss'].update(output['total_loss'], n) return {k: round(v.avg, 4) for k, v in all_losses_meter.items()} def run_model(self, model, sample, return_output=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] mel2ph = sample['mel2ph'] # [B, T_s] f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False) losses = {} self.add_mel_loss(output['mel_out'], target, losses) self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output ############ # losses ############ def add_mel_loss(self, mel_out, target, losses, postfix='', mel_mix_loss=None): if mel_mix_loss is None: for loss_name, lbd in self.loss_and_lambda.items(): if 'l1' == loss_name: l = self.l1_loss(mel_out, target) elif 'mse' == loss_name: raise NotImplementedError elif 'ssim' == loss_name: l = self.ssim_loss(mel_out, target) elif 'gdl' == loss_name: raise NotImplementedError losses[f'{loss_name}{postfix}'] = l * lbd else: raise NotImplementedError def l1_loss(self, decoder_output, target): # decoder_output : B x T x n_mel # target : B x T x n_mel l1_loss = F.l1_loss(decoder_output, target, reduction='none') weights = self.weights_nonzero_speech(target) l1_loss = (l1_loss * weights).sum() / weights.sum() return l1_loss def ssim_loss(self, decoder_output, target, bias=6.0): # decoder_output : B x T x n_mel # target : B x T x n_mel assert decoder_output.shape == target.shape weights = self.weights_nonzero_speech(target) decoder_output = decoder_output[:, None] + bias target = target[:, None] + bias ssim_loss = 1 - ssim(decoder_output, target, size_average=False) ssim_loss = (ssim_loss * weights).sum() / weights.sum() return ssim_loss def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, losses=None): """ :param dur_pred: [B, T], float, log scale :param mel2ph: [B, T] :param txt_tokens: [B, T] :param losses: :return: """ B, T = txt_tokens.shape nonpadding = (txt_tokens != 0).float() dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding is_sil = torch.zeros_like(txt_tokens).bool() for p in self.sil_ph: is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) is_sil = is_sil.float() # [B, T_txt] # phone duration loss if hparams['dur_loss'] == 'mse': losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() dur_pred = (dur_pred.exp() - 1).clamp(min=0) elif hparams['dur_loss'] == 'mog': return NotImplementedError elif hparams['dur_loss'] == 'crf': losses['pdur'] = -self.model.dur_predictor.crf( dur_pred, dur_gt.long().clamp(min=0, max=31), mask=nonpadding > 0, reduction='mean') losses['pdur'] = losses['pdur'] * hparams['lambda_ph_dur'] # use linear scale for sent and word duration if hparams['lambda_word_dur'] > 0: word_id = (is_sil.cumsum(-1) * (1 - is_sil)).long() word_dur_p = dur_pred.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_pred)[:, 1:] word_dur_g = dur_gt.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_gt)[:, 1:] wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') word_nonpadding = (word_dur_g > 0).float() wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] if hparams['lambda_sent_dur'] > 0: sent_dur_p = dur_pred.sum(-1) sent_dur_g = dur_gt.sum(-1) sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] def add_pitch_loss(self, output, sample, losses): if hparams['pitch_type'] == 'ph': nonpadding = (sample['txt_tokens'] != 0).float() pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss losses['f0'] = (pitch_loss_fn(output['pitch_pred'][:, :, 0], sample['f0'], reduction='none') * nonpadding).sum() \ / nonpadding.sum() * hparams['lambda_f0'] return mel2ph = sample['mel2ph'] # [B, T_s] f0 = sample['f0'] uv = sample['uv'] nonpadding = (mel2ph != 0).float() if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] cwt_pred = output['cwt'][:, :, :10] f0_mean_pred = output['f0_mean'] f0_std_pred = output['f0_std'] losses['C'] = self.cwt_loss(cwt_pred, cwt_spec) * hparams['lambda_f0'] if hparams['use_uv']: assert output['cwt'].shape[-1] == 11 uv_pred = output['cwt'][:, :, -1] losses['uv'] = (F.binary_cross_entropy_with_logits(uv_pred, uv, reduction='none') * nonpadding) \ .sum() / nonpadding.sum() * hparams['lambda_uv'] losses['f0_mean'] = F.l1_loss(f0_mean_pred, f0_mean) * hparams['lambda_f0'] losses['f0_std'] = F.l1_loss(f0_std_pred, f0_std) * hparams['lambda_f0'] if hparams['cwt_add_f0_loss']: f0_cwt_ = self.model.cwt2f0_norm(cwt_pred, f0_mean_pred, f0_std_pred, mel2ph) self.add_f0_loss(f0_cwt_[:, :, None], f0, uv, losses, nonpadding=nonpadding) elif hparams['pitch_type'] == 'frame': self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding) def add_f0_loss(self, p_pred, f0, uv, losses, nonpadding): assert p_pred[..., 0].shape == f0.shape if hparams['use_uv']: assert p_pred[..., 1].shape == uv.shape losses['uv'] = (F.binary_cross_entropy_with_logits( p_pred[:, :, 1], uv, reduction='none') * nonpadding).sum() \ / nonpadding.sum() * hparams['lambda_uv'] nonpadding = nonpadding * (uv == 0).float() f0_pred = p_pred[:, :, 0] if hparams['pitch_loss'] in ['l1', 'l2']: pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss losses['f0'] = (pitch_loss_fn(f0_pred, f0, reduction='none') * nonpadding).sum() \ / nonpadding.sum() * hparams['lambda_f0'] elif hparams['pitch_loss'] == 'ssim': return NotImplementedError def cwt_loss(self, cwt_p, cwt_g): if hparams['cwt_loss'] == 'l1': return F.l1_loss(cwt_p, cwt_g) if hparams['cwt_loss'] == 'l2': return F.mse_loss(cwt_p, cwt_g) if hparams['cwt_loss'] == 'ssim': return self.ssim_loss(cwt_p, cwt_g, 20) def add_energy_loss(self, energy_pred, energy, losses): nonpadding = (energy != 0).float() loss = (F.mse_loss(energy_pred, energy, reduction='none') * nonpadding).sum() / nonpadding.sum() loss = loss * hparams['lambda_energy'] losses['e'] = loss ############ # validation plots ############ def plot_mel(self, batch_idx, spec, spec_out, name=None): spec_cat = torch.cat([spec, spec_out], -1) name = f'mel_{batch_idx}' if name is None else name vmin = hparams['mel_vmin'] vmax = hparams['mel_vmax'] self.logger.experiment.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step) def plot_dur(self, batch_idx, sample, model_out): T_txt = sample['txt_tokens'].shape[1] dur_gt = mel2ph_to_dur(sample['mel2ph'], T_txt)[0] dur_pred = self.model.dur_predictor.out2dur(model_out['dur']).float() txt = self.phone_encoder.decode(sample['txt_tokens'][0].cpu().numpy()) txt = txt.split(" ") self.logger.experiment.add_figure( f'dur_{batch_idx}', dur_to_figure(dur_gt, dur_pred, txt), self.global_step) def plot_pitch(self, batch_idx, sample, model_out): f0 = sample['f0'] if hparams['pitch_type'] == 'ph': mel2ph = sample['mel2ph'] f0 = self.expand_f0_ph(f0, mel2ph) f0_pred = self.expand_f0_ph(model_out['pitch_pred'][:, :, 0], mel2ph) self.logger.experiment.add_figure( f'f0_{batch_idx}', f0_to_figure(f0[0], None, f0_pred[0]), self.global_step) return f0 = denorm_f0(f0, sample['uv'], hparams) if hparams['pitch_type'] == 'cwt': # cwt cwt_out = model_out['cwt'] cwt_spec = cwt_out[:, :, :10] cwt = torch.cat([cwt_spec, sample['cwt_spec']], -1) self.logger.experiment.add_figure(f'cwt_{batch_idx}', spec_to_figure(cwt[0]), self.global_step) # f0 f0_pred = cwt2f0(cwt_spec, model_out['f0_mean'], model_out['f0_std'], hparams['cwt_scales']) if hparams['use_uv']: assert cwt_out.shape[-1] == 11 uv_pred = cwt_out[:, :, -1] > 0 f0_pred[uv_pred > 0] = 0 f0_cwt = denorm_f0(sample['f0_cwt'], sample['uv'], hparams) self.logger.experiment.add_figure( f'f0_{batch_idx}', f0_to_figure(f0[0], f0_cwt[0], f0_pred[0]), self.global_step) elif hparams['pitch_type'] == 'frame': # f0 uv_pred = model_out['pitch_pred'][:, :, 1] > 0 pitch_pred = denorm_f0(model_out['pitch_pred'][:, :, 0], uv_pred, hparams) self.logger.experiment.add_figure( f'f0_{batch_idx}', f0_to_figure(f0[0], None, pitch_pred[0]), self.global_step) ############ # infer ############ def test_step(self, sample, batch_idx): spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') txt_tokens = sample['txt_tokens'] mel2ph, uv, f0 = None, None, None ref_mels = None if hparams['profile_infer']: pass else: if hparams['use_gt_dur']: mel2ph = sample['mel2ph'] if hparams['use_gt_f0']: f0 = sample['f0'] uv = sample['uv'] print('Here using gt f0!!') if hparams.get('use_midi') is not None and hparams['use_midi']: outputs = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True, pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) else: outputs = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True) sample['outputs'] = self.model.out2mel(outputs['mel_out']) sample['mel2ph_pred'] = outputs['mel2ph'] if hparams.get('pe_enable') is not None and hparams['pe_enable']: sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred'] # pe predict from Pred mel else: sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) sample['f0_pred'] = outputs.get('f0_denorm') return self.after_infer(sample) def after_infer(self, predictions): if self.saving_result_pool is None and not hparams['profile_infer']: self.saving_result_pool = Pool(min(int(os.getenv('N_PROC', os.cpu_count())), 16)) self.saving_results_futures = [] predictions = utils.unpack_dict_to_list(predictions) t = tqdm(predictions) for num_predictions, prediction in enumerate(t): for k, v in prediction.items(): if type(v) is torch.Tensor: prediction[k] = v.cpu().numpy() item_name = prediction.get('item_name') text = prediction.get('text').replace(":", "%3A")[:80] # remove paddings mel_gt = prediction["mels"] mel_gt_mask = np.abs(mel_gt).sum(-1) > 0 mel_gt = mel_gt[mel_gt_mask] mel2ph_gt = prediction.get("mel2ph") mel2ph_gt = mel2ph_gt[mel_gt_mask] if mel2ph_gt is not None else None mel_pred = prediction["outputs"] mel_pred_mask = np.abs(mel_pred).sum(-1) > 0 mel_pred = mel_pred[mel_pred_mask] mel_gt = np.clip(mel_gt, hparams['mel_vmin'], hparams['mel_vmax']) mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax']) mel2ph_pred = prediction.get("mel2ph_pred") if mel2ph_pred is not None: if len(mel2ph_pred) > len(mel_pred_mask): mel2ph_pred = mel2ph_pred[:len(mel_pred_mask)] mel2ph_pred = mel2ph_pred[mel_pred_mask] f0_gt = prediction.get("f0") f0_pred = prediction.get("f0_pred") if f0_pred is not None: f0_gt = f0_gt[mel_gt_mask] if len(f0_pred) > len(mel_pred_mask): f0_pred = f0_pred[:len(mel_pred_mask)] f0_pred = f0_pred[mel_pred_mask] str_phs = None if self.phone_encoder is not None and 'txt_tokens' in prediction: str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True) gen_dir = os.path.join(hparams['work_dir'], f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}') wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred) if not hparams['profile_infer']: os.makedirs(gen_dir, exist_ok=True) os.makedirs(f'{gen_dir}/wavs', exist_ok=True) os.makedirs(f'{gen_dir}/plot', exist_ok=True) os.makedirs(os.path.join(hparams['work_dir'], 'P_mels_npy'), exist_ok=True) os.makedirs(os.path.join(hparams['work_dir'], 'G_mels_npy'), exist_ok=True) self.saving_results_futures.append( self.saving_result_pool.apply_async(self.save_result, args=[ wav_pred, mel_pred, 'P', item_name, text, gen_dir, str_phs, mel2ph_pred, f0_gt, f0_pred])) if mel_gt is not None and hparams['save_gt']: wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) self.saving_results_futures.append( self.saving_result_pool.apply_async(self.save_result, args=[ wav_gt, mel_gt, 'G', item_name, text, gen_dir, str_phs, mel2ph_gt, f0_gt, f0_pred])) if hparams['save_f0']: import matplotlib.pyplot as plt # f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) f0_pred_ = f0_pred f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) fig = plt.figure() plt.plot(f0_pred_, label=r'$f0_P$') plt.plot(f0_gt_, label=r'$f0_G$') if hparams.get('pe_enable') is not None and hparams['pe_enable']: # f0_midi = prediction.get("f0_midi") # f0_midi = f0_midi[mel_gt_mask] # plt.plot(f0_midi, label=r'$f0_M$') pass plt.legend() plt.tight_layout() plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png') plt.close(fig) t.set_description( f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") else: if 'gen_wav_time' not in self.stats: self.stats['gen_wav_time'] = 0 self.stats['gen_wav_time'] += len(wav_pred) / hparams['audio_sample_rate'] print('gen_wav_time: ', self.stats['gen_wav_time']) return {} @staticmethod def save_result(wav_out, mel, prefix, item_name, text, gen_dir, str_phs=None, mel2ph=None, gt_f0=None, pred_f0=None): item_name = item_name.replace('/', '-') base_fn = f'[{item_name}][{prefix}]' if text is not None: base_fn += text base_fn += ('-' + hparams['exp_name']) np.save(os.path.join(hparams['work_dir'], f'{prefix}_mels_npy', item_name), mel) audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'], norm=hparams['out_wav_norm']) fig = plt.figure(figsize=(14, 10)) spec_vmin = hparams['mel_vmin'] spec_vmax = hparams['mel_vmax'] heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax) fig.colorbar(heatmap) if hparams.get('pe_enable') is not None and hparams['pe_enable']: gt_f0 = (gt_f0 - 100) / (800 - 100) * 80 * (gt_f0 > 0) pred_f0 = (pred_f0 - 100) / (800 - 100) * 80 * (pred_f0 > 0) plt.plot(pred_f0, c='white', linewidth=1, alpha=0.6) plt.plot(gt_f0, c='red', linewidth=1, alpha=0.6) else: f0, _ = get_pitch(wav_out, mel, hparams) f0 = (f0 - 100) / (800 - 100) * 80 * (f0 > 0) plt.plot(f0, c='white', linewidth=1, alpha=0.6) if mel2ph is not None and str_phs is not None: decoded_txt = str_phs.split(" ") dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy() dur = [0] + list(np.cumsum(dur)) for i in range(len(dur) - 1): shift = (i % 20) + 1 plt.text(dur[i], shift, decoded_txt[i]) plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black') plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black', alpha=1, linewidth=1) plt.tight_layout() plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png', dpi=1000) plt.close(fig) ############## # utils ############## @staticmethod def expand_f0_ph(f0, mel2ph): f0 = denorm_f0(f0, None, hparams) f0 = F.pad(f0, [1, 0]) f0 = torch.gather(f0, 1, mel2ph) # [B, T_mel] return f0 if __name__ == '__main__': FastSpeech2Task.start() ================================================ FILE: NeuralSeq/tasks/tts/fs2_adv.py ================================================ from tasks.tts.fs2 import FastSpeech2Task from modules.syntaspeech.multi_window_disc import Discriminator from utils.hparams import hparams from torch import nn import torch import torch.optim import torch.utils.data import utils class FastSpeech2AdvTask(FastSpeech2Task): def build_model(self): self.build_tts_model() if hparams['load_ckpt'] != '': self.load_ckpt(hparams['load_ckpt'], strict=False) utils.print_arch(self.model, 'Generator') self.build_disc_model() if not hasattr(self, 'gen_params'): self.gen_params = list(self.model.parameters()) return self.model def build_disc_model(self): disc_win_num = hparams['disc_win_num'] h = hparams['mel_disc_hidden_size'] self.mel_disc = Discriminator( time_lengths=[32, 64, 128][:disc_win_num], freq_length=80, hidden_size=h, kernel=(3, 3) ) self.disc_params = list(self.mel_disc.parameters()) utils.print_arch(self.mel_disc, model_name='Mel Disc') def _training_step(self, sample, batch_idx, optimizer_idx): log_outputs = {} loss_weights = {} disc_start = hparams['mel_gan'] and self.global_step >= hparams["disc_start_steps"] and \ hparams['lambda_mel_adv'] > 0 if optimizer_idx == 0: ####################### # Generator # ####################### log_outputs, model_out = self.run_model(self.model, sample, return_output=True) self.model_out = {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)} if disc_start: self.disc_cond = disc_cond = self.model_out['decoder_inp'].detach() \ if hparams['use_cond_disc'] else None if hparams['mel_loss_no_noise']: self.add_mel_loss(model_out['mel_out_nonoise'], sample['mels'], log_outputs) mel_p = model_out['mel_out'] if hasattr(self.model, 'out2mel'): mel_p = self.model.out2mel(mel_p) o_ = self.mel_disc(mel_p, disc_cond) p_, pc_ = o_['y'], o_['y_c'] if p_ is not None: log_outputs['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size())) loss_weights['a'] = hparams['lambda_mel_adv'] if pc_ is not None: log_outputs['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size())) loss_weights['ac'] = hparams['lambda_mel_adv'] else: ####################### # Discriminator # ####################### if disc_start and self.global_step % hparams['disc_interval'] == 0: if hparams['rerun_gen']: with torch.no_grad(): _, model_out = self.run_model(self.model, sample, return_output=True) else: model_out = self.model_out mel_g = sample['mels'] mel_p = model_out['mel_out'] if hasattr(self.model, 'out2mel'): mel_p = self.model.out2mel(mel_p) o = self.mel_disc(mel_g, self.disc_cond) p, pc = o['y'], o['y_c'] o_ = self.mel_disc(mel_p, self.disc_cond) p_, pc_ = o_['y'], o_['y_c'] if p_ is not None: log_outputs["r"] = self.mse_loss_fn(p, p.new_ones(p.size())) log_outputs["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size())) if pc_ is not None: log_outputs["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size())) log_outputs["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size())) if len(log_outputs) == 0: return None total_loss = sum([loss_weights.get(k, 1) * v for k, v in log_outputs.items()]) log_outputs['bs'] = sample['mels'].shape[0] return total_loss, log_outputs def configure_optimizers(self): if not hasattr(self, 'gen_params'): self.gen_params = list(self.model.parameters()) optimizer_gen = torch.optim.AdamW( self.gen_params, lr=hparams['lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) optimizer_disc = torch.optim.AdamW( self.disc_params, lr=hparams['disc_lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), **hparams["discriminator_optimizer_params"]) if len(self.disc_params) > 0 else None self.scheduler = self.build_scheduler({'gen': optimizer_gen, 'disc': optimizer_disc}) return [optimizer_gen, optimizer_disc] def build_scheduler(self, optimizer): return { "gen": super().build_scheduler(optimizer['gen']), "disc": torch.optim.lr_scheduler.StepLR( optimizer=optimizer["disc"], **hparams["discriminator_scheduler_params"]) if optimizer["disc"] is not None else None, } def on_before_optimization(self, opt_idx): if opt_idx == 0: nn.utils.clip_grad_norm_(self.gen_params, hparams['generator_grad_norm']) else: nn.utils.clip_grad_norm_(self.disc_params, hparams["discriminator_grad_norm"]) def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx): if optimizer_idx == 0: self.scheduler['gen'].step(self.global_step) else: self.scheduler['disc'].step(max(self.global_step - hparams["disc_start_steps"], 1)) ================================================ FILE: NeuralSeq/tasks/tts/fs2_utils.py ================================================ import matplotlib matplotlib.use('Agg') import glob import importlib from utils.cwt import get_lf0_cwt import os import torch.optim import torch.utils.data from utils.indexed_datasets import IndexedDataset from utils.pitch_utils import norm_interp_f0 import numpy as np from tasks.base_task import BaseDataset import torch import torch.optim import torch.utils.data import utils import torch.distributions from utils.hparams import hparams class FastSpeechDataset(BaseDataset): def __init__(self, prefix, shuffle=False): super().__init__(shuffle) self.data_dir = hparams['binary_data_dir'] self.prefix = prefix self.hparams = hparams self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') self.indexed_ds = None # self.name2spk_id={} # pitch stats f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy' if os.path.exists(f0_stats_fn): hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn) hparams['f0_mean'] = float(hparams['f0_mean']) hparams['f0_std'] = float(hparams['f0_std']) else: hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None if prefix == 'test': if hparams['test_input_dir'] != '': self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir']) else: if hparams['num_test_samples'] > 0: self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids'] self.sizes = [self.sizes[i] for i in self.avail_idxs] if hparams['pitch_type'] == 'cwt': _, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10)) def _get_item(self, index): if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: index = self.avail_idxs[index] if self.indexed_ds is None: self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') return self.indexed_ds[index] def __getitem__(self, index): hparams = self.hparams item = self._get_item(index) max_frames = hparams['max_frames'] spec = torch.Tensor(item['mel'])[:max_frames] energy = (spec.exp() ** 2).sum(-1).sqrt() mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) pitch = torch.LongTensor(item.get("pitch"))[:max_frames] # print(item.keys(), item['mel'].shape, spec.shape) sample = { "id": index, "item_name": item['item_name'], "text": item['txt'], "txt_token": phone, "mel": spec, "pitch": pitch, "energy": energy, "f0": f0, "uv": uv, "mel2ph": mel2ph, "mel_nonpadding": spec.abs().sum(-1) > 0, } if self.hparams['use_spk_embed']: sample["spk_embed"] = torch.Tensor(item['spk_embed']) if self.hparams['use_spk_id']: sample["spk_id"] = item['spk_id'] # sample['spk_id'] = 0 # for key in self.name2spk_id.keys(): # if key in item['item_name']: # sample['spk_id'] = self.name2spk_id[key] # break if self.hparams['pitch_type'] == 'cwt': cwt_spec = torch.Tensor(item['cwt_spec'])[:max_frames] f0_mean = item.get('f0_mean', item.get('cwt_mean')) f0_std = item.get('f0_std', item.get('cwt_std')) sample.update({"cwt_spec": cwt_spec, "f0_mean": f0_mean, "f0_std": f0_std}) elif self.hparams['pitch_type'] == 'ph': f0_phlevel_sum = torch.zeros_like(phone).float().scatter_add(0, mel2ph - 1, f0) f0_phlevel_num = torch.zeros_like(phone).float().scatter_add( 0, mel2ph - 1, torch.ones_like(f0)).clamp_min(1) sample["f0_ph"] = f0_phlevel_sum / f0_phlevel_num return sample def collater(self, samples): if len(samples) == 0: return {} id = torch.LongTensor([s['id'] for s in samples]) item_names = [s['item_name'] for s in samples] text = [s['text'] for s in samples] txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0) f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) pitch = utils.collate_1d([s['pitch'] for s in samples]) uv = utils.collate_1d([s['uv'] for s in samples]) energy = utils.collate_1d([s['energy'] for s in samples], 0.0) mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \ if samples[0]['mel2ph'] is not None else None mels = utils.collate_2d([s['mel'] for s in samples], 0.0) txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples]) mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) batch = { 'id': id, 'item_name': item_names, 'nsamples': len(samples), 'text': text, 'txt_tokens': txt_tokens, 'txt_lengths': txt_lengths, 'mels': mels, 'mel_lengths': mel_lengths, 'mel2ph': mel2ph, 'energy': energy, 'pitch': pitch, 'f0': f0, 'uv': uv, } if self.hparams['use_spk_embed']: spk_embed = torch.stack([s['spk_embed'] for s in samples]) batch['spk_embed'] = spk_embed if self.hparams['use_spk_id']: spk_ids = torch.LongTensor([s['spk_id'] for s in samples]) batch['spk_ids'] = spk_ids if self.hparams['pitch_type'] == 'cwt': cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples]) f0_mean = torch.Tensor([s['f0_mean'] for s in samples]) f0_std = torch.Tensor([s['f0_std'] for s in samples]) batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std}) elif self.hparams['pitch_type'] == 'ph': batch['f0'] = utils.collate_1d([s['f0_ph'] for s in samples]) return batch def load_test_inputs(self, test_input_dir, spk_id=0): inp_wav_paths = glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3') sizes = [] items = [] binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') pkg = ".".join(binarizer_cls.split(".")[:-1]) cls_name = binarizer_cls.split(".")[-1] binarizer_cls = getattr(importlib.import_module(pkg), cls_name) binarization_args = hparams['binarization_args'] for wav_fn in inp_wav_paths: item_name = os.path.basename(wav_fn) ph = txt = tg_fn = '' wav_fn = wav_fn encoder = None item = binarizer_cls.process_item(item_name, ph, txt, tg_fn, wav_fn, spk_id, encoder, binarization_args) items.append(item) sizes.append(item['len']) return items, sizes ================================================ FILE: NeuralSeq/tasks/tts/pe.py ================================================ import matplotlib matplotlib.use('Agg') import torch import numpy as np import os from tasks.base_task import BaseDataset from tasks.tts.fs2 import FastSpeech2Task from modules.fastspeech.pe import PitchExtractor import utils from utils.indexed_datasets import IndexedDataset from utils.hparams import hparams from utils.plot import f0_to_figure from utils.pitch_utils import norm_interp_f0, denorm_f0 class PeDataset(BaseDataset): def __init__(self, prefix, shuffle=False): super().__init__(shuffle) self.data_dir = hparams['binary_data_dir'] self.prefix = prefix self.hparams = hparams self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') self.indexed_ds = None # pitch stats f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy' if os.path.exists(f0_stats_fn): hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn) hparams['f0_mean'] = float(hparams['f0_mean']) hparams['f0_std'] = float(hparams['f0_std']) else: hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None if prefix == 'test': if hparams['num_test_samples'] > 0: self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids'] self.sizes = [self.sizes[i] for i in self.avail_idxs] def _get_item(self, index): if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: index = self.avail_idxs[index] if self.indexed_ds is None: self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') return self.indexed_ds[index] def __getitem__(self, index): hparams = self.hparams item = self._get_item(index) max_frames = hparams['max_frames'] spec = torch.Tensor(item['mel'])[:max_frames] # mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) pitch = torch.LongTensor(item.get("pitch"))[:max_frames] # print(item.keys(), item['mel'].shape, spec.shape) sample = { "id": index, "item_name": item['item_name'], "text": item['txt'], "mel": spec, "pitch": pitch, "f0": f0, "uv": uv, # "mel2ph": mel2ph, # "mel_nonpadding": spec.abs().sum(-1) > 0, } return sample def collater(self, samples): if len(samples) == 0: return {} id = torch.LongTensor([s['id'] for s in samples]) item_names = [s['item_name'] for s in samples] text = [s['text'] for s in samples] f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) pitch = utils.collate_1d([s['pitch'] for s in samples]) uv = utils.collate_1d([s['uv'] for s in samples]) mels = utils.collate_2d([s['mel'] for s in samples], 0.0) mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) # mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \ # if samples[0]['mel2ph'] is not None else None # mel_nonpaddings = utils.collate_1d([s['mel_nonpadding'].float() for s in samples], 0.0) batch = { 'id': id, 'item_name': item_names, 'nsamples': len(samples), 'text': text, 'mels': mels, 'mel_lengths': mel_lengths, 'pitch': pitch, # 'mel2ph': mel2ph, # 'mel_nonpaddings': mel_nonpaddings, 'f0': f0, 'uv': uv, } return batch class PitchExtractionTask(FastSpeech2Task): def __init__(self): super().__init__() self.dataset_cls = PeDataset def build_tts_model(self): self.model = PitchExtractor(conv_layers=hparams['pitch_extractor_conv_layers']) # def build_scheduler(self, optimizer): # return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5) def _training_step(self, sample, batch_idx, _): loss_output = self.run_model(self.model, sample) total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad]) loss_output['batch_size'] = sample['mels'].size()[0] return total_loss, loss_output def validation_step(self, sample, batch_idx): outputs = {} outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=True) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: self.plot_pitch(batch_idx, model_out, sample) return outputs def run_model(self, model, sample, return_output=False, infer=False): f0 = sample['f0'] uv = sample['uv'] output = model(sample['mels']) losses = {} self.add_pitch_loss(output, sample, losses) if not return_output: return losses else: return losses, output def plot_pitch(self, batch_idx, model_out, sample): gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) self.logger.experiment.add_figure( f'f0_{batch_idx}', f0_to_figure(gt_f0[0], None, model_out['f0_denorm_pred'][0]), self.global_step) def add_pitch_loss(self, output, sample, losses): # mel2ph = sample['mel2ph'] # [B, T_s] mel = sample['mels'] f0 = sample['f0'] uv = sample['uv'] # nonpadding = (mel2ph != 0).float() if hparams['pitch_type'] == 'frame' \ # else (sample['txt_tokens'] != 0).float() nonpadding = (mel.abs().sum(-1) > 0).float() # sample['mel_nonpaddings'] # print(nonpadding[0][-8:], nonpadding.shape) self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding) ================================================ FILE: NeuralSeq/tasks/tts/ps.py ================================================ import os import torch import torch.nn.functional as F from torch import nn from modules.portaspeech.portaspeech import PortaSpeech from tasks.tts.fs2 import FastSpeech2Task from utils.tts_utils import mel2token_to_dur from utils.hparams import hparams from utils.tts_utils import get_focus_rate, get_phone_coverage_rate, get_diagonal_focus_rate from utils import num_params import numpy as np from utils.plot import spec_to_figure from data_gen.tts.data_gen_utils import build_token_encoder class PortaSpeechTask(FastSpeech2Task): def __init__(self): super().__init__() data_dir = hparams['binary_data_dir'] self.word_encoder = build_token_encoder(f'{data_dir}/word_set.json') def build_tts_model(self): ph_dict_size = len(self.token_encoder) word_dict_size = len(self.word_encoder) self.model = PortaSpeech(ph_dict_size, word_dict_size, hparams) def on_train_start(self): super().on_train_start() for n, m in self.model.named_children(): num_params(m, model_name=n) if hasattr(self.model, 'fvae'): for n, m in self.model.fvae.named_children(): num_params(m, model_name=f'fvae.{n}') def run_model(self, sample, infer=False, *args, **kwargs): txt_tokens = sample['txt_tokens'] word_tokens = sample['word_tokens'] spk_embed = sample.get('spk_embed') spk_id = sample.get('spk_ids') if not infer: output = self.model(txt_tokens, word_tokens, ph2word=sample['ph2word'], mel2word=sample['mel2word'], mel2ph=sample['mel2ph'], word_len=sample['word_lengths'].max(), tgt_mels=sample['mels'], pitch=sample.get('pitch'), spk_embed=spk_embed, spk_id=spk_id, infer=False, global_step=self.global_step) losses = {} losses['kl_v'] = output['kl'].detach() losses_kl = output['kl'] losses_kl = torch.clamp(losses_kl, min=hparams['kl_min']) losses_kl = min(self.global_step / hparams['kl_start_steps'], 1) * losses_kl losses_kl = losses_kl * hparams['lambda_kl'] losses['kl'] = losses_kl self.add_mel_loss(output['mel_out'], sample['mels'], losses) if hparams['dur_level'] == 'word': self.add_dur_loss( output['dur'], sample['mel2word'], sample['word_lengths'], sample['txt_tokens'], losses) self.get_attn_stats(output['attn'], sample, losses) else: super(PortaSpeechTask, self).add_dur_loss(output['dur'], sample['mel2ph'], sample['txt_tokens'], losses) return losses, output else: use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur']) output = self.model( txt_tokens, word_tokens, ph2word=sample['ph2word'], word_len=sample['word_lengths'].max(), pitch=sample.get('pitch'), mel2ph=sample['mel2ph'] if use_gt_dur else None, mel2word=sample['mel2word'] if use_gt_dur else None, tgt_mels=sample['mels'], infer=True, spk_embed=spk_embed, spk_id=spk_id, ) return output def add_dur_loss(self, dur_pred, mel2token, word_len, txt_tokens, losses=None): T = word_len.max() dur_gt = mel2token_to_dur(mel2token, T).float() nonpadding = (torch.arange(T).to(dur_pred.device)[None, :] < word_len[:, None]).float() dur_pred = dur_pred * nonpadding dur_gt = dur_gt * nonpadding wdur = F.l1_loss((dur_pred + 1).log(), (dur_gt + 1).log(), reduction='none') wdur = (wdur * nonpadding).sum() / nonpadding.sum() if hparams['lambda_word_dur'] > 0: losses['wdur'] = wdur * hparams['lambda_word_dur'] if hparams['lambda_sent_dur'] > 0: sent_dur_p = dur_pred.sum(-1) sent_dur_g = dur_gt.sum(-1) sdur_loss = F.l1_loss(sent_dur_p, sent_dur_g, reduction='mean') losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] def validation_step(self, sample, batch_idx): return super().validation_step(sample, batch_idx) def save_valid_result(self, sample, batch_idx, model_out): super(PortaSpeechTask, self).save_valid_result(sample, batch_idx, model_out) if self.global_step > 0 and hparams['dur_level'] == 'word': self.logger.add_figure(f'attn_{batch_idx}', spec_to_figure(model_out['attn'][0]), self.global_step) def get_attn_stats(self, attn, sample, logging_outputs, prefix=''): # diagonal_focus_rate txt_lengths = sample['txt_lengths'].float() mel_lengths = sample['mel_lengths'].float() src_padding_mask = sample['txt_tokens'].eq(0) target_padding_mask = sample['mels'].abs().sum(-1).eq(0) src_seg_mask = sample['txt_tokens'].eq(self.seg_idx) attn_ks = txt_lengths.float() / mel_lengths.float() focus_rate = get_focus_rate(attn, src_padding_mask, target_padding_mask).mean().data phone_coverage_rate = get_phone_coverage_rate( attn, src_padding_mask, src_seg_mask, target_padding_mask).mean() diagonal_focus_rate, diag_mask = get_diagonal_focus_rate( attn, attn_ks, mel_lengths, src_padding_mask, target_padding_mask) logging_outputs[f'{prefix}fr'] = focus_rate.mean().data logging_outputs[f'{prefix}pcr'] = phone_coverage_rate.mean().data logging_outputs[f'{prefix}dfr'] = diagonal_focus_rate.mean().data def get_plot_dur_info(self, sample, model_out): if hparams['dur_level'] == 'word': T_txt = sample['word_lengths'].max() dur_gt = mel2token_to_dur(sample['mel2word'], T_txt)[0] dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt txt = sample['ph_words'][0].split(" ") else: T_txt = sample['txt_tokens'].shape[1] dur_gt = mel2token_to_dur(sample['mel2ph'], T_txt)[0] dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt txt = self.token_encoder.decode(sample['txt_tokens'][0].cpu().numpy()) txt = txt.split(" ") return {'dur_gt': dur_gt, 'dur_pred': dur_pred, 'txt': txt} def build_optimizer(self, model): self.optimizer = torch.optim.AdamW( self.model.parameters(), lr=hparams['lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) return self.optimizer def build_scheduler(self, optimizer): return FastSpeechTask.build_scheduler(self, optimizer) ############ # infer ############ def test_start(self): super().test_start() if hparams.get('save_attn', False): os.makedirs(f'{self.gen_dir}/attn', exist_ok=True) self.model.store_inverse_all() def test_step(self, sample, batch_idx): assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference' outputs = self.run_model(sample, infer=True) text = sample['text'][0] item_name = sample['item_name'][0] tokens = sample['txt_tokens'][0].cpu().numpy() mel_gt = sample['mels'][0].cpu().numpy() mel_pred = outputs['mel_out'][0].cpu().numpy() mel2ph = sample['mel2ph'][0].cpu().numpy() mel2ph_pred = None str_phs = self.token_encoder.decode(tokens, strip_padding=True) base_fn = f'[{batch_idx:06d}][{item_name.replace("%", "_")}][%s]' if text is not None: base_fn += text.replace(":", "$3A")[:80] base_fn = base_fn.replace(' ', '_') gen_dir = self.gen_dir wav_pred = self.vocoder.spec2wav(mel_pred) self.saving_result_pool.add_job(self.save_result, args=[ wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred]) if hparams['save_gt']: wav_gt = self.vocoder.spec2wav(mel_gt) self.saving_result_pool.add_job(self.save_result, args=[ wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph]) if hparams.get('save_attn', False): attn = outputs['attn'][0].cpu().numpy() np.save(f'{gen_dir}/attn/{item_name}.npy', attn) print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") return { 'item_name': item_name, 'text': text, 'ph_tokens': self.token_encoder.decode(tokens.tolist()), 'wav_fn_pred': base_fn % 'P', 'wav_fn_gt': base_fn % 'G', } ================================================ FILE: NeuralSeq/tasks/tts/ps_adv.py ================================================ import os import torch import torch.nn.functional as F import torch.nn as nn import numpy as np from modules.portaspeech.portaspeech import PortaSpeech from modules.syntaspeech.multi_window_disc import Discriminator from tasks.tts.fs2 import FastSpeech2Task from utils.hparams import hparams from utils.tts_utils import get_focus_rate, get_phone_coverage_rate, get_diagonal_focus_rate, mel2token_to_dur from utils import num_params, tensors_to_scalars from utils.pitch_utils import denorm_f0, norm_f0 from data_gen.tts.data_gen_utils import get_pitch from utils.dtw import dtw as DTW from utils.plot import spec_to_figure from utils.text.text_encoder import build_token_encoder class PortaSpeechAdvTask(FastSpeech2Task): def __init__(self): super().__init__() data_dir = hparams['binary_data_dir'] self.word_encoder = build_token_encoder(f'{data_dir}/word_set.json') self.build_disc_model() self.mse_loss_fn = torch.nn.MSELoss() def build_tts_model(self): ph_dict_size = len(self.token_encoder) word_dict_size = len(self.word_encoder) self.model = PortaSpeech(ph_dict_size, word_dict_size, hparams) self.gen_params = [p for p in self.model.parameters() if p.requires_grad] self.dp_params = [p for k, p in self.model.named_parameters() if (('dur_predictor' in k) and p.requires_grad)] self.gen_params_except_dp = [p for k, p in self.model.named_parameters() if (('dur_predictor' not in k) and p.requires_grad)] self.bert_params = [p for k, p in self.model.named_parameters() if (('bert' in k) and p.requires_grad)] self.gen_params_except_bert_and_dp = [p for k, p in self.model.named_parameters() if ('dur_predictor' not in k) and ('bert' not in k) and p.requires_grad ] self.use_bert = True if len(self.bert_params) > 0 else False def build_disc_model(self): disc_win_num = hparams['disc_win_num'] h = hparams['mel_disc_hidden_size'] self.mel_disc = Discriminator( time_lengths=[32, 64, 128][:disc_win_num], freq_length=80, hidden_size=h, kernel=(3, 3) ) self.disc_params = list(self.mel_disc.parameters()) def on_train_start(self): super().on_train_start() for n, m in self.model.named_children(): num_params(m, model_name=n) if hasattr(self.model, 'fvae'): for n, m in self.model.fvae.named_children(): num_params(m, model_name=f'fvae.{n}') def _training_step(self, sample, batch_idx, optimizer_idx): loss_output = {} loss_weights = {} disc_start = self.global_step >= hparams["disc_start_steps"] and hparams['lambda_mel_adv'] > 0 if optimizer_idx == 0: ####################### # Generator # ####################### loss_output, model_out = self.run_model(sample, infer=False) self.model_out_gt = self.model_out = \ {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)} if disc_start: mel_p = model_out['mel_out'] if hasattr(self.model, 'out2mel'): mel_p = self.model.out2mel(mel_p) o_ = self.mel_disc(mel_p) p_, pc_ = o_['y'], o_['y_c'] if p_ is not None: loss_output['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size())) loss_weights['a'] = hparams['lambda_mel_adv'] if pc_ is not None: loss_output['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size())) loss_weights['ac'] = hparams['lambda_mel_adv'] else: ####################### # Discriminator # ####################### if disc_start and self.global_step % hparams['disc_interval'] == 0: model_out = self.model_out_gt mel_g = sample['mels'] mel_p = model_out['mel_out'] o = self.mel_disc(mel_g) p, pc = o['y'], o['y_c'] o_ = self.mel_disc(mel_p) p_, pc_ = o_['y'], o_['y_c'] if p_ is not None: loss_output["r"] = self.mse_loss_fn(p, p.new_ones(p.size())) loss_output["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size())) if pc_ is not None: loss_output["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size())) loss_output["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size())) total_loss = sum([loss_weights.get(k, 1) * v for k, v in loss_output.items() if isinstance(v, torch.Tensor) and v.requires_grad]) loss_output['batch_size'] = sample['txt_tokens'].size()[0] return total_loss, loss_output def run_model(self, sample, infer=False, *args, **kwargs): txt_tokens = sample['txt_tokens'] word_tokens = sample['word_tokens'] spk_embed = sample.get('spk_embed') spk_id = sample.get('spk_ids') if not infer: output = self.model(txt_tokens, word_tokens, ph2word=sample['ph2word'], mel2word=sample['mel2word'], mel2ph=sample['mel2ph'], word_len=sample['word_lengths'].max(), tgt_mels=sample['mels'], pitch=sample.get('pitch'), spk_embed=spk_embed, spk_id=spk_id, infer=False, global_step=self.global_step, graph_lst=sample['graph_lst'], etypes_lst=sample['etypes_lst'], bert_feats=sample.get("bert_feats"), cl_feats=sample.get("cl_feats") ) losses = {} losses['kl_v'] = output['kl'].detach() losses_kl = output['kl'] losses_kl = torch.clamp(losses_kl, min=hparams['kl_min']) losses_kl = min(self.global_step / hparams['kl_start_steps'], 1) * losses_kl losses_kl = losses_kl * hparams['lambda_kl'] losses['kl'] = losses_kl self.add_mel_loss(output['mel_out'], sample['mels'], losses) if hparams['dur_level'] == 'word': self.add_dur_loss( output['dur'], sample['mel2word'], sample['word_lengths'], sample['txt_tokens'], losses) self.get_attn_stats(output['attn'], sample, losses) else: super(PortaSpeechAdvTask, self).add_dur_loss(output['dur'], sample['mel2ph'], sample['txt_tokens'], losses) return losses, output else: use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur']) output = self.model( txt_tokens, word_tokens, ph2word=sample['ph2word'], word_len=sample['word_lengths'].max(), pitch=sample.get('pitch'), mel2ph=sample['mel2ph'] if use_gt_dur else None, mel2word=sample['mel2word'] if use_gt_dur else None, tgt_mels=sample['mels'], infer=True, spk_embed=spk_embed, spk_id=spk_id, graph_lst=sample['graph_lst'], etypes_lst=sample['etypes_lst'], bert_feats=sample.get("bert_feats"), cl_feats=sample.get("cl_feats") ) return output def add_dur_loss(self, dur_pred, mel2token, word_len, txt_tokens, losses=None): T = word_len.max() dur_gt = mel2token_to_dur(mel2token, T).float() nonpadding = (torch.arange(T).to(dur_pred.device)[None, :] < word_len[:, None]).float() dur_pred = dur_pred * nonpadding dur_gt = dur_gt * nonpadding wdur = F.l1_loss((dur_pred + 1).log(), (dur_gt + 1).log(), reduction='none') wdur = (wdur * nonpadding).sum() / nonpadding.sum() if hparams['lambda_word_dur'] > 0: losses['wdur'] = wdur * hparams['lambda_word_dur'] if hparams['lambda_sent_dur'] > 0: sent_dur_p = dur_pred.sum(-1) sent_dur_g = dur_gt.sum(-1) sdur_loss = F.l1_loss(sent_dur_p, sent_dur_g, reduction='mean') losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] with torch.no_grad(): # calculate word-level abs_dur_error in micro-second abs_word_dur_error = F.l1_loss(dur_pred , dur_gt, reduction='none') abs_word_dur_error = (abs_word_dur_error * nonpadding).sum() / nonpadding.sum() abs_word_dur_error = abs_word_dur_error * hparams['hop_size'] / hparams['audio_sample_rate'] * 1000 losses['abs_word_dur_error'] = abs_word_dur_error # calculate word-level abs_dur_error in second sent_dur_p = dur_pred.sum(-1) sent_dur_g = dur_gt.sum(-1) abs_sent_dur_error = F.l1_loss(sent_dur_p, sent_dur_g, reduction='mean').mean() abs_sent_dur_error = abs_sent_dur_error * hparams['hop_size'] / hparams['audio_sample_rate'] losses['abs_sent_dur_error'] = abs_sent_dur_error def validation_step(self, sample, batch_idx): outputs = {} outputs['losses'] = {} outputs['losses'], model_out = self.run_model(sample) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = tensors_to_scalars(outputs) if self.global_step % hparams['valid_infer_interval'] == 0 \ and batch_idx < hparams['num_valid_plots']: valid_results = self.save_valid_result(sample, batch_idx, model_out) wav_gt = valid_results['wav_gt'] mel_gt = valid_results['mel_gt'] wav_pred = valid_results['wav_pred'] mel_pred = valid_results['mel_pred'] f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) manhattan_distance = lambda x, y: np.abs(x - y) dist, cost, acc, path = DTW(f0_pred_, f0_gt_, manhattan_distance) outputs['losses']['f0_dtw'] = dist / len(f0_gt_) return outputs def save_valid_result(self, sample, batch_idx, model_out): sr = hparams['audio_sample_rate'] f0_gt = None mel_out = model_out['mel_out'] if sample.get('f0') is not None: f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu()) self.plot_mel(batch_idx, sample['mels'], mel_out, f0s=f0_gt) # if self.global_step > 0: wav_pred = self.vocoder.spec2wav(mel_out[0].cpu(), f0=f0_gt) self.logger.add_audio(f'wav_val_{batch_idx}', wav_pred, self.global_step, sr) # with gt duration model_out = self.run_model(sample, infer=True, infer_use_gt_dur=True) dur_info = self.get_plot_dur_info(sample, model_out) del dur_info['dur_pred'] wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt) self.logger.add_audio(f'wav_gdur_{batch_idx}', wav_pred, self.global_step, sr) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_gdur_{batch_idx}', dur_info=dur_info, f0s=f0_gt) # with pred duration if not hparams['use_gt_dur']: model_out = self.run_model(sample, infer=True, infer_use_gt_dur=False) dur_info = self.get_plot_dur_info(sample, model_out) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_pdur_{batch_idx}', dur_info=dur_info, f0s=f0_gt) wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt) self.logger.add_audio(f'wav_pdur_{batch_idx}', wav_pred, self.global_step, sr) # gt wav mel_gt = sample['mels'][0].cpu() wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) if self.global_step <= hparams['valid_infer_interval']: self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, sr) # add attn plot if self.global_step > 0 and hparams['dur_level'] == 'word': self.logger.add_figure(f'attn_{batch_idx}', spec_to_figure(model_out['attn'][0]), self.global_step) return {'wav_gt': wav_gt, 'wav_pred': wav_pred, 'mel_gt': mel_gt, 'mel_pred': model_out['mel_out'][0].cpu()} def get_attn_stats(self, attn, sample, logging_outputs, prefix=''): # diagonal_focus_rate txt_lengths = sample['txt_lengths'].float() mel_lengths = sample['mel_lengths'].float() src_padding_mask = sample['txt_tokens'].eq(0) target_padding_mask = sample['mels'].abs().sum(-1).eq(0) src_seg_mask = sample['txt_tokens'].eq(self.seg_idx) attn_ks = txt_lengths.float() / mel_lengths.float() focus_rate = get_focus_rate(attn, src_padding_mask, target_padding_mask).mean().data phone_coverage_rate = get_phone_coverage_rate( attn, src_padding_mask, src_seg_mask, target_padding_mask).mean() diagonal_focus_rate, diag_mask = get_diagonal_focus_rate( attn, attn_ks, mel_lengths, src_padding_mask, target_padding_mask) logging_outputs[f'{prefix}fr'] = focus_rate.mean().data logging_outputs[f'{prefix}pcr'] = phone_coverage_rate.mean().data logging_outputs[f'{prefix}dfr'] = diagonal_focus_rate.mean().data def get_plot_dur_info(self, sample, model_out): if hparams['dur_level'] == 'word': T_txt = sample['word_lengths'].max() dur_gt = mel2token_to_dur(sample['mel2word'], T_txt)[0] dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt txt = sample['ph_words'][0].split(" ") else: T_txt = sample['txt_tokens'].shape[1] dur_gt = mel2token_to_dur(sample['mel2ph'], T_txt)[0] dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt txt = self.token_encoder.decode(sample['txt_tokens'][0].cpu().numpy()) txt = txt.split(" ") return {'dur_gt': dur_gt, 'dur_pred': dur_pred, 'txt': txt} def build_optimizer(self, model): optimizer_gen = torch.optim.AdamW( self.gen_params, lr=hparams['lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) optimizer_disc = torch.optim.AdamW( self.disc_params, lr=hparams['disc_lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), **hparams["discriminator_optimizer_params"]) if len(self.disc_params) > 0 else None return [optimizer_gen, optimizer_disc] def build_scheduler(self, optimizer): return [ FastSpeechTask.build_scheduler(self, optimizer[0]), # Generator Scheduler torch.optim.lr_scheduler.StepLR(optimizer=optimizer[1], # Discriminator Scheduler **hparams["discriminator_scheduler_params"]), ] def on_before_optimization(self, opt_idx): if opt_idx == 0: nn.utils.clip_grad_norm_(self.dp_params, hparams['clip_grad_norm']) if self.use_bert: nn.utils.clip_grad_norm_(self.bert_params, hparams['clip_grad_norm']) nn.utils.clip_grad_norm_(self.gen_params_except_bert_and_dp, hparams['clip_grad_norm']) else: nn.utils.clip_grad_norm_(self.gen_params_except_dp, hparams['clip_grad_norm']) else: nn.utils.clip_grad_norm_(self.disc_params, hparams["clip_grad_norm"]) def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx): if self.scheduler is not None: self.scheduler[0].step(self.global_step // hparams['accumulate_grad_batches']) self.scheduler[1].step(self.global_step // hparams['accumulate_grad_batches']) ############ # infer ############ def test_start(self): super().test_start() if hparams.get('save_attn', False): os.makedirs(f'{self.gen_dir}/attn', exist_ok=True) self.model.store_inverse_all() def test_step(self, sample, batch_idx): assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference' outputs = self.run_model(sample, infer=True) text = sample['text'][0] item_name = sample['item_name'][0] tokens = sample['txt_tokens'][0].cpu().numpy() mel_gt = sample['mels'][0].cpu().numpy() mel_pred = outputs['mel_out'][0].cpu().numpy() mel2ph = sample['mel2ph'][0].cpu().numpy() mel2ph_pred = None str_phs = self.token_encoder.decode(tokens, strip_padding=True) base_fn = f'[{batch_idx:06d}][{item_name.replace("%", "_")}][%s]' if text is not None: base_fn += text.replace(":", "$3A")[:80] base_fn = base_fn.replace(' ', '_') gen_dir = self.gen_dir wav_pred = self.vocoder.spec2wav(mel_pred) self.saving_result_pool.add_job(self.save_result, args=[ wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred]) if hparams['save_gt']: wav_gt = self.vocoder.spec2wav(mel_gt) self.saving_result_pool.add_job(self.save_result, args=[ wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph]) if hparams.get('save_attn', False): attn = outputs['attn'][0].cpu().numpy() np.save(f'{gen_dir}/attn/{item_name}.npy', attn) # save f0 for pitch dtw f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) np.save(f'{gen_dir}/f0/{item_name}.npy', f0_pred_) np.save(f'{gen_dir}/f0/{item_name}_gt.npy', f0_gt_) print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") return { 'item_name': item_name, 'text': text, 'ph_tokens': self.token_encoder.decode(tokens.tolist()), 'wav_fn_pred': base_fn % 'P', 'wav_fn_gt': base_fn % 'G', } ================================================ FILE: NeuralSeq/tasks/tts/ps_flow.py ================================================ import torch from modules.portaspeech.portaspeech_flow import PortaSpeechFlow from tasks.tts.fs2 import FastSpeech2Task from tasks.tts.ps import PortaSpeechTask from utils.pitch_utils import denorm_f0 from utils.hparams import hparams class PortaSpeechFlowTask(PortaSpeechTask): def __init__(self): super().__init__() self.training_post_glow = False def build_tts_model(self): ph_dict_size = len(self.token_encoder) word_dict_size = len(self.word_encoder) self.model = PortaSpeechFlow(ph_dict_size, word_dict_size, hparams) def _training_step(self, sample, batch_idx, opt_idx): self.training_post_glow = self.global_step >= hparams['post_glow_training_start'] \ and hparams['use_post_flow'] if hparams['two_stage'] and \ ((opt_idx == 0 and self.training_post_glow) or (opt_idx == 1 and not self.training_post_glow)): return None loss_output, _ = self.run_model(sample) total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad]) loss_output['batch_size'] = sample['txt_tokens'].size()[0] if 'postflow' in loss_output and loss_output['postflow'] is None: return None return total_loss, loss_output def run_model(self, sample, infer=False, *args, **kwargs): if not infer: training_post_glow = self.training_post_glow spk_embed = sample.get('spk_embed') spk_id = sample.get('spk_ids') output = self.model(sample['txt_tokens'], sample['word_tokens'], ph2word=sample['ph2word'], mel2word=sample['mel2word'], mel2ph=sample['mel2ph'], word_len=sample['word_lengths'].max(), tgt_mels=sample['mels'], pitch=sample.get('pitch'), spk_embed=spk_embed, spk_id=spk_id, infer=False, forward_post_glow=training_post_glow, two_stage=hparams['two_stage'], global_step=self.global_step, bert_feats=sample.get('bert_feats')) losses = {} self.add_mel_loss(output['mel_out'], sample['mels'], losses) if (training_post_glow or not hparams['two_stage']) and hparams['use_post_flow']: losses['postflow'] = output['postflow'] losses['l1'] = losses['l1'].detach() losses['ssim'] = losses['ssim'].detach() if not training_post_glow or not hparams['two_stage'] or not self.training: losses['kl'] = output['kl'] if self.global_step < hparams['kl_start_steps']: losses['kl'] = losses['kl'].detach() else: losses['kl'] = torch.clamp(losses['kl'], min=hparams['kl_min']) losses['kl'] = losses['kl'] * hparams['lambda_kl'] if hparams['dur_level'] == 'word': self.add_dur_loss( output['dur'], sample['mel2word'], sample['word_lengths'], sample['txt_tokens'], losses) self.get_attn_stats(output['attn'], sample, losses) else: super().add_dur_loss(output['dur'], sample['mel2ph'], sample['txt_tokens'], losses) return losses, output else: use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur']) forward_post_glow = self.global_step >= hparams['post_glow_training_start'] + 1000 \ and hparams['use_post_flow'] spk_embed = sample.get('spk_embed') spk_id = sample.get('spk_ids') output = self.model( sample['txt_tokens'], sample['word_tokens'], ph2word=sample['ph2word'], word_len=sample['word_lengths'].max(), pitch=sample.get('pitch'), mel2ph=sample['mel2ph'] if use_gt_dur else None, mel2word=sample['mel2word'] if hparams['profile_infer'] or hparams['use_gt_dur'] else None, infer=True, forward_post_glow=forward_post_glow, spk_embed=spk_embed, spk_id=spk_id, two_stage=hparams['two_stage'], bert_feats=sample.get('bert_feats')) return output def validation_step(self, sample, batch_idx): self.training_post_glow = self.global_step >= hparams['post_glow_training_start'] \ and hparams['use_post_flow'] return super().validation_step(sample, batch_idx) def save_valid_result(self, sample, batch_idx, model_out): super(PortaSpeechFlowTask, self).save_valid_result(sample, batch_idx, model_out) sr = hparams['audio_sample_rate'] f0_gt = None if sample.get('f0') is not None: f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu()) if self.global_step > 0: # save FVAE result if hparams['use_post_flow']: wav_pred = self.vocoder.spec2wav(model_out['mel_out_fvae'][0].cpu(), f0=f0_gt) self.logger.add_audio(f'wav_fvae_{batch_idx}', wav_pred, self.global_step, sr) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out_fvae'][0], f'mel_fvae_{batch_idx}', f0s=f0_gt) def build_optimizer(self, model): if hparams['two_stage'] and hparams['use_post_flow']: self.optimizer = torch.optim.AdamW( [p for name, p in self.model.named_parameters() if 'post_flow' not in name], lr=hparams['lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) self.post_flow_optimizer = torch.optim.AdamW( self.model.post_flow.parameters(), lr=hparams['post_flow_lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) return [self.optimizer, self.post_flow_optimizer] else: self.optimizer = torch.optim.AdamW( self.model.parameters(), lr=hparams['lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) return [self.optimizer] def build_scheduler(self, optimizer): return FastSpeech2Task.build_scheduler(self, optimizer[0]) ================================================ FILE: NeuralSeq/tasks/tts/synta.py ================================================ import os import torch import torch.nn.functional as F from torch import nn from modules.tts.syntaspeech.syntaspeech import SyntaSpeech from tasks.tts.ps_adv import PortaSpeechAdvTask from utils.hparams import hparams class SyntaSpeechTask(PortaSpeechAdvTask): def build_tts_model(self): ph_dict_size = len(self.token_encoder) word_dict_size = len(self.word_encoder) self.model = SyntaSpeech(ph_dict_size, word_dict_size, hparams) self.gen_params = [p for p in self.model.parameters() if p.requires_grad] self.dp_params = [p for k, p in self.model.named_parameters() if (('dur_predictor' in k) and p.requires_grad)] self.gen_params_except_dp = [p for k, p in self.model.named_parameters() if (('dur_predictor' not in k) and p.requires_grad)] self.bert_params = [p for k, p in self.model.named_parameters() if (('bert' in k) and p.requires_grad)] self.gen_params_except_bert_and_dp = [p for k, p in self.model.named_parameters() if ('dur_predictor' not in k) and ('bert' not in k) and p.requires_grad ] self.use_bert = True if len(self.bert_params) > 0 else False ================================================ FILE: NeuralSeq/tasks/tts/tts.py ================================================ from multiprocessing.pool import Pool import matplotlib from utils.pl_utils import data_loader from utils.training_utils import RSQRTSchedule from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder from modules.fastspeech.pe import PitchExtractor matplotlib.use('Agg') import os import numpy as np from tqdm import tqdm import torch.distributed as dist from tasks.base_task import BaseTask from utils.hparams import hparams from utils.text_encoder import TokenTextEncoder import json import torch import torch.optim import torch.utils.data import utils class TtsTask(BaseTask): def __init__(self, *args, **kwargs): self.vocoder = None self.phone_encoder = self.build_phone_encoder(hparams['binary_data_dir']) self.padding_idx = self.phone_encoder.pad() self.eos_idx = self.phone_encoder.eos() self.seg_idx = self.phone_encoder.seg() self.saving_result_pool = None self.saving_results_futures = None self.stats = {} super().__init__(*args, **kwargs) def build_scheduler(self, optimizer): return RSQRTSchedule(optimizer) def build_optimizer(self, model): self.optimizer = optimizer = torch.optim.AdamW( model.parameters(), lr=hparams['lr']) return optimizer def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None, required_batch_size_multiple=-1, endless=False, batch_by_size=True): devices_cnt = torch.cuda.device_count() if devices_cnt == 0: devices_cnt = 1 if required_batch_size_multiple == -1: required_batch_size_multiple = devices_cnt def shuffle_batches(batches): np.random.shuffle(batches) return batches if max_tokens is not None: max_tokens *= devices_cnt if max_sentences is not None: max_sentences *= devices_cnt indices = dataset.ordered_indices() if batch_by_size: batch_sampler = utils.batch_by_size( indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences, required_batch_size_multiple=required_batch_size_multiple, ) else: batch_sampler = [] for i in range(0, len(indices), max_sentences): batch_sampler.append(indices[i:i + max_sentences]) if shuffle: batches = shuffle_batches(list(batch_sampler)) if endless: batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))] else: batches = batch_sampler if endless: batches = [b for _ in range(1000) for b in batches] num_workers = dataset.num_workers if self.trainer.use_ddp: num_replicas = dist.get_world_size() rank = dist.get_rank() batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0] return torch.utils.data.DataLoader(dataset, collate_fn=dataset.collater, batch_sampler=batches, num_workers=num_workers, pin_memory=False) def build_phone_encoder(self, data_dir): phone_list_file = os.path.join(data_dir, 'phone_set.json') phone_list = json.load(open(phone_list_file)) return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') def build_optimizer(self, model): self.optimizer = optimizer = torch.optim.AdamW( model.parameters(), lr=hparams['lr']) return optimizer def test_start(self): self.saving_result_pool = Pool(8) self.saving_results_futures = [] self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() if hparams.get('pe_enable') is not None and hparams['pe_enable']: self.pe = PitchExtractor().cuda() utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True) self.pe.eval() def test_end(self, outputs): self.saving_result_pool.close() [f.get() for f in tqdm(self.saving_results_futures)] self.saving_result_pool.join() return {} ########## # utils ########## def weights_nonzero_speech(self, target): # target : B x T x mel # Assign weight 1.0 to all labels except for padding (id=0). dim = target.size(-1) return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim) if __name__ == '__main__': TtsTask.start() ================================================ FILE: NeuralSeq/tasks/tts/tts_base.py ================================================ import filecmp import matplotlib from utils.plot import spec_to_figure matplotlib.use('Agg') from data_gen.tts.data_gen_utils import get_pitch from modules.fastspeech.tts_modules import mel2ph_to_dur from tasks.tts.dataset_utils import BaseTTSDataset from utils.tts_utils import sequence_mask from multiprocessing.pool import Pool from tasks.base_task import data_loader, BaseConcatDataset from utils.common_schedulers import RSQRTSchedule, NoneSchedule from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder import os import numpy as np from tqdm import tqdm import torch.distributed as dist from tasks.base_task import BaseTask from utils.hparams import hparams from utils.text_encoder import TokenTextEncoder import json import matplotlib.pyplot as plt import torch import torch.optim import torch.utils.data import utils from utils import audio import pandas as pd class TTSBaseTask(BaseTask): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.dataset_cls = BaseTTSDataset self.max_tokens = hparams['max_tokens'] self.max_sentences = hparams['max_sentences'] self.max_valid_tokens = hparams['max_valid_tokens'] if self.max_valid_tokens == -1: hparams['max_valid_tokens'] = self.max_valid_tokens = self.max_tokens self.max_valid_sentences = hparams['max_valid_sentences'] if self.max_valid_sentences == -1: hparams['max_valid_sentences'] = self.max_valid_sentences = self.max_sentences self.vocoder = None self.phone_encoder = self.build_phone_encoder(hparams['binary_data_dir']) self.padding_idx = self.phone_encoder.pad() self.eos_idx = self.phone_encoder.eos() self.seg_idx = self.phone_encoder.seg() self.saving_result_pool = None self.saving_results_futures = None self.stats = {} @data_loader def train_dataloader(self): if hparams['train_sets'] != '': train_sets = hparams['train_sets'].split("|") # check if all train_sets have the same spk map and dictionary binary_data_dir = hparams['binary_data_dir'] file_to_cmp = ['phone_set.json'] if os.path.exists(f'{binary_data_dir}/word_set.json'): file_to_cmp.append('word_set.json') if hparams['use_spk_id']: file_to_cmp.append('spk_map.json') for f in file_to_cmp: for ds_name in train_sets: base_file = os.path.join(binary_data_dir, f) ds_file = os.path.join(ds_name, f) assert filecmp.cmp(base_file, ds_file), \ f'{f} in {ds_name} is not same with that in {binary_data_dir}.' train_dataset = BaseConcatDataset([ self.dataset_cls(prefix='train', shuffle=True, data_dir=ds_name) for ds_name in train_sets]) else: train_dataset = self.dataset_cls(prefix=hparams['train_set_name'], shuffle=True) return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences, endless=hparams['endless_ds']) @data_loader def val_dataloader(self): valid_dataset = self.dataset_cls(prefix=hparams['valid_set_name'], shuffle=False) return self.build_dataloader(valid_dataset, False, self.max_valid_tokens, self.max_valid_sentences) @data_loader def test_dataloader(self): test_dataset = self.dataset_cls(prefix=hparams['test_set_name'], shuffle=False) self.test_dl = self.build_dataloader( test_dataset, False, self.max_valid_tokens, self.max_valid_sentences, batch_by_size=False) return self.test_dl def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None, required_batch_size_multiple=-1, endless=False, batch_by_size=True): devices_cnt = torch.cuda.device_count() if devices_cnt == 0: devices_cnt = 1 if required_batch_size_multiple == -1: required_batch_size_multiple = devices_cnt def shuffle_batches(batches): np.random.shuffle(batches) return batches if max_tokens is not None: max_tokens *= devices_cnt if max_sentences is not None: max_sentences *= devices_cnt indices = dataset.ordered_indices() if batch_by_size: batch_sampler = utils.batch_by_size( indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences, required_batch_size_multiple=required_batch_size_multiple, ) else: batch_sampler = [] for i in range(0, len(indices), max_sentences): batch_sampler.append(indices[i:i + max_sentences]) if shuffle: batches = shuffle_batches(list(batch_sampler)) if endless: batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))] else: batches = batch_sampler if endless: batches = [b for _ in range(1000) for b in batches] num_workers = dataset.num_workers if self.trainer.use_ddp: num_replicas = dist.get_world_size() rank = dist.get_rank() batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0] return torch.utils.data.DataLoader(dataset, collate_fn=dataset.collater, batch_sampler=batches, num_workers=num_workers, pin_memory=False) def build_phone_encoder(self, data_dir): phone_list_file = os.path.join(data_dir, 'phone_set.json') phone_list = json.load(open(phone_list_file)) return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') def build_scheduler(self, optimizer): if hparams['scheduler'] == 'rsqrt': return RSQRTSchedule(optimizer) else: return NoneSchedule(optimizer) def build_optimizer(self, model): self.optimizer = optimizer = torch.optim.AdamW( model.parameters(), lr=hparams['lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) return optimizer def plot_mel(self, batch_idx, spec, spec_out, name=None): spec_cat = torch.cat([spec, spec_out], -1) name = f'mel_{batch_idx}' if name is None else name vmin = hparams['mel_vmin'] vmax = hparams['mel_vmax'] self.logger.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step) def test_start(self): self.saving_result_pool = Pool(min(int(os.getenv('N_PROC', os.cpu_count())), 16)) self.saving_results_futures = [] self.results_id = 0 self.gen_dir = os.path.join( hparams['work_dir'], f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}') self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() def after_infer(self, predictions, sil_start_frame=0): predictions = utils.unpack_dict_to_list(predictions) assert len(predictions) == 1, 'Only support batch_size=1 in inference.' prediction = predictions[0] prediction = utils.tensors_to_np(prediction) item_name = prediction.get('item_name') text = prediction.get('text') ph_tokens = prediction.get('txt_tokens') mel_gt = prediction["mels"] mel2ph_gt = prediction.get("mel2ph") mel2ph_gt = mel2ph_gt if mel2ph_gt is not None else None mel_pred = prediction["outputs"] mel2ph_pred = prediction.get("mel2ph_pred") f0_gt = prediction.get("f0") f0_pred = prediction.get("f0_pred") str_phs = None if self.phone_encoder is not None and 'txt_tokens' in prediction: str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True) if 'encdec_attn' in prediction: encdec_attn = prediction['encdec_attn'] encdec_attn = encdec_attn[encdec_attn.max(-1).sum(-1).argmax(-1)] txt_lengths = prediction.get('txt_lengths') encdec_attn = encdec_attn.T[:txt_lengths, :len(mel_gt)] else: encdec_attn = None wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred) wav_pred[:sil_start_frame * hparams['hop_size']] = 0 gen_dir = self.gen_dir base_fn = f'[{self.results_id:06d}][{item_name}][%s]' # if text is not None: # base_fn += text.replace(":", "%3A")[:80] base_fn = base_fn.replace(' ', '_') if not hparams['profile_infer']: os.makedirs(gen_dir, exist_ok=True) os.makedirs(f'{gen_dir}/wavs', exist_ok=True) os.makedirs(f'{gen_dir}/plot', exist_ok=True) if hparams.get('save_mel_npy', False): os.makedirs(f'{gen_dir}/npy', exist_ok=True) if 'encdec_attn' in prediction: os.makedirs(f'{gen_dir}/attn_plot', exist_ok=True) self.saving_results_futures.append( self.saving_result_pool.apply_async(self.save_result, args=[ wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred, encdec_attn])) if mel_gt is not None and hparams['save_gt']: wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) self.saving_results_futures.append( self.saving_result_pool.apply_async(self.save_result, args=[ wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph_gt])) if hparams['save_f0']: import matplotlib.pyplot as plt f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) fig = plt.figure() plt.plot(f0_pred_, label=r'$\hat{f_0}$') plt.plot(f0_gt_, label=r'$f_0$') plt.legend() plt.tight_layout() plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png') plt.close(fig) print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") self.results_id += 1 return { 'item_name': item_name, 'text': text, 'ph_tokens': self.phone_encoder.decode(ph_tokens.tolist()), 'wav_fn_pred': base_fn % 'P', 'wav_fn_gt': base_fn % 'G', } @staticmethod def save_result(wav_out, mel, base_fn, gen_dir, str_phs=None, mel2ph=None, alignment=None): audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'], norm=hparams['out_wav_norm']) fig = plt.figure(figsize=(14, 10)) spec_vmin = hparams['mel_vmin'] spec_vmax = hparams['mel_vmax'] heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax) fig.colorbar(heatmap) f0, _ = get_pitch(wav_out, mel, hparams) f0 = f0 / 10 * (f0 > 0) plt.plot(f0, c='white', linewidth=1, alpha=0.6) if mel2ph is not None and str_phs is not None: decoded_txt = str_phs.split(" ") dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy() dur = [0] + list(np.cumsum(dur)) for i in range(len(dur) - 1): shift = (i % 20) + 1 plt.text(dur[i], shift, decoded_txt[i]) plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black') plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black', alpha=1, linewidth=1) plt.tight_layout() plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png') plt.close(fig) if hparams.get('save_mel_npy', False): np.save(f'{gen_dir}/npy/{base_fn}', mel) if alignment is not None: fig, ax = plt.subplots(figsize=(12, 16)) im = ax.imshow(alignment, aspect='auto', origin='lower', interpolation='none') decoded_txt = str_phs.split(" ") ax.set_yticks(np.arange(len(decoded_txt))) ax.set_yticklabels(list(decoded_txt), fontsize=6) fig.colorbar(im, ax=ax) fig.savefig(f'{gen_dir}/attn_plot/{base_fn}_attn.png', format='png') plt.close(fig) def test_end(self, outputs): pd.DataFrame(outputs).to_csv(f'{self.gen_dir}/meta.csv') self.saving_result_pool.close() [f.get() for f in tqdm(self.saving_results_futures)] self.saving_result_pool.join() return {} ########## # utils ########## def weights_nonzero_speech(self, target): # target : B x T x mel # Assign weight 1.0 to all labels except for padding (id=0). dim = target.size(-1) return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim) def make_stop_target(self, target): # target : B x T x mel seq_mask = target.abs().sum(-1).ne(0).float() seq_length = seq_mask.sum(1) mask_r = 1 - sequence_mask(seq_length - 1, target.size(1)).float() return seq_mask, mask_r ================================================ FILE: NeuralSeq/tasks/tts/tts_utils.py ================================================ import importlib from data_gen.tts.base_binarizer import BaseBinarizer from data_gen.tts.base_preprocess import BasePreprocessor from data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls from utils.hparams import hparams def parse_dataset_configs(): max_tokens = hparams['max_tokens'] max_sentences = hparams['max_sentences'] max_valid_tokens = hparams['max_valid_tokens'] if max_valid_tokens == -1: hparams['max_valid_tokens'] = max_valid_tokens = max_tokens max_valid_sentences = hparams['max_valid_sentences'] if max_valid_sentences == -1: hparams['max_valid_sentences'] = max_valid_sentences = max_sentences return max_tokens, max_sentences, max_valid_tokens, max_valid_sentences def parse_mel_losses(): mel_losses = hparams['mel_losses'].split("|") loss_and_lambda = {} for i, l in enumerate(mel_losses): if l == '': continue if ':' in l: l, lbd = l.split(":") lbd = float(lbd) else: lbd = 1.0 loss_and_lambda[l] = lbd print("| Mel losses:", loss_and_lambda) return loss_and_lambda def load_data_preprocessor(): preprocess_cls = hparams["preprocess_cls"] pkg = ".".join(preprocess_cls.split(".")[:-1]) cls_name = preprocess_cls.split(".")[-1] preprocessor: BasePreprocessor = getattr(importlib.import_module(pkg), cls_name)() preprocess_args = {} preprocess_args.update(hparams['preprocess_args']) return preprocessor, preprocess_args def load_data_binarizer(): binarizer_cls = hparams['binarizer_cls'] pkg = ".".join(binarizer_cls.split(".")[:-1]) cls_name = binarizer_cls.split(".")[-1] binarizer: BaseBinarizer = getattr(importlib.import_module(pkg), cls_name)() binarization_args = {} binarization_args.update(hparams['binarization_args']) return binarizer, binarization_args ================================================ FILE: NeuralSeq/tasks/vocoder/dataset_utils.py ================================================ import glob import importlib import os from resemblyzer import VoiceEncoder import numpy as np import torch import torch.distributed as dist from torch.utils.data import DistributedSampler import utils from tasks.base_task import BaseDataset from utils.hparams import hparams from utils.indexed_datasets import IndexedDataset from tqdm import tqdm class EndlessDistributedSampler(DistributedSampler): def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True): if num_replicas is None: if not dist.is_available(): raise RuntimeError("Requires distributed package to be available") num_replicas = dist.get_world_size() if rank is None: if not dist.is_available(): raise RuntimeError("Requires distributed package to be available") rank = dist.get_rank() self.dataset = dataset self.num_replicas = num_replicas self.rank = rank self.epoch = 0 self.shuffle = shuffle g = torch.Generator() g.manual_seed(self.epoch) if self.shuffle: indices = [i for _ in range(1000) for i in torch.randperm( len(self.dataset), generator=g).tolist()] else: indices = [i for _ in range(1000) for i in list(range(len(self.dataset)))] indices = indices[:len(indices) // self.num_replicas * self.num_replicas] indices = indices[self.rank::self.num_replicas] self.indices = indices def __iter__(self): return iter(self.indices) def __len__(self): return len(self.indices) class VocoderDataset(BaseDataset): def __init__(self, prefix, shuffle=False): super().__init__(shuffle) self.hparams = hparams self.prefix = prefix self.data_dir = hparams['binary_data_dir'] self.is_infer = prefix == 'test' self.batch_max_frames = 0 if self.is_infer else hparams['max_samples'] // hparams['hop_size'] self.aux_context_window = hparams['aux_context_window'] self.hop_size = hparams['hop_size'] if self.is_infer and hparams['test_input_dir'] != '': self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir']) self.avail_idxs = [i for i, _ in enumerate(self.sizes)] elif self.is_infer and hparams['test_mel_dir'] != '': self.indexed_ds, self.sizes = self.load_mel_inputs(hparams['test_mel_dir']) self.avail_idxs = [i for i, _ in enumerate(self.sizes)] else: self.indexed_ds = None self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') self.avail_idxs = [idx for idx, s in enumerate(self.sizes) if s - 2 * self.aux_context_window > self.batch_max_frames] print(f"| {len(self.sizes) - len(self.avail_idxs)} short items are skipped in {prefix} set.") self.sizes = [s for idx, s in enumerate(self.sizes) if s - 2 * self.aux_context_window > self.batch_max_frames] def _get_item(self, index): if self.indexed_ds is None: self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') item = self.indexed_ds[index] return item def __getitem__(self, index): index = self.avail_idxs[index] item = self._get_item(index) sample = { "id": index, "item_name": item['item_name'], "mel": torch.FloatTensor(item['mel']), "wav": torch.FloatTensor(item['wav'].astype(np.float32)), } if 'pitch' in item: sample['pitch'] = torch.LongTensor(item['pitch']) sample['f0'] = torch.FloatTensor(item['f0']) if hparams.get('use_spk_embed', False): sample["spk_embed"] = torch.Tensor(item['spk_embed']) if hparams.get('use_emo_embed', False): sample["emo_embed"] = torch.Tensor(item['emo_embed']) return sample def collater(self, batch): if len(batch) == 0: return {} y_batch, c_batch, p_batch, f0_batch = [], [], [], [] item_name = [] have_pitch = 'pitch' in batch[0] for idx in range(len(batch)): item_name.append(batch[idx]['item_name']) x, c = batch[idx]['wav'] if self.hparams['use_wav'] else None, batch[idx]['mel'].squeeze(0) if have_pitch: p = batch[idx]['pitch'] f0 = batch[idx]['f0'] if self.hparams['use_wav']:self._assert_ready_for_upsampling(x, c, self.hop_size, 0) if len(c) - 2 * self.aux_context_window > self.batch_max_frames: # randomly pickup with the batch_max_steps length of the part batch_max_frames = self.batch_max_frames if self.batch_max_frames != 0 else len( c) - 2 * self.aux_context_window - 1 batch_max_steps = batch_max_frames * self.hop_size interval_start = self.aux_context_window interval_end = len(c) - batch_max_frames - self.aux_context_window start_frame = np.random.randint(interval_start, interval_end) start_step = start_frame * self.hop_size if self.hparams['use_wav']:y = x[start_step: start_step + batch_max_steps] c = c[start_frame - self.aux_context_window: start_frame + self.aux_context_window + batch_max_frames] if have_pitch: p = p[start_frame - self.aux_context_window: start_frame + self.aux_context_window + batch_max_frames] f0 = f0[start_frame - self.aux_context_window: start_frame + self.aux_context_window + batch_max_frames] if self.hparams['use_wav']:self._assert_ready_for_upsampling(y, c, self.hop_size, self.aux_context_window) else: print(f"Removed short sample from batch (length={len(x)}).") continue if self.hparams['use_wav']:y_batch += [y.reshape(-1, 1)] # [(T, 1), (T, 1), ...] c_batch += [c] # [(T' C), (T' C), ...] if have_pitch: p_batch += [p] # [(T' C), (T' C), ...] f0_batch += [f0] # [(T' C), (T' C), ...] # convert each batch to tensor, asuume that each item in batch has the same length if self.hparams['use_wav']:y_batch = utils.collate_2d(y_batch, 0).transpose(2, 1) # (B, 1, T) c_batch = utils.collate_2d(c_batch, 0).transpose(2, 1) # (B, C, T') if have_pitch: p_batch = utils.collate_1d(p_batch, 0) # (B, T') f0_batch = utils.collate_1d(f0_batch, 0) # (B, T') else: p_batch, f0_batch = None, None # make input noise signal batch tensor if self.hparams['use_wav']: z_batch = torch.randn(y_batch.size()) # (B, 1, T) else: z_batch=[] return { 'z': z_batch, 'mels': c_batch, 'wavs': y_batch, 'pitches': p_batch, 'f0': f0_batch, 'item_name': item_name } @staticmethod def _assert_ready_for_upsampling(x, c, hop_size, context_window): """Assert the audio and feature lengths are correctly adjusted for upsamping.""" assert len(x) == (len(c) - 2 * context_window) * hop_size def load_test_inputs(self, test_input_dir, spk_id=0): inp_wav_paths = sorted(glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/**/*.mp3')) sizes = [] items = [] binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizer.BaseBinarizer') pkg = ".".join(binarizer_cls.split(".")[:-1]) cls_name = binarizer_cls.split(".")[-1] binarizer_cls = getattr(importlib.import_module(pkg), cls_name) binarization_args = hparams['binarization_args'] for wav_fn in inp_wav_paths: item_name = wav_fn[len(test_input_dir) + 1:].replace("/", "_") item = binarizer_cls.process_item( item_name, wav_fn, binarization_args) items.append(item) sizes.append(item['len']) return items, sizes def load_mel_inputs(self, test_input_dir, spk_id=0): inp_mel_paths = sorted(glob.glob(f'{test_input_dir}/*.npy')) sizes = [] items = [] binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizer.BaseBinarizer') pkg = ".".join(binarizer_cls.split(".")[:-1]) cls_name = binarizer_cls.split(".")[-1] binarizer_cls = getattr(importlib.import_module(pkg), cls_name) binarization_args = hparams['binarization_args'] for mel in inp_mel_paths: mel_input = np.load(mel) mel_input = torch.FloatTensor(mel_input) item_name = mel[len(test_input_dir) + 1:].replace("/", "_") item = binarizer_cls.process_mel_item(item_name, mel_input, None, binarization_args) items.append(item) sizes.append(item['len']) return items, sizes ================================================ FILE: NeuralSeq/tasks/vocoder/vocoder_base.py ================================================ import os import torch import torch.distributed as dist from torch.utils.data import DistributedSampler from tasks.base_task import BaseTask from tasks.base_task import data_loader from tasks.vocoder.dataset_utils import VocoderDataset, EndlessDistributedSampler from utils.hparams import hparams class VocoderBaseTask(BaseTask): def __init__(self): super(VocoderBaseTask, self).__init__() self.max_sentences = hparams['max_sentences'] self.max_valid_sentences = hparams['max_valid_sentences'] if self.max_valid_sentences == -1: hparams['max_valid_sentences'] = self.max_valid_sentences = self.max_sentences self.dataset_cls = VocoderDataset @data_loader def train_dataloader(self): train_dataset = self.dataset_cls('train', shuffle=True) return self.build_dataloader(train_dataset, True, self.max_sentences, hparams['endless_ds']) @data_loader def val_dataloader(self): valid_dataset = self.dataset_cls('valid', shuffle=False) return self.build_dataloader(valid_dataset, False, self.max_valid_sentences) @data_loader def test_dataloader(self): test_dataset = self.dataset_cls('test', shuffle=False) return self.build_dataloader(test_dataset, False, self.max_valid_sentences) def build_dataloader(self, dataset, shuffle, max_sentences, endless=False): world_size = 1 rank = 0 if dist.is_initialized(): world_size = dist.get_world_size() rank = dist.get_rank() sampler_cls = DistributedSampler if not endless else EndlessDistributedSampler train_sampler = sampler_cls( dataset=dataset, num_replicas=world_size, rank=rank, shuffle=shuffle, ) return torch.utils.data.DataLoader( dataset=dataset, shuffle=False, collate_fn=dataset.collater, batch_size=max_sentences, num_workers=dataset.num_workers, sampler=train_sampler, pin_memory=True, ) def test_start(self): self.gen_dir = os.path.join(hparams['work_dir'], f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}') os.makedirs(self.gen_dir, exist_ok=True) def test_end(self, outputs): return {} ================================================ FILE: NeuralSeq/utils/__init__.py ================================================ import glob import logging import re import time from collections import defaultdict import os import sys import shutil import types import numpy as np import torch import torch.nn.functional as F import torch.distributed as dist from torch import nn def tensors_to_scalars(metrics): new_metrics = {} for k, v in metrics.items(): if isinstance(v, torch.Tensor): v = v.item() if type(v) is dict: v = tensors_to_scalars(v) new_metrics[k] = v return new_metrics class AvgrageMeter(object): def __init__(self): self.reset() def reset(self): self.avg = 0 self.sum = 0 self.cnt = 0 def update(self, val, n=1): self.sum += val * n self.cnt += n self.avg = self.sum / self.cnt def collate_1d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None, shift_id=1): """Convert a list of 1d tensors into a padded 2d tensor.""" size = max(v.size(0) for v in values) if max_len is None else max_len res = values[0].new(len(values), size).fill_(pad_idx) def copy_tensor(src, dst): assert dst.numel() == src.numel() if shift_right: dst[1:] = src[:-1] dst[0] = shift_id else: dst.copy_(src) for i, v in enumerate(values): copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)]) return res def collate_2d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None): """Convert a list of 2d tensors into a padded 3d tensor.""" size = max(v.size(0) for v in values) if max_len is None else max_len res = values[0].new(len(values), size, values[0].shape[1]).fill_(pad_idx) def copy_tensor(src, dst): assert dst.numel() == src.numel() if shift_right: dst[1:] = src[:-1] else: dst.copy_(src) for i, v in enumerate(values): copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)]) return res def _is_batch_full(batch, num_tokens, max_tokens, max_sentences): if len(batch) == 0: return 0 if len(batch) == max_sentences: return 1 if num_tokens > max_tokens: return 1 return 0 def batch_by_size( indices, num_tokens_fn, max_tokens=None, max_sentences=None, required_batch_size_multiple=1, distributed=False ): """ Yield mini-batches of indices bucketed by size. Batches may contain sequences of different lengths. Args: indices (List[int]): ordered list of dataset indices num_tokens_fn (callable): function that returns the number of tokens at a given index max_tokens (int, optional): max number of tokens in each batch (default: None). max_sentences (int, optional): max number of sentences in each batch (default: None). required_batch_size_multiple (int, optional): require batch size to be a multiple of N (default: 1). """ max_tokens = max_tokens if max_tokens is not None else sys.maxsize max_sentences = max_sentences if max_sentences is not None else sys.maxsize bsz_mult = required_batch_size_multiple if isinstance(indices, types.GeneratorType): indices = np.fromiter(indices, dtype=np.int64, count=-1) sample_len = 0 sample_lens = [] batch = [] batches = [] for i in range(len(indices)): idx = indices[i] num_tokens = num_tokens_fn(idx) sample_lens.append(num_tokens) sample_len = max(sample_len, num_tokens) assert sample_len <= max_tokens, ( "sentence at index {} of size {} exceeds max_tokens " "limit of {}!".format(idx, sample_len, max_tokens) ) num_tokens = (len(batch) + 1) * sample_len if _is_batch_full(batch, num_tokens, max_tokens, max_sentences): mod_len = max( bsz_mult * (len(batch) // bsz_mult), len(batch) % bsz_mult, ) batches.append(batch[:mod_len]) batch = batch[mod_len:] sample_lens = sample_lens[mod_len:] sample_len = max(sample_lens) if len(sample_lens) > 0 else 0 batch.append(idx) if len(batch) > 0: batches.append(batch) return batches def make_positions(tensor, padding_idx): """Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. """ # The series of casts and type-conversions here are carefully # balanced to both work with ONNX export and XLA. In particular XLA # prefers ints, cumsum defaults to output longs, and ONNX doesn't know # how to handle the dtype kwarg in cumsum. mask = tensor.ne(padding_idx).int() return ( torch.cumsum(mask, dim=1).type_as(mask) * mask ).long() + padding_idx def softmax(x, dim): return F.softmax(x, dim=dim, dtype=torch.float32) def unpack_dict_to_list(samples): samples_ = [] bsz = samples.get('outputs').size(0) for i in range(bsz): res = {} for k, v in samples.items(): try: res[k] = v[i] except: pass samples_.append(res) return samples_ def load_ckpt(cur_model, ckpt_base_dir, prefix_in_ckpt='model', force=True, strict=True): if os.path.isfile(ckpt_base_dir): base_dir = os.path.dirname(ckpt_base_dir) checkpoint_path = [ckpt_base_dir] else: base_dir = ckpt_base_dir checkpoint_path = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0])) if len(checkpoint_path) > 0: checkpoint_path = checkpoint_path[-1] state_dict = torch.load(checkpoint_path, map_location="cpu")["state_dict"] state_dict = {k[len(prefix_in_ckpt) + 1:]: v for k, v in state_dict.items() if k.startswith(f'{prefix_in_ckpt}.')} if not strict: cur_model_state_dict = cur_model.state_dict() unmatched_keys = [] for key, param in state_dict.items(): if key in cur_model_state_dict: new_param = cur_model_state_dict[key] if new_param.shape != param.shape: unmatched_keys.append(key) print("| Unmatched keys: ", key, new_param.shape, param.shape) for key in unmatched_keys: del state_dict[key] cur_model.load_state_dict(state_dict, strict=strict) print(f"| load '{prefix_in_ckpt}' from '{checkpoint_path}'.") else: e_msg = f"| ckpt not found in {base_dir}." if force: assert False, e_msg else: print(e_msg) def remove_padding(x, padding_idx=0): if x is None: return None assert len(x.shape) in [1, 2] if len(x.shape) == 2: # [T, H] return x[np.abs(x).sum(-1) != padding_idx] elif len(x.shape) == 1: # [T] return x[x != padding_idx] class Timer: timer_map = {} def __init__(self, name, print_time=False): if name not in Timer.timer_map: Timer.timer_map[name] = 0 self.name = name self.print_time = print_time def __enter__(self): self.t = time.time() def __exit__(self, exc_type, exc_val, exc_tb): Timer.timer_map[self.name] += time.time() - self.t if self.print_time: print(self.name, Timer.timer_map[self.name]) def print_arch(model, model_name='model'): print(f"| {model_name} Arch: ", model) num_params(model, model_name=model_name) def num_params(model, print_out=True, model_name="model"): parameters = filter(lambda p: p.requires_grad, model.parameters()) parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 if print_out: print(f'| {model_name} Trainable Parameters: %.3fM' % parameters) return parameters ================================================ FILE: NeuralSeq/utils/audio.py ================================================ import subprocess import matplotlib import os matplotlib.use('Agg') import librosa import librosa.filters import numpy as np from scipy import signal from scipy.io import wavfile def save_wav(wav, path, sr, norm=False): if norm: wav = wav / np.abs(wav).max() wav *= 32767 # proposed by @dsmiller wavfile.write(path, sr, wav.astype(np.int16)) def get_hop_size(hparams): hop_size = hparams['hop_size'] if hop_size is None: assert hparams['frame_shift_ms'] is not None hop_size = int(hparams['frame_shift_ms'] / 1000 * hparams['audio_sample_rate']) return hop_size ########################################################################################### def _stft(y, hparams): return librosa.stft(y=y, n_fft=hparams['fft_size'], hop_length=get_hop_size(hparams), win_length=hparams['win_size'], pad_mode='constant') def _istft(y, hparams): return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams['win_size']) def librosa_pad_lr(x, fsize, fshift, pad_sides=1): '''compute right padding (final frame) or both sides padding (first and final frames) ''' assert pad_sides in (1, 2) # return int(fsize // 2) pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0] if pad_sides == 1: return 0, pad else: return pad // 2, pad // 2 + pad % 2 # Conversions def amp_to_db(x): return 20 * np.log10(np.maximum(1e-5, x)) def normalize(S, hparams): return (S - hparams['min_level_db']) / -hparams['min_level_db'] def denormalize(D, hparams): return (D * -hparams['min_level_db']) + hparams['min_level_db'] def rnnoise(filename, out_fn=None, verbose=False, out_sample_rate=22050): assert os.path.exists('./rnnoise/examples/rnnoise_demo'), INSTALL_STR if out_fn is None: out_fn = f"{filename[:-4]}.denoised.wav" out_48k_fn = f"{out_fn}.48000.wav" tmp0_fn = f"{out_fn}.0.wav" tmp1_fn = f"{out_fn}.1.wav" tmp2_fn = f"{out_fn}.2.raw" tmp3_fn = f"{out_fn}.3.raw" if verbose: print("Pre-processing audio...") # wav to pcm raw subprocess.check_call( f'sox "{filename}" -G -r48000 "{tmp0_fn}"', shell=True, stdin=subprocess.PIPE) # convert to raw subprocess.check_call( f'sox -v 0.95 "{tmp0_fn}" "{tmp1_fn}"', shell=True, stdin=subprocess.PIPE) # convert to raw subprocess.check_call( f'ffmpeg -y -i "{tmp1_fn}" -loglevel quiet -f s16le -ac 1 -ar 48000 "{tmp2_fn}"', shell=True, stdin=subprocess.PIPE) # convert to raw if verbose: print("Applying rnnoise algorithm to audio...") # rnnoise subprocess.check_call( f'./rnnoise/examples/rnnoise_demo "{tmp2_fn}" "{tmp3_fn}"', shell=True) if verbose: print("Post-processing audio...") # pcm raw to wav if filename == out_fn: subprocess.check_call(f'rm -f "{out_fn}"', shell=True) subprocess.check_call( f'sox -t raw -r 48000 -b 16 -e signed-integer -c 1 "{tmp3_fn}" "{out_48k_fn}"', shell=True) subprocess.check_call(f'sox "{out_48k_fn}" -G -r{out_sample_rate} "{out_fn}"', shell=True) subprocess.check_call(f'rm -f "{tmp0_fn}" "{tmp1_fn}" "{tmp2_fn}" "{tmp3_fn}" "{out_48k_fn}"', shell=True) if verbose: print("Audio-filtering completed!") ================================================ FILE: NeuralSeq/utils/ckpt_utils.py ================================================ import glob import logging import os import re import torch def get_last_checkpoint(work_dir, steps=None): checkpoint = None last_ckpt_path = None ckpt_paths = get_all_ckpts(work_dir, steps) if len(ckpt_paths) > 0: last_ckpt_path = ckpt_paths[0] checkpoint = torch.load(last_ckpt_path, map_location='cpu') logging.info(f'load module from checkpoint: {last_ckpt_path}') return checkpoint, last_ckpt_path def get_all_ckpts(work_dir, steps=None): if steps is None: ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_*.ckpt' else: ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_{steps}.ckpt' return sorted(glob.glob(ckpt_path_pattern), key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0])) def load_ckpt(cur_model, ckpt_base_dir, model_name='model', force=True, strict=True): if os.path.isfile(ckpt_base_dir): base_dir = os.path.dirname(ckpt_base_dir) ckpt_path = ckpt_base_dir checkpoint = torch.load(ckpt_base_dir, map_location='cpu') else: base_dir = ckpt_base_dir checkpoint, ckpt_path = get_last_checkpoint(ckpt_base_dir) if checkpoint is not None: state_dict = checkpoint["state_dict"] if len([k for k in state_dict.keys() if '.' in k]) > 0: state_dict = {k[len(model_name) + 1:]: v for k, v in state_dict.items() if k.startswith(f'{model_name}.')} else: if '.' not in model_name: state_dict = state_dict[model_name] else: base_model_name = model_name.split('.')[0] rest_model_name = model_name[len(base_model_name) + 1:] state_dict = { k[len(rest_model_name) + 1:]: v for k, v in state_dict[base_model_name].items() if k.startswith(f'{rest_model_name}.')} if not strict: cur_model_state_dict = cur_model.state_dict() unmatched_keys = [] for key, param in state_dict.items(): if key in cur_model_state_dict: new_param = cur_model_state_dict[key] if new_param.shape != param.shape: unmatched_keys.append(key) print("| Unmatched keys: ", key, new_param.shape, param.shape) for key in unmatched_keys: del state_dict[key] cur_model.load_state_dict(state_dict, strict=strict) print(f"| load '{model_name}' from '{ckpt_path}'.") else: e_msg = f"| ckpt not found in {base_dir}." if force: assert False, e_msg else: print(e_msg) ================================================ FILE: NeuralSeq/utils/cwt.py ================================================ import librosa import numpy as np from pycwt import wavelet from scipy.interpolate import interp1d def load_wav(wav_file, sr): wav, _ = librosa.load(wav_file, sr=sr, mono=True) return wav def convert_continuos_f0(f0): '''CONVERT F0 TO CONTINUOUS F0 Args: f0 (ndarray): original f0 sequence with the shape (T) Return: (ndarray): continuous f0 with the shape (T) ''' # get uv information as binary f0 = np.copy(f0) uv = np.float32(f0 != 0) # get start and end of f0 if (f0 == 0).all(): print("| all of the f0 values are 0.") return uv, f0 start_f0 = f0[f0 != 0][0] end_f0 = f0[f0 != 0][-1] # padding start and end of f0 sequence start_idx = np.where(f0 == start_f0)[0][0] end_idx = np.where(f0 == end_f0)[0][-1] f0[:start_idx] = start_f0 f0[end_idx:] = end_f0 # get non-zero frame index nz_frames = np.where(f0 != 0)[0] # perform linear interpolation f = interp1d(nz_frames, f0[nz_frames]) cont_f0 = f(np.arange(0, f0.shape[0])) return uv, cont_f0 def get_cont_lf0(f0, frame_period=5.0): uv, cont_f0_lpf = convert_continuos_f0(f0) # cont_f0_lpf = low_pass_filter(cont_f0_lpf, int(1.0 / (frame_period * 0.001)), cutoff=20) cont_lf0_lpf = np.log(cont_f0_lpf) return uv, cont_lf0_lpf def get_lf0_cwt(lf0): ''' input: signal of shape (N) output: Wavelet_lf0 of shape(10, N), scales of shape(10) ''' mother = wavelet.MexicanHat() dt = 0.005 dj = 1 s0 = dt * 2 J = 9 Wavelet_lf0, scales, _, _, _, _ = wavelet.cwt(np.squeeze(lf0), dt, dj, s0, J, mother) # Wavelet.shape => (J + 1, len(lf0)) Wavelet_lf0 = np.real(Wavelet_lf0).T return Wavelet_lf0, scales def norm_scale(Wavelet_lf0): Wavelet_lf0_norm = np.zeros((Wavelet_lf0.shape[0], Wavelet_lf0.shape[1])) mean = Wavelet_lf0.mean(0)[None, :] std = Wavelet_lf0.std(0)[None, :] Wavelet_lf0_norm = (Wavelet_lf0 - mean) / std return Wavelet_lf0_norm, mean, std def normalize_cwt_lf0(f0, mean, std): uv, cont_lf0_lpf = get_cont_lf0(f0) cont_lf0_norm = (cont_lf0_lpf - mean) / std Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_norm) Wavelet_lf0_norm, _, _ = norm_scale(Wavelet_lf0) return Wavelet_lf0_norm def get_lf0_cwt_norm(f0s, mean, std): uvs = list() cont_lf0_lpfs = list() cont_lf0_lpf_norms = list() Wavelet_lf0s = list() Wavelet_lf0s_norm = list() scaless = list() means = list() stds = list() for f0 in f0s: uv, cont_lf0_lpf = get_cont_lf0(f0) cont_lf0_lpf_norm = (cont_lf0_lpf - mean) / std Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm) # [560,10] Wavelet_lf0_norm, mean_scale, std_scale = norm_scale(Wavelet_lf0) # [560,10],[1,10],[1,10] Wavelet_lf0s_norm.append(Wavelet_lf0_norm) uvs.append(uv) cont_lf0_lpfs.append(cont_lf0_lpf) cont_lf0_lpf_norms.append(cont_lf0_lpf_norm) Wavelet_lf0s.append(Wavelet_lf0) scaless.append(scales) means.append(mean_scale) stds.append(std_scale) return Wavelet_lf0s_norm, scaless, means, stds def inverse_cwt_torch(Wavelet_lf0, scales): import torch b = ((torch.arange(0, len(scales)).float().to(Wavelet_lf0.device)[None, None, :] + 1 + 2.5) ** (-2.5)) lf0_rec = Wavelet_lf0 * b lf0_rec_sum = lf0_rec.sum(-1) lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdim=True)) / lf0_rec_sum.std(-1, keepdim=True) return lf0_rec_sum def inverse_cwt(Wavelet_lf0, scales): b = ((np.arange(0, len(scales))[None, None, :] + 1 + 2.5) ** (-2.5)) lf0_rec = Wavelet_lf0 * b lf0_rec_sum = lf0_rec.sum(-1) lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdims=True)) / lf0_rec_sum.std(-1, keepdims=True) return lf0_rec_sum def cwt2f0(cwt_spec, mean, std, cwt_scales): assert len(mean.shape) == 1 and len(std.shape) == 1 and len(cwt_spec.shape) == 3 import torch if isinstance(cwt_spec, torch.Tensor): f0 = inverse_cwt_torch(cwt_spec, cwt_scales) f0 = f0 * std[:, None] + mean[:, None] f0 = f0.exp() # [B, T] else: f0 = inverse_cwt(cwt_spec, cwt_scales) f0 = f0 * std[:, None] + mean[:, None] f0 = np.exp(f0) # [B, T] return f0 ================================================ FILE: NeuralSeq/utils/dtw.py ================================================ from numpy import array, zeros, full, argmin, inf, ndim from scipy.spatial.distance import cdist from math import isinf def dtw(x, y, dist, warp=1, w=inf, s=1.0): """ Computes Dynamic Time Warping (DTW) of two sequences. :param array x: N1*M array :param array y: N2*M array :param func dist: distance used as cost measure :param int warp: how many shifts are computed. :param int w: window size limiting the maximal distance between indices of matched entries |i,j|. :param float s: weight applied on off-diagonal moves of the path. As s gets larger, the warping path is increasingly biased towards the diagonal Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path. """ assert len(x) assert len(y) assert isinf(w) or (w >= abs(len(x) - len(y))) assert s > 0 r, c = len(x), len(y) if not isinf(w): D0 = full((r + 1, c + 1), inf) for i in range(1, r + 1): D0[i, max(1, i - w):min(c + 1, i + w + 1)] = 0 D0[0, 0] = 0 else: D0 = zeros((r + 1, c + 1)) D0[0, 1:] = inf D0[1:, 0] = inf D1 = D0[1:, 1:] # view for i in range(r): for j in range(c): if (isinf(w) or (max(0, i - w) <= j <= min(c, i + w))): D1[i, j] = dist(x[i], y[j]) C = D1.copy() jrange = range(c) for i in range(r): if not isinf(w): jrange = range(max(0, i - w), min(c, i + w + 1)) for j in jrange: min_list = [D0[i, j]] for k in range(1, warp + 1): i_k = min(i + k, r) j_k = min(j + k, c) min_list += [D0[i_k, j] * s, D0[i, j_k] * s] D1[i, j] += min(min_list) if len(x) == 1: path = zeros(len(y)), range(len(y)) elif len(y) == 1: path = range(len(x)), zeros(len(x)) else: path = _traceback(D0) return D1[-1, -1], C, D1, path def accelerated_dtw(x, y, dist, warp=1): """ Computes Dynamic Time Warping (DTW) of two sequences in a faster way. Instead of iterating through each element and calculating each distance, this uses the cdist function from scipy (https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html) :param array x: N1*M array :param array y: N2*M array :param string or func dist: distance parameter for cdist. When string is given, cdist uses optimized functions for the distance metrics. If a string is passed, the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'. :param int warp: how many shifts are computed. Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path. """ assert len(x) assert len(y) if ndim(x) == 1: x = x.reshape(-1, 1) if ndim(y) == 1: y = y.reshape(-1, 1) r, c = len(x), len(y) D0 = zeros((r + 1, c + 1)) D0[0, 1:] = inf D0[1:, 0] = inf D1 = D0[1:, 1:] D0[1:, 1:] = cdist(x, y, dist) C = D1.copy() for i in range(r): for j in range(c): min_list = [D0[i, j]] for k in range(1, warp + 1): min_list += [D0[min(i + k, r), j], D0[i, min(j + k, c)]] D1[i, j] += min(min_list) if len(x) == 1: path = zeros(len(y)), range(len(y)) elif len(y) == 1: path = range(len(x)), zeros(len(x)) else: path = _traceback(D0) return D1[-1, -1], C, D1, path def _traceback(D): i, j = array(D.shape) - 2 p, q = [i], [j] while (i > 0) or (j > 0): tb = argmin((D[i, j], D[i, j + 1], D[i + 1, j])) if tb == 0: i -= 1 j -= 1 elif tb == 1: i -= 1 else: # (tb == 2): j -= 1 p.insert(0, i) q.insert(0, j) return array(p), array(q) if __name__ == '__main__': w = inf s = 1.0 if 1: # 1-D numeric from sklearn.metrics.pairwise import manhattan_distances import numpy as np x = [0, 0, 1, 1, 2, 4, 2, 1, 2, 0] x = np.array(x).reshape([-1,1,1]) y = [1, 1, 1, 2, 2, 2, 2, 3, 2, 0] y = np.array(y).reshape([-1,1,1]) dist_fun = manhattan_distances w = 1 # s = 1.2 elif 0: # 2-D numeric from sklearn.metrics.pairwise import euclidean_distances x = [[0, 0], [0, 1], [1, 1], [1, 2], [2, 2], [4, 3], [2, 3], [1, 1], [2, 2], [0, 1]] y = [[1, 0], [1, 1], [1, 1], [2, 1], [4, 3], [4, 3], [2, 3], [3, 1], [1, 2], [1, 0]] dist_fun = euclidean_distances else: # 1-D list of strings from nltk.metrics.distance import edit_distance # x = ['we', 'shelled', 'clams', 'for', 'the', 'chowder'] # y = ['class', 'too'] x = ['i', 'soon', 'found', 'myself', 'muttering', 'to', 'the', 'walls'] y = ['see', 'drown', 'himself'] # x = 'we talked about the situation'.split() # y = 'we talked about the situation'.split() dist_fun = edit_distance dist, cost, acc, path = dtw(x, y, dist_fun, w=w, s=s) # Vizualize from matplotlib import pyplot as plt plt.imshow(cost.T, origin='lower', cmap=plt.cm.Reds, interpolation='nearest') plt.plot(path[0], path[1], '-o') # relation plt.xticks(range(len(x)), x) plt.yticks(range(len(y)), y) plt.xlabel('x') plt.ylabel('y') plt.axis('tight') if isinf(w): plt.title('Minimum distance: {}, slope weight: {}'.format(dist, s)) else: plt.title('Minimum distance: {}, window widht: {}, slope weight: {}'.format(dist, w, s)) plt.show() ================================================ FILE: NeuralSeq/utils/hparams.py ================================================ import argparse import os import yaml global_print_hparams = True hparams = {} class Args: def __init__(self, **kwargs): for k, v in kwargs.items(): self.__setattr__(k, v) def override_config(old_config: dict, new_config: dict): for k, v in new_config.items(): if isinstance(v, dict) and k in old_config: override_config(old_config[k], new_config[k]) else: old_config[k] = v def set_hparams(config='', exp_name='', hparams_str='', print_hparams=True, global_hparams=True): if config == '' and exp_name == '': parser = argparse.ArgumentParser(description='') parser.add_argument('--config', type=str, default='', help='location of the data corpus') parser.add_argument('--exp_name', type=str, default='', help='exp_name') parser.add_argument('-hp', '--hparams', type=str, default='', help='location of the data corpus') parser.add_argument('--infer', action='store_true', help='infer') parser.add_argument('--validate', action='store_true', help='validate') parser.add_argument('--reset', action='store_true', help='reset hparams') parser.add_argument('--remove', action='store_true', help='remove old ckpt') parser.add_argument('--debug', action='store_true', help='debug') args, unknown = parser.parse_known_args() print("| Unknow hparams: ", unknown) else: args = Args(config=config, exp_name=exp_name, hparams=hparams_str, infer=False, validate=False, reset=False, debug=False, remove=False) global hparams assert args.config != '' or args.exp_name != '' if args.config != '': assert os.path.exists(args.config) config_chains = [] loaded_config = set() def load_config(config_fn): # deep first inheritance and avoid the second visit of one node if not os.path.exists(config_fn): return {} with open(config_fn) as f: hparams_ = yaml.safe_load(f) loaded_config.add(config_fn) if 'base_config' in hparams_: ret_hparams = {} if not isinstance(hparams_['base_config'], list): hparams_['base_config'] = [hparams_['base_config']] for c in hparams_['base_config']: if c.startswith('.'): c = f'{os.path.dirname(config_fn)}/{c}' c = os.path.normpath(c) if c not in loaded_config: override_config(ret_hparams, load_config(c)) override_config(ret_hparams, hparams_) else: ret_hparams = hparams_ config_chains.append(config_fn) return ret_hparams saved_hparams = {} args_work_dir = '' if args.exp_name != '': args_work_dir = f'{args.exp_name}' # modified ckpt_config_path = f'{args_work_dir}/config.yaml' if os.path.exists(ckpt_config_path): with open(ckpt_config_path) as f: saved_hparams_ = yaml.safe_load(f) if saved_hparams_ is not None: saved_hparams.update(saved_hparams_) hparams_ = {} if args.config != '': hparams_.update(load_config(args.config)) if not args.reset: hparams_.update(saved_hparams) hparams_['work_dir'] = args_work_dir # Support config overriding in command line. Support list type config overriding. # Examples: --hparams="a=1,b.c=2,d=[1 1 1]" if args.hparams != "": for new_hparam in args.hparams.split(","): k, v = new_hparam.split("=") v = v.strip("\'\" ") config_node = hparams_ for k_ in k.split(".")[:-1]: config_node = config_node[k_] k = k.split(".")[-1] if v in ['True', 'False'] or type(config_node[k]) in [bool, list, dict]: if type(config_node[k]) == list: v = v.replace(" ", ",") config_node[k] = eval(v) else: config_node[k] = type(config_node[k])(v) if args_work_dir != '' and args.remove: answer = input("REMOVE old checkpoint? Y/N [Default: N]: ") if answer.lower() == "y": remove_file(args_work_dir) if args_work_dir != '' and (not os.path.exists(ckpt_config_path) or args.reset) and not args.infer: os.makedirs(hparams_['work_dir'], exist_ok=True) with open(ckpt_config_path, 'w') as f: yaml.safe_dump(hparams_, f) hparams_['infer'] = args.infer hparams_['debug'] = args.debug hparams_['validate'] = args.validate hparams_['exp_name'] = args.exp_name global global_print_hparams if global_hparams: hparams.clear() hparams.update(hparams_) if print_hparams and global_print_hparams and global_hparams: print('| Hparams chains: ', config_chains) print('| Hparams: ') for i, (k, v) in enumerate(sorted(hparams_.items())): print(f"\033[;33;m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "") print("") global_print_hparams = False return hparams_ ================================================ FILE: NeuralSeq/utils/indexed_datasets.py ================================================ import pickle from copy import deepcopy import numpy as np class IndexedDataset: def __init__(self, path, num_cache=1): super().__init__() self.path = path self.data_file = None self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets'] self.data_file = open(f"{path}.data", 'rb', buffering=-1) self.cache = [] self.num_cache = num_cache def check_index(self, i): if i < 0 or i >= len(self.data_offsets) - 1: raise IndexError('index out of range') def __del__(self): if self.data_file: self.data_file.close() def __getitem__(self, i): self.check_index(i) if self.num_cache > 0: for c in self.cache: if c[0] == i: return c[1] self.data_file.seek(self.data_offsets[i]) b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i]) item = pickle.loads(b) if self.num_cache > 0: self.cache = [(i, deepcopy(item))] + self.cache[:-1] return item def __len__(self): return len(self.data_offsets) - 1 class IndexedDatasetBuilder: def __init__(self, path): self.path = path self.out_file = open(f"{path}.data", 'wb') self.byte_offsets = [0] def add_item(self, item): s = pickle.dumps(item) bytes = self.out_file.write(s) self.byte_offsets.append(self.byte_offsets[-1] + bytes) def finalize(self): self.out_file.close() np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets}) if __name__ == "__main__": import random from tqdm import tqdm ds_path = '/tmp/indexed_ds_example' size = 100 items = [{"a": np.random.normal(size=[10000, 10]), "b": np.random.normal(size=[10000, 10])} for i in range(size)] builder = IndexedDatasetBuilder(ds_path) for i in tqdm(range(size)): builder.add_item(items[i]) builder.finalize() ds = IndexedDataset(ds_path) for i in tqdm(range(10000)): idx = random.randint(0, size - 1) assert (ds[idx]['a'] == items[idx]['a']).all() ================================================ FILE: NeuralSeq/utils/multiprocess_utils.py ================================================ import os import traceback from multiprocessing import Queue, Process def chunked_worker(worker_id, map_func, args, results_queue=None, init_ctx_func=None): ctx = init_ctx_func(worker_id) if init_ctx_func is not None else None for job_idx, arg in args: try: if ctx is not None: res = map_func(*arg, ctx=ctx) else: res = map_func(*arg) results_queue.put((job_idx, res)) except: traceback.print_exc() results_queue.put((job_idx, None)) def chunked_multiprocess_run(map_func, args, num_workers=None, ordered=True, init_ctx_func=None, q_max_size=1000): args = zip(range(len(args)), args) args = list(args) n_jobs = len(args) if num_workers is None: num_workers = int(os.getenv('N_PROC', os.cpu_count())) results_queues = [] if ordered: for i in range(num_workers): results_queues.append(Queue(maxsize=q_max_size // num_workers)) else: results_queue = Queue(maxsize=q_max_size) for i in range(num_workers): results_queues.append(results_queue) workers = [] for i in range(num_workers): args_worker = args[i::num_workers] p = Process(target=chunked_worker, args=( i, map_func, args_worker, results_queues[i], init_ctx_func), daemon=True) workers.append(p) p.start() for n_finished in range(n_jobs): results_queue = results_queues[n_finished % num_workers] job_idx, res = results_queue.get() assert job_idx == n_finished or not ordered, (job_idx, n_finished) yield res for w in workers: w.join() w.close() def multiprocess_run_tqdm(map_func, args, num_workers=None, ordered=True, init_ctx_func=None, multithread=False, desc=None): for i, res in tqdm(enumerate( multiprocess_run(map_func, args, num_workers, ordered, init_ctx_func, multithread)), total=len(args), desc=desc): yield i, res ================================================ FILE: NeuralSeq/utils/os_utils.py ================================================ import os import subprocess def link_file(from_file, to_file): subprocess.check_call( f'ln -s "`realpath --relative-to="{os.path.dirname(to_file)}" "{from_file}"`" "{to_file}"', shell=True) def move_file(from_file, to_file): subprocess.check_call(f'mv "{from_file}" "{to_file}"', shell=True) def copy_file(from_file, to_file): subprocess.check_call(f'cp -r "{from_file}" "{to_file}"', shell=True) def remove_file(*fns): for f in fns: subprocess.check_call(f'rm -rf "{f}"', shell=True) ================================================ FILE: NeuralSeq/utils/pitch_utils.py ================================================ ######### # world ########## import librosa import numpy as np import torch gamma = 0 mcepInput = 3 # 0 for dB, 3 for magnitude alpha = 0.45 en_floor = 10 ** (-80 / 20) FFT_SIZE = 2048 f0_bin = 256 f0_max = 1100.0 f0_min = 50.0 f0_mel_min = 1127 * np.log(1 + f0_min / 700) f0_mel_max = 1127 * np.log(1 + f0_max / 700) def f0_to_coarse(f0): is_torch = isinstance(f0, torch.Tensor) f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700) f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1 f0_mel[f0_mel <= 1] = 1 f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1 f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int) assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min()) return f0_coarse def norm_f0(f0, uv, hparams): is_torch = isinstance(f0, torch.Tensor) if hparams['pitch_norm'] == 'standard': f0 = (f0 - hparams['f0_mean']) / hparams['f0_std'] if hparams['pitch_norm'] == 'log': f0 = torch.log2(f0) if is_torch else np.log2(f0) if uv is not None and hparams['use_uv']: f0[uv > 0] = 0 return f0 def norm_interp_f0(f0, hparams): is_torch = isinstance(f0, torch.Tensor) if is_torch: device = f0.device f0 = f0.data.cpu().numpy() uv = f0 == 0 f0 = norm_f0(f0, uv, hparams) if sum(uv) == len(f0): f0[uv] = 0 elif sum(uv) > 0: f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) uv = torch.FloatTensor(uv) f0 = torch.FloatTensor(f0) if is_torch: f0 = f0.to(device) return f0, uv def denorm_f0(f0, uv, hparams, pitch_padding=None, min=None, max=None): if hparams['pitch_norm'] == 'standard': f0 = f0 * hparams['f0_std'] + hparams['f0_mean'] if hparams['pitch_norm'] == 'log': f0 = 2 ** f0 if min is not None: f0 = f0.clamp(min=min) if max is not None: f0 = f0.clamp(max=max) if uv is not None and hparams['use_uv']: f0[uv > 0] = 0 if pitch_padding is not None: f0[pitch_padding] = 0 return f0 ================================================ FILE: NeuralSeq/utils/pl_utils.py ================================================ import matplotlib from torch.nn import DataParallel from torch.nn.parallel import DistributedDataParallel matplotlib.use('Agg') import glob import itertools import subprocess import threading import traceback from pytorch_lightning.callbacks import GradientAccumulationScheduler from pytorch_lightning.callbacks import ModelCheckpoint from functools import wraps from torch.cuda._utils import _get_device_index import numpy as np import torch.optim import torch.utils.data import copy import logging import os import re import sys import torch import torch.distributed as dist import torch.multiprocessing as mp import tqdm from torch.optim.optimizer import Optimizer def get_a_var(obj): # pragma: no cover if isinstance(obj, torch.Tensor): return obj if isinstance(obj, list) or isinstance(obj, tuple): for result in map(get_a_var, obj): if isinstance(result, torch.Tensor): return result if isinstance(obj, dict): for result in map(get_a_var, obj.items()): if isinstance(result, torch.Tensor): return result return None def data_loader(fn): """ Decorator to make any fx with this use the lazy property :param fn: :return: """ wraps(fn) attr_name = '_lazy_' + fn.__name__ def _get_data_loader(self): try: value = getattr(self, attr_name) except AttributeError: try: value = fn(self) # Lazy evaluation, done only once. if ( value is not None and not isinstance(value, list) and fn.__name__ in ['test_dataloader', 'val_dataloader'] ): value = [value] except AttributeError as e: # Guard against AttributeError suppression. (Issue #142) traceback.print_exc() error = f'{fn.__name__}: An AttributeError was encountered: ' + str(e) raise RuntimeError(error) from e setattr(self, attr_name, value) # Memoize evaluation. return value return _get_data_loader def parallel_apply(modules, inputs, kwargs_tup=None, devices=None): # pragma: no cover r"""Applies each `module` in :attr:`modules` in parallel on arguments contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword) on each of :attr:`devices`. Args: modules (Module): modules to be parallelized inputs (tensor): inputs to the modules devices (list of int or torch.device): CUDA devices :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and :attr:`devices` (if given) should all have same length. Moreover, each element of :attr:`inputs` can either be a single object as the only argument to a module, or a collection of positional arguments. """ assert len(modules) == len(inputs) if kwargs_tup is not None: assert len(modules) == len(kwargs_tup) else: kwargs_tup = ({},) * len(modules) if devices is not None: assert len(modules) == len(devices) else: devices = [None] * len(modules) devices = list(map(lambda x: _get_device_index(x, True), devices)) lock = threading.Lock() results = {} grad_enabled = torch.is_grad_enabled() def _worker(i, module, input, kwargs, device=None): torch.set_grad_enabled(grad_enabled) if device is None: device = get_a_var(input).get_device() try: with torch.cuda.device(device): # this also avoids accidental slicing of `input` if it is a Tensor if not isinstance(input, (list, tuple)): input = (input,) # --------------- # CHANGE if module.training: output = module.training_step(*input, **kwargs) elif module.testing: output = module.test_step(*input, **kwargs) else: output = module.validation_step(*input, **kwargs) # --------------- with lock: results[i] = output except Exception as e: with lock: results[i] = e # make sure each module knows what training state it's in... # fixes weird bug where copies are out of sync root_m = modules[0] for m in modules[1:]: m.training = root_m.training m.testing = root_m.testing if len(modules) > 1: threads = [threading.Thread(target=_worker, args=(i, module, input, kwargs, device)) for i, (module, input, kwargs, device) in enumerate(zip(modules, inputs, kwargs_tup, devices))] for thread in threads: thread.start() for thread in threads: thread.join() else: _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0]) outputs = [] for i in range(len(inputs)): output = results[i] if isinstance(output, Exception): raise output outputs.append(output) return outputs def _find_tensors(obj): # pragma: no cover r""" Recursively find all tensors contained in the specified object. """ if isinstance(obj, torch.Tensor): return [obj] if isinstance(obj, (list, tuple)): return itertools.chain(*map(_find_tensors, obj)) if isinstance(obj, dict): return itertools.chain(*map(_find_tensors, obj.values())) return [] class DDP(DistributedDataParallel): """ Override the forward call in lightning so it goes to training and validation step respectively """ def parallel_apply(self, replicas, inputs, kwargs): return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) def forward(self, *inputs, **kwargs): # pragma: no cover self._sync_params() if self.device_ids: inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) if len(self.device_ids) == 1: # -------------- # LIGHTNING MOD # -------------- # normal # output = self.module(*inputs[0], **kwargs[0]) # lightning if self.module.training: output = self.module.training_step(*inputs[0], **kwargs[0]) elif self.module.testing: output = self.module.test_step(*inputs[0], **kwargs[0]) else: output = self.module.validation_step(*inputs[0], **kwargs[0]) else: outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs) output = self.gather(outputs, self.output_device) else: # normal output = self.module(*inputs, **kwargs) if torch.is_grad_enabled(): # We'll return the output object verbatim since it is a freeform # object. We need to find any tensors in this object, though, # because we need to figure out which parameters were used during # this forward pass, to ensure we short circuit reduction for any # unused parameters. Only if `find_unused_parameters` is set. if self.find_unused_parameters: self.reducer.prepare_for_backward(list(_find_tensors(output))) else: self.reducer.prepare_for_backward([]) return output class DP(DataParallel): """ Override the forward call in lightning so it goes to training and validation step respectively """ def forward(self, *inputs, **kwargs): if not self.device_ids: return self.module(*inputs, **kwargs) for t in itertools.chain(self.module.parameters(), self.module.buffers()): if t.device != self.src_device_obj: raise RuntimeError("module must have its parameters and buffers " "on device {} (device_ids[0]) but found one of " "them on device: {}".format(self.src_device_obj, t.device)) inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) if len(self.device_ids) == 1: # lightning if self.module.training: return self.module.training_step(*inputs[0], **kwargs[0]) elif self.module.testing: return self.module.test_step(*inputs[0], **kwargs[0]) else: return self.module.validation_step(*inputs[0], **kwargs[0]) replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) outputs = self.parallel_apply(replicas, inputs, kwargs) return self.gather(outputs, self.output_device) def parallel_apply(self, replicas, inputs, kwargs): return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) class GradientAccumulationScheduler: def __init__(self, scheduling: dict): if scheduling == {}: # empty dict error raise TypeError("Empty dict cannot be interpreted correct") for key in scheduling.keys(): if not isinstance(key, int) or not isinstance(scheduling[key], int): raise TypeError("All epoches and accumulation factor must be integers") minimal_epoch = min(scheduling.keys()) if minimal_epoch < 1: msg = f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct" raise IndexError(msg) elif minimal_epoch != 1: # if user didnt define first epoch accumulation factor scheduling.update({1: 1}) self.scheduling = scheduling self.epochs = sorted(scheduling.keys()) def on_epoch_begin(self, epoch, trainer): epoch += 1 # indexing epochs from 1 for i in reversed(range(len(self.epochs))): if epoch >= self.epochs[i]: trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i]) break class LatestModelCheckpoint(ModelCheckpoint): def __init__(self, filepath, monitor='val_loss', verbose=0, num_ckpt_keep=5, save_weights_only=False, mode='auto', period=1, prefix='model', save_best=True): super(ModelCheckpoint, self).__init__() self.monitor = monitor self.verbose = verbose self.filepath = filepath os.makedirs(filepath, exist_ok=True) self.num_ckpt_keep = num_ckpt_keep self.save_best = save_best self.save_weights_only = save_weights_only self.period = period self.epochs_since_last_check = 0 self.prefix = prefix self.best_k_models = {} # {filename: monitor} self.kth_best_model = '' self.save_top_k = 1 self.task = None if mode == 'min': self.monitor_op = np.less self.best = np.Inf self.mode = 'min' elif mode == 'max': self.monitor_op = np.greater self.best = -np.Inf self.mode = 'max' else: if 'acc' in self.monitor or self.monitor.startswith('fmeasure'): self.monitor_op = np.greater self.best = -np.Inf self.mode = 'max' else: self.monitor_op = np.less self.best = np.Inf self.mode = 'min' if os.path.exists(f'{self.filepath}/best_valid.npy'): self.best = np.load(f'{self.filepath}/best_valid.npy')[0] def get_all_ckpts(self): return sorted(glob.glob(f'{self.filepath}/{self.prefix}_ckpt_steps_*.ckpt'), key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0])) def on_epoch_end(self, epoch, logs=None): logs = logs or {} self.epochs_since_last_check += 1 best_filepath = f'{self.filepath}/{self.prefix}_ckpt_best.pt' if self.epochs_since_last_check >= self.period: self.epochs_since_last_check = 0 filepath = f'{self.filepath}/{self.prefix}_ckpt_steps_{self.task.global_step}.ckpt' if self.verbose > 0: logging.info(f'Epoch {epoch:05d}@{self.task.global_step}: saving model to {filepath}') self._save_model(filepath) for old_ckpt in self.get_all_ckpts()[self.num_ckpt_keep:]: subprocess.check_call(f'rm -rf "{old_ckpt}"', shell=True) if self.verbose > 0: logging.info(f'Delete ckpt: {os.path.basename(old_ckpt)}') current = logs.get(self.monitor) if current is not None and self.save_best: if self.monitor_op(current, self.best): self.best = current if self.verbose > 0: logging.info( f'Epoch {epoch:05d}@{self.task.global_step}: {self.monitor} reached' f' {current:0.5f} (best {self.best:0.5f}), saving model to' f' {best_filepath} as top 1') self._save_model(best_filepath) np.save(f'{self.filepath}/best_valid.npy', [self.best]) class BaseTrainer: def __init__( self, logger=True, checkpoint_callback=True, default_save_path=None, gradient_clip_val=0, process_position=0, gpus=-1, log_gpu_memory=None, show_progress_bar=True, track_grad_norm=-1, check_val_every_n_epoch=1, accumulate_grad_batches=1, max_updates=1000, min_epochs=1, val_check_interval=1.0, log_save_interval=100, row_log_interval=10, print_nan_grads=False, weights_summary='full', num_sanity_val_steps=5, resume_from_checkpoint=None, ): self.log_gpu_memory = log_gpu_memory self.gradient_clip_val = gradient_clip_val self.check_val_every_n_epoch = check_val_every_n_epoch self.track_grad_norm = track_grad_norm self.on_gpu = True if (gpus and torch.cuda.is_available()) else False self.process_position = process_position self.weights_summary = weights_summary self.max_updates = max_updates self.min_epochs = min_epochs self.num_sanity_val_steps = num_sanity_val_steps self.print_nan_grads = print_nan_grads self.resume_from_checkpoint = resume_from_checkpoint self.default_save_path = default_save_path # training bookeeping self.total_batch_idx = 0 self.running_loss = [] self.avg_loss = 0 self.batch_idx = 0 self.tqdm_metrics = {} self.callback_metrics = {} self.num_val_batches = 0 self.num_training_batches = 0 self.num_test_batches = 0 self.get_train_dataloader = None self.get_test_dataloaders = None self.get_val_dataloaders = None self.is_iterable_train_dataloader = False # training state self.model = None self.testing = False self.disable_validation = False self.lr_schedulers = [] self.optimizers = None self.global_step = 0 self.current_epoch = 0 self.total_batches = 0 # configure checkpoint callback self.checkpoint_callback = checkpoint_callback self.checkpoint_callback.save_function = self.save_checkpoint self.weights_save_path = self.checkpoint_callback.filepath # accumulated grads self.configure_accumulated_gradients(accumulate_grad_batches) # allow int, string and gpu list self.data_parallel_device_ids = [ int(x) for x in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if x != ''] if len(self.data_parallel_device_ids) == 0: self.root_gpu = None self.on_gpu = False else: self.root_gpu = self.data_parallel_device_ids[0] self.on_gpu = True # distributed backend choice self.use_ddp = False self.use_dp = False self.single_gpu = False self.distributed_backend = 'ddp' if self.num_gpus > 0 else 'dp' self.set_distributed_mode(self.distributed_backend) self.proc_rank = 0 self.world_size = 1 self.node_rank = 0 # can't init progress bar here because starting a new process # means the progress_bar won't survive pickling self.show_progress_bar = show_progress_bar # logging self.log_save_interval = log_save_interval self.val_check_interval = val_check_interval self.logger = logger self.logger.rank = 0 self.row_log_interval = row_log_interval @property def num_gpus(self): gpus = self.data_parallel_device_ids if gpus is None: return 0 else: return len(gpus) @property def data_parallel(self): return self.use_dp or self.use_ddp def get_model(self): is_dp_module = isinstance(self.model, (DDP, DP)) model = self.model.module if is_dp_module else self.model return model # ----------------------------- # MODEL TRAINING # ----------------------------- def fit(self, model): if self.use_ddp: mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model,)) else: model.model = model.build_model() if not self.testing: self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers()) if self.use_dp: model.cuda(self.root_gpu) model = DP(model, device_ids=self.data_parallel_device_ids) elif self.single_gpu: model.cuda(self.root_gpu) self.run_pretrain_routine(model) return 1 def init_optimizers(self, optimizers): # single optimizer if isinstance(optimizers, Optimizer): return [optimizers], [] # two lists elif len(optimizers) == 2 and isinstance(optimizers[0], list): optimizers, lr_schedulers = optimizers return optimizers, lr_schedulers # single list or tuple elif isinstance(optimizers, list) or isinstance(optimizers, tuple): return optimizers, [] def run_pretrain_routine(self, model): """Sanity check a few things before starting actual training. :param model: """ ref_model = model if self.data_parallel: ref_model = model.module # give model convenience properties ref_model.trainer = self # set local properties on the model self.copy_trainer_model_properties(ref_model) # link up experiment object if self.logger is not None: ref_model.logger = self.logger self.logger.save() if self.use_ddp: dist.barrier() # set up checkpoint callback # self.configure_checkpoint_callback() # transfer data loaders from model self.get_dataloaders(ref_model) # track model now. # if cluster resets state, the model will update with the saved weights self.model = model # restore training and model before hpc call self.restore_weights(model) # when testing requested only run test and return if self.testing: self.run_evaluation(test=True) return # check if we should run validation during training self.disable_validation = self.num_val_batches == 0 # run tiny validation (if validation defined) # to make sure program won't crash during val ref_model.on_sanity_check_start() ref_model.on_train_start() if not self.disable_validation and self.num_sanity_val_steps > 0: # init progress bars for validation sanity check pbar = tqdm.tqdm(desc='Validation sanity check', total=self.num_sanity_val_steps * len(self.get_val_dataloaders()), leave=False, position=2 * self.process_position, disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch') self.main_progress_bar = pbar # dummy validation progress bar self.val_progress_bar = tqdm.tqdm(disable=True) self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, self.testing) # close progress bars self.main_progress_bar.close() self.val_progress_bar.close() # init progress bar pbar = tqdm.tqdm(leave=True, position=2 * self.process_position, disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch', file=sys.stdout) self.main_progress_bar = pbar # clear cache before training if self.on_gpu: torch.cuda.empty_cache() # CORE TRAINING LOOP self.train() def test(self, model): self.testing = True self.fit(model) @property def training_tqdm_dict(self): tqdm_dict = { 'step': '{}'.format(self.global_step), } tqdm_dict.update(self.tqdm_metrics) return tqdm_dict # -------------------- # restore ckpt # -------------------- def restore_weights(self, model): """ To restore weights we have two cases. First, attempt to restore hpc weights. If successful, don't restore other weights. Otherwise, try to restore actual weights :param model: :return: """ # clear cache before restore if self.on_gpu: torch.cuda.empty_cache() if self.resume_from_checkpoint is not None: self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu) else: # restore weights if same exp version self.restore_state_if_checkpoint_exists(model) # wait for all models to restore weights if self.use_ddp: # wait for all processes to catch up dist.barrier() # clear cache after restore if self.on_gpu: torch.cuda.empty_cache() def restore_state_if_checkpoint_exists(self, model): did_restore = False # do nothing if there's not dir or callback no_ckpt_callback = (self.checkpoint_callback is None) or (not self.checkpoint_callback) if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath): return did_restore # restore trainer state and model if there is a weight for this experiment last_steps = -1 last_ckpt_name = None # find last epoch checkpoints = os.listdir(self.checkpoint_callback.filepath) for name in checkpoints: if '.ckpt' in name and not name.endswith('part'): if 'steps_' in name: steps = name.split('steps_')[1] steps = int(re.sub('[^0-9]', '', steps)) if steps > last_steps: last_steps = steps last_ckpt_name = name # restore last checkpoint if last_ckpt_name is not None: last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name) self.restore(last_ckpt_path, self.on_gpu) logging.info(f'model and trainer restored from checkpoint: {last_ckpt_path}') did_restore = True return did_restore def restore(self, checkpoint_path, on_gpu): checkpoint = torch.load(checkpoint_path, map_location='cpu') # load model state model = self.get_model() # load the state_dict on the model automatically model.load_state_dict(checkpoint['state_dict'], strict=False) if on_gpu: model.cuda(self.root_gpu) # load training state (affects trainer only) self.restore_training_state(checkpoint) model.global_step = self.global_step del checkpoint try: if dist.is_initialized() and dist.get_rank() > 0: return except Exception as e: print(e) return def restore_training_state(self, checkpoint): """ Restore trainer state. Model will get its change to update :param checkpoint: :return: """ if self.checkpoint_callback is not None and self.checkpoint_callback is not False: self.checkpoint_callback.best = checkpoint['checkpoint_callback_best'] self.global_step = checkpoint['global_step'] self.current_epoch = checkpoint['epoch'] if self.testing: return # restore the optimizers optimizer_states = checkpoint['optimizer_states'] for optimizer, opt_state in zip(self.optimizers, optimizer_states): if optimizer is None: return optimizer.load_state_dict(opt_state) # move optimizer to GPU 1 weight at a time # avoids OOM if self.root_gpu is not None: for state in optimizer.state.values(): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.cuda(self.root_gpu) # restore the lr schedulers lr_schedulers = checkpoint['lr_schedulers'] for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers): scheduler.load_state_dict(lrs_state) # -------------------- # MODEL SAVE CHECKPOINT # -------------------- def _atomic_save(self, checkpoint, filepath): """Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints. This will create a temporary checkpoint with a suffix of ``.part``, then copy it to the final location once saving is finished. Args: checkpoint (object): The object to save. Built to be used with the ``dump_checkpoint`` method, but can deal with anything which ``torch.save`` accepts. filepath (str|pathlib.Path): The path to which the checkpoint will be saved. This points to the file that the checkpoint will be stored in. """ tmp_path = str(filepath) + ".part" torch.save(checkpoint, tmp_path) os.replace(tmp_path, filepath) def save_checkpoint(self, filepath): checkpoint = self.dump_checkpoint() self._atomic_save(checkpoint, filepath) def dump_checkpoint(self): checkpoint = { 'epoch': self.current_epoch, 'global_step': self.global_step } if self.checkpoint_callback is not None and self.checkpoint_callback is not False: checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best # save optimizers optimizer_states = [] for i, optimizer in enumerate(self.optimizers): if optimizer is not None: optimizer_states.append(optimizer.state_dict()) checkpoint['optimizer_states'] = optimizer_states # save lr schedulers lr_schedulers = [] for i, scheduler in enumerate(self.lr_schedulers): lr_schedulers.append(scheduler.state_dict()) checkpoint['lr_schedulers'] = lr_schedulers # add the hparams and state_dict from the model model = self.get_model() checkpoint['state_dict'] = model.state_dict() # give the model a chance to add a few things model.on_save_checkpoint(checkpoint) return checkpoint def copy_trainer_model_properties(self, model): if isinstance(model, DP): ref_model = model.module elif isinstance(model, DDP): ref_model = model.module else: ref_model = model for m in [model, ref_model]: m.trainer = self m.on_gpu = self.on_gpu m.use_dp = self.use_dp m.use_ddp = self.use_ddp m.testing = self.testing m.single_gpu = self.single_gpu def transfer_batch_to_gpu(self, batch, gpu_id): # base case: object can be directly moved using `cuda` or `to` if callable(getattr(batch, 'cuda', None)): return batch.cuda(gpu_id, non_blocking=True) elif callable(getattr(batch, 'to', None)): return batch.to(torch.device('cuda', gpu_id), non_blocking=True) # when list elif isinstance(batch, list): for i, x in enumerate(batch): batch[i] = self.transfer_batch_to_gpu(x, gpu_id) return batch # when tuple elif isinstance(batch, tuple): batch = list(batch) for i, x in enumerate(batch): batch[i] = self.transfer_batch_to_gpu(x, gpu_id) return tuple(batch) # when dict elif isinstance(batch, dict): for k, v in batch.items(): batch[k] = self.transfer_batch_to_gpu(v, gpu_id) return batch # nothing matches, return the value as is without transform return batch def set_distributed_mode(self, distributed_backend): # skip for CPU if self.num_gpus == 0: return # single GPU case # in single gpu case we allow ddp so we can train on multiple # nodes, 1 gpu per node elif self.num_gpus == 1: self.single_gpu = True self.use_dp = False self.use_ddp = False self.root_gpu = 0 self.data_parallel_device_ids = [0] else: if distributed_backend is not None: self.use_dp = distributed_backend == 'dp' self.use_ddp = distributed_backend == 'ddp' elif distributed_backend is None: self.use_dp = True self.use_ddp = False logging.info(f'gpu available: {torch.cuda.is_available()}, used: {self.on_gpu}') def ddp_train(self, gpu_idx, model): """ Entry point into a DP thread :param gpu_idx: :param model: :param cluster_obj: :return: """ # otherwise default to node rank 0 self.node_rank = 0 # show progressbar only on progress_rank 0 self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_idx == 0 # determine which process we are and world size if self.use_ddp: self.proc_rank = self.node_rank * self.num_gpus + gpu_idx self.world_size = self.num_gpus # let the exp know the rank to avoid overwriting logs if self.logger is not None: self.logger.rank = self.proc_rank # set up server using proc 0's ip address # try to init for 20 times at max in case ports are taken # where to store ip_table model.trainer = self model.init_ddp_connection(self.proc_rank, self.world_size) # CHOOSE OPTIMIZER # allow for lr schedulers as well model.model = model.build_model() if not self.testing: self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers()) # MODEL # copy model to each gpu if self.distributed_backend == 'ddp': torch.cuda.set_device(gpu_idx) model.cuda(gpu_idx) # set model properties before going into wrapper self.copy_trainer_model_properties(model) # override root GPU self.root_gpu = gpu_idx if self.distributed_backend == 'ddp': device_ids = [gpu_idx] else: device_ids = None # allow user to configure ddp model = model.configure_ddp(model, device_ids) # continue training routine self.run_pretrain_routine(model) def resolve_root_node_address(self, root_node): if '[' in root_node: name = root_node.split('[')[0] number = root_node.split(',')[0] if '-' in number: number = number.split('-')[0] number = re.sub('[^0-9]', '', number) root_node = name + number return root_node def log_metrics(self, metrics, grad_norm_dic, step=None): """Logs the metric dict passed in. :param metrics: :param grad_norm_dic: """ # added metrics by Lightning for convenience metrics['epoch'] = self.current_epoch # add norms metrics.update(grad_norm_dic) # turn all tensors to scalars scalar_metrics = self.metrics_to_scalars(metrics) step = step if step is not None else self.global_step # log actual metrics if self.proc_rank == 0 and self.logger is not None: self.logger.log_metrics(scalar_metrics, step=step) self.logger.save() def add_tqdm_metrics(self, metrics): for k, v in metrics.items(): if type(v) is torch.Tensor: v = v.item() self.tqdm_metrics[k] = v def metrics_to_scalars(self, metrics): new_metrics = {} for k, v in metrics.items(): if isinstance(v, torch.Tensor): v = v.item() if type(v) is dict: v = self.metrics_to_scalars(v) new_metrics[k] = v return new_metrics def process_output(self, output, train=False): """Reduces output according to the training mode. Separates loss from logging and tqdm metrics :param output: :return: """ # --------------- # EXTRACT CALLBACK KEYS # --------------- # all keys not progress_bar or log are candidates for callbacks callback_metrics = {} for k, v in output.items(): if k not in ['progress_bar', 'log', 'hiddens']: callback_metrics[k] = v if train and self.use_dp: num_gpus = self.num_gpus callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus) for k, v in callback_metrics.items(): if isinstance(v, torch.Tensor): callback_metrics[k] = v.item() # --------------- # EXTRACT PROGRESS BAR KEYS # --------------- try: progress_output = output['progress_bar'] # reduce progress metrics for tqdm when using dp if train and self.use_dp: num_gpus = self.num_gpus progress_output = self.reduce_distributed_output(progress_output, num_gpus) progress_bar_metrics = progress_output except Exception: progress_bar_metrics = {} # --------------- # EXTRACT LOGGING KEYS # --------------- # extract metrics to log to experiment try: log_output = output['log'] # reduce progress metrics for tqdm when using dp if train and self.use_dp: num_gpus = self.num_gpus log_output = self.reduce_distributed_output(log_output, num_gpus) log_metrics = log_output except Exception: log_metrics = {} # --------------- # EXTRACT LOSS # --------------- # if output dict doesn't have the keyword loss # then assume the output=loss if scalar loss = None if train: try: loss = output['loss'] except Exception: if type(output) is torch.Tensor: loss = output else: raise RuntimeError( 'No `loss` value in the dictionary returned from `model.training_step()`.' ) # when using dp need to reduce the loss if self.use_dp: loss = self.reduce_distributed_output(loss, self.num_gpus) # --------------- # EXTRACT HIDDEN # --------------- hiddens = output.get('hiddens') # use every metric passed in as a candidate for callback callback_metrics.update(progress_bar_metrics) callback_metrics.update(log_metrics) # convert tensors to numpy for k, v in callback_metrics.items(): if isinstance(v, torch.Tensor): callback_metrics[k] = v.item() return loss, progress_bar_metrics, log_metrics, callback_metrics, hiddens def reduce_distributed_output(self, output, num_gpus): if num_gpus <= 1: return output # when using DP, we get one output per gpu # average outputs and return if type(output) is torch.Tensor: return output.mean() for k, v in output.items(): # recurse on nested dics if isinstance(output[k], dict): output[k] = self.reduce_distributed_output(output[k], num_gpus) # do nothing when there's a scalar elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0: pass # reduce only metrics that have the same number of gpus elif output[k].size(0) == num_gpus: reduced = torch.mean(output[k]) output[k] = reduced return output def clip_gradients(self): if self.gradient_clip_val > 0: model = self.get_model() torch.nn.utils.clip_grad_norm_(model.parameters(), self.gradient_clip_val) def print_nan_gradients(self): model = self.get_model() for param in model.parameters(): if (param.grad is not None) and torch.isnan(param.grad.float()).any(): logging.info(param, param.grad) def configure_accumulated_gradients(self, accumulate_grad_batches): self.accumulate_grad_batches = None if isinstance(accumulate_grad_batches, dict): self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches) elif isinstance(accumulate_grad_batches, int): schedule = {1: accumulate_grad_batches} self.accumulation_scheduler = GradientAccumulationScheduler(schedule) else: raise TypeError("Gradient accumulation supports only int and dict types") def get_dataloaders(self, model): if not self.testing: self.init_train_dataloader(model) self.init_val_dataloader(model) else: self.init_test_dataloader(model) if self.use_ddp: dist.barrier() if not self.testing: self.get_train_dataloader() self.get_val_dataloaders() else: self.get_test_dataloaders() def init_train_dataloader(self, model): self.fisrt_epoch = True self.get_train_dataloader = model.train_dataloader if isinstance(self.get_train_dataloader(), torch.utils.data.DataLoader): self.num_training_batches = len(self.get_train_dataloader()) self.num_training_batches = int(self.num_training_batches) else: self.num_training_batches = float('inf') self.is_iterable_train_dataloader = True if isinstance(self.val_check_interval, int): self.val_check_batch = self.val_check_interval else: self._percent_range_check('val_check_interval') self.val_check_batch = int(self.num_training_batches * self.val_check_interval) self.val_check_batch = max(1, self.val_check_batch) def init_val_dataloader(self, model): self.get_val_dataloaders = model.val_dataloader self.num_val_batches = 0 if self.get_val_dataloaders() is not None: if isinstance(self.get_val_dataloaders()[0], torch.utils.data.DataLoader): self.num_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders()) self.num_val_batches = int(self.num_val_batches) else: self.num_val_batches = float('inf') def init_test_dataloader(self, model): self.get_test_dataloaders = model.test_dataloader if self.get_test_dataloaders() is not None: if isinstance(self.get_test_dataloaders()[0], torch.utils.data.DataLoader): self.num_test_batches = sum(len(dataloader) for dataloader in self.get_test_dataloaders()) self.num_test_batches = int(self.num_test_batches) else: self.num_test_batches = float('inf') def evaluate(self, model, dataloaders, max_batches, test=False): """Run evaluation code. :param model: PT model :param dataloaders: list of PT dataloaders :param max_batches: Scalar :param test: boolean :return: """ # enable eval mode model.zero_grad() model.eval() # copy properties for forward overrides self.copy_trainer_model_properties(model) # disable gradients to save memory torch.set_grad_enabled(False) if test: self.get_model().test_start() # bookkeeping outputs = [] # run training for dataloader_idx, dataloader in enumerate(dataloaders): dl_outputs = [] for batch_idx, batch in enumerate(dataloader): if batch is None: # pragma: no cover continue # stop short when on fast_dev_run (sets max_batch=1) if batch_idx >= max_batches: break # ----------------- # RUN EVALUATION STEP # ----------------- output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test) # track outputs for collation dl_outputs.append(output) # batch done if test: self.test_progress_bar.update(1) else: self.val_progress_bar.update(1) outputs.append(dl_outputs) # with a single dataloader don't pass an array if len(dataloaders) == 1: outputs = outputs[0] # give model a chance to do something with the outputs (and method defined) model = self.get_model() if test: eval_results_ = model.test_end(outputs) else: eval_results_ = model.validation_end(outputs) eval_results = eval_results_ # enable train mode again model.train() # enable gradients to save memory torch.set_grad_enabled(True) return eval_results def run_evaluation(self, test=False): # when testing make sure user defined a test step model = self.get_model() model.on_pre_performance_check() # select dataloaders if test: dataloaders = self.get_test_dataloaders() max_batches = self.num_test_batches else: # val dataloaders = self.get_val_dataloaders() max_batches = self.num_val_batches # init validation or test progress bar # main progress bar will already be closed when testing so initial position is free position = 2 * self.process_position + (not test) desc = 'Testing' if test else 'Validating' pbar = tqdm.tqdm(desc=desc, total=max_batches, leave=test, position=position, disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch', file=sys.stdout) setattr(self, f'{"test" if test else "val"}_progress_bar', pbar) # run evaluation eval_results = self.evaluate(self.model, dataloaders, max_batches, test) if eval_results is not None: _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output( eval_results) # add metrics to prog bar self.add_tqdm_metrics(prog_bar_metrics) # log metrics self.log_metrics(log_metrics, {}) # track metrics for callbacks self.callback_metrics.update(callback_metrics) # hook model.on_post_performance_check() # add model specific metrics tqdm_metrics = self.training_tqdm_dict if not test: self.main_progress_bar.set_postfix(**tqdm_metrics) # close progress bar if test: self.test_progress_bar.close() else: self.val_progress_bar.close() # model checkpointing if self.proc_rank == 0 and self.checkpoint_callback is not None and not test: self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch, logs=self.callback_metrics) def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False): # make dataloader_idx arg in validation_step optional args = [batch, batch_idx] if test and len(self.get_test_dataloaders()) > 1: args.append(dataloader_idx) elif not test and len(self.get_val_dataloaders()) > 1: args.append(dataloader_idx) # handle DP, DDP forward if self.use_ddp or self.use_dp: output = model(*args) return output # single GPU if self.single_gpu: # for single GPU put inputs on gpu manually root_gpu = 0 if isinstance(self.data_parallel_device_ids, list): root_gpu = self.data_parallel_device_ids[0] batch = self.transfer_batch_to_gpu(batch, root_gpu) args[0] = batch # CPU if test: output = model.test_step(*args) else: output = model.validation_step(*args) return output def train(self): model = self.get_model() # run all epochs for epoch in range(self.current_epoch, 1000000): # set seed for distributed sampler (enables shuffling for each epoch) if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'): self.get_train_dataloader().sampler.set_epoch(epoch) # get model model = self.get_model() # update training progress in trainer and model model.current_epoch = epoch self.current_epoch = epoch total_val_batches = 0 if not self.disable_validation: # val can be checked multiple times in epoch is_val_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0 val_checks_per_epoch = self.num_training_batches // self.val_check_batch val_checks_per_epoch = val_checks_per_epoch if is_val_epoch else 0 total_val_batches = self.num_val_batches * val_checks_per_epoch # total batches includes multiple val checks self.total_batches = self.num_training_batches + total_val_batches self.batch_loss_value = 0 # accumulated grads if self.is_iterable_train_dataloader: # for iterable train loader, the progress bar never ends num_iterations = None else: num_iterations = self.total_batches # reset progress bar # .reset() doesn't work on disabled progress bar so we should check desc = f'Epoch {epoch + 1}' if not self.is_iterable_train_dataloader else '' self.main_progress_bar.set_description(desc) # changing gradient according accumulation_scheduler self.accumulation_scheduler.on_epoch_begin(epoch, self) # ----------------- # RUN TNG EPOCH # ----------------- self.run_training_epoch() # update LR schedulers if self.lr_schedulers is not None: for lr_scheduler in self.lr_schedulers: lr_scheduler.step(epoch=self.current_epoch) self.main_progress_bar.close() model.on_train_end() if self.logger is not None: self.logger.finalize("success") def run_training_epoch(self): # before epoch hook if self.is_function_implemented('on_epoch_start'): model = self.get_model() model.on_epoch_start() # run epoch for batch_idx, batch in enumerate(self.get_train_dataloader()): # stop epoch if we limited the number of training batches if batch_idx >= self.num_training_batches: break self.batch_idx = batch_idx model = self.get_model() model.global_step = self.global_step # --------------- # RUN TRAIN STEP # --------------- output = self.run_training_batch(batch, batch_idx) batch_result, grad_norm_dic, batch_step_metrics = output # when returning -1 from train_step, we end epoch early early_stop_epoch = batch_result == -1 # --------------- # RUN VAL STEP # --------------- should_check_val = ( not self.disable_validation and self.global_step % self.val_check_batch == 0 and not self.fisrt_epoch) self.fisrt_epoch = False if should_check_val: self.run_evaluation(test=self.testing) # when logs should be saved should_save_log = (batch_idx + 1) % self.log_save_interval == 0 or early_stop_epoch if should_save_log: if self.proc_rank == 0 and self.logger is not None: self.logger.save() # when metrics should be logged should_log_metrics = batch_idx % self.row_log_interval == 0 or early_stop_epoch if should_log_metrics: # logs user requested information to logger self.log_metrics(batch_step_metrics, grad_norm_dic) self.global_step += 1 self.total_batch_idx += 1 # end epoch early # stop when the flag is changed or we've gone past the amount # requested in the batches if early_stop_epoch: break if self.global_step > self.max_updates: print("| Training end..") exit() # epoch end hook if self.is_function_implemented('on_epoch_end'): model = self.get_model() model.on_epoch_end() def run_training_batch(self, batch, batch_idx): # track grad norms grad_norm_dic = {} # track all metrics for callbacks all_callback_metrics = [] # track metrics to log all_log_metrics = [] if batch is None: return 0, grad_norm_dic, {} # hook if self.is_function_implemented('on_batch_start'): model_ref = self.get_model() response = model_ref.on_batch_start(batch) if response == -1: return -1, grad_norm_dic, {} splits = [batch] self.hiddens = None for split_idx, split_batch in enumerate(splits): self.split_idx = split_idx # call training_step once per optimizer for opt_idx, optimizer in enumerate(self.optimizers): if optimizer is None: continue # make sure only the gradients of the current optimizer's paramaters are calculated # in the training step to prevent dangling gradients in multiple-optimizer setup. if len(self.optimizers) > 1: for param in self.get_model().parameters(): param.requires_grad = False for group in optimizer.param_groups: for param in group['params']: param.requires_grad = True # wrap the forward step in a closure so second order methods work def optimizer_closure(): # forward pass output = self.training_forward( split_batch, batch_idx, opt_idx, self.hiddens) closure_loss = output[0] progress_bar_metrics = output[1] log_metrics = output[2] callback_metrics = output[3] self.hiddens = output[4] if closure_loss is None: return None # accumulate loss # (if accumulate_grad_batches = 1 no effect) closure_loss = closure_loss / self.accumulate_grad_batches # backward pass model_ref = self.get_model() if closure_loss.requires_grad: model_ref.backward(closure_loss, optimizer) # track metrics for callbacks all_callback_metrics.append(callback_metrics) # track progress bar metrics self.add_tqdm_metrics(progress_bar_metrics) all_log_metrics.append(log_metrics) # insert after step hook if self.is_function_implemented('on_after_backward'): model_ref = self.get_model() model_ref.on_after_backward() return closure_loss # calculate loss loss = optimizer_closure() if loss is None: continue # nan grads if self.print_nan_grads: self.print_nan_gradients() # track total loss for logging (avoid mem leaks) self.batch_loss_value += loss.item() # gradient update with accumulated gradients if (self.batch_idx + 1) % self.accumulate_grad_batches == 0: # track gradient norms when requested if batch_idx % self.row_log_interval == 0: if self.track_grad_norm > 0: model = self.get_model() grad_norm_dic = model.grad_norm( self.track_grad_norm) # clip gradients self.clip_gradients() # calls .step(), .zero_grad() # override function to modify this behavior model = self.get_model() model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx) # calculate running loss for display self.running_loss.append(self.batch_loss_value) self.batch_loss_value = 0 self.avg_loss = np.mean(self.running_loss[-100:]) # activate batch end hook if self.is_function_implemented('on_batch_end'): model = self.get_model() model.on_batch_end() # update progress bar self.main_progress_bar.update(1) self.main_progress_bar.set_postfix(**self.training_tqdm_dict) # collapse all metrics into one dict all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()} # track all metrics for callbacks self.callback_metrics.update({k: v for d in all_callback_metrics for k, v in d.items()}) return 0, grad_norm_dic, all_log_metrics def training_forward(self, batch, batch_idx, opt_idx, hiddens): """ Handle forward for each training case (distributed, single gpu, etc...) :param batch: :param batch_idx: :return: """ # --------------- # FORWARD # --------------- # enable not needing to add opt_idx to training_step args = [batch, batch_idx, opt_idx] # distributed forward if self.use_ddp or self.use_dp: output = self.model(*args) # single GPU forward elif self.single_gpu: gpu_id = 0 if isinstance(self.data_parallel_device_ids, list): gpu_id = self.data_parallel_device_ids[0] batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id) args[0] = batch output = self.model.training_step(*args) # CPU forward else: output = self.model.training_step(*args) # allow any mode to define training_end model_ref = self.get_model() output_ = model_ref.training_end(output) if output_ is not None: output = output_ # format and reduce outputs accordingly output = self.process_output(output, train=True) return output # --------------- # Utils # --------------- def is_function_implemented(self, f_name): model = self.get_model() f_op = getattr(model, f_name, None) return callable(f_op) def _percent_range_check(self, name): value = getattr(self, name) msg = f"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}." if name == "val_check_interval": msg += " If you want to disable validation set `val_percent_check` to 0.0 instead." if not 0. <= value <= 1.: raise ValueError(msg) ================================================ FILE: NeuralSeq/utils/plot.py ================================================ import matplotlib.pyplot as plt import numpy as np import torch LINE_COLORS = ['w', 'r', 'y', 'cyan', 'm', 'b', 'lime'] def spec_to_figure(spec, vmin=None, vmax=None): if isinstance(spec, torch.Tensor): spec = spec.cpu().numpy() fig = plt.figure(figsize=(12, 6)) plt.pcolor(spec.T, vmin=vmin, vmax=vmax) return fig def spec_f0_to_figure(spec, f0s, figsize=None): max_y = spec.shape[1] if isinstance(spec, torch.Tensor): spec = spec.detach().cpu().numpy() f0s = {k: f0.detach().cpu().numpy() for k, f0 in f0s.items()} f0s = {k: f0 / 10 for k, f0 in f0s.items()} fig = plt.figure(figsize=(12, 6) if figsize is None else figsize) plt.pcolor(spec.T) for i, (k, f0) in enumerate(f0s.items()): plt.plot(f0.clip(0, max_y), label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.8) plt.legend() return fig def dur_to_figure(dur_gt, dur_pred, txt): dur_gt = dur_gt.long().cpu().numpy() dur_pred = dur_pred.long().cpu().numpy() dur_gt = np.cumsum(dur_gt) dur_pred = np.cumsum(dur_pred) fig = plt.figure(figsize=(12, 6)) for i in range(len(dur_gt)): shift = (i % 8) + 1 plt.text(dur_gt[i], shift, txt[i]) plt.text(dur_pred[i], 10 + shift, txt[i]) plt.vlines(dur_gt[i], 0, 10, colors='b') # blue is gt plt.vlines(dur_pred[i], 10, 20, colors='r') # red is pred return fig def f0_to_figure(f0_gt, f0_cwt=None, f0_pred=None): fig = plt.figure() f0_gt = f0_gt.cpu().numpy() plt.plot(f0_gt, color='r', label='gt') if f0_cwt is not None: f0_cwt = f0_cwt.cpu().numpy() plt.plot(f0_cwt, color='b', label='cwt') if f0_pred is not None: f0_pred = f0_pred.cpu().numpy() plt.plot(f0_pred, color='green', label='pred') plt.legend() return fig ================================================ FILE: NeuralSeq/utils/text_encoder.py ================================================ import re import six from six.moves import range # pylint: disable=redefined-builtin PAD = "" EOS = "" UNK = "" SEG = "|" RESERVED_TOKENS = [PAD, EOS, UNK] NUM_RESERVED_TOKENS = len(RESERVED_TOKENS) PAD_ID = RESERVED_TOKENS.index(PAD) # Normally 0 EOS_ID = RESERVED_TOKENS.index(EOS) # Normally 1 UNK_ID = RESERVED_TOKENS.index(UNK) # Normally 2 if six.PY2: RESERVED_TOKENS_BYTES = RESERVED_TOKENS else: RESERVED_TOKENS_BYTES = [bytes(PAD, "ascii"), bytes(EOS, "ascii")] # Regular expression for unescaping token strings. # '\u' is converted to '_' # '\\' is converted to '\' # '\213;' is converted to unichr(213) _UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);") _ESCAPE_CHARS = set(u"\\_u;0123456789") def strip_ids(ids, ids_to_strip): """Strip ids_to_strip from the end ids.""" ids = list(ids) while ids and ids[-1] in ids_to_strip: ids.pop() return ids class TextEncoder(object): """Base class for converting from ints to/from human readable strings.""" def __init__(self, num_reserved_ids=NUM_RESERVED_TOKENS): self._num_reserved_ids = num_reserved_ids @property def num_reserved_ids(self): return self._num_reserved_ids def encode(self, s): """Transform a human-readable string into a sequence of int ids. The ids should be in the range [num_reserved_ids, vocab_size). Ids [0, num_reserved_ids) are reserved. EOS is not appended. Args: s: human-readable string to be converted. Returns: ids: list of integers """ return [int(w) + self._num_reserved_ids for w in s.split()] def decode(self, ids, strip_extraneous=False): """Transform a sequence of int ids into a human-readable string. EOS is not expected in ids. Args: ids: list of integers to be converted. strip_extraneous: bool, whether to strip off extraneous tokens (EOS and PAD). Returns: s: human-readable string. """ if strip_extraneous: ids = strip_ids(ids, list(range(self._num_reserved_ids or 0))) return " ".join(self.decode_list(ids)) def decode_list(self, ids): """Transform a sequence of int ids into a their string versions. This method supports transforming individual input/output ids to their string versions so that sequence to/from text conversions can be visualized in a human readable format. Args: ids: list of integers to be converted. Returns: strs: list of human-readable string. """ decoded_ids = [] for id_ in ids: if 0 <= id_ < self._num_reserved_ids: decoded_ids.append(RESERVED_TOKENS[int(id_)]) else: decoded_ids.append(id_ - self._num_reserved_ids) return [str(d) for d in decoded_ids] @property def vocab_size(self): raise NotImplementedError() class ByteTextEncoder(TextEncoder): """Encodes each byte to an id. For 8-bit strings only.""" def encode(self, s): numres = self._num_reserved_ids if six.PY2: if isinstance(s, unicode): s = s.encode("utf-8") return [ord(c) + numres for c in s] # Python3: explicitly convert to UTF-8 return [c + numres for c in s.encode("utf-8")] def decode(self, ids, strip_extraneous=False): if strip_extraneous: ids = strip_ids(ids, list(range(self._num_reserved_ids or 0))) numres = self._num_reserved_ids decoded_ids = [] int2byte = six.int2byte for id_ in ids: if 0 <= id_ < numres: decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)]) else: decoded_ids.append(int2byte(id_ - numres)) if six.PY2: return "".join(decoded_ids) # Python3: join byte arrays and then decode string return b"".join(decoded_ids).decode("utf-8", "replace") def decode_list(self, ids): numres = self._num_reserved_ids decoded_ids = [] int2byte = six.int2byte for id_ in ids: if 0 <= id_ < numres: decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)]) else: decoded_ids.append(int2byte(id_ - numres)) # Python3: join byte arrays and then decode string return decoded_ids @property def vocab_size(self): return 2**8 + self._num_reserved_ids class ByteTextEncoderWithEos(ByteTextEncoder): """Encodes each byte to an id and appends the EOS token.""" def encode(self, s): return super(ByteTextEncoderWithEos, self).encode(s) + [EOS_ID] class TokenTextEncoder(TextEncoder): """Encoder based on a user-supplied vocabulary (file or list).""" def __init__(self, vocab_filename, reverse=False, vocab_list=None, replace_oov=None, num_reserved_ids=NUM_RESERVED_TOKENS): """Initialize from a file or list, one token per line. Handling of reserved tokens works as follows: - When initializing from a list, we add reserved tokens to the vocab. - When initializing from a file, we do not add reserved tokens to the vocab. - When saving vocab files, we save reserved tokens to the file. Args: vocab_filename: If not None, the full filename to read vocab from. If this is not None, then vocab_list should be None. reverse: Boolean indicating if tokens should be reversed during encoding and decoding. vocab_list: If not None, a list of elements of the vocabulary. If this is not None, then vocab_filename should be None. replace_oov: If not None, every out-of-vocabulary token seen when encoding will be replaced by this string (which must be in vocab). num_reserved_ids: Number of IDs to save for reserved tokens like . """ super(TokenTextEncoder, self).__init__(num_reserved_ids=num_reserved_ids) self._reverse = reverse self._replace_oov = replace_oov if vocab_filename: self._init_vocab_from_file(vocab_filename) else: assert vocab_list is not None self._init_vocab_from_list(vocab_list) self.pad_index = self._token_to_id[PAD] self.eos_index = self._token_to_id[EOS] self.unk_index = self._token_to_id[UNK] self.seg_index = self._token_to_id[SEG] if SEG in self._token_to_id else self.eos_index def encode(self, s): """Converts a space-separated string of tokens to a list of ids.""" sentence = s tokens = sentence.strip().split() if self._replace_oov is not None: tokens = [t if t in self._token_to_id else self._replace_oov for t in tokens] ret = [self._token_to_id[tok] for tok in tokens] return ret[::-1] if self._reverse else ret def decode(self, ids, strip_eos=False, strip_padding=False): if strip_padding and self.pad() in list(ids): pad_pos = list(ids).index(self.pad()) ids = ids[:pad_pos] if strip_eos and self.eos() in list(ids): eos_pos = list(ids).index(self.eos()) ids = ids[:eos_pos] return " ".join(self.decode_list(ids)) def decode_list(self, ids): seq = reversed(ids) if self._reverse else ids return [self._safe_id_to_token(i) for i in seq] @property def vocab_size(self): return len(self._id_to_token) def __len__(self): return self.vocab_size def _safe_id_to_token(self, idx): return self._id_to_token.get(idx, "ID_%d" % idx) def _init_vocab_from_file(self, filename): """Load vocab from a file. Args: filename: The file to load vocabulary from. """ with open(filename) as f: tokens = [token.strip() for token in f.readlines()] def token_gen(): for token in tokens: yield token self._init_vocab(token_gen(), add_reserved_tokens=False) def _init_vocab_from_list(self, vocab_list): """Initialize tokens from a list of tokens. It is ok if reserved tokens appear in the vocab list. They will be removed. The set of tokens in vocab_list should be unique. Args: vocab_list: A list of tokens. """ def token_gen(): for token in vocab_list: if token not in RESERVED_TOKENS: yield token self._init_vocab(token_gen()) def _init_vocab(self, token_generator, add_reserved_tokens=True): """Initialize vocabulary with tokens from token_generator.""" self._id_to_token = {} non_reserved_start_index = 0 if add_reserved_tokens: self._id_to_token.update(enumerate(RESERVED_TOKENS)) non_reserved_start_index = len(RESERVED_TOKENS) self._id_to_token.update( enumerate(token_generator, start=non_reserved_start_index)) # _token_to_id is the reverse of _id_to_token self._token_to_id = dict((v, k) for k, v in six.iteritems(self._id_to_token)) def pad(self): return self.pad_index def eos(self): return self.eos_index def unk(self): return self.unk_index def seg(self): return self.seg_index def store_to_file(self, filename): """Write vocab file to disk. Vocab files have one token per line. The file ends in a newline. Reserved tokens are written to the vocab file as well. Args: filename: Full path of the file to store the vocab to. """ with open(filename, "w") as f: for i in range(len(self._id_to_token)): f.write(self._id_to_token[i] + "\n") def sil_phonemes(self): return [p for p in self._id_to_token.values() if not p[0].isalpha()] ================================================ FILE: NeuralSeq/utils/text_norm.py ================================================ # coding=utf-8 # Authors: # 2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git) # 2019.9 Jiayu DU # # requirements: # - python 3.X # notes: python 2.X WILL fail or produce misleading results import sys, os, argparse, codecs, string, re # ================================================================================ # # basic constant # ================================================================================ # CHINESE_DIGIS = u'零一二三四五六七八九' BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖' BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖' SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万' SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬' LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载' LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載' SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万' SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬' ZERO_ALT = u'〇' ONE_ALT = u'幺' TWO_ALTS = [u'两', u'兩'] POSITIVE = [u'正', u'正'] NEGATIVE = [u'负', u'負'] POINT = [u'点', u'點'] # PLUS = [u'加', u'加'] # SIL = [u'杠', u'槓'] # 中文数字系统类型 NUMBERING_TYPES = ['low', 'mid', 'high'] CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \ '里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)' CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)' COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \ '砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \ '针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \ '毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \ '盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \ '纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)' # punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git) CHINESE_PUNC_STOP = '！？｡。' CHINESE_PUNC_NON_STOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏' CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP # ================================================================================ # # basic class # ================================================================================ # class ChineseChar(object): """ 中文字符 每个字符对应简体和繁体, e.g. 简体 = '负', 繁体 = '負' 转换时可转换为简体或繁体 """ def __init__(self, simplified, traditional): self.simplified = simplified self.traditional = traditional # self.__repr__ = self.__str__ def __str__(self): return self.simplified or self.traditional or None def __repr__(self): return self.__str__() class ChineseNumberUnit(ChineseChar): """ 中文数字/数位字符 每个字符除繁简体外还有一个额外的大写字符 e.g. '陆' 和 '陸' """ def __init__(self, power, simplified, traditional, big_s, big_t): super(ChineseNumberUnit, self).__init__(simplified, traditional) self.power = power self.big_s = big_s self.big_t = big_t def __str__(self): return '10^{}'.format(self.power) @classmethod def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False): if small_unit: return ChineseNumberUnit(power=index + 1, simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1]) elif numbering_type == NUMBERING_TYPES[0]: return ChineseNumberUnit(power=index + 8, simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) elif numbering_type == NUMBERING_TYPES[1]: return ChineseNumberUnit(power=(index + 2) * 4, simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) elif numbering_type == NUMBERING_TYPES[2]: return ChineseNumberUnit(power=pow(2, index + 3), simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) else: raise ValueError( 'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type)) class ChineseNumberDigit(ChineseChar): """ 中文数字字符 """ def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None): super(ChineseNumberDigit, self).__init__(simplified, traditional) self.value = value self.big_s = big_s self.big_t = big_t self.alt_s = alt_s self.alt_t = alt_t def __str__(self): return str(self.value) @classmethod def create(cls, i, v): return ChineseNumberDigit(i, v[0], v[1], v[2], v[3]) class ChineseMath(ChineseChar): """ 中文数位字符 """ def __init__(self, simplified, traditional, symbol, expression=None): super(ChineseMath, self).__init__(simplified, traditional) self.symbol = symbol self.expression = expression self.big_s = simplified self.big_t = traditional CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath class NumberSystem(object): """ 中文数字系统 """ pass class MathSymbol(object): """ 用于中文数字系统的数学符号 (繁/简体), e.g. positive = ['正', '正'] negative = ['负', '負'] point = ['点', '點'] """ def __init__(self, positive, negative, point): self.positive = positive self.negative = negative self.point = point def __iter__(self): for v in self.__dict__.values(): yield v # class OtherSymbol(object): # """ # 其他符号 # """ # # def __init__(self, sil): # self.sil = sil # # def __iter__(self): # for v in self.__dict__.values(): # yield v # ================================================================================ # # basic utils # ================================================================================ # def create_system(numbering_type=NUMBERING_TYPES[1]): """ 根据数字系统类型返回创建相应的数字系统，默认为 mid NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型 low: '兆' = '亿' * '十' = $10^{9}$, '京' = '兆' * '十', etc. mid: '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc. high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc. 返回对应的数字系统 """ # chinese number units of '亿' and larger all_larger_units = zip( LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL) larger_units = [CNU.create(i, v, numbering_type, False) for i, v in enumerate(all_larger_units)] # chinese number units of '十, 百, 千, 万' all_smaller_units = zip( SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL) smaller_units = [CNU.create(i, v, small_unit=True) for i, v in enumerate(all_smaller_units)] # digis chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS, BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL) digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)] digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1] # symbols positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x) negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x) point_cn = CM(POINT[0], POINT[1], '.', lambda x, y: float(str(x) + '.' + str(y))) # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y))) system = NumberSystem() system.units = smaller_units + larger_units system.digits = digits system.math = MathSymbol(positive_cn, negative_cn, point_cn) # system.symbols = OtherSymbol(sil_cn) return system def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]): def get_symbol(char, system): for u in system.units: if char in [u.traditional, u.simplified, u.big_s, u.big_t]: return u for d in system.digits: if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]: return d for m in system.math: if char in [m.traditional, m.simplified]: return m def string2symbols(chinese_string, system): int_string, dec_string = chinese_string, '' for p in [system.math.point.simplified, system.math.point.traditional]: if p in chinese_string: int_string, dec_string = chinese_string.split(p) break return [get_symbol(c, system) for c in int_string], \ [get_symbol(c, system) for c in dec_string] def correct_symbols(integer_symbols, system): """ 一百八 to 一百八十 一亿一千三百万 to 一亿 一千万 三百万 """ if integer_symbols and isinstance(integer_symbols[0], CNU): if integer_symbols[0].power == 1: integer_symbols = [system.digits[1]] + integer_symbols if len(integer_symbols) > 1: if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU): integer_symbols.append( CNU(integer_symbols[-2].power - 1, None, None, None, None)) result = [] unit_count = 0 for s in integer_symbols: if isinstance(s, CND): result.append(s) unit_count = 0 elif isinstance(s, CNU): current_unit = CNU(s.power, None, None, None, None) unit_count += 1 if unit_count == 1: result.append(current_unit) elif unit_count > 1: for i in range(len(result)): if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power: result[-i - 1] = CNU(result[-i - 1].power + current_unit.power, None, None, None, None) return result def compute_value(integer_symbols): """ Compute the value. When current unit is larger than previous unit, current unit * all previous units will be used as all previous units. e.g. '两千万' = 2000 * 10000 not 2000 + 10000 """ value = [0] last_power = 0 for s in integer_symbols: if isinstance(s, CND): value[-1] = s.value elif isinstance(s, CNU): value[-1] *= pow(10, s.power) if s.power > last_power: value[:-1] = list(map(lambda v: v * pow(10, s.power), value[:-1])) last_power = s.power value.append(0) return sum(value) system = create_system(numbering_type) int_part, dec_part = string2symbols(chinese_string, system) int_part = correct_symbols(int_part, system) int_str = str(compute_value(int_part)) dec_str = ''.join([str(d.value) for d in dec_part]) if dec_part: return '{0}.{1}'.format(int_str, dec_str) else: return int_str def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False, traditional=False, alt_zero=False, alt_one=False, alt_two=True, use_zeros=True, use_units=True): def get_value(value_string, use_zeros=True): striped_string = value_string.lstrip('0') # record nothing if all zeros if not striped_string: return [] # record one digits elif len(striped_string) == 1: if use_zeros and len(value_string) != len(striped_string): return [system.digits[0], system.digits[int(striped_string)]] else: return [system.digits[int(striped_string)]] # recursively record multiple digits else: result_unit = next(u for u in reversed( system.units) if u.power < len(striped_string)) result_string = value_string[:-result_unit.power] return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:]) system = create_system(numbering_type) int_dec = number_string.split('.') if len(int_dec) == 1: int_string = int_dec[0] dec_string = "" elif len(int_dec) == 2: int_string = int_dec[0] dec_string = int_dec[1] else: raise ValueError( "invalid input num string with more than one dot: {}".format(number_string)) if use_units and len(int_string) > 1: result_symbols = get_value(int_string) else: result_symbols = [system.digits[int(c)] for c in int_string] dec_symbols = [system.digits[int(c)] for c in dec_string] if dec_string: result_symbols += [system.math.point] + dec_symbols if alt_two: liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t, system.digits[2].big_s, system.digits[2].big_t) for i, v in enumerate(result_symbols): if isinstance(v, CND) and v.value == 2: next_symbol = result_symbols[i + 1] if i < len(result_symbols) - 1 else None previous_symbol = result_symbols[i - 1] if i > 0 else None if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))): if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)): result_symbols[i] = liang # if big is True, '两' will not be used and `alt_two` has no impact on output if big: attr_name = 'big_' if traditional: attr_name += 't' else: attr_name += 's' else: if traditional: attr_name = 'traditional' else: attr_name = 'simplified' result = ''.join([getattr(s, attr_name) for s in result_symbols]) # if not use_zeros: # result = result.strip(getattr(system.digits[0], attr_name)) if alt_zero: result = result.replace( getattr(system.digits[0], attr_name), system.digits[0].alt_s) if alt_one: result = result.replace( getattr(system.digits[1], attr_name), system.digits[1].alt_s) for i, p in enumerate(POINT): if result.startswith(p): return CHINESE_DIGIS[0] + result # ^10, 11, .., 19 if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0], SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \ result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]: result = result[1:] return result # ================================================================================ # # different types of rewriters # ================================================================================ # class Cardinal: """ CARDINAL类 """ def __init__(self, cardinal=None, chntext=None): self.cardinal = cardinal self.chntext = chntext def chntext2cardinal(self): return chn2num(self.chntext) def cardinal2chntext(self): return num2chn(self.cardinal) class Digit: """ DIGIT类 """ def __init__(self, digit=None, chntext=None): self.digit = digit self.chntext = chntext # def chntext2digit(self): # return chn2num(self.chntext) def digit2chntext(self): return num2chn(self.digit, alt_two=False, use_units=False) class TelePhone: """ TELEPHONE类 """ def __init__(self, telephone=None, raw_chntext=None, chntext=None): self.telephone = telephone self.raw_chntext = raw_chntext self.chntext = chntext # def chntext2telephone(self): # sil_parts = self.raw_chntext.split('') # self.telephone = '-'.join([ # str(chn2num(p)) for p in sil_parts # ]) # return self.telephone def telephone2chntext(self, fixed=False): if fixed: sil_parts = self.telephone.split('-') self.raw_chntext = ''.join([ num2chn(part, alt_two=False, use_units=False) for part in sil_parts ]) self.chntext = self.raw_chntext.replace('', '') else: sp_parts = self.telephone.strip('+').split() self.raw_chntext = ''.join([ num2chn(part, alt_two=False, use_units=False) for part in sp_parts ]) self.chntext = self.raw_chntext.replace('', '') return self.chntext class Fraction: """ FRACTION类 """ def __init__(self, fraction=None, chntext=None): self.fraction = fraction self.chntext = chntext def chntext2fraction(self): denominator, numerator = self.chntext.split('分之') return chn2num(numerator) + '/' + chn2num(denominator) def fraction2chntext(self): numerator, denominator = self.fraction.split('/') return num2chn(denominator) + '分之' + num2chn(numerator) class Date: """ DATE类 """ def __init__(self, date=None, chntext=None): self.date = date self.chntext = chntext # def chntext2date(self): # chntext = self.chntext # try: # year, other = chntext.strip().split('年', maxsplit=1) # year = Digit(chntext=year).digit2chntext() + '年' # except ValueError: # other = chntext # year = '' # if other: # try: # month, day = other.strip().split('月', maxsplit=1) # month = Cardinal(chntext=month).chntext2cardinal() + '月' # except ValueError: # day = chntext # month = '' # if day: # day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1] # else: # month = '' # day = '' # date = year + month + day # self.date = date # return self.date def date2chntext(self): date = self.date try: year, other = date.strip().split('年', 1) year = Digit(digit=year).digit2chntext() + '年' except ValueError: other = date year = '' if other: try: month, day = other.strip().split('月', 1) month = Cardinal(cardinal=month).cardinal2chntext() + '月' except ValueError: day = date month = '' if day: day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1] else: month = '' day = '' chntext = year + month + day self.chntext = chntext return self.chntext class Money: """ MONEY类 """ def __init__(self, money=None, chntext=None): self.money = money self.chntext = chntext # def chntext2money(self): # return self.money def money2chntext(self): money = self.money pattern = re.compile(r'(\d+(\.\d+)?)') matchers = pattern.findall(money) if matchers: for matcher in matchers: money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext()) self.chntext = money return self.chntext class Percentage: """ PERCENTAGE类 """ def __init__(self, percentage=None, chntext=None): self.percentage = percentage self.chntext = chntext def chntext2percentage(self): return chn2num(self.chntext.strip().strip('百分之')) + '%' def percentage2chntext(self): return '百分之' + num2chn(self.percentage.strip().strip('%')) # ================================================================================ # # NSW Normalizer # ================================================================================ # class NSWNormalizer: def __init__(self, raw_text): self.raw_text = '^' + raw_text + '$' self.norm_text = '' def _particular(self): text = self.norm_text pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))") matchers = pattern.findall(text) if matchers: # print('particular') for matcher in matchers: text = text.replace(matcher[0], matcher[1] + '2' + matcher[2], 1) self.norm_text = text return self.norm_text def normalize(self, remove_punc=True): text = self.raw_text # 规范化日期 pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)") matchers = pattern.findall(text) if matchers: # print('date') for matcher in matchers: text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1) # 规范化金钱 pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)") matchers = pattern.findall(text) if matchers: # print('money') for matcher in matchers: text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1) # 规范化固话/手机号码 # 手机 # http://www.jihaoba.com/news/show/13680 # 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198 # 联通：130、131、132、156、155、186、185、176 # 电信：133、153、189、180、181、177 pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D") matchers = pattern.findall(text) if matchers: # print('telephone') for matcher in matchers: text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1) # 固话 pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D") matchers = pattern.findall(text) if matchers: # print('fixed telephone') for matcher in matchers: text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1) # 规范化分数 pattern = re.compile(r"(\d+/\d+)") matchers = pattern.findall(text) if matchers: # print('fraction') for matcher in matchers: text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1) # 规范化百分数 text = text.replace('％', '%') pattern = re.compile(r"(\d+(\.\d+)?%)") matchers = pattern.findall(text) if matchers: # print('percentage') for matcher in matchers: text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1) # 规范化纯数+量词 pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS) matchers = pattern.findall(text) if matchers: # print('cardinal+quantifier') for matcher in matchers: text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1) # 规范化数字编号 pattern = re.compile(r"(\d{4,32})") matchers = pattern.findall(text) if matchers: # print('digit') for matcher in matchers: text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1) # 规范化纯数 pattern = re.compile(r"(\d+(\.\d+)?)") matchers = pattern.findall(text) if matchers: # print('cardinal') for matcher in matchers: text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1) self.norm_text = text self._particular() text = self.norm_text.lstrip('^').rstrip('$') if remove_punc: # Punctuations removal old_chars = CHINESE_PUNC_LIST + string.punctuation # includes all CN and EN punctuations new_chars = ' ' * len(old_chars) del_chars = '' text = text.translate(str.maketrans(old_chars, new_chars, del_chars)) return text def nsw_test_case(raw_text): print('I:' + raw_text) print('O:' + NSWNormalizer(raw_text).normalize()) print('') def nsw_test(): nsw_test_case('固话：0595-23865596或23880880。') nsw_test_case('固话：0595-23865596或23880880。') nsw_test_case('手机：+86 19859213959或15659451527。') nsw_test_case('分数：32477/76391。') nsw_test_case('百分数：80.03%。') nsw_test_case('编号：31520181154418。') nsw_test_case('纯数：2983.07克或12345.60米。') nsw_test_case('日期：1999年2月20日或09年3月15号。') nsw_test_case('金钱：12块5，34.5元，20.1万') nsw_test_case('特殊：O2O或B2C。') nsw_test_case('3456万吨') nsw_test_case('2938个') nsw_test_case('938') nsw_test_case('今天吃了115个小笼包231个馒头') nsw_test_case('有62％的概率') if __name__ == '__main__': # nsw_test() p = argparse.ArgumentParser() p.add_argument('ifile', help='input filename, assume utf-8 encoding') p.add_argument('ofile', help='output filename') p.add_argument('--to_upper', action='store_true', help='convert to upper case') p.add_argument('--to_lower', action='store_true', help='convert to lower case') p.add_argument('--has_key', action='store_true', help="input text has Kaldi's key as first field.") p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines') args = p.parse_args() ifile = codecs.open(args.ifile, 'r', 'utf8') ofile = codecs.open(args.ofile, 'w+', 'utf8') n = 0 for l in ifile: key = '' text = '' if args.has_key: cols = l.split(maxsplit=1) key = cols[0] if len(cols) == 2: text = cols[1] else: text = '' else: text = l # cases if args.to_upper and args.to_lower: sys.stderr.write('text norm: to_upper OR to_lower?') exit(1) if args.to_upper: text = text.upper() if args.to_lower: text = text.lower() # NSW(Non-Standard-Word) normalization text = NSWNormalizer(text).normalize() # if args.has_key: ofile.write(key + '\t' + text) else: ofile.write(text) n += 1 if n % args.log_interval == 0: sys.stderr.write("text norm: {} lines done.\n".format(n)) sys.stderr.write("text norm: {} lines done in total.\n".format(n)) ifile.close() ofile.close() ================================================ FILE: NeuralSeq/utils/training_utils.py ================================================ from utils.hparams import hparams class RSQRTSchedule(object): def __init__(self, optimizer): super().__init__() self.optimizer = optimizer self.constant_lr = hparams['lr'] self.warmup_updates = hparams['warmup_updates'] self.hidden_size = hparams['hidden_size'] self.lr = hparams['lr'] for param_group in optimizer.param_groups: param_group['lr'] = self.lr self.step(0) def step(self, num_updates): constant_lr = self.constant_lr warmup = min(num_updates / self.warmup_updates, 1.0) rsqrt_decay = max(self.warmup_updates, num_updates) ** -0.5 rsqrt_hidden = self.hidden_size ** -0.5 self.lr = max(constant_lr * warmup * rsqrt_decay * rsqrt_hidden, 1e-7) for param_group in self.optimizer.param_groups: param_group['lr'] = self.lr return self.lr def get_lr(self): return self.optimizer.param_groups[0]['lr'] ================================================ FILE: NeuralSeq/utils/tts_utils.py ================================================ from collections import defaultdict import torch import torch.nn.functional as F def make_positions(tensor, padding_idx): """Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. """ # The series of casts and type-conversions here are carefully # balanced to both work with ONNX export and XLA. In particular XLA # prefers ints, cumsum defaults to output longs, and ONNX doesn't know # how to handle the dtype kwarg in cumsum. mask = tensor.ne(padding_idx).int() return ( torch.cumsum(mask, dim=1).type_as(mask) * mask ).long() + padding_idx def softmax(x, dim): return F.softmax(x, dim=dim, dtype=torch.float32) def sequence_mask(lengths, maxlen, dtype=torch.bool): if maxlen is None: maxlen = lengths.max() mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths).t() mask.type(dtype) return mask INCREMENTAL_STATE_INSTANCE_ID = defaultdict(lambda: 0) def _get_full_incremental_state_key(module_instance, key): module_name = module_instance.__class__.__name__ # assign a unique ID to each module instance, so that incremental state is # not shared across module instances if not hasattr(module_instance, '_instance_id'): INCREMENTAL_STATE_INSTANCE_ID[module_name] += 1 module_instance._instance_id = INCREMENTAL_STATE_INSTANCE_ID[module_name] return '{}.{}.{}'.format(module_name, module_instance._instance_id, key) def get_incremental_state(module, incremental_state, key): """Helper for getting incremental state for an nn.Module.""" full_key = _get_full_incremental_state_key(module, key) if incremental_state is None or full_key not in incremental_state: return None return incremental_state[full_key] def set_incremental_state(module, incremental_state, key, value): """Helper for setting incremental state for an nn.Module.""" if incremental_state is not None: full_key = _get_full_incremental_state_key(module, key) incremental_state[full_key] = value def fill_with_neg_inf(t): """FP16-compatible function that fills a tensor with -inf.""" return t.float().fill_(float('-inf')).type_as(t) def fill_with_neg_inf2(t): """FP16-compatible function that fills a tensor with -inf.""" return t.float().fill_(-1e8).type_as(t) def get_focus_rate(attn, src_padding_mask=None, tgt_padding_mask=None): ''' attn: bs x L_t x L_s ''' if src_padding_mask is not None: attn = attn * (1 - src_padding_mask.float())[:, None, :] if tgt_padding_mask is not None: attn = attn * (1 - tgt_padding_mask.float())[:, :, None] focus_rate = attn.max(-1).values.sum(-1) focus_rate = focus_rate / attn.sum(-1).sum(-1) return focus_rate def get_phone_coverage_rate(attn, src_padding_mask=None, src_seg_mask=None, tgt_padding_mask=None): ''' attn: bs x L_t x L_s ''' src_mask = attn.new(attn.size(0), attn.size(-1)).bool().fill_(False) if src_padding_mask is not None: src_mask |= src_padding_mask if src_seg_mask is not None: src_mask |= src_seg_mask attn = attn * (1 - src_mask.float())[:, None, :] if tgt_padding_mask is not None: attn = attn * (1 - tgt_padding_mask.float())[:, :, None] phone_coverage_rate = attn.max(1).values.sum(-1) # phone_coverage_rate = phone_coverage_rate / attn.sum(-1).sum(-1) phone_coverage_rate = phone_coverage_rate / (1 - src_mask.float()).sum(-1) return phone_coverage_rate def get_diagonal_focus_rate(attn, attn_ks, target_len, src_padding_mask=None, tgt_padding_mask=None, band_mask_factor=5, band_width=50): ''' attn: bx x L_t x L_s attn_ks: shape: tensor with shape [batch_size], input_lens/output_lens diagonal: y=k*x (k=attn_ks, x:output, y:input) 1 0 0 0 1 0 0 0 1 y>=k*(x-width) and y<=k*(x+width):1 else:0 ''' # width = min(target_len/band_mask_factor, 50) width1 = target_len / band_mask_factor width2 = target_len.new(target_len.size()).fill_(band_width) width = torch.where(width1 < width2, width1, width2).float() base = torch.ones(attn.size()).to(attn.device) zero = torch.zeros(attn.size()).to(attn.device) x = torch.arange(0, attn.size(1)).to(attn.device)[None, :, None].float() * base y = torch.arange(0, attn.size(2)).to(attn.device)[None, None, :].float() * base cond = (y - attn_ks[:, None, None] * x) cond1 = cond + attn_ks[:, None, None] * width[:, None, None] cond2 = cond - attn_ks[:, None, None] * width[:, None, None] mask1 = torch.where(cond1 < 0, zero, base) mask2 = torch.where(cond2 > 0, zero, base) mask = mask1 * mask2 if src_padding_mask is not None: attn = attn * (1 - src_padding_mask.float())[:, None, :] if tgt_padding_mask is not None: attn = attn * (1 - tgt_padding_mask.float())[:, :, None] diagonal_attn = attn * mask diagonal_focus_rate = diagonal_attn.sum(-1).sum(-1) / attn.sum(-1).sum(-1) return diagonal_focus_rate, mask def select_attn(attn_logits, type='best'): """ :param attn_logits: [n_layers, B, n_head, T_sp, T_txt] :return: """ encdec_attn = torch.stack(attn_logits, 0).transpose(1, 2) # [n_layers * n_head, B, T_sp, T_txt] encdec_attn = (encdec_attn.reshape([-1, *encdec_attn.shape[2:]])).softmax(-1) if type == 'best': indices = encdec_attn.max(-1).values.sum(-1).argmax(0) encdec_attn = encdec_attn.gather( 0, indices[None, :, None, None].repeat(1, 1, encdec_attn.size(-2), encdec_attn.size(-1)))[0] return encdec_attn elif type == 'mean': return encdec_attn.mean(0) def make_pad_mask(lengths, xs=None, length_dim=-1): """Make mask tensor containing indices of padded part. Args: lengths (LongTensor or List): Batch of lengths (B,). xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor. length_dim (int, optional): Dimension indicator of the above tensor. See the example. Returns: Tensor: Mask tensor containing indices of padded part. dtype=torch.uint8 in PyTorch 1.2- dtype=torch.bool in PyTorch 1.2+ (including 1.2) Examples: With only lengths. >>> lengths = [5, 3, 2] >>> make_non_pad_mask(lengths) masks = [[0, 0, 0, 0 ,0], [0, 0, 0, 1, 1], [0, 0, 1, 1, 1]] With the reference tensor. >>> xs = torch.zeros((3, 2, 4)) >>> make_pad_mask(lengths, xs) tensor([[[0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 1], [0, 0, 0, 1]], [[0, 0, 1, 1], [0, 0, 1, 1]]], dtype=torch.uint8) >>> xs = torch.zeros((3, 2, 6)) >>> make_pad_mask(lengths, xs) tensor([[[0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]], [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], [[0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8) With the reference tensor and dimension indicator. >>> xs = torch.zeros((3, 6, 6)) >>> make_pad_mask(lengths, xs, 1) tensor([[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]], [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]], [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8) >>> make_pad_mask(lengths, xs, 2) tensor([[[0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]], [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], [[0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8) """ if length_dim == 0: raise ValueError("length_dim cannot be 0: {}".format(length_dim)) if not isinstance(lengths, list): lengths = lengths.tolist() bs = int(len(lengths)) if xs is None: maxlen = int(max(lengths)) else: maxlen = xs.size(length_dim) seq_range = torch.arange(0, maxlen, dtype=torch.int64) seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen) seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1) mask = seq_range_expand >= seq_length_expand if xs is not None: assert xs.size(0) == bs, (xs.size(0), bs) if length_dim < 0: length_dim = xs.dim() + length_dim # ind = (:, None, ..., None, :, , None, ..., None) ind = tuple( slice(None) if i in (0, length_dim) else None for i in range(xs.dim()) ) mask = mask[ind].expand_as(xs).to(xs.device) return mask def make_non_pad_mask(lengths, xs=None, length_dim=-1): """Make mask tensor containing indices of non-padded part. Args: lengths (LongTensor or List): Batch of lengths (B,). xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor. length_dim (int, optional): Dimension indicator of the above tensor. See the example. Returns: ByteTensor: mask tensor containing indices of padded part. dtype=torch.uint8 in PyTorch 1.2- dtype=torch.bool in PyTorch 1.2+ (including 1.2) Examples: With only lengths. >>> lengths = [5, 3, 2] >>> make_non_pad_mask(lengths) masks = [[1, 1, 1, 1 ,1], [1, 1, 1, 0, 0], [1, 1, 0, 0, 0]] With the reference tensor. >>> xs = torch.zeros((3, 2, 4)) >>> make_non_pad_mask(lengths, xs) tensor([[[1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 0], [1, 1, 1, 0]], [[1, 1, 0, 0], [1, 1, 0, 0]]], dtype=torch.uint8) >>> xs = torch.zeros((3, 2, 6)) >>> make_non_pad_mask(lengths, xs) tensor([[[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0]], [[1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0]], [[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8) With the reference tensor and dimension indicator. >>> xs = torch.zeros((3, 6, 6)) >>> make_non_pad_mask(lengths, xs, 1) tensor([[[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]], [[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], [[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]], dtype=torch.uint8) >>> make_non_pad_mask(lengths, xs, 2) tensor([[[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0]], [[1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0]], [[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8) """ return ~make_pad_mask(lengths, xs, length_dim) def get_mask_from_lengths(lengths): max_len = torch.max(lengths).item() ids = torch.arange(0, max_len).to(lengths.device) mask = (ids < lengths.unsqueeze(1)).bool() return mask def group_hidden_by_segs(h, seg_ids, max_len): """ :param h: [B, T, H] :param seg_ids: [B, T] :return: h_ph: [B, T_ph, H] """ B, T, H = h.shape h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h) all_ones = h.new_ones(h.shape[:2]) cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous() h_gby_segs = h_gby_segs[:, 1:] cnt_gby_segs = cnt_gby_segs[:, 1:] h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1) return h_gby_segs, cnt_gby_segs def mel2token_to_dur(mel2token, T_txt=None, max_dur=None): is_torch = isinstance(mel2token, torch.Tensor) has_batch_dim = True if not is_torch: mel2token = torch.LongTensor(mel2token) if T_txt is None: T_txt = mel2token.max() if len(mel2token.shape) == 1: mel2token = mel2token[None, ...] has_batch_dim = False B, _ = mel2token.shape dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token)) dur = dur[:, 1:] if max_dur is not None: dur = dur.clamp(max=max_dur) if not is_torch: dur = dur.numpy() if not has_batch_dim: dur = dur[0] return dur def expand_word2ph(word_encoding, ph2word): word_encoding = F.pad(word_encoding,[0,0,1,0]) ph2word_ = ph2word[:, :, None].repeat([1, 1, word_encoding.shape[-1]]) out = torch.gather(word_encoding, 1, ph2word_) # [B, T, H] return out ================================================ FILE: NeuralSeq/vocoders/__init__.py ================================================ from vocoders import hifigan ================================================ FILE: NeuralSeq/vocoders/base_vocoder.py ================================================ import importlib VOCODERS = {} def register_vocoder(cls): VOCODERS[cls.__name__.lower()] = cls VOCODERS[cls.__name__] = cls return cls def get_vocoder_cls(hparams): if hparams['vocoder'] in VOCODERS: return VOCODERS[hparams['vocoder']] else: vocoder_cls = hparams['vocoder'] pkg = ".".join(vocoder_cls.split(".")[:-1]) cls_name = vocoder_cls.split(".")[-1] vocoder_cls = getattr(importlib.import_module(pkg), cls_name) return vocoder_cls class BaseVocoder: def spec2wav(self, mel): """ :param mel: [T, 80] :return: wav: [T'] """ raise NotImplementedError @staticmethod def wav2spec(wav_fn): """ :param wav_fn: str :return: wav, mel: [T, 80] """ raise NotImplementedError ================================================ FILE: NeuralSeq/vocoders/hifigan.py ================================================ import glob import json import os import re import librosa import torch import utils from modules.hifigan.hifigan import HifiGanGenerator from utils.hparams import hparams, set_hparams from vocoders.base_vocoder import register_vocoder from vocoders.pwg import PWG from vocoders.vocoder_utils import denoise def load_model(config_path, checkpoint_path): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ckpt_dict = torch.load(checkpoint_path, map_location="cpu") if '.yaml' in config_path: config = set_hparams(config_path, global_hparams=False) state = ckpt_dict["state_dict"]["model_gen"] elif '.json' in config_path: config = json.load(open(config_path, 'r')) state = ckpt_dict["generator"] model = HifiGanGenerator(config) model.load_state_dict(state, strict=True) model.remove_weight_norm() model = model.eval().to(device) print(f"| Loaded model parameters from {checkpoint_path}.") print(f"| HifiGAN device: {device}.") return model, config, device total_time = 0 @register_vocoder class HifiGAN(PWG): def __init__(self): base_dir = hparams['vocoder_ckpt'] config_path = f'{base_dir}/config.yaml' if os.path.exists(config_path): ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1] print('| load HifiGAN: ', ckpt) self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt) else: config_path = f'{base_dir}/config.json' ckpt = f'{base_dir}/generator_v1' if os.path.exists(config_path): self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt) def spec2wav(self, mel, **kwargs): device = self.device with torch.no_grad(): c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(device) with utils.Timer('hifigan', print_time=hparams['profile_infer']): f0 = kwargs.get('f0') if f0 is not None and hparams.get('use_nsf'): f0 = torch.FloatTensor(f0[None, :]).to(device) y = self.model(c, f0).view(-1) else: y = self.model(c).view(-1) wav_out = y.cpu().numpy() if hparams.get('vocoder_denoise_c', 0.0) > 0: wav_out = denoise(wav_out, v=hparams['vocoder_denoise_c']) return wav_out # @staticmethod # def wav2spec(wav_fn, **kwargs): # wav, _ = librosa.core.load(wav_fn, sr=hparams['audio_sample_rate']) # wav_torch = torch.FloatTensor(wav)[None, :] # mel = mel_spectrogram(wav_torch, hparams).numpy()[0] # return wav, mel.T ================================================ FILE: NeuralSeq/vocoders/pwg.py ================================================ import glob import re import librosa import torch import yaml from sklearn.preprocessing import StandardScaler from torch import nn from modules.parallel_wavegan.models import ParallelWaveGANGenerator from modules.parallel_wavegan.utils import read_hdf5 from utils.hparams import hparams from utils.pitch_utils import f0_to_coarse from vocoders.base_vocoder import BaseVocoder, register_vocoder import numpy as np def load_pwg_model(config_path, checkpoint_path, stats_path): # load config with open(config_path) as f: config = yaml.load(f, Loader=yaml.Loader) # setup if torch.cuda.is_available(): device = torch.device("cuda") else: device = torch.device("cpu") model = ParallelWaveGANGenerator(**config["generator_params"]) ckpt_dict = torch.load(checkpoint_path, map_location="cpu") if 'state_dict' not in ckpt_dict: # official vocoder model.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["model"]["generator"]) scaler = StandardScaler() if config["format"] == "hdf5": scaler.mean_ = read_hdf5(stats_path, "mean") scaler.scale_ = read_hdf5(stats_path, "scale") elif config["format"] == "npy": scaler.mean_ = np.load(stats_path)[0] scaler.scale_ = np.load(stats_path)[1] else: raise ValueError("support only hdf5 or npy format.") else: # custom PWG vocoder fake_task = nn.Module() fake_task.model_gen = model fake_task.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["state_dict"], strict=False) scaler = None model.remove_weight_norm() model = model.eval().to(device) print(f"| Loaded model parameters from {checkpoint_path}.") print(f"| PWG device: {device}.") return model, scaler, config, device @register_vocoder class PWG(BaseVocoder): def __init__(self): if hparams['vocoder_ckpt'] == '': # load LJSpeech PWG pretrained model base_dir = 'wavegan_pretrained' ckpts = glob.glob(f'{base_dir}/checkpoint-*steps.pkl') ckpt = sorted(ckpts, key= lambda x: int(re.findall(f'{base_dir}/checkpoint-(\d+)steps.pkl', x)[0]))[-1] config_path = f'{base_dir}/config.yaml' print('| load PWG: ', ckpt) self.model, self.scaler, self.config, self.device = load_pwg_model( config_path=config_path, checkpoint_path=ckpt, stats_path=f'{base_dir}/stats.h5', ) else: base_dir = hparams['vocoder_ckpt'] print(base_dir) config_path = f'{base_dir}/config.yaml' ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1] print('| load PWG: ', ckpt) self.scaler = None self.model, _, self.config, self.device = load_pwg_model( config_path=config_path, checkpoint_path=ckpt, stats_path=f'{base_dir}/stats.h5', ) def spec2wav(self, mel, **kwargs): # start generation config = self.config device = self.device pad_size = (config["generator_params"]["aux_context_window"], config["generator_params"]["aux_context_window"]) c = mel if self.scaler is not None: c = self.scaler.transform(c) with torch.no_grad(): z = torch.randn(1, 1, c.shape[0] * config["hop_size"]).to(device) c = np.pad(c, (pad_size, (0, 0)), "edge") c = torch.FloatTensor(c).unsqueeze(0).transpose(2, 1).to(device) p = kwargs.get('f0') if p is not None: p = f0_to_coarse(p) p = np.pad(p, (pad_size,), "edge") p = torch.LongTensor(p[None, :]).to(device) y = self.model(z, c, p).view(-1) wav_out = y.cpu().numpy() return wav_out @staticmethod def wav2spec(wav_fn, return_linear=False): from data_gen.tts.data_gen_utils import process_utterance res = process_utterance( wav_fn, fft_size=hparams['fft_size'], hop_size=hparams['hop_size'], win_length=hparams['win_size'], num_mels=hparams['audio_num_mel_bins'], fmin=hparams['fmin'], fmax=hparams['fmax'], sample_rate=hparams['audio_sample_rate'], loud_norm=hparams['loud_norm'], min_level_db=hparams['min_level_db'], return_linear=return_linear, vocoder='pwg', eps=float(hparams.get('wav2spec_eps', 1e-10))) if return_linear: return res[0], res[1].T, res[2].T # [T, 80], [T, n_fft] else: return res[0], res[1].T @staticmethod def wav2mfcc(wav_fn): fft_size = hparams['fft_size'] hop_size = hparams['hop_size'] win_length = hparams['win_size'] sample_rate = hparams['audio_sample_rate'] wav, _ = librosa.core.load(wav_fn, sr=sample_rate) mfcc = librosa.feature.mfcc(y=wav, sr=sample_rate, n_mfcc=13, n_fft=fft_size, hop_length=hop_size, win_length=win_length, pad_mode="constant", power=1.0) mfcc_delta = librosa.feature.delta(mfcc, order=1) mfcc_delta_delta = librosa.feature.delta(mfcc, order=2) mfcc = np.concatenate([mfcc, mfcc_delta, mfcc_delta_delta]).T return mfcc ================================================ FILE: NeuralSeq/vocoders/vocoder_utils.py ================================================ import librosa from utils.hparams import hparams import numpy as np def denoise(wav, v=0.1): spec = librosa.stft(y=wav, n_fft=hparams['fft_size'], hop_length=hparams['hop_size'], win_length=hparams['win_size'], pad_mode='constant') spec_m = np.abs(spec) spec_m = np.clip(spec_m - v, a_min=0, a_max=None) spec_a = np.angle(spec) return librosa.istft(spec_m * np.exp(1j * spec_a), hop_length=hparams['hop_size'], win_length=hparams['win_size']) ================================================ FILE: README.md ================================================ # AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head [![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2304.12995) [![GitHub Stars](https://img.shields.io/github/stars/AIGC-Audio/AudioGPT?style=social)](https://github.com/AIGC-Audio/AudioGPT) ![visitors](https://visitor-badge.glitch.me/badge?page_id=AIGC-Audio.AudioGPT) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/spaces/AIGC-Audio/AudioGPT) We provide our implementation and pretrained models as open source in this repository. ## Get Started Please refer to [run.md](run.md) ## Capabilities Here we list the capability of AudioGPT at this time. More supported models and tasks are coming soon. For prompt examples, refer to [asset](assets/README.md). Currently not every model has repository. ### Speech | Task | Supported Foundation Models | Status | |:--------------------------:|:-------------------------------:|:------:| | Text-to-Speech | [FastSpeech](https://github.com/ming024/FastSpeech2), [SyntaSpeech](https://github.com/yerfor/SyntaSpeech), [VITS](https://github.com/jaywalnut310/vits) | Yes (WIP) | | Style Transfer | [GenerSpeech](https://github.com/Rongjiehuang/GenerSpeech) | Yes | | Speech Recognition | [whisper](https://github.com/openai/whisper), [Conformer](https://github.com/sooftware/conformer) | Yes | | Speech Enhancement | [ConvTasNet]() | Yes (WIP) | | Speech Separation | [TF-GridNet](https://arxiv.org/pdf/2211.12433.pdf) | Yes (WIP) | | Speech Translation | [Multi-decoder](https://arxiv.org/pdf/2109.12804.pdf) | WIP | | Mono-to-Binaural | [NeuralWarp](https://github.com/fdarmon/NeuralWarp) | Yes | ### Sing | Task | Supported Foundation Models | Status | |:-------------------------:|:-------------------------------:|:------:| | Text-to-Sing | [DiffSinger](https://github.com/MoonInTheRiver/DiffSinger), [VISinger](https://github.com/jerryuhoo/VISinger) | Yes (WIP) | ### Audio | Task | Supported Foundation Models | Status | |:----------------------:|:---------------------------:|:------:| | Text-to-Audio | [Make-An-Audio]() | Yes | | Audio Inpainting | [Make-An-Audio]() | Yes | | Image-to-Audio | [Make-An-Audio]() | Yes | | Sound Detection | [Audio-transformer](https://github.com/RetroCirce/HTS-Audio-Transformer) | Yes | | Target Sound Detection | [TSDNet](https://github.com/gy65896/TSDNet) | Yes | | Sound Extraction | [LASSNet](https://github.com/liuxubo717/LASS) | Yes | ### Talking Head | Task | Supported Foundation Models | Status | |:-------------------------:|:-------------------------------:|:----------:| | Talking Head Synthesis | [GeneFace](https://github.com/yerfor/GeneFace) | Yes (WIP) | ## Acknowledgement We appreciate the open source of the following projects: [ESPNet](https://github.com/espnet/espnet)   [NATSpeech](https://github.com/NATSpeech/NATSpeech)   [Visual ChatGPT](https://github.com/microsoft/visual-chatgpt)   [Hugging Face](https://github.com/huggingface)   [LangChain](https://github.com/hwchase17/langchain)   [Stable Diffusion](https://github.com/CompVis/stable-diffusion)   ================================================ FILE: assets/README.md ================================================ # Prompt Example ## Speech ### Text-To-Speech Input Example : Generate a speech with text "here we go"
Output:
![](tts.png)
Audio:

### Style Transfer Text-To-Speech First upload your audio(.wav)
Input Example : Speak using the voice of this audio. The text is "here we go".
Output:
![](style_transfer_tts.png)
### Speech Recognition First upload your audio(.wav)
Audio Example :

Input Example : Generate the text of this speech
Output:
![](asr.png)
## Sing ### Text-To-Sing Input example : please generate a piece of singing voice. Text sequence is 小酒窝长睫毛AP是你最美的记号. Note sequence is C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4. Note duration sequence is 0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340.
Output:
![](t2s.png)
Audio:

## Audio ### Text-To-Audio Input Example : Generate an audio of a piano playing
Output:
![](t2a.png)
Audio:

### Audio Inpainting First upload your audio(.wav)
Audio Example :

Input Example : I want to inpaint this audio.
Output:
![](inpaint-1.png)
Then you can press the "Predict Masked Place" button
Output:
![](inpaint-2.png)
Output Audio:

### Image-To-Audio First upload your image(.png)
Input Example : Generate the audio of this image
Output:
![](i2a-2.png)
Audio:

### Audio-To-Text First upload your audio(.wav)
Audio Example :

Input Example : Please tell me the text description of this audio.
Output:
![](a2i.png)
### Sound Detection First upload your audio(.wav)
Audio Example :

Input Example : What events does this audio include?
Output:
![](detection.png)
### Mono audio to Binaural Audio First upload your audio(.wav)

Input Example: Transfer the mono speech to a binaural one audio.
Output:
![](m2b.png)
### Target Sound Detection Fisrt upload your audio(.wav)

Input Example: please help me detect the target sound in the audio based on desription: “I want to detect Applause event”
Output:
![](tsd.png)
### Sound Extraction First upload your audio(.wav)

Input Example: Please help me extract the sound events from the audio based on the description: "a person shouts nearby and then emergency vehicle sirens sounds"
Output:
![](sound_extraction.png)
================================================ FILE: audio-chatgpt.py ================================================ import sys import os sys.path.append(os.path.dirname(os.path.realpath(__file__))) sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'NeuralSeq')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'text_to_audio/Make_An_Audio')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'audio_detection')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'mono2binaural')) import gradio as gr import matplotlib import librosa import torch from langchain.agents.initialize import initialize_agent from langchain.agents.tools import Tool from langchain.chains.conversation.memory import ConversationBufferMemory from langchain.llms.openai import OpenAI import re import uuid import soundfile from PIL import Image import numpy as np from omegaconf import OmegaConf from einops import repeat from ldm.util import instantiate_from_config from ldm.data.extract_mel_spectrogram import TRANSFORMS_16000 from vocoder.bigvgan.models import VocoderBigVGAN from ldm.models.diffusion.ddim import DDIMSampler import whisper from utils.hparams import set_hparams from utils.hparams import hparams as hp import scipy.io.wavfile as wavfile import librosa from audio_infer.utils import config as detection_config from audio_infer.pytorch.models import PVT import clip import numpy as np AUDIO_CHATGPT_PREFIX = """AudioGPT AudioGPT can not directly read audios, but it has a list of tools to finish different speech, audio, and singing voice tasks. Each audio will have a file name formed as "audio/xxx.wav". When talking about audios, AudioGPT is very strict to the file name and will never fabricate nonexistent files. AudioGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the audio content and audio file name. It will remember to provide the file name from the last tool observation, if a new audio is generated. Human may provide new audios to AudioGPT with a description. The description helps AudioGPT to understand this audio, but AudioGPT should use tools to finish following tasks, rather than directly imagine from the description. Overall, AudioGPT is a powerful audio dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. TOOLS: ------ AudioGPT has access to the following tools:""" AUDIO_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format: ``` Thought: Do I need to use a tool? Yes Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action ``` When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: ``` Thought: Do I need to use a tool? No {ai_prefix}: [your response here] ``` """ AUDIO_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if not exists. You will remember to provide the audio file name loyally if it's provided in the last tool observation. Begin! Previous conversation history: {chat_history} New input: {input} Thought: Do I need to use a tool? {agent_scratchpad}""" def cut_dialogue_history(history_memory, keep_last_n_words = 500): tokens = history_memory.split() n_tokens = len(tokens) print(f"history_memory:{history_memory}, n_tokens: {n_tokens}") if n_tokens < keep_last_n_words: return history_memory else: paragraphs = history_memory.split('\n') last_n_tokens = n_tokens while last_n_tokens >= keep_last_n_words: last_n_tokens = last_n_tokens - len(paragraphs[0].split(' ')) paragraphs = paragraphs[1:] return '\n' + '\n'.join(paragraphs) def merge_audio(audio_path_1, audio_path_2): merged_signal = [] sr_1, signal_1 = wavfile.read(audio_path_1) sr_2, signal_2 = wavfile.read(audio_path_2) merged_signal.append(signal_1) merged_signal.append(signal_2) merged_signal = np.hstack(merged_signal) merged_signal = np.asarray(merged_signal, dtype=np.int16) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, sr_2, merged_signal) return audio_filename class T2I: def __init__(self, device): from transformers import AutoModelForCausalLM, AutoTokenizer from diffusers import StableDiffusionPipeline from transformers import pipeline print("Initializing T2I to %s" % device) self.device = device self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model, tokenizer=self.text_refine_tokenizer, device=self.device) self.pipe.to(device) def inference(self, text): image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"] print(f'{text} refined to {refined_text}') image = self.pipe(refined_text).images[0] image.save(image_filename) print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}") return image_filename class ImageCaptioning: def __init__(self, device): from transformers import BlipProcessor, BlipForConditionalGeneration print("Initializing ImageCaptioning to %s" % device) self.device = device self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device) def inference(self, image_path): inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device) out = self.model.generate(**inputs) captions = self.processor.decode(out[0], skip_special_tokens=True) return captions class T2A: def __init__(self, device): print("Initializing Make-An-Audio to %s" % device) self.device = device self.sampler = self._initialize_model('text_to_audio/Make_An_Audio/configs/text_to_audio/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta40multi_epoch=000085.ckpt', device=device) self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) def _initialize_model(self, config, ckpt, device): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) model = model.to(device) model.cond_stage_model.to(model.device) model.cond_stage_model.device = model.device sampler = DDIMSampler(model) return sampler def txt2audio(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80): SAMPLE_RATE = 16000 prng = np.random.RandomState(seed) start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) uc = self.sampler.model.get_learned_conditioning(n_samples * [""]) c = self.sampler.model.get_learned_conditioning(n_samples * [text]) shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8] # (z_dim, 80//2^x, 848//2^x) samples_ddim, _ = self.sampler.sample(S = ddim_steps, conditioning = c, batch_size = n_samples, shape = shape, verbose = False, unconditional_guidance_scale = scale, unconditional_conditioning = uc, x_T = start_code) x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1] wav_list = [] for idx,spec in enumerate(x_samples_ddim): wav = self.vocoder.vocode(spec) wav_list.append((SAMPLE_RATE,wav)) best_wav = self.select_best_audio(text, wav_list) return best_wav def select_best_audio(self, prompt, wav_list): from wav_evaluation.models.CLAPWrapper import CLAPWrapper clap_model = CLAPWrapper('text_to_audio/Make_An_Audio/useful_ckpts/CLAP/CLAP_weights_2022.pth', 'text_to_audio/Make_An_Audio/useful_ckpts/CLAP/config.yml', use_cuda=torch.cuda.is_available()) text_embeddings = clap_model.get_text_embeddings([prompt]) score_list = [] for data in wav_list: sr, wav = data audio_embeddings = clap_model.get_audio_embeddings([(torch.FloatTensor(wav), sr)], resample=True) score = clap_model.compute_similarity(audio_embeddings, text_embeddings, use_logit_scale=False).squeeze().cpu().numpy() score_list.append(score) max_index = np.array(score_list).argmax() print(score_list, max_index) return wav_list[max_index] def inference(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80): melbins,mel_len = 80,624 with torch.no_grad(): result = self.txt2audio( text = text, H = melbins, W = mel_len ) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, result[1], samplerate = 16000) print(f"Processed T2I.run, text: {text}, audio_filename: {audio_filename}") return audio_filename class I2A: def __init__(self, device): print("Initializing Make-An-Audio-Image to %s" % device) self.device = device self.sampler = self._initialize_model('text_to_audio/Make_An_Audio/configs/img_to_audio/img2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta54_epoch=000216.ckpt', device=device) self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) def _initialize_model(self, config, ckpt, device): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) model = model.to(device) model.cond_stage_model.to(model.device) model.cond_stage_model.device = model.device sampler = DDIMSampler(model) return sampler def img2audio(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80): SAMPLE_RATE = 16000 n_samples = 1 # only support 1 sample prng = np.random.RandomState(seed) start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) uc = self.sampler.model.get_learned_conditioning(n_samples * [""]) #image = Image.fromarray(image) image = Image.open(image) image = self.sampler.model.cond_stage_model.preprocess(image).unsqueeze(0) image_embedding = self.sampler.model.cond_stage_model.forward_img(image) c = image_embedding.repeat(n_samples, 1, 1) shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8] # (z_dim, 80//2^x, 848//2^x) samples_ddim, _ = self.sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, x_T=start_code) x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1] wav_list = [] for idx,spec in enumerate(x_samples_ddim): wav = self.vocoder.vocode(spec) wav_list.append((SAMPLE_RATE,wav)) best_wav = wav_list[0] return best_wav def inference(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80): melbins,mel_len = 80,624 with torch.no_grad(): result = self.img2audio( image=image, H=melbins, W=mel_len ) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, result[1], samplerate = 16000) print(f"Processed I2a.run, image_filename: {image}, audio_filename: {audio_filename}") return audio_filename class TTS: def __init__(self, device=None): from inference.tts.PortaSpeech import TTSInference if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing PortaSpeech to %s" % device) self.device = device self.exp_name = 'checkpoints/ps_adv_baseline' self.set_model_hparams() self.inferencer = TTSInference(self.hp, device) def set_model_hparams(self): set_hparams(exp_name=self.exp_name, print_hparams=False) self.hp = hp def inference(self, text): self.set_model_hparams() inp = {"text": text} out = self.inferencer.infer_once(inp) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, out, samplerate=22050) return audio_filename class T2S: def __init__(self, device= None): from inference.svs.ds_e2e import DiffSingerE2EInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing DiffSinger to %s" % device) self.device = device self.exp_name = 'checkpoints/0831_opencpop_ds1000' self.config= 'NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml' self.set_model_hparams() self.pipe = DiffSingerE2EInfer(self.hp, device) self.default_inp = { 'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP', 'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest', 'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590' } def set_model_hparams(self): set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False) self.hp = hp def inference(self, inputs): self.set_model_hparams() val = inputs.split(",") key = ['text', 'notes', 'notes_duration'] try: inp = {k: v for k, v in zip(key, val)} wav = self.pipe.infer_once(inp) except: print('Error occurs. Generate default audio sample.\n') inp = self.default_inp wav = self.pipe.infer_once(inp) #if inputs == '' or len(val) < len(key): # inp = self.default_inp #else: # inp = {k:v for k,v in zip(key,val)} #wav = self.pipe.infer_once(inp) wav *= 32767 audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16)) print(f"Processed T2S.run, audio_filename: {audio_filename}") return audio_filename class t2s_VISinger: def __init__(self, device=None): from espnet2.bin.svs_inference import SingingGenerate if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing VISingere to %s" % device) tag = 'AQuarterMile/opencpop_visinger1' self.model = SingingGenerate.from_pretrained( model_tag=str_or_none(tag), device=device, ) phn_dur = [[0. , 0.219 ], [0.219 , 0.50599998], [0.50599998, 0.71399999], [0.71399999, 1.097 ], [1.097 , 1.28799999], [1.28799999, 1.98300004], [1.98300004, 7.10500002], [7.10500002, 7.60400009]] phn = ['sh', 'i', 'q', 'v', 'n', 'i', 'SP', 'AP'] score = [[0, 0.50625, 'sh_i', 58, 'sh_i'], [0.50625, 1.09728, 'q_v', 56, 'q_v'], [1.09728, 1.9832100000000001, 'n_i', 53, 'n_i'], [1.9832100000000001, 7.105360000000001, 'SP', 0, 'SP'], [7.105360000000001, 7.604390000000001, 'AP', 0, 'AP']] tempo = 70 tmp = {} tmp["label"] = phn_dur, phn tmp["score"] = tempo, score self.default_inp = tmp def inference(self, inputs): val = inputs.split(",") key = ['text', 'notes', 'notes_duration'] try: # TODO: input will be update inp = {k: v for k, v in zip(key, val)} wav = self.model(text=inp)["wav"] except: print('Error occurs. Generate default audio sample.\n') inp = self.default_inp wav = self.model(text=inp)["wav"] audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, wav, samplerate=self.model.fs) return audio_filename class TTS_OOD: def __init__(self, device): from inference.tts.GenerSpeech import GenerSpeechInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing GenerSpeech to %s" % device) self.device = device self.exp_name = 'checkpoints/GenerSpeech' self.config = 'NeuralSeq/modules/GenerSpeech/config/generspeech.yaml' self.set_model_hparams() self.pipe = GenerSpeechInfer(self.hp, device) def set_model_hparams(self): set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False) f0_stats_fn = f'{hp["binary_data_dir"]}/train_f0s_mean_std.npy' if os.path.exists(f0_stats_fn): hp['f0_mean'], hp['f0_std'] = np.load(f0_stats_fn) hp['f0_mean'] = float(hp['f0_mean']) hp['f0_std'] = float(hp['f0_std']) hp['emotion_encoder_path'] = 'checkpoints/Emotion_encoder.pt' self.hp = hp def inference(self, inputs): self.set_model_hparams() key = ['ref_audio', 'text'] val = inputs.split(",") inp = {k: v for k, v in zip(key, val)} wav = self.pipe.infer_once(inp) wav *= 32767 audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16)) print( f"Processed GenerSpeech.run. Input text:{val[1]}. Input reference audio: {val[0]}. Output Audio_filename: {audio_filename}") return audio_filename class Inpaint: def __init__(self, device): print("Initializing Make-An-Audio-inpaint to %s" % device) self.device = device self.sampler = self._initialize_model_inpaint('text_to_audio/Make_An_Audio/configs/inpaint/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/inpaint7_epoch00047.ckpt') self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) self.cmap_transform = matplotlib.cm.viridis def _initialize_model_inpaint(self, config, ckpt): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) print(model.device, device, model.cond_stage_model.device) sampler = DDIMSampler(model) return sampler def make_batch_sd(self, mel, mask, num_samples=1): mel = torch.from_numpy(mel)[None,None,...].to(dtype=torch.float32) mask = torch.from_numpy(mask)[None,None,...].to(dtype=torch.float32) masked_mel = (1 - mask) * mel mel = mel * 2 - 1 mask = mask * 2 - 1 masked_mel = masked_mel * 2 -1 batch = { "mel": repeat(mel.to(device=self.device), "1 ... -> n ...", n=num_samples), "mask": repeat(mask.to(device=self.device), "1 ... -> n ...", n=num_samples), "masked_mel": repeat(masked_mel.to(device=self.device), "1 ... -> n ...", n=num_samples), } return batch def gen_mel(self, input_audio_path): SAMPLE_RATE = 16000 sr, ori_wav = wavfile.read(input_audio_path) print("gen_mel") print(sr,ori_wav.shape,ori_wav) ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 if len(ori_wav.shape)==2:# stereo ori_wav = librosa.to_mono(ori_wav.T) print(sr,ori_wav.shape,ori_wav) ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE) mel_len,hop_size = 848,256 input_len = mel_len * hop_size if len(ori_wav) < input_len: input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0) else: input_wav = ori_wav[:input_len] mel = TRANSFORMS_16000(input_wav) return mel def gen_mel_audio(self, input_audio): SAMPLE_RATE = 16000 sr,ori_wav = input_audio print("gen_mel_audio") print(sr,ori_wav.shape,ori_wav) ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 if len(ori_wav.shape)==2:# stereo ori_wav = librosa.to_mono(ori_wav.T) print(sr,ori_wav.shape,ori_wav) ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE) mel_len,hop_size = 848,256 input_len = mel_len * hop_size if len(ori_wav) < input_len: input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0) else: input_wav = ori_wav[:input_len] mel = TRANSFORMS_16000(input_wav) return mel def show_mel_fn(self, input_audio_path): crop_len = 500 crop_mel = self.gen_mel(input_audio_path)[:,:crop_len] color_mel = self.cmap_transform(crop_mel) image = Image.fromarray((color_mel*255).astype(np.uint8)) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") image.save(image_filename) return image_filename def inpaint(self, batch, seed, ddim_steps, num_samples=1, W=512, H=512): model = self.sampler.model prng = np.random.RandomState(seed) start_code = prng.randn(num_samples, model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) c = model.get_first_stage_encoding(model.encode_first_stage(batch["masked_mel"])) cc = torch.nn.functional.interpolate(batch["mask"], size=c.shape[-2:]) c = torch.cat((c, cc), dim=1) # (b,c+1,h,w) 1 is mask shape = (c.shape[1]-1,)+c.shape[2:] samples_ddim, _ = self.sampler.sample(S=ddim_steps, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False) x_samples_ddim = model.decode_first_stage(samples_ddim) mel = torch.clamp((batch["mel"]+1.0)/2.0,min=0.0, max=1.0) mask = torch.clamp((batch["mask"]+1.0)/2.0,min=0.0, max=1.0) predicted_mel = torch.clamp((x_samples_ddim+1.0)/2.0,min=0.0, max=1.0) inpainted = (1-mask)*mel+mask*predicted_mel inpainted = inpainted.cpu().numpy().squeeze() inapint_wav = self.vocoder.vocode(inpainted) return inpainted, inapint_wav def inference(self, input_audio, mel_and_mask, seed = 55, ddim_steps = 100): SAMPLE_RATE = 16000 torch.set_grad_enabled(False) mel_img = Image.open(mel_and_mask['image']) mask_img = Image.open(mel_and_mask["mask"]) show_mel = np.array(mel_img.convert("L"))/255 mask = np.array(mask_img.convert("L"))/255 mel_bins,mel_len = 80,848 input_mel = self.gen_mel_audio(input_audio)[:,:mel_len] mask = np.pad(mask,((0,0),(0,mel_len-mask.shape[1])),mode='constant',constant_values=0) print(mask.shape,input_mel.shape) with torch.no_grad(): batch = self.make_batch_sd(input_mel,mask,num_samples=1) inpainted,gen_wav = self.inpaint( batch=batch, seed=seed, ddim_steps=ddim_steps, num_samples=1, H=mel_bins, W=mel_len ) inpainted = inpainted[:,:show_mel.shape[1]] color_mel = self.cmap_transform(inpainted) input_len = int(input_audio[1].shape[0] * SAMPLE_RATE / input_audio[0]) gen_wav = (gen_wav * 32768).astype(np.int16)[:input_len] image = Image.fromarray((color_mel*255).astype(np.uint8)) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") image.save(image_filename) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, gen_wav, samplerate = 16000) return image_filename, audio_filename class ASR: def __init__(self, device): print("Initializing Whisper to %s" % device) self.device = device self.model = whisper.load_model("base", device=device) def inference(self, audio_path): audio = whisper.load_audio(audio_path) audio = whisper.pad_or_trim(audio) mel = whisper.log_mel_spectrogram(audio).to(self.device) _, probs = self.model.detect_language(mel) options = whisper.DecodingOptions() result = whisper.decode(self.model, mel, options) return result.text def translate_english(self, audio_path): audio = self.model.transcribe(audio_path, language='English') return audio['text'] class A2T: def __init__(self, device): from audio_to_text.inference_waveform import AudioCapModel print("Initializing Audio-To-Text Model to %s" % device) self.device = device self.model = AudioCapModel("audio_to_text/audiocaps_cntrstv_cnn14rnn_trm") def inference(self, audio_path): audio = whisper.load_audio(audio_path) caption_text = self.model(audio) return caption_text[0] class GeneFace: def __init__(self, device=None): print("Initializing GeneFace model to %s" % device) from audio_to_face.GeneFace_binding import GeneFaceInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' self.device = device self.geneface_model = GeneFaceInfer(device) print("Loaded GeneFace model") def inference(self, audio_path): audio_base_name = os.path.basename(audio_path)[:-4] out_video_name = audio_path.replace("audio","video").replace(".wav", ".mp4") inp = { 'audio_source_name': audio_path, 'out_npy_name': f'geneface/tmp/{audio_base_name}.npy', 'cond_name': f'geneface/tmp/{audio_base_name}.npy', 'out_video_name': out_video_name, 'tmp_imgs_dir': f'video/tmp_imgs', } self.geneface_model.infer_once(inp) return out_video_name class SoundDetection: def __init__(self, device): self.device = device self.sample_rate = 32000 self.window_size = 1024 self.hop_size = 320 self.mel_bins = 64 self.fmin = 50 self.fmax = 14000 self.model_type = 'PVT' self.checkpoint_path = 'audio_detection/audio_infer/useful_ckpts/audio_detection.pth' self.classes_num = detection_config.classes_num self.labels = detection_config.labels self.frames_per_second = self.sample_rate // self.hop_size # Model = eval(self.model_type) self.model = PVT(sample_rate=self.sample_rate, window_size=self.window_size, hop_size=self.hop_size, mel_bins=self.mel_bins, fmin=self.fmin, fmax=self.fmax, classes_num=self.classes_num) checkpoint = torch.load(self.checkpoint_path, map_location=self.device) self.model.load_state_dict(checkpoint['model']) self.model.to(device) def inference(self, audio_path): # Forward (waveform, _) = librosa.core.load(audio_path, sr=self.sample_rate, mono=True) waveform = waveform[None, :] # (1, audio_length) waveform = torch.from_numpy(waveform) waveform = waveform.to(self.device) # Forward with torch.no_grad(): self.model.eval() batch_output_dict = self.model(waveform, None) framewise_output = batch_output_dict['framewise_output'].data.cpu().numpy()[0] """(time_steps, classes_num)""" # print('Sound event detection result (time_steps x classes_num): {}'.format( # framewise_output.shape)) import numpy as np import matplotlib.pyplot as plt sorted_indexes = np.argsort(np.max(framewise_output, axis=0))[::-1] top_k = 10 # Show top results top_result_mat = framewise_output[:, sorted_indexes[0 : top_k]] """(time_steps, top_k)""" # Plot result stft = librosa.core.stft(y=waveform[0].data.cpu().numpy(), n_fft=self.window_size, hop_length=self.hop_size, window='hann', center=True) frames_num = stft.shape[-1] fig, axs = plt.subplots(2, 1, sharex=True, figsize=(10, 4)) axs[0].matshow(np.log(np.abs(stft)), origin='lower', aspect='auto', cmap='jet') axs[0].set_ylabel('Frequency bins') axs[0].set_title('Log spectrogram') axs[1].matshow(top_result_mat.T, origin='upper', aspect='auto', cmap='jet', vmin=0, vmax=1) axs[1].xaxis.set_ticks(np.arange(0, frames_num, self.frames_per_second)) axs[1].xaxis.set_ticklabels(np.arange(0, frames_num / self.frames_per_second)) axs[1].yaxis.set_ticks(np.arange(0, top_k)) axs[1].yaxis.set_ticklabels(np.array(self.labels)[sorted_indexes[0 : top_k]]) axs[1].yaxis.grid(color='k', linestyle='solid', linewidth=0.3, alpha=0.3) axs[1].set_xlabel('Seconds') axs[1].xaxis.set_ticks_position('bottom') plt.tight_layout() image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") plt.savefig(image_filename) return image_filename class SoundExtraction: def __init__(self, device): from sound_extraction.model.LASSNet import LASSNet from sound_extraction.utils.stft import STFT import torch.nn as nn self.device = device self.model_file = 'sound_extraction/useful_ckpts/LASSNet.pt' self.stft = STFT() self.model = nn.DataParallel(LASSNet(device)).to(device) checkpoint = torch.load(self.model_file) self.model.load_state_dict(checkpoint['model']) self.model.eval() def inference(self, inputs): #key = ['ref_audio', 'text'] from sound_extraction.utils.wav_io import load_wav, save_wav val = inputs.split(",") audio_path = val[0] # audio_path, text text = val[1] waveform = load_wav(audio_path) waveform = torch.tensor(waveform).transpose(1,0) mixed_mag, mixed_phase = self.stft.transform(waveform) text_query = ['[CLS] ' + text] mixed_mag = mixed_mag.transpose(2,1).unsqueeze(0).to(self.device) est_mask = self.model(mixed_mag, text_query) est_mag = est_mask * mixed_mag est_mag = est_mag.squeeze(1) est_mag = est_mag.permute(0, 2, 1) est_wav = self.stft.inverse(est_mag.cpu().detach(), mixed_phase) est_wav = est_wav.squeeze(0).squeeze(0).numpy() #est_path = f'output/est{i}.wav' audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") print('audio_filename ', audio_filename) save_wav(est_wav, audio_filename) return audio_filename class Binaural: def __init__(self, device): from src.models import BinauralNetwork self.device = device self.model_file = 'mono2binaural/useful_ckpts/m2b/binaural_network.net' self.position_file = ['mono2binaural/useful_ckpts/m2b/tx_positions.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions2.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions3.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions4.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions5.txt'] self.net = BinauralNetwork(view_dim=7, warpnet_layers=4, warpnet_channels=64, ) self.net.load_from_file(self.model_file) self.sr = 48000 def inference(self, audio_path): mono, sr = librosa.load(path=audio_path, sr=self.sr, mono=True) mono = torch.from_numpy(mono) mono = mono.unsqueeze(0) import numpy as np import random rand_int = random.randint(0,4) view = np.loadtxt(self.position_file[rand_int]).transpose().astype(np.float32) view = torch.from_numpy(view) if not view.shape[-1] * 400 == mono.shape[-1]: mono = mono[:,:(mono.shape[-1]//400)*400] # if view.shape[1]*400 > mono.shape[1]: m_a = view.shape[1] - mono.shape[-1]//400 rand_st = random.randint(0,m_a) view = view[:,m_a:m_a+(mono.shape[-1]//400)] # # binauralize and save output self.net.eval().to(self.device) mono, view = mono.to(self.device), view.to(self.device) chunk_size = 48000 # forward in chunks of 1s rec_field = 1000 # add 1000 samples as "safe bet" since warping has undefined rec. field rec_field -= rec_field % 400 # make sure rec_field is a multiple of 400 to match audio and view frequencies chunks = [ { "mono": mono[:, max(0, i-rec_field):i+chunk_size], "view": view[:, max(0, i-rec_field)//400:(i+chunk_size)//400] } for i in range(0, mono.shape[-1], chunk_size) ] for i, chunk in enumerate(chunks): with torch.no_grad(): mono = chunk["mono"].unsqueeze(0) view = chunk["view"].unsqueeze(0) binaural = self.net(mono, view).squeeze(0) if i > 0: binaural = binaural[:, -(mono.shape[-1]-rec_field):] chunk["binaural"] = binaural binaural = torch.cat([chunk["binaural"] for chunk in chunks], dim=-1) binaural = torch.clamp(binaural, min=-1, max=1).cpu() #binaural = chunked_forwarding(net, mono, view) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") import torchaudio torchaudio.save(audio_filename, binaural, sr) #soundfile.write(audio_filename, binaural, samplerate = 48000) print(f"Processed Binaural.run, audio_filename: {audio_filename}") return audio_filename class TargetSoundDetection: def __init__(self, device): from target_sound_detection.src import models as tsd_models from target_sound_detection.src.models import event_labels self.device = device self.MEL_ARGS = { 'n_mels': 64, 'n_fft': 2048, 'hop_length': int(22050 * 20 / 1000), 'win_length': int(22050 * 40 / 1000) } self.EPS = np.spacing(1) self.clip_model, _ = clip.load("ViT-B/32", device=self.device) self.event_labels = event_labels self.id_to_event = {i : label for i, label in enumerate(self.event_labels)} config = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/run_config.pth', map_location='cpu') config_parameters = dict(config) config_parameters['tao'] = 0.6 if 'thres' not in config_parameters.keys(): config_parameters['thres'] = 0.5 if 'time_resolution' not in config_parameters.keys(): config_parameters['time_resolution'] = 125 model_parameters = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/run_model_7_loss=-0.0724.pt' , map_location=lambda storage, loc: storage) # load parameter self.model = getattr(tsd_models, config_parameters['model'])(config_parameters, inputdim=64, outputdim=2, time_resolution=config_parameters['time_resolution'], **config_parameters['model_args']) self.model.load_state_dict(model_parameters) self.model = self.model.to(self.device).eval() self.re_embeds = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/text_emb.pth') self.ref_mel = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/ref_mel.pth') def extract_feature(self, fname): import soundfile as sf y, sr = sf.read(fname, dtype='float32') print('y ', y.shape) ti = y.shape[0]/sr if y.ndim > 1: y = y.mean(1) y = librosa.resample(y, sr, 22050) lms_feature = np.log(librosa.feature.melspectrogram(y, **self.MEL_ARGS) + self.EPS).T return lms_feature,ti def build_clip(self, text): text = clip.tokenize(text).to(self.device) # ["a diagram with dog", "a dog", "a cat"] text_features = self.clip_model.encode_text(text) return text_features def cal_similarity(self, target, retrievals): ans = [] #target =torch.from_numpy(target) for name in retrievals.keys(): tmp = retrievals[name] #tmp = torch.from_numpy(tmp) s = torch.cosine_similarity(target.squeeze(), tmp.squeeze(), dim=0) ans.append(s.item()) return ans.index(max(ans)) def inference(self, text, audio_path): from target_sound_detection.src.utils import median_filter, decode_with_timestamps target_emb = self.build_clip(text) # torch type idx = self.cal_similarity(target_emb, self.re_embeds) target_event = self.id_to_event[idx] embedding = self.ref_mel[target_event] embedding = torch.from_numpy(embedding) embedding = embedding.unsqueeze(0).to(self.device).float() #print('embedding ', embedding.shape) inputs,ti = self.extract_feature(audio_path) #print('ti ', ti) inputs = torch.from_numpy(inputs) inputs = inputs.unsqueeze(0).to(self.device).float() #print('inputs ', inputs.shape) decision, decision_up, logit = self.model(inputs, embedding) pred = decision_up.detach().cpu().numpy() pred = pred[:,:,0] frame_num = decision_up.shape[1] time_ratio = ti / frame_num filtered_pred = median_filter(pred, window_size=1, threshold=0.5) #print('filtered_pred ', filtered_pred) time_predictions = [] for index_k in range(filtered_pred.shape[0]): decoded_pred = [] decoded_pred_ = decode_with_timestamps(target_event, filtered_pred[index_k,:]) if len(decoded_pred_) == 0: # neg deal decoded_pred_.append((target_event, 0, 0)) decoded_pred.append(decoded_pred_) for num_batch in range(len(decoded_pred)): # when we test our model,the batch_size is 1 cur_pred = pred[num_batch] # Save each frame output, for later visualization label_prediction = decoded_pred[num_batch] # frame predict # print(label_prediction) for event_label, onset, offset in label_prediction: time_predictions.append({ 'onset': onset*time_ratio, 'offset': offset*time_ratio,}) ans = '' for i,item in enumerate(time_predictions): ans = ans + 'segment' + str(i+1) + ' start_time: ' + str(item['onset']) + ' end_time: ' + str(item['offset']) + '\t' #print(ans) return ans # class Speech_Enh_SS_SC: # """Speech Enhancement or Separation in single-channel # Example usage: # enh_model = Speech_Enh_SS("cuda") # enh_wav = enh_model.inference("./test_chime4_audio_M05_440C0213_PED_REAL.wav") # """ # def __init__(self, device="cuda", model_name="lichenda/chime4_fasnet_dprnn_tac"): # self.model_name = model_name # self.device = device # print("Initializing ESPnet Enh to %s" % device) # self._initialize_model() # def _initialize_model(self): # from espnet_model_zoo.downloader import ModelDownloader # from espnet2.bin.enh_inference import SeparateSpeech # d = ModelDownloader() # cfg = d.download_and_unpack(self.model_name) # self.separate_speech = SeparateSpeech( # train_config=cfg["train_config"], # model_file=cfg["model_file"], # # for segment-wise process on long speech # segment_size=2.4, # hop_size=0.8, # normalize_segment_scale=False, # show_progressbar=True, # ref_channel=None, # normalize_output_wav=True, # device=self.device, # ) # def inference(self, speech_path, ref_channel=0): # speech, sr = soundfile.read(speech_path) # speech = speech[:, ref_channel] # assert speech.dim() == 1 # enh_speech = self.separate_speech(speech[None, ], fs=sr) # if len(enh_speech) == 1: # return enh_speech[0] # return enh_speech # class Speech_Enh_SS_MC: # """Speech Enhancement or Separation in multi-channel""" # def __init__(self, device="cuda", model_name=None, ref_channel=4): # self.model_name = model_name # self.ref_channel = ref_channel # self.device = device # print("Initializing ESPnet Enh to %s" % device) # self._initialize_model() # def _initialize_model(self): # from espnet_model_zoo.downloader import ModelDownloader # from espnet2.bin.enh_inference import SeparateSpeech # d = ModelDownloader() # cfg = d.download_and_unpack(self.model_name) # self.separate_speech = SeparateSpeech( # train_config=cfg["train_config"], # model_file=cfg["model_file"], # # for segment-wise process on long speech # segment_size=2.4, # hop_size=0.8, # normalize_segment_scale=False, # show_progressbar=True, # ref_channel=self.ref_channel, # normalize_output_wav=True, # device=self.device, # ) # def inference(self, speech_path): # speech, sr = soundfile.read(speech_path) # speech = speech.T # enh_speech = self.separate_speech(speech[None, ...], fs=sr) # if len(enh_speech) == 1: # return enh_speech[0] # return enh_speech class Speech_Enh_SS_SC: """Speech Enhancement or Separation in single-channel Example usage: enh_model = Speech_Enh_SS("cuda") enh_wav = enh_model.inference("./test_chime4_audio_M05_440C0213_PED_REAL.wav") """ def __init__(self, device="cuda", model_name="espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw"): self.model_name = model_name self.device = device print("Initializing ESPnet Enh to %s" % device) self._initialize_model() def _initialize_model(self): from espnet_model_zoo.downloader import ModelDownloader from espnet2.bin.enh_inference import SeparateSpeech d = ModelDownloader() cfg = d.download_and_unpack(self.model_name) self.separate_speech = SeparateSpeech( train_config=cfg["train_config"], model_file=cfg["model_file"], # for segment-wise process on long speech segment_size=2.4, hop_size=0.8, normalize_segment_scale=False, show_progressbar=True, ref_channel=None, normalize_output_wav=True, device=self.device, ) def inference(self, speech_path, ref_channel=0): speech, sr = soundfile.read(speech_path) speech = speech[:, ref_channel] # speech = torch.from_numpy(speech) # assert speech.dim() == 1 enh_speech = self.separate_speech(speech[None, ...], fs=sr) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # if len(enh_speech) == 1: soundfile.write(audio_filename, enh_speech[0].squeeze(), samplerate=sr) # return enh_speech[0] # return enh_speech # else: # print("############") # audio_filename_1 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # soundfile.write(audio_filename_1, enh_speech[0].squeeze(), samplerate=sr) # audio_filename_2 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # soundfile.write(audio_filename_2, enh_speech[1].squeeze(), samplerate=sr) # audio_filename = merge_audio(audio_filename_1, audio_filename_2) return audio_filename class Speech_SS: def __init__(self, device="cuda", model_name="lichenda/wsj0_2mix_skim_noncausal"): self.model_name = model_name self.device = device print("Initializing ESPnet SS to %s" % device) self._initialize_model() def _initialize_model(self): from espnet_model_zoo.downloader import ModelDownloader from espnet2.bin.enh_inference import SeparateSpeech d = ModelDownloader() cfg = d.download_and_unpack(self.model_name) self.separate_speech = SeparateSpeech( train_config=cfg["train_config"], model_file=cfg["model_file"], # for segment-wise process on long speech segment_size=2.4, hop_size=0.8, normalize_segment_scale=False, show_progressbar=True, ref_channel=None, normalize_output_wav=True, device=self.device, ) def inference(self, speech_path): speech, sr = soundfile.read(speech_path) enh_speech = self.separate_speech(speech[None, ...], fs=sr) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") if len(enh_speech) == 1: soundfile.write(audio_filename, enh_speech[0], samplerate=sr) else: # print("############") audio_filename_1 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename_1, enh_speech[0].squeeze(), samplerate=sr) audio_filename_2 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename_2, enh_speech[1].squeeze(), samplerate=sr) audio_filename = merge_audio(audio_filename_1, audio_filename_2) return audio_filename class ConversationBot: def __init__(self): print("Initializing AudioGPT") self.llm = OpenAI(temperature=0) self.t2i = T2I(device="cuda:1") self.i2t = ImageCaptioning(device="cuda:0") self.t2a = T2A(device="cuda:0") self.tts = TTS(device="cpu") self.t2s = T2S(device="cpu") self.i2a = I2A(device="cuda:0") self.a2t = A2T(device="cpu") self.asr = ASR(device="cuda:0") self.SE_SS_SC = Speech_Enh_SS_SC(device="cuda:0") # self.SE_SS_MC = Speech_Enh_SS_MC(device="cuda:0") self.SS = Speech_SS(device="cuda:0") self.inpaint = Inpaint(device="cuda:0") self.tts_ood = TTS_OOD(device="cpu") self.geneface = GeneFace(device="cuda:0") self.detection = SoundDetection(device="cpu") self.binaural = Binaural(device="cuda:0") self.extraction = SoundExtraction(device="cuda:0") self.TSD = TargetSoundDetection(device="cuda:0") self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output') def init_tools(self, interaction_type): if interaction_type == 'text': self.tools = [ Tool(name="Generate Image From User Input Text", func=self.t2i.inference, description="useful for when you want to generate an image from a user input text and it saved it to a file. like: generate an image of an object or something, or generate an image that includes some objects. " "The input to this tool should be a string, representing the text used to generate image. "), Tool(name="Get Photo Description", func=self.i2t.inference, description="useful for when you want to know what is inside the photo. receives image_path as input. " "The input to this tool should be a string, representing the image_path. "), Tool(name="Generate Audio From User Input Text", func=self.t2a.inference, description="useful for when you want to generate an audio from a user input text and it saved it to a file." "The input to this tool should be a string, representing the text used to generate audio."), Tool( name="Style Transfer", func= self.tts_ood.inference, description="useful for when you want to generate speech samples with styles (e.g., timbre, emotion, and prosody) derived from a reference custom voice." "Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx." "The input to this tool should be a comma seperated string of two, representing reference audio path and input text."), Tool(name="Generate Singing Voice From User Input Text, Note and Duration Sequence", func= self.t2s.inference, description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) and save it to a file." "If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence ." "If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. " "Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx." "The input to this tool should be a comma seperated string of three, representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided."), Tool(name="Synthesize Speech Given the User Input Text", func=self.tts.inference, description="useful for when you want to convert a user input text into speech audio it saved it to a file." "The input to this tool should be a string, representing the text used to be converted to speech."), # Tool(name="Speech Enhancement Or Separation In Single-Channel", func=self.SE_SS_SC.inference, # description="useful for when you want to enhance the quality of the speech signal by reducing background noise (single-channel), " # "or separate each speech from the speech mixture (single-channel), receives audio_path as input." # "The input to this tool should be a string, representing the audio_path."), Tool(name="Speech Enhancement In Single-Channel", func=self.SE_SS_SC.inference, description="useful for when you want to enhance the quality of the speech signal by reducing background noise (single-channel), receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Speech Separation In Single-Channel", func=self.SS.inference, description="useful for when you want to separate each speech from the speech mixture, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), # Tool(name="Speech Enhancement In Multi-Channel", func=self.SE_SS_MC.inference, # description="useful for when you want to enhance the quality of the speech signal by reducing background noise (multi-channel), receives audio_path as input." # "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate Audio From The Image", func=self.i2a.inference, description="useful for when you want to generate an audio based on an image." "The input to this tool should be a string, representing the image_path. "), Tool(name="Generate Text From The Audio", func=self.a2t.inference, description="useful for when you want to describe an audio in text, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Audio Inpainting", func=self.inpaint.show_mel_fn, description="useful for when you want to inpaint a mel spectrum of an audio and predict this audio, this tool will generate a mel spectrum and you can inpaint it, receives audio_path as input, " "The input to this tool should be a string, representing the audio_path."), Tool(name="Transcribe Speech", func=self.asr.inference, description="useful for when you want to know the text corresponding to a human speech, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate a talking human portrait video given a input Audio", func=self.geneface.inference, description="useful for when you want to generate a talking human portrait video given a input audio." "The input to this tool should be a string, representing the audio_path."), Tool(name="Detect The Sound Event From The Audio", func=self.detection.inference, description="useful for when you want to know what event in the audio and the sound event start or end time, this tool will generate an image of all predict events, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Sythesize Binaural Audio From A Mono Audio Input", func=self.binaural.inference, description="useful for when you want to transfer your mono audio into binaural audio, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Extract Sound Event From Mixture Audio Based On Language Description", func=self.extraction.inference, description="useful for when you extract target sound from a mixture audio, you can describe the target sound by text, receives audio_path and text as input. " "The input to this tool should be a comma seperated string of two, representing mixture audio path and input text."), Tool(name="Target Sound Detection", func=self.TSD.inference, description="useful for when you want to know when the target sound event in the audio happens. You can use language descriptions to instruct the model. receives text description and audio_path as input. " "The input to this tool should be a comma seperated string of two, representing audio path and the text description. ")] self.agent = initialize_agent( self.tools, self.llm, agent="conversational-react-description", verbose=True, memory=self.memory, return_intermediate_steps=True, agent_kwargs={'prefix': AUDIO_CHATGPT_PREFIX, 'format_instructions': AUDIO_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': AUDIO_CHATGPT_SUFFIX}, ) return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False) else: self.tools = [ Tool(name="Generate Audio From User Input Text", func=self.t2a.inference, description="useful for when you want to generate an audio from a user input text and it saved it to a file." "The input to this tool should be a string, representing the text used to generate audio."), Tool( name="Style Transfer", func= self.tts_ood.inference, description="useful for when you want to generate speech samples with styles (e.g., timbre, emotion, and prosody) derived from a reference custom voice." "Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx." "The input to this tool should be a comma seperated string of two, representing reference audio path and input text."), Tool(name="Generate Singing Voice From User Input Text, Note and Duration Sequence", func= self.t2s.inference, description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) and save it to a file." "If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence ." "If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. " "Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx." "The input to this tool should be a comma seperated string of three, representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided."), Tool(name="Synthesize Speech Given the User Input Text", func=self.tts.inference, description="useful for when you want to convert a user input text into speech audio it saved it to a file." "The input to this tool should be a string, representing the text used to be converted to speech."), Tool(name="Generate Text From The Audio", func=self.a2t.inference, description="useful for when you want to describe an audio in text, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate a talking human portrait video given a input Audio", func=self.geneface.inference, description="useful for when you want to generate a talking human portrait video given a input audio." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate Binaural Audio From A Mono Audio Input", func=self.binaural.inference, description="useful for when you want to transfer your mono audio into binaural audio, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Extract Sound Event From Mixture Audio Based On Language Description", func=self.extraction.inference, description="useful for when you extract target sound from a mixture audio, you can describe the target sound by text, receives audio_path and text as input. " "The input to this tool should be a comma seperated string of two, representing mixture audio path and input text."), Tool(name="Target Sound Detection", func=self.TSD.inference, description="useful for when you want to know when the target sound event in the audio happens. You can use language descriptions to instruct the model. receives text description and audio_path as input. " "The input to this tool should be a comma seperated string of two, representing audio path and the text description. ")] self.agent = initialize_agent( self.tools, self.llm, agent="conversational-react-description", verbose=True, memory=self.memory, return_intermediate_steps=True, agent_kwargs={'prefix': AUDIO_CHATGPT_PREFIX, 'format_instructions': AUDIO_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': AUDIO_CHATGPT_SUFFIX}, ) return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) def run_text(self, text, state): print("===============Running run_text =============") print("Inputs:", text, state) print("======>Previous memory:\n %s" % self.agent.memory) self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) res = self.agent({"input": text}) if res['intermediate_steps'] == []: print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) else: tool = res['intermediate_steps'][0][0].tool if tool == "Generate Image From User Input Text" or tool == "Generate Text From The Audio" or tool == "Target Sound Detection": print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'![](/file={m.group(0)})*{m.group(0)}*', res['output']) state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Transcribe Speech": response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Detect The Sound Event From The Audio": image_filename = res['intermediate_steps'][0][1] response = res['output'] + f"![](/file={image_filename})*{image_filename}*" state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Audio Inpainting": audio_filename = res['intermediate_steps'][0][0].tool_input image_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False), gr.Image.update(value=image_filename,visible=True), gr.Button.update(visible=True) elif tool == "Generate a talking human portrait video given a input Audio": video_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(value=video_filename,visible=True), gr.Image.update(visible=False), gr.Button.update(visible=False) print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'![](/file={m.group(0)})*{m.group(0)}*', res['output']) audio_filename = res['intermediate_steps'][0][1] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) def run_image_or_audio(self, file, state, txt): file_type = file.name[-3:] if file_type == "wav": print("===============Running run_audio =============") print("Inputs:", file, state) print("======>Previous memory:\n %s" % self.agent.memory) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # audio_load = whisper.load_audio(file.name) audio_load, sr = soundfile.read(file.name) soundfile.write(audio_filename, audio_load, samplerate = sr) description = self.a2t.inference(audio_filename) Human_prompt = "\nHuman: provide an audio named {}. The description is: {}. This information helps you to understand this audio, but you should use tools to finish following tasks, " \ "rather than directly imagine from my description. If you understand, say \"Received\". \n".format(audio_filename, description) AI_prompt = "Received. " self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) #state = state + [(f"*{audio_filename}*", AI_prompt)] state = state + [(f"*{audio_filename}*", AI_prompt)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False) else: print("===============Running run_image =============") print("Inputs:", file, state) print("======>Previous memory:\n %s" % self.agent.memory) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") print("======>Auto Resize Image...") img = Image.open(file.name) width, height = img.size ratio = min(512 / width, 512 / height) width_new, height_new = (round(width * ratio), round(height * ratio)) img = img.resize((width_new, height_new)) img = img.convert('RGB') img.save(image_filename, "PNG") print(f"Resize image form {width}x{height} to {width_new}x{height_new}") description = self.i2t.inference(image_filename) Human_prompt = "\nHuman: provide a figure named {}. The description is: {}. This information helps you to understand this image, but you should use tools to finish following tasks, " \ "rather than directly imagine from my description. If you understand, say \"Received\". \n".format(image_filename, description) AI_prompt = "Received. " self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) state = state + [(f"![](/file={image_filename})*{image_filename}*", AI_prompt)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False) def speech(self, speech_input, state): input_audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") text = self.asr.translate_english(speech_input) print("Inputs:", text, state) print("======>Previous memory:\n %s" % self.agent.memory) self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) res = self.agent({"input": text}) if res['intermediate_steps'] == []: print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] output_audio_filename = self.tts.inference(response) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) else: tool = res['intermediate_steps'][0][0].tool if tool == "Generate Image From User Input Text" or tool == "Generate Text From The Audio" or tool == "Target Sound Detection": print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'![](/file={m.group(0)})*{m.group(0)}*', res['output']) output_audio_filename = self.tts.inference(res['output']) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Transcribe Speech": print("======>Current memory:\n %s" % self.agent.memory) output_audio_filename = self.tts.inference(res['output']) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Detect The Sound Event From The Audio": print("======>Current memory:\n %s" % self.agent.memory) image_filename = res['intermediate_steps'][0][1] output_audio_filename = self.tts.inference(res['output']) response = res['output'] + f"![](/file={image_filename})*{image_filename}*" state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Generate a talking human portrait video given a input Audio": video_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] output_audio_filename = self.tts.inference(res['output']) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(value=video_filename,visible=True) print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'![](/file={m.group(0)})*{m.group(0)}*', res['output']) audio_filename = res['intermediate_steps'][0][1] Res = "The audio file has been generated and the audio is " output_audio_filename = merge_audio(self.tts.inference(Res), audio_filename) print(output_audio_filename) state = state + [(text, response)] response = res['output'] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) def inpainting(self, state, audio_filename, image_filename): print("===============Running inpainting =============") print("Inputs:", state) print("======>Previous memory:\n %s" % self.agent.memory) new_image_filename, new_audio_filename = self.inpaint.inference(audio_filename, image_filename) AI_prompt = "Here are the predict audio and the mel spectrum." + f"*{new_audio_filename}*" + f"![](/file={new_image_filename})*{new_image_filename}*" output_audio_filename = self.tts.inference(AI_prompt) self.agent.memory.buffer = self.agent.memory.buffer + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) state = state + [(f"Audio Inpainting", AI_prompt)] print("Outputs:", state) return state, state, gr.Image.update(visible=False), gr.Audio.update(value=new_audio_filename, visible=True), gr.Video.update(visible=False), gr.Button.update(visible=False) def clear_audio(self): return gr.Audio.update(value=None, visible=False) def clear_input_audio(self): return gr.Audio.update(value=None) def clear_image(self): return gr.Image.update(value=None, visible=False) def clear_video(self): return gr.Video.update(value=None, visible=False) def clear_button(self): return gr.Button.update(visible=False) if __name__ == '__main__': bot = ConversationBot() with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo: with gr.Row(): gr.Markdown("## AudioGPT") chatbot = gr.Chatbot(elem_id="chatbot", label="AudioGPT", visible=False) state = gr.State([]) with gr.Row() as select_raws: with gr.Column(scale=0.7): interaction_type = gr.Radio(choices=['text', 'speech'], value='text', label='Interaction Type') with gr.Column(scale=0.3, min_width=0): select = gr.Button("Select") with gr.Row(visible=False) as text_input_raws: with gr.Column(scale=0.7): txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(container=False) with gr.Column(scale=0.1, min_width=0): run = gr.Button("🏃‍♂️Run") with gr.Column(scale=0.1, min_width=0): clear_txt = gr.Button("🔄Clear️") with gr.Column(scale=0.1, min_width=0): btn = gr.UploadButton("🖼️Upload", file_types=["image","audio"]) with gr.Row(): outaudio = gr.Audio(visible=False) with gr.Row(): with gr.Column(scale=0.3, min_width=0): outvideo = gr.Video(visible=False) with gr.Row(): show_mel = gr.Image(type="filepath",tool='sketch',visible=False) with gr.Row(): run_button = gr.Button("Predict Masked Place",visible=False) with gr.Row(visible=False) as speech_input_raws: with gr.Column(scale=0.7): speech_input = gr.Audio(source="microphone", type="filepath", label="Input") with gr.Column(scale=0.15, min_width=0): submit_btn = gr.Button("🏃‍♂️Submit") with gr.Column(scale=0.15, min_width=0): clear_speech = gr.Button("🔄Clear️") with gr.Row(): speech_output = gr.Audio(label="Output",visible=False) select.click(bot.init_tools, [interaction_type], [chatbot, select_raws, text_input_raws, speech_input_raws]) txt.submit(bot.run_text, [txt, state], [chatbot, state, outaudio, outvideo, show_mel, run_button]) txt.submit(lambda: "", None, txt) run.click(bot.run_text, [txt, state], [chatbot, state, outaudio, outvideo, show_mel, run_button]) run.click(lambda: "", None, txt) btn.upload(bot.run_image_or_audio, [btn, state, txt], [chatbot, state, outaudio, outvideo]) run_button.click(bot.inpainting, [state, outaudio, show_mel], [chatbot, state, show_mel, outaudio, outvideo, run_button]) clear_txt.click(bot.memory.clear) clear_txt.click(lambda: [], None, chatbot) clear_txt.click(lambda: [], None, state) clear_txt.click(lambda:None, None, txt) clear_txt.click(bot.clear_button, None, run_button) clear_txt.click(bot.clear_image, None, show_mel) clear_txt.click(bot.clear_audio, None, outaudio) clear_txt.click(bot.clear_video, None, outvideo) submit_btn.click(bot.speech, [speech_input, state], [speech_input, speech_output, state, outvideo]) clear_speech.click(bot.clear_input_audio, None, speech_input) clear_speech.click(bot.clear_audio, None, speech_output) clear_speech.click(lambda: [], None, state) clear_speech.click(bot.clear_video, None, outvideo) demo.launch(server_name="0.0.0.0", server_port=7860, share=True) ================================================ FILE: audio_detection/__init__.py 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1oJAVJPX0YY_20.000_30.000.wav 20.000 30.000 Train 26dNsDuIt9Q_340.000_350.000.wav 340.000 350.000 Train 2BMHsKLcb7E_90.000_100.000.wav 90.000 100.000 Train 2RpOd9MJjyQ_10.000_20.000.wav 10.000 20.000 Train 2U4wSdl10to_200.000_210.000.wav 200.000 210.000 Train 2aBV6AZt5nk_570.000_580.000.wav 570.000 580.000 Train 3ntFslTK6hM_90.000_100.000.wav 90.000 100.000 Train ================================================ FILE: audio_detection/audio_infer/metadata/class_labels_indices.csv ================================================ index,mid,display_name 0,/m/09x0r,"Speech" 1,/m/05zppz,"Male speech, man speaking" 2,/m/02zsn,"Female speech, woman speaking" 3,/m/0ytgt,"Child speech, kid speaking" 4,/m/01h8n0,"Conversation" 5,/m/02qldy,"Narration, monologue" 6,/m/0261r1,"Babbling" 7,/m/0brhx,"Speech synthesizer" 8,/m/07p6fty,"Shout" 9,/m/07q4ntr,"Bellow" 10,/m/07rwj3x,"Whoop" 11,/m/07sr1lc,"Yell" 12,/m/04gy_2,"Battle cry" 13,/t/dd00135,"Children shouting" 14,/m/03qc9zr,"Screaming" 15,/m/02rtxlg,"Whispering" 16,/m/01j3sz,"Laughter" 17,/t/dd00001,"Baby laughter" 18,/m/07r660_,"Giggle" 19,/m/07s04w4,"Snicker" 20,/m/07sq110,"Belly laugh" 21,/m/07rgt08,"Chuckle, chortle" 22,/m/0463cq4,"Crying, sobbing" 23,/t/dd00002,"Baby cry, infant cry" 24,/m/07qz6j3,"Whimper" 25,/m/07qw_06,"Wail, moan" 26,/m/07plz5l,"Sigh" 27,/m/015lz1,"Singing" 28,/m/0l14jd,"Choir" 29,/m/01swy6,"Yodeling" 30,/m/02bk07,"Chant" 31,/m/01c194,"Mantra" 32,/t/dd00003,"Male singing" 33,/t/dd00004,"Female singing" 34,/t/dd00005,"Child singing" 35,/t/dd00006,"Synthetic singing" 36,/m/06bxc,"Rapping" 37,/m/02fxyj,"Humming" 38,/m/07s2xch,"Groan" 39,/m/07r4k75,"Grunt" 40,/m/01w250,"Whistling" 41,/m/0lyf6,"Breathing" 42,/m/07mzm6,"Wheeze" 43,/m/01d3sd,"Snoring" 44,/m/07s0dtb,"Gasp" 45,/m/07pyy8b,"Pant" 46,/m/07q0yl5,"Snort" 47,/m/01b_21,"Cough" 48,/m/0dl9sf8,"Throat clearing" 49,/m/01hsr_,"Sneeze" 50,/m/07ppn3j,"Sniff" 51,/m/06h7j,"Run" 52,/m/07qv_x_,"Shuffle" 53,/m/07pbtc8,"Walk, footsteps" 54,/m/03cczk,"Chewing, mastication" 55,/m/07pdhp0,"Biting" 56,/m/0939n_,"Gargling" 57,/m/01g90h,"Stomach rumble" 58,/m/03q5_w,"Burping, eructation" 59,/m/02p3nc,"Hiccup" 60,/m/02_nn,"Fart" 61,/m/0k65p,"Hands" 62,/m/025_jnm,"Finger snapping" 63,/m/0l15bq,"Clapping" 64,/m/01jg02,"Heart sounds, heartbeat" 65,/m/01jg1z,"Heart murmur" 66,/m/053hz1,"Cheering" 67,/m/028ght,"Applause" 68,/m/07rkbfh,"Chatter" 69,/m/03qtwd,"Crowd" 70,/m/07qfr4h,"Hubbub, speech noise, speech babble" 71,/t/dd00013,"Children playing" 72,/m/0jbk,"Animal" 73,/m/068hy,"Domestic animals, pets" 74,/m/0bt9lr,"Dog" 75,/m/05tny_,"Bark" 76,/m/07r_k2n,"Yip" 77,/m/07qf0zm,"Howl" 78,/m/07rc7d9,"Bow-wow" 79,/m/0ghcn6,"Growling" 80,/t/dd00136,"Whimper (dog)" 81,/m/01yrx,"Cat" 82,/m/02yds9,"Purr" 83,/m/07qrkrw,"Meow" 84,/m/07rjwbb,"Hiss" 85,/m/07r81j2,"Caterwaul" 86,/m/0ch8v,"Livestock, farm animals, working animals" 87,/m/03k3r,"Horse" 88,/m/07rv9rh,"Clip-clop" 89,/m/07q5rw0,"Neigh, whinny" 90,/m/01xq0k1,"Cattle, bovinae" 91,/m/07rpkh9,"Moo" 92,/m/0239kh,"Cowbell" 93,/m/068zj,"Pig" 94,/t/dd00018,"Oink" 95,/m/03fwl,"Goat" 96,/m/07q0h5t,"Bleat" 97,/m/07bgp,"Sheep" 98,/m/025rv6n,"Fowl" 99,/m/09b5t,"Chicken, rooster" 100,/m/07st89h,"Cluck" 101,/m/07qn5dc,"Crowing, cock-a-doodle-doo" 102,/m/01rd7k,"Turkey" 103,/m/07svc2k,"Gobble" 104,/m/09ddx,"Duck" 105,/m/07qdb04,"Quack" 106,/m/0dbvp,"Goose" 107,/m/07qwf61,"Honk" 108,/m/01280g,"Wild animals" 109,/m/0cdnk,"Roaring cats (lions, tigers)" 110,/m/04cvmfc,"Roar" 111,/m/015p6,"Bird" 112,/m/020bb7,"Bird vocalization, bird call, bird song" 113,/m/07pggtn,"Chirp, tweet" 114,/m/07sx8x_,"Squawk" 115,/m/0h0rv,"Pigeon, dove" 116,/m/07r_25d,"Coo" 117,/m/04s8yn,"Crow" 118,/m/07r5c2p,"Caw" 119,/m/09d5_,"Owl" 120,/m/07r_80w,"Hoot" 121,/m/05_wcq,"Bird flight, flapping wings" 122,/m/01z5f,"Canidae, dogs, wolves" 123,/m/06hps,"Rodents, rats, mice" 124,/m/04rmv,"Mouse" 125,/m/07r4gkf,"Patter" 126,/m/03vt0,"Insect" 127,/m/09xqv,"Cricket" 128,/m/09f96,"Mosquito" 129,/m/0h2mp,"Fly, housefly" 130,/m/07pjwq1,"Buzz" 131,/m/01h3n,"Bee, wasp, etc." 132,/m/09ld4,"Frog" 133,/m/07st88b,"Croak" 134,/m/078jl,"Snake" 135,/m/07qn4z3,"Rattle" 136,/m/032n05,"Whale vocalization" 137,/m/04rlf,"Music" 138,/m/04szw,"Musical instrument" 139,/m/0fx80y,"Plucked string instrument" 140,/m/0342h,"Guitar" 141,/m/02sgy,"Electric guitar" 142,/m/018vs,"Bass guitar" 143,/m/042v_gx,"Acoustic guitar" 144,/m/06w87,"Steel guitar, slide guitar" 145,/m/01glhc,"Tapping (guitar technique)" 146,/m/07s0s5r,"Strum" 147,/m/018j2,"Banjo" 148,/m/0jtg0,"Sitar" 149,/m/04rzd,"Mandolin" 150,/m/01bns_,"Zither" 151,/m/07xzm,"Ukulele" 152,/m/05148p4,"Keyboard (musical)" 153,/m/05r5c,"Piano" 154,/m/01s0ps,"Electric piano" 155,/m/013y1f,"Organ" 156,/m/03xq_f,"Electronic organ" 157,/m/03gvt,"Hammond organ" 158,/m/0l14qv,"Synthesizer" 159,/m/01v1d8,"Sampler" 160,/m/03q5t,"Harpsichord" 161,/m/0l14md,"Percussion" 162,/m/02hnl,"Drum kit" 163,/m/0cfdd,"Drum machine" 164,/m/026t6,"Drum" 165,/m/06rvn,"Snare drum" 166,/m/03t3fj,"Rimshot" 167,/m/02k_mr,"Drum roll" 168,/m/0bm02,"Bass drum" 169,/m/011k_j,"Timpani" 170,/m/01p970,"Tabla" 171,/m/01qbl,"Cymbal" 172,/m/03qtq,"Hi-hat" 173,/m/01sm1g,"Wood block" 174,/m/07brj,"Tambourine" 175,/m/05r5wn,"Rattle (instrument)" 176,/m/0xzly,"Maraca" 177,/m/0mbct,"Gong" 178,/m/016622,"Tubular bells" 179,/m/0j45pbj,"Mallet percussion" 180,/m/0dwsp,"Marimba, xylophone" 181,/m/0dwtp,"Glockenspiel" 182,/m/0dwt5,"Vibraphone" 183,/m/0l156b,"Steelpan" 184,/m/05pd6,"Orchestra" 185,/m/01kcd,"Brass instrument" 186,/m/0319l,"French horn" 187,/m/07gql,"Trumpet" 188,/m/07c6l,"Trombone" 189,/m/0l14_3,"Bowed string instrument" 190,/m/02qmj0d,"String section" 191,/m/07y_7,"Violin, fiddle" 192,/m/0d8_n,"Pizzicato" 193,/m/01xqw,"Cello" 194,/m/02fsn,"Double bass" 195,/m/085jw,"Wind instrument, woodwind instrument" 196,/m/0l14j_,"Flute" 197,/m/06ncr,"Saxophone" 198,/m/01wy6,"Clarinet" 199,/m/03m5k,"Harp" 200,/m/0395lw,"Bell" 201,/m/03w41f,"Church bell" 202,/m/027m70_,"Jingle bell" 203,/m/0gy1t2s,"Bicycle bell" 204,/m/07n_g,"Tuning fork" 205,/m/0f8s22,"Chime" 206,/m/026fgl,"Wind chime" 207,/m/0150b9,"Change ringing (campanology)" 208,/m/03qjg,"Harmonica" 209,/m/0mkg,"Accordion" 210,/m/0192l,"Bagpipes" 211,/m/02bxd,"Didgeridoo" 212,/m/0l14l2,"Shofar" 213,/m/07kc_,"Theremin" 214,/m/0l14t7,"Singing bowl" 215,/m/01hgjl,"Scratching (performance technique)" 216,/m/064t9,"Pop music" 217,/m/0glt670,"Hip hop music" 218,/m/02cz_7,"Beatboxing" 219,/m/06by7,"Rock music" 220,/m/03lty,"Heavy metal" 221,/m/05r6t,"Punk rock" 222,/m/0dls3,"Grunge" 223,/m/0dl5d,"Progressive rock" 224,/m/07sbbz2,"Rock and roll" 225,/m/05w3f,"Psychedelic rock" 226,/m/06j6l,"Rhythm and blues" 227,/m/0gywn,"Soul music" 228,/m/06cqb,"Reggae" 229,/m/01lyv,"Country" 230,/m/015y_n,"Swing music" 231,/m/0gg8l,"Bluegrass" 232,/m/02x8m,"Funk" 233,/m/02w4v,"Folk music" 234,/m/06j64v,"Middle Eastern music" 235,/m/03_d0,"Jazz" 236,/m/026z9,"Disco" 237,/m/0ggq0m,"Classical music" 238,/m/05lls,"Opera" 239,/m/02lkt,"Electronic music" 240,/m/03mb9,"House music" 241,/m/07gxw,"Techno" 242,/m/07s72n,"Dubstep" 243,/m/0283d,"Drum and bass" 244,/m/0m0jc,"Electronica" 245,/m/08cyft,"Electronic dance music" 246,/m/0fd3y,"Ambient music" 247,/m/07lnk,"Trance music" 248,/m/0g293,"Music of Latin America" 249,/m/0ln16,"Salsa music" 250,/m/0326g,"Flamenco" 251,/m/0155w,"Blues" 252,/m/05fw6t,"Music for children" 253,/m/02v2lh,"New-age music" 254,/m/0y4f8,"Vocal music" 255,/m/0z9c,"A capella" 256,/m/0164x2,"Music of Africa" 257,/m/0145m,"Afrobeat" 258,/m/02mscn,"Christian music" 259,/m/016cjb,"Gospel music" 260,/m/028sqc,"Music of Asia" 261,/m/015vgc,"Carnatic music" 262,/m/0dq0md,"Music of Bollywood" 263,/m/06rqw,"Ska" 264,/m/02p0sh1,"Traditional music" 265,/m/05rwpb,"Independent music" 266,/m/074ft,"Song" 267,/m/025td0t,"Background music" 268,/m/02cjck,"Theme music" 269,/m/03r5q_,"Jingle (music)" 270,/m/0l14gg,"Soundtrack music" 271,/m/07pkxdp,"Lullaby" 272,/m/01z7dr,"Video game music" 273,/m/0140xf,"Christmas music" 274,/m/0ggx5q,"Dance music" 275,/m/04wptg,"Wedding music" 276,/t/dd00031,"Happy music" 277,/t/dd00032,"Funny music" 278,/t/dd00033,"Sad music" 279,/t/dd00034,"Tender music" 280,/t/dd00035,"Exciting music" 281,/t/dd00036,"Angry music" 282,/t/dd00037,"Scary music" 283,/m/03m9d0z,"Wind" 284,/m/09t49,"Rustling leaves" 285,/t/dd00092,"Wind noise (microphone)" 286,/m/0jb2l,"Thunderstorm" 287,/m/0ngt1,"Thunder" 288,/m/0838f,"Water" 289,/m/06mb1,"Rain" 290,/m/07r10fb,"Raindrop" 291,/t/dd00038,"Rain on surface" 292,/m/0j6m2,"Stream" 293,/m/0j2kx,"Waterfall" 294,/m/05kq4,"Ocean" 295,/m/034srq,"Waves, surf" 296,/m/06wzb,"Steam" 297,/m/07swgks,"Gurgling" 298,/m/02_41,"Fire" 299,/m/07pzfmf,"Crackle" 300,/m/07yv9,"Vehicle" 301,/m/019jd,"Boat, Water vehicle" 302,/m/0hsrw,"Sailboat, sailing ship" 303,/m/056ks2,"Rowboat, canoe, kayak" 304,/m/02rlv9,"Motorboat, speedboat" 305,/m/06q74,"Ship" 306,/m/012f08,"Motor vehicle (road)" 307,/m/0k4j,"Car" 308,/m/0912c9,"Vehicle horn, car horn, honking" 309,/m/07qv_d5,"Toot" 310,/m/02mfyn,"Car alarm" 311,/m/04gxbd,"Power windows, electric windows" 312,/m/07rknqz,"Skidding" 313,/m/0h9mv,"Tire squeal" 314,/t/dd00134,"Car passing by" 315,/m/0ltv,"Race car, auto racing" 316,/m/07r04,"Truck" 317,/m/0gvgw0,"Air brake" 318,/m/05x_td,"Air horn, truck horn" 319,/m/02rhddq,"Reversing beeps" 320,/m/03cl9h,"Ice cream truck, ice cream van" 321,/m/01bjv,"Bus" 322,/m/03j1ly,"Emergency vehicle" 323,/m/04qvtq,"Police car (siren)" 324,/m/012n7d,"Ambulance (siren)" 325,/m/012ndj,"Fire engine, fire truck (siren)" 326,/m/04_sv,"Motorcycle" 327,/m/0btp2,"Traffic noise, roadway noise" 328,/m/06d_3,"Rail transport" 329,/m/07jdr,"Train" 330,/m/04zmvq,"Train whistle" 331,/m/0284vy3,"Train horn" 332,/m/01g50p,"Railroad car, train wagon" 333,/t/dd00048,"Train wheels squealing" 334,/m/0195fx,"Subway, metro, underground" 335,/m/0k5j,"Aircraft" 336,/m/014yck,"Aircraft engine" 337,/m/04229,"Jet engine" 338,/m/02l6bg,"Propeller, airscrew" 339,/m/09ct_,"Helicopter" 340,/m/0cmf2,"Fixed-wing aircraft, airplane" 341,/m/0199g,"Bicycle" 342,/m/06_fw,"Skateboard" 343,/m/02mk9,"Engine" 344,/t/dd00065,"Light engine (high frequency)" 345,/m/08j51y,"Dental drill, dentist's drill" 346,/m/01yg9g,"Lawn mower" 347,/m/01j4z9,"Chainsaw" 348,/t/dd00066,"Medium engine (mid frequency)" 349,/t/dd00067,"Heavy engine (low frequency)" 350,/m/01h82_,"Engine knocking" 351,/t/dd00130,"Engine starting" 352,/m/07pb8fc,"Idling" 353,/m/07q2z82,"Accelerating, revving, vroom" 354,/m/02dgv,"Door" 355,/m/03wwcy,"Doorbell" 356,/m/07r67yg,"Ding-dong" 357,/m/02y_763,"Sliding door" 358,/m/07rjzl8,"Slam" 359,/m/07r4wb8,"Knock" 360,/m/07qcpgn,"Tap" 361,/m/07q6cd_,"Squeak" 362,/m/0642b4,"Cupboard open or close" 363,/m/0fqfqc,"Drawer open or close" 364,/m/04brg2,"Dishes, pots, and pans" 365,/m/023pjk,"Cutlery, silverware" 366,/m/07pn_8q,"Chopping (food)" 367,/m/0dxrf,"Frying (food)" 368,/m/0fx9l,"Microwave oven" 369,/m/02pjr4,"Blender" 370,/m/02jz0l,"Water tap, faucet" 371,/m/0130jx,"Sink (filling or washing)" 372,/m/03dnzn,"Bathtub (filling or washing)" 373,/m/03wvsk,"Hair dryer" 374,/m/01jt3m,"Toilet flush" 375,/m/012xff,"Toothbrush" 376,/m/04fgwm,"Electric toothbrush" 377,/m/0d31p,"Vacuum cleaner" 378,/m/01s0vc,"Zipper (clothing)" 379,/m/03v3yw,"Keys jangling" 380,/m/0242l,"Coin (dropping)" 381,/m/01lsmm,"Scissors" 382,/m/02g901,"Electric shaver, electric razor" 383,/m/05rj2,"Shuffling cards" 384,/m/0316dw,"Typing" 385,/m/0c2wf,"Typewriter" 386,/m/01m2v,"Computer keyboard" 387,/m/081rb,"Writing" 388,/m/07pp_mv,"Alarm" 389,/m/07cx4,"Telephone" 390,/m/07pp8cl,"Telephone bell ringing" 391,/m/01hnzm,"Ringtone" 392,/m/02c8p,"Telephone dialing, DTMF" 393,/m/015jpf,"Dial tone" 394,/m/01z47d,"Busy signal" 395,/m/046dlr,"Alarm clock" 396,/m/03kmc9,"Siren" 397,/m/0dgbq,"Civil defense siren" 398,/m/030rvx,"Buzzer" 399,/m/01y3hg,"Smoke detector, smoke alarm" 400,/m/0c3f7m,"Fire alarm" 401,/m/04fq5q,"Foghorn" 402,/m/0l156k,"Whistle" 403,/m/06hck5,"Steam whistle" 404,/t/dd00077,"Mechanisms" 405,/m/02bm9n,"Ratchet, pawl" 406,/m/01x3z,"Clock" 407,/m/07qjznt,"Tick" 408,/m/07qjznl,"Tick-tock" 409,/m/0l7xg,"Gears" 410,/m/05zc1,"Pulleys" 411,/m/0llzx,"Sewing machine" 412,/m/02x984l,"Mechanical fan" 413,/m/025wky1,"Air conditioning" 414,/m/024dl,"Cash register" 415,/m/01m4t,"Printer" 416,/m/0dv5r,"Camera" 417,/m/07bjf,"Single-lens reflex camera" 418,/m/07k1x,"Tools" 419,/m/03l9g,"Hammer" 420,/m/03p19w,"Jackhammer" 421,/m/01b82r,"Sawing" 422,/m/02p01q,"Filing (rasp)" 423,/m/023vsd,"Sanding" 424,/m/0_ksk,"Power tool" 425,/m/01d380,"Drill" 426,/m/014zdl,"Explosion" 427,/m/032s66,"Gunshot, gunfire" 428,/m/04zjc,"Machine gun" 429,/m/02z32qm,"Fusillade" 430,/m/0_1c,"Artillery fire" 431,/m/073cg4,"Cap gun" 432,/m/0g6b5,"Fireworks" 433,/g/122z_qxw,"Firecracker" 434,/m/07qsvvw,"Burst, pop" 435,/m/07pxg6y,"Eruption" 436,/m/07qqyl4,"Boom" 437,/m/083vt,"Wood" 438,/m/07pczhz,"Chop" 439,/m/07pl1bw,"Splinter" 440,/m/07qs1cx,"Crack" 441,/m/039jq,"Glass" 442,/m/07q7njn,"Chink, clink" 443,/m/07rn7sz,"Shatter" 444,/m/04k94,"Liquid" 445,/m/07rrlb6,"Splash, splatter" 446,/m/07p6mqd,"Slosh" 447,/m/07qlwh6,"Squish" 448,/m/07r5v4s,"Drip" 449,/m/07prgkl,"Pour" 450,/m/07pqc89,"Trickle, dribble" 451,/t/dd00088,"Gush" 452,/m/07p7b8y,"Fill (with liquid)" 453,/m/07qlf79,"Spray" 454,/m/07ptzwd,"Pump (liquid)" 455,/m/07ptfmf,"Stir" 456,/m/0dv3j,"Boiling" 457,/m/0790c,"Sonar" 458,/m/0dl83,"Arrow" 459,/m/07rqsjt,"Whoosh, swoosh, swish" 460,/m/07qnq_y,"Thump, thud" 461,/m/07rrh0c,"Thunk" 462,/m/0b_fwt,"Electronic tuner" 463,/m/02rr_,"Effects unit" 464,/m/07m2kt,"Chorus effect" 465,/m/018w8,"Basketball bounce" 466,/m/07pws3f,"Bang" 467,/m/07ryjzk,"Slap, smack" 468,/m/07rdhzs,"Whack, thwack" 469,/m/07pjjrj,"Smash, crash" 470,/m/07pc8lb,"Breaking" 471,/m/07pqn27,"Bouncing" 472,/m/07rbp7_,"Whip" 473,/m/07pyf11,"Flap" 474,/m/07qb_dv,"Scratch" 475,/m/07qv4k0,"Scrape" 476,/m/07pdjhy,"Rub" 477,/m/07s8j8t,"Roll" 478,/m/07plct2,"Crushing" 479,/t/dd00112,"Crumpling, crinkling" 480,/m/07qcx4z,"Tearing" 481,/m/02fs_r,"Beep, bleep" 482,/m/07qwdck,"Ping" 483,/m/07phxs1,"Ding" 484,/m/07rv4dm,"Clang" 485,/m/07s02z0,"Squeal" 486,/m/07qh7jl,"Creak" 487,/m/07qwyj0,"Rustle" 488,/m/07s34ls,"Whir" 489,/m/07qmpdm,"Clatter" 490,/m/07p9k1k,"Sizzle" 491,/m/07qc9xj,"Clicking" 492,/m/07rwm0c,"Clickety-clack" 493,/m/07phhsh,"Rumble" 494,/m/07qyrcz,"Plop" 495,/m/07qfgpx,"Jingle, tinkle" 496,/m/07rcgpl,"Hum" 497,/m/07p78v5,"Zing" 498,/t/dd00121,"Boing" 499,/m/07s12q4,"Crunch" 500,/m/028v0c,"Silence" 501,/m/01v_m0,"Sine wave" 502,/m/0b9m1,"Harmonic" 503,/m/0hdsk,"Chirp tone" 504,/m/0c1dj,"Sound effect" 505,/m/07pt_g0,"Pulse" 506,/t/dd00125,"Inside, small room" 507,/t/dd00126,"Inside, large room or hall" 508,/t/dd00127,"Inside, public space" 509,/t/dd00128,"Outside, urban or manmade" 510,/t/dd00129,"Outside, rural or natural" 511,/m/01b9nn,"Reverberation" 512,/m/01jnbd,"Echo" 513,/m/096m7z,"Noise" 514,/m/06_y0by,"Environmental noise" 515,/m/07rgkc5,"Static" 516,/m/06xkwv,"Mains hum" 517,/m/0g12c5,"Distortion" 518,/m/08p9q4,"Sidetone" 519,/m/07szfh9,"Cacophony" 520,/m/0chx_,"White noise" 521,/m/0cj0r,"Pink noise" 522,/m/07p_0gm,"Throbbing" 523,/m/01jwx6,"Vibration" 524,/m/07c52,"Television" 525,/m/06bz3,"Radio" 526,/m/07hvw1,"Field recording" ================================================ FILE: audio_detection/audio_infer/pytorch/evaluate.py ================================================ from sklearn import metrics from pytorch_utils import forward class Evaluator(object): def __init__(self, model): """Evaluator. Args: model: object """ self.model = model def evaluate(self, data_loader): """Forward evaluation data and calculate statistics. Args: data_loader: object Returns: statistics: dict, {'average_precision': (classes_num,), 'auc': (classes_num,)} """ # Forward output_dict = forward( model=self.model, generator=data_loader, return_target=True) clipwise_output = output_dict['clipwise_output'] # (audios_num, classes_num) target = output_dict['target'] # (audios_num, classes_num) average_precision = metrics.average_precision_score( target, clipwise_output, average=None) auc = metrics.roc_auc_score(target, clipwise_output, average=None) statistics = {'average_precision': average_precision, 'auc': auc} return statistics ================================================ FILE: audio_detection/audio_infer/pytorch/finetune_template.py ================================================ import os import sys sys.path.insert(1, os.path.join(sys.path[0], '../utils')) import numpy as np import argparse import h5py import math import time import logging import matplotlib.pyplot as plt import torch torch.backends.cudnn.benchmark=True torch.manual_seed(0) import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.utils.data from utilities import get_filename from models import * import config class Transfer_Cnn14(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, freeze_base): """Classifier for a new task using pretrained Cnn14 as a sub module. """ super(Transfer_Cnn14, self).__init__() audioset_classes_num = 527 self.base = Cnn14(sample_rate, window_size, hop_size, mel_bins, fmin, fmax, audioset_classes_num) # Transfer to another task layer self.fc_transfer = nn.Linear(2048, classes_num, bias=True) if freeze_base: # Freeze AudioSet pretrained layers for param in self.base.parameters(): param.requires_grad = False self.init_weights() def init_weights(self): init_layer(self.fc_transfer) def load_from_pretrain(self, pretrained_checkpoint_path): checkpoint = torch.load(pretrained_checkpoint_path) self.base.load_state_dict(checkpoint['model']) def forward(self, input, mixup_lambda=None): """Input: (batch_size, data_length) """ output_dict = self.base(input, mixup_lambda) embedding = output_dict['embedding'] clipwise_output = torch.log_softmax(self.fc_transfer(embedding), dim=-1) output_dict['clipwise_output'] = clipwise_output return output_dict def train(args): # Arugments & parameters sample_rate = args.sample_rate window_size = args.window_size hop_size = args.hop_size mel_bins = args.mel_bins fmin = args.fmin fmax = args.fmax model_type = args.model_type pretrained_checkpoint_path = args.pretrained_checkpoint_path freeze_base = args.freeze_base device = 'cuda' if (args.cuda and torch.cuda.is_available()) else 'cpu' classes_num = config.classes_num pretrain = True if pretrained_checkpoint_path else False # Model Model = eval(model_type) model = Model(sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, freeze_base) # Load pretrained model if pretrain: logging.info('Load pretrained model from {}'.format(pretrained_checkpoint_path)) model.load_from_pretrain(pretrained_checkpoint_path) # Parallel print('GPU number: {}'.format(torch.cuda.device_count())) model = torch.nn.DataParallel(model) if 'cuda' in device: model.to(device) print('Load pretrained model successfully!') if __name__ == '__main__': parser = argparse.ArgumentParser(description='Example of parser. ') subparsers = parser.add_subparsers(dest='mode') # Train parser_train = subparsers.add_parser('train') parser_train.add_argument('--sample_rate', type=int, required=True) parser_train.add_argument('--window_size', type=int, required=True) parser_train.add_argument('--hop_size', type=int, required=True) parser_train.add_argument('--mel_bins', type=int, required=True) parser_train.add_argument('--fmin', type=int, required=True) parser_train.add_argument('--fmax', type=int, required=True) parser_train.add_argument('--model_type', type=str, required=True) parser_train.add_argument('--pretrained_checkpoint_path', type=str) parser_train.add_argument('--freeze_base', action='store_true', default=False) parser_train.add_argument('--cuda', action='store_true', default=False) # Parse arguments args = parser.parse_args() args.filename = get_filename(__file__) if args.mode == 'train': train(args) else: raise Exception('Error argument!') ================================================ FILE: audio_detection/audio_infer/pytorch/inference.py ================================================ import os import sys sys.path.insert(1, os.path.join(sys.path[0], '../utils')) import numpy as np import argparse import librosa import matplotlib.pyplot as plt import torch from utilities import create_folder, get_filename from models import * from pytorch_utils import move_data_to_device import config def audio_tagging(args): """Inference audio tagging result of an audio clip. """ # Arugments & parameters sample_rate = args.sample_rate window_size = args.window_size hop_size = args.hop_size mel_bins = args.mel_bins fmin = args.fmin fmax = args.fmax model_type = args.model_type checkpoint_path = args.checkpoint_path audio_path = args.audio_path device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu') classes_num = config.classes_num labels = config.labels # Model Model = eval(model_type) model = Model(sample_rate=sample_rate, window_size=window_size, hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax, classes_num=classes_num) checkpoint = torch.load(checkpoint_path, map_location=device) model.load_state_dict(checkpoint['model']) # Parallel if 'cuda' in str(device): model.to(device) print('GPU number: {}'.format(torch.cuda.device_count())) model = torch.nn.DataParallel(model) else: print('Using CPU.') # Load audio (waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True) waveform = waveform[None, :] # (1, audio_length) waveform = move_data_to_device(waveform, device) # Forward with torch.no_grad(): model.eval() batch_output_dict = model(waveform, None) clipwise_output = batch_output_dict['clipwise_output'].data.cpu().numpy()[0] """(classes_num,)""" sorted_indexes = np.argsort(clipwise_output)[::-1] # Print audio tagging top probabilities for k in range(10): print('{}: {:.3f}'.format(np.array(labels)[sorted_indexes[k]], clipwise_output[sorted_indexes[k]])) # Print embedding if 'embedding' in batch_output_dict.keys(): embedding = batch_output_dict['embedding'].data.cpu().numpy()[0] print('embedding: {}'.format(embedding.shape)) return clipwise_output, labels def sound_event_detection(args): """Inference sound event detection result of an audio clip. """ # Arugments & parameters sample_rate = args.sample_rate window_size = args.window_size hop_size = args.hop_size mel_bins = args.mel_bins fmin = args.fmin fmax = args.fmax model_type = args.model_type checkpoint_path = args.checkpoint_path audio_path = args.audio_path device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu') classes_num = config.classes_num labels = config.labels frames_per_second = sample_rate // hop_size # Paths fig_path = os.path.join('results', '{}.png'.format(get_filename(audio_path))) create_folder(os.path.dirname(fig_path)) # Model Model = eval(model_type) model = Model(sample_rate=sample_rate, window_size=window_size, hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax, classes_num=classes_num) checkpoint = torch.load(checkpoint_path, map_location=device) model.load_state_dict(checkpoint['model']) # Parallel print('GPU number: {}'.format(torch.cuda.device_count())) model = torch.nn.DataParallel(model) if 'cuda' in str(device): model.to(device) # Load audio (waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True) waveform = waveform[None, :] # (1, audio_length) waveform = move_data_to_device(waveform, device) # Forward with torch.no_grad(): model.eval() batch_output_dict = model(waveform, None) framewise_output = batch_output_dict['framewise_output'].data.cpu().numpy()[0] """(time_steps, classes_num)""" print('Sound event detection result (time_steps x classes_num): {}'.format( framewise_output.shape)) sorted_indexes = np.argsort(np.max(framewise_output, axis=0))[::-1] top_k = 10 # Show top results top_result_mat = framewise_output[:, sorted_indexes[0 : top_k]] """(time_steps, top_k)""" # Plot result stft = librosa.core.stft(y=waveform[0].data.cpu().numpy(), n_fft=window_size, hop_length=hop_size, window='hann', center=True) frames_num = stft.shape[-1] fig, axs = plt.subplots(2, 1, sharex=True, figsize=(10, 4)) axs[0].matshow(np.log(np.abs(stft)), origin='lower', aspect='auto', cmap='jet') axs[0].set_ylabel('Frequency bins') axs[0].set_title('Log spectrogram') axs[1].matshow(top_result_mat.T, origin='upper', aspect='auto', cmap='jet', vmin=0, vmax=1) axs[1].xaxis.set_ticks(np.arange(0, frames_num, frames_per_second)) axs[1].xaxis.set_ticklabels(np.arange(0, frames_num / frames_per_second)) axs[1].yaxis.set_ticks(np.arange(0, top_k)) axs[1].yaxis.set_ticklabels(np.array(labels)[sorted_indexes[0 : top_k]]) axs[1].yaxis.grid(color='k', linestyle='solid', linewidth=0.3, alpha=0.3) axs[1].set_xlabel('Seconds') axs[1].xaxis.set_ticks_position('bottom') plt.tight_layout() plt.savefig(fig_path) print('Save sound event detection visualization to {}'.format(fig_path)) return framewise_output, labels if __name__ == '__main__': parser = argparse.ArgumentParser(description='Example of parser. ') subparsers = parser.add_subparsers(dest='mode') parser_at = subparsers.add_parser('audio_tagging') parser_at.add_argument('--sample_rate', type=int, default=32000) parser_at.add_argument('--window_size', type=int, default=1024) parser_at.add_argument('--hop_size', type=int, default=320) parser_at.add_argument('--mel_bins', type=int, default=64) parser_at.add_argument('--fmin', type=int, default=50) parser_at.add_argument('--fmax', type=int, default=14000) parser_at.add_argument('--model_type', type=str, required=True) parser_at.add_argument('--checkpoint_path', type=str, required=True) parser_at.add_argument('--audio_path', type=str, required=True) parser_at.add_argument('--cuda', action='store_true', default=False) parser_sed = subparsers.add_parser('sound_event_detection') parser_sed.add_argument('--sample_rate', type=int, default=32000) parser_sed.add_argument('--window_size', type=int, default=1024) parser_sed.add_argument('--hop_size', type=int, default=320) parser_sed.add_argument('--mel_bins', type=int, default=64) parser_sed.add_argument('--fmin', type=int, default=50) parser_sed.add_argument('--fmax', type=int, default=14000) parser_sed.add_argument('--model_type', type=str, required=True) parser_sed.add_argument('--checkpoint_path', type=str, required=True) parser_sed.add_argument('--audio_path', type=str, required=True) parser_sed.add_argument('--cuda', action='store_true', default=False) args = parser.parse_args() if args.mode == 'audio_tagging': audio_tagging(args) elif args.mode == 'sound_event_detection': sound_event_detection(args) else: raise Exception('Error argument!') ================================================ FILE: audio_detection/audio_infer/pytorch/losses.py ================================================ import torch import torch.nn.functional as F def clip_bce(output_dict, target_dict): """Binary crossentropy loss. """ return F.binary_cross_entropy( output_dict['clipwise_output'], target_dict['target']) def get_loss_func(loss_type): if loss_type == 'clip_bce': return clip_bce ================================================ FILE: audio_detection/audio_infer/pytorch/main.py ================================================ import os import sys sys.path.insert(1, os.path.join(sys.path[0], '../utils')) import numpy as np import argparse import time import logging import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.utils.data from utilities import (create_folder, get_filename, create_logging, Mixup, StatisticsContainer) from models import (PVT, PVT2, PVT_lr, PVT_nopretrain, PVT_2layer, Cnn14, Cnn14_no_specaug, Cnn14_no_dropout, Cnn6, Cnn10, ResNet22, ResNet38, ResNet54, Cnn14_emb512, Cnn14_emb128, Cnn14_emb32, MobileNetV1, MobileNetV2, LeeNet11, LeeNet24, DaiNet19, Res1dNet31, Res1dNet51, Wavegram_Cnn14, Wavegram_Logmel_Cnn14, Wavegram_Logmel128_Cnn14, Cnn14_16k, Cnn14_8k, Cnn14_mel32, Cnn14_mel128, Cnn14_mixup_time_domain, Cnn14_DecisionLevelMax, Cnn14_DecisionLevelAtt, Cnn6_Transformer, GLAM, GLAM2, GLAM3, Cnn4, EAT) #from models_test import (PVT_test) #from models1 import (PVT1) #from models_vig import (VIG, VIG2) #from models_vvt import (VVT) #from models2 import (MPVIT, MPVIT2) #from models_reshape import (PVT_reshape, PVT_tscam) #from models_swin import (Swin, Swin_nopretrain) #from models_swin2 import (Swin2) #from models_van import (Van, Van_tiny) #from models_focal import (Focal) #from models_cross import (Cross) #from models_cov import (Cov) #from models_cnn import (Cnn_light) #from models_twins import (Twins) #from models_cmt import (Cmt, Cmt1) #from models_shunted import (Shunted) #from models_quadtree import (Quadtree, Quadtree2, Quadtree_nopretrain) #from models_davit import (Davit_tscam, Davit, Davit_nopretrain) from pytorch_utils import (move_data_to_device, count_parameters, count_flops, do_mixup) from data_generator import (AudioSetDataset, TrainSampler, BalancedTrainSampler, AlternateTrainSampler, EvaluateSampler, collate_fn) from evaluate import Evaluator import config from losses import get_loss_func def train(args): """Train AudioSet tagging model. Args: dataset_dir: str workspace: str data_type: 'balanced_train' | 'full_train' window_size: int hop_size: int mel_bins: int model_type: str loss_type: 'clip_bce' balanced: 'none' | 'balanced' | 'alternate' augmentation: 'none' | 'mixup' batch_size: int learning_rate: float resume_iteration: int early_stop: int accumulation_steps: int cuda: bool """ # Arugments & parameters workspace = args.workspace data_type = args.data_type sample_rate = args.sample_rate window_size = args.window_size hop_size = args.hop_size mel_bins = args.mel_bins fmin = args.fmin fmax = args.fmax model_type = args.model_type loss_type = args.loss_type balanced = args.balanced augmentation = args.augmentation batch_size = args.batch_size learning_rate = args.learning_rate resume_iteration = args.resume_iteration early_stop = args.early_stop device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu') filename = args.filename num_workers = 8 clip_samples = config.clip_samples classes_num = config.classes_num loss_func = get_loss_func(loss_type) # Paths black_list_csv = None train_indexes_hdf5_path = os.path.join(workspace, 'hdf5s', 'indexes', '{}.h5'.format(data_type)) eval_bal_indexes_hdf5_path = os.path.join(workspace, 'hdf5s', 'indexes', 'balanced_train.h5') eval_test_indexes_hdf5_path = os.path.join(workspace, 'hdf5s', 'indexes', 'eval.h5') checkpoints_dir = os.path.join(workspace, 'checkpoints', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size)) create_folder(checkpoints_dir) statistics_path = os.path.join(workspace, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') create_folder(os.path.dirname(statistics_path)) logs_dir = os.path.join(workspace, 'logs', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size)) create_logging(logs_dir, filemode='w') logging.info(args) if 'cuda' in str(device): logging.info('Using GPU.') device = 'cuda' else: logging.info('Using CPU. Set --cuda flag to use GPU.') device = 'cpu' # Model Model = eval(model_type) model = Model(sample_rate=sample_rate, window_size=window_size, hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax, classes_num=classes_num) total = sum(p.numel() for p in model.parameters()) print("Total params: %.2fM" % (total/1e6)) logging.info("Total params: %.2fM" % (total/1e6)) #params_num = count_parameters(model) # flops_num = count_flops(model, clip_samples) #logging.info('Parameters num: {}'.format(params_num)) # logging.info('Flops num: {:.3f} G'.format(flops_num / 1e9)) # Dataset will be used by DataLoader later. Dataset takes a meta as input # and return a waveform and a target. dataset = AudioSetDataset(sample_rate=sample_rate) # Train sampler if balanced == 'none': Sampler = TrainSampler elif balanced == 'balanced': Sampler = BalancedTrainSampler elif balanced == 'alternate': Sampler = AlternateTrainSampler train_sampler = Sampler( indexes_hdf5_path=train_indexes_hdf5_path, batch_size=batch_size * 2 if 'mixup' in augmentation else batch_size, black_list_csv=black_list_csv) # Evaluate sampler eval_bal_sampler = EvaluateSampler( indexes_hdf5_path=eval_bal_indexes_hdf5_path, batch_size=batch_size) eval_test_sampler = EvaluateSampler( indexes_hdf5_path=eval_test_indexes_hdf5_path, batch_size=batch_size) # Data loader train_loader = torch.utils.data.DataLoader(dataset=dataset, batch_sampler=train_sampler, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True) eval_bal_loader = torch.utils.data.DataLoader(dataset=dataset, batch_sampler=eval_bal_sampler, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True) eval_test_loader = torch.utils.data.DataLoader(dataset=dataset, batch_sampler=eval_test_sampler, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True) mix=0.5 if 'mixup' in augmentation: mixup_augmenter = Mixup(mixup_alpha=mix) print(mix) logging.info(mix) # Evaluator evaluator = Evaluator(model=model) # Statistics statistics_container = StatisticsContainer(statistics_path) # Optimizer optimizer = optim.AdamW(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.05, amsgrad=True) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=4, min_lr=1e-06, verbose=True) train_bgn_time = time.time() # Resume training if resume_iteration > 0: resume_checkpoint_path = os.path.join(workspace, 'checkpoints', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), '{}_iterations.pth'.format(resume_iteration)) logging.info('Loading checkpoint {}'.format(resume_checkpoint_path)) checkpoint = torch.load(resume_checkpoint_path) model.load_state_dict(checkpoint['model']) train_sampler.load_state_dict(checkpoint['sampler']) statistics_container.load_state_dict(resume_iteration) iteration = checkpoint['iteration'] else: iteration = 0 # Parallel print('GPU number: {}'.format(torch.cuda.device_count())) model = torch.nn.DataParallel(model) if 'cuda' in str(device): model.to(device) if resume_iteration: optimizer.load_state_dict(checkpoint['optimizer']) scheduler.load_state_dict(checkpoint['scheduler']) print(optimizer.state_dict()['param_groups'][0]['lr']) time1 = time.time() for batch_data_dict in train_loader: """batch_data_dict: { 'audio_name': (batch_size [*2 if mixup],), 'waveform': (batch_size [*2 if mixup], clip_samples), 'target': (batch_size [*2 if mixup], classes_num), (ifexist) 'mixup_lambda': (batch_size * 2,)} """ # Evaluate if (iteration % 2000 == 0 and iteration >= resume_iteration) or (iteration == 0): train_fin_time = time.time() bal_statistics = evaluator.evaluate(eval_bal_loader) test_statistics = evaluator.evaluate(eval_test_loader) logging.info('Validate bal mAP: {:.3f}'.format( np.mean(bal_statistics['average_precision']))) logging.info('Validate test mAP: {:.3f}'.format( np.mean(test_statistics['average_precision']))) statistics_container.append(iteration, bal_statistics, data_type='bal') statistics_container.append(iteration, test_statistics, data_type='test') statistics_container.dump() train_time = train_fin_time - train_bgn_time validate_time = time.time() - train_fin_time logging.info( 'iteration: {}, train time: {:.3f} s, validate time: {:.3f} s' ''.format(iteration, train_time, validate_time)) logging.info('------------------------------------') train_bgn_time = time.time() # Save model if iteration % 2000 == 0: checkpoint = { 'iteration': iteration, 'model': model.module.state_dict(), 'sampler': train_sampler.state_dict(), 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict()} checkpoint_path = os.path.join( checkpoints_dir, '{}_iterations.pth'.format(iteration)) torch.save(checkpoint, checkpoint_path) logging.info('Model saved to {}'.format(checkpoint_path)) # Mixup lambda if 'mixup' in augmentation: batch_data_dict['mixup_lambda'] = mixup_augmenter.get_lambda( batch_size=len(batch_data_dict['waveform'])) # Move data to device for key in batch_data_dict.keys(): batch_data_dict[key] = move_data_to_device(batch_data_dict[key], device) # Forward model.train() if 'mixup' in augmentation: batch_output_dict = model(batch_data_dict['waveform'], batch_data_dict['mixup_lambda']) """{'clipwise_output': (batch_size, classes_num), ...}""" batch_target_dict = {'target': do_mixup(batch_data_dict['target'], batch_data_dict['mixup_lambda'])} """{'target': (batch_size, classes_num)}""" else: batch_output_dict = model(batch_data_dict['waveform'], None) """{'clipwise_output': (batch_size, classes_num), ...}""" batch_target_dict = {'target': batch_data_dict['target']} """{'target': (batch_size, classes_num)}""" # Loss loss = loss_func(batch_output_dict, batch_target_dict) # Backward loss.backward() optimizer.step() optimizer.zero_grad() if iteration % 10 == 0: print(iteration, loss) #print('--- Iteration: {}, train time: {:.3f} s / 10 iterations ---'\ # .format(iteration, time.time() - time1)) #time1 = time.time() if iteration % 2000 == 0: scheduler.step(np.mean(test_statistics['average_precision'])) print(optimizer.state_dict()['param_groups'][0]['lr']) logging.info(optimizer.state_dict()['param_groups'][0]['lr']) # Stop learning if iteration == early_stop: break iteration += 1 if __name__ == '__main__': parser = argparse.ArgumentParser(description='Example of parser. ') subparsers = parser.add_subparsers(dest='mode') parser_train = subparsers.add_parser('train') parser_train.add_argument('--workspace', type=str, required=True) parser_train.add_argument('--data_type', type=str, default='full_train', choices=['balanced_train', 'full_train']) parser_train.add_argument('--sample_rate', type=int, default=32000) parser_train.add_argument('--window_size', type=int, default=1024) parser_train.add_argument('--hop_size', type=int, default=320) parser_train.add_argument('--mel_bins', type=int, default=64) parser_train.add_argument('--fmin', type=int, default=50) parser_train.add_argument('--fmax', type=int, default=14000) parser_train.add_argument('--model_type', type=str, required=True) parser_train.add_argument('--loss_type', type=str, default='clip_bce', choices=['clip_bce']) parser_train.add_argument('--balanced', type=str, default='balanced', choices=['none', 'balanced', 'alternate']) parser_train.add_argument('--augmentation', type=str, default='mixup', choices=['none', 'mixup']) parser_train.add_argument('--batch_size', type=int, default=32) parser_train.add_argument('--learning_rate', type=float, default=1e-3) parser_train.add_argument('--resume_iteration', type=int, default=0) parser_train.add_argument('--early_stop', type=int, default=1000000) parser_train.add_argument('--cuda', action='store_true', default=False) args = parser.parse_args() args.filename = get_filename(__file__) if args.mode == 'train': train(args) else: raise Exception('Error argument!') ================================================ FILE: audio_detection/audio_infer/pytorch/models.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from torchlibrosa.stft import Spectrogram, LogmelFilterBank from torchlibrosa.augmentation import SpecAugmentation from audio_infer.pytorch.pytorch_utils import do_mixup, interpolate, pad_framewise_output import os import sys import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.parameter import Parameter from torchlibrosa.stft import Spectrogram, LogmelFilterBank from torchlibrosa.augmentation import SpecAugmentation from audio_infer.pytorch.pytorch_utils import do_mixup import torch.utils.checkpoint as checkpoint from timm.models.layers import DropPath, to_2tuple, trunc_normal_ import warnings from functools import partial #from mmdet.models.builder import BACKBONES from mmdet.utils import get_root_logger from mmcv.runner import load_checkpoint os.environ['TORCH_HOME'] = '../pretrained_models' from copy import deepcopy from timm.models.helpers import load_pretrained from torch.cuda.amp import autocast from collections import OrderedDict import io import re from mmcv.runner import _load_checkpoint, load_state_dict import mmcv.runner import copy import random from einops import rearrange from einops.layers.torch import Rearrange, Reduce from torch import nn, einsum def load_checkpoint(model, filename, map_location=None, strict=False, logger=None, revise_keys=[(r'^module\.', '')]): """Load checkpoint from a file or URI. Args: model (Module): Module to load checkpoint. filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for details. map_location (str): Same as :func:`torch.load`. strict (bool): Whether to allow different params for the model and checkpoint. logger (:mod:`logging.Logger` or None): The logger for error message. revise_keys (list): A list of customized keywords to modify the state_dict in checkpoint. Each item is a (pattern, replacement) pair of the regular expression operations. Default: strip the prefix 'module.' by [(r'^module\\.', '')]. Returns: dict or OrderedDict: The loaded checkpoint. """ checkpoint = _load_checkpoint(filename, map_location, logger) new_proj = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2)) new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1)) checkpoint['patch_embed1.proj.weight'] = new_proj.weight # OrderedDict is a subclass of dict if not isinstance(checkpoint, dict): raise RuntimeError( f'No state_dict found in checkpoint file {filename}') # get state_dict from checkpoint if 'state_dict' in checkpoint: state_dict = checkpoint['state_dict'] else: state_dict = checkpoint # strip prefix of state_dict metadata = getattr(state_dict, '_metadata', OrderedDict()) for p, r in revise_keys: state_dict = OrderedDict( {re.sub(p, r, k): v for k, v in state_dict.items()}) state_dict = OrderedDict({k.replace('backbone.',''):v for k,v in state_dict.items()}) # Keep metadata in state_dict state_dict._metadata = metadata # load state_dict load_state_dict(model, state_dict, strict, logger) return checkpoint def init_layer(layer): """Initialize a Linear or Convolutional layer. """ nn.init.xavier_uniform_(layer.weight) if hasattr(layer, 'bias'): if layer.bias is not None: layer.bias.data.fill_(0.) def init_bn(bn): """Initialize a Batchnorm layer. """ bn.bias.data.fill_(0.) bn.weight.data.fill_(1.) class TimeShift(nn.Module): def __init__(self, mean, std): super().__init__() self.mean = mean self.std = std def forward(self, x): if self.training: shift = torch.empty(1).normal_(self.mean, self.std).int().item() x = torch.roll(x, shift, dims=2) return x class LinearSoftPool(nn.Module): """LinearSoftPool Linear softmax, takes logits and returns a probability, near to the actual maximum value. Taken from the paper: A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling https://arxiv.org/abs/1810.09050 """ def __init__(self, pooldim=1): super().__init__() self.pooldim = pooldim def forward(self, logits, time_decision): return (time_decision**2).sum(self.pooldim) / time_decision.sum( self.pooldim) class PVT(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num): super(PVT, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) self.time_shift = TimeShift(0, 10) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001, fdim=64, patch_size=7, stride=4, in_chans=1, num_classes=classes_num, embed_dims=[64, 128, 320, 512], depths=[3, 4, 6, 3], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True, qk_scale=None, drop_rate=0.0, drop_path_rate=0.1, sr_ratios=[8, 4, 2, 1], norm_layer=partial(nn.LayerNorm, eps=1e-6), num_stages=4, #pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth' ) #self.temp_pool = LinearSoftPool() self.avgpool = nn.AdaptiveAvgPool1d(1) self.fc_audioset = nn.Linear(512, classes_num, bias=True) self.init_weights() def init_weights(self): init_bn(self.bn0) init_layer(self.fc_audioset) def forward(self, input, mixup_lambda=None): """Input: (batch_size, times_steps, freq_bins)""" interpolate_ratio = 32 x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) frames_num = x.shape[2] x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: x = self.time_shift(x) x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) #print(x.shape) #torch.Size([10, 1, 1001, 64]) x = self.pvt_transformer(x) #print(x.shape) #torch.Size([10, 800, 128]) x = torch.mean(x, dim=3) x = x.transpose(1, 2).contiguous() framewise_output = torch.sigmoid(self.fc_audioset(x)) #clipwise_output = torch.mean(framewise_output, dim=1) #clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1) x = framewise_output.transpose(1, 2).contiguous() x = self.avgpool(x) clipwise_output = torch.flatten(x, 1) #print(framewise_output.shape) #torch.Size([10, 100, 17]) framewise_output = interpolate(framewise_output, interpolate_ratio) #framewise_output = framewise_output[:,:1000,:] #framewise_output = pad_framewise_output(framewise_output, frames_num) output_dict = {'framewise_output': framewise_output, 'clipwise_output': clipwise_output} return output_dict class PVT2(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num): super(PVT2, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) self.time_shift = TimeShift(0, 10) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001, fdim=64, patch_size=7, stride=4, in_chans=1, num_classes=classes_num, embed_dims=[64, 128, 320, 512], depths=[3, 4, 6, 3], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True, qk_scale=None, drop_rate=0.0, drop_path_rate=0.1, sr_ratios=[8, 4, 2, 1], norm_layer=partial(nn.LayerNorm, eps=1e-6), num_stages=4, pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth' ) #self.temp_pool = LinearSoftPool() self.fc_audioset = nn.Linear(512, classes_num, bias=True) self.init_weights() def init_weights(self): init_bn(self.bn0) init_layer(self.fc_audioset) def forward(self, input, mixup_lambda=None): """Input: (batch_size, times_steps, freq_bins)""" interpolate_ratio = 32 x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) frames_num = x.shape[2] x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: #x = self.time_shift(x) x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) #print(x.shape) #torch.Size([10, 1, 1001, 64]) x = self.pvt_transformer(x) #print(x.shape) #torch.Size([10, 800, 128]) x = torch.mean(x, dim=3) x = x.transpose(1, 2).contiguous() framewise_output = torch.sigmoid(self.fc_audioset(x)) clipwise_output = torch.mean(framewise_output, dim=1) #clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1) #print(framewise_output.shape) #torch.Size([10, 100, 17]) framewise_output = interpolate(framewise_output, interpolate_ratio) #framewise_output = framewise_output[:,:1000,:] #framewise_output = pad_framewise_output(framewise_output, frames_num) output_dict = {'framewise_output': framewise_output, 'clipwise_output': clipwise_output} return output_dict class PVT_2layer(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num): super(PVT_2layer, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) self.time_shift = TimeShift(0, 10) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001, fdim=64, patch_size=7, stride=4, in_chans=1, num_classes=classes_num, embed_dims=[64, 128], depths=[3, 4], num_heads=[1, 2], mlp_ratios=[8, 8], qkv_bias=True, qk_scale=None, drop_rate=0.0, drop_path_rate=0.1, sr_ratios=[8, 4], norm_layer=partial(nn.LayerNorm, eps=1e-6), num_stages=2, pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth' ) #self.temp_pool = LinearSoftPool() self.avgpool = nn.AdaptiveAvgPool1d(1) self.fc_audioset = nn.Linear(128, classes_num, bias=True) self.init_weights() def init_weights(self): init_bn(self.bn0) init_layer(self.fc_audioset) def forward(self, input, mixup_lambda=None): """Input: (batch_size, times_steps, freq_bins)""" interpolate_ratio = 8 x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) frames_num = x.shape[2] x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: x = self.time_shift(x) x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) #print(x.shape) #torch.Size([10, 1, 1001, 64]) x = self.pvt_transformer(x) #print(x.shape) #torch.Size([10, 800, 128]) x = torch.mean(x, dim=3) x = x.transpose(1, 2).contiguous() framewise_output = torch.sigmoid(self.fc_audioset(x)) #clipwise_output = torch.mean(framewise_output, dim=1) #clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1) x = framewise_output.transpose(1, 2).contiguous() x = self.avgpool(x) clipwise_output = torch.flatten(x, 1) #print(framewise_output.shape) #torch.Size([10, 100, 17]) framewise_output = interpolate(framewise_output, interpolate_ratio) #framewise_output = framewise_output[:,:1000,:] #framewise_output = pad_framewise_output(framewise_output, frames_num) output_dict = {'framewise_output': framewise_output, 'clipwise_output': clipwise_output} return output_dict class PVT_lr(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num): super(PVT_lr, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) self.time_shift = TimeShift(0, 10) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001, fdim=64, patch_size=7, stride=4, in_chans=1, num_classes=classes_num, embed_dims=[64, 128, 320, 512], depths=[3, 4, 6, 3], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True, qk_scale=None, drop_rate=0.0, drop_path_rate=0.1, sr_ratios=[8, 4, 2, 1], norm_layer=partial(nn.LayerNorm, eps=1e-6), num_stages=4, pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth' ) self.temp_pool = LinearSoftPool() self.fc_audioset = nn.Linear(512, classes_num, bias=True) self.init_weights() def init_weights(self): init_bn(self.bn0) init_layer(self.fc_audioset) def forward(self, input, mixup_lambda=None): """Input: (batch_size, times_steps, freq_bins)""" interpolate_ratio = 32 x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) frames_num = x.shape[2] x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: x = self.time_shift(x) x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) #print(x.shape) #torch.Size([10, 1, 1001, 64]) x = self.pvt_transformer(x) #print(x.shape) #torch.Size([10, 800, 128]) x = torch.mean(x, dim=3) x = x.transpose(1, 2).contiguous() framewise_output = torch.sigmoid(self.fc_audioset(x)) clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1) #print(framewise_output.shape) #torch.Size([10, 100, 17]) framewise_output = interpolate(framewise_output, interpolate_ratio) #framewise_output = framewise_output[:,:1000,:] #framewise_output = pad_framewise_output(framewise_output, frames_num) output_dict = {'framewise_output': framewise_output, 'clipwise_output': clipwise_output} return output_dict class PVT_nopretrain(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num): super(PVT_nopretrain, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) self.time_shift = TimeShift(0, 10) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001, fdim=64, patch_size=7, stride=4, in_chans=1, num_classes=classes_num, embed_dims=[64, 128, 320, 512], depths=[3, 4, 6, 3], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True, qk_scale=None, drop_rate=0.0, drop_path_rate=0.1, sr_ratios=[8, 4, 2, 1], norm_layer=partial(nn.LayerNorm, eps=1e-6), num_stages=4, #pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth' ) self.temp_pool = LinearSoftPool() self.fc_audioset = nn.Linear(512, classes_num, bias=True) self.init_weights() def init_weights(self): init_bn(self.bn0) init_layer(self.fc_audioset) def forward(self, input, mixup_lambda=None): """Input: (batch_size, times_steps, freq_bins)""" interpolate_ratio = 32 x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) frames_num = x.shape[2] x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: x = self.time_shift(x) x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) #print(x.shape) #torch.Size([10, 1, 1001, 64]) x = self.pvt_transformer(x) #print(x.shape) #torch.Size([10, 800, 128]) x = torch.mean(x, dim=3) x = x.transpose(1, 2).contiguous() framewise_output = torch.sigmoid(self.fc_audioset(x)) clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1) #print(framewise_output.shape) #torch.Size([10, 100, 17]) framewise_output = interpolate(framewise_output, interpolate_ratio) framewise_output = framewise_output[:,:1000,:] #framewise_output = pad_framewise_output(framewise_output, frames_num) output_dict = {'framewise_output': framewise_output, 'clipwise_output': clipwise_output} return output_dict class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.dwconv = DWConv(hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) self.linear = linear if self.linear: self.relu = nn.ReLU() self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x, H, W): x = self.fc1(x) if self.linear: x = self.relu(x) x = self.dwconv(x, H, W) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1, linear=False): super().__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 self.q = nn.Linear(dim, dim, bias=qkv_bias) self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.linear = linear self.sr_ratio = sr_ratio if not linear: if sr_ratio > 1: self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) self.norm = nn.LayerNorm(dim) else: self.pool = nn.AdaptiveAvgPool2d(7) self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1) self.norm = nn.LayerNorm(dim) self.act = nn.GELU() self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x, H, W): B, N, C = x.shape q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) if not self.linear: if self.sr_ratio > 1: x_ = x.permute(0, 2, 1).reshape(B, C, H, W) x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) x_ = self.norm(x_) kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) else: kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) else: x_ = x.permute(0, 2, 1).reshape(B, C, H, W) x_ = self.sr(self.pool(x_)).reshape(B, C, -1).permute(0, 2, 1) x_ = self.norm(x_) x_ = self.act(x_) kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) k, v = kv[0], kv[1] attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class Pooling(nn.Module): """ Implementation of pooling for PoolFormer --pool_size: pooling size """ def __init__(self, pool_size=3): super().__init__() self.pool = nn.AvgPool2d( pool_size, stride=1, padding=pool_size//2, count_include_pad=False) def forward(self, x): return self.pool(x) - x class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, linear=False): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio, linear=linear) #self.norm3 = norm_layer(dim) #self.token_mixer = Pooling(pool_size=3) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, linear=linear) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x, H, W): x = x + self.drop_path(self.attn(self.norm1(x), H, W)) x = x + self.drop_path(self.mlp(self.norm2(x), H, W)) return x class OverlapPatchEmbed(nn.Module): """ Image to Patch Embedding """ def __init__(self, tdim, fdim, patch_size=7, stride=4, in_chans=3, embed_dim=768): super().__init__() img_size = (tdim, fdim) patch_size = to_2tuple(patch_size) self.img_size = img_size self.patch_size = patch_size self.H, self.W = img_size[0] // stride, img_size[1] // stride self.num_patches = self.H * self.W self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=(patch_size[0] // 3, patch_size[1] // 3)) self.norm = nn.LayerNorm(embed_dim) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x): x = self.proj(x) _, _, H, W = x.shape x = x.flatten(2).transpose(1, 2) x = self.norm(x) return x, H, W class PyramidVisionTransformerV2(nn.Module): def __init__(self, tdim=1001, fdim=64, patch_size=16, stride=4, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512], num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], num_stages=2, linear=False, pretrained=None): super().__init__() # self.num_classes = num_classes self.depths = depths self.num_stages = num_stages self.linear = linear dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule cur = 0 for i in range(num_stages): patch_embed = OverlapPatchEmbed(tdim=tdim if i == 0 else tdim // (2 ** (i + 1)), fdim=fdim if i == 0 else tdim // (2 ** (i + 1)), patch_size=7 if i == 0 else 3, stride=stride if i == 0 else 2, in_chans=in_chans if i == 0 else embed_dims[i - 1], embed_dim=embed_dims[i]) block = nn.ModuleList([Block( dim=embed_dims[i], num_heads=num_heads[i], mlp_ratio=mlp_ratios[i], qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j], norm_layer=norm_layer, sr_ratio=sr_ratios[i], linear=linear) for j in range(depths[i])]) norm = norm_layer(embed_dims[i]) cur += depths[i] setattr(self, f"patch_embed{i + 1}", patch_embed) setattr(self, f"block{i + 1}", block) setattr(self, f"norm{i + 1}", norm) #self.n = nn.Linear(125, 250, bias=True) # classification head # self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity() self.apply(self._init_weights) self.init_weights(pretrained) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def init_weights(self, pretrained=None): if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger) def freeze_patch_emb(self): self.patch_embed1.requires_grad = False @torch.jit.ignore def no_weight_decay(self): return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better def get_classifier(self): return self.head def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() def forward_features(self, x): B = x.shape[0] for i in range(self.num_stages): patch_embed = getattr(self, f"patch_embed{i + 1}") block = getattr(self, f"block{i + 1}") norm = getattr(self, f"norm{i + 1}") x, H, W = patch_embed(x) #print(x.shape) for blk in block: x = blk(x, H, W) #print(x.shape) x = norm(x) #if i != self.num_stages - 1: x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() #print(x.shape) return x def forward(self, x): x = self.forward_features(x) # x = self.head(x) return x class DWConv(nn.Module): def __init__(self, dim=768): super(DWConv, self).__init__() self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) def forward(self, x, H, W): B, N, C = x.shape x = x.transpose(1, 2).view(B, C, H, W) x = self.dwconv(x) x = x.flatten(2).transpose(1, 2) return x def _conv_filter(state_dict, patch_size=16): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} for k, v in state_dict.items(): if 'patch_embed.proj.weight' in k: v = v.reshape((v.shape[0], 3, patch_size, patch_size)) out_dict[k] = v return out_dict ================================================ FILE: audio_detection/audio_infer/pytorch/pytorch_utils.py ================================================ import numpy as np import time import torch import torch.nn as nn def move_data_to_device(x, device): if 'float' in str(x.dtype): x = torch.Tensor(x) elif 'int' in str(x.dtype): x = torch.LongTensor(x) else: return x return x.to(device) def do_mixup(x, mixup_lambda): """Mixup x of even indexes (0, 2, 4, ...) with x of odd indexes (1, 3, 5, ...). Args: x: (batch_size * 2, ...) mixup_lambda: (batch_size * 2,) Returns: out: (batch_size, ...) """ out = (x[0 :: 2].transpose(0, -1) * mixup_lambda[0 :: 2] + \ x[1 :: 2].transpose(0, -1) * mixup_lambda[1 :: 2]).transpose(0, -1) return out def append_to_dict(dict, key, value): if key in dict.keys(): dict[key].append(value) else: dict[key] = [value] def forward(model, generator, return_input=False, return_target=False): """Forward data to a model. Args: model: object generator: object return_input: bool return_target: bool Returns: audio_name: (audios_num,) clipwise_output: (audios_num, classes_num) (ifexist) segmentwise_output: (audios_num, segments_num, classes_num) (ifexist) framewise_output: (audios_num, frames_num, classes_num) (optional) return_input: (audios_num, segment_samples) (optional) return_target: (audios_num, classes_num) """ output_dict = {} device = next(model.parameters()).device time1 = time.time() # Forward data to a model in mini-batches for n, batch_data_dict in enumerate(generator): print(n) batch_waveform = move_data_to_device(batch_data_dict['waveform'], device) with torch.no_grad(): model.eval() batch_output = model(batch_waveform) append_to_dict(output_dict, 'audio_name', batch_data_dict['audio_name']) append_to_dict(output_dict, 'clipwise_output', batch_output['clipwise_output'].data.cpu().numpy()) if 'segmentwise_output' in batch_output.keys(): append_to_dict(output_dict, 'segmentwise_output', batch_output['segmentwise_output'].data.cpu().numpy()) if 'framewise_output' in batch_output.keys(): append_to_dict(output_dict, 'framewise_output', batch_output['framewise_output'].data.cpu().numpy()) if return_input: append_to_dict(output_dict, 'waveform', batch_data_dict['waveform']) if return_target: if 'target' in batch_data_dict.keys(): append_to_dict(output_dict, 'target', batch_data_dict['target']) if n % 10 == 0: print(' --- Inference time: {:.3f} s / 10 iterations ---'.format( time.time() - time1)) time1 = time.time() for key in output_dict.keys(): output_dict[key] = np.concatenate(output_dict[key], axis=0) return output_dict def interpolate(x, ratio): """Interpolate data in time domain. This is used to compensate the resolution reduction in downsampling of a CNN. Args: x: (batch_size, time_steps, classes_num) ratio: int, ratio to interpolate Returns: upsampled: (batch_size, time_steps * ratio, classes_num) """ (batch_size, time_steps, classes_num) = x.shape upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1) upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num) return upsampled def pad_framewise_output(framewise_output, frames_num): """Pad framewise_output to the same length as input frames. The pad value is the same as the value of the last frame. Args: framewise_output: (batch_size, frames_num, classes_num) frames_num: int, number of frames to pad Outputs: output: (batch_size, frames_num, classes_num) """ pad = framewise_output[:, -1 :, :].repeat(1, frames_num - framewise_output.shape[1], 1) """tensor for padding""" output = torch.cat((framewise_output, pad), dim=1) """(batch_size, frames_num, classes_num)""" return output def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) def count_flops(model, audio_length): """Count flops. Code modified from others' implementation. """ multiply_adds = True list_conv2d=[] def conv2d_hook(self, input, output): batch_size, input_channels, input_height, input_width = input[0].size() output_channels, output_height, output_width = output[0].size() kernel_ops = self.kernel_size[0] * self.kernel_size[1] * (self.in_channels / self.groups) * (2 if multiply_adds else 1) bias_ops = 1 if self.bias is not None else 0 params = output_channels * (kernel_ops + bias_ops) flops = batch_size * params * output_height * output_width list_conv2d.append(flops) list_conv1d=[] def conv1d_hook(self, input, output): batch_size, input_channels, input_length = input[0].size() output_channels, output_length = output[0].size() kernel_ops = self.kernel_size[0] * (self.in_channels / self.groups) * (2 if multiply_adds else 1) bias_ops = 1 if self.bias is not None else 0 params = output_channels * (kernel_ops + bias_ops) flops = batch_size * params * output_length list_conv1d.append(flops) list_linear=[] def linear_hook(self, input, output): batch_size = input[0].size(0) if input[0].dim() == 2 else 1 weight_ops = self.weight.nelement() * (2 if multiply_adds else 1) bias_ops = self.bias.nelement() flops = batch_size * (weight_ops + bias_ops) list_linear.append(flops) list_bn=[] def bn_hook(self, input, output): list_bn.append(input[0].nelement() * 2) list_relu=[] def relu_hook(self, input, output): list_relu.append(input[0].nelement() * 2) list_pooling2d=[] def pooling2d_hook(self, input, output): batch_size, input_channels, input_height, input_width = input[0].size() output_channels, output_height, output_width = output[0].size() kernel_ops = self.kernel_size * self.kernel_size bias_ops = 0 params = output_channels * (kernel_ops + bias_ops) flops = batch_size * params * output_height * output_width list_pooling2d.append(flops) list_pooling1d=[] def pooling1d_hook(self, input, output): batch_size, input_channels, input_length = input[0].size() output_channels, output_length = output[0].size() kernel_ops = self.kernel_size[0] bias_ops = 0 params = output_channels * (kernel_ops + bias_ops) flops = batch_size * params * output_length list_pooling2d.append(flops) def foo(net): childrens = list(net.children()) if not childrens: if isinstance(net, nn.Conv2d): net.register_forward_hook(conv2d_hook) elif isinstance(net, nn.Conv1d): net.register_forward_hook(conv1d_hook) elif isinstance(net, nn.Linear): net.register_forward_hook(linear_hook) elif isinstance(net, nn.BatchNorm2d) or isinstance(net, nn.BatchNorm1d): net.register_forward_hook(bn_hook) elif isinstance(net, nn.ReLU): net.register_forward_hook(relu_hook) elif isinstance(net, nn.AvgPool2d) or isinstance(net, nn.MaxPool2d): net.register_forward_hook(pooling2d_hook) elif isinstance(net, nn.AvgPool1d) or isinstance(net, nn.MaxPool1d): net.register_forward_hook(pooling1d_hook) else: print('Warning: flop of module {} is not counted!'.format(net)) return for c in childrens: foo(c) # Register hook foo(model) device = device = next(model.parameters()).device input = torch.rand(1, audio_length).to(device) out = model(input) total_flops = sum(list_conv2d) + sum(list_conv1d) + sum(list_linear) + \ sum(list_bn) + sum(list_relu) + sum(list_pooling2d) + sum(list_pooling1d) return total_flops ================================================ FILE: audio_detection/audio_infer/utils/config.py ================================================ import numpy as np import csv sample_rate = 32000 clip_samples = sample_rate * 10 # Audio clips are 10-second # Load label with open('./audio_detection/audio_infer/metadata/class_labels_indices.csv', 'r') as f: reader = csv.reader(f, delimiter=',') lines = list(reader) labels = [] ids = [] # Each label has a unique id such as "/m/068hy" for i1 in range(1, len(lines)): id = lines[i1][1] label = lines[i1][2] ids.append(id) labels.append(label) classes_num = len(labels) lb_to_ix = {label : i for i, label in enumerate(labels)} ix_to_lb = {i : label for i, label in enumerate(labels)} id_to_ix = {id : i for i, id in enumerate(ids)} ix_to_id = {i : id for i, id in enumerate(ids)} full_samples_per_class = np.array([ 937432, 16344, 7822, 10271, 2043, 14420, 733, 1511, 1258, 424, 1751, 704, 369, 590, 1063, 1375, 5026, 743, 853, 1648, 714, 1497, 1251, 2139, 1093, 133, 224, 39469, 6423, 407, 1559, 4546, 6826, 7464, 2468, 549, 4063, 334, 587, 238, 1766, 691, 114, 2153, 236, 209, 421, 740, 269, 959, 137, 4192, 485, 1515, 655, 274, 69, 157, 1128, 807, 1022, 346, 98, 680, 890, 352, 4169, 2061, 1753, 9883, 1339, 708, 37857, 18504, 12864, 2475, 2182, 757, 3624, 677, 1683, 3583, 444, 1780, 2364, 409, 4060, 3097, 3143, 502, 723, 600, 230, 852, 1498, 1865, 1879, 2429, 5498, 5430, 2139, 1761, 1051, 831, 2401, 2258, 1672, 1711, 987, 646, 794, 25061, 5792, 4256, 96, 8126, 2740, 752, 513, 554, 106, 254, 1592, 556, 331, 615, 2841, 737, 265, 1349, 358, 1731, 1115, 295, 1070, 972, 174, 937780, 112337, 42509, 49200, 11415, 6092, 13851, 2665, 1678, 13344, 2329, 1415, 2244, 1099, 5024, 9872, 10948, 4409, 2732, 1211, 1289, 4807, 5136, 1867, 16134, 14519, 3086, 19261, 6499, 4273, 2790, 8820, 1228, 1575, 4420, 3685, 2019, 664, 324, 513, 411, 436, 2997, 5162, 3806, 1389, 899, 8088, 7004, 1105, 3633, 2621, 9753, 1082, 26854, 3415, 4991, 2129, 5546, 4489, 2850, 1977, 1908, 1719, 1106, 1049, 152, 136, 802, 488, 592, 2081, 2712, 1665, 1128, 250, 544, 789, 2715, 8063, 7056, 2267, 8034, 6092, 3815, 1833, 3277, 8813, 2111, 4662, 2678, 2954, 5227, 1472, 2591, 3714, 1974, 1795, 4680, 3751, 6585, 2109, 36617, 6083, 16264, 17351, 3449, 5034, 3931, 2599, 4134, 3892, 2334, 2211, 4516, 2766, 2862, 3422, 1788, 2544, 2403, 2892, 4042, 3460, 1516, 1972, 1563, 1579, 2776, 1647, 4535, 3921, 1261, 6074, 2922, 3068, 1948, 4407, 712, 1294, 1019, 1572, 3764, 5218, 975, 1539, 6376, 1606, 6091, 1138, 1169, 7925, 3136, 1108, 2677, 2680, 1383, 3144, 2653, 1986, 1800, 1308, 1344, 122231, 12977, 2552, 2678, 7824, 768, 8587, 39503, 3474, 661, 430, 193, 1405, 1442, 3588, 6280, 10515, 785, 710, 305, 206, 4990, 5329, 3398, 1771, 3022, 6907, 1523, 8588, 12203, 666, 2113, 7916, 434, 1636, 5185, 1062, 664, 952, 3490, 2811, 2749, 2848, 15555, 363, 117, 1494, 1647, 5886, 4021, 633, 1013, 5951, 11343, 2324, 243, 372, 943, 734, 242, 3161, 122, 127, 201, 1654, 768, 134, 1467, 642, 1148, 2156, 1368, 1176, 302, 1909, 61, 223, 1812, 287, 422, 311, 228, 748, 230, 1876, 539, 1814, 737, 689, 1140, 591, 943, 353, 289, 198, 490, 7938, 1841, 850, 457, 814, 146, 551, 728, 1627, 620, 648, 1621, 2731, 535, 88, 1736, 736, 328, 293, 3170, 344, 384, 7640, 433, 215, 715, 626, 128, 3059, 1833, 2069, 3732, 1640, 1508, 836, 567, 2837, 1151, 2068, 695, 1494, 3173, 364, 88, 188, 740, 677, 273, 1533, 821, 1091, 293, 647, 318, 1202, 328, 532, 2847, 526, 721, 370, 258, 956, 1269, 1641, 339, 1322, 4485, 286, 1874, 277, 757, 1393, 1330, 380, 146, 377, 394, 318, 339, 1477, 1886, 101, 1435, 284, 1425, 686, 621, 221, 117, 87, 1340, 201, 1243, 1222, 651, 1899, 421, 712, 1016, 1279, 124, 351, 258, 7043, 368, 666, 162, 7664, 137, 70159, 26179, 6321, 32236, 33320, 771, 1169, 269, 1103, 444, 364, 2710, 121, 751, 1609, 855, 1141, 2287, 1940, 3943, 289]) ================================================ FILE: audio_detection/audio_infer/utils/crash.py ================================================ import sys class ExceptionHook: instance = None def __call__(self, *args, **kwargs): if self.instance is None: from IPython.core import ultratb self.instance = ultratb.FormattedTB(mode='Plain', color_scheme='Linux', call_pdb=1) return self.instance(*args, **kwargs) sys.excepthook = ExceptionHook() ================================================ FILE: audio_detection/audio_infer/utils/create_black_list.py ================================================ import argparse import csv import os from utilities import create_folder def dcase2017task4(args): """Create black list. Black list is a list of audio ids that will be skipped in training. """ # Augments & parameters workspace = args.workspace # Black list from DCASE 2017 Task 4 test_weak_csv = 'metadata/black_list/groundtruth_weak_label_testing_set.csv' evaluation_weak_csv = 'metadata/black_list/groundtruth_weak_label_evaluation_set.csv' black_list_csv = os.path.join(workspace, 'black_list', 'dcase2017task4.csv') create_folder(os.path.dirname(black_list_csv)) def get_id_sets(csv_path): with open(csv_path, 'r') as fr: reader = csv.reader(fr, delimiter='\t') lines = list(reader) ids_set = [] for line in lines: """line: ['-5QrBL6MzLg_60.000_70.000.wav', '60.000', '70.000', 'Train horn']""" ids_set.append(line[0][0 : 11]) ids_set = list(set(ids_set)) return ids_set test_ids_set = get_id_sets(test_weak_csv) evaluation_ids_set = get_id_sets(evaluation_weak_csv) full_ids_set = test_ids_set + evaluation_ids_set # Write black list fw = open(black_list_csv, 'w') for id in full_ids_set: fw.write('{}\n'.format(id)) print('Write black list to {}'.format(black_list_csv)) if __name__ == '__main__': parser = argparse.ArgumentParser(description='') subparsers = parser.add_subparsers(dest='mode') parser_dcase2017task4 = subparsers.add_parser('dcase2017task4') parser_dcase2017task4.add_argument('--workspace', type=str, required=True) args = parser.parse_args() if args.mode == 'dcase2017task4': dcase2017task4(args) else: raise Exception('Error argument!') ================================================ FILE: audio_detection/audio_infer/utils/create_indexes.py ================================================ import numpy as np import argparse import csv import os import glob import datetime import time import logging import h5py import librosa from utilities import create_folder, get_sub_filepaths import config def create_indexes(args): """Create indexes a for dataloader to read for training. When users have a new task and their own data, they need to create similar indexes. The indexes contain meta information of "where to find the data for training". """ # Arguments & parameters waveforms_hdf5_path = args.waveforms_hdf5_path indexes_hdf5_path = args.indexes_hdf5_path # Paths create_folder(os.path.dirname(indexes_hdf5_path)) with h5py.File(waveforms_hdf5_path, 'r') as hr: with h5py.File(indexes_hdf5_path, 'w') as hw: audios_num = len(hr['audio_name']) hw.create_dataset('audio_name', data=hr['audio_name'][:], dtype='S20') hw.create_dataset('target', data=hr['target'][:], dtype=np.bool) hw.create_dataset('hdf5_path', data=[waveforms_hdf5_path.encode()] * audios_num, dtype='S200') hw.create_dataset('index_in_hdf5', data=np.arange(audios_num), dtype=np.int32) print('Write to {}'.format(indexes_hdf5_path)) def combine_full_indexes(args): """Combine all balanced and unbalanced indexes hdf5s to a single hdf5. This combined indexes hdf5 is used for training with full data (~20k balanced audio clips + ~1.9m unbalanced audio clips). """ # Arguments & parameters indexes_hdf5s_dir = args.indexes_hdf5s_dir full_indexes_hdf5_path = args.full_indexes_hdf5_path classes_num = config.classes_num # Paths paths = get_sub_filepaths(indexes_hdf5s_dir) paths = [path for path in paths if ( 'train' in path and 'full_train' not in path and 'mini' not in path)] print('Total {} hdf5 to combine.'.format(len(paths))) with h5py.File(full_indexes_hdf5_path, 'w') as full_hf: full_hf.create_dataset( name='audio_name', shape=(0,), maxshape=(None,), dtype='S20') full_hf.create_dataset( name='target', shape=(0, classes_num), maxshape=(None, classes_num), dtype=np.bool) full_hf.create_dataset( name='hdf5_path', shape=(0,), maxshape=(None,), dtype='S200') full_hf.create_dataset( name='index_in_hdf5', shape=(0,), maxshape=(None,), dtype=np.int32) for path in paths: with h5py.File(path, 'r') as part_hf: print(path) n = len(full_hf['audio_name'][:]) new_n = n + len(part_hf['audio_name'][:]) full_hf['audio_name'].resize((new_n,)) full_hf['audio_name'][n : new_n] = part_hf['audio_name'][:] full_hf['target'].resize((new_n, classes_num)) full_hf['target'][n : new_n] = part_hf['target'][:] full_hf['hdf5_path'].resize((new_n,)) full_hf['hdf5_path'][n : new_n] = part_hf['hdf5_path'][:] full_hf['index_in_hdf5'].resize((new_n,)) full_hf['index_in_hdf5'][n : new_n] = part_hf['index_in_hdf5'][:] print('Write combined full hdf5 to {}'.format(full_indexes_hdf5_path)) if __name__ == '__main__': parser = argparse.ArgumentParser() subparsers = parser.add_subparsers(dest='mode') parser_create_indexes = subparsers.add_parser('create_indexes') parser_create_indexes.add_argument('--waveforms_hdf5_path', type=str, required=True, help='Path of packed waveforms hdf5.') parser_create_indexes.add_argument('--indexes_hdf5_path', type=str, required=True, help='Path to write out indexes hdf5.') parser_combine_full_indexes = subparsers.add_parser('combine_full_indexes') parser_combine_full_indexes.add_argument('--indexes_hdf5s_dir', type=str, required=True, help='Directory containing indexes hdf5s to be combined.') parser_combine_full_indexes.add_argument('--full_indexes_hdf5_path', type=str, required=True, help='Path to write out full indexes hdf5 file.') args = parser.parse_args() if args.mode == 'create_indexes': create_indexes(args) elif args.mode == 'combine_full_indexes': combine_full_indexes(args) else: raise Exception('Incorrect arguments!') ================================================ FILE: audio_detection/audio_infer/utils/data_generator.py ================================================ import numpy as np import h5py import csv import time import logging from utilities import int16_to_float32 def read_black_list(black_list_csv): """Read audio names from black list. """ with open(black_list_csv, 'r') as fr: reader = csv.reader(fr) lines = list(reader) black_list_names = ['Y{}.wav'.format(line[0]) for line in lines] return black_list_names class AudioSetDataset(object): def __init__(self, sample_rate=32000): """This class takes the meta of an audio clip as input, and return the waveform and target of the audio clip. This class is used by DataLoader. """ self.sample_rate = sample_rate def __getitem__(self, meta): """Load waveform and target of an audio clip. Args: meta: { 'hdf5_path': str, 'index_in_hdf5': int} Returns: data_dict: { 'audio_name': str, 'waveform': (clip_samples,), 'target': (classes_num,)} """ hdf5_path = meta['hdf5_path'] index_in_hdf5 = meta['index_in_hdf5'] with h5py.File(hdf5_path, 'r') as hf: audio_name = hf['audio_name'][index_in_hdf5].decode() waveform = int16_to_float32(hf['waveform'][index_in_hdf5]) waveform = self.resample(waveform) target = hf['target'][index_in_hdf5].astype(np.float32) data_dict = { 'audio_name': audio_name, 'waveform': waveform, 'target': target} return data_dict def resample(self, waveform): """Resample. Args: waveform: (clip_samples,) Returns: (resampled_clip_samples,) """ if self.sample_rate == 32000: return waveform elif self.sample_rate == 16000: return waveform[0 :: 2] elif self.sample_rate == 8000: return waveform[0 :: 4] else: raise Exception('Incorrect sample rate!') class Base(object): def __init__(self, indexes_hdf5_path, batch_size, black_list_csv, random_seed): """Base class of train sampler. Args: indexes_hdf5_path: string batch_size: int black_list_csv: string random_seed: int """ self.batch_size = batch_size self.random_state = np.random.RandomState(random_seed) # Black list if black_list_csv: self.black_list_names = read_black_list(black_list_csv) else: self.black_list_names = [] logging.info('Black list samples: {}'.format(len(self.black_list_names))) # Load target load_time = time.time() with h5py.File(indexes_hdf5_path, 'r') as hf: self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]] self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]] self.indexes_in_hdf5 = hf['index_in_hdf5'][:] self.targets = hf['target'][:].astype(np.float32) (self.audios_num, self.classes_num) = self.targets.shape logging.info('Training number: {}'.format(self.audios_num)) logging.info('Load target time: {:.3f} s'.format(time.time() - load_time)) class TrainSampler(Base): def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None, random_seed=1234): """Balanced sampler. Generate batch meta for training. Args: indexes_hdf5_path: string batch_size: int black_list_csv: string random_seed: int """ super(TrainSampler, self).__init__(indexes_hdf5_path, batch_size, black_list_csv, random_seed) self.indexes = np.arange(self.audios_num) # Shuffle indexes self.random_state.shuffle(self.indexes) self.pointer = 0 def __iter__(self): """Generate batch meta for training. Returns: batch_meta: e.g.: [ {'hdf5_path': string, 'index_in_hdf5': int}, ...] """ batch_size = self.batch_size while True: batch_meta = [] i = 0 while i < batch_size: index = self.indexes[self.pointer] self.pointer += 1 # Shuffle indexes and reset pointer if self.pointer >= self.audios_num: self.pointer = 0 self.random_state.shuffle(self.indexes) # If audio in black list then continue if self.audio_names[index] in self.black_list_names: continue else: batch_meta.append({ 'hdf5_path': self.hdf5_paths[index], 'index_in_hdf5': self.indexes_in_hdf5[index]}) i += 1 yield batch_meta def state_dict(self): state = { 'indexes': self.indexes, 'pointer': self.pointer} return state def load_state_dict(self, state): self.indexes = state['indexes'] self.pointer = state['pointer'] class BalancedTrainSampler(Base): def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None, random_seed=1234): """Balanced sampler. Generate batch meta for training. Data are equally sampled from different sound classes. Args: indexes_hdf5_path: string batch_size: int black_list_csv: string random_seed: int """ super(BalancedTrainSampler, self).__init__(indexes_hdf5_path, batch_size, black_list_csv, random_seed) self.samples_num_per_class = np.sum(self.targets, axis=0) logging.info('samples_num_per_class: {}'.format( self.samples_num_per_class.astype(np.int32))) # Training indexes of all sound classes. E.g.: # [[0, 11, 12, ...], [3, 4, 15, 16, ...], [7, 8, ...], ...] self.indexes_per_class = [] for k in range(self.classes_num): self.indexes_per_class.append( np.where(self.targets[:, k] == 1)[0]) # Shuffle indexes for k in range(self.classes_num): self.random_state.shuffle(self.indexes_per_class[k]) self.queue = [] self.pointers_of_classes = [0] * self.classes_num def expand_queue(self, queue): classes_set = np.arange(self.classes_num).tolist() self.random_state.shuffle(classes_set) queue += classes_set return queue def __iter__(self): """Generate batch meta for training. Returns: batch_meta: e.g.: [ {'hdf5_path': string, 'index_in_hdf5': int}, ...] """ batch_size = self.batch_size while True: batch_meta = [] i = 0 while i < batch_size: if len(self.queue) == 0: self.queue = self.expand_queue(self.queue) class_id = self.queue.pop(0) pointer = self.pointers_of_classes[class_id] self.pointers_of_classes[class_id] += 1 index = self.indexes_per_class[class_id][pointer] # When finish one epoch of a sound class, then shuffle its indexes and reset pointer if self.pointers_of_classes[class_id] >= self.samples_num_per_class[class_id]: self.pointers_of_classes[class_id] = 0 self.random_state.shuffle(self.indexes_per_class[class_id]) # If audio in black list then continue if self.audio_names[index] in self.black_list_names: continue else: batch_meta.append({ 'hdf5_path': self.hdf5_paths[index], 'index_in_hdf5': self.indexes_in_hdf5[index]}) i += 1 yield batch_meta def state_dict(self): state = { 'indexes_per_class': self.indexes_per_class, 'queue': self.queue, 'pointers_of_classes': self.pointers_of_classes} return state def load_state_dict(self, state): self.indexes_per_class = state['indexes_per_class'] self.queue = state['queue'] self.pointers_of_classes = state['pointers_of_classes'] class AlternateTrainSampler(Base): def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None, random_seed=1234): """AlternateSampler is a combination of Sampler and Balanced Sampler. AlternateSampler alternately sample data from Sampler and Blanced Sampler. Args: indexes_hdf5_path: string batch_size: int black_list_csv: string random_seed: int """ self.sampler1 = TrainSampler(indexes_hdf5_path, batch_size, black_list_csv, random_seed) self.sampler2 = BalancedTrainSampler(indexes_hdf5_path, batch_size, black_list_csv, random_seed) self.batch_size = batch_size self.count = 0 def __iter__(self): """Generate batch meta for training. Returns: batch_meta: e.g.: [ {'hdf5_path': string, 'index_in_hdf5': int}, ...] """ batch_size = self.batch_size while True: self.count += 1 if self.count % 2 == 0: batch_meta = [] i = 0 while i < batch_size: index = self.sampler1.indexes[self.sampler1.pointer] self.sampler1.pointer += 1 # Shuffle indexes and reset pointer if self.sampler1.pointer >= self.sampler1.audios_num: self.sampler1.pointer = 0 self.sampler1.random_state.shuffle(self.sampler1.indexes) # If audio in black list then continue if self.sampler1.audio_names[index] in self.sampler1.black_list_names: continue else: batch_meta.append({ 'hdf5_path': self.sampler1.hdf5_paths[index], 'index_in_hdf5': self.sampler1.indexes_in_hdf5[index]}) i += 1 elif self.count % 2 == 1: batch_meta = [] i = 0 while i < batch_size: if len(self.sampler2.queue) == 0: self.sampler2.queue = self.sampler2.expand_queue(self.sampler2.queue) class_id = self.sampler2.queue.pop(0) pointer = self.sampler2.pointers_of_classes[class_id] self.sampler2.pointers_of_classes[class_id] += 1 index = self.sampler2.indexes_per_class[class_id][pointer] # When finish one epoch of a sound class, then shuffle its indexes and reset pointer if self.sampler2.pointers_of_classes[class_id] >= self.sampler2.samples_num_per_class[class_id]: self.sampler2.pointers_of_classes[class_id] = 0 self.sampler2.random_state.shuffle(self.sampler2.indexes_per_class[class_id]) # If audio in black list then continue if self.sampler2.audio_names[index] in self.sampler2.black_list_names: continue else: batch_meta.append({ 'hdf5_path': self.sampler2.hdf5_paths[index], 'index_in_hdf5': self.sampler2.indexes_in_hdf5[index]}) i += 1 yield batch_meta def state_dict(self): state = { 'sampler1': self.sampler1.state_dict(), 'sampler2': self.sampler2.state_dict()} return state def load_state_dict(self, state): self.sampler1.load_state_dict(state['sampler1']) self.sampler2.load_state_dict(state['sampler2']) class EvaluateSampler(object): def __init__(self, indexes_hdf5_path, batch_size): """Evaluate sampler. Generate batch meta for evaluation. Args: indexes_hdf5_path: string batch_size: int """ self.batch_size = batch_size with h5py.File(indexes_hdf5_path, 'r') as hf: self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]] self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]] self.indexes_in_hdf5 = hf['index_in_hdf5'][:] self.targets = hf['target'][:].astype(np.float32) self.audios_num = len(self.audio_names) def __iter__(self): """Generate batch meta for training. Returns: batch_meta: e.g.: [ {'hdf5_path': string, 'index_in_hdf5': int} ...] """ batch_size = self.batch_size pointer = 0 while pointer < self.audios_num: batch_indexes = np.arange(pointer, min(pointer + batch_size, self.audios_num)) batch_meta = [] for index in batch_indexes: batch_meta.append({ 'audio_name': self.audio_names[index], 'hdf5_path': self.hdf5_paths[index], 'index_in_hdf5': self.indexes_in_hdf5[index], 'target': self.targets[index]}) pointer += batch_size yield batch_meta def collate_fn(list_data_dict): """Collate data. Args: list_data_dict, e.g., [{'audio_name': str, 'waveform': (clip_samples,), ...}, {'audio_name': str, 'waveform': (clip_samples,), ...}, ...] Returns: np_data_dict, dict, e.g., {'audio_name': (batch_size,), 'waveform': (batch_size, clip_samples), ...} """ np_data_dict = {} for key in list_data_dict[0].keys(): np_data_dict[key] = np.array([data_dict[key] for data_dict in list_data_dict]) return np_data_dict ================================================ FILE: audio_detection/audio_infer/utils/dataset.py ================================================ import numpy as np import argparse import csv import os import glob import datetime import time import logging import h5py import librosa from utilities import (create_folder, get_filename, create_logging, float32_to_int16, pad_or_truncate, read_metadata) import config def split_unbalanced_csv_to_partial_csvs(args): """Split unbalanced csv to part csvs. Each part csv contains up to 50000 ids. """ unbalanced_csv_path = args.unbalanced_csv unbalanced_partial_csvs_dir = args.unbalanced_partial_csvs_dir create_folder(unbalanced_partial_csvs_dir) with open(unbalanced_csv_path, 'r') as f: lines = f.readlines() lines = lines[3:] # Remove head info audios_num_per_file = 50000 files_num = int(np.ceil(len(lines) / float(audios_num_per_file))) for r in range(files_num): lines_per_file = lines[r * audios_num_per_file : (r + 1) * audios_num_per_file] out_csv_path = os.path.join(unbalanced_partial_csvs_dir, 'unbalanced_train_segments_part{:02d}.csv'.format(r)) with open(out_csv_path, 'w') as f: f.write('empty\n') f.write('empty\n') f.write('empty\n') for line in lines_per_file: f.write(line) print('Write out csv to {}'.format(out_csv_path)) def download_wavs(args): """Download videos and extract audio in wav format. """ # Paths csv_path = args.csv_path audios_dir = args.audios_dir mini_data = args.mini_data if mini_data: logs_dir = '_logs/download_dataset/{}'.format(get_filename(csv_path)) else: logs_dir = '_logs/download_dataset_minidata/{}'.format(get_filename(csv_path)) create_folder(audios_dir) create_folder(logs_dir) create_logging(logs_dir, filemode='w') logging.info('Download log is saved to {}'.format(logs_dir)) # Read csv with open(csv_path, 'r') as f: lines = f.readlines() lines = lines[3:] # Remove csv head info if mini_data: lines = lines[0 : 10] # Download partial data for debug download_time = time.time() # Download for (n, line) in enumerate(lines): items = line.split(', ') audio_id = items[0] start_time = float(items[1]) end_time = float(items[2]) duration = end_time - start_time logging.info('{} {} start_time: {:.1f}, end_time: {:.1f}'.format( n, audio_id, start_time, end_time)) # Download full video of whatever format video_name = os.path.join(audios_dir, '_Y{}.%(ext)s'.format(audio_id)) os.system("youtube-dl --quiet -o '{}' -x https://www.youtube.com/watch?v={}"\ .format(video_name, audio_id)) video_paths = glob.glob(os.path.join(audios_dir, '_Y' + audio_id + '.*')) # If download successful if len(video_paths) > 0: video_path = video_paths[0] # Choose one video # Add 'Y' to the head because some video ids are started with '-' # which will cause problem audio_path = os.path.join(audios_dir, 'Y' + audio_id + '.wav') # Extract audio in wav format os.system("ffmpeg -loglevel panic -i {} -ac 1 -ar 32000 -ss {} -t 00:00:{} {} "\ .format(video_path, str(datetime.timedelta(seconds=start_time)), duration, audio_path)) # Remove downloaded video os.system("rm {}".format(video_path)) logging.info("Download and convert to {}".format(audio_path)) logging.info('Download finished! Time spent: {:.3f} s'.format( time.time() - download_time)) logging.info('Logs can be viewed in {}'.format(logs_dir)) def pack_waveforms_to_hdf5(args): """Pack waveform and target of several audio clips to a single hdf5 file. This can speed up loading and training. """ # Arguments & parameters audios_dir = args.audios_dir csv_path = args.csv_path waveforms_hdf5_path = args.waveforms_hdf5_path mini_data = args.mini_data clip_samples = config.clip_samples classes_num = config.classes_num sample_rate = config.sample_rate id_to_ix = config.id_to_ix # Paths if mini_data: prefix = 'mini_' waveforms_hdf5_path += '.mini' else: prefix = '' create_folder(os.path.dirname(waveforms_hdf5_path)) logs_dir = '_logs/pack_waveforms_to_hdf5/{}{}'.format(prefix, get_filename(csv_path)) create_folder(logs_dir) create_logging(logs_dir, filemode='w') logging.info('Write logs to {}'.format(logs_dir)) # Read csv file meta_dict = read_metadata(csv_path, classes_num, id_to_ix) if mini_data: mini_num = 10 for key in meta_dict.keys(): meta_dict[key] = meta_dict[key][0 : mini_num] audios_num = len(meta_dict['audio_name']) # Pack waveform to hdf5 total_time = time.time() with h5py.File(waveforms_hdf5_path, 'w') as hf: hf.create_dataset('audio_name', shape=((audios_num,)), dtype='S20') hf.create_dataset('waveform', shape=((audios_num, clip_samples)), dtype=np.int16) hf.create_dataset('target', shape=((audios_num, classes_num)), dtype=np.bool) hf.attrs.create('sample_rate', data=sample_rate, dtype=np.int32) # Pack waveform & target of several audio clips to a single hdf5 file for n in range(audios_num): audio_path = os.path.join(audios_dir, meta_dict['audio_name'][n]) if os.path.isfile(audio_path): logging.info('{} {}'.format(n, audio_path)) (audio, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True) audio = pad_or_truncate(audio, clip_samples) hf['audio_name'][n] = meta_dict['audio_name'][n].encode() hf['waveform'][n] = float32_to_int16(audio) hf['target'][n] = meta_dict['target'][n] else: logging.info('{} File does not exist! {}'.format(n, audio_path)) logging.info('Write to {}'.format(waveforms_hdf5_path)) logging.info('Pack hdf5 time: {:.3f}'.format(time.time() - total_time)) if __name__ == '__main__': parser = argparse.ArgumentParser() subparsers = parser.add_subparsers(dest='mode') parser_split = subparsers.add_parser('split_unbalanced_csv_to_partial_csvs') parser_split.add_argument('--unbalanced_csv', type=str, required=True, help='Path of unbalanced_csv file to read.') parser_split.add_argument('--unbalanced_partial_csvs_dir', type=str, required=True, help='Directory to save out split unbalanced partial csv.') parser_download_wavs = subparsers.add_parser('download_wavs') parser_download_wavs.add_argument('--csv_path', type=str, required=True, help='Path of csv file containing audio info to be downloaded.') parser_download_wavs.add_argument('--audios_dir', type=str, required=True, help='Directory to save out downloaded audio.') parser_download_wavs.add_argument('--mini_data', action='store_true', default=True, help='Set true to only download 10 audios for debugging.') parser_pack_wavs = subparsers.add_parser('pack_waveforms_to_hdf5') parser_pack_wavs.add_argument('--csv_path', type=str, required=True, help='Path of csv file containing audio info to be downloaded.') parser_pack_wavs.add_argument('--audios_dir', type=str, required=True, help='Directory to save out downloaded audio.') parser_pack_wavs.add_argument('--waveforms_hdf5_path', type=str, required=True, help='Path to save out packed hdf5.') parser_pack_wavs.add_argument('--mini_data', action='store_true', default=False, help='Set true to only download 10 audios for debugging.') args = parser.parse_args() if args.mode == 'split_unbalanced_csv_to_partial_csvs': split_unbalanced_csv_to_partial_csvs(args) elif args.mode == 'download_wavs': download_wavs(args) elif args.mode == 'pack_waveforms_to_hdf5': pack_waveforms_to_hdf5(args) else: raise Exception('Incorrect arguments!') ================================================ FILE: audio_detection/audio_infer/utils/plot_for_paper.py ================================================ import os import sys import numpy as np import argparse import h5py import time import pickle import matplotlib.pyplot as plt import csv from sklearn import metrics from utilities import (create_folder, get_filename, d_prime) import config def load_statistics(statistics_path): statistics_dict = pickle.load(open(statistics_path, 'rb')) bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) bal_map = np.mean(bal_map, axis=-1) test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) test_map = np.mean(test_map, axis=-1) return bal_map, test_map def crop_label(label): max_len = 16 if len(label) <= max_len: return label else: words = label.split(' ') cropped_label = '' for w in words: if len(cropped_label + ' ' + w) > max_len: break else: cropped_label += ' {}'.format(w) return cropped_label def add_comma(integer): """E.g., 1234567 -> 1,234,567 """ integer = int(integer) if integer >= 1000: return str(integer // 1000) + ',' + str(integer % 1000) else: return str(integer) def plot_classwise_iteration_map(args): # Paths save_out_path = 'results/classwise_iteration_map.pdf' create_folder(os.path.dirname(save_out_path)) # Load statistics statistics_dict = pickle.load(open('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_WavegramLogmelCnn_balanced_mixup_bs32.pkl', 'rb')) mAP_mat = np.array([e['average_precision'] for e in statistics_dict['test']]) mAP_mat = mAP_mat[0 : 300, :] # 300 * 2000 = 600k iterations sorted_indexes = np.argsort(config.full_samples_per_class)[::-1] fig, axs = plt.subplots(1, 3, figsize=(20, 5)) ranges = [np.arange(0, 10), np.arange(250, 260), np.arange(517, 527)] axs[0].set_ylabel('AP') for col in range(0, 3): axs[col].set_ylim(0, 1.) axs[col].set_xlim(0, 301) axs[col].set_xlabel('Iterations') axs[col].set_ylabel('AP') axs[col].xaxis.set_ticks(np.arange(0, 301, 100)) axs[col].xaxis.set_ticklabels(['0', '200k', '400k', '600k']) lines = [] for _ix in ranges[col]: _label = crop_label(config.labels[sorted_indexes[_ix]]) + \ ' ({})'.format(add_comma(config.full_samples_per_class[sorted_indexes[_ix]])) line, = axs[col].plot(mAP_mat[:, sorted_indexes[_ix]], label=_label) lines.append(line) box = axs[col].get_position() axs[col].set_position([box.x0, box.y0, box.width * 1., box.height]) axs[col].legend(handles=lines, bbox_to_anchor=(1., 1.)) axs[col].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) plt.tight_layout(pad=4, w_pad=1, h_pad=1) plt.savefig(save_out_path) print(save_out_path) def plot_six_figures(args): # Arguments & parameters classes_num = config.classes_num labels = config.labels max_plot_iteration = 540000 iterations = np.arange(0, max_plot_iteration, 2000) # Paths class_labels_indices_path = os.path.join('metadata', 'class_labels_indices.csv') save_out_path = 'results/six_figures.pdf' create_folder(os.path.dirname(save_out_path)) # Plot fig, ax = plt.subplots(2, 3, figsize=(14, 7)) bal_alpha = 0.3 test_alpha = 1.0 linewidth = 1. # (a) Comparison of architectures if True: lines = [] # Wavegram-Logmel-CNN (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_WavegramLogmelCnn_balanced_mixup_bs32.pkl') line, = ax[0, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 0].plot(test_map, label='Wavegram-Logmel-CNN', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Cnn14 (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[0, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 0].plot(test_map, label='CNN14', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # MobileNetV1 (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_MobileNetV1_balanced_mixup_bs32.pkl') line, = ax[0, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 0].plot(test_map, label='MobileNetV1', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[0, 0].legend(handles=lines, loc=2) ax[0, 0].set_title('(a) Comparison of architectures') # (b) Comparison of training data and augmentation' if True: lines = [] # Full data + balanced sampler + mixup (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[0, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (1.9m)', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Full data + balanced sampler + mixup in time domain (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_timedomain_bs32.pkl') line, = ax[0, 1].plot(bal_map, color='y', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup-wav (1.9m)', color='y', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Full data + balanced sampler + no mixup (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_nomixup_bs32.pkl') line, = ax[0, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (1.9m)', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Full data + uniform sampler + no mixup (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_nobalanced_nomixup_bs32.pkl') line, = ax[0, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,no-bal,no-mixup (1.9m)', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Balanced data + balanced sampler + mixup (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_balanced_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[0, 1].plot(bal_map, color='m', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (20k)', color='m', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Balanced data + balanced sampler + no mixup (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_balanced_train_Cnn14_balanced_nomixup_bs32.pkl') line, = ax[0, 1].plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (20k)', color='k', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[0, 1].legend(handles=lines, loc=2, fontsize=8) ax[0, 1].set_title('(b) Comparison of training data and augmentation') # (c) Comparison of embedding size if True: lines = [] # Embedding size 2048 (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[0, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 2].plot(test_map, label='CNN14,emb=2048', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Embedding size 128 (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_emb128_balanced_mixup_bs32.pkl') line, = ax[0, 2].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 2].plot(test_map, label='CNN14,emb=128', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Embedding size 32 (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_emb32_balanced_mixup_bs32.pkl') line, = ax[0, 2].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 2].plot(test_map, label='CNN14,emb=32', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[0, 2].legend(handles=lines, loc=2) ax[0, 2].set_title('(c) Comparison of embedding size') # (d) Comparison of amount of training data if True: lines = [] # 100% of full training data (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[1, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 0].plot(test_map, label='CNN14 (100% full)', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # 80% of full training data (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_0.8full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[1, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 0].plot(test_map, label='CNN14 (80% full)', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) # 50% of full training data (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_0.5full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[1, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 0].plot(test_map, label='cnn14 (50% full)', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[1, 0].legend(handles=lines, loc=2) ax[1, 0].set_title('(d) Comparison of amount of training data') # (e) Comparison of sampling rate if True: lines = [] # Cnn14 + 32 kHz (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[1, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 1].plot(test_map, label='CNN14,32kHz', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Cnn14 + 16 kHz (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_16k_balanced_mixup_bs32.pkl') line, = ax[1, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 1].plot(test_map, label='CNN14,16kHz', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Cnn14 + 8 kHz (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_8k_balanced_mixup_bs32.pkl') line, = ax[1, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 1].plot(test_map, label='CNN14,8kHz', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[1, 1].legend(handles=lines, loc=2) ax[1, 1].set_title('(e) Comparison of sampling rate') # (f) Comparison of mel bins number if True: lines = [] # Cnn14 + 128 mel bins (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel128_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[1, 2].plot(bal_map, color='g', alpha=bal_alpha) line, = ax[1, 2].plot(test_map, label='CNN14,128-melbins', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Cnn14 + 64 mel bins (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[1, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 2].plot(test_map, label='CNN14,64-melbins', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # Cnn14 + 32 mel bins (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel32_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl') line, = ax[1, 2].plot(bal_map, color='b', alpha=bal_alpha) line, = ax[1, 2].plot(test_map, label='CNN14,32-melbins', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[1, 2].legend(handles=lines, loc=2) ax[1, 2].set_title('(f) Comparison of mel bins number') for i in range(2): for j in range(3): ax[i, j].set_ylim(0, 0.8) ax[i, j].set_xlim(0, len(iterations)) ax[i, j].set_xlabel('Iterations') ax[i, j].set_ylabel('mAP') ax[i, j].xaxis.set_ticks(np.arange(0, len(iterations), 50)) ax[i, j].xaxis.set_ticklabels(['0', '100k', '200k', '300k', '400k', '500k']) ax[i, j].yaxis.set_ticks(np.arange(0, 0.81, 0.05)) ax[i, j].yaxis.set_ticklabels(['0', '', '0.1', '', '0.2', '', '0.3', '', '0.4', '', '0.5', '', '0.6', '', '0.7', '', '0.8']) ax[i, j].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) ax[i, j].xaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) plt.tight_layout(0, 1, 0) plt.savefig(save_out_path) print('Save figure to {}'.format(save_out_path)) def plot_complexity_map(args): # Paths save_out_path = 'results/complexity_mAP.pdf' create_folder(os.path.dirname(save_out_path)) plt.figure(figsize=(5, 5)) fig, ax = plt.subplots(1, 1) model_types = np.array(['Cnn6', 'Cnn10', 'Cnn14', 'ResNet22', 'ResNet38', 'ResNet54', 'MobileNetV1', 'MobileNetV2', 'DaiNet', 'LeeNet', 'LeeNet18', 'Res1dNet30', 'Res1dNet44', 'Wavegram-CNN', 'Wavegram-\nLogmel-CNN']) flops = np.array([21.986, 28.166, 42.220, 30.081, 48.962, 54.563, 3.614, 2.810, 30.395, 4.741, 26.369, 32.688, 61.833, 44.234, 53.510]) mAPs = np.array([0.343, 0.380, 0.431, 0.430, 0.434, 0.429, 0.389, 0.383, 0.295, 0.266, 0.336, 0.365, 0.355, 0.389, 0.439]) sorted_indexes = np.sort(flops) ax.scatter(flops, mAPs) shift = [[-5.5, -0.004], [1, -0.004], [-1, -0.014], [-2, 0.006], [-7, 0.006], [1, -0.01], [0.5, 0.004], [-1, -0.014], [1, -0.007], [0.8, -0.008], [1, -0.007], [1, 0.002], [-6, -0.015], [1, -0.008], [0.8, 0]] for i, model_type in enumerate(model_types): ax.annotate(model_type, (flops[i] + shift[i][0], mAPs[i] + shift[i][1])) ax.plot(flops[[0, 1, 2]], mAPs[[0, 1, 2]]) ax.plot(flops[[3, 4, 5]], mAPs[[3, 4, 5]]) ax.plot(flops[[6, 7]], mAPs[[6, 7]]) ax.plot(flops[[9, 10]], mAPs[[9, 10]]) ax.plot(flops[[11, 12]], mAPs[[11, 12]]) ax.plot(flops[[13, 14]], mAPs[[13, 14]]) ax.set_xlim(0, 70) ax.set_ylim(0.2, 0.5) ax.set_xlabel('Multi-load_statisticss (million)', fontsize=15) ax.set_ylabel('mAP', fontsize=15) ax.tick_params(axis='x', labelsize=12) ax.tick_params(axis='y', labelsize=12) plt.tight_layout(0, 0, 0) plt.savefig(save_out_path) print('Write out figure to {}'.format(save_out_path)) def plot_long_fig(args): # Paths stats = pickle.load(open('paper_statistics/stats_for_long_fig.pkl', 'rb')) save_out_path = 'results/long_fig.pdf' create_folder(os.path.dirname(save_out_path)) # Load meta N = len(config.labels) sorted_indexes = stats['sorted_indexes_for_plot'] sorted_labels = np.array(config.labels)[sorted_indexes] audio_clips_per_class = stats['official_balanced_training_samples'] + stats['official_unbalanced_training_samples'] audio_clips_per_class = audio_clips_per_class[sorted_indexes] # Prepare axes for plot (ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b) = prepare_plot_long_4_rows(sorted_labels) # plot the number of training samples ax1a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) ax2a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) ax3a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) ax4a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) # Load mAP of different systems """Average instance system of [1] with an mAP of 0.317. [1] Kong, Qiuqiang, Changsong Yu, Yong Xu, Turab Iqbal, Wenwu Wang, and Mark D. Plumbley. "Weakly labelled audioset tagging with attention neural networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 11 (2019): 1791-1802.""" maps_avg_instances = stats['averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations']['eval']['average_precision'] maps_avg_instances = maps_avg_instances[sorted_indexes] # PANNs Cnn14 maps_panns_cnn14 = stats['panns_cnn14']['eval']['average_precision'] maps_panns_cnn14 = maps_panns_cnn14[sorted_indexes] # PANNs MobileNetV1 maps_panns_mobilenetv1 = stats['panns_mobilenetv1']['eval']['average_precision'] maps_panns_mobilenetv1 = maps_panns_mobilenetv1[sorted_indexes] # PANNs Wavegram-Logmel-Cnn14 maps_panns_wavegram_logmel_cnn14 = stats['panns_wavegram_logmel_cnn14']['eval']['average_precision'] maps_panns_wavegram_logmel_cnn14 = maps_panns_wavegram_logmel_cnn14[sorted_indexes] # Plot mAPs _scatter_4_rows(maps_panns_wavegram_logmel_cnn14, ax1b, ax2b, ax3b, ax4b, s=5, c='g') _scatter_4_rows(maps_panns_cnn14, ax1b, ax2b, ax3b, ax4b, s=5, c='r') _scatter_4_rows(maps_panns_mobilenetv1, ax1b, ax2b, ax3b, ax4b, s=5, c='b') _scatter_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, s=5, c='k') linewidth = 0.7 line0te = _plot_4_rows(maps_panns_wavegram_logmel_cnn14, ax1b, ax2b, ax3b, ax4b, c='g', linewidth=linewidth, label='AP with Wavegram-Logmel-CNN') line1te = _plot_4_rows(maps_panns_cnn14, ax1b, ax2b, ax3b, ax4b, c='r', linewidth=linewidth, label='AP with CNN14') line2te = _plot_4_rows(maps_panns_mobilenetv1, ax1b, ax2b, ax3b, ax4b, c='b', linewidth=linewidth, label='AP with MobileNetV1') line3te = _plot_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, c='k', linewidth=linewidth, label='AP with averaging instances (baseline)') # Plot label quality label_quality = stats['label_quality'] sorted_label_quality = np.array(label_quality)[sorted_indexes] for k in range(len(sorted_label_quality)): if sorted_label_quality[k] and sorted_label_quality[k] == 1: sorted_label_quality[k] = 0.99 ax1b.scatter(np.arange(N)[sorted_label_quality != None], sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+') ax2b.scatter(np.arange(N)[sorted_label_quality != None], sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+') ax3b.scatter(np.arange(N)[sorted_label_quality != None], sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+') line_label_quality = ax4b.scatter(np.arange(N)[sorted_label_quality != None], sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+', label='Label quality') ax1b.scatter(np.arange(N)[sorted_label_quality == None], 0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_') ax2b.scatter(np.arange(N)[sorted_label_quality == None], 0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_') ax3b.scatter(np.arange(N)[sorted_label_quality == None], 0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_') ax4b.scatter(np.arange(N)[sorted_label_quality == None], 0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_') plt.legend(handles=[line0te, line1te, line2te, line3te, line_label_quality], fontsize=6, loc=1) plt.tight_layout(0, 0, 0) plt.savefig(save_out_path) print('Save fig to {}'.format(save_out_path)) def prepare_plot_long_4_rows(sorted_lbs): N = len(sorted_lbs) f,(ax1a, ax2a, ax3a, ax4a) = plt.subplots(4, 1, sharey=False, facecolor='w', figsize=(10, 10.5)) fontsize = 5 K = 132 ax1a.set_xlim(0, K) ax2a.set_xlim(K, 2 * K) ax3a.set_xlim(2 * K, 3 * K) ax4a.set_xlim(3 * K, N) truncated_sorted_lbs = [] for lb in sorted_lbs: lb = lb[0 : 25] words = lb.split(' ') if len(words[-1]) < 3: lb = ' '.join(words[0:-1]) truncated_sorted_lbs.append(lb) ax1a.grid(which='major', axis='x', linestyle='-', alpha=0.3) ax2a.grid(which='major', axis='x', linestyle='-', alpha=0.3) ax3a.grid(which='major', axis='x', linestyle='-', alpha=0.3) ax4a.grid(which='major', axis='x', linestyle='-', alpha=0.3) ax1a.set_yscale('log') ax2a.set_yscale('log') ax3a.set_yscale('log') ax4a.set_yscale('log') ax1b = ax1a.twinx() ax2b = ax2a.twinx() ax3b = ax3a.twinx() ax4b = ax4a.twinx() ax1b.set_ylim(0., 1.) ax2b.set_ylim(0., 1.) ax3b.set_ylim(0., 1.) ax4b.set_ylim(0., 1.) ax1b.set_ylabel('Average precision') ax2b.set_ylabel('Average precision') ax3b.set_ylabel('Average precision') ax4b.set_ylabel('Average precision') ax1b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) ax2b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) ax3b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) ax4b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) ax1a.xaxis.set_ticks(np.arange(K)) ax1a.xaxis.set_ticklabels(truncated_sorted_lbs[0:K], rotation=90, fontsize=fontsize) ax1a.xaxis.tick_bottom() ax1a.set_ylabel("Number of audio clips") ax2a.xaxis.set_ticks(np.arange(K, 2*K)) ax2a.xaxis.set_ticklabels(truncated_sorted_lbs[K:2*K], rotation=90, fontsize=fontsize) ax2a.xaxis.tick_bottom() ax2a.set_ylabel("Number of audio clips") ax3a.xaxis.set_ticks(np.arange(2*K, 3*K)) ax3a.xaxis.set_ticklabels(truncated_sorted_lbs[2*K:3*K], rotation=90, fontsize=fontsize) ax3a.xaxis.tick_bottom() ax3a.set_ylabel("Number of audio clips") ax4a.xaxis.set_ticks(np.arange(3*K, N)) ax4a.xaxis.set_ticklabels(truncated_sorted_lbs[3*K:], rotation=90, fontsize=fontsize) ax4a.xaxis.tick_bottom() ax4a.set_ylabel("Number of audio clips") ax1a.spines['right'].set_visible(False) ax1b.spines['right'].set_visible(False) ax2a.spines['left'].set_visible(False) ax2b.spines['left'].set_visible(False) ax2a.spines['right'].set_visible(False) ax2b.spines['right'].set_visible(False) ax3a.spines['left'].set_visible(False) ax3b.spines['left'].set_visible(False) ax3a.spines['right'].set_visible(False) ax3b.spines['right'].set_visible(False) ax4a.spines['left'].set_visible(False) ax4b.spines['left'].set_visible(False) plt.subplots_adjust(hspace = 0.8) return ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b def _scatter_4_rows(x, ax, ax2, ax3, ax4, s, c, marker='.', alpha=1.): N = len(x) ax.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) ax2.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) ax3.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) ax4.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) def _plot_4_rows(x, ax, ax2, ax3, ax4, c, linewidth=1.0, alpha=1.0, label=""): N = len(x) ax.plot(x, c=c, linewidth=linewidth, alpha=alpha) ax2.plot(x, c=c, linewidth=linewidth, alpha=alpha) ax3.plot(x, c=c, linewidth=linewidth, alpha=alpha) line, = ax4.plot(x, c=c, linewidth=linewidth, alpha=alpha, label=label) return line if __name__ == '__main__': parser = argparse.ArgumentParser(description='') subparsers = parser.add_subparsers(dest='mode') parser_classwise_iteration_map = subparsers.add_parser('plot_classwise_iteration_map') parser_six_figures = subparsers.add_parser('plot_six_figures') parser_complexity_map = subparsers.add_parser('plot_complexity_map') parser_long_fig = subparsers.add_parser('plot_long_fig') args = parser.parse_args() if args.mode == 'plot_classwise_iteration_map': plot_classwise_iteration_map(args) elif args.mode == 'plot_six_figures': plot_six_figures(args) elif args.mode == 'plot_complexity_map': plot_complexity_map(args) elif args.mode == 'plot_long_fig': plot_long_fig(args) else: raise Exception('Incorrect argument!') ================================================ FILE: audio_detection/audio_infer/utils/plot_statistics.py ================================================ import os import sys import numpy as np import argparse import h5py import time import _pickle as cPickle import _pickle import matplotlib.pyplot as plt import csv from sklearn import metrics from utilities import (create_folder, get_filename, d_prime) import config def _load_metrics0(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): workspace0 = '/mnt/cephfs_new_wj/speechsv/qiuqiang.kong/workspaces/pub_audioset_tagging_cnn_transfer' statistics_path = os.path.join(workspace0, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) bal_map = np.mean(bal_map, axis=-1) test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) test_map = np.mean(test_map, axis=-1) legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} return bal_map, test_map, legend def _load_metrics0_classwise(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): workspace0 = '/mnt/cephfs_new_wj/speechsv/qiuqiang.kong/workspaces/pub_audioset_tagging_cnn_transfer' statistics_path = os.path.join(workspace0, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) return statistics_dict['test'][300]['average_precision'] def _load_metrics0_classwise2(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): workspace0 = '/mnt/cephfs_new_wj/speechsv/qiuqiang.kong/workspaces/pub_audioset_tagging_cnn_transfer' statistics_path = os.path.join(workspace0, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) k = 270 mAP = np.mean(statistics_dict['test'][k]['average_precision']) mAUC = np.mean(statistics_dict['test'][k]['auc']) dprime = d_prime(mAUC) return mAP, mAUC, dprime def _load_metrics_classwise(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): workspace = '/mnt/cephfs_new_wj/speechsv/kongqiuqiang/workspaces/cvssp/pub_audioset_tagging_cnn' statistics_path = os.path.join(workspace, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) k = 300 mAP = np.mean(statistics_dict['test'][k]['average_precision']) mAUC = np.mean(statistics_dict['test'][k]['auc']) dprime = d_prime(mAUC) return mAP, mAUC, dprime def plot(args): # Arguments & parameters dataset_dir = args.dataset_dir workspace = args.workspace select = args.select classes_num = config.classes_num max_plot_iteration = 1000000 iterations = np.arange(0, max_plot_iteration, 2000) class_labels_indices_path = os.path.join(dataset_dir, 'metadata', 'class_labels_indices.csv') save_out_path = 'results/{}.pdf'.format(select) create_folder(os.path.dirname(save_out_path)) # Read labels labels = config.labels # Plot fig, ax = plt.subplots(1, 1, figsize=(15, 8)) lines = [] def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): statistics_path = os.path.join(workspace, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) bal_map = np.mean(bal_map, axis=-1) test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) test_map = np.mean(test_map, axis=-1) legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} return bal_map, test_map, legend bal_alpha = 0.3 test_alpha = 1.0 lines = [] if select == '1_cnn13': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_no_dropout', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_no_specaug', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_no_specaug', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_no_dropout', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'none', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_no_mixup', color='k', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_mixup_in_wave', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='c', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_mixup_in_wave', color='c', alpha=test_alpha) lines.append(line) elif select == '1_pooling': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_gwrp', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_gmpgapgwrp', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_att', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_gmpgapatt', color='g', alpha=test_alpha) lines.append(line) elif select == '1_resnet': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'ResNet18', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='ResNet18', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'ResNet34', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha) line, = ax.plot(test_map, label='resnet34', color='k', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'ResNet50', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='c', alpha=bal_alpha) line, = ax.plot(test_map, label='resnet50', color='c', alpha=test_alpha) lines.append(line) elif select == '1_densenet': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'DenseNet121', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='densenet121', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'DenseNet201', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='densenet201', color='g', alpha=test_alpha) lines.append(line) elif select == '1_cnn9': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn5', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn9', color='g', alpha=test_alpha) lines.append(line) elif select == '1_hop': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 500, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_hop500', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 640, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_hop640', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 1000, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_hop1000', color='k', alpha=test_alpha) lines.append(line) elif select == '1_emb': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_emb32', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_emb128', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb512', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13_emb512', color='k', alpha=test_alpha) lines.append(line) elif select == '1_mobilenet': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='mobilenetv1', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'MobileNetV2', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='mobilenetv2', color='g', alpha=test_alpha) lines.append(line) elif select == '1_waveform': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_LeeNet', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn1d_LeeNet', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_LeeNet18', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn1d_LeeNet18', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_DaiNet', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn1d_DaiNet', color='k', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='c', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn1d_ResNet34', color='c', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet50', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='m', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn1d_ResNet50', color='m', alpha=test_alpha) lines.append(line) elif select == '1_waveform_cnn2d': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_SpAndWav', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_WavCnn2d', color='g', alpha=test_alpha) lines.append(line) elif select == '1_decision_level': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelMax', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_DecisionLevelMax', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelAvg', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_DecisionLevelAvg', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelAtt', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_DecisionLevelAtt', color='k', alpha=test_alpha) lines.append(line) elif select == '1_transformer': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_Transformer1', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_Transformer1', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_Transformer3', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_Transformer3', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_Transformer6', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_Transformer6', color='k', alpha=test_alpha) lines.append(line) elif select == '1_aug': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,balanced,mixup', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,none,none', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,balanced,none', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup_from_0_epoch', 32) line, = ax.plot(bal_map, color='m', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,balanced,mixup_from_0_epoch', color='m', alpha=test_alpha) lines.append(line) elif select == '1_bal_train_aug': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,balanced,mixup', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,none,none', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,balanced,none', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup_from_0_epoch', 32) line, = ax.plot(bal_map, color='m', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,balanced,mixup_from_0_epoch', color='m', alpha=test_alpha) lines.append(line) elif select == '1_sr': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_16k', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14_16k', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_8k', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14_8k', color='b', alpha=test_alpha) lines.append(line) elif select == '1_time_domain': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_mixup_time_domain', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14_time_domain', color='b', alpha=test_alpha) lines.append(line) elif select == '1_partial_full': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.9_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,partial_0.9', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.8_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,partial_0.8', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.7_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,partial_0.7', color='k', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.5_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='m', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,partial_0.5', color='m', alpha=test_alpha) lines.append(line) elif select == '1_window': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 2048, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14_win2048', color='b', alpha=test_alpha) lines.append(line) elif select == '1_melbins': (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 32, 50, 14000, 'full_train', 'Cnn14_mel32', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14_mel32', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 128, 50, 14000, 'full_train', 'Cnn14_mel128', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14_mel128', color='g', alpha=test_alpha) lines.append(line) elif select == '1_alternate': max_plot_iteration = 2000000 iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'alternate', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14_alternate', color='b', alpha=test_alpha) lines.append(line) elif select == '2_all': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn9', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn5', color='g', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='MobileNetV1', color='k', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn1d_ResNet34', color='grey', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'ResNet34', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='ResNet34', color='grey', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_WavCnn2d', color='m', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_SpAndWav', color='orange', alpha=test_alpha) lines.append(line) elif select == '2_emb': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_emb32', color='r', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_128', color='k', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb512', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='Cnn13_512', color='g', alpha=test_alpha) lines.append(line) elif select == '2_aug': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn13', color='b', alpha=test_alpha) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_no_specaug', 'clip_bce', 'none', 'none', 32) line, = ax.plot(bal_map, color='c', alpha=bal_alpha) line, = ax.plot(test_map, label='cnn14,none,none', color='c', alpha=test_alpha) lines.append(line) ax.set_ylim(0, 1.) ax.set_xlim(0, len(iterations)) ax.xaxis.set_ticks(np.arange(0, len(iterations), 25)) ax.xaxis.set_ticklabels(np.arange(0, max_plot_iteration, 50000)) ax.yaxis.set_ticks(np.arange(0, 1.01, 0.05)) ax.yaxis.set_ticklabels(np.around(np.arange(0, 1.01, 0.05), decimals=2)) ax.grid(color='b', linestyle='solid', linewidth=0.3) plt.legend(handles=lines, loc=2) # box = ax.get_position() # ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) # ax.legend(handles=lines, bbox_to_anchor=(1.0, 1.0)) plt.savefig(save_out_path) print('Save figure to {}'.format(save_out_path)) def plot_for_paper(args): # Arguments & parameters dataset_dir = args.dataset_dir workspace = args.workspace select = args.select classes_num = config.classes_num max_plot_iteration = 1000000 iterations = np.arange(0, max_plot_iteration, 2000) class_labels_indices_path = os.path.join(dataset_dir, 'metadata', 'class_labels_indices.csv') save_out_path = 'results/paper_{}.pdf'.format(select) create_folder(os.path.dirname(save_out_path)) # Read labels labels = config.labels # Plot fig, ax = plt.subplots(1, 1, figsize=(6, 4)) lines = [] def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): statistics_path = os.path.join(workspace, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) bal_map = np.mean(bal_map, axis=-1) test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) test_map = np.mean(test_map, axis=-1) legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} return bal_map, test_map, legend bal_alpha = 0.3 test_alpha = 1.0 lines = [] linewidth = 1. max_plot_iteration = 540000 if select == '2_all': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) # line, = ax.plot(test_map, label='cnn9', color='r', alpha=test_alpha) # lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) # line, = ax.plot(test_map, label='cnn5', color='g', alpha=test_alpha) # lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='MobileNetV1', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) # line, = ax.plot(test_map, label='Cnn1d_ResNet34', color='grey', alpha=test_alpha) # lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) # line, = ax.plot(test_map, label='Wavegram-CNN', color='g', alpha=test_alpha, linewidth=linewidth) # lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='Wavegram-Logmel-CNN', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) elif select == '2_emb': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,emb=2048', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,emb=32', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,emb=128', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn13_emb512', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='g', alpha=bal_alpha) # line, = ax.plot(test_map, label='Cnn13_512', color='g', alpha=test_alpha) # lines.append(line) elif select == '2_bal': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,bal,mixup (1.9m)', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_mixup_time_domain', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='y', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,bal,mixup-wav (1.9m)', color='y', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,no-bal,no-mixup (1.9m)', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,bal,no-mixup (1.9m)', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) line, = ax.plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,bal,no-mixup (20k)', color='k', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='m', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,bal,mixup (20k)', color='m', alpha=test_alpha, linewidth=linewidth) lines.append(line) elif select == '2_sr': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,32kHz', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_16k', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,16kHz', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_8k', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,8kHz', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) elif select == '2_partial': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14 (100% full)', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, # 320, 64, 50, 14000, 'partial_0.9_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) # line, = ax.plot(test_map, label='cnn14,partial_0.9', color='b', alpha=test_alpha, linewidth=linewidth) # lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.8_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14 (80% full)', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) # (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, # 320, 64, 50, 14000, 'partial_0.7_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) # line, = ax.plot(test_map, label='cnn14,partial_0.7', color='k', alpha=test_alpha, linewidth=linewidth) # lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.5_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='cnn14 (50% full)', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) elif select == '2_melbins': iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax.plot(test_map, label='CNN14,64-melbins', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 32, 50, 14000, 'full_train', 'Cnn14_mel32', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='CNN14,32-melbins', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 128, 50, 14000, 'full_train', 'Cnn14_mel128', 'clip_bce', 'balanced', 'mixup', 32) line, = ax.plot(bal_map, color='r', alpha=bal_alpha) line, = ax.plot(test_map, label='CNN14,128-melbins', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax.set_ylim(0, 0.8) ax.set_xlim(0, len(iterations)) ax.set_xlabel('Iterations') ax.set_ylabel('mAP') ax.xaxis.set_ticks(np.arange(0, len(iterations), 50)) # ax.xaxis.set_ticklabels(np.arange(0, max_plot_iteration, 50000)) ax.xaxis.set_ticklabels(['0', '100k', '200k', '300k', '400k', '500k']) ax.yaxis.set_ticks(np.arange(0, 0.81, 0.05)) ax.yaxis.set_ticklabels(['0', '', '0.1', '', '0.2', '', '0.3', '', '0.4', '', '0.5', '', '0.6', '', '0.7', '', '0.8']) # ax.yaxis.set_ticklabels(np.around(np.arange(0, 0.81, 0.05), decimals=2)) ax.yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) ax.xaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) plt.legend(handles=lines, loc=2) plt.tight_layout(0, 0, 0) # box = ax.get_position() # ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) # ax.legend(handles=lines, bbox_to_anchor=(1.0, 1.0)) plt.savefig(save_out_path) print('Save figure to {}'.format(save_out_path)) def plot_for_paper2(args): # Arguments & parameters dataset_dir = args.dataset_dir workspace = args.workspace classes_num = config.classes_num max_plot_iteration = 1000000 iterations = np.arange(0, max_plot_iteration, 2000) class_labels_indices_path = os.path.join(dataset_dir, 'metadata', 'class_labels_indices.csv') save_out_path = 'results/paper2.pdf' create_folder(os.path.dirname(save_out_path)) # Read labels labels = config.labels # Plot fig, ax = plt.subplots(2, 3, figsize=(14, 7)) lines = [] def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): statistics_path = os.path.join(workspace, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) bal_map = np.mean(bal_map, axis=-1) test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) test_map = np.mean(test_map, axis=-1) legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} return bal_map, test_map, legend def _load_metrics0(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): workspace0 = '/mnt/cephfs_new_wj/speechsv/qiuqiang.kong/workspaces/pub_audioset_tagging_cnn_transfer' statistics_path = os.path.join(workspace0, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) bal_map = np.mean(bal_map, axis=-1) test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) test_map = np.mean(test_map, axis=-1) legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} return bal_map, test_map, legend bal_alpha = 0.3 test_alpha = 1.0 lines = [] linewidth = 1. max_plot_iteration = 540000 if True: iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 0].plot(test_map, label='CNN14', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) # line, = ax.plot(test_map, label='cnn9', color='r', alpha=test_alpha) # lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) # line, = ax.plot(test_map, label='cnn5', color='g', alpha=test_alpha) # lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 0].plot(test_map, label='MobileNetV1', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) # line, = ax.plot(test_map, label='Cnn1d_ResNet34', color='grey', alpha=test_alpha) # lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'ResNet34', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax[0, 0].plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) # line, = ax[0, 0].plot(test_map, label='ResNet38', color='k', alpha=test_alpha, linewidth=linewidth) # lines.append(line) # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, # 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32) # line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) # line, = ax.plot(test_map, label='Wavegram-CNN', color='g', alpha=test_alpha, linewidth=linewidth) # lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 0].plot(test_map, label='Wavegram-Logmel-CNN', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[0, 0].legend(handles=lines, loc=2) ax[0, 0].set_title('(a) Comparison of architectures') if True: lines = [] iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (1.9m)', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) line, = ax[0, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,no-bal,no-mixup (1.9m)', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_mixup_time_domain', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 1].plot(bal_map, color='y', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup-wav (1.9m)', color='y', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) line, = ax[0, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (1.9m)', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) line, = ax[0, 1].plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (20k)', color='k', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 1].plot(bal_map, color='m', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (20k)', color='m', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[0, 1].legend(handles=lines, loc=2, fontsize=8) ax[0, 1].set_title('(b) Comparison of training data and augmentation') if True: lines = [] iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 2].plot(test_map, label='CNN14,emb=2048', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 2].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 2].plot(test_map, label='CNN14,emb=32', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[0, 2].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[0, 2].plot(test_map, label='CNN14,emb=128', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[0, 2].legend(handles=lines, loc=2) ax[0, 2].set_title('(c) Comparison of embedding size') if True: lines = [] iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 0].plot(test_map, label='CNN14 (100% full)', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.8_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 0].plot(test_map, label='CNN14 (80% full)', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.5_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 0].plot(test_map, label='cnn14 (50% full)', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[1, 0].legend(handles=lines, loc=2) ax[1, 0].set_title('(d) Comparison of amount of training data') if True: lines = [] iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 1].plot(test_map, label='CNN14,32kHz', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_16k', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 1].plot(test_map, label='CNN14,16kHz', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_8k', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 1].plot(test_map, label='CNN14,8kHz', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[1, 1].legend(handles=lines, loc=2) ax[1, 1].set_title('(e) Comparison of sampling rate') if True: lines = [] iterations = np.arange(0, max_plot_iteration, 2000) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) line, = ax[1, 2].plot(test_map, label='CNN14,64-melbins', color='r', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 32, 50, 14000, 'full_train', 'Cnn14_mel32', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 2].plot(bal_map, color='b', alpha=bal_alpha) line, = ax[1, 2].plot(test_map, label='CNN14,32-melbins', color='b', alpha=test_alpha, linewidth=linewidth) lines.append(line) (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, 320, 128, 50, 14000, 'full_train', 'Cnn14_mel128', 'clip_bce', 'balanced', 'mixup', 32) line, = ax[1, 2].plot(bal_map, color='g', alpha=bal_alpha) line, = ax[1, 2].plot(test_map, label='CNN14,128-melbins', color='g', alpha=test_alpha, linewidth=linewidth) lines.append(line) ax[1, 2].legend(handles=lines, loc=2) ax[1, 2].set_title('(f) Comparison of mel bins number') for i in range(2): for j in range(3): ax[i, j].set_ylim(0, 0.8) ax[i, j].set_xlim(0, len(iterations)) ax[i, j].set_xlabel('Iterations') ax[i, j].set_ylabel('mAP') ax[i, j].xaxis.set_ticks(np.arange(0, len(iterations), 50)) # ax.xaxis.set_ticklabels(np.arange(0, max_plot_iteration, 50000)) ax[i, j].xaxis.set_ticklabels(['0', '100k', '200k', '300k', '400k', '500k']) ax[i, j].yaxis.set_ticks(np.arange(0, 0.81, 0.05)) ax[i, j].yaxis.set_ticklabels(['0', '', '0.1', '', '0.2', '', '0.3', '', '0.4', '', '0.5', '', '0.6', '', '0.7', '', '0.8']) # ax.yaxis.set_ticklabels(np.around(np.arange(0, 0.81, 0.05), decimals=2)) ax[i, j].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) ax[i, j].xaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) plt.tight_layout(0, 1, 0) # box = ax.get_position() # ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) # ax.legend(handles=lines, bbox_to_anchor=(1.0, 1.0)) plt.savefig(save_out_path) print('Save figure to {}'.format(save_out_path)) def table_values(args): # Arguments & parameters dataset_dir = args.dataset_dir workspace = args.workspace select = args.select def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size, iteration): statistics_path = os.path.join(workspace, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) idx = iteration // 2000 mAP = np.mean(statistics_dict['test'][idx]['average_precision']) mAUC = np.mean(statistics_dict['test'][idx]['auc']) dprime = d_prime(mAUC) print('mAP: {:.3f}'.format(mAP)) print('mAUC: {:.3f}'.format(mAUC)) print('dprime: {:.3f}'.format(dprime)) if select == 'cnn13': iteration = 600000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn5': iteration = 440000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn9': iteration = 440000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_decisionlevelmax': iteration = 400000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelMax', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_decisionlevelavg': iteration = 600000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelAvg', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_decisionlevelatt': iteration = 600000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelAtt', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_emb32': iteration = 560000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_emb128': iteration = 560000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_emb512': iteration = 440000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb512', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_hop500': iteration = 440000 _load_metrics('main', 32000, 1024, 500, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_hop640': iteration = 440000 _load_metrics('main', 32000, 1024, 640, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'cnn13_hop1000': iteration = 540000 _load_metrics('main', 32000, 1024, 1000, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'mobilenetv1': iteration = 560000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'mobilenetv2': iteration = 560000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'MobileNetV2', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'resnet18': iteration = 600000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'ResNet18', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'resnet34': iteration = 600000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'ResNet34', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'resnet50': iteration = 600000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'ResNet50', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'dainet': iteration = 600000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_DaiNet', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'leenet': iteration = 540000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_LeeNet', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'leenet18': iteration = 440000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_LeeNet18', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'resnet34_1d': iteration = 500000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'resnet50_1d': iteration = 500000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet50', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'waveform_cnn2d': iteration = 660000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32, iteration) elif select == 'waveform_spandwav': iteration = 700000 _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32, iteration) def crop_label(label): max_len = 16 if len(label) <= max_len: return label else: words = label.split(' ') cropped_label = '' for w in words: if len(cropped_label + ' ' + w) > max_len: break else: cropped_label += ' {}'.format(w) return cropped_label def add_comma(integer): integer = int(integer) if integer >= 1000: return str(integer // 1000) + ',' + str(integer % 1000) else: return str(integer) def plot_class_iteration(args): # Arguments & parameters workspace = args.workspace select = args.select save_out_path = 'results_map/class_iteration_map.pdf' create_folder(os.path.dirname(save_out_path)) def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size, iteration): statistics_path = os.path.join(workspace, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) return statistics_dict iteration = 600000 statistics_dict = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) mAP_mat = np.array([e['average_precision'] for e in statistics_dict['test']]) mAP_mat = mAP_mat[0 : 300, :] sorted_indexes = np.argsort(config.full_samples_per_class)[::-1] fig, axs = plt.subplots(1, 3, figsize=(20, 5)) ranges = [np.arange(0, 10), np.arange(250, 260), np.arange(517, 527)] axs[0].set_ylabel('AP') for col in range(0, 3): axs[col].set_ylim(0, 1.) axs[col].set_xlim(0, 301) axs[col].set_xlabel('Iterations') axs[col].set_ylabel('AP') axs[col].xaxis.set_ticks(np.arange(0, 301, 100)) axs[col].xaxis.set_ticklabels(['0', '200k', '400k', '600k']) lines = [] for _ix in ranges[col]: _label = crop_label(config.labels[sorted_indexes[_ix]]) + \ ' ({})'.format(add_comma(config.full_samples_per_class[sorted_indexes[_ix]])) line, = axs[col].plot(mAP_mat[:, sorted_indexes[_ix]], label=_label) lines.append(line) box = axs[col].get_position() axs[col].set_position([box.x0, box.y0, box.width * 1., box.height]) axs[col].legend(handles=lines, bbox_to_anchor=(1., 1.)) axs[col].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) plt.tight_layout(pad=4, w_pad=1, h_pad=1) plt.savefig(save_out_path) print(save_out_path) def _load_old_metrics(workspace, filename, iteration, data_type): assert data_type in ['train', 'test'] stat_name = "stat_{}_iters.p".format(iteration) # Load stats stat_path = os.path.join(workspace, "stats", filename, data_type, stat_name) try: stats = cPickle.load(open(stat_path, 'rb')) except: stats = cPickle.load(open(stat_path, 'rb'), encoding='latin1') precisions = [stat['precisions'] for stat in stats] recalls = [stat['recalls'] for stat in stats] maps = np.array([stat['AP'] for stat in stats]) aucs = np.array([stat['auc'] for stat in stats]) return {'average_precision': maps, 'AUC': aucs} def _sort(ys): sorted_idxes = np.argsort(ys) sorted_idxes = sorted_idxes[::-1] sorted_ys = ys[sorted_idxes] sorted_lbs = [config.labels[e] for e in sorted_idxes] return sorted_ys, sorted_idxes, sorted_lbs def load_data(hdf5_path): with h5py.File(hdf5_path, 'r') as hf: x = hf['x'][:] y = hf['y'][:] video_id_list = list(hf['video_id_list'][:]) return x, y, video_id_list def get_avg_stats(workspace, bgn_iter, fin_iter, interval_iter, filename, data_type): assert data_type in ['train', 'test'] bal_train_hdf5 = "/vol/vssp/msos/audioset/packed_features/bal_train.h5" eval_hdf5 = "/vol/vssp/msos/audioset/packed_features/eval.h5" unbal_train_hdf5 = "/vol/vssp/msos/audioset/packed_features/unbal_train.h5" t1 = time.time() if data_type == 'test': (te_x, te_y, te_id_list) = load_data(eval_hdf5) elif data_type == 'train': (te_x, te_y, te_id_list) = load_data(bal_train_hdf5) y = te_y prob_dir = os.path.join(workspace, "probs", filename, data_type) names = os.listdir(prob_dir) probs = [] iters = range(bgn_iter, fin_iter, interval_iter) for iter in iters: pickle_path = os.path.join(prob_dir, "prob_%d_iters.p" % iter) try: prob = cPickle.load(open(pickle_path, 'rb')) except: prob = cPickle.load(open(pickle_path, 'rb'), encoding='latin1') probs.append(prob) avg_prob = np.mean(np.array(probs), axis=0) n_out = y.shape[1] stats = [] for k in range(n_out): # around 7 seconds (precisions, recalls, thresholds) = metrics.precision_recall_curve(y[:, k], avg_prob[:, k]) avg_precision = metrics.average_precision_score(y[:, k], avg_prob[:, k], average=None) (fpr, tpr, thresholds) = metrics.roc_curve(y[:, k], avg_prob[:, k]) auc = metrics.roc_auc_score(y[:, k], avg_prob[:, k], average=None) # eer = pp_data.eer(avg_prob[:, k], y[:, k]) skip = 1000 dict = {'precisions': precisions[0::skip], 'recalls': recalls[0::skip], 'AP': avg_precision, 'fpr': fpr[0::skip], 'fnr': 1. - tpr[0::skip], 'auc': auc} stats.append(dict) mAPs = np.array([e['AP'] for e in stats]) aucs = np.array([e['auc'] for e in stats]) print("Get avg time: {}".format(time.time() - t1)) return {'average_precision': mAPs, 'auc': aucs} def _samples_num_per_class(): bal_train_hdf5 = "/vol/vssp/msos/audioset/packed_features/bal_train.h5" eval_hdf5 = "/vol/vssp/msos/audioset/packed_features/eval.h5" unbal_train_hdf5 = "/vol/vssp/msos/audioset/packed_features/unbal_train.h5" (x, y, id_list) = load_data(eval_hdf5) eval_num = np.sum(y, axis=0) (x, y, id_list) = load_data(bal_train_hdf5) bal_num = np.sum(y, axis=0) (x, y, id_list) = load_data(unbal_train_hdf5) unbal_num = np.sum(y, axis=0) return bal_num, unbal_num, eval_num def get_label_quality(): rate_csv = '/vol/vssp/msos/qk/workspaces/pub_audioset_tagging_cnn_transfer/metadata/qa_true_counts.csv' with open(rate_csv, 'r') as f: reader = csv.reader(f, delimiter=',') lis = list(reader) rates = [] for n in range(1, len(lis)): li = lis[n] if float(li[1]) == 0: rate = None else: rate = float(li[2]) / float(li[1]) rates.append(rate) return rates def summary_stats(args): # Arguments & parameters workspace = args.workspace out_stat_path = os.path.join(workspace, 'results', 'stats_for_paper.pkl') create_folder(os.path.dirname(out_stat_path)) # Old workspace old_workspace = '/vol/vssp/msos/qk/workspaces/audioset_classification' # bal_train_metrics = _load_old_metrics(old_workspace, 'tmp127', 20000, 'train') # eval_metrics = _load_old_metrics(old_workspace, 'tmp127', 20000, 'test') bal_train_metrics = get_avg_stats(old_workspace, bgn_iter=10000, fin_iter=50001, interval_iter=5000, filename='tmp127_re', data_type='train') eval_metrics = get_avg_stats(old_workspace, bgn_iter=10000, fin_iter=50001, interval_iter=5000, filename='tmp127_re', data_type='test') maps0te = eval_metrics['average_precision'] (maps0te, sorted_idxes, sorted_lbs) = _sort(maps0te) bal_num, unbal_num, eval_num = _samples_num_per_class() output_dict = { 'labels': config.labels, 'label_quality': get_label_quality(), 'sorted_indexes_for_plot': sorted_idxes, 'official_balanced_trainig_samples': bal_num, 'official_unbalanced_training_samples': unbal_num, 'official_eval_samples': eval_num, 'downloaded_full_training_samples': config.full_samples_per_class, 'averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations': {'bal_train': bal_train_metrics, 'eval': eval_metrics} } def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size, iteration): _workspace = '/vol/vssp/msos/qk/bytedance/workspaces_important/pub_audioset_tagging_cnn_transfer' statistics_path = os.path.join(_workspace, 'statistics', filename, 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 'data_type={}'.format(data_type), model_type, 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 'statistics.pkl') statistics_dict = cPickle.load(open(statistics_path, 'rb')) _idx = iteration // 2000 _dict = {'bal_train': {'average_precision': statistics_dict['bal'][_idx]['average_precision'], 'auc': statistics_dict['bal'][_idx]['auc']}, 'eval': {'average_precision': statistics_dict['test'][_idx]['average_precision'], 'auc': statistics_dict['test'][_idx]['auc']}} return _dict iteration = 600000 output_dict['cnn13_system_iteration60k'] = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) iteration = 560000 output_dict['mobilenetv1_system_iteration56k'] = _load_metrics('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32, iteration) cPickle.dump(output_dict, open(out_stat_path, 'wb')) print('Write stats for paper to {}'.format(out_stat_path)) def prepare_plot_long_4_rows(sorted_lbs): N = len(sorted_lbs) f,(ax1a, ax2a, ax3a, ax4a) = plt.subplots(4, 1,sharey=False, facecolor='w', figsize=(10, 12)) fontsize = 5 K = 132 ax1a.set_xlim(0, K) ax2a.set_xlim(K, 2 * K) ax3a.set_xlim(2 * K, 3 * K) ax4a.set_xlim(3 * K, N) truncated_sorted_lbs = [] for lb in sorted_lbs: lb = lb[0 : 25] words = lb.split(' ') if len(words[-1]) < 3: lb = ' '.join(words[0:-1]) truncated_sorted_lbs.append(lb) ax1a.grid(which='major', axis='x', linestyle='-', alpha=0.3) ax2a.grid(which='major', axis='x', linestyle='-', alpha=0.3) ax3a.grid(which='major', axis='x', linestyle='-', alpha=0.3) ax4a.grid(which='major', axis='x', linestyle='-', alpha=0.3) ax1a.set_yscale('log') ax2a.set_yscale('log') ax3a.set_yscale('log') ax4a.set_yscale('log') ax1b = ax1a.twinx() ax2b = ax2a.twinx() ax3b = ax3a.twinx() ax4b = ax4a.twinx() ax1b.set_ylim(0., 1.) ax2b.set_ylim(0., 1.) ax3b.set_ylim(0., 1.) ax4b.set_ylim(0., 1.) ax1b.set_ylabel('Average precision') ax2b.set_ylabel('Average precision') ax3b.set_ylabel('Average precision') ax4b.set_ylabel('Average precision') ax1b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) ax2b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) ax3b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) ax4b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) ax1a.xaxis.set_ticks(np.arange(K)) ax1a.xaxis.set_ticklabels(truncated_sorted_lbs[0:K], rotation=90, fontsize=fontsize) ax1a.xaxis.tick_bottom() ax1a.set_ylabel("Number of audio clips") ax2a.xaxis.set_ticks(np.arange(K, 2*K)) ax2a.xaxis.set_ticklabels(truncated_sorted_lbs[K:2*K], rotation=90, fontsize=fontsize) ax2a.xaxis.tick_bottom() # ax2a.tick_params(left='off', which='both') ax2a.set_ylabel("Number of audio clips") ax3a.xaxis.set_ticks(np.arange(2*K, 3*K)) ax3a.xaxis.set_ticklabels(truncated_sorted_lbs[2*K:3*K], rotation=90, fontsize=fontsize) ax3a.xaxis.tick_bottom() ax3a.set_ylabel("Number of audio clips") ax4a.xaxis.set_ticks(np.arange(3*K, N)) ax4a.xaxis.set_ticklabels(truncated_sorted_lbs[3*K:], rotation=90, fontsize=fontsize) ax4a.xaxis.tick_bottom() # ax4a.tick_params(left='off', which='both') ax4a.set_ylabel("Number of audio clips") ax1a.spines['right'].set_visible(False) ax1b.spines['right'].set_visible(False) ax2a.spines['left'].set_visible(False) ax2b.spines['left'].set_visible(False) ax2a.spines['right'].set_visible(False) ax2b.spines['right'].set_visible(False) ax3a.spines['left'].set_visible(False) ax3b.spines['left'].set_visible(False) ax3a.spines['right'].set_visible(False) ax3b.spines['right'].set_visible(False) ax4a.spines['left'].set_visible(False) ax4b.spines['left'].set_visible(False) plt.subplots_adjust(hspace = 0.8) return ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b def _scatter_4_rows(x, ax, ax2, ax3, ax4, s, c, marker='.', alpha=1.): N = len(x) ax.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) ax2.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) ax3.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) ax4.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) def _plot_4_rows(x, ax, ax2, ax3, ax4, c, linewidth=1.0, alpha=1.0, label=""): N = len(x) ax.plot(x, c=c, linewidth=linewidth, alpha=alpha) ax2.plot(x, c=c, linewidth=linewidth, alpha=alpha) ax3.plot(x, c=c, linewidth=linewidth, alpha=alpha) line, = ax4.plot(x, c=c, linewidth=linewidth, alpha=alpha, label=label) return line def plot_long_fig(args): # Arguments & parameters workspace = args.workspace # Paths stat_path = os.path.join(workspace, 'results', 'stats_for_paper.pkl') save_out_path = 'results/long_fig.pdf' create_folder(os.path.dirname(save_out_path)) # Stats stats = cPickle.load(open(stat_path, 'rb')) N = len(config.labels) sorted_indexes = stats['sorted_indexes_for_plot'] sorted_labels = np.array(config.labels)[sorted_indexes] audio_clips_per_class = stats['official_balanced_trainig_samples'] + stats['official_unbalanced_training_samples'] audio_clips_per_class = audio_clips_per_class[sorted_indexes] (ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b) = prepare_plot_long_4_rows(sorted_labels) # plot the same data on both axes ax1a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) ax2a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) ax3a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) ax4a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) maps_avg_instances = stats['averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations']['eval']['average_precision'] maps_avg_instances = maps_avg_instances[sorted_indexes] maps_cnn13 = stats['cnn13_system_iteration60k']['eval']['average_precision'] maps_cnn13 = maps_cnn13[sorted_indexes] maps_mobilenetv1 = stats['mobilenetv1_system_iteration56k']['eval']['average_precision'] maps_mobilenetv1 = maps_mobilenetv1[sorted_indexes] maps_logmel_wavegram_cnn = _load_metrics0_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) maps_logmel_wavegram_cnn = maps_logmel_wavegram_cnn[sorted_indexes] _scatter_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, s=5, c='k') _scatter_4_rows(maps_cnn13, ax1b, ax2b, ax3b, ax4b, s=5, c='r') _scatter_4_rows(maps_mobilenetv1, ax1b, ax2b, ax3b, ax4b, s=5, c='b') _scatter_4_rows(maps_logmel_wavegram_cnn, ax1b, ax2b, ax3b, ax4b, s=5, c='g') linewidth = 0.7 line0te = _plot_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, c='k', linewidth=linewidth, label='AP with averaging instances (baseline)') line1te = _plot_4_rows(maps_cnn13, ax1b, ax2b, ax3b, ax4b, c='r', linewidth=linewidth, label='AP with CNN14') line2te = _plot_4_rows(maps_mobilenetv1, ax1b, ax2b, ax3b, ax4b, c='b', linewidth=linewidth, label='AP with MobileNetV1') line3te = _plot_4_rows(maps_logmel_wavegram_cnn, ax1b, ax2b, ax3b, ax4b, c='g', linewidth=linewidth, label='AP with Wavegram-Logmel-CNN') label_quality = stats['label_quality'] sorted_rate = np.array(label_quality)[sorted_indexes] for k in range(len(sorted_rate)): if sorted_rate[k] and sorted_rate[k] == 1: sorted_rate[k] = 0.99 ax1b.scatter(np.arange(N)[sorted_rate != None], sorted_rate[sorted_rate != None], s=12, c='r', linewidth=0.8, marker='+') ax2b.scatter(np.arange(N)[sorted_rate != None], sorted_rate[sorted_rate != None], s=12, c='r', linewidth=0.8, marker='+') ax3b.scatter(np.arange(N)[sorted_rate != None], sorted_rate[sorted_rate != None], s=12, c='r', linewidth=0.8, marker='+') line_label_quality = ax4b.scatter(np.arange(N)[sorted_rate != None], sorted_rate[sorted_rate != None], s=12, c='r', linewidth=0.8, marker='+', label='Label quality') ax1b.scatter(np.arange(N)[sorted_rate == None], 0.5 * np.ones(len(np.arange(N)[sorted_rate == None])), s=12, c='r', linewidth=0.8, marker='_') ax2b.scatter(np.arange(N)[sorted_rate == None], 0.5 * np.ones(len(np.arange(N)[sorted_rate == None])), s=12, c='r', linewidth=0.8, marker='_') ax3b.scatter(np.arange(N)[sorted_rate == None], 0.5 * np.ones(len(np.arange(N)[sorted_rate == None])), s=12, c='r', linewidth=0.8, marker='_') ax4b.scatter(np.arange(N)[sorted_rate == None], 0.5 * np.ones(len(np.arange(N)[sorted_rate == None])), s=12, c='r', linewidth=0.8, marker='_') plt.legend(handles=[line0te, line1te, line2te, line3te, line_label_quality], fontsize=6, loc=1) plt.savefig(save_out_path) print('Save fig to {}'.format(save_out_path)) def plot_flops(args): # Arguments & parameters workspace = args.workspace # Paths save_out_path = 'results_map/flops.pdf' create_folder(os.path.dirname(save_out_path)) plt.figure(figsize=(5, 5)) fig, ax = plt.subplots(1, 1) model_types = np.array(['Cnn6', 'Cnn10', 'Cnn14', 'ResNet22', 'ResNet38', 'ResNet54', 'MobileNetV1', 'MobileNetV2', 'DaiNet', 'LeeNet', 'LeeNet18', 'Res1dNet30', 'Res1dNet44', 'Wavegram-CNN', 'Wavegram-\nLogmel-CNN']) flops = np.array([21.986, 21.986, 42.220, 30.081, 48.962, 54.563, 3.614, 2.810, 30.395, 4.741, 26.369, 32.688, 61.833, 44.234, 53.510]) mAPs = np.array([0.343, 0.380, 0.431, 0.430, 0.434, 0.429, 0.389, 0.383, 0.295, 0.266, 0.336, 0.365, 0.355, 0.389, 0.439]) sorted_indexes = np.sort(flops) ax.scatter(flops, mAPs) shift = [[1, 0.002], [1, -0.006], [-1, -0.014], [-2, 0.006], [-7, 0.006], [1, -0.01], [0.5, 0.004], [-1, -0.014], [1, -0.007], [0.8, -0.008], [1, -0.007], [1, 0.002], [-6, -0.015], [1, -0.008], [0.8, 0]] for i, model_type in enumerate(model_types): ax.annotate(model_type, (flops[i] + shift[i][0], mAPs[i] + shift[i][1])) ax.plot(flops[[0, 1, 2]], mAPs[[0, 1, 2]]) ax.plot(flops[[3, 4, 5]], mAPs[[3, 4, 5]]) ax.plot(flops[[6, 7]], mAPs[[6, 7]]) ax.plot(flops[[9, 10]], mAPs[[9, 10]]) ax.plot(flops[[11, 12]], mAPs[[11, 12]]) ax.plot(flops[[13, 14]], mAPs[[13, 14]]) ax.set_xlim(0, 70) ax.set_ylim(0.2, 0.5) ax.set_xlabel('Multi-adds (million)') ax.set_ylabel('mAP') plt.tight_layout(0, 0, 0) plt.savefig(save_out_path) print('Write out figure to {}'.format(save_out_path)) def spearman(args): # Arguments & parameters workspace = args.workspace # Paths stat_path = os.path.join(workspace, 'results', 'stats_for_paper.pkl') # Stats stats = cPickle.load(open(stat_path, 'rb')) label_quality = np.array([qu if qu else 0.5 for qu in stats['label_quality']]) training_samples = np.array(stats['official_balanced_trainig_samples']) + \ np.array(stats['official_unbalanced_training_samples']) mAP = stats['averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations']['eval']['average_precision'] import scipy samples_spearman = scipy.stats.spearmanr(training_samples, mAP)[0] quality_spearman = scipy.stats.spearmanr(label_quality, mAP)[0] print('Training samples spearman: {:.3f}'.format(samples_spearman)) print('Quality spearman: {:.3f}'.format(quality_spearman)) def print_results(args): (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_mixup_time_domain', 'clip_bce', 'balanced', 'mixup', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) # (mAP, mAUC, dprime) = _load_metrics0_classwise2('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) (mAP, mAUC, dprime) = _load_metrics0_classwise2('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) # partial (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.8_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.5_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) # Sample rate (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_16k', 'clip_bce', 'balanced', 'mixup', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_8k', 'clip_bce', 'balanced', 'mixup', 32) # Mel bins (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 128, 50, 14000, 'full_train', 'Cnn14_mel128', 'clip_bce', 'balanced', 'mixup', 32) (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 32, 50, 14000, 'full_train', 'Cnn14_mel32', 'clip_bce', 'balanced', 'mixup', 32) import crash asdf if __name__ == '__main__': parser = argparse.ArgumentParser(description='') subparsers = parser.add_subparsers(dest='mode') parser_plot = subparsers.add_parser('plot') parser_plot.add_argument('--dataset_dir', type=str, required=True) parser_plot.add_argument('--workspace', type=str, required=True) parser_plot.add_argument('--select', type=str, required=True) parser_plot = subparsers.add_parser('plot_for_paper') parser_plot.add_argument('--dataset_dir', type=str, required=True) parser_plot.add_argument('--workspace', type=str, required=True) parser_plot.add_argument('--select', type=str, required=True) parser_plot = subparsers.add_parser('plot_for_paper2') parser_plot.add_argument('--dataset_dir', type=str, required=True) parser_plot.add_argument('--workspace', type=str, required=True) parser_values = subparsers.add_parser('plot_class_iteration') parser_values.add_argument('--workspace', type=str, required=True) parser_values.add_argument('--select', type=str, required=True) parser_summary_stats = subparsers.add_parser('summary_stats') parser_summary_stats.add_argument('--workspace', type=str, required=True) parser_plot_long = subparsers.add_parser('plot_long_fig') parser_plot_long.add_argument('--workspace', type=str, required=True) parser_plot_flops = subparsers.add_parser('plot_flops') parser_plot_flops.add_argument('--workspace', type=str, required=True) parser_spearman = subparsers.add_parser('spearman') parser_spearman.add_argument('--workspace', type=str, required=True) parser_print = subparsers.add_parser('print') parser_print.add_argument('--workspace', type=str, required=True) args = parser.parse_args() if args.mode == 'plot': plot(args) elif args.mode == 'plot_for_paper': plot_for_paper(args) elif args.mode == 'plot_for_paper2': plot_for_paper2(args) elif args.mode == 'table_values': table_values(args) elif args.mode == 'plot_class_iteration': plot_class_iteration(args) elif args.mode == 'summary_stats': summary_stats(args) elif args.mode == 'plot_long_fig': plot_long_fig(args) elif args.mode == 'plot_flops': plot_flops(args) elif args.mode == 'spearman': spearman(args) elif args.mode == 'print': print_results(args) else: raise Exception('Error argument!') ================================================ FILE: audio_detection/audio_infer/utils/utilities.py ================================================ import os import logging import h5py import soundfile import librosa import numpy as np import pandas as pd from scipy import stats import datetime import pickle def create_folder(fd): if not os.path.exists(fd): os.makedirs(fd) def get_filename(path): path = os.path.realpath(path) na_ext = path.split('/')[-1] na = os.path.splitext(na_ext)[0] return na def get_sub_filepaths(folder): paths = [] for root, dirs, files in os.walk(folder): for name in files: path = os.path.join(root, name) paths.append(path) return paths def create_logging(log_dir, filemode): create_folder(log_dir) i1 = 0 while os.path.isfile(os.path.join(log_dir, '{:04d}.log'.format(i1))): i1 += 1 log_path = os.path.join(log_dir, '{:04d}.log'.format(i1)) logging.basicConfig( level=logging.DEBUG, format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', datefmt='%a, %d %b %Y %H:%M:%S', filename=log_path, filemode=filemode) # Print to console console = logging.StreamHandler() console.setLevel(logging.INFO) formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s') console.setFormatter(formatter) logging.getLogger('').addHandler(console) return logging def read_metadata(csv_path, classes_num, id_to_ix): """Read metadata of AudioSet from a csv file. Args: csv_path: str Returns: meta_dict: {'audio_name': (audios_num,), 'target': (audios_num, classes_num)} """ with open(csv_path, 'r') as fr: lines = fr.readlines() lines = lines[3:] # Remove heads audios_num = len(lines) targets = np.zeros((audios_num, classes_num), dtype=np.bool) audio_names = [] for n, line in enumerate(lines): items = line.split(', ') """items: ['--4gqARaEJE', '0.000', '10.000', '"/m/068hy,/m/07q6cd_,/m/0bt9lr,/m/0jbk"\n']""" audio_name = 'Y{}.wav'.format(items[0]) # Audios are started with an extra 'Y' when downloading label_ids = items[3].split('"')[1].split(',') audio_names.append(audio_name) # Target for id in label_ids: ix = id_to_ix[id] targets[n, ix] = 1 meta_dict = {'audio_name': np.array(audio_names), 'target': targets} return meta_dict def float32_to_int16(x): assert np.max(np.abs(x)) <= 1.2 x = np.clip(x, -1, 1) return (x * 32767.).astype(np.int16) def int16_to_float32(x): return (x / 32767.).astype(np.float32) def pad_or_truncate(x, audio_length): """Pad all audio to specific length.""" if len(x) <= audio_length: return np.concatenate((x, np.zeros(audio_length - len(x))), axis=0) else: return x[0 : audio_length] def d_prime(auc): d_prime = stats.norm().ppf(auc) * np.sqrt(2.0) return d_prime class Mixup(object): def __init__(self, mixup_alpha, random_seed=1234): """Mixup coefficient generator. """ self.mixup_alpha = mixup_alpha self.random_state = np.random.RandomState(random_seed) def get_lambda(self, batch_size): """Get mixup random coefficients. Args: batch_size: int Returns: mixup_lambdas: (batch_size,) """ mixup_lambdas = [] for n in range(0, batch_size, 2): lam = self.random_state.beta(self.mixup_alpha, self.mixup_alpha, 1)[0] mixup_lambdas.append(lam) mixup_lambdas.append(1. - lam) return np.array(mixup_lambdas) class StatisticsContainer(object): def __init__(self, statistics_path): """Contain statistics of different training iterations. """ self.statistics_path = statistics_path self.backup_statistics_path = '{}_{}.pkl'.format( os.path.splitext(self.statistics_path)[0], datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')) self.statistics_dict = {'bal': [], 'test': []} def append(self, iteration, statistics, data_type): statistics['iteration'] = iteration self.statistics_dict[data_type].append(statistics) def dump(self): pickle.dump(self.statistics_dict, open(self.statistics_path, 'wb')) pickle.dump(self.statistics_dict, open(self.backup_statistics_path, 'wb')) logging.info(' Dump statistics to {}'.format(self.statistics_path)) logging.info(' Dump statistics to {}'.format(self.backup_statistics_path)) def load_state_dict(self, resume_iteration): self.statistics_dict = pickle.load(open(self.statistics_path, 'rb')) resume_statistics_dict = {'bal': [], 'test': []} for key in self.statistics_dict.keys(): for statistics in self.statistics_dict[key]: if statistics['iteration'] <= resume_iteration: resume_statistics_dict[key].append(statistics) self.statistics_dict = resume_statistics_dict ================================================ FILE: audio_detection/target_sound_detection/src/models.py ================================================ # !/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2021/3/9 16:33 # @Author : dongchao yang # @File : train.py from itertools import zip_longest import numpy as np from scipy import ndimage import torch import torch.nn as nn import torch.nn.functional as F import time from torchlibrosa.augmentation import SpecAugmentation from torchlibrosa.stft import Spectrogram, LogmelFilterBank import math from sklearn.cluster import KMeans import os import time from functools import partial # import timm # from timm.models.layers import DropPath, to_2tuple, trunc_normal_ import warnings from functools import partial # from timm.models.registry import register_model # from timm.models.vision_transformer import _cfg # from mmdet.utils import get_root_logger # from mmcv.runner import load_checkpoint # from mmcv.runner import _load_checkpoint, load_state_dict # import mmcv.runner import copy from collections import OrderedDict import io import re DEBUG=0 event_labels = ['Alarm', 'Alarm_clock', 'Animal', 'Applause', 'Arrow', 'Artillery_fire', 'Babbling', 'Baby_laughter', 'Bark', 'Basketball_bounce', 'Battle_cry', 'Bell', 'Bird', 'Bleat', 'Bouncing', 'Breathing', 'Buzz', 'Camera', 'Cap_gun', 'Car', 'Car_alarm', 'Cat', 'Caw', 'Cheering', 'Child_singing', 'Choir', 'Chop', 'Chopping_(food)', 'Clapping', 'Clickety-clack', 'Clicking', 'Clip-clop', 'Cluck', 'Coin_(dropping)', 'Computer_keyboard', 'Conversation', 'Coo', 'Cough', 'Cowbell', 'Creak', 'Cricket', 'Croak', 'Crow', 'Crowd', 'DTMF', 'Dog', 'Door', 'Drill', 'Drip', 'Engine', 'Engine_starting', 'Explosion', 'Fart', 'Female_singing', 'Filing_(rasp)', 'Finger_snapping', 'Fire', 'Fire_alarm', 'Firecracker', 'Fireworks', 'Frog', 'Gasp', 'Gears', 'Giggle', 'Glass', 'Glass_shatter', 'Gobble', 'Groan', 'Growling', 'Hammer', 'Hands', 'Hiccup', 'Honk', 'Hoot', 'Howl', 'Human_sounds', 'Human_voice', 'Insect', 'Laughter', 'Liquid', 'Machine_gun', 'Male_singing', 'Mechanisms', 'Meow', 'Moo', 'Motorcycle', 'Mouse', 'Music', 'Oink', 'Owl', 'Pant', 'Pant_(dog)', 'Patter', 'Pig', 'Plop', 'Pour', 'Power_tool', 'Purr', 'Quack', 'Radio', 'Rain_on_surface', 'Rapping', 'Rattle', 'Reversing_beeps', 'Ringtone', 'Roar', 'Run', 'Rustle', 'Scissors', 'Scrape', 'Scratch', 'Screaming', 'Sewing_machine', 'Shout', 'Shuffle', 'Shuffling_cards', 'Singing', 'Single-lens_reflex_camera', 'Siren', 'Skateboard', 'Sniff', 'Snoring', 'Speech', 'Speech_synthesizer', 'Spray', 'Squeak', 'Squeal', 'Steam', 'Stir', 'Surface_contact', 'Tap', 'Tap_dance', 'Telephone_bell_ringing', 'Television', 'Tick', 'Tick-tock', 'Tools', 'Train', 'Train_horn', 'Train_wheels_squealing', 'Truck', 'Turkey', 'Typewriter', 'Typing', 'Vehicle', 'Video_game_sound', 'Water', 'Whimper_(dog)', 'Whip', 'Whispering', 'Whistle', 'Whistling', 'Whoop', 'Wind', 'Writing', 'Yip', 'and_pans', 'bird_song', 'bleep', 'clink', 'cock-a-doodle-doo', 'crinkling', 'dove', 'dribble', 'eructation', 'faucet', 'flapping_wings', 'footsteps', 'gunfire', 'heartbeat', 'infant_cry', 'kid_speaking', 'man_speaking', 'mastication', 'mice', 'river', 'rooster', 'silverware', 'skidding', 'smack', 'sobbing', 'speedboat', 'splatter', 'surf', 'thud', 'thwack', 'toot', 'truck_horn', 'tweet', 'vroom', 'waterfowl', 'woman_speaking'] def load_checkpoint(model, filename, map_location=None, strict=False, logger=None, revise_keys=[(r'^module\.', '')]): """Load checkpoint from a file or URI. Args: model (Module): Module to load checkpoint. filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for details. map_location (str): Same as :func:`torch.load`. strict (bool): Whether to allow different params for the model and checkpoint. logger (:mod:`logging.Logger` or None): The logger for error message. revise_keys (list): A list of customized keywords to modify the state_dict in checkpoint. Each item is a (pattern, replacement) pair of the regular expression operations. Default: strip the prefix 'module.' by [(r'^module\\.', '')]. Returns: dict or OrderedDict: The loaded checkpoint. """ checkpoint = _load_checkpoint(filename, map_location, logger) ''' new_proj = torch.nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1)) checkpoint['patch_embed1.proj.weight'] = new_proj.weight new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=2).unsqueeze(2).repeat(1,1,3,1)) checkpoint['patch_embed1.proj.weight'] = new_proj.weight new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=3).unsqueeze(3).repeat(1,1,1,3)) checkpoint['patch_embed1.proj.weight'] = new_proj.weight ''' new_proj = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2)) new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1)) checkpoint['patch_embed1.proj.weight'] = new_proj.weight # OrderedDict is a subclass of dict if not isinstance(checkpoint, dict): raise RuntimeError( f'No state_dict found in checkpoint file {filename}') # get state_dict from checkpoint if 'state_dict' in checkpoint: state_dict = checkpoint['state_dict'] else: state_dict = checkpoint # strip prefix of state_dict metadata = getattr(state_dict, '_metadata', OrderedDict()) for p, r in revise_keys: state_dict = OrderedDict( {re.sub(p, r, k): v for k, v in state_dict.items()}) state_dict = OrderedDict({k.replace('backbone.',''):v for k,v in state_dict.items()}) # Keep metadata in state_dict state_dict._metadata = metadata # load state_dict load_state_dict(model, state_dict, strict, logger) return checkpoint def init_weights(m): if isinstance(m, (nn.Conv2d, nn.Conv1d)): nn.init.kaiming_normal_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) if m.bias is not None: nn.init.constant_(m.bias, 0) if isinstance(m, nn.Linear): nn.init.kaiming_uniform_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) def init_layer(layer): """Initialize a Linear or Convolutional layer. """ nn.init.xavier_uniform_(layer.weight) if hasattr(layer, 'bias'): if layer.bias is not None: layer.bias.data.fill_(0.) def init_bn(bn): """Initialize a Batchnorm layer. """ bn.bias.data.fill_(0.) bn.weight.data.fill_(1.) class MaxPool(nn.Module): def __init__(self, pooldim=1): super().__init__() self.pooldim = pooldim def forward(self, logits, decision): return torch.max(decision, dim=self.pooldim)[0] class LinearSoftPool(nn.Module): """LinearSoftPool Linear softmax, takes logits and returns a probability, near to the actual maximum value. Taken from the paper: A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling https://arxiv.org/abs/1810.09050 """ def __init__(self, pooldim=1): super().__init__() self.pooldim = pooldim def forward(self, logits, time_decision): return (time_decision**2).sum(self.pooldim) / (time_decision.sum( self.pooldim)+1e-7) class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) self.init_weight() def init_weight(self): init_layer(self.conv1) init_layer(self.conv2) init_bn(self.bn1) init_bn(self.bn2) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = F.relu_(self.bn1(self.conv1(x))) x = F.relu_(self.bn2(self.conv2(x))) if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception('Incorrect argument!') return x class ConvBlock_GLU(nn.Module): def __init__(self, in_channels, out_channels,kernel_size=(3,3)): super(ConvBlock_GLU, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=(1, 1), padding=(1, 1), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.sigmoid = nn.Sigmoid() self.init_weight() def init_weight(self): init_layer(self.conv1) init_bn(self.bn1) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = self.bn1(self.conv1(x)) cnn1 = self.sigmoid(x[:, :x.shape[1]//2, :, :]) cnn2 = x[:,x.shape[1]//2:,:,:] x = cnn1*cnn2 if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 elif pool_type == 'None': pass elif pool_type == 'LP': pass #nn.LPPool2d(4, pool_size) else: raise Exception('Incorrect argument!') return x class Mul_scale_GLU(nn.Module): def __init__(self): super(Mul_scale_GLU,self).__init__() self.conv_block1_1 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(1,1)) # 1*1 self.conv_block1_2 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(3,3)) # 3*3 self.conv_block1_3 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(5,5)) # 5*5 self.conv_block2 = ConvBlock_GLU(in_channels=96, out_channels=128*2) # self.conv_block3 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock_GLU(in_channels=128, out_channels=128*2) self.conv_block4 = ConvBlock_GLU(in_channels=128, out_channels=256*2) self.conv_block5 = ConvBlock_GLU(in_channels=256, out_channels=256*2) self.conv_block6 = ConvBlock_GLU(in_channels=256, out_channels=512*2) self.conv_block7 = ConvBlock_GLU(in_channels=512, out_channels=512*2) self.padding = nn.ReplicationPad2d((0,1,0,1)) def forward(self, input, fi=None): """ Input: (batch_size, data_length)""" x1 = self.conv_block1_1(input, pool_size=(2, 2), pool_type='avg') x1 = x1[:,:,:500,:32] #print('x1 ',x1.shape) x2 = self.conv_block1_2(input,pool_size=(2,2),pool_type='avg') #print('x2 ',x2.shape) x3 = self.conv_block1_3(input,pool_size=(2,2),pool_type='avg') x3 = self.padding(x3) #print('x3 ',x3.shape) # assert 1==2 x = torch.cat([x1,x2],dim=1) x = torch.cat([x,x3],dim=1) #print('x ',x.shape) x = self.conv_block2(x, pool_size=(2, 2), pool_type='None') x = self.conv_block3(x,pool_size=(2,2),pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) # #print('x2,3 ',x.shape) x = self.conv_block4(x, pool_size=(2, 4), pool_type='None') x = self.conv_block5(x,pool_size=(2,4),pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) #print('x4,5 ',x.shape) x = self.conv_block6(x, pool_size=(1, 4), pool_type='None') x = self.conv_block7(x, pool_size=(1, 4), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) # print('x6,7 ',x.shape) # assert 1==2 return x class Cnn14(nn.Module): def __init__(self, sample_rate=32000, window_size=1024, hop_size=320, mel_bins=64, fmin=50, fmax=14000, classes_num=527): super(Cnn14, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) self.fc1 = nn.Linear(2048, 128, bias=True) self.fc_audioset = nn.Linear(128, classes_num, bias=True) self.init_weight() def init_weight(self): init_layer(self.fc1) init_layer(self.fc_audioset) def forward(self, input_, mixup_lambda=None): """ Input: (batch_size, data_length)""" input_ = input_.unsqueeze(1) x = self.conv_block1(input_, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(1, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block5(x, pool_size=(1, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block6(x, pool_size=(1, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) # print(x.shape) # x = torch.mean(x, dim=3) x = x.transpose(1, 2).contiguous().flatten(-2) x = self.fc1(x) # print(x.shape) # assert 1==2 # (x1,_) = torch.max(x, dim=2) # x2 = torch.mean(x, dim=2) # x = x1 + x2 # x = F.dropout(x, p=0.5, training=self.training) # x = F.relu_(self.fc1(x)) # embedding = F.dropout(x, p=0.5, training=self.training) return x class Cnn10_fi(nn.Module): def __init__(self): super(Cnn10_fi, self).__init__() self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) # self.fc1 = nn.Linear(512, 512, bias=True) # self.fc_audioset = nn.Linear(512, classes_num, bias=True) # self.init_weight() def forward(self, input, fi=None): """ Input: (batch_size, data_length)""" x = self.conv_block1(input, pool_size=(2, 2), pool_type='avg') if fi != None: gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x) beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x) x = (gamma)*x + beta x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') if fi != None: gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x) beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x) x = (gamma)*x + beta x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 4), pool_type='avg') if fi != None: gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x) beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x) x = (gamma)*x + beta x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(1, 4), pool_type='avg') if fi != None: gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x) beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x) x = (gamma)*x + beta x = F.dropout(x, p=0.2, training=self.training) return x class Cnn10_mul_scale(nn.Module): def __init__(self,scale=8): super(Cnn10_mul_scale, self).__init__() self.conv_block1_1 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(1,1)) self.conv_block1_2 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(3,3)) self.conv_block1_3 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(5,5)) self.conv_block2 = ConvBlock(in_channels=96, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.scale = scale self.padding = nn.ReplicationPad2d((0,1,0,1)) def forward(self, input, pool_size=(2, 2), pool_type='avg'): """ Input: (batch_size, data_length)""" if self.scale == 8: pool_size1 = (2,2) pool_size2 = (2,2) pool_size3 = (2,4) pool_size4 = (1,4) elif self.scale == 4: pool_size1 = (2,2) pool_size2 = (2,2) pool_size3 = (1,4) pool_size4 = (1,4) elif self.scale == 2: pool_size1 = (2,2) pool_size2 = (1,2) pool_size3 = (1,4) pool_size4 = (1,4) else: pool_size1 = (1,2) pool_size2 = (1,2) pool_size3 = (1,4) pool_size4 = (1,4) # print('input ',input.shape) x1 = self.conv_block1_1(input, pool_size=pool_size1, pool_type='avg') x1 = x1[:,:,:500,:32] #print('x1 ',x1.shape) x2 = self.conv_block1_2(input, pool_size=pool_size1, pool_type='avg') #print('x2 ',x2.shape) x3 = self.conv_block1_3(input, pool_size=pool_size1, pool_type='avg') x3 = self.padding(x3) #print('x3 ',x3.shape) # assert 1==2 m_i = min(x3.shape[2],min(x1.shape[2],x2.shape[2])) #print('m_i ', m_i) x = torch.cat([x1[:,:,:m_i,:],x2[:,:, :m_i,:],x3[:,:, :m_i,:]],dim=1) # x = torch.cat([x,x3],dim=1) # x = self.conv_block1(input, pool_size=pool_size1, pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=pool_size2, pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=pool_size3, pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=pool_size4, pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) return x class Cnn10(nn.Module): def __init__(self,scale=8): super(Cnn10, self).__init__() self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.scale = scale def forward(self, input, pool_size=(2, 2), pool_type='avg'): """ Input: (batch_size, data_length)""" if self.scale == 8: pool_size1 = (2,2) pool_size2 = (2,2) pool_size3 = (2,4) pool_size4 = (1,4) elif self.scale == 4: pool_size1 = (2,2) pool_size2 = (2,2) pool_size3 = (1,4) pool_size4 = (1,4) elif self.scale == 2: pool_size1 = (2,2) pool_size2 = (1,2) pool_size3 = (1,4) pool_size4 = (1,4) else: pool_size1 = (1,2) pool_size2 = (1,2) pool_size3 = (1,4) pool_size4 = (1,4) x = self.conv_block1(input, pool_size=pool_size1, pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=pool_size2, pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=pool_size3, pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=pool_size4, pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) return x class MeanPool(nn.Module): def __init__(self, pooldim=1): super().__init__() self.pooldim = pooldim def forward(self, logits, decision): return torch.mean(decision, dim=self.pooldim) class ResPool(nn.Module): def __init__(self, pooldim=1): super().__init__() self.pooldim = pooldim self.linPool = LinearSoftPool(pooldim=1) class AutoExpPool(nn.Module): def __init__(self, outputdim=10, pooldim=1): super().__init__() self.outputdim = outputdim self.alpha = nn.Parameter(torch.full((outputdim, ), 1)) self.pooldim = pooldim def forward(self, logits, decision): scaled = self.alpha * decision # \alpha * P(Y|x) in the paper return (logits * torch.exp(scaled)).sum( self.pooldim) / torch.exp(scaled).sum(self.pooldim) class SoftPool(nn.Module): def __init__(self, T=1, pooldim=1): super().__init__() self.pooldim = pooldim self.T = T def forward(self, logits, decision): w = torch.softmax(decision / self.T, dim=self.pooldim) return torch.sum(decision * w, dim=self.pooldim) class AutoPool(nn.Module): """docstring for AutoPool""" def __init__(self, outputdim=10, pooldim=1): super().__init__() self.outputdim = outputdim self.alpha = nn.Parameter(torch.ones(outputdim)) self.dim = pooldim def forward(self, logits, decision): scaled = self.alpha * decision # \alpha * P(Y|x) in the paper weight = torch.softmax(scaled, dim=self.dim) return torch.sum(decision * weight, dim=self.dim) # B x C class ExtAttentionPool(nn.Module): def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs): super().__init__() self.inputdim = inputdim self.outputdim = outputdim self.pooldim = pooldim self.attention = nn.Linear(inputdim, outputdim) nn.init.zeros_(self.attention.weight) nn.init.zeros_(self.attention.bias) self.activ = nn.Softmax(dim=self.pooldim) def forward(self, logits, decision): # Logits of shape (B, T, D), decision of shape (B, T, C) w_x = self.activ(self.attention(logits) / self.outputdim) h = (logits.permute(0, 2, 1).contiguous().unsqueeze(-2) * w_x.unsqueeze(-1)).flatten(-2).contiguous() return torch.sum(h, self.pooldim) class AttentionPool(nn.Module): """docstring for AttentionPool""" def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs): super().__init__() self.inputdim = inputdim self.outputdim = outputdim self.pooldim = pooldim self.transform = nn.Linear(inputdim, outputdim) self.activ = nn.Softmax(dim=self.pooldim) self.eps = 1e-7 def forward(self, logits, decision): # Input is (B, T, D) # B, T , D w = self.activ(torch.clamp(self.transform(logits), -15, 15)) detect = (decision * w).sum( self.pooldim) / (w.sum(self.pooldim) + self.eps) # B, T, D return detect class Block2D(nn.Module): def __init__(self, cin, cout, kernel_size=3, padding=1): super().__init__() self.block = nn.Sequential( nn.BatchNorm2d(cin), nn.Conv2d(cin, cout, kernel_size=kernel_size, padding=padding, bias=False), nn.LeakyReLU(inplace=True, negative_slope=0.1)) def forward(self, x): return self.block(x) class AudioCNN(nn.Module): def __init__(self, classes_num): super(AudioCNN, self).__init__() self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.fc1 = nn.Linear(512,128,bias=True) self.fc = nn.Linear(128, classes_num, bias=True) self.init_weights() def init_weights(self): init_layer(self.fc) def forward(self, input): ''' Input: (batch_size, times_steps, freq_bins)''' # [128, 801, 168] --> [128,1,801,168] x = input[:, None, :, :] '''(batch_size, 1, times_steps, freq_bins)''' x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') # 128,64,400,84 x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') # 128,128,200,42 x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') # 128,256,100,21 x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') # 128,512,50,10 '''(batch_size, feature_maps, time_steps, freq_bins)''' x = torch.mean(x, dim=3) # (batch_size, feature_maps, time_stpes) # 128,512,50 (x, _) = torch.max(x, dim=2) # (batch_size, feature_maps) 128,512 x = self.fc1(x) # 128,128 output = self.fc(x) # 128,10 return x,output def extract(self,input): '''Input: (batch_size, times_steps, freq_bins)''' x = input[:, None, :, :] x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') '''(batch_size, feature_maps, time_steps, freq_bins)''' x = torch.mean(x, dim=3) # (batch_size, feature_maps, time_stpes) (x, _) = torch.max(x, dim=2) # (batch_size, feature_maps) x = self.fc1(x) # 128,128 return x def parse_poolingfunction(poolingfunction_name='mean', **kwargs): """parse_poolingfunction A heler function to parse any temporal pooling Pooling is done on dimension 1 :param poolingfunction_name: :param **kwargs: """ poolingfunction_name = poolingfunction_name.lower() if poolingfunction_name == 'mean': return MeanPool(pooldim=1) elif poolingfunction_name == 'max': return MaxPool(pooldim=1) elif poolingfunction_name == 'linear': return LinearSoftPool(pooldim=1) elif poolingfunction_name == 'expalpha': return AutoExpPool(outputdim=kwargs['outputdim'], pooldim=1) elif poolingfunction_name == 'soft': return SoftPool(pooldim=1) elif poolingfunction_name == 'auto': return AutoPool(outputdim=kwargs['outputdim']) elif poolingfunction_name == 'attention': return AttentionPool(inputdim=kwargs['inputdim'], outputdim=kwargs['outputdim']) class conv1d(nn.Module): def __init__(self, nin, nout, kernel_size=3, stride=1, padding='VALID', dilation=1): super(conv1d, self).__init__() if padding == 'VALID': dconv_pad = 0 elif padding == 'SAME': dconv_pad = dilation * ((kernel_size - 1) // 2) else: raise ValueError("Padding Mode Error!") self.conv = nn.Conv1d(nin, nout, kernel_size=kernel_size, stride=stride, padding=dconv_pad) self.act = nn.ReLU() self.init_layer(self.conv) def init_layer(self, layer, nonlinearity='relu'): """Initialize a Linear or Convolutional layer. """ nn.init.kaiming_normal_(layer.weight, nonlinearity=nonlinearity) nn.init.constant_(layer.bias, 0.1) def forward(self, x): out = self.act(self.conv(x)) return out class Atten_1(nn.Module): def __init__(self, input_dim, context=2, dropout_rate=0.2): super(Atten_1, self).__init__() self._matrix_k = nn.Linear(input_dim, input_dim // 4) self._matrix_q = nn.Linear(input_dim, input_dim // 4) self.relu = nn.ReLU() self.context = context self._dropout_layer = nn.Dropout(dropout_rate) self.init_layer(self._matrix_k) self.init_layer(self._matrix_q) def init_layer(self, layer, nonlinearity='leaky_relu'): """Initialize a Linear or Convolutional layer. """ nn.init.kaiming_uniform_(layer.weight, nonlinearity=nonlinearity) if hasattr(layer, 'bias'): if layer.bias is not None: layer.bias.data.fill_(0.) def forward(self, input_x): k_x = input_x k_x = self.relu(self._matrix_k(k_x)) k_x = self._dropout_layer(k_x) # print('k_x ',k_x.shape) q_x = input_x[:, self.context, :] # print('q_x ',q_x.shape) q_x = q_x[:, None, :] # print('q_x1 ',q_x.shape) q_x = self.relu(self._matrix_q(q_x)) q_x = self._dropout_layer(q_x) # print('q_x2 ',q_x.shape) x_ = torch.matmul(k_x, q_x.transpose(-2, -1) / math.sqrt(k_x.size(-1))) # print('x_ ',x_.shape) x_ = x_.squeeze(2) alpha = F.softmax(x_, dim=-1) att_ = alpha # print('alpha ',alpha) alpha = alpha.unsqueeze(2).repeat(1,1,input_x.shape[2]) # print('alpha ',alpha) # alpha = alpha.view(alpha.size(0), alpha.size(1), alpha.size(2), 1) out = alpha * input_x # print('out ', out.shape) # out = out.mean(2) out = out.mean(1) # print('out ',out.shape) # assert 1==2 #y = alpha * input_x #return y, att_ out = input_x[:, self.context, :] + out return out class Fusion(nn.Module): def __init__(self, inputdim, inputdim2, n_fac): super().__init__() self.fuse_layer1 = conv1d(inputdim, inputdim2*n_fac,1) self.fuse_layer2 = conv1d(inputdim2, inputdim2*n_fac,1) self.avg_pool = nn.AvgPool1d(n_fac, stride=n_fac) # 沿着最后一个维度进行pooling def forward(self,embedding,mix_embed): embedding = embedding.permute(0,2,1) fuse1_out = self.fuse_layer1(embedding) # [2, 501, 2560] ,512*5, 1D卷积融合,spk_embeding ,扩大其维度 fuse1_out = fuse1_out.permute(0,2,1) mix_embed = mix_embed.permute(0,2,1) fuse2_out = self.fuse_layer2(mix_embed) # [2, 501, 2560] ,512*5, 1D卷积融合,spk_embeding ,扩大其维度 fuse2_out = fuse2_out.permute(0,2,1) as_embs = torch.mul(fuse1_out, fuse2_out) # 相乘 [2, 501, 2560] # (10, 501, 512) as_embs = self.avg_pool(as_embs) # [2, 501, 512] 相当于 2560//5 return as_embs class CDur_fusion(nn.Module): def __init__(self, inputdim, outputdim, **kwargs): super().__init__() self.features = nn.Sequential( Block2D(1, 32), nn.LPPool2d(4, (2, 4)), Block2D(32, 128), Block2D(128, 128), nn.LPPool2d(4, (2, 4)), Block2D(128, 128), Block2D(128, 128), nn.LPPool2d(4, (1, 4)), nn.Dropout(0.3), ) with torch.no_grad(): rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1] self.gru = nn.GRU(128, 128, bidirectional=True, batch_first=True) self.fusion = Fusion(128,2) self.fc = nn.Linear(256,256) self.outputlayer = nn.Linear(256, outputdim) self.features.apply(init_weights) self.outputlayer.apply(init_weights) def forward(self, x, embedding): # batch, time, dim = x.shape x = x.unsqueeze(1) # (b,1,t,d) x = self.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128) embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) x = self.fusion(embedding,x) #x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.gru.flatten_parameters() x, _ = self.gru(x) # x torch.Size([16, 125, 256]) x = self.fc(x) decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2]) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), # [16, 2, 125] time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0],decision_up class CDur(nn.Module): def __init__(self, inputdim, outputdim,time_resolution, **kwargs): super().__init__() self.features = nn.Sequential( Block2D(1, 32), nn.LPPool2d(4, (2, 4)), Block2D(32, 128), Block2D(128, 128), nn.LPPool2d(4, (2, 4)), Block2D(128, 128), Block2D(128, 128), nn.LPPool2d(4, (2, 4)), nn.Dropout(0.3), ) with torch.no_grad(): rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1] self.gru = nn.GRU(256, 256, bidirectional=True, batch_first=True) self.fc = nn.Linear(512,256) self.outputlayer = nn.Linear(256, outputdim) self.features.apply(init_weights) self.outputlayer.apply(init_weights) def forward(self, x, embedding,one_hot=None): # batch, time, dim = x.shape x = x.unsqueeze(1) # (b,1,t,d) x = self.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128) embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.gru.flatten_parameters() x, _ = self.gru(x) # x torch.Size([16, 125, 256]) x = self.fc(x) decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2]) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), # [16, 2, 125] time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0],decision_up class CDur_big(nn.Module): def __init__(self, inputdim, outputdim, **kwargs): super().__init__() self.features = nn.Sequential( Block2D(1, 64), Block2D(64, 64), nn.LPPool2d(4, (2, 2)), Block2D(64, 128), Block2D(128, 128), nn.LPPool2d(4, (2, 2)), Block2D(128, 256), Block2D(256, 256), nn.LPPool2d(4, (2, 4)), Block2D(256, 512), Block2D(512, 512), nn.LPPool2d(4, (1, 4)), nn.Dropout(0.3),) with torch.no_grad(): rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1] self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True) self.fc = nn.Linear(1024,256) self.outputlayer = nn.Linear(256, outputdim) self.features.apply(init_weights) self.outputlayer.apply(init_weights) def forward(self, x, embedding): # batch, time, dim = x.shape x = x.unsqueeze(1) # (b,1,t,d) x = self.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512) embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.gru.flatten_parameters() x, _ = self.gru(x) # x torch.Size([16, 125, 256]) x = self.fc(x) decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2]) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), # [16, 2, 125] time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0],decision_up class CDur_GLU(nn.Module): def __init__(self, inputdim, outputdim, **kwargs): super().__init__() self.features = Mul_scale_GLU() # with torch.no_grad(): # rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape # rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1] self.gru = nn.GRU(640, 512,1, bidirectional=True, batch_first=True) # previous is 640 # self.gru = LSTMModel(640, 512,1) self.fc = nn.Linear(1024,256) self.outputlayer = nn.Linear(256, outputdim) # self.features.apply(init_weights) self.outputlayer.apply(init_weights) def forward(self, x, embedding,one_hot=None): # batch, time, dim = x.shape x = x.unsqueeze(1) # (b,1,t,d) x = self.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512) # print('x ',x.shape) # assert 1==2 embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.gru.flatten_parameters() x, _ = self.gru(x) # x torch.Size([16, 125, 256]) # x = self.gru(x) # x torch.Size([16, 125, 256]) x = self.fc(x) decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2]) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), # [16, 2, 125] time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0],decision_up class CDur_CNN14(nn.Module): def __init__(self, inputdim, outputdim,time_resolution,**kwargs): super().__init__() if time_resolution==125: self.features = Cnn10(8) elif time_resolution == 250: #print('time_resolution ',time_resolution) self.features = Cnn10(4) elif time_resolution == 500: self.features = Cnn10(2) else: self.features = Cnn10(0) with torch.no_grad(): rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1] # self.features = Cnn10() self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True) # self.gru = LSTMModel(640, 512,1) self.fc = nn.Linear(1024,256) self.outputlayer = nn.Linear(256, outputdim) # self.features.apply(init_weights) self.outputlayer.apply(init_weights) def forward(self, x, embedding,one_hot=None): batch, time, dim = x.shape x = x.unsqueeze(1) # (b,1,t,d) x = self.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512) # print('x ',x.shape) # assert 1==2 embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.gru.flatten_parameters() x, _ = self.gru(x) # x torch.Size([16, 125, 256]) # x = self.gru(x) # x torch.Size([16, 125, 256]) x = self.fc(x) decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2]) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), # [16, 2, 125] time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0],decision_up class CDur_CNN_mul_scale(nn.Module): def __init__(self, inputdim, outputdim,time_resolution,**kwargs): super().__init__() if time_resolution==125: self.features = Cnn10_mul_scale(8) elif time_resolution == 250: #print('time_resolution ',time_resolution) self.features = Cnn10_mul_scale(4) elif time_resolution == 500: self.features = Cnn10_mul_scale(2) else: self.features = Cnn10_mul_scale(0) # with torch.no_grad(): # rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape # rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1] # self.features = Cnn10() self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True) # self.gru = LSTMModel(640, 512,1) self.fc = nn.Linear(1024,256) self.outputlayer = nn.Linear(256, outputdim) # self.features.apply(init_weights) self.outputlayer.apply(init_weights) def forward(self, x, embedding,one_hot=None): # print('x ',x.shape) # assert 1==2 batch, time, dim = x.shape x = x.unsqueeze(1) # (b,1,t,d) x = self.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512) # print('x ',x.shape) # assert 1==2 embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.gru.flatten_parameters() x, _ = self.gru(x) # x torch.Size([16, 125, 256]) # x = self.gru(x) # x torch.Size([16, 125, 256]) x = self.fc(x) decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2]) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), # [16, 2, 125] time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0],decision_up class CDur_CNN_mul_scale_fusion(nn.Module): def __init__(self, inputdim, outputdim, time_resolution,**kwargs): super().__init__() if time_resolution==125: self.features = Cnn10_mul_scale(8) elif time_resolution == 250: #print('time_resolution ',time_resolution) self.features = Cnn10_mul_scale(4) elif time_resolution == 500: self.features = Cnn10_mul_scale(2) else: self.features = Cnn10_mul_scale(0) # with torch.no_grad(): # rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape # rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1] # self.features = Cnn10() self.gru = nn.GRU(512, 512, bidirectional=True, batch_first=True) # self.gru = LSTMModel(640, 512,1) self.fc = nn.Linear(1024,256) self.fusion = Fusion(128,512,2) self.outputlayer = nn.Linear(256, outputdim) # self.features.apply(init_weights) self.outputlayer.apply(init_weights) def forward(self, x, embedding,one_hot=None): # print('x ',x.shape) # assert 1==2 batch, time, dim = x.shape x = x.unsqueeze(1) # (b,1,t,d) x = self.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512) # print('x ',x.shape) # assert 1==2 embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) x = self.fusion(embedding, x) #x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.gru.flatten_parameters() x, _ = self.gru(x) # x torch.Size([16, 125, 256]) # x = self.gru(x) # x torch.Size([16, 125, 256]) x = self.fc(x) decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2]) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), # [16, 2, 125] time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0],decision_up class RaDur_fusion(nn.Module): def __init__(self, model_config, inputdim, outputdim, time_resolution, **kwargs): super().__init__() self.encoder = Cnn14() self.detection = CDur_CNN_mul_scale_fusion(inputdim, outputdim, time_resolution) self.softmax = nn.Softmax(dim=2) #self.temperature = 5 # if model_config['pre_train']: # self.encoder.load_state_dict(torch.load(model_config['encoder_path'])['model']) # self.detection.load_state_dict(torch.load(model_config['CDur_path'])) self.q = nn.Linear(128,128) self.k = nn.Linear(128,128) self.q_ee = nn.Linear(128, 128) self.k_ee = nn.Linear(128, 128) self.temperature = 11.3 # sqrt(128) self.att_pool = model_config['att_pool'] self.enhancement = model_config['enhancement'] self.tao = model_config['tao'] self.top = model_config['top'] self.bn = nn.BatchNorm1d(128) self.EE_fusion = Fusion(128, 128, 4) def get_w(self,q,k): q = self.q(q) k = self.k(k) q = q.unsqueeze(1) attn = torch.bmm(q, k.transpose(1, 2)) attn = attn/self.temperature attn = self.softmax(attn) return attn def get_w_ee(self,q,k): q = self.q_ee(q) k = self.k_ee(k) q = q.unsqueeze(1) attn = torch.bmm(q, k.transpose(1, 2)) attn = attn/self.temperature attn = self.softmax(attn) return attn def attention_pooling(self, embeddings, mean_embedding): att_pool_w = self.get_w(mean_embedding,embeddings) embedding = torch.bmm(att_pool_w, embeddings).squeeze(1) # print(embedding.shape) # print(att_pool_w.shape) # print(att_pool_w[0]) # assert 1==2 return embedding def select_topk_embeddings(self, scores, embeddings, k): _, idx_DESC = scores.sort(descending=True, dim=1) # 根据分数进行排序 top_k = _[:,:k] # print('top_k ', top_k) # top_k = top_k.mean(1) idx_topk = idx_DESC[:, :k] # 取top_k个 # print('index ', idx_topk) idx_topk = idx_topk.unsqueeze(2).expand([-1, -1, embeddings.shape[2]]) selected_embeddings = torch.gather(embeddings, 1, idx_topk) return selected_embeddings,top_k def sum_with_attention(self, embedding, top_k, selected_embeddings): # print('embedding ',embedding) # print('selected_embeddings ',selected_embeddings.shape) att_1 = self.get_w_ee(embedding, selected_embeddings) att_1 = att_1.squeeze(1) #print('att_1 ',att_1.shape) larger = top_k > self.tao # print('larger ',larger) top_k = top_k*larger # print('top_k ',top_k.shape) # print('top_k ',top_k) att_1 = att_1*top_k #print('att_1 ',att_1.shape) # assert 1==2 att_2 = att_1.unsqueeze(2).repeat(1,1,128) Es = selected_embeddings*att_2 return Es def orcal_EE(self, x, embedding, label): batch, time, dim = x.shape mixture_embedding = self.encoder(x) # 8, 125, 128 mixture_embedding = mixture_embedding.transpose(1,2) mixture_embedding = self.bn(mixture_embedding) mixture_embedding = mixture_embedding.transpose(1,2) x = x.unsqueeze(1) # (b,1,t,d) x = self.detection.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128) embedding_pre = embedding.unsqueeze(1) embedding_pre = embedding_pre.repeat(1, x.shape[1], 1) f = self.detection.fusion(embedding_pre, x) # the first stage results #f = torch.cat((x, embedding_pre), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.detection.gru.flatten_parameters() f, _ = self.detection.gru(f) # x torch.Size([16, 125, 256]) f = self.detection.fc(f) decision_time = torch.softmax(self.detection.outputlayer(f),dim=2) # x torch.Size([16, 125, 2]) selected_embeddings, top_k = self.select_topk_embeddings(decision_time[:,:,0], mixture_embedding, self.top) selected_embeddings = self.sum_with_attention(embedding, top_k, selected_embeddings) # add the weight mix_embedding = selected_embeddings.mean(1).unsqueeze(1) # mix_embedding = mix_embedding.repeat(1, x.shape[1], 1) embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) mix_embedding = self.EE_fusion(mix_embedding, embedding) # 使用神经网络进行融合 # mix_embedding2 = selected_embeddings2.mean(1) #mix_embedding = embedding + mix_embedding # 直接相加 # new detection results # embedding_now = mix_embedding.unsqueeze(1) # embedding_now = embedding_now.repeat(1, x.shape[1], 1) f_now = self.detection.fusion(mix_embedding, x) #f_now = torch.cat((x, embedding_now), dim=2) # f_now, _ = self.detection.gru(f_now) # x torch.Size([16, 125, 256]) f_now = self.detection.fc(f_now) decision_time_now = torch.softmax(self.detection.outputlayer(f_now), dim=2) # x torch.Size([16, 125, 2]) top_k = top_k.mean(1) # get avg score,higher score will have more weight larger = top_k > self.tao top_k = top_k * larger top_k = top_k/2.0 # print('top_k ',top_k) # assert 1==2 # print('tok_k[ ',top_k.shape) # print('decision_time ',decision_time.shape) # print('decision_time_now ',decision_time_now.shape) neg_w = top_k.unsqueeze(1).unsqueeze(2) neg_w = neg_w.repeat(1, decision_time_now.shape[1], decision_time_now.shape[2]) # print('neg_w ',neg_w.shape) #print('neg_w ',neg_w[:,0:10,0]) pos_w = 1-neg_w #print('pos_w ',pos_w[:,0:10,0]) decision_time_final = decision_time*pos_w + neg_w*decision_time_now #print('decision_time_final ',decision_time_final[0,0:10,0]) # print(decision_time_final[0,:,:]) #assert 1==2 return decision_time_final def forward(self, x, ref, label=None): batch, time, dim = x.shape logit = torch.zeros(1).cuda() embeddings = self.encoder(ref) mean_embedding = embeddings.mean(1) if self.att_pool == True: mean_embedding = self.bn(mean_embedding) embeddings = embeddings.transpose(1,2) embeddings = self.bn(embeddings) embeddings = embeddings.transpose(1,2) embedding = self.attention_pooling(embeddings, mean_embedding) else: embedding = mean_embedding if self.enhancement == True: decision_time = self.orcal_EE(x, embedding, label) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), # [16, 2, 125] time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0], decision_up, logit x = x.unsqueeze(1) # (b,1,t,d) x = self.detection.features(x) # x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128) embedding = embedding.unsqueeze(1) embedding = embedding.repeat(1, x.shape[1], 1) # x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] x = self.detection.fusion(embedding, x) # embedding = embedding.unsqueeze(1) # embedding = embedding.repeat(1, x.shape[1], 1) # x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim] if not hasattr(self, '_flattened'): self.detection.gru.flatten_parameters() x, _ = self.detection.gru(x) # x torch.Size([16, 125, 256]) x = self.detection.fc(x) decision_time = torch.softmax(self.detection.outputlayer(x),dim=2) # x torch.Size([16, 125, 2]) decision_up = torch.nn.functional.interpolate( decision_time.transpose(1, 2), time, # 501 mode='linear', align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2) return decision_time[:,:,0], decision_up, logit ================================================ FILE: audio_detection/target_sound_detection/src/utils.py ================================================ # !/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2021/3/9 16:33 # @Author : dongchao yang # @File : train.py import collections import sys from loguru import logger from pprint import pformat import numpy as np import pandas as pd import scipy import six import sklearn.preprocessing as pre import torch import tqdm import yaml from scipy.interpolate import interp1d def parse_config_or_kwargs(config_file, **kwargs): """parse_config_or_kwargs :param config_file: Config file that has parameters, yaml format :param **kwargs: Other alternative parameters or overwrites for config """ with open(config_file) as con_read: yaml_config = yaml.load(con_read, Loader=yaml.FullLoader) arguments = dict(yaml_config, **kwargs) return arguments def find_contiguous_regions(activity_array): # in this part, if you cannot understand the binary operation, I think you can write a O(n) complexity method """Find contiguous regions from bool valued numpy.array. Copy of https://dcase-repo.github.io/dcase_util/_modules/dcase_util/data/decisions.html#DecisionEncoder Reason is: 1. This does not belong to a class necessarily 2. Import DecisionEncoder requires sndfile over some other imports..which causes some problems on clusters """ change_indices = np.logical_xor(activity_array[1:], activity_array[:-1]).nonzero()[0] change_indices += 1 if activity_array[0]: # If the first element of activity_array is True add 0 at the beginning change_indices = np.r_[0, change_indices] if activity_array[-1]: # If the last element of activity_array is True, add the length of the array change_indices = np.r_[change_indices, activity_array.size] # print(change_indices.reshape((-1, 2))) # Reshape the result into two columns return change_indices.reshape((-1, 2)) def split_train_cv( data_frame: pd.DataFrame, frac: float = 0.9, y=None, # Only for stratified, computes necessary split **kwargs): """split_train_cv :param data_frame: :type data_frame: pd.DataFrame :param frac: :type frac: float """ if kwargs.get('mode', None) == 'urbansed': # Filenames are DATA_-1 DATA_-2 etc data_frame.loc[:, 'id'] = data_frame.groupby( data_frame['filename'].str.split('_').apply( lambda x: '_'.join(x[:-1]))).ngroup() sampler = np.random.permutation(data_frame['id'].nunique()) num_train = int(frac * len(sampler)) train_indexes = sampler[:num_train] cv_indexes = sampler[num_train:] train_data = data_frame[data_frame['id'].isin(train_indexes)] cv_data = data_frame[data_frame['id'].isin(cv_indexes)] del train_data['id'] del cv_data['id'] elif kwargs.get('mode', None) == 'stratified': # stratified --> 分层的 ? # Use statified sampling from skmultilearn.model_selection import iterative_train_test_split index_train, _, index_cv, _ = iterative_train_test_split( data_frame.index.values.reshape(-1, 1), y, test_size=1. - frac) train_data = data_frame[data_frame.index.isin(index_train.squeeze())] cv_data = data_frame[data_frame.index.isin(index_cv.squeeze())] # cv --> cross validation else: # Simply split train_test train_data = data_frame.sample(frac=frac, random_state=10) cv_data = data_frame[~data_frame.index.isin(train_data.index)] return train_data, cv_data def pprint_dict(in_dict, outputfun=sys.stdout.write, formatter='yaml'): # print yaml file """pprint_dict :param outputfun: function to use, defaults to sys.stdout :param in_dict: dict to print """ if formatter == 'yaml': format_fun = yaml.dump elif formatter == 'pretty': format_fun = pformat for line in format_fun(in_dict).split('\n'): outputfun(line) def getfile_outlogger(outputfile): log_format = "[{time:YYYY-MM-DD HH:mm:ss}] {message}" logger.configure(handlers=[{"sink": sys.stderr, "format": log_format}]) if outputfile: logger.add(outputfile, enqueue=True, format=log_format) return logger # according label, get encoder def train_labelencoder(labels: pd.Series, sparse=True): """encode_labels Encodes labels :param labels: pd.Series representing the raw labels e.g., Speech, Water :param encoder (optional): Encoder already fitted returns encoded labels (many hot) and the encoder """ assert isinstance(labels, pd.Series), "Labels need to be series" if isinstance(labels[0], six.string_types): # In case of using non processed strings, e.g., Vaccum, Speech label_array = labels.str.split(',').values.tolist() # split label according to ',' elif isinstance(labels[0], np.ndarray): # Encoder does not like to see numpy array label_array = [lab.tolist() for lab in labels] elif isinstance(labels[0], collections.Iterable): label_array = labels encoder = pre.MultiLabelBinarizer(sparse_output=sparse) encoder.fit(label_array) return encoder def encode_labels(labels: pd.Series, encoder=None, sparse=True): """encode_labels Encodes labels :param labels: pd.Series representing the raw labels e.g., Speech, Water :param encoder (optional): Encoder already fitted returns encoded labels (many hot) and the encoder """ assert isinstance(labels, pd.Series), "Labels need to be series" instance = labels.iloc[0] if isinstance(instance, six.string_types): # In case of using non processed strings, e.g., Vaccum, Speech label_array = labels.str.split(',').values.tolist() elif isinstance(instance, np.ndarray): # Encoder does not like to see numpy array label_array = [lab.tolist() for lab in labels] elif isinstance(instance, collections.Iterable): label_array = labels # get label_array, it is a list ,contain a lot of label, this label are string type if not encoder: encoder = pre.MultiLabelBinarizer(sparse_output=sparse) # if we encoder is None, we should init a encoder firstly. encoder.fit(label_array) labels_encoded = encoder.transform(label_array) # transform string to digit return labels_encoded, encoder # return pd.arrays.SparseArray( # [row.toarray().ravel() for row in labels_encoded]), encoder def decode_with_timestamps(events,labels: np.array): """decode_with_timestamps Decodes the predicted label array (2d) into a list of [(Labelname, onset, offset), ...] :param encoder: Encoder during training :type encoder: pre.MultiLabelBinarizer :param labels: n-dim array :type labels: np.array """ # print('events ',events) # print('labels ',labels.shape) #assert 1==2 if labels.ndim == 2: #print('...') return [_decode_with_timestamps(events[i],labels[i]) for i in range(labels.shape[0])] else: return _decode_with_timestamps(events,labels) def median_filter(x, window_size, threshold=0.5): """median_filter :param x: input prediction array of shape (B, T, C) or (B, T). Input is a sequence of probabilities 0 <= x <= 1 :param window_size: An integer to use :param threshold: Binary thresholding threshold """ x = binarize(x, threshold=threshold) # transfer to 0 or 1 if x.ndim == 3: size = (1, window_size, 1) elif x.ndim == 2 and x.shape[0] == 1: # Assume input is class-specific median filtering # E.g, Batch x Time [1, 501] size = (1, window_size) elif x.ndim == 2 and x.shape[0] > 1: # Assume input is standard median pooling, class-independent # E.g., Time x Class [501, 10] size = (window_size, 1) return scipy.ndimage.median_filter(x, size=size) def _decode_with_timestamps(events,labels): result_labels = [] # print('.......') # print('labels ',labels.shape) # print(labels) change_indices = find_contiguous_regions(labels) # print(change_indices) # assert 1==2 for row in change_indices: result_labels.append((events,row[0], row[1])) return result_labels def inverse_transform_labels(encoder, pred): if pred.ndim == 3: return [encoder.inverse_transform(x) for x in pred] else: return encoder.inverse_transform(pred) def binarize(pred, threshold=0.5): # Batch_wise if pred.ndim == 3: return np.array( [pre.binarize(sub, threshold=threshold) for sub in pred]) else: return pre.binarize(pred, threshold=threshold) def double_threshold(x, high_thres, low_thres, n_connect=1): """double_threshold Helper function to calculate double threshold for n-dim arrays :param x: input array :param high_thres: high threshold value :param low_thres: Low threshold value :param n_connect: Distance of <= n clusters will be merged """ assert x.ndim <= 3, "Whoops something went wrong with the input ({}), check if its <= 3 dims".format( x.shape) if x.ndim == 3: apply_dim = 1 elif x.ndim < 3: apply_dim = 0 # x is assumed to be 3d: (batch, time, dim) # Assumed to be 2d : (time, dim) # Assumed to be 1d : (time) # time axis is therefore at 1 for 3d and 0 for 2d ( return np.apply_along_axis(lambda x: _double_threshold( x, high_thres, low_thres, n_connect=n_connect), axis=apply_dim, arr=x) def _double_threshold(x, high_thres, low_thres, n_connect=1, return_arr=True): # in nature, double_threshold considers boundary question """_double_threshold Computes a double threshold over the input array :param x: input array, needs to be 1d :param high_thres: High threshold over the array :param low_thres: Low threshold over the array :param n_connect: Postprocessing, maximal distance between clusters to connect :param return_arr: By default this function returns the filtered indiced, but if return_arr = True it returns an array of tsame size as x filled with ones and zeros. """ assert x.ndim == 1, "Input needs to be 1d" high_locations = np.where(x > high_thres)[0] # return the index, where value is greater than high_thres locations = x > low_thres # return true of false encoded_pairs = find_contiguous_regions(locations) # print('encoded_pairs ',encoded_pairs) filtered_list = list( filter( lambda pair: ((pair[0] <= high_locations) & (high_locations <= pair[1])).any(), encoded_pairs)) # find encoded_pair where inclide a high_lacations #print('filtered_list ',filtered_list) filtered_list = connect_(filtered_list, n_connect) # if the distance of two pair is less than n_connect, we can merge them if return_arr: zero_one_arr = np.zeros_like(x, dtype=int) for sl in filtered_list: zero_one_arr[sl[0]:sl[1]] = 1 return zero_one_arr return filtered_list def connect_clusters(x, n=1): if x.ndim == 1: return connect_clusters_(x, n) if x.ndim >= 2: return np.apply_along_axis(lambda a: connect_clusters_(a, n=n), -2, x) def connect_clusters_(x, n=1): """connect_clusters_ Connects clustered predictions (0,1) in x with range n :param x: Input array. zero-one format :param n: Number of frames to skip until connection can be made """ assert x.ndim == 1, "input needs to be 1d" reg = find_contiguous_regions(x) start_end = connect_(reg, n=n) zero_one_arr = np.zeros_like(x, dtype=int) for sl in start_end: zero_one_arr[sl[0]:sl[1]] = 1 return zero_one_arr def connect_(pairs, n=1): """connect_ Connects two adjacent clusters if their distance is <= n :param pairs: Clusters of iterateables e.g., [(1,5),(7,10)] :param n: distance between two clusters """ if len(pairs) == 0: return [] start_, end_ = pairs[0] new_pairs = [] for i, (next_item, cur_item) in enumerate(zip(pairs[1:], pairs[0:])): end_ = next_item[1] if next_item[0] - cur_item[1] <= n: pass else: new_pairs.append((start_, cur_item[1])) start_ = next_item[0] new_pairs.append((start_, end_)) return new_pairs def predictions_to_time(df, ratio): df.onset = df.onset * ratio df.offset = df.offset * ratio return df def upgrade_resolution(arr, scale): print('arr ',arr.shape) x = np.arange(0, arr.shape[0]) f = interp1d(x, arr, kind='linear', axis=0, fill_value='extrapolate') scale_x = np.arange(0, arr.shape[0], 1 / scale) up_scale = f(scale_x) return up_scale # a = [0.1,0.2,0.3,0.8,0.4,0.1,0.3,0.9,0.4] # a = np.array(a) # b = a>0.2 # _double_threshold(a,0.7,0.2) ================================================ FILE: audio_to_text/__init__.py ================================================ ================================================ FILE: audio_to_text/captioning/__init__.py ================================================ ================================================ FILE: audio_to_text/captioning/models/__init__.py ================================================ from .base_model import * from .transformer_model import * ================================================ FILE: audio_to_text/captioning/models/base_model.py ================================================ # -*- coding: utf-8 -*- from typing import Dict import torch import torch.nn as nn from .utils import mean_with_lens, repeat_tensor class CaptionModel(nn.Module): """ Encoder-decoder captioning model. """ pad_idx = 0 start_idx = 1 end_idx = 2 max_length = 20 def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs): super().__init__() self.encoder = encoder self.decoder = decoder self.vocab_size = decoder.vocab_size self.train_forward_keys = ["cap", "cap_len", "ss_ratio"] self.inference_forward_keys = ["sample_method", "max_length", "temp"] freeze_encoder = kwargs.get("freeze_encoder", False) if freeze_encoder: for param in self.encoder.parameters(): param.requires_grad = False self.check_decoder_compatibility() def check_decoder_compatibility(self): compatible_decoders = [x.__class__.__name__ for x in self.compatible_decoders] assert isinstance(self.decoder, self.compatible_decoders), \ f"{self.decoder.__class__.__name__} is incompatible with " \ f"{self.__class__.__name__}, please use decoder in {compatible_decoders} " @classmethod def set_index(cls, start_idx, end_idx): cls.start_idx = start_idx cls.end_idx = end_idx def forward(self, input_dict: Dict): """ input_dict: { (required) mode: train/inference, spec, spec_len, fc, attn, attn_len, [sample_method: greedy], [temp: 1.0] (in case of no teacher forcing) (optional, mode=train) cap, cap_len, ss_ratio, (optional, mode=inference) sample_method: greedy/beam, max_length, temp, beam_size (optional, sample_method=beam), n_best (optional, sample_method=beam), } """ # encoder_input_keys = ["spec", "spec_len", "fc", "attn", "attn_len"] # encoder_input = { key: input_dict[key] for key in encoder_input_keys } encoder_output_dict = self.encoder(input_dict) if input_dict["mode"] == "train": forward_dict = { "mode": "train", "sample_method": "greedy", "temp": 1.0 } for key in self.train_forward_keys: forward_dict[key] = input_dict[key] forward_dict.update(encoder_output_dict) output = self.train_forward(forward_dict) elif input_dict["mode"] == "inference": forward_dict = {"mode": "inference"} default_args = { "sample_method": "greedy", "max_length": self.max_length, "temp": 1.0 } for key in self.inference_forward_keys: if key in input_dict: forward_dict[key] = input_dict[key] else: forward_dict[key] = default_args[key] if forward_dict["sample_method"] == "beam": forward_dict["beam_size"] = input_dict.get("beam_size", 3) forward_dict["n_best"] = input_dict.get("n_best", False) forward_dict["n_best_size"] = input_dict.get("n_best_size", forward_dict["beam_size"]) elif forward_dict["sample_method"] == "dbs": forward_dict["beam_size"] = input_dict.get("beam_size", 6) forward_dict["group_size"] = input_dict.get("group_size", 3) forward_dict["diversity_lambda"] = input_dict.get("diversity_lambda", 0.5) forward_dict["group_nbest"] = input_dict.get("group_nbest", True) forward_dict.update(encoder_output_dict) output = self.inference_forward(forward_dict) else: raise Exception("mode should be either 'train' or 'inference'") return output def prepare_output(self, input_dict): output = {} batch_size = input_dict["fc_emb"].size(0) if input_dict["mode"] == "train": max_length = input_dict["cap"].size(1) - 1 elif input_dict["mode"] == "inference": max_length = input_dict["max_length"] else: raise Exception("mode should be either 'train' or 'inference'") device = input_dict["fc_emb"].device output["seq"] = torch.full((batch_size, max_length), self.end_idx, dtype=torch.long) output["logit"] = torch.empty(batch_size, max_length, self.vocab_size).to(device) output["sampled_logprob"] = torch.zeros(batch_size, max_length) output["embed"] = torch.empty(batch_size, max_length, self.decoder.d_model).to(device) return output def train_forward(self, input_dict): if input_dict["ss_ratio"] != 1: # scheduled sampling training input_dict["mode"] = "train" return self.stepwise_forward(input_dict) output = self.seq_forward(input_dict) self.train_process(output, input_dict) return output def seq_forward(self, input_dict): raise NotImplementedError def train_process(self, output, input_dict): pass def inference_forward(self, input_dict): if input_dict["sample_method"] == "beam": return self.beam_search(input_dict) elif input_dict["sample_method"] == "dbs": return self.diverse_beam_search(input_dict) return self.stepwise_forward(input_dict) def stepwise_forward(self, input_dict): """Step-by-step decoding""" output = self.prepare_output(input_dict) max_length = output["seq"].size(1) # start sampling for t in range(max_length): input_dict["t"] = t self.decode_step(input_dict, output) if input_dict["mode"] == "inference": # decide whether to stop when sampling unfinished_t = output["seq"][:, t] != self.end_idx if t == 0: unfinished = unfinished_t else: unfinished *= unfinished_t output["seq"][:, t][~unfinished] = self.end_idx if unfinished.sum() == 0: break self.stepwise_process(output) return output def decode_step(self, input_dict, output): """Decoding operation of timestep t""" decoder_input = self.prepare_decoder_input(input_dict, output) # feed to the decoder to get logit output_t = self.decoder(decoder_input) logit_t = output_t["logit"] # assert logit_t.ndim == 3 if logit_t.size(1) == 1: logit_t = logit_t.squeeze(1) embed_t = output_t["embed"].squeeze(1) elif logit_t.size(1) > 1: logit_t = logit_t[:, -1, :] embed_t = output_t["embed"][:, -1, :] else: raise Exception("no logit output") # sample the next input word and get the corresponding logit sampled = self.sample_next_word(logit_t, method=input_dict["sample_method"], temp=input_dict["temp"]) output_t.update(sampled) output_t["t"] = input_dict["t"] output_t["logit"] = logit_t output_t["embed"] = embed_t self.stepwise_process_step(output, output_t) def prepare_decoder_input(self, input_dict, output): """Prepare the inp ut dict for the decoder""" raise NotImplementedError def stepwise_process_step(self, output, output_t): """Postprocessing (save output values) after each timestep t""" t = output_t["t"] output["logit"][:, t, :] = output_t["logit"] output["seq"][:, t] = output_t["word"] output["sampled_logprob"][:, t] = output_t["probs"] output["embed"][:, t, :] = output_t["embed"] def stepwise_process(self, output): """Postprocessing after the whole step-by-step autoregressive decoding""" pass def sample_next_word(self, logit, method, temp): """Sample the next word, given probs output by the decoder""" logprob = torch.log_softmax(logit, dim=1) if method == "greedy": sampled_logprob, word = torch.max(logprob.detach(), 1) elif method == "gumbel": def sample_gumbel(shape, eps=1e-20): U = torch.rand(shape).to(logprob.device) return -torch.log(-torch.log(U + eps) + eps) def gumbel_softmax_sample(logit, temperature): y = logit + sample_gumbel(logit.size()) return torch.log_softmax(y / temperature, dim=-1) _logprob = gumbel_softmax_sample(logprob, temp) _, word = torch.max(_logprob.data, 1) sampled_logprob = logprob.gather(1, word.unsqueeze(-1)) else: logprob = logprob / temp if method.startswith("top"): top_num = float(method[3:]) if 0 < top_num < 1: # top-p sampling probs = torch.softmax(logit, dim=1) sorted_probs, sorted_indices = torch.sort(probs, descending=True, dim=1) _cumsum = sorted_probs.cumsum(1) mask = _cumsum < top_num mask = torch.cat([torch.ones_like(mask[:,:1]), mask[:,:-1]], 1) sorted_probs = sorted_probs * mask.to(sorted_probs) sorted_probs = sorted_probs / sorted_probs.sum(1, keepdim=True) logprob.scatter_(1, sorted_indices, sorted_probs.log()) else: # top-k sampling k = int(top_num) tmp = torch.empty_like(logprob).fill_(float('-inf')) topk, indices = torch.topk(logprob, k, dim=1) tmp = tmp.scatter(1, indices, topk) logprob = tmp word = torch.distributions.Categorical(logits=logprob.detach()).sample() sampled_logprob = logprob.gather(1, word.unsqueeze(-1)).squeeze(1) word = word.detach().long() # sampled_logprob: [N,], word: [N,] return {"word": word, "probs": sampled_logprob} def beam_search(self, input_dict): output = self.prepare_output(input_dict) max_length = input_dict["max_length"] beam_size = input_dict["beam_size"] if input_dict["n_best"]: n_best_size = input_dict["n_best_size"] batch_size, max_length = output["seq"].size() output["seq"] = torch.full((batch_size, n_best_size, max_length), self.end_idx, dtype=torch.long) temp = input_dict["temp"] # instance by instance beam seach for i in range(output["seq"].size(0)): output_i = self.prepare_beamsearch_output(input_dict) input_dict["sample_idx"] = i for t in range(max_length): input_dict["t"] = t output_t = self.beamsearch_step(input_dict, output_i) ####################################### # merge with previous beam and select the current max prob beam ####################################### logit_t = output_t["logit"] if logit_t.size(1) == 1: logit_t = logit_t.squeeze(1) elif logit_t.size(1) > 1: logit_t = logit_t[:, -1, :] else: raise Exception("no logit output") logprob_t = torch.log_softmax(logit_t, dim=1) logprob_t = torch.log_softmax(logprob_t / temp, dim=1) logprob_t = output_i["topk_logprob"].unsqueeze(1) + logprob_t if t == 0: # for the first step, all k seq will have the same probs topk_logprob, topk_words = logprob_t[0].topk( beam_size, 0, True, True) else: # unroll and find top logprob, and their unrolled indices topk_logprob, topk_words = logprob_t.view(-1).topk( beam_size, 0, True, True) topk_words = topk_words.cpu() output_i["topk_logprob"] = topk_logprob # output_i["prev_words_beam"] = topk_words // self.vocab_size # [beam_size,] output_i["prev_words_beam"] = torch.div(topk_words, self.vocab_size, rounding_mode='trunc') output_i["next_word"] = topk_words % self.vocab_size # [beam_size,] if t == 0: output_i["seq"] = output_i["next_word"].unsqueeze(1) else: output_i["seq"] = torch.cat([ output_i["seq"][output_i["prev_words_beam"]], output_i["next_word"].unsqueeze(1)], dim=1) # add finished beams to results is_end = output_i["next_word"] == self.end_idx if t == max_length - 1: is_end.fill_(1) for beam_idx in range(beam_size): if is_end[beam_idx]: final_beam = { "seq": output_i["seq"][beam_idx].clone(), "score": output_i["topk_logprob"][beam_idx].item() } final_beam["score"] = final_beam["score"] / (t + 1) output_i["done_beams"].append(final_beam) output_i["topk_logprob"][is_end] -= 1000 self.beamsearch_process_step(output_i, output_t) self.beamsearch_process(output, output_i, input_dict) return output def prepare_beamsearch_output(self, input_dict): beam_size = input_dict["beam_size"] device = input_dict["fc_emb"].device output = { "topk_logprob": torch.zeros(beam_size).to(device), "seq": None, "prev_words_beam": None, "next_word": None, "done_beams": [], } return output def beamsearch_step(self, input_dict, output_i): decoder_input = self.prepare_beamsearch_decoder_input(input_dict, output_i) output_t = self.decoder(decoder_input) output_t["t"] = input_dict["t"] return output_t def prepare_beamsearch_decoder_input(self, input_dict, output_i): raise NotImplementedError def beamsearch_process_step(self, output_i, output_t): pass def beamsearch_process(self, output, output_i, input_dict): i = input_dict["sample_idx"] done_beams = sorted(output_i["done_beams"], key=lambda x: -x["score"]) if input_dict["n_best"]: done_beams = done_beams[:input_dict["n_best_size"]] for out_idx, done_beam in enumerate(done_beams): seq = done_beam["seq"] output["seq"][i][out_idx, :len(seq)] = seq else: seq = done_beams[0]["seq"] output["seq"][i][:len(seq)] = seq def diverse_beam_search(self, input_dict): def add_diversity(seq_table, logprob, t, divm, diversity_lambda, bdash): local_time = t - divm unaug_logprob = logprob.clone() if divm > 0: change = torch.zeros(logprob.size(-1)) for prev_choice in range(divm): prev_decisions = seq_table[prev_choice][..., local_time] for prev_labels in range(bdash): change.scatter_add_(0, prev_decisions[prev_labels], change.new_ones(1)) change = change.to(logprob.device) logprob = logprob - repeat_tensor(change, bdash) * diversity_lambda return logprob, unaug_logprob output = self.prepare_output(input_dict) group_size = input_dict["group_size"] batch_size = output["seq"].size(0) beam_size = input_dict["beam_size"] bdash = beam_size // group_size input_dict["bdash"] = bdash diversity_lambda = input_dict["diversity_lambda"] device = input_dict["fc_emb"].device max_length = input_dict["max_length"] temp = input_dict["temp"] group_nbest = input_dict["group_nbest"] batch_size, max_length = output["seq"].size() if group_nbest: output["seq"] = torch.full((batch_size, beam_size, max_length), self.end_idx, dtype=torch.long) else: output["seq"] = torch.full((batch_size, group_size, max_length), self.end_idx, dtype=torch.long) for i in range(batch_size): input_dict["sample_idx"] = i seq_table = [torch.LongTensor(bdash, 0) for _ in range(group_size)] # group_size x [bdash, 0] logprob_table = [torch.zeros(bdash).to(device) for _ in range(group_size)] done_beams_table = [[] for _ in range(group_size)] output_i = { "prev_words_beam": [None for _ in range(group_size)], "next_word": [None for _ in range(group_size)], "state": [None for _ in range(group_size)] } for t in range(max_length + group_size - 1): input_dict["t"] = t for divm in range(group_size): input_dict["divm"] = divm if t >= divm and t <= max_length + divm - 1: local_time = t - divm decoder_input = self.prepare_dbs_decoder_input(input_dict, output_i) output_t = self.decoder(decoder_input) output_t["divm"] = divm logit_t = output_t["logit"] if logit_t.size(1) == 1: logit_t = logit_t.squeeze(1) elif logit_t.size(1) > 1: logit_t = logit_t[:, -1, :] else: raise Exception("no logit output") logprob_t = torch.log_softmax(logit_t, dim=1) logprob_t = torch.log_softmax(logprob_t / temp, dim=1) logprob_t, unaug_logprob_t = add_diversity(seq_table, logprob_t, t, divm, diversity_lambda, bdash) logprob_t = logprob_table[divm].unsqueeze(-1) + logprob_t if local_time == 0: # for the first step, all k seq will have the same probs topk_logprob, topk_words = logprob_t[0].topk( bdash, 0, True, True) else: # unroll and find top logprob, and their unrolled indices topk_logprob, topk_words = logprob_t.view(-1).topk( bdash, 0, True, True) topk_words = topk_words.cpu() logprob_table[divm] = topk_logprob output_i["prev_words_beam"][divm] = topk_words // self.vocab_size # [bdash,] output_i["next_word"][divm] = topk_words % self.vocab_size # [bdash,] if local_time > 0: seq_table[divm] = seq_table[divm][output_i["prev_words_beam"][divm]] seq_table[divm] = torch.cat([ seq_table[divm], output_i["next_word"][divm].unsqueeze(-1)], -1) is_end = seq_table[divm][:, t-divm] == self.end_idx assert seq_table[divm].shape[-1] == t - divm + 1 if t == max_length + divm - 1: is_end.fill_(1) for beam_idx in range(bdash): if is_end[beam_idx]: final_beam = { "seq": seq_table[divm][beam_idx].clone(), "score": logprob_table[divm][beam_idx].item() } final_beam["score"] = final_beam["score"] / (t - divm + 1) done_beams_table[divm].append(final_beam) logprob_table[divm][is_end] -= 1000 self.dbs_process_step(output_i, output_t) done_beams_table = [sorted(done_beams_table[divm], key=lambda x: -x["score"])[:bdash] for divm in range(group_size)] if group_nbest: done_beams = sum(done_beams_table, []) else: done_beams = [group_beam[0] for group_beam in done_beams_table] for _, done_beam in enumerate(done_beams): output["seq"][i, _, :len(done_beam["seq"])] = done_beam["seq"] return output def prepare_dbs_decoder_input(self, input_dict, output_i): raise NotImplementedError def dbs_process_step(self, output_i, output_t): pass class CaptionSequenceModel(nn.Module): def __init__(self, model, seq_output_size): super().__init__() self.model = model if model.decoder.d_model != seq_output_size: self.output_transform = nn.Linear(model.decoder.d_model, seq_output_size) else: self.output_transform = lambda x: x def forward(self, input_dict): output = self.model(input_dict) if input_dict["mode"] == "train": lens = input_dict["cap_len"] - 1 # seq_outputs: [N, d_model] elif input_dict["mode"] == "inference": if "sample_method" in input_dict and input_dict["sample_method"] == "beam": return output seq = output["seq"] lens = torch.where(seq == self.model.end_idx, torch.zeros_like(seq), torch.ones_like(seq)).sum(dim=1) else: raise Exception("mode should be either 'train' or 'inference'") seq_output = mean_with_lens(output["embed"], lens) seq_output = self.output_transform(seq_output) output["seq_output"] = seq_output return output ================================================ FILE: audio_to_text/captioning/models/decoder.py ================================================ # -*- coding: utf-8 -*- import math from functools import partial import numpy as np import torch import torch.nn as nn from .utils import generate_length_mask, init, PositionalEncoding class BaseDecoder(nn.Module): """ Take word/audio embeddings and output the next word probs Base decoder, cannot be called directly All decoders should inherit from this class """ def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout=0.2): super().__init__() self.emb_dim = emb_dim self.vocab_size = vocab_size self.fc_emb_dim = fc_emb_dim self.attn_emb_dim = attn_emb_dim self.word_embedding = nn.Embedding(vocab_size, emb_dim) self.in_dropout = nn.Dropout(dropout) def forward(self, x): raise NotImplementedError def load_word_embedding(self, weight, freeze=True): embedding = np.load(weight) assert embedding.shape[0] == self.vocab_size, "vocabulary size mismatch" assert embedding.shape[1] == self.emb_dim, "embed size mismatch" # embeddings = torch.as_tensor(embeddings).float() # self.word_embeddings.weight = nn.Parameter(embeddings) # for para in self.word_embeddings.parameters(): # para.requires_grad = tune self.word_embedding = nn.Embedding.from_pretrained(embedding, freeze=freeze) class RnnDecoder(BaseDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs): super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout,) self.d_model = d_model self.num_layers = kwargs.get('num_layers', 1) self.bidirectional = kwargs.get('bidirectional', False) self.rnn_type = kwargs.get('rnn_type', "GRU") self.classifier = nn.Linear( self.d_model * (self.bidirectional + 1), vocab_size) def forward(self, x): raise NotImplementedError def init_hidden(self, bs, device): num_dire = self.bidirectional + 1 n_layer = self.num_layers hid_dim = self.d_model if self.rnn_type == "LSTM": return (torch.zeros(num_dire * n_layer, bs, hid_dim).to(device), torch.zeros(num_dire * n_layer, bs, hid_dim).to(device)) else: return torch.zeros(num_dire * n_layer, bs, hid_dim).to(device) class RnnFcDecoder(RnnDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs): super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs) self.model = getattr(nn, self.rnn_type)( input_size=self.emb_dim * 2, hidden_size=self.d_model, batch_first=True, num_layers=self.num_layers, bidirectional=self.bidirectional) self.fc_proj = nn.Linear(self.fc_emb_dim, self.emb_dim) self.apply(init) def forward(self, input_dict): word = input_dict["word"] state = input_dict.get("state", None) fc_emb = input_dict["fc_emb"] word = word.to(fc_emb.device) embed = self.in_dropout(self.word_embedding(word)) p_fc_emb = self.fc_proj(fc_emb) # embed: [N, T, embed_size] embed = torch.cat((embed, p_fc_emb), dim=-1) out, state = self.model(embed, state) # out: [N, T, hs], states: [num_layers * num_dire, N, hs] logits = self.classifier(out) output = { "state": state, "embeds": out, "logits": logits } return output class Seq2SeqAttention(nn.Module): def __init__(self, hs_enc, hs_dec, attn_size): """ Args: hs_enc: encoder hidden size hs_dec: decoder hidden size attn_size: attention vector size """ super(Seq2SeqAttention, self).__init__() self.h2attn = nn.Linear(hs_enc + hs_dec, attn_size) self.v = nn.Parameter(torch.randn(attn_size)) self.apply(init) def forward(self, h_dec, h_enc, src_lens): """ Args: h_dec: decoder hidden (query), [N, hs_dec] h_enc: encoder memory (key/value), [N, src_max_len, hs_enc] src_lens: source (encoder memory) lengths, [N, ] """ N = h_enc.size(0) src_max_len = h_enc.size(1) h_dec = h_dec.unsqueeze(1).repeat(1, src_max_len, 1) # [N, src_max_len, hs_dec] attn_input = torch.cat((h_dec, h_enc), dim=-1) attn_out = torch.tanh(self.h2attn(attn_input)) # [N, src_max_len, attn_size] v = self.v.repeat(N, 1).unsqueeze(1) # [N, 1, attn_size] score = torch.bmm(v, attn_out.transpose(1, 2)).squeeze(1) # [N, src_max_len] idxs = torch.arange(src_max_len).repeat(N).view(N, src_max_len) mask = (idxs < src_lens.view(-1, 1)).to(h_dec.device) score = score.masked_fill(mask == 0, -1e10) weights = torch.softmax(score, dim=-1) # [N, src_max_len] ctx = torch.bmm(weights.unsqueeze(1), h_enc).squeeze(1) # [N, hs_enc] return ctx, weights class AttentionProj(nn.Module): def __init__(self, hs_enc, hs_dec, embed_dim, attn_size): self.q_proj = nn.Linear(hs_dec, embed_dim) self.kv_proj = nn.Linear(hs_enc, embed_dim) self.h2attn = nn.Linear(embed_dim * 2, attn_size) self.v = nn.Parameter(torch.randn(attn_size)) self.apply(init) def init(self, m): if isinstance(m, nn.Linear): nn.init.kaiming_uniform_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) def forward(self, h_dec, h_enc, src_lens): """ Args: h_dec: decoder hidden (query), [N, hs_dec] h_enc: encoder memory (key/value), [N, src_max_len, hs_enc] src_lens: source (encoder memory) lengths, [N, ] """ h_enc = self.kv_proj(h_enc) # [N, src_max_len, embed_dim] h_dec = self.q_proj(h_dec) # [N, embed_dim] N = h_enc.size(0) src_max_len = h_enc.size(1) h_dec = h_dec.unsqueeze(1).repeat(1, src_max_len, 1) # [N, src_max_len, hs_dec] attn_input = torch.cat((h_dec, h_enc), dim=-1) attn_out = torch.tanh(self.h2attn(attn_input)) # [N, src_max_len, attn_size] v = self.v.repeat(N, 1).unsqueeze(1) # [N, 1, attn_size] score = torch.bmm(v, attn_out.transpose(1, 2)).squeeze(1) # [N, src_max_len] idxs = torch.arange(src_max_len).repeat(N).view(N, src_max_len) mask = (idxs < src_lens.view(-1, 1)).to(h_dec.device) score = score.masked_fill(mask == 0, -1e10) weights = torch.softmax(score, dim=-1) # [N, src_max_len] ctx = torch.bmm(weights.unsqueeze(1), h_enc).squeeze(1) # [N, hs_enc] return ctx, weights class BahAttnDecoder(RnnDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs): """ concatenate fc, attn, word to feed to the rnn """ super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs) attn_size = kwargs.get("attn_size", self.d_model) self.model = getattr(nn, self.rnn_type)( input_size=self.emb_dim * 3, hidden_size=self.d_model, batch_first=True, num_layers=self.num_layers, bidirectional=self.bidirectional) self.attn = Seq2SeqAttention(self.attn_emb_dim, self.d_model * (self.bidirectional + 1) * \ self.num_layers, attn_size) self.fc_proj = nn.Linear(self.fc_emb_dim, self.emb_dim) self.ctx_proj = nn.Linear(self.attn_emb_dim, self.emb_dim) self.apply(init) def forward(self, input_dict): word = input_dict["word"] state = input_dict.get("state", None) # [n_layer * n_dire, bs, d_model] fc_emb = input_dict["fc_emb"] attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] word = word.to(fc_emb.device) embed = self.in_dropout(self.word_embedding(word)) # embed: [N, 1, embed_size] if state is None: state = self.init_hidden(word.size(0), fc_emb.device) if self.rnn_type == "LSTM": query = state[0].transpose(0, 1).flatten(1) else: query = state.transpose(0, 1).flatten(1) c, attn_weight = self.attn(query, attn_emb, attn_emb_len) p_fc_emb = self.fc_proj(fc_emb) p_ctx = self.ctx_proj(c) rnn_input = torch.cat((embed, p_ctx.unsqueeze(1), p_fc_emb.unsqueeze(1)), dim=-1) out, state = self.model(rnn_input, state) output = { "state": state, "embed": out, "logit": self.classifier(out), "attn_weight": attn_weight } return output class BahAttnDecoder2(RnnDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs): """ add fc, attn, word together to feed to the rnn """ super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs) attn_size = kwargs.get("attn_size", self.d_model) self.model = getattr(nn, self.rnn_type)( input_size=self.emb_dim, hidden_size=self.d_model, batch_first=True, num_layers=self.num_layers, bidirectional=self.bidirectional) self.attn = Seq2SeqAttention(self.emb_dim, self.d_model * (self.bidirectional + 1) * \ self.num_layers, attn_size) self.fc_proj = nn.Linear(self.fc_emb_dim, self.emb_dim) self.attn_proj = nn.Linear(self.attn_emb_dim, self.emb_dim) self.apply(partial(init, method="xavier")) def forward(self, input_dict): word = input_dict["word"] state = input_dict.get("state", None) # [n_layer * n_dire, bs, d_model] fc_emb = input_dict["fc_emb"] attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] word = word.to(fc_emb.device) embed = self.in_dropout(self.word_embedding(word)) p_attn_emb = self.attn_proj(attn_emb) # embed: [N, 1, embed_size] if state is None: state = self.init_hidden(word.size(0), fc_emb.device) if self.rnn_type == "LSTM": query = state[0].transpose(0, 1).flatten(1) else: query = state.transpose(0, 1).flatten(1) c, attn_weight = self.attn(query, p_attn_emb, attn_emb_len) p_fc_emb = self.fc_proj(fc_emb) rnn_input = embed + c.unsqueeze(1) + p_fc_emb.unsqueeze(1) out, state = self.model(rnn_input, state) output = { "state": state, "embed": out, "logit": self.classifier(out), "attn_weight": attn_weight } return output class ConditionalBahAttnDecoder(RnnDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs): """ concatenate fc, attn, word to feed to the rnn """ super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs) attn_size = kwargs.get("attn_size", self.d_model) self.model = getattr(nn, self.rnn_type)( input_size=self.emb_dim * 3, hidden_size=self.d_model, batch_first=True, num_layers=self.num_layers, bidirectional=self.bidirectional) self.attn = Seq2SeqAttention(self.attn_emb_dim, self.d_model * (self.bidirectional + 1) * \ self.num_layers, attn_size) self.ctx_proj = nn.Linear(self.attn_emb_dim, self.emb_dim) self.condition_embedding = nn.Embedding(2, emb_dim) self.apply(init) def forward(self, input_dict): word = input_dict["word"] state = input_dict.get("state", None) # [n_layer * n_dire, bs, d_model] fc_emb = input_dict["fc_emb"] attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] condition = input_dict["condition"] word = word.to(fc_emb.device) embed = self.in_dropout(self.word_embedding(word)) condition = torch.as_tensor([[1 - c, c] for c in condition]).to(fc_emb.device) condition_emb = torch.matmul(condition, self.condition_embedding.weight) # condition_embs: [N, emb_dim] # embed: [N, 1, embed_size] if state is None: state = self.init_hidden(word.size(0), fc_emb.device) if self.rnn_type == "LSTM": query = state[0].transpose(0, 1).flatten(1) else: query = state.transpose(0, 1).flatten(1) c, attn_weight = self.attn(query, attn_emb, attn_emb_len) p_ctx = self.ctx_proj(c) rnn_input = torch.cat((embed, p_ctx.unsqueeze(1), condition_emb.unsqueeze(1)), dim=-1) out, state = self.model(rnn_input, state) output = { "state": state, "embed": out, "logit": self.classifier(out), "attn_weight": attn_weight } return output class StructBahAttnDecoder(RnnDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, struct_vocab_size, attn_emb_dim, dropout, d_model, **kwargs): """ concatenate fc, attn, word to feed to the rnn """ super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs) attn_size = kwargs.get("attn_size", self.d_model) self.model = getattr(nn, self.rnn_type)( input_size=self.emb_dim * 3, hidden_size=self.d_model, batch_first=True, num_layers=self.num_layers, bidirectional=self.bidirectional) self.attn = Seq2SeqAttention(self.attn_emb_dim, self.d_model * (self.bidirectional + 1) * \ self.num_layers, attn_size) self.ctx_proj = nn.Linear(self.attn_emb_dim, self.emb_dim) self.struct_embedding = nn.Embedding(struct_vocab_size, emb_dim) self.apply(init) def forward(self, input_dict): word = input_dict["word"] state = input_dict.get("state", None) # [n_layer * n_dire, bs, d_model] fc_emb = input_dict["fc_emb"] attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] structure = input_dict["structure"] word = word.to(fc_emb.device) embed = self.in_dropout(self.word_embedding(word)) struct_emb = self.struct_embedding(structure) # struct_embs: [N, emb_dim] # embed: [N, 1, embed_size] if state is None: state = self.init_hidden(word.size(0), fc_emb.device) if self.rnn_type == "LSTM": query = state[0].transpose(0, 1).flatten(1) else: query = state.transpose(0, 1).flatten(1) c, attn_weight = self.attn(query, attn_emb, attn_emb_len) p_ctx = self.ctx_proj(c) rnn_input = torch.cat((embed, p_ctx.unsqueeze(1), struct_emb.unsqueeze(1)), dim=-1) out, state = self.model(rnn_input, state) output = { "state": state, "embed": out, "logit": self.classifier(out), "attn_weight": attn_weight } return output class StyleBahAttnDecoder(RnnDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs): """ concatenate fc, attn, word to feed to the rnn """ super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs) attn_size = kwargs.get("attn_size", self.d_model) self.model = getattr(nn, self.rnn_type)( input_size=self.emb_dim * 3, hidden_size=self.d_model, batch_first=True, num_layers=self.num_layers, bidirectional=self.bidirectional) self.attn = Seq2SeqAttention(self.attn_emb_dim, self.d_model * (self.bidirectional + 1) * \ self.num_layers, attn_size) self.ctx_proj = nn.Linear(self.attn_emb_dim, self.emb_dim) self.apply(init) def forward(self, input_dict): word = input_dict["word"] state = input_dict.get("state", None) # [n_layer * n_dire, bs, d_model] fc_emb = input_dict["fc_emb"] attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] style = input_dict["style"] word = word.to(fc_emb.device) embed = self.in_dropout(self.word_embedding(word)) # embed: [N, 1, embed_size] if state is None: state = self.init_hidden(word.size(0), fc_emb.device) if self.rnn_type == "LSTM": query = state[0].transpose(0, 1).flatten(1) else: query = state.transpose(0, 1).flatten(1) c, attn_weight = self.attn(query, attn_emb, attn_emb_len) p_ctx = self.ctx_proj(c) rnn_input = torch.cat((embed, p_ctx.unsqueeze(1), style.unsqueeze(1)), dim=-1) out, state = self.model(rnn_input, state) output = { "state": state, "embed": out, "logit": self.classifier(out), "attn_weight": attn_weight } return output class BahAttnDecoder3(RnnDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs): """ concatenate fc, attn, word to feed to the rnn """ super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs) attn_size = kwargs.get("attn_size", self.d_model) self.model = getattr(nn, self.rnn_type)( input_size=self.emb_dim + attn_emb_dim, hidden_size=self.d_model, batch_first=True, num_layers=self.num_layers, bidirectional=self.bidirectional) self.attn = Seq2SeqAttention(self.attn_emb_dim, self.d_model * (self.bidirectional + 1) * \ self.num_layers, attn_size) self.ctx_proj = lambda x: x self.apply(init) def forward(self, input_dict): word = input_dict["word"] state = input_dict.get("state", None) # [n_layer * n_dire, bs, d_model] fc_emb = input_dict["fc_emb"] attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] if word.size(-1) == self.fc_emb_dim: # fc_emb embed = word.unsqueeze(1) elif word.size(-1) == 1: # word word = word.to(fc_emb.device) embed = self.in_dropout(self.word_embedding(word)) else: raise Exception(f"problem with word input size {word.size()}") # embed: [N, 1, embed_size] if state is None: state = self.init_hidden(word.size(0), fc_emb.device) if self.rnn_type == "LSTM": query = state[0].transpose(0, 1).flatten(1) else: query = state.transpose(0, 1).flatten(1) c, attn_weight = self.attn(query, attn_emb, attn_emb_len) p_ctx = self.ctx_proj(c) rnn_input = torch.cat((embed, p_ctx.unsqueeze(1)), dim=-1) out, state = self.model(rnn_input, state) output = { "state": state, "embed": out, "logit": self.classifier(out), "attn_weight": attn_weight } return output class SpecificityBahAttnDecoder(RnnDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs): """ concatenate fc, attn, word to feed to the rnn """ super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, d_model, **kwargs) attn_size = kwargs.get("attn_size", self.d_model) self.model = getattr(nn, self.rnn_type)( input_size=self.emb_dim + attn_emb_dim + 1, hidden_size=self.d_model, batch_first=True, num_layers=self.num_layers, bidirectional=self.bidirectional) self.attn = Seq2SeqAttention(self.attn_emb_dim, self.d_model * (self.bidirectional + 1) * \ self.num_layers, attn_size) self.ctx_proj = lambda x: x self.apply(init) def forward(self, input_dict): word = input_dict["word"] state = input_dict.get("state", None) # [n_layer * n_dire, bs, d_model] fc_emb = input_dict["fc_emb"] attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] condition = input_dict["condition"] # [N,] word = word.to(fc_emb.device) embed = self.in_dropout(self.word_embedding(word)) # embed: [N, 1, embed_size] if state is None: state = self.init_hidden(word.size(0), fc_emb.device) if self.rnn_type == "LSTM": query = state[0].transpose(0, 1).flatten(1) else: query = state.transpose(0, 1).flatten(1) c, attn_weight = self.attn(query, attn_emb, attn_emb_len) p_ctx = self.ctx_proj(c) rnn_input = torch.cat( (embed, p_ctx.unsqueeze(1), condition.reshape(-1, 1, 1)), dim=-1) out, state = self.model(rnn_input, state) output = { "state": state, "embed": out, "logit": self.classifier(out), "attn_weight": attn_weight } return output class TransformerDecoder(BaseDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, **kwargs): super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout=dropout,) self.d_model = emb_dim self.nhead = kwargs.get("nhead", self.d_model // 64) self.nlayers = kwargs.get("nlayers", 2) self.dim_feedforward = kwargs.get("dim_feedforward", self.d_model * 4) self.pos_encoder = PositionalEncoding(self.d_model, dropout) layer = nn.TransformerDecoderLayer(d_model=self.d_model, nhead=self.nhead, dim_feedforward=self.dim_feedforward, dropout=dropout) self.model = nn.TransformerDecoder(layer, self.nlayers) self.classifier = nn.Linear(self.d_model, vocab_size) self.attn_proj = nn.Sequential( nn.Linear(self.attn_emb_dim, self.d_model), nn.ReLU(), nn.Dropout(dropout), nn.LayerNorm(self.d_model) ) # self.attn_proj = lambda x: x self.init_params() def init_params(self): for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def generate_square_subsequent_mask(self, max_length): mask = (torch.triu(torch.ones(max_length, max_length)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) return mask def forward(self, input_dict): word = input_dict["word"] attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] cap_padding_mask = input_dict["cap_padding_mask"] p_attn_emb = self.attn_proj(attn_emb) p_attn_emb = p_attn_emb.transpose(0, 1) # [T_src, N, emb_dim] word = word.to(attn_emb.device) embed = self.in_dropout(self.word_embedding(word)) * math.sqrt(self.emb_dim) # [N, T, emb_dim] embed = embed.transpose(0, 1) # [T, N, emb_dim] embed = self.pos_encoder(embed) tgt_mask = self.generate_square_subsequent_mask(embed.size(0)).to(attn_emb.device) memory_key_padding_mask = ~generate_length_mask(attn_emb_len, attn_emb.size(1)).to(attn_emb.device) output = self.model(embed, p_attn_emb, tgt_mask=tgt_mask, tgt_key_padding_mask=cap_padding_mask, memory_key_padding_mask=memory_key_padding_mask) output = output.transpose(0, 1) output = { "embed": output, "logit": self.classifier(output), } return output class EventTransformerDecoder(TransformerDecoder): def forward(self, input_dict): word = input_dict["word"] # index of word embeddings attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] cap_padding_mask = input_dict["cap_padding_mask"] event_emb = input_dict["event"] # [N, emb_dim] p_attn_emb = self.attn_proj(attn_emb) p_attn_emb = p_attn_emb.transpose(0, 1) # [T_src, N, emb_dim] word = word.to(attn_emb.device) embed = self.in_dropout(self.word_embedding(word)) * math.sqrt(self.emb_dim) # [N, T, emb_dim] embed = embed.transpose(0, 1) # [T, N, emb_dim] embed += event_emb embed = self.pos_encoder(embed) tgt_mask = self.generate_square_subsequent_mask(embed.size(0)).to(attn_emb.device) memory_key_padding_mask = ~generate_length_mask(attn_emb_len, attn_emb.size(1)).to(attn_emb.device) output = self.model(embed, p_attn_emb, tgt_mask=tgt_mask, tgt_key_padding_mask=cap_padding_mask, memory_key_padding_mask=memory_key_padding_mask) output = output.transpose(0, 1) output = { "embed": output, "logit": self.classifier(output), } return output class KeywordProbTransformerDecoder(TransformerDecoder): def __init__(self, emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, keyword_classes_num, **kwargs): super().__init__(emb_dim, vocab_size, fc_emb_dim, attn_emb_dim, dropout, **kwargs) self.keyword_proj = nn.Linear(keyword_classes_num, self.d_model) self.word_keyword_norm = nn.LayerNorm(self.d_model) def forward(self, input_dict): word = input_dict["word"] # index of word embeddings attn_emb = input_dict["attn_emb"] attn_emb_len = input_dict["attn_emb_len"] cap_padding_mask = input_dict["cap_padding_mask"] keyword = input_dict["keyword"] # [N, keyword_classes_num] p_attn_emb = self.attn_proj(attn_emb) p_attn_emb = p_attn_emb.transpose(0, 1) # [T_src, N, emb_dim] word = word.to(attn_emb.device) embed = self.in_dropout(self.word_embedding(word)) * math.sqrt(self.emb_dim) # [N, T, emb_dim] embed = embed.transpose(0, 1) # [T, N, emb_dim] embed += self.keyword_proj(keyword) embed = self.word_keyword_norm(embed) embed = self.pos_encoder(embed) tgt_mask = self.generate_square_subsequent_mask(embed.size(0)).to(attn_emb.device) memory_key_padding_mask = ~generate_length_mask(attn_emb_len, attn_emb.size(1)).to(attn_emb.device) output = self.model(embed, p_attn_emb, tgt_mask=tgt_mask, tgt_key_padding_mask=cap_padding_mask, memory_key_padding_mask=memory_key_padding_mask) output = output.transpose(0, 1) output = { "embed": output, "logit": self.classifier(output), } return output ================================================ FILE: audio_to_text/captioning/models/encoder.py ================================================ # -*- coding: utf-8 -*- import math import copy import torch import torch.nn as nn import torch.nn.functional as F from torchaudio import transforms from torchlibrosa.augmentation import SpecAugmentation from .utils import mean_with_lens, max_with_lens, \ init, pack_wrapper, generate_length_mask, PositionalEncoding def init_layer(layer): """Initialize a Linear or Convolutional layer. """ nn.init.xavier_uniform_(layer.weight) if hasattr(layer, 'bias'): if layer.bias is not None: layer.bias.data.fill_(0.) def init_bn(bn): """Initialize a Batchnorm layer. """ bn.bias.data.fill_(0.) bn.weight.data.fill_(1.) class BaseEncoder(nn.Module): """ Encode the given audio into embedding Base encoder class, cannot be called directly All encoders should inherit from this class """ def __init__(self, spec_dim, fc_feat_dim, attn_feat_dim): super(BaseEncoder, self).__init__() self.spec_dim = spec_dim self.fc_feat_dim = fc_feat_dim self.attn_feat_dim = attn_feat_dim def forward(self, x): ######################### # an encoder first encodes audio feature into embedding, obtaining # `encoded`: { # fc_embs: [N, fc_emb_dim], # attn_embs: [N, attn_max_len, attn_emb_dim], # attn_emb_lens: [N,] # } ######################### raise NotImplementedError class Block2D(nn.Module): def __init__(self, cin, cout, kernel_size=3, padding=1): super().__init__() self.block = nn.Sequential( nn.BatchNorm2d(cin), nn.Conv2d(cin, cout, kernel_size=kernel_size, padding=padding, bias=False), nn.LeakyReLU(inplace=True, negative_slope=0.1)) def forward(self, x): return self.block(x) class LinearSoftPool(nn.Module): """LinearSoftPool Linear softmax, takes logits and returns a probability, near to the actual maximum value. Taken from the paper: A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling https://arxiv.org/abs/1810.09050 """ def __init__(self, pooldim=1): super().__init__() self.pooldim = pooldim def forward(self, logits, time_decision): return (time_decision**2).sum(self.pooldim) / time_decision.sum( self.pooldim) class MeanPool(nn.Module): def __init__(self, pooldim=1): super().__init__() self.pooldim = pooldim def forward(self, logits, decision): return torch.mean(decision, dim=self.pooldim) class AttentionPool(nn.Module): """docstring for AttentionPool""" def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs): super().__init__() self.inputdim = inputdim self.outputdim = outputdim self.pooldim = pooldim self.transform = nn.Linear(inputdim, outputdim) self.activ = nn.Softmax(dim=self.pooldim) self.eps = 1e-7 def forward(self, logits, decision): # Input is (B, T, D) # B, T, D w = self.activ(torch.clamp(self.transform(logits), -15, 15)) detect = (decision * w).sum( self.pooldim) / (w.sum(self.pooldim) + self.eps) # B, T, D return detect class MMPool(nn.Module): def __init__(self, dims): super().__init__() self.avgpool = nn.AvgPool2d(dims) self.maxpool = nn.MaxPool2d(dims) def forward(self, x): return self.avgpool(x) + self.maxpool(x) def parse_poolingfunction(poolingfunction_name='mean', **kwargs): """parse_poolingfunction A heler function to parse any temporal pooling Pooling is done on dimension 1 :param poolingfunction_name: :param **kwargs: """ poolingfunction_name = poolingfunction_name.lower() if poolingfunction_name == 'mean': return MeanPool(pooldim=1) elif poolingfunction_name == 'linear': return LinearSoftPool(pooldim=1) elif poolingfunction_name == 'attention': return AttentionPool(inputdim=kwargs['inputdim'], outputdim=kwargs['outputdim']) def embedding_pooling(x, lens, pooling="mean"): if pooling == "max": fc_embs = max_with_lens(x, lens) elif pooling == "mean": fc_embs = mean_with_lens(x, lens) elif pooling == "mean+max": x_mean = mean_with_lens(x, lens) x_max = max_with_lens(x, lens) fc_embs = x_mean + x_max elif pooling == "last": indices = (lens - 1).reshape(-1, 1, 1).repeat(1, 1, x.size(-1)) # indices: [N, 1, hidden] fc_embs = torch.gather(x, 1, indices).squeeze(1) else: raise Exception(f"pooling method {pooling} not support") return fc_embs class Cdur5Encoder(BaseEncoder): def __init__(self, spec_dim, fc_feat_dim, attn_feat_dim, pooling="mean"): super().__init__(spec_dim, fc_feat_dim, attn_feat_dim) self.pooling = pooling self.features = nn.Sequential( Block2D(1, 32), nn.LPPool2d(4, (2, 4)), Block2D(32, 128), Block2D(128, 128), nn.LPPool2d(4, (2, 4)), Block2D(128, 128), Block2D(128, 128), nn.LPPool2d(4, (1, 4)), nn.Dropout(0.3), ) with torch.no_grad(): rnn_input_dim = self.features( torch.randn(1, 1, 500, spec_dim)).shape rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1] self.gru = nn.GRU(rnn_input_dim, 128, bidirectional=True, batch_first=True) self.apply(init) def forward(self, input_dict): x = input_dict["spec"] lens = input_dict["spec_len"] if "upsample" not in input_dict: input_dict["upsample"] = False lens = torch.as_tensor(copy.deepcopy(lens)) N, T, _ = x.shape x = x.unsqueeze(1) x = self.features(x) x = x.transpose(1, 2).contiguous().flatten(-2) x, _ = self.gru(x) if input_dict["upsample"]: x = nn.functional.interpolate( x.transpose(1, 2), T, mode='linear', align_corners=False).transpose(1, 2) else: lens //= 4 attn_emb = x fc_emb = embedding_pooling(x, lens, self.pooling) return { "attn_emb": attn_emb, "fc_emb": fc_emb, "attn_emb_len": lens } def conv_conv_block(in_channel, out_channel): return nn.Sequential( nn.Conv2d(in_channel, out_channel, kernel_size=3, bias=False, padding=1), nn.BatchNorm2d(out_channel), nn.ReLU(True), nn.Conv2d(out_channel, out_channel, kernel_size=3, bias=False, padding=1), nn.BatchNorm2d(out_channel), nn.ReLU(True) ) class Cdur8Encoder(BaseEncoder): def __init__(self, spec_dim, fc_feat_dim, attn_feat_dim, pooling="mean"): super().__init__(spec_dim, fc_feat_dim, attn_feat_dim) self.pooling = pooling self.features = nn.Sequential( conv_conv_block(1, 64), MMPool((2, 2)), nn.Dropout(0.2, True), conv_conv_block(64, 128), MMPool((2, 2)), nn.Dropout(0.2, True), conv_conv_block(128, 256), MMPool((1, 2)), nn.Dropout(0.2, True), conv_conv_block(256, 512), MMPool((1, 2)), nn.Dropout(0.2, True), nn.AdaptiveAvgPool2d((None, 1)), ) self.init_bn = nn.BatchNorm2d(spec_dim) self.embedding = nn.Linear(512, 512) self.gru = nn.GRU(512, 256, bidirectional=True, batch_first=True) self.apply(init) def forward(self, input_dict): x = input_dict["spec"] lens = input_dict["spec_len"] lens = torch.as_tensor(copy.deepcopy(lens)) x = x.unsqueeze(1) # B x 1 x T x D x = x.transpose(1, 3) x = self.init_bn(x) x = x.transpose(1, 3) x = self.features(x) x = x.transpose(1, 2).contiguous().flatten(-2) x = F.dropout(x, p=0.5, training=self.training) x = F.relu_(self.embedding(x)) x, _ = self.gru(x) attn_emb = x lens //= 4 fc_emb = embedding_pooling(x, lens, self.pooling) return { "attn_emb": attn_emb, "fc_emb": fc_emb, "attn_emb_len": lens } class Cnn10Encoder(BaseEncoder): def __init__(self, spec_dim, fc_feat_dim, attn_feat_dim): super().__init__(spec_dim, fc_feat_dim, attn_feat_dim) self.features = nn.Sequential( conv_conv_block(1, 64), nn.AvgPool2d((2, 2)), nn.Dropout(0.2, True), conv_conv_block(64, 128), nn.AvgPool2d((2, 2)), nn.Dropout(0.2, True), conv_conv_block(128, 256), nn.AvgPool2d((2, 2)), nn.Dropout(0.2, True), conv_conv_block(256, 512), nn.AvgPool2d((2, 2)), nn.Dropout(0.2, True), nn.AdaptiveAvgPool2d((None, 1)), ) self.init_bn = nn.BatchNorm2d(spec_dim) self.embedding = nn.Linear(512, 512) self.apply(init) def forward(self, input_dict): x = input_dict["spec"] lens = input_dict["spec_len"] lens = torch.as_tensor(copy.deepcopy(lens)) x = x.unsqueeze(1) # [N, 1, T, D] x = x.transpose(1, 3) x = self.init_bn(x) x = x.transpose(1, 3) x = self.features(x) # [N, 512, T/16, 1] x = x.transpose(1, 2).contiguous().flatten(-2) # [N, T/16, 512] attn_emb = x lens //= 16 fc_emb = embedding_pooling(x, lens, "mean+max") fc_emb = F.dropout(fc_emb, p=0.5, training=self.training) fc_emb = self.embedding(fc_emb) fc_emb = F.relu_(fc_emb) return { "attn_emb": attn_emb, "fc_emb": fc_emb, "attn_emb_len": lens } class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) self.init_weight() def init_weight(self): init_layer(self.conv1) init_layer(self.conv2) init_bn(self.bn1) init_bn(self.bn2) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = F.relu_(self.bn1(self.conv1(x))) x = F.relu_(self.bn2(self.conv2(x))) if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception('Incorrect argument!') return x class Cnn14Encoder(nn.Module): def __init__(self, sample_rate=32000): super().__init__() sr_to_fmax = { 32000: 14000, 16000: 8000 } # Logmel spectrogram extractor self.melspec_extractor = transforms.MelSpectrogram( sample_rate=sample_rate, n_fft=32 * sample_rate // 1000, win_length=32 * sample_rate // 1000, hop_length=10 * sample_rate // 1000, f_min=50, f_max=sr_to_fmax[sample_rate], n_mels=64, norm="slaney", mel_scale="slaney" ) self.hop_length = 10 * sample_rate // 1000 self.db_transform = transforms.AmplitudeToDB() # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) self.downsample_ratio = 32 self.fc1 = nn.Linear(2048, 2048, bias=True) self.init_weight() def init_weight(self): init_bn(self.bn0) init_layer(self.fc1) def load_pretrained(self, pretrained): checkpoint = torch.load(pretrained, map_location="cpu") if "model" in checkpoint: state_keys = checkpoint["model"].keys() backbone = False for key in state_keys: if key.startswith("backbone."): backbone = True break if backbone: # COLA state_dict = {} for key, value in checkpoint["model"].items(): if key.startswith("backbone."): model_key = key.replace("backbone.", "") state_dict[model_key] = value else: # PANNs state_dict = checkpoint["model"] elif "state_dict" in checkpoint: # CLAP state_dict = checkpoint["state_dict"] state_dict_keys = list(filter( lambda x: "audio_encoder" in x, state_dict.keys())) state_dict = { key.replace('audio_encoder.', ''): state_dict[key] for key in state_dict_keys } else: raise Exception("Unkown checkpoint format") model_dict = self.state_dict() pretrained_dict = { k: v for k, v in state_dict.items() if (k in model_dict) and ( model_dict[k].shape == v.shape) } model_dict.update(pretrained_dict) self.load_state_dict(model_dict, strict=True) def forward(self, input_dict): """ Input: (batch_size, n_samples)""" waveform = input_dict["wav"] wave_length = input_dict["wav_len"] specaug = input_dict["specaug"] x = self.melspec_extractor(waveform) x = self.db_transform(x) # (batch_size, mel_bins, time_steps) x = x.transpose(1, 2) x = x.unsqueeze(1) # (batch_size, 1, time_steps, mel_bins) # SpecAugment if self.training and specaug: x = self.spec_augmenter(x) x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = torch.mean(x, dim=3) attn_emb = x.transpose(1, 2) wave_length = torch.as_tensor(wave_length) feat_length = torch.div(wave_length, self.hop_length, rounding_mode="floor") + 1 feat_length = torch.div(feat_length, self.downsample_ratio, rounding_mode="floor") x_max = max_with_lens(attn_emb, feat_length) x_mean = mean_with_lens(attn_emb, feat_length) x = x_max + x_mean x = F.dropout(x, p=0.5, training=self.training) x = F.relu_(self.fc1(x)) fc_emb = F.dropout(x, p=0.5, training=self.training) output_dict = { 'fc_emb': fc_emb, 'attn_emb': attn_emb, 'attn_emb_len': feat_length } return output_dict class RnnEncoder(BaseEncoder): def __init__(self, spec_dim, fc_feat_dim, attn_feat_dim, pooling="mean", **kwargs): super().__init__(spec_dim, fc_feat_dim, attn_feat_dim) self.pooling = pooling self.hidden_size = kwargs.get('hidden_size', 512) self.bidirectional = kwargs.get('bidirectional', False) self.num_layers = kwargs.get('num_layers', 1) self.dropout = kwargs.get('dropout', 0.2) self.rnn_type = kwargs.get('rnn_type', "GRU") self.in_bn = kwargs.get('in_bn', False) self.embed_dim = self.hidden_size * (self.bidirectional + 1) self.network = getattr(nn, self.rnn_type)( attn_feat_dim, self.hidden_size, num_layers=self.num_layers, bidirectional=self.bidirectional, dropout=self.dropout, batch_first=True) if self.in_bn: self.bn = nn.BatchNorm1d(self.embed_dim) self.apply(init) def forward(self, input_dict): x = input_dict["attn"] lens = input_dict["attn_len"] lens = torch.as_tensor(lens) # x: [N, T, E] if self.in_bn: x = pack_wrapper(self.bn, x, lens) out = pack_wrapper(self.network, x, lens) # out: [N, T, hidden] attn_emb = out fc_emb = embedding_pooling(out, lens, self.pooling) return { "attn_emb": attn_emb, "fc_emb": fc_emb, "attn_emb_len": lens } class Cnn14RnnEncoder(nn.Module): def __init__(self, sample_rate=32000, pretrained=None, freeze_cnn=False, freeze_cnn_bn=False, pooling="mean", **kwargs): super().__init__() self.cnn = Cnn14Encoder(sample_rate) self.rnn = RnnEncoder(64, 2048, 2048, pooling, **kwargs) if pretrained is not None: self.cnn.load_pretrained(pretrained) if freeze_cnn: assert pretrained is not None, "cnn is not pretrained but frozen" for param in self.cnn.parameters(): param.requires_grad = False self.freeze_cnn_bn = freeze_cnn_bn def train(self, mode): super().train(mode=mode) if self.freeze_cnn_bn: def bn_eval(module): class_name = module.__class__.__name__ if class_name.find("BatchNorm") != -1: module.eval() self.cnn.apply(bn_eval) return self def forward(self, input_dict): output_dict = self.cnn(input_dict) output_dict["attn"] = output_dict["attn_emb"] output_dict["attn_len"] = output_dict["attn_emb_len"] del output_dict["attn_emb"], output_dict["attn_emb_len"] output_dict = self.rnn(output_dict) return output_dict class TransformerEncoder(BaseEncoder): def __init__(self, spec_dim, fc_feat_dim, attn_feat_dim, d_model, **kwargs): super().__init__(spec_dim, fc_feat_dim, attn_feat_dim) self.d_model = d_model dropout = kwargs.get("dropout", 0.2) self.nhead = kwargs.get("nhead", self.d_model // 64) self.nlayers = kwargs.get("nlayers", 2) self.dim_feedforward = kwargs.get("dim_feedforward", self.d_model * 4) self.attn_proj = nn.Sequential( nn.Linear(attn_feat_dim, self.d_model), nn.ReLU(), nn.Dropout(dropout), nn.LayerNorm(self.d_model) ) layer = nn.TransformerEncoderLayer(d_model=self.d_model, nhead=self.nhead, dim_feedforward=self.dim_feedforward, dropout=dropout) self.model = nn.TransformerEncoder(layer, self.nlayers) self.cls_token = nn.Parameter(torch.zeros(d_model)) self.init_params() def init_params(self): for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def forward(self, input_dict): attn_feat = input_dict["attn"] attn_feat_len = input_dict["attn_len"] attn_feat_len = torch.as_tensor(attn_feat_len) attn_feat = self.attn_proj(attn_feat) # [bs, T, d_model] cls_emb = self.cls_token.reshape(1, 1, self.d_model).repeat( attn_feat.size(0), 1, 1) attn_feat = torch.cat((cls_emb, attn_feat), dim=1) attn_feat = attn_feat.transpose(0, 1) attn_feat_len += 1 src_key_padding_mask = ~generate_length_mask( attn_feat_len, attn_feat.size(0)).to(attn_feat.device) output = self.model(attn_feat, src_key_padding_mask=src_key_padding_mask) attn_emb = output.transpose(0, 1) fc_emb = attn_emb[:, 0] return { "attn_emb": attn_emb, "fc_emb": fc_emb, "attn_emb_len": attn_feat_len } class Cnn14TransformerEncoder(nn.Module): def __init__(self, sample_rate=32000, pretrained=None, freeze_cnn=False, freeze_cnn_bn=False, d_model="mean", **kwargs): super().__init__() self.cnn = Cnn14Encoder(sample_rate) self.trm = TransformerEncoder(64, 2048, 2048, d_model, **kwargs) if pretrained is not None: self.cnn.load_pretrained(pretrained) if freeze_cnn: assert pretrained is not None, "cnn is not pretrained but frozen" for param in self.cnn.parameters(): param.requires_grad = False self.freeze_cnn_bn = freeze_cnn_bn def train(self, mode): super().train(mode=mode) if self.freeze_cnn_bn: def bn_eval(module): class_name = module.__class__.__name__ if class_name.find("BatchNorm") != -1: module.eval() self.cnn.apply(bn_eval) return self def forward(self, input_dict): output_dict = self.cnn(input_dict) output_dict["attn"] = output_dict["attn_emb"] output_dict["attn_len"] = output_dict["attn_emb_len"] del output_dict["attn_emb"], output_dict["attn_emb_len"] output_dict = self.trm(output_dict) return output_dict ================================================ FILE: audio_to_text/captioning/models/transformer_model.py ================================================ # -*- coding: utf-8 -*- import random import torch import torch.nn as nn from .base_model import CaptionModel from .utils import repeat_tensor import audio_to_text.captioning.models.decoder class TransformerModel(CaptionModel): def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs): if not hasattr(self, "compatible_decoders"): self.compatible_decoders = ( audio_to_text.captioning.models.decoder.TransformerDecoder, ) super().__init__(encoder, decoder, **kwargs) def seq_forward(self, input_dict): cap = input_dict["cap"] cap_padding_mask = (cap == self.pad_idx).to(cap.device) cap_padding_mask = cap_padding_mask[:, :-1] output = self.decoder( { "word": cap[:, :-1], "attn_emb": input_dict["attn_emb"], "attn_emb_len": input_dict["attn_emb_len"], "cap_padding_mask": cap_padding_mask } ) return output def prepare_decoder_input(self, input_dict, output): decoder_input = { "attn_emb": input_dict["attn_emb"], "attn_emb_len": input_dict["attn_emb_len"] } t = input_dict["t"] ############### # determine input word ################ if input_dict["mode"] == "train" and random.random() < input_dict["ss_ratio"]: # training, scheduled sampling word = input_dict["cap"][:, :t+1] else: start_word = torch.tensor([self.start_idx,] * input_dict["attn_emb"].size(0)).unsqueeze(1).long() if t == 0: word = start_word else: word = torch.cat((start_word, output["seq"][:, :t]), dim=-1) # word: [N, T] decoder_input["word"] = word cap_padding_mask = (word == self.pad_idx).to(input_dict["attn_emb"].device) decoder_input["cap_padding_mask"] = cap_padding_mask return decoder_input def prepare_beamsearch_decoder_input(self, input_dict, output_i): decoder_input = {} t = input_dict["t"] i = input_dict["sample_idx"] beam_size = input_dict["beam_size"] ############### # prepare attn embeds ################ if t == 0: attn_emb = repeat_tensor(input_dict["attn_emb"][i], beam_size) attn_emb_len = repeat_tensor(input_dict["attn_emb_len"][i], beam_size) output_i["attn_emb"] = attn_emb output_i["attn_emb_len"] = attn_emb_len decoder_input["attn_emb"] = output_i["attn_emb"] decoder_input["attn_emb_len"] = output_i["attn_emb_len"] ############### # determine input word ################ start_word = torch.tensor([self.start_idx,] * beam_size).unsqueeze(1).long() if t == 0: word = start_word else: word = torch.cat((start_word, output_i["seq"]), dim=-1) decoder_input["word"] = word cap_padding_mask = (word == self.pad_idx).to(input_dict["attn_emb"].device) decoder_input["cap_padding_mask"] = cap_padding_mask return decoder_input class M2TransformerModel(CaptionModel): def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs): if not hasattr(self, "compatible_decoders"): self.compatible_decoders = ( captioning.models.decoder.M2TransformerDecoder, ) super().__init__(encoder, decoder, **kwargs) self.check_encoder_compatibility() def check_encoder_compatibility(self): assert isinstance(self.encoder, captioning.models.encoder.M2TransformerEncoder), \ f"only M2TransformerModel is compatible with {self.__class__.__name__}" def seq_forward(self, input_dict): cap = input_dict["cap"] output = self.decoder( { "word": cap[:, :-1], "attn_emb": input_dict["attn_emb"], "attn_emb_mask": input_dict["attn_emb_mask"], } ) return output def prepare_decoder_input(self, input_dict, output): decoder_input = { "attn_emb": input_dict["attn_emb"], "attn_emb_mask": input_dict["attn_emb_mask"] } t = input_dict["t"] ############### # determine input word ################ if input_dict["mode"] == "train" and random.random() < input_dict["ss_ratio"]: # training, scheduled sampling word = input_dict["cap"][:, :t+1] else: start_word = torch.tensor([self.start_idx,] * input_dict["attn_emb"].size(0)).unsqueeze(1).long() if t == 0: word = start_word else: word = torch.cat((start_word, output["seq"][:, :t]), dim=-1) # word: [N, T] decoder_input["word"] = word return decoder_input def prepare_beamsearch_decoder_input(self, input_dict, output_i): decoder_input = {} t = input_dict["t"] i = input_dict["sample_idx"] beam_size = input_dict["beam_size"] ############### # prepare attn embeds ################ if t == 0: attn_emb = repeat_tensor(input_dict["attn_emb"][i], beam_size) attn_emb_mask = repeat_tensor(input_dict["attn_emb_mask"][i], beam_size) output_i["attn_emb"] = attn_emb output_i["attn_emb_mask"] = attn_emb_mask decoder_input["attn_emb"] = output_i["attn_emb"] decoder_input["attn_emb_mask"] = output_i["attn_emb_mask"] ############### # determine input word ################ start_word = torch.tensor([self.start_idx,] * beam_size).unsqueeze(1).long() if t == 0: word = start_word else: word = torch.cat((start_word, output_i["seq"]), dim=-1) decoder_input["word"] = word return decoder_input class EventEncoder(nn.Module): """ Encode the Label information in AudioCaps and AudioSet """ def __init__(self, emb_dim, vocab_size=527): super(EventEncoder, self).__init__() self.label_embedding = nn.Parameter( torch.randn((vocab_size, emb_dim)), requires_grad=True) def forward(self, word_idxs): indices = word_idxs / word_idxs.sum(dim=1, keepdim=True) embeddings = indices @ self.label_embedding return embeddings class EventCondTransformerModel(TransformerModel): def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs): if not hasattr(self, "compatible_decoders"): self.compatible_decoders = ( captioning.models.decoder.EventTransformerDecoder, ) super().__init__(encoder, decoder, **kwargs) self.label_encoder = EventEncoder(decoder.emb_dim, 527) self.train_forward_keys += ["events"] self.inference_forward_keys += ["events"] # def seq_forward(self, input_dict): # cap = input_dict["cap"] # cap_padding_mask = (cap == self.pad_idx).to(cap.device) # cap_padding_mask = cap_padding_mask[:, :-1] # output = self.decoder( # { # "word": cap[:, :-1], # "attn_emb": input_dict["attn_emb"], # "attn_emb_len": input_dict["attn_emb_len"], # "cap_padding_mask": cap_padding_mask # } # ) # return output def prepare_decoder_input(self, input_dict, output): decoder_input = super().prepare_decoder_input(input_dict, output) decoder_input["events"] = self.label_encoder(input_dict["events"]) return decoder_input def prepare_beamsearch_decoder_input(self, input_dict, output_i): decoder_input = super().prepare_beamsearch_decoder_input(input_dict, output_i) t = input_dict["t"] i = input_dict["sample_idx"] beam_size = input_dict["beam_size"] if t == 0: output_i["events"] = repeat_tensor(self.label_encoder(input_dict["events"])[i], beam_size) decoder_input["events"] = output_i["events"] return decoder_input class KeywordCondTransformerModel(TransformerModel): def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs): if not hasattr(self, "compatible_decoders"): self.compatible_decoders = ( captioning.models.decoder.KeywordProbTransformerDecoder, ) super().__init__(encoder, decoder, **kwargs) self.train_forward_keys += ["keyword"] self.inference_forward_keys += ["keyword"] def seq_forward(self, input_dict): cap = input_dict["cap"] cap_padding_mask = (cap == self.pad_idx).to(cap.device) cap_padding_mask = cap_padding_mask[:, :-1] keyword = input_dict["keyword"] output = self.decoder( { "word": cap[:, :-1], "attn_emb": input_dict["attn_emb"], "attn_emb_len": input_dict["attn_emb_len"], "keyword": keyword, "cap_padding_mask": cap_padding_mask } ) return output def prepare_decoder_input(self, input_dict, output): decoder_input = super().prepare_decoder_input(input_dict, output) decoder_input["keyword"] = input_dict["keyword"] return decoder_input def prepare_beamsearch_decoder_input(self, input_dict, output_i): decoder_input = super().prepare_beamsearch_decoder_input(input_dict, output_i) t = input_dict["t"] i = input_dict["sample_idx"] beam_size = input_dict["beam_size"] if t == 0: output_i["keyword"] = repeat_tensor(input_dict["keyword"][i], beam_size) decoder_input["keyword"] = output_i["keyword"] return decoder_input ================================================ FILE: audio_to_text/captioning/models/utils.py ================================================ import math import numpy as np import torch import torch.nn as nn from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_packed_sequence def sort_pack_padded_sequence(input, lengths): sorted_lengths, indices = torch.sort(lengths, descending=True) tmp = pack_padded_sequence(input[indices], sorted_lengths.cpu(), batch_first=True) inv_ix = indices.clone() inv_ix[indices] = torch.arange(0,len(indices)).type_as(inv_ix) return tmp, inv_ix def pad_unsort_packed_sequence(input, inv_ix): tmp, _ = pad_packed_sequence(input, batch_first=True) tmp = tmp[inv_ix] return tmp def pack_wrapper(module, attn_feats, attn_feat_lens): packed, inv_ix = sort_pack_padded_sequence(attn_feats, attn_feat_lens) if isinstance(module, torch.nn.RNNBase): return pad_unsort_packed_sequence(module(packed)[0], inv_ix) else: return pad_unsort_packed_sequence(PackedSequence(module(packed[0]), packed[1]), inv_ix) def generate_length_mask(lens, max_length=None): lens = torch.as_tensor(lens) N = lens.size(0) if max_length is None: max_length = max(lens) idxs = torch.arange(max_length).repeat(N).view(N, max_length) idxs = idxs.to(lens.device) mask = (idxs < lens.view(-1, 1)) return mask def mean_with_lens(features, lens): """ features: [N, T, ...] (assume the second dimension represents length) lens: [N,] """ lens = torch.as_tensor(lens) if max(lens) != features.size(1): max_length = features.size(1) mask = generate_length_mask(lens, max_length) else: mask = generate_length_mask(lens) mask = mask.to(features.device) # [N, T] while mask.ndim < features.ndim: mask = mask.unsqueeze(-1) feature_mean = features * mask feature_mean = feature_mean.sum(1) while lens.ndim < feature_mean.ndim: lens = lens.unsqueeze(1) feature_mean = feature_mean / lens.to(features.device) # feature_mean = features * mask.unsqueeze(-1) # feature_mean = feature_mean.sum(1) / lens.unsqueeze(1).to(features.device) return feature_mean def max_with_lens(features, lens): """ features: [N, T, ...] (assume the second dimension represents length) lens: [N,] """ lens = torch.as_tensor(lens) mask = generate_length_mask(lens).to(features.device) # [N, T] feature_max = features.clone() feature_max[~mask] = float("-inf") feature_max, _ = feature_max.max(1) return feature_max def repeat_tensor(x, n): return x.unsqueeze(0).repeat(n, *([1] * len(x.shape))) def init(m, method="kaiming"): if isinstance(m, (nn.Conv2d, nn.Conv1d)): if method == "kaiming": nn.init.kaiming_uniform_(m.weight) elif method == "xavier": nn.init.xavier_uniform_(m.weight) else: raise Exception(f"initialization method {method} not supported") if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)): nn.init.constant_(m.weight, 1) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): if method == "kaiming": nn.init.kaiming_uniform_(m.weight) elif method == "xavier": nn.init.xavier_uniform_(m.weight) else: raise Exception(f"initialization method {method} not supported") if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Embedding): if method == "kaiming": nn.init.kaiming_uniform_(m.weight) elif method == "xavier": nn.init.xavier_uniform_(m.weight) else: raise Exception(f"initialization method {method} not supported") class PositionalEncoding(nn.Module): def __init__(self, d_model, dropout=0.1, max_len=100): super(PositionalEncoding, self).__init__() self.dropout = nn.Dropout(p=dropout) pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * \ (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0).transpose(0, 1) # self.register_buffer("pe", pe) self.register_parameter("pe", nn.Parameter(pe, requires_grad=False)) def forward(self, x): # x: [T, N, E] x = x + self.pe[:x.size(0), :] return self.dropout(x) ================================================ FILE: audio_to_text/captioning/utils/README.md ================================================ # Utils Scripts in this directory are used as utility functions. ## BERT Pretrained Embeddings You can load pretrained word embeddings in Google [BERT](https://github.com/google-research/bert#pre-trained-models) instead of training word embeddings from scratch. The scripts in `utils/bert` need a BERT server in the background. We use BERT server from [bert-as-service](https://github.com/hanxiao/bert-as-service). To use bert-as-service, you need to first install the repository. It is recommended that you create a new environment with Tensorflow 1.3 to run BERT server since it is incompatible with Tensorflow 2.x. After successful installation of [bert-as-service](https://github.com/hanxiao/bert-as-service), downloading and running the BERT server needs to execute: ```bash bash scripts/prepare_bert_server.sh zh ``` By default, server based on BERT base Chinese model is running in the background. You can change to other models by changing corresponding model name and path in `scripts/prepare_bert_server.sh`. To extract BERT word embeddings, you need to execute `utils/bert/create_word_embedding.py`. ================================================ FILE: audio_to_text/captioning/utils/__init__.py ================================================ ================================================ FILE: audio_to_text/captioning/utils/bert/create_sent_embedding.py ================================================ import pickle import fire import numpy as np import pandas as pd from tqdm import tqdm class EmbeddingExtractor(object): def extract_sentbert(self, caption_file: str, output: str, dev: bool=True, zh: bool=False): from sentence_transformers import SentenceTransformer lang2model = { "zh": "distiluse-base-multilingual-cased", "en": "bert-base-nli-mean-tokens" } lang = "zh" if zh else "en" model = SentenceTransformer(lang2model[lang]) self.extract(caption_file, model, output, dev) def extract_originbert(self, caption_file: str, output: str, dev: bool=True, ip="localhost"): from bert_serving.client import BertClient client = BertClient(ip) self.extract(caption_file, client, output, dev) def extract(self, caption_file: str, model, output, dev: bool): caption_df = pd.read_json(caption_file, dtype={"key": str}) embeddings = {} if dev: with tqdm(total=caption_df.shape[0], ascii=True) as pbar: for idx, row in caption_df.iterrows(): caption = row["caption"] key = row["key"] cap_idx = row["caption_index"] embedding = model.encode([caption]) embedding = np.array(embedding).reshape(-1) embeddings[f"{key}_{cap_idx}"] = embedding pbar.update() else: dump = {} with tqdm(total=caption_df.shape[0], ascii=True) as pbar: for idx, row in caption_df.iterrows(): key = row["key"] caption = row["caption"] value = np.array(model.encode([caption])).reshape(-1) if key not in embeddings.keys(): embeddings[key] = [value] else: embeddings[key].append(value) pbar.update() for key in embeddings: dump[key] = np.stack(embeddings[key]) embeddings = dump with open(output, "wb") as f: pickle.dump(embeddings, f) def extract_sbert(self, input_json: str, output: str): from sentence_transformers import SentenceTransformer import json import torch from h5py import File device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = SentenceTransformer("paraphrase-MiniLM-L6-v2") model = model.to(device) model.eval() data = json.load(open(input_json))["audios"] with torch.no_grad(), tqdm(total=len(data), ascii=True) as pbar, File(output, "w") as store: for sample in data: audio_id = sample["audio_id"] for cap in sample["captions"]: cap_id = cap["cap_id"] store[f"{audio_id}_{cap_id}"] = model.encode(cap["caption"]) pbar.update() if __name__ == "__main__": fire.Fire(EmbeddingExtractor) ================================================ FILE: audio_to_text/captioning/utils/bert/create_word_embedding.py ================================================ # -*- coding: utf-8 -*- import sys import os from bert_serving.client import BertClient import numpy as np from tqdm import tqdm import fire import torch sys.path.append(os.getcwd()) from utils.build_vocab import Vocabulary def main(vocab_file: str, output: str, server_hostname: str): client = BertClient(ip=server_hostname) vocabulary = torch.load(vocab_file) vocab_size = len(vocabulary) fake_embedding = client.encode(["test"]).reshape(-1) embed_size = fake_embedding.shape[0] print("Encoding words into embeddings with size: ", embed_size) embeddings = np.empty((vocab_size, embed_size)) for i in tqdm(range(len(embeddings)), ascii=True): embeddings[i] = client.encode([vocabulary.idx2word[i]]) np.save(output, embeddings) if __name__ == '__main__': fire.Fire(main) ================================================ FILE: audio_to_text/captioning/utils/build_vocab.py ================================================ import json from tqdm import tqdm import logging import pickle from collections import Counter import re import fire class Vocabulary(object): """Simple vocabulary wrapper.""" def __init__(self): self.word2idx = {} self.idx2word = {} self.idx = 0 def add_word(self, word): if not word in self.word2idx: self.word2idx[word] = self.idx self.idx2word[self.idx] = word self.idx += 1 def __call__(self, word): if not word in self.word2idx: return self.word2idx[""] return self.word2idx[word] def __getitem__(self, word_id): return self.idx2word[word_id] def __len__(self): return len(self.word2idx) def build_vocab(input_json: str, threshold: int, keep_punctuation: bool, host_address: str, character_level: bool = False, zh: bool = True ): """Build vocabulary from csv file with a given threshold to drop all counts < threshold Args: input_json(string): Preprossessed json file. Structure like this: { 'audios': [ { 'audio_id': 'xxx', 'captions': [ { 'caption': 'xxx', 'cap_id': 'xxx' } ] }, ... ] } threshold (int): Threshold to drop all words with counts < threshold keep_punctuation (bool): Includes or excludes punctuation. Returns: vocab (Vocab): Object with the processed vocabulary """ data = json.load(open(input_json, "r"))["audios"] counter = Counter() pretokenized = "tokens" in data[0]["captions"][0] if zh: from nltk.parse.corenlp import CoreNLPParser from zhon.hanzi import punctuation if not pretokenized: parser = CoreNLPParser(host_address) for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): for cap_idx in range(len(data[audio_idx]["captions"])): if pretokenized: tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split() else: caption = data[audio_idx]["captions"][cap_idx]["caption"] # Remove all punctuations if not keep_punctuation: caption = re.sub("[{}]".format(punctuation), "", caption) if character_level: tokens = list(caption) else: tokens = list(parser.tokenize(caption)) data[audio_idx]["captions"][cap_idx]["tokens"] = " ".join(tokens) counter.update(tokens) else: if pretokenized: for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): for cap_idx in range(len(data[audio_idx]["captions"])): tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split() counter.update(tokens) else: from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer captions = {} for audio_idx in range(len(data)): audio_id = data[audio_idx]["audio_id"] captions[audio_id] = [] for cap_idx in range(len(data[audio_idx]["captions"])): caption = data[audio_idx]["captions"][cap_idx]["caption"] captions[audio_id].append({ "audio_id": audio_id, "id": cap_idx, "caption": caption }) tokenizer = PTBTokenizer() captions = tokenizer.tokenize(captions) for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): audio_id = data[audio_idx]["audio_id"] for cap_idx in range(len(data[audio_idx]["captions"])): tokens = captions[audio_id][cap_idx] data[audio_idx]["captions"][cap_idx]["tokens"] = tokens counter.update(tokens.split(" ")) if not pretokenized: json.dump({ "audios": data }, open(input_json, "w"), indent=4, ensure_ascii=not zh) words = [word for word, cnt in counter.items() if cnt >= threshold] # Create a vocab wrapper and add some special tokens. vocab = Vocabulary() vocab.add_word("") vocab.add_word("") vocab.add_word("") vocab.add_word("") # Add the words to the vocabulary. for word in words: vocab.add_word(word) return vocab def process(input_json: str, output_file: str, threshold: int = 1, keep_punctuation: bool = False, character_level: bool = False, host_address: str = "http://localhost:9000", zh: bool = False): logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s" logging.basicConfig(level=logging.INFO, format=logfmt) logging.info("Build Vocab") vocabulary = build_vocab( input_json=input_json, threshold=threshold, keep_punctuation=keep_punctuation, host_address=host_address, character_level=character_level, zh=zh) pickle.dump(vocabulary, open(output_file, "wb")) logging.info("Total vocabulary size: {}".format(len(vocabulary))) logging.info("Saved vocab to '{}'".format(output_file)) if __name__ == '__main__': fire.Fire(process) ================================================ FILE: audio_to_text/captioning/utils/build_vocab_ltp.py ================================================ import json from tqdm import tqdm import logging import pickle from collections import Counter import re import fire class Vocabulary(object): """Simple vocabulary wrapper.""" def __init__(self): self.word2idx = {} self.idx2word = {} self.idx = 0 def add_word(self, word): if not word in self.word2idx: self.word2idx[word] = self.idx self.idx2word[self.idx] = word self.idx += 1 def __call__(self, word): if not word in self.word2idx: return self.word2idx[""] return self.word2idx[word] def __len__(self): return len(self.word2idx) def build_vocab(input_json: str, output_json: str, threshold: int, keep_punctuation: bool, character_level: bool = False, zh: bool = True ): """Build vocabulary from csv file with a given threshold to drop all counts < threshold Args: input_json(string): Preprossessed json file. Structure like this: { 'audios': [ { 'audio_id': 'xxx', 'captions': [ { 'caption': 'xxx', 'cap_id': 'xxx' } ] }, ... ] } threshold (int): Threshold to drop all words with counts < threshold keep_punctuation (bool): Includes or excludes punctuation. Returns: vocab (Vocab): Object with the processed vocabulary """ data = json.load(open(input_json, "r"))["audios"] counter = Counter() pretokenized = "tokens" in data[0]["captions"][0] if zh: from ltp import LTP from zhon.hanzi import punctuation if not pretokenized: parser = LTP("base") for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): for cap_idx in range(len(data[audio_idx]["captions"])): if pretokenized: tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split() else: caption = data[audio_idx]["captions"][cap_idx]["caption"] if character_level: tokens = list(caption) else: tokens, _ = parser.seg([caption]) tokens = tokens[0] # Remove all punctuations if not keep_punctuation: tokens = [token for token in tokens if token not in punctuation] data[audio_idx]["captions"][cap_idx]["tokens"] = " ".join(tokens) counter.update(tokens) else: if pretokenized: for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): for cap_idx in range(len(data[audio_idx]["captions"])): tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split() counter.update(tokens) else: from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer captions = {} for audio_idx in range(len(data)): audio_id = data[audio_idx]["audio_id"] captions[audio_id] = [] for cap_idx in range(len(data[audio_idx]["captions"])): caption = data[audio_idx]["captions"][cap_idx]["caption"] captions[audio_id].append({ "audio_id": audio_id, "id": cap_idx, "caption": caption }) tokenizer = PTBTokenizer() captions = tokenizer.tokenize(captions) for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): audio_id = data[audio_idx]["audio_id"] for cap_idx in range(len(data[audio_idx]["captions"])): tokens = captions[audio_id][cap_idx] data[audio_idx]["captions"][cap_idx]["tokens"] = tokens counter.update(tokens.split(" ")) if not pretokenized: if output_json is None: output_json = input_json json.dump({ "audios": data }, open(output_json, "w"), indent=4, ensure_ascii=not zh) words = [word for word, cnt in counter.items() if cnt >= threshold] # Create a vocab wrapper and add some special tokens. vocab = Vocabulary() vocab.add_word("") vocab.add_word("") vocab.add_word("") vocab.add_word("") # Add the words to the vocabulary. for word in words: vocab.add_word(word) return vocab def process(input_json: str, output_file: str, output_json: str = None, threshold: int = 1, keep_punctuation: bool = False, character_level: bool = False, zh: bool = True): logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s" logging.basicConfig(level=logging.INFO, format=logfmt) logging.info("Build Vocab") vocabulary = build_vocab( input_json=input_json, output_json=output_json, threshold=threshold, keep_punctuation=keep_punctuation, character_level=character_level, zh=zh) pickle.dump(vocabulary, open(output_file, "wb")) logging.info("Total vocabulary size: {}".format(len(vocabulary))) logging.info("Saved vocab to '{}'".format(output_file)) if __name__ == '__main__': fire.Fire(process) ================================================ FILE: audio_to_text/captioning/utils/build_vocab_spacy.py ================================================ import json from tqdm import tqdm import logging import pickle from collections import Counter import re import fire class Vocabulary(object): """Simple vocabulary wrapper.""" def __init__(self): self.word2idx = {} self.idx2word = {} self.idx = 0 def add_word(self, word): if not word in self.word2idx: self.word2idx[word] = self.idx self.idx2word[self.idx] = word self.idx += 1 def __call__(self, word): if not word in self.word2idx: return self.word2idx[""] return self.word2idx[word] def __len__(self): return len(self.word2idx) def build_vocab(input_json: str, output_json: str, threshold: int, keep_punctuation: bool, host_address: str, character_level: bool = False, retokenize: bool = True, zh: bool = True ): """Build vocabulary from csv file with a given threshold to drop all counts < threshold Args: input_json(string): Preprossessed json file. Structure like this: { 'audios': [ { 'audio_id': 'xxx', 'captions': [ { 'caption': 'xxx', 'cap_id': 'xxx' } ] }, ... ] } threshold (int): Threshold to drop all words with counts < threshold keep_punctuation (bool): Includes or excludes punctuation. Returns: vocab (Vocab): Object with the processed vocabulary """ data = json.load(open(input_json, "r"))["audios"] counter = Counter() if retokenize: pretokenized = False else: pretokenized = "tokens" in data[0]["captions"][0] if zh: from nltk.parse.corenlp import CoreNLPParser from zhon.hanzi import punctuation if not pretokenized: parser = CoreNLPParser(host_address) for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): for cap_idx in range(len(data[audio_idx]["captions"])): if pretokenized: tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split() else: caption = data[audio_idx]["captions"][cap_idx]["caption"] # Remove all punctuations if not keep_punctuation: caption = re.sub("[{}]".format(punctuation), "", caption) if character_level: tokens = list(caption) else: tokens = list(parser.tokenize(caption)) data[audio_idx]["captions"][cap_idx]["tokens"] = " ".join(tokens) counter.update(tokens) else: if pretokenized: for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): for cap_idx in range(len(data[audio_idx]["captions"])): tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split() counter.update(tokens) else: import spacy tokenizer = spacy.load("en_core_web_sm", disable=["parser", "ner"]) for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): captions = data[audio_idx]["captions"] for cap_idx in range(len(captions)): caption = captions[cap_idx]["caption"] doc = tokenizer(caption) tokens = " ".join([str(token).lower() for token in doc]) data[audio_idx]["captions"][cap_idx]["tokens"] = tokens counter.update(tokens.split(" ")) if not pretokenized: if output_json is None: json.dump({ "audios": data }, open(input_json, "w"), indent=4, ensure_ascii=not zh) else: json.dump({ "audios": data }, open(output_json, "w"), indent=4, ensure_ascii=not zh) words = [word for word, cnt in counter.items() if cnt >= threshold] # Create a vocab wrapper and add some special tokens. vocab = Vocabulary() vocab.add_word("") vocab.add_word("") vocab.add_word("") vocab.add_word("") # Add the words to the vocabulary. for word in words: vocab.add_word(word) return vocab def process(input_json: str, output_file: str, output_json: str = None, threshold: int = 1, keep_punctuation: bool = False, character_level: bool = False, retokenize: bool = False, host_address: str = "http://localhost:9000", zh: bool = True): logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s" logging.basicConfig(level=logging.INFO, format=logfmt) logging.info("Build Vocab") vocabulary = build_vocab( input_json=input_json, output_json=output_json, threshold=threshold, keep_punctuation=keep_punctuation, host_address=host_address, character_level=character_level, retokenize=retokenize, zh=zh) pickle.dump(vocabulary, open(output_file, "wb")) logging.info("Total vocabulary size: {}".format(len(vocabulary))) logging.info("Saved vocab to '{}'".format(output_file)) if __name__ == '__main__': fire.Fire(process) ================================================ FILE: audio_to_text/captioning/utils/eval_round_robin.py ================================================ import copy import json import numpy as np import fire def evaluate_annotation(key2refs, scorer): if scorer.method() == "Bleu": scores = np.array([ 0.0 for n in range(4) ]) else: scores = 0 num_cap_per_audio = len(next(iter(key2refs.values()))) for i in range(num_cap_per_audio): if i > 0: for key in key2refs: key2refs[key].insert(0, res[key][0]) res = { key: [refs.pop(),] for key, refs in key2refs.items() } score, _ = scorer.compute_score(key2refs, res) if scorer.method() == "Bleu": scores += np.array(score) else: scores += score score = scores / num_cap_per_audio return score def evaluate_prediction(key2pred, key2refs, scorer): if scorer.method() == "Bleu": scores = np.array([ 0.0 for n in range(4) ]) else: scores = 0 num_cap_per_audio = len(next(iter(key2refs.values()))) for i in range(num_cap_per_audio): key2refs_i = {} for key, refs in key2refs.items(): key2refs_i[key] = refs[:i] + refs[i+1:] score, _ = scorer.compute_score(key2refs_i, key2pred) if scorer.method() == "Bleu": scores += np.array(score) else: scores += score score = scores / num_cap_per_audio return score class Evaluator(object): def eval_annotation(self, annotation, output): captions = json.load(open(annotation, "r"))["audios"] key2refs = {} for audio_idx in range(len(captions)): audio_id = captions[audio_idx]["audio_id"] key2refs[audio_id] = [] for caption in captions[audio_idx]["captions"]: key2refs[audio_id].append(caption["caption"]) from fense.fense import Fense scores = {} scorer = Fense() scores[scorer.method()] = evaluate_annotation(copy.deepcopy(key2refs), scorer) refs4eval = {} for key, refs in key2refs.items(): refs4eval[key] = [] for idx, ref in enumerate(refs): refs4eval[key].append({ "audio_id": key, "id": idx, "caption": ref }) from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer tokenizer = PTBTokenizer() key2refs = tokenizer.tokenize(refs4eval) from pycocoevalcap.bleu.bleu import Bleu from pycocoevalcap.cider.cider import Cider from pycocoevalcap.rouge.rouge import Rouge from pycocoevalcap.meteor.meteor import Meteor from pycocoevalcap.spice.spice import Spice scorers = [Bleu(), Rouge(), Cider(), Meteor(), Spice()] for scorer in scorers: scores[scorer.method()] = evaluate_annotation(copy.deepcopy(key2refs), scorer) spider = 0 with open(output, "w") as f: for name, score in scores.items(): if name == "Bleu": for n in range(4): f.write("Bleu-{}: {:6.3f}\n".format(n + 1, score[n])) else: f.write("{}: {:6.3f}\n".format(name, score)) if name in ["CIDEr", "SPICE"]: spider += score f.write("SPIDEr: {:6.3f}\n".format(spider / 2)) def eval_prediction(self, prediction, annotation, output): ref_captions = json.load(open(annotation, "r"))["audios"] key2refs = {} for audio_idx in range(len(ref_captions)): audio_id = ref_captions[audio_idx]["audio_id"] key2refs[audio_id] = [] for caption in ref_captions[audio_idx]["captions"]: key2refs[audio_id].append(caption["caption"]) pred_captions = json.load(open(prediction, "r"))["predictions"] key2pred = {} for audio_idx in range(len(pred_captions)): item = pred_captions[audio_idx] audio_id = item["filename"] key2pred[audio_id] = [item["tokens"]] from fense.fense import Fense scores = {} scorer = Fense() scores[scorer.method()] = evaluate_prediction(key2pred, key2refs, scorer) refs4eval = {} for key, refs in key2refs.items(): refs4eval[key] = [] for idx, ref in enumerate(refs): refs4eval[key].append({ "audio_id": key, "id": idx, "caption": ref }) preds4eval = {} for key, preds in key2pred.items(): preds4eval[key] = [] for idx, pred in enumerate(preds): preds4eval[key].append({ "audio_id": key, "id": idx, "caption": pred }) from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer tokenizer = PTBTokenizer() key2refs = tokenizer.tokenize(refs4eval) key2pred = tokenizer.tokenize(preds4eval) from pycocoevalcap.bleu.bleu import Bleu from pycocoevalcap.cider.cider import Cider from pycocoevalcap.rouge.rouge import Rouge from pycocoevalcap.meteor.meteor import Meteor from pycocoevalcap.spice.spice import Spice scorers = [Bleu(), Rouge(), Cider(), Meteor(), Spice()] for scorer in scorers: scores[scorer.method()] = evaluate_prediction(key2pred, key2refs, scorer) spider = 0 with open(output, "w") as f: for name, score in scores.items(): if name == "Bleu": for n in range(4): f.write("Bleu-{}: {:6.3f}\n".format(n + 1, score[n])) else: f.write("{}: {:6.3f}\n".format(name, score)) if name in ["CIDEr", "SPICE"]: spider += score f.write("SPIDEr: {:6.3f}\n".format(spider / 2)) if __name__ == "__main__": fire.Fire(Evaluator) ================================================ FILE: audio_to_text/captioning/utils/fasttext/create_word_embedding.py ================================================ # coding=utf-8 #!/usr/bin/env python3 import numpy as np import pandas as pd import torch from gensim.models import FastText from tqdm import tqdm import fire import sys import os sys.path.append(os.getcwd()) from utils.build_vocab import Vocabulary def create_embedding(caption_file: str, vocab_file: str, embed_size: int, output: str, **fasttext_kwargs): caption_df = pd.read_json(caption_file) caption_df["tokens"] = caption_df["tokens"].apply(lambda x: [""] + [token for token in x] + [""]) sentences = list(caption_df["tokens"].values) vocabulary = torch.load(vocab_file, map_location="cpu") epochs = fasttext_kwargs.get("epochs", 10) model = FastText(size=embed_size, min_count=1, **fasttext_kwargs) model.build_vocab(sentences=sentences) model.train(sentences=sentences, total_examples=len(sentences), epochs=epochs) word_embeddings = np.zeros((len(vocabulary), embed_size)) with tqdm(total=len(vocabulary), ascii=True) as pbar: for word, idx in vocabulary.word2idx.items(): if word == "" or word == "": continue word_embeddings[idx] = model.wv[word] pbar.update() np.save(output, word_embeddings) print("Finish writing fasttext embeddings to " + output) if __name__ == "__main__": fire.Fire(create_embedding) ================================================ FILE: audio_to_text/captioning/utils/lr_scheduler.py ================================================ import math import torch class ExponentialDecayScheduler(torch.optim.lr_scheduler._LRScheduler): def __init__(self, optimizer, total_iters, final_lrs, warmup_iters=3000, last_epoch=-1, verbose=False): self.total_iters = total_iters self.final_lrs = final_lrs if not isinstance(self.final_lrs, list) and not isinstance( self.final_lrs, tuple): self.final_lrs = [self.final_lrs] * len(optimizer.param_groups) self.warmup_iters = warmup_iters self.bases = [0.0,] * len(optimizer.param_groups) super().__init__(optimizer, last_epoch, verbose) for i, (base_lr, final_lr) in enumerate(zip(self.base_lrs, self.final_lrs)): base = (final_lr / base_lr) ** (1 / ( self.total_iters - self.warmup_iters)) self.bases[i] = base def _get_closed_form_lr(self): warmup_coeff = 1.0 current_iter = self._step_count if current_iter < self.warmup_iters: warmup_coeff = current_iter / self.warmup_iters current_lrs = [] # if not self.linear_warmup: # for base_lr, final_lr, base in zip(self.base_lrs, self.final_lrs, self.bases): # # current_lr = warmup_coeff * base_lr * math.exp(((current_iter - self.warmup_iters) / self.total_iters) * math.log(final_lr / base_lr)) # current_lr = warmup_coeff * base_lr * (base ** (current_iter - self.warmup_iters)) # current_lrs.append(current_lr) # else: for base_lr, final_lr, base in zip(self.base_lrs, self.final_lrs, self.bases): if current_iter <= self.warmup_iters: current_lr = warmup_coeff * base_lr else: # current_lr = warmup_coeff * base_lr * math.exp(((current_iter - self.warmup_iters) / self.total_iters) * math.log(final_lr / base_lr)) current_lr = base_lr * (base ** (current_iter - self.warmup_iters)) current_lrs.append(current_lr) return current_lrs def get_lr(self): return self._get_closed_form_lr() class NoamScheduler(torch.optim.lr_scheduler._LRScheduler): def __init__(self, optimizer, model_size=512, factor=1, warmup_iters=3000, last_epoch=-1, verbose=False): self.model_size = model_size self.warmup_iters = warmup_iters # self.factors = [group["lr"] / (self.model_size ** (-0.5) * self.warmup_iters ** (-0.5)) for group in optimizer.param_groups] self.factor = factor super().__init__(optimizer, last_epoch, verbose) def _get_closed_form_lr(self): current_iter = self._step_count current_lrs = [] for _ in self.base_lrs: current_lr = self.factor * \ (self.model_size ** (-0.5) * min(current_iter ** (-0.5), current_iter * self.warmup_iters ** (-1.5))) current_lrs.append(current_lr) return current_lrs def get_lr(self): return self._get_closed_form_lr() class CosineWithWarmup(torch.optim.lr_scheduler._LRScheduler): def __init__(self, optimizer, total_iters, warmup_iters, num_cycles=0.5, last_epoch=-1, verbose=False): self.total_iters = total_iters self.warmup_iters = warmup_iters self.num_cycles = num_cycles super().__init__(optimizer, last_epoch, verbose) def lr_lambda(self, iteration): if iteration < self.warmup_iters: return float(iteration) / float(max(1, self.warmup_iters)) progress = float(iteration - self.warmup_iters) / float(max(1, self.total_iters - self.warmup_iters)) return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float( self.num_cycles) * 2.0 * progress))) def _get_closed_form_lr(self): current_iter = self._step_count current_lrs = [] for base_lr in self.base_lrs: current_lr = base_lr * self.lr_lambda(current_iter) current_lrs.append(current_lr) return current_lrs def get_lr(self): return self._get_closed_form_lr() if __name__ == "__main__": model = torch.nn.Linear(10, 5) optimizer = torch.optim.Adam(model.parameters(), 5e-4) epochs = 25 iters = 600 scheduler = CosineWithWarmup(optimizer, 600 * 25, 600 * 5,) # scheduler = ExponentialDecayScheduler(optimizer, 600 * 25, 5e-7, 600 * 5) criterion = torch.nn.MSELoss() lrs = [] for epoch in range(1, epochs + 1): for iteration in range(1, iters + 1): optimizer.zero_grad() x = torch.randn(4, 10) y = torch.randn(4, 5) loss = criterion(model(x), y) loss.backward() optimizer.step() scheduler.step() # print(f"lr: {scheduler.get_last_lr()}") # lrs.append(scheduler.get_last_lr()) lrs.append(optimizer.param_groups[0]["lr"]) import matplotlib.pyplot as plt plt.plot(list(range(1, len(lrs) + 1)), lrs, '-o', markersize=1) # plt.legend(loc="best") plt.xlabel("Iteration") plt.ylabel("LR") plt.savefig("lr_curve.png", dpi=100) ================================================ FILE: audio_to_text/captioning/utils/model_eval_diff.py ================================================ import os import sys import copy import pickle import numpy as np import pandas as pd import fire sys.path.append(os.getcwd()) def coco_score(refs, pred, scorer): if scorer.method() == "Bleu": scores = np.array([ 0.0 for n in range(4) ]) else: scores = 0 num_cap_per_audio = len(refs[list(refs.keys())[0]]) for i in range(num_cap_per_audio): if i > 0: for key in refs: refs[key].insert(0, res[key][0]) res = {key: [refs[key].pop(),] for key in refs} score, _ = scorer.compute_score(refs, pred) if scorer.method() == "Bleu": scores += np.array(score) else: scores += score score = scores / num_cap_per_audio for key in refs: refs[key].insert(0, res[key][0]) score_allref, _ = scorer.compute_score(refs, pred) diff = score_allref - score return diff def embedding_score(refs, pred, scorer): num_cap_per_audio = len(refs[list(refs.keys())[0]]) scores = 0 for i in range(num_cap_per_audio): res = {key: [refs[key][i],] for key in refs.keys() if len(refs[key]) == num_cap_per_audio} refs_i = {key: np.concatenate([refs[key][:i], refs[key][i+1:]]) for key in refs.keys() if len(refs[key]) == num_cap_per_audio} score, _ = scorer.compute_score(refs_i, pred) scores += score score = scores / num_cap_per_audio score_allref, _ = scorer.compute_score(refs, pred) diff = score_allref - score return diff def main(output_file, eval_caption_file, eval_embedding_file, output, zh=False): output_df = pd.read_json(output_file) output_df["key"] = output_df["filename"].apply(lambda x: os.path.splitext(os.path.basename(x))[0]) pred = output_df.groupby("key")["tokens"].apply(list).to_dict() label_df = pd.read_json(eval_caption_file) if zh: refs = label_df.groupby("key")["tokens"].apply(list).to_dict() else: refs = label_df.groupby("key")["caption"].apply(list).to_dict() from pycocoevalcap.bleu.bleu import Bleu from pycocoevalcap.cider.cider import Cider from pycocoevalcap.rouge.rouge import Rouge scorer = Bleu(zh=zh) bleu_scores = coco_score(copy.deepcopy(refs), pred, scorer) scorer = Cider(zh=zh) cider_score = coco_score(copy.deepcopy(refs), pred, scorer) scorer = Rouge(zh=zh) rouge_score = coco_score(copy.deepcopy(refs), pred, scorer) if not zh: from pycocoevalcap.meteor.meteor import Meteor scorer = Meteor() meteor_score = coco_score(copy.deepcopy(refs), pred, scorer) from pycocoevalcap.spice.spice import Spice scorer = Spice() spice_score = coco_score(copy.deepcopy(refs), pred, scorer) # from audiocaptioneval.sentbert.sentencebert import SentenceBert # scorer = SentenceBert(zh=zh) # with open(eval_embedding_file, "rb") as f: # ref_embeddings = pickle.load(f) # sent_bert = embedding_score(ref_embeddings, pred, scorer) with open(output, "w") as f: f.write("Diff:\n") for n in range(4): f.write("BLEU-{}: {:6.3f}\n".format(n+1, bleu_scores[n])) f.write("CIDEr: {:6.3f}\n".format(cider_score)) f.write("ROUGE: {:6.3f}\n".format(rouge_score)) if not zh: f.write("Meteor: {:6.3f}\n".format(meteor_score)) f.write("SPICE: {:6.3f}\n".format(spice_score)) # f.write("SentenceBert: {:6.3f}\n".format(sent_bert)) if __name__ == "__main__": fire.Fire(main) ================================================ FILE: audio_to_text/captioning/utils/predict_nn.py ================================================ import json import random import argparse import numpy as np from tqdm import tqdm from h5py import File import sklearn.metrics random.seed(1) parser = argparse.ArgumentParser() parser.add_argument("train_feature", type=str) parser.add_argument("train_corpus", type=str) parser.add_argument("pred_feature", type=str) parser.add_argument("output_json", type=str) args = parser.parse_args() train_embs = [] train_idx_to_audioid = [] with File(args.train_feature, "r") as store: for audio_id, embedding in tqdm(store.items(), ascii=True): train_embs.append(embedding[()]) train_idx_to_audioid.append(audio_id) train_annotation = json.load(open(args.train_corpus, "r"))["audios"] train_audioid_to_tokens = {} for item in train_annotation: audio_id = item["audio_id"] train_audioid_to_tokens[audio_id] = [cap_item["tokens"] for cap_item in item["captions"]] train_embs = np.stack(train_embs) pred_data = [] pred_embs = [] pred_idx_to_audioids = [] with File(args.pred_feature, "r") as store: for audio_id, embedding in tqdm(store.items(), ascii=True): pred_embs.append(embedding[()]) pred_idx_to_audioids.append(audio_id) pred_embs = np.stack(pred_embs) similarity = sklearn.metrics.pairwise.cosine_similarity(pred_embs, train_embs) for idx, audio_id in enumerate(pred_idx_to_audioids): train_idx = similarity[idx].argmax() pred_data.append({ "filename": audio_id, "tokens": random.choice(train_audioid_to_tokens[train_idx_to_audioid[train_idx]]) }) json.dump({"predictions": pred_data}, open(args.output_json, "w"), ensure_ascii=False, indent=4) ================================================ FILE: audio_to_text/captioning/utils/remove_optimizer.py ================================================ import argparse import torch def main(checkpoint): state_dict = torch.load(checkpoint, map_location="cpu") if "optimizer" in state_dict: del state_dict["optimizer"] if "lr_scheduler" in state_dict: del state_dict["lr_scheduler"] torch.save(state_dict, checkpoint) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("checkpoint", type=str) args = parser.parse_args() main(args.checkpoint) ================================================ FILE: audio_to_text/captioning/utils/report_results.py ================================================ from pathlib import Path import argparse import numpy as np parser = argparse.ArgumentParser() parser.add_argument("--input", help="input filename", type=str, nargs="+") parser.add_argument("--output", help="output result file", default=None) args = parser.parse_args() scores = {} for path in args.input: with open(path, "r") as reader: for line in reader.readlines(): metric, score = line.strip().split(": ") score = float(score) if metric not in scores: scores[metric] = [] scores[metric].append(score) if len(scores) == 0: print("No experiment directory found, wrong path?") exit(1) with open(args.output, "w") as writer: print("Average results: ", file=writer) for metric, score in scores.items(): score = np.array(score) mean = np.mean(score) std = np.std(score) print(f"{metric}: {mean:.3f} (±{std:.3f})", file=writer) print("", file=writer) print("Best results: ", file=writer) for metric, score in scores.items(): score = np.max(score) print(f"{metric}: {score:.3f}", file=writer) ================================================ FILE: audio_to_text/captioning/utils/tokenize_caption.py ================================================ import json from tqdm import tqdm import re import fire def tokenize_caption(input_json: str, keep_punctuation: bool = False, host_address: str = None, character_level: bool = False, zh: bool = True, output_json: str = None): """Build vocabulary from csv file with a given threshold to drop all counts < threshold Args: input_json(string): Preprossessed json file. Structure like this: { 'audios': [ { 'audio_id': 'xxx', 'captions': [ { 'caption': 'xxx', 'cap_id': 'xxx' } ] }, ... ] } threshold (int): Threshold to drop all words with counts < threshold keep_punctuation (bool): Includes or excludes punctuation. Returns: vocab (Vocab): Object with the processed vocabulary """ data = json.load(open(input_json, "r"))["audios"] if zh: from nltk.parse.corenlp import CoreNLPParser from zhon.hanzi import punctuation parser = CoreNLPParser(host_address) for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): for cap_idx in range(len(data[audio_idx]["captions"])): caption = data[audio_idx]["captions"][cap_idx]["caption"] # Remove all punctuations if not keep_punctuation: caption = re.sub("[{}]".format(punctuation), "", caption) if character_level: tokens = list(caption) else: tokens = list(parser.tokenize(caption)) data[audio_idx]["captions"][cap_idx]["tokens"] = " ".join(tokens) else: from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer captions = {} for audio_idx in range(len(data)): audio_id = data[audio_idx]["audio_id"] captions[audio_id] = [] for cap_idx in range(len(data[audio_idx]["captions"])): caption = data[audio_idx]["captions"][cap_idx]["caption"] captions[audio_id].append({ "audio_id": audio_id, "id": cap_idx, "caption": caption }) tokenizer = PTBTokenizer() captions = tokenizer.tokenize(captions) for audio_idx in tqdm(range(len(data)), leave=False, ascii=True): audio_id = data[audio_idx]["audio_id"] for cap_idx in range(len(data[audio_idx]["captions"])): tokens = captions[audio_id][cap_idx] data[audio_idx]["captions"][cap_idx]["tokens"] = tokens if output_json: json.dump( { "audios": data }, open(output_json, "w"), indent=4, ensure_ascii=not zh) else: json.dump( { "audios": data }, open(input_json, "w"), indent=4, ensure_ascii=not zh) if __name__ == "__main__": fire.Fire(tokenize_caption) ================================================ FILE: audio_to_text/captioning/utils/train_util.py ================================================ # -*- coding: utf-8 -*- #!/usr/bin/env python3 import os import sys import logging from typing import Callable, Dict, Union import yaml import torch from torch.optim.swa_utils import AveragedModel as torch_average_model import numpy as np import pandas as pd from pprint import pformat def load_dict_from_csv(csv, cols): df = pd.read_csv(csv, sep="\t") output = dict(zip(df[cols[0]], df[cols[1]])) return output def init_logger(filename, level="INFO"): formatter = logging.Formatter( "[ %(levelname)s : %(asctime)s ] - %(message)s") logger = logging.getLogger(__name__ + "." + filename) logger.setLevel(getattr(logging, level)) # Log results to std # stdhandler = logging.StreamHandler(sys.stdout) # stdhandler.setFormatter(formatter) # Dump log to file filehandler = logging.FileHandler(filename) filehandler.setFormatter(formatter) logger.addHandler(filehandler) # logger.addHandler(stdhandler) return logger def init_obj(module, config, **kwargs):# 'captioning.models.encoder' obj_args = config["args"].copy() obj_args.update(kwargs) return getattr(module, config["type"])(**obj_args) def pprint_dict(in_dict, outputfun=sys.stdout.write, formatter='yaml'): """pprint_dict :param outputfun: function to use, defaults to sys.stdout :param in_dict: dict to print """ if formatter == 'yaml': format_fun = yaml.dump elif formatter == 'pretty': format_fun = pformat for line in format_fun(in_dict).split('\n'): outputfun(line) def merge_a_into_b(a, b): # merge dict a into dict b. values in a will overwrite b. for k, v in a.items(): if isinstance(v, dict) and k in b: assert isinstance( b[k], dict ), "Cannot inherit key '{}' from base!".format(k) merge_a_into_b(v, b[k]) else: b[k] = v def load_config(config_file): with open(config_file, "r") as reader: config = yaml.load(reader, Loader=yaml.FullLoader) if "inherit_from" in config: base_config_file = config["inherit_from"] base_config_file = os.path.join( os.path.dirname(config_file), base_config_file ) assert not os.path.samefile(config_file, base_config_file), \ "inherit from itself" base_config = load_config(base_config_file) del config["inherit_from"] merge_a_into_b(config, base_config) return base_config return config def parse_config_or_kwargs(config_file, **kwargs): yaml_config = load_config(config_file) # passed kwargs will override yaml config args = dict(yaml_config, **kwargs) return args def store_yaml(config, config_file): with open(config_file, "w") as con_writer: yaml.dump(config, con_writer, indent=4, default_flow_style=False) class MetricImprover: def __init__(self, mode): assert mode in ("min", "max") self.mode = mode # min: lower -> better; max: higher -> better self.best_value = np.inf if mode == "min" else -np.inf def compare(self, x, best_x): return x < best_x if self.mode == "min" else x > best_x def __call__(self, x): if self.compare(x, self.best_value): self.best_value = x return True return False def state_dict(self): return self.__dict__ def load_state_dict(self, state_dict): self.__dict__.update(state_dict) def fix_batchnorm(model: torch.nn.Module): def inner(module): class_name = module.__class__.__name__ if class_name.find("BatchNorm") != -1: module.eval() model.apply(inner) def load_pretrained_model(model: torch.nn.Module, pretrained: Union[str, Dict], output_fn: Callable = sys.stdout.write): if not isinstance(pretrained, dict) and not os.path.exists(pretrained): output_fn(f"pretrained {pretrained} not exist!") return if hasattr(model, "load_pretrained"): model.load_pretrained(pretrained) return if isinstance(pretrained, dict): state_dict = pretrained else: state_dict = torch.load(pretrained, map_location="cpu") if "model" in state_dict: state_dict = state_dict["model"] model_dict = model.state_dict() pretrained_dict = { k: v for k, v in state_dict.items() if (k in model_dict) and ( model_dict[k].shape == v.shape) } output_fn(f"Loading pretrained keys {pretrained_dict.keys()}") model_dict.update(pretrained_dict) model.load_state_dict(model_dict, strict=True) class AveragedModel(torch_average_model): def update_parameters(self, model): for p_swa, p_model in zip(self.parameters(), model.parameters()): device = p_swa.device p_model_ = p_model.detach().to(device) if self.n_averaged == 0: p_swa.detach().copy_(p_model_) else: p_swa.detach().copy_(self.avg_fn(p_swa.detach(), p_model_, self.n_averaged.to(device))) for b_swa, b_model in zip(list(self.buffers())[1:], model.buffers()): device = b_swa.device b_model_ = b_model.detach().to(device) if self.n_averaged == 0: b_swa.detach().copy_(b_model_) else: b_swa.detach().copy_(self.avg_fn(b_swa.detach(), b_model_, self.n_averaged.to(device))) self.n_averaged += 1 ================================================ FILE: audio_to_text/captioning/utils/word2vec/create_word_embedding.py ================================================ # coding=utf-8 #!/usr/bin/env python3 import numpy as np import pandas as pd import torch import gensim from gensim.models import Word2Vec from tqdm import tqdm import fire import sys import os sys.path.append(os.getcwd()) from utils.build_vocab import Vocabulary def create_embedding(vocab_file: str, embed_size: int, output: str, caption_file: str = None, pretrained_weights_path: str = None, **word2vec_kwargs): vocabulary = torch.load(vocab_file, map_location="cpu") if pretrained_weights_path: model = gensim.models.KeyedVectors.load_word2vec_format( fname=pretrained_weights_path, binary=True, ) if model.vector_size != embed_size: assert embed_size < model.vector_size, f"only reduce dimension, cannot add dimesion {model.vector_size} to {embed_size}" from sklearn.decomposition import PCA pca = PCA(n_components=embed_size) model.vectors = pca.fit_transform(model.vectors) else: caption_df = pd.read_json(caption_file) caption_df["tokens"] = caption_df["tokens"].apply(lambda x: [""] + [token for token in x] + [""]) sentences = list(caption_df["tokens"].values) epochs = word2vec_kwargs.get("epochs", 10) if "epochs" in word2vec_kwargs: del word2vec_kwargs["epochs"] model = Word2Vec(size=embed_size, min_count=1, **word2vec_kwargs) model.build_vocab(sentences=sentences) model.train(sentences=sentences, total_examples=len(sentences), epochs=epochs) word_embeddings = np.random.randn(len(vocabulary), embed_size) if isinstance(model, gensim.models.word2vec.Word2Vec): model = model.wv with tqdm(total=len(vocabulary), ascii=True) as pbar: for word, idx in vocabulary.word2idx.items(): try: word_embeddings[idx] = model.get_vector(word) except KeyError: print(f"word {word} not found in word2vec model, it is random initialized!") pbar.update() np.save(output, word_embeddings) print("Finish writing word2vec embeddings to " + output) if __name__ == "__main__": fire.Fire(create_embedding) ================================================ FILE: audio_to_text/inference_waveform.py ================================================ import sys import os import librosa import numpy as np import torch import audio_to_text.captioning.models import audio_to_text.captioning.models.encoder import audio_to_text.captioning.models.decoder import audio_to_text.captioning.utils.train_util as train_util def load_model(config, checkpoint): ckpt = torch.load(checkpoint, "cpu") encoder_cfg = config["model"]["encoder"] encoder = train_util.init_obj( audio_to_text.captioning.models.encoder, encoder_cfg ) if "pretrained" in encoder_cfg: pretrained = encoder_cfg["pretrained"] train_util.load_pretrained_model(encoder, pretrained, sys.stdout.write) decoder_cfg = config["model"]["decoder"] if "vocab_size" not in decoder_cfg["args"]: decoder_cfg["args"]["vocab_size"] = len(ckpt["vocabulary"]) decoder = train_util.init_obj( audio_to_text.captioning.models.decoder, decoder_cfg ) if "word_embedding" in decoder_cfg: decoder.load_word_embedding(**decoder_cfg["word_embedding"]) if "pretrained" in decoder_cfg: pretrained = decoder_cfg["pretrained"] train_util.load_pretrained_model(decoder, pretrained, sys.stdout.write) model = train_util.init_obj(audio_to_text.captioning.models, config["model"], encoder=encoder, decoder=decoder) train_util.load_pretrained_model(model, ckpt) model.eval() return { "model": model, "vocabulary": ckpt["vocabulary"] } def decode_caption(word_ids, vocabulary): candidate = [] for word_id in word_ids: word = vocabulary[word_id] if word == "": break elif word == "": continue candidate.append(word) candidate = " ".join(candidate) return candidate class AudioCapModel(object): def __init__(self,weight_dir,device='cuda'): config = os.path.join(weight_dir,'config.yaml') self.config = train_util.parse_config_or_kwargs(config) checkpoint = os.path.join(weight_dir,'swa.pth') resumed = load_model(self.config, checkpoint) model = resumed["model"] self.vocabulary = resumed["vocabulary"] self.model = model.to(device) self.device = device def caption(self,audio_list): if isinstance(audio_list,np.ndarray): audio_list = [audio_list] elif isinstance(audio_list,str): audio_list = [librosa.load(audio_list,sr=32000)[0]] captions = [] for wav in audio_list: inputwav = torch.as_tensor(wav).float().unsqueeze(0).to(self.device) wav_len = torch.LongTensor([len(wav)]) input_dict = { "mode": "inference", "wav": inputwav, "wav_len": wav_len, "specaug": False, "sample_method": "beam", } print(input_dict) out_dict = self.model(input_dict) caption_batch = [decode_caption(seq, self.vocabulary) for seq in \ out_dict["seq"].cpu().numpy()] captions.extend(caption_batch) return captions def __call__(self, audio_list): return self.caption(audio_list) ================================================ FILE: download.sh ================================================ mkdir checkpoints mkdir audio mkdir image mkdir text_to_audio # Text to sing wget -P checkpoints/0831_opencpop_ds1000/ -i https://huggingface.co/spaces/Silentlin/DiffSinger/resolve/main/checkpoints/0831_opencpop_ds1000/config.yaml https://huggingface.co/spaces/Silentlin/DiffSinger/resolve/main/checkpoints/0831_opencpop_ds1000/model_ckpt_steps_320000.ckpt wget -P checkpoints/0109_hifigan_bigpopcs_hop128/ -i https://huggingface.co/spaces/Silentlin/DiffSinger/blob/main/checkpoints/0109_hifigan_bigpopcs_hop128/config.yaml https://huggingface.co/spaces/Silentlin/DiffSinger/resolve/main/checkpoints/0109_hifigan_bigpopcs_hop128/model_ckpt_steps_1512000.ckpt wget -P checkpoints/0102_xiaoma_pe/ -i https://huggingface.co/spaces/Silentlin/DiffSinger/blob/main/checkpoints/0102_xiaoma_pe/config.yaml https://huggingface.co/spaces/Silentlin/DiffSinger/resolve/main/checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt # Text to audio cd text_to_audio wget -P text_to_audio/Make_An_Audio/useful_ckpts/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio/resolve/main/useful_ckpts/ta40multi_epoch=000085.ckpt wget -P text_to_audio/Make_An_Audio/useful_ckpts/CLAP/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio/resolve/main/useful_ckpts/CLAP/CLAP_weights_2022.pth wget -P text_to_audio/Make_An_Audio/useful_ckpts/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_img/resolve/main/useful_ckpts/ta54_epoch=000216.ckpt wget -P text_to_audio/Make_An_Audio/useful_ckpts/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_inpaint/resolve/main/useful_ckpts/inpaint7_epoch00047.ckpt # Text to speech wget -P checkpoints/GenerSpeech/ -i https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/blob/main/checkpoints/GenerSpeech/config.yaml https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/checkpoints/GenerSpeech/model_ckpt_steps_300000.ckpt wget -P checkpoints/trainset_hifigan/ -i https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/blob/main/checkpoints/trainset_hifigan/config.yaml https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/checkpoints/trainset_hifigan/model_ckpt_steps_1000000.ckpt wget -P checkpoints/ https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/checkpoints/Emotion_encoder.pt wget -P data/binary/training_set https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/data/binary/training_set/mfa_dict.txt wget -P data/binary/training_set https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/data/binary/training_set/mfa_model.zip wget -P data/binary/training_set https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/data/binary/training_set/phone_set.json wget -P data/binary/training_set https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/data/binary/training_set/spk_map.json wget -P data/binary/training_set https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/data/binary/training_set/train_f0s_mean_std.npy wget -P data/binary/training_set https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/data/binary/training_set/word_set.json wget -P text_to_speech/checkpoints/hifi_lj -i https://huggingface.co/AIGC-Audio/AudioGPT/blob/main/text_to_speech/checkpoints/hifi_lj/config.yaml https://huggingface.co/AIGC-Audio/AudioGPT/resolve/main/text_to_speech/checkpoints/hifi_lj/model_ckpt_steps_2076000.ckpt wget -P text_to_speech/checkpoints/ljspeech/ps_adv_baseline -i https://huggingface.co/AIGC-Audio/AudioGPT/blob/main/text_to_speech/checkpoints/ljspeech/ps_adv_baseline/config.yaml https://huggingface.co/AIGC-Audio/AudioGPT/resolve/main/checkpoints/ljspeech/ps_adv_baseline/model_ckpt_steps_160000.ckpt https://huggingface.co/AIGC-Audio/AudioGPT/resolve/main/checkpoints/ljspeech/ps_adv_baseline/model_ckpt_steps_160001.ckpt # Audio to text wget -P audio_to_text/audiocaps_cntrstv_cnn14rnn_trm -i https://huggingface.co/AIGC-Audio/AudioGPT/blob/main/audio_to_text/audiocaps_cntrstv_cnn14rnn_trm/config.yaml https://huggingface.co/AIGC-Audio/AudioGPT/resolve/main/audio_to_text/audiocaps_cntrstv_cnn14rnn_trm/swa.pth wget -P audio_to_text/clotho_cntrstv_cnn14rnn_trm -i https://huggingface.co/AIGC-Audio/AudioGPT/blob/main/audio_to_text/clotho_cntrstv_cnn14rnn_trm/config.yaml https://huggingface.co/AIGC-Audio/AudioGPT/resolve/main/audio_to_text/clotho_cntrstv_cnn14rnn_trm/swa.pth wget -P audio_to_text/pretrained_feature_extractors https://huggingface.co/AIGC-Audio/AudioGPT/resolve/main/audio_to_text/pretrained_feature_extractors/contrastive_pretrain_cnn14_bertm.pth # Audio detection cd audio_detection/audio_infer/useful_ckpts wget https://huggingface.co/Dongchao/pre_trained_model/resolve/main/audio_detection.pth cd mono2binaural/useful_ckpts wget https://huggingface.co/Dongchao/pre_trained_model/resolve/main/m2b.tar.gz tar -zxvf m2b.tar.gz ./ rm m2b.tar.gz cd audio_detection/target_sound_detection/useful_ckpts wget https://huggingface.co/Dongchao/pre_trained_model/resolve/main/tsd.tar.gz tar -zxvf tsd.tar.gz ./ rm tsd.tar.gz cd sound_extraction/useful_ckpts wget https://huggingface.co/Dongchao/pre_trained_model/resolve/main/LASSNet.pt ================================================ FILE: mono2binaural/src/models.py ================================================ import numpy as np import scipy.linalg from scipy.spatial.transform import Rotation as R import torch as th import torch.nn as nn import torch.nn.functional as F from src.warping import GeometricTimeWarper, MonotoneTimeWarper from src.utils import Net class GeometricWarper(nn.Module): def __init__(self, sampling_rate=48000): super().__init__() self.warper = GeometricTimeWarper(sampling_rate=sampling_rate) def _transmitter_mouth(self, view): # offset between tracking markers and real mouth position in the dataset mouth_offset = np.array([0.09, 0, -0.20]) quat = view[:, 3:, :].transpose(2, 1).contiguous().detach().cpu().view(-1, 4).numpy() # make sure zero-padded values are set to non-zero values (else scipy raises an exception) norms = scipy.linalg.norm(quat, axis=1) eps_val = (norms == 0).astype(np.float32) quat = quat + eps_val[:, None] transmitter_rot_mat = R.from_quat(quat) transmitter_mouth = transmitter_rot_mat.apply(mouth_offset, inverse=True) transmitter_mouth = th.Tensor(transmitter_mouth).view(view.shape[0], -1, 3).transpose(2, 1).contiguous() if view.is_cuda: transmitter_mouth = transmitter_mouth.cuda() return transmitter_mouth def _3d_displacements(self, view): transmitter_mouth = self._transmitter_mouth(view) # offset between tracking markers and ears in the dataset left_ear_offset = th.Tensor([0, -0.08, -0.22]).cuda() if view.is_cuda else th.Tensor([0, -0.08, -0.22]) right_ear_offset = th.Tensor([0, 0.08, -0.22]).cuda() if view.is_cuda else th.Tensor([0, 0.08, -0.22]) # compute displacements between transmitter mouth and receiver left/right ear displacement_left = view[:, 0:3, :] + transmitter_mouth - left_ear_offset[None, :, None] displacement_right = view[:, 0:3, :] + transmitter_mouth - right_ear_offset[None, :, None] displacement = th.stack([displacement_left, displacement_right], dim=1) return displacement def _warpfield(self, view, seq_length): return self.warper.displacements2warpfield(self._3d_displacements(view), seq_length) def forward(self, mono, view): ''' :param mono: input signal as tensor of shape B x 1 x T :param view: rx/tx position/orientation as tensor of shape B x 7 x K (K = T / 400) :return: warped: warped left/right ear signal as tensor of shape B x 2 x T ''' return self.warper(th.cat([mono, mono], dim=1), self._3d_displacements(view)) class Warpnet(nn.Module): def __init__(self, layers=4, channels=64, view_dim=7): super().__init__() self.layers = [nn.Conv1d(view_dim if l == 0 else channels, channels, kernel_size=2) for l in range(layers)] self.layers = nn.ModuleList(self.layers) self.linear = nn.Conv1d(channels, 2, kernel_size=1) self.neural_warper = MonotoneTimeWarper() self.geometric_warper = GeometricWarper() def neural_warpfield(self, view, seq_length): warpfield = view for layer in self.layers: warpfield = F.pad(warpfield, pad=[1, 0]) warpfield = F.relu(layer(warpfield)) warpfield = self.linear(warpfield) warpfield = F.interpolate(warpfield, size=seq_length) return warpfield def forward(self, mono, view): ''' :param mono: input signal as tensor of shape B x 1 x T :param view: rx/tx position/orientation as tensor of shape B x 7 x K (K = T / 400) :return: warped: warped left/right ear signal as tensor of shape B x 2 x T ''' geometric_warpfield = self.geometric_warper._warpfield(view, mono.shape[-1]) neural_warpfield = self.neural_warpfield(view, mono.shape[-1]) warpfield = geometric_warpfield + neural_warpfield # ensure causality warpfield = -F.relu(-warpfield) # the predicted warp warped = self.neural_warper(th.cat([mono, mono], dim=1), warpfield) return warped class BinauralNetwork(Net): def __init__(self, view_dim=7, warpnet_layers=4, warpnet_channels=64, model_name='binaural_network', use_cuda=True): super().__init__(model_name, use_cuda) self.warper = Warpnet(warpnet_layers, warpnet_channels) if self.use_cuda: self.cuda() def forward(self, mono, view): ''' :param mono: the input signal as a B x 1 x T tensor :param view: the receiver/transmitter position as a B x 7 x T tensor :return: out: the binaural output produced by the network intermediate: a two-channel audio signal obtained from the output of each intermediate layer as a list of B x 2 x T tensors ''' # print('mono ', mono.shape) # print('view ', view.shape) warped = self.warper(mono, view) # print('warped ', warped.shape) return warped ================================================ FILE: mono2binaural/src/utils.py ================================================ """ Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. This source code is licensed under the license found in the LICENSE file in the root directory of this source tree. """ import numpy as np import torch as th #import torchaudio as ta class Net(th.nn.Module): def __init__(self, model_name="network", use_cuda=True): super().__init__() self.use_cuda = use_cuda self.model_name = model_name def save(self, model_dir, suffix=''): ''' save the network to model_dir/model_name.suffix.net :param model_dir: directory to save the model to :param suffix: suffix to append after model name ''' if self.use_cuda: self.cpu() if suffix == "": fname = f"{model_dir}/{self.model_name}.net" else: fname = f"{model_dir}/{self.model_name}.{suffix}.net" th.save(self.state_dict(), fname) if self.use_cuda: self.cuda() def load_from_file(self, model_file): ''' load network parameters from model_file :param model_file: file containing the model parameters ''' if self.use_cuda: self.cpu() states = th.load(model_file) self.load_state_dict(states) if self.use_cuda: self.cuda() print(f"Loaded: {model_file}") def load(self, model_dir, suffix=''): ''' load network parameters from model_dir/model_name.suffix.net :param model_dir: directory to load the model from :param suffix: suffix to append after model name ''' if suffix == "": fname = f"{model_dir}/{self.model_name}.net" else: fname = f"{model_dir}/{self.model_name}.{suffix}.net" self.load_from_file(fname) def num_trainable_parameters(self): ''' :return: the number of trainable parameters in the model ''' return sum(p.numel() for p in self.parameters() if p.requires_grad) # class NewbobAdam(th.optim.Adam): # def __init__(self, # weights, # net, # artifacts_dir, # initial_learning_rate=0.001, # decay=0.5, # max_decay=0.01 # ): # ''' # Newbob learning rate scheduler # :param weights: weights to optimize # :param net: the network, must be an instance of type src.utils.Net # :param artifacts_dir: (str) directory to save/restore models to/from # :param initial_learning_rate: (float) initial learning rate # :param decay: (float) value to decrease learning rate by when loss doesn't improve further # :param max_decay: (float) maximum decay of learning rate # ''' # super().__init__(weights, lr=initial_learning_rate) # self.last_epoch_loss = np.inf # self.total_decay = 1 # self.net = net # self.decay = decay # self.max_decay = max_decay # self.artifacts_dir = artifacts_dir # # store initial state as backup # if decay < 1.0: # net.save(artifacts_dir, suffix="newbob") # def update_lr(self, loss): # ''' # update the learning rate based on the current loss value and historic loss values # :param loss: the loss after the current iteration # ''' # if loss > self.last_epoch_loss and self.decay < 1.0 and self.total_decay > self.max_decay: # self.total_decay = self.total_decay * self.decay # print(f"NewbobAdam: Decay learning rate (loss degraded from {self.last_epoch_loss} to {loss})." # f"Total decay: {self.total_decay}") # # restore previous network state # self.net.load(self.artifacts_dir, suffix="newbob") # # decrease learning rate # for param_group in self.param_groups: # param_group['lr'] = param_group['lr'] * self.decay # else: # self.last_epoch_loss = loss # # save last snapshot to restore it in case of lr decrease # if self.decay < 1.0 and self.total_decay > self.max_decay: # self.net.save(self.artifacts_dir, suffix="newbob") # class FourierTransform: # def __init__(self, # fft_bins=2048, # win_length_ms=40, # frame_rate_hz=100, # causal=False, # preemphasis=0.0, # sample_rate=48000, # normalized=False): # self.sample_rate = sample_rate # self.frame_rate_hz = frame_rate_hz # self.preemphasis = preemphasis # self.fft_bins = fft_bins # self.win_length = int(sample_rate * win_length_ms / 1000) # self.hop_length = int(sample_rate / frame_rate_hz) # self.causal = causal # self.normalized = normalized # if self.win_length > self.fft_bins: # print('FourierTransform Warning: fft_bins should be larger than win_length') # def _convert_format(self, data, expected_dims): # if not type(data) == th.Tensor: # data = th.Tensor(data) # if len(data.shape) < expected_dims: # data = data.unsqueeze(0) # if not len(data.shape) == expected_dims: # raise Exception(f"FourierTransform: data needs to be a Tensor with {expected_dims} dimensions but got shape {data.shape}") # return data # def _preemphasis(self, audio): # if self.preemphasis > 0: # return th.cat((audio[:, 0:1], audio[:, 1:] - self.preemphasis * audio[:, :-1]), dim=1) # return audio # def _revert_preemphasis(self, audio): # if self.preemphasis > 0: # for i in range(1, audio.shape[1]): # audio[:, i] = audio[:, i] + self.preemphasis * audio[:, i-1] # return audio # def _magphase(self, complex_stft): # mag, phase = ta.functional.magphase(complex_stft, 1.0) # return mag, phase # def stft(self, audio): # ''' # wrapper around th.stft # audio: wave signal as th.Tensor # ''' # hann = th.hann_window(self.win_length) # hann = hann.cuda() if audio.is_cuda else hann # spec = th.stft(audio, n_fft=self.fft_bins, hop_length=self.hop_length, win_length=self.win_length, # window=hann, center=not self.causal, normalized=self.normalized) # return spec.contiguous() # def complex_spectrogram(self, audio): # ''' # audio: wave signal as th.Tensor # return: th.Tensor of size channels x frequencies x time_steps (channels x y_axis x x_axis) # ''' # self._convert_format(audio, expected_dims=2) # audio = self._preemphasis(audio) # return self.stft(audio) # def magnitude_phase(self, audio): # ''' # audio: wave signal as th.Tensor # return: tuple containing two th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum # ''' # stft = self.complex_spectrogram(audio) # return self._magphase(stft) # def mag_spectrogram(self, audio): # ''' # audio: wave signal as th.Tensor # return: magnitude spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum # ''' # return self.magnitude_phase(audio)[0] # def power_spectrogram(self, audio): # ''' # audio: wave signal as th.Tensor # return: power spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum # ''' # return th.pow(self.mag_spectrogram(audio), 2.0) # def phase_spectrogram(self, audio): # ''' # audio: wave signal as th.Tensor # return: phase spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum # ''' # return self.magnitude_phase(audio)[1] # def mel_spectrogram(self, audio, n_mels): # ''' # audio: wave signal as th.Tensor # n_mels: number of bins used for mel scale warping # return: mel spectrogram as th.Tensor of size channels x n_mels x time_steps for magnitude and phase spectrum # ''' # spec = self.power_spectrogram(audio) # mel_warping = ta.transforms.MelScale(n_mels, self.sample_rate) # return mel_warping(spec) # def complex_spec2wav(self, complex_spec, length): # ''' # inverse stft # complex_spec: complex spectrum as th.Tensor of size channels x frequencies x time_steps x 2 (real part/imaginary part) # length: length of the audio to be reconstructed (in frames) # ''' # complex_spec = self._convert_format(complex_spec, expected_dims=4) # hann = th.hann_window(self.win_length) # hann = hann.cuda() if complex_spec.is_cuda else hann # wav = ta.functional.istft(complex_spec, n_fft=self.fft_bins, hop_length=self.hop_length, win_length=self.win_length, window=hann, length=length, center=not self.causal) # wav = self._revert_preemphasis(wav) # return wav # def magphase2wav(self, mag_spec, phase_spec, length): # ''' # reconstruction of wav signal from magnitude and phase spectrum # mag_spec: magnitude spectrum as th.Tensor of size channels x frequencies x time_steps # phase_spec: phase spectrum as th.Tensor of size channels x frequencies x time_steps # length: length of the audio to be reconstructed (in frames) # ''' # mag_spec = self._convert_format(mag_spec, expected_dims=3) # phase_spec = self._convert_format(phase_spec, expected_dims=3) # complex_spec = th.stack([mag_spec * th.cos(phase_spec), mag_spec * th.sin(phase_spec)], dim=-1) # return self.complex_spec2wav(complex_spec, length) ================================================ FILE: mono2binaural/src/warping.py ================================================ """ Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. This source code is licensed under the license found in the LICENSE file in the root directory of this source tree. """ import torch as th import torch.nn as nn import torch.nn.functional as F class TimeWarperFunction(th.autograd.Function): @staticmethod def forward(ctx, input, warpfield): ''' :param ctx: autograd context :param input: input signal (B x 2 x T) :param warpfield: the corresponding warpfield (B x 2 x T) :return: the warped signal (B x 2 x T) ''' ctx.save_for_backward(input, warpfield) # compute index list to lookup warped input values idx_left = warpfield.floor().type(th.long) idx_right = th.clamp(warpfield.ceil().type(th.long), max=input.shape[-1]-1) # compute weight for linear interpolation alpha = warpfield - warpfield.floor() # linear interpolation output = (1 - alpha) * th.gather(input, 2, idx_left) + alpha * th.gather(input, 2, idx_right) return output @staticmethod def backward(ctx, grad_output): input, warpfield = ctx.saved_tensors # compute index list to lookup warped input values idx_left = warpfield.floor().type(th.long) idx_right = th.clamp(warpfield.ceil().type(th.long), max=input.shape[-1]-1) # warpfield gradient grad_warpfield = th.gather(input, 2, idx_right) - th.gather(input, 2, idx_left) grad_warpfield = grad_output * grad_warpfield # input gradient grad_input = th.zeros(input.shape, device=input.device) alpha = warpfield - warpfield.floor() grad_input = grad_input.scatter_add(2, idx_left, grad_output * (1 - alpha)) + \ grad_input.scatter_add(2, idx_right, grad_output * alpha) return grad_input, grad_warpfield class TimeWarper(nn.Module): def __init__(self): super().__init__() self.warper = TimeWarperFunction().apply def _to_absolute_positions(self, warpfield, seq_length): # translate warpfield from relative warp indices to absolute indices ([1...T] + warpfield) temp_range = th.arange(seq_length, dtype=th.float) temp_range = temp_range.cuda() if warpfield.is_cuda else temp_range return th.clamp(warpfield + temp_range[None, None, :], min=0, max=seq_length-1) def forward(self, input, warpfield): ''' :param input: audio signal to be warped (B x 2 x T) :param warpfield: the corresponding warpfield (B x 2 x T) :return: the warped signal (B x 2 x T) ''' warpfield = self._to_absolute_positions(warpfield, input.shape[-1]) warped = self.warper(input, warpfield) return warped class MonotoneTimeWarper(TimeWarper): def forward(self, input, warpfield): ''' :param input: audio signal to be warped (B x 2 x T) :param warpfield: the corresponding warpfield (B x 2 x T) :return: the warped signal (B x 2 x T), ensured to be monotonous ''' warpfield = self._to_absolute_positions(warpfield, input.shape[-1]) # ensure monotonicity: each warp must be at least as big as previous_warp-1 warpfield = th.cummax(warpfield, dim=-1)[0] # print('warpfield ',warpfield.shape) # warp warped = self.warper(input, warpfield) return warped class GeometricTimeWarper(TimeWarper): def __init__(self, sampling_rate=48000): super().__init__() self.sampling_rate = sampling_rate def displacements2warpfield(self, displacements, seq_length): distance = th.sum(displacements**2, dim=2) ** 0.5 distance = F.interpolate(distance, size=seq_length) warpfield = -distance / 343.0 * self.sampling_rate return warpfield def forward(self, input, displacements): ''' :param input: audio signal to be warped (B x 2 x T) :param displacements: sequence of 3D displacement vectors for geometric warping (B x 3 x T) :return: the warped signal (B x 2 x T) ''' warpfield = self.displacements2warpfield(displacements, input.shape[-1]) # print('Ge warpfield ', warpfield.shape) # assert 1==2 warped = super().forward(input, warpfield) return warped ================================================ FILE: requirements.txt ================================================ --extra-index-url https://download.pytorch.org/whl/cu113 accelerate addict==2.4.0 aiofiles albumentations==1.3.0 appdirs==1.4.4 basicsr==1.4.2 beautifulsoup4==4.10.0 Cython==0.29.24 diffusers einops==0.3.0 espnet espnet_model_zoo ffmpeg-python g2p-en==2.1.0 google==3.0.0 gradio h5py imageio==2.9.0 imageio-ffmpeg==0.4.2 invisible-watermark>=0.1.5 jieba kornia==0.6 langchain==0.0.101 librosa loguru miditoolkit==0.1.7 mmcv==1.5.0 mmdet==2.23.0 mmengine==0.7.2 moviepy==1.0.3 numpy==1.23.1 omegaconf==2.1.1 open_clip_torch==2.0.2 openai openai-whisper opencv-contrib-python==4.3.0.36 praat-parselmouth==0.3.3 prettytable==3.6.0 proglog==0.1.9 pycwt==0.3.0a22 pyloudnorm==0.1.0 pypinyin==0.43.0 pytorch-lightning==1.5.0 pytorch-ssim==0.1 pyworld==0.3.0 resampy==0.2.2 Resemblyzer==0.1.1.dev0 safetensors==0.2.7 sklearn==0.0 soundfile soupsieve==2.3 streamlit==1.12.1 streamlit-drawable-canvas==0.8.0 tensorboardX==2.4 test-tube>=0.7.5 TextGrid==1.5 timm==0.6.12 torch==1.12.1 torchaudio==0.12.1 torch-fidelity==0.3.0 torchlibrosa torchmetrics==0.6.0 torchvision==0.13.1 transformers==4.26.1 typing-extensions==4.0.0 uuid==1.30 webdataset==0.2.5 webrtcvad==2.0.10 yapf==0.32.0 git+https://github.com/openai/CLIP.git ================================================ FILE: run.md ================================================ # Run AudioGPT ``` # create a new environment conda create -n audiogpt python=3.8 # prepare the basic environments pip install -r requirements.txt # download the foundation models you need bash download.sh # prepare your private openAI private key export OPENAI_API_KEY={Your_Private_Openai_Key} # Start AudioGPT ! python audio-chatgpt.py ``` ================================================ FILE: sound_extraction/model/LASSNet.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from .text_encoder import Text_Encoder from .resunet_film import UNetRes_FiLM class LASSNet(nn.Module): def __init__(self, device='cuda'): super(LASSNet, self).__init__() self.text_embedder = Text_Encoder(device) self.UNet = UNetRes_FiLM(channels=1, cond_embedding_dim=256) def forward(self, x, caption): # x: (Batch, 1, T, 128)) input_ids, attns_mask = self.text_embedder.tokenize(caption) cond_vec = self.text_embedder(input_ids, attns_mask)[0] dec_cond_vec = cond_vec mask = self.UNet(x, cond_vec, dec_cond_vec) mask = torch.sigmoid(mask) return mask def get_tokenizer(self): return self.text_embedder.tokenizer ================================================ FILE: sound_extraction/model/film.py ================================================ import torch import torch.nn as nn class Film(nn.Module): def __init__(self, channels, cond_embedding_dim): super(Film, self).__init__() self.linear = nn.Sequential( nn.Linear(cond_embedding_dim, channels * 2), nn.ReLU(inplace=True), nn.Linear(channels * 2, channels), nn.ReLU(inplace=True) ) def forward(self, data, cond_vec): """ :param data: [batchsize, channels, samples] or [batchsize, channels, T, F] or [batchsize, channels, F, T] :param cond_vec: [batchsize, cond_embedding_dim] :return: """ bias = self.linear(cond_vec) # [batchsize, channels] if len(list(data.size())) == 3: data = data + bias[..., None] elif len(list(data.size())) == 4: data = data + bias[..., None, None] else: print("Warning: The size of input tensor,", data.size(), "is not correct. Film is not working.") return data ================================================ FILE: sound_extraction/model/modules.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import math from .film import Film class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, activation, momentum): super(ConvBlock, self).__init__() self.activation = activation padding = (kernel_size[0] // 2, kernel_size[1] // 2) self.conv1 = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=(1, 1), dilation=(1, 1), padding=padding, bias=False, ) self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum) self.conv2 = nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, stride=(1, 1), dilation=(1, 1), padding=padding, bias=False, ) self.bn2 = nn.BatchNorm2d(out_channels, momentum=momentum) self.init_weights() def init_weights(self): init_layer(self.conv1) init_layer(self.conv2) init_bn(self.bn1) init_bn(self.bn2) def forward(self, x): x = act(self.bn1(self.conv1(x)), self.activation) x = act(self.bn2(self.conv2(x)), self.activation) return x class EncoderBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, downsample, activation, momentum): super(EncoderBlock, self).__init__() self.conv_block = ConvBlock( in_channels, out_channels, kernel_size, activation, momentum ) self.downsample = downsample def forward(self, x): encoder = self.conv_block(x) encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) return encoder_pool, encoder class DecoderBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, upsample, activation, momentum): super(DecoderBlock, self).__init__() self.kernel_size = kernel_size self.stride = upsample self.activation = activation self.conv1 = torch.nn.ConvTranspose2d( in_channels=in_channels, out_channels=out_channels, kernel_size=self.stride, stride=self.stride, padding=(0, 0), bias=False, dilation=(1, 1), ) self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum) self.conv_block2 = ConvBlock( out_channels * 2, out_channels, kernel_size, activation, momentum ) def init_weights(self): init_layer(self.conv1) init_bn(self.bn) def prune(self, x): """Prune the shape of x after transpose convolution.""" padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2) x = x[ :, :, padding[0] : padding[0] - self.stride[0], padding[1] : padding[1] - self.stride[1]] return x def forward(self, input_tensor, concat_tensor): x = act(self.bn1(self.conv1(input_tensor)), self.activation) # from IPython import embed; embed(using=False); os._exit(0) # x = self.prune(x) x = torch.cat((x, concat_tensor), dim=1) x = self.conv_block2(x) return x class EncoderBlockRes1B(nn.Module): def __init__(self, in_channels, out_channels, downsample, activation, momentum): super(EncoderBlockRes1B, self).__init__() size = (3,3) self.conv_block1 = ConvBlockRes(in_channels, out_channels, size, activation, momentum) self.conv_block2 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.downsample = downsample def forward(self, x): encoder = self.conv_block1(x) encoder = self.conv_block2(encoder) encoder = self.conv_block3(encoder) encoder = self.conv_block4(encoder) encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) return encoder_pool, encoder class DecoderBlockRes1B(nn.Module): def __init__(self, in_channels, out_channels, stride, activation, momentum): super(DecoderBlockRes1B, self).__init__() size = (3,3) self.activation = activation self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=size, stride=stride, padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1) self.bn1 = nn.BatchNorm2d(in_channels) self.conv_block2 = ConvBlockRes(out_channels * 2, out_channels, size, activation, momentum) self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.conv_block5 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) def init_weights(self): init_layer(self.conv1) def prune(self, x, both=False): """Prune the shape of x after transpose convolution. """ if(both): x = x[:, :, 0 : - 1, 0:-1] else: x = x[:, :, 0: - 1, :] return x def forward(self, input_tensor, concat_tensor,both=False): x = self.conv1(F.relu_(self.bn1(input_tensor))) x = self.prune(x,both=both) x = torch.cat((x, concat_tensor), dim=1) x = self.conv_block2(x) x = self.conv_block3(x) x = self.conv_block4(x) x = self.conv_block5(x) return x class EncoderBlockRes2BCond(nn.Module): def __init__(self, in_channels, out_channels, downsample, activation, momentum, cond_embedding_dim): super(EncoderBlockRes2BCond, self).__init__() size = (3, 3) self.conv_block1 = ConvBlockResCond(in_channels, out_channels, size, activation, momentum, cond_embedding_dim) self.conv_block2 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) self.downsample = downsample def forward(self, x, cond_vec): encoder = self.conv_block1(x, cond_vec) encoder = self.conv_block2(encoder, cond_vec) encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) return encoder_pool, encoder class DecoderBlockRes2BCond(nn.Module): def __init__(self, in_channels, out_channels, stride, activation, momentum, cond_embedding_dim): super(DecoderBlockRes2BCond, self).__init__() size = (3, 3) self.activation = activation self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=size, stride=stride, padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1) self.bn1 = nn.BatchNorm2d(in_channels) self.conv_block2 = ConvBlockResCond(out_channels * 2, out_channels, size, activation, momentum, cond_embedding_dim) self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) def init_weights(self): init_layer(self.conv1) def prune(self, x, both=False): """Prune the shape of x after transpose convolution. """ if(both): x = x[:, :, 0 : - 1, 0:-1] else: x = x[:, :, 0: - 1, :] return x def forward(self, input_tensor, concat_tensor, cond_vec, both=False): x = self.conv1(F.relu_(self.bn1(input_tensor))) x = self.prune(x, both=both) x = torch.cat((x, concat_tensor), dim=1) x = self.conv_block2(x, cond_vec) x = self.conv_block3(x, cond_vec) return x class EncoderBlockRes4BCond(nn.Module): def __init__(self, in_channels, out_channels, downsample, activation, momentum, cond_embedding_dim): super(EncoderBlockRes4B, self).__init__() size = (3,3) self.conv_block1 = ConvBlockResCond(in_channels, out_channels, size, activation, momentum, cond_embedding_dim) self.conv_block2 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) self.conv_block4 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) self.downsample = downsample def forward(self, x, cond_vec): encoder = self.conv_block1(x, cond_vec) encoder = self.conv_block2(encoder, cond_vec) encoder = self.conv_block3(encoder, cond_vec) encoder = self.conv_block4(encoder, cond_vec) encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) return encoder_pool, encoder class DecoderBlockRes4BCond(nn.Module): def __init__(self, in_channels, out_channels, stride, activation, momentum, cond_embedding_dim): super(DecoderBlockRes4B, self).__init__() size = (3, 3) self.activation = activation self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=size, stride=stride, padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1) self.bn1 = nn.BatchNorm2d(in_channels) self.conv_block2 = ConvBlockResCond(out_channels * 2, out_channels, size, activation, momentum, cond_embedding_dim) self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) self.conv_block4 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) self.conv_block5 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) def init_weights(self): init_layer(self.conv1) def prune(self, x, both=False): """Prune the shape of x after transpose convolution. """ if(both): x = x[:, :, 0 : - 1, 0:-1] else: x = x[:, :, 0: - 1, :] return x def forward(self, input_tensor, concat_tensor, cond_vec, both=False): x = self.conv1(F.relu_(self.bn1(input_tensor))) x = self.prune(x,both=both) x = torch.cat((x, concat_tensor), dim=1) x = self.conv_block2(x, cond_vec) x = self.conv_block3(x, cond_vec) x = self.conv_block4(x, cond_vec) x = self.conv_block5(x, cond_vec) return x class EncoderBlockRes4B(nn.Module): def __init__(self, in_channels, out_channels, downsample, activation, momentum): super(EncoderBlockRes4B, self).__init__() size = (3, 3) self.conv_block1 = ConvBlockRes(in_channels, out_channels, size, activation, momentum) self.conv_block2 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.downsample = downsample def forward(self, x): encoder = self.conv_block1(x) encoder = self.conv_block2(encoder) encoder = self.conv_block3(encoder) encoder = self.conv_block4(encoder) encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) return encoder_pool, encoder class DecoderBlockRes4B(nn.Module): def __init__(self, in_channels, out_channels, stride, activation, momentum): super(DecoderBlockRes4B, self).__init__() size = (3,3) self.activation = activation self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=size, stride=stride, padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1) self.bn1 = nn.BatchNorm2d(in_channels) self.conv_block2 = ConvBlockRes(out_channels * 2, out_channels, size, activation, momentum) self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) self.conv_block5 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) def init_weights(self): init_layer(self.conv1) def prune(self, x, both=False): """Prune the shape of x after transpose convolution. """ if(both): x = x[:, :, 0 : - 1, 0:-1] else: x = x[:, :, 0: - 1, :] return x def forward(self, input_tensor, concat_tensor,both=False): x = self.conv1(F.relu_(self.bn1(input_tensor))) x = self.prune(x,both=both) x = torch.cat((x, concat_tensor), dim=1) x = self.conv_block2(x) x = self.conv_block3(x) x = self.conv_block4(x) x = self.conv_block5(x) return x class ConvBlockResCond(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, activation, momentum, cond_embedding_dim): r"""Residual block. """ super(ConvBlockResCond, self).__init__() self.activation = activation padding = [kernel_size[0] // 2, kernel_size[1] // 2] self.bn1 = nn.BatchNorm2d(in_channels) self.bn2 = nn.BatchNorm2d(out_channels) self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=(1, 1), dilation=(1, 1), padding=padding, bias=False) self.film1 = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim) self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, stride=(1, 1), dilation=(1, 1), padding=padding, bias=False) self.film2 = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim) if in_channels != out_channels: self.shortcut = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) self.film_res = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim) self.is_shortcut = True else: self.is_shortcut = False self.init_weights() def init_weights(self): init_bn(self.bn1) init_bn(self.bn2) init_layer(self.conv1) init_layer(self.conv2) if self.is_shortcut: init_layer(self.shortcut) def forward(self, x, cond_vec): origin = x x = self.conv1(F.leaky_relu_(self.bn1(x), negative_slope=0.01)) x = self.film1(x, cond_vec) x = self.conv2(F.leaky_relu_(self.bn2(x), negative_slope=0.01)) x = self.film2(x, cond_vec) if self.is_shortcut: residual = self.shortcut(origin) residual = self.film_res(residual, cond_vec) return residual + x else: return origin + x class ConvBlockRes(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, activation, momentum): r"""Residual block. """ super(ConvBlockRes, self).__init__() self.activation = activation padding = [kernel_size[0] // 2, kernel_size[1] // 2] self.bn1 = nn.BatchNorm2d(in_channels) self.bn2 = nn.BatchNorm2d(out_channels) self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=(1, 1), dilation=(1, 1), padding=padding, bias=False) self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, stride=(1, 1), dilation=(1, 1), padding=padding, bias=False) if in_channels != out_channels: self.shortcut = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) self.is_shortcut = True else: self.is_shortcut = False self.init_weights() def init_weights(self): init_bn(self.bn1) init_bn(self.bn2) init_layer(self.conv1) init_layer(self.conv2) if self.is_shortcut: init_layer(self.shortcut) def forward(self, x): origin = x x = self.conv1(F.leaky_relu_(self.bn1(x), negative_slope=0.01)) x = self.conv2(F.leaky_relu_(self.bn2(x), negative_slope=0.01)) if self.is_shortcut: return self.shortcut(origin) + x else: return origin + x def init_layer(layer): """Initialize a Linear or Convolutional layer. """ nn.init.xavier_uniform_(layer.weight) if hasattr(layer, 'bias'): if layer.bias is not None: layer.bias.data.fill_(0.) def init_bn(bn): """Initialize a Batchnorm layer. """ bn.bias.data.fill_(0.) bn.weight.data.fill_(1.) def init_gru(rnn): """Initialize a GRU layer. """ def _concat_init(tensor, init_funcs): (length, fan_out) = tensor.shape fan_in = length // len(init_funcs) for (i, init_func) in enumerate(init_funcs): init_func(tensor[i * fan_in: (i + 1) * fan_in, :]) def _inner_uniform(tensor): fan_in = nn.init._calculate_correct_fan(tensor, 'fan_in') nn.init.uniform_(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in)) for i in range(rnn.num_layers): _concat_init( getattr(rnn, 'weight_ih_l{}'.format(i)), [_inner_uniform, _inner_uniform, _inner_uniform] ) torch.nn.init.constant_(getattr(rnn, 'bias_ih_l{}'.format(i)), 0) _concat_init( getattr(rnn, 'weight_hh_l{}'.format(i)), [_inner_uniform, _inner_uniform, nn.init.orthogonal_] ) torch.nn.init.constant_(getattr(rnn, 'bias_hh_l{}'.format(i)), 0) def act(x, activation): if activation == 'relu': return F.relu_(x) elif activation == 'leaky_relu': return F.leaky_relu_(x, negative_slope=0.2) elif activation == 'swish': return x * torch.sigmoid(x) else: raise Exception('Incorrect activation!') ================================================ FILE: sound_extraction/model/resunet_film.py ================================================ from .modules import * import numpy as np class UNetRes_FiLM(nn.Module): def __init__(self, channels, cond_embedding_dim, nsrc=1): super(UNetRes_FiLM, self).__init__() activation = 'relu' momentum = 0.01 self.nsrc = nsrc self.channels = channels self.downsample_ratio = 2 ** 6 # This number equals 2^{#encoder_blocks} self.encoder_block1 = EncoderBlockRes2BCond(in_channels=channels * nsrc, out_channels=32, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block2 = EncoderBlockRes2BCond(in_channels=32, out_channels=64, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block3 = EncoderBlockRes2BCond(in_channels=64, out_channels=128, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block4 = EncoderBlockRes2BCond(in_channels=128, out_channels=256, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block5 = EncoderBlockRes2BCond(in_channels=256, out_channels=384, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block6 = EncoderBlockRes2BCond(in_channels=384, out_channels=384, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.conv_block7 = ConvBlockResCond(in_channels=384, out_channels=384, kernel_size=(3, 3), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block1 = DecoderBlockRes2BCond(in_channels=384, out_channels=384, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block2 = DecoderBlockRes2BCond(in_channels=384, out_channels=384, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block3 = DecoderBlockRes2BCond(in_channels=384, out_channels=256, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block4 = DecoderBlockRes2BCond(in_channels=256, out_channels=128, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block5 = DecoderBlockRes2BCond(in_channels=128, out_channels=64, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block6 = DecoderBlockRes2BCond(in_channels=64, out_channels=32, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.after_conv_block1 = ConvBlockResCond(in_channels=32, out_channels=32, kernel_size=(3, 3), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.after_conv2 = nn.Conv2d(in_channels=32, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), bias=True) self.init_weights() def init_weights(self): init_layer(self.after_conv2) def forward(self, sp, cond_vec, dec_cond_vec): """ Args: input: sp: (batch_size, channels_num, segment_samples) Outputs: output_dict: { 'wav': (batch_size, channels_num, segment_samples), 'sp': (batch_size, channels_num, time_steps, freq_bins)} """ x = sp # Pad spectrogram to be evenly divided by downsample ratio. origin_len = x.shape[2] # time_steps pad_len = int(np.ceil(x.shape[2] / self.downsample_ratio)) * self.downsample_ratio - origin_len x = F.pad(x, pad=(0, 0, 0, pad_len)) x = x[..., 0: x.shape[-1] - 2] # (bs, channels, T, F) # UNet (x1_pool, x1) = self.encoder_block1(x, cond_vec) # x1_pool: (bs, 32, T / 2, F / 2) (x2_pool, x2) = self.encoder_block2(x1_pool, cond_vec) # x2_pool: (bs, 64, T / 4, F / 4) (x3_pool, x3) = self.encoder_block3(x2_pool, cond_vec) # x3_pool: (bs, 128, T / 8, F / 8) (x4_pool, x4) = self.encoder_block4(x3_pool, dec_cond_vec) # x4_pool: (bs, 256, T / 16, F / 16) (x5_pool, x5) = self.encoder_block5(x4_pool, dec_cond_vec) # x5_pool: (bs, 512, T / 32, F / 32) (x6_pool, x6) = self.encoder_block6(x5_pool, dec_cond_vec) # x6_pool: (bs, 1024, T / 64, F / 64) x_center = self.conv_block7(x6_pool, dec_cond_vec) # (bs, 2048, T / 64, F / 64) x7 = self.decoder_block1(x_center, x6, dec_cond_vec) # (bs, 1024, T / 32, F / 32) x8 = self.decoder_block2(x7, x5, dec_cond_vec) # (bs, 512, T / 16, F / 16) x9 = self.decoder_block3(x8, x4, cond_vec) # (bs, 256, T / 8, F / 8) x10 = self.decoder_block4(x9, x3, cond_vec) # (bs, 128, T / 4, F / 4) x11 = self.decoder_block5(x10, x2, cond_vec) # (bs, 64, T / 2, F / 2) x12 = self.decoder_block6(x11, x1, cond_vec) # (bs, 32, T, F) x = self.after_conv_block1(x12, cond_vec) # (bs, 32, T, F) x = self.after_conv2(x) # (bs, channels, T, F) # Recover shape x = F.pad(x, pad=(0, 2)) x = x[:, :, 0: origin_len, :] return x if __name__ == "__main__": model = UNetRes_FiLM(channels=1, cond_embedding_dim=16) cond_vec = torch.randn((1, 16)) dec_vec = cond_vec print(model(torch.randn((1, 1, 1001, 513)), cond_vec, dec_vec).size()) ================================================ FILE: sound_extraction/model/text_encoder.py ================================================ import torch import torch.nn as nn from transformers import * import warnings warnings.filterwarnings('ignore') # pretrained model name: (model class, model tokenizer, output dimension, token style) MODELS = { 'prajjwal1/bert-mini': (BertModel, BertTokenizer), } class Text_Encoder(nn.Module): def __init__(self, device): super(Text_Encoder, self).__init__() self.base_model = 'prajjwal1/bert-mini' self.dropout = 0.1 self.tokenizer = MODELS[self.base_model][1].from_pretrained(self.base_model) self.bert_layer = MODELS[self.base_model][0].from_pretrained(self.base_model, add_pooling_layer=False, hidden_dropout_prob=self.dropout, attention_probs_dropout_prob=self.dropout, output_hidden_states=True) self.linear_layer = nn.Sequential(nn.Linear(256, 256), nn.ReLU(inplace=True)) self.device = device def tokenize(self, caption): # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tokenized = self.tokenizer(caption, add_special_tokens=False, padding=True, return_tensors='pt') input_ids = tokenized['input_ids'] attns_mask = tokenized['attention_mask'] input_ids = input_ids.to(self.device) attns_mask = attns_mask.to(self.device) return input_ids, attns_mask def forward(self, input_ids, attns_mask): # input_ids, attns_mask = self.tokenize(caption) output = self.bert_layer(input_ids=input_ids, attention_mask=attns_mask)[0] cls_embed = output[:, 0, :] text_embed = self.linear_layer(cls_embed) return text_embed, output # text_embed: (batch, hidden_size) ================================================ FILE: sound_extraction/utils/create_mixtures.py ================================================ import torch import numpy as np def add_noise_and_scale(front, noise, snr_l=0, snr_h=0, scale_lower=1.0, scale_upper=1.0): """ :param front: front-head audio, like vocal [samples,channel], will be normlized so any scale will be fine :param noise: noise, [samples,channel], any scale :param snr_l: Optional :param snr_h: Optional :param scale_lower: Optional :param scale_upper: Optional :return: scaled front and noise (noisy = front + noise), all_mel_e2e outputs are noramlized within [-1 , 1] """ snr = None noise, front = normalize_energy_torch(noise), normalize_energy_torch(front) # set noise and vocal to equal range [-1,1] # print("normalize:",torch.max(noise),torch.max(front)) if snr_l is not None and snr_h is not None: front, noise, snr = _random_noise(front, noise, snr_l=snr_l, snr_h=snr_h) # remix them with a specific snr noisy, noise, front = unify_energy_torch(noise + front, noise, front) # normalize noisy, noise and vocal energy into [-1,1] # print("unify:", torch.max(noise), torch.max(front), torch.max(noisy)) scale = _random_scale(scale_lower, scale_upper) # random scale these three signal # print("Scale",scale) noisy, noise, front = noisy * scale, noise * scale, front * scale # apply scale # print("after scale", torch.max(noisy), torch.max(noise), torch.max(front), snr, scale) front, noise = _to_numpy(front), _to_numpy(noise) # [num_samples] mixed_wav = front + noise return front, noise, mixed_wav, snr, scale def _random_scale(lower=0.3, upper=0.9): return float(uniform_torch(lower, upper)) def _random_noise(clean, noise, snr_l=None, snr_h=None): snr = uniform_torch(snr_l,snr_h) clean_weight = 10 ** (float(snr) / 20) return clean, noise/clean_weight, snr def _to_numpy(wav): return np.transpose(wav, (1, 0))[0].numpy() # [num_samples] def normalize_energy(audio, alpha = 1): ''' :param audio: 1d waveform, [batchsize, *], :param alpha: the value of output range from: [-alpha,alpha] :return: 1d waveform which value range from: [-alpha,alpha] ''' val_max = activelev(audio) return (audio / val_max) * alpha def normalize_energy_torch(audio, alpha = 1): ''' If the signal is almost empty(determined by threshold), if will only be divided by 2**15 :param audio: 1d waveform, 2**15 :param alpha: the value of output range from: [-alpha,alpha] :return: 1d waveform which value range from: [-alpha,alpha] ''' val_max = activelev_torch([audio]) return (audio / val_max) * alpha def unify_energy(*args): max_amp = activelev(args) mix_scale = 1.0/max_amp return [x * mix_scale for x in args] def unify_energy_torch(*args): max_amp = activelev_torch(args) mix_scale = 1.0/max_amp return [x * mix_scale for x in args] def activelev(*args): ''' need to update like matlab ''' return np.max(np.abs([*args])) def activelev_torch(*args): ''' need to update like matlab ''' res = [] args = args[0] for each in args: res.append(torch.max(torch.abs(each))) return max(res) def uniform_torch(lower, upper): if(abs(lower-upper)<1e-5): return upper return (upper-lower)*torch.rand(1)+lower if __name__ == "__main__": wav1 = torch.randn(1, 32000) wav2 = torch.randn(1, 32000) target, noise, snr, scale = add_noise_and_scale(wav1, wav2) ================================================ FILE: sound_extraction/utils/stft.py ================================================ import torch import numpy as np import torch.nn.functional as F from torch.autograd import Variable from scipy.signal import get_window import librosa.util as librosa_util from librosa.util import pad_center, tiny # from audio_processing import window_sumsquare def window_sumsquare(window, n_frames, hop_length=512, win_length=1024, n_fft=1024, dtype=np.float32, norm=None): """ # from librosa 0.6 Compute the sum-square envelope of a window function at a given hop length. This is used to estimate modulation effects induced by windowing observations in short-time fourier transforms. Parameters ---------- window : string, tuple, number, callable, or list-like Window specification, as in `get_window` n_frames : int > 0 The number of analysis frames hop_length : int > 0 The number of samples to advance between frames win_length : [optional] The length of the window function. By default, this matches `n_fft`. n_fft : int > 0 The length of each analysis frame. dtype : np.dtype The data type of the output Returns ------- wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` The sum-squared envelope of the window function """ if win_length is None: win_length = n_fft n = n_fft + hop_length * (n_frames - 1) x = np.zeros(n, dtype=dtype) # Compute the squared window at the desired length win_sq = get_window(window, win_length, fftbins=True) win_sq = librosa_util.normalize(win_sq, norm=norm)**2 win_sq = librosa_util.pad_center(win_sq, n_fft) # Fill the envelope for i in range(n_frames): sample = i * hop_length x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))] return x class STFT(torch.nn.Module): """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" def __init__(self, filter_length=1024, hop_length=512, win_length=1024, window='hann'): super(STFT, self).__init__() self.filter_length = filter_length self.hop_length = hop_length self.win_length = win_length self.window = window self.forward_transform = None scale = self.filter_length / self.hop_length fourier_basis = np.fft.fft(np.eye(self.filter_length)) cutoff = int((self.filter_length / 2 + 1)) fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]) forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) inverse_basis = torch.FloatTensor( np.linalg.pinv(scale * fourier_basis).T[:, None, :]) if window is not None: assert(filter_length >= win_length) # get window and zero center pad it to filter_length fft_window = get_window(window, win_length, fftbins=True) fft_window = pad_center(fft_window, filter_length) fft_window = torch.from_numpy(fft_window).float() # window the bases forward_basis *= fft_window inverse_basis *= fft_window self.register_buffer('forward_basis', forward_basis.float()) self.register_buffer('inverse_basis', inverse_basis.float()) def transform(self, input_data): num_batches = input_data.size(0) num_samples = input_data.size(1) self.num_samples = num_samples # similar to librosa, reflect-pad the input input_data = input_data.view(num_batches, 1, num_samples) input_data = F.pad( input_data.unsqueeze(1), (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), mode='reflect') input_data = input_data.squeeze(1) forward_transform = F.conv1d( input_data, Variable(self.forward_basis, requires_grad=False), stride=self.hop_length, padding=0) cutoff = int((self.filter_length / 2) + 1) real_part = forward_transform[:, :cutoff, :] imag_part = forward_transform[:, cutoff:, :] magnitude = torch.sqrt(real_part**2 + imag_part**2) phase = torch.autograd.Variable( torch.atan2(imag_part.data, real_part.data)) return magnitude, phase # [batch_size, F(513), T(1251)] def inverse(self, magnitude, phase): recombine_magnitude_phase = torch.cat( [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1) inverse_transform = F.conv_transpose1d( recombine_magnitude_phase, Variable(self.inverse_basis, requires_grad=False), stride=self.hop_length, padding=0) if self.window is not None: window_sum = window_sumsquare( self.window, magnitude.size(-1), hop_length=self.hop_length, win_length=self.win_length, n_fft=self.filter_length, dtype=np.float32) # remove modulation effects approx_nonzero_indices = torch.from_numpy( np.where(window_sum > tiny(window_sum))[0]) window_sum = torch.autograd.Variable( torch.from_numpy(window_sum), requires_grad=False) window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices] # scale by hop ratio inverse_transform *= float(self.filter_length) / self.hop_length inverse_transform = inverse_transform[:, :, int(self.filter_length/2):] inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):] return inverse_transform #[batch_size, 1, sample_num] def forward(self, input_data): self.magnitude, self.phase = self.transform(input_data) reconstruction = self.inverse(self.magnitude, self.phase) return reconstruction if __name__ == '__main__': a = torch.randn(4, 320000) stft = STFT() mag, phase = stft.transform(a) # rec_a = stft.inverse(mag, phase) print(mag.shape) ================================================ FILE: sound_extraction/utils/wav_io.py ================================================ import librosa import librosa.filters import math import numpy as np import scipy.io.wavfile def load_wav(path): max_length = 32000 * 10 wav = librosa.core.load(path, sr=32000)[0] if len(wav) > max_length: audio = wav[0:max_length] # pad audio to max length, 10s for AudioCaps if len(wav) < max_length: # audio = torch.nn.functional.pad(audio, (0, self.max_length - audio.size(1)), 'constant') wav = np.pad(wav, (0, max_length - len(wav)), 'constant') wav = wav[...,None] return wav def save_wav(wav, path): wav *= 32767 / max(0.01, np.max(np.abs(wav))) scipy.io.wavfile.write(path, 32000, wav.astype(np.int16)) ================================================ FILE: text_to_audio/Make_An_Audio/configs/img_to_audio/img2audio_args.yaml ================================================ model: base_learning_rate: 1.0e-05 target: ldm.models.diffusion.ddpm_audio.LatentDiffusion_audio params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: caption image_size: 32 # unused mel_dim: 10 # 80 // 2^3 mel_length: 78 # 624 // 2^3 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_by_std: True use_ema: False scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [10000] cycle_lengths: [10000000000000] f_start: [1.e-6] f_max: [1.] f_min: [ 1.] unet_config: target: ldm.modules.diffusionmodules.custom_openaimodel.UNetModel params: image_size: 32 # ununsed in_channels: 4 out_channels: 4 model_channels: 256 attention_resolutions: - 1 - 2 num_res_blocks: 2 channel_mult: # num_down = len(ch_mult)-1 - 1 - 2 num_head_channels: 32 use_spatial_transformer: true transformer_depth: 1 context_dim: 1024 use_context_project: false first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 848 in_channels: 1 out_ch: 1 ch: 128 ch_mult: [ 1, 2, 2, 4 ] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [106, 212] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenGlobalNormOpenCLIPEmbedder params: freeze: True delvisual: False ================================================ FILE: text_to_audio/Make_An_Audio/configs/inpaint/txt2audio_args.yaml ================================================ model: base_learning_rate: 1.0e-05 target: ldm.models.diffusion.ddpm_audio.LatentDiffusion_audio params: linear_start: 0.0015 linear_end: 0.0205 log_every_t: 100 timesteps: 1000 loss_type: l1 first_stage_key: image cond_stage_key: masked_image image_size: 32 # unused mel_dim: 10 # 80 // 2^3 mel_length: 106 # 848 // 2^3 channels: 4 concat_mode: true monitor: val/loss use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaWarmUpCosineScheduler params: verbosity_interval: 0 warm_up_steps: 1000 max_decay_steps: 50000 lr_start: 0.001 lr_max: 0.1 lr_min: 0.0001 unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # ununsed in_channels: 9 # 4 + 1 + 4 out_channels: 4 model_channels: 320 attention_resolutions: - 1 - 2 num_res_blocks: 2 channel_mult: # num_down = len(ch_mult)-1 - 1 - 2 num_heads: 8 resblock_updown: true first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ckpt_path: # /apdcephfs/share_1316500/nlphuang/results/Text_to_audio/ae15/2022-12-15T22-24-00_mixdata_kl_4_tile/epoch=000009-v2.ckpt ddconfig: double_z: true z_channels: 4 resolution: 848 in_channels: 1 out_ch: 1 ch: 128 ch_mult: [ 1, 2, 2, 4 ] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [106, 212] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: __is_first_stage__ ================================================ FILE: text_to_audio/Make_An_Audio/configs/text_to_audio/clap_args.yaml ================================================ # TEXT ENCODER CONFIG text_model: 'bert-base-uncased' text_len: 100 transformer_embed_dim: 768 freeze_text_encoder_weights: True # AUDIO ENCODER CONFIG audioenc_name: 'Cnn14' out_emb: 2048 sampling_rate: 44100 duration: 9 fmin: 50 fmax: 14000 n_fft: 1028 hop_size: 320 mel_bins: 64 window_size: 1024 # PROJECTION SPACE CONFIG d_proj: 1024 temperature: 0.003 # TRAINING AND EVALUATION CONFIG num_classes: 527 batch_size: 1024 demo: False ================================================ FILE: text_to_audio/Make_An_Audio/configs/text_to_audio/hifigan_args.yaml ================================================ adam_b1: 0.8 adam_b2: 0.99 batch_size: 24 dist_config: dist_backend: nccl dist_url: tcp://localhost:54321 world_size: 1 fmax: 8000 fmax_for_loss: null fmin: 0 hop_size: 256 learning_rate: 0.0002 lr_decay: 0.999 n_fft: 1024 num_gpus: 0 num_mels: 80 num_workers: 4 resblock: '1' resblock_dilation_sizes: - - 1 - 3 - 5 - - 1 - 3 - 5 - - 1 - 3 - 5 resblock_kernel_sizes: - 3 - 7 - 11 sampling_rate: 16000 seed: 1234 segment_size: 8192 upsample_initial_channel: 512 upsample_kernel_sizes: - 16 - 16 - 4 - 4 upsample_rates: - 8 - 8 - 2 - 2 win_size: 1024 ================================================ FILE: text_to_audio/Make_An_Audio/configs/text_to_audio/txt2audio_args.yaml ================================================ model: base_learning_rate: 1.0e-05 target: ldm.models.diffusion.ddpm_audio.LatentDiffusion_audio params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: caption image_size: 32 # unused mel_dim: 10 # 80 // 2^3 mel_length: 78 # 624 // 2^3 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_by_std: True use_ema: False scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [10000] cycle_lengths: [10000000000000] f_start: [1.e-6] f_max: [1.] f_min: [ 1.] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # ununsed in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: - 1 - 2 num_res_blocks: 2 channel_mult: # num_down = len(ch_mult)-1 - 1 - 2 num_heads: 8 use_spatial_transformer: true transformer_depth: 1 context_dim: 1024 use_checkpoint: true legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ckpt_path: ddconfig: double_z: true z_channels: 4 resolution: 848 in_channels: 1 out_ch: 1 ch: 128 ch_mult: [ 1, 2, 2, 4 ] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [106, 212] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLAPEmbedder params: weights_path: useful_ckpts/CLAP/CLAP_weights_2022.pth ckpt_path: useful_ckpts/ta40multi_epoch=000085.ckpt ================================================ FILE: text_to_audio/Make_An_Audio/ldm/data/extract_mel_spectrogram.py ================================================ import argparse import os import os.path as P from copy import deepcopy from functools import partial from glob import glob from multiprocessing import Pool from pathlib import Path import librosa import numpy as np import torchvision class MelSpectrogram(object): def __init__(self, sr, nfft, fmin, fmax, nmels, hoplen, spec_power, inverse=False): self.sr = sr self.nfft = nfft self.fmin = fmin self.fmax = fmax self.nmels = nmels self.hoplen = hoplen self.spec_power = spec_power self.inverse = inverse self.mel_basis = librosa.filters.mel(sr=sr, n_fft=nfft, fmin=fmin, fmax=fmax, n_mels=nmels) def __call__(self, x): if self.inverse: spec = librosa.feature.inverse.mel_to_stft( x, sr=self.sr, n_fft=self.nfft, fmin=self.fmin, fmax=self.fmax, power=self.spec_power ) wav = librosa.griffinlim(spec, hop_length=self.hoplen) return wav else: spec = np.abs(librosa.stft(x, n_fft=self.nfft, hop_length=self.hoplen)) ** self.spec_power mel_spec = np.dot(self.mel_basis, spec) return mel_spec class LowerThresh(object): def __init__(self, min_val, inverse=False): self.min_val = min_val self.inverse = inverse def __call__(self, x): if self.inverse: return x else: return np.maximum(self.min_val, x) class Add(object): def __init__(self, val, inverse=False): self.inverse = inverse self.val = val def __call__(self, x): if self.inverse: return x - self.val else: return x + self.val class Subtract(Add): def __init__(self, val, inverse=False): self.inverse = inverse self.val = val def __call__(self, x): if self.inverse: return x + self.val else: return x - self.val class Multiply(object): def __init__(self, val, inverse=False) -> None: self.val = val self.inverse = inverse def __call__(self, x): if self.inverse: return x / self.val else: return x * self.val class Divide(Multiply): def __init__(self, val, inverse=False): self.inverse = inverse self.val = val def __call__(self, x): if self.inverse: return x * self.val else: return x / self.val class Log10(object): def __init__(self, inverse=False): self.inverse = inverse def __call__(self, x): if self.inverse: return 10 ** x else: return np.log10(x) class Clip(object): def __init__(self, min_val, max_val, inverse=False): self.min_val = min_val self.max_val = max_val self.inverse = inverse def __call__(self, x): if self.inverse: return x else: return np.clip(x, self.min_val, self.max_val) class TrimSpec(object): def __init__(self, max_len, inverse=False): self.max_len = max_len self.inverse = inverse def __call__(self, x): if self.inverse: return x else: return x[:, :self.max_len] class MaxNorm(object): def __init__(self, inverse=False): self.inverse = inverse self.eps = 1e-10 def __call__(self, x): if self.inverse: return x else: return x / (x.max() + self.eps) TRANSFORMS_16000 = torchvision.transforms.Compose([ MelSpectrogram(sr=16000, nfft=1024, fmin=125, fmax=7600, nmels=80, hoplen=1024//4, spec_power=1), LowerThresh(1e-5), Log10(), Multiply(20), Subtract(20), Add(100), Divide(100), Clip(0, 1.0) # TrimSpec(860) ]) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/lr_scheduler.py ================================================ import numpy as np class LambdaWarmUpCosineScheduler: """ note: use with a base_lr of 1.0 """ def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): self.lr_warm_up_steps = warm_up_steps self.lr_start = lr_start self.lr_min = lr_min self.lr_max = lr_max self.lr_max_decay_steps = max_decay_steps self.last_lr = 0. self.verbosity_interval = verbosity_interval def schedule(self, n, **kwargs): if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") if n < self.lr_warm_up_steps: lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start self.last_lr = lr return lr else: t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) t = min(t, 1.0) lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( 1 + np.cos(t * np.pi)) self.last_lr = lr return lr def __call__(self, n, **kwargs): return self.schedule(n,**kwargs) class LambdaWarmUpCosineScheduler2: """ supports repeated iterations, configurable via lists note: use with a base_lr of 1.0. """ def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) self.lr_warm_up_steps = warm_up_steps self.f_start = f_start self.f_min = f_min self.f_max = f_max self.cycle_lengths = cycle_lengths self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) self.last_f = 0. self.verbosity_interval = verbosity_interval def find_in_interval(self, n): interval = 0 for cl in self.cum_cycles[1:]: if n <= cl: return interval interval += 1 def schedule(self, n, **kwargs): cycle = self.find_in_interval(n) n = n - self.cum_cycles[cycle] if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}") if n < self.lr_warm_up_steps[cycle]: f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] self.last_f = f return f else: t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) t = min(t, 1.0) f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( 1 + np.cos(t * np.pi)) self.last_f = f return f def __call__(self, n, **kwargs): return self.schedule(n, **kwargs) class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): def schedule(self, n, **kwargs): cycle = self.find_in_interval(n) n = n - self.cum_cycles[cycle] if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}") if n < self.lr_warm_up_steps[cycle]: f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] self.last_f = f return f else: f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle]) self.last_f = f return f ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/autoencoder.py ================================================ import os import torch import pytorch_lightning as pl import torch.nn.functional as F from contextlib import contextmanager from packaging import version import numpy as np from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.modules.distributions.distributions import DiagonalGaussianDistribution from torch.optim.lr_scheduler import LambdaLR from ldm.util import instantiate_from_config # from icecream import ic class VQModel(pl.LightningModule): def __init__(self, ddconfig, lossconfig, n_embed, embed_dim, ckpt_path=None, ignore_keys=[], image_key="image", colorize_nlabels=None, monitor=None, batch_resize_range=None, scheduler_config=None, lr_g_factor=1.0, remap=None, sane_index_shape=False, # tell vector quantizer to return indices as bhw use_ema=False ): super().__init__() self.embed_dim = embed_dim self.n_embed = n_embed self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, remap=remap, sane_index_shape=sane_index_shape) self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor self.batch_resize_range = batch_resize_range if self.batch_resize_range is not None: print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") self.use_ema = use_ema if self.use_ema: self.model_ema = LitEma(self) print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) self.scheduler_config = scheduler_config self.lr_g_factor = lr_g_factor @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.parameters()) self.model_ema.copy_to(self) if context is not None: print(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.parameters()) if context is not None: print(f"{context}: Restored training weights") def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") print(f"Unexpected Keys: {unexpected}") def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self) def encode(self, x): h = self.encoder(x) h = self.quant_conv(h) quant, emb_loss, info = self.quantize(h) return quant, emb_loss, info def encode_to_prequant(self, x): h = self.encoder(x) h = self.quant_conv(h) return h def decode(self, quant): quant = self.post_quant_conv(quant) dec = self.decoder(quant) return dec def decode_code(self, code_b): quant_b = self.quantize.embed_code(code_b) dec = self.decode(quant_b) return dec def forward(self, input, return_pred_indices=False): quant, diff, (_,_,ind) = self.encode(input) dec = self.decode(quant) if return_pred_indices: return dec, diff, ind return dec, diff def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() if self.batch_resize_range is not None: lower_size = self.batch_resize_range[0] upper_size = self.batch_resize_range[1] if self.global_step <= 4: # do the first few batches with max size to avoid later oom new_resize = upper_size else: new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) if new_resize != x.shape[2]: x = F.interpolate(x, size=new_resize, mode="bicubic") x = x.detach() return x def training_step(self, batch, batch_idx, optimizer_idx): # https://github.com/pytorch/pytorch/issues/37142 # try not to fool the heuristics x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) if optimizer_idx == 0: # autoencode aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train", predicted_indices=ind) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) return aeloss if optimizer_idx == 1: # discriminator discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) return discloss def validation_step(self, batch, batch_idx): log_dict = self._validation_step(batch, batch_idx) with self.ema_scope(): log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") return log_dict def _validation_step(self, batch, batch_idx, suffix=""): x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step, last_layer=self.get_last_layer(), split="val"+suffix, predicted_indices=ind ) discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step, last_layer=self.get_last_layer(), split="val"+suffix, predicted_indices=ind ) rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] self.log(f"val{suffix}/rec_loss", rec_loss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) self.log(f"val{suffix}/aeloss", aeloss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) if version.parse(pl.__version__) >= version.parse('1.4.0'): del log_dict_ae[f"val{suffix}/rec_loss"] self.log_dict(log_dict_ae) self.log_dict(log_dict_disc) return self.log_dict def test_step(self, batch, batch_idx): x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) reconstructions = (xrec + 1)/2 # to mel scale test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') if not os.path.exists(savedir): os.makedirs(savedir) file_names = batch['f_name'] # print(f"reconstructions.shape:{reconstructions.shape}",file_names) reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim for b in range(reconstructions.shape[0]): vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy') np.save(save_img_path,reconstructions[b]) return None def configure_optimizers(self): lr_d = self.learning_rate lr_g = self.lr_g_factor*self.learning_rate print("lr_d", lr_d) print("lr_g", lr_g) opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quantize.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr_g, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)) if self.scheduler_config is not None: scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }, { 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }, ] return [opt_ae, opt_disc], scheduler return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): log = dict() x = self.get_input(batch, self.image_key) x = x.to(self.device) if only_inputs: log["inputs"] = x return log xrec, _ = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["inputs"] = x log["reconstructions"] = xrec if plot_ema: with self.ema_scope(): xrec_ema, _ = self(x) if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) log["reconstructions_ema"] = xrec_ema return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class VQModelInterface(VQModel): def __init__(self, embed_dim, *args, **kwargs): super().__init__(embed_dim=embed_dim, *args, **kwargs) self.embed_dim = embed_dim def encode(self, x):# VQModel的quantize写在encoder里,VQModelInterface则将其写在decoder里 h = self.encoder(x) h = self.quant_conv(h) return h def decode(self, h, force_not_quantize=False): # also go through quantization layer if not force_not_quantize: quant, emb_loss, info = self.quantize(h) else: quant = h quant = self.post_quant_conv(quant) dec = self.decoder(quant) return dec class AutoencoderKL(pl.LightningModule): def __init__(self, ddconfig, lossconfig, embed_dim, ckpt_path=None, ignore_keys=[], image_key="image", colorize_nlabels=None, monitor=None, ): super().__init__() self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) assert ddconfig["double_z"] self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) # self.automatic_optimization = False # hjw for debug def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] self.load_state_dict(sd, strict=False) print(f"Restored from {path}") def encode(self, x): h = self.encoder(x) moments = self.quant_conv(h) posterior = DiagonalGaussianDistribution(moments) return posterior def decode(self, z): z = self.post_quant_conv(z) dec = self.decoder(z) return dec def forward(self, input, sample_posterior=True): posterior = self.encode(input) if sample_posterior: z = posterior.sample() else: z = posterior.mode() dec = self.decode(z) return dec, posterior def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() return x def training_step(self, batch, batch_idx, optimizer_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) if optimizer_idx == 0: # train encoder+decoder+logvar aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) return aeloss if optimizer_idx == 1: # train the discriminator discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) return discloss def validation_step(self, batch, batch_idx): # self.log_images(batch,only_inputs=False,save_dir='mel_result_ae13_26/fake_class') return self.log_dict def test_step(self, batch, batch_idx): test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') os.makedirs(savedir,exist_ok=True) inputs = self.get_input(batch, self.image_key)# inputs shape:(b,c,mel_len,T) or (b,c,h,w) # ic(inputs.shape) # inputs = inputs[...,:624] # ic(inputs.shape) xrec, posterior = self(inputs)# reconstructions:(b,c,mel_len,T) or (b,c,h,w) file_names = batch['f_name'] # print(f"reconstructions.shape:{reconstructions.shape}",file_names) for b in range(len(file_names)): rcon = (xrec[b].squeeze().detach().cpu().numpy() + 1) / 2 # to mel scale,squeeze channel dim vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy') np.save(save_img_path,rcon) return None def configure_optimizers(self): lr = self.learning_rate opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)) return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight @torch.no_grad() def log_images(self, batch, only_inputs=False,save_dir = 'mel_result_ae13_26_debug/fake_class', **kwargs): # 在main.py的on_validation_batch_end中调用 log = dict() x = self.get_input(batch, self.image_key) x = x.to(self.device) if not only_inputs: xrec, posterior = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["samples"] = self.decode(torch.randn_like(posterior.sample())) log["reconstructions"] = xrec log["inputs"] = x return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class IdentityFirstStage(torch.nn.Module): def __init__(self, *args, vq_interface=False, **kwargs): self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff super().__init__() def encode(self, x, *args, **kwargs): return x def decode(self, x, *args, **kwargs): return x def quantize(self, x, *args, **kwargs): if self.vq_interface: return x, None, [None, None, None] return x def forward(self, x, *args, **kwargs): return x ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/autoencoder_multi.py ================================================ """ 与autoencoder.py的区别在于，autoencoder.py计算loss时只有一个discriminator，而此处又多了个multiwindowDiscriminator，所以优化器 优化的参数改为： opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()), lr=lr, betas=(0.5, 0.9)) """ import os import torch import pytorch_lightning as pl import torch.nn.functional as F from contextlib import contextmanager from packaging import version import numpy as np from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.modules.distributions.distributions import DiagonalGaussianDistribution from torch.optim.lr_scheduler import LambdaLR from ldm.util import instantiate_from_config class AutoencoderKL(pl.LightningModule): def __init__(self, ddconfig, lossconfig, embed_dim, ckpt_path=None, ignore_keys=[], image_key="image", colorize_nlabels=None, monitor=None, ): super().__init__() self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) assert ddconfig["double_z"] self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] self.load_state_dict(sd, strict=False) print(f"Restored from {path}") def encode(self, x): h = self.encoder(x) moments = self.quant_conv(h) posterior = DiagonalGaussianDistribution(moments) return posterior def decode(self, z): z = self.post_quant_conv(z) dec = self.decoder(z) return dec def forward(self, input, sample_posterior=True): posterior = self.encode(input) if sample_posterior: z = posterior.sample() else: z = posterior.mode() dec = self.decode(z) return dec, posterior def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() return x def training_step(self, batch, batch_idx, optimizer_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) if optimizer_idx == 0: # train encoder+decoder+logvar aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) return aeloss if optimizer_idx == 1: # train the discriminator discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) return discloss def validation_step(self, batch, batch_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, last_layer=self.get_last_layer(), split="val") discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, last_layer=self.get_last_layer(), split="val") self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) self.log_dict(log_dict_ae) self.log_dict(log_dict_disc) return self.log_dict def test_step(self, batch, batch_idx): inputs = self.get_input(batch, self.image_key)# inputs shape:(b,c,mel_len,T) or (b,c,h,w) reconstructions, posterior = self(inputs)# reconstructions:(b,c,mel_len,T) or (b,c,h,w) reconstructions = (reconstructions + 1)/2 # to mel scale test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') if not os.path.exists(savedir): os.makedirs(savedir) file_names = batch['f_name'] # print(f"reconstructions.shape:{reconstructions.shape}",file_names) reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim for b in range(reconstructions.shape[0]): vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy') np.save(save_img_path,reconstructions[b]) return None def configure_optimizers(self): lr = self.learning_rate opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()), lr=lr, betas=(0.5, 0.9)) return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight @torch.no_grad() def log_images(self, batch, only_inputs=False, **kwargs): log = dict() x = self.get_input(batch, self.image_key) x = x.to(self.device) if not only_inputs: xrec, posterior = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["samples"] = self.decode(torch.randn_like(posterior.sample())) log["reconstructions"] = xrec log["inputs"] = x return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class IdentityFirstStage(torch.nn.Module): def __init__(self, *args, vq_interface=False, **kwargs): self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff super().__init__() def encode(self, x, *args, **kwargs): return x def decode(self, x, *args, **kwargs): return x def quantize(self, x, *args, **kwargs): if self.vq_interface: return x, None, [None, None, None] return x def forward(self, x, *args, **kwargs): return x ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/diffusion/__init__.py ================================================ ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/diffusion/classifier.py ================================================ import os import torch import pytorch_lightning as pl from omegaconf import OmegaConf from torch.nn import functional as F from torch.optim import AdamW from torch.optim.lr_scheduler import LambdaLR from copy import deepcopy from einops import rearrange from glob import glob from natsort import natsorted from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config __models__ = { 'class_label': EncoderUNetModel, 'segmentation': UNetModel } def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self class NoisyLatentImageClassifier(pl.LightningModule): def __init__(self, diffusion_path, num_classes, ckpt_path=None, pool='attention', label_key=None, diffusion_ckpt_path=None, scheduler_config=None, weight_decay=1.e-2, log_steps=10, monitor='val/loss', *args, **kwargs): super().__init__(*args, **kwargs) self.num_classes = num_classes # get latest config of diffusion model diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1] self.diffusion_config = OmegaConf.load(diffusion_config).model self.diffusion_config.params.ckpt_path = diffusion_ckpt_path self.load_diffusion() self.monitor = monitor self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 self.log_time_interval = self.diffusion_model.num_timesteps // log_steps self.log_steps = log_steps self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \ else self.diffusion_model.cond_stage_key assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params' if self.label_key not in __models__: raise NotImplementedError() self.load_classifier(ckpt_path, pool) self.scheduler_config = scheduler_config self.use_scheduler = self.scheduler_config is not None self.weight_decay = weight_decay def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(f"Unexpected Keys: {unexpected}") def load_diffusion(self): model = instantiate_from_config(self.diffusion_config) self.diffusion_model = model.eval() self.diffusion_model.train = disabled_train for param in self.diffusion_model.parameters(): param.requires_grad = False def load_classifier(self, ckpt_path, pool): model_config = deepcopy(self.diffusion_config.params.unet_config.params) model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels model_config.out_channels = self.num_classes if self.label_key == 'class_label': model_config.pool = pool self.model = __models__[self.label_key](**model_config) if ckpt_path is not None: print('#####################################################################') print(f'load from ckpt "{ckpt_path}"') print('#####################################################################') self.init_from_ckpt(ckpt_path) @torch.no_grad() def get_x_noisy(self, x, t, noise=None): noise = default(noise, lambda: torch.randn_like(x)) continuous_sqrt_alpha_cumprod = None if self.diffusion_model.use_continuous_noise: continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) # todo: make sure t+1 is correct here return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise, continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod) def forward(self, x_noisy, t, *args, **kwargs): return self.model(x_noisy, t) @torch.no_grad() def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = rearrange(x, 'b h w c -> b c h w') x = x.to(memory_format=torch.contiguous_format).float() return x @torch.no_grad() def get_conditioning(self, batch, k=None): if k is None: k = self.label_key assert k is not None, 'Needs to provide label key' targets = batch[k].to(self.device) if self.label_key == 'segmentation': targets = rearrange(targets, 'b h w c -> b c h w') for down in range(self.numd): h, w = targets.shape[-2:] targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest') # targets = rearrange(targets,'b c h w -> b h w c') return targets def compute_top_k(self, logits, labels, k, reduction="mean"): _, top_ks = torch.topk(logits, k, dim=1) if reduction == "mean": return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() elif reduction == "none": return (top_ks == labels[:, None]).float().sum(dim=-1) def on_train_epoch_start(self): # save some memory self.diffusion_model.model.to('cpu') @torch.no_grad() def write_logs(self, loss, logits, targets): log_prefix = 'train' if self.training else 'val' log = {} log[f"{log_prefix}/loss"] = loss.mean() log[f"{log_prefix}/acc@1"] = self.compute_top_k( logits, targets, k=1, reduction="mean" ) log[f"{log_prefix}/acc@5"] = self.compute_top_k( logits, targets, k=5, reduction="mean" ) self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True) self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False) self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True) lr = self.optimizers().param_groups[0]['lr'] self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) def shared_step(self, batch, t=None): x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key) targets = self.get_conditioning(batch) if targets.dim() == 4: targets = targets.argmax(dim=1) if t is None: t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long() else: t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() x_noisy = self.get_x_noisy(x, t) logits = self(x_noisy, t) loss = F.cross_entropy(logits, targets, reduction='none') self.write_logs(loss.detach(), logits.detach(), targets.detach()) loss = loss.mean() return loss, logits, x_noisy, targets def training_step(self, batch, batch_idx): loss, *_ = self.shared_step(batch) return loss def reset_noise_accs(self): self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)} def on_validation_start(self): self.reset_noise_accs() @torch.no_grad() def validation_step(self, batch, batch_idx): loss, *_ = self.shared_step(batch) for t in self.noisy_acc: _, logits, _, targets = self.shared_step(batch, t) self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean')) self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean')) return loss def configure_optimizers(self): optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) if self.use_scheduler: scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [optimizer], scheduler return optimizer @torch.no_grad() def log_images(self, batch, N=8, *args, **kwargs): log = dict() x = self.get_input(batch, self.diffusion_model.first_stage_key) log['inputs'] = x y = self.get_conditioning(batch) if self.label_key == 'class_label': y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['labels'] = y if ismap(y): log['labels'] = self.diffusion_model.to_rgb(y) for step in range(self.log_steps): current_time = step * self.log_time_interval _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) log[f'inputs@t{current_time}'] = x_noisy pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) pred = rearrange(pred, 'b h w c -> b c h w') log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred) for key in log: log[key] = log[key][:N] return log ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/diffusion/ddim.py ================================================ """SAMPLING ONLY.""" import torch import numpy as np from tqdm import tqdm from functools import partial from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \ extract_into_tensor class DDIMSampler(object): def __init__(self, model, schedule="linear", **kwargs): super().__init__() self.model = model self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def register_buffer(self, name, attr): if type(attr) == torch.Tensor: # if attr.device != torch.device("cuda"): # attr = attr.to(torch.device("cuda")) attr = attr.to(self.device) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) alphas_cumprod = self.model.alphas_cumprod assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) self.register_buffer('betas', to_torch(self.model.betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) # ddim sampling parameters ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta,verbose=verbose) self.register_buffer('ddim_sigmas', ddim_sigmas) self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) @torch.no_grad() def sample(self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **kwargs ): if conditioning is not None: if isinstance(conditioning, dict): ctmp = conditioning[list(conditioning.keys())[0]] while isinstance(ctmp, list): ctmp = ctmp[0] cbs = ctmp.shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") else: if conditioning.shape[0] != batch_size: print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) # sampling C, H, W = shape size = (batch_size, C, H, W) # print(f'Data shape for DDIM sampling is {size}, eta {eta}') samples, intermediates = self.ddim_sampling(conditioning, size, callback=callback, img_callback=img_callback, quantize_denoised=quantize_x0, mask=mask, x0=x0, ddim_use_original_steps=False, noise_dropout=noise_dropout, temperature=temperature, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, ) return samples, intermediates @torch.no_grad() def ddim_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None,): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] # iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) for i, step in enumerate(time_range): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) if mask is not None: assert x0 is not None img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? img = img_orig * mask + (1. - mask) * img outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning) img, pred_x0 = outs if callback: callback(i) if img_callback: img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates @torch.no_grad() def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None): b, *_, device = *x.shape, x.device if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t] * 2) if isinstance(c, dict): assert isinstance(unconditional_conditioning, dict) c_in = dict() for k in c: if isinstance(c[k], list): c_in[k] = [torch.cat([ unconditional_conditioning[k][i], c[k][i]]) for i in range(len(c[k]))] else: c_in[k] = torch.cat([ unconditional_conditioning[k], c[k]]) elif isinstance(c, list): c_in = list() assert isinstance(unconditional_conditioning, list) for i in range(len(c)): c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) else: c_in = torch.cat([unconditional_conditioning, c])# c/uc shape [b,seq_len=77,dim=1024],c_in shape [b*2,seq_len,dim] e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) if score_corrector is not None: assert self.model.parameterization == "eps" e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas # select parameters corresponding to the currently considered timestep a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 @torch.no_grad() def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): # fast, but does not allow for exact reconstruction # t serves as an index to gather the correct alphas if use_original_steps: sqrt_alphas_cumprod = self.sqrt_alphas_cumprod sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod else: sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas if noise is None: noise = torch.randn_like(x0) return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) @torch.no_grad() def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, use_original_steps=False): timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps timesteps = timesteps[:t_start] time_range = np.flip(timesteps) total_steps = timesteps.shape[0] # print(f"Running DDIM Sampling with {total_steps} timesteps") # iterator = tqdm(time_range, desc='Decoding image', total=total_steps) x_dec = x_latent for i, step in enumerate(time_range): index = total_steps - i - 1 ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning) return x_dec ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/diffusion/ddpm.py ================================================ """ wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py https://github.com/CompVis/taming-transformers -- merci """ import torch import torch.nn as nn import numpy as np import pytorch_lightning as pl from torch.optim.lr_scheduler import LambdaLR from einops import rearrange, repeat from contextlib import contextmanager from functools import partial from tqdm import tqdm from torchvision.utils import make_grid from pytorch_lightning.utilities.distributed import rank_zero_only from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config from ldm.modules.ema import LitEma from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like from ldm.models.diffusion.ddim import DDIMSampler __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self def uniform_on_device(r1, r2, shape, device): return (r1 - r2) * torch.rand(*shape, device=device) + r2 class DDPM(pl.LightningModule): # classic DDPM with Gaussian diffusion, in image space def __init__(self, unet_config, timesteps=1000, beta_schedule="linear", loss_type="l2", ckpt_path=None, ignore_keys=[], load_only_unet=False, monitor="val/loss", use_ema=True, first_stage_key="image", image_size=256, channels=3, log_every_t=100, clip_denoised=True, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, given_betas=None, original_elbo_weight=0., v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta l_simple_weight=1., conditioning_key=None, parameterization="eps", # all config files uses "eps" scheduler_config=None, use_positional_encodings=False, learn_logvar=False, logvar_init=0., ): super().__init__() assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' self.parameterization = parameterization print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") self.cond_stage_model = None self.clip_denoised = clip_denoised self.log_every_t = log_every_t self.first_stage_key = first_stage_key self.image_size = image_size # try conv? self.channels = channels self.use_positional_encodings = use_positional_encodings self.model = DiffusionWrapper(unet_config, conditioning_key) count_params(self.model, verbose=True) self.use_ema = use_ema if self.use_ema: self.model_ema = LitEma(self.model) print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") self.use_scheduler = scheduler_config is not None if self.use_scheduler: self.scheduler_config = scheduler_config self.v_posterior = v_posterior self.original_elbo_weight = original_elbo_weight self.l_simple_weight = l_simple_weight if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) self.loss_type = loss_type self.learn_logvar = learn_logvar self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) if self.learn_logvar: self.logvar = nn.Parameter(self.logvar, requires_grad=True) def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if exists(given_betas): betas = given_betas else: betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( 1. - alphas_cumprod) + self.v_posterior * betas # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_torch(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) if self.parameterization == "eps": lvlb_weights = self.betas ** 2 / ( 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) elif self.parameterization == "x0": lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) else: raise NotImplementedError("mu not supported") # TODO how to choose this term lvlb_weights[0] = lvlb_weights[1] self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) assert not torch.isnan(self.lvlb_weights).all() @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.model.parameters()) self.model_ema.copy_to(self.model) if context is not None: print(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.model.parameters()) if context is not None: print(f"{context}: Restored training weights") def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(f"Unexpected Keys: {unexpected}") def q_mean_variance(self, x_start, t): """ Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diffusion steps (minus 1). Here, 0 means one step. :return: A tuple (mean, variance, log_variance), all of x_start's shape. """ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) return mean, variance, log_variance def predict_start_from_noise(self, x_t, t, noise): return ( extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise ) def q_posterior(self, x_start, x_t, t): posterior_mean = ( extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t ) posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) return posterior_mean, posterior_variance, posterior_log_variance_clipped def p_mean_variance(self, x, t, clip_denoised: bool): model_out = self.model(x, t) if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out if clip_denoised: x_recon.clamp_(-1., 1.) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): b, *_, device = *x.shape, x.device model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) noise = noise_like(x.shape, device, repeat_noise) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def p_sample_loop(self, shape, return_intermediates=False): device = self.betas.device b = shape[0] img = torch.randn(shape, device=device) intermediates = [img] for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), clip_denoised=self.clip_denoised) if i % self.log_every_t == 0 or i == self.num_timesteps - 1: intermediates.append(img) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, batch_size=16, return_intermediates=False): image_size = self.image_size channels = self.channels return self.p_sample_loop((batch_size, channels, image_size, image_size), return_intermediates=return_intermediates) def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) def get_loss(self, pred, target, mean=True): if self.loss_type == 'l1': loss = (target - pred).abs() if mean: loss = loss.mean() elif self.loss_type == 'l2': if mean: loss = torch.nn.functional.mse_loss(target, pred) else: loss = torch.nn.functional.mse_loss(target, pred, reduction='none') else: raise NotImplementedError("unknown loss type '{loss_type}'") return loss def p_losses(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_out = self.model(x_noisy, t) loss_dict = {} if self.parameterization == "eps": target = noise elif self.parameterization == "x0": target = x_start else: raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) log_prefix = 'train' if self.training else 'val' loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) loss_simple = loss.mean() * self.l_simple_weight loss_vlb = (self.lvlb_weights[t] * loss).mean() loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) loss = loss_simple + self.original_elbo_weight * loss_vlb loss_dict.update({f'{log_prefix}/loss': loss}) return loss, loss_dict def forward(self, x, *args, **kwargs): # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() return self.p_losses(x, t, *args, **kwargs) def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = rearrange(x, 'b h w c -> b c h w') x = x.to(memory_format=torch.contiguous_format).float() return x def shared_step(self, batch): x = self.get_input(batch, self.first_stage_key) loss, loss_dict = self(x) return loss, loss_dict def training_step(self, batch, batch_idx): loss, loss_dict = self.shared_step(batch) self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log("global_step", self.global_step, prog_bar=True, logger=True, on_step=True, on_epoch=False) if self.use_scheduler: lr = self.optimizers().param_groups[0]['lr'] self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) return loss @torch.no_grad() def validation_step(self, batch, batch_idx): _, loss_dict_no_ema = self.shared_step(batch) with self.ema_scope(): _, loss_dict_ema = self.shared_step(batch) loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self.model) def _get_rows_from_list(self, samples): n_imgs_per_row = len(samples) denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid @torch.no_grad() def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): log = dict() x = self.get_input(batch, self.first_stage_key) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) x = x.to(self.device)[:N] log["inputs"] = x # get diffusion row diffusion_row = list() x_start = x[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(x_start) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) diffusion_row.append(x_noisy) log["diffusion_row"] = self._get_rows_from_list(diffusion_row) if sample: # get denoise row with self.ema_scope("Plotting"): samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) log["samples"] = samples log["denoise_row"] = self._get_rows_from_list(denoise_row) if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.learn_logvar: params = params + [self.logvar] opt = torch.optim.AdamW(params, lr=lr) return opt class LatentDiffusion(DDPM): """main class""" def __init__(self, first_stage_config, cond_stage_config, num_timesteps_cond=None, cond_stage_key="image",# 'caption' for txt2image, 'masked_image' for inpainting cond_stage_trainable=False, concat_mode=True,# true for inpainting cond_stage_forward=None, conditioning_key=None, # 'crossattn' for txt2image, None for inpainting scale_factor=1.0, scale_by_std=False, *args, **kwargs): self.num_timesteps_cond = default(num_timesteps_cond, 1) self.scale_by_std = scale_by_std assert self.num_timesteps_cond <= kwargs['timesteps'] # for backwards compatibility after implementation of DiffusionWrapper if conditioning_key is None: conditioning_key = 'concat' if concat_mode else 'crossattn' if cond_stage_config == '__is_unconditional__': conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) super().__init__(conditioning_key=conditioning_key, *args, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 except: self.num_downs = 0 if not scale_by_std: self.scale_factor = scale_factor else: self.register_buffer('scale_factor', torch.tensor(scale_factor)) self.instantiate_first_stage(first_stage_config) self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward self.clip_denoised = False self.bbox_tokenizer = None self.restarted_from_ckpt = False if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys) self.restarted_from_ckpt = True def make_cond_schedule(self, ): self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() self.cond_ids[:self.num_timesteps_cond] = ids @rank_zero_only @torch.no_grad() def on_train_batch_start(self, batch, batch_idx, dataloader_idx): # only for very first batch if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' # set rescale weight to 1./std of encodings print("### USING STD-RESCALING ###") x = super().get_input(batch, self.first_stage_key) x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() del self.scale_factor self.register_buffer('scale_factor', 1. / z.flatten().std()) print(f"setting self.scale_factor to {self.scale_factor}") print("### USING STD-RESCALING ###") def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) self.shorten_cond_schedule = self.num_timesteps_cond > 1 if self.shorten_cond_schedule: self.make_cond_schedule() def instantiate_first_stage(self, config): model = instantiate_from_config(config) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): param.requires_grad = False def instantiate_cond_stage(self, config): if not self.cond_stage_trainable: if config == "__is_first_stage__":# inpaint print("Using first stage also as cond stage.") self.cond_stage_model = self.first_stage_model elif config == "__is_unconditional__": print(f"Training {self.__class__.__name__} as an unconditional model.") self.cond_stage_model = None # self.be_unconditional = True else: model = instantiate_from_config(config) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): param.requires_grad = False else: assert config != '__is_first_stage__' assert config != '__is_unconditional__' model = instantiate_from_config(config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): denoise_row = [] for zd in tqdm(samples, desc=desc): denoise_row.append(self.decode_first_stage(zd.to(self.device), force_not_quantize=force_no_decoder_quantization)) n_imgs_per_row = len(denoise_row) denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid def get_first_stage_encoding(self, encoder_posterior): if isinstance(encoder_posterior, DiagonalGaussianDistribution): z = encoder_posterior.sample() elif isinstance(encoder_posterior, torch.Tensor): z = encoder_posterior else: raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") return self.scale_factor * z def get_learned_conditioning(self, c): if self.cond_stage_forward is None: if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): c = self.cond_stage_model.encode(c) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() else: c = self.cond_stage_model(c) else: assert hasattr(self.cond_stage_model, self.cond_stage_forward) c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) return c def meshgrid(self, h, w): y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) arr = torch.cat([y, x], dim=-1) return arr def delta_border(self, h, w): """ :param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and max dist = 0.5 at image center """ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) arr = self.meshgrid(h, w) / lower_right_corner dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] return edge_dist def get_weighting(self, h, w, Ly, Lx, device): weighting = self.delta_border(h, w) weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], self.split_input_params["clip_max_weight"], ) weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) if self.split_input_params["tie_braker"]: L_weighting = self.delta_border(Ly, Lx) L_weighting = torch.clip(L_weighting, self.split_input_params["clip_min_tie_weight"], self.split_input_params["clip_max_tie_weight"]) L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) weighting = weighting * L_weighting return weighting def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code """ :param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) """ bs, nc, h, w = x.shape # number of crops in image Ly = (h - kernel_size[0]) // stride[0] + 1 Lx = (w - kernel_size[1]) // stride[1] + 1 if uf == 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) elif uf > 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), dilation=1, padding=0, stride=(stride[0] * uf, stride[1] * uf)) fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) elif df > 1 and uf == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), dilation=1, padding=0, stride=(stride[0] // df, stride[1] // df)) fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) else: raise NotImplementedError return fold, unfold, normalization, weighting @torch.no_grad() def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, cond_key=None, return_original_cond=False, bs=None): x = super().get_input(batch, k) if bs is not None: x = x[:bs] x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() if self.model.conditioning_key is not None: if cond_key is None: cond_key = self.cond_stage_key if cond_key != self.first_stage_key:# cond_key is not image. for inapint it's masked_img if cond_key in ['caption', 'coordinates_bbox']: xc = batch[cond_key] elif cond_key == 'class_label': xc = batch else: xc = super().get_input(batch, cond_key).to(self.device) else: xc = x if not self.cond_stage_trainable or force_c_encode: if isinstance(xc, dict) or isinstance(xc, list): # import pudb; pudb.set_trace() c = self.get_learned_conditioning(xc) else: c = self.get_learned_conditioning(xc.to(self.device)) else: c = xc if bs is not None: c = c[:bs] if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) ckey = __conditioning_keys__[self.model.conditioning_key] c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} else: c = None xc = None if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) c = {'pos_x': pos_x, 'pos_y': pos_y} out = [z, c] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([x, xrec]) if return_original_cond: out.append(xc) return out @torch.no_grad() def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) # same as above but without decorator def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) @torch.no_grad() def encode_first_stage(self, x): if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) df = self.split_input_params["vqf"] self.split_input_params['original_image_size'] = x.shape[-2:] bs, nc, h, w = x.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) z = unfold(x) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization return decoded else: return self.first_stage_model.encode(x) else: return self.first_stage_model.encode(x) def shared_step(self, batch, **kwargs): x, c = self.get_input(batch, self.first_stage_key) loss = self(x, c) return loss def forward(self, x, c, *args, **kwargs): t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() if self.model.conditioning_key is not None: assert c is not None if self.cond_stage_trainable:# true when use text c = self.get_learned_conditioning(c) # c: string list -> [B, T, Context_dim] if self.shorten_cond_schedule: # TODO: drop this option tc = self.cond_ids[t].to(self.device) c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset def rescale_bbox(bbox): x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) w = min(bbox[2] / crop_coordinates[2], 1 - x0) h = min(bbox[3] / crop_coordinates[3], 1 - y0) return x0, y0, w, h return [rescale_bbox(b) for b in bboxes] def apply_model(self, x_noisy, t, cond, return_ids=False): if isinstance(cond, dict): # hybrid case, cond is exptected to be a dict pass else: if not isinstance(cond, list): cond = [cond] key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' cond = {key: cond} if hasattr(self, "split_input_params"): assert len(cond) == 1 # todo can only deal with one conditioning atm assert not return_ids ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) h, w = x_noisy.shape[-2:] fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) z = unfold(x_noisy) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] if self.cond_stage_key in ["image", "LR_image", "segmentation", 'bbox_img'] and self.model.conditioning_key: # todo check for completeness c_key = next(iter(cond.keys())) # get key c = next(iter(cond.values())) # get value assert (len(c) == 1) # todo extend to list with more than one elem c = c[0] # get element c = unfold(c) c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] elif self.cond_stage_key == 'coordinates_bbox': assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' # assuming padding of unfold is always 0 and its dilation is always 1 n_patches_per_row = int((w - ks[0]) / stride[0] + 1) full_img_h, full_img_w = self.split_input_params['original_image_size'] # as we are operating on latents, we need the factor from the original image size to the # spatial latent size to properly rescale the crops for regenerating the bbox annotations num_downs = self.first_stage_model.encoder.num_resolutions - 1 rescale_latent = 2 ** (num_downs) # get top left postions of patches as conforming for the bbbox tokenizer, therefore we # need to rescale the tl patch coordinates to be in between (0,1) tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) for patch_nr in range(z.shape[-1])] # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) patch_limits = [(x_tl, y_tl, rescale_latent * ks[0] / full_img_w, rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] # tokenize crop coordinates for the bounding boxes of the respective patches patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) for bbox in patch_limits] # list of length l with tensors of shape (1, 2) print(patch_limits_tknzd[0].shape) # cut tknzd crop position from conditioning assert isinstance(cond, dict), 'cond must be dict to be fed into model' cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) print(cut_cond.shape) adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') print(adapted_cond.shape) adapted_cond = self.get_learned_conditioning(adapted_cond) print(adapted_cond.shape) adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) print(adapted_cond.shape) cond_list = [{'c_crossattn': [e]} for e in adapted_cond] else: cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient # apply model by loop over crops output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] assert not isinstance(output_list[0], tuple) # todo cant deal with multiple model outputs check this never happens o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together x_recon = fold(o) / normalization else: x_recon = self.model(x_noisy, t, **cond) if isinstance(x_recon, tuple) and not return_ids: return x_recon[0] else: return x_recon def _predict_eps_from_xstart(self, x_t, t, pred_xstart): return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) def _prior_bpd(self, x_start): """ Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only depends on the encoder. :param x_start: the [N x C x ...] tensor of inputs. :return: a batch of [N] KL values (in bits), one per batch element. """ batch_size = x_start.shape[0] t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) return mean_flat(kl_prior) / np.log(2.0) def p_losses(self, x_start, cond, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_output = self.apply_model(x_noisy, t, cond) loss_dict = {} prefix = 'train' if self.training else 'val' if self.parameterization == "x0": target = x_start elif self.parameterization == "eps": target = noise else: raise NotImplementedError() loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) logvar_t = self.logvar[t].to(self.device) loss = loss_simple / torch.exp(logvar_t) + logvar_t # loss = loss_simple / torch.exp(self.logvar) + self.logvar if self.learn_logvar: loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) loss_dict.update({'logvar': self.logvar.data.mean()}) loss = self.l_simple_weight * loss.mean() loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) loss += (self.original_elbo_weight * loss_vlb) loss_dict.update({f'{prefix}/loss': loss}) return loss, loss_dict def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, return_x0=False, score_corrector=None, corrector_kwargs=None): t_in = t model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) if score_corrector is not None: assert self.parameterization == "eps" model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) if return_codebook_ids: model_out, logits = model_out if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out else: raise NotImplementedError() if clip_denoised: x_recon.clamp_(-1., 1.) if quantize_denoised: x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) if return_codebook_ids: return model_mean, posterior_variance, posterior_log_variance, logits elif return_x0: return model_mean, posterior_variance, posterior_log_variance, x_recon else: return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_codebook_ids=False, quantize_denoised=False, return_x0=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): b, *_, device = *x.shape, x.device outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_codebook_ids=return_codebook_ids, quantize_denoised=quantize_denoised, return_x0=return_x0, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if return_codebook_ids: raise DeprecationWarning("Support dropped.") model_mean, _, model_log_variance, logits = outputs elif return_x0: model_mean, _, model_log_variance, x0 = outputs else: model_mean, _, model_log_variance = outputs noise = noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) if return_codebook_ids: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) if return_x0: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 else: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t timesteps = self.num_timesteps if batch_size is not None: b = batch_size if batch_size is not None else shape[0] shape = [batch_size] + list(shape) else: b = batch_size = shape[0] if x_T is None: img = torch.randn(shape, device=self.device) else: img = x_T intermediates = [] if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', total=timesteps) if verbose else reversed( range(0, timesteps)) if type(temperature) == float: temperature = [temperature] * timesteps for i in iterator: ts = torch.full((b,), i, device=self.device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img, x0_partial = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised, return_x0=True, temperature=temperature[i], noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if mask is not None: assert x0 is not None img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(x0_partial) if callback: callback(i) if img_callback: img_callback(img, i) return img, intermediates @torch.no_grad() def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t device = self.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T intermediates = [img] if timesteps is None: timesteps = self.num_timesteps if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( range(0, timesteps)) if mask is not None: assert x0 is not None assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match for i in iterator: ts = torch.full((b,), i, device=device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised) if mask is not None: img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(img) if callback: callback(i) if img_callback: img_callback(img, i) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, verbose=True, timesteps=None, quantize_denoised=False, mask=None, x0=None, shape=None,**kwargs): if shape is None: shape = (batch_size, self.channels, self.image_size, self.image_size) if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, shape, return_intermediates=return_intermediates, x_T=x_T, verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, mask=mask, x0=x0) @torch.no_grad() def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): if ddim: ddim_sampler = DDIMSampler(self) shape = (self.channels, self.image_size, self.image_size) samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, shape,cond,verbose=False,**kwargs) else: samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True,**kwargs) return samples, intermediates @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, **kwargs): use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=N) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if plot_diffusion_rows: # get diffusion row diffusion_row = list() z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with self.ema_scope("Plotting"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( self.first_stage_model, IdentityFirstStage): # also display when quantizing x0 while sampling with self.ema_scope("Plotting Quantized Denoised"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta, quantize_denoised=True) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, # quantize_denoised=True) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_x0_quantized"] = x_samples if inpaint: # make a simple center square b, h, w = z.shape[0], z.shape[2], z.shape[3] mask = torch.ones(N, h, w).to(self.device) # zeros will be filled in mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. mask = mask[:, None, ...] with self.ema_scope("Plotting Inpaint"): samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_inpainting"] = x_samples log["mask"] = mask # outpaint with self.ema_scope("Plotting Outpaint"): samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_outpainting"] = x_samples if plot_progressive_rows: with self.ema_scope("Plotting Progressives"): img, progressives = self.progressive_denoising(c, shape=(self.channels, self.image_size, self.image_size), batch_size=N) prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") log["progressive_row"] = prog_row if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.cond_stage_trainable: print(f"{self.__class__.__name__}: Also optimizing conditioner params!") params = params + list(self.cond_stage_model.parameters()) if self.learn_logvar: print('Diffusion model optimizing logvar') params.append(self.logvar) opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: assert 'target' in self.scheduler_config scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [opt], scheduler return opt @torch.no_grad() def to_rgb(self, x): x = x.float() if not hasattr(self, "colorize"): self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) x = nn.functional.conv2d(x, weight=self.colorize) x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. return x class DiffusionWrapper(pl.LightningModule): def __init__(self, diff_model_config, conditioning_key): super().__init__() self.diffusion_model = instantiate_from_config(diff_model_config) self.conditioning_key = conditioning_key # 'crossattn' for txt2image, concat for inpainting assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): """param x: tensor with shape:[B,C,mel_len,T]""" if self.conditioning_key is None: out = self.diffusion_model(x, t) elif self.conditioning_key == 'concat': xc = torch.cat([x] + c_concat, dim=1)# channel dim,x shape (b,3,64,64) c_concat shape(b,4,64,64) out = self.diffusion_model(xc, t) elif self.conditioning_key == 'crossattn': cc = torch.cat(c_crossattn, 1)# [b,seq_len,dim] out = self.diffusion_model(x, t, context=cc) elif self.conditioning_key == 'hybrid':# not implemented in the LatentDiffusion xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(xc, t, context=cc) elif self.conditioning_key == 'adm': cc = c_crossattn[0] out = self.diffusion_model(x, t, y=cc) else: raise NotImplementedError() return out class Layout2ImgDiffusion(LatentDiffusion): # TODO: move all layout-specific hacks to this class def __init__(self, cond_stage_key, *args, **kwargs): assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) def log_images(self, batch, N=8, *args, **kwargs): logs = super().log_images(batch=batch, N=N, *args, **kwargs) key = 'train' if self.training else 'validation' dset = self.trainer.datamodule.datasets[key] mapper = dset.conditional_builders[self.cond_stage_key] bbox_imgs = [] map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) for tknzd_bbox in batch[self.cond_stage_key][:N]: bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) bbox_imgs.append(bboximg) cond_img = torch.stack(bbox_imgs, dim=0) logs['bbox_image'] = cond_img return logs ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/diffusion/ddpm_audio.py ================================================ """ wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py https://github.com/CompVis/taming-transformers -- merci """ import os import torch import torch.nn as nn import numpy as np import pytorch_lightning as pl from torch.optim.lr_scheduler import LambdaLR from einops import rearrange, repeat from contextlib import contextmanager from functools import partial from tqdm import tqdm from torchvision.utils import make_grid from pytorch_lightning.utilities.distributed import rank_zero_only from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config from ldm.modules.ema import LitEma from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.ddpm import DDPM, disabled_train from omegaconf import ListConfig __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} class LatentDiffusion_audio(DDPM): """main class""" def __init__(self, first_stage_config, cond_stage_config, num_timesteps_cond=None, mel_dim=80, mel_length=848, cond_stage_key="image", cond_stage_trainable=False, concat_mode=True, cond_stage_forward=None, conditioning_key=None, scale_factor=1.0, scale_by_std=False, *args, **kwargs): self.num_timesteps_cond = default(num_timesteps_cond, 1) self.scale_by_std = scale_by_std assert self.num_timesteps_cond <= kwargs['timesteps'] # for backwards compatibility after implementation of DiffusionWrapper if conditioning_key is None: conditioning_key = 'concat' if concat_mode else 'crossattn' if cond_stage_config == '__is_unconditional__': conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) super().__init__(conditioning_key=conditioning_key, *args, **kwargs) self.concat_mode = concat_mode self.mel_dim = mel_dim self.mel_length = mel_length self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 except: self.num_downs = 0 if not scale_by_std: self.scale_factor = scale_factor else: self.register_buffer('scale_factor', torch.tensor(scale_factor)) self.instantiate_first_stage(first_stage_config) self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward self.clip_denoised = False self.bbox_tokenizer = None self.restarted_from_ckpt = False if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys) self.restarted_from_ckpt = True def make_cond_schedule(self, ): self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() self.cond_ids[:self.num_timesteps_cond] = ids @rank_zero_only @torch.no_grad() def on_train_batch_start(self, batch, batch_idx, dataloader_idx): # only for very first batch if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' # set rescale weight to 1./std of encodings print("### USING STD-RESCALING ###") x = super().get_input(batch, self.first_stage_key) x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() del self.scale_factor self.register_buffer('scale_factor', 1. / z.flatten().std()) print(f"setting self.scale_factor to {self.scale_factor}") print("### USING STD-RESCALING ###") def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) self.shorten_cond_schedule = self.num_timesteps_cond > 1 if self.shorten_cond_schedule: self.make_cond_schedule() def instantiate_first_stage(self, config): model = instantiate_from_config(config) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): param.requires_grad = False def instantiate_cond_stage(self, config): if not self.cond_stage_trainable: if config == "__is_first_stage__": print("Using first stage also as cond stage.") self.cond_stage_model = self.first_stage_model elif config == "__is_unconditional__": print(f"Training {self.__class__.__name__} as an unconditional model.") self.cond_stage_model = None # self.be_unconditional = True else: model = instantiate_from_config(config) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): param.requires_grad = False else: assert config != '__is_first_stage__' assert config != '__is_unconditional__' model = instantiate_from_config(config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): denoise_row = [] for zd in tqdm(samples, desc=desc): denoise_row.append(self.decode_first_stage(zd.to(self.device), force_not_quantize=force_no_decoder_quantization)) n_imgs_per_row = len(denoise_row) denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid def get_first_stage_encoding(self, encoder_posterior): if isinstance(encoder_posterior, DiagonalGaussianDistribution): z = encoder_posterior.sample() elif isinstance(encoder_posterior, torch.Tensor): z = encoder_posterior else: raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") return self.scale_factor * z def get_learned_conditioning(self, c): if self.cond_stage_forward is None: if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): c = self.cond_stage_model.encode(c) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() else: c = self.cond_stage_model(c) else: assert hasattr(self.cond_stage_model, self.cond_stage_forward) c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) return c @torch.no_grad() def get_unconditional_conditioning(self, batch_size, null_label=None): if null_label is not None: xc = null_label if isinstance(xc, ListConfig): xc = list(xc) if isinstance(xc, dict) or isinstance(xc, list): c = self.get_learned_conditioning(xc) else: if hasattr(xc, "to"): xc = xc.to(self.device) c = self.get_learned_conditioning(xc) else: if self.cond_stage_key in ["class_label", "cls"]: xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device) return self.get_learned_conditioning(xc) else: raise NotImplementedError("todo") if isinstance(c, list): # in case the encoder gives us a list for i in range(len(c)): c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device) else: c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device) return c def meshgrid(self, h, w): y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) arr = torch.cat([y, x], dim=-1) return arr def delta_border(self, h, w): """ :param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and max dist = 0.5 at image center """ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) arr = self.meshgrid(h, w) / lower_right_corner dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] return edge_dist def get_weighting(self, h, w, Ly, Lx, device): weighting = self.delta_border(h, w) weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], self.split_input_params["clip_max_weight"], ) weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) if self.split_input_params["tie_braker"]: L_weighting = self.delta_border(Ly, Lx) L_weighting = torch.clip(L_weighting, self.split_input_params["clip_min_tie_weight"], self.split_input_params["clip_max_tie_weight"]) L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) weighting = weighting * L_weighting return weighting def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code """ :param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) """ bs, nc, h, w = x.shape # number of crops in image Ly = (h - kernel_size[0]) // stride[0] + 1 Lx = (w - kernel_size[1]) // stride[1] + 1 if uf == 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) elif uf > 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), dilation=1, padding=0, stride=(stride[0] * uf, stride[1] * uf)) fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) elif df > 1 and uf == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), dilation=1, padding=0, stride=(stride[0] // df, stride[1] // df)) fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) else: raise NotImplementedError return fold, unfold, normalization, weighting @torch.no_grad() def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, cond_key=None, return_original_cond=False, bs=None): x = super().get_input(batch, k) if bs is not None: x = x[:bs] x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() if self.model.conditioning_key is not None: if cond_key is None: cond_key = self.cond_stage_key if cond_key != self.first_stage_key: if cond_key in ['caption', 'coordinates_bbox']: xc = batch[cond_key] elif cond_key == 'class_label': xc = batch else: xc = super().get_input(batch, cond_key).to(self.device) else: xc = x if not self.cond_stage_trainable or force_c_encode: if isinstance(xc, dict) or isinstance(xc, list): # import pudb; pudb.set_trace() c = self.get_learned_conditioning(xc) else: c = self.get_learned_conditioning(xc.to(self.device)) else: c = xc if bs is not None: c = c[:bs] # Testing # if cond_key == 'masked_image': mask = super().get_input(batch, "mask") cc = torch.nn.functional.interpolate(mask, size=c.shape[-2:]) # [B, 1, 10, 106] c = torch.cat((c, cc), dim=1) # [B, 5, 10, 106] # Testing # if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) ckey = __conditioning_keys__[self.model.conditioning_key] c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} else: c = None xc = None if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) c = {'pos_x': pos_x, 'pos_y': pos_y} out = [z, c] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([x, xrec]) if return_original_cond: out.append(xc) return out @torch.no_grad() def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) # same as above but without decorator def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) @torch.no_grad() def encode_first_stage(self, x): if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) df = self.split_input_params["vqf"] self.split_input_params['original_image_size'] = x.shape[-2:] bs, nc, h, w = x.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) z = unfold(x) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization return decoded else: return self.first_stage_model.encode(x) else: return self.first_stage_model.encode(x) def shared_step(self, batch, **kwargs): x, c = self.get_input(batch, self.first_stage_key) loss = self(x, c) return loss def test_step(self,batch,batch_idx): cond = batch[self.cond_stage_key] * self.test_repeat cond = self.get_learned_conditioning(cond) # c: string -> [B, T, Context_dim] batch_size = len(cond) enc_emb = self.sample(cond,batch_size,timesteps=self.test_numsteps)# shape = [batch_size,self.channels,self.mel_dim,self.mel_length] xrec = self.decode_first_stage(enc_emb) reconstructions = (xrec + 1)/2 # to mel scale test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') if not os.path.exists(savedir): os.makedirs(savedir) file_names = batch['f_name'] nfiles = len(file_names) reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim for k in range(reconstructions.shape[0]): b,repeat = k % nfiles, k // nfiles vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition np.save(save_img_path,reconstructions[b]) return None def forward(self, x, c, *args, **kwargs): t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() if self.model.conditioning_key is not None: assert c is not None if self.cond_stage_trainable: c = self.get_learned_conditioning(c) # c: string -> [B, T, Context_dim] if self.shorten_cond_schedule: # TODO: drop this option tc = self.cond_ids[t].to(self.device) c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset def rescale_bbox(bbox): x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) w = min(bbox[2] / crop_coordinates[2], 1 - x0) h = min(bbox[3] / crop_coordinates[3], 1 - y0) return x0, y0, w, h return [rescale_bbox(b) for b in bboxes] def apply_model(self, x_noisy, t, cond, return_ids=False): if isinstance(cond, dict): # hybrid case, cond is exptected to be a dict pass else: if not isinstance(cond, list): cond = [cond] key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' cond = {key: cond} if hasattr(self, "split_input_params"): assert len(cond) == 1 # todo can only deal with one conditioning atm assert not return_ids ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) h, w = x_noisy.shape[-2:] fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) z = unfold(x_noisy) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] if self.cond_stage_key in ["image", "LR_image", "segmentation", 'bbox_img'] and self.model.conditioning_key: # todo check for completeness c_key = next(iter(cond.keys())) # get key c = next(iter(cond.values())) # get value assert (len(c) == 1) # todo extend to list with more than one elem c = c[0] # get element c = unfold(c) c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] elif self.cond_stage_key == 'coordinates_bbox': assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' # assuming padding of unfold is always 0 and its dilation is always 1 n_patches_per_row = int((w - ks[0]) / stride[0] + 1) full_img_h, full_img_w = self.split_input_params['original_image_size'] # as we are operating on latents, we need the factor from the original image size to the # spatial latent size to properly rescale the crops for regenerating the bbox annotations num_downs = self.first_stage_model.encoder.num_resolutions - 1 rescale_latent = 2 ** (num_downs) # get top left postions of patches as conforming for the bbbox tokenizer, therefore we # need to rescale the tl patch coordinates to be in between (0,1) tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) for patch_nr in range(z.shape[-1])] # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) patch_limits = [(x_tl, y_tl, rescale_latent * ks[0] / full_img_w, rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] # tokenize crop coordinates for the bounding boxes of the respective patches patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) for bbox in patch_limits] # list of length l with tensors of shape (1, 2) print(patch_limits_tknzd[0].shape) # cut tknzd crop position from conditioning assert isinstance(cond, dict), 'cond must be dict to be fed into model' cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) print(cut_cond.shape) adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') print(adapted_cond.shape) adapted_cond = self.get_learned_conditioning(adapted_cond) print(adapted_cond.shape) adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) print(adapted_cond.shape) cond_list = [{'c_crossattn': [e]} for e in adapted_cond] else: cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient # apply model by loop over crops output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] assert not isinstance(output_list[0], tuple) # todo cant deal with multiple model outputs check this never happens o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together x_recon = fold(o) / normalization else: x_recon = self.model(x_noisy, t, **cond) if isinstance(x_recon, tuple) and not return_ids: return x_recon[0] else: return x_recon def _predict_eps_from_xstart(self, x_t, t, pred_xstart): return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) def _prior_bpd(self, x_start): """ Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only depends on the encoder. :param x_start: the [N x C x ...] tensor of inputs. :return: a batch of [N] KL values (in bits), one per batch element. """ batch_size = x_start.shape[0] t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) return mean_flat(kl_prior) / np.log(2.0) def p_losses(self, x_start, cond, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_output = self.apply_model(x_noisy, t, cond) loss_dict = {} prefix = 'train' if self.training else 'val' if self.parameterization == "x0": target = x_start elif self.parameterization == "eps": target = noise else: raise NotImplementedError() loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) logvar_t = self.logvar[t].to(self.device) loss = loss_simple / torch.exp(logvar_t) + logvar_t # loss = loss_simple / torch.exp(self.logvar) + self.logvar if self.learn_logvar: loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) loss_dict.update({'logvar': self.logvar.data.mean()}) loss = self.l_simple_weight * loss.mean() loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) loss += (self.original_elbo_weight * loss_vlb) loss_dict.update({f'{prefix}/loss': loss}) return loss, loss_dict def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, return_x0=False, score_corrector=None, corrector_kwargs=None): t_in = t model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) if score_corrector is not None: assert self.parameterization == "eps" model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) if return_codebook_ids: model_out, logits = model_out if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out else: raise NotImplementedError() if clip_denoised: x_recon.clamp_(-1., 1.) if quantize_denoised: x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) if return_codebook_ids: return model_mean, posterior_variance, posterior_log_variance, logits elif return_x0: return model_mean, posterior_variance, posterior_log_variance, x_recon else: return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_codebook_ids=False, quantize_denoised=False, return_x0=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): b, *_, device = *x.shape, x.device outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_codebook_ids=return_codebook_ids, quantize_denoised=quantize_denoised, return_x0=return_x0, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if return_codebook_ids: raise DeprecationWarning("Support dropped.") model_mean, _, model_log_variance, logits = outputs elif return_x0: model_mean, _, model_log_variance, x0 = outputs else: model_mean, _, model_log_variance = outputs noise = noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) if return_codebook_ids: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) if return_x0: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 else: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t timesteps = self.num_timesteps if batch_size is not None: b = batch_size if batch_size is not None else shape[0] shape = [batch_size] + list(shape) else: b = batch_size = shape[0] if x_T is None: img = torch.randn(shape, device=self.device) else: img = x_T intermediates = [] if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', total=timesteps) if verbose else reversed( range(0, timesteps)) if type(temperature) == float: temperature = [temperature] * timesteps for i in iterator: ts = torch.full((b,), i, device=self.device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img, x0_partial = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised, return_x0=True, temperature=temperature[i], noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if mask is not None: assert x0 is not None img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(x0_partial) if callback: callback(i) if img_callback: img_callback(img, i) return img, intermediates @torch.no_grad() def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t device = self.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T intermediates = [img] if timesteps is None: timesteps = self.num_timesteps if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( range(0, timesteps)) if mask is not None: assert x0 is not None assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match for i in iterator: ts = torch.full((b,), i, device=device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised) if mask is not None: img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(img) if callback: callback(i) if img_callback: img_callback(img, i) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, verbose=True, timesteps=None, quantize_denoised=False, mask=None, x0=None, shape=None,**kwargs): if shape is None: shape = (batch_size, self.channels, self.mel_dim, self.mel_length) if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, shape, return_intermediates=return_intermediates, x_T=x_T, verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, mask=mask, x0=x0) @torch.no_grad() def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): if ddim: ddim_sampler = DDIMSampler(self) shape = (self.channels, self.mel_dim, self.mel_length) samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, shape,cond,verbose=False,**kwargs) else: samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True,**kwargs) return samples, intermediates @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, **kwargs): use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=N) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode") and self.cond_stage_key != "masked_image": xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key == "masked_image": log["mask"] = c[:, -1, :, :][:, None, :, :] xc = self.cond_stage_model.decode(c[:, :self.cond_stage_model.embed_dim, :, :]) log["conditioning"] = xc elif self.cond_stage_key in ["caption"]: xc = log_txt_as_img((256, 256), batch["caption"]) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if plot_diffusion_rows: # get diffusion row diffusion_row = list() z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with self.ema_scope("Plotting"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( self.first_stage_model, IdentityFirstStage): # also display when quantizing x0 while sampling with self.ema_scope("Plotting Quantized Denoised"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta, quantize_denoised=True) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, # quantize_denoised=True) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_x0_quantized"] = x_samples if inpaint: # make a simple center square b, h, w = z.shape[0], z.shape[2], z.shape[3] mask = torch.ones(N, h, w).to(self.device) # zeros will be filled in mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. mask = mask[:, None, ...] with self.ema_scope("Plotting Inpaint"): samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_inpainting"] = x_samples log["mask_inpainting"] = mask # outpaint mask = 1 - mask with self.ema_scope("Plotting Outpaint"): samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_outpainting"] = x_samples log["mask_outpainting"] = mask if plot_progressive_rows: with self.ema_scope("Plotting Progressives"): img, progressives = self.progressive_denoising(c, shape=(self.channels, self.mel_dim, self.mel_length), batch_size=N) prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") log["progressive_row"] = prog_row if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.cond_stage_trainable: print(f"{self.__class__.__name__}: Also optimizing conditioner params!") params = params + list(self.cond_stage_model.parameters()) if self.learn_logvar: print('Diffusion model optimizing logvar') params.append(self.logvar) opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: assert 'target' in self.scheduler_config scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [opt], scheduler return opt @torch.no_grad() def to_rgb(self, x): x = x.float() if not hasattr(self, "colorize"): self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) x = nn.functional.conv2d(x, weight=self.colorize) x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. return x class LatentFinetuneDiffusion(LatentDiffusion_audio): """ Basis for different finetunas, such as inpainting or depth2image To disable finetuning mode, set finetune_keys to None """ def __init__(self, concat_keys: tuple, finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", "model_ema.diffusion_modelinput_blocks00weight" ), keep_finetune_dims=4, # if model was trained without concat mode before and we would like to keep these channels c_concat_log_start=None, # to log reconstruction of c_concat codes c_concat_log_end=None, *args, **kwargs ): ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", list()) super().__init__(*args, **kwargs) self.finetune_keys = finetune_keys self.concat_keys = concat_keys self.keep_dims = keep_finetune_dims self.c_concat_log_start = c_concat_log_start self.c_concat_log_end = c_concat_log_end if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint' if exists(ckpt_path): self.init_from_ckpt(ckpt_path, ignore_keys) def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] # make it explicit, finetune by including extra input channels if exists(self.finetune_keys) and k in self.finetune_keys: new_entry = None for name, param in self.named_parameters(): if name in self.finetune_keys: print( f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only") new_entry = torch.zeros_like(param) # zero init assert exists(new_entry), 'did not find matching parameter to modify' new_entry[:, :self.keep_dims, ...] = sd[k] sd[k] = new_entry missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(f"Unexpected Keys: {unexpected}") @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, **kwargs): use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True) c_cat, c = c["c_concat"][0], c["c_crossattn"][0] N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if not (self.c_concat_log_start is None and self.c_concat_log_end is None): log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end]) if plot_diffusion_rows: # get diffusion row diffusion_row = list() z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with self.ema_scope("Sampling"): samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if unconditional_guidance_scale > 1.0: uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label) uc_cat = c_cat uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} with self.ema_scope("Sampling with classifier-free guidance"): samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=uc_full, ) x_samples_cfg = self.decode_first_stage(samples_cfg) log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg return log class LatentInpaintDiffusion(LatentFinetuneDiffusion): """ can either run as pure inpainting model (only concat mode) or with mixed conditionings, e.g. mask as concat and text via cross-attn. To disable finetuning mode, set finetune_keys to None """ def __init__(self, concat_keys=("mask", "masked_image"), masked_image_key="masked_image", *args, **kwargs ): super().__init__(concat_keys, *args, **kwargs) self.masked_image_key = masked_image_key assert self.masked_image_key in concat_keys @torch.no_grad() def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): # note: restricted to non-trainable encoders currently assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting' z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=bs) assert exists(self.concat_keys) c_cat = list() for ck in self.concat_keys: if len(batch[ck].shape) == 3: batch[ck] = batch[ck][..., None] cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() if bs is not None: cc = cc[:bs] cc = cc.to(self.device) bchw = z.shape if ck != self.masked_image_key: cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) else: cc = self.get_first_stage_encoding(self.encode_first_stage(cc)) c_cat.append(cc) c_cat = torch.cat(c_cat, dim=1) all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} if return_first_stage_outputs: return z, all_conds, x, xrec, xc return z, all_conds @torch.no_grad() def log_images(self, *args, **kwargs): log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs) log["masked_image"] = rearrange(args[0]["masked_image"], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() return log ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/diffusion/ddpm_audio_inpaint.py ================================================ """ wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py https://github.com/CompVis/taming-transformers -- merci """ import os import torch import torch.nn as nn import numpy as np import pytorch_lightning as pl from torch.optim.lr_scheduler import LambdaLR from einops import rearrange, repeat from contextlib import contextmanager from functools import partial from tqdm import tqdm from torchvision.utils import make_grid from pytorch_lightning.utilities.distributed import rank_zero_only from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config from ldm.modules.ema import LitEma from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.ddpm import DDPM, disabled_train __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} # add mel_dim and mel_length params to ensure correct shape class LatentDiffusion_audioinpaint(DDPM): """main class""" def __init__(self, first_stage_config, cond_stage_config, num_timesteps_cond=None, mel_dim=80, mel_length=848, cond_stage_key="image", cond_stage_trainable=False, concat_mode=True, cond_stage_forward=None, conditioning_key=None, scale_factor=1.0, scale_by_std=False, test_repeat=1, test_numsteps = None, *args, **kwargs): self.num_timesteps_cond = default(num_timesteps_cond, 1) self.scale_by_std = scale_by_std assert self.num_timesteps_cond <= kwargs['timesteps'] # for backwards compatibility after implementation of DiffusionWrapper if conditioning_key is None: conditioning_key = 'concat' if concat_mode else 'crossattn' if cond_stage_config == '__is_unconditional__': conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) super().__init__(conditioning_key=conditioning_key, *args, **kwargs) self.test_repeat = test_repeat if test_numsteps == None: self.test_numsteps = self.num_timesteps self.concat_mode = concat_mode self.mel_dim = mel_dim self.mel_length = mel_length self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 except: self.num_downs = 0 if not scale_by_std: self.scale_factor = scale_factor else: self.register_buffer('scale_factor', torch.tensor(scale_factor)) self.instantiate_first_stage(first_stage_config) self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward self.clip_denoised = False self.bbox_tokenizer = None self.restarted_from_ckpt = False if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys) self.restarted_from_ckpt = True def make_cond_schedule(self, ): self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() self.cond_ids[:self.num_timesteps_cond] = ids @rank_zero_only @torch.no_grad() def on_train_batch_start(self, batch, batch_idx, dataloader_idx): # only for very first batch if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' # set rescale weight to 1./std of encodings print("### USING STD-RESCALING ###") x = super().get_input(batch, self.first_stage_key) x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() del self.scale_factor self.register_buffer('scale_factor', 1. / z.flatten().std()) print(f"setting self.scale_factor to {self.scale_factor}") print("### USING STD-RESCALING ###") def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) self.shorten_cond_schedule = self.num_timesteps_cond > 1 if self.shorten_cond_schedule: self.make_cond_schedule() def instantiate_first_stage(self, config): model = instantiate_from_config(config) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): param.requires_grad = False def instantiate_cond_stage(self, config): if not self.cond_stage_trainable: if config == "__is_first_stage__":# for no_text inpainting task print("Using first stage also as cond stage.") self.cond_stage_model = self.first_stage_model elif config == "__is_unconditional__":# for unconditional image generation such as human face、ImageNet print(f"Training {self.__class__.__name__} as an unconditional model.") self.cond_stage_model = None # self.be_unconditional = True else: model = instantiate_from_config(config) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): param.requires_grad = False else: assert config != '__is_first_stage__' assert config != '__is_unconditional__' model = instantiate_from_config(config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): denoise_row = [] for zd in tqdm(samples, desc=desc): denoise_row.append(self.decode_first_stage(zd.to(self.device), force_not_quantize=force_no_decoder_quantization)) n_imgs_per_row = len(denoise_row) denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid def get_first_stage_encoding(self, encoder_posterior):# encode_emb from autoencoder if isinstance(encoder_posterior, DiagonalGaussianDistribution): z = encoder_posterior.sample() elif isinstance(encoder_posterior, torch.Tensor): z = encoder_posterior else: raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") return self.scale_factor * z def get_learned_conditioning(self, c): if self.cond_stage_forward is None: if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): c = self.cond_stage_model.encode(c) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() else: c = self.cond_stage_model(c) else: assert hasattr(self.cond_stage_model, self.cond_stage_forward) c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) return c def meshgrid(self, h, w): y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) arr = torch.cat([y, x], dim=-1) return arr def delta_border(self, h, w): """ :param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and max dist = 0.5 at image center """ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) arr = self.meshgrid(h, w) / lower_right_corner dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] return edge_dist def get_weighting(self, h, w, Ly, Lx, device): weighting = self.delta_border(h, w) weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], self.split_input_params["clip_max_weight"], ) weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) if self.split_input_params["tie_braker"]: L_weighting = self.delta_border(Ly, Lx) L_weighting = torch.clip(L_weighting, self.split_input_params["clip_min_tie_weight"], self.split_input_params["clip_max_tie_weight"]) L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) weighting = weighting * L_weighting return weighting def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code """ :param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) """ bs, nc, h, w = x.shape # number of crops in image Ly = (h - kernel_size[0]) // stride[0] + 1 Lx = (w - kernel_size[1]) // stride[1] + 1 if uf == 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) elif uf > 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), dilation=1, padding=0, stride=(stride[0] * uf, stride[1] * uf)) fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) elif df > 1 and uf == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), dilation=1, padding=0, stride=(stride[0] // df, stride[1] // df)) fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) else: raise NotImplementedError return fold, unfold, normalization, weighting @torch.no_grad() def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, cond_key=None, return_original_cond=False, bs=None): x = super().get_input(batch, k) if bs is not None: x = x[:bs] x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() if self.model.conditioning_key is not None:# 'crossattn' for txt2image, 'hybird' for txt_inpaint if cond_key is None: cond_key = self.cond_stage_key # 'caption' for txt_inpaint if self.model.conditioning_key == 'hybrid': xc = {} assert cond_key == 'caption' # only txt_inpaint is implemented now assert 'masked_image' in batch.keys() assert 'mask' in batch.keys() masked_image = super().get_input(batch,'masked_image') mask = super().get_input(batch,'mask') if bs is not None: masked_image,mask = masked_image[:bs],mask[:bs] masked_image,mask = masked_image.to(self.device),mask.to(self.device) masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach() resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:]) xc['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1) xc[cond_key] = batch[cond_key] else: if cond_key != self.first_stage_key: if cond_key in ['caption', 'coordinates_bbox']: xc = batch[cond_key] elif cond_key == 'class_label': xc = batch else: xc = super().get_input(batch, cond_key).to(self.device) else:# cond_key == 'image' xc = x if not self.cond_stage_trainable or force_c_encode:# cond_stage_trainable is true for txt2img，force_c_encoder = True，when called in log_images if isinstance(xc, list): # import pudb; pudb.set_trace() c = self.get_learned_conditioning(xc)# 因为log_images内接下来要调用sample_log,所以需要预先得到处理好的c if isinstance(xc, dict): c = {} c['c_concat'] = xc['c_concat'] c['c_crossattn'] = self.get_learned_conditioning(xc[cond_key]) else: c = self.get_learned_conditioning(xc.to(self.device)) else: c = xc if bs is not None: if isinstance(c,dict): for k in c.keys(): c[k] = c[k][:bs] else: c = c[:bs] if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) ckey = __conditioning_keys__[self.model.conditioning_key] c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} else: c = None xc = None if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) c = {'pos_x': pos_x, 'pos_y': pos_y} out = [z, c] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([x, xrec]) if return_original_cond: out.append(xc) return out @torch.no_grad() def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) # same as above but without decorator def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) @torch.no_grad() def encode_first_stage(self, x): if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) df = self.split_input_params["vqf"] self.split_input_params['original_image_size'] = x.shape[-2:] bs, nc, h, w = x.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) z = unfold(x) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization return decoded else: return self.first_stage_model.encode(x) else: return self.first_stage_model.encode(x) def shared_step(self, batch, **kwargs): x, c = self.get_input(batch, self.first_stage_key)# get latent and condition loss = self(x, c) return loss def test_step(self,batch,batch_idx): # TODO make self.test_repeat work cond = {} cond[self.cond_stage_key] = batch[self.cond_stage_key] cond[self.cond_stage_key] = self.get_learned_conditioning(cond[self.cond_stage_key]) # c: string -> [B, T, Context_dim] cond['c_crossattn'] = cond.pop(self.cond_stage_key) masked_image = super().get_input(batch,'masked_image') mask = super().get_input(batch,'mask') masked_image,mask = masked_image.to(self.device),mask.to(self.device) masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach() resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:]) cond['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1) batch_size = len(batch[self.cond_stage_key]) # shape = [batch_size,self.channels,self.mel_dim,self.mel_length] enc_emb = self.sample(cond,batch_size,timesteps=self.test_numsteps) xrec = self.decode_first_stage(enc_emb) reconstructions = (xrec + 1)/2 # to mel scale test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') if not os.path.exists(savedir): os.makedirs(savedir) file_names = batch['f_name'] nfiles = len(file_names) reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim for k in range(reconstructions.shape[0]): b,repeat = k % nfiles, k // nfiles vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition np.save(save_img_path,reconstructions[b]) return None def forward(self, x, c, *args, **kwargs): t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() if self.model.conditioning_key is not None: assert c is not None if self.cond_stage_trainable: if isinstance(c,dict): c[self.cond_stage_key] = self.get_learned_conditioning(c[self.cond_stage_key]) c['c_crossattn'] = c.pop(self.cond_stage_key) else: c = self.get_learned_conditioning(c) # c: string -> [B, T, Context_dim] if self.shorten_cond_schedule: # TODO: drop this option tc = self.cond_ids[t].to(self.device) c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset def rescale_bbox(bbox): x0 = torch.clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) y0 = torch.clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) w = min(bbox[2] / crop_coordinates[2], 1 - x0) h = min(bbox[3] / crop_coordinates[3], 1 - y0) return x0, y0, w, h return [rescale_bbox(b) for b in bboxes] def apply_model(self, x_noisy, t, cond, return_ids=False): # make values to list to enable concat operation in if isinstance(cond, dict): # hybrid case, cond is exptected to be a dict. (txt2inpaint) cond_tmp = {}# use cond_tmp to avoid inplace edit for k,v in cond.items(): if not isinstance(v, list): cond_tmp[k] = [cond[k]] else: cond_tmp[k] = cond[k] cond = cond_tmp else: if not isinstance(cond, list): cond = [cond] key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' cond = {key: cond} if hasattr(self, "split_input_params"): assert len(cond) == 1 # todo can only deal with one conditioning atm assert not return_ids ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) h, w = x_noisy.shape[-2:] fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) z = unfold(x_noisy) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] if self.cond_stage_key in ["image", "LR_image", "segmentation", 'bbox_img'] and self.model.conditioning_key: # todo check for completeness c_key = next(iter(cond.keys())) # get key c = next(iter(cond.values())) # get value assert (len(c) == 1) # todo extend to list with more than one elem c = c[0] # get element c = unfold(c) c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] elif self.cond_stage_key == 'coordinates_bbox': assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' # assuming padding of unfold is always 0 and its dilation is always 1 n_patches_per_row = int((w - ks[0]) / stride[0] + 1) full_img_h, full_img_w = self.split_input_params['original_image_size'] # as we are operating on latents, we need the factor from the original image size to the # spatial latent size to properly rescale the crops for regenerating the bbox annotations num_downs = self.first_stage_model.encoder.num_resolutions - 1 rescale_latent = 2 ** (num_downs) # get top left postions of patches as conforming for the bbbox tokenizer, therefore we # need to rescale the tl patch coordinates to be in between (0,1) tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) for patch_nr in range(z.shape[-1])] # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) patch_limits = [(x_tl, y_tl, rescale_latent * ks[0] / full_img_w, rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] # tokenize crop coordinates for the bounding boxes of the respective patches patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) for bbox in patch_limits] # list of length l with tensors of shape (1, 2) print(patch_limits_tknzd[0].shape) # cut tknzd crop position from conditioning assert isinstance(cond, dict), 'cond must be dict to be fed into model' cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) print(cut_cond.shape) adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') print(adapted_cond.shape) adapted_cond = self.get_learned_conditioning(adapted_cond) print(adapted_cond.shape) adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) print(adapted_cond.shape) cond_list = [{'c_crossattn': [e]} for e in adapted_cond] else: cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient # apply model by loop over crops output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] assert not isinstance(output_list[0], tuple) # todo cant deal with multiple model outputs check this never happens o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together x_recon = fold(o) / normalization else: # x_noisy is tensor with shape [b,c,mel_len,T] # if condition is caption ,cond['c_crossattn'] is a list, each item shape is [1, 77, 1280] x_recon = self.model(x_noisy, t, **cond)# tensor with shape [b,c,mel_len,T] if isinstance(x_recon, tuple) and not return_ids: return x_recon[0] else: return x_recon def _predict_eps_from_xstart(self, x_t, t, pred_xstart): return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) def _prior_bpd(self, x_start): """ Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only depends on the encoder. :param x_start: the [N x C x ...] tensor of inputs. :return: a batch of [N] KL values (in bits), one per batch element. """ batch_size = x_start.shape[0] t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) return mean_flat(kl_prior) / np.log(2.0) def p_losses(self, x_start, cond, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_output = self.apply_model(x_noisy, t, cond) loss_dict = {} prefix = 'train' if self.training else 'val' if self.parameterization == "x0": target = x_start elif self.parameterization == "eps": target = noise else: raise NotImplementedError() loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) logvar_t = self.logvar[t].to(self.device) loss = loss_simple / torch.exp(logvar_t) + logvar_t # loss = loss_simple / torch.exp(self.logvar) + self.logvar if self.learn_logvar: loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) loss_dict.update({'logvar': self.logvar.data.mean()}) loss = self.l_simple_weight * loss.mean() loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) loss += (self.original_elbo_weight * loss_vlb) loss_dict.update({f'{prefix}/loss': loss}) return loss, loss_dict def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, return_x0=False, score_corrector=None, corrector_kwargs=None): t_in = t model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) if score_corrector is not None: assert self.parameterization == "eps" model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) if return_codebook_ids: model_out, logits = model_out if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out else: raise NotImplementedError() if clip_denoised: x_recon.clamp_(-1., 1.) if quantize_denoised: x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) if return_codebook_ids: return model_mean, posterior_variance, posterior_log_variance, logits elif return_x0: return model_mean, posterior_variance, posterior_log_variance, x_recon else: return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_codebook_ids=False, quantize_denoised=False, return_x0=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): b, *_, device = *x.shape, x.device outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_codebook_ids=return_codebook_ids, quantize_denoised=quantize_denoised, return_x0=return_x0, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if return_codebook_ids: raise DeprecationWarning("Support dropped.") model_mean, _, model_log_variance, logits = outputs elif return_x0: model_mean, _, model_log_variance, x0 = outputs else: model_mean, _, model_log_variance = outputs noise = noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) if return_codebook_ids: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) if return_x0: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 else: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t timesteps = self.num_timesteps if batch_size is not None: b = batch_size if batch_size is not None else shape[0] shape = [batch_size] + list(shape) else: b = batch_size = shape[0] if x_T is None: img = torch.randn(shape, device=self.device) else: img = x_T intermediates = [] if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', total=timesteps) if verbose else reversed( range(0, timesteps)) if type(temperature) == float: temperature = [temperature] * timesteps for i in iterator: ts = torch.full((b,), i, device=self.device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img, x0_partial = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised, return_x0=True, temperature=temperature[i], noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if mask is not None: assert x0 is not None img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(x0_partial) if callback: callback(i) if img_callback: img_callback(img, i) return img, intermediates @torch.no_grad() def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t device = self.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T intermediates = [img] if timesteps is None: timesteps = self.num_timesteps if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( range(0, timesteps)) if mask is not None: assert x0 is not None assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match for i in iterator: ts = torch.full((b,), i, device=device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised) if mask is not None: img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(img) if callback: callback(i) if img_callback: img_callback(img, i) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, verbose=True, timesteps=None, quantize_denoised=False, mask=None, x0=None, shape=None,**kwargs): if shape is None: shape = (batch_size, self.channels, self.mel_dim, self.mel_length) if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, shape, return_intermediates=return_intermediates, x_T=x_T, verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, mask=mask, x0=x0) @torch.no_grad() def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): if ddim: ddim_sampler = DDIMSampler(self) shape = (self.channels, self.mel_dim, self.mel_length) samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, shape,cond,verbose=False,**kwargs) else: samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True,**kwargs) return samples, intermediates @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, **kwargs): use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=N) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x # 原始输入图像 log["reconstruction"] = xrec # 重建得到的图像 if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"):# when cond_stage is first_stage. (bert embedder doesnot have decode) xc = self.cond_stage_model.decode(c)# decoded masked image log["conditioning"] = xc # 重建后的图像 elif self.cond_stage_key in ["caption"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) log["conditioning"] = xc # 含有文本的图像 if self.model.conditioning_key == 'hybrid': log["decoded_maskedimg"] = self.first_stage_model.decode(c['c_concat'][:,:self.first_stage_model.embed_dim])# c_concat is the concat result of masked_img latent and resized mask. get latent here to decode elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['conditioning'] = xc # 文本为类标签的图像 elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if plot_diffusion_rows:# diffusion每一步的图像 # get diffusion row diffusion_row = list() z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample:# # get denoise row with self.ema_scope("Plotting"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( self.first_stage_model, IdentityFirstStage): # also display when quantizing x0 while sampling with self.ema_scope("Plotting Quantized Denoised"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta, quantize_denoised=True) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, # quantize_denoised=True) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_x0_quantized"] = x_samples if inpaint: # make a simple center square b, h, w = z.shape[0], z.shape[2], z.shape[3] mask = torch.ones(N, h, w).to(self.device) # zeros will be filled in mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. mask = mask[:, None, ...]# N,1,H,W with self.ema_scope("Plotting Inpaint"): samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_inpainting"] = x_samples log["mask"] = mask # outpaint with self.ema_scope("Plotting Outpaint"): samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_outpainting"] = x_samples if plot_progressive_rows: with self.ema_scope("Plotting Progressives"): img, progressives = self.progressive_denoising(c, shape=(self.channels, self.mel_dim, self.mel_length), batch_size=N) prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") log["progressive_row"] = prog_row if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.cond_stage_trainable: print(f"{self.__class__.__name__}: Also optimizing conditioner params!") params = params + list(self.cond_stage_model.parameters()) if self.learn_logvar: print('Diffusion model optimizing logvar') params.append(self.logvar) opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: assert 'target' in self.scheduler_config scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [opt], scheduler return opt @torch.no_grad() def to_rgb(self, x): x = x.float() if not hasattr(self, "colorize"): self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) x = nn.functional.conv2d(x, weight=self.colorize) x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. return x ================================================ FILE: text_to_audio/Make_An_Audio/ldm/models/diffusion/plms.py ================================================ """SAMPLING ONLY.""" import torch import numpy as np from tqdm import tqdm from functools import partial from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like class PLMSSampler(object): def __init__(self, model, schedule="linear", **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != torch.device("cuda"): attr = attr.to(torch.device("cuda")) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): if ddim_eta != 0: raise ValueError('ddim_eta must be 0 for PLMS') self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) alphas_cumprod = self.model.alphas_cumprod assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) self.register_buffer('betas', to_torch(self.model.betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) # ddim sampling parameters ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta,verbose=verbose) self.register_buffer('ddim_sigmas', ddim_sigmas) self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) @torch.no_grad() def sample(self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **kwargs ): if conditioning is not None: if isinstance(conditioning, dict): cbs = conditioning[list(conditioning.keys())[0]].shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") else: if conditioning.shape[0] != batch_size: print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) # sampling C, H, W = shape size = (batch_size, C, H, W) print(f'Data shape for PLMS sampling is {size}') samples, intermediates = self.plms_sampling(conditioning, size, callback=callback, img_callback=img_callback, quantize_denoised=quantize_x0, mask=mask, x0=x0, ddim_use_original_steps=False, noise_dropout=noise_dropout, temperature=temperature, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, ) return samples, intermediates @torch.no_grad() def plms_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None,): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] print(f"Running PLMS Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) old_eps = [] for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) if mask is not None: assert x0 is not None img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? img = img_orig * mask + (1. - mask) * img outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, old_eps=old_eps, t_next=ts_next) img, pred_x0, e_t = outs old_eps.append(e_t) if len(old_eps) >= 4: old_eps.pop(0) if callback: callback(i) if img_callback: img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates @torch.no_grad() def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): b, *_, device = *x.shape, x.device def get_model_output(x, t): if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t] * 2) c_in = torch.cat([unconditional_conditioning, c]) e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) if score_corrector is not None: assert self.model.parameterization == "eps" e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) return e_t alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas def get_x_prev_and_pred_x0(e_t, index): # select parameters corresponding to the currently considered timestep a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 e_t = get_model_output(x, t) if len(old_eps) == 0: # Pseudo Improved Euler (2nd order) x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) e_t_next = get_model_output(x_prev, t_next) e_t_prime = (e_t + e_t_next) / 2 elif len(old_eps) == 1: # 2nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (3 * e_t - old_eps[-1]) / 2 elif len(old_eps) == 2: # 3nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 elif len(old_eps) >= 3: # 4nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) return x_prev, pred_x0, e_t ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/attention.py ================================================ from inspect import isfunction import math import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat from ldm.modules.diffusionmodules.util import checkpoint def exists(val): return val is not None def uniq(arr): return{el: True for el in arr}.keys() def default(val, d): if exists(val): return val return d() if isfunction(d) else d def max_neg_value(t): return -torch.finfo(t.dtype).max def init_(tensor): dim = tensor.shape[-1] std = 1 / math.sqrt(dim) tensor.uniform_(-std, std) return tensor # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * F.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def Normalize(in_channels): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) class LinearAttention(nn.Module): def __init__(self, dim, heads=4, dim_head=32): super().__init__() self.heads = heads hidden_dim = dim_head * heads self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) self.to_out = nn.Conv2d(hidden_dim, dim, 1) def forward(self, x): b, c, h, w = x.shape qkv = self.to_qkv(x) q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) k = k.softmax(dim=-1) context = torch.einsum('bhdn,bhen->bhde', k, v) out = torch.einsum('bhde,bhdn->bhen', context, q) out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) return self.to_out(out) class SpatialSelfAttention(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention b,c,h,w = q.shape q = rearrange(q, 'b c h w -> b (h w) c') k = rearrange(k, 'b c h w -> b c (h w)') w_ = torch.einsum('bij,bjk->bik', q, k) w_ = w_ * (int(c)**(-0.5)) w_ = torch.nn.functional.softmax(w_, dim=2) # attend to values v = rearrange(v, 'b c h w -> b c (h w)') w_ = rearrange(w_, 'b i j -> b j i') h_ = torch.einsum('bij,bjk->bik', v, w_) h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) h_ = self.proj_out(h_) return x+h_ class CrossAttention(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):# 如果设置了context_dim就不是自注意力了 super().__init__() inner_dim = dim_head * heads # inner_dim == SpatialTransformer.model_channels context_dim = default(context_dim, query_dim) self.scale = dim_head ** -0.5 self.heads = heads self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_out = nn.Sequential( nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) ) def forward(self, x, context=None, mask=None):# x:(b,h*w,c), context:(b,seq_len,context_dim) h = self.heads q = self.to_q(x)# q:(b,h*w,inner_dim) context = default(context, x) k = self.to_k(context)# (b,seq_len,inner_dim) v = self.to_v(context)# (b,seq_len,inner_dim) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))# n is seq_len for k and v sim = einsum('b i d, b j d -> b i j', q, k) * self.scale # (b*head,h*w,seq_len) if exists(mask):# false mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) # attention, what we cannot get enough of attn = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', attn, v)# (b*head,h*w,inner_dim/head) out = rearrange(out, '(b h) n d -> b n (h d)', h=h)# (b,h*w,inner_dim) return self.to_out(out) class BasicTransformerBlock(nn.Module): def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True): super().__init__() self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.norm3 = nn.LayerNorm(dim) self.checkpoint = checkpoint def forward(self, x, context=None): return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) def _forward(self, x, context=None): x = self.attn1(self.norm1(x)) + x x = self.attn2(self.norm2(x), context=context) + x x = self.ff(self.norm3(x)) + x return x class SpatialTransformer(nn.Module): """ Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image """ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None): super().__init__() self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = Normalize(in_channels) self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim) for d in range(depth)] ) self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) def forward(self, x, context=None): # note: if no context is given, cross-attention defaults to self-attention b, c, h, w = x.shape # such as [2,320,10,106] x_in = x x = self.norm(x)# group norm x = self.proj_in(x)# no shape change x = rearrange(x, 'b c h w -> b (h w) c') for block in self.transformer_blocks: x = block(x, context=context)# context shape [b,seq_len=77,context_dim] x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w) x = self.proj_out(x) return x + x_in ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/__init__.py ================================================ ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/custom_openaimodel.py ================================================ from abc import abstractmethod from functools import partial import math from typing import Iterable import numpy as np import torch as th import torch.nn as nn import torch.nn.functional as F from ldm.modules.diffusionmodules.util import ( checkpoint, conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding, ) from ldm.modules.attention import SpatialTransformer from ldm.modules.diffusionmodules.openaimodel import convert_module_to_f16, convert_module_to_f32, AttentionPool2d, \ TimestepBlock, TimestepEmbedSequential, Upsample, TransposedUpsample, Downsample, ResBlock, AttentionBlock, count_flops_attn, \ QKVAttentionLegacy, QKVAttention class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attention will be used. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, use_fp16=False, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, # custom transformer support transformer_depth=1, # custom transformer support context_dim=None, # custom transformer support n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model legacy=True, use_context_project=False, # custom text to audio support use_context_attn=True # custom text to audio support ): super().__init__() if use_spatial_transformer: assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' if context_dim is not None and not use_context_project: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' from omegaconf.listconfig import ListConfig if type(context_dim) == ListConfig: context_dim = list(context_dim) if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.num_res_blocks = num_res_blocks self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.predict_codebook_ids = n_embed is not None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) if self.num_classes is not None: self.label_emb = nn.Embedding(num_classes, time_embed_dim) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = mult * model_channels if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(num_res_blocks + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = model_channels * mult if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads_upsample, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ) ) if level and i == num_res_blocks: out_ch = ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( normalization(ch), conv_nd(dims, model_channels, n_embed, 1), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) self.use_context_project = use_context_project if use_context_project: self.context_project = linear(context_dim, time_embed_dim) self.use_context_attn = use_context_attn def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.input_blocks.apply(convert_module_to_f16) self.middle_block.apply(convert_module_to_f16) self.output_blocks.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.input_blocks.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32) self.output_blocks.apply(convert_module_to_f32) def forward(self, x, timesteps=None, context=None, y=None,**kwargs): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs. """ assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape == (x.shape[0],) emb = emb + self.label_emb(y) # For text-to-audio using global CLIP if self.use_context_project: context = self.context_project(context) emb = emb + context.squeeze(1) h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb, context if self.use_context_attn else None) hs.append(h) h = self.middle_block(h, emb, context if self.use_context_attn else None) for module in self.output_blocks: h = th.cat([h, hs.pop()], dim=1) h = module(h, emb, context if self.use_context_attn else None) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/model.py ================================================ # pytorch_diffusion + derived encoder decoder import math import torch import torch.nn as nn import numpy as np from einops import rearrange from ldm.util import instantiate_from_config from ldm.modules.attention import LinearAttention def get_timestep_embedding(timesteps, embedding_dim): """ This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ assert len(timesteps.shape) == 1 half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) emb = emb.to(device=timesteps.device) emb = timesteps.float()[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0,1,0,0)) return emb def nonlinearity(x): # swish return x*torch.sigmoid(x) def Normalize(in_channels, num_groups=32): return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) class Upsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") if self.with_conv: x = self.conv(x) return x class Downsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: # no asymmetric padding in torch conv, must do it ourselves self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, x): if self.with_conv: pad = (0,1,0,1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) else: x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) return x class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.use_conv_shortcut = conv_shortcut self.norm1 = Normalize(in_channels) self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels > 0: self.temb_proj = torch.nn.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels) self.dropout = torch.nn.Dropout(dropout) self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, temb): h = x h = self.norm1(h) h = nonlinearity(h) h = self.conv1(h) if temb is not None: h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] h = self.norm2(h) h = nonlinearity(h) h = self.dropout(h) h = self.conv2(h) if self.in_channels != self.out_channels: if self.use_conv_shortcut: x = self.conv_shortcut(x) else: x = self.nin_shortcut(x) return x+h class LinAttnBlock(LinearAttention): """to match AttnBlock usage""" def __init__(self, in_channels): super().__init__(dim=in_channels, heads=1, dim_head=in_channels) class AttnBlock(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention b,c,h,w = q.shape q = q.reshape(b,c,h*w) q = q.permute(0,2,1) # b,hw,c k = k.reshape(b,c,h*w) # b,c,hw w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] w_ = w_ * (int(c)**(-0.5)) w_ = torch.nn.functional.softmax(w_, dim=2) # attend to values v = v.reshape(b,c,h*w) w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] h_ = h_.reshape(b,c,h,w) h_ = self.proj_out(h_) return x+h_ def make_attn(in_channels, attn_type="vanilla"): assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' print(f"making attention of type '{attn_type}' with {in_channels} in_channels") if attn_type == "vanilla": return AttnBlock(in_channels) elif attn_type == "none": return nn.Identity(in_channels) else: return LinAttnBlock(in_channels) class Model(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = self.ch*4 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.use_timestep = use_timestep if self.use_timestep: # timestep embedding self.temb = nn.Module() self.temb.dense = nn.ModuleList([ torch.nn.Linear(self.ch, self.temb_ch), torch.nn.Linear(self.temb_ch, self.temb_ch), ]) # downsampling self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] skip_in = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): if i_block == self.num_res_blocks: skip_in = ch*in_ch_mult[i_level] block.append(ResnetBlock(in_channels=block_in+skip_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, x, t=None, context=None): #assert x.shape[2] == x.shape[3] == self.resolution if context is not None: # assume aligned context, cat along channel axis x = torch.cat((x, context), dim=1) if self.use_timestep: # timestep embedding assert t is not None temb = get_timestep_embedding(t, self.ch) temb = self.temb.dense[0](temb) temb = nonlinearity(temb) temb = self.temb.dense[1](temb) else: temb = None # downsampling hs = [self.conv_in(x)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](hs[-1], temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) hs.append(h) if i_level != self.num_resolutions-1: hs.append(self.down[i_level].downsample(hs[-1])) # middle h = hs[-1] h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block]( torch.cat([h, hs.pop()], dim=1), temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h def get_last_layer(self): return self.conv_out.weight class Encoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", **ignore_kwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels # downsampling self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.in_ch_mult = in_ch_mult self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type))# vanilla attention down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # end self.norm_out = Normalize(block_in)# GroupNorm self.conv_out = torch.nn.Conv2d(block_in, 2*z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # timestep embedding temb = None # downsampling hs = [self.conv_in(x)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](hs[-1], temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) hs.append(h) if i_level != self.num_resolutions-1: hs.append(self.down[i_level].downsample(hs[-1])) # middle h = hs[-1] h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class Decoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, attn_type="vanilla", **ignorekwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.give_pre_end = give_pre_end self.tanh_out = tanh_out # compute in_ch_mult, block_in and curr_res at lowest res in_ch_mult = (1,)+tuple(ch_mult) block_in = ch*ch_mult[self.num_resolutions-1] curr_res = resolution // 2**(self.num_resolutions-1) self.z_shape = (1,z_channels,curr_res,curr_res) print("Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape))) # z to block_in self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, z): #assert z.shape[1:] == self.z_shape[1:] self.last_z_shape = z.shape # timestep embedding temb = None # z to block_in h = self.conv_in(z) # middle h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block](h, temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) # end if self.give_pre_end: return h h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) if self.tanh_out: h = torch.tanh(h) return h class SimpleDecoder(nn.Module): def __init__(self, in_channels, out_channels, *args, **kwargs): super().__init__() self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), ResnetBlock(in_channels=in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=2 * in_channels, out_channels=4 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=4 * in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), nn.Conv2d(2*in_channels, in_channels, 1), Upsample(in_channels, with_conv=True)]) # end self.norm_out = Normalize(in_channels) self.conv_out = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): for i, layer in enumerate(self.model): if i in [1,2,3]: x = layer(x, None) else: x = layer(x) h = self.norm_out(x) h = nonlinearity(h) x = self.conv_out(h) return x class UpsampleDecoder(nn.Module): def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, ch_mult=(2,2), dropout=0.0): super().__init__() # upsampling self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks block_in = in_channels curr_res = resolution // 2 ** (self.num_resolutions - 1) self.res_blocks = nn.ModuleList() self.upsample_blocks = nn.ModuleList() for i_level in range(self.num_resolutions): res_block = [] block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): res_block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out self.res_blocks.append(nn.ModuleList(res_block)) if i_level != self.num_resolutions - 1: self.upsample_blocks.append(Upsample(block_in, True)) curr_res = curr_res * 2 # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # upsampling h = x for k, i_level in enumerate(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.res_blocks[i_level][i_block](h, None) if i_level != self.num_resolutions - 1: h = self.upsample_blocks[k](h) h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class LatentRescaler(nn.Module): def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): super().__init__() # residual block, interpolate, residual block self.factor = factor self.conv_in = nn.Conv2d(in_channels, mid_channels, kernel_size=3, stride=1, padding=1) self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) for _ in range(depth)]) self.attn = AttnBlock(mid_channels) self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) for _ in range(depth)]) self.conv_out = nn.Conv2d(mid_channels, out_channels, kernel_size=1, ) def forward(self, x): x = self.conv_in(x) for block in self.res_block1: x = block(x, None) x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) x = self.attn(x) for block in self.res_block2: x = block(x, None) x = self.conv_out(x) return x class MergedRescaleEncoder(nn.Module): def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): super().__init__() intermediate_chn = ch * ch_mult[-1] self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, z_channels=intermediate_chn, double_z=False, resolution=resolution, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, out_ch=None) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) def forward(self, x): x = self.encoder(x) x = self.rescaler(x) return x class MergedRescaleDecoder(nn.Module): def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): super().__init__() tmp_chn = z_channels*ch_mult[-1] self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, ch_mult=ch_mult, resolution=resolution, ch=ch) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, out_channels=tmp_chn, depth=rescale_module_depth) def forward(self, x): x = self.rescaler(x) x = self.decoder(x) return x class Upsampler(nn.Module): def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): super().__init__() assert out_size >= in_size num_blocks = int(np.log2(out_size//in_size))+1 factor_up = 1.+ (out_size % in_size) print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, out_channels=in_channels) self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, attn_resolutions=[], in_channels=None, ch=in_channels, ch_mult=[ch_mult for _ in range(num_blocks)]) def forward(self, x): x = self.rescaler(x) x = self.decoder(x) return x class Resize(nn.Module): def __init__(self, in_channels=None, learned=False, mode="bilinear"): super().__init__() self.with_conv = learned self.mode = mode if self.with_conv: print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") raise NotImplementedError() assert in_channels is not None # no asymmetric padding in torch conv, must do it ourselves self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=4, stride=2, padding=1) def forward(self, x, scale_factor=1.0): if scale_factor==1.0: return x else: x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) return x class FirstStagePostProcessor(nn.Module): def __init__(self, ch_mult:list, in_channels, pretrained_model:nn.Module=None, reshape=False, n_channels=None, dropout=0., pretrained_config=None): super().__init__() if pretrained_config is None: assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' self.pretrained_model = pretrained_model else: assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' self.instantiate_pretrained(pretrained_config) self.do_reshape = reshape if n_channels is None: n_channels = self.pretrained_model.encoder.ch self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, stride=1,padding=1) blocks = [] downs = [] ch_in = n_channels for m in ch_mult: blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) ch_in = m * n_channels downs.append(Downsample(ch_in, with_conv=False)) self.model = nn.ModuleList(blocks) self.downsampler = nn.ModuleList(downs) def instantiate_pretrained(self, config): model = instantiate_from_config(config) self.pretrained_model = model.eval() # self.pretrained_model.train = False for param in self.pretrained_model.parameters(): param.requires_grad = False @torch.no_grad() def encode_with_pretrained(self,x): c = self.pretrained_model.encode(x) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() return c def forward(self,x): z_fs = self.encode_with_pretrained(x) z = self.proj_norm(z_fs) z = self.proj(z) z = nonlinearity(z) for submodel, downmodel in zip(self.model,self.downsampler): z = submodel(z,temb=None) z = downmodel(z) if self.do_reshape: z = rearrange(z,'b c h w -> b (h w) c') return z ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/openaimodel.py ================================================ from abc import abstractmethod from functools import partial import math from typing import Iterable import numpy as np import torch as th import torch.nn as nn import torch.nn.functional as F from ldm.modules.diffusionmodules.util import ( checkpoint, conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding, ) from ldm.modules.attention import SpatialTransformer # dummy replace def convert_module_to_f16(x): pass def convert_module_to_f32(x): pass ## go class AttentionPool2d(nn.Module): """ Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py """ def __init__( self, spacial_dim: int, embed_dim: int, num_heads_channels: int, output_dim: int = None, ): super().__init__() self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) self.num_heads = embed_dim // num_heads_channels self.attention = QKVAttention(self.num_heads) def forward(self, x): b, c, *_spatial = x.shape x = x.reshape(b, c, -1) # NC(HW) x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) x = self.qkv_proj(x) x = self.attention(x) x = self.c_proj(x) return x[:, :, 0] class TimestepBlock(nn.Module): """ Any module where forward() takes timestep embeddings as a second argument. """ @abstractmethod def forward(self, x, emb): """ Apply the module to `x` given `emb` timestep embeddings. """ class TimestepEmbedSequential(nn.Sequential, TimestepBlock): """ A sequential module that passes timestep embeddings to the children that support it as an extra input. """ def forward(self, x, emb, context=None): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialTransformer): x = layer(x, context) else: x = layer(x) return x class Upsample(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) def forward(self, x): assert x.shape[1] == self.channels if self.dims == 3: x = F.interpolate( x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" ) else: x = F.interpolate(x, scale_factor=2, mode="nearest") if self.use_conv: x = self.conv(x) return x class TransposedUpsample(nn.Module): 'Learned 2x upsampling without padding' def __init__(self, channels, out_channels=None, ks=5): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) def forward(self,x): return self.up(x) class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.op(x) class ResBlock(TimestepBlock): """ A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use a spatial convolution instead of a smaller 1x1 convolution to change the channels in the skip connection. :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_conv=False, use_scale_shift_norm=False, dims=2, use_checkpoint=False, up=False, down=False, ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_conv = use_conv self.use_checkpoint = use_checkpoint self.use_scale_shift_norm = use_scale_shift_norm self.in_layers = nn.Sequential( normalization(channels), nn.SiLU(), conv_nd(dims, channels, self.out_channels, 3, padding=1), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims) self.x_upd = Upsample(channels, False, dims) elif down: self.h_upd = Downsample(channels, False, dims) self.x_upd = Downsample(channels, False, dims) else: self.h_upd = self.x_upd = nn.Identity() self.emb_layers = nn.Sequential( nn.SiLU(), linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, ), ) self.out_layers = nn.Sequential( normalization(self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module( conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = conv_nd( dims, channels, self.out_channels, 3, padding=1 ) else: self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, (x, emb), self.parameters(), self.use_checkpoint ) def _forward(self, x, emb): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] scale, shift = th.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class AttentionBlock(nn.Module): """ An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted to the N-d case. https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. """ def __init__( self, channels, num_heads=1, num_head_channels=-1, use_checkpoint=False, use_new_attention_order=False, ): super().__init__() self.channels = channels if num_head_channels == -1: self.num_heads = num_heads else: assert ( channels % num_head_channels == 0 ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" self.num_heads = channels // num_head_channels self.use_checkpoint = use_checkpoint self.norm = normalization(channels) self.qkv = conv_nd(1, channels, channels * 3, 1) if use_new_attention_order: # split qkv before split heads self.attention = QKVAttention(self.num_heads) else: # split heads before split qkv self.attention = QKVAttentionLegacy(self.num_heads) self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) def forward(self, x): return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! #return pt_checkpoint(self._forward, x) # pytorch def _forward(self, x): b, c, *spatial = x.shape x = x.reshape(b, c, -1) qkv = self.qkv(self.norm(x)) h = self.attention(qkv) h = self.proj_out(h) return (x + h).reshape(b, c, *spatial) def count_flops_attn(model, _x, y): """ A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.profile( model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops}, ) """ b, c, *spatial = y[0].shape num_spatial = int(np.prod(spatial)) # We perform two matmuls with the same number of ops. # The first computes the weight matrix, the second computes # the combination of the value vectors. matmul_ops = 2 * b * (num_spatial ** 2) * c model.total_ops += th.DoubleTensor([matmul_ops]) class QKVAttentionLegacy(nn.Module): """ A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping """ def __init__(self, n_heads): super().__init__() self.n_heads = n_heads def forward(self, qkv): """ Apply QKV attention. :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after attention. """ bs, width, length = qkv.shape assert width % (3 * self.n_heads) == 0 ch = width // (3 * self.n_heads) q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) scale = 1 / math.sqrt(math.sqrt(ch)) weight = th.einsum( "bct,bcs->bts", q * scale, k * scale ) # More stable with f16 than dividing afterwards weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) a = th.einsum("bts,bcs->bct", weight, v) return a.reshape(bs, -1, length) @staticmethod def count_flops(model, _x, y): return count_flops_attn(model, _x, y) class QKVAttention(nn.Module): """ A module which performs QKV attention and splits in a different order. """ def __init__(self, n_heads): super().__init__() self.n_heads = n_heads def forward(self, qkv): """ Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after attention. """ bs, width, length = qkv.shape assert width % (3 * self.n_heads) == 0 ch = width // (3 * self.n_heads) q, k, v = qkv.chunk(3, dim=1) scale = 1 / math.sqrt(math.sqrt(ch)) weight = th.einsum( "bct,bcs->bts", (q * scale).view(bs * self.n_heads, ch, length), (k * scale).view(bs * self.n_heads, ch, length), ) # More stable with f16 than dividing afterwards weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) return a.reshape(bs, -1, length) @staticmethod def count_flops(model, _x, y): return count_flops_attn(model, _x, y) class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attention will be used. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, use_fp16=False, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, # custom transformer support transformer_depth=1, # custom transformer support context_dim=None, # custom transformer support n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model legacy=True, ): super().__init__() if use_spatial_transformer: assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' from omegaconf.listconfig import ListConfig if type(context_dim) == ListConfig: context_dim = list(context_dim) if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.num_res_blocks = num_res_blocks self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.predict_codebook_ids = n_embed is not None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) if self.num_classes is not None: self.label_emb = nn.Embedding(num_classes, time_embed_dim) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1)# conv2d for txt2img/audio ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 # downsample blocks for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = mult * model_channels if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer(# transformer_depth is 1 ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch # upsample blocks self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(num_res_blocks + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = model_channels * mult if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads_upsample, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ) ) if level and i == num_res_blocks: out_ch = ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( normalization(ch), conv_nd(dims, model_channels, n_embed, 1), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.input_blocks.apply(convert_module_to_f16) self.middle_block.apply(convert_module_to_f16) self.output_blocks.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.input_blocks.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32) self.output_blocks.apply(convert_module_to_f32) def forward(self, x, timesteps=None, context=None, y=None,**kwargs): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps,shape [N] :param context: conditioning plugged in via crossattn. for txt2img shape is [N,77,context_dim] :param y: an [N] Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs. """ # print(f"in unet {x.shape}") assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)# shape [N,self.model_channels] emb = self.time_embed(t_emb)# shape [N,context_dim] if self.num_classes is not None:# only for class label assert y.shape == (x.shape[0],) emb = emb + self.label_emb(y) h = x.type(self.dtype)# [N,C,10,106] for module in self.input_blocks: h = module(h, emb, context)# 0:[N,self.model_channels,10,106],1:[N,self.model_channels,10,106],2:[N,self.model_channels,10,106] 3:[N,self.model_channels,5,53] 4:[N,self.model_channels,5,53] 5:[N,self.model_channels*2,5,53] hs.append(h) h = self.middle_block(h, emb, context)# no shape change for module in self.output_blocks: h = th.cat([h, hs.pop()], dim=1)# 在这里c维度乘2或+self.model_channels，其余维度不变 h = module(h, emb, context)# 在这里c维度/2回到之前维度，h,w不变或*2 h = h.type(x.dtype)# 至此h维度和输入x维度回到相同状态 if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h) class EncoderUNetModel(nn.Module): """ The half UNet model with attention and timestep embedding. For usage, see UNet. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, use_checkpoint=False, use_fp16=False, num_heads=1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, pool="adaptive", *args, **kwargs ): super().__init__() if num_heads_upsample == -1: num_heads_upsample = num_heads self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.num_res_blocks = num_res_blocks self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = mult * model_channels if ds in attention_resolutions: layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch self.pool = pool if pool == "adaptive": self.out = nn.Sequential( normalization(ch), nn.SiLU(), nn.AdaptiveAvgPool2d((1, 1)), zero_module(conv_nd(dims, ch, out_channels, 1)), nn.Flatten(), ) elif pool == "attention": assert num_head_channels != -1 self.out = nn.Sequential( normalization(ch), nn.SiLU(), AttentionPool2d( (image_size // ds), ch, num_head_channels, out_channels ), ) elif pool == "spatial": self.out = nn.Sequential( nn.Linear(self._feature_size, 2048), nn.ReLU(), nn.Linear(2048, self.out_channels), ) elif pool == "spatial_v2": self.out = nn.Sequential( nn.Linear(self._feature_size, 2048), normalization(2048), nn.SiLU(), nn.Linear(2048, self.out_channels), ) else: raise NotImplementedError(f"Unexpected {pool} pooling") def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.input_blocks.apply(convert_module_to_f16) self.middle_block.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.input_blocks.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32) def forward(self, x, timesteps): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :return: an [N x K] Tensor of outputs. """ emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) results = [] h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb) if self.pool.startswith("spatial"): results.append(h.type(x.dtype).mean(dim=(2, 3))) h = self.middle_block(h, emb) if self.pool.startswith("spatial"): results.append(h.type(x.dtype).mean(dim=(2, 3))) h = th.cat(results, axis=-1) return self.out(h) else: h = h.type(x.dtype) return self.out(h) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/util.py ================================================ # adopted from # https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py # and # https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py # and # https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py # # thanks! import os import math import torch import torch.nn as nn import numpy as np from einops import repeat from ldm.util import instantiate_from_config def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if schedule == "linear": betas = ( torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 ) elif schedule == "cosine": timesteps = ( torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s ) alphas = timesteps / (1 + cosine_s) * np.pi / 2 alphas = torch.cos(alphas).pow(2) alphas = alphas / alphas[0] betas = 1 - alphas[1:] / alphas[:-1] betas = np.clip(betas, a_min=0, a_max=0.999) elif schedule == "sqrt_linear": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) elif schedule == "sqrt": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 else: raise ValueError(f"schedule '{schedule}' unknown.") return betas.numpy() def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): if ddim_discr_method == 'uniform': c = num_ddpm_timesteps // num_ddim_timesteps ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) elif ddim_discr_method == 'quad': ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) else: raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') # assert ddim_timesteps.shape[0] == num_ddim_timesteps # add one to get the final alpha values right (the ones from first scale to data during sampling) steps_out = ddim_timesteps + 1 if verbose: print(f'Selected timesteps for ddim sampler: {steps_out}') return steps_out def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): # select alphas for computing the variance schedule alphas = alphacums[ddim_timesteps] alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) # according the the formula provided in https://arxiv.org/abs/2010.02502 sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) if verbose: print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') print(f'For the chosen value of eta, which is {eta}, ' f'this results in the following sigma_t schedule for ddim sampler {sigmas}') return sigmas, alphas, alphas_prev def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): """ Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t from 0 to 1 and produces the cumulative product of (1-beta) up to that part of the diffusion process. :param max_beta: the maximum beta to use; use values lower than 1 to prevent singularities. """ betas = [] for i in range(num_diffusion_timesteps): t1 = i / num_diffusion_timesteps t2 = (i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) return np.array(betas) def extract_into_tensor(a, t, x_shape): b, *_ = t.shape out = a.gather(-1, t) return out.reshape(b, *((1,) * (len(x_shape) - 1))) def checkpoint(func, inputs, params, flag): """ Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing. """ if flag: args = tuple(inputs) + tuple(params) return CheckpointFunction.apply(func, len(inputs), *args) else: return func(*inputs) class CheckpointFunction(torch.autograd.Function): @staticmethod def forward(ctx, run_function, length, *args): ctx.run_function = run_function ctx.input_tensors = list(args[:length]) ctx.input_params = list(args[length:]) with torch.no_grad(): output_tensors = ctx.run_function(*ctx.input_tensors) return output_tensors @staticmethod def backward(ctx, *output_grads): ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] with torch.enable_grad(): # Fixes a bug where the first op in run_function modifies the # Tensor storage in place, which is not allowed for detach()'d # Tensors. shallow_copies = [x.view_as(x) for x in ctx.input_tensors] output_tensors = ctx.run_function(*shallow_copies) input_grads = torch.autograd.grad( output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True, ) del ctx.input_tensors del ctx.input_params del output_tensors return (None, None) + input_grads def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): """ Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. """ if not repeat_only: half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half ).to(device=timesteps.device) args = timesteps[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) else: embedding = repeat(timesteps, 'b -> b d', d=dim) return embedding def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def scale_module(module, scale): """ Scale the parameters of a module and return it. """ for p in module.parameters(): p.detach().mul_(scale) return module def mean_flat(tensor): """ Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def normalization(channels): """ Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization. """ return GroupNorm32(32, channels) # PyTorch 1.7 has SiLU, but we support PyTorch 1.5. class SiLU(nn.Module): def forward(self, x): return x * torch.sigmoid(x) class GroupNorm32(nn.GroupNorm): def forward(self, x): return super().forward(x.float()).type(x.dtype) def conv_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D convolution module. """ if dims == 1: return nn.Conv1d(*args, **kwargs) elif dims == 2: return nn.Conv2d(*args, **kwargs) elif dims == 3: return nn.Conv3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") def linear(*args, **kwargs): """ Create a linear module. """ return nn.Linear(*args, **kwargs) def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. """ if dims == 1: return nn.AvgPool1d(*args, **kwargs) elif dims == 2: return nn.AvgPool2d(*args, **kwargs) elif dims == 3: return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") class HybridConditioner(nn.Module): def __init__(self, c_concat_config, c_crossattn_config): super().__init__() self.concat_conditioner = instantiate_from_config(c_concat_config) self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) def forward(self, c_concat, c_crossattn): c_concat = self.concat_conditioner(c_concat) c_crossattn = self.crossattn_conditioner(c_crossattn) return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} def noise_like(shape, device, repeat=False): repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: torch.randn(shape, device=device) return repeat_noise() if repeat else noise() ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/discriminator/model.py ================================================ import functools import torch.nn as nn class ActNorm(nn.Module): def __init__(self, num_features, logdet=False, affine=True, allow_reverse_init=False): assert affine super().__init__() self.logdet = logdet self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) self.allow_reverse_init = allow_reverse_init self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) def initialize(self, input): with torch.no_grad(): flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) mean = ( flatten.mean(1) .unsqueeze(1) .unsqueeze(2) .unsqueeze(3) .permute(1, 0, 2, 3) ) std = ( flatten.std(1) .unsqueeze(1) .unsqueeze(2) .unsqueeze(3) .permute(1, 0, 2, 3) ) self.loc.data.copy_(-mean) self.scale.data.copy_(1 / (std + 1e-6)) def forward(self, input, reverse=False): if reverse: return self.reverse(input) if len(input.shape) == 2: input = input[:, :, None, None] squeeze = True else: squeeze = False _, _, height, width = input.shape if self.training and self.initialized.item() == 0: self.initialize(input) self.initialized.fill_(1) h = self.scale * (input + self.loc) if squeeze: h = h.squeeze(-1).squeeze(-1) if self.logdet: log_abs = torch.log(torch.abs(self.scale)) logdet = height * width * torch.sum(log_abs) logdet = logdet * torch.ones(input.shape[0]).to(input) return h, logdet return h def reverse(self, output): if self.training and self.initialized.item() == 0: if not self.allow_reverse_init: raise RuntimeError( "Initializing ActNorm in reverse direction is " "disabled by default. Use allow_reverse_init=True to enable." ) else: self.initialize(output) self.initialized.fill_(1) if len(output.shape) == 2: output = output[:, :, None, None] squeeze = True else: squeeze = False h = output / self.scale - self.loc if squeeze: h = h.squeeze(-1).squeeze(-1) return h def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: nn.init.normal_(m.weight.data, 0.0, 0.02) elif classname.find('BatchNorm') != -1: nn.init.normal_(m.weight.data, 1.0, 0.02) nn.init.constant_(m.bias.data, 0) class NLayerDiscriminator(nn.Module): """Defines a PatchGAN discriminator as in Pix2Pix --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py """ def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): """Construct a PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int) -- the number of filters in the last conv layer n_layers (int) -- the number of conv layers in the discriminator norm_layer -- normalization layer """ super(NLayerDiscriminator, self).__init__() if not use_actnorm: norm_layer = nn.BatchNorm2d else: norm_layer = ActNorm if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters use_bias = norm_layer.func != nn.BatchNorm2d else: use_bias = norm_layer != nn.BatchNorm2d kw = 4 padw = 1 sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): # gradually increase the number of filters nf_mult_prev = nf_mult nf_mult = min(2 ** n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2 ** n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] # output 1 channel prediction map sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] self.main = nn.Sequential(*sequence) def forward(self, input): """Standard forward.""" return self.main(input) class NLayerDiscriminator1dFeats(NLayerDiscriminator): """Defines a PatchGAN discriminator as in Pix2Pix --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py """ def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): """Construct a PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input feats ndf (int) -- the number of filters in the last conv layer n_layers (int) -- the number of conv layers in the discriminator norm_layer -- normalization layer """ super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm) if not use_actnorm: norm_layer = nn.BatchNorm1d else: norm_layer = ActNorm if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm has affine parameters use_bias = norm_layer.func != nn.BatchNorm1d else: use_bias = norm_layer != nn.BatchNorm1d kw = 4 padw = 1 sequence = [nn.Conv1d(input_nc, input_nc//2, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] nf_mult = input_nc//2 nf_mult_prev = 1 for n in range(1, n_layers): # gradually decrease the number of filters nf_mult_prev = nf_mult nf_mult = max(nf_mult_prev // (2 ** n), 8) sequence += [ nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = max(nf_mult_prev // (2 ** n), 8) sequence += [ nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = max(nf_mult_prev // (2 ** n), 8) sequence += [ nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(nf_mult), nn.LeakyReLU(0.2, True) ] # output 1 channel prediction map sequence += [nn.Conv1d(nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] self.main = nn.Sequential(*sequence) class NLayerDiscriminator1dSpecs(NLayerDiscriminator): """Defines a PatchGAN discriminator as in Pix2Pix --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py """ def __init__(self, input_nc=80, ndf=64, n_layers=3, use_actnorm=False): """Construct a PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input specs ndf (int) -- the number of filters in the last conv layer n_layers (int) -- the number of conv layers in the discriminator norm_layer -- normalization layer """ super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm) if not use_actnorm: norm_layer = nn.BatchNorm1d else: norm_layer = ActNorm if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm has affine parameters use_bias = norm_layer.func != nn.BatchNorm1d else: use_bias = norm_layer != nn.BatchNorm1d kw = 4 padw = 1 sequence = [nn.Conv1d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): # gradually decrease the number of filters nf_mult_prev = nf_mult nf_mult = min(2 ** n, 8) sequence += [ nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2 ** n_layers, 8) sequence += [ nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] # output 1 channel prediction map sequence += [nn.Conv1d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] self.main = nn.Sequential(*sequence) def forward(self, input): """Standard forward.""" # (B, C, L) input = input.squeeze(1) input = self.main(input) return input if __name__ == '__main__': import torch ## FEATURES disc_in_channels = 2048 disc_num_layers = 2 use_actnorm = False disc_ndf = 64 discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) inputs = torch.rand((6, 2048, 212)) outputs = discriminator(inputs) print(outputs.shape) ## AUDIO disc_in_channels = 1 disc_num_layers = 3 use_actnorm = False disc_ndf = 64 discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) inputs = torch.rand((6, 1, 80, 848)) outputs = discriminator(inputs) print(outputs.shape) ## IMAGE disc_in_channels = 3 disc_num_layers = 3 use_actnorm = False disc_ndf = 64 discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) inputs = torch.rand((6, 3, 256, 256)) outputs = discriminator(inputs) print(outputs.shape) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/discriminator/multi_window_disc.py ================================================ import numpy as np import torch import torch.nn as nn class Discriminator2DFactory(nn.Module): def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128, norm_type='bn', reduction='sum'): super(Discriminator2DFactory, self).__init__() padding = (kernel[0] // 2, kernel[1] // 2) def discriminator_block(in_filters, out_filters, first=False): """ Input: (B, in, 2H, 2W) Output:(B, out, H, W) """ conv = nn.Conv2d(in_filters, out_filters, kernel, (2, 2), padding) if norm_type == 'sn': conv = nn.utils.spectral_norm(conv) block = [ conv, # padding = kernel//2 nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25) ] if norm_type == 'bn' and not first: block.append(nn.BatchNorm2d(out_filters, 0.8)) if norm_type == 'in' and not first: block.append(nn.InstanceNorm2d(out_filters, affine=True)) block = nn.Sequential(*block) return block self.model = nn.ModuleList([ discriminator_block(c_in, hidden_size, first=True), discriminator_block(hidden_size, hidden_size), discriminator_block(hidden_size, hidden_size), ]) self.reduction = reduction ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3) if reduction != 'none': # The height and width of downsampled image self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1) else: self.adv_layer = nn.Linear(hidden_size * ds_size[1], 1) def forward(self, x): """ :param x: [B, C, T, n_bins] :return: validity: [B, 1], h: List of hiddens """ h = [] for l in self.model: x = l(x) h.append(x) if self.reduction != 'none': x = x.view(x.shape[0], -1) validity = self.adv_layer(x) # [B, 1] else: B, _, T_, _ = x.shape x = x.transpose(1, 2).reshape(B, T_, -1) validity = self.adv_layer(x)[:, :, 0] # [B, T] return validity, h class MultiWindowDiscriminator(nn.Module): def __init__(self, time_lengths, cond_size=0, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128, norm_type='bn', reduction='sum'): super(MultiWindowDiscriminator, self).__init__() self.win_lengths = time_lengths self.reduction = reduction self.conv_layers = nn.ModuleList() if cond_size > 0: self.cond_proj_layers = nn.ModuleList() self.mel_proj_layers = nn.ModuleList() for time_length in time_lengths: conv_layer = [ Discriminator2DFactory( time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size, norm_type=norm_type, reduction=reduction) ] self.conv_layers += conv_layer if cond_size > 0: self.cond_proj_layers.append(nn.Linear(cond_size, freq_length)) self.mel_proj_layers.append(nn.Linear(freq_length, freq_length)) def forward(self, x, x_len, cond=None, start_frames_wins=None): ''' Args: x (tensor): input mel, (B, c_in, T, n_bins). x_length (tensor): len of per mel. (B,). Returns: tensor : (B). ''' validity = [] if start_frames_wins is None: start_frames_wins = [None] * len(self.conv_layers) h = [] for i, start_frames in zip(range(len(self.conv_layers)), start_frames_wins): x_clip, c_clip, start_frames = self.clip( x, cond, x_len, self.win_lengths[i], start_frames) # (B, win_length, C) start_frames_wins[i] = start_frames if x_clip is None: continue if cond is not None: x_clip = self.mel_proj_layers[i](x_clip) # (B, 1, win_length, C) c_clip = self.cond_proj_layers[i](c_clip)[:, None] # (B, 1, win_length, C) x_clip = x_clip + c_clip x_clip, h_ = self.conv_layers[i](x_clip) h += h_ validity.append(x_clip) if len(validity) != len(self.conv_layers): return None, start_frames_wins, h if self.reduction == 'sum': validity = sum(validity) # [B] elif self.reduction == 'stack': validity = torch.stack(validity, -1) # [B, W_L] elif self.reduction == 'none': validity = torch.cat(validity, -1) # [B, W_sum] return validity, start_frames_wins, h def clip(self, x, cond, x_len, win_length, start_frames=None): '''Ramdom clip x to win_length. Args: x (tensor) : (B, c_in, T, n_bins). cond (tensor) : (B, T, H). x_len (tensor) : (B,). win_length (int): target clip length Returns: (tensor) : (B, c_in, win_length, n_bins). ''' T_start = 0 T_end = x_len.max() - win_length if T_end < 0: return None, None, start_frames T_end = T_end.item() if start_frames is None: start_frame = np.random.randint(low=T_start, high=T_end + 1) start_frames = [start_frame] * x.size(0) else: start_frame = start_frames[0] x_batch = x[:, :, start_frame: start_frame + win_length] c_batch = cond[:, start_frame: start_frame + win_length] if cond is not None else None return x_batch, c_batch, start_frames class Discriminator(nn.Module): def __init__(self, time_lengths=[32, 64, 128], freq_length=80, cond_size=0, kernel=(3, 3), c_in=1, hidden_size=128, norm_type='bn', reduction='sum', uncond_disc=True): super(Discriminator, self).__init__() self.time_lengths = time_lengths self.cond_size = cond_size self.reduction = reduction self.uncond_disc = uncond_disc if uncond_disc: self.discriminator = MultiWindowDiscriminator( freq_length=freq_length, time_lengths=time_lengths, kernel=kernel, c_in=c_in, hidden_size=hidden_size, norm_type=norm_type, reduction=reduction ) if cond_size > 0: self.cond_disc = MultiWindowDiscriminator( freq_length=freq_length, time_lengths=time_lengths, cond_size=cond_size, kernel=kernel, c_in=c_in, hidden_size=hidden_size, norm_type=norm_type, reduction=reduction ) def forward(self, x, cond=None, start_frames_wins=None): """ :param x: [B, T, 80] :param cond: [B, T, cond_size] :param return_y_only: :return: """ if len(x.shape) == 3: x = x[:, None, :, :] x_len = x.sum([1, -1]).ne(0).int().sum([-1]) ret = {'y_c': None, 'y': None} if self.uncond_disc: ret['y'], start_frames_wins, ret['h'] = self.discriminator( x, x_len, start_frames_wins=start_frames_wins) if self.cond_size > 0 and cond is not None: ret['y_c'], start_frames_wins, ret['h_c'] = self.cond_disc( x, x_len, cond, start_frames_wins=start_frames_wins) ret['start_frames_wins'] = start_frames_wins return ret ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/distributions/__init__.py ================================================ ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/distributions/distributions.py ================================================ import torch import numpy as np class AbstractDistribution: def sample(self): raise NotImplementedError() def mode(self): raise NotImplementedError() class DiracDistribution(AbstractDistribution): def __init__(self, value): self.value = value def sample(self): return self.value def mode(self): return self.value class DiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False): self.parameters = parameters self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) def sample(self): x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) return x def kl(self, other=None): if self.deterministic: return torch.Tensor([0.]) else: if other is None: return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) else: return 0.5 * torch.sum( torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, dim=[1, 2, 3]) def nll(self, sample, dims=[1,2,3]): if self.deterministic: return torch.Tensor([0.]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) def mode(self): return self.mean def normal_kl(mean1, logvar1, mean2, logvar2): """ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases. """ tensor = None for obj in (mean1, logvar1, mean2, logvar2): if isinstance(obj, torch.Tensor): tensor = obj break assert tensor is not None, "at least one argument must be a Tensor" # Force variances to be Tensors. Broadcasting helps convert scalars to # Tensors, but it does not work for torch.exp(). logvar1, logvar2 = [ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2) ] return 0.5 * ( -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) ) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/ema.py ================================================ import torch from torch import nn class LitEma(nn.Module): def __init__(self, model, decay=0.9999, use_num_upates=True): super().__init__() if decay < 0.0 or decay > 1.0: raise ValueError('Decay must be between 0 and 1') self.m_name2s_name = {} self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates else torch.tensor(-1,dtype=torch.int)) for name, p in model.named_parameters(): if p.requires_grad: #remove as '.'-character is not allowed in buffers s_name = name.replace('.','') self.m_name2s_name.update({name:s_name}) self.register_buffer(s_name,p.clone().detach().data) self.collected_params = [] def forward(self,model): decay = self.decay if self.num_updates >= 0: self.num_updates += 1 decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) one_minus_decay = 1.0 - decay with torch.no_grad(): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: sname = self.m_name2s_name[key] shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) else: assert not key in self.m_name2s_name def copy_to(self, model): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) else: assert not key in self.m_name2s_name def store(self, parameters): """ Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. """ self.collected_params = [param.clone() for param in parameters] def restore(self, parameters): """ Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the original optimization process. Store the parameters before the `copy_to` method. After validation (or model saving), use this to restore the former parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters. """ for c_param, param in zip(self.collected_params, parameters): param.data.copy_(c_param.data) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/CLAPWrapper.py ================================================ import random import torchaudio from torch._six import string_classes import collections import re import torch.nn.functional as F import numpy as np from transformers import AutoTokenizer from ldm.modules.encoders.CLAP.utils import read_config_as_args from ldm.modules.encoders.CLAP.clap import CLAP import math import torchaudio.transforms as T import os import torch from importlib_resources import files class CLAPWrapper(): """ A class for interfacing CLAP model. """ def __init__(self, model_fp, device): self.np_str_obj_array_pattern = re.compile(r'[SaUO]') self.file_path = os.path.realpath(__file__) self.default_collate_err_msg_format = ( "default_collate: batch must contain tensors, numpy arrays, numbers, " "dicts or lists; found {}") self.config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() self.model_fp = model_fp self.device = device self.clap, self.tokenizer, self.args = self.load_clap() def load_clap(self): r"""Load CLAP model with args from config file""" args = read_config_as_args(self.config_as_str, is_config_str=True) if 'bert' in args.text_model: self.token_keys = ['input_ids', 'token_type_ids', 'attention_mask'] else: self.token_keys = ['input_ids', 'attention_mask'] clap = CLAP( audioenc_name=args.audioenc_name, sample_rate=args.sampling_rate, window_size=args.window_size, hop_size=args.hop_size, mel_bins=args.mel_bins, fmin=args.fmin, fmax=args.fmax, classes_num=args.num_classes, out_emb=args.out_emb, text_model=args.text_model, transformer_embed_dim=args.transformer_embed_dim, d_proj=args.d_proj ) # Load pretrained weights for model model_state_dict = torch.load(self.model_fp, map_location=torch.device('cpu'))['model'] clap.load_state_dict(model_state_dict) clap.eval() # set clap in eval mode tokenizer = AutoTokenizer.from_pretrained(args.text_model) clap = clap.to(self.device) tokenizer = tokenizer.to(self.device) return clap, tokenizer, args def default_collate(self, batch): r"""Puts each data field into a tensor with outer dimension batch size""" elem = batch[0] elem_type = type(elem) if isinstance(elem, torch.Tensor): out = None if torch.utils.data.get_worker_info() is not None: # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = elem.storage()._new_shared(numel) out = elem.new(storage) return torch.stack(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap': # array of string classes and object if self.np_str_obj_array_pattern.search(elem.dtype.str) is not None: raise TypeError( self.default_collate_err_msg_format.format(elem.dtype)) return self.default_collate([torch.as_tensor(b) for b in batch]) elif elem.shape == (): # scalars return torch.as_tensor(batch) elif isinstance(elem, float): return torch.tensor(batch, dtype=torch.float64) elif isinstance(elem, int): return torch.tensor(batch) elif isinstance(elem, string_classes): return batch elif isinstance(elem, collections.abc.Mapping): return {key: self.default_collate([d[key] for d in batch]) for key in elem} elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple return elem_type(*(self.default_collate(samples) for samples in zip(*batch))) elif isinstance(elem, collections.abc.Sequence): # check to make sure that the elements in batch have consistent size it = iter(batch) elem_size = len(next(it)) if not all(len(elem) == elem_size for elem in it): raise RuntimeError( 'each element in list of batch should be of equal size') transposed = zip(*batch) return [self.default_collate(samples) for samples in transposed] raise TypeError(self.default_collate_err_msg_format.format(elem_type)) def load_audio_into_tensor(self, audio_path, audio_duration, resample=False): r"""Loads audio file and returns raw audio.""" # Randomly sample a segment of audio_duration from the clip or pad to match duration audio_time_series, sample_rate = torchaudio.load(audio_path) resample_rate = self.args.sampling_rate if resample: resampler = T.Resample(sample_rate, resample_rate) audio_time_series = resampler(audio_time_series) audio_time_series = audio_time_series.reshape(-1) # audio_time_series is shorter than predefined audio duration, # so audio_time_series is extended if audio_duration*sample_rate >= audio_time_series.shape[0]: repeat_factor = int(np.ceil((audio_duration*sample_rate) / audio_time_series.shape[0])) # Repeat audio_time_series by repeat_factor to match audio_duration audio_time_series = audio_time_series.repeat(repeat_factor) # remove excess part of audio_time_series audio_time_series = audio_time_series[0:audio_duration*sample_rate] else: # audio_time_series is longer than predefined audio duration, # so audio_time_series is trimmed start_index = random.randrange( audio_time_series.shape[0] - audio_duration*sample_rate) audio_time_series = audio_time_series[start_index:start_index + audio_duration*sample_rate] return torch.FloatTensor(audio_time_series) def preprocess_audio(self, audio_files, resample): r"""Load list of audio files and return raw audio""" audio_tensors = [] for audio_file in audio_files: audio_tensor = self.load_audio_into_tensor( audio_file, self.args.duration, resample) audio_tensor = audio_tensor.reshape(1, -1).to(self.device) audio_tensors.append(audio_tensor) return self.default_collate(audio_tensors) def preprocess_text(self, text_queries, text_len=100): r"""Load list of class labels and return tokenized text""" device = next(self.clap.parameters()).device tokenized_texts = [] for ttext in text_queries: tok = self.tokenizer.encode_plus( text=ttext, add_special_tokens=True, max_length=text_len, pad_to_max_length=True, return_tensors="pt") for key in self.token_keys: tok[key] = tok[key].reshape(-1).to(device) tokenized_texts.append(tok) return self.default_collate(tokenized_texts) def get_text_embeddings(self, class_labels): r"""Load list of class labels and return text embeddings""" preprocessed_text = self.preprocess_text(class_labels) text_embeddings = self._get_text_embeddings(preprocessed_text) text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True) return text_embeddings def get_audio_embeddings(self, audio_files, resample): r"""Load list of audio files and return a audio embeddings""" preprocessed_audio = self.preprocess_audio(audio_files, resample) audio_embeddings = self._get_audio_embeddings(preprocessed_audio) audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True) return audio_embeddings def _get_text_embeddings(self, preprocessed_text): r"""Load preprocessed text and return text embeddings""" with torch.no_grad(): text_embeddings = self.clap.caption_encoder(preprocessed_text) text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True) return text_embeddings def _get_audio_embeddings(self, preprocessed_audio): r"""Load preprocessed audio and return a audio embeddings""" with torch.no_grad(): preprocessed_audio = preprocessed_audio.reshape( preprocessed_audio.shape[0], preprocessed_audio.shape[2]) #Append [0] the audio emebdding, [1] has output class probabilities audio_embeddings = self.clap.audio_encoder(preprocessed_audio)[0] audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True) return audio_embeddings def compute_similarity(self, audio_embeddings, text_embeddings): r"""Compute similarity between text and audio embeddings""" logit_scale = self.clap.logit_scale.exp() similarity = logit_scale*text_embeddings @ audio_embeddings.T return similarity.T def _generic_batch_inference(self, func, *args): r"""Process audio and/or text per batch""" input_tmp = args[0] batch_size = args[-1] # args[0] has audio_files, args[1] has class_labels inputs = [args[0], args[1]] if len(args) == 3 else [args[0]] args0_len = len(args[0]) # compute text_embeddings once for all the audio_files batches if len(inputs) == 2: text_embeddings = self.get_text_embeddings(args[1]) inputs = [args[0], args[1], text_embeddings] dataset_idx = 0 for _ in range(math.ceil(args0_len/batch_size)): next_batch_idx = dataset_idx + batch_size # batch size is bigger than available audio/text items if next_batch_idx >= args0_len: inputs[0] = input_tmp[dataset_idx:] return func(*tuple(inputs)) else: inputs[0] = input_tmp[dataset_idx:next_batch_idx] yield func(*tuple(inputs)) dataset_idx = next_batch_idx def get_audio_embeddings_per_batch(self, audio_files, batch_size): r"""Load preprocessed audio and return a audio embeddings per batch""" return self._generic_batch_inference(self.get_audio_embeddings, audio_files, batch_size) def get_text_embeddings_per_batch(self, class_labels, batch_size): r"""Load preprocessed text and return text embeddings per batch""" return self._generic_batch_inference(self.get_text_embeddings, class_labels, batch_size) def classify_audio_files_per_batch(self, audio_files, class_labels, batch_size): r"""Compute classification probabilities for each audio recording in a batch and each class label""" return self._generic_batch_inference(self.classify_audio_files, audio_files, class_labels, batch_size) if __name__ == '__main__': # Load and initialize CLAP weights_path = "/home1/huangrongjie/Project/Diffusion/LatentDiffusion/CLAP/CLAP_weights_2022.pth" clap_model = CLAPWrapper(weights_path, use_cuda=False) y = ["A woman talks nearby as water pours", "Multiple clanging and clanking sounds"] x = ['/home2/huangjiawei/data/audiocaps/train/Yr1nicOVtvkQ.wav', '/home2/huangjiawei/data/audiocaps/train/YUDGBjjwyaqE.wav'] # Computing text embeddings text_embeddings = clap_model.get_text_embeddings(y) import ipdb ipdb.set_trace() # Computing audio embeddings audio_embeddings = clap_model.get_audio_embeddings(x, resample=True) similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/__init__.py ================================================ from . import clap from . import audio from . import utils ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/audio.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from torchlibrosa.stft import Spectrogram, LogmelFilterBank def get_audio_encoder(name: str): if name == "Cnn14": return Cnn14 else: raise Exception('The audio encoder name {} is incorrect or not supported'.format(name)) class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = F.relu_(self.bn1(self.conv1(x))) x = F.relu_(self.bn2(self.conv2(x))) if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception('Incorrect argument!') return x class ConvBlock5x5(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock5x5, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = F.relu_(self.bn1(self.conv1(x))) if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception('Incorrect argument!') return x class AttBlock(nn.Module): def __init__(self, n_in, n_out, activation='linear', temperature=1.): super(AttBlock, self).__init__() self.activation = activation self.temperature = temperature self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) self.bn_att = nn.BatchNorm1d(n_out) def forward(self, x): # x: (n_samples, n_in, n_time) norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1) cla = self.nonlinear_transform(self.cla(x)) x = torch.sum(norm_att * cla, dim=2) return x, norm_att, cla def nonlinear_transform(self, x): if self.activation == 'linear': return x elif self.activation == 'sigmoid': return torch.sigmoid(x) class Cnn14(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, out_emb): super(Cnn14, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) self.bn0 = nn.BatchNorm2d(64) self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) # out_emb is 2048 for best Cnn14 self.fc1 = nn.Linear(2048, out_emb, bias=True) self.fc_audioset = nn.Linear(out_emb, classes_num, bias=True) def forward(self, input, mixup_lambda=None): """ Input: (batch_size, data_length) """ x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = torch.mean(x, dim=3) (x1, _) = torch.max(x, dim=2) x2 = torch.mean(x, dim=2) x = x1 + x2 x = F.dropout(x, p=0.5, training=self.training) x = F.relu_(self.fc1(x)) embedding = F.dropout(x, p=0.5, training=self.training) clipwise_output = torch.sigmoid(self.fc_audioset(x)) output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding} return output_dict ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/clap.py ================================================ import numpy as np import torch import torch.nn.functional as F from torch import nn from transformers import AutoModel from .audio import get_audio_encoder class Projection(nn.Module): def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None: super().__init__() self.linear1 = nn.Linear(d_in, d_out, bias=False) self.linear2 = nn.Linear(d_out, d_out, bias=False) self.layer_norm = nn.LayerNorm(d_out) self.drop = nn.Dropout(p) def forward(self, x: torch.Tensor) -> torch.Tensor: embed1 = self.linear1(x) embed2 = self.drop(self.linear2(F.gelu(embed1))) embeds = self.layer_norm(embed1 + embed2) return embeds class AudioEncoder(nn.Module): def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int, hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None: super().__init__() audio_encoder = get_audio_encoder(audioenc_name) self.base = audio_encoder( sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, d_in) self.projection = Projection(d_in, d_out) def forward(self, x): out_dict = self.base(x) audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output'] projected_vec = self.projection(audio_features) return projected_vec, audio_classification_output class TextEncoder(nn.Module): def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None: super().__init__() self.base = AutoModel.from_pretrained(text_model) self.projection = Projection(transformer_embed_dim, d_out) def forward(self, x): out = self.base(**x)[0] out = out[:, 0, :] # get CLS token output projected_vec = self.projection(out) return projected_vec class CLAP(nn.Module): def __init__(self, # audio audioenc_name: str, sample_rate: int, window_size: int, hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int, out_emb: int, # text text_model: str, transformer_embed_dim: int, # common d_proj: int, ): super().__init__() self.audio_encoder = AudioEncoder( audioenc_name, out_emb, d_proj, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num) self.caption_encoder = TextEncoder( d_proj, text_model, transformer_embed_dim ) self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) def forward(self, audio, text): audio_embed, _ = self.audio_encoder(audio) caption_embed = self.caption_encoder(text) return caption_embed, audio_embed, self.logit_scale.exp() ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/config.yml ================================================ # TEXT ENCODER CONFIG text_model: 'bert-base-uncased' text_len: 100 transformer_embed_dim: 768 freeze_text_encoder_weights: True # AUDIO ENCODER CONFIG audioenc_name: 'Cnn14' out_emb: 2048 sampling_rate: 44100 duration: 5 fmin: 50 fmax: 14000 n_fft: 1028 hop_size: 320 mel_bins: 64 window_size: 1024 # PROJECTION SPACE CONFIG d_proj: 1024 temperature: 0.003 # TRAINING AND EVALUATION CONFIG num_classes: 527 batch_size: 1024 demo: False ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/utils.py ================================================ import argparse import yaml import sys def read_config_as_args(config_path,args=None,is_config_str=False): return_dict = {} if config_path is not None: if is_config_str: yml_config = yaml.load(config_path, Loader=yaml.FullLoader) else: with open(config_path, "r") as f: yml_config = yaml.load(f, Loader=yaml.FullLoader) if args != None: for k, v in yml_config.items(): if k in args.__dict__: args.__dict__[k] = v else: sys.stderr.write("Ignored unknown parameter {} in yaml.\n".format(k)) else: for k, v in yml_config.items(): return_dict[k] = v args = args if args != None else return_dict return argparse.Namespace(**args) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/__init__.py ================================================ ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/modules.py ================================================ import torch import torch.nn as nn from functools import partial from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test from torch.utils.checkpoint import checkpoint from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoTokenizer from importlib_resources import files from ldm.modules.encoders.CLAP.utils import read_config_as_args from ldm.modules.encoders.CLAP.clap import TextEncoder from ldm.util import default, count_params import open_clip class AbstractEncoder(nn.Module): def __init__(self): super().__init__() def encode(self, *args, **kwargs): raise NotImplementedError class ClassEmbedder(nn.Module): def __init__(self, embed_dim, n_classes=1000, key='class'): super().__init__() self.key = key self.embedding = nn.Embedding(n_classes, embed_dim) def forward(self, batch, key=None): if key is None: key = self.key # this is for use in crossattn c = batch[key][:, None]# (bsz,1) c = self.embedding(c) return c class TransformerEmbedder(AbstractEncoder): """Some transformer encoder layers""" def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): super().__init__() self.device = device self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer)) def forward(self, tokens): tokens = tokens.to(self.device) # meh z = self.transformer(tokens, return_embeddings=True) return z def encode(self, x): return self(x) class BERTTokenizer(AbstractEncoder): """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" def __init__(self, device="cuda", vq_interface=True, max_length=77): super().__init__() from transformers import BertTokenizerFast # TODO: add to reuquirements self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") self.device = device self.vq_interface = vq_interface self.max_length = max_length def forward(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) return tokens @torch.no_grad() def encode(self, text): tokens = self(text) if not self.vq_interface: return tokens return None, None, [None, None, tokens] def decode(self, text): return text class BERTEmbedder(AbstractEncoder):# 这里不是用的pretrained bert,是用的transformers的BertTokenizer加自定义的TransformerWrapper """Uses the BERT tokenizr model and add some transformer encoder layers""" def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, device="cuda",use_tokenizer=True, embedding_dropout=0.0): super().__init__() self.use_tknz_fn = use_tokenizer if self.use_tknz_fn: self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) self.device = device self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer), emb_dropout=embedding_dropout) def forward(self, text): if self.use_tknz_fn: tokens = self.tknz_fn(text)#.to(self.device) else: tokens = text z = self.transformer(tokens, return_embeddings=True) return z def encode(self, text): # output of length 77 return self(text) class SpatialRescaler(nn.Module): def __init__(self, n_stages=1, method='bilinear', multiplier=0.5, in_channels=3, out_channels=None, bias=False): super().__init__() self.n_stages = n_stages assert self.n_stages >= 0 assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] self.multiplier = multiplier self.interpolator = partial(torch.nn.functional.interpolate, mode=method) self.remap_output = out_channels is not None if self.remap_output: print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) def forward(self,x): for stage in range(self.n_stages): x = self.interpolator(x, scale_factor=self.multiplier) if self.remap_output: x = self.channel_mapper(x) return x def encode(self, x): return self(x) def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self class FrozenT5Embedder(AbstractEncoder): """Uses the T5 transformer encoder for text""" def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl super().__init__() self.tokenizer = T5Tokenizer.from_pretrained(version) self.transformer = T5EncoderModel.from_pretrained(version) self.device = device self.max_length = max_length # TODO: typical value? if freeze: self.freeze() def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False def forward(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state return z def encode(self, text): return self(text) class FrozenCLAPEmbedder(AbstractEncoder): """Uses the CLAP transformer encoder for text (from huggingface)""" def __init__(self, weights_path, freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 super().__init__() model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model'] match_params = dict() for key in list(model_state_dict.keys()): if 'caption_encoder' in key: match_params[key.replace('caption_encoder.', '')] = model_state_dict[key] config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() args = read_config_as_args(config_as_str, is_config_str=True) # To device self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model self.caption_encoder = TextEncoder( args.d_proj, args.text_model, args.transformer_embed_dim ) self.max_length = max_length self.device = device if freeze: self.freeze() print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") def freeze(self): self.caption_encoder.base = self.caption_encoder.base.eval() for param in self.caption_encoder.base.parameters(): param.requires_grad = False def encode(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.caption_encoder.base(input_ids=tokens) z = self.caption_encoder.projection(outputs.last_hidden_state) return z class FrozenCLAPEmbedderNoLoad(AbstractEncoder): def __init__(self, config, freeze=True, device="cpu", max_length=77): super().__init__() args = config # To device self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model self.caption_encoder = TextEncoder( args.d_proj, args.text_model, args.transformer_embed_dim ) self.max_length = max_length self.device = device if freeze: self.freeze() print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") def freeze(self): self.caption_encoder.base = self.caption_encoder.base.eval() for param in self.caption_encoder.base.parameters(): param.requires_grad = False def encode(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.caption_encoder.base(input_ids=tokens) z = self.caption_encoder.projection(outputs.last_hidden_state) return z class NewFrozenCLAPEmbedder(AbstractEncoder): """Uses the CLAP transformer encoder for text (from huggingface)""" def __init__(self, weights_path, freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 super().__init__() # To device from transformers import RobertaTokenizer from ldm.modules.encoders.open_clap import create_model model, model_cfg = create_model( 'HTSAT-tiny', 'roberta', weights_path, enable_fusion=True, fusion_type='aff_2d' ) del model.audio_branch, model.audio_transform, model.audio_projection self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base') self.model = model self.max_length = max_length self.device = device if freeze: self.freeze() param_num = sum(p.numel() for p in model.parameters() if p.requires_grad) print(f'{self.model.__class__.__name__} comes with: {param_num / 1e+6:.3f} M params.') def freeze(self): self.model = self.model.eval() for param in self.model.parameters(): param.requires_grad = False def encode(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") outputs = self.model.text_branch(input_ids=batch_encoding["input_ids"].to(self.device), attention_mask=batch_encoding["attention_mask"].to(self.device)) z = self.model.text_projection(outputs.last_hidden_state) return z class FrozenFLANEmbedder(AbstractEncoder): """Uses the T5 transformer encoder for text""" def __init__(self, version="google/flan-t5-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl super().__init__() self.tokenizer = T5Tokenizer.from_pretrained(version) self.transformer = T5EncoderModel.from_pretrained(version) self.device = device self.max_length = max_length # TODO: typical value? if freeze: self.freeze() def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False def forward(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state return z def encode(self, text): return self(text) class FrozenGlobalNormOpenCLIPEmbedder(AbstractEncoder): """ Uses the OpenCLIP transformer encoder for text """ def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", freeze=True, delvisual=True): super().__init__() model, _, preprocess = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) if delvisual: del model.visual del preprocess else: self.preprocess = preprocess self.model = model self.device = device if freeze: self.freeze() def freeze(self): self.model = self.model.eval() for param in self.parameters(): param.requires_grad = False def forward(self, text): tokens = open_clip.tokenize(text) z = self.model.encode_text(tokens.to(self.device)) z /= z.norm(dim=-1, keepdim=True) return z.unsqueeze(1) def forward_img(self, image): z = self.model.encode_image(image.to(self.device)) z /= z.norm(dim=-1, keepdim=True) return z.unsqueeze(1) def encode(self, text): return self(text) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/__init__.py ================================================ from .factory import list_models, create_model, create_model_and_transforms, add_model_config from .loss import ClipLoss, gather_features, LPLoss, lp_gather_features, LPMetrics from .model import CLAP, CLAPTextCfg, CLAPVisionCfg, CLAPAudioCfp, convert_weights_to_fp16, trace_model from .openai import load_openai_model, list_openai_models from .pretrained import list_pretrained, list_pretrained_tag_models, list_pretrained_model_tags,\ get_pretrained_url, download_pretrained from .tokenizer import SimpleTokenizer, tokenize from .transform import image_transform ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/bert.py ================================================ from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." def bert_embeddings(text): # text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) return output from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('roberta-base') model = RobertaModel.from_pretrained('roberta-base') text = "Replace me by any text you'd like." def Roberta_embeddings(text): # text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) return output from transformers import BartTokenizer, BartModel tokenizer = BartTokenizer.from_pretrained('facebook/bart-base') model = BartModel.from_pretrained('facebook/bart-base') text = "Replace me by any text you'd like." def bart_embeddings(text): # text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) return output ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/factory.py ================================================ import json import logging import os import pathlib import re from copy import deepcopy from pathlib import Path import torch from .model import CLAP, convert_weights_to_fp16 from .openai import load_openai_model from .pretrained import get_pretrained_url, download_pretrained from .transform import image_transform _MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] _MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs def _natural_key(string_): return [int(s) if s.isdigit() else s for s in re.split(r"(\d+)", string_.lower())] def _rescan_model_configs(): global _MODEL_CONFIGS config_ext = (".json",) config_files = [] for config_path in _MODEL_CONFIG_PATHS: if config_path.is_file() and config_path.suffix in config_ext: config_files.append(config_path) elif config_path.is_dir(): for ext in config_ext: config_files.extend(config_path.glob(f"*{ext}")) for cf in config_files: with open(cf, "r") as f: model_cfg = json.load(f) if all(a in model_cfg for a in ("embed_dim", "audio_cfg", "text_cfg")): _MODEL_CONFIGS[cf.stem] = model_cfg _MODEL_CONFIGS = { k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0])) } _rescan_model_configs() # initial populate of model config registry def load_state_dict(checkpoint_path: str, map_location="cpu", skip_params=True): checkpoint = torch.load(checkpoint_path, map_location=map_location) if isinstance(checkpoint, dict) and "state_dict" in checkpoint: state_dict = checkpoint["state_dict"] else: state_dict = checkpoint if skip_params: if next(iter(state_dict.items()))[0].startswith("module"): state_dict = {k[7:]: v for k, v in state_dict.items()} # for k in state_dict: # if k.startswith('transformer'): # v = state_dict.pop(k) # state_dict['text_branch.' + k[12:]] = v return state_dict def create_model( amodel_name: str, tmodel_name: str, pretrained: str = "", precision: str = "fp32", device: torch.device = torch.device("cpu"), jit: bool = False, force_quick_gelu: bool = False, openai_model_cache_dir: str = os.path.expanduser("~/.cache/clip"), skip_params=True, pretrained_audio: str = "", pretrained_text: str = "", enable_fusion: bool = False, fusion_type: str = 'None' # pretrained_image: bool = False, ): amodel_name = amodel_name.replace( "/", "-" ) # for callers using old naming with / in ViT names pretrained_orig = pretrained pretrained = pretrained.lower() if pretrained == "openai": if amodel_name in _MODEL_CONFIGS: logging.info(f"Loading {amodel_name} model config.") model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name]) else: logging.error( f"Model config for {amodel_name} not found; available models {list_models()}." ) raise RuntimeError(f"Model config for {amodel_name} not found.") logging.info(f"Loading pretrained ViT-B-16 text encoder from OpenAI.") # Hard Code in model name model_cfg["text_cfg"]["model_type"] = tmodel_name model = load_openai_model( "ViT-B-16", model_cfg, device=device, jit=jit, cache_dir=openai_model_cache_dir, enable_fusion=enable_fusion, fusion_type=fusion_type ) # See https://discuss.pytorch.org/t/valueerror-attemting-to-unscale-fp16-gradients/81372 if precision == "amp" or precision == "fp32": model = model.float() else: if amodel_name in _MODEL_CONFIGS: logging.info(f"Loading {amodel_name} model config.") model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name]) else: logging.error( f"Model config for {amodel_name} not found; available models {list_models()}." ) raise RuntimeError(f"Model config for {amodel_name} not found.") if force_quick_gelu: # override for use of QuickGELU on non-OpenAI transformer models model_cfg["quick_gelu"] = True # if pretrained_image: # if 'timm_amodel_name' in model_cfg.get('vision_cfg', {}): # # pretrained weight loading for timm models set via vision_cfg # model_cfg['vision_cfg']['timm_model_pretrained'] = True # else: # assert False, 'pretrained image towers currently only supported for timm models' model_cfg["text_cfg"]["model_type"] = tmodel_name model_cfg["enable_fusion"] = enable_fusion model_cfg["fusion_type"] = fusion_type model = CLAP(**model_cfg) if pretrained: checkpoint_path = "" url = get_pretrained_url(amodel_name, pretrained) if url: checkpoint_path = download_pretrained(url, root=openai_model_cache_dir) elif os.path.exists(pretrained_orig): checkpoint_path = pretrained_orig if checkpoint_path: logging.info(f"Loading pretrained {amodel_name}-{tmodel_name} weights ({pretrained}).") ckpt = load_state_dict(checkpoint_path, skip_params=True) model.load_state_dict(ckpt) param_names = [n for n, p in model.named_parameters()] for n in param_names: print(n, "\t", "Loaded" if n in ckpt else "Unloaded") else: logging.warning( f"Pretrained weights ({pretrained}) not found for model {amodel_name}." ) raise RuntimeError( f"Pretrained weights ({pretrained}) not found for model {amodel_name}." ) if pretrained_audio: if amodel_name.startswith('PANN'): if 'Cnn14_mAP' in pretrained_audio: # official checkpoint audio_ckpt = torch.load(pretrained_audio, map_location='cpu') audio_ckpt = audio_ckpt['model'] keys = list(audio_ckpt.keys()) for key in keys: if 'spectrogram_extractor' not in key and 'logmel_extractor' not in key: v = audio_ckpt.pop(key) audio_ckpt['audio_branch.' + key] = v elif os.path.basename(pretrained_audio).startswith('PANN'): # checkpoint trained via HTSAT codebase audio_ckpt = torch.load(pretrained_audio, map_location='cpu') audio_ckpt = audio_ckpt['state_dict'] keys = list(audio_ckpt.keys()) for key in keys: if key.startswith('sed_model'): v = audio_ckpt.pop(key) audio_ckpt['audio_branch.' + key[10:]] = v elif os.path.basename(pretrained_audio).startswith('finetuned'): # checkpoint trained via linear probe codebase audio_ckpt = torch.load(pretrained_audio, map_location='cpu') else: raise ValueError('Unknown audio checkpoint') elif amodel_name.startswith('HTSAT'): if 'HTSAT_AudioSet_Saved' in pretrained_audio: # official checkpoint audio_ckpt = torch.load(pretrained_audio, map_location='cpu') audio_ckpt = audio_ckpt['state_dict'] keys = list(audio_ckpt.keys()) for key in keys: if key.startswith('sed_model') and ('spectrogram_extractor' not in key and 'logmel_extractor' not in key): v = audio_ckpt.pop(key) audio_ckpt['audio_branch.' + key[10:]] = v elif os.path.basename(pretrained_audio).startswith('HTSAT'): # checkpoint trained via HTSAT codebase audio_ckpt = torch.load(pretrained_audio, map_location='cpu') audio_ckpt = audio_ckpt['state_dict'] keys = list(audio_ckpt.keys()) for key in keys: if key.startswith('sed_model'): v = audio_ckpt.pop(key) audio_ckpt['audio_branch.' + key[10:]] = v elif os.path.basename(pretrained_audio).startswith('finetuned'): # checkpoint trained via linear probe codebase audio_ckpt = torch.load(pretrained_audio, map_location='cpu') else: raise ValueError('Unknown audio checkpoint') else: raise f'this audio encoder pretrained checkpoint is not support' model.load_state_dict(audio_ckpt, strict=False) logging.info(f"Loading pretrained {amodel_name} weights ({pretrained_audio}).") param_names = [n for n, p in model.named_parameters()] for n in param_names: print(n, "\t", "Loaded" if n in audio_ckpt else "Unloaded") model.to(device=device) if precision == "fp16": assert device.type != "cpu" convert_weights_to_fp16(model) if jit: model = torch.jit.script(model) return model, model_cfg def create_model_and_transforms( model_name: str, pretrained: str = "", precision: str = "fp32", device: torch.device = torch.device("cpu"), jit: bool = False, force_quick_gelu: bool = False, # pretrained_image: bool = False, ): model = create_model( model_name, pretrained, precision, device, jit, force_quick_gelu=force_quick_gelu, # pretrained_image=pretrained_image ) preprocess_train = image_transform(model.visual.image_size, is_train=True) preprocess_val = image_transform(model.visual.image_size, is_train=False) return model, preprocess_train, preprocess_val def list_models(): """enumerate available model architectures based on config files""" return list(_MODEL_CONFIGS.keys()) def add_model_config(path): """add model config path or file and update registry""" if not isinstance(path, Path): path = Path(path) _MODEL_CONFIG_PATHS.append(path) _rescan_model_configs() ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/feature_fusion.py ================================================ ''' Feature Fusion for Varible-Length Data Processing AFF/iAFF is referred and modified from https://github.com/YimianDai/open-aff/blob/master/aff_pytorch/aff_net/fusion.py According to the paper: Yimian Dai et al, Attentional Feature Fusion, IEEE Winter Conference on Applications of Computer Vision, WACV 2021 ''' import torch import torch.nn as nn class DAF(nn.Module): ''' 直接相加 DirectAddFuse ''' def __init__(self): super(DAF, self).__init__() def forward(self, x, residual): return x + residual class iAFF(nn.Module): ''' 多特征融合 iAFF ''' def __init__(self, channels=64, r=4, type='2D'): super(iAFF, self).__init__() inter_channels = int(channels // r) if type == '1D': # 本地注意力 self.local_att = nn.Sequential( nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(inter_channels), nn.ReLU(inplace=True), nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(channels), ) # 全局注意力 self.global_att = nn.Sequential( nn.AdaptiveAvgPool1d(1), nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(inter_channels), nn.ReLU(inplace=True), nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(channels), ) # 第二次本地注意力 self.local_att2 = nn.Sequential( nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(inter_channels), nn.ReLU(inplace=True), nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(channels), ) # 第二次全局注意力 self.global_att2 = nn.Sequential( nn.AdaptiveAvgPool1d(1), nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(inter_channels), nn.ReLU(inplace=True), nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(channels), ) elif type == '2D': # 本地注意力 self.local_att = nn.Sequential( nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(inter_channels), nn.ReLU(inplace=True), nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(channels), ) # 全局注意力 self.global_att = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(inter_channels), nn.ReLU(inplace=True), nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(channels), ) # 第二次本地注意力 self.local_att2 = nn.Sequential( nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(inter_channels), nn.ReLU(inplace=True), nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(channels), ) # 第二次全局注意力 self.global_att2 = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(inter_channels), nn.ReLU(inplace=True), nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(channels), ) else: raise f'the type is not supported' self.sigmoid = nn.Sigmoid() def forward(self, x, residual): flag = False xa = x + residual if xa.size(0) == 1: xa = torch.cat([xa,xa],dim=0) flag = True xl = self.local_att(xa) xg = self.global_att(xa) xlg = xl + xg wei = self.sigmoid(xlg) xi = x * wei + residual * (1 - wei) xl2 = self.local_att2(xi) xg2 = self.global_att(xi) xlg2 = xl2 + xg2 wei2 = self.sigmoid(xlg2) xo = x * wei2 + residual * (1 - wei2) if flag: xo = xo[0].unsqueeze(0) return xo class AFF(nn.Module): ''' 多特征融合 AFF ''' def __init__(self, channels=64, r=4, type='2D'): super(AFF, self).__init__() inter_channels = int(channels // r) if type == '1D': self.local_att = nn.Sequential( nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(inter_channels), nn.ReLU(inplace=True), nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(channels), ) self.global_att = nn.Sequential( nn.AdaptiveAvgPool1d(1), nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(inter_channels), nn.ReLU(inplace=True), nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm1d(channels), ) elif type == '2D': self.local_att = nn.Sequential( nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(inter_channels), nn.ReLU(inplace=True), nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(channels), ) self.global_att = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(inter_channels), nn.ReLU(inplace=True), nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(channels), ) else: raise f'the type is not supported.' self.sigmoid = nn.Sigmoid() def forward(self, x, residual): flag = False xa = x + residual if xa.size(0) == 1: xa = torch.cat([xa,xa],dim=0) flag = True xl = self.local_att(xa) xg = self.global_att(xa) xlg = xl + xg wei = self.sigmoid(xlg) xo = 2 * x * wei + 2 * residual * (1 - wei) if flag: xo = xo[0].unsqueeze(0) return xo ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/htsat.py ================================================ # Ke Chen # knutchen@ucsd.edu # HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION # Some layers designed on the model # below codes are based and referred from https://github.com/microsoft/Swin-Transformer # Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf import torch import torch.nn as nn import torch.nn.functional as F from itertools import repeat import collections.abc import math import warnings from torch.nn.init import _calculate_fan_in_and_fan_out import torch.utils.checkpoint as checkpoint import random from torchlibrosa.stft import Spectrogram, LogmelFilterBank from torchlibrosa.augmentation import SpecAugmentation from itertools import repeat from .utils import do_mixup, interpolate from .feature_fusion import iAFF, AFF, DAF # from PyTorch internals def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable): return x return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) to_3tuple = _ntuple(3) to_4tuple = _ntuple(4) to_ntuple = _ntuple def drop_path(x, drop_prob: float = 0., training: bool = False): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, patch_stride = 16, enable_fusion=False, fusion_type='None'): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patch_stride = to_2tuple(patch_stride) self.img_size = img_size self.patch_size = patch_size self.patch_stride = patch_stride self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1]) self.num_patches = self.grid_size[0] * self.grid_size[1] self.flatten = flatten self.in_chans = in_chans self.embed_dim = embed_dim self.enable_fusion = enable_fusion self.fusion_type = fusion_type padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2) if (self.enable_fusion) and (self.fusion_type == 'channel_map'): self.proj = nn.Conv2d(in_chans*4, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding) else: self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): self.mel_conv2d = nn.Conv2d(in_chans, embed_dim, kernel_size=(patch_size[0], patch_size[1]*3), stride=(patch_stride[0], patch_stride[1] * 3), padding=padding) if self.fusion_type == 'daf_2d': self.fusion_model = DAF() elif self.fusion_type == 'aff_2d': self.fusion_model = AFF(channels=embed_dim, type='2D') elif self.fusion_type == 'iaff_2d': self.fusion_model = iAFF(channels=embed_dim, type='2D') def forward(self, x, longer_idx = None): if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): global_x = x[:,0:1,:,:] # global processing B, C, H, W = global_x.shape assert H == self.img_size[0] and W == self.img_size[1], \ f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." global_x = self.proj(global_x) TW = global_x.size(-1) if len(longer_idx) > 0: # local processing local_x = x[longer_idx,1:,:,:].contiguous() B, C, H, W = local_x.shape local_x = local_x.view(B*C,1,H,W) local_x = self.mel_conv2d(local_x) local_x = local_x.view(B,C,local_x.size(1),local_x.size(2),local_x.size(3)) local_x = local_x.permute((0,2,3,1,4)).contiguous().flatten(3) TB,TC,TH,_ = local_x.size() if local_x.size(-1) < TW: local_x = torch.cat([local_x, torch.zeros((TB,TC,TH,TW-local_x.size(-1)), device=global_x.device)], dim=-1) else: local_x = local_x[:,:,:,:TW] global_x[longer_idx] = self.fusion_model(global_x[longer_idx],local_x) x = global_x else: B, C, H, W = x.shape assert H == self.img_size[0] and W == self.img_size[1], \ f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x) if self.flatten: x = x.flatten(2).transpose(1, 2) # BCHW -> BNC x = self.norm(x) return x class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def _no_grad_trunc_normal_(tensor, mean, std, a, b): # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1. + math.erf(x / math.sqrt(2.))) / 2. if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " "The distribution of values may be incorrect.", stacklevel=2) with torch.no_grad(): # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values l = norm_cdf((a - mean) / std) u = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [l, u], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * l - 1, 2 * u - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) return tensor def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): # type: (Tensor, float, float, float, float) -> Tensor r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ return _no_grad_trunc_normal_(tensor, mean, std, a, b) def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) if mode == 'fan_in': denom = fan_in elif mode == 'fan_out': denom = fan_out elif mode == 'fan_avg': denom = (fan_in + fan_out) / 2 variance = scale / denom if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) elif distribution == "normal": tensor.normal_(std=math.sqrt(variance)) elif distribution == "uniform": bound = math.sqrt(3 * variance) tensor.uniform_(-bound, bound) else: raise ValueError(f"invalid distribution {distribution}") def lecun_normal_(tensor): variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') def window_partition(x, window_size): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x class WindowAttention(nn.Module): r""" Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Args: dim (int): Number of input channels. window_size (tuple[int]): The height and width of the window. num_heads (int): Number of attention heads. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0 """ def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.window_size[0]) coords_w = torch.arange(self.window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("relative_position_index", relative_position_index) self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) trunc_normal_(self.relative_position_bias_table, std=.02) self.softmax = nn.Softmax(dim=-1) def forward(self, x, mask=None): """ Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None """ B_, N, C = x.shape qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) q = q * self.scale attn = (q @ k.transpose(-2, -1)) relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = self.proj(x) x = self.proj_drop(x) return x, attn def extra_repr(self): return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' # We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model class SwinTransformerBlock(nn.Module): r""" Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float, optional): Stochastic depth rate. Default: 0.0 act_layer (nn.Module, optional): Activation layer. Default: nn.GELU norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'): super().__init__() self.dim = dim self.input_resolution = input_resolution self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio self.norm_before_mlp = norm_before_mlp if min(self.input_resolution) <= self.window_size: # if window size is larger than input resolution, we don't partition windows self.shift_size = 0 self.window_size = min(self.input_resolution) assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" self.norm1 = norm_layer(dim) self.attn = WindowAttention( dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() if self.norm_before_mlp == 'ln': self.norm2 = nn.LayerNorm(dim) elif self.norm_before_mlp == 'bn': self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2) else: raise NotImplementedError mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) if self.shift_size > 0: # calculate attention mask for SW-MSA H, W = self.input_resolution img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) else: attn_mask = None self.register_buffer("attn_mask", attn_mask) def forward(self, x): # pdb.set_trace() H, W = self.input_resolution # print("H: ", H) # print("W: ", W) # pdb.set_trace() B, L, C = x.shape # assert L == H * W, "input feature has wrong size" shortcut = x x = self.norm1(x) x = x.view(B, H, W, C) # cyclic shift if self.shift_size > 0: shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) else: shifted_x = x # partition windows x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C # W-MSA/SW-MSA attn_windows, attn = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C # reverse cyclic shift if self.shift_size > 0: x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) else: x = shifted_x x = x.view(B, H * W, C) # FFN x = shortcut + self.drop_path(x) x = x + self.drop_path(self.mlp(self.norm2(x))) return x, attn def extra_repr(self): return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" class PatchMerging(nn.Module): r""" Patch Merging Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): super().__init__() self.input_resolution = input_resolution self.dim = dim self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(4 * dim) def forward(self, x): """ x: B, H*W, C """ H, W = self.input_resolution B, L, C = x.shape assert L == H * W, "input feature has wrong size" assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." x = x.view(B, H, W, C) x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C x = self.norm(x) x = self.reduction(x) return x def extra_repr(self): return f"input_resolution={self.input_resolution}, dim={self.dim}" class BasicLayer(nn.Module): """ A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. """ def __init__(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, norm_before_mlp='ln'): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth self.use_checkpoint = use_checkpoint # build blocks self.blocks = nn.ModuleList([ SwinTransformerBlock(dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer, norm_before_mlp=norm_before_mlp) for i in range(depth)]) # patch merging layer if downsample is not None: self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) else: self.downsample = None def forward(self, x): attns = [] for blk in self.blocks: if self.use_checkpoint: x = checkpoint.checkpoint(blk, x) else: x, attn = blk(x) if not self.training: attns.append(attn.unsqueeze(0)) if self.downsample is not None: x = self.downsample(x) if not self.training: attn = torch.cat(attns, dim = 0) attn = torch.mean(attn, dim = 0) return x, attn def extra_repr(self): return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" # The Core of HTSAT class HTSAT_Swin_Transformer(nn.Module): r"""HTSAT based on the Swin Transformer Args: spec_size (int | tuple(int)): Input Spectrogram size. Default 256 patch_size (int | tuple(int)): Patch size. Default: 4 path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4 in_chans (int): Number of input image channels. Default: 1 (mono) num_classes (int): Number of classes for classification head. Default: 527 embed_dim (int): Patch embedding dimension. Default: 96 depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. Default: 8 mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. ape (bool): If True, add absolute position embedding to the patch embedding. Default: False patch_norm (bool): If True, add normalization after patch embedding. Default: True use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False config (module): The configuration Module from config.py """ def __init__(self, spec_size=256, patch_size=4, patch_stride=(4,4), in_chans=1, num_classes=527, embed_dim=96, depths=[2, 2, 6, 2], num_heads=[4, 8, 16, 32], window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, norm_before_mlp='ln', config = None, enable_fusion = False, fusion_type = 'None', **kwargs): super(HTSAT_Swin_Transformer, self).__init__() self.config = config self.spec_size = spec_size self.patch_stride = patch_stride self.patch_size = patch_size self.window_size = window_size self.embed_dim = embed_dim self.depths = depths self.ape = ape self.in_chans = in_chans self.num_classes = num_classes self.num_heads = num_heads self.num_layers = len(self.depths) self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1)) self.drop_rate = drop_rate self.attn_drop_rate = attn_drop_rate self.drop_path_rate = drop_path_rate self.qkv_bias = qkv_bias self.qk_scale = None self.patch_norm = patch_norm self.norm_layer = norm_layer if self.patch_norm else None self.norm_before_mlp = norm_before_mlp self.mlp_ratio = mlp_ratio self.use_checkpoint = use_checkpoint self.enable_fusion = enable_fusion self.fusion_type = fusion_type # process mel-spec ; used only once self.freq_ratio = self.spec_size // self.config.mel_bins window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None self.interpolate_ratio = 32 # Downsampled ratio # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=config.window_size, hop_length=config.hop_size, win_length=config.window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=config.sample_rate, n_fft=config.window_size, n_mels=config.mel_bins, fmin=config.fmin, fmax=config.fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) # 2 2 self.bn0 = nn.BatchNorm2d(self.config.mel_bins) # split spctrogram into non-overlapping patches self.patch_embed = PatchEmbed( img_size=self.spec_size, patch_size=self.patch_size, in_chans=self.in_chans, embed_dim=self.embed_dim, norm_layer=self.norm_layer, patch_stride = patch_stride, enable_fusion=self.enable_fusion, fusion_type=self.fusion_type ) num_patches = self.patch_embed.num_patches patches_resolution = self.patch_embed.grid_size self.patches_resolution = patches_resolution # absolute position embedding if self.ape: self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.embed_dim)) trunc_normal_(self.absolute_pos_embed, std=.02) self.pos_drop = nn.Dropout(p=self.drop_rate) # stochastic depth dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))] # stochastic depth decay rule # build layers self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = BasicLayer(dim=int(self.embed_dim * 2 ** i_layer), input_resolution=(patches_resolution[0] // (2 ** i_layer), patches_resolution[1] // (2 ** i_layer)), depth=self.depths[i_layer], num_heads=self.num_heads[i_layer], window_size=self.window_size, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, qk_scale=self.qk_scale, drop=self.drop_rate, attn_drop=self.attn_drop_rate, drop_path=dpr[sum(self.depths[:i_layer]):sum(self.depths[:i_layer + 1])], norm_layer=self.norm_layer, downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, use_checkpoint=use_checkpoint, norm_before_mlp=self.norm_before_mlp) self.layers.append(layer) self.norm = self.norm_layer(self.num_features) self.avgpool = nn.AdaptiveAvgPool1d(1) self.maxpool = nn.AdaptiveMaxPool1d(1) SF = self.spec_size // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] // self.freq_ratio self.tscam_conv = nn.Conv2d( in_channels = self.num_features, out_channels = self.num_classes, kernel_size = (SF,3), padding = (0,1) ) self.head = nn.Linear(num_classes, num_classes) if (self.enable_fusion) and (self.fusion_type in ['daf_1d','aff_1d','iaff_1d']): self.mel_conv1d = nn.Sequential( nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2), nn.BatchNorm1d(64) ) if self.fusion_type == 'daf_1d': self.fusion_model = DAF() elif self.fusion_type == 'aff_1d': self.fusion_model = AFF(channels=64, type='1D') elif self.fusion_type == 'iaff_1d': self.fusion_model = iAFF(channels=64, type='1D') self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'absolute_pos_embed'} @torch.jit.ignore def no_weight_decay_keywords(self): return {'relative_position_bias_table'} def forward_features(self, x, longer_idx = None): # A deprecated optimization for using a hierarchical output from different blocks frames_num = x.shape[2] x = self.patch_embed(x, longer_idx = longer_idx) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for i, layer in enumerate(self.layers): x, attn = layer(x) # for x x = self.norm(x) B, N, C = x.shape SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1] x = x.permute(0,2,1).contiguous().reshape(B, C, SF, ST) B, C, F, T = x.shape # group 2D CNN c_freq_bin = F // self.freq_ratio x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T) x = x.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1) # get latent_output fine_grained_latent_output = torch.mean(x, dim = 2) fine_grained_latent_output = interpolate(fine_grained_latent_output.permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) latent_output = self.avgpool(torch.flatten(x,2)) latent_output = torch.flatten(latent_output, 1) # display the attention map, if needed x = self.tscam_conv(x) x = torch.flatten(x, 2) # B, C, T fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) x = self.avgpool(x) x = torch.flatten(x, 1) output_dict = { 'framewise_output': fpx, # already sigmoided 'clipwise_output': torch.sigmoid(x), 'fine_grained_embedding': fine_grained_latent_output, 'embedding': latent_output } return output_dict def crop_wav(self, x, crop_size, spe_pos = None): time_steps = x.shape[2] tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device) for i in range(len(x)): if spe_pos is None: crop_pos = random.randint(0, time_steps - crop_size - 1) else: crop_pos = spe_pos tx[i][0] = x[i, 0, crop_pos:crop_pos + crop_size,:] return tx # Reshape the wavform to a img size, if you want to use the pretrained swin transformer model def reshape_wav2img(self, x): B, C, T, F = x.shape target_T = int(self.spec_size * self.freq_ratio) target_F = self.spec_size // self.freq_ratio assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size" # to avoid bicubic zero error if T < target_T: x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True) if F < target_F: x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True) x = x.permute(0,1,3,2).contiguous() x = x.reshape(x.shape[0], x.shape[1], x.shape[2], self.freq_ratio, x.shape[3] // self.freq_ratio) # print(x.shape) x = x.permute(0,1,3,2,4).contiguous() x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4]) return x # Repeat the wavform to a img size, if you want to use the pretrained swin transformer model def repeat_wat2img(self, x, cur_pos): B, C, T, F = x.shape target_T = int(self.spec_size * self.freq_ratio) target_F = self.spec_size // self.freq_ratio assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size" # to avoid bicubic zero error if T < target_T: x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True) if F < target_F: x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True) x = x.permute(0,1,3,2).contiguous() # B C F T x = x[:,:,:,cur_pos:cur_pos + self.spec_size] x = x.repeat(repeats = (1,1,4,1)) return x def forward(self, x: torch.Tensor, mixup_lambda = None, infer_mode = False, device=None):# out_feat_keys: List[str] = None): if self.enable_fusion and x["longer"].sum() == 0: # if no audio is longer than 10s, then randomly select one audio to be longer x["longer"][torch.randint(0, x["longer"].shape[0], (1,))] = True if not self.enable_fusion: x = x["waveform"].to(device=device, non_blocking=True) x = self.spectrogram_extractor(x) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: x = self.spec_augmenter(x) if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) x = self.reshape_wav2img(x) output_dict = self.forward_features(x) else: longer_list = x["longer"].to(device=device, non_blocking=True) x = x["mel_fusion"].to(device=device, non_blocking=True) x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) longer_list_idx = torch.where(longer_list)[0] if self.fusion_type in ['daf_1d','aff_1d','iaff_1d']: new_x = x[:,0:1,:,:].clone().contiguous() if len(longer_list_idx) > 0: # local processing fusion_x_local = x[longer_list_idx,1:,:,:].clone().contiguous() FB,FC,FT,FF = fusion_x_local.size() fusion_x_local = fusion_x_local.view(FB * FC, FT, FF) fusion_x_local = torch.permute(fusion_x_local, (0,2,1)).contiguous() fusion_x_local = self.mel_conv1d(fusion_x_local) fusion_x_local = fusion_x_local.view(FB,FC,FF,fusion_x_local.size(-1)) fusion_x_local = torch.permute(fusion_x_local, (0,2,1,3)).contiguous().flatten(2) if fusion_x_local.size(-1) < FT: fusion_x_local = torch.cat([fusion_x_local, torch.zeros((FB,FF,FT- fusion_x_local.size(-1)), device=device)], dim=-1) else: fusion_x_local = fusion_x_local[:,:,:FT] # 1D fusion new_x = new_x.squeeze(1).permute((0,2,1)).contiguous() new_x[longer_list_idx] = self.fusion_model(new_x[longer_list_idx], fusion_x_local) x = new_x.permute((0,2,1)).contiguous()[:,None,:,:] else: x = new_x elif self.fusion_type in ['daf_2d','aff_2d','iaff_2d','channel_map']: x = x # no change if self.training: x = self.spec_augmenter(x) if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) x = self.reshape_wav2img(x) output_dict = self.forward_features(x, longer_idx = longer_list_idx) # if infer_mode: # # in infer mode. we need to handle different length audio input # frame_num = x.shape[2] # target_T = int(self.spec_size * self.freq_ratio) # repeat_ratio = math.floor(target_T / frame_num) # x = x.repeat(repeats=(1,1,repeat_ratio,1)) # x = self.reshape_wav2img(x) # output_dict = self.forward_features(x) # else: # if x.shape[2] > self.freq_ratio * self.spec_size: # if self.training: # x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size) # x = self.reshape_wav2img(x) # output_dict = self.forward_features(x) # else: # # Change: Hard code here # overlap_size = (x.shape[2] - 1) // 4 # output_dicts = [] # crop_size = (x.shape[2] - 1) // 2 # for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size): # tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos) # tx = self.reshape_wav2img(tx) # output_dicts.append(self.forward_features(tx)) # clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device) # framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device) # for d in output_dicts: # clipwise_output += d["clipwise_output"] # framewise_output += d["framewise_output"] # clipwise_output = clipwise_output / len(output_dicts) # framewise_output = framewise_output / len(output_dicts) # output_dict = { # 'framewise_output': framewise_output, # 'clipwise_output': clipwise_output # } # else: # this part is typically used, and most easy one # x = self.reshape_wav2img(x) # output_dict = self.forward_features(x) # x = self.head(x) # We process the data in the dataloader part, in that here we only consider the input_T < fixed_T return output_dict def create_htsat_model(audio_cfg, enable_fusion=False, fusion_type='None'): try: assert audio_cfg.model_name in ["tiny", "base", "large"], "model name for HTS-AT is wrong!" if audio_cfg.model_name == "tiny": model = HTSAT_Swin_Transformer( spec_size=256, patch_size=4, patch_stride=(4,4), num_classes=audio_cfg.class_num, embed_dim=96, depths=[2,2,6,2], num_heads=[4,8,16,32], window_size=8, config = audio_cfg, enable_fusion = enable_fusion, fusion_type = fusion_type ) elif audio_cfg.model_name == "base": model = HTSAT_Swin_Transformer( spec_size=256, patch_size=4, patch_stride=(4,4), num_classes=audio_cfg.class_num, embed_dim=128, depths=[2,2,12,2], num_heads=[4,8,16,32], window_size=8, config = audio_cfg, enable_fusion = enable_fusion, fusion_type = fusion_type ) elif audio_cfg.model_name == "large": model = HTSAT_Swin_Transformer( spec_size=256, patch_size=4, patch_stride=(4,4), num_classes=audio_cfg.class_num, embed_dim=256, depths=[2,2,12,2], num_heads=[4,8,16,32], window_size=8, config = audio_cfg, enable_fusion = enable_fusion, fusion_type = fusion_type ) return model except: raise RuntimeError(f'Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough.') ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/linear_probe.py ================================================ import numpy as np import torch.nn.functional as F from torch import nn from .model import MLPLayers class LinearProbe(nn.Module): def __init__(self, model, mlp, freeze, in_ch, out_ch, act=None): """ Args: model: nn.Module mlp: bool, if True, then use the MLP layer as the linear probe module freeze: bool, if Ture, then freeze all the CLAP model's layers when training the linear probe in_ch: int, the output channel from CLAP model out_ch: int, the output channel from linear probe (class_num) act: torch.nn.functional, the activation function before the loss function """ super().__init__() in_ch = 512 self.clap_model = model self.clap_model.text_branch = None # to save memory self.freeze = freeze if mlp: self.lp_layer = MLPLayers(units=[in_ch, in_ch * 2, out_ch]) else: self.lp_layer = nn.Linear(in_ch, out_ch) if self.freeze: for param in self.clap_model.parameters(): param.requires_grad = False if act == 'None': self.act = None elif act == 'relu': self.act = nn.ReLU() elif act == 'elu': self.act = nn.ELU() elif act == 'prelu': self.act = nn.PReLU(num_parameters=in_ch) elif act == 'softmax': self.act = nn.Softmax(dim=-1) elif act == 'sigmoid': self.act = nn.Sigmoid() def forward(self, x, mix_lambda=None, device=None): """ Args: x: waveform, torch.tensor [batch, t_samples] / batch of mel_spec and longer list mix_lambda: torch.tensor [batch], the mixup lambda Returns: class_prob: torch.tensor [batch, class_num] """ # batchnorm cancel grandient if self.freeze: self.clap_model.eval() x = self.clap_model.audio_projection( self.clap_model.audio_branch(x, mixup_lambda=mix_lambda, device=device)["embedding"]) out = self.lp_layer(x) if self.act is not None: out = self.act(out) return out ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/loss.py ================================================ from multiprocessing.sharedctypes import Value import torch import torch.distributed.nn from torch import distributed as dist, nn as nn from torch.nn import functional as F import numpy as np from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score try: import horovod.torch as hvd except ImportError: hvd = None def gather_features( audio_features, text_features, audio_features_mlp=None, text_features_mlp=None, local_loss=False, gather_with_grad=False, rank=0, world_size=1, use_horovod=False, mlp_loss=False ): if use_horovod: assert hvd is not None, 'Please install horovod' if gather_with_grad: all_audio_features = hvd.allgather(audio_features) all_text_features = hvd.allgather(text_features) if mlp_loss: all_audio_features_mlp = hvd.allgather(audio_features_mlp) all_text_features_mlp = hvd.allgather(text_features_mlp) else: with torch.no_grad(): all_audio_features = hvd.allgather(audio_features) all_text_features = hvd.allgather(text_features) if mlp_loss: all_audio_features_mlp = hvd.allgather(audio_features_mlp) all_text_features_mlp = hvd.allgather(text_features_mlp) if not local_loss: # ensure grads for local rank when all_* features don't have a gradient gathered_audio_features = list(all_audio_features.chunk(world_size, dim=0)) gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) gathered_audio_features[rank] = audio_features gathered_text_features[rank] = text_features all_audio_features = torch.cat(gathered_audio_features, dim=0) all_text_features = torch.cat(gathered_text_features, dim=0) if mlp_loss: gathered_audio_features_mlp = list(all_audio_features_mlp.chunk(world_size, dim=0)) gathered_text_features_mlp = list(all_text_features_mlp.chunk(world_size, dim=0)) gathered_audio_features_mlp[rank] = audio_features_mlp gathered_text_features_mlp[rank] = text_features_mlp all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0) all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0) else: # We gather tensors from all gpus if gather_with_grad: all_audio_features = torch.cat(torch.distributed.nn.all_gather(audio_features), dim=0) all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) if mlp_loss: all_audio_features_mlp = torch.cat(torch.distributed.nn.all_gather(audio_features_mlp), dim=0) all_text_features_mlp = torch.cat(torch.distributed.nn.all_gather(text_features_mlp), dim=0) else: gathered_audio_features = [torch.zeros_like(audio_features) for _ in range(world_size)] gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] dist.all_gather(gathered_audio_features, audio_features) dist.all_gather(gathered_text_features, text_features) if mlp_loss: gathered_audio_features_mlp = [torch.zeros_like(audio_features_mlp) for _ in range(world_size)] gathered_text_features_mlp = [torch.zeros_like(text_features_mlp) for _ in range(world_size)] dist.all_gather(gathered_audio_features_mlp, audio_features_mlp) dist.all_gather(gathered_text_features_mlp, text_features_mlp) if not local_loss: # ensure grads for local rank when all_* features don't have a gradient gathered_audio_features[rank] = audio_features gathered_text_features[rank] = text_features if mlp_loss: gathered_audio_features_mlp[rank] = audio_features_mlp gathered_text_features_mlp[rank] = text_features_mlp all_audio_features = torch.cat(gathered_audio_features, dim=0) all_text_features = torch.cat(gathered_text_features, dim=0) if mlp_loss: all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0) all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0) if mlp_loss: return all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp else: return all_audio_features, all_text_features class ClipLoss(nn.Module): def __init__( self, local_loss=False, gather_with_grad=False, cache_labels=False, rank=0, world_size=1, use_horovod=False, mlp_loss=False, weight_loss_kappa=0, ): super().__init__() self.local_loss = local_loss self.gather_with_grad = gather_with_grad self.cache_labels = cache_labels self.rank = rank self.world_size = world_size self.use_horovod = use_horovod self.mlp_loss = mlp_loss self.weighted_loss = bool(weight_loss_kappa!=0) self.weight_loss_kappa = weight_loss_kappa # cache state self.prev_num_logits = 0 self.labels = {} def forward(self, audio_features, text_features, logit_scale_a, logit_scale_t=None, audio_features_mlp=None, text_features_mlp=None): device = audio_features.device if self.mlp_loss: if self.world_size > 1: all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp = gather_features( audio_features=audio_features,text_features=text_features, audio_features_mlp=audio_features_mlp,text_features_mlp=text_features_mlp, local_loss=self.local_loss,gather_with_grad=self.gather_with_grad, rank=self.rank,world_size=self.world_size,use_horovod=self.use_horovod, mlp_loss=self.mlp_loss ) if self.local_loss: a_logits_per_audio = logit_scale_a * audio_features @ all_text_features_mlp.T a_logits_per_text = logit_scale_a * text_features_mlp @ all_audio_features.T t_logits_per_audio = logit_scale_t * audio_features_mlp @ all_text_features.T t_logits_per_text = logit_scale_t * text_features @ all_audio_features_mlp.T else: a_logits_per_audio = logit_scale_a * all_audio_features @ all_text_features_mlp.T a_logits_per_text = a_logits_per_audio.T t_logits_per_audio = logit_scale_t * all_audio_features_mlp @ all_text_features.T t_logits_per_text = t_logits_per_audio.T else: a_logits_per_audio = logit_scale_a * audio_features @ text_features_mlp.T a_logits_per_text = logit_scale_a * text_features_mlp @ audio_features.T t_logits_per_audio = logit_scale_t * audio_features_mlp @ text_features.T t_logits_per_text = logit_scale_t * text_features @ audio_features_mlp.T # calculated ground-truth and cache if enabled num_logits = a_logits_per_audio.shape[0] if self.prev_num_logits != num_logits or device not in self.labels: labels = torch.arange(num_logits, device=device, dtype=torch.long) if self.world_size > 1 and self.local_loss: labels = labels + num_logits * self.rank if self.cache_labels: self.labels[device] = labels self.prev_num_logits = num_logits else: labels = self.labels[device] if not self.weighted_loss: total_loss = ( F.cross_entropy(a_logits_per_audio, labels) + F.cross_entropy(a_logits_per_text, labels) + F.cross_entropy(t_logits_per_audio, labels) + F.cross_entropy(t_logits_per_text, labels) ) / 4 else: audio_weight = (audio_features@audio_features.T).detach() audio_weight = (torch.exp(torch.sum(audio_weight, axis=1)/(self.weight_loss_kappa*len(audio_weight)))).detach() text_weight = (text_features@text_features.T).detach() text_weight = (torch.exp(torch.sum(text_weight, axis=1)/(self.weight_loss_kappa*len(text_features)))).detach() total_loss = ( F.cross_entropy(a_logits_per_audio, labels, weight=audio_weight) + F.cross_entropy(a_logits_per_text, labels, weight=audio_weight) + F.cross_entropy(t_logits_per_audio, labels, weight=text_weight) + F.cross_entropy(t_logits_per_text, labels, weight=text_weight) ) / 4 else: if self.world_size > 1: all_audio_features, all_text_features = gather_features( audio_features=audio_features,text_features=text_features, local_loss=self.local_loss,gather_with_grad=self.gather_with_grad, rank=self.rank,world_size=self.world_size,use_horovod=self.use_horovod, mlp_loss=self.mlp_loss ) if self.local_loss: logits_per_audio = logit_scale_a * audio_features @ all_text_features.T logits_per_text = logit_scale_a * text_features @ all_audio_features.T else: logits_per_audio = logit_scale_a * all_audio_features @ all_text_features.T logits_per_text = logits_per_audio.T else: logits_per_audio = logit_scale_a * audio_features @ text_features.T logits_per_text = logit_scale_a * text_features @ audio_features.T # calculated ground-truth and cache if enabled num_logits = logits_per_audio.shape[0] if self.prev_num_logits != num_logits or device not in self.labels: labels = torch.arange(num_logits, device=device, dtype=torch.long) if self.world_size > 1 and self.local_loss: labels = labels + num_logits * self.rank if self.cache_labels: self.labels[device] = labels self.prev_num_logits = num_logits else: labels = self.labels[device] if not self.weighted_loss: total_loss = ( F.cross_entropy(logits_per_audio, labels) + F.cross_entropy(logits_per_text, labels) ) / 2 else: audio_weight = (all_audio_features@all_audio_features.T).detach() audio_weight = (torch.exp(torch.sum(audio_weight, axis=1)/(self.weight_loss_kappa*len(all_audio_features)))).detach() text_weight = (all_text_features@all_text_features.T).detach() text_weight = (torch.exp(torch.sum(text_weight, axis=1)/(self.weight_loss_kappa*len(all_text_features)))).detach() total_loss = ( F.cross_entropy(logits_per_audio, labels, weight=text_weight) + F.cross_entropy(logits_per_text, labels, weight=audio_weight) ) / 2 return total_loss def lp_gather_features( pred, target, world_size=1, use_horovod=False ): if use_horovod: assert hvd is not None, 'Please install horovod' with torch.no_grad(): all_preds = hvd.allgather(pred) all_targets = hvd.allgath(target) else: gathered_preds = [torch.zeros_like(pred) for _ in range(world_size)] gathered_targets = [torch.zeros_like(target) for _ in range(world_size)] dist.all_gather(gathered_preds, pred) dist.all_gather(gathered_targets, target) all_preds = torch.cat(gathered_preds, dim=0) all_targets = torch.cat(gathered_targets, dim=0) return all_preds, all_targets def get_map(pred, target): pred = torch.sigmoid(pred).numpy() target = target.numpy() return np.mean(average_precision_score(target, pred, average=None)) def get_acc(pred, target): pred = torch.argmax(pred,1).numpy() target = torch.argmax(target,1).numpy() return accuracy_score(target, pred) def get_mauc(pred, target): pred = torch.sigmoid(pred).numpy() target = target.numpy() return np.mean(roc_auc_score(target, pred, average=None)) class LPMetrics(object): def __init__(self, metric_names = ['map','acc','mauc']): self.metrics = [] for name in metric_names: self.metrics.append(self.get_metric(name)) self.metric_names = metric_names def get_metric(self,name): if name == 'map': return get_map elif name == 'acc': return get_acc elif name == 'mauc': return get_mauc else: raise ValueError(f'the metric should be at least one of [map, acc, mauc]') def evaluate_mertics(self, pred, target): metric_dict = {} for i in range(len(self.metric_names)): metric_dict[self.metric_names[i]] = self.metrics[i](pred, target) return metric_dict def calc_celoss(pred, target): target = torch.argmax(target, 1).long() return nn.CrossEntropyLoss()(pred, target) class LPLoss(nn.Module): def __init__(self, loss_name): super().__init__() if loss_name == 'bce': self.loss_func = nn.BCEWithLogitsLoss() elif loss_name == 'ce': self.loss_func = calc_celoss elif loss_name == 'mse': self.loss_func = nn.MSELoss() else: raise ValueError(f'the loss func should be at least one of [bce, ce, mse]') def forward(self, pred, target): loss = self.loss_func(pred, target) return loss ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model.py ================================================ """ CLAP Model Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. Adapted to the Audio Task. """ from collections import OrderedDict from dataclasses import dataclass from email.mime import audio from typing import Tuple, Union, Callable, Optional import numpy as np import torch import torch.nn.functional as F from torch import nn from .timm_model import TimmModel import logging from .utils import freeze_batch_norm_2d from .pann_model import create_pann_model from .htsat import create_htsat_model from transformers import BertModel, RobertaModel, BartModel from transformers.tokenization_utils_base import BatchEncoding class MLPLayers(nn.Module): def __init__(self, units=[512, 512, 512], nonlin=nn.ReLU(), dropout=0.1): super(MLPLayers, self).__init__() self.nonlin = nonlin self.dropout = dropout sequence = [] for u0, u1 in zip(units[:-1], units[1:]): sequence.append(nn.Linear(u0, u1)) sequence.append(self.nonlin) sequence.append(nn.Dropout(self.dropout)) sequence = sequence[:-2] self.sequential = nn.Sequential(*sequence) def forward(self, X): X = self.sequential(X) return X class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1): super().__init__() # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = None self.stride = stride if stride > 1 or inplanes != planes * Bottleneck.expansion: # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 self.downsample = nn.Sequential( OrderedDict( [ ("-1", nn.AvgPool2d(stride)), ( "0", nn.Conv2d( inplanes, planes * self.expansion, 1, stride=1, bias=False, ), ), ("1", nn.BatchNorm2d(planes * self.expansion)), ] ) ) def forward(self, x: torch.Tensor): identity = x out = self.relu(self.bn1(self.conv1(x))) out = self.relu(self.bn2(self.conv2(out))) out = self.avgpool(out) out = self.bn3(self.conv3(out)) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class AttentionPool2d(nn.Module): def __init__( self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None ): super().__init__() self.positional_embedding = nn.Parameter( torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5 ) self.k_proj = nn.Linear(embed_dim, embed_dim) self.q_proj = nn.Linear(embed_dim, embed_dim) self.v_proj = nn.Linear(embed_dim, embed_dim) self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) self.num_heads = num_heads def forward(self, x): x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute( 2, 0, 1 ) # NCHW -> (HW)NC x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC x, _ = F.multi_head_attention_forward( query=x, key=x, value=x, embed_dim_to_check=x.shape[-1], num_heads=self.num_heads, q_proj_weight=self.q_proj.weight, k_proj_weight=self.k_proj.weight, v_proj_weight=self.v_proj.weight, in_proj_weight=None, in_proj_bias=torch.cat( [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias] ), bias_k=None, bias_v=None, add_zero_attn=False, dropout_p=0, out_proj_weight=self.c_proj.weight, out_proj_bias=self.c_proj.bias, use_separate_proj_weight=True, training=self.training, need_weights=False, ) return x[0] class ModifiedResNet(nn.Module): """ A ResNet class that is similar to torchvision's but contains the following changes: - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 - The final pooling layer is a QKV attention instead of an average pool """ def __init__(self, layers, output_dim, heads, image_size=224, width=64): super().__init__() self.output_dim = output_dim self.image_size = image_size # the 3-layer stem self.conv1 = nn.Conv2d( 3, width // 2, kernel_size=3, stride=2, padding=1, bias=False ) self.bn1 = nn.BatchNorm2d(width // 2) self.conv2 = nn.Conv2d( width // 2, width // 2, kernel_size=3, padding=1, bias=False ) self.bn2 = nn.BatchNorm2d(width // 2) self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) self.bn3 = nn.BatchNorm2d(width) self.avgpool = nn.AvgPool2d(2) self.relu = nn.ReLU(inplace=True) # residual layers self._inplanes = width # this is a *mutable* variable used during construction self.layer1 = self._make_layer(width, layers[0]) self.layer2 = self._make_layer(width * 2, layers[1], stride=2) self.layer3 = self._make_layer(width * 4, layers[2], stride=2) self.layer4 = self._make_layer(width * 8, layers[3], stride=2) embed_dim = width * 32 # the ResNet feature dimension self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) self.init_parameters() def _make_layer(self, planes, blocks, stride=1): layers = [Bottleneck(self._inplanes, planes, stride)] self._inplanes = planes * Bottleneck.expansion for _ in range(1, blocks): layers.append(Bottleneck(self._inplanes, planes)) return nn.Sequential(*layers) def init_parameters(self): if self.attnpool is not None: std = self.attnpool.c_proj.in_features**-0.5 nn.init.normal_(self.attnpool.q_proj.weight, std=std) nn.init.normal_(self.attnpool.k_proj.weight, std=std) nn.init.normal_(self.attnpool.v_proj.weight, std=std) nn.init.normal_(self.attnpool.c_proj.weight, std=std) for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: for name, param in resnet_block.named_parameters(): if name.endswith("bn3.weight"): nn.init.zeros_(param) def lock(self, unlocked_groups=0, freeze_bn_stats=False): assert ( unlocked_groups == 0 ), "partial locking not currently supported for this model" for param in self.parameters(): param.requires_grad = False if freeze_bn_stats: freeze_batch_norm_2d(self) def stem(self, x): for conv, bn in [ (self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3), ]: x = self.relu(bn(conv(x))) x = self.avgpool(x) return x def forward(self, x): x = self.stem(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.attnpool(x) return x class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor): orig_type = x.dtype x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) return x.to(orig_type) class QuickGELU(nn.Module): # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class ResidualAttentionBlock(nn.Module): def __init__(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU): super().__init__() self.attn = nn.MultiheadAttention(d_model, n_head) self.ln_1 = LayerNorm(d_model) self.mlp = nn.Sequential( OrderedDict( [ ("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", act_layer()), ("c_proj", nn.Linear(d_model * 4, d_model)), ] ) ) self.ln_2 = LayerNorm(d_model) def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): x = x + self.attention(self.ln_1(x), attn_mask=attn_mask) x = x + self.mlp(self.ln_2(x)) return x class Transformer(nn.Module): def __init__( self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU ): super().__init__() self.width = width self.layers = layers self.resblocks = nn.ModuleList( [ ResidualAttentionBlock(width, heads, act_layer=act_layer) for _ in range(layers) ] ) def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): for r in self.resblocks: x = r(x, attn_mask=attn_mask) return x class VisualTransformer(nn.Module): def __init__( self, image_size: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int, act_layer: Callable = nn.GELU, ): super().__init__() self.image_size = image_size self.output_dim = output_dim self.conv1 = nn.Conv2d( in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False, ) scale = width**-0.5 self.class_embedding = nn.Parameter(scale * torch.randn(width)) self.positional_embedding = nn.Parameter( scale * torch.randn((image_size // patch_size) ** 2 + 1, width) ) self.ln_pre = LayerNorm(width) self.text_branch = Transformer(width, layers, heads, act_layer=act_layer) self.ln_post = LayerNorm(width) self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) def lock(self, unlocked_groups=0, freeze_bn_stats=False): assert ( unlocked_groups == 0 ), "partial locking not currently supported for this model" for param in self.parameters(): param.requires_grad = False def forward(self, x: torch.Tensor): x = self.conv1(x) # shape = [*, width, grid, grid] x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] x = torch.cat( [ self.class_embedding.to(x.dtype) + torch.zeros( x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device ), x, ], dim=1, ) # shape = [*, grid ** 2 + 1, width] x = x + self.positional_embedding.to(x.dtype) x = self.ln_pre(x) x = x.permute(1, 0, 2) # NLD -> LND x = self.text_branch(x) x = x.permute(1, 0, 2) # LND -> NLD x = self.ln_post(x[:, 0, :]) if self.proj is not None: x = x @ self.proj return x @dataclass class CLAPVisionCfg: layers: Union[Tuple[int, int, int, int], int] = 12 width: int = 768 patch_size: int = 16 image_size: Union[Tuple[int, int], int] = 224 timm_model_name: str = ( None # a valid model name overrides layers, width, patch_size ) timm_model_pretrained: bool = ( False # use (imagenet) pretrained weights for named model ) timm_pool: str = ( "avg" # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') ) timm_proj: str = ( "linear" # linear projection for timm model output ('linear', 'mlp', '') ) # Audio Config Class @dataclass class CLAPAudioCfp: model_type: str = "PANN" model_name: str = "Cnn14" sample_rate: int = 48000 # Param audio_length: int = 1024 window_size: int = 1024 hop_size: int = 1024 fmin: int = 50 fmax: int = 14000 class_num: int = 527 mel_bins: int = 64 clip_samples: int = 480000 @dataclass class CLAPTextCfg: context_length: int vocab_size: int width: int heads: int layers: int model_type: str class CLAP(nn.Module): def __init__( self, embed_dim: int, audio_cfg: CLAPAudioCfp, text_cfg: CLAPTextCfg, quick_gelu: bool = False, enable_fusion: bool = False, fusion_type: str = 'None', joint_embed_shape: int = 512, mlp_act: str = 'relu', ): super().__init__() if isinstance(audio_cfg, dict): audio_cfg = CLAPAudioCfp(**audio_cfg) if isinstance(text_cfg, dict): text_cfg = CLAPTextCfg(**text_cfg) self.audio_cfg = audio_cfg self.text_cfg = text_cfg self.enable_fusion = enable_fusion self.fusion_type = fusion_type self.joint_embed_shape = joint_embed_shape self.mlp_act = mlp_act self.context_length = text_cfg.context_length # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more # memory efficient in recent PyTorch releases (>= 1.10). # NOTE: timm models always use native GELU regardless of quick_gelu flag. act_layer = QuickGELU if quick_gelu else nn.GELU if mlp_act == 'relu': mlp_act_layer = nn.ReLU() elif mlp_act == 'gelu': mlp_act_layer = nn.GELU() else: raise NotImplementedError # audio branch # audio branch parameters if audio_cfg.model_type == "PANN": self.audio_branch = create_pann_model(audio_cfg, enable_fusion, fusion_type) elif audio_cfg.model_type == "HTSAT": self.audio_branch = create_htsat_model(audio_cfg, enable_fusion, fusion_type) else: logging.error(f"Model config for {audio_cfg.model_type} not found") raise RuntimeError(f"Model config for {audio_cfg.model_type} not found.") # text branch # text branch parameters if text_cfg.model_type == "transformer": self.text_branch = Transformer( width=text_cfg.width, layers=text_cfg.layers, heads=text_cfg.heads, act_layer=act_layer, ) self.vocab_size = text_cfg.vocab_size self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width) self.positional_embedding = nn.Parameter( torch.empty(self.context_length, text_cfg.width) ) self.ln_final = LayerNorm(text_cfg.width) self.text_transform = MLPLayers(units=[self.joint_embed_shape, self.joint_embed_shape, self.joint_embed_shape], dropout=0.1) self.text_projection = nn.Sequential( nn.Linear(text_cfg.width, self.joint_embed_shape), mlp_act_layer, nn.Linear(self.joint_embed_shape, self.joint_embed_shape) ) elif text_cfg.model_type == "bert": self.text_branch = BertModel.from_pretrained("bert-base-uncased") self.text_transform = MLPLayers(units=[self.joint_embed_shape, self.joint_embed_shape, self.joint_embed_shape], dropout=0.1) self.text_projection = nn.Sequential( nn.Linear(768, self.joint_embed_shape), mlp_act_layer, nn.Linear(self.joint_embed_shape, self.joint_embed_shape) ) elif text_cfg.model_type == "roberta": self.text_branch = RobertaModel.from_pretrained('roberta-base') self.text_transform = MLPLayers(units=[self.joint_embed_shape, self.joint_embed_shape, self.joint_embed_shape], dropout=0.1) self.text_projection = nn.Sequential( nn.Linear(768, self.joint_embed_shape), mlp_act_layer, nn.Linear(self.joint_embed_shape, self.joint_embed_shape) ) elif text_cfg.model_type == "bart": self.text_branch = BartModel.from_pretrained('facebook/bart-base') self.text_transform = MLPLayers(units=[self.joint_embed_shape, self.joint_embed_shape, self.joint_embed_shape], dropout=0.1) self.text_projection = nn.Sequential( nn.Linear(768, self.joint_embed_shape), mlp_act_layer, nn.Linear(self.joint_embed_shape, self.joint_embed_shape) ) else: logging.error(f"Model config for {text_cfg.model_type} not found") raise RuntimeError(f"Model config for {text_cfg.model_type} not found.") self.text_branch_type = text_cfg.model_type # text branch parameters # audio branch parameters self.audio_transform = MLPLayers(units=[self.joint_embed_shape, self.joint_embed_shape, self.joint_embed_shape], dropout=0.1) # below here is text branch parameters # ============================================================================================================ self.audio_projection = nn.Sequential( nn.Linear(embed_dim, self.joint_embed_shape), mlp_act_layer, nn.Linear(self.joint_embed_shape, self.joint_embed_shape) ) self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) self.register_buffer("attn_mask", self.build_attention_mask(), persistent=False) self.init_text_branch_parameters() def init_text_branch_parameters(self): if self.text_branch_type == "transformer": nn.init.normal_(self.token_embedding.weight, std=0.02) nn.init.normal_(self.positional_embedding, std=0.01) proj_std = (self.text_branch.width**-0.5) * ( (2 * self.text_branch.layers) ** -0.5 ) attn_std = self.text_branch.width**-0.5 fc_std = (2 * self.text_branch.width) ** -0.5 for block in self.text_branch.resblocks: nn.init.normal_(block.attn.in_proj_weight, std=attn_std) nn.init.normal_(block.attn.out_proj.weight, std=proj_std) nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) if self.text_branch_type == "bert" or self.text_branch_type == "roberta": width = self.text_branch.embeddings.word_embeddings.weight.shape[-1] elif self.text_branch_type == "bart": width = self.text_branch.shared.weight.shape[-1] else: width = self.text_branch.width nn.init.constant_(self.logit_scale_a, np.log(1 / 0.07)) nn.init.constant_(self.logit_scale_t, np.log(1 / 0.07)) # deprecated # if hasattr(self.visual, 'init_parameters'): # self.visual.init_parameters() # if self.text_projection is not None: # nn.init.normal_(self.text_projection, std=width**-0.5) def build_attention_mask(self): # lazily create causal attention mask, with full attention between the vision tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(self.context_length, self.context_length) mask.fill_(float("-inf")) mask.triu_(1) # zero out the lower diagonal return mask def encode_audio(self, audio, device): return self.audio_branch(audio, mixup_lambda=None, device=device) # mix lambda needs to add # def list_of_dict_of_tensor2dict_of_tensor(self, x, device): # tmp = {} # for k in x[0].keys(): # tmp[k] = [] # for i in range(len(x)): # tmp[k].append(x[i][k][:77]) # for k in x[0].keys(): # tmp[k] = torch.tensor(tmp[k]).to(device=device, non_blocking=True) # return tmp def encode_text(self, text, device): if self.text_branch_type == "transformer": text = text.to(device=device, non_blocking=True) x = self.token_embedding(text) # [batch_size, n_ctx, d_model] x = x + self.positional_embedding x = x.permute(1, 0, 2) # NLD -> LND x = self.text_branch(x, attn_mask=self.attn_mask) x = x.permute(1, 0, 2) # LND -> NLD x = self.ln_final(x) # x.shape = [batch_size, n_ctx, transformer.width] # take features from the eot embedding (eot_token is the highest number in each sequence) x = self.text_projection(x[torch.arange(x.shape[0]), text.argmax(dim=-1)]) elif self.text_branch_type == "bert": # text = self.list_of_dict_of_tensor2dict_of_tensor(text, device) # text = BatchEncoding(text) x = self.text_branch( input_ids=text["input_ids"].to(device=device, non_blocking=True), attention_mask=text["attention_mask"].to( device=device, non_blocking=True ), token_type_ids=text["token_type_ids"].to( device=device, non_blocking=True ), )["pooler_output"] x = self.text_projection(x) elif self.text_branch_type == "roberta": x = self.text_branch( input_ids=text["input_ids"].to(device=device, non_blocking=True), attention_mask=text["attention_mask"].to( device=device, non_blocking=True ), )["pooler_output"] x = self.text_projection(x) elif self.text_branch_type == "bart": x = torch.mean(self.text_branch( input_ids=text["input_ids"].to(device=device, non_blocking=True), attention_mask=text["attention_mask"].to( device=device, non_blocking=True ), )["encoder_last_hidden_state"],axis=1) x = self.text_projection(x) else: logging.error(f"Model type {self.text_branch_type} not found") raise RuntimeError(f"Model type {self.text_branch_type} not found.") return x def forward(self, audio, text, device=None): """Forward audio and text into the CLAP Parameters ---------- audio: torch.Tensor (batch_size, audio_length) the time-domain audio input / the batch of mel_spec and longer list. text: torch.Tensor () // need to add the text token input """ if device is None: if audio is not None: device = audio.device elif text is not None: device = text.device if audio is None and text is None: # a hack to get the logit scale return self.logit_scale_a.exp(), self.logit_scale_t.exp() elif audio is None: return self.encode_text(text, device=device) elif text is None: return self.audio_projection(self.encode_audio(audio, device=device)["embedding"]) audio_features = self.audio_projection(self.encode_audio(audio, device=device)["embedding"]) audio_features = F.normalize(audio_features, dim=-1) text_features = self.encode_text( text, device=device ) # print("text_features", text_features) # print("text_features.shape", text_features.shape) # print("text_features.type", type(text_features)) text_features = F.normalize(text_features, dim=-1) audio_features_mlp = self.audio_transform(audio_features) text_features_mlp = self.text_transform(text_features) # Four outputs: audio features (basic & MLP), text features (basic & MLP) return ( audio_features, text_features, audio_features_mlp, text_features_mlp, self.logit_scale_a.exp(), self.logit_scale_t.exp(), ) def get_logit_scale(self): return self.logit_scale_a.exp(), self.logit_scale_t.exp() def get_textual_embedding(self, data): device = next(self.parameters()).device for k in data: data[k] = data[k].to(device) # if self.text_branch_type == "roberta": text_embeds = self.text_branch( input_ids=data["input_ids"].to(device=device, non_blocking=True), attention_mask=data["attention_mask"].to(device=device, non_blocking=True), )["last_hidden_state"] text_embeds = self.text_projection(text_embeds) text_embeds = F.normalize(text_embeds, dim=-1) return text_embeds def get_text_embedding(self, data): """Get the text embedding from the model Parameters ---------- data: torch.Tensor a tensor of text embedding Returns ---------- text_embed: torch.Tensor a tensor of text_embeds (N, D) """ device = next(self.parameters()).device for k in data: data[k] = data[k].to(device) text_embeds = self.encode_text(data, device=device) text_embeds = F.normalize(text_embeds, dim=-1) return text_embeds def get_audio_embedding(self, data): """Get the audio embedding from the model Parameters ---------- data: a list of dict the audio input dict list from 'get_audio_feature' method Returns ---------- audio_embed: torch.Tensor a tensor of audio_embeds (N, D) """ device = next(self.parameters()).device input_dict = {} keys = data[0].keys() for k in keys: input_dict[k] = torch.cat([d[k].unsqueeze(0) for d in data], dim=0).to(device) audio_embeds = self.audio_projection(self.encode_audio(input_dict, device=device)["embedding"]) audio_embeds = F.normalize(audio_embeds, dim=-1) return audio_embeds def audio_infer(self, audio, hopsize=None, device=None): """Forward one audio and produce the audio embedding Parameters ---------- audio: (audio_length) the time-domain audio input, notice that it must be only one input hopsize: int the overlap hopsize as the sliding window Returns ---------- output_dict: { key: [n, (embedding_shape)] if "HTS-AT" or key: [(embedding_shape)] if "PANN" } the list of key values of the audio branch """ assert not self.training, "the inference mode must be run at eval stage" output_dict = {} # PANN if self.audio_cfg.model_type == "PANN": audio_input = audio.unsqueeze(dim=0) output_dict[key] = self.encode_audio(audio_input, device=device)[key].squeeze(dim=0) elif self.audio_cfg.model_type == "HTSAT": # repeat audio_len = len(audio) k = self.audio_cfg.clip_samples // audio_len if k > 1: audio = audio.repeat(k) audio_len = len(audio) if hopsize is None: hopsize = min(hopsize, audio_len) if audio_len > self.audio_cfg.clip_samples: audio_input = [ audio[pos : pos + self.audio_cfg.clip_samples].clone() for pos in range( 0, audio_len - self.audio_cfg.clip_samples, hopsize ) ] audio_input.append(audio[-self.audio_cfg.clip_samples :].clone()) audio_input = torch.stack(audio_input) output_dict[key] = self.encode_audio(audio_input, device=device)[key] else: audio_input = audio.unsqueeze(dim=0) output_dict[key] = self.encode_audio(audio_input, device=device)[key].squeeze(dim=0) return output_dict def convert_weights_to_fp16(model: nn.Module): """Convert applicable model parameters to fp16""" def _convert_weights_to_fp16(l): if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): l.weight.data = l.weight.data.half() if l.bias is not None: l.bias.data = l.bias.data.half() if isinstance(l, nn.MultiheadAttention): for attr in [ *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v", ]: tensor = getattr(l, attr) if tensor is not None: tensor.data = tensor.data.half() for name in ["text_projection", "proj"]: if hasattr(l, name): attr = getattr(l, name) if attr is not None: attr.data = attr.data.half() model.apply(_convert_weights_to_fp16) # Ignore the state dict of the vision part def build_model_from_openai_state_dict(state_dict: dict, model_cfg, enable_fusion: bool = False, fusion_type: str = 'None'): embed_dim = model_cfg["embed_dim"] audio_cfg = model_cfg["audio_cfg"] text_cfg = model_cfg["text_cfg"] context_length = state_dict["positional_embedding"].shape[0] vocab_size = state_dict["token_embedding.weight"].shape[0] transformer_width = state_dict["ln_final.weight"].shape[0] transformer_heads = transformer_width // 64 transformer_layers = len( set( k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks") ) ) audio_cfg = CLAPAudioCfp(**audio_cfg) text_cfg = CLAPTextCfg(**text_cfg) model = CLAP( embed_dim, audio_cfg=audio_cfg, text_cfg=text_cfg, quick_gelu=True, # OpenAI models were trained with QuickGELU enable_fusion=enable_fusion, fusion_type=fusion_type ) state_dict["logit_scale_a"] = state_dict["logit_scale"] state_dict["logit_scale_t"] = state_dict["logit_scale"] pop_keys = list(state_dict.keys())[::] # pop the visual branch saved weights for key in pop_keys: if key.startswith("visual."): state_dict.pop(key, None) for key in ["logit_scale", "input_resolution", "context_length", "vocab_size"]: state_dict.pop(key, None) # not use fp16 # convert_weights_to_fp16(model) model.load_state_dict(state_dict, strict=False) return model.eval() def trace_model(model, batch_size=256, device=torch.device("cpu")): model.eval() audio_length = model.audio_cfg.audio_length example_audio = torch.ones((batch_size, audio_length), device=device) example_text = torch.zeros( (batch_size, model.context_length), dtype=torch.int, device=device ) model = torch.jit.trace_module( model, inputs=dict( forward=(example_audio, example_text), encode_text=(example_text,), encode_image=(example_audio,), ), ) model.audio_cfg.audio_length = audio_length # Question: what does this do? return model ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/HTSAT-base.json ================================================ { "embed_dim": 1024, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "HTSAT", "model_name": "base" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/HTSAT-large.json ================================================ { "embed_dim": 2048, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "HTSAT", "model_name": "large" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/HTSAT-tiny-win-1536.json ================================================ { "embed_dim": 768, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1536, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "HTSAT", "model_name": "tiny" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/HTSAT-tiny.json ================================================ { "embed_dim": 768, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "HTSAT", "model_name": "tiny" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/PANN-10.json ================================================ { "embed_dim": 1024, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "PANN", "model_name": "Cnn10" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/PANN-14-fmax-18k.json ================================================ { "embed_dim": 2048, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 480, "fmin": 50, "fmax": 18000, "class_num": 527, "model_type": "PANN", "model_name": "Cnn14" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/PANN-14-fmax-8k-20s.json ================================================ { "embed_dim": 2048, "audio_cfg": { "audio_length": 1024, "clip_samples": 960000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 360, "fmin": 50, "fmax": 8000, "class_num": 527, "model_type": "PANN", "model_name": "Cnn14" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/PANN-14-tiny-transformer.json ================================================ { "embed_dim": 2048, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "PANN", "model_name": "Cnn14" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 4 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/PANN-14-win-1536.json ================================================ { "embed_dim": 2048, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1536, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "PANN", "model_name": "Cnn14" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/PANN-14.json ================================================ { "embed_dim": 2048, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "PANN", "model_name": "Cnn14" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/PANN-6.json ================================================ { "embed_dim": 512, "audio_cfg": { "audio_length": 1024, "clip_samples": 480000, "mel_bins": 64, "sample_rate": 48000, "window_size": 1024, "hop_size": 480, "fmin": 50, "fmax": 14000, "class_num": 527, "model_type": "PANN", "model_name": "Cnn6" }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/RN101-quickgelu.json ================================================ { "embed_dim": 512, "quick_gelu": true, "vision_cfg": { "image_size": 224, "layers": [ 3, 4, 23, 3 ], "width": 64, "patch_size": null }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/RN101.json ================================================ { "embed_dim": 512, "vision_cfg": { "image_size": 224, "layers": [ 3, 4, 23, 3 ], "width": 64, "patch_size": null }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/RN50-quickgelu.json ================================================ { "embed_dim": 1024, "quick_gelu": true, "vision_cfg": { "image_size": 224, "layers": [ 3, 4, 6, 3 ], "width": 64, "patch_size": null }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/RN50.json ================================================ { "embed_dim": 1024, "vision_cfg": { "image_size": 224, "layers": [ 3, 4, 6, 3 ], "width": 64, "patch_size": null }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/RN50x16.json ================================================ { "embed_dim": 768, "vision_cfg": { "image_size": 384, "layers": [ 6, 8, 18, 8 ], "width": 96, "patch_size": null }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 768, "heads": 12, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/RN50x4.json ================================================ { "embed_dim": 640, "vision_cfg": { "image_size": 288, "layers": [ 4, 6, 10, 6 ], "width": 80, "patch_size": null }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 640, "heads": 10, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/ViT-B-16.json ================================================ { "embed_dim": 512, "vision_cfg": { "image_size": 224, "layers": 12, "width": 768, "patch_size": 16 }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/ViT-B-32-quickgelu.json ================================================ { "embed_dim": 512, "quick_gelu": true, "vision_cfg": { "image_size": 224, "layers": 12, "width": 768, "patch_size": 32 }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/ViT-B-32.json ================================================ { "embed_dim": 512, "vision_cfg": { "image_size": 224, "layers": 12, "width": 768, "patch_size": 32 }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/model_configs/ViT-L-14.json ================================================ { "embed_dim": 768, "vision_cfg": { "image_size": 224, "layers": 24, "width": 1024, "patch_size": 14 }, "text_cfg": { "context_length": 77, "vocab_size": 49408, "width": 768, "heads": 12, "layers": 12 } } ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/openai.py ================================================ """ OpenAI pretrained model functions Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. """ import os import warnings from typing import Union, List import torch from .model import build_model_from_openai_state_dict from .pretrained import get_pretrained_url, list_pretrained_tag_models, download_pretrained __all__ = ["list_openai_models", "load_openai_model"] def list_openai_models() -> List[str]: """Returns the names of available CLIP models""" return list_pretrained_tag_models('openai') def load_openai_model( name: str, model_cfg, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit=True, cache_dir=os.path.expanduser("~/.cache/clip"), enable_fusion: bool = False, fusion_type: str = 'None' ): """Load a CLIP model, preserve its text pretrained part, and set in the CLAP model Parameters ---------- name : str A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict device : Union[str, torch.device] The device to put the loaded model jit : bool Whether to load the optimized JIT model (default) or more hackable non-JIT model. Returns ------- model : torch.nn.Module The CLAP model preprocess : Callable[[PIL.Image], torch.Tensor] A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input """ if get_pretrained_url(name, 'openai'): model_path = download_pretrained(get_pretrained_url(name, 'openai'), root=cache_dir) elif os.path.isfile(name): model_path = name else: raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") try: # loading JIT archive model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() state_dict = None except RuntimeError: # loading saved state dict if jit: warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") jit = False state_dict = torch.load(model_path, map_location="cpu") if not jit: try: model = build_model_from_openai_state_dict(state_dict or model.state_dict(), model_cfg, enable_fusion, fusion_type).to(device) except KeyError: sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} model = build_model_from_openai_state_dict(sd, model_cfg, enable_fusion, fusion_type).to(device) if str(device) == "cpu": model.float() return model # patch the device names device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] def patch_device(module): try: graphs = [module.graph] if hasattr(module, "graph") else [] except RuntimeError: graphs = [] if hasattr(module, "forward1"): graphs.append(module.forward1.graph) for graph in graphs: for node in graph.findAllNodes("prim::Constant"): if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): node.copyAttributes(device_node) model.apply(patch_device) patch_device(model.encode_audio) patch_device(model.encode_text) # patch dtype to float32 on CPU if str(device) == "cpu": float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] float_node = float_input.node() def patch_float(module): try: graphs = [module.graph] if hasattr(module, "graph") else [] except RuntimeError: graphs = [] if hasattr(module, "forward1"): graphs.append(module.forward1.graph) for graph in graphs: for node in graph.findAllNodes("aten::to"): inputs = list(node.inputs()) for i in [1, 2]: # dtype can be the second or third argument to aten::to() if inputs[i].node()["value"] == 5: inputs[i].node().copyAttributes(float_node) model.apply(patch_float) patch_float(model.encode_audio) patch_float(model.encode_text) model.float() model.audio_branch.audio_length = model.audio_cfg.audio_length return model ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/pann_model.py ================================================ # PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition # Reference from https://github.com/qiuqiangkong/audioset_tagging_cnn # Some layers are re-designed for CLAP import os os.environ['NUMBA_CACHE_DIR'] = '/tmp/' import torch import torch.nn as nn import torch.nn.functional as F from torchlibrosa.stft import Spectrogram, LogmelFilterBank from torchlibrosa.augmentation import SpecAugmentation from .utils import do_mixup, interpolate, pad_framewise_output from .feature_fusion import iAFF, AFF, DAF def init_layer(layer): """Initialize a Linear or Convolutional layer. """ nn.init.xavier_uniform_(layer.weight) if hasattr(layer, 'bias'): if layer.bias is not None: layer.bias.data.fill_(0.) def init_bn(bn): """Initialize a Batchnorm layer. """ bn.bias.data.fill_(0.) bn.weight.data.fill_(1.) class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) self.init_weight() def init_weight(self): init_layer(self.conv1) init_layer(self.conv2) init_bn(self.bn1) init_bn(self.bn2) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = F.relu_(self.bn1(self.conv1(x))) x = F.relu_(self.bn2(self.conv2(x))) if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception('Incorrect argument!') return x class ConvBlock5x5(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock5x5, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.init_weight() def init_weight(self): init_layer(self.conv1) init_bn(self.bn1) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = F.relu_(self.bn1(self.conv1(x))) if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception('Incorrect argument!') return x class AttBlock(nn.Module): def __init__(self, n_in, n_out, activation='linear', temperature=1.): super(AttBlock, self).__init__() self.activation = activation self.temperature = temperature self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) self.bn_att = nn.BatchNorm1d(n_out) self.init_weights() def init_weights(self): init_layer(self.att) init_layer(self.cla) init_bn(self.bn_att) def forward(self, x): # x: (n_samples, n_in, n_time) norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1) cla = self.nonlinear_transform(self.cla(x)) x = torch.sum(norm_att * cla, dim=2) return x, norm_att, cla def nonlinear_transform(self, x): if self.activation == 'linear': return x elif self.activation == 'sigmoid': return torch.sigmoid(x) class Cnn14(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, enable_fusion=False, fusion_type='None'): super(Cnn14, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None self.enable_fusion = enable_fusion self.fusion_type = fusion_type # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) if (self.enable_fusion) and (self.fusion_type == 'channel_map'): self.conv_block1 = ConvBlock(in_channels=4, out_channels=64) else: self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) self.fc1 = nn.Linear(2048, 2048, bias=True) self.fc_audioset = nn.Linear(2048, classes_num, bias=True) if (self.enable_fusion) and (self.fusion_type in ['daf_1d','aff_1d','iaff_1d']): self.mel_conv1d = nn.Sequential( nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2), nn.BatchNorm1d(64) # No Relu ) if self.fusion_type == 'daf_1d': self.fusion_model = DAF() elif self.fusion_type == 'aff_1d': self.fusion_model = AFF(channels=64, type='1D') elif self.fusion_type == 'iaff_1d': self.fusion_model = iAFF(channels=64, type='1D') if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): self.mel_conv2d = nn.Sequential( nn.Conv2d(1, 64, kernel_size=(5,5), stride=(6, 2), padding=(2,2)), nn.BatchNorm2d(64), nn.ReLU(inplace=True) ) if self.fusion_type == 'daf_2d': self.fusion_model = DAF() elif self.fusion_type == 'aff_2d': self.fusion_model = AFF(channels=64, type='2D') elif self.fusion_type == 'iaff_2d': self.fusion_model = iAFF(channels=64, type='2D') self.init_weight() def init_weight(self): init_bn(self.bn0) init_layer(self.fc1) init_layer(self.fc_audioset) def forward(self, input, mixup_lambda=None, device=None): """ Input: (batch_size, data_length)""" if self.enable_fusion and input["longer"].sum() == 0: # if no audio is longer than 10s, then randomly select one audio to be longer input["longer"][torch.randint(0, input["longer"].shape[0], (1,))] = True if not self.enable_fusion: x = self.spectrogram_extractor(input['waveform'].to(device=device, non_blocking=True)) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) else: longer_list = input["longer"].to(device=device, non_blocking=True) x = input["mel_fusion"].to(device=device, non_blocking=True) longer_list_idx = torch.where(longer_list)[0] x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.fusion_type in ['daf_1d','aff_1d','iaff_1d']: new_x = x[:,0:1,:,:].clone().contiguous() # local processing if len(longer_list_idx) > 0: fusion_x_local = x[longer_list_idx,1:,:,:].clone().contiguous() FB,FC,FT,FF = fusion_x_local.size() fusion_x_local = fusion_x_local.view(FB * FC, FT, FF) fusion_x_local = torch.permute(fusion_x_local, (0,2,1)).contiguous() fusion_x_local = self.mel_conv1d(fusion_x_local) fusion_x_local = fusion_x_local.view(FB,FC,FF,fusion_x_local.size(-1)) fusion_x_local = torch.permute(fusion_x_local, (0,2,1,3)).contiguous().flatten(2) if fusion_x_local.size(-1) < FT: fusion_x_local = torch.cat([fusion_x_local, torch.zeros((FB,FF,FT- fusion_x_local.size(-1)), device=device)], dim=-1) else: fusion_x_local = fusion_x_local[:,:,:FT] # 1D fusion new_x = new_x.squeeze(1).permute((0,2,1)).contiguous() new_x[longer_list_idx] = self.fusion_model(new_x[longer_list_idx], fusion_x_local) x = new_x.permute((0,2,1)).contiguous()[:,None,:,:] else: x = new_x elif self.fusion_type in ['daf_2d','aff_2d','iaff_2d','channel_map']: x = x # no change if self.training: x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): global_x = x[:,0:1,:,:] # global processing B, C, H, W = global_x.shape global_x = self.conv_block1(global_x, pool_size=(2, 2), pool_type='avg') if len(longer_list_idx) > 0: local_x = x[longer_list_idx,1:,:,:].contiguous() TH = global_x.size(-2) # local processing B, C, H, W = local_x.shape local_x = local_x.view(B*C,1,H,W) local_x = self.mel_conv2d(local_x) local_x = local_x.view(B,C,local_x.size(1),local_x.size(2),local_x.size(3)) local_x = local_x.permute((0,2,1,3,4)).contiguous().flatten(2,3) TB,TC,_,TW = local_x.size() if local_x.size(-2) < TH: local_x = torch.cat([local_x, torch.zeros((TB,TC,TH-local_x.size(-2),TW), device=global_x.device)], dim=-2) else: local_x = local_x[:,:,:TH,:] global_x[longer_list_idx] = self.fusion_model(global_x[longer_list_idx],local_x) x = global_x else: x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = torch.mean(x, dim=3) latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) latent_x = latent_x1 + latent_x2 latent_x = latent_x.transpose(1, 2) latent_x = F.relu_(self.fc1(latent_x)) latent_output = interpolate(latent_x, 32) (x1, _) = torch.max(x, dim=2) x2 = torch.mean(x, dim=2) x = x1 + x2 x = F.dropout(x, p=0.5, training=self.training) x = F.relu_(self.fc1(x)) embedding = F.dropout(x, p=0.5, training=self.training) clipwise_output = torch.sigmoid(self.fc_audioset(x)) output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} return output_dict class Cnn6(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, enable_fusion=False, fusion_type='None'): super(Cnn6, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None self.enable_fusion = enable_fusion self.fusion_type = fusion_type # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512) self.fc1 = nn.Linear(512, 512, bias=True) self.fc_audioset = nn.Linear(512, classes_num, bias=True) self.init_weight() def init_weight(self): init_bn(self.bn0) init_layer(self.fc1) init_layer(self.fc_audioset) def forward(self, input, mixup_lambda=None, device=None): """ Input: (batch_size, data_length)""" x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = torch.mean(x, dim=3) latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) latent_x = latent_x1 + latent_x2 latent_x = latent_x.transpose(1, 2) latent_x = F.relu_(self.fc1(latent_x)) latent_output = interpolate(latent_x, 16) (x1, _) = torch.max(x, dim=2) x2 = torch.mean(x, dim=2) x = x1 + x2 x = F.dropout(x, p=0.5, training=self.training) x = F.relu_(self.fc1(x)) embedding = F.dropout(x, p=0.5, training=self.training) clipwise_output = torch.sigmoid(self.fc_audioset(x)) output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} return output_dict class Cnn10(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, enable_fusion=False, fusion_type='None'): super(Cnn10, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None self.enable_fusion = enable_fusion self.fusion_type = fusion_type # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) # Spec augmenter self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2) self.bn0 = nn.BatchNorm2d(64) self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) self.fc1 = nn.Linear(1024, 1024, bias=True) self.fc_audioset = nn.Linear(1024, classes_num, bias=True) self.init_weight() def init_weight(self): init_bn(self.bn0) init_layer(self.fc1) init_layer(self.fc_audioset) def forward(self, input, mixup_lambda=None, device=None): """ Input: (batch_size, data_length)""" x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = torch.mean(x, dim=3) latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) latent_x = latent_x1 + latent_x2 latent_x = latent_x.transpose(1, 2) latent_x = F.relu_(self.fc1(latent_x)) latent_output = interpolate(latent_x, 32) (x1, _) = torch.max(x, dim=2) x2 = torch.mean(x, dim=2) x = x1 + x2 x = F.dropout(x, p=0.5, training=self.training) x = F.relu_(self.fc1(x)) embedding = F.dropout(x, p=0.5, training=self.training) clipwise_output = torch.sigmoid(self.fc_audioset(x)) output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} return output_dict def create_pann_model(audio_cfg, enable_fusion=False, fusion_type='None'): try: ModelProto = eval(audio_cfg.model_name) model = ModelProto( sample_rate = audio_cfg.sample_rate, window_size = audio_cfg.window_size, hop_size =audio_cfg.hop_size, mel_bins = audio_cfg.mel_bins, fmin = audio_cfg.fmin, fmax = audio_cfg.fmax, classes_num = audio_cfg.class_num, enable_fusion = enable_fusion, fusion_type = fusion_type ) return model except: raise RuntimeError(f'Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough.') ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/pretrained.py ================================================ import hashlib import os import urllib import warnings from tqdm import tqdm _RN50 = dict( openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" ) _RN50_quickgelu = dict( openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" ) _RN101 = dict( openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" ) _RN101_quickgelu = dict( openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" ) _RN50x4 = dict( openai="https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", ) _RN50x16 = dict( openai="https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", ) _RN50x64 = dict( openai="https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt", ) _VITB32 = dict( openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", ) _VITB32_quickgelu = dict( openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", ) _VITB16 = dict( openai="https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", ) _VITL14 = dict( openai="https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", ) _PRETRAINED = { "RN50": _RN50, "RN50-quickgelu": _RN50_quickgelu, "RN101": _RN101, "RN101-quickgelu": _RN101_quickgelu, "RN50x4": _RN50x4, "RN50x16": _RN50x16, "ViT-B-32": _VITB32, "ViT-B-32-quickgelu": _VITB32_quickgelu, "ViT-B-16": _VITB16, "ViT-L-14": _VITL14, } def list_pretrained(as_str: bool = False): """ returns list of pretrained models Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True """ return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] def list_pretrained_tag_models(tag: str): """ return all models having the specified pretrain tag """ models = [] for k in _PRETRAINED.keys(): if tag in _PRETRAINED[k]: models.append(k) return models def list_pretrained_model_tags(model: str): """ return all pretrain tags for the specified model architecture """ tags = [] if model in _PRETRAINED: tags.extend(_PRETRAINED[model].keys()) return tags def get_pretrained_url(model: str, tag: str): if model not in _PRETRAINED: return '' model_pretrained = _PRETRAINED[model] if tag not in model_pretrained: return '' return model_pretrained[tag] def download_pretrained(url: str, root: str = os.path.expanduser("~/.cache/clip")): os.makedirs(root, exist_ok=True) filename = os.path.basename(url) if 'openaipublic' in url: expected_sha256 = url.split("/")[-2] else: expected_sha256 = '' download_target = os.path.join(root, filename) if os.path.exists(download_target) and not os.path.isfile(download_target): raise RuntimeError(f"{download_target} exists and is not a regular file") if os.path.isfile(download_target): if expected_sha256: if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: return download_target else: warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") else: return download_target with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: while True: buffer = source.read(8192) if not buffer: break output.write(buffer) loop.update(len(buffer)) if expected_sha256 and hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") return download_target ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/timm_model.py ================================================ """ timm model adapter Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. """ from collections import OrderedDict import torch.nn as nn try: import timm from timm.models.layers import Mlp, to_2tuple from timm.models.layers.attention_pool2d import RotAttentionPool2d from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d except ImportError as e: timm = None from .utils import freeze_batch_norm_2d class TimmModel(nn.Module): """ timm model adapter # FIXME this adapter is a work in progress, may change in ways that break weight compat """ def __init__( self, model_name, embed_dim, image_size=224, pool='avg', proj='linear', drop=0., pretrained=False): super().__init__() if timm is None: raise RuntimeError("Please `pip install timm` to use timm models.") self.image_size = to_2tuple(image_size) self.trunk = timm.create_model(model_name, pretrained=pretrained) feat_size = self.trunk.default_cfg.get('pool_size', None) feature_ndim = 1 if not feat_size else 2 if pool in ('abs_attn', 'rot_attn'): assert feature_ndim == 2 # if attn pooling used, remove both classifier and default pool self.trunk.reset_classifier(0, global_pool='') else: # reset global pool if pool config set, otherwise leave as network default reset_kwargs = dict(global_pool=pool) if pool else {} self.trunk.reset_classifier(0, **reset_kwargs) prev_chs = self.trunk.num_features head_layers = OrderedDict() if pool == 'abs_attn': head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) prev_chs = embed_dim elif pool == 'rot_attn': head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) prev_chs = embed_dim else: assert proj, 'projection layer needed if non-attention pooling is used.' # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used if proj == 'linear': head_layers['drop'] = nn.Dropout(drop) head_layers['proj'] = nn.Linear(prev_chs, embed_dim) elif proj == 'mlp': head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop) self.head = nn.Sequential(head_layers) def lock(self, unlocked_groups=0, freeze_bn_stats=False): """ lock modules Args: unlocked_groups (int): leave last n layer groups unlocked (default: 0) """ if not unlocked_groups: # lock full model for param in self.trunk.parameters(): param.requires_grad = False if freeze_bn_stats: freeze_batch_norm_2d(self.trunk) else: # NOTE: partial freeze requires latest timm (master) branch and is subject to change try: # FIXME import here until API stable and in an official release from timm.models.helpers import group_parameters, group_modules except ImportError: raise RuntimeError( 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') matcher = self.trunk.group_matcher() gparams = group_parameters(self.trunk, matcher) max_layer_id = max(gparams.keys()) max_layer_id = max_layer_id - unlocked_groups for group_idx in range(max_layer_id + 1): group = gparams[group_idx] for param in group: self.trunk.get_parameter(param).requires_grad = False if freeze_bn_stats: gmodules = group_modules(self.trunk, matcher, reverse=True) gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} freeze_batch_norm_2d(self.trunk, gmodules) def forward(self, x): x = self.trunk(x) x = self.head(x) return x ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/tokenizer.py ================================================ """ CLIP tokenizer Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. """ import gzip import html import os from functools import lru_cache from typing import Union, List import ftfy import regex as re import torch @lru_cache() def default_bpe(): return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a signficant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. """ bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8+n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() def whitespace_clean(text): text = re.sub(r'\s+', ' ', text) text = text.strip() return text class SimpleTokenizer(object): def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') merges = merges[1:49152-256-2+1] merges = [tuple(merge.split()) for merge in merges] vocab = list(bytes_to_unicode().values()) vocab = vocab + [v+'' for v in vocab] for merge in merges: vocab.append(''.join(merge)) if not special_tokens: special_tokens = ['', ''] else: special_tokens = ['', ''] + special_tokens vocab.extend(special_tokens) self.encoder = dict(zip(vocab, range(len(vocab)))) self.decoder = {v: k for k, v in self.encoder.items()} self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {t:t for t in special_tokens} special = "|".join(special_tokens) self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) self.vocab_size = len(self.encoder) self.all_special_ids = [self.encoder[t] for t in special_tokens] def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token[:-1]) + ( token[-1] + '',) pairs = get_pairs(word) if not pairs: return token+'' while True: bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) new_word.extend(word[i:j]) i = j except: new_word.extend(word[i:]) break if word[i] == first and i < len(word)-1 and word[i+1] == second: new_word.append(first+second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = ' '.join(word) self.cache[token] = word return word def encode(self, text): bpe_tokens = [] text = whitespace_clean(basic_clean(text)).lower() for token in re.findall(self.pat, text): token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) return bpe_tokens def decode(self, tokens): text = ''.join([self.decoder[token] for token in tokens]) text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') return text _tokenizer = SimpleTokenizer() def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: """ Returns the tokenized representation of given input string(s) Parameters ---------- texts : Union[str, List[str]] An input string or a list of input strings to tokenize context_length : int The context length to use; all CLIP models use 77 as the context length Returns ------- A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] """ if isinstance(texts, str): texts = [texts] sot_token = _tokenizer.encoder[""] eot_token = _tokenizer.encoder[""] all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) for i, tokens in enumerate(all_tokens): if len(tokens) > context_length: tokens = tokens[:context_length] # Truncate result[i, :len(tokens)] = torch.tensor(tokens) return result ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/transform.py ================================================ from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ CenterCrop def _convert_to_rgb(image): return image.convert('RGB') def image_transform( image_size: int, is_train: bool, mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711) ): normalize = Normalize(mean=mean, std=std) if is_train: return Compose([ RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC), _convert_to_rgb, ToTensor(), normalize, ]) else: return Compose([ Resize(image_size, interpolation=InterpolationMode.BICUBIC), CenterCrop(image_size), _convert_to_rgb, ToTensor(), normalize, ]) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/utils.py ================================================ import numpy as np import torch from torch import nn as nn from torchvision.ops.misc import FrozenBatchNorm2d import logging import h5py from tqdm import tqdm import random import json import os import pathlib # TODO: (yusong) this not a good place to store those information and does not scale. Need to be fixed later. dataset_split = { "audiocaps": ["train", "valid", "test"], "audioset": ["balanced_train", "unbalanced_train", "eval"], "BBCSoundEffects": ["train", "test"], "Clotho": ["train", "test", "valid"], "free_to_use_sounds": ["train", "test"], "paramount_motion": ["train", "test"], "sonniss_game_effects": ["train", "test"], "wesoundeffects": ["train", "test"], "MACS": ["train", "test"], "freesound": ["train", "test"], "FSD50K": ["train", "test", "valid"], "fsd50k_class_label": ["train", "test", "valid"], "esc50": ["train", "test"], "audiostock": ["train", "test"], "freesound_no_overlap_noesc50": ["train", "test"], "epidemic_sound_effects": ["train", "test"], "VGGSound": ["train", "test"], "urbansound8k_class_label": ["train", "test"], "audioset_t5": ["balanced_train", "unbalanced_train", "eval"], "epidemic_sound_effects_t5": ["train", "test"], "WavText5K": ["train", "test"], "esc50_no_overlap": ["train", "test"], "usd8k_no_overlap": ["train", "test"], "fsd50k_200_class_label": ["train", "test", "valid"] } def freeze_batch_norm_2d(module, module_match={}, name=""): """ Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and returned. Otherwise, the module is walked recursively and submodules are converted in place. Args: module (torch.nn.Module): Any PyTorch module. module_match (dict): Dictionary of full module names to freeze (all if empty) name (str): Full module name (prefix) Returns: torch.nn.Module: Resulting module Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 """ res = module is_match = True if module_match: is_match = name in module_match if is_match and isinstance( module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm) ): res = FrozenBatchNorm2d(module.num_features) res.num_features = module.num_features res.affine = module.affine if module.affine: res.weight.data = module.weight.data.clone().detach() res.bias.data = module.bias.data.clone().detach() res.running_mean.data = module.running_mean.data res.running_var.data = module.running_var.data res.eps = module.eps else: for child_name, child in module.named_children(): full_child_name = ".".join([name, child_name]) if name else child_name new_child = freeze_batch_norm_2d(child, module_match, full_child_name) if new_child is not child: res.add_module(child_name, new_child) return res def exist(dataset_name, dataset_type): """ Check if dataset exists """ if dataset_type in dataset_split[dataset_name]: return True else: return False def get_tar_path_from_dataset_name( dataset_names, dataset_types, islocal, dataset_path, proportion=1, full_dataset=None ): """ Get tar path from dataset name and type """ output = [] for n in dataset_names: if full_dataset is not None and n in full_dataset: current_dataset_types = dataset_split[n] else: current_dataset_types = dataset_types for s in current_dataset_types: tmp = [] if islocal: sizefilepath_ = f"{dataset_path}/{n}/{s}/sizes.json" if not os.path.exists(sizefilepath_): sizefilepath_ = f"./json_files/{n}/{s}/sizes.json" else: sizefilepath_ = f"./json_files/{n}/{s}/sizes.json" if not os.path.exists(sizefilepath_): continue sizes = json.load(open(sizefilepath_, "r")) for k in sizes.keys(): if islocal: tmp.append(f"{dataset_path}/{n}/{s}/{k}") else: tmp.append( f"pipe:aws s3 --cli-connect-timeout 0 cp s3://s-laion-audio/webdataset_tar/{n}/{s}/{k} -" ) if proportion != 1: tmp = random.sample(tmp, int(proportion * len(tmp))) output.append(tmp) return sum(output, []) def get_tar_path_from_txts(txt_path, islocal, proportion=1): """ Get tar path from txt path """ if isinstance(txt_path, (list, tuple)): return sum( [ get_tar_path_from_txts( txt_path[i], islocal=islocal, proportion=proportion ) for i in range(len(txt_path)) ], [], ) if isinstance(txt_path, str): with open(txt_path) as f: lines = f.readlines() if islocal: lines = [ lines[i] .split("\n")[0] .replace("pipe:aws s3 cp s3://s-laion-audio/", "/mnt/audio_clip/") for i in range(len(lines)) ] else: lines = [ lines[i].split("\n")[0].replace(".tar", ".tar -") for i in range(len(lines)) ] if proportion != 1: print("Sampling tars with proportion of {}".format(proportion)) lines = random.sample(lines, int(proportion * len(lines))) return lines def get_mix_lambda(mixup_alpha, batch_size): mixup_lambdas = [ np.random.beta(mixup_alpha, mixup_alpha, 1)[0] for _ in range(batch_size) ] return np.array(mixup_lambdas).astype(np.float32) def do_mixup(x, mixup_lambda): """ Args: x: (batch_size , ...) mixup_lambda: (batch_size,) Returns: out: (batch_size, ...) """ out = ( x.transpose(0, -1) * mixup_lambda + torch.flip(x, dims=[0]).transpose(0, -1) * (1 - mixup_lambda) ).transpose(0, -1) return out def interpolate(x, ratio): """Interpolate data in time domain. This is used to compensate the resolution reduction in downsampling of a CNN. Args: x: (batch_size, time_steps, classes_num) ratio: int, ratio to interpolate Returns: upsampled: (batch_size, time_steps * ratio, classes_num) """ (batch_size, time_steps, classes_num) = x.shape upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1) upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num) return upsampled def pad_framewise_output(framewise_output, frames_num): """Pad framewise_output to the same length as input frames. The pad value is the same as the value of the last frame. Args: framewise_output: (batch_size, frames_num, classes_num) frames_num: int, number of frames to pad Outputs: output: (batch_size, frames_num, classes_num) """ pad = framewise_output[:, -1:, :].repeat( 1, frames_num - framewise_output.shape[1], 1 ) """tensor for padding""" output = torch.cat((framewise_output, pad), dim=1) """(batch_size, frames_num, classes_num)""" def process_ipc(index_path, classes_num, filename): # load data logging.info("Load Data...............") ipc = [[] for _ in range(classes_num)] with h5py.File(index_path, "r") as f: for i in tqdm(range(len(f["target"]))): t_class = np.where(f["target"][i])[0] for t in t_class: ipc[t].append(i) print(ipc) np.save(filename, ipc) logging.info("Load Data Succeed...............") def save_to_dict(s, o_={}): sp = s.split(": ") o_.update({sp[0]: float(sp[1])}) return o_ def get_data_from_log(txt_path): """ Output dictionary from out.txt log file """ with open(txt_path) as f: lines = f.readlines() val_data = {} train_data = {} train_losses = [] train_losses_epoch = [] for i in range(len(lines)): if "| INFO |" in lines[i]: if "Eval Epoch" in lines[i]: if "val_loss" in lines[i]: # float(regex.sub("", lines[310].split(" ")[-1]).replace(" ", "")) line = lines[i].split("Eval Epoch: ")[-1] num_epoch = int(line.split(" ")[0].split(" ")[0]) d = { line.split(" ")[0] .split(" ")[1] .replace(":", ""): float(line.split(" ")[0].split(" ")[-1]) } for i in range(1, len(line.split(" "))): d = save_to_dict(line.split(" ")[i], d) val_data[num_epoch] = d elif "Train Epoch" in lines[i]: num_epoch = int(lines[i].split("Train Epoch: ")[1][0]) loss = float(lines[i].split("Loss: ")[-1].split(" (")[0]) train_losses.append(loss) train_losses_epoch.append(num_epoch) for i in range(len(train_losses)): train_data[i] = { "num_epoch": train_losses_epoch[i], "train_loss": train_losses[i], } return train_data, val_data def save_p(obj, filename): import pickle try: from deepdiff import DeepDiff except: os.system("pip install deepdiff") from deepdiff import DeepDiff with open(filename, "wb") as file: pickle.dump(obj, file, protocol=pickle.HIGHEST_PROTOCOL) # highest protocol with open(filename, "rb") as file: z = pickle.load(file) assert ( DeepDiff(obj, z, ignore_string_case=True) == {} ), "there is something wrong with the saving process" return def load_p(filename): import pickle with open(filename, "rb") as file: z = pickle.load(file) return z def save_json(data, name="data.json"): import json with open(name, 'w') as fp: json.dump(data, fp) return def load_json(name): import json with open(name, 'r') as fp: data = json.load(fp) return data from multiprocessing import Process, Manager from multiprocessing import Process, Value, Array from ctypes import c_wchar def load_class_label(path): # https://stackoverflow.com/questions/48004243/how-to-share-large-read-only-dictionary-list-across-processes-in-multiprocessing # https://stackoverflow.com/questions/45693949/storing-strings-in-a-multiprocessing-sharedctypes-array out = None if path is not None: if pathlib.Path(path).suffix in [".pkl", ".pickle"]: out = load_p(path) elif pathlib.Path(path).suffix in [".json", ".txt"]: out = load_json(path) elif pathlib.Path(path).suffix in [".npy", ".npz"]: out = np.load(path) elif pathlib.Path(path).suffix in [".csv"]: import pandas as pd out = pd.read_csv(path) return out # if out is None: # return None # else: # key = Array(c_wchar, '\n'.join(list(out.keys())), lock=False) # val = Array('i', out.values(), lock=False) # return (key, val) from torch import optim def get_optimizer(params, lr, betas, eps, momentum, optimizer_name): if optimizer_name.lower() == "adamw": optimizer = optim.AdamW( params, lr=lr, betas=betas, eps=eps ) elif optimizer_name.lower() == "sgd": optimizer = optim.SGD( params, lr=lr, momentum=momentum ) elif optimizer_name.lower() == "adam": optimizer = optim.Adam( params, lr=lr, betas=betas, eps=eps ) else: raise ValueError("optimizer name is not correct") return optimizer ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/version.py ================================================ __version__ = '0.2.1' ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/image_degradation/__init__.py ================================================ from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/image_degradation/bsrgan.py ================================================ # -*- coding: utf-8 -*- """ # -------------------------------------------- # Super-Resolution # -------------------------------------------- # # Kai Zhang (cskaizhang@gmail.com) # https://github.com/cszn # From 2019/03--2021/08 # -------------------------------------------- """ import numpy as np import cv2 import torch from functools import partial import random from scipy import ndimage import scipy import scipy.stats as ss from scipy.interpolate import interp2d from scipy.linalg import orth import albumentations import ldm.modules.image_degradation.utils_image as util def modcrop_np(img, sf): ''' Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image ''' w, h = img.shape[:2] im = np.copy(img) return im[:w - w % sf, :h - h % sf, ...] """ # -------------------------------------------- # anisotropic Gaussian kernels # -------------------------------------------- """ def analytic_kernel(k): """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" k_size = k.shape[0] # Calculate the big kernels size big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) # Loop over the small kernel to fill the big one for r in range(k_size): for c in range(k_size): big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k # Crop the edges of the big kernel to ignore very small values and increase run time of SR crop = k_size // 2 cropped_big_k = big_k[crop:-crop, crop:-crop] # Normalize to 1 return cropped_big_k / cropped_big_k.sum() def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): """ generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1 : [0.1,50], scaling of eigenvalues l2 : [0.1,l1], scaling of eigenvalues If l1 = l2, will get an isotropic Gaussian kernel. Returns: k : kernel """ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) V = np.array([[v[0], v[1]], [v[1], -v[0]]]) D = np.array([[l1, 0], [0, l2]]) Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) return k def gm_blur_kernel(mean, cov, size=15): center = size / 2.0 + 0.5 k = np.zeros([size, size]) for y in range(size): for x in range(size): cy = y - center + 1 cx = x - center + 1 k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) k = k / np.sum(k) return k def shift_pixel(x, sf, upper_left=True): """shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift direction """ h, w = x.shape[:2] shift = (sf - 1) * 0.5 xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) if upper_left: x1 = xv + shift y1 = yv + shift else: x1 = xv - shift y1 = yv - shift x1 = np.clip(x1, 0, w - 1) y1 = np.clip(y1, 0, h - 1) if x.ndim == 2: x = interp2d(xv, yv, x)(x1, y1) if x.ndim == 3: for i in range(x.shape[-1]): x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) return x def blur(x, k): ''' x: image, NxcxHxW k: kernel, Nx1xhxw ''' n, c = x.shape[:2] p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') k = k.repeat(1, c, 1, 1) k = k.view(-1, 1, k.shape[2], k.shape[3]) x = x.view(1, -1, x.shape[2], x.shape[3]) x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) x = x.view(n, c, x.shape[2], x.shape[3]) return x def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): """" # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var # max_var = 2.5 * sf """ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix lambda_1 = min_var + np.random.rand() * (max_var - min_var) lambda_2 = min_var + np.random.rand() * (max_var - min_var) theta = np.random.rand() * np.pi # random theta noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 # Set COV matrix using Lambdas and Theta LAMBDA = np.diag([lambda_1, lambda_2]) Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) SIGMA = Q @ LAMBDA @ Q.T INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] # Set expectation position (shifting kernel for aligned image) MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) MU = MU[None, None, :, None] # Create meshgrid for Gaussian [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) Z = np.stack([X, Y], 2)[:, :, :, None] # Calcualte Gaussian for every pixel of the kernel ZZ = Z - MU ZZ_t = ZZ.transpose(0, 1, 3, 2) raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) # shift the kernel so it will be centered # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) # Normalize the kernel and return # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) kernel = raw_kernel / np.sum(raw_kernel) return kernel def fspecial_gaussian(hsize, sigma): hsize = [hsize, hsize] siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] std = sigma [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) arg = -(x * x + y * y) / (2 * std * std) h = np.exp(arg) h[h < scipy.finfo(float).eps * h.max()] = 0 sumh = h.sum() if sumh != 0: h = h / sumh return h def fspecial_laplacian(alpha): alpha = max([0, min([alpha, 1])]) h1 = alpha / (alpha + 1) h2 = (1 - alpha) / (alpha + 1) h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] h = np.array(h) return h def fspecial(filter_type, *args, **kwargs): ''' python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py ''' if filter_type == 'gaussian': return fspecial_gaussian(*args, **kwargs) if filter_type == 'laplacian': return fspecial_laplacian(*args, **kwargs) """ # -------------------------------------------- # degradation models # -------------------------------------------- """ def bicubic_degradation(x, sf=3): ''' Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image ''' x = util.imresize_np(x, scale=1 / sf) return x def srmd_degradation(x, k, sf=3): ''' blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} } ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' x = bicubic_degradation(x, sf=sf) return x def dpsr_degradation(x, k, sf=3): ''' bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} } ''' x = bicubic_degradation(x, sf=sf) x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') return x def classical_degradation(x, k, sf=3): ''' blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) st = 0 return x[st::sf, st::sf, ...] def add_sharpening(img, weight=0.5, radius=50, threshold=10): """USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * K + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): """ if radius % 2 == 0: radius += 1 blur = cv2.GaussianBlur(img, (radius, radius), 0) residual = img - blur mask = np.abs(residual) * 255 > threshold mask = mask.astype('float32') soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) K = img + weight * residual K = np.clip(K, 0, 1) return soft_mask * K + (1 - soft_mask) * img def add_blur(img, sf=4): wd2 = 4.0 + sf wd = 2.0 + 0.2 * sf if random.random() < 0.5: l1 = wd2 * random.random() l2 = wd2 * random.random() k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) else: k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') return img def add_resize(img, sf=4): rnum = np.random.rand() if rnum > 0.8: # up sf1 = random.uniform(1, 2) elif rnum < 0.7: # down sf1 = random.uniform(0.5 / sf, 1) else: sf1 = 1.0 img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) return img # def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): # noise_level = random.randint(noise_level1, noise_level2) # rnum = np.random.rand() # if rnum > 0.6: # add color Gaussian noise # img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) # elif rnum < 0.4: # add grayscale Gaussian noise # img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) # else: # add noise # L = noise_level2 / 255. # D = np.diag(np.random.rand(3)) # U = orth(np.random.rand(3, 3)) # conv = np.dot(np.dot(np.transpose(U), D), U) # img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) # img = np.clip(img, 0.0, 1.0) # return img def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) rnum = np.random.rand() if rnum > 0.6: # add color Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: # add grayscale Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: # add noise L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_speckle_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) img = np.clip(img, 0.0, 1.0) rnum = random.random() if rnum > 0.6: img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_Poisson_noise(img): img = np.clip((img * 255.0).round(), 0, 255) / 255. vals = 10 ** (2 * random.random() + 2.0) # [2, 4] if random.random() < 0.5: img = np.random.poisson(img * vals).astype(np.float32) / vals else: img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray img += noise_gray[:, :, np.newaxis] img = np.clip(img, 0.0, 1.0) return img def add_JPEG_noise(img): quality_factor = random.randint(30, 95) img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) img = cv2.imdecode(encimg, 1) img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) return img def random_crop(lq, hq, sf=4, lq_patchsize=64): h, w = lq.shape[:2] rnd_h = random.randint(0, h - lq_patchsize) rnd_w = random.randint(0, w - lq_patchsize) lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] return lq, hq def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') hq = img.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: img = util.imresize_np(img, 1 / 2, True) img = np.clip(img, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_blur(img, sf=sf) elif i == 2: a, b = img.shape[1], img.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') img = img[0::sf, 0::sf, ...] # nearest downsampling img = np.clip(img, 0.0, 1.0) elif i == 3: # downsample3 img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) elif i == 4: # add Gaussian noise img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: img = add_JPEG_noise(img) elif i == 6: # add processed camera sensor noise if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf_ori, lq_patchsize) return img, hq # todo no isp_model? def degradation_bsrgan_variant(image, sf=4, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ image = util.uint2single(image) isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = image.shape[:2] image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = image.shape[:2] hq = image.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: image = util.imresize_np(image, 1 / 2, True) image = np.clip(image, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: image = add_blur(image, sf=sf) elif i == 1: image = add_blur(image, sf=sf) elif i == 2: a, b = image.shape[1], image.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') image = image[0::sf, 0::sf, ...] # nearest downsampling image = np.clip(image, 0.0, 1.0) elif i == 3: # downsample3 image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) image = np.clip(image, 0.0, 1.0) elif i == 4: # add Gaussian noise image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: image = add_JPEG_noise(image) # elif i == 6: # # add processed camera sensor noise # if random.random() < isp_prob and isp_model is not None: # with torch.no_grad(): # img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise image = add_JPEG_noise(image) image = util.single2uint(image) example = {"image":image} return example # TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): """ This is an extended degradation model by combining the degradation models of BSRGAN and Real-ESRGAN ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor use_shuffle: the degradation shuffle use_sharp: sharpening the img Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') if use_sharp: img = add_sharpening(img) hq = img.copy() if random.random() < shuffle_prob: shuffle_order = random.sample(range(13), 13) else: shuffle_order = list(range(13)) # local shuffle for noise, JPEG is always the last one shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_resize(img, sf=sf) elif i == 2: img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 3: if random.random() < poisson_prob: img = add_Poisson_noise(img) elif i == 4: if random.random() < speckle_prob: img = add_speckle_noise(img) elif i == 5: if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) elif i == 6: img = add_JPEG_noise(img) elif i == 7: img = add_blur(img, sf=sf) elif i == 8: img = add_resize(img, sf=sf) elif i == 9: img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 10: if random.random() < poisson_prob: img = add_Poisson_noise(img) elif i == 11: if random.random() < speckle_prob: img = add_speckle_noise(img) elif i == 12: if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) else: print('check the shuffle!') # resize to desired size img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), interpolation=random.choice([1, 2, 3])) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf, lq_patchsize) return img, hq if __name__ == '__main__': print("hey") img = util.imread_uint('utils/test.png', 3) print(img) img = util.uint2single(img) print(img) img = img[:448, :448] h = img.shape[0] // 4 print("resizing to", h) sf = 4 deg_fn = partial(degradation_bsrgan_variant, sf=sf) for i in range(20): print(i) img_lq = deg_fn(img) print(img_lq) img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] print(img_lq.shape) print("bicubic", img_lq_bicubic.shape) print(img_hq.shape) lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) util.imsave(img_concat, str(i) + '.png') ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/image_degradation/bsrgan_light.py ================================================ # -*- coding: utf-8 -*- import numpy as np import cv2 import torch from functools import partial import random from scipy import ndimage import scipy import scipy.stats as ss from scipy.interpolate import interp2d from scipy.linalg import orth import albumentations import ldm.modules.image_degradation.utils_image as util """ # -------------------------------------------- # Super-Resolution # -------------------------------------------- # # Kai Zhang (cskaizhang@gmail.com) # https://github.com/cszn # From 2019/03--2021/08 # -------------------------------------------- """ def modcrop_np(img, sf): ''' Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image ''' w, h = img.shape[:2] im = np.copy(img) return im[:w - w % sf, :h - h % sf, ...] """ # -------------------------------------------- # anisotropic Gaussian kernels # -------------------------------------------- """ def analytic_kernel(k): """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" k_size = k.shape[0] # Calculate the big kernels size big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) # Loop over the small kernel to fill the big one for r in range(k_size): for c in range(k_size): big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k # Crop the edges of the big kernel to ignore very small values and increase run time of SR crop = k_size // 2 cropped_big_k = big_k[crop:-crop, crop:-crop] # Normalize to 1 return cropped_big_k / cropped_big_k.sum() def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): """ generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1 : [0.1,50], scaling of eigenvalues l2 : [0.1,l1], scaling of eigenvalues If l1 = l2, will get an isotropic Gaussian kernel. Returns: k : kernel """ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) V = np.array([[v[0], v[1]], [v[1], -v[0]]]) D = np.array([[l1, 0], [0, l2]]) Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) return k def gm_blur_kernel(mean, cov, size=15): center = size / 2.0 + 0.5 k = np.zeros([size, size]) for y in range(size): for x in range(size): cy = y - center + 1 cx = x - center + 1 k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) k = k / np.sum(k) return k def shift_pixel(x, sf, upper_left=True): """shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift direction """ h, w = x.shape[:2] shift = (sf - 1) * 0.5 xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) if upper_left: x1 = xv + shift y1 = yv + shift else: x1 = xv - shift y1 = yv - shift x1 = np.clip(x1, 0, w - 1) y1 = np.clip(y1, 0, h - 1) if x.ndim == 2: x = interp2d(xv, yv, x)(x1, y1) if x.ndim == 3: for i in range(x.shape[-1]): x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) return x def blur(x, k): ''' x: image, NxcxHxW k: kernel, Nx1xhxw ''' n, c = x.shape[:2] p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') k = k.repeat(1, c, 1, 1) k = k.view(-1, 1, k.shape[2], k.shape[3]) x = x.view(1, -1, x.shape[2], x.shape[3]) x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) x = x.view(n, c, x.shape[2], x.shape[3]) return x def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): """" # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var # max_var = 2.5 * sf """ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix lambda_1 = min_var + np.random.rand() * (max_var - min_var) lambda_2 = min_var + np.random.rand() * (max_var - min_var) theta = np.random.rand() * np.pi # random theta noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 # Set COV matrix using Lambdas and Theta LAMBDA = np.diag([lambda_1, lambda_2]) Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) SIGMA = Q @ LAMBDA @ Q.T INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] # Set expectation position (shifting kernel for aligned image) MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) MU = MU[None, None, :, None] # Create meshgrid for Gaussian [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) Z = np.stack([X, Y], 2)[:, :, :, None] # Calcualte Gaussian for every pixel of the kernel ZZ = Z - MU ZZ_t = ZZ.transpose(0, 1, 3, 2) raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) # shift the kernel so it will be centered # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) # Normalize the kernel and return # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) kernel = raw_kernel / np.sum(raw_kernel) return kernel def fspecial_gaussian(hsize, sigma): hsize = [hsize, hsize] siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] std = sigma [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) arg = -(x * x + y * y) / (2 * std * std) h = np.exp(arg) h[h < scipy.finfo(float).eps * h.max()] = 0 sumh = h.sum() if sumh != 0: h = h / sumh return h def fspecial_laplacian(alpha): alpha = max([0, min([alpha, 1])]) h1 = alpha / (alpha + 1) h2 = (1 - alpha) / (alpha + 1) h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] h = np.array(h) return h def fspecial(filter_type, *args, **kwargs): ''' python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py ''' if filter_type == 'gaussian': return fspecial_gaussian(*args, **kwargs) if filter_type == 'laplacian': return fspecial_laplacian(*args, **kwargs) """ # -------------------------------------------- # degradation models # -------------------------------------------- """ def bicubic_degradation(x, sf=3): ''' Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image ''' x = util.imresize_np(x, scale=1 / sf) return x def srmd_degradation(x, k, sf=3): ''' blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} } ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' x = bicubic_degradation(x, sf=sf) return x def dpsr_degradation(x, k, sf=3): ''' bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} } ''' x = bicubic_degradation(x, sf=sf) x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') return x def classical_degradation(x, k, sf=3): ''' blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) st = 0 return x[st::sf, st::sf, ...] def add_sharpening(img, weight=0.5, radius=50, threshold=10): """USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * K + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): """ if radius % 2 == 0: radius += 1 blur = cv2.GaussianBlur(img, (radius, radius), 0) residual = img - blur mask = np.abs(residual) * 255 > threshold mask = mask.astype('float32') soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) K = img + weight * residual K = np.clip(K, 0, 1) return soft_mask * K + (1 - soft_mask) * img def add_blur(img, sf=4): wd2 = 4.0 + sf wd = 2.0 + 0.2 * sf wd2 = wd2/4 wd = wd/4 if random.random() < 0.5: l1 = wd2 * random.random() l2 = wd2 * random.random() k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) else: k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random()) img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') return img def add_resize(img, sf=4): rnum = np.random.rand() if rnum > 0.8: # up sf1 = random.uniform(1, 2) elif rnum < 0.7: # down sf1 = random.uniform(0.5 / sf, 1) else: sf1 = 1.0 img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) return img # def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): # noise_level = random.randint(noise_level1, noise_level2) # rnum = np.random.rand() # if rnum > 0.6: # add color Gaussian noise # img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) # elif rnum < 0.4: # add grayscale Gaussian noise # img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) # else: # add noise # L = noise_level2 / 255. # D = np.diag(np.random.rand(3)) # U = orth(np.random.rand(3, 3)) # conv = np.dot(np.dot(np.transpose(U), D), U) # img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) # img = np.clip(img, 0.0, 1.0) # return img def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) rnum = np.random.rand() if rnum > 0.6: # add color Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: # add grayscale Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: # add noise L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_speckle_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) img = np.clip(img, 0.0, 1.0) rnum = random.random() if rnum > 0.6: img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_Poisson_noise(img): img = np.clip((img * 255.0).round(), 0, 255) / 255. vals = 10 ** (2 * random.random() + 2.0) # [2, 4] if random.random() < 0.5: img = np.random.poisson(img * vals).astype(np.float32) / vals else: img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray img += noise_gray[:, :, np.newaxis] img = np.clip(img, 0.0, 1.0) return img def add_JPEG_noise(img): quality_factor = random.randint(80, 95) img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) img = cv2.imdecode(encimg, 1) img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) return img def random_crop(lq, hq, sf=4, lq_patchsize=64): h, w = lq.shape[:2] rnd_h = random.randint(0, h - lq_patchsize) rnd_w = random.randint(0, w - lq_patchsize) lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] return lq, hq def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') hq = img.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: img = util.imresize_np(img, 1 / 2, True) img = np.clip(img, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_blur(img, sf=sf) elif i == 2: a, b = img.shape[1], img.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') img = img[0::sf, 0::sf, ...] # nearest downsampling img = np.clip(img, 0.0, 1.0) elif i == 3: # downsample3 img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) elif i == 4: # add Gaussian noise img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: img = add_JPEG_noise(img) elif i == 6: # add processed camera sensor noise if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf_ori, lq_patchsize) return img, hq # todo no isp_model? def degradation_bsrgan_variant(image, sf=4, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ image = util.uint2single(image) isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = image.shape[:2] image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = image.shape[:2] hq = image.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: image = util.imresize_np(image, 1 / 2, True) image = np.clip(image, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: image = add_blur(image, sf=sf) # elif i == 1: # image = add_blur(image, sf=sf) if i == 0: pass elif i == 2: a, b = image.shape[1], image.shape[0] # downsample2 if random.random() < 0.8: sf1 = random.uniform(1, 2 * sf) image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') image = image[0::sf, 0::sf, ...] # nearest downsampling image = np.clip(image, 0.0, 1.0) elif i == 3: # downsample3 image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) image = np.clip(image, 0.0, 1.0) elif i == 4: # add Gaussian noise image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: image = add_JPEG_noise(image) # # elif i == 6: # # add processed camera sensor noise # if random.random() < isp_prob and isp_model is not None: # with torch.no_grad(): # img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise image = add_JPEG_noise(image) image = util.single2uint(image) example = {"image": image} return example if __name__ == '__main__': print("hey") img = util.imread_uint('utils/test.png', 3) img = img[:448, :448] h = img.shape[0] // 4 print("resizing to", h) sf = 4 deg_fn = partial(degradation_bsrgan_variant, sf=sf) for i in range(20): print(i) img_hq = img img_lq = deg_fn(img)["image"] img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) print(img_lq) img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"] print(img_lq.shape) print("bicubic", img_lq_bicubic.shape) print(img_hq.shape) lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) util.imsave(img_concat, str(i) + '.png') ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/image_degradation/utils_image.py ================================================ import os import math import random import numpy as np import torch import cv2 from torchvision.utils import make_grid from datetime import datetime #import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" ''' # -------------------------------------------- # Kai Zhang (github: https://github.com/cszn) # 03/Mar/2019 # -------------------------------------------- # https://github.com/twhui/SRGAN-pyTorch # https://github.com/xinntao/BasicSR # -------------------------------------------- ''' IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] def is_image_file(filename): return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) def get_timestamp(): return datetime.now().strftime('%y%m%d-%H%M%S') def imshow(x, title=None, cbar=False, figsize=None): plt.figure(figsize=figsize) plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') if title: plt.title(title) if cbar: plt.colorbar() plt.show() def surf(Z, cmap='rainbow', figsize=None): plt.figure(figsize=figsize) ax3 = plt.axes(projection='3d') w, h = Z.shape[:2] xx = np.arange(0,w,1) yy = np.arange(0,h,1) X, Y = np.meshgrid(xx, yy) ax3.plot_surface(X,Y,Z,cmap=cmap) #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) plt.show() ''' # -------------------------------------------- # get image pathes # -------------------------------------------- ''' def get_image_paths(dataroot): paths = None # return None if dataroot is None if dataroot is not None: paths = sorted(_get_paths_from_images(dataroot)) return paths def _get_paths_from_images(path): assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) images = [] for dirpath, _, fnames in sorted(os.walk(path)): for fname in sorted(fnames): if is_image_file(fname): img_path = os.path.join(dirpath, fname) images.append(img_path) assert images, '{:s} has no valid image file'.format(path) return images ''' # -------------------------------------------- # split large images into small images # -------------------------------------------- ''' def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): w, h = img.shape[:2] patches = [] if w > p_max and h > p_max: w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) w1.append(w-p_size) h1.append(h-p_size) # print(w1) # print(h1) for i in w1: for j in h1: patches.append(img[i:i+p_size, j:j+p_size,:]) else: patches.append(img) return patches def imssave(imgs, img_path): """ imgs: list, N images of size WxHxC """ img_name, ext = os.path.splitext(os.path.basename(img_path)) for i, img in enumerate(imgs): if img.ndim == 3: img = img[:, :, [2, 1, 0]] new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') cv2.imwrite(new_path, img) def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): """ split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) will be splitted. Args: original_dataroot: taget_dataroot: p_size: size of small images p_overlap: patch size in training is a good choice p_max: images with smaller size than (p_max)x(p_max) keep unchanged. """ paths = get_image_paths(original_dataroot) for img_path in paths: # img_name, ext = os.path.splitext(os.path.basename(img_path)) img = imread_uint(img_path, n_channels=n_channels) patches = patches_from_image(img, p_size, p_overlap, p_max) imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) #if original_dataroot == taget_dataroot: #del img_path ''' # -------------------------------------------- # makedir # -------------------------------------------- ''' def mkdir(path): if not os.path.exists(path): os.makedirs(path) def mkdirs(paths): if isinstance(paths, str): mkdir(paths) else: for path in paths: mkdir(path) def mkdir_and_rename(path): if os.path.exists(path): new_name = path + '_archived_' + get_timestamp() print('Path already exists. Rename it to [{:s}]'.format(new_name)) os.rename(path, new_name) os.makedirs(path) ''' # -------------------------------------------- # read image from path # opencv is fast, but read BGR numpy image # -------------------------------------------- ''' # -------------------------------------------- # get uint8 image of size HxWxn_channles (RGB) # -------------------------------------------- def imread_uint(path, n_channels=3): # input: path # output: HxWx3(RGB or GGG), or HxWx1 (G) if n_channels == 1: img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE img = np.expand_dims(img, axis=2) # HxWx1 elif n_channels == 3: img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G if img.ndim == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG else: img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB return img # -------------------------------------------- # matlab's imwrite # -------------------------------------------- def imsave(img, img_path): img = np.squeeze(img) if img.ndim == 3: img = img[:, :, [2, 1, 0]] cv2.imwrite(img_path, img) def imwrite(img, img_path): img = np.squeeze(img) if img.ndim == 3: img = img[:, :, [2, 1, 0]] cv2.imwrite(img_path, img) # -------------------------------------------- # get single image of size HxWxn_channles (BGR) # -------------------------------------------- def read_img(path): # read image by cv2 # return: Numpy float32, HWC, BGR, [0,1] img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE img = img.astype(np.float32) / 255. if img.ndim == 2: img = np.expand_dims(img, axis=2) # some images have 4 channels if img.shape[2] > 3: img = img[:, :, :3] return img ''' # -------------------------------------------- # image format conversion # -------------------------------------------- # numpy(single) <---> numpy(unit) # numpy(single) <---> tensor # numpy(unit) <---> tensor # -------------------------------------------- ''' # -------------------------------------------- # numpy(single) [0, 1] <---> numpy(unit) # -------------------------------------------- def uint2single(img): return np.float32(img/255.) def single2uint(img): return np.uint8((img.clip(0, 1)*255.).round()) def uint162single(img): return np.float32(img/65535.) def single2uint16(img): return np.uint16((img.clip(0, 1)*65535.).round()) # -------------------------------------------- # numpy(unit) (HxWxC or HxW) <---> tensor # -------------------------------------------- # convert uint to 4-dimensional torch tensor def uint2tensor4(img): if img.ndim == 2: img = np.expand_dims(img, axis=2) return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) # convert uint to 3-dimensional torch tensor def uint2tensor3(img): if img.ndim == 2: img = np.expand_dims(img, axis=2) return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) # convert 2/3/4-dimensional torch tensor to uint def tensor2uint(img): img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) return np.uint8((img*255.0).round()) # -------------------------------------------- # numpy(single) (HxWxC) <---> tensor # -------------------------------------------- # convert single (HxWxC) to 3-dimensional torch tensor def single2tensor3(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() # convert single (HxWxC) to 4-dimensional torch tensor def single2tensor4(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) # convert torch tensor to single def tensor2single(img): img = img.data.squeeze().float().cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) return img # convert torch tensor to single def tensor2single3(img): img = img.data.squeeze().float().cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) elif img.ndim == 2: img = np.expand_dims(img, axis=2) return img def single2tensor5(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) def single32tensor5(img): return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) def single42tensor4(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() # from skimage.io import imread, imsave def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): ''' Converts a torch Tensor into an image Numpy array of BGR channel order Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) ''' tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] n_dim = tensor.dim() if n_dim == 4: n_img = len(tensor) img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 3: img_np = tensor.numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 2: img_np = tensor.numpy() else: raise TypeError( 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) if out_type == np.uint8: img_np = (img_np * 255.0).round() # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. return img_np.astype(out_type) ''' # -------------------------------------------- # Augmentation, flipe and/or rotate # -------------------------------------------- # The following two are enough. # (1) augmet_img: numpy image of WxHxC or WxH # (2) augment_img_tensor4: tensor image 1xCxWxH # -------------------------------------------- ''' def augment_img(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' if mode == 0: return img elif mode == 1: return np.flipud(np.rot90(img)) elif mode == 2: return np.flipud(img) elif mode == 3: return np.rot90(img, k=3) elif mode == 4: return np.flipud(np.rot90(img, k=2)) elif mode == 5: return np.rot90(img) elif mode == 6: return np.rot90(img, k=2) elif mode == 7: return np.flipud(np.rot90(img, k=3)) def augment_img_tensor4(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' if mode == 0: return img elif mode == 1: return img.rot90(1, [2, 3]).flip([2]) elif mode == 2: return img.flip([2]) elif mode == 3: return img.rot90(3, [2, 3]) elif mode == 4: return img.rot90(2, [2, 3]).flip([2]) elif mode == 5: return img.rot90(1, [2, 3]) elif mode == 6: return img.rot90(2, [2, 3]) elif mode == 7: return img.rot90(3, [2, 3]).flip([2]) def augment_img_tensor(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' img_size = img.size() img_np = img.data.cpu().numpy() if len(img_size) == 3: img_np = np.transpose(img_np, (1, 2, 0)) elif len(img_size) == 4: img_np = np.transpose(img_np, (2, 3, 1, 0)) img_np = augment_img(img_np, mode=mode) img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) if len(img_size) == 3: img_tensor = img_tensor.permute(2, 0, 1) elif len(img_size) == 4: img_tensor = img_tensor.permute(3, 2, 0, 1) return img_tensor.type_as(img) def augment_img_np3(img, mode=0): if mode == 0: return img elif mode == 1: return img.transpose(1, 0, 2) elif mode == 2: return img[::-1, :, :] elif mode == 3: img = img[::-1, :, :] img = img.transpose(1, 0, 2) return img elif mode == 4: return img[:, ::-1, :] elif mode == 5: img = img[:, ::-1, :] img = img.transpose(1, 0, 2) return img elif mode == 6: img = img[:, ::-1, :] img = img[::-1, :, :] return img elif mode == 7: img = img[:, ::-1, :] img = img[::-1, :, :] img = img.transpose(1, 0, 2) return img def augment_imgs(img_list, hflip=True, rot=True): # horizontal flip OR rotate hflip = hflip and random.random() < 0.5 vflip = rot and random.random() < 0.5 rot90 = rot and random.random() < 0.5 def _augment(img): if hflip: img = img[:, ::-1, :] if vflip: img = img[::-1, :, :] if rot90: img = img.transpose(1, 0, 2) return img return [_augment(img) for img in img_list] ''' # -------------------------------------------- # modcrop and shave # -------------------------------------------- ''' def modcrop(img_in, scale): # img_in: Numpy, HWC or HW img = np.copy(img_in) if img.ndim == 2: H, W = img.shape H_r, W_r = H % scale, W % scale img = img[:H - H_r, :W - W_r] elif img.ndim == 3: H, W, C = img.shape H_r, W_r = H % scale, W % scale img = img[:H - H_r, :W - W_r, :] else: raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) return img def shave(img_in, border=0): # img_in: Numpy, HWC or HW img = np.copy(img_in) h, w = img.shape[:2] img = img[border:h-border, border:w-border] return img ''' # -------------------------------------------- # image processing process on numpy image # channel_convert(in_c, tar_type, img_list): # rgb2ycbcr(img, only_y=True): # bgr2ycbcr(img, only_y=True): # ycbcr2rgb(img): # -------------------------------------------- ''' def rgb2ycbcr(img, only_y=True): '''same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert if only_y: rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 else: rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def ycbcr2rgb(img): '''same as matlab ycbcr2rgb Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def bgr2ycbcr(img, only_y=True): '''bgr version of rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert if only_y: rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 else: rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def channel_convert(in_c, tar_type, img_list): # conversion among BGR, gray and y if in_c == 3 and tar_type == 'gray': # BGR to gray gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] return [np.expand_dims(img, axis=2) for img in gray_list] elif in_c == 3 and tar_type == 'y': # BGR to y y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] return [np.expand_dims(img, axis=2) for img in y_list] elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] else: return img_list ''' # -------------------------------------------- # metric, PSNR and SSIM # -------------------------------------------- ''' # -------------------------------------------- # PSNR # -------------------------------------------- def calculate_psnr(img1, img2, border=0): # img1 and img2 have range [0, 255] #img1 = img1.squeeze() #img2 = img2.squeeze() if not img1.shape == img2.shape: raise ValueError('Input images must have the same dimensions.') h, w = img1.shape[:2] img1 = img1[border:h-border, border:w-border] img2 = img2[border:h-border, border:w-border] img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) mse = np.mean((img1 - img2)**2) if mse == 0: return float('inf') return 20 * math.log10(255.0 / math.sqrt(mse)) # -------------------------------------------- # SSIM # -------------------------------------------- def calculate_ssim(img1, img2, border=0): '''calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255] ''' #img1 = img1.squeeze() #img2 = img2.squeeze() if not img1.shape == img2.shape: raise ValueError('Input images must have the same dimensions.') h, w = img1.shape[:2] img1 = img1[border:h-border, border:w-border] img2 = img2[border:h-border, border:w-border] if img1.ndim == 2: return ssim(img1, img2) elif img1.ndim == 3: if img1.shape[2] == 3: ssims = [] for i in range(3): ssims.append(ssim(img1[:,:,i], img2[:,:,i])) return np.array(ssims).mean() elif img1.shape[2] == 1: return ssim(np.squeeze(img1), np.squeeze(img2)) else: raise ValueError('Wrong input image dimensions.') def ssim(img1, img2): C1 = (0.01 * 255)**2 C2 = (0.03 * 255)**2 img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) kernel = cv2.getGaussianKernel(11, 1.5) window = np.outer(kernel, kernel.transpose()) mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] mu1_sq = mu1**2 mu2_sq = mu2**2 mu1_mu2 = mu1 * mu2 sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) return ssim_map.mean() ''' # -------------------------------------------- # matlab's bicubic imresize (numpy and torch) [0, 1] # -------------------------------------------- ''' # matlab 'imresize' function, now only support 'bicubic' def cubic(x): absx = torch.abs(x) absx2 = absx**2 absx3 = absx**3 return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): if (scale < 1) and (antialiasing): # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width kernel_width = kernel_width / scale # Output-space coordinates x = torch.linspace(1, out_length, out_length) # Input-space coordinates. Calculate the inverse mapping such that 0.5 # in output space maps to 0.5 in input space, and 0.5+scale in output # space maps to 1.5 in input space. u = x / scale + 0.5 * (1 - 1 / scale) # What is the left-most pixel that can be involved in the computation? left = torch.floor(u - kernel_width / 2) # What is the maximum number of pixels that can be involved in the # computation? Note: it's OK to use an extra pixel here; if the # corresponding weights are all zero, it will be eliminated at the end # of this function. P = math.ceil(kernel_width) + 2 # The indices of the input pixels involved in computing the k-th output # pixel are in row k of the indices matrix. indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( 1, P).expand(out_length, P) # The weights used to compute the k-th output pixel are in row k of the # weights matrix. distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices # apply cubic kernel if (scale < 1) and (antialiasing): weights = scale * cubic(distance_to_center * scale) else: weights = cubic(distance_to_center) # Normalize the weights matrix so that each row sums to 1. weights_sum = torch.sum(weights, 1).view(out_length, 1) weights = weights / weights_sum.expand(out_length, P) # If a column in weights is all zero, get rid of it. only consider the first and last column. weights_zero_tmp = torch.sum((weights == 0), 0) if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): indices = indices.narrow(1, 1, P - 2) weights = weights.narrow(1, 1, P - 2) if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): indices = indices.narrow(1, 0, P - 2) weights = weights.narrow(1, 0, P - 2) weights = weights.contiguous() indices = indices.contiguous() sym_len_s = -indices.min() + 1 sym_len_e = indices.max() - in_length indices = indices + sym_len_s - 1 return weights, indices, int(sym_len_s), int(sym_len_e) # -------------------------------------------- # imresize for tensor image [0, 1] # -------------------------------------------- def imresize(img, scale, antialiasing=True): # Now the scale should be the same for H and W # input: img: pytorch tensor, CHW or HW [0,1] # output: CHW or HW [0,1] w/o round need_squeeze = True if img.dim() == 2 else False if need_squeeze: img.unsqueeze_(0) in_C, in_H, in_W = img.size() out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) kernel_width = 4 kernel = 'cubic' # Return the desired dimension order for performing the resize. The # strategy is to perform the resize first along the dimension with the # smallest scale factor. # Now we do not support this. # get weights and indices weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( in_H, out_H, scale, kernel, kernel_width, antialiasing) weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( in_W, out_W, scale, kernel, kernel_width, antialiasing) # process H dimension # symmetric copying img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) sym_patch = img[:, :sym_len_Hs, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) sym_patch = img[:, -sym_len_He:, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) out_1 = torch.FloatTensor(in_C, out_H, in_W) kernel_width = weights_H.size(1) for i in range(out_H): idx = int(indices_H[i][0]) for j in range(out_C): out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) # process W dimension # symmetric copying out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) sym_patch = out_1[:, :, :sym_len_Ws] inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(2, inv_idx) out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) sym_patch = out_1[:, :, -sym_len_We:] inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(2, inv_idx) out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) out_2 = torch.FloatTensor(in_C, out_H, out_W) kernel_width = weights_W.size(1) for i in range(out_W): idx = int(indices_W[i][0]) for j in range(out_C): out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) if need_squeeze: out_2.squeeze_() return out_2 # -------------------------------------------- # imresize for numpy image [0, 1] # -------------------------------------------- def imresize_np(img, scale, antialiasing=True): # Now the scale should be the same for H and W # input: img: Numpy, HWC or HW [0,1] # output: HWC or HW [0,1] w/o round img = torch.from_numpy(img) need_squeeze = True if img.dim() == 2 else False if need_squeeze: img.unsqueeze_(2) in_H, in_W, in_C = img.size() out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) kernel_width = 4 kernel = 'cubic' # Return the desired dimension order for performing the resize. The # strategy is to perform the resize first along the dimension with the # smallest scale factor. # Now we do not support this. # get weights and indices weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( in_H, out_H, scale, kernel, kernel_width, antialiasing) weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( in_W, out_W, scale, kernel, kernel_width, antialiasing) # process H dimension # symmetric copying img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) sym_patch = img[:sym_len_Hs, :, :] inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(0, inv_idx) img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) sym_patch = img[-sym_len_He:, :, :] inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(0, inv_idx) img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) out_1 = torch.FloatTensor(out_H, in_W, in_C) kernel_width = weights_H.size(1) for i in range(out_H): idx = int(indices_H[i][0]) for j in range(out_C): out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) # process W dimension # symmetric copying out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) sym_patch = out_1[:, :sym_len_Ws, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) sym_patch = out_1[:, -sym_len_We:, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) out_2 = torch.FloatTensor(out_H, out_W, in_C) kernel_width = weights_W.size(1) for i in range(out_W): idx = int(indices_W[i][0]) for j in range(out_C): out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) if need_squeeze: out_2.squeeze_() return out_2.numpy() if __name__ == '__main__': print('---') # img = imread_uint('test.bmp', 3) # img = uint2single(img) # img_bicubic = imresize_np(img, 1/4) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/__init__.py ================================================ from ldm.modules.losses_audio.vqperceptual import DummyLoss # relative imports pain import os import sys path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'vggishish') sys.path.append(path) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import sys sys.path.insert(0, '.') # nopep8 from ldm.modules.losses_audio.vqperceptual import * class LPAPSWithDiscriminator(nn.Module): def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_loss="hinge"): super().__init__() assert disc_loss in ["hinge", "vanilla"] self.kl_weight = kl_weight self.pixel_weight = pixelloss_weight self.perceptual_loss = LPAPS().eval()# LPIPS用于日常图像，而LPAPS用于梅尔谱图 self.perceptual_weight = perceptual_weight # output log variance self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ).apply(weights_init) self.discriminator_iter_start = disc_start if disc_loss == "hinge": self.disc_loss = hinge_d_loss elif disc_loss == "vanilla": self.disc_loss = vanilla_d_loss else: raise ValueError(f"Unknown GAN loss '{disc_loss}'.") print(f"LPAPSWithDiscriminator running with {disc_loss} loss.") self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): if last_layer is not None: nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] else: nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() d_weight = d_weight * self.discriminator_weight return d_weight def forward(self, inputs, reconstructions, posteriors, optimizer_idx, global_step, last_layer=None, cond=None, split="train", weights=None): rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) # print(f"p_loss {p_loss}") rec_loss = rec_loss + self.perceptual_weight * p_loss else: p_loss = torch.tensor([0.0]) nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar weighted_nll_loss = nll_loss if weights is not None: weighted_nll_loss = weights*nll_loss weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] kl_loss = posteriors.kl() kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] # now the GAN part if optimizer_idx == 0: # generator update if cond is None: assert not self.disc_conditional logits_fake = self.discriminator(reconstructions.contiguous()) else: assert self.disc_conditional logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) g_loss = -torch.mean(logits_fake) try: d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) except RuntimeError: assert not self.training d_weight = torch.tensor(0.0) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(), "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/d_weight".format(split): d_weight.detach(), "{}/disc_factor".format(split): torch.tensor(disc_factor), "{}/g_loss".format(split): g_loss.detach().mean(), } return loss, log if optimizer_idx == 1: # second pass for discriminator update if cond is None: logits_real = self.discriminator(inputs.contiguous().detach()) logits_fake = self.discriminator(reconstructions.contiguous().detach()) else: logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), "{}/logits_real".format(split): logits_real.detach().mean(), "{}/logits_fake".format(split): logits_fake.detach().mean() } return d_loss, log ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual_dis.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import sys sys.path.insert(0, '.') # nopep8 from ldm.modules.losses_audio.vqperceptual import * from ldm.modules.discriminator.multi_window_disc import Discriminator class LPAPSWithDiscriminator(nn.Module):# 相比于contperceptual.py添加了MultiWindowDiscriminator def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_loss="hinge"): super().__init__() assert disc_loss in ["hinge", "vanilla"] self.kl_weight = kl_weight self.pixel_weight = pixelloss_weight self.perceptual_loss = LPAPS().eval() self.perceptual_weight = perceptual_weight # output log variance self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ).apply(weights_init) self.discriminator_iter_start = disc_start if disc_loss == "hinge": self.disc_loss = hinge_d_loss elif disc_loss == "vanilla": self.disc_loss = vanilla_d_loss else: raise ValueError(f"Unknown GAN loss '{disc_loss}'.") print(f"LPAPSWithDiscriminator running with {disc_loss} loss.") self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional disc_win_num = 3 mel_disc_hidden_size = 128 self.discriminator_multi = Discriminator(time_lengths=[32, 64, 128][:disc_win_num], freq_length=80, hidden_size=mel_disc_hidden_size, kernel=(3, 3), cond_size=0, norm_type="in", reduction="stack") def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): if last_layer is not None: nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] else: nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() d_weight = d_weight * self.discriminator_weight return d_weight def forward(self, inputs, reconstructions, posteriors, optimizer_idx, global_step, last_layer=None, cond=None, split="train", weights=None): rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) rec_loss = rec_loss + self.perceptual_weight * p_loss else: p_loss = torch.tensor([0.0]) nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar weighted_nll_loss = nll_loss if weights is not None: weighted_nll_loss = weights*nll_loss weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] kl_loss = posteriors.kl() kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] # now the GAN part if optimizer_idx == 0: # generator update if cond is None: assert not self.disc_conditional logits_fake = self.discriminator(reconstructions.contiguous()) else: assert self.disc_conditional logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().squeeze(1).transpose(1, 2)) g_loss = -torch.mean(logits_fake) g_loss_multi = -torch.mean(logits_fake_multi['y']) try: d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) d_weight_multi = self.calculate_adaptive_weight(nll_loss, g_loss_multi, last_layer=last_layer) except RuntimeError: assert not self.training d_weight = d_weight_multi = torch.tensor(0.0) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss + d_weight_multi * disc_factor * g_loss_multi log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(), "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/d_weight".format(split): d_weight.detach(), "{}/disc_factor".format(split): torch.tensor(disc_factor), "{}/g_loss".format(split): g_loss.detach().mean(), "{}/g_loss_multi".format(split): g_loss_multi.detach().mean(), } return loss, log if optimizer_idx == 1: # second pass for discriminator update if cond is None: logits_real = self.discriminator(inputs.contiguous().detach()) logits_fake = self.discriminator(reconstructions.contiguous().detach()) else: logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) logits_real_multi = self.discriminator_multi(inputs.contiguous().detach().squeeze(1).transpose(1, 2)) logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().detach().squeeze(1).transpose(1, 2)) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) d_loss_multi = disc_factor * self.disc_loss(logits_real_multi['y'], logits_fake_multi['y']) log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), "{}/disc_loss_multi".format(split): d_loss_multi.clone().detach().mean(), "{}/logits_real".format(split): logits_real.detach().mean(), "{}/logits_fake".format(split): logits_fake.detach().mean() } return d_loss+d_loss_multi, log ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/lpaps.py ================================================ """ Based on https://github.com/CompVis/taming-transformers/blob/52720829/taming/modules/losses/lpips.py Adapted for spectrograms by Vladimir Iashin (v-iashin) """ from collections import namedtuple import numpy as np import torch import torch.nn as nn import sys sys.path.insert(0, '.') # nopep8 from ldm.modules.losses_audio.vggishish.model import VGGishish from ldm.util import get_ckpt_path class LPAPS(nn.Module): # Learned perceptual metric def __init__(self, use_dropout=True): super().__init__() self.scaling_layer = ScalingLayer() self.chns = [64, 128, 256, 512, 512] # vggish16 features self.net = vggishish16(pretrained=True, requires_grad=False) self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) self.load_from_pretrained() for param in self.parameters(): param.requires_grad = False def load_from_pretrained(self, name="vggishish_lpaps"): ckpt = get_ckpt_path(name, "ldm/modules/autoencoder/lpaps") self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) print("loaded pretrained LPAPS loss from {}".format(ckpt)) @classmethod def from_pretrained(cls, name="vggishish_lpaps"): if name != "vggishish_lpaps": raise NotImplementedError model = cls() ckpt = get_ckpt_path(name) model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) return model def forward(self, input, target): in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target)) outs0, outs1 = self.net(in0_input), self.net(in1_input) feats0, feats1, diffs = {}, {}, {} lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] for kk in range(len(self.chns)): feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))] val = res[0] for l in range(1, len(self.chns)): val += res[l] return val class ScalingLayer(nn.Module): def __init__(self): super(ScalingLayer, self).__init__() # we are gonna use get_ckpt_path to donwload the stats as well stat_path = get_ckpt_path('vggishish_mean_std_melspec_10s_22050hz', 'ldm/modules/autoencoder/lpaps') # if for images we normalize on the channel dim, in spectrogram we will norm on frequency dimension means, stds = np.loadtxt(stat_path, dtype=np.float32).T # the normalization in means and stds are given for [0, 1], but specvqgan expects [-1, 1]: means = 2 * means - 1 stds = 2 * stds # input is expected to be (B, 1, F, T) self.register_buffer('shift', torch.from_numpy(means)[None, None, :, None]) self.register_buffer('scale', torch.from_numpy(stds)[None, None, :, None]) def forward(self, inp): return (inp - self.shift) / self.scale class NetLinLayer(nn.Module): """ A single linear layer which does a 1x1 conv """ def __init__(self, chn_in, chn_out=1, use_dropout=False): super(NetLinLayer, self).__init__() layers = [nn.Dropout(), ] if (use_dropout) else [] layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ] self.model = nn.Sequential(*layers) class vggishish16(torch.nn.Module): def __init__(self, requires_grad=False, pretrained=True): super().__init__() vgg_pretrained_features = self.vggishish16(pretrained=pretrained).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() self.N_slices = 5 for x in range(4): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(4, 9): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(9, 16): self.slice3.add_module(str(x), vgg_pretrained_features[x]) for x in range(16, 23): self.slice4.add_module(str(x), vgg_pretrained_features[x]) for x in range(23, 30): self.slice5.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h = self.slice1(X) h_relu1_2 = h h = self.slice2(h) h_relu2_2 = h h = self.slice3(h) h_relu3_3 = h h = self.slice4(h) h_relu4_3 = h h = self.slice5(h) h_relu5_3 = h vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) return out def vggishish16(self, pretrained: bool = True) -> VGGishish: # loading vggishish pretrained on vggsound num_classes_vggsound = 309 conv_layers = [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512] model = VGGishish(conv_layers, use_bn=False, num_classes=num_classes_vggsound) if pretrained: ckpt_path = get_ckpt_path('vggishish_lpaps', "ldm/modules/autoencoder/lpaps") ckpt = torch.load(ckpt_path, map_location=torch.device("cpu")) model.load_state_dict(ckpt, strict=False) return model def normalize_tensor(x, eps=1e-10): norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True)) return x / (norm_factor+eps) def spatial_average(x, keepdim=True): return x.mean([2, 3], keepdim=keepdim) if __name__ == '__main__': inputs = torch.rand((16, 1, 80, 848)) reconstructions = torch.rand((16, 1, 80, 848)) lpips = LPAPS().eval() loss_p = lpips(inputs.contiguous(), reconstructions.contiguous()) # (16, 1, 1, 1) print(loss_p.shape) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/config/melception.yaml ================================================ seed: 1337 log_code_state: True # patterns to ignore when backing up the code folder patterns_to_ignore: ['logs', '.git', '__pycache__', 'data', 'checkpoints', '*.pt'] # data: mels_path: '/home/nvme/data/vggsound/features/melspec_10s_22050hz/' spec_shape: [80, 860] cropped_size: [80, 848] random_crop: False # train: device: 'cuda:0' batch_size: 8 num_workers: 0 optimizer: adam betas: [0.9, 0.999] momentum: 0.9 learning_rate: 3e-4 weight_decay: 0 num_epochs: 100 patience: 3 logdir: './logs' cls_weights_in_loss: False ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/config/vggish.yaml ================================================ seed: 1337 log_code_state: True # patterns to ignore when backing up the code folder patterns_to_ignore: ['logs', '.git', '__pycache__'] # data: mels_path: '/home/nvme/data/vggsound/features/melspec_10s_22050hz/' spec_shape: [80, 860] cropped_size: [80, 848] random_crop: False # model: # original vgg family except for MP is missing at the end # 'vggish': [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512] # 'vgg11': [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512, 'MP', 512, 512], # 'vgg13': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 'MP', 512, 512, 'MP', 512, 512], # 'vgg16': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512], # 'vgg19': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 256, 'MP', 512, 512, 512, 512, 'MP', 512, 512, 512, 512], conv_layers: [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512] use_bn: False # train: device: 'cuda:0' batch_size: 32 num_workers: 0 optimizer: adam betas: [0.9, 0.999] momentum: 0.9 learning_rate: 3e-4 weight_decay: 0.0001 num_epochs: 100 patience: 3 logdir: './logs' cls_weights_in_loss: False ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/data/train_means_stds_melspec_10s_22050hz.txt ================================================ 0.51234712 0.08187601 0.52630101 0.08393201 0.52002938 0.08533191 0.51831866 0.08651366 0.52457265 0.08795700 0.51129235 0.08924046 0.51403755 0.09011565 0.51189406 0.09138965 0.50142221 0.09215379 0.50632402 0.09251092 0.49724399 0.09356590 0.49062130 0.09333057 0.49971113 0.09446411 0.48442903 0.09400197 0.48598301 0.09477853 0.48681630 0.09487848 0.47436119 0.09424447 0.48031359 0.09417475 0.47422810 0.09498061 0.46369397 0.09362415 0.47413055 0.09453825 0.46062686 0.09481226 0.45677793 0.09359464 0.46135474 0.09437913 0.45246800 0.09384014 0.45232766 0.09438247 0.45208386 0.09419720 0.44351671 0.09340880 0.44667316 0.09423184 0.44447692 0.09344461 0.43676363 0.09265266 0.44002381 0.09307925 0.43772414 0.09299363 0.43061019 0.09236166 0.43120828 0.09156491 0.42603271 0.09161403 0.42863234 0.09150530 0.42296206 0.09102536 0.41733331 0.09048366 0.41804121 0.09025013 0.41605068 0.09078869 0.40875265 0.08985338 0.40666997 0.08877566 0.40407463 0.08961667 0.40353311 0.08859275 0.39708031 0.08827818 0.39375066 0.08833999 0.39301091 0.08760654 0.39047117 0.08812327 0.38461680 0.08782288 0.38145284 0.08645484 0.37985209 0.08718211 0.37419526 0.08644421 0.37080597 0.08532454 0.36786535 0.08592822 0.36569049 0.08452069 0.36336079 0.08474272 0.35775191 0.08476392 0.35504801 0.08334654 0.35284816 0.08412110 0.34594865 0.08367254 0.34112312 0.08252251 0.33784886 0.08320975 0.33095703 0.08257768 0.32559461 0.08171253 0.32003106 0.08204872 0.31506222 0.08098545 0.31138077 0.08152917 0.30403516 0.08209135 0.29969540 0.08073266 0.29578024 0.08225822 0.28861871 0.08324076 0.28581686 0.08058489 0.27922253 0.08515350 0.27444035 0.08355056 0.27339468 0.08067638 0.26571759 0.08536921 0.26280864 0.08107620 0.25664202 0.08357468 0.24853513 0.08556041 ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/data/vggsound.csv ================================================ ---g-f_I2yQ,1,people marching,test --0PQM4-hqg,30,waterfall burbling,train --56QUhyDQM,185,playing tennis,train --5OkAjCI7g,40,people belly laughing,train --8puiAGLhs,30,car engine starting,train --96EN9NUQM,242,alarm clock ringing,train --9O4XZOge4,30,"female speech, woman speaking",train --Aa4M484QM,0,cricket chirping,train --Bu2xe4OSo,430,wind noise,train --CC5pH97q4,4,foghorn,train --CC5pH97q4,33,foghorn,train --CudrykwoE,67,cricket chirping,train --CudrykwoE,78,cricket chirping,train --DTR0_mIGI,11,people battle cry,train --DTR0_mIGI,32,people battle cry,train --EquRwM9IQ,8,playing volleyball,train --FenyW2i_4,0,female singing,train --G-wKyj6JQ,30,playing harpsichord,train --HuBNHXu9I,30,"male speech, man speaking",train --I3Bjp_ptc,254,playing bassoon,train --IOqc-XgYo,0,playing piano,train --L3BCCcGEw,400,people clapping,train --Lj4Y_96f0,120,"bee, wasp, etc. buzzing",train --Nrb6rtheE,10,baby babbling,train --PlJNEnf-s,288,"bee, wasp, etc. buzzing",train --Q8wkZvDZE,150,people whispering,train --QVnZXkb_Y,74,coyote howling,train --QVnZXkb_Y,98,coyote howling,train --R3QLObQ5I,319,metronome,train --SQyOb8eS0,30,playing harp,train --SvivLlKLU,137,airplane,train --SvivLlKLU,662,airplane,train --TF_YkxfvQ,1,rope skipping,train --TF_YkxfvQ,12,rope skipping,train --TKJIv9aY4,210,ambulance siren,train --TKJIv9aY4,282,ambulance siren,train --U7joUcTCo,0,people coughing,test --UE437qBIo,1,pheasant crowing,train --UQXuZfbFY,44,bird wings flapping,train --UQXuZfbFY,59,bird wings flapping,train --UgnJTCTqg,0,people battle cry,train --UgnJTCTqg,101,people battle cry,train --XInAaMS6k,61,cap gun shooting,train --XInAaMS6k,72,cap gun shooting,train --Z4X0SQxBc,290,people clapping,train --ZHUMfueO0,30,female singing,train --_K9nQrmDU,30,child singing,train --bvmgIdDC8,30,female singing,train --byNAHo7tQ,0,"race car, auto racing",train --dMUrmpeqc,30,male singing,train --f4Zx92FcY,30,"male speech, man speaking",train --gJvz9RrfA,13,playing bass guitar,train --i-y1v8Hy8,0,female singing,test --iY3Bf4hDg,30,"playing violin, fiddle",train --nzKFQ7Myg,109,playing bongo,train --q1RQWxQog,93,playing erhu,train --qEoNMshyY,37,tap dancing,train --qXTWmzUKA,30,playing bass guitar,train --rs27ZwZ9M,22,playing electronic organ,train --rs27ZwZ9M,189,playing electronic organ,train --veKG73Di4,39,playing congas,train --veKG73Di4,83,playing congas,train --vpnMyh30c,83,"subway, metro, underground",train --xDffQ9Mwo,30,car passing by,train --xJurJrYQg,220,orchestra,train --yJcmmwiMc,30,playing acoustic guitar,train --z8JpdDKr0,249,hammering nails,train --zLzL0sq3M,30,car passing by,train --zOG2GRGY4,4,duck quacking,train --zOG2GRGY4,14,duck quacking,train -00nar1nEPc,33,playing harmonica,train -011qJFjM4M,56,dog growling,train -011qJFjM4M,96,dog growling,train -021-GT2EIM,174,church bell ringing,train -021-GT2EIM,224,church bell ringing,train -03Hd0MBgQY,0,playing piano,train -03N_1zOM4E,239,playing table tennis,train -03N_1zOM4E,337,playing table tennis,train -0BIyqJj9ZU,30,people belly laughing,test -0DIFwkUpjQ,50,skateboarding,train -0HTFjyC-SI,7,pheasant crowing,train -0HxntVOLW4,508,"playing steel guitar, slide guitar",train -0KPtvNQwSU,188,playing squash,train -0KgYgXKFXs,30,female singing,train -0LtujdJBYA,85,playing bass drum,train -0MDgdtkV0k,10,police radio chatter,train -0MDgdtkV0k,44,police radio chatter,train -0NxpZlO348,30,"male speech, man speaking",train -0SIsA5l5Eg,30,female singing,train -0SQayvXyIQ,110,"male speech, man speaking",train -0TTFAArJ9k,30,horse clip-clop,train -0U3c4PN8sc,30,car passing by,train -0UQF2s0VYI,0,horse neighing,train -0UuUoXQUoI,107,playing double bass,test -0UyIRbzhlw,26,playing cello,train -0VEyRPpHcY,181,people booing,train -0VKonoRviE,197,church bell ringing,train -0VKonoRviE,209,church bell ringing,train -0VQXloTkoo,32,tap dancing,train -0WZoijgWto,30,playing electric guitar,train -0WfESRV74k,0,raining,train -0WxMOK3X5M,16,gibbon howling,train -0WxMOK3X5M,59,gibbon howling,train -0X1juZHG6I,30,toilet flushing,train -0Y5M39myZY,30,"male speech, man speaking",train -0YaPPvFmC8,30,male singing,train -0ZYW2yXKp8,22,bird wings flapping,train -0ZYW2yXKp8,32,bird wings flapping,train -0Zg_dXR_6I,13,missile launch,test -0_vEaaXndY,11,driving buses,train -0aiCtGjNXE,30,"motorboat, speedboat acceleration",train -0dzLw4NyrM,596,playing didgeridoo,train -0gYWIOfqdM,46,playing glockenspiel,train -0hh8Yp02FU,49,skiing,train -0iEMffOYwo,5,playing oboe,train -0jbsobEJg8,0,playing harpsichord,train -0jbsobEJg8,22,playing harpsichord,train -0jeONf82dE,21,horse neighing,test -0jvUCuqxow,15,arc welding,train -0kJ0SVWD7E,30,sheep bleating,train -0legLrA6Ns,51,playing bass drum,train -0legLrA6Ns,61,playing bass drum,train -0lrq0dgfnQ,15,chimpanzee pant-hooting,train -0lrq0dgfnQ,26,chimpanzee pant-hooting,train -0m2awVIc8A,15,police radio chatter,train -0mLdC1zRx8,16,playing cornet,train -0p7hKXZ1ww,30,lighting firecrackers,test -0pDdvzuFHg,142,bowling impact,train -0pDdvzuFHg,298,bowling impact,train -0pJqpNjft4,267,cow lowing,test -0qyuT918Jc,75,planing timber,train -0u9E3dETG0,52,"bee, wasp, etc. buzzing",train -0u9E3dETG0,199,"bee, wasp, etc. buzzing",train -0vPFx-wRRI,30,people finger snapping,test -0xDlYX5LM8,227,arc welding,train -0yQTraiAUY,30,"pigeon, dove cooing",train -0yRK50zyTI,30,fire crackling,test -0zIOEW3mM4,16,pheasant crowing,train -0zIOEW3mM4,26,pheasant crowing,train -10VmSN3WzE,159,playing tabla,train -10VmSN3WzE,218,playing tabla,train -11sNcJejws,3,cupboard opening or closing,train -12PNUDVUjA,29,playing acoustic guitar,train -16xdxkRgXY,1,people battle cry,train -16xdxkRgXY,12,people battle cry,train -17HGDPd8gE,30,waterfall burbling,train -18TGb1WmO8,23,missile launch,train -19HsqaVOHU,3,crow cawing,train -19HsqaVOHU,19,crow cawing,train -1ABuaSkkio,30,male singing,train -1B4--7U1xY,2,"vehicle horn, car horn, honking",train -1B4--7U1xY,64,"vehicle horn, car horn, honking",train -1BLaahvBSM,266,child singing,train -1BLaahvBSM,1048,child singing,train -1BtY81-D54,130,people battle cry,test -1C5GLtgcbY,30,playing washboard,train -1C5GLtgcbY,60,playing washboard,train -1Cvs1_xWW4,0,fire truck siren,train -1EXhfqLLwQ,150,pumping water,test -1EeNriiRN0,443,playing squash,train -1GJQ3c94jQ,97,cat growling,train -1JmE4eV4zE,442,beat boxing,train -1KVzbdvkdg,6,playing bagpipes,train -1KVzbdvkdg,54,playing bagpipes,train -1LGZONdjkY,22,playing trumpet,train -1LGZONdjkY,64,playing trumpet,train -1M0nMXxvHY,43,playing harpsichord,train -1MClsMVlF0,54,lighting firecrackers,train -1NybDGvjoA,36,woodpecker pecking tree,train -1P74acOb3c,50,playing banjo,train -1PkCejU5u8,370,people crowd,train -1QUpHlvDM4,0,chicken crowing,train -1UP2kG24nM,50,"rowboat, canoe, kayak rowing",train -1VcGT09lUI,126,forging swords,train -1VcGT09lUI,208,forging swords,train -1Vd89MCLJU,514,footsteps on snow,train -1Vd89MCLJU,647,footsteps on snow,train -1WK72M4xeg,220,driving motorcycle,train -1WNSmtDVi8,12,owl hooting,train -1WNSmtDVi8,23,owl hooting,train -1X2N5XJckw,0,"vehicle horn, car horn, honking",train -1XBP-nZ1bQ,90,playing acoustic guitar,train -1_f4o5Lqkg,59,mosquito buzzing,train -1cRD9GzcFY,0,air horn,train -1dApgfibc4,0,"child speech, kid speaking",train -1d_TDWib1A,50,slot machine,train -1eOkp74EGU,105,lighting firecrackers,train -1eOkp74EGU,287,lighting firecrackers,train -1fAlgxLC00,3,lions growling,train -1gKqs5p3I8,107,playing flute,train -1iK6drewCE,184,playing synthesizer,train -1iK6drewCE,349,playing synthesizer,train -1oRfe0FQI8,60,"railroad car, train wagon",train -1pHg-ZVssE,30,female singing,train -1pRmoJIGQc,10,car engine knocking,test -1q0XHPxqe8,80,"race car, auto racing",train -1qyr-IzqKY,60,playing accordion,train -1rZFviqTTQ,3,sharpen knife,train -1rZFviqTTQ,28,sharpen knife,train -1ukRC9M6bc,50,playing drum kit,train -1vfxCtpdEI,30,male singing,train -1vr3JMt_tc,249,playing trombone,train -2-wdcN5vOw,17,train whistling,test -20w_sCdwhk,16,reversing beeps,train -239HZbaZ0M,30,singing choir,train -23CeprtibU,30,chainsawing trees,test -25e5qcELvw,11,printer printing,train -25e5qcELvw,21,printer printing,train -26aVYRtEAc,30,female singing,train -26kjr5MxSI,0,chicken clucking,train -28N8VI2emM,76,underwater bubbling,train -28cdRYzR4w,65,child singing,train -28cdRYzR4w,86,child singing,train -2Dm0VjW8oM,1,"pigeon, dove cooing",test -2FKhYSw_zU,99,singing choir,train -2Fbw2plADY,30,people whispering,train -2HXm-Rj3lg,283,people nose blowing,train -2JAK9CdWvY,435,slot machine,train -2JalGJn_4Y,263,train whistling,train -2JomCd5zzY,184,child singing,train -2JomCd5zzY,287,child singing,train -2LHurwr5CQ,45,"subway, metro, underground",train -2LHurwr5CQ,95,"subway, metro, underground",train -2LJWaL2PuA,30,car passing by,train -2LovF10IwM,179,ice cracking,train -2LovF10IwM,282,ice cracking,train -2McrNUTKQQ,30,female singing,train -2MeFbcUUmk,30,male singing,train -2Mt0URu_Xo,247,playing table tennis,train -2OrscQ9MKw,185,playing lacrosse,train -2PCw_b6c2k,116,playing timpani,train -2QyHTr4cJM,16,waterfall burbling,train -2RPPODqLy4,30,tapping guitar,test -2TjevpJuAs,30,playing acoustic guitar,train -2UTeNMKf7g,260,orchestra,train -2Ugsiz1Ro4,88,turkey gobbling,train -2X5YwLlICg,2,door slamming,train -2Z9JGG0Dnw,70,playing saxophone,train -2bOwXoTATM,373,playing bagpipes,train -2fGMsiNgl8,52,bowling impact,train -2fmgdWsRxM,109,"bee, wasp, etc. buzzing",train -2gf4GmIplE,120,playing french horn,train -2jAdijiM14,0,playing bagpipes,train -2jAdijiM14,44,playing bagpipes,train -2jWRtFugLs,0,sea waves,train -2k4RLnPDbo,153,playing clarinet,train -2k4RLnPDbo,333,playing clarinet,train -2pb2WUxF5Q,50,police radio chatter,train -2poGMEmbbk,98,using sewing machines,train -2poGMEmbbk,109,using sewing machines,train -2qJQqXbtZs,42,vacuum cleaner cleaning floors,train -2qJQqXbtZs,128,vacuum cleaner cleaning floors,train -2sE5CH8Wb8,30,reversing beeps,test -2sE_O4zGO0,73,playing castanets,train -2sE_O4zGO0,129,playing castanets,train -2sOH8XovEE,484,playing table tennis,test -2toZf00LvI,12,bowling impact,train -2uGOpRwYzc,2,playing french horn,train -2vPstoITHs,39,playing timpani,train -2vPstoITHs,430,playing timpani,train -2xiZDEuHd8,30,pig oinking,test -2xjeuKOWwQ,120,lawn mowing,train -2xn5g7sfrU,44,basketball bounce,train -2xxVISlg34,30,female singing,train -2zHCY2q4DA,139,eletric blender running,train -3-4qmWSJXU,30,thunder,test -30AswTAoUE,24,singing choir,train -30UVzI7M1Y,2,dog whimpering,train -32EWNC_btk,11,car engine idling,train -32HIdBGdjo,66,"vehicle horn, car horn, honking",train -32HIdBGdjo,78,"vehicle horn, car horn, honking",train -32IrasDuMg,0,people sobbing,train -32R7lVBhoI,30,sheep bleating,train -33dtHMQ38I,422,playing theremin,train -33dtHMQ38I,449,playing theremin,train -376LJuHi_Y,486,playing squash,train -37X0bRVT0g,32,fox barking,train -37X0bRVT0g,122,fox barking,train -39sHTky_6o,0,playing ukulele,train -39sHTky_6o,125,playing ukulele,train -39y2CnhAsU,115,sea waves,train -39y2CnhAsU,125,sea waves,train -3ADZZS5luA,104,car engine starting,train -3EJe3ZMO1k,30,"playing violin, fiddle",train -3EYaEWEfRE,30,"rowboat, canoe, kayak rowing",train -3EwIFTR-Kk,45,tap dancing,train -3F84DMR4fA,3,parrot talking,train -3F84DMR4fA,40,parrot talking,train -3G6bE5PqIQ,2,playing cymbal,train -3G6bE5PqIQ,30,playing cymbal,train -3GsMfOpuRM,145,tapping guitar,train -3Hi-atFiAw,30,people shuffling,train -3KiSKjtdXs,44,people eating noodle,train -3Kv4fdm7Uk,30,playing steelpan,test -3L1rzGAD_o,30,playing acoustic guitar,train -3M-k4nIYIM,30,car passing by,train -3MNphBfq_0,30,car passing by,train -3PdsZqxOwA,40,people farting,train -3Ptg4uALKc,230,playing tabla,train -3Ptg4uALKc,284,playing tabla,train -3Q3Hgr5lhw,370,"child speech, kid speaking",train -3Q5K1PO7ag,51,bowling impact,train -3Q5K1PO7ag,80,bowling impact,train -3RH8_aeZkk,105,running electric fan,test -3SE2nOj6d4,30,male singing,train -3VDc0cjv_4,0,mouse clicking,train -3WPsuawZDE,328,playing electronic organ,train -3WPsuawZDE,345,playing electronic organ,train -3WQytGKbrA,28,playing didgeridoo,train -3Y4NuyZmvA,250,"vehicle horn, car horn, honking",train -3YMCZ2AWW0,30,waterfall burbling,train -3YP7hGz7RI,106,"ice cream truck, ice cream van",train -3YWuPXHknk,30,car engine starting,test -3ZJD-ApzXE,6,playing bassoon,train -3aFttpVvu0,11,chimpanzee pant-hooting,train -3b9gwBYXp8,503,chopping food,test -3cXcRajPSc,30,raining,train -3cuiWZz8FY,30,female singing,train -3cvcq0X_zo,250,turkey gobbling,train -3d7sJpR8MA,22,playing bagpipes,train -3f9zdKh7Oc,168,female singing,train -3fum-y9ouw,70,people whispering,train -3gU1bPU7_8,0,skateboarding,train -3hbLaC4bSs,2,people marching,train -3hbLaC4bSs,13,people marching,train -3iQfkOVi8g,30,male singing,train -3ihfHTdJSI,110,chicken crowing,train -3inDTr5RV0,619,lathe spinning,train -3inDTr5RV0,631,lathe spinning,train -3kt0GobDIM,70,playing accordion,train -3l98ZbqecQ,299,playing tympani,train -3l98ZbqecQ,903,playing tympani,train -3lvuJ1-KsY,70,playing clarinet,train -3mXuQ3qGdo,35,sheep bleating,train -3mXuQ3qGdo,45,sheep bleating,train -3rHVsIj1M8,30,wind noise,test -3sX4pIZa8c,1,canary calling,train -3sX4pIZa8c,22,canary calling,train -3suCV1UqMc,102,sharpen knife,train -3suCV1UqMc,282,sharpen knife,train -3sxPjf6E6k,30,playing trombone,train -3tQW7Zhl9U,380,helicopter,train -3tuTtK-PyA,111,playing sitar,train -3tuTtK-PyA,152,playing sitar,train -3usm60vq2g,240,hammering nails,train -3usm60vq2g,321,hammering nails,train -3uypzLYfSw,0,people screaming,train -3viw1EWLpo,15,baby crying,train -3x09REGA-0,50,playing didgeridoo,train -3xJAtuhxJ4,115,playing bass drum,train -3xJAtuhxJ4,189,playing bass drum,train -3xhrOw45ss,60,playing banjo,train -3yVqL0DWQE,30,car passing by,train -3yfGzvvYhw,248,cell phone buzzing,train -3z5mFRgbxc,30,driving buses,test -4-rtJzyJ1g,30,"male speech, man speaking",train -405Ksmbra8,19,chicken clucking,train -40DA8RPuq0,34,train horning,train -40DA8RPuq0,216,train horning,train -40nDU5Ecgg,130,"donkey, ass braying",test -427zFFU4Kw,30,"male speech, man speaking",train -42aitlKedE,574,striking pool,test -46xqouqMxA,6,cat meowing,train -471M-E0YHg,2,tractor digging,train -471M-E0YHg,376,tractor digging,train -47tt-BTqJY,63,playing hammond organ,train -4B9Xf1b1yI,23,sheep bleating,train -4CtdVLzUHM,20,playing double bass,train -4DpBHTuc88,75,playing glockenspiel,test -4EIZrmnLcE,53,people booing,train -4ELUORuKtk,30,playing acoustic guitar,train -4GGG_1IqE4,211,playing bongo,train -4GGG_1IqE4,239,playing bongo,train -4ItJ9yTz_c,42,train horning,train -4N3GjgnIQ0,260,"subway, metro, underground",train -4ObGigN498,50,lions growling,train -4QuzXilXac,30,playing hammond organ,train -4ST8ru_k9Y,30,"pigeon, dove cooing",train -4T9CjIfm_I,96,striking pool,train -4TJHvjsYrE,30,playing flute,train -4TM7m_07dg,350,planing timber,train -4XHW-rTETw,0,playing accordion,train -4XHW-rTETw,29,playing accordion,train -4XxY6IA8Zo,61,mouse pattering,train -4Yeb7_b-vw,33,beat boxing,train -4YxFFstS9E,29,missile launch,train -4_Ykqfn8Fw,30,playing harpsichord,train -4ano7snqlI,30,"motorboat, speedboat acceleration",train -4bMxEoHn9c,40,people hiccup,train -4bPiXbovf0,8,air conditioning noise,test -4c4jzE5y_c,5,horse neighing,train -4c9EgbBqro,30,"female speech, woman speaking",train -4cRJi8yIMY,187,playing hammond organ,train -4cRJi8yIMY,346,playing hammond organ,train -4cdRRPG3Wg,76,playing tabla,train -4cdRRPG3Wg,115,playing tabla,train -4eXuXHZw_A,30,playing snare drum,train -4f9PIOK2_I,59,woodpecker pecking tree,train -4k2KQXBucU,200,"race car, auto racing",train -4kV3ulsTJc,440,people belly laughing,train -4kkGS4-qVM,10,playing clarinet,test -4lxNixEQ2A,3,penguins braying,train -4lxNixEQ2A,13,penguins braying,train -4n6RJHEu7w,387,playing hockey,train -4o0jRbgHr4,30,cat meowing,train -4p1fU6syyU,72,playing didgeridoo,train -4p4JPT4yaU,118,playing theremin,train -4pUrlMafww,1,people screaming,train -4pmCrSdMhg,30,horse clip-clop,test -4rIk1uat_w,30,female singing,train -4rdRn-FRXo,30,female singing,train -4u6ax5P-o8,4,wood thrush calling,train -4vYvHuiHH8,30,car passing by,train -4viN_EoxOA,0,playing saxophone,test -4wjU1nWdjQ,30,stream burbling,train -4xWt2NNeOI,30,playing bass guitar,train -4yCSY_5Zns,30,"playing marimba, xylophone",train -5-8Vij75C8,46,baby laughter,train -516tRoxniU,20,playing cello,train -51CA8BX7gU,30,female singing,train -532yuAVspQ,69,playing tambourine,train -54fV6gwh7U,177,people screaming,train -584aFK8XSk,30,female singing,train -5A6hBRssh8,52,playing bagpipes,train -5A6hBRssh8,69,playing bagpipes,train -5CGQGSFGyg,60,playing electronic organ,test -5EM5-7njeE,353,singing bowl,train -5F19pIqURQ,30,female singing,train -5F5AyQh1q0,30,male singing,train -5G8-A2YtEU,11,"engine accelerating, revving, vroom",train -5INZ_Tk8RM,30,male singing,train -5Ik428qyGo,74,playing snare drum,train -5KBSlu3DQc,30,"rowboat, canoe, kayak rowing",train -5KPeudJyhk,300,playing drum kit,train -5MpAJy294E,30,turkey gobbling,train -5MpRM5DfFI,160,playing flute,train -5NxigJHFJg,0,playing harp,train -5NxigJHFJg,84,playing harp,train -5QEN0mzCGA,15,car passing by,train -5THbQ6zQM4,218,playing accordion,train -5TlMbSgt6M,30,"male speech, man speaking",train -5Tu_wiNnmg,0,playing bagpipes,train -5Tu_wiNnmg,32,playing bagpipes,train -5U3hgjXAIE,30,singing choir,train -5V3sOxJSVc,120,female singing,train -5VX0RJeXok,30,female singing,train -5VoaeTlokE,107,yodelling,train -5VoaeTlokE,250,yodelling,train -5YLszkC2Ow,30,horse neighing,train -5YpkfJk6vI,30,playing electronic organ,train -5ZIlKvRDXY,30,female singing,train -5Zh8Ab4KX4,6,"child speech, kid speaking",train -5adN5KiLog,0,machine gun shooting,train -5cWCaoEDlE,249,electric grinder grinding,train 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singing,train 01ZhG92x1Z0,0,"vehicle horn, car horn, honking",train 01_ZssoHSyE,22,coyote howling,train 01_ZssoHSyE,51,coyote howling,train 01_fPuKwyFM,20,"fly, housefly buzzing",train 01_sfLC74xs,30,"male speech, man speaking",train 01a4tYk7zKM,30,"engine accelerating, revving, vroom",train 01cQde0EX6I,53,tractor digging,train 01hnGC6JgS4,6,parrot talking,train 01hnGC6JgS4,68,parrot talking,train 01j_x89dowI,481,hammering nails,test 01kxsUBtBZY,100,"ice cream truck, ice cream van",test 01lU03LHruc,10,"race car, auto racing",train 01lp3vQ2VLs,270,typing on typewriter,train 01mcAZ1y8_A,4,opening or closing car doors,train 01mcAZ1y8_A,14,opening or closing car doors,train 01qB_Lg2wcQ,20,playing trombone,train 01qO4NHLmPA,28,playing hammond organ,train 01qO4NHLmPA,92,playing hammond organ,train 01qfwq1SlGo,30,dog bow-wow,train 01rCU4_kliM,333,running electric fan,train 01s-dP713bo,30,male singing,train 01ucV3qaYlA,178,using sewing machines,train 01ucV3qaYlA,197,using sewing machines,train 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028-0RypwzE,290,people whispering,train 0292kQJnKos,30,people cheering,train 029r_iqw_Yg,8,running electric fan,train 029r_iqw_Yg,30,running electric fan,train 02Ak1eIyj3M,30,ambulance siren,test 02CCSi1V49I,313,playing bass drum,train 02CCSi1V49I,427,playing bass drum,train 02DaUDlB82Y,30,church bell ringing,train 02DaUDlB82Y,202,church bell ringing,train 02FlinN0rIw,49,tap dancing,train 02JLBs8BRro,58,playing squash,train 02JVmqDwL1E,20,tap dancing,train 02MRwKtQ_cs,0,ocean burbling,train 02NPzNHN0ho,30,"motorboat, speedboat acceleration",train 02O3-z5wuXU,30,playing electric guitar,train 02OYNmGx79Y,185,lathe spinning,train 02OYNmGx79Y,218,lathe spinning,train 02P3ZXouUVM,11,"engine accelerating, revving, vroom",train 02Pyhg27uMw,169,blowtorch igniting,train 02TKYUQSV9w,45,rapping,train 02TKYUQSV9w,107,rapping,train 02Tq-8slsSs,50,wind rustling leaves,train 02UahUjIv5A,24,chicken crowing,train 02UvvE1oA1I,30,goat bleating,test 02W6VBraJ2g,30,"pigeon, dove cooing",train 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1nzr0Hb_4_Y,55,tap dancing,train 1o-N0LvsQC4,20,frog croaking,train 1o1GGQVxKRg,22,missile launch,train 1o5NEgjOC0o,11,snake hissing,train 1o6XTJ4jDUY,39,electric grinder grinding,train 1o85pGq79Kg,43,penguins braying,train 1o8EUEYCqIY,25,woodpecker pecking tree,train 1o9An8cPLXs,113,typing on typewriter,train 1o9An8cPLXs,133,typing on typewriter,train 1oAo0Y3JBJk,127,playing tabla,train 1oAo0Y3JBJk,192,playing tabla,train 1oCfXFxnfjk,4,playing zither,train 1oCfXFxnfjk,193,playing zither,train 1oEekSTzgeo,30,"playing violin, fiddle",train 1oG4I70RjUQ,40,people hiccup,train 1oGZJN0ZbJM,8,mouse clicking,train 1oH-wBFq1W4,20,playing accordion,train 1oHqeajIE4s,30,playing harpsichord,train 1oJAVJPX0YY,20,"railroad car, train wagon",test 1oJwAHgbKU4,304,volcano explosion,train 1oNdZdDtnxQ,123,playing badminton,train 1oPiseY5V7k,40,playing accordion,train 1oSVFi7oM-0,30,"playing violin, fiddle",train 1oSllDD0YVM,30,male singing,train 1oTyVZZzIQA,300,people clapping,train 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2FaEyGOJwG8,23,lions roaring,train 2FaYBwvwq5k,3,police radio chatter,train 2FaYBwvwq5k,25,police radio chatter,train 2Faj-8gK9Xw,87,playing badminton,train 2Fbt9QiLWWc,370,chicken clucking,test 2Fdau5KTEls,30,"race car, auto racing",test 2FebVhmmuZo,60,playing clarinet,train 2FexpXIrqGo,80,people sniggering,train 2FfKOXztBXU,0,playing double bass,train 2FfKOXztBXU,41,playing double bass,train 2FfmlSJA1Vk,10,playing trumpet,train 2Fh1RZG5xtI,260,eletric blender running,train 2FhCTKOAmKc,530,fireworks banging,train 2FilJ012FCk,120,playing synthesizer,train 2Fkh-PUx2SA,70,playing banjo,train 2FnuJrRQz9U,19,slot machine,train 2Fr1AKOjcEY,30,female singing,train 2FrMCrnNkZw,30,stream burbling,train 2FrXzcm4YMc,70,playing gong,test 2Fs0E3my1kw,30,female singing,train 2Ft1AB0cg7g,67,people slapping,train 2FtVHJ91gDc,45,playing darts,test 2Fv0G9iaeeo,90,"bee, wasp, etc. buzzing",train 2FxRgLF37kw,215,playing drum kit,test 2FysaSPqlys,14,bull bellowing,train 2G-BMlVRTCw,80,thunder,train 2G2H1jm58XU,30,horse clip-clop,train 2G2ZtyZk4eM,135,canary calling,train 2G2ZtyZk4eM,228,canary calling,train 2G2geuss0IM,1,playing tabla,train 2G2geuss0IM,24,playing tabla,train 2G2z0qIcfNo,0,chimpanzee pant-hooting,train 2G3-4D-Mxoo,80,helicopter,train 2G4_eviie4w,30,people screaming,train 2G4lyzAZ1vk,30,female singing,test 2G5tBdHRNcc,106,dinosaurs bellowing,train 2G5tBdHRNcc,119,dinosaurs bellowing,train 2G95ke7F8Fs,90,"railroad car, train wagon",train 2GB8Z903Rqc,30,male singing,train 2GCIWbwQqto,30,male singing,train 2GGNgvdRcAs,60,sliding door,test 2GH8KpzKH5I,47,frog croaking,train 2GH8KpzKH5I,68,frog croaking,train 2GI3UqL5aI8,17,singing bowl,train 2GJLNQs4Az8,52,arc welding,train 2GKNithqwrQ,15,people marching,train 2GOPuGmcHH0,20,lawn mowing,test 2GOn-UqyjeI,30,female singing,train 2GPiI8T-t60,19,playing timbales,train 2GPiI8T-t60,33,playing timbales,train 2GQ7fHIa-dw,280,dog bow-wow,train 2GQMtmYb6ko,70,playing piano,train 2GQlNwxrJgM,30,female singing,train 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2UDkN9BLa-g,82,volcano explosion,train 2UF5AB8PqQw,150,playing drum kit,train 2UFTCXrS1SQ,30,wind noise,train 2UGCVA6U4Lc,130,chicken crowing,train 2UNuMbxz9ds,22,car passing by,train 2UPvOw_EYVs,11,skiing,train 2UQ9v6Uyafg,30,"male speech, man speaking",train 2URxLM0TEOI,5,playing mandolin,train 2URxLM0TEOI,23,playing mandolin,train 2USwMyiJOzY,7,playing bassoon,train 2UUMr1GaomU,70,people babbling,train 2UUmseSWdiA,30,playing bass guitar,train 2UV8DWO8MKg,10,ocean burbling,train 2UVDsCYViMI,40,"vehicle horn, car horn, honking",train 2UWQtOCs0PA,30,playing flute,train 2UXH7NW9ae8,30,sailing,test 2UXW346x7ZU,30,"bird chirping, tweeting",train 2UYBhI3zw-U,1,door slamming,train 2UZNJNAlcW4,20,"motorboat, speedboat acceleration",train 2UZcZRiVvNU,0,people running,train 2U_Qfi_ruZU,86,missile launch,train 2U_Qfi_ruZU,97,missile launch,train 2U_lnDPHb78,27,hail,train 2U_lnDPHb78,59,hail,train 2Udcp_d5d5U,40,playing timpani,train 2UfG3LhFjac,73,playing didgeridoo,train 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2ZkCTUQ7jOw,37,playing hockey,train 2ZkJJcYl09g,200,playing cornet,train 2ZlN1trHbTg,49,woodpecker pecking tree,train 2ZlN1trHbTg,71,woodpecker pecking tree,train 2ZlmHVfeqKc,224,people sneezing,train 2ZlmHVfeqKc,246,people sneezing,train 2ZmyggA9WJU,30,typing on computer keyboard,train 2Zn1f9rPFkA,1,people battle cry,train 2ZnUkaOOzrc,17,car passing by,train 2ZogsGp-T4o,40,playing mandolin,test 2Zp97ZmyGyk,50,playing electronic organ,train 2ZtyzGwmU8I,1,printer printing,train 2Zu9otq2Aek,77,playing oboe,train 2Zu9otq2Aek,257,playing oboe,train 2Zuf1fY1seE,30,female singing,train 2_-SUOI680U,130,playing gong,test 2_1fgJUVuhU,170,"bird chirping, tweeting",train 2_26akyADLs,35,tornado roaring,train 2_26akyADLs,72,tornado roaring,train 2_3-Ch7q_cs,155,playing timbales,train 2_3-Ch7q_cs,180,playing timbales,train 2_3Oc_rW4sI,110,playing synthesizer,train 2_3P73g4M7M,230,"female speech, woman speaking",train 2_3bK8fFleQ,35,playing trumpet,train 2_3bK8fFleQ,45,playing trumpet,train 2_3qcLEjOQI,8,cupboard opening or closing,train 2_3qcLEjOQI,73,cupboard opening or closing,train 2_6OnT5aucY,166,people eating noodle,train 2_6jiYrY1qU,190,"race car, auto racing",train 2_6xnNwKwuY,56,fire truck siren,train 2_6xnNwKwuY,66,fire truck siren,train 2_7j_3PxIKI,280,stream burbling,train 2_7qbucji_0,0,arc welding,train 2_82Nuquy3A,407,airplane flyby,train 2_82Nuquy3A,518,airplane flyby,train 2_8Bmzr9xz4,461,sliding door,train 2_9UtSV9F9I,0,"race car, auto racing",train 2_9WJSzV6fw,30,raining,test 2_AwJzxpf6o,64,playing trombone,train 2_AwJzxpf6o,153,playing trombone,train 2_C5yMbxKpY,11,bowling impact,test 2_DY-BjzM8w,30,printer printing,train 2_DpD0RYFPw,94,gibbon howling,train 2_DpD0RYFPw,157,gibbon howling,train 2_Dqq0YOtm4,317,strike lighter,train 2_F5t0xIJ9M,28,air conditioning noise,train 2_Fl6KD9xZQ,30,cat meowing,train 2_GHk_SBYNo,334,missile launch,train 2_Gxk1n1Um4,402,playing gong,train 2_Gxk1n1Um4,417,playing gong,train 2_Ic6x2VcXE,60,wind noise,train 2_ItcPhh67E,15,dog bow-wow,train 2_J8rK24EZs,30,female singing,train 2_JIKuVe5PM,133,lions roaring,train 2_JlSdRCg7g,30,"male speech, man speaking",train 2_JrT6xKW1I,30,snake hissing,train 2_L5-QLMebA,10,"railroad car, train wagon",train 2_Mk_FWH084,7,playing bagpipes,train 2_Mk_FWH084,25,playing bagpipes,train 2_OG7wY-QNk,27,dog growling,train 2_OG7wY-QNk,55,dog growling,train 2_OMikTYNsE,19,sea lion barking,train 2_OMikTYNsE,31,sea lion barking,train 2_OnVT0HQCc,327,airplane flyby,train 2_OnVT0HQCc,348,airplane flyby,train 2_QViv3vWm8,108,duck quacking,train 2_QViv3vWm8,118,duck quacking,train 2_RltCGlGOA,10,playing piano,test 2_SKjq21NTI,0,playing clarinet,test 2_STnL8sj2g,35,woodpecker pecking tree,train 2_STnL8sj2g,61,woodpecker pecking tree,train 2_VO7vgrm6Q,330,playing bass guitar,train 2_Vyif4X_wE,0,bird squawking,train 2_WoWZxVPTE,30,stream burbling,train 2_WqOmn9Kd8,138,people whispering,train 2_ZU0gzxEmA,408,lighting firecrackers,train 2_ZvlyiEbQo,30,printer printing,train 2__T6Q8rCCA,80,"bee, wasp, etc. buzzing",test 2__VywfLB_U,6,machine gun shooting,train 2__VywfLB_U,21,machine gun shooting,train 2_aXtwlsAUE,5,dog whimpering,train 2_b-XVPpUd4,6,tractor digging,train 2_b-XVPpUd4,16,tractor digging,train 2_cTS4ctWtE,310,playing french horn,test 2_d5Tziof1s,37,tractor digging,train 2_dEkCMxgfw,123,running electric fan,test 2_daAyVcfOE,2856,playing sitar,train 2_daAyVcfOE,3128,playing sitar,train 2_gcAeeqzwQ,1,helicopter,train 2_gogtkUsZ4,503,hail,test 2_k8p3mvc5A,30,car passing by,train 2_kjJWlhxMU,40,"race car, auto racing",train 2_ljDKHdv6g,0,dog barking,train 2_oWlFrhzK4,13,lions growling,train 2_ooKikakQc,15,cheetah chirrup,train 2_s2FSlwT5I,30,playing electric guitar,train 2_s5a9xew18,10,crow cawing,train 2_tV06Cje5M,230,"playing marimba, xylophone",train 2_uzONq4Ed8,30,"male speech, man speaking",train 2_xbORyOjSk,377,people slurping,train 2_xbORyOjSk,412,people slurping,train 2_xuTtIXaNo,4,spraying water,train 2_zlYie952k,30,playing acoustic 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2aKfdR1GdHU,119,playing harpsichord,train 2aOGU44gbLg,14,skateboarding,train 2aOQ2P8hR1c,109,playing mandolin,train 2aOQ2P8hR1c,222,playing mandolin,train 2aOQsWU66u4,30,"railroad car, train wagon",train 2aOS7PDaGWM,92,striking pool,test 2aOm79Zj4f8,1,tap dancing,train 2aPDsvJSXMI,102,otter growling,train 2aPDsvJSXMI,340,otter growling,train 2aQ-GaVu934,17,people farting,train 2aQ-GaVu934,30,people farting,train 2aQ4JPV-pBM,280,chainsawing trees,train 2aRPQ_WaBKg,483,mosquito buzzing,train 2aUNtHWM4VY,32,cheetah chirrup,train 2aUNtHWM4VY,151,cheetah chirrup,train 2aUsiNodEM4,31,dog barking,train 2aUsiNodEM4,41,dog barking,train 2aWjGWhBKGM,4,running electric fan,train 2aWjGWhBKGM,15,running electric fan,train 2aYdg2iSgvY,2,francolin calling,train 2aYdg2iSgvY,47,francolin calling,train 2aYtg-fYR18,62,snake rattling,train 2aYtg-fYR18,113,snake rattling,train 2a_dlEUbFUk,30,people running,train 2a_wIQT5ePk,10,people shuffling,train 2a_wIQT5ePk,134,people shuffling,train 2aczGM1laAI,50,"pigeon, dove cooing",train 2ae8qWdVbFI,44,train horning,train 2afLCdkF7-w,87,bathroom ventilation fan running,train 2ahksspKhaY,30,"race car, auto racing",train 2aic1tw9QDs,70,people clapping,train 2ajYuWne8gE,50,playing snare drum,train 2ajYuWne8gE,213,playing snare drum,train 2ajxNb25WMg,370,"railroad car, train wagon",train 2amLcAGf0Tc,24,playing bagpipes,train 2amLcAGf0Tc,66,playing bagpipes,train 2aoeZxddbn0,0,strike lighter,train 2apQgkVGUfE,41,swimming,train 2at_rdtSoH8,106,playing trombone,train 2avCpTQb5QU,30,driving motorcycle,train 2axX20RQe3A,30,goose honking,train 2b0G46_yPp0,87,people battle cry,train 2b0G46_yPp0,101,people battle cry,train 2b0bPZGe324,28,playing shofar,test 2b0eM8lWGCQ,30,horse neighing,test 2b0sK547IWM,30,"female speech, woman speaking",train 2b0sNxc5Toc,6,people booing,train 2b3wZ2zKHpM,22,"subway, metro, underground",train 2b3wZ2zKHpM,80,"subway, metro, underground",train 2b4ACbRvvkk,21,playing trombone,train 2b4ACbRvvkk,46,playing trombone,train 2b4YG1uPMuc,0,elephant trumpeting,train 2b4YG1uPMuc,15,elephant trumpeting,train 2b4_qmTABrI,120,playing french horn,train 2b4_qmTABrI,273,playing french horn,train 2b8vO9O9Nps,30,church bell ringing,train 2b9wkv7uhBQ,41,barn swallow calling,train 2bA6cdBBbPY,619,writing on blackboard with chalk,test 2bA9-WkLX-A,0,cricket chirping,train 2bA9-WkLX-A,10,cricket chirping,train 2bAYEFawJZs,30,playing piano,train 2bBpql9IcDE,256,driving snowmobile,train 2bBpql9IcDE,620,driving snowmobile,train 2bCuw7U_Rac,390,playing accordion,test 2bDqxE3h270,30,"male speech, man speaking",train 2bEQQ35Wkdc,31,penguins braying,test 2bG7vNnDujU,30,male singing,train 2bGx-xBewt8,35,playing trumpet,train 2bLydNVPBqk,30,playing bass drum,train 2bNdjX930lE,61,blowtorch igniting,train 2bNdjX930lE,84,blowtorch igniting,train 2bQ-fYvnFYI,10,cattle mooing,train 2bQ-fYvnFYI,23,cattle mooing,train 2bQCMGHQjI4,79,playing bass drum,train 2bQCMGHQjI4,89,playing bass drum,train 2bT8BF0XL-s,53,planing 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2bnzTqx2P2g,73,"ice cream truck, ice cream van",train 2bp4VxfjKa0,30,skateboarding,train 2bpLImW7w7c,402,hail,train 2bpLImW7w7c,429,hail,train 2bq2lc3DLwM,30,car passing by,train 2bqkVGDvtVg,30,male singing,train 2br6jJnonew,30,"female speech, woman speaking",train 2brkecipIPQ,72,playing harp,train 2brvA4UqqDM,380,playing cello,train 2bsBaUn2Ztg,51,mouse clicking,train 2bsBaUn2Ztg,409,mouse clicking,train 2bt0yL69XNA,30,"playing violin, fiddle",train 2bt5HDbLE6w,8,"railroad car, train wagon",train 2buf5yR_g_0,0,people battle cry,train 2bv4DbARrVw,56,people crowd,train 2bvGjlgZ9Io,194,playing flute,train 2bvyRryx8ws,84,playing hammond organ,train 2bwq60TMOmg,55,playing erhu,train 2bxEMJM110U,9,"vehicle horn, car horn, honking",train 2bxEMJM110U,21,"vehicle horn, car horn, honking",train 2bxIMl3hwHw,30,playing electric guitar,train 2bymJUGrN8k,1067,playing gong,train 2c1gTxqEtUs,0,mynah bird singing,train 2c1gTxqEtUs,23,mynah bird singing,train 2c1ndTY9RlI,30,playing acoustic guitar,train 2c2V1q1AEQo,104,playing erhu,train 2c5VU69225M,30,dog barking,train 2c6g-8T_mpE,100,playing piano,train 2cBdAvV0_zs,80,toilet flushing,train 2cCpu0j_uR4,63,mosquito buzzing,train 2cDxkcrJeSY,30,turkey gobbling,train 2cEM6Hu90v0,38,playing erhu,test 2cEhWZxAkvc,15,sliding door,train 2cEhWZxAkvc,34,sliding door,train 2cElQswf7R0,16,people coughing,train 2cG5W4y5evg,7,playing electronic organ,train 2cHS2U1XWJM,55,playing harmonica,train 2cI0VceC4LM,8,playing harpsichord,train 2cI0VceC4LM,59,playing harpsichord,train 2cIWozjL-2I,331,slot machine,train 2cMqpe-G9kc,91,toilet flushing,train 2cOZC5HNkTE,2,dog baying,train 2cOZC5HNkTE,13,dog baying,train 2cQs7GHkk8g,20,driving motorcycle,train 2cQyJV1DkC4,10,squishing water,train 2cQyJV1DkC4,34,squishing water,train 2cRPzzZV8u0,155,orchestra,train 2cRtHlmkABA,29,warbler chirping,train 2cRtHlmkABA,82,warbler chirping,train 2cTwMsrofYE,30,"engine accelerating, revving, vroom",train 2cVQZpQMrxY,30,male singing,train 2cVk2TTqH0U,130,playing tabla,train 2cVk2TTqH0U,253,playing tabla,train 2cWM497pH-E,130,playing piano,train 2cWOuVS6Z48,38,child singing,train 2cWOuVS6Z48,86,child singing,train 2cXiij-QU5Q,541,playing badminton,train 2cXuHNQAsSc,9,cat meowing,train 2cZP2alATm8,410,"race car, auto racing",train 2cZWJ_PXIaI,0,chimpanzee pant-hooting,train 2cZWJ_PXIaI,14,chimpanzee pant-hooting,train 2cbdHNaQU5o,30,playing electric guitar,train 2cbdHNaQU5o,46,tapping guitar,train 2ceyaaIqzko,50,"race car, auto racing",train 2cf5AoCB9Co,460,train wheels squealing,train 2cf5AoCB9Co,482,train wheels squealing,train 2cgbCHAx5iM,30,chicken clucking,train 2cgv4p_bht8,205,playing hammond organ,train 2chFiPWG1OA,175,metronome,train 2chmkOzGL1I,45,arc welding,train 2ckKLDFHacM,30,car passing by,train 2clqL24tABc,30,playing acoustic guitar,train 2cmGfVqK8Ng,51,people booing,train 2cmGfVqK8Ng,94,people booing,train 2co1V9LFloY,136,firing muskets,train 2co1V9LFloY,366,firing muskets,train 2cpKd4brn5g,10,lighting 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honking,train 2dJVcd47iJ8,270,people clapping,train 2dJoctdoKig,56,mosquito buzzing,train 2dNEVAF3X2E,30,"playing violin, fiddle",train 2dNpVU8CouE,30,chicken clucking,train 2dPu7QirkbA,470,"child speech, kid speaking",train 2dQK9ttaVVk,30,playing electric guitar,train 2dQZlFSue0g,30,playing acoustic guitar,train 2dQmleM3HxE,30,waterfall burbling,train 2dQzoS6Usew,11,fireworks banging,train 2dT9hA8A18k,78,people babbling,train 2dT9hA8A18k,123,people babbling,train 2dYpLr6_jZQ,30,"rowboat, canoe, kayak rowing",train 2dZN-_9Xb9k,10,playing piano,train 2d_-_dY5pRE,30,"playing marimba, xylophone",train 2da6SvWTVks,13,horse clip-clop,train 2dbFeFiW4iY,30,playing harp,train 2dbSSa7bG9I,30,playing cello,train 2dehkZMjijk,240,fireworks banging,train 2dfshj7PQtI,7,cat purring,train 2dfshj7PQtI,99,cat purring,train 2dg5n2_xdzY,107,woodpecker pecking tree,train 2dg5n2_xdzY,139,woodpecker pecking tree,train 2diTm8p7mTQ,40,orchestra,train 2dih3AAlObo,10,basketball bounce,train 2djRf_d62jM,30,"motorboat, speedboat acceleration",train 2djYfdmB6Mk,60,playing acoustic guitar,train 2djZE8kJRvw,30,"pigeon, dove cooing",train 2dk9xHmkNpI,20,playing table tennis,train 2dkuvWY6-0U,30,goose honking,train 2dkwV5ECo7Y,30,child singing,train 2dlDWVvS1k0,92,playing castanets,train 2dm5g2brcdw,220,"playing marimba, xylophone",train 2doQQrQIYNw,3,singing bowl,train 2doTYxZzbTU,31,dog growling,train 2doTYxZzbTU,83,dog growling,train 2drXOn18U3Q,136,slot machine,test 2dtawTx7ROs,30,"male speech, man speaking",train 2duVt8nj6ZA,30,"female speech, woman speaking",train 2dvEcri6G7k,19,opening or closing drawers,train 2dvgvZ0qNcg,40,driving buses,train 2dw6t-q098U,16,people burping,train 2dxn4R8BGxE,5,playing trombone,train 2dy5JZACfsg,30,female singing,train 2e0HjGI0GlY,0,"male speech, man speaking",train 2e0brTzq_Wg,20,car engine knocking,train 2e1juXs5N7U,30,"male speech, man speaking",train 2e1tRszQIPE,57,bouncing on trampoline,test 2e4TDL3CyiU,30,female singing,train 2e56p1bICcM,90,tractor digging,train 2eAheUWCOjU,1,"vehicle horn, car horn, honking",train 2eBakaPLH-8,84,underwater bubbling,train 2eCf9vVEnX8,19,car passing by,train 2eDP3jKoUd4,30,owl hooting,test 2eDkTcyzh9I,22,playing electric guitar,train 2eE4N0IumN4,13,mouse clicking,train 2eEPiCh9bZo,90,cap gun shooting,train 2eMeVbchV9E,20,people crowd,train 2eN39YWP7Sk,240,people crowd,train 2eNJyPLjnWM,522,playing table tennis,train 2eNg6ANiQJA,128,horse clip-clop,train 2eNg6ANiQJA,162,horse clip-clop,train 2eNxilyt7-o,160,"female speech, woman speaking",train 2ePpNRwWoOc,30,cat meowing,train 2eUMLGwGxRc,30,playing acoustic guitar,train 2eWobSpugTw,17,baby crying,test 2eYaXTKSpMI,4,cat meowing,test 2e_33OW5YJo,160,playing theremin,train 2ea0cOj1j1g,14,playing electronic organ,train 2ea0cOj1j1g,35,playing electronic organ,train 2eawVBlNmL4,21,playing timpani,train 2eawVBlNmL4,31,playing timpani,train 2ebPmPNtK4k,51,bowling impact,train 2edk0gKJzAA,124,rope skipping,train 2eeYRrqBWlE,30,goose honking,train 2efO5eptwWs,30,"male speech, man speaking",train 2egH3WFpjJY,26,bathroom ventilation fan running,train 2egH3WFpjJY,72,bathroom ventilation fan running,train 2ehbByOUJjM,16,francolin calling,test 2ehs70MWQTs,50,waterfall burbling,train 2ei5ygwZySA,52,playing volleyball,train 2ei7yS-2vjw,0,pumping water,train 2ei7yS-2vjw,218,pumping water,train 2ejP5DGhNhI,90,tap dancing,train 2ek_-GrUPaQ,26,missile launch,train 2eneDLL-zN0,13,dog howling,train 2eneDLL-zN0,25,dog howling,train 2eoeJITJeHc,16,volcano explosion,train 2eoz_HkiIBw,30,male singing,train 2ep92qmo1YY,59,cat growling,train 2eqzjhD9o-Q,208,playing mandolin,train 2eqzjhD9o-Q,673,playing mandolin,train 2erACT9n-B4,330,helicopter,train 2es7oZzwLWM,30,playing zither,test 2et3Ton0d94,8,car passing by,train 2ewT1ktBlOA,30,playing electronic organ,train 2ew_E4ivlC8,130,chicken clucking,train 2eyH2sIwmvU,149,playing table tennis,train 2f2P6qkmWiQ,30,"male speech, man speaking",train 2f4C2QcLOjw,20,using sewing 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2ne_qrHsFac,30,"playing violin, fiddle",train 2ngGIq2yiJY,6,francolin calling,train 2ngGIq2yiJY,24,francolin calling,train 2ngbRYHDfWY,25,chicken crowing,train 2ngxzAR5dzM,30,singing choir,train 2niIOtYfozY,5,playing glockenspiel,train 2nmH6YheSt4,261,"vehicle horn, car horn, honking",train 2nmeO60naBA,30,splashing water,train 2nmwWWYJtD4,17,crow cawing,train 2nnO4VGUbDg,30,printer printing,train 2noBxxKAKj4,30,cat purring,train 2np9C3_mTy4,2,crow cawing,train 2np9C3_mTy4,13,crow cawing,train 2nqvjFQN7ZQ,19,"ice cream truck, ice cream van",train 2nqvjFQN7ZQ,59,"ice cream truck, ice cream van",train 2nr4aXKyZJQ,30,"pigeon, dove cooing",train 2nvQdsMdQ6o,0,"fly, housefly buzzing",test 2nyT4fAy8Uo,28,pheasant crowing,train 2nyT4fAy8Uo,43,pheasant crowing,train 2nyh86pbQck,30,"bird chirping, tweeting",train 2nzpQA6yeXc,2,ice cracking,train 2o-kTkbWjQY,30,"bird chirping, tweeting",train 2o0mRc98H8k,36,people marching,train 2o2loo3QKP0,26,"male speech, man speaking",train 2o4-lEtFb2k,60,hair 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2qBbIZsK0nQ,20,playing flute,train 2qCMEZCYG7Y,606,people eating apple,train 2qDW74WT9Xw,30,playing bass guitar,train 2qGjgfpQtFI,0,playing drum kit,test 2qHRJAQDCDo,95,lathe spinning,test 2qHhRB-ngs4,30,playing electric guitar,train 2qLp3Sw98LM,15,playing sitar,train 2qLp3Sw98LM,305,playing sitar,train 2qMVWzk3G_I,15,hail,train 2qMVWzk3G_I,25,hail,train 2qMWck_oljo,30,playing harpsichord,train 2qO-OQtOBK0,30,people running,test 2qPzOTf32sU,30,opening or closing drawers,train 2qRRK9xoA0A,0,"child speech, kid speaking",train 2qSknsEkmZI,30,playing didgeridoo,test 2qUypNph8eQ,90,playing harpsichord,train 2qVII2EZbF4,0,chicken clucking,train 2qW86VaasAU,30,"male speech, man speaking",train 2qXyeiKNsVc,30,car passing by,train 2qZHVjJhQhg,23,cricket chirping,train 2qagYYdOh6o,228,pig oinking,train 2qagYYdOh6o,278,pig oinking,train 2qau31o4FL8,10,pheasant crowing,train 2qau31o4FL8,45,pheasant crowing,train 2qe_mPKLjro,7,mouse squeaking,train 2qe_mPKLjro,20,mouse squeaking,train 2qfUIqAgv-M,140,people babbling,train 2qfohiU4rZ8,11,bouncing on trampoline,train 2qg4J2JNdSs,30,"rowboat, canoe, kayak rowing",train 2qg5P9IJ_9U,70,pumping water,test 2qgOeAgtWi0,50,people sobbing,train 2qh8jLGVaYk,38,playing lacrosse,test 2qhsqZnXmro,30,helicopter,train 2qi-Dc1EkJo,30,female singing,train 2qjrKm2eu18,15,wind chime,train 2qldSK1lSAM,250,telephone bell ringing,train 2qmgGzB6VyA,424,playing bassoon,train 2qoNc9be5Vo,22,playing volleyball,train 2qsYvMVuz3o,326,people cheering,train 2qsYvMVuz3o,413,people cheering,train 2qtDXB4wT8c,20,cat growling,test 2qtdpohFB_I,30,"playing violin, fiddle",train 2quZjuqevWw,53,airplane flyby,train 2quZjuqevWw,104,airplane flyby,train 2qwE7Pj6600,30,playing electric guitar,train 2r-ivyuxOnI,17,basketball bounce,train 2r0iftxu5vc,265,police radio chatter,test 2r2cG0UcYEI,30,child singing,train 2r3NQx2KU5g,67,mosquito buzzing,train 2r3NQx2KU5g,87,mosquito buzzing,train 2r4UK8lfgJc,616,playing glockenspiel,train 2rD3GB29x20,1,cat purring,train 2rD3GB29x20,34,cat purring,train 2rEhwhYebxY,182,train whistling,train 2rEhwhYebxY,192,train whistling,train 2rFMXXUKdBI,8,people shuffling,train 2rFw6q6XxRY,20,police car (siren),train 2rJQT6a0RN4,30,"playing violin, fiddle",train 2rK6HBRC8HQ,40,playing castanets,test 2rL8GgmWhYk,136,playing electric guitar,train 2rOHn25sOHg,30,"male speech, man speaking",train 2rRTUPQOOaM,180,playing clarinet,train 2rSFLrwcvcY,20,pheasant crowing,train 2rTNtCCS3ws,15,horse clip-clop,train 2rV3x4vpi8g,50,chicken crowing,train 2rV5a3l9IyY,1,hammering nails,train 2rW2I6KmWhw,30,dog howling,test 2rW3anXsE3o,30,female singing,train 2rYqQH_iBm8,150,train horning,train 2rZa26Geuhg,60,child singing,train 2rZvDtZIaD4,110,wind rustling leaves,train 2r_3JrPmUVw,60,driving motorcycle,train 2raf5B_kYCs,22,firing muskets,train 2raoaApPfh0,6,"alligators, crocodiles hissing",train 2raoaApPfh0,22,"alligators, crocodiles hissing",train 2rbWnqEycqo,240,waterfall burbling,train 2rciuzaEEWo,25,chimpanzee 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2s3QHQ3oMpc,35,people marching,train 2s3QHQ3oMpc,180,people marching,train 2s4P4sQgYCE,4,playing sitar,train 2s6TxnuKYhc,30,child singing,train 2s7sdW92JRA,238,people eating,train 2s7sdW92JRA,256,people eating,train 2s8JCk4nTwM,22,"engine accelerating, revving, vroom",train 2s8P9kQdQMY,160,people cheering,train 2s8l7_MTk5Q,510,people slurping,train 2s8l7_MTk5Q,522,people slurping,train 2s8tM4yCtbw,418,playing badminton,train 2sA1hqfprh0,16,tapping guitar,train 2sBFY3RZ6uA,185,rope skipping,train 2sBFY3RZ6uA,209,rope skipping,train 2sBdPQ6wx6E,510,playing cello,train 2sC8XYyM4xs,31,"vehicle horn, car horn, honking",train 2sC8XYyM4xs,41,"vehicle horn, car horn, honking",train 2sDFQiVB67I,80,people sniggering,train 2sDfZImf37A,113,missile launch,train 2sF7d4EYxsI,30,car passing by,train 2sGnD9sd1xM,470,typing on typewriter,test 2sJdNGJ4Tig,0,people whistling,train 2sKmLLGGuVw,0,ambulance siren,train 2sKmbnOi5S0,6,playing table tennis,test 2sOHTha-0sY,190,orchestra,train 2sQ61lNvm-g,87,woodpecker pecking tree,train 2sQ61lNvm-g,199,woodpecker pecking tree,train 2sQi5sbiYH8,18,child singing,train 2sR5qCjPF4Q,24,people shuffling,train 2sRndtEVzjY,0,skidding,train 2sSAvURngJY,60,lawn mowing,train 2sSDVIV7kdA,210,people screaming,train 2sTc2C9Uh1w,0,people babbling,train 2sUaBf4GfIg,15,wind chime,train 2sXQeWPD5z4,54,warbler chirping,test 2sY5Q-XVolY,290,playing vibraphone,train 2sZNZ32gRuY,11,sheep bleating,train 2sZNZ32gRuY,21,sheep bleating,train 2sZVqDELp3o,167,dog howling,train 2sZhC_mKeic,30,cat meowing,train 2s_2bndbkHk,8,playing bongo,train 2s_2bndbkHk,72,playing bongo,train 2s_tkksPz_8,0,chimpanzee pant-hooting,train 2s_vkDbVsxo,170,driving buses,train 2sa7fL31GSk,120,female singing,train 2saVQ0sDK_E,30,playing cello,train 2seBiGjMbDc,18,wood thrush calling,train 2seBiGjMbDc,77,wood thrush calling,train 2seHZPN4sjM,30,playing electric guitar,train 2seJaQVkFmg,13,vacuum cleaner cleaning floors,train 2seJaQVkFmg,25,vacuum cleaner cleaning floors,train 2sicAOYVR6s,350,"child speech, kid speaking",train 2sjg8wg1gOA,42,civil defense siren,train 2spLOha2DNc,101,slot machine,train 2spo0y0u7R8,30,female singing,train 2sqpbWv2HC0,115,cricket chirping,train 2sqpbWv2HC0,162,cricket chirping,train 2suUz4Wpg4Q,30,playing acoustic guitar,train 2sw_J5RClOc,346,hair dryer drying,train 2sw_J5RClOc,669,hair dryer drying,train 2swwGdH_Qgc,90,sharpen knife,train 2swwGdH_Qgc,100,sharpen knife,train 2sx5x3p6WHE,10,cheetah chirrup,train 2sx5x3p6WHE,21,cheetah chirrup,train 2sxYW0fvods,5,hail,test 2syCCpacVu0,30,playing cello,train 2syL8-wPM4M,89,playing cornet,train 2syL8-wPM4M,150,playing cornet,train 2szJ9STQPUk,30,male singing,train 2szvfFHZTV0,160,swimming,test 2t1iW_l8Bbw,3,gibbon howling,train 2t1iW_l8Bbw,29,gibbon howling,train 2t1liDKjf-g,30,helicopter,train 2t307aEwFOk,250,"rowboat, canoe, kayak rowing",train 2t5-1tl2ibM,30,"engine accelerating, revving, vroom",train 2t6eqvXT7QI,20,"female speech, woman speaking",train 2t7AJfvbd2s,30,male singing,train 2t8k0lAXoVY,30,male singing,train 2t8m9DMVEpc,1,playing tambourine,train 2t8m9DMVEpc,20,playing tambourine,train 2t8rYnARZuI,30,raining,test 2tDB_s0aHmo,30,"pigeon, dove cooing",train 2tF8E3WGxkk,380,playing cello,train 2tFn61fIwQ0,210,ambulance siren,train 2tJxO3dqjs0,30,horse clip-clop,train 2tLgG0hmUpU,10,toilet flushing,train 2tMwmcoGhus,80,lawn mowing,train 2tOCH1TEjYA,30,male singing,train 2tP5a_zb5sc,20,cat meowing,train 2tPAbgpeYkg,30,"male speech, man speaking",train 2tQjoo4LmXg,510,playing acoustic guitar,train 2tRFcX7HrZw,280,orchestra,train 2tRIWpbNKT8,350,crow cawing,train 2tSO6n23IYg,30,playing trombone,train 2tVVu1EKdJ4,20,goose honking,train 2tVVu1EKdJ4,30,goose honking,train 2tXE7snh6Ak,30,cat meowing,test 2tXX2-Su_vg,100,driving buses,train 2tZjx59rRJM,24,canary calling,train 2tZjx59rRJM,88,canary calling,train 2tagPyDux64,464,children shouting,train 2tasnlJcA08,30,"male speech, man speaking",train 2tcC3sMhjBA,23,people slapping,train 2tcT1CDFM54,9,"rowboat, canoe, kayak rowing",train 2tdHvVXuajw,14,playing sitar,train 2tdHvVXuajw,40,playing sitar,train 2thZbFL5w64,30,people sniggering,train 2tiMSpzfqYI,30,sliding door,train 2tjEckdpSW0,7,pig oinking,train 2tjEckdpSW0,17,pig oinking,train 2tkNfsYlENs,61,mouse clicking,train 2tkNfsYlENs,71,mouse clicking,train 2tlB5xQCbwo,30,people running,train 2tlSUSvX9RY,30,"male speech, man speaking",train 2tmyLqFP41c,30,"male speech, man speaking",train 2toMcw9b0MA,450,"playing marimba, xylophone",train 2tq2OtEiyfw,30,playing electric guitar,train 2tqg64yZqBg,554,playing darts,train 2ts6qo_2fYY,12,mouse squeaking,train 2ts6qo_2fYY,110,mouse squeaking,train 2tvbXTF_MOA,30,cat meowing,train 2tySiVDqMw4,127,airplane flyby,train 2tySiVDqMw4,138,airplane flyby,train 2u2EnV5FxdY,40,elephant trumpeting,train 2u6N3qKNyHw,30,"railroad car, train wagon",train 2u6O2IEo4kU,12,dog bow-wow,train 2u8fEVT8mU8,30,playing bass guitar,train 2u9tk_L7ZZU,210,people screaming,train 2uCaPqc1etI,23,tractor digging,train 2uENvPNxvFw,30,child singing,train 2uFIpLZhMvU,60,"race car, auto racing",train 2uGhMcvJOLA,30,playing drum kit,train 2uH6sLmJgA4,30,wind noise,train 2uHNcokm3Cw,97,playing tympani,train 2uHNcokm3Cw,208,playing tympani,train 2uH_Ohl4TyI,1,lighting firecrackers,train 2uHcWdkV4W4,120,"subway, metro, underground",test 2uI67pirsdA,213,chopping wood,train 2uJaRa0ECEs,30,"motorboat, speedboat acceleration",train 2uL9elcaeZ0,30,car passing by,train 2uNz8CePpg8,30,female singing,train 2uP2y0CQ3QI,90,"playing marimba, xylophone",train 2uPG28ye2Vw,21,duck quacking,train 2uRe-e8RVEM,190,dog bow-wow,test 2uSlSMj8t8Q,30,playing bass guitar,train 2uYTNgvxVwk,257,people humming,test 2uZt59va3Ss,50,"subway, metro, underground",test 2u_7joHhM0k,69,tractor digging,train 2u_7joHhM0k,242,tractor digging,train 2u_KfycvEQw,30,female singing,train 2uagA3ujRtM,0,cat purring,test 2ubvf7fB5co,270,"railroad car, train wagon",train 2ud7bBTRfOs,50,driving motorcycle,train 2ud8L1rbjcs,56,singing bowl,train 2uenFW2fWkE,310,slot machine,train 2uflpI9sq88,218,electric grinder grinding,train 2uflpI9sq88,496,electric grinder grinding,train 2ulXtPz9vs8,396,playing bassoon,train 2unB-S9JXV8,4,playing harpsichord,train 2unB-S9JXV8,14,playing harpsichord,train 2upMYciVz-c,3,playing didgeridoo,train 2upm5NkV2I4,26,pheasant crowing,train 2usxAmDgd-s,30,people cheering,train 2utGBNLT0fg,80,"rowboat, canoe, kayak rowing",train 2utaodyuqRY,50,wind noise,train 2uyog-ze6G4,19,crow cawing,train 2uyog-ze6G4,29,crow cawing,train 2v-znXpWilg,30,raining,train 2v05J1E9Eg8,30,playing bass drum,train 2v1rSA4FqlM,100,pumping water,test 2v24bPK-Nd0,20,people hiccup,train 2v2TfV1MpIU,110,"female speech, woman speaking",train 2v38YjzPuzo,30,male singing,train 2v3l1eZzL_Y,30,wind rustling leaves,train 2v56Xt_bqTg,30,skidding,train 2v5BQq4TPRk,74,playing theremin,train 2v5BQq4TPRk,87,playing theremin,train 2v8kCGNsohM,30,"male speech, man speaking",train 2v99C9bbZ9U,30,male 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3Cz7pNwyFhg,179,playing clarinet,train 3D-GYcWGwhQ,119,playing ukulele,train 3D-GYcWGwhQ,132,playing ukulele,train 3D-h4i1AJok,4,bathroom ventilation fan running,test 3D3oByfZJlk,54,scuba diving,train 3D4IYzc4moA,47,people humming,train 3D4IYzc4moA,74,people humming,train 3D64AmSIdnM,10,playing harp,train 3D6DZfMIfcU,180,playing banjo,train 3D7k7cNcpe4,0,"child speech, kid speaking",train 3D9VWKR-iFc,127,playing table tennis,train 3D9nFlR99Qk,70,driving motorcycle,train 3DBebXDpCus,1,train horning,train 3DBebXDpCus,15,train horning,train 3DC7I_j9UeA,100,lighting firecrackers,train 3DC7I_j9UeA,200,lighting firecrackers,train 3DCowHr8Ays,30,playing acoustic guitar,train 3DErjEh2qKI,119,frog croaking,train 3DErjEh2qKI,149,frog croaking,train 3DFhAixMoV8,82,civil defense siren,train 3DFhAixMoV8,103,civil defense siren,train 3DG6SB4xJAM,12,singing bowl,train 3DGDYmY-7Eg,6,cricket chirping,train 3DHZFKdF47g,101,playing theremin,train 3DHZFKdF47g,116,playing theremin,train 3DHm_I7hRZQ,190,wind noise,train 3DK5YAQAVlI,80,people nose blowing,test 3DK7ovnDdYo,200,driving buses,train 3DKiu2JwRmA,114,slot machine,train 3DLkaqs2N1w,30,female singing,train 3DM9b8yBjd0,30,male singing,train 3DMWwehJT4M,0,people crowd,train 3DMgdToT6HM,30,playing electric guitar,train 3DMijw9ZZ4w,10,ambulance siren,train 3DNK6BxlnyQ,30,playing banjo,train 3DO6o12iY-8,30,"child speech, kid speaking",train 3DOkS4iXBEs,13,playing snare drum,train 3DOkS4iXBEs,124,playing snare drum,train 3DPDSnI0jBE,202,crow cawing,train 3DP_zIFMURw,10,people sobbing,train 3DTZsjKMOyA,289,"playing steel guitar, slide guitar",test 3DUZPfSL5D4,135,mosquito buzzing,train 3DVR27dKuQY,197,chimpanzee pant-hooting,train 3DVaxzv01sc,118,playing didgeridoo,train 3DVaxzv01sc,177,playing didgeridoo,train 3DWHPMv2T88,30,playing electric guitar,train 3DY7nN6Lz7Y,172,fire truck siren,train 3DY7nN6Lz7Y,321,fire truck siren,train 3DYMHbOLVEw,16,frog croaking,train 3DYMHbOLVEw,36,frog croaking,train 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3kR8yIqkzzE,30,male singing,train 3kS4S8otgOc,172,gibbon howling,train 3kSQNjfJnN8,210,playing theremin,test 3kSwAW7KDrs,30,"engine accelerating, revving, vroom",train 3kSzbuRGTaA,80,"rowboat, canoe, kayak rowing",train 3kW99COiygw,107,playing saxophone,train 3kWzeKKT3vc,290,driving motorcycle,train 3kXROE2wcRA,31,bowling impact,train 3kXROE2wcRA,69,bowling impact,train 3kaCL9Zu7NY,230,playing zither,test 3kbzQStO4sI,103,dinosaurs bellowing,train 3kbzQStO4sI,688,dinosaurs bellowing,train 3ke5s3AxbKU,42,bouncing on trampoline,train 3ke5s3AxbKU,95,bouncing on trampoline,train 3kh-ognXZv4,30,female singing,train 3khrTcVL1oY,30,child singing,train 3kmIqkUsMv4,50,driving motorcycle,train 3kmYCPaVNxA,238,people eating crisps,train 3kmYCPaVNxA,352,people eating crisps,train 3kn2YCwns7E,53,typing on typewriter,train 3kn2YCwns7E,125,typing on typewriter,train 3ko8kweSHvg,19,lathe spinning,train 3ko8kweSHvg,104,lathe spinning,train 3ktBJ6qlhIM,4,chicken crowing,train 3kuOt4kZUn0,10,people 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3lNRv1EVmj0,30,"male speech, man speaking",train 3lNsV-UqPKc,1,people giggling,train 3lOBYqiPYb4,200,playing accordion,train 3lOD17f5qZI,30,female singing,train 3lOfIG1h-Zw,30,chicken clucking,train 3lOobqeoGiU,30,playing french horn,train 3lXNVqBnN5o,236,fire truck siren,train 3lXNVqBnN5o,350,fire truck siren,train 3l_RFqq6-bQ,30,dog bow-wow,train 3l_mVPiM9ec,185,playing double bass,train 3la2-1FOBHQ,110,playing harp,train 3la8bsi4P-c,237,rapping,train 3la_W7DqDuA,7,cricket chirping,train 3leo_g4VUoM,92,playing didgeridoo,train 3lfHiWEi9Jo,170,"railroad car, train wagon",train 3lgayA3z3pc,133,roller coaster running,train 3lgayA3z3pc,159,roller coaster running,train 3linUB5ot5k,79,missile launch,train 3linUB5ot5k,91,missile launch,train 3lkI_scF2eU,130,chainsawing trees,test 3lkZPJES45Q,64,playing double bass,train 3lmTC5eaZ3o,10,"child speech, kid speaking",train 3lmZxzC0Bio,12,playing volleyball,train 3loBOieUrjA,2,lathe spinning,train 3loBOieUrjA,73,lathe spinning,train 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3mrC9pAWIZc,80,helicopter,train 3mrXbPGfaA8,30,female singing,train 3mrwN1CoeOw,30,female singing,train 3msLliexfYs,260,skateboarding,train 3mt11BuP5dw,30,people clapping,train 3mtdpXbogzk,180,"motorboat, speedboat acceleration",test 3mvE_CeH9q0,30,helicopter,train 3mwpcqHnhN4,30,horse clip-clop,train 3mxGqUjztEY,30,male singing,train 3my8O8vQxhw,0,child singing,train 3n--reua_a0,27,cat meowing,train 3n05BjV7r7M,5,sliding door,train 3n1sm1ELQ8A,678,playing snare drum,train 3n1sm1ELQ8A,719,playing snare drum,train 3n49iVKodmc,8,swimming,train 3n4ENB_Zj2o,20,people babbling,train 3n768MgJd2Q,46,hail,train 3n768MgJd2Q,87,hail,train 3n9hbYwZZqk,9,playing bagpipes,train 3n9hbYwZZqk,150,playing bagpipes,train 3nAJRVtDL4M,5,fire truck siren,train 3nAc3hMiSvE,210,lions growling,train 3nBeX4lKPWI,173,playing vibraphone,train 3nBeX4lKPWI,183,playing vibraphone,train 3nEpOo2NfCo,14,cat purring,train 3nEpOo2NfCo,24,cat purring,train 3nF3F8IhEu4,30,playing harpsichord,train 3nG7T4p97HE,14,cheetah 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3oRgfwIXPEE,76,playing glockenspiel,train 3oYSlw6l0XA,380,typing on computer keyboard,test 3oZgMPvdxtQ,30,"motorboat, speedboat acceleration",train 3o_pLgWtd6o,46,penguins braying,train 3oaky81Equ8,16,electric grinder grinding,test 3oaky81Equ8,45,electric grinder grinding,test 3obLPXCKYpM,130,"race car, auto racing",train 3obfBkNGtv4,249,golf driving,train 3od9aUtrcz4,40,playing saxophone,test 3oe2YOuCZFM,80,lions growling,train 3oe63RC5LhI,30,playing bass guitar,train 3oef68YabD0,2,"male speech, man speaking",test 3oefh4d24h4,0,ambulance siren,train 3ogvpIfTlhY,0,hail,test 3ogvpIfTlhY,29,hail,test 3oiBBirw4H8,30,playing acoustic guitar,train 3oj_nRQRd_Q,190,splashing water,train 3okoVi3M_WA,3,cat hissing,train 3old2IAT-P0,30,"male speech, man speaking",train 3opGxvlhDGw,35,francolin calling,train 3opGxvlhDGw,111,francolin calling,train 3opYb79_ozE,121,arc welding,train 3opYb79_ozE,158,arc welding,train 3op_kCAYePo,30,playing bass drum,train 3oqAUFCk32s,30,wind noise,train 3orhy2ukQ28,150,swimming,train 3orrFzLPc5Q,39,playing erhu,train 3otUlQ4wvLY,30,male singing,test 3ouNXXs3EHc,80,playing banjo,train 3oxOFICpmio,193,sharpen knife,train 3oxOFICpmio,224,sharpen knife,train 3oyB1Kus8Ls,76,playing cymbal,train 3p0EJX_ALfs,176,playing badminton,train 3p1aDdoCTqo,30,playing acoustic guitar,train 3p1k2UL5Q8A,63,crow cawing,train 3p1k2UL5Q8A,155,crow cawing,train 3p1nUlA1_eo,30,playing bass guitar,train 3p3A4QDXw-g,30,male singing,test 3p5QFkV7auM,32,arc welding,train 3p8RkDtYEgE,50,"motorboat, speedboat acceleration",train 3p9aVzs8aYA,30,female singing,train 3pA50Sm8efU,269,airplane flyby,train 3pBIi9Fjy9g,30,"motorboat, speedboat acceleration",train 3pEbvd3Na8U,60,splashing water,train 3pF_tcWqpPY,30,female singing,train 3pG8P6m0E4I,30,baby crying,train 3pI4o9J6Aag,160,car engine idling,train 3pI4o9J6Aag,200,car engine idling,train 3pIfMXVW43M,50,"bird chirping, tweeting",train 3pJ588Jm-2U,2,cattle mooing,train 3pJ588Jm-2U,43,cattle mooing,train 3pJUVMoajmM,29,lions roaring,train 3pJUVMoajmM,46,lions roaring,train 3pLnDLDC9Ms,0,driving snowmobile,train 3pLnDLDC9Ms,10,driving snowmobile,train 3pM6Qx3bvuw,3,"child speech, kid speaking",train 3pPSrdjQUO8,130,orchestra,train 3pQ8wwMVTbI,121,firing muskets,train 3pR_57IS-fM,30,child singing,train 3pRjN92McPQ,30,playing banjo,train 3pSH7rZ5Etk,29,lathe spinning,train 3pV7d32BfBQ,50,lions roaring,test 3pVA-QGwVco,30,chicken crowing,train 3pW86xNtueI,30,"pigeon, dove cooing",train 3pWTLDHzXQ4,30,playing electric guitar,train 3pYJi5e-i9w,30,"male speech, man speaking",train 3pYWAWVcWwU,13,playing gong,train 3pYWAWVcWwU,324,playing gong,train 3pYxoIFMn2Y,87,rapping,train 3pYxoIFMn2Y,123,rapping,train 3pawubfvFek,107,tap dancing,train 3pawubfvFek,140,tap dancing,train 3pbN1SfMPdU,0,people whistling,train 3pdT176TlfE,363,civil defense siren,train 3pfELcB-wtU,38,cap gun shooting,train 3pfELcB-wtU,48,cap gun shooting,train 3pfcIX2QZP0,57,"motorboat, speedboat acceleration",train 3pfqUnK0x9c,26,train whistling,train 3phypxzGQPI,80,skiing,train 3pkGoSVPczg,16,mouse squeaking,train 3pkGoSVPczg,34,mouse squeaking,train 3pl1Emio5BI,5,chimpanzee pant-hooting,train 3pl1Emio5BI,15,chimpanzee pant-hooting,train 3poJu4RgSGc,73,playing tabla,train 3poJu4RgSGc,117,playing tabla,train 3posAjJWEi4,400,splashing water,test 3prvluhbnOo,30,female singing,train 3psAIQn38nk,34,golf driving,train 3psCxxucT7Q,47,playing harp,train 3pt_QkEmJ98,120,"female speech, woman speaking",train 3ptlEuPhJ-s,30,horse neighing,train 3ptwCmPgGNc,30,male singing,train 3ptxRyVuU0w,520,machine gun shooting,test 3pxoiN6EyUg,22,tap dancing,train 3pzYvGn-m_Q,49,playing tambourine,train 3pzYvGn-m_Q,126,playing tambourine,train 3q-3H26pipU,30,playing acoustic guitar,train 3q-JchFdzwE,30,car passing by,train 3q0cl6qLihQ,173,playing snare drum,train 3q0cl6qLihQ,231,playing snare drum,train 3q120yiwUc4,60,woodpecker pecking tree,train 3q120yiwUc4,79,woodpecker pecking tree,train 3q1DkQ6wbOs,30,male 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3qLonLZHtE8,210,playing cymbal,train 3qMYEZUkvcU,110,playing piano,train 3qNRUI2bdT8,30,"playing violin, fiddle",train 3qNSaQnzWUs,30,horse clip-clop,train 3qO4P2X-oHU,63,cheetah chirrup,test 3qPefjNIBok,30,goat bleating,test 3qTYudQugys,30,playing acoustic guitar,train 3qUbjqwGIx8,50,people sobbing,train 3qViFNEU9ak,30,playing electric guitar,train 3qXCX0ai6Ws,30,"playing violin, fiddle",train 3qaVPWjI7KQ,30,"playing violin, fiddle",train 3qe9RK3sMBs,0,basketball bounce,test 3qebnkmvjiw,152,playing synthesizer,train 3qesirWAGt4,20,dog barking,test 3qeykYF7pjQ,30,car engine knocking,train 3qfJKebhBTU,18,people slapping,train 3qgtQ0qyuts,107,playing badminton,train 3qiHV5i0GbA,72,sliding door,train 3qjbhmzyajY,31,playing hammond organ,train 3qpHpgWjrz0,19,crow cawing,train 3qpHpgWjrz0,29,crow cawing,train 3qpNZkxd-OA,30,car engine starting,train 3qt1J_CBfX0,41,bowling impact,train 3qtnbifHKXU,64,playing tuning fork,test 3qvoFOxxBIw,180,playing french horn,train 3r-61P3WM8U,30,"male 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3rY5SJ5uKSw,64,people booing,train 3rZSvpD5t2Y,143,playing timpani,train 3raMVUoN6Lw,17,goat bleating,train 3raMVUoN6Lw,34,goat bleating,train 3rebHmqy2jM,225,yodelling,train 3rf6-9LMshs,30,playing acoustic guitar,train 3rhGzj6Dt1E,163,cattle mooing,train 3ri9Zxwg_08,0,chimpanzee pant-hooting,train 3rjzRMZs4Q0,30,playing bagpipes,train 3rlxOuTcUC0,209,tapping guitar,train 3rn-tCQafl8,30,dog bow-wow,test 3rns4DQHEn4,110,playing cello,train 3rnvViBVuBU,30,"female speech, woman speaking",train 3roTh_P6biI,200,male singing,train 3rpDHN8QClo,25,civil defense siren,train 3rtvCt-LmuI,60,barn swallow calling,train 3rtvCt-LmuI,290,barn swallow calling,train 3rvgRUCmEpY,30,"playing marimba, xylophone",train 3rwnWLI0qHo,30,snake hissing,train 3rx66BY-i84,30,"engine accelerating, revving, vroom",train 3rxwXCAkvpU,83,playing steelpan,train 3rzLAawdzhg,1,magpie calling,train 3rzLAawdzhg,12,magpie calling,train 3s0QRMuXTnE,60,playing banjo,train 3s0i-tQfrcU,30,female singing,train 3s4uAaSnCz4,13,car 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(siren),train 3sYnYraOuIo,510,fireworks banging,test 3sYx4P33JxA,30,playing acoustic guitar,train 3sZ8cKelDRs,30,"rowboat, canoe, kayak rowing",train 3sZub6gLUew,290,"bee, wasp, etc. buzzing",train 3sdTgEz7oXM,10,lawn mowing,train 3se55vnrD0A,10,"vehicle horn, car horn, honking",train 3seavihOXmk,68,coyote howling,train 3sf-Fa7cZfA,3,horse clip-clop,train 3sflCnetnS0,30,car engine knocking,test 3sg0SrdGsq4,20,"playing marimba, xylophone",train 3shIRyIgzrE,897,fire crackling,train 3shIRyIgzrE,968,fire crackling,train 3shTfMWBhPA,0,lawn mowing,train 3shqIkkhGIQ,30,playing electric guitar,train 3sj_-6uX3xk,30,people sneezing,train 3sln1_psRUU,50,playing synthesizer,train 3sm1AK6l9u8,30,male singing,train 3soNTGz24xo,30,playing electric guitar,train 3sov3vKMpoc,30,"motorboat, speedboat acceleration",train 3sp7-5pJXYg,30,"male speech, man speaking",train 3spIoq7zkew,378,dinosaurs bellowing,train 3spIoq7zkew,390,dinosaurs bellowing,train 3sr974a_5_0,0,stream burbling,train 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4Rt1q4vBCIU,32,lathe spinning,train 4RtWwoh42pc,5,bouncing on trampoline,train 4Rto9BkNv-Y,30,"child speech, kid speaking",train 4Ruk56DiQj8,30,"bird chirping, tweeting",train 4RutoyCbhzY,210,waterfall burbling,train 4RvbUx9fXvM,304,playing congas,train 4RyID93BNuI,192,lighting firecrackers,train 4Rym4DqrhHM,30,cattle mooing,test 4Rz2o9Z9FEM,30,"playing violin, fiddle",train 4RzAe3fyiY4,114,playing badminton,train 4S-Gub71djg,87,playing ukulele,train 4S-Gub71djg,281,playing ukulele,train 4S0Me_0xdsg,30,playing french horn,train 4S3JFXawlq8,65,cupboard opening or closing,train 4S3JFXawlq8,80,cupboard opening or closing,train 4S3KpBZXIRc,94,train wheels squealing,train 4S3YZb6eQdw,0,playing cymbal,train 4S4HAUntfRY,30,"male speech, man speaking",train 4S5tFrk15Q8,0,missile launch,train 4S5tFrk15Q8,10,missile launch,train 4S7B132xDoY,22,"motorboat, speedboat acceleration",train 4S8c9KWmD-k,30,female singing,train 4SB3edrzqKw,2,playing bongo,train 4SB3edrzqKw,13,playing bongo,train 4SBs7SR1mNY,30,playing bagpipes,train 4SCTfaYmwnc,0,people sobbing,train 4SDbvMqY20k,30,sheep bleating,train 4SG5_5E44bU,30,"female speech, woman speaking",train 4SHm6lbVxic,20,fireworks banging,train 4SIbk8cHSWY,30,playing bass guitar,train 4SIuuAQ3YLk,40,toilet flushing,train 4SIvARU12AA,4,rope skipping,train 4SL8XRbXqus,9,"motorboat, speedboat acceleration",train 4SLZ6a-_AxA,936,lathe spinning,test 4SN4qd2DrFY,30,male singing,train 4SQ-JmWEho8,400,people clapping,train 4SQ58QIZU6U,27,playing glockenspiel,train 4SQ58QIZU6U,41,playing glockenspiel,train 4SRnoNRF8AA,30,female singing,train 4STGms-Uyuo,0,fireworks banging,train 4STHMNgCAHM,204,playing harp,train 4SToswHS8bw,141,"fly, housefly buzzing",train 4SUPgmqKe1s,53,"bird chirping, tweeting",train 4SUu2G5CvJQ,7,playing washboard,train 4SUu2G5CvJQ,21,playing washboard,train 4SXMrTBjNc4,14,firing muskets,train 4SXt7WVkOOU,57,yodelling,train 4SY6UlT-tG8,16,"cattle, bovinae cowbell",train 4SY6UlT-tG8,74,"cattle, bovinae cowbell",train 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4aFirNGu_P8,426,planing timber,train 4aI5KBUZRjU,20,lions growling,train 4aJdbteTqlY,30,child singing,train 4aMRgzWp4H4,1,people battle cry,train 4aMRgzWp4H4,29,people battle cry,train 4aNGJhpRN6U,29,playing hammond organ,train 4aNGJhpRN6U,44,playing hammond organ,train 4aNGtCv4cYc,168,playing harmonica,train 4aR88-YLypo,73,playing lacrosse,train 4aR88-YLypo,116,playing lacrosse,train 4aRGLWEJxm8,341,skiing,train 4aRYzKW-_Fg,30,horse neighing,train 4aS5hiMnxek,0,yodelling,train 4aS5hiMnxek,13,yodelling,train 4aTMef_vDxE,0,bird squawking,train 4aTMef_vDxE,10,bird squawking,train 4aU1w7pqJGU,130,"child speech, kid speaking",train 4aUDttwGEWE,170,playing drum kit,train 4aVzh6mImIg,70,playing acoustic guitar,train 4aW_oVXTOIM,38,printer printing,train 4aW_oVXTOIM,50,printer printing,train 4aYK2lPo77c,170,police car (siren),train 4a_1IoqQgQY,30,playing acoustic guitar,train 4a_ZErzvTb0,20,fireworks banging,train 4ab1gIubby8,8,playing tuning fork,train 4ab1gIubby8,96,playing tuning 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4bLFS9KnHJU,30,cat meowing,train 4bNVFBzxtHw,30,male singing,train 4bQdokE2vEw,217,police car (siren),train 4bRRp4e_iDw,450,fireworks banging,train 4bSsg-8kbQo,15,lighting firecrackers,train 4bTSwkyUoYg,268,train whistling,train 4bUL_ttiOdw,21,baby crying,train 4bVuzJsgUe0,153,playing bongo,train 4bYTxN0M6mg,1,playing trombone,train 4bYTxN0M6mg,33,playing trombone,train 4bYvLmTISps,30,playing acoustic guitar,train 4bZMvyzfZkY,80,stream burbling,train 4baGljyzdW4,190,snake hissing,train 4bb6cm_Dg8M,3,playing bongo,train 4bdsT8e3WpA,31,people giggling,train 4be-7SaPD_Y,30,wind rustling leaves,train 4bheVZSrDbs,9,playing synthesizer,train 4bheVZSrDbs,20,playing synthesizer,train 4bhj7TthTlc,104,people marching,train 4bhpXhxP-WU,11,people booing,train 4bhpXhxP-WU,36,people booing,train 4bj5XAzAtPY,30,playing electric guitar,train 4bkhAlgw-44,30,people sniggering,train 4blQ5wRV7XA,30,driving motorcycle,train 4bmH5rXWS7I,30,"male speech, man speaking",train 4bmQQxR4Yxg,460,lawn mowing,train 4bma6GcgXXo,0,skidding,train 4bn0l60xg1w,26,playing tambourine,train 4bn0l60xg1w,43,playing tambourine,train 4btuGoj4P6I,317,skiing,train 4bv8yhZe1tU,2,toilet flushing,train 4bw8qb69PBg,30,people whispering,train 4c-iJIACk1I,30,"male speech, man speaking",train 4c0cARvhiRk,19,people humming,train 4c0cARvhiRk,31,people humming,train 4c0fG9oJ-Qg,9,horse clip-clop,train 4c0uLxgFiVc,420,church bell ringing,train 4c1DZry02ls,30,playing drum kit,train 4c38rC-BdRU,16,warbler chirping,train 4c38rC-BdRU,360,warbler chirping,train 4c5cZx1TLHI,30,"engine accelerating, revving, vroom",train 4c7qCb2aWYk,120,using sewing machines,train 4c9xmfKia44,30,playing acoustic guitar,train 4cClQc-Stks,90,"subway, metro, underground",train 4cDdFIn23GA,28,playing castanets,train 4cDdFIn23GA,75,playing castanets,train 4cEOkkDeJqs,30,horse clip-clop,train 4cEj58DYRGc,10,car engine knocking,train 4cEqsUfA9z0,30,playing electric guitar,train 4cF8-do5MrY,2,frog croaking,train 4cF8-do5MrY,87,frog croaking,train 4cFFwzNNiAk,30,playing trombone,train 4cHXdVw2-90,36,playing harmonica,train 4cHXdVw2-90,317,playing harmonica,train 4cIXwIFpNkE,9,canary calling,train 4cJTY4cHxk0,30,"female speech, woman speaking",train 4cJg6GOkXv4,83,playing ukulele,train 4cJg6GOkXv4,141,playing ukulele,train 4cLGIraee5U,284,playing tympani,train 4cLGIraee5U,523,playing tympani,train 4cLz8nCEek8,398,cricket chirping,train 4cM3J6BcbQw,30,wind noise,train 4cMFDZ_HDEI,30,playing bagpipes,train 4cMIkyP0R78,0,people sobbing,train 4cNEKIg0eOA,46,playing volleyball,train 4cNEKIg0eOA,57,playing volleyball,train 4cTUYfh_nKk,40,people cheering,train 4cVf11NkQBA,120,stream burbling,train 4cZ__FPecxc,460,orchestra,train 4cZlZ3rXVN8,140,playing trombone,train 4cbh7Rd9Fwk,130,horse clip-clop,train 4ccNxzf-SD8,21,playing trombone,train 4cePXII2ycg,47,arc welding,train 4cgDBRTdmq0,221,air horn,test 4ciWIKT9sPQ,80,"female speech, woman speaking",train 4cjgoPQHFS0,290,playing bass guitar,train 4ckZutJGFLQ,90,people crowd,train 4cqRKsgxBAA,30,playing acoustic guitar,train 4ctbltakDP8,105,coyote howling,train 4ctbltakDP8,395,coyote howling,train 4cte5A1reCM,20,people burping,train 4cu3kGDs10I,3,tornado roaring,train 4cu3kGDs10I,47,tornado roaring,train 4cvklHsBPGI,34,beat boxing,train 4cxd0Vs0gvo,144,wood thrush calling,train 4cxd0Vs0gvo,183,wood thrush calling,train 4cya2yZJV0g,16,sliding door,train 4cyaFyQr4V4,20,lawn mowing,train 4cyi9yZXI80,370,"race car, auto racing",train 4cziCvJUhtA,30,"male speech, man speaking",train 4d-dT9YBedc,4,elephant trumpeting,train 4d-dT9YBedc,117,elephant trumpeting,train 4d-dxIFjL4g,1,"playing steel guitar, slide guitar",train 4d-dxIFjL4g,12,"playing steel guitar, slide guitar",train 4d1VDNUCXPA,30,playing acoustic guitar,train 4d1sQJO5Z54,510,wind noise,train 4d213Q_i9OA,9,"donkey, ass braying",train 4d3vFI5UpIc,7,gibbon howling,train 4d7_oaxDNOc,30,"playing violin, fiddle",train 4d8iemra7BE,57,striking pool,train 4d8iemra7BE,92,striking pool,train 4dBM17WHyHM,30,"engine 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4dhVR16Sm64,85,frog croaking,train 4dhVR16Sm64,99,frog croaking,train 4dhyddSUAWg,14,police radio chatter,train 4dhyddSUAWg,175,police radio chatter,train 4diqZHVAGCI,30,"playing violin, fiddle",train 4dkU-c4g1VM,111,dog barking,train 4dkU-c4g1VM,163,dog barking,train 4dmBaDl584M,0,dog barking,train 4dnWjyySxQs,0,"vehicle horn, car horn, honking",train 4do54Decjrs,30,playing bass guitar,train 4dp9-SzcjXA,9,opening or closing car doors,train 4drS_d4kbN0,30,child singing,train 4dtlc2iiVjw,13,"playing steel guitar, slide guitar",train 4dtlc2iiVjw,83,"playing steel guitar, slide guitar",train 4duOIDKlbos,15,coyote howling,train 4duOIDKlbos,30,coyote howling,train 4duQCsZVkD0,16,barn swallow calling,train 4duQCsZVkD0,102,barn swallow calling,train 4dxdVV1SuvI,68,police radio chatter,test 4dy4NVoiygc,47,yodelling,train 4dy4NVoiygc,83,yodelling,train 4dzwpnunIdY,3,wind rustling leaves,train 4e-SX9wMWGE,5,mynah bird singing,train 4e-SX9wMWGE,40,mynah bird singing,train 4e0riwTv0xY,170,eating 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bongo,train 4ecjFo1h4mM,102,playing bongo,train 4ecs1MrI6gY,30,"male speech, man speaking",train 4edK8hTyCXA,30,playing acoustic guitar,train 4eeCIydN--4,3,cat meowing,train 4efSIvhv4lU,125,slot machine,train 4egukLBpAcQ,30,chainsawing trees,test 4ej6SM_caX8,272,popping popcorn,train 4ejLeuJcmoQ,25,helicopter,train 4ej_ZNSW_IY,84,playing guiro,train 4ejiG4iizcI,30,sheep bleating,train 4en3v_n6IWM,30,male singing,train 4enilF4pEko,0,mouse pattering,test 4eoCZhmgi4M,30,playing acoustic guitar,train 4ep09nZl3LA,30,reversing beeps,test 4epl0cI2-Ew,30,people belly laughing,train 4eqRyB5mMlI,6,mosquito buzzing,test 4erYC3tVoDw,40,playing harp,train 4eu5aWxN82I,30,printer printing,train 4euUZgSSuIw,160,woodpecker pecking tree,test 4ewhrrQCJic,120,cap gun shooting,test 4eyg3kCvZ1A,196,playing volleyball,train 4ezVEvHKT9M,295,tapping guitar,train 4ezrR2ufaB0,2,ice cracking,test 4f-OxHchGHU,50,playing didgeridoo,train 4f-iKQbnxm0,30,female singing,train 4f05q_UD-Q0,20,snake rattling,test 4f0FFrKJZkk,100,people babbling,train 4f1R36qriMc,20,chainsawing trees,train 4f2SbVHDPU8,30,cat meowing,train 4f37Eql1YpA,50,playing saxophone,train 4f3YKcLABDk,6,child singing,train 4f4R-i163gg,6,"cattle, bovinae cowbell",train 4f4R-i163gg,21,"cattle, bovinae cowbell",train 4f4T1k-WsEk,30,"playing violin, fiddle",train 4f5oB8auxyo,10,playing squash,train 4f7wTMnWbqQ,726,machine gun shooting,train 4f9UTGjtV5Q,30,"male speech, man speaking",train 4f9wKBo-kRg,30,"motorboat, speedboat acceleration",train 4fArmAGdfSY,0,opening or closing car electric windows,train 4fC1QmVDwsU,355,playing bass drum,train 4fC1QmVDwsU,503,playing bass drum,train 4fDq2HmIVrI,45,playing didgeridoo,train 4fED6esiE3I,30,singing choir,train 4fErKtlMUiQ,5,playing didgeridoo,train 4fFQNjlYN8o,30,dog bow-wow,train 4fFnsCifFRo,32,gibbon howling,train 4fNySMuyr70,0,playing bassoon,train 4fRbyl1DaM0,154,dog baying,train 4fRbyl1DaM0,343,dog baying,train 4fS56fYQ070,0,playing tabla,train 4fSzrI8gKp4,200,people sniggering,train 4fUBy0bT8U8,590,"bee, wasp, etc. buzzing",train 4fXRIXuYOus,150,playing trumpet,train 4fZfFpz7K0w,110,church bell ringing,train 4fbUxBr2A-o,30,people running,train 4fbYb88nno8,30,"pigeon, dove cooing",train 4fdj50cmB2c,120,people sniggering,train 4fevSSIxRgI,30,female singing,train 4fhl9xIVUFw,7,cattle mooing,train 4fhl9xIVUFw,50,cattle mooing,train 4fiKAAsqEMA,2,people farting,train 4fiKAAsqEMA,281,people farting,train 4fiR3qashxE,0,cattle mooing,train 4fkEenNoGY4,131,electric grinder grinding,train 4fkbVVLhJRs,440,playing vibraphone,train 4fnWgdzVKnQ,61,playing theremin,train 4fnWgdzVKnQ,248,playing theremin,train 4foP1NnCaPY,21,dog whimpering,train 4foP1NnCaPY,44,dog whimpering,train 4fpkD4A_t1s,35,playing cymbal,train 4fr32jhXHQE,30,playing acoustic guitar,train 4fti1n2kmss,10,eating with cutlery,train 4ftnQdEaLO8,70,playing banjo,train 4fuT7wlF6jY,20,"playing marimba, xylophone",train 4fwQDqRDHt4,266,playing hockey,train 4fwQDqRDHt4,368,playing hockey,train 4fzJQ0tYOAE,35,sea waves,test 4g3lXNjz0N0,40,playing electric guitar,train 4g3ntrQGgDw,0,vacuum cleaner cleaning floors,train 4g3ntrQGgDw,20,vacuum cleaner cleaning floors,train 4g4LXjjyNgA,79,playing didgeridoo,train 4g4eMbVYS5I,11,sea lion barking,train 4g5UJW0t4uc,30,cat meowing,train 4g6vv14KD4U,49,chimpanzee pant-hooting,train 4g6vv14KD4U,141,chimpanzee pant-hooting,train 4g7ilnLBh_U,30,"male speech, man speaking",train 4g7mLG-8AmQ,28,"engine accelerating, revving, vroom",train 4g9l9Po2J70,0,playing erhu,train 4g9l9Po2J70,42,playing erhu,train 4gD9zx1YGtc,30,female singing,train 4gDn-kUFHLg,59,ambulance siren,train 4gFbCdHle0w,30,pumping water,test 4gH49xUolqM,50,playing harpsichord,train 4gLGlF8-Kcg,73,playing saxophone,train 4gLGlF8-Kcg,120,playing saxophone,train 4gM_5gyM_FA,70,"race car, auto racing",train 4gNhqLdyUuM,2,people farting,train 4gPRrbIDOTY,50,church bell ringing,train 4gSRzRQie2M,1,cat meowing,train 4gT86CN1Rps,0,toilet flushing,train 4gVQSm2dMt4,80,playing harp,train 4gWwOA9JWZ0,530,ocean burbling,train 4gYPtEofhsg,40,"subway, metro, underground",train 4gaviW9QzY4,10,toilet flushing,train 4gbRUNa5nIM,134,playing steelpan,train 4gbRUNa5nIM,159,playing steelpan,train 4gfLeTZOmu8,30,people sniggering,train 4gfmGSw40dM,30,"male speech, man speaking",train 4ggZRf0Jp5g,30,female singing,train 4gggxf5LBeM,30,"playing violin, fiddle",train 4giL85pSzzY,30,playing electric guitar,train 4giafu96v1w,302,arc welding,train 4gij26RHDPs,30,skidding,train 4glPYChurF0,28,playing theremin,train 4gmCSl03pgo,30,playing electronic organ,train 4gmXgqkJm2w,40,"playing marimba, xylophone",train 4gmq40xxXYA,60,driving motorcycle,train 4go6CQ_RhYQ,250,hair dryer drying,test 4gp-2h4dMCw,66,playing timpani,train 4gp-2h4dMCw,118,playing timpani,train 4gp0XVHrSrI,30,"rowboat, canoe, kayak rowing",train 4gpeEUlW2dg,180,people crowd,train 4gq5IKSASSQ,30,dog bow-wow,train 4gqgfYAd7XQ,40,lawn mowing,train 4gr05zu98xc,2,church bell ringing,train 4gr2Se5WKP4,30,"female 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4hFwm8IdhMM,10,police car (siren),train 4hHDosGivJg,22,smoke detector beeping,train 4hHNJrlY-WY,7,bowling impact,train 4hJb45sSO6M,110,lawn mowing,train 4hMpfbdjIjo,40,cat purring,train 4hMvjjbW1ew,33,playing squash,train 4hOmfChTth8,1,fire truck siren,train 4hOmfChTth8,19,fire truck siren,train 4hQgFX4vxi4,30,dog bow-wow,train 4hU6jqQQUto,9,playing harpsichord,train 4hUSCEtJDTU,480,"race car, auto racing",train 4hUysAJFmf8,40,stream burbling,train 4hXl-C8wwfM,70,beat boxing,train 4hYiaSYueCE,60,playing cornet,test 4hYiaSYueCE,216,playing cornet,test 4hZrR-W2Thg,30,child singing,train 4h_xj31Ggso,30,child singing,train 4hcHr-U9cgg,420,people cheering,train 4hd2CLrzCZs,30,"engine accelerating, revving, vroom",test 4heWGT4YjiM,30,people shuffling,test 4heiBUr2Bow,50,playing synthesizer,train 4hew3HeE1LI,0,people screaming,train 4hhGREGdWuY,228,hair dryer drying,train 4hijHihnKzY,20,helicopter,train 4hixl1PkJ0I,18,playing bassoon,test 4hixl1PkJ0I,85,playing bassoon,test 4hjCYiQfmZU,1,planing timber,train 4hjCYiQfmZU,37,planing timber,train 4hjW385XyHE,30,cat meowing,train 4hkwDZ0lzVI,30,male singing,train 4hl-3oay7c0,7,pumping water,test 4hlPEicuOKM,30,playing acoustic guitar,train 4hlWfqP9vzk,27,metronome,train 4hlWfqP9vzk,145,metronome,train 4hllebRhENI,158,"electric shaver, electric razor shaving",train 4hnz9O-0uQ8,58,playing harmonica,train 4hpZtZyh0Xs,4,pheasant crowing,train 4hp_AZbnVEE,30,"motorboat, speedboat acceleration",train 4hsd65MhsS8,40,playing synthesizer,train 4hxOFQ8t6F0,60,"rowboat, canoe, kayak rowing",train 4i-4ftqMeo0,7,sheep bleating,train 4i-iua3hAG0,30,playing acoustic guitar,train 4i13IaeBZgA,32,playing erhu,train 4i5r4V7ZBQI,5,cap gun shooting,train 4i5r4V7ZBQI,16,cap gun shooting,train 4i8NO3X2v74,95,"subway, metro, underground",train 4i8NO3X2v74,718,"subway, metro, underground",train 4i9DgH80kDg,30,playing banjo,test 4iAR1YBLkus,137,playing sitar,train 4iAR1YBLkus,395,playing sitar,train 4iBqpFUnPoA,170,fireworks banging,train 4iFEX0lYXyY,30,"subway, metro, underground",train 4iFT4mdGwH8,30,"vehicle horn, car horn, honking",train 4iG7Y-bP78o,170,playing bassoon,train 4iGsAv1Idrs,15,cat caterwauling,train 4iHGe8w8qc4,30,"playing violin, fiddle",train 4iJGQIUyKhw,70,people hiccup,train 4iLKKmkyN14,110,fireworks banging,test 4iLMcU6Kjl8,87,playing squash,train 4iLUAbDQ2E4,160,skidding,train 4iNH5KcANLw,30,chainsawing trees,train 4iS7MF8mzac,35,cell phone buzzing,test 4iTlAxWW6WU,30,playing electric guitar,train 4iUo257KvbE,202,children shouting,test 4iV2AKbTZaU,8,shot football,train 4iWXpUNpXtw,410,ambulance siren,train 4iX6QcFCbcQ,30,"playing violin, fiddle",train 4iaqYlmr0Ro,0,playing bassoon,train 4idgv63g4Ug,169,skiing,train 4ieUQEjLU1U,129,owl hooting,train 4ieUQEjLU1U,139,owl hooting,train 4ihSgRNfkeM,30,playing electric guitar,train 4iibx3vPLts,30,"motorboat, speedboat acceleration",train 4il63WoPc0s,207,volcano explosion,train 4il63WoPc0s,228,volcano explosion,train 4iliicSkQNM,30,playing flute,train 4inDbYLMsk0,480,"child speech, kid speaking",train 4ipWKLhd39o,30,"playing violin, fiddle",train 4iphOVMeOME,47,playing oboe,train 4iphOVMeOME,79,playing oboe,train 4iq8TLSBnsM,150,people crowd,train 4isFtP23Sh0,25,cat caterwauling,train 4isFtP23Sh0,73,cat caterwauling,train 4isHCjOrIIk,31,chopping wood,test 4ivFJOMviDc,6,pig oinking,train 4iwDHWXzQJg,30,male singing,train 4ixQ_ZeiSyQ,30,male singing,train 4izwBJEV0qA,40,dog barking,train 4j-_8M8qWk0,39,airplane flyby,train 4j0L0W5MkiI,1,cow lowing,train 4j0sWzGR6sg,30,people farting,train 4j20SKEW2Y4,260,"subway, metro, underground",train 4j2scmsFwIc,28,crow cawing,train 4j2scmsFwIc,40,crow cawing,train 4j34Ukcvkyo,30,"motorboat, speedboat acceleration",train 4j5CsFnXIfM,61,train horning,train 4j5CsFnXIfM,95,train horning,train 4j62lG5sTrE,0,"subway, metro, underground",train 4j7GbxZQjB8,24,car engine knocking,train 4j7GbxZQjB8,65,car engine knocking,train 4j8Ne_8bGEU,40,wind noise,train 4jBUfO_IkX0,212,playing harpsichord,train 4jBUfO_IkX0,261,playing harpsichord,train 4jDqaqqvLVQ,30,chicken crowing,test 4jGrLhMLulA,14,sheep bleating,train 4jHC-EX1GX8,30,"motorboat, speedboat acceleration",train 4jHrFbnaVRc,294,firing muskets,train 4jIxOuTJh-k,104,swimming,train 4jJ2mV88v74,30,sailing,train 4jKkc7qQQ88,16,dog baying,test 4jM-Y4QdyOw,81,ripping paper,train 4jNagJvHrJ8,30,people farting,train 4jO7p0wQDi8,30,"playing marimba, xylophone",train 4jRSJjV-afY,50,people sneezing,train 4jSdZ5s_ft0,2,"pigeon, dove cooing",train 4jW7xTLOjtc,43,dog barking,train 4jWllOiVygA,30,cat caterwauling,train 4jY6qjpgxJc,15,dog growling,train 4jZjNhgM7s4,14,dog bow-wow,train 4jaU_R0rhC8,10,dog bow-wow,train 4jcikqJx2yE,178,firing muskets,train 4jgTmzXNKRk,30,duck quacking,train 4jiLdRnqW_s,2,fox barking,train 4jjMZTHIJok,210,"vehicle horn, car horn, honking",train 4jk63c84kr0,130,"female speech, woman speaking",train 4jkUPQA9ApM,30,"male speech, man speaking",train 4jmv1XpnR90,50,baby laughter,train 4jmvM2R2jAY,25,playing cornet,train 4jmvM2R2jAY,54,playing cornet,train 4jnOpT78B7o,10,playing glockenspiel,train 4joy_cww3Qk,30,playing bass guitar,train 4jozrwhD37U,30,"playing violin, fiddle",train 4jrkctbayaM,30,male singing,train 4jt1iKXSd90,480,driving motorcycle,train 4jucrVBcSGI,20,people crowd,test 4jwzcofos5Q,222,planing timber,train 4jyvga149tU,30,horse neighing,train 4jz2LztV7Hk,202,scuba diving,train 4jzIyP5wN_I,100,people finger snapping,train 4k1DSRIjzcE,47,raining,train 4k1DSRIjzcE,127,raining,train 4k2DmuFwQvc,30,female singing,train 4k4Tzdu-Qss,63,canary calling,train 4k5Jfnss-JU,30,male singing,train 4k74Cvw2rLU,160,bird squawking,test 4k8sLuTbzOI,11,popping popcorn,train 4k9eWAhUGkM,51,playing bassoon,train 4kA9zn075bE,9,playing squash,train 4kAoBqK-pFI,110,"race car, auto racing",train 4kCLya_7bLs,9,playing timbales,train 4kCLya_7bLs,25,playing timbales,train 4kCre4Wwh6o,45,pheasant crowing,train 4kGozLe1X_Q,73,playing djembe,test 4kIMv2FIUWY,30,driving buses,train 4kIhJhN_ry8,30,pheasant crowing,train 4kIhJhN_ry8,262,pheasant crowing,train 4kJnHD93fDM,30,"rowboat, canoe, kayak rowing",train 4kKxHaFKeBU,30,"male speech, man speaking",train 4kLGnZxl4Yw,60,"child speech, kid speaking",train 4kLp4qP72yc,0,driving motorcycle,train 4kNAXUqlvAI,295,singing bowl,train 4kOAVSw9Z0A,30,child singing,train 4kQ7ZuaOKDI,46,dinosaurs bellowing,train 4kQ7ZuaOKDI,62,dinosaurs bellowing,train 4kRDE90UGRg,50,wind noise,train 4kT-lzQNJF4,30,horse neighing,train 4kVq6vB04B8,30,driving buses,train 4kX7tJBZneQ,30,people sniggering,train 4kXW4Vds4xg,30,people clapping,test 4kYAgNG3pUM,12,arc welding,train 4kZ-6xON17k,31,playing mandolin,train 4kZ-6xON17k,354,playing mandolin,train 4k_VXcQL06Y,7,car engine knocking,train 4k_VXcQL06Y,17,car engine knocking,train 4kbQgn2OIto,30,playing acoustic guitar,train 4kc4DrKg06M,30,"male speech, man speaking",train 4kcab5Hg-7Q,137,tap dancing,train 4kd48cu404M,30,female singing,train 4kf2_VaTHDY,150,singing choir,train 4kfCcFUE6Pc,1,whale calling,train 4kfOU8PreOo,30,playing acoustic guitar,train 4kfV8c7ibDc,30,"male speech, man speaking",train 4ki2idVRj4Q,321,tractor digging,train 4ki73zg038Q,30,playing acoustic guitar,train 4kkrLlD_7sg,18,playing bugle,test 4kkrLlD_7sg,83,playing bugle,test 4kofZlPsMoc,130,"ice cream truck, ice cream van",train 4kofZlPsMoc,188,"ice cream truck, ice cream van",train 4kq_eW1NcDs,80,ferret dooking,train 4ks0_dkWNGw,10,cat meowing,train 4ksEvXq-i7w,110,cat meowing,train 4ksaGLuzD8U,80,"motorboat, speedboat acceleration",train 4ku0PSzJgiM,3,owl hooting,train 4kvMxffs2V8,0,volcano explosion,train 4kvqtJEFqjw,190,playing bagpipes,train 4kxTV6o1pw4,41,chopping food,train 4kxX9wpOKnA,30,"male speech, man speaking",train 4l1Wz4faxkc,20,snake rattling,train 4l1Wz4faxkc,31,snake rattling,train 4l5x4uyu-uE,310,playing cello,train 4l67XnuQO_M,234,roller coaster running,train 4l6IYZU8ZKI,30,playing piano,train 4l7pVvVK9nI,162,playing table tennis,train 4l91vxM-JQU,10,police car (siren),train 4l9UUFoU9EU,343,playing steelpan,train 4l9UUFoU9EU,449,playing steelpan,train 4lCPeXS52zQ,140,sailing,train 4lDB_cFgoz0,8,people marching,train 4lDB_cFgoz0,37,people marching,train 4lERySWydkU,0,lions growling,train 4lFkbJphYm8,121,playing bass drum,train 4lFkbJphYm8,156,playing bass drum,train 4lH9S8DC8oM,30,"playing violin, fiddle",train 4lK59o053NU,1,rope skipping,train 4lK59o053NU,90,rope skipping,train 4lLkPHKE_Gg,8,playing volleyball,train 4lN1PfKg28k,70,"pigeon, dove cooing",train 4lNaYGaeRFQ,420,orchestra,train 4lNsWi99V5Y,280,ocean burbling,train 4lOTeIsN6wc,17,duck quacking,train 4lOXjW8rpxU,0,people coughing,train 4lOwFWQ-nQc,13,stream burbling,train 4lPLQzBT84E,30,cat meowing,train 4lQ_MjU4QHw,42,typing on typewriter,train 4lQ_MjU4QHw,74,typing on typewriter,train 4lS2ABk5KYU,55,otter growling,train 4lS2ABk5KYU,67,otter growling,train 4lXclEZcAlU,258,playing flute,train 4lY2GgVg-GI,32,cat hissing,test 4lYE_Bgydlg,245,train whistling,train 4lYE_Bgydlg,300,train whistling,train 4lYQD8oNIi8,30,horse neighing,train 4lZNAMJTw3E,30,playing acoustic guitar,train 4l_LVR88DhM,390,playing clarinet,train 4lchD4CmjDc,10,cow lowing,test 4ldID97D-oU,20,people coughing,train 4le_42J5qkU,12,cat meowing,train 4liPv5npftg,194,"heart sounds, heartbeat",test 4llA1fFN26U,12,people burping,train 4llA1fFN26U,43,people burping,train 4lmDnGe6oOM,30,"male speech, man speaking",train 4lmtlR2SOh0,30,singing choir,train 4lnhOIftuMQ,60,people whistling,train 4lnjRvHh_jY,30,male singing,train 4loEscUObuk,351,rapping,train 4lorH6_kV2A,30,horse clip-clop,train 4lpu8RIA6_k,330,people screaming,train 4lqUgzaykg8,160,eating with cutlery,test 4lrXdffoQh4,37,playing congas,train 4lrXdffoQh4,68,playing congas,train 4ltMnqHyJGc,28,cattle mooing,train 4ltRU4gFvNQ,17,cat growling,train 4ltnOsbLIJI,40,toilet flushing,train 4lv9P23EgxA,60,printer printing,train 4lwG34wZYdY,55,barn swallow calling,train 4lwG34wZYdY,86,barn swallow calling,train 4lyT9tez4IE,30,"female speech, woman speaking",train 4lytS3VyzTI,30,chainsawing trees,train 4lzKsazb254,30,playing acoustic guitar,train 4m-kVx4K8cg,74,playing badminton,test 4m2g9OB0SFU,50,playing drum kit,train 4m4OZxceG08,30,female singing,train 4m517eFnzuo,40,"race car, auto racing",train 4m962cRo960,47,cat hissing,train 4m9xANkhsZQ,60,waterfall burbling,train 4mByRFk6iZQ,18,police radio chatter,train 4mByRFk6iZQ,57,police radio chatter,train 4mC1ppSeN28,0,"donkey, ass braying",train 4mCpxmwhci4,49,cat caterwauling,test 4mF4zegsZCw,80,playing bagpipes,train 4mFktqODAMU,355,vacuum cleaner cleaning floors,train 4mGpG2ufZgk,30,"playing violin, fiddle",train 4mH4vFRYTdY,30,fireworks banging,train 4mJ-Wwq5ijw,140,people cheering,train 4mJ8ZDheEM4,16,playing bass drum,train 4mJ8ZDheEM4,27,playing bass drum,train 4mK2qKQjnuc,30,"pigeon, dove cooing",train 4mKHPElrmOo,0,people clapping,train 4mLv61JADOQ,50,skateboarding,train 4mPkPnN-T9o,48,singing choir,train 4mPkPnN-T9o,60,singing choir,train 4mX-FX8A9uQ,30,driving buses,train 4mX4RuO8cZw,110,"child speech, kid speaking",train 4mXEDQ_S5sc,51,playing squash,train 4mXikhkrUXA,10,helicopter,train 4mY_cjqkK0Y,233,"playing steel guitar, slide guitar",train 4m_70-KPC10,18,woodpecker pecking tree,train 4m_70-KPC10,31,woodpecker pecking tree,train 4mbjc7b59gY,25,owl hooting,train 4mbjc7b59gY,57,owl hooting,train 4mbu2ueMe2E,18,underwater bubbling,train 4mbu2ueMe2E,137,underwater bubbling,train 4mcgn3asep8,19,ice cracking,train 4mcgn3asep8,73,ice cracking,train 4mgX8QxQsn0,470,lions growling,train 4mhQ721OnsE,30,child singing,train 4mhuA5d6JOA,171,mouse clicking,train 4mjj6V4Oi4w,28,"playing steel guitar, slide guitar",train 4mjj6V4Oi4w,140,"playing steel guitar, slide guitar",train 4ml0JMCNOR4,31,woodpecker pecking tree,train 4ml0JMCNOR4,164,woodpecker pecking tree,train 4mnrwM3UELM,30,playing electric guitar,train 4msoB74uypo,30,"pigeon, dove cooing",train 4mswzuaZjnQ,30,cat meowing,train 4mtj8-D05TI,30,female singing,train 4muUnvSiceA,30,cat meowing,train 4muu6L6cHs4,30,female singing,train 4mw2w5MmdjQ,144,playing mandolin,train 4mw2w5MmdjQ,155,playing mandolin,train 4n-Te5Ld7mo,0,missile launch,train 4n13mkhnIcw,20,playing bass guitar,train 4n3IRbC0ywg,41,playing theremin,train 4n3IRbC0ywg,81,playing theremin,train 4n4LfWt2dVU,160,fireworks banging,train 4n5663oxxDU,30,driving motorcycle,train 4n657Imjmjo,15,sheep bleating,train 4n6pa7f_T7M,30,female singing,train 4n7X62O0E1k,4,bull bellowing,train 4n7X62O0E1k,95,bull bellowing,train 4nC49mQyEGQ,27,dog whimpering,train 4nD5lXIO_oE,30,skateboarding,train 4nHL61DSM3s,637,people slurping,train 4nHL61DSM3s,648,people slurping,train 4nHUfVnmH-0,0,skidding,train 4nKjtrnLHCs,44,playing bugle,train 4nM9UpBBPhI,266,playing theremin,train 4nN9QuT_OJM,20,toilet flushing,train 4nO5aI4Iq0Q,50,orchestra,train 4nO7SgfHPts,0,"vehicle horn, car horn, honking",train 4nOdVrO0JTE,30,child singing,train 4nP5_uCxD3U,56,singing bowl,train 4nPwg37Vrg4,30,people crowd,train 4nQT1bL3e7A,0,playing darts,train 4nQog3-_BOY,90,fireworks banging,train 4nRQ1QVXurQ,30,wind rustling leaves,train 4nRb6gEpRWo,24,rope skipping,train 4nRb6gEpRWo,127,rope skipping,train 4nTcoySgx1Q,30,"female speech, woman speaking",train 4nTsfDX6-kM,550,wind noise,train 4nV_pfmapH4,30,female singing,train 4nVnNB8op10,80,dog barking,train 4nVoeV3iLhQ,30,playing electric guitar,train 4nZQEWyzumc,30,playing electric guitar,train 4nZ_aZzec14,169,police radio chatter,train 4n_Dl4Mu_5g,90,playing accordion,train 4n_Yztbefi0,35,police radio chatter,train 4n_Yztbefi0,191,police radio chatter,train 4n_g-Qjy2n4,10,zebra braying,test 4n_rCIk2NQU,30,child singing,train 4ndYVaLbpEQ,30,male singing,train 4negcLvXq8A,23,ocean burbling,train 4nhmvjZHhg8,30,"bird chirping, tweeting",train 4nlB8qkQJ9s,20,people whistling,test 4nmkq1dhfDs,220,fireworks banging,train 4nn2IkQcoZ4,4,"donkey, ass braying",train 4nnJpWkUWxQ,10,lawn mowing,train 4noHONJstjw,220,playing drum kit,train 4npuBancLyY,147,wind noise,train 4nqIKfvRb2Y,157,fire crackling,train 4nsoFVx6MZ4,17,cat meowing,train 4nuZXg7fuBA,480,playing cello,train 4nuvSOb5Syc,24,volcano explosion,train 4nvAbP6wXDA,100,mouse pattering,train 4nxbjH-Q8Pg,30,playing electric guitar,train 4nyeCkCZiic,30,playing flute,train 4nyr0ce83ek,30,stream burbling,train 4o0c5uM-370,30,horse clip-clop,train 4o1COd_SWyw,47,"playing steel guitar, slide guitar",train 4o1COd_SWyw,125,"playing steel guitar, slide guitar",train 4o1YlJioDHE,22,"pigeon, dove cooing",train 4o20dEnQifc,30,male singing,train 4o2IRyXi-aY,325,playing harpsichord,train 4o2IRyXi-aY,667,playing harpsichord,train 4o2mmbMIJFs,24,playing vibraphone,train 4o32eYwCCGU,75,gibbon howling,train 4o32eYwCCGU,87,gibbon howling,train 4o32uQXPjbg,30,playing electric guitar,train 4o3E5SLrghQ,400,"bird chirping, tweeting",train 4o3t2ynnWtU,48,playing electronic organ,train 4o3t2ynnWtU,73,playing electronic organ,train 4o4vXix0nGI,49,playing french horn,train 4o7971UYubI,10,playing electric guitar,train 4o8e7uUET24,30,people shuffling,train 4oA4ExnChF0,161,tap dancing,train 4oCdwxmeM2A,8448,wind rustling leaves,train 4oDeIv9gA-k,310,ocean burbling,train 4oEjg-ZPXBc,30,playing badminton,test 4oISdT2t5lU,30,using sewing machines,train 4oLI1ydnsX4,17,train whistling,train 4oNK7MWNZjQ,30,child singing,train 4oNM9Ze60DI,80,people screaming,train 4oNMRGGEGfM,27,"bird chirping, tweeting",train 4oSpF8Fj4_k,29,gibbon howling,train 4oSpF8Fj4_k,72,gibbon howling,train 4oUZprjHLUo,140,"subway, metro, underground",train 4oYvlTxsEVo,30,child singing,train 4o_s3hXDPhM,26,car engine starting,train 4oapd9jq7jI,410,police car (siren),train 4obki3oLFFU,9,pumping water,train 4ocLv3bI-O8,3,"fly, housefly buzzing",train 4ocpom8W-A0,189,people eating apple,train 4ocpom8W-A0,339,people eating apple,train 4odv8-E6PyQ,0,playing tambourine,train 4odv8-E6PyQ,50,playing tambourine,train 4onH3sBeVdU,42,car engine idling,test 4ooEeN51qdQ,93,playing erhu,test 4ooeMv8m-Wg,57,playing tabla,train 4ooeMv8m-Wg,356,playing tabla,train 4oor_OxZw_Q,23,waterfall burbling,train 4oqBCyfd4ro,129,barn swallow calling,train 4oqBCyfd4ro,149,barn swallow calling,train 4oqqXQcDg3w,2,alarm clock ringing,train 4ouDRNzMlOI,26,playing acoustic guitar,train 4oxIoyP7-mY,30,playing bass guitar,train 4oyCLBKl3_s,30,"rowboat, canoe, kayak rowing",train 4p-dncxh1Hw,0,skateboarding,train 4p1AE2_zsHY,30,playing acoustic guitar,train 4p59BuaT5eE,97,playing table tennis,train 4p7BcobBKgk,118,firing muskets,train 4p8T4zQOYIw,30,wind noise,train 4p8hJPMm3D4,13,playing glockenspiel,train 4p8hJPMm3D4,42,playing glockenspiel,train 4p8n4Zf-WMM,190,lighting firecrackers,test 4pCGsyzfPrQ,30,female singing,train 4pDEU96vJIk,30,people sniggering,train 4pEVl2Q86QM,0,playing tuning fork,train 4pKWWCDsvmk,28,crow cawing,train 4pKaybgemUQ,33,tractor digging,train 4pL3dJQxw0Q,244,warbler chirping,train 4pL3dJQxw0Q,301,warbler chirping,train 4pNaGtLFx-o,32,civil defense siren,train 4pNaGtLFx-o,44,civil defense 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4qaPtkgKSNE,74,playing hammond organ,train 4qaPtkgKSNE,101,playing hammond organ,train 4qaiLMEce6Y,25,"bird chirping, tweeting",test 4qb-DnfGCD8,315,playing table tennis,train 4qcTZMFU8p8,31,skiing,train 4qevqi0-WYk,110,people screaming,test 4qgyGAJkvWY,182,train wheels squealing,train 4qgyGAJkvWY,295,train wheels squealing,train 4qhEUr5U48o,21,horse neighing,train 4qhPjDyElJQ,71,playing timpani,train 4qhPjDyElJQ,110,playing timpani,train 4qiKMQOovEc,30,"male speech, man speaking",train 4qsHBw_r5TQ,49,playing bugle,train 4qtzG_66Vxk,13,barn swallow calling,train 4qu0Xxd5U8o,0,dog growling,train 4qu0Xxd5U8o,108,dog growling,train 4qvxpVQ9wHM,30,horse clip-clop,train 4qxIDB3UDrQ,80,lions growling,train 4qxuRgqSqDQ,30,"railroad car, train wagon",train 4r0yP4y_N2A,252,playing timbales,train 4r22sRnrdvc,63,people shuffling,train 4r3SaN7a7pY,30,car engine starting,train 4r3cnueH214,30,playing acoustic guitar,train 4r4YfRgiL-c,30,playing cello,train 4r6EpTvwZKs,100,people burping,train 4r7Lea8y-VU,30,cat meowing,train 4r8Aizy1ePc,30,female singing,train 4r8kZF_CRSk,30,female singing,train 4r8uFqPOpqU,0,toilet flushing,train 4rADsunfYdg,30,playing harpsichord,train 4rAaLc08xJo,30,playing bass guitar,train 4rCLWAky_YY,30,playing acoustic guitar,train 4rDF7G9cO44,30,sliding door,train 4rDe1MsbuT4,30,playing cello,train 4rE3ckqZ2zc,8,cat growling,train 4rE3ckqZ2zc,30,cat growling,train 4rEd0ZR3EJM,126,tornado roaring,test 4rGLZHwGVl4,0,chimpanzee pant-hooting,train 4rGLZHwGVl4,15,chimpanzee pant-hooting,train 4rGhvG3STRE,0,cap gun shooting,train 4rIzM457KnM,172,"bird chirping, tweeting",train 4rJ_JaeuhIU,25,turkey gobbling,train 4rLT5kTehsE,30,missile launch,train 4rNP0Uh5TbQ,8,crow cawing,train 4rNVouEKO1c,63,fire truck siren,train 4rO0BUf85i8,23,playing timpani,train 4rO0BUf85i8,33,playing timpani,train 4rOTlxMqhfA,9,writing on blackboard with chalk,test 4rOX8bLtx5E,363,playing hockey,train 4rPgUfaKrv0,541,people giggling,train 4rRbi3qb2LQ,103,"playing steel guitar, 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4ruTRQwenaA,1,playing theremin,train 4rzV0AJyhBE,30,"rowboat, canoe, kayak rowing",train 4s0Go0kzx1I,92,sea lion barking,train 4s1HppPbBsw,461,parrot talking,train 4s1HppPbBsw,493,parrot talking,train 4s4Nh2B8miM,30,"pigeon, dove cooing",train 4s4raXsk9jk,110,"child speech, kid speaking",train 4s5S8Tju5l4,8,tractor digging,train 4s5S8Tju5l4,21,tractor digging,train 4s6OAuqdPQs,2,scuba diving,train 4s6OAuqdPQs,16,scuba diving,train 4s7NIp-bg0A,15,swimming,train 4s9_61fICT4,130,playing acoustic guitar,train 4sBWMB41gVM,569,fire truck siren,train 4sBYUQQVnk8,5,basketball bounce,train 4sD1HZsypwM,30,playing cello,train 4sDwxr00VSM,18,tap dancing,train 4sEr3mzriKw,260,using sewing machines,train 4sEx5pNroI8,270,people clapping,train 4sGk_gq3ORg,30,female singing,train 4sIdZ_xgTBE,300,printer printing,train 4sJFrYzql_4,0,car engine knocking,train 4sKkzAuoMD8,30,"female speech, woman speaking",train 4sPa8dPpxQo,30,playing accordion,train 4sSsaFCvFfc,157,"electric shaver, electric razor 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4snoveXPGM8,87,cheetah chirrup,train 4sodha2P-c4,7,chicken crowing,train 4sodha2P-c4,27,chicken crowing,train 4sr3JQi4GXs,7,opening or closing car electric windows,train 4ss4NliuCFY,266,ripping paper,train 4ssWujrqMhs,29,alarm clock ringing,train 4sukGdqyw8o,224,singing bowl,train 4t-2zL10y_4,0,"bird chirping, tweeting",train 4t-WdXW3rYs,200,goose honking,test 4t1VqRz4w2g,30,reversing beeps,test 4t4dSXAcZU0,44,skiing,train 4t7-bOEAyZU,20,singing bowl,train 4t7-bOEAyZU,57,singing bowl,train 4t7PjMsNAO8,13,ocean burbling,train 4t7Wc6SIHXo,120,playing hammond organ,train 4t7b588zSvI,13,lions roaring,train 4tASCYb3ySA,0,typing on computer keyboard,train 4tD8JAPjpHU,2,dog bow-wow,train 4tDRgbOA4xI,30,car passing by,train 4tHvsHkl8L0,27,snake hissing,test 4tJSiIFaIm0,45,chopping food,train 4tJYtZCC0Vs,0,wind rustling leaves,train 4tJYtZCC0Vs,25,wind rustling leaves,train 4tK9qybkrhA,10,playing double bass,train 4tKABFCejJo,156,airplane flyby,train 4tKABFCejJo,328,airplane flyby,train 4tKLeXperAo,77,playing volleyball,train 4tKpO_3s05U,40,playing badminton,train 4tKvAMmAUMM,30,reversing beeps,test 4tL1U_xWmo0,296,reversing beeps,train 4tL1U_xWmo0,323,reversing beeps,train 4tM8iv-G4tI,101,people booing,train 4tNCm4RVH3k,64,missile launch,train 4tNWjh4s2qQ,38,playing clarinet,train 4tNWjh4s2qQ,175,playing clarinet,train 4tOFGK47I5E,5,cow lowing,test 4tRO9sTH7Uw,110,playing bass drum,train 4tRQ_XzrXs4,18,people booing,train 4tRlgk--1ro,390,driving motorcycle,train 4tT0QpiWrhY,150,playing harpsichord,test 4tUbt7BgSlM,50,sailing,train 4tVBc9aHvrs,7,playing steelpan,train 4tVBc9aHvrs,240,playing steelpan,train 4tX8ccyUTNo,5,airplane,train 4tYg5yxFdPk,30,female singing,train 4t_Qz9RyUm8,6,alarm clock ringing,train 4tboypzMMkI,17,chimpanzee pant-hooting,train 4tceS9Lr4y0,110,using sewing machines,train 4tgd0Is70so,30,police car (siren),train 4thEVesl7yA,1,driving snowmobile,test 4tj_DVfp0ME,10,toilet flushing,train 4tkYYWTqh4I,199,police radio chatter,train 4tl24kZkJ7E,19,playing didgeridoo,train 4tl24kZkJ7E,67,playing didgeridoo,train 4tlvzARr7lQ,20,"railroad car, train wagon",train 4tmZF1TY2HM,30,playing drum kit,train 4tmi7jMMiJM,100,playing cymbal,train 4tmi7jMMiJM,150,playing cymbal,train 4tndgAxVQJU,20,playing timbales,train 4tnwJe6vvrE,30,raining,test 4toXA3a5PSU,30,female singing,train 4tsbMQETqQw,20,people screaming,train 4tu8J1iAgXo,140,skateboarding,train 4tv4VySahgQ,30,male singing,train 4twsvQvrhvc,50,playing sitar,train 4twsvQvrhvc,280,playing sitar,train 4txRL-0_s8s,88,woodpecker pecking tree,train 4u0ndYQGlTM,30,"playing marimba, xylophone",train 4u1E2SBAWRc,30,female singing,train 4u1Q7AK-x3Q,34,tap dancing,train 4u8wrORW09M,30,playing bass guitar,train 4uCT8NgRUtI,30,female singing,train 4uFk2XUg2Qs,30,"rowboat, canoe, kayak rowing",train 4uIABJ-XtSo,30,male singing,train 4uILfAUkvlg,178,playing bassoon,train 4uJ1FTeH_Wg,52,hammering nails,train 4uJPjs11who,22,playing electronic organ,train 4uLtndu3Uc4,30,playing cello,train 4uNheq2wBbE,2,church bell ringing,train 4uRizWpSXAc,30,toilet flushing,train 4uS1k7WEg9w,7,cattle mooing,train 4uSbpeUmxAk,30,printer printing,train 4uTU0OXWUHM,89,lathe spinning,test 4uVzgXlMLzo,80,chicken crowing,train 4uWyU-YQF44,340,playing trombone,train 4uZEIiH3rvQ,50,playing electronic organ,train 4uap7zZ9pLM,30,"motorboat, speedboat acceleration",train 4ueITaPM08M,30,"playing marimba, xylophone",train 4ueJP_4ivxg,78,eagle screaming,train 4ueJP_4ivxg,164,eagle screaming,train 4ufA8j3XAhc,18,airplane flyby,train 4ufLjQfuEAI,39,horse clip-clop,train 4ufLjQfuEAI,382,horse clip-clop,train 4ugAsVs28Wk,9,"motorboat, speedboat acceleration",train 4uihL55gcWo,28,elephant trumpeting,test 4umEvJi2rRE,110,playing banjo,train 4umOlKW1GIw,30,playing electric guitar,train 4un9PzN6o5E,157,lathe spinning,train 4unAT0OouX8,30,playing acoustic guitar,train 4ur5U1k9omM,30,"motorboat, speedboat acceleration",train 4ur5pqQkYGU,30,toilet flushing,train 4ursgQETEJQ,20,chicken crowing,train 4usk-3Gqdu0,30,playing electric guitar,train 4uucOhaW5ow,30,chicken clucking,train 4uuxHwLf49w,21,horse clip-clop,train 4uvmPgXYLCQ,0,people crowd,train 4uwr6wcM2xQ,30,playing acoustic guitar,train 4uyCODx5Sow,30,horse clip-clop,train 4uyJumj_Dso,1,hail,train 4uz1nnaIxDs,30,people crowd,train 4v1HX8V90iU,197,vacuum cleaner cleaning floors,train 4v1VgwCvm_U,30,female singing,test 4v3XfIpKpvE,106,playing glockenspiel,train 4v3i_OQ33bo,290,"race car, auto racing",train 4v4BR80DLhQ,130,frog croaking,train 4v4BR80DLhQ,146,frog croaking,train 4v4Uo4DJEK8,30,male singing,train 4v6G5PJkMkY,30,chainsawing trees,train 4v7vm2XKqfI,410,mouse pattering,train 4v9O60HOe50,30,horse clip-clop,train 4vBuAoZJiiw,22,fire truck siren,train 4vF2cfinado,39,scuba diving,train 4vF2cfinado,238,scuba diving,train 4vFHOgUKYvM,0,people crowd,train 4vFPPpXSlk0,560,chainsawing trees,train 4vGhbpP-l8I,8,slot machine,train 4vHhCVKja0U,50,playing drum kit,train 4vI7dH_rPy0,30,people whispering,train 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55R9x4FWuAc,4,fire truck siren,train 55S7vnPhIDY,16,singing bowl,train 55TvaNVBO8Q,143,playing cornet,train 55VB_nTTNDU,219,"bee, wasp, etc. buzzing",train 55VB_nTTNDU,274,"bee, wasp, etc. buzzing",train 55VafmDNbjE,30,waterfall burbling,test 55XnC-7cJwI,30,playing electric guitar,train 55Ypr_uAdpU,28,dog howling,train 55Ypr_uAdpU,39,dog howling,train 55ZEaojcD84,30,sloshing water,train 55ZxQ6G7mkw,18,skidding,train 55ZxQ6G7mkw,49,skidding,train 55b4KDqVhx0,30,chicken crowing,train 55bGnVpdr9w,30,"male speech, man speaking",train 55bSR917TyM,30,"male speech, man speaking",train 55cDQffhucw,410,people belly laughing,train 55fiHaTdTPQ,53,people sniggering,train 55fiHaTdTPQ,140,people sniggering,train 55iPOaquWuM,38,firing muskets,train 55jkvcFVbYQ,30,sliding door,train 55kTgMoMO54,39,airplane flyby,train 55kTgMoMO54,90,airplane flyby,train 55l9j-CggDQ,30,cat meowing,test 55lop0TiOws,47,singing bowl,train 55mmn9rkbk8,180,"bird chirping, tweeting",train 55qNcv9ReD8,26,ferret dooking,train 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5oqA2MST-44,103,mynah bird singing,train 5oqh3_qYr6I,89,dog baying,train 5orYrZI-sCU,30,playing acoustic guitar,train 5oseYvmykn8,30,"female speech, woman speaking",train 5otmyiCRQZw,49,civil defense siren,train 5ott6Y1gZcY,144,people cheering,test 5ott6Y1gZcY,218,people cheering,test 5owAMw3f8iQ,5,people whistling,train 5owzIXTe-gY,30,playing bagpipes,train 5oyJb-UZKjQ,30,"male speech, man speaking",train 5oyi3Y7pSI8,50,toilet flushing,train 5ozIHcsN2RE,30,playing acoustic guitar,train 5p0BeKfgfqo,30,playing drum kit,train 5p61xtcXv20,2,smoke detector beeping,train 5p61xtcXv20,14,smoke detector beeping,train 5pAL5p9PPQs,35,goose honking,train 5pBGoEzEm5U,30,playing electric guitar,train 5pBdB98cEhk,100,people burping,train 5pD1F83TATo,30,playing acoustic guitar,train 5pDbUFv5cJw,30,"male speech, man speaking",train 5pIje_zRg5c,90,playing trumpet,train 5pMoDgOaPE0,31,"bee, wasp, etc. buzzing",train 5pPL4tH-axw,4,playing bongo,train 5pPL4tH-axw,52,playing bongo,train 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6rdiCN5Au00,136,planing timber,train 6rdiQBsbXRg,110,playing squash,train 6resAnZWrkk,918,machine gun shooting,train 6resAnZWrkk,930,machine gun shooting,train 6rgLZ_unLQw,426,slot machine,train 6rhJ9DfFBJs,70,playing trombone,train 6ri3coZWpv8,8,playing volleyball,train 6rj99iki74w,30,female singing,train 6rkigce_m8o,82,dog howling,train 6rkigce_m8o,110,dog howling,train 6rlxQfCLLlQ,185,skiing,train 6rlxQfCLLlQ,265,skiing,train 6rnjZymx8GY,88,playing harmonica,train 6rpRjYMA3n4,73,disc scratching,test 6rq30Ie8Ttk,20,driving buses,train 6rr-8pKtca8,30,female singing,train 6rtTUIy8x50,30,playing french horn,train 6rtaNO8IxNg,26,lions roaring,train 6rtaNO8IxNg,41,lions roaring,train 6ruj1_Q3SlQ,150,playing bagpipes,train 6rwzCqnRTEk,8,driving snowmobile,train 6rxYuIM6tH8,94,lighting firecrackers,train 6rxYuIM6tH8,135,lighting firecrackers,train 6rzV_S8Rvjg,0,skidding,train 6s0ydfe8lVk,13,dog whimpering,train 6s2pBWctGDM,6,snake rattling,test 6s3MBS9IYNg,135,car engine idling,train 6s5aL2T08q0,30,"male speech, man speaking",train 6s6m-fMtmFk,3,pig oinking,train 6s6zo-vLVpI,382,black capped chickadee calling,train 6s6zo-vLVpI,523,black capped chickadee calling,train 6sAJzjJ6ujE,592,bathroom ventilation fan running,test 6sBLQEumyDU,51,fire crackling,train 6sBLQEumyDU,61,fire crackling,train 6sD2dh7ZJdY,30,female singing,train 6sDLBu92Qh4,395,"motorboat, speedboat acceleration",train 6sEjETtEWRw,44,hail,train 6sEjETtEWRw,72,hail,train 6sG0Qe2839Q,384,playing bongo,train 6sGwBelfXQ8,20,playing trumpet,train 6sHQYwNKJzU,410,waterfall burbling,test 6sJHHUVWvcc,403,singing bowl,train 6sPXX8hTY1c,0,playing saxophone,train 6sQ8Y7mFyXM,30,"playing marimba, xylophone",train 6sQnZuxAAN0,30,ambulance siren,train 6sRQnpEEQ0E,390,lighting firecrackers,test 6sT-n5_Ihdo,168,mynah bird singing,train 6sT-n5_Ihdo,201,mynah bird singing,train 6sVEjiEbjSI,39,playing glockenspiel,train 6sWY1FBit5Y,13,planing timber,train 6sWY1FBit5Y,33,planing timber,train 6sWkgLiif3o,3,elephant 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8TCdnr4F6tw,303,playing glockenspiel,test 8TGaHgRdIjI,424,tapping guitar,train 8THEyz540pk,30,cat meowing,train 8TNLqJ0dJDA,297,skiing,test 8TOCcbGnHEE,30,wind noise,train 8TPLOn32v3E,153,playing cello,train 8TR0IBpd3W8,410,playing piano,train 8TSnOcdBovA,30,playing electronic organ,train 8TVOesBBLPg,23,horse neighing,test 8TW6eA6hIS4,84,alarm clock ringing,train 8TWXyry2Kc4,245,slot machine,test 8TWtLCjUI5I,0,dog barking,test 8TXIrXiyW6o,194,bird squawking,train 8TX_NCcvVK8,50,playing acoustic guitar,train 8TX_VqAPcrY,111,playing congas,train 8TYTcASOrHA,8,sliding door,train 8TYYkaS-qaY,87,playing bass drum,train 8TYdYVcYcYo,468,golf driving,train 8TZcN7vYW8E,30,"playing violin, fiddle",train 8TZxA9eY9dw,0,printer printing,train 8TbLa8P3Tgk,110,"railroad car, train wagon",train 8Td4ORMB7eI,100,using sewing machines,train 8TddUGafopE,16,cap gun shooting,train 8TddUGafopE,20,cap gun shooting,train 8Te61qq79j4,61,playing bagpipes,train 8Te61qq79j4,184,playing bagpipes,train 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8rTu0h4hx6s,90,chainsawing trees,train 8rWuGBPA7bE,11,"vehicle horn, car horn, honking",train 8rYlqJpx57I,30,"rowboat, canoe, kayak rowing",train 8rZVz3O47yo,55,"playing steel guitar, slide guitar",train 8rZVz3O47yo,164,"playing steel guitar, slide guitar",train 8raHEGgGVQc,39,"cattle, bovinae cowbell",train 8raHEGgGVQc,50,"cattle, bovinae cowbell",train 8rbRmWmgtR8,8,playing cornet,train 8rd7e3TBid0,179,playing table tennis,train 8rdFWmDjL9c,30,horse clip-clop,test 8rdzxfhTHL4,6,wood thrush calling,train 8rdzxfhTHL4,29,wood thrush calling,train 8rekc-V4A8c,22,mouse squeaking,train 8rekc-V4A8c,41,mouse squeaking,train 8rf1Mk_BHmI,30,"motorboat, speedboat acceleration",train 8rgCE4KYCO4,33,cat purring,train 8rhKBsR-lHk,236,opening or closing car doors,train 8rj_0zB6RHE,33,dog growling,train 8rlCRBYe6II,10,whale calling,train 8rmoYAfr0t8,10,car passing by,test 8rrO4Hy9yKU,30,playing flute,train 8rsdciKRbl4,100,playing clarinet,train 8rwrbDUH6uM,20,machine gun shooting,train 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AWBz0TZNY-o,22,playing bagpipes,train AWClq1Pr7hM,76,playing electronic organ,train AWE73KP-hiE,30,"motorboat, speedboat acceleration",train AWESKpWsz44,250,people clapping,train AWH7nE0vekg,20,mouse pattering,train AWJYBzRVc_U,241,cap gun shooting,train AWKAW8k98Pg,32,playing sitar,train AWKCTE-ahlA,110,hail,train AWKCTE-ahlA,146,hail,train AWKdMdlD9wg,45,pheasant crowing,train AWMLktP-bds,8,playing volleyball,train AWNoelEfXZo,507,playing hammond organ,train AWOjrxjftQM,46,singing choir,train AWOjrxjftQM,173,singing choir,train AWPXkWnuY-Q,449,playing sitar,train AWPXkWnuY-Q,562,playing sitar,train AWQb44koUZw,0,people sobbing,train AWQsYKbbWWw,30,playing flute,train AWSrbuErOy0,60,people cheering,train AWSrbuErOy0,71,people cheering,train AWUOoqSsU34,42,"cattle, bovinae cowbell",train AWUOoqSsU34,122,"cattle, bovinae cowbell",train AWUsylEsXk8,30,playing cello,train AWWOFum__kY,312,playing volleyball,train AWZQiVd-HAs,110,wind rustling leaves,train AWZfk-e6vSc,40,playing piano,train 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A_eWeEm78ao,40,tapping guitar,test A_gBxm_E9mg,30,"male speech, man speaking",train A_h6IWgim9I,76,striking pool,train A_h6IWgim9I,86,striking pool,train A_jWehat1-k,36,parrot talking,train A_jorvNqilc,72,playing french horn,train A_jorvNqilc,83,playing french horn,train A_k_um_Stz8,60,playing accordion,train A_kpEvj4bLE,16,hail,train A_nbVfDVD5I,30,people crowd,train A_nycfCJidc,1,mosquito buzzing,train A_nycfCJidc,217,mosquito buzzing,train A_oDbD4CiR4,85,metronome,test A_oaLt-n4fQ,220,"playing violin, fiddle",test A_q2UiACSaY,97,slot machine,train A_qAdH_RGH8,293,striking pool,train A_qp6T2pAek,30,"rowboat, canoe, kayak rowing",train A_syLVXfAPQ,162,pheasant crowing,train A_uldWErog4,40,playing washboard,test A_xvegMIGrI,40,cheetah chirrup,train Aa02sGer9_g,10,playing mandolin,train Aa02sGer9_g,44,playing mandolin,train Aa0QWZ9brik,10,wind noise,train Aa0US1znMOg,3,yodelling,train Aa0US1znMOg,223,yodelling,train Aa37Dh4LRHE,30,playing bagpipes,train Aa68KCtc3Ek,30,people 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purring,train AjeGfcPOrPQ,34,cat purring,train AjhPr5GGfHk,220,playing hammond organ,train AjiJWKxmd7E,1,people whistling,train AjiJWKxmd7E,15,people whistling,train AjjQqd0eLzw,30,playing theremin,test Ajl5A3YbR88,120,playing timpani,test AjqpT62eZZU,0,car engine knocking,train Ajug0GCvSqY,153,playing tympani,train Ajvgls4yE58,14,machine gun shooting,train AjvrUIFe4-M,49,playing banjo,train AjxBgZV2__k,7,sheep bleating,train AjyMyr_A9lw,30,playing hammond organ,train AjzjxR0gZhY,8,canary calling,train AjzjxR0gZhY,20,canary calling,train AjznRVgZ_9Y,245,warbler chirping,train AjznRVgZ_9Y,267,warbler chirping,train AjzrFt5Oe-c,4,playing mandolin,train Ak0fm9geF1s,46,playing didgeridoo,train Ak0p5momxzY,82,police car (siren),train Ak0wxUETZS8,0,ambulance siren,train Ak5AuVWQU7U,30,playing drum kit,train Ak68t2pvwo8,110,dog barking,train Ak6R1APC2Zg,308,playing cornet,train Ak7fgTNgFGY,27,cheetah chirrup,train AkAVeSxvjhE,51,cricket chirping,train AkAdDFHQars,30,fire truck siren,test 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AkXQyXqfj0k,191,mosquito buzzing,train AkXTp7cusoo,74,air conditioning noise,train AkXTp7cusoo,97,air conditioning noise,train AkZAMlpCfBI,30,playing bagpipes,train Akb_KqIuXug,31,playing badminton,train AkcijRIqm1I,259,train wheels squealing,train AkdRFBa_HSw,306,people eating crisps,train Akg7gkpmlf0,460,"railroad car, train wagon",train AkiTPf5aJn4,150,people clapping,train AkkHLhZXwgE,12,wind rustling leaves,train Akmt_1N_tuU,30,raining,test AkpqrEOj2GA,201,volcano explosion,train AkpqrEOj2GA,246,volcano explosion,train AkqOw291COE,30,orchestra,train AkrxaFwDbmE,55,baby babbling,train AkttDEmHk6c,20,playing timbales,train Akujp3soPo0,30,"engine accelerating, revving, vroom",train Akva1CjEDM8,30,playing acoustic guitar,train AkwGkljvxuU,90,dog whimpering,train AkwGkljvxuU,100,dog whimpering,train AkxP0-Nwipw,0,chipmunk chirping,train AkxP0-Nwipw,16,chipmunk chirping,train AkxYA06oKG4,90,people nose blowing,train Aky0DF507fw,170,playing didgeridoo,test Akzl9v1T7Fs,30,"motorboat, 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AlXx7c8DyX0,102,civil defense siren,train AlZUmhHgF8g,8,chipmunk chirping,test AlZv9bRtnws,30,playing bass guitar,test Al_MtuxxQmY,260,chicken clucking,train Al_r9Q2kemY,440,playing french horn,train AlaZJPiXbks,30,driving buses,train Alafxv2O2FU,150,people crowd,train AlbAOGUV52U,59,playing badminton,train AlbsaV0DXwk,160,playing electric guitar,train AlcRFk7qexo,0,"child speech, kid speaking",train AleBXKXc7BI,50,police car (siren),train AleDj31jU0U,30,"engine accelerating, revving, vroom",train AleIQXvHCQ4,17,"alligators, crocodiles hissing",train AleIQXvHCQ4,109,"alligators, crocodiles hissing",train AlebU-Vdy18,22,playing theremin,train AlebU-Vdy18,33,playing theremin,train AlfButqVXcs,30,playing bass guitar,train AlfyBgCDCF4,11,civil defense siren,train AlgjleXmAhg,330,"child speech, kid speaking",train AliSNrQrW0Y,67,playing table tennis,train AliXPlZzbpc,4,playing steelpan,train AliXPlZzbpc,16,playing steelpan,train Alj6G_oFY4E,25,"rowboat, canoe, kayak rowing",test AlnFwT2U1xM,163,parrot talking,test AloVGi156T4,230,playing bass guitar,train AlqCbPTpKUg,19,ice cracking,test AlqWTkItYzY,150,bull bellowing,train AlqWTkItYzY,161,bull bellowing,train AlraMYyGJRg,15,baby laughter,train AlsDSDTiaWI,30,skidding,test AlsTrpFt2fU,294,playing hammond organ,train AlsWo2mEv80,9,horse neighing,train Alu3Xvifc_c,30,"playing violin, fiddle",train Alv7N6Ynm1Y,117,playing saxophone,train Alv7N6Ynm1Y,128,playing saxophone,train AlwGUO8Zu_U,163,sloshing water,train Alwpvv60ppI,60,playing piano,train Am6ffaEoGvc,151,lawn mowing,train Am9yLJ6nuRk,200,people whispering,train AmAAQSGg22Q,70,"bee, wasp, etc. buzzing",train AmAThmRphk0,30,playing banjo,test AmC1pCa1GfE,40,baby babbling,test AmCLMhqU_NQ,330,people clapping,train AmDDp-cCo24,0,cat caterwauling,test AmDfKTq_cu8,101,dinosaurs bellowing,train AmDfKTq_cu8,120,dinosaurs bellowing,train AmLJ-mfWmvQ,38,opening or closing car electric windows,train AmLYldI6bH0,3,golf driving,train AmLYldI6bH0,14,golf driving,train 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AmtF_rAphg0,1,basketball bounce,train AmuX-Lv7OeM,580,cattle mooing,train AmvzgDPwg50,11,baby babbling,train Amwv9i3FA5M,30,"female speech, woman speaking",train Amz3wvG4iWw,91,playing gong,train Amz3wvG4iWw,197,playing gong,train An-4jPvUT14,60,skateboarding,test An05S__zc-U,26,people humming,train An0ZimXa54M,290,stream burbling,train An32hX6i1gs,30,"male speech, man speaking",test An4ZXnZAaoo,120,police car (siren),train An6OiOMGjMM,30,orchestra,train An6rhCwAOZI,30,"female speech, woman speaking",train AnAmM5BgNUg,150,people crowd,train AnCRFGoLN5E,146,car engine idling,train AnCRFGoLN5E,158,car engine idling,train AnDbC4puNyQ,0,lions growling,train AnE0CpdZmDk,110,playing bagpipes,train AnEpnvDnCqU,16,people giggling,train AnEpnvDnCqU,223,people giggling,train AnFLES8rJfw,10,people coughing,train AnG3crvRXng,100,playing harp,train AnGDWthZpUM,3,playing bass drum,train AnGYl7BIqC4,25,sea lion barking,test AnISvHJbLuY,97,playing didgeridoo,train AnKrC55uXhQ,30,"bee, wasp, etc. 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AnqonqH4PSA,3,tap dancing,train AnqyqcFL1ho,1,underwater bubbling,train AnqyqcFL1ho,13,underwater bubbling,train AnrcPBN7yms,10,people coughing,train AnrfffrNd38,20,people sniggering,train AnsAjksOTJs,38,playing volleyball,train AnsWBhwvumo,30,duck quacking,train Antt8lhlmMU,178,eletric blender running,train Anusf3_0GSE,28,"engine accelerating, revving, vroom",train Anv2glMa9SM,240,lawn mowing,train Anvt3HcBojI,100,ambulance siren,train Anw-UkkrrD8,415,playing bassoon,train AnwUnhBvZTE,560,"race car, auto racing",train Anyv7uCBghI,138,volcano explosion,train Anyv7uCBghI,181,volcano explosion,train Ao-JEw0QpRk,23,reversing beeps,train Ao-JEw0QpRk,45,reversing beeps,train Ao-lUtXtmvA,6,woodpecker pecking tree,train Ao0n1cqfFDw,20,people coughing,train Ao4c295afvI,64,people whispering,train Ao4c295afvI,212,people whispering,train Ao4ls4kFejw,50,dog barking,train Ao5t9Yq3LvU,21,playing volleyball,train Ao66xMk6lO0,310,"female speech, woman speaking",train Ao6_gAiv5bg,2,ambulance siren,train AoCQU4VJZuU,47,wood thrush calling,train AoCQU4VJZuU,81,wood thrush calling,train AoENedRm66U,4,"vehicle horn, car horn, honking",train AoEgLMNTXcY,93,underwater bubbling,train AoEgLMNTXcY,194,underwater bubbling,train AoG0OMBtaSI,30,cat meowing,train AoGisMI5cb8,30,pig oinking,train AoHIzq2Gac8,267,strike lighter,train AoIHTerxdas,0,lawn mowing,test AoOTpDe2MHQ,402,civil defense siren,train AoRuxdVDyhY,269,playing badminton,train AoSn89Wt6pE,10,people sneezing,train AoT1gMVzi_U,20,"race car, auto racing",train AoTDOno9DFk,90,lawn mowing,train AoUbsMewv2M,8,tapping guitar,train AoUbsMewv2M,25,tapping guitar,train AoUn8I-RtRk,30,horse neighing,test AoXQva2Qajo,320,people clapping,train AoY3PpFWQqY,520,goose honking,train Ao_3hNlU0EA,20,air conditioning noise,train AobOKrXlNRE,10,train horning,train AobrYbdAXgE,30,orchestra,train Aod4kKMxp24,150,helicopter,train AojhBQuHRtQ,187,dog growling,train AokCFdV24Vs,370,playing cello,train AooPHEbMeAk,351,bowling impact,train 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BCpa-X77_Vg,30,"female speech, woman speaking",train BCrAKtHcl_k,68,playing squash,train BCvNyY6EU7I,61,people eating crisps,train BCvNyY6EU7I,95,people eating crisps,train BCx-qhmHoZI,50,people coughing,train BCzUvCRiBZs,20,machine gun shooting,test BCzVNe73_B0,30,playing piano,train BD-tvzLCjD0,25,playing darts,train BD-tvzLCjD0,52,playing darts,train BD1BzaXNqDw,30,duck quacking,train BD29f_ddG4w,50,playing saxophone,train BD2CR9PJp-U,42,singing bowl,train BD2zIq3TNJM,53,tapping guitar,train BD2zIq3TNJM,236,tapping guitar,train BD3aEZknenQ,550,people clapping,train BD3b_3wrLxk,220,"playing marimba, xylophone",train BD3ddOHUj_E,117,playing timpani,train BD3ddOHUj_E,261,playing timpani,train BD6zXOhQEMQ,6,air conditioning noise,test BD79NEVyRTU,125,tapping guitar,train BDAM1O5WBUc,3,people whistling,train BDCBkS7JYqA,30,"male speech, man speaking",train BDD6a0EPaQU,190,playing drum kit,train BDEn9P9_S-s,30,stream burbling,test BDIDl9aCpCk,50,people whispering,train BDMv6AOI5jY,175,chicken crowing,train BDMv6AOI5jY,190,chicken crowing,train BDN1S8MBgdA,0,chainsawing trees,train BDNaHFGZA7w,30,singing choir,train BDOxmZ-2O88,30,"motorboat, speedboat acceleration",train BDPCOVG7TR8,30,singing choir,train BDQ2yiowIBQ,1,train horning,train BDRbRjjMLDw,69,dog howling,train BDS5KpghF8c,200,playing trumpet,train BDSCQD3wElA,200,"female speech, woman speaking",train BDTHP1yhfBk,30,"playing violin, fiddle",train BDTkuYjCh64,0,playing piano,train BDTlaUbJcrI,30,ambulance siren,train BDVnkNawZz4,30,people running,train BDWHXBe5YWE,63,cap gun shooting,train BDWHXBe5YWE,158,cap gun shooting,train BDYPBHGz9pk,50,dog barking,train BDYpXqMsg5k,150,helicopter,train BDZA0jNtJCE,13,skateboarding,train BD_4CJoxufY,244,chopping wood,train BD_4CJoxufY,282,chopping wood,train BDaqnMJ5Ems,13,mynah bird singing,train BDaqnMJ5Ems,56,mynah bird singing,train BDb9c4alPJA,37,striking pool,train BDb9c4alPJA,339,striking pool,train BDduRe11_wo,30,helicopter,train BDh_NsRFwdM,286,smoke detector beeping,train BDh_NsRFwdM,299,smoke detector beeping,train BDmNQtRC6QM,210,playing bagpipes,train BDmYnlmKC-8,30,playing banjo,train BDnQ4F7srQA,318,playing darts,train BDp-Hlo89Qo,110,people sniggering,train BDsDhrZ3Dvo,471,using sewing machines,train BDsDhrZ3Dvo,485,using sewing machines,train BDsPJ0oqgT4,60,playing cymbal,train BDsV_SxqFZs,136,crow cawing,train BDxc1Luo-8s,413,playing hockey,train BDz8ezGGSzo,93,playing guiro,train BDz8ezGGSzo,116,playing guiro,train BDzSHpf_Rl4,30,"playing marimba, xylophone",train BE0qaVGwpLE,278,mouse clicking,test BE1gj7PG2q4,40,playing accordion,train BE1qG9iQZlg,190,eating with cutlery,test BE50tP0I6hQ,8,playing bongo,train BE50tP0I6hQ,22,playing bongo,train BE66p64LLJ4,20,chicken crowing,train BE6A4J-uduI,170,playing squash,train BE6A4J-uduI,431,playing squash,train BE7IhNnKzZU,230,people cheering,train BE8Mv5ww4BM,171,playing hammond organ,test BEBcGGsE7v8,120,people sniggering,train BECqoogkuRQ,0,playing 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BF_dz_-rNMY,76,people humming,train BF_dz_-rNMY,309,people humming,train BFamCCNvAzA,210,"race car, auto racing",train BFc26Yl7kaI,22,typing on computer keyboard,train BFc26Yl7kaI,79,typing on computer keyboard,train BFgCp6GBu70,6,lighting firecrackers,train BFgCp6GBu70,18,lighting firecrackers,train BFgpQEdpi1g,400,train horning,train BFhl6dQO1Ag,30,people whispering,train BFjEVcbFlrU,30,"subway, metro, underground",train BFm-qJ-J7sU,30,"rowboat, canoe, kayak rowing",train BFmLRo8muBU,130,playing drum kit,train BFnWuw4dAGk,70,lions growling,train BFqHyCoypfM,16,people screaming,test BFu7OQ9O1MU,37,playing saxophone,train BFu7OQ9O1MU,112,playing saxophone,train BFuaBQX7ixY,20,"rowboat, canoe, kayak rowing",train BFyA47oNp94,18,people screaming,train BFzC73MTEQc,241,running electric fan,train BFzC73MTEQc,489,running electric fan,train BFzDqOp-TKk,28,wind noise,train BG-aWOCKlPY,30,lions growling,train BG-lnBgvnao,70,alarm clock ringing,train BG-lnBgvnao,85,alarm clock ringing,train BG1-yAZ8m1o,48,squishing water,train BG1-yAZ8m1o,164,squishing water,train BG23o0x_Qeg,13,playing glockenspiel,train BG23o0x_Qeg,76,playing glockenspiel,train BG7JDNp0xoQ,59,playing french horn,train BG7kR72vLM0,19,woodpecker pecking tree,train BG8SQ3Pq508,4,playing tambourine,train BG8SQ3Pq508,55,playing tambourine,train BG9oq6EH8xE,30,people coughing,train BGBhdmaeIjE,9,"playing violin, fiddle",train BGCtuZt-SUw,116,gibbon howling,train BGCtuZt-SUw,129,gibbon howling,train BGEu5qwDl60,4,alarm clock ringing,train BGEu5qwDl60,15,alarm clock ringing,train BGF8AsLrB7c,35,cupboard opening or closing,train BGF8AsLrB7c,69,cupboard opening or closing,train BGQKsAPmuPs,120,eating with cutlery,train BGQNzn68rgo,28,playing french horn,train BGQNzn68rgo,39,playing french horn,train BGR10BFJO30,0,chicken clucking,train BGUsFNNKdIs,530,using sewing machines,train BGVT6ozn17E,26,sea lion barking,test BGXkkeqyUWg,25,baby laughter,train BGXkkeqyUWg,36,baby laughter,train BGYJzLkKals,162,coyote 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BHLstQL7_sY,1,underwater bubbling,train BHMVhbiZPv8,93,lathe spinning,train BHPYgbsCEtk,0,baby laughter,test BHZcfW4o0HE,0,horse clip-clop,train BH_Y9BKj7yk,160,playing trombone,train BH_ngeh2_dI,182,striking bowling,train BH_ngeh2_dI,194,striking bowling,train BHajqlL5wmw,500,opening or closing drawers,test BHbl6V8SZm0,42,bathroom ventilation fan running,train BHdH4Lxc_PQ,89,playing squash,train BHdi8AH1McE,50,dog bow-wow,train BHe2EpB39S0,280,"race car, auto racing",train BHf4pvm-gGM,30,playing cello,test BHi1tfQxHqE,0,"race car, auto racing",train BHk6PBNyvFo,210,skidding,train BHk6mhbm72o,30,playing flute,train BHkkbaFNB94,30,printer printing,train BHlxLoeMpFo,166,playing bassoon,train BHokVDycxUk,0,train horning,train BHpNtqUXOWk,50,playing cymbal,train BHsRDRQ9sbA,57,opening or closing drawers,train BHsRDRQ9sbA,69,opening or closing drawers,train BHtGtRljQVY,0,chicken crowing,train BHtukWoETTs,30,police car (siren),train BHu13EUEdyc,50,playing drum kit,train BHv64mp5f7Q,12,playing tabla,train BI0B8dv8G8Y,21,sliding door,train BI4quuGz_zk,30,"motorboat, speedboat acceleration",test BI512MzgiMk,131,playing vibraphone,train BI8CGzXgBvY,30,wind noise,train BI8PGwXqauk,128,people booing,train BI8YQ3ueD24,30,tapping guitar,test BI96CRWSZd4,223,ripping paper,test BI96CRWSZd4,374,ripping paper,test BI9eRg88Dd0,30,"male speech, man speaking",train BI9zy-5gGLY,30,people sniggering,train BIAkLC-gy2Y,250,helicopter,train BIFzpJ9D6-w,63,people eating crisps,test BIG65vPSHlo,5,gibbon howling,train BIGdC7p29Pg,36,dog whimpering,train BIGdC7p29Pg,70,dog whimpering,train BIIQ31BlVhE,147,playing trombone,train BILTdRT6eJI,190,waterfall burbling,train BINanfoPH2o,150,church bell ringing,train BIOaT_Fs1yU,39,playing squash,train BIPMekfvC_4,0,ambulance siren,train BIT-3hb7kIM,110,chainsawing trees,train BITOpJi3deA,0,people farting,test BIUFS-_wR_U,30,"subway, metro, underground",train BIWfOGF9YII,48,telephone bell ringing,train BIWfOGF9YII,70,telephone bell ringing,train BIZCRkW1VWU,161,raining,train BIZTvT21e5A,100,playing trombone,train BIalOjDwZzU,0,car passing by,train BIc1Cxx815A,370,playing lacrosse,train BIdHpHU42Ak,36,playing glockenspiel,train BIjx92h6R2U,145,playing mandolin,train BIlRBML107k,40,chicken crowing,train BIn8CogzivA,20,playing trombone,test BIr4z6rYYvY,1,train horning,train BItnjQ0QLko,10,playing saxophone,train BIvGhdx_lWQ,260,people eating noodle,train BIvGhdx_lWQ,354,people eating noodle,train BIvhQRpnObk,18,hail,train BIvhQRpnObk,41,hail,train BIwDAToKdIk,40,playing harmonica,train BIy1bsz0E4o,30,"female speech, woman speaking",train BIyFT6voGk0,30,playing piano,train BIyFdiF6M-I,90,people farting,train BIz9kd6M4os,30,people eating,train BJ-QduG8VQM,50,people screaming,train BJ1rHiaa590,95,civil defense siren,test BJ2k53KFZmk,55,playing oboe,train BJ31LCL3Dy4,100,crow cawing,train BJ33tSFYRUU,8,people booing,train BJ6Dym0CPTw,30,using sewing machines,train BJ7_yh9_19U,6,duck quacking,train BJ7mww-NnRc,30,playing trombone,train BJA9uHZv5Yo,200,orchestra,train BJEaIuaFBzY,49,airplane flyby,train BJGen_ARjzc,16,dog growling,train BJGvBCD7V1c,30,cattle mooing,train BJHrEjlb5S8,30,ambulance siren,train BJHrv7oDwRA,158,playing bagpipes,train BJHrv7oDwRA,212,playing bagpipes,train BJK2ne9wz70,370,people whispering,train BJNjHtS6WZU,11,"ice cream truck, ice cream van",train BJOu83Fawow,192,playing timbales,train BJOu83Fawow,224,playing timbales,train BJQPnGKD9X0,49,lip smacking,test BJQv65l6E70,20,"race car, auto racing",train BJRg1SmxfLk,44,footsteps on snow,test BJUogLG1t5k,38,wood thrush calling,train BJUogLG1t5k,50,wood thrush calling,train BJX2fm61IKY,30,wind rustling leaves,test BJZ_RnITfJU,30,chainsawing trees,train BJarZ3vb2hs,29,playing theremin,train BJarZ3vb2hs,218,playing theremin,train BJdtH2BfTe4,30,playing trombone,train BJediht8lKg,8,fire truck siren,train BJiU61jxqCk,5,beat boxing,train BJldV1OrwlA,79,skiing,train BJlpvO8avgg,30,chicken clucking,train BJoZbC3ItYg,10,playing piano,train BJss50VVKwo,30,toilet flushing,train BJtFgX3d8hk,30,people burping,train BJv_oZOmlwA,172,lions roaring,train BK0roBXvTIc,14,people marching,train BK1B9KMu6T8,181,playing badminton,train BK1wj4ucyy4,286,playing guiro,train BK1wj4ucyy4,317,playing guiro,train BK2wTF4WSvU,0,splashing water,train BK3kJ-hV0SY,150,playing theremin,train BK3kJ-hV0SY,190,playing theremin,train BK4gm3HgM5E,30,"rowboat, canoe, kayak rowing",train BK5dVH8P9uM,7,owl hooting,train BK5dVH8P9uM,24,owl hooting,train BK6-3kej65g,30,"female speech, woman speaking",train BK8rx80RgyM,130,playing banjo,train BK9E6UoAotI,48,playing table tennis,train BK9E6UoAotI,135,playing table tennis,train BKD-wbClWnY,23,mynah bird singing,train BKE_Z-iGErM,41,"electric shaver, electric razor shaving",test BKEf8SlEMYU,30,"male speech, man speaking",train BKExZZNvzJQ,50,typing on computer keyboard,train BKJl9_SsA68,36,tap dancing,train BKMle8hodOQ,23,police car (siren),train BKQVdfoRhzc,15,running electric fan,train BKQVdfoRhzc,257,running electric fan,train BKR2-pD_LWg,123,playing glockenspiel,train BKR2-pD_LWg,426,playing glockenspiel,train BKRs2BYSYYQ,337,playing darts,train BKVcTXsztAU,30,opening or closing drawers,test BKVrl0ZP0lI,30,"railroad car, train wagon",train BKWMNKkXlB0,30,people whistling,train BKWhBaSv2Ec,116,slot machine,train BKXLZ5T1VDU,0,dog baying,train BKXLZ5T1VDU,13,dog baying,train BKZ4wX_SAwY,2,"electric shaver, electric razor shaving",train BK_TJmKerJc,110,"rowboat, canoe, kayak rowing",train BK_hqG0d5QE,30,car engine knocking,test BKaX1l9PZ2E,30,"male speech, man speaking",train BKaY-5O5AKU,30,"male speech, man speaking",train BKbJ25_BE6k,71,crow cawing,train BKbJ25_BE6k,114,crow cawing,train BKbsn9hxLIE,260,orchestra,train BKg-ou7mnX8,6,lions roaring,train BKg-ou7mnX8,65,lions roaring,train BKgKt28yFZI,2,dog howling,train BKgKt28yFZI,18,dog howling,train BKgwRJP-wDI,10,wind rustling leaves,test BKhI_-c32rI,626,lathe spinning,train BKilsIM2Qbo,360,"race car, auto 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BNermhLzCLs,30,typing on computer keyboard,train BNfclSQ7ZKk,30,people sobbing,train BNfeHeas6hA,76,lions growling,test BNjrwopS5F4,30,"male speech, man speaking",train BNm5AKqrq1Q,0,alarm clock ringing,train BNqedIgXi8w,30,"female speech, woman speaking",train BNrmFFXqea8,77,skiing,train BNrmXcZQ-p4,30,helicopter,test BNuIRyShkgs,114,mouse clicking,train BNxn1bNPkZE,0,footsteps on snow,test BNxowL6utbQ,30,playing harp,test BNyHLGuV6to,106,bowling impact,train BNzrif2Z9xQ,30,playing drum kit,train BO0GyCFqdsc,21,cat hissing,train BO0psQFOOiQ,62,playing bongo,train BO1K4wXy2CI,299,mosquito buzzing,train BO555bbZkDw,550,playing vibraphone,test BOA7ziGMulU,140,"motorboat, speedboat acceleration",train BOB65Nd0pXo,180,helicopter,train BOCd3N8juF0,101,playing double bass,train BOCd3N8juF0,119,playing double bass,train BOESGPWlwT0,275,slot machine,train BOId12fDHNQ,30,"subway, metro, underground",train BOJxTetn3Fs,69,cap gun shooting,train BOJxTetn3Fs,79,cap gun shooting,train BOMAzVa5QRk,30,thunder,train BOMhs1h6J1Y,92,people gargling,train BOQqI7HoPjQ,0,people hiccup,train BOTMv61wlEs,30,stream burbling,test BOVyRevryDI,5,"vehicle horn, car horn, honking",train BOW_TmbrTt8,30,playing bagpipes,train BOWctSZXFcU,70,"playing marimba, xylophone",train BOZASRG2DyQ,23,people shuffling,train BO_1EuEydjM,165,people whispering,train BO_1EuEydjM,200,people whispering,train BOc69eMmwRw,192,francolin calling,train BOc69eMmwRw,253,francolin calling,train BOczdUKyyq8,30,car passing by,train BOdItCYjfv8,197,arc welding,train BOdItCYjfv8,238,arc welding,train BOgYLyIbVN8,31,dog growling,test BOhnGTZVN1o,27,owl hooting,train BOhnGTZVN1o,136,owl hooting,train BOjXEY8N7z4,135,people burping,train BOt9nRqboZA,271,"alligators, crocodiles hissing",train BOtOhr1SSIQ,11,playing electronic organ,train BOtOhr1SSIQ,166,playing electronic organ,train BOuMmi32-64,30,"pigeon, dove cooing",train BOvH0ICyVLY,109,whale calling,test BOvv1l0H_to,60,typing on computer keyboard,train BOyOFZVuCcE,94,cap gun shooting,train BOyOFZVuCcE,104,cap gun shooting,train BP0iZLks5EY,34,lions growling,test BP15mo8Q7ak,450,playing cymbal,train BP2HxUGBnNs,30,"female speech, woman speaking",train BP37C-enwTE,30,playing trombone,train BP3ePJlZBEU,221,tap dancing,train BP6a3GPGBT8,298,playing squash,train BP7tLbj4fOI,30,car engine starting,test BPD7Qj1U_Bo,86,playing theremin,train BPD7Qj1U_Bo,131,playing theremin,train BPDlyKkCIp0,148,"child speech, kid speaking",train BPF5A-zl0Ik,158,playing table tennis,train BPFadhq3kNo,30,"subway, metro, underground",train BPGYuOrNch4,3,sea lion barking,train BPGg2wCtGgg,10,people sobbing,train BPHQ3MOGdmA,30,playing vibraphone,train BPIAQNuqwXM,183,airplane flyby,train BPIAQNuqwXM,219,airplane flyby,train BPJNfS-jpjk,110,playing electronic organ,train BPPdGEG60Io,30,playing harpsichord,train BPQOTj6zgNg,295,basketball bounce,train BPTayUs7eIo,10,people sniggering,train BPXptOeqaqQ,80,driving motorcycle,train BPaeXNtLMvA,30,church bell ringing,train BPcFdofkcFw,62,singing bowl,train BPeLfS4NPEI,60,"bee, wasp, etc. buzzing",test BPe_XzuAznU,74,mosquito buzzing,train BPifVCUVLGA,510,people babbling,train BPlGa-qJ-90,0,volcano explosion,train BPlzQl2Cy48,30,"playing violin, fiddle",train BPmBF4MZJnU,0,driving motorcycle,train BPpRl7qWfvA,20,fireworks banging,train BPptenn6CWc,30,sailing,train BPpzeDb1xY4,21,slot machine,train BPq38_R7K6A,590,forging swords,train BPq38_R7K6A,827,forging swords,train BPrLBrRTRpE,133,typing on typewriter,train BPrLBrRTRpE,337,typing on typewriter,train BPs5Za2cNOg,130,people crowd,train BPtuekvJdhs,21,baby laughter,train BPtuekvJdhs,80,baby laughter,train BPvFw65onEg,87,people booing,train BPvFw65onEg,97,people booing,train BPxW7nP4loQ,3,eagle screaming,train BPxW7nP4loQ,36,eagle screaming,train BPyI5tsSgLA,213,hail,train BPyI5tsSgLA,228,hail,train BQ-dwfQPVtI,10,typing on computer keyboard,train BQ0_TuaVsaU,91,shot football,train BQ1a_xJ9lXA,33,mouse clicking,train BQ2jSKthb98,20,splashing water,train BQ2om2mrIps,298,disc scratching,test BQ3ZpRIsskU,16,mosquito buzzing,train BQ3ZpRIsskU,91,mosquito buzzing,train BQ5Vdu40iUg,34,machine gun shooting,train BQ5Vdu40iUg,44,machine gun shooting,train BQ5_882rP_E,50,wind noise,train BQ5nKgbGVEE,91,electric grinder grinding,train BQ5nKgbGVEE,110,electric grinder grinding,train BQ7Nj7WJL8U,70,eating with cutlery,train BQ86IY0M860,30,sailing,test BQ8xzFthR4A,30,playing badminton,train BQC-9_M8JG0,69,pheasant crowing,train BQDvQ2nbJOc,3,playing tambourine,train BQEV_8eNU0Q,44,playing djembe,train BQEV_8eNU0Q,126,playing djembe,train BQGQnEkC314,33,zebra braying,train BQLwKNmSFps,231,child singing,train BQLwKNmSFps,267,child singing,train BQOeDujTlSQ,20,"race car, auto racing",train BQPk4cmN__4,30,dog barking,test BQQzUbV1tyQ,20,police car (siren),train BQSVLgKQGM8,23,sheep bleating,train BQVAAEqUT6s,310,singing bowl,train BQW-qa8ltPk,30,"rowboat, canoe, kayak rowing",train BQY0ETj5HzY,13,pig oinking,train BQY0ETj5HzY,34,pig oinking,train BQYGJUpVQCA,400,"bird chirping, tweeting",train BQZ6Xf8zdVA,8,frog croaking,train BQZstoZcKmY,30,sailing,train BQ_BJNFGmTg,16,playing tabla,train BQ_BJNFGmTg,30,playing tabla,train BQ_egQf-9Ew,85,opening or closing drawers,train BQ_egQf-9Ew,143,opening or closing drawers,train BQ_pkvO5ia8,41,magpie calling,train BQ_pkvO5ia8,52,magpie calling,train BQbhG1T0fqc,30,sliding door,train BQd0UwK-gio,60,mouse pattering,train BQd4G_ZeBJs,3,civil defense siren,train BQdGYBt4ook,76,people burping,train BQdIeHYZiCs,40,playing banjo,train BQe6ZyIFt7s,10,"male speech, man speaking",train BQeI4i2Nzw8,86,people burping,train BQfIyahHrzU,30,"motorboat, speedboat acceleration",train BQgU2ncKCcc,27,hammering nails,train BQintSKffhY,110,playing saxophone,train BQl2vpUYFmU,27,sea waves,train BQlSMv7L80E,70,basketball bounce,train BQlaxJccFP4,159,playing harpsichord,train BQlaxJccFP4,247,playing harpsichord,train BQo9bIH4Dv0,30,skateboarding,train BQoWzwgdVsQ,90,playing hammond organ,train BQp3mKHHX3U,10,barn swallow calling,train BQpa8whzwAE,30,train horning,test BQqbFrrVgIo,30,"male speech, man speaking",train BQr6na4zPjw,30,horse clip-clop,train BQsQzaDrHKY,155,"alligators, crocodiles hissing",test BQsQzaDrHKY,210,"alligators, crocodiles hissing",test BQt2tShMHLs,5,playing glockenspiel,train BQt2tShMHLs,24,playing glockenspiel,train BQzwOyjPqGc,31,typing on typewriter,train BQzwOyjPqGc,94,typing on typewriter,train BR-35Wx41xM,250,people whispering,train BR-R852SC7k,30,ambulance siren,train BR0-kSmLMXY,16,crow cawing,train BR0-kSmLMXY,44,crow cawing,train BR3U7WaCP-4,5,people nose blowing,test BR3pOHMDDY0,212,playing gong,train BR3pOHMDDY0,329,playing gong,train BR5NGmkK8w4,56,police car (siren),test BR8iVJ9SRAQ,30,"male speech, man speaking",train BRBoT3VyrBA,160,people cheering,train BRCrFArdLUo,30,"male speech, man speaking",train BRF88GkMa-o,30,ambulance siren,train BRIGveLMsJ4,0,scuba diving,train BRIGveLMsJ4,51,scuba diving,train BRK__gCrups,5,sliding door,train BRK__gCrups,64,sliding door,train BRL7fOLmeRM,80,orchestra,train BRM5rIA6xXY,50,fireworks banging,train BRMeRLcfq4k,240,"race car, auto racing",train BRN-PSchNr4,10,"child speech, kid speaking",train BRNk4sTuDcs,161,canary calling,train BRNk4sTuDcs,347,canary calling,train BRQIMf5c-Jk,50,cat purring,train BRRyMm2KYCY,18,underwater bubbling,train BRRyMm2KYCY,95,underwater bubbling,train BRTOWkKiKu0,30,people eating,test BRX_rnUt9NM,30,driving buses,train BRYaBO_jiTQ,101,playing bugle,train BRYaBO_jiTQ,112,playing bugle,train BR_GZnsBJT4,45,bull bellowing,test BRaUCYxsvGE,30,"subway, metro, underground",train BRf30tyY-Ps,17,goose honking,train BRf30tyY-Ps,36,goose honking,train BRhvg3c0Jq4,30,playing acoustic guitar,test BRi_JxhLpIU,41,playing tambourine,train BRi_JxhLpIU,83,playing tambourine,train BRieV0pFOHA,30,male singing,train BRilC6wK8Rg,30,"pigeon, dove cooing",train BRj47W5y1f0,60,people cheering,train BRk0uSa7LW8,25,"railroad car, train 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cello,train BSoRS11wGnc,280,driving buses,train BSroniM1gz4,0,toilet flushing,train BStmOULYCAw,30,"female speech, woman speaking",train BSuBTKbulgQ,362,bouncing on trampoline,train BSxqZYnYzZE,30,playing electric guitar,train BSyUK-SV08w,30,"playing violin, fiddle",train BT1Mt78RuWU,90,"pigeon, dove cooing",train BT2JPFpMrzM,2,lions growling,test BT2JPFpMrzM,18,lions growling,test BT2Z0tftfl0,31,playing volleyball,train BT32WToEqYU,250,"child speech, kid speaking",train BT66M2qTIGc,30,horse clip-clop,train BT6_F86gXf8,10,ambulance siren,train BT7h7bpL0wE,30,chicken clucking,train BT9Dhl_-3lI,3,elk bugling,train BT9Dhl_-3lI,17,elk bugling,train BTDfLUVb6Lk,30,stream burbling,train BTDm_YTYHoY,30,playing cymbal,train BTGrmSzfO3U,25,opening or closing drawers,train BTGrmSzfO3U,50,opening or closing drawers,train BTHtb2nqivY,30,people whistling,train BTJI_j4XS4M,60,ocean burbling,train BTLaMEsdQEg,68,ferret dooking,train BTMCTPQ2-d0,20,fireworks banging,train BTNz6NftP34,30,driving motorcycle,test BTOSqSB1YkU,10,baby crying,train BTOo592Zuv0,220,people sniggering,train BTRkn_ivpHA,121,"alligators, crocodiles hissing",train BTSOyzWePsE,226,slot machine,train BTSa72kwiao,19,playing bassoon,train BTSndFd9MAg,50,skiing,test BTU7dvn_6Lg,70,chicken crowing,train BTV1_YWa--A,49,people burping,train BTY5tEwm-54,97,playing clarinet,train BTY5tEwm-54,283,playing clarinet,train BTZmHbLgSMY,27,people slapping,train BTaGlq_OIlA,28,cupboard opening or closing,test BTaR6V1MO6I,30,"playing violin, fiddle",train BTaTbW2CSe0,45,people eating,train BTaTbW2CSe0,381,people eating,train BTbrNgkqxZg,150,vacuum cleaner cleaning floors,train BTdIM1mncyA,30,"female speech, woman speaking",train BTdYAA04kDQ,100,sloshing water,train BTfOgoiEFfA,80,car passing by,train BTfkrELdOHA,30,"female speech, woman speaking",train BTi6AO7BOvo,4,people farting,train BTlAlQNqHt0,480,people running,train BTnTqJ-m_Io,50,chicken crowing,test BTo0ZD3l0U0,0,playing cymbal,train BToDpVXv1Ug,0,cattle 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Ba-us2952hg,44,playing glockenspiel,train Ba0HBrORXe0,30,baby laughter,train Ba0qmGx4IKQ,709,"playing steel guitar, slide guitar",train Ba2r9vrbiAM,2,firing cannon,train Ba2r9vrbiAM,18,firing cannon,train Ba36c1XVqU0,20,people babbling,train Ba8B8CuykUk,30,wind noise,train BaBNjfuhtM0,171,baby babbling,train BaBT2r8L6E4,13,"pigeon, dove cooing",test BaCKlONJXKw,70,people whispering,train BaDwTIoimY4,10,disc scratching,train BaEx0qmyJks,183,playing accordion,train BaFAzMCXtWw,51,playing squash,train BaG4cQ-bnKc,0,people sobbing,train BaHAI1jtGaY,40,tractor digging,train BaKFEJ6mHRI,40,police car (siren),train BaKrsmiD1VE,12,beat boxing,train BaMIYW1bZGc,30,basketball bounce,train BaNEVBXSNmk,30,driving motorcycle,train BaP6qRkjVKM,30,horse neighing,train BaUSDbLhpc0,30,"engine accelerating, revving, vroom",train BaUynrJl_8k,20,"female speech, woman speaking",train BaVmCSzgfZI,30,"male speech, man speaking",train BaYe54NXLSM,617,playing bongo,train BaZGT-2NhEA,157,playing cornet,test 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Bgh_mgO2VZU,150,helicopter,train Bgihg2J-1Is,12,fox barking,train BgjCl_aZeW8,35,arc welding,train BgmnIOiLalA,180,"child speech, kid speaking",train BgnEBEkSMXo,18,playing squash,train Bgnaur8-fas,160,playing bongo,test BgtyMgPJdMw,61,playing bongo,train BgtyMgPJdMw,89,playing bongo,train Bgu9bzrgN-c,20,cheetah chirrup,train Bgu9bzrgN-c,89,cheetah chirrup,train Bgu9fBatQso,10,pig oinking,train Bgu9fBatQso,22,pig oinking,train BgyP7fzI7GM,217,alarm clock ringing,train Bh--nnGdwX0,89,squishing water,train Bh-1ajFQr6g,120,skidding,train Bh-PzOM0ad0,4,"vehicle horn, car horn, honking",train Bh-o_G4wNZU,10,people babbling,test Bh05MyZ9uSw,43,tap dancing,train Bh09EQkv-rs,40,telephone bell ringing,train Bh09EQkv-rs,185,telephone bell ringing,train Bh0zigNPoec,64,playing electronic organ,train Bh0zigNPoec,255,playing electronic organ,train Bh2Fk5T0Xmo,30,playing saxophone,train Bh2GaErefPU,240,playing saxophone,train Bh2ggNbvNK0,19,electric grinder grinding,train Bh2ggNbvNK0,46,electric 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car doors,train BlDAvLaO4UU,30,printer printing,train BlGB75_IQp8,24,raining,train BlHoprSIS7c,0,"bee, wasp, etc. buzzing",train BlIZWzpaEdA,31,cuckoo bird calling,train BlIZWzpaEdA,46,cuckoo bird calling,train BlL3ylriTys,30,driving buses,train BlLPTLJfJv8,30,sloshing water,test BlM-m0-yc3Q,260,bouncing on trampoline,train BlNHTYxW49s,260,playing synthesizer,train BlOgHKDq3f0,112,playing darts,train BlSQiV4cxKQ,10,otter growling,train BlU3CyIZN44,26,baby crying,train BlXWW0tleY0,175,bouncing on trampoline,train BlXWW0tleY0,209,bouncing on trampoline,train BlXaABxVPSs,30,orchestra,train Bl_FyZ4c_pI,53,beat boxing,train BleBqimXGdw,12,chicken crowing,train BlgKvjO7vH8,584,people eating noodle,train BlhUt8AJJO8,30,skateboarding,test BlipbBuIAUc,180,male singing,train Bljgy68mRTM,30,people whispering,train BlkTAJgLUKI,8,vacuum cleaner cleaning floors,train BlkTAJgLUKI,120,vacuum cleaner cleaning floors,train Bll-sQvnKGo,351,mouse clicking,train Bllfhno5dLA,30,"male speech, man 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BnVxtkKiXd4,275,playing guiro,train BnY_93ZaFgY,3,"playing steel guitar, slide guitar",train BnY_93ZaFgY,170,"playing steel guitar, slide guitar",train Bn_4VF71brY,217,playing table tennis,train Bn_4VF71brY,227,playing table tennis,train Bn_vV9thaNo,312,golf driving,train BnccD3gqbKI,4,golf driving,test BndvWLg8hkM,80,stream burbling,train Bnf8YjlIFGQ,36,playing tambourine,train Bnf8YjlIFGQ,125,playing tambourine,train BngzM8s9WYs,508,playing ukulele,train BngzM8s9WYs,544,playing ukulele,train BniijKHywXM,103,playing tambourine,train BnjqGhAlFzs,30,playing harpsichord,test BnkDQXlrIX4,240,playing sitar,test BnmVJUFOisQ,170,playing squash,train BnnI4OEIIrI,1,civil defense siren,train BnnI4OEIIrI,51,civil defense siren,train Bnq573SNAeg,60,playing flute,train BnrocXZueJI,500,"child speech, kid speaking",train Bns-ir_npe4,24,owl hooting,train Bnsv3UTvEz4,148,playing tympani,train Bnsv3UTvEz4,185,playing tympani,train BnuyupfSBZA,40,stream burbling,train Bnyj8gKl9v4,30,"playing violin, 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growling,train BoXJR6_zAwQ,71,cat growling,train BoXorZKh7v8,270,people clapping,train BoY6Mw7PaFg,0,wind rustling leaves,train BoYDDn8nOhk,30,"playing violin, fiddle",train BoYqls3XfaA,766,scuba diving,train BoYviiH4HpQ,29,"electric shaver, electric razor shaving",train BoZq15USPso,30,playing drum kit,test BobKIVLfYyg,2,tap dancing,train BokrfYASP28,30,baby laughter,test Bonrrh9Gq5E,10,playing vibraphone,train Bonrrh9Gq5E,68,playing vibraphone,train BooOzz6kGa0,52,pheasant crowing,train BooOzz6kGa0,64,pheasant crowing,train BookISwJRqQ,19,pig oinking,test Boow3AumZH0,50,playing bass guitar,train BoqxtDpaqXM,72,"playing steel guitar, slide guitar",train BoqxtDpaqXM,244,"playing steel guitar, slide guitar",train BotjPWQZrQ4,8,car engine knocking,train BotjPWQZrQ4,19,car engine knocking,train Bou54QBgIYw,30,"ice cream truck, ice cream van",train Bou6FtHLp_0,0,skateboarding,train BouWWmc9Bgw,101,playing table tennis,train Bov2v3pQVrU,5,skateboarding,train Bov2v3pQVrU,126,skateboarding,train BoxuXMoXC8M,400,playing cello,train Boz-Ice4AMM,2,cupboard opening or closing,train Boz-Ice4AMM,19,cupboard opening or closing,train Bozdlum_p3A,30,"playing marimba, xylophone",train Bp6GfJajhF0,36,snake hissing,test Bp6k9pStgxU,127,"playing marimba, xylophone",train Bp7ohtEe7kE,380,playing cello,train BpATV7c5xCw,204,basketball bounce,train BpAZVHn6Y3M,110,disc scratching,train BpBBVyd28e8,26,chainsawing trees,train BpDsEmgy4qY,30,playing drum kit,train BpFBJtxcKws,10,baby laughter,train BpGskGpbv5E,30,driving motorcycle,train BpJgzIfK9a0,0,"fly, housefly buzzing",train BpNN9k4brXM,9,toilet flushing,train BpNayTl2zzM,30,playing accordion,train BpNzK4jfY04,631,scuba diving,train BpNzK4jfY04,663,scuba diving,train BpOT-yDjqZc,50,lawn mowing,train BpPRKj4JFIk,198,people burping,train BpRiAEeE4Ro,35,singing bowl,train BpTNcUQ_lg4,0,"bird chirping, tweeting",train BpV7n-YUtos,248,rope skipping,train BpVAPPzj3ZI,71,playing table tennis,train BpVN2-CV3a0,17,lathe spinning,train BpVN2-CV3a0,50,lathe spinning,train BpVpIktA6ys,5,people coughing,train BpVpIktA6ys,60,people coughing,train BpY5kW3UJcI,90,playing cymbal,train BpZTCPr75dc,30,"playing violin, fiddle",train Bpao4M5dNsU,12,machine gun shooting,train Bpc_A26bN9A,30,bird squawking,test Bpe9dVP8krk,30,lawn mowing,train Bpei0sQeqq4,65,children shouting,train Bpei0sQeqq4,157,children shouting,train BpfM3evN6H8,9,people eating apple,train BpfM3evN6H8,155,people eating apple,train Bpmn-1SOFNY,19,playing tabla,train Bpmn-1SOFNY,88,playing tabla,train BpnANQ2624Q,2,volcano explosion,train Bpq4P7nmfKE,30,printer printing,train BpqPYZfCpLw,283,chicken crowing,train Bpr2EDFu0-g,515,sharpen knife,train BpsLlRNO7Xg,320,"child speech, kid speaking",train BptW9UWJSsg,30,orchestra,train BpuKKpk92dc,110,male singing,train Bpw53tN6h8E,30,singing bowl,test BpzAsX9y43k,189,sailing,train BpzHFszqXVc,53,arc welding,train Bq10zG74Wc0,40,playing drum kit,test Bq2bggPDulg,30,sloshing 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BraofYWqX84,7,"engine accelerating, revving, vroom",train BrauyhCx3iA,0,playing bass drum,train Brc0bDoCjfM,30,stream burbling,train Breb9mvHXhc,33,hail,train Brh4TWNKhwY,354,tap dancing,train Brh4TWNKhwY,380,tap dancing,train BrjXD7rdihs,497,playing bass drum,test BrmFlrMVBqE,0,people finger snapping,train BrmFlrMVBqE,20,people finger snapping,train BrtKgAjXv7g,30,mouse pattering,test BrultP5tQvw,0,crow cawing,train BrzyYybRULM,22,typing on typewriter,train Bs-zj5vOkcQ,79,penguins braying,train Bs-zj5vOkcQ,90,penguins braying,train Bs1u4mn17jU,30,"race car, auto racing",train Bs6SMNq51Gw,140,playing cello,train Bs6a7tENmJQ,40,dog barking,train Bs9olZfNiTg,4,playing squash,train BsAevOQQ3zQ,2,gibbon howling,train BsAevOQQ3zQ,36,gibbon howling,train BsD0rfqtg_g,32,playing badminton,train BsHgr_sj6ec,58,playing bongo,train BsIQFOwV7xI,140,playing drum kit,train BsJoOE9x8s4,209,playing sitar,train BsJoOE9x8s4,254,playing sitar,train BsLVpoK1DKg,50,toilet flushing,train BsLgruLdjDI,39,train whistling,train BsLgruLdjDI,208,train whistling,train BsOFfQIi6MA,1,mynah bird singing,train BsPBVoBaJ40,30,playing banjo,train BsSSaXXuTsI,30,horse clip-clop,train BsU4Gtd6FpA,12,playing hammond organ,train BsYFCAuPjwE,30,chainsawing trees,test BsZ-mT0VGm8,1,beat boxing,train Bsb8e96xnxE,20,"race car, auto racing",train BsceC-igcTY,78,playing hockey,train BschKmTilLA,152,people sneezing,train Bscvr8eoteU,7,swimming,train Bsd7nIHXzac,40,dog bow-wow,train BsgAcQKLqGc,2,arc welding,train BsgAcQKLqGc,87,arc welding,train BsgYWC8QM4g,4,wind chime,train BsgYWC8QM4g,190,wind chime,train BsjoMDuHniE,10,driving motorcycle,train BskXhYLmvLo,20,typing on typewriter,train BslzZEEwFa8,30,people whistling,test BsnRFlOVr2I,30,wind noise,train BsnjK6DypAg,30,playing didgeridoo,test BsoEBcaCrlg,35,train horning,train BspAnDl4bEw,30,people babbling,train BspRb5sspsY,78,dinosaurs bellowing,train BspRb5sspsY,108,dinosaurs bellowing,train BspTAvJUJBQ,30,lawn mowing,train 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CCXN6QjFPyE,62,playing darts,train CCX_4cW_SAU,0,"subway, metro, underground",test CCZvSObNOL4,260,playing trombone,train CCdFTFaxrJU,150,chinchilla barking,train CCdIv4n1CA0,200,eating with cutlery,train CCe50Ed2sWM,30,dog bow-wow,train CCeiXaCDgHI,831,playing hockey,train CCfE2bMLxFI,31,train wheels squealing,train CCffUseBsfM,30,horse clip-clop,test CCiW5Km55rA,180,playing tambourine,train CCiW5Km55rA,197,playing tambourine,train CCkduyQIPog,30,playing bass guitar,train CCl-8Eu8Src,17,cat purring,train CCl-8Eu8Src,27,cat purring,train CCnV8AO8moM,1,goose honking,train CCnV8AO8moM,18,goose honking,train CCpzixMf_Xo,1,"engine accelerating, revving, vroom",train CCqBDTbY6Rg,315,airplane flyby,test CCqIisSpuVY,183,mosquito buzzing,train CCrRIQZ1NPg,46,hammering nails,train CCriJ0jOrIY,70,skidding,train CCrpqe1ijKI,300,skiing,train CCsBnN1Tyhg,30,playing flute,train CCsQ3Ioa4ls,110,basketball bounce,train CCstODfDFKY,60,helicopter,train CCv0iVNgAqg,5,chipmunk chirping,train 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CEGOtonophg,10,cat meowing,train CEIDpwB1ZFE,30,playing synthesizer,test CEJcYnPp3q8,9,tornado roaring,train CEJcYnPp3q8,63,tornado roaring,train CENJ57WEMfU,210,chainsawing trees,train CEOC7a6DjqQ,37,lighting firecrackers,train CEOC7a6DjqQ,111,lighting firecrackers,train CEPqJQMX-TI,200,rope skipping,train CEQIzR9TpZA,30,playing electronic organ,train CESRrHfXako,220,"railroad car, train wagon",train CEU-5tpB5xg,127,playing bass drum,train CEV000ZGHCw,50,playing table tennis,train CEWnNoK8LVQ,90,playing volleyball,test CEWvYJiAxgg,69,bouncing on trampoline,test CEYDkGy1uAY,294,people eating crisps,test CE_cEThCA1M,99,pheasant crowing,test CE_cEThCA1M,151,pheasant crowing,test CEdldBsuagY,273,people burping,train CEeS448fSus,30,"motorboat, speedboat acceleration",train CEj6NV3Xzk0,142,beat boxing,train CEkd4qntoD8,27,people booing,train CEmDoh7jaIg,30,people sniggering,train CEqWnXQKmHM,30,singing choir,train CEt91kPhjis,296,playing steelpan,train CEuML1KBLnE,0,bathroom ventilation fan 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CFqcbj_9kMI,190,train horning,train CFrR2FlASfo,220,playing accordion,train CFsme5oy2BA,50,people crowd,train CFuaMowPdT4,213,playing vibraphone,train CFuaMowPdT4,329,playing vibraphone,train CFw01uH8p4M,50,wood thrush calling,train CFwUzfuwpH0,71,missile launch,test CFyUs8PScdU,30,people farting,test CG-2XtQI6sM,0,cat hissing,train CG-62M13z_Y,41,yodelling,train CG2nCAPv5Dc,509,swimming,train CG3h38A9f-8,0,volcano explosion,train CG5EsomqSFQ,10,"vehicle horn, car horn, honking",train CG7Z7ZBMk6I,30,people sobbing,train CG81WWzF3PA,30,"female speech, woman speaking",train CG8pTm82NF0,4,people whistling,train CG8pTm82NF0,24,people whistling,train CGAvGtSlk3U,14,baby babbling,train CGAvGtSlk3U,69,baby babbling,train CGBhkXlhccE,0,people crowd,train CGCVxKFlRzQ,15,cricket chirping,train CGEBxfiJ9yI,60,chicken clucking,train CGEnKh0ocb0,13,cricket chirping,train CGEnKh0ocb0,41,cricket chirping,train CGFEXCxElJA,51,tap dancing,train CGFjIOYwvmE,30,people sniggering,train CGGi6Q8xvO0,6,train 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CHt2dcVfcIY,161,church bell ringing,train CHt2dcVfcIY,183,church bell ringing,train CHx8uTt2u3c,73,whale calling,train CHxOPxrzSfQ,214,warbler chirping,test CI1OKSC7B8E,43,"subway, metro, underground",train CI2Hh36KS4U,10,lawn mowing,train CI2dgMgpyBI,110,playing snare drum,train CI5aF4TyF0E,0,people farting,train CI6MFNaE3hE,14,horse neighing,train CI6MFNaE3hE,24,horse neighing,train CI6uv9SfUhQ,20,church bell ringing,train CIAd34BDDHg,15,underwater bubbling,test CIAd34BDDHg,29,underwater bubbling,test CICMa4PjfvM,6,people burping,train CID9NCPIBNY,264,playing bassoon,train CIDVb_gSs7s,21,cat hissing,train CIDVb_gSs7s,101,cat hissing,train CIH67LU9DaA,10,"bird chirping, tweeting",train CIJ9L83uKtE,30,lawn mowing,test CIJhL5kL8Sg,788,driving snowmobile,test CILRl2BB1bU,30,sailing,train CIMZELXrQ0k,34,typing on typewriter,train CIMZELXrQ0k,57,typing on typewriter,train CIMb0SSPDYs,90,lawn mowing,train CIN14f0ybzQ,360,wind noise,train CINS1OiNH44,30,"race car, auto racing",train CIN_pzwxrCE,69,ferret dooking,test CIOTqXLHB0c,111,fire crackling,train CIOTqXLHB0c,121,fire crackling,train CIOyzuncSdk,0,playing tennis,train CIQ5daJVfPI,80,car passing by,train CIRBYei04i0,30,wind noise,train CIRU6b1ZNDg,10,playing acoustic guitar,train CIaS9g6pn6k,21,playing glockenspiel,train CIaS9g6pn6k,84,playing glockenspiel,train CIbAcL2RiEs,50,playing banjo,train CIbJOUOd8WM,30,"playing marimba, xylophone",train CIfcvnqfuXc,65,yodelling,train CIiA39U8UTw,30,fire truck siren,test CIjjpfPKh4M,223,singing choir,train CIlNi_e9SJ0,120,sharpen knife,test CImW_P9jdxk,4,opening or closing car doors,train CImW_P9jdxk,250,opening or closing car doors,train CIo6-8awLRs,130,cattle mooing,train CIo9rp_xraQ,40,"race car, auto racing",train CIpihtmITgs,58,electric grinder grinding,train CIw3fdJqJ9o,30,orchestra,train CIxMFQX2LgU,29,people nose blowing,train CJ02NxxZvoc,30,baby crying,train CJ0cuUPDpMY,173,playing volleyball,train CJ0kz4TA8eE,372,dog growling,train CJ0kz4TA8eE,389,dog 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CJuqRsvF_1M,20,wind chime,train CJvEP-8jQOI,113,playing harp,train CJwSH6LNTV0,10,people crowd,train CJyJ6rnaAFk,50,"female speech, woman speaking",train CJyW4f1oKHk,23,lighting firecrackers,train CJzHrfSPi90,82,orchestra,train CJzzhXoHkp0,3,parrot talking,train CJzzhXoHkp0,33,parrot talking,train CK-Mfef1kLw,10,train horning,train CK-oCooXxa8,210,playing bagpipes,train CK04S0xSebQ,60,stream burbling,train CK3zi8p7wZM,149,playing steelpan,train CK3zi8p7wZM,1035,playing steelpan,train CK5D-skzB5Y,170,dinosaurs bellowing,test CK5PEK5xU2o,190,"electric shaver, electric razor shaving",train CK5yGvj_tOU,30,"female speech, woman speaking",train CK6hEXL5w9M,110,police car (siren),train CK7_1E8cNMM,43,playing tympani,test CKAd79fy-CU,30,playing french horn,train CKAhsHKst9Q,9,owl hooting,train CKBM6AmkWX4,25,playing badminton,train CKEt34jgBKo,59,air conditioning noise,train CKJBlAO6XZ0,180,"railroad car, train wagon",train CKRW-uZfeHI,56,turkey gobbling,train CKTU641d7lE,95,air conditioning 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CMCXDzo08VQ,30,playing trombone,train CMCtge02AnU,260,playing hammond organ,test CMCuZmgEVSk,610,playing bassoon,train CMDzYEKKzhI,10,"race car, auto racing",train CMJkKnoGIus,70,playing timpani,train CMJkKnoGIus,168,playing timpani,train CMKnSInoA7I,3,horse clip-clop,test CMQArl9dSHY,20,police car (siren),train CMR5qHb-X9M,30,stream burbling,train CMSeOc8Nypc,470,"subway, metro, underground",train CMU37ljrYWE,273,"electric shaver, electric razor shaving",train CMUsN0k5XpQ,26,people sneezing,train CMUsN0k5XpQ,38,people sneezing,train CMUx4c5m6Qk,30,"male speech, man speaking",train CMVlcgyX_no,111,slot machine,train CMWoAvgD0A0,9,people screaming,test CMXmmp2RmyI,90,"child speech, kid speaking",train CM_7aAq0zNI,354,planing timber,train CMbFe9o_ID8,13,"motorboat, speedboat acceleration",train CMbdIGgqxVc,30,ambulance siren,train CMfE3W4Ynk0,160,"railroad car, train wagon",train CMfvZvYL1Co,10,fireworks banging,train CMgS157FM00,30,turkey gobbling,train CMhmiVXSMoU,30,people running,train CMifv1ntm8E,159,playing bassoon,train CMin_yNlwv0,356,civil defense siren,train CMjEOnT4aMQ,72,pheasant crowing,train CMjEOnT4aMQ,212,pheasant crowing,train CMkQi6yLxgQ,140,stream burbling,train CMoJV9bMyZ8,390,car engine idling,train CMoJV9bMyZ8,485,car engine idling,train CMoOp8mf8uw,4,fire truck siren,train CMpe34Aign4,0,"bird chirping, tweeting",train CMpe34Aign4,109,"bird chirping, tweeting",train CMsPzzzOKJU,327,playing darts,train CMvgidD4cBc,0,playing piano,train CMx9nGyUVz0,480,helicopter,train CMzLTmZsUGo,0,baby babbling,test CMzlxMzoQJE,20,typing on computer keyboard,train CMzyWdYklFs,160,chicken clucking,train CN0Bi4MDpA4,21,police car (siren),train CN0qhAFvzH0,87,people humming,train CN0s5zluRbg,62,electric grinder grinding,train CN1qntICn2E,30,"pigeon, dove cooing",train CN2ppTU6zxo,67,beat boxing,train CN3TcLhslxU,30,telephone bell ringing,train CN4TTTXLcVU,30,playing banjo,train CN5k-jr8oAw,13,police car (siren),train CN7qu_1k3F8,550,lawn mowing,train CNAsjF-sses,5,baby babbling,train CNAsjF-sses,15,baby babbling,train CNAw6JD-gX0,30,"male speech, man speaking",train CNBNExwqjbs,10,people sneezing,train CNEN26JetZo,68,civil defense siren,train CNFaXg2gaug,164,playing badminton,train CNHIs4v6EmU,191,playing didgeridoo,train CNHSWKOACOc,30,car engine starting,train CNNoQ-0b7pk,5,child singing,train CNNoQ-0b7pk,33,child singing,train CNPDH0QoNX4,30,playing vibraphone,train CNQmjYx14Fo,100,playing accordion,train CNTOvE2Hzqo,30,"male speech, man speaking",train CNU0j7FMDqo,10,people whispering,train CNURAn-l9NI,217,bowling impact,train CNURAn-l9NI,247,bowling impact,train CNWGLjGSxvU,47,opening or closing drawers,train CNWGLjGSxvU,131,opening or closing drawers,train CNWMy-ZI8NQ,8,elk bugling,train CNWRE8HnSmU,23,opening or closing car doors,test CNX4-EN36eU,50,people whispering,train CNX7DluUdyg,10,"railroad car, train wagon",train CNZj2Ai6Wts,105,people marching,train CNZj2Ai6Wts,131,people marching,train CNbMz2uiHws,52,playing erhu,train CNbMz2uiHws,91,playing erhu,train CNbeBv-j6tY,580,playing double bass,train CNcLTTnYRtc,23,playing squash,train CNcr2qmwLHg,17,playing table tennis,train CNcxzB9F-Q8,100,skateboarding,test CNglJTNGK0Q,118,firing muskets,train CNkV9_EPEnQ,86,playing erhu,train CNmf1vehl-4,200,"child speech, kid speaking",train CNoP-Y19lRI,4,whale calling,test CNosN4iZWXA,95,playing cornet,train CNrkDt9Hvco,11,playing bongo,train CNteFuxbbZs,30,mouse squeaking,test CNx0gV0K1kA,273,playing didgeridoo,train CNxIeasZD74,20,dog barking,train CNy35bD66fQ,170,orchestra,train CO-K8uaLhhg,168,cap gun shooting,train CO0CK_M_Nk0,20,playing saxophone,train CO1M63tvnrw,30,people sneezing,train CO30dUWfl5M,47,"cattle, bovinae cowbell",train CO30dUWfl5M,57,"cattle, bovinae cowbell",train CO5O7C7YfJc,0,horse clip-clop,train CO5Xu_vzYOc,110,playing trumpet,train CO7SB41wQ9c,16,firing muskets,train CO7SB41wQ9c,91,firing muskets,train CO8AzGO9j08,192,playing table tennis,train CO9AXwmgg1g,40,train horning,train CO9AXwmgg1g,64,train horning,train CO9sZl5tJVA,17,raining,train COCYZmtHWHg,247,"playing steel guitar, slide guitar",train COCYZmtHWHg,531,"playing steel guitar, slide guitar",train COD_WfqQT5Y,80,driving buses,train COE7-slXfTY,59,"railroad car, train wagon",train COG0-wbXIPE,298,beat boxing,train COHl887nx60,3,playing badminton,test COIZ9xZCRyI,9,people nose blowing,test COJIFU6e3Xk,12,crow cawing,train COLqfiud9pw,26,parrot talking,train COLqfiud9pw,48,parrot talking,train COM6r9isaxY,5,playing trumpet,train COMArU-coE0,71,tap dancing,train COMY8VWnKOQ,20,playing harp,train COSqUkkPniA,9,bull bellowing,train COSqUkkPniA,63,bull bellowing,train COY2vaJHKb0,50,"race car, auto racing",train COYm1lXIMfM,170,people crowd,train CO_SwoaO7SE,30,"playing violin, fiddle",train CO_onAOxX4Q,112,people burping,train CObFdYKG0G4,156,chipmunk chirping,train CObWMGQEalk,30,playing electronic organ,train COdrxNdndGU,30,"male speech, man speaking",train COe3wDHLKtg,1,playing harmonica,train COe3wDHLKtg,41,playing harmonica,train COhPkkb8I0c,96,cat growling,test COlFbCXF7GQ,105,magpie calling,train COlFbCXF7GQ,194,magpie calling,train COllwlh-jXo,22,black capped chickadee calling,train COllwlh-jXo,86,black capped chickadee calling,train COmphOZc-YI,3,dog growling,train COmq6eCydDg,9,squishing water,train COmq6eCydDg,181,squishing water,train COnY5HC8byk,48,missile launch,train COoFKQhF8ho,473,tap dancing,train COoFKQhF8ho,485,tap dancing,train COpjE2vz3nA,30,"playing marimba, xylophone",train COt-QuxvoIE,100,people crowd,train COtxBtwFyeA,10,people sobbing,train COu67aiDOyg,30,playing drum kit,train COvPKvoAz_c,0,cat growling,train COwOSdMOcv0,30,female singing,train COye9W7MzkY,134,playing double bass,train CP13uQo5oC0,101,beat boxing,train CP3Kh4RJyPo,45,playing cornet,train CP3Kh4RJyPo,56,playing cornet,train CP4AdJG80gY,70,machine gun shooting,train CP5BtER96PQ,13,missile launch,train CP5BtER96PQ,24,missile launch,train CPAtpochQAk,23,civil defense siren,train CPAwdF-PBp0,30,"pigeon, dove cooing",train CPFPv7oSez0,15,church bell ringing,train CPFPv7oSez0,54,church bell ringing,train CPF_KlBowoo,4,raining,train CPF_KlBowoo,18,raining,train CPFuAg2QaQQ,78,church bell ringing,train CPFuAg2QaQQ,96,church bell ringing,train CPJoCqYFZ6c,30,"female speech, woman speaking",train CPMMiLhqpao,258,cat meowing,train CPQaXeB-Rzc,54,canary calling,train CPQaXeB-Rzc,110,canary calling,train CPR74K2MZ1M,254,pheasant crowing,train CPR74K2MZ1M,276,pheasant crowing,train CPRTXk9BkbU,320,playing cello,train CPRUkY2QVh8,30,chainsawing trees,train CPRv_HM_5Cs,52,dog howling,train CPRv_HM_5Cs,99,dog howling,train CPTYsnBdZRQ,27,playing timpani,train CPTYsnBdZRQ,50,playing timpani,train CPU3L4C8Y98,26,dog bow-wow,train CPV-FGtCDaY,21,horse neighing,train CPVO7EbzYeI,192,squishing water,train CPVPmzfWJhQ,20,playing tympani,test CPVSpl2eqRc,220,chicken crowing,train CPZVdYECX0g,136,squishing water,train CPZVdYECX0g,165,squishing water,train CPZujERalLY,0,canary 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chirping,test CQ9bDn6Xqsk,8,playing mandolin,train CQ9bDn6Xqsk,18,playing mandolin,train CQC1wSV5FwA,10,toilet flushing,train CQCAQ2dMWGY,46,train horning,train CQCmi-CTYMI,30,"railroad car, train wagon",train CQDY8CdIfMw,30,chainsawing trees,train CQDxBt3Remo,4,magpie calling,train CQDxBt3Remo,15,magpie calling,train CQFEY9RIRJA,2,cricket chirping,train CQFEY9RIRJA,14,cricket chirping,train CQFnNp1MjEs,40,people burping,train CQGQV-JxPoI,30,cat meowing,test CQHDA4nXmTs,30,"male speech, man speaking",train CQMoTk48xPA,60,playing darts,train CQNxQ_dD040,5,horse clip-clop,test CQO0MYbxhho,26,playing darts,train CQO0MYbxhho,45,playing darts,train CQO6w2XV3xY,30,typing on computer keyboard,train CQQUkFPujFA,2,train whistling,train CQS3nOWUbT4,7,crow cawing,train CQTUoapmD_s,30,"motorboat, speedboat acceleration",test CQVCIRA09mk,18,police car (siren),train CQVpQwmbPLI,190,people crowd,train CQX9AlQg-kk,62,"cattle, bovinae cowbell",train CQX9AlQg-kk,80,"cattle, bovinae cowbell",train CQ_C6C-Loa8,250,"female speech, woman speaking",train CQ_HCfeU7nM,30,playing french horn,train CQ_HCfeU7nM,42,playing french horn,train CQ_ZtRZszDM,52,pheasant crowing,train CQ_ZtRZszDM,76,pheasant crowing,train CQaeWFMNfQA,30,playing bass guitar,train CQe_TjjJasM,60,canary calling,train CQg7RhMCk-E,19,playing timpani,train CQg7RhMCk-E,371,playing timpani,train CQpGLOiNpnI,590,playing saxophone,test CQqJYxXIP5c,181,spraying water,train CQqdkWliRKs,4,"vehicle horn, car horn, honking",train CQqjv5jpGR8,30,chainsawing trees,train CQsvbSaaafc,45,playing volleyball,train CQvTthq4ukk,11,magpie calling,train CQzSSPNZoY8,129,people marching,train CQzqhM81T_M,0,people sobbing,train CQzu2V7N67A,28,parrot talking,train CQzu2V7N67A,161,parrot talking,train CR-QgaCcbB8,19,barn swallow calling,train CR-QgaCcbB8,29,barn swallow calling,train CR4OxwCR6aQ,14,printer printing,train CR6L5R9chy0,20,lions growling,train CR6L5R9chy0,75,lions growling,train CR6wbyGorPg,72,wind chime,test CR6yVUxXnyA,82,singing choir,train CR7c9dAnMVg,99,baby laughter,train CR9sOWFWuDc,141,ripping paper,train CR9sOWFWuDc,186,ripping paper,train CRAG1rh2rf8,310,playing saxophone,train CRAYl0Aro0A,114,playing didgeridoo,train CRAgkesZIQY,30,chicken clucking,train CRBgBz9I1ys,30,female singing,train CRBxroF27g4,30,fireworks banging,train CRCj2Du3JiU,150,people whispering,train CRFwma6Sn1Y,350,orchestra,train CRM51PSM0B0,30,people whispering,train CROMGcx932M,30,"playing marimba, xylophone",train CROqUhmRysU,100,toilet flushing,train CROvmDevCyM,1,lighting firecrackers,train CRQgbT-tDxg,39,reversing beeps,train CRQgbT-tDxg,67,reversing beeps,train CRXDKODoIgk,3,canary calling,train CRXDKODoIgk,17,canary calling,train CRXWJk6Jx-w,3,dinosaurs bellowing,train CRY1DYa7tsg,30,sliding door,train CRYSh17PbsA,305,playing bassoon,train CRZqoEw4LX8,18,singing bowl,train CRaKdtto9As,21,playing hockey,test CRatTuWdT_Q,22,playing saxophone,train CRcRNHVak7s,129,sharpen knife,train CRdma6NINIw,30,typing on typewriter,test CRdoqNj0Wxk,6,"subway, metro, underground",train CRdrO7hA_R4,300,lions growling,train CRegJ_OVMv8,1,"motorboat, speedboat acceleration",train CRgVfjvTVa4,13,chinchilla barking,train CRgVfjvTVa4,44,chinchilla barking,train CRkUzygY0fo,207,playing double bass,train CRkUzygY0fo,363,playing double bass,train CRlC9US2Vgs,30,horse neighing,test CRm_856NGI4,27,tap dancing,train CRpMIfmsvlk,10,people crowd,train CRxIWxG11rU,45,black capped chickadee calling,train CRxIWxG11rU,57,black capped chickadee calling,train CRy2FhOj_H0,80,skidding,train CRzuNMG55nQ,10,fire truck siren,train CRzuNMG55nQ,159,fire truck siren,train CS32tCoZujM,30,playing trumpet,train CS3vhj7pQjU,30,"female speech, woman speaking",train CS4S51lQ2_8,0,police radio chatter,train CS4cJBRBE6I,0,chainsawing trees,train CS5bAvngHcc,140,playing hammond organ,train CS6HHWCORiE,380,playing cymbal,train CS6gtfNSJ1E,30,"playing violin, fiddle",train CS7HGv9opEI,230,car engine knocking,train CS8EwDPmlkI,6,dog bow-wow,train CSAGAusAJXI,19,horse neighing,train CSEWnDLoCNA,21,cat hissing,test CSEqIdBIXAY,30,using sewing machines,test CSF9ReuCVkQ,8,bathroom ventilation fan running,train CSHRE2ce59w,570,people eating apple,train CSHRE2ce59w,743,people eating apple,train CSIb3bYDuYI,60,"female speech, woman speaking",train CSJN6VmiIyk,40,driving buses,train CSMYl_WaYdM,75,singing choir,train CSMYl_WaYdM,122,singing choir,train CSOfixnDpmk,30,sailing,train CSQ6B4PGVKg,580,"bird chirping, tweeting",train CSTatJYxND8,0,coyote howling,train CSTatJYxND8,12,coyote howling,train CSVQcod3ilA,0,splashing water,train CSWQUunh5jI,14,sailing,train CSXguQWRCT8,80,playing flute,train CSXkBsS5Ryw,120,playing cello,train CSYikpL3vJI,30,cat purring,train CSauTkrlmic,30,playing accordion,train CSbYnwftfAc,201,mouse pattering,train CSbYnwftfAc,419,mouse pattering,train CSc9azy7cgU,160,driving motorcycle,train CScQCxNACyc,130,chicken crowing,test CSdGqDZ09pA,0,people sneezing,train CSezApZRDcw,200,"race car, auto 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CTHLav0r8kg,23,baby laughter,train CTJAqGAoFiI,20,chicken crowing,train CTK3fl2GaSw,230,"bee, wasp, etc. buzzing",train CTLXacD9ero,7,wind noise,train CTLlFAhVG1I,140,"railroad car, train wagon",train CTMLiej_6Fw,100,"race car, auto racing",train CTMn3L0C5kI,80,dog barking,train CTMtd045iK8,280,waterfall burbling,train CTORcFY7oOM,80,"railroad car, train wagon",train CTQWa_KvB9w,218,cell phone buzzing,test CTSTTr0Gjdc,70,pig oinking,train CTTPaz8O06k,110,playing drum kit,train CTUJFYxuRwM,76,lighting firecrackers,train CTU_ZD8nQ5I,30,printer printing,train CTVRmnklMHI,340,playing trombone,train CTVyvfZfJJg,7,chicken crowing,train CTVyvfZfJJg,30,chicken crowing,train CTWtiwntfgw,30,fire truck siren,test CTYO9dLDun4,20,sliding door,test CTa-wlpZppY,11,civil defense siren,train CTa-wlpZppY,145,civil defense siren,train CTcP7nZ_nNs,390,skateboarding,train CTgD1J-pbAQ,501,bouncing on trampoline,train CThe_IxJs-I,30,"motorboat, speedboat acceleration",train CTi0qH3C81k,40,playing harp,train CTiA87IP-X4,44,footsteps on snow,train CTiA87IP-X4,86,footsteps on snow,train CTloWTO7bqQ,61,people eating apple,train CTloWTO7bqQ,185,people eating apple,train CTmRRq187y4,30,wind noise,train CTmeYSIdosA,100,eating with cutlery,train CToB66zxFqA,12,parrot talking,train CToB66zxFqA,144,parrot talking,train CTp0IjcFOg8,576,air conditioning noise,test CTqu3lM8GXc,112,singing bowl,train CTrbAT1vFa0,30,playing banjo,train CTsgfQKFtxA,41,playing bongo,train CTsgfQKFtxA,51,playing bongo,train CTsnrMlKJLc,0,frog croaking,train CTsnrMlKJLc,12,frog croaking,train CTtc7R99ur8,30,playing accordion,train CTuTiD0wjIM,500,"railroad car, train wagon",train CTurVRuyfHA,10,people clapping,train CTv3Xr4IMuo,115,baby babbling,train CTvhe9ME-wk,1,gibbon howling,train CTvhe9ME-wk,15,gibbon howling,train CTwaTWyiLCA,65,playing tabla,train CTwaTWyiLCA,98,playing tabla,train CTx62ed7QCg,110,playing banjo,train CTxq1zDlg2s,30,"pigeon, dove cooing",train CTzaeyoqQuo,201,playing badminton,train CU0AVPge-Og,61,people burping,train CU0kyxIiIC0,0,horse neighing,train CU0kyxIiIC0,4,horse neighing,train CU1sfLCpH78,30,ocean burbling,train CU2TPSXkc5g,30,people shuffling,train CU52O6Hhjus,50,eating with cutlery,train CU5NW2rzBoc,77,playing vibraphone,train CU5NW2rzBoc,180,playing vibraphone,train CU6KzqG_GjA,10,"engine accelerating, revving, vroom",train CU72qSlceqM,30,chicken crowing,test CU8HcUG0vrU,44,vacuum cleaner cleaning floors,train CU8HcUG0vrU,81,vacuum cleaner cleaning floors,train CUBSkreMjxQ,662,"heart sounds, heartbeat",train CUBSkreMjxQ,764,"heart sounds, heartbeat",train CUCvzqNLKDs,17,police radio chatter,train CUECynBifc4,443,underwater bubbling,train CUECynBifc4,498,underwater bubbling,train CUFBalzotgk,30,people crowd,train CUFhXylk4QE,930,playing darts,train CUJ1MnvyKOw,40,dog growling,test CUL0UrnVfZI,30,singing choir,test CULhzgFtHrg,30,wind noise,train CULsELht9bs,90,lions growling,train CUMXpHasweE,17,"donkey, ass braying",train CUMXpHasweE,38,"donkey, ass braying",train CUMunjwsO2U,20,playing piano,test CUPjM8RTvWo,14,ferret dooking,train CUPjM8RTvWo,57,ferret dooking,train CUQ8qOTO8fI,14,wind noise,train CUSBC7acLd4,154,skiing,train CUSBC7acLd4,240,skiing,train CUSCoXBezvg,30,playing harp,train CUSEEGsYDPY,14,car passing by,train CUTePwfZ5EQ,30,horse clip-clop,train CUTfPci11jI,30,playing electronic organ,train CUTuPw81MRY,13,"male speech, man speaking",train CUUrDwx93lU,229,mouse clicking,train CUUrDwx93lU,293,mouse clicking,train CUWVOZrmaMk,450,"engine accelerating, revving, vroom",train CUWfvJiJFLQ,30,playing bagpipes,test CUY3hob5V_o,10,opening or closing car doors,train CUY3hob5V_o,258,opening or closing car doors,train CUb8VehohVk,115,singing bowl,train CUbzJWylbb4,468,playing theremin,train CUcml-g1D6U,74,police radio chatter,train CUcsXLZhXfk,21,cheetah chirrup,test CUdOm0db2Yc,7,"engine accelerating, revving, vroom",train CUdvxcQIEnE,15,mouse clicking,train CUg3l00Jhdw,63,playing tambourine,train CUg3l00Jhdw,73,playing tambourine,train CUgPtcrfP8s,10,dog growling,train CUjFD0Y_2jE,66,playing erhu,train CUjFD0Y_2jE,148,playing erhu,train CUjZKCFqDdY,19,slot machine,train CUjhT_SDh2E,233,playing badminton,train CUlZAipx4zw,30,playing clarinet,train CUlyRLxqW1Q,17,horse clip-clop,train CUnjwBtbbd4,6,cap gun shooting,train CUnjwBtbbd4,17,cap gun shooting,train CUqga7lwvfM,80,"subway, metro, underground",train CUrulzw7D-M,50,people cheering,train CUt0oGFuxL8,53,woodpecker pecking tree,test CUuFZupAjew,30,orchestra,train CUvBlafR-eQ,320,people hiccup,train CUvIqWXgc7w,598,playing volleyball,train CUvR7KBSn8k,8,dog baying,train CUyb9VAaGe0,76,playing erhu,train CUyb9VAaGe0,126,playing erhu,train CUyh6xIDmKo,50,playing cymbal,train CV0GijLCbvU,130,playing tabla,train CV0GijLCbvU,196,playing tabla,train CV1MMVIbLP0,10,playing cello,train CV2Zm2EZKuw,42,baby babbling,train CV2Zm2EZKuw,94,baby babbling,train CV3EAozMSzw,47,parrot talking,test CV4v18JU7rg,50,child singing,train CV824GP7LXg,140,people slurping,train CV824GP7LXg,219,people slurping,train CV8_hjJGiU4,220,playing banjo,train CV8nDzeeL74,70,helicopter,train CVAVJ0DuUJg,51,playing cornet,train CVArsu9YFv8,0,people hiccup,train CVD1upoJKAQ,20,"rowboat, canoe, kayak rowing",train CVL_4sHnTzk,32,toilet flushing,train CVLqZyUwqv8,200,playing sitar,test CVOSvaWIajQ,150,"motorboat, speedboat acceleration",train CVPb-vauNGo,99,people nose blowing,train CVPb-vauNGo,192,people nose blowing,train CVQPA9xDCHg,31,swimming,train CVQPA9xDCHg,41,swimming,train CVT4i-GXoqw,48,dog growling,train CVT4i-GXoqw,74,dog growling,train CVV3Q32RLDc,103,woodpecker pecking tree,train CVV3Q32RLDc,138,woodpecker pecking tree,train CVVrs_KA6sU,30,"rowboat, canoe, kayak rowing",train CVYlabkgLj8,330,people belly laughing,train CVcJV8kyxVE,30,"motorboat, speedboat acceleration",train CVdN1Ckti9A,502,typing on typewriter,train CVdN1Ckti9A,570,typing on typewriter,train CVeGmthT028,0,driving buses,train CVeLV4bqoN0,30,playing electric guitar,train CVfIWr2niI8,3,"vehicle horn, car horn, honking",train CVgLrsBPt4o,105,playing didgeridoo,train CViouHw-mfQ,122,playing cello,train CVlI85dM5RY,170,playing vibraphone,train CVlI85dM5RY,350,playing vibraphone,train CVlPYjGPm1E,45,civil defense siren,train CVldpy1ryr8,95,airplane flyby,train CVls2S-1BuA,88,playing bugle,train CVmk3PcyXHc,82,mynah bird singing,train CVmk3PcyXHc,94,mynah bird singing,train CVtWW-uQUIg,103,tap dancing,train CVvSq6zl87k,300,playing cymbal,train CVxeB259h3I,30,"child speech, kid speaking",train CVxvdpOhffM,165,playing erhu,train CVy7vvomWXo,490,orchestra,train CVyFs95Q0Zg,61,playing bassoon,train CW1kKoIhZxs,90,playing clarinet,train CW397QKBGtU,46,bathroom ventilation fan running,train CW3Y1SBFfLk,81,pumping water,train CW3Y1SBFfLk,528,pumping water,train CW49y8rkiqo,370,"race car, auto racing",train CW5pZPEl1KU,27,playing hammond organ,train CW6dKG2JedI,140,"child speech, kid speaking",train CW7f-NtUWuk,252,car engine idling,train CW7f-NtUWuk,352,car engine idling,train CWA2pRIDHuI,0,snake rattling,test CWACwP-cvNI,80,playing double bass,train CWAfPZakgzU,61,tap dancing,train CWC9wVplIEc,30,playing vibraphone,train CWF2G3RMGk8,51,electric grinder grinding,train CWFb9W7M1U4,157,beat boxing,train CWFnfL-5X-M,30,basketball bounce,train CWH53Ep7lfU,0,"ice cream truck, ice cream van",train CWH53Ep7lfU,15,"ice cream truck, ice cream van",train CWHD3CxFWkQ,43,people humming,test CWHKc9QByN4,30,"engine accelerating, revving, vroom",train CWJGSnWotW4,30,people whistling,train CWJJ1Fm6-d0,183,driving snowmobile,train CWKPEl5AsFE,117,playing volleyball,train CWM9JSCk2WI,30,turkey gobbling,train CWMLP1OHIW8,30,people sneezing,test CWMsVY6pp3k,350,"pigeon, dove cooing",train CWNAocJJIKw,30,people hiccup,train CWOLPke8cr0,10,playing flute,train CWQ8P0yJdL8,45,bowling impact,train CWQ8P0yJdL8,75,bowling impact,train CWSH3GzXtFU,3,dog whimpering,train CWSH3GzXtFU,13,dog whimpering,train CWVvrSKZd1U,30,sailing,train CWY1Y5hjfpo,30,playing bass guitar,test CWYQDitVT14,116,playing sitar,train CWYQDitVT14,556,playing sitar,train CW_HpVt8EpE,16,opening or closing car doors,train CW_HpVt8EpE,61,opening or closing car doors,train CW_Mf9OrqEM,17,"bird chirping, tweeting",train CW_UrQzAqzU,25,canary calling,test CWctSkH4jR0,78,car engine idling,test CWdlYKBIAn0,43,ice cracking,train CWdlYKBIAn0,325,ice cracking,train CWdm18N1GWM,49,eagle screaming,train CWdxuVEGE-I,47,people booing,train CWfZCubBROk,20,striking pool,train CWfZCubBROk,57,striking pool,train CWi_968aD3M,17,rope skipping,test CWjh7YhXNjQ,30,child singing,train CWlNJlc72zM,30,police car (siren),train CWnBiSzxAYI,360,"subway, metro, underground",train CWnRnwZqzJw,5,magpie calling,train CWqrT9gtjXM,30,playing harp,train CWrsHXE93zA,30,playing saxophone,train CWsCtFc_pT8,30,people shuffling,train CWuenU7Xmo4,47,playing steelpan,train CWuenU7Xmo4,80,playing steelpan,train CWv80hsVFmQ,129,mosquito buzzing,train CWxZNhucY8o,573,people eating apple,test CWzYmmP0xlw,130,using sewing machines,train CWzzP8Ox_ts,64,police radio chatter,train CWzzP8Ox_ts,115,police radio chatter,train CX-gGuT3iEs,230,chicken crowing,test CX27q0WPWKw,10,horse neighing,train CX2bsgtQoJU,30,"motorboat, speedboat acceleration",train CX4kv67HpeE,30,"male speech, man speaking",train CX95CBc93MQ,32,chainsawing trees,train CXG5GsoQfYY,4,cattle mooing,train CXGTGLVxiWU,34,magpie calling,train CXHUsoiBbgA,155,playing cymbal,train CXHyc7PJj4o,4,train whistling,train CXHyc7PJj4o,51,train whistling,train CXISjTeWfTE,200,cap gun shooting,test CXIbJ-9MNMU,1,car engine starting,train CXJ0uOej2oQ,7,chimpanzee pant-hooting,train CXJ0uOej2oQ,22,chimpanzee pant-hooting,train CXKHEiMPMgA,4,cat meowing,train CXO_q6FyFKc,60,toilet flushing,train CXRemJ1ZLC8,250,"child speech, kid speaking",test CXS7Diqeav4,30,sea lion barking,train CXS7Diqeav4,211,sea lion barking,train CXVXMdU84G4,299,train horning,train CXVXMdU84G4,338,train horning,train CXWYOlareRc,27,tractor digging,train CXWYOlareRc,38,tractor digging,train CXYfryU_6qY,30,horse clip-clop,train CXZC7bNNA0s,30,"male speech, man speaking",train CXZHkKB1wXc,238,playing harmonica,train CXZkbV0ABjo,490,"race car, auto racing",train CXbKO-XqmZA,30,playing double bass,train CXdsBxwn9II,30,"male speech, man speaking",train CXdvqD8RNlo,30,people sniggering,train CXflSD-v3rM,226,skiing,train CXfxDP-UyiE,30,people whistling,test CXgJ51zVKrY,640,playing squash,train CXh9OSQYiCY,0,cat hissing,train CXjrsFSUhuw,46,playing didgeridoo,train CXjxUcCAy5c,180,"railroad car, train wagon",train CXndqNRc4LM,0,sheep bleating,train CXnspnV1tZw,20,horse neighing,train CXnspnV1tZw,48,horse neighing,train CXoPzPLaDAw,10,fireworks banging,train CXq5MGxKM0k,0,playing volleyball,train CXr238ry0qM,38,"donkey, ass braying",train CXuTZ-y2JsU,12,car passing by,train CXvP-PYsNaI,103,driving snowmobile,train CXvP-PYsNaI,115,driving snowmobile,train CXz7jYa3cNk,21,playing vibraphone,train CY-pqhHMy9o,160,playing saxophone,train CY1frWB5CYM,58,plastic bottle crushing,train CY4NP22N5j4,0,lawn mowing,train CY6U0_vGc1E,190,"playing violin, fiddle",train CY6xP2-6e3Q,30,goat bleating,test CY8ycxk-s6o,30,printer printing,train CY8zq68WPZ8,30,"male speech, man speaking",train CYBtjIoqnWI,37,spraying water,train CYCLbkiS8rw,5,sloshing water,train CYCOliCnITo,30,"pigeon, dove cooing",train CYDFwj0KWpM,274,tapping guitar,train CYDFwj0KWpM,490,tapping guitar,train CYEHNi_Pxyw,30,playing harpsichord,train CYFJwAgxbkQ,0,"donkey, ass braying",train CYFKuOIEun8,3,horse neighing,train CYIgHS75i8k,37,mynah bird singing,train CYM4xW9F-gs,30,"rowboat, canoe, kayak rowing",train CYNZTKputok,122,cap gun shooting,train CYNZTKputok,209,cap gun shooting,train CYPEomFJPMs,240,female singing,train CYQDPwKvchg,30,"subway, metro, underground",train CYRnb2F9ekQ,280,goose honking,test CYTTsSPohw0,87,planing timber,train CYTTsSPohw0,122,planing timber,train CYUxdTyZYg4,11,people giggling,train CYUxdTyZYg4,26,people giggling,train CYVqIQ2PNgY,34,playing zither,train CYVyhlHmeSg,30,wind rustling leaves,train CYWB9m7YbNc,30,"rowboat, canoe, kayak rowing",train CYWXrXmk9a8,8,driving buses,train CYWt9k1oDmY,30,"male speech, man speaking",train CYWtrdH6iBQ,3,"ice cream truck, ice cream van",train CYWtrdH6iBQ,13,"ice cream truck, ice cream van",train CYZ8TrVcthM,30,thunder,train CYZTjXQMiV8,570,bird wings flapping,test CYhBfou48kE,30,playing vibraphone,train CYlnEYDsuD4,151,train whistling,train CYru9Gs9HMg,49,cat purring,train CYru9Gs9HMg,130,cat purring,train CYrxqIcBs9A,6,police radio chatter,train CYs9GRLtMOo,30,ambulance siren,train CYu7bAfEbVQ,14,people burping,train CYv6-qTDdss,290,playing piano,train CYwNAbJ-ygk,130,lawn mowing,train CYyIccX_3zw,110,"race car, auto racing",train CYys94_AZqI,0,frog croaking,train CYys94_AZqI,10,frog croaking,train CZ--1inKLVI,310,skidding,train CZ-cU-ZWqjY,80,ambulance siren,train CZ1Nz3UOOas,3,playing bassoon,train CZ1Nz3UOOas,50,playing bassoon,train CZ2u7O2OQkU,57,playing bass drum,train CZ4scHrRlVA,53,people eating noodle,test CZ5Cx2EStQc,260,people screaming,train CZ5TAtK89sk,20,chicken clucking,train CZ5U_N4sB4s,10,"child speech, kid speaking",train CZB6WXDuM1g,4,"ice cream truck, ice cream van",train CZB6WXDuM1g,26,"ice cream truck, ice cream van",train CZCK-9H_MeE,67,car engine idling,train CZCK-9H_MeE,267,car engine idling,train CZFMj95X_sQ,70,people whistling,train CZFRiZ1O9AI,30,"rowboat, canoe, kayak rowing",train CZGYS93Rke0,118,people eating noodle,train CZHP_LNyxms,222,rope skipping,train CZHP_LNyxms,246,rope skipping,train CZHiQHnNH3Y,30,people sniggering,train CZIdGCpsuog,28,horse neighing,train CZIrziAE3Cc,0,woodpecker pecking tree,train CZIrziAE3Cc,44,woodpecker pecking tree,train CZKa0Mavl1o,0,volcano explosion,train CZKa0Mavl1o,10,volcano explosion,train CZMSdiWdI48,5,car engine knocking,train CZP4gf3qvxY,57,playing cymbal,train CZQ1bedI5Wo,27,chainsawing trees,train CZQ1bedI5Wo,44,chainsawing trees,train CZQa3m-bJtE,30,cap gun shooting,train CZREdcbM3MQ,30,orchestra,train CZRPRgfwuCo,168,vacuum cleaner cleaning floors,train CZRPRgfwuCo,186,vacuum cleaner cleaning floors,train CZRaMly8YLU,631,people slurping,train CZSPx5sNFlc,8,ocean burbling,train CZSg1Eaysno,30,singing choir,train CZTqSpwCQ9s,30,horse clip-clop,train CZVMFTPG_RE,30,dog bow-wow,train CZVm6QNKE4E,30,people cheering,train CZVnrWXyqGM,20,fireworks banging,train CZXrroS4h7s,27,playing bass drum,train CZZNrpHh3UM,0,people sneezing,test CZdjlDB-meY,380,driving motorcycle,train CZf-yC8pkwk,106,people gargling,test CZf-yC8pkwk,204,people gargling,test CZfDH8vKEsU,28,lathe spinning,train CZfDH8vKEsU,54,lathe spinning,train CZg-F8GYRC4,18,train whistling,train CZg-F8GYRC4,53,train whistling,train CZgx_6XaEkg,30,driving motorcycle,test CZiZUrFOV_g,137,air horn,train CZilvdWN8PI,79,planing timber,test CZj_Io9jr70,0,machine gun shooting,train CZl0tKcJkj0,89,playing volleyball,train CZlQbIE6FXQ,50,wind noise,test CZlTpRf_NVg,170,playing cello,train CZo-fDNS13Q,0,chimpanzee pant-hooting,train CZoPTJNmiCw,100,"playing steel guitar, slide guitar",train CZoPTJNmiCw,111,"playing steel guitar, slide guitar",train CZpF7Cs6G94,0,frog croaking,train CZpzlNUH_Hc,30,cricket chirping,test CZs6MipvwMg,30,cat hissing,train CZs6MipvwMg,44,cat hissing,train CZv5DpnS-P8,329,ripping paper,train CZv5DpnS-P8,367,ripping paper,train CZvOHDZEBnM,74,playing trumpet,train CZvOHDZEBnM,88,playing trumpet,train CZv_yRWyT74,377,plastic bottle crushing,test CZxkTPDarHM,10,"female speech, woman speaking",train CZyE91tdJPg,10,playing cymbal,train C_-axMbCVcg,49,firing cannon,train C_-bt-pcz9c,18,playing snare drum,train C_-bt-pcz9c,55,playing snare drum,train C_1N58WOkuY,14,roller coaster running,train C_1vUaruCYI,30,ocean burbling,test C_70fUJJXfY,250,playing tabla,train C_8va834LMc,30,playing flute,train C_A2lur6bf4,602,playing banjo,train C_Bi8DC-pf4,15,sliding door,train C_F0btVljgs,41,singing bowl,train C_GSeZOpoRo,30,using sewing machines,test C_HzWDFy4zo,14,"vehicle horn, car horn, honking",train C_IAeOZGYQU,33,wood thrush calling,train C_INgMNFDoQ,248,playing squash,train C_LJsj6oq3E,130,playing banjo,train C_Ld0QrpaxQ,1,horse clip-clop,train C_NqNUqZa1w,162,rope skipping,train C_NqNUqZa1w,194,rope skipping,train C_TOZBmeifA,30,"playing marimba, xylophone",train C_TP1j8ROSA,30,fireworks banging,train C_XLWF1NlA8,230,church bell ringing,train C_XzIj4daAg,89,playing glockenspiel,train C_ZjPPSeXO8,80,toilet flushing,train C_gwIpEhhps,167,tap dancing,train C_hoX170mSM,0,chicken crowing,train C_j9wk4iU2o,100,playing double bass,train C_jRrJ_bPLI,11,tap dancing,train C_jegfW0R8U,60,helicopter,train C_kGXRnzv0s,183,skidding,train C_kGXRnzv0s,197,skidding,train C_mXH5ftdMs,30,"male speech, man speaking",train C_pnsyNXphA,30,"race car, auto racing",test C_q3_Dk8f_g,306,hedge trimmer running,train C_q3_Dk8f_g,318,hedge trimmer running,train C_r9hCERb68,30,church bell ringing,train C_xY8hOpWz0,40,"female speech, woman speaking",train C_zGm7PavBE,34,pig oinking,train C_zGm7PavBE,48,pig 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CaYNuuW9rt4,11,horse neighing,test Ca_Ma5uwJCc,300,people clapping,train Caa5S3Y8hNs,1485,"heart sounds, heartbeat",train CabS6kMykGw,40,"bird chirping, tweeting",train CabjugcYktA,3,fire truck siren,train CabjugcYktA,16,fire truck siren,train Cac949WVIog,240,sharpen knife,train CacM2nOinxE,60,playing accordion,train CaeMb35PVeI,30,"male speech, man speaking",train Cah4MJiVUFI,30,people crowd,test CajHMHppjmU,121,playing squash,train CajHMHppjmU,160,playing squash,train CajYMXL4BcI,140,eletric blender running,train CalM7Iwy13w,74,playing djembe,train CalQByLEuH0,30,"male speech, man speaking",train CalrKBSTdEo,6,playing tabla,train CalrKBSTdEo,16,playing tabla,train Camg3w9ElXc,120,"child speech, kid speaking",train CaqwDChGTms,170,"subway, metro, underground",train CasGkf7VeXM,5,wood thrush calling,train CasGkf7VeXM,21,wood thrush calling,train Cat9ZZ_ehWI,0,skidding,train CatMCNUQz08,46,playing steelpan,train CauIbuCpgqM,400,male singing,train CayfgOUTSx4,30,horse clip-clop,train Cb0DSmup2o0,270,wind noise,train Cb0NWVDCttA,30,playing harpsichord,train Cb0NWVDCttA,247,playing harpsichord,train Cb1D5MkFoSM,417,typing on typewriter,train Cb2k9JwDkDQ,30,goose honking,test Cb5mn7IX4O8,30,waterfall burbling,train Cb6LNTERjmw,30,people farting,train Cb8DxhooHqM,1,hail,train Cb8DxhooHqM,53,hail,train CbA9JHkkaAk,58,playing banjo,train CbAnwLhS6BA,30,playing trombone,train CbBVBaDjedg,21,chicken crowing,train CbBVBaDjedg,55,chicken crowing,train CbD3MlWBlhU,160,car engine idling,train CbGBQPE9t6g,30,"motorboat, speedboat acceleration",train CbGUGjpAD80,17,people burping,train CbGUGjpAD80,111,people burping,train CbL1ie6c61o,7,playing snare drum,train CbL1ie6c61o,63,playing snare drum,train CbNsFZ6QeZU,30,car engine knocking,train CbQLKLbqt4s,30,playing harpsichord,train CbRmM-CFnv0,150,playing bagpipes,train CbToYp_cJKs,171,playing erhu,train CbVtmfIamX0,97,child singing,train CbVtmfIamX0,117,child singing,train CbW1nNBqVnI,24,playing harpsichord,train CbW1nNBqVnI,196,playing harpsichord,train CbYYXYtFRuw,52,firing muskets,train CbYYXYtFRuw,73,firing muskets,train CbZ2SM5-kgs,90,playing bassoon,train CbZ2SM5-kgs,109,playing bassoon,train Cb_NG2TsAM0,51,people battle cry,train Cb_NG2TsAM0,89,people battle cry,train CbabViJ0eEY,445,opening or closing drawers,train CbdJ7WK7nbs,34,playing cymbal,train CbeRxQazD4g,1,lighting firecrackers,train CbhHl9x0e_Q,10,fireworks banging,train Cbha15TRFC4,6,sloshing water,train Cbi9PGAQzZw,30,playing harpsichord,train CbjwG807kC8,10,machine gun shooting,train Cbk_j2aHpEs,30,dog bow-wow,train Cboit9vTmlU,30,orchestra,train CbomWMJU3ro,30,opening or closing drawers,test Cbse-gNw-PM,160,stream burbling,train CbuheLOSZq0,20,sliding door,train Cbvp4ZD6QDI,30,"male speech, man speaking",train Cbyc7mscFlA,21,striking pool,train Cbyc7mscFlA,410,striking pool,train CbzPomBXoaU,30,"male speech, man speaking",train Cc4IpE8mGm0,239,people gargling,train Cc4IpE8mGm0,323,people gargling,train CcB6a1oY0As,2,opening 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CdrDq9Y5zAw,133,ripping paper,train CdrDq9Y5zAw,144,ripping paper,train Cds-q4BGjps,86,running electric fan,train Cdt_IONjSDQ,0,ambulance siren,train CdxlOobmOW0,6,playing mandolin,train CdxlOobmOW0,20,playing mandolin,train CdyHQN4MAPo,10,police car (siren),train Ce09mKzzlmA,280,people whispering,train Ce1P47VwTAI,30,"female speech, woman speaking",test Ce2DcIxIW90,6,playing bongo,train Ce2HdI4L3Ww,30,"rowboat, canoe, kayak rowing",train Ce2fbtvF_TI,39,playing glockenspiel,test Ce2fbtvF_TI,136,playing glockenspiel,test Ce4UjupnAhk,28,police radio chatter,train Ce5Oz-0s6TM,4,telephone bell ringing,train Ce6PfY9hMD4,10,driving motorcycle,train Ce6yOfEJbZo,0,missile launch,train Ce6yOfEJbZo,10,missile launch,train Ce7SG8sk6eE,298,beat boxing,train Ce7w1C0gxqk,50,driving motorcycle,train CeA8--FUxYg,300,playing cymbal,test CeD0DWsDQDA,30,raining,train CeD6RlRSr8M,42,cat purring,train CeD6RlRSr8M,99,cat purring,train CeDxmj5R3RY,109,playing squash,train CeDxmj5R3RY,252,playing squash,train CeFVakrcaUg,25,driving snowmobile,train CeKyVk-iidw,558,lighting firecrackers,train CeKyVk-iidw,662,lighting firecrackers,train CeLKj9WN9Ys,0,tapping guitar,train CeLKj9WN9Ys,13,tapping guitar,train CeOCvd2MX5Q,292,splashing water,train CeOXbjg0Jfo,31,train horning,train CeOXbjg0Jfo,70,train horning,train CeP56Xgi6EA,48,car passing by,train CeP56Xgi6EA,136,car passing by,train CePF4SlQJF4,10,skidding,train CeRmdEmqV68,19,baby crying,train CeTEU-Ad-5s,3,toilet flushing,train CeTbzVsGfRw,30,"subway, metro, underground",train CecOgy69bq4,12,machine gun shooting,train CecOgy69bq4,29,machine gun shooting,train CecabqlQ07M,15,playing oboe,train CedeUlDcZtE,30,wind noise,train Ceeys4960Sw,182,skiing,train CefFMA3klxk,150,"vehicle horn, car horn, honking",train Cefeb2q4KSE,110,playing gong,test CegaaCotBjE,124,playing erhu,train CeizF1_jKYk,0,turkey gobbling,train CeizF1_jKYk,11,turkey gobbling,train Cej5WLJdYl0,22,driving motorcycle,train CejhQC9hUO8,63,baby babbling,train CemDdFycEwI,37,running electric fan,train CemDdFycEwI,122,running electric fan,train CemU9Ocvc_8,0,cat meowing,train CemzBvXmwxo,590,fireworks banging,train CequClAbdLc,0,playing hammond organ,train Cer1un9vciI,30,singing choir,test CerhH_FzZCA,248,hammering nails,train Cerpl_TLxB8,30,playing harp,train CetVBCi16H0,80,lawn mowing,train Ceu0T2G3axs,63,elephant trumpeting,train Cev7ZrC-Yb0,560,lions growling,train Cew4YcrZCms,19,playing trombone,train CewaO3Aug84,80,firing muskets,test CewfEDYubBs,67,printer printing,train CewfEDYubBs,291,printer printing,train CewyQvcluec,63,skiing,train CewyQvcluec,76,skiing,train CeyAeQS-pQU,0,planing timber,train CezVHSslo_M,0,opening or closing car doors,train CezZxDVpBB4,330,arc welding,train CezhegjE3zI,42,"electric shaver, electric razor shaving",train Cf-JJ_vLNOs,356,playing table tennis,train Cf0MHLBJ0IE,410,fireworks banging,train Cf0qCCo8ItA,219,car engine idling,test Cf0qyMmU_9s,30,"motorboat, speedboat acceleration",train Cf1N4pGAj6A,125,playing volleyball,train Cf2WXNoA2tw,179,volcano explosion,train Cf2WXNoA2tw,200,volcano explosion,train Cf3eLRyjI2Q,27,playing harpsichord,train Cf78fh5uvag,133,dog bow-wow,train Cf7z0Yiw2H4,30,playing saxophone,train Cf94bR8Whqo,88,arc welding,train Cf9Aa1ZkRZw,56,typing on computer keyboard,train Cf9JUwCUwds,0,bird squawking,train Cf9jnHBXybI,115,playing trombone,train Cf9u8yM02Ek,9,magpie calling,train Cf9u8yM02Ek,48,magpie calling,train CfARy6C6AK4,10,"race car, auto racing",train CfEHqU1Tq-4,30,ocean burbling,train CfG0-W3Ejo8,7,cattle mooing,train CfG9k54ztuU,510,driving buses,train CfGnxXdDEPI,40,playing cymbal,train CfHPXNUZHL4,30,"male speech, man speaking",train CfJDYeKdWXs,30,"male speech, man speaking",train CfK0AJ0WJ-U,641,"playing steel guitar, slide guitar",train CfLjeMrn2eM,77,tap dancing,train CfM98Eed_6w,67,playing badminton,train CfO-lBWDvko,17,dog barking,train CfO6YkN8rWM,821,playing bugle,train CfO6YkN8rWM,929,playing bugle,train CfOUt1kKuCM,177,playing erhu,train CfOmpgxf6CM,12,"bee, wasp, etc. buzzing",train CfOmpgxf6CM,50,"bee, wasp, etc. buzzing",train CfPbKzvW0SQ,70,playing trombone,test CfRekpxyTjw,30,playing electronic organ,train CfS97Vs1RCM,27,"pigeon, dove cooing",train CfT6tLRMFoE,1,tapping guitar,train CfUlR_ghqXk,28,baby crying,train CfWz4S0o--o,280,car engine starting,train CfX59stwfQ4,50,people marching,train CfYmWirI2A8,30,playing electric guitar,train CfYpzLMBTyY,10,playing tuning fork,train CfYpzLMBTyY,20,playing tuning fork,train CfZlw_8w_QA,409,airplane,train Cf_oLD9r_kA,178,eating with cutlery,train Cff-UT8famY,26,playing glockenspiel,train Cff-UT8famY,55,playing glockenspiel,train CfgHSXq0H5A,12,wind noise,train CfifGyLjn_0,0,people battle cry,train Cfij00R-8Zs,280,eating with cutlery,train CfjgrLnuTnc,1,skiing,train Cfl6CYZUzFQ,40,playing clarinet,train CflKSgVMAew,21,playing bagpipes,train CflKSgVMAew,84,playing bagpipes,train CfldmU4WIoQ,85,beat boxing,train CfmYClV-gQY,30,playing bassoon,train CfnFpH_Xe4Y,13,"ice cream truck, ice cream van",test CfnG4jrhvbY,420,people sniggering,train Cfong6fYc6U,350,lawn mowing,train CfpY0InwT1g,20,raining,train Cfqdeu5tDD4,79,mosquito buzzing,train Cfs7cp5r38U,67,volcano explosion,test CfsB0hE9Z-4,170,chainsawing trees,train CftBSEbId4o,20,playing erhu,train CftPdpiRMm8,79,cheetah chirrup,train CfywJyLsQ3U,5,people whistling,train Cg-6sXWQ0mA,74,playing cornet,train Cg-6sXWQ0mA,214,playing cornet,train Cg-lWXNuNeM,81,electric grinder grinding,train Cg00ul1y6s0,427,golf driving,train Cg07QFTiJtI,30,"bird chirping, tweeting",test Cg0cRkx4bcw,130,dog bow-wow,train Cg1KplXy9aI,25,playing harpsichord,train Cg1KplXy9aI,185,playing harpsichord,train Cg2nZSgA3p8,11,playing bassoon,train Cg3MLVO4PKE,72,planing timber,train Cg3MLVO4PKE,267,planing timber,train Cg3XzrFzzpM,60,playing accordion,train Cg4hAlGGABI,220,playing drum kit,train Cg70pKaWmac,50,playing double bass,train Cg8J1IVC3Ic,13,mouse pattering,test Cg8hMsx5Heo,0,duck 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CgUcQCJT7xU,30,playing trumpet,train CgUcQCJT7xU,66,playing trumpet,train CgXG-hmQ-7w,61,playing flute,train CgXInoMkFus,30,lions growling,train CgYxsJpoGHM,210,car engine starting,train CgZ3djkJzco,30,car passing by,train CgZY6h-Z8-4,14,people farting,train CgZY6h-Z8-4,32,people farting,train Cg_I1pc51kg,70,chicken crowing,train CgaqFGoBpwA,11,tapping guitar,train CgaqFGoBpwA,117,tapping guitar,train Cgb7x_fbmHk,14,scuba diving,test CgbRp-U26Kg,83,playing theremin,train Cgd4TRwDEpM,360,playing vibraphone,train CgevwvZLE3c,107,running electric fan,train CgevwvZLE3c,219,running electric fan,train Cgk6FEWKwDU,200,"child speech, kid speaking",train CgkeGvvGdwo,28,ambulance siren,train CgnAZUpa9ZM,62,roller coaster running,train CgnAZUpa9ZM,133,roller coaster running,train CgptWbgXPjA,30,"playing violin, fiddle",train CgrFzqunnEk,62,dog barking,train Cgt4DEBQy50,97,playing harp,train Cgtmk5RO9pE,3,horse neighing,train CguEYgNPpd4,13,people battle cry,train CguEYgNPpd4,28,people battle 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CjHGvAKsAK0,41,gibbon howling,train CjHgjklgXnw,33,people shuffling,train CjHgjklgXnw,54,people shuffling,train CjI_BUlyd6E,30,playing electronic organ,train CjJra7kXgtk,69,dog whimpering,train CjO1ZZZI8CE,30,"female speech, woman speaking",test CjOPFlelcUA,135,eletric blender running,train CjOT8bmPGww,30,ocean burbling,train CjOw-PyOtqg,50,toilet flushing,train CjPmQdDKXjQ,21,horse clip-clop,train CjQa054tuQs,75,playing volleyball,train CjSlgK74Lzw,175,playing snare drum,train CjU3tXmneOk,30,playing electric guitar,train CjUaysxepr4,90,playing synthesizer,train CjV7-o9Ys6w,28,volcano explosion,train CjWRpycOoc0,153,"electric shaver, electric razor shaving",train CjX9G0xf3AU,140,train horning,train CjYnF-uyrPY,63,people burping,train CjZ0zjXlzTk,56,yodelling,train CjZ0zjXlzTk,124,yodelling,train Cj_0SK2wgLM,20,people shuffling,train Cj_0SK2wgLM,45,people shuffling,train CjbPGAiP37s,10,playing saxophone,train CjcvUdmrKdw,142,train wheels squealing,train CjcvUdmrKdw,194,train wheels 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CmPXqV2h6JQ,358,people eating apple,train CmQkmpqE_rI,16,"donkey, ass braying",train CmQkmpqE_rI,34,"donkey, ass braying",train CmQqwYDDfcY,802,blowtorch igniting,train CmRTIeYaGNM,5,"motorboat, speedboat acceleration",train CmTgg0QEqWs,178,"electric shaver, electric razor shaving",train CmXA759VLZo,7,baby crying,train CmYvnJ0hZQk,30,playing saxophone,train CmatqqnxjS8,310,"railroad car, train wagon",train Cmb7oRnRLio,34,whale calling,test Cmb7oRnRLio,83,whale calling,test CmdNvVEElts,8,frog croaking,train CmeKmN6Jr9I,30,driving buses,train CmgsQJ_8hxY,30,"bird chirping, tweeting",train Cmh023qRXcM,100,people hiccup,train CmhEp3OZAF8,56,playing timpani,train Cmhpj4MJ_hQ,20,people babbling,test CmkJiZwb1mg,72,rapping,train CmkJiZwb1mg,171,rapping,train Cmm6FqqQcFI,7,wind rustling leaves,train CmmOzB0BZos,510,people whispering,train CmpE_B5E9BM,30,goose honking,train Cmr5eUth66c,30,driving motorcycle,train CmtXArKMJdU,40,playing drum kit,train Cmu09s5NjGo,101,playing bagpipes,train Cmz0InA1eM4,30,singing choir,test Cn4a6xsBfa4,106,civil defense siren,train Cn8-CibBKkE,212,playing clarinet,train Cn8YPaPB6rA,17,"vehicle horn, car horn, honking",train Cn9qLiuvBIY,9,police car (siren),train CnCI7tYpTCM,290,goose honking,train CnFNmem0Apo,30,"male speech, man speaking",train CnG4XKUvFZE,180,singing choir,train CnJ7JRPVS0M,30,"female speech, woman speaking",train CnJC4GE-ATA,120,playing accordion,train CnJkiXUYJaA,30,"male speech, man speaking",train CnKijcQz2n8,12,sheep bleating,train CnLblC-RQTQ,39,volcano explosion,train CnMu7QmoODI,13,people shuffling,train CnMu7QmoODI,31,people shuffling,train CnN4q0gvDD8,57,bowling impact,train CnQAW7qQ_jk,30,horse clip-clop,train CnRbOkyhD8E,60,cat hissing,train CnRbOkyhD8E,135,cat hissing,train CnViLJZjHCo,70,wind rustling leaves,train CnYWRFDGFuA,40,playing bass guitar,train Cn_vGe_7_x4,0,reversing beeps,train CnbUcxEwMPE,8,crow cawing,train CnbUcxEwMPE,38,crow cawing,train CngsPppEunk,10,people sobbing,train 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CpX9kzdO7Sk,10,dog growling,train CpX9kzdO7Sk,119,dog growling,train Cp_wgmmliuk,210,playing cello,test CpbfwvjiUH8,30,car engine knocking,train CpfMp3bcgTM,10,"subway, metro, underground",train CphvE8f5GNE,70,"rowboat, canoe, kayak rowing",train CpiDw7Fd8Y0,27,arc welding,train Cplz_y5hJow,420,people crowd,train Cpm2q3-yIt0,170,playing drum kit,train CpoAi0CS9Xw,30,wind noise,train CpoUHOCPaNw,20,playing harp,test CpoX-mbf2sA,383,opening or closing drawers,train CpoX-mbf2sA,494,opening or closing drawers,train Cpo_PbyTOcY,20,eating with cutlery,train CpobEqZ9ra0,52,horse neighing,train CpoiMmYEFGI,4,barn swallow calling,train CpoiMmYEFGI,142,barn swallow calling,train CppYZAqFyZQ,70,playing clarinet,train Cpqj3gwmU4c,62,playing trombone,train Cputgvnjz-k,25,cow lowing,train CpvZ69tkW3w,57,people battle cry,train CpvZ69tkW3w,186,people battle cry,train Cpv_L9epCbU,65,running electric fan,train Cpv_L9epCbU,278,running electric fan,train CpvhvcYqm34,90,people burping,train 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Cslm76GqtrU,292,cat purring,train CslyyelkQx0,39,police radio chatter,train CsoNvGpEC84,40,skidding,train CsqoaGRJhfM,30,chicken crowing,test Csr7c9uFvQk,28,dog whimpering,train CsrB8B5aiR0,14,zebra braying,test CsrB8B5aiR0,49,zebra braying,test CstJ-46M1Fk,50,toilet flushing,train CsvtHy2q2Og,30,typing on computer keyboard,train Csx4dJHYIiQ,390,"playing marimba, xylophone",train CsyYWeKBOzU,0,playing bass guitar,train CszHuyG_yLE,142,roller coaster running,train CszHuyG_yLE,257,roller coaster running,train Ct0BPe1h-Sk,10,playing flute,train Ct0DGnrbEoA,19,"motorboat, speedboat acceleration",train Ct0DTSW4mvw,181,firing muskets,test Ct1A5s2g_7w,57,barn swallow calling,train Ct1J38dieXk,10,cattle mooing,train Ct2MyTLm0Xo,40,playing cymbal,train Ct55jhgsgac,48,beat boxing,train Ct6lFX0YVMc,6,zebra braying,train Ct6lFX0YVMc,26,zebra braying,train Ct7XprHhuu0,52,airplane flyby,train Ct9ltnuEPvc,38,playing washboard,test CtACwFf1Fcs,17,frog croaking,train CtACwFf1Fcs,320,frog croaking,train CtEVHZZ4tQM,36,tractor digging,train CtEVHZZ4tQM,272,tractor digging,train CtGQY2NFqIc,67,cat purring,train CtGQY2NFqIc,78,cat purring,train CtHn-g2nG8g,595,playing darts,train CtID8dV7BDo,163,playing ukulele,train CtJn201KEYI,9,playing glockenspiel,train CtMlhwN3wAI,30,chainsawing trees,train CtN48OU5faI,491,typing on typewriter,train CtN48OU5faI,568,typing on typewriter,train CtNPXe1lY-4,8,arc welding,train CtPTHqVydUA,20,playing didgeridoo,train CtQDDc8kU2c,50,dog barking,train CtTWTf_Nf8o,30,playing drum kit,train CtUZ9HljxMQ,1,dog growling,train CtUZ9HljxMQ,21,dog growling,train CtYxhxt6JJE,341,"heart sounds, heartbeat",train CtYxhxt6JJE,587,"heart sounds, heartbeat",train Ctau5S5KUtc,100,"race car, auto racing",train CtcmYF7mufk,30,rapping,test CtendBTVJ5U,355,playing squash,train Ctf7P2dXgoM,1,frog croaking,train Ctf7P2dXgoM,109,frog croaking,train CtfH80K7m-k,30,"playing violin, fiddle",train CthIjd4u5bI,38,dog whimpering,train CtjOyMSiAqI,10,raining,train CtkviPJ9QT8,120,fireworks banging,train CtlUCtnaqwg,12,gibbon howling,train CtlUCtnaqwg,30,gibbon howling,train Ctmo-rZ3Vws,312,hammering nails,train CtmwhXJr570,220,playing djembe,train CtmwhXJr570,490,playing djembe,train Ctnf51mWdPI,24,hail,test CtoshZDIKBY,30,chicken crowing,train CtpP0QJle3Y,30,ambulance siren,train CtqrXwEYNoc,149,slot machine,train CtrGN7mR3fw,30,"male speech, man speaking",train Ctrg2wyS1M8,621,bull bellowing,train CtsAprziDT8,12,reversing beeps,train Ctu_wOdQYGo,10,toilet flushing,train Ctuu2FLKFMU,13,train horning,train Ctuu2FLKFMU,212,train horning,train Ctx-hCu359Y,134,car engine idling,train CtxbVh2n0WY,135,playing table tennis,train Ctykf8qh288,65,playing hammond organ,train CtzL6U3p7UY,264,cap gun shooting,train Cu1d354hi0o,41,scuba diving,train Cu3JkFmIPvM,23,tapping guitar,train Cu3JkFmIPvM,76,tapping guitar,train Cu3R-CNNygI,30,duck quacking,train Cu3Y18649pE,520,"child speech, kid speaking",train Cu3bgBVyrNw,27,owl hooting,train Cu3bgBVyrNw,43,owl 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DAh8JEeoKOs,4,dog barking,train DAnYL58ADIU,10,cat meowing,train DAp6w2uI5cM,341,opening or closing drawers,train DApu95qRhu4,364,slot machine,train DAqIsagCgFk,320,police car (siren),train DAr1B-IN_MQ,30,"playing violin, fiddle",train DArQ4wGJyrI,17,playing flute,train DAu-7TnQyGk,30,"male speech, man speaking",train DAu13OXZcGM,0,"cattle, bovinae cowbell",train DAu13OXZcGM,10,"cattle, bovinae cowbell",train DAubFydKLOE,8,police radio chatter,test DAxjaVWyY9k,30,waterfall burbling,train DAy2M5vW4_w,120,planing timber,train DAy2M5vW4_w,396,planing timber,train DAy8UB9SgUs,23,playing bass drum,train DAy8UB9SgUs,33,playing bass drum,train DAy_bV1d9c4,46,playing squash,train DB0WPKC18JA,14,ocean burbling,train DB2ZA23QsSY,0,people crowd,train DB38NRSHw9A,30,"male speech, man speaking",test DB3Ao8x9Om8,39,people battle cry,test DB4S8Ja9AZ8,3,cuckoo bird calling,train DB5IuFiuOvQ,0,typing on typewriter,train DB5ZKxnM1xE,0,hair dryer drying,train DB5cVFz8HJE,30,driving buses,train DB5yX-AzF8Y,30,ambulance siren,train DB8Cw-S3B-A,510,ocean burbling,train DB9UP1AHi44,32,playing squash,train DBD0GjGP6Ew,57,fire crackling,train DBD0GjGP6Ew,168,fire crackling,train DBEipd5z2q4,157,using sewing machines,train DBEsw81m3I8,10,wind noise,train DBEsw81m3I8,110,wind noise,train DBG_ekcuFfs,14,sheep bleating,train DBHMICVNaws,10,lions growling,train DBHtYH1zpPw,6,sliding door,train DBQLnc_UdT0,19,duck quacking,train DBQLnc_UdT0,51,duck quacking,train DBQyMqrSRdE,39,train whistling,train DBQyMqrSRdE,62,train whistling,train DBRY_PLvI4Y,20,people battle cry,train DBRY_PLvI4Y,41,people battle cry,train DBTiFISQzz4,60,playing trombone,train DBUTIL4CLzA,0,people hiccup,train DBW6rwVoIcQ,56,"heart sounds, heartbeat",test DBWzijilWkQ,30,playing accordion,train DBYHy9sg9ls,40,male singing,train DBZyRBNb3no,77,playing clarinet,train DBZyRBNb3no,106,playing clarinet,train DBfN_P84wCc,161,running electric fan,train DBfN_P84wCc,201,running electric fan,train DBh-oPTYJ7M,39,chopping food,test DBiGZEP5d-k,244,playing trombone,train DBiGZEP5d-k,473,playing trombone,train DBih8YtVf3U,5,fire truck siren,train DBjQUbJNHwc,30,"female speech, woman speaking",train DBk5ksrAU50,17,playing bongo,train DBk5ksrAU50,67,playing bongo,train DBkpo9eesb0,19,"ice cream truck, ice cream van",train DBkpo9eesb0,43,"ice cream truck, ice cream van",train DBkw1O1v9Ls,19,singing bowl,train DBlmvswc8ZY,30,"pigeon, dove cooing",train DBm8YB8QM_U,190,orchestra,train DBn0Ggw6ATs,50,"playing violin, fiddle",train DBp8CMXWMmM,221,"electric shaver, electric razor shaving",train DBqsvYy4uiU,20,"bird chirping, tweeting",train DBsPqaD5eXc,135,rope skipping,train DBsPqaD5eXc,242,rope skipping,train DBwlUSkJEuQ,9,"alligators, crocodiles hissing",train DBxaCgkkBrA,1,running electric fan,test DByNvRcTTeo,280,"child speech, kid speaking",train DByW9JuyRn8,30,"male speech, man speaking",train DBz-jxGV9-k,25,yodelling,train DBz-jxGV9-k,36,yodelling,train DBzeal6ptag,133,car engine starting,train DBzeal6ptag,181,car engine starting,train DC1U9GAjIgw,130,"subway, metro, underground",train DC2q5iyvKFM,60,playing cornet,test DC2vAS8zMy0,30,"male speech, man speaking",train DC4_1CHRcx8,196,dinosaurs bellowing,test DC4mdAoRfK8,58,machine gun shooting,train DC4mdAoRfK8,92,machine gun shooting,train DC5kWOcR1ko,47,people booing,train DC5srBwx4pw,100,female singing,train DC6GBUUBPc0,30,"female speech, woman speaking",train DCC9JN97G1E,13,sliding door,train DCCVsJc05Og,240,people clapping,train DCEOM-_KZAo,179,playing guiro,train DCEOM-_KZAo,233,playing guiro,train DCFAWgQGr8c,24,baby laughter,train DCFAWgQGr8c,63,baby laughter,train DCFMA7p2pvg,30,"male speech, man speaking",train DCGqaHqNJfA,97,slot machine,train DCIOwV5DRLo,10,baby crying,train DCISHo7PK6k,128,mynah bird singing,train DCK554zzEQ0,30,"playing violin, fiddle",train DCOCJkGYCWk,70,playing snare drum,train DCPjlNWs-_I,118,canary calling,train DCPjlNWs-_I,193,canary calling,train DCRmDxAuLc0,0,disc scratching,train DCRmDxAuLc0,22,disc scratching,train DCSt3wF-5gA,230,using sewing machines,train DCXSxkexB04,0,people battle cry,train DCXSxkexB04,33,people battle cry,train DCY6vXkGiVs,0,lawn mowing,train DCZeMextoMU,34,parrot talking,test DC_EOd5OsOo,18,snake hissing,train DC_EOd5OsOo,32,snake hissing,train DCaL4Ra7g78,277,people hiccup,train DCcQVDU0sDU,30,"male speech, man speaking",train DCcTfozwuKo,140,eating with cutlery,train DCeON4JhY8I,0,playing harpsichord,train DCeON4JhY8I,10,playing harpsichord,train DCePXADCM78,3,chimpanzee pant-hooting,train DCePXADCM78,14,chimpanzee pant-hooting,train DCftdpZ9lIs,162,dinosaurs bellowing,test DCgggkJLB3I,41,playing cymbal,train DCkVNCYFAis,290,bird wings flapping,train DCmn93c5MBE,309,playing bassoon,train DCmn93c5MBE,323,playing bassoon,train DCnMWm2AwBs,2,playing harmonica,train DCpRZ3QAaRQ,10,"engine accelerating, revving, vroom",train DCpWtyLjKmM,30,mouse squeaking,test DCpt--jHKH0,38,chicken crowing,train DCqu2l1Uqic,109,parrot talking,train DCrG_vS-4l0,97,playing harpsichord,train DCrct29xGEY,30,lions roaring,test DCs0-s3tmxg,30,playing harpsichord,train DCu5Ob61dRk,2,dog barking,train DCvouEcI1OM,22,car engine starting,train DCvouEcI1OM,51,car engine starting,train DCwQByjftG8,0,"ice cream truck, ice cream van",train DCxBh25xlpQ,60,singing choir,train DCx_5fHoST0,95,playing ukulele,train DCy-5juiFuc,1,playing bass drum,train DD-TGbF4vjI,350,"child speech, kid speaking",train DD0iSzxHJ_Q,0,helicopter,train DD10XUUctRA,105,people marching,train DD10XUUctRA,121,people marching,train DD2i983uNLM,157,sharpen knife,train DD2i983uNLM,236,sharpen knife,train DD6HeCQrAoo,170,playing piano,train DD84ndAZFpI,220,playing drum kit,train DD8zqjElvvY,192,people eating noodle,train DDAQLIRlZqA,1,car engine knocking,train DDAQLIRlZqA,16,car engine knocking,train DDAb3_R8gYQ,143,yodelling,train DDAb3_R8gYQ,159,yodelling,train DDB250xVlbM,30,skidding,train DDC6t5mwDlE,6,playing bagpipes,train DDC6t5mwDlE,36,playing bagpipes,train DDDlxmsciqY,34,child singing,train DDDlxmsciqY,152,child singing,train DDHgNv6ALDg,4,playing bagpipes,train DDHgNv6ALDg,15,playing bagpipes,train DDILS8etIAQ,165,dinosaurs bellowing,train DDKmVgSHEcY,30,"male speech, man speaking",train DDL2vJqSA1o,32,civil defense siren,test DDMn93cddnE,30,"playing marimba, xylophone",train DDMuNgzmkRg,328,bowling impact,train DDN-n4t2YDk,60,mosquito buzzing,train DDPccAVpirM,30,wind noise,train DDVBLLoqQHk,30,female singing,train DDZJg2NchTw,8,people marching,train DDZJg2NchTw,49,people marching,train DDZlMjb-0i4,150,helicopter,test DD_ohpfno8w,200,"race car, auto racing",train DDeAjcedfyE,190,playing piano,train DDelecxB7p8,0,bowling impact,train DDelecxB7p8,68,bowling impact,train DDgT1J61hbU,0,playing tennis,train DDgT1J61hbU,198,playing tennis,train DDgjUz_ATkA,4,pig oinking,train DDgjUz_ATkA,18,pig oinking,train DDhKU_VH0Js,228,canary calling,test DDlAcAppYtE,26,parrot talking,test DDlWvzk4qfo,30,people whistling,train DDm-Lb8n1rs,30,"male 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DEPVeBydhWQ,30,playing flute,train DEQA0rMExqA,30,people sobbing,train DEVG0hNft_4,0,wind noise,train DEXE_V8oBYI,30,driving buses,test DEXmFmFQNJY,30,orchestra,train DEYfBf6B_po,150,child singing,train DEdIa0QLk4s,96,airplane flyby,train DEiRx6SglWU,16,playing bassoon,train DEksqA1baos,30,people hiccup,train DEqPVor5JJY,110,people burping,train DEqdX5GtXPU,1,hammering nails,train DEqdX5GtXPU,12,hammering nails,train DEuA6rfibS4,0,rope skipping,train DEuA6rfibS4,175,rope skipping,train DEuXlL3BLAo,8,playing mandolin,train DEuXlL3BLAo,25,playing mandolin,train DEv1p6ncG-g,0,playing electric guitar,train DEyImP2Y6_g,2,playing djembe,train DF0AL3gaNLQ,30,raining,test DF0mkS63iIQ,133,sharpen knife,train DF3RPgRYw7s,172,playing bassoon,train DF3RPgRYw7s,255,playing bassoon,train DF57PcyquR0,84,people burping,train DF5LtYwhU9M,0,people screaming,train DF6yQD0HwOo,33,"cattle, bovinae cowbell",train DF6yQD0HwOo,45,"cattle, bovinae cowbell",train DFA8FqKLI7g,7,"engine accelerating, revving, 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DGCG2aKOk7Y,30,"pigeon, dove cooing",train DGDPUgukvMs,14,playing trumpet,train DGFzHPES0yk,29,barn swallow calling,train DGFzHPES0yk,45,barn swallow calling,train DGGvzc9WFco,3,cuckoo bird calling,train DGGvzc9WFco,14,cuckoo bird calling,train DGGwyqSZYR4,150,female singing,train DGI7xPh_qBE,24,playing tabla,train DGI7xPh_qBE,93,playing tabla,train DGKusEuU9Kk,60,playing washboard,train DGLnx6Qjwb4,10,playing bagpipes,train DGLnx6Qjwb4,45,playing bagpipes,train DGMRZAitMFw,520,orchestra,train DGPXdUslprQ,30,"rowboat, canoe, kayak rowing",train DGQYtgLiQdU,79,playing bongo,train DGQYtgLiQdU,89,playing bongo,train DGRrI5D6Rd8,39,goat bleating,train DGRrI5D6Rd8,50,goat bleating,train DGS6j6k14ak,105,popping popcorn,test DGU-HbuX6rs,230,people crowd,train DGUX1hc8LK8,354,"playing steel guitar, slide guitar",train DGV-sHosGBk,4,writing on blackboard with chalk,train DGV-sHosGBk,17,writing on blackboard with chalk,train DGXRruZNJD0,30,"male speech, man speaking",train DGY42yUamaE,0,canary 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chirping,train DHDn79ee98Q,30,playing theremin,test DHEUiHo0V_Q,234,dinosaurs bellowing,train DHFkwF6B6ZE,59,elk bugling,train DHFqpPEMl9k,48,tractor digging,train DHHhgQTYUw8,183,bouncing on trampoline,test DHHxAhb-_2E,20,people shuffling,train DHILIMc2DMQ,428,bowling impact,train DHILIMc2DMQ,461,bowling impact,train DHK1lEGyOV4,30,duck quacking,test DHK3IGuuKw4,30,playing cello,train DHLBMEIV1eA,217,playing badminton,train DHNE_ImwGfU,2,"cattle, bovinae cowbell",train DHQwGDIUqU8,12,tap dancing,train DHRHQbuBiz4,20,chicken crowing,train DHSOhv8q3U4,30,"male speech, man speaking",train DHSYC4eaO2I,347,playing lacrosse,train DHSr8GMQSqw,28,playing tennis,test DHSrb_xxFd4,50,church bell ringing,train DHUaM7P5hB0,290,playing vibraphone,train DHV51pWjxCY,47,playing sitar,train DHV51pWjxCY,69,playing sitar,train DHV_KwdPehs,63,playing bongo,train DHV_KwdPehs,79,playing bongo,train DHXgiZqSoTg,270,playing gong,test DHapCRPNc1M,30,"female speech, woman speaking",train DHb6b5PjeVM,82,playing erhu,train DHb6b5PjeVM,149,playing erhu,train DHbl8ELtJY8,15,playing djembe,train DHbl8ELtJY8,33,playing djembe,train DHby_UW4fDQ,30,driving buses,train DHcivLcp9fc,33,sharpen knife,train DHcivLcp9fc,46,sharpen knife,train DHezpuoYiLA,55,playing tabla,train DHezpuoYiLA,65,playing tabla,train DHgK7_AUskA,409,typing on computer keyboard,train DHgdDd6tYI8,6,people booing,train DHhWMcQQWsQ,10,machine gun shooting,train DHlPxjJealI,120,playing drum kit,train DHoVXJe-MXY,293,playing glockenspiel,test DHoVXJe-MXY,729,playing glockenspiel,test DHsxI3rBR-I,0,sailing,train DHu6l7QUocg,317,playing synthesizer,train DHu6l7QUocg,331,playing synthesizer,train DHvbAz3nJG4,20,"child speech, kid speaking",train DHwhQs0Dozk,21,playing bass drum,train DHwhQs0Dozk,430,playing bass drum,train DHxHB5-bG_o,51,tractor digging,train DHyYhgyKSgQ,30,police car (siren),train DHzoE8_Xqgk,23,horse clip-clop,train DI-Hh94RFLI,20,playing banjo,train DI08kzfwnlE,17,dog howling,train DI08kzfwnlE,28,dog howling,train DI0rRY3wnOE,584,tractor digging,train DI1UW_uxTjw,100,playing clarinet,train DI4m3AEy3RI,54,playing erhu,train DI6wxP7dQWs,110,goose honking,train DIAS8gDM8Hg,3,dog baying,test DIBpl7npZ8c,21,blowtorch igniting,test DIF8Cd8YwMg,450,typing on typewriter,train DIFol-4j0aI,14,sliding door,train DIG8XzctOME,0,"bird chirping, tweeting",train DIGMegi9Uhs,250,"female speech, woman speaking",train DIKJTssDrb0,0,people farting,train DIKZP8-TnOQ,20,cap gun shooting,test DIK_y189Q64,60,playing piano,train DINUoBQK80g,247,cheetah chirrup,train DINjQrp9PgM,70,train horning,train DIPMHV4V5C8,152,people eating crisps,train DIQ_n4wSktQ,0,frog croaking,train DIRgllzpEtQ,43,canary calling,train DIRgllzpEtQ,408,canary calling,train DIU3xS6u7hw,80,playing cymbal,train DIUcpkMtLEo,16,eagle screaming,train DIXRbv8PprE,30,crow cawing,train DIYczgV9W1g,30,"playing violin, fiddle",train DIkH3opVS8o,30,raining,test DIkpvHv0QHo,10,skidding,train DIl050cgX_U,30,chicken crowing,train DIogRxvHtbc,76,tap 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DJQHER44njs,230,"playing marimba, xylophone",train DJQMIEG5oBY,160,people cheering,train DJQXmv2nVgw,36,playing badminton,train DJRpHO7J9q0,60,playing bass drum,train DJSLsPydAB0,12,sliding door,train DJUreQlLnE0,26,arc welding,train DJV_eXkAK6k,260,ice cracking,train DJV_eXkAK6k,314,ice cracking,train DJW9SHSWko0,29,wind noise,train DJXQV_oKyjE,30,ambulance siren,test DJYnTgPJRVU,312,airplane flyby,train DJan9OSSF7c,60,plastic bottle crushing,train DJan9OSSF7c,78,plastic bottle crushing,train DJbFAoB4-_o,170,people hiccup,test DJcgckYTlc4,35,singing bowl,train DJclHUgiz_U,30,horse clip-clop,train DJew-iu_Wjs,12,cat growling,test DJewp10-KLs,0,skidding,train DJgNqEySfJI,80,playing piano,train DJjP05WC4Ak,144,playing bongo,train DJjP05WC4Ak,212,playing bongo,train DJk1D4hTk6U,17,chainsawing trees,train DJn1wOC-gnE,85,horse clip-clop,train DJnOiAvi9io,13,typing on typewriter,train DJnOiAvi9io,47,typing on typewriter,train DJoFVhjQeDE,30,people running,test DJolvOvyJzA,30,train 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DLp0eqrHUe4,30,"female speech, woman speaking",train DLpG-RezecE,11,"playing steel guitar, slide guitar",train DLpG-RezecE,53,"playing steel guitar, slide guitar",train DLpmZEm6WHA,30,chicken crowing,train DLpo7rIyxQI,240,orchestra,train DLupjf31s7c,193,playing darts,train DLur1dQzoV8,20,cat purring,train DLyJAVC_ies,120,playing glockenspiel,train DLyJAVC_ies,133,playing glockenspiel,train DM0qDWgFzO8,2,yodelling,train DM32RsmS59w,109,playing tambourine,train DM39tc7aKA4,12,playing snare drum,train DM39tc7aKA4,80,playing snare drum,train DM3jH3OZJRM,10,cutting hair with electric trimmers,train DM3jH3OZJRM,248,cutting hair with electric trimmers,train DM4-O5TsdvA,6,"engine accelerating, revving, vroom",train DM7l2cIdc9s,10,basketball bounce,train DM8TK6Ah6FI,22,"motorboat, speedboat acceleration",train DMAFbu8iaYY,222,playing hammond organ,train DMAFbu8iaYY,281,playing hammond organ,train DMBhLhFloO4,107,bouncing on trampoline,train DMBhLhFloO4,128,bouncing on trampoline,train DMCRlHYJDiU,36,elk bugling,train DMF-j1T0CO8,6,train horning,train DMF-j1T0CO8,21,train horning,train DMF8AdyiilI,210,people clapping,train DMFhzHjUcxM,30,toilet flushing,train DMFqqbDkQW0,0,toilet flushing,train DMKqPQLjN0s,4,"cattle, bovinae cowbell",train DMKqPQLjN0s,16,"cattle, bovinae cowbell",train DMLHdO7y4LQ,81,lighting firecrackers,train DMLHdO7y4LQ,98,lighting firecrackers,train DMLf0s4VWIk,30,"playing violin, fiddle",train DMNFJ_mNz3M,15,playing volleyball,test DMNPkx5Yyn8,10,people clapping,train DMOOvIqqt5M,353,rope skipping,train DMU38cv6JWs,50,sloshing water,train DMUEIXXp5b0,110,"railroad car, train wagon",train DMURz6gtnJw,101,machine gun shooting,train DMURz6gtnJw,146,machine gun shooting,train DMUw6uA8elc,30,police car (siren),train DMVWs7xXA7w,96,skiing,train DMVmy7UvFEc,20,fireworks banging,train DMWJo8E2kMA,65,bull bellowing,train DMWJo8E2kMA,264,bull bellowing,train DMZ0i0Gva2M,2,francolin calling,train DMZ0i0Gva2M,33,francolin calling,train DMZcvqcVKbg,120,playing drum kit,train DMbLpqQlYjc,30,using sewing machines,test DMc_Y0x1zSM,16,chicken crowing,train DMgSyzR-82s,80,playing banjo,train DMgY_-WUHJ8,70,playing saxophone,train DMgplSlWFC4,39,people giggling,train DMgplSlWFC4,49,people giggling,train DMiK4JLMCc0,7,gibbon howling,train DMjznhTMwns,1,"pigeon, dove cooing",train DMk6F_m3ts0,30,playing french horn,train DMvU7iM4YFM,21,playing sitar,train DMvU7iM4YFM,57,playing sitar,train DMvf7NyvSz4,38,"playing steel guitar, slide guitar",train DMxvSt9i-xU,187,slot machine,train DMyEFoDXRCQ,40,playing trumpet,train DMycNFY3cys,8,playing darts,train DMycNFY3cys,24,playing darts,train DN-9XlR3qxg,29,police car (siren),train DN-OEf_uv8k,180,playing bass guitar,train DN2ZVSlKDG8,81,lathe spinning,train DN2ZVSlKDG8,189,lathe spinning,train DN8L4RgrAo0,160,printer printing,train DN9iVrbj1G4,26,roller coaster running,train DNA4jGl2SXo,3,people booing,train DNBpRJMEm4s,30,playing electric guitar,test DNDz_sjBBY0,230,lawn 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DNgiaNDwA9s,30,"male speech, man speaking",train DNj4jw0wjyU,62,car engine knocking,train DNjLiQy6nUs,290,playing trombone,train DNjlqZzM8iw,0,car passing by,train DNk3NWnTGyo,30,horse neighing,train DNkzxmcMEKU,226,swimming,test DNmRabPDTOQ,190,female singing,train DNr4RPLJQgU,276,playing badminton,train DNrufgsuEDI,430,"race car, auto racing",train DNs8nJ-FSiM,108,roller coaster running,train DNsdcmDoDY0,80,playing french horn,train DNt94Nj_czU,19,playing trombone,train DNwNccsO2hE,7,people battle cry,train DNwNccsO2hE,35,people battle cry,train DNwWLZCupMc,300,mouse squeaking,test DNxo99pYHcw,236,spraying water,train DNyElNcEr1M,0,people belly laughing,train DNz1Lcp25sQ,70,splashing water,train DNz3D68ikyU,20,fireworks banging,train DO-9yuU9brk,28,sea lion barking,train DO1AqNRXZGo,370,playing bass guitar,train DO1kiAhC0f0,41,ice cracking,train DO1kiAhC0f0,71,ice cracking,train DO3W0TK3S3g,310,people sniggering,train DO5ZE3t5G50,8,baby crying,train DO93G9gc78c,0,playing acoustic 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snowmobile,train DOi8fGCT5G0,20,gibbon howling,train DOi8fGCT5G0,32,gibbon howling,train DOi9a2IS0ws,40,playing accordion,train DOidA_6Fips,49,train wheels squealing,train DOidA_6Fips,86,train wheels squealing,train DOjwJ5ZDTqw,21,francolin calling,train DOjwJ5ZDTqw,216,francolin calling,train DOkYKNCFCWM,27,civil defense siren,train DOl3YIx5wwE,350,arc welding,train DOl3YIx5wwE,467,arc welding,train DOodvwXcpwQ,0,dog bow-wow,test DOolMLMjK7U,53,fire truck siren,train DOolMLMjK7U,65,fire truck siren,train DOotwwASsNE,30,people belly laughing,train DOrELY7Pfh4,507,police radio chatter,train DOrELY7Pfh4,528,police radio chatter,train DOrXkT4N5ic,48,people booing,train DOsBP-y-VV4,30,horse clip-clop,train DOue5Xe9cTU,119,playing bass drum,train DOue5Xe9cTU,137,playing bass drum,train DOvhD6NofnI,30,people burping,train DOvmu9ZNI64,520,ripping paper,train DOwB4BzrC-8,0,lawn mowing,train DOwkjBCah5A,590,using sewing machines,test DOxEOSckwgY,12,"female speech, woman speaking",train DP-MlXGWLEI,69,singing bowl,train DP0-le2mbZg,40,frog croaking,train DP0jL-Ll8v8,16,baby laughter,train DP39sTllcPU,170,helicopter,train DP59jSKJoGk,572,tractor digging,train DP59jSKJoGk,732,tractor digging,train DP6Fi7akaLE,210,playing cello,train DPBZSmjc2po,54,playing didgeridoo,train DPFVLHDDPx8,119,bouncing on trampoline,train DPFVLHDDPx8,139,bouncing on trampoline,train DPH7c2UHeeg,171,people burping,train DPIoiTbSPek,10,"vehicle horn, car horn, honking",train DPLGqj3cwrE,19,elephant trumpeting,train DPLGqj3cwrE,32,elephant trumpeting,train DPMAYai9WYY,4,volcano explosion,train DPMEoIfxrZY,30,"playing violin, fiddle",train DPMrlyqRvGg,30,"female speech, woman speaking",train DPNmUOnE83o,9,dog growling,train DPPPYgju9IU,22,"child speech, kid speaking",train DPVdz-ioLuI,7,bird squawking,train DPVofFsl66E,60,"playing violin, fiddle",train DPYOFo9xZmA,30,"female speech, woman speaking",train DPZJqHfYV60,21,people slurping,train DPZJqHfYV60,42,people slurping,train DP_ZPAfVk_g,10,people farting,train DPaaIZDxce4,70,child singing,train DPbQ9UpjNjw,30,male singing,train DPcGzqHoo7Y,30,turkey gobbling,test DPdAsDw3chc,30,fire truck siren,test DPdddPbtCfU,30,"female speech, woman speaking",train DPddqF9Clw0,30,"motorboat, speedboat acceleration",train DPgCDFu1Gqc,33,tractor digging,train DPgCDFu1Gqc,54,tractor digging,train DPlDZTcVI7c,24,squishing water,test DPlSp0M9Mgs,9,lions roaring,train DPlSp0M9Mgs,20,lions roaring,train DPmQfp49nWo,30,horse clip-clop,train DPp4E85YjAs,88,playing tabla,train DPp4E85YjAs,359,playing tabla,train DPpDgM5ElNw,89,opening or closing drawers,train DPpDgM5ElNw,691,opening or closing drawers,train DPpmV4JqYsM,30,goat bleating,test DPpo_Whnuqc,22,missile launch,train DPszRazVlU0,46,playing erhu,train DPuPLIDOIA4,30,chicken crowing,train DPx8lE8I3zc,0,"bird chirping, tweeting",train DPymsBFV1Uk,10,"railroad car, train wagon",train DQ-tXGj6I5Y,20,"pigeon, dove cooing",train DQ1-Jj3kEP0,61,hammering nails,test DQ1bXxKPF9U,315,sailing,test DQ1o_3vGXaE,29,mynah bird singing,train DQ1o_3vGXaE,39,mynah bird singing,train DQ5Elbvvr1M,243,printer printing,train DQ5qoElNdZ0,47,striking pool,train DQ5vJVlRFs8,18,dog howling,train DQ6chSmkLfA,0,playing timpani,train DQ8tagAN5EI,180,chicken clucking,train DQ9CDTTDQSU,59,skiing,train DQBstimVS84,22,people marching,train DQBstimVS84,125,people marching,train DQBz86mVEmY,0,lawn mowing,train DQC78JSBJoo,0,slot machine,test DQCcDIxpY9c,131,playing hockey,train DQCcDIxpY9c,203,playing hockey,train DQCwfadKuEo,258,francolin calling,train DQCwfadKuEo,829,francolin calling,train DQD8Eb9F8dw,51,playing clarinet,train DQD8Eb9F8dw,131,playing clarinet,train DQE6bGhc5RM,55,woodpecker pecking tree,train DQE6bGhc5RM,146,woodpecker pecking tree,train DQFC76IHiKs,30,"bee, wasp, etc. buzzing",test DQGH_6EIaQ8,10,using sewing machines,train DQHiagV2Ln0,40,playing cello,train DQIDA6od_nM,60,playing vibraphone,train DQIwRVrlYqI,159,baltimore oriole calling,test DQJeoZlknZk,330,opening or closing drawers,train DQJwCqR08Tg,170,helicopter,train DQLEFiVZBnA,22,sheep bleating,train DQLtNSPC7P0,480,playing saxophone,test DQQM6n9p3Jg,26,playing steelpan,train DQQwF7ApETY,160,swimming,train DQQwF7ApETY,282,swimming,train DQRNT4VF-q0,20,gibbon howling,train DQRNT4VF-q0,46,gibbon howling,train DQRapqt_69c,128,sharpen knife,test DQTyOCKrW2o,370,playing double bass,train DQV9Z2cpne4,30,playing drum kit,train DQVVjXM7G_E,187,singing choir,train DQVlm6cRowU,271,playing volleyball,train DQWw45SlINk,30,playing accordion,train DQd5YOtzlyI,140,playing drum kit,train DQdJDxCHQ3o,60,playing drum kit,train DQelhAtUyHY,30,playing electric guitar,train DQg8b91HjRk,30,"engine accelerating, revving, vroom",test DQgDkZX3GSU,40,ocean burbling,train DQgjJcFjm98,30,"playing violin, fiddle",train DQiq1PDGhd8,222,parrot talking,train DQkQZaH592g,0,fireworks banging,train DQnlsuyVGk4,90,playing djembe,train DQnlsuyVGk4,149,playing djembe,train DQoArE2P-4k,119,singing bowl,train DQsaIFZCfFs,31,pheasant crowing,train DQsaIFZCfFs,46,pheasant crowing,train DQvgFaXA7uM,100,playing banjo,train DQyTJV-NcHs,54,bowling impact,train DQyTJV-NcHs,156,bowling impact,train DQyssqMj_8c,270,people cheering,train DR0oSKaer9g,40,"playing marimba, xylophone",train DR1i4GYntsM,470,male singing,train DR3oja4NVDo,2,sheep bleating,train DR4WxNQO9EA,5,people booing,train DR66Fj-Yz10,30,people sobbing,train DR7TdSc2ahQ,30,sliding door,train DR8wsqJ68uk,128,typing on typewriter,train DR9BhZidfss,100,people burping,train DR9C1LAFrZw,30,"playing violin, fiddle",train DRAE6T2bL5g,30,police car (siren),train DRAkXEFXpgE,13,playing squash,train DRC7KTs-BLc,8,using sewing machines,test DRCozm65eZI,460,typing on typewriter,train DRGD6sMSGKw,30,"playing violin, fiddle",train DRHGGhCkixE,30,people running,train DRJmupJHOgk,210,people clapping,train DRLYGLRCo7o,158,playing trombone,train DRMUo8bv45U,410,"pigeon, dove cooing",train DRP51rNKDTU,78,foghorn,train DRP51rNKDTU,88,foghorn,train DRPlHayqsbw,30,"female speech, woman speaking",train DRQPYwom_mY,40,people crowd,train DRSs6q_sTr4,0,bird squawking,test DRVQD-qckPk,10,scuba diving,train DRVQD-qckPk,76,scuba diving,train DRayNzyw_TQ,22,duck quacking,train DRhJDhG2w2k,24,cap gun shooting,train DRhfucrNK0k,21,bird squawking,train DRiOHJsHlI0,6,cat meowing,train DRjWu503U3I,170,playing banjo,train DRnIWAFMJOI,7,"ice cream truck, ice cream van",train DRnSZIUhxsA,130,bird wings flapping,train DRtFsqMZEDw,20,beat boxing,train DRt_Oz3bf-o,300,church bell ringing,train DRtxPMsdKv4,238,car engine starting,train DRtxPMsdKv4,313,car engine starting,train DRz5kTGD4t4,20,playing drum kit,train DS-2gDqsYBo,22,"alligators, crocodiles hissing",train DS3VR_InLEQ,1059,opening or closing car doors,train DS4sPXFfRTg,250,ambulance siren,train DS5yCGeCrbw,93,arc welding,train DS9VRNABi1I,30,playing vibraphone,train DSEY4EVn1U0,79,penguins braying,test DSGMDArb1QI,30,"motorboat, speedboat acceleration",test DSHtSIWAHhM,62,hail,test DSJDZ9tftb4,35,tapping guitar,train DSJDZ9tftb4,62,tapping guitar,train DSKtUgmuaQY,320,playing drum kit,train DSLO9BKHSEk,40,helicopter,test DSMnx8xEMtA,30,people whistling,train DSNeK0S43rQ,110,"bird chirping, tweeting",train DSOQnoi3l5M,30,"male speech, man speaking",train DSOadme3cM8,40,ambulance siren,train DSPokyX3KJc,60,"female speech, woman speaking",train DSPvFTwJ3WA,40,driving buses,train DSQXq-xOVu4,65,playing bagpipes,train DSQXq-xOVu4,87,playing bagpipes,train DSRP9aZE5S4,147,hail,train DSRP9aZE5S4,232,hail,train DSS-cd080wc,925,playing tympani,train DSS-cd080wc,1137,playing tympani,train DSSdOSlRmsQ,30,people cheering,train DSThhOKXU-c,250,playing bass guitar,train DSVfDPjZE0I,30,"pigeon, dove cooing",train DSW2P4qw8cs,20,chicken crowing,train DS_NTKQtMFI,54,playing gong,train DS_NTKQtMFI,151,playing gong,train DS_kzdgXoV4,30,playing accordion,train DSbzsgroTrg,130,lions growling,train DSc3646beZQ,52,playing erhu,train DSc3646beZQ,126,playing erhu,train DScPeHqgZP4,490,"rowboat, canoe, kayak rowing",train DSdCxzO0q0g,77,cutting hair with electric trimmers,test DSef4-e07Dc,0,zebra braying,train DSgKhbtDWWo,400,playing flute,train DShnW2mZR-0,2,opening or closing car electric windows,test DSir0_W7SFg,530,"child speech, kid speaking",train DSkPr--ZFxA,51,electric grinder grinding,train DSkPr--ZFxA,68,electric grinder grinding,train DSl6ZNhg4i0,30,"playing violin, fiddle",train DSmDCeC6IT0,30,"engine accelerating, revving, vroom",train DSnJrKiurY8,42,playing squash,train DSnhM5tKZSQ,0,"donkey, ass braying",test DSoP_VA_dfU,110,"engine accelerating, revving, vroom",train DSp4wteM0Cs,247,hammering nails,train DSp4wteM0Cs,263,hammering nails,train DSpXn80aU64,121,slot machine,train DSqPiPumORo,94,disc scratching,train DSqPiPumORo,105,disc scratching,train DStN6b2qUd4,21,gibbon howling,train DSv6Jce5gd0,122,playing mandolin,train DSv6Jce5gd0,159,playing mandolin,train DSwEi6cwPjM,170,playing cello,train DSwl-mSXsJ8,6,alarm clock ringing,train DSyFGJCRt_c,564,playing table tennis,train DSzLO-mc4NY,40,skidding,train DSztGsbZkmY,197,arc welding,test DT-UIIpvVVs,245,opening or closing drawers,train DT0VbmY28ew,21,missile launch,train DT1CTUruDyQ,160,chainsawing trees,train DT1WrAVRtX8,20,people sobbing,train DT5wTed21Tk,300,playing cello,train DTBJhSfEPJE,30,"female speech, woman speaking",train DTBz0ljgQb4,350,playing piano,train DTCcy7Al-Ag,10,fireworks banging,train DTCrTafN98o,6,francolin calling,train DTCrTafN98o,90,francolin calling,train DTCvxgSXT-E,220,people eating apple,train DTCvxgSXT-E,895,people eating apple,train DTEW9xRIYVw,160,playing snare drum,train DTK4eRzcRyI,15,typing on typewriter,train DTK4eRzcRyI,195,typing on typewriter,train DTNQt1qO8xA,30,cricket chirping,test DTPRspgWEeM,22,civil defense siren,train DTPRspgWEeM,48,civil defense siren,train DTRSz0XDKFM,40,playing timpani,train DTRSz0XDKFM,63,playing timpani,train DTU30MNjCx0,36,airplane flyby,train DTU30MNjCx0,76,airplane flyby,train DTVyQel5xfI,76,playing ukulele,train DTVyQel5xfI,103,playing ukulele,train DTX8eqtAGYE,30,playing harpsichord,train DTZ4khupsCM,56,beat boxing,test DTZC7Lj_S8A,30,"pigeon, dove cooing",train DTZHOC7ApwQ,0,cricket chirping,train DTZHOC7ApwQ,11,cricket chirping,train DTZTaKxS0GA,48,playing didgeridoo,train DTZTaKxS0GA,567,playing didgeridoo,train DT_92w0rSro,2,playing guiro,train DT_92w0rSro,30,playing guiro,train DT_Dpgaycjg,190,parrot talking,train DTavxCg13eI,30,typing on computer keyboard,train DTc70NOKD6Q,88,owl hooting,train DTcwwL4AQ9w,6,owl hooting,train DTcwwL4AQ9w,27,owl hooting,train DTjPLSQ370I,270,wind noise,train DTk5EFwnFPc,410,playing badminton,train DTkKGYCRMlc,80,orchestra,test DTkZOdtLp5o,20,playing piano,train DTkc7PSmJ90,30,playing hammond organ,test DTknpR3yFqA,187,skiing,train DTliXQ0GxB0,174,"playing marimba, xylophone",train DTnCrtCro44,94,playing didgeridoo,train DToM9JiNt5U,10,"motorboat, speedboat acceleration",train DToYgw2Ajzo,100,people sniggering,train DTokoi5hIu4,190,black capped chickadee calling,train DTokoi5hIu4,549,black capped chickadee calling,train DTqY8UQUv-o,37,civil defense siren,train DTs7D4ZsY_0,0,zebra braying,train DTsxw2Ci66c,16,"cattle, bovinae cowbell",train DTtGA6AyEBo,38,car engine idling,train DTuaUC-gT34,40,playing bass guitar,train DTwF07IOD3o,7,penguins braying,train DTwF07IOD3o,18,penguins braying,train DTzILdRUnuQ,10,dog barking,train DU1DilqxBGM,30,toilet flushing,train DU3Vlaa8rU0,372,playing hammond organ,train DU3zRwIMISQ,30,black capped chickadee calling,test DU61SQ1H824,7,people booing,train DU8A5r6nJiY,587,golf driving,test DUAGfqb3UvY,30,typing on computer keyboard,train DUDZxeNXB6k,0,playing glockenspiel,train DUDZxeNXB6k,93,playing glockenspiel,train DUI2WorAoC0,20,pheasant crowing,train DUI2WorAoC0,37,pheasant crowing,train DUJZ3PAYOxY,154,singing choir,train DUJZ3PAYOxY,259,singing choir,train DULWnX_HVQM,30,mouse pattering,train DULYgjnsKDk,379,playing erhu,train DUNOn71oGCw,40,tapping guitar,test DUSKxRMntZY,268,slot machine,test DUShpEZz7Sg,0,playing piano,train DUVfuXWArGM,30,playing drum kit,train DUW7iK2vWCQ,30,"race car, auto racing",train DUWeKLvfrwc,80,eating with cutlery,test DUWmB0kUk8o,32,cat purring,train DUX6kGHReyc,11,playing squash,train DUa2Hu8O_iM,90,tapping guitar,train DUa2Hu8O_iM,119,tapping guitar,train DUc3n_C_L48,50,playing snare drum,train DUduRpexDM0,30,"rowboat, canoe, kayak rowing",train DUf0I--lTF0,20,owl hooting,train DUf0I--lTF0,59,owl hooting,train DUf9efYEKIc,30,sliding door,train DUiO3wRYYeQ,676,"railroad car, train wagon",train DUk_kghCYXQ,18,fox barking,train DUkvDu7I2r4,69,swimming,train DUkvDu7I2r4,115,swimming,train DUlqHdaW4jc,40,driving motorcycle,train DUlxiX4ri1Q,45,alarm clock ringing,test DUmVzuuDCzo,27,singing bowl,train DUn_ilzLlkE,0,people whistling,train DUsivUkD3ik,4,"rowboat, canoe, kayak rowing",test DUuju3MBB30,90,"railroad car, train wagon",train DUuuaRBBwRQ,60,"bird chirping, tweeting",train DUxBS-hhQRw,500,orchestra,train DUxKXPvd6UU,21,bull bellowing,train DUxKXPvd6UU,41,bull bellowing,train DUxxLxD_xv0,20,people hiccup,train DUyCqcaKaEU,266,playing electronic organ,train DV-rskMKYSc,260,orchestra,train DV1C1XbdG7A,220,playing harmonica,test DV4g-G6JUp0,18,missile launch,train DV6cfqlLlfY,10,people crowd,train DV7VaYjfSps,30,"female speech, woman speaking",train DV8I3wOF-MU,380,playing cello,train DV8bLuo3z7Q,86,tractor digging,train DV9DC4wwhfo,50,eating with cutlery,test DV9U9uSkZVo,141,typing on typewriter,train DV9XRZkBxBQ,11,horse neighing,train DVAfQPhd9ck,250,police car (siren),train DVDQyIt56nQ,200,child singing,test DVEvol7LpSU,19,sheep bleating,train DVF3rKX-opo,30,playing zither,test DVG2nBpQbiE,8,striking pool,train DVG2nBpQbiE,52,striking pool,train DVGCIRVpNlA,67,mouse clicking,train DVGQtmNDRic,20,"bird chirping, tweeting",train DVISRP3_0H0,119,playing hammond organ,train DVIV9n6cfv4,60,people marching,train DVIV9n6cfv4,124,people marching,train 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DVqKW1ob9UI,96,playing squash,train DVsbEC8MgLk,0,"child speech, kid speaking",train DVsz0OY8DkU,34,people marching,train DVsz0OY8DkU,252,people marching,train DVuWm53IlVw,30,playing acoustic guitar,test DVvPRU8oOTU,150,cap gun shooting,train DVvPRU8oOTU,218,cap gun shooting,train DVyqYTg1roU,495,playing table tennis,train DVyqYTg1roU,694,playing table tennis,train DW-BHnG-rKI,0,splashing water,train DW0d_Z3SW08,30,police car (siren),train DW1K8H9dlAc,30,"female speech, woman speaking",train DW2hvIp0mns,60,toilet flushing,train DW7iF35FG64,30,printer printing,train DW9q69vo4pc,60,playing piano,train DWALHZzps-E,19,arc welding,train DWALHZzps-E,43,arc welding,train DWAb_kAkK2o,0,people belly laughing,train DWBCU0G6HGU,10,mouse pattering,test DWCJQAymU4Q,0,playing tympani,train DWCJQAymU4Q,45,playing tympani,train DWCxrR90MqY,139,playing darts,train DWCxrR90MqY,189,playing darts,train DWF0Klkry4A,2,printer printing,train DWF4VrLqKFU,183,car engine idling,train DWF4VrLqKFU,351,car engine 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DWbz9Vzktj4,7,playing tambourine,train DWbz9Vzktj4,77,playing tambourine,train DWdTkqrgH2M,30,pig oinking,train DWgZ6zyQXtg,16,horse neighing,train DWglVAgMkD0,50,lions growling,train DWiiZodMqPk,61,gibbon howling,train DWiiZodMqPk,158,gibbon howling,train DWj7WUnXPWU,30,"playing violin, fiddle",train DWjrhCewhCg,30,"male speech, man speaking",train DWk7uPVObYo,90,"bird chirping, tweeting",train DWk_7rw81D0,15,cutting hair with electric trimmers,test DWmLuZ5s3KQ,30,waterfall burbling,train DWmzLNQpgtA,7,playing mandolin,train DWmzLNQpgtA,361,playing mandolin,train DWp96JHQd2o,9,playing bugle,train DWp96JHQd2o,64,playing bugle,train DWphfP8IXqo,65,door slamming,train DWpiobxlB1Q,20,people cheering,train DWtXpVI7SqI,86,wood thrush calling,train DWu9TYzOXuQ,210,people clapping,train DWumfOF-SuM,30,playing acoustic guitar,train DWwoI0zWu0A,65,barn swallow calling,train DWwoI0zWu0A,168,barn swallow calling,train DX21AjqfOIo,60,police radio chatter,train DX21AjqfOIo,70,police radio chatter,train DX2JytS-moE,30,playing harpsichord,train DX35XbFuFMw,80,"pigeon, dove cooing",train DX3T74terp8,0,"vehicle horn, car horn, honking",train DX5Kzbqd2SQ,30,horse clip-clop,train DX5VbKSF4qY,7,pig oinking,train DX5VbKSF4qY,17,pig oinking,train DX5_AglGFMw,349,metronome,train DX62MLFGPUE,110,"female speech, woman speaking",train DX6qiEryKZw,20,turkey gobbling,train DXCblN0SaTw,106,playing french horn,train DXCblN0SaTw,128,playing french horn,train DXEsn0w7gSo,20,driving motorcycle,train DXGbTR7SURU,30,playing harpsichord,train DXHAHZHwrAU,116,baby laughter,train DXHyW_LwlCk,17,tap dancing,train DXJ4iTEhMVg,257,people slurping,train DXJ4iTEhMVg,464,people slurping,train DXJGQfd1T5A,7,snake hissing,train DXJGQfd1T5A,83,snake hissing,train DXKjzOkNQfE,2,baby laughter,train DXKw_eTEt3Y,120,people whistling,train DXM-kf0Xomc,328,hammering nails,test DXNMtIwvk_Q,117,rapping,train DXPQFOt9zf8,28,"electric shaver, electric razor shaving",train DXQiubaBRSo,25,owl hooting,train DXQiubaBRSo,42,owl hooting,train DXT6zby3jKY,164,playing saxophone,train DXTkY938Cvg,60,"child speech, kid speaking",train DXUAyRRkI6k,192,cat caterwauling,train DXUQquTjRzM,90,playing banjo,train DXV2fIU6ADA,30,"male speech, man speaking",train DXWEVucEPvU,6,alarm clock ringing,test DXXGotNNVcI,136,tap dancing,train DXXW74z_GX0,30,"race car, auto racing",train DXZVgRaY8VI,179,playing gong,train DXZVgRaY8VI,219,playing gong,train DX_LNgP2J80,40,basketball bounce,train DXbVj39Ns10,84,tornado roaring,train DXdzSJdVudM,3,"bird chirping, tweeting",train DXeaNCdkQ4Q,0,wood thrush calling,train DXeaNCdkQ4Q,41,wood thrush calling,train DXfTYgSGLac,177,"alligators, crocodiles hissing",train DXfTYgSGLac,371,"alligators, crocodiles hissing",train DXfyoVutnU4,6,dog growling,train DXfyoVutnU4,20,dog growling,train DXg8oM6TX1k,235,playing gong,train DXja5QSkP3k,40,toilet flushing,train DXops1QGV90,0,people farting,train DXowhNibTlc,29,"motorboat, speedboat acceleration",train DXrs7QVkT-w,276,playing sitar,train DXrs7QVkT-w,410,playing sitar,train DXrykPu_baA,30,"female speech, woman speaking",train DXwZ8K6n9sA,30,playing accordion,train DXxhYjLQwb4,30,people sobbing,train DY-80XawmHQ,6,baby crying,train DY02v5u6Aqc,176,playing hockey,train DY29Zj2Pcs4,230,people farting,train DY2ckrTZsLA,16,playing bassoon,train DY4xb_YcEYE,370,playing gong,train DY5N_hh_fyw,80,ocean burbling,train DY9Xm0boaeA,30,"playing violin, fiddle",train DYA-zwZRIX4,1,horse clip-clop,train DYB_eAmtqFs,10,lawn mowing,train DYCZEs43JBE,20,driving motorcycle,train DYCtWMJ4INE,30,"male speech, man speaking",train DYG5i8YHqJ4,270,lawn mowing,train DYHU6DKgVso,76,stream burbling,train DYHU6DKgVso,270,stream burbling,train DYK9wxYXK1U,160,ocean burbling,train DYMdmyHFWNg,0,driving motorcycle,train DYNbFbJDz8g,30,helicopter,train DYON5w_ntE4,90,"child speech, kid speaking",train DYPF2U0X5ic,81,playing cornet,train DYPF2U0X5ic,91,playing cornet,train DYPNx9Y47VM,122,playing erhu,train 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DkEr3QczKWY,30,orchestra,train DkF8b_hGOqo,30,pumping water,train DkID_3HqNpI,48,elephant trumpeting,train DkJ6OScQshI,0,chicken crowing,test DkJIuTcJaNo,202,playing trumpet,train DkKaDfJPVjM,0,"subway, metro, underground",train DkNxtDBYiUY,36,elk bugling,train DkORoInXHDA,30,wind noise,train DkSFdTiSDa8,30,people sneezing,train DkSkLdqeE1U,210,"subway, metro, underground",train DkUfRxjExYU,57,"playing steel guitar, slide guitar",train DkUfRxjExYU,165,"playing steel guitar, slide guitar",train DkW4qZ_-4fI,0,lions growling,train DkWAn4rtLa4,160,"vehicle horn, car horn, honking",test DkYT-3NTpl4,318,skiing,train DkYqBDuTmKU,79,bouncing on trampoline,train DkYqBDuTmKU,233,bouncing on trampoline,train DkZJOB6lk-g,30,wind rustling leaves,train DkaANehqdPU,30,"female speech, woman speaking",train DkbOS78Xb5g,346,slot machine,train DkeCmXk5A-4,155,car engine knocking,train DkfVECixy9w,78,playing bassoon,train Dkfcal9MGPs,400,bird squawking,test Dkg9aauGj_E,182,people eating,train 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ELvE1WPpTmQ,19,running electric fan,train ELvE1WPpTmQ,31,running electric fan,train ELvsokKcydM,30,goat bleating,test ELz3HBHK9xg,100,dog barking,train ELzkopVUqxs,370,driving motorcycle,train EM0R1zvfr5o,50,"race car, auto racing",train EM0yqNSnB7A,2,zebra braying,train EM1vGu1ZjC8,3,playing bugle,train EM2AkbQkAiY,12,fire truck siren,train EM2AkbQkAiY,70,fire truck siren,train EM2O8RPZcuk,0,"cattle, bovinae cowbell",test EM2tEFE_Bpo,8,"electric shaver, electric razor shaving",train EM8GPWL_x54,65,playing badminton,train EM8SyoZQuzE,1,goose honking,train EM9WYjXtDKE,310,helicopter,train EMAArbPyHEo,30,chicken crowing,train EMB9fenVJZM,180,lions growling,train EMENiImb_1Y,30,church bell ringing,train EMF1qyVp7o8,19,playing bugle,train EMF1qyVp7o8,44,playing bugle,train EMGNyRa5pEc,280,"playing marimba, xylophone",test EMIiVIF7za8,193,playing tabla,train EMIiVIF7za8,219,playing tabla,train EMKTB-YkNhY,28,playing table tennis,train EML37lGE1iI,12,electric grinder grinding,train EML37lGE1iI,246,electric grinder grinding,train EMLpB_ZP0c0,30,"male speech, man speaking",test EMNzuJ6rZxg,0,wind chime,train EMOi0zx0X8c,18,striking pool,train EMOi0zx0X8c,149,striking pool,train EMQcJEaFw2I,173,skiing,train EMT1qKVS3aA,30,"male speech, man speaking",train EMUI7ZCX2w8,24,wind chime,train EMUI7ZCX2w8,67,wind chime,train EMUhJi30xJU,30,crow cawing,test EMVqctEZfsg,8,people booing,train EMVqctEZfsg,404,people booing,train EM_6ft3-HNU,0,"subway, metro, underground",train EMadTSIdCc8,29,dog whimpering,train EMdU0DgkpFg,34,"playing steel guitar, slide guitar",train EMdU0DgkpFg,58,"playing steel guitar, slide guitar",train EMeKaxAPIws,30,people running,train EMi1ExaxEmE,6,bull bellowing,train EMi1ExaxEmE,22,bull bellowing,train EMjKRdRaZO0,90,playing french horn,train EMjKRdRaZO0,117,playing french horn,train EMjsPq0Ar8k,30,goose honking,train EMlUVQlPb34,19,frog croaking,train EMlUVQlPb34,41,frog croaking,train EMn6GIEwv4Y,81,playing bugle,train EMnv_LWMC8U,0,playing bass drum,train EMocj3sITnE,47,tractor digging,train EMqAXt_l8Hc,200,people sobbing,train EMrgl0l3ko4,184,playing harpsichord,train EMu8PTcJ-Qw,0,"bird chirping, tweeting",train EMxmq6xcuSw,24,playing mandolin,train EMxmq6xcuSw,43,playing mandolin,train EN00CYMmzkU,15,tapping guitar,train EN00CYMmzkU,36,tapping guitar,train EN08Qy6ihNE,30,ambulance siren,train EN1BT3qaaIE,160,playing accordion,train EN3EZvD-UKA,10,ocean burbling,train EN3cOtb-8s8,67,people cheering,train EN3cOtb-8s8,83,people cheering,train EN3pztu-Y1Y,30,people shuffling,train EN50BTjjnrs,8,baby crying,train EN5AwM98F5A,10,toilet flushing,train EN5rfAftUWw,50,driving motorcycle,train EN5v0IGCGKc,30,pumping water,train EN6Um-1FMz0,30,"female speech, woman speaking",train EN6mxwNRVwA,21,sea lion barking,train EN89KybPotA,30,horse clip-clop,train EN8W__NIerc,10,"vehicle horn, car horn, honking",train ENAJjEpGj8Y,38,playing squash,train ENAJjEpGj8Y,125,playing squash,train ENBV0FcXwsQ,122,ripping paper,train ENCPkF4K9zU,30,"female speech, woman speaking",test ENCo87TxtWU,44,people marching,train ENCo87TxtWU,103,people marching,train ENDXhI_PeAc,80,driving motorcycle,train ENDfOGqPAPA,41,people marching,train ENFRqdOudJA,30,printer printing,train ENFlRdXc-GQ,14,chopping wood,train ENHpFkKiyrU,44,civil defense siren,train ENJ0HrOYFuc,43,cat hissing,train ENJ0HrOYFuc,53,cat hissing,train ENK60Gv9vOw,30,playing french horn,train ENPpKSaqTcw,385,dinosaurs bellowing,train ENPpKSaqTcw,615,dinosaurs bellowing,train ENQBXODItVI,100,basketball bounce,train ENQli94rpHM,0,people crowd,test ENQuAXyve5Y,325,people burping,train ENRLn7chJcg,170,female singing,train ENRMPv0Ujaw,30,playing trombone,train ENSMDHYewy4,30,chicken crowing,train ENVxkLsBePg,13,police radio chatter,train ENVxkLsBePg,43,police radio chatter,train ENW8dt3hiqg,6,cheetah chirrup,train ENWNe64RqFY,50,people screaming,train ENX0totqysA,30,playing piano,test ENXtT_-9UbQ,131,playing double bass,train ENZQLnCEMh0,390,ocean burbling,train EN_dvVYOot0,80,basketball bounce,train EN_fOMt-IVE,60,singing choir,test EN_xBgrR7Ps,23,playing djembe,train EN_xBgrR7Ps,97,playing djembe,train ENbtI8vWvA8,231,hammering nails,train ENhniny87Qs,0,cricket chirping,train ENhniny87Qs,365,cricket chirping,train ENim4Igx6ms,61,tractor digging,train ENjSYuaNt3Q,5,dog howling,train ENjSYuaNt3Q,27,dog howling,train ENnH9h1p2wk,13,"motorboat, speedboat acceleration",train ENnY-h8S0Pg,60,volcano explosion,train ENpkwl3xLrU,28,popping popcorn,train ENpkwl3xLrU,58,popping popcorn,train ENq8KlN9JOw,20,dog barking,train ENq_rrKiAzo,33,rope skipping,train ENr5NQ-Q5aM,0,fire crackling,test ENwOUpDI1vY,60,playing squash,train ENzeA0AtdwI,157,parrot talking,train ENzihkm9wn0,450,"vehicle horn, car horn, honking",train EO0iTL39_y0,7,wind noise,train EO1-qOw889s,154,playing tabla,train EO1-qOw889s,260,playing tabla,train EO2y901299I,30,"male speech, man speaking",test EO4qoQ_zoiw,4,"motorboat, speedboat acceleration",train EO5iFr1HmAg,90,lions growling,train EO60eRfUyUI,47,playing steelpan,train EO7qcVc4GeQ,0,playing erhu,train EO8K9wfx3SY,110,police radio chatter,train EO8K9wfx3SY,963,police radio chatter,train EO8QXtVa8b0,240,singing bowl,train EO8nwfjhVtQ,30,playing harmonica,test EO9OcJIU8Eg,17,pig oinking,train EOBZrI1VLAE,58,playing double bass,train EOBZrI1VLAE,208,playing double bass,train EOGVVhl9TxA,7,playing glockenspiel,train EOGVVhl9TxA,165,playing glockenspiel,train EOHjf43uSB0,80,"female speech, woman speaking",train EOI4gbmM4Ks,126,playing cymbal,train EOInyR8PQGE,85,crow cawing,train EOInyR8PQGE,209,crow cawing,train EOKUV6hgMhM,122,helicopter,test EOMH_FQjigQ,350,people cheering,train EOMO4YYRWfE,102,bowling impact,train EOMO4YYRWfE,516,bowling impact,train EON_rdhgxfg,21,toilet flushing,train EOPG9LHhzis,1,playing sitar,train EOPG9LHhzis,51,playing sitar,train EOSB-mXWEHA,92,tractor digging,test EOU--3jPTA0,335,"playing steel guitar, slide guitar",train EOU--3jPTA0,402,"playing steel guitar, slide 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shaving",train EOxtnBabMLg,54,playing erhu,train EOxtnBabMLg,77,playing erhu,train EOzMqSLNheA,130,playing tabla,train EOzMqSLNheA,152,playing tabla,train EOzXkp3xQnk,10,duck quacking,train EP4mNLfwbfA,30,playing snare drum,train EP8QFFUA3xE,5,hail,test EP8p7y-ue-s,21,people finger snapping,train EP8p7y-ue-s,46,people finger snapping,train EPB4u-BfYFA,48,"bird chirping, tweeting",train EPBLwW3SYSo,109,playing squash,train EPE1FDeM_S8,170,driving motorcycle,train EPGpTB9F6EI,431,car engine starting,train EPHhAplooqo,322,reversing beeps,train EPIGyorzXAU,34,cheetah chirrup,train EPJcLGY5CDI,232,playing table tennis,train EPJp93NxFGM,70,swimming,test EPLa4BUpoqY,186,tap dancing,train EPLgaEuASU8,150,toilet flushing,train EPMy4aKf6Tk,560,playing hockey,train EPNCDINUmsM,32,playing electronic organ,train EPOBpBggw_8,0,turkey gobbling,train EPSPjAR8ztY,30,people clapping,train EPTN39_4gOM,30,stream burbling,train EPYCgIMyUCU,250,fire truck siren,train EPZ6tp4Ffxg,0,people whistling,train EPZhtjORB-k,295,"electric shaver, electric razor shaving",train EP_Sgbkk4Z4,30,"female speech, woman speaking",train EPbf6hJx3-w,3,writing on blackboard with chalk,train EPcBaOcQOE0,34,tap dancing,train EPd2oA202SI,30,"bird chirping, tweeting",train EPeB_VsfU-U,10,fireworks banging,train EPeGf7PwV9s,150,playing flute,train EPgxbgiPjUs,15,people shuffling,train EPh-ys3ypr0,76,fox barking,train EPmKDRAraCQ,30,air horn,train EPoJdBYFGc4,22,slot machine,train EPqJH1Cvae8,130,playing banjo,train EPrEIOGuTWo,69,playing squash,train EPs0mPdiLXU,170,skidding,train EPsCPRsJjYg,137,playing gong,train EPsCPRsJjYg,148,playing gong,train EPsXvd-QpAE,30,"female speech, woman speaking",train EPsc5jAGzdQ,1,people nose blowing,train EPslcLneAjc,392,playing timbales,train EPslcLneAjc,463,playing timbales,train EPwe8rqaZVw,123,playing bugle,train EPzNwL4OVYI,263,playing congas,test EPzOm1_V9WQ,271,volcano explosion,train EPztDADcdMY,210,playing cymbal,train EQ-lnted3yk,356,cricket chirping,train EQ-lnted3yk,409,cricket chirping,train EQ0A3_edsfo,6,goose honking,train EQ0A3_edsfo,40,goose honking,train EQ0kUMWbY4U,360,playing clarinet,train EQ1vXDzmpKc,10,"subway, metro, underground",train EQ2E5wlLrqw,30,playing vibraphone,train EQ2LdntrmrM,268,playing badminton,train EQ2S35bforg,52,air horn,train EQ3FRdi-AMM,3,playing ukulele,train EQ3FRdi-AMM,17,playing ukulele,train EQ4HQ3KBth8,47,playing mandolin,train EQ4HQ3KBth8,70,playing mandolin,train EQ648GMdOZg,170,using sewing machines,train EQ7ekQG3Sk4,4,chipmunk chirping,train EQ7ekQG3Sk4,39,chipmunk chirping,train EQ91XVsmq-I,30,"female speech, woman speaking",test EQFPbqB0jc0,0,"vehicle horn, car horn, honking",train EQFXDLBz31A,7,"vehicle horn, car horn, honking",train EQHrQIaQNv8,30,"playing steel guitar, slide guitar",test EQJHgbw5LFE,0,door slamming,test EQKs1YLMUuI,4,playing oboe,test EQLA6AXVpEQ,286,beat boxing,train EQNyJp6oePU,269,missile launch,train EQNyJp6oePU,305,missile launch,train EQOS-sQeUkQ,30,playing saxophone,train EQOs-tpgiAY,3,"heart sounds, heartbeat",train EQOs-tpgiAY,94,"heart sounds, heartbeat",train EQSwgoOfYUM,0,opening or closing drawers,train EQSwgoOfYUM,10,opening or closing drawers,train EQTa-Zl1YWg,50,"child speech, kid speaking",train EQTeF8RWoMg,160,turkey gobbling,train EQV0Cg7KmZI,20,horse clip-clop,train EQV6rVGE7Zg,526,mynah bird singing,train EQVZ14O8we8,25,warbler chirping,train EQVZ14O8we8,69,warbler chirping,train EQVZLtlDkHw,30,playing acoustic guitar,test EQWSZKpbv6w,8,golf driving,train EQWSZKpbv6w,19,golf driving,train EQWlMJg_un4,30,"motorboat, speedboat acceleration",train EQ_8X70mQ4s,200,"railroad car, train wagon",train EQbRqBZEIx8,39,playing bassoon,train EQd8uEzbCCc,310,"race car, auto racing",train EQevqCUnthY,40,fireworks banging,train EQfZjJ_XwT4,40,lions growling,train EQg2BXZrw7Q,90,playing piano,train EQh-EIlfCe8,30,driving buses,test EQjczS2mbgY,91,fire crackling,train EQjczS2mbgY,354,fire crackling,train EQkYsCRkPrE,14,baby crying,train EQl7JH5u8KI,30,"male speech, man speaking",train EQlJQYneXWQ,109,tap dancing,train EQljP2jWJYQ,493,beat boxing,train EQmPGxcKU80,25,chicken clucking,train EQomhuMkoJs,418,playing shofar,train EQowNjI_jvI,30,"male speech, man speaking",train EQrpQGyLR1Y,2,wind chime,test EQuUepsdemc,183,playing volleyball,train EQuaioBhvMU,3,lions roaring,train EQuaioBhvMU,25,lions roaring,train EQxLc4oDpgA,150,stream burbling,train EQxb-FJFXds,210,"female speech, woman speaking",train EQzQ6VUDg_c,110,playing banjo,train EQzSzKAPvCA,30,goat bleating,test EQzkiFF2tEg,37,baby babbling,train EQzkiFF2tEg,52,baby babbling,train ER-GV5_15uI,30,skateboarding,train ER1c1h89GjA,30,horse neighing,test ER1fTL9-pQw,30,people finger snapping,test ER1nrv5LcsI,64,car engine knocking,train ER1nrv5LcsI,101,car engine knocking,train ER2x_QMrxeU,860,playing flute,test ER3oiyrocu4,70,people sobbing,train ER3udhm3ORs,30,"playing violin, fiddle",train ER49MNmOo28,30,turkey gobbling,train ER4H2iXe0aY,98,slot machine,train ER4JlOeKCpo,70,driving buses,train ER5JQwhdmRY,0,bouncing on trampoline,train ER8IwnCAGRY,30,playing flute,train ER8sTBWviZM,80,playing cello,train ERDFCb11y5g,75,singing choir,train ERDFCb11y5g,123,singing choir,train ERFb32Lz9tg,30,"male speech, man speaking",train ERGfpSkxEv0,160,basketball bounce,train ERH43oqF9cQ,55,hammering nails,train ERH43oqF9cQ,66,hammering nails,train ERHZYuFN1yM,188,playing mandolin,train ERHZYuFN1yM,204,playing mandolin,train ERLJQmqc1Wo,410,playing trumpet,train ERMA1NLFh2k,150,snake rattling,train ERMA1NLFh2k,274,snake rattling,train EROTUjI5wN8,70,female singing,train EROVgv7HE18,159,yodelling,train ERSGymAqVdk,1,playing congas,train ERSGymAqVdk,31,playing congas,train ERSmiGXdHWY,14,cap gun shooting,train ERSvGqYUrlM,9,pig oinking,train ERTa0cp5w5M,30,playing flute,train ERTvDybU-pc,30,"male speech, man speaking",train ERU0drXzYRM,30,"male speech, man speaking",train ERUgkFvw65E,28,skiing,train ERVOYGFtJQI,4,playing mandolin,train ERVOYGFtJQI,42,playing mandolin,train ERVngmuVN3Q,450,skiing,train ERYUB760gi0,193,playing erhu,train ERZAEDpbHVo,30,"female speech, woman speaking",train ERaXFb5yucw,10,splashing water,train ERbL-zEC5lA,4,mynah bird singing,train ERbL-zEC5lA,16,mynah bird singing,train ERbvR-fqZPo,491,skiing,train ERejBg5LMVw,11,arc welding,train ERfCd5axxy0,490,ocean burbling,train ERhTtHyXnKo,40,"bird chirping, tweeting",train ERjtCDLt4sQ,20,playing cello,train ERkeSKwKfYE,18,singing bowl,train ERktJImUbI0,30,"male speech, man speaking",train ERm2BHHpaMk,91,goose honking,train ERm2BHHpaMk,101,goose honking,train ERnnXHI7_UE,30,orchestra,train ERpkJpNX7zY,30,"female speech, woman speaking",train ERra12KuEvw,153,machine gun shooting,train ERsTmg4EzRg,279,yodelling,train ERsTmg4EzRg,503,yodelling,train ERsozcHtl44,30,"pigeon, dove cooing",train ERt1uZBbXfE,30,"pigeon, dove cooing",train ERthdENWAfo,10,duck quacking,train ERxSZJ2rYXY,52,frog croaking,train ERxSZJ2rYXY,137,frog croaking,train ERxlveTpCnw,137,sharpen knife,test ERxpa14WnSo,55,machine gun shooting,train ERxpa14WnSo,166,machine gun shooting,train ES3QpDN2AyY,21,playing snare drum,train ES3QpDN2AyY,72,playing snare drum,train ES6piySS22g,30,horse clip-clop,train ES6uOsjPUoA,130,raining,train ESC9tSqHoX8,20,printer printing,train ESFx2o6vBEw,50,playing harp,train ESG_Js02R9o,16,cutting hair with electric trimmers,train ESHbujZ0W-s,3,volcano explosion,train ESHbujZ0W-s,18,volcano explosion,train ESI5IS8_lRA,13,singing bowl,train ESKallQizu8,19,"playing marimba, xylophone",test ESLVjO1LMGQ,70,lions growling,train ESM3-dvF4KU,50,"cattle, bovinae cowbell",train ESMXYy0WoVc,27,cat meowing,train ESOu5e09YMU,30,"female speech, woman speaking",train ESPEIAXP6qQ,49,arc welding,train ESQKMvVQxOM,10,"child speech, kid speaking",train ESRG74Q_r6k,49,basketball bounce,train ESRiXGhb-Ww,69,dog howling,train ESRiXGhb-Ww,95,dog howling,train ESTA2rs6M80,20,helicopter,train ESUfWZMOdhA,30,mouse pattering,train ESVLbzlvySk,87,playing bass guitar,train ESXgEitdYaQ,30,ambulance siren,train ES_52GyiJRw,100,playing drum kit,train ESaJ7Qcpoqw,30,goose honking,train EScf6Anv-8E,30,dog barking,test ESemz3nDJ9s,30,female singing,train ESjJnbgktbQ,22,basketball bounce,train ESjpq2_MP4Y,330,typing on computer keyboard,train ESkTYqDqM1o,0,lions growling,train ESlGCaUX9WY,30,"pigeon, dove cooing",train ESlYQyNmlc8,30,"male speech, man speaking",train ESnr0SFXGnk,111,yodelling,train ESqbpJKEVQg,230,chainsawing trees,train ESrN-rZ19SI,289,frog croaking,train ESrN-rZ19SI,372,frog croaking,train ESrx_YETREM,16,typing on typewriter,train ESrx_YETREM,31,typing on typewriter,train ESsUj8ehKBk,40,stream burbling,train ESwZz_8F6gY,33,playing timpani,train ESwZz_8F6gY,62,playing timpani,train ESzi6uKbBNE,32,children shouting,train ESzi6uKbBNE,154,children shouting,train ET-FTargOKc,100,missile launch,train ET6nfwzi4gg,260,fireworks banging,train ET7ZBJ0yBuQ,48,eating with cutlery,train ET9aU1AGm0I,234,bowling impact,train ET9kOjyP_tk,30,"male speech, man speaking",train ETDUCrX7DL4,190,"bird chirping, tweeting",train ETJOLbXNiwA,7,airplane,test ETMBVRa7YIs,106,hammering nails,train ETMBVRa7YIs,126,hammering nails,train ETOUmGbeIEk,173,scuba diving,train ETOUmGbeIEk,315,scuba diving,train ETOorNNFxFw,3,people sniggering,train ETQVt8auIsk,30,"female speech, woman speaking",train ETRMThbrvJ0,30,"male speech, man speaking",train ETRouhCOVd8,20,fireworks banging,train ETSs8PC2ce8,107,playing trombone,train ETU02ii0RoI,30,"male speech, man speaking",train ETU0q9hkX0M,30,"playing violin, fiddle",train ETUPrxEC8I4,7,dog growling,train ETUPrxEC8I4,38,dog growling,train ETW1gA1L6uw,40,people hiccup,train ETYCjtwiTSM,28,cat caterwauling,train ETb9EIQOMAA,30,"female speech, woman speaking",train ETbXNJxQrQU,30,train horning,test ETcFOSD7zaA,79,tapping guitar,train ETcOGmdUTS0,99,children shouting,train ETcwLdOldMg,0,blowtorch igniting,train ETcwLdOldMg,24,blowtorch igniting,train ETdXETTxsyk,0,"cattle, 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Eq2WpGjcpYo,5,car engine knocking,train Eq6UPUhm7mU,210,playing hockey,train Eq6UPUhm7mU,273,playing hockey,train EqACP6NqfLA,141,playing table tennis,train EqAs8roxLWE,727,missile launch,train EqAtAK-8hM8,30,machine gun shooting,train EqBdyk1K2m8,40,chicken clucking,train EqEjeB-bybo,33,typing on typewriter,train EqEjeB-bybo,46,typing on typewriter,train EqFttu-PgP0,30,playing clarinet,test EqHZ1KHoZbo,399,police radio chatter,train EqHZ1KHoZbo,489,police radio chatter,train EqIdWYi5Odo,517,"child speech, kid speaking",train EqJiVBHZfXE,97,basketball bounce,train EqJiVBHZfXE,255,basketball bounce,train EqJvx65U5KE,0,playing guiro,train EqJvx65U5KE,21,playing guiro,train EqM7FGmFkOY,2,hedge trimmer running,train EqO2tReGQ_0,0,cap gun shooting,train EqPtQBbZIvo,144,playing bassoon,train EqRLucGfvYA,190,playing trombone,train EqRc7MTIAQk,80,typing on computer keyboard,train EqSS8aAOVtE,106,beat boxing,train EqW0-KBhWEM,367,electric grinder grinding,train EqWyIpHsP-U,210,playing drum 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FFFG5d5EESE,14,wind noise,train FFHS7_Wmbyc,6,penguins braying,train FFJHFj3Jpoc,20,playing saxophone,train FFJJ8Y2Xs1I,754,airplane,train FFOYTJ9lT8I,6,train wheels squealing,train FFObKY673R0,12,dog bow-wow,train FFQPxQAoulI,56,playing volleyball,train FFQahZpC1Vo,51,beat boxing,train FFSWmryaZ60,30,"engine accelerating, revving, vroom",test FFSe-f-rotY,367,roller coaster running,train FFSe-f-rotY,663,roller coaster running,train FFUSdzyhgZo,290,playing timpani,test FFVOPAvBdfU,14,"vehicle horn, car horn, honking",train FFVoNfpWNUo,13,dog bow-wow,train FFVqYj5OXLQ,50,car passing by,train FFYIcy6QY00,62,skateboarding,train FFYIcy6QY00,228,skateboarding,train FF_Cg4zjivs,69,ambulance siren,train FF_V67Kl9Og,39,electric grinder grinding,train FF_V67Kl9Og,181,electric grinder grinding,train FFb-xUphILQ,0,elk bugling,train FFbHL55f9DM,17,baby laughter,train FFbHL55f9DM,83,baby laughter,train FFbek4AyAG8,60,"race car, auto racing",train FFcICFIuQ9g,70,"child speech, kid speaking",train FFf9f2jEiAo,0,playing synthesizer,train FFfnl-GAny8,347,playing badminton,train FFhP3QnSgr4,39,"donkey, ass braying",train FFmaYNNL1-c,33,playing volleyball,train FFnnORgl-yU,124,fire truck siren,train FFnnORgl-yU,137,fire truck siren,train FFpzSZfPhP8,30,goose honking,train FFqXcpeaAk4,4,people sneezing,train FFqXcpeaAk4,17,people sneezing,train FFsQyJjVcPQ,30,"playing marimba, xylophone",train FFtbyvZ4tl8,17,yodelling,train FFtbyvZ4tl8,56,yodelling,train FFve7h-Ccts,49,cricket chirping,train FFvraS-rx0U,11,forging swords,train FFyNhc547GA,13,arc welding,train FFzGypgcnjI,20,slot machine,train FFzKd2m_PEU,0,"motorboat, speedboat acceleration",train FG0-y-b1Jb0,30,playing accordion,train FG1z3tJg084,20,stream burbling,train FG3AsChgzas,22,train horning,train FG9lLFEg__Y,286,ice cracking,test FG9vTn5F3wg,30,police car (siren),train FG9y0fPdhTs,73,yodelling,train FGCKPPryTX4,30,horse clip-clop,train FGCfTn--lCU,7,people sneezing,train FGCfTn--lCU,34,people sneezing,train FGCzEhD_Eok,11,playing bassoon,train FGEcmA0EFpI,0,whale calling,test FGEvKWujnOk,47,dog howling,train FGJQ3KC-Geo,110,chainsawing trees,train FGMkqfhlUd4,30,"railroad car, train wagon",train FGNu14tRwO4,30,wind noise,train FGPWq4t43p4,134,slot machine,train FGPeEBmDvkU,20,waterfall burbling,train FGPx2E089VU,19,frog croaking,test FGQ7BwsixYg,340,playing cello,train FGRrlxh8-vY,58,people marching,train FGRrlxh8-vY,116,people marching,train FGS5TllmBDQ,23,child singing,train FGWcwpr_SeM,98,fire truck siren,train FGWcwpr_SeM,133,fire truck siren,train FGXVaZO9-Dg,182,slot machine,train FGXZK7piMf4,30,car passing by,train FGXe8T2BrHA,140,underwater bubbling,train FGXfdOXOQZk,480,people clapping,train FGePTAdRuQE,300,playing hammond organ,train FGigv85KX_8,0,pheasant crowing,train FGlymPOoYhs,18,mouse clicking,train FGlymPOoYhs,152,mouse clicking,train FGoXt7LIK3U,10,police car (siren),train FGoxQnQ-oe0,62,people marching,train FGpPnT8p_bs,11,horse clip-clop,train FGs-H19DZbQ,16,sliding door,train FGsrHUgkwbI,28,tractor digging,train FGyuBIfX_II,85,playing tabla,train FGyuBIfX_II,108,playing tabla,train FGzLl301eEk,121,lighting firecrackers,train FGzLl301eEk,133,lighting firecrackers,train FGz_-95vo0c,118,people nose blowing,test FH0MZoQHSro,30,driving motorcycle,test FH1cbyFO5Z0,96,people nose blowing,train FH5wnNFAUeo,23,car engine knocking,train FH7JbGtgudE,30,"pigeon, dove cooing",train FH7KEa8jSSw,30,playing trombone,train FH7qqzgCWW4,40,ocean burbling,train FHBRxDWHHfM,110,using sewing machines,train FHBpISMSnhg,52,people marching,train FHBpISMSnhg,81,people marching,train FHChU52nch0,10,bird squawking,test FHCn-NCJdnM,16,driving buses,train FHD4uibbUw0,83,arc welding,train FHDFt90vW2U,30,people running,train FHGfA16-nvs,132,missile launch,train FHGfA16-nvs,147,missile launch,train FHHItLYeb40,9,bowling impact,train FHHJeASlCGA,98,hail,train FHHL1mhlkcs,29,police radio chatter,train FHJdKw8D0eg,299,alarm clock ringing,train FHK2RotEKGw,170,people clapping,train FHLRsleW_hw,50,people marching,train FHMSC_zdouc,0,baby laughter,train FHQwP8G9oC0,30,wind rustling leaves,train FHSIW4GSvjI,529,people humming,train FHUMzvoqnwo,30,"pigeon, dove cooing",train FHUaKxRam9s,57,cat purring,train FHUaKxRam9s,117,cat purring,train FHWFCOW4sIM,20,people burping,train FHWpzsHQMHg,100,"electric shaver, electric razor shaving",train FH_QX0UcSrg,140,playing acoustic guitar,train FHa9Tnk9zno,86,playing steelpan,train FHa9Tnk9zno,96,playing steelpan,train FHaWu2rcP94,80,"bee, wasp, etc. buzzing",test FHbaWfXp_AE,95,mouse clicking,train FHcO1-1TOvU,0,ambulance siren,train FHcsjO-lVbw,60,people whistling,test FHdOYVJomSc,299,vacuum cleaner cleaning floors,train FHeYqlO0hVU,100,"playing marimba, xylophone",train FHgtLE-TzQ4,30,singing bowl,test FHhZzDbLmw0,16,snake hissing,test FHlR_Rcj5Mg,110,ocean burbling,train FHlRlupe2JY,30,wind noise,train FHlly60mYqE,242,bouncing on trampoline,train FHnzqKaX93s,60,toilet flushing,train FHoXCE3kKI8,32,volcano explosion,train FHohYlcdQkc,306,playing bass guitar,train FHr8zO7FIdg,9,stream burbling,train FHrLulSgRpI,39,scuba diving,test FHs7zOf2V4Y,79,hammering nails,train FHuAVY2LuKA,139,dinosaurs bellowing,train FHuxuM-iRo4,160,eating with cutlery,test FHxZat9m5qQ,0,playing bass guitar,train FHz8YQy4q5A,27,tractor digging,train FHz9Jw8usSE,283,dinosaurs bellowing,train FHzLlPxMziY,70,wind rustling leaves,train FI--PhlnZjg,24,missile launch,train FI--PhlnZjg,50,missile launch,train FI-mwoODWC0,6,"electric shaver, electric razor shaving",train FI1UQPVaF9Y,200,cap gun shooting,train FI1hEB4_TrM,0,ambulance siren,train FI5kFWqwYgc,30,children shouting,train FI6SwkcxekM,540,opening or closing drawers,test FI6qxYW4WMs,15,volcano explosion,train FI8HGcMk004,72,playing vibraphone,train FI8HGcMk004,194,playing vibraphone,train FI8pI6jSoEk,94,fire crackling,train FI8pI6jSoEk,265,fire crackling,train FIAVnu8cBxM,76,playing hammond organ,train FIEy-SwDFoI,30,playing piano,train FIHge8DZnu0,10,bird wings flapping,train FIJTyjxYYVs,30,"motorboat, speedboat acceleration",train FIKZ47cKwck,3,children shouting,train FIKZ47cKwck,59,children shouting,train FILQ86izycw,0,fox barking,train FINmeftH3mg,255,playing badminton,train FIO-QGbT1-g,0,skateboarding,train FIOyB6sjTms,0,dog howling,train FIOyB6sjTms,17,dog howling,train FIPPjbUbVa4,51,"playing steel guitar, slide guitar",train FIPPjbUbVa4,92,"playing steel guitar, slide guitar",train FIPu0jd8I28,30,people screaming,train FIPvchztENM,30,playing accordion,train FIRzhfnzngo,2,smoke detector beeping,train FIS8W5nBpao,40,driving buses,train FIV8PQt6ZOc,20,"bird chirping, tweeting",train FIX38sEagtE,42,fire crackling,train FIXv2xPat1Y,4,slot machine,train FI_XlBfPmEY,70,dog whimpering,train FI_XlBfPmEY,118,dog whimpering,train FIaEnn89hrQ,11,police radio chatter,train FIaEnn89hrQ,69,police radio chatter,train FIdKxNdAIMc,6,people booing,train FIgNdwihlG8,30,playing cello,train FIktp5ggvX4,10,dog barking,test FIm20O_OSaI,978,"ice cream truck, ice cream van",train FImoR2zbanw,10,playing acoustic guitar,train FIpCyWCy9Qc,30,"playing violin, fiddle",train FIsc4nmZv34,30,people sobbing,train FIxfnbLFcdg,178,singing bowl,train FIxfnbLFcdg,199,singing bowl,train FIzw4A5LawI,30,playing flute,train FIzwGbdwzAA,30,"rowboat, canoe, kayak rowing",train FJ5oGRYlgnQ,70,"pigeon, dove cooing",train FJ6cTKn5MUw,10,waterfall burbling,test FJH2eiElMYo,276,cat purring,train FJH2eiElMYo,374,cat purring,train FJHzpQbSM-w,283,dinosaurs bellowing,train FJIwqY2zFwk,10,"subway, metro, underground",train FJJ5f8_pf8I,30,chicken clucking,train FJKWcPcRf_U,1,beat boxing,train FJMdkycIvRY,372,vacuum cleaner cleaning floors,train FJMdkycIvRY,387,vacuum cleaner cleaning floors,train FJNNtgukxUs,53,opening or closing car doors,test FJNNtgukxUs,122,opening or closing car doors,test FJO6LeTuaII,30,playing drum kit,train FJOqBrW13bk,38,people slurping,train FJQtNhFIKFY,39,tapping guitar,train FJQtNhFIKFY,53,tapping guitar,train FJTi6S-qrSc,17,playing theremin,train FJU_Z8ITaIU,74,arc welding,train FJVXrSRD5ko,2,ambulance siren,train FJVZvPhciFk,20,ambulance siren,train FJX5Yxk7a_g,30,wind noise,train FJY2zm3JD24,490,parrot talking,train FJYWS_q96Vg,30,"rowboat, canoe, kayak rowing",train FJ_DJc7-Sms,30,playing trumpet,train FJbaw0iXKuo,31,fox barking,train FJcq-6ED8EM,40,dog barking,train FJd8u-f4L8U,354,airplane,train FJeBCrhavT4,80,singing bowl,train FJeQBOfG7p8,60,chainsawing trees,train FJftvSQPOAo,30,playing harpsichord,train FJjeSoPQjeU,30,playing drum kit,train FJkEg6VaTd0,7,mouse clicking,train FJkEg6VaTd0,35,mouse clicking,train FJksOZP9SzU,23,playing congas,train FJksOZP9SzU,40,playing congas,train FJm1mFcfvBk,75,fire truck siren,train FJmnucB-Wik,30,"bird chirping, tweeting",test FJpUgwFhVGM,250,playing cello,train FJr7uvG71i8,22,fire crackling,train FJr7uvG71i8,113,fire crackling,train FJrkwKQAClI,71,mouse clicking,train FJtVcf6sTEU,167,air horn,train FJtVcf6sTEU,287,air horn,train FJzKgF-nR0E,7,baby laughter,train FK1DCWrW02s,30,people whistling,test FK38PMVIGuw,2,car engine knocking,train FK4MiC9uvWM,30,"rowboat, canoe, kayak rowing",test FK8FodQIRyc,40,cattle mooing,train FKAGLsgllJE,319,golf driving,train FKBgB2AYbDQ,7,wind chime,train FKDD5t8FIY0,16,penguins braying,train FKDD5t8FIY0,33,penguins braying,train FKElIiHW0Ws,1,chipmunk chirping,test FKFKZf4tZWA,490,mouse clicking,train FKGmkI_Vfo8,12,alarm clock ringing,test FKGpGPgtwe0,0,people hiccup,train FKLiINxnzK0,4,dinosaurs bellowing,test FKO9SVVk2jk,75,metronome,test FKOOHwPkeeY,280,playing hammond organ,train FKObuu8N3NQ,30,"rowboat, canoe, kayak rowing",train FKQIdqjY9nI,200,playing bagpipes,test FKUJHprym2A,9,people belly laughing,train FKWArdlknOk,220,eating with cutlery,train FKX5QaJ9xoU,130,"playing violin, fiddle",train FKXV4EwZ9dY,20,sea waves,train FKXV4EwZ9dY,86,sea waves,train FKbldjY5sJw,20,helicopter,test FKc0bDJeOJA,30,"motorboat, speedboat acceleration",train FKcBnx0LCCs,60,eating with cutlery,train FKcQI2j3GUE,20,rapping,train FKcQI2j3GUE,121,rapping,train FKdgnfe990M,577,people eating noodle,train FKfTVGHGEys,370,stream burbling,train FKi_TyITeSM,30,playing hammond organ,train FKjjWCqGQhs,39,machine gun shooting,train FKjjWCqGQhs,103,machine gun shooting,train FKl2Wkymh4c,170,people crowd,train FKlDt5hkltY,80,beat boxing,train FKnubh4xKU0,460,playing drum kit,train FKtYWCWWZEo,50,playing trombone,train FKu2kkbTzWI,30,cattle mooing,train FKu8iGqIxhY,140,people whispering,train FKxDVNKzlA8,140,playing snare drum,train FL-xuApjbGc,49,people shuffling,train FL29Xezp3jk,10,playing accordion,train FL4fh2dz3oM,96,reversing beeps,train FL4fh2dz3oM,127,reversing beeps,train FL59fF9nnTw,216,roller coaster running,train FL59fF9nnTw,299,roller coaster running,train FL5ETr8pdT0,20,door slamming,train FL6geOEPFco,30,playing harp,train FL8HO0lsdX8,30,"pigeon, dove cooing",train FL8RHi87Ud0,2,playing glockenspiel,train FL8RHi87Ud0,46,playing glockenspiel,train FL9890DvOBQ,41,dog whimpering,train FLDAjOn6Yak,8,civil defense siren,train FLDDsWfIq28,15,francolin calling,train FLDDsWfIq28,25,francolin calling,train FLEBlLMOW64,30,playing piano,train FLGL5t5blWQ,421,skiing,train FLGNNxsyuVw,109,sharpen knife,train FLJTIUfOe-c,30,playing flute,train FLLOoVLYIUU,17,people slapping,test FLLpW_Yvj2k,21,frog croaking,train FLLpW_Yvj2k,38,frog croaking,train FLMVB0B1_Ts,17,playing timpani,train FLMrGbAJoxU,0,foghorn,train FLO2c3nrKXI,486,turkey gobbling,train FLOwOPYypPo,0,horse clip-clop,train FLQyPz3vRVM,2,people booing,train FLRjkuYIbjw,325,airplane,train FLU4hbBTFVU,30,"playing violin, fiddle",train FLUtaFs5gsc,80,skidding,train FLXBsUitGoE,30,pig oinking,train FLYjmRy39_g,10,lawn mowing,train FLZmXRGElWs,0,people belly laughing,train FLb3rJLGH9o,30,crow cawing,test FLd9sgIr7jI,30,"rowboat, canoe, kayak rowing",train FLf9RwgPGuM,5,alarm clock ringing,train FLfRPHFr7mY,11,footsteps on snow,test FLhuFKCiX0A,480,"female speech, woman speaking",train FLiB2OzvuSQ,25,baby laughter,train FLiB2OzvuSQ,41,baby laughter,train FLjHIfRfrBM,70,cricket chirping,train FLjHIfRfrBM,95,cricket chirping,train FLkNs2KvURw,711,scuba diving,train FLkNs2KvURw,730,scuba diving,train FLnxZKHc0oM,71,people burping,train FLnxZKHc0oM,92,people burping,train FLqRmIJmI_I,74,people booing,train FLuJ7cahR1E,66,"vehicle horn, car horn, honking",train FLuiWytkGd4,29,snake hissing,test FLwualktyac,133,skiing,train FLyJYCs1xVk,1,people booing,train FLyJYCs1xVk,12,people booing,train FLzR_bQZRlw,10,skateboarding,train FM-9uXZjsZU,40,ambulance siren,train FM0Gj7OmyIc,42,people whispering,train FM0Gj7OmyIc,463,people whispering,train FM0hZB9yVbw,169,police radio chatter,train FM0hZB9yVbw,179,police radio chatter,train FM13Khnaces,30,playing banjo,train FM1hhKgg434,21,toilet flushing,train FM1rJBmPXqs,10,people sobbing,train FM4UOM5OA6I,30,sheep bleating,train FM4Wvd__lZA,30,car engine knocking,train FM5DeAtuV4k,436,striking pool,train FM5DeAtuV4k,479,striking pool,train FM5a8kD-fXY,208,frog croaking,train FM63-dOYX74,140,playing drum kit,train FM6fd7gGaJg,151,car engine idling,train FM7IZ_JjL2s,174,striking bowling,train FM7IZ_JjL2s,190,striking bowling,train FM8qkCKCSTo,32,playing squash,train FMBl6BUTTIY,69,playing timbales,train FMIdcSrlwgo,30,playing banjo,train FMJSz5_b9E0,35,people burping,train FMJnKZwTMDI,11,pheasant crowing,train FMLhPfYb-oY,30,lions roaring,test FMN4CgE8VUI,197,arc welding,train FMPMfuLM800,28,volcano explosion,train FMQRDj3O3J8,6,printer printing,train FMQRDj3O3J8,18,printer printing,train FMRs5hFPffA,65,lions roaring,train FMRvi_i_JlM,63,running electric fan,train FMRvi_i_JlM,113,running electric fan,train FMSusnIyFEw,36,electric grinder grinding,train FMU7-_2FFXk,15,skidding,train FMW_NPEhdUs,507,playing badminton,train FMZmVfcQ5MY,84,playing didgeridoo,train FMd0-2PuEKQ,47,playing bass drum,train FMd0-2PuEKQ,351,playing bass drum,train FMdlAzav_Ow,10,using sewing machines,train FMei7iWJm9E,32,planing timber,train FMei7iWJm9E,68,planing timber,train FMfTSJGsk5M,62,people gargling,test FMfZWfpusVk,130,people crowd,test FMgPAqd8p_Y,36,playing glockenspiel,train FMgPAqd8p_Y,46,playing glockenspiel,train FMkBELT-d7I,140,playing flute,train FMm8n_9uuMI,10,car engine knocking,train FMnFYLZy1Jk,550,ocean burbling,train FMpafcGqufg,160,orchestra,train FMpkclQ4hGI,400,helicopter,train FMqROYx-1ys,222,eletric blender running,train FMqROYx-1ys,269,eletric blender running,train FMqqZAej7w0,50,playing electronic organ,train FMr1WHtXJbc,30,tapping guitar,test FMs3LTtgwY8,35,playing djembe,train FMuNyhUSwtE,2,tractor digging,train FMxsRuoDPyE,30,playing banjo,train FMyn1ItURys,103,playing mandolin,train FMyn1ItURys,202,playing mandolin,train FMz9_nwYG8g,14,missile launch,train FMzsVBOOMvg,0,people sobbing,train FN-ZVe9PTGk,28,playing glockenspiel,train FN-ZVe9PTGk,50,playing glockenspiel,train FN0hq8H1b8o,100,"bee, wasp, etc. buzzing",train FN1E2ak7U94,73,cricket chirping,train FN1E2ak7U94,88,cricket chirping,train FN1rC23Rrlg,18,fire truck siren,train FN37xYdMOQc,0,"railroad car, train wagon",train FN4jPxZxa04,90,playing piano,train FN4rvrPvHRM,30,car engine knocking,train FN5hQz_UsHc,218,skiing,train FN6pdfdz1kk,45,lions growling,train FN6pdfdz1kk,55,lions growling,train FN7OcEhMA7I,132,playing badminton,test FN7iy28h4H4,50,opening or closing drawers,train FNADrZNlI-c,273,skiing,train FNB7YwtFqVc,30,horse clip-clop,train FNCJFdB8gtM,29,chicken clucking,train FNCJFdB8gtM,39,chicken clucking,train FNCaRK2k4GU,70,"race car, auto racing",train FNFgjJ00J9w,30,chicken crowing,test FNFxvieawH8,12,woodpecker pecking tree,train FNHGzY44MHE,38,playing bagpipes,train FNHGzY44MHE,77,playing bagpipes,train FNI2U4Nsi-U,623,opening or closing car electric windows,train FNI2U4Nsi-U,638,opening or closing car electric windows,train FNIu_2GyCAo,55,running electric fan,train FNIu_2GyCAo,73,running electric fan,train FNJMGR_9A1w,8,playing bassoon,train FNK83RgV1Y8,30,"motorboat, speedboat acceleration",train FNKPIo6TBKY,60,playing cello,train FNLsG0fb8Z8,318,playing volleyball,train FNMGJPkE5X8,10,playing trombone,train FNQYEi91RXU,130,lawn mowing,train FNR_4MzJdd0,27,hammering nails,train FNTRT6vbrVM,173,wind noise,train FNTRT6vbrVM,219,wind noise,train FNUGQ2XeOsc,144,playing erhu,train FNUGQ2XeOsc,215,playing erhu,train FNWNogI-8HE,43,lions growling,train FNWNogI-8HE,89,lions growling,train FNYFv72QFpg,63,people battle cry,train FNYFv72QFpg,317,people battle cry,train FNZQJNDQ9gQ,18,people eating noodle,train FNZQJNDQ9gQ,60,people eating noodle,train FNZZ776f-0I,107,playing bass drum,train FN_3DH1JUUM,115,cattle mooing,train FN_g7XK4lMU,74,lip smacking,train FN_g7XK4lMU,153,lip smacking,train FNb5C0D81uw,2,car engine knocking,train FNb5C0D81uw,36,car engine knocking,train FNdhe0cZq-4,55,train whistling,train FNeSOj6cs4M,20,fireworks banging,train FNiKJZkdDTA,164,arc welding,train FNk4h39VPCM,5,tornado roaring,train FNk4h39VPCM,36,tornado roaring,train FNloM9CNQQg,29,"motorboat, speedboat acceleration",train FNmOqib1QmM,80,ocean burbling,train FNosnxnkpgk,20,people farting,train FNq6QLj7UQo,0,playing bass guitar,train FNqaFtHizxc,87,beat boxing,train FNsrRF3qPBE,40,people farting,train FNtePcDQL-8,264,playing table tennis,train FNv-ToebU8U,81,people screaming,train FNvn4-qGrjc,6,bowling impact,train FNvx6TfrLz4,2,vacuum cleaner cleaning floors,train FNvx6TfrLz4,89,vacuum cleaner cleaning floors,train FNwEPkLhRRI,236,singing bowl,train FO-_aqhmNN4,20,"male speech, man speaking",train FO02t_Gswww,0,"engine accelerating, revving, vroom",train FO0nFGUAIZQ,8,horse clip-clop,train FO0u1akOwOM,0,playing badminton,test FO1CJtzzMRg,20,dog barking,train FO1KkUIG3Sw,530,people giggling,train FO1ho1R18P8,140,playing vibraphone,test FO1vBXcoNVA,16,dog bow-wow,train FO4xeN0ydVE,40,orchestra,train FO5LAnTjuGo,30,playing cymbal,train FO5wh_fJS44,20,car engine knocking,train FO7t3IjPt8U,160,playing cello,train FOA5xvjCaYE,30,driving buses,train FOAZUzwSgis,41,"fly, housefly buzzing",train FOAsVHwMDN4,107,people burping,train FOBJ6tMrx3M,295,reversing beeps,train FOBse-U8z9I,30,playing bagpipes,train FOCCi3YLA34,27,police radio chatter,train FOEBsn_WVYQ,30,"motorboat, speedboat acceleration",train FOE_E1qn65U,43,frog croaking,train FOE_E1qn65U,59,frog croaking,train FOFGOd7qrkQ,29,basketball bounce,train FOGwxDSEgcs,30,police car (siren),train FOI9HHH1-n8,50,playing trombone,train FOIc5yr0M2Y,90,mouse clicking,train FOIr0siM2Nc,506,bowling impact,train FOIr0siM2Nc,528,bowling impact,train FOIr3YDU9p8,486,airplane,train FOIr3YDU9p8,518,airplane,train FOJHLNb2jJM,80,playing erhu,train FOJgJN4K50Y,297,playing ukulele,train FOJgJN4K50Y,329,playing ukulele,train FOK98rljaDc,22,playing french horn,train FON0KSG0Dkg,160,sliding door,train FONKpzlXUoc,172,roller coaster running,test FONo5c5KY5w,138,yodelling,train FOQEgU0-fR8,10,door slamming,train FORooIR-LCY,23,parrot talking,train FOS2IZGOGoI,119,tractor digging,train FOTk7wukpBw,90,"male speech, man speaking",train FOUDk3LFmvg,6,dog bow-wow,train FOV6bHcfdEw,125,playing oboe,train FOV6bHcfdEw,278,playing oboe,train FOX_S8z84-w,30,playing flute,train FOY2xjkeUrc,390,stream burbling,train FOZPD9Bxl-I,151,alarm clock ringing,train FOcDASPMHuQ,19,wind noise,train FOfcuFTgP-U,30,using sewing machines,train FOi9Bpuke_o,205,mosquito buzzing,train FOid23PpHKQ,70,thunder,train FOnnca2cwcQ,312,owl hooting,train FOnnca2cwcQ,530,owl hooting,train FOouVxXD67k,1012,playing bassoon,train FOqVo-sJQ-E,30,lawn mowing,train FOseBorBQ_I,460,people belly laughing,test FOtTiYfkKng,123,electric grinder grinding,train FOtfGsjYmx0,204,playing badminton,train FOu4wP7ONW4,79,cheetah chirrup,train FOwv5kR1e-U,427,people slurping,train FP0bmYM069s,85,arc welding,train FP3HK3s3yBw,80,"engine accelerating, revving, vroom",train FPA2VDDE-Uk,420,people clapping,train FPBE68OiNQM,10,skidding,train FPBFx0-LJNY,89,playing tambourine,train FPDDyRF7eSg,30,playing clarinet,train FPDJwRtqJQs,41,playing snare drum,train FPDJwRtqJQs,68,playing snare drum,train FPDsx70WKPQ,130,chicken crowing,train FPEg4NAnRKA,30,"railroad car, train wagon",train FPGDW5FoJ-8,0,people whistling,train FPHS1EHC1Q0,100,playing banjo,train FPJzr0CYHmY,96,playing bassoon,train FPJzr0CYHmY,114,playing bassoon,train FPMs8dhb7To,130,playing bass guitar,train FPNP4MCDBYY,9,sheep bleating,train FPQ3ttYOU9o,30,police car (siren),train FPRJ9H5efN8,220,playing electronic organ,train FPRJ9H5efN8,268,playing electronic organ,train FPRsJLplb4M,170,"female speech, woman speaking",train FPTqmeVs7s0,138,playing zither,train FPTqmeVs7s0,290,playing zither,train FPU3rCOPT5A,9,police car (siren),train FP_rJYMKbSM,30,dog barking,train FPaT4KAPg3s,25,baby babbling,train FPaT4KAPg3s,66,baby babbling,train FPbBk4TFQVc,7,playing harp,train FPd7Kw2I2tE,10,skateboarding,train FPdbWs2pEWs,4,coyote howling,train FPdbWs2pEWs,29,coyote howling,train FPefk_0ine8,20,skateboarding,train FPgmRsiwN2U,30,"motorboat, 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FQEVb1bYY0g,72,chipmunk chirping,train FQGllj4BjbA,238,"ice cream truck, ice cream van",train FQGllj4BjbA,568,"ice cream truck, ice cream van",train FQHhMhW4sHw,40,beat boxing,test FQIZHO6l0IY,30,sailing,train FQOEFL6kP9w,40,"female speech, woman speaking",train FQPPueG9d5o,0,people sneezing,train FQRBUmbebmA,0,cat meowing,train FQSAHUw_fUc,0,police car (siren),train FQTthmVUZKM,17,chopping wood,train FQTthmVUZKM,36,chopping wood,train FQUn70b4dUQ,30,baby laughter,train FQV43mo_bCU,270,people clapping,train FQZOlzHKzPo,80,tractor digging,train FQZOlzHKzPo,94,tractor digging,train FQZmPzbXE8s,20,elephant trumpeting,train FQZmPzbXE8s,185,elephant trumpeting,train FQblJKqk-yE,6,"donkey, ass braying",train FQeBtb6K7aA,30,printer printing,train FQfsqcX3_BI,30,"motorboat, speedboat acceleration",train FQgdj99QsRQ,33,"electric shaver, electric razor shaving",train FQqwy9PPhX4,60,basketball bounce,train FQrAMqXBtGA,292,playing cornet,train FQreavA9fBM,48,duck quacking,train FQreavA9fBM,207,duck quacking,train FQu2j4el-6g,30,skateboarding,train FQuRQH5b9hY,30,people shuffling,train FQvOzn1FvFI,55,people eating noodle,train FQvOzn1FvFI,156,people eating noodle,train FQwA6ZD-mIE,30,playing acoustic guitar,train FQx8EY6tiS8,67,slot machine,train FQxz6v2Y9cA,41,playing bongo,train FQxz6v2Y9cA,92,playing bongo,train FR-3728aOfw,60,"playing marimba, xylophone",train FR-wbPTHHmM,90,people crowd,train FR0XWAgdcVY,0,baltimore oriole calling,train FR1NmlPnRsg,23,playing bugle,train FR1NmlPnRsg,54,playing bugle,train FR4y1RXYQNM,30,cat purring,train FR6dxI97RJc,220,people whispering,train FR8CL5WbWYA,40,people hiccup,train FR8QdwTIBFg,0,lawn mowing,train FR9qGkCa78M,140,eating with cutlery,test FRJMxnIJ11I,115,mosquito buzzing,train FRJMxnIJ11I,125,mosquito buzzing,train FRLV9WnpnuQ,510,people sniggering,train FRRpYaRQhCU,96,playing darts,train FRRpYaRQhCU,172,playing darts,train FRSQ8cm0Xbw,4,playing guiro,train FRSQ8cm0Xbw,26,playing guiro,train FRZW3Qh6K28,70,cattle mooing,train FRZW3Qh6K28,107,cattle mooing,train FRb9sSaF6EY,10,thunder,train FRdrLB6J4bM,133,eating with cutlery,train FRdrLB6J4bM,179,eating with cutlery,train FRfzf61i6Cs,50,skidding,train FRiSKdEk9uw,30,printer printing,train FRia9keFeXs,8,wind chime,train FRipKtQVr4w,206,playing volleyball,train FRmaoECmtkw,44,skiing,train FRoKR8h1_nE,12,baltimore oriole calling,train FRoMl49q5gc,160,people crowd,test FRr154O9PeI,60,people babbling,train FRu6azEgTvU,285,writing on blackboard with chalk,test FRwFfq3Tl1g,310,skateboarding,test FRxNI559-Xs,280,"railroad car, train wagon",train FRyT1KqVAH8,310,dog baying,train FRyT1KqVAH8,402,dog baying,train FRzHx9ibb9I,240,car engine starting,test FS3_UDNEbG0,312,playing gong,train FS3_UDNEbG0,327,playing gong,train FS6zjNL_Dd0,296,hail,train FS75JZ6aCEA,20,cat meowing,train FS7HdtEQz-Y,80,"railroad car, train wagon",train FS7kLyDBcXw,20,"bird chirping, tweeting",train FS8YXwJnXiw,30,sailing,train FS9QBLvmLz0,3,playing 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FUP-qrrwTFc,30,printer printing,train FUSGfjtfVCg,30,"playing violin, fiddle",train FUVXK29tUwQ,0,owl hooting,train FUXSq44CbHo,133,people burping,train FUaZ9DiCBaM,97,playing guiro,train FUaZ9DiCBaM,119,playing guiro,train FUdBBBCXORg,40,singing bowl,train FUh65Huom48,50,mouse clicking,train FUh65Huom48,119,mouse clicking,train FUhyWzVd71Y,30,helicopter,train FUiAOhrm63E,15,chicken crowing,train FUxzR6cdovU,30,playing harp,train FUyNP1-21Ic,30,"railroad car, train wagon",train FUySDnBMums,30,"playing violin, fiddle",train FV1KJw9IaIk,51,cap gun shooting,train FV20Hhv-H10,94,"subway, metro, underground",train FV20Hhv-H10,136,"subway, metro, underground",train FV4xykR3nVo,30,horse clip-clop,train FV6KSQuYkE0,0,playing bagpipes,train FV6_g_HDdiA,30,"subway, metro, underground",train FV6rKmY8I4o,30,police car (siren),train FV7J7_JdYqs,34,playing trombone,train FV7J7_JdYqs,45,playing trombone,train FV7oQAP-Z20,400,"railroad car, train wagon",train FV8MMevoDCA,0,driving motorcycle,train FV9g6thgJ08,122,train whistling,train FV9g6thgJ08,139,train whistling,train FVAUcrBpJec,40,orchestra,train FVI7M1T0D90,99,cap gun shooting,train FVJuVxvwYns,30,playing bass drum,train FVKCPEDAXA8,90,"engine accelerating, revving, vroom",train FVL372sQVG4,50,chainsawing trees,train FVLnxsthNf8,20,playing snare drum,train FVMe2sMLNPQ,30,typing on computer keyboard,train FVOHV7IjwLg,72,playing accordion,train FVOzZC_ePY4,182,mosquito buzzing,train FVOzZC_ePY4,199,mosquito buzzing,train FVU1tJvmzo0,21,snake hissing,train FVU1tJvmzo0,31,snake hissing,train FVVfs9cZLZM,393,ice cracking,train FVVfs9cZLZM,467,ice cracking,train FVXdvx8eBp8,38,writing on blackboard with chalk,test FVY4gFFJBFQ,0,fireworks banging,train FVZr5OXn_hg,60,playing accordion,train FV_vnbV-S1I,13,francolin calling,train FV_vnbV-S1I,37,francolin calling,train FVahCy5NGZ8,26,roller coaster running,train FVc9n56y8-g,60,"pigeon, dove cooing",train FVcz_xUPeeE,10,chopping wood,train FVcz_xUPeeE,35,chopping wood,train 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Fkmsju2mOEU,40,playing trumpet,train Fkp_KZz1wvw,30,playing accordion,train FksrdMc3lZU,70,car engine knocking,train FktsFcooIG8,939,"child speech, kid speaking",train FkxJIcs5BhQ,398,playing synthesizer,train Fl0yc47JSWM,25,chicken clucking,test Fl3T9_rOaNI,26,people booing,train Fl3XlOm4KYY,1,wind rustling leaves,train Fl95Zz5wssE,80,arc welding,test Fl9FZxewjEY,23,driving snowmobile,train FlASw7qL1kY,180,chainsawing trees,train FlClO2HslBI,140,playing harp,train FlD90YAkqLg,11,cat growling,train FlDfv2UJyRw,50,playing saxophone,train FlDmMlMIXxQ,30,duck quacking,train FlEPYihl6lM,90,playing volleyball,train FlG1zt1-pW8,153,tap dancing,train FlKh9ywfT5k,320,baby babbling,train FlKh9ywfT5k,396,baby babbling,train FlM0tg_Mm0U,226,playing didgeridoo,train FlM85DliPCo,38,cat growling,train FlM85DliPCo,125,cat growling,train FlOuJSfIDMY,43,playing bass drum,train FlQ-WKTY8oE,30,car passing by,train FlRrFM78vg0,28,parrot talking,train FlTxKFel_J0,0,"electric shaver, electric razor 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GE6eG121KaA,315,playing saxophone,train GE8kN-X0h5M,0,dog whimpering,train GE9HJJduEyM,37,playing tympani,train GE9HJJduEyM,55,playing tympani,train GEA2O3wMrro,2,duck quacking,train GEA2O3wMrro,14,duck quacking,train GEAZn65ZFlk,25,baby babbling,train GEAZn65ZFlk,39,baby babbling,train GEBDw8f5nM0,30,"motorboat, speedboat acceleration",train GEBMPrzW0NI,30,"male speech, man speaking",train GEBwIdRRhEg,0,driving motorcycle,train GEFv6l1XtQI,91,hammering nails,train GEL7JhHm1go,30,"pigeon, dove cooing",train GEMVGHoenXM,22,basketball bounce,train GEOvdsvnnIM,0,people screaming,train GEQBizds5T4,67,bowling impact,train GEQBizds5T4,95,bowling impact,train GESrnHr-SjU,21,owl hooting,test GEThMNr8pxk,90,people sobbing,train GEWK9pDM31g,64,writing on blackboard with chalk,train GEWK9pDM31g,91,writing on blackboard with chalk,train GEXBt_eRZXM,65,"fly, housefly buzzing",train GEZofK3iWv8,23,scuba diving,train GEZofK3iWv8,37,scuba diving,train GE_GdFha3xo,13,coyote howling,train 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GGVJ4517Ypo,1,woodpecker pecking tree,train GGVeTZUthhM,330,playing cymbal,train GGXkjwNZD0g,0,barn swallow calling,train GGZ0HfO87zE,290,orchestra,train GGZ3EPoirKQ,320,fireworks banging,train GG_-n-CdCP0,3,playing tuning fork,train GGaMTz7vAN4,19,baby babbling,train GGa_vj__TsA,30,turkey gobbling,train GGbmTFmeQq4,352,firing cannon,train GGf2EbHoL_w,0,machine gun shooting,train GGfQFDIVkOk,4,horse clip-clop,train GGfhnsS9Pk0,100,cattle mooing,train GGnnOevKVy4,39,cricket chirping,train GGnnOevKVy4,54,cricket chirping,train GGnxSnqjM0w,140,playing saxophone,train GGp62K6WF_k,229,typing on typewriter,train GGp62K6WF_k,244,typing on typewriter,train GGpS5Zychoc,53,lathe spinning,test GGpS5Zychoc,222,lathe spinning,test GGpaLV0l1ys,70,mouse clicking,test GGpsYonZ3XE,8,baby laughter,train GGpsYonZ3XE,30,baby laughter,train GGqC1H9BeTg,180,electric grinder grinding,test GGqLhtwd8Fo,358,playing erhu,train GGqy4BtaLgQ,94,volcano explosion,train GGr5RlDlG7U,90,chainsawing trees,train 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saxophone,train GJLPOtnAerI,0,"pigeon, dove cooing",train GJOprBTkhtc,180,playing harp,train GJQ4YbOAtKM,15,"electric shaver, electric razor shaving",train GJQ4YbOAtKM,30,"electric shaver, electric razor shaving",train GJSRLVKwD3c,10,"railroad car, train wagon",train GJSoIY45TAo,300,people whispering,train GJUSgtPnvf4,350,"railroad car, train wagon",train GJUpi-uz6J8,220,turkey gobbling,train GJZxsKMG4zQ,210,playing trombone,train GJbCOfvIE58,30,playing french horn,train GJchm5m3CaM,30,"playing violin, fiddle",train GJeAqIzSoQc,33,lighting firecrackers,train GJeQuhOjqqE,10,playing bagpipes,train GJgOtB76Bqg,24,train wheels squealing,train GJjaYkSjSo0,30,playing harp,train GJjl9LHOTjM,30,chicken clucking,train GJnue_2COrU,30,using sewing machines,train GJpOLvZfKyE,515,train whistling,train GJthiCjIO40,134,playing vibraphone,train GJthiCjIO40,155,playing vibraphone,train GJwLTcCZhzM,90,"playing marimba, xylophone",train GJxrTAevrcM,310,people eating crisps,train GK0U5FjrYss,99,playing 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HDShSIiXH6Q,10,car engine knocking,train HDShSIiXH6Q,35,car engine knocking,train HDTOCdXSE3E,270,people whispering,train HDUWiEzDGpk,50,playing flute,train HDWZu2roOgU,32,people giggling,test HDWdBUcT_lc,173,playing tambourine,train HDWdBUcT_lc,194,playing tambourine,train HD_7BsKYGyQ,25,wind rustling leaves,train HDbSr1CZgJE,0,sloshing water,train HDbxyN08niU,30,horse clip-clop,train HDcqKuQDQck,160,people sobbing,train HDeyzzDBgIk,10,driving buses,train HDghRVL0ZOI,526,arc welding,train HDlFE2ETelY,22,horse clip-clop,train HDo1lZix5OU,206,eletric blender running,train HDpjudS990k,19,singing bowl,train HDqMojvTFqY,20,skidding,train HDsgtH73Zmk,27,ambulance siren,train HDv3VAcHym4,57,playing french horn,train HDv3VAcHym4,69,playing french horn,train HDvPMEU33HM,89,people cheering,test HDyNMAnRKi0,375,beat boxing,train HDynce_yjno,145,"electric shaver, electric razor shaving",train HDzYSpSSnvE,10,chicken crowing,train HDzmxOIHaiM,30,horse clip-clop,train HE0uI4Xy4Xc,45,playing mandolin,train HE0uI4Xy4Xc,58,playing mandolin,train HE3N0LVSlBQ,280,playing mandolin,train HE3N0LVSlBQ,293,playing mandolin,train HE41fjWcPXw,52,canary calling,train HE41fjWcPXw,78,canary calling,train HEAG7FJN83Y,5,church bell ringing,train HEAG7FJN83Y,89,church bell ringing,train HEBbCfH6xME,3,telephone bell ringing,test HEC5CKnS1rM,2,singing bowl,train HECfM2scY0k,8,playing cornet,train HEClHl_yGmo,30,playing bass guitar,train HEEg6VYebvE,30,goose honking,train HEGKuYkLTJg,242,warbler chirping,train HEGKuYkLTJg,382,warbler chirping,train HEL09B00AJY,139,car passing by,train HEL09B00AJY,249,car passing by,train HEN6TOCTbp8,10,female singing,train HESc87zC1u8,50,driving buses,train HEUIZWyieAk,109,"playing steel guitar, slide guitar",train HEWWYQtbN_Y,63,playing erhu,train HEWWYQtbN_Y,236,playing erhu,train HE_RSGb4dpE,107,rapping,train HE_RSGb4dpE,120,rapping,train HEaUr6c0IfA,198,people eating noodle,train HEaijrw5v1g,110,people belly laughing,train HEavWDeq1kM,230,wind noise,train HEcSEZvAecY,70,playing acoustic guitar,train HEfowm2XpXY,46,"ice cream truck, ice cream van",train HEfowm2XpXY,63,"ice cream truck, ice cream van",train HEgEQ8Z3n0w,140,people eating,train HEgEQ8Z3n0w,301,people eating,train HEga6SUO8TU,20,playing didgeridoo,train HEkrRrTDxlc,40,playing bass guitar,train HEmuGKgTEq8,248,dinosaurs bellowing,train HEmuGKgTEq8,279,dinosaurs bellowing,train HEpnQWX-NqE,394,swimming,train HEpnQWX-NqE,1007,swimming,train HEtQWBRfMW0,148,wind chime,train HEtQWBRfMW0,605,wind chime,train HExmYu7eRe0,2,people giggling,train HExxXz2cWCE,70,playing bass guitar,train HF-b_4-VgOo,570,skidding,train HF8mUW0rUJ0,33,vacuum cleaner cleaning floors,train HF8mUW0rUJ0,94,vacuum cleaner cleaning floors,train HFB_Z1xRRzI,117,mynah bird singing,train HFB_Z1xRRzI,140,mynah bird singing,train HFBhWjKDbso,238,francolin calling,train HFBhWjKDbso,300,francolin calling,train HFDKfk56dQ8,161,people eating noodle,train HFF-uyR7bPM,30,train horning,train HFFHAj4MDfc,30,horse neighing,train HFFx-QXlT-4,30,orchestra,train HFIN3vwxeBU,110,playing ukulele,train HFJGkg7H8j4,1,goose honking,train HFLhyn5GtPs,0,frog croaking,train HFLhyn5GtPs,17,frog croaking,train HFLp66p1MWE,29,"pigeon, dove cooing",train HFN1L8HIS3E,105,playing french horn,train HFN1L8HIS3E,124,playing french horn,train HFPb4dJWqhw,1,playing djembe,train HFPb4dJWqhw,12,playing djembe,train HFQrHE2H8Ho,80,playing piano,test HFQsjUx6SbA,57,typing on typewriter,train HFTMqomoqL8,11,owl hooting,train HFTMqomoqL8,28,owl hooting,train HFTfE-IZKhw,762,people eating noodle,train HFU884o9Dw8,91,singing bowl,train HFVIKa0XcPg,30,church bell ringing,train HFW8GrQxsr0,10,"engine accelerating, revving, vroom",train HFWHkePFf_4,0,horse clip-clop,train HFZHOOhhtTM,30,ambulance siren,train HFdlbxUUwfk,230,"race car, auto racing",train HFebKem6z10,23,"cattle, bovinae cowbell",train HFg49xzk1pA,10,"cattle, bovinae cowbell",train HFg49xzk1pA,65,"cattle, bovinae cowbell",train HFh-RH4IQoM,9,dog howling,train HFhv6boVE50,23,snake hissing,train HFhv6boVE50,77,snake hissing,train HFi-z3dCVZE,0,playing trumpet,test HFiAyxKa2UI,291,baby babbling,train HFlaYsxovAc,94,owl hooting,train HFlzbHJBaH0,30,horse neighing,train HFnrfu_8tNU,150,driving motorcycle,train HFpSy8N15Uo,9,playing timbales,train HFpSy8N15Uo,50,playing timbales,train HFqNU7bPv8Y,368,playing french horn,train HFqNU7bPv8Y,406,playing french horn,train HFqj77fcTBc,150,dog bow-wow,train HFqvSm9x8SE,70,playing banjo,train HFrsBMXipDk,258,singing choir,train HFt4LJmimes,50,people hiccup,train HFuDwdCmmAw,429,people burping,train HFwY5Ggp1fY,120,yodelling,train HFwY5Ggp1fY,167,yodelling,train HFyfYZsuMD8,162,beat boxing,train HG2nZRIweUI,269,chopping food,train HG34Zw-1KTQ,20,"engine accelerating, revving, vroom",train HGBdJOttt9A,11,people babbling,train HGC2Gnq7hX4,0,air horn,train HGDGDWiCWCY,40,"bee, wasp, etc. buzzing",train HGE4FaX-Roc,9,cat hissing,test HGGI3eeRky4,117,playing didgeridoo,train HGH-qq7Hung,340,helicopter,train HGJgGPcyNAU,6,horse clip-clop,train HGNm9LpqlBY,99,car engine idling,train HGVVUyDLJaM,390,people clapping,train HGX7vxcMn_0,0,arc welding,train HGXasGbxtIM,30,"engine accelerating, revving, vroom",train HGXtWRckqhE,440,people crowd,train HGa7IM5VYxI,247,driving snowmobile,train HGcrOm1ozkc,30,playing bagpipes,train HGgU3LRS_PU,16,horse clip-clop,train HGlv3xtLnTs,24,people booing,test HGlv3xtLnTs,58,people booing,test HGnRESIiExw,40,"bird chirping, tweeting",train HGoEfc_tXts,214,people whispering,train HGoKSDynJu8,30,horse clip-clop,train HGpEAOwHBuI,4,"electric shaver, electric razor shaving",test HGqVT06pS70,147,train whistling,train HGqVT06pS70,260,train whistling,train HGs1Gzvh3iI,400,police radio chatter,train HGs1Gzvh3iI,415,police radio chatter,train HGsYBmzGzJw,507,dinosaurs bellowing,train HGsYBmzGzJw,518,dinosaurs bellowing,train HGtSaIsU7cc,289,tap dancing,train HGv7Lu8IuV4,30,playing accordion,train HGwkogFOlKU,30,"rowboat, canoe, kayak rowing",train HGxVMI_Snp8,210,"race 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HHp4h8wHDPA,30,playing trombone,train HHpUYZEDFlQ,140,chicken crowing,train HHpZd44tmqw,293,people eating noodle,train HHpc7Z6xA8k,153,playing mandolin,train HHpc7Z6xA8k,562,playing mandolin,train HHppMX_PmUM,70,playing banjo,train HHrMwGyKhPs,50,helicopter,train HHtQm-YDzxE,222,lighting firecrackers,train HI1FHmDXlGk,30,typing on computer keyboard,train HI1hd1sXk_w,30,"motorboat, speedboat acceleration",train HI4-yL1BrmY,2,police radio chatter,train HI4-yL1BrmY,65,police radio chatter,train HI615WGKOo4,7,chipmunk chirping,train HI615WGKOo4,51,chipmunk chirping,train HI7vbz6L_qo,263,lighting firecrackers,train HI7vbz6L_qo,388,lighting firecrackers,train HIDb-C5YRa4,10,people cheering,test HIFv8XUBB2o,323,machine gun shooting,train HIFv8XUBB2o,401,machine gun shooting,train HIMqfEFAq7Y,3,dog howling,train HIMqfEFAq7Y,84,dog howling,train HINQ5Td2jZc,90,people sniggering,test HIPheOplqJ8,340,basketball bounce,train HIPviUsXLYA,50,people hiccup,train HIQ63_WoKwg,30,playing trombone,test HIQq6nxHI_M,30,"motorboat, speedboat acceleration",train HISa-nj-XUU,0,black capped chickadee calling,train HIT2YWOALto,2,dinosaurs bellowing,test HIVOTzX2UCg,0,wind noise,train HI_A6HjULqU,229,playing bassoon,train HIc0hDiU6r8,30,machine gun shooting,train HIcEcJ7R1Qg,160,strike lighter,test HId7yOxZvlM,23,people running,train HIdaO6nnMfA,34,dinosaurs bellowing,train HIdaO6nnMfA,52,dinosaurs bellowing,train HIe5tb863Mo,98,scuba diving,train HIeMlTCkfu0,203,singing bowl,train HIeTAbw6w24,32,lathe spinning,train HIeTAbw6w24,60,lathe spinning,train HIer2K4IUcA,457,sharpen knife,train HIhA7pSbe64,118,mouse clicking,train HIhcuUsl88Q,250,machine gun shooting,train HIiNR4bsZIA,24,playing table tennis,train HIjv3QUMMTo,100,"child speech, kid speaking",train HIn8Gt_bc5Y,30,"pigeon, dove cooing",test HIoTFbiL8YI,9,"vehicle horn, car horn, honking",train HIp1bPkvaDg,460,driving motorcycle,train HIpS6nTzZAs,30,goose honking,train HIs8SK2A15M,30,people crowd,train HIsCIup94Qc,35,missile launch,train HIsCIup94Qc,45,missile launch,train HIu0Ejgzbtk,30,playing snare drum,train HIuKo1w81NU,60,"heart sounds, heartbeat",train HIuKo1w81NU,289,"heart sounds, heartbeat",train HIvjBlAgzns,341,bouncing on trampoline,test HIvtuTrO4ec,110,"playing marimba, xylophone",train HIwggEBLU8E,118,"electric shaver, electric razor shaving",train HIypcNPjbaA,1,car engine knocking,train HJ-k0zP0uGE,110,playing sitar,train HJ2oZVVyM4s,510,playing clarinet,train HJ2vlYRTxkg,207,dinosaurs bellowing,train HJ2vlYRTxkg,218,dinosaurs bellowing,train HJ4mpaRJU3Y,301,dinosaurs bellowing,train HJ4mpaRJU3Y,361,dinosaurs bellowing,train HJ52lwAE0ko,0,"motorboat, speedboat acceleration",train HJ6T0inw2mo,12,wood thrush calling,test HJ7rcdhb_kM,109,cap gun shooting,train HJ7rcdhb_kM,317,cap gun shooting,train HJCEfuXVnd8,127,people booing,train HJD6jVlvpKk,11,horse clip-clop,train HJGtRMubleU,30,dog whimpering,test HJMo1CHcT38,69,cupboard opening or closing,train HJR7Kyen6ks,21,"pigeon, dove cooing",train HJSxD7NJQ0g,19,people burping,train HJSxD7NJQ0g,102,people burping,train HJT_2UhDFWg,6,fox barking,train HJT_2UhDFWg,21,fox barking,train HJXQHCc9MsY,380,sailing,train HJYCdrNitZs,131,playing table tennis,train HJYCdrNitZs,231,playing table tennis,train HJZnkN-TAnM,8,owl hooting,train HJZnkN-TAnM,69,owl hooting,train HJ_Gd8zcqgo,47,train whistling,train HJfNakzGo3E,16,canary calling,train HJfNakzGo3E,278,canary calling,train HJg4jZNv1pc,150,people coughing,test HJhFxiKjBoo,30,horse clip-clop,test HJlJH6e9LtY,220,"race car, auto racing",train HJlLoOZZx10,9,"railroad car, train wagon",train HJrV6ckJKoo,120,people crowd,train HJrYNAIm1AY,110,dog baying,test HJtOIx6GDwE,0,playing piano,train HJtntCh7ljc,10,playing clarinet,train HJv4fPrSWns,30,horse clip-clop,train HJwkLcIU264,110,thunder,train HJxW5SGaPMA,90,chainsawing trees,train HJxeh3jvPEg,0,fireworks banging,train HJytt74Rg-A,8,train whistling,train HJytt74Rg-A,22,train whistling,train HK0r4-3lwtc,30,people running,train HK2KTmXVhpc,129,playing bassoon,train HK4VzNUdkCA,25,opening or closing drawers,train HK6HLZqAe9I,142,people battle cry,test HK8XAEJ25SQ,151,opening or closing car electric windows,train HK9jBKK4ZtY,438,arc welding,train HK9jBKK4ZtY,454,arc welding,train HKB6DqdXLOo,17,volcano explosion,train HKEFr8rpBXg,40,lions growling,train HKHZPT0l1_g,10,people whistling,train HKKhcfT2goA,90,playing clarinet,train HKN2548Ts6A,30,typing on computer keyboard,test HKROcP0Omv8,147,"cattle, bovinae cowbell",train HKROcP0Omv8,170,"cattle, bovinae cowbell",train HKRWrODrY44,230,female singing,train HKTSaezB4p8,50,playing bongo,train HKbkJd6I62o,170,playing piano,train HKdPfxirTBk,7,mynah bird singing,train HKdPfxirTBk,35,mynah bird singing,train HKdtztWDuCo,80,chicken crowing,train HKeP8gO2wMM,13,child singing,train HKeP8gO2wMM,105,child singing,train HKgIZLUhxJA,79,chopping food,train HKgby13GaZM,60,helicopter,train HKhAbwXJpdc,62,"fly, housefly buzzing",train HKhBeY4I830,9,cat hissing,test HKhIXwQ_jdU,70,"bee, wasp, etc. buzzing",train HKkTmjvMq38,143,people sniggering,test HKkihPXCIr8,11,francolin calling,train HKkihPXCIr8,32,francolin calling,train HKkpuguS48c,51,people eating noodle,train HKkpuguS48c,314,people eating noodle,train HKlccLAzhI4,30,playing bagpipes,train HKmcoHRPhrc,23,writing on blackboard with chalk,train HKpQ85g0Tw8,370,"female speech, woman speaking",train HKqz2eUv92k,15,smoke detector beeping,train HKqz2eUv92k,37,smoke detector beeping,train HKt34k7B7BA,40,playing volleyball,test HKvc2fgaeak,189,playing tambourine,test HKvimMUXe7c,181,people eating crisps,test HKxnn8YdnMM,34,penguins braying,train HKy0NSF4oRQ,7,"cattle, bovinae cowbell",train HKy0NSF4oRQ,17,"cattle, bovinae cowbell",train HKyC1S5vKTw,3,wind noise,train HKz3Fg-ceL0,26,mosquito buzzing,train HL16LUCUXQQ,30,police car (siren),train HL36YvzbFYs,210,goose honking,train HL52g8irOtk,7,lathe spinning,train HL52g8irOtk,24,lathe spinning,train HL5HnA_ZOcw,0,playing bass guitar,train HL6te-mR4ZM,70,people sniggering,train HL8XpYoU10w,0,horse clip-clop,train HL9MW4_cLLY,540,skiing,train HL9MW4_cLLY,811,skiing,train HLDItHIUVKQ,30,playing cymbal,train HLENBXmc1VI,291,playing oboe,train HLJ5z1cMn1g,83,"electric shaver, electric razor shaving",train HLKbbnyH_h0,20,using sewing machines,train HLNIbgt6gJc,13,playing tennis,train HLPcZlZqdKo,30,helicopter,train HLULbyqKleE,303,people sneezing,train HLUhrTtdZrg,90,helicopter,train HLXuOt_JUbk,30,printer printing,train HLZqYW0soKI,10,lawn mowing,train HL_E1j069EI,30,"female speech, woman speaking",train HLaphaV-Hq4,3,gibbon howling,train HLay8gvNJWI,30,people sniggering,train HLbHNqrLFFQ,343,slot machine,train HLcN40WOBlM,57,striking pool,train HLgZcNhBmXc,10,dog barking,train HLmnlRlD4Jk,30,wind rustling leaves,train HLn-Q2t8NH8,0,playing washboard,test HLncJNvEBH8,30,wind rustling leaves,test HLsRePLObfI,30,playing steelpan,test HLtnf4PKGFs,10,baby babbling,train HLtnf4PKGFs,30,baby babbling,train HLtwxQuuyKI,0,people sobbing,train HLuB_-dVQhM,130,"playing marimba, xylophone",train HLuYLN_k4lA,30,playing harpsichord,train HLvMDpc_btY,230,canary calling,train HLvMDpc_btY,246,canary calling,train HLvVyfCFyzY,60,dog barking,train HLw8lwXkM84,140,orchestra,train HLxIwN7KAks,20,people sneezing,test HLyeGb5Vux8,30,"playing violin, fiddle",train HLz3N5nG8fQ,30,people clapping,test HM0tEh4oGbU,19,playing banjo,train HM1I7IMe8pY,81,gibbon howling,train HM1I7IMe8pY,107,gibbon howling,train HM259nrg69k,30,"motorboat, speedboat acceleration",train HM2NNkJjWpw,0,dog bow-wow,train HM2cskmdTj8,16,"vehicle horn, car horn, honking",train HM4O8Xh1E5A,30,playing drum kit,train HM5YBO5CF_0,0,cat growling,train HM6ZGx9WB48,0,people sobbing,train HM8ObQ8dG9A,10,toilet flushing,train HMClRBBC6iY,26,singing bowl,train HMClRBBC6iY,79,singing bowl,train HMDCaaa8iQU,30,playing synthesizer,train HMF7YxjVg6w,30,playing electronic organ,test HMFAe77dB78,90,raining,train HMIGxs1GVCg,30,wind rustling leaves,train HMLPHqlZz_0,0,skidding,train HMLsJvJPwjU,29,"alligators, crocodiles hissing",train HMLsJvJPwjU,546,"alligators, crocodiles hissing",train HMMFB8RNP7E,162,playing volleyball,train HMO0ibdFAUw,7,airplane flyby,train HMO0ibdFAUw,85,airplane flyby,train HMOOCJv15eU,390,slot machine,train HMSaSTWFzWU,36,airplane flyby,train HMSaSTWFzWU,64,airplane flyby,train HMUwh4XTSMU,400,playing trombone,train HMVVq68-K7o,185,dinosaurs bellowing,train HMfGIhrYnZs,248,planing timber,train HMfiK6CBa3c,10,"female speech, woman speaking",train HMgD6ezml7U,20,playing oboe,train HMgTLxHOq7s,100,people crowd,train HMhAM5aicvI,30,playing electronic organ,test HMisYO72Q-8,23,goat bleating,train HMisYO72Q-8,33,goat bleating,train HMjbW-qhtHw,150,playing banjo,train HMnF8n9c5-Y,30,people coughing,test HMp465M22Yw,30,sailing,train HMr-ie6GLEE,18,vacuum cleaner cleaning floors,train HMr-ie6GLEE,96,vacuum cleaner cleaning floors,train HMrb7WWQcmY,1,basketball bounce,train HMvUuOGgHl8,507,people eating crisps,train HMxNxpw0Lrg,280,people clapping,train HMxxGXjfrpU,85,rope skipping,test HMyKmgWt5c4,100,"railroad car, train wagon",train HMyOVE7JRSM,27,driving buses,train HMyRA33XJRM,77,playing glockenspiel,test HN1rFi57fEY,43,slot machine,train HN4KMLVaT74,61,magpie calling,test HN4qMdzurZ4,378,playing harpsichord,train HN4r_ZUzl5g,57,playing tambourine,train HN7u55_WqBk,30,playing drum kit,train HNCDZVUHsS8,54,people eating crisps,train HND0THZUqOY,41,beat boxing,train HND8lhdYWmM,29,air conditioning noise,train HND8lhdYWmM,136,air conditioning noise,train HNEGSr6z4oQ,591,people marching,test HNErst-w60U,35,frog croaking,train HNErst-w60U,47,frog croaking,train HNFwHuI84Sg,99,"electric shaver, electric razor shaving",train HNHaIkaFlsY,30,playing cymbal,train HNIjhvZa6nY,37,missile launch,train HNJP9IsA65Q,12,civil defense siren,train HNK5kF8O8ww,32,singing bowl,train HNKkEcIH3Yc,5,basketball bounce,train HNLgOocZAM8,42,missile launch,train HNN96ylX7gI,68,playing cornet,train HNN96ylX7gI,99,playing 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HO8Lt7F8uKg,51,cattle mooing,train HO9oG0qy2SY,20,toilet flushing,train HOD29VAXJD8,30,car engine knocking,train HOFz5CofxJc,51,bouncing on trampoline,train HOGj4kLWmxs,30,playing hammond organ,test HOI0ZaKLAMM,30,fireworks banging,train HOI7KapLzz4,30,"playing violin, fiddle",train HOJDVqm29KM,32,playing theremin,train HOJDVqm29KM,64,playing theremin,train HON-M2R0hJg,30,horse clip-clop,train HONjTzR2dDk,40,playing double bass,train HONkDXUDjwQ,78,playing squash,train HOOK6E_Hogg,30,ambulance siren,test HOObl4Nv2zI,20,playing flute,train HOQc5Bn6-yI,60,"child speech, kid speaking",train HOTCl7R7UPA,20,people belly laughing,train HOTIBJemUB8,87,playing bongo,test HOTXPQ6HbII,113,bowling impact,train HO_dmqI-nmw,152,ice cracking,test HOb1EP6TzP0,28,wind noise,train HObPAU94Fd0,294,tapping guitar,train HOdHHv3mV-4,30,"rowboat, canoe, kayak rowing",train HOeYeDRiwds,220,playing trumpet,train HOfsCvdfyzk,81,people eating crisps,train HOghNCgJvnk,40,ocean burbling,train HOh2dGbYOpA,20,playing french horn,train HOhpKjQWqxc,30,people belly laughing,test HOiHcbRB3xQ,400,cutting hair with electric trimmers,train HOm3GkVl8D8,40,chinchilla barking,test HOpNAZQ5WKU,30,"rowboat, canoe, kayak rowing",train HOqx0IZGgBk,286,train whistling,test HOs8RElj6yc,13,owl hooting,train HOs8RElj6yc,26,owl hooting,train HOsw5Ly21MA,0,civil defense siren,train HOsw5Ly21MA,27,civil defense siren,train HOtQseh7MIM,120,typing on computer keyboard,train HOupeg-QhHk,73,yodelling,train HOw3C1IRkI4,607,basketball bounce,train HOwg8fXpyA0,30,orchestra,train HOxtY15Otpg,5,toilet flushing,train HOyov3OS0a0,54,underwater bubbling,test HP0DrupEb4Y,524,people burping,train HP4bUf_w4hU,522,people eating noodle,test HP5IYVg9xxA,183,beat boxing,train HPAf26FbtTk,169,mynah bird singing,train HPB6h2STTrU,27,playing table tennis,train HPBBxLAhYGo,30,"motorboat, speedboat acceleration",train HPBZicZZ4mA,295,playing washboard,train HPCXWDW7nZw,98,baby babbling,train HPFCdS2zF04,30,playing flute,train HPFqUBIoYp4,60,playing cello,train HPH-14FTE6A,119,dinosaurs bellowing,train HPH-14FTE6A,171,dinosaurs bellowing,train HPI4kBvKdGk,28,singing bowl,train HPIR2TbnJl8,120,playing banjo,train HPJZjx8DUF4,192,wood thrush calling,train HPJZjx8DUF4,221,wood thrush calling,train HPNTMpVyJx8,18,playing gong,train HPNTMpVyJx8,35,playing gong,train HPO6f3BqWbw,30,driving buses,train HPOBl_BzybA,5,door slamming,test HPOznS141RY,120,slot machine,train HPPMKJ68j6w,70,basketball bounce,train HPQQl0cHQ14,31,chinchilla barking,train HPQQl0cHQ14,70,chinchilla barking,train HPQzJFAXiLc,1185,singing bowl,train HPQzJFAXiLc,1214,singing bowl,train HPSV1Spyuf4,178,playing erhu,train HPVNhLMIJIY,56,yodelling,train HPVNhLMIJIY,124,yodelling,train HPYAsDO_ykM,50,chainsawing trees,train HPYJkSOkkdc,30,"pigeon, dove cooing",train HPblIiAP47o,3,"motorboat, speedboat acceleration",train HPfBYRU8tVM,30,"female speech, woman speaking",test HPgh02VwZAI,17,chicken clucking,train HPhPfOyOguw,240,people 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HQMWk3gAHpk,30,chicken clucking,train HQONTOdUvT8,30,playing bass guitar,train HQOb1LjygAU,30,sloshing water,test HQPym956zwQ,8,planing timber,train HQQcYFWOsnU,10,"female speech, woman speaking",train HQRnSdn-OqM,30,horse clip-clop,train HQRp7fgP6r0,38,wood thrush calling,train HQRp7fgP6r0,58,wood thrush calling,train HQSHJVH2x4w,24,lathe spinning,test HQSSYxEhEpI,20,playing cello,train HQSafj2aCNI,100,playing banjo,train HQT_rIesjyE,30,playing electric guitar,train HQVK7LA0aro,0,metronome,train HQVK7LA0aro,11,metronome,train HQYDO1uOreg,150,people whistling,train HQ_LmoQUeV0,10,ocean burbling,train HQb2jhmw1BE,310,playing timpani,test HQfF_JXrUUI,30,sliding door,test HQi_FnTchrM,30,people running,train HQigVNt3Wog,40,people crowd,train HQih8kN9S0A,30,car passing by,train HQjcUBuO1Lg,30,people booing,train HQlV2jYCz5k,30,"playing violin, fiddle",train HQm3NrLs1wY,185,people eating apple,train HQmFhsEbinU,28,playing snare drum,train HQmFhsEbinU,120,playing snare drum,train HQmV5gCORLk,285,arc welding,train HQmjczT0K70,55,wind rustling leaves,train HQmjczT0K70,290,wind rustling leaves,train HQq7bBFDyok,12,yodelling,train HQq7bBFDyok,46,yodelling,train HQqJl_Cq3C4,2,civil defense siren,train HQr1YvLzoZo,146,disc scratching,test HQrlkBZ23E8,1,footsteps on snow,train HQsB856J4SQ,8,playing sitar,train HQsB856J4SQ,33,playing sitar,train HQwaKBt71HM,12,playing guiro,train HQwaKBt71HM,199,playing guiro,train HQxEVnGeIbY,4,people gargling,train HQxEVnGeIbY,15,people gargling,train HQy0U1Cz3XM,22,people whistling,train HQynuQkl5mE,130,car engine starting,train HQzSDDJ3xDo,20,fireworks banging,train HQzY3V0aU5I,31,people burping,train HQzZqfH7RfM,316,hammering nails,train HR25et9y2cA,30,horse neighing,train HR2jp1VmA-g,414,arc welding,train HR35d67Dhts,108,singing bowl,train HR3C0qciAic,80,playing harp,train HR6tyAVCeKw,72,singing choir,train HR6tyAVCeKw,117,singing choir,train HR6u8bxP2ak,425,forging swords,train HR7G0NbSQqM,517,tap dancing,train HR7U2DrHkLE,118,otter growling,train HR7U2DrHkLE,157,otter growling,train HR7s2KGaowc,13,cattle mooing,train HR9eRE54Id0,10,raining,train HRCk1keB6lk,252,male singing,train HREgdi88SL0,35,chopping food,test HRFdIu5TgN8,286,playing table tennis,train HRFlqAkNeck,225,playing lacrosse,train HRFlqAkNeck,792,playing lacrosse,train HRGbypY727Y,38,beat boxing,train HRI31s40P0E,19,people sneezing,train HRIUhSHDIJA,430,ambulance siren,train HRIm5DT7zpY,75,dog howling,train HRJ5zYDSGgo,339,singing bowl,train HRJg0-VVUnE,24,train whistling,train HRJinN9ULaA,118,playing didgeridoo,train HRKZwa1CFg8,179,rope skipping,train HRKZwa1CFg8,254,rope skipping,train HRPIkTgl_EQ,30,"motorboat, speedboat acceleration",train HRQmCA3h7mE,75,disc scratching,train HRRiiNlbIHk,310,people crowd,train HRRv0X3euOg,34,roller coaster running,train HRRv0X3euOg,146,roller coaster running,train HRUMdh_jkqc,30,people sniggering,train HRWdvBmMr1k,152,playing tabla,train HRWdvBmMr1k,163,playing tabla,train HRZWQYvCijU,30,baby crying,train HR_8lYLA81Q,0,playing piano,train HR_PbjsImqE,2,people sneezing,train HRaGv5q3P3E,0,opening or closing drawers,train HRaMaCCoRCY,210,"child speech, kid speaking",test HRbTw9w6e2U,42,playing cornet,train HRbTw9w6e2U,166,playing cornet,train HRcFk47jSu4,16,playing trombone,train HRd-_j0Uvzw,21,playing gong,train HRd-_j0Uvzw,79,playing gong,train HRe6-Vgsj6Y,225,playing trombone,train HRf5yR_6Nj4,26,playing squash,train HRf5yR_6Nj4,64,playing squash,train HRivTI-ayP8,47,hail,train HRivTI-ayP8,59,hail,train HRkM8bV-Vtg,54,car engine starting,train HRkYFqZqhIs,10,people whispering,train HRkoHSBbte0,30,people sobbing,train HRoBuZPVTzw,30,wind noise,train HRoiXDAp4k8,10,"child speech, kid speaking",train HRuuSVGbbns,0,mouse squeaking,test HRvLVi2GGIc,20,"bird chirping, tweeting",train HRx7qCP-mZw,260,turkey gobbling,train HRyEHAP8BBQ,30,playing flute,train HRyOW7hmeEg,48,rapping,train HRyOW7hmeEg,181,rapping,train HS100sqEvVk,2,writing on blackboard with chalk,test HS1OLc1WWnY,124,male singing,train HS1uLhSG478,40,wood thrush calling,train HS1uLhSG478,53,wood thrush calling,train HS28EUWt8dE,110,people screaming,test HS2g_q5PFX8,7,bowling impact,train HS8kJm9g9lg,129,playing cornet,train HS9OIBifQVw,55,whale calling,train HS9OIBifQVw,110,whale calling,train HSBJDZqYUwY,262,playing piano,train HSDfR4NTly4,152,skiing,train HSEz645ojUA,119,electric grinder grinding,train HSEz645ojUA,142,electric grinder grinding,train HSFaoOPifSM,0,chipmunk chirping,train HSG2Flnc1Rs,904,machine gun shooting,train HSG2Flnc1Rs,1215,machine gun shooting,train HSGCkDnF-8E,30,playing acoustic guitar,train HSGejM8DhX4,93,cupboard opening or closing,test HSHlRaQBjys,11,people booing,train HSHlRaQBjys,114,people booing,train HSJwXo4JqdY,39,fire truck siren,train HSJwXo4JqdY,54,fire truck siren,train HSKK3r1AscE,75,wood thrush calling,train HSKK3r1AscE,85,wood thrush calling,train HSLw55gJieU,69,playing cymbal,train HSMAUXsU6Fk,148,playing harp,train HSM_PNshtHI,3,bull bellowing,train HSMxBQAbdSM,0,mouse pattering,train HSPBrLMYWQA,30,cat meowing,test HSPNgLSCvf0,92,hail,train HSPQt8u0mwc,160,playing accordion,train HSRcYEkdziE,20,people farting,train HSc5ANoET3Q,854,"heart sounds, heartbeat",train HSc5ANoET3Q,953,"heart sounds, heartbeat",train HSfcY8i95u8,10,typing on computer keyboard,train HShbaC9ORSo,410,driving motorcycle,test HSioMid2FrI,40,"bird chirping, tweeting",train HSk-dOqVqSg,30,orchestra,train HSmpRg2LQto,9,rope skipping,train HSonfW-Aawc,0,playing flute,train HSqBsOD5XGE,45,canary calling,train HSqBsOD5XGE,107,canary calling,train HSroGvRrDjA,80,people sniggering,train HSsuZH_Hz6w,70,cat purring,train HStmDF5gx_g,50,"vehicle horn, car horn, honking",train HStsQmXv_U4,32,beat boxing,train HSw40rXMQYs,421,people burping,train HSxi_GnjYNw,36,pheasant crowing,train HSxi_GnjYNw,46,pheasant crowing,train HSz1ZnYrdUk,212,people eating noodle,train HSz1ZnYrdUk,264,people eating noodle,train HSzKX0GZmpA,7,turkey gobbling,train HT41_XzXQVE,30,people sniggering,train HT4SFkl2FJM,421,parrot talking,train HTB-4d-bH3E,30,wind noise,train HTBzzt2VDlk,0,people babbling,train HTDqRGxzPU8,107,wind rustling leaves,train HTDqRGxzPU8,117,wind rustling leaves,train HTGUvmdO8TI,370,playing tabla,test HTIQiWoMLLE,180,"child speech, kid speaking",train HTJG6WItOVY,11,pheasant crowing,train HTJG6WItOVY,29,pheasant crowing,train HTKlTxUjV_0,0,playing tympani,train HTM6oZp9Jg8,140,"bird chirping, tweeting",test HTPHilkU_BY,10,playing zither,test HTQUbz_gAkk,30,dog whimpering,train HTQqXgwc6Io,93,"playing steel guitar, slide guitar",train HTRRMT1NQOc,60,playing saxophone,train HTRYiv5quSY,10,"race car, auto racing",train HTRw_rsg9HM,463,wind chime,train HTRw_rsg9HM,477,wind chime,train HTUXByy6Ezc,0,people sobbing,train HTVaHt5GKpE,5,wind noise,train HTXB0VW74eY,107,people giggling,train HTakErbPX64,30,hammering nails,train HTakErbPX64,50,hammering nails,train HTbusHXoIUk,0,skidding,train HTbzsDqmxQQ,10,skateboarding,train HTcCT2bBOsA,0,baby crying,train HTd__10ayCc,30,orchestra,train HTeb4PocfB8,30,"playing marimba, xylophone",train HTeh7kizm6c,119,cutting hair with electric trimmers,train HTeh7kizm6c,131,cutting hair with electric trimmers,train HTfbsi1hcpc,30,"playing violin, fiddle",train HToX7PfMrY8,11,yodelling,train HToX7PfMrY8,43,yodelling,train HTogJsbCE3o,550,"playing marimba, xylophone",train HTpz3cJ9P7M,140,people crowd,train HTqUtEGJ0As,30,people whistling,train HTsICRj_uwk,17,"playing steel guitar, slide guitar",train HTsICRj_uwk,53,"playing steel guitar, slide guitar",train HTtlo0oQpBg,208,swimming,train HTvG7s0Ryk4,30,"engine accelerating, revving, vroom",train HTvxR-W5_W4,67,hail,train HTvxR-W5_W4,109,hail,train HTyKo9q2qHo,30,playing drum kit,train HU2je5GvISo,40,driving motorcycle,train HU5vANycheQ,140,firing cannon,train HU5vANycheQ,156,firing cannon,train HU7oqkJeItQ,0,playing ukulele,test HU7tSduQ7m4,34,singing bowl,train HU8EqjWqt3E,30,horse clip-clop,train HU8MeJcb6nQ,30,"engine accelerating, revving, vroom",train HU8dZBS8CfI,0,people sneezing,train HU8mXO7tqwQ,212,bird wings flapping,train HU8wVzy3AVc,430,playing snare drum,train HU9dmBNTYB4,0,playing acoustic guitar,train HUA-Ad4W8FQ,30,chicken crowing,train HUDYHBPtrG8,33,people eating crisps,train HUEGlr0i-4Y,230,sailing,train HUGMUQATQGY,88,opening or closing car electric windows,train HUGMUQATQGY,265,opening or closing car electric windows,train HUH7m4FBSrQ,190,playing bass guitar,train HUNRuPfeOv8,110,rope skipping,train HUNnyQ9UFII,20,skidding,train HUOXygZMYyw,35,fire truck siren,train HUOXygZMYyw,52,fire truck siren,train HUP72tlgzyE,66,playing badminton,train HUQ8fCzYZPM,69,playing badminton,train HUSmc0VzTj0,170,playing saxophone,train HUU3HdxOqZs,45,arc welding,train HUU3HdxOqZs,97,arc welding,train HUWudrZ-opQ,50,skateboarding,train HUWvhtKby-A,33,car engine knocking,train HUXE2XqHvQQ,17,"engine accelerating, revving, vroom",train HUXoJfwc3Dk,110,people sniggering,train HU_6y-lM-pU,13,wind rustling leaves,train HUbIqDsoDUg,25,dog whimpering,train HUbwy9eoJWI,193,strike lighter,train HUdYgNalCEk,16,lions growling,train HUdneXvZWGA,10,police car (siren),train HUfcTlTNidU,69,playing harpsichord,train HUfcTlTNidU,144,playing harpsichord,train HUg9y9YlppI,11,playing acoustic guitar,train HUgKjkp-7M8,13,owl hooting,test HUidkBAoMCI,181,tap dancing,train HUikGXJ2ALE,3,playing glockenspiel,train HUlSVwhLTr0,30,"race car, auto racing",train HUm2vzjI6DQ,220,vacuum cleaner cleaning floors,test HUmHY73M3vI,119,bowling impact,train HUmHY73M3vI,165,bowling impact,train HUoClhZEwNA,455,playing darts,train HUoPiOdipoo,40,playing hammond organ,train HUqkf7wl01g,20,frog croaking,train HUshg3DsGD8,68,forging swords,train HUvA4DbfZoI,80,people humming,train HUvA4DbfZoI,157,people humming,train HUwbgSqLHQ4,190,lawn mowing,train HUxGbWDChFk,1,chicken crowing,train HUxGbWDChFk,30,chicken crowing,train HUy61ioZy_w,9,chicken crowing,train HUy61ioZy_w,21,chicken crowing,train HUyzxJCsM3k,220,playing clarinet,test HV1J_actdHE,110,cap gun shooting,test HV2vtdIzHqM,101,vacuum cleaner cleaning floors,train HV2vtdIzHqM,130,vacuum cleaner cleaning floors,train HV52MTha2YQ,308,swimming,train HV7MIG4y6_E,70,driving motorcycle,train HV7tFDJXGEc,621,baby laughter,train HV8ZILgsTnQ,371,"ice cream truck, ice cream van",train HV8ZILgsTnQ,404,"ice cream truck, ice cream van",train HV8cjZxpo9Q,110,playing cello,train HVC2u85qrKI,92,eletric blender running,train HVC2u85qrKI,103,eletric blender running,train HVCSEr3SFZQ,6,missile launch,train HVCSEr3SFZQ,34,missile launch,train HVDwMeFIfHA,0,volcano explosion,train HVF2W7EWKLs,368,rope skipping,train HVF2W7EWKLs,401,rope skipping,train HVF8a59C8w0,30,people sniggering,train HVHe2kibCQU,5,cupboard opening or closing,test HVHe2kibCQU,19,cupboard opening or closing,test HVP4jvqQAqA,1,pheasant crowing,train HVP4jvqQAqA,12,pheasant crowing,train HVSNN1S9ykI,100,"subway, metro, 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HXe6ofss7hU,30,"rowboat, canoe, kayak rowing",train HXfcD0aKrXY,30,horse clip-clop,train HXj5NUoTohs,130,playing didgeridoo,test HXm8JdC4k4c,56,playing bagpipes,train HXokFsqqI0U,140,playing saxophone,train HXq7yu1jWJE,35,sheep bleating,test HXqj_9oZReQ,14,disc scratching,test HXvge-6i_mk,30,chicken crowing,train HXvj7oE2Ze8,30,playing bass drum,train HXw_VxOsKbY,3,planing timber,train HXw_VxOsKbY,17,planing timber,train HY-4ilSDR1o,41,people gargling,test HY-buBIbIQc,49,cheetah chirrup,train HY1VQKcDosE,11,playing glockenspiel,train HY1VQKcDosE,38,playing glockenspiel,train HY4UcH1BAZ8,30,"playing violin, fiddle",train HY9MluCd8f8,185,cap gun shooting,train HY9MluCd8f8,255,cap gun shooting,train HY9wWqsbjWw,21,bird wings flapping,train HYBo4SLZxFU,30,horse clip-clop,train HYBxO5Fy1PY,27,chicken clucking,train HYI-tonwxZ8,26,civil defense siren,train HYI5ag1fptk,76,chimpanzee pant-hooting,train HYLv2bjajlQ,160,"male speech, man speaking",train HYNnJpdc3Vg,302,basketball bounce,train HYO4XU70NA8,6,police car (siren),train HYO_BVfdOFk,10,skateboarding,train HYOgM9dLUNQ,108,playing volleyball,train HYOlZb2bmmU,228,hedge trimmer running,train HYOlZb2bmmU,329,hedge trimmer running,train HYQ8F3qtKEA,83,playing erhu,test HYTovmZaLYQ,7,horse neighing,train HYVq4q-Ugqw,30,chicken clucking,train HY_lPyHyqLU,183,machine gun shooting,train HY_ydY-ko6Q,5,cat meowing,train HYb3UuzX04o,37,playing synthesizer,train HYb3UuzX04o,59,playing synthesizer,train HYdq8Z4Fg9o,70,"bird chirping, tweeting",train HYgsw0YuK14,85,playing glockenspiel,train HYgsw0YuK14,99,playing glockenspiel,train HYiVTA9iSHw,101,driving snowmobile,train HYiVTA9iSHw,167,driving snowmobile,train HYirUQnBUBE,170,female singing,train HYj4_-EroNo,78,"female speech, woman speaking",train HYjgeGXTxEE,30,"pigeon, dove cooing",train HYmjrTzXHSY,10,fireworks banging,train HYoOPT-ohJo,30,people babbling,train HYojEoDJiQc,49,hammering nails,test HYotVrl2lmc,50,driving buses,train HYpVi0G8sKI,50,tapping guitar,train 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HZ9xuABVaBY,353,planing timber,train HZAIM6dpzsw,7,church bell ringing,train HZBNSwmHsBY,182,playing erhu,train HZDIyvYqEC8,1,magpie calling,test HZDIyvYqEC8,66,magpie calling,test HZDTg4CuPFg,1,sliding door,train HZDTg4CuPFg,11,sliding door,train HZEvYfr8UMk,131,people eating,train HZEvYfr8UMk,146,people eating,train HZGsJYlQmDQ,1,plastic bottle crushing,train HZHb51xCZ24,94,children shouting,test HZHg4FvoAtk,50,playing glockenspiel,train HZOlXAiDsvg,45,slot machine,train HZQkO9ZJwfk,0,rope skipping,train HZRJxOXw2os,207,people marching,test HZWl58g59I4,88,playing glockenspiel,train HZWl58g59I4,160,playing glockenspiel,train HZXBjzykwuY,13,"engine accelerating, revving, vroom",train HZ_7tjx8VdI,30,"pigeon, dove cooing",train HZ_ccSiWsq8,147,slot machine,train HZbYh7KTqi4,38,playing didgeridoo,train HZd8jSzlo7E,115,tractor digging,train HZePq5a8Obo,30,playing flute,train HZf-xTiPeS0,11,striking pool,train HZf02v6DyyY,30,fire crackling,test HZfA--TfBuE,121,playing cornet,train HZhGj5zG-Q0,86,playing didgeridoo,train HZiI4OC7SCM,21,printer printing,train HZm39zraaRc,347,lathe spinning,train HZm39zraaRc,359,lathe spinning,train HZm7-D82vNQ,425,playing badminton,train HZnLrWB-yaI,30,"playing violin, fiddle",train HZpu3ldMgbU,1,telephone bell ringing,train HZr3fI72eyQ,20,arc welding,train HZsViW29xXI,0,woodpecker pecking tree,train HZsViW29xXI,56,woodpecker pecking tree,train HZw-0UgIq0I,393,arc welding,train HZxl5ot0Luk,10,typing on computer keyboard,train H_-2X4LNDH0,1,typing on typewriter,test H_-AsgYvF2o,112,airplane flyby,train H_-AsgYvF2o,131,airplane flyby,train H_-FF15yaFQ,30,"motorboat, speedboat acceleration",train H_3WItJ525I,66,plastic bottle crushing,train H_3WItJ525I,83,plastic bottle crushing,train H_4RyfaFPhA,38,lions growling,train H_4RyfaFPhA,50,lions growling,train H_9lP_C048s,114,warbler chirping,train H_9lP_C048s,248,warbler chirping,train H_A5WMp9fcg,9,lighting firecrackers,train H_AyLpJ882E,95,beat boxing,train H_DwA0vzKXI,260,mosquito buzzing,test H_IQFD0nTJY,726,vacuum cleaner cleaning floors,train H_IQFD0nTJY,924,vacuum cleaner cleaning floors,train H_Sjk3Y5U3o,26,waterfall burbling,train H_W6swWxWAo,140,opening or closing drawers,train H_WMxqB5R00,18,playing congas,train H_WMxqB5R00,43,playing congas,train H_ZAN5aDPFM,120,playing bass guitar,train H_ZdA-xMhCk,200,driving buses,train H_ZqoIMdMcY,9,strike lighter,train H__6P-rZ3Lo,50,helicopter,train H__lr6k27sw,71,gibbon howling,train H__lr6k27sw,85,gibbon howling,train H__wRhX66pc,30,people screaming,train H_bUwWFPLsc,131,hammering nails,train H_bUwWFPLsc,267,hammering nails,train H_eFbId-WLQ,2,owl hooting,train H_fmKOl4zA8,30,sailing,train H_foifR2C6A,20,playing badminton,train H_fviksWGjo,2,chinchilla barking,train H_htVEiV8dA,80,"railroad car, train wagon",train H_iMLZZsgT8,30,chicken crowing,train H_jNen_c0kU,742,"electric shaver, electric razor shaving",train H_jNen_c0kU,759,"electric shaver, electric razor shaving",train H_jj3mRx28k,15,playing squash,train H_pK_3oXHqk,91,cheetah chirrup,train H_qfRJ9aF7s,1,elephant trumpeting,train H_qfRJ9aF7s,28,elephant trumpeting,train H_rLJuETP-k,1,lions roaring,train H_rLJuETP-k,15,lions roaring,train H_rYM_x08Iw,70,basketball bounce,test H_tUOwjMW_I,7,playing tambourine,train H_zwpk8XgRw,15,fox barking,train H_zwpk8XgRw,25,fox barking,train Ha1jyRRI9fI,81,dinosaurs bellowing,train Ha29IISBBRM,70,lions roaring,train Ha9rHBjtQ3o,20,sailing,train Ha9sIboXnBg,280,playing snare drum,train HaABMNzUOvo,30,wind rustling leaves,train HaCfZHSzX9c,10,baby laughter,train HaGQnzWOAUo,434,hedge trimmer running,test HaIQWATIkZo,20,people screaming,train HaJarMSG9g8,550,train horning,train HaKezfKkwRk,30,"motorboat, speedboat acceleration",train HaLZD-2ppdU,8,lions growling,test HaMAXmXlCPI,7,cricket chirping,train HaMYMbMfUEA,60,chopping wood,train HaN05gSaEIA,69,child singing,train HaNg49GsCKY,220,lip smacking,train HaNg49GsCKY,671,lip smacking,train HaOuwBXoeIU,124,striking pool,train HaOuwBXoeIU,278,striking pool,train HaOyCAxvHlU,9,playing bassoon,train HaP9U9vnP0U,7,cat hissing,train HaP9U9vnP0U,26,cat hissing,train HaPN9LrUfsw,0,police radio chatter,train HaQNEB6XGss,60,people clapping,train HaQxKJ1EDng,4,playing guiro,train HaR6dHxe2B0,9,people giggling,test HaUDQcyN0io,30,playing harpsichord,train HaWMdjbsgw0,0,police radio chatter,train HaYA-ur4oxE,40,playing trumpet,train Ha_ZKFGG0jg,23,driving snowmobile,train Haa191Sk2fg,48,"electric shaver, electric razor shaving",train HaaEUs9tDyQ,20,people running,train HaeLlGQx9iw,39,magpie calling,train HaeLlGQx9iw,175,magpie calling,train Hag7M9EgGZA,32,people cheering,train Hag7M9EgGZA,74,people cheering,train HaiIELUJdec,978,dog baying,train Haj9xcxCWPQ,28,volcano explosion,train Haj9xcxCWPQ,38,volcano explosion,train HajJAkQj_HU,1,sheep bleating,train HajJKWI8tFs,236,beat boxing,train HajY_r6RNy0,90,playing accordion,test HakqARzlniY,380,playing hammond organ,train Hakqd6g2jaY,110,helicopter,train Hal5TuhjNDE,23,playing bass drum,train Hal5TuhjNDE,180,playing bass drum,train HamY4xk9QBE,193,tractor digging,train HamoOAaXcs8,62,playing french horn,train HamoOAaXcs8,188,playing french horn,train Hapz6uAKwp8,26,missile launch,train Hapz6uAKwp8,62,missile launch,train Har9LqPyQLo,23,tapping guitar,train Har9LqPyQLo,88,tapping guitar,train Hasc2nx7tO4,8,lions roaring,train HavgWNdxnZg,16,civil defense siren,train Haw5B6w-x8s,15,"railroad car, train wagon",train HaxK5z56kQk,25,bird squawking,train HaxrfCqNrqM,0,splashing water,train Hb2CiHHREfQ,68,"fly, housefly buzzing",train Hb2CiHHREfQ,78,"fly, housefly buzzing",train Hb4Fb50945Y,256,beat boxing,train Hb6sqtiRT-w,20,pig oinking,train Hb7w7a4RATg,28,hail,train Hb7w7a4RATg,68,hail,train Hb85aFzdR_I,520,people crowd,test Hb8vaImqLR8,407,mouse clicking,train Hb9RJlHvjXQ,11,baby laughter,train Hb9RJlHvjXQ,25,baby laughter,train Hb9ZvV2gnR4,185,sharpen knife,train HbA-Abyc6bU,30,helicopter,train HbCqkKPESLk,30,playing trombone,train HbDyj9DuNG0,39,playing glockenspiel,train HbFMmA5MEIE,20,playing trombone,train HbFXeX78W0Q,70,splashing water,train HbHnid_RnUs,130,playing drum kit,train HbKm3MFLDjQ,0,opening or closing car electric windows,test HbN_XhgHyys,30,"pigeon, dove cooing",train HbOi09w6J10,511,forging swords,train HbPUjzTAFLk,202,reversing beeps,train HbQFsVHafCY,58,mynah bird singing,train HbSm0b-NCgU,110,cap gun shooting,train HbTXI-9Y2Us,330,playing badminton,train HbV9Coascfc,70,goose honking,train HbXsZnHJzOg,5,basketball bounce,train HbY_u2-Oaqs,3,pig oinking,train Hba62E0mRQs,350,stream burbling,train HbdqYnSPi9Q,349,tractor digging,train HbgLcK3NgD8,0,car engine knocking,train Hbjs3l7oEvU,90,playing trombone,train HbksrKQpPFI,94,bowling impact,train HbmOyGIVpXw,19,playing double bass,train HbmOyGIVpXw,32,playing double bass,train HbmaTG_fTpo,140,"railroad car, train wagon",train HbmxnbsDzAA,40,playing theremin,train HbmxnbsDzAA,240,playing theremin,train Hbp4cnuQS_Q,80,playing harmonica,test Hbp9cvlqlWU,90,playing cello,train HbpuDuYSZkc,12,"motorboat, speedboat acceleration",train HbsniEGbyik,30,chicken crowing,train HbsniEGbyik,51,chicken crowing,train HbuZI65Zko8,30,barn swallow calling,train HbuZI65Zko8,57,barn swallow calling,train Hbvm3Fi1Yts,60,stream burbling,train Hbx-UBI95pw,240,"bird chirping, tweeting",train Hc1of-osBtk,24,snake hissing,train Hc1of-osBtk,39,snake hissing,train Hc364rm_suQ,57,bowling impact,train Hc4tkgra5hA,10,cattle mooing,train HcCK0MAofyk,40,"subway, metro, underground",train HcEX6Sfc7GI,30,"playing violin, fiddle",train HcFH-8xJ5rw,120,playing acoustic guitar,train HcKs8qp4wW4,0,people screaming,train HcO60nHH4W0,23,playing bass drum,train HcOO67Gw0A0,390,waterfall burbling,train HcOiYh9FEGc,82,playing bassoon,train HcSIzCCx9Lw,85,playing erhu,train HcSIzCCx9Lw,149,playing erhu,train HcTcFbxtiLU,332,forging swords,train HcTcFbxtiLU,473,forging swords,train HcU7vIoD-ds,331,vacuum cleaner cleaning floors,train HcUouOFqwGE,30,chainsawing trees,train HcZ-jbkIhDY,19,people burping,train Hc_DktFujm0,390,car engine starting,train Hc_T-fFFa0I,59,fire truck siren,train Hc_T-fFFa0I,75,fire truck siren,train Hc_UM8l_sTg,30,"playing steel guitar, slide guitar",test HcatlQREpLc,29,tap dancing,train HcatlQREpLc,74,tap dancing,train HceKeXpbIR4,20,fire truck siren,train HceKeXpbIR4,45,fire truck siren,train Hcgh5O6DGqc,557,planing timber,train Hch5_Go_-y0,145,playing volleyball,train HckqMrtU3dg,56,playing double bass,train HckqMrtU3dg,133,playing double bass,train HclKywBSYJ0,0,basketball bounce,train Hcl_te_R7us,1,people booing,train Hcl_te_R7us,24,people booing,train Hcm7lXelcbk,48,missile launch,train HcmqePry1mg,0,planing timber,train HcmqePry1mg,16,planing timber,train HcmzzM7q6-4,60,"child speech, kid speaking",test HcnsDpA-5f0,241,lighting firecrackers,train HcuXEYAbw5s,213,lighting firecrackers,train HcuXEYAbw5s,252,lighting firecrackers,train Hcwc3UlAuTY,390,driving motorcycle,train HcyAerQ0TbQ,29,car engine knocking,train HczZ2ij0k5Y,0,playing synthesizer,train HczyKAQCUu8,69,coyote howling,train HczyKAQCUu8,79,coyote howling,train Hd1q2XwgXQU,6,dog howling,train Hd1wio1YueI,70,"motorboat, speedboat acceleration",train Hd2Fs51iOrc,15,playing hockey,train Hd2Fs51iOrc,117,playing hockey,train Hd2pGcqgE9c,105,telephone bell ringing,train Hd4Lrewto7Q,20,playing accordion,train Hd5M86oGZdw,677,scuba diving,test Hd5kbFcn5_Q,30,horse clip-clop,train Hd5noiSGfC4,201,chainsawing trees,train Hd9k6tnbTfc,23,turkey gobbling,train Hd9z1A9DbYM,0,"race car, auto racing",train HdASUfcAR9A,30,helicopter,train HdCWsxc-HIA,67,slot machine,train HdGwVEwVd2g,15,"motorboat, speedboat acceleration",train HdKF2nxsV9U,30,"playing violin, fiddle",train HdKs8tVSOTU,715,playing darts,train HdOeLKqorQU,50,dog barking,train HdRiAojEdZQ,250,"child speech, kid speaking",train HdTs6HsfMZM,73,train whistling,train HdWCXFkPLks,447,playing steelpan,train HdWCXFkPLks,662,playing steelpan,train HdWOYFnRiz0,447,playing banjo,train HdWsYEaKAOM,160,"ice cream truck, ice cream van",train HdZG8kcjRPw,30,wind noise,train HdavUxdZ6g8,98,cap gun shooting,train HdavUxdZ6g8,113,cap gun shooting,train HdbcCjMkkEg,51,playing badminton,train Hdc5AhPehh4,26,people burping,train Hdc5AhPehh4,41,people burping,train Hdd8a7w3yAs,20,"bird chirping, tweeting",train HddhwL1gVH8,40,people crowd,train HdfWsgtjXMs,330,stream burbling,train HdfvJsm19h8,420,orchestra,train Hdg05LxCud0,250,stream burbling,train HdgmT8_o5zQ,60,bird wings flapping,test HdiFVoglTZc,50,fireworks banging,train HdiHG0_x7f4,133,slot machine,train HdiusrvXuZk,315,playing saxophone,train HdjMLMiTU_o,233,playing theremin,train HdjMLMiTU_o,464,playing theremin,train HdmEmfVaBmw,211,singing choir,train HdmEmfVaBmw,230,singing choir,train HdmoLs50WFI,291,firing cannon,train HdmoLs50WFI,503,firing cannon,train HdoWq76rxDw,149,bowling impact,train HdoWq76rxDw,162,bowling impact,train Hdokl0xe_j8,30,playing clarinet,train Hdrn_jN8goA,363,police radio chatter,test Hdu21U_FVs8,20,dog barking,train HdxjPTwud-k,18,sea waves,train HdxjPTwud-k,50,sea waves,train Hdyo-yBB4Q0,32,black capped chickadee calling,train Hdyo-yBB4Q0,72,black capped chickadee calling,train He-JTZ6Tngo,230,cat purring,train He4e6VXiKbk,13,horse neighing,train He4e6VXiKbk,35,horse neighing,train He7Tm7pF9Z0,34,snake hissing,train He7Tm7pF9Z0,112,snake hissing,train He7jOq1MZH8,30,skidding,test He9_MdIqh5M,7,playing tennis,train He9_MdIqh5M,142,playing tennis,train He9vFFxC4nk,133,"playing marimba, xylophone",train HeBkHBn5k7k,30,"subway, metro, underground",train HeEAELVuOuI,30,people farting,train HeHDHCtcfbI,30,"playing violin, fiddle",train HeHaL9UxOFY,110,skateboarding,train HeHmyhIObVg,801,playing didgeridoo,train HeJE0SH2_vc,20,playing cymbal,train HeLV1buhzpI,30,people belly laughing,test HeO_lx7I0c4,17,"rowboat, canoe, kayak rowing",train HePIWl6hQ4s,1,bull bellowing,train HePIWl6hQ4s,12,bull bellowing,train HeQch-tqle4,50,"child speech, kid speaking",train HeSsGP8y2AM,141,civil defense siren,test HeU2sqhkuUE,300,helicopter,train HeWAnmDLQZY,60,waterfall burbling,train HeX0uXT3oVA,9,lathe spinning,train HeX0uXT3oVA,26,lathe spinning,train HeZr6sKmSgM,320,church bell ringing,train HeZzSAhK8J0,47,mynah bird singing,train HeZzSAhK8J0,159,mynah bird singing,train He_1P_OtbDE,437,sharpen knife,train He_1P_OtbDE,447,sharpen knife,train HebxWsaO-LA,115,train whistling,train HebxWsaO-LA,144,train whistling,train Hec1D7VKUwI,29,playing theremin,train Hec1D7VKUwI,59,playing theremin,train HecjeydJh0A,20,ocean burbling,train Heg9QZf0Qrc,96,gibbon howling,train Heg9QZf0Qrc,161,gibbon howling,train HehLGhBhRQs,44,playing bongo,train HehLGhBhRQs,68,playing bongo,train HehWviK6M7Y,368,playing didgeridoo,train Hei85ZhvQhg,19,plastic bottle crushing,train Hei85ZhvQhg,38,plastic bottle crushing,train HejDlgS37cw,288,playing tambourine,test HelWXyTrzbY,180,people clapping,train HemIatG0W6A,642,playing synthesizer,train Henm5fCboCg,71,airplane flyby,train HenwLBq3A0s,30,"playing violin, fiddle",train HeoJjxOudaE,57,bowling impact,train HeoJjxOudaE,102,bowling impact,train Hep_JoJt67w,16,sharpen knife,train HepzhOzkL7A,43,people whispering,train HepzhOzkL7A,650,people whispering,train Her4dLwN19o,60,playing french horn,train Her4dLwN19o,84,playing french horn,train HesPISmVfLM,30,playing electric guitar,train HewqDf3mGOE,30,printer printing,train HexYOBRDRmU,38,playing volleyball,train Hexc6YWqptw,5,magpie calling,test Hez8gs_Lo78,12,playing double bass,train Hez8gs_Lo78,178,playing double bass,train Hezt6_2Iu2g,52,"electric shaver, electric razor shaving",test Hf-uMkF-gyA,296,playing drum kit,test Hf1DS6TAH_k,43,skiing,train Hf1uCTQ9MFs,37,hammering nails,train Hf1uCTQ9MFs,78,hammering nails,train Hf4941jTIsY,110,skidding,train Hf4Vcq5nxic,17,fire truck siren,train Hf4zT02INYI,40,playing tabla,train Hf4zT02INYI,118,playing tabla,train Hf75QQI3gyM,0,dog barking,train Hf7CWmNHHRo,110,playing snare drum,train Hf7fK6mmzKs,323,slot machine,train Hf89tWVqNSA,30,"playing violin, fiddle",train Hf8zYMgVJWQ,116,playing volleyball,train Hf9Id3EkyfI,22,playing volleyball,train HfBsCTiD0Eo,18,"electric shaver, electric razor shaving",train HfBsCTiD0Eo,191,"electric shaver, electric razor shaving",train HfD1GeaAp78,180,lawn mowing,train HfGq4L0vloM,674,bowling impact,train HfGuAUPiwqw,893,playing lacrosse,test HfMOBHKi9Dw,1,arc welding,test HfNxSd0qbts,479,basketball bounce,train HfPWJI2b3D8,276,playing table tennis,train HfSYwRQq7fs,60,lawn mowing,train HfTZM6_YWNc,30,playing accordion,train HfX6wDk3RzQ,3,basketball bounce,train Hf_-oVv-Fxo,3,horse clip-clop,train Hf_g1O4vnT8,158,playing zither,train Hf_g1O4vnT8,213,playing zither,train Hfa-bfdZtKI,19,"motorboat, speedboat acceleration",test Hfb2eAq2Moc,30,"rowboat, canoe, kayak rowing",train HfdcIEYWu1E,11,singing bowl,train HfiJ2PX9XvQ,46,slot machine,train Hfim8HbXRr0,3,playing volleyball,train HflZQW4kg4I,10,wind noise,train HfnGMq0S-vo,19,popping popcorn,test HfnHUJVgBQU,30,wind noise,train HfoM_YBKe6k,467,police radio chatter,train HfoM_YBKe6k,525,police radio chatter,train HfpKXlRds3g,7,people shuffling,train HfpLXTo59vQ,10,"pigeon, dove cooing",train HfpwZhka6O8,26,playing glockenspiel,train HfpwZhka6O8,40,playing glockenspiel,train Hfskv_LpQH4,78,missile launch,train HftUHV8Gf9w,8,chinchilla barking,train HftUHV8Gf9w,48,chinchilla barking,train HfwTq-8DHNM,200,mouse pattering,train HfwZ-nqwxcw,30,playing banjo,train HfxTKZnuK7E,745,beat boxing,train Hg34d1siONc,106,playing bagpipes,train Hg3vxyrXt0g,120,raining,train Hg68OTpBIsg,70,playing bagpipes,test Hg6H8AWJxvQ,160,playing flute,train Hg6HxylRGDo,29,ambulance siren,train Hg7I4F4_sPg,200,people clapping,train Hg7OTQFOuh0,456,typing on typewriter,train Hg7OTQFOuh0,598,typing on typewriter,train Hg7oPTHDgSo,484,people marching,train Hg9thtsQwCQ,0,chopping wood,test HgCforzKvyQ,90,people cheering,train HgFiPSS2MW4,2,"vehicle horn, car horn, honking",train HgGAzBDE454,115,tap dancing,train HgGAzBDE454,222,tap dancing,train HgGoPxJljao,318,playing harmonica,train HgGoPxJljao,394,playing harmonica,train HgHMkBk262o,75,swimming,train HgHMkBk262o,106,swimming,train HgJaV3YXwnM,30,playing harp,test HgNaHA4LNxc,160,people crowd,test HgRHussns4I,10,skiing,train HgRHussns4I,38,skiing,train HgRQvjGz5pM,20,"race car, auto racing",train HgRZdfWim_0,0,driving motorcycle,train HgSqYIGfpiQ,210,"female speech, woman speaking",train HgSqmsr5GOQ,30,fire crackling,test HgT_xT587MY,30,owl hooting,test HgTvI6n2Rvw,19,car engine knocking,train HgWQkH38LL4,230,stream burbling,test HgWSoxnpCLs,380,car engine starting,train HgZ2M-tAtos,50,"pigeon, dove cooing",train HgaNzT-qHLI,30,"pigeon, dove cooing",train Hgc7pA43DgI,0,metronome,train HgcTrJd9B_Q,80,ambulance siren,train Hgd5Q2-Hu_U,110,playing synthesizer,train HggjNCPnkS4,11,door slamming,train HggjNCPnkS4,44,door slamming,train HghXlPghzR8,22,people sniggering,train HgjV6Vp_sBw,30,ocean burbling,train Hgkor3Legc8,187,people marching,train HglBsVRAC_8,0,playing drum kit,train HglasZ-0Mgc,29,machine gun shooting,train Hglu_TgnaM8,240,people sniggering,train HgnFzTstUWk,30,playing snare drum,train HgnxMHFzs2A,30,wind noise,train HgotF15JrkI,1,skiing,train HgprrPSJNGw,22,singing choir,train HgprrPSJNGw,135,singing choir,train HgqRiglHTLs,419,skiing,train Hgt15uZWyQk,7,dog howling,train HgtyZOU2M-Y,0,"cattle, bovinae cowbell",train HgtyZOU2M-Y,13,"cattle, bovinae cowbell",train HgutlGwgFwY,10,driving buses,train HgvHOTml91A,66,playing glockenspiel,train HgvYPYlOF54,226,plastic bottle crushing,train HgvYPYlOF54,268,plastic bottle crushing,train HgwCuVENrsU,260,"railroad car, train wagon",train HgyKiDhqHRk,63,roller coaster running,train HgyKiDhqHRk,112,roller coaster running,train HgznL-AjthM,30,playing bass guitar,train Hh-Yz3wyfYc,170,male singing,train Hh1__trSyRs,170,chicken crowing,train Hh1m9WQHhCo,16,slot machine,train Hh3OYDF8BZI,30,"pigeon, dove cooing",train Hh527hfajR4,30,playing drum kit,train Hh86JwsZDQk,93,rapping,train Hh86JwsZDQk,106,rapping,train HhAVN1pJWHA,110,using sewing machines,train HhBb76CNDu4,106,playing bugle,train HhBb76CNDu4,203,playing bugle,train HhESQEt_bP4,30,playing accordion,train HhEvn8h_CK0,37,parrot talking,train HhFYnONzvGU,96,turkey gobbling,train HhFYnONzvGU,170,turkey gobbling,train HhFtx9TTlm0,35,playing volleyball,train HhGEy-CYXhU,3,lions roaring,train HhGEy-CYXhU,21,lions roaring,train HhHkS0YII3w,10,police radio chatter,train HhHkS0YII3w,118,police radio chatter,train HhImfa-Wt1E,162,bouncing on trampoline,train HhInmmqxgGU,60,"race car, auto racing",train HhJNtJYbWkk,32,fireworks banging,test HhK3H03kEAs,57,bull bellowing,train HhMdJl8GW1A,70,driving motorcycle,train HhMompUPH00,10,playing electric guitar,train HhQ41vxrZmE,30,dog barking,train HhQLp6dE2B4,93,ripping paper,train HhQLp6dE2B4,128,ripping paper,train HhQSVf5374c,30,horse clip-clop,train HhRoNuedKsM,90,playing piano,train HhTDVGp5e3c,40,people burping,train HhUYxDk7_98,18,horse clip-clop,test HhVetWYzY_w,393,playing castanets,train HhVetWYzY_w,481,playing castanets,train HhWB0knbNqQ,30,horse clip-clop,train HhWRklMHffo,190,"race car, auto racing",train Hh_NXJ72P3c,203,baby laughter,train Hh_NXJ72P3c,270,baby laughter,train Hh_kU32kioM,8,civil defense siren,train HhbE_XODRMw,34,woodpecker pecking tree,train HhcCVomKeZo,32,playing volleyball,train HhcDQUcuJdM,30,sloshing water,train Hhcaum73-qE,31,dog howling,train Hhce0wCLOVQ,24,playing bass drum,train Hheph8KXKGU,49,driving snowmobile,train Hheph8KXKGU,80,driving snowmobile,train HhhXGOhtJL4,78,mosquito buzzing,train Hhi73xZk7nU,6,"vehicle horn, car horn, honking",train HhjA64JO4nU,113,gibbon howling,train HhjA64JO4nU,202,gibbon howling,train HhkkT1eqwqI,15,chicken clucking,train Hhkx8KaB2mo,18,playing steelpan,train Hhkx8KaB2mo,171,playing steelpan,train HhlebKBv7hM,570,driving buses,train HhnDdPU7KZE,104,sharpen knife,train HhnDdPU7KZE,117,sharpen knife,train HhoNDfMa13w,35,children shouting,train HhoNDfMa13w,58,children shouting,train Hhqrajt0syo,32,car engine starting,train Hhqrajt0syo,68,car engine starting,train Hhqvvc4qu2Y,310,church bell ringing,train HhrEn0BtklQ,52,"female speech, woman speaking",train HhrGyeQa4ks,200,orchestra,train HhrcF14RHQU,30,car passing by,train HhrgwH415Zo,100,playing bassoon,train HhsGpNE4abY,300,playing hammond organ,test Hhtd8cUZcIo,139,dinosaurs bellowing,train HhvCh6gOZS8,372,cat hissing,test HhwVVRmjB4g,98,playing guiro,train HhwbgeftMSk,70,fireworks banging,train HhxPpI0FgLQ,36,people booing,train Hi-Ukxwijmc,30,sloshing water,train Hi1HVjOqJa8,120,ocean burbling,train Hi2Ww4lscA0,6,"motorboat, speedboat acceleration",train Hi4Ulcpdeb4,19,church bell ringing,train Hi4Ulcpdeb4,44,church bell ringing,train Hi4je0tyGEc,70,printer printing,test Hi5zEuHjtrg,58,missile launch,train Hi6_Bzk63Fc,30,playing electric guitar,train Hi6yZLHQ2SA,0,bathroom ventilation fan running,test Hi8GA42aa6w,10,using sewing machines,train Hi8XKFqnmt0,635,wind chime,train Hi8XKFqnmt0,763,wind chime,train HiB09gOhhtw,29,sheep bleating,train HiF0XL42VZM,45,playing vibraphone,train HiH5s5lpVEI,11,sea lion barking,train HiHDkNI2NLc,0,frog croaking,train HiHDkNI2NLc,10,frog croaking,train HiJcBocppus,88,train horning,train HiKH2JyBI_A,57,playing bassoon,train HiLBo43GMKE,41,frog croaking,train HiLBo43GMKE,52,frog croaking,train HiM0cqYAV7Q,20,lions roaring,test HiNx-R9hCYA,30,"race car, auto racing",train HiOpePZmSMU,166,planing timber,train HiOpePZmSMU,465,planing timber,train HiSgQqpcVTE,27,people cheering,train HiT20dkqFbM,41,playing squash,train HiT5sHl-Z-c,208,skiing,train HiTc0RzLE80,10,playing bass guitar,train HiWpyiIU8lE,477,people eating crisps,train HiZH9nFihpg,425,playing bugle,train HiZH9nFihpg,453,playing bugle,train HiZMgIsK3Lw,137,car engine idling,train HiZo4TDHBnY,111,playing theremin,train HiZo4TDHBnY,124,playing theremin,train Hi_1fC2fZig,30,playing harpsichord,train Hib44-JtbYc,11,child singing,train Hib44-JtbYc,66,child singing,train HienF50JvOw,0,car passing by,train HigTx0yxhIE,130,fireworks banging,test HijtYCEY1wg,0,people babbling,train HinmSr4nxEs,18,hammering nails,train HinmSr4nxEs,84,hammering nails,train HiosTOHR3Po,6,"male speech, man speaking",train HipoTPs-rRE,30,goose honking,train His7pbIckOQ,26,wind noise,train Hiw32i6UO_8,0,playing ukulele,train HiyIu2qCBF4,1,duck quacking,train Hj-EI65FsZI,150,driving buses,train Hj-R0wBJ7bo,140,skateboarding,train Hj2gy6RnDzk,49,people coughing,train Hj2gy6RnDzk,91,people coughing,train Hj5EYl2b91Q,30,chainsawing trees,train Hj5x9gTrxsA,30,playing trombone,train Hj6SLH_KCIs,50,toilet flushing,train Hj9XMlY6uKk,220,"child speech, kid speaking",train Hj9s82zlliM,6,foghorn,train Hj9s82zlliM,78,foghorn,train HjB2Xu1Uvic,32,tapping guitar,train HjB2Xu1Uvic,46,tapping guitar,train HjDMFIjR-s8,513,"heart sounds, heartbeat",train HjDMFIjR-s8,679,"heart sounds, heartbeat",train HjDhIgLSjrk,8,playing bagpipes,train HjDhIgLSjrk,36,playing bagpipes,train HjFChqYG2kY,44,playing timpani,train HjFChqYG2kY,162,playing timpani,train HjHEu6aovFw,22,playing clarinet,train HjHEu6aovFw,235,playing clarinet,train HjIoQ3z_cf4,10,typing on computer keyboard,train HjPztpjAFb8,0,tapping guitar,train HjPztpjAFb8,45,tapping guitar,train HjRt_p-xsYM,30,playing electric guitar,train HjSUesNoWO4,107,bird squawking,train HjYoyHCg3Oc,190,yodelling,train Hjb9r5TCKGc,30,"rowboat, canoe, kayak rowing",train HjcFaQ3Q8hg,2,baby laughter,train HjdpYIHdd4w,38,cricket chirping,train HjdpYIHdd4w,53,cricket chirping,train Hjh4w8yqbP8,391,playing saxophone,train Hjj6LamDw20,30,"motorboat, speedboat acceleration",test HjjO8coYGWY,30,playing tambourine,train HjjO8coYGWY,184,playing tambourine,train HjjningiK0w,120,playing flute,train Hjn-3E0iD-Q,30,driving buses,train HjnqKdsDAk4,19,printer printing,train HjtZptK3jDk,40,people cheering,train Hjx-S-6U9k0,0,elephant trumpeting,test Hjx-S-6U9k0,50,elephant trumpeting,test Hjx3ZUm0KnU,483,police radio chatter,train HjxDdFAhz3A,157,gibbon howling,train Hjyl1n_1muU,6,cat meowing,test HjzXJCRoUU8,80,people sobbing,train Hk1XCAhHxBM,30,playing bagpipes,train Hk3bjXUPLLI,120,orchestra,train Hk67NTn6QxA,142,skiing,train Hk7KpspDimc,0,lions roaring,train Hk7KpspDimc,11,lions roaring,train Hk8N0oXixHU,270,car engine starting,test HkCt4hh_x58,30,"rowboat, canoe, kayak rowing",train HkDAT7DjGjc,241,spraying water,train HkEhaayH8oc,181,metronome,train HkEhaayH8oc,269,metronome,train HkFCkbC4f1Y,410,car engine idling,train HkFIRKpANRY,37,crow cawing,train HkFIRKpANRY,48,crow cawing,train HkFjchxJevQ,14,people sobbing,train HkGJNsOLODM,173,parrot talking,train HkH1ZXtBiWk,100,playing harpsichord,train HkISIrBn5rI,390,people clapping,train HkIiMIA_nw4,195,people gargling,train HkIk61IMK7U,326,rope skipping,train HkLLvBQoMoQ,7,hail,train HkLLvBQoMoQ,34,hail,train HkMHloYQkaI,14,playing cornet,train HkMHloYQkaI,52,playing cornet,train HkNbWAXjnAw,430,skidding,train HkQHdrxx_v0,10,people sobbing,test 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HnAOIp0BZhQ,33,tap dancing,train HnAn-XFTXps,70,playing trumpet,train HnBydptCDHk,58,gibbon howling,train HnBydptCDHk,136,gibbon howling,train HnDDsq617A8,14,playing harp,train HnDW0n298Gk,40,people babbling,train HnDcaWMbyS4,7,horse clip-clop,train HnFqcjskgQA,132,cat growling,train HnGYT7_JTb0,30,mouse pattering,train HnIxZ8xC0Vg,300,baby crying,train HnOUK3wyxMI,30,typing on computer keyboard,train HnQ9ldBgV9g,83,tractor digging,train HnRqjImDRAc,30,chicken crowing,train HnSX2KK91ak,30,horse clip-clop,train HnSYc22c7H4,30,playing harpsichord,train HnSbmmkPHB0,0,skiing,train HnURtEzECx8,57,basketball bounce,train HnWLG-z5zMA,913,typing on typewriter,train HnjLi_S-Zkc,240,driving motorcycle,train Hnmk4vK4wZs,38,frog croaking,train Hnmk4vK4wZs,141,frog croaking,train HnoV4ttujRI,28,people marching,train HnoV4ttujRI,98,people marching,train HnqXdu-HAKA,30,"playing violin, fiddle",train HnuVpiEFi9c,27,playing tambourine,train HnuVpiEFi9c,59,playing tambourine,train HnwAL8E_HUs,30,"playing violin, fiddle",train HnzVgxFIC-w,170,rope skipping,train Ho-AkPmmMVk,30,skateboarding,test Ho2g8rlLcVI,117,cap gun shooting,train HoBfULM1h6Q,30,telephone bell ringing,train HoC3cEojE7g,15,bird squawking,train HoDOZ_tJ5k8,86,playing bassoon,train HoDo9TiTRP0,48,slot machine,train HoEZVMys3ls,40,typing on computer keyboard,train HoEcEbgJuik,1334,child singing,train HoG5GIRYhfA,17,blowtorch igniting,train HoG5GIRYhfA,138,blowtorch igniting,train HoIx2OncOGI,30,wind noise,train HoJJ9jdljtw,292,playing trumpet,test HoN-x_PxtH8,7,"fly, housefly buzzing",train HoN-x_PxtH8,18,"fly, housefly buzzing",train HoO07sydAZI,30,"bird chirping, tweeting",test HoQjIPYQ_ZY,30,"playing violin, fiddle",train HoRv8AH1LdM,72,playing tambourine,train HoSPR3JdSTI,43,playing theremin,train HoSPR3JdSTI,74,playing theremin,train HoTtn5FUjRg,29,mosquito buzzing,train HoTtn5FUjRg,46,mosquito buzzing,train HoUGwp-wV7U,1,skidding,train HoVAI-jH5CE,210,playing flute,train HoWlVscFcOE,251,missile launch,train HoWlVscFcOE,276,missile launch,train HoYfpoOt3nE,10,lions growling,train HoaP9CdbvD4,80,people crowd,train HocsqZoaVsQ,30,"railroad car, train wagon",train HoczhM-CEY8,222,playing bongo,test Hoe9BaoEfC0,608,driving snowmobile,train HofBEl7IjDk,30,cat caterwauling,test HogdqQjJgfg,10,"child speech, kid speaking",train HogebVN7Z5Y,8,chicken clucking,train HohAivF6Wdc,16,splashing water,train HojYokrZKsk,619,cell phone buzzing,train Hok1xQzMEe0,467,swimming,train Hok1xQzMEe0,479,swimming,train HokTklE1urA,240,playing piano,train Holr54eF4LY,30,sheep bleating,train Honj-TQHx3U,29,airplane,train Honj-TQHx3U,129,airplane,train Hook5wxdRqE,10,people eating crisps,train Hook5wxdRqE,104,people eating crisps,train Hos3L-BPLlI,30,playing harp,train Hos9lLBRLPc,30,"race car, auto racing",train HouMTQ9SIFs,99,"fly, housefly buzzing",train HouMTQ9SIFs,402,"fly, housefly buzzing",train Houip5i0OMU,40,playing saxophone,train HovtaizOH-k,34,pheasant crowing,train HovxPoqvyKY,0,penguins braying,train Hox-kMFGgO8,798,ripping paper,train HoxcuqaZbBg,8,missile launch,train HoypRk_30q4,0,toilet flushing,train Hozjwza7JfY,255,firing muskets,train Hozjwza7JfY,343,firing muskets,train Hp-9hVWWpIw,260,ocean burbling,test Hp33VJU5-TI,450,people whistling,train Hp5KwtsauHk,9,playing table tennis,train HpA4ldGoHRQ,20,toilet flushing,train HpAbxUb5XEA,101,lathe spinning,train HpAbxUb5XEA,123,lathe spinning,train HpDgnUgkNTo,120,"railroad car, train wagon",test HpEDfK0s3IA,7,woodpecker pecking tree,train HpEDfK0s3IA,20,woodpecker pecking tree,train HpFCHRZdGi4,30,goose honking,train HpLJ5utM9wo,0,alarm clock ringing,train HpQMqgDSuio,0,"vehicle horn, car horn, honking",train HpQS72_DP3Y,30,chicken clucking,train HpRJPPs0mdY,7,footsteps on snow,train HpRJPPs0mdY,50,footsteps on snow,train HpRZXnx-ckk,30,ambulance siren,train HpTG-E8HTQg,60,playing trumpet,train HpVtLvPIjzs,121,wind chime,train HpVtLvPIjzs,136,wind chime,train HpWgE39uRzE,180,thunder,train HpXi8bzk60I,30,"rowboat, canoe, kayak rowing",train HpXo1UR6zqM,20,disc scratching,train HpYKnpOma-w,13,playing mandolin,train HpYKnpOma-w,51,playing mandolin,train Hp_l6sICOzE,13,airplane flyby,train Hp_l6sICOzE,30,airplane flyby,train HpceHTbwbSQ,220,playing bass guitar,train Hpcta5kO0qA,13,playing french horn,train Hpcta5kO0qA,23,playing french horn,train HpdkgpaVDgI,500,typing on computer keyboard,train HpiYzXgg3YY,83,horse neighing,train Hpltst6Y2f4,4,playing cymbal,train HpnNPWrwfOw,63,volcano explosion,train HpnNPWrwfOw,92,volcano explosion,train Hpnja1gcKN0,30,ambulance siren,train Hpqueb0nov4,141,firing muskets,test Hpqueb0nov4,270,firing muskets,test HprEd7CUowg,2,striking bowling,train HprEd7CUowg,92,striking bowling,train HpsGzhqvt90,521,police radio chatter,train HpsQwLqJDAM,435,people cheering,train HpseKJL6jRQ,129,sharpen knife,test HptCvAkQvNc,500,wind rustling leaves,train HptgOC7kfkw,4,otter growling,test HpvGvwC3PAQ,23,fire truck siren,train HpwTxEttH7c,5,bouncing on trampoline,train Hq-x93bupbM,35,people giggling,train Hq-x93bupbM,49,people giggling,train Hq0FJIKLitI,30,horse clip-clop,train Hq0ZkxHHLMk,400,printer printing,train Hq46Ab2SoAA,140,"bee, wasp, etc. buzzing",train Hq9IDfISrwA,40,"subway, metro, underground",train Hq9g6-RovMs,299,"electric shaver, electric razor shaving",train HqAxo7HigE4,90,sailing,train HqBrIk32h7Y,290,people whispering,train HqBrIk32h7Y,644,people whispering,train HqCu8_P0QLw,73,playing volleyball,train HqEtZGXtJnQ,200,playing acoustic guitar,train HqF7uiKEjUA,50,cap gun shooting,test HqFVxWfVtoo,106,raining,train HqHNS2jKgu8,115,cat hissing,test HqIZ3VEiJH4,36,"playing marimba, xylophone",train HqJHsYe7IhU,80,playing acoustic guitar,train HqKVoaR75z0,30,train horning,train HqLEO32nczg,17,bathroom ventilation fan running,train HqMbDE6QkVE,291,writing on blackboard with chalk,train HqMbDE6QkVE,343,writing on blackboard with chalk,train HqMvfGGPAac,30,playing hammond organ,train HqNJVk3FgYg,30,people running,train HqP3nvOa8Qs,196,hail,train HqPgf8ewa60,12,frog croaking,train HqPgf8ewa60,67,frog croaking,train HqQ0Soufdpg,10,lawn mowing,train HqS6-PER_Kc,2,lions roaring,train HqS6-PER_Kc,23,lions roaring,train HqWG4KGktI4,178,people burping,train HqWG4KGktI4,398,people burping,train HqZhlvWkNf4,30,"motorboat, speedboat acceleration",train Hq_awz6Vkng,0,planing timber,train Hq_awz6Vkng,45,planing timber,train Hqd9ZfS605g,30,male singing,train HqfvzcK-pS4,180,crow cawing,train Hqg6M-i_FJs,6,playing guiro,train HqgKog6_7Ps,24,barn swallow calling,train Hqh-Y4Icd6w,80,helicopter,train Hqhi7LioGyM,30,"playing marimba, xylophone",train Hqk9mqSGIeY,238,playing erhu,train Hqm0aZ5Dq9Q,413,people eating apple,train Hqm0aZ5Dq9Q,545,people eating apple,train HqmB_1mW2Uw,30,opening or closing drawers,train HqmbLPQc0QY,7,people cheering,train HqoK2LXEykM,40,"engine accelerating, revving, vroom",train HqoZXkQ6bto,15,sheep bleating,train HqtdpO_BVAc,20,playing harp,train HqtqOCxsxAE,40,playing cornet,train HquG1U1_uLk,286,hedge trimmer running,train HquG1U1_uLk,314,hedge trimmer running,train HqwA_fQeh5M,30,playing banjo,train HqyJhgcTRig,3,"vehicle horn, car horn, honking",train HqyJhgcTRig,13,"vehicle horn, car horn, honking",train HqyyRRCyw_s,0,door slamming,train Hr0nDpDW0Fw,30,horse clip-clop,train Hr36Lf8JwoI,80,playing squash,train Hr3ifPuUU7w,59,owl hooting,train Hr3ifPuUU7w,85,owl hooting,train Hr9FP93o8Ro,714,playing banjo,train Hr9ZJe4WTWE,41,train horning,train Hr9ZJe4WTWE,127,train horning,train HrALXsVvXlc,230,"railroad car, train wagon",train HrCuyJBt444,30,"playing violin, fiddle",train HrEUzoS9ErQ,0,people hiccup,test HrIJyC1wAUU,230,police car (siren),train HrIRQU3cEdE,13,machine gun shooting,train HrIRQU3cEdE,26,machine gun shooting,train HrJ1f6xJ6YY,30,"playing violin, fiddle",train HrNnLrFCrbo,50,people crowd,train HrOIF7dgIwA,30,"playing marimba, xylophone",train HrOY-NV8yAc,30,people babbling,train HrOgTciaJ6A,96,owl hooting,train HrPnGYGrvm0,100,playing banjo,test 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HuJRhoFY1VE,50,train whistling,train HuLsUhhbZ-s,22,striking bowling,train HuLsUhhbZ-s,35,striking bowling,train HuNyiPQtKl0,37,beat boxing,train HuP9RekGOdI,0,mynah bird singing,train HuP9RekGOdI,74,mynah bird singing,train HuPle-nxPH8,30,sailing,train HuRKiXKU3iM,520,people clapping,train HuRMjkH-NuQ,547,wind rustling leaves,train HuXByaGHxss,4,people belly laughing,train HuXByaGHxss,18,people belly laughing,train HuXH7Ry6NZE,177,cricket chirping,train HuXi5qGwxmY,30,church bell ringing,train HuYfq0OdJgk,210,playing cymbal,train Hug-uy6Pj-g,10,splashing water,train Hujimw8rHlQ,23,skiing,train Hujj6Q1Et3k,8,playing congas,train Hujj6Q1Et3k,25,playing congas,train Hujl_1FNDJM,34,playing vibraphone,train Hum53_V1zw8,1,wind noise,train Hup8_wHHAK0,30,dog bow-wow,train HupUeD8ZSRs,30,horse clip-clop,train HupjSIzGMDU,40,basketball bounce,train HuqAKNXhQmo,8,playing badminton,train Huqd3u3UPl4,1,playing cornet,train HuqgCD5UXto,20,playing drum kit,train HuruKe0EZ4U,15,reversing beeps,train HuuFMUV4dWM,266,tractor digging,train HuvE7tMCgR8,66,playing gong,train HuvE7tMCgR8,101,playing gong,train HuwE0QmcT3c,30,people running,train HuxAEeQUPhI,41,playing didgeridoo,train HuyX__O69Wg,140,playing french horn,train HuzCzz7_pk0,74,playing tympani,train HuzTP9tI23c,30,"playing violin, fiddle",train HuzaEBh6Phg,60,rope skipping,train Hv-GSUhEiCY,72,playing bassoon,train Hv-f9i5cFUk,30,playing drum kit,train Hv1YjZAvTm0,30,raining,train Hv46ZVtvWJQ,30,"playing violin, fiddle",train Hv4G3il73fY,70,people sniggering,train Hv546RqGR9U,51,chimpanzee pant-hooting,train Hv6-RrVRiWA,90,skidding,train Hv8xCitMZa8,0,people babbling,train HvFts3KRhjw,310,playing cornet,train HvFxCGKsfL8,20,skidding,train HvGwWk3wjM4,307,shot football,test HvIRlZz32nQ,30,"bird chirping, tweeting",train HvJV8X975qM,30,ambulance siren,train HvM0UuvZxSM,30,"pigeon, dove cooing",train HvMZHdzvmSM,20,people farting,test HvO7hFHwXZw,30,pig oinking,train HvR47ZOuYNA,22,printer printing,train 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HzR5tPwXpoI,0,basketball bounce,train HzSOK5rklH8,77,missile launch,test HzUoNHiqKhM,20,"engine accelerating, revving, vroom",train HzVGh0xbPXI,0,dog barking,train HzXWXYxXyYA,50,playing bass guitar,test HzYUtghwGJ0,30,chicken clucking,train HzZUCLCCVOM,33,people eating apple,train HzaHK3s-m-U,30,playing flute,train HzboAacgzkM,100,waterfall burbling,train HzdHrKjho9A,26,sliding door,train Hzf_-LTeToc,30,raining,train HzgY7I9waq0,459,vacuum cleaner cleaning floors,train HzhLUVRp9NE,480,"subway, metro, underground",train HzhiWSedtyQ,23,child singing,test Hzj5Ha_6d8E,32,arc welding,train Hzj5Ha_6d8E,43,arc welding,train HzlP2U_JeRk,76,rope skipping,train Hzo4vw9onzk,160,"bird chirping, tweeting",train HzoXGUBZJSc,33,playing hammond organ,train HzoXGUBZJSc,48,playing hammond organ,train HzxXEADHPN8,2,playing tuning fork,train HzyRF594p-A,30,playing vibraphone,train Hzzq5D5IWvw,260,orchestra,train I-1k-q-otiM,46,civil defense siren,train I-3qYNL2V0I,39,playing sitar,train 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piano,train I34yatMC-pM,57,arc welding,train I35_Hl46fGo,25,duck quacking,train I377YwSvD_Q,5,dinosaurs bellowing,train I377YwSvD_Q,42,dinosaurs bellowing,train I37HOATyw78,8,gibbon howling,train I37HOATyw78,20,gibbon howling,train I38XSoc9FJs,403,hair dryer drying,train I38XSoc9FJs,467,hair dryer drying,train I3Bw-OMBnv8,60,church bell ringing,train I3DRb27TX_M,20,ambulance siren,train I3EtEa77uCk,529,playing badminton,train I3Fgc2ltgQA,112,singing bowl,train I3Fgc2ltgQA,138,singing bowl,train I3HT4CPOl1M,200,female singing,train I3Hw5adoiHE,30,"playing violin, fiddle",train I3La9lomP54,230,"pigeon, dove cooing",train I3MKefS8Ea4,30,car passing by,train I3OYmetiXqA,597,people slurping,test I3PusH9Y2LE,13,"engine accelerating, revving, vroom",train I3RbMcdh-1A,62,spraying water,train I3RbMcdh-1A,181,spraying water,train I3TqeCU2Nuc,242,planing timber,train I3TqeCU2Nuc,263,planing timber,train I3UiTltUvj4,122,playing bongo,train I3UiTltUvj4,188,playing bongo,train 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I5pX2EFs4zQ,3,skidding,train I5wV1AFabIA,29,frog croaking,train I5yMAPfZsBQ,360,vacuum cleaner cleaning floors,test I61-Ex5gx8k,41,arc welding,train I61-Ex5gx8k,73,arc welding,train I622XDJu1EE,1,baby laughter,train I638TWm-cE4,47,fire truck siren,train I63wkpl1i5o,0,lawn mowing,train I644GaVj71s,0,lawn mowing,train I65aqG7z_LA,305,ripping paper,train I65aqG7z_LA,380,ripping paper,train I66OsMa1sKs,60,"railroad car, train wagon",train I68BQGzdVkI,30,goose honking,train I6BoigdgEBc,30,rapping,test I6CApeTzvGs,14,underwater bubbling,train I6CApeTzvGs,24,underwater bubbling,train I6DjQ1-T3z8,130,playing french horn,train I6HyEJloIZI,90,"child speech, kid speaking",train I6IWqqszDH0,30,playing zither,test I6Ly1zg0ux8,0,"vehicle horn, car horn, honking",train I6Ly1zg0ux8,11,"vehicle horn, car horn, honking",train I6MyIm5btGM,20,playing flute,train I6Nly5f8NkU,303,playing guiro,test I6VTDmfyhs8,30,lawn mowing,train I6YjJDUYakM,140,playing harp,train I6ZpW5udNZY,19,elk bugling,train I6ZpW5udNZY,36,elk bugling,train I6_30m_TQ2o,0,playing tuning fork,train I6azdulfZSw,30,playing drum kit,train I6bNIhsP0DY,218,disc scratching,test I6cbopg_Lic,194,dinosaurs bellowing,train I6eTdPG3jo8,10,playing theremin,train I6eTdPG3jo8,47,playing theremin,train I6f_QnSnwj8,500,splashing water,train I6g96wQAID0,96,tractor digging,train I6gmSF6mmyM,50,cell phone buzzing,train I6k0X86qIvQ,30,goose honking,train I6kM5lT04Zc,80,"female speech, woman speaking",train I6kwtevZJ3s,0,people sniggering,train I6mGZ7pHSRk,150,people whispering,train I6mbf9EaxWc,2,cheetah chirrup,train I6mbf9EaxWc,63,cheetah chirrup,train I6nOJYm3mhM,26,train whistling,train I6nOJYm3mhM,36,train whistling,train I6nRHMGe4Q8,129,zebra braying,test I6nlWsAjfkE,82,playing glockenspiel,train I6oUGatDmrY,0,ripping paper,train I6oUGatDmrY,10,ripping paper,train I6ra3cdOUWY,180,people cheering,train I6tAn2a2Vv4,81,playing volleyball,train I6tErxpo138,450,playing drum kit,train I6tXhdnv8UQ,110,chicken crowing,train I6txVvxtUO0,42,playing vibraphone,train I6txVvxtUO0,110,playing vibraphone,train I6u6WZce-W4,6,striking bowling,train I6u6WZce-W4,57,striking bowling,train I6v8Ve_dxgE,202,playing badminton,train I6w9JATPcz8,187,singing bowl,train I6xOIslAQSE,10,playing harp,train I6xg7TmkoJ4,88,firing muskets,train I70DGW_g26g,100,people slapping,test I72ZD-aVo7I,153,driving snowmobile,train I72ZD-aVo7I,262,driving snowmobile,train I755otRJJuM,40,"bird chirping, tweeting",train I783IrVZdbw,30,playing cello,train I7AWclzpjQ4,123,woodpecker pecking tree,train I7Bev-ufyg8,30,chicken crowing,train I7Ev7bQF3bs,24,people eating crisps,train I7F9PbcP3Zo,2,opening or closing drawers,train I7F9PbcP3Zo,29,opening or closing drawers,train I7HDd_c_4u4,13,yodelling,train I7IlnxgRgnM,526,pig oinking,train I7JquYRmkao,15,crow cawing,train I7JquYRmkao,35,crow cawing,train I7L5sIAE1qc,360,sailing,train I7LyNEcRIbo,18,"vehicle horn, car horn, honking",train I7M80_q3oSU,0,playing snare drum,train I7MWLCAI9sY,120,playing 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I7vMYYXGQiI,517,people sneezing,train I7vb32VzyYU,30,turkey gobbling,train I7wYg-bKW7Y,121,playing table tennis,train I7zgwA9BOrI,10,dog barking,train I7ziS4ErEqs,70,turkey gobbling,train I8-mgqpnSXU,74,crow cawing,train I8-mgqpnSXU,150,crow cawing,train I827IpiD9CQ,50,playing trumpet,train I82H2RLpX70,279,yodelling,train I82yLYwdiO8,79,train whistling,train I83QYLHFbDU,30,playing vibraphone,train I84sJtBW2QY,30,"motorboat, speedboat acceleration",train I86BSCJlkQg,455,lighting firecrackers,train I86ZT0F9K3I,420,telephone bell ringing,train I86ZT0F9K3I,574,telephone bell ringing,train I86lwfQPg00,310,"subway, metro, underground",train I88Da1UWKgw,80,crow cawing,train I88Da1UWKgw,103,crow cawing,train I8Af3ncHGJU,170,people sneezing,train I8DSF5M8vYU,13,mynah bird singing,train I8DSF5M8vYU,23,mynah bird singing,train I8DkmAVTZuc,176,child singing,train I8DvheI_3gc,487,playing timpani,train I8DvheI_3gc,559,playing timpani,train I8EZK8WiyCo,30,wind rustling leaves,train I8EmCDUIdVE,119,mouse clicking,train I8FKBgwo-VQ,150,civil defense siren,train I8FKBgwo-VQ,162,civil defense siren,train I8GGPj1c41o,26,pheasant crowing,train I8GGPj1c41o,65,pheasant crowing,train I8HVWmz-prk,91,"cattle, bovinae cowbell",train I8HVWmz-prk,102,"cattle, bovinae cowbell",train I8IsSrpqvSo,92,chicken crowing,train I8MWSZzaSHM,64,waterfall burbling,train I8MWSZzaSHM,133,waterfall burbling,train I8OEau20VUw,30,people sniggering,train I8PzwWI-gxU,40,using sewing machines,train I8S4VeghFZw,124,playing bassoon,train I8YBRY2uypA,30,skateboarding,train I8a6GwMJpuM,200,helicopter,train I8bnbYWpncU,32,playing guiro,train I8bnbYWpncU,69,playing guiro,train I8dpjXeiE1Y,492,typing on typewriter,train I8dpjXeiE1Y,517,typing on typewriter,train I8ggbwzUhiM,30,"motorboat, speedboat acceleration",train I8jDjGBV9Bw,16,wind noise,train I8jKzLbz3vQ,0,basketball bounce,train I8jmTB7dBqM,88,playing bassoon,train I8mELIan_Ak,10,toilet flushing,train I8n45ojNCFQ,175,playing table tennis,train I8q1M4pl0mY,50,people coughing,train I8r4hsBwoRQ,51,playing bass drum,train I8usi0X-ys0,39,writing on blackboard with chalk,train I8usi0X-ys0,175,writing on blackboard with chalk,train I8wGJhigo-c,16,arc welding,train I8xtQWuexPM,4,people gargling,train I8ysH_GNAYs,30,people whispering,train I8zhB7QYHK8,20,fireworks banging,train I939NKndAbk,26,"pigeon, dove cooing",train I940b6YrGg4,252,playing tympani,train I94mE4_hwT8,380,"race car, auto racing",train I98Rcxvx-JI,9,chimpanzee pant-hooting,train I98Rcxvx-JI,96,chimpanzee pant-hooting,train I99Fszcy6pQ,0,playing bagpipes,train I9DM2EBBuQ4,30,cat meowing,train I9DXY3uVflY,119,opening or closing car doors,test I9DXY3uVflY,152,opening or closing car doors,test I9DZ5KwHdbU,10,ambulance siren,train I9FFR1oMn-M,41,mynah bird singing,train I9F_1qi3-wQ,37,eagle screaming,train I9F_1qi3-wQ,66,eagle screaming,train I9G6Nu0OXxA,290,playing piano,train I9GLG8Bh5dE,30,people screaming,train I9GnRxVEk9U,10,"heart sounds, heartbeat",train I9H49JIKSJQ,380,driving buses,train I9IXh15T54w,22,civil defense siren,train I9If2WzGNOg,82,singing choir,train I9If2WzGNOg,160,singing choir,train I9JycY8QtuY,30,printer printing,train I9OOInjCX2Q,158,fire truck siren,train I9OOInjCX2Q,227,fire truck siren,train I9PO4PSobX8,16,vacuum cleaner cleaning floors,train I9PO4PSobX8,48,vacuum cleaner cleaning floors,train I9SnfySBwNA,30,wind rustling leaves,train I9U48Jxd_bg,120,playing banjo,train I9VHosptp-A,0,splashing water,train I9W4OTz2iuw,40,woodpecker pecking tree,train I9WHv9nnq0Q,140,playing trumpet,train I9WmOHZUuZg,21,printer printing,train I9X7DSK_d3g,0,playing washboard,train I9Zk4ypA834,93,playing badminton,train I9auMdqQlmY,30,typing on computer keyboard,test I9gELPxPG8Q,72,airplane flyby,train I9gELPxPG8Q,94,airplane flyby,train I9gzjPLh-IA,66,playing bongo,train I9hdR7tKFso,93,yodelling,train I9hdR7tKFso,108,yodelling,train I9lGEguTfss,18,"engine accelerating, revving, vroom",train I9nzy1j_M4A,30,"motorboat, speedboat 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IAG9PZmTGD4,208,foghorn,test IAIDjULy7kg,20,"child speech, kid speaking",train IAJLCRaO_OM,114,playing erhu,train IAJLCRaO_OM,207,playing erhu,train IAKiCdD0hkE,180,"female speech, woman speaking",train IAMb0vDC-wA,22,civil defense siren,train IARt1vLoHEE,100,playing vibraphone,train IAUMAuANoKo,15,eletric blender running,train IAUOYg9aPUM,160,people coughing,train IAVy63qJAx8,106,playing timbales,train IAVy63qJAx8,177,playing timbales,train IAW82EdOmMk,222,arc welding,train IAW82EdOmMk,287,arc welding,train IAXQeg7IkHI,40,people screaming,train IAXnoicEPzQ,18,vacuum cleaner cleaning floors,train IAXnoicEPzQ,49,vacuum cleaner cleaning floors,train IAYRm0ggMjg,260,"female speech, woman speaking",train IAZKS6bZdLU,30,"female speech, woman speaking",train IAZfBOEmwNc,5,lions roaring,train IAZfBOEmwNc,25,lions roaring,train IAZrXuaaQu8,37,tap dancing,train IAbZXolV6fQ,366,sharpen knife,train IAcI9eyCY20,340,people clapping,train IAcIk-LL7CA,14052,"bird chirping, tweeting",train IAcVCW7WKIA,29,helicopter,train IAdVPfd3qT0,312,footsteps on snow,train IAdVPfd3qT0,453,footsteps on snow,train IAf3HUIy0lA,30,fire crackling,test IAg539E9spE,43,playing darts,train IAg539E9spE,115,playing darts,train IAj-meCa5N4,30,police car (siren),train IAkC33Oblk0,140,playing hockey,train IAkC33Oblk0,484,playing hockey,train IAkfSQROmP8,32,playing tympani,train IAkfSQROmP8,55,playing tympani,train IAlYlq8L4ug,10,playing flute,train IArDDikQbAg,30,cap gun shooting,test IAuNCE5AOrI,180,people clapping,train IAyFaOOu1TA,238,playing synthesizer,train IAyXUcw02Yo,1,skiing,train IAzgZ8LqbFQ,0,turkey gobbling,train IAzrTg2UrAM,30,playing bagpipes,train IB10i5gjd64,60,playing cello,train IB1M4B0LypE,45,civil defense siren,train IB2YDmEtKPo,170,helicopter,train IB3EV1mSeVE,584,firing cannon,train IB3EV1mSeVE,595,firing cannon,train IB4K1JFxVd8,180,"bird chirping, tweeting",train IB5GpLct3c4,42,playing tambourine,test IB6nvQEPM3g,95,arc welding,train IB72ly4OH3Q,30,"pigeon, dove 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IBsHxnbfO-w,500,splashing water,train IBtTRYVbp3I,11,people booing,train IBtouB3hoFg,7,elk bugling,train IBtuh3JerOQ,71,francolin calling,train IBtuh3JerOQ,176,francolin calling,train IButQ7xfhzk,76,arc welding,train IBy30oL3yxw,399,playing harpsichord,train IBy30oL3yxw,427,playing harpsichord,train IC08PcUq6jQ,30,playing french horn,train IC0SBe-NNfw,470,bouncing on trampoline,train IC2FODu6bt0,789,skiing,train IC4Rpl0_dW8,20,lighting firecrackers,test IC4h_HtFkGI,18,printer printing,train IC5RtMKUQGU,71,playing flute,train IC5x2ks6_ww,164,car engine idling,test IC9hh-3zlQY,792,wind chime,test ICB0gmlDnzE,165,playing cymbal,train ICBJAU36KN8,5,police car (siren),train ICBSkcHJH2Y,250,playing harp,train ICEvW9u8J8c,3,frog croaking,train ICFS5X9Tm-I,13,goose honking,train ICFS5X9Tm-I,25,goose honking,train ICGEg2YBvQc,30,waterfall burbling,train ICI95xzFxGs,30,playing table tennis,train ICIG9ubOR0Q,54,underwater bubbling,test ICKLJGr9k-U,98,playing oboe,train ICKLJGr9k-U,156,playing oboe,train ICLRKVqOf_k,30,car passing by,train ICNlA2IlwuI,236,smoke detector beeping,train ICPIVuPrjWk,60,cat purring,train ICPIVuPrjWk,363,cat purring,train ICPivY-cze4,9,beat boxing,train ICSs4gbJRIc,42,people marching,train ICSs4gbJRIc,131,people marching,train ICUSDdWJ_Is,0,playing cornet,train ICZiUsEaoiU,40,playing hammond organ,train ICajcUYAan8,410,people babbling,train ICbAyC3CDrA,0,mynah bird singing,train ICcK-wl6Gog,30,waterfall burbling,train ICcTfrj8YNs,0,fireworks banging,train ICeDT4g9WUI,5,playing volleyball,train ICecx83kVx8,40,playing electric guitar,train ICiRZcE5Uhc,430,ripping paper,train ICjN7lDvelQ,23,wind rustling leaves,train ICk5X6bcvtM,351,slot machine,train IClqeKMGVdQ,58,slot machine,test ICmMMd-nGS8,140,canary calling,train ICmMMd-nGS8,207,canary calling,train ICnSSbDDUCg,370,playing french horn,train ICoxzwibZJ8,84,lighting firecrackers,train ICoxzwibZJ8,133,lighting firecrackers,train ICpY96DeTgA,75,people humming,train ICpY96DeTgA,98,people humming,train ICpahMYO2xQ,20,typing on computer keyboard,train ICppPduV1fo,111,cat purring,train ICppPduV1fo,270,cat purring,train ICqaONjF-cA,1629,lip smacking,train ICr3g9tNi24,30,playing cornet,train ICr3g9tNi24,425,playing cornet,train ICrLXKUyBn8,110,waterfall burbling,train ICrXyOyRevM,90,playing bagpipes,train ICy1HVbfgNs,117,"vehicle horn, car horn, honking",train ICz4UtYnG7w,30,playing french horn,train ID00wk34PhI,12,playing zither,train ID00wk34PhI,137,playing zither,train ID44l9VqqGQ,26,playing accordion,train ID45LgG8LAk,109,playing tympani,train ID45LgG8LAk,120,playing tympani,train ID4E3P2YpUM,293,tractor digging,train ID4E3P2YpUM,599,tractor digging,train ID61eWTeQNo,819,playing electronic organ,train ID7NdijmMvE,90,playing banjo,train ID7lrmjrRxA,3,playing snare drum,train ID7lrmjrRxA,13,playing snare drum,train ID9mfxZSK6M,420,typing on computer keyboard,train ID9p52w2ah4,79,playing erhu,test IDCk1OAWSHY,30,"motorboat, speedboat acceleration",train IDCr6u_523k,80,"rowboat, canoe, kayak rowing",train IDDEpnjgTyw,30,people sniggering,train IDFaBYPBn90,63,sharpen knife,train IDFaBYPBn90,81,sharpen knife,train IDFvkp08li4,30,car passing by,train IDG4kgU31Gc,363,swimming,train IDGas4YWRHs,11,playing hockey,train IDGas4YWRHs,90,playing hockey,train IDGpCj3TOq0,540,"rowboat, canoe, kayak rowing",train IDIQUplab0k,130,helicopter,train IDJ4Xt44W4o,60,playing saxophone,train IDJTLOxZoCY,30,wind noise,train IDJVy6-R0H0,580,"engine accelerating, revving, vroom",train IDKEFgj7TaY,1032,playing badminton,train IDKT9T0NpwQ,39,francolin calling,train IDLHTubG-lU,30,"race car, auto racing",train IDLRYPqa010,239,underwater bubbling,train IDNjtuhsyok,5,wood thrush calling,test IDVwQY6qwxE,13,parrot talking,train IDWWfZ86akg,100,driving motorcycle,train IDYTuFR9WQ8,460,"child speech, kid speaking",train IDYdzPRct_I,303,playing congas,train IDYdzPRct_I,467,playing congas,train IDbj0dPpflc,30,typing on computer keyboard,train IDfaiqtHiEw,80,chimpanzee pant-hooting,test IDg45ezSvZE,30,"motorboat, speedboat acceleration",train IDhLvheBVM4,11,"rowboat, canoe, kayak rowing",train IDjZnEy6m00,25,chicken crowing,train IDnEyHDoVrU,1,duck quacking,train IDnYogceaA0,92,hammering nails,train IDrVguzuW8c,30,playing double bass,train IDs65n3FNuU,28,snake hissing,train IDs65n3FNuU,39,snake hissing,train IDuWdFF-2-0,70,"railroad car, train wagon",train IDvzopNK304,0,chainsawing trees,train IDxB0gesAa0,188,blowtorch igniting,train IDxrkNsZths,0,helicopter,train IDzUjPhY080,340,people burping,test IE-r6NDZ3aw,107,cutting hair with electric trimmers,train IE-r6NDZ3aw,217,cutting hair with electric trimmers,train IE0R6lK9n5Q,30,"railroad car, train wagon",train IE0sJnaEKvs,21,missile launch,train IE2LO4UkHHE,10,"pigeon, dove cooing",train IE2MjjzhuxY,0,lions growling,train IE4C5-gWx_8,0,playing bass guitar,train IE4fF36EIhU,13,pumping water,train IE4fF36EIhU,23,pumping water,train IE4mG3DA1nY,52,fire truck siren,train IE4pLXxV7MQ,0,bull bellowing,test IE4pLXxV7MQ,36,bull bellowing,test IE6M6eeyXLk,30,people crowd,train IE7oMAok9cA,253,beat boxing,train IE9g1dwEkNA,37,basketball bounce,train IEBY_sjA7iE,393,strike lighter,train IEB_QE5x9L4,400,"race car, auto racing",train IEBc019d1uo,242,playing tabla,train IECZE0mUXaA,195,singing bowl,train IED1SktHzNA,40,typing on computer keyboard,train IEEy9YYmxXs,17,"cattle, bovinae cowbell",train IEEy9YYmxXs,42,"cattle, bovinae cowbell",train IEFxlEeaQcM,5,sliding door,train IEHH2JYeJZI,60,playing acoustic guitar,train IEJGDoi_QwI,13,ferret dooking,test IEJGDoi_QwI,32,ferret dooking,test IEKrdzzZS_M,110,lawn mowing,train IEKy-qK_Py8,14465,"bird chirping, tweeting",train IEKy-qK_Py8,20578,"bird chirping, tweeting",train IEL-ZRC8hXU,20,train horning,train IEL-ZRC8hXU,49,train horning,train IELO5IfWFnk,218,people eating noodle,train IELO5IfWFnk,471,people eating noodle,train IEL_tTYEDeQ,120,ambulance siren,train IERQwxXxb2E,103,cutting hair with electric trimmers,test IERQwxXxb2E,163,cutting hair with electric trimmers,test IEUgLQwLgt0,30,driving buses,train IEVTCgOShEg,144,beat boxing,train IEVTx9wVSGI,155,roller coaster running,train IEVVHo9nr7g,30,rapping,test IEYLKRh6cEI,15,playing didgeridoo,train IEYy-OE1R2w,40,"pigeon, dove cooing",train IEZImL0QZEs,1,playing tabla,train IEZImL0QZEs,21,playing tabla,train IEdaJIbAHvQ,87,playing harpsichord,train IEeRzZGREz4,101,magpie calling,train IEf_lSt-a48,50,"subway, metro, underground",train IEg89aPi2JE,187,playing erhu,train IEgKwPiDpZQ,32,missile launch,train IEgXdfMcE3U,50,driving motorcycle,train IEi2m9ekyd8,30,playing bass drum,train IEiseWb8Tao,80,playing acoustic guitar,train IEjTV6_vzsQ,22,"playing steel guitar, slide guitar",train IEjTV6_vzsQ,34,"playing steel guitar, slide guitar",train IEjaZmXmm4c,20,telephone bell ringing,test IEmJWs9FL3c,11,playing mandolin,train IEmJWs9FL3c,22,playing mandolin,train IEoySXEns9A,30,"bird chirping, tweeting",train IEpswDTC1CA,30,turkey gobbling,train IEq9D1iMvFQ,154,train whistling,train IEtugDK9ugc,105,shot football,train IEwX4HhetxM,30,"rowboat, canoe, kayak rowing",test IEwkHkRqkL0,0,arc welding,train IEyAS4N4HkA,42,mynah bird singing,train IEyAS4N4HkA,55,mynah bird singing,train IEz41644hzU,30,playing drum kit,train IEzsYaOCMXI,171,airplane,train IF-77lLlMzE,30,duck quacking,test IF-Fxq9etvc,463,"rowboat, canoe, kayak rowing",train IF0oBhpPDRw,220,"playing marimba, xylophone",train IF44JsBICls,260,"bee, wasp, etc. buzzing",train IF4cFJeQIro,5,playing glockenspiel,train IF4cFJeQIro,15,playing glockenspiel,train IF5TdAAOjqE,40,chicken crowing,train IF5TdAAOjqE,51,chicken crowing,train IF6537UnoLY,146,people eating crisps,train IF6pEraHXjI,145,playing washboard,test IF780vXZ8LA,80,chicken crowing,train IF7L4BUR-og,10,playing table tennis,train IF87P1LmXus,30,playing french horn,train IF8Z5ITjt4Q,427,people eating crisps,train IF8Z5ITjt4Q,482,people eating crisps,train IF92YmTMtdk,89,"cattle, bovinae cowbell",train IF92YmTMtdk,105,"cattle, bovinae cowbell",train IFAKJtuWCV4,100,"electric shaver, electric razor shaving",train IFAKJtuWCV4,112,"electric shaver, electric razor shaving",train IFCIUF1PG-M,107,people burping,train IFCzZMQ5lbk,17,baby laughter,train IFGCaL0TrSw,172,hail,train IFGCaL0TrSw,468,hail,train IFGbGcs3bQQ,34,chinchilla barking,train IFGbGcs3bQQ,51,chinchilla barking,train IFICeWzDGqg,236,playing gong,train IFLy09zPXFI,109,playing bassoon,train IFM2fxyKzGU,51,volcano explosion,test IFQ76msqUA4,1,machine gun shooting,train IFR7h0EFItM,59,bathroom ventilation fan running,test IFVU1mFIcok,149,tractor digging,train IFVeQqSwXrY,570,sailing,train IFWxRvYRibk,30,"playing violin, fiddle",train IFXLc3VZldw,30,playing zither,test IFcZ_KZ5S4o,128,playing electric guitar,train IFcg8mEco2o,30,playing acoustic guitar,train IFclJMPODCA,221,blowtorch igniting,test IFdYNwdwhYk,198,planing timber,train IFg0tmz75O8,30,"playing marimba, xylophone",train IFh4XSLdgDE,60,playing harp,train IFi2TjB-DN0,30,"female speech, woman speaking",train IFiWCAJXAwQ,20,yodelling,train IFin18hjcqI,2,people booing,train IFkLB68mx0U,30,driving buses,train IFlCOAKzU3U,26,ferret dooking,train IFlVLHua0x8,175,airplane,train IFpiPt42434,30,"engine accelerating, revving, vroom",train IFsSyQHwZJc,117,"subway, metro, underground",train IFsSyQHwZJc,544,"subway, metro, underground",train IFshEqjzKyM,11,dog baying,train IFvvPCjqjQM,290,playing cymbal,train IG2MM3AYP9E,2,typing on typewriter,train IG5-s_G2csM,30,playing electronic organ,test IG6xVFAMS8A,5,playing darts,train IG7LgYs17Ak,20,"female speech, woman speaking",train IGBJHTVtyKM,340,"railroad car, train wagon",train IGBz-D8eV5Q,30,people eating,train IGCmCRZnsRs,317,playing volleyball,train IGE20HaWFbw,35,"railroad car, train wagon",train IGE3AvBMcK4,4,sharpen knife,train IGE3AvBMcK4,14,sharpen knife,train IGF__xZmktU,167,parrot talking,test IGG2xMicLG0,360,driving motorcycle,train IGKGJkWa1qc,220,"race car, auto racing",train IGKcvwjDKic,80,beat boxing,train IGLL0_qVomg,13,sea waves,test IGMBon0T3Wg,101,electric grinder grinding,train IGMBon0T3Wg,128,electric grinder grinding,train IGQv-05ILA8,34,tractor digging,train IGQv-05ILA8,68,tractor digging,train IGRQ57WD7Pg,100,dog barking,train IGS8gcF0A0Y,270,people sniggering,train IGTMu3MlafI,163,black capped chickadee calling,train IGTZX6hiIOw,31,singing bowl,train IGU4x63VjAU,96,baby babbling,train IGaF7wK8hvM,98,playing bassoon,test IGcqALaU3Yw,240,people whispering,train IGfwhMu7FOE,10,underwater bubbling,train IGfwhMu7FOE,21,underwater bubbling,train IGikBxNZsf0,11,baby babbling,train IGikBxNZsf0,46,baby babbling,train IGilUdalDOg,30,helicopter,train IGm_C8qG9-M,11,police car (siren),train IGoB2DkbZKc,30,playing banjo,train IGu-tLc0CUo,177,playing bongo,train IGuhcXoT2ls,804,playing darts,train IGv-gHYEKl8,6,typing on typewriter,train IGv-gHYEKl8,17,typing on typewriter,train IGwZbtXCaFM,30,horse clip-clop,train IGyeI9enP_Q,30,car passing by,train IH5NqdX4RWE,1,horse neighing,train IH6ED91P95U,40,people hiccup,train IH7-WzwcKKs,570,cap gun shooting,train IHGdJJtGSgY,30,helicopter,train IHKUiHcxnKc,80,people crowd,train IHKjoCS9gs4,28,chinchilla barking,test IHMFdks4CGg,258,playing flute,train IHOpfOtDnp4,300,typing on computer keyboard,train IHOqNe8k0Ck,0,sliding door,train IHOqNe8k0Ck,10,sliding door,train IHPKIibjte8,0,people farting,train IHPNPQgbLCo,22,playing volleyball,train IHS3h8E7MaQ,30,"playing violin, fiddle",train IHSblFkJ6-Y,29,cheetah chirrup,test IHSblFkJ6-Y,39,cheetah chirrup,test IHVNnkU7-To,140,driving buses,train IHVy1roVH28,0,people giggling,train IHVy1roVH28,22,people giggling,train IHYmcWev4RM,26,people burping,train IHZgNzX-BPs,8,playing guiro,train IHaWOJuekYY,109,tap dancing,train IHaeb18ynFo,30,"playing violin, fiddle",train IHeOIx7Vt_k,20,ambulance siren,train IHg3nrJZQ1I,100,lawn mowing,test IHl4CxHfO8c,56,firing cannon,train IHl4CxHfO8c,72,firing cannon,train IHl8o_iL02E,80,printer printing,train IHlciYhuweo,170,playing saxophone,train IHlgJYdUxKo,30,crow cawing,test IHoEjgpwHmQ,132,people booing,train IHplK2PQ0wg,120,people sobbing,train IHqCkKtcZZ0,584,blowtorch igniting,train IHrz8URnta4,86,airplane flyby,train IHrz8URnta4,139,airplane flyby,train IHsAs7VAS20,40,playing drum kit,train IHsenuyeSFQ,176,playing glockenspiel,train IHsenuyeSFQ,186,playing glockenspiel,train IHstpsby6d4,158,penguins braying,train IHtB1vkBT1A,30,playing trombone,train IHvajW2S44c,213,playing badminton,train IHvtpbQJR3c,10,people whistling,train IHw2Wom8yLA,17,cat meowing,train IHw2Wom8yLA,87,cat meowing,train II0KddTM6H0,105,turkey gobbling,test II11q1nDqdA,0,"vehicle horn, car horn, honking",train II1ZL1W-hQQ,129,police radio chatter,train II1ZL1W-hQQ,190,police radio chatter,train II3Er-VitEc,42,sheep bleating,train II3Er-VitEc,83,sheep bleating,train II51i1JynL8,42,lathe spinning,train II51i1JynL8,56,lathe spinning,train II5Mqxbc7vY,48,playing squash,train II5UmxdlyZE,89,playing squash,train II72zzkaBaY,21,using sewing machines,train II7R8Bu9rBM,95,parrot talking,train II85Ieg2TYc,70,chicken crowing,train IIAYqMumkqg,245,playing sitar,train IIAYqMumkqg,294,playing sitar,train IICmzYnPVYM,35,playing trombone,train IID1k7AERws,0,lighting firecrackers,train IID1k7AERws,45,lighting firecrackers,train IID8ylEYH5o,62,canary calling,test IIK56-kUxc0,264,mouse pattering,train IIKc_1iCxMQ,14,playing bassoon,test IILZkLn6etw,80,rope skipping,train IILmblGTzGQ,10,playing lacrosse,train IINqN6L2NsY,285,tapping guitar,train IIRg_apJQpQ,226,air conditioning noise,train IIRnqLFHzY0,147,missile launch,train IIRnqLFHzY0,158,missile launch,train IIYDIxvVSjM,197,slot machine,train IIamk2tYJHY,365,playing oboe,test IIaqHCtUW5A,19,baby crying,train IIdwBaHYhE4,70,"electric shaver, electric razor shaving",test IIdwIxA5sfo,77,singing bowl,train IIhLozwtaWY,40,"race car, auto racing",train IIm5ZKHuf0U,80,male singing,train IInBRjAPYIs,30,orchestra,train IIq2M4Ksk3I,15,"alligators, crocodiles hissing",train IIqXmRP7O1U,23,cuckoo bird calling,train IIqXmRP7O1U,48,cuckoo bird calling,train IIv1mYMKQB0,82,playing volleyball,test IIw9Glx8j0w,140,playing snare drum,train IIwUyz187WE,35,cat caterwauling,train IIzMqbTNYOA,410,ambulance siren,test IIzN5tWA0Qo,10,playing bagpipes,train IJ0jeaVklt8,16,playing snare drum,test IJ0v0t2qB10,3,slot machine,train IJ1E4bYBvXw,42,people babbling,train IJ1E4bYBvXw,60,people babbling,train IJ1aeBxbn4g,58,basketball bounce,train IJ2qbwmUrCM,80,basketball bounce,train IJ2qbwmUrCM,184,basketball bounce,train IJ3jYxFXJyM,30,playing cymbal,train IJ4w3IQNITs,0,police car (siren),train IJ5QADtaMJE,30,"rowboat, canoe, kayak rowing",train IJ5UJ_-3sY8,19,"vehicle horn, car horn, honking",train IJ5m2mpSfm8,5,"cattle, bovinae cowbell",train IJ8-cf4hNAg,280,people clapping,train IJ83FEhSk9w,127,lions growling,train IJ83FEhSk9w,162,lions growling,train IJ9WJQ0dj3g,20,helicopter,train IJASJFiWN3A,308,tap dancing,train IJCIcvTqm3s,100,"vehicle horn, car horn, honking",train IJDZ85O0fkg,30,people babbling,train IJD_RBMwAHw,160,playing saxophone,train IJFRLHi0PRw,440,fireworks banging,train IJFkecE1yq8,19,playing electronic organ,train IJFkecE1yq8,57,playing electronic organ,train IJHDhkDsztU,30,police car (siren),train IJHy3Iz_SVQ,5,playing tambourine,train IJI4VNsKSUg,0,helicopter,train IJIFSDMPT_8,0,basketball bounce,train IJIP1LubfHQ,126,playing glockenspiel,train IJIP1LubfHQ,150,playing glockenspiel,train IJM_CdTilmg,290,sloshing water,train IJNf5hJzf5M,6,door slamming,test IJOJPj10cgs,80,dog barking,train IJPFmylnJ1s,38,ambulance siren,train IJRHXISVhlc,30,playing acoustic guitar,train IJRX9a3o17I,30,horse clip-clop,train IJSYa0U9E7A,52,barn swallow calling,train IJSYa0U9E7A,131,barn swallow calling,train IJTq1kMJgUQ,43,reversing beeps,train IJTq1kMJgUQ,380,reversing beeps,train IJWXr8cdCDs,160,wind noise,train IJXMLAcshzY,34,playing djembe,train IJXMLAcshzY,139,playing djembe,train IJXrQCDz-wc,233,people eating noodle,test IJYZ6hGSaiw,197,male singing,train IJYt5jMdqj4,10,people crowd,train IJ_JOEQkYUM,100,airplane,train IJ_Ueyp9h0s,338,tractor digging,train IJap0sFiIcE,116,"heart sounds, heartbeat",train IJap0sFiIcE,155,"heart sounds, heartbeat",train IJbayDf5SB8,11,tapping guitar,train IJc2An2hClc,9,"cattle, bovinae cowbell",train IJc2An2hClc,20,"cattle, bovinae cowbell",train IJcUDAQtctc,13,horse clip-clop,train IJf8MeTnILE,30,door slamming,train IJfZZhI5TDI,4,people eating apple,train IJiH2wBJY4g,193,beat boxing,train IJjtEns0Fxk,86,playing cymbal,train IJkAvCHwmTA,95,barn swallow calling,train IJmuDKiE33c,30,playing banjo,train IJn4Uc9vf28,30,people crowd,train IJngO9uln94,270,playing french horn,train IJquEOrTj1Y,150,playing didgeridoo,test IJrlj2Jp1gA,440,playing vibraphone,train IJrlj2Jp1gA,552,playing vibraphone,train IJsTAdWRHQs,20,fireworks banging,train IJtWQfFRhmQ,30,driving motorcycle,train IJtYFtBmpCU,560,people clapping,train IJv0lS_bIe8,51,cell phone buzzing,train IJv0lS_bIe8,217,cell phone buzzing,train IJvYFkrfjBg,28,tornado roaring,train IJvYFkrfjBg,49,tornado roaring,train IJyVRkeyBA8,70,"child speech, kid speaking",train IK0axvMo1I0,1,duck quacking,train IK0axvMo1I0,10,duck quacking,train IK1OPO6Llf8,5,pig oinking,train IK6KzFOUENI,0,zebra braying,train IK8QyhprUlk,16,vacuum cleaner cleaning floors,train IKDGba7vsOA,44,baby laughter,test IKEt6XNhyk8,48,child singing,train IKEt6XNhyk8,62,child singing,train IKGIdg6ohP8,0,playing cello,train IKHgv7lcAPQ,30,playing acoustic guitar,train IKOtjdmU89I,30,playing accordion,train IKQBehM7Q2c,30,pig oinking,train IKQjtVBPUZ0,99,skateboarding,train IKQjtVBPUZ0,360,skateboarding,train IKRHxvXjIuc,29,reversing beeps,train IKRHxvXjIuc,49,reversing beeps,train IKRPW3-bqSk,31,elephant trumpeting,train IKTblEUO_Zs,12,canary calling,train IKTblEUO_Zs,23,canary calling,train IKUVgbi1e2U,0,playing congas,train IKUVgbi1e2U,51,playing congas,train IKVBMpnp48s,30,"rowboat, canoe, kayak rowing",train IKVaMbh4Ue0,23,people booing,train IKY9y5SvTaE,39,people booing,train IKaxw1EDGUQ,736,playing volleyball,train IKbPgWZ_gww,70,using sewing machines,train IKdDjrqrzoY,160,squishing water,test IKemjiZjzpc,1001,playing erhu,train IKevuIosyL0,21,mouse pattering,train IKj9E33H8e8,12,pig oinking,train IKjNfEf9F-Y,130,playing piano,train IKk7RiqtOF0,14,chicken crowing,train IKk7RiqtOF0,20,chicken crowing,train IKkkAR4EWKA,30,"rowboat, canoe, kayak rowing",train IKkkix_DHVE,27,playing zither,train IKkkix_DHVE,37,playing zither,train IKmA4opS4es,10,scuba diving,train IKmA4opS4es,34,scuba diving,train IKpJJIibEgo,131,planing timber,train IKt4Qp-lnGo,70,playing hammond organ,train IKu6Akp4iBU,35,missile launch,train IKu6Akp4iBU,46,missile launch,train IKx5YbMQSP0,30,toilet flushing,train IKxTq84PvY4,7,fireworks banging,train IL-EAE-7HJU,30,chicken clucking,train IL0hnXdZgZY,227,airplane,train IL0hnXdZgZY,266,airplane,train IL3ThGKdOMk,221,tractor digging,test IL4Luz1dBGw,6,playing timbales,train IL4_fpYj0Is,3,opening or closing car doors,train IL5ARDGrfWQ,100,planing timber,train IL5ARDGrfWQ,191,planing timber,train IL5IujOTavY,50,electric grinder grinding,train IL5IujOTavY,73,electric grinder grinding,train ILBWV9AFKDU,115,playing ukulele,train ILBWV9AFKDU,138,playing ukulele,train ILCKGnhlzT0,65,playing gong,train ILCKGnhlzT0,342,playing gong,train ILCOAF3HDso,30,orchestra,train ILDt5_ntP0s,30,toilet flushing,train ILE3qw72rkc,200,machine gun shooting,train ILGttENN29k,20,"child speech, kid speaking",train ILHUnfxj7Cw,51,arc welding,train ILHW8B5dfMQ,33,dog whimpering,train ILIh--LeKHI,275,chimpanzee pant-hooting,test ILKdRNizGCs,594,people slurping,test ILLASxBC1h4,30,playing cymbal,train ILOGhQwN_KA,25,playing cymbal,train ILQoE0nox84,11,baby laughter,train ILR0MTxWCSo,44,skiing,train ILRHyaxIBI0,435,playing badminton,train ILRMVaPMGOg,12,bowling impact,train ILRMVaPMGOg,57,bowling impact,train ILRrSTpzzLg,59,francolin calling,train ILRrSTpzzLg,116,francolin calling,train ILV9U54DWyk,21,playing mandolin,train ILVb9cQ-7JM,5,wind rustling leaves,train ILXPPbAZBaY,68,playing table tennis,train ILXhvRIlIXM,155,playing theremin,train ILYfegayIsQ,260,dog bow-wow,train IL_SGJjcUdo,20,"race car, auto racing",train ILblRfNLj58,20,people giggling,train ILcubQfUPa8,30,wind noise,train ILen-duaTSM,240,wood thrush calling,train ILen-duaTSM,252,wood thrush calling,train ILjCxvRSgzc,40,using sewing machines,train ILlu_6T-L6U,30,playing harpsichord,train ILm4AbCmeTE,64,singing choir,train ILm4AbCmeTE,110,singing choir,train ILmJuuV8W6U,20,"subway, metro, underground",train ILnH26kEEWk,70,"bee, wasp, etc. buzzing",train ILnH26kEEWk,120,"bee, wasp, etc. buzzing",train ILo3y2yo0ho,4,goose honking,train ILo3y2yo0ho,14,goose honking,train ILofaPGsD6w,41,playing djembe,train ILofaPGsD6w,80,playing djembe,train ILq6u0HON8g,90,"engine accelerating, revving, vroom",train ILqT2sZF8OA,107,skiing,train ILqT2sZF8OA,295,skiing,train ILq_7niK90M,0,civil defense siren,train ILqcqREuxi8,10,underwater bubbling,train ILqcqREuxi8,40,underwater bubbling,train ILrDsnIyj6M,4,crow cawing,train ILrDsnIyj6M,30,crow cawing,train ILtFiRpHhF8,69,rope skipping,train ILtFiRpHhF8,228,rope skipping,train ILv7AQF-ltI,30,"bird chirping, tweeting",test ILwYjUMzP7g,7,beat boxing,train ILybfxFIpSU,50,driving motorcycle,train IM-kd7Wsno4,130,female singing,train IM-xD0Xf-24,10,splashing water,train IM0wyMNcLtY,124,tap dancing,train IM1-ypyrUBo,45,"ice cream truck, ice cream van",train IM1-ypyrUBo,195,"ice cream truck, ice cream van",train IM192eAncKE,20,people hiccup,train IM3XAO8xzzU,240,playing synthesizer,train IM3gZfpkAMs,142,hail,train IM3gZfpkAMs,166,hail,train IM6GwctAsMA,20,"railroad car, train wagon",train IM88gSXv2e8,310,people humming,test IM8V-nhXkJ4,361,skiing,train IMAuRc4z0AQ,270,playing harp,train IMB24hwbfg0,227,people eating apple,train IMB24hwbfg0,296,people eating apple,train IMCPtjWhKdw,20,"engine accelerating, revving, vroom",train IMFsWfdTQ9c,19,basketball bounce,train IMGxFGNfevk,123,bouncing on trampoline,train IMICMWEyi5E,123,playing squash,train IMIuEADfRTQ,140,bird wings flapping,test IMKIBJD-JSM,13,disc scratching,test IMLVJnySOAM,186,electric grinder grinding,train IMLVJnySOAM,202,electric grinder grinding,train IMM2rV0CZq0,8,playing tabla,train IMMfo_brvf4,9,reversing beeps,train IMN42wrdwfI,125,mouse pattering,train IMN42wrdwfI,147,mouse pattering,train IMNTwlKQM08,343,police radio chatter,train IMNTwlKQM08,464,police radio chatter,train IMO3P07aU9M,40,playing bassoon,train IMO7ZsPYNAE,2,"subway, metro, underground",train IMO7ZsPYNAE,12,"subway, metro, underground",train IMOAsgB8pK8,128,mouse clicking,train IMOAsgB8pK8,148,mouse clicking,train IMOQv1QHxSQ,41,chipmunk chirping,test IMPJ60OJvYM,338,singing bowl,train IMPOWm90ELo,60,stream burbling,train IMPZAq8cQKY,30,"playing marimba, xylophone",train IMPrYSvy0ks,65,singing bowl,train IMQtdwaG8I8,101,forging swords,test IMTCYbIC8C4,92,playing oboe,train IMTCYbIC8C4,109,playing oboe,train IMTv4Ym0p6A,23,"vehicle horn, car horn, honking",test IMV24W8j48M,21,reversing beeps,train IMWPb20akf8,51,slot machine,test IMWghaglXg0,2,church bell ringing,train IMWghaglXg0,19,church bell ringing,train IMYZ_q8fDhI,17,pumping water,train IMYZ_q8fDhI,29,pumping water,train IMZ9b45CRJs,58,baby crying,train IMbEfV42v0c,5,pig oinking,train IMbLXHGE7wY,27,lathe spinning,train IMbLXHGE7wY,68,lathe spinning,train IMbrg9npgbA,32,playing hammond organ,train IMcMi3JCmVk,104,playing double bass,train IMcMi3JCmVk,114,playing double bass,train IMcXGgxPjl4,0,people belly laughing,train IMdOc6tuH8o,28,playing timbales,train IMdOc6tuH8o,116,playing timbales,train IMh14SVVQQ8,73,mouse pattering,train IMh14SVVQQ8,427,mouse pattering,train IMhj3aWYz60,575,opening or closing drawers,train IMiHxrJreuQ,476,driving snowmobile,train IMiHxrJreuQ,585,driving snowmobile,train IMjpwQHZaWo,33,bathroom ventilation fan running,test IMkAKQsD5sg,260,crow cawing,train IMkBXHBYcMU,48,owl hooting,train 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IPCpCk055mI,35,alarm clock ringing,train IPCpCk055mI,111,alarm clock ringing,train IPDIjAVxW0w,153,playing french horn,train IPDt_iDc6o0,80,chicken crowing,train IPDt_iDc6o0,96,chicken crowing,train IPFvrKR5KUo,30,playing harp,train IPGCkdTtbhg,100,cattle mooing,train IPIS6TUVpSM,30,vacuum cleaner cleaning floors,test IPKcMm8Lmms,139,canary calling,train IPKcMm8Lmms,243,canary calling,train IPNSrRyGttU,7,dog baying,train IPNSrRyGttU,78,dog baying,train IPPEJRnBBNA,130,playing cymbal,train IPRSfGhKCk4,30,police car (siren),train IPSzXisZuD4,30,"rowboat, canoe, kayak rowing",train IPUw8tohizs,0,mynah bird singing,train IPUw8tohizs,11,mynah bird singing,train IPWg7K8zf9g,0,splashing water,train IPXwsIzKC20,110,lawn mowing,train IPYHy8k9Z34,50,playing piano,test IPZc3vHtDhA,190,skidding,train IPatThnRs2w,108,sharpen knife,train IPatThnRs2w,206,sharpen knife,train IPdB13O5Z6s,53,missile launch,train IPdZmFcWiAQ,462,swimming,train IPehEfMBLM0,100,chopping wood,train IPhu-6QWums,50,church 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ISRUTS1uqpY,127,rope skipping,train ISRUTS1uqpY,219,rope skipping,train ISSYF0u4lE4,20,playing trumpet,train ISSr0mmq150,26,francolin calling,train ISSr0mmq150,44,francolin calling,train ISXdFhTvGh8,4,dog howling,train ISYLqeureiM,180,thunder,train ISYr6jW2nkw,88,tractor digging,train ISbweX-xGbs,9,woodpecker pecking tree,train ISbweX-xGbs,21,woodpecker pecking tree,train ISczptiVLYk,57,tractor digging,train ISdguQ3flPA,33,bouncing on trampoline,test ISeBh08an7o,80,ocean burbling,train ISeCp56ud4I,64,tap dancing,train ISeCp56ud4I,81,tap dancing,train ISh4xMml57A,31,playing bugle,train ISknJkNd4e0,230,lathe spinning,train ISknJkNd4e0,243,lathe spinning,train ISlP7Mq27EA,39,electric grinder grinding,train ISmPPPoe_kk,222,planing timber,train ISmo5KouU0Q,318,lathe spinning,train ISoElB3H3tE,30,dog barking,train ISoO6g69dzc,19,people booing,train ISpO-T6bavY,42,golf driving,train ISq4fKAacv0,30,playing acoustic guitar,train ISrb23zuWcY,180,helicopter,train ISxOV4i0CTI,140,sliding 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razor shaving",train IT_hfR-jqLI,30,"pigeon, dove cooing",train ITaflypbuAs,15,playing shofar,test ITc6Bnh2q0c,21,"rowboat, canoe, kayak rowing",train ITc_gC-oIUc,64,playing mandolin,train ITd53w6IXlM,30,sliding door,train ITd5pesVjpY,167,singing choir,train ITfUBCYUWY8,140,parrot talking,train ITfUBCYUWY8,172,parrot talking,train IThCRPbXPMI,17,whale calling,test ITiWSraf-qg,39,cuckoo bird calling,train ITiWSraf-qg,116,cuckoo bird calling,train ITjVA8TQPXs,30,"pigeon, dove cooing",train ITjqXtLFsLU,30,"playing violin, fiddle",train ITks35MInZI,30,"pigeon, dove cooing",train ITlbHsh59y8,22,"donkey, ass braying",train ITnp7kkGUJU,30,stream burbling,train ITqJAI4xCB0,32,tapping guitar,train ITqJAI4xCB0,42,tapping guitar,train ITtiXLwV0gc,10,train wheels squealing,train ITtiXLwV0gc,26,train wheels squealing,train ITv-2kfYMTk,150,driving buses,train ITvfU2G-IVo,320,people sniggering,train ITwKfIU9MKM,30,dog howling,train ITxHwlf92U8,64,airplane,train ITxKPZN4_v8,30,ocean burbling,train ITym6X1Cvzs,0,duck quacking,test IU-WlKHvnG4,202,"cattle, bovinae cowbell",train IU-WlKHvnG4,216,"cattle, bovinae cowbell",train IU0ZuMyg05g,80,"bee, wasp, etc. buzzing",train IU2VRTyAmDg,30,horse clip-clop,train IU4XC9ZWhzw,34,dog whimpering,train IU4XC9ZWhzw,49,dog whimpering,train IU5l2tcG78U,30,zebra braying,train IU5l2tcG78U,85,zebra braying,train IU61q6O2aiQ,140,playing bass guitar,train IU6jKQVQWkw,393,elephant trumpeting,train IU7E31cfniU,44,playing theremin,train IU7E31cfniU,147,playing theremin,train IU7G1gnORJU,125,playing volleyball,train IUAdc4VQfek,782,scuba diving,train IUAgX3BGjj8,20,playing harp,train IUAzgF0QW-k,0,cell phone buzzing,train IUBXcZ4ae8U,33,chimpanzee pant-hooting,train IUBXcZ4ae8U,44,chimpanzee pant-hooting,train IUCFDTu--Us,0,chicken clucking,train IUCNGNGn4GY,30,"playing marimba, xylophone",train IUCWs3HjsgI,756,bowling impact,train IUCWs3HjsgI,799,bowling impact,train IUCbnO8J9yY,140,people crowd,train IUEdovEi1mc,290,people clapping,train 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IVGD0NIQV8A,30,mouse pattering,train IVHhX5g3pAU,142,playing volleyball,train IVHyosaA8Eo,71,barn swallow calling,train IVJ8T-SM1O0,30,goose honking,train IVJkeUVwUaY,37,playing erhu,test IVJkeUVwUaY,145,playing erhu,test IVM2EsJg8Ag,14,playing cornet,train IVO-hM-FOkE,199,people eating noodle,train IVPJLrzhUjA,30,car engine knocking,train IVQQ--Kz38k,29,people coughing,train IVQQ--Kz38k,45,people coughing,train IVRZ48sQOro,75,elephant trumpeting,train IVRsljoz4FI,680,slot machine,train IVSVTVzhFWg,43,cheetah chirrup,train IVSVTVzhFWg,88,cheetah chirrup,train IVTRZ2LOCxw,2,strike lighter,train IVWAYYPiHe0,330,sailing,train IVZQRMQULlQ,100,playing cello,test IVZRQbAG5Xs,97,disc scratching,train IVZRQbAG5Xs,131,disc scratching,train IVb0xn1T4OE,68,playing squash,train IVctunvuTjM,10,people babbling,train IVdazTeNu08,36,playing table tennis,train IVdp0uBdrMM,17,playing cornet,train IVeHhIHMC7w,50,"bee, wasp, etc. buzzing",train IVj52dHn4Hc,102,"bee, wasp, etc. buzzing",train 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IYzE-9Ng2oE,763,rapping,train IZ2hLZCF5dw,0,people coughing,train IZ3GBQx5g3I,150,waterfall burbling,train IZ3vSgL_CLA,40,driving buses,train IZ51tUuibq8,30,owl hooting,test IZ5ta34oUWg,30,wind noise,train IZ6IgSG2AEU,60,male singing,train IZ7DHivuA-I,352,"railroad car, train wagon",train IZ9JfjL_Qj8,554,playing bassoon,train IZAasx5KIKE,10,fireworks banging,train IZC5-yGRThA,2,owl hooting,train IZC5-yGRThA,24,owl hooting,train IZCAxLMALHM,110,playing banjo,train IZFXnHlsITs,114,foghorn,test IZIWAhVkXzc,45,playing timpani,train IZIxQ465y7Q,100,people burping,train IZLn7IjGJKI,0,people crowd,test IZLpZ41_Vj8,424,eletric blender running,train IZQPvlIJEoI,30,"rowboat, canoe, kayak rowing",test IZQv6lPBdLQ,100,playing accordion,train IZR4G9Hhy5U,30,playing cello,test IZR6yuDevUc,101,bowling impact,train IZR6yuDevUc,171,bowling impact,train IZ_FwYrGGXA,29,chinchilla barking,test IZ_ZbhVjfMw,475,turkey gobbling,train IZ_ZbhVjfMw,534,turkey gobbling,train IZdBjkqyhhc,27,dog bow-wow,train IZffL8aQW9w,30,horse neighing,train IZhiA0OGa5Y,110,skidding,train IZiEWWNZ594,2,"pigeon, dove cooing",train IZiz8hNStYY,80,singing bowl,train IZj-NCD3RKI,197,playing table tennis,train IZpyBGV1p6M,220,skateboarding,train IZpyBGV1p6M,440,skateboarding,train IZr_1vTYTHQ,27,fox barking,train IZtiMmPN0Xc,0,playing harp,test IZu3c1eNw2Y,12,airplane,train IZu3c1eNw2Y,25,airplane,train IZuuoY5_8oU,30,skateboarding,train IZwy6SkDAVU,30,sailing,train I_0VX1yYQdU,11,playing cymbal,train I_0p9BDtdvk,168,scuba diving,train I_0p9BDtdvk,192,scuba diving,train I_2Wi0y7dqU,82,firing muskets,train I_2Wi0y7dqU,163,firing muskets,train I_34oVPN34w,3,"donkey, ass braying",train I_34oVPN34w,26,"donkey, ass braying",train I_3q_dWuk0E,2,dog baying,train I_3q_dWuk0E,12,dog baying,train I_5TsFIAB5A,397,car engine idling,train I_5TsFIAB5A,454,car engine idling,train I_9vhjV6b0g,15,arc welding,train I_B_bBusjoU,18,singing choir,train I_B_bBusjoU,95,singing choir,train I_D5VluKCdg,3,arc welding,train I_D5VluKCdg,25,arc welding,train I_Dqr6WdBHU,8,woodpecker pecking tree,train I_Dqr6WdBHU,23,woodpecker pecking tree,train I_E5FmPkq-M,430,people sniggering,train I_G4uEBkfEU,136,playing double bass,train I_I0aKu9hbs,450,"child speech, kid speaking",train I_J4gmcdiN8,30,church bell ringing,train I_JPRIJj53E,0,disc scratching,train I_ND-Uw-mNk,30,airplane flyby,train I_QA4FKGWHc,19,lathe spinning,train I_QUt72H_VA,7,playing glockenspiel,train I_QUt72H_VA,17,playing glockenspiel,train I_QqrPynbqs,43,volcano explosion,train I_R6SATSgXU,15,playing ukulele,train I_R_6gRPR40,80,playing flute,train I_RqRi4nRXs,70,printer printing,train I_SzdlhWsyQ,15,lighting firecrackers,train I_WJAC1RvFg,10,playing double bass,train I_WJAC1RvFg,32,playing double bass,train I__AnlUCtNg,23,playing timbales,train I__AnlUCtNg,84,playing timbales,train I_fnP-4bh6g,172,cricket chirping,train I_fnP-4bh6g,346,cricket chirping,train I_h193IuPKY,17,"vehicle horn, car horn, honking",train I_jvVCAadYo,166,playing 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IbSYTVRyOLw,160,people whistling,test IbSzAEABHFY,359,playing electronic organ,train IbSzAEABHFY,495,playing electronic organ,train IbUS2L6ny8I,67,crow cawing,train IbUS2L6ny8I,84,crow cawing,train Ibas2aHHcnI,48,shot football,train Ibas2aHHcnI,88,shot football,train Ibbl6TewkHo,220,thunder,train Ibex2TX-iFI,30,fire truck siren,train Ibex2TX-iFI,57,fire truck siren,train IbfKWNsNZGw,0,playing vibraphone,train IbfKWNsNZGw,18,playing vibraphone,train Ibgc-DVgllI,30,"playing violin, fiddle",train IblhEF_MiH8,400,"race car, auto racing",test Ibr70BZuYZ0,30,pig oinking,train IbrsNWp8GIo,133,playing harmonica,train IbsALIlSPZU,30,"pigeon, dove cooing",train Iby8hIuJcqY,124,slot machine,train Ic-DNuZ4Gr0,350,"subway, metro, underground",train Ic-T5M7LAaI,147,people eating apple,test Ic1NEpwOvcQ,119,playing harmonica,train Ic2mi7SiCP4,150,driving buses,train Ic3CVwevSi0,21,airplane,train Ic3UBfhI49I,170,playing trombone,train Ic4BZspmb6Q,63,squishing water,train Ic4BZspmb6Q,76,squishing 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Ig44XGQRxXI,30,playing bass guitar,train Ig5IzqiO-UY,66,sea waves,train Ig8Gmdns4Rg,30,people shuffling,train Ig8yFpXwTIE,37,opening or closing car doors,train IgASaWbXwII,170,people crowd,train IgCge-RtPCw,103,firing cannon,train IgCphQCkHSk,65,male singing,train IgD6Jnw4BZc,30,"bird chirping, tweeting",train IgGRilpvJTI,78,playing snare drum,train IgGRilpvJTI,108,playing snare drum,train IgMsw4Usa7c,140,playing banjo,train IgO_Zb3wxvQ,304,bird squawking,train IgP-FA06k5o,30,playing piano,train IgSz8aZUJsw,0,horse neighing,train IgU4YzlfJ6k,30,"playing violin, fiddle",train IgVdF1Igzis,110,frog croaking,test IgVkmn7ZMYg,99,beat boxing,train IgYKpvbtKtA,21,chicken crowing,train IgYKpvbtKtA,32,chicken crowing,train IgYfNFKwiGs,94,chopping food,train IgYfNFKwiGs,139,chopping food,train IgZ6SeMFFDE,5,squishing water,test IgZuLdndWww,47,lions roaring,train IgZuLdndWww,59,lions roaring,train Igak2b93OeI,0,baltimore oriole calling,train Igak2b93OeI,13,baltimore oriole calling,train IgbGOMBaGmw,13,chimpanzee pant-hooting,test Igbpx26YE3k,34,playing vibraphone,train Igbpx26YE3k,46,playing vibraphone,train IgePf95SUgQ,30,"rowboat, canoe, kayak rowing",train IgiMaj7k4D4,160,crow cawing,train IgiaGUeiVe4,411,bowling impact,test IgjmUOZmRBk,689,people slurping,train IgjmUOZmRBk,731,people slurping,train Igk69C6svwg,38,"playing steel guitar, slide guitar",train Iglv2Kru0mw,40,"fly, housefly buzzing",test IgnWzDZ917E,231,playing hockey,train IgnWzDZ917E,375,playing hockey,train IgpZc6-OuKo,140,"race car, auto racing",train IgpcP8uwyGg,60,playing banjo,train IgqgBEcSpRo,60,orchestra,train IgrTu3Ms5E8,380,using sewing machines,train Iguc_tZCwjQ,50,people sobbing,train Ih0EUGHl2z0,83,playing trombone,train Ih0pk4PHp8M,2,sliding door,train Ih0xy7tt2JY,30,helicopter,test Ih3hZZRw9Fg,146,bathroom ventilation fan running,test Ih3hZZRw9Fg,415,bathroom ventilation fan running,test Ih4LSoeKCkE,21,gibbon howling,train Ih4LSoeKCkE,43,gibbon howling,train Ih4pv180BdQ,96,playing sitar,train Ih4pv180BdQ,495,playing sitar,train Ih6yFXOZ4iw,100,playing saxophone,train Ih7sLCzExYU,30,"bird chirping, tweeting",test Ih93KqW-kxQ,109,canary calling,train Ih93KqW-kxQ,158,canary calling,train Ih9XB-nt-DI,50,"bee, wasp, etc. buzzing",train IhKG0G9gfuY,51,playing squash,train IhLglo-knXk,63,playing congas,train IhLglo-knXk,96,playing congas,train IhRG_F50p88,0,"vehicle horn, car horn, honking",train IhRmwx5w7pw,3,playing glockenspiel,train IhS6dZJsI9E,0,gibbon howling,train IhT-WxWAauI,30,duck quacking,train IhWgOiqyk1c,500,driving buses,train IhWpN_nbQ9k,30,horse clip-clop,train Ihaf67Urzp8,137,people eating crisps,train Ihaf67Urzp8,296,people eating crisps,train IheB9Wg2VdY,65,playing glockenspiel,train IheB9Wg2VdY,201,playing glockenspiel,train IhesEJJid7g,100,driving motorcycle,train IhfmN-OT2C8,35,skiing,train IhfmN-OT2C8,46,skiing,train IhjS6l-BTio,13,"motorboat, speedboat acceleration",train IhjW0KKkjLQ,30,opening or closing drawers,train IhkA4_KpHSg,30,"playing 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IiCRuL4iIhQ,472,golf driving,train IiDtJLfrD9E,3,"fly, housefly buzzing",test IiGasa2Vl4Y,30,orchestra,train IiHTkYhxSRg,470,stream burbling,train IiHqZH0Nfdw,2,woodpecker pecking tree,test IiJciyiQBDw,10,opening or closing drawers,test IiKfTN7WS6w,30,wind rustling leaves,train IiMWHiZ2lmk,30,lawn mowing,train IiOj18HpE8E,30,chainsawing trees,train IiPFL45UAmc,49,chopping wood,test IiQ9HLByqUY,76,wood thrush calling,train IiQ9HLByqUY,118,wood thrush calling,train IiRyJQ_7BGI,291,ripping paper,train IiRyJQ_7BGI,398,ripping paper,train IiTAMq0bnrI,30,horse clip-clop,train IiUTRIyV444,527,whale calling,test IiXJj6fzNV8,338,splashing water,train IiXJj6fzNV8,478,splashing water,train IiXYFBV0Qsk,395,opening or closing car electric windows,train IiX_3XnzkKo,1,air horn,train IiYSoGSDUY8,30,playing harpsichord,test IiZ3mFhN2oA,20,fireworks banging,train IiZE96L6ZzY,28,people belly laughing,train IiZE96L6ZzY,38,people belly laughing,train IiaFSYoAtKo,35,playing squash,train IicM8tOXAFg,132,pheasant crowing,train IicM8tOXAFg,146,pheasant crowing,train IicdaT_VaT8,349,"bird chirping, tweeting",train IidXCLA4rd0,8,people eating apple,train IidXCLA4rd0,418,people eating apple,train IieMyEQ9wcU,90,ambulance siren,train IieuTH2TeCs,35,lathe spinning,train IieuTH2TeCs,59,lathe spinning,train Iij-OW1GM7c,199,ripping paper,train Iij-OW1GM7c,666,ripping paper,train Iij3pwrZcjg,30,people running,train Iil6A-PM0bk,4,planing timber,test Iil6A-PM0bk,139,planing timber,test IiqVDLbeF8g,30,child singing,train IisvMeU0iKQ,180,helicopter,test IitBwOUBK-I,2,playing tambourine,train Iiu1mQCp1xs,104,elk bugling,train Iiu1mQCp1xs,125,elk bugling,train Iiufr2wezns,30,people crowd,train IixugYA0nKg,135,people marching,test IiyyCWSAt1k,156,people whistling,train IiyyCWSAt1k,337,people whistling,train IizUP5xQlKQ,170,mosquito buzzing,train IiznaL4D-L4,198,tap dancing,train Ij-1lR0fnMU,3,"heart sounds, heartbeat",train Ij-1lR0fnMU,92,"heart sounds, heartbeat",train Ij0V9MHIQDs,40,playing electric guitar,train Ij4kiBg9FQM,4,people booing,train IjDIUVeTglA,50,car engine knocking,train IjFL0N2wiT8,21,car passing by,train IjFYatQ7LkE,60,"playing violin, fiddle",train IjHCyUOtYYI,54,wind chime,train IjHCyUOtYYI,72,wind chime,train IjHfakNXy0o,20,playing acoustic guitar,train IjHmm02lV4A,524,forging swords,train IjHmm02lV4A,543,forging swords,train IjLR6lE2ydw,0,wind noise,train IjLseOa0e5A,201,arc welding,train IjLseOa0e5A,216,arc welding,train IjOKUlm9OYo,12,playing tennis,test IjP0FPlhMGo,16,singing bowl,train IjP30CoPX14,98,hair dryer drying,train IjP30CoPX14,334,hair dryer drying,train IjPtt5UTImE,4,reversing beeps,train IjPtt5UTImE,14,reversing beeps,train IjQYJJo-cnw,3,playing bongo,test IjSBSAcFK6E,0,chicken crowing,train IjSBSAcFK6E,55,chicken crowing,train IjSF15tfhfY,10,toilet flushing,train IjToWcWV0EE,225,"ice cream truck, ice cream van",train IjToWcWV0EE,267,"ice cream truck, ice cream van",train IjUCS1Qm5_o,8,coyote howling,train 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banjo,train IpTegSY4HY8,30,"rowboat, canoe, kayak rowing",train IpTo9MnufLM,360,"race car, auto racing",train IpWIs9sMx0g,180,church bell ringing,train IpWRflndp7s,80,sliding door,train IpbPmyChqC0,21,gibbon howling,train IpbPmyChqC0,85,gibbon howling,train IpdpUArkLng,75,playing badminton,train Ipev8h84BZA,10,"donkey, ass braying",train Ipev8h84BZA,163,"donkey, ass braying",train IpfkOr8tKtI,15,baby babbling,train IpfkOr8tKtI,25,baby babbling,train Ipg9iwUDps4,38,horse neighing,train Ipg9iwUDps4,99,horse neighing,train IphL7mUqZ6A,400,car engine starting,test IplcY5mIpEM,30,playing banjo,train IpoAYuf8X0s,0,metronome,train IpoMqMp3QkU,10,sliding door,train IpoMqMp3QkU,126,sliding door,train IpqtJmqY4-w,200,dog howling,test IptFuv-Ud4E,30,playing french horn,train IptXKPsJ-Dc,386,police radio chatter,train Iptuyr30Q2w,200,ambulance siren,train Ipv6_Qz3oBI,90,people babbling,train IpyI5otVTes,382,scuba diving,train Ipzfx50Le3c,30,"rowboat, canoe, kayak rowing",train IpzneSHvBlY,160,"bird chirping, tweeting",train Iq04gwNVBKY,0,ferret dooking,train Iq04gwNVBKY,21,ferret dooking,train Iq4JhQIDRH0,94,tap dancing,train Iq5eTbzhwQY,60,cat growling,train Iq5eTbzhwQY,85,cat growling,train Iq5ndbJx8Wo,1,ferret dooking,train Iq6HNq1s8jM,30,basketball bounce,train Iq6zmmwXjMk,145,planing timber,train Iq8Tp8f8alc,2,people booing,train Iq93uacbKwI,90,ambulance siren,train Iq9KWM00hX0,29,wind noise,train IqAuy_6ja28,30,turkey gobbling,train IqCRbzhPkvU,0,lawn mowing,train IqG7MqzI1Qo,164,snake hissing,test IqGMJ8VGWQA,382,playing didgeridoo,train IqH1Bf93Lok,22,fireworks banging,train IqHowLVNyq8,158,beat boxing,train IqIMmMTajxI,97,singing bowl,train IqIbGncnNdE,30,cap gun shooting,train IqJ4Y8DHpqg,189,skiing,train IqKi5MFiMoY,30,playing drum kit,train IqLZpNET3Bk,58,dog howling,train IqMNDteHHX8,10,car engine knocking,train IqNkvPgQU4I,35,dog whimpering,train IqQE2gbMFNQ,30,"playing violin, fiddle",train IqQa2LHUOls,100,chicken clucking,train IqS-NrgokYY,50,printer printing,train IqS6mQ_z_Go,150,machine gun shooting,train IqT74kx1O34,30,"female speech, woman speaking",train IqTSyx3L0j8,97,"engine accelerating, revving, vroom",train IqUzh80HYVg,30,eating with cutlery,train IqVEMfbM-D4,117,"electric shaver, electric razor shaving",train IqVYIkNUZR0,316,"alligators, crocodiles hissing",train IqVYIkNUZR0,476,"alligators, crocodiles hissing",train IqWLzYZ2aQc,3,playing tambourine,train IqWSnNzRkG8,48,beat boxing,train IqX2IAt6bQc,30,playing harpsichord,train IqXQDDgv1eY,75,people shuffling,train IqXQDDgv1eY,89,people shuffling,train Iq_y4aZY6EA,37,hammering nails,train IqcwGClaunU,49,wind chime,train IqcwGClaunU,94,wind chime,train IqdNnGxhG0o,175,playing tympani,train IqdfQJxX7KI,8,cat hissing,train IqdfQJxX7KI,21,cat hissing,train IqeMhIETBFI,12,sliding door,train IqfoWPRARtg,47,people slapping,train IqfoWPRARtg,76,people slapping,train IqhcwbBmTU8,66,chopping wood,train IqhcwbBmTU8,154,chopping wood,train 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JnizmPXNfDs,38,playing gong,train JnjQzxzaVL0,180,playing trombone,train JnmAs0ILDn0,245,writing on blackboard with chalk,train JnmAs0ILDn0,635,writing on blackboard with chalk,train JnmXmi4fb-8,112,"playing marimba, xylophone",train JnoonNii9g8,60,helicopter,train JnpBKdwHkjM,0,people farting,train JnpxAzxLo_M,150,people hiccup,train JnzJAckPzQ0,79,playing erhu,train JnzwhYy8nY0,30,ambulance siren,train Jo-Y6ADNs_Q,30,wind noise,train Jo4fAcY9Xng,30,car passing by,train Jo7noH-2ocM,327,playing shofar,train Jo8il_Kg_j8,50,thunder,train Jo9ZI-74cp8,1,bowling impact,train JoDHjeWzZjY,150,playing flute,train JoE7tDOr6wk,20,people crowd,train JoE9F2eO_jE,80,using sewing machines,test JoEPH_TrHdo,140,playing clarinet,train JoF8ykq9fow,1,fire crackling,train JoF8ykq9fow,11,fire crackling,train JoFMnnPzB6Q,288,splashing water,train JoFMnnPzB6Q,317,splashing water,train JoIsbgmfiv0,0,playing bass drum,train JoKMKFX12x8,3,dog baying,test JoKYZZLOWIQ,30,"engine accelerating, revving, 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Jok1SdpXFg8,30,"playing violin, fiddle",train JolRyxW4Xwk,53,car engine idling,train Jom3pPmbKic,120,orchestra,train JomN2AH80LA,69,dog growling,train JooCrHsN6jU,90,"female speech, woman speaking",train Joox3Ycl0Sk,489,arc welding,train JorNR3IXDyc,60,"playing marimba, xylophone",test Jost8ZlNABk,43,fire truck siren,train Jost8ZlNABk,78,fire truck siren,train Jovlf_bjlZk,10,toilet flushing,train JovrM71dLA4,17,cat caterwauling,train JowRAi5YGi4,40,baby laughter,train Joyk_EMtzn0,283,playing tabla,train Joyk_EMtzn0,468,playing tabla,train Jp-4vhmhJBA,4,"motorboat, speedboat acceleration",train Jp1w6gdj1Fk,70,arc welding,train Jp1w6gdj1Fk,125,arc welding,train Jp3A3ItE7QU,62,playing sitar,train Jp3ggRQzpHM,72,train whistling,test Jp4BuR7ECuo,30,ocean burbling,train Jp4Lxknb5S4,73,bowling impact,train Jp4Lxknb5S4,93,bowling impact,train Jp4n17HxutU,2022,playing harpsichord,train Jp4n17HxutU,2059,playing harpsichord,train Jp5dJVHnLu4,111,slot machine,train Jp64Whpr3BA,50,using sewing 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JplLGOopyXU,79,playing tambourine,train JplLGOopyXU,102,playing tambourine,train Jpn2nG1N0IE,120,sliding door,train Jpo0qvXUcL8,23,playing shofar,train JpoI3sn-oNI,364,tapping guitar,train JpqxMvQVUFk,30,horse clip-clop,train Jpr42ZzjC6A,113,playing didgeridoo,train Jpr8pIHzFuU,30,playing bagpipes,train JprwmO63dg4,60,"bee, wasp, etc. buzzing",train JpuPbowSHBI,88,playing guiro,train JpwAOJ-FaJQ,245,playing squash,test JpwHoGB_RJ4,30,wind noise,train JpxuZL7U6wE,7,"heart sounds, heartbeat",train Jpy3Cf6O7CA,28,volcano explosion,train Jq-I5fUa4pE,37,chinchilla barking,train Jq-I5fUa4pE,96,chinchilla barking,train Jq123QYJ7iI,2,"rowboat, canoe, kayak rowing",train Jq123QYJ7iI,3,"rowboat, canoe, kayak rowing",train JqC3gZpgBEI,100,writing on blackboard with chalk,train JqC3gZpgBEI,161,writing on blackboard with chalk,train JqCUpR5ZwlY,127,dinosaurs bellowing,train JqCUpR5ZwlY,221,dinosaurs bellowing,train JqDwovf5SDw,30,people running,train JqFmTe2uZ7c,9,horse clip-clop,train JqGGlr_icXU,30,helicopter,train JqJaY6SXPiQ,29,chopping food,train JqJbDj8fQ88,206,singing choir,train JqK8R08i5_M,280,people crowd,train JqMZ5jnjGN0,30,plastic bottle crushing,train JqOthdX0yQg,49,mouse pattering,train JqOthdX0yQg,126,mouse pattering,train JqSqwNdakmo,30,playing harpsichord,train JqW9neEt2GA,40,playing harpsichord,train JqW9neEt2GA,64,playing harpsichord,train JqWN0hkVtE0,30,"pigeon, dove cooing",train Jq_JlpSnwrc,385,arc welding,train Jqe9TikMdxI,23,playing tambourine,train Jqe9TikMdxI,98,playing tambourine,train JqfJ5xfAMbE,30,horse clip-clop,test JqfWmGGrP8Q,69,airplane flyby,train JqfWmGGrP8Q,115,airplane flyby,train JqfhG1dFMT4,30,chainsawing trees,train JqhHVfNQmHg,551,playing synthesizer,train JqmOqYtQqB8,30,people shuffling,test Jqmirlih-xg,75,playing hammond organ,train JqnS0skIZlI,596,swimming,train JqnS0skIZlI,612,swimming,train JqpBnYq1pnM,130,cap gun shooting,train Jqpd7KNnzz8,30,"race car, auto racing",train JqsNvJPUa_g,30,helicopter,train JqtaJF4jyTw,30,"playing violin, fiddle",train JqtaThuiBSg,6,coyote howling,train JquzIbIjDgo,50,playing bass guitar,train JqvMsUrp40c,0,people hiccup,train JqwQIAL9L_A,30,car engine knocking,train Jqwl7uvN65w,150,playing trumpet,train Jqwl7uvN65w,259,playing trumpet,train JqxqAMVGU3A,825,playing hockey,train JqxtHK8t_k8,24,car engine knocking,train Jr2KHPn2yUA,30,horse clip-clop,train Jr2XqXub28A,30,sloshing water,test Jr45XZVspPg,0,frog croaking,train Jr5bzWiBdCU,16,chicken clucking,train Jr60re79KxU,4,basketball bounce,train Jr7kBKM7Hn4,30,orchestra,train JrD-s5Zs1HE,48,snake hissing,train JrD-s5Zs1HE,94,snake hissing,train JrD3mJeqjZc,30,playing acoustic guitar,train JrFKFAI-R78,0,typing on typewriter,train JrFKFAI-R78,107,typing on typewriter,train JrFaJ2xVokU,1,driving snowmobile,train JrFaJ2xVokU,53,driving snowmobile,train JrFqSShiV8E,21,playing trombone,train JrGD2jdHUek,30,playing cornet,test JrGZF1EncK0,27,beat boxing,train JrLvzjU5je0,100,playing double bass,train JrMLg7s6tT8,84,playing didgeridoo,train JrN-voVbbYg,320,lions growling,train JrNZ3aHvkYA,289,sharpen knife,train JrNtNmEXZlc,195,pheasant crowing,train JrO94oKkeY8,30,playing french horn,train JrPXRMnZWH4,117,popping popcorn,test JrQDMmAN3_o,249,playing cymbal,train JrQzNKi9DSw,2,canary calling,train JrQzNKi9DSw,83,canary calling,train JrRI_tVzeGQ,1,cat purring,train JrRI_tVzeGQ,32,cat purring,train JrRnP4q4vE0,37,playing accordion,train JrYxlP1336E,36,scuba diving,train JrZDEWOAFsk,196,playing flute,train JrZZVNhUDtY,127,driving snowmobile,test JrbVM5zjdug,30,tapping guitar,test JrckrQryUV4,35,playing glockenspiel,train JrckrQryUV4,147,playing glockenspiel,train Jrdarw46ALY,306,blowtorch igniting,test Jre2f0ixPnU,27,playing zither,test JrfDWKVDa3w,200,playing bass guitar,train JrfXTTPIjew,80,sharpen knife,train Jrj7ArPoqlA,19,woodpecker pecking tree,train Jrj7ArPoqlA,124,woodpecker pecking tree,train JrjNd3mNj10,30,"playing violin, fiddle",train JrkvvWuv7Ug,380,playing double 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trees,train JsQdaHQekZs,226,singing choir,train JsS2NEeleXU,120,playing trumpet,train JsSF-wDNF5g,117,playing vibraphone,train JsTj-8bNObw,331,chicken clucking,train JsTj-8bNObw,694,chicken clucking,train JsWqh5ekS14,35,people babbling,train JsXUSL8kzdk,30,people running,train JsXnNvxxuRo,561,playing volleyball,train JsXyqBLUVIM,20,playing saxophone,train JsZN_AE3OPY,81,playing erhu,test Js_3Aa214xY,30,playing timpani,test Js_JxGdO-lg,32,playing squash,train Js_JxGdO-lg,87,playing squash,train Jsa5o3JX018,40,"race car, auto racing",train JsaV64W-mZY,30,singing bowl,test Jsb9BnfWlbI,592,driving snowmobile,train Jsb_CONyugk,4,people nose blowing,train JscdHxor7Wc,10,"railroad car, train wagon",train Jse-XQrz7MA,30,dog bow-wow,train JserMSH0rDo,95,playing trombone,train JserMSH0rDo,108,playing trombone,train JsjIde3ioco,99,elk bugling,train JsjIde3ioco,414,elk bugling,train JsjOH1ujhWY,30,"pigeon, dove cooing",train Jskt1d0Vlhs,200,using sewing machines,train Jsu41L8WgvE,37,mosquito 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KQGJCKwsFbA,157,sharpen knife,test KQH0FiRzrvE,23,fire truck siren,train KQKVkbfGqO8,264,elk bugling,train KQKVkbfGqO8,413,elk bugling,train KQNDRWNkC2g,9,people cheering,train KQPCsXTBwjE,50,dog bow-wow,train KQRnsZqwOKo,9,mynah bird singing,train KQRnsZqwOKo,121,mynah bird singing,train KQTnapN7HPA,123,owl hooting,train KQTnapN7HPA,146,owl hooting,train KQURtVRW1JY,24,playing djembe,train KQURtVRW1JY,65,playing djembe,train KQUkDQzqRhU,20,playing harp,train KQX6xlnSd0w,30,"rowboat, canoe, kayak rowing",train KQXoBL4qLiE,99,playing badminton,train KQbCjNzlYPs,1,writing on blackboard with chalk,train KQbCjNzlYPs,82,writing on blackboard with chalk,train KQd5scW8yf0,200,"subway, metro, underground",train KQdTp3XploQ,20,people coughing,train KQiBIb_klT8,16,playing harpsichord,train KQiBIb_klT8,64,playing harpsichord,train KQjsqc1Ls58,2,playing tuning fork,train KQkCJ9e4O18,20,car engine knocking,train KQkCJ9e4O18,77,car engine knocking,train KQmSy7ux-oc,80,"race car, auto racing",train 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KS_5Qiec4AI,9,warbler chirping,test KS_5Qiec4AI,200,warbler chirping,test KS_b47l_Mwg,622,chimpanzee pant-hooting,train KSbbVzoyMyg,30,"bird chirping, tweeting",train KSdNLpbMPjM,480,car engine idling,train KSfjfM22eCg,30,sheep bleating,train KSgWEfplTHc,60,helicopter,train KSgolImRApk,45,whale calling,test KSh5t4oznJ4,4,zebra braying,train KSikyKc_YSI,0,ambulance siren,train KSjJPBB8R-Y,30,duck quacking,train KSmL4L5SI6E,3,machine gun shooting,train KSmL4L5SI6E,125,machine gun shooting,train KSnKNiu7u1g,21,sliding door,train KSnrf1KkEdo,0,people humming,train KSokFTcjDog,250,playing saxophone,train KSpsLu32E3s,194,playing tambourine,train KSpsLu32E3s,225,playing tambourine,train KSt0yIxppK8,30,"playing violin, fiddle",train KSt7_lqNxVg,30,car passing by,train KSu1DMCQRZU,9,tap dancing,train KSvb5cQ-ACE,15,dog growling,train KSvb5cQ-ACE,34,dog growling,train KSx1Y32_Wd0,62,playing oboe,test KSyS5UvfRik,26,airplane flyby,train KSzohgqJFec,0,playing double bass,train KSzzceIaXd4,36,civil 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KXK8edltsYg,410,"child speech, kid speaking",train KXMjrVMdiXQ,20,playing piano,train KXN_I4ODZL8,30,"motorboat, speedboat acceleration",train KXO4lC4LjxI,11,lathe spinning,test KXO4lC4LjxI,95,lathe spinning,test KXOJyVJ0fvM,128,singing choir,train KXOJyVJ0fvM,218,singing choir,train KXPzLHbJLn8,20,"playing marimba, xylophone",train KXQ0oFeq_8Q,10,playing cello,train KXQF2eVHeA0,230,dog growling,train KXQF2eVHeA0,511,dog growling,train KXRngbBe0bg,80,tapping guitar,test KXVWbk9RRRs,30,playing steelpan,test KXW90ZYDtBg,36,chicken crowing,train KXW90ZYDtBg,53,chicken crowing,train KXY1DWeHnk8,5,"electric shaver, electric razor shaving",train KXYEQBAelKA,30,wind noise,train KXYeIQntJ8s,13,blowtorch igniting,train KXYeIQntJ8s,132,blowtorch igniting,train KXfN9Z4U7KY,38,playing tennis,train KXgkl1THwjY,50,chainsawing trees,train KXgnw3fjv7k,12,skateboarding,train KXiGGV67BMs,28,yodelling,train KXkPJ_xgPZg,4,cat hissing,test KXlPBLIMDAk,10,firing cannon,train KXlPBLIMDAk,21,firing 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KaQmoNsM-08,30,playing bass drum,train KaRUxmw35lI,266,machine gun shooting,train KaSatb7Hz_4,0,people shuffling,train KaT5aMPgWgQ,69,owl hooting,train KaTJFNfVxqc,69,cricket chirping,train KaTJFNfVxqc,115,cricket chirping,train KaTo0OEVZwI,10,toilet flushing,train KaTo0OEVZwI,80,toilet flushing,train KaUfUlBkQWU,49,singing bowl,train KaUfUlBkQWU,79,singing bowl,train KaVHCudVkRQ,70,playing snare drum,train KaWojaQvsBA,410,goose honking,train KaaLQKnxHOc,301,playing darts,train KaaLQKnxHOc,796,playing darts,train Kac7HhD0LkA,400,driving motorcycle,train Kac8qiE-yAc,232,playing harpsichord,train Kac8qiE-yAc,247,playing harpsichord,train KacVC3bg_QI,0,playing bass drum,train KacYEZwBTY4,50,playing timbales,test Kadf-COTu20,310,typing on typewriter,train Kae8or9eMPM,30,"motorboat, speedboat acceleration",train Kah6q-zXqPU,30,horse clip-clop,train Kai40zuI9VQ,17,playing theremin,train KailpxclbHo,307,playing didgeridoo,train KaiwGew41iE,30,people sniggering,train KalWGl-IoAo,98,playing 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KbBE0skXK68,143,playing tambourine,train KbDDlGGnwbM,12,sheep bleating,train KbDyzsVa9Uk,0,mouse squeaking,train KbDyzsVa9Uk,11,mouse pattering,train KbHPe6PdKhA,1,basketball bounce,train KbIIrQtBjk8,70,owl hooting,test KbIw9tsxoUk,10,people coughing,train KbL8SpU1djs,44,airplane,train KbMpDHwexaI,84,people eating crisps,train KbOySeP46l4,570,people belly laughing,test KbRIx25mE3A,24,woodpecker pecking tree,train KbRIx25mE3A,86,woodpecker pecking tree,train KbS00pEsy9s,14,dog growling,train KbS00pEsy9s,49,dog growling,train KbSGOnGv48Q,30,"pigeon, dove cooing",train KbSHithKfl4,10,skidding,train KbTzJLFcN2Q,0,fireworks banging,train KbViq9fsi8E,130,helicopter,train KbVu2lBSUWE,100,beat boxing,train KbX-JykZZYc,159,people humming,train KbXUhdWqdvk,40,"race car, auto racing",train KbZVGga6w5U,50,playing banjo,train Kb_VOBHIncc,40,chicken clucking,train KbbW3YBHxW0,651,arc welding,train KbbtsFrTNCI,108,playing tabla,train Kbc8ioemPlA,81,tractor digging,train Kbc8ioemPlA,147,tractor digging,train KbdK-oXOoDk,1,baby babbling,train KbdK-oXOoDk,17,baby babbling,train KbegYu2ZFQQ,7,civil defense siren,train KbqSyf7pJJc,40,playing cornet,train KbqSyf7pJJc,58,playing cornet,train KbqYB8Xn27g,99,"alligators, crocodiles hissing",train KbqYB8Xn27g,406,"alligators, crocodiles hissing",train Kbs3ILYpKKs,225,playing trombone,train KbtChZNVHS4,80,female singing,train KbwN44PG_0Y,651,bowling impact,train KbwN44PG_0Y,787,bowling impact,train Kbx3Ddg6ito,30,"bird chirping, tweeting",train KbxnfvXug9w,30,"motorboat, speedboat acceleration",train Kby08jHmSQA,200,skidding,train Kc-qw2FX6UM,55,missile launch,train Kc0zpdJRjDo,110,playing synthesizer,train Kc1yfZngUms,9,child singing,test Kc22akwKcg8,30,playing cello,train Kc2VV3qO4Ns,8,"vehicle horn, car horn, honking",train Kc4Zc5HJvA4,50,"race car, auto racing",train Kc4moqpQ9ZU,18,mynah bird singing,train Kc4moqpQ9ZU,57,mynah bird singing,train Kc4xrWpOrUM,30,sailing,train Kc66Krp4BWU,30,typing on computer keyboard,train Kc79x7oWP8s,30,turkey gobbling,train Kc7PzpNXPmw,0,otter growling,train Kc9ZwuuvkTk,130,playing djembe,train Kc9mICcGqp8,76,arc welding,test KcAVA5SadCc,30,"rowboat, canoe, kayak rowing",train KcC_g1wPovM,80,waterfall burbling,test KcD5R2atk1Y,5,people nose blowing,train KcG1Y4hMU-Y,126,baby laughter,train KcG1Y4hMU-Y,295,baby laughter,train KcIWglVKHCE,20,"bird chirping, tweeting",train KcIvudkMTBE,292,beat boxing,train KcLIsnXinOw,74,people burping,train KcO-TMFCmRI,100,playing volleyball,train KcPtruBDqmw,30,people sniggering,train KcRd3Kg_MLQ,30,baby crying,train KcRmfJn09bo,120,chainsawing trees,train KcV64MycgT4,0,"alligators, crocodiles hissing",test KcV64MycgT4,19,"alligators, crocodiles hissing",test KcVNOUkORR4,108,playing tympani,train KcVNOUkORR4,238,playing tympani,train KcVxt7NLR7c,40,playing double bass,train KcWPqFhrna0,30,baby laughter,test KcWcNy_3xkE,330,singing bowl,train KcYCxfTPfY0,0,sea lion barking,test KcYdL20iDjg,30,playing harp,train KcZZlt-zucU,23,car engine 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Kd0TBs2yTzs,195,opening or closing car doors,train Kd0gbI_5sCQ,5370,frog croaking,train Kd0v9LD9Edc,40,skateboarding,train Kd2P8uMUoTU,70,typing on computer keyboard,train Kd3fRWZSwHQ,37,lathe spinning,train Kd3fRWZSwHQ,81,lathe spinning,train Kd5fom_lr1I,149,playing squash,train Kd7aHdOwh0I,30,playing electric guitar,test Kd97kU73QPo,15,warbler chirping,train Kd97kU73QPo,57,warbler chirping,train Kd9IClxbSWQ,30,playing hammond organ,train Kd9UTRyyfsM,197,people booing,train KdBwrU3jrHw,613,ripping paper,test KdD8xho7ymw,37,cat purring,train KdDGn-fzzgY,10,lions growling,train KdDociD4Il8,200,chainsawing trees,train KdEjNsZYJc4,36,missile launch,train KdFbCaI4Hes,90,playing piano,train KdGYrdnLYN0,59,dinosaurs bellowing,train KdGYrdnLYN0,167,dinosaurs bellowing,train KdIIocq3jeM,409,lip smacking,test KdJytEWgnEY,130,mouse clicking,train KdJytEWgnEY,182,mouse clicking,train KdKwPvfYBj0,2,people burping,train KdLdOtzGI8U,96,bouncing on trampoline,train KdLdOtzGI8U,136,bouncing on 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LCzeX8MADxs,232,francolin calling,train LD1AFNeDg8M,50,bull bellowing,train LD1AFNeDg8M,206,bull bellowing,train LD6MlX75dOE,30,orchestra,train LD6nP2qlsRI,30,people shuffling,test LD7MIjQWWi8,350,church bell ringing,train LD7TaAY5gRI,196,playing hammond organ,train LD8lSS3sLGY,10,wind noise,train LD8vntp39uA,119,arc welding,train LD9tpiEoauI,150,car engine starting,train LDASZopsyt8,10,wood thrush calling,train LDASZopsyt8,25,wood thrush calling,train LDCHOGG9jqo,10,people coughing,train LDCjqa4l7Ho,250,fireworks banging,train LDCrM5ItPQM,0,people giggling,train LDCrM5ItPQM,12,people giggling,train LDD41NEniMY,35,rope skipping,train LDDOczcTYxI,115,bull bellowing,test LDDOczcTYxI,147,bull bellowing,test LDDp7L2nUHY,152,child singing,train LDDp7L2nUHY,393,child singing,train LDEWSADK27Y,20,fireworks banging,train LDGPRNM2tJI,310,skidding,train LDGPdo4jIS0,400,people whispering,train LDGzSk_vzPo,260,bird wings flapping,test LDIO2iseceA,250,arc welding,train LDIO2iseceA,317,arc 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LDv3Z0hd37Y,230,playing hockey,train LDv3Z0hd37Y,328,playing hockey,train LDwvklDKXro,30,"rowboat, canoe, kayak rowing",train LDxjSL1hGYg,30,people screaming,train LDxpdFKuGb4,339,playing guiro,train LDykvT8PuQE,475,magpie calling,test LDz3wd663kg,30,frog croaking,test LDz50tam4vg,490,people babbling,train LDzb6mVK4nk,127,playing volleyball,test LE2tFN6DOtk,129,playing timbales,train LE2tFN6DOtk,190,playing timbales,train LE49c8e5VMU,37,mynah bird singing,train LE49c8e5VMU,49,mynah bird singing,train LE5NuPvHLB4,15,turkey gobbling,train LE5NuPvHLB4,45,turkey gobbling,train LE5xN_UR26A,54,roller coaster running,train LE5xN_UR26A,67,roller coaster running,train LE6bwX6GVL8,297,singing bowl,train LE9ntDAYGjA,1,black capped chickadee calling,train LE9ntDAYGjA,30,black capped chickadee calling,train LE9tSNREtSs,5,playing guiro,train LEC7aPQfmUs,40,male singing,train LECMvwr9-8E,90,"railroad car, train wagon",train LEFSHa_n9jE,287,people cheering,test LEG7xkYOsWA,20,playing ukulele,test LEGIZE5Ky8A,22,lions roaring,train LEJyA_IDpxg,30,chicken crowing,test LEKAHkNrSTc,3,playing theremin,train LEKPbIpYdEE,71,playing glockenspiel,train LEKyqtVgngo,30,playing french horn,train LEMhcRo3skk,30,goose honking,train LEOhHFRPXe8,44,pheasant crowing,train LEOhHFRPXe8,55,pheasant crowing,train LEPKdbZu3K8,1,elk bugling,train LEPjx8ua0Vg,5,scuba diving,train LEUKYOndizA,23,coyote howling,train LEUkbkdBupE,4,playing castanets,test LEUtV-ghj3U,320,"subway, metro, underground",train LEZFcGFwJlc,30,"male speech, man speaking",test LEZfZZmiVsY,11,people running,test LEaHZwCWE5M,38,"bird chirping, tweeting",train LEazryUgd1w,310,goose honking,train LEdnH6ZJR40,4,car engine knocking,train LEdnH6ZJR40,19,car engine knocking,train LEeocOjQ8b0,30,machine gun shooting,train LEf8YRG2l34,30,cat meowing,train LEfOSP8Dpa8,5,goose honking,train LEfOSP8Dpa8,16,goose honking,train LEgH4nKdeJU,97,playing sitar,train LEgH4nKdeJU,115,playing sitar,train LEkZReqWPQc,72,sharpen knife,train LEkZReqWPQc,106,sharpen knife,train LEkedQqDBwU,90,chimpanzee pant-hooting,train LEkoeC8YPZA,280,car passing by,train LElqGsIiT3o,0,"donkey, ass braying",train LEn3f97acaw,30,people clapping,test LEnIUJpCuUU,170,rapping,train LEnIUJpCuUU,276,rapping,train LEnZ2fTT1cE,307,skiing,test LEpQ2FsAcMg,77,sharpen knife,train LEpQ2FsAcMg,112,sharpen knife,train LEpzp8DnWyY,26,sharpen knife,train LEpzp8DnWyY,140,sharpen knife,train LEqJhYzoRio,70,playing flute,train LErTvptWpXU,70,lawn mowing,train LEtaIu8L53k,260,typing on computer keyboard,train LEwwgCUGraQ,180,bouncing on trampoline,train LEwwgCUGraQ,226,bouncing on trampoline,train LExYeHF-ypk,130,playing cello,test LExpQz32kLY,100,helicopter,train LF-5BAUGvWI,30,rapping,test LF1t9KbTNeA,22,playing accordion,train LF3C6NlbbYk,15,playing glockenspiel,train LF3GkqFzqRk,170,people cheering,train LF4bs0MUoQg,80,helicopter,train LF5TEWQQoSs,0,people whistling,train LF6RUWsUUrw,70,police radio chatter,train LF6x7B0Ppvo,20,"race car, auto 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LGIUEjBk_9w,194,crow cawing,train LGLnSKuKS98,30,goose honking,train LGMZ9c7q8tE,48,cat purring,train LGMZ9c7q8tE,168,cat purring,train LGO04YtusJ8,149,playing tabla,train LGO04YtusJ8,222,playing tabla,train LGPS4wqwjlY,30,playing drum kit,train LGROeInZI5o,30,"pigeon, dove cooing",train LGSgOXywYto,30,sliding door,train LGWA6yziPGQ,38,baby laughter,train LGWA6yziPGQ,171,baby laughter,train LG_Qh325ueA,150,playing snare drum,train LGcAfeZ54qE,26,baby crying,train LGdZx_Ng2OU,70,"race car, auto racing",train LGeLv_bEBSA,420,orchestra,train LGedkzCqAak,43,rope skipping,train LGhIEKIwy6U,5,francolin calling,train LGhIEKIwy6U,28,francolin calling,train LGk1hK4BDfM,30,chicken clucking,train LGmsnRbaS68,1,black capped chickadee calling,train LGmsnRbaS68,18,black capped chickadee calling,train LGrpsZ7BsQA,30,duck quacking,test LGvgNhfKcN4,30,"vehicle horn, car horn, honking",train LGwiDm3w-Xs,0,civil defense siren,train LGx0Q_YRCtw,110,"bee, wasp, etc. buzzing",train LGxAsKXazQ0,126,fire 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LHdbtxx9pMs,340,firing muskets,test LHeZe3OG61g,11,baby babbling,train LHffmONgAiU,170,playing flute,train LHhpWHRvKsI,110,"bee, wasp, etc. buzzing",train LHjIPTqS94k,30,dog barking,train LHmVKhar4H8,10,"subway, metro, underground",train LHoUMnwjTU4,12,fireworks banging,train LHou2kyoLns,36,dinosaurs bellowing,train LHozDajmbwU,30,people running,train LHqflvg3_TM,150,playing trumpet,train LHvER6Tj8jM,53,playing cello,train LHvER6Tj8jM,69,playing cello,train LHwxKe2Ck9A,0,people screaming,train LHxJZmqcIxE,1,frog croaking,train LI-UBG1Z77o,30,frog croaking,test LI46aCaqKQg,108,playing congas,test LI85dkBB3Ho,30,"pigeon, dove cooing",test LI8z11HmROA,140,lawn mowing,train LIBNF0ye2G8,140,playing cello,train LIDdYpALUgE,70,people booing,train LIDincQ4uiw,97,bouncing on trampoline,train LIEFQ2Lo0uc,163,beat boxing,train LIHraswGNLc,19,missile launch,train LILHnSfoQ_8,0,baby laughter,train LILtqwDL4bg,40,sailing,train LINkHBnBkM4,125,driving snowmobile,train LINkHBnBkM4,178,driving snowmobile,train LIRckRNjPNk,37,playing didgeridoo,train LISpHNrMrwU,13,beat boxing,train LIU7EYJEbYQ,10,driving buses,test LIU94Xivf9Q,67,baby babbling,train LIU94Xivf9Q,138,baby babbling,train LIUowNjRNwE,125,machine gun shooting,train LIUowNjRNwE,130,machine gun shooting,train LIW-8UOdx6k,245,striking pool,train LIW5rTr0CRc,150,lions growling,train LIWLLPsx3r8,58,owl hooting,train LIZLgIFV3Jk,40,"female speech, woman speaking",train LI_sDy80oIY,65,gibbon howling,train LI_sDy80oIY,77,gibbon howling,train LIc5lj_AHBw,22,playing snare drum,train LIeIhd25B8k,41,playing glockenspiel,train LIhExivQcSo,410,"railroad car, train wagon",train LIie5ZYob_E,1,pheasant crowing,train LIie5ZYob_E,74,pheasant crowing,train LIluVun_dJk,30,turkey gobbling,test LImpMouen9M,5,horse clip-clop,train LIneudLfJPg,40,train horning,train LInus-K0lKA,43,lighting firecrackers,train LInus-K0lKA,135,lighting firecrackers,train LIo3C81rKVQ,21,"bird chirping, tweeting",train LIp8L-3FGR8,30,playing bagpipes,train LItix3t1bO4,14,cheetah chirrup,train LItpwrpcmoI,30,playing flute,train LIvad3qKUm8,184,driving snowmobile,train LIvad3qKUm8,218,driving snowmobile,train LIwFJsQKBUY,260,playing banjo,train LIwY4qX8voM,47,tractor digging,train LIyH7JfRvqk,227,playing vibraphone,train LIyH7JfRvqk,448,playing vibraphone,train LJ1vwuBVc3I,250,people clapping,train LJ2DRcqyYDA,60,"bee, wasp, etc. buzzing",train LJ2GzUF7YsY,10,playing snare drum,train LJ2fi9gsbwo,107,underwater bubbling,train LJ2fi9gsbwo,185,underwater bubbling,train LJ4cnz7mpbs,45,mouse clicking,train LJ4cnz7mpbs,75,mouse clicking,train LJ5P8TkTAJ0,21,airplane flyby,train LJ6ILhwg-L8,13,owl hooting,train LJ8xszg_bnE,90,air conditioning noise,train LJ9fWTag5Wo,19,playing tuning fork,train LJCVfn607oA,30,playing harpsichord,train LJFgjBp1t8k,86,"subway, metro, underground",train LJFgjBp1t8k,108,"subway, metro, underground",train LJHX_7ACFlI,6,civil defense siren,train LJHX_7ACFlI,18,civil defense siren,train LJJ6MoA89Gs,34,playing zither,train LJJ6MoA89Gs,85,playing zither,train LJK-gdwl3Jk,376,people eating noodle,train LJLKpqVHXRo,0,ambulance siren,train LJPBzpoEJtw,30,chainsawing trees,train LJRU18iZ1Uc,190,playing trombone,train LJXhL0GhHGw,15,playing steelpan,train LJXhL0GhHGw,218,playing steelpan,train LJZzd2UMg_U,42,train horning,train LJZzd2UMg_U,57,train horning,train LJ_YzqtklqI,17,elephant trumpeting,train LJb7crmmsPQ,90,people clapping,train LJbM0DMRM4Q,0,horse clip-clop,test LJe46iGMlS8,199,scuba diving,train LJgt5dUlv5k,212,beat boxing,train LJhK1uL4LWY,69,mouse clicking,train LJiXsZyxE9k,0,people burping,train LJkokwDPNiA,131,alarm clock ringing,test LJlqOniAPo0,1,civil defense siren,train LJo8jderfA0,48,cheetah chirrup,train LJo8jderfA0,89,cheetah chirrup,train LJoKVqX7ChU,0,"cattle, bovinae cowbell",train LJoKVqX7ChU,62,"cattle, bovinae cowbell",train LJppR-V0Wdc,2,machine gun shooting,train LJppR-V0Wdc,24,machine gun shooting,train LJsSbG5A1y0,0,lighting firecrackers,train LJtiz9zxi-Q,20,skidding,train LJu_awNp1dU,92,black capped chickadee calling,train LJu_awNp1dU,186,black capped chickadee calling,train LJv4wiYf6YI,98,cheetah chirrup,train LJxzk8gQykY,7,raining,train LK0um3j89a0,30,ocean burbling,test LK1R99gvkfI,100,"pigeon, dove cooing",train LK1exTizTH8,83,bird squawking,train LK4Izj5rWyQ,5,people humming,train LK4Izj5rWyQ,250,people humming,train LK4jHphrSco,30,male singing,train LK5gifFxUH8,168,playing bongo,train LK5gifFxUH8,197,playing bongo,train LK7g-pZZFiA,4,owl hooting,train LK84gMnPoNE,157,playing badminton,train LK8NpgU9EZE,30,people whispering,test LKCrURlIPEc,30,ambulance siren,train LKEnzK53rkM,414,singing bowl,train LKKRNiPkulA,1,playing zither,train LKN-K6Bsj8U,470,stream burbling,train LKNWSFCXzNY,330,fireworks banging,train LKQsW_OrVP0,94,bowling impact,train LKT3H-EKZ4k,0,dog growling,train LKVho_0UfF8,30,skateboarding,train LKZQQWl-e_s,13,"playing steel guitar, slide guitar",train LK_oH-4NoE4,28,driving buses,train LKaJGXalsyw,373,vacuum cleaner cleaning floors,train LKaJGXalsyw,414,vacuum cleaner cleaning floors,train LKaSVqcWi5c,90,people cheering,train LKbXNuCFoPc,31,playing snare drum,train LKbXNuCFoPc,49,playing snare drum,train LKe5DgwMtvg,131,playing badminton,train LKi_DwqbGKc,30,printer printing,train LKjCiJhyWnI,80,people whispering,train LKkJ1a6MfCc,152,playing squash,train LKl9MAeN_U8,70,people cheering,train LKoHHk6XzQQ,70,ambulance siren,train LKp2j3aiF-s,230,playing cello,train LKp7X_F-8jE,30,people whistling,train LKp_PzNEAo0,120,playing electric guitar,train LKqGRO4GWko,0,"engine accelerating, revving, vroom",train LKqwsWi5UAo,21,goose honking,train LKqwsWi5UAo,31,goose honking,train LKuAK-CCkv0,120,rapping,train LKv5qJfYkGM,120,owl hooting,train LKw-ak3wab8,37,lathe spinning,train LKw-ak3wab8,56,lathe spinning,train LKxUjv-HFF8,216,playing table tennis,train LKxyl8crle0,30,playing drum kit,train LKyG1Mx6dZk,23,baby crying,train LL3QnWVLG8c,56,rope skipping,train LL3QnWVLG8c,138,rope skipping,train LL436Co8SA8,519,dinosaurs bellowing,test LL618LsL2zY,30,pig oinking,train LL7FkfTYRLU,30,"motorboat, speedboat acceleration",train LLAjvllcjZ8,291,ice cracking,test LLBH0ARrqvc,30,playing accordion,train LLEYtiKvaMc,2,penguins braying,train LLFeRPBeOaM,100,people sobbing,train LLFttupRXJM,20,car passing by,train LLHtaUr94sw,28,playing cornet,train LLHtaUr94sw,63,playing cornet,train LLLEkpfrwSg,95,playing bassoon,train LLLEkpfrwSg,114,playing bassoon,train LLPaxKzTTw8,30,horse clip-clop,train LLS0Shv6qVQ,31,"cattle, bovinae cowbell",train LLS0Shv6qVQ,80,"cattle, bovinae cowbell",train LLSFepj3LEk,34,chinchilla barking,train LLTPXNtfLvs,300,people cheering,train LLXvx7UTe9I,23,playing zither,train LLXvx7UTe9I,66,playing zither,train LLZgmJDJEmk,30,"motorboat, speedboat acceleration",train LL_sYpFWLQI,25,dog growling,train LLfgor44ASw,99,singing bowl,train LLgtu7YH_hg,9,"vehicle horn, car horn, honking",train LLh-quhF9QM,59,scuba diving,train LLh-quhF9QM,432,scuba diving,train LLhnmEUWg1A,122,sea lion barking,train LLiZI8k2xnk,211,people eating crisps,train LLiZI8k2xnk,260,people eating crisps,train LLkNFGrrgUo,30,"engine accelerating, revving, vroom",test LLkNsHcG3oA,4,pig oinking,train LLl-cqybths,290,car engine idling,test LLm512F49X4,54,playing squash,train LLmztIHvpbo,267,male singing,train LLnxJkUwnNI,555,basketball bounce,train LLoAfWorzmM,30,chicken clucking,train LLq-nMBVAVk,227,sea lion barking,train LLrgl50VhMM,100,spraying water,train LLt7PvFbq5c,23,rapping,train LLt7PvFbq5c,73,rapping,train LLwRuoCfNP8,61,playing volleyball,train LLy9SA-v61o,100,driving motorcycle,train LLyrzrpWYYY,100,playing hammond organ,train LM-2P7D25G8,30,orchestra,train LM-l1I4cc5c,8,airplane,train LM-l1I4cc5c,61,airplane,train LM1gZ_7DWlk,10,playing bass guitar,train LM6ZI79S7vU,30,playing drum kit,train LM6vqXpAQOM,220,bowling impact,train LM6vqXpAQOM,443,bowling impact,train LM731_FdcNw,56,raining,train LM731_FdcNw,70,raining,train LM7r6mlq0a4,0,duck quacking,train LM8m_O4656I,210,playing cymbal,train LMCouoWmJYA,217,people booing,train LMFM61h_9a4,44,horse neighing,train LMFexwOPWk4,192,singing choir,train LMJk7dDQlW4,0,black capped chickadee calling,train LMLbNd-zAt8,347,hair dryer drying,train LMO4xIuCyVI,29,arc welding,train LMOmUef5FQ0,2,people shuffling,train LMOmUef5FQ0,29,people shuffling,train LMRNPdm6ybQ,30,wind noise,train LMUOVQAI2ew,59,playing table tennis,train LMUOVQAI2ew,79,playing table tennis,train LMW6S_qNxVY,20,typing on computer keyboard,train LMZt1Saz0J0,160,train horning,train LMbyOx04l9E,25,"vehicle horn, car horn, honking",train LMbyOx04l9E,36,"vehicle horn, car horn, honking",train LMcCvGwYIS4,22,gibbon howling,test LMfD-ZsuzOU,30,playing clarinet,test LMfiNbqrzYE,70,"playing marimba, xylophone",train LMhZUDcPUR4,31,woodpecker pecking tree,train LMhZUDcPUR4,63,woodpecker pecking tree,train LMiatBGqFOE,8,cricket chirping,train LMiatBGqFOE,88,cricket chirping,train LMjurhBPGY8,409,people 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LNHBMFCzznE,237,"female speech, woman speaking",train LNHNq5uQQ5s,155,people cheering,train LNHNq5uQQ5s,173,people cheering,train LNHWLAdsxfo,13,ferret dooking,test LNIhJN4uzpY,10,roller coaster running,train LNIhJN4uzpY,78,roller coaster running,train LNM9zAriJvU,53,striking pool,train LNM9zAriJvU,73,striking pool,train LNPMe91gyFc,7,tractor digging,train LNPMe91gyFc,83,tractor digging,train LNPOYOchZwM,65,driving snowmobile,train LNPOYOchZwM,242,driving snowmobile,train LNQ7fzfdLiY,150,ambulance siren,test LNQL7xrn5oU,20,people farting,train LNQkIjxdSwg,30,wind noise,train LNRC5gZVz1s,0,playing accordion,train LNYEJd6TYho,1,playing snare drum,train LNYEJd6TYho,134,playing snare drum,train LNZvPEzKgb0,30,printer printing,train LNaoXd_PoS4,142,tap dancing,train LNaoXd_PoS4,292,tap dancing,train LNeVmkI8zj4,0,"donkey, ass braying",train LNf7s1uPB1c,222,chimpanzee pant-hooting,test LNgwQ8fypt0,0,playing trombone,train LNgwQ8fypt0,56,playing trombone,train LNhj87xZ5aU,55,bathroom 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LOE3pidTXLA,200,"race car, auto racing",train LOHogbPn2vs,30,playing flute,train LOHw8cZZ9-U,450,people belly laughing,test LOJs23D5h1U,380,"subway, metro, underground",train LOLFOiNiS1o,67,sharpen knife,train LOLFOiNiS1o,77,sharpen knife,train LOLqoLD8HtM,36,playing gong,train LOLqoLD8HtM,241,playing gong,train LOS0kA_O6IQ,67,playing djembe,train LOS0kA_O6IQ,167,playing djembe,train LOTibagId-E,30,chainsawing trees,train LOVkJdMSrqs,78,slot machine,train LO_9mAMFlO0,250,ocean burbling,train LOc4IDtoiWU,30,"playing violin, fiddle",train LOdLWZ9C6Qo,45,playing timbales,train LOhQ4YSNnfI,100,people burping,train LOj1kNzRycU,196,swimming,test LOnt8b9cPFg,48,playing vibraphone,train LOnt8b9cPFg,65,playing vibraphone,train LOoPZn7V_TQ,0,duck quacking,train LOoPZn7V_TQ,31,duck quacking,train LOqu7XHB290,110,playing accordion,train LOr90qaqsaA,40,mouse pattering,train LOrRbaJxEPE,76,people marching,train LOtPb1yU8TM,30,"motorboat, speedboat acceleration",train LOu4KNdImkI,0,police radio 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LPS8GvxWDZU,133,blowtorch igniting,train LPTsZZVr06o,30,people eating,train LPUBIiA6iHg,22,playing volleyball,test LPXXpU06itM,90,playing accordion,train LP_4TrSjQXs,40,playing trumpet,train LP_LVcw0gOw,500,playing drum kit,train LPaRBMnW6MI,30,"playing violin, fiddle",train LPadA2UrxvQ,0,people hiccup,train LPahP_DkF9I,30,"pigeon, dove cooing",train LPcMl8ECF44,2,horse neighing,train LPcMl8ECF44,12,horse neighing,train LPdJmqyA2pM,33,pig oinking,train LPdjqW4UvHw,243,"playing steel guitar, slide guitar",train LPeWcFOc9J8,27,hail,train LPfrSeIPPCY,19,hammering nails,train LPiCAPibtr4,15,woodpecker pecking tree,train LPiCAPibtr4,27,woodpecker pecking tree,train LPjblqE3xHk,218,fox barking,test LPjufeH3iqs,60,"railroad car, train wagon",train LPkJoOnqplU,10,"motorboat, speedboat acceleration",train LPmkUnCv6ow,20,dog barking,train LPn7CtfZlT0,19,car engine starting,train LPn7CtfZlT0,89,car engine starting,train LPor11i_sAY,31,dog whimpering,train LPor11i_sAY,44,dog whimpering,train LPp7IhATsuU,30,"motorboat, speedboat acceleration",train LPpTOlfyy0M,50,playing trumpet,train LPrPknMiPhQ,280,"playing violin, fiddle",train LPrlvkTcbeA,350,sailing,train LPsRAZwvkbw,141,playing table tennis,train LPsUImLIxqc,30,"playing violin, fiddle",train LPvJFjsxMj8,30,cat meowing,test LPwwx-pZszc,60,playing badminton,train LPydM-Gilpg,133,cutting hair with electric trimmers,test LQ-G_4rcA48,10,playing drum kit,train LQ-KewVW1YI,47,electric grinder grinding,train LQ1iB7gJSkw,140,mouse pattering,train LQ4_PGhyHCw,94,playing french horn,train LQ4_PGhyHCw,216,playing french horn,train LQ4nI5kF_yc,210,playing cymbal,train LQ73KFe5IPk,30,driving buses,train LQ7nbKZwpvA,50,missile launch,train LQ7nbKZwpvA,127,missile launch,train LQ8MgjYCl8o,4,sliding door,train LQ8yfLvqe3k,82,car engine idling,train LQAsnYwSFoY,30,firing muskets,train LQCNmM3O9KQ,820,playing timpani,train LQCkP_KRBnk,289,"playing steel guitar, slide guitar",train LQCkP_KRBnk,715,"playing steel guitar, slide 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LRhGAkeY8YA,91,missile launch,test LRiNKxZ8v_A,30,"playing violin, fiddle",train LRlYimasLkc,50,playing volleyball,train LRlrFudaEs8,0,"bee, wasp, etc. buzzing",test LRm_U8JmtZw,0,"race car, auto racing",train LRoZg5pgDj4,340,lions growling,train LRovcTNgUc8,140,cap gun shooting,train LRrSyPviods,30,horse clip-clop,train LRragvvrZcg,6,"bee, wasp, etc. buzzing",train LRrdIoE-UD8,53,alarm clock ringing,train LRu9O56jfJg,67,bowling impact,train LRvmSJE8nDg,100,"male speech, man speaking",train LS06FnMkXgU,28,child singing,train LS13yc8djcs,33,civil defense siren,train LS1BpvHTIeg,7,baby crying,train LS2MPW1D-JU,5,beat boxing,train LS2YHzjELb0,80,"playing marimba, xylophone",train LS5KKiBdk0k,130,people babbling,train LS6BIaF2-2w,3,lions roaring,train LS6BIaF2-2w,22,lions roaring,train LS6efNeNaFs,187,wind chime,test LS6qPERMibg,86,people marching,train LS7RM5iaibg,107,tractor digging,train LS7RM5iaibg,147,tractor digging,train LS7qx2Fh-6Q,180,wind rustling leaves,train LS8mDeIxxdk,80,door 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knocking,train LSXoi2-LOuU,382,roller coaster running,test LSZUZoZaT5Q,64,squishing water,train LSZUZoZaT5Q,103,squishing water,train LSaLPObrnZw,10,playing timpani,test LSdLFRi_h-g,90,waterfall burbling,test LSe5WTYa-Ho,42,arc welding,train LSe6hHPMC68,77,swimming,train LSe6hHPMC68,252,swimming,train LSeA3blFGCE,17,car engine knocking,train LSeA3blFGCE,39,car engine knocking,train LSeDA9tLvzM,70,skateboarding,train LShVRPL7yAE,81,civil defense siren,train LSk35Fi_43I,119,rope skipping,train LSk35Fi_43I,238,rope skipping,train LSnY_IYwUCg,40,sailing,train LSoXrR2Xec4,20,bird squawking,train LSoxVk2XdJ8,30,"playing violin, fiddle",train LSqdkL9Tt1E,8,playing cornet,train LSsLxwvgB9g,126,owl hooting,train LSsYBN_RvPc,122,rapping,train LSsYBN_RvPc,257,rapping,train LSxvSU0syjg,21,playing table tennis,train LT-i2mpMatI,24,"rowboat, canoe, kayak rowing",train LT19zaddcig,157,playing volleyball,train LT2JlrCBl_o,100,playing saxophone,train LT2tovTFHSc,48,playing bass drum,train LT3uQigQCQo,0,scuba diving,train LT3uQigQCQo,28,scuba diving,train LT4uxV4ppLo,1,hammering nails,train LT5b_9j7Pxw,19,disc scratching,train LT6fwvMhbms,30,wind noise,train LT80mm7UBJk,200,stream burbling,train LTA3ncpTnUo,30,using sewing machines,test LTCmg2nPV6o,11,"rowboat, canoe, kayak rowing",train LTD5rh_DOTY,260,owl hooting,test LTEEjl4BW6U,152,popping popcorn,train LTEEjl4BW6U,165,popping popcorn,train LTEvLRNOcIc,30,people running,train LTGCVbXAzRk,30,"engine accelerating, revving, vroom",train LTIZRqL7X3Y,4,car engine knocking,train LTJ7hbP8y8g,21,chopping wood,test LTJJqIPPxi0,0,lions growling,train LTJj9cAzlw4,140,playing snare drum,train LTKXl24ekd8,30,"motorboat, speedboat acceleration",train LTLRjL8Myc0,72,airplane,train LTLRjL8Myc0,86,airplane,train LTLi43TyqTI,20,raining,train LTLuDFO8QpA,40,helicopter,train LTMpqJNTDGc,187,skiing,train LTNJm66gkIw,0,cat growling,train LTNgncM_otw,10,plastic bottle crushing,test LTNxwcaFGME,103,playing badminton,train 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LZEHPE5NjWo,20,ocean burbling,train LZEu6O7vCAo,0,mynah bird singing,train LZFfZoHh-Vs,30,thunder,test LZGE-904f9E,0,wood thrush calling,train LZGE-904f9E,17,wood thrush calling,train LZGVm_okTm0,40,sharpen knife,train LZGVm_okTm0,143,sharpen knife,train LZJis3ahSx4,66,tap dancing,train LZJlMsvKgFk,345,dinosaurs bellowing,train LZJmNr8L5d0,200,pumping water,test LZMKjVcy0PA,30,"engine accelerating, revving, vroom",train LZMLFdjS3rs,5,people shuffling,train LZMLFdjS3rs,15,people shuffling,train LZNqxwQg0ys,27,francolin calling,train LZNqxwQg0ys,91,francolin calling,train LZPBd-rcFAo,24,francolin calling,train LZPBd-rcFAo,38,francolin calling,train LZRj04MWEo8,2,pheasant crowing,test LZSQDZ1B5-o,30,playing acoustic guitar,train LZSsYfPl3lg,20,ferret dooking,train LZSsYfPl3lg,35,ferret dooking,train LZT90iZ5gLY,0,playing zither,train LZT90iZ5gLY,29,playing zither,train LZTGraAMpzM,30,horse clip-clop,train LZUjv6CFsDs,516,playing volleyball,train LZXbXdqyvG4,30,chicken clucking,train 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Lg9CqI9xl_g,152,opening or closing drawers,train Lg9I7HyyQBk,1,"ice cream truck, ice cream van",train Lg9mealmU3Q,0,playing zither,train Lg9mealmU3Q,38,playing zither,train LgEdRFbdjH4,4,dog baying,test LgFktHEa5u0,47,singing bowl,train LgPWTQNtSFE,35,baby laughter,train LgPWTQNtSFE,59,baby laughter,train LgRNpbP7DXk,30,playing drum kit,train LgS4kbZ4JRc,220,machine gun shooting,train LgX6oXlXwjA,79,mynah bird singing,train LgX6oXlXwjA,125,mynah bird singing,train LgagvyI9h2k,145,elk bugling,train LgbsI0f0QdA,0,people eating,test LgdtTzvKnT4,30,"rowboat, canoe, kayak rowing",train LgetdAglQ2E,16,playing harp,train LgirOC0mfkU,30,wind noise,train Lgj_4xyUt2s,103,playing tabla,train LgnLozA45Go,30,wind noise,train Lgn_t_Jj21s,27,playing volleyball,train LgoGcCeyTFo,55,playing squash,train Lgp2PgROBsM,325,skiing,train LgpehXwUqYo,40,using sewing machines,train LgrxLv0DenA,43,spraying water,train LgsskXHlt6g,437,forging swords,train LgsskXHlt6g,459,forging swords,train 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LhT0L5FBLVs,567,cattle mooing,train LhTULmMIfFs,162,toilet flushing,train LhUr6xCmYHQ,110,chainsawing trees,train LhWoZWuUg_s,30,printer printing,train LhYaUSu_zX0,2,car engine knocking,train LhZAOIR90BM,30,baby crying,train Lh_iDoJABdM,380,lathe spinning,train Lhc5eE9X-ak,30,cat purring,test Lhd0A3gu2gA,270,ocean burbling,train LhdWt-1Cd38,10,playing vibraphone,train LhedwI5h3Z0,10,"engine accelerating, revving, vroom",train LheyT6WSxkw,220,playing piano,train LhfBQAes28g,12,lawn mowing,train LhgaG9ZtdwI,3,alarm clock ringing,test Lhi2ULgR2Fk,94,playing accordion,train Lhi9SJ6V8NQ,14,smoke detector beeping,test LhicdC_0z18,30,toilet flushing,train LhkgXPKpZGo,30,printer printing,train Lhks-KUCZ9Y,30,orchestra,train LhpHdLZwc48,120,people sniggering,train Lhpiwmswibk,0,using sewing machines,train LhpzNXhZ44c,30,"motorboat, speedboat acceleration",test LhqNXgIRspc,9,"alligators, crocodiles hissing",train LhsdlB1SXio,73,mynah bird singing,train Lhu_8H6MMFk,12,bowling impact,train Lhuh4mKOTqY,109,foghorn,test LhyM78BIZHs,9,cupboard opening or closing,train LhyM78BIZHs,19,cupboard opening or closing,train Lhz-1v9k2qM,530,people sniggering,train LhzEFv55O2A,0,beat boxing,train Li0ILVhZE3o,30,"pigeon, dove cooing",train Li14hNb14wc,190,"playing marimba, xylophone",train Li3EiRv9Iu4,123,volcano explosion,train Li3nk9blh3M,30,"female speech, woman speaking",train Li4HFMzmOoE,40,basketball bounce,train Li4fgVkszto,30,people running,train Li7OMYGLaOU,69,canary calling,train Li7vbezLonM,30,"playing violin, fiddle",train Li83STyiyYM,28,playing guiro,train Li83STyiyYM,39,playing guiro,train LiEdORNK_5A,30,"playing violin, fiddle",train LiFrFhxR5VM,34,airplane flyby,train LiGROhA4a3c,130,playing saxophone,train LiGjElnn_iY,15,dog howling,train LiGjElnn_iY,26,dog howling,train LiHQ6oUWCNM,70,people sneezing,test LiIo3fRZhjY,29,people booing,train LiIuyWaSP90,280,stream burbling,train LiN230GLuRw,30,"bird chirping, tweeting",train LiRgBYhkeOA,30,people whistling,train 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LkbZkF_t7u4,70,skidding,train Lkbi5rBO06E,62,rapping,train Lkbi5rBO06E,150,rapping,train LkcSqM2prHE,22,magpie calling,train LkcWI0sov1s,346,vacuum cleaner cleaning floors,train LkcWI0sov1s,356,vacuum cleaner cleaning floors,train LkeQWD67MvE,141,civil defense siren,train LknBdjt06DY,20,"railroad car, train wagon",train LksDJWHBxkA,22,people burping,train LksEctPkEvg,40,"rowboat, canoe, kayak rowing",train Lkw8wWtwGzk,129,people cheering,train Lkw8wWtwGzk,140,people cheering,train LkwghKk20CU,0,pheasant crowing,train LkwghKk20CU,18,pheasant crowing,train Lkz3lkZCAQ4,52,whale calling,train Lkz5Es_FfNQ,0,dog howling,train Ll-6nb8hn2A,3,telephone bell ringing,train Ll-yxQeojbY,133,coyote howling,train Ll3m35ff8LU,30,"playing violin, fiddle",train Ll4gN_OGDzg,27,basketball bounce,train Ll7Kt04js4U,96,playing badminton,train Ll7tdQWMw50,22,playing bongo,train LlAwycmnVvk,180,"heart sounds, heartbeat",train LlAwycmnVvk,243,"heart sounds, heartbeat",train LlFCu8KEMQs,30,driving buses,train LlG9Ntnnz4k,25,tap dancing,train LlGEXC69z3Q,30,playing drum kit,train LlGMBEUYQds,0,people whistling,train LlHvZoTWfu4,1,playing cornet,train LlHvZoTWfu4,110,playing cornet,train LlI2SQxGXBs,270,splashing water,train LlICTLO9aSA,30,door slamming,test LlKwEc4Br1M,0,helicopter,train LlL8A1YuIO4,120,playing cello,train LlRZR8xPOEw,21,frog croaking,train LlRZR8xPOEw,32,frog croaking,train LlTy_WMYt7w,144,missile launch,train LlU4FuIJT2k,55,rapping,train LlU4FuIJT2k,88,rapping,train LlU5NzH20bA,30,playing banjo,train LlZUcpAWK2A,0,playing shofar,train Ll_8IubMoeg,0,chicken clucking,train LlbH6CZvbKE,30,goose honking,train Lldp769fKTk,107,airplane,train Llemx-jjVLg,30,people babbling,train LlfLc92ry3c,115,people burping,train Llffd6cn5jY,80,skidding,train LlhQXN7vlv8,80,playing piano,train LliQJxISm68,165,skiing,train LliThslFpjg,190,playing banjo,train LllgjDqtKc8,30,"rowboat, canoe, kayak rowing",train LlozjQG4Zyo,48,vacuum cleaner cleaning floors,train LlpeMFJtdB8,53,playing 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LmC34A2ioGI,275,barn swallow calling,train LmFTa1huZV0,50,orchestra,train LmGDryn6Jbs,580,playing drum kit,train LmI9xv_S2Ug,12,missile launch,train LmI9xv_S2Ug,93,missile launch,train LmNULnY7lkE,109,mouse clicking,train LmNULnY7lkE,150,mouse clicking,train LmQNDC-1Ew0,210,playing table tennis,train LmR7PmWCLGs,40,toilet flushing,train LmSnKdx4W_8,0,"race car, auto racing",test LmT3VlPzVz8,30,wind noise,train LmUE68RukYQ,200,"playing marimba, xylophone",train LmVXpy1QWkc,156,playing erhu,train LmWE3BoSoGM,90,playing trumpet,train LmWgMdpm7W4,4,cat caterwauling,train LmWgMdpm7W4,63,cat caterwauling,train LmYDd861ABA,20,playing banjo,train LmYVanmnrW8,2,gibbon howling,train LmYVanmnrW8,27,gibbon howling,train LmaGFSc6jmw,216,"playing steel guitar, slide guitar",train LmcF42o3ikc,30,people burping,train Lmgw3C240pQ,2,opening or closing car electric windows,train Lmgw3C240pQ,61,opening or closing car electric windows,train LmhK0p-7Ljw,43,playing darts,train LmhK0p-7Ljw,54,playing 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Loj3IDd7VGA,13,squishing water,train Loj3IDd7VGA,23,squishing water,train Lok8UEJm-Xw,247,playing snare drum,train LokKlY1gwDI,1011,rapping,train LopuqC456yg,90,people sniggering,train Lou52lJSBVI,0,people crowd,train LovjUadPj4M,390,playing clarinet,train Lowb1I1qUV8,5,cow lowing,train Loyx41TB17Y,1,electric grinder grinding,train Loyx41TB17Y,86,electric grinder grinding,train Lp-kKzgjH88,10,"race car, auto racing",train Lp2iclrG_yM,140,"playing marimba, xylophone",train Lp4VlV1iebo,8,coyote howling,train Lp4VlV1iebo,82,coyote howling,train Lp5AKGTP7DY,117,playing french horn,train Lp5AKGTP7DY,185,playing french horn,train Lp6JViuKWlo,20,baby crying,train Lp7-GR1vLwA,410,opening or closing drawers,test Lp72mKGAe10,41,playing gong,train Lp72mKGAe10,77,playing gong,train LpAIjJ6hjU0,10,"male speech, man speaking",train LpBq1W8zLTY,353,vacuum cleaner cleaning floors,train LpBq1W8zLTY,655,vacuum cleaner cleaning floors,train LpD-nbvv99Q,142,strike lighter,train LpDRwDB6w7U,30,"motorboat, 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Lpc7Ws6PSfg,2,baby crying,train LpeUNO651d4,115,mynah bird singing,train LpfewILEuUU,20,dog barking,train LphSS-BomWw,308,playing cymbal,train LphmNfEyJho,8,penguins braying,test Lpjc2_2weO0,60,playing cello,train LpoBrR9TSaw,30,typing on typewriter,train LppSgg2G60U,30,chicken crowing,train Lpp_75qV4T0,29,car engine idling,train LprsVxsYWi0,24,fire truck siren,train LpswiH_Vkes,21,frog croaking,test LpthkDojmy8,30,playing harpsichord,train Lpto0z0MQOk,310,playing snare drum,train Lpus_fxffrE,156,playing tabla,train Lpus_fxffrE,172,playing tabla,train LpvRYLxyNL4,130,playing cello,train Lpxf3h1898s,0,playing flute,train LpyGsxCZfj4,35,playing snare drum,train Lpyf4MOamio,188,playing double bass,train Lpyf4MOamio,323,playing double bass,train Lq--IqXIB2I,160,people burping,train Lq-d8F79aCQ,510,playing synthesizer,train Lq05z1p5aC8,30,playing harp,train Lq0L2A7qLE8,498,hedge trimmer running,train Lq6-uDTBUuY,0,fireworks banging,train Lq71WlQjDII,74,slot machine,train LqB9SQFGCSs,30,car passing by,train LqDMCieTGqY,490,skidding,train LqEkssWoBqw,90,car engine starting,train LqEq7TJXS8s,15,air horn,train LqFBUprTCg0,250,people whispering,train LqGFZnVPJnA,148,vacuum cleaner cleaning floors,train LqH1ult87Ug,180,lions growling,train LqInaHKSdvo,30,dog barking,train LqJ_7WCwaao,316,playing tuning fork,train LqJ_7WCwaao,572,playing tuning fork,train LqKqauhtq9w,200,basketball bounce,train LqNygs6uTSc,21,goose honking,train LqRM-S2Ss_c,41,cat hissing,train LqSWI5SLido,92,playing erhu,train LqTZUgSifzc,30,baby crying,train LqVUU-V1BeI,3,wind noise,train LqYu2qXHRkc,10,toilet flushing,train Lq_NbIUNZ2s,14,"electric shaver, electric razor shaving",train Lq_niRlFCaI,27,police car (siren),train LqaYNGcDuEE,16,pheasant crowing,train LqaYNGcDuEE,63,pheasant crowing,train LqbnamMXZP4,0,"race car, auto racing",train LqennQNZKCg,430,people cheering,train LqhN-ntklwo,338,golf driving,train LqhjQLbxtcI,0,ocean burbling,train LqjMBnhbM7k,151,playing electronic organ,train LqjMBnhbM7k,176,playing electronic organ,train LqjueODAG5U,10,"male speech, man speaking",train LqkmiYK1Zys,40,skidding,train Lqm_8Z0F_ng,72,playing squash,train LqsJbF2yEuU,30,playing cello,train Lqt0q4fyE_I,118,playing bagpipes,train Lqt0q4fyE_I,216,playing bagpipes,train LquCrc_r7KM,30,sailing,train LqxWkSDycdA,14,horse clip-clop,train LqyJFK7rfRk,51,black capped chickadee calling,train LqyJFK7rfRk,69,black capped chickadee calling,train Lr18-dzxl1k,60,rope skipping,train Lr18-dzxl1k,71,rope skipping,train Lr1LlNv2jn8,30,turkey gobbling,train Lr1_t0FpxME,0,"subway, metro, underground",train Lr3pctubAEA,55,barn swallow calling,train Lr7IaXUJsxI,37,ice cracking,train Lr7IaXUJsxI,71,ice cracking,train Lr8YOKuagNM,0,"bee, wasp, etc. buzzing",train LrC_s7x50vY,25,"motorboat, speedboat acceleration",train LrD2YEIIIFs,100,people babbling,train LrF1D-wSHHI,84,people slapping,train LrLLSlnDo-w,1,skiing,train LrLLSlnDo-w,47,skiing,train LrLTtlB2yWc,160,playing banjo,train LrLnJVIub9o,300,"subway, metro, underground",train LrM4VhPRHAE,1,cheetah chirrup,train LrMyRNZna6Y,300,sailing,train LrN3196Om30,39,"subway, metro, underground",train LrOzd4OE6UU,70,helicopter,train LrQiEXOiEto,78,playing zither,train LrQiEXOiEto,184,playing zither,train LrRS8tjN8B0,11,scuba diving,train LrRS8tjN8B0,25,scuba diving,train LrT96quJM5U,155,sharpen knife,train LrT96quJM5U,166,sharpen knife,train LrTTUePnxhY,179,playing erhu,train LrTTUePnxhY,192,playing erhu,train LrUjrgxe0X0,30,playing piano,test LrUx2l2aR7c,10,toilet flushing,train LrWwi0-wcrg,30,chainsawing trees,train Lr_pKhp6lXI,40,"bird chirping, tweeting",train LraC9zXnE60,140,"playing violin, fiddle",train LrcvUfmRN8k,30,"female speech, woman speaking",test LrhrN7LUFCA,30,chicken crowing,train Lri_tk0Wh2A,6,singing bowl,train Lri_tk0Wh2A,18,singing bowl,train LriokFTJZt4,40,car engine knocking,train LrjQrZr-qek,165,basketball bounce,train Lrko8zztrzc,62,dog growling,train Lrko8zztrzc,84,dog growling,train 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MGZgC343fWg,450,ocean burbling,train MG_SfVPSgMk,118,"playing steel guitar, slide guitar",train MG_SfVPSgMk,133,"playing steel guitar, slide guitar",train MGaM-X5x8H4,8,airplane flyby,train MGePrjhGlIk,30,"pigeon, dove cooing",train MGfanXBmjXE,30,female singing,train MGg-bDY46a0,30,dog whimpering,train MGgLUzCQnsc,100,slot machine,train MGjR2zgbstI,5,playing hammond organ,train MGke2jauexU,142,playing volleyball,test MGlXtoSD6SA,33,sea lion barking,test MGlwdoXLQIc,14,foghorn,train MGmTwvFeCMo,7,people finger snapping,train MGmTwvFeCMo,48,people finger snapping,train MGmtyU2P5l8,0,splashing water,train MGnItgaw4vw,10,black capped chickadee calling,train MGtVHEIcCTw,148,playing table tennis,train MGuyIHXGseM,35,popping popcorn,test MGuyIHXGseM,45,popping popcorn,test MGxS7Qs4AJc,50,playing banjo,train MGxjzhNtH6A,2,fox barking,train MGxjzhNtH6A,20,dog barking,train MGxy0Y8SakA,129,beat boxing,train MGyzZ4kVXb0,213,woodpecker pecking tree,train MH0M0LA0Rhw,8,playing badminton,train MH1MqlYsIKc,1,playing tabla,train MH1MqlYsIKc,37,playing tabla,train MH2xwQLsUJI,9,lighting firecrackers,train MH2xwQLsUJI,39,lighting firecrackers,train MH5ZWgG5IVc,500,"railroad car, train wagon",train MH6jMDL7smk,121,playing squash,train MH8chavmTOI,50,playing drum kit,train MH9L55k7wF0,146,rapping,train MH9L55k7wF0,210,rapping,train MHAJqiKZmI0,204,singing bowl,train MHAJqiKZmI0,303,singing bowl,train MHAYFT28nV0,30,playing snare drum,train MHAZd_4Ww0Y,24,warbler chirping,train MHAZd_4Ww0Y,43,warbler chirping,train MHB0W5qJHzk,250,orchestra,train MHEZrTwE_qM,50,mynah bird singing,train MHFWFdBZKkQ,9,playing tuning fork,train MHFWFdBZKkQ,33,playing tuning fork,train MHHjP7XrBq0,170,singing choir,train MHJ6Fru5kSo,73,crow cawing,train MHJ6Fru5kSo,85,crow cawing,train MHJJzaCwLdg,320,playing bass guitar,train MHKs4ulWAvI,215,playing table tennis,train MHNATdBHPGY,270,playing synthesizer,train MHQg-o5ZAmg,19,"vehicle horn, car horn, honking",train MHS-QSRBm38,80,"bird chirping, 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MHq9YldNuP0,139,people marching,train MHqKIOwIXeM,146,car engine starting,train MHr8Jvx5aoI,2,"cattle, bovinae cowbell",train MHr8Jvx5aoI,25,"cattle, bovinae cowbell",train MHsRa_x7fpA,29,chainsawing trees,train MHu7IYl3Zy8,64,skiing,train MHuITs1e-5I,44,playing bugle,train MHy6atcu4zs,13,dog growling,train MHy6atcu4zs,26,dog growling,train MHykFW1zgoA,121,lathe spinning,test MHzp59m-3ek,550,ocean burbling,train MI0-z5n5SMI,30,sheep bleating,train MI1lgCg3YpQ,136,playing didgeridoo,train MI2ybOGJzXo,36,tap dancing,train MI6AgoqRlZg,69,chicken crowing,test MI6mYvpTgCM,83,alarm clock ringing,test MI81v1R8Nl0,28,playing accordion,train MI93WTScO9M,41,playing glockenspiel,train MI93WTScO9M,195,playing glockenspiel,train MIA6JhJEto0,50,lathe spinning,train MIFh-KEiq4w,30,wind rustling leaves,train MIH6vCpmH3o,29,rope skipping,train MIK-lTPhEtw,30,duck quacking,train MIKSO7t2IC8,55,playing hockey,train MIKSQT-oXfc,114,playing tympani,train MILB0ojcL-o,68,playing trombone,train MILB0ojcL-o,84,playing trombone,train MIS03lny-xU,0,fireworks banging,train MISZHZTjfuw,61,alarm clock ringing,train MITJWqaDX0U,486,people eating,train MIUpzWvCErQ,44,playing didgeridoo,train MIVCAMVyi_8,0,wind noise,train MIYBdV60ZIc,30,playing piano,train MI_TK1ELZk8,39,playing darts,train MI_TK1ELZk8,51,playing darts,train MIbtv3lms8Q,30,"motorboat, speedboat acceleration",train MIfBpzbPIjY,151,people eating crisps,train MIfBpzbPIjY,166,people eating crisps,train MIhxn88-8FY,3,mosquito buzzing,train MIj9l8N27wY,25,missile launch,train MIjHhIJHJfU,3,mouse clicking,train MIk0BxhTHWo,32,civil defense siren,train MInPDok3qRQ,15,air horn,test MIowYYjCTGw,30,machine gun shooting,train MIp8Urq0NBU,20,playing hammond organ,train MItFhDUKcno,190,lions growling,train MIuTJVNnIJc,470,waterfall burbling,train MIx6I90Y-LU,15,cap gun shooting,train MIx_gbyG5CM,6,volcano explosion,train MIx_gbyG5CM,31,volcano explosion,train MIxsGKpk5s0,38,"electric shaver, electric razor shaving",train MIyLv7YcZKc,26,sloshing water,train MJ-wuiqNzv0,0,police car (siren),train MJ3Ho0eM9Zw,60,toilet flushing,train MJ3Rvy0cAeo,0,police radio chatter,train MJ3Rvy0cAeo,21,police radio chatter,train MJ4LfITd-HA,142,car engine idling,train MJ6esCBhTVU,30,"motorboat, speedboat acceleration",train MJ7F76eKeIM,1,playing timbales,train MJ7F76eKeIM,31,playing timbales,train MJAizVcs_gY,22,playing squash,train MJCrHvZZn2E,20,helicopter,train MJDLONul7IA,160,playing drum kit,train MJE78HKFuEI,1,"ice cream truck, ice cream van",train MJE806Idm5A,40,people babbling,train MJEE2eBqZfc,330,playing harp,test MJEKrMdCW2w,80,people babbling,train MJEjBU0ZRyU,0,horse clip-clop,train MJEuGgSzeEE,43,playing didgeridoo,train MJLB4Qv38vM,1891,fire crackling,train MJLB4Qv38vM,4952,fire crackling,train MJOnx-9jMjI,554,skateboarding,train MJRcEwEAsuw,135,striking pool,train MJVr6A2xaHg,46,playing snare drum,train MJVr6A2xaHg,57,playing snare drum,train MJY80F7Uyn0,468,cat purring,train MJY80F7Uyn0,916,cat 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MK35p2gRrUs,1,woodpecker pecking tree,train MK35p2gRrUs,38,woodpecker pecking tree,train MK3CQN3WgC0,50,playing trumpet,train MK3CQN3WgC0,99,playing trumpet,train MK6GR1_GAXg,18,reversing beeps,train MK6GR1_GAXg,42,reversing beeps,train MK6Gfm0jElc,0,baby crying,test MK7OIYV8ML8,40,"race car, auto racing",train MK8Ho-d4Hqc,30,"race car, auto racing",train MKB_BmZSvqw,40,playing saxophone,train MKC9LvRivTM,30,playing french horn,test MKCapHpr7Tk,0,crow cawing,train MKCapHpr7Tk,108,crow cawing,train MKEP6WhrvYg,13,"cattle, bovinae cowbell",train MKH5kqirNY8,19,hail,train MKH5kqirNY8,42,hail,train MKO7AELuHqo,0,"subway, metro, underground",train MKOfso3MJNc,0,francolin calling,train MKQWCVr4q8g,27,playing zither,train MKQWCVr4q8g,46,playing zither,train MKRuxljwsOs,30,mosquito buzzing,test MKS_SPDcaRU,163,playing volleyball,train MKXeCiPtZwo,280,child singing,test MKXpiFZFcUM,54,playing double bass,train MKXpiFZFcUM,100,playing double bass,train MKYXrSz_YY4,28,playing hammond organ,train MKcdqW9AAXs,150,playing electric guitar,train MKeuXUprnfw,61,smoke detector beeping,train MKeuXUprnfw,81,smoke detector beeping,train MKfhI4Zlgj4,11,"engine accelerating, revving, vroom",train MKhoXSqPYGk,19,car engine knocking,train MKhoXSqPYGk,91,car engine knocking,train MKiAImPY9M0,90,ocean burbling,train MKiV9vfUuJk,55,tap dancing,train MKikHxKeodA,30,opening or closing drawers,test MKja1VePlyc,39,volcano explosion,train MKocTMZZnTs,40,"male speech, man speaking",test MKoh3xOKiNM,15,wind noise,train MKpr8QJqO-I,0,sharpen knife,train MKrQ6akI730,0,sheep bleating,train MKsk2ub1rdk,297,people eating noodle,train MKsk2ub1rdk,364,people eating noodle,train MKtmTL61HX8,12,dog howling,train MKvtEAVHV-I,80,people whispering,train MKyj8BBAX6Y,87,playing badminton,train MKzBNrmcwHE,40,"motorboat, speedboat acceleration",train MKzKTIEjbO8,0,driving motorcycle,train MKzQMsNp2PY,29,playing theremin,train MKzQMsNp2PY,94,playing theremin,train ML0uOLoXHFc,16,goose honking,train ML17yAKZN-g,30,firing muskets,train ML38h-r3DvU,229,playing flute,train ML5fyHB7bNo,510,"railroad car, train wagon",train ML66Le-Zf78,0,lions roaring,train ML66Le-Zf78,25,lions roaring,train ML6QPGbfMS4,15,chicken clucking,train ML6xJBRSsPw,230,chainsawing trees,train ML7FojlK3jo,10,fireworks banging,train ML8ZVFSC5I0,335,people giggling,test MLDbfT0Pb4I,91,people marching,train MLESi_Rqwiw,21,mynah bird singing,train MLESi_Rqwiw,88,mynah bird singing,train MLElJRdcIDY,20,playing bagpipes,train MLFBpDH5dMM,10,playing drum kit,train MLFNXG-MEoU,176,woodpecker pecking tree,train MLFNXG-MEoU,235,woodpecker pecking tree,train MLFfE90Av2Q,30,ambulance siren,train MLGDSpj7EHI,5,people burping,train MLHEeVVOx-E,181,chipmunk chirping,test MLIqLt475Bw,97,cat purring,train MLIqLt475Bw,108,cat purring,train MLJHuFgS_AA,26,sliding door,train MLJvuEjyTTk,57,people humming,test MLJzHwcECZs,74,skiing,train MLK8yDZ6o9A,198,playing tympani,train MLK8yDZ6o9A,251,playing tympani,train MLMRHScRnhs,42,gibbon howling,test MLRF4jaQq-w,110,playing banjo,train MLYaxnJOfco,30,duck quacking,train MLZCG_lI7oY,6,people sneezing,train MLZCG_lI7oY,20,people sneezing,train MLZ_o2stcLw,20,playing drum kit,train MLbAJtqdG-I,52,playing steelpan,train MLbAJtqdG-I,209,playing steelpan,train MLg4597C380,20,playing synthesizer,train MLh8iiJgFZs,216,planing timber,train MLh8iiJgFZs,227,planing timber,train MLnh7GfXXYM,65,cricket chirping,train MLnq3WOdlgs,230,church bell ringing,train MLoHwqhdt70,290,people clapping,train MLpeo_X06FU,33,underwater bubbling,train MLpeo_X06FU,44,underwater bubbling,train MLtiAoMQuUg,10,people clapping,train MLuTS53-4Gc,45,playing tambourine,train MLuTS53-4Gc,79,playing tambourine,train MLxH7Ue_2jM,30,"bird chirping, tweeting",train MLxdreyjd54,375,playing erhu,train MLxdreyjd54,389,playing erhu,train MLxttVd-Jc4,210,bird squawking,test MM-K9Yj5bgo,77,beat boxing,train MM-RnWEBd14,5,people nose blowing,test MM-bVAblNHs,198,mynah bird singing,train MM-bVAblNHs,223,mynah bird singing,train MM0sn9Efq2A,212,playing hammond organ,train MM1e5JFxBi8,187,playing table tennis,train MM4bGimL-Gs,300,"ice cream truck, ice cream van",train MM6DTFoesew,7,playing squash,train MM754Sj7Y20,19,"engine accelerating, revving, vroom",train MM78BP5nkUs,417,lip smacking,test MM8g2yMwcXE,60,toilet flushing,train MM8ieQ5u2Hk,3,playing badminton,train MM919r_fU3I,150,driving motorcycle,train MM9KDPTBo-s,2,baby babbling,train MM9KDPTBo-s,47,baby babbling,train MM9iJCD-gvs,30,wind noise,train MMB-vKPWadU,401,playing table tennis,train MMBEDuEmNMo,25,train whistling,train MMBEDuEmNMo,151,train whistling,train MMCrV1ia670,30,mouse squeaking,test MMFjRD-RUdU,160,playing piano,train MMJRk_cGwDg,93,reversing beeps,train MMJRk_cGwDg,187,reversing beeps,train MMLlnAvxPKQ,23,tornado roaring,train MMLlnAvxPKQ,50,tornado roaring,train MMQL7BIzzXo,0,chicken crowing,train MMQLD910Rpw,0,ambulance siren,train MMSEW658XRM,50,"race car, auto racing",train MMTvsiahcsc,2,fire crackling,train MMUQckEa_ME,30,turkey gobbling,test MMVkRbIJ8Ms,0,"subway, metro, underground",test MMXF2f9gzuo,34,basketball bounce,train MMbA6h8apf4,220,machine gun shooting,train MMbnehN8iNs,30,"pigeon, dove cooing",train MMc-gyj2QIM,65,people eating crisps,train MMc-gyj2QIM,293,people eating crisps,train MMcIAn91Zrc,5,wind chime,test MMjEF4SKf6s,75,people shuffling,train MMjEIFDYQvc,98,yodelling,train MMjEIFDYQvc,117,yodelling,train MMoFRoRCFrM,62,vacuum cleaner cleaning floors,train MMoFRoRCFrM,77,vacuum cleaner cleaning floors,train MMqf5uH8rxI,20,people farting,train MMu4fMyOvuE,220,people crowd,train MMucG31g4og,470,playing flute,train MMul9mjuboY,40,"railroad car, train wagon",train MMupe-duLlg,167,slot machine,train MMw4iZq3_Ec,79,ambulance siren,train MMz80Wog-Ac,112,dog growling,train MMz80Wog-Ac,167,dog growling,train MMzui9B5kXg,30,playing flute,train MN-NfHbxdQE,107,playing tympani,test MN4D-0ukfJY,1,hammering nails,train MN4D-0ukfJY,11,hammering nails,train MN56LjjbB8k,18,playing glockenspiel,test MN6bkbOPB0o,85,car engine idling,train MN6bkbOPB0o,102,car engine idling,train MN7F9kqUeRk,410,playing vibraphone,train MN7nyLGcFPw,0,coyote howling,train MNB9PrFaZls,22,air conditioning noise,train MNEuyIAtj3U,172,cow lowing,test MNFMIN8IOXQ,189,airplane,train MNJl29wscDQ,5,lions growling,train MNN2LtdpPc0,170,waterfall burbling,test MNNYGt_fCdk,2,vacuum cleaner cleaning floors,train MNNYGt_fCdk,17,vacuum cleaner cleaning floors,train MNO-OJX76Nw,34,cutting hair with electric trimmers,train MNOk2dorDtQ,0,people crowd,train MNP4OL70WNQ,30,lawn mowing,train MNR6OkOMF14,0,underwater bubbling,train MNSRBa4kaf8,50,printer printing,train MNXrgwjf4CY,356,chopping wood,test MNYUKikYNvc,15,dog howling,train MNYUKikYNvc,38,dog howling,train MNZfV3fxWBI,40,sheep bleating,train MNZgKyCnKlY,350,telephone bell ringing,train MNZn_3NRXzM,170,female singing,train MN_JuWJ3jYE,50,playing electric guitar,train MNbRj2qJQuM,30,playing banjo,train MNg18qaYU9o,13,opening or closing car doors,train MNg18qaYU9o,29,opening or closing car doors,train MNgEO78i24E,30,ambulance siren,train MNgWQJ8Ugmk,10,stream burbling,train MNhse4Q3JGM,221,playing didgeridoo,train MNjU-b6VUlg,493,slot machine,train MNjbk9I1Iaw,287,slot machine,train MNkBaKb6IcI,565,playing harp,train MNmNy2ezR9o,3,tractor digging,train MNmNy2ezR9o,68,tractor digging,train MNmSMhjYLs8,14,playing glockenspiel,train MNmSMhjYLs8,62,playing glockenspiel,train MNnlykTxbB0,30,"motorboat, speedboat acceleration",test MNnogKQYh78,270,thunder,train MNptHL4Bn4k,90,printer printing,train MNptJtM7jLY,83,playing tuning fork,train MNsQzMFz7_k,208,playing washboard,train MNsQzMFz7_k,372,playing washboard,train MNvEsayOfnA,8,dog growling,train MNvEsayOfnA,19,dog growling,train MNxzi0QgpCA,10,"subway, metro, underground",train MO016m1JHFI,8,playing piano,train MO2sYpqteQk,30,"pigeon, dove cooing",train MO3bj9tC3F0,46,airplane,train MO6vQZrz1_M,325,people slurping,train MODmVOcRAO4,7,"railroad car, train wagon",train MOE-FqxfnJk,119,playing gong,train MOE-FqxfnJk,184,playing gong,train MOEE9uo6MCk,20,toilet flushing,test MOG1EzzM2IA,606,people whistling,train MOG1EzzM2IA,935,people whistling,train MOGbbue6MSc,230,"railroad car, train wagon",train MOJ_XL8jXgU,501,toilet flushing,train MOMJpUOkYgA,30,snake rattling,train MOMqco6mO30,30,people sniggering,train MON5q_Vvgx8,62,fox barking,test MOOHvvxeJ90,80,using sewing machines,train MOQ3Sch50nk,147,lions growling,test MOQ9XfVc54A,110,cat growling,train MOUIhPB365c,30,wind noise,train MOYN-z_PO0A,35,ice cracking,train MOYN-z_PO0A,47,ice cracking,train MO__hCCvmUk,1,playing badminton,train MObnJSvH7TU,30,playing banjo,train MOcRvP3i5Vc,32,driving snowmobile,train MOe28qaQUOQ,13,frog croaking,train MOe28qaQUOQ,178,frog croaking,train MOf9nra9Ixk,100,driving buses,train MOgKZR-Rmgo,6,splashing water,train MOoQo1U0VkM,160,female singing,train MOr1AtMw1bw,40,people screaming,test MOtlrCOboKY,4,airplane flyby,train MOvXMi-pJ8I,50,"ice cream truck, ice cream van",train MOw6dvN9ICY,63,playing volleyball,train MOwYn4wwLLU,129,hail,train MOwYn4wwLLU,149,hail,train MP-PQ78Agig,86,people burping,train MP0DFVO16Zk,101,barn swallow calling,train MP0DFVO16Zk,118,barn swallow calling,train MP0zCsnIKbs,190,playing bassoon,train MP3xQWyapDI,22,playing synthesizer,train MP3xQWyapDI,39,playing synthesizer,train MP6xL1x98G4,83,playing theremin,train MP6xL1x98G4,108,playing theremin,train MPAb8d7muhM,50,playing accordion,train MPC5L1pCM5s,108,francolin calling,train MPC5L1pCM5s,127,francolin calling,train MPCrgyAJuL8,178,people giggling,test MPF-nNshbB8,70,people hiccup,train MPIm7rCKVfM,10,people sniggering,train MPSf7dJpV5w,30,train horning,train MPSrT1O0frg,30,horse clip-clop,train MPU_pdP4JpI,440,playing drum kit,train MPW-geOjDG4,197,tap dancing,test MPWsyATAwUU,20,opening or closing drawers,train MPcL4LqvCzg,134,plastic bottle crushing,train MPe6ztPtF0Y,30,playing acoustic guitar,test MPfnSaOw-kU,40,machine gun shooting,train MPgMuzSk_ME,570,people eating apple,train MPgMuzSk_ME,743,people eating apple,train MPhiBEdZY9g,2,vacuum cleaner cleaning floors,train MPhiBEdZY9g,16,vacuum cleaner cleaning floors,train MPqT3M8d2AA,10,"railroad car, train wagon",train MPqru_ruKxo,70,driving snowmobile,train MPqru_ruKxo,109,driving snowmobile,train MPtHe2WVUG0,47,playing snare drum,train MPtHe2WVUG0,161,playing snare drum,train MPwnx2Ck_34,0,"vehicle horn, car horn, honking",train MPypW5ZffzY,50,playing cymbal,train MQ0YasvMcuQ,1,people screaming,test MQ2kTI9oLkg,133,basketball bounce,train MQ2kTI9oLkg,180,basketball bounce,train MQ2xDnY3n0E,166,popping popcorn,train MQ2xDnY3n0E,199,popping popcorn,train MQ4FqwEYkck,30,"pigeon, dove cooing",train MQ4zH2jPK9o,34,slot machine,train MQ6b3LIQC3g,117,cap gun shooting,train MQ6ckvo-f2k,50,lighting firecrackers,test MQ7kp6ss5Xk,132,machine gun shooting,train MQ8DTDN4s5I,30,mouse pattering,train MQ8svM3S9t0,102,playing harpsichord,train MQ8svM3S9t0,243,playing harpsichord,train MQ9klWTwbbI,110,people sobbing,train MQ9q67yhFfM,6,playing table tennis,train MQEgNzMQ8mY,99,penguins braying,test MQJccVBY4MY,0,raining,train MQNthRVr0Gg,80,playing piano,train MQOGykg6mJI,40,using sewing machines,train MQPNvRDVuUs,100,playing french horn,train MQPggq37uX8,3,scuba diving,train MQPggq37uX8,30,scuba diving,train MQPsGHsZLnI,96,playing cymbal,train MQTw8tsFBTM,5,"pigeon, dove cooing",train MQU2ZC_mmHk,30,driving buses,train MQVv8qD8RqQ,30,mouse pattering,train MQWpXS_hJgA,70,playing piano,train MQWwTcBxl_U,50,playing banjo,train MQa-urj2M0U,170,playing flute,train MQa47ICQHgs,298,opening or closing drawers,train MQa47ICQHgs,315,opening or closing drawers,train MQcCibmWHT8,43,playing shofar,test MQcJWqlpiFo,11,bull bellowing,train MQcOCBe870I,28,scuba diving,train MQcOCBe870I,53,scuba diving,train MQcS6DqCjKQ,30,playing vibraphone,train MQiLD1ylq2Q,30,turkey gobbling,train MQjxYhhhAIU,70,"race car, auto 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MREyrwXOYxQ,35,footsteps on snow,train MRFsJIQMxs0,250,playing tabla,test MRGFUrg1pd4,19,magpie calling,train MRGFUrg1pd4,41,magpie calling,train MRHsr-C7c9k,90,"race car, auto racing",train MRIG4BRXk7I,10,helicopter,train MRIrhVXOJvM,30,crow cawing,train MRJ1yZlSPpg,30,goose honking,train MRKD16B9zLg,76,missile launch,train MRKD16B9zLg,89,missile launch,train MRM2Tr7xYZM,107,playing erhu,train MRM2Tr7xYZM,160,playing erhu,train MRMgeULTDso,30,police car (siren),train MRObJ6HI-gs,180,"playing marimba, xylophone",train MRRr7POGC3w,40,playing cello,train MRWHn2TG6h8,71,playing volleyball,train MRWN7B_6dQQ,32,mouse clicking,train MRZ1FZsAp7U,77,playing bass drum,train MR_SSRoEsf0,0,playing mandolin,test MR_x_vrSZBM,76,playing bass drum,train MR_x_vrSZBM,253,playing bass drum,train MRaKYkOfKDw,9,people booing,train MRazdvVjmbQ,3,lions growling,train MRdIB6BqNuw,40,playing harp,train MRfWuurA5YY,27,horse clip-clop,train MRiPIWYhZuU,50,people crowd,train MRk9T7dQoY8,75,slot machine,train MRkfL-6lspI,357,mouse clicking,train MRl6F2Cf9jc,39,car engine knocking,train MRlnWfB7uus,360,people whispering,train MRnL5_02oLI,64,foghorn,train MRnnE9MTm64,13,driving snowmobile,train MRnnE9MTm64,52,driving snowmobile,train MRnpKEhDQNE,10,dog howling,test MRoPGplsR0o,10,playing timpani,train MRoPGplsR0o,106,playing timpani,train MRpC0H7UaP0,30,"motorboat, speedboat acceleration",train MRsarUB6ZuQ,550,eating with cutlery,train MRszvEBk9u8,38,yodelling,train MRszvEBk9u8,100,yodelling,train MRt_i6-o-Gw,30,playing vibraphone,train MRuLtJzoUTw,30,printer printing,train MRy9OWS-hL0,0,playing badminton,train MRzEscxFTwk,20,people coughing,train MS-IJrai-Mk,80,fireworks banging,train MS1MKQj2L5o,20,helicopter,train MS1U-dCigxI,30,orchestra,train MS1spN8Xb1Q,74,cheetah chirrup,train MS30QIfCMjE,190,orchestra,train MS4q6wtYrwA,20,"male speech, man speaking",train MS7OIzKL0FU,100,skateboarding,train MS7qdoXghiY,37,"bee, wasp, etc. buzzing",train MS7qdoXghiY,92,"bee, wasp, etc. buzzing",train MS9eyI2nmK8,58,lathe spinning,train MSB_mC9UOSE,117,playing didgeridoo,train MSDd1LxNV-Q,10,toilet flushing,train MSGX3yfPfTw,0,playing accordion,train MSH0GwZj8BI,85,machine gun shooting,train MSH0GwZj8BI,189,machine gun shooting,train MSMG_OTJRCo,50,driving motorcycle,train MSOcuE3pHqE,190,stream burbling,train MSQ6MoR8bnQ,5,"vehicle horn, car horn, honking",train MSR_ONjR7fw,280,people whispering,train MSSXU2oUydk,14,lions roaring,train MSSXU2oUydk,24,lions roaring,train MSTj1UJKGFc,209,playing snare drum,train MSTj1UJKGFc,325,playing snare drum,train MSWbH1VzjXg,569,arc welding,train MSWbH1VzjXg,654,arc welding,train MSZXGiSsdU8,288,airplane flyby,train MSZry-BWyks,127,playing erhu,train MSZry-BWyks,160,playing erhu,train MScGcBjNoL0,30,playing piano,train MScmUIc0K0g,0,bird wings flapping,train MSdSPgaKMv4,50,cattle mooing,train MSmg48orZqI,8,gibbon howling,train MSmg48orZqI,65,gibbon howling,train MSoNO0bcWhs,0,people belly laughing,train MSq8Lt8UIv4,30,horse clip-clop,train MSqRrbmZWdc,0,car engine starting,train MSq_FvYoTXw,20,fireworks banging,train MSs-8U8wjYk,100,"female speech, woman speaking",train MSsWPWskqNk,40,playing table tennis,train MStmrPtlnR0,171,playing bass guitar,train MSuQ8DH5Enc,77,planing timber,train MSxmUaGlK6E,137,playing bass drum,train MSziND26UTA,130,"bee, wasp, etc. buzzing",train MT-tMyUbijY,5,"fly, housefly buzzing",train MT0ujxJyAvs,108,eletric blender running,test MT2Xe4C2qHc,60,ocean burbling,train MT6Odyb4acA,64,"alligators, crocodiles hissing",train MT6Odyb4acA,96,"alligators, crocodiles hissing",train MT8hvzdeYDI,0,playing electric guitar,train MT9sM_6FDhU,28,playing congas,train MT9sM_6FDhU,43,playing congas,train MTD6-1mrtP8,72,owl hooting,train MTD6-1mrtP8,229,owl hooting,train MTDR8ALMDco,410,train whistling,train MTGoWjhe46g,30,machine gun shooting,train MTHqXTF2VSo,73,opening or closing car doors,train MTHtlHmcGRs,58,beat boxing,train MTHtlHmcGRs,158,beat boxing,train MTIF_l_8d4Q,0,people sobbing,train MTK5UcuO72E,64,driving snowmobile,train MTL8-cVoP64,169,machine gun shooting,train MTL8-cVoP64,171,machine gun shooting,train MTRtiH3YlFA,70,yodelling,train MTRtiH3YlFA,91,yodelling,train MTRzo13-oC0,24,ambulance siren,train MTTBJaElCCY,30,playing banjo,train MTUM74Fz5lo,30,police car (siren),train MTYcXmN72L4,167,basketball bounce,train MTYcXmN72L4,235,basketball bounce,train MTZYN5K2-sE,30,duck quacking,train MT_TpWVeVQk,30,"child speech, kid speaking",train MTaGPbG1aCM,44,mouse clicking,train MTaGPbG1aCM,328,mouse clicking,train MTaLknnq4wc,60,people whistling,train MTanYXO51lA,80,toilet flushing,train MTi0XWcAnNM,7,bowling impact,train MTi0XWcAnNM,101,bowling impact,train MTkvHwqj9v4,161,wind noise,test MTkvHwqj9v4,305,wind noise,test MTlP-9CpSDg,383,tractor digging,train MTlmurAe4QU,25,missile launch,train MToZPtAjm2c,373,lathe spinning,train MTojyhiHCcE,81,playing didgeridoo,train MTpldq2GEY8,24,car engine knocking,train MTq-TJeKeq4,41,civil defense siren,train MTqWK8IfmiI,500,sliding door,train MTrElumD3eA,3,wind chime,train MTrElumD3eA,42,wind chime,train MTtWX1Tm1S8,3,"playing steel guitar, slide guitar",train MTtWX1Tm1S8,15,"playing steel guitar, slide guitar",train MTus6p-Im5g,64,tap dancing,train MTvPyRwzzko,80,fireworks banging,train MTvQzs4-CPo,23,magpie calling,train MTw5NCC0SU8,9,elk bugling,train MTw5NCC0SU8,26,elk bugling,train MTzP5GuHQRI,51,fire truck siren,train MU0hzql664M,30,"playing violin, fiddle",train MU1383zAS_8,0,thunder,train MU1CbyoeMfY,61,playing harpsichord,train MU1CbyoeMfY,269,playing harpsichord,train MU1NcKt8O4w,30,playing accordion,train MU2DSS1m9m4,49,"electric shaver, electric razor shaving",train MU6V78mysgQ,56,playing snare drum,train MU8Uiv2rYsM,74,lighting firecrackers,train MU99JIYhvGA,12,frog croaking,train MU99JIYhvGA,22,frog croaking,train MUAXuelXJ0A,10,car engine starting,train MUBB_1nwANQ,10,lawn mowing,train MUBCm2L4wj0,18,mynah bird singing,train MUBRGZZIxUc,530,fireworks banging,train MUC3GjvBI7I,30,horse clip-clop,test MUFLQgRcE14,30,"motorboat, speedboat acceleration",train MUGf2TEbPfw,68,playing french horn,train MUJmWnjGEBk,0,dog howling,test MUOEQDw_9uU,77,bowling impact,train MUPayTxCSFw,0,goat bleating,train MUPayTxCSFw,43,goat bleating,train MUPwu3O5Qaw,20,dog barking,train MUQ7z_NKWD0,403,ice cracking,train MURfGUHNyNA,30,"pigeon, dove cooing",train MUS3o_lBX6E,20,"child speech, kid speaking",train MUUGV2STsl8,30,playing harpsichord,train MUWFIMX5azw,31,train whistling,train MUWFIMX5azw,53,train whistling,train MUXASTFxsds,20,"race car, auto racing",train MUa1Yf6_CoU,243,electric grinder grinding,train MUa5SVsbp_M,20,"bee, wasp, etc. buzzing",train MUcwm720oM0,175,lighting firecrackers,train MUdHSIGA4To,1,people booing,train MUdHSIGA4To,24,people booing,train MUdMO4lgB4I,103,lathe spinning,train MUdMO4lgB4I,233,lathe spinning,train MUfK-F-xbU8,30,"playing violin, fiddle",train MUgo4GloKw0,159,airplane,train MUgo4GloKw0,314,airplane,train MUhlTa6fvm4,110,"pigeon, dove cooing",train MUiL7gayhnQ,190,playing bass guitar,train MUm2xdBsyMI,177,canary calling,train MUm2xdBsyMI,211,canary calling,train MUmLR8cyg-E,21,frog croaking,train MUmLR8cyg-E,63,frog croaking,train MUojOzC9Q6k,57,pheasant crowing,train MUpTIY4puZc,10,stream burbling,train MUrZExMV1R0,39,"playing steel guitar, slide guitar",train MUtpqKfatIE,30,horse clip-clop,train MUumU2VEZuQ,580,people crowd,train MUuo495CdA0,30,train horning,train MUvK7osPbgw,30,wind noise,train MUwh8hHnigg,6,underwater bubbling,train MUwh8hHnigg,17,underwater bubbling,train MUywj4wIkXI,10,wind noise,train MV-PoLxtL0o,15,civil defense siren,train MV1qOqhaLXk,16,playing table tennis,test MV3oOUCBNWM,439,playing snare drum,test MV4a9UE-REY,4,tap dancing,train MV6-aXoxqFc,96,cat growling,train MV74Z_AJyAo,10,playing synthesizer,train MV74Z_AJyAo,202,playing synthesizer,train MVDGpFGKQqs,390,people belly laughing,train MVDR9JeJA6o,30,turkey gobbling,test MVDWEcrt5yM,140,playing hammond 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marimba, xylophone",train MWL1Vm7DZG4,30,"motorboat, speedboat acceleration",train MWN0T8-x_cg,151,arc welding,train MWNXngcmxFM,330,ocean burbling,train MWODeQJ5wYQ,0,playing clarinet,train MWRwk19xzVw,320,church bell ringing,train MWTTe0M9vi4,30,lawn mowing,test MWWihdWE2FE,60,typing on computer keyboard,test MWYaSa583gw,91,playing synthesizer,train MWYaSa583gw,116,playing synthesizer,train MW_qtWvHtT8,30,playing bass drum,train MW_uh8_uMaI,30,sailing,train MWbr9K1V1_o,136,squishing water,train MWc4s4LWJXc,50,orchestra,train MWdoXUhJhc8,20,"subway, metro, underground",train MWeI85Q5usQ,20,playing harp,train MWhqBMJ_iUM,230,people clapping,train MWijbtY52As,80,people booing,train MWjllkdbbLY,1012,child singing,train MWjllkdbbLY,1056,child singing,train MWmA7X7crmk,30,orchestra,train MWoK4Wyrzy0,103,skiing,train MWoNfQzHUFc,140,playing cymbal,train MWopgb0XY34,30,"playing violin, fiddle",train MWqtVBr4RFo,124,playing glockenspiel,train MWqtVBr4RFo,135,playing glockenspiel,train MWsPUFmT0hI,30,toilet flushing,train MWsxogG56k4,30,wind noise,train MWvpipcNaHA,167,playing tympani,train MWvpipcNaHA,205,playing tympani,train MWyNhlGiF6s,20,playing bass guitar,train MWyp1vjkF8Q,200,"vehicle horn, car horn, honking",train MX-JzznHHwM,30,driving buses,train MX-udyJ1ai8,0,fireworks banging,train MX0EfdSVaB8,4,dog howling,train MX0EfdSVaB8,17,dog howling,train MX0mj0mzZ8s,30,"playing violin, fiddle",train MX3pWUmr-ak,72,scuba diving,train MX3pWUmr-ak,90,scuba diving,train MX4tHPT63sI,0,firing muskets,train MX5ei7dDgPc,137,parrot talking,test MX7SSgk5KMs,80,playing electric guitar,train MXAO47hr8mU,74,playing hockey,train MXAO47hr8mU,96,playing hockey,train MXPxusnSW0U,274,hammering nails,train MXPxusnSW0U,520,hammering nails,train MXQV6FHa_MM,50,"child speech, kid speaking",train MXS7_y6eV3o,248,people burping,train MXVL_C3o1Lc,80,playing hammond organ,train MXVrmLk-nL0,33,playing squash,train MXXIRH0sE0s,25,cattle mooing,train MXZ2ShPfo90,30,car passing by,train MXZAy2RQhPM,5,dog growling,train MXeQnYI6EhU,30,people crowd,train MXikVLdYKWw,30,turkey gobbling,train MXj_gRUEb8U,490,baby laughter,train MXjuFU8Pv98,4,fox barking,train MXk9kmVFW4A,67,hammering nails,train MXkqKt-o3Tk,30,people sniggering,train MXmetP4F-EU,19,door slamming,train MXmetP4F-EU,29,door slamming,train MXnWMs5R2JM,176,playing badminton,train MXoUiFrXVak,11,"electric shaver, electric razor shaving",test MXpCW25taqo,4,playing table tennis,train MXrgKeMxjEw,196,lighting firecrackers,train MXrgKeMxjEw,247,lighting firecrackers,train MXt20EKY8nM,6,"engine accelerating, revving, vroom",train MXtIsfktzdI,120,"playing marimba, xylophone",train MXtktxGEdOY,49,"playing steel guitar, slide guitar",train MXtktxGEdOY,105,"playing steel guitar, slide guitar",train MXwjpZSQdH4,30,"pigeon, dove cooing",train MY-bxnrlbYE,39,train horning,train MY-bxnrlbYE,185,train horning,train MY0PsDE3xHs,30,"male speech, man speaking",test MY19seQZndY,26,people booing,train MY4s8uxP7Qk,4,people 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MdROu4LlR1E,10,cat meowing,test MdRvEvLFw-I,10,people sobbing,train MdUG2H5K5eg,117,roller coaster running,train MdUG2H5K5eg,149,roller coaster running,train MdVexSX41ow,20,driving buses,train MdXGGoBN5KQ,20,"race car, auto racing",train MdYoe_APBuo,560,people belly laughing,train MdZOxntKwxA,34,cat hissing,train MdZe4NARyD0,390,people crowd,train Md_CHA8dz5Q,210,orchestra,train Md_f9VNsumo,0,driving motorcycle,train Mda39locghA,30,ocean burbling,train Mdc6HgDQNyI,4,playing shofar,train Mdc6HgDQNyI,32,playing shofar,train Mdf-8iRUN4A,208,playing oboe,train MdhgC5gR9Fc,9,people whistling,train MdhgC5gR9Fc,27,people whistling,train Mdk5TxNRlvY,128,people marching,train Mdk5TxNRlvY,151,people marching,train MdlNA0sJz38,28,dog whimpering,test MdlXv_6-W7Q,120,playing drum kit,train MdoVZI0jRaE,20,playing darts,test MdofVXugy1g,147,playing timbales,train MdtyI8EpoHY,10,playing bagpipes,train MdvcWT126mE,121,people battle cry,train MdvcWT126mE,140,people battle cry,train Mdw3cJ4vr8c,84,striking pool,test MdwLy7XXAkI,101,playing bassoon,test Mdwn6x9edFI,352,sea lion barking,test Mdwn6x9edFI,530,sea lion barking,test MdxUVZg4zGc,30,"rowboat, canoe, kayak rowing",train MdxpV_F1cCo,379,"subway, metro, underground",train MdxpV_F1cCo,401,"subway, metro, underground",train MdzGKumKYVw,26,playing bassoon,train MdzGKumKYVw,38,playing bassoon,train Me0_rzOpr-c,41,baby crying,train Me0_rzOpr-c,52,baby crying,train Me1HZ0DBGYY,93,playing theremin,train Me5Gtqf7J6Q,34,people humming,train Me5Gtqf7J6Q,74,people humming,train Me6bx7jn_-o,140,"playing marimba, xylophone",train MeCMU_eLrI8,1,frog croaking,train MeF_9KT_T8k,40,lions growling,test MeF_9KT_T8k,71,lions growling,test MeH0NWoeZS0,0,woodpecker pecking tree,train MeLZ-FoXuXg,56,people eating apple,train MeLZ-FoXuXg,248,people eating apple,train MeO3gTWjF-0,56,playing erhu,train MeO3gTWjF-0,68,playing erhu,train MeSab2AIb1k,30,dog bow-wow,train MeW9OdcWP3M,2,people nose blowing,test MeYMwX9-lAE,168,hammering 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Mj49QUYvkxY,225,playing congas,train Mj49QUYvkxY,478,playing congas,train Mj51bVlY8Fg,150,people babbling,test Mj6ezUbjRHE,133,cow lowing,test MjAp25BGYcI,2,otter growling,train MjAp25BGYcI,18,otter growling,train MjCXkVaj6SM,0,sailing,train MjCjCjiXBZQ,0,lions growling,train MjF0lmPftnk,10,people crowd,train MjHF1JLOJPo,12,playing sitar,train MjHF1JLOJPo,23,playing sitar,train MjKSocnL3rA,30,horse clip-clop,train MjKcIQflrR4,8,dog growling,train MjKcIQflrR4,22,dog growling,train MjMJr1zzO1Y,110,playing saxophone,train MjP5s-w1K_Y,200,people crowd,train MjTuAV036yY,80,thunder,test MjVTveERWzU,0,"bird chirping, tweeting",train MjWzxfIKWdo,22,dog growling,train MjX-8im4e2s,30,playing harp,train MjXA90jDR0E,3,horse neighing,train MjY1NPUUp9U,252,playing saxophone,train MjY1NPUUp9U,378,playing saxophone,train MjYzsL4q-7c,30,"motorboat, speedboat acceleration",train MjZfE0zq0Bw,0,car engine starting,train MjZhpUrhsZI,57,playing theremin,train MjZhpUrhsZI,109,playing theremin,train Mj_BO-iK1G4,310,using sewing machines,train MjbbL_v6VFc,236,"playing marimba, xylophone",train Mjen5oQLvlA,10,lions growling,train Mjf34K-cGf0,80,playing saxophone,train Mjfnz0piLvM,30,church bell ringing,train MjgUwbwjOzQ,30,fire crackling,test Mjlfmb0m9AE,31,singing bowl,train Mjlxg1cSYuk,14,owl hooting,train Mjlxg1cSYuk,45,owl hooting,train Mjmt_HBO__s,30,"pigeon, dove cooing",train MjosnOTCa9U,142,people slapping,train MjpAvGl_xM0,1,playing french horn,train MjpAvGl_xM0,14,playing french horn,train MjrDF8uUtBs,43,playing zither,train MjrDF8uUtBs,60,playing zither,train Mjti5KoMVps,26,mynah bird singing,test Mju2mwj3O9Q,8,sharpen knife,train Mju2mwj3O9Q,42,sharpen knife,train Mk07G3pNQ50,273,playing squash,train Mk2WpqHNQ7s,5,baby laughter,train Mk2WpqHNQ7s,46,baby laughter,train Mk6PkNfg_vE,39,ferret dooking,test Mk8KIPDdBDg,530,sailing,train Mk8fhA3DAsA,30,turkey gobbling,train MkD0Ya9A50U,50,"subway, metro, underground",train MkFSp56wlH4,48,people belly laughing,train MkJVNXKVqp4,30,"playing violin, fiddle",train MkJ_aJ7t6Ek,233,slot machine,train MkJuGDph-1k,70,dog barking,train MkK0XQo7aIQ,380,church bell ringing,train MkKX_5HN4C4,60,people whispering,train MkMPuP4RNy0,30,playing electric guitar,train MkNFN4rIKus,30,fire crackling,train MkQbJf_u9ps,30,playing harp,train MkRTLP2_wgQ,60,ocean burbling,train MkRaqj1FQno,0,vacuum cleaner cleaning floors,train MkUe-2-AhHY,30,people whistling,train MkVdwt0hpS8,46,singing bowl,train Mk_LYufk1JI,30,people clapping,train Mk_hbTS9vfk,0,opening or closing car doors,train Mka-WngcU_8,30,car passing by,train MkhiAepcYT8,149,mouse clicking,train MkhiAepcYT8,204,mouse clicking,train MklAHsSFP38,50,mynah bird singing,train MklAHsSFP38,95,mynah bird singing,train Mkld6TJfZ7M,85,playing double bass,train Mkld6TJfZ7M,342,playing double bass,train MknL0KJxA8g,113,bowling impact,train MkpJ3_ik4ag,290,playing cello,train MkrFhq3F_z4,100,playing accordion,train Mku-MDzwXTA,30,printer printing,train MkvSjd9CVrc,42,mynah 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MlNp4-xdQoQ,45,"heart sounds, heartbeat",train MlQ2L2LyGPw,0,lighting firecrackers,train MlRLia2u-Wk,70,mouse pattering,train MlS0Ddcxpi0,235,police radio chatter,train MlS0Ddcxpi0,249,police radio chatter,train MlSTEqBn2Bw,21,playing harpsichord,train MlSTEqBn2Bw,47,playing harpsichord,train MlTEMxzj0WI,627,playing banjo,train MlVeufbjdDc,11,people eating noodle,train MlX7I-OZIyk,15,playing timpani,train MlX7I-OZIyk,62,playing timpani,train Mlb1RW4-LRg,10,skidding,train Mlbhom2FcZ4,28,playing cymbal,train MlcOOc6j90U,0,splashing water,train MlfB1hHZKDk,230,people running,train MlgDyRysR_E,30,driving buses,train Mlh4Sa1VzhQ,16,arc welding,test Mlm7RbMOFko,30,people running,train Mln0mA-PCSo,30,people screaming,train MlnBzUzGhlA,10,lawn mowing,train MlooTw6qHp4,0,dog barking,train Mlq9EE12zHE,80,"railroad car, train wagon",train MlsC1361WcE,80,"railroad car, train wagon",train MlxORICa7nQ,93,playing tabla,train MlxORICa7nQ,215,playing tabla,train Mlz5dx4fETU,193,child singing,train Mm2ybCWF_bg,1,dog howling,train Mm2ybCWF_bg,13,dog howling,train Mm7C71TVhHw,12,playing sitar,train Mm7C71TVhHw,162,playing sitar,train Mm7gi453_Iw,259,child singing,train Mm7gi453_Iw,312,child singing,train Mm95l7Aq3dU,121,bull bellowing,train Mm9zyJLl1s4,33,people hiccup,train MmB9Kq6Ec7Y,80,ocean burbling,train MmBjXKefcpw,70,toilet flushing,train MmBrM1jZ6xM,250,"child speech, kid speaking",train MmDB6B4d5rY,237,child singing,train MmDB6B4d5rY,299,child singing,train MmFXxZ6suLY,884,playing tuning fork,train MmGsyQBb-CM,32,underwater bubbling,train MmGsyQBb-CM,43,underwater bubbling,train MmHJttzfisw,20,dog growling,train MmHJttzfisw,43,dog growling,train MmIIjAzgw30,180,"race car, auto racing",train MmIjDn4MnkA,454,"bird chirping, tweeting",train MmLPXeY2HT4,41,wood thrush calling,train MmMtqNIzmjg,75,people eating noodle,test MmQ79KteHac,203,bouncing on trampoline,train MmQ79KteHac,223,bouncing on trampoline,train MmQbRaLKkNU,81,baby babbling,train MmQbRaLKkNU,91,baby babbling,train MmRLGPVScxc,280,people sobbing,train MmU9WdP0qFc,49,playing bongo,train MmU9WdP0qFc,68,playing bongo,train MmXfiPeSnIU,61,woodpecker pecking tree,train MmXfiPeSnIU,111,woodpecker pecking tree,train Mm_sF4fvrZc,14,yodelling,train Mma2Labgdw0,75,playing gong,train Mma2Labgdw0,86,playing gong,train Mman_xxqfoY,10,playing trumpet,train Mme__3Xlnq4,460,eating with cutlery,train MmgJAGpHOek,190,mouse pattering,test Mmh2sx2Si6g,110,playing harp,train MmiAwvIQeRY,151,playing timbales,train MmiAwvIQeRY,161,playing timbales,train MmiImX_7cj4,70,people belly laughing,train Mmic5xqaDeg,33,playing didgeridoo,train MmkFLysGeYk,0,machine gun shooting,train MmkHAlhCvWg,96,people booing,train MmkHAlhCvWg,132,people booing,train MmmgFYdnY5M,5,people burping,train Mmnq-iAT9lg,11,playing trombone,train Mmnq-iAT9lg,38,playing trombone,train Mmpx4uysOCM,30,people running,train Mmq9YFHY_80,0,ripping paper,test MmtbKxvPRKU,6,gibbon howling,train MmvvHG1JvM0,491,male singing,train MmxRSlNyoxU,200,using sewing machines,train MmydLMDt1Fs,120,playing french horn,train MmygeqACnGw,29,tractor digging,train Mmyr6Gpclbk,70,"bird chirping, tweeting",train Mn-4WiUemII,125,playing washboard,train Mn1Vvg-VWeo,89,playing badminton,train Mn3qROqDwgI,30,people screaming,train Mn638FEXFm0,72,playing bassoon,train Mn8WGSW8jHY,65,eletric blender running,test Mn9Kc49yIAU,0,chicken crowing,train MnAibI6Oi0E,3,ferret dooking,train MnD5hZssyIg,15,baby laughter,train MnD5hZssyIg,130,baby laughter,train MnDmqGmBPcE,333,vacuum cleaner cleaning floors,train MnEZwnRuQR8,30,firing cannon,test MnGVz1Xu4L4,46,train wheels squealing,test MnGVz1Xu4L4,75,train wheels squealing,test MnH4tzgKXVc,30,chainsawing trees,test MnIR5gyERFw,12,wind rustling leaves,train MnL2ArrEm_o,33,playing theremin,train MnL2ArrEm_o,83,playing theremin,train MnNC-xl7cqM,22,chicken crowing,train MnOn0UKwAjk,70,people booing,train MnOweV1VCfU,187,people slurping,train MnOweV1VCfU,365,people slurping,train MnPRF4_N-LY,43,warbler chirping,test MnR2K_dVNfA,146,singing choir,train MnRkrapf4wI,11,sheep bleating,train MnSZAzs8BtU,25,cattle mooing,test MncE0mbYOYY,201,playing bongo,train MngPLoigZ98,30,"motorboat, speedboat acceleration",train Mnj4CeLObLs,115,"subway, metro, underground",train Mnj4CeLObLs,126,"subway, metro, underground",train MnjBeC1w-rM,90,people sobbing,train Mnk6590abfY,30,playing steelpan,test MnkYB-kZszE,30,orchestra,train MnlVtbfFtxI,0,car engine starting,train Mnok_52JuCA,50,slot machine,train MnqgzY6qW34,4,dog howling,train MnqmkjXBte8,53,people eating crisps,train MnqmkjXBte8,136,people eating crisps,train Mnv4KVEt18I,18,people giggling,train MnvrPsQ6CjQ,33,beat boxing,train MnvrPsQ6CjQ,63,beat boxing,train MnwQrKoYugI,30,wind noise,train Mo-wwcL3B-Y,224,goat bleating,train Mo-wwcL3B-Y,235,goat bleating,train Mo0FaMMJsfU,11,singing bowl,train Mo0WiG_6T80,11,playing gong,test Mo1Y-XaXPow,271,skiing,train Mo2Gd_cD1i4,46,smoke detector beeping,train Mo2Gd_cD1i4,71,smoke detector beeping,train Mo6wJYejW4I,65,bathroom ventilation fan running,train Mo6wQDEFsTo,50,stream burbling,train Mo72PVhD2Ek,0,singing bowl,train Mo7wPrmKJC0,220,orchestra,train MoAb4jXtJIo,140,"bee, wasp, etc. buzzing",train MoBOUstH9d8,33,people booing,train MoDbMzPv5FE,11,bird squawking,train MoG0nt_0iSw,0,door slamming,test MoGVz88f8DI,2,playing bongo,train MoJvvL89tqE,220,playing piano,train MoLIMDBI-uo,54,spraying water,train MoLx_fOx-Qc,30,playing drum kit,train MoN0UpTxxk4,188,"ice cream truck, ice cream van",train MoOQct5zBBM,450,splashing water,train MoS2ECeKVj0,311,arc welding,train MoS8-mVelV4,391,driving snowmobile,train MoS8-mVelV4,440,driving snowmobile,train MoVcXbU_PBQ,30,"pigeon, dove cooing",train MoWhCwxV-Dk,60,playing piano,train MoWyfzqzZrk,19,black capped chickadee calling,test MoXmJmnpEc8,30,playing clarinet,train MoYPhggPrPg,74,snake hissing,test MoZc4IsdhiU,20,helicopter,train Moaum5JOBnw,30,"motorboat, speedboat acceleration",train MobIwjeG6ec,3,basketball 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MxafvDBd2FM,4,children shouting,train Mxb9Aj4toCQ,48,playing oboe,train Mxb9Aj4toCQ,71,playing oboe,train MxcQARYSiKg,50,stream burbling,train Mxctj-MoYbw,4,"subway, metro, underground",train Mxfiktq7M_M,5,snake hissing,train MxjT19YwzTs,30,driving buses,train MxkqAVx-x6Y,80,playing vibraphone,train MxkqAVx-x6Y,106,playing vibraphone,train MxlAcvDtzqI,110,skidding,train MxlG--30VvM,8,car engine idling,test MxlKNay7Kcs,4,frog croaking,train MxlKNay7Kcs,32,frog croaking,train MxlQ5p20CYE,48,playing badminton,train Mxln7MDA8iI,0,playing glockenspiel,train MxnBobj0ZHA,0,fireworks banging,train MxqdvI_78Ls,130,machine gun shooting,train MxtLWYV0OM8,30,"playing violin, fiddle",train MxtnOYYbLiI,7,dog howling,train MxtnOYYbLiI,20,dog howling,train MxwkQQei_-4,0,scuba diving,train MxxJWqd5948,30,playing clarinet,test Mxy5wCh1YE4,0,gibbon howling,train Mxy5wCh1YE4,13,gibbon howling,train Mxyfto_wlPE,290,helicopter,train My0YmR6tYAk,60,vacuum cleaner cleaning floors,test My7bHhOuhwY,322,ripping 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NMI-cjORSWM,61,swimming,train NMI304MvAmk,20,chicken clucking,train NMJS0gYRjzY,117,"bird chirping, tweeting",train NMJS0gYRjzY,165,"bird chirping, tweeting",train NMKreZ6qbUE,70,"female speech, woman speaking",train NMNZ19KsBqs,191,playing synthesizer,train NMOHGaxPlR8,434,people eating noodle,train NMOHGaxPlR8,462,people eating noodle,train NMPi62qSExI,139,"playing steel guitar, slide guitar",train NMQiKrTkl10,39,volcano explosion,test NMRg80qQYy0,0,crow cawing,train NMRg80qQYy0,23,crow cawing,train NMSJHtmQLQM,30,basketball bounce,test NMVPs3h0hNw,110,printer printing,test NMYn_muhxfo,30,"engine accelerating, revving, vroom",train NMZOwBoQZiw,100,playing electric guitar,train NMcyEuj4JiI,11,children shouting,train NMfSkNH-Ikc,353,duck quacking,train NMfSkNH-Ikc,726,duck quacking,train NMl7j-Q1DEE,35,playing bassoon,test NMlfyPv2AqM,15,baby crying,train NMmKhX576Mo,286,playing volleyball,train NMmxaEYQXt4,110,church bell ringing,train NMng8HLUfBg,492,"heart sounds, heartbeat",train 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OT1JUueUxmc,71,hedge trimmer running,train OT1XkHtyZbQ,220,people whistling,test OT3bgv__E0M,40,playing piano,train OT4bnMtDYhs,36,fire truck siren,train OT4bnMtDYhs,120,fire truck siren,train OT6Xpe4gDoY,52,dinosaurs bellowing,train OT6Xpe4gDoY,98,dinosaurs bellowing,train OT7sMbT2zdk,13,playing congas,train OT7sMbT2zdk,86,playing congas,train OT8fj8N-2_c,526,chainsawing trees,train OTAx86vnAqo,52,beat boxing,train OTBViYZQZtU,81,popping popcorn,train OTBujHqhKhI,0,snake rattling,test OTKH2-5t54w,1,coyote howling,train OTMNVZjCTeg,32,ice cracking,train OTMNVZjCTeg,43,ice cracking,train OTOZOx8ZqUI,60,playing bass guitar,train OTPJ-RSyijg,169,playing darts,train OTRAK5JyZew,16,playing clarinet,train OTUWwSzYDAI,6,sheep bleating,train OTVFQoNRQTs,60,playing bass guitar,train OTWD-PbcYss,30,playing cymbal,train OTXxwsPCnNU,50,printer printing,train OTYxCDBLmcA,160,"cattle, bovinae cowbell",train OTZDYafhtKY,634,playing lacrosse,test OTattrwc_Qc,300,basketball bounce,train 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PDnJe8aqo_A,41,rope skipping,train PDnJe8aqo_A,116,rope skipping,train PDoTNan9uz4,30,goose honking,train PDod_wuXg7s,353,sharpen knife,train PDrlBB2nyfI,0,skiing,train PDrlBB2nyfI,12,skiing,train PDrqeGo6qRU,150,playing bass guitar,train PDwYe1vUgkA,165,volcano explosion,train PE-kjgKBbI0,149,people screaming,train PE0ujuMgJO0,75,air conditioning noise,train PE4du0Qz_EI,30,opening or closing drawers,test PE4qQfZ22Ws,113,bathroom ventilation fan running,test PE6UaH9OqSA,250,playing clarinet,train PE7KmRHXIz8,40,driving motorcycle,train PE7Mu6VmOac,131,eagle screaming,train PE8pBWEkxrM,30,stream burbling,train PE9EyCIVsyg,23,planing timber,train PE9EyCIVsyg,61,planing timber,train PEABLpVs3gg,10,church bell ringing,test PEAIVf2F8eA,30,waterfall burbling,train PECUpJuUeAQ,265,playing bugle,train PECpA9a_2zQ,46,"playing steel guitar, slide guitar",train PECpA9a_2zQ,56,"playing steel guitar, slide guitar",train PEDhkB-ewqY,150,playing harpsichord,train PEEAl9laoUg,5,wind rustling 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PIRTxO7cjkI,33,hail,train PIRTxO7cjkI,59,hail,train PIYtNwz4AGs,9,vacuum cleaner cleaning floors,train PIZFlCE3-eg,31,singing bowl,train PIZyzsm7Za0,0,mouse clicking,train PIbT5NnPrIU,30,"female speech, woman speaking",train PIf8AQu-DeA,10,playing bagpipes,train PIgN2TZiL5M,26,chicken clucking,test PIgWwkS9HSU,90,wind rustling leaves,test PIgb1MuXf4I,195,playing tympani,train PIgb1MuXf4I,224,playing tympani,train PIgjdcyOWJc,30,"bird chirping, tweeting",train PIndoxUM_4c,18,goose honking,train PIvb0MoczTo,2,people clapping,train PIy1YLKNb2g,58,"playing marimba, xylophone",train PJ12md3DdvY,30,typing on computer keyboard,train PJ1BNO2ix98,7,lions growling,train PJ4JtOikQjQ,88,playing cymbal,train PJ789dhgVYw,5,magpie calling,train PJ789dhgVYw,87,magpie calling,train PJ8QzVjylxg,157,parrot talking,train PJAQLxfRAec,1,playing darts,train PJAQLxfRAec,86,playing darts,train PJBFnqfLM3c,0,lions roaring,train PJBFnqfLM3c,12,lions roaring,train PJBRvSClhzY,4,singing bowl,train PJCxjG3dxqw,12,singing bowl,train PJHNzWpMCyQ,170,playing saxophone,train PJHg_DV7HGE,30,"engine accelerating, revving, vroom",test PJNtaEK1RCY,0,fox barking,train PJOL__OmuJc,6,opening or closing car doors,train PJPv_nz-Jpk,92,arc welding,train PJQoAIyh0io,15,skidding,train PJQoAIyh0io,85,skidding,train PJR7rtjJdGg,0,people burping,train PJStuIi8WBc,140,wind noise,train PJUO6ietZOQ,27,frog croaking,train PJUy17bXlhc,40,"railroad car, train wagon",test PJWp2WywIgE,10,machine gun shooting,train PJc98NdlzU0,0,lions growling,train PJc98NdlzU0,13,lions growling,train PJdKij1OZ24,236,sharpen knife,train PJdKij1OZ24,417,sharpen knife,train PJdT9_dU8MM,420,wind rustling leaves,train PJeyzd-ZDGM,0,owl hooting,train PJeyzd-ZDGM,35,owl hooting,train PJf5M6ETV98,347,forging swords,train PJgahfTB7iY,94,disc scratching,train PJkDaZ861uU,30,driving motorcycle,train PJm2kXek0vY,30,playing electronic organ,train PJmIra-c8cc,173,"playing violin, fiddle",test PJn2A21RDK0,62,playing cornet,train 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PNmIoaNKwN8,60,missile launch,train PNneH-eaWgM,190,"playing marimba, xylophone",train PNoTKVe2_LY,30,playing trumpet,test PNwLEr_lLVQ,10,ambulance siren,train PNwwIx8_mT0,40,turkey gobbling,train PO2aADVm3ls,0,black capped chickadee calling,train PO36mY_acGM,30,skateboarding,train PO3B8e2k1wI,38,playing hammond organ,train PO6d5FWeh1g,10,driving motorcycle,train PO8UG4uV0D0,200,people whispering,train PO94QmBo2Bg,430,"railroad car, train wagon",train PO9UcAxu-0c,46,playing timbales,train PO9UcAxu-0c,74,playing timbales,train POAbQTYHT98,30,duck quacking,train POCoI3MD2dw,238,skiing,train POJOgimzKN0,17,cat purring,train POJOgimzKN0,46,cat purring,train POJwWQgrq34,10,cat purring,train POK-k4FuFGw,165,playing volleyball,train POKeNjFBAsg,15,people belly laughing,train PONG-v-y1HE,50,cap gun shooting,train POO8ggq2xIM,115,coyote howling,train POO8ggq2xIM,238,coyote howling,train POOrEcn6b7A,6,squishing water,train POOrEcn6b7A,18,squishing water,train POU8CnRvd_A,36,airplane,train POU8CnRvd_A,46,airplane,train POUA6r5PyTI,270,mosquito buzzing,train POYRdof8qBQ,70,"child speech, kid speaking",train PO_LL2YBh7U,421,people whispering,train PO_LL2YBh7U,644,people whispering,train POaBsehllg4,260,skateboarding,test PObLY87wyIU,190,baby babbling,test PObXOPHfoLw,30,playing electronic organ,train POdVBPX8QNQ,29,lions roaring,train POdVBPX8QNQ,46,lions roaring,train POiEA2ISnAs,0,eagle screaming,train POktPynubck,58,volcano explosion,train POo_y8hQb6Y,10,playing tambourine,train POqr1st4gmc,111,playing djembe,train POt5agYdY9A,0,"race car, auto racing",train POtYaGfqU5s,84,striking bowling,train POuhGn61ZR8,350,raining,test PP-wlVycr7w,102,playing accordion,train PP02v544Qbo,30,"rowboat, canoe, kayak rowing",train PP0SUjhV1Bc,120,"race car, auto racing",train PP0yUp5XWPg,83,people burping,train PP4e9mz5nxY,51,singing choir,train PP4e9mz5nxY,64,singing choir,train PP6nSzTzVZk,218,firing muskets,train PP78Ka1xthw,0,fireworks banging,train PPCiswbldl4,244,popping 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PPR5XTCLJfY,29,yodelling,train PPR5XTCLJfY,120,yodelling,train PPSbZLlSFJE,8,playing snare drum,train PPSbZLlSFJE,30,playing snare drum,train PPT40oiF-SE,4,tap dancing,train PPTlyK9zrqI,0,wind noise,train PPXg0vOX4oE,140,people cheering,train PPYAoQjHA7k,148,horse neighing,train PPZ3UnpB8ew,34,black capped chickadee calling,train PPb9RaZ3e3g,55,black capped chickadee calling,test PPbp1ARS-nU,30,ocean burbling,train PPc4RszVAjE,4,warbler chirping,train PPc4RszVAjE,40,warbler chirping,train PPcmcMjk7eM,14,lawn mowing,train PPd3u6-DPeI,270,"female speech, woman speaking",train PPewZ5c6zg8,12,civil defense siren,train PPfzvibfLgg,169,male singing,train PPgnK3vp8Ms,174,airplane,train PPgnK3vp8Ms,361,airplane,train PPgptTC2rE8,156,popping popcorn,train PPgqiihGkSU,41,lighting firecrackers,train PPgqiihGkSU,63,lighting firecrackers,train PPk77JN0dLc,90,scuba diving,train PPn8-6x5Dv0,100,people cheering,train PPo4EmxR0dw,11,ambulance siren,train PPp1GiEnP28,140,dog growling,train PPrvYk7id7c,30,chainsawing trees,train PPsZwcWeNyg,30,playing harpsichord,train PPsuFTOjbVg,22,"donkey, ass braying",train PPvL4bcxdF0,83,playing hammond organ,train PPvyhFeIfn4,50,"subway, metro, underground",train PPxOMzXA3Hk,27,playing badminton,train PPxtZe692Ow,52,playing bagpipes,train PPxtZe692Ow,115,playing bagpipes,train PQ-Kq6eMnrs,443,playing bass drum,train PQ1StoH4vg0,240,horse clip-clop,train PQ1s0fJHf9Q,19,singing bowl,train PQ2ADTS8670,390,playing drum kit,train PQ2vqE0zc3E,0,mosquito buzzing,test PQ3sslf55eE,200,pheasant crowing,train PQ9DXmFYjhE,196,tractor digging,train PQ9EmxbdIb4,7,mouse squeaking,train PQ9EmxbdIb4,18,mouse squeaking,train PQBRSwZiYS4,14,playing banjo,train PQDJ1ar_aEI,100,lawn mowing,train PQF_6uyVxEA,22,snake rattling,train PQGYbVBY2mA,30,orchestra,train PQKLMe0XmXE,69,warbler chirping,test PQKMxBkU994,51,firing cannon,train PQKMxBkU994,109,firing cannon,train PQLFDcm6h3g,44,playing shofar,train PQLFDcm6h3g,69,playing shofar,train 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PR4lzoObEJE,21,"fly, housefly buzzing",test PR6blkQnH7Q,80,playing trumpet,train PR9xqyUljKQ,4,hail,train PR9xqyUljKQ,36,hail,train PREsgySYnBI,140,"bird chirping, tweeting",train PRGQHCPtpt8,2,playing table tennis,train PRMVYsDTgzY,20,people screaming,train PRQebpwoOg8,0,"bird chirping, tweeting",train PRTxPJfCKhY,88,playing timpani,train PRTxPJfCKhY,139,playing timpani,train PRU9zAtcQDs,0,dog howling,test PRWtIQUfoag,163,volcano explosion,train PRYUzm_tcmE,550,"child speech, kid speaking",train PRYrQno6QlQ,150,people crowd,train PR_-hLTo7HU,0,people screaming,train PRbLDDyX2mk,13,people nose blowing,train PRbLDDyX2mk,105,people nose blowing,train PRbUBKp5wuc,107,elk bugling,test PRc1SSTAmog,10,people crowd,train PRehPQIwf1E,20,bowling impact,train PRj3CWvV95c,62,skiing,train PRlFQELvNcE,30,waterfall burbling,train PRlReh9ILKg,65,francolin calling,train PRlReh9ILKg,78,francolin calling,train PRmRDckg58Y,30,opening or closing drawers,train PRnU3HUvleY,180,playing glockenspiel,train PRouijtFA4k,13,striking bowling,train PRouijtFA4k,23,striking bowling,train PRrO0ljpBsU,15,zebra braying,train PRrO0ljpBsU,30,zebra braying,train PRriMdEO23I,179,playing shofar,test PRtH2Gx_0IE,431,playing volleyball,train PRtufglGUQ8,40,playing harpsichord,train PRvCGwI1zJY,10,"electric shaver, electric razor shaving",train PRv_fsi1c30,260,bird wings flapping,train PRw-tq19Vv4,30,sailing,train PRyCl-EuATM,469,playing squash,train PS07qOV9lEo,78,playing congas,train PS07qOV9lEo,220,playing congas,train PS0td0Q6d60,120,singing bowl,train PS1mmNPt7LI,61,playing volleyball,train PS4gOE1TZiY,30,female singing,train PS6OZs-a3TY,9,dog barking,train PS6OZs-a3TY,19,dog barking,train PSBU8NcboBc,106,playing didgeridoo,train PSCOjQOTGmo,22,crow cawing,train PSCOjQOTGmo,47,crow cawing,train PSC_ukU73Qk,2,cheetah chirrup,train PSEUYHgKejY,30,playing flute,train PSEd6-Nya_A,36,sharpen knife,train PSHINj-Wy5s,0,frog croaking,train PSKfEkWWewA,20,goose honking,train PSLvH52zZQ8,30,sheep 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PSldRyhH7Q4,30,playing cello,train PSoOq28g2t0,30,people shuffling,test PSpznDznL-c,40,people sobbing,train PSs6U21YRtA,76,planing timber,train PSs6U21YRtA,112,planing timber,train PSsXtlAsKy8,80,pig oinking,train PSt0xAYgf4g,0,reversing beeps,test PSt_gdjwmvY,140,people whistling,test PSuJpX1NAZI,190,bull bellowing,train PSuJpX1NAZI,201,bull bellowing,train PSvEFwTVCYQ,451,"bird chirping, tweeting",train PSw5uqkTPzs,314,playing bass drum,test PSxkZwlhs8c,80,dinosaurs bellowing,train PSyKmm5CT7M,460,"railroad car, train wagon",train PT1J9Eke49k,0,"rowboat, canoe, kayak rowing",train PT3IFeGi7Fk,30,goose honking,train PT75YqQ7ZTg,55,playing timbales,train PT75YqQ7ZTg,79,playing timbales,train PT8NDpewk4I,30,using sewing machines,train PT91KEbyF4Q,170,playing harp,train PTCf54Kci6Q,9,frog croaking,train PTCf54Kci6Q,19,frog croaking,train PTCuyqS72F4,40,chopping food,train PTEo-s2Intw,140,"subway, metro, underground",train PTH7W49yqW4,162,slot machine,train PTJewrg2m1w,50,playing hammond organ,train PTNpq0ggTuo,59,playing table tennis,train PTOF-fb9S5k,10,ambulance siren,train PTPS1vUShlw,136,playing snare drum,train PTPS1vUShlw,195,playing snare drum,train PTYYZK7gg0g,0,"vehicle horn, car horn, honking",train PTYaHFAdxXo,77,child singing,train PTYaHFAdxXo,113,child singing,train PTZGpTJtmnc,61,people finger snapping,train PTZGpTJtmnc,254,people finger snapping,train PT_D_ND5WtU,270,playing harp,train PTbK33IvHRw,122,plastic bottle crushing,train PTbK33IvHRw,141,plastic bottle crushing,train PTdYGyme-f4,83,arc welding,train PTee0M-CjHc,30,playing piano,train PThjNYIKTH0,73,hedge trimmer running,train PTjNb3y7X1k,160,playing accordion,train PTjnZXm5E0c,120,people sneezing,train PTlnAgea9AU,49,airplane flyby,train PTmetoJBQok,22,police car (siren),train PToE46GGCU8,60,people cheering,train PTpndH6GHps,285,arc welding,train PTwMae_To4I,320,horse neighing,train PTwkbdlp3go,4,scuba diving,train PTwkbdlp3go,15,scuba diving,train PTwq7y4Fn3I,30,driving motorcycle,train PTxm0fp49zk,30,goose honking,train PTz5Sl5Beks,136,bouncing on trampoline,train PTz5Sl5Beks,243,bouncing on trampoline,train PU2Twev6jHs,60,fireworks banging,train PU5n9RW5THM,30,playing accordion,train PU6NmaI_JOc,30,people farting,test PU7s_lmOQ4c,9,dog howling,train PU9YY2sEg20,9,lions growling,train PUA3P_YwMOA,220,playing sitar,train PUA4fKKo4nA,122,missile launch,train PUAOOtaP-9Q,240,writing on blackboard with chalk,train PUAOOtaP-9Q,251,writing on blackboard with chalk,train PUEer5h_RDw,80,using sewing machines,train PUI0Am7G-jo,21,playing cello,train PUI0Am7G-jo,227,playing cello,train PUIDyHJmt1A,62,female singing,train PUIdrmzyBr8,23,gibbon howling,train PULXiLv4cGM,24,sliding door,train PULijjqMrVQ,169,sharpen knife,train PUO_wx5rAsI,81,driving snowmobile,train PUO_wx5rAsI,97,driving snowmobile,train PUPO1fZFLNs,40,people sniggering,train PUQ7VQDo9bA,28,air horn,train PURNt0pI8kM,30,chainsawing trees,train PUT3dRnTz2I,120,lighting firecrackers,train PUT3dRnTz2I,151,lighting 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PWpj93J7mE4,0,elephant trumpeting,train PWq0d81qsJ8,170,mosquito buzzing,train PWwD7zzMOWE,540,"playing marimba, xylophone",test PWxH3l6Iri4,0,wind chime,test PWyG6GYErRM,0,playing squash,train PWzVmJBBB0M,38,driving snowmobile,train PWzVmJBBB0M,48,driving snowmobile,train PX1P9sCoZKI,270,"engine accelerating, revving, vroom",train PX1rz-NdaaI,0,pheasant crowing,train PX2_QP0lMXY,30,playing flute,train PX2xcfpGynA,9,"subway, metro, underground",test PX3Pd2LJZ4o,30,printer printing,train PX8PcsCKIh4,250,playing trombone,train PX9kGT0E7B0,30,dog barking,test PXBvcucFV3A,60,driving buses,train PXCYl2vJtDw,10,"child speech, kid speaking",train PXIQRNcTVlk,40,people crowd,train PXItSEVgUYs,7,typing on typewriter,train PXLKCiSigsw,16,"rowboat, canoe, kayak rowing",train PXMYoOp4IZw,105,sharpen knife,train PXQtk1RqdDY,30,playing clarinet,train PXSUPKo3T7M,469,golf driving,train PXWZ5EIjKQs,180,playing cello,train PXWeqYmg6zk,190,people whispering,train PXYyDjuL5I8,30,playing harp,train PXZ9SHtRGAc,3,playing glockenspiel,train PXZ9SHtRGAc,14,playing glockenspiel,train PXaP1A_wSKY,84,yodelling,train PXbqV8Qfh7s,60,playing electronic organ,train PXdHv6GA8ec,0,dog barking,train PXdQrPW_YyY,0,typing on computer keyboard,train PXdQrPW_YyY,11,typing on computer keyboard,train PXiK9bK5V4o,0,yodelling,train PXjNNs6lmvs,70,playing electric guitar,train PXkG_tRi3XQ,85,singing bowl,train PXlloE5Rb4Q,24,civil defense siren,train PXlloE5Rb4Q,65,civil defense siren,train PXm5b0Kkgrk,10,black capped chickadee calling,train PXm5b0Kkgrk,35,black capped chickadee calling,train PXmv6GJpEzo,30,playing drum kit,train PXnNOBj26lk,61,people clapping,train PXoE-TLvxc8,60,playing flute,train PXud6JIU3D4,140,parrot talking,train PXvygAB5RKg,300,people running,test PXxMW3PFhAc,36,"playing steel guitar, slide guitar",train PXxMW3PFhAc,47,"playing steel guitar, slide guitar",train PY-qb2c0Q_g,190,orchestra,train PY2G7zneDhs,2,bull bellowing,train PY2G7zneDhs,42,bull bellowing,train PY3XnCBezm8,384,skiing,train PY401MV0EAc,26,owl hooting,train PY401MV0EAc,39,owl hooting,train PY4304sV5fY,105,playing piano,train PY53bDROYYw,60,lawn mowing,train PY5Uwb2HZsg,2,playing table tennis,train PY5cAuyieVM,7,playing didgeridoo,train PY5o-Q3A_fU,2,lions growling,train PY7SiMNxPrY,83,pheasant crowing,train PY7SiMNxPrY,131,pheasant crowing,train PY9LqGPITPU,48,playing theremin,train PY9LqGPITPU,78,playing theremin,train PYB0tnC2sxY,20,playing acoustic guitar,train PYB2y3j9QM0,60,skiing,train PYBGLz-lsvo,90,playing cymbal,train PYD4CKCSV-8,520,"female speech, woman speaking",train PYDzO-W1zW8,11,swimming,train PYEuYK9Bbr8,9,dog howling,train PYEuYK9Bbr8,24,dog howling,train PYFUsdNXTXg,313,airplane,train PYFUsdNXTXg,402,airplane,train PYHq2uE8wis,0,running electric fan,train PYHq2uE8wis,89,running electric fan,train PYHtxuXTILY,19,playing synthesizer,train PYHtxuXTILY,82,playing synthesizer,train PYI-xEv2R5o,65,playing volleyball,train PYIMNmqJhYc,11,airplane flyby,train PYJZY9Z3yfo,204,golf driving,train PYMZszqrfHs,247,playing cymbal,train PYNETgMvo7I,190,playing clarinet,train PYNzu3fZ-3Q,271,"electric shaver, electric razor shaving",train PYOPQuhdoQM,335,playing bassoon,test PYOPQuhdoQM,366,playing bassoon,test PYRC5MGJUz4,0,slot machine,train PYT7A8naPqk,420,driving buses,train PYT_uyMPF-U,267,child singing,train PYT_uyMPF-U,289,child singing,train PYWyfP_l7tU,27,playing saxophone,train PYYbTjLkU4M,221,playing electronic organ,train PYYbTjLkU4M,377,playing electronic organ,train PYYgbYwbPEQ,111,people eating noodle,train PYYgbYwbPEQ,134,people eating noodle,train PYYggAMBkq0,10,dog barking,train PYZ1LtBRgq4,30,cat growling,train PYacH8VUhXc,10,"pigeon, dove cooing",train PYgxoMjrObc,0,people slapping,train PYislCydAPQ,30,playing french horn,test PYkYPLNqhBM,30,dog bow-wow,train PYlCPdwBOlA,14,people giggling,train PYlCPdwBOlA,38,people giggling,train PYlIscXuIWM,246,playing timbales,test PYlu5B-Qva8,21,electric grinder grinding,train PYlu5B-Qva8,36,electric grinder grinding,train PYn8KKYiX24,45,tractor digging,train PYnP4e9bJ_I,0,underwater bubbling,train PYnln-xwWUU,375,people eating apple,train PYnln-xwWUU,434,people eating apple,train PYo7P9sWaAE,66,airplane flyby,train PYo7P9sWaAE,105,airplane flyby,train PYqPEeh56fg,81,air conditioning noise,test PYqjZWAFU6I,110,plastic bottle crushing,train PYs1p7kvjT4,0,singing choir,train PYs1p7kvjT4,333,singing choir,train PYsE0XVBr7o,26,dog baying,train PYt5F1WyFHA,25,cat growling,train PYt5F1WyFHA,38,cat growling,train PYtRirJih6k,200,"female speech, woman speaking",train PYteL5vS-xM,248,playing squash,train PYvZ4KPGhHs,0,"playing marimba, xylophone",test PYvas8lpXtQ,0,horse neighing,train PYvas8lpXtQ,88,horse neighing,train PYwnh-INyWs,30,"motorboat, speedboat acceleration",train PYxfPtFymHg,30,playing cymbal,train PYyOddqpw10,0,playing bass drum,train PYz-Dytp0uc,107,playing timpani,train PYz-Dytp0uc,130,playing timpani,train PZ-qUWM5OLY,70,ambulance siren,train PZ1HBXnwIVE,16,sea lion barking,train PZ5JWB6iNa4,30,people running,train PZ5zOeFS0Dg,232,playing congas,train PZ5zOeFS0Dg,359,playing congas,train PZ79Au6qZSA,51,horse neighing,train PZ7lARjDQXQ,4,chinchilla barking,train PZ7lARjDQXQ,18,chinchilla barking,train PZ8JKqeAXJQ,30,"motorboat, speedboat acceleration",train PZ9WxD7K6ng,8,playing glockenspiel,train PZACLpEasFU,30,typing on computer keyboard,test PZAXmiMsa2k,3,horse neighing,train PZC_HSy31sQ,32,plastic bottle crushing,train PZC_HSy31sQ,274,plastic bottle crushing,train PZChY40DQt8,30,turkey gobbling,train PZDAz2nEZys,16,dog howling,train PZDAz2nEZys,56,dog howling,train PZFFaQWzjBo,50,"railroad car, train wagon",train PZH-l6Rn9yM,570,wind noise,train PZIfsItgfNc,10,"engine accelerating, revving, vroom",train PZIi9nkHlc4,514,singing choir,train PZIi9nkHlc4,857,singing choir,train PZJIZrfZmXw,36,francolin calling,train PZJIZrfZmXw,176,francolin calling,train PZJtBkLPDbM,460,arc welding,test PZLsTEMIuW4,2,"donkey, ass 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barking,train PZkS9IVg0e4,30,chopping wood,test PZm7QLVcVRs,104,playing bongo,train PZm7QLVcVRs,119,playing bongo,train PZq54xp87Ks,6,lighting firecrackers,train PZq54xp87Ks,35,lighting firecrackers,train PZspE60vpqE,50,"race car, auto racing",train PZsw1ONxRuw,6,canary calling,train PZt-LLS7hBs,70,warbler chirping,train PZt-LLS7hBs,80,warbler chirping,train P_-tkTKaLls,107,"heart sounds, heartbeat",train P_-tkTKaLls,148,"heart sounds, heartbeat",train P_2nWhBOvBY,159,sharpen knife,train P_2nWhBOvBY,216,sharpen knife,train P_5BP-o7sD8,0,"electric shaver, electric razor shaving",train P_5BP-o7sD8,53,"electric shaver, electric razor shaving",train P_81cZt6LNU,30,cat meowing,train P_8sR3jDZ9E,120,helicopter,train P_9hIZhtjBA,110,"playing marimba, xylophone",train P_AesAaJfWY,63,pheasant crowing,train P_AesAaJfWY,100,pheasant crowing,train P_BjFvh__1I,20,gibbon howling,test P_ECtmnjjAs,37,tapping guitar,train P_Gv5_4yNOM,110,playing ukulele,test P_HYjo4gR0E,10,mynah bird singing,test P_MTMEaTxbM,1,slot machine,train P_MlP2Q2L88,30,playing accordion,train P_T1GK-jRQM,5,airplane flyby,train P_Uxma3VR9Q,52,lathe spinning,train P_Uxma3VR9Q,99,lathe spinning,train P_XVyfJwsEU,30,baby crying,train P__oyJXFZ2A,10,police car (siren),train P_bO3pB7Hl0,10,playing accordion,train P_dnD6MMFrY,85,cattle mooing,train P_dnD6MMFrY,177,cattle mooing,train P_dpxVdZCVQ,105,magpie calling,train P_g2RXFxusg,69,playing badminton,train P_i3VWRnJMs,0,playing bagpipes,train P_i3VWRnJMs,24,playing bagpipes,train P_iS0s1COPs,320,skidding,train P_k1wxugOwE,0,"motorboat, speedboat acceleration",train P_mLPm2_G0g,0,playing ukulele,train P_m_FQhNeKo,28,air horn,train P_mbqLjnhio,390,goose honking,train P_oq6uB0wms,379,tapping guitar,train P_oq6uB0wms,407,tapping guitar,train P_qNYofNfEk,1,basketball bounce,train P_snJiyefD0,0,mouse clicking,train P_snJiyefD0,14,mouse clicking,train P_spMkYQct8,200,people crowd,train P_tktMUTH04,260,eating with cutlery,train P_utET7TCMQ,20,people babbling,test P_zAbDRxHzs,50,"male speech, man speaking",train P_zYjBcBUSg,150,playing accordion,train Pa-ZG3QOFdM,1,church bell ringing,train Pa0oHNLU0Xk,57,skiing,train Pa4tApc0vpc,30,playing electric guitar,train Pa5uJFBEauA,28,slot machine,train Pa7IKLTNB_I,196,playing synthesizer,train Pa9wX5rm49Q,59,cheetah chirrup,train PaAg2L4dd04,0,chainsawing trees,train PaE5-YikuaI,14,driving snowmobile,train PaE5-YikuaI,27,driving snowmobile,train PaF003XihkA,150,using sewing machines,train PaFSypqIYgI,70,playing accordion,train PaFyQudGuDo,133,tap dancing,train PaJatNJhmsQ,0,church bell ringing,test PaKdVbNHpRI,30,"bird chirping, tweeting",train PaOuM5N4StM,69,shot football,train PaOuM5N4StM,193,shot football,train PaQGXIh94uc,150,playing ukulele,test PaUKC70Eavk,120,playing djembe,train PaUKC70Eavk,207,playing djembe,train PaWSFbyz-34,0,playing badminton,train PaXrL0mrnTI,20,people babbling,train PaYhemY3bqk,30,car engine knocking,train PaZ4aVEsPsE,19,"playing violin, fiddle",test Pa_7DkJIFIs,10,playing cello,train Pa_V3wLTkQk,11,pheasant crowing,train PacYskI7Ewc,4,lions roaring,train PacYskI7Ewc,28,lions roaring,train PagL5w4mR-Y,27,cat hissing,train PagL5w4mR-Y,43,cat hissing,train PahZ3VCQNrg,220,playing drum kit,train Pair_NsHdTc,30,driving motorcycle,test PakRIOibhD4,0,goose honking,train Palc0qoKA60,20,eating with cutlery,train PaoTT5FsvZQ,511,dinosaurs bellowing,train Pappo2xJhQQ,30,playing accordion,train PatPjxyHqvY,0,"motorboat, speedboat acceleration",train PatkcLn3NSk,30,playing cello,train PauJJstWSUU,250,people babbling,train PaupGh4ZNpM,250,playing french horn,train PavKY6YlSl4,26,people whistling,train PawUc0pqf9M,260,car engine starting,train PawqhPA0NVY,10,people sneezing,train PayI-T_mgto,30,car engine starting,test Payu3YBHABc,30,helicopter,train Pb0ZLgHLHFc,10,people burping,train Pb1AZvbd0Jc,111,bull bellowing,test Pb1AZvbd0Jc,174,bull bellowing,test Pb1Ck3HS_5M,10,people hiccup,train Pb2V-iTXckA,470,fireworks banging,train Pb36NPWN_7M,30,playing bagpipes,train Pb4oNDhz8fk,53,yodelling,train Pb5hh6RWJ9I,96,mouse clicking,train Pb5hh6RWJ9I,144,mouse clicking,train Pb6MqpdX5Jw,40,thunder,train Pb8tWxrWwPI,29,tornado roaring,train Pb8tWxrWwPI,245,tornado roaring,train Pb8vhbhLwBM,8,cricket chirping,train Pb8vhbhLwBM,46,cricket chirping,train PbASjQJaFaA,10,people sobbing,train PbEAHzwEhJQ,30,"playing violin, fiddle",train PbGHLLnSeNw,57,playing saxophone,train PbGHLLnSeNw,395,playing saxophone,train PbIfUvf5hNo,40,playing flute,train PbLKBDzeE-8,7,rapping,train PbLKBDzeE-8,29,rapping,train PbLND2U9FWs,30,"playing violin, fiddle",train PbLaCqBxMoU,344,playing darts,train PbQpcGkbdd0,83,playing bassoon,train PbRtr9WZuaQ,60,toilet flushing,train PbWm2wozeN4,5,coyote howling,train PbXhwnptMh4,236,people hiccup,train Pb_2O6yAltQ,17,playing harpsichord,train Pb_2O6yAltQ,87,playing harpsichord,train PbcbSK_GBgU,116,playing mandolin,train PbcbSK_GBgU,214,playing mandolin,train PbgR89WJbFo,0,people whistling,train PblK1qc8FGc,20,driving motorcycle,train PblL4pDSqKM,6,"engine accelerating, revving, vroom",train PbmbH0NWOSg,51,playing tambourine,train PbmzUJaE_wc,273,playing table tennis,train PbnASv2DPto,29,cat meowing,train PbnQgQMgbMU,30,printer printing,train Pbnx5vn5iOc,3,playing theremin,train Pbnx5vn5iOc,100,playing theremin,train PboDGCcBGGo,0,dog growling,train PboIASxaflM,10,chainsawing trees,train PbomocKzqKU,99,splashing water,train PbomocKzqKU,109,splashing water,train Pbp9IQcq2SM,40,playing cymbal,train PbsHFz8yxpM,0,lions growling,train PbsfBjvUED8,21,elephant trumpeting,train PbvaOdsrZVQ,240,people crowd,train Pbw3N3BBzHU,10,cuckoo bird calling,train PbwZ_e-zk5w,432,playing timpani,train PbwZ_e-zk5w,699,playing timpani,train Pbwh5K-DndE,70,orchestra,train PbxmnRMGPww,16,francolin calling,train PbxmnRMGPww,256,francolin calling,train Pbykt6qWaOQ,9,typing on typewriter,train PbzpyQWMapw,85,coyote howling,train Pc-XnRIIgqE,5,fireworks banging,test Pc3ZOp0v9dk,5,cat meowing,train Pc6d7q9UGpE,150,chainsawing trees,train Pc78O2hAygQ,31,playing harp,train Pc7asKTC-z4,40,people crowd,train Pc81NvSwB78,143,"electric shaver, electric razor shaving",train Pc96-b1Ps0k,0,"vehicle horn, car horn, honking",train Pc9UgVPYNQo,218,"engine accelerating, revving, vroom",train PcAoj73jjd0,22,planing timber,train PcAoj73jjd0,132,planing timber,train PcBeQcVgYpA,30,reversing beeps,train PcDMVeEotEM,116,playing electronic organ,train PcDMVeEotEM,127,playing electronic organ,train PcELumLdzEs,100,dog barking,train PcFDXx01-Lo,49,playing sitar,train PcIgEjeqkyg,20,people whispering,train PcKUdXJYzzE,48,playing cymbal,train PcKrWmot2pQ,140,sheep bleating,train PcNL--ladHw,75,playing bugle,train PcO85V8_y20,90,scuba diving,train PcOsdbvOP_w,156,playing didgeridoo,train PcPDSWC2egs,365,tractor digging,train PcRTetx2nYA,208,people eating noodle,train PcWA9h6a8LA,28,mosquito buzzing,train PcWA9h6a8LA,153,mosquito buzzing,train PcWE2oZjQwE,44,playing bass drum,train PcWE2oZjQwE,59,playing bass drum,train PcXGEObW3CM,17,people booing,train PcXGEObW3CM,91,people booing,train PcYwEMoS7oQ,451,"heart sounds, heartbeat",train PcYwEMoS7oQ,774,"heart sounds, heartbeat",train PceHOu6zCUo,37,pig oinking,train PceW8rVajAA,50,people giggling,train PceW8rVajAA,153,people giggling,train PciUdLBCwVI,30,"motorboat, speedboat acceleration",train PcjQkCcaZxg,174,playing timpani,train PcjQkCcaZxg,185,playing timpani,train PckLzWYMVSk,5,playing bass drum,train PckLzWYMVSk,29,playing bass drum,train PclM4OKGxDg,30,playing hammond organ,train PclYMwkHYiM,60,"child speech, kid speaking",train Pco26HSFSos,160,sailing,train Pcub59Q79EI,53,cell phone buzzing,train Pcub59Q79EI,66,cell phone buzzing,train PcwLsQKDvzw,310,playing banjo,train Pd-dkQtpu8Q,80,using sewing machines,train Pd-so26kW1A,188,cricket chirping,train Pd-so26kW1A,203,cricket chirping,train Pd4xAWBgFME,170,people cheering,train Pd5GUdUvOQM,30,turkey gobbling,train Pd5pT3JMtgc,24,firing cannon,train Pd5pT3JMtgc,73,firing 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PdjrgwJVRiw,85,car engine starting,train PdpvpKt-r7k,38,"bird chirping, tweeting",train PdpvpKt-r7k,53,"bird chirping, tweeting",train PdqAEA7gqsI,61,plastic bottle crushing,train PdqAEA7gqsI,88,plastic bottle crushing,train Pdq_2YWF1uM,120,mouse pattering,train PduA7h3_bsU,167,church bell ringing,train PduA7h3_bsU,246,church bell ringing,train PduTMr-xtOE,90,playing banjo,train PdujPQaEl6U,106,swimming,train Pdw0eHhrCv4,90,sailing,train PdxXNBCAySs,190,playing synthesizer,train PdxXNBCAySs,281,playing synthesizer,train PdzDGNMpPZc,520,horse neighing,test Pe1UAkHgeW8,31,hair dryer drying,train Pe1UAkHgeW8,52,hair dryer drying,train Pe2BE5r17yc,14,civil defense siren,train Pe6HQWtK8h0,152,planing timber,train Pe809Y_kRrE,25,toilet flushing,train PeEeRpIyMZs,9,playing bongo,train PeEeRpIyMZs,51,playing bongo,train PeEy6rXMLyg,35,arc welding,test PeFuWuES7LQ,101,playing bassoon,train PeG13itARjs,210,playing cello,train PeHYeTnyhIk,280,ocean burbling,train PeHs2gHO520,472,people 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PfJE4zsiN18,0,telephone bell ringing,train PfLGxpFOeBg,400,people clapping,train PfLdPsmqMiI,30,"ice cream truck, ice cream van",train PfMJtjUZjZw,430,church bell ringing,train PfMppHvc45E,62,bird wings flapping,train PfP1Fhg3h1g,10,playing drum kit,test PfPGMbzTRow,30,people shuffling,train PfRLolIra8U,15,mosquito buzzing,train PfRLolIra8U,25,mosquito buzzing,train PfT8GxIOSrY,192,playing table tennis,train PfTeqgGPlSI,297,playing bugle,test PfTfUpBoVZ0,174,chopping food,train PfUFeX-Kt2g,30,playing cymbal,train PfXdcsW8dJI,500,eating with cutlery,test PfZgWnbLXLE,108,playing volleyball,train Pf_0KozajWU,20,splashing water,train Pf_2RK3fdbQ,30,"playing violin, fiddle",train Pf_5TncWkIM,0,playing ukulele,train Pf_5TncWkIM,31,playing ukulele,train PfaniREjsVE,27,playing bagpipes,train PffJOlEBENw,10,playing french horn,train PfhBG-jGQXo,127,woodpecker pecking tree,train PfhBG-jGQXo,158,woodpecker pecking tree,train PfiCO5179Jk,111,splashing water,train Pfj5Bov8U04,290,people crowd,train Pfj6DXzeIoU,727,chainsawing trees,train PfjQeYyJwjo,444,planing timber,train Pflj3QD1gtE,149,playing cymbal,train PfnKTi3pL8g,7,mouse clicking,train PfnKTi3pL8g,48,mouse clicking,train PfrHb8eia8E,5,wood thrush calling,train PfriI_DDifE,0,people booing,train PftKLIWo5jQ,261,playing trombone,train PfuWJ1tOX5I,19,playing mandolin,train PfuWJ1tOX5I,53,playing mandolin,train PfwBOCxEst8,210,cheetah chirrup,train PfwBOCxEst8,243,cheetah chirrup,train PfyuVCT8anw,60,goose honking,train Pg0dzAGvGoo,130,playing piano,train Pg1kWopoOwo,0,playing didgeridoo,train Pg1kWopoOwo,53,playing didgeridoo,train Pg41Zz4TUX8,100,eating with cutlery,train Pg4_tKYeC3Y,29,dog howling,train Pg4_tKYeC3Y,41,dog howling,train Pg69FhliCIg,20,"motorboat, speedboat acceleration",train Pg8ZU1tV850,27,penguins braying,train Pg8ZU1tV850,50,penguins braying,train PgB-kjdulEs,10,people crowd,train PgEB2YrWi-o,136,playing zither,train PgEB2YrWi-o,146,playing zither,train PgG2-gpemT0,10,elephant trumpeting,train PgG2-gpemT0,35,elephant trumpeting,train PgGVeTRSGMI,129,playing tabla,train PgGVeTRSGMI,178,playing tabla,train PgLT9HSWxEk,330,stream burbling,test PgLsFRfKMwU,58,canary calling,test PgNdRQL7Oio,5,people booing,train PgNdRQL7Oio,36,people booing,train PgRuNr9GOOs,5,driving buses,train PgSg11RQRFE,205,slot machine,train PgSlWz78gps,553,scuba diving,train PgTHbXjI50U,410,people sniggering,train PgTpHEpo44g,0,cap gun shooting,train PgV-b6EpfoA,30,playing timpani,train PgV-b6EpfoA,53,playing timpani,train PgYPznYEodM,35,canary calling,train PgYPznYEodM,58,canary calling,train PgYXewF7fok,0,woodpecker pecking tree,train PgYXewF7fok,40,woodpecker pecking tree,train Pg_2jS6izLI,5,sea waves,train Pg_2jS6izLI,21,sea waves,train Pgbt_hmuzIE,27,lathe spinning,train PgcdFqEJrnk,75,playing synthesizer,train Pgco2o9mAXE,21,arc welding,train Pgco2o9mAXE,40,arc welding,train Pgf_As0Y-_M,86,pheasant crowing,train Pgf_As0Y-_M,135,pheasant crowing,train Pgi0eo9zQe0,68,sharpen knife,train Pgi0eo9zQe0,85,sharpen knife,train Pgi61qnTKfE,22,skiing,train PgiVYe0HbNg,290,people sniggering,train PgkZgpf6sc8,76,sharpen knife,train PgkZgpf6sc8,91,sharpen knife,train Pgm0bybZZZ4,30,"playing marimba, xylophone",train Pgm5rSAVKWk,115,slot machine,train PgoalJe2WYM,0,playing tuning fork,test Pgoy24J9ycU,301,cap gun shooting,train PgqZzq398UI,0,canary calling,train PgqgO7kBzis,18,"electric shaver, electric razor shaving",train PguXa1Qg6nk,111,playing badminton,train Pgw5p6gLKWQ,70,playing accordion,train PgzigM-nenk,269,tapping guitar,train PgzigM-nenk,442,tapping guitar,train Ph142rGl4oQ,430,playing flute,train Ph2SI18iz7o,10,people sneezing,test Ph30_CQK6BA,30,playing bass drum,train Ph3or5bl_WM,210,helicopter,train Ph56RzL_-2s,0,machine gun shooting,train Ph62Q9QGQzU,113,bull bellowing,train PhCYs4eq2R0,310,skidding,train PhGtPyuJYiY,30,playing acoustic guitar,train PhL0wn_3tBc,231,tap dancing,train PhNDkgnx3IU,30,raining,train PhPL2UDExnY,1,dog howling,train PhPL2UDExnY,28,dog 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PiBtyYom3gY,130,people farting,train PiCxuXVr3ZE,282,missile launch,train PiFRO003klU,36,slot machine,train PiFpVdJCmfc,0,"playing marimba, xylophone",train PiHB-GqmMAQ,13,playing squash,train PiHu2zncZa4,40,toilet flushing,train PiIDZQo55lo,36,scuba diving,train PiIkl6EF6pY,120,"female speech, woman speaking",train PiIqOQR8ifA,134,playing harp,train PiJQiyuyqCg,243,cat purring,train PiJQiyuyqCg,286,cat purring,train PiMVlo6SLOU,24,playing cymbal,train PiMVlo6SLOU,55,playing cymbal,train PiP3iigeKgo,250,people crowd,train PiPEJTLBRNY,228,bowling impact,train PiQzM3HrV38,15,tap dancing,train PiRG35lMDgo,470,people clapping,train PiRcMM-ZsU0,60,car passing by,train PiSCs8mZhOE,510,"female speech, woman speaking",train PiSVyCGhX70,9,footsteps on snow,train PiSsn2xVzsM,20,lawn mowing,train PiTYkVJNgL4,320,"subway, metro, underground",train PiU0epdkiGA,182,people eating apple,train PiV3CexoH5I,310,eating with cutlery,train Pi_0HqRCZdo,781,playing bongo,train Pi_0HqRCZdo,792,playing bongo,train Pi_2255ToZg,111,playing squash,test Pi_NkKDYf_E,41,people booing,train Pi_gwED-gQw,114,playing hammond organ,train Pi_gwED-gQw,231,playing hammond organ,train PibCpuiwBpM,6,playing bassoon,train PibCpuiwBpM,145,playing bassoon,train PidvpaoeB-s,76,chimpanzee pant-hooting,train PidvpaoeB-s,88,chimpanzee pant-hooting,train PifhBU-mSVA,10,baby crying,train PiiEjFXwnDg,38,missile launch,train PiiM-uHJBus,9,people shuffling,train PiiM-uHJBus,25,people shuffling,train PinJoeOrZvA,30,"engine accelerating, revving, vroom",train Piog69eJLJg,290,stream burbling,train PiqEsvZfLUA,0,playing harmonica,train PiqEsvZfLUA,6,playing harmonica,train PivW9LtnGTA,0,lawn mowing,train Piy0C2b2MAk,60,planing timber,train Piz8z7C6NJE,22,driving snowmobile,train Piz8z7C6NJE,34,driving snowmobile,train Pj1NvamGXIw,40,lawn mowing,train Pj1q78Fx-28,30,"railroad car, train wagon",train Pj2t-6P7dDQ,30,pig oinking,test Pj6d357RZ9A,29,police car (siren),train Pj8IFKkW-xc,30,"playing violin, fiddle",train Pj8timZ_Y5I,2,striking bowling,train PjADZJahgYY,10,playing sitar,train PjADZJahgYY,130,playing sitar,train PjAanUL2H5E,30,duck quacking,train PjDUsRFRj08,120,driving motorcycle,train PjEW-yavRMg,34,playing squash,train PjEW-yavRMg,46,playing squash,train PjEuPHoGyAc,6,opening or closing drawers,train PjFAqValSbQ,294,playing bass guitar,train PjG5UFKF-yo,240,chicken crowing,train PjGbMmYyLGk,231,dog howling,train PjGbMmYyLGk,317,dog howling,train PjHc3Py5xNg,327,penguins braying,train PjIkgSK2bbM,320,sailing,train PjK77Fekmnc,120,people sneezing,train PjMpv9k5MhA,210,"cattle, bovinae cowbell",test PjNF7HoQ6yY,75,bowling impact,train PjNF7HoQ6yY,87,bowling impact,train PjPp-KMQpEc,60,playing clarinet,train PjRqQr5NDho,4,car engine knocking,train PjSzkI2MWXc,120,playing erhu,train PjSzkI2MWXc,156,playing erhu,train PjThS6Qtslk,117,rope skipping,train PjThS6Qtslk,146,rope skipping,train PjW1uD2Rdhw,30,"pigeon, dove cooing",train PjYUODTUsvc,71,playing piano,train PjanC5Y1KVU,4,opening or closing car electric windows,train PjboOZnzD5Q,1,barn swallow calling,test PjbxRjKvzw4,30,wind noise,train Pjc7jN6Lt3M,54,playing volleyball,train Pjd88LyReAs,179,playing tympani,train Pjg71GYeYIQ,110,people sneezing,train PjhKFnOvJII,30,printer printing,test Pjhx1F9xehE,120,"bird chirping, tweeting",train PjjoJqYvatA,15,chicken clucking,train PjjoJqYvatA,25,chicken clucking,train PjjojjUn0bk,185,wind rustling leaves,train PjljP9lQiAU,12,playing sitar,train PjnQDGhdZHI,10,people crowd,train PjnQRX5t928,30,driving buses,train Pjnf6ZC0P5Q,90,"child speech, kid speaking",train Pjt1J7a6Glc,30,"playing violin, fiddle",train PjukklH2NoQ,3,chipmunk chirping,train PjuzvRmhPnc,56,civil defense siren,train PjuzvRmhPnc,71,civil defense siren,train Pjw2A3QU8Qg,0,playing trumpet,test PjwsI-2LCeA,170,playing piano,train PjyE14xpsq4,7,smoke detector beeping,train Pk-xRDgJ76U,356,golf driving,train Pk1fF_kiUIo,259,people gargling,train Pk1gIi3N2U0,85,people marching,train Pk1gIi3N2U0,120,people 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ukulele,train PkSJFoNUGIE,80,playing ukulele,train PkSnLg3JnJw,0,cricket chirping,train PkUH4erhyLY,228,airplane,train PkV-dIbY9ic,84,playing darts,test PkWPkWI3iCI,415,striking bowling,train PkYZfCJEwTA,190,"railroad car, train wagon",train PkYume3SDZ0,21,cell phone buzzing,train PkZ--3VqG1w,3,blowtorch igniting,train Pk_qRlJyXrc,168,lions growling,train Pk_s14ykHSI,14,ferret dooking,train PkaJPt7AE7c,510,basketball bounce,train Pkb7lLbH540,0,"heart sounds, heartbeat",train PkbcoGwaX0k,122,playing table tennis,train PkcGAwxHbyA,184,people marching,train PkcGAwxHbyA,435,people marching,train Pkg0zpu4VEg,20,people hiccup,train PkkdzlUjIS8,490,singing bowl,test PklWG8k_cqs,19,frog croaking,train PklWG8k_cqs,31,frog croaking,train Pkm1TV6aD4g,345,people eating apple,train Pkm1TV6aD4g,380,people eating apple,train Pkm7v1pHu6Y,30,"engine accelerating, revving, vroom",test PkreH_mETSc,337,playing flute,train Pks4dq4ek2c,13,rapping,train Pks4dq4ek2c,138,rapping,train PktlLZlDX7k,30,people 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PlNj11K8Iyw,10,"race car, auto racing",train PlOfLgGXNFE,550,playing banjo,train PlOz6mEHmLw,8,dog whimpering,train PlP0NaovbvM,200,cap gun shooting,train PlPhpnyoKGc,0,police car (siren),train PlRB61MufUU,83,mosquito buzzing,train PlRH75G-_bI,30,ocean burbling,train PlRiyuRPCNo,295,playing table tennis,train PlRiyuRPCNo,385,playing table tennis,train PlTDatLu_0Y,154,people whispering,train PlTDatLu_0Y,958,people whispering,train PlWaWGn_Gsk,130,playing saxophone,train PlX1lssY8xQ,0,ambulance siren,train PlXQEmeTeYY,90,female singing,train PlY6X2o6r0w,240,people clapping,train PlZqOnfVa_4,0,playing bagpipes,train PlZxvS4aHM0,2022,playing bass guitar,train Pl_UFaYEfyc,504,people eating,train Pl_UFaYEfyc,703,people eating,train PlaTHuf1_rI,0,fireworks banging,train Ple9riB3S3w,347,lathe spinning,train Ple9riB3S3w,412,lathe spinning,train PlgMdnZrAgA,10,people sobbing,train PlgZyXU_8Eo,425,playing accordion,train PlhkwEVRA9s,0,playing bagpipes,train PljFeREhXVM,50,cattle mooing,train PljFeREhXVM,202,cattle mooing,train PljzqcsQ62U,30,"bird chirping, tweeting",test Plki1Ld1vFw,0,people sneezing,train Pll-TpbHen4,67,airplane,train Pll-TpbHen4,316,airplane,train Pll7PhlRmGE,30,"railroad car, train wagon",train Pllz9qSURGg,30,cat meowing,train Pln7cARoVlI,30,driving buses,train PlniFHSJkJ8,154,cat purring,train PlniFHSJkJ8,307,cat purring,train PlrV0NcUkqE,58,train horning,test PlsgUJqm8HE,30,chicken crowing,train PltJDdkD1Nw,30,"pigeon, dove cooing",train PluNxt4xEv8,160,playing banjo,train PluONSpL-Tk,70,people coughing,train PlueL79AuMs,30,people shuffling,train Plv4-mn8S_s,30,bird wings flapping,train PlvPkaWgSjk,0,toilet flushing,train PlwsY37UxBo,0,skateboarding,train Pm-LKw7pZ5A,0,lions growling,train Pm199wiFoug,180,using sewing machines,train Pm1pTvRwouM,70,playing accordion,train Pm380jkCVng,30,orchestra,train Pm3Nzpz8S0c,260,skateboarding,train Pm4vdSVr6wY,10,"bird chirping, tweeting",train Pm6vRblouxc,170,playing timpani,test Pm9bMFHsKLg,772,playing hockey,train Pm9bMFHsKLg,797,playing hockey,train PmJwUYwP-Ts,500,orchestra,train PmKVuli9v0Y,240,eagle screaming,train PmKnH4y_7Zc,0,cupboard opening or closing,train PmKnH4y_7Zc,12,cupboard opening or closing,train PmM8bo_gwvs,40,playing synthesizer,train PmNzvRtespc,60,driving buses,train PmO7_1t8n2Y,13,sliding door,train PmOvNCYlP1Q,2,mosquito buzzing,train PmQO6kKpCek,0,playing french horn,train PmQOS3KXCxA,30,playing double bass,train PmQoIaftfBA,120,people crowd,train PmQukx7q97M,80,church bell ringing,train PmRayk0uWpc,80,pumping water,train PmSApcExnY4,4,people sneezing,train PmSApcExnY4,68,people sneezing,train PmYaGfEW13I,30,dog bow-wow,train PmZ2kdlUQD8,60,using sewing machines,train PmaUwtX1ONI,7,volcano explosion,train Pmj7x-dc-9M,1,pheasant crowing,train Pmlkt7-BWGA,8,fox barking,train Pmow_aB00Ww,195,forging swords,test Pms5yukX_h8,60,playing harp,train Pmt4mkQ__GU,1,ocean burbling,train Pmt4mkQ__GU,14,ocean burbling,train Pmv9oPvqD5Y,150,"race car, auto racing",train Pmzf20r01Z0,545,arc welding,train PmzpFkzcyuI,29,mouse clicking,train PmzpFkzcyuI,143,mouse clicking,train Pn-J9-LQRw8,30,singing bowl,test Pn-j14rAdOg,6,playing bass drum,train Pn4TRXnIT6c,26,crow cawing,train Pn4aDGGqRfg,30,"motorboat, speedboat acceleration",train Pn8vmMbFJMs,154,playing tabla,train Pn8vmMbFJMs,168,playing tabla,train PnB1MPjLmNM,32,playing squash,train PnCcYESZ89Q,136,playing steelpan,train PnCcYESZ89Q,150,playing steelpan,train PnDQGrMCnts,280,"railroad car, train wagon",test PnDQHZr8tbc,497,"cattle, bovinae cowbell",train PnDQHZr8tbc,507,"cattle, bovinae cowbell",train PnF9B_ipllk,88,playing tabla,train PnF9B_ipllk,108,playing tabla,train PnHZC3F0xdw,23,train whistling,train PnHZC3F0xdw,398,train whistling,train PnIl8h50Cws,110,hair dryer drying,train PnMUZJMxd4I,722,forging swords,train PnOaTdjd80A,110,playing bagpipes,train PnR47zX_HXs,21,sloshing water,train PnR5LPRkDKs,31,baby babbling,train PnR5LPRkDKs,42,baby babbling,train PnSx_B_SX0A,78,raining,train PnWbkw3hxtc,361,horse neighing,train PncqYV0kGdg,10,playing vibraphone,train PneeIrUj7uE,100,playing flute,train PnhPfcUQDOw,30,sailing,train PnhSgHzDSYc,61,playing vibraphone,train PniqNWep-n0,0,car engine knocking,train PniqNWep-n0,26,car engine knocking,train Pnjue53VDKs,6,people booing,train PnlvBFdNje4,145,playing washboard,test Pnmm7nIINxc,207,slot machine,train Pnq9Vjc6Zws,30,duck quacking,train PnqJTDMaYZk,158,striking bowling,train PnqJTDMaYZk,193,striking bowling,train PnrVVc5ax_w,11,playing squash,train PnrsC9lVBx0,30,people running,train PnsTszABZco,15,penguins braying,train PnsTszABZco,42,penguins braying,train PntiXZ692xM,30,people running,train PnvAu3C0Gb4,448,playing bagpipes,train PnvAu3C0Gb4,459,playing bagpipes,train Pnw8qqcD-oI,200,ambulance siren,train PnwTlG1aBdU,86,playing cello,train PnwxuspBTW4,100,"bee, wasp, etc. buzzing",train PnxCQgtxxJU,0,people burping,train PnxROAEuwQk,0,snake rattling,train Pny1CWL93Ic,432,playing ukulele,train Pny1CWL93Ic,481,playing ukulele,train Po08ljFXElg,77,playing squash,train Po0BiwykVJw,20,using sewing machines,train Po1J63SueE8,13,chicken crowing,train Po1kUPiC19o,210,"subway, metro, underground",train Po2FioVjUX4,90,playing drum kit,train Po2kalrDi-w,55,barn swallow calling,test Po40UtlYskc,33,warbler chirping,train Po4DUmZQUE4,20,toilet flushing,train Po6ZZ0sGfUw,11,pheasant crowing,train Po8Y0J8T2Zs,150,people farting,train PoAwaR2XjTY,14,fire truck siren,train PoAwaR2XjTY,45,fire truck siren,train PoBC1F6MhWE,14,swimming,train PoD9gVdeIS4,10,playing electronic organ,test PoE8rtJJnnY,45,"playing steel guitar, slide guitar",train PoE8rtJJnnY,55,"playing steel guitar, slide guitar",train PoEZuVCuydM,30,"vehicle horn, car horn, honking",train PoFq-kBh8M0,180,"female speech, woman speaking",train PoHKNFGbwjM,629,playing french horn,train PoHp9UDAVjc,80,"electric shaver, electric razor shaving",train PoIYw0426uM,35,dog howling,train PoIYw0426uM,70,dog howling,train PoLGVBFrEHE,37,mynah bird singing,train PoLRMBEOvWU,40,"bee, wasp, etc. buzzing",train PoLlYMVKXWA,161,playing didgeridoo,train PoO-HQQ4GXw,321,"fly, housefly buzzing",train PoO-HQQ4GXw,331,"fly, housefly buzzing",train PoPf-7a0EpA,342,baby laughter,train PoRhdg3tdpo,30,playing acoustic guitar,train PoSKE-GS4iU,188,civil defense siren,train PoShfpfhAPw,30,skateboarding,train PoSoLU7ZEDE,30,woodpecker pecking tree,train PoX7raahm2w,110,playing bass guitar,train PoaVoAeDBiY,7,playing hammond organ,train PoaVoAeDBiY,187,playing hammond organ,train PofOSIgOwKY,90,lawn mowing,train PojJMH8Agt4,30,playing flute,train PolsKxOgA64,14,reversing beeps,train PolsKxOgA64,32,reversing beeps,train PonfK7S7xgc,180,people whispering,train Poo5yVJGA7o,20,ambulance siren,train PooqFrcVt4I,29,playing french horn,train PoqBKimpunU,470,people slurping,train PoqBKimpunU,481,people slurping,train PovPsoVl7hw,6,fox barking,test PovPsoVl7hw,45,fox barking,test PovceEcM0HY,50,dog barking,train Pp3TAyeu0Lk,0,ambulance siren,train Pp61sP7bols,76,cap gun shooting,train Pp61sP7bols,98,cap gun shooting,train Pp6J-qekXIE,83,otter growling,train Pp6J-qekXIE,93,otter growling,train Pp6K29hnu4Q,22,people humming,train Pp6K29hnu4Q,46,people humming,train Pp8xJVlQLiM,135,playing banjo,train PpDEBVB9S8U,330,ambulance siren,train PpF3MODQVOI,84,playing timpani,train PpFiuRrVg2A,0,people babbling,train PpH7SykHL9c,61,people farting,train PpH7SykHL9c,106,people farting,train PpHSbCr9w8Y,350,playing drum kit,train PpISdNxSSSM,260,printer printing,train PpJwU5cJQDg,210,wind chime,train PpK09wcJWVE,30,sailing,test PpLTN658fjo,290,people eating,train PpM8yYTUzkE,2,dog growling,train PpQo4AXuAlc,30,ambulance siren,train PpTFmMSjMqQ,30,whale calling,train PpTFmMSjMqQ,85,whale calling,train PpUMdcZZxlk,34,chicken crowing,train PpXUoR4JgNk,30,playing flute,train PpYX0gZo_Ow,131,gibbon howling,train PpccpglnNf0,51,sheep bleating,train PpdfTLC0Ox4,63,roller coaster running,train Ppdtm-TBi-4,182,"ice cream truck, ice cream van",train Ppdtm-TBi-4,239,"ice 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motorcycle,train PqEzb44fiPE,7,horse neighing,train PqEzb44fiPE,24,horse neighing,train PqLknuoxR4E,10,"child speech, kid speaking",train PqMVDVBKcHw,237,skiing,test PqPZ4idfr8Y,0,"subway, metro, underground",train PqPiOAUxwyU,14,people shuffling,train PqPiOAUxwyU,43,people shuffling,train PqQ9CMXkAIU,13,underwater bubbling,train PqSzzThW06A,10,dog growling,train PqSzzThW06A,34,dog growling,train PqU1rMMwsMw,4,cow lowing,train PqVctp-MUgA,98,dog howling,train PqVctp-MUgA,114,dog howling,train PqW1Gj_Aef4,10,horse clip-clop,train PqYkYaX-8BE,7,bouncing on trampoline,train Pq_R0jmLdOg,51,playing djembe,train Pqaci4LI8Nw,103,tap dancing,train PqdO-QbA1X0,125,people booing,train PqdO-QbA1X0,140,people booing,train Pqe5TGDjyHo,590,wind rustling leaves,train Pqf-jjyJRi4,24,"vehicle horn, car horn, honking",train PqgwjSOMLOo,30,people crowd,train PqkzA4iD4Vg,90,playing piano,train PqlmyFDIeUM,39,playing mandolin,test Pqmf5NnRpEk,30,mosquito buzzing,train PqoogPfdI9o,60,"bird chirping, 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PrJjpNYBXgA,30,cat meowing,train PrLGFyf59oQ,0,fireworks banging,train PrMHt7ZCBsk,140,playing erhu,train PrNaeQohH9g,200,typing on computer keyboard,train PrNcgd-VR4k,50,people nose blowing,train PrPuxo0Veqk,153,beat boxing,train PrSKZWr__XI,102,lions roaring,train PrSKZWr__XI,123,lions roaring,train PrTKTX9kFvo,0,hair dryer drying,train PrTKTX9kFvo,102,hair dryer drying,train PrVROB_aBks,274,playing volleyball,train PrVROB_aBks,510,playing volleyball,train PrWsecbfFeQ,9,pheasant crowing,train PrWy40DYZQU,30,firing cannon,test PrY4HEQPnBk,221,roller coaster running,train PrY4HEQPnBk,231,roller coaster running,train PrYmqZsWLfU,103,lathe spinning,train PrYmqZsWLfU,120,lathe spinning,train PrZB5RVfreA,5,playing castanets,train PrZB5RVfreA,64,playing castanets,train Pr_HvUgeha8,30,"playing steel guitar, slide guitar",train Pr_HvUgeha8,51,"playing steel guitar, slide guitar",train PraRD5bHRBQ,390,ocean burbling,train Prakf1cuKio,193,underwater bubbling,train Prakf1cuKio,212,underwater 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RFtQBf9aUR4,23,dog growling,train RFtQBf9aUR4,105,dog growling,train RFujlj3J23k,119,train wheels squealing,train RFujlj3J23k,214,train wheels squealing,train RFw2FNX1_8Q,30,"engine accelerating, revving, vroom",train RG-IZL3z5FA,10,"railroad car, train wagon",train RG-Tky7XSqw,460,playing electronic organ,test RG09MAO6R0s,8,playing glockenspiel,train RG2sjK0Zsng,19,sheep bleating,train RG31m3LE8iM,97,playing table tennis,train RG3JF7GRfRE,832,tornado roaring,test RG3oorUYydE,160,playing synthesizer,train RG44HB5d5_U,32,warbler chirping,train RG44HB5d5_U,225,warbler chirping,train RG7I0ccOjhg,258,reversing beeps,train RGAwOehgBlo,23,lions roaring,train RGAwOehgBlo,39,lions roaring,train RGB0pMtcGhc,90,playing saxophone,test RGDXPHTlfp0,120,playing cymbal,train RGH3W7WaZtE,30,playing synthesizer,train RGIAV23shwc,58,tap dancing,train RGIBHzedGO8,110,playing bass guitar,train RGK2m-KvHBM,90,dog barking,train RGN-FOoHzPM,107,horse neighing,train RGOjpIPnVFo,96,people eating,train RGOjpIPnVFo,258,people eating,train RGRphncp3jc,22,playing volleyball,train RGRphncp3jc,261,playing volleyball,train RGTWqZoswAo,30,playing didgeridoo,test RGT_P6zLSAc,22,playing badminton,train RGVXAYKxLkI,52,bird wings flapping,train RGWC7icTM_E,31,male singing,test RG_FIFAaO2s,190,playing piano,train RG_FSCaHCrE,317,playing harpsichord,train RG_FSCaHCrE,330,playing harpsichord,train RGalQMCZg8s,30,playing banjo,train RGbFZcXDZJw,8,slot machine,test RGf_iSqR4I0,84,beat boxing,train RGjFmFvOU2M,30,"playing violin, fiddle",train RGjWYrsXkyo,1,lions roaring,train RGjWYrsXkyo,13,lions roaring,train RGjZiguLpSU,30,"subway, metro, underground",train RGmGjwnIaN4,6,sliding door,train RGnRD1Bcqg0,30,"playing violin, fiddle",train RGoMJq3VNns,30,toilet flushing,train RGrYgnF7Ce8,65,"electric shaver, electric razor shaving",train RGsh7tdPxfo,6,bird squawking,train RGtGjM3uSsk,25,playing cymbal,train RGtcWTTG9sg,319,arc welding,train RGurTRr5gOY,0,playing clarinet,train RGurTRr5gOY,49,playing clarinet,train RGwL0-NK8Zk,409,strike lighter,train RGw_L1hqR1k,0,"vehicle horn, car horn, honking",train RGw_L1hqR1k,12,"vehicle horn, car horn, honking",train RGxnyfd4VXU,27,playing timbales,train RGxnyfd4VXU,72,playing timbales,train RH-2breIx-g,58,machine gun shooting,train RH1q8tAbo5k,76,chimpanzee pant-hooting,test RH1sdVjZ0QE,0,"cattle, bovinae cowbell",train RH1sdVjZ0QE,19,"cattle, bovinae cowbell",train RH2Mm7FUEBg,307,playing cornet,train RH2Mm7FUEBg,323,playing cornet,train RH2T6HebvWQ,174,lighting firecrackers,train RH2T6HebvWQ,189,lighting firecrackers,train RH7IVn9jk6k,24,machine gun shooting,train RH7IVn9jk6k,84,machine gun shooting,train RHCSy4sx1hs,45,playing harpsichord,train RHCSy4sx1hs,126,playing harpsichord,train RHDA2lhZzPU,30,playing trumpet,train RHE06zjk5BU,27,electric grinder grinding,train RHE06zjk5BU,68,electric grinder grinding,train RHIiYOH0pfE,44,playing saxophone,train RHIiYOH0pfE,130,playing saxophone,train RHKosalOvNk,187,missile launch,train RHLK3qz-Dug,210,singing choir,train RHMYTbavcOg,30,goose honking,train RHOEkXGi8sE,441,ripping paper,train RHOEkXGi8sE,475,ripping paper,train RHOVVtkE6ao,20,playing piano,train RHSeU23UoV0,30,"playing violin, fiddle",train RHTcPuPlCLE,2,sharpen knife,train RHTcPuPlCLE,20,sharpen knife,train RHTp09_QPDg,245,ice cracking,train RHTp09_QPDg,260,ice cracking,train RHVOwaPojnA,2,"playing steel guitar, slide guitar",train RHVOwaPojnA,148,"playing steel guitar, slide guitar",train RHVSg9xMuGY,30,playing banjo,train RHWT8VqnRGo,27,reversing beeps,train RHYNFIUe7H8,1,canary calling,test RHYVy33kuik,30,people whistling,train RHahPl9Tq0U,55,skiing,train RHbATxP6pic,3,chipmunk chirping,train RHekSxZzOH8,90,"race car, auto racing",train RHfFQSSPmCo,290,cap gun shooting,test RHfuy9GO5NA,150,"railroad car, train wagon",train RHgz_XHqQ1Y,58,people marching,train RHi70Kk_L0o,370,basketball bounce,train RHiFGaNBBXI,170,playing trombone,train RHiZ_L7-Lkc,30,"playing violin, fiddle",train RHmHLI3BRx4,96,roller coaster running,train RHmHLI3BRx4,161,roller coaster running,train RHo32uETLPo,30,orchestra,train RHqJkbRZWd8,33,"vehicle horn, car horn, honking",train RHqJkbRZWd8,68,"vehicle horn, car horn, honking",train RHsOi7PrPrM,30,playing tambourine,train RHtP2X9yFvE,30,"engine accelerating, revving, vroom",train RHv7gZU5x7k,23,playing steelpan,train RHv7gZU5x7k,40,playing steelpan,train RHv99_pQAPE,21,arc welding,train RHxmnJAn9ys,48,playing didgeridoo,train RI01_heS550,50,wind noise,train RI07NUYheXA,90,"child speech, kid speaking",train RI0BybPnNN4,20,printer printing,train RI1Tbj7Lr9Q,538,playing hockey,train RI1vwad55YQ,175,striking bowling,train RI1vwad55YQ,227,striking bowling,train RI2T1BBL_8I,30,people shuffling,test RI2crpgBLYw,468,lathe spinning,train RI5t2xw-6AA,168,playing cornet,train RI5tC5yDY20,7,missile launch,train RI5tC5yDY20,25,missile launch,train RI6JtTcM-Ts,204,dinosaurs bellowing,train RI7oxJ-YTYQ,150,"bee, wasp, etc. buzzing",train RI7tkiBOaGI,40,lions growling,train RI7yRZpOCAQ,73,playing harmonica,train RI9hqFJXGLw,80,orchestra,train RIBHgoib6Z0,30,"engine accelerating, revving, vroom",train RIF75FsxLxA,30,cap gun shooting,train RIG9kbNMsgA,65,playing volleyball,train RIGQjzXbvv4,140,volcano explosion,test RIHfqLujZw0,60,playing electronic organ,train RIIReItxKso,0,people clapping,train RIIjP8gOxTQ,110,playing hammond organ,train RIJT4Vzy40A,16,playing bassoon,train RIKqRVSx2-w,0,"cattle, bovinae cowbell",train RIKqRVSx2-w,80,"cattle, bovinae cowbell",train RILIg_iO94A,91,playing hockey,train RILIg_iO94A,142,playing hockey,train RIMBQ0oZodU,233,slot machine,train RIMncNmZoZA,30,people babbling,train RIP-02Nt6Q0,90,singing bowl,train RIQzc8Gx0q4,26,electric grinder grinding,test RIS3__VRdLg,365,eletric blender running,train RIazUtLxjyA,22,roller coaster running,train RIazUtLxjyA,35,roller coaster running,train RIerEKqkVgU,190,playing cymbal,train RIhDeDI4eaY,87,bouncing on trampoline,train RIhDeDI4eaY,112,bouncing on trampoline,train RIlsjOn1gFE,90,chainsawing trees,train RIplVUGwgjo,157,train whistling,train RIplVUGwgjo,192,train whistling,train RIrwRBosuIw,0,underwater bubbling,test RIs-uHCS4Co,298,playing table tennis,train RIt26vGVwVA,30,people crowd,train RIuHB7VURyk,30,people babbling,train RIx7OxBoqh0,32,planing timber,train RIx7OxBoqh0,43,planing timber,train RJ-L831MBhc,0,penguins braying,train RJ3CLEZmDWA,30,orchestra,train RJ44KbKJek0,30,turkey gobbling,train RJ68N9KDYIA,10,ocean burbling,train RJ7w4kth6S8,230,playing clarinet,train RJ9QIXwDJAQ,0,people screaming,train RJAeqAoTsu0,8,people cheering,train RJAeqAoTsu0,32,people cheering,train RJDiKLC5HIw,0,playing didgeridoo,train RJDiKLC5HIw,106,playing didgeridoo,train RJFMqcRLz0I,76,lions growling,train RJFdWGEdzP4,111,fox barking,test RJFdWGEdzP4,160,fox barking,test RJFuksJPGYU,220,opening or closing drawers,train RJIQdBdH4d0,465,"heart sounds, heartbeat",train RJIQdBdH4d0,487,"heart sounds, 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RJfWYY9BNEQ,263,playing tabla,train RJg2vDsysbA,30,typing on computer keyboard,train RJgZGJ1AdMw,30,ambulance siren,train RJkLEW8g8lw,30,"subway, metro, underground",train RJlF5YycahI,408,francolin calling,test RJmH-Tx9XuQ,145,playing tennis,test RJnlFcqsLsg,3,baby crying,train RJnou9A2OKc,30,typing on computer keyboard,train RJosrxUnqxQ,5,tap dancing,train RJrJAyUZwc4,40,dog bow-wow,train RJtfFAk0c-0,1,"ice cream truck, ice cream van",train RJtn6Td3rco,68,missile launch,test RJv6517esW8,62,tapping guitar,train RJwnZT7tuZY,0,playing bass drum,train RJwnZT7tuZY,50,playing bass drum,train RJyEc4h54Tw,30,people crowd,train RJygciSu91w,84,playing vibraphone,train RJygciSu91w,160,playing vibraphone,train RK-e59tROJM,0,playing didgeridoo,train RK-e59tROJM,4,playing didgeridoo,train RK3Fqnmo3rs,6,cow lowing,train RK4gmRmuQJ8,130,lathe spinning,train RK7JrYO4joM,510,driving buses,train RK7JwiiFHek,4,playing clarinet,train RK7JwiiFHek,71,playing clarinet,train RK8UJgvsFks,58,playing bass 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sewing machines,train RL5BRO_e1g4,21,bouncing on trampoline,train RL8F6782uuo,14,"heart sounds, heartbeat",test RL9xBAFVHWA,208,tractor digging,train RLBROHO86No,121,people burping,train RLC6BxdpsqM,150,church bell ringing,test RLFF-Tstiy0,230,"motorboat, speedboat acceleration",train RLJ3UfB1ZNc,64,playing hammond organ,train RLJYigWchf0,30,wind noise,test RLL4p_0Yyf8,399,playing mandolin,train RLL4p_0Yyf8,411,playing mandolin,train RLLwqA3eoXY,269,playing timpani,train RLQQRxMAbbU,1,playing didgeridoo,train RLTjJkSYes0,352,people marching,train RLUx3cmOnME,57,tap dancing,train RLVJCUALSQI,78,vacuum cleaner cleaning floors,train RLVJCUALSQI,88,vacuum cleaner cleaning floors,train RLWuHd5z9JQ,56,beat boxing,train RLYTxLkwwv0,24,bull bellowing,train RLYTxLkwwv0,227,bull bellowing,train RLZrbPGux3k,30,mynah bird singing,train RL_wAS82diQ,40,car engine starting,train RLazzGrRLlI,42,playing darts,train RLe18UDaOQY,83,playing trumpet,train RLe18UDaOQY,162,playing trumpet,train RLeF2krlYvE,5,people sniggering,train RLiHf1zIuXY,50,baby laughter,train RLij2FHY6VE,9,dog bow-wow,test RLjMRHiY0UU,0,"vehicle horn, car horn, honking",train RLkcrup70I8,260,driving buses,train RLmj4N2JP44,139,people farting,train RLnAIkBccZk,6,penguins braying,test RLoW9yQW5K4,30,"railroad car, train wagon",train RLu3qvQBUuo,18,baby crying,train RLv1HWLWjRo,0,people farting,train RLx4EkCzkd8,30,playing clarinet,train RLygFh12Y2c,0,train horning,train RM0t3n_tO0w,5,running electric fan,train RM0t3n_tO0w,22,running electric fan,train RM4Axre_16E,40,playing bass guitar,train RM4Kawctw68,43,skateboarding,train RM4Kawctw68,76,skateboarding,train RM5hQ3Qtf-8,20,cat purring,train RM5hQ3Qtf-8,32,cat purring,train RM6uf-sdVQI,43,playing bass drum,train RM6uf-sdVQI,107,playing bass drum,train RM7NRwcFVLU,33,playing cello,train RM7qL9gXDLM,0,wind noise,train RM8hTwrdvPQ,0,playing harmonica,train RM8hTwrdvPQ,2,playing harmonica,train RMD49rgtDC8,91,sliding door,train RMDifbpWFsY,30,baby 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RM_z2dAI0hY,115,playing bass drum,train RM_z2dAI0hY,209,playing bass drum,train RMcpZd3YpfA,23,train horning,train RMcpZd3YpfA,33,train horning,train RMhxg8dOPrI,150,ocean burbling,train RMjwlqXpizs,30,playing piano,train RMkMmbCwScE,110,ocean burbling,train RMlHPm-nrL4,20,playing banjo,train RMmULmkjwZM,30,playing drum kit,train RMme7Xc39kI,213,playing tympani,train RMnNSgRaoo0,30,"playing violin, fiddle",train RMoVhwyMZyU,350,driving motorcycle,train RModQ5O_s0M,40,playing bass guitar,train RMp8ph0hbUk,6,playing harmonica,train RMp8ph0hbUk,95,playing harmonica,train RMsqMhneVl4,10,people screaming,train RMtEFzM9AqA,13,chimpanzee pant-hooting,train RMtEFzM9AqA,25,chimpanzee pant-hooting,train RMu8nI6HSP4,4,people marching,train RMu8nI6HSP4,14,people marching,train RMu_zoagGU8,109,"bee, wasp, etc. buzzing",train RMu_zoagGU8,128,"bee, wasp, etc. buzzing",train RMw45VAQ_Go,60,printer printing,train RMxuJgz9WTg,272,forging swords,train RMxuJgz9WTg,287,forging swords,train RMyI354P9Ho,30,people sniggering,train RMzeKw2V0Mg,20,playing cello,train RN0DNglJ-cQ,0,police car (siren),train RN0SO5Q4_SQ,16,goose honking,train RN1Zb10P-Uw,12,squishing water,train RN1Zb10P-Uw,40,squishing water,train RN1ho4G-W0o,40,wind noise,test RN3Lw-GLXrY,9,canary calling,train RN3Lw-GLXrY,37,canary calling,train RN3mbS_-xi8,90,male singing,train RN45OKuPBLc,43,rope skipping,train RN7AAPoGUl4,30,dog bow-wow,train RN7NH6WaEa0,0,dog baying,train RN96eLdMN_I,5,bull bellowing,train RN96eLdMN_I,45,bull bellowing,train RN9vY8QehsQ,0,bathroom ventilation fan running,train RNASIFWJNsg,30,"motorboat, speedboat acceleration",train RNBLlPAxhVA,0,playing ukulele,train RNBLlPAxhVA,50,playing ukulele,train RNBoH2LHQEM,370,people clapping,train RNGfqFe1vgk,480,playing drum kit,train RNGji8RfRXQ,30,playing french horn,train RNHgQNpT0iY,104,singing choir,train RNHgQNpT0iY,117,singing choir,train RNJ0kG5-df4,324,playing didgeridoo,train RNLFSKy0xhA,8,dog barking,train RNVhw-I8iXc,110,"bird 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RNo8OpI4XEg,20,"race car, auto racing",train RNrTjwhrzSk,7,people eating crisps,train RNtVMEEOOLQ,30,duck quacking,train RNu0msBwvqg,459,playing snare drum,train RNu0msBwvqg,567,playing snare drum,train RNuJWhPdHpk,43,mosquito buzzing,train RNvIxiLiuV0,199,fire truck siren,train RNwfG-x2iRk,30,baby laughter,train RNxwurjyY10,140,people hiccup,train RNzEhD2IC1c,90,fireworks banging,train RO1j8CM0FaQ,0,chicken clucking,train RO9MSicoRT0,303,opening or closing drawers,train RO9MSicoRT0,388,opening or closing drawers,train ROAFt1tNLu0,5,turkey gobbling,train ROAFt1tNLu0,16,turkey gobbling,train ROBegNai4Fw,3,tap dancing,train ROCl79fqrSM,519,playing tennis,test RODFiuImAak,88,baby babbling,train RODFiuImAak,146,baby babbling,train ROE6mMMp9Zw,30,dog bow-wow,train ROEsrCtBv0U,30,orchestra,train ROEtA4E43rc,5,mosquito buzzing,train ROEtA4E43rc,18,mosquito buzzing,train ROJ1PJrZidM,20,people screaming,train RONnDnp1dpU,26,playing table tennis,train ROXS_MAMaxI,44,yodelling,train ROYibXMMJVo,7,wind chime,train ROaYj8o3Tic,0,dog barking,train RObYRwt7b8U,189,playing tambourine,test ROei6zu0KVk,30,stream burbling,train ROevIcvVKAQ,28,frog croaking,train ROex7e3gCE8,10,frog croaking,train ROex7e3gCE8,44,frog croaking,train ROgb-PGx_l0,1,playing volleyball,train ROgb-PGx_l0,32,playing volleyball,train ROhlD0G2_ho,17,wind rustling leaves,test ROnDOA_R0DE,35,people booing,train ROnDOA_R0DE,139,people booing,train ROootH-mtEI,130,stream burbling,train ROsAOQe62gs,50,playing electronic organ,train ROsVbzFlnp0,24,bathroom ventilation fan running,test ROsXWMvGiJA,207,playing sitar,train ROsXWMvGiJA,721,playing sitar,train ROt9hElCgXI,220,singing choir,train ROtaeOZoTqU,196,train wheels squealing,test ROtmQ7XKE-A,90,basketball bounce,train ROxBQbgudVI,321,police radio chatter,train ROxBQbgudVI,338,police radio chatter,train RP-QSTvP4WA,43,warbler chirping,train RP02g6BZazY,30,"playing violin, fiddle",train RP1QQR3vICo,72,playing oboe,train RP3r2le3wt8,51,playing cornet,train RP40MftqkRk,67,chopping food,train RP4abiHdQpc,2,baby laughter,train RP4abiHdQpc,28,baby laughter,train RP5mput8V9A,0,frog croaking,test RP6-ST5JR0Q,90,using sewing machines,train RP6FPSbnjbk,65,otter growling,train RP6FPSbnjbk,128,otter growling,train RP8JCcPSSxQ,22,lighting firecrackers,train RP8PNWMKYyA,18,ambulance siren,train RP8dkYgavQI,198,dog baying,train RP8dkYgavQI,210,dog baying,train RPA-QYPV_os,30,fire crackling,test RPD2qc5zN9w,230,playing french horn,train RPGK0rIy8NM,50,waterfall burbling,train RPHCOGFASX8,142,popping popcorn,train RPIR8nwhLJ8,27,sliding door,train RPJ2qonuYlQ,30,"pigeon, dove cooing",train RPJcsFuGTEo,30,cattle mooing,train RPM1Wg4kzZw,50,dog howling,train RPM1Wg4kzZw,72,dog howling,train RPMicSPIAlg,30,"female speech, woman speaking",test RPMqBN6rWow,30,bird wings flapping,train RPN3yIkJkg4,217,electric grinder grinding,train RPN3yIkJkg4,230,electric grinder grinding,train RPWDMb2iYVE,52,playing tambourine,train RPczE44xuDw,10,"playing 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guitar,train RQ4Yrgukqkw,80,"rowboat, canoe, kayak rowing",train RQ51sHzSQdo,158,playing table tennis,train RQ6o5C5KoxA,330,beat boxing,train RQ85Tkl8i2k,8,fireworks banging,test RQ8idRIzvQM,20,wind noise,train RQ95RtzIBYc,25,airplane,train RQAULiyzMUs,106,slot machine,train RQCzIGuypaU,0,civil defense siren,train RQDtL6TpUiI,70,dinosaurs bellowing,train RQDtL6TpUiI,99,dinosaurs bellowing,train RQFySnj-NbM,67,playing clarinet,train RQHZOYLB5bA,30,"motorboat, speedboat acceleration",train RQHsbSHEnYc,9,running electric fan,test RQIVLqDLQzA,50,using sewing machines,test RQInNBTsxYI,30,playing cello,train RQJO4qaFu7g,230,"railroad car, train wagon",train RQJTMfIDOmY,30,driving buses,train RQKOPOY23SM,90,"race car, auto racing",train RQKlFpOesFA,40,playing clarinet,train RQMNAMGIKWk,37,playing french horn,train RQMNAMGIKWk,64,playing french horn,train RQMUz0NFx6o,420,"playing marimba, xylophone",test RQOmNv-vysE,8,police car (siren),train RQOzaqt9IIY,30,dog barking,train RQRipbhO86I,40,playing volleyball,test RQVQcJf-yVU,30,"playing violin, fiddle",train RQXRGp0NDCI,541,scuba diving,train RQXRGp0NDCI,595,scuba diving,train RQXyVYZV8jk,28,magpie calling,train RQXyVYZV8jk,38,magpie calling,train RQ_Tg4wGbk0,309,playing clarinet,train RQ_Z6j5gsS0,69,beat boxing,train RQbHlgbL-RM,20,sailing,train RQdSNk4ASCo,410,"railroad car, train wagon",train RQeXcKxVraY,17,typing on computer keyboard,train RQgs7m3gkgU,157,playing double bass,train RQgs7m3gkgU,1007,playing double bass,train RQkrgWSEqY0,3,playing guiro,train RQkrgWSEqY0,13,playing guiro,train RQmdXi_s9_o,0,playing acoustic guitar,train RQniUyKnRWQ,24,owl hooting,train RQniUyKnRWQ,35,owl hooting,train RQo7HHPTihg,0,helicopter,train RQqhMzWrdtY,0,ambulance siren,train RQuN7usNGdE,110,orchestra,train RQuX0B0-AwQ,195,playing cello,train RQvPU8_aL7o,30,playing harpsichord,train RQvbm3l3wmA,110,playing drum kit,test RQw2ENh3eBc,86,missile launch,train RQxDepmgGnc,30,people running,train RQyYOppVLCI,30,"rowboat, canoe, kayak rowing",train RR09SGoMmoE,30,"pigeon, dove cooing",train RR1Z1-YVE_M,70,helicopter,train RR46q_jvw8A,30,"bird chirping, tweeting",train RR5BtXP0s0o,74,machine gun shooting,train RR5BtXP0s0o,214,machine gun shooting,train RRAIWVRaTU4,453,playing tympani,train RRAIWVRaTU4,474,playing tympani,train RRAw_ogOfrk,0,people farting,test RRBCpehbzBE,18,playing volleyball,train RRCEWfZl3nA,13,playing bugle,train RRDABWhg-xI,52,yodelling,train RRFgyPeRPuI,141,singing bowl,train RRGP-oQRnjU,30,"playing marimba, xylophone",train RRGrXBMxMmw,130,ocean burbling,train RRHBZU6NEjY,7,dog growling,train RRHNu65iY6U,192,playing badminton,train RRL-uTwfh6E,56,playing djembe,train RRMnkWKIEGQ,40,"heart sounds, heartbeat",train RRMnkWKIEGQ,52,"heart sounds, heartbeat",train RRMut97mg2A,25,"motorboat, speedboat acceleration",train RRNdA_RwzBQ,57,woodpecker pecking tree,train RRNdA_RwzBQ,76,woodpecker pecking tree,train RRSFuzQR9VU,2,baby babbling,train RRSFuzQR9VU,19,baby 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RSbO6Dy3qU8,18,playing hockey,train RSe-Ci8oSlg,183,baby babbling,train RSfCgL4THgk,30,"motorboat, speedboat acceleration",train RSfVmslc_b8,140,playing bagpipes,train RSgmATBcCwE,110,cow lowing,test RShU_-BXj-s,44,francolin calling,train RShU_-BXj-s,69,francolin calling,train RSoyWV3tYDQ,30,playing cello,train RSr8O3CELbQ,13,skiing,train RSr8O3CELbQ,28,skiing,train RSrEgP7hMWo,17,baby babbling,train RSrEgP7hMWo,37,baby babbling,train RSryuuvUfDM,50,playing acoustic guitar,test RSsewzGIpDY,90,ocean burbling,train RSvhifzdmQM,163,playing harpsichord,train RSvuhSQuSRc,156,playing double bass,test RSxSeOkQ6xY,47,playing harpsichord,train RSxSeOkQ6xY,225,playing harpsichord,train RSysEonRPj4,20,splashing water,train RSz1MKGjY1U,60,fire truck siren,train RSz1MKGjY1U,95,fire truck siren,train RSz9M9RgcP8,250,beat boxing,train RSzoXOX0iIU,137,train wheels squealing,train RT-ecxduz3o,30,"rowboat, canoe, kayak rowing",train RT234P5SzJo,460,lions roaring,test RT3u7H2XWjM,417,playing cymbal,train RT4oQOc2-eI,30,orchestra,train RT6TY-5SzMI,230,people clapping,train RT8_iyDgFnI,20,playing electric guitar,train RTBkCAIhyH8,120,people cheering,train RTD6V07bMtY,29,snake hissing,train RTDWS0Xa6UE,40,playing saxophone,train RTDr2L_OT0M,30,printer printing,test RTHhGX4lb6g,0,dog whimpering,train RTJ3-PrJi-o,82,fire crackling,train RTJB7hzZtBo,16,people booing,train RTLGAJZ8WzM,219,airplane,test RTNawnsw_OU,11,police radio chatter,train RTNawnsw_OU,75,police radio chatter,train RTPvz6DEWXc,21,people sniggering,train RTQrEpoyKpc,20,"child speech, kid speaking",train RTSi-s8uzy4,11,playing harp,train RTSuByDiDo4,1,lions roaring,train RTSuByDiDo4,11,lions roaring,train RTVmWmh82Z4,27,playing didgeridoo,train RTXYG9NoOKY,67,mynah bird singing,train RTXYG9NoOKY,92,mynah bird singing,train RTYGX9tCQU0,260,playing double bass,train RTYPgxVrwIc,20,playing electric guitar,train RTZ-x1tRhjQ,10,playing bass guitar,train RTZ0uoKFahw,493,people slurping,train RTZ0uoKFahw,528,people slurping,train RTZN_SN-YLg,39,lighting firecrackers,train RTZN_SN-YLg,252,lighting firecrackers,train RT_MG7uPvgg,3,playing sitar,train RTbHdYJeRO0,70,people farting,train RTdHyXvkOOw,2,coyote howling,train RTdHyXvkOOw,13,coyote howling,train RTdSUXLwGb4,30,people clapping,train RTfIJgpMsMM,121,electric grinder grinding,train RTfIJgpMsMM,189,electric grinder grinding,train RTn4k8cOc6I,85,playing cornet,train RTn4k8cOc6I,123,playing cornet,train RTrJjdiTYWA,280,people eating noodle,train RTtc-QplpWk,1,lions roaring,train RTtc-QplpWk,11,lions roaring,train RTx4ndKeIKU,65,toilet flushing,train RU0w5Tb_8JY,260,playing piano,train RU1VfnGOdAU,46,dog growling,train RU20kqorozs,30,playing tennis,train RU597QkD7_8,0,singing bowl,train RU5bDSJvNfE,70,playing bassoon,train RUAomPTkvYM,100,mynah bird singing,test RUKBMBlXrTM,10,people sneezing,train RUKBMBlXrTM,28,people sneezing,train RUNozQyH5_A,17,dog barking,train RUS-pW7rfnU,137,missile launch,train RUS-pW7rfnU,162,missile launch,train 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SGKYmf3POkE,48,lip smacking,test SGMxxqGj8jM,230,tap dancing,train SGMy2Zsmq-E,94,pheasant crowing,train SGN0K4eAdm8,4,cheetah chirrup,train SGN0K4eAdm8,63,cheetah chirrup,train SGO2_vZyp5I,4,playing bugle,train SGO2_vZyp5I,22,playing bugle,train SGU2KnZeRyo,4,playing table tennis,train SGWo9F3NbCE,377,cheetah chirrup,test SG_aifKvyj4,222,sharpen knife,train SG_aifKvyj4,249,sharpen knife,train SGaIIMZ3hzg,80,"bee, wasp, etc. buzzing",train SGaIvgwwWSE,30,thunder,train SGaQSzRTmNY,432,people slurping,train SGaQSzRTmNY,443,people slurping,train SGa_Tx6dAk0,25,car engine idling,train SGb_Uw_pKzk,70,bird wings flapping,test SGdSYIOgeSg,30,chainsawing trees,train SGf6c2DG8RM,102,frog croaking,train SGf6c2DG8RM,116,frog croaking,train SGkzdDWFIHI,53,playing bass drum,train SGkzdDWFIHI,85,playing bass drum,train SGnZxcS7VKA,10,playing trumpet,test SGopC095eoE,26,fire crackling,train SGrV4P_O5Sw,150,playing banjo,train SGs-Nqs39gc,22,playing tambourine,train SGs-Nqs39gc,117,playing 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SHl6GRDG7hg,140,helicopter,train SHm4zS66WB4,127,people burping,train SHmReBlUtPU,67,rope skipping,train SHmReBlUtPU,130,rope skipping,train SHo4tB6Tu08,30,"pigeon, dove cooing",train SHqf5widI0w,30,ocean burbling,train SHrzYaxWGoM,3,playing volleyball,train SHsEVcouAFw,0,train horning,train SHsUuLkcOD0,78,playing badminton,train SHtmd5K46Qg,130,playing cello,train SHuQTeX-3Ms,209,wind chime,test SHukm5yoMQ0,287,typing on typewriter,train SHukm5yoMQ0,377,typing on typewriter,train SHzZp_qdKD8,588,lip smacking,train SHzZp_qdKD8,1045,lip smacking,train SI00PE02Aaw,12,sheep bleating,train SI0QKrIOp-A,115,playing steelpan,train SI0QKrIOp-A,163,playing steelpan,train SI1JZVA6oMg,86,mouse clicking,train SI3bGPXfBew,40,"engine accelerating, revving, vroom",train SI76XSPWeW0,0,duck quacking,train SI76XSPWeW0,29,duck quacking,train SIBvsE1K7GE,0,chicken crowing,train SILr2thbbfo,0,"railroad car, train wagon",train SIO0ArR1pBo,25,playing snare drum,train SIO0ArR1pBo,52,playing snare drum,train SIOke4cxjTQ,10,police car (siren),train SIOvpobbwS4,50,people babbling,train SIPdfY5W2LM,40,playing saxophone,train SIQd9SsOWA8,248,ice cracking,train SISMQT3T9R4,6,door slamming,train SIV8Ll6-Rzs,10,people gargling,train SIVfUkxEhsM,6,chinchilla barking,train SIVfUkxEhsM,18,chinchilla barking,train SIWbjgPYcJY,22,otter growling,train SIWbjgPYcJY,33,otter growling,train SIWdClR0dJI,57,people sneezing,train SIYOznYuaDE,109,playing didgeridoo,train SIZ5WUVzAMk,216,driving snowmobile,test SIZ6WkNXP0c,33,sharpen knife,train SIZ6WkNXP0c,52,sharpen knife,train SIZhuyT1g6w,30,"rowboat, canoe, kayak rowing",train SIc2mWVfFfY,96,lathe spinning,train SIfqy_WU-pE,161,playing bongo,train SIilbRdyIDc,160,basketball bounce,train SIjpy6DoB4Q,150,chicken crowing,train SIk0-1qWui0,30,"playing violin, fiddle",train SImCS0yjnRY,50,lawn mowing,train SIv3rhii510,175,lighting firecrackers,train SIv3rhii510,190,lighting firecrackers,train SIvBuk5xiCc,21,people booing,train SIvRaJ-0vy0,132,playing table 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SJSevhQs-VM,389,mouse clicking,train SJSpVMcz7HU,148,rope skipping,train SJUCzO7kOEc,100,police car (siren),train SJUl98voKZo,3,woodpecker pecking tree,test SJVJCfXMxtw,32,playing mandolin,train SJVJCfXMxtw,47,playing mandolin,train SJVuopldMyU,30,"bird chirping, tweeting",test SJWHCN-JAWo,76,writing on blackboard with chalk,train SJWHCN-JAWo,350,writing on blackboard with chalk,train SJX2-RXK8z8,30,playing cello,train SJZEAJLA1HM,251,playing badminton,train SJajgv9hhJM,140,playing accordion,train SJc1x4NWbr4,250,playing cello,train SJdTgAQJhRo,0,people sobbing,train SJg5k-CJpww,246,airplane flyby,train SJgIQHuEETA,65,chopping wood,train SJk-ifLO5Xo,40,playing drum kit,train SJpEIKnO9U4,95,police radio chatter,train SJpupJFyWEQ,80,playing synthesizer,train SJqqDBeKZwk,15,raining,train SJr33LjDyPM,25,baltimore oriole calling,train SJrVTQPbMXs,16,"fly, housefly buzzing",test SJws8LT72OA,550,church bell ringing,train SJyqw3w0rUA,47,"cattle, bovinae cowbell",train SK-8Kma2u-o,243,beat boxing,train SK0COQEuo-0,37,people eating apple,train SK3RWNJvBVg,10,people belly laughing,train SK3n2xCz8nQ,110,smoke detector beeping,train SK5f-F_j-bs,33,lathe spinning,train SK5f-F_j-bs,43,lathe spinning,train SK68J_xCvhA,60,stream burbling,train SKArI_nysos,3,roller coaster running,train SKArI_nysos,16,roller coaster running,train SKBT4d1cvbc,60,playing electric guitar,train SKDAS1s7p0I,18,dog growling,train SKDozX6K6Sw,11,playing double bass,train SKDozX6K6Sw,25,playing double bass,train SKFW6ede0dk,30,orchestra,train SKG3d6xTtrY,0,lawn mowing,train SKGSYhcoOgI,30,"pigeon, dove cooing",train SKGwJqVfvXQ,95,people eating noodle,train SKGwJqVfvXQ,197,people eating noodle,train SKK-sj5krTc,73,people booing,test SKKwIcjv-5g,30,people coughing,train SKOPcKu1VK0,11,playing badminton,train SKPwClfO2po,210,playing cello,train SKQyhdeYZlg,10,slot machine,train SKRc_yI3FYM,129,airplane flyby,train SKRc_yI3FYM,190,airplane flyby,train SKT5ts1R-F4,121,parrot talking,train SKTxtJL3yWU,410,playing cello,train SKUwqSACWzU,105,people booing,train SKVkkt-yDCU,27,"electric shaver, electric razor shaving",train SKVkkt-yDCU,50,"electric shaver, electric razor shaving",train SKW-XMrygaQ,0,people sobbing,train SKWgjwBVgPg,0,dinosaurs bellowing,train SKXw4tcWw_k,265,using sewing machines,train SKXw4tcWw_k,396,using sewing machines,train SKYGG_WdRz0,56,beat boxing,train SKZYwt_cyfw,110,female singing,train SKbw-7f7msw,300,people sniggering,train SKdeZ137uJQ,9,machine gun shooting,train SKeSRDMRpY0,240,sharpen knife,train SKeSRDMRpY0,254,sharpen knife,train SKkg53r4F84,21,people shuffling,train SKpVSv3oUc8,20,sliding door,train SKpbq2B8J_c,40,driving motorcycle,train SKpxQWl_6CY,0,"bee, wasp, etc. buzzing",train SKso0pzsrHE,0,parrot talking,train SKso0pzsrHE,29,parrot talking,train SKt_3Nv2DHo,30,pig oinking,test SKvS_18cQXE,180,tap dancing,train SKwiRhAqagU,16,playing electronic organ,train SKwiRhAqagU,198,playing electronic organ,train SKz0l2kMBXc,35,playing synthesizer,train SKz0l2kMBXc,59,playing synthesizer,train SKz3Ga23W7Q,42,fireworks banging,train SKzerhkLfiE,30,"child speech, kid speaking",train SL0guolHL4I,5,arc welding,train SL0guolHL4I,22,arc welding,train SL2dGtsWBxQ,50,playing trombone,train SL3wB5sDcdw,350,wind rustling leaves,train SL42CbatTWc,29,"heart sounds, heartbeat",train SL42CbatTWc,147,"heart sounds, heartbeat",train SL4nhUGSYIM,201,playing snare drum,train SL52ykhLo8A,70,playing piano,test SL5xjLgQp1c,70,playing flute,test SL6NGiHGsE0,78,playing volleyball,train SL6euTEK8zA,220,"child speech, kid speaking",train SL8DCasPm0Q,51,playing djembe,train SL8DCasPm0Q,92,playing djembe,train SL8rg2qeD0M,30,playing bass drum,train SL90sfXc-jQ,42,people booing,test SLAXSx4prwU,91,playing sitar,train SLAmK__qyXM,10,firing cannon,test SLDq23GJYOI,18,"playing steel guitar, slide guitar",train SLDq23GJYOI,64,"playing steel guitar, slide guitar",train SLE9CGDgvC4,220,playing clarinet,train SLHUqeriDwQ,30,cat purring,train SLNrw88b6rE,17,pheasant crowing,train SLNrw88b6rE,28,pheasant crowing,train SLQAve85lDU,188,disc scratching,test SLZ2O9m7OQE,30,"playing violin, fiddle",train SLajIWg3BY8,94,chopping food,test SLajIWg3BY8,117,chopping food,test SLc5_3K9TDI,10,"vehicle horn, car horn, honking",train SLcBc1uVLxA,570,"female speech, woman speaking",train SLfgyNBabbA,550,"race car, auto racing",train SLfww_Lj2dM,4,airplane flyby,train SLg9tx3N5w0,160,playing trombone,train SLl3-jvTmBk,68,hammering nails,train SLnB-o8xNK0,7,dog howling,train SLnB-o8xNK0,17,dog howling,train SLrb_8ksUq4,0,sea lion barking,train SLsFOd-Xv1o,0,fireworks banging,train SLx_FzNfOTI,67,playing bongo,train SLx_FzNfOTI,82,playing bongo,train SM10Hrl5d7U,10,playing electronic organ,test SM3HpMR5mXk,150,child singing,train SM5Hw63E2GU,11,planing timber,train SM5Hw63E2GU,418,planing timber,train SM5NmE7h9dM,206,lighting firecrackers,train SM6m56nzTWM,7,pheasant crowing,test SM7-EH2-Qm4,130,lawn mowing,train SM756Ki9S9g,164,people cheering,test SMAOB1Wt6QQ,406,playing tennis,test SMAOB1Wt6QQ,426,playing tennis,test SMAUvuW-7U4,30,train horning,train SMCq6c5-ImY,59,arc welding,train SMFXX3-7QgA,30,playing saxophone,train SMHB9zDEhrQ,496,train whistling,train SMJp9PeDE_c,1,dog whimpering,train SMKCFYD7OMI,30,chainsawing trees,train SMKiX8loOGE,67,missile launch,train SMMbpFMAF2o,185,playing glockenspiel,train SMNZtLC7GC4,3,air conditioning noise,train SMOdtkgnssc,30,"rowboat, canoe, kayak rowing",train SMQLAAE5z54,65,playing timbales,train SMQLAAE5z54,159,playing timbales,train SMQh5_vTk7k,30,"female speech, woman speaking",train SMSG3bbiFY8,119,playing badminton,train SMXSbe7ESgY,305,cutting hair with electric trimmers,train SMXSbe7ESgY,354,cutting hair with electric trimmers,train SMYv226ofrY,50,civil defense siren,train SMaUgHxx_dY,25,beat boxing,train SMbBYR_lplE,48,playing harpsichord,train SMbBYR_lplE,150,playing harpsichord,train SMeEI7c4O0E,100,helicopter,train SMfjUon5v7c,40,crow cawing,train SMfjUon5v7c,52,crow cawing,train SMjUdE9YXXU,0,opening or closing drawers,train SMkVCM9GfO8,30,playing harpsichord,test SMo3neynJU4,435,alarm clock ringing,train SMsaGSsiJcg,44,slot machine,train SMsmM0pznrI,0,people burping,train SMt0CPjfNaU,30,people whispering,train SMw-s0vINYM,434,people eating,train SMw-s0vINYM,951,people eating,train SMw6bAeB684,360,"child speech, kid speaking",train SMwDCvHSQnU,40,"subway, metro, underground",train SMzqiVdkooM,30,playing vibraphone,train SN12UrzADw4,280,playing harp,train SN4Aii-9FtA,77,playing washboard,train SN5biaYaHP8,190,"child speech, kid speaking",train SN8DrnnFJpg,3,people booing,train SN8pdPVZED0,6,tap dancing,train SNA6ceTxJTQ,50,people whistling,test SNA9thaHvM8,24,playing timbales,train SNA9thaHvM8,56,playing timbales,train SNBAaiLZqfY,82,people marching,train SNBAaiLZqfY,334,people marching,train SNBh5fJRP-k,30,"playing violin, fiddle",train SNFhscI0RMM,14,striking bowling,train SNJ7FtAEMJc,80,using sewing machines,train SNLYL1xH6ms,15,"electric shaver, electric razor shaving",train SNLYL1xH6ms,29,"electric shaver, electric razor shaving",train SNOTWjL9n1A,53,coyote howling,train SNOWQ3aba30,60,sliding door,train SNOWQ3aba30,119,sliding door,train SNPuQKAKLd4,270,helicopter,train SNQK5gkqktw,30,"rowboat, canoe, kayak rowing",train SNSNCkqwgn0,18,cuckoo bird calling,train SNSNCkqwgn0,94,cuckoo bird calling,train SNSrsP-J3NY,70,toilet flushing,train SNVGPmo1xuA,3,woodpecker pecking tree,train SNWYi_Sxb4c,120,cattle mooing,train SNWsSV6CY5k,98,playing table tennis,train SNXyA1AncDc,320,"bee, wasp, etc. buzzing",train SNY3nC_2Wso,150,playing cello,train SNY5uEPwgh4,210,people crowd,train SNZOpdPhp9Q,100,"female speech, woman speaking",train SNanDCQuatA,189,wind chime,train SNbQWJoCvo8,0,playing tennis,train SNbQWJoCvo8,28,playing tennis,train SNbeOaDj648,0,mouse clicking,train SNbeOaDj648,164,mouse clicking,train SNc93-hbKjw,26,smoke detector beeping,train SNc93-hbKjw,45,smoke detector beeping,train SNhKNaH47bU,295,tap dancing,train SNhfvhWPXNc,30,playing electric guitar,test SNiIBZBoVT4,124,chimpanzee pant-hooting,train SNjarxyIjlA,150,"male speech, man speaking",train SNl6ebKdLXc,72,mouse clicking,train SNl6ebKdLXc,358,mouse clicking,train SNmLa_TnyE8,206,lathe spinning,train SNmLa_TnyE8,247,lathe spinning,train SNndVYIMO-s,129,sharpen knife,train SNndVYIMO-s,158,sharpen knife,train SNoMRiUTxqc,136,playing didgeridoo,train SNp2MhaGueY,30,"playing violin, fiddle",train SNrwSjGGtUI,30,baby crying,train SNsWpmHpGAU,89,fire truck siren,train SNsWpmHpGAU,254,fire truck siren,train SNt6zvI-Qzo,30,people clapping,test SNunid-6FhI,170,ambulance siren,train SNyrF4o3kpA,30,"pigeon, dove cooing",train SO-ozEm3N2k,52,baby laughter,train SO1FskSFulg,22,dog howling,train SO1FskSFulg,32,dog howling,train SO1SiqH6H9w,523,"heart sounds, heartbeat",train SO4etSgwPVE,10,people coughing,train SOA7hn1VH50,140,playing bagpipes,train SOAp5DLI8yg,200,cat purring,train SOAp5DLI8yg,239,cat purring,train SOCnHo6nlrQ,209,wind chime,test SOFo5qUBwCA,110,eating with cutlery,train SOIEYuj6XQc,0,car engine knocking,train SOIzWlAQeio,29,volcano explosion,train SOJ4Kw1SR5E,12,"engine accelerating, revving, vroom",train SOK-zynRiGo,151,reversing beeps,train SOK-zynRiGo,267,reversing beeps,train SOM53RgpyD8,2,woodpecker pecking tree,train SOMG-Xx7Y-E,0,missile launch,train SOP4zO5Tk3w,161,striking bowling,test SOQG19evQdQ,21,fire crackling,train SOQG19evQdQ,83,fire crackling,train SOTplPfdSG0,30,printer printing,train SOWC-jX_D1c,6,playing cornet,train SOWC-jX_D1c,20,playing cornet,train SOYjBDuLOy8,658,ripping paper,train SOYjBDuLOy8,681,ripping paper,train SOZVVzV_D_I,170,people farting,train SObG51xihGc,210,people running,test SObgC3fTdwc,200,child singing,train SOfMv6mDA_4,0,"pigeon, dove cooing",train SOgj3xPTII4,447,lawn mowing,train SOivECNvewA,12,playing glockenspiel,train SOnS4V10cfk,0,people babbling,train SOnaorXAq3w,300,eating with cutlery,train SOoDcaw1Xog,251,electric grinder grinding,train SOoDcaw1Xog,326,electric grinder grinding,train SOoge-_tuUQ,126,dinosaurs bellowing,train SOq_bQ3iX6g,10,ambulance siren,train SOr0nxRmf8o,130,cat growling,test SOvdoq3ijuc,10,toilet flushing,train SP2h2_EDLgA,91,slot machine,train SPAaM-x7IKA,0,church bell ringing,train SPBgSTN8LXo,60,"female speech, woman speaking",train SPD7dfBWolc,110,stream burbling,train SPEXQrPERN0,30,female singing,train SPMU7hg2-5c,25,chainsawing trees,train SPMidzJ-5h8,30,playing banjo,train SPOQzd_LaGM,300,playing cello,train SPTbcABgueI,274,missile launch,train SPTrALVrz90,2,scuba diving,train SPXhFGrgAoc,91,hedge trimmer running,test SPXuLDnpk6k,36,mosquito buzzing,train SPY_KBOKQmQ,316,playing erhu,train SPY_KBOKQmQ,503,playing erhu,train SPZVTvhuS5Y,40,playing banjo,train SPZwYtstx_I,80,playing cello,train SPc81SK-LAE,166,playing tabla,train SPcjjVUox74,30,singing bowl,train SPeZr3ElIiM,138,playing squash,train SPg60Aiuqdo,453,bowling impact,train SPiCJj-DN8M,16,mouse squeaking,train SPiCJj-DN8M,27,mouse squeaking,train SPizIaBPhSg,74,male singing,train SPm4-LYkdJs,400,"railroad car, train wagon",train SPnZIDCnKwM,30,orchestra,train SPpJfaoMZwM,2,horse neighing,train SPpJfaoMZwM,12,horse neighing,train SPt0EDZm7Go,10,playing cymbal,train SPtUehqbdn8,43,pheasant crowing,train SPtUehqbdn8,147,pheasant crowing,train SPvKV-bFKbE,20,ocean burbling,train SPyyUMPdkCA,18,crow cawing,train SQ-pR4Kp9kc,10,penguins braying,test SQ31Y8vSNF8,45,playing gong,train SQ71Y_g116A,90,ocean burbling,test SQ9QI8Ns3tM,50,playing congas,train SQFoUylmwl0,20,machine gun shooting,train SQGYIwL6O5Q,56,people marching,train SQHomHUqFF4,255,playing table tennis,train SQKCDQrAKho,20,squishing water,test SQNCsQqjuGw,130,door slamming,test SQPQBndxiB4,87,disc scratching,test SQRBJ6Eo5D0,163,scuba diving,train SQRBJ6Eo5D0,221,scuba diving,train SQThTAr_2Hw,29,playing sitar,train SQUIUrGB_Uo,30,duck quacking,train SQUzddh-A1c,22,"engine accelerating, revving, vroom",train SQWKklcMKbE,159,chinchilla barking,test SQaFz1SxUyo,42,playing bass drum,test SQdbw-ezy-4,147,playing harpsichord,train SQe1slBe8ww,150,people crowd,train SQfTvzvUJ6k,6,hammering nails,test SQfTvzvUJ6k,29,hammering nails,test SQfXXTIqjnw,60,playing trumpet,train SQjt8CMaIBs,30,playing accordion,train SQkXWwsg-yc,30,car passing by,train SQknlti6WjE,90,eating with cutlery,train SQkoFk_gnE8,5,air horn,test SQnDdH9U-VE,13,playing zither,train SQnDdH9U-VE,38,playing zither,train SQpamzcNvgc,480,people whispering,train SQsGjWAufoM,8,civil defense siren,train SQt0fMAmvR8,250,air conditioning noise,train SQt0fMAmvR8,281,air conditioning noise,train SQtOiWZOXXs,135,tractor digging,train SQtOiWZOXXs,270,tractor digging,train SQtP27xFjRk,200,playing drum kit,train SQvIXKCYli4,33,playing cymbal,train SQywkZ3RxN8,510,people babbling,train SQz29-4O6u0,30,playing piano,train SQzU9MaABSw,30,"pigeon, dove cooing",train SR1Z-U6xNig,183,francolin calling,train SR1a5VJqF8w,80,playing cello,test SR3VwhVsgE0,10,driving motorcycle,train SR5vl-d1A3g,20,driving motorcycle,train SR7olYPb_78,0,skiing,train SR8JlAXuB4w,40,chainsawing trees,train SR9jJQ9ldgc,90,people babbling,train SR9oxnm66bY,401,playing clarinet,train SR9oxnm66bY,481,playing clarinet,train SRCenC3nBDk,260,playing drum kit,train SRGAN0te_8Y,1,chimpanzee pant-hooting,train SRGnZ_pw00k,59,tapping guitar,train SRGnZ_pw00k,196,tapping guitar,train SRGwMWX8lwA,109,playing ukulele,train SRJ8I_ng9RY,34,dog whimpering,train SRJ8I_ng9RY,56,dog whimpering,train SRJd9uC8EIU,117,cricket chirping,train SRKWKWz9M18,70,"pigeon, dove cooing",train SRMAGOHnzhs,30,playing harpsichord,train SROH5NB6ARQ,30,"pigeon, dove cooing",train SRPrQ6W_CgM,30,people sobbing,train SRRqpjvCDZM,169,sharpen knife,train SRU4M9aaZLQ,14,woodpecker pecking tree,train SRU4M9aaZLQ,38,woodpecker pecking tree,train SRUbxGVXDds,10,"bird chirping, tweeting",train SRVJXeltDNk,127,elk bugling,train SRVJXeltDNk,163,elk bugling,train SRVxkhLC3tM,11,police radio chatter,train SRVxkhLC3tM,100,police radio chatter,train SRXAVY7Z7WU,20,"female speech, woman speaking",train SRYjTwQ1km4,61,eagle screaming,train SRgNvQX10W4,10,playing djembe,train SRgOaqY0BLg,6,cheetah chirrup,train SRjqugSLcSk,215,parrot talking,train SRjqugSLcSk,275,parrot talking,train SRjsxeOWFnw,30,"motorboat, speedboat acceleration",test SRlBWSavtYU,160,driving motorcycle,train SRoQ5vA0eMY,10,dog whimpering,train SRoQ5vA0eMY,23,dog whimpering,train SRwoNyPXwoU,44,cricket chirping,train SRwoNyPXwoU,55,cricket chirping,train SRyRCbTD0Fw,26,people sneezing,train SRyRCbTD0Fw,54,people sneezing,train SS-6WbRaov0,170,mouse clicking,train SS04Rh6abxg,2,playing bongo,train SS1N4x910Cw,30,playing double bass,train SS2ccXhZI-8,148,playing vibraphone,train SS2ccXhZI-8,205,playing vibraphone,train SS2iZu7tgFg,37,"alligators, crocodiles hissing",train SS3tzY5eZq4,143,train horning,train SS3tzY5eZq4,211,train horning,train SS53enMsEZg,17,lions growling,train SS6iMabGB1Y,6,chimpanzee pant-hooting,train SS6iMabGB1Y,20,chimpanzee pant-hooting,train SS82HvMadRM,30,raining,train SS8ovfbxzmM,190,stream burbling,train SSCmi9zMv0w,360,playing flute,train SSEbqG20G7w,0,car engine knocking,train SSFDy3BzR5I,3,dog howling,train SSFDy3BzR5I,13,dog howling,train SSFFe1cVwgU,80,"vehicle horn, car horn, honking",test SSG3ss-ea7U,68,missile launch,train SSHtma_dv4s,35,playing electronic organ,train SSI1_7U1nfM,121,playing bassoon,train SSINSojVVZ0,30,wind rustling leaves,train SSJamz54lbo,118,bowling impact,train SSO72AXVGfA,27,people shuffling,train SSP4gBysDZ0,240,helicopter,train SSVTzCeBeU8,17,scuba diving,train SSWx8LMwPz8,30,people whispering,train SSZ0sQoirx4,0,car engine knocking,train SSZ0sQoirx4,10,car engine knocking,train SSZlXw0EGLw,30,orchestra,train SS_LK-dxR2g,30,chicken clucking,train SSbUrHeTXCs,43,basketball bounce,train SSbUrHeTXCs,62,basketball bounce,train SSbdKR5CvAg,205,playing badminton,train SSctjV--wAA,50,playing didgeridoo,test SSe_TE8W2pE,25,hammering nails,test SSiuPNxkqrw,0,playing volleyball,train SSor5MN-QJM,56,barn swallow calling,test SSp6S5yNcBU,0,using sewing machines,train SStyuWS2PZc,13,people sobbing,train SSuREgQbIMY,76,people burping,train SSvJ1oAHir8,30,people sniggering,train SSvSM_CeQu0,11,striking pool,train SSvSM_CeQu0,46,striking pool,train SSvx7pLqilw,330,machine gun shooting,train SSwkpDULf04,30,driving buses,train SSxdHLFNJao,112,tap dancing,train ST06L3u-WOo,240,cricket chirping,test ST22onBWu48,53,slot machine,train ST33aEP5Hbc,6,train horning,train ST33aEP5Hbc,22,train horning,train ST34BKENxUQ,390,"child speech, kid speaking",train ST3z_40gdHg,5,pheasant crowing,train ST3z_40gdHg,63,pheasant crowing,train ST5bojWT3Jo,108,sharpen knife,test ST7oYnZ4sHY,11,singing bowl,train STATfuf_Hps,381,"playing steel guitar, slide guitar",train STAqnKYiieI,31,playing sitar,train STAqnKYiieI,41,playing sitar,train STBKUszmXe0,103,playing tabla,train STCoVBgkOzM,47,woodpecker pecking tree,train STD9dx2cz_0,0,"subway, metro, underground",train STEk6xebH28,100,"playing marimba, xylophone",train STIZTWWJf24,40,lathe spinning,train STIZTWWJf24,97,lathe spinning,train STJ5zksfXGU,0,children shouting,train STJ5zksfXGU,10,children shouting,train STMY3SRCTXY,30,"playing violin, fiddle",train STNgYrg6P0A,520,goose honking,train STPjzf4YCwk,370,machine gun shooting,train STSx2uJQvZI,30,playing snare drum,train STV7yQz46H8,74,playing harp,train STcBJsLZVmU,134,slot machine,train STcdHl5XylU,57,playing sitar,train STdAr1JNXIU,190,helicopter,test STesNg0U8Ko,124,skiing,train STg6Uyytdgc,30,"playing marimba, xylophone",train STm83fDhyxg,86,playing harpsichord,train STm83fDhyxg,159,playing harpsichord,train STmZ6joYPmk,30,"motorboat, speedboat acceleration",train STnJkHshBas,10,mosquito buzzing,test STqWci9qTs0,280,people crowd,train STr4-FxNNyQ,0,dog barking,train STrIfVNpIGc,6,playing shofar,test STtUuX9XOc0,10,orchestra,train STuJgvE1c8g,139,cap gun shooting,train STuouZHd5e4,30,playing bass guitar,train 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TSzfRaudi8Q,49,train whistling,train TSzfRaudi8Q,90,train whistling,train TT-OX27yDU8,66,people burping,train TT2abhLPLgk,91,playing banjo,train TT3wkoagzFk,170,"subway, metro, underground",train TT4vjP_UgOI,2,playing cornet,train TT8-1OvQuqI,30,"race car, auto racing",train TT8FXOJtlyc,90,playing trombone,train TT8xhjDC3Cg,250,typing on typewriter,test TTARAT2Rcuw,11,"vehicle horn, car horn, honking",train TTAheqvgits,30,playing synthesizer,train TTElms_ZWqI,360,hair dryer drying,train TTElms_ZWqI,428,hair dryer drying,train TTEtcymhyms,137,rapping,train TTEtcymhyms,245,rapping,train TTGReOIGen8,10,skateboarding,train TTK8_AY5iYg,320,goose honking,train TTNKZDJwudg,21,"bee, wasp, etc. buzzing",train TTNKZDJwudg,77,"bee, wasp, etc. buzzing",train TTNan0_J9Js,30,"playing violin, fiddle",train TTO2nrWJtBw,0,playing badminton,train TTQOWdcbqe8,14,singing bowl,train TTQhlqcsIMc,53,playing bassoon,train TTSQ3pl6ZXA,40,playing harp,train TTUWNnHKjV8,153,playing squash,train TTUaHXPSvlg,30,playing drum kit,train TTW8Sthg2fc,359,"pigeon, dove cooing",train TTXJ-htgDNE,169,"electric shaver, electric razor shaving",train TT_l2Ylr5HU,153,"electric shaver, electric razor shaving",train TT_tnA_fYoU,60,people crowd,train TTaOTkBplYY,151,people slapping,train TTaOTkBplYY,207,people slapping,train TTbbBJe4eiA,139,plastic bottle crushing,train TTe5ZULNSx4,109,playing table tennis,train TTf1SCNAiHo,30,"bee, wasp, etc. buzzing",train TTfyqahHVzc,50,chainsawing trees,train TTgg982ZTlc,60,raining,train TThTcY9wMms,35,chimpanzee pant-hooting,train TThYysuKcFo,80,"motorboat, speedboat acceleration",train TTkEwALEoj0,16,playing bugle,train TTkzAOi9le0,96,warbler chirping,train TTkzAOi9le0,268,warbler chirping,train TTm28g9w9uA,30,goose honking,train TTmWAaX1yBE,20,francolin calling,train TTmWAaX1yBE,42,francolin calling,train TTqXHL8qeL4,20,dog barking,train TTstWFDMmqc,30,people whistling,train TU1AsAAJen4,7,cat growling,train TU1BKNbFTQw,10,train horning,train 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TkQt5DKAIZs,2,crow cawing,train TkTpYVcb3JA,243,skiing,train TkW5oy5eC2I,30,sloshing water,train TkW6Cf5OxAc,400,playing clarinet,train TkWpyw3VipM,30,ambulance siren,train TkZGsc-s2BE,0,using sewing machines,train Tk_xOZpwDTw,10,playing banjo,train Tkch_y2YQDI,17,playing volleyball,train TkdLSt0HzEQ,80,helicopter,train Tke_RFWNYLo,30,"pigeon, dove cooing",train TkhHf2_jfp8,110,playing bass drum,train TkjHHBwnnmM,80,"pigeon, dove cooing",train TkjzSou5z7w,169,playing harpsichord,train TkjzSou5z7w,244,playing harpsichord,train TklEupQlzDk,14,playing glockenspiel,train TklEupQlzDk,43,playing glockenspiel,train Tkl_MzLp5xo,30,"pigeon, dove cooing",train TklzW9RmC3o,320,eating with cutlery,train Tkn9-qpYlEI,10,using sewing machines,train TkrYBLL1Eu0,120,playing trumpet,train TkrtTUPxHNI,30,playing harpsichord,test TkxMGNL8Fhw,30,playing badminton,train TkyFHuRi6xU,0,francolin calling,train Tl-hvN3KMes,10,chainsawing trees,train Tl0qxOEm9gw,107,yodelling,train Tl1pnIJLvr4,10,playing 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mandolin,train VLVpgiAaCao,36,playing mandolin,train VLWbeAYnumM,123,slot machine,train VLWnw19iarI,37,pig oinking,train VLX4Bq9aKWw,218,rope skipping,train VLX4Bq9aKWw,243,rope skipping,train VLYst8G4b0w,293,underwater bubbling,train VLawS2dt3rs,130,helicopter,train VLawbfIv71c,240,playing flute,train VLbtXTxAgng,113,lighting firecrackers,train VLbtXTxAgng,123,lighting firecrackers,train VLcLq4531tc,10,people screaming,train VLf92iZ6sW4,145,people slurping,train VLf92iZ6sW4,406,people slurping,train VLgNw_87asA,0,people crowd,train VLlDboQc7W0,200,people crowd,train VLlmSFLfFdY,30,playing bagpipes,train VLmA3aSryUU,62,arc welding,train VLmA3aSryUU,73,arc welding,train VLoybl-FJbE,0,splashing water,train VLr-E7RAuWU,120,playing double bass,train VLr-E7RAuWU,131,playing double bass,train VLtblnr2xRk,5,ferret dooking,train VLtblnr2xRk,40,ferret dooking,train VLuerVXFD84,169,air conditioning noise,train VLuerVXFD84,223,air conditioning noise,train VLyLt6Rfw6U,360,machine gun shooting,test VM-U7MyZAck,30,playing bass guitar,test VM-UevP3TbA,0,playing ukulele,train VM-UevP3TbA,88,playing ukulele,train VM0acV45zHk,176,playing electronic organ,train VM4j2n1clH4,1,airplane,train VM55GWl_Nlk,120,playing cornet,train VM55GWl_Nlk,173,playing cornet,train VM9heJTKWxY,15,arc welding,train VM9heJTKWxY,76,arc welding,train VM9qv3xrtTw,70,people sobbing,train VMAUek2HQH0,60,"bird chirping, tweeting",train VMCBMairJgU,104,driving snowmobile,train VMCBMairJgU,120,driving snowmobile,train VMCLwzIJE6c,260,fireworks banging,train VMDD0ZErfMM,30,"playing violin, fiddle",train VME3sfDxM04,110,people booing,train VMFOs_ekgko,30,playing snare drum,train VMJ4Qp2uDk8,83,cupboard opening or closing,train VMJ4Qp2uDk8,108,cupboard opening or closing,train VMJncve5fB8,315,singing bowl,train VMPFS-ppp78,90,people clapping,train VMR6h4OzQA4,121,hedge trimmer running,train VMR6h4OzQA4,132,hedge trimmer running,train VMSTzfF3udY,196,airplane,train VMS_gOrUXXo,30,playing drum kit,train VMVxhZuZSeQ,30,people shuffling,train VMWmX19efbw,324,playing lacrosse,test VMXbqYX47LI,11,cheetah chirrup,train VMaczONM3RU,10,opening or closing drawers,train VMazAn03018,60,playing clarinet,train VMbJTgzMhKE,30,waterfall burbling,test VMh0rAdXP6s,0,skidding,train VMheTWhW3Nw,216,rope skipping,train VMheTWhW3Nw,446,rope skipping,train VMlTFLMl3IA,37,playing double bass,train VMnJ2f5MyFw,14,playing bongo,train VMnJ2f5MyFw,79,playing bongo,train VMunhPU_TWE,88,beat boxing,train VMxEwhbaoa0,230,lions growling,train VMzHtezrQ5Y,10,toilet flushing,train VN0FRmNf8R4,30,helicopter,train VN2kFrz6jhA,242,playing harp,train VN3yhzZWfeg,63,playing steelpan,train VN3yhzZWfeg,136,playing steelpan,train VN4AS5J25os,40,playing hammond organ,test VN4SqiL7q6I,306,basketball bounce,train VN57ziCj0Pk,95,parrot talking,train VN57ziCj0Pk,253,parrot talking,train VN5W9piaNOw,63,swimming,test VN5els00U0U,2,train whistling,train VN82K8yN9rI,0,dog growling,train VN82K8yN9rI,17,dog growling,train VN9Vq3wM-vI,13,underwater bubbling,test VNBIEaE4-BU,400,people clapping,train VNGReSXnsBo,25,fire truck siren,train VNGReSXnsBo,86,fire truck siren,train VNGosgG2KZM,9,frog croaking,train VNGosgG2KZM,29,frog croaking,train VNHHksC53Is,110,"race car, auto racing",train VNHy4EZUobU,74,hammering nails,train VNHy4EZUobU,92,hammering nails,train VNIrzn-_2q8,30,people sneezing,train VNKRfDJMLSs,30,people shuffling,train VNKgbflCAvI,60,"vehicle horn, car horn, honking",train VNQHojXWV9E,30,"motorboat, speedboat acceleration",train VNTJglF_TeU,4,cheetah chirrup,train VNUAW_5hKvs,0,people sneezing,train VNUwrtWZLBU,30,"rowboat, canoe, kayak rowing",train VNWkz4TeE10,125,sea waves,train VNXtnsAlxS8,10,police car (siren),train VNXyOQGJync,129,fire crackling,train VNXyOQGJync,139,fire crackling,train VNZl3XAcjk8,1,cat growling,train VNZl3XAcjk8,30,cat growling,train VN_4Z0j3Qrw,30,people shuffling,train VN_mHMrLy0M,80,lawn mowing,train VNa4wT02XlY,410,train horning,train VNahNEGSgnU,115,playing table tennis,train VNdNEui-7BM,35,canary calling,train VNdNEui-7BM,163,canary calling,train VNfvXuW9O1U,34,playing squash,train VNgw3odATDg,30,sailing,train VNj1PR4n-AA,40,playing electric guitar,train VNj4fnWVJqc,21,sloshing water,train VNkDk_7TQ4Q,40,playing cello,train VNl6awOmiQQ,30,printer printing,train VNlfJLC00Ds,330,playing flute,train VNnZ5H3SmS4,257,people eating apple,train VNnZ5H3SmS4,335,people eating apple,train VNoK4zpW3uI,28,frog croaking,train VNpyoGatOxA,9,mosquito buzzing,train VNpyoGatOxA,52,mosquito buzzing,train VNqZMljUbrQ,30,car engine knocking,train VNs8qiFvwfk,30,chicken crowing,train VNtUol6uIAg,220,people clapping,train VNzEAjztgxg,30,playing synthesizer,test VO1QA-Ihceo,368,playing guiro,test VO4ZYraEeTM,30,people running,train VO5ZwFwNYJY,1,airplane flyby,train VO7p7I3TfAA,295,playing hammond organ,train VO8finuNVeQ,35,tap dancing,train VO8wt0-nXxE,47,playing theremin,train VODYoF0HFrc,30,singing choir,train VODsvTur3Po,50,people sniggering,train 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hammond organ,train VRL4-i1XlyI,7,people cheering,train VRLwxCQ5Ooo,390,horse clip-clop,train VRN-Knq8-eU,10,skateboarding,train VRPAAwXDC4g,0,playing harmonica,train VRPAAwXDC4g,4,playing harmonica,train VRR42vwH9rE,34,owl hooting,train VRR42vwH9rE,49,owl hooting,train VRSjLkj0VMw,31,"playing marimba, xylophone",train VRTk6hTsttc,430,playing double bass,train VRXZx3M6bd0,40,machine gun shooting,train VRZmcY-Mnqk,30,cat meowing,train VRZn5DsghD0,450,playing synthesizer,train VRaLq9Gb9xI,180,using sewing machines,test VRfD7xFIUCw,250,people clapping,train VRgihFvQ1dg,49,playing erhu,train VRlu77IkhHM,205,fire crackling,train VRlu77IkhHM,431,fire crackling,train VRm8ShxYz5Y,0,ambulance siren,train VRnZ0lWPacg,20,orchestra,train VRo6T0j_vAI,4,people booing,train VRo6T0j_vAI,32,people booing,train VRpdj-k4VTE,1,pig oinking,train VRrELISNqOI,170,"alligators, crocodiles hissing",train VRrELISNqOI,225,"alligators, crocodiles hissing",train VRsXejBBi_c,380,people clapping,train VRtti7CpV-E,90,people whispering,train VRv4AGaeDxw,360,"subway, metro, underground",train VRz0aZ2elWc,13,mynah bird singing,train VRz0aZ2elWc,84,mynah bird singing,train VRzO1cDfjeo,30,thunder,train VS18Gt2TJp4,0,mouse pattering,train VS27CqYYTrE,9,sea lion barking,train VS27CqYYTrE,63,sea lion barking,train VS2BMll4cKU,0,people booing,train VS3vEkNN8uM,100,police radio chatter,test VS4aLgaiSZo,90,driving buses,train VS5_qH4VdEs,10,people crowd,train VS5iIUx_qzw,30,"pigeon, dove cooing",train VS5tEdU-ITE,35,writing on blackboard with chalk,train VS6xerCFr7M,39,playing cornet,train VS81egrQqC0,24,opening or closing car doors,test VS8OoTXmKXs,330,tractor digging,train VS8OoTXmKXs,355,tractor digging,train VS9R3iOc4Vk,13,pheasant crowing,train VS9R3iOc4Vk,27,pheasant crowing,train VS9ickCCcTg,65,playing squash,train VSA5_6TLbt4,26,train horning,train VSA5_6TLbt4,61,train horning,train VSCAtKddPWw,21,toilet flushing,train VSDB4p7b90w,30,chainsawing trees,train VSGfl-dZKCY,30,"bee, wasp, 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_MXYzvCjPAA,1,people sneezing,train _MYMoWQCiAM,289,people slurping,train _MYMoWQCiAM,414,people slurping,train _M_fE31sgi4,146,people whistling,train _M_fE31sgi4,274,people whistling,train _M_h4GcCR3c,0,car engine knocking,train _M_h4GcCR3c,43,car engine knocking,train _MdwGoJiOZ0,60,playing accordion,train _MeImC-roPc,147,splashing water,train _MhpIsfqNL4,97,skiing,train _MkMdlfl8Hg,89,playing french horn,train _Mnq--ENmzU,200,"bee, wasp, etc. buzzing",train _MqJPuHdpUA,359,people eating noodle,train _MrWjiIzjFk,66,ferret dooking,test _Mvbu0OLwRU,40,playing flute,train _N0jQsh3osY,30,"playing violin, fiddle",train _N1JsKu4He0,70,"rowboat, canoe, kayak rowing",train _NIWrFdbZdE,450,missile launch,train _NJq0p2U_Mk,25,playing bagpipes,train _NMGrV9Y7Sg,30,people screaming,train _NMmnC8XETg,50,machine gun shooting,train _NRBf_Jd1l0,227,rope skipping,train _NRBf_Jd1l0,245,rope skipping,train _NRFt6UyL7g,27,wood thrush calling,train _NROsRGhIZw,50,"motorboat, speedboat acceleration",train _NShiXyBmsY,270,train wheels squealing,train _NTY_84ULb4,21,playing harpsichord,train _NVQDzZNQyo,185,playing electronic organ,train _NW6RuwbP1Q,20,people giggling,test _NblwewWOiA,7,eletric blender running,train _NcO7A8jwWg,70,fireworks banging,train _Ndp_pj1i2g,77,playing bassoon,train _NeqFFZCzEg,30,"bird chirping, tweeting",train _Nffivgc2Qc,19,baltimore oriole calling,train _NgVUWxZh7c,0,cat purring,train _NhGiSQdg-k,42,people marching,train _NhmzumoxCQ,0,car engine starting,train _Nhs12jGmd0,61,playing flute,train _NjjOdDRhfI,30,"playing marimba, xylophone",train _Nk6TOM9CHg,19,playing guiro,train _NmGGuew9yg,0,mouse clicking,train _NmGGuew9yg,21,mouse clicking,train _NmIyTpmTgE,70,playing banjo,train _NptAqzg_u8,170,people babbling,train _NvCMlLCDPQ,30,printer printing,test _Nz7aasD2tk,247,playing piano,train _O0MKN31iXk,117,beat boxing,train _O1LosjUrJM,448,playing bassoon,train _O5NaJziPSo,490,train horning,train _O6AmnKPBC8,67,hail,train _O6AmnKPBC8,86,hail,train _O7JwtSY4yA,30,driving buses,train _O9VCVqOQAE,0,playing drum kit,train _OAmHm2hHGk,0,people screaming,train _OBormXvB0w,148,playing table tennis,train _OFyfcFXi8o,20,splashing water,train _OII0mDYqJU,2,snake hissing,train _OII0mDYqJU,16,snake hissing,train _OK6s5Djius,12,playing snare drum,train _OK6s5Djius,25,playing snare drum,train _OKZleBfj5Y,4,mouse clicking,train _OLtKOPyOF4,30,ambulance siren,train _OMD0fqsBjI,166,beat boxing,train _ONjC3LJuRM,240,"female speech, woman speaking",train _OOCcxvETVg,30,turkey gobbling,train _ORmL-PlU_I,1,cat growling,train _OSrbf91BLA,10,people crowd,train _OV3RWqTPrg,30,police car (siren),train _OV4wuEUvuE,136,writing on blackboard with chalk,test _ObtURsKv-U,1,alarm clock ringing,test _OciOkwWgaw,36,sliding door,train _OciOkwWgaw,63,sliding door,train _OjFxe9REKk,133,playing darts,train _OlZxmuIiLg,0,playing mandolin,train _OlZxmuIiLg,63,playing mandolin,train _Oniex0A4Qk,540,people crowd,train _OocUoF31oE,59,playing tambourine,test 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_PNkhP8Qo7s,140,people whistling,train _PPSaWDcmes,90,ocean burbling,train _PR74a-PN6s,30,chopping food,test _PSaSRAubU0,150,playing saxophone,train _PT3Mb3NaLc,31,lions roaring,train _PT_SXYPGrk,0,helicopter,train _PUM6n6KFcs,37,opening or closing car doors,test _PUN_CLPzUQ,0,golf driving,train _PUNeXHLib8,30,"rowboat, canoe, kayak rowing",train _PX_ZKpotMU,10,door slamming,train _PX_ZKpotMU,41,door slamming,train _PZZCdKz9lU,30,frog croaking,test _PcHCcSlhDQ,30,playing trumpet,train _PdAlY9N0as,29,people shuffling,train _PfRSr0F9eM,35,playing sitar,train _PfRSr0F9eM,171,playing sitar,train _PhdfL1SHIw,183,hail,train _PhdfL1SHIw,461,hail,train _PjBTtIUlpg,20,driving motorcycle,train _PkwbLXY46w,30,playing harp,train _Pm4A9bNmXk,1,"alligators, crocodiles hissing",train _Pm4A9bNmXk,22,"alligators, crocodiles hissing",train _Pmv8Q5rcJw,180,people cheering,train _Pn7Zvx0scw,30,driving motorcycle,train _PnPolAU5rA,30,"pigeon, dove cooing",train _PqQJ9OUqEs,28,penguins braying,train _PqQJ9OUqEs,38,penguins braying,train _PtI9SZZCqo,99,slot machine,train _PuBoGoSpog,280,stream burbling,test _Pw-XHrWkj8,331,police radio chatter,train _PwfkuNdqQs,80,people sobbing,train _PwucS53AsY,110,playing mandolin,train _PwucS53AsY,188,playing mandolin,train _PyplT2u9K8,205,playing sitar,train _PyplT2u9K8,400,playing sitar,train _PzX-D95Q0s,27,golf driving,test _Q-3bl5JqkA,30,"motorboat, speedboat acceleration",train _Q-g-HiVEGU,0,horse neighing,test _Q14s0426Wk,34,skiing,train _Q3K8-CUC7E,120,playing saxophone,train _Q43GCi1kDs,80,lawn mowing,train _Q4XJNTbHVM,130,playing banjo,train _Q686aLcMvM,30,"playing violin, fiddle",train _Q717C1SJDs,0,playing ukulele,train _Q717C1SJDs,4,playing ukulele,train _Q7rBy99QTE,49,people slurping,train _Q7rBy99QTE,151,people slurping,train _QAcFbjJaiI,5,crow cawing,train _QAcFbjJaiI,17,crow cawing,train _QDvxPWPGbY,114,crow cawing,train _QFLqsl_m4c,103,playing guiro,train _QFLqsl_m4c,161,playing guiro,train _QGI4aRWqWc,46,playing snare 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_R2GqFuFoYQ,50,toilet flushing,train _R3VE_hpeLI,3,playing bugle,train _R3VE_hpeLI,14,playing bugle,train _R65RzskAgg,18,golf driving,train _R65RzskAgg,73,golf driving,train _R6pY_ivHxQ,70,skidding,train _R8AR4yKfSA,40,playing theremin,test _R8a5J5vcfo,47,shot football,train _R8f1lYktl0,30,playing electronic organ,train _R8td2L0Bx4,270,playing piano,train _R9-NgGmnb8,58,otter growling,train _R9-NgGmnb8,131,otter growling,train _R9Ma9rjEWg,30,playing ukulele,test _R9ezw0ow9w,60,playing electric guitar,train _RB5ohjmR6U,15,playing gong,train _RB9txkXq1o,53,machine gun shooting,train _RB9txkXq1o,66,machine gun shooting,train _RBV4hHznP0,80,playing piano,train _REg3JRsI88,240,playing clarinet,train _RGRlCB8g6s,26,woodpecker pecking tree,train _RHUQzPObtk,0,playing clarinet,train _RHUQzPObtk,113,playing clarinet,train _RIhuEAzbW0,152,woodpecker pecking tree,train _RJ1I_zRhKA,190,people crowd,train _RKhYWkBN_g,20,printer printing,train _RKlwsyRIfo,90,driving motorcycle,train 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_awRNo1PGHc,129,cricket chirping,train _awRNo1PGHc,397,cricket chirping,train _awxRCtO1PQ,0,train horning,train _ay5fBI9Izg,30,raining,train _b-RCWL4X44,669,basketball bounce,train _b0V0AVqyWU,10,"vehicle horn, car horn, honking",train _b0oajVzd9Q,140,"playing marimba, xylophone",train _b3IN1zKsLk,16,barn swallow calling,train _b3IN1zKsLk,27,barn swallow calling,train _b6Tb9SkmLg,208,playing didgeridoo,train _b8BfaPb3lg,42,bowling impact,train _b8BfaPb3lg,56,bowling impact,train _b9hppX1RKs,310,fireworks banging,train _bAVmK7n0fs,130,"male speech, man speaking",test _bBUW5C5yW4,53,elephant trumpeting,test _bBUW5C5yW4,79,elephant trumpeting,test _bCTc6OBaM0,208,opening or closing car doors,test _bFKw61jn0c,6,car engine idling,train _bH_0GEjBEw,200,playing cello,train _bHvov8wEuY,193,singing choir,train _bIE0Cnyaf4,56,snake hissing,test _bRYwy6Qk18,90,people babbling,train _bSkBUAWrKw,30,"child speech, kid speaking",train _bTgFkmACf0,53,frog croaking,train _b_LoQme1fw,19,elephant trumpeting,train _bahlW-5CjU,81,cheetah chirrup,train _bbO8yTFRQ4,80,"race car, auto racing",train _bbQMnvdEeQ,14,dog growling,train _bctdebXOFk,30,driving buses,train _bdJm7_sJwc,197,people cheering,train _bdJm7_sJwc,350,people cheering,train _be6UpSq_LU,40,planing timber,train _be6UpSq_LU,201,planing timber,train _be6agljRCs,30,playing cymbal,train _bkLypz1uds,5,"donkey, ass braying",train _bkMO3cOoyg,129,people eating apple,train _bkMO3cOoyg,566,people eating apple,train _bkX5VkZg8U,189,"race car, auto racing",test _bmyOvumxjQ,3,lighting firecrackers,train _bouDnx68b4,6,bouncing on trampoline,train _btHG3n5REg,523,police radio chatter,train _buSeHemW4c,537,playing guiro,train _buSeHemW4c,784,playing guiro,train _bwi6VRGtKo,30,sheep bleating,train _c0dwXSTgj4,51,playing tambourine,train _c0dwXSTgj4,73,playing tambourine,train _c2DFDqOFwc,30,people whispering,test _c35HShJyGE,530,people whispering,train _c4eMTw9zIg,1,pig oinking,train _c5jxSdC3OQ,43,whale calling,train _c765cuwx5s,4,turkey gobbling,train _cDmniDzNSo,118,striking pool,train _cES7Twcq18,6,playing cornet,test _cFacI3X6x4,150,driving motorcycle,train _cM07D-JDCU,2,people burping,train _cO2JQZW8FQ,91,mouse clicking,train _cO2JQZW8FQ,129,mouse clicking,train _cP2ZtKFm_Y,100,toilet flushing,train _cT4gpvsLqY,30,orchestra,train _cVmIi8gjdg,73,mosquito buzzing,train _cW-ybXSseo,7,frog croaking,train _cW-ybXSseo,17,frog croaking,train _cWuLBzHZrk,20,playing tympani,train _cWuLBzHZrk,42,playing tympani,train _caFr8RrH-Q,30,cat meowing,train _cb_U_z_Ct0,30,lawn mowing,test _cgMmZfpV8o,30,sheep bleating,train _cg_IfaD1C0,27,playing didgeridoo,train _ckZ081SIgI,30,playing piano,train _ckj60RJ1fo,164,bouncing on trampoline,train _cqtOdzjwq8,133,francolin calling,train _csR8OOB8WI,0,roller coaster running,train _ctYxJG78H4,29,people sneezing,train _cvucKdFb5I,43,people booing,train _cvucKdFb5I,92,people booing,train _cwV3qV_lgU,706,people slurping,train _cwV3qV_lgU,717,people slurping,train _czw2dB1eRE,95,playing gong,train _czw2dB1eRE,131,playing gong,train _d2Sz-B6eIU,117,people burping,train _d3Av_Hdang,30,chainsawing trees,train _d6d7zyAuT4,1,wind rustling leaves,train _d7GVxP-fMw,20,mouse pattering,train _d7qNQzYQIY,180,playing clarinet,train _d7wY9TDWTI,30,coyote howling,train _d7wY9TDWTI,88,coyote howling,train _d9KqoxetNg,70,"engine accelerating, revving, vroom",train _dAGlAQF4HA,2,cell phone buzzing,train _dIrLK5i5Os,30,playing harp,train _dIzu78Ld2w,166,lathe spinning,train _dLpgs0tCOc,5,"engine accelerating, revving, vroom",train _dMKUFMq2n8,202,rope skipping,test _dMuRqKDu3Q,48,cat caterwauling,train _dN5mROd8QE,34,lighting firecrackers,train _dN5mROd8QE,47,lighting firecrackers,train _dO4dnyfvow,186,firing muskets,train _dO4dnyfvow,227,firing muskets,train _dQop-gc2hA,30,skidding,train _dR8_cQ2Cy8,150,playing harp,train _dRo_bkA2bM,40,playing flute,train _dRqfb20DdU,0,playing ukulele,train _dRqfb20DdU,183,playing ukulele,train _dSikYkY28s,18,people sniggering,train _dUGoBdFSa8,70,helicopter,train _dVDFVDqWz8,177,tornado roaring,train _dWjXXGbTX4,116,people coughing,train _dWjXXGbTX4,138,people coughing,train _davW0SUcLo,20,playing bass guitar,train _daxTTHmXbA,50,helicopter,train _dbH01jeTzM,0,playing tennis,train _dbH01jeTzM,7,playing tennis,train _dbl7AJkX4Q,79,machine gun shooting,train _dc_OE5O8Cs,9,lighting firecrackers,train _deq3Z1pHqw,107,woodpecker pecking tree,train _deq3Z1pHqw,117,woodpecker pecking tree,train _dh3u5cMEhs,121,playing washboard,train _dh3u5cMEhs,145,playing washboard,train _dj794C7yvY,30,"rowboat, canoe, kayak rowing",train _dl-OakpcJc,35,rope skipping,train _dl-OakpcJc,112,rope skipping,train _dlpgziE7CU,10,"cattle, bovinae cowbell",test _do1KN1Z3Vc,10,people humming,train _dqBNyuClaI,50,"playing marimba, xylophone",train _dsu_ZznF2M,2,playing djembe,train _dsu_ZznF2M,25,playing djembe,train _dvVfWzJlDM,100,chainsawing trees,test _dw9kYTSzRM,30,"rowboat, canoe, kayak rowing",train _dwGCXLU_28,0,dog barking,train _dwwBAa2WfE,52,baltimore oriole calling,train _e-8LARTan4,46,playing bugle,train _e2hAGRuLpc,4,people booing,train _e30BqdPnaA,230,skateboarding,train _e494e6o39s,178,cat purring,train _e494e6o39s,359,cat purring,train _e4GZ6p6nCY,20,people coughing,train _e4_mvCMlkk,1,playing tuning fork,test _e65m2a8--E,294,bowling impact,train _e65m2a8--E,331,bowling impact,train _e6ZQLbfmdM,268,singing choir,train _e8MH62ZmSw,40,wind noise,train _eBPydLCBjQ,241,plastic bottle crushing,train _eBPydLCBjQ,252,plastic bottle crushing,train _eBYRnly4m8,97,playing cornet,test _eCKiqkOudc,40,bowling impact,train _eE3lDQtM5I,60,bowling impact,train _eEpQoCWHY0,28,volcano explosion,train _eEvIRCorxE,200,sailing,train _eIe1isk5JM,30,people sniggering,train _eIoUd0BNBk,450,people clapping,train _eJr86Ywo9Y,17,typing on computer keyboard,train _eLVrgvrVBQ,20,vacuum cleaner cleaning floors,test _eNSyBa4yEg,30,playing electronic organ,train _eNVk82qHyY,10,"subway, metro, underground",train _eOCm8F0tmk,3,dinosaurs bellowing,test _eU8eWizcDw,98,ice cracking,train _eU8eWizcDw,249,ice cracking,train _eVt1GbNtgA,256,playing badminton,train _eWg4NgyWts,14,spraying water,train _eX_uZrOUik,30,duck quacking,test _eaTr5qR7bM,50,female singing,train _ea_RZJl2iI,450,"subway, metro, underground",train _ecMAW8uw0A,10,playing electric guitar,train _ecMZXO_JEo,30,duck quacking,test _eceNIlHt4E,204,train horning,test _ectpPOax4k,30,"bird chirping, tweeting",train _ed1AXUv-kw,1,people booing,train _ed1AXUv-kw,30,people booing,train _egFkIE-tqY,0,playing bass drum,train _ek505nTA00,30,playing bass guitar,train _eniUqeGMmg,48,playing table tennis,train _eo2lCLyqSQ,30,people sniggering,train _eq8srydX9I,16,playing theremin,train _esOx1VPkYM,3,hammering nails,train _esQmr3FyAk,71,playing bassoon,train _esQxfIgLGc,198,rapping,train _esQxfIgLGc,482,rapping,train _ev9pC-oTPM,0,woodpecker pecking tree,train _ev9pC-oTPM,47,woodpecker pecking tree,train _evex4Vvaxo,70,chicken crowing,train _exHiLyiLgw,100,playing clarinet,train _f-7q5uU30k,4,dog growling,train _f-7q5uU30k,19,dog growling,train _f4eLauQM5A,44,playing mandolin,train _f4eLauQM5A,60,playing mandolin,train _f5UukHhYaM,88,tornado roaring,train _f7qCwXJQmQ,35,playing cello,train _f8VwdL5sk0,63,playing bagpipes,train _f8VwdL5sk0,111,playing bagpipes,train _f8qhPkavbs,200,"playing marimba, xylophone",train _fBovTvf-F4,43,playing timbales,train _fBovTvf-F4,84,playing timbales,train _fDPfdFyKyU,30,playing acoustic guitar,train _fFqYWo7b4I,70,people babbling,train _fH2iVg6qPs,30,"bird chirping, tweeting",train _fKTbfYFQKM,11,crow cawing,train _fKntnlIYTQ,219,playing steelpan,train _fKntnlIYTQ,338,playing steelpan,train _fMyflalfSo,359,dinosaurs bellowing,train _fMyflalfSo,618,dinosaurs bellowing,train _fTONy_pqik,30,dog barking,test _fUpJR6YKMo,0,"cattle, bovinae cowbell",train _fUpJR6YKMo,49,"cattle, bovinae cowbell",train _fV9SiTt1YI,113,cat hissing,train _fWFbxwuGJA,56,playing timpani,train _fWQ_r7UHgs,0,cricket chirping,train _fX-4hMWUHQ,110,skidding,train _fX5M_NQJIc,117,playing bongo,train _fX5M_NQJIc,324,playing bongo,train _faURfqNXSM,50,train horning,train _fawinc5ssE,29,playing theremin,train _fdY5ZOOBWI,0,lions roaring,train _fduNroJq2I,56,civil defense siren,train _feNEmomM7k,130,playing piano,train _fejow1b8zU,30,printer printing,train _fg0mAQwTc4,118,sea waves,train _fg0mAQwTc4,133,sea waves,train _fhCuBfDnWw,0,lions roaring,train _fji_yRt11A,84,airplane flyby,train _fji_yRt11A,134,airplane flyby,train _fmX7Y7JxMA,60,people farting,test _fmtZt7iGfQ,13,people booing,train _fokP9gJPbQ,196,playing tambourine,train _fp7W-nVb3s,60,train horning,train _fprJMwmYoU,221,cuckoo bird calling,test _fwLaGA_4Kk,30,people running,train _fxCpMoW2Tw,25,cap gun shooting,train _fxL9BKkLbo,30,"playing marimba, xylophone",train _fxNfwJOGas,27,driving snowmobile,train _fxNfwJOGas,111,driving snowmobile,train _fyNX6HmJo0,71,basketball bounce,train _g05NZ3rro8,51,rope skipping,train _g05NZ3rro8,65,rope skipping,train _g3twT5pIzk,20,playing acoustic guitar,train _g47--qTsB4,169,tractor digging,train _g47--qTsB4,183,tractor digging,train _g4uYCDQTmA,20,skateboarding,train _g6rYlBap-8,30,printer printing,train _gCFHs1B_cI,200,playing synthesizer,train _gCdHyG3rFM,61,playing mandolin,train _gCdHyG3rFM,103,playing mandolin,train _gDe4J2XCiM,57,vacuum cleaner cleaning floors,train _gDe4J2XCiM,71,vacuum cleaner cleaning floors,train _gDw8q2sRLk,48,rope skipping,train _gEaoE8Upj0,30,fireworks banging,train _gG0KNGD47M,30,reversing beeps,test _gJrRO59Fu0,110,"child speech, kid speaking",train _gJzRoAPDrA,69,electric grinder grinding,train _gL7TZNt86U,100,playing acoustic guitar,train _gNtrscg-YM,25,"bird chirping, tweeting",train _gQFB_Utuf0,77,cat caterwauling,train _gQFB_Utuf0,90,cat caterwauling,train _gRU1gswDtU,120,orchestra,train _gSNEPUZXAc,89,bowling impact,train _gWR0jVR1Sk,230,playing bass drum,train _gY3Z2pWGsA,150,people farting,test _gZHUSy9uUY,79,foghorn,train _gau111HdQQ,60,sailing,train _gcC-GBDgyY,71,swimming,train _gdZO5DnO_0,0,using sewing machines,train _gesDZ5_rAg,17,firing cannon,test _gfjWcVBG4k,21,rapping,train _gfjWcVBG4k,35,rapping,train _gflpty8bDg,50,lawn mowing,train _ghr7AmGTdE,37,beat boxing,train _gjBxWy7v4Y,30,sheep bleating,train _gjDI3-zVZg,1,dog growling,train _gjDI3-zVZg,11,dog growling,train _glf345Z7D8,9,playing trombone,train _gnhJDTygck,70,people hiccup,train _gnzzMjbOpw,280,owl hooting,train _gurg1magkE,150,skidding,train _gxaPRDhlbc,1,playing vibraphone,train _gxaPRDhlbc,64,playing vibraphone,train _h-kwHMFZEs,30,mouse pattering,train _h3eyraAjn8,0,skiing,train _h3nrupHcmE,0,fire truck siren,train _h4LK9mpoi4,112,playing theremin,train _h4LK9mpoi4,177,playing theremin,train _h5GPk81CCc,58,missile launch,train _h5GPk81CCc,76,missile launch,train _h5xxCC6r6E,22,cat purring,train _h5xxCC6r6E,42,cat purring,train _h7jBOGtky0,59,lighting firecrackers,train _h8yXGBIj2I,17,squishing water,train _hBjiZDitVQ,59,civil defense siren,train _hCqN397QrA,10,duck quacking,train _hFT4HkPrSs,10,lathe spinning,train _hFT4HkPrSs,32,lathe spinning,train _hFiksUx6ic,344,bowling impact,train _hFiksUx6ic,398,bowling impact,train _hR6hsfuUaQ,50,"bird chirping, tweeting",train _hSH7asdtag,30,"engine accelerating, revving, vroom",train _hTa_9bP63g,25,zebra braying,train _hTa_9bP63g,37,zebra braying,train _hYBs0xee9Y,30,playing mandolin,test _hYgrHyzbog,318,people slurping,train _hYgrHyzbog,458,people slurping,train _h_0T05F0aI,590,typing on computer keyboard,test _hbAg0Qg5FQ,277,fire truck siren,train _hbDwMvDAdg,10,cat purring,train _hbstEIzzmo,1,pheasant crowing,train _hd1tNgkZz8,9,"playing violin, fiddle",test _hePi9K5YWA,101,playing cymbal,train _hho_Yg81vo,291,tap dancing,train _hjg2Oc4mFM,30,ocean burbling,train _hkYuWImJuY,300,people crowd,train _hkngjEgHgk,183,child singing,train _hlItnCTAOU,32,playing glockenspiel,train _hm8Toscrjg,0,woodpecker pecking tree,train _hm8Toscrjg,14,woodpecker pecking tree,train _hpHt7J3Hrg,130,chicken crowing,train _hpO6Ftvvr8,8160,"bird chirping, tweeting",train _hpPqluWdeQ,164,pumping water,train _hptdlGvSV4,10,people coughing,train _hwjTpeed20,140,basketball bounce,train _hwxmSoitOY,5,snake hissing,train _hzF8vuD7JA,10,lawn mowing,train _hzLiaVc7P4,322,cutting hair with electric trimmers,train _i-fFlNB99k,88,striking bowling,test _i08-zW29xg,30,orchestra,train _i3u5pgPas4,18,playing squash,train _i4LB1MpHj4,110,"playing violin, fiddle",train _i6X-ayXF_M,30,"playing marimba, xylophone",train _i7RKuWNrzk,40,playing electronic organ,train _iAk-fbq0vQ,39,yodelling,train _iIhHW_afdY,123,spraying water,train _iIhHW_afdY,138,spraying water,train _iJRWNX-oEw,396,"subway, metro, underground",train _iJRWNX-oEw,512,"subway, metro, underground",train _iPYbr2frgA,57,playing cornet,train _iPYbr2frgA,68,playing cornet,train _iQ2hXonRsY,460,people belly laughing,test _iSgAhxOWjU,100,"vehicle horn, car horn, honking",train _iSqNROxsO0,50,wind noise,train _iTeRIUAEvo,260,church bell ringing,train _iV3NRlzI3Q,0,"vehicle horn, car horn, honking",train _iWEPorE5h4,18,dog whimpering,train _iYOxNoyw0Q,30,playing saxophone,train _iYaYMFUBf4,63,missile launch,train _iYaYMFUBf4,81,missile launch,train _ie-zZQXq9s,462,child singing,train _if557QC-zE,30,chainsawing trees,train _ifYax8Smoc,36,cat hissing,train _iitnxgV29M,30,male singing,train _ikGrzqjv1g,20,duck quacking,train _il7CA-nMEI,11,dinosaurs bellowing,train _il7CA-nMEI,25,dinosaurs bellowing,train _imIQdMKbpI,0,playing clarinet,train _imIQdMKbpI,190,playing clarinet,train _inZkKi62n8,30,orchestra,train _irJTHxhC24,23,playing zither,train _itDi5yOznQ,30,car engine knocking,train _itXqMyxXcc,431,yodelling,train _itwTub5wQU,23,"bee, wasp, etc. buzzing",train _itwTub5wQU,35,"bee, wasp, etc. buzzing",train _iubsILN3BY,141,"playing steel guitar, slide guitar",train _iubsILN3BY,167,"playing steel guitar, slide guitar",train _ivm5nIZBko,210,people cheering,train _iywvGPzgA4,130,playing trombone,train _izB_qnZH3s,164,playing bass guitar,train _izdlkhrh1g,83,horse neighing,train _j1DBw30be0,35,otter growling,train _j1DBw30be0,80,otter growling,train _j2i3nrW7vo,440,coyote howling,train _j5C4pY7NMg,517,playing mandolin,train _j5C4pY7NMg,590,playing mandolin,train _j5qjm9kvvw,70,sailing,train _j62xxyQbzo,84,skiing,test _j641GsLX0U,12,missile launch,train _j8zzvBts98,0,splashing water,train _jAOfZ99lEo,41,playing volleyball,train _jB-IM_77lI,0,playing tennis,test _jC9HdDGRqc,26,playing volleyball,train _jCJO4w-lkw,1011,parrot talking,train _jCJO4w-lkw,1235,parrot talking,train _jE0dGlq710,170,"race car, auto racing",train _jEIZwyeoXA,290,people burping,train _jGUaJe_ys8,43,firing cannon,train _jHXhU5oK_Q,10,"subway, metro, underground",train _jJlubOzGN8,21,child singing,train _jKyXrB82ZE,87,slot machine,train _jLo8iosF_E,8,dog whimpering,train _jN2wZp83JM,150,people crowd,train _jNM1xGsbB8,95,people humming,train _jNM1xGsbB8,139,people humming,train _jP7A1uV5O4,56,people 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leaves,test _kTrCCdC5CY,20,goose honking,train _kWbVJfzStc,55,playing sitar,train _kWbVJfzStc,115,playing sitar,train _kX7ALlIhN4,30,"engine accelerating, revving, vroom",train _kY-d3XBDRM,701,police radio chatter,train _kZi3M9i5zU,21,playing cymbal,train _kZxDjK072I,76,playing steelpan,train _kZxDjK072I,102,playing steelpan,train _kbZdBbZB7I,1,dog baying,train _kbZdBbZB7I,20,dog baying,train _kcI7pQqPtY,477,pumping water,train _kcI7pQqPtY,490,pumping water,train _kcsfDD9eio,68,people nose blowing,train _kdj4cVnRWI,68,shot football,train _kgVaDLEXMQ,47,snake hissing,test _kiAg6_20Ds,97,strike lighter,train _kjB88Ls4g4,5,playing bugle,train _kjeVEhUsmg,170,playing accordion,train _kkdAGTlhn0,102,playing bugle,test _kl2mdRYyK4,62,rapping,train _kl2mdRYyK4,77,rapping,train _klUrw_uf70,27,people eating noodle,train _klUrw_uf70,107,people eating noodle,train _knj_9JEm-U,0,chainsawing trees,train _ko_ucGKALY,87,roller coaster running,train _kyD4DmInkI,10,"race car, auto racing",train _kzlkLsoYEE,82,tractor digging,train _l0Cv37sTZg,637,playing squash,train _l0Fscoo4MM,70,chopping wood,train _l3HTM_I9Zw,433,crow cawing,train _l3rjC9t25c,898,lip smacking,test _l3rjC9t25c,935,lip smacking,test _l4Al779h3Q,20,"race car, auto racing",train _l4M84_WbJA,156,playing hammond organ,train _l5aj9nhvEg,150,playing clarinet,train _l5zh2nikMk,2,"alligators, crocodiles hissing",train _l5zh2nikMk,299,"alligators, crocodiles hissing",train _l80H-FL3II,26,people gargling,test _l9MrG0YEMs,10,"bird chirping, tweeting",train _lAmUZm_h3k,71,playing badminton,train _lCrr8sNqww,110,people cheering,train _lDi3nLFQdw,59,yodelling,train _lEgkRyf6j8,10,"electric shaver, electric razor shaving",train _lF16jzhM-A,60,playing cymbal,train _lFcPAJBIoQ,50,playing piano,train _lKGsWAoK80,120,orchestra,train _lNJ0U_lcKA,70,tractor digging,train _lOI2UlRWEE,124,volcano explosion,train _lThcl6HObo,22,tap dancing,train _lVoUIrA-lw,30,playing electronic organ,train _lWKSo_MziU,140,people belly 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electric fan,train _lt2xGK8kgE,179,playing volleyball,train _ltrwGQkIr0,0,skidding,train _lv3tjLbvUE,30,sloshing water,test _lvdxzdMU18,11,people burping,train _lyrXjJF6JQ,0,snake hissing,train _lyrXjJF6JQ,11,snake hissing,train _lzsCN9Q-Zw,27,printer printing,train _m1bnJn9Ngc,2,opening or closing car electric windows,train _m4RUlsBf0I,61,playing snare drum,train _m4RUlsBf0I,84,playing snare drum,train _m4aRAMuLlc,50,people babbling,train _m695EFZwOE,30,canary calling,train _m695EFZwOE,55,canary calling,train _m6lwfMU8Eo,272,"electric shaver, electric razor shaving",train _m6lwfMU8Eo,393,"electric shaver, electric razor shaving",train _mB-DrTf79o,130,lawn mowing,test _mB3Hul3uvw,366,bowling impact,train _mBFr_5_Exc,30,using sewing machines,train _mBsB6rij7E,260,sailing,train _mC0eOGgpx4,839,wood thrush calling,train _mC0eOGgpx4,855,wood thrush calling,train _mCy7y70WQc,19,people crowd,train _mG9p_XN2L0,330,"bird chirping, tweeting",train _mGXFFx7ooo,0,canary calling,train _mGrwBl_BMw,10,singing bowl,test _mHH3D36VV0,30,car passing by,train _mN4UvtRReI,500,driving buses,train _mND7cM7vXc,54,playing timpani,train _mND7cM7vXc,214,playing timpani,train _mOHMo8-sg0,30,people whistling,train _mQRtpuzpWY,10,playing synthesizer,train _mQxtXpvbcI,130,lawn mowing,train _mTjcvZTj1I,18,playing sitar,train _mTjcvZTj1I,492,playing sitar,train _mUgv5zktjA,343,playing zither,train _mUgv5zktjA,460,playing zither,train _mVW8tgGY_w,142,strike lighter,train _mVW8tgGY_w,158,strike lighter,train _mVk_DahMBc,534,playing bass drum,train _mWBTssGnJE,44,"heart sounds, heartbeat",train _mWBTssGnJE,79,"heart sounds, heartbeat",train _mWFylQlyxk,108,baby crying,train _mYkWRuA4Ig,44,playing harpsichord,train _mYkWRuA4Ig,54,playing harpsichord,train _maNgGFgQes,7,yodelling,train _mah4Q004sg,17,dog howling,train _mazflpVg2s,38,"ice cream truck, ice cream van",train _mdK8HFvLGo,32,singing bowl,train _mdbDli9Zzk,449,playing hammond organ,train _mjfkZkpoGE,10,skateboarding,train _mkQWll5ufI,460,goose honking,train _mn5oMc1EBk,126,people booing,train _msO2UQFTNE,34,zebra braying,train _msO2UQFTNE,46,zebra braying,train _mseUi-DGfo,27,playing saxophone,train _mseUi-DGfo,89,playing saxophone,train _muV5ylO41o,28,people booing,train _muV5ylO41o,55,people booing,train _mvJPZzcoS4,0,bowling impact,train _mxCHcrDlxo,0,mosquito buzzing,train _n-kUOZk0A8,130,people screaming,train _n3Fig3O9Io,110,playing electronic organ,train _n5BGo_HUYs,0,fireworks banging,train _n5GtoEuO9c,60,"bee, wasp, etc. buzzing",train _nBvp8vd8nk,30,playing trumpet,train _nELHMKBc_s,2,playing steelpan,train _nELHMKBc_s,148,playing steelpan,train _nFo6r6CPTY,530,"subway, metro, underground",train _nJGColykyY,39,driving snowmobile,train _nKpEymxdyk,81,penguins braying,test _nMkSRVn6G0,12,baltimore oriole calling,test _nPtAhohJ2Q,30,people sniggering,train _nUaENqaw2E,0,playing clarinet,train _nUaENqaw2E,26,playing clarinet,train _nV7oGzAT0I,30,cat purring,train _nWRAYt0_H8,246,police radio chatter,train _nWRAYt0_H8,259,police radio chatter,train _nX4NSHlb0Y,29,mouse clicking,train _nZdrLaMez0,0,people screaming,train _naSi3YAHxw,26,people burping,train _nbIqoREEt4,2,frog croaking,train _nbIqoREEt4,308,frog croaking,train _nc73WaicZA,20,singing bowl,train _ndfvbeSUQI,21,"engine accelerating, revving, vroom",train _ndvkNuB76U,51,dog growling,train _nfVMH-FsPM,0,playing bass guitar,train _nfkvQ_a3uU,25,playing mandolin,train _nfkvQ_a3uU,37,playing mandolin,train _ngcmanTBmo,35,plastic bottle crushing,train _nlaU3VUfe0,60,lions growling,train _nnIU26WiUQ,30,playing hammond organ,train _nnPVbe3AOs,30,printer printing,train _nrSxOilRIU,166,dog baying,train _nsluz3vxXA,437,arc welding,train _nukhX-fSNA,10,duck quacking,train _nukhX-fSNA,55,duck quacking,train _nvBNi1pNFE,30,"engine accelerating, revving, vroom",train _nxJQ6L6ij0,157,reversing beeps,train _o0qE5K1uwU,5,skidding,train _o4xUJyXrVY,60,playing flute,train _o54wuYNIWI,19,lathe spinning,train _o54wuYNIWI,29,lathe spinning,train _o5xtnLwtRc,0,people coughing,train _o8yh1Zufic,270,people crowd,train _oAl1SXNYfI,180,people sniggering,train _oCAeUk3YWo,10,fireworks banging,train _oFh8VzSq7Y,250,"subway, metro, underground",train _oLqB2s4YAA,30,playing french horn,train _oM7gcez7sE,0,playing drum kit,train _oNtWZJu-5Q,161,tractor digging,train _oOACZfnzUU,302,people shuffling,train _oOACZfnzUU,331,people shuffling,train _oSObzOOqMA,0,playing harp,train _oSObzOOqMA,59,playing harp,train _oVcC1E_pbE,150,wood thrush calling,train _oWQDtLPJLk,30,playing drum kit,train _oZ1JuoFwiI,30,baby crying,train _oZ80GPWdk8,78,driving snowmobile,train _oZ80GPWdk8,369,driving snowmobile,train _o_-wOIbysY,30,playing snare drum,train _obGj4Vnr7c,260,playing saxophone,train _oel7mDllgs,20,"bee, wasp, etc. buzzing",train _ogUEPqs1PU,31,owl hooting,train _ogUEPqs1PU,41,owl hooting,train _oivzQNTr6Y,247,playing saxophone,train _olMBq9ihZU,30,baby crying,train _olx6WTK2BA,0,stream burbling,test _om8omzWQew,13,"donkey, ass braying",train _omRlNWlc0U,262,slot machine,train _opVkCWq0ek,101,chimpanzee pant-hooting,train _opVkCWq0ek,124,chimpanzee pant-hooting,train _oqTatuKj-I,390,orchestra,train _oufCa8iplU,30,"rowboat, canoe, kayak rowing",train _ov2YNxuSJE,194,elk bugling,train _ov2YNxuSJE,209,elk bugling,train _ovFsqbWmpQ,40,playing piano,train _ow1e8UqmH8,304,missile launch,train _oyYkHkEn-U,18,police radio chatter,train _p-Ff4jU1G0,47,bouncing on trampoline,train _p-Ff4jU1G0,366,bouncing on trampoline,train _p-OXHZFKPs,160,dog whimpering,test _p04SYXuBSQ,10,dog barking,train _p2dP1Jfi7o,26,people nose blowing,train _p33XpbKyz4,21,pheasant crowing,train _p4TkjDTsdU,29,playing cymbal,train _p4TkjDTsdU,40,playing cymbal,train _p5k9xAk1OI,115,wood thrush calling,train _p8J0PpvA08,144,slot machine,train _pD2AB_oHQM,4,"alligators, crocodiles hissing",train _pD2AB_oHQM,38,"alligators, crocodiles hissing",train _pEahYnekTA,120,train horning,train _pFSFgPWnjE,252,frog croaking,train _pGWT7NWrTY,18,mouse clicking,test _pGWT7NWrTY,40,mouse clicking,test _pHaCx58Qfg,442,opening or closing car electric windows,train _pLQGhHDB5Y,72,playing bongo,train _pLQGhHDB5Y,119,playing bongo,train _pMIvJB5ciQ,70,driving buses,train _pNzy2jflXM,119,playing sitar,train _pNzy2jflXM,144,playing sitar,train _pOWGjZBJUY,30,driving buses,train _pP90iTjUXA,102,tap dancing,train _pSMw5FKHX0,40,people sobbing,train _pTTYvkYw6w,36,playing volleyball,train _pUs3xkPm2U,40,spraying water,train _pVqKXvQiiU,4,foghorn,train _pXrH3QuCNQ,24,playing tuning fork,test _pYgK3S2w4o,30,printer printing,train _pa-9nYl1QE,37,canary calling,test _pajRZhyzks,6,elk bugling,train _pajRZhyzks,22,elk bugling,train _pdD7YiV9Bo,67,volcano explosion,train _pdvWZ8Gwn8,170,chainsawing trees,train _pe7k8ShK40,450,"pigeon, dove cooing",train _peXjzFQ2_c,30,playing trumpet,test _pfccpy7Cqc,103,typing on typewriter,train _pfccpy7Cqc,180,typing on typewriter,train _pgO6AUf_DU,24,volcano explosion,train _ph3ZIM4dOc,87,chopping wood,train _phX1_CDv7M,38,playing harp,train _pj89Ayh08w,142,people booing,train _pj89Ayh08w,157,people booing,train _pkB5RpsuXM,110,lawn mowing,train _pn64nJ1rJg,110,using sewing machines,train _pnFy389pW8,30,mouse pattering,train _po72IgykUU,215,playing oboe,train _po72IgykUU,230,playing oboe,train _pol5sqnKEI,0,gibbon howling,train _porrCRmOWo,18,playing cymbal,train _pq4iu_vhd0,240,driving motorcycle,train _pquL-kF_FM,229,people cheering,test _ps4kaW40dU,51,bouncing on trampoline,train _ps4kaW40dU,217,bouncing on trampoline,train _psmDlbLisM,30,people crowd,train _pul8X1-TiU,75,firing muskets,train _pul8X1-TiU,101,firing muskets,train _puzUOwS0Xc,20,playing trumpet,train _pvCVTApfJI,290,playing piano,train _pw29z2UMms,30,goose honking,train _pyO0emZM5s,9,cricket chirping,train _pzvPXCV6ds,30,people burping,test _q0GyjC6le8,0,playing harmonica,train _q0GyjC6le8,179,playing harmonica,train _q39Tu7wOzw,4,francolin calling,train _q39Tu7wOzw,16,francolin calling,train _q3W8TiNpEM,204,playing hockey,train _q6QHT58A-k,88,playing french horn,train _q6QHT58A-k,249,playing french horn,train _q6VJXgWli4,369,beat boxing,train _q6hgUOR6Lk,90,lawn mowing,train _q8s93klams,51,playing bass drum,train _q8s93klams,91,playing bass drum,train _q8tY-22408,30,church bell ringing,train _q9VfFFVeCw,8,zebra braying,train _qDSR5skY0c,70,people coughing,train _qEAh8sL3UU,1,cuckoo bird calling,train _qEAh8sL3UU,32,cuckoo bird calling,train _qFYpIUWFTc,0,hail,train _qFYpIUWFTc,16,hail,train _qH2H45MnEE,121,playing cornet,train _qH2H45MnEE,131,playing cornet,train _qIiScutmXY,115,playing badminton,train _qJTgi-q4s0,30,printer printing,test _qKfm2Z2lYg,219,playing badminton,train _qKgCBJnCZo,114,reversing beeps,train _qKgCBJnCZo,214,reversing beeps,train _qKofVJp9g4,350,"female speech, woman speaking",train _qL20ImF8bE,14,people giggling,test _qL20ImF8bE,47,people giggling,test _qMAxS0uW7Q,4,playing glockenspiel,train _qMAxS0uW7Q,17,playing glockenspiel,train _qMU3-j4GSE,0,crow cawing,train 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_tFuxC74XDc,326,playing synthesizer,train _tGsFhqma44,150,metronome,train _tKvChohkqs,337,"alligators, crocodiles hissing",train _tO7YdyrHRs,2,electric grinder grinding,train _tO7YdyrHRs,206,electric grinder grinding,train _tQ8Tzd5LLM,22,dog howling,train _tQ8Tzd5LLM,33,dog howling,train _tQCvahXU-4,46,cupboard opening or closing,train _tQCvahXU-4,63,cupboard opening or closing,train _tQeEjenC_8,0,arc welding,train _tSDd9sR1tc,34,playing table tennis,train _tSDd9sR1tc,47,playing table tennis,train _tST2Jz7TxM,14,airplane flyby,train _tST2Jz7TxM,101,airplane flyby,train _tT1Vto2x30,0,"engine accelerating, revving, vroom",train _tTi5909uJc,131,playing theremin,train _tTpw443G6c,246,missile launch,train _tTpw443G6c,348,missile launch,train _tUYIyZZ2TE,535,hedge trimmer running,test _tUhZPFkwak,51,playing tambourine,train _tW5yHOoHoE,5,people cheering,train _tWDtqmgwhQ,30,"rowboat, canoe, kayak rowing",train _tYpZAIT_rc,23,playing mandolin,train _tYpZAIT_rc,46,playing mandolin,train _t_j0QXeQJ0,120,lathe spinning,train _t_j0QXeQJ0,572,lathe spinning,train _taEBDVQBYQ,913,striking pool,train _taEBDVQBYQ,941,striking pool,train _tc2-F86kjc,254,snake hissing,train _tgQqCryi0Q,736,ripping paper,train _tgQqCryi0Q,846,ripping paper,train _tgcJfLGBoM,8144,"bird chirping, tweeting",train _tgkYLwcQKk,243,police radio chatter,train _tgkYLwcQKk,280,police radio chatter,train _tjSmRjvNCI,0,car engine knocking,train _tjSmRjvNCI,11,car engine knocking,train _tljezjFF30,30,"playing marimba, xylophone",train _tp3R4iJPLQ,126,playing bongo,train _tp3R4iJPLQ,138,playing bongo,train _tqIYge0JKg,215,playing volleyball,train _tqvL6FDk9A,90,duck quacking,train _tr1NRiNriM,30,firing muskets,train _trIoYTPVjc,1,firing muskets,train _trIoYTPVjc,78,firing muskets,train _tuNjGHr9G4,65,rope skipping,train _tuNjGHr9G4,75,rope skipping,train _tupGTBFqms,46,tapping guitar,train _tupGTBFqms,102,tapping guitar,train _tzXSoaZ644,21,hammering nails,train _u-1tLuIwkU,220,"race car, auto racing",train 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aFhkbvjEBFU,76,black capped chickadee calling,train aFhkbvjEBFU,87,black capped chickadee calling,train aFiR4B_icSA,130,playing bagpipes,train aFimfm3XVBM,183,mynah bird singing,train aFimfm3XVBM,206,mynah bird singing,train aFm8de9dd0U,69,people eating apple,train aFm8de9dd0U,98,people eating apple,train aFoEDxoBijQ,61,playing glockenspiel,test aFoH8IxhYlE,53,warbler chirping,train aFoH8IxhYlE,64,warbler chirping,train aFpy5ux1gFo,24,dog growling,train aFpy5ux1gFo,71,dog growling,train aFtDubWKeEE,118,people sneezing,train aFv9k9jru9g,28,cricket chirping,train aFv9k9jru9g,39,cricket chirping,train aFwAO0NFMt4,150,volcano explosion,train aFwJ9lA_4Xg,30,people belly laughing,train aFwaEuhjcWA,230,lawn mowing,train aFyZ0bQ5m4s,0,train whistling,train aFynKC57CfI,23,typing on typewriter,train aFynKC57CfI,35,typing on typewriter,train aG-ShZ1hN90,160,playing trombone,train aG0XYaNIMec,0,air conditioning noise,train aG0XYaNIMec,20,air conditioning noise,train aG1wGSIqGR4,13,frog 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aHBRB0KY6zI,111,duck quacking,train aHBXN6K6O9s,100,baby crying,train aHBXN6K6O9s,125,baby crying,train aHC1kSQT75o,43,people gargling,train aHC1kSQT75o,75,people gargling,train aHEAK0iWqKk,200,"playing marimba, xylophone",test aHHbbqwcWn0,450,ambulance siren,train aHI_NnR92kw,57,people booing,train aHJHxmi08Mk,434,playing hammond organ,train aHL7RxI88z0,474,hedge trimmer running,train aHOhqkCw_UY,185,fox barking,train aHPaUxMHjrs,15,wind rustling leaves,train aHQ42pgXh6E,540,turkey gobbling,train aHQExTl0xMw,164,bouncing on trampoline,train aHQeamPCfVU,60,lions growling,train aHTfYcSa71w,126,playing ukulele,train aHTfYcSa71w,158,playing ukulele,train aHUoOSKCYQo,13,people booing,train aHWgt0HgnFY,54,playing cornet,train aHWgt0HgnFY,66,playing cornet,train aHWu9GY1JIs,3,air horn,train aH_GIons4KU,30,stream burbling,train aH_H-5W9We0,222,"heart sounds, heartbeat",train aH_rpUA1IOI,168,"alligators, crocodiles hissing",train aH_rpUA1IOI,179,"alligators, crocodiles hissing",train aH_xbzU6MDk,190,playing clarinet,train aHbNPDLP-go,160,wind noise,train aHeePaXGHUw,40,people screaming,train aHfhMnWzix0,60,playing electric guitar,train aHgZbroI-Co,28,"engine accelerating, revving, vroom",train aHgZsPIpAOk,30,driving motorcycle,train aHjbiC80YD0,109,people slapping,train aHlF-wuzdc4,130,"race car, auto racing",train aHoo_xrD-Tc,20,playing cymbal,train aHrAVnZ2Srs,0,train horning,train aHsLEHTgVmI,50,playing trombone,train aHuoX3agbFk,20,owl hooting,train aHuoX3agbFk,62,owl hooting,train aHusVDD5HdI,40,playing trumpet,train aHv0hpGKd6w,16,mosquito buzzing,train aHwbJLJW9o0,20,dog barking,train aHx50zHbHbE,13,"playing violin, fiddle",test aHzkCSXsrqg,27,vacuum cleaner cleaning floors,train aHzkCSXsrqg,38,vacuum cleaner cleaning floors,train aI2PD0lUW68,114,playing ukulele,train aI2PD0lUW68,239,playing ukulele,train aI3QJk7ZtE4,60,sheep bleating,train aI3QJk7ZtE4,91,sheep bleating,train aI6QnrV8qGE,39,duck quacking,train aI6QnrV8qGE,95,duck quacking,train 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water,train aISujlXZako,27,gibbon howling,test aITbvftspRA,0,church bell ringing,train aIUYLw8-acY,168,playing mandolin,train aIUYXVm9fuc,12,playing vibraphone,train aIUYXVm9fuc,100,playing vibraphone,train aIVgkJHeLGQ,53,underwater bubbling,test aIX6ODNzqPA,8,mouse squeaking,train aIXSYngXa3w,16,playing guiro,train aIXSYngXa3w,48,playing guiro,train aIb8fCweUkg,513,people eating crisps,train aIb8fCweUkg,529,people eating crisps,train aIfK7d1uOXs,30,car engine knocking,train aIgE5nRA26o,1,fire truck siren,train aIgE5nRA26o,12,fire truck siren,train aIhwzi_SKXA,40,playing bagpipes,train aIjQkQ36AVQ,80,people burping,train aIjsfoA2V7s,56,playing double bass,train aIkyvU5ohow,20,volcano explosion,train aIoze-Ni8Nw,33,lathe spinning,train aIoze-Ni8Nw,44,lathe spinning,train aItgQR5cFl0,62,playing cornet,train aItgQR5cFl0,76,playing cornet,train aIvfUWk-XCE,173,"playing steel guitar, slide guitar",train aIycUyMRpGA,20,tractor digging,train aIzKawCLyeU,96,playing oboe,train aIzKawCLyeU,132,playing oboe,train aJ41sea1s0U,80,people farting,train aJ68e-T-i8E,40,wind noise,train aJ6f-3q4cjI,0,people screaming,train aJ8PQ8xqw9Q,286,parrot talking,train aJ9Ml3rrtmE,6,warbler chirping,train aJAyYNGhmKU,70,playing flute,train aJBG1VNtqNg,30,"pigeon, dove cooing",test aJBHU0__eZI,413,playing bongo,test aJCOGMiW0SY,73,people coughing,train aJCOGMiW0SY,109,people coughing,train aJCdsoRa9vY,0,playing ukulele,train aJCdsoRa9vY,6,playing ukulele,train aJEJ__EDvdo,35,dog growling,train aJFX8CH0dBQ,144,people eating crisps,train aJFX8CH0dBQ,158,people eating crisps,train aJHg_H95js0,30,playing banjo,train aJJ0Ai6qbb0,443,ripping paper,train aJJ0Ai6qbb0,607,ripping paper,train aJJf0N75gpE,7,people sneezing,train aJKu_6M6WMw,484,playing badminton,test aJNiSRqdCco,11,people booing,test aJS0s_P-rlI,36,playing timbales,train aJS0s_P-rlI,94,playing timbales,train aJS5pPCq6A4,30,orchestra,train aJa3DYnAjqU,61,tap dancing,train aJa4JzaMzYE,40,people humming,train aJb09Ttp69Q,70,chainsawing trees,train aJdMWg3SXvs,70,"railroad car, train wagon",train aJeBQ1vFwHg,269,chimpanzee pant-hooting,train aJeKrxDd864,24,"child speech, kid speaking",train aJeV-_eW6Bg,130,elephant trumpeting,train aJiV7DVaxDI,23,alarm clock ringing,train aJiudk77Ghw,360,playing bugle,train aJknLNnAOJ4,110,people hiccup,train aJnWxozj9k8,320,playing piano,train aJne-lUSga8,30,people running,train aJnr-wHqZuE,120,roller coaster running,train aJnr-wHqZuE,245,roller coaster running,train aJoauH7KaGw,175,playing shofar,test aJpAmCQy3HY,57,train wheels squealing,test aJqKImEhTxc,200,wind noise,train aJrKckrVxnQ,0,ocean burbling,train aJrOmGLF2Wo,1,zebra braying,train aJrXtD-wz84,20,ambulance siren,train aJvi1Iy3p0w,170,female singing,train aJxrzNtTuq0,6,playing timpani,train aJxrzNtTuq0,27,playing timpani,train aJy80ff2TbU,30,wind rustling leaves,test aJzihMLJpzk,40,waterfall burbling,train aK1QpClccKU,30,playing accordion,train aK6WrCIJ_BQ,44,rope skipping,train aK6WrCIJ_BQ,73,rope skipping,train aKAcjWZ-Q5Q,90,lawn mowing,train aKBE8AgFKxI,30,playing trombone,train aKBV47TsckE,30,printer printing,train aKC7ks478v4,101,playing tympani,train aKC7ks478v4,210,playing tympani,train aKFlkWoQxvc,224,running electric fan,train aKFlkWoQxvc,253,running electric fan,train aKIjdaOrz_8,0,playing harp,train aKLdZwzT2Cc,350,"race car, auto racing",train aKLqt2Z-5nY,235,people nose blowing,train aKOb0Qm-aWI,70,playing trumpet,train aKRSM_r4TR4,289,ambulance siren,train aKRtUL-klhc,704,playing saxophone,train aKSVfbHOOsc,66,playing table tennis,train aKT7RRIU9FM,1,car engine starting,train aKT7RRIU9FM,35,car engine starting,train aKTPehuBw1E,217,tractor digging,train aKTPehuBw1E,239,tractor digging,train aKWG9CWMoWo,696,mouse clicking,test aKZ3NJi8GVU,0,baltimore oriole calling,test aKZjwdcZVUA,170,playing cello,train aKdPOb-VW7g,437,"electric shaver, electric razor shaving",test aKdV5FvXLuI,81,rapping,train aKdV5FvXLuI,114,rapping,train aKevq_vcE0M,30,people hiccup,train aKhkrAVCd1w,270,people clapping,train aKiE7Z76T2Q,0,rope skipping,train aKiYt30HO0k,4,pheasant crowing,test aKlWvoKoZ_U,0,tractor digging,train aKncHNorvhQ,0,frog croaking,train aKow8vjqBvI,0,stream burbling,train aKqXEI4bJNs,420,singing bowl,test aKqw1r_K2Ic,53,playing didgeridoo,train aKr_fqkbfr0,0,helicopter,train aKsed7AdsT0,13,playing glockenspiel,train aKt_wRjScYs,26,francolin calling,train aKxgQNrgZ1s,10,ocean burbling,train aKyK1Xqdu8w,53,fire truck siren,train aKyK1Xqdu8w,64,fire truck siren,train aKykxxu_7jQ,99,hail,train aKykxxu_7jQ,109,hail,train aL1NhFQH3R8,30,"race car, auto racing",train aL5y26yO99k,0,car engine knocking,train aL7-RUJ7ykk,80,chicken crowing,train aL9mS5JflEc,732,people slurping,train aL9mS5JflEc,909,people slurping,train aLBpUZ2oH5s,40,machine gun shooting,train aLE3wepex9s,6,parrot talking,train aLE8m0yKXkA,221,people giggling,train aLEJqXxfwxs,77,playing table tennis,train aLEJqXxfwxs,116,playing table tennis,train aLHbOk5Opw0,127,bowling impact,train aLJkS5JbkLo,0,fireworks banging,train aLK0qpljWDo,77,people eating crisps,train aLL-B4ul-Dg,620,people eating noodle,test aLL1L91Dr48,30,"pigeon, dove cooing",train aLRX0JnMJ90,41,mouse clicking,train aLTJsWCbDJ4,71,playing oboe,train aLTJsWCbDJ4,103,playing oboe,train aLTgXdcI_0c,90,stream burbling,train aLVo_B-rB9I,30,playing harp,train aL_GVLoeWnQ,0,goat bleating,train aL_OmG_cgxE,9,playing accordion,train aL__8TbYW0M,30,playing piano,train aLaWeNb6yHM,460,"race car, auto racing",train aLavVixFQRA,300,raining,train aLbd6Of6DHA,107,car engine idling,train aLcBwn-VOMg,177,dinosaurs bellowing,train aLcBwn-VOMg,263,dinosaurs bellowing,train aLcuT4ddBRY,90,stream burbling,train aLfAsIZhC0c,46,black capped chickadee calling,train aLlZVgTCq6U,189,playing badminton,test aLsL7dxlXbA,256,playing badminton,train aLssySylb0k,32,tractor digging,train aLtDcku3DsE,45,wind rustling leaves,train aLtDcku3DsE,157,wind rustling leaves,train aLtoKiT0qHM,249,lighting firecrackers,train aLtoKiT0qHM,315,lighting firecrackers,train aLvtTXSUagk,2,airplane flyby,train aLvtTXSUagk,14,airplane flyby,train aM-4JzJxeKE,41,civil defense siren,train aM-4JzJxeKE,59,civil defense siren,train aM0JMoGABgk,322,bird wings flapping,train aM0SFYL5A6c,19,"ice cream truck, ice cream van",train aM1ZYRR7h1A,75,owl hooting,train aM4KYTr7adQ,223,playing cello,train aM4vSbv9UCc,0,skidding,train aM6F0Jfsb1A,1,pheasant crowing,train aM9-Kv8KU9I,347,volcano explosion,train aM9-Kv8KU9I,363,volcano explosion,train aM9fEuqoIyE,14,baby laughter,train aM9fEuqoIyE,92,baby laughter,train aMCZ-JbSCmE,0,thunder,train aMDAoaCyB1c,73,roller coaster running,test aMFjOF48PgY,0,playing accordion,train aMI37J-JJjs,188,basketball bounce,train aMIC-rQjCMg,30,people running,train aMJFXtaFGKE,124,wood thrush calling,train aML_KcTrcNo,87,tornado roaring,train aML_KcTrcNo,157,tornado roaring,train aMLiQ2_0MLM,299,playing bass guitar,train aMMQe2pMDEE,440,playing harp,train aMOObhu2HBg,240,playing cello,train aMOWWNNLVns,40,people shuffling,train aMQ8RikNfc4,10,playing bagpipes,train aMQJ0sFLC8A,8,pheasant crowing,train aMScHLRsuiM,30,"subway, metro, underground",train aMUm-tO74tc,145,playing table tennis,train aM_udRtQXAM,30,people sniggering,train aMacjQ9_4FY,30,church bell ringing,train aMbHyI-WC0M,19,playing timbales,train aMbHyI-WC0M,363,playing timbales,train aMcYLl4Xq_U,18,police car (siren),train aMd2L3UJtZ0,280,sailing,train aMeUT6WdKh0,340,playing synthesizer,train aMfJHPmmjus,0,dog howling,train aMfJHPmmjus,51,dog howling,train aMgRBk6PvDs,75,fire truck siren,train aMgRBk6PvDs,158,fire truck siren,train aMleAYsEwsE,245,playing volleyball,train aMlw0VrE0tk,30,car engine knocking,train aMpghH8chf0,27,cell phone buzzing,train aMqDyHLGo10,30,"playing violin, fiddle",train aMt0gIQ0vco,145,baby crying,train aMv_kCEpECM,30,"playing marimba, xylophone",train aMwWAwa_mqM,70,cattle mooing,train aMx96sTccdg,7,frog croaking,train aMx96sTccdg,19,frog croaking,train aMxYWSmKjZY,183,planing timber,train aN4Ot87kqQU,166,playing sitar,train aN4Ot87kqQU,245,playing sitar,train aN4VaaPbptE,137,car engine idling,test aN5E6snD5vw,30,playing bass guitar,train aN5pSfdJOOw,0,playing bagpipes,train aN5pSfdJOOw,15,playing bagpipes,train aN6-n9Fas9I,0,horse neighing,train aN6-n9Fas9I,22,horse neighing,train aN7l0CcB92A,0,playing saxophone,train aN8gUXvQXok,137,playing vibraphone,train aN8kpSurTa4,64,playing bassoon,train aNArqTW4cbc,25,"vehicle horn, car horn, honking",train aNArqTW4cbc,35,"vehicle horn, car horn, honking",train aNDaoF4QxM0,207,playing ukulele,train aNDmHGRsmnM,27,typing on computer keyboard,train aNEPOLrcg5M,22,cricket chirping,train aNEPOLrcg5M,45,cricket chirping,train aNGu4UBaNmw,30,playing cymbal,train aNJSALehmxc,0,civil defense siren,train aNJSALehmxc,10,civil defense siren,train aNKJs1Y59RU,92,sliding door,train aNKWalYyU0g,12,chicken clucking,test aNKjWWMQpew,70,"playing marimba, xylophone",train aNM0sDFgGrI,0,playing bagpipes,train aNM0sDFgGrI,18,playing 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aO3CKc-ia8c,0,people screaming,train aO4ommBFnLM,87,playing tabla,train aO4ommBFnLM,107,playing tabla,train aO5LRQS08E4,87,smoke detector beeping,test aO6HP_IPKg0,110,helicopter,train aOA30aOIBds,8,underwater bubbling,train aOCuFzPYFoU,8,playing cornet,train aOEPCvd_0_0,0,playing glockenspiel,test aOFZG4JnDtA,0,"vehicle horn, car horn, honking",train aOGvHTXY2Us,849,playing oboe,test aOH6ZezITMc,16,popping popcorn,test aOHO1k3e2hI,20,people whistling,train aOI5Zb2dchU,49,playing didgeridoo,train aOLaAeeVs5o,0,playing table tennis,train aOM-Ru3rjXA,20,"playing violin, fiddle",train aOMX_iLw47c,10,opening or closing drawers,train aONNz0qFBUg,91,playing sitar,train aONNz0qFBUg,145,playing sitar,train aOOW_MFCxkA,36,francolin calling,train aOOW_MFCxkA,118,francolin calling,train aOOqSWgmRJM,33,"playing violin, fiddle",test aOPbCQR1nyo,19,playing harp,train aOPnek98MkQ,70,playing electric guitar,train aOQYoz6C-8k,51,mouse pattering,train aOR4wN_vTv8,30,toilet flushing,train aOW5jvddKbU,30,typing on computer keyboard,train aOXDpDW4AiM,15,fireworks banging,train aOZLQhoACtQ,60,lions growling,train aOa2KHuEPgs,137,playing lacrosse,test aOaCU-8rHSc,39,swimming,train aOb74I6Yfz0,56,roller coaster running,train aOg-kmmRnP4,1,tornado roaring,train aOg-kmmRnP4,166,tornado roaring,train aOhvTB_9GAQ,30,singing choir,test aOirMMXO6Og,9,lighting firecrackers,train aOk2XUtCeKg,291,sharpen knife,train aOm2f_anBxM,591,"child speech, kid speaking",train aOnjJYPfmCs,0,crow cawing,train aOqleVQCmwI,30,fire truck siren,test aOutPKIT0gQ,27,playing squash,train aOv6a7um-0s,30,chopping wood,train aP-zlo8AsOI,11,missile launch,train aP0nFHftn9A,70,typing on typewriter,test aP1JcrmLeRs,0,playing ukulele,train aP1JcrmLeRs,45,playing ukulele,train aP3YC0i0yyY,82,hedge trimmer running,test aP4t-BGt2O0,33,eletric blender running,test aP4t-BGt2O0,43,eletric blender running,test aP7lK2dtwXQ,26,rope skipping,train aP7lK2dtwXQ,78,rope skipping,train aP8WWNc66fs,121,playing squash,train aP957d3FJDw,0,printer printing,train aPAYn1g9hrg,2,duck quacking,train aPAYn1g9hrg,20,duck quacking,train aPBK07__7u0,60,playing accordion,train aPEPCC4pPkk,0,helicopter,train aPGfndmW1zM,263,playing table tennis,test aPIM34FLfXc,20,"engine accelerating, revving, vroom",train aPJN04vC5lM,0,mosquito buzzing,train aPJajhgd6bU,280,lions growling,train aPJzYMe4aW4,30,"rowboat, canoe, kayak rowing",train aPK7X-vq9HU,70,people coughing,train aPMtVspavDo,10,thunder,train aPOJ5yOElMs,269,airplane flyby,train aPONneIX9UE,480,opening or closing drawers,test aPPNBRdMKZQ,30,playing acoustic guitar,train aPQTrv2B1sw,250,male singing,test aPQtK3lZPVw,20,playing clarinet,test aPRH3YE7Lpo,253,"fly, housefly buzzing",test aPRPrBUU4j4,5,wind chime,train aPS7f_jeYVU,49,playing snare drum,train aPUV-cIxi2M,30,"subway, metro, underground",train aPVfekPf13w,30,playing drum kit,train aPXlevfejuo,26,sailing,train aPZVh9_6xGA,110,playing badminton,train aPavkNeB2YY,50,chicken clucking,train aPcRHkPHqs8,87,airplane,train aPfTldlfdAw,12,people burping,train aPgZLg6Oqe0,5,bouncing on trampoline,train aPgZLg6Oqe0,18,bouncing on trampoline,train aPhUHrny1to,6,playing squash,train aPiku3leRto,40,"engine accelerating, revving, vroom",train aPlNiMG2Gpo,52,playing bongo,train aPmnFbjsZWQ,217,slot machine,train aPn32IeVM14,108,slot machine,train aPnlWBUlbLE,90,"playing marimba, xylophone",train aPqTlhfdz8k,88,tap dancing,train aPqTlhfdz8k,125,tap dancing,train aPrBLO9cqXo,280,splashing water,train aPxoG5P7ra8,18,lathe spinning,train aPz1C1VxSJM,8,dog baying,train aPz1C1VxSJM,21,dog baying,train aPzVoDPUd5k,90,church bell ringing,train aPzWyqfKZ_8,0,bird squawking,train aQ2IqzZvwZ8,4,dog howling,train aQ4OZzkjjEE,47,spraying water,train aQ6yO9-BAtI,40,cap gun shooting,train aQ71LojkkiU,150,"bee, wasp, etc. buzzing",train aQ8829oJP1E,1,owl hooting,train aQ8829oJP1E,13,owl hooting,train aQCCfEsWZY0,4,fox barking,train aQG92U3aevk,93,playing french horn,train aQGDsMyNXvo,205,hedge 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aQjit5p0j6I,575,car engine starting,train aQjit5p0j6I,602,car engine starting,train aQlLu0JvsOI,0,people screaming,train aQpMRrqK_wU,192,singing choir,train aQpMRrqK_wU,203,singing choir,train aQpis4W9Z9Q,10,dog growling,train aQpis4W9Z9Q,22,dog growling,train aQsqrzPtA1c,30,"rowboat, canoe, kayak rowing",train aQwTwSfJDa4,30,dog whimpering,train aQwTwSfJDa4,44,dog whimpering,train aQxRm0vuLXk,95,people burping,train aQzDvbkWcNU,150,playing saxophone,train aQzcyT2BaPc,50,helicopter,train aR-FuK-8i7w,80,people screaming,train aR0Ajqb_Wzk,73,bowling impact,train aR2xrECuGQE,10,firing cannon,train aR2xrECuGQE,199,firing cannon,train aR413QGq2E8,326,people eating noodle,train aR8ZjITwYBs,20,dog growling,train aRCj4BjgICo,30,people sniggering,train aRCjKBpR9GY,101,singing bowl,train aRF0f0qxZc4,0,slot machine,train aRFbYqp4hy0,270,playing hammond organ,train aRGkw6NLW3c,30,"vehicle horn, car horn, honking",train aRI4l67ZlYQ,63,planing timber,train aRI4l67ZlYQ,135,planing timber,train aRIn4E2KqS4,330,playing accordion,train aRNCCMPDmpc,84,pheasant crowing,train aRNCCMPDmpc,128,pheasant crowing,train aRQZl6LLdfs,101,playing didgeridoo,train aRR0IVC3da4,200,playing banjo,train aRRrszC_gy0,11,cheetah chirrup,train aRSOFp0Y-4k,310,singing bowl,train aRVYOqN1-T8,10,toilet flushing,train aRVhEV5wSYA,240,playing cello,train aRYGbTPEZjE,230,playing synthesizer,train aRYncGVvTPY,393,bowling impact,train aRZMQEFsvek,30,turkey gobbling,train aR_1YsQBjiA,68,woodpecker pecking tree,train aR_1YsQBjiA,81,woodpecker pecking tree,train aR_vxJKLJ54,4,horse neighing,train aRbesDf7X2Q,20,children shouting,train aRbiOq2gdRo,30,people shuffling,train aRcsnB-lUHQ,80,car engine starting,train aRfC5YUoDxc,19,firing muskets,train aRgu2m5FHiI,225,arc welding,train aRgu2m5FHiI,247,arc welding,train aRhx2JtCjYs,0,tap dancing,train aRhx2JtCjYs,152,tap dancing,train aRpV4uaLdIc,87,people booing,train aRvagX7Y2ro,34,"subway, metro, underground",train aRvtzhi_JCw,150,people screaming,train aRxLlentNO0,0,elephant trumpeting,train aRzE8c_o7cQ,244,playing badminton,train aS1Ej3SDg38,536,typing on typewriter,train aS1hsJDiX3Y,16,people whistling,train aS310i8wy_Q,0,coyote howling,train aS310i8wy_Q,11,coyote howling,train aS4pa9XV7yA,30,people whispering,test aS5eOryeLbM,210,singing bowl,test aS72WtEXFAc,60,helicopter,train aSADpcWgGu8,390,"subway, metro, underground",train aSDfi0CMrKg,63,lions growling,test aSDoEYTHGUA,30,"pigeon, dove cooing",test aSEF_AOenW8,245,cutting hair with electric trimmers,train aSEF_AOenW8,278,cutting hair with electric trimmers,train aSF4PVmgU0c,30,people crowd,train aSJKZRwMsxE,50,female singing,test aSKxhLzd9NA,70,playing bass guitar,train aSL5oKrmOMg,150,car passing by,train aSNvXR_ErH0,0,tapping guitar,train aSNvXR_ErH0,94,tapping guitar,train aSOq9SBc7Os,280,playing drum kit,train aSPzTOxWV_Y,29,cattle mooing,train aSPzTOxWV_Y,49,cattle mooing,train aSQbxVpm2f4,381,playing volleyball,train aSRl_33lPhI,245,chainsawing trees,train aSSVxZYiGgA,259,playing badminton,train aSUocHI9tJw,210,people clapping,train aSW0gGcx_AA,220,"race car, auto racing",train aSW7XTLeBHk,274,rope skipping,train aSW7XTLeBHk,327,rope skipping,train aSWcCSj2N7s,98,people cheering,train aSXOoOhp_Ng,11,"engine accelerating, revving, vroom",train aSXfQh7FHBE,38,chimpanzee pant-hooting,train aSYCwv_hda8,30,"subway, metro, underground",train aS_rrOm6zUk,91,magpie calling,train aS_rrOm6zUk,123,magpie calling,train aSaK_yeY5jw,4,wood thrush calling,train aSaK_yeY5jw,64,wood thrush calling,train aSbEp3De3BQ,28,tractor digging,train aSf4paTGiz4,80,chicken crowing,train aSgmEBPgGuA,500,"railroad car, train wagon",train aSheN1oz4Io,0,people clapping,train aSizMziKwBs,30,"motorboat, speedboat acceleration",train aSjFm48NuLg,30,driving buses,train aSkVQ21SeQg,1,basketball bounce,train aSleAKgkDDk,0,playing accordion,train aSmlkEMpkDA,27,people burping,train aSoO6_2r86M,53,playing tambourine,train aSqATVMq_Us,81,police radio chatter,train aSqATVMq_Us,122,police radio chatter,train aStXYvL9qIk,74,people eating crisps,train aSuq-GgibAU,46,eagle screaming,train aSuq-GgibAU,56,eagle screaming,train aSvReJlw71I,30,goose honking,train aSw9lSxX924,30,splashing water,train aSzPZHOW3-E,30,chicken crowing,train aT-Mx4gUsGE,0,playing drum kit,train aT2H95zTChA,38,ferret dooking,test aT3gUDiDGBI,36,people finger snapping,train aT3gUDiDGBI,54,people finger snapping,train aT4vp8WK0QM,101,chopping wood,train aT4vp8WK0QM,112,chopping wood,train aT5tuQWMuLs,17,canary calling,train aT5tuQWMuLs,199,canary calling,train aT5vkweKaCU,200,playing vibraphone,train aT6bc2MM0Xg,30,toilet flushing,train aT7Ube1VuB4,48,playing glockenspiel,train aT7Ube1VuB4,114,playing glockenspiel,train aT7Xt3KsAzk,55,playing tabla,train aT7Xt3KsAzk,168,playing tabla,train aTAArJcL_rQ,30,"playing marimba, xylophone",train aTBRxhGDyfM,0,playing bagpipes,train aTCbCEFGFiw,265,people cheering,train aTCbCEFGFiw,287,people cheering,train aTDz4zcKnpc,39,chipmunk chirping,train aTDz4zcKnpc,55,chipmunk chirping,train aTF-L57U7Js,0,driving motorcycle,train aTFzwmxadaI,688,car engine idling,train aTFzwmxadaI,796,car engine idling,train aTHIF_WR1b8,0,people farting,train aTJ6bswqjgM,19,plastic bottle crushing,train aTJ6bswqjgM,31,plastic bottle crushing,train aTKFwo1ziWo,30,playing cello,train aTNAtCsnr0g,30,playing snare drum,train aTP96yE7cMY,544,"subway, metro, underground",train aTRM6_A5MGY,20,helicopter,train aTRwnFJqTmI,80,using sewing machines,train aTUBskS1TDc,0,chicken crowing,train aTVmj2Tti1s,135,police radio chatter,train aTWdYwhQ64I,98,foghorn,train aTY-qvtDbrc,24,playing castanets,test aTYxcO2D3W4,30,"engine accelerating, revving, vroom",train aTZJUJqpmas,113,people marching,train aTZtMne7a8U,18,playing congas,train aTZtMne7a8U,49,playing congas,train aTZwsApKf1k,105,wood thrush calling,train aTZwsApKf1k,191,wood thrush calling,train aT_XRmu0aSU,9,people giggling,train aTabkVOyP3w,165,people slurping,train aTabkVOyP3w,384,people slurping,train aTbHFrtjyTM,510,people farting,train aTdlRufHll8,44,rapping,train aTdlRufHll8,66,rapping,train aTdpU4HGxXA,109,alarm clock ringing,test aTk0LCf1_Kw,9,basketball bounce,train aTk0LCf1_Kw,31,basketball bounce,train aTky4OLxfP8,30,people booing,train aTkyITpN8oE,78,dog growling,train aTkyITpN8oE,121,dog growling,train aTllQx-v5Uk,79,car engine knocking,train aTllQx-v5Uk,113,car engine knocking,train aTp6RxFMrxU,0,playing electric guitar,train aTplTs_ffX8,30,driving buses,train aTrsVGf3ecM,54,people slapping,train aTsk8DGCcmc,42,airplane,train aTuRI_iWsOc,10,people farting,train aTvoYQ7nRJc,70,basketball bounce,train aTzCrxi1a-Y,30,raining,train aU17f5EdvqE,85,playing washboard,train aU17f5EdvqE,186,playing washboard,train aU1d28eLqVk,5,playing gong,train aU1d28eLqVk,15,playing gong,train aU3OP1m0TBw,11,dinosaurs bellowing,train aU3OP1m0TBw,40,dinosaurs bellowing,train aU3PT6cU-5g,395,playing tambourine,test aU3Rag5zhc0,22,chinchilla barking,train aU3Rag5zhc0,76,chinchilla barking,train aU4cW2jRYwk,490,writing on blackboard with chalk,train aU4cW2jRYwk,561,writing on blackboard with chalk,train aU4qvvyI_gk,12,skiing,train aU728tK9TY8,103,beat boxing,train aU8dxXh2yqk,390,chicken crowing,test aUASiqfolH0,230,playing cymbal,train aUB5vLIz8xg,102,police radio chatter,train aUCsTswOOMk,1,airplane flyby,train aUCsTswOOMk,60,airplane flyby,train aUCvBsCtJMs,260,lawn mowing,train aUDssmruZ78,170,dinosaurs bellowing,train aUDssmruZ78,602,dinosaurs bellowing,train aUEA7Evrh-A,68,people gargling,train aUEA7Evrh-A,154,people gargling,train aUG81LArlcM,49,baltimore oriole calling,test aUHfOzhQK7s,2,playing tabla,train aUHfOzhQK7s,356,playing tabla,train aUIWfd8qOFw,1,people booing,train aUJDmAXsEvs,24,otter growling,train aUJDmAXsEvs,38,otter growling,train aUMmP6x3hiE,30,"pigeon, dove cooing",train aURKi3rs4yY,228,singing bowl,train aUT4OweoT9Y,93,driving snowmobile,train aUTjDDraUaY,278,chimpanzee pant-hooting,train aUTjDDraUaY,412,chimpanzee pant-hooting,train aUTwsvJOu7Q,754,slot machine,train aUVT2tUbPN8,187,spraying water,test aUVx-O4zacE,28,opening or closing car doors,train aUVx-O4zacE,39,opening or closing car doors,train aUX7NmOHq7I,60,toilet flushing,train aUb0z7LjP8A,7,owl hooting,train aUb0z7LjP8A,18,owl hooting,train aUcWb56z-cE,390,people clapping,train aUcmfXMLD4E,0,horse neighing,train aUeysGoPFTk,28,playing cello,train aUeysGoPFTk,61,playing cello,train aUfDxRelPHg,22,lions roaring,test aUfuuj7HO1c,67,tractor digging,train aUiSdiYKKCU,29,horse neighing,train aUkyM2vPI-o,30,goose honking,train aUleDsFtQjs,100,thunder,train aUpV1hL3wuE,30,playing bassoon,train aUpcJRI1NyE,430,stream burbling,train aUq3toj7jDo,18,people running,test aUs67fcQPXc,28,baby crying,train aUsV4aAZqwQ,118,playing oboe,train aUvnIbyLGHk,58,canary calling,train aUvnIbyLGHk,100,canary calling,train aUx0xMF9pwU,20,playing vibraphone,test aUykDHLJ4WY,0,playing synthesizer,train aUzBBPndSzU,51,alarm clock ringing,train aV-b339_4rk,160,playing cello,train aV-wXhi_heM,1,rope skipping,train aV-wXhi_heM,47,rope skipping,train aV15HQTIGeg,63,"electric shaver, electric razor shaving",train aV1UcxVdX2U,200,splashing water,train aV2lph0a9DM,50,people farting,train aV4B4_IypmQ,100,playing harp,train aV6bqF2S0iI,31,playing harp,train aV6hrXe0IQw,22,woodpecker pecking tree,train aV6hrXe0IQw,33,woodpecker pecking tree,train aV71qwSxyEE,35,dog howling,train aV71qwSxyEE,114,dog howling,train aV8X6TwigFE,30,goose honking,train aVEq5k6uy8I,0,mynah bird singing,train aVGH42EpsSA,94,car engine knocking,train aVH7hvDd4xA,0,turkey gobbling,train aVI-YoccwW8,92,playing theremin,train aVLm9VuZwh8,8,"vehicle horn, car horn, honking",train aVLrCy_892Q,30,typing on computer keyboard,train aVMIVDv2Scg,110,playing badminton,train aVNiqpChepM,22,playing harpsichord,train aVRAjLKg8mI,30,playing hammond organ,train aVSPjwbQvpM,200,chicken clucking,train aVSrBoOZNjs,62,playing bassoon,train aVUiu1FklVs,332,strike lighter,train aVVYr0kEBIg,355,whale calling,train 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aZf2b3FBwMc,413,playing bongo,train aZfszZqR7i4,120,"subway, metro, underground",test aZfznvoQeCs,29,footsteps on snow,train aZfznvoQeCs,64,footsteps on snow,train aZgemH7reJU,260,playing bass drum,train aZglCwIbknc,283,tractor digging,train aZhsfeNxqMs,87,playing bongo,train aZj3L_Pjz8A,100,children shouting,test aZkBQ8-NZNc,110,playing saxophone,train aZkxBMMS848,30,playing electronic organ,train aZlH9GX2daw,189,playing bassoon,train aZmN_hzwJAs,26,people battle cry,test aZnIdOtKVSg,35,playing table tennis,train aZqCW-NZCnc,0,people babbling,train aZqwav3PYpY,36,airplane flyby,train aZr2w4U0Pow,331,slot machine,train aZsMmmczKm0,48,playing bass guitar,train aZtGzz9nUxg,34,playing french horn,train aZtGzz9nUxg,101,playing french horn,train aZtUKfuGKjU,180,playing accordion,train aZuIsWDbw8s,28,yodelling,train aZwdq_1q27E,160,bird wings flapping,train aZxm-nYkpNs,3,cricket chirping,test aZyCkWKCX3w,162,playing glockenspiel,train aZyCkWKCX3w,267,playing glockenspiel,train aZylgzEv-Ck,80,skateboarding,train a_0c7fSu_O0,23,printer printing,train a_0luv2NBvM,23,ambulance siren,train a_9DVkmfG5M,270,playing electric guitar,train a_9XjRL1kZo,30,people whistling,train a_A3Ig-3ZR8,30,playing drum kit,train a_AXL-fBPnI,42,opening or closing car electric windows,train a_EW6DgJMTU,56,hedge trimmer running,train a_EbiX2hGSY,18,chainsawing trees,train a_G5PjYSwTw,20,wind noise,train a_GvHsLJ1JQ,7,sea lion barking,train a_H3ARhV1o4,37,shot football,train a_HZ5bMXveM,33,baltimore oriole calling,test a_HzXa0YVIo,170,"rowboat, canoe, kayak rowing",test a_IEy0PjSzw,0,basketball bounce,train a_JxbHMx2pM,30,printer printing,train a_LEqw0beNU,30,people whispering,train a_R8QxsUSr4,144,blowtorch igniting,train a_WQFseeFYA,15,cricket chirping,train a_WQFseeFYA,74,cricket chirping,train a_Wvjj6Yq1Y,2,crow cawing,train a_Wvjj6Yq1Y,12,crow cawing,train a_YY_qkoHz8,510,stream burbling,train a__69686u5k,12,bird wings flapping,train a__6TKTOqXE,257,vacuum cleaner cleaning floors,train a__dMjObbJY,109,slot machine,train a__wFAetAUE,350,"railroad car, train wagon",train a_aHTNC78JY,55,people burping,train a_dHJjMT2BE,1,"bee, wasp, etc. buzzing",train a_e3HqyG1Lk,30,machine gun shooting,train a_kgbg3eDAE,66,air horn,test a_mvCDVzm9Q,0,"electric shaver, electric razor shaving",train a_nUNftOssw,7,airplane flyby,train a_q55lP1H30,30,playing hammond organ,train a_rSPKmxjzI,16,"vehicle horn, car horn, honking",train a_ruWnMK1MA,350,"bird chirping, tweeting",test a_s54dmp6Xc,200,female singing,train a_tTA5W6FAo,47,people cheering,test a_tTA5W6FAo,176,people cheering,test a_xNwy2iOVw,6,ice cracking,train a_xNwy2iOVw,17,ice cracking,train a_xgmS6wZKo,0,police car (siren),test a_ygQh90O4A,46,playing didgeridoo,train aa-UmbdFDMA,30,"bee, wasp, etc. buzzing",train aa-eCqhz9wg,25,playing timbales,train aa-eCqhz9wg,107,playing timbales,train aa09xLmz7sY,23,cat growling,test aa0Zd7yWzYc,140,playing double bass,train aa0Zd7yWzYc,274,playing double bass,train aa1ss4PNNgA,5,frog croaking,train aa1ss4PNNgA,23,frog croaking,train aa8M-f7zCEk,100,singing choir,train aaA_khSU0BE,3,zebra braying,test aaBj0_exx8w,11,playing volleyball,train aaCT_bJ5LEY,10,"child speech, kid speaking",train aaGL5l7WEZc,30,chicken crowing,train aaGctbzDQ8w,30,police car (siren),train aaKf6P2nhKg,146,singing choir,train aaLy5b6Vhx0,0,playing cello,train aaNETotuNTM,33,hammering nails,train aaWs--ahZUg,10,playing electronic organ,test aaaqrxugf1k,205,chimpanzee pant-hooting,train aabuViDSLXg,0,basketball bounce,train aacBGX9fbcM,42,scuba diving,train aaeQVNCHhhQ,30,playing trumpet,train aaf7Z4elJvo,48,"subway, metro, underground",train aaf7Z4elJvo,99,"subway, metro, underground",train aafitYhrit4,230,"pigeon, dove cooing",train aaiz1-DGz98,226,playing sitar,train aaiz1-DGz98,250,playing sitar,train aajx_w7vgjA,3,cat growling,test aam23lhFc5w,26,tap dancing,train aaoF7AJwQC4,123,tapping guitar,train aaptyQW3VkE,19,"playing marimba, xylophone",train aaq191jB0So,1,francolin calling,train aaq191jB0So,16,francolin calling,train aas49Wo_jHM,430,playing table tennis,train aawZ8n-c8Fs,31,playing volleyball,train aawZ8n-c8Fs,41,playing volleyball,train aaxIs0TCbQw,80,playing piano,train aayG11FAvxo,30,using sewing machines,train aaz-kLU71HU,90,people babbling,train ab-YlwCQHqk,16,playing sitar,train ab-YlwCQHqk,36,playing sitar,train ab1WSSXzbIM,88,playing badminton,train ab2Nakcs2_w,80,playing harp,train ab3X6tqW-wg,251,slot machine,test ab55E5sJPlA,0,"child speech, kid speaking",train ab6RK6ATSXU,30,"motorboat, speedboat acceleration",train ab6dwESYFpY,0,opening or closing drawers,train ab6dwESYFpY,124,opening or closing drawers,train ab6f4R2XMT8,19,baby babbling,train ab9Wp9BPxR0,400,playing trombone,train abCZxGW77og,0,people running,train abEzAdnpGus,14,cupboard opening or closing,train abEzAdnpGus,82,cupboard opening or closing,train abG-LyZ40m8,1,"cattle, bovinae cowbell",train abG-LyZ40m8,24,"cattle, bovinae cowbell",train abJ13gwTHYc,41,playing squash,train abJO0t2--NI,83,black capped chickadee calling,test abJeNt0pnbM,306,slot machine,train abKDftdakVI,37,francolin calling,train abKDftdakVI,118,francolin calling,train abLVc3MF1ZQ,4,electric grinder grinding,train abPUBIXQ81I,140,"playing marimba, xylophone",train abT8JlfWIag,74,playing hammond organ,train abWncZQ8X4Q,44,playing gong,train abWncZQ8X4Q,74,playing gong,train abZAUD8g0H8,40,people running,train abZzTy4SqQk,228,foghorn,train abc2lxFXOHI,7,goose honking,train abc2lxFXOHI,19,goose honking,train abei9raEPxg,452,"ice cream truck, ice cream van",train abfGgGwEhWw,160,cat purring,train abimqHHMUrM,30,playing accordion,train abjjKJanf_M,40,slot machine,train abkHKDE-IDc,30,playing ukulele,test abldPDMmD2k,83,crow cawing,train aboMiRrOc0s,150,train horning,train abp0UHxxTNE,30,"pigeon, dove cooing",train abqANHyTUTQ,0,chicken crowing,train abs-wuQzR2g,130,ambulance siren,train abs1VLjetQs,20,driving motorcycle,train absvwmyF88A,82,francolin 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acDOVydwdFs,9,people babbling,train acDnXb3hzQ8,0,people whistling,train acFcrJ3Ixl0,160,playing cello,train acFx_XAnowM,170,fireworks banging,train acG2456R7rE,60,playing flute,train acGYSVFY7U0,140,people coughing,train acHtR7NBmek,65,playing erhu,train acHtR7NBmek,83,playing erhu,train acInDQ5iBBM,1,chipmunk chirping,train acInDQ5iBBM,21,chipmunk chirping,train acKLW0ZV_Eg,0,playing trombone,train acKLW0ZV_Eg,25,playing trombone,train acLR8eDYB7A,72,chinchilla barking,test acLqVC8R5fA,27,tap dancing,train acN6cUh-Qdg,11,frog croaking,train acNZTjF0Aw0,25,tapping guitar,train acP7dY5v-6w,230,people babbling,train acT0ofNQeq4,0,"race car, auto racing",train acVU6ERFxZM,10,toilet flushing,train acXGvTAOKx0,115,fire crackling,train acXGvTAOKx0,273,fire crackling,train acY1nKrHIlM,20,sloshing water,train acYUq-Rn6VQ,15,"vehicle horn, car horn, honking",train acYbaNUGVRE,190,playing drum kit,train acYp_SYmHs8,11,running electric fan,train acYp_SYmHs8,164,running electric fan,train 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ade_sNV8nu8,89,singing bowl,train adh99_Q735w,140,playing gong,test adi-h7zRZzM,0,dog barking,train adiChN67pk4,3,dog howling,train adli9Jl63V0,46,tap dancing,train adli9Jl63V0,99,tap dancing,train admUg-BomeI,3,lions roaring,train admUg-BomeI,54,lions roaring,train adnbNvLOI48,306,skiing,test adnssBz-gck,20,ambulance siren,train adpfKuhE8Go,136,playing flute,train adrIgljD0Kg,180,people crowd,train advxQyTIoiU,68,playing oboe,train advxQyTIoiU,142,playing oboe,train ae-0CmRei-k,63,eletric blender running,test ae0rWKKp-Cs,110,playing vibraphone,train ae0rWKKp-Cs,147,playing vibraphone,train ae3O5BCpdTk,44,running electric fan,train ae3O5BCpdTk,76,running electric fan,train ae3_lm5E2GE,70,skidding,train ae3fFtV6kUo,40,people belly laughing,train ae5GZL0kH7g,30,cat meowing,train ae5Nh96JD90,1,playing glockenspiel,train ae8MxPu11KA,3,people farting,train ae8MxPu11KA,67,people farting,train ae8XeWzzw-Y,71,train horning,train ae8XeWzzw-Y,89,train horning,train aeBgUVUMt6I,29,playing 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akW5zMHFDwI,61,playing electronic organ,train akZTVIeM2EM,70,helicopter,train akZYnqmKbYI,212,smoke detector beeping,test ak_4thDgdeI,90,police car (siren),train akddYbDgHM4,100,playing drum kit,train akfJ6J-24dk,1,slot machine,train akgdKQEVYrg,30,playing drum kit,train akiBCisKkkw,580,people coughing,train akjMGNYTAdA,30,fire crackling,test akj_rU5NGy8,30,playing bass guitar,train akjqOhO0IM0,10,machine gun shooting,train akkm188V7FU,19,playing table tennis,train akmik5SvfUc,20,playing saxophone,train akrHf5T4hAI,7,chopping wood,train akrHf5T4hAI,86,chopping wood,train akv9b-fQ4rI,30,playing accordion,train akvTg0dQ_70,0,playing sitar,train akvv4ApYMVE,333,arc welding,train akvv4ApYMVE,560,arc welding,train akwJwcvfjLA,47,playing squash,train akzilvhnMkk,12,wind rustling leaves,train al-f_WWoHI4,176,chimpanzee pant-hooting,train al0JjG6Utis,220,"child speech, kid speaking",train al15ghuR3s0,30,goose honking,train al1q01CZgtc,4,playing sitar,train al1q01CZgtc,24,playing sitar,train al5JLR2skWY,90,people babbling,test al6QOU1Hm2k,6,wind rustling leaves,train al6QOU1Hm2k,21,wind rustling leaves,train al6dZRTwOZQ,0,whale calling,train al6dZRTwOZQ,10,whale calling,train al6fkLsm61U,102,cupboard opening or closing,train al6fkLsm61U,112,cupboard opening or closing,train al6i9NNtFqU,550,"race car, auto racing",train al7bGtNVe4E,161,barn swallow calling,test al89crXNtjg,180,stream burbling,train al8F0vOpqcU,9,dog whimpering,train al8riq_OJvI,50,playing piano,train al9TDjqIU7k,30,playing accordion,train alAfvm3oHiY,110,tractor digging,train alAfvm3oHiY,145,tractor digging,train alBcRErUVVg,7,chopping wood,test alDHzNnVbow,3,waterfall burbling,train alDahq2CW2Y,2,playing bass drum,train alDahq2CW2Y,274,playing bass drum,train alDl2wQC1NE,3,playing bass drum,train alElu39QjCc,288,playing badminton,train alIAUBQ9TaU,166,chainsawing trees,train alIVv081LtI,30,"female speech, woman speaking",train alMbWx4lVL4,30,playing accordion,test alNvjD9uU5o,85,playing didgeridoo,train 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ameFsdgECqE,34,lathe spinning,train amftivL45V4,6,pheasant crowing,train amftivL45V4,36,pheasant crowing,train amg27b4tKKk,100,"race car, auto racing",train amnTHP7uB40,4,children shouting,train amndBfuwwDE,30,chicken crowing,train amniAzV2-jM,30,people burping,test amubQ-YCNTQ,26,arc welding,train amunlTZVHo8,3,strike lighter,test an0CVmZ9rG4,30,"playing marimba, xylophone",train an0VNdQSAoc,83,squishing water,train an0VNdQSAoc,99,squishing water,train an0WS2W8uMc,30,playing harp,train an2mZpaPTpA,410,chainsawing trees,test an4D1tfthos,0,francolin calling,train an4D1tfthos,177,francolin calling,train an6DRN4flZM,146,police radio chatter,train an7_uRghQhk,430,skidding,train an8FO5lhuPo,30,sailing,train an8oA_gWuok,18,cat hissing,train an8sNYncBao,450,people babbling,test an9qioJSdPA,45,"bird chirping, tweeting",train anB4oZ5n1Mw,300,skateboarding,test anBdQofPQbY,216,people marching,train anBnE04uKGk,41,playing squash,train anCJwAMXjRw,5,tapping guitar,train anCJwAMXjRw,57,tapping 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apK-D0fa4ME,56,playing harmonica,train apK-D0fa4ME,102,playing harmonica,train apKAuos-C9o,285,golf driving,train apR5I0jzNsc,57,"motorboat, speedboat acceleration",train apRefH-tXPE,30,"playing marimba, xylophone",train apTvGua1-FY,271,playing guiro,train apVH3jJR5RM,0,people sobbing,train apVvwmls0tA,74,playing badminton,train apY2K7CLQsw,36,playing table tennis,train apYjEK3rgyI,690,playing hockey,train apZT-WEJ--A,175,people slurping,test apZlK9A3viY,6,"engine accelerating, revving, vroom",train apc4TDFvnZM,108,people humming,train apce09ojNjY,299,"heart sounds, heartbeat",train apce09ojNjY,343,"heart sounds, heartbeat",train apfz_TMTnDc,121,yodelling,train apfz_TMTnDc,140,yodelling,train apgMmrI6C6k,30,train horning,train apgxqR4UL0A,267,playing ukulele,train apgxqR4UL0A,309,playing ukulele,train apiwldD6mRo,20,cow lowing,test apkba25uw1o,0,ripping paper,train apkba25uw1o,10,ripping paper,train aplZLyL0V8o,4,alarm clock ringing,train apn2Jjs8sg4,58,playing lacrosse,train appr3mdGJpI,27,tractor digging,train appxTPE_hgs,82,ambulance siren,train aps21UHdZFw,203,planing timber,train apyd6PnxmP0,480,playing banjo,train aq-7duYaeKA,251,popping popcorn,train aq1yqmOcP9s,103,electric grinder grinding,train aq3a5PGLM1o,141,tapping guitar,train aq3vov8-fw8,64,playing tambourine,train aq4mLiE4WYc,11,horse neighing,train aq4mLiE4WYc,152,horse neighing,train aq6OoqQu2tc,142,parrot talking,train aq6OoqQu2tc,155,parrot talking,train aq9DCY3wtEU,64,baltimore oriole calling,train aqAOyp3z2Ks,50,"child speech, kid speaking",train aqB3NH7tdWk,122,tapping guitar,train aqB3NH7tdWk,158,tapping guitar,train aqEwOpo9kQc,100,playing snare drum,train aqEwOpo9kQc,217,playing snare drum,train aqG-7HD_PHI,24,fire crackling,train aqG-7HD_PHI,74,fire crackling,train aqGVIZmVLuQ,150,playing piano,train aqHiEs78wdg,110,sharpen knife,train aqHiEs78wdg,138,sharpen knife,train aqIw4YZ0ZxE,51,playing glockenspiel,train aqIxpk8uR4E,177,slot machine,train aqLy67ZDIT0,45,train wheels squealing,train aqLy67ZDIT0,113,train wheels squealing,train aqNB9q_cZFI,330,"playing marimba, xylophone",train aqOSowdUE8Y,32,playing hammond organ,train aqOSowdUE8Y,308,playing hammond organ,train aqPXJEAxriU,50,playing bass guitar,train aqQSRXvaQAs,11,ripping paper,train aqQSRXvaQAs,22,ripping paper,train aqRi6eFKBcc,253,eletric blender running,test aqRi6eFKBcc,264,eletric blender running,test aqRuqys5StA,91,people shuffling,train aqRuqys5StA,101,people shuffling,train aqS-DVutR9M,10,strike lighter,train aqTawOdIVAc,90,helicopter,train aqVbLLLy1P8,44,yodelling,train aqVcE_VxMPw,1,coyote howling,train aqVcE_VxMPw,16,coyote howling,train aqVyNqDHCu4,396,mynah bird singing,train aqVyNqDHCu4,406,mynah bird singing,train aqWitkxL7yE,21,horse neighing,train aqah3iaXsDE,1,pumping water,train aqah3iaXsDE,56,pumping water,train aqai2CWfKrs,45,volcano explosion,test aqdLm5qss4U,20,ambulance siren,train aqgzIyE7lig,3,singing bowl,train aqhELp8-o_c,260,chainsawing trees,train aqhhfkXKJ1o,10,playing electric guitar,test aqiwate2fHE,9,playing guiro,train aqlRDAqlhW0,10,"engine accelerating, revving, vroom",train aqlnDYyA6vQ,127,skiing,train aqnWCkNcPRs,204,singing bowl,train aqnoVSQa48k,170,playing cello,train aqqPJb7Ex2Y,43,foghorn,train aqqPJb7Ex2Y,57,foghorn,train aqt_sGrWBBk,63,"alligators, crocodiles hissing",train aqt_sGrWBBk,109,"alligators, crocodiles hissing",train aqyDFgvBz0E,111,scuba diving,train aqyDFgvBz0E,172,scuba diving,train aqyGS_R6gtM,50,"pigeon, dove cooing",train ar0QhTx1bKI,58,ice cracking,train ar1BqJ4ipXk,30,people shuffling,train arAoTC7ohnU,161,fire crackling,train arAoTC7ohnU,176,fire crackling,train arF67Bldxf0,50,elephant trumpeting,train arF67Bldxf0,62,elephant trumpeting,train arG4hMtr9uA,291,bowling impact,train arGG0kirK_Y,31,playing clarinet,train arJ_VSx21uk,115,volcano explosion,train arPk2rqGzTk,63,cat purring,train arPk2rqGzTk,113,cat purring,train arR2WTMpEWk,14,rope skipping,train arUOQ4yF5hw,0,"vehicle horn, car horn, 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sitar,train arx7RzI3Q5c,18,chimpanzee pant-hooting,train arxedRShpdE,34,tornado roaring,train arxedRShpdE,171,tornado roaring,train arytWut8KmU,470,"race car, auto racing",train arz14LS9CF8,61,elk bugling,train arz14LS9CF8,97,elk bugling,train as5Hvy_UMgM,204,chopping food,train as6GvlKVjRc,51,people sneezing,train as6GvlKVjRc,107,people sneezing,train as7MhTe961k,170,playing saxophone,test as83p3KWrbA,34,arc welding,train as8KNZb6Mfs,90,orchestra,train as8vzD9s6u0,20,playing flute,train asB02zVQuy4,30,orchestra,train asBHuUH8GIs,80,helicopter,train asCmWtCBtSk,30,"rowboat, canoe, kayak rowing",train asDnoj5L9Ok,0,"ice cream truck, ice cream van",train asDvJ29sgS4,1,playing timpani,train asH3XiyveZE,40,skateboarding,train asHyyfH_vU0,187,hail,train asHyyfH_vU0,209,hail,train asIKYiwe1-0,30,lawn mowing,train asIO8-Asni4,120,cattle mooing,train asKUTMr1r1s,102,playing timpani,train asKUTMr1r1s,161,playing timpani,train asKmxjxcrWA,470,chimpanzee pant-hooting,train asKuYgAcyIk,30,car 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beaHUXxN7jk,94,chicken crowing,train beb6mgkt16g,29,writing on blackboard with chalk,test bege8UgDdWE,22,gibbon howling,train bege8UgDdWE,35,gibbon howling,train behtgkZntMg,30,playing didgeridoo,train bei2Pve4H2w,30,"engine accelerating, revving, vroom",train beqpYyULyD4,30,people sniggering,train betIn2CIoDw,0,basketball bounce,test betY2k332kI,10,playing banjo,train bewhF_4lCHc,28,tap dancing,train bezUdnkWlSs,4,chicken crowing,train bezUdnkWlSs,10,chicken crowing,train bf458LxcGlE,7,woodpecker pecking tree,train bf458LxcGlE,79,woodpecker pecking tree,train bf4KmAmgEDU,160,skateboarding,train bf4QQkDPEUk,3,magpie calling,train bf4QQkDPEUk,38,magpie calling,train bf551OyAH-Y,30,"cattle, bovinae cowbell",test bf7kDDca10o,158,singing choir,train bf7kDDca10o,229,singing choir,train bf7t0XTqfwc,8,"cattle, bovinae cowbell",train bf81gVdcgUg,436,running electric fan,test bf81gVdcgUg,451,running electric fan,test bf9prVAYH00,132,playing saxophone,train bf9prVAYH00,149,playing 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biFWGPixVqI,54,train wheels squealing,train biFpuf2UYgw,113,basketball bounce,train biG8oNmCnNM,245,chainsawing trees,train biHbeulrVEQ,7,people shuffling,train biKTQt6qIgo,7,people cheering,train biO2iCgjlew,119,playing synthesizer,train biO2iCgjlew,484,playing synthesizer,train biONQT7E0DM,468,playing squash,train biPJwkBezxc,100,people babbling,train biR9GQgVVPg,30,"railroad car, train wagon",train biRDakHDvGY,6,playing flute,train biROU5YSAPg,134,playing table tennis,train biU2FTQLoRA,159,lawn mowing,train biU2FTQLoRA,175,lawn mowing,train biV8Gxzgods,37,lathe spinning,train biWk1rKGpgI,47,baby babbling,train biX0gr2HwPo,83,playing flute,train biYS-B_I44s,116,swimming,train biYedDLibQs,64,tap dancing,train bi_GLOAbrKA,60,bird wings flapping,test bi_YN5AM6Ck,0,splashing water,train bibfIc7Uj7w,30,people running,train bieUxRdZumw,26,cattle mooing,train bieUxRdZumw,46,cattle mooing,train bij9l3w33zA,65,electric grinder grinding,train bijBE5NUOPA,30,goose honking,train bijnFACfm8Q,410,playing acoustic guitar,train binVbFH3LPU,211,skiing,train bio0x_qHgpU,132,volcano explosion,train bioCY2cN4e4,257,playing squash,train birCPPom65k,40,"engine accelerating, revving, vroom",train bisrDRABzBI,30,playing hammond organ,train biwWyeZFJMA,295,people battle cry,train biwWyeZFJMA,316,people battle cry,train biwasX5SD78,245,eletric blender running,train biwyqBjfPU8,70,child singing,train bizdOeh0nWg,149,francolin calling,train bizlRj6yVx4,11,eating with cutlery,train bizlRj6yVx4,23,eating with cutlery,train bizmBpWUwFk,130,"electric shaver, electric razor shaving",test bj-PurxvG4c,30,typing on computer keyboard,train bj0hVqsecbw,344,playing harp,train bj1wYMwZOyU,107,wood thrush calling,train bj1wYMwZOyU,135,wood thrush calling,train bjA3gCODpnI,0,people sobbing,train bjAf1FxAmp8,30,"rowboat, canoe, kayak rowing",train bjDSOZy0ox8,15,hammering nails,train bjDSOZy0ox8,100,hammering nails,train bjDZDmj8u2A,0,people cheering,train bjG6Sa59TrE,3,dog growling,train bjH-ahdq8M8,40,people sniggering,train bjHPNqGWmcE,0,thunder,train bjMwJQLKmv4,10,people booing,train bjOStn6-kGw,91,playing mandolin,train bjOStn6-kGw,180,playing mandolin,train bjP0DOt4-VI,0,owl hooting,train bjP0DOt4-VI,31,owl hooting,train bjR9lv5Q00g,0,driving snowmobile,train bjR9lv5Q00g,10,driving snowmobile,train bjSmlirR3EA,150,"female speech, woman speaking",train bjSuO7h7YTQ,41,missile launch,test bjVfnULGpQs,7,baby laughter,train bjVfnULGpQs,24,baby laughter,train bjVnG7ofQQw,0,"race car, auto racing",train bjWNumTcQpM,124,driving snowmobile,train bjWNumTcQpM,142,driving snowmobile,train bjXmRL4cpH4,15,wind chime,train bjb7QtMEBUg,283,tornado roaring,test bjcc0IaF0Is,120,"child speech, kid speaking",train bjkcIxZ4Dks,30,car engine knocking,train bjoe5H1Jmec,30,people crowd,train bjqQzuEFAB0,60,sailing,train bjs2vx_ZN20,300,waterfall burbling,train bjvnTxWP7vo,90,playing cymbal,test bjwbYDSQyzE,30,playing drum kit,train bjxwmytVnMg,22,cheetah chirrup,train 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buses,train bmJFbfIdQJ8,28,lions roaring,train bmK9BbOkylA,0,fireworks banging,train bmKtI808DsU,9,lions growling,test bmL_Zsoi3nM,40,canary calling,train bmNjHkoEmLQ,0,playing trombone,train bmNjHkoEmLQ,1,playing trombone,train bmUVSBqOBRI,0,basketball bounce,train bmX9lmfi188,94,mosquito buzzing,train bmYCghjgGR8,22,frog croaking,train bmakSi9M2Ww,220,"cattle, bovinae cowbell",test bmdRXSbL3io,463,people slurping,train bmdRXSbL3io,557,people slurping,train bmeRROzi_4k,430,machine gun shooting,train bmeRROzi_4k,464,machine gun shooting,train bmefVbk2-lM,114,playing djembe,test bmfKIUyls84,96,cap gun shooting,train bmk2DsZH9ZE,1168,"heart sounds, heartbeat",train bmmEufdm_4U,30,driving buses,train bmnSFKrPV08,1,pheasant crowing,train bmnbvE7YAZk,10,driving motorcycle,train bmnvxtJp34w,0,alarm clock ringing,train bmoDb6w_IKg,4,parrot talking,train bmqa15B5ra0,10,skateboarding,train bmvCcudPSs8,0,playing ukulele,train bmvCcudPSs8,268,playing ukulele,train bmyKEak01OE,8,"electric shaver, 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boETEUNxZJY,67,people humming,train boETEUNxZJY,177,people humming,train boEXiizBQDE,43,playing glockenspiel,train boEvuyQ50dM,278,bouncing on trampoline,train boFABikOwDI,45,playing hockey,train boK9z1hSasQ,208,child singing,train boK9z1hSasQ,218,child singing,train boM0PUo-2ww,29,playing squash,train boOQ1zWLQ2E,30,goose honking,train boPZxCznYbc,30,crow cawing,test boQ2-m5Tiek,180,"pigeon, dove cooing",train boSzdRZoqz0,0,car engine starting,train boWKyNs9gQs,140,train horning,train boWKyNs9gQs,236,train horning,train boW_X0g5wBE,7,chainsawing trees,train boXzdQsJe2k,11,playing clarinet,train boXzdQsJe2k,23,playing clarinet,train boYCHQeENzg,110,playing piano,train bofgHp2-JHk,0,people crowd,train bogIYcMmesk,89,squishing water,train bohDK3VIPV8,59,elk bugling,train bohDK3VIPV8,220,elk bugling,train bohvqDymNzo,270,helicopter,train boiGR9GXOmQ,7,playing tambourine,train boiGR9GXOmQ,20,playing tambourine,train bok39asc620,87,vacuum cleaner cleaning floors,train bokQgOSQ2OA,1,playing 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cG69R-DdbK0,150,people clapping,train cG6OF9vcGPE,81,people humming,test cG6aF-7g4Dw,50,cutting hair with electric trimmers,train cG73ujzOl4c,0,playing didgeridoo,train cG7MewpScjk,30,chainsawing trees,train cG8SfX25CSQ,90,thunder,train cGBtWNj5WfU,0,skidding,train cGCNCneidp8,5,snake hissing,train cGFLzzjB4hM,190,people whispering,train cGFVM7XiyF4,3,playing badminton,train cGNB4LFmsa0,171,rope skipping,train cGNLoPOKZy0,400,people booing,test cGOnlafTCn4,30,playing snare drum,train cGQxYk4Anxw,90,driving motorcycle,train cGSHtXIYvq0,167,volcano explosion,train cGUhG5PZp0A,30,"male speech, man speaking",test cGUvC8Q0Vxk,230,playing banjo,train cGWmdSBmeZI,56,singing bowl,train cGXoh8VX3Qo,23,driving buses,train cG_w_8hL0uM,55,basketball bounce,train cGdHoroAQgU,160,"playing violin, fiddle",train cGdNIDfBKJk,124,strike lighter,train cGeuU0n-RRo,54,singing bowl,train cGfDnNip2cY,30,helicopter,train cGgb9_F6VZU,4,"playing steel guitar, slide guitar",train cGgb9_F6VZU,60,"playing steel 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cJN7pAVIxSM,430,playing saxophone,train cJSWXGTJMcc,18,"rowboat, canoe, kayak rowing",train cJTDchgcDbg,16,dog barking,train cJTDchgcDbg,20,dog barking,train cJWX2dLCRDM,73,mosquito buzzing,train cJX_A7OLPLw,10,fireworks banging,train cJZAENqxvs0,30,playing bugle,train cJZAENqxvs0,69,playing bugle,train cJZsuL9iRVc,321,writing on blackboard with chalk,test cJalVAaZpeg,30,"pigeon, dove cooing",train cJdxY_mEiUg,70,driving motorcycle,train cJeosfNArnw,50,fireworks banging,train cJh1OwXjFZc,40,church bell ringing,train cJikohRKBKI,216,coyote howling,train cJjIiQy1uM4,3,baby babbling,train cJmzBd-LGe8,5,playing volleyball,train cJq8AXUqz4E,386,playing bongo,train cJq8AXUqz4E,461,playing bongo,train cJq8MhEZE2Q,0,magpie calling,train cJuWEvjeVX0,120,playing piano,train cJxLxr8C89k,30,"rowboat, canoe, kayak rowing",train cK20g76V_-k,112,singing bowl,train cK4PJ78ubaY,24,people belly laughing,train cK4PJ78ubaY,39,people belly laughing,train cK5TqkZqjss,70,ocean burbling,train cK5aT07gOis,151,"playing steel guitar, slide guitar",test cK6tMNO0LZs,11,crow cawing,train cK8CMqiMQA0,10,skidding,train cK9JgR-LGfU,11,"alligators, crocodiles hissing",test cK9JgR-LGfU,46,"alligators, crocodiles hissing",test cKBrnjxlKgU,254,playing bassoon,test cKDCuyo3LjQ,0,"playing steel guitar, slide guitar",train cKDCuyo3LjQ,10,"playing steel guitar, slide guitar",train cKDulGswypk,80,playing djembe,train cKDulGswypk,91,playing djembe,train cKDzBHtK8zo,30,"motorboat, speedboat acceleration",train cKFKX45sBbY,30,printer printing,train cKJZVL2kxAg,1,helicopter,train cKO4YitBMwc,39,pheasant crowing,train cKYetsSaZuo,40,people sniggering,train cKYwBXScWZ0,6,"engine accelerating, revving, vroom",train cK_hw1SsdEM,10,people coughing,train cKc0zgaLXCI,43,playing tympani,train cKc0zgaLXCI,104,playing tympani,train cKcNyfXbQzQ,439,"child speech, kid speaking",train cKdk4ByDQ88,242,playing bongo,train cKdk4ByDQ88,254,playing bongo,train cKf9PzXMSI0,0,tap dancing,train 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cLC4dupIafk,61,airplane flyby,train cLENEeFtOmI,410,horse clip-clop,train cLHZdTCcilQ,98,playing theremin,train cLI_xxNdmjU,26,playing tennis,train cLKm-sWI4NI,50,people screaming,train cLLR-MDHaqQ,70,goose honking,train cLMvr2qKG6Y,50,cap gun shooting,test cLOIO94QUXE,426,singing bowl,train cLRF5b8jtsE,100,"bird chirping, tweeting",train cLRZd6MhTdg,67,typing on typewriter,train cLRa3W-RiWY,29,lip smacking,train cLRa3W-RiWY,44,lip smacking,train cLRbutgJLXs,29,firing cannon,train cLUaGB6J7VU,30,car passing by,train cLUxuK1_3HY,30,dog barking,train cLWd1kuGxGQ,217,bowling impact,train cLXxIk6HwR4,34,playing ukulele,train cLYPlr2lOeQ,3,playing ukulele,train cLYPlr2lOeQ,150,playing ukulele,train cLYWOjOh3Jw,81,golf driving,train cLYWW0LwRhI,265,typing on typewriter,train cLYWW0LwRhI,307,typing on typewriter,train cLZRfmGhjdE,30,dog bow-wow,train cL_nCiBnlbk,1,playing bugle,train cLagB_Uv_RA,329,rope skipping,train cLagB_Uv_RA,361,rope skipping,train cLakDigr-tk,87,playing 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didgeridoo,train cM8ce-aKJys,17,people crowd,train cMEfLsqwyWU,5,cheetah chirrup,train cMEfLsqwyWU,16,cheetah chirrup,train cMG9nG6XcpM,520,ocean burbling,train cMIgtKIXzuc,270,people screaming,train cMM6FrAbI7o,80,planing timber,train cMM6FrAbI7o,97,planing timber,train cMPbKL9ONp0,0,playing didgeridoo,train cMPbKL9ONp0,1,playing didgeridoo,train cMPfmAUIbB0,150,playing electronic organ,train cMR6Hxh82iY,580,lawn mowing,train cMRaLnKuUKk,132,ice cracking,train cMRaLnKuUKk,162,ice cracking,train cMSId-uhMoA,0,playing harp,train cMSId-uhMoA,171,playing harp,train cMSRgqvRgxI,57,tornado roaring,train cMU1lZo54ik,1,scuba diving,train cMU1lZo54ik,11,scuba diving,train cMWwG5FQN3A,30,playing trumpet,train cM_VbOBRWK8,0,playing harmonica,test cMdiYuzHVZ4,58,playing french horn,train cMdiYuzHVZ4,175,playing french horn,train cMdnie91zp4,0,playing trombone,train cMdnie91zp4,238,playing trombone,train cMeze7Znmug,160,playing table tennis,train cMgG9PH4AJQ,28,playing volleyball,train cMlCZQPAAgo,30,typing on typewriter,train cMoWY-kTPw8,72,playing hammond organ,train cMoWY-kTPw8,96,playing hammond organ,train cMrCne9HMMQ,30,driving buses,train cMs5XfNWu6Y,10,lawn mowing,train cMsmQHDkMJc,150,fireworks banging,train cMtGwk5tWgk,102,tap dancing,train cMtGwk5tWgk,113,tap dancing,train cMxMPXKwhlQ,100,playing harp,train cMzERBmtlrs,10,thunder,train cN-JQk7LFZw,43,playing volleyball,train cN-trXs8VgY,6,dog baying,train cN-trXs8VgY,16,dog baying,train cN0WcVoOII0,30,playing banjo,train cN5IsXBc59w,30,chainsawing trees,train cN5WLE0XTsE,185,playing timbales,test cN8OTJq7ZI8,35,playing mandolin,train cN8OTJq7ZI8,48,playing mandolin,train cN8RaAxUWj8,30,playing french horn,train cNHyMIt7gTc,266,rope skipping,train cNHyMIt7gTc,377,rope skipping,train cNI5NQsi4zc,60,playing piano,train cNIAcXMUE6w,190,"playing marimba, xylophone",train cNJJArQyeK8,36,civil defense siren,train cNJjjRYSroQ,11,playing tabla,train cNKbSwSUle4,0,cat purring,train cNLrZCjlup0,17,arc welding,train 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cerQLUONnZw,24,cheetah chirrup,train ces7TGK8qCc,220,helicopter,train ces9pc_r6Wo,15,child singing,train ces9pc_r6Wo,36,child singing,train cesINtdSOfM,102,people coughing,train cesINtdSOfM,113,people coughing,train ceubSv-OKR0,30,playing banjo,train cev4oTRBWAw,100,playing flute,train cevCSfsj9LE,150,orchestra,train cew5sMlRxtM,60,playing bass guitar,train cew6UO_TiWI,60,splashing water,train cexa9HM2-T0,350,chicken crowing,train cexpcGF-xss,30,"bird chirping, tweeting",train cezdJCeghj0,30,church bell ringing,train cf-Qx5yVdoU,150,"child speech, kid speaking",train cf1H3aDRsm8,316,playing erhu,train cf1H3aDRsm8,328,playing erhu,train cf21xOlwAhc,30,chainsawing trees,train cf4ETZOgjTI,40,ambulance siren,train cf5L4Zx0SMs,37,vacuum cleaner cleaning floors,train cf7EomtWaiE,84,mynah bird singing,train cf7EomtWaiE,100,mynah bird singing,train cf9LYApV-Uk,92,people shuffling,train cf9LYApV-Uk,168,people shuffling,train cfA6lbIsEL8,280,playing hammond organ,train cfEs8Cw5RjE,70,toilet flushing,train cfFDwQJ4Mtg,189,typing on typewriter,train cfGI__OMLLE,0,playing cymbal,train cfIbcxG9V0k,30,car passing by,train cfJwmvhV0Ws,13,electric grinder grinding,train cfJwmvhV0Ws,201,electric grinder grinding,train cfL89bDpo6I,12,playing tabla,train cfL89bDpo6I,77,playing tabla,train cfMSQoLfLZk,70,basketball bounce,train cfOYD6xZZHE,30,singing bowl,train cfPRG8ugC4g,112,playing bassoon,train cfPpL9drZ7k,30,"rowboat, canoe, kayak rowing",train cfQkG0vJDic,184,playing tympani,train cfQkG0vJDic,241,playing tympani,train cfRE5bMFqhc,114,slot machine,train cfSY3HIaG7g,88,tap dancing,train cfVgYPGoixg,54,playing djembe,train cfWsBBSC434,70,playing piano,train cfX8KYvvR5o,1,driving buses,train cfZhOlNMWn0,130,people gargling,train cf_vyJGl8as,7,crow cawing,train cf_vyJGl8as,26,crow cawing,train cfaBPxE-A5k,6,chopping wood,train cfaBPxE-A5k,27,chopping wood,train cfcN8mJAnjU,60,people sniggering,train cfg6VvzZD30,69,baltimore oriole calling,train cfg6VvzZD30,100,baltimore oriole calling,train cfh8Gq27tJ8,30,playing accordion,train cfi_mCKjbyw,40,people belly laughing,test cfjmJF4hR5o,61,wind rustling leaves,train cfm3g8IHxcM,20,people sniggering,train cfoKzOwXPxI,30,singing bowl,train cfpKhn4y0Ng,29,lions roaring,train cfrRRhKJakg,93,playing lacrosse,train cfsITR9vibc,30,"motorboat, speedboat acceleration",train cfsOIuZ9G9k,52,playing bongo,test cfvsgEgZlyE,28,tap dancing,train cfwUg_ojUGM,49,playing cymbal,train cfx-5B8GB2c,91,playing cello,train cg-4JbOcB5Y,0,waterfall burbling,train cg-WQl3YCVY,0,playing saxophone,train cg-WQl3YCVY,205,playing saxophone,train cg25DwGFBpw,165,playing snare drum,test cg8BnIv-N50,120,"subway, metro, underground",train cgAVHFjKmg0,92,playing oboe,train cgCDTAPCwP4,120,"playing marimba, xylophone",train cgEErzQKLdQ,20,playing bongo,train cgEErzQKLdQ,59,playing bongo,train cgEH8IViugg,160,"race car, auto racing",train cgFXyJ3hZwQ,30,baby crying,train cgHwD1sOZZg,50,cap gun shooting,train cgLHn7xt_GY,130,bird wings flapping,test cgNhX6KO3xo,79,crow cawing,train cgNkDdgfpEA,349,scuba diving,train cgPLe5oRfQc,70,opening or closing car doors,train cgPLe5oRfQc,99,opening or closing car doors,train cgR-F99YjNQ,19,lawn mowing,train cgTWFpav_zU,50,people running,train cgUQ-obUH64,12,footsteps on snow,train cgUQ-obUH64,22,footsteps on snow,train cgW0QB63SDE,133,"bee, wasp, etc. buzzing",train cgW0QB63SDE,248,"bee, wasp, etc. buzzing",train cgXrWN0FDaI,72,black capped chickadee calling,train cgXrWN0FDaI,98,black capped chickadee calling,train cgaFhhFsDXQ,26,wood thrush calling,train cgaFhhFsDXQ,162,wood thrush calling,train cgaqfrGPykE,111,pumping water,train cgaqfrGPykE,134,pumping water,train cgbPnmFBwNU,280,playing cello,train cgl7maSy4-Y,150,people screaming,train cgm4G0U8uV0,86,people burping,train cgmeQ2BOwr0,10,sailing,train cgo_c4gm7vk,479,lip smacking,train cgo_c4gm7vk,600,lip smacking,train cgqUXxN-BWA,40,chicken crowing,train cgsjamA3Tv4,219,car engine idling,train cgsjamA3Tv4,232,car engine idling,train cgszuuPVJtc,30,goose honking,train cgthAi5wbf8,50,orchestra,train cgyJ14uIVbs,12,chicken crowing,train cgydX42UE14,106,vacuum cleaner cleaning floors,train cgydX42UE14,173,vacuum cleaner cleaning floors,train ch-8uFphP9E,21,playing accordion,train ch-8uFphP9E,25,playing accordion,train ch-C3dLvxbA,39,typing on computer keyboard,train ch-C3dLvxbA,62,typing on computer keyboard,train ch24Wa5eJ0o,30,duck quacking,train ch4uegReLhc,30,"bird chirping, tweeting",test ch5KBnIIbyY,30,"motorboat, speedboat acceleration",train ch5Tu4OcdO4,18,mouse clicking,train ch71KS9DYec,30,metronome,test ch95AXuMsM0,1,people nose blowing,test chA0UZDZLuY,8,hail,train chBNUlCt3Nk,28,eagle screaming,train chCQAz0nQ8A,7,chimpanzee pant-hooting,train chD6MpKh0P0,60,playing drum kit,train chGi9RDXWm0,3,cupboard opening or closing,train chGi9RDXWm0,171,cupboard opening or closing,train chIcsnCHHh8,40,ocean burbling,train chJ-CVAmQc8,130,playing acoustic guitar,train chL6t5TpzjU,16,baby laughter,train chL6t5TpzjU,56,baby laughter,train chLVH1zf-AA,4,dog growling,train chOE6-ryI20,40,basketball bounce,train chP180PR-9g,41,cat growling,test chSVUuN9vP0,128,basketball bounce,train chTlNzQvTDE,110,playing clarinet,train chU1pYEF1b8,149,cat purring,train chU1pYEF1b8,330,cat purring,train chUbOnduMp8,238,bowling impact,train chUbOnduMp8,323,bowling impact,train cha2McaIsGM,30,playing electronic organ,train chaqcpOHlbo,40,playing cymbal,train chb-Akdr07s,10,wind rustling leaves,train chb4eQZuna0,74,people burping,train chddVpfBcwY,17,playing timbales,train cheaB7StWjY,19,police car (siren),train checz2pEGBc,20,fireworks banging,train chjBZGbJy9c,146,planing timber,train chjBZGbJy9c,177,planing timber,train chjb1YwdgS4,250,cap gun shooting,test chlq2bb-JM4,1044,lip smacking,train chlq2bb-JM4,1071,lip smacking,train chqnTo5-BZk,14,"fly, housefly buzzing",test cht2_0Fi-r4,290,fireworks banging,train chtrHNfaT9U,70,people belly laughing,test chxCmnrAlWw,52,wood thrush calling,train chxCmnrAlWw,107,wood thrush calling,train chzNFmwEajw,20,tornado roaring,test ci0Plnhm81A,195,bouncing on trampoline,train ci0Plnhm81A,310,bouncing on trampoline,train ci1HEIiHx-U,210,playing double bass,train ci1HEIiHx-U,212,playing double bass,train ci4EVMQaRg8,21,turkey gobbling,train ci4XgPNza0w,0,snake hissing,train ci4XgPNza0w,17,snake hissing,train ci5F8LW22PE,101,gibbon howling,train ci5F8LW22PE,136,gibbon howling,train ci6XqAg-9Lc,32,skiing,train ci735P6cYfo,30,playing accordion,train ciA3Uhn9JuI,233,playing bass drum,train ciA3Uhn9JuI,285,playing bass drum,train ciEKtA13bm8,0,cat growling,test ciF042MLvz0,20,people babbling,train ciFTHLVRC0E,130,people hiccup,train ciFftfRWii0,7,bird squawking,train ciFftfRWii0,20,bird squawking,train ciKHhffmI34,150,playing accordion,train ciLtHzEyRyk,110,police car (siren),train ciM6QCEK-xw,104,fire truck siren,train ciM6QCEK-xw,880,fire truck siren,train ciM_nMj-avk,100,playing bass guitar,train ciMjm50WoC8,30,car engine knocking,train ciMk3LxO1Es,1,baby crying,train ciUPB6K5mfI,258,playing bongo,train ciVsS-oZEPc,103,elephant trumpeting,test ciVsS-oZEPc,144,elephant trumpeting,test ciXEbIYMYWo,11,vacuum cleaner cleaning floors,train ciYkA4WRxuI,30,"railroad car, train wagon",train ciZ4HqGjZ1E,0,skidding,train ciZjHNKOozg,210,church bell ringing,test cibho-Ggiyk,10,church bell ringing,train cif60rMQ1Eg,210,people clapping,train cifGioDxcz4,0,people burping,test cigzFlh0QUk,30,playing hammond organ,train cihXeGYe1hw,31,roller coaster running,train cihXeGYe1hw,46,roller coaster running,train cijhisH_mIA,84,"bee, wasp, etc. buzzing",train cijhisH_mIA,121,"bee, wasp, etc. buzzing",train cirSNqQZpio,1,playing table tennis,train cislECXF_Mg,82,planing timber,train cislECXF_Mg,98,planing timber,train cisoJDMJlC0,170,"subway, metro, underground",train cisy1T9EljU,88,zebra braying,train ciu_2a5Ug0A,14,police radio chatter,train civgrVO-DF8,12,playing badminton,train civrS6FiynQ,29,playing volleyball,train civwA5z8nME,63,chicken clucking,train civwA5z8nME,88,chicken clucking,train ciwjRi80E1w,208,beat boxing,train ciwvj0fhvTY,3,wind chime,train cizByru14jc,47,playing didgeridoo,train cizByru14jc,59,playing didgeridoo,train cj0UgoBQldM,112,civil defense siren,train cj1dwByhWKI,20,people burping,train cj3VU7koYZg,30,chopping food,test cj62Zn5fFlM,38,mynah bird singing,train cj62Zn5fFlM,75,mynah bird singing,train cj720O6NLbc,1,horse clip-clop,train cj9LDEnrohw,0,people farting,train cjBvP6Bzv7Y,181,fire crackling,train cjCvjnTObIY,48,fire crackling,train cjCvjnTObIY,104,fire crackling,train cjEG771KA3o,0,hail,train cjEG771KA3o,15,hail,train cjEJYgOdPFI,30,playing cello,train cjF2fREZGhA,3,magpie calling,train cjF2fREZGhA,13,magpie calling,train cjIJ-SV-8pU,162,playing tambourine,train cjIJ-SV-8pU,189,playing tambourine,train cjIUmxFy6Gg,30,"playing violin, fiddle",train cjJIl4y87pU,66,playing didgeridoo,train cjJTKKLtxUA,140,lawn mowing,train cjMac1G67tg,146,blowtorch igniting,train cjNeMOntLLk,87,playing timbales,train cjNeMOntLLk,106,playing timbales,train cjS-sZb1HqU,310,driving buses,train cjSTp2d8mBI,291,sea waves,test cjSTp2d8mBI,459,sea waves,test cjUEtP5Xysc,0,playing harmonica,train cjUEtP5Xysc,8,playing harmonica,train cjUPNnvzixo,110,playing bass guitar,train cjUTUcbe_0A,112,spraying water,train cjUn0IOgz1w,181,pheasant crowing,train cjUq5_HBWaM,4,airplane flyby,train cjWPVzmGm_8,0,playing mandolin,train cjXHHF97Nh8,198,playing tympani,train cjXziEc2ymE,36,cutting hair with electric trimmers,train cjaj_qaeySI,362,fox barking,train cjaj_qaeySI,436,fox barking,train cjdk4ItALAg,80,"child speech, kid speaking",train cjgYOOPFats,20,cattle mooing,train cjiRMJ0TeoE,72,train whistling,train cjkEA5qjNJo,210,francolin calling,test cjoNKQftyF4,0,cuckoo bird calling,train cjp_hT6udCg,190,"pigeon, dove cooing",train cjpcSVAKRjQ,92,door slamming,train cjt0MAsGRIo,344,playing harmonica,train cjt0MAsGRIo,429,playing harmonica,train cjukW9ChpV8,38,playing harpsichord,train cjukW9ChpV8,104,playing harpsichord,train cjuu7IHROus,20,playing saxophone,train cjwrRgMmXZ8,240,chainsawing trees,train cjxnDtyw9pE,30,ocean burbling,train cjzDdOWhTDY,50,ocean burbling,train ck13el7VmW8,9,sea waves,test ck2OfAEQIck,56,slot machine,train ck5S__5JJvE,5,bull bellowing,test ck7aw6nSAEg,11,playing guiro,train ck7aw6nSAEg,22,playing guiro,train ckAp0xkhYls,100,wind noise,train ckC7FLdHHCo,114,sharpen knife,train ckHC-rOZNnk,30,dog bow-wow,train ckHzSVkWpII,120,playing cornet,train ckIK43SCbkw,36,"alligators, crocodiles hissing",test ckK5DjJOpCk,44,woodpecker pecking tree,train ckK5DjJOpCk,74,woodpecker pecking tree,train ckKQWp_GP8c,194,playing bassoon,train ckNV5Oos3ac,109,playing darts,test ckOCDVj6rUM,40,playing trombone,train ckQW9dX3_lM,70,playing squash,train ckVZNjaTAPg,124,lighting firecrackers,train ckVw8ZVhAxQ,25,sloshing water,train ckYO-hEIzqY,435,playing squash,train ck_lkaFec80,59,fire truck siren,train ck_lkaFec80,70,fire truck siren,train ck_vqKgnWy4,0,tornado roaring,train ck_vqKgnWy4,26,tornado roaring,train ckbZAk4KdKY,260,goose honking,train ckf0NJCkyq8,390,mouse clicking,train ckf0NJCkyq8,425,mouse clicking,train cki0nt9y6Gs,15,beat boxing,train ckj1dckEdMs,20,fireworks banging,train ckkf4UcEGME,30,"rowboat, canoe, kayak rowing",train ckmdF3t7jUA,30,ambulance siren,train ckmqxWhL-Kw,69,tap dancing,train ckpW2xU10JA,33,playing timbales,train ckpW2xU10JA,44,playing timbales,train ckpj7O0Et28,180,door slamming,test cksBhor2zqU,0,sailing,train cktSGt8nXYY,53,rope skipping,train cktSGt8nXYY,391,rope skipping,train cku_hmDB0T8,943,forging swords,test ckwEyopmfKs,13,crow cawing,train ckwEyopmfKs,24,crow cawing,train ckwnO3ihiHg,9,playing steelpan,train ckwnO3ihiHg,25,playing steelpan,train ckyG0O27hAA,182,playing timbales,train ckyG0O27hAA,214,playing timbales,train ckykq54RCfw,30,baby crying,train ckzLHmauwI8,30,chainsawing trees,test ckzo6GTZ13w,80,playing bass guitar,train cl-_zog-Bfg,215,playing cymbal,train cl2YV8ZZEmQ,3,sloshing water,train cl4hzarjGhk,0,people sneezing,train cl8UGK8KFnU,3,bathroom ventilation fan running,train clBiBzvvwR8,167,playing squash,train clBtYo-ufbw,139,playing hammond organ,train clDTxTSTO0M,218,crow cawing,train clDTxTSTO0M,230,crow cawing,train clEMwdfe_TY,0,people booing,train clEMwdfe_TY,14,people booing,train clF1Gb85Vbc,110,child singing,train clF1Gb85Vbc,207,child singing,train clFyre-E-TI,500,"child speech, kid speaking",train clGOyIGphZ0,40,"rowboat, canoe, kayak rowing",train clHyLk4LrKo,20,female singing,train clI-M4vXZxo,20,toilet flushing,train clIG_BJW2o8,30,playing trombone,train clK-tGbZt6k,14,alarm clock ringing,train clL55xOkRCQ,10,horse clip-clop,train clLiUz1x6N4,12,tractor digging,train clOjjWNs53o,0,playing ukulele,train clOjjWNs53o,3,playing ukulele,train clSPSECcz3w,130,people clapping,train clTtwpS3lz8,0,"motorboat, speedboat acceleration",train clX4fPaR-w4,20,ocean burbling,train clXo5manRMw,25,playing bassoon,train cl_BqV-XzEE,30,people running,train clel1GRLYfk,197,train horning,train clel1GRLYfk,242,train horning,train clpyFTbjGLs,130,people clapping,train clq64QNfU5k,30,playing drum kit,train clqILW5PUC4,0,sheep bleating,train clqpHTYJ3Bw,70,chainsawing trees,train clryZe_CkC4,0,playing bagpipes,train clryZe_CkC4,16,playing bagpipes,train cltogsqy1ng,121,"playing steel guitar, slide guitar",train cltogsqy1ng,231,"playing steel guitar, slide guitar",train clv_ENL66nc,30,"pigeon, dove cooing",train cm-eDKSfHSY,56,missile launch,train cm-eDKSfHSY,66,missile launch,train cm1_HNVqQe4,42,police radio chatter,train cm1_HNVqQe4,148,police radio chatter,train cm1lsulgGmU,212,"electric shaver, electric razor shaving",train cm4pTS-BrN0,137,cheetah chirrup,train cm4pTS-BrN0,263,cheetah chirrup,train cm5bys9BRyI,22,lawn mowing,train cm62raVagEE,60,playing timpani,test cm8hgNnpwxM,49,airplane,train cm9QmzMNrRQ,30,printer printing,train cmACYQzLHFE,25,scuba diving,train cmACYQzLHFE,129,scuba diving,train cmAdMnYEchI,142,yodelling,train cmAdMnYEchI,249,yodelling,train cmB8l4mQ1XQ,30,playing accordion,test cmF0xTw5Mkc,30,playing trombone,train cmFfPsHJGuY,20,people whistling,train cmGp4B-zTyc,30,sailing,train cmMnTHokLQo,624,playing bagpipes,train cmN8sXLcEcY,170,people crowd,train cmOQQpQOsJ0,44,striking pool,train cmObHWICFwM,490,printer printing,train cmOuCD0BHTk,85,car engine idling,train cmOuCD0BHTk,231,car engine idling,train cmRtUbrjKow,6,cattle mooing,train cmUXCpLoeMA,80,skateboarding,train cmW2XPBaj0s,98,"electric shaver, electric razor shaving",train cmW8xbQft9k,0,cupboard opening or closing,train cmW8xbQft9k,133,cupboard opening or closing,train cmbJz59PuoM,20,people screaming,train cmbSarEBcGI,40,beat boxing,train cmgBsMl_6NE,21,elk bugling,train cmgBsMl_6NE,31,elk bugling,train cmkEW0KJDYI,165,arc welding,train cmmZH_uQxKI,2,cuckoo bird calling,train cmmZH_uQxKI,116,cuckoo bird calling,train cmnt4ZB76SU,30,"male speech, man speaking",train cmpLgEpWvJg,0,baltimore oriole 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dNfwHHBNXig,300,"railroad car, train wagon",train dNgDfv_HNqc,4,canary calling,train dNgDfv_HNqc,15,canary calling,train dNhDGUGxEXI,30,"female speech, woman speaking",train dNhd3J5Z5fY,2,frog croaking,train dNjynbUy1RM,17,dog baying,train dNjynbUy1RM,28,dog baying,train dNkBC5l6YAA,0,splashing water,train dNnFoCeabg0,30,chainsawing trees,train dNnq9CMP-x4,30,"playing marimba, xylophone",train dO1Enu4uTtU,105,airplane flyby,train dO3YxuWSBmA,26,playing tambourine,train dO3YxuWSBmA,37,playing tambourine,train dOB2FNE_53Q,6,mosquito buzzing,train dOCv5w5Jgxw,27,dog bow-wow,train dOG3NiSMiVA,390,people sniggering,train dOIBMqseMXk,100,"subway, metro, underground",train dOIFmY0q34s,4,machine gun shooting,train dOKX46DsIZI,5,rope skipping,train dOKX46DsIZI,30,rope skipping,train dOLCKv8bFoU,0,people burping,train dONYIjG0RJY,40,"child speech, kid speaking",train dOPe_X5apPc,7,people clapping,train dOQCPeT0IU8,80,chainsawing trees,train dOQfuOw6okA,381,airplane,test dOU2R5ornPU,122,people 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dR6SgaiL-4c,30,playing flute,train dR8-pvH1j7k,25,parrot talking,train dRCLEDTTRAE,62,playing volleyball,train dREswLVv4WM,30,telephone bell ringing,train dRGOYAWcb1U,34,dog whimpering,train dRGOYAWcb1U,61,dog whimpering,train dRIU42KFp0o,28,penguins braying,train dRIUORLU1sM,31,tornado roaring,train dRIUORLU1sM,45,tornado roaring,train dRLnj5GjZhs,20,playing bass guitar,train dRLrFZIpgto,280,pheasant crowing,train dRN_XT8IUfQ,30,typing on computer keyboard,train dRPoxeAPvtE,134,tap dancing,train dRRE6L8cJQs,1,volcano explosion,train dRT8bSF12XY,270,playing saxophone,train dRTQglfs7-Y,37,people eating,train dRTQglfs7-Y,83,people eating,train dRTjmQjRVAI,30,dog whimpering,train dRTr1wCVRv8,0,playing bagpipes,train dRULggc7_3E,270,dog barking,train dRXzO9tDKTY,220,female singing,train dRayFItxHUo,7,barn swallow calling,train dRc6xyQ45t0,0,machine gun shooting,train dRc6xyQ45t0,29,machine gun shooting,train dRgUzNf78K4,2,mynah bird singing,train dRgUzNf78K4,43,mynah bird singing,train 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water,train dSpwPEC4CSA,30,driving buses,train dSqII72-OxA,20,machine gun shooting,train dSrBO5QlxSA,0,wind noise,train dStS2ndc8LA,134,playing tuning fork,train dStS2ndc8LA,160,playing tuning fork,train dSuI-Z-9efE,120,thunder,train dSwbey83ENE,0,playing banjo,train dSxMOGCQJZU,54,playing erhu,train dSxMOGCQJZU,144,playing erhu,train dSxQ4rKYbAQ,80,playing hammond organ,train dSyf7SaUFkQ,74,rope skipping,train dSyf7SaUFkQ,91,rope skipping,train dSzZWgbJ378,30,train horning,test dT-sPs8jSww,30,"playing violin, fiddle",train dT-u2IdC99E,90,playing electronic organ,train dT25eYToJ2Y,62,playing squash,train dT4dvRrlBL0,272,ice cracking,test dT4dvRrlBL0,321,ice cracking,test dT6y2DuGY-I,22,playing cornet,train dT7lwaU9-uo,24,cricket chirping,train dT7lwaU9-uo,40,cricket chirping,train dTAvJ90kBoQ,32,playing zither,train dTBwiYDG4zs,34,popping popcorn,train dTCaXIVbOKc,3,"vehicle horn, car horn, honking",train dTDj8M5lPN4,39,basketball bounce,train dTFtUC_4kR8,1,dog howling,train dTFtUC_4kR8,30,dog howling,train dTGUepoDklU,3,toilet flushing,train dTK36ESqU7w,180,people clapping,train dTK4lER2iEs,71,arc welding,train dTK4lER2iEs,262,arc welding,train dTPuw5EkE-Q,550,sloshing water,train dTSJd5d0PTE,193,elk bugling,train dTSJd5d0PTE,241,elk bugling,train dTV7UbA_QB8,3,whale calling,test dTVNg-neYlQ,71,fire truck siren,train dTVNg-neYlQ,128,fire truck siren,train dTVudV4Q0nk,80,playing electronic organ,train dTWpRXsK5m0,11,people burping,train dTZXj3L0zU0,470,bird wings flapping,train dTboTs2Y5og,654,scuba diving,train dTbq0LFV6wM,32,playing bugle,train dTbq0LFV6wM,56,playing bugle,train dTdggJhDVcI,188,lions growling,train dTdnoiKMsgM,143,playing theremin,train dTdnoiKMsgM,153,playing theremin,train dTehUQA0KTA,30,playing bagpipes,train dThOBJ_4YI0,19,playing mandolin,train dThOBJ_4YI0,30,playing mandolin,train dTlAkkMLPDg,30,"playing marimba, xylophone",test dTmAiWAi1Wo,72,church bell ringing,train dTmAiWAi1Wo,120,church bell ringing,train dTnfV1L6ixw,90,playing tympani,train dTnfV1L6ixw,128,playing tympani,train dTnxFgmQLKc,30,singing choir,train dTqxDu_GWsI,38,church bell ringing,train dTqzBhVvv1w,500,people cheering,train dU26nQpPPZk,30,crow cawing,train dU2Xf0z3hng,70,people sniggering,train dU2rLhpaMAY,87,"bee, wasp, etc. buzzing",train dU3q3DzSeSA,30,"rowboat, canoe, kayak rowing",train dU4HrME-x30,6,tapping guitar,train dU4HrME-x30,69,tapping guitar,train dU4arf3o4Nc,30,duck quacking,train dU5GJkZ2Ltw,44,parrot talking,test dU5kCZVSfEY,30,printer printing,train dU6WfMrLoW0,30,police car (siren),train dU6mz0osfOc,130,splashing water,train dU6zrdvUIFI,4,airplane flyby,train dU89ZSGtg4Q,67,cutting hair with electric trimmers,train dU93EFHvO9k,80,people crowd,train dU97UxsYbq4,40,airplane flyby,train dU97UxsYbq4,77,airplane flyby,train dUDxXq6n0qE,15,yodelling,train dUEGUl0Rqx4,40,"child speech, kid speaking",train dUEmTBzdT9E,61,playing bass guitar,train dUHZWTKpbbA,90,driving motorcycle,train dUM7Wjnfu0E,110,dog 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dcpsUS0y2Zk,48,people whistling,train dcsO9cBNxUM,52,tap dancing,train dcsSiM5yrnE,90,lawn mowing,train dcxBDy83_mA,30,helicopter,train dcy3L3JRMcc,430,people belly laughing,train dczkx4fRRNc,160,"bee, wasp, etc. buzzing",train dd-sVUe_yVk,50,"bird chirping, tweeting",train dd0hTAb3fDo,16,male singing,train dd8ExHtH4qM,300,driving motorcycle,train ddAkcDFK2mg,45,playing volleyball,test ddBouFOP0Fc,100,driving motorcycle,train ddCHG6RddI4,222,planing timber,train ddEnXMK4sKw,13,playing ukulele,train ddEnXMK4sKw,26,playing ukulele,train ddHLzZUstLk,10,sloshing water,train ddHMstEKZko,10,playing clarinet,test ddHgRMPxRJw,100,people shuffling,train ddHgRMPxRJw,113,people shuffling,train ddQkr_JvXH8,30,wind noise,train ddRUaHaKy4M,6,cap gun shooting,train ddRUaHaKy4M,60,cap gun shooting,train ddSJDyfFgnE,0,ambulance siren,train ddSKmM5ydQA,0,people slapping,train ddSgLvAzdpQ,124,vacuum cleaner cleaning floors,train ddSzt-qOo7Q,16,tap dancing,train ddTiNFm-jvo,217,hair dryer drying,train ddTiNFm-jvo,381,hair dryer drying,train ddU9u9pBszc,6,playing shofar,train ddU9u9pBszc,232,playing shofar,train ddVYzX5d1cw,550,wind noise,train ddWPV8B6QuA,0,skiing,train ddX6vZnPW44,120,child singing,train ddY5X5OZe9Y,20,people farting,train ddb9bUx3bOg,123,ripping paper,train ddbFJnspjbc,30,playing vibraphone,train ddbIpqxNADc,34,turkey gobbling,train ddbIpqxNADc,55,turkey gobbling,train ddeSMfFLYTI,420,telephone bell ringing,test ddfJEYdP3ng,30,people running,train ddgAGEWxtxA,137,striking bowling,train ddl4fgbjsk0,30,waterfall burbling,train ddnuXhoj7Sg,91,people finger snapping,train ddnuXhoj7Sg,253,people finger snapping,train ddo49ckg_B8,550,people screaming,train ddrPr5hBDLY,211,opening or closing car electric windows,test ddsNp72uA_8,0,lawn mowing,train ddseKM9mOjc,328,playing bass drum,train ddshpQOcrG4,120,playing erhu,test ddtYMeuLPoI,30,"male speech, man speaking",train ddtqWAxjBI8,31,elk bugling,test ddtqWAxjBI8,49,elk bugling,test ddyJJrg0Upo,20,"subway, metro, 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pant-hooting,train dePEs7zIJD0,550,people clapping,train dePmlg5yC0Y,30,car passing by,train deSs0MV_mJw,60,train whistling,test deUfVHsTRZg,199,raining,train deUfVHsTRZg,282,raining,train deVspQ1UpS0,51,chimpanzee pant-hooting,train deVyPq-AHfc,430,playing harpsichord,test deX7R9RbmX0,120,ocean burbling,test deYy8qU_SRQ,30,people whistling,train dedGLMGPGxM,250,toilet flushing,train deh1Ki4eiIs,15,playing erhu,train deh1Ki4eiIs,349,playing erhu,train deiUQ-5FhsU,362,eating with cutlery,train deiUQ-5FhsU,373,eating with cutlery,train deo3n0780J8,14,lathe spinning,train deo3n0780J8,74,lathe spinning,train deoQgSRB6ow,179,mosquito buzzing,train depYG7b6YyE,60,toilet flushing,train detypm4KVYc,187,planing timber,train deuQ0cOAgeU,296,lions roaring,train deuQ0cOAgeU,315,lions roaring,train deuo3XfR6jw,41,playing volleyball,train dew16_07TiU,28,playing badminton,train dewbSh89uvA,80,playing accordion,train dexMfOlM1W0,35,cell phone buzzing,test dexNJa7pg60,0,playing harmonica,train deyihxBk1zs,0,zebra braying,train deyvkq2LZoY,6,fox barking,test df-C3QzzG3Q,239,horse neighing,train df12eFgXZZ4,34,cap gun shooting,train df28kqhcTxc,11,dog baying,train df4PKpleovw,13,playing badminton,test df5PVsSoGh0,154,slot machine,train df5ujuCtSNE,0,playing glockenspiel,train dfA32yXUIHE,20,playing clarinet,train dfAIzOXe4Ew,30,people sniggering,train dfBfL37iQ2g,9,playing glockenspiel,train dfBfL37iQ2g,27,playing glockenspiel,train dfDqyT6Yu5A,123,hair dryer drying,train dfGmGghyWJc,60,playing saxophone,train dfLBeKsqjhI,48,telephone bell ringing,train dfLQaboaa84,30,playing drum kit,train dfM_xjOwaNA,85,planing timber,train dfM_xjOwaNA,142,planing timber,train dfMcmALFrCc,10,printer printing,train dfOY_KWKScM,30,playing french horn,train dfQO1xOeZzU,266,slot machine,train dfQTvsnoVLw,42,mosquito buzzing,train dfQTvsnoVLw,53,mosquito buzzing,train dfRtPbBFoGg,198,playing cello,train dfUXxwXWObk,210,people sniggering,train dfUp0pbSoL8,44,francolin calling,train dfUp0pbSoL8,58,francolin calling,train dfV2i9N8Kc0,30,ocean burbling,train dfWayWAZp1k,67,dog howling,train dfWjWNmZ-YY,35,wood thrush calling,train dfWjWNmZ-YY,47,wood thrush calling,train dfX5kzP5ooY,77,people burping,train dfXl7_lnt0U,109,bouncing on trampoline,test dfYYRopvVx0,30,"female speech, woman speaking",test df_SYY4pb3I,130,wind rustling leaves,train df_Xv2M5WT4,0,playing tennis,train df_Xv2M5WT4,226,playing tennis,train dfdBjk9Geac,28,"female speech, woman speaking",train dfdMwK17oiE,18,parrot talking,train dfdMwK17oiE,98,parrot talking,train dfdpO7l7U8k,133,people slapping,train dfhEUYqckOw,90,people cheering,train dfiEQ8ZimiU,320,lawn mowing,train dfmob3AkPdk,0,playing ukulele,train dfmob3AkPdk,122,playing ukulele,train dfo6r6irzyE,38,people marching,train dfo6r6irzyE,54,people marching,train dfp21mqQxEk,30,sloshing water,train dfpYiYOLN_o,0,"bee, wasp, etc. buzzing",train dfr1OFz20sI,0,goat bleating,train dfrps0BtQQQ,80,playing vibraphone,test dfs9jrD2PBc,3,tractor digging,train dfsvT5xImNg,80,people screaming,test dfud78qpHiA,110,ocean burbling,train dfwr8wgZU8M,40,car passing by,test dfz7xf4CoaY,20,"motorboat, speedboat acceleration",train dfzZvahCmpU,140,people whistling,train dg-eCb99xAo,127,tap dancing,train dg4VeesS6_I,25,elk bugling,train dg4VeesS6_I,62,elk bugling,train dg5RolBS79U,150,swimming,train dg7Prc6Qh3w,370,playing piano,train dg7oSv2R4AE,44,playing trumpet,test dg9OQY51vog,100,people crowd,train dgCGmjVTh2k,30,driving motorcycle,train dgDSfNGnN98,175,playing timpani,train dgEK7zHiuuM,26,people burping,train dgGElmZFDtI,30,sliding door,train dgGElmZFDtI,40,sliding door,train dgJis2C20os,117,lip smacking,train dgMTijqN77o,30,ambulance siren,train dgP5ldlYItU,363,car engine starting,train dgP5ldlYItU,380,car engine starting,train dgQwPKvkhKQ,290,fireworks banging,train dgSOnxqNtFE,132,people coughing,train dgSOnxqNtFE,246,people coughing,train dgS_Fy1FiNA,110,people burping,train dgSpR-wx9Yk,39,singing bowl,train dgStV59P5ms,67,police radio chatter,train dgaYDOi4-LU,149,scuba diving,train dgbXKwYZngU,70,sliding door,train dgcBcIi8SZo,115,"electric shaver, electric razor shaving",train dgeBLAt5dTI,100,playing bagpipes,train dgfIgYRMvmU,90,playing synthesizer,train dgfcbbHHjvU,10,playing banjo,train dgiMhvqriZQ,108,electric grinder grinding,train dgiMhvqriZQ,176,electric grinder grinding,train dgjf14_SYe0,48,"pigeon, dove cooing",train dgnWKrBl4OQ,150,dog bow-wow,train dgoncIz632s,30,typing on computer keyboard,train dgoqslmJkSc,5,wind rustling leaves,train dgpEiV4huf4,690,striking bowling,test dgtKGoboNV8,69,playing tambourine,train dgv5-j64bXw,121,playing oboe,train dgv5-j64bXw,256,playing oboe,train dgw0YljeQVg,20,playing double bass,train dgw9FOtLa5I,170,people whistling,train dh4QGeay2Kc,192,"subway, metro, underground",train dh4QGeay2Kc,408,"subway, metro, underground",train dhBG1DMp3kc,6,playing bass drum,train dhBG1DMp3kc,132,playing bass drum,train dhCB2ubN8ho,9,vacuum cleaner cleaning floors,train dhCB2ubN8ho,34,vacuum cleaner cleaning floors,train dhFH5LR7kbU,96,"cattle, bovinae cowbell",train dhFH5LR7kbU,224,"cattle, bovinae cowbell",train dhG_GSGW_RI,4,volcano explosion,train dhHYQufAG6w,233,playing bassoon,train dhHpIGxOjeQ,12,playing mandolin,train dhHpIGxOjeQ,30,playing mandolin,train dhJX4RKs7So,0,cutting hair with electric trimmers,train dhJqeZMu8Co,7,baby crying,train dhM3hPZ_yd0,153,tap dancing,train dhOu_-baBkk,161,playing saxophone,train dhOujxEL7jo,12,canary calling,train dhOujxEL7jo,51,canary calling,train dhPJw4WRHoU,3,playing timbales,train dhPJw4WRHoU,32,playing timbales,train dhU-DJSBplM,55,playing tabla,train dhU-DJSBplM,176,playing tabla,train dhUlWRJZONw,88,dog howling,train dhUlWRJZONw,118,dog howling,train dhVO8tws-9U,30,playing electric guitar,train dhWm0-7gzAg,130,"race car, auto racing",train dhYVes3jMZM,150,orchestra,train dhaK1_BrPxA,10,playing accordion,train dhbzQa4xoM0,44,footsteps on snow,train dhbzQa4xoM0,90,footsteps on snow,train dhdipSwS8D8,18,playing bagpipes,train dhdipSwS8D8,58,playing bagpipes,train dhfqxQGWUaw,20,ocean burbling,train dhg2s0ODqek,447,mouse squeaking,train dhg2s0ODqek,486,mouse squeaking,train dhgVMgOFMus,20,playing cello,train dhgd0aR2WRA,30,firing muskets,train dhgd0aR2WRA,49,firing muskets,train dhgiozdRDCY,230,people clapping,train dhiChQCco3A,117,tap dancing,train dhic2cE57iM,86,playing erhu,train dhic2cE57iM,109,playing erhu,train dhkilgAEiVI,0,lions growling,train dhlj80EGWos,120,playing drum kit,train dhmDHTn83Rw,30,playing harp,train dhpa4N31Fys,1,mosquito buzzing,train dht9Lmw8vcY,40,ambulance siren,train dhtreET00fk,30,"pigeon, dove cooing",train dhvZfhfH4HA,62,playing erhu,test dhy3Cf2gcX8,294,forging swords,train dhy3Cf2gcX8,375,forging swords,train dhzgcdKpfDk,16,cattle mooing,train dhzgcdKpfDk,28,cattle mooing,train di01T0hGboU,51,arc welding,train di01T0hGboU,359,arc welding,train di0AQmiKcKY,54,playing harpsichord,train di0AQmiKcKY,229,playing harpsichord,train di2PQHMTDLk,200,people sniggering,train di5fAzqmTGo,100,playing bass guitar,train di6RXQFxUNU,30,cat meowing,train di7eVZNlpkw,40,car passing by,test diF_lD6WxEU,368,"playing steel guitar, slide guitar",train diHn7q-YQEo,61,blowtorch igniting,test diKozlTCyzU,40,playing cello,train diLG-4lJows,13,people sobbing,train diLG-4lJows,27,people sobbing,train diLN_kU0Kd4,8,"vehicle horn, car horn, honking",train diQl0RqhUzE,185,cat hissing,train diSAlQiITcI,15,duck quacking,train diSZzd4jM0E,30,orchestra,train diWMnukdKQw,30,people running,train diYUQv6OxFc,24,"vehicle horn, car horn, honking",train dijwGXaE4jc,10,toilet flushing,train dilieZz-umQ,20,"pigeon, dove cooing",train dimiHRN4ro8,24,sea waves,test dip-RVUt0fM,290,playing cymbal,train dipQLKuWaVY,0,skateboarding,train dirG5WCAkus,19,people slurping,train dixL-wW1R5s,12,scuba diving,train dizGy3xpVhI,70,playing harp,train dj0g8lt-PfA,26,cheetah chirrup,train dj3OG-Dxcx8,140,people screaming,train dj6248tpgiw,10,lions growling,train dj6BWT51zfk,170,playing synthesizer,train dj87X8-FciY,24,arc welding,train djA1ESqYncU,70,playing volleyball,train djBWuJ0xPtg,390,people babbling,train djG5rw7zmeA,100,mouse pattering,train djGVz1a7ue8,258,playing glockenspiel,train djGVz1a7ue8,281,playing glockenspiel,train djHhz6cHabU,290,"playing marimba, xylophone",train djJ4ETLTBWE,90,wood thrush calling,test djJV4GQBOkk,60,singing bowl,test djLTOhhyuNg,23,playing french horn,train djLTOhhyuNg,254,playing french horn,train djMk1aSjnuc,70,tapping guitar,train djMk1aSjnuc,143,tapping guitar,train djNp6Or8J_8,0,"engine accelerating, revving, vroom",train djOD4pV-ihc,67,beat boxing,train djOj127-bxs,52,tornado roaring,train djOj127-bxs,109,tornado roaring,train djOyUafZTZU,277,tractor digging,train djQQ5W4HQUg,24,"bird chirping, tweeting",train djTikXkqE44,96,people screaming,train djTikXkqE44,118,people screaming,train djVbM5r_2W4,150,"child speech, kid speaking",train djXLI95GJKQ,30,people finger snapping,train djXLI95GJKQ,196,people finger snapping,train djZVhKAb3Lg,7,volcano explosion,train dj_LvYzI0t4,50,playing piano,train djaF3A_xXk4,66,tap dancing,train djbA047pwRA,111,playing didgeridoo,train djdSKJeKqog,9,"cattle, bovinae cowbell",train djdSKJeKqog,24,"cattle, bovinae cowbell",train djdbIEu2K-Y,110,sloshing water,test djf5seRZHiw,30,"pigeon, dove cooing",train djhi1gzyD_A,48,horse neighing,train djhi1gzyD_A,69,horse neighing,train djjpoKY1gMM,70,skateboarding,train djk_jsMJ7CY,1,whale calling,train djk_jsMJ7CY,14,whale calling,train djoF51W8zqw,48,people giggling,test djqv5W15zyg,84,electric grinder grinding,train djqv5W15zyg,107,electric grinder grinding,train djt9_7Prvnc,290,using sewing machines,train djttN6OzZDw,0,ambulance siren,train djwgIScjHtA,80,playing clarinet,train djwtgG9VFr0,260,"female speech, woman speaking",train djxWYck7feM,0,playing ukulele,train djyyv0DxkOA,30,playing snare drum,train djzJjPkt4iI,70,"child speech, kid speaking",train djzh_U3MOoQ,160,orchestra,train dk2lk_HOkDc,70,playing theremin,train dk4wscDSRwc,39,civil defense siren,train dk5RqoGEcTM,100,chainsawing trees,train dk5ep1vKXL4,0,"race car, auto racing",train dk7i18S0KjM,30,"race car, auto racing",train dk99Dm1x3NY,30,people screaming,train dkDOYCjJI08,30,playing harpsichord,train dkDTSJH2Kd4,20,people farting,train dkDYiQ3d4ts,187,tap dancing,train dkFAuyDDqRc,201,playing table tennis,train dkGSmDV3SgI,0,tap dancing,train dkI6clikRIc,10,playing cymbal,train dkIqRw8Zse4,121,warbler chirping,train dkJAxg12nFA,16,civil defense siren,train dkJAxg12nFA,32,civil defense siren,train dkJNxaWpsSs,220,"child speech, kid speaking",train dkPR_5Go9f4,73,foghorn,train dkT2PZ8xwy4,14,cricket chirping,train dkT2PZ8xwy4,80,cricket chirping,train dkYNd7QqjxY,100,"pigeon, dove cooing",train dkekKNvr5AM,41,slot machine,train dkgtYKebAdM,1,playing cornet,train dkgtYKebAdM,14,playing cornet,train dkiK_0-9LRY,70,playing piano,train dkmORzUggaM,30,baby crying,train dkpGb48_Y0A,26,tap dancing,train dkpRsM44hd8,0,coyote howling,train dkpRsM44hd8,14,coyote howling,train dkpsxpeLY-M,150,lighting firecrackers,test dktDmSB21JQ,0,stream burbling,train dky6_4_dwbg,90,chainsawing trees,train dlA8yt3AqME,33,vacuum cleaner cleaning floors,train dlA8yt3AqME,79,vacuum cleaner cleaning floors,train dlBO8GIs3F8,61,playing gong,train dlBO8GIs3F8,310,playing gong,train dlCIbRhI84o,72,playing squash,train dlEOVeJMhXc,270,chicken crowing,train dlEWv6eNOCI,3,splashing water,train dlEWv6eNOCI,13,splashing water,train dlFXGfbcYvg,27,wood thrush calling,test dlIS3IwjfmY,3,cat purring,train dlIS3IwjfmY,14,cat purring,train dlITLvX2RV8,158,playing snare drum,train dlIpdUyw2Sk,0,playing saxophone,train dlIpdUyw2Sk,31,playing saxophone,train dlJm9R5t_qg,30,playing hammond organ,train dlK8Ma4uvDw,70,people sniggering,train dlLzIE8Gqus,1,train horning,train dlLzIE8Gqus,19,train horning,train dlMp8V5OFJM,30,printer printing,train dlN4uttJaOE,22,playing snare drum,train dlNd0EFUzrU,140,people belly laughing,train dlO_XsQIdgY,499,vacuum cleaner cleaning floors,train dlO_XsQIdgY,606,vacuum cleaner cleaning floors,train dlPJjuy0tGI,30,crow cawing,train dlTE7W6vPJE,22,tornado roaring,test dlTTDZ0KwTA,2,playing tambourine,train dlTTDZ0KwTA,16,playing tambourine,train dlWDFGjrOvY,222,machine gun shooting,train dlWDFGjrOvY,445,machine gun shooting,train dlWrMn_RDg0,120,playing bassoon,train dlYRZXZl3H0,98,toilet flushing,train dlYenG7pldI,30,playing cello,train dlZKNonIpSQ,230,"child speech, kid speaking",train dleU7fMUiDc,0,telephone bell ringing,train dlgFTQnAG68,10,playing acoustic guitar,train dlgTjhOlLOY,30,playing trumpet,train dlhIHrtXQXE,25,"heart sounds, heartbeat",train dlhIHrtXQXE,38,"heart sounds, heartbeat",train dliaevGyFuE,40,helicopter,train dlj9hv18to0,49,playing didgeridoo,train dllM4ZAyO2o,212,playing cornet,train dllM4ZAyO2o,229,playing cornet,train dllb2bYsQlU,135,parrot talking,train dllb2bYsQlU,145,parrot talking,train dlltzeDUUvA,82,arc welding,train dln3mKCm29o,70,playing trombone,test dlrs8Nu_7DI,70,people farting,train dlt024a8CAs,62,wood thrush calling,train dlt024a8CAs,79,wood thrush calling,train dltItIGDKCQ,30,playing flute,train dlveqtmiblA,380,people clapping,train dm-6BfncKrA,1,civil defense siren,train dm-AAiDSLjk,65,people marching,train dm-AAiDSLjk,110,people marching,train dm0UmJcbAGM,185,"playing steel guitar, slide guitar",test dm2FkV6WMYU,140,playing cello,train dm5DJNfGJTs,32,playing djembe,test dm5DJNfGJTs,122,playing djembe,test dm5aTtfzevE,30,playing cello,train dm9NY8Kw3vg,121,swimming,train dm9NY8Kw3vg,280,swimming,train dmCQkosIa2k,0,lawn mowing,train dmCQkosIa2k,38,lawn mowing,train dmDn2Tqsyeg,30,hair dryer drying,test dmFjNULc-Gc,90,playing acoustic guitar,train dmGJxG_C8Do,11,playing french horn,train dmGJxG_C8Do,25,playing french horn,train dmGq1L76xgQ,71,people eating noodle,train dmGq1L76xgQ,211,people eating noodle,train dmHMgX58pRk,25,playing harp,train dmHcDWrMH-8,60,"playing violin, fiddle",test dmJnYB-RfHw,30,playing french horn,train dmNjLHLIbK8,48,playing bongo,train dmOYEzKEWew,85,train wheels squealing,test dmOYEzKEWew,158,train wheels squealing,test dmRSzVZTDZI,52,snake hissing,train dmT1K-Vummk,63,cat purring,train dmTOYevacJU,2,penguins braying,train dmVLc12DyxU,38,playing cornet,train dmVLc12DyxU,66,playing cornet,train dmWrpXW_TXQ,105,parrot talking,train dmY_1y3RXYA,30,duck quacking,train dmZLoblB83E,154,basketball bounce,train dmZLoblB83E,401,basketball bounce,train dmbx596522o,107,airplane,train dmdMvf8RkSQ,30,fire crackling,test dmdjEompTFI,14,baby laughter,train dmdjEompTFI,36,baby laughter,train dmejISTIaeM,0,tap dancing,train dmh2ZmD9H70,157,playing bongo,train dmh2ZmD9H70,167,playing bongo,train dmiQmGyPdds,0,helicopter,train dmkzop356wU,40,ambulance siren,train dmmMVyNfprk,22,footsteps on snow,train dmn3Y9jiYmo,20,playing hammond organ,train dmn7cEAxIZM,94,civil defense siren,train dmn7cEAxIZM,116,civil defense siren,train dmp9N7U2jOY,76,reversing beeps,train dmr962k2978,0,dog howling,train dmr962k2978,15,dog howling,train dmsbctKNfOI,0,playing timbales,train dmsbctKNfOI,24,playing timbales,train dmug87W1kqg,115,playing sitar,train dmuseIB42ls,80,people babbling,train dmz7cOjJAYE,388,people humming,train dmzjzo1MVTQ,0,stream burbling,train dn198KfiOwI,30,people running,train dn1EPJABsOE,302,strike lighter,train dn1HKb7-CDQ,12,fire truck siren,train dn1HKb7-CDQ,22,fire truck siren,train dn1YMcsP0SQ,30,"vehicle horn, car horn, honking",train dn2ldIXXbaI,81,fire truck siren,train dn3mv9gWH8c,34,snake hissing,train dn4R3ZMdJ_0,0,sailing,train dn5n8s4FITc,10,playing piano,train dn60AwYbB3E,50,chainsawing trees,train dn7-LVyM8-k,30,orchestra,train dn7CK_jxV_U,148,"race car, auto racing",test dn9U54X-WF8,40,playing piano,train dnBRWaI4E38,20,cat meowing,test dnGW30q_7IA,30,church bell ringing,train dnIXEaoTH0s,180,playing electronic organ,train dnKbIGvpAqA,30,cat purring,train dnNLak_mdzg,80,playing erhu,train dnQvS3jATno,150,duck quacking,test dnUjf6BD7XQ,2,playing harp,train dnV_hpwfkvI,0,playing timpani,train dnWZREiV5d4,49,cap gun shooting,train dnYBT3-G1AA,40,splashing water,train dnZUZVLHNIY,21,civil defense siren,train dnc1a9uGTvI,30,playing banjo,train dncL9WpiDa8,30,"pigeon, dove cooing",train dniA8Xl1-io,19,zebra braying,train dniA8Xl1-io,42,zebra braying,train dniAV5Cjjis,48,sloshing water,train dniAV5Cjjis,60,sloshing water,train dnjPwzmnCbk,293,black capped chickadee calling,test dnl9KUEmUAY,15,playing glockenspiel,train dnl9KUEmUAY,26,playing glockenspiel,train dnvQ3HX0-pA,160,child singing,train dnvQ3HX0-pA,178,child singing,train dnwIcunmiNY,1,mynah bird singing,train dnwIcunmiNY,50,mynah bird singing,train dnygdMFQabg,90,"child speech, kid speaking",train dnyjhIfzBEI,30,baby crying,train dnzaWIToBq0,20,playing trumpet,train do-5QrFLF_4,0,driving motorcycle,train do-GJ2_K1Tg,78,cell phone buzzing,train do-GJ2_K1Tg,135,cell phone buzzing,train do-iArQ2elw,150,playing cello,train do1y1bkL14M,300,"bee, wasp, etc. buzzing",test do2VZgYrFbk,30,fireworks banging,train do5WhNDBwXc,0,playing saxophone,train do5WhNDBwXc,82,playing saxophone,train do6eS6rPew0,30,car engine idling,train do6eS6rPew0,40,car engine idling,train do7N_PshIPU,30,police car (siren),train do8fVVvSvTQ,130,basketball bounce,train do9gD6984uM,122,tap dancing,train doA1nCWTgv8,153,roller coaster running,train doAZt2wBHI4,12,cat purring,test doCIE49-WLU,167,people humming,train doCIE49-WLU,177,people humming,train doDlXlIYamE,38,"playing steel guitar, slide guitar",train doDlXlIYamE,181,"playing steel guitar, slide guitar",train doFrrF9ybU8,240,hair dryer drying,test doGQktH_6mM,340,"bird chirping, tweeting",train doJfRXv_S_g,30,playing french horn,train doLp1Cd9azM,30,people farting,train doSWrqMRuBs,69,chicken crowing,train doS_7NMUGjY,0,baby crying,test doSpIZi-ye8,0,people crowd,train doWcMxaNfRE,116,sharpen knife,train doWcMxaNfRE,172,sharpen knife,train doX0PC5Bhc4,5,playing tympani,train doX8FjlNPf8,30,playing sitar,test doX8y9IdmkY,303,playing cornet,train doX8y9IdmkY,407,playing cornet,train doZgTCe9EnE,79,people screaming,train doZwqc3SwQg,20,"child speech, kid speaking",train do_dil0uppo,200,sailing,train doaMCMATX4w,341,squishing water,train doaMCMATX4w,433,squishing water,train doaQC-S8de8,0,playing trumpet,test dodSXZAIi-c,10,"playing marimba, xylophone",train dodjuYbRJLg,159,people booing,train dodjuYbRJLg,261,people booing,train dogcO2FHoL8,27,playing zither,train dogcO2FHoL8,102,playing zither,train dohGU1yfvZc,30,frog croaking,train doiSifpXB9U,2,planing timber,train doiynMzZ8AY,3,playing glockenspiel,train dojb4SiwMdE,20,playing synthesizer,train dojb4SiwMdE,371,playing synthesizer,train dojzVUgdyxY,20,"playing marimba, xylophone",train donkH2x20no,50,playing accordion,train dopAvVFW754,65,playing tuning fork,train dotbvLbRN_E,30,playing drum kit,train douS4hS9tTQ,149,playing theremin,train douS4hS9tTQ,178,playing theremin,train dowlTXv1qXY,1,"railroad car, 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siren,train dpblKiOjOlA,30,playing cello,train dpdQjMwtlgQ,132,playing synthesizer,train dpdQjMwtlgQ,159,playing synthesizer,train dpeUDViV6oA,43,people giggling,test dpf2YeIWE1g,60,chicken crowing,train dpg67sSZ9ag,180,slot machine,train dpgUHkKvOfk,30,playing electronic organ,train dpiSRhhuMfk,170,playing french horn,train dpjqZhiJeHM,80,playing banjo,train dpkacj27X8U,40,bird wings flapping,train dplCiHpuYUo,60,"playing marimba, xylophone",train dpnzm2DPoBI,40,people sobbing,train dpt-V9gcTnw,6,playing glockenspiel,train dpt-V9gcTnw,18,playing glockenspiel,train dpuMJYwAhN4,14,"bee, wasp, etc. buzzing",train dpuMJYwAhN4,31,"bee, wasp, etc. buzzing",train dpwkmJ5UeME,0,beat boxing,train dpwmaH1PruU,80,playing bagpipes,train dpxjhlwjuGI,149,skidding,train dpxmmcZRtMI,30,waterfall burbling,train dpzMqhVszbc,119,playing badminton,train dpzhENWtxiM,190,frog croaking,train dpzjpWyNuKY,30,playing acoustic guitar,train dq-_xJPMGdo,30,sloshing water,train dq32VQZwRx0,2,civil defense siren,test dq3fhZsVgS0,311,beat boxing,train dq3m_R3Lnu4,46,playing bagpipes,train dq3m_R3Lnu4,150,playing bagpipes,train dq54-kvwZ6g,21,people whistling,train dq54-kvwZ6g,30,people whistling,train dq6gt4o4lCg,120,"female speech, woman speaking",train dq75lff004o,1,elk bugling,test dq8LwAhzmlA,23,playing cornet,train dq8LwAhzmlA,63,playing cornet,train dqA-nXvGjuU,51,playing bassoon,train dqCGrrovEsE,543,playing acoustic guitar,train dqCXGM8wFBc,0,duck quacking,train dqEdAaEHNr8,30,playing drum kit,train dqH8dGsmyBI,175,playing table tennis,train dqJSTTS7HTY,30,playing acoustic guitar,test dqMymPns7no,10,people crowd,train dqR4-FeFQKA,0,car passing by,train dqSlDD4cIaY,40,"motorboat, speedboat acceleration",train dqSvcR5398s,130,"bee, wasp, etc. buzzing",train dqVH3OFcXso,65,splashing water,train dqWXEswBphE,30,playing bagpipes,train dqXj3JhYaso,34,hammering nails,train dqXj3JhYaso,48,hammering nails,train dqYkkhQeNb4,329,crow cawing,train dqYkkhQeNb4,363,crow cawing,train dqYzzLG-lO8,150,skateboarding,train dqcfDrvV-aE,24,pheasant crowing,train dqcfDrvV-aE,75,pheasant crowing,train dqiVZJJ2SOE,30,playing piano,train dqjU1exQN0w,16,baby babbling,train dqjU1exQN0w,75,baby babbling,train dqkRz896XIw,9,"vehicle horn, car horn, honking",train dqrXqd00roE,37,people eating noodle,train dqtWOGgIuHo,20,people sobbing,train dqt_MzUQ20M,80,chicken clucking,test dqu_dkIqqrw,85,lathe spinning,test dqvbJLNW8cI,214,typing on typewriter,train dqwWaqWo_Rw,92,ripping paper,train dqwWaqWo_Rw,103,ripping paper,train dqyUdzrFzCI,113,child singing,train dqyUgT9TPr8,107,playing castanets,train dqyUgT9TPr8,210,playing castanets,train dqymshfwGEE,30,playing saxophone,train dr0xT6hStDI,20,"motorboat, speedboat acceleration",train dr1XVFnZVrw,0,playing harmonica,train dr1XVFnZVrw,5,playing harmonica,train dr1XegYL_mw,368,playing tambourine,train dr1XegYL_mw,384,playing tambourine,train dr2OhNuZpSg,61,playing electric guitar,train dr2Z806GCbE,80,playing saxophone,train dr2nDPdhCrs,42,magpie calling,train dr2nDPdhCrs,68,magpie calling,train dr5n5e0CvuU,70,waterfall burbling,train dr7UEDJiStg,52,train horning,train dr8tviLni_o,94,opening or closing car doors,train dr8tviLni_o,104,opening or closing car doors,train dr9ZhGSnBKE,540,people sniggering,train drBgAVF0tyQ,72,beat boxing,train drCH_OyXxQ4,20,playing saxophone,train drDAR7zKxfA,65,tap dancing,train drH2CnKMZeU,38,playing volleyball,train drIqxiioHYg,190,chainsawing trees,train drJgSKwdD9E,9,dog whimpering,train drLM28M7x8k,10,train horning,train drLVJM8TXeg,457,beat boxing,train drPcYht-AIc,0,playing harmonica,train drPcYht-AIc,192,playing harmonica,train drQwfmZD_uY,13,playing synthesizer,train drSWaJf-U48,551,playing cornet,train drYdl6Mcq7U,34,"electric shaver, electric razor shaving",train drYfXPitJCs,50,dog bow-wow,train drdKm7wie4k,12,playing tambourine,train drdKm7wie4k,30,playing tambourine,train drdcwOg-TR4,30,chainsawing trees,train dretHc7fv18,10,playing drum kit,train driy6y51j2c,0,playing harp,train driy6y51j2c,3,playing harp,train drkBJf6GMTM,22,bouncing on trampoline,train drkBJf6GMTM,99,bouncing on trampoline,train drkBRYuGmU0,30,dog bow-wow,train drkWe7zM9po,0,pig oinking,train drkWe7zM9po,12,pig oinking,train drmOQuyhe6M,48,playing badminton,train drmvHg4qj3c,0,playing timpani,train drnOPQCN3tY,21,mouse clicking,train drnOPQCN3tY,36,mouse clicking,train drnnulHw5CM,30,mouse pattering,test droXM6ux7iQ,17,"engine accelerating, revving, vroom",train drq_ww7Ytzw,6,cat purring,train drq_ww7Ytzw,17,cat purring,train drt87f8a60Y,30,"race car, auto racing",train drtGMjP5lC8,164,tractor digging,train drtdbbfKhBk,20,sloshing water,train drw7pQH_eBA,30,owl hooting,train drwJFRm1YvY,190,playing saxophone,train drxFVUZWFFI,1,bowling impact,train drxmFBf8B-A,101,rapping,train drxmFBf8B-A,148,rapping,train dry7-SJUnnY,3,people burping,train ds-nE2iRIRw,106,swimming,test ds1-9NqmZCo,290,wind noise,train ds3I3sE3IT8,16,playing erhu,train ds5m2-nRB5o,50,female singing,train ds6kfBtmu58,120,playing harp,train ds7hptpzr58,263,slot machine,train dsES5-w0ws4,31,pheasant crowing,train dsFznt4ekVA,30,using sewing machines,train dsFznt4ekVA,134,using sewing machines,train dsGv8jG2vwE,52,playing theremin,train dsGv8jG2vwE,84,playing theremin,train dsIaHbw2sCY,20,people cheering,train dsKhL1vcSEM,70,driving motorcycle,train dsLIRlWz_VY,0,car engine starting,train dsOwGGeTlzo,302,bowling impact,train dsQTxO_94YA,236,fire truck siren,train dsQTxO_94YA,331,fire truck siren,train dsRVKEu7-Mw,17,civil defense siren,train dsRWhQbB82w,33,cat growling,train dsRWhQbB82w,44,cat growling,train dsSgZhFd8Ho,0,playing harp,train dsVxIfmjKpc,12,duck quacking,train dsVxIfmjKpc,23,duck quacking,train dsW8k4dYFBw,440,playing trombone,train dsY0ZOuLQ28,4,car engine idling,train dsYG-vJeLGE,30,orchestra,train dsZSiHzpiT4,24,playing acoustic guitar,train dsZkBxKbGrw,0,fire crackling,train dsa76YLjmmw,8,skiing,test dscFlqmuk4k,8,eagle screaming,train dscQJaLwtF8,30,helicopter,train dseVDEv5Xy8,118,lions roaring,train dsfva4E02Ls,437,playing volleyball,train dsinqF-wXvQ,156,pheasant crowing,train dsjPSk5ZP9Y,40,playing flute,train dsl3naGEjr0,78,arc welding,train dsl3naGEjr0,102,arc welding,train dsp2f5M6q8I,30,skateboarding,train dsp3ykl0p8g,30,chicken crowing,train dsrhHQ7BY_Q,107,tractor digging,train dsrhHQ7BY_Q,131,tractor digging,train dsyPOqxn0lE,38,"railroad car, train wagon",train dsz_3qWFr9M,193,striking pool,train dsz_3qWFr9M,224,striking pool,train dt0ZQyuztDs,100,cap gun shooting,train dt1Yy97psLg,0,goose honking,train dt7MmOz6AgE,36,sea waves,train dt7MmOz6AgE,93,sea waves,train dt9d70BGio4,19,playing squash,test dtA5izZbekc,29,hammering nails,train dtA5izZbekc,176,hammering nails,train dtCrrVDEcMY,270,people farting,train dtE4BjkoQ9A,90,people crowd,train dtF69EXyvHw,0,playing tambourine,train dtGBMnR-GBY,103,gibbon howling,train dtGBMnR-GBY,174,gibbon howling,train dtGFGSzL9Hc,73,"playing steel guitar, slide guitar",train dtGFGSzL9Hc,121,"playing steel guitar, slide guitar",train dtGl5xh6g0g,138,tractor digging,test dtHVjFWdBhw,308,"heart sounds, heartbeat",test dtK3Gb1x4us,40,fireworks banging,train dtKtO5rBadE,10,driving buses,train dtLram2pV7A,30,sheep bleating,train dtNyNi4kksg,480,playing flute,train dtRLrQDmG2A,227,playing badminton,train dtUlMzBIr-U,6,chicken clucking,train dtUlMzBIr-U,22,chicken clucking,train dtV4lyjHfvc,247,people finger snapping,train dtV4lyjHfvc,331,people finger snapping,train dtXN8Hr7TZw,42,elk bugling,train dtXN8Hr7TZw,88,elk bugling,train dtXd6qYchw0,10,car passing by,train dt_BStmnAWk,187,people gargling,test dt_t4GjZmQE,310,"female speech, woman speaking",train dtdQ4tFU4EU,55,"ice cream truck, ice cream van",train dtjPWoaOz60,450,ripping paper,test dtkxYy0b0Cw,298,car engine idling,train dtl3-4tEMI4,0,playing trumpet,train dtnvVS64ASU,17,dog howling,train dtnvVS64ASU,42,dog howling,train dtpCaWeQ6Oc,0,tapping guitar,train dtq8xSegJrg,26,pheasant 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eS3Sa7oP7Yo,102,playing bassoon,train eS4LHsBbLAc,164,car engine idling,train eS4LHsBbLAc,177,car engine idling,train eS4bx7RKdzU,20,"female speech, woman speaking",train eS57rKK3vKY,4,singing bowl,train eS7Mfq8uZ4w,5,"alligators, crocodiles hissing",train eS7Mfq8uZ4w,227,"alligators, crocodiles hissing",train eS85W-c8zf0,0,playing harmonica,train eS85W-c8zf0,63,playing harmonica,train eS8Tf1hfwxk,115,sea waves,train eS8Tf1hfwxk,205,sea waves,train eSAlPrmWYKY,32,arc welding,train eSB8TpIN-bI,290,ocean burbling,train eSEIPV-qSj0,20,squishing water,train eSEIPV-qSj0,33,squishing water,train eSEWJoWf_UE,330,lawn mowing,train eSFYsFnFUME,488,scuba diving,train eSFYsFnFUME,521,scuba diving,train eSGGXzBO4r4,0,"bird chirping, tweeting",train eSGj3tP4HyI,20,mouse pattering,train eSKFXcMdFXI,606,"alligators, crocodiles hissing",test eSKFXcMdFXI,716,"alligators, crocodiles hissing",test eSMCZfnJ5gI,30,dog barking,test eSOT5an0BsU,600,striking bowling,train eSQfT6atP_E,10,playing hammond 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eZh5AI2RXq4,3,missile launch,train eZjZDRMMUoE,30,driving buses,train eZlwJf9ogRA,47,beat boxing,train eZm24b1rKXQ,89,chainsawing trees,train eZm5FEqaAUg,30,police car (siren),train eZnOVrSzUDg,24,people burping,train eZnOVrSzUDg,86,people burping,train eZottcFH4p4,134,playing clarinet,train eZpWgZkb-G8,80,playing accordion,train eZpdCU0u5sA,6,people burping,train eZqotpr9hrU,135,cricket chirping,train eZtNB4BKYRo,173,playing erhu,train eZvK9ni9wSc,10,ocean burbling,train e_-lAh6TQzg,0,chicken crowing,train e_0MXNf11bE,187,playing didgeridoo,train e_1Ek9B-NMA,30,people whispering,train e_2-tPyNgcw,90,playing saxophone,train e_2FygdWh9w,10,"race car, auto racing",train e_2gAZvQTxY,30,orchestra,train e_34bu1mIDU,155,people burping,train e_3GUZmPFBI,20,playing erhu,train e_3GUZmPFBI,48,playing erhu,train e_5zLs-7iuc,51,playing oboe,test e_9Z9r1Rrzc,30,"playing violin, fiddle",train e_DJblp9B-0,88,people whistling,train e_DJblp9B-0,165,people whistling,train e_DeEDpWnP4,83,playing 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f3dZm67Hfao,191,lathe spinning,train f3iOuKzhUlQ,30,printer printing,test f3ipEX-KOoE,93,woodpecker pecking tree,train f3izfk5hs_Q,109,playing guiro,train f3izfk5hs_Q,181,playing guiro,train f3mx0Czyk2A,30,playing cello,train f3n_Hw64Rik,30,people running,train f3pvsBmsN0c,17,people booing,train f3r8brZT6n8,189,hedge trimmer running,test f3rVl9lWtbw,214,volcano explosion,train f3ym_Of8TzQ,30,"playing marimba, xylophone",train f4-1lc_P_eI,30,eating with cutlery,train f40KcLU1yWc,65,gibbon howling,train f460AI72HF4,89,pheasant crowing,train f460AI72HF4,100,pheasant crowing,train f464z4A8fVM,13,car engine knocking,train f47R512PE7U,110,"railroad car, train wagon",train f483jo13XVE,30,playing harpsichord,train f49l4gk1i64,78,police radio chatter,train f49n1tF1jFQ,146,barn swallow calling,train f49n1tF1jFQ,261,barn swallow calling,train f4ADxVSigaE,20,playing banjo,train f4BDxEci8Nk,40,stream burbling,train f4CxHwqbn3M,100,people coughing,train f4EAmPipiHQ,2,horse neighing,train 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f4r-NtpJYQ8,90,playing electronic organ,train f4rYtbdn85U,40,dog bow-wow,train f4tZs3Hsjvc,16,baby babbling,train f4uG5tnWuXM,0,stream burbling,train f4w6aJMNXSk,30,car passing by,test f50ZoBhaNeQ,0,playing cymbal,train f52NbtQr2KQ,10,playing french horn,test f53aPI-C2Rc,97,"ice cream truck, ice cream van",train f53aPI-C2Rc,298,"ice cream truck, ice cream van",train f5DWxusexpI,34,vacuum cleaner cleaning floors,train f5DWxusexpI,47,vacuum cleaner cleaning floors,train f5GMSdTvPfI,18,snake hissing,train f5GMSdTvPfI,31,snake hissing,train f5GZGSOb0vQ,50,people sniggering,train f5IE89ZXgP8,53,playing table tennis,train f5IE89ZXgP8,173,playing table tennis,train f5JrwhMyulM,20,playing bagpipes,train f5K5AkVPD6Y,290,ocean burbling,train f5R1-VCZbLQ,48,"rowboat, canoe, kayak rowing",train f5RrGFBbbSY,20,chicken clucking,test f5Tk3bAr1yk,280,"motorboat, speedboat acceleration",train f5VZ-f0qZ8k,150,people whistling,train f5VqRC769mc,10,playing tuning fork,test f5VqRC769mc,21,playing tuning 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f6M2h-hZG04,30,orchestra,train f6NDR23Vmx0,160,church bell ringing,train f6OhmrIdB-g,30,people sniggering,train f6RTMrYm1ZY,10,"race car, auto racing",train f6TR5MTD91k,200,playing electric guitar,train f6W00VHecIQ,16,singing bowl,train f6WK-3Qo-h8,569,playing bassoon,train f6Wl-9pzib0,32,cattle mooing,train f6Wl-9pzib0,61,cattle mooing,train f6XsgBGadxk,77,playing bongo,train f6XsgBGadxk,149,playing bongo,train f6iZjy8s40M,19,playing saxophone,train f6jVPNOjCpU,105,people booing,train f6jtqRdb99Y,20,people sniggering,train f6lmyKpJQOw,46,tapping guitar,train f6lmyKpJQOw,107,tapping guitar,train f6mdUS1u2HU,30,turkey gobbling,train f6o8T8OSASw,30,cat meowing,train f6ptpMdXCSw,11,people burping,train f6q9eXn8xcQ,15,playing zither,train f6q9eXn8xcQ,57,playing zither,train f6seh-ObNrc,27,playing zither,train f6tNFhw1BP0,40,"cattle, bovinae cowbell",train f6tNFhw1BP0,53,"cattle, bovinae cowbell",train f6wk1t3QqK8,68,playing tabla,train f6wrXzvByrg,30,playing banjo,test 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digging,train f7YwIpi9y7g,73,dog growling,train f7YwIpi9y7g,85,dog growling,train f7_l3P6CDz0,57,sea waves,train f7_l3P6CDz0,82,sea waves,train f7bKNUF9ONg,30,"motorboat, speedboat acceleration",train f7bSMX2A79Y,109,chicken crowing,train f7cV2C8dRhA,7,playing theremin,train f7cV2C8dRhA,41,playing theremin,train f7eBdjOntpY,60,people crowd,train f7gTNZ92m5o,0,playing harmonica,train f7gTNZ92m5o,7,playing harmonica,train f7i6XxU5Mtg,16,car engine knocking,train f7i6XxU5Mtg,32,car engine knocking,train f7ksCbubuZM,11,reversing beeps,train f7ksCbubuZM,46,reversing beeps,train f7lQaR_hFx8,280,cutting hair with electric trimmers,train f7lQaR_hFx8,307,cutting hair with electric trimmers,train f7pP17SYfm0,0,people burping,train f7pxdUWKvnc,100,people farting,train f7qFqZLE7x8,49,playing tambourine,train f7qFqZLE7x8,220,playing tambourine,train f7qumTdyS-s,260,helicopter,train f7sQ_pGL_1k,240,playing flute,train f7tUlXb984k,130,people slurping,train f7uto-NtFbA,72,playing volleyball,train f7yUwZbNfbc,2,wind rustling leaves,train f7yUwZbNfbc,16,wind rustling leaves,train f7yxhLLTbXM,176,lighting firecrackers,train f7zGvxYf6fU,40,turkey gobbling,train f7zuDllh_e0,20,"cattle, bovinae cowbell",train f7zuDllh_e0,76,"cattle, bovinae cowbell",train f80rbeRjJkc,35,"heart sounds, heartbeat",train f80rbeRjJkc,52,"heart sounds, heartbeat",train f82mJAmLeCg,30,playing trombone,train f83XQ9OF_Jg,0,vacuum cleaner cleaning floors,train f880O2NUBmA,43,fox barking,train f880O2NUBmA,319,fox barking,train f88e2QUKhDI,35,lathe spinning,train f88v-5u48eI,30,mouse pattering,train f89qWtff9fQ,47,playing snare drum,train f8Cy_dEdexk,64,magpie calling,train f8Cy_dEdexk,94,magpie calling,train f8DM9mew6tA,30,lawn mowing,train f8K9zdzcDhI,180,playing acoustic guitar,train f8MFow7FEgU,91,playing erhu,test f8MQnSOtUb4,22,playing accordion,train f8MgO54MrUg,214,chainsawing trees,train f8N81AxeoLs,14,playing bongo,train f8N81AxeoLs,27,playing bongo,train f8OLWuwues0,28,sheep bleating,train 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fBmRsXNTtHc,310,"female speech, woman speaking",train fBoHQo14rcM,30,"rowboat, canoe, kayak rowing",train fBr7mJou0I4,84,foghorn,train fBsCcSE5nFA,20,playing hammond organ,train fBsDFIe_TWc,7,lighting firecrackers,test fBtAHYVPzmk,30,lawn mowing,train fC-mmvvz4WU,5,frog croaking,train fC-mmvvz4WU,18,frog croaking,train fC4NrlOALfg,2,otter growling,train fC4NrlOALfg,13,otter growling,train fC5qEPTMArQ,210,playing synthesizer,train fC6nEnG3eJo,70,turkey gobbling,train fCAnce0whF4,79,hammering nails,train fCG6BC4hUuU,350,playing acoustic guitar,train fCH4Sm8ozqc,81,volcano explosion,train fCMb0VsR9ZI,2,lions growling,train fCPX0V2WkTk,144,singing bowl,train fCRWu20zpLs,40,"motorboat, speedboat acceleration",train fCRjn-VetQU,30,"pigeon, dove cooing",train fCRo_inzb2A,30,"female speech, woman speaking",train fCSdVnS-88M,320,"subway, metro, underground",train fCUl-ED2fkk,4,rope skipping,train fCUl-ED2fkk,55,rope skipping,train fCUptrKUv5Q,250,church bell ringing,train fCV3wzCYIJM,67,people 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fD3sYU-cWiY,495,slot machine,train fD6FbZLRwpY,322,people slurping,train fD6FbZLRwpY,738,people slurping,train fD7OpfzE87w,89,bowling impact,test fD8b3ofdles,15,duck quacking,train fD8zypHLhFA,64,tractor digging,train fD8zypHLhFA,96,tractor digging,train fDBWTLAjAm0,120,chicken crowing,train fDBydpPplzQ,63,roller coaster running,train fDEVsK7Ei30,6,pig oinking,train fDEVsK7Ei30,40,pig oinking,train fDHSko3HN4w,48,roller coaster running,train fDHZBZBAhOg,806,people whispering,train fDHZBZBAhOg,980,people whispering,train fDJyoZ2XLs4,31,beat boxing,train fDLyhmA3Bxs,40,people babbling,train fDODWiSNH-E,87,airplane flyby,train fDODWiSNH-E,118,airplane flyby,train fDOltW7Ycys,12,people booing,train fDP492OthUc,21,playing tympani,train fDP492OthUc,172,playing tympani,train fDSLWYHuj4g,31,lathe spinning,train fDSLWYHuj4g,90,lathe spinning,train fDSRbrz1hXA,320,thunder,train fDSlwpZO3jM,140,fireworks banging,train fDUvhvXE0CQ,40,bowling impact,train fDUvhvXE0CQ,178,bowling impact,train fDVLLIeXXoY,80,tap dancing,train fDX5PdL9VfY,40,playing synthesizer,train fDXQkWceWd8,38,skiing,train fDXQkWceWd8,147,skiing,train fDa7q28_a9Y,42,arc welding,train fDb3GYw6bvI,90,playing flute,train fDgBNYl-Wwo,276,people eating noodle,train fDgZeBuQMyU,47,people marching,train fDhd2gvWn0I,30,chicken crowing,train fDkSz06yDbw,30,female singing,train fDkaClqvHXo,24,"pigeon, dove cooing",train fDkv-IZxcPw,8,playing zither,train fDkv-IZxcPw,53,playing zither,train fDkxEFhhd7U,56,playing squash,train fDlrnpAi-0E,11,scuba diving,train fDlrnpAi-0E,861,scuba diving,train fDm0QzUKKaU,162,arc welding,train fDm0QzUKKaU,300,arc welding,train fDo-_WYKUW0,19,gibbon howling,train fDo-_WYKUW0,67,gibbon howling,train fDojYgwghNg,166,playing tabla,train fDojYgwghNg,182,playing tabla,train fDpyiEpIj_Y,30,playing accordion,train fDqJ-GsUQgo,1,gibbon howling,train fDsrQ1hyWeY,10,"vehicle horn, car horn, honking",train fDv1tq94SYc,60,playing cymbal,train fDvtJYVVYr0,44,canary calling,train fDvtJYVVYr0,359,canary calling,train fDyATRWLEUU,30,sheep bleating,train fDzez6qsEZk,230,playing clarinet,train fE-A00yN-Zk,20,driving buses,train fE-gAW1ntzo,46,raining,train fE2annxR4s0,10,sailing,train fE3w-GXt7wQ,34,people burping,train fE4Z9fZnQOI,340,playing mandolin,train fE4Z9fZnQOI,365,playing mandolin,train fE5RV_XeCis,0,"child speech, kid speaking",train fE7-2gllNvE,230,using sewing machines,test fEA5TenQ0Tg,10,"race car, auto racing",train fECOqptfjlI,0,stream burbling,train fEGavnthiVQ,190,playing drum kit,train fEKSFYM-YKM,114,tap dancing,train fELL9dgIBgM,0,playing banjo,train fELy1qpCJGY,30,playing banjo,train fEMY4fV2f2c,38,playing timpani,train fEMY4fV2f2c,67,playing timpani,train fEQYLOlcUw8,22,playing bugle,train fEQYLOlcUw8,48,playing bugle,train fESL2PLuIwA,140,ocean burbling,test fESiXEhMRfI,30,playing electronic organ,train fETc4ntLv8Q,30,playing electronic organ,train fEU8-7EUAAw,15,baby crying,train fEVoZEeJM28,100,playing timbales,train fEXG4HDbLUo,28,playing badminton,train fEYdQcKJqBw,32,singing bowl,train fE_tEm160yY,70,playing flute,train fEfe8jznp5Q,30,duck quacking,test fEflkQxJhNI,58,playing bassoon,train fEgkNrEcwE4,20,people screaming,train fEhEH5etOaM,405,fire truck siren,train fEhEH5etOaM,571,fire truck siren,train fEi7fu2TUSA,34,rapping,train fEi7fu2TUSA,109,rapping,train fEjnkgBilWY,226,people burping,train fEk_247I5eA,280,chainsawing trees,train fEnJ887QI00,110,playing badminton,train fEszpvy4yFw,239,people burping,train fEt2lf7L13g,101,playing mandolin,train fEt2lf7L13g,256,playing mandolin,train fEtjGNHQxRI,0,driving motorcycle,train fEuitN-94sg,15,striking pool,test fEv3GPgcPdI,280,fireworks banging,train fEvbhqxggVc,29,people booing,train fF0Ji5XwoiY,30,playing electronic organ,train fF1Z0WtvGxc,1,yodelling,train fF1Z0WtvGxc,32,yodelling,train fF3u_xLxF9Q,120,squishing water,test fF6VgbK_vrA,18,people marching,train fF6VgbK_vrA,68,people marching,train fF6kfYa1cmY,25,playing glockenspiel,train fF9xCdJjQC0,140,chainsawing trees,train fFA5mzWoj6I,121,playing harp,train fFA9slK__v4,12,baby crying,train fFBFVohVqt0,31,scuba diving,train fFBN3PsRvto,2,cat hissing,train fFD_0eqZSm0,9,underwater bubbling,train fFH4RKjZau0,16,lions roaring,train fFH4RKjZau0,32,lions roaring,train fFKL_Njts_U,371,playing drum kit,test fFLNq-j9af8,224,playing table tennis,train fFM7xgRj6y8,21,hail,train fFM7xgRj6y8,141,hail,train fFMIOI2GoSA,155,singing choir,train fFMIOI2GoSA,195,singing choir,train fFOBH2l57IU,21,people whistling,train fFOBH2l57IU,48,people whistling,train fFP6iXiohsg,30,sailing,train fFPhmNVPqtY,172,eagle screaming,train fFPn4nfp0YA,30,lawn mowing,test fFQmyJ39gQs,530,people finger snapping,test fFSWQ2uyYAA,30,playing hammond organ,train fFSnuSjcrzw,39,"alligators, crocodiles hissing",test fFTFyawoWiE,54,train whistling,train fFTFyawoWiE,67,train whistling,train fFVT_CtL62M,20,"vehicle horn, car horn, honking",test fFVpTF0FBGk,416,frog croaking,train fFVpTF0FBGk,426,frog croaking,train fFWHlFZEm3M,148,sailing,train fFWHlFZEm3M,159,sailing,train fFX-G5ruQaI,4,"donkey, ass braying",train fFX-G5ruQaI,19,"donkey, ass braying",train fFX04tOPv8s,0,dog whimpering,test fFdcj2zFoiw,14,lathe spinning,train fFdcj2zFoiw,96,lathe spinning,train fFdyzV93Lt4,24,people whistling,train fFe333jegTA,48,people booing,train fFeMmjWKdng,140,playing cello,train fFgPwBnxW2c,20,bathroom ventilation fan running,train fFn2P7ZRIeM,480,playing clarinet,train fFnnQtG7Nv0,13,people humming,train fFnnQtG7Nv0,73,people humming,train fFoYjqavGVU,70,chainsawing trees,train fFpirWDT-rw,310,playing electric guitar,train fFqYSzW0xvw,117,sharpen knife,train fFqYSzW0xvw,127,sharpen knife,train fFsM496bVAA,90,orchestra,train fFtGRO2mhpk,1,people burping,train fFu6-om7EU4,270,hair dryer drying,test fFucfdLwSPM,10,people cheering,test fFucfdLwSPM,42,people cheering,test fFvnBRmkUtA,30,people running,train fFwDczC3dwc,30,people running,train fFxqK4Yz5ww,540,playing harpsichord,train fG-aVBWhi1Q,70,lions growling,train fG-nLzfSMrw,60,playing cello,train fG1X4CTixQ4,4,chicken crowing,train fG2pbLjvjU4,258,tap dancing,train fG4SrPjQwno,0,playing trumpet,train fGAEfj8_DJE,96,skiing,train fGAl2WS0aUY,12,airplane flyby,train fGCH85hraXw,466,people slurping,train fGCH85hraXw,478,people slurping,train fGCPIHfAGz0,295,playing squash,train fGD5z3WHOG8,0,bull bellowing,train fGD5z3WHOG8,44,bull bellowing,train fGEDRNxWL54,29,people eating crisps,train fGEDRNxWL54,110,people eating crisps,train fGHWs5wMxnU,150,"race car, auto racing",train fGIJNPLkhCc,39,playing zither,train fGIJNPLkhCc,49,playing zither,train fGJMbeE9G6g,214,arc welding,train fGLYUo-wUiY,80,people whistling,train fGLsqnEJTKk,280,"bird chirping, tweeting",test fGNGhYq90ek,4,penguins braying,train fGNGhYq90ek,56,penguins braying,train fGRRGsLsNjQ,80,playing hammond organ,train fGWIeFFViyM,22,"vehicle horn, car horn, honking",train fGYxUycPW_o,2,"donkey, ass braying",train fGZM48Q6yOY,18,opening or closing car doors,train fGZM48Q6yOY,119,opening or closing car doors,train fGbGPazyrq0,860,ripping paper,train fGcYW5Udv8U,30,playing flute,train fGclsoEE9Hs,220,chicken crowing,train fGedUv2oGoI,120,playing hammond organ,train fGfIQ0PJwjg,30,playing hammond organ,train fGnwGT-LuY0,10,"vehicle horn, car horn, honking",train fGo2V0WayQA,60,playing accordion,train fGpQ0JK0fcI,53,mouse clicking,train fGsElmf0dew,18,roller coaster running,train fGsElmf0dew,28,roller coaster running,train fGv9x_5jUcQ,13,skiing,train fGvMDXGNFMI,16,playing congas,train fGvMDXGNFMI,145,playing congas,train fGvhcD5G82M,7,dog whimpering,test fGwwoRmOXls,10,people burping,train fH-e5SHIxVw,0,fire crackling,train fH1RbSwYqik,4,"vehicle horn, car horn, honking",train fH48t5kNSsg,8,woodpecker pecking tree,train fH4p_x1ZvN0,124,otter growling,test fH9N48SYlzk,10,playing electric guitar,train fHAYVvv769c,44,people burping,train fHBC1g8S_Bw,28,police car (siren),train fHCVGpMGkrA,97,cat purring,test fHD5X6Uw1Gw,32,playing snare drum,test fHFkuDWcXDA,91,dinosaurs bellowing,train fHGRUsHY9mQ,177,bouncing on trampoline,train fHGwT0mbCB8,240,singing choir,train fHHgppmnCqk,71,playing gong,train fHIDUwofCCM,50,helicopter,train fHKxTh8p4T0,90,playing guiro,train fHKxTh8p4T0,107,playing guiro,train fHMu6A7GWUg,109,"heart sounds, heartbeat",train fHMu6A7GWUg,120,"heart sounds, heartbeat",train fHNmUqFs1xc,50,playing accordion,train fHQBImAMHRc,138,playing bassoon,train fHQNnc9F9hU,100,"playing marimba, xylophone",train fHUAnuezb_0,0,orchestra,train fHUi_DMJar8,13,rope skipping,train fHUi_DMJar8,51,rope skipping,train fHVXvdkQYtk,200,playing double bass,train fHXIQk3sUs0,207,sea waves,train fHZLKS9Ik5E,560,people clapping,train fH_6CAH1xyw,515,smoke detector beeping,train fHaG_1NaM7c,125,beat boxing,train fHaQPHCjyfA,30,ambulance siren,train fHdEaNT0gA0,30,typing on typewriter,train fHfRRRzOemM,12,door slamming,train fHg1Gh0w0gc,9,otter growling,train fHjFBuD58rY,151,volcano explosion,train fHkpdB8yGKA,0,cat purring,train fHkpdB8yGKA,30,cat purring,train fHnIVsGd38I,40,chicken clucking,train fHnT48Ke_lE,160,"bird chirping, tweeting",train fHngXc4xLcQ,30,"rowboat, canoe, kayak rowing",train fHoBm-v7-mE,6,eagle screaming,train fHoBm-v7-mE,18,eagle screaming,train fHqD6dh11HY,20,playing tambourine,train fHrn_Qd4nGc,160,machine gun shooting,train fHsxAeWCFpE,0,"vehicle horn, car horn, honking",train fHuGssfuOdM,149,playing bassoon,train fHv7PtlUuIA,18,fire truck siren,train fHwNuVL7QTw,296,playing bassoon,train fHyGWF0Kh-E,813,people eating apple,train fHykkbd05R0,1,chimpanzee pant-hooting,train fHykkbd05R0,58,chimpanzee pant-hooting,train fHz_bjTZXr8,30,"rowboat, canoe, kayak rowing",train fI4jsNBD52A,1,playing table tennis,train fI5TzD_xzDg,144,electric grinder grinding,train fIATenI7nKQ,29,baby laughter,train fIATenI7nKQ,65,baby laughter,train fIDq3VsjHhw,0,lions roaring,train fIDq3VsjHhw,11,lions roaring,train fIE-MfnOcdc,25,sharpen knife,train fIE-MfnOcdc,78,sharpen knife,train fIFl1EcMKK8,50,car 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fK3vB7mI7ns,39,alarm clock ringing,train fK4Pq0ZV2CM,30,sloshing water,train fK4QBQZ6i7w,290,people sniggering,train fK4kD8fXswE,37,playing accordion,train fK5UcGq7C9I,490,playing acoustic guitar,train fK6q832dos4,20,"rowboat, canoe, kayak rowing",train fK9v-WqcoJA,22,striking pool,test fKDWi8Q4THk,436,playing electronic organ,train fKFH7GuhFH4,80,dog barking,train fKGtXtma7nM,12,crow cawing,train fKHZ8oiaLY0,15,lions growling,train fKHZ8oiaLY0,26,lions growling,train fKKcYwIbFd4,0,wind chime,train fKKcYwIbFd4,27,wind chime,train fKL6FNnhFRA,30,playing harp,train fKSORtIbiQM,29,playing accordion,train fKTjtodXxaA,6,people booing,train fKXa-XIkLPk,115,playing harp,train fKZfdUah_48,0,playing bass guitar,train fKZzIM0gt3M,30,female singing,train fKaMqsG4DXw,180,playing piano,train fKf4XvM1B80,350,"bee, wasp, etc. buzzing",train fKfIRGfWKHE,0,playing electric guitar,train fKfoyYLgazw,30,playing acoustic guitar,train fKgLRlCjaYA,0,"male speech, man speaking",train fKiJHquoAJ0,730,male 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fLqKgbl9WyU,25,baby babbling,train fLrhcpiujHw,368,train horning,train fLrhcpiujHw,516,train horning,train fLskMCwav-0,126,playing accordion,train fLvOILMmk7M,32,dog whimpering,train fLyz28viow4,0,playing piano,train fLz1lB4Ni94,75,playing snare drum,train fLzo66H78EQ,10,chainsawing trees,train fM-V2NIhl3U,182,playing bassoon,train fM0wwjjotjw,15,woodpecker pecking tree,train fM0wwjjotjw,27,woodpecker pecking tree,train fM1L9NLudSY,33,sea waves,train fM2gi9BIZWo,54,sliding door,train fM2gi9BIZWo,84,sliding door,train fM5a-MPpMGE,30,printer printing,train fM5cxwzSSX4,24,playing double bass,train fM5cxwzSSX4,96,playing double bass,train fM7R2wYKMbk,35,wood thrush calling,train fMAod4k2P6c,30,playing cymbal,train fMBXz9RlOe0,1,wind rustling leaves,train fMD6WvHjrHA,0,playing tennis,train fMD6WvHjrHA,15,playing tennis,train fMIPxIiSuoI,0,people hiccup,train fMLuzDSxdK0,260,playing saxophone,train fMM7W5vph3I,30,people whistling,train fMMInlGxCDU,311,people finger snapping,train fMMInlGxCDU,343,people finger snapping,train fMMPSkluvAw,2,playing harp,train fMMldz9TqAc,40,skidding,train fMOPpA4W-Ds,48,playing steelpan,train fMOPpA4W-Ds,274,playing steelpan,train fMRHd1klMJ0,170,sailing,train fMRkwcjjozw,250,playing steelpan,test fMSX20utRHI,60,chainsawing trees,train fMTDhan1C7I,300,eating with cutlery,train fMTEdh2mczQ,178,playing didgeridoo,train fMWYnZDq3Bo,87,"playing steel guitar, slide guitar",train fMWYnZDq3Bo,130,"playing steel guitar, slide guitar",train fMXsP6wer_Q,8,people clapping,train fMZIyPbuH9U,270,driving buses,train fMZaRGIpPIE,35,hail,test fMeG6mk568Q,83,hammering nails,train fMeG6mk568Q,151,hammering nails,train fMeaggq0_rA,0,playing bagpipes,train fMeqVvydOd0,90,"bird chirping, tweeting",train fMfpkqpjOHI,310,helicopter,train fMg5fovtooI,120,blowtorch igniting,train fMg5fovtooI,197,blowtorch igniting,train fMj98h7qb8M,240,police car (siren),train fMjZSAv_Ec4,340,basketball bounce,train fMp_fHRaNs8,460,chicken crowing,train fMqCV2R-PF0,95,francolin calling,test fMtJPzrUsJQ,24,playing lacrosse,test fMv6Ww80auE,162,playing badminton,train fMz4zmg-OOA,70,"race car, auto racing",train fN15GqY0LxE,30,chicken clucking,train fN25nqizhbU,164,playing hockey,test fN2WxrtcB4w,0,owl hooting,train fN2WxrtcB4w,13,owl hooting,train fN2kobB_k1w,494,lawn mowing,train fN3sHctBIrA,7,people slapping,train fN4rmyXw4sQ,5,squishing water,train fN4rmyXw4sQ,73,squishing water,train fN6GeBI-Fdw,240,people battle cry,train fN73vXZ1VHo,0,"child speech, kid speaking",train fN9kwTETzfw,61,running electric fan,train fNCKpe1deeo,50,civil defense siren,train fNE44nzpSPE,85,typing on typewriter,train fNE44nzpSPE,95,typing on typewriter,train fNFXzg0YYqI,399,playing piano,train fNLS25qblX0,7,playing table tennis,train fNNhE0Cz2QY,0,door slamming,test fNPYjqqdY1o,445,playing bassoon,train fNSXGwnZweE,48,sharpen knife,train fNWjxc5K3Gw,51,spraying water,test fNZL1F8C6Bo,201,playing bassoon,train fNbCb5bOjPk,40,"race car, auto racing",train fNcWkInULmY,30,playing bass guitar,train fNfs1k_gGj8,130,male singing,train fNgXgJZHxDE,140,pumping water,test fNhQLDhS1WY,9,playing didgeridoo,train fNhWxH_NUh8,97,playing bagpipes,train fNhWxH_NUh8,117,playing bagpipes,train fNlGlh1GaeA,13,"heart sounds, heartbeat",train fNlGlh1GaeA,67,"heart sounds, heartbeat",train fNlxlPvqLmw,45,baby laughter,train fNnTcAxK0Uc,1,people farting,train fNnTcAxK0Uc,84,people farting,train fNpPix_p5qw,50,"railroad car, train wagon",train fNrG3KVS5js,0,cat hissing,train fNrQAmHDiFM,20,printer printing,train fNrptrMP3O0,40,playing timpani,train fNrptrMP3O0,77,playing timpani,train fNsb_BdIHhI,50,wind noise,train fNttvad732o,6,otter growling,test fNwovuulJiM,13,car engine starting,train fNwovuulJiM,54,car engine starting,train fNy394ucW-s,290,playing bass guitar,train fNyPWqo8CAU,330,playing trombone,train fNyZ4V0M_cg,0,metronome,train fNyZ4V0M_cg,174,metronome,train fO0dW4DUscg,57,fire truck siren,train fO1dAAgYq2I,140,using sewing machines,train fO250kjXV7U,0,chainsawing trees,train fO2HS7_fZb0,26,dog barking,train fO4pfWKx8Bk,306,"ice cream truck, ice cream van",train fO5iMURwOdw,68,"playing steel guitar, slide guitar",train fO5iMURwOdw,152,"playing steel guitar, slide guitar",train fO5zkVD7HO0,141,people marching,train fO5zkVD7HO0,370,people marching,train fO6-i9SwY3Q,201,eletric blender running,train fO8UudOPgHk,30,printer printing,train fOA7mEljjPU,22,playing sitar,train fOA7mEljjPU,62,playing sitar,train fODf_0SPQ0I,2,people marching,train fODf_0SPQ0I,119,people marching,train fOHbm-39i4U,26,playing glockenspiel,train fOHbm-39i4U,37,playing glockenspiel,train fOJHXXW5f5c,290,people screaming,train fONIA4brCpM,111,beat boxing,train fOPeVeiO9DY,30,playing drum kit,train fORTwxb5tcY,25,people sniggering,train fOUcyeqZqgI,25,"race car, auto racing",train fOV5aHmFqZ8,67,playing bassoon,train fOVVEorwoLc,100,chicken clucking,train fOVsAMJ3Yms,30,train horning,train fOa9p-Lfhyg,2,dog howling,train fOa9p-Lfhyg,13,dog howling,train fOawd-8FREE,2,people belly laughing,train fOc6JNNdLaQ,30,people eating,test fOd66b_bIWE,0,playing tennis,train fOd66b_bIWE,5,playing tennis,train fOhQ77qVUnE,20,playing harp,train fOkWZBUsKZ4,10,playing trombone,train fOnP76j7-2U,94,child singing,train fOnP76j7-2U,193,child singing,train fOpp44y0cYM,30,typing on computer keyboard,train fOsJkeeuMro,4,"donkey, ass braying",train fOtjQ5goqho,289,ripping paper,train fOtjQ5goqho,299,ripping paper,train fOuQWo-5hqo,39,chimpanzee pant-hooting,train fOwLtOomVBI,15,playing glockenspiel,train fOxsITNBW9g,383,chainsawing trees,train fP5Gwl5CPbc,30,mouse pattering,train fPAsWpYozPM,112,chipmunk chirping,test fPBiEe9rOsw,50,people crowd,train fPCWhYDECWY,308,yodelling,train fPCwL7tr72A,1,frog croaking,train fPCwL7tr72A,19,frog croaking,train fPFaEBGBaN0,30,toilet flushing,train fPFpRsKbzfU,0,owl hooting,test fPGjpjfyJk4,390,people shuffling,train fPL97WEor_g,0,frog croaking,train fPMwOPwurKU,10,playing vibraphone,test fPOWwvB6uLQ,30,lions growling,train fPOkhviH7OU,13,yodelling,train fPOkhviH7OU,103,yodelling,train fPSHEBDjKr8,254,rapping,train fPT4JNeYCZU,100,driving motorcycle,train fPUaeUq7K4Q,20,raining,train fPUhmwkkwpA,210,playing drum kit,train fPUvmDuCE0c,30,people whistling,train fPV1g05NMfE,78,tap dancing,test fPXFskM-qYs,2,roller coaster running,train fPXh640TC5c,0,playing harmonica,train fPXh640TC5c,39,playing harmonica,train fPYVORr8BuY,2,hedge trimmer running,train fPYVORr8BuY,13,hedge trimmer running,train fPYbbH4DvAs,240,mouse pattering,train fPZaee3NDVU,400,"race car, auto racing",train fPZb1IB-U_k,19,tap dancing,train fPZe_fNLkLI,30,"race car, auto racing",train fPdC4E0VUeM,205,train wheels squealing,train fPdC4E0VUeM,222,train wheels squealing,train fPeWY5OrQ6Y,59,volcano explosion,train fPgjE_H2k9s,10,playing castanets,train fPgjE_H2k9s,30,playing castanets,train fPiRBxijEok,694,lip smacking,test fPkpRXRXKiY,160,playing drum kit,train fPn-nSUqea8,60,sailing,train 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fQQiWVijmo0,15,chicken crowing,train fQQiWVijmo0,28,chicken crowing,train fQRh2QW5RX8,30,"bird chirping, tweeting",test fQUgDf1NlFY,8,"heart sounds, heartbeat",train fQUgDf1NlFY,23,"heart sounds, heartbeat",train fQWT4MDmVGw,200,people crowd,train fQWcbHMeEMc,20,cheetah chirrup,train fQWcbHMeEMc,80,cheetah chirrup,train fQY0ETam8y4,111,"rowboat, canoe, kayak rowing",train fQYjh6sCDVM,58,rope skipping,train fQ_XsoxWoHA,7,people finger snapping,train fQ_XsoxWoHA,264,people finger snapping,train fQfMd1E4HS0,73,playing tabla,train fQfMd1E4HS0,157,playing tabla,train fQfXPIzxAqU,379,playing cornet,train fQfXPIzxAqU,458,playing cornet,train fQiWrv2IVh8,30,"pigeon, dove cooing",train fQtAmM3oUhE,4,scuba diving,train fQtAmM3oUhE,19,scuba diving,train fQtPx7U7x9I,117,cutting hair with electric trimmers,train fQtPx7U7x9I,283,cutting hair with electric trimmers,train fQude3KXkEg,34,air conditioning noise,train fQyBZP3EkSo,334,ripping paper,train fQyBZP3EkSo,587,ripping paper,train 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faYV1ZtY9Ok,0,toilet flushing,train faZ26sVFwtc,56,dinosaurs bellowing,train fa_5UMSzHjc,35,cricket chirping,train fa_5UMSzHjc,55,cricket chirping,train faeK4ATuCnE,1,raining,train fahl5bcFosI,30,playing harpsichord,train fakJJOOLBhw,83,elk bugling,test fakJJOOLBhw,140,elk bugling,test faoVtOUk4Gw,257,people finger snapping,train faptc0UqNmg,429,swimming,train fas7QX3GQr0,30,male singing,train fat2VS3qQBw,15,playing bugle,train fat2VS3qQBw,25,playing bugle,train favWHteInt0,448,wind chime,test faxwUueWnTk,456,car engine starting,train fazkK55fvpY,10,chipmunk chirping,train fazkK55fvpY,35,chipmunk chirping,train fazsaDnIEn0,17,people burping,train fb0hw346Nb0,4,crow cawing,train fb1hA3uG5wc,31,sharpen knife,train fb1hA3uG5wc,81,sharpen knife,train fb24Z2vWVvU,30,playing harpsichord,train fb3ia8KOT3s,12,police car (siren),train fb3ywhdT_m0,93,people eating noodle,train fb3ywhdT_m0,430,people eating noodle,train fb4iW6WYAco,30,playing trombone,train fb4jb9TYUxw,8,sliding door,train 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piano,train fbZsEWDdrA4,100,wood thrush calling,train fb_cvUpOpVk,1,car engine starting,train fb_cvUpOpVk,209,car engine starting,train fbdz2U-fJsk,110,owl hooting,test fbfKPvkYDaY,60,playing bass guitar,train fbjHO8McLxk,530,church bell ringing,train fbjKbMefk88,30,people running,train fbmriH12IMg,77,people humming,test fbnmnk0Oj-E,110,skateboarding,train fbnsQ7TAC6M,170,playing drum kit,train fbqfaEkK9bI,0,playing ukulele,train fbqfaEkK9bI,146,playing ukulele,train fbqiN9hHyJo,288,rapping,train fbqre1i9zBk,9,sheep bleating,train fbtKwyrHaV0,15,sharpen knife,train fbtKwyrHaV0,118,sharpen knife,train fbvCzLuoFUk,110,basketball bounce,train fbvCzLuoFUk,170,basketball bounce,train fbvndUYdbX4,3,wind rustling leaves,train fc2IK-6ZjUg,70,"bee, wasp, etc. buzzing",train fc2YddmHFik,80,"playing violin, fiddle",train fc6FIZbrvFU,322,people marching,train fc6N1xWZsgk,59,people humming,train fc7peYF01zs,0,"child speech, kid speaking",train fc9x20wj8ss,76,telephone bell ringing,test fcBELYV6pjE,6,lions roaring,train fcBzKuNDdkI,130,playing harpsichord,train fcBzKuNDdkI,152,playing harpsichord,train fcDDZmBWfWo,0,chainsawing trees,train fcH-2C_25Q8,10,driving motorcycle,train fcLtznBKpBA,39,basketball bounce,train fcPfJ8mx6ek,30,"pigeon, dove cooing",train fcQ8JHt-mDc,0,splashing water,train fcQXysMKfrA,30,sloshing water,train fcSI6Gf9rmo,187,playing erhu,train fcSI6Gf9rmo,198,playing erhu,train fcSlEy-3XSw,1,hammering nails,train fcYQiRWbHIY,97,snake rattling,train fcYQiRWbHIY,148,snake rattling,train fcYtATqhA90,50,tapping guitar,train fcYtATqhA90,80,tapping guitar,train fca5SrxoC0Y,48,people marching,train fca5SrxoC0Y,115,people marching,train fcap3D6kSHQ,30,playing bass drum,train fcbIXLhpBvA,26,people booing,train fcbIXLhpBvA,46,people booing,train fcdmgCl5ZbM,30,dog whimpering,train fcgj812_tbg,227,lighting firecrackers,train fcgj812_tbg,333,lighting firecrackers,train fcgqQMzcPoQ,136,people shuffling,train fcgqQMzcPoQ,148,people shuffling,train fcnDBEQaJ8k,80,"subway, metro, underground",train fcpHx7xFyTU,37,bowling impact,train fcpTJaQGbcA,20,fireworks banging,train fcr6qS6mZ_g,45,singing choir,train fcr6qS6mZ_g,211,singing choir,train fcrDevkLMWY,420,people eating,train fctAf41jeLI,58,alarm clock ringing,train fcuIVIDCiJg,210,people clapping,train fcv8P6nbcPk,0,playing didgeridoo,train fcv8P6nbcPk,29,playing didgeridoo,train fcxriIFvzH0,30,people farting,train fcyUlEGvMdc,37,playing volleyball,train fd2Sm_ee1Mc,0,car engine starting,train fd2e-mmeU_w,47,playing flute,train fd5LCBRsO3s,30,playing vibraphone,train fd6lJbQMTdI,7,playing glockenspiel,train fd6lJbQMTdI,32,playing glockenspiel,train fdCOuABle8A,0,dog barking,train fdDdhBENG28,10,"child speech, kid speaking",train fdE2v8v4K1c,24,roller coaster running,train fdE2v8v4K1c,54,roller coaster running,train fdEY_XAtt6c,1,dog baying,train fdEY_XAtt6c,86,dog baying,train fdFk48aKQ_c,239,people eating crisps,test fdHSEV6Ki5Q,30,"motorboat, speedboat acceleration",train fdN_D5tW398,30,ocean burbling,train fdOYVf1E8O4,5,playing tennis,test fdQIKKY0j8s,30,"pigeon, dove cooing",train fdVKLUSmzgY,173,tractor digging,train fdVPxKhssHE,0,wind rustling leaves,train fdXiiwlmRNQ,0,"child speech, kid speaking",train fdYPgx0vNJI,158,playing erhu,train fdYWSHKBcYE,110,waterfall burbling,train fdZy6OcW9ak,43,playing bugle,train fd_1rOr4gok,40,mynah bird singing,train fdadZ_KrZVw,90,playing timpani,test fdbSeQ5eF88,30,volcano explosion,train fdbwZu1J9e0,27,dog howling,train fdbwZu1J9e0,68,dog howling,train fdeou2jOoT0,529,playing congas,train fdeou2jOoT0,838,playing congas,train fdfXIluCs-Y,580,"railroad car, train wagon",train fdh9MgE7vac,3,"playing steel guitar, slide guitar",train fdh9MgE7vac,142,"playing steel guitar, slide guitar",train fdi7c3YBtg0,380,playing electric guitar,train fdkNxMyL01w,5,cow lowing,train fdkNxMyL01w,101,cow lowing,train fdkrYkpQ09k,117,sliding door,train fdmlZep96fw,350,orchestra,train fdoSpr2hpIA,121,cupboard opening or closing,test fdoxeAfVnEI,30,playing banjo,train fdqScxG9D1M,130,playing cymbal,train fdqYnI55kVw,20,typing on computer keyboard,train fdrD6n99bXk,360,playing piano,train fds6Q7YcRl0,89,people humming,train fds6Q7YcRl0,138,people humming,train fdsY0gHaK3E,150,"subway, metro, underground",train fdspeq8mGRQ,30,playing double bass,train fdtO8Izr5pw,80,"race car, auto racing",train fdxlA8UoNB0,30,playing banjo,test fdyyW84Iakg,30,car passing by,train fe-FMEwCU7E,7,snake hissing,train fe-FMEwCU7E,18,snake hissing,train fe0-2PHj_E8,252,playing electronic organ,train fe0-2PHj_E8,271,playing electronic organ,train fe2hGyMztU4,164,cattle mooing,train fe3Db43jMG8,560,wind noise,train fe3dZmkaq0Q,70,tap dancing,test fe6kCCCVMiI,140,playing badminton,train feBkKRpweHc,128,slot machine,train feC0L9MtghM,190,playing sitar,test feFJuNFY6Dw,30,ambulance siren,train feHkSH3Wixw,0,"ice cream truck, ice cream van",train feHkSH3Wixw,131,"ice cream truck, ice cream van",train feMC18447Ec,24,airplane flyby,test feVv07CkdWE,7,"playing steel guitar, slide guitar",train feVv07CkdWE,119,"playing steel guitar, slide guitar",train feZKO-jZXr0,478,wood thrush calling,train feZKO-jZXr0,675,wood thrush calling,train feaWVXoRz34,8,frog croaking,train feaWVXoRz34,18,frog croaking,train febHB6T3jjs,10,car engine knocking,train fefS2xv_Mhg,10,playing didgeridoo,train fegQklfWKtU,32,playing tabla,train fegQklfWKtU,145,playing tabla,train fehagyXsF3c,51,playing bassoon,train fehagyXsF3c,67,playing bassoon,train felD9q1GY6U,30,people whispering,train feq4LcuTdec,310,typing on typewriter,train ferTWBGi8so,221,parrot talking,train ferTWBGi8so,280,parrot talking,train fesUcNK6Nf0,104,hammering nails,train fesUcNK6Nf0,117,hammering nails,train fetkrPkQuRg,143,playing badminton,train ff21Bursam0,160,cattle mooing,test ff2_VJjkGDw,60,playing harp,train ff68_2YIAvE,329,striking bowling,test ff7CHJ9kvnk,3,playing djembe,test ff9WoS0sBdU,50,"child speech, kid speaking",train ffBqrLOBIQ8,3,stream burbling,train ffDYsmU7tPM,142,typing on typewriter,train ffDYsmU7tPM,153,typing on typewriter,train ffE8ONZ6krU,19,tractor digging,train ffE8ONZ6krU,108,tractor digging,train ffEZyEybPDk,0,"bird chirping, tweeting",train ffEtLvm2qiU,30,playing bass guitar,train ffFcyA6Cyis,92,planing timber,train ffHesVqzJs8,10,ambulance siren,train ffLk4jfhXMs,9,footsteps on snow,train ffLk4jfhXMs,21,footsteps on snow,train ffM2RaFp_SQ,7,cheetah chirrup,train ffMofGnrQlA,5,"fly, housefly buzzing",train ffOaFS3XWtA,0,car engine starting,train ffOpwtBMA28,90,playing synthesizer,train ffRSeubjW-E,50,people coughing,train ffX7uMHCo4w,91,owl hooting,train ffYkw1lMOLo,39,eagle screaming,train ffZWb54oJH4,336,hair dryer drying,train ffZWb54oJH4,376,hair dryer drying,train ffZoY75S_-k,70,chicken clucking,train ffaKOcg7ZhM,9,civil defense siren,train ffcZz0QSZTE,90,chicken crowing,train ffi5lMuuLKc,131,playing erhu,train ffi5lMuuLKc,150,playing erhu,train ffk76nggkWg,0,playing trombone,train ffkvYlRwUSY,15,beat boxing,train ffo1YQzdmhk,71,playing badminton,train ffpSZU8M70E,150,"playing marimba, xylophone",train ffrm2_qqb3Y,56,bouncing on trampoline,train ffrm2_qqb3Y,112,bouncing on trampoline,train ffrz81vItcQ,327,tap dancing,train fftbphh3Swc,122,planing timber,train fftbphh3Swc,151,planing timber,train ffyCVRX1v8M,37,lighting firecrackers,train ffyCVRX1v8M,88,lighting firecrackers,train ffydN8foQU4,200,playing piano,train ffzTeT7M7Ro,2,playing didgeridoo,train fg2BS7H_dAU,35,playing bass guitar,train fg2BS7H_dAU,83,playing bass guitar,train fg2LRdn3DZc,252,tap dancing,train fg2LRdn3DZc,436,tap dancing,train fg2Nvzq1rVU,80,skateboarding,train fg2wvdy8uVM,30,playing french horn,train fg2xsB3kWHA,87,francolin calling,test fg8ttPtDvzU,228,penguins braying,train fgBDOxUJ1QM,36,tractor digging,train fgFTL6xcUFs,47,slot machine,train fgLPt9i7dHc,7,singing bowl,train fgN_arkIa5E,290,church bell ringing,train fgO6xlaHaOc,21,playing harpsichord,train fgO_jdUl3ZU,0,chicken crowing,train fgOrZ3jSrB8,3,owl 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fhLlUKAbdSM,140,playing theremin,train fhLlUKAbdSM,169,playing theremin,train fhLqmoC4ap4,40,playing bagpipes,train fhMFdhv4BlI,56,"fly, housefly buzzing",train fhPv4x7y93E,106,playing snare drum,train fhQTjKUdUSk,42,playing tennis,train fhQTjKUdUSk,121,playing tennis,train fhU_MWaclKQ,1,penguins braying,train fhVy75I226w,20,telephone bell ringing,test fhWZ8pXiGk8,80,playing piano,train fhXqrvg1enM,260,striking pool,train fhYkZRoOObI,253,playing congas,train fhZ4Tl5ba1g,20,thunder,train fh_WNoqq6a0,97,playing glockenspiel,train fhbz1D9svTI,20,playing bassoon,train fhcZ6b2FSsk,69,child singing,train fhcZ6b2FSsk,122,child singing,train fhd8mYk8WAA,30,playing bass drum,train fhfEYHLL4Po,116,firing muskets,train fhi4TRcYfgQ,20,frog croaking,train fhi4TRcYfgQ,76,frog croaking,train fhiB_bilIZE,150,sloshing water,train fhlZG8x-NO4,110,orchestra,train fho-QynG3pI,3,firing muskets,train fhpuzQvZfwc,41,bowling impact,train fhrpCxYp_JE,30,sheep bleating,train fhrwGiZrvio,144,parrot talking,train fhvDFoXRhu8,45,spraying water,test fhxoTeXbkSM,170,playing saxophone,train fhyPfsSGXzA,62,dog growling,train fi18TNUw0Yo,35,civil defense siren,train fi25iGxC18g,280,driving motorcycle,train fi2rMBcmwgU,43,playing cymbal,train fi3tlqrYklE,252,playing congas,train fi3tlqrYklE,304,playing congas,train fi6ax6szIoY,174,playing badminton,train fi6osPCZ6dY,140,playing drum kit,train fiAFZvMSqYU,20,playing oboe,train fiAFZvMSqYU,167,playing oboe,train fiAJqNva0oA,30,playing clarinet,test fiAWta1100o,30,typing on computer keyboard,train fiBkv8nWLA4,122,playing flute,train fiCzT1jPaJY,127,swimming,train fiDi1MhbQgw,12,playing glockenspiel,train fiDi1MhbQgw,35,playing glockenspiel,train fiFFi1Ulgek,257,striking bowling,train fiFFi1Ulgek,273,striking bowling,train fiH2VsH73g0,30,"bird chirping, tweeting",train fiIxQzrDy3Y,30,raining,train fiJiFr_XVTA,50,people farting,train fiLMwCQevN8,41,air horn,train fiLMwCQevN8,109,air horn,train fiLwl2YEViU,60,people farting,train fiOW7Gv5y4o,60,toilet 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tympani,train fitvl1TDa1Q,35,lions roaring,train fitvl1TDa1Q,47,lions roaring,train fivHX77rMRw,0,playing clarinet,train fivHX77rMRw,43,playing clarinet,train fixF5rqZyYA,29,playing oboe,train fixF5rqZyYA,129,playing oboe,train fiz6sxnpraQ,73,slot machine,train fj-fBzwxdYw,48,tapping guitar,train fj-hBepkjwY,47,basketball bounce,train fj0qlDdWt1M,158,playing squash,train fj1fknZPxUA,0,police car (siren),train fj3eP2mYz8k,21,playing lacrosse,train fj5NoJtxdsU,30,playing cello,train fj6iszfEmu4,100,playing bagpipes,train fj7r6L9Le2E,200,people sniggering,train fj86q6kA5r4,30,dog bow-wow,train fj9UYbKJIJo,20,playing volleyball,train fj9e8Ogc6xs,0,playing bagpipes,train fj9e8Ogc6xs,2,playing bagpipes,train fj9fdomOoXQ,30,"rowboat, canoe, kayak rowing",train fjCF72q3qLY,4,civil defense siren,train fjEuY5ocMtA,45,tornado roaring,train fjEuY5ocMtA,55,tornado roaring,train fjFg3Xjo1G8,34,tapping guitar,train fjFoRXobYsk,30,sailing,train fjGSPg_osj0,119,missile launch,train fjJ6DRj5iTY,42,chopping wood,train fjJ6DRj5iTY,53,chopping wood,train fjJPvFZswhg,30,singing bowl,test fjJbMGAjQZM,59,barn swallow calling,train fjJbMGAjQZM,134,barn swallow calling,train fjKiUxdMyC4,3,fire truck siren,train fjKiUxdMyC4,58,fire truck siren,train fjMCFj0y-vY,113,electric grinder grinding,train fjN23DwzgFM,77,air horn,train fjOBnPjzBj0,140,dinosaurs bellowing,train fjOBnPjzBj0,303,dinosaurs bellowing,train fjPI8qbXf8M,115,arc welding,train fjPQzldJAt4,51,canary calling,train fjSHs2IjH6I,433,people slurping,train fjWN2sQcR2s,30,ambulance siren,test fjWpbEr6Fhw,45,playing cornet,train fjWpbEr6Fhw,63,playing cornet,train fjYsSn1fOtM,30,singing choir,train fja-2hs3MFg,156,scuba diving,train fjb3sxOxt9o,65,playing volleyball,train fjbi6dDWkDs,163,playing gong,train fjbi6dDWkDs,249,playing gong,train fjcTeGysmbI,21,playing glockenspiel,train fjcTeGysmbI,53,playing glockenspiel,train fjdWNEpim4w,31,tap dancing,train fjeMnEoIJG4,30,playing drum kit,train fjfyYz-iCIY,105,hail,train fjfziT7BD6o,140,playing synthesizer,train fjhQlXNjyGI,170,helicopter,train fjhqZxE-USo,12,hammering nails,train fjhqZxE-USo,24,hammering nails,train fji8KKZL05Y,27,tractor digging,train fjiv34te9vc,30,cat meowing,test fjj1NqEPR5U,0,playing cello,train fjlC5B-YOC8,60,dog growling,train fjn7GS4WtBo,180,driving buses,train fjtYm7UI7LM,114,frog croaking,train fjtYm7UI7LM,125,frog croaking,train fjxe_aO4e4w,162,playing trombone,train fjxe_aO4e4w,194,playing trombone,train fjxnJKcM0zU,90,playing saxophone,train fjy4Qao_YoI,350,"motorboat, speedboat acceleration",train fk3MurO42_U,50,fireworks banging,train fk5mNZ1pYc8,29,people screaming,train fk5mNZ1pYc8,51,people screaming,train fk6bTuDgr2c,50,skateboarding,train fkGKQAb8Tt0,0,hammering nails,train fkGPfJmuElE,42,playing glockenspiel,test fkGPfJmuElE,52,playing glockenspiel,test fkHQ-o3-XZM,90,playing bassoon,train fkIJhnb9uuU,4,people giggling,train fkIJhnb9uuU,49,people giggling,train fkJIyJMl9PA,42,missile 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fnFoX_fBXRE,30,lions growling,train fnG4thuFieA,0,tapping guitar,train fnG4thuFieA,137,tapping guitar,train fnG5eGra-VI,70,"bee, wasp, etc. buzzing",train fnKOxXbtbWM,0,woodpecker pecking tree,train fnKOxXbtbWM,47,woodpecker pecking tree,train fnMLkL7E2MA,63,playing glockenspiel,train fnMLkL7E2MA,97,playing glockenspiel,train fnMhUPewk6c,35,playing banjo,train fnWQZ3e7T2s,100,playing double bass,train fnWQsPPDFvU,123,whale calling,train fnWgkdamy2w,231,machine gun shooting,train fnWgkdamy2w,383,machine gun shooting,train fnWq9eVVE2Q,30,playing bass drum,train fnX6gC95scE,308,lathe spinning,train fnYBe5M_z3A,390,thunder,train fnZSwHZW1ro,330,"railroad car, train wagon",train fnZcGQ2K2xg,11,opening or closing drawers,train fn__c2O0cac,30,duck quacking,train fndoOosNlgA,60,playing zither,train fndoOosNlgA,129,playing zither,train fneLVe9pnfA,101,chainsawing trees,train fneYz8aPxg8,566,wind rustling leaves,train fngngpYccro,84,singing bowl,train fnj0Bhpamqo,115,squishing water,test fnkLZaSKBpg,30,printer printing,train fnnz5bY9dWE,400,typing on typewriter,train fnstwdUAlu0,15,sloshing water,train fntO4pw_CNI,9,"bird chirping, tweeting",train fnyq6iSDCwU,33,playing didgeridoo,train fnzeJRH5XUc,30,driving buses,train fo-22OQipE8,44,cheetah chirrup,train fo-22OQipE8,54,cheetah chirrup,train fo-Df3ITVh0,85,hair dryer drying,train fo-Df3ITVh0,98,hair dryer drying,train fo0EBnM1Pd0,150,fireworks banging,train fo1B4JOsq38,20,splashing water,train fo5zCqy-FBU,4,"vehicle horn, car horn, honking",train foATQY3wovI,12,playing harmonica,train foATQY3wovI,33,playing harmonica,train foCQnR39lvI,30,people sniggering,train foCxXd7nev4,268,spraying water,test foE5hUpYKMc,60,playing hammond organ,train foEJ3UrBQX4,0,police car (siren),train foEQnzkfwp4,12,baby laughter,train foEQnzkfwp4,196,baby laughter,train foFpNHb8-VE,70,playing cello,train foFzF6jGSII,6,car passing by,train foGge065_kI,0,"child speech, kid speaking",train foKMoQC4j0M,12,mouse squeaking,train foKMoQC4j0M,26,mouse squeaking,train foMCFJxrWnE,270,"bee, wasp, etc. buzzing",test foMjdFy_2s8,30,ocean burbling,train foMunuvUuZ0,193,planing timber,train foMvW_cEeQE,30,people shuffling,train foNZ9lXVozo,154,"ice cream truck, ice cream van",train foNZNgtioeY,180,playing accordion,train foNpH3TLA2s,190,driving buses,train foOESy4QhOI,100,"motorboat, speedboat acceleration",train foQ8lV0bsb4,13,"fly, housefly buzzing",test foQ8lV0bsb4,71,"fly, housefly buzzing",test foR6o-5czLg,6,typing on typewriter,train foSPi5DnrMM,80,"pigeon, dove cooing",train foSZCZ_Za5Q,23,magpie calling,train foSZCZ_Za5Q,34,magpie calling,train foUdXsVw3bw,230,splashing water,train foW3kchexVU,9,scuba diving,train foWcWVrE10c,3,tornado roaring,train foX8A0WjwGg,26,"rowboat, canoe, kayak rowing",train foZD7He0BSs,11,playing timbales,train foZD7He0BSs,21,playing timbales,train focGYBclWlY,11,singing bowl,train fogMF4pNKv0,32,fire truck siren,train foiG74VSHno,30,"pigeon, dove cooing",train fojW3Jey1NE,151,playing bagpipes,train fojW3Jey1NE,199,playing bagpipes,train fojdyF_3m4w,30,typing on typewriter,train fojyzit09fc,80,people screaming,train foqf8FssN1Q,104,chainsawing trees,train foqrHdDS0Go,40,playing badminton,train forJeXwrTzM,10,people booing,train forJeXwrTzM,103,people booing,train fotWLA-z6Eg,0,playing harp,train fou5l8zaNcQ,0,dog barking,train fou66e3D0VM,13,sheep bleating,test fovoqLDggGo,62,elk bugling,train fovoqLDggGo,77,elk bugling,train fowc2nadbvE,0,playing trumpet,test fowp05ctgvw,0,playing bagpipes,train fowp05ctgvw,26,playing bagpipes,train foxnHjXLbPU,11,goose honking,train fozBCIllOFs,10,printer printing,train fozKmM4A8hE,146,playing bongo,train fozKmM4A8hE,729,playing bongo,train fp-22oFC-IE,15,people burping,train fp1eO0c02Lc,26,playing guiro,train fp4r6uEzUOE,88,tap dancing,train fp6pBJ1Iygk,26,dog barking,train fp86HeXZCdo,68,fox barking,test fp8O56_p-3A,80,chainsawing trees,train fp9LFqu3LVQ,84,strike lighter,test fpA_hVmF4H4,30,dog 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gfYDnI6FFUc,50,church bell ringing,train gfZ13eVKElA,190,slot machine,train gfZ30wRF-JQ,10,woodpecker pecking tree,train gfZd1LNNRKM,6,people cheering,train gfZd1LNNRKM,17,people cheering,train gf_FjJ4oRkQ,50,playing piano,train gfb5kqqHtl0,137,chipmunk chirping,train gfchMx_Wb5c,38,"subway, metro, underground",train gfdnzLVn2WE,280,helicopter,train gff-5bjEUM0,500,sliding door,train gffIccrzYjw,63,playing bass guitar,train gfgv17hOPIM,40,"playing marimba, xylophone",train gfjHiY820ug,130,playing acoustic guitar,train gflvYpShFzo,1,playing tuning fork,test gfp3_fGgV_s,80,playing bass guitar,train gfpZ8kIukNc,0,people slapping,train gfqBYh33xxk,0,"race car, auto racing",train gfr1AIQVfm4,147,singing bowl,train gfruZ_gT9fE,53,running electric fan,train gfruZ_gT9fE,111,running electric fan,train gft_TyJhxFk,18,playing didgeridoo,train gfuNXd6e3uw,30,printer printing,train gfvwLcwtyA0,142,train wheels squealing,train gfvwLcwtyA0,226,train wheels squealing,train gfw6P3-c7I0,280,helicopter,train gfwiHOeOio8,9,playing guiro,train gfxXRH-tV2U,100,thunder,train gfyCJyn4SSY,50,stream burbling,train gg-TxJSYA6I,63,playing hammond organ,train gg-TxJSYA6I,259,playing hammond organ,train gg1Jj7saq7g,190,playing trumpet,train gg4i6BCK6XQ,60,"playing marimba, xylophone",train gg5e5NFNboE,96,tractor digging,train gg5e5NFNboE,107,tractor digging,train gg6yoeBoYxg,0,children shouting,test ggBmY9IWDu4,30,"engine accelerating, revving, vroom",train ggDsLmQDQlE,30,male singing,train ggEdDzI27VY,30,sloshing water,train ggGqpXhjXKc,9,black capped chickadee calling,train ggGqpXhjXKc,19,black capped chickadee calling,train ggIo6oarDhw,0,people cheering,train ggJRLJXAJBg,220,chainsawing trees,train ggLnSRSKjYo,120,lighting firecrackers,train ggN-JSq6cN4,40,toilet flushing,train ggN4-K5AgoM,90,toilet flushing,train ggN7z3_DJnE,138,car engine idling,train ggOszPBATek,30,driving buses,train ggRM5r7VqUU,0,"vehicle horn, car horn, honking",train ggRoK7Ftqzo,197,"heart 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ggoTJxYV4BQ,30,people whistling,train ggqwf2hAuao,10,lathe spinning,train ggqwf2hAuao,104,lathe spinning,train ggsFp99brsU,100,ambulance siren,train ggxUVGUzzn4,38,male singing,train ggxmIYMuG4U,270,playing synthesizer,train ggyZ0YuXNZI,155,tractor digging,train gh4oE_5g4YY,439,people slurping,test gh5YBFqpKB0,150,"child speech, kid speaking",train gh5zjxsCcOs,48,typing on typewriter,train gh5zjxsCcOs,62,typing on typewriter,train gh7LqhtRJhs,8,chicken crowing,train gh7LqhtRJhs,19,chicken crowing,train ghC-fGSwYmQ,30,"pigeon, dove cooing",train ghCYzI27vfU,57,planing timber,train ghCYzI27vfU,199,planing timber,train ghC_5IXZtaM,8,playing cello,train ghEm0ZmUGGA,88,mouse clicking,train ghEm0ZmUGGA,191,mouse clicking,train ghN933D4uG0,92,mynah bird singing,train ghNAV1cDnHE,53,"alligators, crocodiles hissing",train ghOSez-sHMg,0,cricket chirping,train ghOfjHwwA-o,223,playing tennis,test ghRMGoLsP90,8,hail,train ghRMGoLsP90,27,hail,train ghRyW0bkt8c,105,smoke detector beeping,test ghTuSMFbfvY,73,opening or closing car doors,train ghUjUAXCbE0,82,playing oboe,train ghUjUAXCbE0,92,playing oboe,train gh_4GSIBZZI,30,goose honking,train ghb17tdhiKQ,160,lions growling,train ghb3PM86q0U,28,tractor digging,train ghb3PM86q0U,139,tractor digging,train ghbKqYdEFlo,21,baby crying,train ghcy_oUmfns,30,people running,train ghdFORranKw,3,wood thrush calling,train ghdFORranKw,15,wood thrush calling,train gheps9Lo3j0,52,people giggling,train gheps9Lo3j0,76,people giggling,train ghgEdrSNnPc,27,driving snowmobile,train ghgEdrSNnPc,90,driving snowmobile,train ghi6w-owZDI,83,people humming,train ghi6w-owZDI,94,people humming,train ghiHH8W3-lk,181,singing bowl,train ghjsa1KV5e0,120,playing piano,train ghl33n26d44,85,volcano explosion,train ghmfVk3a2jg,10,"playing marimba, xylophone",train gho_Qa2ttno,21,elk bugling,train gho_Qa2ttno,31,elk bugling,train ghpakT0ziHc,130,playing vibraphone,train ghpakT0ziHc,189,playing vibraphone,train ghuuGT4cQSg,121,airplane,train ghvwyp-vlGc,90,"subway, metro, underground",train ghz6s0fWy3Q,0,machine gun shooting,train ghzWeEbaAJc,235,playing volleyball,train ghzqieFlNyk,205,tap dancing,train gi-IX72RoxA,90,people babbling,train gi0F_4rV__o,80,wind noise,train gi0KWWHtOGA,90,"rowboat, canoe, kayak rowing",train gi209gt-JbA,88,playing harp,train gi799B4XHLs,22,pheasant crowing,train gi799B4XHLs,32,pheasant crowing,train giATGEw8nAk,107,police radio chatter,test giB6sNmgkvA,75,swimming,train giB6sNmgkvA,100,swimming,train giCuR5vNO2A,19,bouncing on trampoline,train giCuR5vNO2A,91,bouncing on trampoline,train giLww9DlXaE,260,"rowboat, canoe, kayak rowing",train giWiqKTcFnA,389,baby laughter,train giWiqKTcFnA,507,baby laughter,train giZ0AeaiKG0,129,lawn mowing,train gie1geeOlRE,1,ripping paper,train gif5KpUlVT0,20,driving motorcycle,train gifJv_gGUWY,30,duck quacking,train gigAXFw9yzQ,223,police radio chatter,test gigI5cXVpkg,190,mouse pattering,train gihNVuDY-OQ,127,playing congas,test giiSE7V-kl4,332,playing bass drum,train gik6iWY6K5g,30,duck quacking,train gikP1bTjZIw,15,dinosaurs bellowing,train gikP1bTjZIw,25,dinosaurs bellowing,train gimu_XYwHLM,27,people eating noodle,train gimu_XYwHLM,198,people eating noodle,train ginJRiiSkxw,10,driving motorcycle,train ginJRiiSkxw,39,car engine knocking,train gipn4HAxRo4,403,"heart sounds, heartbeat",train gipn4HAxRo4,692,"heart sounds, heartbeat",train giqLq6L9YEM,6,volcano explosion,train girRhNM8ePw,60,people whispering,train girRhNM8ePw,758,people whispering,train giuHZQpdxKQ,32,playing accordion,train giwFZVqUTlU,20,playing acoustic guitar,train giyXqJv4dxg,42,arc welding,train gj3c5rHiRxM,7,playing bugle,train gj4L3gtkW5Q,140,helicopter,train gj4SNi_o9cE,30,typing on computer keyboard,train gj57rI1_JX8,30,crow cawing,test gj5hHMKRK58,12,spraying water,train gj7PVwCVj6k,136,tap dancing,train gj7xoDnvc1c,42,playing mandolin,train gj7xoDnvc1c,52,playing mandolin,train gjA7XGtvLDE,30,typing on computer keyboard,train gjAd1pSznsU,0,tornado roaring,train gjB-IOQ6krI,30,driving buses,train gjD9y5CxKVQ,160,playing hammond organ,train gjDQGGvpHZI,30,baby laughter,train gjE4b3zAwP8,61,toilet flushing,train gjEC3KI3L6c,40,printer printing,train gjF3O4c_dl8,13,fire truck siren,train gjF3O4c_dl8,24,fire truck siren,train gjGy7dor4uQ,30,playing drum kit,train gjHbZERgs9o,90,horse neighing,train gjHbZERgs9o,135,horse neighing,train gjI7yBurcjQ,74,"donkey, ass braying",train gjJ4nqwlgnE,10,playing hammond organ,train gjR4-yHwUZw,120,playing trombone,train gjRMEGGfYNg,150,playing electric guitar,train gjS5H7Fzgaw,10,hail,train gjS5H7Fzgaw,22,hail,train gjSAI-3SFFo,0,playing didgeridoo,train gjSAI-3SFFo,20,playing didgeridoo,train gjV09ltKnuo,107,playing squash,train gjV09ltKnuo,174,playing squash,train gjVNmTsvyXo,120,playing flute,train gjXiJFY4K9w,6,squishing water,train gjXiJFY4K9w,54,squishing water,train gjYdRgH73ww,1,singing bowl,train gjZhpGhT77o,230,sliding door,train gj_7zg1bQdA,30,"engine accelerating, revving, vroom",train gj_sLsgnOB4,15,missile launch,train gj_sLsgnOB4,29,missile launch,train gjaU809Q5As,30,pig oinking,train gjarTbqbnIs,15,cap gun shooting,train gjarTbqbnIs,25,cap gun shooting,train gjb_5xhrlIY,14,underwater bubbling,train gjb_5xhrlIY,53,underwater bubbling,train gjdM7Xdv8vU,30,chicken crowing,train gjoris65JEA,30,printer printing,train gjpndNPlxvc,180,thunder,train gjridbg7TkQ,175,underwater bubbling,train gjridbg7TkQ,287,underwater bubbling,train gjvnnykzvtA,33,"ice cream truck, ice cream van",train gjx1BmOGv24,0,playing saxophone,train gjx1BmOGv24,4,playing saxophone,train gjyGk-wLyEU,659,golf driving,train gjyNJdw8rjs,250,male singing,train gjyp-WpxyqM,20,typing on computer keyboard,train gk-fN4y5XM4,320,skiing,train gk6YR9sREcc,43,playing badminton,train gk6_0RcFc0A,10,people sneezing,train gk9lvbUtfOE,83,tap dancing,train gkD_JJojoeI,270,driving motorcycle,train gkE3euCRW04,360,orchestra,train gkEmGO8QXz4,538,playing tabla,train gkH_Zxasn8o,40,"subway, metro, 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gkn6wM6B1CI,140,waterfall burbling,train gkoWeXVdGfI,774,people slurping,train gkqLzhOT1pY,30,driving buses,train gkr1DCmi7Sg,0,playing ukulele,train gkr1DCmi7Sg,7,playing ukulele,train gkrJHaLcUuE,30,car passing by,train gksGyZQWxgw,90,printer printing,test gksMR7IL-vg,3,"cattle, bovinae cowbell",train gktiZhnNVvY,177,alarm clock ringing,train gktpGCfcSRQ,0,skateboarding,train gkwJu2BtXZc,60,playing cello,train gkzPfToJRuc,7,slot machine,train gl-LjZR_4GI,14,typing on typewriter,train gl-LjZR_4GI,50,typing on typewriter,train gl2i9fUzf60,219,playing sitar,train gl2i9fUzf60,249,playing sitar,train gl32MiX7g5U,17,playing volleyball,train gl4fSba68bc,16,dog bow-wow,train gl4vIRRCdB4,120,"race car, auto racing",train gl5TpKebtMI,841,blowtorch igniting,train gl5TpKebtMI,927,blowtorch igniting,train gl6Mycv9Gl8,3,playing cornet,train gl6XeH95yIA,1,lighting firecrackers,train gl6XeH95yIA,28,lighting firecrackers,train gl7a9KJQ-Es,36,police radio chatter,train gl7a9KJQ-Es,47,police radio 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oinking,train glUwjQSTDO0,240,opening or closing drawers,train glVYbBv1ihQ,35,crow cawing,train glVYbBv1ihQ,52,crow cawing,train glWBiosfTFc,26,cell phone buzzing,train glWBiosfTFc,39,cell phone buzzing,train glcCbWxptJs,51,snake rattling,train glgBqCeC68M,30,cat meowing,train gljo9-7ay8A,30,driving motorcycle,train gll4hdiTtUw,6,cattle mooing,train gll4hdiTtUw,24,cattle mooing,train glm2XQNzv8M,30,playing harpsichord,train glxgYAfs_UE,5,"heart sounds, heartbeat",train glxgYAfs_UE,535,"heart sounds, heartbeat",train glyDXkh-vVM,90,people cheering,train glz5AwUtBGc,62,playing castanets,train glz7zUvkI6I,45,slot machine,train glzG9lizkSc,138,airplane flyby,train glzG9lizkSc,154,airplane flyby,train gm-U_oCATbE,6,reversing beeps,train gm-U_oCATbE,16,reversing beeps,train gm-eGYfi1go,78,gibbon howling,train gm-eGYfi1go,112,gibbon howling,train gm0HkvshnPk,340,cattle mooing,train gm0_zIghr9Y,6,"vehicle horn, car horn, honking",train gm0_zIghr9Y,44,"vehicle horn, car horn, honking",train 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gnFUxz9-ZCI,28,baby laughter,train gnG1SuYmYEg,184,playing didgeridoo,train gnKpkijb2A0,64,playing bassoon,train gnMDpBQ_bDQ,438,playing piano,train gnMaGj5Jm-w,25,"playing steel guitar, slide guitar",train gnMaGj5Jm-w,35,"playing steel guitar, slide guitar",train gnNbtbWe9pM,19,alarm clock ringing,train gnOs2mgRhZE,0,tap dancing,train gnOs2mgRhZE,22,tap dancing,train gnTozabzPY0,274,popping popcorn,train gnYZK6Mto78,290,arc welding,train gnZnWJslANs,0,cap gun shooting,train gnZvqbJR91U,322,rapping,train gnZvqbJR91U,520,rapping,train gnf7zkrZAkg,400,stream burbling,train gnkCCLn5FFw,20,sharpen knife,train gnniUnHnq0Q,4,people burping,train gnpuI5DQSAc,17,playing zither,train gnpuI5DQSAc,64,playing zither,train gnrZHiy3knU,12,"engine accelerating, revving, vroom",train gnr_7SIu8go,55,cat purring,train gnr_7SIu8go,66,cat purring,train gns46STqta0,0,playing harmonica,train gns46STqta0,2,playing harmonica,train gnshLYrONVU,120,chicken crowing,train gnvDy8YMizY,100,"race car, auto 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goz-IQ8s6uk,50,skidding,train gozC2bGmpzY,40,woodpecker pecking tree,train gozC2bGmpzY,114,woodpecker pecking tree,train gp-cl6Fdevw,74,machine gun shooting,train gp-cl6Fdevw,85,machine gun shooting,train gp-xv-PHbgk,4,duck quacking,train gp0I18lq5pM,0,pheasant crowing,train gp0I18lq5pM,35,pheasant crowing,train gp0c3ZfVjzo,370,"railroad car, train wagon",train gp0urM0Y2HA,330,lions growling,train gp5OFYkwkl8,9,playing bagpipes,train gp5OFYkwkl8,60,playing bagpipes,train gp6_jkwClm0,244,toilet flushing,train gp8FcYyZPYE,64,playing squash,train gpCURo8_zwk,0,playing ukulele,train gpCURo8_zwk,25,playing ukulele,train gpCtHNDQxlk,507,playing electric guitar,train gpDP17dbX9w,10,people babbling,train gpDkuPTITZI,49,airplane flyby,train gpDkuPTITZI,118,airplane flyby,train gpHOHI3fuAc,50,playing piano,train gpIWcn9yiXg,350,playing harp,test gpJ-f3PGPOs,10,playing piano,train gpJFhQtQ4cs,133,playing tympani,test gpLe7H5RxnM,13,elephant trumpeting,test gpMWvDgf9ig,360,"female speech, woman 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gpqerD-h7hI,176,arc welding,train gpqerD-h7hI,197,arc welding,train gpsXKzlirxg,197,yodelling,train gpsXKzlirxg,267,yodelling,train gpv2JV8uyOQ,10,dog barking,train gpvYlHv1Ra4,300,rapping,test gpy0tsYP8SY,30,people sobbing,train gpyUJCmt3vE,363,playing trumpet,test gq0fjAAZJ1s,0,ocean burbling,train gq134v-M8P4,30,"engine accelerating, revving, vroom",train gq1F9rKHGlY,0,playing ukulele,train gq1F9rKHGlY,176,playing ukulele,train gq9m8f9dBag,340,"child speech, kid speaking",train gqAg5JKGauI,115,foghorn,train gqDkZE-9SUc,180,skateboarding,train gqE8fkrtBjc,212,air conditioning noise,test gqEvTcyXt6A,13,cupboard opening or closing,train gqEvTcyXt6A,42,cupboard opening or closing,train gqGdj9TKel8,155,electric grinder grinding,train gqHdhUBcmWg,18,lions growling,train gqHmjw64ioQ,26,playing bongo,train gqHnItkPmoQ,30,"rowboat, canoe, kayak rowing",train gqIVab46KV8,405,playing bass guitar,train gqIcQNmwwsQ,30,eating with cutlery,train gqJu7J9Cd2A,295,slot machine,train gqKCwwl8Rks,3,chipmunk chirping,train gqLB2ntSJYI,102,arc welding,train gqLB2ntSJYI,371,arc welding,train gqMjkR8i2GM,14,black capped chickadee calling,train gqMjkR8i2GM,30,black capped chickadee calling,train gqORliVV674,0,singing bowl,train gqPL3q-DzTU,0,skidding,train gqQzYm2ghQ8,65,air conditioning noise,train gqRON4F0hDA,48,"heart sounds, heartbeat",train gqRON4F0hDA,59,"heart sounds, heartbeat",train gqXU_MDxRX4,0,playing guiro,train gqXYzEaLryQ,70,playing trombone,train gqXg3kxxBrA,5,fireworks banging,train gqYkg6Y3MHg,11,playing table tennis,test gqYklYaxHZ0,39,dinosaurs bellowing,train gq_mPxAntjI,40,car engine starting,train gq_xeYxxgQ8,30,people sniggering,train gqbYZ-RVAo0,90,helicopter,train gqeGF31mZvk,180,people crowd,train gqeu03mjjRc,517,baby laughter,train gqf4xmzoQ4w,30,frog croaking,test gqfo078B23o,86,bouncing on trampoline,test gqipvEMxP6k,60,helicopter,train gqmR94t_rRk,320,people whispering,train gqocm0HSRN8,100,playing hammond organ,train gqs4yxcqug8,90,horse clip-clop,train gqvl7BVJhzA,20,ambulance siren,train gqyhe8ubZ_I,0,dog howling,train gqyhe8ubZ_I,14,dog howling,train gqzRsP0mwsc,140,people sobbing,train gr251OhS5_E,72,vacuum cleaner cleaning floors,train gr251OhS5_E,83,vacuum cleaner cleaning floors,train gr3Hz_C1Xzk,77,playing bugle,train gr3Hz_C1Xzk,151,playing bugle,train gr5AxJGqVeM,14,"electric shaver, electric razor shaving",train gr5AxJGqVeM,26,"electric shaver, electric razor shaving",train gr5xrOpN1qc,80,dog bow-wow,train gr64-ldZges,210,wind noise,train gr6FrcaYjN4,30,orchestra,train grAoG-J363U,5,yodelling,train grAoG-J363U,231,yodelling,train grBJMAA_3M4,4,lighting firecrackers,train grBJMAA_3M4,40,lighting firecrackers,train grBni6fk40Y,30,"engine accelerating, revving, vroom",train grEEcC2HRQE,14,lions roaring,train grEEcC2HRQE,35,lions roaring,train grG6GPjDgU8,30,chicken crowing,test grG9TcsNEBQ,449,playing volleyball,train grIV_KatprA,220,playing vibraphone,train grJk5Cego6E,340,people clapping,train grLGgTeY5-o,80,cattle mooing,train grMD-kp6le4,373,"electric shaver, electric razor shaving",train grMo27xIRx8,119,playing saxophone,train grNcuFcNPDs,97,mynah bird singing,train grNcuFcNPDs,264,mynah bird singing,train grRmgU4fJMo,108,tap dancing,train grRuw1cE9LU,10,whale calling,train grW9Hr7sy_M,234,tractor digging,train grWWv43Y4XE,30,people whispering,train grXXPSNjOak,0,"playing marimba, xylophone",test grZDPCKORGg,20,playing shofar,train gr_HIVIYa90,638,people slurping,train gr_HIVIYa90,649,people slurping,train gr_L99v63FI,1,playing glockenspiel,train gr_L99v63FI,35,playing glockenspiel,train grd67war0eM,30,"rowboat, canoe, kayak rowing",train greDhc1Tl-M,80,playing piano,train greRyvV0ECs,0,people farting,train grfEjG6jlUw,4,playing bugle,train grfr3IrdlGA,290,orchestra,test grhq5OG_ovo,31,vacuum cleaner cleaning floors,train grhs1IGBtZg,60,"ice cream truck, ice cream van",train griyZ1JUOs0,304,people gargling,test grjWQ5eHHmI,80,ocean burbling,train grlUiog7-iQ,30,people sniggering,train grmLj1-KMzQ,1,playing theremin,train grmLj1-KMzQ,124,playing theremin,train grmpwbsR3TA,190,playing saxophone,train grrrxORXna0,0,playing cymbal,train grryyLLuWk0,34,wind chime,test gru4zjucK-8,15,ice cracking,train gru4zjucK-8,25,ice cracking,train grvW6WudjqA,167,train whistling,train grvW6WudjqA,189,train whistling,train grwCTB1cVuQ,8,people babbling,train grwCTB1cVuQ,19,people babbling,train grwLuzfleWw,52,striking pool,test grygJyKvYgs,30,basketball bounce,test grzORJCkf3o,30,playing harpsichord,train gs-lSymjBVE,30,"ice cream truck, ice cream van",test gs20o_SBuUk,10,playing acoustic guitar,train gs4g1ZoKAmk,15,playing tuning fork,train gs4g1ZoKAmk,42,playing tuning fork,train gs6GcOCNLuI,120,playing accordion,train gsBJcSLIu5k,70,playing cornet,train gsEgN-dbQjo,320,people eating,test gsGtiLTs4pY,410,playing drum kit,train gsJEMH_emBM,30,metronome,train gsLXTvgWTTo,25,shot football,train gsLXTvgWTTo,188,shot football,train gsMN8_AbcIk,7,frog croaking,train gsMN8_AbcIk,221,frog croaking,train gsNSotF4neU,4,lions roaring,train gsNs4zExX-0,14,opening or closing car doors,train gsQDnAbVAVo,104,people screaming,train gsQDnAbVAVo,120,people screaming,train gsQJ9LtI1aQ,13,driving buses,train gsS_NXsiFDM,30,driving buses,train gsVfu2D304g,3,slot machine,train gsYzYcQmXcM,516,parrot talking,train gsYzYcQmXcM,638,parrot talking,train gs_y9xPG2ME,152,hail,train gs_y9xPG2ME,164,hail,train gsaOQoO82-I,23,barn swallow calling,train gsaOQoO82-I,58,barn swallow calling,train gsaV1KGA7Pw,10,fireworks banging,train gsa_biT_mUo,150,"vehicle horn, car horn, honking",train gsbUPxT3JO8,89,airplane flyby,train gsbaE0ZYdvY,20,black capped chickadee calling,train gsbaE0ZYdvY,51,black capped chickadee calling,train gsbagzVDQFo,0,black capped chickadee calling,train gsbagzVDQFo,10,black capped chickadee calling,train gsdU-YJs7RE,59,playing glockenspiel,train gseikrqMhAY,29,owl hooting,test gsfXO34EhQw,14,singing bowl,train gslFx-NP7wk,30,driving motorcycle,train 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ha-5LhgpVmQ,932,playing tympani,train ha-5LhgpVmQ,946,playing tympani,train ha1sw8u03i8,1,mouse clicking,train ha1sw8u03i8,11,mouse clicking,train ha480LIytfs,260,people clapping,train ha5Ar2EODmM,44,driving snowmobile,train ha5Ar2EODmM,130,driving snowmobile,train ha5DEObj9dw,15,"vehicle horn, car horn, honking",train ha5FkzGTg0g,79,train whistling,train ha8CT4nfwh8,340,people sniggering,train ha8lvNZ7n7c,46,playing oboe,test haCVclnTvXc,18,tap dancing,train haDTvzNUokk,260,people crowd,train haEhBq0E5Eo,119,eletric blender running,test haKkRRJiOgI,27,missile launch,train haKkRRJiOgI,43,missile launch,train haMTnq0TXlM,108,"playing steel guitar, slide guitar",train haMTnq0TXlM,134,"playing steel guitar, slide guitar",train haNExhn3XVY,12,"ice cream truck, ice cream van",train haQTnQPWy7M,48,planing timber,train haVBNvKQsEI,20,mynah bird singing,test haWU_SLZCgg,280,playing synthesizer,train haWpYhULT8A,30,playing cymbal,train haXW-GSXO_4,0,dinosaurs bellowing,train 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hb3bxOTCP_0,0,cow lowing,train hb3c8wvwXY8,113,arc welding,train hb3c8wvwXY8,345,arc welding,train hb4qBPMsjWE,7,lighting firecrackers,train hb6gD0LszbM,30,playing bagpipes,train hb7VP1LnjRc,363,car engine idling,train hb7dzgbprNU,30,car engine knocking,train hb9EBUqHvno,160,people marching,train hb9EBUqHvno,172,people marching,train hb9bttG7rnw,0,driving motorcycle,train hbBHOYFA9_U,4,gibbon howling,train hbBHOYFA9_U,19,gibbon howling,train hbBRyruL25w,7,snake hissing,train hbBRyruL25w,34,snake hissing,train hbBavS7wBq0,30,driving motorcycle,train hbCCLtLSgCM,39,singing bowl,train hbCWAXt0x1w,160,ambulance siren,train hbD-gNUKLJs,65,playing accordion,train hbGePrj-ZQg,400,playing electric guitar,train hbIAN3-5vhM,30,orchestra,train hbJXREtiW44,30,people running,train hbLLrDicXi4,0,"pigeon, dove cooing",train hbLS-Dlgj_o,2,opening or closing car electric windows,test hbQKbtinQv8,258,swimming,train hbS5vHR1r2c,40,playing flute,train hbV443Ypky0,110,playing accordion,train hbWMNXHD61s,4,barn swallow calling,train hbXiMBr--XE,387,playing squash,train hbXyGfTLsGU,10,printer printing,train hbXyGfTLsGU,22,printer printing,train hb_R-HgAoDY,18,skiing,train hb_WCGZJgN0,30,orchestra,train hb_jzFweFuk,22,playing harp,train hbapY_ACX2o,349,bouncing on trampoline,train hbapY_ACX2o,383,bouncing on trampoline,train hbfnmtfXJzo,130,child singing,train hblhOm7sAk8,170,people eating noodle,train hbm-2oz3EGw,36,"heart sounds, heartbeat",train hbm8V49j7KY,180,sailing,train hbmfBT_vE1w,120,playing piano,train hbngc8DNW_k,106,dinosaurs bellowing,train hboxHDBnQrA,23,cupboard opening or closing,test hbvYCE90NYM,192,beat boxing,train hbvtb98oVkU,90,chicken crowing,train hbwm-MC8RbQ,30,duck quacking,train hbx0FqVoB_o,6,airplane flyby,train hbxjsNXN8eQ,30,playing harpsichord,train hbyCTofi4Ak,120,driving buses,train hbzobVwq0eE,57,"electric shaver, electric razor shaving",train hbzobVwq0eE,69,"electric shaver, electric razor shaving",train hc-bUzcySJg,0,"ice cream truck, ice 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hcBkc3nhryA,46,playing shofar,train hcBn4asffoA,30,dog barking,train hcDRANyx95E,20,whale calling,train hcF51SRl4TE,160,"railroad car, train wagon",train hcGQrvcYZc8,22,crow cawing,train hcGQrvcYZc8,41,crow cawing,train hcHC1bz1m-0,160,playing zither,train hcHC1bz1m-0,773,playing zither,train hcHHHVRv_wg,96,playing glockenspiel,train hcI8rwJX04w,21,people belly laughing,train hcKRBwr6Pbs,16,skiing,train hcOkk03PnZE,110,playing electronic organ,test hcQakiomZs4,98,tap dancing,train hcQakiomZs4,152,tap dancing,train hcR4BiG8sZs,150,playing clarinet,train hcRVhFASJv8,20,driving motorcycle,train hcT--c7ObWo,30,chicken crowing,train hcU3i-Y2qJQ,7,people marching,train hcVTbCCp1h4,0,machine gun shooting,train hcWZexmBe20,80,playing hammond organ,test hcWc0qHRROs,2,car engine knocking,train hcXJYfagUOQ,30,typing on typewriter,train hcXJYfagUOQ,60,typing on typewriter,train hcXpsKh1SrY,87,dog growling,train hcXpsKh1SrY,109,dog growling,train hccA_tAzCq0,25,bathroom ventilation fan running,train hcd1frpOXJE,6,goat bleating,train hcd1frpOXJE,25,goat bleating,train hcds7hVt3-c,1,"playing steel guitar, slide guitar",train hceVcQHeeHc,40,playing banjo,train hcfl3lYQy9M,191,playing harpsichord,train hcfngx-FMxs,97,playing double bass,train hcfngx-FMxs,218,playing double bass,train hcgSKUmrF9s,73,playing table tennis,train hcj5DRPnGt0,20,people marching,train hcj5DRPnGt0,48,people marching,train hclq1ywrhjs,180,people clapping,train hcm-ur-7Ilw,3,playing glockenspiel,train hcmet53kp5A,0,"child speech, kid speaking",train hco9vPqpXbQ,106,playing squash,train hco9vPqpXbQ,321,playing squash,train hcpojFqDOyI,429,people eating noodle,train hcpojFqDOyI,479,people eating noodle,train hcqmcM5m020,1,vacuum cleaner cleaning floors,train hcqmcM5m020,37,vacuum cleaner cleaning floors,train hcsB0jEBz9A,8,scuba diving,train hcsB0jEBz9A,21,scuba diving,train hcvTo42lKDQ,735,ripping paper,train hcvTo42lKDQ,811,ripping paper,train hcvZQlaM8ZY,30,driving motorcycle,train hd-3UmM2o3k,70,people marching,test hd-lgDdyrcE,30,"bird chirping, tweeting",train hd2sYZRk6Zw,288,missile launch,train hd2sYZRk6Zw,325,missile launch,train hd4n23cIFv4,432,horse clip-clop,train hd7CBO_lVeE,13,playing djembe,train hd7CBO_lVeE,31,playing djembe,train hd7CQvbcteg,340,using sewing machines,train hd7Ie27-nYE,30,playing double bass,train hdBFRiNBziY,0,people clapping,train hdCFEG7x81w,150,playing accordion,train hdCVGPjYQc0,70,lions growling,train hdCVGPjYQc0,158,lions growling,train hdGxmrVLGdU,130,basketball bounce,train hdKdzevVPHM,57,barn swallow calling,train hdKdzevVPHM,122,barn swallow calling,train hdP6mXKtsb4,110,people sneezing,train hdPMwU2r48g,41,playing banjo,train hdQ3H39cd-g,6,eletric blender running,train hdRbK0latc0,210,people whistling,train hdS1pOu9tv0,50,playing trumpet,train hdSKTNqFX-s,20,people hiccup,train hdSlIt6azSE,30,playing drum kit,train hdXKCV5BMcs,100,"male speech, man speaking",train hdYkHs2vjGM,29,playing volleyball,train hd_JE5o2HWo,5,playing cymbal,train hd_w0MHqpQo,450,cattle mooing,test hde9o_Jq2Bs,4,playing tympani,train hdebdzPVZdQ,101,playing bongo,test hdfObPes4_I,10,people clapping,train hdfvK23HyKE,250,orchestra,train hdhKXTwLaTY,30,playing piano,train hdm26mqvr2I,30,playing acoustic guitar,train hdm8wWubtIQ,314,cutting hair with electric trimmers,train hdoZBIq6dyI,208,people humming,train hdphUn6ihrA,450,lawn mowing,train hdqCHBTwnuQ,253,playing badminton,train hdstykfxcbw,480,church bell ringing,train hdv9-6tBpoE,30,"race car, auto racing",train he-7oT0u6Hs,10,ambulance siren,train he-meSbj7Yk,18,playing squash,train he0g3HIWJF0,52,playing volleyball,train he0jAuOS6qE,93,yodelling,train he0jAuOS6qE,163,yodelling,train he2xu4J1bHg,9,people sneezing,train he3bYB8MKbU,234,playing erhu,train he58w8f6gBc,108,vacuum cleaner cleaning floors,train he7Ku48D4rw,30,"male speech, man speaking",train heB8RxTwkWk,152,playing badminton,train heBbg2oU26o,63,playing table tennis,train heCXVPR-bG0,20,lighting firecrackers,train heCXVPR-bG0,56,lighting firecrackers,train heJXuI8hdZ4,30,orchestra,train heJZe0OTDnA,288,playing table tennis,train heKkRcAj1PA,4,lions roaring,train heKkRcAj1PA,19,lions roaring,train heKzKtokUFo,0,"fly, housefly buzzing",train heMBt20pO20,327,playing bagpipes,train heMBt20pO20,342,playing bagpipes,train heMPfFBpYrs,70,wind noise,train heMVML9K4Ek,33,"heart sounds, heartbeat",train heMVML9K4Ek,59,"heart sounds, heartbeat",train heNIx-8d2-E,149,sharpen knife,train heNIx-8d2-E,161,sharpen knife,train heObG1ks1xQ,60,playing accordion,train heOioQf8HiM,1,sliding door,train heOsZ9H4vzQ,31,cricket chirping,train heOsZ9H4vzQ,79,cricket chirping,train hePjl4JAH94,566,people eating,train heSi5Zq6LXY,2,fox barking,train heVCbAAY8sU,33,playing cymbal,train heVuMUwnyfw,10,playing banjo,train heWuhDADCu8,295,machine gun shooting,train heWuhDADCu8,309,machine gun shooting,train heYwdizfylw,30,people eating,test heZw1TTrtTU,80,using sewing machines,test hebR1fM7Fo8,8,elk bugling,train hefrj9tJOaw,1165,forging swords,train hefrj9tJOaw,1284,forging swords,train hej-H5OKfqU,3,people belly laughing,train hej-H5OKfqU,13,people belly laughing,train hekfjJRfSXM,32,golf driving,test hel2rE-Ju_Y,1,bird wings flapping,train hel2rE-Ju_Y,31,bird wings flapping,train helxEeY04rU,80,car passing by,train henkGn8ybWg,180,playing drum kit,train heqDfamBfCM,39,playing table tennis,train hetGyfo1NgY,330,typing on computer keyboard,train hevwLgGlWVQ,0,"race car, auto racing",train hewWv0Fw9UI,70,"female speech, woman speaking",train hewiPFOOZc4,0,"motorboat, speedboat acceleration",train heyFbeNIDGo,139,playing cornet,train hf1MO-TLIMU,30,playing harpsichord,train hf3VV_PmxhU,30,driving motorcycle,train hf6qJx4tZ6k,5,chinchilla barking,test hf7KD_7SA-g,30,people running,train hf8QTreyNx0,70,chicken crowing,train hf9pKuTuPcA,20,chicken crowing,train hfBoJTiVETk,10,underwater bubbling,train hfBoJTiVETk,52,underwater bubbling,train hfBuRqBEBo4,250,people eating noodle,test hfE7ft18_-g,60,"railroad car, train wagon",train hfFLveFjKm8,30,crow cawing,train hfFNn0n66lA,79,cat purring,train hfFNn0n66lA,164,cat purring,train hfGPgIdj0WM,55,eletric blender running,test hfIfBPkH8Fo,300,waterfall burbling,test hfJLH2-7_Ls,0,toilet flushing,train hfLO3wC9JD8,45,people booing,train hfLSI8JefI4,80,cat meowing,train hfRHV9RzLu8,70,playing bass guitar,train hfSjbxirFOQ,23,"vehicle horn, car horn, honking",train hfVwZxG11d4,1,playing guiro,train hfWEOH_91cI,0,people hiccup,train hfWjVLZOAok,18,skiing,train hfWmS7ZJAt0,80,bird squawking,train hfWmS7ZJAt0,103,bird squawking,train hfXdPqtSNwM,23,mynah bird singing,train hf_FpFzThOk,7,playing glockenspiel,train hf_FpFzThOk,79,playing glockenspiel,train hf_UnBEO4hE,24,tap dancing,train hfcEI_RsDOY,120,child singing,train hfdMfiv2R9w,58,people giggling,test hfdWrvYgPww,11,baby crying,train hfdYtv3Gs9A,40,dog bow-wow,train hfewjg0CTQM,60,church bell ringing,test hfhIZsXdNTg,58,airplane flyby,train hfhIZsXdNTg,86,airplane flyby,train hfhordJxh9Y,30,sliding door,test hfibyjr6OYc,30,ocean burbling,train hfkgVfzjW90,0,playing trumpet,train hflElSv6ESM,14,shot football,train hflLiDViHYM,0,playing synthesizer,train hflYLQsp9VY,2,people slapping,test hfloy__8cxY,60,chicken crowing,train hfnOQRpbp-s,70,"child speech, kid speaking",train hfqUU1lUFLw,10,chinchilla barking,test hfrEWge9aLM,30,sailing,train hftcIkVhGMQ,0,"rowboat, canoe, kayak rowing",train hfuajF8qiOc,280,"race car, auto racing",train hfuk-dabp0k,192,cupboard opening or closing,train hfuk-dabp0k,405,cupboard opening or closing,train hfyr0VizVIU,41,"electric shaver, electric razor shaving",train hfzkKk7nijg,100,chicken crowing,train hg0kUxkOEr8,305,playing cello,train hg1YZJ2BpuM,0,fireworks banging,train hg7mTvZj0JE,14,"vehicle horn, car horn, honking",train hg8saMFSS-Q,30,"railroad car, train wagon",train hg9uOqlmlV0,30,people running,train hgBpK3AEpOM,10,"bee, wasp, etc. buzzing",train hgBpK3AEpOM,30,"bee, wasp, etc. buzzing",train hgCn2ZH8twg,160,pumping water,test hgDXOM75eEQ,105,barn swallow calling,train hgGO3HXXuj0,4,dog growling,train hgG_Gci8op0,30,hair dryer drying,test hgHFHvhfcT8,60,playing trumpet,train hgHgfziP1iY,1,playing tambourine,train hgKQiPRHlQQ,30,playing french horn,train hgLCSID144o,95,playing banjo,train hgMQZlCfwSU,30,cat purring,train hgN_D2dWDCM,60,"motorboat, speedboat acceleration",train hgR14DCHVHM,52,people booing,train hgSh0xFVjKI,47,"cattle, bovinae cowbell",train hgSh0xFVjKI,71,"cattle, bovinae cowbell",train hgXnQssvyYY,16,woodpecker pecking tree,train hgXnQssvyYY,43,woodpecker pecking tree,train hg__ORMvZhs,240,stream burbling,train hg_nvHU26Gk,19,"donkey, ass braying",train hg_x8H8ffWY,15,horse clip-clop,train hg_x8H8ffWY,69,horse clip-clop,train hgcLJFz2WKQ,0,police radio chatter,train hgcLJFz2WKQ,512,police radio chatter,train hgchjfiQz1Q,38,tractor digging,test hggJibID7fI,76,airplane flyby,test hgiPphyUS7I,30,basketball bounce,test hgjC68KOjSQ,14,baby laughter,train 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hhwv-Hos8xA,210,airplane,train hhx8Nc0y0fk,290,playing snare drum,train hhxmGc1-qLs,26,baby laughter,train hhxmGc1-qLs,39,baby laughter,train hi3uJOOJYJs,160,playing electronic organ,train hi3uJOOJYJs,490,playing electronic organ,train hi4zH7QcXeU,30,playing saxophone,train hi5DLemK2Ew,30,sailing,train hi6U1tBkJno,82,slot machine,train hi6XaqUjKTA,32,mouse clicking,train hi6XaqUjKTA,127,mouse clicking,train hi70ZZXForc,440,"bird chirping, tweeting",train hi8ft0rb-vs,80,playing accordion,train hi9BYyB4CUE,20,sheep bleating,train hi9GjH2vuRM,280,opening or closing drawers,train hiAsbFK0YuQ,250,skiing,train hiB46bg1QDo,30,typing on computer keyboard,train hiDZ3dQHP2k,155,civil defense siren,train hiDsYpjK3Uk,49,raining,train hiDsYpjK3Uk,75,raining,train hiE9ODB1eEs,22,mouse squeaking,train hiE9ODB1eEs,61,mouse squeaking,train hiKqSHYhfXc,133,people humming,train hiKqSHYhfXc,159,people humming,train hiMRCYNRYtw,280,people burping,train hiOY9mF6L6Q,30,playing trumpet,train hiOn9xwCG_M,8,sliding door,train hiPTHjIwfwc,10,splashing water,train hiQjYZe1RwU,8,frog croaking,train hiS9_IvQcd4,220,"ice cream truck, ice cream van",train hiSeGNEnv9E,9,francolin calling,train hiSeGNEnv9E,88,francolin calling,train hiU8NlFzt0c,60,driving buses,train hiUE_Q2RclA,10,chainsawing trees,train hiWjTqJP3zE,11,mynah bird singing,train hiXKcpHSc4M,0,playing table tennis,train hiard4Cg4DY,50,"railroad car, train wagon",train hicxibqJJ0I,30,"playing violin, fiddle",train hidrmMfy49s,14,playing trumpet,test hiectAqHc9s,41,ripping paper,train hiectAqHc9s,187,ripping paper,train higFVEW_e8k,136,slot machine,train hijKheYmgrg,70,helicopter,train hioouIHVgmg,10,playing banjo,train hiqQIhOMhs4,245,playing badminton,train hirkeAwbrfY,34,hammering nails,train hiuqK7BZmJA,110,"child speech, kid speaking",train hiwMfncZafA,613,snake rattling,train hiy7mHFnU1Y,30,duck quacking,test hiyX6r804Kc,90,driving buses,train hizN7823a6c,30,typing on typewriter,test hj0CCoBMKXo,173,train 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hkp5NDhqA_4,0,machine gun shooting,train hkr8KzMLwDA,500,typing on computer keyboard,test hksYQRPwvc8,34,plastic bottle crushing,test hksl0iXZlqg,20,"bird chirping, tweeting",train hktBbspxbOQ,110,skidding,train hkvmrojwrzU,40,playing harmonica,test hkxDD3iQNtI,0,magpie calling,train hkxO5z3JV58,50,people babbling,train hkzOOk4r3cw,30,horse clip-clop,train hkzmDTt9aWE,340,people farting,test hl19xAqJqoQ,27,sailing,test hl1ALkOUeq0,30,"rowboat, canoe, kayak rowing",train hl1YNHeN22k,110,ocean burbling,train hl2MtwNZZQI,0,dog growling,train hl2MtwNZZQI,18,dog growling,train hl3zzbSwzTU,30,playing harp,train hl4CItu_we8,0,door slamming,test hl4W7hDMn9I,6,wind noise,train hl6DOlVU-CQ,24,telephone bell ringing,test hl6ct1oqA4E,43,playing harmonica,train hl6ct1oqA4E,242,playing harmonica,train hl6gd7iQizQ,210,basketball bounce,train hl9AzFQ4Hus,21,ocean burbling,test hlDgSJ958cw,7,"vehicle horn, car horn, honking",train hlEwIZ8h4hE,217,firing cannon,train hlF5Cyqpz7Y,300,chainsawing 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hnoWfqp3nMo,0,dog barking,train hnoWfqp3nMo,15,dog barking,train hnpnc72pQOQ,677,police car (siren),test hnqPltW10Zo,81,dog barking,train hnqPltW10Zo,92,dog barking,train hnqz6PfZB1Q,173,cat growling,train hnuXlulPTRw,330,waterfall burbling,train hnuwVonqIwU,114,playing double bass,test hnvSwfChnmg,84,playing tambourine,train hnxOy_4M_cU,105,airplane flyby,train hnz86eMFjkQ,2,people sneezing,test ho-Z_TwhUXE,110,playing cymbal,train ho-ssSGyDFM,10,"child speech, kid speaking",train ho0rgHyQ9Jo,0,playing clarinet,train ho0rgHyQ9Jo,599,playing clarinet,train ho17UqBvls0,47,ripping paper,train ho17UqBvls0,147,ripping paper,train ho2iJOrAs4Y,30,people crowd,train ho2trfBD204,30,"child speech, kid speaking",train ho3acaLjdf8,53,volcano explosion,train ho7tm9n5iCw,126,smoke detector beeping,test hoAcvqLvWA4,200,"railroad car, train wagon",train hoDC2SrHgOk,91,canary calling,train hoDC2SrHgOk,159,canary calling,train hoHhy4lXNt4,158,missile launch,train hoHhy4lXNt4,193,missile launch,train hoJuhFL23U8,70,church bell ringing,train hoNcpx6J01k,1,train horning,train hoNcpx6J01k,87,train horning,train hoOwwU30m0I,67,alarm clock ringing,test hoRwM_QW48Q,1,tap dancing,train hoSWCa2xThE,50,stream burbling,train hoUv-9dfXNM,282,"electric shaver, electric razor shaving",train hoV9W2pscuw,4,dog whimpering,train hoWBMoFQB3o,40,sailing,train ho_PRhIzSYw,150,playing acoustic guitar,train hof2RTFmcog,130,people farting,train hofcTAFCaMA,0,turkey gobbling,train hogsqoh6HwE,56,arc welding,train hoh56kTCijo,50,playing glockenspiel,train hoh56kTCijo,72,playing glockenspiel,train hoj7_LnBrZQ,200,parrot talking,train hoj7_LnBrZQ,232,parrot talking,train hojU-Q9-LZo,300,playing zither,train hojU-Q9-LZo,543,playing zither,train hok9GxMU6fg,0,playing harmonica,train hok9GxMU6fg,109,playing harmonica,train hokDmxyQs5I,16,dog bow-wow,train holPzdyGASA,13,people crowd,train hoq-HM2nFwc,328,playing table tennis,train hornh-NQBHY,240,lawn mowing,train hot-p-Mta6k,30,toilet flushing,train howDFMkLSUg,23,ice cracking,train hoy-6i7ddh8,27,playing glockenspiel,train hp-1ncwHRto,26,people cheering,test hp05xXtSP-E,0,playing harmonica,train hp05xXtSP-E,352,playing harmonica,train hp1Y42faH2Y,2,footsteps on snow,train hp1Y42faH2Y,16,footsteps on snow,train hp2cv-anD2Y,203,"electric shaver, electric razor shaving",train hp2tEoZiFjA,30,orchestra,train hp3CPgNRXV4,30,playing electric guitar,train hp4SeY4S24c,240,playing flute,train hp6DMIi3CNI,54,writing on blackboard with chalk,test hp7qmQGbodA,34,lighting firecrackers,train hp7qmQGbodA,244,lighting firecrackers,train hpBRerDG3QY,120,people babbling,train hpCJY8O8R2E,25,people humming,train hpDY7u8B8hE,5,horse neighing,train hpEJkj85bzY,0,lathe spinning,train hpHPDkedSCk,289,train horning,train hpHPDkedSCk,537,train horning,train hpJX_lIZMD0,0,lighting firecrackers,train hpLvqSrTRTQ,30,basketball bounce,test hpMea9iDL6Y,26,machine gun shooting,train hpNR19Kopg8,10,"railroad car, train wagon",train hpNzM89T2ec,50,"male speech, 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pattering,test hsCHnb10l9M,50,driving buses,train hsCrB45dG9M,76,baby babbling,train hsCrB45dG9M,120,baby babbling,train hsDB4sN8J-o,180,playing clarinet,train hsFCa3QTuTk,18,people clapping,train hsFQTaALioY,3,wind rustling leaves,train hsK-2g2rvww,40,singing choir,train hsLSKowl_k8,11,playing accordion,train hsLgj_vZFz4,2,fire truck siren,train hsOdGHnCIng,0,"bird chirping, tweeting",train hsRGcmw1kUY,93,playing shofar,train hsS6uVUbrBs,156,skiing,train hsUwQBvZGSI,530,bird squawking,test hsWx3U6etY8,18,cricket chirping,test hsXtgiga44s,13,playing squash,train hsYMpQr7M2U,200,chainsawing trees,train hs_lo4GhVdA,50,car passing by,train hsaGiaXTS4g,78,playing tabla,train hsaGiaXTS4g,105,playing tabla,train hsabG8qlqvE,70,playing acoustic guitar,train hsbmi2p_q5U,70,driving buses,train hsbpxtwqG-E,11,playing squash,train hsc8NIZYlFo,8,playing glockenspiel,train hsc8NIZYlFo,47,playing glockenspiel,train hscHP77dcHU,42,mosquito buzzing,train hsch-EsQFlU,21,eagle screaming,test hsgwLAQOnH0,10,pheasant crowing,train hsgwLAQOnH0,24,pheasant crowing,train hsjUKW7FMsQ,22,horse neighing,train hskuHJbi_LA,0,playing tennis,train hskuHJbi_LA,49,playing tennis,train hsmouFWvfeY,310,people clapping,train hsmrvZEc_uw,150,playing electronic organ,test hsnQ-t9zK2U,160,printer printing,train hso6w8KbEkM,10,stream burbling,train hsoC7bFItps,60,fireworks banging,train hsr_nOdRx2w,168,airplane flyby,train hss6tuc0cnw,133,playing djembe,train hss6tuc0cnw,156,playing djembe,train hssewRO0yPU,0,playing saxophone,train hssewRO0yPU,2,playing saxophone,train hsvzzolvwMo,155,playing erhu,train hsvzzolvwMo,181,playing erhu,train hsx4t3lpQyY,120,lawn mowing,train hsx5FsH9CO4,30,chainsawing trees,train hsxxxA-l-Qc,30,playing acoustic guitar,train ht-l3ZKVZY8,30,baby crying,train ht3jNf66nbo,113,missile launch,train ht3jNf66nbo,286,missile launch,train ht4GN2h5bYo,440,"playing marimba, xylophone",train ht6Wn8eU-Pg,30,"ice cream truck, ice cream van",train ht8a14cuQPY,57,train 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hutVC3wlxjk,94,otter growling,train huwEqYYD2TU,20,typing on computer keyboard,train huxUiLoIDrk,43,rope skipping,train hv-gJtaC_uo,302,"electric shaver, electric razor shaving",test hv0rAXB2638,22,people battle cry,train hv0rAXB2638,70,people battle cry,train hv43bHDvo1M,93,playing sitar,train hv43dyPFQuw,28,opening or closing car electric windows,train hv5h0-TRwQM,1,people marching,train hv5h0-TRwQM,41,people marching,train hv5jiU5RxT8,153,pheasant crowing,train hv5jiU5RxT8,212,pheasant crowing,train hvBFjdYnW4U,30,mouse squeaking,test hvCj8Dk0Su4,0,playing bagpipes,train hvCj8Dk0Su4,2,playing bagpipes,train hvDg0j5JAwQ,0,playing clarinet,train hvFQrKDrsfs,8,duck quacking,train hvGl6WbMT3Q,60,playing cymbal,train hvHQLQiCbxY,50,playing accordion,train hvL5P6yjrlg,120,playing cello,train hvL90R4ufTM,1,crow cawing,train hvL90R4ufTM,14,crow cawing,train hvMVOIyDgvc,203,dog barking,train hvMVOIyDgvc,271,dog barking,train hvNVJmoghag,100,basketball bounce,train hvQLw35v4GY,30,squishing 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i1F74NBBZk8,226,playing volleyball,train i1Fk0RT1Zzs,71,skiing,train i1J3EXcLIts,46,basketball bounce,train i1J3EXcLIts,148,basketball bounce,train i1J9nL1lFXs,0,wind noise,train i1LesV4QhlM,96,car engine starting,train i1P-D2VIT9c,41,pheasant crowing,train i1PJ9fKlyUU,30,printer printing,train i1PQka4KJ6I,53,people slapping,train i1Qu7neFrAs,10,playing synthesizer,train i1SVG9vYELU,20,people belly laughing,train i1WaQ2aZ1Ys,4,canary calling,train i1WaQ2aZ1Ys,22,canary calling,train i1YCcw_VpCs,7,playing timbales,train i1YCcw_VpCs,17,playing timbales,train i1YhhtejsS4,4,crow cawing,train i1YhhtejsS4,35,crow cawing,train i1Zt6KmI260,0,playing trumpet,train i1b5A665W_g,40,dog barking,train i1bXJOe6PW0,30,playing bass drum,train i1cYnKF62wo,0,tap dancing,train i1cYnKF62wo,251,tap dancing,train i1eRW16-038,166,mosquito buzzing,train i1fLd18q20U,13,playing guiro,train i1fLd18q20U,27,playing guiro,train i1hlaGlTD0c,493,"heart sounds, heartbeat",train i1hlaGlTD0c,514,"heart sounds, 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i2x7kPNfXpI,384,volcano explosion,train i2y9fubO3Sk,257,skateboarding,train i2zR26JOJLQ,175,singing choir,train i2zR26JOJLQ,207,singing choir,train i3-8Aja8Ajs,24,rope skipping,train i3022Z7Sm-A,123,"electric shaver, electric razor shaving",test i32yzlIbaIU,258,playing cornet,train i32yzlIbaIU,399,playing cornet,train i34M-tBZLtA,300,eating with cutlery,train i368QdYnafg,1,"alligators, crocodiles hissing",train i368QdYnafg,11,"alligators, crocodiles hissing",train i36NA2bDjTo,2,cat hissing,train i38F9XkmoWI,90,playing acoustic guitar,train i38GhHirIeg,0,playing bagpipes,train i39ZIS_58UY,0,lighting firecrackers,test i3F5Npl5k8o,14,baby babbling,train i3F5Npl5k8o,41,baby babbling,train i3FIAR0bfBM,104,yodelling,train i3FIAR0bfBM,135,yodelling,train i3Fj-0RbbJc,30,toilet flushing,train i3Gf0eOCHSQ,150,door slamming,train i3H15lKKzjo,30,people clapping,train i3HSf0rgDu8,137,tap dancing,train i3HsITnarf4,30,chainsawing trees,test i3OBlkyogJ4,90,playing tympani,train i3PRxFpHnUw,14,wood 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iBznvs2SJGg,585,playing tennis,train iC-tmniz3Ag,60,helicopter,train iC0iQ1QKan4,785,lip smacking,test iC1jyQEyhWw,30,chicken crowing,train iC3yFGTYPkY,295,whale calling,train iC7eHZeYY4w,30,printer printing,train iCC4Cow4iUk,72,train whistling,train iCC4Cow4iUk,87,train whistling,train iCCfnUL-KW0,40,lions growling,train iCCmZ0dEB6Q,86,civil defense siren,train iCCujhZEiwI,50,skidding,train iCDRMwVl0YM,33,yodelling,train iCDRMwVl0YM,109,yodelling,train iCED_QpkDVA,70,toilet flushing,train iCFgoEIWamQ,86,playing theremin,train iCFgoEIWamQ,120,playing theremin,train iCG0cNYGaWE,30,"race car, auto racing",train iCIa_pmLDqs,30,playing banjo,test iCJvraD2KPk,12,wood thrush calling,train iCMWxxeNgNI,30,people hiccup,train iCOG41rKjrE,430,playing cello,train iCOiqusHfs8,39,striking pool,train iCQ4vHhQS90,2,mynah bird singing,train iCQ4vHhQS90,162,mynah bird singing,train iCS6D5GoHWc,86,playing accordion,train iCUskN_oVW4,74,playing cymbal,train iCV9tbrNaOY,115,people humming,train 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iKLYNYcQeQU,180,playing snare drum,train iKPKs2Xd9ew,27,frog croaking,train iKPKs2Xd9ew,60,frog croaking,train iKSO-5Ueq-4,402,owl hooting,train iKTlIbkjnRE,60,"subway, metro, underground",train iKVvuNTvAbo,22,owl hooting,train iKWKyW-pOMI,187,playing bassoon,train iKWclBFnR2M,1,mynah bird singing,test iK_CFXlP_Bo,7,playing glockenspiel,train iK_CFXlP_Bo,27,playing glockenspiel,train iK_QZLB74HI,100,child singing,train iK_QZLB74HI,124,child singing,train iKco428HveQ,3,playing glockenspiel,train iKcwzbK_s8A,30,ocean burbling,train iKfgPbiD5Eg,46,wind chime,train iKh5G2-yhcE,226,playing badminton,train iKhEXJm1J18,30,duck quacking,train iKn4rXZ45YA,50,driving motorcycle,train iKorq7dE4gM,442,playing electronic organ,train iKorq7dE4gM,457,playing electronic organ,train iKpSpL5hTd4,25,beat boxing,train iKtLV_5A2Rk,93,basketball bounce,train iKv9ymIxFWA,30,"bird chirping, tweeting",train iKvxvnTZqVU,231,people eating crisps,train iKvxvnTZqVU,242,people eating crisps,train iKwWzXGr_NY,330,crow cawing,test iKwhvVVhBN8,605,horse neighing,train iKwhvVVhBN8,726,horse neighing,train iKxVsZ4AZ8g,5,crow cawing,train iKxVsZ4AZ8g,17,crow cawing,train iKxYxoxe-WE,30,playing accordion,train iKyCsb-Tgmg,30,baby crying,train iKz3ONFLA0c,40,train horning,train iL1_y8SxBFs,240,playing piano,train iL44i-VSUE0,7,playing sitar,train iL6PBVpdMpg,0,dog growling,train iL6wMYqcI6Q,239,elephant trumpeting,train iLChjgAPKzQ,252,"railroad car, train wagon",train iLDAGNiPRro,53,fox barking,train iLEQ3ITdzOE,142,disc scratching,train iLEQ3ITdzOE,163,disc scratching,train iLF_Ir3L800,815,people crowd,train iLFm8FZ8Ybs,17,volcano explosion,train iLGcDWzJObM,13,car engine knocking,train iLIR8xbxu64,30,people sniggering,train iLMz9MkuhHQ,53,"bee, wasp, etc. buzzing",train iLWMBUMohvA,2,dog howling,train iLZSHjE1eJs,17,plastic bottle crushing,train iLa4BUFARnI,79,bowling impact,train iLaDu_GNqb4,13,"ice cream truck, ice cream van",test iLaHS4NFcKo,350,ripping paper,train 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iT_AFcMvBjE,30,duck quacking,train iTagabgQ1jk,36,playing bongo,train iTagabgQ1jk,163,playing bongo,train iTcudjb-reM,9,people booing,train iTd4LaHgg6Q,30,"motorboat, speedboat acceleration",train iTd7hOI27BE,48,playing harp,train iTdWzFiqPf0,30,woodpecker pecking tree,train iTdWzFiqPf0,108,woodpecker pecking tree,train iTdlEJPKT94,300,"railroad car, train wagon",train iTe3DLQmJAg,2,chicken crowing,train iTfglfAWy2E,1,baby laughter,train iTgiQ_KWOP8,72,playing glockenspiel,train iTkzTbXi23U,75,smoke detector beeping,test iTleLSmcP6o,10,playing cymbal,train iTm3Tw2FeBQ,6,lions roaring,train iTm3Tw2FeBQ,24,lions roaring,train iTmNN9uGRfs,314,snake hissing,test iTpHBFwNTsM,60,striking pool,train iTpHBFwNTsM,70,striking pool,train iTsFU1N-YP8,75,squishing water,train iTsFU1N-YP8,110,squishing water,train iTuk4xplBSw,135,lions roaring,train iTuk4xplBSw,343,lions roaring,train iTxbvNfKc3E,30,playing bongo,train iTyWBukpwkk,3,"rowboat, canoe, kayak rowing",train iTyeUJD6POQ,200,playing 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cutlery,train iUUUxjEAkSA,318,electric grinder grinding,train iUUUxjEAkSA,338,electric grinder grinding,train iUVnS10DXpc,200,playing electric guitar,train iUYQuxQ3A8Q,30,people running,train iUZW915vbq4,50,people hiccup,train iUbTO-VODX0,190,pig oinking,train iUcNi8XnzI4,115,singing choir,train iUcepAhtxcc,147,playing hockey,test iUdD3znsLuE,191,dinosaurs bellowing,train iUdD3znsLuE,210,dinosaurs bellowing,train iUdkKNKx4DA,61,singing choir,train iUdkKNKx4DA,187,singing choir,train iUdwFhtGcTg,1049,playing harp,train iUhDA35kY7Y,290,"rowboat, canoe, kayak rowing",train iUhlIlzLT8g,8,striking bowling,train iUi1DG5m20A,190,skidding,train iUi5-o2oSLU,110,playing piano,train iUiSYGEcyx8,90,"race car, auto racing",train iUkTcIHBUc4,62,playing theremin,train iUlC9rWkIr4,0,dog bow-wow,train iUoFC-GUq8o,113,sloshing water,test iUoVIvq0yIE,149,cap gun shooting,train iUoVIvq0yIE,277,cap gun shooting,train iUoiSto-UFc,203,bouncing on trampoline,train iUoiSto-UFc,219,bouncing on trampoline,train 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iZhHrVkEQV0,0,people burping,train iZm1gxfHYdI,186,playing volleyball,train iZmW9EYzHhI,30,pig oinking,test iZpuOn86PYI,10,playing tabla,test iZq0v1uC_dY,30,playing cymbal,train iZqJEypHKcM,111,rope skipping,train iZqJEypHKcM,121,rope skipping,train iZqemiYznPQ,454,telephone bell ringing,test iZqpnIylN6E,30,mouse squeaking,test iZsSiNOh6Os,255,planing timber,train iZspwZ6HU3s,40,"playing marimba, xylophone",train iZuORZnBFR0,30,playing electronic organ,train iZxPrZAZeXY,400,sailing,test i_-LCRDriig,30,ocean burbling,train i_4MUf3nbGs,310,playing piano,train i_4nyIsPBYY,145,black capped chickadee calling,train i_4nyIsPBYY,186,black capped chickadee calling,train i_7pN4F7JeE,280,cat purring,train i_7pN4F7JeE,453,cat purring,train i_8ZeIY2uxM,97,shot football,train i_8ZeIY2uxM,119,shot football,train i_8fgAkHuq8,30,people running,train i_ASjhacyjU,51,playing glockenspiel,train i_CrIsEufjs,2,francolin calling,train i_CrIsEufjs,24,francolin calling,train i_ER8O87unM,20,turkey gobbling,train i_FPgzfbB6A,203,playing squash,train i_G-KciH8XM,28,sea waves,test i_HOO3MaiVw,400,"railroad car, train wagon",train i_HjrNoS78o,13,playing bugle,train i_HjrNoS78o,23,playing bugle,train i_HkWWVmK00,0,"alligators, crocodiles hissing",train i_I-7dkQLlI,150,ocean burbling,train i_Kc3ON9fUk,30,playing hammond organ,train i_LryMZKMso,162,rope skipping,train i_NAMempMvs,105,playing table tennis,train i_RBnnsCI3c,4,arc welding,train i_TVxkPAxgo,0,people sniggering,train i_U686Rpswc,130,mynah bird singing,train i_U686Rpswc,166,mynah bird singing,train i_WGY6IBjRs,19,yodelling,train i_WGY6IBjRs,53,yodelling,train i_ZjJ8-CaWo,4,playing glockenspiel,train i_eGERxyy0Y,88,civil defense siren,train i_gtk2tAjsA,70,lawn mowing,train i_hI3gVoRr0,121,people coughing,train i_h_1ozF_bw,70,"playing marimba, xylophone",train i_hhSKWxzeU,38,frog croaking,train i_hhSKWxzeU,51,frog croaking,train i_ivQ4L3b54,119,playing bassoon,train i_ivQ4L3b54,131,playing bassoon,train i_jX_uwK3CM,41,slot machine,train i_jqjzV3sMo,130,dinosaurs bellowing,train i_jqjzV3sMo,153,dinosaurs bellowing,train i_rQfvOrtrY,120,helicopter,train i_sLmdfpC94,276,writing on blackboard with chalk,train i_sQfy5iYNM,139,playing bass guitar,train i_u1dryFDIA,108,firing muskets,train i_u1dryFDIA,228,firing muskets,train i_yrseEyOWw,0,playing electric guitar,train ia3opw_Vu54,54,swimming,test ia4BQR23r4I,250,pumping water,test ia4WowUevCc,0,gibbon howling,train ia4WowUevCc,10,gibbon howling,train ia6jrhFJzOk,35,people burping,train ia8IrM5BKBU,176,disc scratching,test ia8JbxQqdMM,230,basketball bounce,train iaB5VTVs8As,206,wind rustling leaves,train iaCFiSWBEYo,262,cell phone buzzing,train iaDaJvPYtSU,117,playing volleyball,train iaFnWqVk_r8,24,basketball bounce,train iaJuWf1KcUY,30,people shuffling,train iaQ-eldpvYU,0,toilet flushing,train iaSE6Ee-ev4,199,playing volleyball,train iaTe4kzhv80,50,playing clarinet,train iaUwr0fTHzA,23,air horn,train iaXDjD9OYqQ,73,yodelling,train iaYE28LE7a0,30,"engine accelerating, 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ib3ZwehDw-Y,22,"ice cream truck, ice cream van",train ib3ZwehDw-Y,145,"ice cream truck, ice cream van",train ib4BgSuw2B8,96,playing snare drum,train ib4EcuFd7sI,71,fox barking,train ib4EcuFd7sI,101,fox barking,train ib5CAqv-q-k,110,using sewing machines,train ib8tVilhc64,37,missile launch,train ibCQVMd2SBM,30,mouse squeaking,test ibCQwzpQ3kk,130,playing drum kit,train ibKSki68gHw,30,playing bass drum,train ibNKuL8geCk,30,"female speech, woman speaking",train ibRxF3ACxEc,0,using sewing machines,train ibSs8fg97xg,108,volcano explosion,train ibVJNcncWlU,33,people booing,train ibVpmn8sOKA,20,toilet flushing,train ibZXI6oDCsc,20,playing bass guitar,train ibbYO6kfcnM,95,playing electric guitar,train ibbyRjr7kEs,110,playing bass guitar,train ibd7CKcSiTI,65,playing bass drum,train ibd7CKcSiTI,122,playing bass drum,train ibgOMh9HgiE,252,playing volleyball,train ibgTcJSRpjM,104,horse neighing,train ibjwrgRaIes,306,skiing,test ibkUTqqXMTk,0,playing badminton,train iblN6lyZRmc,30,police car (siren),train ibpIcnGvJAc,736,turkey gobbling,train ibpIcnGvJAc,749,turkey gobbling,train ibxKyROk9gA,422,tapping guitar,train ibxKyROk9gA,434,tapping guitar,train ic2PdsQFCDs,22,firing cannon,train ic63KHggKPw,407,cutting hair with electric trimmers,train ic63KHggKPw,477,cutting hair with electric trimmers,train ic9P8BU0-Wc,53,playing table tennis,train icBR2It66-A,2,playing harp,train icBuuz9dPS0,320,"railroad car, train wagon",train icC9Fa-kcNk,30,cattle mooing,train icCAAIpxAh8,30,driving motorcycle,train icDMsiE9zJ4,470,using sewing machines,train icDmhCW650M,30,printer printing,train icE89KjRArs,30,"playing steel guitar, slide guitar",train icHwOLcHffU,152,playing glockenspiel,train icHwOLcHffU,200,playing glockenspiel,train icI1JzU08vY,5,skiing,train icJZHa6pGmY,8,chicken clucking,train icJZHa6pGmY,21,chicken clucking,train icK4IQb2KsE,0,hail,train icK4IQb2KsE,10,hail,train icKQTkBJMV8,3,cuckoo bird calling,train icKSNpk534s,83,playing french horn,train icKSNpk534s,126,playing french horn,train icKojlQtWv0,400,ocean burbling,test icNGUFeLDAM,297,chopping wood,train icN_D6KGvMI,30,playing flute,train icQe_N-fBi8,698,people eating apple,train icRsdVfWGA0,140,people crowd,train icSTah6oMJ8,38,playing oboe,train icSTah6oMJ8,53,playing oboe,train icWkyedURkM,124,playing banjo,train icYlNmbJLVQ,10,playing drum kit,train ickMnnc50Nk,10,cat purring,train icnRMW6P9nc,331,train horning,train icstndUp2EU,170,"child speech, kid speaking",train icuT0Zcoq3g,8,eletric blender running,train icuT0Zcoq3g,41,eletric blender running,train icui3nlZaRk,20,fireworks banging,train icuqUv4a5Vs,78,skidding,train icwfgqmENJs,0,coyote howling,train icxTfoZvVM0,6,playing glockenspiel,train icxTfoZvVM0,22,playing glockenspiel,train iczNqmkwaMo,130,opening or closing drawers,train iczSNXRUaJs,344,playing bassoon,train iczSNXRUaJs,404,playing bassoon,train id-GVH4Hcig,540,people clapping,train id-RLDdMNd0,60,helicopter,train id-fOnv0ZM4,30,playing accordion,train id1QK90fbQ8,80,people clapping,train id5Xv18xiew,120,"heart sounds, heartbeat",train id5Xv18xiew,160,"heart sounds, heartbeat",train id5_X_3oTNw,358,playing saxophone,train id5_X_3oTNw,373,playing saxophone,train id6eBTnmO6A,38,playing squash,train id7Kq8CEYdI,0,church bell ringing,train id7Kq8CEYdI,10,church bell ringing,train id7schBC4ng,86,black capped chickadee calling,train idBqPBHeVnM,237,playing banjo,train idFx2X2L8BU,230,orchestra,train idJsQ3F071k,140,playing sitar,test idKNZgediIQ,257,bowling impact,train idMhqYA4zKA,160,"playing marimba, xylophone",train idOdL52LXaA,68,people shuffling,train idPIRRspMp0,121,playing castanets,test idQtpYxaKAU,70,car engine starting,train idRWFJDmnSM,83,typing on typewriter,train idRWFJDmnSM,98,typing on typewriter,train idS-vH-vTw0,111,people slapping,test idTOjw-rAoA,51,cat growling,train idTYxBUreGs,20,people farting,train idTzZaMtGy8,41,playing lacrosse,train idUBlAGxIuM,40,people belly laughing,train idUZsNLnyDg,30,tapping guitar,test idUcjmYkMW4,112,beat 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ie2vtKriqdk,30,cat purring,train ie47-Wjj4Zw,27,ferret dooking,test ie47-Wjj4Zw,63,ferret dooking,test ie5H6Qfiy-s,30,driving buses,train ie73SB6WFX8,8,car engine idling,train ie9A5OpaUHE,57,car engine idling,train ieAa6ookkdk,106,playing french horn,train ieAa6ookkdk,147,playing french horn,train ieD57ToCv3A,22,frog croaking,train ieD57ToCv3A,109,frog croaking,train ieExTxwVRHM,0,playing harp,train ieIMfiF3t7s,0,civil defense siren,train ieKkn-YMUh8,570,sailing,train ieMl03vLaa0,11,fireworks banging,train ieNRF7wGbBQ,100,people crowd,train iePXnZ6uRSY,110,stream burbling,train ieQ4cIfU1_w,0,playing timbales,train ieRU5f5P4B8,350,cap gun shooting,train ieVf0iT5ftg,85,parrot talking,train ieVf0iT5ftg,99,parrot talking,train ieVpvcgaSgQ,50,people cheering,train ieXdQlIBgLk,30,"playing marimba, xylophone",train ieZmQbzQYtw,0,people belly laughing,test iebVJ-UA7-Y,79,lathe spinning,train iecSEaFLsf0,227,police radio chatter,train iecSEaFLsf0,321,police radio chatter,train ied6a8WECLI,20,dog howling,train ied6a8WECLI,30,dog howling,train ieiDrKToeg0,200,people clapping,train iekf8109w1E,1,car engine knocking,train iemiraTRmtI,92,chimpanzee pant-hooting,train ien-8SoDmtU,6,cricket chirping,train iepDfEI-h4k,43,crow cawing,train iepssPWnNR0,8,dog whimpering,train iepssPWnNR0,24,dog whimpering,train ierB8KCA3Ro,60,fireworks banging,train ierTwpxGZVU,0,playing harmonica,train ierTwpxGZVU,4,playing harmonica,train ieuAWfo60OM,4,playing cornet,train ieuAWfo60OM,20,playing cornet,train iev1me2TSnE,0,zebra braying,train iexA85JGhtA,20,ambulance siren,train iezBJHQ109k,303,playing oboe,train iezuz2Cjqlw,230,people crowd,train if-oZbLDNE0,20,people clapping,train if0TmhrpKUM,145,lathe spinning,train if6to_bUkoM,33,playing bassoon,train if75RoVHBic,111,eletric blender running,train ifBVBvRLUj8,0,playing tennis,train ifCmvLNQl3s,30,crow cawing,test ifD14kCAPc4,0,playing squash,train ifD75TTksYc,224,child singing,train ifEJBKPGmi8,100,fireworks banging,train ifGofuOYq8k,1,civil defense siren,train ifGuIUPl1hE,30,lions roaring,test ifHRDBx-ctw,30,playing accordion,test ifHS9_-I1B0,18,civil defense siren,train ifJYy--cj6A,3,playing glockenspiel,train ifLZ2S49hVs,53,vacuum cleaner cleaning floors,train ifLZ2S49hVs,63,vacuum cleaner cleaning floors,train ifN9LdEE994,206,opening or closing drawers,train ifOWEpjlcZ4,30,playing bagpipes,train ifOpsuLbIJ0,10,toilet flushing,train ifPpBLSfXbc,139,child singing,train ifPpBLSfXbc,199,child singing,train ifQMabGP0ck,19,mouse pattering,train ifQMabGP0ck,81,mouse pattering,train ifR960G4jSw,216,driving snowmobile,train ifUVNprmFVc,157,slot machine,train ifWhFgoXK0k,30,baby crying,train ifYcY4V_1FQ,110,"bird chirping, tweeting",train if_8D3-Mgik,30,playing flute,train ifbDEDXnh5A,3,playing tambourine,train ifbrZdW82DQ,29,people slapping,train iffkuJiLrCc,420,playing trombone,train ifhMpF5le8E,125,playing badminton,train ifhOD4VYS_Q,130,orchestra,train ifjDWKMNlIk,250,"subway, metro, underground",train ifqgtqeTf-M,5,air conditioning noise,train ifrS8m7WJKQ,204,reversing beeps,train ifrjd5bZHIY,10,car passing by,train ifuD7inq3HQ,42,people humming,train ifuD7inq3HQ,80,people humming,train ifuvQu-yEWM,30,people running,train ifw_BcHJvYc,9,cheetah chirrup,train ifw_BcHJvYc,50,cheetah chirrup,train ig2nyzgJLfY,5,airplane flyby,train ig4gGdMB348,30,sailing,train ig4o_tsUvbM,82,"bee, wasp, etc. buzzing",train ig59gOwKj0k,105,popping popcorn,test ig59gOwKj0k,153,popping popcorn,test igC2mWONVZQ,163,playing trombone,train igDsu5QWhpo,215,playing sitar,train igDsu5QWhpo,318,playing sitar,train igEjCNYSd1Q,220,playing snare drum,train igGAsklLY5U,38,plastic bottle crushing,train igGAsklLY5U,66,plastic bottle crushing,train igGRoOpgyzY,30,cattle mooing,train igPiKZUKwVA,160,"playing marimba, xylophone",test igS2rilFsFE,187,playing timbales,train igTZmll9MN8,21,firing cannon,test igU11WRJNrU,65,lions growling,test igU11WRJNrU,106,lions growling,test igdJhAnZcV4,0,chicken clucking,train ige9smfD6aM,81,tornado roaring,train ige9smfD6aM,96,tornado roaring,train igiHMwU_HZ0,184,playing castanets,train igiHMwU_HZ0,243,playing castanets,train igk8O6OfwdQ,90,people coughing,train igkXM7rZAKo,11,playing bugle,train igkoOIizTiY,30,playing cymbal,train igsItJYgcCY,4,gibbon howling,train igsOixfbCxQ,270,cattle mooing,train iguA4nZde6c,151,tapping guitar,train iguA4nZde6c,262,tapping guitar,train igv8pJHFcP4,510,"race car, auto racing",train igx0uqb_o2g,115,playing timbales,train igx0uqb_o2g,180,playing timbales,train ih-zaNodTEE,100,thunder,train ih1fb0HFuqI,310,playing synthesizer,train ih2swSOH3XY,189,turkey gobbling,train ih5d5cfUOCE,112,playing bongo,train ih5v23G0ej0,0,playing acoustic guitar,train ih6yf3B8cJ0,39,typing on computer keyboard,train ih6yf3B8cJ0,56,typing on computer keyboard,train ih7g1UFPtIw,72,rope skipping,train ih7s-x6rk7A,168,playing volleyball,train ih95nuWYUbo,30,playing accordion,train ih9IJlPqt6Q,73,singing choir,train ih9IJlPqt6Q,417,singing choir,train ihDeBPa4V6k,380,playing flute,train ihDy9IwP3Mo,0,people screaming,train ihJjtBVagh8,40,writing on blackboard with chalk,train ihKNl0K6gXY,433,dinosaurs bellowing,train ihNcChnW2eQ,23,people booing,train ihNenFnQ5N4,6,lathe spinning,train ihNenFnQ5N4,17,lathe spinning,train ihO7ht8fgyw,280,playing drum kit,train ihOOXfgxrGc,3,cat purring,train ihOOXfgxrGc,22,cat purring,train ihPPA36u1rU,37,playing trombone,train ihQiGrQMR3U,6,air conditioning noise,test ihR3eaN89qA,50,playing bass guitar,train ihTdvLaea4M,11,frog croaking,train ihUQ3ZE4kB8,77,dog growling,train ihVpn6ES2Gk,1,mouse squeaking,train ihWwNdUlr7Q,10,"subway, metro, underground",train ihaR8HJKp78,240,ocean burbling,train ihaySbLbkGk,30,raining,train ihccFyVF8OU,42,vacuum cleaner cleaning floors,train ihccFyVF8OU,54,vacuum cleaner cleaning floors,train iheSre-jkNU,333,playing squash,train ihjplPk65Tc,10,people whispering,train ihm1HBwvr_4,0,cat growling,train ihmoNm-mx8g,21,playing steelpan,train ihnW25xmMWI,160,playing timpani,test ihoUhy8XwAo,167,bouncing on trampoline,train ihoUhy8XwAo,245,bouncing on trampoline,train ihorndQTyGE,177,arc welding,train ihorndQTyGE,217,arc welding,train ihpKRENw2N4,43,horse neighing,train ihrCFkCPQZU,0,people sniggering,train ihuAByGN8HQ,480,playing cymbal,test ihuCB0j-nsg,30,female singing,train ihw3jEb-9Q4,99,playing didgeridoo,train ihycWBCtuY0,160,people sniggering,train ihzI8nJ0PnM,336,skiing,train ii3Geza3hAU,210,using sewing machines,train ii3ac-jc454,40,ambulance siren,train ii5w1LER-Bo,30,printer printing,train ii63OTMlhK4,101,fox barking,train ii63OTMlhK4,418,fox barking,train ii6jZ2dOjiA,50,people farting,train ii8AxRh4jy8,6,playing badminton,train ii8ewfa96EE,30,"playing violin, fiddle",train ii9UOxwbF3I,10,police car (siren),train iiAK295_-jk,21,baby crying,test iiAWiz5IGMw,425,sharpen knife,test iiD-5FX5Bns,28,missile launch,train iiKXHd-enWM,4,goat bleating,train iiNMKCDHSsE,79,cat growling,train iiNMKCDHSsE,101,cat growling,train iiO5RrDlmEc,29,"pigeon, dove cooing",train iiOtUMMHqTY,64,dog barking,train iiPCPlK7CT4,220,playing flute,train iiPnJHtsbb0,55,swimming,train iiPnJHtsbb0,69,swimming,train iiSx5bLo7ac,30,ambulance siren,train iiTJJ-LE45Y,0,people sneezing,train iiUvfvkeo0c,237,disc scratching,train iiUvfvkeo0c,290,disc scratching,train ii_Ce3Lwy4E,50,train horning,train iiaC4YUVU2g,73,yodelling,train iiaC4YUVU2g,113,yodelling,train iieUG0ej0bo,48,people burping,train iigve_6jGW4,12,people burping,train iihNY1QMXxc,22,playing bongo,train iihNY1QMXxc,51,playing bongo,train iii0asgPqBc,92,arc welding,train iiil-_55LcA,200,"railroad car, train wagon",train iikAZGWnW_Y,90,playing theremin,train iikAZGWnW_Y,134,playing theremin,train iikhslprd_A,9,chimpanzee pant-hooting,test iil9jPCMI4s,180,"engine accelerating, revving, vroom",train iiqJFQmKNy4,100,"engine accelerating, revving, vroom",train ij-0CG5vZAA,130,yodelling,train ij-0CG5vZAA,173,yodelling,train ij2TSw0ud9Q,1,"donkey, ass braying",train ij4YyxVV9PI,114,playing cymbal,train ij4tD7cMifA,390,playing trombone,train ij64ij5OA-Q,10,playing bagpipes,train ij69DK5uq1I,112,playing guiro,train ij69DK5uq1I,201,playing guiro,train ij6Sq3FJ7hs,210,"race car, auto racing",train ij85kbIJzBE,120,striking bowling,test ij9L0FdpOxw,88,people eating crisps,train ijFPMrptrwE,7,"child speech, kid speaking",train ijJg9KA_zw4,120,people hiccup,train ijLv0PKlD7Q,103,slot machine,train ijMFpC2aJnE,111,playing tambourine,train ijQpLrUpIOs,50,sloshing water,train ijRR09p3Qrw,10,wind rustling leaves,train ijRTRcvKlrs,104,opening or closing car doors,train ijRTRcvKlrs,151,opening or closing car doors,train ijTDR6YQ9V4,30,people burping,test ijTf9dtXB4I,56,playing accordion,train ijWcrK7RRrc,10,skidding,train ijXQ0XKVZjk,492,playing hockey,train ijXeVMx0hTk,59,slot machine,train ijYIoH36ZQ0,22,playing tambourine,train ijYIoH36ZQ0,60,playing tambourine,train ijYfaWPfepE,265,bowling impact,train ij_BPhSGMd0,1,playing volleyball,train ij_BPhSGMd0,45,playing volleyball,train ijbTvQr3JIU,713,playing erhu,train ijc6Ma6c4O0,90,door slamming,test ijdSNE1YG78,93,writing on blackboard with chalk,train ijid9IXLxXw,70,eating with cutlery,train ijirbb9m05k,285,swimming,train ijirbb9m05k,470,swimming,train ijlEJc3dz3Q,100,helicopter,train ijo60CPex6U,20,people babbling,train ijoUCRhaDJo,10,"subway, metro, underground",train ijpj6J6cVRU,70,"subway, metro, underground",train ijrq1gwi3YI,30,"pigeon, dove cooing",train ijs7AJ85E58,28,tap dancing,train ijs7kJPMk_s,2,"playing steel guitar, slide guitar",train ijs7kJPMk_s,142,"playing steel guitar, slide guitar",train ijtlPVjTTyY,0,tornado roaring,train ijuPb7QCssc,261,playing cornet,train ijvr4LEl-JE,212,train wheels squealing,train ijvr4LEl-JE,241,train wheels squealing,train ijxTAufMGO8,128,tap dancing,train ijzq_FNlf7A,80,"rowboat, canoe, kayak rowing",train ik-C5hhrRKA,322,mouse clicking,train ik0gBO1kSV8,13,playing tennis,train ik0gBO1kSV8,79,playing tennis,train ik0hDu3fMYE,224,dog growling,train ik3SNmU2v9k,135,pheasant crowing,test ik4gTQg1wdM,414,bowling impact,train ik9X4ONr4E0,0,scuba diving,train ik9X4ONr4E0,16,scuba diving,train ikDPLMzzWy0,85,arc welding,train ikDcNYHFi_Q,70,fireworks banging,train ikGKQzNz-ok,30,sheep bleating,train ikGYzAVpwNk,240,church bell ringing,train ikHhzHXtGAQ,11,civil defense siren,train ikHhzHXtGAQ,187,civil defense siren,train ikIVo68HLwY,10,bird squawking,test ikIj6yjVnJ0,30,ocean burbling,train ikJKSqnTylI,70,playing piano,test ikJOIP0IyOA,10,dog barking,train ikJlmuxCMgo,560,"railroad car, train wagon",train ikKld7LGBZ0,449,striking bowling,train ikKld7LGBZ0,486,striking bowling,train ikOdqgpCobc,30,"pigeon, dove cooing",train ikQ6ot1qN3M,16,people humming,train ikQ6ot1qN3M,26,people humming,train ikQJw_espoc,39,vacuum cleaner cleaning floors,train ikQp36DD1zY,120,playing banjo,train ikRR0vg8xLc,260,opening or closing drawers,test ikV1DiLtCQE,40,singing bowl,train ikY-mwUoGSE,30,car engine knocking,train ikY72s_7y78,230,waterfall burbling,train ikca5mOwlUM,24,lathe spinning,train ikfwKrGw9Co,155,bird wings flapping,train ikhBs__x9xk,330,"railroad car, train wagon",train ikj5ELPC6L4,18,crow cawing,train ikj5ELPC6L4,72,crow cawing,train ikkKlW18s1E,70,fire crackling,test ikmE_kRvDAc,30,car engine knocking,test ikn8T6srnzs,162,skiing,train iknfedXdCPY,15,pig oinking,train ikoJFBWfv5s,70,playing french horn,train ikoRoQqCCcE,126,canary calling,train ikqBgjLcakE,20,mosquito buzzing,train ikt79aj76hE,178,playing didgeridoo,train ikxOD_Q_C7U,144,vacuum cleaner cleaning floors,train ikyuvMEe1Bw,30,baby crying,train ikzCU789jow,2,people booing,train il17TclY-Mg,2,airplane,train il17TclY-Mg,146,airplane,train il1Jf4JAysQ,29,"heart sounds, heartbeat",test il1s5HHLfI8,152,slot machine,test il3Kzxy95gs,1,"vehicle horn, car horn, honking",train il3TRZx-Z-w,72,beat boxing,train il6_H0WXfyY,26,playing darts,train il7o_yNrbQY,30,toilet flushing,train il9f-258jgc,6,sheep bleating,train ilABCp0Ks5s,23,people sneezing,train ilABCp0Ks5s,36,people sneezing,train ilAO2AiSGVQ,30,police car (siren),train ilAnhNLYjhk,378,skiing,train ilJamXBW2vM,20,people farting,train ilM8hnM6mlQ,131,francolin calling,train ilM8hnM6mlQ,191,francolin calling,train ilMvSaiVsQU,55,playing tuning fork,test ilOFH-GJVIg,229,playing didgeridoo,train ilPZORsv9dk,28,people humming,train ilRXMMIRCdc,240,lawn mowing,train ilRYI0yXCkw,162,playing snare drum,train ilRYI0yXCkw,374,playing snare drum,train ilT2xn2JpEg,46,playing theremin,train ilXHjQ2lNdY,18,hail,train ilXHjQ2lNdY,28,hail,train ilY2kyhwXvU,49,chinchilla barking,train ilZzqohLi9g,70,people booing,train ilZzqohLi9g,128,people booing,train il_XiwwxFrs,240,people whistling,train ilbVvdzNEcI,50,playing badminton,train ilc7aczildg,3,chicken crowing,train ildAKsY9dcI,170,playing cymbal,train ildteJbZ9Jw,513,machine gun shooting,train ilfOKmWrpTk,20,fireworks banging,train ilfqn0RfTXI,25,missile launch,train ilfqn0RfTXI,36,missile launch,train illlK3qd_5k,244,playing hammond organ,train ilm0DVntUSM,140,people whispering,train ilnwc0GckYw,200,people clapping,train iltQQqzoRM8,30,driving buses,train ilu_CR7r8GQ,30,"engine accelerating, revving, vroom",train ilxR4F9iHMU,13,playing harp,train ilz5nOX7vV0,106,beat boxing,train ilzA_NxxGZA,360,"race car, auto racing",train im-KxmkghO0,450,"female speech, woman speaking",train im2xedMFZdQ,6,people burping,train im33PxE-mYU,5,mosquito buzzing,train im3linVIcSs,30,playing flute,train im4gQvLscm0,50,car engine knocking,train im5VmBjvEp0,14,barn swallow calling,train im5VmBjvEp0,51,barn swallow calling,train im98W-KlqrQ,10,bird wings flapping,test imBC2uql1ok,30,playing hammond organ,train imCZXQBgqfU,141,playing cornet,train imCZXQBgqfU,184,playing cornet,train imEAf0HPA5I,190,playing banjo,train imGhU5icVDg,170,ocean burbling,train imKn02Zk958,480,"race car, auto racing",train imKnvlQEY1E,41,playing shofar,train imKnvlQEY1E,52,playing shofar,train imLAZfWj_ug,155,bathroom ventilation fan running,train imO0K_8ZkUA,209,"electric shaver, electric razor shaving",train imPCuCbUSgk,80,splashing water,train imQBmHaqii0,30,people whistling,train imSlWafkXi4,1,wood thrush calling,train imSlWafkXi4,61,wood thrush calling,train imSnOLLDJj8,10,driving motorcycle,train imVNg9j7rvU,85,playing table tennis,train imW8jOAperU,19,playing cymbal,train imWBF3jXFhU,470,playing synthesizer,train im_t2KbYX8M,10,helicopter,train imaWlPW-Oe8,30,chainsawing trees,train imaqQjyxExM,21,slot machine,train imcK9G6McI4,246,playing table tennis,train imcKyN3AflI,83,playing timpani,train imcKyN3AflI,177,playing timpani,train imfimq2hHXE,3,sheep bleating,train imfimq2hHXE,13,sheep bleating,train imhLfPNduNg,150,playing trumpet,train iml7D1XsMe8,10,playing electric guitar,train imm1dI4Xg9g,12,rope skipping,train imo0-muPdy0,30,cat purring,test imo37WpIRaM,30,singing choir,train imoc85nxbCc,368,lighting firecrackers,train imoc85nxbCc,438,lighting firecrackers,train imsxRhbuwfI,5,driving motorcycle,train imtci9Ud5Q8,43,playing mandolin,train imtci9Ud5Q8,118,playing mandolin,train imueBzMdGH4,32,playing castanets,train imxRdTkEh30,157,lions roaring,train in1xuvOCYUg,0,pheasant crowing,train in1xuvOCYUg,21,pheasant crowing,train in34xQXdiPA,280,cattle mooing,test in3i3xxJgD0,320,playing harp,train in6iQB0cW6Y,29,driving buses,train in6yD9cMxz0,0,playing timpani,test in8NNzwFa-s,96,people humming,test inAGPLejHUQ,539,strike lighter,test inAdgQNSCBI,106,"alligators, crocodiles hissing",train inAdgQNSCBI,258,"alligators, crocodiles hissing",train inDGZmbsZW4,3,playing djembe,train inDGZmbsZW4,16,playing djembe,train inDZgkJxPOY,69,playing snare drum,train inDZgkJxPOY,90,playing snare drum,train inEb8oN1hls,50,singing choir,train inEx29XDLE8,71,tractor digging,train inFgvE2PFQM,20,dog bow-wow,train inJSb15tTc0,30,orchestra,train inK-u0Elgx4,10,chicken crowing,train inQRYMeva2k,285,parrot talking,train inRKgjIVxgY,0,arc welding,train inU2StYzOdg,0,playing ukulele,train inU2StYzOdg,153,playing ukulele,train inaB8VTs13E,186,playing erhu,train inbjXJpB7UQ,23,playing vibraphone,train inbjXJpB7UQ,158,playing vibraphone,train inbq6rkW5-M,5,"fly, housefly buzzing",test inbz7sL_St8,280,playing clarinet,train incCY12s0Kc,3,dog growling,train incba3jU7sg,0,playing electronic organ,train incfoBByeok,42,playing accordion,train ineYcKcfmJQ,31,playing timpani,train ineYcKcfmJQ,80,playing timpani,train ineaiD8MHs0,15,church bell ringing,train iniaMkJ7-UA,260,singing choir,train injo3XS5eJg,0,car engine knocking,train injo3XS5eJg,10,car engine knocking,train inkJk79d7yg,10,baby laughter,train inkJk79d7yg,52,baby laughter,train inmc0k7Q-g8,24,playing gong,train inmc0k7Q-g8,75,playing gong,train inofm2HUyUE,30,lawn mowing,train inpnJ5drG8c,90,toilet flushing,train insoe8pmNhM,0,"bird chirping, tweeting",train intZNoJUF-E,190,fireworks banging,train inuub0TOp0Q,660,lighting firecrackers,train inuub0TOp0Q,673,lighting firecrackers,train inwbva8svYA,30,playing electric guitar,train io-1vaZW9RI,10,people whistling,train io-67uJ6BoI,24,hail,train io04Saz73Q0,169,cat growling,train io04Saz73Q0,227,cat growling,train io2HiajFlps,30,goose honking,train io2ZMAWqkeQ,10,lions growling,test io2ppax3UPI,20,printer printing,train io3WpjWTMc8,19,zebra braying,test io4MBcSoMvY,15,air conditioning noise,train io67Pd0ZAeU,212,sharpen knife,train io82JT7bZbs,109,playing volleyball,train io94WhQJZ5k,95,playing banjo,train io9HqH69mR4,87,hail,train io9HqH69mR4,98,hail,train ioBOW6ynH28,10,playing cello,test ioDAVNkgY5I,50,"child speech, kid speaking",train ioEf5F_ugHc,2,cricket chirping,train ioF9TeiOzkM,258,playing table tennis,train ioF9TeiOzkM,432,playing table tennis,train ioFHSGihTBs,220,plastic bottle crushing,train ioGdL9E9BvQ,8,yodelling,train ioH2zmdxER4,5,female singing,train ioHfi2Vqe4k,10,driving motorcycle,train ioHvJCcblz0,59,cat growling,train ioHvJCcblz0,82,cat growling,train ioJyHLUr1ro,30,cat purring,train ioKcaLG7y_A,30,car engine knocking,test ioL0YATWkBc,383,ripping paper,train ioL0YATWkBc,705,ripping paper,train ioMM0NuBG_w,526,playing bassoon,train ioMM0NuBG_w,547,playing bassoon,train ioMlfh9azX0,40,footsteps on snow,train ioOOWGqe_LA,372,planing timber,train ioOwfGf0KZ8,347,wood thrush calling,train ioOwfGf0KZ8,517,wood thrush calling,train ioPmtO-PJcI,30,"subway, metro, underground",train ioQdfK6Ae9k,0,playing djembe,test ioQdfK6Ae9k,10,playing djembe,test ioTfSjloids,12,people booing,train ioTmuyoOeV8,18,squishing water,test ioU4Eh9EsrM,30,driving buses,train ioULyFQT0Rw,120,people eating noodle,train ioUmOfcA_Lw,85,people booing,train ioVAbpKCDxI,476,child singing,train ioaYipDMEXI,0,volcano explosion,train iobl3n3QevY,20,dog barking,train iofz3mKt2AA,25,playing didgeridoo,train iog6SAy5whk,80,opening or closing car doors,train ioi0fU8Xsso,11,bull bellowing,train ioj6AP00MaM,310,playing timpani,test iojt4cbNovM,105,airplane,train iokra0y__ck,13,wind chime,train iolWQv3_Y44,82,yodelling,train iolWQv3_Y44,125,yodelling,train ionclXVrPu4,330,eating with cutlery,train ioo3vA8dvZE,192,cap gun shooting,train ioooDiS8MiQ,10,playing banjo,train ior1xeHJ4Uc,334,tapping guitar,train ior1xeHJ4Uc,363,tapping guitar,train iosDKvAHUCI,12,"ice cream truck, ice cream van",test iotIxAR-eH8,240,bird squawking,test ioxpOCpeGoY,0,people clapping,train ioyVklTZVcw,94,playing steelpan,train ioyVklTZVcw,248,playing steelpan,train iozjWOjyjyw,147,planing timber,train iozjWOjyjyw,158,planing timber,train ip0JOk0hfvs,3,disc scratching,train ip58MhELJ7c,131,playing squash,train ip5ar6sQVNs,20,car engine starting,train ip5lXT8DtS0,176,playing clarinet,train ip6sz3xorFo,10,people farting,test ip7YVEb2fPM,135,playing badminton,train ip9RgNs6tBo,110,people whistling,train ip9gUcLWFr0,1,"engine accelerating, revving, vroom",train ipIVURrJYXA,0,playing harmonica,train ipIVURrJYXA,57,playing harmonica,train ipJABc4oXSo,20,chicken crowing,train ipJsMv8cFjY,130,playing harpsichord,train ipKCHDizaQI,30,people sneezing,test ipLJB7xPA4Q,43,arc welding,train ipMyrDBjkxo,65,air conditioning noise,train ipO62mI7oe4,33,playing accordion,train ipPaK8tCnXw,1,people eating crisps,train ipPm7WJa0us,510,playing clarinet,train ipQEbg1ORDU,29,playing sitar,train ipSnR0fR_uA,44,pheasant crowing,test ipSnR0fR_uA,58,pheasant crowing,test ipUI_bwfNgM,165,typing on typewriter,train ipUI_bwfNgM,180,typing on typewriter,train ipVIZpSG2Ss,88,slot machine,train ipVVIkve0m4,41,hammering nails,train ipVVIkve0m4,55,hammering nails,train ipVs-QHEdyE,8,cuckoo bird calling,train ipVs-QHEdyE,19,cuckoo bird calling,train ipYHAFFd4NM,0,bull bellowing,train ipYHAFFd4NM,43,bull bellowing,train ipZazak-Lxw,30,sheep bleating,train ip_n3wIF0Q0,206,arc welding,train ipd449yLlfs,0,playing harp,train ipd449yLlfs,55,playing harp,train ipdIiXHbU88,131,playing saxophone,train ipdIiXHbU88,161,playing saxophone,train ipdJ-BZVYaA,40,skidding,train ipfmCUYrto8,10,people screaming,train ipfu-qxU0G8,60,chainsawing trees,train iphJ27WkYF0,334,yodelling,train ipifwjZeolE,20,"engine accelerating, revving, vroom",train ipjKpmNCSIs,60,people clapping,train ipjVGsysc5I,70,"subway, metro, underground",train ipjaZ5J8HSU,30,driving buses,train ipjzbJtdebs,0,horse neighing,train iplU8Q0HZI0,160,cap gun shooting,train ipo5U5Grsno,20,people cheering,train iposG4-jOM4,30,playing hammond organ,train ippY5JBmULQ,24,roller coaster running,train ippY5JBmULQ,104,roller coaster running,train ippcVEkspfk,304,striking pool,train ippcVEkspfk,316,striking pool,train ipsrdjA1tZo,130,skidding,train ipvR3bWESRs,190,playing saxophone,train ipvbEhJCzks,10,ocean burbling,train ipxCuhWJ3oA,3,chinchilla barking,test ipx_reb1Kfc,0,baby babbling,test ipxaxb8LQ8o,487,squishing water,train ipxaxb8LQ8o,534,squishing water,train ipybT8elX30,210,people crowd,train iq0icr5inDE,30,"playing violin, fiddle",train iq1JnoLt_N4,76,lathe spinning,train iq1JnoLt_N4,119,lathe spinning,train iq2OSXWlIag,116,playing table tennis,train iq2U_8cBUOM,9,playing squash,train iq5Lh7AB1Ns,19,playing glockenspiel,test iq9XDOhXJ_k,18,owl hooting,train iqB8lcLGDB4,1,car engine knocking,train iqB8lcLGDB4,47,car engine knocking,train iqBB0MK6Mi4,0,thunder,train iqDnYvNGkFM,100,driving buses,test iqJAqRB-N0M,30,lions growling,train iqJYD7gzsHY,510,car passing by,train iqKZ8YaQA2s,140,"child speech, kid speaking",train iqLKbk08-3w,74,playing bongo,train iqLKbk08-3w,228,playing bongo,train iqOA2P1OHZg,137,playing mandolin,train iqPfHp8a87I,17,underwater bubbling,train iqPfHp8a87I,27,underwater bubbling,train iqSzx9Csmi8,2,smoke detector beeping,test iqVOPmhQiDY,101,playing volleyball,train iqWZOwaECdE,30,sheep bleating,train iqbiV-ttlA8,40,typing on computer keyboard,train iqdKq67xkKQ,120,dog baying,train iqdocD4Aqo4,10,toilet flushing,train iqendqTkb9w,46,beat boxing,train iqj50ztI-mg,24,playing french horn,train iqj50ztI-mg,48,playing french horn,train iqr3a2__kdU,6,mouse clicking,train iqr3a2__kdU,22,mouse clicking,train iqx7rZgDC4k,50,people crowd,train iqy8xobVjwE,8,chicken crowing,train ir-3f6P7k8Y,70,playing electric guitar,train ir-BpjFj4ZQ,20,fireworks banging,train ir-oBeMltLY,260,playing gong,test ir0fpcOOCms,519,hair dryer drying,train ir0fpcOOCms,703,hair dryer drying,train ir2FGL_Hx4Q,30,printer printing,train ir2Ks23vIXU,121,playing hockey,train ir3ipI5k1kI,30,chainsawing trees,train ir5BB3_pY2k,52,playing hockey,train ir5BB3_pY2k,102,playing hockey,train ir5W2E2Dieg,140,eating with cutlery,train ir90aX-XOeo,4,dog growling,train ir9ICch6GTo,62,volcano explosion,train irBKwy3NcFI,106,playing didgeridoo,train irC7RX1XlXU,160,playing harp,train irFRNJMB3UQ,74,playing volleyball,train irFbwedgv_8,0,playing bassoon,train irHVR7HFtn4,34,"heart sounds, heartbeat",test irIm5KYixHg,88,beat boxing,train irLYadwQThQ,23,playing volleyball,train irN1qoE3-uQ,4,baby babbling,train irO6I_5ioQE,27,playing harp,train irOhHVL146s,40,playing bass guitar,train irQJm4HoChA,86,playing lacrosse,train irQsMzIx6-A,30,wind noise,train irRzA6Ee8XQ,0,car engine knocking,train irRzA6Ee8XQ,10,car engine knocking,train irSWl1estNM,131,arc welding,train irT_u5UDIxo,30,"bird chirping, tweeting",train irUkV1DP7Cs,30,playing cello,train irVIUvDTTB0,49,rapping,train irVIUvDTTB0,235,rapping,train irVnSDULRNQ,240,bathroom ventilation fan running,train irWVkRLG-Dk,160,playing synthesizer,train irWfYY8aKb4,15,playing darts,train irWfYY8aKb4,145,playing darts,train irXW4VuPu_c,30,people sniggering,train irYyUBdxYq0,6,playing tambourine,train ir_lVCz-hKs,80,"bee, wasp, etc. buzzing",train iraFMFO7KB0,143,black capped chickadee calling,train iraFMFO7KB0,156,black capped chickadee calling,train iramP9ihj_w,200,chicken crowing,test irapaW4ABPE,32,pumping water,train irapaW4ABPE,43,pumping water,train irevtBHmuT8,6,playing bassoon,train irf06TA7m4k,189,playing cornet,train irf9Z-k7dFM,183,playing vibraphone,train irf9Z-k7dFM,208,playing vibraphone,train irfgx1O9twM,25,hedge trimmer running,train irhsdhRIUwI,10,fireworks banging,train irjyQoXw-Lk,190,playing saxophone,train irk-Frn0a54,40,chopping food,test irn1M3ZIYFY,60,playing glockenspiel,train irwCFJewlYY,10,train horning,train iryMredYzj8,140,people screaming,train is02lZjvVsg,103,arc welding,train is02lZjvVsg,173,arc welding,train is1sBotAnqA,40,sailing,train is1zUPpxNno,70,playing lacrosse,test is2UZ3IxXk0,81,playing erhu,train is2UZ3IxXk0,93,playing erhu,train is2cP57W4EQ,100,"race car, auto racing",train is2gWcJIMek,47,airplane,train is2gWcJIMek,212,airplane,train is4FxgL_kiM,20,playing piano,train is68rlOzEio,43,playing flute,train is6hIqHBndU,30,wind noise,train is73kRaH_m0,49,spraying water,train is7BYzVQnfs,20,people farting,train is7dPshLAtE,60,toilet flushing,train is7iUfqD5FE,110,people coughing,train is8EC4o-N8M,2,horse neighing,train is9Cl94qW-U,26,hail,train is9Cl94qW-U,53,hail,train is9OWb982uE,2,horse neighing,train is9i9uA7dsg,118,"alligators, crocodiles hissing",train isCIJS0cH3o,122,cat hissing,train isHE1NZwgAA,21,canary calling,train isHE1NZwgAA,40,canary calling,train isI7ayfoyLQ,50,black capped chickadee calling,train isI7ayfoyLQ,60,black capped chickadee calling,train isNvzMhpdJc,32,bouncing on trampoline,train isNvzMhpdJc,53,bouncing on trampoline,train isUCIXYjOXE,18,striking pool,train isZ-1wPVNP4,127,chinchilla barking,train isf7h2nGFGc,48,air conditioning noise,train isipqYSJGQ4,345,singing bowl,train ismpeZIzpTc,41,playing synthesizer,train ismpeZIzpTc,84,playing synthesizer,train isqkQX9EtXc,60,wind noise,train issN5Qxi0og,45,playing erhu,train istiXxCOR54,1,turkey gobbling,train iswfKoOb1fY,51,playing squash,train iswsP1025PM,51,sharpen knife,train iszPpQxUWJo,260,singing bowl,train iszfu4NKxaE,0,spraying water,train iszfu4NKxaE,253,spraying water,train it0GQPiGzcU,290,tap dancing,train it0U4Vscl5o,208,people slurping,test it3HTmm1qNg,67,playing bagpipes,train it3R0pOwS8Q,5,fire crackling,train it3R0pOwS8Q,30,fire crackling,train it4yWzrzMJ4,29,"playing violin, fiddle",train it54F1z2Fc4,30,mouse pattering,train it68H2Pm-8Q,30,"engine accelerating, revving, vroom",train it6ZiU61lCI,150,people cheering,train it8_3gt0uUo,2,people finger snapping,test it8alDfVrmk,71,arc welding,train it8alDfVrmk,94,arc welding,train itAOGRiYRLI,0,playing snare drum,test itAx5zRDf9g,54,sharpen knife,train itBivMiYlhM,30,people sniggering,train itDV0L9FE1g,22,police radio chatter,train itDV0L9FE1g,49,police radio chatter,train itGMDjiSJDI,4,playing tambourine,train itGMDjiSJDI,146,playing tambourine,train itH-fbb9Ook,250,ambulance siren,train itLh_yWsOX0,2521,playing harpsichord,train itLh_yWsOX0,2533,playing harpsichord,train itLupzULR6A,280,beat boxing,train itM_gBN_PCo,30,playing flute,train itTQe0xQUyw,7,elephant trumpeting,train itTQe0xQUyw,19,elephant trumpeting,train itTdhYKSINs,10,fireworks banging,train itW6dQFRD7I,114,playing volleyball,train itXT9PriVx8,40,playing flute,train itXfA2ObL_Y,111,playing erhu,train itY5L_Wi7h0,125,playing erhu,train it_00sAaUtI,38,playing bassoon,train it_FODMv-g4,30,playing cello,train it_HQtAkops,130,skidding,train itcl4WATij8,2,people babbling,train itcl4WATij8,45,people babbling,train itiMF-W5vN8,160,people whispering,train itiT0I-6YZs,15,train horning,train itiT0I-6YZs,25,train horning,train itj7uPCWRD4,280,playing tympani,train itjMCAhhTMA,32,lathe spinning,train itqUFDR5gsc,190,"playing marimba, xylophone",train itqeu3duYtY,70,playing cymbal,train its_qTxf1nY,78,playing volleyball,train ituIlDoHB6I,60,male singing,train ituWrX-XTas,4,dog whimpering,train ituznB1Y3H4,53,chicken crowing,train ityBhBX136s,74,playing harmonica,train ityBhBX136s,102,playing harmonica,train iu-98K5QwyM,0,playing didgeridoo,train iu-98K5QwyM,106,playing didgeridoo,train iu-yVH5JPw0,0,playing trumpet,train iu0krT-9JmY,8,playing theremin,train iu0krT-9JmY,18,playing theremin,train iu1hNlQ6ZDU,125,forging swords,train iu1hNlQ6ZDU,342,forging swords,train iu23lGWo1Fo,30,playing drum kit,train iu4Q9PQcc08,30,female singing,train iu6PYugufTQ,60,playing electronic organ,train iu9l1V_2ID0,10,singing bowl,train iuBOgKOB6Wc,293,playing badminton,train iuCNian_-H8,0,playing didgeridoo,train iuCNian_-H8,84,playing didgeridoo,train iuDRw9hIe6U,30,playing drum kit,train iuDyN5z1Kzk,45,playing theremin,train iuDyN5z1Kzk,68,playing theremin,train iuEgA98cn_8,0,"playing steel guitar, slide guitar",train iuF6kF5yuXk,30,skateboarding,train iuFGMiUE-uI,12,people cheering,test iuFiONKXfUw,220,"motorboat, speedboat acceleration",train iuHG1hALYQk,1,playing squash,test iuIqwNM12PU,110,playing flute,train iuJLFkoDtJA,30,playing snare drum,train iuJRviEqWWU,139,missile launch,train iuJRviEqWWU,152,missile launch,train iuKp72_fPus,122,playing djembe,train iuKp72_fPus,145,playing djembe,train iuRGURTgqjU,90,people sneezing,train iuRqQWqwfqg,36,writing on blackboard with chalk,train iuRqQWqwfqg,54,writing on blackboard with chalk,train iuTfmOekZ0k,14,lions roaring,train iuTfmOekZ0k,26,lions roaring,train iuUDC7-ZiaA,18,playing erhu,train iuUDC7-ZiaA,282,playing erhu,train iuXb1ckkpwE,246,"fly, housefly buzzing",test iuY0TjxILx4,70,"female speech, woman speaking",train iuYp5_f4J4o,20,toilet flushing,train iua8SG7ZLfU,3,cricket chirping,train iugl0iQwPe8,0,"electric shaver, electric razor shaving",train iugl0iQwPe8,10,"electric shaver, electric razor shaving",train iukBDVKJsYE,254,children shouting,train iul0rfq-c5g,0,"child speech, kid speaking",train iulm_UtK1mo,30,"electric shaver, electric razor shaving",train iuozNmF5TcI,37,hammering nails,train iuozNmF5TcI,52,hammering nails,train ius0G0BIzVg,1,scuba diving,train iuuICKDReI4,4,playing badminton,train iuudC21ScB4,40,"race car, auto racing",train iuvsrxg9_pc,163,firing muskets,train iuvsrxg9_pc,244,firing muskets,train iuwf5R9TiPQ,80,male singing,train iuy2cZTagCQ,0,snake rattling,train iuyWD_kEEtY,11,people whistling,train iuyZbAs8O08,30,"rowboat, canoe, kayak rowing",train iuywbu1d5P8,6,slot machine,train iuz-Be2vWFs,25,playing shofar,train iuz-Be2vWFs,51,playing shofar,train iv0JCJyS5P0,48,playing ukulele,train iv577wPxY18,153,playing vibraphone,train iv577wPxY18,217,playing vibraphone,train iv63T_fGAds,0,frog croaking,train iv9Fhl14iwM,45,playing erhu,train iv9Fhl14iwM,167,playing erhu,train ivAw6QGOR7Q,179,"electric shaver, electric razor shaving",train ivBMsZ9h3x4,30,car engine knocking,train ivBpNGwvrpg,6,"ice cream truck, ice cream van",train ivCGWGvceuw,8,"subway, metro, underground",train ivCGWGvceuw,18,"subway, metro, underground",train ivJmbhteIKE,28,airplane,test ivKeAXf2H_g,11,playing guiro,test ivL2Iuzqb7o,50,playing banjo,train ivO2J4Cx-sw,249,black capped chickadee calling,train ivO2J4Cx-sw,347,black capped chickadee calling,train ivO3uWmRTa8,326,sea waves,test ivONn_ECYwQ,147,playing hammond organ,train ivTiwPkLWQM,90,francolin calling,test ivWvv3G2YdI,86,playing bongo,train ivX_dCgAgmY,492,playing washboard,train ivZnUkicVkU,93,dog howling,train ivZnUkicVkU,149,dog howling,train ivbdRpKd8zQ,30,people finger snapping,train ive4rDgFU_k,80,playing banjo,train ivfPMdM_9oE,20,playing table tennis,train ivftkMSwp5A,60,people sneezing,train ivjfP3SLxgE,30,ambulance siren,train ivlCwlkle3A,30,orchestra,train ivmTP1YxO4k,328,playing drum kit,test ivmkvpNBRqs,180,"bird chirping, tweeting",train ivn383o8m7A,0,playing harmonica,train ivn383o8m7A,98,playing harmonica,train ivnVXdPL5vk,177,"electric shaver, electric razor shaving",train ivnw8o6g07k,45,ambulance siren,train ivpIIn2PlSs,375,"vehicle horn, car horn, honking",train ivq5YlypcLw,270,playing cello,train ivqGiMpjT14,72,playing washboard,train ivrp8Nlp97I,32,playing lacrosse,test ivuAO95MJSc,60,spraying water,test ivvMaVfs0BM,2,dog howling,train ivyw1MwLaUg,30,playing bass drum,train ivzxvL2yasI,98,people farting,train iw-UUHSpBpw,36,playing guiro,train iw-UUHSpBpw,75,playing guiro,train iw-dr_2s9wA,17,beat boxing,train iw0NuCdxC4k,5,people slapping,train iw2FBnTeeUw,49,parrot talking,train iw2FBnTeeUw,209,parrot talking,train iw6LZzIBIts,13,cattle 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j17CUVoONsQ,34,whale calling,test j186aIFLE9E,290,playing saxophone,train j1Bt0_YqGMw,40,police car (siren),train j1DOCjw8sTQ,91,playing saxophone,train j1GerCyGENw,400,orchestra,train j1JKgVWm_gA,130,playing harp,train j1JUsTMZcuY,4,people giggling,test j1JUsTMZcuY,42,people giggling,test j1JtHfcLG14,120,people coughing,train j1McfbLS5R8,85,"ice cream truck, ice cream van",train j1Nd-G5ZLW8,24,gibbon howling,train j1Nd-G5ZLW8,127,gibbon howling,train j1NgLtsilag,10,cap gun shooting,train j1PhBcmXG2Y,580,playing synthesizer,train j1PrEuMq-_M,0,typing on typewriter,test j1UoqruVDT4,7,planing timber,train j1VCugNeomE,20,orchestra,train j1VIxzZdkzQ,208,playing volleyball,train j1W0GAaRe2w,18,elk bugling,train j1W0GAaRe2w,29,elk bugling,train j1XRFRcgdVU,30,car engine knocking,train j1ZofUM27gM,26,chipmunk chirping,train j1bWFZ4g-uU,47,singing bowl,train j1d0rmakDN4,361,mouse clicking,test j1d9XSqzyA0,120,playing accordion,train j1fDOCuqAlE,0,"vehicle horn, car horn, honking",train j1g4fhdhXXs,60,using sewing machines,train j1gPu_osPN0,70,ambulance siren,train j1ihOd2CGDk,30,dog barking,train j1kM-hC44Ok,2,squishing water,test j1lvqo2enAM,2,crow cawing,train j1n4qEYYuOc,108,bouncing on trampoline,train j1oAe49f6uc,19,chipmunk chirping,train j1qY1Dy6odI,53,"railroad car, train wagon",train j1ro7QUID-w,100,playing trumpet,train j1tH0A4UgeQ,78,people eating noodle,train j1wY3xZ1CVg,251,"electric shaver, electric razor shaving",train j1xCOkTfneI,176,electric grinder grinding,train j1xMc-c6UKE,160,"child speech, kid speaking",train j1z57tdPQyA,43,lathe spinning,train j1z57tdPQyA,274,lathe spinning,train j20pULM1-x4,69,tapping guitar,train j20pULM1-x4,79,tapping guitar,train j21rQvDd4K0,70,people clapping,train j233WrRShRY,30,lighting firecrackers,test j23Ak3xk5zk,2,yodelling,train j23Ak3xk5zk,66,yodelling,train j23E2bk7rGo,1,coyote howling,train j23E2bk7rGo,53,coyote howling,train j25k6xzhpl8,1,running electric fan,train j25k6xzhpl8,11,running electric fan,train j27aVT9equ0,14,frog croaking,train j27aVT9equ0,87,frog croaking,train j2AFqijZgq4,173,airplane,train j2AFqijZgq4,213,airplane,train j2Bg2skmC4A,5,civil defense siren,train j2Bg2skmC4A,19,civil defense siren,train j2EOdcxmIyU,87,playing snare drum,train j2FFSM09lLg,34,golf driving,train j2FFSM09lLg,102,golf driving,train j2FVrUNvtsg,570,"playing marimba, xylophone",train j2FaxiRXFjI,10,machine gun shooting,train j2FpwYSQvQo,446,scuba diving,train j2FpwYSQvQo,582,scuba diving,train j2GOki5I9_I,179,yodelling,train j2JoL0Bmx4M,13,people sneezing,train j2JoL0Bmx4M,24,people sneezing,train j2Kb0mdm7nE,12,"vehicle horn, car horn, honking",train j2Kb0mdm7nE,200,"vehicle horn, car horn, honking",train j2LlvzRp1a0,390,"railroad car, train wagon",train j2NMKlNZE9M,258,"electric shaver, electric razor shaving",train j2OhKQ6sm0o,66,people eating noodle,train j2OhKQ6sm0o,77,people eating noodle,train j2PKAEaLTh8,210,church bell ringing,train j2Sc4FYNJc8,30,people belly laughing,train j2YiBd7P2Y4,30,playing banjo,train j2YqidDJ1gA,32,lions roaring,train j2YqidDJ1gA,66,lions roaring,train j2_M3jd_i-s,227,fire crackling,train j2_M3jd_i-s,247,fire crackling,train j2exNc7fC38,20,playing flute,train j2fSnxTqXXQ,85,playing trumpet,train j2fmvgr4od0,0,playing banjo,train j2haTwD3zC4,4,cat caterwauling,train j2haTwD3zC4,47,cat caterwauling,train j2io9De3dLY,160,playing vibraphone,train j2lfi6eR51w,26,playing tambourine,train j2lfi6eR51w,45,playing tambourine,train j2lg4WxyUi4,220,"playing violin, fiddle",train j2nTl3mtd60,270,"railroad car, train wagon",train j2oAnGKHYK8,130,typing on computer keyboard,train j2rVFBPE6Q0,210,playing bassoon,train j2s5jWTmFB4,70,driving buses,train j2sLyZGlGJc,125,playing theremin,train j2sLyZGlGJc,144,playing theremin,train j2t2kpJ5nrY,48,playing bassoon,train j2tDH-Tshr0,165,people burping,train j2u7bQ5BbAc,1,chicken clucking,train j2xMKV0MZfM,70,driving motorcycle,train j2zrJONUyds,400,playing clarinet,train j32gZWkBHUw,0,playing 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jan1zTwfwfY,16,chopping wood,train jan6ID-B7Xs,90,"bird chirping, tweeting",train janLE0vSsCs,136,playing mandolin,train janLE0vSsCs,174,playing mandolin,train janR5dzeg_g,40,toilet flushing,train janfyzt4Qyk,50,fire truck siren,train jaoinIuEwko,30,car engine knocking,train jaoinIuEwko,87,car engine knocking,train jarc5U7yQCY,273,skiing,train jas53wmLSNU,188,arc welding,test jaxaWcqBmQI,197,ferret dooking,train jb20hKUYHYo,13,chicken crowing,train jb28u-YoO-Q,84,skiing,train jb385WrfZJ0,130,playing hammond organ,train jb3vjhlybhE,17,police radio chatter,test jb4ePVcdHGk,200,driving buses,train jb6N8wAYDSo,4,mynah bird singing,train jb6N8wAYDSo,21,mynah bird singing,train jb7eDNGJEkw,262,playing badminton,train jb8cgOMd_nI,150,orchestra,train jb92NmGYNbU,279,police radio chatter,train jb92NmGYNbU,444,police radio chatter,train jbAGrf7BPMA,49,baby crying,train jbAsOVsXqX0,169,playing hockey,train jbAsOVsXqX0,279,playing hockey,train jbB9WuJhXik,30,people whistling,train jbEYZDagytA,30,people sobbing,test jbFTmpM4kMU,93,playing table tennis,train jbGm3ZIIYqw,77,reversing beeps,train jbH4PifIg-g,6,playing tambourine,test jbIGuzaaex4,187,fire truck siren,train jbIGuzaaex4,205,fire truck siren,train jbKjdfEE1HE,216,singing bowl,train jbLQYEpVXEw,35,magpie calling,train jbLQYEpVXEw,45,magpie calling,train jbMkptTaVa0,246,playing clarinet,train jbOON-96xlw,24,sharpen knife,train jbQWvSwZaUE,460,horse neighing,train jbSw-uLTd6U,732,playing badminton,train jbThONNvPUQ,110,playing french horn,train jbVBpTeK53M,140,bird squawking,test jbV_oqXRtOo,258,people eating crisps,train jbV_oqXRtOo,533,people eating crisps,train jbW3gjzpFLI,60,playing drum kit,train jbWmLV7LLPU,29,playing harmonica,train jbWmLV7LLPU,80,playing harmonica,train jb_Q8UJXgPo,99,"electric shaver, electric razor shaving",train jb_Q8UJXgPo,114,"electric shaver, electric razor shaving",train jb_XoDKYVMY,30,duck quacking,train jbaZyROpitQ,10,people hiccup,test jbb3qbxzrLQ,137,missile launch,train jbb3qbxzrLQ,153,missile launch,train jbbKwsvDQZY,360,"race car, auto racing",train jbfeFGnTb5I,21,"vehicle horn, car horn, honking",train jbffNajOX7M,19,chimpanzee pant-hooting,test jbgqboYhJag,480,basketball bounce,train jbkGb99g7Fc,136,"donkey, ass braying",train jbkehrDlSV8,71,squishing water,train jbkehrDlSV8,105,squishing water,train jbkgV6Fx2yI,98,playing accordion,train jbktMs1lxrY,24,tap dancing,train jbp_3YyM66s,15,"heart sounds, heartbeat",train jbp_3YyM66s,47,"heart sounds, heartbeat",train jbqDy1340rk,1,duck quacking,train jbqDy1340rk,45,duck quacking,train jbrZ0Yj8EHM,30,"engine accelerating, revving, vroom",train jbsXmX-GIFU,17,elephant trumpeting,train jbsXmX-GIFU,27,elephant trumpeting,train jbub_6bAPlk,150,"bird chirping, tweeting",train jbui-QBAbTw,360,playing drum kit,train jbv0pgsOkxc,3,dinosaurs bellowing,train jbv0pgsOkxc,136,dinosaurs bellowing,train jbx-LzgrhZk,108,playing tabla,train jc1CQQpJY7Q,278,whale calling,test jc4_k0x6o9Q,30,"motorboat, speedboat 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jjD3bDnaTdY,10,people giggling,train jjD505NAVpw,0,typing on computer keyboard,train jjDLQTx27HU,35,civil defense siren,train jjEfz072T5U,380,driving buses,train jjJSVMREql8,25,cattle mooing,train jjJUL748_cI,126,chicken crowing,train jjL70W4FhCc,200,wind noise,train jjOm5_DTkQ8,288,sharpen knife,train jjRv-GJP4WQ,0,chainsawing trees,train jjVtf8aEPRA,14,playing tabla,train jjVtf8aEPRA,130,playing tabla,train jj_DQxq6AOA,63,mynah bird singing,train jj_DQxq6AOA,249,mynah bird singing,train jjbZ3R3Sp-E,4,gibbon howling,train jjbZ3R3Sp-E,19,gibbon howling,train jjbpboFhhm8,0,playing zither,train jjbpboFhhm8,50,playing zither,train jjcEhwfAhFU,60,cattle mooing,train jjdFRiGFvYo,77,vacuum cleaner cleaning floors,train jjdFRiGFvYo,89,vacuum cleaner cleaning floors,train jjdWRzkoiUE,30,duck quacking,train jjdv54-GdLQ,46,tapping guitar,train jjdv54-GdLQ,73,tapping guitar,train jjfAXVf8e4M,60,basketball bounce,train jjkB91FBcBI,119,"fly, housefly buzzing",test jjmPOFfh5PM,177,slot machine,train 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k03to4tKkAU,102,cat hissing,train k03to4tKkAU,126,cat hissing,train k04bQSznqk4,173,playing badminton,train k06rTkPCOGI,30,playing snare drum,train k07v2Rm0EVA,320,typing on typewriter,train k07v2Rm0EVA,331,typing on typewriter,train k0DKJ2NvqwE,128,"heart sounds, heartbeat",train k0DKJ2NvqwE,212,"heart sounds, heartbeat",train k0FKVp8Jsnk,588,underwater bubbling,train k0FKVp8Jsnk,949,underwater bubbling,train k0FSHGo0TB4,0,slot machine,train k0F_Z8quZ98,940,playing hockey,test k0HTHhaZo20,20,cattle mooing,train k0IKk-52Dek,160,playing banjo,train k0LK8irJeEg,129,ice cracking,test k0MciO71Ozs,173,playing cymbal,train k0Ut6k1F1f4,20,machine gun shooting,train k0VT4cSm4n8,560,people sniggering,train k0W7fAW7Tek,10,people crowd,train k0WHAxSf8wo,62,civil defense siren,train k0XnfNXYyO4,30,driving motorcycle,train k0YT5ZNdXj0,280,roller coaster running,test k0Z95PrMVOI,64,mynah bird singing,test k0_TJ-FStfU,36,car engine knocking,train k0bmHwRYJ-w,220,playing harp,train k0cCIoPYbGA,9,dog 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ljQ1uYYhtxM,27,telephone bell ringing,test ljQ1uYYhtxM,164,telephone bell ringing,test ljRfeBTwa0M,3,goose honking,train ljSTN1wFSm4,0,people clapping,train ljTH0trGEhU,37,mouse clicking,train ljTH0trGEhU,51,mouse clicking,train ljTNG7lbHIc,30,people babbling,train ljTr5Kemgmw,15,beat boxing,train ljUTopU6c-Q,30,playing bagpipes,train ljUu7ZnlKZA,31,"pigeon, dove cooing",test ljVynCoua2k,59,hail,train ljVynCoua2k,72,hail,train ljXTXoBG9rg,21,pheasant crowing,train ljXTXoBG9rg,77,pheasant crowing,train lj_LLLT_Kt4,0,fire crackling,train lj_LLLT_Kt4,15,fire crackling,train ljeHSBU0imI,26,cow lowing,train ljeHSBU0imI,127,cow lowing,train ljeHtxwNib4,2,baby crying,train ljf5_dpjFMQ,235,playing cornet,train ljf5_dpjFMQ,421,playing cornet,train ljfwx2TddYs,30,playing harpsichord,train ljgATU7mU4Q,13,playing glockenspiel,train ljgATU7mU4Q,39,playing glockenspiel,train ljjEcbgjVEg,20,dog bow-wow,train ljjUj5fQZgs,450,skidding,train ljjWDXhYU9A,18,"electric shaver, electric razor shaving",test 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lm8Q2xGOvbQ,16,playing bugle,test lm8W8jMtQyA,530,playing cello,train lmBVOMmDNRI,30,"rowboat, canoe, kayak rowing",train lmC0W0ko_-Y,51,people cheering,train lmC0W0ko_-Y,122,people cheering,train lmCd83p5bFk,30,"pigeon, dove cooing",train lmEdKY-qYyw,642,"alligators, crocodiles hissing",train lmFxzp45DOc,288,machine gun shooting,train lmH3-uHLxig,6,airplane flyby,train lmH3-uHLxig,59,airplane flyby,train lmHENtHW9O0,59,machine gun shooting,train lmHHdp5G3XQ,27,hair dryer drying,train lmHHdp5G3XQ,397,hair dryer drying,train lmKavdebSxE,151,playing table tennis,train lmOBXLY5kNc,100,helicopter,train lmQYt37NI9U,42,civil defense siren,train lmRmrw53-mk,96,reversing beeps,train lmRmrw53-mk,126,reversing beeps,train lmSgxskAcX8,40,helicopter,train lmSmwPsWvII,395,people finger snapping,train lmSmwPsWvII,454,people finger snapping,train lmSsHDBZWSE,40,"subway, metro, underground",train lmVxGH2WUhU,14,popping popcorn,train lmVxGH2WUhU,31,popping popcorn,train lmXan-o-EaA,58,playing snare 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lnAlyRdc_hE,37,church bell ringing,train lnBBb_YsTYY,70,"subway, metro, underground",train lnE7Ay9_dd0,683,ripping paper,train lnE7Ay9_dd0,711,ripping paper,train lnGe0IotDM0,0,playing bagpipes,train lnGe0IotDM0,26,playing bagpipes,train lnH1aIsIBag,92,playing gong,train lnH1aIsIBag,295,playing gong,train lnHdEtuXU8w,37,hair dryer drying,train lnHmq6OJiUo,18,playing sitar,train lnHmq6OJiUo,46,playing sitar,train lnIpd7ZYaz0,20,using sewing machines,train lnNk2OQ5aq4,30,playing bass guitar,train lnPEUbYqJl8,40,mouse pattering,train lnQXGnBbV0Q,33,tractor digging,train lnWP_zWFpBg,30,playing steelpan,test lnYOC9tKUBs,13,playing theremin,train lnYOC9tKUBs,24,playing theremin,train lnYrARRL43U,40,cap gun shooting,train lnZakZnnpMo,80,people finger snapping,test lnaUeiMlwvk,90,people whispering,train lnatlhCU5kI,384,yodelling,train lnatlhCU5kI,420,singing choir,train lnc8_4DWAbM,290,playing piano,train lncPvAjigS4,12,car engine knocking,train lncujsNmWUo,37,dog howling,train lncujsNmWUo,52,dog howling,train lnd5lHMYiXQ,180,people battle cry,train lnd5lHMYiXQ,194,people battle cry,train lnd7II7YeoQ,14,chainsawing trees,train lndowS7ZYlA,166,playing darts,test lnfqtfD4sfc,131,playing erhu,train lnftHT7-Ez0,160,"railroad car, train wagon",train lnhHid8yZQs,16,wind rustling leaves,train lnjFEkSXm8E,10,playing bagpipes,train lnjFEkSXm8E,216,playing bagpipes,train lnjnubaZnIk,101,playing mandolin,train lnkNRENd2Sk,13,playing trombone,train lnkjT4cLCKM,215,yodelling,train lnkjT4cLCKM,274,yodelling,train lnmPm5vGz34,0,underwater bubbling,train lnmPm5vGz34,11,underwater bubbling,train lnnF0hco4Ds,210,air horn,train lno7uQmaibY,170,lighting firecrackers,train lno7uQmaibY,190,lighting firecrackers,train lnouwu8AszI,22,people battle cry,train lnouwu8AszI,95,people battle cry,train lnrj2GUEI0M,156,cat growling,train lntQthtqTH8,380,police car (siren),train lnugDqGqqu4,3,ambulance siren,train lnwYaorKM7Y,180,skiing,train lnxFNgbVQgM,80,playing banjo,train 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mGWN39ipNvo,1288,wood thrush calling,train mGWhEGSKqD0,126,playing timpani,train mGWhEGSKqD0,284,playing timpani,train mGanQnrDLrE,54,popping popcorn,train mGbzcTD69A8,6,airplane,train mGeDx37m0Mo,30,people shuffling,train mGft_69MPpM,143,people shuffling,train mGgCmLMq60g,90,"subway, metro, underground",train mGhW136QZZM,30,car engine knocking,train mGiBIhDVDbo,85,tap dancing,train mGiMv5M9-ks,8,playing badminton,train mGiaUbQpv4Q,13,opening or closing car electric windows,train mGlPkkvNgrM,79,playing volleyball,train mGlPkkvNgrM,339,playing volleyball,train mGnz5SnArvg,340,playing cello,train mGqokiMZZTU,8,printer printing,train mGsJcB1IhHs,2,cat purring,train mGtTbSLfuNg,30,sheep bleating,train mGz9_Ly4A-0,3,"cattle, bovinae cowbell",train mGz9_Ly4A-0,18,"cattle, bovinae cowbell",train mH0qxsxLypw,80,cat meowing,test mH27QUJwxjA,190,playing piano,train mH4ggWfRYUk,30,playing flute,train mH9hRO-RwJ0,104,playing oboe,train mH9hRO-RwJ0,410,playing oboe,train mHAhfX7iRjs,8,arc 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mH_FF14fE-U,370,driving buses,train mHbEEL9B8NM,0,cat meowing,train mHbEEL9B8NM,11,cat meowing,train mHbsKzezfFQ,55,cutting hair with electric trimmers,train mHbsKzezfFQ,126,cutting hair with electric trimmers,train mHcS_ucQANc,0,underwater bubbling,train mHcS_ucQANc,11,underwater bubbling,train mHcpa2SoFC0,440,basketball bounce,train mHe7UH_smrY,63,slot machine,train mHekWRPMgQQ,420,driving motorcycle,train mHfZ7rFTVUs,9,playing hockey,train mHiFmxbGTwc,8,police car (siren),train mHiOGhxbmdo,30,firing cannon,test mHig-h2K2-w,480,"race car, auto racing",train mHnvikXi1HI,30,baby crying,train mHplrd_f7WA,100,male singing,train mHsnKuUWqFQ,60,playing cello,train mHt1q-jIdA0,30,door slamming,train mHu4pQgUsTw,0,skidding,train mHvIsK2sY3Q,45,playing trombone,train mHvp-Hx6uJU,0,playing trombone,train mHvp-Hx6uJU,63,playing trombone,train mHw8P8NnUvI,21,playing trombone,train mHw8P8NnUvI,87,playing trombone,train mI2P76hEzGg,0,baby laughter,train mI3rRMsV-8Y,9,playing tympani,train mIBWsa8mEzQ,10,playing accordion,train mID97k7JkG0,15,fire crackling,train mID97k7JkG0,233,fire crackling,train mIDhigiJqe0,0,cat purring,train mIDhigiJqe0,275,cat purring,train mIGikTEfIfs,11,"engine accelerating, revving, vroom",train mIJJmp3wJOY,318,baby laughter,train mIJJmp3wJOY,454,baby laughter,train mINZOofPPdA,0,baby laughter,train mIOl3hWWfLo,145,arc welding,train mIP23lmL9pQ,85,arc welding,train mIRNVGGbeV0,2,sea lion barking,train mIULs8Cl_aE,150,playing bass guitar,train mIWRagXwP3A,80,people sniggering,train mIeCWNN4su0,30,"pigeon, dove cooing",train mIewOjsg9WQ,300,playing bass guitar,train mIhV91drl-s,161,playing cello,train mIjec3t2C58,226,volcano explosion,train mIjec3t2C58,347,volcano explosion,train mInTDyk6c2A,12,writing on blackboard with chalk,train mIoH-l5balg,30,"subway, metro, underground",train mIr_3HAVCd8,81,playing didgeridoo,train mIr_3HAVCd8,141,playing didgeridoo,train mItGACLF2MY,250,"motorboat, speedboat acceleration",train mJ-G1t9Y1Pg,1,crow 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mJURqFqrJX0,67,police radio chatter,train mJZboy0-FIU,48,bowling impact,train mJZboy0-FIU,285,bowling impact,train mJaX4ZpfULM,30,playing trombone,test mJee-gSMul0,304,missile launch,train mJee-gSMul0,350,missile launch,train mJgULzfw0aU,30,people sniggering,train mJkXhoArOgs,40,cuckoo bird calling,train mJmtX2nUTDI,3,baby babbling,train mJmtX2nUTDI,38,baby babbling,train mJn1j_oMboI,10,wood thrush calling,train mJn1j_oMboI,113,wood thrush calling,train mJniZrbkFIU,0,people burping,train mJqDJD4S-mA,28,playing tympani,train mJxtHhIlUaA,30,goose honking,test mJyX5yVBIlY,625,volcano explosion,train mK-KS9OfG0s,30,cat meowing,train mK4_P5pykjw,30,sheep bleating,train mK5RXuYYF3U,33,"alligators, crocodiles hissing",train mK7rnFse9TM,26,playing oboe,train mK7rnFse9TM,45,playing oboe,train mKAtxfzd5U0,15,goose honking,test mKDaCoEM0I4,291,"cattle, bovinae cowbell",train mKDaCoEM0I4,313,"cattle, bovinae cowbell",train mKEJRZtNx9o,44,baltimore oriole calling,test mKFEC824ffs,18,hedge trimmer running,train mKFEC824ffs,29,hedge trimmer running,train mKFzx_4f93U,1,canary calling,train mKGBKWNPREY,25,people sniggering,train mKGeZUBnPns,10,mouse pattering,test mKHXxKujlNc,3,plastic bottle crushing,train mKHXxKujlNc,16,plastic bottle crushing,train mKKaKEeLxRc,350,skidding,train mKOZKuxbCRc,30,"subway, metro, underground",train mKPwf4CrxDE,437,blowtorch igniting,train mKQBEMoHOc0,740,driving snowmobile,train mKQjB68SziY,0,playing didgeridoo,train mKRpmUv7Tjc,98,chainsawing trees,train mKRpmUv7Tjc,124,chainsawing trees,train mKRzq-UhKQs,606,playing synthesizer,train mKRzq-UhKQs,766,playing synthesizer,train mKVAgtsoQjA,180,using sewing machines,test mKYvf1p1-pY,170,fireworks banging,train mK_y20VBdZM,307,cell phone buzzing,test mKbTOyD_dVw,10,playing trumpet,train mKcOihnUhyo,154,"playing steel guitar, slide guitar",train mKcOihnUhyo,161,"playing steel guitar, slide guitar",train mKdXH78CU4A,304,playing volleyball,train mKgkEvtuJRI,2,mynah bird singing,train mKhul69YhzY,160,people booing,train mKjScDN29fY,131,strike lighter,test mKkvQdNuOd8,125,playing bagpipes,train mKli_UBXPww,81,sharpen knife,train mKn9zqGpsuc,30,playing flute,train mKpLW6TOBKA,72,"playing marimba, xylophone",train mKqfcR4mXMI,104,mynah bird singing,train mKqfcR4mXMI,128,mynah bird singing,train mKsQ3oXlP7E,150,playing drum kit,train mKtkibF-4wE,2,lighting firecrackers,train mKuvTMKAwoc,19,gibbon howling,train mKuvTMKAwoc,43,gibbon howling,train mKvNy9yMqDw,230,playing cello,train mL-t6UGlrCs,234,airplane,train mL1XlPf8X_o,245,planing timber,train mL2bGfP2EqY,50,splashing water,train mL2r6E1E7sM,22,playing gong,train mL2r6E1E7sM,73,playing gong,train mL9BZYbu-vI,59,missile launch,train mLAfkAOnnnw,244,snake rattling,train mLB4qblMi9M,104,playing tabla,train mLB6Wq5ZLlk,84,reversing beeps,train mLB6Wq5ZLlk,153,reversing beeps,train mLBNtJnQ0nY,140,splashing water,train mLBdOzH7yQg,23,playing theremin,train mLF3AqXP4E8,160,playing acoustic guitar,train mLIT_VpecJQ,0,playing electronic organ,train mLJi6qCPOcA,0,playing ukulele,train mLJi6qCPOcA,31,playing ukulele,train mLOltd6lNhc,133,playing squash,train mLOyn9q_ees,11,missile launch,train mLPcIsRdcWs,30,playing bass guitar,train mLQzvV8SdMg,38,mynah bird singing,train mLQzvV8SdMg,48,mynah bird singing,train mLT8t3il0qA,40,driving motorcycle,train mLTVKEqjxDg,95,people gargling,train mLXyNQspexc,110,playing volleyball,train mLYFtJ2CFvQ,460,people crowd,train mLYPfSZ40qY,19,pheasant crowing,train mLZMtM2VkjM,220,playing acoustic guitar,train mL_qeflgEEI,93,playing table tennis,train mLaon9oK1OA,30,male singing,test mLe6rfB6u28,92,basketball bounce,train mLfUdVK1CqM,240,"railroad car, train wagon",train mLg_BrqZW3o,286,train wheels squealing,train mLg_BrqZW3o,326,train wheels squealing,train mLiVFz0cU9E,30,playing banjo,train mLkpr0wjdM0,40,"race car, auto racing",train mLmbZJAjeoI,179,playing harpsichord,train mLntKIYrU2g,480,"railroad car, train wagon",train mLog-a8me8E,37,wood 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mMJwdHPQ7sY,0,police car (siren),train mMM8go9n2Cs,140,chainsawing trees,train mMNI9jZrDYI,135,metronome,train mMNI9jZrDYI,170,metronome,train mMOD6aymIqc,20,"bee, wasp, etc. buzzing",train mMOUlqEMpSU,235,people burping,train mMOq_9gW5yY,30,people shuffling,train mMPedK-yZkw,30,"bird chirping, tweeting",train mMR8S8qxGTg,166,playing mandolin,test mMS8axHh9D8,530,people hiccup,train mMYmVrfUM6Y,265,playing synthesizer,train mMYq-6zXOfg,30,goose honking,train mMdu56oDND4,110,people shuffling,train mMf4vJFT8Fw,30,playing bass guitar,test mMgsS5D68hk,1,fire truck siren,train mMgsS5D68hk,211,fire truck siren,train mMiISjAM8-Y,169,writing on blackboard with chalk,test mMiPfNjEjLI,10,using sewing machines,train mMiw8s9hTWs,10,people cheering,train mMkXFGgQD-k,116,people marching,train mMkXFGgQD-k,616,people marching,train mMqRFspSg10,184,lip smacking,test mMtNvmZ4N5o,77,slot machine,train mMwGJ_zHhsw,190,playing cello,train mMym9RTRS-M,10,driving buses,train mMzKb0ZxiZY,35,frog 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launch,train mNwvSq3mZ4M,0,people whistling,train mO0DygyK7so,18,chipmunk chirping,train mO3DSSIBimQ,2,woodpecker pecking tree,train mO8ZongSYLA,3,playing trumpet,train mOCIgiY1TU4,30,toilet flushing,train mOCcRyrl0co,130,bird wings flapping,train mOGsMOMkS-c,120,police car (siren),train mOHPhSz4vMA,0,raining,train mOI4Zd9AqMI,40,"race car, auto racing",train mOKmK7naCoo,310,chainsawing trees,train mOMOtoR8M_4,0,car engine knocking,train mOMkvizXHHc,10,"vehicle horn, car horn, honking",train mOO0dBIQD_g,325,volcano explosion,train mOO0dBIQD_g,538,volcano explosion,train mOOEhgTAP9c,410,playing cymbal,train mOOx10X0iG4,39,skiing,test mOP9JeWaLQo,198,skiing,train mOPJRy2Nv_o,134,tractor digging,test mOQ-mC2SE1s,16,people belly laughing,train mOQl1VFTC_A,22,woodpecker pecking tree,train mOQl1VFTC_A,85,woodpecker pecking tree,train mOVt9sF_o6I,10,using sewing machines,train mOXa_eKJBFM,0,playing clarinet,train mOXa_eKJBFM,21,playing clarinet,train mOZlvUN29QM,23,chipmunk chirping,train mOZlvUN29QM,78,chipmunk chirping,train mO_SWl8TVrY,290,"child speech, kid speaking",train mO_iLrKaMWw,30,playing flute,train mOda8QPZ36s,56,car engine knocking,train mOfXZlXa0Pg,97,people burping,train mOg8lVftxE8,530,playing bass guitar,train mOh-HvsWnc0,40,train horning,train mOhSSkUWYHk,3,chimpanzee pant-hooting,train mOhSSkUWYHk,70,chimpanzee pant-hooting,train mOj_TMJj1Zg,300,basketball bounce,train mOmJi93VycQ,60,playing accordion,train mOqMEfP_b14,59,crow cawing,train mOqMEfP_b14,168,crow cawing,train mOuVmQdTI9U,196,playing erhu,train mOvRqpiY4SQ,26,mosquito buzzing,train mOw5YNDk4xM,33,lathe spinning,train mOwjXOqRzmY,66,tapping guitar,train mOwjXOqRzmY,97,tapping guitar,train mOyCbfUoSUk,180,playing bagpipes,train mOyCbfUoSUk,200,playing bagpipes,train mOz7A6uXIMg,30,ambulance siren,train mP0Zk_wWFiM,72,goose honking,train mP2CRn9fv_4,0,playing harp,train mP2wToU4VLQ,45,tractor digging,train mP2wToU4VLQ,58,tractor digging,train mP4HYlm7kjg,290,playing trombone,train mP7-RStLkxk,270,"bird chirping, tweeting",train mP7jX_pNKpg,20,lions growling,train mP8PkfAG25s,30,playing accordion,train mPC-Gu9F6yQ,250,"rowboat, canoe, kayak rowing",train mPFlcmSh55k,190,people crowd,train mPG84oq6sd4,137,crow cawing,train mPGSnrm65Vk,10,playing didgeridoo,train mPGSnrm65Vk,31,playing didgeridoo,train mPJf_TNz2Rs,142,bouncing on trampoline,train mPJf_TNz2Rs,152,bouncing on trampoline,train mPJmCLJ0kzs,2,parrot talking,train mPMS4rFtnnk,0,skateboarding,train mPMS4rFtnnk,169,skateboarding,train mPMhtjINDF8,30,car passing by,train mPNLATITg0c,449,playing timpani,train mPP75Ke_RdI,30,playing accordion,train mPQorpKX-Pg,7,airplane flyby,train mPR8KSfpTvU,31,cat purring,train mPRegKImtq8,240,people clapping,train mPSz9XZPIcU,19,playing harmonica,train mPSz9XZPIcU,30,playing harmonica,train mPTi7u5WguU,42,"vehicle horn, car horn, honking",train mPTi7u5WguU,55,"vehicle horn, car horn, honking",train mPU9j7icxs0,123,"electric shaver, electric razor shaving",test mPV5DmvANVo,140,ocean burbling,train mPVHmrMoTFs,160,waterfall burbling,train mPX61tX_08g,110,typing on computer keyboard,train mPXxgdGrRX8,0,pheasant crowing,train mPXxgdGrRX8,13,pheasant crowing,train mP_MN28KoJM,14,baby crying,train mPaXjo_KZF0,18,"motorboat, speedboat acceleration",train mPbv5q6jByw,18,opening or closing car doors,train mPbv5q6jByw,29,opening or closing car doors,train mPbxUT0LqaI,272,writing on blackboard with chalk,train mPbxUT0LqaI,452,writing on blackboard with chalk,train mPc84ZLSKr8,140,"child speech, kid speaking",train mPejTHGbBO0,19,dog growling,train mPf3TRcdH-E,130,"bird chirping, tweeting",train mPihyTX-RUw,40,toilet flushing,train mPmpE3oonhU,500,playing bass drum,train mPnRdL1sC48,61,people eating crisps,train mPnRdL1sC48,240,people eating crisps,train mPqs2V6Pen8,37,people eating crisps,train mPqs2V6Pen8,115,people eating crisps,train mPstdrGqg4M,280,playing harpsichord,train mPtIJOdWFso,30,pig oinking,test mPtV55xd2dU,30,"rowboat, canoe, kayak 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mQJcObz1k_E,30,"vehicle horn, car horn, honking",test mQL-GSy4-hU,23,goose honking,train mQL-GSy4-hU,40,goose honking,train mQLm03LKlY4,606,lathe spinning,train mQMHQvVF5s4,200,driving motorcycle,train mQMRgoyRWFc,2,playing timpani,train mQMRgoyRWFc,14,playing timpani,train mQNEQk-xbu0,170,playing banjo,train mQUXqC3WUVg,3,playing cornet,train mQWht9mv7sE,10,playing synthesizer,test mQaDawDcEEY,71,firing muskets,train mQaDawDcEEY,240,firing muskets,train mQdxeOfMM_s,201,slot machine,train mQe2S0997s0,3,crow cawing,train mQfRl-UiUKw,180,playing trumpet,train mQjIPBhM8qU,27,mynah bird singing,train mQjIPBhM8qU,124,mynah bird singing,train mQnxi53vVpI,10,playing zither,test mQrS4oWjp6g,30,male singing,train mQs3Ze2DQps,3,"heart sounds, heartbeat",train mQs3Ze2DQps,14,"heart sounds, heartbeat",train mQuLCF_oFvc,114,playing washboard,train mQuLCF_oFvc,181,playing washboard,train mQy6LIR4IRI,30,printer printing,train mQydulCA90M,290,people whispering,train mQz1Qe9Zru4,36,fire truck 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electric guitar,train mS76yJUGZ18,3,playing table tennis,train mS8q-AW-3FU,160,orchestra,train mS9XwiM4vbI,40,playing piano,train mS9xiEJ2Mvg,130,"female speech, woman speaking",train mSApWEbr0TA,257,scuba diving,train mSGG5IZvHv8,183,writing on blackboard with chalk,train mSGrH6PcdYQ,90,playing acoustic guitar,train mSHxFybELNY,15,playing cornet,test mSHxFybELNY,30,playing cornet,test mSL8MpANRBE,13,lathe spinning,test mSLcJ6S5w1U,68,"alligators, crocodiles hissing",train mSMTOP3auD8,59,police radio chatter,train mSMTOP3auD8,179,police radio chatter,train mSRrB-GAUo8,0,people clapping,train mSSnkAorimo,30,playing banjo,train mSSoramRw_I,123,police radio chatter,train mSSoramRw_I,135,police radio chatter,train mSTHICFPkv8,0,people sniggering,train mSb0rBo13x0,14,francolin calling,train mSdoNJzz3m0,17,metronome,train mSdoNJzz3m0,91,metronome,train mShJgNrNwpM,30,playing trumpet,train mSidbz4LB_A,6,"playing steel guitar, slide guitar",train mSidbz4LB_A,121,"playing steel guitar, slide 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miKMRo-c9GQ,282,scuba diving,train miLp3xAP0lg,210,playing cello,train miM-Rn6CpAY,87,people marching,train miOjzLVtFJU,30,people whispering,train miOkoD95clY,40,"playing marimba, xylophone",train miP7R_EpdXw,110,"female speech, woman speaking",train miRcAsySwdo,388,dog howling,train miRcAsySwdo,401,dog howling,train miSz5gV8HQo,21,people burping,train miT69L7G9iA,140,people babbling,test mi_ba6tsCNw,30,playing flute,train mi_gpL8cL-s,29,playing didgeridoo,train miaOI1RGX3A,3,playing gong,train miaOI1RGX3A,40,playing gong,train micPV6xzz3c,18,alarm clock ringing,train mieTATLd-u4,300,writing on blackboard with chalk,train mif-X1rvPDg,145,striking pool,train mif-X1rvPDg,214,striking pool,train mifCHSaworI,80,playing flute,train mig7paZEzwM,230,"playing marimba, xylophone",train migjLAWDriI,30,playing hammond organ,train mihBEBVDt1c,30,printer printing,train miiolPHuokU,32,playing didgeridoo,train mijlXzClIhE,453,"ice cream truck, ice cream van",train mikkq9fD5ok,122,mouse 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mjRVoRGBGws,331,chopping wood,test mjSf1QzLPOI,10,playing bassoon,train mjTjFGexjew,235,hedge trimmer running,train mjTjFGexjew,250,hedge trimmer running,train mjTmBkad3uw,0,mouse clicking,train mjTmBkad3uw,60,mouse clicking,train mjVFu7dALVA,60,waterfall burbling,test mj_xqejnFGI,306,cap gun shooting,train mjbQikzRuoo,440,playing electric guitar,train mjbR8VZhJyk,19,sharpen knife,train mjbR8VZhJyk,30,sharpen knife,train mjcZfcr4jkk,0,playing ukulele,train mjcZfcr4jkk,4,playing ukulele,train mjcd2_hY0Wg,0,goose honking,train mjeer87Ftmk,150,typing on typewriter,train mjfUF-Ik8Vg,70,fireworks banging,train mjh0tT3A3oQ,30,playing harp,train mjhkG_JXINI,30,people shuffling,train mjhlzNVpnXs,26,air horn,test mjjYBySgYok,105,playing hockey,train mjkBbCiDMls,41,lions roaring,train mjkBbCiDMls,75,lions roaring,train mjkaaWMznVk,20,people farting,test mjlFKV5zV4w,298,warbler chirping,train mjo-2dXv58E,0,toilet flushing,train mjqrp8HsT3E,55,reversing beeps,train mjqrp8HsT3E,68,reversing beeps,train mjrlByT7QK8,34,frog croaking,train mjsWJpR0We8,226,playing electronic organ,train mjsWJpR0We8,289,playing electronic organ,train mjuS0Mwt5pA,70,skateboarding,train mjzJiHWlgXc,6,cupboard opening or closing,test mk0gCiY0OHg,25,civil defense siren,test mk2YGxdhIVs,41,cat purring,train mk2YGxdhIVs,55,cat purring,train mk49WjhCHC4,31,playing french horn,train mk49WjhCHC4,53,playing french horn,train mk5DDeTaMeA,40,driving motorcycle,train mk65xof54iA,35,sharpen knife,train mk65xof54iA,76,sharpen knife,train mk67RcMHbCU,100,chainsawing trees,train mkBX7AIljsQ,80,chicken crowing,train mkC5y0VYvXc,69,eagle screaming,train mkDIrir-tkY,273,pheasant crowing,train mkDIrir-tkY,431,pheasant crowing,train mkG0xeNt-1w,37,playing didgeridoo,train mkGqqyVW6cM,98,francolin calling,train mkGqqyVW6cM,118,francolin calling,train mkHZoAzCjbk,38,playing volleyball,train mkIilVe2qkg,110,cricket chirping,test mkJL9v2271Y,40,train whistling,train mkJL9v2271Y,52,train whistling,train mkM95UNSgBc,130,using sewing machines,train mkN33PFvJVQ,0,barn swallow calling,test mkTMLeK14h4,120,people sobbing,train mkTgqCbEV_c,30,cat meowing,train mkVcFFOe3LI,30,fire crackling,test mkWRu46Tc7Q,580,male singing,train mkYGE9YzzT4,37,civil defense siren,train mkYGE9YzzT4,49,civil defense siren,train mkYmIx_V1LU,34,gibbon howling,train mkYmIx_V1LU,181,gibbon howling,train mkZ1R1whmF0,27,playing harpsichord,train mkZCF9azVOs,169,car engine starting,train mkZnRjmP-qg,30,frog croaking,train mkZnRjmP-qg,230,frog croaking,train mk_PvYtsEVY,40,toilet flushing,train mkaJujvQBpM,34,mynah bird singing,train mkaJujvQBpM,45,mynah bird singing,train mkayu6dgMQs,190,playing gong,test mkb0UJeTuM8,30,playing drum kit,train mkc5ZACi0IA,102,lions roaring,train mkc5ZACi0IA,112,lions roaring,train mkgvfD0Cfms,396,playing tabla,train mklEEDvsr8g,9,coyote howling,train mklEEDvsr8g,50,coyote howling,train mkmWvUlN6BE,38,"donkey, ass braying",train mknQ4q-U5Sg,310,playing clarinet,train mknhJBBtt2U,5,frog croaking,train mknhJBBtt2U,52,frog croaking,train mkrUuLgbObo,30,"railroad car, train wagon",train mktf-U4zinA,150,rapping,train mktf-U4zinA,822,rapping,train ml07DEX7png,30,machine gun shooting,test ml29mUXt2Sk,30,driving buses,train ml37zic4pg8,30,playing harpsichord,train ml3sa-f7MBY,3,car engine knocking,train ml3sa-f7MBY,13,car engine knocking,train ml4b-43t5Io,30,playing drum kit,train ml524Qm1DMM,53,frog croaking,train ml524Qm1DMM,68,frog croaking,train ml5VKB8fBvY,30,sheep bleating,train ml7Fn1uBiig,229,playing darts,train ml7Fn1uBiig,359,playing darts,train ml9iImnTrtM,30,playing acoustic guitar,train mlAVPC3odKI,0,playing bagpipes,train mlAVPC3odKI,15,playing bagpipes,train mlBZKyxtKQg,5,owl hooting,train mlBZKyxtKQg,32,owl hooting,train mlFAfoQ5FZM,41,playing castanets,train mlFAfoQ5FZM,64,playing castanets,train mlFunrmHAcU,133,basketball bounce,train mlI6dSFpAhs,250,playing saxophone,test mlIb5J97ERs,43,fire truck siren,train mlJWPrrJOkg,925,playing trumpet,train mlJk-25bl5o,126,"electric shaver, electric razor shaving",train mlKh90ty32c,8,people cheering,train mlKh90ty32c,50,people cheering,train mlOa1e5dnOs,40,"bee, wasp, etc. buzzing",train mlP6bSDMRg4,80,cheetah chirrup,train mlPXydnL0mA,30,playing bass drum,train mlQXNCbgL24,254,cutting hair with electric trimmers,train mlQcEXLki9Y,81,typing on typewriter,train mlRlYiQfXfY,192,frog croaking,train mlS9LLiMIG8,30,police car (siren),train mlSSM1ipX8w,16,cat growling,train mlSg4079dHs,24,driving motorcycle,train mlUJRa-_T-E,21,sailing,train mlW-e4gWNmM,312,cupboard opening or closing,test mlaAuQGv2J4,30,car engine knocking,train mlafEULpjGI,286,people eating noodle,train mlafEULpjGI,347,people eating noodle,train mlbfhZZ1YLE,80,canary calling,train mlbfhZZ1YLE,133,canary calling,train mlbhjHfHzbw,2,dog baying,train mlbhjHfHzbw,41,dog baying,train mleKIXnSL2Q,3,police car (siren),train mlfpMzBpfHw,14,dog bow-wow,train mlihNhHFGTM,30,playing harpsichord,train mlkpn5A2bNA,460,spraying water,train mll24xrpuaQ,123,cow lowing,train mll24xrpuaQ,187,cow lowing,train mllpUg5bKNI,51,woodpecker pecking tree,train mlmpEjHF3Fk,20,fireworks banging,train mlnroDsO95Y,5,swimming,test mlpF226Gvlo,0,cat purring,test mlsm0tW5dho,226,cat purring,train mlsm0tW5dho,302,cat purring,train mlwutymyMyc,100,playing accordion,train mm-pIBD8HXY,113,playing didgeridoo,train mm0QJccjD7Q,738,child singing,train mm1wKBBXhsM,10,skateboarding,train mm3QCfZ1t9g,37,barn swallow calling,train mm3QCfZ1t9g,112,barn swallow calling,train mm3g9xWFxHc,136,lathe spinning,train mm48vh_TmMI,558,dinosaurs bellowing,test mm5vqyML7z0,178,playing didgeridoo,train mm5vqyML7z0,302,playing didgeridoo,train mm8LLpjSz08,30,"playing marimba, xylophone",train mmAigxjQbtE,2,machine gun shooting,train mmAigxjQbtE,3,machine gun shooting,train mmCIu4-jA_4,0,fire truck siren,train mmCoiCH4V6k,17,opening or closing drawers,train mmCoiCH4V6k,57,opening or closing drawers,train mmGIuOvHvko,30,ocean burbling,train mmJ-4MGA7WQ,32,playing cymbal,train mmJim2h-byQ,166,playing cello,train mmKr1smjBE0,2,reversing beeps,train mmKr1smjBE0,16,reversing beeps,train mmLfCczuNYg,260,arc welding,train mmP4bZYx8vA,43,playing tuning fork,train mmP4bZYx8vA,74,playing tuning fork,train mmPHyB4iAPA,470,"pigeon, dove cooing",train mmRq-BqKkHA,30,mouse pattering,train mmTCB7Bs2Gc,0,tornado roaring,train mmTCB7Bs2Gc,11,tornado roaring,train mmTcPNiQ2iY,39,"alligators, crocodiles hissing",train mmTcPNiQ2iY,332,"alligators, crocodiles hissing",train mmUh0MraY-k,213,dinosaurs bellowing,train mmUxB3e9cQ4,140,"child speech, kid speaking",train mmVbG5-gQcc,0,dog bow-wow,train mmXK94_cxFw,0,cat purring,train mmXK94_cxFw,15,cat purring,train mmZTHEEPPjI,1,penguins braying,train mmi92uc3MDA,221,bird squawking,train mmi92uc3MDA,232,bird squawking,train mmiThgzYX5E,525,playing tabla,train mmiThgzYX5E,550,playing tabla,train mmkqzl_NHKs,184,playing bugle,test mmlBCGv9tl4,50,dog barking,train mmlyXQcPHSc,196,crow cawing,train mmqhvs3rbS4,0,"race car, auto racing",train mmrFsxwz1x4,187,cattle mooing,train mmrFsxwz1x4,210,cattle mooing,train mmrhmAbYyvA,50,driving motorcycle,train mmsa5TPqaDw,0,"race car, auto racing",train mmsglloa5zw,530,machine gun shooting,train mmufEtX1xJU,140,skidding,train mmutLVUoe1I,140,machine gun shooting,train mmutLVUoe1I,254,machine gun shooting,train mmz5pr4OwY4,115,playing snare drum,train mn6oiVizbBA,480,sailing,train mnCgs153dko,270,playing drum kit,train mnD9jcnMd2k,8,bowling impact,train mnDbXdEp4K0,137,planing timber,train mnERlSxzAG4,142,playing darts,train mnERlSxzAG4,185,playing darts,train mnFNYD0Ax_g,132,train whistling,train mnGf-2p8Prc,30,people running,train mnH4PFFbD5s,30,"pigeon, dove cooing",train mnH8IKZXmMs,160,playing squash,test mnH8IKZXmMs,257,playing squash,test mnJBDZh0wI4,11,"heart sounds, heartbeat",train mnJBDZh0wI4,43,"heart sounds, heartbeat",train mnJBqcUFjwg,114,playing cymbal,train mnJKMcr6Yb8,30,helicopter,train mnN7cbxHM0M,40,fireworks banging,train mnOFHlMEoJI,30,playing tambourine,train mnOFHlMEoJI,203,playing tambourine,train mnS0y3yPHwM,62,duck quacking,train mnS0y3yPHwM,370,duck quacking,train mnSP_ONVS7k,50,rapping,test mnTZy6H9yb0,30,"child speech, kid speaking",train mnUE9pbOaOk,162,canary calling,train mnUE9pbOaOk,273,canary calling,train mnVH8POcOoc,200,people coughing,train mnWYh3iPoy8,20,chipmunk chirping,test mnWlv9VUb3c,8,sea lion barking,train mnXNgsZXMdo,41,chicken clucking,train mnZWrMBn-8U,70,people belly laughing,train mn_bJZVcNpo,290,playing squash,train mn_zA58LGdo,77,people nose blowing,train mn_zA58LGdo,195,people nose blowing,train mnauuKUdjPE,29,"vehicle horn, car horn, honking",train mncrdQEVAuI,61,playing gong,train mncrdQEVAuI,100,playing gong,train mnfToxaQAgc,270,chainsawing trees,train mngNdp0ulDg,81,slot machine,train mngkOGEgHJk,40,playing drum kit,train mngxAJxra58,90,"vehicle horn, car horn, honking",train mnhlL3xdlvI,60,playing hammond organ,train mnia5CPrZvU,10,lawn mowing,train mnjfau-RHzk,74,bowling impact,train mnjfau-RHzk,127,bowling impact,train mnkn-r_eGdE,10,toilet flushing,train mnn72rSTiOA,70,"bird chirping, tweeting",train mno_MA6Hb00,25,playing congas,train mno_MA6Hb00,56,playing congas,train mnovnoxd4zA,30,police car (siren),train mnpRNyjIWZc,37,crow cawing,train mnpRNyjIWZc,58,crow cawing,train mnqax8bwmdQ,4,people coughing,train mntPbojPUe4,30,orchestra,train mntQ19zUhTg,70,playing cymbal,train mntYJdpbwfc,26,civil defense siren,train mo0FIU77w78,250,"rowboat, canoe, kayak rowing",train mo17cWDx1i8,22,coyote howling,train mo17cWDx1i8,100,coyote howling,train mo1ZQjQp5lU,40,"rowboat, canoe, kayak rowing",train mo1lJ5s7vi8,17,playing vibraphone,train mo1lJ5s7vi8,525,playing vibraphone,train mo3TPns4410,2,skiing,train mo8WarZmI_g,297,yodelling,train mo8kDBz_h3Y,26,black capped chickadee calling,train mo8kDBz_h3Y,59,black capped chickadee calling,train moGJY4reglU,0,"railroad car, train wagon",train moJBNjJpEy4,115,rope skipping,train moJBNjJpEy4,179,rope skipping,train moL6N4ss6VA,3,elk bugling,train moL6N4ss6VA,44,elk bugling,train moLATfN79Yo,40,lighting firecrackers,test moLZR7XXTMg,80,playing accordion,train moSFcbZH5_w,50,playing cymbal,train moXkrEGszIo,30,sheep bleating,train moZ49NEGqfk,310,lawn mowing,test mo_x30i1gns,99,playing glockenspiel,test moeIPMKRfK4,30,"pigeon, dove cooing",train mofxODOJLAU,30,sloshing water,test mohlfADrrP0,0,church bell ringing,test moiUAO3CKbM,420,cattle mooing,test molJBIzC-z4,18,playing squash,train molzJ-iOKrI,21,foghorn,test mom3iMrY-3U,16,civil defense siren,train momJJOUPFzU,17,wood thrush calling,test mopIsvlpU_8,0,hail,train moq0MdIY588,7,chicken clucking,train moq0MdIY588,18,chicken clucking,train morfgB1WD5M,30,playing flute,train movVOFdA2KA,65,playing tabla,train movVOFdA2KA,281,playing tabla,train mox0hJ7K9_Q,56,tap dancing,train mox5KqBn4d0,18,snake rattling,train mox5KqBn4d0,42,snake rattling,train moxP6Mj9zvo,290,using sewing machines,train moyH6HVbzLs,20,"bee, wasp, etc. buzzing",train moymtl3YhRA,150,volcano explosion,train mp6HkbXmJ-E,30,singing bowl,train mp7wVm-rawg,30,female singing,train mp8-O-SDxmQ,150,thunder,train mpAlgyTfIGM,101,people burping,train mpDicN3UqaI,30,"pigeon, dove cooing",train mpEJnnKa6qE,30,playing cello,train mpEh3S2rspM,3,striking bowling,test mpFIaxugJxo,17,"playing marimba, xylophone",train mpGhTIpo94E,80,"playing marimba, xylophone",train mpHT-sNXwqg,1360,wind chime,train mpID7KdWyy0,30,people running,train mpIrtUC0VF4,7,pheasant crowing,train mpIrtUC0VF4,26,pheasant crowing,train mpMZqjJIIbY,189,people slapping,train mpNHgChd5II,80,people crowd,train mpNxnTHMCHs,164,playing tambourine,test mpOIzOoJsaw,51,playing lacrosse,train mpP26NCqP9k,20,people hiccup,train mpPrCXLKfGU,2,playing tuning fork,test mpRE-yxlMjg,10,toilet flushing,train mpRs7iEJ_yw,140,playing electronic organ,train mpSwGJyidTM,320,raining,train mpV-6a0J6xY,120,playing french horn,train mpVH8hS8Ng4,230,machine gun shooting,train mpXgIbx8Tbs,6,chopping wood,train mpXv8WKFHRc,435,strike lighter,test mpbLPANcMBA,10,machine gun shooting,train mpbLPANcMBA,38,machine gun shooting,train mpbtQHp8LXU,67,playing hockey,train mphVIh_JgD0,20,dog barking,train mpjr-hd_9uw,110,playing saxophone,train mpmBhEI7bfg,56,playing bassoon,train mpnW1782ZmA,19,singing bowl,train mpoV-SHNr08,30,"child speech, kid speaking",train mprpLute6lo,216,planing timber,train mprpLute6lo,520,planing timber,train mptVpOKdxlc,30,playing drum kit,train mptbemZf66M,7,sailing,train mpvV3wweq0Y,92,mynah bird singing,train mpvV3wweq0Y,404,mynah bird singing,train mpvV90xswJw,368,frog croaking,train mpvV90xswJw,436,frog croaking,train mpvaoCLUvHk,20,printer printing,train mpwfyT4JJ6k,30,"rowboat, canoe, kayak rowing",train mpxDwMUL0Uk,332,electric grinder grinding,train mpx_menHyuI,61,cutting hair with electric trimmers,train mpx_menHyuI,81,cutting hair with electric trimmers,train mpxo-8MopWw,78,eletric blender running,train mpydrldgvog,30,playing cello,train mq51icEniGY,0,stream burbling,train mq61rFzFzy0,160,helicopter,train mq6FQZ6BkMs,46,"heart sounds, heartbeat",train mq6FQZ6BkMs,63,"heart sounds, heartbeat",train mq7eh1p9m4Y,30,dog barking,train mq8x4d5xoJQ,490,playing cello,train mqDrGExngRI,132,eletric blender running,train mqFu7C-hGow,19,chinchilla barking,train mqFu7C-hGow,75,chinchilla barking,train mqHTQRqY2hc,13,lighting firecrackers,train mqHTQRqY2hc,98,lighting firecrackers,train mqLukBIlzAw,30,playing bagpipes,train mqNHhsRsZvw,30,sheep bleating,train mqPvBWf5cGU,31,planing timber,train mqPvBWf5cGU,49,planing timber,train mqWbUmnWtAQ,10,skidding,train mqlUhKGYHIQ,4,dog whimpering,train mqlfGTGj658,81,yodelling,train mqmlwWb3_2M,32,playing mandolin,train mqmlwWb3_2M,140,playing mandolin,train mqqQIeeD1kY,6,elk bugling,train mqqQIeeD1kY,31,elk bugling,train mqrhvHavDOw,3,"donkey, ass braying",train mqrhvHavDOw,26,"donkey, ass braying",train mqrwtk6u9PE,50,lawn mowing,train mqsPMeJ0w5M,43,playing timbales,train mqsPMeJ0w5M,54,playing timbales,train mqtP7MebVFc,30,"railroad car, train wagon",train mqtZsKw0JaQ,0,mouse clicking,train mqtZsKw0JaQ,10,mouse clicking,train mqu1uI6LMu0,50,fireworks banging,train mqut9H7g31M,80,playing hammond organ,train mr2kmEmMjlA,185,missile launch,train mr3niRxyZZE,210,skidding,train mr55aLd_WrQ,152,lathe spinning,train mr68a_FbhAg,40,people clapping,train mr7z6RN9eqA,16,train whistling,train mr8bkOpji0M,0,playing tennis,train mr8bkOpji0M,27,playing tennis,train mr8uEQKL1GY,161,running electric fan,test mr8uEQKL1GY,178,running electric fan,test mrDBSU5EBDI,30,playing cello,train mrH7SmFVytc,10,"child speech, kid speaking",train mrLnNUCJ34A,30,people sniggering,train mrLoI0MacqE,40,church bell ringing,train mrOAC7FigIA,18,toilet flushing,train mrP3qhBldac,216,"heart sounds, heartbeat",train mrP3qhBldac,228,"heart sounds, heartbeat",train mrQ3J123uLU,30,toilet flushing,train mrQWjWsbJgU,516,airplane,train mrS70JOySn4,20,underwater bubbling,train mrTmlBZgP2o,9,tapping guitar,train mrTmlBZgP2o,29,tapping guitar,train mrV2byQFC1E,353,playing lacrosse,train mrW9ESExAuQ,0,"child speech, kid speaking",train mrWO0Ib5HE4,570,hair dryer drying,test mrcePXm-R0Y,110,mouse pattering,train mrdA3fE64hA,5,volcano explosion,train mrdrQkCIYAg,25,turkey gobbling,train mrdrQkCIYAg,58,turkey gobbling,train mre6Zvi1urU,86,playing guiro,train mre6Zvi1urU,96,playing guiro,train mrhB5k_j1ns,248,opening or closing drawers,train mrhB5k_j1ns,268,opening or closing drawers,train mrnOmdJ4ntY,192,swimming,train mrnOmdJ4ntY,230,swimming,train mrpAFoZ76HM,545,blowtorch igniting,train mrrbohlTSeI,52,dog baying,train mrrbohlTSeI,69,dog baying,train mrsCCJ3ddwE,171,airplane,train mrsCCJ3ddwE,391,airplane,train mrsezc3ZqBU,452,playing darts,train mrsezc3ZqBU,468,playing darts,train mrswvofDhMM,13,playing sitar,train mrswvofDhMM,25,playing sitar,train mrv0RhqOzJ0,200,driving snowmobile,train mrv0RhqOzJ0,274,driving snowmobile,train mrwaQdkhAy4,140,planing timber,train ms-hFPj-sR4,13,beat boxing,train ms2Fl2qhurY,560,"railroad car, train wagon",train ms4lJoKssRY,196,playing tabla,train ms75d9J4k1c,40,baby laughter,train ms7zbtZ4c3s,20,pig oinking,train msAGHQAonxg,11,playing mandolin,train msAGHQAonxg,40,playing mandolin,train msCo-ZBwYVo,151,playing bongo,train msCo-ZBwYVo,610,playing bongo,train msHf6vI5oto,7,wind rustling leaves,train msJEUyccJMU,60,people sobbing,train msK6nZqOQvQ,5,playing tabla,train msK6nZqOQvQ,36,playing tabla,train msL63WjBAC4,40,train wheels squealing,train msL63WjBAC4,72,train wheels squealing,train msLQCP9TWe8,360,people whispering,train msM1M4cncfg,0,people belly laughing,train msUQbnlLjQM,6,reversing beeps,train msUQbnlLjQM,25,reversing beeps,train msW2V5bmino,333,beat boxing,train msYdbvtyyAQ,70,ocean burbling,train msfsgbFEEgs,460,toilet flushing,train mshE6_C4DwE,216,door slamming,train mshE6_C4DwE,414,door slamming,train mskCXmokqk4,120,mouse pattering,train mspcwfkhwY4,17,tractor digging,train msrdvZ8P8Og,0,pheasant crowing,train mstqzQk_CDk,97,playing darts,train mstqzQk_CDk,112,playing darts,train msu_3th08J0,2,hail,train msu_3th08J0,14,hail,train mswwtU3T6eM,30,thunder,train msyUhGH2-rE,13,owl hooting,train mt08MzzB8hs,190,singing choir,train mt0NTgrqIXU,30,playing bass drum,train mt13n4XleGY,30,orchestra,train mt1AGjtEOcQ,8,fire truck siren,train mt1AGjtEOcQ,115,fire truck siren,train mt1aAY6NkC0,931,slot machine,train mt2A98C2wHk,280,people eating apple,train mt2jS_qjpkk,613,tornado roaring,test mt6Pa7pBz_A,210,train whistling,test mt88hb4BCSE,210,"child speech, kid speaking",train mt8LBMss8Uw,29,opening or closing car electric windows,train mt8LBMss8Uw,111,opening or closing car electric windows,train mtAZi-ZYFAk,40,pheasant crowing,train mtAc_bMYBsM,21,playing castanets,test mtAdhpTKmgg,8,frog croaking,train mtAdhpTKmgg,51,frog croaking,train mtCPLW4qlQk,94,bowling impact,train mtCPLW4qlQk,544,bowling impact,train mtDKHMmKKV4,53,playing hammond organ,train mtDk6wtKp8Q,0,"rowboat, canoe, kayak rowing",test mtG5wEZup3A,100,"heart sounds, heartbeat",train mtG5wEZup3A,120,"heart sounds, heartbeat",train mtGUMl4z6FE,10,helicopter,train mtGqD-5Jq7Q,360,singing choir,train mtH-Z8fNwDM,0,people coughing,train mtJFsGXZbhw,27,playing didgeridoo,train mtJFsGXZbhw,37,playing didgeridoo,train mtKf6REJ5vs,80,opening or closing car doors,train mtN04JZr4Hk,164,playing squash,train mtOSNRd6k1A,70,playing banjo,train mtQvLN0Lx9g,101,sharpen knife,train mtQvLN0Lx9g,133,sharpen knife,train mtSIukfXSU0,7,playing sitar,train mtSIukfXSU0,46,playing sitar,train mtSPua6By-k,132,francolin calling,train mtW1sYaap7M,37,playing tabla,train mtW1sYaap7M,65,playing tabla,train mtYOKzA6Rzc,1103,planing timber,train mtZYmdNAVvc,250,playing bass guitar,train mtZnAsHMdfw,5,parrot talking,train mt_uKVIWJQY,0,running electric fan,train mt_uKVIWJQY,110,running electric fan,train mtbmxTJ5oWc,0,cat hissing,train mtc5VQNhvDs,0,dog barking,train mtc5VQNhvDs,11,dog barking,train mteZAEwpYZs,74,playing cornet,train mthH4ZsqFTs,160,playing acoustic guitar,train mthJBUF1IP8,56,"bee, wasp, etc. buzzing",train mthJBUF1IP8,72,"bee, wasp, etc. buzzing",train mtiU3XU1zQI,78,playing trumpet,train mtj1tICxsY4,10,chicken crowing,train mtj8ovlWcGQ,104,snake rattling,test mtjUgt-IUV8,30,cat growling,train mtkMOJi99t4,12,goose honking,train mtkMOJi99t4,22,goose honking,train mtlItYghvNw,24,"ice cream truck, ice cream van",train mtmf-Gpet-s,240,"railroad car, train wagon",train mtuohGRYcWo,565,people slurping,train mtv6cAiDdII,0,people burping,train mu-23cbjdmw,367,playing bongo,train mu0PngnrM90,30,chicken crowing,train mu1DEnSlEi8,10,playing cello,train mu2ppw_GbxE,18,playing trumpet,train mu2ppw_GbxE,112,playing trumpet,train mu6WQE2xzbc,0,playing cornet,train mu6WQE2xzbc,39,playing cornet,train mu7K3DFy_ts,180,playing bass drum,train mu936W04j5U,180,people booing,train muAobo0ShNQ,8,otter growling,train muEJOWzBod8,21,striking bowling,test muEqLs4xcvs,30,"female speech, woman speaking",test muG5I3ZmSlM,3,singing bowl,train muH-7-n-xsY,261,playing hockey,train muHo26SaMjk,210,"child speech, kid speaking",train muI78m0GDgk,520,church bell ringing,train muLwbuJW4Rw,59,playing table tennis,train muOFEAcVKHo,90,playing snare drum,train muO_71HSdh0,0,ambulance siren,train muQCppEIMtY,3,playing guiro,train muQCppEIMtY,13,playing guiro,train muQm_S77_E0,70,turkey gobbling,test muQxkt2Mle4,30,playing saxophone,train muSBENgoLhU,231,playing theremin,train muTKg60EWFU,182,ice cracking,train muTKg60EWFU,357,ice cracking,train muVtDAIwxkg,30,church bell ringing,train mu_ph3QlpsE,80,car engine starting,train muafTafMbis,114,vacuum cleaner cleaning floors,train mucCRdwztqM,30,"motorboat, speedboat acceleration",train mueE8croeIw,110,playing trumpet,train muf4YZ6tDDk,9,lathe spinning,train muhutaJJeng,0,dog howling,train muhutaJJeng,10,dog howling,train mujFIWTfvm0,100,playing harp,train mumfDDO-zZQ,63,people marching,train mumfDDO-zZQ,144,people marching,train mun14agONGg,13,basketball bounce,train munKAGqATNY,16,playing cornet,train munKAGqATNY,169,playing cornet,train muou0sMXtCw,160,skidding,train mupYr1D_bQ0,180,people clapping,train mupZWL85t0g,93,popping popcorn,train muq8TGLaiJM,85,footsteps on snow,test muqStFW1OHg,19,lathe spinning,train muqStFW1OHg,68,lathe spinning,train murafPife2k,250,playing trumpet,train mutU0BATb4Q,0,ice cracking,train muuSxbHjipw,112,alarm clock ringing,test muxG7Kf5KD8,10,toilet flushing,train muxjFOPvR1I,320,chicken clucking,test muy6n1TmB18,110,"playing marimba, xylophone",train mv-KFu7uhrE,221,ice cracking,train mv06XrJDY24,38,crow cawing,train mv1wOyvWlMY,81,woodpecker pecking tree,train mv6BaXqJTog,51,police radio chatter,train mv7KNCrRX14,528,missile launch,train mv7z8lWgtks,50,stream burbling,train mv9bGV1lOpc,40,roller coaster running,train mv9bGV1lOpc,56,roller coaster running,train mvADpTONO7E,149,car engine idling,train 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mvmdvrNT8mw,144,tap dancing,train mvmeuVOhAR4,30,"bird chirping, tweeting",train mvoHcwuaMCQ,40,playing french horn,train mvqOR8po0aA,33,"engine accelerating, revving, vroom",train mvtXX1CzKaM,626,tapping guitar,train mvxhKLhUGMI,100,female singing,train mw1-wxA3wTc,129,crow cawing,train mw1VGZoYQts,70,machine gun shooting,test mw2YINxAWcI,1,"heart sounds, heartbeat",train mw3MhiKhGao,30,"engine accelerating, revving, vroom",train mw4_GvnhGbo,73,cheetah chirrup,train mw7DYT62dCM,0,scuba diving,train mw7DYT62dCM,16,scuba diving,train mw8hwxZJHsA,6,"bird chirping, tweeting",train mw9uP9kGGhg,30,people farting,train mwDBvvipkD4,9,playing synthesizer,train mwDBvvipkD4,192,playing synthesizer,train mwGJx-OBgLE,126,strike lighter,train mwGJx-OBgLE,148,strike lighter,train mwI2-699oC8,20,basketball bounce,train mwLrABPxvgA,16,playing steelpan,train mwLrABPxvgA,54,playing steelpan,train mwOakmMiGN4,90,playing shofar,train mwPEC8Z7gUY,236,playing clarinet,train mwRDZB_B-IU,15,bathroom 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mxsm5ZAj_nA,0,fireworks banging,train mxvAcbmNiKU,0,cricket chirping,train mxvAcbmNiKU,26,cricket chirping,train mxvAhlxrW4E,20,"railroad car, train wagon",train mxvDz3knNtQ,9,playing squash,train mxweYk5FHSQ,20,playing trombone,train mxy5qcJckDQ,195,playing bass drum,test mxzFV3nINYg,90,orchestra,train my09_8tUpeM,13,church bell ringing,train my09_8tUpeM,109,church bell ringing,train my0T24S2d9E,170,playing cymbal,train my3lBD6pVr0,50,people burping,train my3wYWd2LqY,274,playing volleyball,train my4W18hF-ks,613,popping popcorn,train my4W18hF-ks,673,popping popcorn,train my5fHix5zG8,0,driving motorcycle,train my6ifQLHFG8,186,singing choir,train my6j-4v6y_8,0,ripping paper,train my7NHSVLQW4,100,driving buses,train my7VwjfpR6I,90,lions growling,train my8cPgjGrK4,131,playing ukulele,train my8cPgjGrK4,270,playing ukulele,train myAkSqcPi5Y,30,duck quacking,train myBRFJP7Z2U,1,basketball bounce,train myD-He_i2Ao,199,elk bugling,test myDTpiCmSO8,30,playing harpsichord,train myGslGTBbaQ,68,yodelling,train myHXSQNFkNc,12,children shouting,train myKkGpWXEFI,50,people belly laughing,train myMF5P871rQ,310,"electric shaver, electric razor shaving",train myOH_8igzTw,161,playing djembe,test myPHIXIEs7Q,2,foghorn,train myPHIXIEs7Q,18,foghorn,train myR36564V9Y,130,playing hammond organ,train myT3mat4-5k,307,people slurping,train myUTxYx37sM,35,tap dancing,train myV0lplq5xo,286,lathe spinning,train myV0lplq5xo,300,lathe spinning,train myWxkFuyzy0,189,singing bowl,train myYN9mDqcEg,17,playing cornet,train mycDd1c2190,30,duck quacking,train mydw8pGo6CA,480,"bird chirping, tweeting",train myiaSS53jjI,489,black capped chickadee calling,train myiaSS53jjI,526,black capped chickadee calling,train mylHTk33srA,164,beat boxing,train mymFNbKJWEU,380,skidding,train mymMye4purU,927,baby laughter,train mymXqXg9LvY,170,female singing,train mymn1bgyZwY,360,people screaming,train mypNlBd6tsI,82,cupboard opening or closing,train mypNlBd6tsI,113,cupboard opening or closing,train myqJzbxvgYI,30,playing acoustic guitar,train myqg4e6_WnI,30,waterfall burbling,train mysG5c5i9cI,190,orchestra,train myueH8g0OsE,4,tap dancing,train myv35irEZtk,31,playing volleyball,train myviqHIZ_q0,193,tap dancing,train mywsmUl_07o,20,playing cello,test myxDbg0vhdQ,28,swimming,train myy-OtJbCfQ,100,ocean burbling,train myyVaRjM-0c,424,playing darts,train myyzG5TnW1g,90,stream burbling,train mz-ngMcGVOs,3,lathe spinning,train mz-ngMcGVOs,16,lathe spinning,train mz0mfHGCAAA,90,"child speech, kid speaking",train mz0tNCXgCYM,255,magpie calling,train mz0tNCXgCYM,291,magpie calling,train mz1TX5pQ0QM,30,playing acoustic guitar,train mz2hGCcgZuc,20,"male speech, man speaking",train mz418O6-sls,119,playing electronic organ,train mz4DrrsTgG4,30,playing accordion,train mz5_PaMwZmI,14,woodpecker pecking tree,train mz8ke0_OhH8,22,zebra braying,train mz8ke0_OhH8,39,zebra braying,train mzAfTmC3It0,30,playing snare drum,test mzB1VGEGcSU,113,rapping,train mzB1VGEGcSU,190,rapping,train mzEG8_6TmzA,0,playing piano,train mzK5tY6GElw,20,"engine accelerating, revving, vroom",train mzKYZncQBL8,0,owl hooting,train mzKYZncQBL8,24,owl hooting,train mzKrddLnEOo,20,horse neighing,train mzKrddLnEOo,37,horse neighing,train mzOAE_FPqx0,20,people sniggering,train mzOUgwsQ_hM,399,driving snowmobile,train mzOUgwsQ_hM,409,driving snowmobile,train mzOtmoqTB9A,193,owl hooting,train mzOtmoqTB9A,210,owl hooting,train mzPDQnCiMfk,108,cricket chirping,train mzPbegRJQQ4,170,playing drum kit,train mzR-mmHXG4M,2,barn swallow calling,train mzRU-olm3ZA,30,pig oinking,train mzU7GEhDxz4,30,ambulance siren,train mzW3yYyb3x8,30,playing drum kit,train mzY_qcsrneA,211,playing harp,train mzZe8MePGB8,10,"railroad car, train wagon",train mz_geEM7Sq8,70,chicken crowing,train mzaRWAYgfLY,140,people clapping,train mza_bg2MWzo,5,playing volleyball,train mzfNmMn-4Cg,489,people booing,train mzi60x3qRV8,30,helicopter,train mzjPED4K-s4,127,cuckoo bird calling,test mzjkQ1BzaS4,541,playing harmonica,train mzjkQ1BzaS4,683,playing harmonica,train mzlD7sLXNxU,437,people slurping,train mzlD7sLXNxU,465,people slurping,train mzlzdyqPlHs,3,"engine accelerating, revving, vroom",train mzmOWvHKv7o,40,using sewing machines,train mzmXrJjY3j0,338,francolin calling,train mzmXrJjY3j0,586,francolin calling,train mzn3hiem4gQ,30,female singing,train mzpu_EIuj3s,160,people crowd,train mzsSSFrTJaQ,323,playing didgeridoo,train mztEA-UKIQI,30,playing trumpet,train mzuUjCDhO-k,89,bowling impact,train mzw5MWr-SZY,300,mosquito buzzing,test mzw5P9ZCUiE,120,playing saxophone,train mzyK41fo0rQ,731,electric grinder grinding,train mzz8idc5_mY,10,driving buses,train mzzbl7X37RI,221,cap gun shooting,train n--0rmSSn0A,4,playing table tennis,train n-1ho8xiSGU,2,beat boxing,test n-2ScoKHfhQ,72,airplane flyby,train n-5ABJBpho8,80,playing accordion,train n-5w-Xaj5A8,124,playing harp,train n-93tsH4WGs,210,driving motorcycle,train n-A3BJEpmz8,10,sailing,test n-BHoBig0b8,0,playing didgeridoo,train 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p3a6KYUqbjs,110,playing drum kit,train p3aqegPlif8,180,lions growling,train p3auwpZvzPg,1,francolin calling,train p3auwpZvzPg,33,francolin calling,train p3b-MBaa3bg,4,chimpanzee pant-hooting,train p3b-MBaa3bg,15,chimpanzee pant-hooting,train p3eeHR601CA,130,playing synthesizer,train p3geNMaF8_M,100,playing accordion,train p3iSZv8AuLs,40,playing electronic organ,train p3j0F29_TG4,10,rope skipping,train p3j0F29_TG4,23,rope skipping,train p3jKnTA1fjc,10,toilet flushing,train p3jWlG1zSVA,30,typing on computer keyboard,train p3k_wRv0E58,134,chimpanzee pant-hooting,train p3lQV8P2V7c,71,playing harp,train p3ntv6VIzE8,119,playing erhu,train p3rjVpHqVeI,3,sliding door,train p3vPWLTC6XE,334,skiing,train p3w5n1XUM0o,2,playing bassoon,train p3wyCHIAO7A,109,car engine idling,test p3xooA7N5y4,3,tap dancing,train p3z2BerIU7M,210,playing trumpet,train p3zMPsRqb6A,39,reversing beeps,train p3zMPsRqb6A,50,reversing beeps,train p4-_lJMAeY4,24,planing timber,train p4-_lJMAeY4,53,planing timber,train 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pAM-l6P8HHI,20,squishing water,train pAM-l6P8HHI,58,squishing water,train pAMPMfQfVn8,0,snake hissing,train pAPRrYtE3sk,130,playing drum kit,train pAQedLx6Piw,30,"playing marimba, xylophone",train pATEJV81ReU,52,reversing beeps,train pATEJV81ReU,116,reversing beeps,train pAV-dPDJv-w,30,"pigeon, dove cooing",train pAVydfXDGLU,319,electric grinder grinding,train pAVydfXDGLU,627,electric grinder grinding,train pAXrCxM2rmM,332,playing table tennis,train pAYe3SoWQW4,3,chicken crowing,train pAa7k8VFrUU,10,ambulance siren,train pAaYNIDYomM,47,sharpen knife,train pAc14GuYAHE,80,"child speech, kid speaking",train pAd3wb5chwY,8,machine gun shooting,train pAd3wb5chwY,594,machine gun shooting,train pAe8kcpjZII,10,playing theremin,train pAe8kcpjZII,48,playing theremin,train pAe9_o3wN8k,30,playing hammond organ,train pAfIutmME04,115,people cheering,test pAfNf_o5WTw,20,people crowd,train pAl_Y8VaS50,1,cupboard opening or closing,train pAl_Y8VaS50,14,cupboard opening or closing,train 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pCftquaBRWo,20,writing on blackboard with chalk,train pCftquaBRWo,35,writing on blackboard with chalk,train pCg5ZQiPa_Y,26,people burping,train pCg5ZQiPa_Y,47,people burping,train pChPmwCl9KI,63,"playing steel guitar, slide guitar",train pChPmwCl9KI,87,"playing steel guitar, slide guitar",train pCjdpTnhauY,121,tap dancing,train pCo0zDiq5nM,30,pig oinking,test pCoebl4yIhI,40,"pigeon, dove cooing",train pCpNYoBRI5w,150,playing timpani,test pCpWuhqlI6A,0,stream burbling,train pCqyd-NzpBk,3,civil defense siren,train pCqyd-NzpBk,141,civil defense siren,train pCrduiDDFL0,13,dog growling,train pCvnSJoshZY,4,"electric shaver, electric razor shaving",train pCvnSJoshZY,15,"electric shaver, electric razor shaving",train pCwsM86k1Mw,250,lawn mowing,train pCxNNPY4KFM,230,"playing marimba, xylophone",train pCzS-CocB04,24,dinosaurs bellowing,train pCzS-CocB04,89,dinosaurs bellowing,train pD-7dfr_lo8,15,bird squawking,train pD-7dfr_lo8,32,bird squawking,train pD-Luxq1DLs,50,"bird chirping, 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pLObOvNq6pg,20,turkey gobbling,train pLQpeUxmhrE,0,basketball bounce,train pLQpeUxmhrE,52,basketball bounce,train pLRDKPfg3Eo,81,barn swallow calling,train pLST4KU6BUU,30,"bird chirping, tweeting",train pLTm5OAsZ5g,490,"playing marimba, xylophone",train pL_tRSvjDjQ,210,waterfall burbling,train pLab3p8IdW8,66,playing harpsichord,train pLab3p8IdW8,99,playing harpsichord,train pLatqIP_U3A,10,fireworks banging,train pLfezoYt7DY,130,people screaming,train pLgTi41dCm0,1,cricket chirping,train pLgTi41dCm0,20,cricket chirping,train pLhgFvut4nI,145,rope skipping,train pLhgFvut4nI,238,rope skipping,train pLl-McANzSI,490,wind rustling leaves,train pLlHIfTP5R4,30,playing cello,train pLqcmZz-2RQ,78,slot machine,train pLrjkMpkmu8,30,chainsawing trees,train pLtll3iMeJE,30,ocean burbling,train pLvTCgG-jvc,107,mynah bird singing,train pLvTCgG-jvc,363,mynah bird singing,train pLyFouZRBZM,30,car passing by,train pM4cJb9NQcw,10,helicopter,train pM8lCnxs9WY,20,dog barking,train pMCBe8WKLEc,4,pheasant 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pQFYUW-lz9Y,95,playing accordion,train pQH2_gOjtyg,43,canary calling,train pQH2_gOjtyg,66,canary calling,train pQI2-e4t67U,15,volcano explosion,train pQI2UUXA2fU,630,playing tabla,train pQI2UUXA2fU,642,playing tabla,train pQLU1mVpRh4,0,cow lowing,train pQLcumTLgp8,0,people screaming,train pQMjcTYSVI8,29,playing glockenspiel,train pQMjcTYSVI8,89,playing glockenspiel,train pQN0UgvjBWQ,170,waterfall burbling,train pQO4Qyn8dMs,60,elephant trumpeting,train pQO4Qyn8dMs,77,elephant trumpeting,train pQOnmZNBDG8,150,playing clarinet,train pQOxMCTGtuc,11,singing bowl,train pQS7cTH9atg,53,playing glockenspiel,train pQS7cTH9atg,124,playing glockenspiel,train pQXl36jUQBM,572,sliding door,train pQf5ukMlmWI,558,car engine starting,train pQfPZdO_2xc,30,playing french horn,train pQgRSbPonH0,370,canary calling,train pQk4a5fJrBc,199,"bird chirping, tweeting",train pQlD03cMPzQ,10,chainsawing trees,train pQlPn8Kj-EQ,10,toilet flushing,train pQm2pV5VVEI,237,playing guiro,train pQm2pV5VVEI,268,playing 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pRKnYxoEeaM,103,people eating crisps,train pRLwsnambSs,89,playing didgeridoo,train pRNr1pqekUw,25,machine gun shooting,train pRNr1pqekUw,417,machine gun shooting,train pRQkghTD_mM,68,playing clarinet,train pRQkghTD_mM,87,playing clarinet,train pRQoVnkttQs,90,people shuffling,train pRSLb0XogD8,527,bowling impact,train pRSLb0XogD8,567,bowling impact,train pRUPbQKFpDw,96,singing bowl,train pRWjRnlmdwU,205,playing harpsichord,train pRWjRnlmdwU,326,playing harpsichord,train pRXSGD99S6s,100,playing harp,train pRZCn0JWvpo,60,playing banjo,train pR_-ClnXGDg,11,zebra braying,train pR_B9XGdkjc,107,playing timbales,train pR_B9XGdkjc,131,playing timbales,train pR_qrdp_b7Q,17,crow cawing,train pR_qrdp_b7Q,27,crow cawing,train pRb2dIjQQAg,37,volcano explosion,train pRbb-ad6iis,615,playing flute,train pRc_dHSgMJw,15,cap gun shooting,train pRc_dHSgMJw,35,cap gun shooting,train pRdi3oChUR4,20,baltimore oriole calling,train pRfJEAw3AHg,65,children shouting,train pRfJEAw3AHg,78,children shouting,train pRgT3eA0CxU,150,child singing,train pRhIIQVpzKM,279,slot machine,train pRiSh8UqHrU,280,playing clarinet,train pRiTNRHzOt0,330,"female speech, woman speaking",train pRieSN1rl8Q,1,bouncing on trampoline,train pRieSN1rl8Q,408,bouncing on trampoline,train pRkn8GNWlE8,140,"child speech, kid speaking",train pRlWXA5axBE,20,people screaming,train pRm4t8VZxKw,208,cuckoo bird calling,test pRoKqxkPlZU,85,playing trombone,train pRpAQYM8QU8,242,sharpen knife,train pRpAQYM8QU8,356,sharpen knife,train pRtEeqLGvEQ,40,playing electronic organ,train pRuOqPQYvHM,2,typing on typewriter,train pRuOqPQYvHM,15,typing on typewriter,train pRwaEwxraWw,130,playing accordion,test pRxkeqaElk8,30,playing trumpet,train pRxzgwe2Dtc,30,people screaming,train pS2KzNeRamQ,70,people burping,train pS9Fh_yqx6M,16,reversing beeps,train pS9Fh_yqx6M,41,reversing beeps,train pSFBhgKBAT4,160,people belly laughing,train pSHXPEMfEvs,120,playing electronic organ,train pSJqundqJV8,469,singing bowl,train pSKEB6LTA0c,108,people eating 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pSzrH_pvIX0,0,people farting,train pT-ejMpAILU,0,playing ukulele,train pT-ejMpAILU,109,playing ukulele,train pT0PaRFjGds,30,"pigeon, dove cooing",train pT1-C0h8As4,30,playing cello,train pT19lSxFOkY,33,tapping guitar,train pT19lSxFOkY,106,tapping guitar,train pT8hbXXw848,95,running electric fan,train pT8hbXXw848,207,running electric fan,train pTAuLfGIlkQ,6,lions roaring,train pTAuLfGIlkQ,25,lions roaring,train pTBuadWggJs,2,people sneezing,train pTCIpHeILlI,30,people sniggering,train pTDyKXTxG18,16,frog croaking,train pTDyKXTxG18,31,frog croaking,train pTIfOoY032Q,30,duck quacking,train pTIukM9zmRk,210,eating with cutlery,train pTLFN9k7PsY,798,lip smacking,train pTO5_gkaZN8,21,black capped chickadee calling,train pTO5_gkaZN8,187,black capped chickadee calling,train pTOzPI_IKQQ,150,"ice cream truck, ice cream van",train pTRiK6Cc9fo,0,wind noise,test pTSnDchWW78,36,police radio chatter,train pTTrNqCpcdc,160,snake hissing,train pTVwrWbHCLg,26,lathe spinning,train pTY56E9D_cE,154,squishing 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pUn8YqN2meU,59,playing cornet,test pUonKZN2ICs,11,airplane flyby,train pV-eMBejgw8,310,sliding door,train pV149D1LRDE,102,lions roaring,train pV149D1LRDE,116,lions roaring,train pV4K74dC_l0,30,"female speech, woman speaking",train pVDaEDd4HDI,460,"pigeon, dove cooing",train pVI2k_TVAvY,88,beat boxing,train pVJY1Q137cw,681,cat purring,train pVJY1Q137cw,695,cat purring,train pVJaBfY4c5M,30,ocean burbling,train pVL33KberLU,57,canary calling,train pVL33KberLU,215,canary calling,train pVQ7i3eAgYQ,50,playing piano,train pVRzseznfzs,56,civil defense siren,train pVRzseznfzs,145,civil defense siren,train pVVyCPXAZsk,271,people booing,train pVYODPI-YAA,20,using sewing machines,train pV_il0PoD_0,0,playing badminton,train pVaAK-5uED8,52,raining,train pVaAK-5uED8,76,raining,train pVcXAW1jP1Q,118,playing bassoon,train pVcXAW1jP1Q,194,playing bassoon,train pVcg6fayDEQ,0,playing zither,train pVcg6fayDEQ,60,playing zither,train pVeVkuromvg,361,squishing water,train pVeVkuromvg,447,squishing water,train 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pfWUc735KA0,250,francolin calling,train pfZpFTHWpEI,197,typing on computer keyboard,train pfZpFTHWpEI,238,typing on computer keyboard,train pf_BAEqKyzE,0,bull bellowing,train pfaUSak-dfc,181,vacuum cleaner cleaning floors,train pfaUSak-dfc,238,vacuum cleaner cleaning floors,train pfdRzXjJtWM,183,bouncing on trampoline,train pfiTberwz8Y,80,"race car, auto racing",train pfixwZ6ldSk,38,playing squash,train pflDBGb5HLo,0,"race car, auto racing",train pfnk3-NjPe4,14,playing badminton,train pfo30EDYCEw,757,forging swords,train pfo30EDYCEw,975,forging swords,train pfpPFMKh6Qw,0,playing harp,train pfpPFMKh6Qw,56,playing harp,train pfro7KdENnI,35,playing vibraphone,train pfsu4HwaRPY,3,lions roaring,train pfteERYuKuA,60,penguins braying,test pfwLtHjFMD8,5,cat hissing,train pfwLtHjFMD8,40,cat hissing,train pfxvjBcVk6U,230,"motorboat, speedboat acceleration",train pfz77dq_XGM,67,otter growling,test pg07llgat-0,84,playing badminton,train pg1LXjei-1Q,43,dog howling,train pg2iqCurlSA,40,cap gun shooting,train pg4tTVGU30g,50,playing cymbal,train pg82S3tyzNE,280,people crowd,train pg85b7sv-0U,0,people hiccup,train pg8ikCUEMBs,22,people gargling,train pgEBXKF2LuU,30,car passing by,train pgELlMHWRnM,155,machine gun shooting,train pgGC_xJtQ1M,0,"bee, wasp, etc. buzzing",train pgGC_xJtQ1M,18,"bee, wasp, etc. buzzing",train pgGUEwjYzUk,13,cricket chirping,train pgGUEwjYzUk,50,cricket chirping,train pgGoOOwXip0,72,people booing,train pgJGenFo4hk,48,beat boxing,train pgKvK-3-tpY,13,yodelling,train pgKvK-3-tpY,75,yodelling,train pgLSY6J7O1w,30,people screaming,train pgOiFNMuXQs,126,playing table tennis,train pgPTMcr6qLg,40,driving motorcycle,train pgQxDv8Iugo,160,people eating noodle,test pgRoG9riugQ,66,opening or closing drawers,train pgRoG9riugQ,76,opening or closing drawers,train pgTZBjwf3qM,9,lions roaring,train pgTZBjwf3qM,26,lions roaring,train pgVmGldPRZU,30,"rowboat, canoe, kayak rowing",train pgWAWwuAYos,20,people babbling,train pgj7cxZcTrg,430,people eating,train pgkntouYr08,286,planing timber,train pgkwRMvN5QM,10,playing electric guitar,train pgp9Rb2bhPA,24,playing double bass,train pgp9Rb2bhPA,38,playing double bass,train pgw1xkCjyCI,373,skiing,train pgyQIR8ThQQ,11,playing trombone,train ph0-RkqW1vM,0,playing piano,test ph0C41a25Hk,27,people whistling,train ph0C41a25Hk,37,people whistling,train ph1Gk4eSLCA,681,missile launch,train ph1Gk4eSLCA,810,missile launch,train ph23dyYaMfA,32,playing squash,train ph2g0DZNiZI,94,people slapping,test ph4fZU2MJok,13,eagle screaming,train ph4von08IIk,44,"electric shaver, electric razor shaving",train ph5lJPxQfZY,10,playing piano,train ph5nv-s8diM,6,firing cannon,train ph5nv-s8diM,25,firing cannon,train ph7UKSlDprk,60,playing saxophone,train ph7dj2YXhP4,20,people babbling,train ph8TUYLKki0,8,reversing beeps,train ph8TUYLKki0,50,reversing beeps,train phAZ2LMRK8w,98,cat purring,train phAZ2LMRK8w,134,cat purring,train phAldSBAkFo,0,playing clarinet,train phAldSBAkFo,32,playing clarinet,train phBEHeaSVkg,110,people sniggering,train phDYaKuzl1Y,111,"playing steel guitar, slide guitar",train phDYaKuzl1Y,121,"playing steel guitar, slide guitar",train phHQ25XYOO8,231,playing volleyball,train phIzLSf1gUU,30,playing flute,train phK4yXwVSF8,10,"bird chirping, tweeting",train phLO4XTYpwA,24,vacuum cleaner cleaning floors,train phLO4XTYpwA,207,vacuum cleaner cleaning floors,train phLQ4OZMQ4g,3,metronome,test phM9g1LKwUk,222,parrot talking,train phM9g1LKwUk,266,parrot talking,train phMBJ4j0vBs,80,skiing,train phMvbmvpa80,71,playing squash,train phOFmSfQ_Jg,194,tractor digging,train phOFmSfQ_Jg,231,tractor digging,train phPbhNNIS4g,90,playing electric guitar,train phU7NHhN4Cw,223,playing squash,train phUlmT4mgxU,58,arc welding,train phUlmT4mgxU,181,arc welding,train phUs9mkhe5E,11,tornado roaring,train phWfXmcDeRw,150,"female speech, woman speaking",train phX99TsHrs8,30,playing accordion,train phXPDjhaJTU,15,chicken crowing,train phYGFrQc2lI,13,lions roaring,train phYGFrQc2lI,28,lions roaring,train phZGGakLzNk,45,electric grinder grinding,train phZGGakLzNk,182,electric grinder grinding,train phaLkeYRyuE,33,snake hissing,train phaLkeYRyuE,101,snake hissing,train phe0N7mKyy4,116,basketball bounce,train phib36cTLiI,62,elk bugling,test phkVRHq_d8A,10,playing congas,train phkVRHq_d8A,20,playing congas,train phnRNhUpx4c,10,magpie calling,train phnRNhUpx4c,38,magpie calling,train phuC8kePWyg,80,printer printing,train phwlgogDg5c,40,playing saxophone,train phyubv_95kM,629,playing theremin,train phyubv_95kM,655,playing theremin,train phzqBd-jikY,330,chainsawing trees,train pi-11wtRzqA,30,church bell ringing,train pi-oIIqZAAg,7,arc welding,train pi-oIIqZAAg,50,arc welding,train pi0FHhi9I88,30,fire crackling,train pi1VVY8985Q,4,helicopter,train pi2Vyqa-wgE,87,dinosaurs bellowing,test pi3mAgddKHY,330,police car (siren),train pi81tDAaOhg,560,fireworks banging,train pi8NqFxIWX4,75,people eating crisps,train pi8NqFxIWX4,201,people eating crisps,train piAGWRxvsBU,20,"bird chirping, tweeting",train piAYp3PRf3I,426,playing darts,train piAYp3PRf3I,525,playing darts,train piCHPHw3ijI,15,people belly laughing,train piGt3HlNbi8,293,lighting firecrackers,train piLcnRXAZRM,90,playing trumpet,train piNRRg7WuG8,208,skiing,train piOeE6oZsio,187,bouncing on trampoline,train piOeE6oZsio,197,bouncing on trampoline,train piQW8vasVMg,244,volcano explosion,train piQW8vasVMg,261,volcano explosion,train piSDcXaGfBs,13,people nose blowing,train piX8NKVa3iY,209,spraying water,train piXadEmr7rI,0,playing acoustic guitar,train piYKrS14dxA,113,mynah bird singing,train pi_iJvFICtY,130,playing piano,train piauKrtpW-E,168,tractor digging,train pic3oNrfgnU,350,skidding,train pif-tFpaVbk,119,people crowd,train pif-tFpaVbk,140,people crowd,train pifgcGqkiaY,60,playing cello,train pig_ry-nabQ,20,skidding,train pignXMaONpY,411,playing bongo,test piiYKJrYAVU,234,rapping,train piiYKJrYAVU,256,rapping,train pilp5t4vlf4,83,sharpen knife,train pimRLKoxEjk,60,typing on computer keyboard,train pim_LgDO9lk,7,police car (siren),train pin1y-0G1aI,150,playing drum kit,train pipj1muSr7w,746,slot machine,train pir4vAH_CL8,11,alarm clock ringing,train piuHFWYfo1c,30,"engine accelerating, revving, vroom",train piuqAyNa82c,13,driving buses,train piv4N9m2hrQ,2,mouse squeaking,train piv4N9m2hrQ,41,mouse squeaking,train pivO7QUj1_w,77,beat boxing,train piwzGI1_O2g,30,people babbling,train piy4mvQkHr4,20,playing snare drum,train piyECZa_kX8,357,"race car, auto racing",test piywlZZAwJ8,135,swimming,train piywlZZAwJ8,152,swimming,train pj615mU9BHQ,186,slot machine,train pj7mpAzimCk,0,bouncing on trampoline,train pj9fkZUmMIA,13,singing bowl,train pjGtwc3Imao,410,typing on computer keyboard,train pjLCrPdE6Ak,0,wind noise,train pjMfkmimn38,30,playing hammond organ,train pjMgYJteDD4,10,skateboarding,train pjO-MK87KI8,113,"electric shaver, electric razor shaving",train pjO-MK87KI8,185,"electric shaver, electric razor shaving",train pjQuG_z0WdQ,5,playing darts,train pjQuG_z0WdQ,25,playing darts,train pjQxAPO5yrg,0,mouse pattering,train pjRs_WT8VzM,59,cat caterwauling,train pjUKyAda058,0,rope skipping,train pjZxxOexp5s,0,hail,train pjZxxOexp5s,10,hail,train pj_q_UlQe9M,97,playing tympani,train pjaQx3EkIvw,70,playing cello,train pjcC2W3cJio,190,lawn mowing,train pjeIK0cIkIY,73,playing tympani,train pjeIK0cIkIY,456,playing tympani,train pjf8FOOm_d8,3,francolin calling,train pjf8FOOm_d8,19,francolin calling,train pjf_fQAF0fY,65,playing squash,train pjfeiHxkyMA,482,mosquito buzzing,train pjgrQdXs1tg,104,metronome,train pjhuq33mFR0,8,fire crackling,train pjhuq33mFR0,27,fire crackling,train pjlKOANbtnw,50,driving snowmobile,train pjlKOANbtnw,63,driving snowmobile,train pjoQKIvf4ns,80,playing french horn,train pjpwB_DAJvI,30,"playing violin, fiddle",train pjrK11MwQcs,30,mouse pattering,test pjtJGwhmi9I,66,running electric fan,test pjuHVmcoVFc,36,playing oboe,test pjuyGQPd4S4,39,playing djembe,test pjvfw_nyR2E,27,playing cymbal,train pjyAecm4TDI,3,"electric shaver, electric 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bowling,train pkq6Naw3PUQ,17,striking bowling,train pkqOfgJhfUw,179,cap gun shooting,train pkqOfgJhfUw,193,cap gun shooting,train pku8OuVjl6A,100,machine gun shooting,train pkuG97o6neQ,566,tractor digging,train pkuLZebvmVg,377,planing timber,train pkxSg04bSF8,506,playing trombone,train pky5XWfOc3I,0,playing saxophone,train pkyKj0H4UFA,84,roller coaster running,train pkyKj0H4UFA,147,roller coaster running,train pkyikyp5pco,15,people shuffling,train pkyqYiwJleI,7,wood thrush calling,train pkyqYiwJleI,20,wood thrush calling,train pkz0rojApas,240,"male speech, man speaking",train pkzIxNi-ECc,131,playing bassoon,train pl-85Jbkpc8,1,canary calling,train pl-85Jbkpc8,126,canary calling,train pl4s6Fn4VII,130,chicken clucking,train pl8tq3Z76is,1,opening or closing car doors,train plAxljMqv5k,340,playing clarinet,train plCmQDYQEN8,17,chipmunk chirping,train plD784lDwdM,65,hammering nails,train plD784lDwdM,79,hammering nails,train plGboStZiiY,2,wood thrush calling,train plHy53mTMU4,32,sea lion barking,train plKaQfqt6Sw,29,people humming,train plKaQfqt6Sw,128,people humming,train plLo6qDutTo,260,people whispering,train plO31AO9tZ4,1,police radio chatter,train plO31AO9tZ4,39,police radio chatter,train plOQRrSNc3E,25,playing hammond organ,train plOQRrSNc3E,258,playing hammond organ,train plQ5W_yiydc,370,lions growling,train plSqyQGuwWc,134,pumping water,train plSqyQGuwWc,191,pumping water,train plU51cWbvno,30,playing trombone,train plUZ35iyOGc,79,wood thrush calling,train plUZ35iyOGc,364,wood thrush calling,train plZiyVzYIL8,1,"engine accelerating, revving, vroom",train plaEM6IoJOg,28,parrot talking,train plaEM6IoJOg,40,parrot talking,train plbDdwUgcqk,30,pig oinking,train plboimUNc4o,100,playing washboard,test plcHnf-z4Eo,21,fire crackling,train plcHnf-z4Eo,32,fire crackling,train pldcfQAiPK0,350,splashing water,test pleTVeIjNYY,2,playing theremin,train pleTVeIjNYY,38,playing theremin,train plfLL0Niy9k,110,lighting firecrackers,test plgXNiPtk-A,14,people sniggering,train plgeWko78Ns,29,car engine knocking,train plgeWko78Ns,30,car engine knocking,train plgicp3H7J4,26,"fly, housefly buzzing",train plgicp3H7J4,47,"fly, housefly buzzing",train plgy59fxesk,0,playing ukulele,train plgy59fxesk,88,playing ukulele,train pljnedma8Wg,854,striking bowling,train pljnedma8Wg,984,striking bowling,train pljrosvt-o8,17,cat hissing,test plkaNq2TRKY,20,car engine idling,train plkaNq2TRKY,234,car engine idling,train pllBx4j_t34,20,skidding,train plszOzrIoLs,6,playing mandolin,train plszOzrIoLs,42,playing mandolin,train plu_FtxhnLw,103,playing didgeridoo,train plu_FtxhnLw,317,playing didgeridoo,train plv9UHlHMPI,285,tapping guitar,train plx2C-ssLQ4,23,people marching,train plx2C-ssLQ4,33,people marching,train ply9qfGhSkk,43,baby babbling,train ply9qfGhSkk,57,baby babbling,train plyEn8_gRkM,83,"ice cream truck, ice cream van",train plyOxwC_6HU,20,"playing marimba, xylophone",train plz9-OYCLYU,49,singing bowl,train pm-F_H45p3c,87,playing cornet,train pm-F_H45p3c,124,playing cornet,train pm-KnI-_frM,430,playing trumpet,train pm-M3i6ZmPI,73,volcano explosion,train pm-mXgqunFk,30,train horning,train pm3PQABVuOA,260,people sobbing,train pm3n1DgGURo,8,chinchilla barking,test pm49-8jN6uk,20,playing banjo,train pm6P94BJ8c8,60,dog barking,train pm738skMIU0,36,playing timpani,train pm75S0gS-AI,52,civil defense siren,train pm75S0gS-AI,62,civil defense siren,train pm8cRdPlFz8,120,mouse pattering,train pmA7oFDQBRg,99,playing table tennis,train pmAxXC84bAk,33,playing erhu,train pmAxXC84bAk,163,playing erhu,train pmCbEUGTlAM,180,police car (siren),train pmDkOx5lMy8,110,thunder,train pmEvamIhlEk,102,playing badminton,train pmFEQS_wBKo,79,hail,train pmFEQS_wBKo,101,hail,train pmFX22vnRmM,170,"child speech, kid speaking",train pmI1Fh1cG-g,3,dinosaurs bellowing,train pmR0jFGUsKQ,30,using sewing machines,train pmR6v5UFcJ4,150,"male speech, man speaking",train pmRHP2yyhDE,30,duck quacking,train pmSBv2QHSaI,1,"rowboat, canoe, kayak rowing",train pmSl--z-KQg,188,cat growling,train pmTVoCl7GB8,2,people humming,test pm_nIkBB-7Q,52,playing tambourine,train pmaAB8SjFig,104,"electric shaver, electric razor shaving",train pmaAB8SjFig,232,"electric shaver, electric razor shaving",train pmbLhGZYFbk,5,baby laughter,train pmbLhGZYFbk,35,baby laughter,train pmdBrMc8xIk,77,singing choir,train pmdBrMc8xIk,98,singing choir,train pmdoDcNBt0E,30,female singing,test pmeqeD6B77A,150,playing flute,train pmfqS_Q1RTg,30,car passing by,train pmgvv1-IKi8,205,playing badminton,train pmhi-GZ_ag4,135,playing theremin,train pmhi-GZ_ag4,170,playing theremin,train pmi24uYJ41U,290,"engine accelerating, revving, vroom",train pmke5kLZWB4,117,playing didgeridoo,train pmkinPPhkIY,67,civil defense siren,train pmrEL-0_z4I,2,playing synthesizer,train pmrEL-0_z4I,88,playing synthesizer,train pmskTn5rGmI,18,"heart sounds, heartbeat",train pmskTn5rGmI,56,"heart sounds, heartbeat",train pmso1Ye6isk,30,police car (siren),train pmtCA3fecWs,20,playing clarinet,train pmvDNOhpXlo,162,volcano 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crowing,train rNPbB-wAmHQ,482,people eating crisps,train rNPbB-wAmHQ,749,people eating crisps,train rNQ0EBdxPvM,30,using sewing machines,test rNQBarDe0xQ,72,francolin calling,test rNSVGIVB7g8,91,bouncing on trampoline,train rNUbmiz-b6g,258,basketball bounce,train rNVDzdGFX2U,11,planing timber,train rNVDzdGFX2U,30,planing timber,train rNWFNYsUHK0,22,bathroom ventilation fan running,train rNWFNYsUHK0,34,bathroom ventilation fan running,train rN_O6dLjm9I,171,"subway, metro, underground",train rN_kU9tSb-8,10,disc scratching,train rNam7W4Df64,156,people whispering,train rNam7W4Df64,579,people whispering,train rNdXezVKeOA,36,playing trombone,train rNdXezVKeOA,69,playing trombone,train rNekOGB8Dw8,20,playing synthesizer,train rNfYmmWlqQE,30,stream burbling,train rNgWga8VnWo,43,playing cornet,train rNh4Zhx_dQw,42,playing cornet,train rNh4Zhx_dQw,160,playing cornet,train rNhpHsoBuO0,10,skateboarding,train rNimTpxoQ6o,80,"bee, wasp, etc. buzzing",train rNizdUYevHw,16,playing volleyball,train rNk2p_S1zOk,10,using sewing machines,train rNmeUxNwyoQ,60,playing volleyball,train rNra5Bj5bpM,40,skidding,train rNsKxHEAlyA,48,chimpanzee pant-hooting,train rNt2cPXtLyI,6,playing squash,train rNvBF_Tt-gA,71,playing bassoon,train rNvFPr3LgKU,30,playing flute,train rNw5WWGsWAQ,582,playing flute,train rNyJbdv2KF4,60,airplane,test rNyXz1dpJuI,89,playing erhu,train rNyXz1dpJuI,100,playing erhu,train rNyiSl6wIzw,314,car engine starting,train rO-Do6tPc2Y,199,rope skipping,train rO-Do6tPc2Y,391,rope skipping,train rO-Xz_2RBTk,34,playing table tennis,train rO0jmZHwV8Q,120,people sniggering,test rO14B9nIQuE,27,chicken clucking,train rO2UIDbE-D0,39,playing timpani,train rO2x6FK9cCk,90,playing snare drum,train rO5PxbbB1Rk,344,playing clarinet,train rO5PxbbB1Rk,363,playing clarinet,train rO5l-XS96V0,10,"male speech, man speaking",train rODIVG_9JlY,201,civil defense siren,train rOE0EaK0wlM,149,bouncing on trampoline,train rOExRzDGPuY,496,basketball bounce,train rOFF7-VVZYI,232,bowling impact,train rOFMi9nnUlc,11,tap dancing,train rOFMi9nnUlc,144,tap dancing,train rOFTDCoxedc,340,singing bowl,test rOFXt0osG6c,96,playing bass drum,train rOGWjfzN7D8,23,playing tuning fork,test rOHAqMgtfhI,9,skidding,train rOHOfupyq8c,205,beat boxing,train rOMPs1zM6PA,9,baby babbling,train rOMU791E9zg,230,bird wings flapping,test rOMgHF0AakI,92,"fly, housefly buzzing",train rOMgHF0AakI,102,"fly, housefly buzzing",train rOPRhNQBg2E,16,frog croaking,train rOPRhNQBg2E,26,frog croaking,train rOPzARRwEyE,0,fireworks banging,train rOQYaea3h7M,500,orchestra,train rOQcAli9JXY,209,playing mandolin,train rOQcAli9JXY,675,playing mandolin,train rOQt6_E-x_0,0,people humming,test rOTafaReN_o,60,helicopter,train rOU39ws1gHo,30,wind noise,train rOVu2u8BeMI,25,sea lion barking,train rOVw7SJ-aQI,50,skateboarding,train rOX4kGYVP70,2,baby laughter,train rOX4kGYVP70,13,baby laughter,train rOXJ9YHpYns,20,people sobbing,train rOXgqK8qGsM,1,electric grinder grinding,train rOXgqK8qGsM,24,electric grinder grinding,train rOXiddbrdj8,0,playing saxophone,train rOYmHMbSg0E,200,eating with cutlery,train rO_8CrvvmiU,48,dinosaurs bellowing,train rO_8CrvvmiU,128,dinosaurs bellowing,train rObb2DPWzyw,30,people sniggering,train rObiA-8Yy1Q,147,hedge trimmer running,train rOcfEi3PMPU,11,"engine accelerating, revving, vroom",train rOfof7J8WK4,162,slot machine,train rOgFeRDm_sc,93,bouncing on trampoline,train rOhU6Uw-p_U,11,hedge trimmer running,train rOjOkAere4c,220,playing electric guitar,train rOjb2iITYhg,18,bathroom ventilation fan running,train rOjujlD6J3Q,202,playing table tennis,train rOkLNRHPloQ,260,airplane,train rOlmYrqLRz0,30,"playing violin, fiddle",train rOoRertCd2E,8,airplane flyby,train rOolowH8rI4,23,chimpanzee pant-hooting,test rOyx_mJy-JQ,40,"race car, auto racing",train rOzkacFHUWQ,29,singing bowl,train rP0YceY0ZkA,0,opening or closing car doors,train rP0YceY0ZkA,25,opening or closing car doors,train rP11v3K4qxs,440,"child speech, kid speaking",train rP1RVEJ--2A,116,elk bugling,train rP1RVEJ--2A,248,elk bugling,train rP2kRxO1jR8,333,bowling impact,train rP2kRxO1jR8,343,bowling impact,train rP4ol9zX7cg,111,roller coaster running,train rP4ol9zX7cg,174,roller coaster running,train rP4rXh_p0uA,54,spraying water,train rP4rXh_p0uA,92,spraying water,train rP6Trr4XROc,330,using sewing machines,train rP7iQsOCBuo,10,toilet flushing,train rPAfAyow6Zw,211,people giggling,train rPAoVjlN_qc,40,fire truck siren,train rPB9W8vYexw,240,using sewing machines,train rPCcb_BjHtM,180,splashing water,train rPEf7r9p9vc,104,tractor digging,train rPGWhdC-Pmk,0,playing didgeridoo,train rPGgxEE2hjk,91,pheasant crowing,test rPGgxEE2hjk,118,pheasant crowing,test rPKtsryFkjs,30,orchestra,train rPLkg-IHdqE,40,playing french horn,train rPMCSLGnm8Y,190,people crowd,train rPMx7vOsaVU,140,printer printing,train rPNLkHeIom0,85,planing timber,train rPNLkHeIom0,137,planing timber,train rPRuyWERrGc,29,playing lacrosse,train rPSY_ZYncns,30,duck quacking,train rPTuh-gMiDg,30,"rowboat, canoe, kayak rowing",train rPa9mZsprbw,160,ocean burbling,train rPcEVmPIxJY,250,orchestra,train rPeTVSdX8UQ,10,helicopter,train rPh0_OE43rc,102,"playing steel guitar, slide guitar",train rPh0_OE43rc,239,"playing steel guitar, slide guitar",train rPjsC1qCSXU,44,playing double bass,train rPjsC1qCSXU,67,playing double bass,train rPlvUTUBClc,10,"child speech, kid speaking",train rPrslEDRR7s,0,ambulance siren,train rPsTwOKcMFE,78,baby laughter,train rPsTwOKcMFE,107,baby laughter,train rPtpgDwjZqc,30,train horning,train rPwbNkRFQdc,60,ocean burbling,train rPx7ajphjPA,168,playing erhu,train rQ2Tq9oHCjE,0,skidding,train rQ3IPqTLi-I,58,tap dancing,test rQ3gsBc8h4Q,444,playing acoustic guitar,train rQ7KUK7bDLQ,9,people whistling,train rQ7o2cWGOGI,20,playing zither,train rQ7o2cWGOGI,67,playing zither,train rQBzcMybFoY,10,driving buses,train rQC750bL4iE,60,cat purring,train rQC750bL4iE,112,cat purring,train rQH-3bwmLa0,2,pig oinking,train rQHwKI3CuE0,30,"vehicle horn, car horn, honking",train rQIfT6gJtxI,3,people eating,train rQKB-Sfd3wU,177,child singing,train rQKe0EgpRF4,50,toilet flushing,train rQMYsPdqjgY,844,elk bugling,train rQOGxgzVlXU,80,people screaming,train rQPhhJpUea4,3,playing harp,train rQUz1GGqc6E,333,playing double bass,test rQV5hHwzj4Y,30,mosquito buzzing,test rQV9d7sR4JA,16,playing banjo,train rQVfmBb6Ays,30,playing harpsichord,train rQXLitOylto,117,child singing,train rQYpGUSGOqQ,14,people farting,train rQbj9uvYL8I,50,airplane,train rQdWe3MIIUM,30,playing bass guitar,train rQeiSlCbcMI,30,printer printing,train rQlwfcMuD7o,40,people crowd,train rQmOOSlJ74g,10,"vehicle horn, car horn, honking",train rQnLCgQ4xgc,57,people cheering,train rQndwrc8gCI,20,typing on computer keyboard,train rQnnw8ZV4vY,233,"bee, wasp, etc. buzzing",train rQnnw8ZV4vY,603,"bee, wasp, etc. buzzing",train rQqXogZWS1U,0,playing harp,train rQrFKiYu0rg,52,playing erhu,train rQsWLagDp4A,70,playing harp,train rQthEYYXM-k,30,people sniggering,train rQuqxKPiw14,118,playing tabla,train rQuqxKPiw14,265,playing tabla,train rQwOIwziqwk,100,driving motorcycle,train rQxKIE7VrqE,40,playing trumpet,train rQxUrb9vpjE,22,cupboard opening or closing,train rQxUrb9vpjE,37,cupboard opening or closing,train rR0_80jfUmo,57,playing squash,test rR1h4DuOER0,108,playing badminton,test rR39rDVGPXk,166,ice cracking,test rR4iWSLeIcE,5,people belly laughing,train rR5d1km7Nnw,4,bird wings flapping,train rR5d1km7Nnw,21,bird wings flapping,train rR6dJHhInaM,197,opening or closing car doors,train rR6r8ZaKGLg,30,"pigeon, dove cooing",train rREFG_r1w94,30,"pigeon, dove cooing",train rREf2jL0jQs,10,playing snare drum,test rRFC5MFVfQ4,281,rope skipping,train rRH5dD4UCRI,30,police car (siren),train rRHscx9mGJI,23,frog croaking,train rRHscx9mGJI,72,frog croaking,train rRI7TnMI8mQ,115,people whispering,train rRI7TnMI8mQ,548,people whispering,train rRKZgh36MCg,30,playing drum kit,train rRL_pA5D2wQ,30,people sobbing,train rRN0fzK640U,30,"playing violin, fiddle",train rRNDhai3_to,2,sheep bleating,train rRP810El--s,588,fire truck siren,train rRP810El--s,958,fire truck siren,train rRPpAZYWqLc,113,penguins braying,train rRQ0HbleWa4,108,car engine idling,train rRQ0HbleWa4,142,car engine idling,train rRTvmcwXciE,150,eating with cutlery,train rRV5vAmnE-I,35,playing bass guitar,train rRVTA8SlJvM,233,lathe spinning,test rRVqHBvEqCc,140,"bee, wasp, etc. buzzing",train rRZkl6tMkPQ,150,people running,train rRaFEsaoqr4,21,francolin calling,train rRaFEsaoqr4,54,francolin calling,train rRfwg5-gOG4,378,skiing,train rRgOjMXOUuY,30,playing cymbal,train rRrTLpJLxUs,143,playing glockenspiel,train rRs4AQTqk34,0,crow cawing,train rRs4AQTqk34,10,crow cawing,train rRsNOVobya8,0,people burping,train rRvtQ18RBH8,0,waterfall burbling,test rRzWGIhwWAI,30,playing flute,test rRzWxGZOB4I,194,parrot talking,train rS-1yeP-Hm0,20,"bird chirping, tweeting",train rS3pxYv0aes,43,people eating crisps,train rS5NkSqRGZA,0,people sobbing,train rS6KeSWuXpk,30,playing harp,train rS6uqXbYd6Q,30,cat caterwauling,train rSA8bF0_Rm4,175,playing harp,train rSCcHpbeJyM,266,beat boxing,train rSEBvEoYntM,39,sharpen knife,train rSEBvEoYntM,100,sharpen knife,train rSHPCQEJd8s,30,driving buses,train rSHvW5dGanw,150,fireworks banging,train rSHwcfVGhDo,51,playing timpani,train rSHwcfVGhDo,78,playing timpani,train rSI7NbneIRY,126,civil defense siren,train rSJHLpGFa5Q,70,typing on computer keyboard,train rSJvgwWCkqs,29,vacuum cleaner cleaning floors,train rSJvgwWCkqs,117,vacuum cleaner cleaning floors,train rSNHn9tNQt4,0,fireworks banging,train rSSMIBfdPWU,9,playing bass drum,train rSSMIBfdPWU,176,playing bass drum,train rSTR-we8M4k,16,"vehicle horn, car horn, honking",train rSUt-uTj0V0,0,people nose blowing,train rSV2oBUOWgo,16,rapping,train rSV2oBUOWgo,100,rapping,train rSVF8kA9eXo,59,yodelling,train rSVSOmswxSM,10,people screaming,train rSWPVWkAbec,0,"bee, wasp, etc. buzzing",train rS_s8NrilHs,164,people booing,train rSaRvxmLXLc,712,bowling impact,train rSe06h1sKxE,0,people clapping,train rSeAuIzgmjY,30,playing accordion,train rSg9QozksLs,15,playing badminton,train rShZIer2a2E,456,slot machine,train rShdWkSramo,111,baby laughter,train rShdWkSramo,136,baby laughter,train rShwuaCxAfM,15,people cheering,train rSiBRO7eprU,0,helicopter,train rSn4FykeLPU,20,golf driving,train rSoIF_UBKUs,160,skidding,train rSp0RJD6too,15,playing harp,train rSp4bLIuGrs,10,typing on computer keyboard,train rSq9_TKjkGc,0,lawn mowing,train rSu1Cr43S2s,10,lions growling,train rSuXnm1YBcc,245,fire truck siren,train rSwNKxmXp-g,3,fire truck siren,train rSwzwMAVc14,4,missile launch,train rSwzwMAVc14,22,missile launch,train rSx7qlyAUuM,19,crow cawing,train rSx7qlyAUuM,37,crow cawing,train rSxhCMLavc8,40,playing double bass,train rSz-pNrp69g,10,"vehicle horn, car horn, honking",train rT-FoZt95D4,163,"playing steel guitar, slide guitar",train rT2FNg2SDtk,70,playing snare drum,train rT3qvDGT7WQ,400,playing cello,train rT4_onlQVXM,4,striking pool,train rT4_onlQVXM,126,striking pool,train rT6CTP_8fTc,70,chicken clucking,train rT9WOZbp74c,20,cattle mooing,train rT9tzUNdzjM,33,rope skipping,test rTB7xMknkmM,280,"playing marimba, xylophone",train rTBQmP6Vt0g,10,playing steelpan,test rTDDuG4ajt4,40,playing snare drum,train rTEtQ2p0998,3,sheep bleating,train rTEtQ2p0998,19,sheep bleating,train rTJ5J_MCb9A,90,"bird chirping, tweeting",train rTJepOCBQeQ,0,"female speech, woman speaking",train rTLZBshyIok,240,driving motorcycle,train rTNSzUXd3wk,180,playing double bass,train rTNbbpIaDVQ,0,police car (siren),train rTNkSWXTKUY,0,"ice cream truck, ice cream van",test rTQwTQ5nd2s,59,playing bassoon,train rTWWqky66Mc,5,people whistling,train rTXLzTwUf-0,0,playing harp,train rTXLzTwUf-0,191,playing harp,train rTZI1YEcCEQ,10,toilet flushing,train rTZPoaMLHEY,304,playing gong,train rTZPoaMLHEY,439,playing gong,train rTcb7ihHSKo,177,playing harp,train rTgDtI1yweg,18,lathe spinning,train rTgh8pttpC4,0,church bell ringing,train rTgh8pttpC4,10,church bell ringing,train rTh92nlG9io,30,typing on computer keyboard,test rTjGvbNsTa8,30,using sewing machines,train rTj_jOeXd3k,170,playing saxophone,train rTpePVriBrM,57,typing on typewriter,train rTsGeNI9SXg,30,people sobbing,train rTyQuGidCBU,40,airplane flyby,test rU04CxbwzJA,10,sailing,train rU2kgj41-1w,110,ocean burbling,train rU6rdQa7xVg,73,"playing steel guitar, slide guitar",train rU6rdQa7xVg,112,"playing steel guitar, slide guitar",train rU7WZkmqSGc,72,baltimore oriole calling,test rUBBfqfV76Y,124,roller coaster running,test rUBTBuHolKk,150,playing cello,train rUEOteG3ZpE,239,children shouting,train rUH8DuNmvxg,19,beat boxing,train rUI4dry_5S4,963,playing table tennis,train rUIGOcQMaSE,40,playing vibraphone,test rUO24e5QM_I,53,people cheering,train rUOY4VPBUNk,92,dog baying,train rURiTqnKe1Q,32,volcano explosion,train rUU-ScAcnW4,30,playing hammond organ,test rUUYR89aPmg,47,playing bongo,train rUUYR89aPmg,116,playing bongo,train rUWCKgJBKV0,25,ambulance siren,train rUa1L-90XXE,32,swimming,train 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rgdMDo5TBic,355,playing squash,train rgeNP5nGQek,200,dog barking,train rgfAABfBT7Y,23,"alligators, crocodiles hissing",train rgfAABfBT7Y,51,"alligators, crocodiles hissing",train rgfOWw8Rxq4,24,swimming,train rgfOWw8Rxq4,34,swimming,train rghkZKnVzaI,113,vacuum cleaner cleaning floors,train rgji6mjO0zI,190,playing badminton,train rglibN33aks,12,opening or closing car electric windows,test rgoYcuInIfo,30,playing cymbal,train rgpDhy0Nwc0,14,"bird chirping, tweeting",train rgqJFceVM2I,66,chicken clucking,test rgqtGsKA_Js,0,"bird chirping, tweeting",train rgrNIBA0yx4,30,playing electronic organ,train rgtrTjn74yo,4,dinosaurs bellowing,train rgtrTjn74yo,38,dinosaurs bellowing,train rgwNz-tnGBQ,0,toilet flushing,train rgx-gISdsyg,70,lawn mowing,train rgznBn8QmNY,10,"subway, metro, underground",train rh42gVF7geg,23,ripping paper,train rh42gVF7geg,103,ripping paper,train rh7OV4m_shI,30,playing accordion,train rh7nDHH08qg,0,people sobbing,train rh9-F99SW1k,32,bowling impact,train rh9-F99SW1k,60,bowling impact,train rh9KDzNkpSI,772,playing hammond organ,train rh9SmhazI5k,3,baltimore oriole calling,train rhAwhUVsWRo,5,goat bleating,test rhBb41L3PYs,287,tap dancing,train rhBb41L3PYs,437,tap dancing,train rhC0pxeuKWc,470,people running,train rhFKHaA40Y4,3,popping popcorn,train rhGSqHtz_C4,30,chainsawing trees,train rhGvg4po4kw,353,planing timber,train rhGvg4po4kw,376,planing timber,train rhHezkpxU7o,0,tapping guitar,train rhHezkpxU7o,45,tapping guitar,train rhHyb4sL0hY,37,opening or closing drawers,train rhHyb4sL0hY,81,opening or closing drawers,train rhK5JBYHrWU,9,bowling impact,train rhMpRPQlwmY,30,firing cannon,test rhQwPnEYWI4,30,people sniggering,test rhRrboWtlH4,30,baby crying,train rhSH8KnqkBQ,450,car passing by,train rhT8kzRAEEM,117,playing washboard,test rhTjyTLYdYk,120,playing clarinet,train rhUccyfn8po,19,playing bassoon,train rhW_qFnBst8,230,hair dryer drying,train rhW_qFnBst8,490,hair dryer drying,train rhWrIKdn8ic,116,people whispering,test rh_rFJsOmis,340,singing bowl,test rhgBTv9bDQk,30,"pigeon, dove cooing",test rhivN_ULK4c,123,playing ukulele,train rhivN_ULK4c,178,playing ukulele,train rhnT1UeL8BI,12,planing timber,test rho50JrK1I0,310,"bee, wasp, etc. buzzing",train rho50JrK1I0,400,"bee, wasp, etc. buzzing",train rhrNqhaH6jc,30,lions growling,train ri1vy6MjKpo,90,"race car, auto racing",train ri2DVH0Q-so,40,basketball bounce,train ri2fX8XU0Tc,444,sharpen knife,train ri2fX8XU0Tc,462,sharpen knife,train ri3f1UNKUJU,84,airplane,train ri3yJEs3HrI,232,playing french horn,train ri62f10f4x8,134,typing on typewriter,train ri6F7zosOPg,49,penguins braying,train ri70pmgQ4b8,40,"race car, auto racing",train ri7I6QWJreM,20,driving motorcycle,train ri7S5Qncubg,3,chicken crowing,train ri7S5Qncubg,65,chicken crowing,train ri7_zyidR1I,0,bird squawking,train riA0tk1i86g,290,"railroad car, train wagon",train riAJa6PUM4E,4,"vehicle horn, car horn, honking",train riAUfYw25t0,110,wind noise,train riBduOWWoJk,227,lip smacking,train riBduOWWoJk,324,lip smacking,train riHWid1NZas,219,playing bassoon,train riI4CyB18mU,10,ambulance siren,train riIE2xqnHyI,80,people sobbing,train riJD63Xfmio,360,playing didgeridoo,train riJD63Xfmio,465,playing didgeridoo,train riJs7DlphEM,4,goose honking,train riJs7DlphEM,15,goose honking,train riLxPF82qhA,11,planing timber,test riMI767O89Q,128,hedge trimmer running,train riMI767O89Q,143,hedge trimmer running,train riOBvxQsGbk,30,sliding door,train riT0l1tLOGA,88,hair dryer drying,train riT0l1tLOGA,572,hair dryer drying,train riZWOToUZ18,30,playing cornet,train riZWOToUZ18,56,playing cornet,train ricVfprV0Lo,30,playing trombone,train rie7C5444pM,57,playing double bass,train riguAWoUU6c,9,playing glockenspiel,train rihIlsk3KRI,0,skidding,train riku-enFqus,26,mosquito buzzing,train rilbNPyjMDI,45,people farting,train rilbNPyjMDI,168,people farting,train rilqUvykJzQ,294,lighting firecrackers,train rin612CdVI4,334,turkey gobbling,train riokFEd3nZY,10,chainsawing trees,train rip8xwobksc,314,striking pool,train rip8xwobksc,396,striking pool,train riwgcn0LVJQ,496,people eating crisps,train riznQ-3rjts,60,chicken crowing,train rj1FsDTd1Eg,40,people babbling,train rj505jDI4nQ,253,"heart sounds, heartbeat",train rj505jDI4nQ,319,"heart sounds, heartbeat",train rj5DZMMg3eQ,192,tornado roaring,test rj8Qex3xn0M,40,playing trumpet,train rj936-vl15o,160,driving motorcycle,train rj9HIP9lpbA,340,helicopter,train rjCbMgN5dFc,110,"subway, metro, underground",train rjG5Mcl7Ji0,10,machine gun shooting,train rjGGEUXj_0I,205,people nose blowing,train rjGjz5DolWA,30,dog barking,test rjMg2k1tJss,388,people slurping,train rjPUfa5T_oQ,381,people slurping,train rjPUfa5T_oQ,409,people slurping,train rjPoX4KQ5dw,22,frog croaking,train rjQLHQktyPg,227,playing hammond organ,train rjQLHQktyPg,260,playing hammond organ,train rjR_613gDsw,50,driving motorcycle,train rjT8KXyEsjg,130,playing flute,train rjUI97LT0SM,20,duck quacking,train rjUI97LT0SM,32,duck quacking,train rjV0GXup6pY,330,people whispering,train rjVMwYGoSbc,0,playing bongo,train rjVrsuAwovc,5,civil defense siren,train rjXgj_5KP-c,179,hair dryer drying,train rjYARQtW5jg,79,cat meowing,train rjYARQtW5jg,124,cat meowing,train rjYPJUBcoN8,33,"donkey, ass braying",test rjYPJUBcoN8,50,"donkey, ass braying",test rjYn3wnWpiU,124,parrot talking,train rjYn3wnWpiU,143,parrot talking,train rjZNtMmr5jY,170,"railroad car, train wagon",train rj_TSXwlhGQ,30,"motorboat, speedboat acceleration",train rjeObu4jfOc,19,"donkey, ass braying",train rjeObu4jfOc,33,"donkey, ass braying",train rjednQ1tay8,40,car engine knocking,train rjfkh7BB_K8,13,firing cannon,train rjhsLNnKX3k,27,playing hammond organ,train rjl2sJx8RuQ,0,playing glockenspiel,train rjl2sJx8RuQ,59,playing glockenspiel,train rjleL6j-Ny8,174,child singing,train rjm7BvZELvA,2,people whistling,train rjm_phhaTqg,12,cat meowing,train rjnO9Ua8fEY,56,playing snare drum,train rjnhTp1MvBQ,55,running electric fan,train rjnhTp1MvBQ,66,running electric fan,train rjqN_oB89pc,380,playing drum kit,train rjued58Yqpc,109,air horn,test rjxeShUI_g8,18,playing harpsichord,train rjxeShUI_g8,64,playing harpsichord,train rjzHrx_1xmA,130,playing synthesizer,train rjzmutRaKDo,49,cricket chirping,train rjzmutRaKDo,98,cricket chirping,train rk-HJLn1zLA,11,hail,train rk-h78dVltc,298,planing timber,train rk1CY76YNyE,290,playing banjo,train rk7JDvfIAq8,80,eating with cutlery,train rk9b_1SDi_A,30,"subway, metro, underground",train rkCEG_9wYHs,210,"child speech, kid speaking",train rkCJGVIDJZs,310,people whistling,train rkEXoDLhz0g,130,helicopter,train rkI21qU8YV0,2,cheetah chirrup,train rkI21qU8YV0,16,cheetah chirrup,train rkLEY5-eveE,30,"pigeon, dove cooing",train rkMa9cC-zDQ,22,playing guiro,test rkMgvtv4G2s,80,lawn mowing,train rkO-YO6NGMM,65,tapping guitar,train rkO-YO6NGMM,83,tapping guitar,train rkO7RJMz_fw,43,missile launch,train rkOSeGg9n08,20,ocean burbling,train rkP0LyzfYiU,74,fire crackling,train rkP0LyzfYiU,146,fire crackling,train rkQr7lgIjpY,179,alarm clock ringing,test rkS7ve7KCig,0,reversing beeps,train rkSgAacF3EA,59,wood thrush calling,train rkVbucSEReo,110,sloshing water,train rkWmicYDrqM,30,helicopter,train rk_iOIumShQ,17,car engine knocking,train rk_iOIumShQ,31,car engine knocking,train rk_qLtk0m2c,103,child singing,train rk_tfiTnSDw,105,playing harp,train rkaM1yOncZ4,0,baby crying,test rkeazz3aMQs,0,ferret dooking,train rkghf6x-X_Y,12,skidding,train rki4wHdj3e4,130,playing banjo,train rknMqCdmKic,30,goose honking,train rkolACeUZMg,61,baby laughter,train rkolACeUZMg,161,baby laughter,train rkomdEYl9qA,460,"subway, metro, underground",train rkt-udQn2e0,0,"race car, auto racing",train rktJa04Bfr4,370,"subway, metro, underground",train rkw6PdThaxQ,30,police car (siren),train rkxPaXs2Pn8,24,chicken crowing,train rky6lT6pkaw,0,scuba diving,train rkyOp-eEwEw,4,cat hissing,train rkz3exeN6nE,30,horse neighing,train rkzCm0aODlE,220,playing trombone,train rl1PnyPqVG0,9,"bird chirping, tweeting",train rl1ytzZOHug,179,coyote howling,train rl1ytzZOHug,199,coyote howling,train rl5LXtrqAtw,80,toilet flushing,train rl5zJlrEjRg,0,air horn,train rl9YHu9ta20,180,chainsawing trees,train rlCBvkm3fWM,30,"rowboat, canoe, kayak rowing",train rlCetbDzOfE,61,people marching,test rlDK_wTtQps,11,sailing,train rlKVXk7znpQ,38,playing steelpan,train rlKVXk7znpQ,103,playing steelpan,train rlKflLrxDeA,60,"subway, metro, underground",train rlLVaef0_80,0,"playing steel guitar, slide guitar",train rlNDdIdLpIo,520,playing electronic organ,train rlPm_5WwWLk,30,playing harp,train rlTL8b0ik0U,18,tornado roaring,train rlTL8b0ik0U,28,tornado roaring,train rlTLzCghEA8,68,skidding,train rlTwy8rxUFs,450,"race car, auto racing",train rlW8-TNarIw,120,playing saxophone,train rlXQ-sxZwfs,30,"playing marimba, xylophone",train rlaK50nlNRY,959,swimming,train rlaaSo9EOJs,30,sloshing water,train rlba2Vs65Cw,8,owl hooting,train rlba2Vs65Cw,49,owl hooting,train rlrO8gHIAEY,1,playing snare drum,train rlrO8gHIAEY,11,playing snare drum,train rlv5Jk0z5V4,30,"playing marimba, xylophone",train rlxmevufILI,40,playing harmonica,test rm06AkLvGmA,41,wood thrush calling,train rm06AkLvGmA,68,wood thrush calling,train rm5jhCIEI34,240,orchestra,train rm6kd2LO8FQ,71,playing washboard,train rm7yHnusPRI,10,"engine accelerating, revving, vroom",train rm9Bwksyl_A,30,chicken crowing,train rmB71qaXQHk,30,"motorboat, speedboat acceleration",train rmBGiKhRLkE,250,printer printing,train rmDJpgs3GaU,30,"pigeon, dove cooing",train rmDOGLbVhTU,0,skateboarding,train rmI5PeG9tSY,30,"playing violin, fiddle",test rmK-nmkmQ7U,40,wind rustling leaves,train rmL21Z7wZuk,20,blowtorch igniting,test rmPfJgDBeQ8,20,stream burbling,test rmQ5d7Q-Dy0,149,playing timbales,train rmQ5d7Q-Dy0,208,playing timbales,train rmQhPCwioJw,0,dog howling,test rmSx_mvP60o,2,dog howling,train rmSx_mvP60o,12,dog howling,train rmV41ZaJ6PI,30,driving buses,train rmdf2QeOUJM,48,train whistling,train rmdf2QeOUJM,142,train whistling,train rmfebxg-k2Y,40,people crowd,train rmgSQx8oUt4,6,zebra braying,train rmgSQx8oUt4,57,zebra braying,train rmmDnHNevbE,0,playing didgeridoo,train rmmDnHNevbE,27,playing didgeridoo,train rmmH7ktQlSo,0,playing bass guitar,train rmpD3GHh-Gw,240,skateboarding,train rmpkTEpF38s,210,playing clarinet,train rmtJy83QmOM,30,fireworks banging,train rmu5YzaPM2g,0,playing squash,train rmwhCL-rJ_w,13,goose honking,train rmxRhByMBMU,30,car engine knocking,test rmyjT-6mWwk,170,playing flute,test rmzDww6glq8,200,playing electronic organ,test rmzYDIM8xsM,28,woodpecker pecking tree,test rn2MzWR8H_8,240,splashing water,train rn381TUMxyE,286,arc welding,train rn381TUMxyE,298,arc welding,train rn77tAch-WE,448,people shuffling,train rn9MQhLs1zc,190,playing trombone,train rnAR_z3mw48,132,people nose blowing,test rnFbBItICj4,30,wind noise,train rnFu9UrmEDw,30,playing piano,train rnHYhyCbcWw,0,playing sitar,train rnJHuIZuevA,11,barn swallow calling,train rnJHuIZuevA,39,barn swallow calling,train rnKrSnPxBGc,317,tap dancing,train rnQV6Kfeaoc,30,"playing violin, fiddle",train rnRwoghjuUQ,100,basketball bounce,train rnSEFgXPzfA,0,"race car, auto racing",train rnSSF1DaXww,420,cap gun shooting,test rnTU7MMSAOs,56,tractor digging,train rnUMYBWOMMQ,34,playing tambourine,train rnUMYBWOMMQ,121,playing tambourine,train rnUcQpH4418,490,playing tabla,train rnUiX7njOV4,0,sailing,train rnV-AEJIvuY,2,cricket chirping,train rnV-AEJIvuY,14,cricket chirping,train rnWPIQBf54c,30,"cattle, bovinae cowbell",test rn_cDJfkxSo,60,"female speech, woman speaking",train rnaOR_WsXPc,0,wind chime,train rnaOR_WsXPc,28,wind chime,train rnaYB3pfi9g,182,playing badminton,train rndjd-fSGhs,289,male singing,train rneiIdBa20E,0,lawn mowing,train rniWSHyhKGc,63,cutting hair with electric trimmers,train rniee1rCniw,460,"playing marimba, xylophone",train rnlWFDC7vt0,27,playing squash,train rnm8TjglYlA,30,fireworks banging,train rnnWO3iyUHs,53,pheasant crowing,train rnoII0JtN38,124,vacuum cleaner cleaning floors,train rnoII0JtN38,201,vacuum cleaner 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sOA-ZjDqSp0,22,dog howling,train sOA-ZjDqSp0,36,dog howling,train sOGCVzpctA4,30,sloshing water,train sOGNSW3yg-M,739,skateboarding,train sOGNSW3yg-M,906,skateboarding,train sOI5njsTJeQ,525,"playing steel guitar, slide guitar",train sOI5njsTJeQ,592,"playing steel guitar, slide guitar",train sOKx1TErHAU,30,fireworks banging,train sOLJsB7nAg8,0,"bird chirping, tweeting",train sOMPREEcxCU,0,waterfall burbling,train sONLqRrNINs,13,tornado roaring,train sONLqRrNINs,118,tornado roaring,train sOYs3ZKlmIs,120,playing flute,train sOfOSuPZ6yM,0,wood thrush calling,train sOg4MNTWx_0,0,skateboarding,train sOisfqKmCvw,520,driving buses,train sOksEBvs9-o,40,smoke detector beeping,train sOnXONU-8Lw,170,printer printing,train sOsmty7Qio4,0,machine gun shooting,train sOtpvMlQ2EQ,10,stream burbling,train sOuJG_t_2OI,90,eating with cutlery,train sOwc2jAgqOQ,16,horse clip-clop,test sOxcrEkKeI4,161,lathe spinning,train sOxcrEkKeI4,180,lathe spinning,train sOyRmfDjJyY,150,basketball bounce,train sP0anQjatZQ,32,chipmunk chirping,test sP0ogCn7f2I,12,tap dancing,train sP3ilUyk-Co,170,people cheering,train sP57075HwDA,83,playing erhu,train sP7rwKQvEOQ,0,scuba diving,train sPCjS-CqXHM,5,snake rattling,train sPH9jDebxM8,30,playing acoustic guitar,train sPIS8vtVqv4,1,reversing beeps,train sPIS8vtVqv4,16,reversing beeps,train sPIXpmZwTz8,4,people farting,train sPIXpmZwTz8,42,people farting,train sPKWMYG40Qw,40,fireworks banging,train sPLbsz2VgRk,60,people babbling,train sPMkHqGzhKM,0,skidding,train sPOMOD8QGFg,29,cap gun shooting,train sPQUe6V2loU,60,skateboarding,train sPUDX9GOQ5s,160,"child speech, kid speaking",train sP_1xkLM4DI,0,playing zither,train sP_1xkLM4DI,22,playing zither,train sP_91NkL5RE,48,playing bongo,train sP_91NkL5RE,58,playing bongo,train sPauFjmGmos,270,playing bagpipes,train sPbpgnbJG8k,39,playing timpani,train sPeu5IRj5nc,0,police car (siren),train sPgbUtTecxg,7,lions roaring,train sPkHFXlUZRc,50,chainsawing trees,train sPkrAc9Qj0s,354,playing bass drum,train sPkrAc9Qj0s,384,playing bass drum,train sPm0vT9fyoY,8,lions roaring,train sPm0vT9fyoY,19,lions roaring,train sPorGErEw3M,30,singing choir,train sPtvIlLI3nc,375,playing congas,train sPtvIlLI3nc,572,playing congas,train sPwIGAuTIPE,50,people hiccup,train sPyuBIixJOQ,80,playing trumpet,train sPzLOL404PA,10,playing acoustic guitar,train sPzbEy3-K7A,9,train horning,train sPzbEy3-K7A,21,train horning,train sPzn4VbEeIY,30,baby crying,train sQ0PHw32Flc,11,door slamming,train sQ0PHw32Flc,44,door slamming,train sQ0rDqlM0Vs,262,"playing steel guitar, slide guitar",train sQ0rDqlM0Vs,626,"playing steel guitar, slide guitar",train sQ3NiBJoqtc,112,playing shofar,test sQ8vnFGXNpg,0,"heart sounds, heartbeat",train sQ9fyZsa978,400,playing saxophone,train sQDAOfrE2WE,66,playing clarinet,train sQDAOfrE2WE,81,playing clarinet,train sQE5I6VZftM,24,playing hammond organ,train sQLiMTl8ebQ,288,male singing,train sQNdEhz_yNg,110,playing cymbal,test sQP4rxwR7sg,0,playing ukulele,train sQP4rxwR7sg,6,playing 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s_u0WplPOhM,16,cat growling,train s_xMq4kjwXI,0,goose honking,train s_xMq4kjwXI,10,goose honking,train s_xqaHzQ8tk,6,cat meowing,train sa2nLSD1uoQ,255,hair dryer drying,train sa2nLSD1uoQ,281,hair dryer drying,train sa6B5XyFYIg,40,playing bagpipes,train sa6z0mxsBo8,30,stream burbling,train saEds-qtjPM,20,"motorboat, speedboat acceleration",train saKIY-YXo14,320,people whistling,train saVgk8CPL3g,52,singing choir,train saWm_7jsO0g,30,car passing by,train saX4T5hPaGA,20,"ice cream truck, ice cream van",train sabX0KrNeVg,62,playing bongo,train sabX0KrNeVg,150,playing bongo,train saccQ-X4fdY,39,singing bowl,train saccQ-X4fdY,60,singing bowl,train saevFB0acTY,30,people screaming,train sahVbvsD5mQ,80,eating with cutlery,train samyE_r0sXs,130,goose honking,test sb-uIO_-rRg,3,toilet flushing,train sb1SNvhD21Q,17,playing gong,train sb1SNvhD21Q,34,playing gong,train sb53n9aRHpE,60,wood thrush calling,train sb53n9aRHpE,71,wood thrush calling,train sb6yqx_maIk,1,people giggling,train sb7LEpuA9a4,570,orchestra,train sb9a9os60PE,410,"race car, auto racing",train sbARpvG6uxk,226,playing badminton,train sbF8ImvxLr8,138,playing zither,train sbF8ImvxLr8,157,playing zither,train sbFWCaYOgeg,156,"playing steel guitar, slide guitar",train sbFWCaYOgeg,184,"playing steel guitar, slide guitar",train sbHLGchgOjA,29,duck quacking,train sbHLGchgOjA,111,duck quacking,train sbHZcgkyJjw,0,"electric shaver, electric razor shaving",train sbHrDFJNLcw,0,people sneezing,train sbIkUq6Ont4,88,playing harp,train sbPuBN-DTEk,30,police car (siren),train sbR5wae59AU,30,people crowd,train sbTt25S5XdE,30,playing snare drum,train sbVM9JWv4ME,70,driving motorcycle,train sbXO_esnNeU,203,police radio chatter,train sbXO_esnNeU,394,police radio chatter,train sbYr-F13QMo,0,playing flute,train sbZ5Ls141fg,30,baby laughter,test sbZNqAu0A38,30,police car (siren),train sbZvuz7EV8s,36,playing snare drum,train sbZvuz7EV8s,46,playing snare drum,train sbaA4n2j2l0,0,parrot talking,train sbfZLQXc8RE,8,playing 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sc7pB6VvJT8,13,people finger snapping,train sc7pB6VvJT8,143,people finger snapping,train scEI6RTQyQc,300,people crowd,train scLBNZl0RYo,50,"playing marimba, xylophone",train scNqWrE8RrU,30,"playing violin, fiddle",train scO6dsXcDoQ,510,playing trombone,train scRVEzEjYBo,453,ripping paper,train scT5F9QO0YM,11,playing trombone,train scVI4yViNq0,109,playing snare drum,train scVI4yViNq0,199,playing snare drum,train scVkWn3Y8so,150,playing synthesizer,train scYRUkrFLiQ,30,people whistling,train scZFDIaYXl4,93,"playing steel guitar, slide guitar",train scZFDIaYXl4,142,"playing steel guitar, slide guitar",train sc_1fFSOar0,30,playing harp,train scaTRuK6YFM,58,"heart sounds, heartbeat",train scaagRtcIAg,1130,people slurping,train scafeyAuUAA,0,driving motorcycle,train scbTRD3Tllo,0,"race car, auto racing",train scebm0Y_cbU,398,machine gun shooting,train scebm0Y_cbU,422,machine gun shooting,train scklK0pTQxE,10,train horning,train scm7r0uBepU,467,mouse clicking,train scnvukJfGJQ,2,"engine 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sdRnH2OwTqg,10,driving motorcycle,train sdTVbdxw-Uk,30,playing drum kit,train sdX3dnphqms,30,people sniggering,train sdY5_sgNeno,90,people eating crisps,train sdY5_sgNeno,309,people eating crisps,train sdbkTEs6-i0,230,orchestra,train sdcE6OXaOII,50,typing on computer keyboard,train sdeEJU7BENE,20,playing saxophone,train sdeFl8zKgK4,30,helicopter,train sdstnr_UXpI,120,police car (siren),train sdukgCY_XTs,110,playing trombone,train se4G4iWRUM8,100,toilet flushing,train se4GheHCJ7w,45,cat purring,train se4GheHCJ7w,162,cat purring,train se62EZurO8Q,70,lawn mowing,test seBRyClhFy0,10,"race car, auto racing",train seC3qjuaVi8,30,"playing violin, fiddle",train seFJhtrrmfQ,70,dog barking,train seI83m0BbqQ,30,playing cymbal,train seR-fi8-wdU,440,"bee, wasp, etc. buzzing",train seRZjbGT4vQ,130,playing accordion,train seVieyiTBbk,711,pumping water,train seWrLzl6fXA,160,police car (siren),train seYSDU7k4T0,0,playing hammond organ,train secD9nmx6lc,84,"cattle, bovinae cowbell",train sei4UDLQf_M,41,dog baying,train sejMb_FB_ag,180,playing clarinet,train set3L1-rjdI,230,toilet flushing,train sewxnah4Oyc,360,playing cello,train sf0qUfZcCvU,146,parrot talking,train sf0qUfZcCvU,161,parrot talking,train sf5YHajXWAU,20,playing bass drum,train sf66H0WDuI4,90,turkey gobbling,train sf7L2RMgrx8,3,playing harmonica,train sf8eCd9dpBc,53,cheetah chirrup,train sfA0tZgtSeg,35,cat caterwauling,train sfA0tZgtSeg,49,cat caterwauling,train sfAvvZwdLCY,20,toilet flushing,train sfF3xoU8M9A,110,people farting,train sfHDnAqoaXg,0,playing ukulele,train sfHDnAqoaXg,101,playing ukulele,train sfIeGQsr7eg,0,driving buses,train sfK9hO3aD4c,107,playing table tennis,train sfK9hO3aD4c,280,playing table tennis,train sfKzHpsQkxo,175,playing synthesizer,train sfKzHpsQkxo,288,playing synthesizer,train sfMWYJpAScA,30,goose honking,train sfNcOxMZ85w,40,male singing,train sfRRSe0mHmk,90,"subway, metro, underground",train sfT5sQNSO5w,20,people whispering,train sfXt8h7KAHM,30,pig oinking,test sf_n4szstWY,0,skateboarding,train sf_n4szstWY,46,skateboarding,train sfbNmWPvO8Q,62,playing gong,train sfbNmWPvO8Q,122,playing gong,train sfh7Jly68qU,210,playing acoustic guitar,train sfhDo-x0VX0,20,driving buses,train sfmAeijj5cM,30,playing ukulele,test sfmr4Oq3DVQ,160,fireworks banging,train sfqC-lxj0BE,54,playing mandolin,train sfqC-lxj0BE,135,playing mandolin,train sfrT1SAfdlI,210,playing piano,train sg0-HUaLodo,53,dog growling,train sg7xGSPJDRM,30,playing clarinet,train sg8SuJ2gxoY,20,driving motorcycle,train sgANY48F4NY,50,fireworks banging,train sgAU2U8n80Y,413,swimming,train sgDsVSjKs70,8,fox barking,train sgGMi1eVvOc,8,playing harp,train sgGMqATJ0JA,10,"race car, auto racing",train sgGYT95y538,140,stream burbling,train sgHhe3K63pA,120,"railroad car, train wagon",train sgJ3fg7JjAc,30,"pigeon, dove cooing",train sgKKBbe8CW0,140,playing piano,train sgKmfDTMB7w,10,otter growling,train sgKmfDTMB7w,23,otter growling,train sgLg-48i12E,50,playing drum kit,train sgNdM-n1bcw,89,playing electronic organ,train sgNdM-n1bcw,102,playing electronic organ,train sgO17DWwtoY,280,playing cymbal,test sgPnDNsc_wA,30,toilet flushing,train sgQThg0MYA8,43,frog croaking,train sgQThg0MYA8,67,frog croaking,train sgQYvRE2KHI,30,playing bagpipes,train sgRZraBax-E,12,cheetah chirrup,train sgRZraBax-E,52,cheetah chirrup,train sgSOxltPP2g,309,playing table tennis,train sgSwxkcZtDY,128,train horning,train sgSwxkcZtDY,180,train horning,train sgTlVp3Dr-Q,110,toilet flushing,train sgXLpQNZFJ4,0,skateboarding,train sgXLpQNZFJ4,18,skateboarding,train sgXjQ9WcL5A,4,pumping water,train sgYW6Bx5QsI,0,playing clarinet,train sgYW6Bx5QsI,115,playing clarinet,train sgZXt8zkBl8,85,tap dancing,train sg_uyXvNPAw,47,canary calling,test sg_xmf4UTNI,64,ambulance siren,train sgcSs2LcQos,9,cat growling,train sgcSs2LcQos,23,cat growling,train sgdPlDG1-8k,188,people battle cry,test sgfHBNMTkzw,47,mosquito buzzing,train sgf_5QwqlyM,0,playing ukulele,train sgf_5QwqlyM,100,playing ukulele,train sgjqvcLZ5rc,127,playing castanets,train sgjqvcLZ5rc,137,playing castanets,train sgkpK9adagY,30,people running,train sgkwq9a7mgU,0,toilet flushing,test sgncsdwSoM8,21,people burping,train sgpAvoN2Y34,130,playing erhu,train sgq6DGyAtJ8,310,"motorboat, speedboat acceleration",train sgqkxJ9rVwI,184,shot football,train sgvQUPj-k1A,25,tractor digging,train sgwhtE8HdjM,20,"child speech, kid speaking",train sgzZLNwqvdQ,70,playing bagpipes,train sh-XNZSVfo4,530,lawn mowing,train sh-XrCP0L5U,60,woodpecker pecking tree,train sh-XrCP0L5U,430,woodpecker pecking tree,train sh2qM6xK3c4,270,fireworks banging,train sh4EQFVAE04,108,playing theremin,train sh4EQFVAE04,138,playing theremin,train sh62CI534DE,41,penguins braying,train sh7-gbR4PvQ,162,lip smacking,train sh7-gbR4PvQ,176,lip smacking,train sh7EmimK9nI,19,playing vibraphone,train sh7EmimK9nI,66,playing vibraphone,train sh92-75RR3E,21,baby crying,train shBWUrHo52I,30,orchestra,train shLlgN8PZQY,501,playing mandolin,train shLlgN8PZQY,578,playing mandolin,train shQqZPRjOxc,30,people eating,test shREvJq6l1M,48,playing synthesizer,train shREvJq6l1M,58,playing synthesizer,train shRNXFTbd6M,55,playing tennis,test shRNXFTbd6M,206,playing tennis,test sh_fN1v9q4M,60,playing synthesizer,train sh_fN1v9q4M,127,playing synthesizer,train shalDdBHulI,30,people shuffling,train shhryqZxMH0,80,"rowboat, canoe, kayak rowing",train shiPEqY22PY,82,dog howling,test shkxVcpYF8c,222,mouse clicking,test shlkS_hqQT8,30,driving motorcycle,train shpIyi9NX88,23,chimpanzee pant-hooting,train shpOybfQQ7k,0,"bird chirping, tweeting",train shtnKiig4r0,160,orchestra,train si-66Kv2m0g,30,"rowboat, canoe, kayak rowing",train si7TjSMmnfg,210,using sewing machines,train siEhzf9cop8,4,baby crying,train siHwQkurDXA,19,playing bugle,train siJzPBPSp_U,208,forging swords,train siLiB-LMtsU,6,playing oboe,train siLiB-LMtsU,26,playing oboe,train siRFsLZpoGI,11,playing guiro,train siRFsLZpoGI,23,playing guiro,train siW1qQvWwmQ,27,reversing beeps,train siW1qQvWwmQ,38,reversing beeps,train si_IAMPOXlQ,170,playing timpani,test sibakQm_9QE,190,sliding door,train sigkXkNHoH0,40,playing cello,train simd0Xwhd2Q,5,scuba diving,train simd0Xwhd2Q,40,scuba diving,train siq-wRzZ4eo,26,ambulance siren,train siqnJ2OqV9w,50,skateboarding,train sircGPq9cck,330,"child speech, kid speaking",train sisQu83C2-M,21,printer printing,train sj-JIxks5us,16,pheasant crowing,train sj-JIxks5us,30,pheasant crowing,train sj-llLXPc-Y,11,pheasant crowing,train sj0Ab0Q_uoc,2,scuba diving,train sj0XcMzo3pc,30,"playing marimba, xylophone",train sj4P_fuC1to,24,snake hissing,train sj4P_fuC1to,67,snake hissing,train sj6Z5B4YcgQ,30,"railroad car, train wagon",train sjAptVRNrm8,30,"motorboat, speedboat acceleration",train sjCUAcJ-fF8,434,playing tabla,train sjCUAcJ-fF8,525,playing tabla,train sjDBac4tZOw,0,playing tennis,train sjElLpSO270,4,splashing water,train sjGifl2m_3k,280,using sewing machines,train sjJ4JaHPZ4s,184,slot machine,train sjK0jXcXy8Y,66,"cattle, bovinae cowbell",train sjK0jXcXy8Y,86,"cattle, bovinae cowbell",train sjLCAcWcS1s,550,"child speech, kid speaking",train sjLDkEyych4,54,playing accordion,train sjO0kS94P7w,0,"vehicle horn, car horn, honking",train sjO5epSBSdY,47,skiing,train sjSCTSfxVMw,3,firing cannon,train sjYDo5Bj3AY,30,"race car, auto racing",train sjaukTNqD6w,134,lip smacking,train sjaukTNqD6w,650,lip smacking,train sjiyEais66Q,10,lawn mowing,train sjkkZekCmDw,24,playing cornet,train sjlfb1b5Y5Y,490,helicopter,train sjpGLZ46Xa0,40,playing trombone,train sjqJVKvOhsY,30,"engine accelerating, revving, vroom",train sjre61a6Gyg,30,sloshing water,train sjt8Yy68URo,5,playing timpani,train sjt8Yy68URo,99,playing timpani,train sjw9-Ktitdg,170,chainsawing trees,train sjwvhUcd28w,90,"playing marimba, xylophone",train sjy7igy9mto,3,playing clarinet,train sjy7igy9mto,13,playing clarinet,train sk5DEsTdrlI,277,playing banjo,train sk7p0LnJmdA,72,playing harp,train skBkKnABzWs,0,playing clarinet,train skBkKnABzWs,94,playing clarinet,train skFciG1k_Yc,110,printer printing,train skG_36Abhos,208,people marching,train skHVtZyzpQw,75,disc scratching,train skIX1tAZFOc,30,people running,train skK5GFATGEU,250,"subway, metro, underground",train skNY67l43h4,30,"pigeon, dove cooing",train skSyu-Q5nZI,0,cat purring,test skTjGgCiWkA,180,people sniggering,train skUZ2-bfaPM,4,"vehicle horn, car horn, honking",train skeFawZe__U,30,playing piano,train skecFfZZaFo,72,playing bongo,train skeuMCAn3uo,90,playing snare drum,train skgVkVbw5SM,30,dog bow-wow,train skis75hLNcY,410,driving buses,train skjJ4tg-BA4,20,"playing violin, fiddle",train skkGUmLWD8I,80,car engine knocking,train skpkNEHlC7E,50,playing drum kit,train skuCXAN5ItA,23,dog bow-wow,train skwl9axYSIE,400,playing ukulele,test skxOyyYReQk,30,sailing,test sl1MkTiYIWM,18,people sniggering,train sl37XQfkJCQ,30,playing synthesizer,test sl69mwbSLxA,30,"vehicle horn, car horn, honking",train sl6si75Yhg0,90,thunder,train sl8tK1ZZvSg,370,playing clarinet,train slDdvHuLA1I,25,playing bagpipes,train slEDoEMYMoA,1,child singing,train slFRHUHoadw,180,turkey gobbling,train slHp8BZgT9I,10,helicopter,train slHsIJhWSkE,60,people whispering,train slOgYIKuAx4,240,playing glockenspiel,test slPFW-Nca5c,70,lawn mowing,train slSdFf7bknM,30,playing drum kit,train slYpq10hLkg,0,baby laughter,train slbhA0hwhFY,71,air horn,test slfngqS0n9w,250,playing drum kit,train slgLmYc4XiY,0,telephone bell ringing,train slgeb5sziSE,260,singing choir,train slm6duI4p5U,0,dog howling,train slnV_vl1o20,43,playing accordion,train slo5xSuRNvA,80,"railroad car, train wagon",train sloQazQLtaQ,30,wind noise,train sloroLWSiXM,11,mouse squeaking,train sloroLWSiXM,25,mouse squeaking,train slvLIvuABjo,28,playing harpsichord,train slyPrSW_3Jg,16,toilet flushing,train slzl17ljtNo,110,playing piano,train sm-KbDzKs7U,120,playing cello,train sm6f9sHOANc,90,playing saxophone,train sm7eBFHtdeA,30,wind rustling leaves,test smA0346vG5c,395,playing accordion,train smA3WWc6wpc,91,dinosaurs bellowing,train smAxunXH-hU,7,"vehicle horn, car horn, honking",train smBHJiEPCRI,30,duck quacking,train smCSy7hyyRg,18,"vehicle horn, car horn, honking",train smDzBBXB3UI,30,playing vibraphone,train smJRVhMRxIQ,7,chinchilla barking,test smKcEKB9W24,189,"heart sounds, heartbeat",train smKcEKB9W24,210,"heart sounds, heartbeat",train smO9IMfUHnM,10,playing drum kit,train smS9aJidlrc,30,driving buses,train smT6VmSIJro,0,playing ukulele,train smT6VmSIJro,108,playing ukulele,train smTiGSAko_o,0,"bird chirping, tweeting",train smUs3hcmlS4,8,people finger snapping,train smUs3hcmlS4,19,people finger snapping,train smVWhQdC0b4,160,playing synthesizer,train smZHAeNF3Q4,17,people eating,train sm_ADflSf80,1,"bird chirping, tweeting",train smbjzYj9hB0,20,people crowd,train smeLYVlh_6Q,30,"rowboat, canoe, kayak rowing",train smeYbLGXcIU,1,gibbon howling,train smgjDWe5ys4,30,lawn mowing,train smhsE-r2Vl4,30,"bird chirping, tweeting",train smieK7KlMkk,11,air conditioning noise,train smjiSAnuR6s,64,popping popcorn,train smkUcJMkoaM,36,playing volleyball,train smmcKxu1Q5A,170,rope skipping,train smmcKxu1Q5A,181,rope skipping,train smn2v3aUtOs,160,"child speech, kid speaking",train smqKcAZ4OMI,30,"rowboat, canoe, kayak rowing",train smqcBKzX2-4,120,"child speech, kid speaking",train smqi7G8wcOM,30,goose honking,train smrm9TSWvKg,30,"pigeon, dove cooing",train smyp6lUaMEw,60,playing cornet,train smyp6lUaMEw,189,playing cornet,train sn17APs9mho,97,playing tabla,train sn17APs9mho,199,playing tabla,train sn1Ws1MDHVw,156,"playing steel guitar, slide guitar",train sn1Ws1MDHVw,218,"playing steel guitar, slide guitar",train sn3Sqa4nRtA,30,people sniggering,test sn4ogk9eSUQ,4,slot machine,train sn8rWgWNXMw,0,roller coaster running,train sn8rWgWNXMw,69,roller coaster running,train snAV_ccYfRM,70,playing accordion,train snBD5mCJLb4,0,opening or closing car doors,train snBInfu7nZ0,114,using sewing machines,train snCeyazbGLM,757,playing clarinet,train snEOLNsNVi4,76,playing double bass,train snG_XWvTU0s,10,stream burbling,train snLQmH-M7N4,40,"bee, wasp, etc. buzzing",train snMHDoaKPIY,57,airplane flyby,train snOVG0lAgkQ,470,eating with cutlery,test snPTIuJrKXk,20,male singing,train snQFKlNjaWU,152,penguins braying,train snR9TvRupjU,0,cat caterwauling,test snSl73J_PJk,110,"playing violin, fiddle",train snbtH1P3MVA,34,playing timbales,train snbtH1P3MVA,119,playing timbales,train snfXTcyofxc,30,"engine accelerating, revving, vroom",train snfZ9quf-Jk,30,sailing,train snh7E7llb48,70,dog bow-wow,train sniCPTArbrg,348,skiing,train snjAw1blbXU,30,playing cymbal,train snnUrLb6KwQ,136,tap dancing,train snv3X4PUPOY,7,"electric shaver, electric razor shaving",train snxMjHP5agw,1,people cheering,test snxOSGEkIIU,30,playing bagpipes,train snyzyJlTBbg,3,dog baying,train snyzyJlTBbg,13,dog baying,train snzXqe7bN_4,0,"railroad car, train wagon",train so1u_Cr2oE8,30,driving motorcycle,train so7Mm3SraO4,52,church bell ringing,train so7Mm3SraO4,410,church bell ringing,train so8iWTZhO0o,34,playing sitar,train so8iWTZhO0o,55,playing sitar,train so9oXNF-0hg,30,playing harpsichord,train soBP2g8GSTs,20,playing hammond organ,train soCh9U2TU48,44,lathe spinning,train soCh9U2TU48,75,lathe spinning,train soDn2puEuL8,13,playing trombone,train soDn2puEuL8,79,playing trombone,train soEfiSk4XUM,30,fireworks banging,train soHRQlHoj8c,11,roller coaster running,train soHRQlHoj8c,84,roller coaster running,train soHXA8fMzKU,20,playing bass guitar,train soI-zt1Dkhk,330,"playing marimba, xylophone",train soNR0-Clr64,380,people crowd,train soO2iBO3BZg,240,chicken crowing,train soOMXu8PW-g,20,fireworks banging,train soOWdd9lqsQ,16,spraying water,test soQAsg_7CYw,0,people hiccup,test soSikoW-S5Q,2,cricket chirping,train soTOh3zYJfY,40,skidding,train soXBLBCaClA,30,people babbling,train sodyFG2eU9U,60,people crowd,train soeGx6QCHlQ,615,tapping guitar,train soeGx6QCHlQ,628,tapping guitar,train sofXB5-nlb8,80,chicken crowing,train sofnQSYWJJQ,100,people crowd,train sohv_bp-lMg,8,singing choir,train sohv_bp-lMg,84,singing choir,train soiUxPgRNNA,83,scuba diving,train solIBis6mlc,50,printer printing,train son1mFjaZUY,344,scuba diving,train sooyEN3jHtw,121,child singing,train sooyEN3jHtw,151,child singing,train sopItEUX7XM,100,chainsawing trees,train sos_Ppbhn5I,323,basketball bounce,train sosmMya-v6k,782,people cheering,test sotvgwFm-MI,55,playing zither,train sotvgwFm-MI,139,playing zither,train soxaB6KCkDU,90,helicopter,train sozRijF3xAU,7,child singing,train sozRijF3xAU,22,child singing,train sozTC0jj7nM,30,mosquito buzzing,train sp-dvFzVxgM,30,playing hammond organ,train sp2v1Wjr3NI,101,dinosaurs bellowing,train sp50oz6P8AE,10,"playing marimba, xylophone",train sp63vL5h2yY,40,door slamming,train sp69Ndnhdbk,30,lawn mowing,train sp6_5r9121Y,59,wood thrush calling,train spEp5a0F1Ag,40,people crowd,train spID7HYqDRA,38,squishing water,train spK6WUqfWxI,20,baltimore oriole calling,train spK6WUqfWxI,71,baltimore oriole calling,train spNFTWUltZU,196,hail,train spNFTWUltZU,207,hail,train spNt2qlGKH0,160,playing saxophone,train spOySZFD2MA,108,baby laughter,train spOySZFD2MA,193,baby laughter,train spPxCn3I4V8,30,playing accordion,test spXO-P4avEA,30,people crowd,train spd1qsgCPZI,480,playing tabla,train spdVuqK0udQ,30,playing harpsichord,train spkB5ezAt0g,0,people babbling,train spkoMj3FT3k,220,playing vibraphone,train spmF_OOWQGY,30,splashing water,train spmS-g58UZA,520,"rowboat, canoe, kayak rowing",train spmT_T3-55o,437,lawn mowing,train spo_77QF8pA,30,mouse pattering,train spplzADWm90,2,scuba diving,train spplzADWm90,36,scuba diving,train spqqoTH48IM,53,playing steelpan,train spqqoTH48IM,157,playing steelpan,train spxPGPVPetw,10,typing on computer keyboard,train sq-PQBO4fM8,30,orchestra,train sq3rxtrgTew,0,playing harp,train sq6SZnx2mx4,103,playing erhu,train sqG_x30NQ8A,120,orchestra,train sqGwflGZk4Y,80,playing badminton,test sqH1jp_Ghb4,235,"playing steel guitar, slide guitar",train sqH1jp_Ghb4,285,"playing steel guitar, slide guitar",train sqLsPS_Eb1g,283,mynah bird singing,train sqOfHbmmnik,70,people whispering,train sqPAx1sBSlc,75,"playing steel guitar, slide guitar",train sqPAx1sBSlc,128,"playing steel guitar, slide guitar",train sqRN7gvO3kQ,0,playing bass guitar,train sqU08GCWfSY,30,printer printing,train sqWGev3ce4s,50,people hiccup,train sqXu3mqXVN0,30,helicopter,train sqcgsDa1vNw,122,"playing steel guitar, slide guitar",train sqcgsDa1vNw,232,"playing steel guitar, slide guitar",train sqfrEyGDmmE,180,playing harp,train sqgwzQUfnDk,10,driving motorcycle,train sqk1-q8gXcY,282,people booing,train sqlAHP2VXaY,154,slot machine,train sqoHNt9vm-Y,0,playing ukulele,train sqoHNt9vm-Y,47,playing ukulele,train sqpnbKYsG50,60,cap gun shooting,train sqroigBGc98,216,air conditioning noise,train sqsI2UyrcBQ,30,"engine accelerating, revving, vroom",train sqt0isejnYU,60,playing cymbal,train sqvoBCJfdl4,30,printer printing,train sr1sn6RS-F4,154,strike lighter,train sr1sn6RS-F4,371,strike 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tj5LEgXL2PQ,48,playing castanets,train tj5LEgXL2PQ,58,playing castanets,train tj77zzeFPsc,267,people eating noodle,test tj7VlXZFQKU,28,playing glockenspiel,train tj7VlXZFQKU,72,playing glockenspiel,train tj9FCzWZLB4,19,police car (siren),train tjAYSDA1p8k,0,black capped chickadee calling,train tjAYSDA1p8k,191,black capped chickadee calling,train tjCUQxvIgvU,6,alarm clock ringing,train tjCUQxvIgvU,16,alarm clock ringing,train tjHArZ1s3fw,32,wood thrush calling,train tjKUXtuuC5g,0,playing trombone,train tjKUXtuuC5g,87,playing trombone,train tjKzkajbKc0,30,smoke detector beeping,test tjOt2iRa6kA,90,people screaming,train tjUvPuD-ozA,50,dog bow-wow,train tjWF3t8W92g,448,sharpen knife,train tjWHkAha6Nk,1,skiing,train tjWHkAha6Nk,15,skiing,train tj_LDYbOdkY,4,"heart sounds, heartbeat",train tjejFIPXdZY,160,playing drum kit,train tjgOiqaAf8w,452,squishing water,train tjgOiqaAf8w,544,squishing water,train tjkLaNs8zHY,250,ambulance siren,train tjnxoIUnUeo,100,orchestra,train tjpAM5YWeQU,44,playing cornet,train tjq0Z9IJw7Q,250,pumping water,train tjqdjuysKOg,278,playing badminton,train tjqqCEt7A8c,224,people humming,test tjr4sjm6VRo,45,chainsawing trees,train tjrXCEIhAzg,30,wind noise,train tjsasTWAZAQ,0,"female speech, woman speaking",train tjvKfKFSjJI,270,people clapping,train tjw-lkoU3Ho,2,playing harp,train tjwevixQeR0,291,playing guiro,train tjwevixQeR0,437,playing guiro,train tjwxro6yei4,50,bowling impact,train tjx_DIDB_4A,110,"playing marimba, xylophone",train tk18D_6j3Ho,0,playing harmonica,train tk18D_6j3Ho,29,playing harmonica,train tk1WqHXdPuY,15,train whistling,test tk22F4q4zGU,180,"railroad car, train wagon",train tk2TV_3iVKQ,330,"female speech, woman speaking",train tk3sdVGyNvw,0,mynah bird singing,train tk3sdVGyNvw,11,mynah bird singing,train tk4rESQBxnE,330,fireworks banging,train tk4s-nlhmEQ,30,baby crying,test tk7JAJdFpAs,50,people farting,train tk7JSiFR9lU,0,hail,train tk7JSiFR9lU,51,hail,train tk7y-AIoimE,10,people sobbing,train tk82jYD47Ps,130,lawn mowing,train tk89xHucsJ0,377,cap gun shooting,train tk9XNkHJvxg,4,playing trombone,train tk9XNkHJvxg,32,playing trombone,train tkAY3tXAoHc,83,dinosaurs bellowing,test tkB_FTfPffc,144,crow cawing,train tkB_FTfPffc,180,crow cawing,train tkCgTRVpLbs,70,"playing marimba, xylophone",train tkDf6AmEIT0,20,driving motorcycle,train tkDyKNZFxgw,86,playing zither,train tkEfCrmfLUM,30,dog barking,train tkGRMMUhgi8,91,"electric shaver, electric razor shaving",train tkHBAn3He1A,138,playing djembe,train tkLfBBTIchs,3,underwater bubbling,train tkMWbq49s3g,1,arc welding,train tkMsqSIEbsM,24,people whispering,train tkO3IfbRU6E,330,"bee, wasp, etc. buzzing",train tkRqFjQ8AyE,30,playing clarinet,train tkSxRQxEcYo,894,people eating,train tkSxRQxEcYo,972,people eating,train tkURaMMDT0E,97,playing table tennis,train tkUc_cdTXHw,55,playing squash,train tkUxSPT6_D8,20,chicken crowing,train tkVfghdys4o,340,playing piano,train tk_UGyo4KGo,30,driving buses,train tk_ojTGyt9o,4,playing volleyball,train tkb0ZjArACE,130,playing electric guitar,train tkhNWB_DyjE,55,playing tennis,train tklHdWOhulw,10,ocean burbling,train tkq3HREJFi8,71,playing trumpet,test tkqB7BDPccc,30,playing accordion,train tkv9GULBI14,15,sliding door,train tkwIwIwifm8,20,pheasant crowing,train tkwIwIwifm8,62,pheasant crowing,train tkxRdkNmTvg,6,civil defense siren,train tkzV2MG2wo0,137,child singing,train tkzqwPULcw0,130,mouse pattering,train tl1GLaYclaQ,0,chicken crowing,train tl3SLx8etII,63,train whistling,train tl3SLx8etII,219,train whistling,train tl55Upqp-nk,30,roller coaster running,train tl55Upqp-nk,71,roller coaster running,train tl5ZbA7KPCc,203,playing double bass,train tl5ZbA7KPCc,353,playing double bass,train tl5v1u9BKbE,1,mynah bird singing,train tl5v1u9BKbE,36,mynah bird singing,train tl8ULnKzsww,30,orchestra,train tlAF7JMN-VU,40,"child speech, kid speaking",train tlBEmLO1-Do,9,people cheering,train tlCV0paDDIo,8,dog howling,train tlCV0paDDIo,94,dog howling,train tlDYcdgAJ4I,160,bird 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tluHydiqDHQ,137,playing djembe,train tlv2oNsgqOU,20,dog bow-wow,train tlyLCgkcW8s,65,turkey gobbling,train tlzeviwP65I,70,skateboarding,train tm-_TGYMzUE,209,bouncing on trampoline,train tm0Em1IlvsA,318,people eating apple,test tm1mczAypbk,0,lawn mowing,train tm7k9yddJMg,94,mouse clicking,train tm870k_Vr2s,0,basketball bounce,train tm9rnG0455k,10,skidding,train tmAmx7C_sGc,10,stream burbling,train tmK5wHRmSws,6,people marching,train tmMiCfi82Zg,43,foghorn,train tmNCbf6E6XA,207,playing double bass,train tmNCbf6E6XA,218,playing double bass,train tmPGHC6QblE,120,playing cymbal,train tmRF04lvBCU,250,"male speech, man speaking",train tmRYOFtQIRk,30,playing harpsichord,train tmRYOFtQIRk,41,playing harpsichord,train tmXq_W2sb-A,3,playing tuning fork,test tm_fzEvkBkI,30,stream burbling,test tm_q91nNRVo,5,police radio chatter,train tmabzx6yxqs,30,pig oinking,test tmcIbRTyU1E,146,hammering nails,train tmcIbRTyU1E,199,hammering nails,train tmd6hANSxbY,10,playing french horn,train tmd6hANSxbY,48,playing french horn,train tmdrHw6-yUY,181,dog bow-wow,test tmhNdSi-CvI,81,cat growling,train tmkOomSM_So,80,"cattle, bovinae cowbell",test tmlv4VNzmMs,30,police car (siren),train tmoUUYi3kKw,20,playing cymbal,train tmpG8tXQT0U,3,airplane,train tmrJbI17lBg,30,"playing violin, fiddle",train tmrm8UEuG6A,240,"race car, auto racing",train tmtcqBvgfw0,12,hail,train tmu8kr7w1Zk,30,"pigeon, dove cooing",train tmuHJbUF150,3,pig oinking,train tmuHJbUF150,15,pig oinking,train tmuy7tBoXTc,62,rope skipping,train tmuy7tBoXTc,86,rope skipping,train tmvB-xUOTlo,20,playing acoustic guitar,train tmxZEDTqjcE,18,people burping,train tmxuH2KaQUk,139,squishing water,train tmxuH2KaQUk,288,squishing water,train tmziKIRUbac,108,playing hockey,test tn-4C0xPvLY,7,"ice cream truck, ice cream van",train tn-4C0xPvLY,188,"ice cream truck, ice cream van",train tn12VTX-mj8,383,playing erhu,train tn1zRhEePbw,0,opening or closing car doors,train tn2hKU_70aA,64,playing bongo,train tn2hKU_70aA,89,playing bongo,train tn6PfpOIjvs,122,slot machine,train tnEkJ_f6Pz0,110,beat boxing,train tnGfNm0SnOg,120,ambulance siren,train tnJ3hrdbW08,10,dog howling,train tnJ3hrdbW08,31,dog howling,train tnJUBZxdEfs,19,playing tambourine,train tnJUBZxdEfs,42,playing tambourine,train tnJdPCCkTKs,30,printer printing,train tnKusaVl_08,0,bird wings flapping,train tnOBDqalweM,286,sharpen knife,train tnOBDqalweM,484,sharpen knife,train tnRowui7b0c,18,elk bugling,train tnS1K4cPnK0,293,horse neighing,train tnWFaTF6a2Y,5,playing harp,train tnX9gdsOI0w,1,cheetah chirrup,train tnbTAYahG4E,40,skateboarding,train tnepPZChA5U,219,"playing violin, fiddle",test tngK6y_GUrg,30,playing drum kit,train tnl1we3UKwc,49,playing tabla,train tnl1we3UKwc,62,playing tabla,train tnmwXN4aTyw,123,"donkey, ass braying",test tnmwXN4aTyw,165,"donkey, ass braying",test tnnckybwbRU,90,cattle mooing,train tnq1qScIcYE,0,people screaming,train tnr3diicTLI,230,mosquito buzzing,test tntf6QdfYqM,126,cat purring,train tntf6QdfYqM,501,cat purring,train tnwSyTPo5lc,20,using sewing machines,train tny0fc-jrrU,50,"railroad car, train wagon",train to0SjMpVxas,243,people eating noodle,train to2RF7hOTFw,40,eating with cutlery,train to2_gf0NbHA,80,helicopter,train to4-MFk0AOA,23,parrot talking,train to4-MFk0AOA,36,parrot talking,train to4YJmaJ19k,0,typing on computer keyboard,train to4zKffZexs,26,ferret dooking,train to5Ezlpp_F8,10,chicken crowing,train to5gqQnATOY,22,playing volleyball,train to6QpP3JB9g,62,tap dancing,train to6oY6CeRVA,76,train horning,train to7dq-F5xQM,64,playing darts,test to9hHNCxa-8,30,playing bass drum,train toA2Jdrj03Q,30,male singing,train toCCxNKPpqU,170,people burping,train toDDCvIk9kM,90,playing piano,train toEK_StoaPI,30,goose honking,test toEpG7exMO4,6,children shouting,train toFMxTLZxHc,90,playing piano,train toGmDm3ZEjM,80,"subway, metro, underground",train toHKgxaOCE0,220,people eating,train toHdjKr9700,67,playing mandolin,train toHdjKr9700,127,playing mandolin,train toI_x9spR8Y,70,helicopter,train toJjkgCRMmI,30,"engine accelerating, revving, vroom",train toJmpt9YWbA,5,playing darts,train toJmpt9YWbA,20,playing darts,train toJsJPelkiM,79,eletric blender running,train toLAdHV2dzY,317,playing bassoon,train toMmCVptG1g,60,playing trombone,train toOQjfejT_o,66,playing bongo,train toOQjfejT_o,135,playing bongo,train toPVcfGy1kk,32,basketball bounce,train toQn_L1TU0U,324,cat purring,train toTu8BntJPg,17,elk bugling,test toTu8BntJPg,97,elk bugling,test toVnU8P3F6I,18,missile launch,train toXTI-BYvb4,30,driving buses,train toZ96bl1kDA,60,people crowd,train to_bt6njNzU,26,playing sitar,train to_bt6njNzU,41,playing sitar,train to_o90OW1j0,7,playing squash,train toaAuv7pELo,4,dog barking,train tocHiK3Wdas,129,volcano explosion,train tocyamm_e44,110,driving buses,train togkfPsg4PA,390,using sewing machines,train toi7wTemtks,210,people crowd,train tooYAQxOpUY,9,wood thrush calling,train tooxxFporX4,396,playing shofar,train tooxxFporX4,752,playing shofar,train tosfDmPIZ6s,70,bird squawking,train totopJl4HF8,6,smoke detector beeping,train tou-JOBT-oM,17,"donkey, ass braying",train tou-JOBT-oM,30,"donkey, ass braying",train touCRIG1_og,43,raining,train tow_-g-MrxU,82,people booing,train toxk6T85Rh4,20,playing piano,train toy_SB-Cn6Q,30,playing trumpet,train toys4WZydc0,284,people cheering,train toys4WZydc0,534,people cheering,train tp27leoiCeQ,80,helicopter,train tp5u-T-EX8o,114,striking bowling,train tpBPyk7_8_A,10,sheep bleating,train tpD4HYQ_Xeo,20,playing acoustic guitar,train tpDBQunheiw,53,wood thrush calling,train tpDBQunheiw,117,wood thrush calling,train tpEdmgus3yM,160,playing drum kit,train tpG82NEVCM4,30,pig oinking,train tpHkiLTJQOI,66,arc welding,train tpJP4ZzSIKk,530,"rowboat, canoe, kayak rowing",train tpK9T7Wx7ck,50,playing acoustic guitar,train tpKGgusdwlk,33,canary calling,train tpKGgusdwlk,48,canary calling,train tpLWbgo7nHM,3,playing cornet,train tpM_xCXcxrI,8,"electric shaver, electric razor shaving",train tpMkAZeyYeU,90,bowling impact,train tpNCHBzj3aM,64,rapping,train tpNCHBzj3aM,122,rapping,train tpOTGSCX3ro,31,raining,train tpOZrrursiw,42,beat boxing,train tpRijq7q21E,24,arc welding,train tpVGKHwWdEU,30,ambulance siren,train tpVLOxEh6Cc,320,"female speech, woman speaking",train tpX1Bgd65Vo,185,playing erhu,train tpXTRVMDYsw,41,blowtorch igniting,test tpYeN1N9FBk,61,playing didgeridoo,train tpZZlw96VnE,30,people clapping,train tpa6lKxq_qM,80,people whistling,train tpb0_LOHQkA,30,"pigeon, dove cooing",train tpbdbCtj86c,21,"playing steel guitar, slide guitar",test tpbjUGSLOvo,21,child singing,train tpbjUGSLOvo,34,child singing,train tpcB84xIngk,250,playing hammond organ,train tpcMlbbKTJI,7,gibbon howling,train tpcMlbbKTJI,31,gibbon howling,train tpcbmMDS8ZU,725,striking bowling,train tpcbmMDS8ZU,820,striking bowling,train tpfg2kIhGXc,365,playing bassoon,train tpfoVRYJ5FY,48,playing tympani,train tpfoVRYJ5FY,67,playing tympani,train tpg1wU3iG8Q,110,playing electronic organ,train tpg5uUr_6uk,2,people shuffling,train tpg5uUr_6uk,16,people shuffling,train tphkP-IGUO4,110,playing bass guitar,train tpjUuZP40HA,300,"subway, metro, underground",train tpkiIAELa1k,71,people eating apple,test tpqnXKgJnKY,190,helicopter,train tprdR0aOg10,180,lawn mowing,train tpsmyWKLk_0,0,people slurping,train tptRRiuT6B8,80,chainsawing trees,train tptTsP3_G3w,834,"ice cream truck, ice cream van",test tpuXjp8znX0,0,basketball bounce,train tpvay_njSds,130,playing cymbal,train tpw-ATsNiZo,139,playing mandolin,train tpw-ATsNiZo,152,playing mandolin,train tpxtAn-oWmY,30,playing bass guitar,train tq08GQXtG0U,0,"fly, housefly buzzing",train tq0UVY5VnxU,48,playing gong,train tq2dxJ5xiGE,360,"railroad car, train wagon",train tq3Hlgw2ZB0,180,sharpen knife,test tq6VaSor2Pc,118,playing timpani,train tq6hvveMDgk,10,"child speech, kid speaking",train tq87dtIgjXE,30,"pigeon, dove cooing",train tq9ca-I4H80,10,ocean burbling,train tqAQ6D45520,34,"heart sounds, heartbeat",train tqD1AGViY1c,21,dog howling,train tqDYOAy5K7s,30,frog croaking,test tqI7G4kxBQU,50,playing cymbal,train tqI_MI3_Lqc,30,fireworks banging,train tqIpdvWOZbg,70,splashing water,train tqKhXfb7sms,400,playing trumpet,train tqNBJkT_ri0,2,civil defense siren,train tqPKoNaeqq8,5,playing ukulele,train tqPKoNaeqq8,113,playing ukulele,train tqQX9yUxKsc,167,playing electronic organ,train tqQX9yUxKsc,177,playing electronic organ,train tqQY9jMv8TM,150,playing cello,train tqR406bGi_E,40,toilet flushing,train tqU0jFhrt84,4,people farting,train tqUDfvhpHQA,43,people marching,train tqUDfvhpHQA,599,people marching,train tqWABeEX7Bg,7,people sniggering,train tqZeqHXYSoU,140,chicken crowing,train tq_4mfI3FYM,30,playing theremin,train tq_4mfI3FYM,56,playing theremin,train tq_6qXm2ZF8,133,playing table tennis,train tqbxnsUkL6o,70,people screaming,train tqcDJDsyfC0,19,foghorn,train tqcDJDsyfC0,30,foghorn,train tqcVPsyLmYM,239,playing badminton,train tqchIi4KzF4,9,frog croaking,train tqchIi4KzF4,88,frog croaking,train 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u3SS43zS4Gs,45,airplane flyby,train u3U-8Aqyjb0,30,cattle mooing,train u3U1xKR2714,391,cap gun shooting,train u3UOgcPbenQ,106,bird wings flapping,train u3UOgcPbenQ,123,bird wings flapping,train u3YqsCTzdj0,70,lawn mowing,train u3a19ujBq-Q,39,turkey gobbling,train u3aHTY2luqA,30,playing drum kit,train u3cNehU5S9E,36,playing squash,train u3cqHEyi1KY,6,"electric shaver, electric razor shaving",train u3fTQHv01rE,163,people giggling,test u3gc1VmsV1A,6,frog croaking,train u3lR-6BMyiQ,278,car engine idling,test u3rfFUdJlZI,0,playing washboard,train u3sufEGKNsI,180,playing french horn,train u3tcxFdBhUw,0,shot football,train u3vBJgEVJvk,0,skateboarding,train u3yYpMwG4Us,30,ambulance siren,train u3zGTadOs9A,280,police car (siren),train u3zeS8e20a0,0,lions roaring,train u4-byoISRZI,30,police car (siren),train u40UhoSPegQ,30,cat purring,test u40XTIfZiis,17,squishing water,train u40XTIfZiis,112,squishing water,train u41s8MBZLKw,124,playing hockey,train u45ds9-wWYU,160,printer printing,train 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uIRzwL9K6FI,32,playing theremin,train uIRzwL9K6FI,216,playing theremin,train uIW00hNwkgo,200,playing harpsichord,train uIWbMmokuiA,150,ocean burbling,train uIYeg7xasjg,115,car engine idling,train uIYeg7xasjg,151,car engine idling,train uIZMRRkalHc,82,ripping paper,train uIZMRRkalHc,106,ripping paper,train uIZdR7DpdM8,140,playing harp,train uI_ZsvGR-y0,1,duck quacking,train uIbAGeYlUpM,210,"vehicle horn, car horn, honking",train uIg0I7pAjvM,30,"race car, auto racing",train uIigdIRmHXA,137,arc welding,train uIijT8U-cFM,23,squishing water,train uIijT8U-cFM,169,squishing water,train uInycnRkFYk,51,snake rattling,test uIoVOR15_1M,59,playing bassoon,train uIosf0OzhNw,30,playing electronic organ,train uIosf0OzhNw,246,playing electronic organ,train uIsNtXye_Q8,30,typing on typewriter,train uIvML-TrnNQ,405,"female speech, woman speaking",train uIwz1htyi0Q,30,wind noise,train uJ-NdAVXHXU,116,"heart sounds, heartbeat",train uJ-NdAVXHXU,241,"heart sounds, heartbeat",train uJ1c2gXiv4s,10,people clapping,train uJ1tFbrIkYo,490,people clapping,train uJ2LJVcuoaM,40,machine gun shooting,train uJ2LJVcuoaM,188,machine gun shooting,train uJ4gkjQ6Jt0,190,playing theremin,test uJ6LYnCn-iA,80,"ice cream truck, ice cream van",train uJ6LYnCn-iA,156,"ice cream truck, ice cream van",train uJ8cvMdSPrQ,16,gibbon howling,train uJ8uqRekpr4,1,car engine idling,train uJ8uqRekpr4,13,car engine idling,train uJ9G9SEVzH8,2,hedge trimmer running,train uJAkeShzkbI,290,skateboarding,train uJBzpue-BnA,58,wind chime,train uJDs89LfKqU,6,people burping,train uJIVnoMFXO4,7,duck quacking,train uJJ494MYSmo,7,sea waves,test uJJ494MYSmo,19,sea waves,test uJJLcSR4WZw,230,"playing marimba, xylophone",train uJKhZf5vfuE,96,alarm clock ringing,train uJLdbb10s8s,224,playing badminton,train uJNudEm4RMQ,82,wind chime,test uJSDmIF4dhE,260,driving buses,train uJTcazr3ZCE,40,horse clip-clop,train uJV8NDaHqqk,100,"bee, wasp, etc. buzzing",train uJVVofrRkRQ,16,cat purring,train uJVVofrRkRQ,27,cat purring,train uJVbPv6Wvqo,6,"railroad car, train wagon",train uJWAKubD1ig,30,car engine knocking,train uJWjZtd8p3M,30,sharpen knife,train uJWjZtd8p3M,41,sharpen knife,train uJ_WO6S_5DY,30,tractor digging,train uJbr4c92O4s,30,people running,train uJdle96PWz4,88,singing choir,train uJgS2FdsKIE,21,ice cracking,train uJgS2FdsKIE,196,ice cracking,train uJhRlq8zOhM,20,"alligators, crocodiles hissing",train uJhvprf9pIU,263,hedge trimmer running,train uJhx-_8SQ1U,20,chicken crowing,train uJkw2pAMaBI,20,lions roaring,test uJluGLhLX8Y,10,people hiccup,train uJnhpp-2qsA,2,owl hooting,train uJnhpp-2qsA,12,owl hooting,train uJoRcO3AdwU,1,horse neighing,train uJqpIDZ3PRc,2,gibbon howling,train uJrx4el8zOE,30,fireworks banging,train uJszdt1FN6U,270,playing trombone,train uJxnF8H9-qY,4,fire truck siren,train uK00rOIAzIk,0,splashing water,train uK0jcVxT-Pg,30,driving buses,train uK3K91aTE0I,20,"railroad car, train wagon",train uK4naF3yDQk,70,waterfall burbling,train uK6WIs_TOxI,10,driving motorcycle,train 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uLjXIGmswjE,30,driving buses,train uLlpGD7eh0E,289,playing tympani,train uLm5oUt3XG4,31,playing tabla,train uLm5oUt3XG4,78,playing tabla,train uLmrI89xy58,30,police car (siren),train uLpEdF2wkqs,27,playing sitar,train uLpEdF2wkqs,69,playing sitar,train uLuV2PuRVVg,256,playing tabla,train uLuV2PuRVVg,307,playing tabla,train uLuzXceGSls,93,playing bassoon,train uLuzXceGSls,142,playing bassoon,train uLvBselbiRE,16,woodpecker pecking tree,train uLz7gC_gBEI,520,"race car, auto racing",train uLzkoKDgRC0,48,zebra braying,test uM1QkCMyv9g,30,ambulance siren,train uM2f5L1Ljrw,130,dog growling,train uM4_9hjW4KM,0,typing on typewriter,train uM5xCVfgCBA,20,skateboarding,train uM6Ec4bCKIk,42,skiing,train uM6dJqhUwFI,39,yodelling,train uM6dJqhUwFI,51,yodelling,train uM79caL7SlM,510,driving buses,train uM7pAdSvMr8,30,people crowd,train uM9uhshQwl8,124,fire crackling,train uMQKjo60iDM,2,chicken clucking,train uMQKjo60iDM,23,chicken clucking,train uMQNOPpvkgk,40,playing tabla,train 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uO34zKivHzI,260,playing oboe,test uOAo8NcPxQE,0,chicken crowing,train uOLwekYCbVU,20,playing flute,train uOOGqzu-I_U,128,people eating noodle,test uOP99lHSlTs,200,playing trombone,train uORJXs9ZFEI,7,skateboarding,train uORJXs9ZFEI,171,skateboarding,train uORYyic9kNs,234,singing choir,train uOTFqs7qdvY,110,playing bass guitar,train uOYGrPa00jg,48,cat growling,train uOYvNQ5wEsI,123,"rowboat, canoe, kayak rowing",train uO_FKfOSJ00,14,chicken crowing,train uOd9cxtri7U,72,skiing,train uOd9cxtri7U,86,skiing,train uOemF8wmIKY,41,playing oboe,train uOemF8wmIKY,89,playing oboe,train uOgjT98WKLI,30,playing electric guitar,train uOhPVEvG8yw,74,airplane flyby,train uOl0o6OnUP8,49,dog whimpering,train uOlKuaCiaOY,20,lawn mowing,train uOlWSbNB2j0,69,train whistling,train uOlWSbNB2j0,80,train whistling,train uOpAbMljiA0,42,roller coaster running,test uOsgPKGznQk,19,cat hissing,train uOuPhJKpil0,108,tapping guitar,train uOuPhJKpil0,139,tapping guitar,train uOw-1KfYv8g,9,fire truck siren,train uOw-1KfYv8g,19,fire truck siren,train uOwKP7vCiJA,25,spraying water,test uOzDoqZZGnc,69,playing tambourine,train uOzDoqZZGnc,89,playing tambourine,train uP-51M6Wb4Q,140,door slamming,train uP1r214VkvM,30,male singing,train uP2Dkhy6yIQ,250,playing vibraphone,train uP2ez8Qi-r4,1,playing snare drum,train uP2ez8Qi-r4,80,playing snare drum,train uP3tVvMJ-n4,25,singing bowl,train uP87XUaQHFo,20,fireworks banging,train uPBgeFQ9g60,80,chimpanzee pant-hooting,train uPBgeFQ9g60,204,chimpanzee pant-hooting,train uPBjQjqH95s,230,francolin calling,train uPCkHe7y6T4,60,playing trombone,train uPGoMt7wSIc,40,people hiccup,train uPH5KjJ-MVI,30,playing flute,train uPIulmVCfNw,0,"child speech, kid speaking",train uPIxCX-g26o,370,eating with cutlery,train uPKcg9wZO5k,2,fire truck siren,train uPKcg9wZO5k,12,fire truck siren,train uPLKmrBgCz4,30,driving buses,train uPMN5IGCbug,29,skiing,train uPSrlVOVBhs,51,arc welding,train uPUk0gCy-OA,84,children shouting,test uPXECTRJdXk,170,playing didgeridoo,train 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hockey,train uQ31XbyhtvE,10,singing bowl,train uQ6ELiwOD6g,158,"playing steel guitar, slide guitar",train uQ6ELiwOD6g,160,"playing steel guitar, slide guitar",train uQC1ACxvWfI,2,"vehicle horn, car horn, honking",train uQCXa9USAYs,30,"railroad car, train wagon",train uQDvr-sG63w,30,people whistling,train uQHFDeFuk0Q,19,scuba diving,train uQHFDeFuk0Q,53,scuba diving,train uQIEj5wj8Mc,30,playing snare drum,train uQIYrVyPkAw,10,people belly laughing,test uQLXSdLU5Zs,380,"female speech, woman speaking",train uQQl-jP0aQ0,0,helicopter,train uQSCG1UtNjo,37,dog growling,train uQSCG1UtNjo,53,dog growling,train uQT5cF-Ph0g,2,cap gun shooting,train uQT5cF-Ph0g,227,cap gun shooting,train uQVlRLSUDcM,30,baby crying,train uQVluj0K7Ps,30,"pigeon, dove cooing",train uQWBA8q8JEs,30,singing bowl,train uQX4B1mfV5U,54,playing banjo,train uQXIGPlWCiY,30,people running,train uQYPbGWPzFk,7,dog howling,train uQZPDu5Y0RQ,30,people sniggering,train uQ_bkiu0HCI,190,playing table tennis,train uQ_bkiu0HCI,225,playing table tennis,train uQfYOhiKroY,50,people clapping,train uQkNJlUCfq0,56,playing didgeridoo,train uQkNJlUCfq0,72,playing didgeridoo,train uQlTyQCuE-U,9,police car (siren),train uQnGP6zL-gc,8,"vehicle horn, car horn, honking",train uQndfA09C84,1,pig oinking,train uQnqdSscps8,30,driving buses,train uQpyOsW2xrI,100,waterfall burbling,train uQvayEgcdCE,4,underwater bubbling,train uQvayEgcdCE,49,underwater bubbling,train uQw4BjJHlLQ,0,people screaming,train uQw8Ns1k0cA,56,playing cymbal,train uQxoth_kdJQ,30,people sniggering,train uQxtRp51LHE,16,telephone bell ringing,train uQxtRp51LHE,28,telephone bell ringing,train uR0W234PpW4,17,owl hooting,test uR0dejIus1c,10,people farting,train uR1Lg-lL0dw,30,playing harpsichord,train uRC1UkOH-zk,100,"female speech, woman speaking",train uRG1YRkSD60,30,playing snare drum,train uROv_aLrLa8,30,duck quacking,train uRSfijSnYl0,0,police car (siren),train uRSj8M5ZIXs,228,people whispering,train uRSj8M5ZIXs,519,people whispering,train uR_avhFBd5U,30,people sniggering,train uR_jZOh9bMA,3,horse neighing,train uRap9q0MUyY,25,baby crying,train uReq_M_IwdY,169,train whistling,train uRhiZy8FsSU,30,female singing,test uRirG4V8djI,20,"engine accelerating, revving, vroom",train uRoS8OQsFM4,40,playing electric guitar,train uRp0HMiA14A,18,toilet flushing,train uRpWARk73uU,460,people whispering,train uRpkjgpWS4Y,8,people sneezing,test uRqDEPGkp6g,268,playing bongo,train uRuCFyjvj3g,17,playing congas,train uRuCFyjvj3g,36,playing congas,train uRyLEBGX5ag,20,machine gun shooting,test uS-ppcpwkwI,370,people clapping,train uS7wisNXqa8,0,people farting,train uSAzmyH_hps,72,playing timpani,train uSAzmyH_hps,83,playing timpani,train uSBP9aYwfhU,18,owl hooting,test uSCpYOTSQuI,30,"pigeon, dove cooing",train uSEzqgeCEhw,117,playing harp,train uSHjmtDpLAA,50,playing accordion,train uSI8OOsj898,410,"race car, auto racing",train uSIWVZMLDU8,79,playing bongo,train uSIWVZMLDU8,234,playing bongo,train uSJV8H-8UxA,30,mouse pattering,train uSNQuqeXn8w,220,playing cymbal,train uSOFzkCAzpY,16,scuba diving,train uSQlT2HQ9zI,76,people marching,train uSRcSa1-d5o,98,parrot talking,test uSY9KlCpZko,55,playing bongo,train uSY9KlCpZko,83,playing bongo,train uSaIfpM0imo,28,"railroad car, train wagon",train uSb68_iAx0g,6,police car (siren),train uSbtXKeRHT4,30,playing drum kit,train uSdYQJi1q1Y,400,playing piano,train uSeRbvjrrj0,236,playing ukulele,train uSeRbvjrrj0,271,playing ukulele,train uSghmuPTlzI,70,driving motorcycle,train uShsRc-QEYE,190,people whistling,train uSlw29Advt4,470,people crowd,train uSmXQ1kQvIU,133,bird squawking,train uSmXQ1kQvIU,163,bird squawking,train uSmduC6gJxg,50,"rowboat, canoe, kayak rowing",train uSp4bhw9GXE,300,playing trumpet,train uSsQDO_yhEY,20,fireworks banging,train uSvrecEs-J4,30,chainsawing trees,train uT2DzjAKoaQ,30,"race car, auto racing",train uT42qImw7Ow,30,people whistling,test uTEwt4UTA-Q,380,people whispering,train uTF_Xl_fVDc,13,cat hissing,train uTG7nAS9fzY,0,"race car, auto 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ud3UAgTveOE,30,playing bass drum,train ud5mtzhFSuw,0,playing ukulele,train ud5mtzhFSuw,134,playing ukulele,train ud9_q8CqxDQ,158,playing erhu,train udBB7gOsHQE,251,playing bass drum,train udDPJhg9Cso,236,swimming,train udDPJhg9Cso,318,swimming,train udEDlhc2X2M,164,car engine starting,train udEDlhc2X2M,207,car engine starting,train udH8V5gsHE8,3,air horn,train udJGn6qlq-o,250,waterfall burbling,train udLcXF_lnGw,72,tap dancing,train udMVp6BFi-I,37,playing harmonica,train udMVp6BFi-I,65,playing harmonica,train udTCedm43qA,14,goose honking,train udTCedm43qA,29,goose honking,train udTooXwgVDs,120,people whistling,train udUkONVo5EM,52,playing oboe,train udVRKeL8L0I,99,singing bowl,train udVSYrFacsc,62,playing cornet,train udVSYrFacsc,72,playing cornet,train udVrsSkomAA,70,dog barking,train ud_NQExWZE0,27,playing double bass,train ud_NQExWZE0,45,playing double bass,train ud_S57P470U,30,thunder,train ud_Y_WXPgHI,56,playing timbales,train udahUCPtzLs,60,playing drum kit,train udef-VkX6RI,28,woodpecker pecking tree,train udef-VkX6RI,56,woodpecker pecking tree,train uderPP64WDw,140,playing acoustic guitar,train udfbqq8-lIM,347,driving snowmobile,train udiQcCbPPBI,0,"ice cream truck, ice cream van",train udlTitDlBCA,0,train horning,train udlfNLNq7V4,13,playing gong,train udlfNLNq7V4,25,playing gong,train udoz5HwKSQ4,150,shot football,train udqp4-lwOA8,130,singing bowl,train udsAqTwur5Q,3,playing tambourine,train udsAqTwur5Q,15,playing tambourine,train udu93mo_ohw,20,fireworks banging,train udw8BzI_wLM,105,driving snowmobile,train udw8BzI_wLM,342,driving snowmobile,train udx0qwvkwFY,27,basketball bounce,train udxYbbWHwGI,0,ice cracking,train ue-lVK6Y5pI,0,people crowd,train ue4Cd1FxR4w,540,"bird chirping, tweeting",train ue8igi9pNSI,8,"engine accelerating, revving, vroom",train ueFsBRn7dx4,80,"subway, metro, underground",train ueHibRtasQ0,127,arc welding,train ueHibRtasQ0,200,arc welding,train ueIn_Vho05Q,21,baby crying,train ueKKohFYF8g,0,telephone bell ringing,test ueLB2kIE04E,73,police radio chatter,test ueMZGwp1iWE,470,orchestra,train ueXAtuZS4Oo,10,turkey gobbling,train ueYQcJQxvUA,180,playing table tennis,train ueZj_98Fu8o,50,"donkey, ass braying",train ueZuSVRInZs,8,baby crying,train ue_LksfXSOc,0,dog barking,train uegvMPjZizA,10,people running,test uegyeSRxX6o,13,"playing steel guitar, slide guitar",train uegyeSRxX6o,145,"playing steel guitar, slide guitar",train ueki62069vg,11,golf driving,test ueki62069vg,80,golf driving,test uelHwf8o7_U,192,rapping,train uelHwf8o7_U,218,rapping,train uelhY3NhecI,30,"motorboat, speedboat acceleration",train uemrn6fV0l4,6,"bee, wasp, etc. buzzing",train uepnAunw5cA,124,playing oboe,train uepnAunw5cA,175,playing oboe,train ueqpEirvZuE,420,"male speech, man speaking",train uerCAfjVPEI,3,machine gun shooting,train uerCAfjVPEI,18,machine gun shooting,train uercY3R8Tjc,361,people eating apple,test uesBQfArswA,158,playing table tennis,train uesBQfArswA,200,playing table tennis,train ueu5VMj5Wsk,30,"playing marimba, xylophone",train ueubLQIRPoI,12,woodpecker pecking tree,train uez5VKwhMTY,100,train horning,train uez5VKwhMTY,255,train horning,train uf4dx0NgFro,30,duck quacking,train uf8J4MaZdbU,19,playing flute,train ufBIW1G1q1c,30,"pigeon, dove cooing",train ufB_pFfO3Ts,30,playing cello,train ufCPGoDcXLQ,190,dog barking,train ufFYrFBDWLI,635,playing zither,train ufFYrFBDWLI,705,playing zither,train ufG4WNuKo7M,50,playing flute,train ufHhAo0a2pk,130,stream burbling,train ufSSCltfOAI,2,playing clarinet,train uf_0F44_gvU,0,"playing marimba, xylophone",train ufaGzKo9EI4,30,toilet flushing,train ufao9Uv1LzU,50,goose honking,train ufdQXq8lzMc,0,people crowd,train ufdlvlzPpwk,4,air horn,train ufdmVZ2Ythw,10,orchestra,train uffaX_UFlUg,130,playing trombone,train ufmRpaObx0w,60,wind noise,train ufoqrr-ToiI,51,cat caterwauling,train uftso8xdvtY,80,singing choir,train uftso8xdvtY,118,singing choir,train ufwgYqQFtrc,30,"motorboat, speedboat acceleration",train 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ukjqp9zXSoA,62,playing cornet,train ukkbR60-rUQ,2,mynah bird singing,train ukoG6Hv0emc,30,playing accordion,train ukrBEKDd6Ys,60,playing harp,train uksHj9QkCnY,13,canary calling,train uksHj9QkCnY,26,canary calling,train ukuEHc_Zg4Q,15,magpie calling,train ukyX7TUP0po,15,"motorboat, speedboat acceleration",train ukzknEpb4rI,47,vacuum cleaner cleaning floors,train ukzknEpb4rI,81,vacuum cleaner cleaning floors,train ukzoInaDH_o,100,people crowd,train ul0EHJptlqY,66,tractor digging,train ul14johUAls,49,slot machine,train ul1AVE9wuFw,30,playing drum kit,train ul1pcZLD4wI,86,"playing steel guitar, slide guitar",train ul1pcZLD4wI,159,"playing steel guitar, slide guitar",train ul285Kv4Zzw,30,sailing,train ul2YgaO5GnM,42,playing bassoon,train ul3umTtB-Xo,14,air conditioning noise,test ul4t6Fv8GWo,11,"alligators, crocodiles hissing",train ul4t6Fv8GWo,35,"alligators, crocodiles hissing",train ulAiyqCUI5g,0,coyote howling,train ulAiyqCUI5g,18,coyote howling,train ulDSh_Nv-Cw,290,skidding,train 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car (siren),train unx2clX6S7c,7,baby laughter,train unxKQM6W1Lo,296,ripping paper,train unxKQM6W1Lo,321,ripping paper,train unzrOnHJgko,70,playing bass drum,train uo0e5E72oyA,70,playing clarinet,train uo3CfFrXIyA,82,playing bugle,test uo6FKrioJ-Y,59,wind chime,train uo88SuaQ-vg,250,playing vibraphone,train uo8upaZnLNU,33,cattle mooing,train uo8upaZnLNU,59,cattle mooing,train uo94852cDjM,4,people belly laughing,train uoBWhzshB6w,24,playing hockey,train uoBWhzshB6w,106,playing hockey,train uoCay5JXnn4,0,toilet flushing,test uoDjFW1Vxvs,0,machine gun shooting,train uoEv64bCevk,30,"pigeon, dove cooing",train uoFaqp3Fn4w,179,playing erhu,train uoGtLDb-Jqs,30,wind noise,train uoHISMEYvVA,320,playing bass guitar,train uoJ3V2sGMEI,30,sailing,train uoWGPHwUG8w,4,people crowd,train uoYDmb6ev0M,0,swimming,train uoYTJWCgNoo,20,playing bass guitar,train uoZwu9hqEuI,61,playing oboe,test uojeevMO44k,0,playing ukulele,train uojeevMO44k,40,playing ukulele,train uolsxlM2Gao,25,church bell ringing,train uolsxlM2Gao,36,church bell ringing,train uor7D8mqyhY,30,"rowboat, canoe, kayak rowing",train uottc28_UQ8,0,goose honking,train uouMndiZtDc,120,people sobbing,test uox2JurcsWM,4,baby crying,train uoxpoAqsjk8,110,raining,train uoyYyKlZu4Q,0,woodpecker pecking tree,train up0tDbwlWaQ,5,sliding door,train up7R_x0B87M,60,playing drum kit,train upFIsN4lfqs,94,male singing,train upJFosA2f7w,861,hair dryer drying,train upJFosA2f7w,921,hair dryer drying,train upKVnN3tVaQ,0,driving motorcycle,train upMv7oZ-uLs,116,child singing,train upMv7oZ-uLs,131,child singing,train upPXpYMnC48,26,snake hissing,train upPXpYMnC48,55,snake hissing,train upSEMWlVLqY,50,people sniggering,train upTOTcANkBo,100,playing ukulele,test upX4cNt_Reo,10,cat purring,test upXbTJCN0EQ,190,"railroad car, train wagon",train upZ0sKmaZrI,142,playing lacrosse,train upZ0sKmaZrI,167,playing lacrosse,train upb6XeTytS4,30,skidding,train upc7twfFCuM,15,"motorboat, speedboat acceleration",train uph-j5eKEfM,30,people whistling,train upkyfMoU_Kw,240,playing piano,train upnnpm5F3yY,0,coyote howling,train uppxs6U3Ous,30,lawn mowing,train uprimXsNE6A,30,playing hammond organ,train upsRhJGKobg,361,playing clarinet,train upsRhJGKobg,373,playing clarinet,train upw13z61S08,50,car engine knocking,train uq0r8jbn-YE,29,ambulance siren,train uq0r8jbn-YE,138,ambulance siren,train uqA51C6Mfw0,25,chinchilla barking,train uqCAwgJ-cuE,70,playing flute,train uqEJKIDlNxk,10,fireworks banging,train uqHsyWjEDvw,257,fire crackling,train uqHsyWjEDvw,490,fire crackling,train uqIH5caLke8,0,playing harmonica,train uqJObMTwfeA,250,splashing water,train uqP_6BmMIEE,30,playing timbales,train uqX427VbwYY,2,chicken crowing,train uqiR6ZJL2XA,2,playing flute,train uqjnMighzJ0,30,playing bass guitar,train uqkQdbRjmyk,15,playing snare drum,train uqkQdbRjmyk,95,playing snare drum,train ur22l6Iw2Zo,81,cat purring,train ur33zPD9BCc,70,playing piano,train ur3xz6SkVyQ,400,"subway, metro, underground",train ur6eNNX9x5w,30,fireworks banging,train 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vpNsT8AFtzI,361,playing didgeridoo,train vpU2noreEyc,227,playing gong,train vpU2noreEyc,263,playing gong,train vp_7wjtk5BQ,10,fireworks banging,train vpf8CUabw_w,100,door slamming,train vpfYsBsVEeI,320,playing piano,train vpoVXmonad8,220,"playing marimba, xylophone",train vpqIoMmCojo,0,playing electronic organ,train vpu4Bwq-xHI,27,playing flute,train vpyjAR_k70Y,66,car engine starting,test vq4jQd5-PL0,30,people sniggering,test vqE5FngWLUk,10,driving motorcycle,train vqG1UtO9bJU,30,people running,train vqHvNLNrsik,270,goose honking,train vqJhSaVb2F8,1,chicken crowing,train vqPfKGUR0EY,102,train whistling,train vqTxZpM7D54,20,playing djembe,train vqTxZpM7D54,73,playing djembe,train vqVFkDOr2zE,65,playing erhu,train vqVFkDOr2zE,244,playing erhu,train vqVG4igY33E,30,playing harp,train vqYSCdBE0wA,210,chainsawing trees,train vqYe16JY4LI,30,helicopter,train vqZuVbG6-HI,130,helicopter,train vq_BNgQV3Dw,30,playing electric guitar,train vq_tCjFmVzI,103,lighting firecrackers,train vq_tCjFmVzI,257,lighting firecrackers,train vqbBCxuspwU,689,people slurping,train vqbBCxuspwU,718,people slurping,train vqdiwr-haOY,40,chainsawing trees,train vqe3HBWgnek,3,opening or closing car electric windows,train vqfmaBEP71A,172,playing bongo,train vqfmaBEP71A,212,playing bongo,train vqhA3Skdmuw,1,coyote howling,train vqhA3Skdmuw,16,coyote howling,train vqkb1hBfLkw,90,using sewing machines,train vql0TfQ1Rzw,3,elk bugling,train vqmv0H7kNlE,8,playing cornet,train vqpcywnZzoU,410,"rowboat, canoe, kayak rowing",train vqqCcdpOUik,30,orchestra,train vqqoMkWSVHI,31,frog croaking,train vqqoMkWSVHI,73,frog croaking,train vqskeDEsWDM,46,vacuum cleaner cleaning floors,train vqx0m6uNic8,0,people cheering,train vr18I1PCECg,0,playing french horn,test vr1QQYkbiD4,60,playing saxophone,train vr1XDvg7kNw,20,fireworks banging,train vr3zbDsuYs8,6,car engine knocking,train vr3zbDsuYs8,19,car engine knocking,train vr7FAYEzWBc,30,playing flute,train vr7mYnXnWOc,139,playing table tennis,train 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zxId45CV2e4_300000_310000 zxR0XDlKt_8_0_10000 zxUbjNFrgWg_131000_141000 zxlZbpGNJPI_30000_40000 zyzEV89uLOc_13000_23000 zz9HCdnTIec_161000_171000 zzVYJYUPNG8_30000_40000 zzc_C1D8iqQ_1000_11000 zzc_C1D8iqQ_28000_38000 zzmWVOQ2iIE_535000_545000 zzrK3Q6rDu4_52000_62000 zzrK3Q6rDu4_8000_18000 zzsUhaDSqzI_115000_125000 zzsUhaDSqzI_145000_155000 ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/dataset.py ================================================ import collections import csv import logging import os import random from glob import glob from pathlib import Path import numpy as np import torch import torchvision logger = logging.getLogger(f'main.{__name__}') class VGGSound(torch.utils.data.Dataset): def __init__(self, split, specs_dir, transforms=None, splits_path='./data', meta_path='./data/vggsound.csv'): super().__init__() self.split = split self.specs_dir = specs_dir self.transforms = transforms self.splits_path = splits_path self.meta_path = meta_path vggsound_meta = list(csv.reader(open(meta_path), quotechar='"')) unique_classes = sorted(list(set(row[2] for row in vggsound_meta))) self.label2target = {label: target for target, label in enumerate(unique_classes)} self.target2label = {target: label for label, target in self.label2target.items()} self.video2target = {row[0]: self.label2target[row[2]] for row in vggsound_meta} split_clip_ids_path = os.path.join(splits_path, f'vggsound_{split}.txt') if not os.path.exists(split_clip_ids_path): self.make_split_files() clip_ids_with_timestamp = open(split_clip_ids_path).read().splitlines() clip_paths = [os.path.join(specs_dir, v + '_mel.npy') for v in clip_ids_with_timestamp] self.dataset = clip_paths # self.dataset = clip_paths[:10000] # overfit one batch # 'zyTX_1BXKDE_16000_26000'[:11] -> 'zyTX_1BXKDE' vid_classes = [self.video2target[Path(path).stem[:11]] for path in self.dataset] class2count = collections.Counter(vid_classes) self.class_counts = torch.tensor([class2count[cls] for cls in range(len(class2count))]) # self.sample_weights = [len(self.dataset) / class2count[self.video2target[Path(path).stem[:11]]] for path in self.dataset] def __getitem__(self, idx): item = {} spec_path = self.dataset[idx] # 'zyTX_1BXKDE_16000_26000' -> 'zyTX_1BXKDE' video_name = Path(spec_path).stem[:11] item['input'] = np.load(spec_path) item['input_path'] = spec_path # if self.split in ['train', 'valid']: item['target'] = self.video2target[video_name] item['label'] = self.target2label[item['target']] if self.transforms is not None: item = self.transforms(item) return item def __len__(self): return len(self.dataset) def make_split_files(self): random.seed(1337) logger.info(f'The split files do not exist @ {self.splits_path}. Calculating the new ones.') # The downloaded videos (some went missing on YouTube and no longer available) available_vid_paths = sorted(glob(os.path.join(self.specs_dir, '*_mel.npy'))) logger.info(f'The number of clips available after download: {len(available_vid_paths)}') # original (full) train and test sets vggsound_meta = list(csv.reader(open(self.meta_path), quotechar='"')) train_vids = {row[0] for row in vggsound_meta if row[3] == 'train'} test_vids = {row[0] for row in vggsound_meta if row[3] == 'test'} logger.info(f'The number of videos in vggsound train set: {len(train_vids)}') logger.info(f'The number of videos in vggsound test set: {len(test_vids)}') # class counts in test set. We would like to have the same distribution in valid unique_classes = sorted(list(set(row[2] for row in vggsound_meta))) label2target = {label: target for target, label in enumerate(unique_classes)} video2target = {row[0]: label2target[row[2]] for row in vggsound_meta} test_vid_classes = [video2target[vid] for vid in test_vids] test_target2count = collections.Counter(test_vid_classes) # now given the counts from test set, sample the same count for validation and the rest leave in train train_vids_wo_valid, valid_vids = set(), set() for target, label in enumerate(label2target.keys()): class_train_vids = [vid for vid in train_vids if video2target[vid] == target] random.shuffle(class_train_vids) count = test_target2count[target] valid_vids.update(class_train_vids[:count]) train_vids_wo_valid.update(class_train_vids[count:]) # make file with a list of available test videos (each video should contain timestamps as well) train_i = valid_i = test_i = 0 with open(os.path.join(self.splits_path, 'vggsound_train.txt'), 'w') as train_file, \ open(os.path.join(self.splits_path, 'vggsound_valid.txt'), 'w') as valid_file, \ open(os.path.join(self.splits_path, 'vggsound_test.txt'), 'w') as test_file: for path in available_vid_paths: path = path.replace('_mel.npy', '') vid_name = Path(path).name # 'zyTX_1BXKDE_16000_26000'[:11] -> 'zyTX_1BXKDE' if vid_name[:11] in train_vids_wo_valid: train_file.write(vid_name + '\n') train_i += 1 elif vid_name[:11] in valid_vids: valid_file.write(vid_name + '\n') valid_i += 1 elif vid_name[:11] in test_vids: test_file.write(vid_name + '\n') test_i += 1 else: raise Exception(f'Clip {vid_name} is neither in train, valid nor test. Strange.') logger.info(f'Put {train_i} clips to the train set and saved it to ./data/vggsound_train.txt') logger.info(f'Put {valid_i} clips to the valid set and saved it to ./data/vggsound_valid.txt') logger.info(f'Put {test_i} clips to the test set and saved it to ./data/vggsound_test.txt') if __name__ == '__main__': from transforms import Crop, StandardNormalizeAudio, ToTensor specs_path = '/home/nvme/data/vggsound/features/melspec_10s_22050hz/' transforms = torchvision.transforms.transforms.Compose([ StandardNormalizeAudio(specs_path), ToTensor(), Crop([80, 848]), ]) datasets = { 'train': VGGSound('train', specs_path, transforms), 'valid': VGGSound('valid', specs_path, transforms), 'test': VGGSound('test', specs_path, transforms), } print(datasets['train'][0]) print(datasets['valid'][0]) print(datasets['test'][0]) print(datasets['train'].class_counts) print(datasets['valid'].class_counts) print(datasets['test'].class_counts) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/logger.py ================================================ import logging import os import time from shutil import copytree, ignore_patterns import torch from omegaconf import OmegaConf from torch.utils.tensorboard import SummaryWriter, summary class LoggerWithTBoard(SummaryWriter): def __init__(self, cfg): # current time stamp and experiment log directory self.start_time = time.strftime('%y-%m-%dT%H-%M-%S', time.localtime()) self.logdir = os.path.join(cfg.logdir, self.start_time) # init tboard super().__init__(self.logdir) # backup the cfg OmegaConf.save(cfg, os.path.join(self.log_dir, 'cfg.yaml')) # backup the code state if cfg.log_code_state: dest_dir = os.path.join(self.logdir, 'code') copytree(os.getcwd(), dest_dir, ignore=ignore_patterns(*cfg.patterns_to_ignore)) # init logger which handles printing and logging mostly same things to the log file self.print_logger = logging.getLogger('main') self.print_logger.setLevel(logging.INFO) msgfmt = '[%(levelname)s] %(asctime)s - %(name)s \n %(message)s' datefmt = '%d %b %Y %H:%M:%S' formatter = logging.Formatter(msgfmt, datefmt) # stdout sh = logging.StreamHandler() sh.setLevel(logging.DEBUG) sh.setFormatter(formatter) self.print_logger.addHandler(sh) # log file fh = logging.FileHandler(os.path.join(self.log_dir, 'log.txt')) fh.setLevel(logging.INFO) fh.setFormatter(formatter) self.print_logger.addHandler(fh) self.print_logger.info(f'Saving logs and checkpoints @ {self.logdir}') def log_param_num(self, model): param_num = sum(p.numel() for p in model.parameters() if p.requires_grad) self.print_logger.info(f'The number of parameters: {param_num/1e+6:.3f} mil') self.add_scalar('num_params', param_num, 0) return param_num def log_iter_loss(self, loss, iter, phase): self.add_scalar(f'{phase}/loss_iter', loss, iter) def log_epoch_loss(self, loss, epoch, phase): self.add_scalar(f'{phase}/loss', loss, epoch) self.print_logger.info(f'{phase} ({epoch}): loss {loss:.3f};') def log_epoch_metrics(self, metrics_dict, epoch, phase): for metric, val in metrics_dict.items(): self.add_scalar(f'{phase}/{metric}', val, epoch) metrics_dict = {k: round(v, 4) for k, v in metrics_dict.items()} self.print_logger.info(f'{phase} ({epoch}) metrics: {metrics_dict};') def log_test_metrics(self, metrics_dict, hparams_dict, best_epoch): allowed_types = (int, float, str, bool, torch.Tensor) hparams_dict = {k: v for k, v in hparams_dict.items() if isinstance(v, allowed_types)} metrics_dict = {f'test/{k}': round(v, 4) for k, v in metrics_dict.items()} exp, ssi, sei = summary.hparams(hparams_dict, metrics_dict) self.file_writer.add_summary(exp) self.file_writer.add_summary(ssi) self.file_writer.add_summary(sei) for k, v in metrics_dict.items(): self.add_scalar(k, v, best_epoch) self.print_logger.info(f'test ({best_epoch}) metrics: {metrics_dict};') def log_best_model(self, model, loss, epoch, optimizer, metrics_dict): model_name = model.__class__.__name__ self.best_model_path = os.path.join(self.logdir, f'{model_name}-{self.start_time}.pt') checkpoint = { 'loss': loss, 'metrics': metrics_dict, 'epoch': epoch, 'optimizer': optimizer.state_dict(), 'model': model.state_dict(), } torch.save(checkpoint, self.best_model_path) self.print_logger.info(f'Saved model in {self.best_model_path}') ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/loss.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim class WeightedCrossEntropy(nn.CrossEntropyLoss): def __init__(self, weights, **pytorch_ce_loss_args) -> None: super().__init__(reduction='none', **pytorch_ce_loss_args) self.weights = weights def __call__(self, outputs, targets, to_weight=True): loss = super().__call__(outputs, targets) if to_weight: return (loss * self.weights[targets]).sum() / self.weights[targets].sum() else: return loss.mean() if __name__ == '__main__': x = torch.randn(10, 5) target = torch.randint(0, 5, (10,)) weights = torch.tensor([1., 2., 3., 4., 5.]) # criterion_weighted = nn.CrossEntropyLoss(weight=weights) # loss_weighted = criterion_weighted(x, target) # criterion_weighted_manual = nn.CrossEntropyLoss(reduction='none') # loss_weighted_manual = criterion_weighted_manual(x, target) # print(loss_weighted, loss_weighted_manual.mean()) # loss_weighted_manual = (loss_weighted_manual * weights[target]).sum() / weights[target].sum() # print(loss_weighted, loss_weighted_manual) # print(torch.allclose(loss_weighted, loss_weighted_manual)) pytorch_weighted = nn.CrossEntropyLoss(weight=weights) pytorch_unweighted = nn.CrossEntropyLoss() custom = WeightedCrossEntropy(weights) assert torch.allclose(pytorch_weighted(x, target), custom(x, target, to_weight=True)) assert torch.allclose(pytorch_unweighted(x, target), custom(x, target, to_weight=False)) print(custom(x, target, to_weight=True), custom(x, target, to_weight=False)) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/metrics.py ================================================ import logging import numpy as np import scipy import torch from sklearn.metrics import average_precision_score, roc_auc_score logger = logging.getLogger(f'main.{__name__}') def metrics(targets, outputs, topk=(1, 5)): """ Adapted from https://github.com/hche11/VGGSound/blob/master/utils.py Calculate statistics including mAP, AUC, and d-prime. Args: output: 2d tensors, (dataset_size, classes_num) - before softmax target: 1d tensors, (dataset_size, ) topk: tuple Returns: metric_dict: a dict of metrics """ metrics_dict = dict() num_cls = outputs.shape[-1] # accuracy@k _, preds = torch.topk(outputs, k=max(topk), dim=1) correct_for_maxtopk = preds == targets.view(-1, 1).expand_as(preds) for k in topk: metrics_dict[f'accuracy_{k}'] = float(correct_for_maxtopk[:, :k].sum() / correct_for_maxtopk.shape[0]) # avg precision, average roc_auc, and dprime targets = torch.nn.functional.one_hot(targets, num_classes=num_cls) # ids of the predicted classes (same as softmax) targets_pred = torch.softmax(outputs, dim=1) targets = targets.numpy() targets_pred = targets_pred.numpy() # one-vs-rest avg_p = [average_precision_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)] try: roc_aucs = [roc_auc_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)] except ValueError: logger.warning('Weird... Some classes never occured in targets. Do not trust the metrics.') roc_aucs = np.array([0.5]) avg_p = np.array([0]) metrics_dict['mAP'] = np.mean(avg_p) metrics_dict['mROCAUC'] = np.mean(roc_aucs) # Percent point function (ppf) (inverse of cdf — percentiles). metrics_dict['dprime'] = scipy.stats.norm().ppf(metrics_dict['mROCAUC']) * np.sqrt(2) return metrics_dict if __name__ == '__main__': targets = torch.tensor([3, 3, 1, 2, 1, 0]) outputs = torch.tensor([ [1.2, 1.3, 1.1, 1.5], [1.3, 1.4, 1.0, 1.1], [1.5, 1.1, 1.4, 1.3], [1.0, 1.2, 1.4, 1.5], [1.2, 1.3, 1.1, 1.1], [1.2, 1.1, 1.1, 1.1], ]).float() metrics_dict = metrics(targets, outputs, topk=(1, 3)) print(metrics_dict) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/model.py ================================================ import torch import torch.nn as nn class VGGishish(nn.Module): def __init__(self, conv_layers, use_bn, num_classes): ''' Mostly from https://pytorch.org/vision/0.8/_modules/torchvision/models/vgg.html ''' super().__init__() layers = [] in_channels = 1 # a list of channels with 'MP' (maxpool) from config for v in conv_layers: if v == 'MP': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1, stride=1) if use_bn: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v self.features = nn.Sequential(*layers) self.avgpool = nn.AdaptiveAvgPool2d((5, 10)) self.flatten = nn.Flatten() self.classifier = nn.Sequential( nn.Linear(512 * 5 * 10, 4096), nn.ReLU(True), nn.Linear(4096, 4096), nn.ReLU(True), nn.Linear(4096, num_classes) ) # weight init self.reset_parameters() def forward(self, x): # adding channel dim for conv2d (B, 1, F, T) <- x = x.unsqueeze(1) # backbone (B, 1, 5, 53) <- (B, 1, 80, 860) x = self.features(x) # adaptive avg pooling (B, 1, 5, 10) <- (B, 1, 5, 53) – if no MP is used as the end of VGG x = self.avgpool(x) # flatten x = self.flatten(x) # classify x = self.classifier(x) return x def reset_parameters(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) if __name__ == '__main__': num_classes = 309 inputs = torch.rand(3, 80, 848) conv_layers = [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512] # conv_layers = [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512, 'MP'] model = VGGishish(conv_layers, use_bn=False, num_classes=num_classes) outputs = model(inputs) print(outputs.shape) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/predict.py ================================================ import os from torch.utils.data import DataLoader import torchvision from tqdm import tqdm from dataset import VGGSound import torch import torch.nn as nn from metrics import metrics from omegaconf import OmegaConf from model import VGGishish from transforms import Crop, StandardNormalizeAudio, ToTensor if __name__ == '__main__': cfg_cli = OmegaConf.from_cli() print(cfg_cli.config) cfg_yml = OmegaConf.load(cfg_cli.config) # the latter arguments are prioritized cfg = OmegaConf.merge(cfg_yml, cfg_cli) OmegaConf.set_readonly(cfg, True) print(OmegaConf.to_yaml(cfg)) # logger = LoggerWithTBoard(cfg) transforms = [ StandardNormalizeAudio(cfg.mels_path), ToTensor(), ] if cfg.cropped_size not in [None, 'None', 'none']: transforms.append(Crop(cfg.cropped_size)) transforms = torchvision.transforms.transforms.Compose(transforms) datasets = { 'test': VGGSound('test', cfg.mels_path, transforms), } loaders = { 'test': DataLoader(datasets['test'], batch_size=cfg.batch_size, num_workers=cfg.num_workers, pin_memory=True) } device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu') model = VGGishish(cfg.conv_layers, cfg.use_bn, num_classes=len(datasets['test'].target2label)) model = model.to(device) optimizer = torch.optim.Adam(model.parameters(), lr=cfg.learning_rate) criterion = nn.CrossEntropyLoss() # loading the best model folder_name = os.path.split(cfg.config)[0].split('/')[-1] print(folder_name) ckpt = torch.load(f'./logs/{folder_name}/vggishish-{folder_name}.pt', map_location='cpu') model.load_state_dict(ckpt['model']) print((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}')) # Testing the model model.eval() running_loss = 0 preds_from_each_batch = [] targets_from_each_batch = [] for i, batch in enumerate(tqdm(loaders['test'])): inputs = batch['input'].to(device) targets = batch['target'].to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize with torch.set_grad_enabled(False): outputs = model(inputs) loss = criterion(outputs, targets) # loss running_loss += loss.item() # for metrics calculation later on preds_from_each_batch += [outputs.detach().cpu()] targets_from_each_batch += [targets.cpu()] # logging metrics preds_from_each_batch = torch.cat(preds_from_each_batch) targets_from_each_batch = torch.cat(targets_from_each_batch) test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) test_metrics_dict['avg_loss'] = running_loss / len(loaders['test']) test_metrics_dict['param_num'] = sum(p.numel() for p in model.parameters() if p.requires_grad) # TODO: I have no idea why tboard doesn't keep metrics (hparams) in a tensorboard when # I run this experiment from cli: `python main.py config=./configs/vggish.yaml` # while when I run it in vscode debugger the metrics are present in the tboard (weird) print(test_metrics_dict) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/train_melception.py ================================================ import random import numpy as np import torch import torchvision from omegaconf import OmegaConf from torch.utils.data.dataloader import DataLoader from torchvision.models.inception import BasicConv2d, Inception3 from tqdm import tqdm from dataset import VGGSound from logger import LoggerWithTBoard from loss import WeightedCrossEntropy from metrics import metrics from transforms import Crop, StandardNormalizeAudio, ToTensor # TODO: refactor ./evaluation/feature_extractors/melception.py to handle this class as well. # So far couldn't do it because of the difference in outputs class Melception(Inception3): def __init__(self, num_classes, **kwargs): # inception = Melception(num_classes=309) super().__init__(num_classes=num_classes, **kwargs) # the same as https://github.com/pytorch/vision/blob/5339e63148/torchvision/models/inception.py#L95 # but for 1-channel input instead of RGB. self.Conv2d_1a_3x3 = BasicConv2d(1, 32, kernel_size=3, stride=2) # also the 'hight' of the mel spec is 80 (vs 299 in RGB) we remove all max pool from Inception self.maxpool1 = torch.nn.Identity() self.maxpool2 = torch.nn.Identity() def forward(self, x): x = x.unsqueeze(1) return super().forward(x) def train_inception_scorer(cfg): logger = LoggerWithTBoard(cfg) random.seed(cfg.seed) np.random.seed(cfg.seed) torch.manual_seed(cfg.seed) torch.cuda.manual_seed_all(cfg.seed) # makes iterations faster (in this case 30%) if your inputs are of a fixed size # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3 torch.backends.cudnn.benchmark = True meta_path = './data/vggsound.csv' train_ids_path = './data/vggsound_train.txt' cache_path = './data/' splits_path = cache_path transforms = [ StandardNormalizeAudio(cfg.mels_path, train_ids_path, cache_path), ] if cfg.cropped_size not in [None, 'None', 'none']: logger.print_logger.info(f'Using cropping {cfg.cropped_size}') transforms.append(Crop(cfg.cropped_size)) transforms.append(ToTensor()) transforms = torchvision.transforms.transforms.Compose(transforms) datasets = { 'train': VGGSound('train', cfg.mels_path, transforms, splits_path, meta_path), 'valid': VGGSound('valid', cfg.mels_path, transforms, splits_path, meta_path), 'test': VGGSound('test', cfg.mels_path, transforms, splits_path, meta_path), } loaders = { 'train': DataLoader(datasets['train'], batch_size=cfg.batch_size, shuffle=True, drop_last=True, num_workers=cfg.num_workers, pin_memory=True), 'valid': DataLoader(datasets['valid'], batch_size=cfg.batch_size, num_workers=cfg.num_workers, pin_memory=True), 'test': DataLoader(datasets['test'], batch_size=cfg.batch_size, num_workers=cfg.num_workers, pin_memory=True), } device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu') model = Melception(num_classes=len(datasets['train'].target2label)) model = model.to(device) param_num = logger.log_param_num(model) if cfg.optimizer == 'adam': optimizer = torch.optim.Adam( model.parameters(), lr=cfg.learning_rate, betas=cfg.betas, weight_decay=cfg.weight_decay) elif cfg.optimizer == 'sgd': optimizer = torch.optim.SGD( model.parameters(), lr=cfg.learning_rate, momentum=cfg.momentum, weight_decay=cfg.weight_decay) else: raise NotImplementedError if cfg.cls_weights_in_loss: weights = 1 / datasets['train'].class_counts else: weights = torch.ones(len(datasets['train'].target2label)) criterion = WeightedCrossEntropy(weights.to(device)) # loop over the train and validation multiple times (typical PT boilerplate) no_change_epochs = 0 best_valid_loss = float('inf') early_stop_triggered = False for epoch in range(cfg.num_epochs): for phase in ['train', 'valid']: if phase == 'train': model.train() else: model.eval() running_loss = 0 preds_from_each_batch = [] targets_from_each_batch = [] prog_bar = tqdm(loaders[phase], f'{phase} ({epoch})', ncols=0) for i, batch in enumerate(prog_bar): inputs = batch['input'].to(device) targets = batch['target'].to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize with torch.set_grad_enabled(phase == 'train'): # inception v3 if phase == 'train': outputs, aux_outputs = model(inputs) loss1 = criterion(outputs, targets) loss2 = criterion(aux_outputs, targets) loss = loss1 + 0.4*loss2 loss = criterion(outputs, targets, to_weight=True) else: outputs = model(inputs) loss = criterion(outputs, targets, to_weight=False) if phase == 'train': loss.backward() optimizer.step() # loss running_loss += loss.item() # for metrics calculation later on preds_from_each_batch += [outputs.detach().cpu()] targets_from_each_batch += [targets.cpu()] # iter logging if i % 50 == 0: logger.log_iter_loss(loss.item(), epoch*len(loaders[phase])+i, phase) # tracks loss in the tqdm progress bar prog_bar.set_postfix(loss=loss.item()) # logging loss epoch_loss = running_loss / len(loaders[phase]) logger.log_epoch_loss(epoch_loss, epoch, phase) # logging metrics preds_from_each_batch = torch.cat(preds_from_each_batch) targets_from_each_batch = torch.cat(targets_from_each_batch) metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) logger.log_epoch_metrics(metrics_dict, epoch, phase) # Early stopping if phase == 'valid': if epoch_loss < best_valid_loss: no_change_epochs = 0 best_valid_loss = epoch_loss logger.log_best_model(model, epoch_loss, epoch, optimizer, metrics_dict) else: no_change_epochs += 1 logger.print_logger.info( f'Valid loss hasnt changed for {no_change_epochs} patience: {cfg.patience}' ) if no_change_epochs >= cfg.patience: early_stop_triggered = True if early_stop_triggered: logger.print_logger.info(f'Training is early stopped @ {epoch}') break logger.print_logger.info('Finished Training') # loading the best model ckpt = torch.load(logger.best_model_path) model.load_state_dict(ckpt['model']) logger.print_logger.info(f'Loading the best model from {logger.best_model_path}') logger.print_logger.info((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}')) # Testing the model model.eval() running_loss = 0 preds_from_each_batch = [] targets_from_each_batch = [] for i, batch in enumerate(loaders['test']): inputs = batch['input'].to(device) targets = batch['target'].to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize with torch.set_grad_enabled(False): outputs = model(inputs) loss = criterion(outputs, targets, to_weight=False) # loss running_loss += loss.item() # for metrics calculation later on preds_from_each_batch += [outputs.detach().cpu()] targets_from_each_batch += [targets.cpu()] # logging metrics preds_from_each_batch = torch.cat(preds_from_each_batch) targets_from_each_batch = torch.cat(targets_from_each_batch) test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) test_metrics_dict['avg_loss'] = running_loss / len(loaders['test']) test_metrics_dict['param_num'] = param_num # TODO: I have no idea why tboard doesn't keep metrics (hparams) when # I run this experiment from cli: `python train_melception.py config=./configs/vggish.yaml` # while when I run it in vscode debugger the metrics are logger (wtf) logger.log_test_metrics(test_metrics_dict, dict(cfg), ckpt['epoch']) logger.print_logger.info('Finished the experiment') if __name__ == '__main__': # input = torch.rand(16, 1, 80, 848) # output, aux = inception(input) # print(output.shape, aux.shape) # Expected input size: (3, 299, 299) in RGB -> (1, 80, 848) in Mel Spec # train_inception_scorer() cfg_cli = OmegaConf.from_cli() cfg_yml = OmegaConf.load(cfg_cli.config) # the latter arguments are prioritized cfg = OmegaConf.merge(cfg_yml, cfg_cli) OmegaConf.set_readonly(cfg, True) print(OmegaConf.to_yaml(cfg)) train_inception_scorer(cfg) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/train_vggishish.py ================================================ from loss import WeightedCrossEntropy import random import numpy as np import torch import torchvision from omegaconf import OmegaConf from torch.utils.data.dataloader import DataLoader from tqdm import tqdm from dataset import VGGSound from transforms import Crop, StandardNormalizeAudio, ToTensor from logger import LoggerWithTBoard from metrics import metrics from model import VGGishish if __name__ == "__main__": cfg_cli = OmegaConf.from_cli() cfg_yml = OmegaConf.load(cfg_cli.config) # the latter arguments are prioritized cfg = OmegaConf.merge(cfg_yml, cfg_cli) OmegaConf.set_readonly(cfg, True) print(OmegaConf.to_yaml(cfg)) logger = LoggerWithTBoard(cfg) random.seed(cfg.seed) np.random.seed(cfg.seed) torch.manual_seed(cfg.seed) torch.cuda.manual_seed_all(cfg.seed) # makes iterations faster (in this case 30%) if your inputs are of a fixed size # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3 torch.backends.cudnn.benchmark = True transforms = [ StandardNormalizeAudio(cfg.mels_path), ] if cfg.cropped_size not in [None, 'None', 'none']: logger.print_logger.info(f'Using cropping {cfg.cropped_size}') transforms.append(Crop(cfg.cropped_size)) transforms.append(ToTensor()) transforms = torchvision.transforms.transforms.Compose(transforms) datasets = { 'train': VGGSound('train', cfg.mels_path, transforms), 'valid': VGGSound('valid', cfg.mels_path, transforms), 'test': VGGSound('test', cfg.mels_path, transforms), } loaders = { 'train': DataLoader(datasets['train'], batch_size=cfg.batch_size, shuffle=True, drop_last=True, num_workers=cfg.num_workers, pin_memory=True), 'valid': DataLoader(datasets['valid'], batch_size=cfg.batch_size, num_workers=cfg.num_workers, pin_memory=True), 'test': DataLoader(datasets['test'], batch_size=cfg.batch_size, num_workers=cfg.num_workers, pin_memory=True), } device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu') model = VGGishish(cfg.conv_layers, cfg.use_bn, num_classes=len(datasets['train'].target2label)) model = model.to(device) param_num = logger.log_param_num(model) if cfg.optimizer == 'adam': optimizer = torch.optim.Adam( model.parameters(), lr=cfg.learning_rate, betas=cfg.betas, weight_decay=cfg.weight_decay) elif cfg.optimizer == 'sgd': optimizer = torch.optim.SGD( model.parameters(), lr=cfg.learning_rate, momentum=cfg.momentum, weight_decay=cfg.weight_decay) else: raise NotImplementedError if cfg.cls_weights_in_loss: weights = 1 / datasets['train'].class_counts else: weights = torch.ones(len(datasets['train'].target2label)) criterion = WeightedCrossEntropy(weights.to(device)) # loop over the train and validation multiple times (typical PT boilerplate) no_change_epochs = 0 best_valid_loss = float('inf') early_stop_triggered = False for epoch in range(cfg.num_epochs): for phase in ['train', 'valid']: if phase == 'train': model.train() else: model.eval() running_loss = 0 preds_from_each_batch = [] targets_from_each_batch = [] prog_bar = tqdm(loaders[phase], f'{phase} ({epoch})', ncols=0) for i, batch in enumerate(prog_bar): inputs = batch['input'].to(device) targets = batch['target'].to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) loss = criterion(outputs, targets, to_weight=phase == 'train') if phase == 'train': loss.backward() optimizer.step() # loss running_loss += loss.item() # for metrics calculation later on preds_from_each_batch += [outputs.detach().cpu()] targets_from_each_batch += [targets.cpu()] # iter logging if i % 50 == 0: logger.log_iter_loss(loss.item(), epoch*len(loaders[phase])+i, phase) # tracks loss in the tqdm progress bar prog_bar.set_postfix(loss=loss.item()) # logging loss epoch_loss = running_loss / len(loaders[phase]) logger.log_epoch_loss(epoch_loss, epoch, phase) # logging metrics preds_from_each_batch = torch.cat(preds_from_each_batch) targets_from_each_batch = torch.cat(targets_from_each_batch) metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) logger.log_epoch_metrics(metrics_dict, epoch, phase) # Early stopping if phase == 'valid': if epoch_loss < best_valid_loss: no_change_epochs = 0 best_valid_loss = epoch_loss logger.log_best_model(model, epoch_loss, epoch, optimizer, metrics_dict) else: no_change_epochs += 1 logger.print_logger.info( f'Valid loss hasnt changed for {no_change_epochs} patience: {cfg.patience}' ) if no_change_epochs >= cfg.patience: early_stop_triggered = True if early_stop_triggered: logger.print_logger.info(f'Training is early stopped @ {epoch}') break logger.print_logger.info('Finished Training') # loading the best model ckpt = torch.load(logger.best_model_path) model.load_state_dict(ckpt['model']) logger.print_logger.info(f'Loading the best model from {logger.best_model_path}') logger.print_logger.info((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}')) # Testing the model model.eval() running_loss = 0 preds_from_each_batch = [] targets_from_each_batch = [] for i, batch in enumerate(loaders['test']): inputs = batch['input'].to(device) targets = batch['target'].to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize with torch.set_grad_enabled(False): outputs = model(inputs) loss = criterion(outputs, targets, to_weight=False) # loss running_loss += loss.item() # for metrics calculation later on preds_from_each_batch += [outputs.detach().cpu()] targets_from_each_batch += [targets.cpu()] # logging metrics preds_from_each_batch = torch.cat(preds_from_each_batch) targets_from_each_batch = torch.cat(targets_from_each_batch) test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) test_metrics_dict['avg_loss'] = running_loss / len(loaders['test']) test_metrics_dict['param_num'] = param_num # TODO: I have no idea why tboard doesn't keep metrics (hparams) when # I run this experiment from cli: `python train_vggishish.py config=./configs/vggish.yaml` # while when I run it in vscode debugger the metrics are logger (wtf) logger.log_test_metrics(test_metrics_dict, dict(cfg), ckpt['epoch']) logger.print_logger.info('Finished the experiment') ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/transforms.py ================================================ import logging import os from pathlib import Path import albumentations import numpy as np import torch from tqdm import tqdm logger = logging.getLogger(f'main.{__name__}') class StandardNormalizeAudio(object): ''' Frequency-wise normalization ''' def __init__(self, specs_dir, train_ids_path='./data/vggsound_train.txt', cache_path='./data/'): self.specs_dir = specs_dir self.train_ids_path = train_ids_path # making the stats filename to match the specs dir name self.cache_path = os.path.join(cache_path, f'train_means_stds_{Path(specs_dir).stem}.txt') logger.info('Assuming that the input stats are calculated using preprocessed spectrograms (log)') self.train_stats = self.calculate_or_load_stats() def __call__(self, item): # just to generalizat the input handling. Useful for FID, IS eval and training other staff if isinstance(item, dict): if 'input' in item: input_key = 'input' elif 'image' in item: input_key = 'image' else: raise NotImplementedError item[input_key] = (item[input_key] - self.train_stats['means']) / self.train_stats['stds'] elif isinstance(item, torch.Tensor): # broadcasts np.ndarray (80, 1) to (1, 80, 1) because item is torch.Tensor (B, 80, T) item = (item - self.train_stats['means']) / self.train_stats['stds'] else: raise NotImplementedError return item def calculate_or_load_stats(self): try: # (F, 2) train_stats = np.loadtxt(self.cache_path) means, stds = train_stats.T logger.info('Trying to load train stats for Standard Normalization of inputs') except OSError: logger.info('Could not find the precalculated stats for Standard Normalization. Calculating...') train_vid_ids = open(self.train_ids_path) specs_paths = [os.path.join(self.specs_dir, f'{i.rstrip()}_mel.npy') for i in train_vid_ids] means = [None] * len(specs_paths) stds = [None] * len(specs_paths) for i, path in enumerate(tqdm(specs_paths)): spec = np.load(path) means[i] = spec.mean(axis=1) stds[i] = spec.std(axis=1) # (F) <- (num_files, F) means = np.array(means).mean(axis=0) stds = np.array(stds).mean(axis=0) # saving in two columns np.savetxt(self.cache_path, np.vstack([means, stds]).T, fmt='%0.8f') means = means.reshape(-1, 1) stds = stds.reshape(-1, 1) return {'means': means, 'stds': stds} class ToTensor(object): def __call__(self, item): item['input'] = torch.from_numpy(item['input']).float() # if 'target' in item: item['target'] = torch.tensor(item['target']) return item class Crop(object): def __init__(self, cropped_shape=None, random_crop=False): self.cropped_shape = cropped_shape if cropped_shape is not None: mel_num, spec_len = cropped_shape if random_crop: self.cropper = albumentations.RandomCrop else: self.cropper = albumentations.CenterCrop self.preprocessor = albumentations.Compose([self.cropper(mel_num, spec_len)]) else: self.preprocessor = lambda **kwargs: kwargs def __call__(self, item): item['input'] = self.preprocessor(image=item['input'])['image'] return item if __name__ == '__main__': cropper = Crop([80, 848]) item = {'input': torch.rand([80, 860])} outputs = cropper(item) print(outputs['input'].shape) ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vqperceptual.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import sys from ldm.util import exists sys.path.insert(0, '.') # nopep8 from ldm.modules.discriminator.model import (NLayerDiscriminator, NLayerDiscriminator1dFeats, NLayerDiscriminator1dSpecs, weights_init) from ldm.modules.losses_audio.lpaps import LPAPS from ldm.modules.losses.vqperceptual import l1, l2, measure_perplexity, hinge_d_loss, vanilla_d_loss, adopt_weight class DummyLoss(nn.Module): def __init__(self): super().__init__() class VQLPAPSWithDiscriminator(nn.Module): def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_ndf=64, disc_loss="hinge", n_classes=None, pixel_loss="l1"): super().__init__() assert disc_loss in ["hinge", "vanilla"] self.codebook_weight = codebook_weight self.pixel_weight = pixelloss_weight self.perceptual_loss = LPAPS().eval() self.perceptual_weight = perceptual_weight if pixel_loss == "l1": self.pixel_loss = l1 else: self.pixel_loss = l2 self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf ).apply(weights_init) self.discriminator_iter_start = disc_start if disc_loss == "hinge": self.disc_loss = hinge_d_loss elif disc_loss == "vanilla": self.disc_loss = vanilla_d_loss else: raise ValueError(f"Unknown GAN loss '{disc_loss}'.") print(f"VQLPAPSWithDiscriminator running with {disc_loss} loss.") self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional self.n_classes = n_classes def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): if last_layer is not None: nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] else: nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() d_weight = d_weight * self.discriminator_weight return d_weight def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, global_step, last_layer=None, cond=None, split="train", predicted_indices=None): if not exists(codebook_loss): codebook_loss = torch.tensor([0.]).to(inputs.device) rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) rec_loss = rec_loss + self.perceptual_weight * p_loss else: p_loss = torch.tensor([0.0]) nll_loss = rec_loss # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] nll_loss = torch.mean(nll_loss) # now the GAN part if optimizer_idx == 0: # generator update if cond is None: assert not self.disc_conditional logits_fake = self.discriminator(reconstructions.contiguous()) else: assert self.disc_conditional logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) g_loss = -torch.mean(logits_fake) try: d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) except RuntimeError: assert not self.training d_weight = torch.tensor(0.0) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/quant_loss".format(split): codebook_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/p_loss".format(split): p_loss.detach().mean(), "{}/d_weight".format(split): d_weight.detach(), "{}/disc_factor".format(split): torch.tensor(disc_factor), "{}/g_loss".format(split): g_loss.detach().mean(), } # if predicted_indices is not None: # assert self.n_classes is not None # with torch.no_grad(): # perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes) # log[f"{split}/perplexity"] = perplexity # log[f"{split}/cluster_usage"] = cluster_usage return loss, log if optimizer_idx == 1: # second pass for discriminator update if cond is None: logits_real = self.discriminator(inputs.contiguous().detach()) logits_fake = self.discriminator(reconstructions.contiguous().detach()) else: logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), "{}/logits_real".format(split): logits_real.detach().mean(), "{}/logits_fake".format(split): logits_fake.detach().mean() } return d_loss, log ================================================ FILE: text_to_audio/Make_An_Audio/ldm/modules/x_transformer.py ================================================ """shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" import torch from torch import nn, einsum import torch.nn.functional as F from functools import partial from inspect import isfunction from collections import namedtuple from einops import rearrange, repeat, reduce # constants DEFAULT_DIM_HEAD = 64 Intermediates = namedtuple('Intermediates', [ 'pre_softmax_attn', 'post_softmax_attn' ]) LayerIntermediates = namedtuple('Intermediates', [ 'hiddens', 'attn_intermediates' ]) class AbsolutePositionalEmbedding(nn.Module): def __init__(self, dim, max_seq_len): super().__init__() self.emb = nn.Embedding(max_seq_len, dim) self.init_() def init_(self): nn.init.normal_(self.emb.weight, std=0.02) def forward(self, x): n = torch.arange(x.shape[1], device=x.device) return self.emb(n)[None, :, :] class FixedPositionalEmbedding(nn.Module): def __init__(self, dim): super().__init__() inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer('inv_freq', inv_freq) def forward(self, x, seq_dim=1, offset=0): t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) return emb[None, :, :] # helpers def exists(val): return val is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def always(val): def inner(*args, **kwargs): return val return inner def not_equals(val): def inner(x): return x != val return inner def equals(val): def inner(x): return x == val return inner def max_neg_value(tensor): return -torch.finfo(tensor.dtype).max # keyword argument helpers def pick_and_pop(keys, d): values = list(map(lambda key: d.pop(key), keys)) return dict(zip(keys, values)) def group_dict_by_key(cond, d): return_val = [dict(), dict()] for key in d.keys(): match = bool(cond(key)) ind = int(not match) return_val[ind][key] = d[key] return (*return_val,) def string_begins_with(prefix, str): return str.startswith(prefix) def group_by_key_prefix(prefix, d): return group_dict_by_key(partial(string_begins_with, prefix), d) def groupby_prefix_and_trim(prefix, d): kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) return kwargs_without_prefix, kwargs # classes class Scale(nn.Module): def __init__(self, value, fn): super().__init__() self.value = value self.fn = fn def forward(self, x, **kwargs): x, *rest = self.fn(x, **kwargs) return (x * self.value, *rest) class Rezero(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn self.g = nn.Parameter(torch.zeros(1)) def forward(self, x, **kwargs): x, *rest = self.fn(x, **kwargs) return (x * self.g, *rest) class ScaleNorm(nn.Module): def __init__(self, dim, eps=1e-5): super().__init__() self.scale = dim ** -0.5 self.eps = eps self.g = nn.Parameter(torch.ones(1)) def forward(self, x): norm = torch.norm(x, dim=-1, keepdim=True) * self.scale return x / norm.clamp(min=self.eps) * self.g class RMSNorm(nn.Module): def __init__(self, dim, eps=1e-8): super().__init__() self.scale = dim ** -0.5 self.eps = eps self.g = nn.Parameter(torch.ones(dim)) def forward(self, x): norm = torch.norm(x, dim=-1, keepdim=True) * self.scale return x / norm.clamp(min=self.eps) * self.g class Residual(nn.Module): def forward(self, x, residual): return x + residual class GRUGating(nn.Module): def __init__(self, dim): super().__init__() self.gru = nn.GRUCell(dim, dim) def forward(self, x, residual): gated_output = self.gru( rearrange(x, 'b n d -> (b n) d'), rearrange(residual, 'b n d -> (b n) d') ) return gated_output.reshape_as(x) # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * F.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) # attention. class Attention(nn.Module): def __init__( self, dim, dim_head=DEFAULT_DIM_HEAD, heads=8, causal=False, mask=None, talking_heads=False, sparse_topk=None, use_entmax15=False, num_mem_kv=0, dropout=0., on_attn=False ): super().__init__() if use_entmax15: raise NotImplementedError("Check out entmax activation instead of softmax activation!") self.scale = dim_head ** -0.5 self.heads = heads self.causal = causal self.mask = mask inner_dim = dim_head * heads self.to_q = nn.Linear(dim, inner_dim, bias=False) self.to_k = nn.Linear(dim, inner_dim, bias=False) self.to_v = nn.Linear(dim, inner_dim, bias=False) self.dropout = nn.Dropout(dropout) # talking heads self.talking_heads = talking_heads if talking_heads: self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) # explicit topk sparse attention self.sparse_topk = sparse_topk # entmax #self.attn_fn = entmax15 if use_entmax15 else F.softmax self.attn_fn = F.softmax # add memory key / values self.num_mem_kv = num_mem_kv if num_mem_kv > 0: self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) # attention on attention self.attn_on_attn = on_attn self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) def forward( self, x, context=None, mask=None, context_mask=None, rel_pos=None, sinusoidal_emb=None, prev_attn=None, mem=None ): b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device kv_input = default(context, x) q_input = x k_input = kv_input v_input = kv_input if exists(mem): k_input = torch.cat((mem, k_input), dim=-2) v_input = torch.cat((mem, v_input), dim=-2) if exists(sinusoidal_emb): # in shortformer, the query would start at a position offset depending on the past cached memory offset = k_input.shape[-2] - q_input.shape[-2] q_input = q_input + sinusoidal_emb(q_input, offset=offset) k_input = k_input + sinusoidal_emb(k_input) q = self.to_q(q_input) k = self.to_k(k_input) v = self.to_v(v_input) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) input_mask = None if any(map(exists, (mask, context_mask))): q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) k_mask = q_mask if not exists(context) else context_mask k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) q_mask = rearrange(q_mask, 'b i -> b () i ()') k_mask = rearrange(k_mask, 'b j -> b () () j') input_mask = q_mask * k_mask if self.num_mem_kv > 0: mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) k = torch.cat((mem_k, k), dim=-2) v = torch.cat((mem_v, v), dim=-2) if exists(input_mask): input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale mask_value = max_neg_value(dots) if exists(prev_attn): dots = dots + prev_attn pre_softmax_attn = dots if talking_heads: dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() if exists(rel_pos): dots = rel_pos(dots) if exists(input_mask): dots.masked_fill_(~input_mask, mask_value) del input_mask if self.causal: i, j = dots.shape[-2:] r = torch.arange(i, device=device) mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') mask = F.pad(mask, (j - i, 0), value=False) dots.masked_fill_(mask, mask_value) del mask if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: top, _ = dots.topk(self.sparse_topk, dim=-1) vk = top[..., -1].unsqueeze(-1).expand_as(dots) mask = dots < vk dots.masked_fill_(mask, mask_value) del mask attn = self.attn_fn(dots, dim=-1) post_softmax_attn = attn attn = self.dropout(attn) if talking_heads: attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() out = einsum('b h i j, b h j d -> b h i d', attn, v) out = rearrange(out, 'b h n d -> b n (h d)') intermediates = Intermediates( pre_softmax_attn=pre_softmax_attn, post_softmax_attn=post_softmax_attn ) return self.to_out(out), intermediates class AttentionLayers(nn.Module): def __init__( self, dim, depth, heads=8, causal=False, cross_attend=False, only_cross=False, use_scalenorm=False, use_rmsnorm=False, use_rezero=False, rel_pos_num_buckets=32, rel_pos_max_distance=128, position_infused_attn=False, custom_layers=None, sandwich_coef=None, par_ratio=None, residual_attn=False, cross_residual_attn=False, macaron=False, pre_norm=True, gate_residual=False, **kwargs ): super().__init__() ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) self.dim = dim self.depth = depth self.layers = nn.ModuleList([]) self.has_pos_emb = position_infused_attn self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None self.rotary_pos_emb = always(None) assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' self.rel_pos = None self.pre_norm = pre_norm self.residual_attn = residual_attn self.cross_residual_attn = cross_residual_attn norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm norm_class = RMSNorm if use_rmsnorm else norm_class norm_fn = partial(norm_class, dim) norm_fn = nn.Identity if use_rezero else norm_fn branch_fn = Rezero if use_rezero else None if cross_attend and not only_cross: default_block = ('a', 'c', 'f') elif cross_attend and only_cross: default_block = ('c', 'f') else: default_block = ('a', 'f') if macaron: default_block = ('f',) + default_block if exists(custom_layers): layer_types = custom_layers elif exists(par_ratio): par_depth = depth * len(default_block) assert 1 < par_ratio <= par_depth, 'par ratio out of range' default_block = tuple(filter(not_equals('f'), default_block)) par_attn = par_depth // par_ratio depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper par_width = (depth_cut + depth_cut // par_attn) // par_attn assert len(default_block) <= par_width, 'default block is too large for par_ratio' par_block = default_block + ('f',) * (par_width - len(default_block)) par_head = par_block * par_attn layer_types = par_head + ('f',) * (par_depth - len(par_head)) elif exists(sandwich_coef): assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef else: layer_types = default_block * depth self.layer_types = layer_types self.num_attn_layers = len(list(filter(equals('a'), layer_types))) for layer_type in self.layer_types: if layer_type == 'a': layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) elif layer_type == 'c': layer = Attention(dim, heads=heads, **attn_kwargs) elif layer_type == 'f': layer = FeedForward(dim, **ff_kwargs) layer = layer if not macaron else Scale(0.5, layer) else: raise Exception(f'invalid layer type {layer_type}') if isinstance(layer, Attention) and exists(branch_fn): layer = branch_fn(layer) if gate_residual: residual_fn = GRUGating(dim) else: residual_fn = Residual() self.layers.append(nn.ModuleList([ norm_fn(), layer, residual_fn ])) def forward( self, x, context=None, mask=None, context_mask=None, mems=None, return_hiddens=False ): hiddens = [] intermediates = [] prev_attn = None prev_cross_attn = None mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): is_last = ind == (len(self.layers) - 1) if layer_type == 'a': hiddens.append(x) layer_mem = mems.pop(0) residual = x if self.pre_norm: x = norm(x) if layer_type == 'a': out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos, prev_attn=prev_attn, mem=layer_mem) elif layer_type == 'c': out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) elif layer_type == 'f': out = block(x) x = residual_fn(out, residual) if layer_type in ('a', 'c'): intermediates.append(inter) if layer_type == 'a' and self.residual_attn: prev_attn = inter.pre_softmax_attn elif layer_type == 'c' and self.cross_residual_attn: prev_cross_attn = inter.pre_softmax_attn if not self.pre_norm and not is_last: x = norm(x) if return_hiddens: intermediates = LayerIntermediates( hiddens=hiddens, attn_intermediates=intermediates ) return x, intermediates return x class Encoder(AttentionLayers): def __init__(self, **kwargs): assert 'causal' not in kwargs, 'cannot set causality on encoder' super().__init__(causal=False, **kwargs) class TransformerWrapper(nn.Module): def __init__( self, *, num_tokens, max_seq_len, attn_layers, emb_dim=None, max_mem_len=0., emb_dropout=0., num_memory_tokens=None, tie_embedding=False, use_pos_emb=True ): super().__init__() assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' dim = attn_layers.dim emb_dim = default(emb_dim, dim) self.max_seq_len = max_seq_len self.max_mem_len = max_mem_len self.num_tokens = num_tokens self.token_emb = nn.Embedding(num_tokens, emb_dim) self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( use_pos_emb and not attn_layers.has_pos_emb) else always(0) self.emb_dropout = nn.Dropout(emb_dropout) self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() self.attn_layers = attn_layers self.norm = nn.LayerNorm(dim) self.init_() self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() # memory tokens (like [cls]) from Memory Transformers paper num_memory_tokens = default(num_memory_tokens, 0) self.num_memory_tokens = num_memory_tokens if num_memory_tokens > 0: self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) # let funnel encoder know number of memory tokens, if specified if hasattr(attn_layers, 'num_memory_tokens'): attn_layers.num_memory_tokens = num_memory_tokens def init_(self): nn.init.normal_(self.token_emb.weight, std=0.02) def forward( self, x, return_embeddings=False, mask=None, return_mems=False, return_attn=False, mems=None, **kwargs ): b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens x = self.token_emb(x) x += self.pos_emb(x) x = self.emb_dropout(x) x = self.project_emb(x) if num_mem > 0: mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) x = torch.cat((mem, x), dim=1) # auto-handle masking after appending memory tokens if exists(mask): mask = F.pad(mask, (num_mem, 0), value=True) x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) x = self.norm(x) mem, x = x[:, :num_mem], x[:, num_mem:] out = self.to_logits(x) if not return_embeddings else x if return_mems: hiddens = intermediates.hiddens new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems)) return out, new_mems if return_attn: attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) return out, attn_maps return out ================================================ FILE: text_to_audio/Make_An_Audio/ldm/util.py ================================================ import importlib import torch import numpy as np from tqdm import tqdm from inspect import isfunction from PIL import Image, ImageDraw, ImageFont import hashlib import requests import os URL_MAP = { 'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt', 'vggishish_mean_std_melspec_10s_22050hz': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/train_means_stds_melspec_10s_22050hz.txt', 'melception': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt', } CKPT_MAP = { 'vggishish_lpaps': 'vggishish16.pt', 'vggishish_mean_std_melspec_10s_22050hz': 'train_means_stds_melspec_10s_22050hz.txt', 'melception': 'melception-21-05-10T09-28-40.pt', } MD5_MAP = { 'vggishish_lpaps': '197040c524a07ccacf7715d7080a80bd', 'vggishish_mean_std_melspec_10s_22050hz': 'f449c6fd0e248936c16f6d22492bb625', 'melception': 'a71a41041e945b457c7d3d814bbcf72d', } def download(url, local_path, chunk_size=1024): os.makedirs(os.path.split(local_path)[0], exist_ok=True) with requests.get(url, stream=True) as r: total_size = int(r.headers.get("content-length", 0)) with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: with open(local_path, "wb") as f: for data in r.iter_content(chunk_size=chunk_size): if data: f.write(data) pbar.update(chunk_size) def md5_hash(path): with open(path, "rb") as f: content = f.read() return hashlib.md5(content).hexdigest() def log_txt_as_img(wh, xc, size=10): # wh a tuple of (width, height) # xc a list of captions to plot b = len(xc) txts = list() for bi in range(b): txt = Image.new("RGB", wh, color="white") draw = ImageDraw.Draw(txt) font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) nc = int(40 * (wh[0] / 256)) lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) try: draw.text((0, 0), lines, fill="black", font=font) except UnicodeEncodeError: print("Cant encode string for logging. Skipping.") txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 txts.append(txt) txts = np.stack(txts) txts = torch.tensor(txts) return txts def ismap(x): if not isinstance(x, torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] > 3) def isimage(x): if not isinstance(x,torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def mean_flat(tensor): """ https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def count_params(model, verbose=False): total_params = sum(p.numel() for p in model.parameters()) if verbose: print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") return total_params def instantiate_from_config(config,reload=False): if not "target" in config: if config == '__is_first_stage__': return None elif config == "__is_unconditional__": return None raise KeyError("Expected key `target` to instantiate.") return get_obj_from_str(config["target"],reload=reload)(**config.get("params", dict())) def get_obj_from_str(string, reload=False): module, cls = string.rsplit(".", 1) if reload: module_imp = importlib.import_module(module) importlib.reload(module_imp) return getattr(importlib.import_module(module, package=None), cls) def get_ckpt_path(name, root, check=False): assert name in URL_MAP path = os.path.join(root, CKPT_MAP[name]) if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) download(URL_MAP[name], path) md5 = md5_hash(path) assert md5 == MD5_MAP[name], md5 return path ================================================ FILE: text_to_audio/Make_An_Audio/useful_ckpts/CLAP/config.yml ================================================ # TEXT ENCODER CONFIG text_model: 'bert-base-uncased' text_len: 100 transformer_embed_dim: 768 freeze_text_encoder_weights: True # AUDIO ENCODER CONFIG audioenc_name: 'Cnn14' out_emb: 2048 sampling_rate: 44100 duration: 9 fmin: 50 fmax: 14000 n_fft: 1028 hop_size: 320 mel_bins: 64 window_size: 1024 # PROJECTION SPACE CONFIG d_proj: 1024 temperature: 0.003 # TRAINING AND EVALUATION CONFIG num_classes: 527 batch_size: 1024 demo: False ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/bigvgan/__init__.py ================================================ ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/bigvgan/activations.py ================================================ # Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. # LICENSE is in incl_licenses directory. import torch from torch import nn, sin, pow from torch.nn import Parameter class Snake(nn.Module): ''' Implementation of a sine-based periodic activation function Shape: - Input: (B, C, T) - Output: (B, C, T), same shape as the input Parameters: - alpha - trainable parameter References: - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195 Examples: >>> a1 = snake(256) >>> x = torch.randn(256) >>> x = a1(x) ''' def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): ''' Initialization. INPUT: - in_features: shape of the input - alpha: trainable parameter alpha is initialized to 1 by default, higher values = higher-frequency. alpha will be trained along with the rest of your model. ''' super(Snake, self).__init__() self.in_features = in_features # initialize alpha self.alpha_logscale = alpha_logscale if self.alpha_logscale: # log scale alphas initialized to zeros self.alpha = Parameter(torch.zeros(in_features) * alpha) else: # linear scale alphas initialized to ones self.alpha = Parameter(torch.ones(in_features) * alpha) self.alpha.requires_grad = alpha_trainable self.no_div_by_zero = 0.000000001 def forward(self, x): ''' Forward pass of the function. Applies the function to the input elementwise. Snake ∶= x + 1/a * sin^2 (xa) ''' alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] if self.alpha_logscale: alpha = torch.exp(alpha) x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) return x class SnakeBeta(nn.Module): ''' A modified Snake function which uses separate parameters for the magnitude of the periodic components Shape: - Input: (B, C, T) - Output: (B, C, T), same shape as the input Parameters: - alpha - trainable parameter that controls frequency - beta - trainable parameter that controls magnitude References: - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195 Examples: >>> a1 = snakebeta(256) >>> x = torch.randn(256) >>> x = a1(x) ''' def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): ''' Initialization. INPUT: - in_features: shape of the input - alpha - trainable parameter that controls frequency - beta - trainable parameter that controls magnitude alpha is initialized to 1 by default, higher values = higher-frequency. beta is initialized to 1 by default, higher values = higher-magnitude. alpha will be trained along with the rest of your model. ''' super(SnakeBeta, self).__init__() self.in_features = in_features # initialize alpha self.alpha_logscale = alpha_logscale if self.alpha_logscale: # log scale alphas initialized to zeros self.alpha = Parameter(torch.zeros(in_features) * alpha) self.beta = Parameter(torch.zeros(in_features) * alpha) else: # linear scale alphas initialized to ones self.alpha = Parameter(torch.ones(in_features) * alpha) self.beta = Parameter(torch.ones(in_features) * alpha) self.alpha.requires_grad = alpha_trainable self.beta.requires_grad = alpha_trainable self.no_div_by_zero = 0.000000001 def forward(self, x): ''' Forward pass of the function. Applies the function to the input elementwise. SnakeBeta ∶= x + 1/b * sin^2 (xa) ''' alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] beta = self.beta.unsqueeze(0).unsqueeze(-1) if self.alpha_logscale: alpha = torch.exp(alpha) beta = torch.exp(beta) x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) return x ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/__init__.py ================================================ # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 # LICENSE is in incl_licenses directory. from .filter import * from .resample import * from .act import * ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/act.py ================================================ # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 # LICENSE is in incl_licenses directory. import torch.nn as nn from .resample import UpSample1d, DownSample1d class Activation1d(nn.Module): def __init__(self, activation, up_ratio: int = 2, down_ratio: int = 2, up_kernel_size: int = 12, down_kernel_size: int = 12): super().__init__() self.up_ratio = up_ratio self.down_ratio = down_ratio self.act = activation self.upsample = UpSample1d(up_ratio, up_kernel_size) self.downsample = DownSample1d(down_ratio, down_kernel_size) # x: [B,C,T] def forward(self, x): x = self.upsample(x) x = self.act(x) x = self.downsample(x) return x ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/filter.py ================================================ # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 # LICENSE is in incl_licenses directory. import torch import torch.nn as nn import torch.nn.functional as F import math if 'sinc' in dir(torch): sinc = torch.sinc else: # This code is adopted from adefossez's julius.core.sinc under the MIT License # https://adefossez.github.io/julius/julius/core.html # LICENSE is in incl_licenses directory. def sinc(x: torch.Tensor): """ Implementation of sinc, i.e. sin(pi * x) / (pi * x) __Warning__: Different to julius.sinc, the input is multiplied by `pi`! """ return torch.where(x == 0, torch.tensor(1., device=x.device, dtype=x.dtype), torch.sin(math.pi * x) / math.pi / x) # This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License # https://adefossez.github.io/julius/julius/lowpass.html # LICENSE is in incl_licenses directory. def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size] even = (kernel_size % 2 == 0) half_size = kernel_size // 2 #For kaiser window delta_f = 4 * half_width A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 if A > 50.: beta = 0.1102 * (A - 8.7) elif A >= 21.: beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.) else: beta = 0. window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio if even: time = (torch.arange(-half_size, half_size) + 0.5) else: time = torch.arange(kernel_size) - half_size if cutoff == 0: filter_ = torch.zeros_like(time) else: filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) # Normalize filter to have sum = 1, otherwise we will have a small leakage # of the constant component in the input signal. filter_ /= filter_.sum() filter = filter_.view(1, 1, kernel_size) return filter class LowPassFilter1d(nn.Module): def __init__(self, cutoff=0.5, half_width=0.6, stride: int = 1, padding: bool = True, padding_mode: str = 'replicate', kernel_size: int = 12): # kernel_size should be even number for stylegan3 setup, # in this implementation, odd number is also possible. super().__init__() if cutoff < -0.: raise ValueError("Minimum cutoff must be larger than zero.") if cutoff > 0.5: raise ValueError("A cutoff above 0.5 does not make sense.") self.kernel_size = kernel_size self.even = (kernel_size % 2 == 0) self.pad_left = kernel_size // 2 - int(self.even) self.pad_right = kernel_size // 2 self.stride = stride self.padding = padding self.padding_mode = padding_mode filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) self.register_buffer("filter", filter) #input [B, C, T] def forward(self, x): _, C, _ = x.shape if self.padding: x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode) out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) return out ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/resample.py ================================================ # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 # LICENSE is in incl_licenses directory. import torch.nn as nn from torch.nn import functional as F from .filter import LowPassFilter1d from .filter import kaiser_sinc_filter1d class UpSample1d(nn.Module): def __init__(self, ratio=2, kernel_size=None): super().__init__() self.ratio = ratio self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size self.stride = ratio self.pad = self.kernel_size // ratio - 1 self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size) self.register_buffer("filter", filter) # x: [B, C, T] def forward(self, x): _, C, _ = x.shape x = F.pad(x, (self.pad, self.pad), mode='replicate') x = self.ratio * F.conv_transpose1d( x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) x = x[..., self.pad_left:-self.pad_right] return x class DownSample1d(nn.Module): def __init__(self, ratio=2, kernel_size=None): super().__init__() self.ratio = ratio self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, stride=ratio, kernel_size=self.kernel_size) def forward(self, x): xx = self.lowpass(x) return xx ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/bigvgan/models.py ================================================ # Copyright (c) 2022 NVIDIA CORPORATION. # Licensed under the MIT license. # Adapted from https://github.com/jik876/hifi-gan under the MIT license. # LICENSE is in incl_licenses directory. import torch import torch.nn.functional as F import torch.nn as nn from torch.nn import Conv1d, ConvTranspose1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm import numpy as np from .activations import Snake,SnakeBeta from .alias_free_torch import * import os from omegaconf import OmegaConf LRELU_SLOPE = 0.1 def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(mean, std) def get_padding(kernel_size, dilation=1): return int((kernel_size*dilation - dilation)/2) class AMPBlock1(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): super(AMPBlock1, self).__init__() self.h = h self.convs1 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2]))) ]) self.convs1.apply(init_weights) self.convs2 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))) ]) self.convs2.apply(init_weights) self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=Snake(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=SnakeBeta(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) else: raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.") def forward(self, x): acts1, acts2 = self.activations[::2], self.activations[1::2] for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): xt = a1(x) xt = c1(xt) xt = a2(xt) xt = c2(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs1: remove_weight_norm(l) for l in self.convs2: remove_weight_norm(l) class AMPBlock2(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None): super(AMPBlock2, self).__init__() self.h = h self.convs = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))) ]) self.convs.apply(init_weights) self.num_layers = len(self.convs) # total number of conv layers if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=Snake(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing self.activations = nn.ModuleList([ Activation1d( activation=SnakeBeta(channels, alpha_logscale=h.snake_logscale)) for _ in range(self.num_layers) ]) else: raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.") def forward(self, x): for c, a in zip (self.convs, self.activations): xt = a(x) xt = c(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs: remove_weight_norm(l) class BigVGAN(torch.nn.Module): # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks. def __init__(self, h): super(BigVGAN, self).__init__() self.h = h self.num_kernels = len(h.resblock_kernel_sizes) self.num_upsamples = len(h.upsample_rates) # pre conv self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)) # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2 # transposed conv-based upsamplers. does not apply anti-aliasing self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): self.ups.append(nn.ModuleList([ weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2)) ])) # residual blocks using anti-aliased multi-periodicity composition modules (AMP) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = h.upsample_initial_channel // (2 ** (i + 1)) for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): self.resblocks.append(resblock(h, ch, k, d, activation=h.activation)) # post conv if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing activation_post = Snake(ch, alpha_logscale=h.snake_logscale) self.activation_post = Activation1d(activation=activation_post) elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing activation_post = SnakeBeta(ch, alpha_logscale=h.snake_logscale) self.activation_post = Activation1d(activation=activation_post) else: raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.") self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) # weight initialization for i in range(len(self.ups)): self.ups[i].apply(init_weights) self.conv_post.apply(init_weights) def forward(self, x): # pre conv x = self.conv_pre(x) for i in range(self.num_upsamples): # upsampling for i_up in range(len(self.ups[i])): x = self.ups[i][i_up](x) # AMP blocks xs = None for j in range(self.num_kernels): if xs is None: xs = self.resblocks[i * self.num_kernels + j](x) else: xs += self.resblocks[i * self.num_kernels + j](x) x = xs / self.num_kernels # post conv x = self.activation_post(x) x = self.conv_post(x) x = torch.tanh(x) return x def remove_weight_norm(self): print('Removing weight norm...') for l in self.ups: for l_i in l: remove_weight_norm(l_i) for l in self.resblocks: l.remove_weight_norm() remove_weight_norm(self.conv_pre) remove_weight_norm(self.conv_post) class DiscriminatorP(torch.nn.Module): def __init__(self, h, period, kernel_size=5, stride=3, use_spectral_norm=False): super(DiscriminatorP, self).__init__() self.period = period self.d_mult = h.discriminator_channel_mult norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv2d(1, int(32*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(int(32*self.d_mult), int(128*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(int(128*self.d_mult), int(512*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(int(512*self.d_mult), int(1024*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(int(1024*self.d_mult), int(1024*self.d_mult), (kernel_size, 1), 1, padding=(2, 0))), ]) self.conv_post = norm_f(Conv2d(int(1024*self.d_mult), 1, (3, 1), 1, padding=(1, 0))) def forward(self, x): fmap = [] # 1d to 2d b, c, t = x.shape if t % self.period != 0: # pad first n_pad = self.period - (t % self.period) x = F.pad(x, (0, n_pad), "reflect") t = t + n_pad x = x.view(b, c, t // self.period, self.period) for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap class MultiPeriodDiscriminator(torch.nn.Module): def __init__(self, h): super(MultiPeriodDiscriminator, self).__init__() self.mpd_reshapes = h.mpd_reshapes print("mpd_reshapes: {}".format(self.mpd_reshapes)) discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes] self.discriminators = nn.ModuleList(discriminators) def forward(self, y, y_hat): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): y_d_r, fmap_r = d(y) y_d_g, fmap_g = d(y_hat) y_d_rs.append(y_d_r) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs class DiscriminatorR(nn.Module): def __init__(self, cfg, resolution): super().__init__() self.resolution = resolution assert len(self.resolution) == 3, \ "MRD layer requires list with len=3, got {}".format(self.resolution) self.lrelu_slope = LRELU_SLOPE norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm if hasattr(cfg, "mrd_use_spectral_norm"): print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm)) norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm self.d_mult = cfg.discriminator_channel_mult if hasattr(cfg, "mrd_channel_mult"): print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult)) self.d_mult = cfg.mrd_channel_mult self.convs = nn.ModuleList([ norm_f(nn.Conv2d(1, int(32*self.d_mult), (3, 9), padding=(1, 4))), norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 3), padding=(1, 1))), ]) self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1))) def forward(self, x): fmap = [] x = self.spectrogram(x) x = x.unsqueeze(1) for l in self.convs: x = l(x) x = F.leaky_relu(x, self.lrelu_slope) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap def spectrogram(self, x): n_fft, hop_length, win_length = self.resolution x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect') x = x.squeeze(1) x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True) x = torch.view_as_real(x) # [B, F, TT, 2] mag = torch.norm(x, p=2, dim =-1) #[B, F, TT] return mag class MultiResolutionDiscriminator(nn.Module): def __init__(self, cfg, debug=False): super().__init__() self.resolutions = cfg.resolutions assert len(self.resolutions) == 3,\ "MRD requires list of list with len=3, each element having a list with len=3. got {}".\ format(self.resolutions) self.discriminators = nn.ModuleList( [DiscriminatorR(cfg, resolution) for resolution in self.resolutions] ) def forward(self, y, y_hat): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): y_d_r, fmap_r = d(x=y) y_d_g, fmap_g = d(x=y_hat) y_d_rs.append(y_d_r) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs def feature_loss(fmap_r, fmap_g): loss = 0 for dr, dg in zip(fmap_r, fmap_g): for rl, gl in zip(dr, dg): loss += torch.mean(torch.abs(rl - gl)) return loss*2 def discriminator_loss(disc_real_outputs, disc_generated_outputs): loss = 0 r_losses = [] g_losses = [] for dr, dg in zip(disc_real_outputs, disc_generated_outputs): r_loss = torch.mean((1-dr)**2) g_loss = torch.mean(dg**2) loss += (r_loss + g_loss) r_losses.append(r_loss.item()) g_losses.append(g_loss.item()) return loss, r_losses, g_losses def generator_loss(disc_outputs): loss = 0 gen_losses = [] for dg in disc_outputs: l = torch.mean((1-dg)**2) gen_losses.append(l) loss += l return loss, gen_losses class VocoderBigVGAN(object): def __init__(self, ckpt_vocoder,device='cuda'): vocoder_sd = torch.load(os.path.join(ckpt_vocoder,'best_netG.pt'), map_location='cpu') vocoder_args = OmegaConf.load(os.path.join(ckpt_vocoder,'args.yml')) self.generator = BigVGAN(vocoder_args) self.generator.load_state_dict(vocoder_sd['generator']) self.generator.eval() self.device = device self.generator.to(self.device) def vocode(self, spec): with torch.no_grad(): if isinstance(spec,np.ndarray): spec = torch.from_numpy(spec).unsqueeze(0) spec = spec.to(dtype=torch.float32,device=self.device) return self.generator(spec).squeeze().cpu().numpy() def __call__(self, wav): return self.vocode(wav) ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/hifigan/modules.py ================================================ import os import torch import torch.nn.functional as F import torch.nn as nn from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from pathlib import Path import yaml import numpy as np from argparse import Namespace LRELU_SLOPE = 0.1 def get_padding(kernel_size, dilation=1): return int((kernel_size*dilation - dilation)/2) def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(mean, std) class ResBlock1(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): super(ResBlock1, self).__init__() self.h = h self.convs1 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2]))) ]) self.convs1.apply(init_weights) self.convs2 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))) ]) self.convs2.apply(init_weights) def forward(self, x): for c1, c2 in zip(self.convs1, self.convs2): xt = F.leaky_relu(x, LRELU_SLOPE) xt = c1(xt) xt = F.leaky_relu(xt, LRELU_SLOPE) xt = c2(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs1: remove_weight_norm(l) for l in self.convs2: remove_weight_norm(l) class ResBlock2(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): super(ResBlock2, self).__init__() self.h = h self.convs = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))) ]) self.convs.apply(init_weights) def forward(self, x): for c in self.convs: xt = F.leaky_relu(x, LRELU_SLOPE) xt = c(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs: remove_weight_norm(l) class Generator(torch.nn.Module): def __init__(self, h): super(Generator, self).__init__() self.h = h self.num_kernels = len(h.resblock_kernel_sizes) self.num_upsamples = len(h.upsample_rates) self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)) resblock = ResBlock1 if h.resblock == '1' else ResBlock2 self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): self.ups.append(weight_norm( ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)), k, u, padding=(k-u)//2))) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = h.upsample_initial_channel//(2**(i+1)) for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): self.resblocks.append(resblock(h, ch, k, d)) self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) self.ups.apply(init_weights) self.conv_post.apply(init_weights) def forward(self, x): x = self.conv_pre(x) for i in range(self.num_upsamples): x = F.leaky_relu(x, LRELU_SLOPE) x = self.ups[i](x) xs = None for j in range(self.num_kernels): if xs is None: xs = self.resblocks[i*self.num_kernels+j](x) else: xs += self.resblocks[i*self.num_kernels+j](x) x = xs / self.num_kernels x = F.leaky_relu(x) x = self.conv_post(x) x = torch.tanh(x) return x def remove_weight_norm(self): print('Removing weight norm...') for l in self.ups: remove_weight_norm(l) for l in self.resblocks: l.remove_weight_norm() remove_weight_norm(self.conv_pre) remove_weight_norm(self.conv_post) class DiscriminatorP(torch.nn.Module): def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): super(DiscriminatorP, self).__init__() self.period = period norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), ]) self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) def forward(self, x): fmap = [] # 1d to 2d b, c, t = x.shape if t % self.period != 0: # pad first n_pad = self.period - (t % self.period) x = F.pad(x, (0, n_pad), "reflect") t = t + n_pad x = x.view(b, c, t // self.period, self.period) for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap class MultiPeriodDiscriminator(torch.nn.Module): def __init__(self): super(MultiPeriodDiscriminator, self).__init__() self.discriminators = nn.ModuleList([ DiscriminatorP(2), DiscriminatorP(3), DiscriminatorP(5), DiscriminatorP(7), DiscriminatorP(11), ]) def forward(self, y, y_hat): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): y_d_r, fmap_r = d(y) y_d_g, fmap_g = d(y_hat) y_d_rs.append(y_d_r) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs class DiscriminatorS(torch.nn.Module): def __init__(self, use_spectral_norm=False): super(DiscriminatorS, self).__init__() norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv1d(1, 128, 15, 1, padding=7)), norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), ]) self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) def forward(self, x): fmap = [] for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap class MultiScaleDiscriminator(torch.nn.Module): def __init__(self): super(MultiScaleDiscriminator, self).__init__() self.discriminators = nn.ModuleList([ DiscriminatorS(use_spectral_norm=True), DiscriminatorS(), DiscriminatorS(), ]) self.meanpools = nn.ModuleList([ AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2) ]) def forward(self, y, y_hat): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): if i != 0: y = self.meanpools[i-1](y) y_hat = self.meanpools[i-1](y_hat) y_d_r, fmap_r = d(y) y_d_g, fmap_g = d(y_hat) y_d_rs.append(y_d_r) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs def feature_loss(fmap_r, fmap_g): loss = 0 for dr, dg in zip(fmap_r, fmap_g): for rl, gl in zip(dr, dg): loss += torch.mean(torch.abs(rl - gl)) return loss*2 def discriminator_loss(disc_real_outputs, disc_generated_outputs): loss = 0 r_losses = [] g_losses = [] for dr, dg in zip(disc_real_outputs, disc_generated_outputs): r_loss = torch.mean((1-dr)**2) g_loss = torch.mean(dg**2) loss += (r_loss + g_loss) r_losses.append(r_loss.item()) g_losses.append(g_loss.item()) return loss, r_losses, g_losses def generator_loss(disc_outputs): loss = 0 gen_losses = [] for dg in disc_outputs: l = torch.mean((1-dg)**2) gen_losses.append(l) loss += l return loss, gen_losses class VocoderHifigan(object): def __init__(self, ckpt_vocoder,device='cuda'): with open(os.path.join(ckpt_vocoder,'args.yml'), 'r') as f: vocoder_args = Namespace(**yaml.load(f, Loader=yaml.UnsafeLoader)) self.generator = Generator(vocoder_args) netG_path = os.path.join(ckpt_vocoder,'best_netG.pt') if os.path.exists(netG_path): vocoder_sd = torch.load(netG_path, map_location='cpu') self.generator.load_state_dict(vocoder_sd['generator']) self.generator.eval() self.device = device self.generator.to(self.device) def vocode(self, spec, global_step=None): with torch.no_grad(): if isinstance(spec,np.ndarray): spec = torch.from_numpy(spec).unsqueeze(0) spec = spec.to(dtype=torch.float32,device=self.device) return self.generator(spec).squeeze().cpu().numpy() class VocoderHifigan_noload(object): def __init__(self, vocoder_args,device='cuda'): self.generator = Generator(vocoder_args) self.generator.eval() self.device = device self.generator.to(self.device) def vocode(self, spec, global_step=None): with torch.no_grad(): if isinstance(spec,np.ndarray): spec = torch.from_numpy(spec).unsqueeze(0) spec = spec.to(dtype=torch.float32,device=self.device) return self.generator(spec).squeeze().cpu().numpy() ================================================ FILE: text_to_audio/Make_An_Audio/vocoder/logs/hifi_0127/args.yml ================================================ adam_b1: 0.8 adam_b2: 0.99 batch_size: 24 dist_config: dist_backend: nccl dist_url: tcp://localhost:54321 world_size: 1 fmax: 8000 fmax_for_loss: null fmin: 0 hop_size: 256 learning_rate: 0.0002 lr_decay: 0.999 n_fft: 1024 num_gpus: 0 num_mels: 80 num_workers: 4 resblock: '1' resblock_dilation_sizes: - - 1 - 3 - 5 - - 1 - 3 - 5 - - 1 - 3 - 5 resblock_kernel_sizes: - 3 - 7 - 11 sampling_rate: 16000 seed: 1234 segment_size: 8192 upsample_initial_channel: 512 upsample_kernel_sizes: - 16 - 16 - 4 - 4 upsample_rates: - 8 - 8 - 2 - 2 win_size: 1024 ================================================ FILE: text_to_audio/Make_An_Audio/wav_evaluation/models/CLAPWrapper.py ================================================ import random import torchaudio from torch._six import string_classes import collections import re import torch.nn.functional as F import numpy as np from transformers import AutoTokenizer from wav_evaluation.models.utils import read_config_as_args from wav_evaluation.models.clap import CLAP import math import torchaudio.transforms as T import os import torch from importlib_resources import files class CLAPWrapper(): """ A class for interfacing CLAP model. """ def __init__(self, model_fp,config_path, use_cuda=False): self.np_str_obj_array_pattern = re.compile(r'[SaUO]') self.file_path = os.path.realpath(__file__) self.default_collate_err_msg_format = ( "default_collate: batch must contain tensors, numpy arrays, numbers, " "dicts or lists; found {}") with open(config_path,'r') as f: self.config_as_str = f.read() self.model_fp = model_fp self.use_cuda = use_cuda self.clap, self.tokenizer, self.args = self.load_clap() def load_clap(self): r"""Load CLAP model with args from config file""" args = read_config_as_args(self.config_as_str, is_config_str=True) if 'bert' in args.text_model: self.token_keys = ['input_ids', 'token_type_ids', 'attention_mask'] else: self.token_keys = ['input_ids', 'attention_mask'] clap = CLAP( audioenc_name=args.audioenc_name, sample_rate=args.sampling_rate, window_size=args.window_size, hop_size=args.hop_size, mel_bins=args.mel_bins, fmin=args.fmin, fmax=args.fmax, classes_num=args.num_classes, out_emb=args.out_emb, text_model=args.text_model, transformer_embed_dim=args.transformer_embed_dim, d_proj=args.d_proj ) # Load pretrained weights for model model_state_dict = torch.load(self.model_fp, map_location=torch.device('cpu'))['model'] clap.load_state_dict(model_state_dict) clap.eval() # set clap in eval mode tokenizer = AutoTokenizer.from_pretrained(args.text_model) if self.use_cuda and torch.cuda.is_available(): clap = clap.cuda() return clap, tokenizer, args def default_collate(self, batch): r"""Puts each data field into a tensor with outer dimension batch size""" elem = batch[0] elem_type = type(elem) if isinstance(elem, torch.Tensor): out = None if torch.utils.data.get_worker_info() is not None: # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) storage = elem.storage()._new_shared(numel) out = elem.new(storage) return torch.stack(batch, 0, out=out) elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap': # array of string classes and object if self.np_str_obj_array_pattern.search(elem.dtype.str) is not None: raise TypeError( self.default_collate_err_msg_format.format(elem.dtype)) return self.default_collate([torch.as_tensor(b) for b in batch]) elif elem.shape == (): # scalars return torch.as_tensor(batch) elif isinstance(elem, float): return torch.tensor(batch, dtype=torch.float64) elif isinstance(elem, int): return torch.tensor(batch) elif isinstance(elem, string_classes): return batch elif isinstance(elem, collections.abc.Mapping): return {key: self.default_collate([d[key] for d in batch]) for key in elem} elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple return elem_type(*(self.default_collate(samples) for samples in zip(*batch))) elif isinstance(elem, collections.abc.Sequence): # check to make sure that the elements in batch have consistent size it = iter(batch) elem_size = len(next(it)) if not all(len(elem) == elem_size for elem in it): raise RuntimeError( 'each element in list of batch should be of equal size') transposed = zip(*batch) return [self.default_collate(samples) for samples in transposed] raise TypeError(self.default_collate_err_msg_format.format(elem_type)) def resample_and_duration(self,wav_sr,audio_duration,resample=False): audio_time_series,sample_rate = wav_sr resample_rate = self.args.sampling_rate if resample: resampler = T.Resample(sample_rate, resample_rate) audio_time_series = resampler(audio_time_series) audio_time_series = audio_time_series.reshape(-1) # audio_time_series is shorter than predefined audio duration, # so audio_time_series is extended if audio_duration*sample_rate >= audio_time_series.shape[0]: repeat_factor = int(np.ceil((audio_duration*sample_rate) / audio_time_series.shape[0])) # Repeat audio_time_series by repeat_factor to match audio_duration audio_time_series = audio_time_series.repeat(repeat_factor) # remove excess part of audio_time_series audio_time_series = audio_time_series[0:audio_duration*sample_rate] else: # audio_time_series is longer than predefined audio duration, # so audio_time_series is trimmed start_index = random.randrange( audio_time_series.shape[0] - audio_duration*sample_rate) audio_time_series = audio_time_series[start_index:start_index + audio_duration*sample_rate] return torch.FloatTensor(audio_time_series) def load_audio_into_tensor(self, audio_path, audio_duration, resample=False): r"""Loads audio file and returns raw audio.""" # Randomly sample a segment of audio_duration from the clip or pad to match duration audio_time_series, sample_rate = torchaudio.load(audio_path) return self.resample_and_duration((audio_time_series, sample_rate),audio_duration,resample) def preprocess_audio(self, audio_files, resample): r"""Load list of audio files and return raw audio""" audio_tensors = [] for audio_file in audio_files: if isinstance(audio_file,str): audio_tensor = self.load_audio_into_tensor(audio_file, self.args.duration, resample) elif isinstance(audio_file,tuple): audio_tensor = self.resample_and_duration(audio_file, self.args.duration, resample) else: raise TypeError(f"type of audiofile is {type(audio_file)},which is not supported") audio_tensor = audio_tensor.reshape( 1, -1).cuda() if self.use_cuda and torch.cuda.is_available() else audio_tensor.reshape(1, -1) audio_tensors.append(audio_tensor) return self.default_collate(audio_tensors) def preprocess_text(self, text_queries): r"""Load list of class labels and return tokenized text""" tokenized_texts = [] for ttext in text_queries: tok = self.tokenizer.encode_plus( text=ttext, add_special_tokens=True, max_length=self.args.text_len, padding="max_length", return_tensors="pt") # max_length=self.args.text_len, padding=True, for key in self.token_keys: tok[key] = tok[key].reshape(-1).cuda() if self.use_cuda and torch.cuda.is_available() else tok[key].reshape(-1) tokenized_texts.append(tok) return self.default_collate(tokenized_texts) def get_text_embeddings(self, class_labels): r"""Load list of class labels and return text embeddings""" preprocessed_text = self.preprocess_text(class_labels) text_embeddings = self._get_text_embeddings(preprocessed_text) text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True) return text_embeddings def get_audio_embeddings(self, audio_files, resample): r"""Load list of audio files and return a audio embeddings""" preprocessed_audio = self.preprocess_audio(audio_files, resample) audio_embeddings = self._get_audio_embeddings(preprocessed_audio) audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True) return audio_embeddings def _get_text_embeddings(self, preprocessed_text): r"""Load preprocessed text and return text embeddings""" with torch.no_grad(): text_embeddings = self.clap.caption_encoder(preprocessed_text) text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True) return text_embeddings def _get_audio_embeddings(self, preprocessed_audio): r"""Load preprocessed audio and return a audio embeddings""" with torch.no_grad(): preprocessed_audio = preprocessed_audio.reshape( preprocessed_audio.shape[0], preprocessed_audio.shape[2]) #Append [0] the audio emebdding, [1] has output class probabilities audio_embeddings = self.clap.audio_encoder(preprocessed_audio)[0] audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True) return audio_embeddings def compute_similarity(self, audio_embeddings, text_embeddings,use_logit_scale = True): r"""Compute similarity between text and audio embeddings""" if use_logit_scale: logit_scale = self.clap.logit_scale.exp() similarity = logit_scale*text_embeddings @ audio_embeddings.T else: similarity = text_embeddings @ audio_embeddings.T return similarity.T def cal_clap_score(self,txt,audio_path): text_embeddings = self.get_text_embeddings([txt])# 经过了norm的embedding audio_embeddings = self.get_audio_embeddings([audio_path], resample=True)# 这一步比较耗时，读取音频并重采样到44100 score = self.compute_similarity(audio_embeddings, text_embeddings,use_logit_scale=False).squeeze().cpu().numpy() return score def _generic_batch_inference(self, func, *args): r"""Process audio and/or text per batch""" input_tmp = args[0] batch_size = args[-1] # args[0] has audio_files, args[1] has class_labels inputs = [args[0], args[1]] if len(args) == 3 else [args[0]] args0_len = len(args[0]) # compute text_embeddings once for all the audio_files batches if len(inputs) == 2: text_embeddings = self.get_text_embeddings(args[1]) inputs = [args[0], args[1], text_embeddings] dataset_idx = 0 for _ in range(math.ceil(args0_len/batch_size)): next_batch_idx = dataset_idx + batch_size # batch size is bigger than available audio/text items if next_batch_idx >= args0_len: inputs[0] = input_tmp[dataset_idx:] return func(*tuple(inputs)) else: inputs[0] = input_tmp[dataset_idx:next_batch_idx] yield func(*tuple(inputs)) dataset_idx = next_batch_idx def get_audio_embeddings_per_batch(self, audio_files, batch_size): r"""Load preprocessed audio and return a audio embeddings per batch""" return self._generic_batch_inference(self.get_audio_embeddings, audio_files, batch_size) def get_text_embeddings_per_batch(self, class_labels, batch_size): r"""Load preprocessed text and return text embeddings per batch""" return self._generic_batch_inference(self.get_text_embeddings, class_labels, batch_size) def classify_audio_files_per_batch(self, audio_files, class_labels, batch_size): r"""Compute classification probabilities for each audio recording in a batch and each class label""" return self._generic_batch_inference(self.classify_audio_files, audio_files, class_labels, batch_size) ================================================ FILE: text_to_audio/Make_An_Audio/wav_evaluation/models/__init__.py ================================================ from . import clap from . import audio from . import utils ================================================ FILE: text_to_audio/Make_An_Audio/wav_evaluation/models/audio.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from torchlibrosa.stft import Spectrogram, LogmelFilterBank def get_audio_encoder(name: str): if name == "Cnn14": return Cnn14 else: raise Exception('The audio encoder name {} is incorrect or not supported'.format(name)) class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = F.relu_(self.bn1(self.conv1(x))) x = F.relu_(self.bn2(self.conv2(x))) if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception('Incorrect argument!') return x class ConvBlock5x5(nn.Module): def __init__(self, in_channels, out_channels): super(ConvBlock5x5, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False) self.bn1 = nn.BatchNorm2d(out_channels) def forward(self, input, pool_size=(2, 2), pool_type='avg'): x = input x = F.relu_(self.bn1(self.conv1(x))) if pool_type == 'max': x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg': x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == 'avg+max': x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception('Incorrect argument!') return x class AttBlock(nn.Module): def __init__(self, n_in, n_out, activation='linear', temperature=1.): super(AttBlock, self).__init__() self.activation = activation self.temperature = temperature self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) self.bn_att = nn.BatchNorm1d(n_out) def forward(self, x): # x: (n_samples, n_in, n_time) norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1) cla = self.nonlinear_transform(self.cla(x)) x = torch.sum(norm_att * cla, dim=2) return x, norm_att, cla def nonlinear_transform(self, x): if self.activation == 'linear': return x elif self.activation == 'sigmoid': return torch.sigmoid(x) class Cnn14(nn.Module): def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, out_emb): super(Cnn14, self).__init__() window = 'hann' center = True pad_mode = 'reflect' ref = 1.0 amin = 1e-10 top_db = None # Spectrogram extractor self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True) self.bn0 = nn.BatchNorm2d(64) self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) # out_emb is 2048 for best Cnn14 self.fc1 = nn.Linear(2048, out_emb, bias=True) self.fc_audioset = nn.Linear(out_emb, classes_num, bias=True) def forward(self, input, mixup_lambda=None): """ Input: (batch_size, data_length) """ x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg') x = F.dropout(x, p=0.2, training=self.training) x = torch.mean(x, dim=3) (x1, _) = torch.max(x, dim=2) x2 = torch.mean(x, dim=2) x = x1 + x2 x = F.dropout(x, p=0.5, training=self.training) x = F.relu_(self.fc1(x)) embedding = F.dropout(x, p=0.5, training=self.training) clipwise_output = torch.sigmoid(self.fc_audioset(x)) output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding} return output_dict ================================================ FILE: text_to_audio/Make_An_Audio/wav_evaluation/models/clap.py ================================================ import numpy as np import torch import torch.nn.functional as F from torch import nn from transformers import AutoModel from .audio import get_audio_encoder class Projection(nn.Module): def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None: super().__init__() self.linear1 = nn.Linear(d_in, d_out, bias=False) self.linear2 = nn.Linear(d_out, d_out, bias=False) self.layer_norm = nn.LayerNorm(d_out) self.drop = nn.Dropout(p) def forward(self, x: torch.Tensor) -> torch.Tensor: embed1 = self.linear1(x) embed2 = self.drop(self.linear2(F.gelu(embed1))) embeds = self.layer_norm(embed1 + embed2) return embeds class AudioEncoder(nn.Module): def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int, hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None: super().__init__() audio_encoder = get_audio_encoder(audioenc_name) self.base = audio_encoder( sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, d_in) self.projection = Projection(d_in, d_out) def forward(self, x): out_dict = self.base(x) audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output'] projected_vec = self.projection(audio_features) return projected_vec, audio_classification_output class TextEncoder(nn.Module): def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None: super().__init__() self.base = AutoModel.from_pretrained(text_model) self.projection = Projection(transformer_embed_dim, d_out) def forward(self, x): out = self.base(**x)[0] out = out[:, 0, :] # get CLS token output projected_vec = self.projection(out) return projected_vec class CLAP(nn.Module): def __init__(self, # audio audioenc_name: str, sample_rate: int, window_size: int, hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int, out_emb: int, # text text_model: str, transformer_embed_dim: int, # common d_proj: int, ): super().__init__() self.audio_encoder = AudioEncoder( audioenc_name, out_emb, d_proj, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num) self.caption_encoder = TextEncoder( d_proj, text_model, transformer_embed_dim ) self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) def forward(self, audio, text): audio_embed, _ = self.audio_encoder(audio) caption_embed = self.caption_encoder(text) return caption_embed, audio_embed, self.logit_scale.exp() ================================================ FILE: text_to_audio/Make_An_Audio/wav_evaluation/models/utils.py ================================================ import argparse import yaml import sys def read_config_as_args(config_path,args=None,is_config_str=False): return_dict = {} if config_path is not None: if is_config_str: yml_config = yaml.load(config_path, Loader=yaml.FullLoader) else: with open(config_path, "r") as f: yml_config = yaml.load(f, Loader=yaml.FullLoader) if args != None: for k, v in yml_config.items(): if k in args.__dict__: args.__dict__[k] = v else: sys.stderr.write("Ignored unknown parameter {} in yaml.\n".format(k)) else: for k, v in yml_config.items(): return_dict[k] = v args = args if args != None else return_dict return argparse.Namespace(**args)