[ { "path": ".gitignore", "content": "**/**/__pycache__\n**/.ipynb_checkpoints\nTensorFlowTTS/build\nTensorFlowTTS/TensorFlowTTS.egg-info\nTensorFlowTTS/.eggs\negs" }, { "path": "README-CN.md", "content": "## Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning\n[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](http://choosealicense.com/licenses/mit/)\n\n> 中文 | [English](README.md)\n\n:exclamation: 提供推理代码和预训练模型，你可以生成想要的文本语音。\n\n:star: 模型只在正常情绪的语料上训练，没有使用其他任何强烈情感的语料。\n\n:star: 受到训练语料的限制，一般的说话人编码或者非监督风格学习方法都很难模仿未见过的语音。训练数据分布范围外的风格迁移仍具有很大的挑战。\n\n:star: 依赖Unet网络和AdaIN层，我们的方法在未见风格上有很强的迁移能力。\n\n:sparkles:强烈推荐使用[在线notebook](https://colab.research.google.com/drive/1sEDvKTJCY7uosb7TvTqwyUdwNPiv3pBW#scrollTo=puzhCI99LY_a)进行推理。\n\n[Demo results](https://cmsmartvoice.github.io/Unet-TTS/)\n\n[Paper link](https://arxiv.org/abs/2109.11115)\n\n![](./pics/structure.png)\n\n---\n:star: 现在只需要输入一条参考语音就可以进行克隆TTS，而不再需要手动输入参考语音的时长统计信息。\n\n:smile: 我们正在准备基于aishell3数据的训练流程，敬请期待。\n\n流程包括:\n- [x] 一句话语音克隆推理\n- [x] 参考音频的时长统计信息可以有训练的Style_Encoder估计\n- [ ] 基于说话人编码的多说话人TTS，它可以提供不错的Content Encoder\n- [ ] Unet-TTS训练\n- [ ] C++推理\n\n---\n### Install Requirements\n- 暂时只支持Linux系统\n- Install the appropriate TensorFlow and tensorflow-addons versions according to CUDA version. \n- The default is TensorFlow 2.6 and tensorflow-addons 0.14.0.\n```shell\ncd One-Shot-Voice-Cloning/TensorFlowTTS\npip install . \n(or python setup.py install)\n```\n\n### Usage\n方法1: 在UnetTTS_syn.py文件中修改要克隆的参考语音. (更多细节参见此文件)\n```shell\ncd One-Shot-Voice-Cloning\nCUDA_VISIBLE_DEVICES=0 python UnetTTS_syn.py\n```\n\n方法2: Notebook\n\n**Note**: 请将One-Shot-Voice-Cloning目录添加到系统路径中，否则UnetTTS类无法从UnetTTS_syn.py文件中载入.\n```python\nimport sys\nsys.path.append(\"/One-Shot-Voice-Cloning\")\nfrom UnetTTS_syn import UnetTTS\n\nfrom tensorflow_tts.audio_process import preprocess_wav\n\n\"\"\"初始化模型\"\"\"\nmodels_and_params = {\"duration_param\": \"train/configs/unetts_duration.yaml\",\n \"duration_model\": \"models/duration4k.h5\",\n \"acous_param\": \"train/configs/unetts_acous.yaml\",\n \"acous_model\": \"models/acous12k.h5\",\n \"vocoder_param\": \"train/configs/multiband_melgan.yaml\",\n \"vocoder_model\": \"models/vocoder800k.h5\"}\n\nfeats_yaml = \"train/configs/unetts_preprocess.yaml\"\n\ntext2id_mapper = \"models/unetts_mapper.json\"\n\nTts_handel = UnetTTS(models_and_params, text2id_mapper, feats_yaml)\n\n\"\"\"根据目标语音，生成任意文本的克隆语音\"\"\" \nwav_fpath = \"./reference_speech.wav\"\nref_audio = preprocess_wav(wav_fpath, source_sr=16000, normalize=True, trim_silence=True, is_sil_pad=True,\n vad_window_length=30,\n vad_moving_average_width=1,\n vad_max_silence_length=1)\n\n# 文本中插入#3标识，可以当作标点符号，合成语音中会产生停顿\ntext = \"一句话#3风格迁移#3语音合成系统\"\n\nsyn_audio, _, _ = Tts_handel.one_shot_TTS(text, ref_audio)\n```\n\n### Reference\nhttps://github.com/TensorSpeech/TensorFlowTTS\n\nhttps://github.com/CorentinJ/Real-Time-Voice-Cloning\n" }, { "path": "README.md", "content": "## Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning\n[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](http://choosealicense.com/licenses/mit/)\n\n> English | [中文](README-CN.md)\n\n:exclamation: Now we provide inferencing code and pre-training models. You could generate any text sounds you want.\n\n:star: The model training only uses the corpus of neutral emotion, and does not use any strongly emotional speech.\n\n:star: There are still great challenges in out-of-domain style transfer. Limited by the training corpus, it is difficult for the speaker-embedding or unsupervised style learning (like GST) methods to imitate the unseen data.\n\n:star: With the help of Unet network and AdaIN layer, our proposed algorithm has powerful speaker and style transfer capabilities.\n\n[Demo results](https://cmsmartvoice.github.io/Unet-TTS/)\n\n[Paper link](https://arxiv.org/abs/2109.11115)\n\n:sparkles:[Colab notebook](https://colab.research.google.com/drive/1sEDvKTJCY7uosb7TvTqwyUdwNPiv3pBW?usp=sharing) is Highly Recommended for test.\n\n![](./pics/structure.png)\n\n---\n:star: Now, you only need to use the reference speech for one-shot voice cloning and no longer need to manually enter the duration statistics additionally.\n\n:smile: The authors are preparing simple, clear, and well-documented training process of Unet-TTS based on Aishell3.\n\nIt contains:\n\n- [x] One-shot Voice cloning inference\n- [x] The duration statistics of the reference speech can be estimated Automatically using Style_Encoder.\n- [ ] Multi-speaker TTS with speaker_embedding-Instance-Normalization, and this model provides pre-training Content Encoder.\n- [ ] Unet-TTS training\n- [ ] C++ inference\n\n Stay tuned!\n\n---\n### Install Requirements\n- Only support Linux system\n- Install the appropriate TensorFlow and tensorflow-addons versions according to CUDA version. \n- The default is TensorFlow 2.6 and tensorflow-addons 0.14.0.\n```shell\ncd One-Shot-Voice-Cloning/TensorFlowTTS\npip install . \n(or python setup.py install)\n```\n\n### Usage\nOption 1: Modify the reference audio file to be cloned in the UnetTTS_syn.py file. (See this file for more details)\n```shell\ncd One-Shot-Voice-Cloning\nCUDA_VISIBLE_DEVICES=0 python UnetTTS_syn.py\n```\n\nOption 2: Notebook\n\n**Note**: Please add the One-Shot-Voice-Cloning path to the system path. Otherwise the required class UnetTTS cannot be imported from the UnetTTS_syn.py file.\n```python\nimport sys\nsys.path.append(\"/One-Shot-Voice-Cloning\")\nfrom UnetTTS_syn import UnetTTS\n\nfrom tensorflow_tts.audio_process import preprocess_wav\n\n\"\"\"Inint models\"\"\"\nmodels_and_params = {\"duration_param\": \"train/configs/unetts_duration.yaml\",\n \"duration_model\": \"models/duration4k.h5\",\n \"acous_param\": \"train/configs/unetts_acous.yaml\",\n \"acous_model\": \"models/acous12k.h5\",\n \"vocoder_param\": \"train/configs/multiband_melgan.yaml\",\n \"vocoder_model\": \"models/vocoder800k.h5\"}\n\nfeats_yaml = \"train/configs/unetts_preprocess.yaml\"\n\ntext2id_mapper = \"models/unetts_mapper.json\"\n\nTts_handel = UnetTTS(models_and_params, text2id_mapper, feats_yaml)\n\n\"\"\"Synthesize arbitrary text cloning voice using a reference speech\"\"\" \nwav_fpath = \"./reference_speech.wav\"\nref_audio = preprocess_wav(wav_fpath, source_sr=16000, normalize=True, trim_silence=True, is_sil_pad=True,\n vad_window_length=30,\n vad_moving_average_width=1,\n vad_max_silence_length=1)\n\n# Inserting #3 marks into text is regarded as punctuation, and synthetic speech can produce pause.\ntext = \"一句话#3风格迁移#3语音合成系统\"\n\nsyn_audio, _, _ = Tts_handel.one_shot_TTS(text, ref_audio)\n```\n\n### Reference\nhttps://github.com/TensorSpeech/TensorFlowTTS\n\nhttps://github.com/CorentinJ/Real-Time-Voice-Cloning\n" }, { "path": "TensorFlowTTS/LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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\n

:yum: TensorFlowTTS\n

\n \n \"Build\"\n \n \n \"GitHub\"\n \n \n \"Colab\"\n \n

\n

\n

\n

Real-Time State-of-the-art Speech Synthesis for Tensorflow 2\n

\n\n:zany_face: TensorFlowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron-2, Melgan, Multiband-Melgan, FastSpeech, FastSpeech2 based-on TensorFlow 2. With Tensorflow 2, we can speed-up training/inference progress, optimizer further by using [fake-quantize aware](https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide) and [pruning](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras), make TTS models can be run faster than real-time and be able to deploy on mobile devices or embedded systems.\n\n## What's new\n- 2020/08/23 **(NEW!)** Add Parallel WaveGAN tensorflow implementation. See [here](https://github.com/TensorSpeech/TensorFlowTTS/tree/master/examples/parallel_wavegan)\n- 2020/08/23 **(NEW!)** Add MBMelGAN G + ParallelWaveGAN G example. See [here](https://github.com/TensorSpeech/TensorFlowTTS/tree/master/examples/multiband_pwgan)\n- 2020/08/20 **(NEW!)** Add C++ inference code. Thank [@ZDisket](https://github.com/ZDisket). See [here](https://github.com/TensorSpeech/TensorFlowTTS/tree/master/examples/cppwin)\n- 2020/08/18 **(NEW!)** Update [new base processor](https://github.com/TensorSpeech/TensorFlowTTS/blob/master/tensorflow_tts/processor/base_processor.py). Add [AutoProcessor](https://github.com/TensorSpeech/TensorFlowTTS/blob/master/tensorflow_tts/inference/auto_processor.py) and [pretrained processor](https://github.com/TensorSpeech/TensorFlowTTS/blob/master/tensorflow_tts/processor/pretrained/) json file.\n- 2020/08/14 **(NEW!)** Support Chinese TTS. Pls see the [colab](https://colab.research.google.com/drive/1YpSHRBRPBI7cnTkQn1UcVTWEQVbsUm1S?usp=sharing). Thank [@azraelkuan](https://github.com/azraelkuan).\n- 2020/08/05 **(NEW!)** Support Korean TTS. Pls see the [colab](https://colab.research.google.com/drive/1ybWwOS5tipgPFttNulp77P6DAB5MtiuN?usp=sharing). Thank [@crux153](https://github.com/crux153).\n- 2020/07/17 Support MultiGPU for all Trainer.\n- 2020/07/05 Support Convert Tacotron-2, FastSpeech to Tflite. Pls see the [colab](https://colab.research.google.com/drive/1HudLLpT9CQdh2k04c06bHUwLubhGTWxA?usp=sharing). Thank @jaeyoo from the TFlite team for his support.\n- 2020/06/20 [FastSpeech2](https://arxiv.org/abs/2006.04558) implementation with Tensorflow is supported.\n- 2020/06/07 [Multi-band MelGAN (MB MelGAN)](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/multiband_melgan/) implementation with Tensorflow is supported.\n\n\n## Features\n- High performance on Speech Synthesis.\n- Be able to fine-tune on other languages.\n- Fast, Scalable, and Reliable.\n- Suitable for deployment.\n- Easy to implement a new model, based-on abstract class.\n- Mixed precision to speed-up training if possible.\n- Support both Single/Multi GPU in base trainer class.\n- TFlite conversion for all supported models.\n- Android example.\n- Support many languages (currently, we support Chinese, Korean, English.)\n- Support C++ inference.\n- Support Convert weight for some models from PyTorch to TensorFlow to accelerate speed.\n\n## Requirements\nThis repository is tested on Ubuntu 18.04 with:\n\n- Python 3.7+\n- Cuda 10.1\n- CuDNN 7.6.5\n- Tensorflow 2.2/2.3\n- [Tensorflow Addons](https://github.com/tensorflow/addons) >= 0.10.0\n\nDifferent Tensorflow version should be working but not tested yet. This repo will try to work with the latest stable TensorFlow version. **We recommend you install TensorFlow 2.3.0 to training in case you want to use MultiGPU.**\n\n## Installation\n### With pip\n```bash\n$ pip install TensorFlowTTS\n```\n### From source\nExamples are included in the repository but are not shipped with the framework. Therefore, to run the latest version of examples, you need to install the source below.\n```bash\n$ git clone https://github.com/TensorSpeech/TensorFlowTTS.git\n$ cd TensorFlowTTS\n$ pip install .\n```\nIf you want to upgrade the repository and its dependencies:\n```bash\n$ git pull\n$ pip install --upgrade .\n```\n\n# Supported Model architectures\nTensorFlowTTS currently provides the following architectures:\n\n1. **MelGAN** released with the paper [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://arxiv.org/abs/1910.06711) by Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brebisson, Yoshua Bengio, Aaron Courville.\n2. **Tacotron-2** released with the paper [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/abs/1712.05884) by Jonathan Shen, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ Skerry-Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis, Yonghui Wu.\n3. **FastSpeech** released with the paper [FastSpeech: Fast, Robust, and Controllable Text to Speech](https://arxiv.org/abs/1905.09263) by Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu.\n4. **Multi-band MelGAN** released with the paper [Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech](https://arxiv.org/abs/2005.05106) by Geng Yang, Shan Yang, Kai Liu, Peng Fang, Wei Chen, Lei Xie.\n5. **FastSpeech2** released with the paper [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://arxiv.org/abs/2006.04558) by Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu.\n6. **Parallel WaveGAN** released with the paper [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://arxiv.org/abs/1910.11480) by Ryuichi Yamamoto, Eunwoo Song, Jae-Min Kim.\n\nWe are also implementing some techniques to improve quality and convergence speed from the following papers:\n\n2. **Guided Attention Loss** released with the paper [Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention\n](https://arxiv.org/abs/1710.08969) by Hideyuki Tachibana, Katsuya Uenoyama, Shunsuke Aihara.\n\n\n# Audio Samples\nHere in an audio samples on valid set. [tacotron-2](https://drive.google.com/open?id=1kaPXRdLg9gZrll9KtvH3-feOBMM8sn3_), [fastspeech](https://drive.google.com/open?id=1f69ujszFeGnIy7PMwc8AkUckhIaT2OD0), [melgan](https://drive.google.com/open?id=1mBwGVchwtNkgFsURl7g4nMiqx4gquAC2), [melgan.stft](https://drive.google.com/open?id=1xUkDjbciupEkM3N4obiJAYySTo6J9z6b), [fastspeech2](https://drive.google.com/drive/u/1/folders/1NG7oOfNuXSh7WyAoM1hI8P5BxDALY_mU), [multiband_melgan](https://drive.google.com/drive/folders/1DCV3sa6VTyoJzZmKATYvYVDUAFXlQ_Zp)\n\n# Tutorial End-to-End\n\n## Prepare Dataset\n\nPrepare a dataset in the following format:\n```\n|- [NAME_DATASET]/\n| |- metadata.csv\n| |- wav/\n| |- file1.wav\n| |- ...\n```\n\nWhere `metadata.csv` has the following format: `id|transcription`. This is a ljspeech-like format; you can ignore preprocessing steps if you have other format datasets.\n\nNote that `NAME_DATASET` should be `[ljspeech/kss/baker/libritts]` for example.\n\n## Preprocessing\n\nThe preprocessing has two steps:\n\n1. Preprocess audio features\n - Convert characters to IDs\n - Compute mel spectrograms\n - Normalize mel spectrograms to [-1, 1] range\n - Split the dataset into train and validation\n - Compute the mean and standard deviation of multiple features from the **training** split\n2. Standardize mel spectrogram based on computed statistics\n\nTo reproduce the steps above:\n```\ntensorflow-tts-preprocess --rootdir ./[ljspeech/kss/baker/libritts] --outdir ./dump_[ljspeech/kss/baker/libritts] --config preprocess/[ljspeech/kss/baker]_preprocess.yaml --dataset [ljspeech/kss/baker/libritts]\ntensorflow-tts-normalize --rootdir ./dump_[ljspeech/kss/baker/libritts] --outdir ./dump_[ljspeech/kss/baker/libritts] --config preprocess/[ljspeech/kss/baker/libritts]_preprocess.yaml --dataset [ljspeech/kss/baker/libritts]\n```\n\nRight now we only support [`ljspeech`](https://keithito.com/LJ-Speech-Dataset/), [`kss`](https://www.kaggle.com/bryanpark/korean-single-speaker-speech-dataset), [`baker`](https://weixinxcxdb.oss-cn-beijing.aliyuncs.com/gwYinPinKu/BZNSYP.rar) and [`libritts`](http://www.openslr.org/60/) for dataset argument. In the future, we intend to support more datasets.\n\n**Note**: To run `libritts` preprocessing, please first read the instruction in [examples/fastspeech2_libritts](https://github.com/TensorSpeech/TensorFlowTTS/tree/master/examples/fastspeech2_libritts). We need to reformat it first before run preprocessing.\n\nAfter preprocessing, the structure of the project folder should be:\n```\n|- [NAME_DATASET]/\n| |- metadata.csv\n| |- wav/\n| |- file1.wav\n| |- ...\n|- dump_[ljspeech/kss/baker/libritts]/\n| |- train/\n| |- ids/\n| |- LJ001-0001-ids.npy\n| |- ...\n| |- raw-feats/\n| |- LJ001-0001-raw-feats.npy\n| |- ...\n| |- raw-f0/\n| |- LJ001-0001-raw-f0.npy\n| |- ...\n| |- raw-energies/\n| |- LJ001-0001-raw-energy.npy\n| |- ...\n| |- norm-feats/\n| |- LJ001-0001-norm-feats.npy\n| |- ...\n| |- wavs/\n| |- LJ001-0001-wave.npy\n| |- ...\n| |- valid/\n| |- ids/\n| |- LJ001-0009-ids.npy\n| |- ...\n| |- raw-feats/\n| |- LJ001-0009-raw-feats.npy\n| |- ...\n| |- raw-f0/\n| |- LJ001-0001-raw-f0.npy\n| |- ...\n| |- raw-energies/\n| |- LJ001-0001-raw-energy.npy\n| |- ...\n| |- norm-feats/\n| |- LJ001-0009-norm-feats.npy\n| |- ...\n| |- wavs/\n| |- LJ001-0009-wave.npy\n| |- ...\n| |- stats.npy\n| |- stats_f0.npy\n| |- stats_energy.npy\n| |- train_utt_ids.npy\n| |- valid_utt_ids.npy\n|- examples/\n| |- melgan/\n| |- fastspeech/\n| |- tacotron2/\n| ...\n```\n\n- `stats.npy` contains the mean and std from the training split mel spectrograms\n- `stats_energy.npy` contains the mean and std of energy values from the training split\n- `stats_f0.npy` contains the mean and std of F0 values in the training split\n- `train_utt_ids.npy` / `valid_utt_ids.npy` contains training and validation utterances IDs respectively\n\nWe use suffix (`ids`, `raw-feats`, `raw-energy`, `raw-f0`, `norm-feats`, and `wave`) for each input type.\n\n\n**IMPORTANT NOTES**:\n- This preprocessing step is based on [ESPnet](https://github.com/espnet/espnet) so you can combine all models here with other models from ESPnet repository.\n- Regardless of how your dataset is formatted, the final structure of the `dump` folder **SHOULD** follow the above structure to be able to use the training script, or you can modify it by yourself 😄.\n\n## Training models\n\nTo know how to train model from scratch or fine-tune with other datasets/languages, please see detail at example directory.\n\n- For Tacotron-2 tutorial, pls see [examples/tacotron2](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/tacotron2)\n- For FastSpeech tutorial, pls see [examples/fastspeech](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/fastspeech)\n- For FastSpeech2 tutorial, pls see [examples/fastspeech2](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/fastspeech2)\n- For FastSpeech2 + MFA tutorial, pls see [examples/fastspeech2_libritts](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/fastspeech2_libritts)\n- For MelGAN tutorial, pls see [examples/melgan](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/melgan)\n- For MelGAN + STFT Loss tutorial, pls see [examples/melgan.stft](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/melgan.stft)\n- For Multiband-MelGAN tutorial, pls see [examples/multiband_melgan](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/multiband_melgan)\n- For Parallel WaveGAN tutorial, pls see [examples/parallel_wavegan](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/parallel_wavegan)\n- For Multiband-MelGAN Generator + Parallel WaveGAN Discriminator tutorial, pls see [examples/multiband_pwgan](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/multiband_pwgan)\n# Abstract Class Explaination\n\n## Abstract DataLoader Tensorflow-based dataset\n\nA detail implementation of abstract dataset class from [tensorflow_tts/dataset/abstract_dataset](https://github.com/tensorspeech/TensorFlowTTS/blob/master/tensorflow_tts/datasets/abstract_dataset.py). There are some functions you need overide and understand:\n\n1. **get_args**: This function return argumentation for **generator** class, normally is utt_ids.\n2. **generator**: This function have an inputs from **get_args** function and return a inputs for models. **Note that we return a dictionary for all generator functions with the keys that exactly match with the model's parameters because base_trainer will use model(\\*\\*batch) to do forward step.**\n3. **get_output_dtypes**: This function need return dtypes for each element from **generator** function.\n4. **get_len_dataset**: Return len of datasets, normaly is len(utt_ids).\n\n**IMPORTANT NOTES**:\n\n- A pipeline of creating dataset should be: cache -> shuffle -> map_fn -> get_batch -> prefetch.\n- If you do shuffle before cache, the dataset won't shuffle when it re-iterate over datasets.\n- You should apply map_fn to make each element return from **generator** function have the same length before getting batch and feed it into a model.\n\nSome examples to use this **abstract_dataset** are [tacotron_dataset.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/tacotron2/tacotron_dataset.py), [fastspeech_dataset.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/fastspeech/fastspeech_dataset.py), [melgan_dataset.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/melgan/audio_mel_dataset.py), [fastspeech2_dataset.py](https://github.com/TensorSpeech/TensorFlowTTS/blob/master/examples/fastspeech2/fastspeech2_dataset.py)\n\n\n## Abstract Trainer Class\n\nA detail implementation of base_trainer from [tensorflow_tts/trainer/base_trainer.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/tensorflow_tts/trainers/base_trainer.py). It include [Seq2SeqBasedTrainer](https://github.com/tensorspeech/TensorFlowTTS/blob/master/tensorflow_tts/trainers/base_trainer.py#L265) and [GanBasedTrainer](https://github.com/tensorspeech/TensorFlowTTS/blob/master/tensorflow_tts/trainers/base_trainer.py#L149) inherit from [BasedTrainer](https://github.com/tensorspeech/TensorFlowTTS/blob/master/tensorflow_tts/trainers/base_trainer.py#L16). All trainer support both single/multi GPU. There a some functions you **MUST** overide when implement new_trainer:\n\n- **compile**: This function aim to define a models, and losses.\n- **generate_and_save_intermediate_result**: This function will save intermediate result such as: plot alignment, save audio generated, plot mel-spectrogram ...\n- **compute_per_example_losses**: This function will compute per_example_loss for model, note that all element of the loss **MUST** has shape [batch_size].\n\nAll models on this repo are trained based-on **GanBasedTrainer** (see [train_melgan.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/melgan/train_melgan.py), [train_melgan_stft.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/melgan.stft/train_melgan_stft.py), [train_multiband_melgan.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/multiband_melgan/train_multiband_melgan.py)) and **Seq2SeqBasedTrainer** (see [train_tacotron2.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/tacotron2/train_tacotron2.py), [train_fastspeech.py](https://github.com/tensorspeech/TensorFlowTTS/blob/master/examples/fastspeech/train_fastspeech.py)).\n\n# End-to-End Examples\nYou can know how to inference each model at [notebooks](https://github.com/tensorspeech/TensorFlowTTS/tree/master/notebooks) or see a [colab](https://colab.research.google.com/drive/1akxtrLZHKuMiQup00tzO2olCaN-y3KiD?usp=sharing) (for English), [colab](https://colab.research.google.com/drive/1ybWwOS5tipgPFttNulp77P6DAB5MtiuN?usp=sharing) (for Korean). Here is an example code for end2end inference with fastspeech and melgan.\n\n```python\nimport numpy as np\nimport soundfile as sf\nimport yaml\n\nimport tensorflow as tf\n\nfrom tensorflow_tts.inference import AutoConfig\nfrom tensorflow_tts.inference import TFAutoModel\nfrom tensorflow_tts.inference import AutoProcessor\n\n# initialize fastspeech model.\nfs_config = AutoConfig.from_pretrained('/examples/fastspeech/conf/fastspeech.v1.yaml')\nfastspeech = TFAutoModel.from_pretrained(\n config=fs_config,\n pretrained_path=\"./examples/fastspeech/pretrained/model-195000.h5\"\n)\n\n\n# initialize melgan model\nmelgan_config = AutoConfig.from_pretrained('./examples/melgan/conf/melgan.v1.yaml')\nmelgan = TFAutoModel.from_pretrained(\n config=melgan_config,\n pretrained_path=\"./examples/melgan/checkpoint/generator-1500000.h5\"\n)\n\n\n# inference\nprocessor = AutoProcessor.from_pretrained(pretrained_path=\"./test/files/ljspeech_mapper.json\")\n\nids = processor.text_to_sequence(\"Recent research at Harvard has shown meditating for as little as 8 weeks, can actually increase the grey matter in the parts of the brain responsible for emotional regulation, and learning.\")\nids = tf.expand_dims(ids, 0)\n# fastspeech inference\n\nmasked_mel_before, masked_mel_after, duration_outputs = fastspeech.inference(\n ids,\n speaker_ids=tf.zeros(shape=[tf.shape(ids)[0]], dtype=tf.int32),\n speed_ratios=tf.constant([1.0], dtype=tf.float32)\n)\n\n# melgan inference\naudio_before = melgan.inference(masked_mel_before)[0, :, 0]\naudio_after = melgan.inference(masked_mel_after)[0, :, 0]\n\n# save to file\nsf.write('./audio_before.wav', audio_before, 22050, \"PCM_16\")\nsf.write('./audio_after.wav', audio_after, 22050, \"PCM_16\")\n```\n\n# Contact\n[Minh Nguyen Quan Anh](https://github.com/tensorspeech): nguyenquananhminh@gmail.com, [erogol](https://github.com/erogol): erengolge@gmail.com, [Kuan Chen](https://github.com/azraelkuan): azraelkuan@gmail.com, [Dawid Kobus](https://github.com/machineko): machineko@protonmail.com, [Takuya Ebata](https://github.com/MokkeMeguru): meguru.mokke@gmail.com, [Trinh Le Quang](https://github.com/l4zyf9x): trinhle.cse@gmail.com, [Yunchao He](https://github.com/candlewill): yunchaohe@gmail.com, [Alejandro Miguel Velasquez](https://github.com/ZDisket): xml506ok@gmail.com\n\n# License\nOverall, Almost models here are licensed under the [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0) for all countries in the world, except in **Viet Nam** this framework cannot be used for production in any way without permission from TensorFlowTTS's Authors. There is an exception, Tacotron-2 can be used with any purpose. If you are Vietnamese and want to use this framework for production, you **Must** contact us in advance.\n\n# Acknowledgement\nWe want to thank [Tomoki Hayashi](https://github.com/kan-bayashi), who discussed with us much about Melgan, Multi-band melgan, Fastspeech, and Tacotron. This framework based-on his great open-source [ParallelWaveGan](https://github.com/kan-bayashi/ParallelWaveGAN) project.\n" }, { "path": "TensorFlowTTS/setup.cfg", "content": "[aliases]\ntest=pytest\n\n[tool:pytest]\naddopts = --verbose --durations=0\ntestpaths = test\n\n[flake8]\nignore = H102,W504,H238,D104,H306,H405,D205\n# 120 is a workaround, 79 is good\nmax-line-length = 120\n" }, { "path": "TensorFlowTTS/setup.py", "content": "\"\"\"Setup Tensorflow TTS libarary.\"\"\"\n\nimport os\nimport sys\nfrom distutils.version import LooseVersion\n\nimport pip\nfrom setuptools import find_packages, setup\n\nif LooseVersion(sys.version) < LooseVersion(\"3.6\"):\n raise RuntimeError(\n \"Tensorflow TTS requires python >= 3.6, \"\n \"but your Python version is {}\".format(sys.version)\n )\n\nif LooseVersion(pip.__version__) < LooseVersion(\"19\"):\n raise RuntimeError(\n \"pip>=19.0.0 is required, but your pip version is {}. \"\n 'Try again after \"pip install -U pip\"'.format(pip.__version__)\n )\n\n# TODO(@dathudeptrai) update requirement if needed.\nrequirements = {\n \"install\": [\n \"tensorflow-gpu==2.6.0\",\n \"tensorflow-addons==0.14.0\",\n \"keras==2.6.0\",\n \"setuptools>=38.5.1\",\n \"librosa>=0.7.0\",\n \"soundfile>=0.10.2\",\n \"matplotlib>=3.1.0\",\n \"PyYAML>=3.12\",\n \"tqdm>=4.26.1\",\n \"h5py>=2.10.0\",\n \"unidecode>=1.1.1\",\n \"inflect>=4.1.0\",\n \"scikit-learn>=0.22.0\",\n \"pyworld>=0.2.10\",\n \"numba<=0.48\", # Fix No module named \"numba.decorators\"\n \"jamo>=0.4.1\",\n \"pypinyin\",\n \"g2pM\",\n \"textgrid\",\n \"click\",\n \"g2p_en\",\n \"dataclasses\",\n \"pysptk\",\n \"webrtcvad\",\n ],\n \"setup\": [\"numpy\", \"pytest-runner\",],\n \"test\": [\n \"pytest>=3.3.0\",\n \"hacking>=1.1.0\",\n ],\n}\n\n# TODO(@dathudeptrai) update console_scripts.\nentry_points = {\n \"console_scripts\": [\n \"tensorflow-tts-preprocess-unetts-duration=tensorflow_tts.bin.preprocess_unetts:preprocess_duration\",\n \"tensorflow-tts-preprocess-unetts-acous=tensorflow_tts.bin.preprocess_unetts:preprocess_acous\",\n \"tensorflow-tts-preprocess-unetts-vocoder=tensorflow_tts.bin.preprocess_unetts:preprocess_vocoder\",\n ]\n}\n\ninstall_requires = requirements[\"install\"]\nsetup_requires = requirements[\"setup\"]\ntests_require = requirements[\"test\"]\nextras_require = {\n k: v for k, v in requirements.items() if k not in [\"install\", \"setup\"]\n}\n\ndirname = os.path.dirname(__file__)\nsetup(\n name=\"TensorFlowTTS\",\n version=\"0.0.0\",\n url=\"https://github.com/tensorspeech/TensorFlowTTS\",\n author=\"Minh Nguyen Quan Anh, Eren Gölge, Kuan Chen, Dawid Kobus, Takuya Ebata, Trinh Le Quang, Yunchao He, Alejandro Miguel Velasquez\",\n author_email=\"nguyenquananhminh@gmail.com\",\n description=\"TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2\",\n long_description=open(os.path.join(dirname, \"README.md\"), encoding=\"utf-8\").read(),\n long_description_content_type=\"text/markdown\",\n license=\"Apache-2.0\",\n packages=find_packages(include=[\"tensorflow_tts*\"]),\n install_requires=install_requires,\n setup_requires=setup_requires,\n tests_require=tests_require,\n extras_require=extras_require,\n entry_points=entry_points,\n classifiers=[\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Intended Audience :: Science/Research\",\n \"Operating System :: POSIX :: Linux\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n ],\n)\n" }, { "path": "TensorFlowTTS/tensorflow_tts/__init__.py", "content": "__version__ = \"0.0\"\n" }, { "path": "TensorFlowTTS/tensorflow_tts/audio_process/__init__.py", "content": "from tensorflow_tts.audio_process.audio import preprocess_wav, melbasis_make, mel_make\nfrom tensorflow_tts.audio_process import audio_spec" }, { "path": "TensorFlowTTS/tensorflow_tts/audio_process/audio.py", "content": "import struct\nfrom pathlib import Path\nfrom typing import Optional, Union\n\nimport librosa\nimport numpy as np\nfrom scipy.ndimage.morphology import binary_dilation\n\ntry:\n import webrtcvad\nexcept:\n print(\"Unable to import 'webrtcvad'. This package enables noise removal and is recommended.\")\n webrtcvad=None\n\n\n# ## Voice Activation Detection\n# # Window size of the VAD. Must be either 10, 20 or 30 milliseconds.\n# # This sets the granularity of the VAD. Should not need to be changed.\n# vad_window_length = 30 # In milliseconds\n# # Number of frames to average together when performing the moving average smoothing.\n# # The larger this value, the larger the VAD variations must be to not get smoothed out. \n# vad_moving_average_width = 8\n# # Maximum number of consecutive silent frames a segment can have.\n# vad_max_silence_length = 6\n\nint16_max = (2 ** 15) - 1\nsampling_rate = 16000\n\ndef preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],\n source_sr: Optional[int] = None,\n normalize: Optional[bool] = True,\n trim_silence: Optional[bool] = True,\n is_sil_pad: Optional[bool] = True,\n vad_window_length = 30,\n vad_moving_average_width = 8,\n vad_max_silence_length = 6):\n \"\"\"\n Applies the preprocessing operations used in training the Speaker Encoder to a waveform \n either on disk or in memory. The waveform will be resampled to match the data hyperparameters.\n\n :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not \n just .wav), either the waveform as a numpy array of floats.\n :param source_sr: if passing an audio waveform, the sampling rate of the waveform before \n preprocessing. After preprocessing, the waveform's sampling rate will match the data \n hyperparameters. If passing a filepath, the sampling rate will be automatically detected and \n this argument will be ignored.\n \"\"\"\n # Load the wav from disk if needed\n if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):\n wav, source_sr = librosa.load(str(fpath_or_wav), sr=None)\n else:\n wav = fpath_or_wav\n\n # Resample the wav if needed\n if source_sr is not None and source_sr != sampling_rate:\n wav = librosa.resample(wav, source_sr, sampling_rate)\n\n # Apply the preprocessing: normalize volume and shorten long silences \n if normalize:\n wav = normalize_volume(wav)\n\n if trim_silence:\n wav = trim_long_silences(wav, vad_window_length, vad_moving_average_width, vad_max_silence_length)\n\n if is_sil_pad:\n wav = sil_pad(wav)\n\n return wav\n\ndef normalize_volume(wav, ratio=0.6):\n return wav / np.max(np.abs(wav)) * ratio\n\ndef sil_pad(wav, pad_length=100):\n pad_length = int(sampling_rate / 1000 * pad_length)\n return np.pad(wav, (pad_length, pad_length))\n\ndef trim_long_silences(wav, vad_window_length, vad_moving_average_width, vad_max_silence_length):\n \"\"\"\n Ensures that segments without voice in the waveform remain no longer than a \n threshold determined by the VAD parameters in params.py.\n\n :param wav: the raw waveform as a numpy array of floats \n :return: the same waveform with silences trimmed away (length <= original wav length)\n \"\"\"\n # Compute the voice detection window size\n samples_per_window = (vad_window_length * sampling_rate) // 1000\n\n # Trim the end of the audio to have a multiple of the window size\n wav = wav[:len(wav) - (len(wav) % samples_per_window)]\n\n # Convert the float waveform to 16-bit mono PCM\n pcm_wave = struct.pack(\"%dh\" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))\n\n # Perform voice activation detection\n voice_flags = []\n vad = webrtcvad.Vad(mode=3)\n for window_start in range(0, len(wav), samples_per_window):\n window_end = window_start + samples_per_window\n voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],\n sample_rate=sampling_rate))\n voice_flags = np.array(voice_flags)\n\n # Smooth the voice detection with a moving average\n def moving_average(array, width):\n array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))\n ret = np.cumsum(array_padded, dtype=float)\n ret[width:] = ret[width:] - ret[:-width]\n return ret[width - 1:] / width\n\n audio_mask = moving_average(voice_flags, vad_moving_average_width)\n audio_mask = np.round(audio_mask).astype(np.bool)\n\n # Dilate the voiced regions\n audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))\n audio_mask = np.repeat(audio_mask, samples_per_window)\n\n return wav[audio_mask == True]\n\ndef melbasis_make(sr=16000, n_fft=1024, n_mels=80, fmin=80, fmax=7600):\n return librosa.filters.mel(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)\n\ndef mel_make(filepath: str, sr=16000, n_fft=1024, framesize=256, mel_basis=None, fn=None):\n if fn is None:\n audio, _ = librosa.load(filepath, sr=sr)\n else:\n audio = fn(filepath, trim_silence=False, is_sil_pad=False)\n\n D = librosa.stft(audio, n_fft=n_fft, hop_length=framesize)\n S, _ = librosa.magphase(D)\n\n if mel_basis:\n mel = np.log10(np.maximum(np.dot(mel_basis, S), 1e-10)).T\n return audio, mel\n else:\n return audio, S" }, { "path": "TensorFlowTTS/tensorflow_tts/audio_process/audio_spec.py", "content": "import librosa\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import signal\nimport soundfile as sf\n\ndef preemphasis(wav, k, preemphasize=True):\n if preemphasize:\n return signal.lfilter([1, -k], [1], wav)\n return wav\n\ndef inv_preemphasis(wav, k, inv_preemphasize=True):\n if inv_preemphasize:\n return signal.lfilter([1], [1, -k], wav)\n return wav\n\nclass AudioMelSpec():\n '''\n Audio to Mel_Spec\n '''\n def __init__(\n self, sample_rate=16000, n_fft=800, num_mels=80, hop_size=200, win_size=800,\n fmin=55, fmax=7600, min_level_db=-100, ref_level_db=20, max_abs_value=4.,\n preemphasis=0.97, preemphasize=True,\n signal_normalization=True, allow_clipping_in_normalization=True, symmetric_mels=True,\n power=1.5, griffin_lim_iters=60,\n rescale=True, rescaling_max=0.9\n ):\n self.sample_rate = sample_rate\n self.n_fft = n_fft\n self.num_mels = num_mels\n self.hop_size = hop_size\n self.win_size = win_size\n self.fmin = fmin\n self.fmax = fmax\n self.min_level_db = min_level_db\n self.ref_level_db = ref_level_db\n self.max_abs_value = max_abs_value\n self.preemphasis = preemphasis\n self.preemphasize = preemphasize\n\n self.signal_normalization = signal_normalization\n self.symmetric_mels = symmetric_mels\n self.allow_clipping_in_normalization = allow_clipping_in_normalization\n\n self.power = power\n self.griffin_lim_iters = griffin_lim_iters\n\n self.rescale = rescale\n self.rescaling_max = rescaling_max\n\n self._mel_basis_create()\n\n def _mel_basis_create(self):\n self._mel_basis = librosa.filters.mel(self.sample_rate, self.n_fft, self.num_mels, self.fmin, self.fmax)\n self._inv_mel_basis = np.linalg.pinv(self._mel_basis)\n\n def _stft(self, y):\n return librosa.stft(y=y, n_fft=self.n_fft, hop_length=self.hop_size, win_length=self.win_size)\n\n def _istft(self, y):\n return librosa.istft(y, hop_length=self.hop_size, win_length=self.win_size)\n\n def _linear_to_mel(self, spectogram):\n return np.dot(self._mel_basis, spectogram)\n\n def _mel_to_linear(self, mel_spectrogram):\n return np.maximum(1e-10, np.dot(self._inv_mel_basis, mel_spectrogram))\n\n def _amp_to_db(self, x):\n min_level = np.exp(self.min_level_db / 20 * np.log(10))\n return 20 * np.log10(np.maximum(min_level, x))\n\n def _db_to_amp(self, x):\n return np.power(10.0, (x) * 0.05)\n\n def _normalize(self, S):\n if self.allow_clipping_in_normalization:\n if self.symmetric_mels:\n return np.clip((2 * self.max_abs_value) * ((S - self.min_level_db) / (-self.min_level_db)) - self.max_abs_value,\n -self.max_abs_value, self.max_abs_value)\n else:\n return np.clip(self.max_abs_value * ((S - self.min_level_db) / (-self.min_level_db)), 0, self.max_abs_value)\n \n assert S.max() <= 0 and S.min() - self.min_level_db >= 0\n if self.symmetric_mels:\n return (2 * self.max_abs_value) * ((S - self.min_level_db) / (-self.min_level_db)) - self.max_abs_value\n else:\n return self.max_abs_value * ((S - self.min_level_db) / (-self.min_level_db))\n\n def _denormalize(self, D):\n if self.allow_clipping_in_normalization:\n if self.symmetric_mels:\n return (((np.clip(D, -self.max_abs_value,\n self.max_abs_value) + self.max_abs_value) * -self.min_level_db / (2 * self.max_abs_value))\n + self.min_level_db)\n else:\n return ((np.clip(D, 0, self.max_abs_value) * -self.min_level_db / self.max_abs_value) + self.min_level_db)\n \n if self.symmetric_mels:\n return (((D + self.max_abs_value) * -self.min_level_db / (2 * self.max_abs_value)) + self.min_level_db)\n else:\n return ((D * -self.min_level_db / self.max_abs_value) + self.min_level_db)\n\n def _griffin_lim(self, S):\n \"\"\"librosa implementation of Griffin-Lim\n Based on https://github.com/librosa/librosa/issues/434\n \"\"\"\n angles = np.exp(2j * np.pi * np.random.rand(*S.shape))\n S_complex = np.abs(S).astype(np.complex)\n y = self._istft(S_complex * angles)\n for i in range(self.griffin_lim_iters):\n angles = np.exp(1j * np.angle(self._stft(y)))\n y = self._istft(S_complex * angles)\n return y\n\n def load_wav(self, wav_fpath):\n wav, _ = librosa.load(wav_fpath, sr=self.sample_rate)\n if self.rescale:\n wav = wav / np.abs(wav).max() * self.rescaling_max\n return wav\n\n def save_wav(self, wav, fpath):\n if self.rescale:\n wav = wav / np.abs(wav).max() * self.rescaling_max\n sf.write(fpath, wav, self.sample_rate, subtype=\"PCM_16\")\n\n def melspectrogram(self, wav):\n D = self._stft(preemphasis(wav, self.preemphasis, self.preemphasize))\n S = self._amp_to_db(self._linear_to_mel(np.abs(D))) - self.ref_level_db\n \n if self.signal_normalization:\n return self._normalize(S.T)\n return S.T\n\n def inv_mel_spectrogram(self, mel_spectrogram):\n \"\"\"Converts mel spectrogram to waveform using librosa\"\"\"\n if self.signal_normalization:\n D = self._denormalize(mel_spectrogram.T)\n else:\n D = mel_spectrogram.T\n \n S = self._mel_to_linear(self._db_to_amp(D + self.ref_level_db)) # Convert back to linear\n \n return inv_preemphasis(self._griffin_lim(S ** self.power), self.preemphasis, self.preemphasize)\n\n def compare_plot(self, targets, preds, filepath=None, frame_real_len=None, text=None):\n if frame_real_len:\n targets = targets[:frame_real_len]\n preds = preds[:frame_real_len]\n\n fig = plt.figure(figsize=(14,10))\n\n if text:\n fig.text(0.4, 0.48, text, horizontalalignment=\"center\", fontsize=16)\n\n ax1 = fig.add_subplot(211)\n ax2 = fig.add_subplot(212)\n\n im = ax1.imshow(targets.T, aspect='auto', origin=\"lower\", interpolation=\"none\")\n ax1.set_title(\"Target Mel-Spectrogram\")\n fig.colorbar(mappable=im, shrink=0.65, ax=ax1)\n \n im = ax2.imshow(preds.T, aspect='auto', origin=\"lower\", interpolation=\"none\")\n ax2.set_title(\"Pred Mel-Spectrogram\")\n fig.colorbar(mappable=im, shrink=0.65, ax=ax2)\n\n plt.tight_layout()\n if filepath is None:\n plt.show()\n else:\n plt.savefig(filepath)\n plt.close()\n\n def melspec_plot(self, mels):\n plt.figure(figsize=(10,6))\n plt.imshow(mels.T, aspect='auto', origin=\"lower\", interpolation=\"none\")\n plt.colorbar()\n plt.show()\n\nclass AudioSpec():\n ''' # TODO\n Now just for sqrt(sp) from world\n '''\n def __init__(self, sr, nfft, mel_dim=80, f0_min=71, f0_max=7800,\n min_level_db=-120., ref_level_db=-5., max_abs_value=4.,\n is_norm=True, is_symmetric=True, is_clipping_in_normalization=False):\n self.sr = sr\n self.nfft = nfft\n self.mel_dim = mel_dim\n self.f0_min = f0_min\n self.f0_max = f0_max\n\n self.min_level_db = min_level_db\n self.min_level_amp = np.exp((self.min_level_db + 0.1) / 20 * np.log(10))\n\n # sp from world, self.ref_level_db should be less than zero\n # otherwise, is_clipping_in_normalization should be true\n self.ref_level_db = ref_level_db\n self.max_abs_value = max_abs_value\n \n self.is_norm = is_norm\n self.is_symmetric = is_symmetric\n self.is_clipping_in_normalization = is_clipping_in_normalization\n\n if self.ref_level_db > 0.:\n try:\n assert self.is_norm and self.is_clipping_in_normalization\n except:\n self.is_clipping_in_normalization = True\n\n self._mel_basis_create()\n\n def _mel_basis_create(self):\n self._mel_basis = librosa.filters.mel(self.sr, self.nfft, self.mel_dim, self.f0_min, self.f0_max)\n self._inv_mel_basis = np.linalg.pinv(self._mel_basis)\n\n def _normalize(self, log_sepc, is_symmetric, is_clipping_in_normalization):\n if is_clipping_in_normalization:\n if is_symmetric:\n return np.clip((2 * self.max_abs_value) * ((log_sepc - self.min_level_db) / (-self.min_level_db)) - self.max_abs_value,\n -self.max_abs_value, self.max_abs_value)\n else:\n return np.clip(self.max_abs_value * ((log_sepc - self.min_level_db) / (-self.min_level_db)), 0, self.max_abs_value)\n\n assert log_sepc.max() <= 0 and log_sepc.min() >= self.min_level_db\n if is_symmetric:\n return (2 * self.max_abs_value) * ((log_sepc - self.min_level_db) / (-self.min_level_db)) - self.max_abs_value\n else:\n return self.max_abs_value * ((log_sepc - self.min_level_db) / (-self.min_level_db))\n\n def _denormalize(self, log_sepc, is_symmetric, is_clipping_in_normalization):\n if is_clipping_in_normalization:\n if is_symmetric:\n return (((np.clip(log_sepc, -self.max_abs_value,\n self.max_abs_value) + self.max_abs_value) * -self.min_level_db / (2 * self.max_abs_value))\n + self.min_level_db)\n else:\n return ((np.clip(log_sepc, 0, self.max_abs_value) * -self.min_level_db / self.max_abs_value) + self.min_level_db)\n\n if is_symmetric:\n return (((log_sepc + self.max_abs_value) * -self.min_level_db / (2 * self.max_abs_value)) + self.min_level_db)\n else:\n return ((log_sepc * -self.min_level_db / self.max_abs_value) + self.min_level_db)\n\n def ampspec2logspec(self, amp_spec):\n mel_spec = np.dot(amp_spec, self._mel_basis.T)\n log_sepc = 20 * np.log10(np.maximum(self.min_level_amp, mel_spec)) - self.ref_level_db\n\n if self.is_norm:\n log_sepc = self._normalize(log_sepc, self.is_symmetric, self.is_clipping_in_normalization)\n\n return log_sepc\n\n def logspec2ampspec(self, log_spec):\n if self.is_norm:\n log_spec = self._denormalize(log_spec, self.is_symmetric, self.is_clipping_in_normalization)\n\n log_spec += self.ref_level_db\n\n amp_spec = np.maximum(self.min_level_amp**2, np.dot(np.power(10.0, log_spec * 0.05), self._inv_mel_basis.T))\n return amp_spec\n\nclass VariableNormProcess():\n '''\n Variable, like duration, f0 and bap from world\n '''\n def __init__(self, var_min, var_max, max_abs_value=4.0, is_symmetric=True):\n self.var_min = var_min\n self.var_max = var_max\n self.scale = var_max - var_min\n self.max_abs_value = max_abs_value\n self.is_symmetric = is_symmetric\n\n assert self.scale > 0\n\n def normalize(self, var):\n if self.is_symmetric:\n return np.clip((2 * self.max_abs_value) * ((var - self.var_min) / self.scale) - self.max_abs_value,\n -self.max_abs_value, self.max_abs_value)\n else:\n return np.clip(self.max_abs_value * ((var - self.var_min) / self.scale), 0, self.max_abs_value)\n\n def denormalize(self, nvar):\n if self.is_symmetric:\n return (((np.clip(nvar, -self.max_abs_value, self.max_abs_value)\n + self.max_abs_value) * self.scale / (2 * self.max_abs_value))\n + self.var_min)\n else:\n return ((np.clip(nvar, 0, self.max_abs_value) * self.scale / self.max_abs_value) + self.var_min)\n" }, { "path": "TensorFlowTTS/tensorflow_tts/bin/__init__.py", "content": "" }, { "path": "TensorFlowTTS/tensorflow_tts/bin/preprocess_unetts.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Perform preprocessing, with raw feature extraction and normalization of train/valid split.\"\"\"\n\nimport argparse\nimport logging\nimport os\nimport yaml\n\nimport numpy as np\n\nfrom functools import partial\nfrom multiprocessing import Pool\nfrom sklearn.model_selection import train_test_split\nfrom tqdm import tqdm\n\nfrom tensorflow_tts.processor.multispk_voiceclone import MultiSPKVoiceCloneProcessor\nfrom tensorflow_tts.processor.multispk_voiceclone import AISHELL_CHN_SYMBOLS\n\nfrom tensorflow_tts.audio_process.audio_spec import AudioMelSpec\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n\nimport random\nimport tensorflow as tf\n\nSEED = 2021\nrandom.seed(SEED)\nnp.random.seed(SEED)\ntf.random.set_seed(SEED)\n\n_feats_handle = None\n\ndef parse_and_config():\n \"\"\"Parse arguments and set configuration parameters.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Preprocess audio and text features \"\n \"(See detail in tensorflow_tts/bin/preprocess_dataset.py).\"\n )\n parser.add_argument(\n \"--rootdir\",\n default=None,\n type=str,\n required=True,\n help=\"Directory containing the dataset files.\",\n )\n parser.add_argument(\n \"--outdir\",\n default=None,\n type=str,\n required=True,\n help=\"Output directory where features will be saved.\",\n )\n parser.add_argument(\n \"--dataset\",\n type=str,\n default=\"multispk_voiceclone\",\n choices=[\"multispk_voiceclone\"],\n help=\"Dataset to preprocess.\",\n )\n parser.add_argument(\n \"--during_train\",\n type=int,\n default=0,\n choices=[0, 1],\n help=\"0-False, 1-True: trainging during model\"\n )\n parser.add_argument(\n \"--all_train\",\n type=int,\n default=0,\n choices=[0, 1],\n help=\"0-False, 1-True: trainging f0 model\"\n )\n parser.add_argument(\n \"--mfaed_txt\",\n type=str,\n default=None,\n required=True,\n help=\"mfa results txt\"\n )\n parser.add_argument(\n \"--wavs_dir\",\n type=str,\n default=None,\n required=True,\n help=\"wav dir\"\n )\n parser.add_argument(\n \"--spkinfo_dir\",\n type=str,\n default=None,\n required=True,\n help=\"spkinfo dir\"\n )\n parser.add_argument(\n \"--embed_dir\",\n type=str,\n default=None,\n required=True,\n help=\"embed dir\"\n )\n parser.add_argument(\n \"--unseen_dir\",\n type=str,\n default=None,\n required=True,\n help=\"unseen speaker dir\"\n )\n parser.add_argument(\n \"--config\", type=str, required=True, help=\"YAML format configuration file.\"\n )\n parser.add_argument(\n \"--n_cpus\",\n type=int,\n default=4,\n required=False,\n help=\"Number of CPUs to use in parallel.\",\n )\n parser.add_argument(\n \"--test_size\",\n type=float,\n default=0.05,\n required=False,\n help=\"Proportion of files to use as test dataset.\",\n )\n parser.add_argument(\n \"--verbose\",\n type=int,\n default=0,\n choices=[0, 1, 2],\n help=\"Logging level. 0: DEBUG, 1: INFO and WARNING, 2: INFO, WARNING, and ERROR\",\n )\n args = parser.parse_args()\n\n # set logger\n FORMAT = \"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\"\n log_level = {0: logging.DEBUG, 1: logging.WARNING, 2: logging.ERROR}\n logging.basicConfig(level=log_level[args.verbose], format=FORMAT)\n\n # load config\n config = yaml.load(open(args.config), Loader=yaml.Loader)\n config.update(vars(args))\n # config checks\n assert config[\"format\"] == \"npy\", \"'npy' is the only supported format.\"\n return config\n\n'''\n###############################################################################\n############################# Duration #######################################\n###############################################################################\n'''\n\ndef preprocess_duration():\n \"\"\"Run preprocessing process and compute statistics for normalizing.\"\"\"\n config = parse_and_config()\n\n dataset_processor = {\n \"multispk_voiceclone\": MultiSPKVoiceCloneProcessor,\n }\n\n dataset_symbol = {\n \"multispk_voiceclone\": AISHELL_CHN_SYMBOLS,\n }\n\n dataset_cleaner = {\n \"multispk_voiceclone\": None,\n }\n\n logging.info(f\"Selected '{config['dataset']}' processor.\")\n processor = dataset_processor[config[\"dataset\"]](\n config[\"rootdir\"],\n symbols = dataset_symbol[config[\"dataset\"]],\n cleaner_names = dataset_cleaner[config[\"dataset\"]],\n during_train = True if config[\"during_train\"] else False,\n mfaed_txt = config[\"mfaed_txt\"],\n wavs_dir = config[\"wavs_dir\"],\n embed_dir = config[\"embed_dir\"],\n spkinfo_dir = config[\"spkinfo_dir\"],\n unseen_dir = config[\"unseen_dir\"]\n )\n\n # check output directories\n build_dir = lambda x: [\n os.makedirs(os.path.join(config[\"outdir\"], x, y), exist_ok=True)\n for y in [\"ids\", \"raw-durations\", \"stat-durations\"]\n ]\n build_dir(\"train\")\n build_dir(\"valid\")\n\n # save pretrained-processor to feature dir\n processor._save_mapper(\n os.path.join(config[\"outdir\"], f\"{config['dataset']}_mapper.json\"),\n extra_attrs_to_save={\"pinyin_dict\": processor.pinyin_dict}\n if config[\"dataset\"] == \"multispk_voiceclone\" else {},\n )\n\n # build train test split\n _Y = [i[0] for i in processor.items]\n train_split, valid_split = train_test_split(\n processor.items,\n test_size=config[\"test_size\"],\n random_state=42,\n shuffle=True,\n stratify=_Y\n )\n logging.info(f\"Training items: {len(train_split)}\")\n logging.info(f\"Validation items: {len(valid_split)}\")\n\n train_utt_ids = [x[1] for x in train_split]\n valid_utt_ids = [x[1] for x in valid_split]\n\n # save train and valid utt_ids to track later\n np.save(os.path.join(config[\"outdir\"], \"train_utt_ids.npy\"), train_utt_ids, allow_pickle=False)\n np.save(os.path.join(config[\"outdir\"], \"valid_utt_ids.npy\"), valid_utt_ids, allow_pickle=False)\n\n config[\"none_pinyin_symnum\"] = processor.none_pinyin_symnum\n\n # define map iterator\n def iterator_data(items_list):\n for item in items_list:\n yield processor.get_one_sample(item)\n\n train_iterator_data = iterator_data(train_split)\n valid_iterator_data = iterator_data(valid_split)\n\n p = Pool(config[\"n_cpus\"])\n\n # preprocess train files and get statistics for normalizing\n partial_fn = partial(gen_duration_features, config=config)\n train_map = p.imap(\n partial_fn,\n tqdm(train_iterator_data, total=len(train_split), desc=\"[Preprocessing train]\"),\n chunksize=10,\n )\n\n for item in train_map:\n save_duration_to_file(item, \"train\", config)\n\n # preprocess valid files\n partial_fn = partial(gen_duration_features, config=config)\n valid_map = p.imap(\n partial_fn,\n tqdm(valid_iterator_data, total=len(valid_split), desc=\"[Preprocessing valid]\"),\n chunksize=10,\n )\n for item in valid_map:\n save_duration_to_file(item, \"valid\", config)\n\n \"\"\"\n sample = {\n \"speaker_name\": spkname,\n \"filename\" : filename,\n \"wav_path\" : wav_path,\n \"text_ids\" : text_ids,\n \"durs\" : durs,\n \"embed_path\" : embed_path,\n \"rate\" : self.target_rate,\n }\n \"\"\"\ndef gen_duration_features(item, config):\n text_ids = item[\"text_ids\"]\n durs = item[\"durs\"]\n assert len(text_ids) == len(durs)\n none_phnum = config[\"none_pinyin_symnum\"]\n\n shengmu = []\n yunmu = []\n is_shengmu = True\n for t_id, dur in zip(text_ids, durs):\n if t_id < none_phnum:\n continue\n\n if is_shengmu:\n shengmu.append(dur)\n is_shengmu = False\n else:\n yunmu.append(dur)\n is_shengmu = True\n\n assert len(shengmu) == len(yunmu)\n\n dur_stats = np.array([np.mean(shengmu), np.std(shengmu), np.mean(yunmu), np.std(yunmu)])\n\n item[\"text_ids\"] = np.array(text_ids)\n item[\"durs\"] = np.array(durs)\n item[\"dur_stats\"] = dur_stats\n\n return item\n\ndef save_duration_to_file(features, subdir, config):\n filename = features[\"filename\"]\n\n if config[\"format\"] == \"npy\":\n save_list = [\n (features[\"text_ids\"], \"ids\", \"ids\", np.int32),\n (features[\"durs\"], \"raw-durations\", \"raw-durations\", np.float32),\n (features[\"dur_stats\"], \"stat-durations\", \"stat-durations\", np.float32),\n ]\n for item, name_dir, name_file, fmt in save_list:\n np.save(\n os.path.join(\n config[\"outdir\"], subdir, name_dir, f\"{filename}-{name_file}.npy\"\n ),\n item.astype(fmt),\n allow_pickle=False,\n )\n else:\n raise ValueError(\"'npy' is the only supported format.\")\n\n\n'''\n###############################################################################\n################################ Acous ########################################\n###############################################################################\n'''\ndef preprocess_acous():\n \"\"\"Run preprocessing process and compute statistics for normalizing.\"\"\"\n config = parse_and_config()\n\n dataset_processor = {\n \"multispk_voiceclone\": MultiSPKVoiceCloneProcessor,\n }\n\n dataset_symbol = {\n \"multispk_voiceclone\": AISHELL_CHN_SYMBOLS,\n }\n\n dataset_cleaner = {\n \"multispk_voiceclone\": None,\n }\n\n logging.info(f\"Selected '{config['dataset']}' processor.\")\n processor = dataset_processor[config[\"dataset\"]](\n config[\"rootdir\"],\n symbols = dataset_symbol[config[\"dataset\"]],\n cleaner_names = dataset_cleaner[config[\"dataset\"]],\n all_train = True if config[\"all_train\"] else False,\n mfaed_txt = config[\"mfaed_txt\"],\n wavs_dir = config[\"wavs_dir\"],\n embed_dir = config[\"embed_dir\"],\n spkinfo_dir = config[\"spkinfo_dir\"],\n unseen_dir = config[\"unseen_dir\"]\n )\n\n # check output directories\n build_dir = lambda x: [\n os.makedirs(os.path.join(config[\"outdir\"], x, y), exist_ok=True)\n for y in [\"ids\", \"raw-durations\", \n \"raw-mels\", \"embeds\"]\n ]\n build_dir(\"train\")\n build_dir(\"valid\")\n\n # save pretrained-processor to feature dir\n processor._save_mapper(\n os.path.join(config[\"outdir\"], f\"{config['dataset']}_mapper.json\"),\n extra_attrs_to_save={\"pinyin_dict\": processor.pinyin_dict}\n if config[\"dataset\"] == \"multispk_voiceclone\" else {},\n )\n\n # build train test split\n _Y = [i[0] for i in processor.items]\n train_split, valid_split = train_test_split(\n processor.items,\n test_size=config[\"test_size\"],\n random_state=42,\n shuffle=True,\n stratify=_Y\n )\n logging.info(f\"Training items: {len(train_split)}\")\n logging.info(f\"Validation items: {len(valid_split)}\")\n\n train_utt_ids = [x[1] for x in train_split]\n valid_utt_ids = [x[1] for x in valid_split]\n\n # save train and valid utt_ids to track later\n np.save(os.path.join(config[\"outdir\"], \"train_utt_ids.npy\"), train_utt_ids, allow_pickle=False)\n np.save(os.path.join(config[\"outdir\"], \"valid_utt_ids.npy\"), valid_utt_ids, allow_pickle=False)\n\n # config[\"none_pinyin_symnum\"] = processor.none_pinyin_symnum\n\n # define map iterator\n def iterator_data(items_list):\n for item in items_list:\n yield processor.get_one_sample(item)\n\n train_iterator_data = iterator_data(train_split)\n valid_iterator_data = iterator_data(valid_split)\n\n p = Pool(config[\"n_cpus\"])\n\n # preprocess train files and get statistics for normalizing\n partial_fn = partial(gen_acous_features, config=config)\n train_map = p.imap_unordered(\n partial_fn,\n tqdm(train_iterator_data, total=len(train_split), desc=\"[Preprocessing train]\"),\n chunksize=10,\n )\n\n for item in train_map:\n save_acous_to_file(item, \"train\", config)\n\n # preprocess valid files\n partial_fn = partial(gen_acous_features, config=config)\n valid_map = p.imap_unordered(\n partial_fn,\n tqdm(valid_iterator_data, total=len(valid_split), desc=\"[Preprocessing valid]\"),\n chunksize=10,\n )\n for item in valid_map:\n save_acous_to_file(item, \"valid\", config)\n\n \"\"\"\n sample = {\n \"speaker_name\": spkname,\n \"filename\" : filename,\n \"wav_path\" : wav_path,\n \"text_ids\" : text_ids,\n \"durs\" : durs,\n \"embed_path\" : embed_path,\n \"rate\" : self.target_rate,\n }\n \"\"\"\ndef gen_acous_features(item, config):\n text_ids = item[\"text_ids\"]\n durs = item[\"durs\"]\n assert len(text_ids) == len(durs)\n\n global _feats_handle\n if _feats_handle is None:\n _feats_handle = AudioMelSpec(**config[\"feat_params\"])\n\n audio = _feats_handle.load_wav(item[\"wav_path\"])\n mel = _feats_handle.melspectrogram(audio)\n\n assert len(mel) == sum(durs)\n\n item[\"text_ids\"] = np.array(text_ids)\n item[\"durs\"] = np.array(durs)\n item[\"mels\"] = mel\n item[\"embeds\"] = np.load(item[\"embed_path\"])\n\n return item\n\ndef save_acous_to_file(features, subdir, config):\n filename = features[\"filename\"]\n\n if config[\"format\"] == \"npy\":\n save_list = [\n (features[\"text_ids\"], \"ids\", \"ids\", np.int32),\n (features[\"durs\"], \"raw-durations\", \"raw-durations\", np.int32),\n (features[\"mels\"], \"raw-mels\", \"raw-mels\", np.float32),\n (features[\"embeds\"], \"embeds\", \"embeds\", np.float32),\n ]\n for item, name_dir, name_file, fmt in save_list:\n np.save(\n os.path.join(\n config[\"outdir\"], subdir, name_dir, f\"{filename}-{name_file}.npy\"\n ),\n item.astype(fmt),\n allow_pickle=False,\n )\n else:\n raise ValueError(\"'npy' is the only supported format.\")\n\n'''\n###############################################################################\n################################ Vocoder ######################################\n###############################################################################\n'''\ndef preprocess_vocoder():\n \"\"\"Run preprocessing process and compute statistics for normalizing.\"\"\"\n config = parse_and_config()\n\n dataset_processor = {\n \"multispk_voiceclone\": MultiSPKVoiceCloneProcessor,\n }\n\n dataset_symbol = {\n \"multispk_voiceclone\": AISHELL_CHN_SYMBOLS,\n }\n\n dataset_cleaner = {\n \"multispk_voiceclone\": None,\n }\n\n logging.info(f\"Selected '{config['dataset']}' processor.\")\n processor = dataset_processor[config[\"dataset\"]](\n config[\"rootdir\"],\n symbols = dataset_symbol[config[\"dataset\"]],\n cleaner_names = dataset_cleaner[config[\"dataset\"]],\n during_train = True if config[\"during_train\"] else False,\n mfaed_txt = config[\"mfaed_txt\"],\n wavs_dir = config[\"wavs_dir\"],\n embed_dir = config[\"embed_dir\"],\n spkinfo_dir = config[\"spkinfo_dir\"]\n )\n\n # check output directories\n build_dir = lambda x: [\n os.makedirs(os.path.join(config[\"outdir\"], x, y), exist_ok=True)\n for y in [\"norm-feats\", \"wavs\"]\n ]\n build_dir(\"train\")\n build_dir(\"valid\")\n\n # save pretrained-processor to feature dir\n processor._save_mapper(\n os.path.join(config[\"outdir\"], f\"{config['dataset']}_mapper.json\"),\n extra_attrs_to_save={\"pinyin_dict\": processor.pinyin_dict}\n if config[\"dataset\"] == \"multispk_voiceclone\" else {},\n )\n\n # build train test split\n _Y = [i[0] for i in processor.items]\n train_split, valid_split = train_test_split(\n processor.items,\n test_size=config[\"test_size\"],\n random_state=42,\n shuffle=True,\n stratify=_Y\n )\n logging.info(f\"Training items: {len(train_split)}\")\n logging.info(f\"Validation items: {len(valid_split)}\")\n\n train_utt_ids = [x[1] for x in train_split]\n valid_utt_ids = [x[1] for x in valid_split]\n\n # save train and valid utt_ids to track later\n np.save(os.path.join(config[\"outdir\"], \"train_utt_ids.npy\"), train_utt_ids, allow_pickle=False)\n np.save(os.path.join(config[\"outdir\"], \"valid_utt_ids.npy\"), valid_utt_ids, allow_pickle=False)\n\n # config[\"none_pinyin_symnum\"] = processor.none_pinyin_symnum\n\n # define map iterator\n def iterator_data(items_list):\n for item in items_list:\n yield processor.get_one_sample(item)\n\n train_iterator_data = iterator_data(train_split)\n valid_iterator_data = iterator_data(valid_split)\n\n p = Pool(config[\"n_cpus\"])\n\n # preprocess train files and get statistics for normalizing\n partial_fn = partial(gen_vocoder, config=config)\n train_map = p.imap_unordered(\n partial_fn,\n tqdm(train_iterator_data, total=len(train_split), desc=\"[Preprocessing train]\"),\n chunksize=10,\n )\n\n for item in train_map:\n save_vocoder_to_file(item, \"train\", config)\n\n # preprocess valid files\n partial_fn = partial(gen_vocoder, config=config)\n valid_map = p.imap_unordered(\n partial_fn,\n tqdm(valid_iterator_data, total=len(valid_split), desc=\"[Preprocessing valid]\"),\n chunksize=10,\n )\n for item in valid_map:\n save_vocoder_to_file(item, \"valid\", config)\n\n \"\"\"\n sample = {\n \"speaker_name\": spkname,\n \"filename\" : filename,\n \"wav_path\" : wav_path,\n \"text_ids\" : text_ids,\n \"durs\" : durs,\n \"embed_path\" : embed_path,\n \"rate\" : self.target_rate,\n }\n \"\"\"\ndef gen_vocoder(item, config):\n global _feats_handle\n if _feats_handle is None:\n _feats_handle = AudioMelSpec(**config[\"feat_params\"])\n\n audio = _feats_handle.load_wav(item[\"wav_path\"])\n mel = _feats_handle.melspectrogram(audio)\n\n # check audio and feature length\n audio = np.pad(audio, (0, _feats_handle.n_fft), mode=\"edge\")\n audio = audio[: len(mel) * _feats_handle.hop_size]\n assert len(mel) * _feats_handle.hop_size == len(audio)\n\n item[\"audio\"] = audio\n item[\"mels\"] = mel\n\n return item\n\ndef save_vocoder_to_file(features, subdir, config):\n filename = features[\"filename\"]\n\n if config[\"format\"] == \"npy\":\n save_list = [\n (features[\"audio\"], \"wavs\", \"wave\", np.float32),\n (features[\"mels\"], \"norm-feats\", \"norm-feats\", np.float32),\n ]\n for item, name_dir, name_file, fmt in save_list:\n np.save(\n os.path.join(\n config[\"outdir\"], subdir, name_dir, f\"{filename}-{name_file}.npy\"\n ),\n item.astype(fmt),\n allow_pickle=False,\n )\n else:\n raise ValueError(\"'npy' is the only supported format.\")" }, { "path": "TensorFlowTTS/tensorflow_tts/configs/__init__.py", "content": "from tensorflow_tts.configs.melgan import (\n MelGANDiscriminatorConfig,\n MelGANGeneratorConfig,\n)\nfrom tensorflow_tts.configs.mb_melgan import (\n MultiBandMelGANDiscriminatorConfig,\n MultiBandMelGANGeneratorConfig,\n)\nfrom tensorflow_tts.configs.unetts import UNETTSDurationConfig, UNETTSAcousConfig\n" }, { "path": "TensorFlowTTS/tensorflow_tts/configs/mb_melgan.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Multi-band MelGAN Config object.\"\"\"\n\nfrom tensorflow_tts.configs import MelGANDiscriminatorConfig, MelGANGeneratorConfig\n\n\nclass MultiBandMelGANGeneratorConfig(MelGANGeneratorConfig):\n \"\"\"Initialize Multi-band MelGAN Generator Config.\"\"\"\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.subbands = kwargs.pop(\"subbands\", 4)\n self.taps = kwargs.pop(\"taps\", 62)\n self.cutoff_ratio = kwargs.pop(\"cutoff_ratio\", 0.142)\n self.beta = kwargs.pop(\"beta\", 9.0)\n\n\nclass MultiBandMelGANDiscriminatorConfig(MelGANDiscriminatorConfig):\n \"\"\"Initialize Multi-band MelGAN Discriminator Config.\"\"\"\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n" }, { "path": "TensorFlowTTS/tensorflow_tts/configs/melgan.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"MelGAN Config object.\"\"\"\n\n\nclass MelGANGeneratorConfig(object):\n \"\"\"Initialize MelGAN Generator Config.\"\"\"\n\n def __init__(\n self,\n out_channels=1,\n kernel_size=7,\n filters=512,\n use_bias=True,\n upsample_scales=[8, 8, 2, 2],\n stack_kernel_size=3,\n stacks=3,\n nonlinear_activation=\"LeakyReLU\",\n nonlinear_activation_params={\"alpha\": 0.2},\n padding_type=\"REFLECT\",\n use_final_nolinear_activation=True,\n is_weight_norm=True,\n initializer_seed=42,\n **kwargs\n ):\n \"\"\"Init parameters for MelGAN Generator model.\"\"\"\n self.out_channels = out_channels\n self.kernel_size = kernel_size\n self.filters = filters\n self.use_bias = use_bias\n self.upsample_scales = upsample_scales\n self.stack_kernel_size = stack_kernel_size\n self.stacks = stacks\n self.nonlinear_activation = nonlinear_activation\n self.nonlinear_activation_params = nonlinear_activation_params\n self.padding_type = padding_type\n self.use_final_nolinear_activation = use_final_nolinear_activation\n self.is_weight_norm = is_weight_norm\n self.initializer_seed = initializer_seed\n\n\nclass MelGANDiscriminatorConfig(object):\n \"\"\"Initialize MelGAN Discriminator Config.\"\"\"\n\n def __init__(\n self,\n out_channels=1,\n scales=3,\n downsample_pooling=\"AveragePooling1D\",\n downsample_pooling_params={\"pool_size\": 4, \"strides\": 2,},\n kernel_sizes=[5, 3],\n filters=16,\n max_downsample_filters=1024,\n use_bias=True,\n downsample_scales=[4, 4, 4, 4],\n nonlinear_activation=\"LeakyReLU\",\n nonlinear_activation_params={\"alpha\": 0.2},\n padding_type=\"REFLECT\",\n is_weight_norm=True,\n initializer_seed=42,\n **kwargs\n ):\n \"\"\"Init parameters for MelGAN Discriminator model.\"\"\"\n self.out_channels = out_channels\n self.scales = scales\n self.downsample_pooling = downsample_pooling\n self.downsample_pooling_params = downsample_pooling_params\n self.kernel_sizes = kernel_sizes\n self.filters = filters\n self.max_downsample_filters = max_downsample_filters\n self.use_bias = use_bias\n self.downsample_scales = downsample_scales\n self.nonlinear_activation = nonlinear_activation\n self.nonlinear_activation_params = nonlinear_activation_params\n self.padding_type = padding_type\n self.is_weight_norm = is_weight_norm\n self.initializer_seed = initializer_seed\n" }, { "path": "TensorFlowTTS/tensorflow_tts/configs/unetts.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"UnetTTS Config object.\"\"\"\n\nimport collections\n\nfrom tensorflow_tts.processor.multispk_voiceclone import AISHELL_CHN_SYMBOLS as aishell_symbols\n\n\nSelfAttentionParams = collections.namedtuple(\n \"SelfAttentionParams\",\n [\n \"hidden_size\",\n \"num_hidden_layers\",\n \"num_attention_heads\",\n \"attention_head_size\",\n \"intermediate_size\",\n \"intermediate_kernel_size\",\n \"hidden_act\",\n \"output_attentions\",\n \"output_hidden_states\",\n \"initializer_range\",\n \"hidden_dropout_prob\",\n \"attention_probs_dropout_prob\",\n \"layer_norm_eps\",\n ],\n)\n\nSelfAttentionConditionalParams = collections.namedtuple(\n \"SelfAttentionParams\",\n [\n \"hidden_size\",\n \"num_hidden_layers\",\n \"num_attention_heads\",\n \"attention_head_size\",\n \"intermediate_size\",\n \"intermediate_kernel_size\",\n \"hidden_act\",\n \"output_attentions\",\n \"output_hidden_states\",\n \"initializer_range\",\n \"hidden_dropout_prob\",\n \"attention_probs_dropout_prob\",\n \"layer_norm_eps\",\n \"conditional_norm_type\",\n ],\n)\n\nclass UNETTSDurationConfig(object):\n \"\"\"Initialize UNETTSDuration Config.\"\"\"\n\n def __init__(\n self,\n dataset = 'multispk_voiceclone',\n vocab_size = len(aishell_symbols),\n encoder_hidden_size = 384,\n encoder_num_hidden_layers = 4,\n encoder_num_attention_heads = 2,\n encoder_attention_head_size = 192,\n encoder_intermediate_size = 1024,\n encoder_intermediate_kernel_size = 3,\n encoder_hidden_act = \"mish\",\n output_attentions = True,\n output_hidden_states = True,\n hidden_dropout_prob = 0.1,\n attention_probs_dropout_prob = 0.1,\n initializer_range = 0.02,\n layer_norm_eps = 1e-5,\n num_duration_conv_layers = 2,\n duration_predictor_filters = 256,\n duration_predictor_kernel_sizes = 3,\n duration_predictor_dropout_probs = 0.1,\n **kwargs\n ):\n \"\"\"Init parameters for UNETTSDuration model.\"\"\"\n if dataset == \"multispk_voiceclone\":\n self.vocab_size = len(aishell_symbols)\n else:\n raise ValueError(\"No such dataset: {}\".format(dataset))\n self.initializer_range = initializer_range\n # self.max_position_embeddings = max_position_embeddings\n self.layer_norm_eps = layer_norm_eps\n\n # encoder params\n self.encoder_self_attention_params = SelfAttentionParams(\n hidden_size = encoder_hidden_size,\n num_hidden_layers = encoder_num_hidden_layers,\n num_attention_heads = encoder_num_attention_heads,\n attention_head_size = encoder_attention_head_size,\n hidden_act = encoder_hidden_act,\n intermediate_size = encoder_intermediate_size,\n intermediate_kernel_size = encoder_intermediate_kernel_size,\n output_attentions = output_attentions,\n output_hidden_states = output_hidden_states,\n initializer_range = initializer_range,\n hidden_dropout_prob = hidden_dropout_prob,\n attention_probs_dropout_prob = attention_probs_dropout_prob,\n layer_norm_eps = layer_norm_eps,\n )\n\n self.duration_predictor_dropout_probs = duration_predictor_dropout_probs\n self.num_duration_conv_layers = num_duration_conv_layers\n self.duration_predictor_filters = duration_predictor_filters\n self.duration_predictor_kernel_sizes = duration_predictor_kernel_sizes\n\nclass UNETTSAcousConfig(object):\n \"\"\"Initialize UNETTSAcou Config.\"\"\"\n\n def __init__(\n self,\n dataset = 'multispk_voiceclone',\n vocab_size = len(aishell_symbols),\n encoder_hidden_size = 384,\n encoder_num_hidden_layers = 4,\n encoder_num_attention_heads = 2,\n encoder_attention_head_size = 192,\n encoder_intermediate_size = 1024,\n encoder_intermediate_kernel_size = 3,\n encoder_hidden_act = \"mish\",\n output_attentions = True,\n output_hidden_states = True,\n hidden_dropout_prob = 0.1,\n attention_probs_dropout_prob = 0.1,\n initializer_range = 0.02,\n layer_norm_eps = 1e-5,\n addfeatures_num = 3,\n isaddur = True,\n num_mels = 80,\n content_latent_dim = 132,\n n_conv_blocks = 6,\n adain_filter_size = 256,\n enc_kernel_size = 5,\n dec_kernel_size = 5,\n gen_kernel_size = 5,\n decoder_hidden_size = 384,\n decoder_num_hidden_layers = 4,\n decoder_num_attention_heads = 2,\n decoder_attention_head_size = 192,\n decoder_intermediate_size = 1024,\n decoder_intermediate_kernel_size = 3,\n decoder_hidden_act = \"mish\",\n decoder_conditional_norm_type = \"Layer\",\n decoder_is_conditional = True,\n num_variant_conv_layers = 2,\n variant_predictor_dropout_probs = 0.1,\n variant_predictor_filters = 256,\n variant_predictor_kernel_sizes = 3,\n n_conv_postnet = 5,\n postnet_conv_filters = 512,\n postnet_conv_kernel_sizes = 5,\n postnet_dropout_rate = 0.1,\n **kwargs\n ):\n \"\"\"Init parameters for UNETTSAcou model.\"\"\"\n if dataset == \"multispk_voiceclone\":\n self.vocab_size = len(aishell_symbols)\n else:\n raise ValueError(\"No such dataset: {}\".format(dataset))\n self.initializer_range = initializer_range\n # self.max_position_embeddings = max_position_embeddings\n self.layer_norm_eps = layer_norm_eps\n\n self.num_mels = num_mels\n\n # encoder params\n self.encoder_self_attention_params = SelfAttentionParams(\n hidden_size = encoder_hidden_size,\n num_hidden_layers = encoder_num_hidden_layers,\n num_attention_heads = encoder_num_attention_heads,\n attention_head_size = encoder_attention_head_size,\n hidden_act = encoder_hidden_act,\n intermediate_size = encoder_intermediate_size,\n intermediate_kernel_size = encoder_intermediate_kernel_size,\n output_attentions = output_attentions,\n output_hidden_states = output_hidden_states,\n initializer_range = initializer_range,\n hidden_dropout_prob = hidden_dropout_prob,\n attention_probs_dropout_prob = attention_probs_dropout_prob,\n layer_norm_eps = layer_norm_eps,\n )\n\n self.content_latent_dim = content_latent_dim\n self.n_conv_blocks = n_conv_blocks\n self.adain_filter_size = adain_filter_size\n self.enc_kernel_size = enc_kernel_size\n self.dec_kernel_size = dec_kernel_size\n self.gen_kernel_size = gen_kernel_size\n\n self.decoder_is_conditional = decoder_is_conditional\n\n self.decoder_self_attention_conditional_params = SelfAttentionConditionalParams(\n hidden_size = decoder_hidden_size,\n num_hidden_layers = decoder_num_hidden_layers,\n num_attention_heads = decoder_num_attention_heads,\n attention_head_size = decoder_attention_head_size,\n hidden_act = decoder_hidden_act,\n intermediate_size = decoder_intermediate_size,\n intermediate_kernel_size = decoder_intermediate_kernel_size,\n output_attentions = output_attentions,\n output_hidden_states = output_hidden_states,\n initializer_range = initializer_range,\n hidden_dropout_prob = hidden_dropout_prob,\n attention_probs_dropout_prob = attention_probs_dropout_prob,\n layer_norm_eps = layer_norm_eps,\n conditional_norm_type = decoder_conditional_norm_type,\n )\n\n self.decoder_self_attention_params = SelfAttentionParams(\n hidden_size = decoder_hidden_size,\n num_hidden_layers = decoder_num_hidden_layers,\n num_attention_heads = decoder_num_attention_heads,\n attention_head_size = decoder_attention_head_size,\n hidden_act = decoder_hidden_act,\n intermediate_size = decoder_intermediate_size,\n intermediate_kernel_size = decoder_intermediate_kernel_size,\n output_attentions = output_attentions,\n output_hidden_states = output_hidden_states,\n initializer_range = initializer_range,\n hidden_dropout_prob = hidden_dropout_prob,\n attention_probs_dropout_prob = attention_probs_dropout_prob,\n layer_norm_eps = layer_norm_eps,\n )\n\n self.num_variant_conv_layers = num_variant_conv_layers\n self.variant_predictor_dropout_probs = variant_predictor_dropout_probs\n self.variant_predictor_filters = variant_predictor_filters\n self.variant_predictor_kernel_sizes = variant_predictor_kernel_sizes\n\n # postnet\n self.n_conv_postnet = n_conv_postnet\n self.postnet_conv_filters = postnet_conv_filters\n self.postnet_conv_kernel_sizes = postnet_conv_kernel_sizes\n self.postnet_dropout_rate = postnet_dropout_rate\n\n self.addfeatures_num = addfeatures_num\n self.isaddur = isaddur" }, { "path": "TensorFlowTTS/tensorflow_tts/datasets/__init__.py", "content": "from tensorflow_tts.datasets.abstract_dataset import AbstractDataset\nfrom tensorflow_tts.datasets.audio_dataset import AudioDataset\nfrom tensorflow_tts.datasets.mel_dataset import MelDataset\n" }, { "path": "TensorFlowTTS/tensorflow_tts/datasets/abstract_dataset.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Abstract Dataset modules.\"\"\"\n\nimport abc\n\nimport tensorflow as tf\n\n\nclass AbstractDataset(metaclass=abc.ABCMeta):\n \"\"\"Abstract Dataset module for Dataset Loader.\"\"\"\n\n @abc.abstractmethod\n def get_args(self):\n \"\"\"Return args for generator function.\"\"\"\n pass\n\n @abc.abstractmethod\n def generator(self):\n \"\"\"Generator function, should have args from get_args function.\"\"\"\n pass\n\n @abc.abstractmethod\n def get_output_dtypes(self):\n \"\"\"Return output dtypes for each element from generator.\"\"\"\n pass\n\n @abc.abstractmethod\n def get_len_dataset(self):\n \"\"\"Return number of samples on dataset.\"\"\"\n pass\n\n def create(\n self,\n allow_cache=False,\n batch_size=1,\n is_shuffle=False,\n map_fn=None,\n reshuffle_each_iteration=True,\n ):\n \"\"\"Create tf.dataset function.\"\"\"\n output_types = self.get_output_dtypes()\n datasets = tf.data.Dataset.from_generator(\n self.generator, output_types=output_types, args=(self.get_args())\n )\n\n if allow_cache:\n datasets = datasets.cache()\n\n if is_shuffle:\n datasets = datasets.shuffle(\n self.get_len_dataset(),\n reshuffle_each_iteration=reshuffle_each_iteration,\n )\n\n if batch_size > 1 and map_fn is None:\n raise ValueError(\"map function must define when batch_size > 1.\")\n\n if map_fn is not None:\n datasets = datasets.map(map_fn, tf.data.experimental.AUTOTUNE)\n\n datasets = datasets.batch(batch_size)\n datasets = datasets.prefetch(tf.data.experimental.AUTOTUNE)\n\n return datasets\n" }, { "path": "TensorFlowTTS/tensorflow_tts/datasets/audio_dataset.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Audio modules.\"\"\"\n\nimport logging\nimport os\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_tts.datasets.abstract_dataset import AbstractDataset\nfrom tensorflow_tts.utils import find_files\n\n\nclass AudioDataset(AbstractDataset):\n \"\"\"Tensorflow compatible audio dataset.\"\"\"\n\n def __init__(\n self,\n root_dir,\n audio_query=\"*-wave.npy\",\n audio_load_fn=np.load,\n audio_length_threshold=0,\n ):\n \"\"\"Initialize dataset.\n\n Args:\n root_dir (str): Root directory including dumped files.\n audio_query (str): Query to find feature files in root_dir.\n audio_load_fn (func): Function to load feature file.\n audio_length_threshold (int): Threshold to remove short feature files.\n return_utt_id (bool): Whether to return the utterance id with arrays.\n\n \"\"\"\n # find all of mel files.\n audio_files = sorted(find_files(root_dir, audio_query))\n audio_lengths = [audio_load_fn(f).shape[0] for f in audio_files]\n\n # assert the number of files\n assert len(audio_files) != 0, f\"Not found any mel files in ${root_dir}.\"\n\n if \".npy\" in audio_query:\n suffix = audio_query[1:]\n utt_ids = [os.path.basename(f).replace(suffix, \"\") for f in audio_files]\n\n # set global params\n self.utt_ids = utt_ids\n self.audio_files = audio_files\n self.audio_lengths = audio_lengths\n self.audio_load_fn = audio_load_fn\n self.audio_length_threshold = audio_length_threshold\n\n def get_args(self):\n return [self.utt_ids]\n\n def generator(self, utt_ids):\n for i, utt_id in enumerate(utt_ids):\n audio_file = self.audio_files[i]\n audio = self.audio_load_fn(audio_file)\n audio_length = self.audio_lengths[i]\n\n items = {\"utt_ids\": utt_id, \"audios\": audio, \"audio_lengths\": audio_length}\n\n yield items\n\n def get_output_dtypes(self):\n output_types = {\n \"utt_ids\": tf.string,\n \"audios\": tf.float32,\n \"audio_lengths\": tf.float32,\n }\n return output_types\n\n def create(\n self,\n allow_cache=False,\n batch_size=1,\n is_shuffle=False,\n map_fn=None,\n reshuffle_each_iteration=True,\n ):\n \"\"\"Create tf.dataset function.\"\"\"\n output_types = self.get_output_dtypes()\n datasets = tf.data.Dataset.from_generator(\n self.generator, output_types=output_types, args=(self.get_args())\n )\n\n datasets = datasets.filter(\n lambda x: x[\"audio_lengths\"] > self.audio_length_threshold\n )\n\n if allow_cache:\n datasets = datasets.cache()\n\n if is_shuffle:\n datasets = datasets.shuffle(\n self.get_len_dataset(),\n reshuffle_each_iteration=reshuffle_each_iteration,\n )\n\n # define padded shapes\n padded_shapes = {\n \"utt_ids\": [],\n \"audios\": [None],\n \"audio_lengths\": [],\n }\n\n datasets = datasets.padded_batch(batch_size, padded_shapes=padded_shapes)\n datasets = datasets.prefetch(tf.data.experimental.AUTOTUNE)\n return datasets\n\n def get_len_dataset(self):\n return len(self.utt_ids)\n\n def __name__(self):\n return \"AudioDataset\"\n" }, { "path": "TensorFlowTTS/tensorflow_tts/datasets/mel_dataset.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Dataset modules.\"\"\"\n\nimport logging\nimport os\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_tts.datasets.abstract_dataset import AbstractDataset\nfrom tensorflow_tts.utils import find_files\n\n\nclass MelDataset(AbstractDataset):\n \"\"\"Tensorflow compatible mel dataset.\"\"\"\n\n def __init__(\n self,\n root_dir,\n mel_query=\"*-raw-feats.h5\",\n mel_load_fn=np.load,\n mel_length_threshold=0,\n ):\n \"\"\"Initialize dataset.\n\n Args:\n root_dir (str): Root directory including dumped files.\n mel_query (str): Query to find feature files in root_dir.\n mel_load_fn (func): Function to load feature file.\n mel_length_threshold (int): Threshold to remove short feature files.\n\n \"\"\"\n # find all of mel files.\n mel_files = sorted(find_files(root_dir, mel_query))\n mel_lengths = [mel_load_fn(f).shape[0] for f in mel_files]\n\n # assert the number of files\n assert len(mel_files) != 0, f\"Not found any mel files in ${root_dir}.\"\n\n if \".npy\" in mel_query:\n suffix = mel_query[1:]\n utt_ids = [os.path.basename(f).replace(suffix, \"\") for f in mel_files]\n\n # set global params\n self.utt_ids = utt_ids\n self.mel_files = mel_files\n self.mel_lengths = mel_lengths\n self.mel_load_fn = mel_load_fn\n self.mel_length_threshold = mel_length_threshold\n\n def get_args(self):\n return [self.utt_ids]\n\n def generator(self, utt_ids):\n for i, utt_id in enumerate(utt_ids):\n mel_file = self.mel_files[i]\n mel = self.mel_load_fn(mel_file)\n mel_length = self.mel_lengths[i]\n\n items = {\"utt_ids\": utt_id, \"mels\": mel, \"mel_lengths\": mel_length}\n\n yield items\n\n def get_output_dtypes(self):\n output_types = {\n \"utt_ids\": tf.string,\n \"mels\": tf.float32,\n \"mel_lengths\": tf.int32,\n }\n return output_types\n\n def create(\n self,\n allow_cache=False,\n batch_size=1,\n is_shuffle=False,\n map_fn=None,\n reshuffle_each_iteration=True,\n ):\n \"\"\"Create tf.dataset function.\"\"\"\n output_types = self.get_output_dtypes()\n datasets = tf.data.Dataset.from_generator(\n self.generator, output_types=output_types, args=(self.get_args())\n )\n\n datasets = datasets.filter(\n lambda x: x[\"mel_lengths\"] > self.mel_length_threshold\n )\n\n if allow_cache:\n datasets = datasets.cache()\n\n if is_shuffle:\n datasets = datasets.shuffle(\n self.get_len_dataset(),\n reshuffle_each_iteration=reshuffle_each_iteration,\n )\n\n # define padded shapes\n padded_shapes = {\n \"utt_ids\": [],\n \"mels\": [None, 80],\n \"mel_lengths\": [],\n }\n\n datasets = datasets.padded_batch(batch_size, padded_shapes=padded_shapes)\n datasets = datasets.prefetch(tf.data.experimental.AUTOTUNE)\n return datasets\n\n def get_len_dataset(self):\n return len(self.utt_ids)\n\n def __name__(self):\n return \"MelDataset\"\n" }, { "path": "TensorFlowTTS/tensorflow_tts/inference/__init__.py", "content": "from tensorflow_tts.inference.auto_model import TFAutoModel\nfrom tensorflow_tts.inference.auto_config import AutoConfig\nfrom tensorflow_tts.inference.auto_processor import AutoProcessor\n" }, { "path": "TensorFlowTTS/tensorflow_tts/inference/auto_config.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 The HuggingFace Inc. team and Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tensorflow Auto Config modules.\"\"\"\n\nimport logging\nimport yaml\nfrom collections import OrderedDict\n\nfrom tensorflow_tts.configs import (\n MelGANGeneratorConfig,\n MultiBandMelGANGeneratorConfig,\n UNETTSDurationConfig,\n UNETTSAcousConfig,\n)\n\nCONFIG_MAPPING = OrderedDict(\n [\n (\"multiband_melgan_generator\", MultiBandMelGANGeneratorConfig),\n (\"melgan_generator\", MelGANGeneratorConfig),\n (\"unetts_duration\", UNETTSDurationConfig),\n (\"unetts_acous\", UNETTSAcousConfig),\n ]\n)\n\n\nclass AutoConfig:\n def __init__(self):\n raise EnvironmentError(\n \"AutoConfig is designed to be instantiated \"\n \"using the `AutoConfig.from_pretrained(pretrained_path)` method.\"\n )\n\n @classmethod\n def from_pretrained(cls, pretrained_path, **kwargs):\n with open(pretrained_path) as f:\n config = yaml.load(f, Loader=yaml.Loader)\n\n try:\n model_type = config[\"model_type\"]\n config_class = CONFIG_MAPPING[model_type]\n config_class = config_class(**config[model_type + \"_params\"], **kwargs)\n return config_class\n except Exception:\n raise ValueError(\n \"Unrecognized config in {}. \"\n \"Should have a `model_type` key in its config.yaml, or contain one of the following strings \"\n \"in its name: {}\".format(\n pretrained_path, \", \".join(CONFIG_MAPPING.keys())\n )\n )\n" }, { "path": "TensorFlowTTS/tensorflow_tts/inference/auto_model.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 The HuggingFace Inc. team and Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tensorflow Auto Model modules.\"\"\"\n\nimport logging\nimport warnings\nfrom collections import OrderedDict\n\nfrom tensorflow_tts.configs import (\n MelGANGeneratorConfig,\n MultiBandMelGANGeneratorConfig,\n UNETTSDurationConfig,\n UNETTSAcousConfig,\n)\n\nfrom tensorflow_tts.models import (\n TFMelGANGenerator,\n TFMBMelGANGenerator,\n TFUNETTSDuration,\n TFUNETTSAcous,\n)\n\n\nTF_MODEL_MAPPING = OrderedDict(\n [\n (MultiBandMelGANGeneratorConfig, TFMBMelGANGenerator),\n (MelGANGeneratorConfig, TFMelGANGenerator),\n (UNETTSDurationConfig, TFUNETTSDuration),\n (UNETTSAcousConfig, TFUNETTSAcous),\n ]\n)\n\n\nclass TFAutoModel(object):\n \"\"\"General model class for inferencing.\"\"\"\n\n def __init__(self):\n raise EnvironmentError(\"Cannot be instantiated using `__init__()`\")\n\n @classmethod\n def from_pretrained(cls, config, pretrained_path=None, **kwargs):\n is_build = kwargs.pop(\"is_build\", True)\n for config_class, model_class in TF_MODEL_MAPPING.items():\n if isinstance(config, config_class) and str(config_class.__name__) in str(\n config\n ):\n model = model_class(config=config, **kwargs)\n if is_build:\n model._build()\n if pretrained_path is not None and \".h5\" in pretrained_path:\n model.load_weights(pretrained_path)\n return model\n raise ValueError(\n \"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\\n\"\n \"Model type should be one of {}.\".format(\n config.__class__,\n cls.__name__,\n \", \".join(c.__name__ for c in TF_MODEL_MAPPING.keys()),\n )\n )\n" }, { "path": "TensorFlowTTS/tensorflow_tts/inference/auto_processor.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 The TensorFlowTTS Team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tensorflow Auto Processor modules.\"\"\"\n\nimport logging\nimport json\nfrom collections import OrderedDict\n\nfrom tensorflow_tts.processor import (\n MultiSPKVoiceCloneProcessor,\n)\n\nCONFIG_MAPPING = OrderedDict(\n [\n (\"MultiSPKVoiceCloneProcessor\", MultiSPKVoiceCloneProcessor),\n ]\n)\n\n\nclass AutoProcessor:\n def __init__(self):\n raise EnvironmentError(\n \"AutoProcessor is designed to be instantiated \"\n \"using the `AutoProcessor.from_pretrained(pretrained_path)` method.\"\n )\n\n @classmethod\n def from_pretrained(cls, pretrained_path, **kwargs):\n with open(pretrained_path, \"r\") as f:\n config = json.load(f)\n\n try:\n processor_name = config[\"processor_name\"]\n processor_class = CONFIG_MAPPING[processor_name]\n processor_class = processor_class(\n data_dir=None, loaded_mapper_path=pretrained_path\n )\n return processor_class\n except Exception:\n raise ValueError(\n \"Unrecognized processor in {}. \"\n \"Should have a `processor_name` key in its config.json, or contain one of the following strings \"\n \"in its name: {}\".format(\n pretrained_path, \", \".join(CONFIG_MAPPING.keys())\n )\n )\n" }, { "path": "TensorFlowTTS/tensorflow_tts/losses/__init__.py", "content": "from tensorflow_tts.losses.spectrogram import TFMelSpectrogram\nfrom tensorflow_tts.losses.stft import TFMultiResolutionSTFT\n" }, { "path": "TensorFlowTTS/tensorflow_tts/losses/spectrogram.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Spectrogram-based loss modules.\"\"\"\n\nimport tensorflow as tf\n\n\nclass TFMelSpectrogram(tf.keras.layers.Layer):\n \"\"\"Mel Spectrogram loss.\"\"\"\n\n def __init__(\n self,\n n_mels=80,\n f_min=80.0,\n f_max=7600,\n frame_length=1024,\n frame_step=256,\n fft_length=1024,\n sample_rate=16000,\n **kwargs\n ):\n \"\"\"Initialize.\"\"\"\n super().__init__(**kwargs)\n self.frame_length = frame_length\n self.frame_step = frame_step\n self.fft_length = fft_length\n\n self.linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(\n n_mels, fft_length // 2 + 1, sample_rate, f_min, f_max\n )\n\n def _calculate_log_mels_spectrogram(self, signals):\n \"\"\"Calculate forward propagation.\n Args:\n signals (Tensor): signal (B, T).\n Returns:\n Tensor: Mel spectrogram (B, T', 80)\n \"\"\"\n stfts = tf.signal.stft(\n signals,\n frame_length=self.frame_length,\n frame_step=self.frame_step,\n fft_length=self.fft_length,\n )\n linear_spectrograms = tf.abs(stfts)\n mel_spectrograms = tf.tensordot(\n linear_spectrograms, self.linear_to_mel_weight_matrix, 1\n )\n mel_spectrograms.set_shape(\n linear_spectrograms.shape[:-1].concatenate(\n self.linear_to_mel_weight_matrix.shape[-1:]\n )\n )\n log_mel_spectrograms = tf.math.log(mel_spectrograms + 1e-6) # prevent nan.\n return log_mel_spectrograms\n\n def call(self, y, x):\n \"\"\"Calculate forward propagation.\n Args:\n y (Tensor): Groundtruth signal (B, T).\n x (Tensor): Predicted signal (B, T).\n Returns:\n Tensor: Mean absolute Error Spectrogram Loss.\n \"\"\"\n y_mels = self._calculate_log_mels_spectrogram(y)\n x_mels = self._calculate_log_mels_spectrogram(x)\n return tf.reduce_mean(\n tf.abs(y_mels - x_mels), axis=list(range(1, len(x_mels.shape)))\n )\n" }, { "path": "TensorFlowTTS/tensorflow_tts/losses/stft.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"STFT-based loss modules.\"\"\"\n\nimport tensorflow as tf\n\n\nclass TFSpectralConvergence(tf.keras.layers.Layer):\n \"\"\"Spectral convergence loss.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize.\"\"\"\n super().__init__()\n\n def call(self, y_mag, x_mag):\n \"\"\"Calculate forward propagation.\n Args:\n y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).\n x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).\n Returns:\n Tensor: Spectral convergence loss value.\n \"\"\"\n return tf.norm(y_mag - x_mag, ord=\"fro\", axis=(-2, -1)) / tf.norm(\n y_mag, ord=\"fro\", axis=(-2, -1)\n )\n\n\nclass TFLogSTFTMagnitude(tf.keras.layers.Layer):\n \"\"\"Log STFT magnitude loss module.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize.\"\"\"\n super().__init__()\n\n def call(self, y_mag, x_mag):\n \"\"\"Calculate forward propagation.\n Args:\n y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).\n x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).\n Returns:\n Tensor: Spectral convergence loss value.\n \"\"\"\n return tf.abs(tf.math.log(y_mag) - tf.math.log(x_mag))\n\n\nclass TFSTFT(tf.keras.layers.Layer):\n \"\"\"STFT loss module.\"\"\"\n\n def __init__(self, frame_length=600, frame_step=120, fft_length=1024):\n \"\"\"Initialize.\"\"\"\n super().__init__()\n self.frame_length = frame_length\n self.frame_step = frame_step\n self.fft_length = fft_length\n self.spectral_convergenge_loss = TFSpectralConvergence()\n self.log_stft_magnitude_loss = TFLogSTFTMagnitude()\n\n def call(self, y, x):\n \"\"\"Calculate forward propagation.\n Args:\n y (Tensor): Groundtruth signal (B, T).\n x (Tensor): Predicted signal (B, T).\n Returns:\n Tensor: Spectral convergence loss value (pre-reduce).\n Tensor: Log STFT magnitude loss value (pre-reduce).\n \"\"\"\n x_mag = tf.abs(\n tf.signal.stft(\n signals=x,\n frame_length=self.frame_length,\n frame_step=self.frame_step,\n fft_length=self.fft_length,\n )\n )\n y_mag = tf.abs(\n tf.signal.stft(\n signals=y,\n frame_length=self.frame_length,\n frame_step=self.frame_step,\n fft_length=self.fft_length,\n )\n )\n\n # add small number to prevent nan value.\n # compatible with pytorch version.\n x_mag = tf.clip_by_value(tf.math.sqrt(x_mag ** 2 + 1e-7), 1e-7, 1e3)\n y_mag = tf.clip_by_value(tf.math.sqrt(y_mag ** 2 + 1e-7), 1e-7, 1e3)\n\n sc_loss = self.spectral_convergenge_loss(y_mag, x_mag)\n mag_loss = self.log_stft_magnitude_loss(y_mag, x_mag)\n\n return sc_loss, mag_loss\n\n\nclass TFMultiResolutionSTFT(tf.keras.layers.Layer):\n \"\"\"Multi resolution STFT loss module.\"\"\"\n\n def __init__(\n self,\n fft_lengths=[1024, 2048, 512],\n frame_lengths=[600, 1200, 240],\n frame_steps=[120, 240, 50],\n ):\n \"\"\"Initialize Multi resolution STFT loss module.\n Args:\n frame_lengths (list): List of FFT sizes.\n frame_steps (list): List of hop sizes.\n fft_lengths (list): List of window lengths.\n \"\"\"\n super().__init__()\n assert len(frame_lengths) == len(frame_steps) == len(fft_lengths)\n self.stft_losses = []\n for frame_length, frame_step, fft_length in zip(\n frame_lengths, frame_steps, fft_lengths\n ):\n self.stft_losses.append(TFSTFT(frame_length, frame_step, fft_length))\n\n def call(self, y, x):\n \"\"\"Calculate forward propagation.\n Args:\n y (Tensor): Groundtruth signal (B, T).\n x (Tensor): Predicted signal (B, T).\n Returns:\n Tensor: Multi resolution spectral convergence loss value.\n Tensor: Multi resolution log STFT magnitude loss value.\n \"\"\"\n sc_loss = 0.0\n mag_loss = 0.0\n for f in self.stft_losses:\n sc_l, mag_l = f(y, x)\n sc_loss += tf.reduce_mean(sc_l, axis=list(range(1, len(sc_l.shape))))\n mag_loss += tf.reduce_mean(mag_l, axis=list(range(1, len(mag_l.shape))))\n\n sc_loss /= len(self.stft_losses)\n mag_loss /= len(self.stft_losses)\n\n return sc_loss, mag_loss\n" }, { "path": "TensorFlowTTS/tensorflow_tts/models/__init__.py", "content": "from tensorflow_tts.models.melgan import (\n TFMelGANDiscriminator,\n TFMelGANGenerator,\n TFMelGANMultiScaleDiscriminator,\n)\nfrom tensorflow_tts.models.mb_melgan import TFPQMF\nfrom tensorflow_tts.models.mb_melgan import TFMBMelGANGenerator\nfrom tensorflow_tts.models.unetts import TFUNETTSDuration, TFUNETTSAcous, TFUNETTSContentPretrain" }, { "path": "TensorFlowTTS/tensorflow_tts/models/mb_melgan.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 The Multi-band MelGAN Authors , Minh Nguyen (@dathudeptrai) and Tomoki Hayashi (@kan-bayashi)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n#\n# Compatible with https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/parallel_wavegan/layers/pqmf.py.\n\"\"\"Multi-band MelGAN Modules.\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\nfrom scipy.signal import kaiser\n\nfrom tensorflow_tts.models import TFMelGANGenerator\n\n\ndef design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):\n \"\"\"Design prototype filter for PQMF.\n This method is based on `A Kaiser window approach for the design of prototype\n filters of cosine modulated filterbanks`_.\n Args:\n taps (int): The number of filter taps.\n cutoff_ratio (float): Cut-off frequency ratio.\n beta (float): Beta coefficient for kaiser window.\n Returns:\n ndarray: Impluse response of prototype filter (taps + 1,).\n .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:\n https://ieeexplore.ieee.org/abstract/document/681427\n \"\"\"\n # check the arguments are valid\n assert taps % 2 == 0, \"The number of taps mush be even number.\"\n assert 0.0 < cutoff_ratio < 1.0, \"Cutoff ratio must be > 0.0 and < 1.0.\"\n\n # make initial filter\n omega_c = np.pi * cutoff_ratio\n with np.errstate(invalid=\"ignore\"):\n h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) / (\n np.pi * (np.arange(taps + 1) - 0.5 * taps)\n )\n # fix nan due to indeterminate form\n h_i[taps // 2] = np.cos(0) * cutoff_ratio\n\n # apply kaiser window\n w = kaiser(taps + 1, beta)\n h = h_i * w\n\n return h\n\n\nclass TFPQMF(tf.keras.layers.Layer):\n \"\"\"PQMF module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Initilize PQMF module.\n Args:\n config (class): MultiBandMelGANGeneratorConfig\n \"\"\"\n super().__init__(**kwargs)\n subbands = config.subbands\n taps = config.taps\n cutoff_ratio = config.cutoff_ratio\n beta = config.beta\n\n # define filter coefficient\n h_proto = design_prototype_filter(taps, cutoff_ratio, beta)\n h_analysis = np.zeros((subbands, len(h_proto)))\n h_synthesis = np.zeros((subbands, len(h_proto)))\n for k in range(subbands):\n h_analysis[k] = (\n 2\n * h_proto\n * np.cos(\n (2 * k + 1)\n * (np.pi / (2 * subbands))\n * (np.arange(taps + 1) - (taps / 2))\n + (-1) ** k * np.pi / 4\n )\n )\n h_synthesis[k] = (\n 2\n * h_proto\n * np.cos(\n (2 * k + 1)\n * (np.pi / (2 * subbands))\n * (np.arange(taps + 1) - (taps / 2))\n - (-1) ** k * np.pi / 4\n )\n )\n\n # [subbands, 1, taps + 1] == [filter_width, in_channels, out_channels]\n analysis_filter = np.expand_dims(h_analysis, 1)\n analysis_filter = np.transpose(analysis_filter, (2, 1, 0))\n\n synthesis_filter = np.expand_dims(h_synthesis, 0)\n synthesis_filter = np.transpose(synthesis_filter, (2, 1, 0))\n\n # filter for downsampling & upsampling\n updown_filter = np.zeros((subbands, subbands, subbands), dtype=np.float32)\n for k in range(subbands):\n updown_filter[0, k, k] = 1.0\n\n self.subbands = subbands\n self.taps = taps\n self.analysis_filter = analysis_filter.astype(np.float32)\n self.synthesis_filter = synthesis_filter.astype(np.float32)\n self.updown_filter = updown_filter.astype(np.float32)\n\n @tf.function(\n experimental_relax_shapes=True,\n input_signature=[tf.TensorSpec(shape=[None, None, 1], dtype=tf.float32)],\n )\n def analysis(self, x):\n \"\"\"Analysis with PQMF.\n Args:\n x (Tensor): Input tensor (B, T, 1).\n Returns:\n Tensor: Output tensor (B, T // subbands, subbands).\n \"\"\"\n x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]])\n x = tf.nn.conv1d(x, self.analysis_filter, stride=1, padding=\"VALID\")\n x = tf.nn.conv1d(x, self.updown_filter, stride=self.subbands, padding=\"VALID\")\n return x\n\n @tf.function(\n experimental_relax_shapes=True,\n input_signature=[tf.TensorSpec(shape=[None, None, None], dtype=tf.float32)],\n )\n def synthesis(self, x):\n \"\"\"Synthesis with PQMF.\n Args:\n x (Tensor): Input tensor (B, T // subbands, subbands).\n Returns:\n Tensor: Output tensor (B, T, 1).\n \"\"\"\n x = tf.nn.conv1d_transpose(\n x,\n self.updown_filter * self.subbands,\n strides=self.subbands,\n output_shape=(\n tf.shape(x)[0],\n tf.shape(x)[1] * self.subbands,\n self.subbands,\n ),\n )\n x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]])\n return tf.nn.conv1d(x, self.synthesis_filter, stride=1, padding=\"VALID\")\n\n\nclass TFMBMelGANGenerator(TFMelGANGenerator):\n \"\"\"Tensorflow MBMelGAN generator module.\"\"\"\n\n def __init__(self, config, **kwargs):\n super().__init__(config, **kwargs)\n self.pqmf = TFPQMF(config=config, name=\"pqmf\")\n\n def call(self, mels, **kwargs):\n \"\"\"Calculate forward propagation.\n Args:\n c (Tensor): Input tensor (B, T, channels)\n Returns:\n Tensor: Output tensor (B, T ** prod(upsample_scales), out_channels)\n \"\"\"\n return self.inference(mels)\n\n @tf.function(\n input_signature=[\n tf.TensorSpec(shape=[None, None, 80], dtype=tf.float32, name=\"mels\")\n ]\n )\n def inference(self, mels):\n mb_audios = self.melgan(mels)\n return self.pqmf.synthesis(mb_audios)\n\n @tf.function(\n input_signature=[\n tf.TensorSpec(shape=[1, None, 80], dtype=tf.float32, name=\"mels\")\n ]\n )\n def inference_tflite(self, mels):\n mb_audios = self.melgan(mels)\n return self.pqmf.synthesis(mb_audios)\n" }, { "path": "TensorFlowTTS/tensorflow_tts/models/melgan.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 The MelGAN Authors and Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"MelGAN Modules.\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_tts.utils import GroupConv1D, WeightNormalization\n\n\ndef get_initializer(initializer_seed=42):\n \"\"\"Creates a `tf.initializers.glorot_normal` with the given seed.\n Args:\n initializer_seed: int, initializer seed.\n Returns:\n GlorotNormal initializer with seed = `initializer_seed`.\n \"\"\"\n return tf.keras.initializers.GlorotNormal(seed=initializer_seed)\n\n\nclass TFReflectionPad1d(tf.keras.layers.Layer):\n \"\"\"Tensorflow ReflectionPad1d module.\"\"\"\n\n def __init__(self, padding_size, padding_type=\"REFLECT\", **kwargs):\n \"\"\"Initialize TFReflectionPad1d module.\n\n Args:\n padding_size (int)\n padding_type (str) (\"CONSTANT\", \"REFLECT\", or \"SYMMETRIC\". Default is \"REFLECT\")\n \"\"\"\n super().__init__(**kwargs)\n self.padding_size = padding_size\n self.padding_type = padding_type\n\n def call(self, x):\n \"\"\"Calculate forward propagation.\n Args:\n x (Tensor): Input tensor (B, T, C).\n Returns:\n Tensor: Padded tensor (B, T + 2 * padding_size, C).\n \"\"\"\n return tf.pad(\n x,\n [[0, 0], [self.padding_size, self.padding_size], [0, 0]],\n self.padding_type,\n )\n\n\nclass TFConvTranspose1d(tf.keras.layers.Layer):\n \"\"\"Tensorflow ConvTranspose1d module.\"\"\"\n\n def __init__(\n self,\n filters,\n kernel_size,\n strides,\n padding,\n is_weight_norm,\n initializer_seed,\n **kwargs\n ):\n \"\"\"Initialize TFConvTranspose1d( module.\n Args:\n filters (int): Number of filters.\n kernel_size (int): kernel size.\n strides (int): Stride width.\n padding (str): Padding type (\"same\" or \"valid\").\n \"\"\"\n super().__init__(**kwargs)\n self.conv1d_transpose = tf.keras.layers.Conv2DTranspose(\n filters=filters,\n kernel_size=(kernel_size, 1),\n strides=(strides, 1),\n padding=\"same\",\n kernel_initializer=get_initializer(initializer_seed),\n )\n if is_weight_norm:\n self.conv1d_transpose = WeightNormalization(self.conv1d_transpose)\n\n def call(self, x):\n \"\"\"Calculate forward propagation.\n Args:\n x (Tensor): Input tensor (B, T, C).\n Returns:\n Tensor: Output tensor (B, T', C').\n \"\"\"\n x = tf.expand_dims(x, 2)\n x = self.conv1d_transpose(x)\n x = tf.squeeze(x, 2)\n return x\n\n\nclass TFResidualStack(tf.keras.layers.Layer):\n \"\"\"Tensorflow ResidualStack module.\"\"\"\n\n def __init__(\n self,\n kernel_size,\n filters,\n dilation_rate,\n use_bias,\n nonlinear_activation,\n nonlinear_activation_params,\n is_weight_norm,\n initializer_seed,\n **kwargs\n ):\n \"\"\"Initialize TFResidualStack module.\n Args:\n kernel_size (int): Kernel size.\n filters (int): Number of filters.\n dilation_rate (int): Dilation rate.\n use_bias (bool): Whether to add bias parameter in convolution layers.\n nonlinear_activation (str): Activation function module name.\n nonlinear_activation_params (dict): Hyperparameters for activation function.\n \"\"\"\n super().__init__(**kwargs)\n self.blocks = [\n getattr(tf.keras.layers, nonlinear_activation)(\n **nonlinear_activation_params\n ),\n TFReflectionPad1d((kernel_size - 1) // 2 * dilation_rate),\n tf.keras.layers.Conv1D(\n filters=filters,\n kernel_size=kernel_size,\n dilation_rate=dilation_rate,\n use_bias=use_bias,\n kernel_initializer=get_initializer(initializer_seed),\n ),\n getattr(tf.keras.layers, nonlinear_activation)(\n **nonlinear_activation_params\n ),\n tf.keras.layers.Conv1D(\n filters=filters,\n kernel_size=1,\n use_bias=use_bias,\n kernel_initializer=get_initializer(initializer_seed),\n ),\n ]\n self.shortcut = tf.keras.layers.Conv1D(\n filters=filters,\n kernel_size=1,\n use_bias=use_bias,\n kernel_initializer=get_initializer(initializer_seed),\n name=\"shortcut\",\n )\n\n # apply weightnorm\n if is_weight_norm:\n self._apply_weightnorm(self.blocks)\n self.shortcut = WeightNormalization(self.shortcut)\n\n def call(self, x):\n \"\"\"Calculate forward propagation.\n Args:\n x (Tensor): Input tensor (B, T, C).\n Returns:\n Tensor: Output tensor (B, T, C).\n \"\"\"\n _x = tf.identity(x)\n for layer in self.blocks:\n _x = layer(_x)\n shortcut = self.shortcut(x)\n return shortcut + _x\n\n def _apply_weightnorm(self, list_layers):\n \"\"\"Try apply weightnorm for all layer in list_layers.\"\"\"\n for i in range(len(list_layers)):\n try:\n layer_name = list_layers[i].name.lower()\n if \"conv1d\" in layer_name or \"dense\" in layer_name:\n list_layers[i] = WeightNormalization(list_layers[i])\n except Exception:\n pass\n\n\nclass TFMelGANGenerator(tf.keras.Model):\n \"\"\"Tensorflow MelGAN generator module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Initialize TFMelGANGenerator module.\n Args:\n config: config object of Melgan generator.\n \"\"\"\n super().__init__(**kwargs)\n\n # check hyper parameter is valid or not\n assert config.filters >= np.prod(config.upsample_scales)\n assert config.filters % (2 ** len(config.upsample_scales)) == 0\n\n # add initial layer\n layers = []\n layers += [\n TFReflectionPad1d(\n (config.kernel_size - 1) // 2,\n padding_type=config.padding_type,\n name=\"first_reflect_padding\",\n ),\n tf.keras.layers.Conv1D(\n filters=config.filters,\n kernel_size=config.kernel_size,\n use_bias=config.use_bias,\n kernel_initializer=get_initializer(config.initializer_seed),\n ),\n ]\n\n for i, upsample_scale in enumerate(config.upsample_scales):\n # add upsampling layer\n layers += [\n getattr(tf.keras.layers, config.nonlinear_activation)(\n **config.nonlinear_activation_params\n ),\n TFConvTranspose1d(\n filters=config.filters // (2 ** (i + 1)),\n kernel_size=upsample_scale * 2,\n strides=upsample_scale,\n padding=\"same\",\n is_weight_norm=config.is_weight_norm,\n initializer_seed=config.initializer_seed,\n name=\"conv_transpose_._{}\".format(i),\n ),\n ]\n\n # ad residual stack layer\n for j in range(config.stacks):\n layers += [\n TFResidualStack(\n kernel_size=config.stack_kernel_size,\n filters=config.filters // (2 ** (i + 1)),\n dilation_rate=config.stack_kernel_size ** j,\n use_bias=config.use_bias,\n nonlinear_activation=config.nonlinear_activation,\n nonlinear_activation_params=config.nonlinear_activation_params,\n is_weight_norm=config.is_weight_norm,\n initializer_seed=config.initializer_seed,\n name=\"residual_stack_._{}._._{}\".format(i, j),\n )\n ]\n # add final layer\n layers += [\n getattr(tf.keras.layers, config.nonlinear_activation)(\n **config.nonlinear_activation_params\n ),\n TFReflectionPad1d(\n (config.kernel_size - 1) // 2,\n padding_type=config.padding_type,\n name=\"last_reflect_padding\",\n ),\n tf.keras.layers.Conv1D(\n filters=config.out_channels,\n kernel_size=config.kernel_size,\n use_bias=config.use_bias,\n kernel_initializer=get_initializer(config.initializer_seed),\n ),\n ]\n if config.use_final_nolinear_activation:\n layers += [tf.keras.layers.Activation(\"tanh\")]\n\n if config.is_weight_norm is True:\n self._apply_weightnorm(layers)\n\n self.melgan = tf.keras.models.Sequential(layers)\n\n def call(self, mels, **kwargs):\n \"\"\"Calculate forward propagation.\n Args:\n c (Tensor): Input tensor (B, T, channels)\n Returns:\n Tensor: Output tensor (B, T ** prod(upsample_scales), out_channels)\n \"\"\"\n return self.inference(mels)\n\n @tf.function(\n input_signature=[\n tf.TensorSpec(shape=[None, None, 80], dtype=tf.float32, name=\"mels\")\n ]\n )\n def inference(self, mels):\n return self.melgan(mels)\n\n @tf.function(\n input_signature=[\n tf.TensorSpec(shape=[1, None, 80], dtype=tf.float32, name=\"mels\")\n ]\n )\n def inference_tflite(self, mels):\n return self.melgan(mels)\n\n def _apply_weightnorm(self, list_layers):\n \"\"\"Try apply weightnorm for all layer in list_layers.\"\"\"\n for i in range(len(list_layers)):\n try:\n layer_name = list_layers[i].name.lower()\n if \"conv1d\" in layer_name or \"dense\" in layer_name:\n list_layers[i] = WeightNormalization(list_layers[i])\n except Exception:\n pass\n\n def _build(self):\n \"\"\"Build model by passing fake input.\"\"\"\n fake_mels = tf.random.uniform(shape=[1, 100, 80], dtype=tf.float32)\n self(fake_mels)\n\n\nclass TFMelGANDiscriminator(tf.keras.layers.Layer):\n \"\"\"Tensorflow MelGAN generator module.\"\"\"\n\n def __init__(\n self,\n out_channels=1,\n kernel_sizes=[5, 3],\n filters=16,\n max_downsample_filters=1024,\n use_bias=True,\n downsample_scales=[4, 4, 4, 4],\n nonlinear_activation=\"LeakyReLU\",\n nonlinear_activation_params={\"alpha\": 0.2},\n padding_type=\"REFLECT\",\n is_weight_norm=True,\n initializer_seed=0.02,\n **kwargs\n ):\n \"\"\"Initilize MelGAN discriminator module.\n Args:\n out_channels (int): Number of output channels.\n kernel_sizes (list): List of two kernel sizes. The prod will be used for the first conv layer,\n and the first and the second kernel sizes will be used for the last two layers.\n For example if kernel_sizes = [5, 3], the first layer kernel size will be 5 * 3 = 15.\n the last two layers' kernel size will be 5 and 3, respectively.\n filters (int): Initial number of filters for conv layer.\n max_downsample_filters (int): Maximum number of filters for downsampling layers.\n use_bias (bool): Whether to add bias parameter in convolution layers.\n downsample_scales (list): List of downsampling scales.\n nonlinear_activation (str): Activation function module name.\n nonlinear_activation_params (dict): Hyperparameters for activation function.\n padding_type (str): Padding type (support only \"REFLECT\", \"CONSTANT\", \"SYMMETRIC\")\n \"\"\"\n super().__init__(**kwargs)\n discriminator = []\n\n # check kernel_size is valid\n assert len(kernel_sizes) == 2\n assert kernel_sizes[0] % 2 == 1\n assert kernel_sizes[1] % 2 == 1\n\n # add first layer\n discriminator = [\n TFReflectionPad1d(\n (np.prod(kernel_sizes) - 1) // 2, padding_type=padding_type\n ),\n tf.keras.layers.Conv1D(\n filters=filters,\n kernel_size=int(np.prod(kernel_sizes)),\n use_bias=use_bias,\n kernel_initializer=get_initializer(initializer_seed),\n ),\n getattr(tf.keras.layers, nonlinear_activation)(\n **nonlinear_activation_params\n ),\n ]\n\n # add downsample layers\n in_chs = filters\n with tf.keras.utils.CustomObjectScope({\"GroupConv1D\": GroupConv1D}):\n for downsample_scale in downsample_scales:\n out_chs = min(in_chs * downsample_scale, max_downsample_filters)\n discriminator += [\n GroupConv1D(\n filters=out_chs,\n kernel_size=downsample_scale * 10 + 1,\n strides=downsample_scale,\n padding=\"same\",\n use_bias=use_bias,\n groups=in_chs // 4,\n kernel_initializer=get_initializer(initializer_seed),\n )\n ]\n discriminator += [\n getattr(tf.keras.layers, nonlinear_activation)(\n **nonlinear_activation_params\n )\n ]\n in_chs = out_chs\n\n # add final layers\n out_chs = min(in_chs * 2, max_downsample_filters)\n discriminator += [\n tf.keras.layers.Conv1D(\n filters=out_chs,\n kernel_size=kernel_sizes[0],\n padding=\"same\",\n use_bias=use_bias,\n kernel_initializer=get_initializer(initializer_seed),\n )\n ]\n discriminator += [\n getattr(tf.keras.layers, nonlinear_activation)(\n **nonlinear_activation_params\n )\n ]\n discriminator += [\n tf.keras.layers.Conv1D(\n filters=out_channels,\n kernel_size=kernel_sizes[1],\n padding=\"same\",\n use_bias=use_bias,\n kernel_initializer=get_initializer(initializer_seed),\n )\n ]\n\n if is_weight_norm is True:\n self._apply_weightnorm(discriminator)\n\n self.disciminator = discriminator\n\n def call(self, x, **kwargs):\n \"\"\"Calculate forward propagation.\n Args:\n x (Tensor): Input noise signal (B, T, 1).\n Returns:\n List: List of output tensors of each layer.\n \"\"\"\n outs = []\n for f in self.disciminator:\n x = f(x)\n outs += [x]\n return outs\n\n def _apply_weightnorm(self, list_layers):\n \"\"\"Try apply weightnorm for all layer in list_layers.\"\"\"\n for i in range(len(list_layers)):\n try:\n layer_name = list_layers[i].name.lower()\n if \"conv1d\" in layer_name or \"dense\" in layer_name:\n list_layers[i] = WeightNormalization(list_layers[i])\n except Exception:\n pass\n\n\nclass TFMelGANMultiScaleDiscriminator(tf.keras.Model):\n \"\"\"MelGAN multi-scale discriminator module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Initilize MelGAN multi-scale discriminator module.\n Args:\n config: config object for melgan discriminator\n \"\"\"\n super().__init__(**kwargs)\n self.discriminator = []\n\n # add discriminator\n for i in range(config.scales):\n self.discriminator += [\n TFMelGANDiscriminator(\n out_channels=config.out_channels,\n kernel_sizes=config.kernel_sizes,\n filters=config.filters,\n max_downsample_filters=config.max_downsample_filters,\n use_bias=config.use_bias,\n downsample_scales=config.downsample_scales,\n nonlinear_activation=config.nonlinear_activation,\n nonlinear_activation_params=config.nonlinear_activation_params,\n padding_type=config.padding_type,\n is_weight_norm=config.is_weight_norm,\n initializer_seed=config.initializer_seed,\n name=\"melgan_discriminator_scale_._{}\".format(i),\n )\n ]\n self.pooling = getattr(tf.keras.layers, config.downsample_pooling)(\n **config.downsample_pooling_params\n )\n\n def call(self, x, **kwargs):\n \"\"\"Calculate forward propagation.\n Args:\n x (Tensor): Input noise signal (B, T, 1).\n Returns:\n List: List of list of each discriminator outputs, which consists of each layer output tensors.\n \"\"\"\n outs = []\n for f in self.discriminator:\n outs += [f(x)]\n x = self.pooling(x)\n return outs\n" }, { "path": "TensorFlowTTS/tensorflow_tts/models/moduls/__init__.py", "content": "from tensorflow_tts.models.moduls import (\n core, core2, conditional, adain_en_de_code\n)" }, { "path": "TensorFlowTTS/tensorflow_tts/models/moduls/adain_en_de_code.py", "content": "import tensorflow as tf\nimport tensorflow_addons as tfa\nfrom tensorflow_tts.models.moduls.conditional import MaskInstanceNormalization\n\ndef get_initializer(initializer_range=0.02):\n \"\"\"Creates a `tf.initializers.truncated_normal` with the given range.\n\n Args:\n initializer_range: float, initializer range for stddev.\n\n Returns:\n TruncatedNormal initializer with stddev = `initializer_range`.\n\n \"\"\"\n return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)\n\nclass ConvModul(tf.keras.layers.Layer):\n def __init__(self, hidden_size, kernel_size, initializer_range, layer_norm_eps=1e-5, **kwargs):\n super().__init__(**kwargs)\n\n self.conv_0 = tf.keras.layers.Conv1D(\n filters = hidden_size,\n kernel_size = kernel_size,\n kernel_initializer = get_initializer(initializer_range),\n padding = 'same',\n )\n\n self.conv_1 = tf.keras.layers.Conv1D(\n filters = hidden_size,\n kernel_size = kernel_size,\n kernel_initializer = get_initializer(initializer_range),\n padding = 'same',\n )\n\n self.atc = tf.keras.layers.Activation(tf.nn.relu)\n\n self.batch_norm = tf.keras.layers.BatchNormalization(epsilon=layer_norm_eps) # TODO\n\n def call(self, x):\n y = self.conv_0(x)\n y = self.batch_norm(y)\n y = self.atc(y)\n y = self.conv_1(y)\n return y\n\nclass EncConvBlock(tf.keras.layers.Layer):\n def __init__(self, config, **kwargs):\n super().__init__(**kwargs)\n\n self.conv = ConvModul(\n config.adain_filter_size,\n config.enc_kernel_size,\n config.initializer_range,\n config.layer_norm_eps)\n\n def call(self, x):\n return x + self.conv(x)\n\nclass DecConvBlock(tf.keras.layers.Layer):\n def __init__(self, config, **kwargs):\n super().__init__(**kwargs)\n\n self.dec_conv = ConvModul(\n config.adain_filter_size,\n config.dec_kernel_size,\n config.initializer_range,\n config.layer_norm_eps)\n\n self.gen_conv = ConvModul(\n config.adain_filter_size,\n config.gen_kernel_size,\n config.initializer_range,\n config.layer_norm_eps)\n\n def call(self, x):\n y = self.dec_conv(x)\n y = y + self.gen_conv(y)\n return x + y\n\nclass AadINEncoder(tf.keras.Model):\n def __init__(self, config, **kwargs):\n super().__init__(**kwargs)\n\n self.config = config\n self.in_hidden_size = config.adain_filter_size # 256\n self.out_hidden_size = config.content_latent_dim # content_latent_dim\n self.n_conv_blocks = config.n_conv_blocks\n\n self.in_conv = tf.keras.layers.Conv1D(\n filters = self.in_hidden_size,\n kernel_size = 1,\n kernel_initializer = get_initializer(config.initializer_range),\n padding = 'same',\n )\n\n self.out_conv = tf.keras.layers.Conv1D(\n filters = self.out_hidden_size,\n kernel_size = 1,\n kernel_initializer = get_initializer(config.initializer_range),\n padding = 'same',\n )\n\n self.inorm = MaskInstanceNormalization(config.layer_norm_eps)\n\n self.conv_blocks = [\n EncConvBlock(config) for _ in range(self.n_conv_blocks)\n ]\n\n def call(self, x, mask):\n means = []\n stds = []\n\n y = self.in_conv(x) # 80 -> 256\n\n for block in self.conv_blocks:\n y = block(y)\n y, mean, std = self.inorm(y, mask, return_mean_std=True)\n means.append(mean)\n stds.append(std)\n\n y = self.out_conv(y) # 256 -> 128 + 4\n\n # TODO sigmoid\n\n means.reverse()\n stds.reverse()\n\n return y, means, stds\n\n\nclass AdaINDecoder(tf.keras.Model):\n def __init__(self, config, **kwargs):\n super().__init__(**kwargs)\n\n self.config = config\n self.in_hidden_size = config.adain_filter_size # 256\n self.out_hidden_size = config.num_mels # 80\n self.n_conv_blocks = config.n_conv_blocks\n\n self.in_conv = tf.keras.layers.Conv1D(\n filters = self.in_hidden_size,\n kernel_size = 1,\n kernel_initializer = get_initializer(config.initializer_range),\n padding = 'same',\n )\n\n self.out_conv = tf.keras.layers.Conv1D(\n filters = self.out_hidden_size,\n kernel_size = 1,\n kernel_initializer = get_initializer(config.initializer_range),\n padding = 'same',\n )\n\n self.inorm = MaskInstanceNormalization(config.layer_norm_eps)\n\n self.conv_blocks = [\n DecConvBlock(config) for _ in range(self.n_conv_blocks)\n ]\n\n def call(self, enc, cond, mask):\n _, means, stds = cond\n \n # TODO\n # y, means, stds = cond\n # _, mean, std = self.inorm(y, mask, return_mean_std=True)\n # enc = self.inorm(enc, mask)\n # enc = enc * tf.expand_dims(std, 1) + tf.expand_dims(mean, 1)\n\n y = self.in_conv(enc) # 132 -> 256\n\n for block, mean, std in zip(self.conv_blocks, means, stds):\n y = self.inorm(y, mask)\n y = y * tf.expand_dims(std, 1) + tf.expand_dims(mean, 1)\n y = block(y)\n\n y = self.out_conv(y) # 256 -> 80\n\n return y\n" }, { "path": "TensorFlowTTS/tensorflow_tts/models/moduls/conditional.py", "content": "import tensorflow as tf\nimport tensorflow_addons as tfa\nimport numpy as np\n\ndef get_initializer(initializer_range=0.02):\n \"\"\"Creates a `tf.initializers.truncated_normal` with the given range.\n\n Args:\n initializer_range: float, initializer range for stddev.\n\n Returns:\n TruncatedNormal initializer with stddev = `initializer_range`.\n\n \"\"\"\n return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)\n\nclass MaskInstanceNormalization(tf.keras.layers.Layer):\n def __init__(self, layer_norm_eps, **kwargs):\n super().__init__(**kwargs)\n self.layer_norm_eps = layer_norm_eps\n\n def _cal_mean_std(self, inputs, mask):\n expend_mask = tf.cast(tf.expand_dims(mask, axis=2), inputs.dtype)\n sums = tf.math.reduce_sum(tf.cast(mask, inputs.dtype), axis=-1, keepdims=True)\n\n mean = tf.math.reduce_sum(inputs * expend_mask, axis=1) / sums\n\n std = tf.math.sqrt(\n tf.math.reduce_sum(\n tf.math.pow(inputs - tf.expand_dims(mean, 1), 2) * expend_mask, axis = 1\n ) / sums + self.layer_norm_eps\n )\n\n return mean, std, expend_mask\n\n def call(self, inputs, mask, return_mean_std=False):\n '''\n inputs: [B, T, hidden_size]\n mask: [B, T]\n '''\n mean, std, expend_mask = self._cal_mean_std(inputs, mask)\n\n outputs = (inputs - tf.expand_dims(mean, 1)) / tf.expand_dims(std, 1) * expend_mask\n\n if return_mean_std:\n return outputs, mean, std\n else:\n return outputs\n\n# TODO\nclass ConditionalNormalization(tf.keras.layers.Layer):\n def __init__(self, config, **kwargs):\n super().__init__(**kwargs)\n\n self.config = config\n\n self.scale = tf.keras.layers.Dense(\n config.hidden_size,\n use_bias = False,\n kernel_initializer = get_initializer(config.initializer_range),\n name = \"Scale\",\n )\n\n self.mean = tf.keras.layers.Dense(\n config.hidden_size,\n use_bias = False,\n kernel_initializer = get_initializer(config.initializer_range),\n name = \"Mean\",\n )\n\n if config.conditional_norm_type == \"Layer\":\n self.norm_layer = tf.keras.layers.LayerNormalization(\n center = False,\n scale = False,\n epsilon = config.layer_norm_eps,\n name = \"LayerNorm\",\n )\n elif config.conditional_norm_type == \"Instance\":\n # self.norm_layer = tfa.layers.InstanceNormalization(\n # center = False,\n # scale = False,\n # epsilon = config.layer_norm_eps,\n # name = \"InstanceNorm\",\n # )\n self.norm_layer = MaskInstanceNormalization(config.layer_norm_eps)\n else:\n print(f\"Not support norm type {config.conditional_norm_type} !\")\n exit(0)\n\n def call(self, inputs, conds, mask):\n '''\n inputs: [B, T, hidden_size]\n conds: [B, 1, C']\n mask: [B, T]\n '''\n if self.config.conditional_norm_type == \"Layer\":\n tmp = self.norm_layer(inputs)\n elif self.config.conditional_norm_type == \"Instance\":\n tmp = self.norm_layer(inputs, mask)\n\n scale = self.scale(conds)\n mean = self.mean(conds)\n\n return tmp * scale + mean" }, { "path": "TensorFlowTTS/tensorflow_tts/models/moduls/core.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 The FastSpeech Authors, The HuggingFace Inc. team and Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tensorflow Model modules for FastSpeech.\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\nimport scipy.stats\n\n\ndef get_initializer(initializer_range=0.02):\n \"\"\"Creates a `tf.initializers.truncated_normal` with the given range.\n\n Args:\n initializer_range: float, initializer range for stddev.\n\n Returns:\n TruncatedNormal initializer with stddev = `initializer_range`.\n\n \"\"\"\n return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)\n\n\ndef gelu(x):\n \"\"\"Gaussian Error Linear unit.\"\"\"\n cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))\n return x * cdf\n\n\ndef gelu_new(x):\n \"\"\"Smoother gaussian Error Linear Unit.\"\"\"\n cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\n return x * cdf\n\n\ndef swish(x):\n \"\"\"Swish activation function.\"\"\"\n return x * tf.sigmoid(x)\n\n\ndef mish(x):\n return x * tf.math.tanh(tf.math.softplus(x))\n\n\nACT2FN = {\n \"identity\": tf.keras.layers.Activation(\"linear\"),\n \"tanh\": tf.keras.layers.Activation(\"tanh\"),\n \"gelu\": tf.keras.layers.Activation(gelu),\n \"relu\": tf.keras.activations.relu,\n \"swish\": tf.keras.layers.Activation(swish),\n \"gelu_new\": tf.keras.layers.Activation(gelu_new),\n \"mish\": tf.keras.layers.Activation(mish),\n}\n\n\nclass TFEmbedding(tf.keras.layers.Embedding):\n \"\"\"Faster version of embedding.\"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def call(self, inputs):\n inputs = tf.cast(inputs, tf.int32)\n outputs = tf.gather(self.embeddings, inputs)\n return outputs\n\n\nclass TFFastSpeechEmbeddings(tf.keras.layers.Layer):\n \"\"\"Construct charactor/phoneme/positional/speaker embeddings.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.vocab_size = config.vocab_size\n self.hidden_size = config.encoder_self_attention_params.hidden_size\n self.initializer_range = config.initializer_range\n self.config = config\n\n def build(self, input_shape):\n \"\"\"Build shared charactor/phoneme embedding layers.\"\"\"\n with tf.name_scope(\"charactor_embeddings\"):\n self.charactor_embeddings = self.add_weight(\n \"weight\",\n shape=[self.vocab_size, self.hidden_size],\n initializer=get_initializer(self.initializer_range),\n )\n super().build(input_shape)\n\n def call(self, input_ids):\n return tf.gather(self.charactor_embeddings, input_ids)\n\n\nclass TFFastSpeechSelfAttention(tf.keras.layers.Layer):\n \"\"\"Self attention module for fastspeech.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads)\n )\n self.output_attentions = config.output_attentions\n self.num_attention_heads = config.num_attention_heads\n self.all_head_size = self.num_attention_heads * config.attention_head_size\n\n self.query = tf.keras.layers.Dense(\n self.all_head_size,\n kernel_initializer=get_initializer(config.initializer_range),\n name=\"query\",\n )\n self.key = tf.keras.layers.Dense(\n self.all_head_size,\n kernel_initializer=get_initializer(config.initializer_range),\n name=\"key\",\n )\n self.value = tf.keras.layers.Dense(\n self.all_head_size,\n kernel_initializer=get_initializer(config.initializer_range),\n name=\"value\",\n )\n\n self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)\n self.config = config\n\n # TODO\n # self.half_win = config.local_attention_halfwin_size\n # self.frames_max = 100\n # self.local_maxs = self._local_attention_mask()\n # self.local_ones = tf.ones([self.frames_max, self.frames_max], tf.float32)\n\n def transpose_for_scores(self, x, batch_size):\n \"\"\"Transpose to calculate attention scores.\"\"\"\n x = tf.reshape(\n x,\n (batch_size, -1, self.num_attention_heads, self.config.attention_head_size),\n )\n return tf.transpose(x, perm=[0, 2, 1, 3])\n\n # def _local_attention_mask(self, frames_num):\n # xv, yv = tf.meshgrid(tf.range(frames_num), tf.range(frames_num), indexing=\"ij\")\n # f32_matrix = tf.cast(yv - xv, tf.float32)\n\n # val = f32_matrix[0][self.half_win]\n\n # local1 = tf.math.greater_equal(f32_matrix, -val)\n # local2 = tf.math.less_equal(f32_matrix, val)\n \n # return tf.cast(tf.logical_and(local1, local2), tf.float32)\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\"\"\"\n hidden_states, attention_mask = inputs\n\n batch_size = tf.shape(hidden_states)[0]\n mixed_query_layer = self.query(hidden_states)\n mixed_key_layer = self.key(hidden_states)\n mixed_value_layer = self.value(hidden_states)\n\n query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)\n key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)\n value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)\n\n attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)\n dk = tf.cast(tf.shape(key_layer)[-1], attention_scores.dtype) # scale attention_scores\n attention_scores = attention_scores / tf.math.sqrt(dk)\n\n if attention_mask is not None:\n # extended_attention_masks for self attention encoder.\n extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]\n extended_attention_mask = tf.cast(extended_attention_mask, attention_scores.dtype)\n extended_attention_mask = (1.0 - extended_attention_mask) * -1e9\n attention_scores = attention_scores + extended_attention_mask\n\n # TODO\n # frames_num = tf.shape(attention_mask)[-1]\n # local_attention_mask = tf.cond(tf.greater(frames_num, self.half_win + 1),\n # lambda: self._local_attention_mask(frames_num),\n # lambda: tf.ones([frames_num, frames_num], tf.float32))\n # local_attention_mask = (1.0 - local_attention_mask) * -1e9\n # attention_scores = attention_scores + local_attention_mask\n\n # Normalize the attention scores to probabilities.\n attention_probs = tf.nn.softmax(attention_scores, axis=-1)\n attention_probs = self.dropout(attention_probs, training=training)\n\n context_layer = tf.matmul(attention_probs, value_layer)\n context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])\n context_layer = tf.reshape(context_layer, (batch_size, -1, self.all_head_size))\n\n outputs = (\n (context_layer, attention_probs)\n if self.output_attentions\n else (context_layer,)\n )\n return outputs\n\n\nclass TFFastSpeechSelfOutput(tf.keras.layers.Layer):\n \"\"\"Fastspeech output of self attention module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.dense = tf.keras.layers.Dense(\n config.hidden_size,\n kernel_initializer=get_initializer(config.initializer_range),\n name=\"dense\",\n )\n self.LayerNorm = tf.keras.layers.LayerNormalization(\n epsilon=config.layer_norm_eps, name=\"LayerNorm\"\n )\n self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\"\"\"\n hidden_states, input_tensor = inputs\n\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states, training=training)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\nclass TFFastSpeechAttention(tf.keras.layers.Layer):\n \"\"\"Fastspeech attention module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.self_attention = TFFastSpeechSelfAttention(config, name=\"self\")\n self.dense_output = TFFastSpeechSelfOutput(config, name=\"output\")\n\n def call(self, inputs, training=False):\n input_tensor, attention_mask = inputs\n\n self_outputs = self.self_attention(\n [input_tensor, attention_mask], training=training\n )\n attention_output = self.dense_output(\n [self_outputs[0], input_tensor], training=training\n )\n masked_attention_output = attention_output * tf.cast(\n tf.expand_dims(attention_mask, 2), dtype=attention_output.dtype\n )\n outputs = (masked_attention_output,) + self_outputs[\n 1:\n ] # add attentions if we output them\n return outputs\n\n\nclass TFFastSpeechIntermediate(tf.keras.layers.Layer):\n \"\"\"Intermediate representation module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.conv1d_1 = tf.keras.layers.Conv1D(\n config.intermediate_size,\n kernel_size=config.intermediate_kernel_size,\n kernel_initializer=get_initializer(config.initializer_range),\n padding=\"same\",\n name=\"conv1d_1\",\n )\n self.conv1d_2 = tf.keras.layers.Conv1D(\n config.hidden_size,\n kernel_size=config.intermediate_kernel_size,\n kernel_initializer=get_initializer(config.initializer_range),\n padding=\"same\",\n name=\"conv1d_2\",\n )\n if isinstance(config.hidden_act, str):\n self.intermediate_act_fn = ACT2FN[config.hidden_act]\n else:\n self.intermediate_act_fn = config.hidden_act\n\n def call(self, inputs):\n \"\"\"Call logic.\"\"\"\n hidden_states, attention_mask = inputs\n\n hidden_states = self.conv1d_1(hidden_states)\n hidden_states = self.intermediate_act_fn(hidden_states)\n hidden_states = self.conv1d_2(hidden_states)\n\n masked_hidden_states = hidden_states * tf.cast(\n tf.expand_dims(attention_mask, 2), dtype=hidden_states.dtype\n )\n return masked_hidden_states\n\n\nclass TFFastSpeechOutput(tf.keras.layers.Layer):\n \"\"\"Output module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.LayerNorm = tf.keras.layers.LayerNormalization(\n epsilon=config.layer_norm_eps, name=\"LayerNorm\"\n )\n self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\"\"\"\n hidden_states, input_tensor = inputs\n\n hidden_states = self.dropout(hidden_states, training=training)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\nclass TFFastSpeechLayer(tf.keras.layers.Layer):\n \"\"\"Fastspeech module (FFT module on the paper).\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.attention = TFFastSpeechAttention(config, name=\"attention\")\n self.intermediate = TFFastSpeechIntermediate(config, name=\"intermediate\")\n self.bert_output = TFFastSpeechOutput(config, name=\"output\")\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\"\"\"\n hidden_states, attention_mask = inputs\n\n attention_outputs = self.attention(\n [hidden_states, attention_mask], training=training\n )\n attention_output = attention_outputs[0]\n intermediate_output = self.intermediate(\n [attention_output, attention_mask], training=training\n )\n layer_output = self.bert_output(\n [intermediate_output, attention_output], training=training\n )\n masked_layer_output = layer_output * tf.cast(\n tf.expand_dims(attention_mask, 2), dtype=layer_output.dtype\n )\n outputs = (masked_layer_output,) + attention_outputs[\n 1:\n ] # add attentions if we output them\n return outputs\n\n\nclass TFFastSpeechEncoder(tf.keras.layers.Layer):\n \"\"\"Fast Speech encoder module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.output_attentions = config.output_attentions\n self.output_hidden_states = config.output_hidden_states\n self.layer = [\n TFFastSpeechLayer(config, name=\"layer_._{}\".format(i))\n for i in range(config.num_hidden_layers)\n ]\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\"\"\"\n hidden_states, attention_mask = inputs\n\n all_hidden_states = ()\n all_attentions = ()\n for _, layer_module in enumerate(self.layer):\n if self.output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states,)\n\n layer_outputs = layer_module(\n [hidden_states, attention_mask], training=training\n )\n hidden_states = layer_outputs[0]\n\n if self.output_attentions:\n all_attentions = all_attentions + (layer_outputs[1],)\n\n # Add last layer\n if self.output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states,)\n\n outputs = (hidden_states,)\n if self.output_hidden_states:\n outputs = outputs + (all_hidden_states,)\n if self.output_attentions:\n outputs = outputs + (all_attentions,)\n return outputs # outputs, (hidden states), (attentions)\n\n\nclass TFFastSpeechDecoder(TFFastSpeechEncoder):\n \"\"\"Fast Speech decoder module.\"\"\"\n\n def __init__(self, config, **kwargs):\n self.is_compatible_encoder = kwargs.pop(\"is_compatible_encoder\", True)\n\n super().__init__(config, **kwargs)\n self.config = config\n\n if self.is_compatible_encoder is False:\n self.project_compatible_decoder = tf.keras.layers.Dense(\n units=config.hidden_size, name=\"project_compatible_decoder\"\n )\n\n def call(self, inputs, training=False):\n hidden_states, encoder_mask = inputs\n\n if self.is_compatible_encoder is False:\n hidden_states = self.project_compatible_decoder(hidden_states)\n\n return super().call([hidden_states, encoder_mask], training=training)\n\n\nclass TFTacotronPostnet(tf.keras.layers.Layer):\n \"\"\"Tacotron-2 postnet.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.conv_batch_norm = []\n for i in range(config.n_conv_postnet):\n conv = tf.keras.layers.Conv1D(\n filters=config.postnet_conv_filters\n if i < config.n_conv_postnet - 1\n else config.num_mels,\n kernel_size=config.postnet_conv_kernel_sizes,\n padding=\"same\",\n name=\"conv_._{}\".format(i),\n )\n batch_norm = tf.keras.layers.BatchNormalization(\n axis=-1, name=\"batch_norm_._{}\".format(i)\n )\n self.conv_batch_norm.append((conv, batch_norm))\n self.dropout = tf.keras.layers.Dropout(\n rate=config.postnet_dropout_rate, name=\"dropout\"\n )\n self.activation = [tf.nn.tanh] * (config.n_conv_postnet - 1) + [tf.identity]\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\"\"\"\n outputs, mask = inputs\n extended_mask = tf.cast(tf.expand_dims(mask, axis=2), outputs.dtype)\n for i, (conv, bn) in enumerate(self.conv_batch_norm):\n outputs = conv(outputs)\n outputs = bn(outputs)\n outputs = self.activation[i](outputs)\n outputs = self.dropout(outputs, training=training)\n return outputs * extended_mask\n\n# TODO Drop infer trainning=False\nclass TFFastSpeechVariantPredictor(tf.keras.layers.Layer):\n \"\"\"FastSpeech variant predictor module.\"\"\"\n\n def __init__(self, config, sub_name=\"f0\", is_sigmod=False, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.is_sigmod = is_sigmod\n self.conv_layers = []\n for i in range(config.num_variant_conv_layers):\n self.conv_layers.append(\n tf.keras.layers.Conv1D(\n config.variant_predictor_filters,\n config.variant_predictor_kernel_sizes,\n padding=\"same\",\n name=\"{}_conv_._{}\".format(sub_name, i),\n )\n )\n\n self.conv_layers.append(tf.keras.layers.Activation(tf.nn.relu))\n\n self.conv_layers.append(\n tf.keras.layers.LayerNormalization(\n epsilon=config.layer_norm_eps, name=\"{}_LayerNorm_._{}\".format(sub_name, i)\n )\n )\n\n self.conv_layers.append(\n tf.keras.layers.Dropout(config.variant_predictor_dropout_probs)\n )\n self.conv_layers_sequence = tf.keras.Sequential(self.conv_layers, name=sub_name)\n self.output_layer = tf.keras.layers.Dense(1)\n\n if self.is_sigmod:\n self.sigmod_layer = tf.keras.layers.Activation(tf.nn.sigmoid)\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\"\"\"\n encoder_hidden_states, attention_mask = inputs\n attention_mask = tf.cast(tf.expand_dims(attention_mask, 2), encoder_hidden_states.dtype)\n\n # mask encoder hidden states\n masked_encoder_hidden_states = encoder_hidden_states * attention_mask\n\n # pass though first layer\n outputs = self.conv_layers_sequence(masked_encoder_hidden_states)\n outputs = self.output_layer(outputs)\n\n if self.is_sigmod:\n outputs = self.sigmod_layer(outputs)\n\n masked_outputs = outputs * attention_mask\n\n return tf.squeeze(masked_outputs, -1)\n\nclass TFFastSpeechDurationPredictor(tf.keras.layers.Layer):\n \"\"\"FastSpeech duration predictor module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.conv_layers = []\n for i in range(config.num_duration_conv_layers):\n self.conv_layers.append(\n tf.keras.layers.Conv1D(\n config.duration_predictor_filters,\n config.duration_predictor_kernel_sizes,\n padding=\"same\",\n name=\"conv_._{}\".format(i),\n )\n )\n\n self.conv_layers.append(tf.keras.layers.Activation(tf.nn.relu))\n\n self.conv_layers.append(\n tf.keras.layers.LayerNormalization(\n epsilon=config.layer_norm_eps, name=\"LayerNorm_._{}\".format(i)\n )\n )\n\n self.conv_layers.append(\n tf.keras.layers.Dropout(config.duration_predictor_dropout_probs)\n )\n\n self.conv_layers_sequence = tf.keras.Sequential(self.conv_layers)\n\n self.output_layer = tf.keras.layers.Dense(1)\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\"\"\"\n encoder_hidden_states, attention_mask = inputs\n attention_mask = tf.cast(tf.expand_dims(attention_mask, 2), encoder_hidden_states.dtype)\n\n # mask encoder hidden states\n masked_encoder_hidden_states = encoder_hidden_states * attention_mask\n\n # pass though first layer\n outputs = self.conv_layers_sequence(masked_encoder_hidden_states)\n outputs = self.output_layer(outputs)\n masked_outputs = outputs * attention_mask\n # return tf.squeeze(tf.nn.relu(masked_outputs), -1) # make sure positive value.\n return tf.squeeze(masked_outputs, -1)\n\nclass TFFastSpeechLengthRegulator(tf.keras.layers.Layer):\n \"\"\"FastSpeech lengthregulator module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n self.enable_tflite_convertible = kwargs.pop(\"enable_tflite_convertible\", False)\n super().__init__(**kwargs)\n self.config = config\n\n self.addfeatures_num = 0\n\n if config.addfeatures_num > 0:\n self._compute_coarse_coding_features()\n self.addfeatures_num = config.addfeatures_num\n if config.isaddur:\n self.addfeatures_num += 1\n\n def _compute_coarse_coding_features(self):\n npoints = 600\n\n x1 = np.linspace(-1.5, 1.5, npoints)\n x2 = np.linspace(-1.0, 2.0, npoints)\n x3 = np.linspace(-0.5, 2.5, npoints)\n x4 = np.linspace(0.0, 3.0, npoints)\n\n mu1 = 0.0\n mu2 = 0.5\n mu3 = 1.0\n mu4 = 1.5\n\n sigma = 0.4\n\n self.cc_features0 = tf.convert_to_tensor(scipy.stats.norm.pdf(x1, mu1, sigma), tf.float32)\n self.cc_features1 = tf.convert_to_tensor(scipy.stats.norm.pdf(x2, mu2, sigma), tf.float32)\n self.cc_features2 = tf.convert_to_tensor(scipy.stats.norm.pdf(x3, mu3, sigma), tf.float32)\n self.cc_features3 = tf.convert_to_tensor(scipy.stats.norm.pdf(x4, mu4, sigma), tf.float32)\n\n def call(self, inputs, training=False):\n \"\"\"Call logic.\n Args:\n 1. encoder_hidden_states, Tensor (float32) shape [batch_size, length, hidden_size]\n 2. durations_gt, Tensor (float32/int32) shape [batch_size, length]\n \"\"\"\n encoder_hidden_states, durations_gt = inputs\n outputs, encoder_masks = self._length_regulator(\n encoder_hidden_states, durations_gt\n )\n return outputs, encoder_masks\n\n def _length_regulator(self, encoder_hidden_states, durations_gt):\n \"\"\"Length regulator logic.\"\"\"\n sum_durations = tf.reduce_sum(durations_gt, axis=-1) # [batch_size]\n max_durations = tf.reduce_max(sum_durations)\n\n input_shape = tf.shape(encoder_hidden_states)\n batch_size = input_shape[0]\n hidden_size = input_shape[-1]\n\n # initialize output hidden states and encoder masking.\n # TODO add tflite_infer for coarse_coding\n if self.enable_tflite_convertible:\n # There is only 1 batch in inference, so we don't have to use\n # `tf.While` op with 3-D output tensor.\n repeats = durations_gt[0]\n real_length = tf.reduce_sum(repeats)\n pad_size = max_durations - real_length\n # masks : [max_durations]\n masks = tf.sequence_mask([real_length], max_durations, dtype=tf.int32)\n repeat_encoder_hidden_states = tf.repeat(\n encoder_hidden_states[0], repeats=repeats, axis=0\n )\n repeat_encoder_hidden_states = tf.expand_dims(\n tf.pad(repeat_encoder_hidden_states, [[0, pad_size], [0, 0]]), 0\n ) # [1, max_durations, hidden_size]\n\n outputs = repeat_encoder_hidden_states\n encoder_masks = masks\n else:\n outputs = tf.zeros(shape=[0, max_durations, hidden_size + self.addfeatures_num], dtype=encoder_hidden_states.dtype)\n # outputs = tf.zeros(shape=[0, max_durations, hidden_size], dtype=encoder_hidden_states.dtype)\n encoder_masks = tf.zeros(shape=[0, max_durations], dtype=tf.int32)\n\n def condition(\n i,\n batch_size,\n outputs,\n encoder_masks,\n encoder_hidden_states,\n durations_gt,\n max_durations,\n ):\n return tf.less(i, batch_size)\n\n def body(\n i,\n batch_size,\n outputs,\n encoder_masks,\n encoder_hidden_states,\n durations_gt,\n max_durations,\n ):\n\n############################### ori ##################################\n # repeats = durations_gt[i]\n # real_length = tf.reduce_sum(repeats)\n # pad_size = max_durations - real_length\n # masks = tf.sequence_mask([real_length], max_durations, dtype=tf.int32)\n # repeat_encoder_hidden_states = tf.repeat(\n # encoder_hidden_states[i], repeats=repeats, axis=0\n # )\n # repeat_encoder_hidden_states = tf.expand_dims(\n # tf.pad(repeat_encoder_hidden_states, [[0, pad_size], [0, 0]]), 0\n # ) # [1, max_durations, hidden_size]\n # outputs = tf.concat([outputs, repeat_encoder_hidden_states], axis=0)\n # encoder_masks = tf.concat([encoder_masks, masks], axis=0)\n\n############################### add duration info ##################################\n repeats = durations_gt[i]\n real_length = tf.reduce_sum(repeats)\n pad_size = max_durations - real_length\n masks = tf.sequence_mask([real_length], max_durations, dtype=tf.int32)\n repeat_encoder_hidden_states = tf.repeat(\n encoder_hidden_states[i], repeats=repeats, axis=0\n )\n\n if self.addfeatures_num > 0:\n # duration sum per phone\n durdur = tf.repeat(repeats[:, tf.newaxis], repeats=repeats, axis=0)\n durdur = tf.cast(durdur, encoder_hidden_states.dtype)\n\n # acc duration\n maskbool = tf.sequence_mask(repeats, tf.reduce_max(repeats))\n durindex = tf.cumsum(tf.cast(maskbool, encoder_hidden_states.dtype), -1)\n durindex = tf.boolean_mask(durindex, maskbool)[:, tf.newaxis]\n\n # duration/(sum)\n durindex = (durindex - 1) / durdur\n\n # coarse_coding\n indexs = tf.cast(durindex*100, tf.int32)\n cc0 = tf.gather(self.cc_features0, 400+indexs)\n cc1 = tf.gather(self.cc_features1, 300+indexs)\n cc2 = tf.gather(self.cc_features2, 200+indexs)\n cc3 = tf.gather(self.cc_features3, 100+indexs)\n ccc = tf.concat([cc0, cc1, cc2, cc3], axis=-1)\n\n if self.config.isaddur:\n repeat_encoder_hidden_states = tf.concat([repeat_encoder_hidden_states, durdur], -1)\n\n repeat_encoder_hidden_states = tf.concat([repeat_encoder_hidden_states, ccc], -1)\n\n repeat_encoder_hidden_states = tf.expand_dims(\n tf.pad(repeat_encoder_hidden_states, [[0, pad_size], [0, 0]]), 0\n ) # [1, max_durations, hidden_size]\n outputs = tf.concat([outputs, repeat_encoder_hidden_states], axis=0)\n encoder_masks = tf.concat([encoder_masks, masks], axis=0)\n return [\n i + 1,\n batch_size,\n outputs,\n encoder_masks,\n encoder_hidden_states,\n durations_gt,\n max_durations,\n ]\n\n # initialize iteration i.\n i = tf.constant(0, dtype=tf.int32)\n _, _, outputs, encoder_masks, _, _, _, = tf.while_loop(\n condition,\n body,\n [\n i,\n batch_size,\n outputs,\n encoder_masks,\n encoder_hidden_states,\n durations_gt,\n max_durations,\n ],\n shape_invariants=[\n i.get_shape(),\n batch_size.get_shape(),\n tf.TensorShape(\n [\n None,\n None,\n self.config.content_latent_dim,\n ]\n ),\n tf.TensorShape([None, None]),\n encoder_hidden_states.get_shape(),\n durations_gt.get_shape(),\n max_durations.get_shape(),\n ],\n )\n\n return outputs, encoder_masks" }, { "path": "TensorFlowTTS/tensorflow_tts/models/moduls/core2.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 The FastSpeech Authors, The HuggingFace Inc. team and Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tensorflow Model modules for FastSpeech.\"\"\"\n\nimport tensorflow as tf\nfrom tensorflow_tts.models.moduls.core import *\nfrom tensorflow_tts.models.moduls.conditional import ConditionalNormalization\n\nclass TFFastSpeechConditionalSelfOutput(tf.keras.layers.Layer):\n \"\"\"Fastspeech output of self attention module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.dense = tf.keras.layers.Dense(\n config.hidden_size,\n kernel_initializer=get_initializer(config.initializer_range),\n name=\"dense\",\n )\n\n self.normlayer = ConditionalNormalization(config)\n\n self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)\n\n def call(self, inputs, training=False):\n '''\n hidden_states: [B, T, C]\n input_tensor: [B, T, C]\n conds: [B, 1, T]\n attention_mask:[B, T]\n '''\n hidden_states, input_tensor, conds, attention_mask = inputs\n\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states, training=training)\n hidden_states = self.normlayer(hidden_states + input_tensor, conds, attention_mask)\n\n return hidden_states\n\nclass TFFastSpeechConditionalAttention(tf.keras.layers.Layer):\n \"\"\"Fastspeech attention module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.self_attention = TFFastSpeechSelfAttention(config, name=\"self\")\n self.dense_output = TFFastSpeechConditionalSelfOutput(config, name=\"output\")\n\n def call(self, inputs, training=False):\n '''\n input_tensor: [B, T, C]\n conds: [B, 1, C']\n attention_mask: [B, T]\n '''\n input_tensor, conds, attention_mask = inputs\n\n self_outputs = self.self_attention(\n [input_tensor, attention_mask], training=training\n )\n attention_output = self.dense_output(\n [self_outputs[0], input_tensor, conds, attention_mask], training=training\n )\n masked_attention_output = attention_output * tf.cast(\n tf.expand_dims(attention_mask, 2), dtype=attention_output.dtype\n )\n outputs = (masked_attention_output,) + self_outputs[\n 1:\n ] # add attentions if we output them\n return outputs\n\nclass TFFastSpeechConditionalOutput(tf.keras.layers.Layer):\n \"\"\"Output module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.normlayer = ConditionalNormalization(config)\n self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)\n\n def call(self, inputs, training=False):\n '''\n hidden_states: [B, T, C]\n input_tensor: [B, T, C]\n conds: [B, 1, T]\n attention_mask:[B, T]\n '''\n hidden_states, input_tensor, conds, attention_mask = inputs\n\n hidden_states = self.dropout(hidden_states, training=training)\n hidden_states = self.normlayer(hidden_states + input_tensor, conds, attention_mask)\n return hidden_states\n\n\nclass TFFastSpeechConditionalLayer(tf.keras.layers.Layer):\n \"\"\"Fastspeech module (FFT module on the paper).\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.attention = TFFastSpeechConditionalAttention(config, name=\"attention\")\n self.intermediate = TFFastSpeechIntermediate(config, name=\"intermediate\")\n self.bert_output = TFFastSpeechConditionalOutput(config, name=\"output\")\n\n def call(self, inputs, training=False):\n '''\n hidden_states: [B, T, C]\n conds: [B, 1, C']\n attention_mask: [B, T]\n '''\n hidden_states, conds, attention_mask = inputs\n\n attention_outputs = self.attention(\n [hidden_states, conds, attention_mask], training=training\n )\n attention_output = attention_outputs[0]\n intermediate_output = self.intermediate(\n [attention_output, attention_mask], training=training\n )\n layer_output = self.bert_output(\n [intermediate_output, attention_output, conds, attention_mask], training=training\n )\n masked_layer_output = layer_output * tf.cast(\n tf.expand_dims(attention_mask, 2), dtype=layer_output.dtype\n )\n outputs = (masked_layer_output,) + attention_outputs[\n 1:\n ] # add attentions if we output them\n return outputs\n\nclass TFFastSpeechConditionalEncoder(tf.keras.layers.Layer):\n \"\"\"Fast Speech encoder module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init variables.\"\"\"\n super().__init__(**kwargs)\n self.output_attentions = config.output_attentions\n self.output_hidden_states = config.output_hidden_states\n self.layer = [\n TFFastSpeechConditionalLayer(config, name=\"layer_._{}\".format(i))\n for i in range(config.num_hidden_layers)\n ]\n\n def call(self, inputs, training=False):\n '''\n hidden_states: [B, T, C]\n conds: [B, 1, C']\n attention_mask: [B, T]\n '''\n hidden_states, conds, attention_mask = inputs\n\n all_hidden_states = ()\n all_attentions = ()\n for _, layer_module in enumerate(self.layer):\n if self.output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states,)\n\n layer_outputs = layer_module(\n [hidden_states, conds, attention_mask], training=training\n )\n hidden_states = layer_outputs[0]\n\n if self.output_attentions:\n all_attentions = all_attentions + (layer_outputs[1],)\n\n # Add last layer\n if self.output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states,)\n\n outputs = (hidden_states,)\n if self.output_hidden_states:\n outputs = outputs + (all_hidden_states,)\n if self.output_attentions:\n outputs = outputs + (all_attentions,)\n return outputs # outputs, (hidden states), (attentions)\n\n\nclass TFFastSpeechConditionalDecoder(TFFastSpeechConditionalEncoder):\n \"\"\"Fast Speech decoder module.\"\"\"\n\n def __init__(self, config, **kwargs):\n self.is_compatible_encoder = kwargs.pop(\"is_compatible_encoder\", True)\n\n super().__init__(config, **kwargs)\n self.config = config\n\n if self.is_compatible_encoder is False:\n self.project_compatible_decoder = tf.keras.layers.Dense(\n units=config.hidden_size, name=\"project_compatible_decoder\"\n )\n\n def call(self, inputs, training=False):\n '''\n hidden_states: [B, T, C]\n conds: [B, 1, C']\n encoder_mask: [B, T]\n '''\n hidden_states, conds, encoder_mask = inputs\n\n if self.is_compatible_encoder is False:\n hidden_states = self.project_compatible_decoder(hidden_states)\n\n return super().call([hidden_states, conds, encoder_mask], training=training)" }, { "path": "TensorFlowTTS/tensorflow_tts/models/unetts.py", "content": "import tensorflow as tf\nimport numpy as np\n\nfrom tensorflow_tts.models.moduls.core import (\n TFFastSpeechEmbeddings,\n TFFastSpeechEncoder,\n TFFastSpeechDecoder,\n TFTacotronPostnet,\n TFFastSpeechLengthRegulator,\n TFFastSpeechVariantPredictor,\n TFFastSpeechDurationPredictor\n)\nfrom tensorflow_tts.models.moduls.core2 import TFFastSpeechConditionalDecoder\nfrom tensorflow_tts.models.moduls.adain_en_de_code import (\n AadINEncoder, AdaINDecoder\n)\n\n'''\n###############################################################################\n############################# Duration #######################################\n###############################################################################\n'''\n\nclass TFUNETTSDuration(tf.keras.Model):\n def __init__(self, config, **kwargs):\n \"\"\"Init layers for UNETTSDuration.\"\"\"\n self.enable_tflite_convertible = kwargs.pop(\"enable_tflite_convertible\", False)\n super().__init__(**kwargs)\n\n self.embeddings = TFFastSpeechEmbeddings(config, name=\"embeddings\")\n\n self.encoder = TFFastSpeechEncoder(\n config.encoder_self_attention_params, name = \"encoder\"\n )\n\n self.duration_predictor = TFFastSpeechDurationPredictor(\n config, name = \"duration_predictor\"\n )\n\n self.duration_stat_cal = tf.keras.layers.Dense(4, use_bias=False,\n kernel_initializer=tf.constant_initializer(\n [[0.97, 0.01, 0.01, 0.01],\n [0.01, 0.97, 0.01, 0.01],\n [0.01, 0.01, 0.97, 0.01],\n [0.01, 0.01, 0.01, 0.97]]\n ),\n kernel_constraint=tf.keras.constraints.NonNeg(),\n name=\"duration_stat_cal\")\n\n self.setup_inference_fn()\n\n def _build(self):\n \"\"\"Dummy input for building model.\"\"\"\n # fake inputs\n char_ids = tf.convert_to_tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], tf.int32)\n duration_stat = tf.convert_to_tensor([[1., 1., 1., 1.]], tf.float32)\n self(char_ids, duration_stat)\n\n def call(\n self, char_ids, duration_stat, training=False, **kwargs,\n ):\n \"\"\"Call logic.\"\"\"\n attention_mask = tf.math.not_equal(char_ids, 0)\n sheng_mask = char_ids < 27\n yun_mask = char_ids > 26\n\n duration_stat = self.duration_stat_cal(duration_stat)\n\n sheng_mean, sheng_std, yun_mean, yun_std = \\\n duration_stat[:,0][:, None], duration_stat[:,1][:, None], duration_stat[:,2][:, None], duration_stat[:,3][:, None]\n\n embedding_output = self.embeddings(char_ids)\n\n encoder_output = self.encoder([embedding_output, attention_mask], training=training)\n last_encoder_hidden_states = encoder_output[0]\n\n duration_outputs = self.duration_predictor([last_encoder_hidden_states, attention_mask])\n\n sheng_outputs = duration_outputs * sheng_std + sheng_mean\n sheng_outputs = sheng_outputs * tf.cast(sheng_mask, tf.float32)\n\n yun_outputs = duration_outputs * yun_std + yun_mean\n yun_outputs = yun_outputs * tf.cast(yun_mask, tf.float32)\n\n duration_outputs = sheng_outputs + yun_outputs\n duration_outputs = tf.nn.relu(duration_outputs * tf.cast(attention_mask, tf.float32))\n\n return duration_outputs\n\n def _inference(self, char_ids, duration_stat, **kwargs):\n \"\"\"Call logic.\"\"\"\n attention_mask = tf.math.not_equal(char_ids, 0)\n sheng_mask = char_ids < 27\n yun_mask = char_ids > 26\n\n duration_stat = self.duration_stat_cal(duration_stat)\n\n sheng_mean, sheng_std, yun_mean, yun_std = \\\n duration_stat[:,0][:, None], duration_stat[:,1][:, None], duration_stat[:,2][:, None], duration_stat[:,3][:, None]\n\n embedding_output = self.embeddings(char_ids, training=False)\n\n encoder_output = self.encoder([embedding_output, attention_mask], training=False)\n last_encoder_hidden_states = encoder_output[0]\n\n duration_outputs = self.duration_predictor([last_encoder_hidden_states, attention_mask])\n\n sheng_outputs = duration_outputs * sheng_std + sheng_mean\n sheng_outputs = sheng_outputs * tf.cast(sheng_mask, tf.float32)\n\n yun_outputs = duration_outputs * yun_std + yun_mean\n yun_outputs = yun_outputs * tf.cast(yun_mask, tf.float32)\n\n duration_outputs = sheng_outputs + yun_outputs\n duration_outputs = tf.nn.relu(duration_outputs * tf.cast(attention_mask, tf.float32))\n\n return duration_outputs\n\n def setup_inference_fn(self):\n self.inference = tf.function(\n self._inference,\n experimental_relax_shapes=True,\n input_signature=[\n tf.TensorSpec(shape=[None, None], dtype=tf.int32, name=\"char_ids\"),\n tf.TensorSpec(shape=[None, None], dtype=tf.float32, name=\"duration_stat\"),\n ],\n )\n\n self.inference_tflite = tf.function(\n self._inference,\n experimental_relax_shapes=True,\n input_signature=[\n tf.TensorSpec(shape=[1, None], dtype=tf.int32, name=\"char_ids\"),\n tf.TensorSpec(shape=[1, None], dtype=tf.float32, name=\"duration_stat\"),\n ],\n )\n\n'''\n###############################################################################\n################################ Acous ########################################\n###############################################################################\n'''\n\nclass ContentEncoder(tf.keras.Model):\n def __init__(self, config, **kwargs):\n \"\"\"Init layers for ContentEncoder.\"\"\"\n self.enable_tflite_convertible = kwargs.pop(\"enable_tflite_convertible\", False)\n super().__init__(**kwargs)\n\n self.embeddings = TFFastSpeechEmbeddings(config, name=\"embeddings\")\n\n self.encoder = TFFastSpeechEncoder(\n config.encoder_self_attention_params, name=\"encoder\"\n )\n\n self.length_regulator = TFFastSpeechLengthRegulator(\n config,\n enable_tflite_convertible = self.enable_tflite_convertible,\n name = \"length_regulator\",\n )\n\n def call(self, char_ids, duration_gts, training=False):\n attention_mask = tf.math.not_equal(char_ids, 0)\n\n embedding_output = self.embeddings(char_ids)\n\n encoder_output = self.encoder([embedding_output, attention_mask], training=training)\n last_encoder_hidden_states = encoder_output[0]\n\n length_regulator_outputs, encoder_masks = self.length_regulator(\n [last_encoder_hidden_states, duration_gts], training=training\n )\n\n return length_regulator_outputs, encoder_masks\n\nclass TFUNETTSAcous(tf.keras.Model):\n \"\"\"TF UNETTSAcous module.\"\"\"\n\n def __init__(self, config, **kwargs):\n \"\"\"Init layers for UNETTSAcous.\"\"\"\n self.enable_tflite_convertible = kwargs.pop(\"enable_tflite_convertible\", False)\n super().__init__(**kwargs)\n\n self.config = config\n\n self.content_encoder = ContentEncoder(\n config,\n enable_tflite_convertible=self.enable_tflite_convertible,\n name=\"content_encoder\"\n )\n\n self.src_mel_encoder = AadINEncoder(config, name=\"src_mel_encoder\")\n\n self.tar_mel_decoder = AdaINDecoder(config, name=\"tar_mel_decoder\")\n\n self.setup_inference_fn()\n\n def _build(self):\n \"\"\"Dummy input for building model.\"\"\"\n # fake inputs\n char_ids = tf.convert_to_tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], tf.int32)\n duration_gts = tf.convert_to_tensor([[3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], tf.int32)\n mel_src = tf.random.normal([1, 30, self.config.num_mels], dtype=tf.float32)\n self(char_ids, duration_gts, mel_src)\n\n def text_encoder_weight_load(self, content_encoder_path):\n self.content_encoder.load_weights(content_encoder_path)\n\n def freezen_encoder(self):\n self.content_encoder.trainable = False\n\n def call(\n self, char_ids, duration_gts, mel_src, training=False, **kwargs,\n ):\n \"\"\"Call logic.\"\"\"\n content_latents, encoder_masks = self.content_encoder(char_ids, duration_gts, training=False)\n content_latents = content_latents * tf.cast(tf.expand_dims(encoder_masks, axis=2), content_latents.dtype)\n\n content_latent_pred, means, stds = self.src_mel_encoder(mel_src, encoder_masks)\n content_latent_pred = content_latent_pred * tf.cast(tf.expand_dims(encoder_masks, axis=2), content_latent_pred.dtype)\n\n mel_before = self.tar_mel_decoder(content_latents, (content_latent_pred, means, stds), encoder_masks)\n mel_before = mel_before * tf.cast(tf.expand_dims(encoder_masks, axis=2), mel_before.dtype)\n\n return (mel_before, content_latents, content_latent_pred)\n\n def _inference(self, char_ids, duration_gts, mel_src, **kwargs):\n \"\"\"Call logic.\"\"\"\n content_latents, encoder_masks = self.content_encoder(char_ids, duration_gts, training=False)\n\n tmp_masks = tf.ones([tf.shape(mel_src)[0], tf.shape(mel_src)[1]], dtype=tf.bool)\n _, means, stds = self.src_mel_encoder(mel_src, tmp_masks)\n\n mel_before = self.tar_mel_decoder(content_latents, (_, means, stds), encoder_masks)\n\n return mel_before, means, stds\n\n def extract_dur_pos_embed(self, mel_src):\n tmp_masks = tf.ones([tf.shape(mel_src)[0], tf.shape(mel_src)[1]], dtype=tf.bool)\n content_latent_pred, _, _ = self.src_mel_encoder(mel_src, tmp_masks)\n return content_latent_pred[:, :, -4:]\n\n def setup_inference_fn(self):\n self.inference = tf.function(\n self._inference,\n experimental_relax_shapes=True,\n input_signature=[\n tf.TensorSpec(shape=[None, None], dtype=tf.int32, name=\"char_ids\"),\n tf.TensorSpec(shape=[None, None], dtype=tf.int32, name=\"duration_gts\"),\n tf.TensorSpec(shape=[None, None, None], dtype=tf.float32, name=\"mel_src\"),\n ],\n )\n\n self.inference_tflite = tf.function(\n self._inference,\n experimental_relax_shapes=True,\n input_signature=[\n tf.TensorSpec(shape=[1, None], dtype=tf.int32, name=\"char_ids\"),\n tf.TensorSpec(shape=[1, None], dtype=tf.int32, name=\"duration_gts\"),\n tf.TensorSpec(shape=[1, None, None], dtype=tf.float32, name=\"mel_src\"),\n ],\n )\n\nclass TFUNETTSContentPretrain(tf.keras.Model):\n \"\"\"UNETTSContentPretrain\"\"\"\n\n def __init__(self, config, **kwargs):\n self.enable_tflite_convertible = kwargs.pop(\"enable_tflite_convertible\", False)\n super().__init__(**kwargs)\n\n self.config = config\n\n self.content_encoder = ContentEncoder(\n config,\n enable_tflite_convertible=self.enable_tflite_convertible,\n name=\"content_encoder\"\n )\n\n if self.config.decoder_is_conditional:\n self.decoder = TFFastSpeechConditionalDecoder(\n config.decoder_self_attention_conditional_params,\n is_compatible_encoder = True,\n name = \"decoder\",\n )\n else:\n self.decoder = TFFastSpeechDecoder(\n config.decoder_self_attention_params,\n is_compatible_encoder = False,\n name = \"decoder\",\n )\n\n self.mel_dense = tf.keras.layers.Dense(units=config.num_mels, dtype=tf.float32, name=\"mel_before\")\n self.postnet = TFTacotronPostnet(config=config, dtype=tf.float32, name=\"postnet\")\n\n self.setup_inference_fn()\n\n def _build(self):\n \"\"\"Dummy input for building model.\"\"\"\n # fake inputs\n char_ids = tf.convert_to_tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], tf.int32)\n duration_gts = tf.convert_to_tensor([[3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], tf.int32)\n embed = tf.random.normal([1, 256], dtype=tf.float32)\n self(char_ids, duration_gts, embed)\n\n def content_encoder_weight_save(self, path):\n self.content_encoder.save_weights(path)\n\n def call(\n self, char_ids, duration_gts, embed, training=False, **kwargs,\n ):\n \"\"\"Call logic.\"\"\"\n content_latents, encoder_masks = self.content_encoder(char_ids, duration_gts, training=training)\n\n if self.config.decoder_is_conditional:\n decoder_output = self.decoder(\n [content_latents, tf.expand_dims(embed, 1), encoder_masks],\n training=training,\n )\n else:\n # TODO\n frame_num = tf.reduce_max(tf.reduce_sum(duration_gts, 1))\n expand_embeds = tf.tile(tf.expand_dims(embed, 1), [1, frame_num, 1])\n content_latents = tf.concat([content_latents, expand_embeds], -1)\n\n decoder_output = self.decoder(\n [content_latents, encoder_masks],\n training=training,\n )\n\n last_decoder_hidden_states = decoder_output[0]\n\n spebap_before = self.mel_dense(last_decoder_hidden_states)\n spebap_before = spebap_before * tf.cast(tf.expand_dims(encoder_masks, axis=2), spebap_before.dtype)\n\n spebap_after = self.postnet([spebap_before, encoder_masks], training=training) + spebap_before\n\n outputs = (spebap_before, spebap_after)\n return outputs\n\n def _inference(self, char_ids, duration_gts, embed, **kwargs):\n \"\"\"Call logic.\"\"\"\n content_latents, encoder_masks = self.content_encoder(char_ids, duration_gts, training=False)\n\n if self.config.decoder_is_conditional:\n decoder_output = self.decoder(\n [content_latents, tf.expand_dims(embed, 1), encoder_masks],\n training=False,\n )\n else:\n # TODO\n frame_num = tf.reduce_max(tf.reduce_sum(duration_gts, 1))\n expand_embeds = tf.tile(tf.expand_dims(embed, 1), [1, frame_num, 1])\n content_latents = tf.concat([content_latents, expand_embeds], -1)\n\n decoder_output = self.decoder(\n [content_latents, encoder_masks],\n training=False,\n )\n\n last_decoder_hidden_states = decoder_output[0]\n\n spebap_before = self.mel_dense(last_decoder_hidden_states)\n spebap_before = spebap_before * tf.cast(tf.expand_dims(encoder_masks, axis=2), spebap_before.dtype)\n\n spebap_after = self.postnet([spebap_before, encoder_masks], training=False) + spebap_before\n\n outputs = (spebap_before, spebap_after)\n return outputs\n\n def setup_inference_fn(self):\n self.inference = tf.function(\n self._inference,\n experimental_relax_shapes=True,\n input_signature=[\n tf.TensorSpec(shape=[None, None], dtype=tf.int32, name=\"char_ids\"),\n tf.TensorSpec(shape=[None, None], dtype=tf.int32, name=\"duration_gts\"),\n tf.TensorSpec(shape=[None, None], dtype=tf.float32, name=\"embed\")\n ],\n )\n\n self.inference_tflite = tf.function(\n self._inference,\n experimental_relax_shapes=True,\n input_signature=[\n tf.TensorSpec(shape=[1, None], dtype=tf.int32, name=\"char_ids\"),\n tf.TensorSpec(shape=[1, None], dtype=tf.int32, name=\"duration_gts\"),\n tf.TensorSpec(shape=[1, None], dtype=tf.float32, name=\"embed\")\n ],\n )" }, { "path": "TensorFlowTTS/tensorflow_tts/optimizers/__init__.py", "content": "from tensorflow_tts.optimizers.adamweightdecay import AdamWeightDecay, WarmUp\n" }, { "path": "TensorFlowTTS/tensorflow_tts/optimizers/adamweightdecay.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"AdamW for training self-attention.\"\"\"\n\n\nimport re\n\nimport tensorflow as tf\n\n\nclass WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):\n \"\"\"Applys a warmup schedule on a given learning rate decay schedule.\"\"\"\n\n def __init__(\n self,\n initial_learning_rate,\n decay_schedule_fn,\n warmup_steps,\n power=1.0,\n name=None,\n ):\n super(WarmUp, self).__init__()\n self.initial_learning_rate = initial_learning_rate\n self.warmup_steps = warmup_steps\n self.power = power\n self.decay_schedule_fn = decay_schedule_fn\n self.name = name\n\n def __call__(self, step):\n with tf.name_scope(self.name or \"WarmUp\") as name:\n # Implements polynomial warmup. i.e., if global_step < warmup_steps, the\n # learning rate will be `global_step/num_warmup_steps * init_lr`.\n global_step_float = tf.cast(step, tf.float32)\n warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)\n warmup_percent_done = global_step_float / warmup_steps_float\n warmup_learning_rate = self.initial_learning_rate * tf.math.pow(\n warmup_percent_done, self.power\n )\n return tf.cond(\n global_step_float < warmup_steps_float,\n lambda: warmup_learning_rate,\n lambda: self.decay_schedule_fn(step),\n name=name,\n )\n\n def get_config(self):\n return {\n \"initial_learning_rate\": self.initial_learning_rate,\n \"decay_schedule_fn\": self.decay_schedule_fn,\n \"warmup_steps\": self.warmup_steps,\n \"power\": self.power,\n \"name\": self.name,\n }\n\n\nclass AdamWeightDecay(tf.keras.optimizers.Adam):\n \"\"\"Adam enables L2 weight decay and clip_by_global_norm on gradients.\n Just adding the square of the weights to the loss function is *not* the\n correct way of using L2 regularization/weight decay with Adam, since that will\n interact with the m and v parameters in strange ways.\n\n Instead we want ot decay the weights in a manner that doesn't interact with\n the m/v parameters. This is equivalent to adding the square of the weights to\n the loss with plain (non-momentum) SGD.\n \"\"\"\n\n def __init__(\n self,\n learning_rate=0.001,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=1e-7,\n amsgrad=False,\n weight_decay_rate=0.0,\n include_in_weight_decay=None,\n exclude_from_weight_decay=None,\n name=\"AdamWeightDecay\",\n **kwargs\n ):\n super(AdamWeightDecay, self).__init__(\n learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs\n )\n self.weight_decay_rate = weight_decay_rate\n self._include_in_weight_decay = include_in_weight_decay\n self._exclude_from_weight_decay = exclude_from_weight_decay\n\n @classmethod\n def from_config(cls, config):\n \"\"\"Creates an optimizer from its config with WarmUp custom object.\"\"\"\n custom_objects = {\"WarmUp\": WarmUp}\n return super(AdamWeightDecay, cls).from_config(\n config, custom_objects=custom_objects\n )\n\n def _prepare_local(self, var_device, var_dtype, apply_state):\n super(AdamWeightDecay, self)._prepare_local(var_device, var_dtype, apply_state)\n apply_state[\"weight_decay_rate\"] = tf.constant(\n self.weight_decay_rate, name=\"adam_weight_decay_rate\"\n )\n\n def _decay_weights_op(self, var, learning_rate, apply_state):\n do_decay = self._do_use_weight_decay(var.name)\n if do_decay:\n return var.assign_sub(\n learning_rate * var * apply_state[\"weight_decay_rate\"],\n use_locking=self._use_locking,\n )\n return tf.no_op()\n\n def apply_gradients(self, grads_and_vars, clip_norm=0.5, **kwargs):\n grads, tvars = list(zip(*grads_and_vars))\n (grads, _) = tf.clip_by_global_norm(grads, clip_norm=clip_norm)\n return super(AdamWeightDecay, self).apply_gradients(zip(grads, tvars), **kwargs)\n\n def _get_lr(self, var_device, var_dtype, apply_state):\n \"\"\"Retrieves the learning rate with the given state.\"\"\"\n if apply_state is None:\n return self._decayed_lr_t[var_dtype], {}\n\n apply_state = apply_state or {}\n coefficients = apply_state.get((var_device, var_dtype))\n if coefficients is None:\n coefficients = self._fallback_apply_state(var_device, var_dtype)\n apply_state[(var_device, var_dtype)] = coefficients\n\n return coefficients[\"lr_t\"], dict(apply_state=apply_state)\n\n def _resource_apply_dense(self, grad, var, apply_state=None):\n lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)\n decay = self._decay_weights_op(var, lr_t, apply_state)\n with tf.control_dependencies([decay]):\n return super(AdamWeightDecay, self)._resource_apply_dense(\n grad, var, **kwargs\n )\n\n def _resource_apply_sparse(self, grad, var, indices, apply_state=None):\n lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)\n decay = self._decay_weights_op(var, lr_t, apply_state)\n with tf.control_dependencies([decay]):\n return super(AdamWeightDecay, self)._resource_apply_sparse(\n grad, var, indices, **kwargs\n )\n\n def get_config(self):\n config = super(AdamWeightDecay, self).get_config()\n config.update(\n {\"weight_decay_rate\": self.weight_decay_rate,}\n )\n return config\n\n def _do_use_weight_decay(self, param_name):\n \"\"\"Whether to use L2 weight decay for `param_name`.\"\"\"\n if self.weight_decay_rate == 0:\n return False\n\n if self._include_in_weight_decay:\n for r in self._include_in_weight_decay:\n if re.search(r, param_name) is not None:\n return True\n\n if self._exclude_from_weight_decay:\n for r in self._exclude_from_weight_decay:\n if re.search(r, param_name) is not None:\n return False\n return True\n" }, { "path": "TensorFlowTTS/tensorflow_tts/processor/__init__.py", "content": "from tensorflow_tts.processor.base_processor import BaseProcessor\nfrom tensorflow_tts.processor.multispk_voiceclone import MultiSPKVoiceCloneProcessor" }, { "path": "TensorFlowTTS/tensorflow_tts/processor/base_processor.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 TensorFlowTTS Team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Base Processor for all processor.\"\"\"\n\nimport abc\nimport json\nimport os\nfrom typing import Dict, List, Union\n\nfrom dataclasses import dataclass, field\n\n\nclass DataProcessorError(Exception):\n pass\n\n\n@dataclass\nclass BaseProcessor(abc.ABC):\n data_dir: str\n symbols: List[str] = field(default_factory=list)\n speakers_map: Dict[str, int] = field(default_factory=dict)\n train_f_name: str = \"train.txt\"\n delimiter: str = \"|\"\n positions = {\n \"file\": 0,\n \"text\": 1,\n \"speaker_name\": 2,\n } # positions of file,text,speaker_name after split line\n f_extension: str = \".wav\"\n saved_mapper_path: str = None\n loaded_mapper_path: str = None\n # extras\n items: List[List[str]] = field(default_factory=list) # text, wav_path, speaker_name\n symbol_to_id: Dict[str, int] = field(default_factory=dict)\n id_to_symbol: Dict[int, str] = field(default_factory=dict)\n\n def __post_init__(self):\n\n if self.loaded_mapper_path is not None:\n self._load_mapper(loaded_path=self.loaded_mapper_path)\n if self.setup_eos_token():\n self.add_symbol(\n self.setup_eos_token()\n ) # if this eos token not yet present in symbols list.\n self.eos_id = self.symbol_to_id[self.setup_eos_token()]\n return\n\n if self.symbols.__len__() < 1:\n raise DataProcessorError(\"Symbols list is empty but mapper isn't loaded\")\n\n self.create_symbols()\n self.create_items()\n self.create_speaker_map()\n self.reverse_speaker = {v: k for k, v in self.speakers_map.items()}\n if self.saved_mapper_path is not None:\n self._save_mapper(saved_path=self.saved_mapper_path)\n\n # processor name. usefull to use it for AutoProcessor\n self._processor_name = type(self).__name__\n\n if self.setup_eos_token():\n self.add_symbol(\n self.setup_eos_token()\n ) # if this eos token not yet present in symbols list.\n self.eos_id = self.symbol_to_id[self.setup_eos_token()]\n\n def __getattr__(self, name: str) -> Union[str, int]:\n if \"_id\" in name: # map symbol to id\n return self.symbol_to_id[name.replace(\"_id\", \"\")]\n return self.symbol_to_id[name] # map symbol to value\n\n def create_speaker_map(self):\n \"\"\"\n Create speaker map for dataset.\n \"\"\"\n sp_id = 0\n for i in self.items:\n speaker_name = i[-1]\n if speaker_name not in self.speakers_map:\n self.speakers_map[speaker_name] = sp_id\n sp_id += 1\n\n def get_speaker_id(self, name: str) -> int:\n return self.speakers_map[name]\n\n def get_speaker_name(self, speaker_id: int) -> str:\n return self.speakers_map[speaker_id]\n\n def create_symbols(self):\n self.symbol_to_id = {s: i for i, s in enumerate(self.symbols)}\n self.id_to_symbol = {i: s for i, s in enumerate(self.symbols)}\n\n def create_items(self):\n \"\"\"\n Method used to create items from training file\n items struct example => text, wav_file_path, speaker_name.\n Note that the speaker_name should be a last.\n \"\"\"\n with open(\n os.path.join(self.data_dir, self.train_f_name), mode=\"r\", encoding=\"utf-8\"\n ) as f:\n for line in f:\n parts = line.strip().split(self.delimiter)\n wav_path = os.path.join(self.data_dir, parts[self.positions[\"file\"]])\n wav_path = (\n wav_path + self.f_extension\n if wav_path[-len(self.f_extension) :] != self.f_extension\n else wav_path\n )\n text = parts[self.positions[\"text\"]]\n speaker_name = parts[self.positions[\"speaker_name\"]]\n self.items.append([text, wav_path, speaker_name])\n\n def add_symbol(self, symbol: Union[str, list]):\n if isinstance(symbol, str):\n if symbol in self.symbol_to_id:\n return\n self.symbols.append(symbol)\n symbol_id = len(self.symbol_to_id)\n self.symbol_to_id[symbol] = symbol_id\n self.id_to_symbol[symbol_id] = symbol\n\n elif isinstance(symbol, list):\n for i in symbol:\n self.add_symbol(i)\n else:\n raise ValueError(\"A new_symbols must be a string or list of string.\")\n\n @abc.abstractmethod\n def get_one_sample(self, item):\n \"\"\"Get one sample from dataset items.\n Args:\n item: one item in Dataset items.\n Dataset items may include (raw_text, speaker_id, wav_path, ...)\n\n Returns:\n sample (dict): sample dictionary return all feature used for preprocessing later.\n \"\"\"\n sample = {\n \"raw_text\": None,\n \"text_ids\": None,\n \"audio\": None,\n \"utt_id\": None,\n \"speaker_name\": None,\n \"rate\": None,\n }\n return sample\n\n @abc.abstractmethod\n def text_to_sequence(self, text: str):\n return []\n\n @abc.abstractmethod\n def setup_eos_token(self):\n \"\"\"Return eos symbol of type string.\"\"\"\n return \"eos\"\n\n def convert_symbols_to_ids(self, symbols: Union[str, list]):\n sequence = []\n if isinstance(symbols, str):\n sequence.append(self._symbol_to_id[symbols])\n return sequence\n elif isinstance(symbols, list):\n for s in symbols:\n if isinstance(s, str):\n sequence.append(self._symbol_to_id[s])\n else:\n raise ValueError(\"All elements of symbols must be a string.\")\n else:\n raise ValueError(\"A symbols must be a string or list of string.\")\n\n return sequence\n\n def _load_mapper(self, loaded_path: str = None):\n \"\"\"\n Save all needed mappers to file\n \"\"\"\n loaded_path = (\n os.path.join(self.data_dir, \"mapper.json\")\n if loaded_path is None\n else loaded_path\n )\n with open(loaded_path, \"r\") as f:\n data = json.load(f)\n self.speakers_map = data[\"speakers_map\"]\n self.symbol_to_id = data[\"symbol_to_id\"]\n self.id_to_symbol = {int(k): v for k, v in data[\"id_to_symbol\"].items()}\n self._processor_name = data[\"processor_name\"]\n\n # other keys\n all_data_keys = data.keys()\n for key in all_data_keys:\n if key not in [\"speakers_map\", \"symbol_to_id\", \"id_to_symbol\"]:\n setattr(self, key, data[key])\n\n def _save_mapper(self, saved_path: str = None, extra_attrs_to_save: dict = None):\n \"\"\"\n Save all needed mappers to file\n \"\"\"\n saved_path = (\n os.path.join(self.data_dir, \"mapper.json\")\n if saved_path is None\n else saved_path\n )\n with open(saved_path, \"w\") as f:\n full_mapper = {\n \"symbol_to_id\": self.symbol_to_id,\n \"id_to_symbol\": self.id_to_symbol,\n \"speakers_map\": self.speakers_map,\n \"processor_name\": self._processor_name,\n }\n if extra_attrs_to_save:\n full_mapper = {**full_mapper, **extra_attrs_to_save}\n json.dump(full_mapper, f)\n" }, { "path": "TensorFlowTTS/tensorflow_tts/processor/multispk_voiceclone.py", "content": "# -*- coding: utf-8 -*-\r\n# Copyright 2020 TensorFlowTTS Team.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\"\"\"Perform preprocessing and raw feature extraction for Aishell3 dataset.\"\"\"\r\n\r\nimport os\r\nimport re\r\nfrom typing import Dict, List, Union, Tuple, Any\r\n\r\nfrom dataclasses import dataclass, field\r\nfrom pypinyin import Style\r\nfrom pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin\r\nfrom pypinyin.converter import DefaultConverter\r\nfrom pypinyin.core import Pinyin\r\nfrom tensorflow_tts.processor import BaseProcessor\r\nfrom tqdm import tqdm\r\n\r\nfrom g2p_en import G2p\r\ng2p = G2p()\r\n\r\n_pad = [\"pad\"]\r\n_eos = [None]\r\n\r\n_pause = [\"sil\", \"#0\", \"#1\", \"#3\"]\r\n\r\nCHN_WORD = \"#0\"\r\nENG_WORD = \"#1\"\r\nPUC_SYM = \"#3\"\r\n\r\n_initials = [\r\n \"^\",\r\n \"b\",\r\n \"c\",\r\n \"ch\",\r\n \"d\",\r\n \"f\",\r\n \"g\",\r\n \"h\",\r\n \"j\",\r\n \"k\",\r\n \"l\",\r\n \"m\",\r\n \"n\",\r\n \"p\",\r\n \"q\",\r\n \"r\",\r\n \"s\",\r\n \"sh\",\r\n \"t\",\r\n \"x\",\r\n \"z\",\r\n \"zh\",\r\n]\r\n\r\n_tones = [\"1\", \"2\", \"3\", \"4\", \"5\"]\r\n\r\n_finals = [\r\n \"a\",\r\n \"ai\",\r\n \"an\",\r\n \"ang\",\r\n \"ao\",\r\n \"e\",\r\n \"ei\",\r\n \"en\",\r\n \"eng\",\r\n \"er\",\r\n \"i\",\r\n \"ia\",\r\n \"ian\",\r\n \"iang\",\r\n \"iao\",\r\n \"ie\",\r\n \"ii\",\r\n \"iii\",\r\n \"in\",\r\n \"ing\",\r\n \"iong\",\r\n \"iou\",\r\n \"o\",\r\n \"ong\",\r\n \"ou\",\r\n \"u\",\r\n \"ua\",\r\n \"uai\",\r\n \"uan\",\r\n \"uang\",\r\n \"uei\",\r\n \"uen\",\r\n \"ueng\",\r\n \"uo\",\r\n \"v\",\r\n \"van\",\r\n \"ve\",\r\n \"vn\",\r\n]\r\n\r\nAISHELL_CHN_SYMBOLS = _pad + _pause + _initials + [i + j for i in _finals for j in _tones]\r\n\r\n\r\nPINYIN_DICT = {\r\n \"a\": (\"^\", \"a\"),\r\n \"ai\": (\"^\", \"ai\"),\r\n \"an\": (\"^\", \"an\"),\r\n \"ang\": (\"^\", \"ang\"),\r\n \"ao\": (\"^\", \"ao\"),\r\n \"ba\": (\"b\", \"a\"),\r\n \"bai\": (\"b\", \"ai\"),\r\n \"ban\": (\"b\", \"an\"),\r\n \"bang\": (\"b\", \"ang\"),\r\n \"bao\": (\"b\", \"ao\"),\r\n \"be\": (\"b\", \"e\"),\r\n \"bei\": (\"b\", \"ei\"),\r\n \"ben\": (\"b\", \"en\"),\r\n \"beng\": (\"b\", \"eng\"),\r\n \"bi\": (\"b\", \"i\"),\r\n \"bian\": (\"b\", \"ian\"),\r\n \"biao\": (\"b\", \"iao\"),\r\n \"bie\": (\"b\", \"ie\"),\r\n \"bin\": (\"b\", \"in\"),\r\n \"bing\": (\"b\", \"ing\"),\r\n \"bo\": (\"b\", \"o\"),\r\n \"bu\": (\"b\", \"u\"),\r\n \"ca\": (\"c\", \"a\"),\r\n \"cai\": (\"c\", \"ai\"),\r\n \"can\": (\"c\", \"an\"),\r\n \"cang\": (\"c\", \"ang\"),\r\n \"cao\": (\"c\", \"ao\"),\r\n \"ce\": (\"c\", \"e\"),\r\n \"cen\": (\"c\", \"en\"),\r\n \"ceng\": (\"c\", \"eng\"),\r\n \"cha\": (\"ch\", \"a\"),\r\n \"chai\": (\"ch\", \"ai\"),\r\n \"chan\": (\"ch\", \"an\"),\r\n \"chang\": (\"ch\", \"ang\"),\r\n \"chao\": (\"ch\", \"ao\"),\r\n \"che\": (\"ch\", \"e\"),\r\n \"chen\": (\"ch\", \"en\"),\r\n \"cheng\": (\"ch\", \"eng\"),\r\n \"chi\": (\"ch\", \"iii\"),\r\n \"chong\": (\"ch\", \"ong\"),\r\n \"chou\": (\"ch\", \"ou\"),\r\n \"chu\": (\"ch\", \"u\"),\r\n \"chua\": (\"ch\", \"ua\"),\r\n \"chuai\": (\"ch\", \"uai\"),\r\n \"chuan\": (\"ch\", \"uan\"),\r\n \"chuang\": (\"ch\", \"uang\"),\r\n \"chui\": (\"ch\", \"uei\"),\r\n \"chun\": (\"ch\", \"uen\"),\r\n \"chuo\": (\"ch\", \"uo\"),\r\n \"ci\": (\"c\", \"ii\"),\r\n \"cong\": (\"c\", \"ong\"),\r\n \"cou\": (\"c\", \"ou\"),\r\n \"cu\": (\"c\", \"u\"),\r\n \"cuan\": (\"c\", \"uan\"),\r\n \"cui\": (\"c\", \"uei\"),\r\n \"cun\": (\"c\", \"uen\"),\r\n \"cuo\": (\"c\", \"uo\"),\r\n \"da\": (\"d\", \"a\"),\r\n \"dai\": (\"d\", \"ai\"),\r\n \"dan\": (\"d\", \"an\"),\r\n \"dang\": (\"d\", \"ang\"),\r\n \"dao\": (\"d\", \"ao\"),\r\n \"de\": (\"d\", \"e\"),\r\n \"dei\": (\"d\", \"ei\"),\r\n \"den\": (\"d\", \"en\"),\r\n \"deng\": (\"d\", \"eng\"),\r\n \"di\": (\"d\", \"i\"),\r\n \"dia\": (\"d\", \"ia\"),\r\n \"dian\": (\"d\", \"ian\"),\r\n \"diao\": (\"d\", \"iao\"),\r\n \"die\": (\"d\", \"ie\"),\r\n \"ding\": (\"d\", \"ing\"),\r\n \"diu\": (\"d\", \"iou\"),\r\n \"dong\": (\"d\", \"ong\"),\r\n \"dou\": (\"d\", \"ou\"),\r\n \"du\": (\"d\", \"u\"),\r\n \"duan\": (\"d\", \"uan\"),\r\n \"dui\": (\"d\", \"uei\"),\r\n \"dun\": (\"d\", \"uen\"),\r\n \"duo\": (\"d\", \"uo\"),\r\n \"e\": (\"^\", \"e\"),\r\n \"ei\": (\"^\", \"ei\"),\r\n \"en\": (\"^\", \"en\"),\r\n \"ng\": (\"^\", \"en\"),\r\n \"eng\": (\"^\", \"eng\"),\r\n \"er\": (\"^\", \"er\"),\r\n \"fa\": (\"f\", \"a\"),\r\n \"fan\": (\"f\", \"an\"),\r\n \"fang\": (\"f\", \"ang\"),\r\n \"fei\": (\"f\", \"ei\"),\r\n \"fen\": (\"f\", \"en\"),\r\n \"feng\": (\"f\", \"eng\"),\r\n \"fo\": (\"f\", \"o\"),\r\n \"fou\": (\"f\", \"ou\"),\r\n \"fu\": (\"f\", \"u\"),\r\n \"ga\": (\"g\", \"a\"),\r\n \"gai\": (\"g\", \"ai\"),\r\n \"gan\": (\"g\", \"an\"),\r\n \"gang\": (\"g\", \"ang\"),\r\n \"gao\": (\"g\", \"ao\"),\r\n \"ge\": (\"g\", \"e\"),\r\n \"gei\": (\"g\", \"ei\"),\r\n \"gen\": (\"g\", \"en\"),\r\n \"geng\": (\"g\", \"eng\"),\r\n \"gong\": (\"g\", \"ong\"),\r\n \"gou\": (\"g\", \"ou\"),\r\n \"gu\": (\"g\", \"u\"),\r\n \"gua\": (\"g\", \"ua\"),\r\n \"guai\": (\"g\", \"uai\"),\r\n \"guan\": (\"g\", \"uan\"),\r\n \"guang\": (\"g\", \"uang\"),\r\n \"gui\": (\"g\", \"uei\"),\r\n \"gun\": (\"g\", \"uen\"),\r\n \"guo\": (\"g\", \"uo\"),\r\n \"ha\": (\"h\", \"a\"),\r\n \"hai\": (\"h\", \"ai\"),\r\n \"han\": (\"h\", \"an\"),\r\n \"hang\": (\"h\", \"ang\"),\r\n \"hao\": (\"h\", \"ao\"),\r\n \"he\": (\"h\", \"e\"),\r\n \"hei\": (\"h\", \"ei\"),\r\n \"hen\": (\"h\", \"en\"),\r\n \"heng\": (\"h\", \"eng\"),\r\n \"hong\": (\"h\", \"ong\"),\r\n \"hou\": (\"h\", \"ou\"),\r\n \"hu\": (\"h\", \"u\"),\r\n \"hua\": (\"h\", \"ua\"),\r\n \"huai\": (\"h\", \"uai\"),\r\n \"huan\": (\"h\", \"uan\"),\r\n \"huang\": (\"h\", \"uang\"),\r\n \"hui\": (\"h\", \"uei\"),\r\n \"hun\": (\"h\", \"uen\"),\r\n \"huo\": (\"h\", \"uo\"),\r\n \"ji\": (\"j\", \"i\"),\r\n \"jia\": (\"j\", \"ia\"),\r\n \"jian\": (\"j\", \"ian\"),\r\n \"jiang\": (\"j\", \"iang\"),\r\n \"jiao\": (\"j\", \"iao\"),\r\n \"jie\": (\"j\", \"ie\"),\r\n \"jin\": (\"j\", \"in\"),\r\n \"jing\": (\"j\", \"ing\"),\r\n \"jiong\": (\"j\", \"iong\"),\r\n \"jiu\": (\"j\", \"iou\"),\r\n \"ju\": (\"j\", \"v\"),\r\n \"juan\": (\"j\", \"van\"),\r\n \"jue\": (\"j\", \"ve\"),\r\n \"jun\": (\"j\", \"vn\"),\r\n \"ka\": (\"k\", \"a\"),\r\n \"kai\": (\"k\", \"ai\"),\r\n \"kan\": (\"k\", \"an\"),\r\n \"kang\": (\"k\", \"ang\"),\r\n \"kao\": (\"k\", \"ao\"),\r\n \"ke\": (\"k\", \"e\"),\r\n \"kei\": (\"k\", \"ei\"),\r\n \"ken\": (\"k\", \"en\"),\r\n \"keng\": (\"k\", \"eng\"),\r\n \"kong\": (\"k\", \"ong\"),\r\n \"kou\": (\"k\", \"ou\"),\r\n \"ku\": (\"k\", \"u\"),\r\n \"kua\": (\"k\", \"ua\"),\r\n \"kuai\": (\"k\", \"uai\"),\r\n \"kuan\": (\"k\", \"uan\"),\r\n \"kuang\": (\"k\", \"uang\"),\r\n \"kui\": (\"k\", \"uei\"),\r\n \"kun\": (\"k\", \"uen\"),\r\n \"kuo\": (\"k\", \"uo\"),\r\n \"la\": (\"l\", \"a\"),\r\n \"lai\": (\"l\", \"ai\"),\r\n \"lan\": (\"l\", \"an\"),\r\n \"lang\": (\"l\", \"ang\"),\r\n \"lao\": (\"l\", \"ao\"),\r\n \"le\": (\"l\", \"e\"),\r\n \"lei\": (\"l\", \"ei\"),\r\n \"leng\": (\"l\", \"eng\"),\r\n \"li\": (\"l\", \"i\"),\r\n \"lia\": (\"l\", \"ia\"),\r\n \"lian\": (\"l\", \"ian\"),\r\n \"liang\": (\"l\", \"iang\"),\r\n \"liao\": (\"l\", \"iao\"),\r\n \"lie\": (\"l\", \"ie\"),\r\n \"lin\": (\"l\", \"in\"),\r\n \"ling\": (\"l\", \"ing\"),\r\n \"liu\": (\"l\", \"iou\"),\r\n \"lo\": (\"l\", \"o\"),\r\n \"long\": (\"l\", \"ong\"),\r\n \"lou\": (\"l\", \"ou\"),\r\n \"lu\": (\"l\", \"u\"),\r\n \"lv\": (\"l\", \"v\"),\r\n \"luan\": (\"l\", \"uan\"),\r\n \"lve\": (\"l\", \"ve\"),\r\n \"lue\": (\"l\", \"ve\"),\r\n \"lun\": (\"l\", \"uen\"),\r\n \"luo\": (\"l\", \"uo\"),\r\n \"ma\": (\"m\", \"a\"),\r\n \"mai\": (\"m\", \"ai\"),\r\n \"man\": (\"m\", \"an\"),\r\n \"mang\": (\"m\", \"ang\"),\r\n \"mao\": (\"m\", \"ao\"),\r\n \"me\": (\"m\", \"e\"),\r\n \"mei\": (\"m\", \"ei\"),\r\n \"men\": (\"m\", \"en\"),\r\n \"meng\": (\"m\", \"eng\"),\r\n \"mi\": (\"m\", \"i\"),\r\n \"mian\": (\"m\", \"ian\"),\r\n \"miao\": (\"m\", \"iao\"),\r\n \"mie\": (\"m\", \"ie\"),\r\n \"min\": (\"m\", \"in\"),\r\n \"ming\": (\"m\", \"ing\"),\r\n \"miu\": (\"m\", \"iou\"),\r\n \"mo\": (\"m\", \"o\"),\r\n \"mou\": (\"m\", \"ou\"),\r\n \"mu\": (\"m\", \"u\"),\r\n \"na\": (\"n\", \"a\"),\r\n \"nai\": (\"n\", \"ai\"),\r\n \"nan\": (\"n\", \"an\"),\r\n \"nang\": (\"n\", \"ang\"),\r\n \"nao\": (\"n\", \"ao\"),\r\n \"ne\": (\"n\", \"e\"),\r\n \"nei\": (\"n\", \"ei\"),\r\n \"nen\": (\"n\", \"en\"),\r\n \"neng\": (\"n\", \"eng\"),\r\n \"ni\": (\"n\", \"i\"),\r\n \"nia\": (\"n\", \"ia\"),\r\n \"nian\": (\"n\", \"ian\"),\r\n \"niang\": (\"n\", \"iang\"),\r\n \"niao\": (\"n\", \"iao\"),\r\n \"nie\": (\"n\", \"ie\"),\r\n \"nin\": (\"n\", \"in\"),\r\n \"ning\": (\"n\", \"ing\"),\r\n \"niu\": (\"n\", \"iou\"),\r\n \"nong\": (\"n\", \"ong\"),\r\n \"nou\": (\"n\", \"ou\"),\r\n \"nu\": (\"n\", \"u\"),\r\n \"nv\": (\"n\", \"v\"),\r\n \"nuan\": (\"n\", \"uan\"),\r\n \"nve\": (\"n\", \"ve\"),\r\n \"nue\": (\"n\", \"ve\"),\r\n \"nuo\": (\"n\", \"uo\"),\r\n \"o\": (\"^\", \"o\"),\r\n \"ou\": (\"^\", \"ou\"),\r\n \"pa\": (\"p\", \"a\"),\r\n \"pai\": (\"p\", \"ai\"),\r\n \"pan\": (\"p\", \"an\"),\r\n \"pang\": (\"p\", \"ang\"),\r\n \"pao\": (\"p\", \"ao\"),\r\n \"pe\": (\"p\", \"e\"),\r\n \"pei\": (\"p\", \"ei\"),\r\n \"pen\": (\"p\", \"en\"),\r\n \"peng\": (\"p\", \"eng\"),\r\n \"pi\": (\"p\", \"i\"),\r\n \"pian\": (\"p\", \"ian\"),\r\n \"piao\": (\"p\", \"iao\"),\r\n \"pie\": (\"p\", \"ie\"),\r\n \"pin\": (\"p\", \"in\"),\r\n \"ping\": (\"p\", \"ing\"),\r\n \"po\": (\"p\", \"o\"),\r\n \"pou\": (\"p\", \"ou\"),\r\n \"pu\": (\"p\", \"u\"),\r\n \"qi\": (\"q\", \"i\"),\r\n \"qia\": (\"q\", \"ia\"),\r\n \"qian\": (\"q\", \"ian\"),\r\n \"qiang\": (\"q\", \"iang\"),\r\n \"qiao\": (\"q\", \"iao\"),\r\n \"qie\": (\"q\", \"ie\"),\r\n \"qin\": (\"q\", \"in\"),\r\n \"qing\": (\"q\", \"ing\"),\r\n \"qiong\": (\"q\", \"iong\"),\r\n \"qiu\": (\"q\", \"iou\"),\r\n \"qu\": (\"q\", \"v\"),\r\n \"quan\": (\"q\", \"van\"),\r\n \"que\": (\"q\", \"ve\"),\r\n \"qun\": (\"q\", \"vn\"),\r\n \"ran\": (\"r\", \"an\"),\r\n \"rang\": (\"r\", \"ang\"),\r\n \"rao\": (\"r\", \"ao\"),\r\n \"re\": (\"r\", \"e\"),\r\n \"ren\": (\"r\", \"en\"),\r\n \"reng\": (\"r\", \"eng\"),\r\n \"ri\": (\"r\", \"iii\"),\r\n \"rong\": (\"r\", \"ong\"),\r\n \"rou\": (\"r\", \"ou\"),\r\n \"ru\": (\"r\", \"u\"),\r\n \"rua\": (\"r\", \"ua\"),\r\n \"ruan\": (\"r\", \"uan\"),\r\n \"rui\": (\"r\", \"uei\"),\r\n \"run\": (\"r\", \"uen\"),\r\n \"ruo\": (\"r\", \"uo\"),\r\n \"sa\": (\"s\", \"a\"),\r\n \"sai\": (\"s\", \"ai\"),\r\n \"san\": (\"s\", \"an\"),\r\n \"sang\": (\"s\", \"ang\"),\r\n \"sao\": (\"s\", \"ao\"),\r\n \"se\": (\"s\", \"e\"),\r\n \"sen\": (\"s\", \"en\"),\r\n \"seng\": (\"s\", \"eng\"),\r\n \"sha\": (\"sh\", \"a\"),\r\n \"shai\": (\"sh\", \"ai\"),\r\n \"shan\": (\"sh\", \"an\"),\r\n \"shang\": (\"sh\", \"ang\"),\r\n \"shao\": (\"sh\", \"ao\"),\r\n \"she\": (\"sh\", \"e\"),\r\n \"shei\": (\"sh\", \"ei\"),\r\n \"shen\": (\"sh\", \"en\"),\r\n \"sheng\": (\"sh\", \"eng\"),\r\n \"shi\": (\"sh\", \"iii\"),\r\n \"shou\": (\"sh\", \"ou\"),\r\n \"shu\": (\"sh\", \"u\"),\r\n \"shua\": (\"sh\", \"ua\"),\r\n \"shuai\": (\"sh\", \"uai\"),\r\n \"shuan\": (\"sh\", \"uan\"),\r\n \"shuang\": (\"sh\", \"uang\"),\r\n \"shui\": (\"sh\", \"uei\"),\r\n \"shun\": (\"sh\", \"uen\"),\r\n \"shuo\": (\"sh\", \"uo\"),\r\n \"si\": (\"s\", \"ii\"),\r\n \"song\": (\"s\", \"ong\"),\r\n \"sou\": (\"s\", \"ou\"),\r\n \"su\": (\"s\", \"u\"),\r\n \"suan\": (\"s\", \"uan\"),\r\n \"sui\": (\"s\", \"uei\"),\r\n \"sun\": (\"s\", \"uen\"),\r\n \"suo\": (\"s\", \"uo\"),\r\n \"ta\": (\"t\", \"a\"),\r\n \"tai\": (\"t\", \"ai\"),\r\n \"tan\": (\"t\", \"an\"),\r\n \"tang\": (\"t\", \"ang\"),\r\n \"tao\": (\"t\", \"ao\"),\r\n \"te\": (\"t\", \"e\"),\r\n \"tei\": (\"t\", \"ei\"),\r\n \"teng\": (\"t\", \"eng\"),\r\n \"ti\": (\"t\", \"i\"),\r\n \"tian\": (\"t\", \"ian\"),\r\n \"tiao\": (\"t\", \"iao\"),\r\n \"tie\": (\"t\", \"ie\"),\r\n \"ting\": (\"t\", \"ing\"),\r\n \"tong\": (\"t\", \"ong\"),\r\n \"tou\": (\"t\", \"ou\"),\r\n \"tu\": (\"t\", \"u\"),\r\n \"tuan\": (\"t\", \"uan\"),\r\n \"tui\": (\"t\", \"uei\"),\r\n \"tun\": (\"t\", \"uen\"),\r\n \"tuo\": (\"t\", \"uo\"),\r\n \"wa\": (\"^\", \"ua\"),\r\n \"wai\": (\"^\", \"uai\"),\r\n \"wan\": (\"^\", \"uan\"),\r\n \"wang\": (\"^\", \"uang\"),\r\n \"wei\": (\"^\", \"uei\"),\r\n \"wen\": (\"^\", \"uen\"),\r\n \"weng\": (\"^\", \"ueng\"),\r\n \"wo\": (\"^\", \"uo\"),\r\n \"wu\": (\"^\", \"u\"),\r\n \"xi\": (\"x\", \"i\"),\r\n \"xia\": (\"x\", \"ia\"),\r\n \"xian\": (\"x\", \"ian\"),\r\n \"xiang\": (\"x\", \"iang\"),\r\n \"xiao\": (\"x\", \"iao\"),\r\n \"xie\": (\"x\", \"ie\"),\r\n \"xin\": (\"x\", \"in\"),\r\n \"xing\": (\"x\", \"ing\"),\r\n \"xiong\": (\"x\", \"iong\"),\r\n \"xiu\": (\"x\", \"iou\"),\r\n \"xu\": (\"x\", \"v\"),\r\n \"xuan\": (\"x\", \"van\"),\r\n \"xue\": (\"x\", \"ve\"),\r\n \"xun\": (\"x\", \"vn\"),\r\n \"ya\": (\"^\", \"ia\"),\r\n \"yan\": (\"^\", \"ian\"),\r\n \"yang\": (\"^\", \"iang\"),\r\n \"yao\": (\"^\", \"iao\"),\r\n \"ye\": (\"^\", \"ie\"),\r\n \"yi\": (\"^\", \"i\"),\r\n \"yin\": (\"^\", \"in\"),\r\n \"ying\": (\"^\", \"ing\"),\r\n \"yo\": (\"^\", \"iou\"),\r\n \"yong\": (\"^\", \"iong\"),\r\n \"you\": (\"^\", \"iou\"),\r\n \"yu\": (\"^\", \"v\"),\r\n \"yuan\": (\"^\", \"van\"),\r\n \"yue\": (\"^\", \"ve\"),\r\n \"yun\": (\"^\", \"vn\"),\r\n \"za\": (\"z\", \"a\"),\r\n \"zai\": (\"z\", \"ai\"),\r\n \"zan\": (\"z\", \"an\"),\r\n \"zang\": (\"z\", \"ang\"),\r\n \"zao\": (\"z\", \"ao\"),\r\n \"ze\": (\"z\", \"e\"),\r\n \"zei\": (\"z\", \"ei\"),\r\n \"zen\": (\"z\", \"en\"),\r\n \"zeng\": (\"z\", \"eng\"),\r\n \"zha\": (\"zh\", \"a\"),\r\n \"zhai\": (\"zh\", \"ai\"),\r\n \"zhan\": (\"zh\", \"an\"),\r\n \"zhang\": (\"zh\", \"ang\"),\r\n \"zhao\": (\"zh\", \"ao\"),\r\n \"zhe\": (\"zh\", \"e\"),\r\n \"zhei\": (\"zh\", \"ei\"),\r\n \"zhen\": (\"zh\", \"en\"),\r\n \"zheng\": (\"zh\", \"eng\"),\r\n \"zhi\": (\"zh\", \"iii\"),\r\n \"zhong\": (\"zh\", \"ong\"),\r\n \"zhou\": (\"zh\", \"ou\"),\r\n \"zhu\": (\"zh\", \"u\"),\r\n \"zhua\": (\"zh\", \"ua\"),\r\n \"zhuai\": (\"zh\", \"uai\"),\r\n \"zhuan\": (\"zh\", \"uan\"),\r\n \"zhuang\": (\"zh\", \"uang\"),\r\n \"zhui\": (\"zh\", \"uei\"),\r\n \"zhun\": (\"zh\", \"uen\"),\r\n \"zhuo\": (\"zh\", \"uo\"),\r\n \"zi\": (\"z\", \"ii\"),\r\n \"zong\": (\"z\", \"ong\"),\r\n \"zou\": (\"z\", \"ou\"),\r\n \"zu\": (\"z\", \"u\"),\r\n \"zuan\": (\"z\", \"uan\"),\r\n \"zui\": (\"z\", \"uei\"),\r\n \"zun\": (\"z\", \"uen\"),\r\n \"zuo\": (\"z\", \"uo\"),\r\n}\r\n\r\nzh_pattern = re.compile(r\"([\\u4e00-\\u9fa5]+)\")\r\nen_pattern = re.compile(r\"([a-zA-Z]+)\")\r\n\r\ndef is_zh(word):\r\n global zh_pattern\r\n match = zh_pattern.search(word)\r\n return match is not None\r\n\r\ndef is_en(word):\r\n global en_pattern\r\n match = en_pattern.search(word)\r\n return match is not None\r\n\r\nclass MyConverter(NeutralToneWith5Mixin, DefaultConverter):\r\n pass\r\n\r\n\r\n@dataclass\r\nclass MultiSPKVoiceCloneProcessor(BaseProcessor):\r\n\r\n pinyin_dict : Dict[str, Tuple[str, str]] = field(default_factory=lambda: PINYIN_DICT)\r\n cleaner_names : str = None\r\n target_rate : int = 16000\r\n speaker_name : str = \"multispk_voiceclone\"\r\n none_pinyin_symnum : int = len(_pad + _pause)\r\n during_train : bool = False\r\n f0_train : bool = False\r\n all_train : bool = False\r\n mfaed_txt : str = \"\"\r\n wavs_dir : str = \"\"\r\n embed_dir : str = \"\"\r\n spkinfo_dir : str = \"\"\r\n unseen_dir : str = \"\"\r\n\r\n def __post_init__(self):\r\n self.pinyin_parser = self.get_pinyin_parser()\r\n\r\n if self.spkinfo_dir:\r\n self.create_speaker_info()\r\n\r\n if self.unseen_dir:\r\n self.create_unseen_speaker()\r\n\r\n super().__post_init__()\r\n\r\n def setup_eos_token(self):\r\n return _eos[0]\r\n\r\n def create_speaker_info(self):\r\n self.spk2sex_dict = {}\r\n with open(self.spkinfo_dir, \"r\") as fr:\r\n lines = fr.readlines()\r\n\r\n for line in lines:\r\n spk_name, _, sex, *_ = line.strip().split()\r\n if spk_name not in self.spk2sex_dict.keys():\r\n self.spk2sex_dict[spk_name] = sex\r\n \r\n print(\"*\"*50)\r\n print(f\"Have {len(self.spk2sex_dict)} Speakers\")\r\n print(\"*\"*50)\r\n\r\n def create_unseen_speaker(self):\r\n self.unseen_spk = []\r\n with open(self.unseen_dir, \"r\") as fr:\r\n lines = fr.readlines()\r\n\r\n for line in lines:\r\n self.unseen_spk.append(line.strip())\r\n \r\n self.unseen_spk_num = 0\r\n\r\n print(\"*\"*50)\r\n print(f\"Have {len(self.unseen_spk)} UNSeen Speakers\")\r\n print(\"*\"*50)\r\n\r\n # TODO now just support for aishell3\r\n def create_items(self):\r\n items = []\r\n if self.data_dir:\r\n with open(\r\n os.path.join(self.data_dir, self.mfaed_txt),\r\n encoding=\"utf-8\",\r\n ) as ttf:\r\n lines = ttf.readlines()\r\n for line in tqdm(lines):\r\n filename, phoneseq, durseq = line.strip().split(\"|\")\r\n spkname = filename[:7] if filename[0] == \"S\" else filename.split(\"_\")[0]\r\n\r\n if self.spkinfo_dir and self.spk2sex_dict[spkname] == \"male\":\r\n continue\r\n\r\n # if self.unseen_dir and (spkname in self.unseen_spk or \"_\" in filename):\r\n if self.unseen_dir and spkname in self.unseen_spk:\r\n self.unseen_spk_num += 1\r\n continue\r\n\r\n wav_path = os.path.join(self.data_dir, self.wavs_dir, f\"{spkname}\", f\"{filename}.wav\")\r\n embed_path = os.path.join(self.data_dir, self.embed_dir, f\"{filename}-embed.npy\")\r\n\r\n if ((self.during_train or self.f0_train) and os.path.exists(wav_path)) or \\\r\n (self.all_train and os.path.exists(wav_path) and os.path.exists(embed_path)):\r\n try:\r\n text_ids = [self.symbol_to_id[phone] for phone in phoneseq.split()]\r\n durs = [int(dur) for dur in durseq.split()]\r\n embed_path = embed_path if self.all_train else None\r\n assert len(text_ids) == len(durs)\r\n except Exception:\r\n print(\"error: generate sequence ids\", filename)\r\n continue\r\n \r\n items.append([spkname, filename, wav_path, text_ids, durs, embed_path])\r\n\r\n self.items = items\r\n print(\"*\"*50)\r\n print(f\"Have {len(self.items)} samples\")\r\n if self.unseen_dir:\r\n print(f\"Have {self.unseen_spk_num} UNSeen samples\")\r\n print(\"*\"*50)\r\n\r\n def get_phoneme_from_char_and_pinyin(self, txt, pinyin):\r\n txt = txt.replace(\"'\", \"\")\r\n txt = txt.replace(\"-\", \"\")\r\n\r\n phrase_list = re.split(\"#\\d\", txt)\r\n rhythms_list = re.findall(\"#\\d\", txt)\r\n\r\n pinyin_index = 0\r\n last_phrase_type = 'chn'\r\n\r\n result = [\"sil\"]\r\n\r\n phrase = phrase_list[0]\r\n\r\n if is_zh(phrase):\r\n length = len(phrase)\r\n\r\n for pinyin_one in pinyin[pinyin_index:pinyin_index+length]:\r\n tone = pinyin_one[-1]\r\n a1, a2 = self.pinyin_dict[pinyin_one[:-1]]\r\n result.append(a1)\r\n result.append(a2+tone)\r\n \r\n pinyin_index += length\r\n\r\n last_phrase_type = 'chn'\r\n \r\n elif is_en(phrase):\r\n pinyin_one = pinyin[pinyin_index]\r\n\r\n result += [ph[:-1] if ph[-1].isdigit() else ph for ph in pinyin_one.split(\"*\")]\r\n\r\n pinyin_index += 1\r\n\r\n last_phrase_type = 'eng'\r\n\r\n # TODO\r\n elif phrase == \"\" or phrase == \" \":\r\n pass\r\n\r\n else:\r\n print(\"Error: processed text ->\", txt)\r\n assert False\r\n\r\n phrase_list = phrase_list[1:]\r\n\r\n for rhythm, phrase in zip(rhythms_list, phrase_list):\r\n\r\n if is_zh(phrase):\r\n if rhythm == \"#3\":\r\n result.append(PUC_SYM)\r\n elif last_phrase_type == 'eng':\r\n result.append(ENG_WORD)\r\n else:\r\n result.append(CHN_WORD)\r\n\r\n length = len(phrase)\r\n\r\n for pinyin_one in pinyin[pinyin_index:pinyin_index+length]:\r\n tone = pinyin_one[-1]\r\n a1, a2 = self.pinyin_dict[pinyin_one[:-1]]\r\n result.append(a1)\r\n result.append(a2+tone)\r\n \r\n pinyin_index += length\r\n\r\n last_phrase_type = 'chn'\r\n \r\n elif is_en(phrase):\r\n if rhythm == \"#3\":\r\n result.append(PUC_SYM)\r\n else:\r\n result.append(ENG_WORD)\r\n\r\n pinyin_one = pinyin[pinyin_index]\r\n\r\n result += [ph[:-1] if ph[-1].isdigit() else ph for ph in pinyin_one.split(\"*\")]\r\n\r\n pinyin_index += 1\r\n\r\n last_phrase_type = 'eng'\r\n\r\n # TODO\r\n elif phrase == \"\" or phrase == \" \":\r\n continue\r\n\r\n else:\r\n print(\"Error: processed text ->\", txt)\r\n assert False\r\n\r\n # result.append(PUC_SYM)\r\n result.append(\"sil\")\r\n\r\n assert pinyin_index == len(pinyin)\r\n\r\n return result\r\n\r\n def get_one_sample(self, item): \r\n spkname, filename, wav_path, text_ids, durs, embed_path = item\r\n\r\n sample = {\r\n \"speaker_name\": spkname,\r\n \"filename\" : filename,\r\n \"wav_path\" : wav_path,\r\n \"text_ids\" : text_ids,\r\n \"durs\" : durs,\r\n \"embed_path\" : embed_path,\r\n \"rate\" : self.target_rate,\r\n }\r\n\r\n return sample\r\n\r\n def get_pinyin_parser(self):\r\n my_pinyin = Pinyin(MyConverter())\r\n pinyin = my_pinyin.pinyin\r\n return pinyin\r\n\r\n def text_to_sequence(self, text, inference=False):\r\n if inference:\r\n pinyin = self.pinyin_parser(text, style=Style.TONE3)\r\n # print(pinyin)\r\n new_pinyin = []\r\n for x in pinyin:\r\n x = \"\".join(x)\r\n if \"#\" not in x:\r\n new_pinyin.append(x)\r\n else:\r\n if len(x) == 2:\r\n continue\r\n else:\r\n eng_list = re.split(\"#\\d\", x)\r\n if eng_list[0] == \"\":\r\n eng_list = eng_list[1:]\r\n if eng_list[-1] == \"\":\r\n eng_list = eng_list[:-1]\r\n\r\n for e in eng_list:\r\n new_pinyin.append(\"*\".join(g2p(e)))\r\n\r\n print(new_pinyin)\r\n\r\n phonemes = self.get_phoneme_from_char_and_pinyin(text, new_pinyin)\r\n text = \" \".join(phonemes)\r\n print(f\"phoneme seq: {text}\")\r\n\r\n sequence = []\r\n #print(\"text\",text)\r\n for symbol in text.split():\r\n idx = self.symbol_to_id[symbol]\r\n sequence.append(idx)\r\n\r\n return sequence\r\n\r\n def create_speaker_map(self):\r\n pass" }, { "path": "TensorFlowTTS/tensorflow_tts/trainers/__init__.py", "content": "from tensorflow_tts.trainers.base_trainer import GanBasedTrainer, Seq2SeqBasedTrainer\n" }, { "path": "TensorFlowTTS/tensorflow_tts/trainers/base_trainer.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Based Trainer.\"\"\"\n\nimport abc\nimport logging\nimport os\n\nimport tensorflow as tf\nfrom tqdm import tqdm\n\nimport random\nimport numpy as np\n\nSEED = 2021\nrandom.seed(SEED)\nnp.random.seed(SEED)\ntf.random.set_seed(SEED)\n\nclass BasedTrainer(metaclass=abc.ABCMeta):\n \"\"\"Customized trainer module for all models.\"\"\"\n\n def __init__(self, steps, epochs, config):\n self.steps = steps\n self.epochs = epochs\n self.config = config\n self.finish_train = False\n self.writer = tf.summary.create_file_writer(config[\"outdir\"])\n self.train_data_loader = None\n self.eval_data_loader = None\n self.train_metrics = None\n self.eval_metrics = None\n self.list_metrics_name = None\n\n def init_train_eval_metrics(self, list_metrics_name):\n \"\"\"Init train and eval metrics to save it to tensorboard.\"\"\"\n self.train_metrics = {}\n self.eval_metrics = {}\n for name in list_metrics_name:\n self.train_metrics.update(\n {name: tf.keras.metrics.Mean(name=\"train_\" + name, dtype=tf.float32)}\n )\n self.eval_metrics.update(\n {name: tf.keras.metrics.Mean(name=\"eval_\" + name, dtype=tf.float32)}\n )\n\n def reset_states_train(self):\n \"\"\"Reset train metrics after save it to tensorboard.\"\"\"\n for metric in self.train_metrics.keys():\n self.train_metrics[metric].reset_states()\n\n def reset_states_eval(self):\n \"\"\"Reset eval metrics after save it to tensorboard.\"\"\"\n for metric in self.eval_metrics.keys():\n self.eval_metrics[metric].reset_states()\n\n def update_train_metrics(self, dict_metrics_losses):\n for name, value in dict_metrics_losses.items():\n self.train_metrics[name].update_state(value)\n\n def update_eval_metrics(self, dict_metrics_losses):\n for name, value in dict_metrics_losses.items():\n self.eval_metrics[name].update_state(value)\n\n def set_train_data_loader(self, train_dataset):\n \"\"\"Set train data loader (MUST).\"\"\"\n self.train_data_loader = train_dataset\n\n def get_train_data_loader(self):\n \"\"\"Get train data loader.\"\"\"\n return self.train_data_loader\n\n def set_eval_data_loader(self, eval_dataset):\n \"\"\"Set eval data loader (MUST).\"\"\"\n self.eval_data_loader = eval_dataset\n\n def get_eval_data_loader(self):\n \"\"\"Get eval data loader.\"\"\"\n return self.eval_data_loader\n\n @abc.abstractmethod\n def compile(self):\n pass\n\n @abc.abstractmethod\n def create_checkpoint_manager(self, saved_path=None, max_to_keep=10):\n \"\"\"Create checkpoint management.\"\"\"\n pass\n\n def run(self):\n \"\"\"Run training.\"\"\"\n self.tqdm = tqdm(\n initial=self.steps, total=self.config[\"train_max_steps\"], desc=\"[train]\"\n )\n while True:\n self._train_epoch()\n\n if self.finish_train:\n break\n\n self.tqdm.close()\n logging.info(\"Finish training.\")\n\n @abc.abstractmethod\n def save_checkpoint(self):\n \"\"\"Save checkpoint.\"\"\"\n pass\n\n @abc.abstractmethod\n def load_checkpoint(self, pretrained_path):\n \"\"\"Load checkpoint.\"\"\"\n pass\n\n def _train_epoch(self):\n \"\"\"Train model one epoch.\"\"\"\n for train_steps_per_epoch, batch in enumerate(self.train_data_loader, 1):\n # one step training\n self._train_step(batch)\n\n # check interval\n self._check_log_interval()\n self._check_eval_interval()\n self._check_save_interval()\n\n # check wheter training is finished\n if self.finish_train:\n return\n\n # update\n self.epochs += 1\n self.train_steps_per_epoch = train_steps_per_epoch\n logging.info(\n f\"(Steps: {self.steps}) Finished {self.epochs} epoch training \"\n f\"({self.train_steps_per_epoch} steps per epoch).\"\n )\n\n @abc.abstractmethod\n def _eval_epoch(self):\n \"\"\"One epoch evaluation.\"\"\"\n pass\n\n @abc.abstractmethod\n def _train_step(self, batch):\n \"\"\"One step training.\"\"\"\n pass\n\n @abc.abstractmethod\n def _check_log_interval(self):\n \"\"\"Save log interval.\"\"\"\n pass\n\n @abc.abstractmethod\n def fit(self):\n pass\n\n def _check_eval_interval(self):\n \"\"\"Evaluation interval step.\"\"\"\n if self.steps % self.config[\"eval_interval_steps\"] == 0:\n self._eval_epoch()\n\n def _check_save_interval(self):\n \"\"\"Save interval checkpoint.\"\"\"\n if self.steps % self.config[\"save_interval_steps\"] == 0:\n self.save_checkpoint()\n logging.info(f\"Successfully saved checkpoint @ {self.steps} steps.\")\n\n def generate_and_save_intermediate_result(self, batch):\n \"\"\"Generate and save intermediate result.\"\"\"\n pass\n\n def _write_to_tensorboard(self, list_metrics, stage=\"train\"):\n \"\"\"Write variables to tensorboard.\"\"\"\n with self.writer.as_default():\n for key, value in list_metrics.items():\n tf.summary.scalar(stage + \"/\" + key, value.result(), step=self.steps)\n self.writer.flush()\n\n\nclass GanBasedTrainer(BasedTrainer):\n \"\"\"Customized trainer module for GAN TTS training (MelGAN, GAN-TTS, ParallelWaveGAN).\"\"\"\n\n def __init__(\n self,\n steps,\n epochs,\n config,\n strategy,\n is_generator_mixed_precision=False,\n is_discriminator_mixed_precision=False,\n ):\n \"\"\"Initialize trainer.\n\n Args:\n steps (int): Initial global steps.\n epochs (int): Initial global epochs.\n config (dict): Config dict loaded from yaml format configuration file.\n\n \"\"\"\n super().__init__(steps, epochs, config)\n self._is_generator_mixed_precision = is_generator_mixed_precision\n self._is_discriminator_mixed_precision = is_discriminator_mixed_precision\n self._strategy = strategy\n self._already_apply_input_signature = False\n\n def init_train_eval_metrics(self, list_metrics_name):\n with self._strategy.scope():\n super().init_train_eval_metrics(list_metrics_name)\n\n def get_n_gpus(self):\n return self._strategy.num_replicas_in_sync\n\n def _get_train_element_signature(self):\n return self.train_data_loader.element_spec\n\n def _get_eval_element_signature(self):\n return self.eval_data_loader.element_spec\n\n def set_gen_model(self, generator_model):\n \"\"\"Set generator class model (MUST).\"\"\"\n self._generator = generator_model\n\n def get_gen_model(self):\n \"\"\"Get generator model.\"\"\"\n return self._generator\n\n def set_dis_model(self, discriminator_model):\n \"\"\"Set discriminator class model (MUST).\"\"\"\n self._discriminator = discriminator_model\n\n def get_dis_model(self):\n \"\"\"Get discriminator model.\"\"\"\n return self._discriminator\n\n def set_gen_optimizer(self, generator_optimizer):\n \"\"\"Set generator optimizer (MUST).\"\"\"\n self._gen_optimizer = generator_optimizer\n if self._is_generator_mixed_precision:\n self._gen_optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(\n self._gen_optimizer, \"dynamic\"\n )\n\n def get_gen_optimizer(self):\n \"\"\"Get generator optimizer.\"\"\"\n return self._gen_optimizer\n\n def set_dis_optimizer(self, discriminator_optimizer):\n \"\"\"Set discriminator optimizer (MUST).\"\"\"\n self._dis_optimizer = discriminator_optimizer\n if self._is_discriminator_mixed_precision:\n self._dis_optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(\n self._dis_optimizer, \"dynamic\"\n )\n\n def get_dis_optimizer(self):\n \"\"\"Get discriminator optimizer.\"\"\"\n return self._dis_optimizer\n\n def compile(self, gen_model, dis_model, gen_optimizer, dis_optimizer):\n self.set_gen_model(gen_model)\n self.set_dis_model(dis_model)\n self.set_gen_optimizer(gen_optimizer)\n self.set_dis_optimizer(dis_optimizer)\n\n def _train_step(self, batch):\n if self._already_apply_input_signature is False:\n train_element_signature = self._get_train_element_signature()\n eval_element_signature = self._get_eval_element_signature()\n self.one_step_forward = tf.function(\n self._one_step_forward, input_signature=[train_element_signature]\n )\n self.one_step_evaluate = tf.function(\n self._one_step_evaluate, input_signature=[eval_element_signature]\n )\n self.one_step_predict = tf.function(\n self._one_step_predict, input_signature=[eval_element_signature]\n )\n self._already_apply_input_signature = True\n\n # run one_step_forward\n self.one_step_forward(batch)\n\n # update counts\n self.steps += 1\n self.tqdm.update(1)\n self._check_train_finish()\n\n def _one_step_forward(self, batch):\n per_replica_losses = self._strategy.run(\n self._one_step_forward_per_replica, args=(batch,)\n )\n return self._strategy.reduce(\n tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None\n )\n\n @abc.abstractmethod\n def compute_per_example_generator_losses(self, batch, outputs):\n \"\"\"Compute per example generator losses and return dict_metrics_losses\n Note that all element of the loss MUST has a shape [batch_size] and \n the keys of dict_metrics_losses MUST be in self.list_metrics_name.\n\n Args:\n batch: dictionary batch input return from dataloader\n outputs: outputs of the model\n \n Returns:\n per_example_losses: per example losses for each GPU, shape [B]\n dict_metrics_losses: dictionary loss.\n \"\"\"\n per_example_losses = 0.0\n dict_metrics_losses = {}\n return per_example_losses, dict_metrics_losses\n\n @abc.abstractmethod\n def compute_per_example_discriminator_losses(self, batch, gen_outputs):\n \"\"\"Compute per example discriminator losses and return dict_metrics_losses\n Note that all element of the loss MUST has a shape [batch_size] and \n the keys of dict_metrics_losses MUST be in self.list_metrics_name.\n\n Args:\n batch: dictionary batch input return from dataloader\n outputs: outputs of the model\n \n Returns:\n per_example_losses: per example losses for each GPU, shape [B]\n dict_metrics_losses: dictionary loss.\n \"\"\"\n per_example_losses = 0.0\n dict_metrics_losses = {}\n return per_example_losses, dict_metrics_losses\n\n def _one_step_forward_per_replica(self, batch):\n per_replica_gen_losses = 0.0\n per_replica_dis_losses = 0.0\n\n # one step generator.\n with tf.GradientTape() as g_tape:\n outputs = self._generator(**batch, training=True)\n (\n per_example_losses,\n dict_metrics_losses,\n ) = self.compute_per_example_generator_losses(batch, outputs)\n\n per_replica_gen_losses = tf.nn.compute_average_loss(\n per_example_losses,\n global_batch_size=self.config[\"batch_size\"] * self.get_n_gpus(),\n )\n\n if self._is_generator_mixed_precision:\n scaled_per_replica_gen_losses = self._gen_optimizer.get_scaled_loss(\n per_replica_gen_losses\n )\n\n if self._is_generator_mixed_precision:\n scaled_gradients = g_tape.gradient(\n scaled_per_replica_gen_losses, self._generator.trainable_variables\n )\n gradients = self._gen_optimizer.get_unscaled_gradients(scaled_gradients)\n else:\n gradients = g_tape.gradient(\n per_replica_gen_losses, self._generator.trainable_variables\n )\n\n self._gen_optimizer.apply_gradients(\n zip(gradients, self._generator.trainable_variables)\n )\n\n # accumulate loss into metrics\n self.update_train_metrics(dict_metrics_losses)\n\n # one step discriminator\n # recompute y_hat after 1 step generator for discriminator training.\n if self.steps >= self.config[\"discriminator_train_start_steps\"]:\n with tf.GradientTape() as d_tape:\n (\n per_example_losses,\n dict_metrics_losses,\n ) = self.compute_per_example_discriminator_losses(\n batch, self._generator(**batch)\n )\n\n per_replica_dis_losses = tf.nn.compute_average_loss(\n per_example_losses,\n global_batch_size=self.config[\"batch_size\"] * self.get_n_gpus(),\n )\n\n if self._is_discriminator_mixed_precision:\n scaled_per_replica_dis_losses = self._dis_optimizer.get_scaled_loss(\n per_replica_dis_losses\n )\n\n if self._is_discriminator_mixed_precision:\n scaled_gradients = d_tape.gradient(\n scaled_per_replica_dis_losses,\n self._discriminator.trainable_variables,\n )\n gradients = self._dis_optimizer.get_unscaled_gradients(scaled_gradients)\n else:\n gradients = d_tape.gradient(\n per_replica_dis_losses, self._discriminator.trainable_variables\n )\n\n self._dis_optimizer.apply_gradients(\n zip(gradients, self._discriminator.trainable_variables)\n )\n\n # accumulate loss into metrics\n self.update_train_metrics(dict_metrics_losses)\n\n return per_replica_gen_losses + per_replica_dis_losses\n\n def _eval_epoch(self):\n \"\"\"Evaluate model one epoch.\"\"\"\n logging.info(f\"(Steps: {self.steps}) Start evaluation.\")\n\n # calculate loss for each batch\n for eval_steps_per_epoch, batch in enumerate(\n tqdm(self.eval_data_loader, desc=\"[eval]\"), 1\n ):\n # eval one step\n self.one_step_evaluate(batch)\n\n if eval_steps_per_epoch <= self.config[\"num_save_intermediate_results\"]:\n # save intermedia\n self.generate_and_save_intermediate_result(batch)\n\n logging.info(\n f\"(Steps: {self.steps}) Finished evaluation \"\n f\"({eval_steps_per_epoch} steps per epoch).\"\n )\n\n # average loss\n for key in self.eval_metrics.keys():\n logging.info(\n f\"(Steps: {self.steps}) eval_{key} = {self.eval_metrics[key].result():.4f}.\"\n )\n\n # record\n self._write_to_tensorboard(self.eval_metrics, stage=\"eval\")\n\n # reset\n self.reset_states_eval()\n\n def _one_step_evaluate_per_replica(self, batch):\n ################################################\n # one step generator.\n outputs = self._generator(**batch, training=False)\n _, dict_metrics_losses = self.compute_per_example_generator_losses(\n batch, outputs\n )\n\n # accumulate loss into metrics\n self.update_eval_metrics(dict_metrics_losses)\n\n ################################################\n # one step discriminator\n if self.steps >= self.config[\"discriminator_train_start_steps\"]:\n _, dict_metrics_losses = self.compute_per_example_discriminator_losses(\n batch, outputs\n )\n\n # accumulate loss into metrics\n self.update_eval_metrics(dict_metrics_losses)\n\n ################################################\n\n def _one_step_evaluate(self, batch):\n self._strategy.run(self._one_step_evaluate_per_replica, args=(batch,))\n\n def _one_step_predict_per_replica(self, batch):\n outputs = self._generator(**batch, training=False)\n return outputs\n\n def _one_step_predict(self, batch):\n outputs = self._strategy.run(self._one_step_predict_per_replica, args=(batch,))\n return outputs\n\n @abc.abstractmethod\n def generate_and_save_intermediate_result(self, batch):\n return\n\n def create_checkpoint_manager(self, saved_path=None, max_to_keep=10):\n \"\"\"Create checkpoint management.\"\"\"\n if saved_path is None:\n saved_path = self.config[\"outdir\"] + \"/checkpoints/\"\n\n os.makedirs(saved_path, exist_ok=True)\n\n self.saved_path = saved_path\n self.ckpt = tf.train.Checkpoint(\n steps=tf.Variable(1),\n epochs=tf.Variable(1),\n gen_optimizer=self.get_gen_optimizer(),\n dis_optimizer=self.get_dis_optimizer(),\n )\n self.ckp_manager = tf.train.CheckpointManager(\n self.ckpt, saved_path, max_to_keep=max_to_keep\n )\n\n def save_checkpoint(self):\n \"\"\"Save checkpoint.\"\"\"\n self.ckpt.steps.assign(self.steps)\n self.ckpt.epochs.assign(self.epochs)\n self.ckp_manager.save(checkpoint_number=self.steps)\n self._generator.save_weights(\n self.saved_path + \"generator-{}.h5\".format(self.steps)\n )\n self._discriminator.save_weights(\n self.saved_path + \"discriminator-{}.h5\".format(self.steps)\n )\n\n def load_checkpoint(self, pretrained_path):\n \"\"\"Load checkpoint.\"\"\"\n self.ckpt.restore(pretrained_path)\n self.steps = self.ckpt.steps.numpy()\n self.epochs = self.ckpt.epochs.numpy()\n self._gen_optimizer = self.ckpt.gen_optimizer\n # re-assign iterations (global steps) for gen_optimizer.\n self._gen_optimizer.iterations.assign(tf.cast(self.steps, tf.int64))\n # re-assign iterations (global steps) for dis_optimizer.\n try:\n discriminator_train_start_steps = self.config[\n \"discriminator_train_start_steps\"\n ]\n discriminator_train_start_steps = tf.math.maximum(\n 0, discriminator_train_start_steps - self.steps\n )\n except Exception:\n discriminator_train_start_steps = self.steps\n self._dis_optimizer = self.ckpt.dis_optimizer\n self._dis_optimizer.iterations.assign(\n tf.cast(discriminator_train_start_steps, tf.int64)\n )\n\n # load weights.\n self._generator.load_weights(\n self.saved_path + \"generator-{}.h5\".format(self.steps)\n )\n self._discriminator.load_weights(\n self.saved_path + \"discriminator-{}.h5\".format(self.steps)\n )\n\n def _check_train_finish(self):\n \"\"\"Check training finished.\"\"\"\n if self.steps >= self.config[\"train_max_steps\"]:\n self.finish_train = True\n\n if (\n self.steps != 0\n and self.steps == self.config[\"discriminator_train_start_steps\"]\n ):\n self.finish_train = True\n logging.info(\n f\"Finished training only generator at {self.steps}steps, pls resume and continue training.\"\n )\n\n def _check_log_interval(self):\n \"\"\"Log to tensorboard.\"\"\"\n if self.steps % self.config[\"log_interval_steps\"] == 0:\n for metric_name in self.list_metrics_name:\n logging.info(\n f\"(Step: {self.steps}) train_{metric_name} = {self.train_metrics[metric_name].result():.4f}.\"\n )\n self._write_to_tensorboard(self.train_metrics, stage=\"train\")\n\n # reset\n self.reset_states_train()\n\n def fit(self, train_data_loader, valid_data_loader, saved_path, resume=None):\n self.set_train_data_loader(train_data_loader)\n self.set_eval_data_loader(valid_data_loader)\n self.train_data_loader = self._strategy.experimental_distribute_dataset(\n self.train_data_loader\n )\n self.eval_data_loader = self._strategy.experimental_distribute_dataset(\n self.eval_data_loader\n )\n with self._strategy.scope():\n self.create_checkpoint_manager(saved_path=saved_path, max_to_keep=10000)\n if len(resume) > 1:\n self.load_checkpoint(resume)\n logging.info(f\"Successfully resumed from {resume}.\")\n self.run()\n\n\nclass Seq2SeqBasedTrainer(BasedTrainer, metaclass=abc.ABCMeta):\n \"\"\"Customized trainer module for Seq2Seq TTS training (Tacotron, FastSpeech).\"\"\"\n\n def __init__(\n self, steps, epochs, config, strategy, is_mixed_precision=False,\n ):\n \"\"\"Initialize trainer.\n\n Args:\n steps (int): Initial global steps.\n epochs (int): Initial global epochs.\n config (dict): Config dict loaded from yaml format configuration file.\n strategy (tf.distribute): Strategy for distributed training.\n is_mixed_precision (bool): Use mixed_precision training or not.\n\n \"\"\"\n super().__init__(steps, epochs, config)\n self._is_mixed_precision = is_mixed_precision\n self._strategy = strategy\n\n # check if we already apply input_signature for train_step.\n self._already_apply_input_signature = False\n\n def init_train_eval_metrics(self, list_metrics_name):\n with self._strategy.scope():\n super().init_train_eval_metrics(list_metrics_name)\n\n def set_model(self, model):\n \"\"\"Set generator class model (MUST).\"\"\"\n self._model = model\n\n def get_model(self):\n \"\"\"Get generator model.\"\"\"\n return self._model\n\n def set_optimizer(self, optimizer):\n \"\"\"Set optimizer (MUST).\"\"\"\n self._optimizer = optimizer\n if self._is_mixed_precision:\n self._optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(\n self._optimizer, \"dynamic\"\n )\n\n def get_optimizer(self):\n \"\"\"Get optimizer.\"\"\"\n return self._optimizer\n\n def get_n_gpus(self):\n return self._strategy.num_replicas_in_sync\n\n def compile(self, model, optimizer):\n self.set_model(model)\n self.set_optimizer(optimizer)\n\n def _get_train_element_signature(self):\n return self.train_data_loader.element_spec\n\n def _get_eval_element_signature(self):\n return self.eval_data_loader.element_spec\n\n def _train_step(self, batch):\n if self._already_apply_input_signature is False:\n train_element_signature = self._get_train_element_signature()\n eval_element_signature = self._get_eval_element_signature()\n self.one_step_forward = tf.function(\n self._one_step_forward, input_signature=[train_element_signature]\n )\n self.one_step_evaluate = tf.function(\n self._one_step_evaluate, input_signature=[eval_element_signature]\n )\n self.one_step_predict = tf.function(\n self._one_step_predict, input_signature=[eval_element_signature]\n )\n self._already_apply_input_signature = True\n\n # run one_step_forward\n self.one_step_forward(batch)\n\n # update counts\n self.steps += 1\n self.tqdm.update(1)\n self._check_train_finish()\n\n def _one_step_forward(self, batch):\n per_replica_losses = self._strategy.run(\n self._one_step_forward_per_replica, args=(batch,)\n )\n return self._strategy.reduce(\n tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None\n )\n\n def _one_step_forward_per_replica(self, batch):\n with tf.GradientTape() as tape:\n outputs = self._model(**batch, training=True)\n per_example_losses, dict_metrics_losses = self.compute_per_example_losses(\n batch, outputs\n )\n per_replica_losses = tf.nn.compute_average_loss(\n per_example_losses,\n global_batch_size=self.config[\"batch_size\"] * self.get_n_gpus(),\n )\n\n if self._is_mixed_precision:\n scaled_per_replica_losses = self._optimizer.get_scaled_loss(\n per_replica_losses\n )\n\n if self._is_mixed_precision:\n scaled_gradients = tape.gradient(\n scaled_per_replica_losses, self._model.trainable_variables\n )\n gradients = self._optimizer.get_unscaled_gradients(scaled_gradients)\n else:\n gradients = tape.gradient(\n per_replica_losses, self._model.trainable_variables\n )\n\n self._optimizer.apply_gradients(\n zip(gradients, self._model.trainable_variables), 1.0\n )\n\n # accumulate loss into metrics\n self.update_train_metrics(dict_metrics_losses)\n\n return per_replica_losses\n\n @abc.abstractmethod\n def compute_per_example_losses(self, batch, outputs):\n \"\"\"Compute per example losses and return dict_metrics_losses\n Note that all element of the loss MUST has a shape [batch_size] and \n the keys of dict_metrics_losses MUST be in self.list_metrics_name.\n\n Args:\n batch: dictionary batch input return from dataloader\n outputs: outputs of the model\n \n Returns:\n per_example_losses: per example losses for each GPU, shape [B]\n dict_metrics_losses: dictionary loss.\n \"\"\"\n per_example_losses = 0.0\n dict_metrics_losses = {}\n return per_example_losses, dict_metrics_losses\n\n def _eval_epoch(self):\n \"\"\"Evaluate model one epoch.\"\"\"\n logging.info(f\"(Steps: {self.steps}) Start evaluation.\")\n\n # calculate loss for each batch\n for eval_steps_per_epoch, batch in enumerate(\n tqdm(self.eval_data_loader, desc=\"[eval]\"), 1\n ):\n # eval one step\n self.one_step_evaluate(batch)\n\n if eval_steps_per_epoch <= self.config[\"num_save_intermediate_results\"]:\n # save intermedia\n self.generate_and_save_intermediate_result(batch)\n\n logging.info(\n f\"(Steps: {self.steps}) Finished evaluation \"\n f\"({eval_steps_per_epoch} steps per epoch).\"\n )\n\n # average loss\n for key in self.eval_metrics.keys():\n logging.info(\n f\"(Steps: {self.steps}) eval_{key} = {self.eval_metrics[key].result():.4f}.\"\n )\n\n # record\n self._write_to_tensorboard(self.eval_metrics, stage=\"eval\")\n\n # reset\n self.reset_states_eval()\n\n def _one_step_evaluate_per_replica(self, batch):\n outputs = self._model(**batch, training=False)\n _, dict_metrics_losses = self.compute_per_example_losses(batch, outputs)\n\n self.update_eval_metrics(dict_metrics_losses)\n\n def _one_step_evaluate(self, batch):\n self._strategy.run(self._one_step_evaluate_per_replica, args=(batch,))\n\n def _one_step_predict_per_replica(self, batch):\n outputs = self._model(**batch, training=False)\n return outputs\n\n def _one_step_predict(self, batch):\n outputs = self._strategy.run(self._one_step_predict_per_replica, args=(batch,))\n return outputs\n\n @abc.abstractmethod\n def generate_and_save_intermediate_result(self, batch):\n return\n\n def create_checkpoint_manager(self, saved_path=None, max_to_keep=10):\n \"\"\"Create checkpoint management.\"\"\"\n if saved_path is None:\n saved_path = self.config[\"outdir\"] + \"/checkpoints/\"\n\n os.makedirs(saved_path, exist_ok=True)\n\n self.saved_path = saved_path\n self.ckpt = tf.train.Checkpoint(\n steps=tf.Variable(1), epochs=tf.Variable(1), optimizer=self.get_optimizer()\n )\n self.ckp_manager = tf.train.CheckpointManager(\n self.ckpt, saved_path, max_to_keep=max_to_keep\n )\n\n def save_checkpoint(self):\n \"\"\"Save checkpoint.\"\"\"\n self.ckpt.steps.assign(self.steps)\n self.ckpt.epochs.assign(self.epochs)\n self.ckp_manager.save(checkpoint_number=self.steps)\n self._model.save_weights(self.saved_path + \"model-{}.h5\".format(self.steps))\n\n def load_checkpoint(self, pretrained_path):\n \"\"\"Load checkpoint.\"\"\"\n self.ckpt.restore(pretrained_path)\n self.steps = self.ckpt.steps.numpy()\n self.epochs = self.ckpt.epochs.numpy()\n self._optimizer = self.ckpt.optimizer\n # re-assign iterations (global steps) for optimizer.\n self._optimizer.iterations.assign(tf.cast(self.steps, tf.int64))\n\n # load weights.\n self._model.load_weights(self.saved_path + \"model-{}.h5\".format(self.steps))\n\n def _check_train_finish(self):\n \"\"\"Check training finished.\"\"\"\n if self.steps >= self.config[\"train_max_steps\"]:\n self.finish_train = True\n\n def _check_log_interval(self):\n \"\"\"Log to tensorboard.\"\"\"\n if self.steps % self.config[\"log_interval_steps\"] == 0:\n for metric_name in self.list_metrics_name:\n logging.info(\n f\"(Step: {self.steps}) train_{metric_name} = {self.train_metrics[metric_name].result():.4f}.\"\n )\n self._write_to_tensorboard(self.train_metrics, stage=\"train\")\n\n # reset\n self.reset_states_train()\n\n def fit(self, train_data_loader, valid_data_loader, saved_path, resume=None):\n self.set_train_data_loader(train_data_loader)\n self.set_eval_data_loader(valid_data_loader)\n self.train_data_loader = self._strategy.experimental_distribute_dataset(\n self.train_data_loader\n )\n self.eval_data_loader = self._strategy.experimental_distribute_dataset(\n self.eval_data_loader\n )\n with self._strategy.scope():\n self.create_checkpoint_manager(saved_path=saved_path, max_to_keep=10000)\n if len(resume) > 1:\n self.load_checkpoint(resume)\n logging.info(f\"Successfully resumed from {resume}.\")\n self.run()\n\n\nclass StreamBasedTrainer(Seq2SeqBasedTrainer):\n def __init__(\n self, steps, epochs, config, strategy, is_mixed_precision=False,\n ):\n super().__init__(steps, epochs, config, strategy, is_mixed_precision)\n\n def _one_step_evaluate_per_replica(self, batch):\n # TODO self._model.stream(**batch)\n # outputs = self._model(**batch, training=False)\n _, dict_metrics_losses = self.compute_per_example_losses(batch, outputs)\n\n self.update_eval_metrics(dict_metrics_losses)" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/__init__.py", "content": "from tensorflow_tts.utils.cleaners import (\n basic_cleaners,\n collapse_whitespace,\n convert_to_ascii,\n english_cleaners,\n expand_abbreviations,\n expand_numbers,\n lowercase,\n transliteration_cleaners,\n)\nfrom tensorflow_tts.utils.decoder import dynamic_decode\nfrom tensorflow_tts.utils.griffin_lim import TFGriffinLim, griffin_lim_lb\nfrom tensorflow_tts.utils.group_conv import GroupConv1D\nfrom tensorflow_tts.utils.number_norm import normalize_numbers\nfrom tensorflow_tts.utils.outliers import remove_outlier\nfrom tensorflow_tts.utils.strategy import (\n calculate_2d_loss,\n calculate_3d_loss,\n calculate_loss_norm_lens,\n return_strategy,\n)\nfrom tensorflow_tts.utils.utils import find_files\nfrom tensorflow_tts.utils.weight_norm import WeightNormalization\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/cleaners.py", "content": "# -*- coding: utf-8 -*-\n# Copyright (c) 2017 Keith Ito\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\nimport re\n\nfrom tensorflow_tts.utils.korean import tokenize as ko_tokenize\nfrom tensorflow_tts.utils.number_norm import normalize_numbers\nfrom unidecode import unidecode\n\n# Regular expression matching whitespace:\n_whitespace_re = re.compile(r\"\\s+\")\n\n# List of (regular expression, replacement) pairs for abbreviations:\n_abbreviations = [\n (re.compile(\"\\\\b%s\\\\.\" % x[0], re.IGNORECASE), x[1])\n for x in [\n (\"mrs\", \"misess\"),\n (\"mr\", \"mister\"),\n (\"dr\", \"doctor\"),\n (\"st\", \"saint\"),\n (\"co\", \"company\"),\n (\"jr\", \"junior\"),\n (\"maj\", \"major\"),\n (\"gen\", \"general\"),\n (\"drs\", \"doctors\"),\n (\"rev\", \"reverend\"),\n (\"lt\", \"lieutenant\"),\n (\"hon\", \"honorable\"),\n (\"sgt\", \"sergeant\"),\n (\"capt\", \"captain\"),\n (\"esq\", \"esquire\"),\n (\"ltd\", \"limited\"),\n (\"col\", \"colonel\"),\n (\"ft\", \"fort\"),\n ]\n]\n\n\ndef expand_abbreviations(text):\n for regex, replacement in _abbreviations:\n text = re.sub(regex, replacement, text)\n return text\n\n\ndef expand_numbers(text):\n return normalize_numbers(text)\n\n\ndef lowercase(text):\n return text.lower()\n\n\ndef collapse_whitespace(text):\n return re.sub(_whitespace_re, \" \", text)\n\n\ndef convert_to_ascii(text):\n return unidecode(text)\n\n\ndef basic_cleaners(text):\n \"\"\"Basic pipeline that lowercases and collapses whitespace without transliteration.\"\"\"\n text = lowercase(text)\n text = collapse_whitespace(text)\n return text\n\n\ndef transliteration_cleaners(text):\n \"\"\"Pipeline for non-English text that transliterates to ASCII.\"\"\"\n text = convert_to_ascii(text)\n text = lowercase(text)\n text = collapse_whitespace(text)\n return text\n\n\ndef english_cleaners(text):\n \"\"\"Pipeline for English text, including number and abbreviation expansion.\"\"\"\n text = convert_to_ascii(text)\n text = lowercase(text)\n text = expand_numbers(text)\n text = expand_abbreviations(text)\n text = collapse_whitespace(text)\n return text\n\n\ndef korean_cleaners(text):\n \"\"\"Pipeline for Korean text, including number and abbreviation expansion.\"\"\"\n text = ko_tokenize(\n text\n ) # '존경하는' --> ['ᄌ', 'ᅩ', 'ᆫ', 'ᄀ', 'ᅧ', 'ᆼ', 'ᄒ', 'ᅡ', 'ᄂ', 'ᅳ', 'ᆫ']\n return text\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/decoder.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 TensorFlow Authors, All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Optional, Tuple, Union\n\nimport tensorflow as tf\nfrom tensorflow.python.ops import control_flow_util\nfrom tensorflow_addons.seq2seq import Decoder\nfrom tensorflow_addons.seq2seq.decoder import (\n BaseDecoder,\n _prepend_batch,\n _transpose_batch_time,\n)\nfrom tensorflow_addons.utils.types import Number, TensorLike\n\n\ndef dynamic_decode(\n decoder: Union[Decoder, BaseDecoder],\n output_time_major: bool = False,\n impute_finished: bool = False,\n maximum_iterations: Optional[TensorLike] = None,\n parallel_iterations: int = 32,\n swap_memory: bool = False,\n training: Optional[bool] = None,\n scope: Optional[str] = None,\n enable_tflite_convertible: bool = False,\n **kwargs\n) -> Tuple[Any, Any, Any]:\n \"\"\"Perform dynamic decoding with `decoder`.\n Calls initialize() once and step() repeatedly on the Decoder object.\n Args:\n decoder: A `Decoder` instance.\n output_time_major: Python boolean. Default: `False` (batch major). If\n `True`, outputs are returned as time major tensors (this mode is\n faster). Otherwise, outputs are returned as batch major tensors (this\n adds extra time to the computation).\n impute_finished: Python boolean. If `True`, then states for batch\n entries which are marked as finished get copied through and the\n corresponding outputs get zeroed out. This causes some slowdown at\n each time step, but ensures that the final state and outputs have\n the correct values and that backprop ignores time steps that were\n marked as finished.\n maximum_iterations: A strictly positive `int32` scalar, the maximum\n allowed number of decoding steps. Default is `None` (decode until the\n decoder is fully done).\n parallel_iterations: Argument passed to `tf.while_loop`.\n swap_memory: Argument passed to `tf.while_loop`.\n training: Python boolean. Indicates whether the layer should behave\n in training mode or in inference mode. Only relevant\n when `dropout` or `recurrent_dropout` is used.\n scope: Optional name scope to use.\n enable_tflite_convertible: Python boolean. If `True`, then the variables\n of `TensorArray` become of 1-D static shape. Also zero pads in the\n output tensor will be discarded. Default: `False`.\n **kwargs: dict, other keyword arguments for dynamic_decode. It might\n contain arguments for `BaseDecoder` to initialize, which takes all\n tensor inputs during call().\n Returns:\n `(final_outputs, final_state, final_sequence_lengths)`.\n Raises:\n ValueError: if `maximum_iterations` is provided but is not a scalar.\n \"\"\"\n with tf.name_scope(scope or \"decoder\"):\n is_xla = not tf.executing_eagerly() and control_flow_util.GraphOrParentsInXlaContext(\n tf.compat.v1.get_default_graph()\n )\n\n if maximum_iterations is not None:\n maximum_iterations = tf.convert_to_tensor(\n maximum_iterations, dtype=tf.int32, name=\"maximum_iterations\"\n )\n if maximum_iterations.shape.ndims != 0:\n raise ValueError(\"maximum_iterations must be a scalar\")\n tf.debugging.assert_greater(\n maximum_iterations,\n 0,\n message=\"maximum_iterations should be greater than 0\",\n )\n elif is_xla:\n raise ValueError(\"maximum_iterations is required for XLA compilation.\")\n\n if isinstance(decoder, Decoder):\n initial_finished, initial_inputs, initial_state = decoder.initialize()\n else:\n # For BaseDecoder that takes tensor inputs during call.\n decoder_init_input = kwargs.pop(\"decoder_init_input\", None)\n decoder_init_kwargs = kwargs.pop(\"decoder_init_kwargs\", {})\n initial_finished, initial_inputs, initial_state = decoder.initialize(\n decoder_init_input, **decoder_init_kwargs\n )\n\n if enable_tflite_convertible:\n # Assume the batch_size = 1 for inference.\n # So we can change 2-D TensorArray into 1-D by reshaping it.\n zero_outputs = tf.nest.map_structure(\n lambda shape, dtype: tf.reshape(\n tf.zeros(_prepend_batch(decoder.batch_size, shape), dtype=dtype),\n [-1],\n ),\n decoder.output_size,\n decoder.output_dtype,\n )\n else:\n zero_outputs = tf.nest.map_structure(\n lambda shape, dtype: tf.zeros(\n _prepend_batch(decoder.batch_size, shape), dtype=dtype\n ),\n decoder.output_size,\n decoder.output_dtype,\n )\n\n if maximum_iterations is not None:\n initial_finished = tf.logical_or(initial_finished, 0 >= maximum_iterations)\n initial_sequence_lengths = tf.zeros_like(initial_finished, dtype=tf.int32)\n initial_time = tf.constant(0, dtype=tf.int32)\n\n def _shape(batch_size, from_shape):\n if not isinstance(from_shape, tf.TensorShape) or from_shape.ndims == 0:\n return None\n else:\n batch_size = tf.get_static_value(\n tf.convert_to_tensor(batch_size, name=\"batch_size\")\n )\n if enable_tflite_convertible:\n # Since we can't use 2-D TensoArray and assume `batch_size` = 1,\n # we use `from_shape` dimension only.\n return from_shape\n return tf.TensorShape([batch_size]).concatenate(from_shape)\n\n dynamic_size = maximum_iterations is None or not is_xla\n # The dynamic shape `TensoArray` is not allowed in TFLite yet.\n dynamic_size = dynamic_size and (not enable_tflite_convertible)\n\n def _create_ta(s, d):\n return tf.TensorArray(\n dtype=d,\n size=0 if dynamic_size else maximum_iterations,\n dynamic_size=dynamic_size,\n element_shape=_shape(decoder.batch_size, s),\n )\n\n initial_outputs_ta = tf.nest.map_structure(\n _create_ta, decoder.output_size, decoder.output_dtype\n )\n\n def condition(\n unused_time,\n unused_outputs_ta,\n unused_state,\n unused_inputs,\n finished,\n unused_sequence_lengths,\n ):\n return tf.logical_not(tf.reduce_all(finished))\n\n def body(time, outputs_ta, state, inputs, finished, sequence_lengths):\n \"\"\"Internal while_loop body.\n Args:\n time: scalar int32 tensor.\n outputs_ta: structure of TensorArray.\n state: (structure of) state tensors and TensorArrays.\n inputs: (structure of) input tensors.\n finished: bool tensor (keeping track of what's finished).\n sequence_lengths: int32 tensor (keeping track of time of finish).\n Returns:\n `(time + 1, outputs_ta, next_state, next_inputs, next_finished,\n next_sequence_lengths)`.\n ```\n \"\"\"\n (next_outputs, decoder_state, next_inputs, decoder_finished) = decoder.step(\n time, inputs, state, training\n )\n decoder_state_sequence_lengths = False\n if decoder.tracks_own_finished:\n next_finished = decoder_finished\n lengths = getattr(decoder_state, \"lengths\", None)\n if lengths is not None:\n # sequence lengths are provided by decoder_state.lengths;\n # overwrite our sequence lengths.\n decoder_state_sequence_lengths = True\n sequence_lengths = tf.cast(lengths, tf.int32)\n else:\n next_finished = tf.logical_or(decoder_finished, finished)\n\n if decoder_state_sequence_lengths:\n # Just pass something through the loop; at the next iteration\n # we'll pull the sequence lengths from the decoder_state again.\n next_sequence_lengths = sequence_lengths\n else:\n next_sequence_lengths = tf.where(\n tf.logical_not(finished),\n tf.fill(tf.shape(sequence_lengths), time + 1),\n sequence_lengths,\n )\n\n tf.nest.assert_same_structure(state, decoder_state)\n tf.nest.assert_same_structure(outputs_ta, next_outputs)\n tf.nest.assert_same_structure(inputs, next_inputs)\n\n # Zero out output values past finish\n if impute_finished:\n\n def zero_out_finished(out, zero):\n if finished.shape.rank < zero.shape.rank:\n broadcast_finished = tf.broadcast_to(\n tf.expand_dims(finished, axis=-1), zero.shape\n )\n return tf.where(broadcast_finished, zero, out)\n else:\n return tf.where(finished, zero, out)\n\n emit = tf.nest.map_structure(\n zero_out_finished, next_outputs, zero_outputs\n )\n else:\n emit = next_outputs\n\n # Copy through states past finish\n def _maybe_copy_state(new, cur):\n # TensorArrays and scalar states get passed through.\n if isinstance(cur, tf.TensorArray):\n pass_through = True\n else:\n new.set_shape(cur.shape)\n pass_through = new.shape.ndims == 0\n if not pass_through:\n broadcast_finished = tf.broadcast_to(\n tf.expand_dims(finished, axis=-1), new.shape\n )\n return tf.where(broadcast_finished, cur, new)\n else:\n return new\n\n if impute_finished:\n next_state = tf.nest.map_structure(\n _maybe_copy_state, decoder_state, state\n )\n else:\n next_state = decoder_state\n\n if enable_tflite_convertible:\n # Reshape to 1-D.\n emit = tf.nest.map_structure(lambda x: tf.reshape(x, [-1]), emit)\n\n outputs_ta = tf.nest.map_structure(\n lambda ta, out: ta.write(time, out), outputs_ta, emit\n )\n return (\n time + 1,\n outputs_ta,\n next_state,\n next_inputs,\n next_finished,\n next_sequence_lengths,\n )\n\n res = tf.while_loop(\n condition,\n body,\n loop_vars=(\n initial_time,\n initial_outputs_ta,\n initial_state,\n initial_inputs,\n initial_finished,\n initial_sequence_lengths,\n ),\n parallel_iterations=parallel_iterations,\n maximum_iterations=maximum_iterations,\n swap_memory=swap_memory,\n )\n\n final_outputs_ta = res[1]\n final_state = res[2]\n final_sequence_lengths = res[5]\n\n final_outputs = tf.nest.map_structure(lambda ta: ta.stack(), final_outputs_ta)\n\n try:\n final_outputs, final_state = decoder.finalize(\n final_outputs, final_state, final_sequence_lengths\n )\n except NotImplementedError:\n pass\n\n if not output_time_major:\n if enable_tflite_convertible:\n # Reshape the output to the original shape.\n def _restore_batch(x):\n return tf.expand_dims(x, [1])\n\n final_outputs = tf.nest.map_structure(_restore_batch, final_outputs)\n\n final_outputs = tf.nest.map_structure(_transpose_batch_time, final_outputs)\n\n return final_outputs, final_state, final_sequence_lengths\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/griffin_lim.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Griffin-Lim phase reconstruction algorithm from mel spectrogram.\"\"\"\n\nimport os\n\nimport librosa\nimport numpy as np\nimport soundfile as sf\nimport tensorflow as tf\nfrom sklearn.preprocessing import StandardScaler\n\n\ndef griffin_lim_lb(\n mel_spec, stats_path, dataset_config, n_iter=32, output_dir=None, wav_name=\"lb\"\n):\n \"\"\"Generate wave from mel spectrogram with Griffin-Lim algorithm using Librosa.\n Args:\n mel_spec (ndarray): array representing the mel spectrogram.\n stats_path (str): path to the `stats.npy` file containing norm statistics.\n dataset_config (Dict): dataset configuration parameters.\n n_iter (int): number of iterations for GL.\n output_dir (str): output directory where audio file will be saved.\n wav_name (str): name of the output file.\n Returns:\n gl_lb (ndarray): generated wave.\n \"\"\"\n scaler = StandardScaler()\n scaler.mean_, scaler.scale_ = np.load(stats_path)\n\n mel_spec = np.power(10.0, scaler.inverse_transform(mel_spec)).T\n mel_basis = librosa.filters.mel(\n dataset_config[\"sampling_rate\"],\n n_fft=dataset_config[\"fft_size\"],\n n_mels=dataset_config[\"num_mels\"],\n fmin=dataset_config[\"fmin\"],\n fmax=dataset_config[\"fmax\"],\n )\n mel_to_linear = np.maximum(1e-10, np.dot(np.linalg.pinv(mel_basis), mel_spec))\n gl_lb = librosa.griffinlim(\n mel_to_linear,\n n_iter=n_iter,\n hop_length=dataset_config[\"hop_size\"],\n win_length=dataset_config[\"win_length\"] or dataset_config[\"fft_size\"],\n )\n if output_dir:\n output_path = os.path.join(output_dir, f\"{wav_name}.wav\")\n sf.write(output_path, gl_lb, dataset_config[\"sampling_rate\"], \"PCM_16\")\n return gl_lb\n\n\nclass TFGriffinLim(tf.keras.layers.Layer):\n \"\"\"Griffin-Lim algorithm for phase reconstruction from mel spectrogram magnitude.\"\"\"\n\n def __init__(self, stats_path, dataset_config, normalized: bool = True):\n \"\"\"Init GL params.\n Args:\n stats_path (str): path to the `stats.npy` file containing norm statistics.\n dataset_config (Dict): dataset configuration parameters.\n \"\"\"\n super().__init__()\n self.normalized = normalized\n if normalized:\n scaler = StandardScaler()\n scaler.mean_, scaler.scale_ = np.load(stats_path)\n self.scaler = scaler\n self.ds_config = dataset_config\n self.mel_basis = librosa.filters.mel(\n self.ds_config[\"sampling_rate\"],\n n_fft=self.ds_config[\"fft_size\"],\n n_mels=self.ds_config[\"num_mels\"],\n fmin=self.ds_config[\"fmin\"],\n fmax=self.ds_config[\"fmax\"],\n ) # [num_mels, fft_size // 2 + 1]\n\n def save_wav(self, gl_tf, output_dir, wav_name):\n \"\"\"Generate WAV file and save it.\n Args:\n gl_tf (tf.Tensor): reconstructed signal from GL algorithm.\n output_dir (str): output directory where audio file will be saved.\n wav_name (str): name of the output file.\n \"\"\"\n encode_fn = lambda x: tf.audio.encode_wav(x, self.ds_config[\"sampling_rate\"])\n gl_tf = tf.expand_dims(gl_tf, -1)\n if not isinstance(wav_name, list):\n wav_name = [wav_name]\n\n if len(gl_tf.shape) > 2:\n bs, *_ = gl_tf.shape\n assert bs == len(wav_name), \"Batch and 'wav_name' have different size.\"\n tf_wav = tf.map_fn(encode_fn, gl_tf, dtype=tf.string)\n for idx in tf.range(bs):\n output_path = os.path.join(output_dir, f\"{wav_name[idx]}.wav\")\n tf.io.write_file(output_path, tf_wav[idx])\n else:\n tf_wav = encode_fn(gl_tf)\n tf.io.write_file(os.path.join(output_dir, f\"{wav_name[0]}.wav\"), tf_wav)\n\n @tf.function(\n input_signature=[\n tf.TensorSpec(shape=[None, None, None], dtype=tf.float32),\n tf.TensorSpec(shape=[], dtype=tf.int32),\n ]\n )\n def call(self, mel_spec, n_iter=32):\n \"\"\"Apply GL algorithm to batched mel spectrograms.\n Args:\n mel_spec (tf.Tensor): normalized mel spectrogram.\n n_iter (int): number of iterations to run GL algorithm.\n Returns:\n (tf.Tensor): reconstructed signal from GL algorithm.\n \"\"\"\n # de-normalize mel spectogram\n if self.normalized:\n mel_spec = tf.math.pow(\n 10.0, mel_spec * self.scaler.scale_ + self.scaler.mean_\n )\n else:\n mel_spec = tf.math.pow(\n 10.0, mel_spec\n ) # TODO @dathudeptrai check if its ok without it wavs were too quiet\n inverse_mel = tf.linalg.pinv(self.mel_basis)\n\n # [:, num_mels] @ [fft_size // 2 + 1, num_mels].T\n mel_to_linear = tf.linalg.matmul(mel_spec, inverse_mel, transpose_b=True)\n mel_to_linear = tf.cast(tf.math.maximum(1e-10, mel_to_linear), tf.complex64)\n\n init_phase = tf.cast(\n tf.random.uniform(tf.shape(mel_to_linear), maxval=1), tf.complex64\n )\n phase = tf.math.exp(2j * np.pi * init_phase)\n for _ in tf.range(n_iter):\n inverse = tf.signal.inverse_stft(\n mel_to_linear * phase,\n frame_length=self.ds_config[\"win_length\"] or self.ds_config[\"fft_size\"],\n frame_step=self.ds_config[\"hop_size\"],\n fft_length=self.ds_config[\"fft_size\"],\n window_fn=tf.signal.inverse_stft_window_fn(self.ds_config[\"hop_size\"]),\n )\n phase = tf.signal.stft(\n inverse,\n self.ds_config[\"win_length\"] or self.ds_config[\"fft_size\"],\n self.ds_config[\"hop_size\"],\n self.ds_config[\"fft_size\"],\n )\n phase /= tf.cast(tf.maximum(1e-10, tf.abs(phase)), tf.complex64)\n\n return tf.signal.inverse_stft(\n mel_to_linear * phase,\n frame_length=self.ds_config[\"win_length\"] or self.ds_config[\"fft_size\"],\n frame_step=self.ds_config[\"hop_size\"],\n fft_length=self.ds_config[\"fft_size\"],\n window_fn=tf.signal.inverse_stft_window_fn(self.ds_config[\"hop_size\"]),\n )\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/group_conv.py", "content": "# -*- coding: utf-8 -*-\n# This code is copy from https://github.com/tensorflow/tensorflow/pull/36773.\n\"\"\"Group Convolution Modules.\"\"\"\n\nfrom tensorflow.python.framework import tensor_shape\nfrom tensorflow.python.keras import activations, constraints, initializers, regularizers\nfrom tensorflow.python.keras.engine.base_layer import Layer\nfrom tensorflow.python.keras.engine.input_spec import InputSpec\nfrom tensorflow.python.keras.layers import Conv1D, SeparableConv1D\nfrom tensorflow.python.keras.utils import conv_utils\nfrom tensorflow.python.ops import array_ops, nn, nn_ops\n\n\nclass Convolution(object):\n \"\"\"Helper class for convolution.\n Note that this class assumes that shapes of input and filter passed to\n __call__ are compatible with input_shape and filter_shape passed to the\n constructor.\n Arguments\n input_shape: static shape of input. i.e. input.get_shape().\n filter_shape: static shape of the filter. i.e. filter.get_shape().\n padding: see convolution.\n strides: see convolution.\n dilation_rate: see convolution.\n name: see convolution.\n data_format: see convolution.\n \"\"\"\n\n def __init__(\n self,\n input_shape,\n filter_shape,\n padding,\n strides=None,\n dilation_rate=None,\n name=None,\n data_format=None,\n ):\n \"\"\"Helper function for convolution.\"\"\"\n num_total_dims = filter_shape.ndims\n if num_total_dims is None:\n num_total_dims = input_shape.ndims\n if num_total_dims is None:\n raise ValueError(\"rank of input or filter must be known\")\n\n num_spatial_dims = num_total_dims - 2\n\n try:\n input_shape.with_rank(num_spatial_dims + 2)\n except ValueError:\n raise ValueError(\"input tensor must have rank %d\" % (num_spatial_dims + 2))\n\n try:\n filter_shape.with_rank(num_spatial_dims + 2)\n except ValueError:\n raise ValueError(\"filter tensor must have rank %d\" % (num_spatial_dims + 2))\n\n if data_format is None or not data_format.startswith(\"NC\"):\n input_channels_dim = tensor_shape.dimension_at_index(\n input_shape, num_spatial_dims + 1\n )\n spatial_dims = range(1, num_spatial_dims + 1)\n else:\n input_channels_dim = tensor_shape.dimension_at_index(input_shape, 1)\n spatial_dims = range(2, num_spatial_dims + 2)\n\n filter_dim = tensor_shape.dimension_at_index(filter_shape, num_spatial_dims)\n if not (input_channels_dim % filter_dim).is_compatible_with(0):\n raise ValueError(\n \"number of input channels is not divisible by corresponding \"\n \"dimension of filter, {} % {} != 0\".format(\n input_channels_dim, filter_dim\n )\n )\n\n strides, dilation_rate = nn_ops._get_strides_and_dilation_rate(\n num_spatial_dims, strides, dilation_rate\n )\n\n self.input_shape = input_shape\n self.filter_shape = filter_shape\n self.data_format = data_format\n self.strides = strides\n self.padding = padding\n self.name = name\n self.dilation_rate = dilation_rate\n self.conv_op = nn_ops._WithSpaceToBatch(\n input_shape,\n dilation_rate=dilation_rate,\n padding=padding,\n build_op=self._build_op,\n filter_shape=filter_shape,\n spatial_dims=spatial_dims,\n data_format=data_format,\n )\n\n def _build_op(self, _, padding):\n return nn_ops._NonAtrousConvolution(\n self.input_shape,\n filter_shape=self.filter_shape,\n padding=padding,\n data_format=self.data_format,\n strides=self.strides,\n name=self.name,\n )\n\n def __call__(self, inp, filter):\n return self.conv_op(inp, filter)\n\n\nclass Conv(Layer):\n \"\"\"Abstract N-D convolution layer (private, used as implementation base).\n This layer creates a convolution kernel that is convolved\n (actually cross-correlated) with the layer input to produce a tensor of\n outputs. If `use_bias` is True (and a `bias_initializer` is provided),\n a bias vector is created and added to the outputs. Finally, if\n `activation` is not `None`, it is applied to the outputs as well.\n Note: layer attributes cannot be modified after the layer has been called\n once (except the `trainable` attribute).\n Arguments:\n rank: An integer, the rank of the convolution, e.g. \"2\" for 2D convolution.\n filters: Integer, the dimensionality of the output space (i.e. the number\n of filters in the convolution).\n kernel_size: An integer or tuple/list of n integers, specifying the\n length of the convolution window.\n strides: An integer or tuple/list of n integers,\n specifying the stride length of the convolution.\n Specifying any stride value != 1 is incompatible with specifying\n any `dilation_rate` value != 1.\n padding: One of `\"valid\"`, `\"same\"`, or `\"causal\"` (case-insensitive).\n data_format: A string, one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch_size, ..., channels)` while `channels_first` corresponds to\n inputs with shape `(batch_size, channels, ...)`.\n dilation_rate: An integer or tuple/list of n integers, specifying\n the dilation rate to use for dilated convolution.\n Currently, specifying any `dilation_rate` value != 1 is\n incompatible with specifying any `strides` value != 1.\n groups: Integer, the number of channel groups controlling the connections\n between inputs and outputs. Input channels and `filters` must both be\n divisible by `groups`. For example,\n - At `groups=1`, all inputs are convolved to all outputs.\n - At `groups=2`, the operation becomes equivalent to having two\n convolutional layers side by side, each seeing half the input\n channels, and producing half the output channels, and both\n subsequently concatenated.\n - At `groups=input_channels`, each input channel is convolved with its\n own set of filters, of size `input_channels / filters`\n activation: Activation function to use.\n If you don't specify anything, no activation is applied.\n use_bias: Boolean, whether the layer uses a bias.\n kernel_initializer: An initializer for the convolution kernel.\n bias_initializer: An initializer for the bias vector. If None, the default\n initializer will be used.\n kernel_regularizer: Optional regularizer for the convolution kernel.\n bias_regularizer: Optional regularizer for the bias vector.\n activity_regularizer: Optional regularizer function for the output.\n kernel_constraint: Optional projection function to be applied to the\n kernel after being updated by an `Optimizer` (e.g. used to implement\n norm constraints or value constraints for layer weights). The function\n must take as input the unprojected variable and must return the\n projected variable (which must have the same shape). Constraints are\n not safe to use when doing asynchronous distributed training.\n bias_constraint: Optional projection function to be applied to the\n bias after being updated by an `Optimizer`.\n trainable: Boolean, if `True` the weights of this layer will be marked as\n trainable (and listed in `layer.trainable_weights`).\n name: A string, the name of the layer.\n \"\"\"\n\n def __init__(\n self,\n rank,\n filters,\n kernel_size,\n strides=1,\n padding=\"valid\",\n data_format=None,\n dilation_rate=1,\n groups=1,\n activation=None,\n use_bias=True,\n kernel_initializer=\"glorot_uniform\",\n bias_initializer=\"zeros\",\n kernel_regularizer=None,\n bias_regularizer=None,\n activity_regularizer=None,\n kernel_constraint=None,\n bias_constraint=None,\n trainable=True,\n name=None,\n **kwargs\n ):\n super(Conv, self).__init__(\n trainable=trainable,\n name=name,\n activity_regularizer=regularizers.get(activity_regularizer),\n **kwargs\n )\n self.rank = rank\n if filters is not None and not isinstance(filters, int):\n filters = int(filters)\n self.filters = filters\n self.groups = groups or 1\n if filters is not None and filters % self.groups != 0:\n raise ValueError(\n \"The number of filters must be evenly divisible by the number of \"\n \"groups. Received: groups={}, filters={}\".format(groups, filters)\n )\n self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, \"kernel_size\")\n if not all(self.kernel_size):\n raise ValueError(\n \"The argument `kernel_size` cannot contain 0(s). \"\n \"Received: %s\" % (kernel_size,)\n )\n self.strides = conv_utils.normalize_tuple(strides, rank, \"strides\")\n self.padding = conv_utils.normalize_padding(padding)\n if self.padding == \"causal\" and not isinstance(self, (Conv1D, SeparableConv1D)):\n raise ValueError(\n \"Causal padding is only supported for `Conv1D`\"\n \"and ``SeparableConv1D`.\"\n )\n self.data_format = conv_utils.normalize_data_format(data_format)\n self.dilation_rate = conv_utils.normalize_tuple(\n dilation_rate, rank, \"dilation_rate\"\n )\n self.activation = activations.get(activation)\n self.use_bias = use_bias\n self.kernel_initializer = initializers.get(kernel_initializer)\n self.bias_initializer = initializers.get(bias_initializer)\n self.kernel_regularizer = regularizers.get(kernel_regularizer)\n self.bias_regularizer = regularizers.get(bias_regularizer)\n self.kernel_constraint = constraints.get(kernel_constraint)\n self.bias_constraint = constraints.get(bias_constraint)\n self.input_spec = InputSpec(ndim=self.rank + 2)\n\n def build(self, input_shape):\n input_shape = tensor_shape.TensorShape(input_shape)\n input_channel = self._get_input_channel(input_shape)\n if input_channel % self.groups != 0:\n raise ValueError(\n \"The number of input channels must be evenly divisible by the number \"\n \"of groups. Received groups={}, but the input has {} channels \"\n \"(full input shape is {}).\".format(\n self.groups, input_channel, input_shape\n )\n )\n kernel_shape = self.kernel_size + (input_channel // self.groups, self.filters)\n\n self.kernel = self.add_weight(\n name=\"kernel\",\n shape=kernel_shape,\n initializer=self.kernel_initializer,\n regularizer=self.kernel_regularizer,\n constraint=self.kernel_constraint,\n trainable=True,\n dtype=self.dtype,\n )\n if self.use_bias:\n self.bias = self.add_weight(\n name=\"bias\",\n shape=(self.filters,),\n initializer=self.bias_initializer,\n regularizer=self.bias_regularizer,\n constraint=self.bias_constraint,\n trainable=True,\n dtype=self.dtype,\n )\n else:\n self.bias = None\n channel_axis = self._get_channel_axis()\n self.input_spec = InputSpec(\n ndim=self.rank + 2, axes={channel_axis: input_channel}\n )\n\n self._build_conv_op_input_shape = input_shape\n self._build_input_channel = input_channel\n self._padding_op = self._get_padding_op()\n self._conv_op_data_format = conv_utils.convert_data_format(\n self.data_format, self.rank + 2\n )\n self._convolution_op = Convolution(\n input_shape,\n filter_shape=self.kernel.shape,\n dilation_rate=self.dilation_rate,\n strides=self.strides,\n padding=self._padding_op,\n data_format=self._conv_op_data_format,\n )\n self.built = True\n\n def call(self, inputs):\n if self._recreate_conv_op(inputs):\n self._convolution_op = Convolution(\n inputs.get_shape(),\n filter_shape=self.kernel.shape,\n dilation_rate=self.dilation_rate,\n strides=self.strides,\n padding=self._padding_op,\n data_format=self._conv_op_data_format,\n )\n self._build_conv_op_input_shape = inputs.get_shape()\n\n # Apply causal padding to inputs for Conv1D.\n if self.padding == \"causal\" and self.__class__.__name__ == \"Conv1D\":\n inputs = array_ops.pad(inputs, self._compute_causal_padding())\n\n outputs = self._convolution_op(inputs, self.kernel)\n\n if self.use_bias:\n if self.data_format == \"channels_first\":\n if self.rank == 1:\n # nn.bias_add does not accept a 1D input tensor.\n bias = array_ops.reshape(self.bias, (1, self.filters, 1))\n outputs += bias\n else:\n outputs = nn.bias_add(outputs, self.bias, data_format=\"NCHW\")\n else:\n outputs = nn.bias_add(outputs, self.bias, data_format=\"NHWC\")\n\n if self.activation is not None:\n return self.activation(outputs)\n return outputs\n\n def compute_output_shape(self, input_shape):\n input_shape = tensor_shape.TensorShape(input_shape).as_list()\n if self.data_format == \"channels_last\":\n space = input_shape[1:-1]\n new_space = []\n for i in range(len(space)):\n new_dim = conv_utils.conv_output_length(\n space[i],\n self.kernel_size[i],\n padding=self.padding,\n stride=self.strides[i],\n dilation=self.dilation_rate[i],\n )\n new_space.append(new_dim)\n return tensor_shape.TensorShape(\n [input_shape[0]] + new_space + [self.filters]\n )\n else:\n space = input_shape[2:]\n new_space = []\n for i in range(len(space)):\n new_dim = conv_utils.conv_output_length(\n space[i],\n self.kernel_size[i],\n padding=self.padding,\n stride=self.strides[i],\n dilation=self.dilation_rate[i],\n )\n new_space.append(new_dim)\n return tensor_shape.TensorShape([input_shape[0], self.filters] + new_space)\n\n def get_config(self):\n config = {\n \"filters\": self.filters,\n \"kernel_size\": self.kernel_size,\n \"strides\": self.strides,\n \"padding\": self.padding,\n \"data_format\": self.data_format,\n \"dilation_rate\": self.dilation_rate,\n \"groups\": self.groups,\n \"activation\": activations.serialize(self.activation),\n \"use_bias\": self.use_bias,\n \"kernel_initializer\": initializers.serialize(self.kernel_initializer),\n \"bias_initializer\": initializers.serialize(self.bias_initializer),\n \"kernel_regularizer\": regularizers.serialize(self.kernel_regularizer),\n \"bias_regularizer\": regularizers.serialize(self.bias_regularizer),\n \"activity_regularizer\": regularizers.serialize(self.activity_regularizer),\n \"kernel_constraint\": constraints.serialize(self.kernel_constraint),\n \"bias_constraint\": constraints.serialize(self.bias_constraint),\n }\n base_config = super(Conv, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))\n\n def _compute_causal_padding(self):\n \"\"\"Calculates padding for 'causal' option for 1-d conv layers.\"\"\"\n left_pad = self.dilation_rate[0] * (self.kernel_size[0] - 1)\n if self.data_format == \"channels_last\":\n causal_padding = [[0, 0], [left_pad, 0], [0, 0]]\n else:\n causal_padding = [[0, 0], [0, 0], [left_pad, 0]]\n return causal_padding\n\n def _get_channel_axis(self):\n if self.data_format == \"channels_first\":\n return 1\n else:\n return -1\n\n def _get_input_channel(self, input_shape):\n channel_axis = self._get_channel_axis()\n if input_shape.dims[channel_axis].value is None:\n raise ValueError(\n \"The channel dimension of the inputs \"\n \"should be defined. Found `None`.\"\n )\n return int(input_shape[channel_axis])\n\n def _get_padding_op(self):\n if self.padding == \"causal\":\n op_padding = \"valid\"\n else:\n op_padding = self.padding\n if not isinstance(op_padding, (list, tuple)):\n op_padding = op_padding.upper()\n return op_padding\n\n def _recreate_conv_op(self, inputs):\n \"\"\"Recreate conv_op if necessary.\n Check if the input_shape in call() is different from that in build().\n For the values that are not None, if they are different, recreate\n the _convolution_op to avoid the stateful behavior.\n Args:\n inputs: The input data to call() method.\n Returns:\n `True` or `False` to indicate whether to recreate the conv_op.\n \"\"\"\n call_input_shape = inputs.get_shape()\n for axis in range(1, len(call_input_shape)):\n if (\n call_input_shape[axis] is not None\n and self._build_conv_op_input_shape[axis] is not None\n and call_input_shape[axis] != self._build_conv_op_input_shape[axis]\n ):\n return True\n return False\n\n\nclass GroupConv1D(Conv):\n \"\"\"1D convolution layer (e.g. temporal convolution).\n This layer creates a convolution kernel that is convolved\n with the layer input over a single spatial (or temporal) dimension\n to produce a tensor of outputs.\n If `use_bias` is True, a bias vector is created and added to the outputs.\n Finally, if `activation` is not `None`,\n it is applied to the outputs as well.\n When using this layer as the first layer in a model,\n provide an `input_shape` argument\n (tuple of integers or `None`, e.g.\n `(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,\n or `(None, 128)` for variable-length sequences of 128-dimensional vectors.\n Examples:\n >>> # The inputs are 128-length vectors with 10 timesteps, and the batch size\n >>> # is 4.\n >>> input_shape = (4, 10, 128)\n >>> x = tf.random.normal(input_shape)\n >>> y = tf.keras.layers.Conv1D(\n ... 32, 3, activation='relu',input_shape=input_shape)(x)\n >>> print(y.shape)\n (4, 8, 32)\n Arguments:\n filters: Integer, the dimensionality of the output space\n (i.e. the number of output filters in the convolution).\n kernel_size: An integer or tuple/list of a single integer,\n specifying the length of the 1D convolution window.\n strides: An integer or tuple/list of a single integer,\n specifying the stride length of the convolution.\n Specifying any stride value != 1 is incompatible with specifying\n any `dilation_rate` value != 1.\n padding: One of `\"valid\"`, `\"causal\"` or `\"same\"` (case-insensitive).\n `\"causal\"` results in causal (dilated) convolutions, e.g. `output[t]`\n does not depend on `input[t+1:]`. Useful when modeling temporal data\n where the model should not violate the temporal order.\n See [WaveNet: A Generative Model for Raw Audio, section\n 2.1](https://arxiv.org/abs/1609.03499).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n groups: Integer, the number of channel groups controlling the connections\n between inputs and outputs. Input channels and `filters` must both be\n divisible by `groups`. For example,\n - At `groups=1`, all inputs are convolved to all outputs.\n - At `groups=2`, the operation becomes equivalent to having two\n convolutional layers side by side, each seeing half the input\n channels, and producing half the output channels, and both\n subsequently concatenated.\n - At `groups=input_channels`, each input channel is convolved with its\n own set of filters, of size `input_channels / filters`\n dilation_rate: an integer or tuple/list of a single integer, specifying\n the dilation rate to use for dilated convolution.\n Currently, specifying any `dilation_rate` value != 1 is\n incompatible with specifying any `strides` value != 1.\n activation: Activation function to use.\n If you don't specify anything, no activation is applied (\n see `keras.activations`).\n use_bias: Boolean, whether the layer uses a bias vector.\n kernel_initializer: Initializer for the `kernel` weights matrix (\n see `keras.initializers`).\n bias_initializer: Initializer for the bias vector (\n see `keras.initializers`).\n kernel_regularizer: Regularizer function applied to\n the `kernel` weights matrix (see `keras.regularizers`).\n bias_regularizer: Regularizer function applied to the bias vector (\n see `keras.regularizers`).\n activity_regularizer: Regularizer function applied to\n the output of the layer (its \"activation\") (\n see `keras.regularizers`).\n kernel_constraint: Constraint function applied to the kernel matrix (\n see `keras.constraints`).\n bias_constraint: Constraint function applied to the bias vector (\n see `keras.constraints`).\n Input shape:\n 3D tensor with shape: `(batch_size, steps, input_dim)`\n Output shape:\n 3D tensor with shape: `(batch_size, new_steps, filters)`\n `steps` value might have changed due to padding or strides.\n Returns:\n A tensor of rank 3 representing\n `activation(conv1d(inputs, kernel) + bias)`.\n Raises:\n ValueError: when both `strides` > 1 and `dilation_rate` > 1.\n \"\"\"\n\n def __init__(\n self,\n filters,\n kernel_size,\n strides=1,\n padding=\"valid\",\n data_format=\"channels_last\",\n dilation_rate=1,\n groups=1,\n activation=None,\n use_bias=True,\n kernel_initializer=\"glorot_uniform\",\n bias_initializer=\"zeros\",\n kernel_regularizer=None,\n bias_regularizer=None,\n activity_regularizer=None,\n kernel_constraint=None,\n bias_constraint=None,\n **kwargs\n ):\n super().__init__(\n rank=1,\n filters=filters,\n kernel_size=kernel_size,\n strides=strides,\n padding=padding,\n data_format=data_format,\n dilation_rate=dilation_rate,\n groups=groups,\n activation=activations.get(activation),\n use_bias=use_bias,\n kernel_initializer=initializers.get(kernel_initializer),\n bias_initializer=initializers.get(bias_initializer),\n kernel_regularizer=regularizers.get(kernel_regularizer),\n bias_regularizer=regularizers.get(bias_regularizer),\n activity_regularizer=regularizers.get(activity_regularizer),\n kernel_constraint=constraints.get(kernel_constraint),\n bias_constraint=constraints.get(bias_constraint),\n **kwargs\n )\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/korean.py", "content": "# -*- coding: utf-8 -*-\r\n# Copyright 2020 TensorFlowTTS Team, Jaehyoung Kim(@crux153) and Taehoon Kim(@carpedm20)\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n# Code based on https://github.com/carpedm20/multi-speaker-tacotron-tensorflow\r\n\"\"\"Korean related helpers.\"\"\"\r\n\r\nimport ast\r\nimport json\r\nimport os\r\nimport re\r\n\r\nfrom jamo import h2j, hangul_to_jamo, j2h, jamo_to_hcj\r\n\r\netc_dictionary = {\r\n \"2 30대\": \"이삼십대\",\r\n \"20~30대\": \"이삼십대\",\r\n \"20, 30대\": \"이십대 삼십대\",\r\n \"1+1\": \"원플러스원\",\r\n \"3에서 6개월인\": \"3개월에서 육개월인\",\r\n}\r\n\r\nenglish_dictionary = {\r\n \"Devsisters\": \"데브시스터즈\",\r\n \"track\": \"트랙\",\r\n # krbook\r\n \"LA\": \"엘에이\",\r\n \"LG\": \"엘지\",\r\n \"KOREA\": \"코리아\",\r\n \"JSA\": \"제이에스에이\",\r\n \"PGA\": \"피지에이\",\r\n \"GA\": \"지에이\",\r\n \"idol\": \"아이돌\",\r\n \"KTX\": \"케이티엑스\",\r\n \"AC\": \"에이씨\",\r\n \"DVD\": \"디비디\",\r\n \"US\": \"유에스\",\r\n \"CNN\": \"씨엔엔\",\r\n \"LPGA\": \"엘피지에이\",\r\n \"P\": \"피\",\r\n \"L\": \"엘\",\r\n \"T\": \"티\",\r\n \"B\": \"비\",\r\n \"C\": \"씨\",\r\n \"BIFF\": \"비아이에프에프\",\r\n \"GV\": \"지비\",\r\n # JTBC\r\n \"IT\": \"아이티\",\r\n \"IQ\": \"아이큐\",\r\n \"JTBC\": \"제이티비씨\",\r\n \"trickle down effect\": \"트리클 다운 이펙트\",\r\n \"trickle up effect\": \"트리클 업 이펙트\",\r\n \"down\": \"다운\",\r\n \"up\": \"업\",\r\n \"FCK\": \"에프씨케이\",\r\n \"AP\": \"에이피\",\r\n \"WHERETHEWILDTHINGSARE\": \"\",\r\n \"Rashomon Effect\": \"\",\r\n \"O\": \"오\",\r\n \"OO\": \"오오\",\r\n \"B\": \"비\",\r\n \"GDP\": \"지디피\",\r\n \"CIPA\": \"씨아이피에이\",\r\n \"YS\": \"와이에스\",\r\n \"Y\": \"와이\",\r\n \"S\": \"에스\",\r\n \"JTBC\": \"제이티비씨\",\r\n \"PC\": \"피씨\",\r\n \"bill\": \"빌\",\r\n \"Halmuny\": \"하모니\", #####\r\n \"X\": \"엑스\",\r\n \"SNS\": \"에스엔에스\",\r\n \"ability\": \"어빌리티\",\r\n \"shy\": \"\",\r\n \"CCTV\": \"씨씨티비\",\r\n \"IT\": \"아이티\",\r\n \"the tenth man\": \"더 텐쓰 맨\", ####\r\n \"L\": \"엘\",\r\n \"PC\": \"피씨\",\r\n \"YSDJJPMB\": \"\", ########\r\n \"Content Attitude Timing\": \"컨텐트 애티튜드 타이밍\",\r\n \"CAT\": \"캣\",\r\n \"IS\": \"아이에스\",\r\n \"K\": \"케이\",\r\n \"Y\": \"와이\",\r\n \"KDI\": \"케이디아이\",\r\n \"DOC\": \"디오씨\",\r\n \"CIA\": \"씨아이에이\",\r\n \"PBS\": \"피비에스\",\r\n \"D\": \"디\",\r\n \"PPropertyPositionPowerPrisonP\" \"S\": \"에스\",\r\n \"francisco\": \"프란시스코\",\r\n \"I\": \"아이\",\r\n \"III\": \"아이아이\", ######\r\n \"No joke\": \"노 조크\",\r\n \"BBK\": \"비비케이\",\r\n \"LA\": \"엘에이\",\r\n \"Don\": \"\",\r\n \"t worry be happy\": \" 워리 비 해피\",\r\n \"NO\": \"엔오\", #####\r\n \"it was our sky\": \"잇 워즈 아워 스카이\",\r\n \"it is our sky\": \"잇 이즈 아워 스카이\", ####\r\n \"NEIS\": \"엔이아이에스\", #####\r\n \"IMF\": \"아이엠에프\",\r\n \"apology\": \"어폴로지\",\r\n \"humble\": \"험블\",\r\n \"M\": \"엠\",\r\n \"Nowhere Man\": \"노웨어 맨\",\r\n \"The Tenth Man\": \"더 텐쓰 맨\",\r\n \"PBS\": \"피비에스\",\r\n \"BBC\": \"비비씨\",\r\n \"MRJ\": \"엠알제이\",\r\n \"CCTV\": \"씨씨티비\",\r\n \"Pick me up\": \"픽 미 업\",\r\n \"DNA\": \"디엔에이\",\r\n \"UN\": \"유엔\",\r\n \"STOP\": \"스탑\", #####\r\n \"PRESS\": \"프레스\", #####\r\n \"not to be\": \"낫 투비\",\r\n \"Denial\": \"디나이얼\",\r\n \"G\": \"지\",\r\n \"IMF\": \"아이엠에프\",\r\n \"GDP\": \"지디피\",\r\n \"JTBC\": \"제이티비씨\",\r\n \"Time flies like an arrow\": \"타임 플라이즈 라이크 언 애로우\",\r\n \"DDT\": \"디디티\",\r\n \"AI\": \"에이아이\",\r\n \"Z\": \"제트\",\r\n \"OECD\": \"오이씨디\",\r\n \"N\": \"앤\",\r\n \"A\": \"에이\",\r\n \"MB\": \"엠비\",\r\n \"EH\": \"이에이치\",\r\n \"IS\": \"아이에스\",\r\n \"TV\": \"티비\",\r\n \"MIT\": \"엠아이티\",\r\n \"KBO\": \"케이비오\",\r\n \"I love America\": \"아이 러브 아메리카\",\r\n \"SF\": \"에스에프\",\r\n \"Q\": \"큐\",\r\n \"KFX\": \"케이에프엑스\",\r\n \"PM\": \"피엠\",\r\n \"Prime Minister\": \"프라임 미니스터\",\r\n \"Swordline\": \"스워드라인\",\r\n \"TBS\": \"티비에스\",\r\n \"DDT\": \"디디티\",\r\n \"CS\": \"씨에스\",\r\n \"Reflecting Absence\": \"리플렉팅 앱센스\",\r\n \"PBS\": \"피비에스\",\r\n \"Drum being beaten by everyone\": \"드럼 빙 비튼 바이 에브리원\",\r\n \"negative pressure\": \"네거티브 프레셔\",\r\n \"F\": \"에프\",\r\n \"KIA\": \"기아\",\r\n \"FTA\": \"에프티에이\",\r\n \"Que sais-je\": \"\",\r\n \"UFC\": \"유에프씨\",\r\n \"P\": \"피\",\r\n \"DJ\": \"디제이\",\r\n \"Chaebol\": \"채벌\",\r\n \"BBC\": \"비비씨\",\r\n \"OECD\": \"오이씨디\",\r\n \"BC\": \"삐씨\",\r\n \"C\": \"씨\",\r\n \"B\": \"씨\",\r\n \"KY\": \"케이와이\",\r\n \"K\": \"케이\",\r\n \"CEO\": \"씨이오\",\r\n \"YH\": \"와이에치\",\r\n \"IS\": \"아이에스\",\r\n \"who are you\": \"후 얼 유\",\r\n \"Y\": \"와이\",\r\n \"The Devils Advocate\": \"더 데빌즈 어드보카트\",\r\n \"YS\": \"와이에스\",\r\n \"so sorry\": \"쏘 쏘리\",\r\n \"Santa\": \"산타\",\r\n \"Big Endian\": \"빅 엔디안\",\r\n \"Small Endian\": \"스몰 엔디안\",\r\n \"Oh Captain My Captain\": \"오 캡틴 마이 캡틴\",\r\n \"AIB\": \"에이아이비\",\r\n \"K\": \"케이\",\r\n \"PBS\": \"피비에스\",\r\n # IU\r\n \"ASMR\": \"에이에스엠알\",\r\n \"V\": \"브이\",\r\n \"PD\": \"피디\",\r\n \"CD\": \"씨디\",\r\n \"ANR\": \"에이엔알\",\r\n \"Twenty Three\": \"투엔티 쓰리\",\r\n \"Through The Night\": \"쓰루 더 나잇\",\r\n \"MD\": \"엠디\",\r\n}\r\n\r\nnum_to_kor = {\r\n \"0\": \"영\",\r\n \"1\": \"일\",\r\n \"2\": \"이\",\r\n \"3\": \"삼\",\r\n \"4\": \"사\",\r\n \"5\": \"오\",\r\n \"6\": \"육\",\r\n \"7\": \"칠\",\r\n \"8\": \"팔\",\r\n \"9\": \"구\",\r\n}\r\n\r\nunit_to_kor1 = {\"%\": \"퍼센트\", \"cm\": \"센치미터\", \"mm\": \"밀리미터\", \"km\": \"킬로미터\", \"kg\": \"킬로그람\"}\r\nunit_to_kor2 = {\"m\": \"미터\"}\r\n\r\nupper_to_kor = {\r\n \"A\": \"에이\",\r\n \"B\": \"비\",\r\n \"C\": \"씨\",\r\n \"D\": \"디\",\r\n \"E\": \"이\",\r\n \"F\": \"에프\",\r\n \"G\": \"지\",\r\n \"H\": \"에이치\",\r\n \"I\": \"아이\",\r\n \"J\": \"제이\",\r\n \"K\": \"케이\",\r\n \"L\": \"엘\",\r\n \"M\": \"엠\",\r\n \"N\": \"엔\",\r\n \"O\": \"오\",\r\n \"P\": \"피\",\r\n \"Q\": \"큐\",\r\n \"R\": \"알\",\r\n \"S\": \"에스\",\r\n \"T\": \"티\",\r\n \"U\": \"유\",\r\n \"V\": \"브이\",\r\n \"W\": \"더블유\",\r\n \"X\": \"엑스\",\r\n \"Y\": \"와이\",\r\n \"Z\": \"지\",\r\n}\r\n\r\n\r\n\"\"\"\r\n초성과 종성은 같아보이지만, 다른 character이다.\r\n\r\n'_-!'(),-.:;? ᄀᄁᄂᄃᄄᄅᄆᄇᄈᄉᄊᄋᄌᄍᄎᄏᄐᄑ하ᅢᅣᅤᅥᅦᅧᅨᅩᅪᅫᅬᅭᅮᅯᅰᅱᅲᅳᅴᅵᆨᆩᆪᆫᆬᆭᆮᆯᆰᆱᆲᆳᆴᆵᆶᆷᆸᆹᆺᆻᆼᆽᆾᆿᇀᇁᇂ~'\r\n\r\n'_': 0, '-': 7, '!': 2, \"'\": 3, '(': 4, ')': 5, ',': 6, '.': 8, ':': 9, ';': 10,\r\n'?': 11, ' ': 12, 'ᄀ': 13, 'ᄁ': 14, 'ᄂ': 15, 'ᄃ': 16, 'ᄄ': 17, 'ᄅ': 18, 'ᄆ': 19, 'ᄇ': 20,\r\n'ᄈ': 21, 'ᄉ': 22, 'ᄊ': 23, 'ᄋ': 24, 'ᄌ': 25, 'ᄍ': 26, 'ᄎ': 27, 'ᄏ': 28, 'ᄐ': 29, 'ᄑ': 30,\r\n'ᄒ': 31, 'ᅡ': 32, 'ᅢ': 33, 'ᅣ': 34, 'ᅤ': 35, 'ᅥ': 36, 'ᅦ': 37, 'ᅧ': 38, 'ᅨ': 39, 'ᅩ': 40,\r\n'ᅪ': 41, 'ᅫ': 42, 'ᅬ': 43, 'ᅭ': 44, 'ᅮ': 45, 'ᅯ': 46, 'ᅰ': 47, 'ᅱ': 48, 'ᅲ': 49, 'ᅳ': 50,\r\n'ᅴ': 51, 'ᅵ': 52, 'ᆨ': 53, 'ᆩ': 54, 'ᆪ': 55, 'ᆫ': 56, 'ᆬ': 57, 'ᆭ': 58, 'ᆮ': 59, 'ᆯ': 60,\r\n'ᆰ': 61, 'ᆱ': 62, 'ᆲ': 63, 'ᆳ': 64, 'ᆴ': 65, 'ᆵ': 66, 'ᆶ': 67, 'ᆷ': 68, 'ᆸ': 69, 'ᆹ': 70,\r\n'ᆺ': 71, 'ᆻ': 72, 'ᆼ': 73, 'ᆽ': 74, 'ᆾ': 75, 'ᆿ': 76, 'ᇀ': 77, 'ᇁ': 78, 'ᇂ': 79, '~': 80\r\n\"\"\"\r\n\r\n_pad = \"pad\"\r\n_eos = \"eos\"\r\n_punctuation = \"!'(),-.:;? \"\r\n_special = \"-\"\r\n\r\n_jamo_leads = [chr(_) for _ in range(0x1100, 0x1113)]\r\n_jamo_vowels = [chr(_) for _ in range(0x1161, 0x1176)]\r\n_jamo_tails = [chr(_) for _ in range(0x11A8, 0x11C3)]\r\n\r\n_letters = _jamo_leads + _jamo_vowels + _jamo_tails\r\n\r\nsymbols = [_pad] + list(_special) + list(_punctuation) + _letters + [_eos]\r\n\r\n_symbol_to_id = {c: i for i, c in enumerate(symbols)}\r\n_id_to_symbol = {i: c for i, c in enumerate(symbols)}\r\n\r\nquote_checker = \"\"\"([`\"'＂“‘])(.+?)([`\"'＂”’])\"\"\"\r\n\r\n\r\ndef is_lead(char):\r\n return char in _jamo_leads\r\n\r\n\r\ndef is_vowel(char):\r\n return char in _jamo_vowels\r\n\r\n\r\ndef is_tail(char):\r\n return char in _jamo_tails\r\n\r\n\r\ndef get_mode(char):\r\n if is_lead(char):\r\n return 0\r\n elif is_vowel(char):\r\n return 1\r\n elif is_tail(char):\r\n return 2\r\n else:\r\n return -1\r\n\r\n\r\ndef _get_text_from_candidates(candidates):\r\n if len(candidates) == 0:\r\n return \"\"\r\n elif len(candidates) == 1:\r\n return jamo_to_hcj(candidates[0])\r\n else:\r\n return j2h(**dict(zip([\"lead\", \"vowel\", \"tail\"], candidates)))\r\n\r\n\r\ndef jamo_to_korean(text):\r\n text = h2j(text)\r\n\r\n idx = 0\r\n new_text = \"\"\r\n candidates = []\r\n\r\n while True:\r\n if idx >= len(text):\r\n new_text += _get_text_from_candidates(candidates)\r\n break\r\n\r\n char = text[idx]\r\n mode = get_mode(char)\r\n\r\n if mode == 0:\r\n new_text += _get_text_from_candidates(candidates)\r\n candidates = [char]\r\n elif mode == -1:\r\n new_text += _get_text_from_candidates(candidates)\r\n new_text += char\r\n candidates = []\r\n else:\r\n candidates.append(char)\r\n\r\n idx += 1\r\n return new_text\r\n\r\n\r\ndef compare_sentence_with_jamo(text1, text2):\r\n return h2j(text1) != h2j(text2)\r\n\r\n\r\ndef tokenize(text, as_id=False):\r\n # jamo package에 있는 hangul_to_jamo를 이용하여 한글 string을 초성/중성/종성으로 나눈다.\r\n text = normalize(text)\r\n tokens = list(\r\n hangul_to_jamo(text)\r\n ) # '존경하는' --> ['ᄌ', 'ᅩ', 'ᆫ', 'ᄀ', 'ᅧ', 'ᆼ', 'ᄒ', 'ᅡ', 'ᄂ', 'ᅳ', 'ᆫ', '~']\r\n\r\n if as_id:\r\n return [_symbol_to_id[token] for token in tokens]\r\n else:\r\n return [token for token in tokens]\r\n\r\n\r\ndef tokenizer_fn(iterator):\r\n return (token for x in iterator for token in tokenize(x, as_id=False))\r\n\r\n\r\ndef normalize(text):\r\n text = text.strip()\r\n\r\n text = re.sub(\"\\(\\d+일\\)\", \"\", text)\r\n text = re.sub(\"\\([⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+\\)\", \"\", text)\r\n\r\n text = normalize_with_dictionary(text, etc_dictionary)\r\n text = normalize_english(text)\r\n text = re.sub(\"[a-zA-Z]+\", normalize_upper, text)\r\n\r\n text = normalize_quote(text)\r\n text = normalize_number(text)\r\n\r\n return text\r\n\r\n\r\ndef normalize_with_dictionary(text, dic):\r\n if any(key in text for key in dic.keys()):\r\n pattern = re.compile(\"|\".join(re.escape(key) for key in dic.keys()))\r\n return pattern.sub(lambda x: dic[x.group()], text)\r\n else:\r\n return text\r\n\r\n\r\ndef normalize_english(text):\r\n def fn(m):\r\n word = m.group()\r\n if word in english_dictionary:\r\n return english_dictionary.get(word)\r\n else:\r\n return word\r\n\r\n text = re.sub(\"([A-Za-z]+)\", fn, text)\r\n return text\r\n\r\n\r\ndef normalize_upper(text):\r\n text = text.group(0)\r\n\r\n if all([char.isupper() for char in text]):\r\n return \"\".join(upper_to_kor[char] for char in text)\r\n else:\r\n return text\r\n\r\n\r\ndef normalize_quote(text):\r\n def fn(found_text):\r\n from nltk import sent_tokenize # NLTK doesn't along with multiprocessing\r\n\r\n found_text = found_text.group()\r\n unquoted_text = found_text[1:-1]\r\n\r\n sentences = sent_tokenize(unquoted_text)\r\n return \" \".join([\"'{}'\".format(sent) for sent in sentences])\r\n\r\n return re.sub(quote_checker, fn, text)\r\n\r\n\r\nnumber_checker = \"([+-]?\\d[\\d,]*)[\\.]?\\d*\"\r\ncount_checker = \"(시|명|가지|살|마리|포기|송이|수|톨|통|점|개|벌|척|채|다발|그루|자루|줄|켤레|그릇|잔|마디|상자|사람|곡|병|판)\"\r\n\r\n\r\ndef normalize_number(text):\r\n text = normalize_with_dictionary(text, unit_to_kor1)\r\n text = normalize_with_dictionary(text, unit_to_kor2)\r\n text = re.sub(\r\n number_checker + count_checker, lambda x: number_to_korean(x, True), text\r\n )\r\n text = re.sub(number_checker, lambda x: number_to_korean(x, False), text)\r\n return text\r\n\r\n\r\nnum_to_kor1 = [\"\"] + list(\"일이삼사오육칠팔구\")\r\nnum_to_kor2 = [\"\"] + list(\"만억조경해\")\r\nnum_to_kor3 = [\"\"] + list(\"십백천\")\r\n\r\n# count_to_kor1 = [\"\"] + [\"하나\",\"둘\",\"셋\",\"넷\",\"다섯\",\"여섯\",\"일곱\",\"여덟\",\"아홉\"]\r\ncount_to_kor1 = [\"\"] + [\"한\", \"두\", \"세\", \"네\", \"다섯\", \"여섯\", \"일곱\", \"여덟\", \"아홉\"]\r\n\r\ncount_tenth_dict = {\r\n \"십\": \"열\",\r\n \"두십\": \"스물\",\r\n \"세십\": \"서른\",\r\n \"네십\": \"마흔\",\r\n \"다섯십\": \"쉰\",\r\n \"여섯십\": \"예순\",\r\n \"일곱십\": \"일흔\",\r\n \"여덟십\": \"여든\",\r\n \"아홉십\": \"아흔\",\r\n}\r\n\r\n\r\ndef number_to_korean(num_str, is_count=False):\r\n if is_count:\r\n num_str, unit_str = num_str.group(1), num_str.group(2)\r\n else:\r\n num_str, unit_str = num_str.group(), \"\"\r\n\r\n num_str = num_str.replace(\",\", \"\")\r\n num = ast.literal_eval(num_str)\r\n\r\n if num == 0:\r\n return \"영\"\r\n\r\n check_float = num_str.split(\".\")\r\n if len(check_float) == 2:\r\n digit_str, float_str = check_float\r\n elif len(check_float) >= 3:\r\n raise Exception(\" [!] Wrong number format\")\r\n else:\r\n digit_str, float_str = check_float[0], None\r\n\r\n if is_count and float_str is not None:\r\n raise Exception(\" [!] `is_count` and float number does not fit each other\")\r\n\r\n digit = int(digit_str)\r\n\r\n if digit_str.startswith(\"-\"):\r\n digit, digit_str = abs(digit), str(abs(digit))\r\n\r\n kor = \"\"\r\n size = len(str(digit))\r\n tmp = []\r\n\r\n for i, v in enumerate(digit_str, start=1):\r\n v = int(v)\r\n\r\n if v != 0:\r\n if is_count:\r\n tmp += count_to_kor1[v]\r\n else:\r\n tmp += num_to_kor1[v]\r\n\r\n tmp += num_to_kor3[(size - i) % 4]\r\n\r\n if (size - i) % 4 == 0 and len(tmp) != 0:\r\n kor += \"\".join(tmp)\r\n tmp = []\r\n kor += num_to_kor2[int((size - i) / 4)]\r\n\r\n if is_count:\r\n if kor.startswith(\"한\") and len(kor) > 1:\r\n kor = kor[1:]\r\n\r\n if any(word in kor for word in count_tenth_dict):\r\n kor = re.sub(\r\n \"|\".join(count_tenth_dict.keys()),\r\n lambda x: count_tenth_dict[x.group()],\r\n kor,\r\n )\r\n\r\n if not is_count and kor.startswith(\"일\") and len(kor) > 1:\r\n kor = kor[1:]\r\n\r\n if float_str is not None:\r\n kor += \"쩜 \"\r\n kor += re.sub(\"\\d\", lambda x: num_to_kor[x.group()], float_str)\r\n\r\n if num_str.startswith(\"+\"):\r\n kor = \"플러스 \" + kor\r\n elif num_str.startswith(\"-\"):\r\n kor = \"마이너스 \" + kor\r\n\r\n return kor + unit_str\r\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/number_norm.py", "content": "# -*- coding: utf-8 -*-\n# Copyright (c) 2017 Keith Ito\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\"\"\"Number norm module.\"\"\"\n\n\nimport re\n\nimport inflect\n\n_inflect = inflect.engine()\n_comma_number_re = re.compile(r\"([0-9][0-9\\,]+[0-9])\")\n_decimal_number_re = re.compile(r\"([0-9]+\\.[0-9]+)\")\n_pounds_re = re.compile(r\"£([0-9\\,]*[0-9]+)\")\n_dollars_re = re.compile(r\"\\$([0-9\\.\\,]*[0-9]+)\")\n_ordinal_re = re.compile(r\"[0-9]+(st|nd|rd|th)\")\n_number_re = re.compile(r\"[0-9]+\")\n\n\ndef _remove_commas(m):\n return m.group(1).replace(\",\", \"\")\n\n\ndef _expand_decimal_point(m):\n return m.group(1).replace(\".\", \" point \")\n\n\ndef _expand_dollars(m):\n match = m.group(1)\n parts = match.split(\".\")\n if len(parts) > 2:\n return match + \" dollars\" # Unexpected format\n dollars = int(parts[0]) if parts[0] else 0\n cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0\n if dollars and cents:\n dollar_unit = \"dollar\" if dollars == 1 else \"dollars\"\n cent_unit = \"cent\" if cents == 1 else \"cents\"\n return \"%s %s, %s %s\" % (dollars, dollar_unit, cents, cent_unit)\n elif dollars:\n dollar_unit = \"dollar\" if dollars == 1 else \"dollars\"\n return \"%s %s\" % (dollars, dollar_unit)\n elif cents:\n cent_unit = \"cent\" if cents == 1 else \"cents\"\n return \"%s %s\" % (cents, cent_unit)\n else:\n return \"zero dollars\"\n\n\ndef _expand_ordinal(m):\n return _inflect.number_to_words(m.group(0))\n\n\ndef _expand_number(m):\n num = int(m.group(0))\n if num > 1000 and num < 3000:\n if num == 2000:\n return \"two thousand\"\n elif num > 2000 and num < 2010:\n return \"two thousand \" + _inflect.number_to_words(num % 100)\n elif num % 100 == 0:\n return _inflect.number_to_words(num // 100) + \" hundred\"\n else:\n return _inflect.number_to_words(\n num, andword=\"\", zero=\"oh\", group=2\n ).replace(\", \", \" \")\n else:\n return _inflect.number_to_words(num, andword=\"\")\n\n\ndef normalize_numbers(text):\n text = re.sub(_comma_number_re, _remove_commas, text)\n text = re.sub(_pounds_re, r\"\\1 pounds\", text)\n text = re.sub(_dollars_re, _expand_dollars, text)\n text = re.sub(_decimal_number_re, _expand_decimal_point, text)\n text = re.sub(_ordinal_re, _expand_ordinal, text)\n text = re.sub(_number_re, _expand_number, text)\n return text\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/outliers.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Outliers detection and remove.\"\"\"\nimport numpy as np\n\n\ndef is_outlier(x, p25, p75):\n \"\"\"Check if value is an outlier.\"\"\"\n lower = p25 - 1.5 * (p75 - p25)\n upper = p75 + 1.5 * (p75 - p25)\n return x <= lower or x >= upper\n\n\ndef remove_outlier(x, p_bottom: int = 25, p_top: int = 75):\n \"\"\"Remove outlier from x.\"\"\"\n p_bottom = np.percentile(x, p_bottom)\n p_top = np.percentile(x, p_top)\n\n indices_of_outliers = []\n for ind, value in enumerate(x):\n if is_outlier(value, p_bottom, p_top):\n indices_of_outliers.append(ind)\n\n x[indices_of_outliers] = 0.0\n\n # replace by mean f0.\n x[indices_of_outliers] = np.max(x)\n return x\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/strategy.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Strategy util functions\"\"\"\nimport tensorflow as tf\n\n\ndef return_strategy():\n physical_devices = tf.config.list_physical_devices(\"GPU\")\n if len(physical_devices) == 0:\n return tf.distribute.OneDeviceStrategy(device=\"/cpu:0\")\n elif len(physical_devices) == 1:\n return tf.distribute.OneDeviceStrategy(device=\"/gpu:0\")\n else:\n return tf.distribute.MirroredStrategy()\n\n\ndef calculate_3d_loss(y_gt, y_pred, loss_fn):\n \"\"\"Calculate 3d loss, normally it's mel-spectrogram loss.\"\"\"\n y_gt_T = tf.shape(y_gt)[1]\n y_pred_T = tf.shape(y_pred)[1]\n\n # there is a mismath length when training multiple GPU.\n # we need slice the longer tensor to make sure the loss\n # calculated correctly.\n if y_gt_T > y_pred_T:\n y_gt = tf.slice(y_gt, [0, 0, 0], [-1, y_pred_T, -1])\n elif y_pred_T > y_gt_T:\n y_pred = tf.slice(y_pred, [0, 0, 0], [-1, y_gt_T, -1])\n\n loss = loss_fn(y_gt, y_pred)\n if isinstance(loss, tuple) is False:\n loss = tf.reduce_mean(loss, list(range(1, len(loss.shape)))) # shape = [B]\n else:\n loss = list(loss)\n for i in range(len(loss)):\n loss[i] = tf.reduce_mean(\n loss[i], list(range(1, len(loss[i].shape)))\n ) # shape = [B]\n return loss\n\n\ndef calculate_2d_loss(y_gt, y_pred, loss_fn):\n \"\"\"Calculate 2d loss, normally it's durrations/f0s/energys loss.\"\"\"\n y_gt_T = tf.shape(y_gt)[1]\n y_pred_T = tf.shape(y_pred)[1]\n\n # there is a mismath length when training multiple GPU.\n # we need slice the longer tensor to make sure the loss\n # calculated correctly.\n if y_gt_T > y_pred_T:\n y_gt = tf.slice(y_gt, [0, 0], [-1, y_pred_T])\n elif y_pred_T > y_gt_T:\n y_pred = tf.slice(y_pred, [0, 0], [-1, y_gt_T])\n\n loss = loss_fn(y_gt, y_pred)\n if isinstance(loss, tuple) is False:\n loss = tf.reduce_mean(loss, list(range(1, len(loss.shape)))) # shape = [B]\n else:\n loss = list(loss)\n for i in range(len(loss)):\n loss[i] = tf.reduce_mean(\n loss[i], list(range(1, len(loss[i].shape)))\n ) # shape = [B]\n\n return loss\n\ndef calculate_loss_norm_lens(y_gt, y_pred, loss_fn, norm_lens):\n if len(y_gt.shape) == 2:\n y_gt = tf.expand_dims(y_gt, -1)\n y_pred = tf.expand_dims(y_pred, -1)\n\n loss = loss_fn(y_gt, y_pred)\n loss = tf.reduce_sum(loss, list(range(1, len(loss.shape))))\n loss = loss / tf.cast(norm_lens, loss.dtype)\n\n return loss\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/utils.py", "content": "# -*- coding: utf-8 -*-\n\n# Copyright 2019 Tomoki Hayashi\n# MIT License (https://opensource.org/licenses/MIT)\n\"\"\"Utility functions.\"\"\"\n\nimport fnmatch\nimport os\n\n\ndef find_files(root_dir, query=\"*.wav\", include_root_dir=True):\n \"\"\"Find files recursively.\n Args:\n root_dir (str): Root root_dir to find.\n query (str): Query to find.\n include_root_dir (bool): If False, root_dir name is not included.\n Returns:\n list: List of found filenames.\n \"\"\"\n files = []\n for root, _, filenames in os.walk(root_dir, followlinks=True):\n for filename in fnmatch.filter(filenames, query):\n files.append(os.path.join(root, filename))\n if not include_root_dir:\n files = [file_.replace(root_dir + \"/\", \"\") for file_ in files]\n\n return files\n" }, { "path": "TensorFlowTTS/tensorflow_tts/utils/weight_norm.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2019 The TensorFlow Probability Authors and Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Weight Norm Modules.\"\"\"\n\nimport warnings\n\nimport tensorflow as tf\n\n\nclass WeightNormalization(tf.keras.layers.Wrapper):\n \"\"\"Layer wrapper to decouple magnitude and direction of the layer's weights.\n This wrapper reparameterizes a layer by decoupling the weight's\n magnitude and direction. This speeds up convergence by improving the\n conditioning of the optimization problem. It has an optional data-dependent\n initialization scheme, in which initial values of weights are set as functions\n of the first minibatch of data. Both the weight normalization and data-\n dependent initialization are described in [Salimans and Kingma (2016)][1].\n #### Example\n ```python\n net = WeightNorm(tf.keras.layers.Conv2D(2, 2, activation='relu'),\n input_shape=(32, 32, 3), data_init=True)(x)\n net = WeightNorm(tf.keras.layers.Conv2DTranspose(16, 5, activation='relu'),\n data_init=True)\n net = WeightNorm(tf.keras.layers.Dense(120, activation='relu'),\n data_init=True)(net)\n net = WeightNorm(tf.keras.layers.Dense(num_classes),\n data_init=True)(net)\n ```\n #### References\n [1]: Tim Salimans and Diederik P. Kingma. Weight Normalization: A Simple\n Reparameterization to Accelerate Training of Deep Neural Networks. In\n _30th Conference on Neural Information Processing Systems_, 2016.\n https://arxiv.org/abs/1602.07868\n \"\"\"\n\n def __init__(self, layer, data_init=True, **kwargs):\n \"\"\"Initialize WeightNorm wrapper.\n Args:\n layer: A `tf.keras.layers.Layer` instance. Supported layer types are\n `Dense`, `Conv2D`, and `Conv2DTranspose`. Layers with multiple inputs\n are not supported.\n data_init: `bool`, if `True` use data dependent variable initialization.\n **kwargs: Additional keyword args passed to `tf.keras.layers.Wrapper`.\n Raises:\n ValueError: If `layer` is not a `tf.keras.layers.Layer` instance.\n \"\"\"\n if not isinstance(layer, tf.keras.layers.Layer):\n raise ValueError(\n \"Please initialize `WeightNorm` layer with a `tf.keras.layers.Layer` \"\n \"instance. You passed: {input}\".format(input=layer)\n )\n\n layer_type = type(layer).__name__\n if layer_type not in [\n \"Dense\",\n \"Conv2D\",\n \"Conv2DTranspose\",\n \"Conv1D\",\n \"GroupConv1D\",\n ]:\n warnings.warn(\n \"`WeightNorm` is tested only for `Dense`, `Conv2D`, `Conv1D`, `GroupConv1D`, \"\n \"`GroupConv2D`, and `Conv2DTranspose` layers. You passed a layer of type `{}`\".format(\n layer_type\n )\n )\n\n super().__init__(layer, **kwargs)\n\n self.data_init = data_init\n self._track_trackable(layer, name=\"layer\")\n self.filter_axis = -2 if layer_type == \"Conv2DTranspose\" else -1\n\n def _compute_weights(self):\n \"\"\"Generate weights with normalization.\"\"\"\n # Determine the axis along which to expand `g` so that `g` broadcasts to\n # the shape of `v`.\n new_axis = -self.filter_axis - 3\n\n self.layer.kernel = tf.nn.l2_normalize(\n self.v, axis=self.kernel_norm_axes\n ) * tf.expand_dims(self.g, new_axis)\n\n def _init_norm(self):\n \"\"\"Set the norm of the weight vector.\"\"\"\n kernel_norm = tf.sqrt(\n tf.reduce_sum(tf.square(self.v), axis=self.kernel_norm_axes)\n )\n self.g.assign(kernel_norm)\n\n def _data_dep_init(self, inputs):\n \"\"\"Data dependent initialization.\"\"\"\n # Normalize kernel first so that calling the layer calculates\n # `tf.dot(v, x)/tf.norm(v)` as in (5) in ([Salimans and Kingma, 2016][1]).\n self._compute_weights()\n\n activation = self.layer.activation\n self.layer.activation = None\n\n use_bias = self.layer.bias is not None\n if use_bias:\n bias = self.layer.bias\n self.layer.bias = tf.zeros_like(bias)\n\n # Since the bias is initialized as zero, setting the activation to zero and\n # calling the initialized layer (with normalized kernel) yields the correct\n # computation ((5) in Salimans and Kingma (2016))\n x_init = self.layer(inputs)\n norm_axes_out = list(range(x_init.shape.rank - 1))\n m_init, v_init = tf.nn.moments(x_init, norm_axes_out)\n scale_init = 1.0 / tf.sqrt(v_init + 1e-10)\n\n self.g.assign(self.g * scale_init)\n if use_bias:\n self.layer.bias = bias\n self.layer.bias.assign(-m_init * scale_init)\n self.layer.activation = activation\n\n def build(self, input_shape=None):\n \"\"\"Build `Layer`.\n Args:\n input_shape: The shape of the input to `self.layer`.\n Raises:\n ValueError: If `Layer` does not contain a `kernel` of weights\n \"\"\"\n if not self.layer.built:\n self.layer.build(input_shape)\n\n if not hasattr(self.layer, \"kernel\"):\n raise ValueError(\n \"`WeightNorm` must wrap a layer that\"\n \" contains a `kernel` for weights\"\n )\n\n self.kernel_norm_axes = list(range(self.layer.kernel.shape.ndims))\n self.kernel_norm_axes.pop(self.filter_axis)\n\n self.v = self.layer.kernel\n\n # to avoid a duplicate `kernel` variable after `build` is called\n self.layer.kernel = None\n self.g = self.add_weight(\n name=\"g\",\n shape=(int(self.v.shape[self.filter_axis]),),\n initializer=\"ones\",\n dtype=self.v.dtype,\n trainable=True,\n )\n self.initialized = self.add_weight(\n name=\"initialized\", dtype=tf.bool, trainable=False\n )\n self.initialized.assign(False)\n\n super().build()\n\n def call(self, inputs):\n \"\"\"Call `Layer`.\"\"\"\n if not self.initialized:\n if self.data_init:\n self._data_dep_init(inputs)\n else:\n # initialize `g` as the norm of the initialized kernel\n self._init_norm()\n\n self.initialized.assign(True)\n\n self._compute_weights()\n output = self.layer(inputs)\n return output\n\n def compute_output_shape(self, input_shape):\n return tf.TensorShape(self.layer.compute_output_shape(input_shape).as_list())\n" }, { "path": "UnetTTS_syn.py", "content": "import re\nfrom pathlib import Path\n\nimport numpy as np\nimport soundfile as sf\nimport tensorflow as tf\nimport yaml\nfrom tensorflow_tts.audio_process import preprocess_wav\nfrom tensorflow_tts.audio_process.audio_spec import AudioMelSpec\nfrom tensorflow_tts.inference import AutoConfig, AutoProcessor, TFAutoModel\n\n\nclass UnetTTS():\n def __init__(self, models_and_params, text2id_mapper, feats_yaml):\n self.models_and_params = models_and_params\n self.text2id_mapper = text2id_mapper\n self.feats_yaml = feats_yaml\n self.rhythm_txt_pat = re.compile(\"[^\\u4e00-\\u9fa5^a-z^A-Z^'^\\-^#\\d]\")\n self.duration_stats_default = np.array([8.7, 2.8, 10.4, 4.7])\n self.text_id_start = [1, 17, 79, 12, 49]\n self.text_id_end = [25, 35, 13, 90, 1]\n self.phone_dur_min = 5\n self.phone_dur_max = 20\n self.__init_models()\n\n def one_shot_TTS(self, text, src_audio, duration_stats=None, is_wrap_txt=True):\n char_ids = self.txt2ids(text)\n # print(char_ids)\n\n mel_src = self.mel_feats_extractor(src_audio)\n\n if duration_stats is None:\n print(\"The statistics of the reference speech duration is calculated using the Style_Encoder.\")\n duration_stats = self.infer_duration_stats(mel_src)\n print(\"Duration statistics equal to {}\".format(duration_stats))\n elif len(duration_stats) != 4:\n print(\"Warning: The dimension of the reference speech duration'statistics of is not equal to 4, use default.\")\n duration_stats = self.duration_stats_default\n\n if is_wrap_txt:\n char_ids = self.text_id_start + char_ids + self.text_id_end\n\n dur_pred = self.duration_model.inference(\n char_ids = tf.expand_dims(tf.convert_to_tensor(char_ids, dtype=tf.int32), 0),\n duration_stat = tf.expand_dims(tf.convert_to_tensor(duration_stats, dtype=tf.float32), 0)\n )\n dur_gts = np.round(dur_pred[0].numpy()).astype(np.int32)\n\n mel_pred, _, _ = self.acous_model.inference(\n char_ids = tf.expand_dims(tf.convert_to_tensor(char_ids, dtype=tf.int32), 0),\n duration_gts = tf.expand_dims(tf.convert_to_tensor(dur_gts, dtype=tf.int32), 0),\n mel_src = tf.expand_dims(tf.convert_to_tensor(mel_src, dtype=tf.float32), 0)\n )\n\n if is_wrap_txt:\n start_dur = sum([dur_gts[i] for i in range(len(self.text_id_start))])\n end_dur = sum([dur_gts[-i] for i in range(1, len(self.text_id_end)+1)])\n audio = self.vocoder_model.inference(mel_pred[:, start_dur:-end_dur, :])[0, :, 0].numpy()\n\n return audio, mel_pred.numpy()[0][start_dur:-end_dur], mel_src\n else:\n audio = self.vocoder_model.inference(mel_pred)[0, :, 0].numpy()\n\n return audio, mel_pred.numpy()[0], mel_src\n\n def __init_models(self):\n self.processor = AutoProcessor.from_pretrained(pretrained_path=self.text2id_mapper)\n self.feats_config = yaml.load(open(self.feats_yaml), Loader=yaml.Loader)\n self.feats_handle = AudioMelSpec(**self.feats_config[\"feat_params\"])\n# print(self.feats_config)\n\n self.duration_model = TFAutoModel.from_pretrained(config=AutoConfig.from_pretrained(self.models_and_params[\"duration_param\"]), \n pretrained_path=self.models_and_params[\"duration_model\"],\n name=\"Normalized_duration_predictor\")\n print(\"duration model load finished.\")\n\n self.acous_model = TFAutoModel.from_pretrained(config=AutoConfig.from_pretrained(self.models_and_params[\"acous_param\"]), \n pretrained_path=self.models_and_params[\"acous_model\"],\n name=\"Unet-TTS\")\n print(\"acoustics model load finished.\")\n\n self.vocoder_model = TFAutoModel.from_pretrained(config=AutoConfig.from_pretrained(self.models_and_params[\"vocoder_param\"]),\n pretrained_path=self.models_and_params[\"vocoder_model\"],\n name=\"Mb_MelGan\")\n print(\"vocode model load finished.\")\n\n def _stats_duration(self, dur_pos_embed):\n dur_pos_embed = dur_pos_embed[0].numpy()\n embed_num = dur_pos_embed.shape[-1] # 4\n\n mean = []\n std = []\n for i in range(embed_num):\n dur_pred = []\n phone_num = 0\n last = dur_pos_embed[1:, i][0]\n\n for j in dur_pos_embed[2:-1, i]:\n phone_num += 1\n if (phone_num >= self.phone_dur_max) or \\\n (i <= 1 and j > last and phone_num >= self.phone_dur_min) or \\\n (i > 1 and j < last and phone_num >= self.phone_dur_min):\n dur_pred.append(phone_num)\n phone_num = 0\n\n last = j\n\n mean.append(np.mean(dur_pred))\n std.append(np.std(dur_pred))\n\n return np.mean(mean)*1.0, np.mean(std)*0.8, np.mean(mean)*1.2, np.mean(std)*1.5\n\n def mel_feats_extractor(self, audio):\n return self.feats_handle.melspectrogram(audio)\n\n def txt2ids(self, input_text):\n assert re.search(self.rhythm_txt_pat, input_text) == None, \"Remove punctuation\"\n\n input_ids = self.processor.text_to_sequence(input_text, inference=True)\n return input_ids\n \n def infer_duration_stats(self, mel_src):\n dur_pos_embed = self.acous_model.extract_dur_pos_embed(\n tf.expand_dims(tf.convert_to_tensor(mel_src, dtype=tf.float32), 0)\n )\n return self._stats_duration(dur_pos_embed)\n\nif __name__ == '__main__':\n \"\"\"\n More examples can be seen in notebook.\n \"\"\"\n\n models_and_params = {\"duration_param\": \"train/configs/unetts_duration.yaml\",\n \"duration_model\": \"models/duration4k.h5\",\n \"acous_param\": \"train/configs/unetts_acous.yaml\",\n \"acous_model\": \"models/acous12k.h5\",\n \"vocoder_param\": \"train/configs/multiband_melgan.yaml\",\n \"vocoder_model\": \"models/vocoder800k.h5\"}\n\n feats_yaml = \"train/configs/unetts_preprocess.yaml\"\n\n text2id_mapper = \"models/unetts_mapper.json\"\n\n emotional_src_wav = {\"neutral\":{\"wav\": \"test_wavs/neutral.wav\",\n \"dur_stat\": \"test_wavs/neutral_dur_stat.npy\",\n \"text\": \"现在全城的人都要向我借钱了\"},\n \"happy\": {\"wav\": \"test_wavs/happy.wav\",\n \"dur_stat\": \"test_wavs/happy_dur_stat.npy\",\n \"text\": \"我参加了一个有关全球变暖的集会\"},\n \"surprise\": {\"wav\": \"test_wavs/surprise.wav\",\n \"dur_stat\": \"test_wavs/surprise_dur_stat.npy\",\n \"text\": \"沙尘暴好像给每个人都带来了麻烦\"},\n \"angry\": {\"wav\": \"test_wavs/angry.wav\",\n \"dur_stat\": \"test_wavs/angry_dur_stat.npy\",\n \"text\": \"不管怎么说主队好象是志在夺魁\"},\n \"sad\": {\"wav\": \"test_wavs/sad.wav\",\n \"dur_stat\": \"test_wavs/sad_dur_stat.npy\",\n \"text\": \"我必须再次感谢您的慷慨相助\"},\n }\n\n Tts_handel = UnetTTS(models_and_params, text2id_mapper, feats_yaml)\n\n emotion_type = \"neutral\"\n # Inserting #3 marks into text is regarded as punctuation, and synthetic speech can produce pause.\n text = emotional_src_wav[emotion_type][\"text\"]\n\n wav_fpath = Path(emotional_src_wav[emotion_type][\"wav\"])\n src_audio = preprocess_wav(wav_fpath, source_sr=16000, normalize=True, trim_silence=True, is_sil_pad=True,\n vad_window_length=30,\n vad_moving_average_width=1,\n vad_max_silence_length=1)\n\n \"\"\"\n * The phoneme duration statistis of reference speech are composed of the initial and vowel of Chinese Pinyin, \n including their respective mean and standard deviation. They will scale and bias the predicted duration of \n phonemes and control the speed style of speech.\n * dur_stat = [initial_mean, initial_std, vowel_mean, vowel_std], like dur_stat = [10., 2., 8., 4.]\n * The value is the frame length, and the frame shift of this model is 200.\n * The accurate value of phoneme duration can be extracted by ASR, MFA and other tools, \n or the approximate value can be estimated using Style_Encoder.\n \"\"\"\n Using_Style_Encoder = True\n\n if Using_Style_Encoder:\n syn_audio, _, _ = Tts_handel.one_shot_TTS(text, src_audio)\n else:\n # or dur_stat = None, or dur_stat = np.array([10., 2., 8., 4.])\n dur_stat = np.load(emotional_src_wav[emotion_type][\"dur_stat\"])\n print(\"dur_stat:\", dur_stat)\n\n syn_audio, _, _ = Tts_handel.one_shot_TTS(text, src_audio, dur_stat)\n\n sf.write(\"./syn.wav\", syn_audio, 16000, subtype='PCM_16')" }, { "path": "models/unetts_mapper.json", "content": "{\"symbol_to_id\": {\"pad\": 0, \"sil\": 1, \"#0\": 2, \"#1\": 3, \"#3\": 4, \"^\": 5, \"b\": 6, \"c\": 7, \"ch\": 8, \"d\": 9, \"f\": 10, \"g\": 11, \"h\": 12, \"j\": 13, \"k\": 14, \"l\": 15, \"m\": 16, \"n\": 17, \"p\": 18, \"q\": 19, \"r\": 20, \"s\": 21, \"sh\": 22, \"t\": 23, \"x\": 24, \"z\": 25, \"zh\": 26, \"a1\": 27, \"a2\": 28, \"a3\": 29, \"a4\": 30, \"a5\": 31, \"ai1\": 32, \"ai2\": 33, \"ai3\": 34, \"ai4\": 35, \"ai5\": 36, \"an1\": 37, \"an2\": 38, \"an3\": 39, \"an4\": 40, \"an5\": 41, \"ang1\": 42, \"ang2\": 43, \"ang3\": 44, \"ang4\": 45, \"ang5\": 46, \"ao1\": 47, \"ao2\": 48, \"ao3\": 49, \"ao4\": 50, \"ao5\": 51, \"e1\": 52, \"e2\": 53, \"e3\": 54, \"e4\": 55, \"e5\": 56, \"ei1\": 57, \"ei2\": 58, \"ei3\": 59, \"ei4\": 60, \"ei5\": 61, \"en1\": 62, \"en2\": 63, \"en3\": 64, \"en4\": 65, \"en5\": 66, \"eng1\": 67, \"eng2\": 68, \"eng3\": 69, \"eng4\": 70, \"eng5\": 71, \"er1\": 72, \"er2\": 73, \"er3\": 74, \"er4\": 75, \"er5\": 76, \"i1\": 77, \"i2\": 78, \"i3\": 79, \"i4\": 80, \"i5\": 81, \"ia1\": 82, \"ia2\": 83, \"ia3\": 84, \"ia4\": 85, \"ia5\": 86, \"ian1\": 87, \"ian2\": 88, \"ian3\": 89, \"ian4\": 90, \"ian5\": 91, \"iang1\": 92, \"iang2\": 93, \"iang3\": 94, \"iang4\": 95, \"iang5\": 96, \"iao1\": 97, \"iao2\": 98, \"iao3\": 99, \"iao4\": 100, \"iao5\": 101, \"ie1\": 102, \"ie2\": 103, \"ie3\": 104, \"ie4\": 105, \"ie5\": 106, \"ii1\": 107, \"ii2\": 108, \"ii3\": 109, \"ii4\": 110, \"ii5\": 111, \"iii1\": 112, \"iii2\": 113, \"iii3\": 114, \"iii4\": 115, \"iii5\": 116, \"in1\": 117, \"in2\": 118, \"in3\": 119, \"in4\": 120, \"in5\": 121, \"ing1\": 122, \"ing2\": 123, \"ing3\": 124, \"ing4\": 125, \"ing5\": 126, \"iong1\": 127, \"iong2\": 128, \"iong3\": 129, \"iong4\": 130, \"iong5\": 131, \"iou1\": 132, \"iou2\": 133, \"iou3\": 134, \"iou4\": 135, \"iou5\": 136, \"o1\": 137, \"o2\": 138, \"o3\": 139, \"o4\": 140, \"o5\": 141, \"ong1\": 142, \"ong2\": 143, \"ong3\": 144, \"ong4\": 145, \"ong5\": 146, \"ou1\": 147, \"ou2\": 148, \"ou3\": 149, \"ou4\": 150, \"ou5\": 151, \"u1\": 152, \"u2\": 153, \"u3\": 154, \"u4\": 155, \"u5\": 156, \"ua1\": 157, \"ua2\": 158, \"ua3\": 159, \"ua4\": 160, \"ua5\": 161, \"uai1\": 162, \"uai2\": 163, \"uai3\": 164, \"uai4\": 165, \"uai5\": 166, \"uan1\": 167, \"uan2\": 168, \"uan3\": 169, \"uan4\": 170, \"uan5\": 171, \"uang1\": 172, \"uang2\": 173, \"uang3\": 174, \"uang4\": 175, \"uang5\": 176, \"uei1\": 177, \"uei2\": 178, \"uei3\": 179, \"uei4\": 180, \"uei5\": 181, \"uen1\": 182, \"uen2\": 183, \"uen3\": 184, \"uen4\": 185, \"uen5\": 186, \"ueng1\": 187, \"ueng2\": 188, \"ueng3\": 189, \"ueng4\": 190, \"ueng5\": 191, \"uo1\": 192, \"uo2\": 193, \"uo3\": 194, \"uo4\": 195, \"uo5\": 196, \"v1\": 197, \"v2\": 198, \"v3\": 199, \"v4\": 200, \"v5\": 201, \"van1\": 202, \"van2\": 203, \"van3\": 204, \"van4\": 205, \"van5\": 206, \"ve1\": 207, \"ve2\": 208, \"ve3\": 209, \"ve4\": 210, \"ve5\": 211, \"vn1\": 212, \"vn2\": 213, \"vn3\": 214, \"vn4\": 215, \"vn5\": 216}, \"id_to_symbol\": {\"0\": \"pad\", \"1\": \"sil\", \"2\": \"#0\", \"3\": \"#1\", 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"path": "notebook/OneShotVoiceClone_Inference.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"e0ebb61b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os, sys\\n\",\n \"sys.path.append(\\\"..\\\")\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import IPython.display as ipd\\n\",\n \"from pathlib import Path\\n\",\n \"\\n\",\n \"from tensorflow_tts.audio_process import preprocess_wav\\n\",\n \"from UnetTTS_syn import UnetTTS\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"d2e5af55\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]= \\\"0\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"e2721028\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"models_and_params = {\\\"duration_param\\\": \\\"../train/configs/unetts_duration.yaml\\\",\\n\",\n \" \\\"duration_model\\\": \\\"../models/duration4k.h5\\\",\\n\",\n \" \\\"acous_param\\\": \\\"../train/configs/unetts_acous.yaml\\\",\\n\",\n \" \\\"acous_model\\\": \\\"../models/acous12k.h5\\\",\\n\",\n \" \\\"vocoder_param\\\": \\\"../train/configs/multiband_melgan.yaml\\\",\\n\",\n \" \\\"vocoder_model\\\": \\\"../models/vocoder800k.h5\\\"}\\n\",\n \"\\n\",\n \"feats_yaml = \\\"../train/configs/unetts_preprocess.yaml\\\"\\n\",\n \"\\n\",\n \"text2id_mapper = \\\"../models/unetts_mapper.json\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"efa01cf3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"duration model load finished.\\n\",\n \"acoustics model load finished.\\n\",\n \"vocode model load finished.\\n\"\n ]\n }\n ],\n \"source\": [\n \"Tts_handel = UnetTTS(models_and_params, text2id_mapper, feats_yaml)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"76b70ddc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"emotional_src_wav = {\\\"neutral\\\":{\\\"wav\\\": \\\"../test_wavs/neutral.wav\\\",\\n\",\n \" \\\"dur_stat\\\": \\\"../test_wavs/neutral_dur_stat.npy\\\",\\n\",\n \" \\\"text\\\": \\\"现在全城的人都要向我借钱了\\\"},\\n\",\n \" \\\"happy\\\": {\\\"wav\\\": \\\"../test_wavs/happy.wav\\\",\\n\",\n \" \\\"dur_stat\\\": \\\"../test_wavs/happy_dur_stat.npy\\\",\\n\",\n \" \\\"text\\\": \\\"我参加了一个有关全球变暖的集会\\\"},\\n\",\n \" \\\"surprise\\\": {\\\"wav\\\": \\\"../test_wavs/surprise.wav\\\",\\n\",\n \" \\\"dur_stat\\\": \\\"../test_wavs/surprise_dur_stat.npy\\\",\\n\",\n \" \\\"text\\\": \\\"沙尘暴好像给每个人都带来了麻烦\\\"},\\n\",\n \" \\\"angry\\\": {\\\"wav\\\": \\\"../test_wavs/angry.wav\\\",\\n\",\n \" \\\"dur_stat\\\": \\\"../test_wavs/angry_dur_stat.npy\\\",\\n\",\n \" \\\"text\\\": \\\"不管怎么说主队好象是志在夺魁\\\"},\\n\",\n \" \\\"sad\\\": {\\\"wav\\\": \\\"../test_wavs/sad.wav\\\",\\n\",\n \" \\\"dur_stat\\\": \\\"../test_wavs/sad_dur_stat.npy\\\",\\n\",\n \" \\\"text\\\": \\\"我必须再次感谢您的慷慨相助\\\"},\\n\",\n \" }\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3d1686ba\",\n \"metadata\": {},\n \"source\": [\n \"# 1. Same Text as Reference\\n\",\n \"- The phoneme duration statistis of reference speech are composed of the initial and vowel of Chinese Pinyin, including their respective mean and standard deviation. They will scale and bias the duration of phonemes and control the speed style of speech.\\n\",\n \"- dur_stat = [initial_mean, initial_std, vowel_mean, vowel_std], like dur_stat = [10., 2., 8., 4.]\\n\",\n \"- The value is the frame length, and the frame shift of this model is 200.\\n\",\n \"- The accurate value of phoneme duration can be extracted by ASR, MFA and other tools, or the approximate value can be estimated manually.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"8e035519\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# change emotion name[\\\"neutral\\\", \\\"happy\\\", \\\"surprise\\\", \\\"angry\\\", \\\"sad\\\"]\\n\",\n \"emotion_type = \\\"angry\\\"\\n\",\n \"text = emotional_src_wav[emotion_type][\\\"text\\\"]\\n\",\n \"\\n\",\n \"wav_fpath = Path(emotional_src_wav[emotion_type][\\\"wav\\\"])\\n\",\n \"src_audio = preprocess_wav(wav_fpath, source_sr=16000, normalize=True, trim_silence=True, is_sil_pad=True,\\n\",\n \" vad_window_length=30,\\n\",\n \" vad_moving_average_width=1,\\n\",\n \" vad_max_silence_length=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c812fe24\",\n \"metadata\": {},\n \"source\": [\n \"## 1.1 *You can Only use the reference speech for one-shot voice cloning, and the duration statistics of the reference speech can be estimated Automatically using Style_Encoder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"b29cbb6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['bu4', 'guan3', 'zen3', 'me5', 'shuo1', 'zhu3', 'dui4', 'hao3', 'xiang4', 'shi4', 'zhi4', 'zai4', 'duo2', 'kui2']\\n\",\n \"phoneme seq: sil b u4 g uan3 z en3 m e5 sh uo1 zh u3 d uei4 h ao3 x iang4 sh iii4 zh iii4 z ai4 d uo2 k uei2 sil\\n\",\n \"The statistics of the reference speech duration is calculated using the Style_Encoder.\\n\",\n \"Duration statistics equal to (7.2711597542242705, 2.0288354905751587, 8.725391705069125, 3.804066544828422)\\n\"\n ]\n }\n ],\n \"source\": [\n \"syn_audio, mel_pred, mel_src = Tts_handel.one_shot_TTS(text, src_audio, is_wrap_txt=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b9fd9924\",\n \"metadata\": {},\n \"source\": [\n \"### Refence_audio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"791be4c2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# plt.plot(src_audio)\\n\",\n \"Tts_handel.feats_handle.melspec_plot(mel_src)\\n\",\n \"ipd.Audio(src_audio, rate=16000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1fa53c42\",\n \"metadata\": {},\n \"source\": [\n \"### Synthetic audio\\n\",\n \"- For faster inferencing, the multi-bank melgan vocoder is used here.\\n\",\n \"- Using other vocoders, such as HiFi-Gan, WaveNet or WaveRNN can improve sound quality.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"3666aefc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# plt.plot(syn_audio)\\n\",\n \"Tts_handel.feats_handle.melspec_plot(mel_pred)\\n\",\n \"ipd.Audio(syn_audio, rate=16000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0ec898b0\",\n \"metadata\": {},\n \"source\": [\n \"## 1.2 Or, you can input the exact duration statistics of the reference speech to improve the similarity.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"ea5edb73\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dur_stat: [7.214286 2.623481 7.142857 4.2904735]\\n\",\n \"['bu4', 'guan3', 'zen3', 'me5', 'shuo1', 'zhu3', 'dui4', 'hao3', 'xiang4', 'shi4', 'zhi4', 'zai4', 'duo2', 'kui2']\\n\",\n \"phoneme seq: sil b u4 g uan3 z en3 m e5 sh uo1 zh u3 d uei4 h ao3 x iang4 sh iii4 zh iii4 z ai4 d uo2 k uei2 sil\\n\"\n ]\n }\n ],\n \"source\": [\n \"dur_stat = np.load(emotional_src_wav[emotion_type][\\\"dur_stat\\\"])\\n\",\n \"print(\\\"dur_stat:\\\", dur_stat)\\n\",\n \"\\n\",\n \"syn_audio, mel_pred, mel_src = Tts_handel.one_shot_TTS(text, src_audio, dur_stat, False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"07801d72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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bxNRS6S0mooe4UMEN7yoGm1YCkZ6RsY04RrbjuNL8TEZFObHpHkq1H5Xguyw7Ge/xMk7tAMEZCzjK7gwZsgUdJUmPTSBeM9tCuQOKW6HggSKdmfyDekrwahN6Wao3Fg9meR3VCqG7WQ5JJSnBcJnTFw8cImqUCaloG+qUTpyQdc3yDhljKj81jUnxTGYwl+jWsgbSurfbMETo5FiGlql6saBj6hWOFulRdfO7FY0/xPZotztZaZNWeXpyBbEwhs2UvGCwjnaaihqmoWjqWi8iqc3nN9Q9Or+LPPhCJtQ3FATokX6AEuQhHCZwdJ34WRCfzJ81VPZVPRIysajAYDAaDYWWw0IzBYDAYDGcEXpxUp+wZg92IGAwGg8FwhmAckZPgQdVW2qZMCR8giPLQjKynKQ3DQjwdadkBN2RLB2x8SfMut2H2xLh+iH36+HPdGmhwRIqDOABaDDXtJgZQ5KuUMM6qi/julZyBqo90AP4HeB/1EGJjs7ZjFtsPjkiXaRzD96nxFctMTO0oLrWIZyDSxNBLcmoGTV3cmsaTM8bNISxVTrRwN46D2YG3QhGlhIfjQ5rUIbSZY5NwTqQtRKVp1YnzaOLn+EzOS5ftw3xY04wc5kVeQFwq07SCgFeNlFwMEZGsq9fSQDAItOF7imMFrhGnXmJeRsEqckT8Er4P+ysIwLX7jbyJZPxSoS0KdNEEkuuqICdkHPMNliHwPo4+bTFOKGQ1J2yWcESI0P8tYkbGccK4hm9yzvslf1kmPKWUU8TjDvOLaz6j0WMQ/GrVD31L6geLpsBZsz7IO8mi4z4YNMbXV2vYR8gR4XrBugQNRKqdpi7cRzLyKRLzu4CEv9F0F/lbcV1S3kvgjKT8H2lxPQI/KTkevoeAGdYRjQd5vDMiPwz8prQNKUcxLKAFbSZvMb0mGOglk672Dzj5HxynkSNiT0QMBoPBYDgzcMHt+rTgdNXWYDAYDAbDmYI9ETEYDAaD4YzAi0h9yp4xrOZGJH0XPTUKSrHA3Co1OAuaFWl8+BjjpPCuPfgYsq4ZFojrkwPg0utbcT/yA0qGD6fgCyA+7IeoQhKHZ4y2woV+lkVVbd6fj9uUwXjKlS2OCLswmNvF5mRBooMaFTThwufA44BBGLkhHKoSnJG64kv7mtQ91GWtCXb3BhoAz9B35ETMwKuoRDu9HjHGzXaSzxKTRML1KDtwKjgtQgwZdW4NdviORoOJ1gvj6YyfkztRraNdmA+9PtsEY7KcbSOvAwWV8wFhGs1RN4N9LWEs4jHJ0jTor8QciNAPOK/C/JlOYjGLum3yRg4LOVXduL6siyfHoaIWA2P66CcG2sOcxPUzzmkYzWHMCs7ZVlmBj5SOL9cBjeW4bpL0pHozdeA9LTe9czkJOahnEY/vvIYJGw4uRBdzm11POgf7c5LorcxxKhZwHwInhJwxnNzBpAW5o065FNSGodYPNZGYFnTkI98nx/ULeAXkSXDDS/bihoOVakBJ1F6akDZ8MFn8ua1HVMQ8k3Auj4cx04RzlP1GA0Jq3aR8FrfMuC60oT04x7ifplwRInNPXEdExDgiBoPBYDAYVgTvjSNiMBgMBoPBcGIceyPinPugc+5zrX97zrk/75y74Jz7tHPuS0jPP4kKGwwGg8FgWI5a3AP/WwWODc14739bRL5FRMQ5l4vIWyLy0yLyCRH5ee/9jzjnPoHPP3R0ZhJriBz17nX7e6L1Xje9LhgXJBi6ZWyS8fV8whSxzU78Pd9BL8s4rpjTrwCcAPIeFvlV1F0NdFY9poxZMr6KOiYcAc9YZBVzBlKOQD6l/wXqMm2JEKCeOXgXRYiLgtvBeHDSL46xf/BUyFfJMk3RbYE7wvg969oQNZqqMI+Kuh6IybMMSWP8ITxPPQC2M+HQsChwcAK3gnyh0H+yHCwrSUP8mCsCceis4Lgnnil1zKkJ/QKtlPbZPkvu98lLmEDXADoHxSHSseC45lKM8BlzPp16vL4cgQPBuHw9P0epeRPqlooqcGzSSwO3gW1CGzhmXEeIy1dT8D46WkC2QI+mynXgpujDKTxBZJpwRGL6RpOm+wZ5C6FO4IaAFzOc6aKvWv3C8QvNzOPxrpP5HfhdoR2xDgl1ZjJqAaEtVUEeBolkLK9Vfe4H7ONQTYwv9pO6q/2U03MmpdwlY8j9xpMX5uM1zH2GvDKRhn/m5/q4js4lN8inay6hVARdnXzBnJQWv6Xl0USOHXlM5KMJdVCo5YMs2dec1FwX1A4KHj0pN6Q8asMIDYg/L9PCWnR8JToipyvY8U45Ih8Rka947193zn1URD6M458UkX8mx92IGAwGg8FgeIw4fRyRd3oj8gMi8vfw/6ve++v4/w0RubroAufcx0Xk4yIi/WzjQepoMBgMBoPhBDiNr++euLbOua6I/DER+fvpd16f3y18AOW9f9l7/5L3/qWu6z9wRQ0Gg8FgMJw9vJMnIn9YRD7rvb+Jzzedc9e899edc9dE5NYjr10ab2vFK8kjIG8ijSMzwNpwRMgpoQYH3zVHnB5x+Qpx9hDipGcNYsfUjyiyJgjqEYMcgwsyRqyyXNPuZYwySA/AO8LhvGBbMacnwTb4OJ1C46Rq9w/PqdAe5M1YfiwtIfk00YeYUAsF8VhUKuhlQEeEmgPF2EXH66yZSvSpEZeMXxnHplOeBtsT+D8Ym5KcEMTXA7cCBJbg30FNg3bIN8TJkQaeQUImSkE/F9R1Bv5CKk8TvFTQtsD/iWwqYlEF1oVjUJAjAi5IMYRHxpAckZg7E3w6iniOZxjzilofC/40cNToYPuSP0VCb6RLL2gwIKWcDM9jv5LXgXLIweA8aoP1o75HxXlTx/005x0TuFaJvhBMepp5EPN4JrP57Y4aLyFvTt0srm/IE2mVaFa4nn7RX9PFXRT6eVLowiM1J9BTMJeztkBRFs+uMEZBAwdldsgzifePoOmC+UDOSZ7FmjCBBxX2SK6fBROGxkfBACn2t2nOk6jOgXeCsaF2DseMbeH+usizKMw18kbADcn6mnneib1gSuxBNfbV4DtGXZUirrNPtVHmPGdacyA9J+WMLMNx+iOPCdUpM717J89v/pQ0YRkRkU+JyMfw/4+JyM88qkoZDAaDwWB45/DipJLsgf+tAid6IuKcWxeRPyAi/4fW4R8RkZ90zv2giLwuIt//6KtnMBgMBoPhnaA+i2RV7/2hiFxMjt0VfYvGYDAYDAbDU4Cvh9d3Hw5O5rVBRObf6ybSc/MmkBh8EZgEbQGkgRPgo+8ZZ6dvwbwuAMtGkXhXfdDTwOtmX4kJa51pq5qa53CmL76/Db7AbAOxb3B0Q+yWcyS8W099CNSNuhv0VKAeR+KVIEXbCyE+N/XYaTwy5GiEQlw720b/IeV1MATaDoWSJ0EeAf04Ej2VpKi5KrjkQJgeoew4lj5H4JBW7JnhX8aL0Yc1YtT0GqrXdEJk8M7p9jTNssWx3ip48KCOwS+oqUTwbWG7JR7PVKOi0XWQ6DqmnLuNpwhTxrGRb56sJ2nF211yDUA/HGpNVMm64lr1Wazjk/azpGMIZC3eUDONY42eqqBuxGIBBsb6Gy+WJPZ/zBzPW2PJ9c0GFkXMBSvJDUraFeY2uCGDdd0XLm6osdSg0MU+Gugg7fZ0ExiN9XPJdNKsYUee0YT8NaTgEAWNlnW9Nj/QyRs8rdIxOKEnD8eybv0aUHPDYc8NfIqc/AvMj2QOhn024ZuEtR48jjTl3jjb1PPL9Rb3jmuxD/4N/J4GPd17Bx34gmE8D6ea6f18XUREplPtn+ke9uN1bWCnp+dl8OrxqY5IlgyyyDzfLf1tWqZ9VZ+uG4KTwjn3oyLyR0Xklvf+dz5sfmezlwwGg8Fg+DqEFyeVf/B/J8TfEZHvfVR1NtM7g8FgMBjOEB63joj3/p87597zqPKzGxGDwWAwGM4IvJeHVVa95Jz7TOvzy977lx+yWkfiyd+IONcE+90JHwMt0/VvIeUbNHoHjCfHGgyBIxDeNcf3SHPEfnv9mBtyoa8x4I0OXVhEMgRGd7I1ERG509MY5ZgeCXzvX1gH1g3cgS50QUKsFtwSeq30mSIuixixa/cL+QdoX9XFud1Yc6JmjDbUgY3wUeoQE6Xnhq9jbkEK13qkF+LJbnGevmJAWeI08FgWFxKuX8YtWcCD4fgyTk7/H1atJDcEPhauH497j3HoRAeDdSnJEQFfZUZfnKpVCdabnJYZtRiObq8kcfUKc5ZtaOYF8sd8ky60bnqcV03dqRPjUe/A30HdPAahChoLqEriW9Pwc5I5TQ4K5nYOPY088WoSESngNZOjXR3oQpRYD0GCISFozD09bohMmgQOFhL0P7khecs3iHofGerAceY1zsXzPlAFeBh5cZ6c66sYzEah+8O4Ai8Bbd3vKG9hiDZOpw0PoYInDjV9Gj0M6uZQnwhaPj1453CNd5t2iTRj5Mnj6KRcEP1M7x3OJxGRGn5ZgZcHny8HXgXnYMq5ok5I+jvI88oBy9LP5IbMtjEOm7NwzQCaLJsD7cvNHtKOij6tgYcT9t/pWtSe+9CEmm51kWql+hvgiByyUmXUxjmuohYSf055jYu4jyLKqTldkh4iIne89y89yQLtiYjBYDAYDGcGq3PRfVDYjYjBYDAYDGcEXh46NPPEcbpqazAYDAaD4Ug8bmVV59zfE5F/LSIfdM69CWHTB8aTfSLiRYUJsiS+RqRckPRz1XgLMBbpqvgRFGOV9FPga9z0e6kDZyTmTqSxbcayGdvtFRpHXCs0brmFOGUbe9OB5sn4Ob1jEn2I4A0yYQwYcdgOeArQMqCOQgWtixIx3Azvw1MTRctKdC5SD5FE94PeGUyrRAejsfFAP7EtwaMGGfs4ViwiItR3YJ4Snyuppw76w8EHiJ467D/WibyLoK8wSa5PNS9ahfMPBI7FnObCEhpSFngFGBtyZ8j7CJ484IyAY+AXLWjWpRHliL9PtRaKxTopc/4w9PEAp8hP6TkDP5OWx0bwVpkz+pmvbnQ4mT9B4yRNy3jsK/i80GvFtwgedaKxQE8YT28hrI/gCwV/o2KMMRiXvBB5p/sHqgLezrSc3+7I0fCoSxg/UmSGek2XWh5cuyP0cR8+NgM9b4Iy+nnDdRCZ56lwX5Fuqy5Ig39RnfAwyMNJ+AjN2mYny8I0dH3gg+F40KVpzROu4ZT7kHBEmIZ20henjI/7hJMWuHkcEuy/bf+YNWo3gRuy3VX+zTb23l4e63+UIL4NunrdDvZT7u3N3p9FKT2sPH9fFnnNuEA4koVYpiOyiG/ymOHFBa+wx1aG93/qUeZnT0QMBoPBYDCsDMYRMRgMBoPhDMEk3g0Gg8FgMKwEXs6o6d0jg5PlMTZZ8F0ad2vrLeC/wXcDcfBSJTykHOA0ciEQpJ5tZNH5sw2cv6EnFusaX9xY1zjkxXXVDbk62Ne0tyciIoNWDPiAJA6ghB5APmXZ5LPwM1L6dCCmT8+HvA/PjQF0FVDn6VYsuJFPW74dSUyW2gJMA+Y0GHC4juPSTZpoYPAyzHP2v++04qnQr6AWC3kW5ARUNKhgjDroPnCc47oFzwzWgZyIhO9ynKdGVGaIi8faG13oh2yvazz6PHQhulkTu26DOhEu8aKoWtwlTtvgSwNeAnMsZzGnKGjcILZNfhOrEDRyGMYn12YSz48ajazaY8N6ob0ZvyPXh7oimdaxdGFwtI51zJUI/jlLODeBY0StiqrlrZJ4K4Xvqrg/siR1KXfMxXMz/Rw4F4vki1i/iik5I+TdgGdD/5+EKyNYu+ORkj0O1jWl1wyR44J+EfMasha3K8wh1GnGsZou+VEpyDHTjzV9g2LZFakG6GfsK+SikStSYayrXlMONYtcFySWGerdwZzi/tKPy+IarpP5wH15xv15DWVvYq/YAB9kveHeXRjo3nuxfygiIufAEVkHOayDDZV//U+wFvPUFyZBI0tzDI/Ctfr9OO7Hot+olcFJZa/vGgwGg8FgWAVO4xOR01Vbg8FgMBgMZwr2RMRgMBgMhjMEC808CFLdfuIIPgnjfMFDBXHSCiFN+i6QT5BySfjOfA16h1vTeOPmhsYhL69rXPLy4EBEGm7I+Y7GLbMWIWGv1EDpYamFz8Za2NqIeheoS8JpCNoL1H+g3gN9LhDLraAnUiJuW4wkyk8/oF/QZ2XiBRG4IEu0KN4xUk2CTtMfnb7Gk+nXQg2F6QzxZXjp+IQjEjRgEqmL1Fxkma/QIsyXoWnw2kG9M3oLdbXuG13oxXQ1Zk1OEG2y+eizwGAyPj2DFkZZz1eKHIgSDZvR/wYcgCzQCqh3gPnDupNrlMiQUI+FnCSXdIyv5tcR2914rSBmX1BPA7wChzELWiUsi1yRhNcTCkBCDgl1OVqeNeyPVDeDCFygoFESpwJ+ha+p8RLzE8K0oa9Q2fi6hDpAY6SeJd9xrQZeSpxX4Irge/J+xpjj466mRRZ716Rp2/fGJdo05ERJwomihlJof6LNQd2MwIPC3ubI2WpzhkSkSr2PZF6rJNV+SvVjGo2TuC7BYwZcEvoi1WuJtxM0Q/qdhkND7SbqhXSzmF8zrHW/pX7Ifqmb+eFUC/Xw7CEXL6tQ5nFcsvyoDeUYras86TeXyZM2m/HenbrQzNNxI2IwGAwGg+GRwCTeDQaDwWAwGE4IeyJiMBgMBsMZgRcx990joe8VLfniCCzwpmmsMsi/SLQWZrHeAXkabe0NkZZ2A+KwjO1PEXdkOkGwcwgSyqRuuu7eVF+O35+AcDKJdUSKSVwm4+r0TGkED/A+f57UsV4yqVqHQ1g5+JTE6bI4exNvpygFM0LsvIrj1IETEMtPNFwRkYbjwpg3DldJXsyj0WaIORDBqyfhIQTdjFQ/JOGOLEQa4yeHCBwB8liGMx3vTqbB7WHWMgWRhiNEHZERzp/g+ranSp7HPjUui9PghUFvmU5Sx9STh5yQSXwdOyCMLfq3nrUniiakGUwLLawAN4Z0LXJGavge+Q55BDGPJ4why+SYsF9Lmvyg+NY8IV+EOiI1Y/pj6ORM2E4Xf8YadtS2mGrHePigpH449K4p2d+t9eRHuGbKhkuEbLxYN4VeSzm0JkqM4cFQ50sHY97NY/0ZcozIJZlVDTFjMtGxKNEPbqxpQa4ZuGH5GPNppnn7ePhbfCj0dZ7MO5we1jZ1Wmh2IyIZfJ/8DH1LfkUJvSXMvWJIfhsuTLloRfw58DM4Nih7NsW6K5p11qH+EBo4gv7QHFcE3Lwbh1siIrK3v6ZtGGIfhjQJ10s2wVzH/CHHaI7/0dYEqRIvr2VI8/CVnEzg6FHCnbrQjD0RMRgMBoPhjED/3rcnIgaDwWAwGFaE0+Y1c7pqazAYDAaD4UzhyT8R8QtibS5bfA6P0++kzRFJ4sAZvgu8CyDEcpP4MkEeh0zADUGsctxDzBLxx0PELosFniN18IZgfdmOuK5zT8t8cl4wH2Fc3cffB20DH6Xtc0J8OI/TcNqSUGcoKtXuSGL+5GtQRyLoBbS8bnwd60OQG5ElMetwGxzKTNobvGYSfZXgPSPRZx/TERa2L9VR8XlcJn2ChhMdb/pWUNOA3BC2aQZSTpXohrS9Z1wi/MF+cNTqQBrGjDoQ7NNEV8Ul84Uhc8gqzPdPe8onvj0E6xTqhouos+GDB41EaWhjMpeXIfqa8yThDjV8Exdd5JLxbuZLHaeSnFeRBzavq9L4FpH7snjNBY5VKDsRKQlWI4s7gLohVaqJ0/rs0/lexv3RcKOohxGX3ewjuC7RqQlDz3UI/ZDiQCde56CpVzFEg8G/EXJFJrphFiPNjfttSv3z9PtJuWn02cJ+W0HPhuuOHCsRkcNcJ3TYP+hNBJ5S4WKNlvWO1o36RYf0ycmSjSH1g1nIW5SY77F048yS7+e1ap40vDgLzRgMBoPBYFgd6lMW7LAbEYPBYDAYzgi8n3/y9rTDbkQMBoPBYDhDOG2hmdP1/MZgMBgMBsOZwpN9IuJEJM8bclBqrMTjKXmVl7dIRoEsl974JYJDFCIKRK9ZTEYL50FEaTpUgtMuyXk+IUYtYONRxW69q4Xd69OsTr+vKFCVkCqDkBUJn11WCmm9mKw31xaRFqGRhmnIAuZ+jhwqioxh5BtSa0IgzZO6sK2oI/3Zqk0Q5wYNiTeHkVowM0vuzoOQVyBpss6x+RbF2DxNurL4eDD0I1LyawtLjfKS+cNLg7AdBKf4FwZNzDoQqqp9TNZL29o+FgTNQhoXmoqspWTmubYkhOS54+ynFn8uNDuInVGQC+JZGDuKsFHobAbDRRqkVVg3FUnLHCsWQH5gIt7W7p1G4I1iYvHcpNljM84QHexjvvT1C9eJxeZSgnbo2KQuIg1x1QclN1xSxZ/nENYR8kLa6Wj/9UFu5jxpSM7xfGrPl1mWt5vZFBXM/7gvLKkTz09IuiSGBk5m0ka3YD9xNNTz8YsCrkZ7IHiWwUjQJ+NPIi1NAxthSa51jC0NP1EXGiGKiFQ1XwJIjCTRTcFAMCGQ51ijNOIsB/pxNsDc7UE4rwgZSZQx0Sao5tyUkt+u9DcrJcKmAmdPAEpWPV3PGCw0YzAYDAbDGUJlEu8Gg8FgMBhWgdOorHqi5zfOuXPOuZ9yzv2Wc+6LzrnvcM5dcM592jn3JaTnH3dlDQaDwWAwnC2c9InI/ygi/8R7/yedc10RWRORvywiP++9/xHn3CdE5BMi8kNH5qK3as3nZVyRVDxmEWckEXVKharqLD5OQ7VsBk4D4o/5GHFFxLpnZRy7ZdyxRMYzpL2WsBl5BIdTBLPLuD00JcsY4w11SkgCwbmOMeE4vloMEYfeh/HUuKkDRX6qTnxtI8S0JFa57Ma5OvqOOpgLwpCrKpr46iyIYtFIDfHgwHmJjdNCfH6JoFnop0TQKqSB2BEfbyOY/6XXMCX3AUsC/mIhZt0hdwLx5zwR/mIcm22MtJB8LPBWI8+KJm+I4efoyyKYmwk++yjlmJYUSEPbsjROz7FvL5+03UETLKkbYv8UNAviWqHvj/mLi3yoMhbTWjjfgoCXnpsKeC3jyoRM03h9wqli/n7RnF7SjsBfSgTcXJWeh//k8XxIEfYPzJNpybTZgkuKeUGALB9zj0IREGXMZrHYmFtiAunm2ksOFuoa+BtIWwKJLMNPSQaqopTnBt4a9iiu6YYPF8/ZZt5gTCA2Vna0P2Ytw89h0pfkZ5EbUmKC8DgNBgfg6u2DWzYn8liQawJzQZgl+rJM2rpgI0l/q+bm3pPnhMzj9HFEjq2tc25bRH6fiPxtERHv/dR7vyMiHxWRT+K0T4rI9z2eKhoMBoPBYDgpanEP/G8VOMkTkRdF5LaI/H+cc98sIr8qIn9ORK5676/jnBsicnXRxc65j4vIx0VE+tnGQ1fYYDAYDAbDYpxGQbOTPL8pROTbRORveO+/VUQORcMwAV7f7Vr4TMp7/7L3/iXv/Utd13/Y+hoMBoPBYDgCtc8e+N8qcJInIm+KyJve+1/G558SvRG56Zy75r2/7py7JiK3TlxqygFJfeQSPoP3iN11G+GINMbPmGSOd+GDdABCnB3wK4pDmDchtleMNU/G56t9mN4hlnlnFpuaMcZLfYA2qCOys6b1rXuaN3UQqPGRmpalqYCvkiHtHGraPdATOvtaTj5pcUQQ96y7fFeewdqkkqmJVzCjSmOfmoSRCNcxtitR29rlUJtiUsVCHzQdCzFwtrNivNnHn1m3hHMTNAkSrZhmLrQJGjG3IZ+Ar0LeDnUN2C6URb2MEeLH4y50NaATwTRwRGCWSPOu9m25SzRpyJtwI2h3DDEXD/X8zgHmKrgBnLv5NF43jK/T7I6xfo4F9VnalUm5QxUEIGrUZdZZrGXD+RHKSP7sCAZ8ialZoAVVS+ajSDO3ZjG/aU5PI+UOMR4f4vN1/H0V5+dnR7kiLq7TPF9J08CrwBysMafHIx2MPfAWipx6HHo+uSEzptNmCyZnyHFdpBpIXLPgb7iyis+bxWPL42x/oKg5rjv9XAw17Ry2OCLDKeo0kTbqkU7KfAh+xhjzJ9G+CXwTlFUlhozcf6lzVEPHqW5xzcpCT+aeO0uMJckN4Rsio1L3mxG4emF6dGI9Gu6RUqSCMwBJVUWrPOinzBnkkSPC37SkjuLrJX+iG9o49vbHe39DRN5wzn0Qhz4iIl8QkU+JyMdw7GMi8jOPpYYGg8FgMBhOBLrvPui/VeCkb838FyLyY3hj5lUR+d+J3sT8pHPuB0XkdRH5/sdTRYPBYDAYDCfFqkinD4oT3Yh47z8nIi8t+Oojj7Q2BoPBYDAYHhinUdBsNcqqS7xk5rgjiL853N25bMF1IR6uaeN5Qa8IxAeDL0cWfR/Gi/nAS8X1wceA10ajH6HpRqeJnaZ+JARjk4Gv4eO6BM+UPo7Do0YQG63hPcH4KvOp1nBh3pQXYq5dpPhurj9C3BQfT+w5g+/RJsb8Q8y3rccQ+oFxYiTkSjBl/yBmW8MDgnVPPXl4XdWF9wS4z8GiY06fpRUXRjvLAfsJx7txXQI3gnQClJ1jTEKK2H/QSCnieLW05wIrxr4KlcPX1HNIuQBLNGDIb6m6SdpjmzStBuivfvv6uL2+S/JMzAmhjkzghkzj8W7qglzD/ECax2O/yLtoTj6GOiv0XHLJGkXdOe51VwvPuaZ9Mm+S+eN67NBWA1iddI6iHyr0Q9kHn4dUAPrhDKBtsaGEjM0NFYHZ7CnHIvUsSuGypkMdMvdY5+U654WmszWUuaEDnI37aB/bGzemDjori9uaos1TJP8oHaTg98XD3Hdy8t/i/kvPm/t9pGcPyy6a/uAa68K3Z62jfbzVUZ7KJtIeFs6V/oGIiAwKPe9V9P1wTyflbB08v3Udi+6a9mM+hGdRRY8siiS11s0yHabUa2bOP+106XmsCibxbjAYDAbDGcJpEzSzGxGDwWAwGM4KVkg6fVDYjYjBYDAYDGcEXs4oWfWRwTmRopjnggQwqLskHpe33jFHHHW2HsciU05APmGMG54GA82DGgsV482II1PTxE+pxwFvGeiGbHU1LtkNRhgNLyALhjdIQnw0bl5IGR/lnEG8vjPQGGcJr4QZPCcm29Tf0MZls/m4M/uDKduXzkvyTthPwvaHOuJznrSJ7+TzM+P53WZMWf9Ua6OmFkuXPibkNqCvyRUBByJwQzoUb0CdwXkoU/2EBZ4kjJ/XaAe5Dg1HBiejDIc0Q9oFR6jXgW4C4tYDxKupJ0Otg9FMM56Uzdiw3SX0QyiLwvbXPeqBUMOEk5m6KnEcnpyQcg2fB8gvcETITUq4R20k3KjgLUQfEvp0sE4d1JF1Ji+D3CK2qU/uCfoVvIwMsX/X8hLheuH6oRpksIhJ5i49Qso1lA1OUdgXymZN6vkoi7yeDnk+jf6OT/5y5HjTM2gIbY+6w30jztv3NM+1NeWEkBuy1dN9ghyRSaUTkfOFHjPT1jyZom+m5LXhOK1lig1yHMCNGeoibjhkoVUiIuI4H7gGOCaoO+cfuUiztRa3ak3ncbamxBT6sLiuFkY+F3lr5O1QK4m+RyknLXDYWGek7Me814zNNvg2z27siYjI82s7+rmn6YVCOSEzLPKvTS5o3pxHx/gh8TfAQ0/EdcAV4feR1wz5Rcf8wJMrwt+43C3l5DxOnLYnIqcrkGQwGAwGg+FMwUIzBoPBYDCcEdjruwaDwWAwGFYKuxE5Er7R7G+DhIP0HWxiQVyOegaBlhF4F8n39F0oW8QBEfHUYgjv7COGfV5jvOsbqhNyfk3jlBf6agRCbkgva/m8oPAe3ndnmLDxfECsP3AamLro+8AZCHFWxIoZ80ccdgYtjLw1eqmHQzie8FXS87PAicEJia5IALuP5VB3Y1MbM0CMXESkKOIxrirqPOBaalSk+hnohzz0Fzoy8bFw6RRa4osj0tJWSDgRzfxw0bXkhrANHaR9+AhRy2C9M0XReuW+aEB+jNh/7lrzjRo06NOKczHoqqDaIZYf17FErJtjFbgg5Lmk820azw/OdZEmFp+tYx73Z9IGx6oCd6EaI37OMZvGYxaOs1/pbwIPkeDhQ82baGyQ1un4xp85hmHOBn8k6D5MZ8woyQcFgZtTTcHzWKTxAozgFVMHPyDwsiAbFPyNqKsCLtkUeZdL/FBm2ONYcgGuSN7SEcmSukzIDZqRryZIyaVaElln95HPQs0Ycq2Cxgv5UuTBtOYJjjlMWnJEJOiBxLogYf8lJ4T7B7lDXc4Dia4LHYIx7nSaxd1FH2WJeM0MmexWyl8ZYyHsgahyONMxnMBHLHg50bNpjL0+8exphFcWIOiIHPNbJUkey/iOjxGUeD9NsCciBoPBYDCcIZy2t2aMrGowGAwGg2FlsBsRg8FgMBjOCrw8dvdd59z3Oud+2zn3ZefcJx62yqsJzaS6/Yy3LdPzpz/GrIln51M9xrgfw4ip9wXjycUE5480HlgjTt850AuKA63DtK9xxRF1JxC35fv/a/AxqFtaKG8dbouIyBu39D12ua7B3N6O5tk9QFx+Ftcxhz5IgVhv0LzoUnOBMe/YQ4PcANeaNK6KNUnoTxJi+AmvgrFaxvQzxn7JGeE79lU8JtlYKxH4ClONx47WmqnkBtrpnT58etAOvtdfI55eIK9ixNitnp/h+8ClGXMwkZCnQP0QDkXwmmm1s477jrH+8BkcgJK6EORjIGYdYvlp/B5cED4CJTdkBm5FWx+iJN8C/AvG/MldCHWlBxHHtyIfA1whLhNmTakX8hckRsn+aZVD7RHqqszQziyPY9shtM1CwefJp4y3u6jsdG6Sl0LPkuAD09IRCZ5D5ISgX1zQUdEkw7VcLxnWvitJosEih1cI13o2QX6TmKNUDZue4hrywagICdrb2eccFbQ31qMhZ8aDG0JOSFtnSKThe3WwcCpqy7SkuKm3E/xpIMYRLGLC/CaHChyKhHsmyTzxqSZQ0AZy0flt/R12lg+dFvNx8gnWBfhc1P5JIwLpnA48F/ogwauH3l7kYok0Wiznu9r5AxB0uOb2QZojZ4RcPfYt95ke5mrnEPwv/AZkI5Q1w1iBIxLa3OYzcr9PfdLmfrMW/IY9YZrI435rxjmXi8hfF5E/ICJvisivOOc+5b3/woPmaRwRg8FgMBjOEB4zWfXbReTL3vtXRUSccz8uIh8VkQe+EbHQjMFgMBgMhpPiORF5o/X5TRx7YNgTEYPBYDAYzggeweu7l5xzn2l9ftl7//JDVutIPNkbEQ1ePfj1C64NehCJbshSF2RP7gRioNR2oFfCAPoReJ99Dd4R24hTjuEZcXO0GbKkvwg9LGbduJ7eJUHYhMsQdDQQby4Z08Z7/dmS19ujuZYlMVoXnzPnwxL6LeHnsK7p++/pR54GjQLyQkRE+uvaZ/Rn4aIgT6DMYz5LAMp2HKNUKyacl7TxqOd6S9rPeRLShBuyMYCOTF/HnRoxU3jKzGrGpV1UJaJu6UnU1FGhpkYdV6pGH5ZrOB/x9qArwnlAuYxUE4a6GglXJth9tDQPQsye2hRIyd8JdazIY4nLZllyTH+m36fzTqSl5xF4TLE2RWrb4QJPBfNjWsYn5DFLJl1fNfvBt8/h+KHsTjLRk30llYkIp6HukyquQxeckH4erwVyQ6ZVswUXqHCJPCaF7iszrpd0nmOdZGEOoy7MMp2Uob/xNccW2ijke+h3yIzcEHLOqLWRrNFQBMeM+2rwBMPnNXCTNlDWlvbL5paus6ub+yGvqwP9/3qha5F9N4RBzQQNvTXRvfj1vfMiInL73pbW7ZAaOKybi9Jjkc17eR3Lb0w/V5U8cZKIyJyH0jvEHe/9S0d8/5aIvND6/DyOPTDsiYjBYDAYDGcIj1lH5FdE5APOuRdFb0B+QET+k4fJ0G5EDAaDwWA4I/D+8ZJVvfelc+7Pisg/FX1Z70e9959/mDztRsRgMBgMBsOJ4b3/RyLyjx5Vfk/2RsSJxtDSONtJeSPtWHcSi5wrivFhxLaLEbQ8ptAR6cbf8/y8q99f2FRvmW28yz6t467q5Q0nYgJTD+oABN4F68J46tEyKU2cHf4eQq2UA2o3xHVu++cErxm2mzoOSzxjltZlGRJPiND/5Ii0Tp2MNLY9nWjKfglaEmWs/9HwC2KvkBNXLeUrtHkI6bmM9S/5gyGNrZIbsoE4NVfMFByR/Sn8LaCnMplBT2TazJdggRI8VWI+TzqX617Sx+DOBApEwp1xsV1MqzH4vp4/Ri2JoqftKwo9aYp6VyPo6+xqoZ39WDckXTdcT8ci6t9Yu4NaHlmVzA/yOIJvFC4PvCYKzBzzV2DCaxIR8Vky58j5gfZKFjgyCW8p7C/gfECzYjjRjhj2NV3rH4hI7E2lVQenJDkuItItSPpZzNMKdSCPI/FNCvJDYa2CBxQuRJsmcUqNFBGRbKwD7KcgkPhYY4Nlk2vHMQtTnHM58U8qwRHxG9rG7kDLIZ+svf4Oy3hSUZulg3YcgitCv6fLa7pnk2NzB/to3c2juoR1hX6Zp6phn2rvHu/0N6vdX0+eIvKwHJEnDnsiYjAYDAbDmYGZ3hkMBoPBYFgh7ImIwWAwGAyGleBxS7w/DjydNyJprJfvsLvmeHinnvYJ08XaHTyeTRDThPZAhthm51DT3j3NcJwNRETk7QPlN9xcx3vuG/qe+5UNjfm2X4/aGSlPYLKjaWcYv58fYtrU5mAMkm1IdSHImZjqCfTYYAyX7/u3/WMYTk7oKU1IPqEnMIZL74/AJaEvRVq3hM+QM658Wy/0RUNGqcBxqNYQk+5pRcPwIf6eahCkaDgS/MyYd8LBSbQr2jYfaR9z3Hk8vZZ8DnI9dsY6H6Yd+N9Q+wOVGpU6T8Y4nxyL4CfTbs+yWHEWjwHnbpjisRVGGKKE1iBuLti9oChwGvJ9LaycavtmRVw5akykvBWXbHCkOIS6p/OGn1np9uX8f6LB4MmR4SVTnp+sm5PqQQjrtoAglWaxTA8lPT/VpwG60BKi5ww9inqJ98xsgfjNOIu347nxZJVCXyd58PzAJSG3BmOXeDPRL6gD7lkxntcRqRMuRMOfWIKwz1ATiMdTjgW1hZLLW+dRV2WQK4/kHEhy9Ou5jFrkOO/+bC3K62CkxjbTLfC2NuHvs64d2MWaFu5dia/OQq+ZUNGk7/k99YMeRi/r6xBP542IwWAwGAyGdw5/xB89TynsRsRgMBgMhjOExyxo9shhNyIGg8FgMJwReDGy6tFQFk3zeQkXZKmefytoGnQ/ED8uRuCClDGPwAWPBJaB6yV5rx+vrFfrejyDd8rauuqIXNrQd9SpH3HvsIlHzsAP4DV8b73xNkCZ85IBEeZ8c8gJmPNFQRp5ZqD91BhhPJiv4mdxOhfLp9ZHEmdOg8FpP/L6tswKY/wBE/RH4hky51uSIgnp++R4qr/CtnNOiLT5JThnmnxmXdBfNfUghhpfLuG5cs/peE/xPb1k0u6qy9hXJq4vO4snxx4rcwSf0F9x+xZIT8TlpFiwJwX+TZeEAa49nJB4pgRtnBDzT/JJKQ/L9sGII5L4c3AO5sn3iMf7pZ4rSXw+KavxW8J/ylYlUq8UllGkdUGSrM0mHz1hWsb9ViU/CAUGL0s5B9Lwj0KWmHsu0VWZu477QqLtEo4Hvk+cT65bm3T2oSkzbBUwizktjVZLotmSElkCTwv9NyNfjnosbBvWG8aMnKxxpxOyGubQCUn65XJXPWjWIGpzZ6ZeM186uCIiIq/fPy8iItMJNqWUF8e6hz0wboOnnlH7eEERkiWLrE76ReBz4+vlZB9DgD0RMRgMBoPhzMB0RAwGg8FgMKwQZ5Ks6px7TUT2RV9uK733LznnLojIT4jIe0TkNRH5fu/9/cdTTYPBYDAYDCfBaeOIzL/Mvhzf7b3/Fu/9S/j8CRH5ee/9B0Tk5/HZYDAYDAbDiuC93og86L9V4GFCMx8VkQ/j/58UkX8mIj905BVOYoLqOxV9yRsiWA1TN5Ikqy7JU5qwP3MaggUOEQmkFNVKxMFwXg9mTJt9JUTNEvLelc2D8P/RTAlWt6oNrVMi5lR1XFSnlCiaCncFpCJkbGsnJuLq/+P2B4OnPE6XCpslZNY5JARRnl93WLdWXWimlZAvfTCd8vHnUEeQEhOS7wLtJy0HY0ZyagZDNgq+icz3eQFRuNCHJLqCTOeHEPpKysoLKnfhOtaBRocsj21rN71MOx3Xsn+WkZRJ9DuGrJqK1hF1nnwvzTjVPQhuQbAvyzXTkmZ9MAyb61sSgZN5kBr3BfGwo5CSd4PhXNJf/DpdJykJkPNm8eWtOrfqlqyLRi3OReemeS4rYway6hT7BcW4SFLtYMLx9cqRb4zdphWEtyoSouP+aMinSKlwl5DXeR7JqEQglPM8krvp5zhqJpYbx2Z3roCBJUibXKvB1C7tR35MifasO9pE4T+SwmctwjH7iGJwJKle6eyJiMgQbxiQD3Guq4Jn++tKND8E4ZzrL9SBcYtAGodBXVlGbY7iGyf9reKcZDNSArVhIU56I+JF5Ged7rp/y3v/sohc9d5fx/c3ROTq46igwWAwGAyGk+OsklW/y3v/lnPuioh82jn3W+0vvffeucXG8s65j4vIx0VE+tnGQ1XWYDAYDAbD0ThtZNUTPTfy3r+F9JaI/LSIfLuI3HTOXRMRQXprybUve+9f8t6/1M36j6bWBoPBYDAYFuLMcUScc+siknnv9/H/Pygi/2cR+ZSIfExEfgTpz5y41GXxtndgYsXYZDmIuSI5hXMgqDMn/pTE8Bh/7+5o6nOYlx3q05u3N9UUrHdeA64vXNQT14tGNWtnBOOwocYsC8Y/E/5F4D4cYxAWBNDq+POcgV3e7q/YVCxoQaV5pCkvD8dTMbn4I3kdjPV2DmLBonaZFUO0a7FI1DIxrNA/xeLvJe3PtB9DJZv/uvRY6nu2RLgtmOTl2pjNNQ2k9wptOE3NJojn7w31Jrsed5MCpFllnIsUMCM/hRwAElPIESE/YxanjHGH8U95C+yfIk7byA8hMjdGjD41PQQ/gRygch3cki55C3EzeV7gITC/PDZRdK35Ff5q47oPBow+vhb9U/U0k6qPedLVwlynG+ezhDMT5l+v2RQ6/bgh5Mj4YSp6hTyWCQKirv3uDFXRz1N0yCCQ1RQVMs5aKmXdvIyuZV/ViZBbGFeatqXrgdQRnhcITDg9EQCkEWS0BkJfJkJdy3g4yR4V9p+KQpMxTyPwWrK43873R6EKz6/tiIjIsz1Nr3U07WDy9aHg1sF4MhzxljsnIiIVhBR7GMsCBqf5SAvPYIAqVaIUx88LROfm+oPgb9pT8CjCy+puKB4UJwnNXBWRn4brYiEif9d7/0+cc78iIj/pnPtBEXldRL7/8VXTYDAYDAbDWcSxNyLe+1dF5JsXHL8rIh95HJUyGAwGg8HwYFj9c5l3BlNWNRgMBoPhrAA6IqcJqzW9S3Gc2V1LN2DOUC65tM5j7gjj6b7gBYyBazrb0sPTbY0LVhuaFpsah7yyrbohGe41v7Z7LpQ1BC8gR6yyWocOwC7i8IyXpyZvSZ3n+Bohvh7XtarnY7oZ+yrRd1iKNI6eaFs0cWXGp5NYf01uyHzW5BOQI9LWGInqHeLuHBuWHS+iYHqX6CXM8TzStp0Ec7wbxOcLxJHRbr4UNuiAA4BCGZfO8ySe3NZQCXohjJOnKarAOHpiWhb0HmZxQ6uUG7CEx1B3musClwM6Ir6bdibrAp2IoAOBsWGMn3OaY5PH+fjFL9HFZYQpG3OHAn+rq4VxXtQjrifUBRyJjPozy+LzR8wHjl/Ky0gN9gIfZ4k+isP4r4HrcKE3jL4nV6RGo0sM2rTlFlnSSDHRpkk5QHXgBqUkmDgNGjk8zKJSo79k3rTzdtBuckuM25YOc+CAIE20cXJyk4ZaqcMetD+g2yQiMqp0su6WysGrUME+FkQP6VuT8yIi8qt33yUiIl+7fkHL2umgLM2PGkLFITbiKRZWVcVpINm0N9glnJAUYVInm9aTxil7JGJqKwaDwWAwGFYGC80YDAaDwXCGYKEZg8FgMBgMK8NT8BbxO8JqbkSOi7el3y/oVb77Tp+EzgHif2PGePV489764ve8g94IwoWBc7KmccQB9CPGpXbV/lhjmW0h2UtbhyIiMpxqTPL+vqZp7JX6D+RXzHEcwvnxgcAxSbHgpjfNM7zHj5MDdeEYnkVCEWk4IZMsui7oS7RmUt1lO/Uz48JC+wrEuIOOAcdgCl7GLNGAYZiV8VfGnxMfiyaf+YawfswzcBokHotsCm4E+AiMWM+gL3Gn2kQGuJqcEpYD74zgLyMyzw2hbgi9ZKDBwrnMfgux7XHcrnqZzkrS5kUeNOSL+E2d353BLDp5NoKnCLQXqBPDlPohc15DtEwJOhKYu0ftiIELgXN5nDo8IKKQf9HwT5J86EGFmH+Q5phbCyhn0iyoCp46lHaRTlJW6teT8JQCRwacGu4P0w29cKuTGL4ABSZt4Zq6UJuGfBVq2JSBf5Nkwv0E857tox5NPgVXq7uYTxd8tgInqTVWc1oa4OHMyujauT5O/JIk8UkK+3EyLcix6raEnyp0+l6pGj0TTHz2z42xEvte31OOyJ37ujb9oZ7X3dfru7uaX28XvL99XWhuqGPjx1h4Qc8GY7Jo3z3Ocyad7+7Jsx+82BMRg8FgMBgMq4KXBXfrTzeMrGowGAwGg2FlsCciBoPBYDCcIRhH5CQI8cXkZfP0+yO4ImkssuEj+ChrHzQ4EOOE5gBjnPQdGNzCcYgzjA811ntwToPgB4ipn7+4LyIiH7xwO9RlWGqA/JWDy5rHLK53iKcn3iBzSH0bZHFM3C16NT2N1S7T1lhS9lzMO5y/+AJ+Tw+JTmMRIVkVX0MeQdWL+SSpX8ciToNIq71Z8nXS1jl9kfY55JNg3IP3Ba1GEp8XVsajLRXbRH4B+6mvGeddxPfBMWiH22VGnQvWP9Z8CVVNNU0S/445j52kH12iOxLmXeq31GrHDOkyTRPmQU0Yxx2D7Q86PUm6TGCidbzxnWEa64GQf5N83SpryWRewlcIJbfWD7lPjf4OPnMc+5rWXRTKeZ7op2TQEOoWmk4reMyA2EOvGeqWdMCF2Mgb3Qz6zux2VDdjh3lBh6cGD4d6Qqxr2tXsn3Ae+XKpvkjC0XKLdDOCt0qSujjvUEbC35Il+3QYDPQf+41eTm2UMO2irsjOdE1ERG4cKCdkOIGOE/IoO+TOoK/hMdPZB7/lkNwQpFMdI2qmHOl5tuy3adlvmOmInAj2RMRgMBgMhjODs2l6ZzAYDAaD4bTglD0RMbKqwWAwGAyGlWG1T0TSuFrKGUnjcW2vmeANomnZj2OaQVOijG8NA0cEZdHHourH+VHjgXHG7rrGEV/Y0pfS6xaR4eZwQ+swgxcGYpR1j34VQVwhrntoA86nxkMR6wJQVyR4sjC7VvgxaAAkuh/Bp2UZh2SJlslSUOMAvIfejh4uhvO34LN1+GqsQc+gz/ahSLQvcGfyOF6/9K4+fVV/iXdGfFL8Xcq3IAeiXtPM3EDjyRnGIoPGg1uD10wWH+ej0JIeLS3+QtDSSHgF6bxIO38Zz4ex/6DhEfg6OC3RV2kj9YShp4wkPBz2Q411USbaJ6keROAKJJ5EQVeEnj150yiX+rZgIAtqSvS0QTOsq+moiPL0CzyoFqEZa5RXtCZMN548rk7ySvRSUn4TNXM2N5Rv8IHzyh270FWvGXJDLnWUW1bjwgqNmLlmCx6CBNLJY77JCHwV7g+hDkU81wLnKhmbMFbpvhHWQsw50Twwr+G/4rBvUmPDJ9eyDtkSXtvcuuMeR/4LThiVnXBNN+tKG/cnyg3ZG/dRRZSJNTiFhktxjzoiyOcQ8+oQOjMj5eVQE2UOi7RCjtIWWQT25SJ+1uOGNx0Rg8FgMBgMq8QpC83YjYjBYDAYDGcKp+uJiHFEDAaDwWAwrAxP9omIE5G8de8zp7qyJACXMT7XXDv3rrxj/BS+HfDvKMYaH6ReSHM9uCE9TSfbiFE+A/2ACxo/PHfxQEREXjx/V6tCn4PDrZBXDzHdFy7fFxGRmz19v3001rSzh1gmfU6KOKbLmG8NzQIHbQoP3xNPDZREs6HNIWji5ciL/YN2hjI6cZpqeQQETYvU5wIcgYSXUa41Gcw0lCvVgNwXXMn4O30nWHfUrUramWqfpNyaOf2RBY8j5/oY86VKeBZ1jxwQeAytaxx5c6DphYHG/K8ONPDMWPjdybqIiNw41LHeH2mc2tfNXK3BhaB3CjkjvoznbIjlV0mdOV9ycm1wGkLo6Vg03iH43NJ18eA0bFxRf6RBVwdjVmrdDkea6exQU3eox/MxOSLIJ+VOdBJuSCgQyTJ9G5mbWnMnZQmXJIx3yh3L4jUwN7fJb+o3gfsNcDvYDyP4RY3YD8M8yotrN6wfzJvNvs6TF9Z0D3ixp1yRsdcTOxjc9Vzn0WGt82Qo8ygxd+hnFbRpMN7BO6az+O/IOd2hoJESf7907YuEvdYlehnkirBs7jNB4+UYrlnYt8Gfy7vaL+z/rLWI96c60fem6CvohVTonxr7yWhfv+/c1O/7t8Hr2yWfDXnWSzQ9qI3SIQfpiKcJ6W/WMh5jm/+4iocTFpoxGAwGg8GwMtiNiMFgMBgMhpXAy5JXB59e2I2IwWAwGAxnCOY1cxzaPbQsFvcOetFBPyNXmQ/JJ3g/PfUfqRgvjF/sLsAd6e5pTG+2iff8wRmZVRrUfPtgW/PHO+tX1/ZDHh/Y1HjwYamxyv0JYpp87z/Re5h7bBZi3ny3ntoUyXU8PfHS0GN8fz+Ok8/FJ1M9EeZRxhoeDHEGqwSGQulBAr7C5Bxi5r2mUfSUIeeh4RfE14a6Bw0Lt/A4tVzQ9WGsM6Yzasbg+9n8/AmaA/yO7ZnGejEe5+XQstjuKYfgUl+5QusonL4gMwTa92c65pOZLqmybPhOfhGpJ2ro4sON5svi61LPkHyk5xVaZalwnBwTkaadBTpzraMnHWKS1eQnjLXOHayLzj76D31MvgI1YoIGBfk/yTxpNGNasfNkHgj6vsK2NEv4SdlIr+X4O65lxv7r2DMlzIcJ6wJuQdZse4eZjltZkZcRV4m8JubF+RN4N2wfzr8zUU2h57vKFek77ZB9CLKkOiL3Z+uhrHvwUNkHJ+JgqGl1CF0MznfKX3BPo2ZSFfN4gscOp+KcL5dE10deMynXIYv5KHM+N4nHTJZIdKTH6cdVQceG3Jyqxa2aYg1NsabICeEane2CG7Kj5xWH8R4Wxh88QTdJ5gu0QdxJ/GAC5yM5N/BskolDg6fan7owySpgT0QMBoPBYDhLOGU3P3YjYjAYDAbDWcIp44iYjojBYDAYDGcIzj/4v4cu27n/2Dn3eedc7Zx76STXPNknIl4SHf/kHexFGv8iEoQVqiY+l8Y5O/A66RzSl4ExaWTdQ3y4oweyEnFDZN09AJ/jNnUyNGY5HKo+xPiSBsW/8fnrIiLyvZc/H+rSdRqM/MWdD+o1k9gjIby/n8UxzPA9KQTQNwgeG/ChaDwm4jZF+Rx3Bxx4KMnncH3yOfhRJLFgft1lsYwvN98xVks+Cfs41fRgXcg3CHoZbnE/NXyfWJdlzmumfXu9JI/0WnpfULOBHIopOvvmUHVjvlZhTNDfG13Vj1jvaAC/GoAD0BqPMWUM6AmSarMkug4NZwbXsV+ot9KNz2+4EEinsZgDj4s0PIvdXfh27A80L8TqPTyEWAeO82yD4xxzP5qxTHg/pGLR84i+SUVzXvClIR0h8XkJ3yd8noanwUomfjGp5w75HRPsAa2xqaHVM8yhm4I1mEHfgvVN11wYI/RDv9BCDjE416fKKXtXT/WH1kBoul82nBARkQOaHInI3lTHYkxOREUjrZjHxHaT05GuzeN8pNLzG7+Y1sIhJyRbvK9Qh2lORyRNubWDJ1dSWwheRp2O9jM5OrNyXkuKfC2PTKdjzSzwTMBJq7rkK+l53T1UjRyYqf5Y+Bl+NKrECGau41pIfZFSkMeV6oqchH/yqOFl1aGZ3xSRPyEif+ukF1hoxmAwGAwGwyOB9/6LIiLuGDPKNuxGxGAwGAyGMwP3sByRS865z7Q+v+y9f/khK3Uk7EbEYDAYDIazhIcLzdzx3h/J7XDO/ZyIPLPgqx/23v/MOy1wBToi9bxIRf0AnNkkVhv0RCaIn1IXAzH7xq8DsU0ENau+fp5ualoOkD9DfF3N73n4yHz44isiIrKeNYH3f733fhER+fxdHZfRIUUWFvMrqAvB9/5581ogLr0G3wr6UEyKLuouSRtaGaccDsRkGYJmGrxBiviCpSSlwBWJy6E2Q6GWJdLZbzIgb4eeKAiXB82Jqh8XFvg8nUQDhZiLabMN8efwffuR4JKYdYivJ+3L4BtEn4/dkTZiONYxmA5p8KLfb5/TUXpue1dERNbAFdnPG57QxFFIJlQwqks4TE0Kfp/6uZAbQksMch+ooQNuSOCIhLEKVZGcGiPQCZEeOBHgxghSj7ErN1CJGSuT6iWgLvg+S3REAs2Dbc3bY+Oj78JYBd8ajFEZ5832UEfEkyPiyfvi+kI2iaZJxEVJ+EgVeCTVRioogsTHqZCvhAODXCt5e7oZXf5cT/ePGRbx3anqjezMBuGc2yPlj+wf6LFqqAOdjbOo3WxXmDiJPkjqxZS2oRkLXJ4vOH/OUyXWakkR1hU5IYlfVDqHBftqDs0k6tcskpkqMX6zCTIln2mdA4yL9tBfQV+IlUvaUOrC8eSIgFMS9ETcgt+jtN3LuB+Lrl0FHjNHxHv/PY8yv6ek1wwGg8FgMHw9wm5EDAaDwWA4S/AP8e8h4Zz74865N0XkO0Tk/+ec+6fHXXPiGxHnXO6c+7fOuX+Izy86537ZOfdl59xPOOe6x+VhMBgMBoPhMcKLxsoe9N/DFu/9T3vvn/fe97z3V733f+i4a94JR+TPicgXRWQLn/+KiPw17/2PO+f+poj8oIj8jWNzcVnr3XQGKZfchmVJ4HEBGOcsB7H+QYiX8laLcfcO4ouIWU62NIPD5/D5GQ0srl/S2P93XH1bRET+4EXVDaF3xK8cvBjq8NrhBb0GZhCzrZGIiOwgxlsXFIhAVSTmADDgzHjpFjgiHbw/f3MNnhM9aKCEGGjTL8GPhK+zF3zHHp/JFQnv2vPCqApzmgNpHRlfd4l2RTu+XK6Td6Ofp+e1HdVGzENgWeVeF3XNojJTvRHqSpAjQU4JeT7kfdCLRKQVD+c1Bc+N20f+QI6UfU/vGManPTgE1I+gF1GJGHcXHJN+pzHbGBd6rCr03Io8DI4VOVJZqrURc0nqoLei3xf0O0mWx5w3USKXICLSP69kkQ9cUZ+kjUIHcgdaFneGylfYBV9hOsSCmcXtD+C8mCTrMNWwaNeT18xxFxbvB9QiIQckcCMc+4n9GOfXFBjXpX0OeRfFCFyxLNGzSNZH49EU/y33rsE9ERGZgXixB9LZBhYK9UTexuQ+mDU6ItQ3of+KIzeEXk3Ba4b6IeTS4HpWmVyyMLeTdnOryOK1zPUnIuL70FXJuUfH/iwN5w7nc325uJCwD4FzVGP/cVjLVRXPgTZK9EONORd8oNZ1D86g9TPb1z7s39PK9O5jfUy4XyDzLuZwBwupWsLz8Av4MKmeypwZV3otB2U1oh6PQpjsSeJET0Scc8+LyH8oIv9vfHYi8vtF5KdwyidF5PseQ/0MBoPBYDC8E6wwNPMgOGlo5n8Qkb8ozTsUF0Vkx3vPP/veFJHnHm3VDAaDwWAwnHUceyPinPujInLLe/+rD1KAc+7jzrnPOOc+M/XjB8nCYDAYDAbDGcVJOCLfKSJ/zDn3R0SkL8oR+R9F5JxzrsBTkedF5K1FF0OR7WURke3i8imLXBkMBoPBcLpw2jgix96IeO//koj8JRER59yHReS/8t7/p865vy8if1JEflxEPiYix6upORHJFzyEyVO2HRlhyfEWYYgkqaoLMhQ1xPqx+BENwTojCOckZc0R+2B69a7zKkD0jZtqcncItudvjJ4XEZHr4+1wzeX+gYiIXIVQ15fqyyIispMpWzOIIJH8lAhbpZUYFFr5Lhh0d9e0EVVfyVbFEESxFk+KpLBARCvilCJiQciMRVLcqYoPzyEV36LO1Trzb66cbaGvITjkIF5EkbAs1EHTck3bxTGcEzBLBJh8QsZrxNtIAm6rIvFcEN1wLo3TwmnohzoR7Op3MZFI0d6Kj691NO3nOkY1CuxkzeAUIL7OcKwKhmIg00HAqyKnOREBy4IIncQY4fsyFvBKCaIRUEYHZNqLPVWk2yzGUf1vH6rg1mykhWe7sdEYSYcU/AtBW5pRMmiLRgQKY2tsAt8vabdDR/hJ3ACXGBWSrOlT8a1UdCwlsLdOpflaRbGzWVxmMNoL7ZGFGM20f8aYjM93df+YYNC+MtQ94VLvIL6u7IT/lzS5I7EXRM48qUMgq0LQLZhKJvxaDolPhP9CP1bx93W3tb/CHNQVIGmXZKdD8I0moriGc5O7O+dR2JchZlgPIGTWSYTMaKbYXn88Rk4urgkkVQhHdu5q4RTrIzmVe10j8Ia5yN+ggh2TCJkdZVRH8m7KsA51TiZI5uSIXfXx4RG8/fIk8TA6Ij8kIn/BOfdlUc7I3340VTIYDAaDwfBAeBii6oqepLwjiXfv/T8TkX+G/78qIt/+6KtkMBgMBoPh6wVmemcwGAwGw1nCWeOIrBQpV6QVf2PMn/yAwBnpJfFUGIAF4yuKiwXjOU27u3q8vKlckC93NKY7nGkcsgcOQAeCVd+wdSPU5X39WyIi8pXxFREROZw+q9WeQpgIdXFJDD9PzJkoZEQxpEwodKYXlgxpMhbcaGY1XAgKvNG0bAD+QTcWP2rEoFIFM4nQxNVjsa3JxYQj0G9UswoaqbGaE1RqBjEkmpOBO0FxI/JcyN9ouDVx1XzCLSACd2RBfDQ1vSOfIhiJgY9Qoo6jqU6sTYjLvXBuR0RErvb3RURkHQJgh6XOl7uTNRERGZddtLEVb2e6JG4bBLzIn2BsHHWsEzWwIGyFNMyv+uhUpBHH2r+h/KV/MXyfiIgMBppZgfj7aKLt9zRuxPUcE85ZDz4H11dYd+RapOH2Ns+L/03HkdyZWdJutrNRgtO0ihXbXMJXcYn5Xd1p7SOYqwV5WBTPOoSBGs382OcJv4KDe/9Qhcs+e+8FLeO8ltVDpQ9Bltg/vCgiDf9rWjfEjkMYK1LAK0/KCu1nCo5Iloz/nOFc4Ithz5vG85BVKNuCZj1wgjokmbEyiehgKp6WjGkjPojjCV+Ma4JtbnMbuC90+tpACj5OIK5X3NU0H8WiacyCc32Oa8jPeVKmWzIhFyHlJRGp8NmDGLo+Apw5sqrBYDAYDIZThFN2I2KmdwaDwWAwGFYGeyJiMBgMBsNZwil7IvJkb0S8LDcais5LzuH73WVDigjaCYwjT2UhgiZDYgDFd/RTMN4629d47fVMhSMub+v7/99x/qsiIvLhrS+Ga+6Wqrnwxb1nRETk3h2Nv3f2EesNZlVJ3ZIYcIl4O43UyEvJsphD4Bc4RDVGYIjVp5oLbC94NsFALOURpJSRhBwQ9CN6uIAckVZ/0sQuLYPg8HroaXQm5BckehhzbUjyScy8Qiy9pRES6k+zu0TTJcTXMY+qsWZ6IBpwnsL0LsOF/VzJEZOaZneaThGIn1QwyVsQG3bLArcuTn1iGObIISG3aIR5BX4L55dLNAwWUVLI7ejcB1/nUNt5sKXx9t62ijEUiMfLlhYyA5ci8H3Af8pgzNb0fWykxrEMWjCtbkmrF77rxNwQjn+WcKpkRre2eIJlM+hNJNwJCfyVVh2GGEdWEHM2g3kfuUM5uWbkL5E7g/ZOwO948945PR+FfvN51Xl8pr8nIiKvHShHZFxBn6U1J2jyJqM8Kpv1zUfgjI2hHxJ0RJI1neiJBKTrkPQP8OqqXuvULuoQ9DJS8tiSNGQep6GZ6Lcwj6gbMptfL+SfUWukxFjJbic6b3JR+4F6PMWenke9JZpjct9sDEK5Z6b8uQXrNOV+JLykOZxEk+QxwXnjiBgMBoPBYFglTpmgmd2IGAwGg8FwlnDKnogYWdVgMBgMBsPK8HQ+EXFL7o8WvLtNXgHj5YxV853yCvFB6mrQI4Fx1emGfj++pJ9nz2ggevP8UERErm2qbsTvvfSqiIj87vWviIjI3Woj1OEf3PxdIiLy+VefExGRzi2NYXZ2EeMlfyXlX6T6CfQ7wWO1HJoOPfiCjHvkaaAt7fBj4pES3u8PaeqtEpcdYrmBv5F8nfIxwKXId3QKFaPW+//URYG/zWxbM/Vr0D2A5kjwl8jgZxG4L4tv50OdyFehx0w37uf2U0nqzQTdgyI+zqICLwWx6xrx9jHa+dZEL7x+d1vb29ELLmyqV8v5vhq/0CeoanFEqM1BHYSKPhtJ+1yWcDzK2IeD8XV/uCwwjzbOjX0rz0RvpsYY5eta74tb2p6Lg2GU5yH0dHZGupD2wS2Z7SmxICvjrWTO5yU9LvPaE0EHI4vHlxwJ+gFxzQdwLOe8nBbXJehLiEhGbRvoC3n6+nR8lPp88dxkndKg/K0D3R9e710QEZFv2FDdocO+9tcbh+dQ5aYuHcypMrQTWSd8m9B+bABzUkAc96TTWVfuBcEfilSJ1hDWHeyTHejJgIjkCs2c+2qd+CClOj3cq3zCcyInJGgtTWOdIj0VvjYJHynU/xnd9Lmuyl0tjNyQfMKxQ36dxCdmERdEZDGvg5QQ/jalv1G8JvBN2p+f/OMJ44gYDAaDwWBYHexGxGAwGAwGw0pwCt+aMY6IwWAwGAyGleHJPhFxovr+qYdMivR7fs6bYHcTo6RWBWLdiW4BY5SMI1eMr+OE6TY4JM+pfsLvft9rIiLyneeUC/KBnsZ2L+SqI/LqVP1kfvrWt4a6/OZb17QOh4tf4J+L4abWB4zdzug1E98fdgq+Jw9uAeO3ZSsj+izQ1mVDCy3XUDhjr+yfoB8SazbMVx7ns0ocCmgcFIex1kH7muADtKGF9jc1ptsF52VWamUnvS7qjn5I9FaIJvYdazlQ26QczHtLpPoxwddnkrSL/TEhpyQeLH8PGUDDY4oyb17RJdS/qpVe72gsnfwe/T/0DTCO9BQigqdGJ9YmmE01b2qbuCl4OajjnK4K+TAu+dwqjmNSXtSOOH9ZOVDPgAt1baB6FwWC/Pen6qGzN9EJVlbJHE94Lak2TvqXWUTz4jLnx+Q/HOeUnxTKwpoPWdI7JLEMSTVM2vwo8jDIcYIljEy3JSq78VRx0fHAMcJ4bwx0YlVYV6/vnRcRke2Ocoi2ihEuPCciIodTblAiObyXPDhU5K2wHWkdOM/r3EXfz/lKhQ7ycT+AJ0dORfQ4P/Rh8rcq9uCUf8R5FeQz8MtSroPPsZboDpXxHha4aW2vGerGTFhh8Hawn1DrhrpFa1/TQtdu6nndfXCzRuDSUPsj5RqyPzM2hnyWtklTHp+T8kgC0SnNu5blm+tjxCl7ImKhGYPBYDAYzhLsRsRgMBgMBsOqYBwRg8FgMBgMhhNiNU9EjuOGnOC6OZ2LEEaMPWjCu/j0YwjcCHBKqD0BfkYXF1wuNFY+RRD0Fw8/JCIi/+LuB0RE5JWbl0PZNWL3fgPcB8QT81ERlU1+QhOjleg4tTkOxqo10APhhfFmwfvzjL+2byODTkYnTiV5fz/0yzTtnzhOHGK9OX0s+Jm8A2oR6PFqralLyXjwtjZsY0v5N91CC2MceALxAdYp+HmUcZku8QghL6jRi6AGAerS8sxI+QFFFsdy+ZcDeRfMw/foPaJJ766OTWefZej3o1zj07fWVDfi0obqcExbXIopuDAVvIR8QhIiN4DcmSz5cyZ488BExCV6ElnwGom5AXM+Q61r6alCvZMhdELe8kqOOJhqJ9470IEd7epA05uFHisFYvz5iGOIKjDlvMniNPp/oiPCPKgPQ3D8g45GnWg3SNIPbD/ndKLL0S6LmhPkW4Q5hWEsxsncrKlVgX6EHskF6K/0MNfvjrT/XttXj5kPbN0WEZFLfZ0nO+NBqEuJeeKKhBuTaPzMeaVI3E6i0ZPhvhHXvdiP9XeyNucoyctx7wUPh/3UrC+uJ/BVuHzIVwE3hL5JfkIiGesoc+C+wPrX66ggP9/SObl2U+vUvwfOyzhOOwc6Fm6kDfWp31nqGxO4Rq2fxzmvmfn6LjxvgeeUYR4WmjEYDAaD4SzhlIVm7EbEYDAYDIazglOoI2I3IgaDwWAwnCXYjcgR8JIYpKTfJ98FosL8NSGeyXAwYpYVuRFJqM6Hd+7BDUGWGfQk6JnyG7eeFRGRYakx8yl4DG/snBMRkf0Djem61i3n+YtKHOgg0zv3N7Uuu+COIG6YVfQYwbvzaewXMdEp9CMmffhg1IkgQmhU89+G+4KU/IpZ/J5+htgs/TYCPyXx56B3hOPr89RhQay3gu9NhdhwvtEIiVzc0jj5Vl+5ITPwJfbBfRlNdJCmY00LckCQ5jMffc6oF8IxRx3oCyL08pFYT0GkiT2nWi7hisC3iNufcmroZZSP4/xy6IoMD7VtB+B5uAV/kqTckFQ/ZK2nDSE3qMB82gWfo0w8RFyoY8xf4HwLPKFWVQrUv3dDB/hwTzkhe+s6Z90AmjW8ZqSFFntIhwt0Y9ptSsYy8DUC96J1ciq5kMTdg3cQ5iLbGXRAsJ58GQvPZDPwEeqYExG+L9vnYv6iX3JUqgY/hXyjVBelWbOa1kOt5BST74PrN0Wk4ZzdGmr/fm2ouiIXe8oRGXSajrwzVZ6RH8VcIKaBQzVF+0p4N5ErVMb7St1H//S4+PHFKBZmCdoy/aZfyoGe0+uw88kRoTePfgycKkpwoIww1VMu3yzRD4kpbNHYBN7bph7M1zStoBvSvwVe365EZc4GMX+HHCL2l+dGkvrDLCV+SPNblPoahcou+V1bkdfMabsRMSaNwWAwGAyGlcFCMwaDwWAwnBE4MY6IwWAwGAyGVcJuRB4Ay+Jr6fHW+97BZyKLY7mM7QbNCXIcmGWwE6BPCT6D80DNhy/dVZ2Qw6FmXI21qzoDzfi9V++EunxoW+PBXzvU+O+dnQ1UEnkXrEvs65JqXITPQSbARXXl5Ao+OuQAtL6rO0s0CKpYNyRoMpAjwq6e8+WIK0UfjPNXlBfz/LYGaK8NdkNVBrlmemei/fDlnUsi0vBrqgkqN81QB/JYKCogC1OXTgfqlWCQO/y+xRGpizh+nnrmuBDrXfx9iDsH7x6UjflWbml/cGgnmD9FtoDXhGMZTiaPhPoqG10lRXRAlhjOdIBLcIbyA60ceR4upkY0SMawHYBlH9IjiFyhGXRlSvoXdWJdiNRbRAIHBGk6Rj4+7pPPrVOac1L+Dk9Yxjspl3UAM1h8uK1ZUa7FHjMsY5GuxVFlUAPoxs6WiIi8f0v3h/dtaFpijt4ZrWs5aH0vL9OsJAPvKHgKpbwU8uywXkI/cflwPwGXKhtAv4c6NlgT1QB7XtBxaTqs6uNcckSCxwx0ROgjlcyLsGSpF9JJSCAsI5kn+TTe40QanxrO3+pAC+3sxBya2Say7MR7f4b2DroJAyHldxDkgVTkSTXnkSu3lEayLM9V4BS+NWMcEYPBYDAYDCvD0/FExGAwGAwGw6PBKXsiYjciBoPBYDCcJdiNyBFwMq/FL3JyPf6sOS/ohsCqgTH84A1xGHMiBDoQVSfWmmDMX7Y0sLjW0/RgpCQA+sgM4JfyHc+/JiIiv3v71VCXt6bKDXl99716zR29tnsYc0KCNkf6CjrjqyA50Nahnguaxyn9HUREfBFrAoTYbDgBl9aJrkiix9JwQ+KUZebQmSA35Pm1HRERmVTNVHp1pNoUb+1pvPz+PXBmdjQQnyehfWp0pByQFE2dwFuA/ka1GXLSNrU0Lhpvk5hT1Hwfc4wYNw99DO7Q7IJmun1e9R/WoflBT5nJDDwO8EDy1tg4BLM5nuSGNPYd0LJAA3kePYZ8FY9Z2k9sQ5hHWXK8tbw474MOROJNRM0J8grYD9WaZkJ+T34IHgN0RYpRqksTcwMCp6tF3HDJsqcmBesWtFxSKkgQnYAmRbFkG0s4RuyHqrF3kdkWdEPAm2A7yJ0hDyf1ygkpOWho93hHxTi+fE55UR+5eldERH7n9tsiIvI5/7yIiOzPdMJ1WuIpG+u6x+z0QVjZA08jcMKwxgtyPahTFNeFPDGHscwLTUvyM7DPVNtoew9crWlDjCmhxeEHWpfQx+hzek6xLKZh3dAvp8uNhhlzLnO+xPOGXB0REaE3DrVsdmNO2egq1su5eILk97EWwUGb3gW3hP1KLxl2Pflc3BsyeIQt8kRb5oeWburMq6pkKVnpMeK0cUTsiYjBYDAYDGcJp+xGxMiqBoPBYDAYVoZjb0Scc33n3L9xzv2ac+7zzrn/E46/6Jz7Zefcl51zP+Gc6x6Xl8FgMBgMhscI/5D/VoCThGYmIvL7vfcHzrmOiPxL59w/FpG/ICJ/zXv/4865vykiPygif+PY3Jybj7Mt4o3wXJHm/DZHBLc9JeK9fOe8GiBu2Im9MYrkutC4C3r+i8/q+/7v37otIiJvQBNkcl6vfOni10RE5Ls3vygiIm/Nzoc8/tUd5Ybce+uciIj07yKOPkIz+H4/K8HYZML5EPrgwGOkxHvwVcqhSfgeC/NKz020BpZ+DpoCcRqyw+fbw/Uo3T1sAu/joXayh/9GNgafYJxoBTAkm+hDLOOKBMpM8BrBgR64Ijxv2PQX9VP4ZRNPjzk1gRtSUJAF5yGefvnynoiIfPsVnQeXu6qjcnOqPJi3h8qL2Z0qR4DcEZHGaydLuCENVyReDzwvcEfQvnINWiaI3xdD1BEcG482kj8VvDryZlJw/k/PgyewAU4IvILOwyfo/JpO3gKDMSq1ow4mmsHBIdp5X7kO7g7W22gJn4VVaK19n2iUkB8w24z5Xnngn+D8ghMnWRfkxKRrINFVYT+KiJTn4WOyru2f7ms78z3oBu3HdUz7NvRxP+bEXL+v8+LGOZ0XL21+VUREJlua72fvvSAiIsNZsyFtD5QjcnBO+7Y84D4Sa53UPVSm2zmyTpLF86rowpsGWkich+Nd8OF6TX+WPeTZxRoGRyToiCR8njm9EHLNKO5DDxrq1CQ6IoGjtdEsfnLgin3ohuCr6SVtx/bzylPrwd/p1h3tc/ppUWekQlvqNXDUOtpv3sPM6CQcxUBoSjdE/jYFYt/xeT0BnDaOyLEj4BUH+NjBPy8iv19EfgrHPyki3/c4KmgwGAwGg+Ed4JQ9ETkRR8Q5lzvnPicit0Tk0yLyFRHZ8d6TrvymiDy35NqPO+c+45z7zLQeP4IqGwwGg8FgWAbnH/zfQ5ft3F91zv2Wc+7XnXM/7Zw7d9w1J7oR8d5X3vtvEZHnReTbReRDJ62U9/5l7/1L3vuXuln/+AsMBoPBYDCcVnxaRH6n9/6bROQVEflLx13wjl7f9d7vOOd+QUS+Q0TOOecKPBV5XkTeOmEm76RIRSO4EA5RD6GJPeMjNRj6GssLuhGIAzLeGuLS0If4rstfERGR37fxWyIisn9eOQ8ZYoJXcuUE3Kg05vvpe98Y6vLqddUMKHZjHYvUd4Ox67kQdiA3IBbOeKqn/wcD0WlbW5kk3Ro8QKokJpucv9QjhHVNPEVq+D3cKM/pAeZftngZ1Aqo4ph91fVxmbw26EUsnhtzXhtMZ9A0YNHgUrSbSr2LlDiQ6mmEdk8T7Q1wRrq55j3BBUOQLS50oCuyqUSN62OdHzdHQdxEdlt8kTZ8qhfCzxx3cISoZRP0NNJuSvQyjvqrpgQXxj+j9X33lfsiIvLMunJgNgs9XqID7k9UaIccEdbRkX9AHYlkJ1nGEZnjbyxA4F10Y94BfaR8+ucTiUthzsfzbG5ttCcI5mCNucQ/zWpyzSacY1wQMX8l1HEDXBNwIkp4Dv323hUREfng2g0REfnQ4LqIiLwxUI7ZV+5faqqCedDpal5jeCn5ndjIpfGoijsiaAOBS1PS0wl6O+fI/+kr/2d3on8Yjvd7Ub4iDXeKXLuc+iFBRwR1oF4INT84lxP+Uwb+W8V5k4xxtY7Kd5rByQ7AT8F+OtvS7wbPKFOAnJq37pzTS9/Qdqy9rXXo7oFjNU4mALg1UnJTS4Rq6gUTh/VepC0SXbPk85PGCov33v9s6+MvicifPO6ak7w1c5mPVpxzAxH5AyLyRRH5hVYBHxORn3mH9TUYDAaDwfAo8TD8EL2BuUQ6Bf59/CFq85+LyD8+7qSTPBG5JiKfdM7lojcuP+m9/4fOuS+IyI875/6vIvJvReRvP0RlDQaDwWAwPCScPLSW6x3v/UtHluHcz4nIMwu++mHv/c/gnB8W9c7+seMKPPZGxHv/6yLyrQuOvyrKFzEYDAaDwfB1Au/99xz1vXPuT4vIHxWRj3h/PB/jyUq8e4ljZ8v0Q8L5SVxuQXvIYQhaA4jx1/AICTHIPmOZSZmIp7450pjtjf45ERHZzEbRaZ8bv0tERP7V7vtEROTXrz8bvqv3NObIOBffiWfMNsfr6lnCt0hj+yQ3ZPA+YFoh3uzgnZCFWHhTv8DhoJ9Nlug58JqUT1Cxbuif0D3UbGAb0K/QNvDQBnGJPodmhmOMHyPu63MG+1G3MS8it4bf+6hOLvFameMFoV+Omu6pp0XoQ/b5NJ4XYb4gz1v3lfNxd191U/pdzegDF1V35v3rml7tKddib9YQs3e88o2oB1Oz/QmZg5/Jw6D+iBxC0wJeG9QPSduSVXGdQ7+1O4Z0K2jV9GD8Q3+b22P1Bbo51PTurrZ3dgDPkTHJVS4uexZzkVzSv6H41jxJtWy4TjoH4MrMFu8PIc9S6+6n6Iga62ZWLawD1x+1hUREBPon9G0J3KE+9Yj0c+PBFBM1OKe7a1r5zTXl2IzhPXT3UDk2v36gHjMfPqcctG/aVErdzWHDJXrrrvKLygnFOdKy2Q6si5k2iOOeldwDkSH6rwBH5Mq6civ6ufbXjX2UPYnXsn5AmsV7L+dS6m8TOBSc2+SHgd9UBwIPTqMHDZcJP0+bCcI1SY2o4oruyfQDe+OW7tnF65rJ+pvI8r7mVYw07RxqP7kJhUUoEoR9CfPIkYOYmiC1sWyTIU+J14bftvyhH088EFbIEXHOfa+I/EUR+Q+898OTXGNeMwaDwWAwnCGsWNDsfxKRnoh8GsaBv+S9/zNHXWA3IgaDwWAwnCWs9q2Z97/Ta+xGxGAwGAyGs4QVvz38TmHuuwaDwWAwGFaGJ/xExItUVUzkOQqpsEzZsKlS4ifJlPzMU/36fBXa1xe72gX/+s33iIjIawcXRERkvaOMrxIEw3sjJZ3dS8h7WibIdRBBIiHWQZAKfDfJpyRfog6z+PoMRK0ZiG59mDmxF0jeIjFwEVmVhLW6s+SWmIS3UAeI/kzj0yr2vUsN1DSlaZxLCHUiLaInCGjZGsSeQJAMQm0Opl1FPA8C+Y5kTIyVn8XEuSCiRAG1EUi9rX4JJFW0j2OQTyh2BEIfhrOiwBXF1yAKVcEETXD+HoTBPg8icf9Zrey5jhLq6pY61BTnTEHcq2Yg54LoWCemWzWJwon6F/s6JSu70F8x4ThHG8qWOWIOQ7DxbSX4vTLSN/DyPgh7OK88hPkbRPr6ByBKT+K6zBMp47oFEnQgPbYaxHHknMI4drg2VSsuGPVFZMr29VX8hQNZNZDEp3Fds5Z+VT5nkKbp5DwInMwz2TdCSkJooR1wbVPJyjQ95L7xpd3LIiKyVej8eKF/T89f2wt1oTCXgPxeQIwvFUjMZjXaWSZ1QZu4F9asW9w/FKkbDmFYuIAUHMioyVdc9+FP2LBFU7UQdaVYIQXhynj8XXo+1hdfOhBpieRd0km3ASLwvR3dg4vXdA4Pbuo1FC5LSb0ZSKqOpOaSG8oSEbJsgevmSYzxFuUl8uSfTviVc0TeMSw0YzAYDAbDWYLdiBgMBoPBYFgV7ImIwWAwGAyG1cFuRI6Ci0XM3qkxUCsWnAolpccZ+w3x5VRoqSRHQD+P7mjc9NUdKOykMVDyEGiOVTR1d+c1CN3taexxOtZunXmNwRYjvaa7R44HYpdBiAopYrtTCBqVvcTUigJVqaleu32MFydCY3Pmdkl/BMO50CxyQ9BPGLcKxmM1jpMzUq21KrOtFaQRXK+jn8l5mJbavoqmdUUHeSMuH4TMUDfyVxjCHaAu4P+QK5KDx1C0tOjYp+QLBBO/mtwYtIPzhVpS9DjD9b17sZhYuabHhxs6b762qdyiKczj9iaNoNl4ou2bjWi2hXbTIG2gnysMVpnEo31fO2K2AePGCWPi+D6ZqxSdSue8iEiOvunfgpDXXfBWBhgD8HvyRPyL17Fvg5Ad+R2J4FfKqeA8av+lRp5AY2AZ7wfkPFBULFxXxfwlRx4T212Cm8Q1nnBEfMu4rJkHKIvjn1EYEeeFOUnxMNaRZevnd63dFxGRy101yfzSoZrevban8+Pzu9f0OrT1Uu8g1GVjXQd0B/wd8pfC+JFmUVBMLYvakHJoKLbG1u6MVVhvf6yNorCe71L8sXUt92lyQoLYV0oaidOwR4U6c8+KRQm5vrg+O4cuOi4iUl7RTAYDHZwDclpuatq7F++nrP+U88HpxCqGeVSXVOhzqZCZX/Ijc9Q5/Bz9tp2yu4IVwJ6IGAwGg8FwhmChGYPBYDAYDKuBl1P3EMZuRAwGg8FgOEuwG5Ej4EQkz1vvaz+8GxBj1FUvNl1qTmiVLS0TqzzWiwgoKZiBy2huhRg34/Xnr+yHS775ytsiItJF4Pjz9zQO/NZE48KzQw1eznBJGicNRlvUrghyGYiB4r32oCeS6AZoPZEHTe9SYz1+TMKgc9yRNOabXE8OweySVqKzpTyQq9uH4ZyLAyVSFMh0d6ox73swAJtBR8NDZyVolHRplBVrlDT6CZoW0Boot3B+LzbVyyZNIwvwKMgzmXtkmfRbuYF4MzRhssQQbI6bBO7QvaHG33OM1eG0ITaUaK+QX0RDsCzWC8kdzQ7Jy4FeBLgk7Puql/B1ijg/l+ojtJZE4HQk3I7UBDHoQCR5pOc3GcuRCLojR5yT8plycqmo+0HeDjkieR6lDhyywAFJ5k/g/SyoBdtdBG6MpqHosB/gQMK/GY+wxtGIl9ZfFRGRF3u3RETkF7MPiYjIW8NtERG5OVHDuWv9RkfkvefviojIv93TdVINY65IBQ5EOdDJWgxapA5tmFaNGkLgTs2gY7ODOcr1RzM8Oa/pVBpe02yjiMrq9LSsuqefA5eGXLl0202ML4PWT2KWSL4Tx4brT0TEr2vvs2/lDrkh8SY23Ub/JGajnX2WBV2WPT0hP9DK0+xuKdqcEf5WpbxGnpOa3pH35GuZ6xzDHOyJiMFgMBgMZwROjCNiMBgMBoNhlbAbEYPBYDAYDKvCXHj2KcdqbkQYb5t7J/3knccYJLkhjC0y9k29CI94aUUJh/U4Jjnbgi/FphIQasbv4THicWvZ2VCSwYeevSkiIn/i6mdDXd7TuSMiIr8yelFERF7ZVe0AVzCmjzqu8b13vY6x/XINdT+nX6zBU6EDj4huV9NhL/btiPgKQWOAeUuENLbPd+7JmakTLwj2Y00uDftvWwu9+rzqJXzDBe2PS91GD2GCwt8cnhMRkd2Rxp4PDzWtyZUoQ9AeZbGuib8N2xI4Appm4zg2XPe1bnWvmVeeMeiEGxE0BfC5hCbJ7Bx8KQbw+Zl14v6gJ00/jsOT5zGr5v2TwjRfMr3JAepDvKOLdG+qDduXQdRugmMSOCOzOA5PTgG5JFpvtHOzjvKgL1DQx8HHCpyp2Qb1WGItG3obBQ4OtSnIqWIIPaQtLxGOCfk5fRd95vf5hGXN56EHko5NygwcrE483/QkpPQpoT9JGZfBtVp14joGntehHvjynnrK5Bc0nw+vvSYiIuvooF/Iv0FERG6MlSOyMxuEMp5f2xERkbtXdTK+NsI+At0dckXKNXCrevBqStZo0APB8/nJWL9wGJNuVzvywoYSYQbQ+XnDnQt1md3ZiMsCH6Va07xKeC2RnyXYZ33Sb34A3g73FfRTcUgNGeyNW7j+QkN8K7DvVffBDblPEpEmoyu49gK8rMApqbB3z27BLwk8r949nfzZLjYMes/MSLpLCGD5/Fqe4zUGT64s/j7wlNyTp4icwrdmzH3XYDAYDAbDymChGYPBYDAYzhCMrGowGAwGg2F1sBuRI+Alfg+bPgZpjDfVGUm5JCINJ4Jx0TXEIjP4cYxijQpqTAQ+BsJ/nQsqNPHMeRX5mMHn4wB+DAXiht9y9S0REfnfX/lFERH51m7zDvqvTzWz3zqAfsidc/rFrlYu1UGYe+ceo9Bd0zjy1kDrVFNfgjoiPcb10ahxOxMkCVckgBYIiOUyXt5oUMRCD4FTEvoPKbgE612t6wwB+K8cXApF3RlpfPnugeohDPc0NusOKXKCJPG9WQq2KQnZ5tATqftZVLeyCbtLNonbUU3Z7ngekMfj+jRRifkW1Hihzwc5Ig5xbIc/QTh/vF8wZ5M8Pbkc0FMp0ekM8fdyzTvLExGOlMfDNYC6ZRJzidraMUGLZDPmRpGXtNkHPwlzjnOQ7RpOtHYjaDuM9zXt3NOxJbeihg5HflR8PHCD0A8Yt+k2eBoquSGdA/iR7IPjAE8h6XA+0bhkcaQ59EPKE5J5XZigWUNvGeoIBe5V0qekLYBL89Y9rfQvX36fiIj8zq5qg/x7fdUaul0qN+TWRHVFbo62QtlXB6op8sKG8q/uXtb1czDWczoH8AXqcg4WUV1CnShpMdLza9Qt29CxPg9uyAfP3cLpmt/OqFk49/vrUVnUD6n6qAO4Rg57kiswX6asjCZ5n3MYfkmkh2Xg4GFud87rvKPfjojIAThl2Shes7NLWtb2u3ZFRORF6K+UGNjX7p/X64fab9WAfkpYm+vId6x72FI9kfbvTqoXQoQ+f7p++e2JiMFgMBgMhtXhlN2IGFnVYDAYDAbDymBPRAwGg8FgOCvwFpo5Hr5u4m3VMeQAxt1yxtSb84NOCMPD1H3oat4VmuaCPgbftQdHArHya+dU/+K7n3lFRESudBrvBxGRZwqNQ/5uxHifL5T/8OvTpi5/9953iojIL731bhERKW9prJXvvQeNBb6uzrojPsq4dJ7rfza7Gi89gI7EZJq0hWh1H7VJ2v4zIg33g/oOLDt4Z1TUTcB5QauCcXUeB78B3Juvvq2ckK96Tf2omUqO/ixsF7UmWLc4jBz8YPIp6jKrk8+IifM6dAN1FdzdOAac6m2ISBM3rxfzLQLvYqh5ZWPNK+gd0OeDXKQNaLz0Me9QqTE8ZqZlQ0SoE58NjiO5CyXOnVSLl2O3px03Wte8K7Q79elIaSmhrS1SUgX9hwvP6rz+lss6r19cUy2cHgZpCHGWIRo+ghDPvalyB+6MNX2zd07Pm+q66OzC94U0ryT8Xrdi6Q13A5wWF/NvynXymTCuod/YrHQ9gK9QwrOHejP0pknmkeataUnOQxgj/UwNk8ZrJp6Toe/RhtlEx/Cf336/iIh8sH9dRER+d/8NERH5UE8//2bveRER+dzd50Jdbo+0T7t5PIHp1xK8clJeS+ITRC8mh46iZkwHvLZr67rHrRfauBvgoIxnrTUc9pqYr9dwY+JfOl9y7eE61IWaQdTKyXv6RY29rtPTOm2CGzKZNR5NM+iHdDHfZ+f0mkvvvSciIr/3ma+KiMgUQkP/9o725f5d7cfOfrz/No07obBHxF08jsjG09hfJzv9scFuRAwGg8FgMKwCTk7fE5FV37cZDAaDwWD4OoY9ETEYDAaD4SzBvGaOgVvwEIZ6IcvexQ5GBU3stJgwMKrXrm9qjHG9pwHBO119X7/c0xi3myaaJEjoDdJBgPVDPY2ZvwfckEvwG5ghQPvzeDf/x+98JNTll95WbsjwNmKT4BXk5D4gzlyMGV9G7Ju6D4g319BqONcbiYjIWqFtubuv+Tp6ibA7WqOXvt4eeBKod4iXTxfXLZ+QKyJxGVnsJdK9j/f/9xh/RZ1a5QePj048nmynzHE9kjpMoHEBvgXj9Y6+KJSPQJs6h5oyNt7WzWCsNOhDgKeSz2L+BOvW2dP25UPqR+h508t64bPPaXyacfYxuBP3x8oLos7GbNZwREL8nDwDtgdrocS5nIvU7hjDYyRDPD5b1zqU4OkUB/F84BgHL55kLEUaP573QXvh925/WURENnOdc2/PVIPh5lR5AzdGuo7uT9aiOpWoa1lysOMyQz+TS8VxKNraDPgqI+cF14zIFcH3iSdNaE/YF+j3Az4TPES4zsK8E3JPmjpM4AkzOaefO/v6HXlLgSIR+EpsH3VoUCY4ajn0NG7tK2fmp279LhERGV/WefLermp3vH9NPZq+vN/o73ztnvb9dAIuEDQ5soRzFXhdkxKfsceFNY6qc19F/60PKKqjeGVPvWzeuK+NH95bC98NuIdNY95NAD2FKAwCbgjHjpy0ssDcJketo5Wkj0wHn/ehGTLb6Yciin1cC3+rzRd0zX0I/lZ3JuDr3VL9psNXVcNl/ZbWqQP7q94O+CljtGVKkl7qUfQOTGF4beCcpf2TeM88YZy20Iw9ETEYDAaD4azgFJre2Y2IwWAwGAxnCOmbVU87jiWrOudecM79gnPuC865zzvn/hyOX3DOfdo59yWk5x9/dQ0Gg8FgMJwlnOSJSCki/6X3/rPOuU0R+VXn3KdF5E+LyM9773/EOfcJEfmEiPzQOyr9IeJnvOMjz4DckA+cuy0iIhvwQnm7p7Hu4V3EP+ELQ/+CO7f1+5/rqPfD7iWN9f87a+otkyH4/fkR3vu/p+lXb10MdeH77gW8IHLyJhCzzcENCVwIxJmpYeDweQq9kBLxxct9DXK+Dv+PqYArQm2G1uO3OR8NUmF4Th1fEzgTCZ8gxL5xi5pjhjB+T9+PwD1J2iIiAgkKcXlSieS21yV1Ydw11TYhn4P+OIzL05tEUKdiyDY0ZQQeTVJW+hcD4+qBT4DzJ5f1xPe+W2P7//GzvyoijR7EK9NnRETkV/ZeFBGRL9y/qlU6bGLdNNggx4dlcH6UEx20w6nG+gcd6IaAIzOGr0sNrZaC16ecE/Zb4CDFx0UartRbBxpP/5fFB7SK8Ay6Dn+O6zuajhCzp29J0JdIZCY6w4TnQU7FNNbKWcQlymkdA66Pp/dMj2k8J4PfC/gHktMwCL4/JfxNypj3xOsrmvmIyOwiuB1rmg73tc+LHXDH9pO5VcUeNNQjGmxow/tdHbspuDRfvKXzYVp/m4iIfPii6hVRr+XaWqNb9ObOOa0f6kAtG+rlEOyfVKuF7eXgUI9F4DEz6OiJd6EB89ZdnQPUPeoMmwVaYCw4lwIPJymb3JB0bgduzST2XqIXTQ1fqNkIPDDsne06lOt6zsUX1XvnGy7eEBGRexOt/xff1LVXvK5zdPOmltHdxb6BudcZQlcG3DOZJeI2nIApZ2SR3sgyEqhbLSdkDqcsNHPsExHv/XXv/Wfx/30R+aKIPCciHxWRT+K0T4rI9z2mOhoMBoPBYDghnH/wf6vAO+KIOOfeIyLfKiK/LCJXvffX8dUNEbm65JqPi8jHRUT62cYDV9RgMBgMBsMx8HJ2X991zm2IyP8qIn/ee7/nWo+tvPfeucX3Ut77l0XkZRGR7eLy6eodg8FgMBhOGc7k67vOuY7oTciPee//AQ7fdM5d895fd85dE5Fbj7x2R+mL4BBj0ntDjRPurcHnJdc44NaaEjNGB0pccIjdFohp19B9+Nq+PtB546Zybv/JxjeISOMDMt7T67M9vb4dt+1P4pj/XDMQkqT2Cd/NrwvEgBG3rw4071tD1W7Y7GjjukWccdAlGbb6hboo63GMtonNJ7yCwA2J+RjkE+TgTDAezzLnOCiBf9D0R+CLYDXQp8Un/hTBL4jj7Bh/jrkOoWzkN0N/MYY8w4M29mOx2+JEkEtEOgE5MJQ0oT/HWKLj5TquP68d8Z4N1Q+5XOyLiMiFXEkD7+tC02CgY/bqvnKH6rrpD/IyWD96xNQ5iSg6Dw5GOscqXDumngQ4Ax3wFgryFkZoQ8IRaNJYA0ZEpLujZb39qupXvJ1rfR21TtiHWB9rh3FZLgmv1934M+dkwxGBdkPghjTR4DzMpdi3hR45oK0E3Q+OITkefg1ruosJN4uNltL+qHMnc8CcKsCfkE2UieMOW2R9P15XwWMGXIfLm0qqoAYQfWNugofx+TdU6+LeSLlq33RR9YoGeVPn7TW99rDQPSwDd6gImh4oM+j0ZFH7uKbJDSq3tG79db1wgr3v1o4umOCJdQ+8udY8yRMdlTBGHLPAT8IcI59llGgF8XxyR8I8iD2JOK/ohSQi0r+mffpNl7SvZmj4l25eFhGR4qu656/dgAbQIcaSnDzqyHAultREQUdVyYbNP66PepqQnrOME9L+zTplNwWrwEnemnEi8rdF5Ive+/++9dWnRORj+P/HRORnHn31DAaDwWAwvCP4h/i3Apzkich3isj/VkR+wzn3ORz7yyLyIyLyk865HxSR10Xk+x9LDQ0Gg8FgMJwIp9H07tgbEe/9v5T2c/cYH1ly3GAwGAwGw5OG92eXrPpI4ERjaoyfLfOWScE4XCumx/f5O/saXRq+pXHPL4LTcX5zGGdRxFoWjd4BY5fw/Sg17ny4q4HoDL4efcZpR/PVYxy0XIs5EYFfMWbcPWlv+to69CbuDzV2e7+v8eROBk5JT1OfITbcDomH+HpSRCL7sKzsOaR1Y2wcbWUcmjFTaoeIiMw29ORyE/XdgFZDD/oOWawhMBnrxeUaPFQO4WfDwCH5QIhhU2+C3AH2O+xRAr9BpNEUkITbQD2ElEvjSTdAfH2wBr8faBf8w3vfLCIil7qq8XKtu6vno2PITWpLEFBDIdVySUFeCXVk6DEj4JKEcD3/wzFhPD7Eq6l1EWt4iDRzsbOLGH0VH1/qQUSPlSSsXiX+P42/zZJGto/7eFbyGnJoOokOCPlPQUekq4W6LNETyeKIc6qZU7S2huKONmAKPobv4GRq1UCLo+6RI5FwJbCvPL+xIyIiH9pQrYtXuurjsou1fHgL2h2HysnZx5z/4KWGWkf/ot0L8C061PZ19mIOWphO3TyqC/uFdfZ9vYDz6t59EJ/uatld5rtAl6jRS+ECQbtTTkQV6wkFXSHugd14HnBtptoos03U+Wrjh/PceV1bN8dK3HnlhnJD/GvajsEOeW8YI8zBqsu6ok5B1wl1gY6IH5MIA3D+1MkCE2l+g1ySLtMeyVvfPyXSIk8zTOLdYDAYDIYzhDMXmjEYDAaDwXCKYDciBoPBYDAYVgV7IvIgWPYudqr1H4QHpPG4UFkHGVzX76Z4f//WNW3axpaSOshLqIok5slQHmPdiCsG7QeELF3iqcKYpohIeVm/XD83Qtb63cE9+Ns4eEfQjwGaDSGmS10EaBKQUzAqoR+RIzg80LTcgBZKK87K+Oec5wx5BFXSboZDk5j3Mi0U9tMMYebZFmLoeO+/3mpIGIMtjb0+u6U8CmorFAnB4PohfE0ONPY7Ok/ui7ab3jJ1MksL6AX07sffMzY+2W7NmzqOI1NPpdFi0LSENczkoo5B8YISUTbg8/Pq/QsiInJ48KzmA/7Gsxc1jk2OQI2OylvaL3XQT0l8OOjTgWsYbp7NwH3APOpta39OoZcx7uuE8fDIcMinmMR6E0chzHPyJ6h1A/5EMUq0GFL9kEDHwBjE9JRmXkErR0oKT7SEELNkLIp4TQZ/I3Cr0jpTRyOnjgh5Hsf4fbS5WoPbqD80WmYbmufsPNYi9osSvkZlP+apuFzPe26wIyIi373xBRER+Ya+al/UqPS/mbxbr78HvaMbynv4UusXg3yR9168KyIir2CfmHiK2uhn+j1Vfa1zxXXCfYRrH74uk/vwCxrDz+UwXvPcy9q8Dfr8sJ3UPEpoPUt/8Gr0W72pEycDX2U6xNxG3Tx4b+eu6Eb+vgt3Qh598K1+45ZqsJQ3dT8lX4+8tHIt4YRgry5GcRu6+9CA6raMsY5Cex4t8p05CvSekfrI0x4LvJycf/mU4FgdEYPBYDAYDIaTwDn3f3HO/bpz7nPOuZ91zj173DV2I2IwGAwGw1nCagXN/qr3/pu8998iIv9QRP7r4y54OkIzBoPBYDAYHglWyRHx3u+1Pq7LCW5v7EbEYDAYDIazhBULmjnn/jsR+c9EZFdEvvu481dzI7LMzG6OYLPcWIiCOxRcIoGLYkgjgTAZiIIp1ygYQ0F8a3ZFGU4U3aLRmAcBjqRMktguvedeyOu7n/2SHgNz9gsgNP6qe15ERA4OtkWkMRzzeySOakKSZWdL2XkkSB7OlH02nIJcVZF8BaJot2kU292Y3MXtDcRIcCiD6V1FwSqkU5L0aMhHsp5eT5Jq/W4loL7/qpLLfue5t0NZV7r7Udl34Ep3b6qku92ZkufGIGWS6UiyHQmBwRArEe4icbJ3PyaB0vzOtzjNJY7RnIskTMJjYnAeyDXt++cuKAl1DKOwgx0lymW3tZLsvzcOdWx2L2ubaFBYV62oZ9KOMDYcK4xdOdWyZhgrKdlgzGGIbNV9vZBj0unFZL3GkEzmQIJfEL3COTQrS8nOvmLeMWGUc4/rKG1bEMQCSZWfvW8mZh7KAnk7JwkxVuELpn4gIXLNuyCMiDlbghgJU7Mwp8u47lwr7e9S0bQhotZc94HUnhBmPQigX4VQ2Y31cyIi8kJHCaffvPWGiIi8eVGPf20KIbldnTc717dCXb6AQq5t6vqhKOPN83puCVNE9jlJmI24VtK++7EwoO9gD9tG5Te1Q4uu9tdkr3EwzECUL/vYs3IS7RORvJSLGcaMmxuIoj3t4M6GEq9JwL+8rqTw925qf2WtP55/e09F4fZ3YQLIMQK5toLppdueRmXMsG5GIAbXHe2H7oG2r9jFWh5pXfyUDp9H/B6dlMRQcQHELwc8aTzkE5FLzrnPtD6/7L1/OcrfuZ8TkWcWXPvD3vuf8d7/sIj8sHPuL4nInxWR/+aoAu2JiMFgMBgMBuKO9/6lo07w3n/PCfP6MRH5R3LMjYiRVQ0Gg8FgOCt4GKLqI4joOOc+0Pr4URH5reOusSciBoPBYDCcETg5wuvpyeBHnHMfFA3cvS4if+a4C578jUg7ZpY+j0m5IyGenEuKIFqUxGzJAejCEGm4qbFOt0Z3L02CydLzGif85ueV49AvNG76yl0V2dpZU15DB/HHb3tWz/uei18MdblQqHDXVycwuppqbLIiT4CxfYRgKUDEODzj0M+e19jwsxvKT7g9ArfiAMJoMMULcfp+0x8U5gomXYlAGXkljqJRGPkgUJTE9inCFQylKOR2XjN8/1WN6f57F18XEZF39e6GutAA7q3JeREReWN0PmrP/kRj3bv72q4gpDTHCUkEy7pxv5Er0N1BXatY6EikEaJqzO3AFaFIFvKmyd0VjEGGStzb1/F397UDurtxXcVpBnte25av6zypps3knuPnJOZ3jKdXVAXDoGT72lAKM/lkvQQuQJJf6D8crlv6TeQHZJfHUVmjdRgQXqCoXMzTaRTL2CiUBd4GOVr9e+hXfA5ckRni+XmTT+DRUMhtQ78bX/JR2eSvUGyNXAHG/iOhQ5FgakaRv8CbYv+39uhgClmzHfH+MWGfs6u5rrAuih0d/8++/i4REdmd6IT7vZdeFRGRNaiyvbil62MPZnf3x8oNyfeaLfhwrFyyL53TddGF4WLgBnWYglNDng6bzy0RlAeOYbmu102xdi88q/vLBy/c1jZAaPCV+1dCXW5NlfMyvaFlDW7nUT9xDIJsH4eSdaAJHgwsp+sQmBxof1wY6GBudPTzrbGuH4ociojcuK39IXs6N6sNLe38NX0p4/dc073nd228JiIiY6hSfv7wORER+eUbOiY7UxUj7O5qJbt7Oka9Q+1nt485S35HzU3zBD/mqfldymdc1Q3BCnTUCO/9f/ROr7HQjMFgMBgMhpXBQjMGg8FgMJwhrDg0845hNyIGg8FgMJwVPDqF1CeGp+NGJI2zheNJ5ChrYsHkWcxoeISWMC5K468cMcoK2gsC8yVyB37HMxon/YFn/o2IiFzIle/xa9saX7wzU3MqaoR8sHddRJp4pIjIrxy8V0REPndfdUOu72qcc3TQIiu0yqRWAePMrPu5vmpzvHdNtTkKBGRvH4CnwDg9ZSYG87Ot3CQfBTF58koQ0/VB9wH9BH5GMYaBGvkF6GrqCFCzQgrNl+Zurw01lvzlw8uhDnfHWt+7iMEeQv+gnMWx/Bp1yxEDr1BW4IIELox+nm6iToh5s+5d6PjRJK29CAN3Zg0aCuRfgJdBEz+BOVcX+gZ30OdjaBH09tlfKIL0BNTBg88RYuZFUwmfU8ci4ZcA1PvwqJPrMhCPrA7jsoN3XhKPT83jaBoYdDlExF/QTvp3nrshIiLnuhqrP5jpGO1D42UE48Gyhgkc+Bw1PnP8D4d63ew2CUv6PXktOXQ2chfP3XZ9Od7UgZk+Q6M0TScjnbSduzBMQ9nTHa1jcU4nhpvR7Qxj0Y3nUxX6o6lDo8VDHR39xP2DnAhyr2abiV4PeCjVXSWAvbKvGkLX97VO3/HsayIi8oE1NbTLruj5vwx9muF0I9SlQ52hkeY13dRzPPYs12Ud9PzJATU+9DPnetANIR8OvKBLzyk35MPQPXpPX/eZ69NzIiJyZ9zU5dam8jPGF7SzBuj7lKfEOUqeStgWg/YLeCoH+sX9TPeE4QQ6TV7LnoxhdLnX7KsZ9iQPbsgH36/8vD/zwi+KiMj3DLT+PZiK/sJI5yA5Ih0YEta4fnQJJqKoSz7UjuyAG+KoKwI+k7T4TOG3yC8hX/C3id9HvKUnrSXiVy5o9k7xdNyIGAwGg8FgeCRYpcT7g8DIqgaDwWAwGFYGeyJiMBgMBsNZgoVmjoCXRou/jeCRkXTeAo+ZkBVjk4j3Nu/3I0WMkvoGdT+PymLcnn4ut0vldVwulHDwjf23RERkp6NcgXswLfmlg/eLiMi/vvNiqMvXbuh76n5X88qg71DQ/4USJojxB78GHkc8/e4I8dMNzaeD9/tn8GTJEROmxgHj2CLzXIjQp45eGbHmADVNghZBh5odMecmjTtn0D149atXNa3UbiA/bOlmoN2Br4OxcPT3QR0deDuBpwINGPrbMKZPTgX1VqZXYhOV2T3NuH8P+Rw237GPZ3U8FmH+0F4D/XQXmi3Du9ByuKV5s8+pM8F+a8K/iGezyr1mLmcQo8hDv0TVD+2v0Lf1GrxTwBWhvoZL9UiCNkrslxI8WGRef4R6OBd72knbnVFUlxLEgklJ3xu9eDLrROflmdat09HKjHuoc8Z+SPon4bUs+i71R2JdBel0pmNSwUOFc9b3ta5ZXzcD3wGfIY/bH3R7Wh5NQWMFa5N9Sp+a1IOHczHNM+jT3IfPyc45ERH5hdHvEBGR73xRdUWe6++IiMh7L6quyBdGjb+LP1SOQ0ENlil9rjhZURb5F8G/J24neWKzbeiGPK9lfsczX9U69O6LiMiXRrqGP3dXuW03djZDXQRzMegQkQKRpQMZ79lh/rNfSS3bQwY7SpSbCfxjsIf1ZtyHmvwmV7Wh3/zBr4mIyH/x/M+JiMg3dpTr8tvYu3/xUIU8f+72h0RE5JXrqodS39P50LsLrxlyyabUqUnqXpMQRCOh1oRMtWrmfquO8Kl50vDza+lphz0RMRgMBoPhLOGUPRExjojBYDAYDIaVwZ6IGAwGg8FwlnC6Hog8ZTciR3BCRKTxAJBWjD99h54+Cwkvo7MbP/xhvP2t+pKIiPyt4XeJiMjVLdUL2YQHAn1j3rqr79WXd+FTcLOJGW7uIM9UqyOmMjT+FAjLM0ZJD4TrN9ST5fMdJRFsd/XEHO/Dz8BrIfeiu9vMNmojTLcY04ZnyJTx5qQu7Mo5nxL8p6Tehn6kr0v/NvoRaYH+zUctTsQsbi/jldRRmZynB4bm0dmnHoieWAzBN1in7wnyoc4GdBUuXlDNl8kVHYv9Gxrj7t1spnXnIG5vaHfgV+Dzvk6g0a6mPWg69O6DGwI/imIMjgj7E0HxELeHNkrb34V93xnGn8lPIS8heMqQGwCeCXk/4y4JCvRx0Y/dDrVgkM84XkdZax5OoYvyr0Q5Th7chtkQFQafpTiAz81BnDfnS4nTa2i6kOlQpDwo8hqomdPWZqDkAmL2PXgGVW9r5jNoANXbNLTRC8qBHi+hj1L3wBHpoRbQEeFcZvt9iOO3dFUSj5RmjZITFF9CHlMJ/aLA05lxnjDV47M95Zj9s33lL7znRdUTubamhIX3QcdIRORVp3vR5EYfeWAshgnXDHo5XGcVPaywvqrndBDe88w9ERH5d8+r/sYg1wv+1f33iYjIb1xXzZPJLXg+7Td75GBPy+ztxrwtzuuwnjjvy/g4tX7KLWigYF717tHvBW3BemIbDp8PVZBn3608mg9f+m0REdmvtYF//d43iojIz76lfXr7Dd03u/DDWQdXjB5UvT2tQ3cf6V3sq/dBJoN+SOB15MmkWIRlv1XkRvG3yjlZxV2BKasaDAaDwWBYHexGxGAwGAwGw0rgZaXuuw8CI6saDAaDwWBYGZ7sExEnGltb9o61OyImJxI/bsKp1JYI/hGOmhT4iFAdY7cN50HTYgi9hHuqI/JGoWmIXyPePkBMs7uHmPhBc8tJDkOd+N+kPAS+90++BePT1L3Ibmug9Ktd9W955oIW2gNn5HADMXJwJ/IWF4AxQeoesGyeEzRHEn8SxryLMTU9wM9gqLNDjkmcT+jXA7YpVEXymY/yCP41WcxtIE+HdWEcOtQB5+drKAv9NIEfRbmt/fCe86qLcK+vlbixtRXqMtmBtssUfBTwcUI7OB8OyEdBvwzZAewHTcsgesE2xJyCwMdoTVWON/uI84G+JcErBCk5HuRnEOQeVeSKJJIOnE8cm6wE52bUiv2/Da2S28pd6KKMzd3UawXtmnBekPPB9QZ9EYwBtU7Y1iK0WSuTIXVZe86yXeBVYHwHoE2QIzGB1gbX+pxXD71CZiX6g7wd6vWw7vO6KuTp0M8o9ZjpHMaaPx2sF+rtzCHwfbBPgCvSBffo7VvKy/jae1R76Nql3XDpVaz32+CETe4rV4QaPfQc4sAH3g36dLap15E79f4t9WKhNsxn76nmz6tvKxclu67593fR/8OmGR3sc4N7mmdvRxfGbE3nTwEPIerpkLcWfI/gtdTZ1klPj6KJ0w73CQ9jel47eP2F/VCH33FOJ8IXD6+JiMj/9/o3iYjIV1H/4m3d9LduJpwQcEHINSuG+LyvdSE3xO9jwpUkuhzx+/NOfptEWr9x/olTRJx444gYDAaDwWBYIexGxGAwGAwGw8pwym5EjuWIOOd+1Dl3yzn3m61jF5xzn3bOfQnp+cdbTYPBYDAYDMeCZNUH/bcCnOSJyN8Rkf9JRP7n1rFPiMjPe+9/xDn3CXz+oROXepxeyAnu5hijnVzGu+EX9F1wemOM9zQWydh/Zy/mAAQOCOP2VRwjLhAb7jDOOI75C+R7iDQ8iqqLuCl9WpAGbYUx+QTgeqzBlwO3gyx7dkdjn7ec6mJsrGsG+aZWfnyF5TX3kfTUYWybHIegORC0FCTqB/I5yHMhr4Bx9nyK+LuP6yo+TqkbIdLE5Bn7D/1BPxz6A/Xi86gxUXepxRFzKFjnLrwjdsDnKRBTv7qh8eWNZxrCyt0t5ULs7CnRpKzg50HeROJzEngEOD49p+lsA3Xuxys1H1GvhZwI9FvLwoV5srOyRF+GZZEDQe5EH7ym3r1YkwNNkHIQ81KaOaprgv3ZLo/1IneIea/dRRz9QNN8hnHH+Gezti6CSAWugMOguoqmK2wztV84vxbscORygG/Ca1hHcqfYp9TISflOwdemRB2DjohEaRjT7aYK42dKlA1ezl7Mx+DaJf8g6BeRM0RfIHikzNAPrPPgDrgWd+DNg3yH+6qJ8fa7Gq+Z9Wd1/l7eVo5Huamkjfv7OnenN/WaDnxbquAThTqBO1ShLV/eUy7F3UO9fv+WTuLedb2grzIdcxwmEZHufswNyfemaD/m1ETzSLljXKM8zs7f3NBBnfb1xOklvb6AV9GzmzrY5/rNwnltX3k0b9zWv3Pdm9r+9duYu/exRx+Cx0ReE/bowA3CnubAU5IpKkmtD3rK1NwcWfdjfqfaWHauX9Ev+ynDsU9EvPf/XETuJYc/KiKfxP8/KSLf92irZTAYDAaD4UHgvH/gf6vAg3JErnrvr+P/N0Tk6rITnXMfF5GPi4j0s40HLM5gMBgMBsOJcMo4Ig9NVvXee+fc0lZ7718WkZdFRLY7l09X7xgMBoPBcKrgv25uRG4656557687566JyK0TX9mOpZ20s1IPAGl4B9mGxvs+cFXfOd+AR8wePGJuHuhTmHs3NDBc3dYLyakIehvUT0C8tEuOyAFj5ORKsPymHbM1aioglr2d6ENQJ6Rk3Blx9k4c42UMuwCvpYJ5xM55mtcgTj3Q2OY0b/FUeloYdSBCjBYIOisAu7JO+BmMATPuzrSJiaMq9L2gl02b90DOQzfWVZlB3mO6rX1Z98kJ0LpPNmOuDetSNWF0EWk0YvKvab/cG2ssef9ZbeSlrcNw7kZP50MFnYv769CkgJ5Io7mhKfUPyCOontXrn7+qWiXPrO9Fdbk90vl1a0/T4a6OWbbTLK2g5QLeAP1LsiSuLhjP4NMBrxVSgTgng6cMdB/IrWBcP4f2B7k2bcsMzsmUP5FyfYLuB9MaXIgClaEmDuLxnU48f4I/UEVzmjrKR0Qko+8MBFK4HqiXkqN91O4JujNlrMcTyvDx51RHhNoVk0uNZ9Xld+u4bve1Uw+mOj92D3Uc9+/ohO9fh34GuFfTc+iwczp43Z5OoCn8bnwG7gw6vwu5EM63tRvYX/Ybz6rhfZ10b7+ge9d56IFsgSN2ZxMePFtoxxDjSw7VWD9zr7uH/aazAw+WHdYlnkdEm+cVvILI7QmyH+SEJJwoctEwp+nRdLip/Xh4UT+vr+mJvY72Vw7/nym4Na/euRjqMELfD97Svl+7Hs9zatyEvQp1LAfkHMXty8FrkUNMMOow5Xl84km4IY4dEs+9APJM6mPZDwZ5cGXVT4nIx/D/j4nIzzya6hgMBoPBYHhgeNEbowf9twIc+0TEOff3ROTDInLJOfemiPw3IvIjIvKTzrkfFJHXReT7H2clDQaDwWAwnBCn7GWdY29EvPd/aslXH3nEdTEYDAaDwfCQMIn34+B9E4NjetJOW3BePYJXDGKMmwONQT7T11j+ua4SCn6j1PMOR5u4MuYIsC7kVpTgVJDXUVMjBOHE6WYT1Roj/jm+iNj+VqJNQM4HYpdlz7WrICVeJqKORIa4K7UM6hlIEkk8n3wGEZEa/6d3RoiPo/4h6kmOS/CgIW+FehhIGS+mRMOAnhD0gYFmQR+6C1ttIoKL2jPbAmdhW+Pp69sanOZwjrx2QDGEPgLaTY2TwDnBmJAzQq7I+te0DtNdHdu3L66FqmQXdD50u7F4R8qRIAdmBs+L/kXN/AOXVGzhhXXlEgxybUMHE+dKT+P4F/ta9qsdjXHfrznPRLjMmrLiuHpOnRloklSbmvf4GU3Zx6PDxT455Jx0huRYJDHu1kf2IePn5BeQz1MOYj2QLHiCUEcDc7IX83iyhI+RQysnm4JjAh2S9gbJY+QnkVvFsWCZKX+Lfi/Uj2AZy8A1Tl6Z7zV/Lp6HbsW7NnR8OzBCOjink+yLffVnuVOfExGR7j1t9ww8p80tvf7d8DuaYJ95fUO1Lw66qmPTuws9I47Vvraht9fUheM3uaMckb1rOljVNZ0oQS5FaRcCCllrHoATcqgd2NUqSXef/Yh1iPlRQo+GPjvc69oIvApgToeIfCVo2GSJV8/am9pf5T1omawNonwCb2oP2iAtCtbGAcc75j4RYc5ifYT9oUdNJfL8qIEEbRfMu4xzcaz96+k5w+NtT7Q57at6+blPA07ZjYgxaQwGg8FgMKwM5jVjMBgMBsNZgZen7wnNMbAbEYPBYDAYzgy+fnREHgxeRKpq/h1sfpZjOq9qYsEF4sS9G9qEV/w1ERH56rZqSlw5p8HYXhFzA+gJQZ8TalnwdflynXwOxGtnGuOcQgNjckXr0LtyEPJ85pwGNtc7GiAt8e74vZHGRe/tapz4cE2DmDXeW6d2Bf1ysvMaqyzH2qZsF5on8Bzp34l1ANraHTPoZIwvxBomc14zSagzZ7wdeimdPQ3+ZlPwXBB4LQdal+5zh1GbB4WeP6ubd/FrFJKBFMG4e5FpGWuF9tPbB6p38EYPPjDgJzDu7MABYWycHivVGto/Q79AZ6IPI4L6a03Ecba1hn7BOYiDd3fwPfg5s4va3ne/R/Vo3rWpme1NNab9mZsviIjI/iG8agpty/Z6y1RGRKYliQhNRzMOXiCGHzgh4MAUOHftBrgSQ82jXNcyyLUJ/B9qnwQ/jzi+HjRAqN3REmIp1+I1NiEnCn3vMI7eKTeA84gxfZ8Ec8kVCOtxl/MJ8wfzKBui0a0NMitZhq4PcqxmW5qOL6HfRuw/XhfXIeTJ/QF+Uzn6gZwZ8qA6d5tt75VCOSCvDtSXhb5OlzZ0noc/LDuxlgk9rMbgUHDOX+zrdfll7fu3wE3a29Q2lrdxPnk8LfOMLjgR/R2t99otLePgrs652Qb6FnyKwe2Yl8NIO5ab9O+Rz6WfJ+f0P4fPoZ+vgu80wBrea+ZJ7ya4d+uabrzFsaiiunIuBp8jzGl6fnGOk9dD/ZGgQ4QxYr6u5UnkO9BLOa99dnBNJ+PoKvbmC2jflu4neQ954PryAFwZtKVcox6TjsXAcx/BhJphMnMetZ8qcD0f91tVxXwlX1WruSmwGxGDwWAwGAwrwym7ETGyqsFgMBgMhpXBnogYDAaDwXBWYGTVY+BEdf2Dfsg71BNphd/4/v3a2xo37N1F7LWj6Y1ryg3In1fDCupIZJsaB4R0hWxuxjHh4Uyvn8y0a+iFcAGeJeSBZC2fP3JC9iYay6VvAs/ZRNx5ryIHADwD6j50wTfY1LquX9Q63tnSWObotrbFVagTeB+de00dijvUDcE78xhZ+pF0hoin5rF/SzGKY/r5SMsOsf1+zBUgJ+L9W3dERGQAkYed2SDUZVhq5vvw+7k/1u/Ypxs9vaYil2SgZc02wY2ZMaav+TGePoGGibuqHTC8ihjyHS1vcJv8j6ZfqNdAPZWgjwHdC3rnbILzc2VtX0RE7ox1gvz2W2os3fmKtmHtLuuk6b3zqhcyO4fY9ibiza35UXf4f+ob6CfqIJD7wuP9O/h8m/oH+jl4FxVxSvhE64BVYP4ijSbF9jmdGOQ2cGzSJcilmefoe2RalvAHGsJT5b6OQf8mNR2UW7SOOH/3Ps4b0VhHRDgXe3rODDH/cy/siEgzT/bHmtfODe3rck3zIm9ptqEErsEW1tU+dCE41tDP6IF7sf5mo41RYe44r+lkQyfEl9+j49/doCFQzC3j2i1f1zX6+T2tY3dLy95Y08m7hjb0ntF5sY86HpzHXnGnGcTePe0H+rRQN6V3P9YXIg+nexDrgnDNcylS62gCfpS/pmv3mUtqfLPd0zpW2L/e7G6Hugy9touZ+kzHe3A39i9y8dSWGbSSRpehCUPKFDRNBtiz6FFUjOllBG7JdjM2+8/pxXvv08/5i7pGX7i4o/2QY9/AfnsIvaUR5vK+046YgRs0OdDzOki7mLNZQbMeauikfBBpecdQxGmJx0xKwktJVU8EvqnfKYE9ETEYDAaD4SzBOCIGg8FgMBgMJ4M9ETEYDAaD4azAOCIngHMn/+6Ix0vkNKzfokcMtTU0PbyN+OKBxnoPL9LXAnn24eOB+Cj5HBcHGjvPoLfAGPoImgd3hho7vbOzEepS39P4MLUF+Jypgg4EOSACngKlTYLXyK7GJvfWNT597oqKC1yDVscN1G1Y6Pd1V9tW9RrtjmKI+mJEqSVAbw9yQ0LcvGSaahHE4Pms661bGkf+FweIv061wPqgie3m9EShn804Htfdi+iXCxo/92PEbFFGEXQ2NK0xZh7x+SsXtF/oE3L/Ga3LrTvKFRje6TV1GcW+E4ztU9uD/I0p2vFbd66IiMj+PXiEgE+w8Sa1SsCVQHeV1+kzpG0YXoOfUEuvg9oTjKNzjMjdmG7HPh39e+AC3Il1JegfVINbMoPmTZ3TYwTeK9BiCF4ureeeDn252dfOzqHtcjCBXgz0H8h78pgAwVKjjh+iFl0tYwZuzBS6GuMheT4ktKD/i+b6jDon+C7DPBlP0efgV1xY1zU5uVjge/pEUY+HJBrwVNAfnNNMqVWxfrOpA8eR3jj09dkZgW/wDHhZXDfUhAH3qruLL65TH0TTww3dH+5j3yku6qReX9N+7z+jk3t0vsWJONAxONyBZs8u/Z/YX5rWgSuEeUDNo3OYy9Al6mxrWZe2lf9GTkiNiXj7UOs4Qn+PDpt146bxmqU/UtWN9wnuDxW+n21qevg8+CvrFfKGBgi8egp8dhX4PuBqjS833Ib8mnbye7gfrim3pcaEvj3S+t/YV+7Qwb4uKO5F+VDP6++QexP7+5APJ1XCp+Bk9y1SYqojErSwEp+jnIPDBZOK3jwhnLLQjD0RMRgMBoPhLMFuRAwGg8FgMKwGp0/i3ciqBoPBYDAYVgZ7ImIwGAwGw1mBF5HadESWw4sSgxLhpbnHSCnjtyjmjmcQwimGEKnZV+JRvj/GZ4gbQWRsBDJhCbEfEj7LvpLSXusr0bERn1IEcyYQ6YpDTbdbYmLdPRJlY9GwEsS32XpDKhUR6e7H5LnOPoiOEP56rb4oIo3w0HmIiG0OlHx2H0Zuu+cbparufZLAaE6lx2drJGUK6qYpiaC9nVhki0ZpOVOYUq1fBxGy7OF6TbfR9u5+M/HZLgpJkTBLcu3BNe374TXUH125dkP/s34DZlxDJXqNLusYHYJ0tnOg7Sf57tq6kld7uZa7u9X0ywFM6mYwkqtABOyAWAw9NinfUHJqDTLiNgzo1m5qG/p3tU4F6kSCpQfBurvPfoFJ4nbzsJHkQpJwOaegnSfldmKUVZDIByI2yJidEU3M4jErUsKxj1Oa7ImIuJtazzcOLutniseBYEwyJudHMLWbRFUMRNt6HXMZAnlcJ2E+kYhakpDakPdIpu3d02ObX9W6jQ50Lb55Xsckv6SFU0wtGFfSo42E6inbS8PGpD+AQWsfcZij+X7cwGymdTjYy9FerisfpzR7m5AMrW0ikXS6rfPt4Jq25eBZGDxe0Ym3ttUMzvlLKqY3PQdSLgiz1b5OlA7WOAX/AnkV84jmiP2LuvgvbNK4T89/a1eJ5od3tQ40/+OY9VvTkARZEjzXb2Gv2gM5eUvrMt6GSSKme8l5cU4nzqXL+6iDHj8cYYwhiFd0NL9NCMC9uN6YiW50IFwIgcRX7iuR/M59Jaf6W5pXDyJrm7pdNvsrxqZzqB3Vu6P5dW7qiX5f+8fP0Ng83qcX4jihML5wQU5rnjcufE8ST0Foxjn3X4rI/11ELnvv7xx1rj0RMRgMBoPhLGHFNyLOuRdE5A+KyNdOcr5xRAwGg8FgODPw+gjqQf89Gvw1EfmLEp53Hw17ImIwGAwGg4G45Jz7TOvzy977l096sXPuoyLylvf+19xRumEtrMD0LlvurHXc46S8JURUxWJIvJNzY8TywXHYwPHBLZo3oSgqFCUdRTGoDNyIILrEeDNIQFnbvKskkQQiUJ08Susey6bAF4K7FU2tNLDa24Ww0B3litx4N3gHVzV+em5LBX4ubGlsc9yfhirsnwMXYoqyZ2woTuigHRQHw/eHu1q30U1N127o9f0dLbsY0TAsThkL7+xrHdyk6Q83YfA6jqf6AUymphAL2y+iMvq3tJ3ZjrZPwJWoc43Xb31Frx8eaIz4K+dV0Ki8rHXow6Bs0Gv6ZQOGg7M+eDhdbdcEZn6923p8/U2IgoHzMrgH/sJ9zKcDmB1y3CkUl0EsKUwjzX+228xVCk4xVhxEoHBREI9CXL2ECeAuuAL774kFvIIKl+d8Qsk72ibO9e4hOSWhKrLxmn4owAsI47oLjtUkFk8L64ziaJjj5YaOxRgiY5NzMLsDX4Fx+s4+uFy7Gp/PDluEFcyPLqLHF76oafka+VxaR/IqxpfAR0J3dfaRHuD4nrYhw1hx/RGu0rq0j9Lc0U0wj6eaxxoeKHd36EiYCnxhTDy5L2jnXeU4OOwJg472z+arMLk7B9Gya9p/B883ImK7VyECtqV1KvrgNlxQzsd0oJ27vwnRuPvkOcUOdOO7un9cv6H9VuzHZnqXbyVCeRQ/bIka5uDOFFjf+X1dk24GM8wXlMdGkzsKnpFz5Q7AA9vSuq71tF831xKyEeATHouIyN6etiN7W/tuDeKBF++i/ve14l3MLZodOs4xjA3Hwu/qhKn2NfUlxh4GjdkAY01O4jsJb/B3KP1dyXN54iQRL+IfzvTujvf+paNOcM79nIg8s+CrHxaRvywaljkx7ImIwWAwGAxnCY9Z4t17/z2Ljjvn/l0ReVFE+DTkeRH5rHPu2733N5blZzciBoPBYDCcJayIrOq9/w0RucLPzrnXROSl496aMbKqwWAwGAyGleEJPxFxahZEoyA+Pkpvh6gzwu8Z72q9580weYhl8w6QsbmSsWmNr+b7cUxX0vP5GdcxFrr0zrJtlEQuBPgCDtwGxtOzThF9HzglBbU/YOB3XaJ0803wNy5qrHS8CQ2CdWQzaOkh8L31Av3B46gatSmowSDkiqBLqQtB86lqhLrP4nfx2d8ZeCDZIWK+0zZHhG51KByxeTfWAHKPGiUjxIDxOTvQ2C7j9GwdOTWb4HGs34g5F7M17afppsbdZxtNTHZyAdU7hzLQ9dTLIL+iu6Olrd1B3Bl8gzzlhnBeFDH/IJuCM3NInYxmaVEPhLwkjkmHWhTQwpmc03QGEzyaJtZrnF/x2PFzBdNAjjG5I1UXHIIWnWlwB7yMA6Q74MDsoe/HNAKLtU1c8qg3h7ZFcaATp7uncfZqEM8bzm3yM9q8IfJPZKR93L2tH7vJmlx/C7yeCyijRx0NckPAqQBXgHwPySA0wnVHbZNZ07bGGI9zFfNkT/lYHfZHyhHZGqAueXx9ch7rko+1boM7+v3gVa3ThY1G82Z2EVyYC9q3wysw0HsOJ2xyz8LaJZUBVRzciLkg/ftap84BdHkwp4t74HsMW3ydFNyD0S4/QyFd9mlUlaDh0gd/IwMHrfqKcj4mgZsXF0O9o+IQ14+bebYxJocO+h/39eRsHxeNtP6e++lkEte9Q3EbFA7uR7ahG6gn/4ljRm5IalzXRl3NH2sjuENijMryyT+d8P6pETTz3r/nJOdZaMZgMBgMhrOEp0DQ7J3AbkQMBoPBYDhD8E/JE5GTwm5EDAaDwWA4M/D2RORo+FirP0veuU55G+Gd7HlOLXkIBU89QHwYsdjA4SA/IcSJSZpgcDNfeNwnAxniiEijO840lph46YSymTJe3kGcnZ4g4ApQ26B3Ex4K1CVBfwRdkk7TL4xVkxdA/kTwRMnIG0Dag+YJw6kj+n4gnnxPY+SBM0Aw3hr0JRZohrDv2N50fGeM6SPWS17OSMfOT6lNommB64vdYmE+ARl1XJppXW1Dv2Fb+zroxyToHGhe3VuInx9o+9vcl0VlkQ8kGLvsEL4gvU44te7HOjISQv36n00cr7vQz1gDZ2SbehqItyPkTY4EvVaC5wzi7Gu3tT/7GEs3a/MyMNfAeXEHOgZzvJ5wAbkCCWcE8fkC1+f3URn2Rzo/pvPckxCjx5wihyjlJ5CvUdxOtqtEJ4LzwaOsbJpogCzanFMOBNvHfaNK+oO6IegnB20c8k4C74L9uGxfYf7wOxER6d2FZ1JX587mpvJQyB2hbw2RYZ8I+jTot95d6GrcOYjrlJRNHY20bXpR0mfcR/B1Ds+lYqR15RzMJ/qfdfhGBf7XCFyhGfWfYn2aMPbtNZ3WF9f4oB2Vcg3b2h3tbJLvoRsSeEv8fUk90Nqf6+S3KemXufMWcKIMy2FPRAwGg8FgOCvw8th1RB41Hur1Xefc9zrnfts592Xn3CceVaUMBoPBYDA8IHz94P9WgAd+IuKcy0Xkr4vIHxCRN0XkV5xzn/Lef+FRVc5gMBgMBsPJ4aUVvjoleJjQzLeLyJe996+KiDjnflxEPioix9+IuGXcjzo+zrszpnXLa4Y6FmnMLuUlMGadmu8wdpceZ9z6GLJP4H2088qTspblEd5Xj+tK35JQowk1LGKeR8ayW/l3GC8P2it+7pyoLPZLEb8zH2L6TJO4a8gtzb9eEF9OP2dL+iX53lGrIOSN/iUXgG1NeEDMx7V4HRni48XtuL2+gEcGPGcCjyJwGZK/DFKOUahzMofJd2lfmujMBASOQ9y+Ls5fC1ypmGNEnkvqacQxyIbgf7AtLc2TmtyVtB35kjg5kY5pMjYhxp8nbayStrfqMlcS5xg5I+l4c04yz5TjwPWT1mFZG0REwCcK16R8g3StLZsXWXJdnXzPucnriiO2Xvq57Cp/pAfdjC76LvQLORMb0Bc6p5ySnOO/TAupYJsTUY9FfIb0WlxDbkc+wd7EaQSuGbV/gpfPOKkT12yZ8FUW/YByX0i5g+k4u3ivmhvvdG6zH8lB4dgu0gqZ+83ivF5yHlEdozvyOOATLuYpwMOEZp4TkTdan9/EMYPBYDAYDIYT4bGTVZ1zHxeRj4uI9LONx12cwWAwGAxf1/h6Cs28JSIvtD4/j2MRvPcvi8jLIiLbncunq3cMBoPBYDhtOGWhGZfqZZz4QucKEXlFRD4iegPyKyLyn3jvP3/ENbdF5FBEjnTiO0W4JGenLSJnqz1nqS0iZ6s91panF2epPU9LW97tvb/8pApzzv0T0bY/KO5477/3UdXnJHjgGxEREefcHxGR/0FEchH5Ue/9f3eCaz7jvX/pgQt9inCW2iJyttpzltoicrbaY215enGW2nOW2nLW8VAcEe/9PxKRf/SI6mIwGAwGg+HrDA8laGYwGAwGg8HwMFjFjcjLKyjzceEstUXkbLXnLLVF5Gy1x9ry9OIstecsteVM46E4IgaDwWAwGAwPAwvNGAwGg8FgWBme2I3IaTfIc8694Jz7BefcF5xzn3fO/Tkc/2+dc2855z6Hf39k1XU9CZxzrznnfgN1/gyOXXDOfdo59yWk51ddz5PAOffBVv9/zjm355z786dlbJxzP+qcu+Wc+83WsYVj4RT/T6yjX3fOfdvqar4YS9rzV51zv4U6/7Rz7hyOv8c5N2qN0d9cWcUXYElbls4r59xfwtj8tnPuD62m1ouxpC0/0WrHa865z+H4Uz0uIkfuyad27Xzdwnv/2P+Jvt77FRF5r4h0ReTXROQbn0TZj7AN10Tk2/D/TVENlW8Ukf9WRP6rVdfvAdrzmohcSo7930TkE/j/J0Tkr6y6ng/QrlxEbojIu0/L2IjI7xORbxOR3zxuLETkj4jIPxa1Lfk9IvLLq67/CdvzB0WkwP//Sqs972mf97T9W9KWhfMK+8GviUhPRF7Enpevug1HtSX5/v8hIv/1aRgX1HHZnnxq187X678n9UQkGOR576ciQoO8UwPv/XXv/Wfx/30R+aKcPW+dj4rIJ/H/T4rI962uKg+Mj4jIV7z3r6+6IieF9/6fi8i95PCysfioiPzPXvFLInLOOXftiVT0hFjUHu/9z3rv6VL3S6JKzE89lozNMnxURH7cez/x3n9VRL4suvc9FTiqLU4d5b5fRP7eE63UQ+CIPfnUrp2vVzypG5EzZZDnnHuPiHyriPwyDv1ZPOr70dMSzhA10v1Z59yvOvUDEhG56r2/jv/fEJGrq6naQ+EHJN5MT+PYiCwfi7Owlv5z0b9MiRedc//WOfeLzrl/f1WVeodYNK9O89j8+yJy03v/pdaxUzMuyZ58ltfOmYSRVd8hnHMbIvK/isif997vicjfEJH3ici3iMh10cebpwHf5b3/NhH5wyLyf3TO/b72l957L3qzcmrgnOuKyB8Tkb+PQ6d1bCKcxrFYBufcD4tIKSI/hkPXReRd3vtvFZG/ICJ/1zm3tar6nRBnYl4l+FMS38CfmnFZsCcHnKW1c5bxpG5ETmSQ97TDOdcRnfA/5r3/ByIi3vub3vvKe1+LyP9LnqJHsUfBe/8W0lsi8tOi9b7JR5VIb62uhg+EPywin/Xe3xQ5vWMDLBuLU7uWnHN/WkT+qIj8p/iBEIQx7uL/vyrKq/gdK6vkCXDEvDqVY+PUN+xPiMhP8NhpGZdFe7KcwbVz1vGkbkR+RUQ+4Jx7EX+1/oCIfOoJlf1IgBjq3xaRL3rv//vW8XaM8Y+LyG+m1z5tcM6tO+c2+X9RIuFvio7Jx3Dax0TkZ1ZTwwdG9FfdaRybFpaNxadE5D/DGwC/R0R2W4+hn1o4575XRP6iiPwx7/2wdfyycy7H/98rIh8QkVdXU8uT4Yh59SkR+QHnXM8596JoW/7Nk67fA+B7ROS3vPdv8sBpGJdle7KcsbXzdYEnxYoVZSy/Inpn/cOrZuk+QP2/S/QR36+LyOfw74+IyP8iIr+B458SkWurrusJ2vJeUXb/r4nI5zkeInJRRH5eRL4kIj8nIhdWXdd30KZ1EbkrItutY6dibERvnq6LyEw0bv2Dy8ZClPH/17GOfkNEXlp1/U/Yni+Lxue5dv4mzv2PMAc/JyKfFZH/zarrf4K2LJ1XIvLDGJvfFpE/vOr6H9cWHP87IvJnknOf6nFBHZftyad27Xy9/jNlVYPBYDAYDCuDkVUNBoPBYDCsDHYjYjAYDAaDYWWwGxGDwWAwGAwrg92IGAwGg8FgWBnsRsRgMBgMBsPKYDciBoPBYDAYVga7ETEYDAaDwbAy2I2IwWAwGAyGleH/Dy26/0LeBaJ4AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# plt.plot(syn_audio)\\n\",\n \"Tts_handel.feats_handle.melspec_plot(mel_pred)\\n\",\n \"ipd.Audio(syn_audio, rate=16000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"95d06cd7\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"source\": [\n \"# 2. *Arbitrary Text\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c1c18921\",\n \"metadata\": {},\n \"source\": [\n \"- Because the silence at both ends of the reference audio may cause pronunciation errors at the beginning and end of the generated speech. \\n\",\n \"- So we add some characters at both ends of the input text as the new input of the synthesizer. \\n\",\n \"- After the synthesis is completed, the audio of the added character is removed from the speech according to their phoneme frame length.\\n\",\n \"- Inserting #3 marks into text is regarded as punctuation, and synthetic speech can produce pause.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"6de3e38d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Inserting #3 marks into text is regarded as punctuation, and synthetic speech can produce pause.\\n\",\n \"a_text = \\\"一句话#3风格迁移#3语音合成系统\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"bd749ed6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['yi1', 'ju4', 'hua4', 'feng1', 'ge2', 'qian1', 'yi2', 'yu3', 'yin1', 'he2', 'cheng2', 'xi4', 'tong3']\\n\",\n \"phoneme seq: sil ^ i1 j v4 h ua4 #3 f eng1 g e2 q ian1 ^ i2 #3 ^ v3 ^ in1 h e2 ch eng2 x i4 t ong3 sil\\n\",\n \"The statistics of the reference speech duration is calculated using the Style_Encoder.\\n\",\n \"Duration statistics equal to (7.955729166666667, 2.4811523765991477, 9.546875, 4.652160706123402)\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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1omqYEv7WQVWdCGJNMyqpAQWJc2hz0Qlzr6pSY2lexDt2EDmFybS7otBmS7KStQiRUkmxGvMvJqsXGwZO0zLqS2KciWLFurFdODoziUCnxb8iFzAX0VCDuMUaH7gKxtXwu74LOe5LBlRSeknFrGbdRXaLIDBq3M03L3SlohvScjA8a6w7AgaKFWSY5STfjvgM79o/HmOrlIn9KblYSbipcWOxQlvKexFIk5zayVtZHss73UiL6sB00yCNRnoTKflTCK1k12wOy9rL8Fg7dJPyt8z4TAuuWvDqaXTKf3NxRRQFl7vY6XgNifLd7Pl5EFPZusN9nc0vHSS5hn7VFJPtiIN4W2p4ulqINJxarsGmogOIQW78ZOyZT/jt2CaTV1/GJiJFVDQaDwWAwXBosNGMwGAwGwxWBh0P3Ep8xOOc+AeA/B3Ab4QHM57z3f/l58rQbEYPBYDAYrhBeMkekBfBnvfc/55w7APBPnHM/5b3/lWfN8BXfiPgQM8vjZsM4ssbYMmMhjcfJ1+UglpsJmfkshjws/iK4QTwdGIpRyReNU14FY6AsT4XFVHwoGkg5DTUKD2IVPlWS0hwrmqlFjkgeR98ymHscLjo357EU2+cMDerCNVnHeQ/XxvqGY7vLUUO7mmJyUVRN88/4JeTGcHp24wLlJPxdifAQz2CM3OfclFnkhvSj3bFyjRvLuO3iiHgKcTE+LHXnuA3NtPwmLaDI5gv7aMhtyk3GCJfHzAf9qfPrKQ0O4WO9c2GzraHdMbdyDoBv0pNa+dxNpM9GVRQ02+JspFwOim91Y2wLpl0wL7fWxY66XmjqN7g255R5NV/0yeeEI0I6DgUIfX4tuT2SR++iiOBAYG6Y8vte5rbyNSZ+myPCS7lzSz9268id2zKsu4CmwLr2FdDuS5mj9Bw1GCQfSMXmiu12ZNyQfOyHwmM5B6rI+FQ5/6/wgzqoOl3WoHzPqeKeTJQqoijltxSLFP6b7DPuvIBrMyXCJ+FJxqmvCC+bI+K9/wDAB/L3mXPuVwF8HMDrciNiMBgMBoPh5cGh25bzfTklOfcpAL8LwM88Tz52I2IwGAwGg4G45Zz7wuDz57z3n8tPcs7tA/gvAfwZ7/3p8xRoNyIGg8FgMFwReAD985FV73vvP/O4E5xzNcJNyI947/+r5ykMuDQdkV1fMXibB8vlvfUqVLUfhVheN3JqxtbtMDzbhX6of0BdCJcaKGmxme6G732MBzN+qToAmZ7HIK/cvKmQsGPZ0OwuBEWrRSfleXQSJyX/Qc8daH8AQEm9CuGX+NZtmX9dxItQDY3GqZ6H5iufy03Og5CLBtoIefxbY8oDI7lh3X2/rTeATGdD+RkyRsWkQD9OB6juyP8Q0zvlokgeU49uQmO83TFl6hp08n3fOq0DTfpQS8y4LLELqifSOfRN+p0a4rHP85j2wFAuF0iJBl/yeTDHoiFeyhVBrh2zy/nKp9yG7PDOL+J4yXws00pz3ui4VcU2N4RQbohcI3yIdhY1H/pMJ+VCvofWEVtGeduGeVk64OeQc5PzMZSHMeRckB/jduuWbK8Hv4N/Fg5wfeVjPqzrxVpJ6ThRV6QbFcoxi9o0uzU7/GDNUJtINXfYHupulOkguL5AtU55R0XGIfLyRYG0PsPxUh7VBdyQfrDsdP/OjRNZs2yd+WKwNrI8XJV+7mmQKTydsiy3109ugJljOBeUxHY5ChkvkyPinHMAfhjAr3rv/6MXkac9ETEYDAaD4YrA+5fOEfm9AP4dAL/onPt5OfYXvPd/61kztBsRg8FgMBgMTwXv/T/E0727+dR44m2Tc+5bnHM/P/jv1Dn3Z5xzN5xzP+Wc+6Kk119kxQwGg8FgMHx09HDP/N9l4IlPRLz3/xzA7wQA51wJ4D0APw7gBwH8tPf+h5xzPyiff+CpSiVPZJcO/xapQj4LR6SbhKBfOxv4l4h2xJZ+SC5Xshm8a6+cFAYns3rseEdd4/QXeDlo5Hzga1CuwpfVPByr5+HL+iwEVMtFIFOUSwnGeo9iI7wEklBkmDQezTi/xGQ7+tmMfeSp5PMp041Q7ka7/f4/Y9d8556fnXBEXNPquLhMB4X6Gn2b6QSweQOOCGPKF8XzhzoHyhdZD/xusCN2PtAtUW5ImcbiY5ekwXlfe7QyN5s94Q6NQ4cqR4Q+F7s0LjIdmxjTDmlH3RmJw6tH0QBx7ua6EQMeknJDLhAfeayYkR/8f3DNRTwMH7QwAKiOTlw7IeG6ot/Phf4yg+/If2qnTGN/sF36dJn9qnXO0iFHhHym3MtE+ReRV0P+AeeJat1QsyPnILQOjvIofcqPybU7dA/o4trIOSKaZvyqyMlyW2OqGjKcY1Jn8nP6UeQ54QI+iV4z4bgN5uQoPbfItDvaZF2nXA3lEpEfxn3SZ3Ms8fmRcunTRE0m1YiSsVk7tJNs/j2GM8TUZVIgOY8kjle6jyVcRf5NLaHcT+brRD+ECDoir5d7y0cNzXwXgF/33n/VOfc9AH6/HP88gL+Pp70RMRgMBoPB8BLw6nREXhQ+6o3Ivw3gr8vft0VhDQA+RNCd34Jz7rMAPgsAk2L/WepoMBgMBoPhKfACXt995Xjq2jrnRgD+GID/Iv/Oh2dvO94VBLz3n/Pef8Z7/5lRMX3mihoMBoPBYLh6+ChPRP4IgJ/z3t+Rz3ecc2977z9wzr0N4O6Ts/DY6TWTnJJxQxgPrEQ/ZBxjy92UsU65tGCcL3s3fZ3yFfraAfQQIV8lD/NlMeXwwSWn5tyGGOuN8eNqEY6NzkM+9bnohsxDZYpFCDK7tQRW+151OgrRTKFvRfSlSSsZdSW2NSbydmlMdOgjk3FO+kwHIOo4DDLLYqk570Pj3BfwQIAYoy+yd/rVh2LgY7PN/+mTcnN6ROKhk8XItzgGg8rxr050S7zoCmh/5Z4tmqeP3AZyDzzj9mnd2pnEvaf9Tk5GqFL6hfJBSh/bk7crx6DTtmQ9Mm6Ik8+5zE/fOfiO80E0W+TfL5zn21oXbrB+Mz5YsfuaMHez/ruA66Dfl9trk/Ouz7gBeu2w3JxrU8ucqiRlfdj+poBvZD6IRwn9hXKdkliw3+KGRJ+inCuyY//KtTHY5IwbpZ4qLnI04rWsY9oHqtExsI3Z9jaSOpGDsmZ5LnJqMlpfzkPT+tTxNKVxcPy5RoZcKEQ+S1kOdZoynlZWPj8XXfxb92bVSiIPTsZCPhcb0XNq2gEnRDos1xLS37EdGkO7NHxeIbqXa3r3wvFRnt/8ScSwDAD8JIDvk7+/D8BPvKhKGQwGg8Fg+OjwcOhQPPN/l4GneiLinNsD8IcB/K8Hh38IwI85574fwFcBfO+Lr57BYDAYDIaPgv4qklW993MAN7NjDxDeojEYDAaDwfB1gN8Mr+8+J1yIs+l72QwmDuJZuecMvWCEI9JOwud2Gv4DgH4mniLkiFBDgN4YkjU1PXyJGPfTWHZa0xj3HJATGJPPNAriu+jU1pCs26EmgMQk1xJ/XjMWOYhJst1VF/8e1C3GxqkpkB2vsO2vsuU/Qj4IY74u+jHQu4aXMt6+ZnmSSVkM6rY7FpnHoRNNjSymSyqI+k50ad6u276G8e8troHoIPhJh2IWGlBkehuuCBcXnC+DKbfGSPIlYYXBclZWxo+6KcqBcZFTo3ozcgm1EahXsR8uKqYMnkd4FYigBoPkL20oSo9C6u+0/tvtSPLacY7mkXNEMvJL1xVoGll7pejZ8DsZr7IerCsAGOr05O1zLkm1fglxgHVK2xN5WyxHPpdeT+pV42GYcXYuENaFcELKkaSy7ipJOT/Yj01TolmJ55UQFlryPMgLk5QcBL9xOsGViqRpyrFQ/hv9d/a86np4puR+sR3kr8wHY+05N9O+cBcIb3iHLW5I5CQlVVU+XreOHlUX6Xnk+6Iu9HIw1BxLaRd9w/pxuo+5rlBuyEW/sTlXJdGXybhq2xyv/LMbLo40LXb7ToVzWMfsN87wWJjEu8FgMBgMVwQe7rUjq9qNiMFgMBgMVwivm46I3YgYDAaDwXBF4D2uvLLqi4FqUEjArsNAvz/77gK4Psbn6WuikiDdNlcDiDoirkN8R1zjtKkeQHwZfhDHTakhMazos1jsIFXtANW7uCBmGAP7WieXxRd74YbEd+xD2o0lvjrpNZaMSgPDaaVzwYBhu8o0Va+XMu2jYfzUX8CxcVknqQfMyEdvikzngJyKkqFY1VuI3JpyFQaTnJqoxZDmOfRbcRkfosj0N+jV07UlsAqNp8+O24RyfCvl5uM30Ewgx4VVIF9ANVYcy5Hz2kJ5HrmeBzkAGrovsu8B9N3uzYachiGNJ+eAkEtxEXdEy+gLbHnX5PoNmYZG4jWjXk4ZT0f1Z2IfkdNAbxKlZ5FvRK0QsA1ZPZL8pVidgHogpuynx281MU/ndbz6XPNka+3E1GX9x2p7n+ph6Lrmeh55ePnbC58l901yMgc494rGx7WgOh7k8KRrhXsS3A7+ilaSe6lwXzbkwADlanebI1dIsldxHuY52F7p2UN/GtkvO2kX86oWDtVS9gPx5Sobn+SR64r4wdR1g2PAgLtGvp3uraLdVBZwfTZ3NU1/OxQFoB34lL9jhgB7ImIwGAwGw5XB5bnoPivsRsRgMBgMhisCDwvNGAwGg8FguESYjshj4YGu246xlbg4ppbF4VS/oR34BtC/QgKB6nci31eLcLyeh2urZQ+/kpfgOwZSpfjcM2IgI6Lx7JwmkMeLGR+uncbLyU8p1+FL17Lt8j3fke97+HEIFnfTcG4zi9opaSrx6tGgHwm+u7/FEdmO90e/ConPjtM683Piv6KeQJJtlmqMnHFvekhUsS4a0pX4MGPA6sszF25H61GtwjiVc/HmkfErV+SOSBeIVoxrHHrxA1ENDhm4TjU6yNmQdi1LlOehEyuZK24Ryuk5Xw4v4IgM5qNyQ9oYT0/6og7ldRjoNWT8I/T5OA36OzfWeRKGE1Z5HSk3RD/n/jUe8IzX02eF6SDmP6xOrhEyLEhj8eQf6fyIvIstTYtsLe5qtsv0VxiqZ3N8xuNC6eDlQy8kFFJuIg+nT67tu0Ln1C4+WDg5r9g2LyF2lCRyvJ3KfJzIWpn2gGicOPrgkOPTkOQgxchaLdb9tteLjlOqdTIct9zXJ983cq+Weu4xPgsHuxHHltemew+pFom2DKdzk6bkClJ7h31XrqDlucVK2pG2j/sutUh8EdvTZzxBzkNyzLjH0VtKdaaAuNfxt6Ijd0SNu6QQHxsm3/mu3+JHvWx4OOVTvS54vW6bDAaDwWAwXClYaMZgMBgMhisEC80YDAaDwWC4FHhcUdO7FwcX4mmqxz/gg6iOSNaBqlOxrVsReQlZnL3L4mNZnNO1vcb7PON+Wh7zlOrU0ePBSVzbt2n+Mb6a8Vk6h3aVxsRJ5ODneh6GoFrWel2zF46tboRzlzdDZdY3Q/6baxJ/nErd6T8x4ALkYfot+QvyJGqHfhx5FaHN1DdIuQ7dVI6PKvXI8Vnsv5W8NoeSHoWCm0Opc+mVa+NXIb+S3BoOLb05JH5brnsU9OYRXQ+08pmaK7xG/TXcljZGmXnO5J4sHYbeMWl/ubpiZuGaHfop6qmhGhksSJLMFweV3x47NR6Rc3IZgh7bg8t5V+aDPKxcNjdzLohes/vwMI+43tI+SIrI6yhrvh+Jbw15TnshbfZ85Drl/kisGo9zixic53PfKs5LXrzLa0b6vhD+RVWHuVWJBk8p82bYLOpb9A3bQ80PyXbDNSRzqo45FMh0Ukg9kHPZJ514Z7lZi2qc1olzdaN9kvqe+NJt+7hQCySfj6SZjD064ZvpnsJ+Yjs3ZZLnUKNja85kHJUim/9D3ZI8ZVY5v6VcAeVKvm259yTNi8XLUm1nXvfv6AUkddqkvyeFcH/q83BxORuj4Bqnv1Sb/VYMOY5A/K0C9HfMOb9T5+blwqGz13cNBoPBYDBcBl7HJyKvV20NBoPBYDBcKdgTEYPBYDAYrhAsNPM4OMCVBXybHS92dNrQhwaAkzhdoVyAQXyx2x2rVt0PPU+Kaz2wCS+U00NEi73offpiEKMmLrIRSGLlcoicE4kl871115NzUOn5m4MQdFwfhu+afblmInmNpWCJcbtyuyJbHiasGnkM8r138aGYciZUF4VxVDk+HCblQWS8C8ad6/Sz1qN3MV6bxYEZv6WHBLVDinWHYi1eL6sQHffrIM7hGpkXqtMS0mrp0IhvRMNYvMTZ2WRtb7urgRly34kcbtBvW3oU1Chgvw7yUMmKXAcj073IykrrlnJTtvJ229f4XKeEx3dxR3iuejhlKT2cGH/veo3jq+YCdUrUR0b6RDVDHkNOyaua99ngmO4FSjjy6TXDlEM69BrCQDekTAtu2yLOFc7hJkvV30p4Id2Osp8E1qst0GUEKq0bNXLUw0rSzm/vpxn/KKcj9RUiV2nIodmRReKj1affxZPT/Le0mRD3BaWx5JysnCPSeJQb4ck0TVpuPrfZrtqjnygRLBzjuNVsh/BzluFzKzy4elKp14zy0sg/KtmgTE9kKOREHRHvn37cXxC8d69daMaeiBgMBoPBcIXwukm8v161NRgMBoPBcKVgT0QMBoPBYLgiCFFH44hcDOeAuo5x477bPucirxmJ12nstQGKNePMGR+ic3rOMI1xR7+tH8Iq5j4NDQOdMZ7o1OMgjbnm78+7PsaOK4lB0qvB7S4evnDqSZHzK7SObard4ZWUgO2A7QV8AuUItC7G/kUzII9374wFkyNC759Nqp2hKfPYxL5in6o3C/kd9A4SvQBqhxSbLmi/AJF7IBwfclSiPoDksXSqsdB11HK5QKdC+qJYFFvjQ25STy5Rl+qXJPH/nF+U6TX47Dg8lGvAOqgWSeYPguEcz/MhNK/s+EfZk5QuM9CkyTgi1GCgr4/yctYyB5p+wKlJuV6FxPnpK1SfR65Urxwa8pgkiwua4/MDg4N6jurc7OA9sTxpV8t5IBo/ncwftcZqSmAj2jeLkNLHiuubfaHzvvdbmjRabfIfpNKc/8VatDtKoM/nLvNiPZayZtXnyG/vG8rPYqekn10X16Qv0ofkTsuRdi7C8Wo14GyUKcfKZ+Xumn9be+Zj6gbIft9wD2jTcnw8Jymjdfpl1I8JiU5p6Tf106JvTlWo74wjL2gXl3GIfsDPyX63Xi3caxeasSciBoPBYDBcEQQdEXsiYjAYDAaD4ZLwunnNvF61NRgMBoPBcKXw6r1mygJOYopJ7JeBWJe9o517zQz1B3Lvjsx7xuk793K8YuqidwiL5y0ZiyWPgfF9Hyu85VvQpHolGHAqnOYnqfJHyG3Y1uPYej8+i/Eqb0X6KtGlUD+QLLOLtCd6F7kuub9Jzg1R/Yb+Qg2V3Dsi13dwfphfyrFR3xjyQXZwUnQ+5OkuZOOlHjfkBnCe7NDUiHXMtTkybszQmybTZ+B8o56I+hbRU6X2sR8ZX8/mrsaaOberQQc+6fGr9uv2eT7nEj1mvujSzM6NYejIo9LUpXNU0ZMPIekqFhT9mLK6ZutcUx3HQeUu0rTQPGJ/busCZRodPHeH9kis2w7uybBc5wYaKnnD5NTM34hzqm8dnHS2eukw+9xPi8e9V62RyHPKuEMsdzg/dF6n+epUYzsrclYculGoG71yeu6vF3DbYqaDYVJPL5aDJK+4x0YPLFfXaTn53j3glfQban9klblIAwVsg4u+UvTcyv2TtD0DDokaXGUT7xXCw1loxmAwGAwGw+Whf82CHXYjYjAYDAbDFYH3QGdPRAwGg8FgMFwWXrfQzOv1/MZgMBgMBsOVwit+IuIB74MRUA4l/Mh3XXZchYnCx74GOiH99eOUoEnSVxSQEnKpCKC10xKj8VjKSdlnRS7ENeCrKdlVBbFSsalIiox5JEZ7iCQ9CjqVNHaT7/vKoVxTLCkeC3+k+asunDTF1300wqtp8Jb2NcWbfDdQi8pJWJkwV04WRFFoR0W9rZTEqaJyFF8TMq4fXEOyYV+nYkLdpJS85LxRATeSY400frWWPFKinJoKTj26PRFcmnRJn1Qj6fPMLLBrS6zPAhGuPg9pdxQcB939aTiJRLkiJSf6YjA3dT6mBMB+IqJ8e42UH4mFvQ6m5Jvr0glxsqp6lJW0h9MiE6nrZGz9LkKjdn6Wv4651Hkwb5ivF8G7jgJWMkFaIU83e1L+rEI9EvbhKnMYZHmZYJXrBjz13HAy03RTUzim5XBu+rSZPE5Bs6GoHdeKmCGWaooon8u0zm1TonVhovVqlJetebZLj3tdA6BhpxIluSbSMdFSSx/WNAA3ljnLNUMRNiHdN6uQtpNS95It4q4SnqU6criv/TaZNxOA45xq1nEv1XlNom61vSbC98x7kLp0bZCr33NPJ9GbxyuHyaOQwXQ8SstNfyK03eXGbROcs9+IaJKYlheO7SaqK3Ii9o7vXDms2KtBIKu+3GcMzrm/BuCPArjrvf9tz5ufPRExGAwGg+EKoYN75v+eEv8ZgO9+UfU1jojBYDAYDFcEr0JZ1Xv/D5xzn3pR+T3VExHn3JFz7m86537NOferzrnf45y74Zz7KefcFyW9/qIqZTAYDAaD4VJwyzn3hcF/n33ZBT7tE5G/DOC/9d7/CefcCMAMwF8A8NPe+x9yzv0ggB8E8ANPzGkozDM0uGP8rc8Dm/J1Jlw1NJLKOQcaHquZF42hyBEp4CpVm8oKYnmSMu4/8jFW3WTXUEiHvBKK8HQDQR41ymOaxYtFxKtwxZagmcY3JezekQchHAgwflz3KDTGnysvSTxXuQ0Si0UZxZKymKv2a35z7QdCXJp9Jk6WcW20XytoHL93u2PKGtcf5k0RvDp8Wcj49XK8z/tmFOPrGu+WeHS7Eb4JBYsoJtY5NXZT5LfqWZzYD+LuGt8eZwpg7IPcnLHooy6btJnTUYth3ShS5Tx6idfzXz19Q06IVCYXsBrWhdwP6RNyTwrhR1TCPykH/Jm+p3CVcELK0PfkGFDQr5uQ61NEIzSNo6d9siVi17stw8RcjIxjTAM48oGGa3MLKjiWkQBKDydtZNvrURioWvqgKoUHJf28KSv9uxMzuLjnZIJmOc8Fg/Wk7ZG8mE7k+ETyrPtYfxFZ6ziXKDy2JeAWxcmKjLfSK79OLiJXZOrhZ8I7GqdunCyXwmA6BnXkrm3REXIROfK31NQwnqPmkRfVUVBMHNqp9PlkLPml5XA+EH2FrfWrcyzbp9hXKuw2WOfuoxjX5TyS3mN7kF42npsjct97/5kXVZunwRNr65y7BuB/AOCHAcB7v/HeHwP4HgCfl9M+D+CPv5wqGgwGg8FgeFr0cM/832XgaZ6IfBrAPQD/L+fctwP4JwD+NIDb3vsP5JwPAdzedbE81vksAEzKg+eusMFgMBgMht14HQXNnub5TQXgXwXwV7z3vwvAHCEMo/DeX/j8yXv/Oe/9Z7z3nxmV0+etr8FgMBgMhseg98Uz//c0cM79dQD/CMC3OOfedc59//PU92meiLwL4F3v/c/I57+JcCNyxzn3tvf+A+fc2wDuPjEnj2CY1mVCGMDjzct2wHmgyM3aMt2BLCwd44JDmorURfPIXx1njLb2kWtAgyYxffIrvkcvTdlQdyHm57OYaNGIVsFa+B2bkPadR7kWHoTEocu1NEfyL1n+shg2E94BXjQySql3kemIUA+ANtGu71UrQNunxAUk6fAmW3VDslh/PCG9NhqwQTkMNAIsVyGtRTelXGdxWjfQKBhJJ1SlfjdMNR497VHshYB6VVN3I2pxDD8z7t+2BTYuBJq7cciInJQnzs7hrfhFIeEyHRPnIm+FfA/9LLwPNNnG4BH1ZNS4MCRbBl7KMfIX/pMjSnMI/yPXNRnWrUvTXPdiy4QsNEjSdG5xLnfjyI/IY/5R+4HaH1KfjEfgqx3ty8aCuh5aR+/iKZJ/2+7upIKmhb1D36Z9rsXpOtvmnEWNnd1101MzrhvqIY+Fcyd85ph0slf0Y0krp+tqmz+SaoMk4NhyvvEUGnpyrx2YWrqLqBN53+8wz/T5uRxzjmmmxdN2Du2U55Dfl7VL53vKg0vqwqnEvWcjqew5ZSNj1fRR3ORJ4BzvAJQZwa1weIrd47WD9/5Pvsj8nnj7473/EMDXnHPfIoe+C8CvAPhJAN8nx74PwE+8yIoZDAaDwWD4aKD77rP+dxl42rdm/jcAfkTemPkygP8lwk3Mj8kjma8C+N6XU0WDwWAwGAxPi8sinT4rnupGxHv/8wB2vc7zXS+0NgaDwWAwGJ4Zr0LQ7EXj1XvN9P32u9ZDfgj/Tl9nH+gNxKx8FiMcFpMc3hWLzQQbcs6DnsvgVemj34PE+tVvQngE/TrVK3GN03glj/Vn5F9kghGDPqEmR4yjSzrhZyl/kuqIVOMWk2kIes5GIQg6Ei0ETsxWNCGWm0A02TQVNmyPk3ZIPXguPXroBeOrYltPo2SdXVZnybuO5xb67r70DTVWSCdgGF50K/pBTLvYhE4pxMuE56gnxojx9ejJUmXeLLX0icbbe+qJYDvenHOXyCki14c6MQ1Qrhmbpm8SO0eSStrDeDwKLahX7Q9JyVfY4oG4bY5Bn9ZR/ZFUrwTwPDnTEaGGhuqJSJ8MeSzkD/Q+5eW4bLNL+swNCgdUo4ZchmYm83EmzZ155ffkUB+hCb2DpM4jikJEvQsIr4qcEO0jVof93En/I3Zfod4ywt+SvqjKyMtoRJumrdN5Tc5BrsEzrEPkU6Wp8mbI/SIfZNQpv4lzmGhbWavUwsk4UkPodOF+kvnu+GmHchYmMvVjyFVqxFvHr7nOItdnZ1sH0L2U6x3kGMV+Ui+ZzNfHZ5o7cF41gyDrSPVD2GbVZwnXdmOvbXQZn5D8Kq07i9PvfdQPyTWudM/OSDJD75kLfscMu2ES7waDwWAwXCG8bNO7Fw27ETEYDAaD4argEkmnzwq7ETEYDAaD4YrA44qSVV8cHFDseGQ09HvJ+SPZ8aKJWhMl9TvK3THWrffYM++FpOwsbhtj/9R1cOqf4YtUl0LfV8/jjcNjLE51DSR2KR4zrhHewqAvNKYr3JDmmsRvrwceyP5hEOA4nIZ0UrWospf7OSHJ9yDGdatV7enBwmsY3iRdQeLv7YyaDxXcOudoSJ0zj434bn/UgmCdyEPItSWoGaL0iDr2SSccg3JvIp/TWG9BbsCmQLMQTZA6DdQuJVWPmSbyC8p5+Ht0KvktpVcYL87m73C+kEvDRaV9wj6QdvTidVOMOuUlVLWSQMK5o5RP0jfhGr8pBjyHNO69c/7JeQXbSk4B/Vq4VtS/ReY2vWgGWgx9l08MGcdMk8H1fsDBookIPYFEx0Z8ado90YmY+aghwTqx7Pxfd9QEaTlhBhwArVPOCZBTBxolvaxBhv474R5U9H5R7pDUtRuMvUs5ZS7zkirayCEq2T9ragela5+8KvZjsSI3qkIj7WrFjKgnF4ZaQ3Jufc72D6qY+6isU96M0u16p5wQ6gtxjmIZ0krWRTWXOb702o7c66WgjgeHj+tgh+6IrtvM88sJB4b7Rrl0KDfpb4O2j3t0nU98RH2SbC4pf2qLtzMog3VSzlc2D9Wjq4zfF49p7CvE6/ZE5PUKJBkMBoPBYLhSsNCMwWAwGAxXBPb6rsFgMBgMhkuF3Yg8CW4QR+NL1sP3r/3TvXjt3XZHk9fBGLLGECU2Sk+Tct3Dr0NA229CYLboGLclB0VioXMlOaBjvF4Ci6Xod5TTkDKE3DGmuCxQLqVsiW9WK4kTr1qpW0jdRirfedTzwL+oz4WbMUl1PDbit7KsglgHuSr92GFcZvyVTD9EU6ls3++IzqVyCpru0tZw7LcLvHp0TAbxaXJ76vNwaHwcLhqdMYbeJ+UVfqDbIZ48yqnZRM5Q+Czx9lUROSakHNCHh9oZ6iMkfVSV6DvhvmQrw7fCqZH5Am03542LbVUNDSlf4tzkRZD7UJQerqBug4xHm8XoyV9RLoKDy/w/ooeIlL9D34G6DNRPIJdC9TY4PAuJzbNvduhhqP4L+zr3X+kROTUCJ/yqXGeGvAI/8lpm1ACReH7GY0GmOYGmgGNd1qxTWuctz6NuoGtBToPwIdbU0BilPkNdW8BTp0TOLRcyl+fpnK7nki57lCuZo6suSTmXS/GdGh8H3hO5WK0v0YvOCiZyTZ2uDfX9Advlt9YkORTIPG+ctqyMHjrsJzmnXAg3RNsZjo/OPeplOKkjp0bKKUjdIS0j5zABW/ou5PeV1GDy6QnVwmF0Luv2fCnnXpP2yZl9NpfdgHLYp/Oi4r4shDH1uZI2Fas27snqeeXSNOeM7PhNSn7bXhEo8f46wZ6IGAwGg8FwhfC6vTVjZFWDwWAwGAyXBnsiYjAYDAbDVYE3jsgT4IG2Bfqn4IG49N1tn8WYfTl4P73MLiX1JPMyIU+DMdQhiob8BMkziykXjUMn8eGNBDS7PYlnZrol8d317ToV5DKIPoWbB+KKWwXOiisL1KJnMJkwXp/GJosuDFt7Fr4/n4SA+9msQ70f8tmbhYbMxuEzPWeKzEzHe4euyx6MZTwCxm3JwyjXHQqJn3rRtyiE01CtqDegLU/6pmihMfN6HtLJo1C38cO19E0I+nrhwnSTauDZIx0pHJ+K8fZGPGhUs8GhUC0C6r9Iv9Fjo87mQQ+Uy0LaLLUXbxkvKfUBVH9goEPAeeaz8XcujXurBUdbwIt3jltRF0Ji8Zt07jKG3tcevfjpqNfQBdCY+aCZyrdQvs9uDZzhZ5f54LiBv06SUhtnqCPitBNCdtmaVX0ZN6hTNi5avoxNkQveYHs8cj4L6Whsb19HDZWoWyLnCC+HnDBqqngP9QAqdJ6TSxaurcg14FpZeVTLjBuyEF7aOqTkP9XzwBGhJkiog+iHbLj/pVyfYsl1Ec4vN33kiGQcr5zLo+0qEPk35OnIfCSfS/l1q9g+8ikc+Wb1ID9sawslvL58vlHbhJIc2Q9puQLqOTkiCwADvp3sPbw2zgGnPzU5X7AOWWB0Kn0v/JNKxsYt1nCtXES+U0MeH7Vx2GcDEl3++1/sOPaSYW/NGAwGg8FguFS8bjcixhExGAwGg8FwabAnIgaDwWAwXBHY67tPA+cGPhTbXA2FavXLu/y9T1OPrfiixiK3PGdSHQ5fOrhK8q0rudYleTCez7iqL6D1rqTX+jbtPr6vr7ola6CS99SZkoviGHdkX1SR6OKFI9KLj0o7DcebPUn35f19SSHaAq7qVRdksQoCDY3EmGtyRCSe2crxTVOhoY8JfSxybsFW/H173Nh/qvfRpLHr4bv91NPQvu45xoHrUkn7E98HgnwZ0WrpRvROSU8r1y5qYWRjGzk36TWuG+glnMs8W4rejGac8nbIWeproFMeh5RTZjFz8jGkn30D1bwh+qlojszIRZG6s/MLACPROsg8dLzE6ql14QdcKZd5r+g17BvG98nPqEgsGLRd6toX5Gqk88Q9nrKSnKMp9Uw2sa2aD7NXroHwO+jN0sa55shTyfx31AdF+SCDPNn1TKXNFfWBKuEHUYunKZWroWD+HHvRRek3PO5UG6YQbxlUHP80L/poUZPEeYdW+qfbpHOJ0yHncLjeR12UYVsHF3HcCuV+AV2Z1WWZjnE+Jn3l1BMq91Ta8pm6YL0lUE6PfBzovYQ6+jgRszW41SfC83M90LH+yvWTVDk1nENRPwQAXNNGXhN/K9oLuI27tELYgH5Q71cIbzciBoPBYDAYLguvm46I3YgYDAaDwXBF4F/D13eNrGowGAwGg+HS8OqfiHifxs+A3cHlPO6mnA3G9HyMX2bxYI1RkiKSvc+elpN5NVwQz/MV0M4Yv0/j6Rp/13ftQ1qfA6OzVCujmkdPmXBA4p0Drkg/DsPS5zFYSakjUeyHgOd0FgKeRdGrJkgrGhqrVQheL7sQvPZ9GsD1rVPOguqGkOOyTOPP1EZwba+dq/XWvs/8HmSGtZP0MxB5JDnfot6X+HQzGOuNaAbwYmqDVLvjxOhjnegb0wn/wo9ppOHjuQDcpoATjo3+g+JxPCYgjf+yvFHODZE080opRp3yEGrhe4yq3V5BHXkKbYlGtEe6JucrpPmT6+MrH8tmW7N/MflciGPIE+KxTCeHuhHk/JAnsdNzQ31OJKWuAzVB4CJPQHVJMv7MLp4Hj5MX0ab5K38s4zH0lYeXcYLMh3oSKjOZhvVUSrs3spbaTRnXDf2Dxqyz9MEqnY9wfosrQQ6HK7O9J9sGfRHb3E04d9P5SI0cao+43u/whhrw6nagaACccwEhOZc+Sco7GfBAyM/qa5ekPsuD+7zmUQD5P9iVY6N8lvT7VINkNzdFpzB1bvrteaCaUtRz0vmSdc6gPOWq6Q+K+P1wTg/5jP4piFKvAMYRMRgMBoPBcEmwt2YMBoPBYDBcIuyJiMFgMBgMhkuBSbx/VBTDuB+DqlG/IIHP4tR+6PORalhoHJxxaNUYYFlQHQrHd9LLLLbLYhgPLwdxzJlockxDwLEXHY7GVUm55Fok4CHGh/s8GOrVT4X+B5MqPYceKptVIF7MD0PFir0GI4lzk3NQCeeAWiEd/VfaHXWjBkOTcUXknftiGEfNeQCZlkvUG8iO+8G48F1+ckE68k6kOgPdlxjrzwcozZ/lDeeP8hCE89JTb2PM2DW5B15j/u1Myt4LfexGgWPjZN74x8X3NTYPzRcAIN4we0dBVGZ/skYlui7kgKzFM2e1CWPaCn+nlDo659GT+7GU+bbJdBp47mAzUk8X8mOoE5JLPDBverS0DhDejHq+dCkXgO2L44U4P7jGSwb0pbxcR8T5AcdGipmmdVSvm6317bbz5briuMi5nY454EV/pxhRNyR8bmQMGhbLcgcaLr5hx8l35CWQc0AOVYHoYUNuA7kiVerDE7kI0JQ+T8xDpVvoNyRpKzyddlKiXGf9lut6UAdJOD7tzKOfprwpJ3op1anwQJSPQz6X1/7vLuB3bHFjhh40PjtXru0mPJ7qEfV1PCeH8sNkDIZaOVsUr/zzLj4TL8wv7lP+h+NexLm9g8eCAo/XTzEAuOwbEYPBYDAYDC8OO+6hvt5hNyIGg8FgMFwhmKCZwWAwGAyGS4GHkVUfD+/h2xboSKIYBP34/jWfKfEcckc60ZFYiR/EMr7Dr/wA+sJk75UrR4AhPg+N9+m74F2qP+AY9utjHuQ0FIvwpcaOM/0NN+BfkFfSTsOxahK6vFiLpwH9J6Q+btOgaETPoBGex1I8WEQTpJ4LJ+VRuLTZk5j2YYn1dcnneqjsbC8YLxzsraSY0FnURmg2VdBHAOBlOiiHYpX2Z4zDe0B8F1xH7xxIH4hfBseAGi8MmXZDLRhIu0TH4Vxi9avUcMIXTuPpTvrErcPFRaYbwbHuGwBVFr+X8ryMo19uzxf1yGl3P9v0Mk7qTaHzxsUYNTlJpCwxhl1KO8X3Z9OWOF4FI6H1XDgoC5kfq4GOB4CWGh2VV80PckDoGcK653ofw2OeE1vj9ZEzAQw0J3Zp7mTcjC29mXU6FgDgLuASRT2TwbnUeNB2CD9glGp2KHdEUGycegSxLhxr5Sqx/3ycEx31WGQd0xaJ2i6VcEZq4ZJUVYelVKKQa+vTcO34kcz7ZdS+Yap7Ss5zU/+WcKBahXR0GtJyA3TqN5W2T7VqSMFhewdww/1u0Bc5Ao+F+6703yqdW1teM6UjzS7ZI4fN4q+LhgkG7c41TVTfQzSLlIM1nB+cV22bfM45Q8q1cU7zjZykXGcm0zjRtNC97UJoOfyhAbZ81C7Ja+Z1gz0RMRgMBoPhysB0RAwGg8FgMFwiriRZ1Tn3FQBnCC/Btd77zzjnbgD4GwA+BeArAL7Xe//o5VTTYDAYDAbD0+B144h8FNO7P+C9/53e+8/I5x8E8NPe+28G8NPy2WAwGAwGwyUhSKC4Z/7vMvA8oZnvAfD75e/PA/j7AH7g8Ze4QFDNSUCu2DYLKner15SrwD4anVfKqCNpr52Fc8TfLQr3yLUUSuprF/MnSbSjCVKsKhBFelwXyYDlknUpJU2PV/x+7ZU0t+XrR3EjihlRKKt28ONQ0fYgEOPW1wPTbnUkQmYH4ZpumtWxdajEvKpFyGMugliNGOONRu2weLjCD4SPUuJiR9GkMUWFUhGnIXIhs5y8qs32gegGREOvaO5HgpwIxi02el0/G6Xl1UI0pPFWlZLbinagZTVNyY4k+mmdhqS3nFiXP+PsUpIuSYnlxqPfZIJVdXapjAXFyppNhe5MxOhWFA3Ly8/m3MbpMYpz6T8n5COJm0pQrQdk4Uz0yecGYhwTioqNBuZwmofUSeY953u9kDquO+0nJYNzndH0bgeRUk3swLWYlbNgnUu9hqmSlbOUBFheGw0QXSxc1l4ngoTltXDx/jQQvSdVyORsNcayT4nB2o/Md5TNwy6WSUNFEiZR5QaLPI9ERxfry/EYp2s070fnIyk2zmvOy5QsS1J9tRyI40l20UiU84GkWZbndM1TkDAKTMo5HAsSQpUw7baIszqkzCMjG/sy1gF1nXynyPegzusH7VOk5/giz4R5FanRHuIerUzugizdx/x4F9ttNWzjaW9EPIC/44Il6P/Te/85ALe99x/I9x8CuP0yKmgwGAwGg+HpcVXJqv997/17zrk3AfyUc+7Xhl96771zu02mnXOfBfBZAJgU+89VWYPBYDAYDI/HyyarOue+G8BfBlAC+E+99z/0PPk9FUfEe/+epHcB/DiA7wBwxzn3tlTqbQB3L7j2c977z3jvPzMqprtOMRgMBoPB8ILwMjkizrkSwH8C4I8A+DYAf9I5923PU98nPhFxzu0BKLz3Z/L3vw7g/wzgJwF8H4AfkvQnnqrEPFanBWWmd/ktXSMCVssQr62WvXIWaLqkMT1qhDFGr/FASUsHVCEo6YRrwLgq4528qJQYaTd1ahJFzoHGXOV+rlinceNiA4zEuG58LPVehHaokBmbLfXxRYH2erhhW7wdSA2LWyJCdk264pqYVe2LKFvNPnPRRIzibiehEzbnoZ3rahBMzq6heFGZpbkoWV8VKCQumj8HI1+FQkybfQowQVM1pvMpx2AkBluT/ZBJfR7aXy7ayOHZMOhfJuVwrP0g1pyb3TE4rnwIikKRS+S8muo1+8I9EW5KOUoJHxy/cuO1DSoGJjH5Vgy8lD8j/dxRNKrs4A6lXdcpniWibhJ/Xotw1vpUCC4PaoyOw5/1XPLL+APK5aCAWw/0FAObpEJYOn45bYvd3DkVtlMhMwqobdJzE0NKWec+E31S4cB8G/ADYUKpY1uldeV4cp1V0v76HKhESEzF6zKxK845msP1dRRkU27KWMa8CwdaEfbrhgaHRbb2ZN9qZZ7XZ+G00UlsNvui57ircRw5bum4UbysOXA6h7h+yDvSvYx0EjWecyhVvEuqLOkukbDQBqAn96Rmn7M/pa4L4bOQDzEQ7+qz8d8SGFMhMi0x7iWst/SJmt4RcWtDI/3ip+PkFOUFNWkKH9Z0uJ771TZnLfzBdg3+ba5cG6kERTZzPiPn+OO4Iq8QHi+ddPodAL7kvf8yADjnfhSBM/orz5rh04RmbgP4cVFIrAD8v733/61z7mcB/Jhz7vsBfBXA9z5rJQwGg8FgMHxd4JZz7guDz58TXijxcQBfG3x+F8Dvfp4Cn3gjInc9377j+AMA3/U8hRsMBoPBYHixeE6KyP2BTMcrgSmrGgwGg8FwVeBfuqDZewA+Mfj8jhx7ZlzOjYjGGYWnUca4nG9zwQ057lOOQNH0KESXgTFr3geWa/mkRl4Sm235fR+Dq5leST5+w/f0lUuwL1omByFI3omB3OIwBDy7e+HEyX2Has14cK4xoZWTvuj1eDcV/kOmh6JaBeQCHIZg6OFhIFnMxhsUEvQ8W4U46nIR0lZ0AtBQy4BBZqjRlcZLGevNYryJFkqRaiAw/qzcENE6WR9JnWfU8vCxyaLF4KkBAmqrCCdmL6T1ssToNFSiFkMwzhPqNigHgn1WbreDnACNxZOTMBNtkEmHXuLQm0XIaHMtpLNp4O3Q9I6I8XCvuheesWrOl56dJNwbMZo72l/g5jQIXBzWwe2rkoD3og3lfjg/BADck2s3bYFyFTIe3QvZ0nCN/AeNs1OuYgl44eGQf6CcA0ojZFwRN+i7XHvkQhM15Z0M/j3GtS7rjPOEXCJqyfgq5qvmfeQ2cKoWKaeHXK2gI0S+UapZQW5IziEq2sE4kbsg66Cbh4tOfBAmWo5lzyl6VKLD42+Ejmqn4Vx/LFwz7kky19o26oeQW9BJJSKPRoofcY8In10X+VPkQbSqmUS+Wri4kTKag1KNGCP/gm0nt02O00zwWod6P+xljhwYMcLs5yGTXvIkXaLYuJj/KOMQrakDI+3YsaXHfSM9zr1N9zhBuXJoZ+SIjJJ29Xkeo7jOVcNF57fMP5kgyq/i2hmHzIqyiMao8vvkKslMNaj4G1LocZo8enZUzid5VXi5b838LIBvds59GuEG5N8G8D97ngztiYjBYDAYDIangve+dc79ewD+NsLru3/Ne//Lz5On3YgYDAaDwXCF8LKl2r33fwvA33pR+dmNiMFgMBgMVwhX0n33hcEFPoj2EePHQ03/PP7Wd0jQhMBjuWxRVeldX9umMdaINGZZn7fwq0Ak8RvqeqQeGIzfFm2MPVO3oLkmcWB5YX9PPCnOy0BU2IghQ7kq1fejnoZrKnkfH2vxfGnoXcL29qhPwzVTclsYyyYXRuLBqyIEOJeiPXFjb4FPHTwMdSpDzHcuQdA7ywMAwP3FXji+CsebTYW2EU7KWuLCSw3KS50zz4Wujx499A5h/+U8gjQruN5t6UFQj0JT1YKQWHfp0InGQ8V4rXBEVA6F5ahehI+eMqofIzHykfjEzEJBk4lovFQdGumLdlzLNdm/LLS9afsTKNchbRf5F9NxOPD23ilujhfJpQ/WgZfw/nkQjXl4EsarOQ/j5Rqn7aCODrUrpqJZk3NFvHOoRO9kfJzWUTU0yCPI/DqGviA+H0u2k1oqK1kzA6+ZLeQ+RcoViNyhyO0htwvSTll36uXE8wbzMJt/PId1YzubWeQ4kJNSzcPnXrRbOln7K1m7o1mD/dk6qf5xu5fU1WXN9kXkMBQl96dM20L3JxmjE6nr3CsvYpNdS00jauH0TWyfan0M+jZcE+sEDDyXCo9ONFM8/ZLER4vtivyfQcrLuV43KUcvajJJubl2DaDzgddwj6PeTT/mOi+2fGGUb6c6QEjamZST89/6LFU+C/dhH+eqmuRka137Wfakwdwm79EP+EuvCl7LfX1gT0QMBoPBYLgq8Nh+6+LrHE8l8W4wGAwGg8HwMmBPRAwGg8FguEIwjsjj4EUPpM/MS5zbjh3n719LzNm1THuNqaoPicT5GDuPvISQknPgWh9j2EwZY2bKusk11dJh8oCxwtBtqya4Ca+uCRmAQUmJCbd7Ho3oaWzWEt8WDYiC3BBJyX1x6x7lMnAIJuchrU8C96Sei/eKvONPHZXVJvAKfqOND7i+9egOAOCbZ8GL8LfvB72ZlQRQHzQhtn1vs4/7q9COh8uQz8k8cFxWUi6KcE19Ftoy26tRLGjokIJ6B9RzwKNwzehE4uE9lCvB8aNWwviMWiGd5BVO7KsC3Uh8QDSt5LtwLePC1B/wZYyf+z3hgohWwsEsaHZMKjW/AABsuhInwu/R2LjGljPOEvVEHrPgdd4x/iycg4Xwc3794S388lq8gObCATkLDWJfjySdDqgJeZs3h3LuafhcL2Q9SBuamddrWBd65HDKRk0GWTvkjtR+y89HvZREL2J0HtL6XMZt2QAthS/YeI4/6yZ5NHGtRs+XlB+gXU29BnoUCb1mfNJHfkrG9SKoLSHDi6IN2hTD9nB82j0pX+aanwhPwXl0ws+azwMBx90P41afpHUiZwp+yMOR8qh9I+uV+1euxdNOgfWR6PHcEH2Pa1HzBoi6H/1GNIxGhZajyHRflCtF36nOoZ+TJJQSOXiu26TrYcjDqxZcx+kYcMw1SrDj+TvnIecuTy5E72hzPR7WfV00mFSLJJ0WqpFTNNttz+kaQx0gIHK/0EetKfWa4UQkjyT7jfKFizoil30nYDciBoPBYDAYLgcv3fTuhcNuRAwGg8FguEp4zZ6IGFnVYDAYDAbDpeFynojkfJDHgfE41RiIt3oazlQOiBxnTDaLdxcaEPfRNCH3DlENA0l5vPcQaQ59572eSxzzSPxcpjEuzHrlsXfGb/tK4sOim+IqqUeL2D9yjpeUseOhXwYQ+QSNG+MrmzcAAA/mge/xDUchyPqN+/cBAG+OgujEzTqIJoyLFqVLb58ZB6e+SLuXpZMSNfU1GFvVNouWAGkz59IlO2K01NmoF6Hto5PQoPpBCLQ74cqgrtBeY3Cf3iWMozNNPSpcHzQ3AMCvqQ0S0qZNzSnajlybGuul8GGoAcL4/RNivs4PuBMSI48hZOp9yHwpwti0a4f6JBw7FC2QcpnyZkbzTj4LT6H16ETXYnkjtGNzzSUpUQmHwxcOzb5wDa6ndUKup5DrYJQDP5BMH6VaMaV+BOvYb3GvtP9UpyHNC3BaBz+nPkSq/aF1ohbIAY8UymVgPxWZT43yC2RtVas4TtRdYTtVv0R0MKgnslnVWD8K3JDyVLg8S61+KIf8hI6eIx6dTvpUXCfXrtjsC//kbVnPBx7tTHgyB6E/y73QYYV4U/WdaP8MeBL0+olaGWn5Op7Sv8WsRVmF/CeTZtgcLFdhPTQI7abeSLlyOu6js1AQPytnI7PT6geLP/papfov0a+GdZe9aM8P+B4uzUP1Q2S8PPfY7XnN+aZTmr8rmm7/NqnXTPGEf7f3Ay2chEfyih9PeNMRMRgMBoPBcJl4zUIzdiNiMBgMBsOVwuv1RMQ4IgaDwWAwGC4Nr95rpijgd72HfVEMPo/ZCaej2HQoVyHwRw6FnkIORfZ+udtRRB773+IyDLwV+B3jjKNTxjcl7jxN9UuKJp4zEh+QaiFaC+qVkr1zP6rRXQvx2OWbgXuyvCmcgDdSTYHukBUZ9KNoE5zdCdogv3Ic+AhfnAbuCH1xxnXU0Fg3YRos1+I/Q++ZBTVPUj0AXzp9lz/n+9D/Ieo2pBoU3XgYi5fqnwoXppQAu3RJLRokbtOiOl1p2SGV2LjqXcil1BFpHUrxsinFN6M/D/16Mh5LHplmTOu0TvTXUT2IzFOG5avfxbAbMp5RKdeOH8o8OQ/XlusYC+c1vfRbQ1kHelZI/qPTDuP7G6ljOGneiBbJtVQrgxyOyXGHUnRsqDnCcfEpXWbbu6eIXAZ6h6gvSO4lwrFxUU8hX7/qT6PaP+maCeXIuTIGRRbfZ7mqFeIjj6SdFJJvxpOgHpCk3cjBj8hFCefkWivq7XQmPK6HlfKxuD80+yG/5lD2pRU1XYQXdO5QrlIyAzkhLNdlkjwsv1o47Z+WHiwyjh3XvPA8tJfdgA8hGh1cv8pfkfxJB6pHLW4dBs7YVMhRZ2vZe4QjAnLYHDkiwOhM5sMmnQdRwyVr92AD1nGhlEk2D1Vb6KG0YTPoxwt+K9ifnlvbYFvc4j5l/EI9tx/WMSsnn9Mk5jyO8+gKXMrTCQvNGAwGg8FguDTYjYjBYDAYDIZLgcdrZ3pnNyIGg8FgMFwhXLbC/EfFq/eayXQ7gIynkcfl8s/0mmk6FBvhW2wYYAwxO1oo9IwVMrTHmOmm3faYEV2RPL4ZvTgcujqNwVPXo5vIOZkXxzCOSh+OchkqVSzlff2htwEAjGtsroWM5reFG/JWKGd1O1w7ezPEcz92FAwaDupABli0I9ybBw+Z45OQ9mchr/Y4pKfdDFvoGbtG0uaaYWiJ1dfzzEsCA16AhEs7iUNveWTMRBNi2gPqXyEaAQ/FP4a6KUXIZDQL41qfNqjEd8dtUn+YC1F6HY9C2seY/9DfZJiGtrHNkkq5fhX6mNwHR+8jch06wDPWn/Es1IJolVaxr4E2DJPqoNBnRXky5yGT0Unoi/GjArM7Tv4OZI1r81Dp9Y3Qb6sj4a+QK9V4TM9D5SaP2NcpP4Jcl8i5iX2Ue+Zwl1N+TJbupMB3XKvClVrGPgh1jHOHx7g7+SaeA8R+VN7Vso9eIZmuTT4W3aSM7VddFGqQZHoiLTkinDcx382h5LcX6kA/GtX8kbldrp3mw7ptgetMOBCzO3EOcI/R9SQ6Hs0RT0m5KeXaK3euEl8r9jX1RFSPSJq3Lsa4K/n2wnsgP8wJJ4VrZiRrdXrPq34IkXO/cj0d1YlxLnLjEI+FusmBjNdXLYF6yX1DfLnYn0pey8sf7MUiSaTaLVkdqYHD3xTXtNu/5hmfL04uHseO368unme4EPZExGAwGAyGq4TX7N7HbkQMBoPBYLhKMI6IwWAwGAyGy8IuqYqvZ7z6G5Hep/ohOfgd427kcmR6BL4q0I9D9TXGn/kWlFm8jvFp1/Zb+iF8Z5z6Br2+8y8nlPE9/OjtIfyHfYkPjySPFbkORdSyEL+TUupQNDRiYHxRtDumNdpp6i3TTiTfgxAkJzfkdxy9BwD4xuk9AEDtOpx0gcDya+dvAwB++eFbAIC790JQ2x+HRjAe7lpsPcZT35iWdUeSFpserqGxT6rXQB7N5kj8M94OdZ5eC4H96XijPgjnixAQXpeBZOOlD/o6tL8WzYt6UWJ8Euo9fhiCvcU6BHuVl5A1wlcefhrq2BdSx+wfCToFRAcGmwKYl0l7yB3iPGQs26kODPT8QjkAGQdFhprx7/V16ZsbHcqj0J7pVDgwkuFqKXyPU9F22Q+ZrI8KNHuBRHHtKyG/yfuB0DJdkXcUeECrm+Ga1VGJMXUtzsTPZ50KK/Tiw9OPOHcHPIKBfkxoh/BJdN0haW/S0ZlXlHKxyK2R2H3RACV5I6OUL6AaIUJvih5SkYwyOhMOVkPOFZJy25pzK65rcjJ6ckFkQnTiPUNegbavBNay5pujlBtC+IJ5SLmbeL1qZqiXDbkjaTuVv1NE3ZfVG1LudTl5LGnH+cg69yhkbMl9UT2UjDejPKCqRCtaO07OLbNzyRMbH4fPo3kf+yX/FcmujXocUtfBWvUZn4h90G35/wz8aGTtU78k1wih54x3LvKaBJRD0TldZGuVc3c4h/l3LySlbE9Q55phW/g71vtXHybxeO1CM6asajAYDAaD4dJgoRmDwWAwGK4MnHFEDAaDwWAwXCJes9DMK/aacXCjGn4jsTaJoznntjgbMbac3dkxLtf5HbyOWA4wcDqQ8xg/dk23rWfCazRGiCR1fvAOOmksjCtKnLjalxi9xJGXkzF8SUMJRsHqtE4spqEuRY96HvKbPmAMN1w7n4Sg+fvTwPeYVqlJxacmD/DJ0X0AwCdvhPRf2f8AAPCL1z8OAPj1k1sAgIdnQcBis67gxZ/Gkysh8eZiKXoUJ9SyGPQJ9RqQcnrUqySjbrRSxnk3QSu6L/089EV1Ru+VNGbOvm+nDn1J7ka4ZvxAuDYDHQ8A8BLjdt6h35M+3g9fjsahv6qqkzoJh2Iu5J9FqZoL1ULa06QBaKceM6kHzPBv1Ujo0zg39Wc2N0Ke9Y0V9oQb0svFC+HNtMINKdk3Aw0Salicf4xeQCHj0b1Q+cl9+vKE46sbJZY3SqmDaO0sU65G9PCB5Mk2xO94bZ/tGpGLEDlY8Utqi6RmIi6lDYQ2CgeJ+iAEORPUOEHGvVkfFmiFU1CtyMXKOEOZl0m58ZH/peOWrX1eK+3dHHpsbpKzJm0+T+duvv93Ez+YGKl+h+vZn8ILuhE+r6+H75t9j/aG6L8chTEdlaHS5BB1p7KGhPNVNl7nn/r60Hcn+1dy1IopYv9Iu3ry3aRd9XmqJeRd5AopV0P9fVK9DdXTocfOQEeEY1DLfOwrcpXYfyEt+0E56422FdjmiFCPxtU+yHgAKMu07dzLOafUr4zroCq3dES49nVO508dfD/Y5y8Zr9mNyNdJrxkMBoPBYPjNCAvNGAwGg8FwlXBVn4g450rn3D91zv3X8vnTzrmfcc59yTn3N5xzo5dXTYPBYDAYDE+ERwgbPet/l4CP8kTkTwP4VQASocb/HcBf8t7/qHPurwL4fgB/5alyYoytf4r7oEwLxKsOQfSaUS2JMo3BMrbN262eugO5oMTgGo0P53eU3qOix4VyJeQdfxe6sZVY795RiNWXN3usRfxAY9crxuglvi/v/NM/oVg0mEi76pMQ/508Er7AMnxenIch+KVr+wCAX5y+AwAYH67xyZuPAADfevQhAODj4/D5dx68CwD49OwBAODDGyGPB+s9nGwCl+B8E8qZryU9DwHajRtLu0XfZFREHoTEhRmHVt+OhcS/H4Y698ehveXaYbxgPB9ybriG8WdyZBgT7sZOOQBdpnOh2gIS8y1kAKulg5e4ditx/OWB+GeMRANCOBxY0xekiP4iSOPcFyHhxGjomDwVn9S1Ez0YjEP57abC8XHo4/o41HHyMJw7Opa+WKSx9K4Gmj3RajkI6fFvCX08OxQ/mofUWeBaKbAW/sHqptNjQPSPiZ4bkDTyDKgNox4w0gyOT5HNAezwkyJcn8b1tV0jF+P1klLng31Q5BwEQV+6gYZJmkc+P6hh0U0cmpl4HQl3p52l+iXK7RGuUXOjBWTuVPfCGpncFZ6E9FvHvKbMwyvHRLkLbDNlKaR/yQ1ZfkJ4IddXuLUfNGImVWjA6SpUbrkYSfsyjRDE+ad6Ky49J2rkgJ2Cbk/2oZmkwkXpZe/h/qheMCh0jpBvxPlGkIOS12sI1XwSrtIYov0jui/UE+mHHJ8uLScX76Jfky+AUjx4OM85h6lVo5/JQ5Jyi6qM5XwUB7lcB+uS8LoJmj3VExHn3DsA/k0A/6l8dgD+IIC/Kad8HsAffwn1MxgMBoPB8FHgn+O/S8DThmb+HwD+HOKLKTcBHHvvqVv3LoCPv9iqGQwGg8FguOp44o2Ic+6PArjrvf8nz1KAc+6zzrkvOOe+sOmXz5KFwWAwGAyGK4qn4Yj8XgB/zDn3PwQwQeCI/GUAR865Sp6KvAPgvV0Xe+8/B+BzAHBtdPs1i1wZDAaDwfB64XXjiDzxRsR7/+cB/HkAcM79fgD/O+/9/9w5918A+BMAfhTA9wH4iScX54MgWS5W5twTyT1eRXoeY5hHkAurgj0Zaaou8yu2zLhIfFVynXMqoDN5JMSqEzGCEoLh4lxInh8XQurhBp7ExAlNt6QOJObRdImCXS62rxAxrWItZDCSw+ZCwKJxHph3jS/tB6GyL954EwBw88Y5AOATh4G0enO8SNpdFT1Kl/YpTekcxY1qIa6JGZ2vnBKBtb9IhGP3UZzqkdSVRl8boFoKMZJkVSUlhnJozFbNA2mvH5XYXGPHSZKZVZG0VyDmXZ+lRLVuIsRZki4FSnQdGABGw7OUgOnbcLLrdxDm2AdI+4Sg6FX1oNY6jo5lDnFOiXlbJYTdaiXkQZq5OYf1jXD92cfD8l3dErLjUZgHszuhU/buhLpOHnU6LmsxUSNZL5p9STM5L6dyXjUgfkoVOG6lkC1V0Ix1HJIJlVgq7RCxszIzZPPOq0BWIyRckkcjKVJEtWSesI9GqzYSWPPtgYRhEaxq90TYbVxEUz3uE9m2QJJpu8eGeyWp7r0rpOLTSIwEtkXDutptC6QVg++G55LMLOtuvaxxZ3EtXCOmmRRQ4x5Q0qROHjYXax/3DYoO5tqNuu9KubMe5bUwmPUozJm1iPzRmC83/KzO47pVkjITmvZ9hBcwOC/qU/lMgUIRwuPcCI3lpOW1aUr4cY+OxPGObyykhGQSiHU9yDzxdQnI74QK9C3JSM4U1Ia/Lypoxk6/pDuC10zi/XkEzX4AwL/vnPsSAmfkh19MlQwGg8FgMDwTnoeoekn3TR9J0Mx7//cB/H35+8sAvuPFV8lgMBgMBsNvFpiyqsFgMBgMVwlXjSPyQuEKYDJW0yJFEQWyPGNsEicFBYnUjK7Qa/oRTYgkIV9BYpa+FI5DlYn+7OKjFKlQVjQwk3QQZ62Es1EtQuWm92gIFWL3RRO6dflWoXpYKh6UcRzYBo1xdwW6scSx90N+y1tizvaGxNAP2VfhEvIZyqVTXoS/G4SyTg5CMPT+9aNwzoEYv9UxoErzt25Fd6/QF07iw9Wcgl/sCz8Q+iJ3J60T+6to0nRIotJzaFzXkN8h4m6nS/m+Q3UswkqM4dKkCtMkL8bf+yrG+AtyGrJ4vopgDfgtPMa6MD7ck6OkRl5SD5mCfTngIumYM4YePo8k/l1LfL8+9Wr2lYuDxT6SPlmGDizO16gfCAdpfgAAOP7m0DfkilC0jCJ6szsN9r8WKjEdi9BcLgyXia/R4K6ZRo4DuRqscyGfo3BaFHNSU0lyuiheSN6CpMoV6QaiYIzfT1KuSCsGaBQiq6WO4zOH6ryT/FgeO5DrTDgik9juZD4PzuV4yTJGuZT5/6jE/tfIDwvldcItoMgc538x8KPstylpchKSa7hHVCICWC5d5OPIGt/iQZTp90XT65xV4TKmuvek5bnGoTsPje26UPbokQgwLlL+DNtSrTxG0geliDL2siYppKbictxT+WvjAXTZBluy74X384i/EWFubw6KaMSYCaPR8LIQMcJhH/kRBew476QdTdquLf+6stQ68SJHbkqZDejwtykX6yz8FkfoVeAyyarOuf8pgP8jgG8F8B3e+y886RozvTMYDAaD4SrhcjkivwTgfwzgHzztBRaaMRgMBoPB8ELgvf9VYBDFeArYjYjBYDAYDFcJz/dk45ZzbhhO+Zzogb00vGKOCAIXo2Agv09TYBBTlph1xudwfafXaExaIkzUdig2TBnHTzUv3KpBL3oQyHRJ1Mytp44Iy/WRU5CdS52P8anE1R9Qg6FALzFKNRMj90Dj0lIN1cVwaEQzY347DM/itsT+bwtfYSIx4IZ3nGJg1gxMzERXgKZP9anEgCchZb3ggFKuqTdO8wEGsVepO/VTqkWnJn3aFxKbZ2xSNRroyazci4EZV5nzSlL+AOeFO1/APzgOf1diFrgfzATrReBJUI+C8dpu4iH+YNgccvyTKsf48EAng3oM1DpRTQyO10V6Nw64yDCRvI9qmX4/mvvIRblo48g1IJoW/jhowUwXK8n/FgDg5NOhwevroitCzYdVhemdMIjje+Fa1UZglci9GYmB455wBcZFDOMvJVbe5ZVibJ7zsoXfyCRiv6kJnWhcCCemrKPWBbkGGsevU75KO06NyjjHllWJSs6p1tQnSblL5ImRjzQ57nTcm2mqS0EuA8unlsbkvsfenS7Jb7PP+ZZpkQw0Loq8uzgPubfo2kg5DvU87hvkZvTkz4zTvFSbZ93DNexH9m3KkYucLNkbzgr4JfVIZM5QbohaI1JeHWSJMLvXYXJ/JX1B7oTsw6msSMSQH5atI+UsVWn5nHPj3qM+l46RuaUcF+5T5MpJ2rdOdZy4Z/oVOXlp1XT9+cG8oclim+0BWmm9SCrgt8kmvX/lxFHnn5sjct97/5nHluHc3wXw1o6v/qL3/ic+aoH2RMRgMBgMhquElyxo5r3/Qy8yP7sRMRgMBoPhKuE1e33X3poxGAwGg8HwQuCc+x85594F8HsA/DfOub/9pGte7RMRjxCDIyekGNwH5fr9OXhNI14fqw0K8QJQ7xjJjzFLel/4jhyBGBh08i643jj6NLC5yy9ENRZm1Cagbkg4iRoFjFmWK+gjsqhz4dKUuhQSX+3GhXJDTn9LuKb9ZCAXfPyN45CvBJ1PhQRxvggCC8uzEYqzcG11LjHfFT0ppE4n2gPh/x4aqy405prqXzD2OjoTzYtlC7TpeCk3RHQVNkfSJwfC11lGHov2z5oxatFNkVg9Y/Mj0Vip6gruURDh8CsJmkuqfhdCSVCtghpopsJBqfOAcPqRmgbl2ik3SLVnGPe+SENgCNUj2c3/0dMkq2bqgGmqsUAuAPu+XpSShrlWXZ9g9Cj4CRUnIZA+/o2HAICj9ggAcPbJMB/W10JBq2sFik0g60xkDZSnIlDBNUHeDqk29P5oeo2F6/rZmsN5P/gt7hWvYb7qSwPqesS1UDap387oUfbPuzJysACgmxQ6Z5Q3kPGNqP1DnRHvHJoD6XRqf8i1rXBQlH8hfIVq5VXDhLwj8nHaGZK+oP5H0WzzEXT+DTRv2AdA5G81zqHZk+/IZ6rSvuD6Zt9h0I+KXAtJ5pZyKVaIGipcR9yaJSv2wfR+1PkgFwV1kZyrPAtdACnvLoEcYr+2wrWpxFunomZN46PfkvxWxDFGUnfyxVzj4IXbgpFwRaijU6XrnL4/nn5azsV1nPPClC/GtUMClIuEHz9YK7/JdES89z8O4Mc/yjUWmjEYDAaD4SrhNQvN2I2IwWAwGAxXBc//1swrh3FEDAaDwWAwXBpeuY6ILws4ckN2Ka/luiGZt8fwPH2/u4j8CiD6t2iIkjoH9A6oyhjXy7gpuV8IqHVROLTTVDuA8cXcp6NXHQLAU/NAqkq/jG7MmGyMkQPA5lqFJT1l3gmB5s986jcAAN95/dcBADMhbSxEpONEAtTvrq7jK+c3AADvPboGAFjeD9+NHgjX4IzcEWnmUMJFvS/Ia0k1LtRTZN3BCVfHV8K1kb5QP5DroV9nb8zD9xI877oCXmLFq3XoqNVJ6KTNQ8bfw+ex+F1MrtUY74dgb/lQhAyEo6L8BOU2SF2XLupEzKTeU/GwkTh7L9wQv5ABW5fRU0P0T9TTpki5Il45FRfwJHaA/JlW4v7tFGj2xQtjn4QE6Wz6/Jxx3EId6/MKkwehcoe/EdLR1x6F9P3jcLwPY3/+DcGHZ33oML8tPJyZ6K+cSX/KmPaMjaveR0jLdRf1Ncpq57mqh8F5MlirnvH8C7g1wzVDfRnyPeq5eJks2lgXAJB1X3HtOrc1TheNB89rD2rlJUS+gGRP35hsy9nsOzSy9jfXhQO1J5yTkpoTKQHE9dscEdatz+ZaO5M8j2QSVx6uyvRehDtRyrzgOFHHpBuXqKpsjnKXz/yulFvRxbp0Upd+TP5FyGN6N3wenXOwHVrxsaIGiM6LjAMTeXE7OoG8GNknyb3ZyIKulpwLHtWSRjdVkq9ybMijGfJb2H1yTid7QSf59gv2G/cv6de9Wn1vCnJeappX6Y+D9kVIBw1UrsgTuI8vC6/ZExELzRgMBoPBcJVgNyIGg8FgMBguC8YRMRgMBoPBYHhKvGKOiAPGI9UC2enfwXibxNbUa0Z0RMgVcc7p3xofJreBnA2kn5VT0naRG0IehLwTz5g544/kSfR11C3YXKOGgMT3JwOvAUSegi/inWlVpLFIvrdekmfiYrw6j023fTiwlkBqLcHdozK83P+JOuhIfGbvy+huhHO/+tYbAICfPf0kAOAX734MAHB8fz9UVTgHxcoN/HSkGW3qO8HnfKrV4H3Uc9G+zTxTJLZ9fS9ooIyrcP6iqTFfh0D0grwR0V9hbH7dpjoRzZ7D5jAQK2Yz6YOHImygfJbwkdoIxQao5+TDhLZujqg7QVEXuWYhPiULp94eW740nC/0A5H5Qh+ZogX6KvqmDPvCy7yg/sz8nXBtfXuJt64FzsteHSp+3oS+eXgW2rsahzh8Nw3x6M21QuPofRUIOUe4HvL7IGitVPfOQp6MzX9igpXMi/MD6m1Q10Xqr5yQkFIzBt5rH3P8yXUhB4F5+EUkZvhucD0Q15vMG+p5UCeir13UoDmQdbZP/xjRxlnINQvRGVkE4YjifIOC+jI5uBeI5k97M/Bmmv0ytkPWm/IshCdRrZhHSFY3HVYfk31pRt8T6UfRe1G/mB3/Is3XmWoLKc8jXDS9GdbMdLzRazet+E6dh/ngFynnRvNsfdT3yKxQopZRSJUTMwUa0fvp9riQRH/loewTuq5kvEYFulG6UTkthxOfZBgutDg/cq8t5dFQV0R4VOTkdCOH6QMO1G7eRb7e+nEfOVeEaAq1MreazFun2edvSY1StGf4I+lWFCohuSffJIaEO/5uvXqvmdcRFpoxGAwGg+Eq4TW7+bEbEYPBYDAYrgpeQx0RuxExGAwGg+EqwW5EHgPn4EsXdUTyGFtyLrkg5HvIYeo4jGp08h67z2KVqmtQZJok/Fi4yC0oU37JhfDRt8LRM4HNIOdgKjwT8TWoqk41M9bzEKP2jv40jCVLjLmlFglQLSX7u6F9/6x8BwDw1RPhApQh/4nwLt6cBU7Atx58iO/YC1ojv2/vnwMAfvfsSwCAL994EwDwS8uQ15fmgUPy4fwQJ8vANVhtRM9DOBX9efjc3Jd360VsYPxojNExX8yXtme+D070Dh7NQ0y+F57L6mwMRz8cagQwTivtLiVl3LgbO6yuy7GO/TeR/sROVEuP0Vmo09774Rh1YKgVQB5Q5Hl4LbNaZnOHOhi5/0TuQwFE/Rr52AoviP4747dDQPqdG8d6yd3zwN0hh6e6H9o5Fc8g8o76ceQSnH+DcDdGoS8O9kK/Tu6E/Cvxk9l/36HowlxaXc94HsJR8pnXEtvQj4ptbgiXjHJh0j7wZQFHrYe8a2S+kFNEzkGxid5DzZ7wYYQztJJxc9dkHrairbIUvtD5BPW5aMSI1sjQVwoAOuEWra8LV2QavUSoccN2kRtSrlJuz+qtDuM3Q99yjRR3AqFkfJxOxOG8zDlf0ZMo+yxVXq+krsta12A5D5nUi5S/Ff1VJC8XuTs5WCfqbXRhaWJzvQduBY7NqA79tzkP7apWKc+u2Ze1UzrVHimFI1Su0z1gy2PF7fib9BG5lutO1+qefO6celFRA0p1bPJy2N91j1K0g3zmc9NTt6kRDyflinA/jvu9aktlvlM+91NK2po18FXjNbsRsbdmDAaDwWAwXBosNGMwGAwGwxWBg3FEDAaDwWAwXCbsRuQx8D7E3fodsTX1fqGYBXtS4sKM09UhdunHlXJDOvE4yO8Cox+IZMlYZlmqb0CuRcJ4eNQRkbycQ7mRGP9DyUfip81ByGt9Xfw83giB29neCnuiBbCSGPVxexiacR6uoe6F6ld4oFqInsA9yV84HKfCBVBuirwj/9VZ4H/84s2P4RdvB72Q33PjywCAf3X6FQDAt4/fS9J7h8Fz5MubN/Gl1W0AwHurIwDAB4tQxw9OQ3paBd7CSngum4Ma9TRwDtxagtTUA6BlyolwQtYH4bPEtvdPHOpzxpSlOTIu1ZrH0/nR7BVoZqkOS7MvPJZsBg9j8xwvalWMxKYm1z9g+a7zURuDuijLMECec7YWro/MV/pr9FX0LCE4Hxnvpn4JS3/v0TUsH4VAfX0vNOTwbjh3InOsXqQ8qs1egeXtcM7yzZDfSRgmrG6Euh1+JfT5/ruhg8t5g9n7kt9ZLXVJPY6ihoxoMEhbNvux7xmTJy+hXpDnQS2ewbgJR0R7hJomjK+Tm0IdnW6oDyI6K+f0xxHNB+EIUOdjTV+SgxJFIx4sm6jrEgqQOH/Gmyg38bt+L9snZE2yD1a3JM+ba/TCNXDC35p9ED6PTkVTKBxWTZJ8fgI7uCEyxPWpzKVVWOf1mcPoOHxXrXL+hdSdY9Iy7aPui3peSZ2LlPMQvaUAvxJejnBSxneFD/aQeYR08Wb0seG4j86kSuQMkU71GN4d+4B5VCvhvT2SE6RyKxfrSo4SRBOG7WLd2BfaR51DVaXrpxV+USFcmG5PNIxmobM4fn454IZolaKG1e5GFQMfmuHx3ae/NLyGb80YR8RgMBgMBsOlwUIzBoPBYDBcJbxmT0TsRsRgMBgMhqsEuxF5BjgX9RgYY9OArZzTp1Ek13k4ahAwjlhS70BirRd4YtTTWnUOXB8C3j7XQmBcT+rTlzGf0Vw4HA/Fd0LOWYqfx0LewT9rHbqbQXdgxFjlOKSMSZI/MPSfYGyadXDivVIpX0WqRimPLsQ9180M//QkeMv8yuFbAIC/c/PbAAC/4yhwQ755egcAMJFA/3E3w1qCyetO4qQSny0L0XzI6tzsFegnoY0cFV+nnAPVCBEdjGrO4165IYznqw6BxMHJEShXIfA9Oi2V/6C8n4px8JS/wDhxs+fgZM7U48frKrAfywYxbi8xa7eR+bEzhwEcon5ILiEgfVJsRFPmTuDnlOcF9h6lHAPyg8hrYcq5OzpuUa2F5yF9vvyE+PgcStx7yni36It8rUB1FogPk/syZ5U/lekrjIUrcCR5jAqNwWtTySdRXZ0d/cF1nMXMc32PyF+I2jrlKtRxPA99PyojLwGIc416MN0kap3EOqa6KPS24Rxzba/crpxnwWsb8eVprsl89A7t+2Hs9r8W6jA+zvRm2DfCo4HHYA9jH8g57D/JgmuFOkXjhx6Tk9QXKWq6yOGMy+ZaH/dSdkmeEnJatXTwMqfI5Ro9Yn4hpb9WO5O6b4ARtVOo9aScEzc8HMcC8Xvv0nUL7QMZp3XKEdwcRi8iL/y0nvMhIxiQu1csS/gj4VyNuc9LOzjowrNTvRJy0RZOdY2ijghTbjIc5IicP+JdgVdPEnn9OCJfHzciBoPBYDAYXgxesxsRI6saDAaDwWC4NDzxRsQ5N3HO/f+cc//MOffLzrn/kxz/tHPuZ5xzX3LO/Q3n3OjlV9dgMBgMBsOF8M/53yXgaUIzawB/0Ht/7pyrAfxD59z/B8C/D+Avee9/1Dn3VwF8P4C/8tic6DXzNDVjnLOTgCrjjGW8d4q+FUroCJ/pe9Jn/AHlFRQoqC+g+bPcC6pTxhilck1Ec6FchjwKeUfdSYHe1Vh0ewCA1YHEExsKN+x+px8Aerml6yb8nPmA8Dy+r19Sx8GhXAh/QLxKfv3DENT99WvBW2Z2EAgaM9E3KZzHugnTYClx4lY+d+IX4xai0cDYa7dNCtB2ZDHfbe8SB19kB/mJtjXkCkiMtjpvMOF0mNBvZLfPA/kMmyOg2Rd+Cuu9ScvxZZqij7HpmWi4TEYZQULrKnNP59qAK5Hf3tO/5pwcCPFKOdv2tGnF/4OaBdSEqM/DHBudtNj/Gv1URPTAhfFavS0+IUehwPnb1NaosSf1rE7X0hcX+GTIvyeKTbi2WvnAnUHkUJSblLdCaCzdue11S8hxcgQ66vYMeTzkLjTCzZiHOuuYy/wb+bgn+JpEj5RPws852r068kqU15RyXjbX5OuNnPD+BIdfFf7Goz5pM9eq6rII/6OAi8d8+h25IjmnSDkk1EJB1NBo9sjVSLkitfDWfOV0zuR7i0slNVDQF6qJK7Ggto+U7eUXgvOyG5HL5FRHRtuhfBUSWiT/QTsAwDkf+STl7mupJTQ6o75Tquk0bJfmm/VfuXRo1qEBe9PQsLFwRbqWoiNSVfLtZBzbsUM1Fq7cSPbzKvMlc2zEYzxnfI/L+HV/3TgiT3wi4gNECgq1/OcB/EEAf1OOfx7AH38ZFTQYDAaDwfAR8Jo9EXkqjohzrnTO/TyAuwB+CsCvAzj23vNe910AH7/g2s86577gnPvCpp2/gCobDAaDwWC4CM4/+3+Xgae6EfHed9773wngHQDfAeBfedoCvPef895/xnv/mVG192y1NBgMBoPBcCXxkV7f9d4fO+f+HoDfA+DIOVfJU5F3ALz3xOsdgkcHY2zD2Bq9PPKYMuNwomnBd7h9FXX9GXPl7Rz9MhikdB3Lk6w2LdCEhzm+beUcxt3TYp2PfBONfWpcUbgHVVoPxmsn9x18Gbp4I3XQSPjQFGVQt5CvpLVPTtWYLttDngnz9gPPCdGsqITf4Y9DgzajwCtYMe8ylq1aBJJfLXlRW4AeO/V5H/oQiOOV30nnzRv4WuQcIXcBP0fj/I1HdS6clg1ju+SrpHwT9lk385Fb05Pbw/LIFZI+qKkt4VCfybltqPDBVILw5Br0KU8h6bucFyPgfKS2Cv/VUQ74IT7TjmG7Yiw96qdUJyHeffAvW8k/3OCfLEPf0BulnYV0ebNAKfyfKXlNi9CfnLOMf9MLhmNSrfrIWWj65Br1j9nSp/BAm82PXR4cAIa+KM0s1aIhX4WbVLFqpHyph6xhLFpdp7q3kEtGzoh4S/WHgezQ7FfY7Kd1oq5NruMzkrUzfgDsfUhCREjow0M+RE5/8j7yIXT/4Tru03PVM4U8pwOHdkovlFTHo5ukk4xrNvdH2YnslKJzW3sA6Ufkq5GHVs3jXjA+zTRhuDQz36ItHZHCRV6JDK7OIW5tupcKF6csMD6VPXotczffL9jnnDAe8OLpNR+LN9BE9pFSNHeyn0C2oR8FnggAlMIVqXROccAy0g161Sm5dFw1johz7g3n3JH8PQXwhwH8KoC/B+BPyGnfB+AnXlIdDQaDwWAwPA2ehx9ySTcwT/NE5G0An3fOlQg3Lj/mvf+vnXO/AuBHnXP/VwD/FMAPv8R6GgwGg8FgeAIcLkPL9fnwxBsR7/0vAPhdO45/GYEvYjAYDAaDwfBMeMUS7w6oCviKehuM4fsYgy/0ZXH5LJeKb4irQwC1r0uNZ0efmqy0zMuB8WqNcQ9BfoRyQpCkQTMh981Iyykk/3oR4959zffgQ9pNGftnyqBobDZjxMNYJxD1UpTrwM/rQZ22/Cz4OfNAGJR7UVhTdSNWIR3TD2UeOTbse5f5jqh2gcZ8Ja+N1/ozhsyU+hRMdZw6rxweDbcX5HLIgexV/r728PviVTMTz5KR8IGy4PJmI1478xEaHzq/2Zdxm4TPhfKastj8RRyZQR9o3DvjkPjCxfmmcyh8R80TanUol6OIPh3FaRBa2f+y9E1zAAA4YXuuS/sOgXnHAH4I+pP3QA8W+tb0Eg8nT8L1g1h/PsbSEA3v6/rq4XOuF0Efki4de184nffLvVDX5Q3RW1mGL0ZnoZ3VnGkY12K+BjYpf0Qx0BoBgOZQOFLXS9XgqGR+s183h7J+hSdRBbsojI+9zmPyuNjmok11dHT+99v6HfkaJahhwT2iuTbkPHEh81qZA8I7InejrwugS8crX+DKOxrul9yKa9ZB0hHXpnBDjsPx6cMe1SI0hHMFNddkXn7W/sEa2uJUcCtvqSNCvZ4K9VzW7yrlN+neSRrXiKlXDZiN+H+VpeTHYjNvoMgj28XPyje1rO4udig9Zy6NqvGacUTMa8ZgMBgMhiuE103QzG5EDAaDwWC4SrAbEYPBYDAYDJeG1+xGxNx3DQaDwWAwXBpe8RMRn5L9SPYp9H+R5KOGQmRdyrlCdO3HlRqgae4kFV2kncTzqgJOzMxUyEyJs3LuFsHMbx2LZEsR3WHdhWxZrRzqcyGbTcN368zsSYlVFPYpBsZL05QcGEWHUtJqKcr5/Mz67ro2niCHh15jxXA8BoRCIU5S8AnebxO1kLZDSbnSTop5eTcgldGEkOTcTSo8NyS1RZM7YaeKyRnJnNoXK5IGC7QyvdsqXEvjq30x/GuEAE2yKgaEXjUe3AuVKyeBuehX69gHGBI4/YCMms7DPFVzrt6jpBFfJnLFvqYJn5rUeQBluhacCH1N7wTyal/PpJzw/fo6sLoVLmn3aLgnZNUNEuTlVys/IOTqCkrasW1w6ICaqnwX/NPMs31ioLdxakLHdUTxrqWIeq2uc95Xcm0Yk3oxU+JktZL13KSk1XY/jONKCLDdJLadbaXA2fpIqiiE8mj26NV8Tkmd/MzmKslXxq0brEF2T0aEJ7hmurfCHKtGLfYmYWzHoi44X4WJuTgJbNK+FaE2ztdxofMjF07T/alk3aVvJj6SbIUc248pGidk2Dsyb2Q/K5e9rklXpCJ4+Xxwu6ZPJmDGsW9l8ylloyzWYny47uF0Deze29SgT4T8uoNOxQwhpNXVQkirFVmqaR05J4oN1OxxSyxPRfPS359hXwwODBr7iuCNI2IwGAwGg+Ey8ZrdiFhoxmAwGAyGK4TLNL1zzv2Hzrlfc879gnPux6nM/jjYjYjBYDAYDFcJF8m3P81/z4+fAvDbvPe/A8C/APDnn3TBKw3NOB9i/VuiQ87hiT2g5lliYlU5FQnLDZM0W43F5jFtvx3faykKJSI9W+pobovbwPs4GtuRX0KuivMhxg4A9WlqShUFkdK6d2OHzZGI+bwdyB+VxDNXqxCYXp4Lb+FM4ranYsq0GJjeNZlwFAV7yHPReP6Aw6Aia2ndtvgKbQ+QW9MylX6TWG9zjUpj0jWjUNdyFQ22WCfyR0YnIpokZlM07CtXvXIa3IYmhYzBs07MK6T1wqF/GK7fnATSzfGNEB9+NKUqldRDjLFGc6dmgY6ebXUqpOeXq6TdWn439MBiDDufkPIt51HttkTPVLBKym+FB1WtCi2vmpbSX9I/cxF4WgpX5O5a8g/t7qtCxc3WN0Pd+JnzJBevG50OmiKVLGQBuSJdq0VuglcWAw7R49c1OT7lulUhrlrmdXMYxoXGcu0k42fI5821Ek5MCskfKdLpt9XP5TrO607m7Po6uSlSDxEJKzZxXdAIrTmQdI91kezJNVhRaCzuAS5be+QekA/XTcIJewdhEPbGG5SicNcJn6lphBckBoflOuVV9ZWDzzgb3P9075H+a/fC8faNBuP9MGem4zCHWinv/G5oYCGCeOyzYigKyTmb77f5vqtzfHsvXR9y7oS0Ew7f+ETGtR2WF/fXXfn3E+ELHTQqmNbNw7zwa+EIZWuzz4ToitajWorA4op8GTr1sbPTee+GvDme44dumL854L3/O4OP/xjRk+5CGEfEYDAYDIYrhOcMsdxyzn1h8Plz3vvPPWNe/ysAf+NJJ9mNiMFgMBgMVwXPH2K5773/zONOcM79XQBv7fjqL3rvf0LO+YsIrzn+yJMKtBsRg8FgMBiuEl5yNMh7/4ce971z7k8B+KMAvst7/8TaXM6NSF6vYWxNzcVy8yr53Eu8rvWqreCrlH+x5aO0i0OS6xv4NJ6Zv/veVw7NXgggrq8xKBqSosk0LSQ+7fqhdgT1IMjdQJIyVtocRG7IH/z0FwEAn5w8AACcSSD6g9U1AMB7i5DePd8P359P0a7CkLoz4T2IudnoJJRDHQAafbneD96PlzazT5TTwP6V473fwfMJSSdGYZNbgazxxuE5AGC+ER2HTa1x27XodyxOA3ejFk7H+Fh4EQumHuOTcO74USvHmp11Y5y6PveoFxIDfyB9e48aEik/iLyIovE6XpEXEwbIS3t9xwEb6Hog1UfRWDzjzlWqi9FNRKuhBjqJZ3tJUUmGomniluT/UFehUG7I3ochnX0Y+qY+C1yRYhPqOL1HkZARikY0UxiLzw0VKdfTpJ+dH+hOFNlCctm5whuo6ipysNqMrEFkOjSu9ShED6UUMztyn7pZmDutcGM60RUhX6OvnfZ1/kian8nzqJdSx0Wv7ZrfriRfOVfaQ7M7ft4cOKxuyty9JZyow9C+YhRO6jfCpXgo87Uo4GRSkWOlc8aT60AeV/g8Pw3r/LybAeR6yfiVMg/Gi3Sxqh5N77deQdjSD5E12l4Ldb7xxilu758n13xwepiUO9QoAoB2WigfTLVTunxvYQV2/A5xv5A8NkeyRve9pMKJEX7Q+MzDCU+kFo0a3ZuV38HBDslkusGoCuNzVoTBbRbC9aKgk3BFqJ/Sko80dWpeusV1yaEcnPgTo7+9T/4NvnJwzn03gD8H4Pd57xdPc409ETEYDAaD4YrA4dIFzf5jAGMAPyUE3n/svf93H3eB3YgYDAaDwXCVcIk3It77b/qo19iNiMFgMBgMVwjuNQsJvdIbEe8AXxWqBUItiuQcjb1n72rnnI7eo+gyzQ+2JueK0IOG3iajCmUpGiB5+WUaT2V8tRs75YYs3hb9gf2UJ+FaiQUvqYPhVA9Cqy3v8JNHEj0qQro56vFb3wickD92/Z8CAL599CDJ40wadiaZnfUhpvyg28eHbeCN/PL84wCAn7v3DgDgzgdHAID6XrimFs2Oahl1PaDv9qd1pteMExLE+GEFT58TcidqaiGEcz99K9T5j7z5y+EaCTKfdFM0UsDd5gAA8KWzNwAAX3l4AwBw/iCQKYq5xNuXDvUp+RChnP0PpDzGqcU3Qz18qshdGM2F7yEcnqgDI+1rqXsQ483USXDt9rwLX6S6B30dvVKYh+o1CPdg9WaoUP1m4M98w81jfONB6KdPT++H9pVBz+FRG/Qbfn0e+ubd8yMAwOlqjONH4bvN0VjKC43fuyNjeh76uhDflendDepz0eYQnlM35hpJeQrss1L5Th69ajukKfs6cqJE12Zco6TezwVcLPZbK3wPX9SQ7lKuSJ7W7HPuH+z7Uak6NVt8Jmo8yDiWq7gg1zfGUod0PlTn4bP6RMm4Lj7u0X86hLw/fjOQrvZHYbyo8/FwGebufQSORbOplRdGPkIpC04pFAX3jfDZfSAeOmcOtUTYc58rto98D37vCxe1lshZyvYYcpTcLPTF3qhBL3vKg0WYW8f3A+9s/LBI6sY5vrxZKp+CqObkUgiPT8c+Pc8Xg3HKeCvUUFKdFkk3J4U2ZHR3ItemeXC9aTne4Wi60jYCwJ0i7DmbUymQ1EPliHgtt9zQj0k4gMJJ5NxW3qL+6JcJj07TV31P8OKEyV4ZTFnVYDAYDAbDpcFCMwaDwWAwXCGY+67BYDAYDIbLg92IPAbOoR+VKMgvyPkgu0DtAhI+GP8sHbo6/r37Wrk0i0f6UQGId4ij3oG7II/BYY35H4WA7OyNoPdxtBcCqKXchlIz4+x8iuZcyAvUA2jJHxF9iDO+qy5ljLx6ehx3Id58pwvGH7Wc1EmlZkJA+Vgdvr8xWWPswt/3D34NAPAz128CAP7+W98KAPjCg28AALz/MHBJFvMRsNkdoVPOy5wiHSGdPKxQP5T38ZtQB9UqkFMPRyE2+43jOwCAt8pQrwfdnvJYemnHySSQKE73Quy360TzYhqmZ9s7NDfCOPUybozbMmbdZRwRHLioCSO0AI2Vc75wStGfZzPQPuA15BiMQwFOPDCilwTSdABeS/2QvXfOAADf/clfDem1X8BvF1OXmQsVv9eFgn+luQUAqCX4v1cFLsKmr3DvMMTv/8XoTQDAiQ9x/b4KdZtKXL+ah2vLpsfoRHgWZ+S0cEy36w0MNBrqAt2+tEO8XcgD4rqjfg79cPy4BCrp7NzTST53Y9HkOZS9wHvlrYxOWP9QZ7eWVPyMHNJ9o1gMeCPqKZPxSbLj7f4I62uiRyJ+MS7TD+FcWN+Sa79pjn/jG/85AOATk4cAgK+tAq/pK/MbSTNdKdypAtsB8Hyu6Dyk3kj4PD4ByqVPzlFuD7ct+ruQQ+IG3Il8bFUXSD6K7saqrXTPOj4OnVHK+uZ8IYdoTb2PvcinUL8b4cCM5vzM9qa8qgTsCw6p6Oj4/dCgdaB0oJ1V6jd1MK13t495ST9u1hUq6aDb+2HtNcLlucv9Y871nGbWV5Hb1eyRqyYeNjWNwpQQst0uonAXrrGXCXsiYjAYDAaD4fLwmt2IGFnVYDAYDAbDpcGeiBgMBoPBcFXgLTTzeDiEOGHJICWDlv5i/xkel9iyH15L/gi1EKj5PzQAwVBbgjoPH+FBkHpxePVbKMT/Y70O8cVuGoK6bx8cAwBuXg/cEdwG7iyDnsCdRYjrPzwVDYhHE8k3amUAwUvkS3dDUPqvbn5f+K4Qbkif1ntcBj7BG9PgE/Hth1/Dvzb7FwCAb5LQ5x+YBA7Cd47/OwDA1278IwDAFzeBX/Cl9W3c3YRA7HETyAxnbXjH/sEq1PXdB0cAgGUXPu99WGIyClOnoPeP+mWEcr9yEmLm/83od4a6y1i8tzjCB2ehvPkylLNZSmXPQ1qsZFzJ6dlv4aYSM74ZDi6FR1Aol0NSmdFtAbieHhjhGHUUeI7SjsjbWUY/kMS7A4hzLOM8UMum6HwsL5te7TTk8S03gmbIdx3+CgDgG6sT3JPx/8X12wCAnz3/NADgF46DDsyHp6GvyJvZm2xwYxpIDN94O2iPfLUKdXg0C3Os+Y3QwL07ovty3KISzxfyLEp6BXGd9SkXoR+HerX70WOJmg7kSrkBtwZAqv3Dv7M1yvVLjor674wL1fNoJ+KPJNox9ZmsEfH9Abki5Jh1uwg6Pj1HBrvbE8+jmzVWN6QuMqS12K2MzsK1G2nv8htDA/+t3/Kr+M6DLwEAfnX1MQDAzz8I4/TBPeE9CeegIAds5Qa+PSmvKPezyj2x+gpAxglh3zPlvNcxQOQmXQTlo61CpifnU3StcCbOQsYT4XkwX2qGrG6J3sZBH6kRJ0rACde03Au4NwzaM2wggFL2i1GgcKB7FOqxEcpNsR9O6A4dNoey386qNJusP6nb0pyO8ME0rJ+9OuzRh6L78lC8gTbS3oraT/PhXiD5UlMo08Rx1KIiP6dwcFxP5OkULmnvK4PdiBgMBoPBYLgMOLx+T0SMI2IwGAwGg+HSYE9EDAaDwWC4SjCvmcegB4pNBzQ0WmGw8uIYr36X6fq7rkfR7I69Mf7cF9SYoC9J+L4eFZGnQojvDeOanoF+2lo0MXY8Fb+T5jy8aH7vIPA97r8R4pGfeDNoDHzT4X18bBY8Kaoi5L+S9/U35VjKYXtDUi0cmvcCF+NrkpYrxmtTzRGGHv/lKFz83x39Vvzwre8EAHyj+NX87htfAQB8Zu/LAICPl6E+n6oDv2CvWONuHep9vw18lpMutOvDUfi8bsM0eX8/HG/HBVDR7ELiwOLDUAs95s571wEAf/thyLsX/kd1XKKml4fEn8cSJ6ZmAvkZjIOvbo6wvsnxT7/TuLDEaRvx0fCT2EHlIH4ObOsPMP7eTiOPKPcIQhMq6WWe0GPHyXwpWq+xcS23Sed1KwH9X1wF/5+/d/at+LmHnwAA/MsPAy+ofxD0SmqJu7OvOFvPZsD926Fyt78hzDP6+nw4Dg09nhxJu6jXU+q8LpfSMMa9VceBnkHU+ZBY/V6BZj/VDyE3gPF99i/b65o++khx/ZLa1bL/or9PqCvUy2lzSH0S6omE+TeW9VdTH2UZ21Rs2iRf3VtYZfGi2RwJR+R6of4mVZC8weiE/jrh8+rN8PmTnwhr5ROTh8rh+btf+xYAwPlXAjdk9DDlLLFd5RooV5Kv9hPP4V52kZjLNkcp50TlvCf4uIdxTShHhfQF2U/Kk3DRpi20DtW5tEP4EVxn65viwXJb/H/qDv4kzNVyw3XGdtLbifs720uOngueYwAq2dfHj8jnkrxWoW6rN6TStR/oABVyrk/a2dMnifyukwpnPuw/X5RFf028Z4qC81/KE44IuSr1mUdFvhhT8Slyq9A5njwr/jb1hXJDFLt+214BXrfQjD0RMRgMBoPhquA1NL2zGxGDwWAwGK4QXP/kc76e8ESyqnPuE865v+ec+xXn3C875/60HL/hnPsp59wXJb3+8qtrMBgMBoPhKuFpnoi0AP6s9/7nnHMHAP6Jc+6nAPwpAD/tvf8h59wPAvhBAD/w+Kw80PYaJ1aUReQaUG8gv1Q5G/Ludl0o90PfxxdOCOP89GWgD0lXx+NevEOoT7IV81evCsZoC4zOs7j23ZDS72R1M3AovvrxtwAAd9/ex5uHQZzASyxyKdojbh3qzvjqMLbsfBqnJe+CsUr1lUgpCfBliV74Hl89DOmvvRU4CD/69n8PAPANNx4BiNojheuxkUDzRjQt+HnZhrqerkIwnd4z0iA5JtwaiZ/WEscf3a2k/yS+T17IPMbM+Z4+286YbLVM+SCjswJLiV3T/4F+IGy7aiPQD+hah+YQSb0Zo96yhhjejst34wfh4Oy+cDbeE0LBXAqmfopoWhQbj7LK5ofMx2oRUurD3DkPY/Pw0R7cnZDv+CE5IdK+ufAhFmm83ztgeS/06b1VyG/96WMAwK39MFGad8I4Mj7ui1L9dSaPZN4tMk6FdEIn/j7NPnU+nPIQVDdEuSHkVGS+O8P+zDUtPMeenCJyOoCOOiKiLbIRTgo5Kusb9GKRNmjsfoR6meZLngLRTcM1q6NC8+S8q89TDZyVlLP5WJigt2St/Ozxp/AL7wf9kParQbNldpdjnDaTfVG0XjkLqq/RpudQX0S9sKgZMopaSaofImNBfku7F9rbix9UO3HRA0j1PFLBEtVDWtOTptA5y7qxvM31cG13O/TFZBYuXs9HqqdE7otyUciLIA+oy8r3BTzXD/1pZH+o1tTxIFeL3B4/WOP0v8nK7bj/ey2Oei7nD2VSUZ9EtJl8NSApDfqq3Hj9W/VQlH+U8RaJJ+i3vFK8ZqGZJz4R8d5/4L3/Ofn7DMCvAvg4gO8B8Hk57fMA/vhLqqPBYDAYDIanhPPP/t9l4CNxRJxznwLwuwD8DIDb3vsP5KsPAdy+4JrPAvgsAExG1565ogaDwWAwGJ4Aj9fu9d2nFjRzzu0D+C8B/Bnv/enwO+/9hTxd7/3nvPef8d5/pq5mz1VZg8FgMBgMj8eVfCLinKsRbkJ+xHv/X8nhO865t733Hzjn3gZw94n5eMA13bZGSBXvh/yT7uTI3XBOY/C8grFRxhD5rj1jiKUGZQE/Dk2nX0DUJ2GsuZNyYt1ZnsZ8NfYaksmxeGOch3Rxeoiv3AhaIBhJnHEp5xxTI0HqtmL7gJ75S8oYdkU9gnZ3HxVtjJGzruuvhnKWt4Q7ciOkXzqQ88YePp99WajTbUIe0/vCY1j2YRyBqL8iOg6M1Y/oP8Ehlhh6uY4xc+UYNGxfL+dSJyLkOX7gMHkQgtbr6+SehDwa8gqmqSdHeW2DvVmIa09HIa5d0bOHseqs3Zu2wkI4PMtR4AAs3g8Zzo7CZ3ciQgN9yikq173OP7aZU2t0Go7PfyPMhUcupONHBUYn0j8cW8bvB7HqJF33ml+1CH1xvAo88fknA3Fgfy9MpuqNkM67qeo2tBPxEjlOeRbst2YmHArxl/HFNjdpmyMSPitTvyiUe7XlGSVcLGoyjIWzMn4knAgA60PhBVzj2IZLyY9gSv8T1wLrhmsz5UHkvBX6ylQLj2oZ/q6Fk7SRNtNPZe9a6L8P5oFs9MHdI1Tvhj6e3RfO0znHBzvhuqivwnXrMoocvY60feM4Jpzn3YzrVeYd95NS6v4ocJma/QJ9Tf2YfHzC5z7jpfnWxbGT/mr3wh/trdCRs/2wllbLUE5xv8bkgfSBLAlyvCIXJuVReeFw+N5HPp8YSpVc+xyTc/ZN6JRF41TvJUfkpoRU+TSzHo7aS+chn/MiTKZqzB8F4cCI/lA3isJO0Xcq1bC6CK6I3BdF3+G1I2xcAp7mrRkH4IcB/Kr3/j8afPWTAL5P/v4+AD/x4qtnMBgMBoPhI8E/x3+XgKd5IvJ7Afw7AH7ROffzcuwvAPghAD/mnPt+AF8F8L0vpYYGg8FgMBieCq+j6d0Tb0S89/8QWw/rFd/1YqtjMBgMBoPhmeH9a0dWfbXKqt7Ddd3O4xd2nOr4M8Ys/IFVq+/f03ugF10RehIUHd83l5hrx/ixj1oSLo1O6bn6jr+k3cBLRGKr5JG4NsbvQ/6hAtWywPpR+JvaCLylo0YItTQYb/cuvg9PngB5F/WcGgl9UjfyWIrW63eFcDgYg5/doz5EiMk2s+jDo/oF6l+Raheonsdp+GP8sIFbSlCcfguMjcq5hWonQOoeeS65JwXf069WMrbn4eLiPHSOmy9RV6GSo7eOAACba6IDc1M0UK6l98pV1eHtw8Cp/q2Hgb70iUnwZplJ5UqZBCddGJz31kf48nnQ5viV+Vj6K4hZdHuhvErmmuogDOdtNmcI6qZM7tEjIxwfnfqoTcNlQU6S+mik3KVi02N0EvqFWiejc+GenEn6iVD3Yi/E9/ujBvNKxn8v9OP6OHymv49q0VALgUnrlb+k46a6DT5prxv6apAbsuXHkfIGatEzKRct3Fr4COMw8ZoD4T0ccM5GrQwgrnNfpFwuHkvqJvNwLP09Ou9RLkNDuG/M3xRuys1w8qwM3995EN72q39jjNkHontxmo2P+qlgC5EbsnusOW+6femTNwJRYjRqcSjeKDengWQ1q8LcpebPo3WYu1/1N6WPJnEfVD5dVh6LJY2r9HEPEO2W/lA4PMINaZpwgn8YxmTvgwKTB9z3uE9xbHfv5To2w69VByjjl3CP0/6stB3cZ7mX5b473Z7w1g4a9PMwl4qVnCS+RW2b7vvdLJTLfaSeO9SZNoyujdynTE/w2xojvTeKyFPAJN4NBoPBYLhCuHKhGYPBYDAYDK8R7EbEYDAYDAbDZcGeiDwOzsHXJVCLhke3I6C6dY3EO32IWRaNpMsm+nyIRkInQWMvGgZ8JzzGb6lfAn1x2Qn3gB42WxhyRPg+eRbXdKqNIBwKHu8rVBKb3BzQRyON227Flgfxbo3haiyXfUL9hH5YHcBHLRXyYaj3UYkHDD1GRiOJr1YDPRaWKxwRjcXTM0Xe9a/PG7iNCDWQ89OlvBWiz/gnvthuj+/TcrbGouvglyFuXsl3rg26Hu0s8CKoG1GLZ8Xq4RRfdcFYgj4/awkiH4ogQS/H76xFJ2JxiEeLoDPgNynfqJuI/stURC3aVtpH/RK3rb8iY0qNCWqpMA5ebrY5Ber5oZodGb/Aex3T8iSI0ByuRcNlHvrk+DTExRcfC3Xub7ZwR8ItOJS+WInWxIb9KTocwgep6A107pQbEv1IUv2cXIMinOTSlJo7mc7CcO6R10T+0fg0jPlY1qjqtBQpBwKlQ8/5XBdJvlpH9QSSMjzQjcM1q5vSX2/LuEhfLVaBD+HvBs7N5IFTrhP7JPc60j1h8HnLg0X5aWnqroVyf9c77wIAvmnvHq7JpOll0Txsw3z/6kJMUwSF6GH0NdBNMt4b6QplmlI7o7/ZYCJckL1JqMPeKKRcD6f3wtya3C+lLzwmx+TISTnKl2E7L+KKeN1stL/4mVwozmkZt2kRdWaoQeOUxBbgxWMGk3DN4eESq0k4d3Uq5jziU8R5Tw0l7n2daLi0M4eGXjrCjynWXJTSv9TK6ckrHLSX3wEXv+rxsuBxYd9/veKplVUNBoPBYDAYXjQsNGMwGAwGw1XC6/VAxG5EDAaDwWC4SjCOiMFgMBgMhsuDCZo9A4aCZjnJRkhnNKfzk0A66vZG8BSWUUOrlPRIUmROKCu6gaDZBVAiKKvlAJAIl4s/1Sl5jseLjcfoXEhyIqDTrHhtXqeQdiPAj5JqR5EwmmeJABjJUyR8+cKpuRlJe3l7KDBGQyol/GEg5ib9WmfEP5LEypMlsBYGZjbhXT5+7KvhGGQkx5zsqAJMJBJPxlE4TEiyxUJEz5og6ERCIE0Ep1+r0N8NBLsvXgsEv3+x91Y4d5SKr/m1EDebAqDY2JmYEi5SsiguINS6zg/Iy0hSmpq5He3OCaA5OTUXSeurAv3+SM5JyZ3T9+ZyPLS3WonR16bG+k2Zk/vSf/tCtqXh2obiUELM20QTwWg2l7LuLiKv7oSKQQmBdkyBtZD2g/lazmVuCTGdc61QsaiM+Np7lBcRnVWZ0CVpd20Pm9uBiHn2CSGUvxn6pi5oJBfUvaYPaFDptw3rOF4cU5+NW7dN4tSxJdgM2SMercKc/jJuYSPM0rPNRL4LdT5fhn1wIyaNvQiNles472jWRiNAuHT8eiGr7l1b4uPXgvvipAx9ftaE/Jdiclc/COM0vSeE/JMe1SIVm9T5QEJwk4r+kWzsUGijHc0JZfx5rq4VmmnOC3QkVsu+pwKSIu6mYyNE/brqMBuHuSPaa1i3oR+LU5nnUn61FEK+mpD6rf0pknAf8+NRZMzUssSrZ6u+fk9EjKxqMBgMBoPh0vD18UTEYDAYDAbD88PDyKoGg8FgMBguB8F99/LuRJxz/xcA34MQf7sL4E95799/3DWXYHrno5AZY8DeD0zTMpM7grHlfYmNXh9pzDo3kepriXtXKVekGMR3NW6ZmZcNRZKAeGPZj0s0h2WSbx7XV46IinhFkSueo/QEinqxzgOjuU40s1RYTIzxaolj0jBP68hyK6ciTblBVLkUESDheag42xCaD83ZSL4RESeJ3bvTOfxKKiFmaryW7WilDYyv1hJ7hceWMZmaVo1YLl0LZXxHFYqpCBLJ3PH1QDAIiAJ1knd9DlT3w9+z96VuoxDvpg6ScohYnzIeE80zjM4oVidztSUBJI27+9LF+VamqXIsVH9Jvl/5yDfK5gEyPkY/2Fh4zohGYYsQ6KbQ2fheEMFyEg+vFjWWYnK3PgophZu4AVCUqlzR9EuqMfcqyKYmk/m6Iu+o4xwuUF4kECi8n+YwlDx/i4ulxOhGODY6Y73FvEzMEFXwjPwt+Yym3Sbn5Z85TtMwB9ZvTnH6yVDe4h3JbxLya45D+ZMPw/fjRyGLIECXchi0ODZDhbqk2M5j21CQB2T8yOm4H+r2L9Yfk++BgqJam5RnpHNX0tGCooNReJHrKedpKWVBxqtpSjxcBl7KWgw7z07DAi7uhHW3L2todi9UdnTaKs+MpqP5j1/ky5DwIZyz0ikPjUJ0NLDrpM6lcEa4f/UDoznXk++WCsSxj0rhfxwfTPHW9TMAwNF+EMe7u5JFeBbOqaR/OcbjY5oxepQi4Fgto4gmgLgH5AaufRE5IpctKPYUWqEvEf+h9/5/DwDOuf8tgP8DgH/3cRcYR8RgMBgMBsMLgff+dPBxD08RKLLQjMFgMBgMVwiXGZoBAOfc/w3A/wLACYA/8KTz7YmIwWAwGAxXBf45/wNuOee+MPjvs3kRzrm/65z7pR3/fQ8AeO//ovf+EwB+BMC/96QqX84TEeVhyH3Q0PzOMfCc64lIDFE0FNaHpXJBGCvM3/tmHJXmd4zoFW2J+iDEPqvzkPrMWMtn2iDNQYnVkdRB6AqFxM5LGnpp/DbG0DWW/IQb1E60Q7qJQzuTdtBMioZk8h499VIK6hO0g3wmKVeD+iX1uWghCP8jch76rff8yRHpyRWhTgnN9vo+cnnYVjEypFFec5DG0iP3wKvZHIday1WujVyzlvPqEt1endalIO/DJf037Ds1m1vKsQWvRZpKfL+dOHQTOVcmi3I3yEkZkWDik+PdqEA3Zr9BjpEvI33CunG69wMOEccw0w2JGitxTjGOTu2N+jzUiboyjN0zvj590KGWtrfZ/FCNHZqNcU51cU35bJw66YLIfWHdaA42QnkuHAPhEvkmxNf9OAwU19LituQ58TpXSzEmK5ehINadmi7VWtIl+Tu98iKUf8b1xvETLsJGODJnH6tw/gkZwwPpfDECHN0L6fgBpJzB4s0kIZTfdIHGii+hYxfHNBXMoQnd6FTyOBGuzAnUZI/tIc+o2Zc5dSh5ynzty2h6x5Tzr6vTMS+Fc7Z+OMU94cW4pbT9oXAnHoZzpw+EL7GI/dvJ/OvHhR4DAHrPUe+I/D/dV+pS+SvkhixvcK+RPIS7oeaCiP1XiYaKrrcREtSn4fh6NMX9KmT45uE5AGD/MGwGZwvRr1mkBJpS5la57FWHxZGLkuu/cAMjBhoiqnt1KU8m/DZH6qPhvvf+M48twfs/9JR5/QiAvwXgP3jcSfZExGAwGAyGKwTnn/2/5y7buW8efPweAL/2pGuMI2IwGAwGg+FF4Yecc9+C8O7OV/GEN2YAuxExGAwGg+Fq4RLJqt77/8lHveYV64ggvPNPzZAhV4QdVzCuKNcwnipBpHYi753vOY3Bl/J6N30DGOfLHzNp7L52yn9gHRhb1vf0edFAA4DcEMZnyRHpqbPA8KnG3bfrEPUF+JmxeafXdpOUK6Ex5l5ivqssVi6f9b36QQPcgH4TTsrqCOh4RO6CfOfSmLJySdoOXjRgXBYnZby7H/nkWnJXmpnb4j0Q5NrUjNtK+UXT66nKB6InReZz0dfyuYi6HjxH5wXnFLVPKEni47FyxbpI366FR9AMCDkYzpsdCz/jKXiJ7zuRYHHeD+YMG5hd2qX9CIjvEYBuzLkbGlCSa5P5CLm2V65BtZD8M0+M3CNIvy9iLJ6yMvmc5boaalt40f2h/g8amnqQZxW5IQDQ7nv0406vT+q2oX4OeQP8LFoQq9gu8kjILVCOV7Dfweqm8CJu9einMnbkhtwP6fROOHd8St4JpJ0u+lnJXhB1PVJtFZ3j6zivL/IRUh0RmRfUCZre7zE+ZeGSyJ63krZTa0j5GStse9mA5UjfCF9rfCzaI4sqejUJn2p0Es6diK7G6Ez4R9QfckAn/Cjl6m3IWct0XnRvp0hPrF8zEz7H9XQ+1mesM+d4XItsM/f/yHOSawMdBOWqxHp5AAB4/xPCfZnIPKw5QLJ2pO4cq3LdR08veufkHjPUOco8fORg2vZXCb9j3/86hz0RMRgMBoPhKuE1c981sqrBYDAYDIZLgz0RMRgMBoPhKuH1eiDyim9E+h5uvowa/YwfF0XkjVyg0a/vZVcxLk39hz7nXVC7Yp3GZMkZqNb9hXFUxvyLdcojKGalvo+/vpFeS50P1RVhuh7ETfv0O+U4pJICaPaB7p1gdHJ0LRh+rJtALjhbhhfmu7PwuaQ2yEL8KNZO9ShK9UoJBYzUmiUMecV4cdcDDdueeic4T9+aHTHQC7xE2pnEb6+L/4nEYufTUO7y7SLyVzheHXkZ1MUIx+szad8qjmWt2hEpB2DoFwOE/iZ3ZnwSCqLuhOobCKjLUXSV5sNxGh+HdpTnErhv2cGhIMc4cuNVd6DgfCCtZI9zNtUxaTqnuhDKXxIdk6iNkLavaOLYkg8xOheNFpkH1VzaOw8VqBo82fuCuizUhmAcflJGrQzWtUz5EFpTFjHs3l1zB3E8J6JX0Z071b1o9sN33V5ohxcuRyd6H2otJVmvOgdsuAZEj6JRYlOoknCW/Cj2Q3lGzYxw7vSu1OkkjikQuQjNzCmXgePE/Ug5V9TBEK+e8TEwPmWbo+5JOFfm9FzafV7K53B8+qBFfbyW9tBjiZ0edFqaPfoyhWR01qMSb55uKn1BLyDV6JB5cxrrXIs+SDWnv4pwQjJtGs6Pdr9GK/wO9Rxq0j2N4YGcK1IOuBYddWVuyTjRA+mMWkncGwB/L1wzoZ9WSZEYaRfpH2ecW8D0bjg2Pw0koflb/O3hb0L4GLV+6O3jUVCTRn3QpGHcAySlfg4KN+A0ksjV4TLuCi5bWfWjwp6IGAwGg8FwlWA3IgaDwWAwGC4FHpftvvuRYWRVg8FgMBgMl4ZX+0TEOaCusOU1M+QbKFdEPjPW5tJ4YLkZaEpQfiClOGh4vVDuSHyfnzHPrSpmXhuMQ/aVw/p6OKf7VAjST2chfluXoYBG9ByWAy4H49CVaGOMTiRl3HiV1rWbeLx5M3z5nW/+SwDAYRVOKiUAfb/ZD+k6pBsRwui9w3kTBA6OVyGG/PA0GJw0p+KtcxyGvD4VH4955FtQhyXXQ6FOy/ReaNfMe7gHx+Fgm+pqSNVw63Zow2+5fj/kLXHScdniTOp4tgmB9nkT8j1dheOLeTh+Lr4X2BSoJH4+ehTqP36Ucm+UW8EZPeBEcJ6UEu929JDg40tfSfs9nIh0KP+InkMyRx15TdTo8HG+qOeP6iSkfiDNJ8J8ObgWxBqqskNdSkxeAvjjqpXqh2s3Mqc60ZDZtCUWq9BfZxzTB2EsJw/EH0TS2f2Qjk5blEvRfWmyRVJkvkLqO0S/Eof1AbkbshaEv+Kz9YWz2Ofat7p+KZAi/AThIrgPw+Fy43UNbPakrYeVlBuOd1PyPSB1Zl2BLQ8YjoFya4RzMCePxukaHJ0KL2GZXrO+Jn44b4Vr5x/vMf5YIDAd7S+keeHk06XM5bOQNnelkkWhc7RohH9AXZE1OUXSRRlvzLU+6uRk/C3lY/Qp/y3n7QCD8WFftKnuULnxqM/DSZwnWzyqWRiL1c3QruX1As1Byl+aPMrbJw3L9lS4XtuoHlHXw7n7bwRyTdvKvBf/odXJGH0pe5f4xHCN5j5NqivSe0zO2Fapm+S7Pkr5Wusj5kGPsZiP1l/Wju4B2d7nnNvZ/68aDt44IgaDwWAwGC4RdiNiMBgMBoPh0vCa3Yg88UGSc+6vOefuOud+aXDshnPup5xzX5T0+sutpsFgMBgMhieCZNVn/e8S8DRPRP4zAP8xgP98cOwHAfy09/6HnHM/KJ9/4Ik5FQ5+NgE2ojHRx3i0xkcd38vPgr6VVJW8hbVX7gL1G6hvwPf+20n2jr/GSJ3G/gt5j518FXJC1C9BRQuiFgG5IZ84OgYA3J4GY4RrdYj9t8LZOG6meG9+DQBw5yR4HpzdDZyN6QfCdXjAOsX48Mk88Du+eP4mAGC/CuVVEuzdCBFi1QZuwKwKohcHoxVujkOM9ZMSVy/fEC0E6YRlF6453YQyzpox5pvQB+tG8t2EczYricUeCzfkvXD88PAG9t4L7agfhTZ3B4GvwBhpJ1yLqQwSUwCYtyqSIe0iP0J0I6R/3Z7oAdSt1u3sOJS7vi8cl7NUy6A9pC+Ew3IhWh+t9LXoVFDDglB9mGsF1kepBgjj0eUy9BcXDGP2vlaBlsiv8KmHzfp6OP7Jj4XB/u3X3wcA7JdrdPJvgbM2cAs4PifKnwn9WkofzaoNDurQP40UcH8VNBI+OA1klAf3wuCfvy/z48MSYxlD6kXkvjtcI71oqmyEF7I6KrA5Ct81BwNOBuLaqOZR6wEI2hraL9RbGdVSDteXXEtPn7MWpfASxtQWEY8b+qvQ0yTXMelrp9yg3Pcm93aq1Kep3+KYcb9YHYWLT79JmvBNYX1/2xv38am9hxji/WXo86WsGQX7s8RW3Vh/aiOpTsl+ul+1sxJOCDGFzH+OT7NfpteoHkaBjjobRZpf0cc9JvwRNZnaaa7NEU7aXAsZz98W/tub4Xhz4OFLerFQW4U8jHCNE3OuUuZCsRKe0uBf67q/ivYO94I3rwcuzrgM17w3vYYzIVuNhHdHbpuCe8A08ryoB0ROkv+AOiHyGzFlRaTZsjU1+075JE45ZjLG4pfkuuzns6q2uRnOXailY4h44hMR7/0/APAwO/w9AD4vf38ewB9/sdUyGAwGg8HwLHDeP/N/l4Fn5Yjc9t5/IH9/COD2RSc65z4L4LMAMKkPn7E4g8FgMBgMT4XXjCPy3GRV7713Lje7T77/HIDPAcC16duvV+8YDAaDwfBawf+muRG545x723v/gXPubQB3n+oqF2LHrpXAYCcxw66/uOP4znbG4QBifFv1G1T7IY3/8R171QZxBcqDWvKYpd8V6bnOR38IalmcPwrXvCv3X0vhatyepVyRo3qJYj/ViXhXArarbpZUuha/kHIJbL4SYvy/9GGI/TuJZ1ILwWVSEN1EtARmPdw0dMZoGjgZNw/nSd2uj0Ld3pkd6/WNBNTJPaHOxwPhHnw4kTh4uyf1KNCNQ+dOhavBeDu1GR69G7gx/9/zcJ4Xzki/KeHmjBlTu0IqQqIUNVXEY2R+fYPRROaKxKV7tlm0H+iDUl0Tvsz+EieHoY+X74TAL715qJeiHIPRwNtEvHGKU3KSxFtjEjgbkyNyU6Q+5BL1PsaSlXMg7ZH2rSR2/pX5TQDAuq1wsg4nHUs/bZbCpZiLtsmGPAmp10GDN26GsXxrL6Rvz06T9M61wEf62q0jAMCjj+2hPJF5Ju0ahUs1zk4uBdcOtTuafR/1O9gl5FptUs0Vch02+wWqg9DnTtpHPkQ/De3rJimfq2jLgd4GeSy5MFAAOQ8aey/iuiX/Avk6Zkr9IOeUg9LuibbENeHrfDKcUn2zcENuf6hlf+nsFgDg3jyshUcPQ0e5B+TgCNdBeDP1OVAvol4HABSZzxW9iMglYt+c9SXKtXA3qvTcTVheWN8ULxjZG6pFgZHsU+045SboGHO8plGjKWoxSZ/Id/OPyX71Tlg09UHgJ5UA2nWoVCc8qk7W1/qQ+5Vws2RM+GNTLjYoFsIdexT2mtm74dsTHxq2elP21GthDMqih6/ZP2xP5LgAcQ9gX7nW6VwZn4SUHCn6QXH+qV8O+6HxKJt0nHTeUfeKv03UeOm6bQ8u44c8FZ5VfuUnAXyf/P19AH7ixVTHYDAYDAbDM8Mj/MP+Wf+7BDzxiYhz7q8D+P0Abjnn3gXwHwD4IQA/5pz7fgBfBfC9L7OSBoPBYDAYnhKvmdfME29EvPd/8oKvvusF18VgMBgMBsNzwiTeH4e+h1us4Vbycjc7y7ntR0IuixoVjNVLbG/sNC7cMXY4Tt/HV48KeTfcdYxpO1QrvgseAovlivoT5Iak/gL1WYvZhxK3lwybUUg/qI4AAF+bST0OwjXVQYPROOpnAMBmzYBmqqdQLqmnAMw+kJhqCMdifBxub0dnIa9imXoc9CPxZZiUaPdD/mvR9Ti/EfgCD268Fep8TTgWU4lrjvoYxlSTC/lM642l8ArmjEN71WNg3Llchvz2P5CYtbyD713gaYzO6efRa5xWuUKES+O17VS0LK5PsHxDODQH6TyplhIzF55HVYc837l2gneunSTnkstDTyD6uTCtih6teLq8f/co5HsSAtKRn0OeiXgILTlPepTZFK6FbzG9G/rv7j9/I6R9SEenBUaPwrn70j815wFj2ZvQHnI31ocTLG8GPskvvx3y8e8EAY+P3ToGANyYBB+UT14PmZ/OljgTH5/5IqSnwkXBhh4wUmmOPRvjoRoP5PSUa/lM7pIs5yFlXT03sph5Pw59sdmnVgi/iedp/g3nCXke5ONoKXoN57DLvKmiV4voSFA7Y1ygES+b87eE+/VJ4XJ8OmhYfPx6mD8fzANH6oM7R3APhQtyFvLZk3R0Qk8TVk3yajxG56Hs3M+F1W/2Qyc0t8L6bifhvOU3Rn5BOZY9RbR2iiJcvC+fz0/CnGjuTyI3hHSZXC9FeSbUWxp4K8leuTkSns4t8UcSbZ9GeB/r+QgQHlNF/56F1HXNciVtOJeFV7Vq4MRTbCp76rVRqD/9ktbXw771Gx8TLt1hC0e/oDXLkf2WOjNSd/Jm4ICemirCUxmfpDo6yt8R359qQc7N4JGCzCHyWqiD5ek1M/C1gnABncx73/fDafrq8JrdiHwdWPQYDAaDwWD4zQrzmjEYDAaD4arAI31K8xrAbkQMBoPBYLgy+M2jI/Js8B5utYFfrZPDrizg2XF8J5ufJb7pr8m78eJnwPfpAcAx1i9hb+o30BuDOhGMaddzN/CdkRghfRCEE+IW4nfShONV6TATzYxqLfoX1D1gyJceEZNK6lijJU9FYuFiFYFadBz23w+ZTB5SU8VrPvV5iEVWd4M+hH8UYtZ+uZQuEl6EcFVGezNgGhq/L34IXrx0+v2QdlN5519ip31Vaiw56kFIHw88G4DI85i9v0J9L8TR3YaiHJz4QSOjaKSv5qE/x/dDnd35MnJDyAkpi+SzpnL8YFRhczPEihe3xQfnkHySlNyweCA+PXgDdR36lB42rfA76LvTCo+lFb5HWfWgNp9fUMdD2n4qbb8nOi33pD2cr0URfVQk/l2sQ58fCNelnqeciunDFuMH4QPnHzrOVcafhdsgfh2zcY2+Et2Xo5D/+cfDmN97520AwNfeCtfW18Icnkw3qEXc5Ma1oCsD0aEgyJOhp89yKTyo8xFK0X0ZnQpPh8tXdT9CWs1FS+asR30a2lWchPL8Qubs7cC3WF9P9TCWtxzqhfST8omovwEpN43vk9dVrDvVB+F08DqnRb9E+q85EO+U2xXOPylt/lRo0JtvhvXFOfDVe9cBAN2HYe7tvVtg8jCtW7VO+R+F+F4N9Y7KhewhJzJnltKBSs4SfR7RADo6Cn02rVusRXuGc5f8praVObARP6gl+WtRr4R1ooYKfWrW4h+jvjG3WlR7ohMyCnWYSh9sxF/l7E7YyOpH4dq9hw6VTCWOx/gklDe9J3mJD1VxKuSRZeAy+abRtleS7sk4Te/JeIn20/mdUP7i7bFyLWZ3hTN3zvkgexk1Qcayp95YoRNu2fFbogMkHl+zD0Nmk0dS92PhAt4PdXXrjXrKKOS3QLkhTDmOXeSVkL+IpgX8JbzCYjciBoPBYDAYLg2v2Y2IkVUNBoPBYDBcGuyJiMFgMBgMVwVGVn0CVHqWfg/xgYwjX4DHfKYxITHEVl4r3xxFjxX1XvGM0SM5Tl+BYiC/0dXpu/bkhjBGr3E/6omsGkzuh2D16ET0FDJeCdGPhIcxrdTHgroJ1EFhXWqNc8byqX1QnoVYsj+TOLtwQ3ryMvpwjd9IEL3r4Bi3FB8ERqEreT9/54D7tM1+L/As+muhs7tZ4AsUa4l13z+DPxHeCnUa9sUXR+Kk5IaMjkMbivvCb5kv4LtsbFX7Qd69b1N+hKsrTB4GXYH6LMTtl7dDHekPwph88eXQQvfFA+UySBg9aluQg9OnaXMArG/KeOjcCSnj4KOHIc5dPhKSD/UCxjUiK0CukX6d3hOflzPRHhFuw+jOOdxZiEkrb6of+FYM4GQ8i7pGKX9X9wLxaHIv9P3hV8Ln1a0QV1/eDMfX1/ewuCaaC/td2mhCOAfFUuaJ8Flmc6e+NOSAsE/o8cGGU1um3PQ6VxhX58bY7lVSR/n8DaE/q1EHtngu3J1OtH6odVIIN6tYCb9gJT5H68hbYco1xHlB36nNkXAfbrWY3Qp9/7G9sK42wr+49/4RAGD61TDvDz+QcXzQYfxI+A/Hod7kknGuohbORh35Bepvw3XLPhEOQiVaFpDxfHQayn3UOtXJKVephoYTztn6DfJApBpl1NUYq34If5REQ0i+b2UujA7XKMXDiZyQ7iSkU+FUHN6RPngo/IzTTrU2yI2i5k11GvqG3BA/X0q7wz7lu173e/Jl6gdzJJB9pGgCicj1o+gnRQ2jlvNR+Dri8+MamSdFj2t7If/FJPT92WhP+iBk1s6EN1aHPp/Vsrcv2ri/M2WdKxlj/tgX+cpH3FMLB2ztDC8b/nJ4Kc8BeyJiMBgMBsNVgnFEDAaDwWAwGJ4O9kTEYDAYDIarAuOIPAHOAVUJNx5vf9f32+cC+szGS1yccf6+9hoz5KmVhCL1/XbRDemzVtZzr3FF9VORWL/zqcaFVme9QXX/XE6WGOVi8F48or9AKdod1XiEWnwkRtTxqFmO1FF8JzTeWro4iai3wXgf68i+4HGpj99soseGniPfCedA+Rn8PJiw9Olw5yGgXq6C5kMxE2EWalqcnsGLJkDORWE7SvUJ6ZPyUDggo4h4vn/fiYeDvq/faFv46K6k3gB1I5pQ16JLB3l02mF8X2LVm7RAX6UPAvnO//qNCU4+KTFy4RSMjlPvF+UDMc5PL5VykKf6FAmnRvgC1bnErs+l7+4+QD8PcfSc96PI52FVwwkPwc2FIyEcoumdMMcmXwvjdTgLc6+fVokfEQDVIsl9haiLUooehuu8jqFyDSibQE2aEXV1pK9ON3BL4S2R7yNz1eX7o+Q1GrWY1NTsEU0OOZkaJ17STvyAuj5+boRXshJNmL5Px5jaIKVoyszKXvP78GGY5/3D0F+z90MeU+FFzB4Il+KkRXUinCfqo5wv0nK4VmSMhuNH/pQ+NpfPs3sy5r8s82TgMVXPZX/YpNduroWxPv5m6uqEr6tl7GOOC8eSa5I6LaOHokmymQHCQZmId85Y9FL27obyJ3dCpapj6mw08NQqmoa66B6a84PIDdlE3y3u5457p+ylupfJuhjr78J1dFPyl0J/dWMpr2N/CY/lvZD3en2AxZ5w18S/pxiFdHM7XNPORPNJOHxr0eYZn1TqS1Ofybych7qWrGs7IB0CqT+a/o5cUtDhNQvN2BMRg8FgMBiuEuxGxGAwGAwGw+Xg9ZN4N7KqwWAwGAyGS4M9ETEYDAaD4arAY5tz+XWOV3sjUhbo92fBUAgD8lbXAcr7ycWuhNQkRCeaTvmiQD+SU4TcVYvg0vhMRLWEYNiLeBnFxFwfiGcAUIloWDFPyVJqzMY6OqeES5IQ/UrIqjRzkiqTTIiqQnkeCHAkfFZCZAXJgn1KBByaZbnMZIkkTiX+VYGo5kiULIpIniRIMM0IoLse3XmkBDIKqDmey7wGJC2XkynFrI1yTm5NAqqkm2Z7kWR1ie0ZxTIoUCXiSBQLGyvpkoJ44eP40RrVneNwjOQyQUEzKyHMKbEQwN40Nd0anYlp3wOZJw/PpB6BrOimwmrFKPY95wlFmZaRpAcE4z9gQFDFYM70UqdckIikt8LFtkr+jvNP56eQ6oRIWVZlnMfZWCo5UI8z7eJnCjZxLUr/1WKw6KdhjpME7FaNClXpXKFB41moG4m1yz7ksRpNsKiFnDqRuol5GQmGZSWfRXyrks9l0aMWEmolKcmqfU+DQzGJW4Y14xcV6pPQjsmxEDXvpQTN8cOwT1XHwoLfNLomkQvQETKmuirKMpI6ZV6T1M4+mb2/lPKFXHouc+3esa5BJYXXYiZ5Iwh9dVMR+LsRrh2de8w+lHpTEFHI0k5EEw9+Q0if0hd9BYyEAFrLfK/OxIyRRn0nYd6TXA3n4GYy9/sgfEiCPPd3v5byu+0fRa5xnxG7da+TOc1+HNdVnF9ChO6PAhFVDeZIfD4T8cipQyvmnuvrYvR3W+b7vvw2yFxbB69OFYNrp4X+XsxkSXKPLkQMLa9z+JI15g7YvXo9M8BCMwaDwWAwGC4R3j/7fy8Izrk/65zzzrlbTzrXQjMGg8FgMFwZ+EvXEXHOfQLAvw7gN57mfHsiYjAYDAaD4UXiLwH4c4iMhcfiFZveebi+345TAwMhLhqfpXFFCt4c/MsQm5/dqWPMbk3RH4mRixEVeSVeYnpeBJ586VCcrZJ8VZxGDI342Uu8E6NaBb+wI+bJ9gFAT7Gv3sOVwiVYh3hmcbAfTs3i6sNU+SIsj/yIjN9B8yWUErMcjaKwlqNwWh+/A7bM1EJ2O8YDqQBRyES4Cc0gJkqeBU/RcROjKHJEdvRZNLdLzaNcJso2rKOjONKKBlddWu5QrCzjr+iYSh4J7wJA1XY4WEiMn+UJ1wGLpZQbxzZtuIt9L/n5creBIs9zozryijguaXduI2/ToDzOS0/CFft8GMNmvXMOypCvlYOHCuFfrGROkS8gXJFC+Ato2zi2mehTdS+YJd74NRGO+7KIvLXR7JGcrm5E8TWmPC6p6CJ2Y6d8MYoXUtSrkuInSxqjhc+jeY/6PHxZn5ALImJdp2HN9qfCiyAfBICbhEJ17Uma8LQGqZ+M4Ed0XeT4yyDLeisWofyCc+4kCCf2j461H9VoTfqYs3zvN0TE7p5wR05XymMi96k/nCXljR4upS9krndeRfZ0PyTPQ+Y7+0A5HGWJgutVeG80v8v3R93TsQPMr8s4eRRclHoUp/NojCq8GeVq9DImUkB9uo7tkv2hFUFJNYQUTs3mUEQvZerS0LFaApXMmXIpXCTheilPiPvljqcP2ubLML3ziGKXlwDn3PcAeM97/89yDuFFsNCMwWAwGAxXCc8XmrnlnPvC4PPnvPefG57gnPu7AN7ace1fBPAXEMIyTw27ETEYDAaD4Srh+Uin9733n3l89v4P7TrunPvtAD4NgE9D3gHwc8657/Def3hRfnYjYjAYDAaD4bnhvf9FAG/ys3PuKwA+472//7jrXu2NSNfBPTyJmhKMH7liEOfOYoWF8CMkVll98AgAUI7rqENCHQXeBTLuyMdTEkssGP8sC9WW8Bk3xI3VZS85HvKltkIWA63TbnQSqPbDeDvLIe+CPIGuTD7DuXg3y3izmAQWuZ6I6jsMuAl8l1/1RKhhwNgrtS5iGzSKJ3HFLT4H+7Hc5m6wbxPTt10oBrHCTLMixofl6+xSV7gYk3cp14UclGpJ4y3hL6wHfe+zuDPLy7kxTQu3EY0blkftlHXGKxFDsWR+ZGZmuVbHTn4HkWurFIP5sOs4Bhou+hg2M0nMODCPxQUxZe/9djnsE5qccV7yoq6LfaB8COE/iA7M+MPAgyB/olg2cW2wvHxOkSOgRoPyuYr6OT5rKnknatxH08K2i7H+nA9BbpnMBUURtYSiKadL2h7XUjS9U22gzACNmju6r2QInC/pP85dqVMvY1DeT3WK3Gqj53Bf0vI5VzvyrTJ+FTAwcUz3PzfK9qQhpP5uiwtVJp9139i1DjKuGepMI6ks4t7Wiv5JSX6H8IxotCntKxcbONGHGj8IvJmR6NfsT1I9J5pA8jfFdf3WnFGT0zMxP835VK7QOes51n2Hp+Rrvjh4b4JmBoPBYDAYLhFfJ4Jm3vtPPc15diNiMBgMBsMVgrcnIgaDwWAwGC4Hr5/77qu9Eel9jLUDGuMFEONteQeq7oHESM/kHfllFbkMu7QPgK34tPI0OrcdExfPCPUl8Vl8v98R6btosPl+fVFtx+kzvxqNDzNuumk0rq3tYpx0L2iRuMf5kLBqTeYpk+tHPMvrXcPYb6b5od/Ra2Mtn8nfGWqSMNbL9KLy6MNTlpH/k2lmMG5bMi/27+lcY7lRr+SCfyUM4+LkrYhPhvZtmXFrqNnBePywbtrXGTeE3ApqkqyjPgWheikX1HHXe/k+m4eOV3OsGx/zJc/igrm75R20ozyXx/Pz+Qps81LIP6IGxMOMG9V2UTNDuWO7Z4bb5aeUc2pyfknGb0HfwyvnS9JMH0LbqcZJLuVjJfnL/kHdHtlGXNejWNdJ3dTHil4lTarXw7Xih5Lb+fiTE0NPFuXHdbGtkn+xSv1+3Er24KEnDOdztj9cpDE0rCcQdFe2eB4X7TmF2+LLRC+kjF/Fcdw0QEnfLOp5iAfRhjof4pPzIKx7d3quvzc622Xcisw3qcw4Yeg65anoWPO3q0m1cbb4jcCT9YAMCeyJiMFgMBgMVwUely7x/lHxXBLvzrnvds79c+fcl5xzP/iiKmUwGAwGg+EZ4ftn/+8S8MxPRJxzJYD/BMAfBvAugJ91zv2k9/5XXlTlDAaDwWAwPD08BtIVrwmeJzTzHQC+5L3/MgA4534UwPcAePobEcYffbf9KCnXQuBhcgP+/+3dXYhUZRzH8e9vNQsyekGRKCkJb+xm80oowggqvbFuwoKSCOxCoaCb6qZugm56oYsEI9GgF4SSvBArJOiq0kLyjV4gI8W0CCqI1HX/XZznnDl7Zs667sye0+z+PrDszJk5e/7zP88Mz87zP88zfr4zJlm51r5WMX48rxiDL+bX6Dp+99wP1bHrGKnUoFSNCPLnjFTGvStzcxTjt+NRrDGjqFzTX4zX1nyRNR49alsuvTZElTkSumKf15nrJEpxT5CPpRfzsyyYGFfvA9c+p+vvX5hY06N8PpM8rnPnJ87j0iu2ihgbq6+/mVdTG5AfY2ys++9W6wnycek8F+X988N1xVqdGKM7dlVfZyVXUZpXIGpee9c5n47ye6Za36F8Dak0vp6v2VN+7+bnrmbfwkiP/9ryuo5ivZNqjUq15iE6tSHV/NXkCMrzWkwyJwaluo+xMUjrtBTrjxTzzVTns+lRa1M3j0xey5DqjIozXn5d1fqOaq1SOgfx79nO50+xflWlfmuy/5SLOTMqdVx5rvLJXVRql9UanspnWzE3U4ojxsc79XR5LVGaQ2jkXCW2/Fz3WhMsj1EpB6k5dl5nZ1+Rz/lUKfio1oKV6mmK92I/76N+RUx+vv6H+hmauQH4pXT/RNpmZmZmNiUzXqwqaSOwEeCKkYUzfTgzM7M5bS4NzZwElpbu35i2TZBW7dsKcPX8xcOVHTMzs2EzZEMzqptP4KI7SvOB74G7yTog+4GHI+LIJPv8RnbR+aQL4NjALcI5b5pz3jznvHnO+cXdFBGLmzqYpL1k52W6fo+I+wYVz1RMuyMCIGkt8BrZ9C3bIuLFKexz4GJLDNtgOefNc86b55w3zzm3QeirRiQi9gB7BhSLmZmZzTF9TWhmZmZm1o82OiJbWzjmXOecN885b55z3jzn3PrWV42ImZmZWT88NGNmZmataawj4gXymiHpuKRDkg5KOpC2XSfpU0k/pN/Xth3nMJO0TdIZSYdL23rmWJnXU7v/VtLK9iIfXjU5f0HSydTWD6ar+PLHnk05/07Sve1EPdwkLZX0maSjko5IejJtd1u3gWqkI1JaIG8NsAJ4SNKKJo49R90VEaOly+qeAfZFxHJgX7pv07cdqF5nX5fjNcDy9LMR2NJQjLPNdrpzDvBqauuj6So+0mfLeuDWtM8b6TPILs0Y8HRErABWAZtSbt3WbaCa+kakWCAvIs4B+QJ51ox1wI50ewdwf3uhDL+I+Bz4o7K5LsfrgLcj8wVwjaTrGwl0FqnJeZ11wPsRcTYifgJ+JPsMsksQEaci4pt0+2/gGNl6Ym7rNlBNdUS8QF5zAvhE0tdpnR+AJRFxKt3+FVjSTmizWl2O3fZn1uY0DLCtNOTonA+YpJuB24AvcVu3AXOx6uxzR0SsJPuadJOkO8sPRnaZlC+VmkHOcWO2ALcAo8Ap4OVWo5mlJC0EPgCeioi/yo+5rdsgNNURmdICeda/iDiZfp8BdpF9JX06/4o0/T7TXoSzVl2O3fZnSEScjogLETEOvEln+MU5HxBJl5F1Qt6JiA/TZrd1G6imOiL7geWSlklaQFZItruhY88Zkq6UdFV+G7gHOEyW6w3paRuAj9qJcFary/Fu4NF0RcEq4M/S19rWh0r9wQNkbR2ynK+XdLmkZWTFk181Hd+wkyTgLeBYRLxSesht3Qaqr7VmpioixiRtBj6ms0Be7Sq9Nm1LgF3Z5wfzgXcjYq+k/cBOSY8DPwMPthjj0JP0HrAaWCTpBPA88BK9c7wHWEtWMPkP8FjjAc8CNTlfLWmUbGjgOPAEQEQckbQTOEp25cemiLjQQtjD7nbgEeCQpINp23O4rduAeWZVMzMza42LVc3MzKw17oiYmZlZa9wRMTMzs9a4I2JmZmatcUfEzMzMWuOOiJmZmbXGHREzMzNrjTsiZmZm1pr/APKSt6Zgt60fAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# change emotion name[\\\"neutral\\\", \\\"happy\\\", \\\"surprise\\\", \\\"angry\\\", \\\"sad\\\"]\\n\",\n \"emotion_type = \\\"happy\\\"\\n\",\n \"\\n\",\n \"wav_fpath = Path(emotional_src_wav[emotion_type][\\\"wav\\\"])\\n\",\n \"src_audio = preprocess_wav(wav_fpath, source_sr=16000, normalize=True, trim_silence=True, is_sil_pad=True,\\n\",\n \" vad_window_length=30,\\n\",\n \" vad_moving_average_width=1,\\n\",\n \" vad_max_silence_length=1)\\n\",\n \"\\n\",\n \"text = a_text\\n\",\n \"\\n\",\n \"syn_audio, mel_pred, mel_src = Tts_handel.one_shot_TTS(text, src_audio)\\n\",\n \"\\n\",\n \"## or\\n\",\n \"# dur_stat = np.load(emotional_src_wav[emotion_type][\\\"dur_stat\\\"])\\n\",\n \"# print(\\\"dur_stat:\\\", dur_stat)\\n\",\n \"# syn_audio, mel_pred, mel_src = Tts_handel.one_shot_TTS(text, src_audio, dur_stat)\\n\",\n \"\\n\",\n \"# plt.plot(syn_audio)\\n\",\n \"Tts_handel.feats_handle.melspec_plot(mel_pred)\\n\",\n \"ipd.Audio(syn_audio, rate=16000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c032ae3f\",\n \"metadata\": {},\n \"source\": [\n \"# 3. Adjust speech speed according to the statistics of phoneme duration\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"c07b16d5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"a_text = \\\"一句话#3风格迁移#3语音合成系统\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"a11ce2b5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# change emotion name[\\\"neutral\\\", \\\"happy\\\", \\\"surprise\\\", \\\"angry\\\", \\\"sad\\\"]\\n\",\n \"emotion_type = \\\"happy\\\"\\n\",\n \"\\n\",\n \"wav_fpath = Path(emotional_src_wav[emotion_type][\\\"wav\\\"])\\n\",\n \"\\n\",\n \"src_audio = preprocess_wav(wav_fpath, source_sr=16000, normalize=True, trim_silence=True, is_sil_pad=True,\\n\",\n \" vad_window_length=30,\\n\",\n \" vad_moving_average_width=1,\\n\",\n \" vad_max_silence_length=1)\\n\",\n \"\\n\",\n \"text = a_text\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"35072516\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['yi1', 'ju4', 'hua4', 'feng1', 'ge2', 'qian1', 'yi2', 'yu3', 'yin1', 'he2', 'cheng2', 'xi4', 'tong3']\\n\",\n \"phoneme seq: sil ^ i1 j v4 h ua4 #3 f eng1 g e2 q ian1 ^ i2 #3 ^ v3 ^ in1 h e2 ch eng2 x i4 t ong3 sil\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dur_stat = np.array([15, 6, 18, 9])\\n\",\n \"\\n\",\n \"syn_audio, mel_pred, mel_src = Tts_handel.one_shot_TTS(text, src_audio, dur_stat, True)\\n\",\n \"\\n\",\n \"# plt.plot(syn_audio)\\n\",\n \"Tts_handel.feats_handle.melspec_plot(mel_pred)\\n\",\n \"ipd.Audio(syn_audio, rate=16000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"9f7a67ea\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['yi1', 'ju4', 'hua4', 'feng1', 'ge2', 'qian1', 'yi2', 'yu3', 'yin1', 'he2', 'cheng2', 'xi4', 'tong3']\\n\",\n \"phoneme seq: sil ^ i1 j v4 h ua4 #3 f eng1 g e2 q ian1 ^ i2 #3 ^ v3 ^ in1 h e2 ch eng2 x i4 t ong3 sil\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dur_stat = np.array([10, 4, 12, 6])\\n\",\n \"\\n\",\n \"syn_audio, mel_pred, mel_src = Tts_handel.one_shot_TTS(text, src_audio, dur_stat, True)\\n\",\n \"\\n\",\n \"# plt.plot(syn_audio)\\n\",\n \"Tts_handel.feats_handle.melspec_plot(mel_pred)\\n\",\n \"ipd.Audio(syn_audio, rate=16000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5cc68224\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"37dd49ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "train/configs/multiband_melgan.yaml", "content": "\n# This is the hyperparameter configuration file for Multi-Band MelGAN.\n# Please make sure this is adjusted for the Baker dataset. If you want to\n# apply to the other dataset, you might need to carefully change some parameters.\n# This configuration performs 1000k iters.\n\n###########################################################\n# FEATURE EXTRACTION SETTING #\n###########################################################\nsampling_rate: 16000\nhop_size: 200 # Hop size.\nformat: \"npy\"\n\n\n###########################################################\n# GENERATOR NETWORK ARCHITECTURE SETTING #\n###########################################################\nmodel_type: \"multiband_melgan_generator\"\n\nmultiband_melgan_generator_params:\n out_channels: 4 # Number of output channels (number of subbands).\n kernel_size: 7 # Kernel size of initial and final conv layers.\n filters: 384 # Initial number of channels for conv layers.\n upsample_scales: [5, 5, 2] # List of Upsampling scales.\n stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack.\n stacks: 4 # Number of stacks in a single residual stack module.\n is_weight_norm: false # Use weight-norm or not.\n\n###########################################################\n# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #\n###########################################################\nmultiband_melgan_discriminator_params:\n out_channels: 1 # Number of output channels.\n scales: 3 # Number of multi-scales.\n downsample_pooling: \"AveragePooling1D\" # Pooling type for the input downsampling.\n downsample_pooling_params: # Parameters of the above pooling function.\n pool_size: 4\n strides: 2\n kernel_sizes: [5, 3] # List of kernel size.\n filters: 16 # Number of channels of the initial conv layer.\n max_downsample_filters: 512 # Maximum number of channels of downsampling layers.\n downsample_scales: [4, 4, 4] # List of downsampling scales.\n nonlinear_activation: \"LeakyReLU\" # Nonlinear activation function.\n nonlinear_activation_params: # Parameters of nonlinear activation function.\n alpha: 0.2\n is_weight_norm: false # Use weight-norm or not.\n\n###########################################################\n# STFT LOSS SETTING #\n###########################################################\nstft_loss_params:\n fft_lengths: [1024, 2048, 512] # List of FFT size for STFT-based loss.\n frame_steps: [120, 240, 50] # List of hop size for STFT-based loss\n frame_lengths: [600, 1200, 240] # List of window length for STFT-based loss.\n\nsubband_stft_loss_params:\n fft_lengths: [384, 683, 171] # List of FFT size for STFT-based loss.\n frame_steps: [30, 60, 10] # List of hop size for STFT-based loss\n frame_lengths: [150, 300, 60] # List of window length for STFT-based loss.\n\n###########################################################\n# ADVERSARIAL LOSS SETTING #\n###########################################################\nlambda_feat_match: 10.0 # Loss balancing coefficient for feature matching loss\nlambda_adv: 2.5 # Loss balancing coefficient for adversarial loss.\n\n###########################################################\n# DATA LOADER SETTING #\n###########################################################\nbatch_size: 64 # Batch size.\nbatch_max_steps: 6400 # Length of each audio in batch for training. Make sure dividable by hop_size.\nbatch_max_steps_valid: 32000 # Length of each audio for validation. Make sure dividable by hope_size.\nremove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.\nallow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.\nis_shuffle: true # shuffle dataset after each epoch.\n\n###########################################################\n# OPTIMIZER & SCHEDULER SETTING #\n###########################################################\ngenerator_optimizer_params:\n lr_fn: \"PiecewiseConstantDecay\"\n lr_params: \n boundaries: [100000, 200000, 300000, 400000, 500000, 600000, 700000]\n # values: [0.001, 0.0005, 0.00025, 0.000125, 0.0000625, 0.00003125, 0.000015625, 0.000001]\n values: [0.0001, 0.0001, 0.0001, 0.0001, 0.0000625, 0.00003125, 0.000015625, 0.000001]\n amsgrad: false\n\ndiscriminator_optimizer_params:\n lr_fn: \"PiecewiseConstantDecay\"\n lr_params: \n boundaries: [100000, 200000, 300000, 400000, 500000]\n # values: [0.00025, 0.000125, 0.0000625, 0.00003125, 0.000015625, 0.000001]\n values: [0.0001, 0.0001, 0.0000625, 0.00003125, 0.000015625, 0.000001]\n amsgrad: false\n\n###########################################################\n# INTERVAL SETTING #\n###########################################################\ndiscriminator_train_start_steps: 200000 # steps begin training discriminator\ntrain_max_steps: 4000000 # Number of training steps.\nsave_interval_steps: 20000 # Interval steps to save checkpoint.\neval_interval_steps: 5000 # Interval steps to evaluate the network.\nlog_interval_steps: 200 # Interval steps to record the training log.\n\n###########################################################\n# OTHER SETTING #\n###########################################################\nnum_save_intermediate_results: 1 # Number of batch to be saved as intermediate results.\n" }, { "path": "train/configs/unetts_acous.yaml", "content": "###########################################################\n# FEATURE EXTRACTION SETTING #\n###########################################################\nhop_size: 200 # Hop size.\nformat: \"npy\"\n\n\n###########################################################\n# NETWORK ARCHITECTURE SETTING #\n###########################################################\nmodel_type: \"unetts_acous\"\n\nunetts_acous_params:\n dataset: multispk_voiceclone\n\n # content encoder\n encoder_hidden_size: 128\n encoder_num_hidden_layers: 2\n encoder_num_attention_heads: 2\n encoder_attention_head_size: 64\n\n encoder_intermediate_size: 512\n encoder_intermediate_kernel_size: 3\n encoder_hidden_act: \"mish\"\n\n addfeatures_num: 4\n isaddur: False\n\n # AdaIN encoder and decoder\n content_latent_dim: 132 # content_latent_dim = proj(encoder_output) + (3 + (1 if isaddur else 0) if addfeatures_num else 0)\n n_conv_blocks: 6\n adain_filter_size: 256\n enc_kernel_size: 5\n dec_kernel_size: 5\n gen_kernel_size: 5\n\n num_mels: 80\n hidden_dropout_prob: 0.2\n attention_probs_dropout_prob: 0.1\n\n initializer_range: 0.02\n output_attentions: False\n output_hidden_states: False\n\nunetts_acous_context_pre_params:\n dataset: multispk_voiceclone\n\n encoder_hidden_size: 128\n encoder_num_hidden_layers: 2\n encoder_num_attention_heads: 2\n encoder_attention_head_size: 64\n\n encoder_intermediate_size: 512\n encoder_intermediate_kernel_size: 3\n encoder_hidden_act: \"mish\"\n\n addfeatures_num: 4\n isaddur: False\n\n content_latent_dim: 132 # content_latent_dim = proj(encoder_output) + (3 + (1 if isaddur else 0) if addfeatures_num else 0)\n\n decoder_is_conditional: True\n decoder_conditional_norm_type: \"Instance\" # \"Layer\" or \"Instance\"\n\n decoder_hidden_size: 132\n decoder_num_hidden_layers: 3\n decoder_num_attention_heads: 2\n decoder_attention_head_size: 66\n\n decoder_intermediate_size: 512\n decoder_intermediate_kernel_size: 9\n decoder_hidden_act: \"mish\"\n\n num_mels: 80\n hidden_dropout_prob: 0.2\n attention_probs_dropout_prob: 0.1\n\n initializer_range: 0.02\n output_attentions: False\n output_hidden_states: False\n\n###########################################################\n# DATA LOADER SETTING #\n###########################################################\nbatch_size: 32 # Batch size.\n# remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.\nallow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.\n# mel_length_threshold: 32 # remove all targets has mel_length <= 32 \nis_shuffle: true # shuffle dataset after each epoch.\n###########################################################\n# OPTIMIZER & SCHEDULER SETTING #\n###########################################################\noptimizer_params:\n initial_learning_rate: 0.001\n end_learning_rate: 0.00001\n decay_steps: 150000 # < train_max_steps is recommend.\n warmup_proportion: 0.02\n weight_decay: 0.001\n \n \n###########################################################\n# INTERVAL SETTING #\n###########################################################\ntrain_max_steps: 200000 # Number of training steps.\nsave_interval_steps: 10000 # Interval steps to save checkpoint.\neval_interval_steps: 2000 # Interval steps to evaluate the network.\nlog_interval_steps: 250 # Interval steps to record the training log.\n###########################################################\n# OTHER SETTING #\n###########################################################\nnum_save_intermediate_results: 1 # Number of batch to be saved as intermediate results.\nresults_num: 10\nwav_output_epochs: 20\n" }, { "path": "train/configs/unetts_duration.yaml", "content": "###########################################################\n# FEATURE EXTRACTION SETTING #\n###########################################################\nhop_size: 200 # Hop size.\nformat: \"npy\"\n\n###########################################################\n# NETWORK ARCHITECTURE SETTING #\n###########################################################\nmodel_type: \"unetts_duration\"\n\nunetts_duration_params:\n dataset: multispk_voiceclone\n\n encoder_hidden_size: 128\n encoder_num_hidden_layers: 2\n encoder_num_attention_heads: 2\n encoder_attention_head_size: 64\n\n encoder_intermediate_size: 512\n encoder_intermediate_kernel_size: 3\n encoder_hidden_act: \"mish\"\n\n hidden_dropout_prob: 0.2\n attention_probs_dropout_prob: 0.1\n\n num_duration_conv_layers: 2\n duration_predictor_filters: 128\n duration_predictor_kernel_sizes: 3\n duration_predictor_dropout_probs: 0.1\n\n initializer_range: 0.02\n output_attentions: False\n output_hidden_states: False\n\n###########################################################\n# DATA LOADER SETTING #\n###########################################################\nbatch_size: 32 # Batch size.\nallow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.\nis_shuffle: true # shuffle dataset after each epoch.\n\n###########################################################\n# OPTIMIZER & SCHEDULER SETTING #\n###########################################################\noptimizer_params:\n initial_learning_rate: 0.001\n end_learning_rate: 0.00001\n decay_steps: 75000 # < train_max_steps is recommend.\n warmup_proportion: 0.02\n weight_decay: 0.001\n\n###########################################################\n# INTERVAL SETTING #\n###########################################################\ntrain_max_steps: 100000 # Number of training steps.\nsave_interval_steps: 10000 # Interval steps to save checkpoint.\neval_interval_steps: 1000 # Interval steps to evaluate the network.\nlog_interval_steps: 500 # Interval steps to record the training log.\n\n###########################################################\n# OTHER SETTING #\n###########################################################\nnum_save_intermediate_results: 1 # Number of batch to be saved as intermediate results.\n" }, { "path": "train/configs/unetts_preprocess.yaml", "content": "###########################################################\n# FEATURE EXTRACTION SETTING #\n###########################################################\nfeat_params:\n sample_rate: 16000\n n_fft: 800\n num_mels: 80\n hop_size: 200\n win_size: 800\n fmin: 55\n fmax: 7600\n min_level_db: -100\n ref_level_db: 20\n max_abs_value: 4.0\n preemphasis: 0.97\n preemphasize: True\n signal_normalization: True\n allow_clipping_in_normalization: True\n symmetric_mels: True\n power: 1.5\n griffin_lim_iters: 60\n rescale: True\n rescaling_max: 0.9\n\nformat: \"npy\" # Feature file format. Only \"npy\" is supported." }, { "path": "train/train_multiband_melgan.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Train Multi-Band MelGAN.\"\"\"\n\nimport tensorflow as tf\n\nphysical_devices = tf.config.list_physical_devices(\"GPU\")\nfor i in range(len(physical_devices)):\n tf.config.experimental.set_memory_growth(physical_devices[i], True)\n\nimport sys\n\nsys.path.append(\"../..\")\n\nimport argparse\nimport logging\nimport os\n\nimport numpy as np\nimport soundfile as sf\nimport yaml\nfrom tensorflow.keras.mixed_precision import experimental as mixed_precision\n\nimport tensorflow_tts\nfrom examples.melgan.audio_mel_dataset import AudioMelDataset\nfrom examples.melgan.train_melgan import MelganTrainer, collater\nfrom tensorflow_tts.configs import (MultiBandMelGANDiscriminatorConfig,\n MultiBandMelGANGeneratorConfig)\nfrom tensorflow_tts.losses import TFMultiResolutionSTFT\nfrom tensorflow_tts.models import (TFPQMF, TFMelGANGenerator,\n TFMelGANMultiScaleDiscriminator)\nfrom tensorflow_tts.utils import (calculate_2d_loss, calculate_3d_loss,\n return_strategy)\n\n\nclass MultiBandMelganTrainer(MelganTrainer):\n \"\"\"Multi-Band MelGAN Trainer class based on MelganTrainer.\"\"\"\n\n def __init__(\n self,\n config,\n strategy,\n steps=0,\n epochs=0,\n is_generator_mixed_precision=False,\n is_discriminator_mixed_precision=False,\n ):\n \"\"\"Initialize trainer.\n\n Args:\n steps (int): Initial global steps.\n epochs (int): Initial global epochs.\n config (dict): Config dict loaded from yaml format configuration file.\n is_generator_mixed_precision (bool): Use mixed precision for generator or not.\n is_discriminator_mixed_precision (bool): Use mixed precision for discriminator or not.\n\n \"\"\"\n super(MultiBandMelganTrainer, self).__init__(\n config=config,\n steps=steps,\n epochs=epochs,\n strategy=strategy,\n is_generator_mixed_precision=is_generator_mixed_precision,\n is_discriminator_mixed_precision=is_discriminator_mixed_precision,\n )\n\n # define metrics to aggregates data and use tf.summary logs them\n self.list_metrics_name = [\n \"adversarial_loss\",\n \"subband_spectral_convergence_loss\",\n \"subband_log_magnitude_loss\",\n \"fullband_spectral_convergence_loss\",\n \"fullband_log_magnitude_loss\",\n \"gen_loss\",\n \"real_loss\",\n \"fake_loss\",\n \"dis_loss\",\n ]\n\n self.init_train_eval_metrics(self.list_metrics_name)\n self.reset_states_train()\n self.reset_states_eval()\n\n def compile(self, gen_model, dis_model, gen_optimizer, dis_optimizer, pqmf):\n super().compile(gen_model, dis_model, gen_optimizer, dis_optimizer)\n # define loss\n self.sub_band_stft_loss = TFMultiResolutionSTFT(\n **self.config[\"subband_stft_loss_params\"]\n )\n self.full_band_stft_loss = TFMultiResolutionSTFT(\n **self.config[\"stft_loss_params\"]\n )\n\n # define pqmf module\n self.pqmf = pqmf\n\n def compute_per_example_generator_losses(self, batch, outputs):\n \"\"\"Compute per example generator losses and return dict_metrics_losses\n Note that all element of the loss MUST has a shape [batch_size] and \n the keys of dict_metrics_losses MUST be in self.list_metrics_name.\n\n Args:\n batch: dictionary batch input return from dataloader\n outputs: outputs of the model\n \n Returns:\n per_example_losses: per example losses for each GPU, shape [B]\n dict_metrics_losses: dictionary loss.\n \"\"\"\n dict_metrics_losses = {}\n per_example_losses = 0.0\n\n audios = batch[\"audios\"]\n y_mb_hat = outputs\n y_hat = self.pqmf.synthesis(y_mb_hat)\n\n y_mb = self.pqmf.analysis(tf.expand_dims(audios, -1))\n y_mb = tf.transpose(y_mb, (0, 2, 1)) # [B, subbands, T//subbands]\n y_mb = tf.reshape(y_mb, (-1, tf.shape(y_mb)[-1])) # [B * subbands, T']\n\n y_mb_hat = tf.transpose(y_mb_hat, (0, 2, 1)) # [B, subbands, T//subbands]\n y_mb_hat = tf.reshape(\n y_mb_hat, (-1, tf.shape(y_mb_hat)[-1])\n ) # [B * subbands, T']\n\n # calculate sub/full band spectral_convergence and log mag loss.\n sub_sc_loss, sub_mag_loss = calculate_2d_loss(\n y_mb, y_mb_hat, self.sub_band_stft_loss\n )\n sub_sc_loss = tf.reduce_mean(\n tf.reshape(sub_sc_loss, [-1, self.pqmf.subbands]), -1\n )\n sub_mag_loss = tf.reduce_mean(\n tf.reshape(sub_mag_loss, [-1, self.pqmf.subbands]), -1\n )\n full_sc_loss, full_mag_loss = calculate_2d_loss(\n audios, tf.squeeze(y_hat, -1), self.full_band_stft_loss\n )\n\n # define generator loss\n gen_loss = 0.5 * (sub_sc_loss + sub_mag_loss) + 0.5 * (\n full_sc_loss + full_mag_loss\n )\n\n if self.steps >= self.config[\"discriminator_train_start_steps\"]:\n p_hat = self._discriminator(y_hat)\n p = self._discriminator(tf.expand_dims(audios, 2))\n adv_loss = 0.0\n for i in range(len(p_hat)):\n adv_loss += calculate_3d_loss(\n tf.ones_like(p_hat[i][-1]), p_hat[i][-1], loss_fn=self.mse_loss\n )\n adv_loss /= i + 1\n gen_loss += self.config[\"lambda_adv\"] * adv_loss\n\n dict_metrics_losses.update({\"adversarial_loss\": adv_loss},)\n\n dict_metrics_losses.update({\"gen_loss\": gen_loss})\n dict_metrics_losses.update({\"subband_spectral_convergence_loss\": sub_sc_loss})\n dict_metrics_losses.update({\"subband_log_magnitude_loss\": sub_mag_loss})\n dict_metrics_losses.update({\"fullband_spectral_convergence_loss\": full_sc_loss})\n dict_metrics_losses.update({\"fullband_log_magnitude_loss\": full_mag_loss})\n\n per_example_losses = gen_loss\n return per_example_losses, dict_metrics_losses\n\n def compute_per_example_discriminator_losses(self, batch, gen_outputs):\n \"\"\"Compute per example discriminator losses and return dict_metrics_losses\n Note that all element of the loss MUST has a shape [batch_size] and \n the keys of dict_metrics_losses MUST be in self.list_metrics_name.\n\n Args:\n batch: dictionary batch input return from dataloader\n outputs: outputs of the model\n \n Returns:\n per_example_losses: per example losses for each GPU, shape [B]\n dict_metrics_losses: dictionary loss.\n \"\"\"\n y_mb_hat = gen_outputs\n y_hat = self.pqmf.synthesis(y_mb_hat)\n (\n per_example_losses,\n dict_metrics_losses,\n ) = super().compute_per_example_discriminator_losses(batch, y_hat)\n return per_example_losses, dict_metrics_losses\n\n def generate_and_save_intermediate_result(self, batch):\n \"\"\"Generate and save intermediate result.\"\"\"\n import matplotlib.pyplot as plt\n\n y_mb_batch_ = self.one_step_predict(batch) # [B, T // subbands, subbands]\n y_batch = batch[\"audios\"]\n\n # convert to tensor.\n # here we just take a sample at first replica.\n try:\n y_mb_batch_ = y_mb_batch_.values[0].numpy()\n y_batch = y_batch.values[0].numpy()\n except Exception:\n y_mb_batch_ = y_mb_batch_.numpy()\n y_batch = y_batch.numpy()\n\n y_batch_ = self.pqmf.synthesis(y_mb_batch_).numpy() # [B, T, 1]\n\n # check directory\n utt_ids = batch[\"utt_ids\"].numpy()\n dirname = os.path.join(self.config[\"outdir\"], f\"predictions/{self.steps}steps\")\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n for idx, (y, y_) in enumerate(zip(y_batch, y_batch_), 0):\n # convert to ndarray\n y, y_ = tf.reshape(y, [-1]).numpy(), tf.reshape(y_, [-1]).numpy()\n\n # plit figure and save it\n utt_id = utt_ids[idx]\n figname = os.path.join(dirname, f\"{utt_id}.png\")\n plt.subplot(2, 1, 1)\n plt.plot(y)\n plt.title(\"groundtruth speech\")\n plt.subplot(2, 1, 2)\n plt.plot(y_)\n plt.title(f\"generated speech @ {self.steps} steps\")\n plt.tight_layout()\n plt.savefig(figname)\n plt.close()\n\n # save as wavefile\n y = np.clip(y, -1, 1)\n y_ = np.clip(y_, -1, 1)\n sf.write(\n figname.replace(\".png\", \"_ref.wav\"),\n y,\n self.config[\"sampling_rate\"],\n \"PCM_16\",\n )\n sf.write(\n figname.replace(\".png\", \"_gen.wav\"),\n y_,\n self.config[\"sampling_rate\"],\n \"PCM_16\",\n )\n\n\ndef main():\n \"\"\"Run training process.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Train MultiBand MelGAN (See detail in examples/multiband_melgan/train_multiband_melgan.py)\"\n )\n parser.add_argument(\n \"--train-dir\",\n default=None,\n type=str,\n help=\"directory including training data. \",\n )\n parser.add_argument(\n \"--dev-dir\",\n default=None,\n type=str,\n help=\"directory including development data. \",\n )\n parser.add_argument(\n \"--use-norm\", default=1, type=int, help=\"use norm mels for training or raw.\"\n )\n parser.add_argument(\n \"--outdir\", type=str, required=True, help=\"directory to save checkpoints.\"\n )\n parser.add_argument(\n \"--config\", type=str, required=True, help=\"yaml format configuration file.\"\n )\n parser.add_argument(\n \"--resume\",\n default=\"\",\n type=str,\n nargs=\"?\",\n help='checkpoint file path to resume training. (default=\"\")',\n )\n parser.add_argument(\n \"--verbose\",\n type=int,\n default=1,\n help=\"logging level. higher is more logging. (default=1)\",\n )\n parser.add_argument(\n \"--generator_mixed_precision\",\n default=0,\n type=int,\n help=\"using mixed precision for generator or not.\",\n )\n parser.add_argument(\n \"--discriminator_mixed_precision\",\n default=0,\n type=int,\n help=\"using mixed precision for discriminator or not.\",\n )\n parser.add_argument(\n \"--pretrained\",\n default=\"\",\n type=str,\n nargs=\"?\",\n help='path of .h5 mb-melgan generator to load weights from',\n )\n args = parser.parse_args()\n\n # return strategy\n STRATEGY = return_strategy()\n\n # set mixed precision config\n if args.generator_mixed_precision == 1 or args.discriminator_mixed_precision == 1:\n tf.config.optimizer.set_experimental_options({\"auto_mixed_precision\": True})\n\n args.generator_mixed_precision = bool(args.generator_mixed_precision)\n args.discriminator_mixed_precision = bool(args.discriminator_mixed_precision)\n\n args.use_norm = bool(args.use_norm)\n\n # set logger\n if args.verbose > 1:\n logging.basicConfig(\n level=logging.DEBUG,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n elif args.verbose > 0:\n logging.basicConfig(\n level=logging.INFO,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n else:\n logging.basicConfig(\n level=logging.WARN,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n logging.warning(\"Skip DEBUG/INFO messages\")\n\n # check directory existence\n if not os.path.exists(args.outdir):\n os.makedirs(args.outdir)\n\n # check arguments\n if args.train_dir is None:\n raise ValueError(\"Please specify --train-dir\")\n if args.dev_dir is None:\n raise ValueError(\"Please specify either --valid-dir\")\n\n # load and save config\n with open(args.config) as f:\n config = yaml.load(f, Loader=yaml.Loader)\n config.update(vars(args))\n config[\"version\"] = tensorflow_tts.__version__\n with open(os.path.join(args.outdir, \"config.yml\"), \"w\") as f:\n yaml.dump(config, f, Dumper=yaml.Dumper)\n for key, value in config.items():\n logging.info(f\"{key} = {value}\")\n\n # get dataset\n if config[\"remove_short_samples\"]:\n mel_length_threshold = config[\"batch_max_steps\"] // config[\n \"hop_size\"\n ] + 2 * config[\"multiband_melgan_generator_params\"].get(\"aux_context_window\", 0)\n else:\n mel_length_threshold = None\n\n if config[\"format\"] == \"npy\":\n audio_query = \"*-wave.npy\"\n mel_query = \"*-raw-feats.npy\" if args.use_norm is False else \"*-norm-feats.npy\"\n audio_load_fn = np.load\n mel_load_fn = np.load\n else:\n raise ValueError(\"Only npy are supported.\")\n\n # define train/valid dataset\n train_dataset = AudioMelDataset(\n root_dir=args.train_dir,\n audio_query=audio_query,\n mel_query=mel_query,\n audio_load_fn=audio_load_fn,\n mel_load_fn=mel_load_fn,\n mel_length_threshold=mel_length_threshold,\n ).create(\n is_shuffle=config[\"is_shuffle\"],\n map_fn=lambda items: collater(\n items,\n batch_max_steps=tf.constant(config[\"batch_max_steps\"], dtype=tf.int32),\n hop_size=tf.constant(config[\"hop_size\"], dtype=tf.int32),\n ),\n allow_cache=config[\"allow_cache\"],\n batch_size=config[\"batch_size\"] * STRATEGY.num_replicas_in_sync,\n )\n\n valid_dataset = AudioMelDataset(\n root_dir=args.dev_dir,\n audio_query=audio_query,\n mel_query=mel_query,\n audio_load_fn=audio_load_fn,\n mel_load_fn=mel_load_fn,\n mel_length_threshold=mel_length_threshold,\n ).create(\n is_shuffle=config[\"is_shuffle\"],\n map_fn=lambda items: collater(\n items,\n batch_max_steps=tf.constant(\n config[\"batch_max_steps_valid\"], dtype=tf.int32\n ),\n hop_size=tf.constant(config[\"hop_size\"], dtype=tf.int32),\n ),\n allow_cache=config[\"allow_cache\"],\n batch_size=config[\"batch_size\"] * STRATEGY.num_replicas_in_sync,\n )\n\n # define trainer\n trainer = MultiBandMelganTrainer(\n steps=0,\n epochs=0,\n config=config,\n strategy=STRATEGY,\n is_generator_mixed_precision=args.generator_mixed_precision,\n is_discriminator_mixed_precision=args.discriminator_mixed_precision,\n )\n\n with STRATEGY.scope():\n # define generator and discriminator\n generator = TFMelGANGenerator(\n MultiBandMelGANGeneratorConfig(**config[\"multiband_melgan_generator_params\"]),\n name=\"multi_band_melgan_generator\",\n )\n\n discriminator = TFMelGANMultiScaleDiscriminator(\n MultiBandMelGANDiscriminatorConfig(**config[\"multiband_melgan_discriminator_params\"]),\n name=\"multi_band_melgan_discriminator\",\n )\n\n pqmf = TFPQMF(\n MultiBandMelGANGeneratorConfig(**config[\"multiband_melgan_generator_params\"]), name=\"pqmf\"\n )\n\n # dummy input to build model.\n fake_mels = tf.random.uniform(shape=[1, 100, 80], dtype=tf.float32)\n y_mb_hat = generator(fake_mels)\n y_hat = pqmf.synthesis(y_mb_hat)\n discriminator(y_hat)\n \n if len(args.pretrained) > 1:\n generator.load_weights(args.pretrained)\n logging.info(f\"Successfully loaded pretrained weight from {args.pretrained}.\")\n\n generator.summary()\n discriminator.summary()\n\n # define optimizer\n generator_lr_fn = getattr(\n tf.keras.optimizers.schedules, config[\"generator_optimizer_params\"][\"lr_fn\"]\n )(**config[\"generator_optimizer_params\"][\"lr_params\"])\n discriminator_lr_fn = getattr(\n tf.keras.optimizers.schedules,\n config[\"discriminator_optimizer_params\"][\"lr_fn\"],\n )(**config[\"discriminator_optimizer_params\"][\"lr_params\"])\n\n gen_optimizer = tf.keras.optimizers.Adam(\n learning_rate=generator_lr_fn,\n amsgrad=config[\"generator_optimizer_params\"][\"amsgrad\"],\n )\n dis_optimizer = tf.keras.optimizers.Adam(\n learning_rate=discriminator_lr_fn,\n amsgrad=config[\"discriminator_optimizer_params\"][\"amsgrad\"],\n )\n\n trainer.compile(\n gen_model=generator,\n dis_model=discriminator,\n gen_optimizer=gen_optimizer,\n dis_optimizer=dis_optimizer,\n pqmf=pqmf,\n )\n\n # start training\n try:\n trainer.fit(\n train_dataset,\n valid_dataset,\n saved_path=os.path.join(config[\"outdir\"], \"checkpoints/\"),\n resume=args.resume,\n )\n except KeyboardInterrupt:\n trainer.save_checkpoint()\n logging.info(f\"Successfully saved checkpoint @ {trainer.steps}steps.\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "train/train_unetts_acous.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Train Unet-TTS\"\"\"\n\nimport tensorflow as tf\n\nphysical_devices = tf.config.list_physical_devices(\"GPU\")\nfor i in range(len(physical_devices)):\n tf.config.experimental.set_memory_growth(physical_devices[i], True)\n\nimport sys\n\n# sys.path.append(\"../..\")\n\nimport argparse\nimport logging\nimport os\nimport traceback\n\nimport numpy as np\nimport yaml\n\nimport tensorflow_tts\nfrom train.unetts_dataset import UNETTSAcousDataset\nfrom tensorflow_tts.configs import UNETTSAcousConfig\nfrom tensorflow_tts.models import TFUNETTSContentPretrain, TFUNETTSAcous\nfrom tensorflow_tts.optimizers import AdamWeightDecay, WarmUp\nfrom tensorflow_tts.trainers import Seq2SeqBasedTrainer\nfrom tensorflow_tts.utils import calculate_loss_norm_lens, return_strategy\nfrom tensorflow_tts.audio_process.audio_spec import AudioMelSpec\n\n\nclass UNETTSAcousTrainer(Seq2SeqBasedTrainer):\n \"\"\"UNETTSAcousTrainer.\"\"\"\n\n def __init__(\n self, config, strategy, steps=0, epochs=0, is_mixed_precision=False,\n ):\n \"\"\"Initialize trainer.\n Args:\n steps (int): Initial global steps.\n epochs (int): Initial global epochs.\n config (dict): Config dict loaded from yaml format configuration file.\n is_mixed_precision (bool): Use mixed precision or not.\n \"\"\"\n super(UNETTSAcousTrainer, self).__init__(\n steps=steps,\n epochs=epochs,\n config=config,\n strategy=strategy,\n is_mixed_precision=is_mixed_precision,\n )\n # define metrics to aggregates data and use tf.summary logs them\n self.list_metrics_name = [\n \"mel_before\",\n \"content_loss\",\n ]\n self.init_train_eval_metrics(self.list_metrics_name)\n self.reset_states_train()\n self.reset_states_eval()\n\n self.feature_handle = AudioMelSpec(**config[\"feat_params\"])\n\n def compile(self, model, optimizer):\n super().compile(model, optimizer)\n self.mse = tf.keras.losses.MeanSquaredError(\n reduction=tf.keras.losses.Reduction.NONE\n )\n self.mae = tf.keras.losses.MeanAbsoluteError(\n reduction=tf.keras.losses.Reduction.NONE\n )\n # self.bce = tf.keras.losses.BinaryCrossentropy(\n # reduction=tf.keras.losses.Reduction.NONE\n # )\n\n def compute_per_example_losses(self, batch, outputs):\n \"\"\"Compute per example losses and return dict_metrics_losses\n Note that all element of the loss MUST has a shape [batch_size] and \n the keys of dict_metrics_losses MUST be in self.list_metrics_name.\n\n Args:\n batch: dictionary batch input return from dataloader\n outputs: outputs of the model\n \n Returns:\n per_example_losses: per example losses for each GPU, shape [B]\n dict_metrics_losses: dictionary loss.\n \"\"\"\n mel_before, content_latents, content_latent_pred = outputs\n\n # mse, 0.01; mae, 0.05\n mel_loss_before = calculate_loss_norm_lens(batch[\"mel_gts\"], mel_before, self.mae, batch[\"mel_lengths\"])\n content_loss = calculate_loss_norm_lens(content_latents, content_latent_pred, self.mse, batch[\"mel_lengths\"])\n\n per_example_losses = (\n mel_loss_before + content_loss\n )\n\n dict_metrics_losses = {\n \"mel_before\": mel_loss_before,\n \"content_loss\" : content_loss,\n }\n\n return per_example_losses, dict_metrics_losses\n\n def generate_and_save_intermediate_result(self, batch):\n \"\"\"Generate and save intermediate result.\"\"\"\n\n # predict with tf.function.\n mel_before, *_ = self.one_step_predict(batch)\n\n mel_gts = batch[\"mel_gts\"].numpy()\n frame_real_length = batch[\"mel_lengths\"].numpy()\n\n # convert to tensor.\n # here we just take a sample at first replica.\n try: \n mel_before = mel_before.values[0].numpy()\n except Exception: \n mel_before = mel_before.numpy()\n\n # check directory\n utt_ids = batch[\"utt_ids\"].numpy()\n dirname = os.path.join(self.config[\"outdir\"], f\"predictions/{self.steps}steps\")\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n for i, utt in enumerate(utt_ids):\n figname = os.path.join(dirname, f\"{utt}.png\")\n wavname = os.path.join(dirname, f\"{utt}.wav\")\n\n self.feature_handle.compare_plot(mel_gts[i],\n mel_before[i],\n filepath=figname,\n frame_real_len=frame_real_length[i],\n text=None)\n\n if self.epochs > self.config[\"wav_output_epochs\"] and i < self.config[\"results_num\"]:\n audio = self.feature_handle.inv_mel_spectrogram(mel_before[i][:frame_real_length[i]])\n self.feature_handle.save_wav(audio, wavname)\n\n\nclass UNETTSContentPreTrainer(Seq2SeqBasedTrainer):\n \"\"\"UNETTSContentPreTrainer\"\"\"\n\n def __init__(\n self, config, strategy, steps=0, epochs=0, is_mixed_precision=False,\n ):\n \"\"\"Initialize trainer.\n Args:\n steps (int): Initial global steps.\n epochs (int): Initial global epochs.\n config (dict): Config dict loaded from yaml format configuration file.\n is_mixed_precision (bool): Use mixed precision or not.\n \"\"\"\n super(UNETTSContentPreTrainer, self).__init__(\n steps=steps,\n epochs=epochs,\n config=config,\n strategy=strategy,\n is_mixed_precision=is_mixed_precision,\n )\n # define metrics to aggregates data and use tf.summary logs them\n self.list_metrics_name = [\n \"mel_before\",\n \"mel_after\",\n ]\n self.init_train_eval_metrics(self.list_metrics_name)\n self.reset_states_train()\n self.reset_states_eval()\n\n self.feature_handle = AudioMelSpec(**config[\"feat_params\"])\n\n def compile(self, model, optimizer):\n super().compile(model, optimizer)\n self.mse = tf.keras.losses.MeanSquaredError(\n reduction=tf.keras.losses.Reduction.NONE\n )\n self.mae = tf.keras.losses.MeanAbsoluteError(\n reduction=tf.keras.losses.Reduction.NONE\n )\n # self.bce = tf.keras.losses.BinaryCrossentropy(\n # reduction=tf.keras.losses.Reduction.NONE\n # )\n\n def compute_per_example_losses(self, batch, outputs):\n \"\"\"Compute per example losses and return dict_metrics_losses\n Note that all element of the loss MUST has a shape [batch_size] and \n the keys of dict_metrics_losses MUST be in self.list_metrics_name.\n\n Args:\n batch: dictionary batch input return from dataloader\n outputs: outputs of the model\n \n Returns:\n per_example_losses: per example losses for each GPU, shape [B]\n dict_metrics_losses: dictionary loss.\n \"\"\"\n mel_before, mel_after = outputs\n\n # mse, 0.01; mae, 0.05\n mel_loss_before = calculate_loss_norm_lens(batch[\"mel_gts\"], mel_before, self.mae, batch[\"mel_lengths\"])\n mel_loss_after = calculate_loss_norm_lens(batch[\"mel_gts\"], mel_after, self.mae, batch[\"mel_lengths\"])\n\n per_example_losses = (\n mel_loss_before + mel_loss_after\n )\n\n dict_metrics_losses = {\n \"mel_before\": mel_loss_before,\n \"mel_after\" : mel_loss_after,\n }\n\n return per_example_losses, dict_metrics_losses\n\n def generate_and_save_intermediate_result(self, batch):\n \"\"\"Generate and save intermediate result.\"\"\"\n\n # predict with tf.function.\n outputs = self.one_step_predict(batch)\n\n mel_before, mel_after = outputs\n\n mel_gts = batch[\"mel_gts\"].numpy()\n frame_real_length = batch[\"mel_lengths\"].numpy()\n\n # convert to tensor.\n # here we just take a sample at first replica.\n try: \n mel_before = mel_before.values[0].numpy()\n mel_after = mel_after.values[0].numpy()\n except Exception: \n mel_before = mel_before.numpy()\n mel_after = mel_after.numpy()\n\n # check directory\n utt_ids = batch[\"utt_ids\"].numpy()\n dirname = os.path.join(self.config[\"outdir\"], f\"predictions/{self.steps}steps\")\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n for i, utt in enumerate(utt_ids):\n figname = os.path.join(dirname, f\"{utt}.png\")\n wavname = os.path.join(dirname, f\"{utt}.wav\")\n\n self.feature_handle.compare_plot(mel_gts[i],\n mel_after[i],\n filepath=figname,\n frame_real_len=frame_real_length[i],\n text=None)\n\n if self.epochs > self.config[\"wav_output_epochs\"] and i < self.config[\"results_num\"]:\n audio = self.feature_handle.inv_mel_spectrogram(mel_after[i][:frame_real_length[i]])\n self.feature_handle.save_wav(audio, wavname)\n\ndef main():\n \"\"\"Run training process.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Train model (See detail in tensorflow_tts/models)\"\n )\n parser.add_argument(\n \"--train-dir\",\n default=None,\n type=str,\n help=\"directory including training data. \",\n )\n parser.add_argument(\n \"--dev-dir\",\n default=None,\n type=str,\n help=\"directory including development data. \",\n )\n parser.add_argument(\n \"--content_training\", default=0, type=int, help=\"is need to training context_model\"\n )\n parser.add_argument(\n \"--content_pretrained_path\", type=str, required=True, help=\"directory to content_pretrained_path\"\n )\n parser.add_argument(\n \"--outdir\", type=str, required=True, help=\"directory to save checkpoints.\"\n )\n parser.add_argument(\n \"--config\", type=str, required=True, help=\"yaml format configuration file.\"\n )\n parser.add_argument(\n \"--data_config\", type=str, required=True, help=\"yaml format data_process file.\"\n )\n parser.add_argument(\n \"--resume\",\n default=\"\",\n type=str,\n nargs=\"?\",\n help='checkpoint file path to resume training. (default=\"\")',\n )\n parser.add_argument(\n \"--verbose\",\n type=int,\n default=1,\n help=\"logging level. higher is more logging. (default=1)\",\n )\n parser.add_argument(\n \"--mixed_precision\",\n default=0,\n type=int,\n help=\"using mixed precision for generator or not.\",\n )\n parser.add_argument(\n \"--pretrained\",\n default=\"\",\n type=str,\n nargs=\"?\",\n help='pretrained weights .h5 file to load weights from. Auto-skips non-matching layers',\n )\n \n\n args = parser.parse_args()\n\n # return strategy\n STRATEGY = return_strategy()\n\n # set mixed precision config\n if args.mixed_precision == 1:\n tf.config.optimizer.set_experimental_options({\"auto_mixed_precision\": True})\n\n args.mixed_precision = bool(args.mixed_precision)\n # args.use_norm = bool(args.use_norm)\n\n # set logger\n if args.verbose > 1:\n logging.basicConfig(\n level=logging.DEBUG,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n elif args.verbose > 0:\n logging.basicConfig(\n level=logging.INFO,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n else:\n logging.basicConfig(\n level=logging.WARN,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n logging.warning(\"Skip DEBUG/INFO messages\")\n\n # check directory existence\n if not os.path.exists(args.outdir):\n os.makedirs(args.outdir)\n\n # check arguments\n if args.train_dir is None:\n raise ValueError(\"Please specify --train-dir\")\n if args.dev_dir is None:\n raise ValueError(\"Please specify --valid-dir\")\n\n # load and save config\n with open(args.config) as f:\n config = yaml.load(f, Loader=yaml.Loader)\n with open(args.data_config) as f:\n data_config = yaml.load(f, Loader=yaml.Loader)\n config.update(data_config)\n\n config.update(vars(args))\n config[\"version\"] = tensorflow_tts.__version__\n with open(os.path.join(args.outdir, \"config.yml\"), \"w\") as f:\n yaml.dump(config, f, Dumper=yaml.Dumper)\n for key, value in config.items():\n logging.info(f\"{key} = {value}\")\n\n config[\"content_training\"] = True if config[\"content_training\"] else False\n\n if config[\"content_training\"]:\n print(\"*\"*50)\n print(\"*\"*20 + \"Training Content Encoder ......\" + \"*\"*20)\n print(\"*\"*50)\n assert config[\"unetts_acous_context_pre_params\"][\"num_mels\"] == config[\"feat_params\"][\"num_mels\"]\n else:\n assert config[\"unetts_acous_params\"][\"num_mels\"] == config[\"feat_params\"][\"num_mels\"]\n\n # get dataset\n # if config[\"remove_short_samples\"]:\n # mel_length_threshold = config[\"mel_length_threshold\"]\n # else:\n # mel_length_threshold = None\n\n # if config[\"format\"] == \"npy\":\n # charactor_query = \"*-ids.npy\"\n # mel_query = \"*-raw-feats.npy\" if args.use_norm is False else \"*-norm-feats.npy\"\n # duration_query = \"*-durations.npy\"\n # lf0_query = \"*-raw-lf0.npy\"\n # energy_query = \"*-raw-energy.npy\"\n # else:\n # raise ValueError(\"Only npy are supported.\")\n\n # define train/valid dataset\n train_dataset = UNETTSAcousDataset(\n root_dir=args.train_dir,\n ).create(\n is_shuffle=config[\"is_shuffle\"],\n allow_cache=config[\"allow_cache\"],\n batch_size=config[\"batch_size\"] * STRATEGY.num_replicas_in_sync,\n )\n\n valid_dataset = UNETTSAcousDataset(\n root_dir=args.dev_dir,\n ).create(\n is_shuffle=config[\"is_shuffle\"],\n allow_cache=config[\"allow_cache\"],\n batch_size=config[\"batch_size\"] * STRATEGY.num_replicas_in_sync,\n )\n\n # define trainer\n if config[\"content_training\"]:\n trainer = UNETTSContentPreTrainer(\n config=config,\n strategy=STRATEGY,\n steps=0,\n epochs=0,\n is_mixed_precision=args.mixed_precision,\n )\n else:\n trainer = UNETTSAcousTrainer(\n config=config,\n strategy=STRATEGY,\n steps=0,\n epochs=0,\n is_mixed_precision=args.mixed_precision,\n )\n\n with STRATEGY.scope():\n # define model\n if config[\"content_training\"]:\n model = TFUNETTSContentPretrain(\n config=UNETTSAcousConfig(**config[\"unetts_acous_context_pre_params\"])\n )\n else:\n model = TFUNETTSAcous(\n config=UNETTSAcousConfig(**config[\"unetts_acous_params\"])\n )\n\n model._build()\n\n if not config[\"content_training\"]:\n logging.info(\"Model content_pretrained_path: {}\".format(config[\"content_pretrained_path\"]))\n try:\n # TODO\n model.text_encoder_weight_load(config[\"content_pretrained_path\"])\n model.freezen_encoder()\n except:\n logging.error(\"Error: Model embedding and text_encoder\")\n exit(1)\n\n model.summary()\n\n if len(args.pretrained) > 1:\n model.load_weights(args.pretrained, by_name=True, skip_mismatch=True)\n logging.info(f\"Successfully loaded pretrained weight from {args.pretrained}.\")\n\n # AdamW for model\n learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(\n initial_learning_rate=config[\"optimizer_params\"][\"initial_learning_rate\"],\n decay_steps=config[\"optimizer_params\"][\"decay_steps\"],\n end_learning_rate=config[\"optimizer_params\"][\"end_learning_rate\"],\n )\n\n learning_rate_fn = WarmUp(\n initial_learning_rate=config[\"optimizer_params\"][\"initial_learning_rate\"],\n decay_schedule_fn=learning_rate_fn,\n warmup_steps=int(\n config[\"train_max_steps\"]\n * config[\"optimizer_params\"][\"warmup_proportion\"]\n ),\n )\n\n optimizer = AdamWeightDecay(\n learning_rate=learning_rate_fn,\n weight_decay_rate=config[\"optimizer_params\"][\"weight_decay\"],\n beta_1=0.9,\n beta_2=0.98,\n epsilon=1e-6,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"],\n )\n\n _ = optimizer.iterations\n\n # compile trainer\n trainer.compile(model=model, optimizer=optimizer)\n\n # start training\n try:\n trainer.fit(\n train_dataset,\n valid_dataset,\n saved_path=os.path.join(config[\"outdir\"], \"checkpoints/\"),\n resume=args.resume,\n )\n except KeyboardInterrupt:\n trainer.save_checkpoint()\n logging.info(f\"Successfully saved checkpoint @ {trainer.steps}steps.\")\n except:\n error = traceback.format_exc()\n print(error)\n with open(os.path.join(config[\"outdir\"], \"error.txt\"), 'a') as fw:\n fw.write(error + \"\\n\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "train/train_unetts_duration.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Train MultiSPKCLDuration.\"\"\"\n\nimport tensorflow as tf\n\nphysical_devices = tf.config.list_physical_devices(\"GPU\")\nfor i in range(len(physical_devices)):\n tf.config.experimental.set_memory_growth(physical_devices[i], True)\n\nimport sys\n\nsys.path.append(\"../..\")\n\nimport argparse\nimport logging\nimport os\nimport traceback\n\nimport numpy as np\nimport yaml\nimport matplotlib.pyplot as plt\n\nimport tensorflow_tts\nfrom train.unetts_dataset import UNETTSDurationDataset\nfrom tensorflow_tts.configs import UNETTSDurationConfig\nfrom tensorflow_tts.models import TFUNETTSDuration\nfrom tensorflow_tts.optimizers import AdamWeightDecay, WarmUp\nfrom tensorflow_tts.trainers import Seq2SeqBasedTrainer\nfrom tensorflow_tts.utils import (calculate_loss_norm_lens, return_strategy)\n\n\nclass UNETTSDurationTrainer(Seq2SeqBasedTrainer):\n \"\"\"UNETTSDurationTrainer\"\"\"\n\n def __init__(\n self, config, strategy, steps=0, epochs=0, is_mixed_precision=False,\n ):\n \"\"\"Initialize trainer.\n Args:\n steps (int): Initial global steps.\n epochs (int): Initial global epochs.\n config (dict): Config dict loaded from yaml format configuration file.\n is_mixed_precision (bool): Use mixed precision or not.\n \"\"\"\n super(UNETTSDurationTrainer, self).__init__(\n steps=steps,\n epochs=epochs,\n config=config,\n strategy=strategy,\n is_mixed_precision=is_mixed_precision,\n )\n # define metrics to aggregates data and use tf.summary logs them\n self.list_metrics_name = [\n \"dur_loss\",\n ]\n self.init_train_eval_metrics(self.list_metrics_name)\n self.reset_states_train()\n self.reset_states_eval()\n\n def compile(self, model, optimizer):\n super().compile(model, optimizer)\n self.mse = tf.keras.losses.MeanSquaredError(\n reduction=tf.keras.losses.Reduction.NONE\n )\n self.mae = tf.keras.losses.MeanAbsoluteError(\n reduction=tf.keras.losses.Reduction.NONE\n )\n self.huber = tf.keras.losses.Huber(\n delta = 2.0,\n reduction=tf.keras.losses.Reduction.NONE\n )\n\n def compute_per_example_losses(self, batch, outputs):\n \"\"\"Compute per example losses and return dict_metrics_losses\n Note that all element of the loss MUST has a shape [batch_size] and \n the keys of dict_metrics_losses MUST be in self.list_metrics_name.\n\n Args:\n batch: dictionary batch input return from dataloader\n outputs: outputs of the model\n \n Returns:\n per_example_losses: per example losses for each GPU, shape [B]\n dict_metrics_losses: dictionary loss.\n \"\"\"\n\n # log_duration = tf.math.log(\n # tf.cast(tf.math.add(batch[\"duration_gts\"], 1), tf.float32)\n # )\n\n per_example_losses = calculate_loss_norm_lens(batch[\"duration_gts\"], outputs, self.mae, batch[\"char_lengths\"])\n\n dict_metrics_losses = {\n \"dur_loss\": per_example_losses,\n }\n\n return per_example_losses, dict_metrics_losses\n\n def generate_and_save_intermediate_result(self, batch):\n \"\"\"Generate and save intermediate result.\"\"\"\n\n outputs = self.one_step_predict(batch)\n\n try:\n dur_preds = outputs.values[0].numpy()\n except Exception:\n dur_preds = outputs.numpy()\n\n dur_gts = batch[\"duration_gts\"].numpy()\n char_lengths = batch[\"char_lengths\"].numpy()\n utt_ids = batch[\"utt_ids\"].numpy()\n\n dirname = os.path.join(self.config[\"outdir\"], f\"predictions/{self.steps}steps\")\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n for i, utt_id in enumerate(utt_ids):\n figname = os.path.join(dirname, f\"{utt_id}.png\")\n plt.figure(figsize=(10, 4))\n plt.plot(dur_gts[i][:char_lengths[i]], 'b--o', markersize=6)\n plt.plot(dur_preds[i][:char_lengths[i]], 'r-x', markersize=10)\n plt.grid()\n plt.legend((\"gst\", \"pred\"))\n plt.tight_layout()\n plt.savefig(figname)\n plt.close()\n\n\ndef main():\n \"\"\"Run training process.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Train model (See detail in tensorflow_tts/models)\"\n )\n parser.add_argument(\n \"--train-dir\",\n default=None,\n type=str,\n help=\"directory including training data. \",\n )\n parser.add_argument(\n \"--dev-dir\",\n default=None,\n type=str,\n help=\"directory including development data. \",\n )\n parser.add_argument(\n \"--outdir\", type=str, required=True, help=\"directory to save checkpoints.\"\n )\n parser.add_argument(\n \"--config\", type=str, required=True, help=\"yaml format configuration file.\"\n )\n parser.add_argument(\n \"--resume\",\n default=\"\",\n type=str,\n nargs=\"?\",\n help='checkpoint file path to resume training. (default=\"\")',\n )\n parser.add_argument(\n \"--verbose\",\n type=int,\n default=1,\n help=\"logging level. higher is more logging. (default=1)\",\n )\n parser.add_argument(\n \"--mixed_precision\",\n default=0,\n type=int,\n help=\"using mixed precision for generator or not.\",\n )\n parser.add_argument(\n \"--pretrained\",\n default=\"\",\n type=str,\n nargs=\"?\",\n help='pretrained weights .h5 file to load weights from. Auto-skips non-matching layers',\n )\n \n\n args = parser.parse_args()\n\n # return strategy\n STRATEGY = return_strategy()\n\n # set mixed precision config\n if args.mixed_precision == 1:\n tf.config.optimizer.set_experimental_options({\"auto_mixed_precision\": True})\n\n args.mixed_precision = bool(args.mixed_precision)\n # args.use_norm = bool(args.use_norm)\n\n # set logger\n if args.verbose > 1:\n logging.basicConfig(\n level=logging.DEBUG,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n elif args.verbose > 0:\n logging.basicConfig(\n level=logging.INFO,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n else:\n logging.basicConfig(\n level=logging.WARN,\n stream=sys.stdout,\n format=\"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s\",\n )\n logging.warning(\"Skip DEBUG/INFO messages\")\n\n # check directory existence\n if not os.path.exists(args.outdir):\n os.makedirs(args.outdir)\n\n # check arguments\n if args.train_dir is None:\n raise ValueError(\"Please specify --train-dir\")\n if args.dev_dir is None:\n raise ValueError(\"Please specify --valid-dir\")\n\n # load and save config\n with open(args.config) as f:\n config = yaml.load(f, Loader=yaml.Loader)\n config.update(vars(args))\n config[\"version\"] = tensorflow_tts.__version__\n with open(os.path.join(args.outdir, \"config.yml\"), \"w\") as f:\n yaml.dump(config, f, Dumper=yaml.Dumper)\n for key, value in config.items():\n logging.info(f\"{key} = {value}\")\n\n # # get dataset\n # if config[\"remove_short_samples\"]:\n # mel_length_threshold = config[\"mel_length_threshold\"]\n # else:\n # mel_length_threshold = None\n\n # if config[\"format\"] == \"npy\":\n # charactor_query = \"*-ids.npy\"\n # mel_query = \"*-raw-feats.npy\" if args.use_norm is False else \"*-norm-feats.npy\"\n # duration_query = \"*-durations.npy\"\n # lf0_query = \"*-raw-lf0.npy\"\n # energy_query = \"*-raw-energy.npy\"\n # else:\n # raise ValueError(\"Only npy are supported.\")\n\n # define train/valid dataset\n train_dataset = UNETTSDurationDataset(\n root_dir=args.train_dir\n ).create(\n is_shuffle=config[\"is_shuffle\"],\n allow_cache=config[\"allow_cache\"],\n batch_size=config[\"batch_size\"] * STRATEGY.num_replicas_in_sync,\n )\n\n valid_dataset = UNETTSDurationDataset(\n root_dir=args.dev_dir\n ).create(\n is_shuffle=config[\"is_shuffle\"],\n allow_cache=config[\"allow_cache\"],\n batch_size=config[\"batch_size\"] * STRATEGY.num_replicas_in_sync,\n )\n\n # define trainer\n trainer = UNETTSDurationTrainer(\n config=config,\n strategy=STRATEGY,\n steps=0,\n epochs=0,\n is_mixed_precision=args.mixed_precision,\n )\n\n with STRATEGY.scope():\n # define model\n model = TFUNETTSDuration(\n config=UNETTSDurationConfig(**config[\"unetts_duration\"])\n )\n model._build()\n model.summary()\n if len(args.pretrained) > 1:\n model.load_weights(args.pretrained, by_name=True, skip_mismatch=True)\n logging.info(f\"Successfully loaded pretrained weight from {args.pretrained}.\")\n\n # AdamW for model\n learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(\n initial_learning_rate=config[\"optimizer_params\"][\"initial_learning_rate\"],\n decay_steps=config[\"optimizer_params\"][\"decay_steps\"],\n end_learning_rate=config[\"optimizer_params\"][\"end_learning_rate\"],\n )\n\n learning_rate_fn = WarmUp(\n initial_learning_rate=config[\"optimizer_params\"][\"initial_learning_rate\"],\n decay_schedule_fn=learning_rate_fn,\n warmup_steps=int(\n config[\"train_max_steps\"]\n * config[\"optimizer_params\"][\"warmup_proportion\"]\n ),\n )\n\n optimizer = AdamWeightDecay(\n learning_rate=learning_rate_fn,\n weight_decay_rate=config[\"optimizer_params\"][\"weight_decay\"],\n beta_1=0.9,\n beta_2=0.98,\n epsilon=1e-6,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"],\n )\n\n _ = optimizer.iterations\n\n # compile trainer\n trainer.compile(model=model, optimizer=optimizer)\n\n # start training\n try:\n trainer.fit(\n train_dataset,\n valid_dataset,\n saved_path=os.path.join(config[\"outdir\"], \"checkpoints/\"),\n resume=args.resume,\n )\n except KeyboardInterrupt:\n trainer.save_checkpoint()\n logging.info(f\"Successfully saved checkpoint @ {trainer.steps}steps.\")\n except:\n error = traceback.format_exc()\n print(error)\n with open(os.path.join(config[\"outdir\"], \"error.txt\"), 'a') as fw:\n fw.write(error + \"\\n\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "train/unetts_dataset.py", "content": "# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Dataset modules.\"\"\"\n\nimport itertools\nimport logging\nimport os\nimport random\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_tts.datasets.abstract_dataset import AbstractDataset\nfrom tensorflow_tts.utils import find_files\n\nclass UNETTSDurationDataset(AbstractDataset):\n \"\"\"UNETTSDurationDataset\"\"\"\n\n def __init__(\n self,\n root_dir,\n charactor_query = \"*-ids.npy\",\n duration_query = \"*-raw-durations.npy\",\n stat_dur_query = \"*-stat-durations.npy\",\n charactor_load_fn = np.load,\n duration_load_fn = np.load,\n stat_dur_load_fn = np.load,\n ):\n \"\"\"Initialize dataset.\n\n Args:\n root_dir (str): Root directory including dumped files.\n ...\n \"\"\"\n # find all of charactor and mel files.\n charactor_files = sorted(find_files(root_dir, charactor_query))\n duration_files = sorted(find_files(root_dir, duration_query))\n stat_dur_files = sorted(find_files(root_dir, stat_dur_query))\n\n # assert the number of files\n assert len(duration_files) != 0, f\"Not found any mels files in ${root_dir}.\"\n assert (\n len(charactor_files)\n == len(duration_files)\n == len(stat_dur_files)\n ), f\"Number of charactor, duration and its stats files are different\"\n\n if \".npy\" in charactor_query:\n suffix = charactor_query[1:]\n utt_ids = [os.path.basename(f).replace(suffix, \"\") for f in charactor_files]\n\n # set global params\n self.utt_ids = utt_ids\n self.charactor_files = charactor_files\n self.duration_files = duration_files\n self.stat_dur_files = stat_dur_files\n self.charactor_load_fn = charactor_load_fn\n self.duration_load_fn = duration_load_fn\n self.stat_dur_load_fn = stat_dur_load_fn\n\n def get_args(self):\n return [self.utt_ids]\n\n def generator(self, utt_ids):\n for i, utt_id in enumerate(utt_ids): \n charactor_file = self.charactor_files[i]\n duration_file = self.duration_files[i]\n stat_dur_file = self.stat_dur_files[i]\n\n items = {\n \"utt_ids\" : utt_id,\n \"charactor_files\": charactor_file,\n \"duration_files\" : duration_file,\n \"stat_dur_files\" : stat_dur_file,\n }\n\n yield items\n\n @tf.function\n def _load_data(self, items):\n charactor = tf.numpy_function(np.load, [items[\"charactor_files\"]], tf.int32)\n duration = tf.numpy_function(np.load, [items[\"duration_files\"]], tf.float32)\n durstats = tf.numpy_function(np.load, [items[\"stat_dur_files\"]], tf.float32)\n\n items = {\n \"utt_ids\" : items[\"utt_ids\"],\n \"char_ids\" : charactor,\n \"char_lengths\" : len(charactor),\n \"duration_gts\" : duration,\n \"duration_stat\": durstats\n }\n\n return items\n\n def create(\n self,\n allow_cache=False,\n batch_size=1,\n is_shuffle=False,\n map_fn=None,\n reshuffle_each_iteration=True,\n ):\n \"\"\"Create tf.dataset function.\"\"\"\n output_types = self.get_output_dtypes()\n datasets = tf.data.Dataset.from_generator(\n self.generator, output_types=output_types, args=(self.get_args())\n )\n\n # load data\n datasets = datasets.map(\n lambda items: self._load_data(items), tf.data.experimental.AUTOTUNE\n )\n\n if allow_cache:\n datasets = datasets.cache()\n\n if is_shuffle:\n datasets = datasets.shuffle(\n self.get_len_dataset(),\n reshuffle_each_iteration=reshuffle_each_iteration,\n )\n\n # define padded shapes\n padded_shapes = {\n \"utt_ids\" : [],\n \"char_ids\" : [None],\n \"char_lengths\" : [],\n \"duration_gts\" : [None],\n \"duration_stat\": [None],\n }\n\n datasets = datasets.padded_batch(batch_size, padded_shapes=padded_shapes)\n datasets = datasets.prefetch(tf.data.experimental.AUTOTUNE)\n return datasets\n\n def get_output_dtypes(self):\n output_types = {\n \"utt_ids\" : tf.string,\n \"charactor_files\": tf.string,\n \"duration_files\" : tf.string,\n \"stat_dur_files\" : tf.string,\n }\n return output_types\n\n def get_len_dataset(self):\n return len(self.utt_ids)\n\n def __name__(self):\n return \"UNETTSDurationDataset\"\n\nclass UNETTSAcousDataset(AbstractDataset):\n \"\"\"UNETTSAcousDataset\"\"\"\n\n def __init__(\n self,\n root_dir,\n charactor_query = \"*-ids.npy\",\n duration_query = \"*-raw-durations.npy\",\n mel_query = \"*-raw-mels.npy\",\n embed_query = \"*-embeds.npy\",\n mel_load_fn = np.load,\n charactor_load_fn = np.load,\n duration_load_fn = np.load,\n embed_load_fn = np.load,\n mel_length_threshold = 0,\n ):\n \"\"\"Initialize dataset.\n\n Args:\n root_dir (str): Root directory including dumped files.\n ...\n \"\"\"\n # find all of charactor and mel files.\n charactor_files = sorted(find_files(root_dir, charactor_query))\n duration_files = sorted(find_files(root_dir, duration_query))\n mel_files = sorted(find_files(root_dir, mel_query))\n embed_files = sorted(find_files(root_dir, embed_query))\n\n # assert the number of files\n assert len(mel_files) != 0, f\"Not found any mels files in ${root_dir}.\"\n assert (\n len(charactor_files)\n == len(duration_files)\n == len(mel_files)\n == len(embed_files)\n ), f\"Number of charactor, duration and its stats files are different\"\n\n if \".npy\" in charactor_query:\n suffix = charactor_query[1:]\n utt_ids = [os.path.basename(f).replace(suffix, \"\") for f in charactor_files]\n\n # set global params\n self.utt_ids = utt_ids\n self.charactor_files = charactor_files\n self.duration_files = duration_files\n self.mel_files = mel_files\n self.embed_files = embed_files\n self.charactor_load_fn = charactor_load_fn\n self.duration_load_fn = duration_load_fn\n self.mel_load_fn = mel_load_fn\n self.embed_load_fn = embed_load_fn\n self.mel_length_threshold = mel_length_threshold\n\n def get_args(self):\n return [self.utt_ids]\n\n def generator(self, utt_ids):\n for i, utt_id in enumerate(utt_ids):\n charactor_file = self.charactor_files[i]\n duration_file = self.duration_files[i]\n mel_file = self.mel_files[i]\n embed_file = self.embed_files[i]\n\n items = {\n \"utt_ids\" : utt_id,\n \"charactor_files\": charactor_file,\n \"duration_files\" : duration_file,\n \"mel_files\" : mel_file,\n \"embed_files\" : embed_file,\n }\n\n yield items\n\n @tf.function\n def _load_data(self, items):\n charactor = tf.numpy_function(np.load, [items[\"charactor_files\"]], tf.int32)\n duration = tf.numpy_function(np.load, [items[\"duration_files\"]], tf.int32)\n mel = tf.numpy_function(np.load, [items[\"mel_files\"]], tf.float32)\n embed = tf.numpy_function(np.load, [items[\"embed_files\"]], tf.float32)\n\n items = {\n \"utt_ids\" : items[\"utt_ids\"],\n \"char_ids\" : charactor,\n \"char_lengths\": len(charactor),\n \"mel_gts\" : mel,\n \"mel_lengths\" : len(mel),\n \"duration_gts\": duration,\n \"embed\" : embed,\n }\n\n return items\n\n def create(\n self,\n allow_cache=False,\n batch_size=1,\n is_shuffle=False,\n map_fn=None,\n reshuffle_each_iteration=True,\n ):\n \"\"\"Create tf.dataset function.\"\"\"\n output_types = self.get_output_dtypes()\n datasets = tf.data.Dataset.from_generator(\n self.generator, output_types=output_types, args=(self.get_args())\n )\n\n # load data\n datasets = datasets.map(\n lambda items: self._load_data(items), tf.data.experimental.AUTOTUNE\n )\n\n # datasets = datasets.filter(\n # lambda x: x[\"spe_lengths\"] > self.mel_length_threshold\n # )\n\n if allow_cache:\n datasets = datasets.cache()\n\n if is_shuffle:\n datasets = datasets.shuffle(\n self.get_len_dataset(),\n reshuffle_each_iteration=reshuffle_each_iteration,\n )\n\n # define padded shapes\n padded_shapes = {\n \"utt_ids\" : [],\n \"char_ids\" : [None],\n \"char_lengths\": [],\n \"mel_gts\" : [None, None],\n \"mel_lengths\" : [],\n \"duration_gts\": [None],\n \"embed\" : [None],\n }\n\n datasets = datasets.padded_batch(batch_size, padded_shapes=padded_shapes)\n datasets = datasets.prefetch(tf.data.experimental.AUTOTUNE)\n return datasets\n\n def get_output_dtypes(self):\n output_types = {\n \"utt_ids\" : tf.string,\n \"charactor_files\": tf.string,\n \"mel_files\" : tf.string,\n \"duration_files\" : tf.string,\n \"embed_files\" : tf.string,\n }\n return output_types\n\n def get_len_dataset(self):\n return len(self.utt_ids)\n\n def __name__(self):\n return \"UNETTSAcousDataset\"" } ]