Repository: billwuhao/ComfyUI_MegaTTS3 Branch: main Commit: bad7ccb58afc Files: 40 Total size: 284.9 KB Directory structure: gitextract_pb8s4_gh/ ├── .github/ │ └── workflows/ │ └── publish_action.yml ├── .gitignore ├── LICENSE ├── README-CN.md ├── README.md ├── __init__.py ├── megatts3node.py ├── pyproject.toml ├── requirements.txt ├── tts/ │ ├── frontend_function.py │ ├── modules/ │ │ ├── aligner/ │ │ │ └── whisper_small.py │ │ ├── ar_dur/ │ │ │ ├── ar_dur_predictor.py │ │ │ └── commons/ │ │ │ ├── layers.py │ │ │ ├── nar_tts_modules.py │ │ │ ├── rel_transformer.py │ │ │ ├── rot_transformer.py │ │ │ ├── seq_utils.py │ │ │ └── transformer.py │ │ ├── llm_dit/ │ │ │ ├── cfm.py │ │ │ ├── dit.py │ │ │ ├── time_embedding.py │ │ │ └── transformer.py │ │ └── wavvae/ │ │ ├── decoder/ │ │ │ ├── diag_gaussian.py │ │ │ ├── hifigan_modules.py │ │ │ ├── seanet_encoder.py │ │ │ └── wavvae_v3.py │ │ └── encoder/ │ │ └── common_modules/ │ │ ├── conv.py │ │ ├── lstm.py │ │ └── seanet.py │ └── utils/ │ ├── audio_utils/ │ │ ├── align.py │ │ ├── io.py │ │ └── plot.py │ ├── commons/ │ │ ├── ckpt_utils.py │ │ └── hparams.py │ └── text_utils/ │ ├── dict.json │ ├── ph_tone_convert.py │ ├── split_text.py │ └── text_encoder.py └── workflow-examples/ ├── 单人语音.json └── 双人会话.json ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/publish_action.yml ================================================ name: Publish to Comfy registry on: workflow_dispatch: push: branches: - master - main paths: - "pyproject.toml" jobs: publish-node: name: Publish Custom Node to registry runs-on: ubuntu-latest steps: - name: Check out code uses: actions/checkout@v4 - name: Publish Custom Node uses: Comfy-Org/publish-node-action@main with: ## Add your own personal access token to your Github Repository secrets and reference it here. personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [2025] ByteDance Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README-CN.md ================================================ [中文](README-CN.md) | [English](README.md) # ComfyUI 的 MegaTTS3 声音克隆节点 声音克隆质量非常高, 支持中英文, 并可跨语言克隆. **支持自定义音色!!! 超长文本!!! 双人对话!!! Windows 正常安装 pynini, 不再是阉割版 TTS!!!**. ## 📣 更新 [2025-06-07]⚒️: v2.0.0. **支持自定义音色, 支持超长文本, 支持双人对话, Windows 正常安装 pynini, 不再是阉割版 TTS!**. ``` [S1] MegaTTS 真开源版本来了，效果666 [S2] 晕 xuan4 是一种 gan3 觉 [S1] 我爱你！I love you!“我爱你”的英语是“I love you” [S2] 2.5平方电线,共465篇，约315万字 [S1] 2002年的第一场雪，下在了2003年 ``` https://github.com/user-attachments/assets/b734e6bd-9303-4311-b3a4-618241ca6535 [2025-04-28]⚒️: 新增预览音色节点, 先预览音色, 满意再进行克隆. 感谢 @chenpipi0807 的 idea😍. 可在 `speakers` 文件夹下分门别类建更多文件夹. [2025-04-06]⚒️: 发布 v1.0.0. ## 使用 - 单人克隆(超长文本用空行隔开): ![image](https://github.com/billwuhao/ComfyUI_MegaTTS3/blob/main/images/2025-04-06_13-52-57.png) - 双人对话: ![image](https://github.com/billwuhao/ComfyUI_MegaTTS3/blob/main/images/2025-04-06_14-49-12.png) ## 安装 - **Windows 先安装以下依赖**: [pynini-windows-wheels](https://github.com/billwuhao/pynini-windows-wheels/releases/tag/v2.1.6.post1) 下载相应 python 版本的 pynini 轮子. 示例: ``` D:\AIGC\python\py310\python.exe -m pip install pynini-2.1.6.post1-cp3xx-cp3xx-win_amd64.whl D:\AIGC\python\py310\python.exe -m pip install importlib_resources D:\AIGC\python\py310\python.exe -m pip install WeTextProcessing>=1.0.4 --no-deps ``` - **然后正常进行下列安装**: ``` cd ComfyUI/custom_nodes git clone https://github.com/billwuhao/ComfyUI_MegaTTS3.git cd ComfyUI_MegaTTS3 pip install -r requirements.txt # python_embeded ./python_embeded/python.exe -m pip install -r requirements.txt ``` ## 模型下载 - 模型和音色需要手动下载放到 `ComfyUI\models\TTS` 路径下: [MegaTTS3](https://huggingface.co/ByteDance/MegaTTS3/tree/main) 整个文件夹全部下载放到 `TTS` 文件夹下. - **VAE 编码模型, 加微信公众号获取, 放到 `TTS\MegaTTS3\wavvae` 文件夹下, 即可自定义音色而无需 `.npy` 文件**: - [Google 云盘](https://drive.google.com/drive/folders/1p9GNdNJqeK_94lIJW8lew_G3EazU-9Wx?usp=sharing) - 请将音频放到 `TTS\speakers` 目录下. 我将会把所有 TTS 节点的说话者音频全部统一放到 `ComfyUI\models\TTS\speakers` 路径下, 这些节点包括 `IndexTTS, CSM, Dia, KokoroTTS, MegaTTS, QuteTTS, SparkTTS, StepAudioTTS` 等. 结构如下: ``` . │ .gitattributes │ config.json │ README.md │ ├─aligner_lm │ config.yaml │ model_only_last.ckpt │ ├─diffusion_transformer │ config.yaml │ model_only_last.ckpt │ ├─duration_lm │ config.yaml │ model_only_last.ckpt │ ├─g2p │ added_tokens.json │ config.json │ generation_config.json │ latest │ merges.txt │ model.safetensors │ special_tokens_map.json │ tokenizer.json │ tokenizer_config.json │ trainer_state.json │ vocab.json │ └─wavvae config.yaml decoder.ckpt model_only_last.ckpt ``` ## 鸣谢 - [MegaTTS3](https://github.com/bytedance/MegaTTS3) ## 打赏 您的赞赏是我最大的动力! 感谢您支持我一杯咖啡! ================================================ FILE: README.md ================================================ [中文](README-CN.md) | [English](README.md) # MegaTTS3 Voice Cloning Nodes for ComfyUI High-quality voice cloning, supporting both Chinese and English, with cross-lingual cloning capabilities. **Supports custom voice cloning!!! Extra-long text!!! Two-person dialogue!!! Full pynini installation on Windows, no more stripped-down TTS!!!**. ## 📣 Updates [2025-06-07]⚒️: v2.0.0. **Supports custom voice cloning, extra-long text, two-person dialogue, and full pynini installation on Windows, no more stripped-down TTS!**. ``` [S1] MegaTTS 真开源版本来了，效果666 [S2] 晕 xuan4 是一种 gan3 觉 [S1] 我爱你！I love you!“我爱你”的英语是“I love you” [S2] 2.5平方电线,共465篇，约315万字 [S1] 2002年的第一场雪，下在了2003年 ``` https://github.com/user-attachments/assets/b734e6bd-9303-4311-b3a4-618241ca6535 [2025-04-28]⚒️: Added a voice preview node. Preview the voice first, then clone if you're satisfied. Thanks to @chenpipi0807 for the idea😍. You can create categorized subfolders within the `speakers` folder. [2025-04-06]⚒️: Released v1.0.0. ## Usage - Single-person cloning (separate long text with blank lines): ![image](https://github.com/billwuhao/ComfyUI_MegaTTS3/blob/main/images/2025-04-06_13-52-57.png) - Two-person dialogue: ![image](https://github.com/billwuhao/ComfyUI_MegaTTS3/blob/main/images/2025-04-06_14-49-12.png) ## Installation - **For Windows, install the following dependencies first**: [pynini-windows-wheels](https://github.com/billwuhao/pynini-windows-wheels/releases/tag/v2.1.6.post1) Download the pynini wheel file corresponding to your Python version. Example: ``` D:\AIGC\python\py310\python.exe -m pip install pynini-2.1.6.post1-cp3xx-cp3xx-win_amd64.whl D:\AIGC\python\py310\python.exe -m pip install importlib_resources D:\AIGC\python\py310\python.exe -m pip install WeTextProcessing>=1.0.4 --no-deps ``` - **Then, proceed with the normal installation**: ``` cd ComfyUI/custom_nodes git clone https://github.com/billwuhao/ComfyUI_MegaTTS3.git cd ComfyUI_MegaTTS3 pip install -r requirements.txt # For python_embeded ./python_embeded/python.exe -m pip install -r requirements.txt ``` ## Model Download - Models and voices need to be downloaded manually and placed in the `ComfyUI\models\TTS` directory: [MegaTTS3](https://huggingface.co/ByteDance/MegaTTS3/tree/main) Download the entire folder and place it in the `TTS` directory. - **For the VAE encoder model, which enables custom voice cloning without `.npy` files, please follow our WeChat Official Account to obtain it. Place it in the `TTS\MegaTTS3\wavvae` folder**: - [Google Cloud Drive](https://drive.google.com/drive/folders/1p9GNdNJqeK_94lIJW8lew_G3EazU-9Wx?usp=sharing) - Please place the audio in the `TTS\speakers` directory. I will unify all speaker audios for TTS nodes into the `ComfyUI\models\TTS\speakers` path. These nodes include `IndexTTS, CSM, Dia, KokoroTTS, MegaTTS, QuteTTS, SparkTTS, StepAudioTTS`, etc. The structure is as follows: ``` . │ .gitattributes │ config.json │ README.md │ ├─aligner_lm │ config.yaml │ model_only_last.ckpt │ ├─diffusion_transformer │ config.yaml │ model_only_last.ckpt │ ├─duration_lm │ config.yaml │ model_only_last.ckpt │ ├─g2p │ added_tokens.json │ config.json │ generation_config.json │ latest │ merges.txt │ model.safetensors │ special_tokens_map.json │ tokenizer.json │ tokenizer_config.json │ trainer_state.json │ vocab.json │ └─wavvae config.yaml decoder.ckpt model_only_last.ckpt ``` ## Credits - [MegaTTS3](https://github.com/bytedance/MegaTTS3) ## Donation Your appreciation is my greatest motivation! Thank you for supporting me with a cup of coffee! ================================================ FILE: __init__.py ================================================ from .megatts3node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] ================================================ FILE: megatts3node.py ================================================ import json import os import librosa import numpy as np import torch import torchaudio from typing import List, Union, Optional from tn.chinese.normalizer import Normalizer as ZhNormalizer from tn.english.normalizer import Normalizer as EnNormalizer from langdetect import detect as classify_language import pyloudnorm as pyln import folder_paths import gc import re import sys current_dir = os.path.dirname(os.path.abspath(__file__)) if current_dir not in sys.path: sys.path.append(current_dir) from tts.modules.ar_dur.commons.nar_tts_modules import LengthRegulator from tts.frontend_function import g2p, align, make_dur_prompt, dur_pred, prepare_inputs_for_dit from tts.utils.audio_utils.io import convert_to_wav_bytes, combine_audio_segments from tts.utils.commons.ckpt_utils import load_ckpt from tts.utils.commons.hparams import set_hparams, hparams from tts.utils.text_utils.text_encoder import TokenTextEncoder from tts.utils.text_utils.split_text import chunk_text_chinese, chunk_text_english, chunk_text_chinesev2 from tts.utils.commons.hparams import hparams, set_hparams models_dir = folder_paths.models_dir model_path = os.path.join(models_dir, "TTS") speakers_dir = os.path.join(model_path, "speakers") cache_dir = folder_paths.get_temp_directory() def get_all_files( root_dir: str, return_type: str = "list", extensions: Optional[List[str]] = None, exclude_dirs: Optional[List[str]] = None, relative_path: bool = False ) -> Union[List[str], dict]: """ 递归获取目录下所有文件路径 :param root_dir: 要遍历的根目录 :param return_type: 返回类型 - "list"(列表) 或 "dict"(按目录分组) :param extensions: 可选的文件扩展名过滤列表 (如 ['.py', '.txt']) :param exclude_dirs: 要排除的目录名列表 (如 ['__pycache__', '.git']) :param relative_path: 是否返回相对路径 (相对于root_dir) :return: 文件路径列表或字典 """ file_paths = [] file_dict = {} # 规范化目录路径 root_dir = os.path.normpath(root_dir) for dirpath, dirnames, filenames in os.walk(root_dir): # 处理排除目录 if exclude_dirs: dirnames[:] = [d for d in dirnames if d not in exclude_dirs] current_files = [] for filename in filenames: # 扩展名过滤 if extensions: if not any(filename.lower().endswith(ext.lower()) for ext in extensions): continue # 构建完整路径 full_path = os.path.join(dirpath, filename) # 处理相对路径 if relative_path: full_path = os.path.relpath(full_path, root_dir) current_files.append(full_path) if return_type == "dict": # 使用相对路径或绝对路径作为键 dict_key = os.path.relpath(dirpath, root_dir) if relative_path else dirpath if current_files: file_dict[dict_key] = current_files else: file_paths.extend(current_files) return file_dict if return_type == "dict" else file_paths def get_speakers(): if not os.path.exists(speakers_dir): os.makedirs(speakers_dir, exist_ok=True) return [] speakers = get_all_files(speakers_dir, extensions=[".wav", ".mp3", ".flac", ".mp4", ".WAV", ".MP3", ".FLAC", ".MP4"], relative_path=True) return speakers class MegaTTS3DiTInfer(): def __init__( self, device=None, ckpt_root=os.path.join(model_path, "MegaTTS3"), dit_exp_name='diffusion_transformer', frontend_exp_name='aligner_lm', wavvae_exp_name='wavvae', dur_ckpt_path='duration_lm', g2p_exp_name='g2p', precision=torch.float16, **kwargs ): self.sr = 24000 self.fm = 8 if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' self.device = device self.precision = precision # build models self.dit_exp_name = os.path.join(ckpt_root, dit_exp_name) self.frontend_exp_name = os.path.join(ckpt_root, frontend_exp_name) self.wavvae_exp_name = os.path.join(ckpt_root, wavvae_exp_name) self.dur_exp_name = os.path.join(ckpt_root, dur_ckpt_path) self.g2p_exp_name = os.path.join(ckpt_root, g2p_exp_name) self.build_model(self.device) # init text normalizer self.zh_normalizer = ZhNormalizer(overwrite_cache=False, remove_erhua=False, remove_interjections=False) self.en_normalizer = EnNormalizer(overwrite_cache=False) # loudness meter self.loudness_meter = pyln.Meter(self.sr) self.ph_ref = None self.tone_ref = None self.mel2ph_ref = None self.vae_latent = None self.ctx_dur_tokens = None self.incremental_state_dur_prompt = None self.audio_bytes = None def clean(self): import gc self.dur_model = None self.dit= None self.g2p_model = None self.wavvae_en = None self.wavvae_de = None self.aligner_lm = None self.audio_bytes = None self.ph_ref = None self.tone_ref = None self.mel2ph_ref = None self.vae_latent = None self.ctx_dur_tokens = None self.incremental_state_dur_prompt = None gc.collect() torch.cuda.empty_cache() def build_model(self, device): set_hparams(exp_name=self.dit_exp_name, print_hparams=False) ''' Load Dict ''' current_dir = os.path.dirname(os.path.abspath(__file__)) ling_dict = json.load(open(f"{current_dir}/tts/utils/text_utils/dict.json", encoding='utf-8-sig')) self.ling_dict = {k: TokenTextEncoder(None, vocab_list=ling_dict[k], replace_oov='') for k in ['phone', 'tone']} self.token_encoder = token_encoder = self.ling_dict['phone'] ph_dict_size = len(token_encoder) ''' Load Duration LM ''' from tts.modules.ar_dur.ar_dur_predictor import ARDurPredictor hp_dur_model = self.hp_dur_model = set_hparams(f'{self.dur_exp_name}/config.yaml', global_hparams=False) hp_dur_model['frames_multiple'] = hparams['frames_multiple'] self.dur_model = ARDurPredictor( hp_dur_model, hp_dur_model['dur_txt_hs'], hp_dur_model['dur_model_hidden_size'], hp_dur_model['dur_model_layers'], ph_dict_size, hp_dur_model['dur_code_size'], use_rot_embed=hp_dur_model.get('use_rot_embed', False)) self.length_regulator = LengthRegulator() load_ckpt(self.dur_model, f'{self.dur_exp_name}', 'dur_model') self.dur_model.eval() self.dur_model.to(device) ''' Load Diffusion Transformer ''' from tts.modules.llm_dit.dit import Diffusion self.dit = Diffusion() load_ckpt(self.dit, f'{self.dit_exp_name}', 'dit', strict=False) self.dit.eval() self.dit.to(device) self.cfg_mask_token_phone = 302 - 1 self.cfg_mask_token_tone = 32 - 1 ''' Load Frontend LM ''' from tts.modules.aligner.whisper_small import Whisper self.aligner_lm = Whisper() load_ckpt(self.aligner_lm, f'{self.frontend_exp_name}', 'model') self.aligner_lm.eval() self.aligner_lm.to(device) self.kv_cache = None self.hooks = None ''' Load G2P LM''' from transformers import AutoTokenizer, AutoModelForCausalLM g2p_tokenizer = AutoTokenizer.from_pretrained(self.g2p_exp_name, padding_side="right") g2p_tokenizer.padding_side = "right" self.g2p_model = AutoModelForCausalLM.from_pretrained(self.g2p_exp_name).eval().to(device) self.g2p_tokenizer = g2p_tokenizer self.speech_start_idx = g2p_tokenizer.encode('')[0] ''' Wav VAE ''' self.hp_wavvae = hp_wavvae = set_hparams(f'{self.wavvae_exp_name}/config.yaml', global_hparams=False) from tts.modules.wavvae.decoder.wavvae_v3 import WavVAE_V3 self.wavvae_en = WavVAE_V3(hparams=hp_wavvae) self.wavvae_de = WavVAE_V3(hparams=hp_wavvae) if os.path.exists(f'{self.wavvae_exp_name}/model_only_last.ckpt'): load_ckpt(self.wavvae_en, f'{self.wavvae_exp_name}/model_only_last.ckpt', 'model_gen', strict=True) self.has_vae_encoder = True self.wavvae_en.eval() self.wavvae_en.to(device) else: load_ckpt(self.wavvae_de, f'{self.wavvae_exp_name}/decoder.ckpt', 'model_gen', strict=False) self.has_vae_encoder = False self.wavvae_de.eval() self.wavvae_de.to(device) self.vae_stride = hp_wavvae.get('vae_stride', 4) self.hop_size = hp_wavvae.get('hop_size', 4) def preprocess(self, audio_bytes, latent_file=None, topk_dur=1, **kwargs): if self.audio_bytes != audio_bytes: self.audio_bytes = audio_bytes wav_bytes = convert_to_wav_bytes(audio_bytes) ''' Load wav ''' wav, _ = librosa.core.load(wav_bytes, sr=self.sr) # Pad wav if necessary ws = hparams['win_size'] if len(wav) % ws < ws - 1: wav = np.pad(wav, (0, ws - 1 - (len(wav) % ws)), mode='constant', constant_values=0.0).astype(np.float32) wav = np.pad(wav, (0, 12000), mode='constant', constant_values=0.0).astype(np.float32) self.loudness_prompt = self.loudness_meter.integrated_loudness(wav.astype(float)) ''' obtain alignments with aligner_lm ''' ph_ref, tone_ref, mel2ph_ref = align(self, wav) self.kv_cache = None self.hooks = None with torch.inference_mode(): ''' Forward WaveVAE to obtain: prompt latent ''' if self.has_vae_encoder: if latent_file is None: wav = torch.FloatTensor(wav)[None].to(self.device) vae_latent = self.wavvae_en.encode_latent(wav) else: vae_latent = torch.from_numpy(np.load(latent_file)).to(self.device) vae_latent = vae_latent[:, :mel2ph_ref.size(1)//4] else: assert latent_file is not None, "WaveVAE encode model does not exist, an npy file must be provided!!!" vae_latent = torch.from_numpy(np.load(latent_file)).to(self.device) vae_latent = vae_latent[:, :mel2ph_ref.size(1)//4] ''' Duration Prompting ''' self.dur_model.hparams["infer_top_k"] = topk_dur if topk_dur > 1 else None incremental_state_dur_prompt, ctx_dur_tokens = make_dur_prompt(self, mel2ph_ref, ph_ref, tone_ref) self.ph_ref = ph_ref.to(self.device) self.tone_ref = tone_ref.to(self.device) self.mel2ph_ref = mel2ph_ref.to(self.device) self.vae_latent = vae_latent.to(self.device) self.ctx_dur_tokens = ctx_dur_tokens.to(self.device) self.incremental_state_dur_prompt = incremental_state_dur_prompt def forward(self, texts, time_step, p_w, t_w, dur_disturb=0.1, dur_alpha=1.0, **kwargs): with torch.inference_mode(): ''' Generating ''' waveforms = [] for input_text in texts: wav_pred_ = [] language_type = classify_language(input_text) if language_type == 'en': input_text = self.en_normalizer.normalize(input_text) text_segs = chunk_text_english(input_text, max_chars=130) else: input_text = self.zh_normalizer.normalize(input_text) text_segs = chunk_text_chinesev2(input_text, limit=60) for seg_i, text in enumerate(text_segs): ''' G2P ''' ph_pred, tone_pred = g2p(self, text) ''' Duration Prediction ''' mel2ph_pred = dur_pred(self, self.ctx_dur_tokens, self.incremental_state_dur_prompt, ph_pred, tone_pred, seg_i, dur_disturb, dur_alpha, is_first=seg_i==0, is_final=seg_i==len(text_segs)-1) inputs = prepare_inputs_for_dit(self, self.mel2ph_ref, mel2ph_pred, self.ph_ref, self.tone_ref, ph_pred, tone_pred, self.vae_latent) # Speech dit inference with torch.cuda.amp.autocast(dtype=self.precision, enabled=True): x = self.dit.inference(inputs, timesteps=time_step, seq_cfg_w=[p_w, t_w]).float() # WavVAE decode x[:, :self.vae_latent.size(1)] = self.vae_latent if self.has_vae_encoder: wav_pred = self.wavvae_en.decode(x)[0,0].to(torch.float32) else: wav_pred = self.wavvae_de.decode(x)[0,0].to(torch.float32) ''' Post-processing ''' # Trim prompt wav wav_pred = wav_pred[self.vae_latent.size(1)*self.vae_stride*self.hop_size:].cpu().numpy() # Norm generated wav to prompt wav's level meter = pyln.Meter(self.sr) # create BS.1770 meter loudness_pred = self.loudness_meter.integrated_loudness(wav_pred.astype(float)) wav_pred = pyln.normalize.loudness(wav_pred, loudness_pred, self.loudness_prompt) if np.abs(wav_pred).max() >= 1: wav_pred = wav_pred / np.abs(wav_pred).max() * 0.95 # Apply hamming window wav_pred_.append(wav_pred) gc.collect() torch.cuda.empty_cache() wav_pred = combine_audio_segments(wav_pred_, sr=self.sr).astype(np.float32) waveform = torch.tensor(wav_pred) waveforms.append(waveform.cpu()) return torch.cat(waveforms, dim=0), self.sr class MegaTTS3SpeakersPreview: @classmethod def INPUT_TYPES(s): speakers = get_speakers() return { "required": {"speaker":(speakers,),},} RETURN_TYPES = ("AUDIO", "STRING", ) RETURN_NAMES = ("audio", "npy_file", ) FUNCTION = "preview" CATEGORY = "🎤MW/MW-MegaTTS3" def preview(self, speaker): wav_path = os.path.join(speakers_dir, speaker) latent_file = wav_path.rsplit('.', 1)[0] + '.npy' if not os.path.exists(latent_file): latent_file = "" waveform, sample_rate = torchaudio.load(wav_path) waveform = waveform.unsqueeze(0) output_audio = { "waveform": waveform, "sample_rate": sample_rate } return (output_audio, latent_file) def cache_audio_tensor( cache_dir, audio_tensor: torch.Tensor, sample_rate: int, filename_prefix: str = "cached_audio_", audio_format: Optional[str] = ".wav" ) -> str: import tempfile try: with tempfile.NamedTemporaryFile( prefix=filename_prefix, suffix=audio_format, dir=cache_dir, delete=False ) as tmp_file: temp_filepath = tmp_file.name torchaudio.save(temp_filepath, audio_tensor, sample_rate) return temp_filepath except Exception as e: raise Exception(f"Error caching audio tensor: {e}") def statistical_compare(tensor1, tensor2): """通过统计特征快速比较""" stats1 = { 'mean': tensor1.mean(), 'std': tensor1.std(), 'max': tensor1.max(), 'min': tensor1.min() } stats2 = { 'mean': tensor2.mean(), 'std': tensor2.std(), 'max': tensor2.max(), 'min': tensor2.min() } return all(torch.allclose(stats1[k], stats2[k], rtol=1e-3) for k in stats1) INFER_INS_CACHE = None class MegaTTS3Run: def __init__(self): self.resource_context = None self.audio_tensor = None self.audio_prompt = None @classmethod def INPUT_TYPES(s): return { "required": { "audio": ("AUDIO",), "text": ("STRING", {"forceInput": True}), "time_step": ("INT", {"default": 32, "min": 1,}), "p_w": ("FLOAT", {"default":1.6, "min": 0.1,}), "t_w": ("FLOAT", {"default": 2.5, "min": 0.1,}), "unload_model": ("BOOLEAN", {"default": True}), }, "optional": { "dialogue_audio_s2":("AUDIO",), "audio_npy_file": ("STRING", {"forceInput": True, "tooltip": "No `npy_file` will use VAE to encode audio. 不提供 .npy 文件, 将使用 WaveVAE 编码音频"}), "audio_s2_npy_file": ("STRING", {"forceInput": True, "tooltip": "No `npy_file` will use VAE to encode audio. 不提供 .npy 文件, 将使用 WaveVAE 编码音频"}), } } RETURN_TYPES = ("AUDIO",) RETURN_NAMES = ("audio",) FUNCTION = "clone" CATEGORY = "🎤MW/MW-MegaTTS3" def clone(self, audio, text, time_step, p_w, t_w, unload_model, audio_npy_file=None, dialogue_audio_s2=None, audio_s2_npy_file=None): if not os.path.exists(os.path.join(model_path, "MegaTTS3", 'wavvae', 'model_only_last.ckpt')): print("WaveVAE encode model does not exist, an npy file must be provided!!!") waveform = audio["waveform"].squeeze(0) global INFER_INS_CACHE if INFER_INS_CACHE is None: INFER_INS_CACHE = MegaTTS3DiTInfer() latent_file = audio_npy_file if audio_npy_file else None try: import gc if dialogue_audio_s2 is None: # 只有音频改变时, 才重新预处理 if self.audio_tensor is None or self.audio_prompt is None or statistical_compare(self.audio_tensor, waveform) == False: self.audio_tensor = waveform self.audio_prompt = cache_audio_tensor(cache_dir, waveform, audio["sample_rate"]) texts = [i.strip() for i in re.split(r'\n\s*\n', text.strip()) if i.strip()] with open(self.audio_prompt, 'rb') as file: file_content = file.read() INFER_INS_CACHE.preprocess(file_content, latent_file=latent_file) del file_content gc.collect() torch.cuda.empty_cache() waveform, sr = INFER_INS_CACHE.forward(texts=texts, time_step=time_step, p_w=p_w, t_w=t_w) gc.collect() torch.cuda.empty_cache() else: latent_file_2 = audio_s2_npy_file if audio_s2_npy_file else None audio_1 = cache_audio_tensor(cache_dir, waveform, audio["sample_rate"]) audio_2 = cache_audio_tensor(cache_dir, dialogue_audio_s2["waveform"].squeeze(0), dialogue_audio_s2["sample_rate"]) with open(audio_1, 'rb') as file: file_content_1 = file.read() with open(audio_2, 'rb') as file: file_content_2 = file.read() gc.collect() torch.cuda.empty_cache() ress = [] for t, a, n in self.get_speaker_text_audio(text, audio_1, audio_2): texts = [i.strip() for i in re.split(r'\n\s*\n', t.strip()) if i.strip()] if a == audio_1: INFER_INS_CACHE.preprocess(file_content_1, latent_file=latent_file) res_sub, sr = INFER_INS_CACHE.forward(texts=texts, time_step=time_step, p_w=p_w, t_w=t_w) ress.append([res_sub, n]) else: INFER_INS_CACHE.preprocess(file_content_2, latent_file=latent_file_2) res_sub, sr = INFER_INS_CACHE.forward(texts=texts, time_step=time_step, p_w=p_w, t_w=t_w) ress.append([res_sub, n]) del file_content_1 del file_content_2 gc.collect() torch.cuda.empty_cache() waveform = torch.cat(list(zip(*sorted(ress, key=lambda x: x[1])))[0], dim=0) except Exception as e: if unload_model: import gc INFER_INS_CACHE.clean() INFER_INS_CACHE = None self.resource_context = None gc.collect() torch.cuda.empty_cache() raise e if unload_model: import gc INFER_INS_CACHE.clean() INFER_INS_CACHE = None self.resource_context = None gc.collect() torch.cuda.empty_cache() return ({"waveform": waveform.unsqueeze(0).unsqueeze(0), "sample_rate": sr},) def get_speaker_text_audio(self, text, audio_1, audio_2): pattern = r'(\[s?S?1\]|\[s?S?2\])\s*([\s\S]*?)(?=\[s?S?[12]\]|$)' matches = re.findall(pattern, text) if len(matches) == 0: raise ValueError("No speaker tags found in the text: [S1]... [S2]...") labels = [] contents = [] audios = [] for label, content in matches: labels.append(label) contents.append(content) audios = [ audio_1 if i.lower() == '[s1]' else audio_2 for i in labels ] return sorted(zip(contents, audios, range(len(contents))), key=lambda x: x[1]) class MultiLinePromptMG: @classmethod def INPUT_TYPES(cls): return { "required": { "multi_line_prompt": ("STRING", { "multiline": True, "default": ""}), }, } CATEGORY = "🎤MW/MW-MegaTTS3" RETURN_TYPES = ("STRING",) RETURN_NAMES = ("text",) FUNCTION = "promptgen" def promptgen(self, multi_line_prompt: str): return (multi_line_prompt.strip(),) NODE_CLASS_MAPPINGS = { "MegaTTS3SpeakersPreview": MegaTTS3SpeakersPreview, "MegaTTS3Run": MegaTTS3Run, "MultiLinePromptMG": MultiLinePromptMG, } NODE_DISPLAY_NAME_MAPPINGS = { "MegaTTS3SpeakersPreview": "MegaTTS3 Speakers Preview", "MegaTTS3Run": "MegaTTS3 Run", "MultiLinePromptMG": "Multi Line Text", } ================================================ FILE: pyproject.toml ================================================ [project] name = "megatts3-mw" description = "Lightweight and Efficient, 🎧Ultra High-Quality Voice Cloning, Chinese and English." version = "2.0.0" license = {file = "LICENSE"} dependencies = ["setproctitle", "attrdict", "librosa", "pydub", "pyloudnorm", "x-transformers", "torchdiffeq", "openai-whisper>=20240930"] [project.urls] Repository = "https://github.com/billwuhao/ComfyUI_MegaTTS3" # Used by Comfy Registry https://comfyregistry.org [tool.comfy] PublisherId = "mw" DisplayName = "MW-ComfyUI_MegaTTS3" Icon = "" ================================================ FILE: requirements.txt ================================================ setproctitle attrdict librosa pyloudnorm x-transformers torchdiffeq openai-whisper>=20240930 langdetect pynini==2.1.6; platform_system!="Windows" WeTextProcessing>=1.0.3; platform_system!="Windows" ================================================ FILE: tts/frontend_function.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn.functional as F import whisper import librosa from copy import deepcopy from tts.utils.text_utils.ph_tone_convert import split_ph_timestamp, split_ph from tts.utils.audio_utils.align import mel2token_to_dur ''' Graphme to phoneme function ''' def g2p(self, text_inp): # prepare inputs txt_token = self.g2p_tokenizer('' + text_inp + '')['input_ids'] input_ids = torch.LongTensor([txt_token+[145+self.speech_start_idx]]).to(self.device) # model forward with torch.cuda.amp.autocast(dtype=self.precision, enabled=True): outputs = self.g2p_model.generate(input_ids, max_new_tokens=256, do_sample=True, top_k=1, eos_token_id=800+1+self.speech_start_idx) # process outputs ph_tokens = outputs[:, len(txt_token):-1]-self.speech_start_idx ph_pred, tone_pred = split_ph(ph_tokens[0]) ph_pred, tone_pred = ph_pred[None, :].to(self.device), tone_pred[None, :].to(self.device) return ph_pred, tone_pred ''' Get phoneme2mel align of prompt speech ''' def align(self, wav): with torch.inference_mode(): whisper_wav = librosa.resample(wav, orig_sr=self.sr, target_sr=16000) mel = torch.FloatTensor(whisper.log_mel_spectrogram(whisper_wav).T).to(self.device)[None].transpose(1,2) prompt_max_frame = mel.size(2) // self.fm * self.fm mel = mel[:, :, :prompt_max_frame] token = torch.LongTensor([[798]]).to(self.device) audio_features = self.aligner_lm.embed_audio(mel) for i in range(768): with torch.cuda.amp.autocast(dtype=self.precision, enabled=True): logits = self.aligner_lm.logits(token, audio_features, None) token_pred = torch.argmax(F.softmax(logits[:, -1], dim=-1), 1)[None] token = torch.cat([token, token_pred], dim=1) if token_pred[0] == 799: break alignment_tokens = token ph_ref, tone_ref, dur_ref, _ = split_ph_timestamp(deepcopy(alignment_tokens)[0, 1:-1]) ph_ref = torch.Tensor(ph_ref)[None].to(self.device) tone_ref = torch.Tensor(tone_ref)[None].to(self.device) if dur_ref.sum() < prompt_max_frame: dur_ref[-1] += prompt_max_frame - dur_ref.sum() elif dur_ref.sum() > prompt_max_frame: len_diff = dur_ref.sum() - prompt_max_frame while True: for i in range(len(dur_ref)): dur_ref[i] -= 1 len_diff -= 1 if len_diff == 0: break if len_diff == 0: break mel2ph_ref = self.length_regulator(dur_ref[None]).to(self.device) mel2ph_ref = mel2ph_ref[:, :mel2ph_ref.size(1)//self.fm*self.fm] return ph_ref, tone_ref, mel2ph_ref ''' Duration Prompting ''' def make_dur_prompt(self, mel2ph_ref, ph_ref, tone_ref): dur_tokens_2d_ = mel2token_to_dur(mel2ph_ref, ph_ref.shape[1]).clamp( max=self.hp_dur_model['dur_code_size'] - 1) + 1 ctx_dur_tokens = dur_tokens_2d_.clone().flatten(0, 1).to(self.device) txt_tokens_flat_ = ph_ref.flatten(0, 1) ctx_dur_tokens = ctx_dur_tokens[txt_tokens_flat_ > 0][None] last_dur_pos_prompt = ctx_dur_tokens.shape[1] dur_spk_pos_ids_flat = range(0, last_dur_pos_prompt) dur_spk_pos_ids_flat = torch.LongTensor([dur_spk_pos_ids_flat]).to(self.device) with torch.cuda.amp.autocast(dtype=self.precision, enabled=True): _, incremental_state_dur_prompt = self.dur_model.infer( ph_ref, {'tone': tone_ref}, None, None, None, ctx_vqcodes=ctx_dur_tokens, spk_pos_ids_flat=dur_spk_pos_ids_flat, return_state=True) return incremental_state_dur_prompt, ctx_dur_tokens ''' Duration Prediction ''' def dur_pred(self, ctx_dur_tokens, incremental_state_dur_prompt, ph_pred, tone_pred, seg_i, dur_disturb, dur_alpha, is_first, is_final): last_dur_token = ctx_dur_tokens[:, -1:] last_dur_pos_prompt = ctx_dur_tokens.shape[1] incremental_state_dur = deepcopy(incremental_state_dur_prompt) txt_len = ph_pred.shape[1] dur_spk_pos_ids_flat = range(last_dur_pos_prompt, last_dur_pos_prompt + txt_len) dur_spk_pos_ids_flat = torch.LongTensor([dur_spk_pos_ids_flat]).to(self.device) last_dur_pos_prompt = last_dur_pos_prompt + txt_len with torch.cuda.amp.autocast(dtype=self.precision, enabled=True): dur_pred = self.dur_model.infer( ph_pred, {'tone': tone_pred}, None, None, None, incremental_state=incremental_state_dur, first_decoder_inp=last_dur_token, spk_pos_ids_flat=dur_spk_pos_ids_flat, ) dur_pred = dur_pred - 1 dur_pred = dur_pred.clamp(0, self.hp_dur_model['dur_code_size'] - 1) # if is_final: # dur_pred[:, -1] = dur_pred[:, -1].clamp(64, 128) # else: # dur_pred[:, -1] = dur_pred[:, -1].clamp(48, 128) # if seg_i > 0: # dur_pred[:, 0] = 0 # ['。', '！', '？', 'sil'] # for sil_token in [148, 153, 166, 145]: # dur_pred[ph_pred==sil_token].clamp_min(32) # # ['，', '；'] # for sil_token in [163, 165]: # dur_pred[ph_pred==sil_token].clamp_min(16) if not is_final: # add 0.32ms for crossfade dur_pred[:, -1] = dur_pred[:, -1] + 32 else: dur_pred[:, -1] = dur_pred[:, -1].clamp(64, 128) ''' DiT target speech generation ''' dur_disturb_choice = (torch.rand_like(dur_pred.float()) > 0.5).float() dur_disturb_r = 1 + torch.rand_like(dur_pred.float()) * dur_disturb dur_pred = dur_pred * dur_disturb_r * dur_disturb_choice + \ dur_pred / dur_disturb_r * (1 - dur_disturb_choice) dur_pred = torch.round(dur_pred * dur_alpha).clamp(0, 127) # ['。', '！', '？', 'sil'] for sil_token in [148, 153, 166, 145]: dur_pred[ph_pred==sil_token] = dur_pred[ph_pred==sil_token].clamp_min(64) # ['，', '；'] for sil_token in [163, 165]: dur_pred[ph_pred==sil_token] = dur_pred[ph_pred==sil_token].clamp_min(32) if is_first: dur_pred[:, 0] = 8 dur_sum = dur_pred.sum() npad = self.fm - dur_sum % self.fm if npad < self.fm: dur_pred[:, -1] += npad mel2ph_pred = self.length_regulator(dur_pred).to(self.device) return mel2ph_pred def prepare_inputs_for_dit(self, mel2ph_ref, mel2ph_pred, ph_ref, tone_ref, ph_pred, tone_pred, vae_latent): # Prepare duration token mel2ph_pred = torch.cat((mel2ph_ref, mel2ph_pred+ph_ref.size(1)), dim=1) mel2ph_pred = mel2ph_pred[:, :mel2ph_pred.size(1)//self.fm*self.fm].repeat(3, 1) # Prepare phone and tone token ph_pred = torch.cat((ph_ref, ph_pred), dim=1) tone_pred = torch.cat((tone_ref, tone_pred), dim=1) # Disable the English tone (set them to 3)""" en_tone_idx = ~((tone_pred == 4) | ( (11 <= tone_pred) & (tone_pred <= 15)) | (tone_pred == 0)) tone_pred[en_tone_idx] = 3 # Prepare cfg inputs ph_seq = torch.cat([ph_pred, ph_pred, torch.full(ph_pred.size(), self.cfg_mask_token_phone, device=self.device)], 0) tone_seq = torch.cat([tone_pred, tone_pred, torch.full(tone_pred.size(), self.cfg_mask_token_tone, device=self.device)], 0) target_size = mel2ph_pred.size(1)//self.vae_stride vae_latent_ = vae_latent.repeat(3, 1, 1) ctx_mask = torch.ones_like(vae_latent_[:, :, 0:1]) vae_latent_ = F.pad(vae_latent_, (0, 0, 0, target_size - vae_latent.size(1)), mode='constant', value=0) vae_latent_[1:] = 0.0 ctx_mask = F.pad(ctx_mask, (0, 0, 0, target_size - vae_latent.size(1)), mode='constant', value=0) return { 'phone': ph_seq, 'tone': tone_seq, "lat_ctx": vae_latent_ * ctx_mask, "ctx_mask": ctx_mask, "dur": mel2ph_pred, } ================================================ FILE: tts/modules/aligner/whisper_small.py ================================================ # MIT License # Copyright (c) 2022 OpenAI # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Copyright (c) [2022] [OpenAI] # Copyright (c) [2025] [Ziyue Jiang] # SPDX-License-Identifier: MIT # This file has been modified by Ziyue Jiang on 2025/03/19 # Original file was released under MIT, with the full license text # available at https://github.com/openai/whisper/blob/v20240930/LICENSE. # This modified file is released under the same license. from contextlib import contextmanager from typing import Dict, Iterable, Optional, Tuple import numpy as np import torch import torch.nn.functional as F from torch import Tensor, nn from torch.nn.functional import scaled_dot_product_attention SDPA_AVAILABLE = True class LayerNorm(nn.LayerNorm): def forward(self, x: Tensor) -> Tensor: return super().forward(x.float()).type(x.dtype) class Linear(nn.Linear): def forward(self, x: Tensor) -> Tensor: return F.linear( x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype), ) class Conv1d(nn.Conv1d): def _conv_forward( self, x: Tensor, weight: Tensor, bias: Optional[Tensor] ) -> Tensor: return super()._conv_forward( x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) ) def sinusoids(length, channels, max_timescale=10000): """Returns sinusoids for positional embedding""" assert channels % 2 == 0 log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) @contextmanager def disable_sdpa(): prev_state = MultiHeadAttention.use_sdpa try: MultiHeadAttention.use_sdpa = False yield finally: MultiHeadAttention.use_sdpa = prev_state class MultiHeadAttention(nn.Module): use_sdpa = True def __init__(self, n_state: int, n_head: int): super().__init__() self.n_head = n_head self.query = Linear(n_state, n_state) self.key = Linear(n_state, n_state, bias=False) self.value = Linear(n_state, n_state) self.out = Linear(n_state, n_state) def forward( self, x: Tensor, xa: Optional[Tensor] = None, mask: Optional[Tensor] = None, kv_cache: Optional[dict] = None, casual: Optional[bool] = None ): q = self.query(x) if kv_cache is None or xa is None or self.key not in kv_cache: # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; # otherwise, perform key/value projections for self- or cross-attention as usual. k = self.key(x if xa is None else xa) v = self.value(x if xa is None else xa) else: # for cross-attention, calculate keys and values once and reuse in subsequent calls. k = kv_cache[self.key] v = kv_cache[self.value] wv = self.qkv_attention(q, k, v, mask, casual) return self.out(wv) def qkv_attention( self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None, casual: Optional[bool] = None ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: n_batch, n_ctx, n_state = q.shape scale = (n_state // self.n_head) ** -0.25 q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) a = scaled_dot_product_attention( q, k, v, is_causal=casual and n_ctx > 1, attn_mask=mask[:, None, None, :] if mask is not None else None ) out = a.permute(0, 2, 1, 3).flatten(start_dim=2) return out class ResidualAttentionBlock(nn.Module): def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): super().__init__() self.attn = MultiHeadAttention(n_state, n_head) self.attn_ln = LayerNorm(n_state) self.cross_attn = ( MultiHeadAttention(n_state, n_head) if cross_attention else None ) self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None n_mlp = n_state * 4 self.mlp = nn.Sequential( Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state) ) self.mlp_ln = LayerNorm(n_state) def forward( self, x: Tensor, xa: Optional[Tensor] = None, mask: Optional[Tensor] = None, kv_cache: Optional[dict] = None, casual: Optional[bool] = None, ): x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache, casual=casual) if self.cross_attn: # TODO: Cross attention mask x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache, casual=False) x = x + self.mlp(self.mlp_ln(x)) return x class AudioEncoder(nn.Module): def __init__( self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int ): super().__init__() self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] ) self.ln_post = LayerNorm(n_state) def forward(self, x: Tensor, attn_mask: Tensor): """ x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) the mel spectrogram of the audio """ x = F.gelu(self.conv1(x)) x = F.gelu(self.conv2(x)) x = x.permute(0, 2, 1) # assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" x = (x + self.positional_embedding[:x.size(1)]).to(x.dtype) for block in self.blocks: x = block(x, mask=attn_mask, casual=False) x = self.ln_post(x) return x class TextDecoder(nn.Module): def __init__( self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int ): super().__init__() self.token_embedding = nn.Embedding(n_vocab, n_state) self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( [ ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer) ] ) self.ln = LayerNorm(n_state) self.out_proj = nn.Linear(n_state, n_vocab) def forward(self, x: Tensor, attn_mask: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): """ x : torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state) the encoded audio features to be attended on """ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 x = ( self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] ) x = x.to(xa.dtype) for block in self.blocks: x = block(x, xa, mask=attn_mask, kv_cache=kv_cache, casual=True) x = self.ln(x) # logits = ( # x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1) # ).float() logits = self.out_proj(x) return logits class Whisper(nn.Module): def __init__(self): super().__init__() self.n_vocab = 6800 self.n_text_layer = 6 self.n_text_head = 8 self.n_text_ctx = 2048 self.encoder = AudioEncoder( n_mels=80, n_ctx=3000, n_state=512, n_head=8, n_layer=6, ) self.decoder = TextDecoder( n_vocab=6800, n_ctx=2048, n_state=512, n_head=8, n_layer=6, ) def embed_audio(self, mel: torch.Tensor): return self.encoder(mel, None) def logits(self, tokens, audio_features, kv_cache=None): return self.decoder(tokens, None, audio_features, kv_cache=kv_cache) def forward( self, mel, mel_len, token, token_len ) -> Dict[str, torch.Tensor]: attn_mask_enc = self.sequence_mask(mel_len//2, device=mel.device) > 0 attn_mask_dec = self.sequence_mask(token_len, device=mel.device) > 0 return self.decoder(token, attn_mask_dec, self.encoder(mel, attn_mask_enc)) @property def device(self): return next(self.parameters()).device def install_kv_cache_hooks(self, cache: Optional[dict] = None): """ The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value tensors calculated for the previous positions. This method returns a dictionary that stores all caches, and the necessary hooks for the key and value projection modules that save the intermediate tensors to be reused during later calculations. Returns ------- cache : Dict[nn.Module, torch.Tensor] A dictionary object mapping the key/value projection modules to its cache hooks : List[RemovableHandle] List of PyTorch RemovableHandle objects to stop the hooks to be called """ cache = {**cache} if cache is not None else {} hooks = [] def save_to_cache(module, _, output): if module not in cache or output.shape[1] > self.n_text_ctx: # save as-is, for the first token or cross attention cache[module] = output else: cache[module] = torch.cat([cache[module], output], dim=1).detach() return cache[module] def install_hooks(layer: nn.Module): if isinstance(layer, MultiHeadAttention): hooks.append(layer.key.register_forward_hook(save_to_cache)) hooks.append(layer.value.register_forward_hook(save_to_cache)) self.decoder.apply(install_hooks) return cache, hooks def sequence_mask(self, seq_lens, max_len=None, device='cpu'): b = seq_lens.shape[0] if max_len is None: max_len = seq_lens.max() mask = torch.arange(max_len).unsqueeze(0).to(device) # [1, t] mask = mask < (seq_lens.unsqueeze(1)) # [1, t] + [b, 1] = [b, t] mask = mask.float() return mask ================================================ FILE: tts/modules/ar_dur/ar_dur_predictor.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random from copy import deepcopy import torch import torch.nn.functional as F from torch import nn from torch.nn import Linear from tqdm import tqdm from tts.modules.ar_dur.commons.layers import Embedding, LayerNorm from tts.modules.ar_dur.commons.nar_tts_modules import PosEmb from tts.modules.ar_dur.commons.rot_transformer import RotTransformerDecoderLayer from tts.modules.ar_dur.commons.transformer import SinusoidalPositionalEmbedding from tts.modules.ar_dur.commons.rel_transformer import RelTransformerEncoder FS_ENCODERS = { 'rel_fft': lambda hp, dict_size: RelTransformerEncoder( dict_size, hp['hidden_size'], hp['hidden_size'], hp['ffn_hidden_size'], hp['num_heads'], hp['enc_layers'], hp['enc_ffn_kernel_size'], hp['dropout'], prenet=hp['enc_prenet'], pre_ln=hp['enc_pre_ln']), } def fill_with_neg_inf2(t): """FP16-compatible function that fills a tensor with -inf.""" return t.float().fill_(-1e8).type_as(t) def expand_states(h, mel2token): h = F.pad(h, [0, 0, 1, 0]) mel2token_ = mel2token[..., None].repeat([1, 1, h.shape[-1]]) h = torch.gather(h, 1, mel2token_) # [B, T, H] return h class CodePredictor(nn.Module): def __init__(self, hparams, hidden_size, dec_hidden_size, lm_num_layers, dict_size, code_size): super().__init__() self.hparams = deepcopy(hparams) self.hparams['hidden_size'] = hidden_size self.hidden_size = hidden_size char_dict_size = hparams.get('char_dict_size', 4000) if not hparams.get('lm_use_enc'): self.encoder = nn.Embedding(dict_size, self.hidden_size, padding_idx=0) if hparams.get('mega_use_char', True): self.char_encoder = nn.Embedding(char_dict_size, self.hidden_size, padding_idx=0) else: self.encoder = FS_ENCODERS[self.hparams['encoder_type']](self.hparams, dict_size) if hparams.get('mega_use_char', True): self.char_encoder = FS_ENCODERS[self.hparams['encoder_type']](self.hparams, char_dict_size) if hparams['use_ph_pos_embed']: self.ph_pos_embed = PosEmb(self.hidden_size) self.char_empty_embed = nn.Embedding(1, self.hidden_size) if hparams.get('use_bert_input'): self.bert_input_proj = nn.Linear(768, self.hidden_size) self.ling_label_embed_layers = nn.ModuleDict() for k, s in zip(hparams['ling_labels'], hparams['ling_label_dict_size']): self.ling_label_embed_layers[k] = Embedding(s + 3, self.hidden_size, padding_idx=0) self.dec_hidden_size = dec_hidden_size self.enc_proj = nn.Linear(self.hidden_size, dec_hidden_size) self.code_emb = Embedding(code_size + 2, dec_hidden_size, 0) self.use_pos_embed = hparams.get('use_pos_embed', False) if self.use_pos_embed: self.embed_positions = SinusoidalPositionalEmbedding(dec_hidden_size, 0, init_size=1024) self.use_post_ln = hparams.get('use_post_ln', False) self.layers = None if not self.use_post_ln: self.layer_norm = LayerNorm(dec_hidden_size) self.code_size = code_size self.project_out_dim = Linear(dec_hidden_size, code_size + 1, bias=True) def forward_ling_encoder( self, txt_tokens, ling_feas, char_tokens, ph2char, bert_embed, spk_id, spk_embed, mels_timbre): ph_tokens = txt_tokens hparams = self.hparams ph_nonpadding = (ph_tokens > 0).float()[:, :, None] # [B, T_phone, 1] x_spk = self.forward_style_embed(spk_embed, spk_id, mels_timbre) # enc_ph if not hparams.get('lm_use_enc'): x_ph = self.encoder(ph_tokens) x_ph = x_ph + sum( [self.ling_label_embed_layers[k](ling_feas[k]) for k in hparams['ling_labels']]) \ if len(hparams['ling_labels']) > 0 else 0 x_ph = x_ph + x_spk else: # enc_ph ph_enc_oembed = sum( [self.ling_label_embed_layers[k](ling_feas[k]) for k in hparams['ling_labels']]) \ if len(hparams['ling_labels']) > 0 else 0 ph_enc_oembed = ph_enc_oembed + self.ph_pos_embed( torch.arange(0, ph_tokens.shape[1])[None,].to(ph_tokens.device)) ph_enc_oembed = ph_enc_oembed + x_spk ph_enc_oembed = ph_enc_oembed * ph_nonpadding x_ph = self.encoder(ph_tokens, other_embeds=ph_enc_oembed) # enc_char if char_tokens is not None and ph2char is not None: char_nonpadding = (char_tokens > 0).float()[:, :, None] x_char = self.char_encoder(char_tokens) empty_char = (ph2char > 100000).long() ph2char = ph2char * (1 - empty_char) x_char_phlevel = \ expand_states(x_char * char_nonpadding, ph2char) \ * (1 - empty_char)[..., None] + \ self.char_empty_embed(torch.zeros_like(ph_tokens)) * empty_char[..., None] else: x_char_phlevel = 0 # x_ling x_ling = x_ph + x_char_phlevel x_ling = x_ling * ph_nonpadding x_ling = self.enc_proj(x_ling) return x_ling def sample_one_step(self, vq_pred): hparams = self.hparams if hparams.get('infer_top_k'): top_k = hparams.get('infer_top_k') temperature = hparams.get('infer_temperature', 1) vq_pred = vq_pred[:, -1] / temperature # optionally crop the logits to only the top k options if top_k is not None: v, _ = torch.topk(vq_pred, min(top_k, vq_pred.size(-1))) vq_pred[vq_pred < v[:, [-1]]] = -float('Inf') # apply softmax to convert logits to (normalized) probabilities probs = F.softmax(vq_pred, dim=-1) # sample from the distribution vq_pred = torch.multinomial(probs, num_samples=1) else: vq_pred = torch.argmax(F.softmax(vq_pred[:, -1], dim=-1), 1) return vq_pred def forward_style_embed(self, spk_embed=None, spk_id=None, mel_ref=None): # add spk embed style_embed = 0 if self.hparams['use_spk_embed']: style_embed = style_embed + self.spk_embed_proj(spk_embed)[:, None, :] if self.hparams['use_spk_id']: style_embed = style_embed + self.spk_id_proj(spk_id)[:, None, :] if self.hparams['use_spk_enc']: style_embed = style_embed + self.spk_enc(mel_ref)[:, None, :] return style_embed def buffered_future_mask(self, tensor): dim = tensor.size(0) if ( not hasattr(self, '_future_mask') or self._future_mask is None or self._future_mask.device != tensor.device or self._future_mask.size(0) < dim ): self._future_mask = torch.triu(fill_with_neg_inf2(tensor.new(dim, dim)), 1) return self._future_mask[:dim, :dim] class ARDurPredictor(CodePredictor): def __init__(self, hparams, hidden_size, dec_hidden_size, lm_num_layers, dict_size, code_size, use_rot_embed=True, op_version=1): super().__init__(hparams, hidden_size, dec_hidden_size, lm_num_layers, dict_size, code_size) self.use_rot_embed = use_rot_embed bias = hparams.get('lm_bias', True) if self.use_rot_embed: self.layers = nn.ModuleList([]) self.layers.extend([ RotTransformerDecoderLayer( dec_hidden_size, 0.0, kernel_size=1, ffn_hidden_size=dec_hidden_size * 4, post_ln=self.use_post_ln, op_version=op_version, bias=bias) for _ in range(lm_num_layers) ]) if hparams['dur_model_type'] == 'ar_mse': self.project_out_dim = nn.Sequential(torch.nn.Linear(dec_hidden_size, 1), nn.Softplus()) else: self.project_out_dim = torch.nn.Linear(dec_hidden_size, code_size + 1) def forward(self, txt_tokens, ling_feas, char_tokens, ph2char, bert_embed, prev_code, spk_id=None, spk_embed=None, mels_timbre=None, mel2ph=None, incremental_state=None, x_ling=None, attn_mask=None, spk_pos_ids_flat=None, prompt_length=None, cache_size=20, streaming=False): x = self.code_emb(prev_code) if x_ling is None: x_ling = self.forward_ling_encoder( txt_tokens, ling_feas, char_tokens, ph2char, bert_embed, spk_id, spk_embed, mels_timbre) x_ling = x_ling.flatten(0, 1) txt_tokens = txt_tokens.flatten(0, 1) x_ling = x_ling[txt_tokens > 0][None] # run decoder self_attn_padding_mask = None if self.use_pos_embed: positions = self.embed_positions( prev_code, incremental_state=incremental_state ) if incremental_state is not None: x_ling = x_ling[:, x.shape[1] - 1:x.shape[1]] if spk_pos_ids_flat is not None: spk_pos_ids_flat = spk_pos_ids_flat[:, x.shape[1] - 1:x.shape[1]] x = x[:, -1:] if self.use_pos_embed: positions = positions[:, -1:] if streaming: # Shift Pos: query pos is min(cache_size, idx) spk_pos_ids_flat = torch.min(torch.LongTensor([prompt_length + cache_size]).to(x.device), spk_pos_ids_flat) # # B x T x C -> T x B x C if self.use_pos_embed: x = x + positions x_ling = x_ling[:, :self.hparams['max_tokens']].contiguous() T = min(self.hparams.get('max_tokens_per_item', 1e9), x_ling.shape[1]) x_ling = x_ling.reshape(-1, T, x_ling.shape[-1]) x = x + x_ling x = x.transpose(0, 1) for idx, layer in enumerate(self.layers): if incremental_state is None: self_attn_mask = self.buffered_future_mask(x) if attn_mask is not None: self_attn_mask = self_attn_mask + (1 - attn_mask.float()) * -1e8 self_attn_mask = self_attn_mask.clamp_min(-1e8) else: self_attn_mask = None x, attn_weights = layer( x, incremental_state=incremental_state, self_attn_mask=self_attn_mask, self_attn_padding_mask=self_attn_padding_mask, spk_pos_ids_flat=spk_pos_ids_flat ) if streaming and incremental_state != {}: for k, v in incremental_state.items(): if 'attn_state' in k: prev_key, prev_value = incremental_state[k]['prev_key'], incremental_state[k]['prev_value'] cur_length = prev_key.shape[2] if cur_length - prompt_length > cache_size: prev_key = torch.cat((prev_key[:, :, :prompt_length], prev_key[:, :, -cache_size:]), dim=2) prev_value = torch.cat((prev_value[:, :, :prompt_length], prev_value[:, :, -cache_size:]), dim=2) incremental_state[k]['prev_key'], incremental_state[k]['prev_value'] = prev_key, prev_value if not self.use_post_ln: x = self.layer_norm(x) # T x B x C -> B x T x C x = x.transpose(0, 1) x = self.project_out_dim(x) return x def infer(self, txt_tokens, ling_feas, char_tokens, ph2char, bert_embed, spk_id=None, spk_embed=None, mels_timbre=None, incremental_state=None, ctx_vqcodes=None, spk_pos_ids_flat=None, return_state=False, first_step_min=0, return_probs=False, first_decoder_inp=None, dur_disturb=0.0, **kwargs): if incremental_state is None: incremental_state = {} x_ling = self.forward_ling_encoder( txt_tokens, ling_feas, char_tokens, ph2char, bert_embed, spk_id, spk_embed, mels_timbre) x_ling = x_ling.flatten(0, 1) txt_tokens_ori = txt_tokens txt_tokens_withpad = txt_tokens = txt_tokens.flatten(0, 1) x_ling = x_ling[txt_tokens > 0][None] txt_tokens = txt_tokens[txt_tokens > 0][None] decoded = torch.zeros_like(txt_tokens) decoded = F.pad(decoded, [1, 0], value=self.code_size + 1) if incremental_state != {}: if first_decoder_inp is None: assert ctx_vqcodes is not None decoded[:, :ctx_vqcodes.shape[1]] = ctx_vqcodes ctx_vqcodes = None else: decoded[:, :1] = first_decoder_inp probs = [] for step in range(decoded.shape[1] - 1): vq_pred = self(txt_tokens, None, None, None, None, decoded[:, :step + 1], None, None, None, incremental_state=incremental_state, x_ling=x_ling, spk_pos_ids_flat=spk_pos_ids_flat, **kwargs) probs.append(vq_pred.cpu()) if ctx_vqcodes is None or step >= ctx_vqcodes.shape[1]: if self.hparams['dur_model_type'] == 'ar_mse': d = vq_pred[:, -1, 0] if dur_disturb > 0 and step >= 1: if random.random() > 0.5: d = d * (1 + random.random() * dur_disturb) else: d = d / (1 + random.random() * dur_disturb) d = torch.clamp_max(d, self.code_size - 1) vq_pred = torch.round(d).long() else: vq_pred = self.sample_one_step(vq_pred) decoded[:, step + 1] = torch.clamp_min(vq_pred, 1) if step == 0: decoded[:, step + 1] = torch.clamp_min(vq_pred, first_step_min) else: decoded[:, step + 1] = ctx_vqcodes[:, step] decoded = decoded[:, 1:] decoded_2d = torch.zeros_like(txt_tokens_ori) decoded_2d.flatten(0, 1)[txt_tokens_withpad > 0] = decoded if return_state: return decoded_2d, incremental_state if return_probs: return decoded_2d, torch.cat(probs, 1) return decoded_2d def streaming_infer(self, txt_tokens, ling_feas, char_tokens, ph2char, bert_embed, spk_id=None, spk_embed=None, mels_timbre=None, incremental_state=None, ctx_vqcodes=None, spk_pos_ids_flat=None, return_state=False, **kwargs): if incremental_state is None: incremental_state = {} x_ling = self.forward_ling_encoder( txt_tokens, ling_feas, char_tokens, ph2char, bert_embed, spk_id, spk_embed, mels_timbre) x_ling = x_ling.flatten(0, 1) txt_tokens_ori = txt_tokens txt_tokens_withpad = txt_tokens = txt_tokens.flatten(0, 1) x_ling = x_ling[txt_tokens > 0][None] txt_tokens = txt_tokens[txt_tokens > 0][None] vq_decoded = torch.zeros_like(txt_tokens) vq_decoded = F.pad(vq_decoded, [1, 0], value=self.code_size + 1) if incremental_state != {}: assert ctx_vqcodes is not None vq_decoded[:, :ctx_vqcodes.shape[1]] = ctx_vqcodes ctx_vqcodes = None prompt_length = list(incremental_state.items())[0][1]['prev_key'].shape[2] for step in tqdm(range(vq_decoded.shape[1] - 1), desc='AR Duration Predictor inference...'): vq_pred = self(txt_tokens, None, None, None, None, vq_decoded[:, :step + 1], None, None, None, incremental_state=incremental_state, x_ling=x_ling, spk_pos_ids_flat=spk_pos_ids_flat, prompt_length=prompt_length, streaming=True, **kwargs) if ctx_vqcodes is None or step >= ctx_vqcodes.shape[1]: if self.hparams['dur_model_type'] == 'ar_mse': vq_pred = torch.round(vq_pred[:, -1, 0]).long() else: vq_pred = self.sample_one_step(vq_pred) vq_decoded[:, step + 1] = vq_pred else: vq_decoded[:, step + 1] = ctx_vqcodes[:, step] vq_decoded = vq_decoded[:, 1:] vq_decoded_2d = torch.zeros_like(txt_tokens_ori) vq_decoded_2d.flatten(0, 1)[txt_tokens_withpad > 0] = vq_decoded if return_state: return vq_decoded_2d, incremental_state return vq_decoded_2d ================================================ FILE: tts/modules/ar_dur/commons/layers.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from torch import nn class LayerNorm(torch.nn.LayerNorm): """Layer normalization module. :param int nout: output dim size :param int dim: dimension to be normalized """ def __init__(self, nout, dim=-1, eps=1e-5): """Construct an LayerNorm object.""" super(LayerNorm, self).__init__(nout, eps=eps) self.dim = dim def forward(self, x): """Apply layer normalization. :param torch.Tensor x: input tensor :return: layer normalized tensor :rtype torch.Tensor """ if self.dim == -1: return super(LayerNorm, self).forward(x) return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1) class Reshape(nn.Module): def __init__(self, *args): super(Reshape, self).__init__() self.shape = args def forward(self, x): return x.view(self.shape) class Permute(nn.Module): def __init__(self, *args): super(Permute, self).__init__() self.args = args def forward(self, x): return x.permute(self.args) def Embedding(num_embeddings, embedding_dim, padding_idx=None): m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) if padding_idx is not None: nn.init.constant_(m.weight[padding_idx], 0) return m ================================================ FILE: tts/modules/ar_dur/commons/nar_tts_modules.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import torch from torch import nn import torch.nn.functional as F class LengthRegulator(torch.nn.Module): def __init__(self, pad_value=0.0): super(LengthRegulator, self).__init__() self.pad_value = pad_value def forward(self, dur, dur_padding=None, alpha=1.0): """ Example (no batch dim version): 1. dur = [2,2,3] 2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4] 3. token_mask = [[1,1,0,0,0,0,0], [0,0,1,1,0,0,0], [0,0,0,0,1,1,1]] 4. token_idx * token_mask = [[1,1,0,0,0,0,0], [0,0,2,2,0,0,0], [0,0,0,0,3,3,3]] 5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3] :param dur: Batch of durations of each frame (B, T_txt) :param dur_padding: Batch of padding of each frame (B, T_txt) :param alpha: duration rescale coefficient :return: mel2ph (B, T_speech) assert alpha > 0 """ dur = torch.round(dur.float() * alpha).long() if dur_padding is not None: dur = dur * (1 - dur_padding.long()) token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device) dur_cumsum = torch.cumsum(dur, 1) dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0) pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device) token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None]) mel2token = (token_idx * token_mask.long()).sum(1) return mel2token class PosEmb(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim half_dim = self.dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim) * -emb) self.emb = emb # TODO def forward(self, x): emb = x[:, :, None] * self.emb[None, None, :].to(x.device) emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb ================================================ FILE: tts/modules/ar_dur/commons/rel_transformer.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import torch from torch import nn from torch.nn import functional as F from tts.modules.ar_dur.commons.layers import Embedding def convert_pad_shape(pad_shape): l = pad_shape[::-1] pad_shape = [item for sublist in l for item in sublist] return pad_shape def shift_1d(x): x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] return x def sequence_mask(length, max_length=None): if max_length is None: max_length = length.max() x = torch.arange(max_length, dtype=length.dtype, device=length.device) return x.unsqueeze(0) < length.unsqueeze(1) class Encoder(nn.Module): def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=None, block_length=None, pre_ln=False, **kwargs): super().__init__() self.hidden_channels = hidden_channels self.filter_channels = filter_channels self.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout self.window_size = window_size self.block_length = block_length self.pre_ln = pre_ln self.drop = nn.Dropout(p_dropout) self.attn_layers = nn.ModuleList() self.norm_layers_1 = nn.ModuleList() self.ffn_layers = nn.ModuleList() self.norm_layers_2 = nn.ModuleList() for i in range(self.n_layers): self.attn_layers.append( MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size, p_dropout=p_dropout, block_length=block_length)) self.norm_layers_1.append(LayerNorm(hidden_channels)) self.ffn_layers.append( FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) self.norm_layers_2.append(LayerNorm(hidden_channels)) if pre_ln: self.last_ln = LayerNorm(hidden_channels) def forward(self, x, x_mask, attn_mask=1): if isinstance(attn_mask, torch.Tensor): attn_mask = attn_mask[:, None] attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) * attn_mask for i in range(self.n_layers): x = x * x_mask x_ = x if self.pre_ln: x = self.norm_layers_1[i](x) y = self.attn_layers[i](x, x, attn_mask) y = self.drop(y) x = x_ + y if not self.pre_ln: x = self.norm_layers_1[i](x) x_ = x if self.pre_ln: x = self.norm_layers_2[i](x) y = self.ffn_layers[i](x, x_mask) y = self.drop(y) x = x_ + y if not self.pre_ln: x = self.norm_layers_2[i](x) if self.pre_ln: x = self.last_ln(x) x = x * x_mask return x class MultiHeadAttention(nn.Module): def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0., block_length=None, proximal_bias=False, proximal_init=False): super().__init__() assert channels % n_heads == 0 self.channels = channels self.out_channels = out_channels self.n_heads = n_heads self.window_size = window_size self.heads_share = heads_share self.block_length = block_length self.proximal_bias = proximal_bias self.p_dropout = p_dropout self.attn = None self.k_channels = channels // n_heads self.conv_q = nn.Conv1d(channels, channels, 1) self.conv_k = nn.Conv1d(channels, channels, 1) self.conv_v = nn.Conv1d(channels, channels, 1) if window_size is not None: n_heads_rel = 1 if heads_share else n_heads rel_stddev = self.k_channels ** -0.5 self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.conv_o = nn.Conv1d(channels, out_channels, 1) self.drop = nn.Dropout(p_dropout) nn.init.xavier_uniform_(self.conv_q.weight) nn.init.xavier_uniform_(self.conv_k.weight) if proximal_init: self.conv_k.weight.data.copy_(self.conv_q.weight.data) self.conv_k.bias.data.copy_(self.conv_q.bias.data) nn.init.xavier_uniform_(self.conv_v.weight) def forward(self, x, c, attn_mask=None): q = self.conv_q(x) k = self.conv_k(c) v = self.conv_v(c) x, self.attn = self.attention(q, k, v, mask=attn_mask) x = self.conv_o(x) return x def attention(self, query, key, value, mask=None): # reshape [b, d, t] -> [b, n_h, t, d_k] b, d, t_s, t_t = (*key.size(), query.size(2)) query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) if self.window_size is not None: assert t_s == t_t, "Relative attention is only available for self-attention." key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) rel_logits = self._relative_position_to_absolute_position(rel_logits) scores_local = rel_logits / math.sqrt(self.k_channels) scores = scores + scores_local if self.proximal_bias: assert t_s == t_t, "Proximal bias is only available for self-attention." scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) if mask is not None: scores = scores.masked_fill(mask == 0, -1e4) if self.block_length is not None: block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) scores = scores * block_mask + -1e4 * (1 - block_mask) p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] p_attn = self.drop(p_attn) output = torch.matmul(p_attn, value) if self.window_size is not None: relative_weights = self._absolute_position_to_relative_position(p_attn) value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] return output, p_attn def _matmul_with_relative_values(self, x, y): """ x: [b, h, l, m] y: [h or 1, m, d] ret: [b, h, l, d] """ ret = torch.matmul(x, y.unsqueeze(0)) return ret def _matmul_with_relative_keys(self, x, y): """ x: [b, h, l, d] y: [h or 1, m, d] ret: [b, h, l, m] """ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) return ret def _get_relative_embeddings(self, relative_embeddings, length): max_relative_position = 2 * self.window_size + 1 # Pad first before slice to avoid using cond ops. pad_length = max(length - (self.window_size + 1), 0) slice_start_position = max((self.window_size + 1) - length, 0) slice_end_position = slice_start_position + 2 * length - 1 if pad_length > 0: padded_relative_embeddings = F.pad( relative_embeddings, convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) else: padded_relative_embeddings = relative_embeddings used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] return used_relative_embeddings def _relative_position_to_absolute_position(self, x): """ x: [b, h, l, 2*l-1] ret: [b, h, l, l] """ batch, heads, length, _ = x.size() # Concat columns of pad to shift from relative to absolute indexing. x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) # Concat extra elements so to add up to shape (len+1, 2*len-1). x_flat = x.view([batch, heads, length * 2 * length]) x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) # Reshape and slice out the padded elements. x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:] return x_final def _absolute_position_to_relative_position(self, x): """ x: [b, h, l, l] ret: [b, h, l, 2*l-1] """ batch, heads, length, _ = x.size() # padd along column x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) x_flat = x.view([batch, heads, -1]) # add 0's in the beginning that will skew the elements after reshape x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] return x_final def _attention_bias_proximal(self, length): """Bias for self-attention to encourage attention to close positions. Args: length: an integer scalar. Returns: a Tensor with shape [1, 1, length, length] """ r = torch.arange(length, dtype=torch.float32) diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) class FFN(nn.Module): def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.filter_channels = filter_channels self.kernel_size = kernel_size self.p_dropout = p_dropout self.activation = activation self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1) self.drop = nn.Dropout(p_dropout) def forward(self, x, x_mask): x = self.conv_1(x * x_mask) if self.activation == "gelu": x = x * torch.sigmoid(1.702 * x) else: x = torch.relu(x) x = self.drop(x) x = self.conv_2(x * x_mask) return x * x_mask class LayerNorm(nn.Module): def __init__(self, channels, eps=1e-4): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = nn.Parameter(torch.zeros(channels)) def forward(self, x): n_dims = len(x.shape) mean = torch.mean(x, 1, keepdim=True) variance = torch.mean((x - mean) ** 2, 1, keepdim=True) x = (x - mean) * torch.rsqrt(variance + self.eps) shape = [1, -1] + [1] * (n_dims - 2) x = x * self.gamma.view(*shape) + self.beta.view(*shape) return x class ConvReluNorm(nn.Module): def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.out_channels = out_channels self.kernel_size = kernel_size self.n_layers = n_layers self.p_dropout = p_dropout assert n_layers > 1, "Number of layers should be larger than 0." self.conv_layers = nn.ModuleList() self.norm_layers = nn.ModuleList() self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.relu_drop = nn.Sequential( nn.ReLU(), nn.Dropout(p_dropout)) for _ in range(n_layers - 1): self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.proj.weight.data.zero_() self.proj.bias.data.zero_() def forward(self, x, x_mask): x_org = x for i in range(self.n_layers): x = self.conv_layers[i](x * x_mask) x = self.norm_layers[i](x) x = self.relu_drop(x) x = x_org + self.proj(x) return x * x_mask class RelTransformerEncoder(nn.Module): def __init__(self, n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout=0.0, window_size=4, block_length=None, in_channels=None, prenet=True, pre_ln=True, ): super().__init__() self.n_vocab = n_vocab self.out_channels = out_channels self.hidden_channels = hidden_channels self.filter_channels = filter_channels self.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout self.window_size = window_size self.block_length = block_length self.prenet = prenet if n_vocab > 0: self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) if prenet: if in_channels is None: in_channels = hidden_channels self.pre = ConvReluNorm(in_channels, in_channels, in_channels, kernel_size=5, n_layers=3, p_dropout=0) if in_channels is not None and in_channels != hidden_channels: self.encoder_inp_proj = nn.Conv1d(in_channels, hidden_channels, 1) self.encoder = Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, window_size=window_size, block_length=block_length, pre_ln=pre_ln, ) def forward(self, x, x_mask=None, other_embeds=0, attn_mask=1): if self.n_vocab > 0: x_lengths = (x > 0).long().sum(-1) x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] else: x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) x = x + other_embeds x = torch.transpose(x, 1, -1) # [b, h, t] x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) if self.prenet: x = self.pre(x, x_mask) self.prenet_out = x.transpose(1, 2) if hasattr(self, 'encoder_inp_proj'): x = self.encoder_inp_proj(x) * x_mask x = self.encoder(x, x_mask, attn_mask) return x.transpose(1, 2) ================================================ FILE: tts/modules/ar_dur/commons/rot_transformer.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import torch from typing import Optional, Tuple from torch import nn from torch.nn import Parameter, Linear from tts.modules.ar_dur.commons.layers import LayerNorm, Embedding from tts.modules.ar_dur.commons.transformer import TransformerFFNLayer, MultiheadAttention from tts.modules.ar_dur.commons.seq_utils import get_incremental_state, set_incremental_state, softmax, make_positions import torch.nn.functional as F DEFAULT_MAX_SOURCE_POSITIONS = 3000 DEFAULT_MAX_TARGET_POSITIONS = 3000 class SinusoidalPositionalEmbedding(nn.Module): """This module produces sinusoidal positional embeddings of any length. Padding symbols are ignored. """ def __init__(self, embedding_dim, padding_idx, init_size=1024): super().__init__() self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.weights = SinusoidalPositionalEmbedding.get_embedding( init_size, embedding_dim, padding_idx, ) self.register_buffer('_float_tensor', torch.FloatTensor(1)) @staticmethod def get_embedding(num_embeddings, embedding_dim, padding_idx=None): """Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) if embedding_dim % 2 == 1: # zero pad emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) if padding_idx is not None: emb[padding_idx, :] = 0 return emb def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs): """Input is expected to be of size [bsz x seqlen].""" bsz, seq_len = input.shape[:2] max_pos = self.padding_idx + 1 + seq_len if self.weights is None or max_pos > self.weights.size(0): # recompute/expand embeddings if needed self.weights = SinusoidalPositionalEmbedding.get_embedding( max_pos, self.embedding_dim, self.padding_idx, ) self.weights = self.weights.to(self._float_tensor) if incremental_state is not None: # positions is the same for every token when decoding a single step pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) positions = make_positions(input, self.padding_idx) if positions is None else positions return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() def max_positions(self): """Maximum number of supported positions.""" return int(1e5) # an arbitrary large number class RotaryEmbeddings(nn.Module): cos: torch.Tensor sin: torch.Tensor theta: torch.Tensor def __init__( self, width: int, *, seq_len: int = 40000, base: int = 10000, device: Optional[torch.device] = None, ): """Rotary embeddings (Su et al., 2021) layer. The rotary embedding will be precomputed for up to 'seq _len' positions. The embedding will be recomputed when a longer sequence is found in the input. :param width: Rotary embedding dimensionality, must be even. :param seq_len: Number of positons to initially precompute. :param base: The base used for Θ_i, determines the cycle length of the embeddings. :param device: Device on which the module is to be initialized. """ super().__init__() if width % 2: raise ValueError(f"Width of rotary embeddings must be even, was: {width}") # Ignore allocations on the meta device as we don't persist our buffer, # i.e., we don't expect the backing tensor to be replaced with pretrained weights. if device is not None and device.type == "meta": device = None # Θ_i = 10000^(-2(i-1)/d) theta = torch.pow( base, -torch.arange(0, width, 2, dtype=torch.float, device=device) / width ) self.register_buffer("theta", theta, persistent=False) self._create_rotary_embed(width=width, length=seq_len) def _create_rotary_embed(self, *, width: int, length: int): # mΘ position = torch.arange(length, device=self.theta.device).unsqueeze(1) m_theta = position * self.theta.unsqueeze(0) # We apply both sin and cos twice (see Eq 15, 34), but the ordering # is changed for compatibility with most common implementations. m_theta = torch.cat([m_theta, m_theta], dim=-1) re_cos = m_theta.cos().view([length, width]) re_sin = m_theta.sin().view([length, width]) self.register_buffer("cos", re_cos, persistent=False) self.register_buffer("sin", re_sin, persistent=False) def _rotate(self, input: torch.Tensor): """Rotate the input tensor by half of its innermost width. input (Tensor): array to rotate. RETURNS (Tensor): rotated array. Shapes: input - (..., width) output - (..., width) """ half_idx = input.shape[-1] // 2 input_1 = -input[..., half_idx:] input_2 = input[..., :half_idx] return torch.cat([input_1, input_2], dim=-1) def forward(self, input: torch.Tensor, *, positions: Optional[torch.Tensor] = None): """ Apply rotary embeddings to an array. :param input: Array to apply the rotary embeddings to. :param positions: positions of the inputs. If no positions are provided, they are assumed to be [0, seq_len). :return: Array with the rotary embeddings applied. Shapes: input - (batch_size, num_heads, seq_len, width_per_head) positions - (batch_size, seq_len) output - (batch_size, num_heads, seq_len, width_per_head) """ batch_size, _, seq_len, width = input.shape if positions is None: # Fastpath: positions from [0..seq_len), avoid indexing. if self.cos.size(-2) < seq_len: self._create_rotary_embed(width=width, length=seq_len) rot_cos = self.cos[:seq_len, :].view(1, 1, seq_len, width) rot_sin = self.sin[:seq_len, :].view(1, 1, seq_len, width) else: max_len = int(positions.max()) + 1 if self.cos.size(-2) < max_len: self._create_rotary_embed(width=width, length=max_len) # Flatten positions to index cos/sin arrays, then unflatten. # # Example shapes: # # positions_flat - (batch_size * seq_len) # self.cos - (max_len, width) # rot_cos - (batch_size, seq_len, width) positions_flat = positions.view(-1) rot_cos = self.cos[positions_flat].view(batch_size, 1, seq_len, width) rot_sin = self.sin[positions_flat].view(batch_size, 1, seq_len, width) # Eq 34 with ordering changed for compatibility. return rot_cos * input + rot_sin * self._rotate(input) class RotMultiheadAttention(MultiheadAttention): def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, self_attention=False, encoder_decoder_attention=False): super().__init__(embed_dim, num_heads, kdim=kdim, vdim=vdim, dropout=dropout, bias=bias, add_bias_kv=add_bias_kv, add_zero_attn=add_zero_attn, self_attention=self_attention, encoder_decoder_attention=encoder_decoder_attention) self.rotary_embeds = RotaryEmbeddings(width=embed_dim // num_heads) def forward( self, query, key, value, spk_pos_ids_flat=None, key_padding_mask=None, incremental_state=None, need_weights=True, static_kv=False, attn_mask=None, before_softmax=False, need_head_weights=False, enc_dec_attn_constraint_mask=None, reset_attn_weight=None ): """Input shape: Time x Batch x Channel Args: key_padding_mask (ByteTensor, optional): mask to exclude keys that are pads, of shape `(batch, src_len)`, where padding elements are indicated by 1s. need_weights (bool, optional): return the attention weights, averaged over heads (default: False). attn_mask (ByteTensor, optional): typically used to implement causal attention, where the mask prevents the attention from looking forward in time (default: None). before_softmax (bool, optional): return the raw attention weights and values before the attention softmax. need_head_weights (bool, optional): return the attention weights for each head. Implies *need_weights*. Default: return the average attention weights over all heads. """ if need_head_weights: need_weights = True tgt_len, bsz, embed_dim = query.size() assert embed_dim == self.embed_dim assert list(query.size()) == [tgt_len, bsz, embed_dim] if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_key' in saved_state: # previous time steps are cached - no need to recompute # key and value if they are static if static_kv: assert self.encoder_decoder_attention and not self.self_attention key = value = None else: saved_state = None if self.self_attention: # self-attention q, k, v = self.in_proj_qkv(query) elif self.encoder_decoder_attention: # encoder-decoder attention q = self.in_proj_q(query) if key is None: assert value is None k = v = None else: k = self.in_proj_k(key) v = self.in_proj_v(key) else: q = self.in_proj_q(query) k = self.in_proj_k(key) v = self.in_proj_v(value) q = q * self.scaling if self.bias_k is not None: assert self.bias_v is not None k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1) q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) if k is not None: k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) if v is not None: v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) # Apply rot embedding and store incremental_state q = self.rotary_embeds(q[None, :], positions=spk_pos_ids_flat)[0] if saved_state is not None: # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) if 'prev_key' in saved_state: prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim) if static_kv: k = prev_key else: k = torch.cat((prev_key, k), dim=1) if 'prev_value' in saved_state: prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim) if static_kv: v = prev_value else: v = torch.cat((prev_value, v), dim=1) saved_state['prev_key'], saved_state['prev_value'] = k.view(bsz, self.num_heads, -1, self.head_dim), v.view( bsz, self.num_heads, -1, self.head_dim) self._set_input_buffer(incremental_state, saved_state) if incremental_state is not None: key_pos = torch.arange(k.shape[-2], device=q.device).unsqueeze(0) else: key_pos = spk_pos_ids_flat k = self.rotary_embeds(k[None, :], positions=key_pos)[0] src_len = k.size(1) # This is part of a workaround to get around fork/join parallelism # not supporting Optional types. if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]): key_padding_mask = None if key_padding_mask is not None: assert key_padding_mask.size(0) == bsz assert key_padding_mask.size(1) == src_len if self.add_zero_attn: src_len += 1 k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1) attn_weights = torch.bmm(q, k.transpose(1, 2)) attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] if attn_mask is not None: if len(attn_mask.shape) == 2: attn_mask = attn_mask.unsqueeze(0) elif len(attn_mask.shape) == 3: attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape( bsz * self.num_heads, tgt_len, src_len) attn_weights = attn_weights + attn_mask if enc_dec_attn_constraint_mask is not None: # bs x head x L_kv attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.masked_fill( enc_dec_attn_constraint_mask.unsqueeze(2).bool(), -1e8, ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if key_padding_mask is not None: # don't attend to padding symbols attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.masked_fill( key_padding_mask.unsqueeze(1).unsqueeze(2), -1e8, ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) if before_softmax: return attn_weights, v attn_weights_float = softmax(attn_weights, dim=-1) attn_weights = attn_weights_float.type_as(attn_weights) attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training) if reset_attn_weight is not None: if reset_attn_weight: self.last_attn_probs = attn_probs.detach() else: assert self.last_attn_probs is not None attn_probs = self.last_attn_probs attn = torch.bmm(attn_probs, v) assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) attn = self.out_proj(attn) if need_weights: attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0) if not need_head_weights: # average attention weights over heads attn_weights = attn_weights.mean(dim=0) else: attn_weights = None return attn, (attn_weights, attn_logits) class RotMultiheadAttention2(MultiheadAttention): def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, self_attention=False, encoder_decoder_attention=False): super().__init__(embed_dim, num_heads, kdim=kdim, vdim=vdim, dropout=dropout, bias=bias, add_bias_kv=add_bias_kv, add_zero_attn=add_zero_attn, self_attention=self_attention, encoder_decoder_attention=encoder_decoder_attention) self.rotary_embeds = RotaryEmbeddings(width=embed_dim // num_heads) def forward( self, query, key, value, spk_pos_ids_flat=None, key_padding_mask=None, incremental_state=None, need_weights=True, static_kv=False, attn_mask=None, before_softmax=False, need_head_weights=False, enc_dec_attn_constraint_mask=None, reset_attn_weight=None ): """Input shape: Time x Batch x Channel Args: key_padding_mask (ByteTensor, optional): mask to exclude keys that are pads, of shape `(batch, src_len)`, where padding elements are indicated by 1s. need_weights (bool, optional): return the attention weights, averaged over heads (default: False). attn_mask (ByteTensor, optional): typically used to implement causal attention, where the mask prevents the attention from looking forward in time (default: None). before_softmax (bool, optional): return the raw attention weights and values before the attention softmax. need_head_weights (bool, optional): return the attention weights for each head. Implies *need_weights*. Default: return the average attention weights over all heads. """ if need_head_weights: need_weights = True tgt_len, bsz, embed_dim = query.size() assert embed_dim == self.embed_dim assert list(query.size()) == [tgt_len, bsz, embed_dim] if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_key' in saved_state: # previous time steps are cached - no need to recompute # key and value if they are static if static_kv: assert self.encoder_decoder_attention and not self.self_attention key = value = None else: saved_state = None if self.self_attention: # self-attention q, k, v = self.in_proj_qkv(query) elif self.encoder_decoder_attention: # encoder-decoder attention q = self.in_proj_q(query) if key is None: assert value is None k = v = None else: k = self.in_proj_k(key) v = self.in_proj_v(key) else: q = self.in_proj_q(query) k = self.in_proj_k(key) v = self.in_proj_v(value) if self.bias_k is not None: assert self.bias_v is not None k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1) q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) if k is not None: k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) if v is not None: v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) # Apply rot embedding and store incremental_state q = self.rotary_embeds(q[None, :], positions=spk_pos_ids_flat)[0] if saved_state is not None: # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) if 'prev_key' in saved_state: prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim) if static_kv: k = prev_key else: k = torch.cat((prev_key, k), dim=1) if 'prev_value' in saved_state: prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim) if static_kv: v = prev_value else: v = torch.cat((prev_value, v), dim=1) saved_state['prev_key'], saved_state['prev_value'] = k.view(bsz, self.num_heads, -1, self.head_dim), v.view( bsz, self.num_heads, -1, self.head_dim) self._set_input_buffer(incremental_state, saved_state) key_pos = torch.arange(k.shape[-2], device=q.device).unsqueeze(0) k = self.rotary_embeds(k[None, :], positions=key_pos)[0] src_len = k.size(1) # This is part of a workaround to get around fork/join parallelism # not supporting Optional types. if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]): key_padding_mask = None if key_padding_mask is not None: assert key_padding_mask.size(0) == bsz assert key_padding_mask.size(1) == src_len if attn_mask is not None: if len(attn_mask.shape) == 2: attn_mask = attn_mask.unsqueeze(0) elif len(attn_mask.shape) == 3: attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape( bsz * self.num_heads, tgt_len, src_len) attn = torch.nn.functional.scaled_dot_product_attention( q, k, v, attn_mask=attn_mask, dropout_p=0, is_causal=False) assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) attn_logits = None attn_weights = None return attn, (attn_weights, attn_logits) class RotDecSALayer(nn.Module): def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, ffn_hidden_size=1024, act='gelu', post_ln=False, bias=True): super().__init__() self.c = c self.dropout = dropout self.layer_norm1 = LayerNorm(c) self.self_attn = RotMultiheadAttention( c, num_heads, self_attention=True, dropout=attention_dropout, bias=False ) self.layer_norm2 = LayerNorm(c) self.ffn = TransformerFFNLayer( c, ffn_hidden_size, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act, bias=bias) self.post_ln = post_ln def forward( self, x, encoder_out=None, encoder_padding_mask=None, incremental_state=None, self_attn_mask=None, self_attn_padding_mask=None, attn_out=None, reset_attn_weight=None, spk_pos_ids_flat=None, **kwargs, ): layer_norm_training = kwargs.get('layer_norm_training', None) if layer_norm_training is not None: self.layer_norm1.training = layer_norm_training self.layer_norm2.training = layer_norm_training residual = x if not self.post_ln: x = self.layer_norm1(x) x, (attn_weights, _) = self.self_attn( query=x, key=x, value=x, key_padding_mask=self_attn_padding_mask, incremental_state=incremental_state, attn_mask=self_attn_mask, spk_pos_ids_flat=spk_pos_ids_flat ) x = F.dropout(x, self.dropout, training=self.training) x = residual + x if self.post_ln: x = self.layer_norm1(x) residual = x if not self.post_ln: x = self.layer_norm2(x) x = self.ffn(x, incremental_state=incremental_state) x = F.dropout(x, self.dropout, training=self.training) x = residual + x if self.post_ln: x = self.layer_norm2(x) return x, attn_weights def clear_buffer(self, input, encoder_out=None, encoder_padding_mask=None, incremental_state=None): self.encoder_attn.clear_buffer(incremental_state) self.ffn.clear_buffer(incremental_state) def set_buffer(self, name, tensor, incremental_state): return set_incremental_state(self, incremental_state, name, tensor) class RotDecSALayer2(RotDecSALayer): def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, ffn_hidden_size=1024, act='gelu', post_ln=False): super().__init__(c, num_heads, dropout, attention_dropout, relu_dropout, kernel_size, ffn_hidden_size, act, post_ln) self.self_attn = RotMultiheadAttention2( c, num_heads, self_attention=True, dropout=attention_dropout, bias=False ) class RotTransformerDecoderLayer(nn.Module): def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=8, ffn_hidden_size=1024, post_ln=False, op_version=1, bias=True): super().__init__() self.hidden_size = hidden_size self.dropout = dropout self.num_heads = num_heads if op_version == 1: self.op = RotDecSALayer( hidden_size, num_heads, dropout=dropout, attention_dropout=0.0, relu_dropout=dropout, kernel_size=kernel_size, ffn_hidden_size=ffn_hidden_size, post_ln=post_ln, bias=bias) else: self.op = RotDecSALayer2( hidden_size, num_heads, dropout=dropout, attention_dropout=0.0, relu_dropout=dropout, kernel_size=kernel_size, ffn_hidden_size=ffn_hidden_size, post_ln=post_ln) def forward(self, x, **kwargs): return self.op(x, **kwargs) def clear_buffer(self, *args): return self.op.clear_buffer(*args) def set_buffer(self, *args): return self.op.set_buffer(*args) ================================================ FILE: tts/modules/ar_dur/commons/seq_utils.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict import torch import torch.nn.functional as F def make_positions(tensor, padding_idx): """Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. """ # The series of casts and type-conversions here are carefully # balanced to both work with ONNX export and XLA. In particular XLA # prefers ints, cumsum defaults to output longs, and ONNX doesn't know # how to handle the dtype kwarg in cumsum. mask = tensor.ne(padding_idx).int() return ( torch.cumsum(mask, dim=1).type_as(mask) * mask ).long() + padding_idx def softmax(x, dim): return F.softmax(x, dim=dim, dtype=torch.float32) def sequence_mask(lengths, maxlen=None, dtype=torch.bool): if maxlen is None: maxlen = lengths.max() mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths).t() mask.type(dtype) return mask def weights_nonzero_speech(target): # target : B x T x mel # Assign weight 1.0 to all labels except for padding (id=0). dim = target.size(-1) return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim) INCREMENTAL_STATE_INSTANCE_ID = defaultdict(lambda: 0) def _get_full_incremental_state_key(module_instance, key): module_name = module_instance.__class__.__name__ # assign a unique ID to each module instance, so that incremental state is # not shared across module instances if not hasattr(module_instance, '_instance_id'): INCREMENTAL_STATE_INSTANCE_ID[module_name] += 1 module_instance._instance_id = INCREMENTAL_STATE_INSTANCE_ID[module_name] return '{}.{}.{}'.format(module_name, module_instance._instance_id, key) def get_incremental_state(module, incremental_state, key): """Helper for getting incremental state for an nn.Module.""" full_key = _get_full_incremental_state_key(module, key) if incremental_state is None or full_key not in incremental_state: return None return incremental_state[full_key] def set_incremental_state(module, incremental_state, key, value): """Helper for setting incremental state for an nn.Module.""" if incremental_state is not None: full_key = _get_full_incremental_state_key(module, key) incremental_state[full_key] = value def fill_with_neg_inf(t): """FP16-compatible function that fills a tensor with -inf.""" return t.float().fill_(float('-inf')).type_as(t) def fill_with_neg_inf2(t): """FP16-compatible function that fills a tensor with -inf.""" return t.float().fill_(-1e8).type_as(t) def select_attn(attn_logits, type='best'): """ :param attn_logits: [n_layers, B, n_head, T_sp, T_txt] :return: """ encdec_attn = torch.stack(attn_logits, 0).transpose(1, 2) # [n_layers * n_head, B, T_sp, T_txt] encdec_attn = (encdec_attn.reshape([-1, *encdec_attn.shape[2:]])).softmax(-1) if type == 'best': indices = encdec_attn.max(-1).values.sum(-1).argmax(0) encdec_attn = encdec_attn.gather( 0, indices[None, :, None, None].repeat(1, 1, encdec_attn.size(-2), encdec_attn.size(-1)))[0] return encdec_attn elif type == 'mean': return encdec_attn.mean(0) def make_pad_mask(lengths, xs=None, length_dim=-1): """Make mask tensor containing indices of padded part. Args: lengths (LongTensor or List): Batch of lengths (B,). xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor. length_dim (int, optional): Dimension indicator of the above tensor. See the example. Returns: Tensor: Mask tensor containing indices of padded part. dtype=torch.uint8 in PyTorch 1.2- dtype=torch.bool in PyTorch 1.2+ (including 1.2) Examples: With only lengths. >>> lengths = [5, 3, 2] >>> make_non_pad_mask(lengths) masks = [[0, 0, 0, 0 ,0], [0, 0, 0, 1, 1], [0, 0, 1, 1, 1]] With the reference tensor. >>> xs = torch.zeros((3, 2, 4)) >>> make_pad_mask(lengths, xs) tensor([[[0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 1], [0, 0, 0, 1]], [[0, 0, 1, 1], [0, 0, 1, 1]]], dtype=torch.uint8) >>> xs = torch.zeros((3, 2, 6)) >>> make_pad_mask(lengths, xs) tensor([[[0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]], [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], [[0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8) With the reference tensor and dimension indicator. >>> xs = torch.zeros((3, 6, 6)) >>> make_pad_mask(lengths, xs, 1) tensor([[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]], [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]], [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8) >>> make_pad_mask(lengths, xs, 2) tensor([[[0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]], [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], [[0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8) """ if length_dim == 0: raise ValueError("length_dim cannot be 0: {}".format(length_dim)) if not isinstance(lengths, list): lengths = lengths.tolist() bs = int(len(lengths)) if xs is None: maxlen = int(max(lengths)) else: maxlen = xs.size(length_dim) seq_range = torch.arange(0, maxlen, dtype=torch.int64) seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen) seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1) mask = seq_range_expand >= seq_length_expand if xs is not None: assert xs.size(0) == bs, (xs.size(0), bs) if length_dim < 0: length_dim = xs.dim() + length_dim # ind = (:, None, ..., None, :, , None, ..., None) ind = tuple( slice(None) if i in (0, length_dim) else None for i in range(xs.dim()) ) mask = mask[ind].expand_as(xs).to(xs.device) return mask def make_non_pad_mask(lengths, xs=None, length_dim=-1): """Make mask tensor containing indices of non-padded part. Args: lengths (LongTensor or List): Batch of lengths (B,). xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor. length_dim (int, optional): Dimension indicator of the above tensor. See the example. Returns: ByteTensor: mask tensor containing indices of padded part. dtype=torch.uint8 in PyTorch 1.2- dtype=torch.bool in PyTorch 1.2+ (including 1.2) Examples: With only lengths. >>> lengths = [5, 3, 2] >>> make_non_pad_mask(lengths) masks = [[1, 1, 1, 1 ,1], [1, 1, 1, 0, 0], [1, 1, 0, 0, 0]] With the reference tensor. >>> xs = torch.zeros((3, 2, 4)) >>> make_non_pad_mask(lengths, xs) tensor([[[1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 0], [1, 1, 1, 0]], [[1, 1, 0, 0], [1, 1, 0, 0]]], dtype=torch.uint8) >>> xs = torch.zeros((3, 2, 6)) >>> make_non_pad_mask(lengths, xs) tensor([[[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0]], [[1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0]], [[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8) With the reference tensor and dimension indicator. >>> xs = torch.zeros((3, 6, 6)) >>> make_non_pad_mask(lengths, xs, 1) tensor([[[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]], [[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], [[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]], dtype=torch.uint8) >>> make_non_pad_mask(lengths, xs, 2) tensor([[[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0]], [[1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0]], [[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8) """ return ~make_pad_mask(lengths, xs, length_dim) def get_mask_from_lengths(lengths): max_len = torch.max(lengths).item() ids = torch.arange(0, max_len).to(lengths.device) mask = (ids < lengths.unsqueeze(1)).bool() return mask def group_hidden_by_segs(h, seg_ids, max_len): """ :param h: [B, T, H] :param seg_ids: [B, T] :return: h_ph: [B, T_ph, H] """ B, T, H = h.shape h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h) all_ones = h.new_ones(h.shape[:2]) cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous() h_gby_segs = h_gby_segs[:, 1:] cnt_gby_segs = cnt_gby_segs[:, 1:] h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1) return h_gby_segs, cnt_gby_segs def expand_by_repeat_times(source_encoding, lengths): """ source_encoding: [T, C] lengths, list of int, [T,], how many times each token should repeat return: expanded_encoding: [T_expand, C] """ hid_dim = source_encoding.shape[1] out2source = [] for i, length in enumerate(lengths): out2source += [i for _ in range(length)] out2source = torch.LongTensor(out2source).to(source_encoding.device) out2source_ = out2source[:, None].repeat([1, hid_dim]) expanded_encoding = torch.gather(source_encoding, 0, out2source_) # [B, T, H] return expanded_encoding def expand_word2ph(word_encoding, ph2word): word_encoding = F.pad(word_encoding,[0,0,1,0]) ph2word_ = ph2word[:, :, None].repeat([1, 1, word_encoding.shape[-1]]) out = torch.gather(word_encoding, 1, ph2word_) # [B, T, H] return out ================================================ FILE: tts/modules/ar_dur/commons/transformer.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import torch from torch import nn from torch.nn import Parameter, Linear from tts.modules.ar_dur.commons.layers import LayerNorm, Embedding from tts.modules.ar_dur.commons.seq_utils import get_incremental_state, set_incremental_state, softmax, make_positions import torch.nn.functional as F DEFAULT_MAX_SOURCE_POSITIONS = 3000 DEFAULT_MAX_TARGET_POSITIONS = 3000 class SinusoidalPositionalEmbedding(nn.Module): """This module produces sinusoidal positional embeddings of any length. Padding symbols are ignored. """ def __init__(self, embedding_dim, padding_idx, init_size=1024): super().__init__() self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.weights = SinusoidalPositionalEmbedding.get_embedding( init_size, embedding_dim, padding_idx, ) self.register_buffer('_float_tensor', torch.FloatTensor(1)) @staticmethod def get_embedding(num_embeddings, embedding_dim, padding_idx=None): """Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) if embedding_dim % 2 == 1: # zero pad emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) if padding_idx is not None: emb[padding_idx, :] = 0 return emb def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs): """Input is expected to be of size [bsz x seqlen].""" bsz, seq_len = input.shape[:2] max_pos = self.padding_idx + 1 + seq_len if self.weights is None or max_pos > self.weights.size(0): # recompute/expand embeddings if needed self.weights = SinusoidalPositionalEmbedding.get_embedding( max_pos, self.embedding_dim, self.padding_idx, ) self.weights = self.weights.to(self._float_tensor) if incremental_state is not None: # positions is the same for every token when decoding a single step pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) positions = make_positions(input, self.padding_idx) if positions is None else positions return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() def max_positions(self): """Maximum number of supported positions.""" return int(1e5) # an arbitrary large number class TransformerFFNLayer(nn.Module): def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu', bias=True): super().__init__() self.kernel_size = kernel_size self.dropout = dropout self.act = act if padding == 'SAME': self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2, bias=bias) elif padding == 'LEFT': self.ffn_1 = nn.Sequential( nn.ConstantPad1d((kernel_size - 1, 0), 0.0), nn.Conv1d(hidden_size, filter_size, kernel_size, bias=bias) ) self.ffn_2 = Linear(filter_size, hidden_size, bias=bias) def forward(self, x, incremental_state=None): # x: T x B x C if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_input' in saved_state: prev_input = saved_state['prev_input'] x = torch.cat((prev_input, x), dim=0) x = x[-self.kernel_size:] saved_state['prev_input'] = x self._set_input_buffer(incremental_state, saved_state) x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1) x = x * self.kernel_size ** -0.5 if incremental_state is not None: x = x[-1:] if self.act == 'gelu': x = F.gelu(x) if self.act == 'relu': x = F.relu(x) x = F.dropout(x, self.dropout, training=self.training) x = self.ffn_2(x) return x def _get_input_buffer(self, incremental_state): return get_incremental_state( self, incremental_state, 'f', ) or {} def _set_input_buffer(self, incremental_state, buffer): set_incremental_state( self, incremental_state, 'f', buffer, ) def clear_buffer(self, incremental_state): if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_input' in saved_state: del saved_state['prev_input'] self._set_input_buffer(incremental_state, saved_state) class MultiheadAttention(nn.Module): def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, self_attention=False, encoder_decoder_attention=False): super().__init__() self.embed_dim = embed_dim self.kdim = kdim if kdim is not None else embed_dim self.vdim = vdim if vdim is not None else embed_dim self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" self.scaling = self.head_dim ** -0.5 self.self_attention = self_attention self.encoder_decoder_attention = encoder_decoder_attention assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \ 'value to be of the same size' if self.qkv_same_dim: self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim)) else: self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim)) self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim)) self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim)) if bias: self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim)) else: self.register_parameter('in_proj_bias', None) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) if add_bias_kv: self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) else: self.bias_k = self.bias_v = None self.add_zero_attn = add_zero_attn self.reset_parameters() self.enable_torch_version = False self.last_attn_probs = None def reset_parameters(self): if self.qkv_same_dim: nn.init.xavier_uniform_(self.in_proj_weight) else: nn.init.xavier_uniform_(self.k_proj_weight) nn.init.xavier_uniform_(self.v_proj_weight) nn.init.xavier_uniform_(self.q_proj_weight) nn.init.xavier_uniform_(self.out_proj.weight) if self.in_proj_bias is not None: nn.init.constant_(self.in_proj_bias, 0.) nn.init.constant_(self.out_proj.bias, 0.) if self.bias_k is not None: nn.init.xavier_normal_(self.bias_k) if self.bias_v is not None: nn.init.xavier_normal_(self.bias_v) def forward( self, query, key, value, key_padding_mask=None, incremental_state=None, need_weights=True, static_kv=False, attn_mask=None, before_softmax=False, need_head_weights=False, enc_dec_attn_constraint_mask=None, reset_attn_weight=None ): """Input shape: Time x Batch x Channel Args: key_padding_mask (ByteTensor, optional): mask to exclude keys that are pads, of shape `(batch, src_len)`, where padding elements are indicated by 1s. need_weights (bool, optional): return the attention weights, averaged over heads (default: False). attn_mask (ByteTensor, optional): typically used to implement causal attention, where the mask prevents the attention from looking forward in time (default: None). before_softmax (bool, optional): return the raw attention weights and values before the attention softmax. need_head_weights (bool, optional): return the attention weights for each head. Implies *need_weights*. Default: return the average attention weights over all heads. """ if need_head_weights: need_weights = True tgt_len, bsz, embed_dim = query.size() assert embed_dim == self.embed_dim assert list(query.size()) == [tgt_len, bsz, embed_dim] if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None: if self.qkv_same_dim: return F.multi_head_attention_forward(query, key, value, self.embed_dim, self.num_heads, self.in_proj_weight, self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, self.training, key_padding_mask, need_weights, attn_mask) else: return F.multi_head_attention_forward(query, key, value, self.embed_dim, self.num_heads, torch.empty([0]), self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, self.training, key_padding_mask, need_weights, attn_mask, use_separate_proj_weight=True, q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight, v_proj_weight=self.v_proj_weight) if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_key' in saved_state: # previous time steps are cached - no need to recompute # key and value if they are static if static_kv: assert self.encoder_decoder_attention and not self.self_attention key = value = None else: saved_state = None if self.self_attention: # self-attention q, k, v = self.in_proj_qkv(query) elif self.encoder_decoder_attention: # encoder-decoder attention q = self.in_proj_q(query) if key is None: assert value is None k = v = None else: k = self.in_proj_k(key) v = self.in_proj_v(key) else: q = self.in_proj_q(query) k = self.in_proj_k(key) v = self.in_proj_v(value) q = q * self.scaling if self.bias_k is not None: assert self.bias_v is not None k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1) q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) if k is not None: k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) if v is not None: v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) if saved_state is not None: # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) if 'prev_key' in saved_state: prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim) if static_kv: k = prev_key else: k = torch.cat((prev_key, k), dim=1) if 'prev_value' in saved_state: prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim) if static_kv: v = prev_value else: v = torch.cat((prev_value, v), dim=1) if 'prev_key_padding_mask' in saved_state and saved_state['prev_key_padding_mask'] is not None: prev_key_padding_mask = saved_state['prev_key_padding_mask'] if static_kv: key_padding_mask = prev_key_padding_mask else: key_padding_mask = torch.cat((prev_key_padding_mask, key_padding_mask), dim=1) saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim) saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim) saved_state['prev_key_padding_mask'] = key_padding_mask self._set_input_buffer(incremental_state, saved_state) src_len = k.size(1) # This is part of a workaround to get around fork/join parallelism # not supporting Optional types. if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]): key_padding_mask = None if key_padding_mask is not None: assert key_padding_mask.size(0) == bsz assert key_padding_mask.size(1) == src_len if self.add_zero_attn: src_len += 1 k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) if attn_mask is not None: attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) if key_padding_mask is not None: key_padding_mask = torch.cat( [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1) attn_weights = torch.bmm(q, k.transpose(1, 2)) attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] if attn_mask is not None: if len(attn_mask.shape) == 2: attn_mask = attn_mask.unsqueeze(0) elif len(attn_mask.shape) == 3: attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape( bsz * self.num_heads, tgt_len, src_len) attn_weights = attn_weights + attn_mask if enc_dec_attn_constraint_mask is not None: # bs x head x L_kv attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.masked_fill( enc_dec_attn_constraint_mask.unsqueeze(2).bool(), -1e8, ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if key_padding_mask is not None: # don't attend to padding symbols attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.masked_fill( key_padding_mask.unsqueeze(1).unsqueeze(2), -1e8, ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) if before_softmax: return attn_weights, v attn_weights_float = softmax(attn_weights, dim=-1) attn_weights = attn_weights_float.type_as(attn_weights) attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training) if reset_attn_weight is not None: if reset_attn_weight: self.last_attn_probs = attn_probs.detach() else: assert self.last_attn_probs is not None attn_probs = self.last_attn_probs attn = torch.bmm(attn_probs, v) assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) attn = self.out_proj(attn) if need_weights: attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0) if not need_head_weights: # average attention weights over heads attn_weights = attn_weights.mean(dim=0) else: attn_weights = None return attn, (attn_weights, attn_logits) def in_proj_qkv(self, query): return self._in_proj(query).chunk(3, dim=-1) def in_proj_q(self, query): if self.qkv_same_dim: return self._in_proj(query, end=self.embed_dim) else: bias = self.in_proj_bias if bias is not None: bias = bias[:self.embed_dim] return F.linear(query, self.q_proj_weight, bias) def in_proj_k(self, key): if self.qkv_same_dim: return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim) else: weight = self.k_proj_weight bias = self.in_proj_bias if bias is not None: bias = bias[self.embed_dim:2 * self.embed_dim] return F.linear(key, weight, bias) def in_proj_v(self, value): if self.qkv_same_dim: return self._in_proj(value, start=2 * self.embed_dim) else: weight = self.v_proj_weight bias = self.in_proj_bias if bias is not None: bias = bias[2 * self.embed_dim:] return F.linear(value, weight, bias) def _in_proj(self, input, start=0, end=None): weight = self.in_proj_weight bias = self.in_proj_bias weight = weight[start:end, :] if bias is not None: bias = bias[start:end] return F.linear(input, weight, bias) def _get_input_buffer(self, incremental_state): return get_incremental_state( self, incremental_state, 'attn_state', ) or {} def _set_input_buffer(self, incremental_state, buffer): set_incremental_state( self, incremental_state, 'attn_state', buffer, ) def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): return attn_weights def clear_buffer(self, incremental_state=None): if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if 'prev_key' in saved_state: del saved_state['prev_key'] if 'prev_value' in saved_state: del saved_state['prev_value'] self._set_input_buffer(incremental_state, saved_state) class EncSALayer(nn.Module): def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, padding='SAME', act='gelu', ffn_hidden_size=1024): super().__init__() self.c = c self.dropout = dropout self.num_heads = num_heads if num_heads > 0: self.layer_norm1 = LayerNorm(c) self.self_attn = MultiheadAttention( self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False) self.layer_norm2 = LayerNorm(c) self.ffn = TransformerFFNLayer( c, ffn_hidden_size, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act) def forward(self, x, encoder_padding_mask=None, **kwargs): layer_norm_training = kwargs.get('layer_norm_training', None) if layer_norm_training is not None: self.layer_norm1.training = layer_norm_training self.layer_norm2.training = layer_norm_training if self.num_heads > 0: residual = x x = self.layer_norm1(x) x, _, = self.self_attn( query=x, key=x, value=x, key_padding_mask=encoder_padding_mask ) x = F.dropout(x, self.dropout, training=self.training) x = residual + x x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] residual = x x = self.layer_norm2(x) x = self.ffn(x) x = F.dropout(x, self.dropout, training=self.training) x = residual + x x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] return x class DecSALayer(nn.Module): def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, ffn_hidden_size=1024, act='gelu', post_ln=False): super().__init__() self.c = c self.dropout = dropout self.layer_norm1 = LayerNorm(c) self.self_attn = MultiheadAttention( c, num_heads, self_attention=True, dropout=attention_dropout, bias=False ) self.layer_norm2 = LayerNorm(c) self.encoder_attn = MultiheadAttention( c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False, ) self.layer_norm3 = LayerNorm(c) self.ffn = TransformerFFNLayer( c, ffn_hidden_size, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act) self.post_ln = post_ln def forward( self, x, encoder_out=None, encoder_padding_mask=None, incremental_state=None, self_attn_mask=None, self_attn_padding_mask=None, attn_out=None, reset_attn_weight=None, **kwargs, ): layer_norm_training = kwargs.get('layer_norm_training', None) if layer_norm_training is not None: self.layer_norm1.training = layer_norm_training self.layer_norm2.training = layer_norm_training self.layer_norm3.training = layer_norm_training residual = x if not self.post_ln: x = self.layer_norm1(x) x, _ = self.self_attn( query=x, key=x, value=x, key_padding_mask=self_attn_padding_mask, incremental_state=incremental_state, attn_mask=self_attn_mask ) x = F.dropout(x, self.dropout, training=self.training) x = residual + x if self.post_ln: x = self.layer_norm1(x) attn_logits = None if encoder_out is not None or attn_out is not None: residual = x if not self.post_ln: x = self.layer_norm2(x) if encoder_out is not None: x, attn = self.encoder_attn( query=x, key=encoder_out, value=encoder_out, key_padding_mask=encoder_padding_mask, incremental_state=incremental_state, static_kv=True, enc_dec_attn_constraint_mask=get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'), reset_attn_weight=reset_attn_weight ) attn_logits = attn[1] elif attn_out is not None: x = self.encoder_attn.in_proj_v(attn_out) if encoder_out is not None or attn_out is not None: x = F.dropout(x, self.dropout, training=self.training) x = residual + x if self.post_ln: x = self.layer_norm2(x) residual = x if not self.post_ln: x = self.layer_norm3(x) x = self.ffn(x, incremental_state=incremental_state) x = F.dropout(x, self.dropout, training=self.training) x = residual + x if self.post_ln: x = self.layer_norm3(x) return x, attn_logits def clear_buffer(self, input, encoder_out=None, encoder_padding_mask=None, incremental_state=None): self.encoder_attn.clear_buffer(incremental_state) self.ffn.clear_buffer(incremental_state) def set_buffer(self, name, tensor, incremental_state): return set_incremental_state(self, incremental_state, name, tensor) class TransformerEncoderLayer(nn.Module): def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2, ffn_hidden_size=1024): super().__init__() self.hidden_size = hidden_size self.dropout = dropout self.num_heads = num_heads self.op = EncSALayer( hidden_size, num_heads, dropout=dropout, attention_dropout=0.0, relu_dropout=dropout, kernel_size=kernel_size, ffn_hidden_size=ffn_hidden_size) def forward(self, x, **kwargs): return self.op(x, **kwargs) class TransformerDecoderLayer(nn.Module): def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2, ffn_hidden_size=1024, post_ln=False): super().__init__() self.hidden_size = hidden_size self.dropout = dropout self.num_heads = num_heads self.op = DecSALayer( hidden_size, num_heads, dropout=dropout, attention_dropout=0.0, relu_dropout=dropout, kernel_size=kernel_size, ffn_hidden_size=ffn_hidden_size, post_ln=post_ln) def forward(self, x, **kwargs): return self.op(x, **kwargs) def clear_buffer(self, *args): return self.op.clear_buffer(*args) def set_buffer(self, *args): return self.op.set_buffer(*args) class FFTBlocks(nn.Module): def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=0.0, num_heads=2, use_pos_embed=True, use_last_norm=True, use_pos_embed_alpha=True, ffn_hidden_size=1024): super().__init__() self.num_layers = num_layers embed_dim = self.hidden_size = hidden_size self.dropout = dropout self.use_pos_embed = use_pos_embed self.use_last_norm = use_last_norm if use_pos_embed: self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS self.padding_idx = 0 self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1 self.embed_positions = SinusoidalPositionalEmbedding( embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, ) self.layers = nn.ModuleList([]) self.layers.extend([ TransformerEncoderLayer(self.hidden_size, self.dropout, kernel_size=ffn_kernel_size, num_heads=num_heads, ffn_hidden_size=ffn_hidden_size) for _ in range(self.num_layers) ]) if self.use_last_norm: self.layer_norm = nn.LayerNorm(embed_dim) else: self.layer_norm = None def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False): """ :param x: [B, T, C] :param padding_mask: [B, T] :return: [B, T, C] or [L, B, T, C] """ padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1] if self.use_pos_embed: positions = self.pos_embed_alpha * self.embed_positions(x[..., 0]) x = x + positions x = F.dropout(x, p=self.dropout, training=self.training) # B x T x C -> T x B x C x = x.transpose(0, 1) * nonpadding_mask_TB hiddens = [] for layer in self.layers: x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB hiddens.append(x) if self.use_last_norm: x = self.layer_norm(x) * nonpadding_mask_TB if return_hiddens: x = torch.stack(hiddens, 0) # [L, T, B, C] x = x.transpose(1, 2) # [L, B, T, C] else: x = x.transpose(0, 1) # [B, T, C] return x class FastSpeechEncoder(FFTBlocks): def __init__(self, dict_size, hidden_size=256, num_layers=4, kernel_size=9, dropout=0.0, num_heads=2, ffn_hidden_size=1024): super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads, use_pos_embed=False, dropout=dropout, ffn_hidden_size=ffn_hidden_size) self.embed_tokens = Embedding(dict_size, hidden_size, 0) self.embed_scale = math.sqrt(hidden_size) self.padding_idx = 0 self.embed_positions = SinusoidalPositionalEmbedding( hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, ) def forward(self, txt_tokens, attn_mask=None, other_embeds=0): """ :param txt_tokens: [B, T] :return: { 'encoder_out': [B x T x C] } """ encoder_padding_mask = txt_tokens.eq(self.padding_idx).data x = self.forward_embedding(txt_tokens) + other_embeds # [B, T, H] if self.num_layers > 0: x = super(FastSpeechEncoder, self).forward(x, encoder_padding_mask, attn_mask=attn_mask) return x def forward_embedding(self, txt_tokens): # embed tokens and positions x = self.embed_scale * self.embed_tokens(txt_tokens) if self.use_pos_embed: positions = self.embed_positions(txt_tokens) x = x + positions x = F.dropout(x, p=self.dropout, training=self.training) return x ================================================ FILE: tts/modules/llm_dit/cfm.py ================================================ # MIT License # Copyright (c) 2023 Alexander Tong # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Copyright (c) [2023] [Alexander Tong] # Copyright (c) [2025] [Ziyue Jiang] # SPDX-License-Identifier: MIT # This file has been modified by Ziyue Jiang on 2025/03/19 # Original file was released under MIT, with the full license text # available at https://github.com/atong01/conditional-flow-matching/blob/1.0.7/LICENSE. # This modified file is released under the same license. import math import torch from typing import Union from torch.distributions import LogisticNormal class LogitNormalTrainingTimesteps: def __init__(self, T=1000.0, loc=0.0, scale=1.0): assert T > 0 self.T = T self.dist = LogisticNormal(loc, scale) def sample(self, size, device): t = self.dist.sample(size)[..., 0].to(device) return t def pad_t_like_x(t, x): """Function to reshape the time vector t by the number of dimensions of x. Parameters ---------- x : Tensor, shape (bs, *dim) represents the source minibatch t : FloatTensor, shape (bs) Returns ------- t : Tensor, shape (bs, number of x dimensions) Example ------- x: Tensor (bs, C, W, H) t: Vector (bs) pad_t_like_x(t, x): Tensor (bs, 1, 1, 1) """ if isinstance(t, (float, int)): return t return t.reshape(-1, *([1] * (x.dim() - 1))) class ConditionalFlowMatcher: """Base class for conditional flow matching methods. This class implements the independent conditional flow matching methods from [1] and serves as a parent class for all other flow matching methods. It implements: - Drawing data from gaussian probability path N(t * x1 + (1 - t) * x0, sigma) function - conditional flow matching ut(x1|x0) = x1 - x0 - score function $\nabla log p_t(x|x0, x1)$ """ def __init__(self, sigma: Union[float, int] = 0.0): r"""Initialize the ConditionalFlowMatcher class. It requires the hyper-parameter $\sigma$. Parameters ---------- sigma : Union[float, int] """ self.sigma = sigma self.time_sampler = LogitNormalTrainingTimesteps() def compute_mu_t(self, x0, x1, t): """ Compute the mean of the probability path N(t * x1 + (1 - t) * x0, sigma), see (Eq.14) [1]. Parameters ---------- x0 : Tensor, shape (bs, *dim) represents the source minibatch x1 : Tensor, shape (bs, *dim) represents the target minibatch t : FloatTensor, shape (bs) Returns ------- mean mu_t: t * x1 + (1 - t) * x0 References ---------- [1] Improving and Generalizing Flow-Based Generative Models with minibatch optimal transport, Preprint, Tong et al. """ t = pad_t_like_x(t, x0) return t * x1 + (1 - t) * x0 def compute_sigma_t(self, t): """ Compute the standard deviation of the probability path N(t * x1 + (1 - t) * x0, sigma), see (Eq.14) [1]. Parameters ---------- t : FloatTensor, shape (bs) Returns ------- standard deviation sigma References ---------- [1] Improving and Generalizing Flow-Based Generative Models with minibatch optimal transport, Preprint, Tong et al. """ del t return self.sigma def sample_xt(self, x0, x1, t, epsilon): """ Draw a sample from the probability path N(t * x1 + (1 - t) * x0, sigma), see (Eq.14) [1]. Parameters ---------- x0 : Tensor, shape (bs, *dim) represents the source minibatch x1 : Tensor, shape (bs, *dim) represents the target minibatch t : FloatTensor, shape (bs) epsilon : Tensor, shape (bs, *dim) noise sample from N(0, 1) Returns ------- xt : Tensor, shape (bs, *dim) References ---------- [1] Improving and Generalizing Flow-Based Generative Models with minibatch optimal transport, Preprint, Tong et al. """ mu_t = self.compute_mu_t(x0, x1, t) sigma_t = self.compute_sigma_t(t) sigma_t = pad_t_like_x(sigma_t, x0) return mu_t + sigma_t * epsilon def compute_conditional_flow(self, x0, x1, t, xt): """ Compute the conditional vector field ut(x1|x0) = x1 - x0, see Eq.(15) [1]. Parameters ---------- x0 : Tensor, shape (bs, *dim) represents the source minibatch x1 : Tensor, shape (bs, *dim) represents the target minibatch t : FloatTensor, shape (bs) xt : Tensor, shape (bs, *dim) represents the samples drawn from probability path pt Returns ------- ut : conditional vector field ut(x1|x0) = x1 - x0 References ---------- [1] Improving and Generalizing Flow-Based Generative Models with minibatch optimal transport, Preprint, Tong et al. """ del t, xt return x1 - x0 def sample_noise_like(self, x): return torch.randn_like(x) def sample_location_and_conditional_flow(self, x0, x1, t=None, return_noise=False): """ Compute the sample xt (drawn from N(t * x1 + (1 - t) * x0, sigma)) and the conditional vector field ut(x1|x0) = x1 - x0, see Eq.(15) [1]. Parameters ---------- x0 : Tensor, shape (bs, *dim) represents the source minibatch x1 : Tensor, shape (bs, *dim) represents the target minibatch (optionally) t : Tensor, shape (bs) represents the time levels if None, drawn from uniform [0,1] return_noise : bool return the noise sample epsilon Returns ------- t : FloatTensor, shape (bs) xt : Tensor, shape (bs, *dim) represents the samples drawn from probability path pt ut : conditional vector field ut(x1|x0) = x1 - x0 (optionally) eps: Tensor, shape (bs, *dim) such that xt = mu_t + sigma_t * epsilon References ---------- [1] Improving and Generalizing Flow-Based Generative Models with minibatch optimal transport, Preprint, Tong et al. """ if t is None: # t = torch.rand(x0.shape[0]).type_as(x0) t = self.time_sampler.sample([x0.shape[0]], x0.device).type_as(x0) assert len(t) == x0.shape[0], "t has to have batch size dimension" eps = self.sample_noise_like(x0) xt = self.sample_xt(x0, x1, t, eps) ut = self.compute_conditional_flow(x0, x1, t, xt) if return_noise: return t, xt, ut, eps else: return t, xt, ut def compute_lambda(self, t): """Compute the lambda function, see Eq.(23) [3]. Parameters ---------- t : FloatTensor, shape (bs) Returns ------- lambda : score weighting function References ---------- [4] Simulation-free Schrodinger bridges via score and flow matching, Preprint, Tong et al. """ sigma_t = self.compute_sigma_t(t) return 2 * sigma_t / (self.sigma**2 + 1e-8) class VariancePreservingConditionalFlowMatcher(ConditionalFlowMatcher): """Albergo et al. 2023 trigonometric interpolants class. This class inherits the ConditionalFlowMatcher and override the compute_mu_t and compute_conditional_flow functions in order to compute [3]'s trigonometric interpolants. [3] Stochastic Interpolants: A Unifying Framework for Flows and Diffusions, Albergo et al. """ def compute_mu_t(self, x0, x1, t): r"""Compute the mean of the probability path (Eq.5) from [3]. Parameters ---------- x0 : Tensor, shape (bs, *dim) represents the source minibatch x1 : Tensor, shape (bs, *dim) represents the target minibatch t : FloatTensor, shape (bs) Returns ------- mean mu_t: cos(pi t/2)x0 + sin(pi t/2)x1 References ---------- [3] Stochastic Interpolants: A Unifying Framework for Flows and Diffusions, Albergo et al. """ t = pad_t_like_x(t, x0) return torch.cos(math.pi / 2 * t) * x0 + torch.sin(math.pi / 2 * t) * x1 def compute_conditional_flow(self, x0, x1, t, xt): r"""Compute the conditional vector field similar to [3]. ut(x1|x0) = pi/2 (cos(pi*t/2) x1 - sin(pi*t/2) x0), see Eq.(21) [3]. Parameters ---------- x0 : Tensor, shape (bs, *dim) represents the source minibatch x1 : Tensor, shape (bs, *dim) represents the target minibatch t : FloatTensor, shape (bs) xt : Tensor, shape (bs, *dim) represents the samples drawn from probability path pt Returns ------- ut : conditional vector field ut(x1|x0) = pi/2 (cos(pi*t/2) x1 - sin(\pi*t/2) x0) References ---------- [3] Stochastic Interpolants: A Unifying Framework for Flows and Diffusions, Albergo et al. """ del xt t = pad_t_like_x(t, x0) return math.pi / 2 * (torch.cos(math.pi / 2 * t) * x1 - torch.sin(math.pi / 2 * t) * x0) ================================================ FILE: tts/modules/llm_dit/dit.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from torch import nn from tts.modules.llm_dit.cfm import ConditionalFlowMatcher from tts.modules.ar_dur.commons.layers import Embedding from tts.modules.ar_dur.commons.nar_tts_modules import PosEmb from tts.modules.ar_dur.commons.rel_transformer import RelTransformerEncoder from tts.modules.ar_dur.ar_dur_predictor import expand_states from tts.modules.llm_dit.transformer import Transformer from tts.modules.llm_dit.time_embedding import TimestepEmbedding class Diffusion(nn.Module): def __init__(self): super().__init__() # Hparams # cond dim self.local_cond_dim = 512 self.ctx_mask_dim = 16 self.in_channels = 32 self.out_channels = 32 # LLM self.encoder_dim = 1024 self.encoder_n_layers = 24 self.encoder_n_heads = 16 self.max_seq_len = 16384 self.multiple_of = 256 self.ctx_mask_proj = nn.Linear(1, self.ctx_mask_dim) self.local_cond_project = nn.Linear( self.out_channels + self.ctx_mask_dim, self.local_cond_dim) self.encoder = Transformer(self.encoder_n_layers, self.encoder_dim, self.encoder_n_heads, self.max_seq_len) self.x_prenet = nn.Linear(self.in_channels, self.encoder_dim) self.prenet = nn.Linear(self.local_cond_dim, self.encoder_dim) self.postnet = nn.Linear(self.encoder_dim, self.out_channels) self.flow_matcher = ConditionalFlowMatcher(sigma=0.0) # The implementation of TimestepEmbedding is a modified version from F5-TTS (https://github.com/SWivid/F5-TTS), # which is licensed under the MIT License. self.f5_time_embed = TimestepEmbedding(self.encoder_dim) # text encoder self.ph_encoder = RelTransformerEncoder( 302, self.encoder_dim, self.encoder_dim, self.encoder_dim * 2, 4, 6, 3, 0.0, prenet=True, pre_ln=True) self.tone_embed = Embedding(32, self.encoder_dim, padding_idx=0) self.ph_pos_embed = PosEmb(self.encoder_dim) self.ling_pre_net = torch.nn.Sequential(*[ torch.nn.Conv1d(self.encoder_dim, self.encoder_dim, kernel_size=s * 2, stride=s, padding=s // 2) for i, s in enumerate([2, 2]) ]) def forward(self, inputs, sigmas=None, x_noisy=None): ctx_mask = inputs['ctx_mask'] ctx_feature = inputs['lat_ctx'] * ctx_mask """ local conditioning (prompt_latent + spk_embed) """ ctx_mask_emb = self.ctx_mask_proj(ctx_mask) # ctx_feature = ctx_feature * (1 - inputs["spk_cfg_mask"][:, :, None]) local_cond = torch.cat([ctx_feature, ctx_mask_emb], dim=-1) local_cond = self.local_cond_project(local_cond) """ diffusion target latent """ x = inputs['lat'] # Here, x is x1 in CFM x0 = torch.randn_like(x) t, xt, ut = self.flow_matcher.sample_location_and_conditional_flow(x0, x) # define noisy_input and target t = t.bfloat16() x_noisy = (xt * (1 - ctx_mask)).bfloat16() target = ut # concat condition. x_ling = self.forward_ling_encoder(inputs["phone"], inputs["tone"]) x_ling = self.ling_pre_net(expand_states(x_ling, inputs['mel2ph']).transpose(1, 2)).transpose(1, 2) x_noisy = self.x_prenet(x_noisy) + self.prenet(local_cond) + x_ling encoder_out = self.encoder(x_noisy, self.f5_time_embed(t), attn_mask=inputs["text_mel_mask"], do_checkpoint=False) pred = self.postnet(encoder_out) return pred, target def forward_ling_encoder(self, txt_tokens, tone_tokens): ph_tokens = txt_tokens ph_nonpadding = (ph_tokens > 0).float()[:, :, None] # [B, T_phone, 1] # enc_ph ph_enc_oembed = self.tone_embed(tone_tokens) ph_enc_oembed = ph_enc_oembed + self.ph_pos_embed( torch.arange(0, ph_tokens.shape[1])[None,].to(ph_tokens.device)) ph_enc_oembed = ph_enc_oembed ph_enc_oembed = ph_enc_oembed * ph_nonpadding x_ling = self.ph_encoder(ph_tokens, other_embeds=ph_enc_oembed) * ph_nonpadding return x_ling def _forward(self, x, local_cond, x_ling, timesteps, ctx_mask, dur=None, seq_cfg_w=[1.0,1.0]): """ When we use torchdiffeq, we need to include the CFG process inside _forward() """ x = x * (1 - ctx_mask) x = self.x_prenet(x) + self.prenet(local_cond) + x_ling pred_v = self.encoder(x, self.f5_time_embed(timesteps), attn_mask=torch.ones((x.size(0), x.size(1)), device=x.device)) pred = self.postnet(pred_v) """ Perform multi-cond CFG """ cond_spk_txt, cond_txt, uncond = pred.chunk(3) pred = uncond + seq_cfg_w[0] * (cond_txt - uncond) + seq_cfg_w[1] * (cond_spk_txt - cond_txt) return pred @torch.no_grad() def inference(self, inputs, timesteps=20, seq_cfg_w=[1.0, 1.0], **kwargs): # txt embedding x_ling = self.forward_ling_encoder(inputs["phone"], inputs["tone"]) x_ling = self.ling_pre_net(expand_states(x_ling, inputs['dur']).transpose(1, 2)).transpose(1, 2) # speaker embedding ctx_feature = inputs['lat_ctx'] ctx_feature[1:, :, :] = 0 # prefix spk cfg ctx_mask_emb = self.ctx_mask_proj(inputs['ctx_mask']) # local conditioning. local_cond = torch.cat([ctx_feature, ctx_mask_emb], dim=-1) local_cond = self.local_cond_project(local_cond) ''' Euler ODE solver ''' bsz, device, frm_len = (local_cond.size(0), local_cond.device, local_cond.size(1)) # Sway sampling from F5-TTS (https://github.com/SWivid/F5-TTS), # which is licensed under the MIT License. sway_sampling_coef = -1.0 t_schedule = torch.linspace(0, 1, timesteps + 1, device=device, dtype=x_ling.dtype) if sway_sampling_coef is not None: t_schedule = t_schedule + sway_sampling_coef * (torch.cos(torch.pi / 2 * t_schedule) - 1 + t_schedule) # AMO sampling implementation for "AMO Sampler: Enhancing Text Rendering with Overshooting" (https://arxiv.org/pdf/2411.19415) def amo_sampling(z_t, t, t_next, v): # Upcast to avoid precision issues when computing prev_sample z_t = z_t.to(torch.float32) # Constant definition in Algorithm 1 s = t_next c = 3 # Line 7 in Algorithm 1 o = min(t_next + c * (t_next - t), 1) pred_z_o = z_t + (o - t) * v # Line 11 in Algorithm 1 a = s / o b = ((1 - s) ** 2 - (a * (1 - o)) ** 2) ** 0.5 noise_i = torch.randn(size=z_t.shape, device=z_t.device) z_t_next = a * pred_z_o + b * noise_i return z_t_next.to(v.dtype) x = torch.randn([1, frm_len, self.out_channels], device=device) for step_index in range(timesteps): x = x.to(torch.float32) sigma = t_schedule[step_index].to(x_ling.dtype) sigma_next = t_schedule[step_index + 1] model_out = self._forward(torch.cat([x] * bsz), local_cond, x_ling, timesteps=sigma.unsqueeze(0), ctx_mask=inputs['ctx_mask'], dur=inputs['dur'], seq_cfg_w=seq_cfg_w) x = amo_sampling(x, sigma, sigma_next, model_out) # Cast sample back to model compatible dtype x = x.to(model_out.dtype) return x ================================================ FILE: tts/modules/llm_dit/time_embedding.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import torch from torch import nn class SinusPositionEmbedding(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, x, scale=1000): device = x.device half_dim = self.dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb class TimestepEmbedding(nn.Module): def __init__(self, dim, freq_embed_dim=256): super().__init__() self.time_embed = SinusPositionEmbedding(freq_embed_dim) self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) def forward(self, timestep): # noqa: F821 time_hidden = self.time_embed(timestep) time_hidden = time_hidden.to(timestep.dtype) time = self.time_mlp(time_hidden) # b d return time ================================================ FILE: tts/modules/llm_dit/transformer.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Optional, Tuple import torch import torch.nn.functional as F from torch import nn def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0): freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) t = torch.arange(end, device=freqs.device) # type: ignore freqs = torch.outer(t, freqs).float() # type: ignore freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 return freqs_cis def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): ndim = x.ndim assert 0 <= 1 < ndim assert freqs_cis.shape == (x.shape[1], x.shape[-1]) shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis.view(*shape) def apply_rotary_emb( xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor]: xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) freqs_cis = reshape_for_broadcast(freqs_cis, xq_) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) return xq_out.type_as(xq), xk_out.type_as(xk) class AdaLNZero(nn.Module): def __init__(self, dim): super().__init__() self.silu = nn.SiLU() self.linear = nn.Linear(dim, dim * 6) self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) def forward(self, x, emb=None): emb = self.linear(self.silu(emb)) shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1) x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] return x, gate_msa, shift_mlp, scale_mlp, gate_mlp class AdaLNZero_Out(nn.Module): def __init__(self, dim): super().__init__() self.silu = nn.SiLU() self.linear = nn.Linear(dim, dim * 2) self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) def forward(self, x, emb): emb = self.linear(self.silu(emb)) scale, shift = torch.chunk(emb, 2, dim=1) x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :] return x class Attention(nn.Module): def __init__(self, encoder_dim, encoder_n_heads, max_seq_len): super().__init__() self.encoder_n_kv_heads = encoder_n_heads model_parallel_size = 1 self.n_local_heads = encoder_n_heads // model_parallel_size self.n_local_kv_heads = self.encoder_n_kv_heads // model_parallel_size self.n_rep = self.n_local_heads // self.n_local_kv_heads self.head_dim = encoder_dim // encoder_n_heads self.wq = nn.Linear( encoder_dim, encoder_n_heads * self.head_dim, ) self.wk = nn.Linear( encoder_dim, self.encoder_n_kv_heads * self.head_dim, ) self.wv = nn.Linear( encoder_dim, self.encoder_n_kv_heads * self.head_dim, ) self.wo = nn.Linear( encoder_n_heads * self.head_dim, encoder_dim, ) def forward( self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor], ): bsz, seqlen, _ = x.shape xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis) xq = xq.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim) keys = xk.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim) values = xv.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim) output = F.scaled_dot_product_attention(xq, keys, values, mask[:, None, None, :], is_causal=False) output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1) return self.wo(output) class FeedForward(nn.Module): def __init__( self, dim: int, hidden_dim: int, multiple_of: int, ffn_dim_multiplier: Optional[float], ): super().__init__() if ffn_dim_multiplier is not None: hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) self.w1 = nn.Linear( dim, hidden_dim ) self.w2 = nn.Linear( hidden_dim, dim ) def forward(self, x): return self.w2(F.silu(self.w1(x))) class TransformerBlock(nn.Module): def __init__(self, encoder_dim, encoder_n_heads, max_seq_len): super().__init__() self.encoder_n_heads = encoder_n_heads self.encoder_dim = encoder_dim self.head_dim = encoder_dim // encoder_n_heads self.attention = Attention(encoder_dim, encoder_n_heads, max_seq_len) self.feed_forward = FeedForward( dim=encoder_dim, hidden_dim=2 * encoder_dim, multiple_of=256, ffn_dim_multiplier=None, ) self.attention_norm = AdaLNZero(encoder_dim) self.ffn_norm = nn.LayerNorm(encoder_dim, elementwise_affine=False, eps=1e-6) def forward( self, x: torch.Tensor, t: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor], ): """ Perform a forward pass through the TransformerBlock. Args: x (torch.Tensor): Input tensor. start_pos (int): Starting position for attention caching. freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. mask (torch.Tensor, optional): Masking tensor for attention. Defaults to None. Returns: torch.Tensor: Output tensor after applying attention and feedforward layers. """ # pre-norm & modulation for attention input norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attention_norm(x, emb=t) # attention attn_output = self.attention(norm, start_pos, freqs_cis, mask=mask) # process attention output for input x h = x + gate_msa.unsqueeze(1) * attn_output norm = self.ffn_norm(h) * (1 + scale_mlp[:, None]) + shift_mlp[:, None] ff_output = self.feed_forward(norm) out = h + gate_mlp.unsqueeze(1) * ff_output return out class Transformer(nn.Module): def __init__(self, encoder_n_layers, encoder_dim, encoder_n_heads, max_seq_len): super().__init__() # Decoder self.layers = torch.nn.ModuleList() for _ in range(encoder_n_layers): self.layers.append(TransformerBlock(encoder_dim, encoder_n_heads, max_seq_len)) self.norm = AdaLNZero_Out(encoder_dim) self.out_proj = nn.Linear(encoder_dim, encoder_dim) # Rope embedding freqs_cis = precompute_freqs_cis( encoder_dim // encoder_n_heads, max_seq_len ) self.register_buffer("freqs_cis", torch.view_as_real(freqs_cis), persistent=False) def forward(self, x, t, attn_mask, start_pos=0): freqs_cis = torch.view_as_complex(self.freqs_cis.float())[start_pos: start_pos + x.size(1)] for i, layer in enumerate(self.layers): x = layer(x, t, start_pos, freqs_cis, attn_mask) x = self.norm(x, t) x = self.out_proj(x) return x ================================================ FILE: tts/modules/wavvae/decoder/diag_gaussian.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import numpy as np class DiagonalGaussianDistribution(object): def __init__(self, parameters: torch.Tensor, deterministic: bool = False): self.parameters = parameters self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: self.var = self.std = torch.zeros_like( self.mean, device=self.parameters.device, dtype=self.parameters.dtype ) def sample(self, generator=None) -> torch.Tensor: # make sure sample is on the same device as the parameters and has same dtype sample = torch.randn( self.mean.shape, generator=generator, device=self.parameters.device, dtype=self.parameters.dtype, ) x = self.mean + self.std * sample return x def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: if self.deterministic: return torch.Tensor([0.0]) else: if other is None: return 0.5 * torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar else: return 0.5 * ( torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar ) def nll(self, sample, dims) -> torch.Tensor: if self.deterministic: return torch.Tensor([0.0]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims, ) def mode(self) -> torch.Tensor: return self.mean ================================================ FILE: tts/modules/wavvae/decoder/hifigan_modules.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch.nn as nn import torch.nn.functional as F import torch import torch.utils.data from librosa.filters import mel as librosa_mel_fn from torch.nn.utils import weight_norm, remove_weight_norm from torch.nn import Conv1d import numpy as np def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(mean, std) def get_padding(kernel_size, dilation=1): return int((kernel_size*dilation - dilation)/2) class Upsample(nn.Module): def __init__(self, mult, r): super(Upsample, self).__init__() self.r = r self.upsample = nn.Sequential(nn.Upsample(mode="nearest", scale_factor=r), nn.LeakyReLU(0.2), nn.ReflectionPad1d(3), nn.utils.weight_norm(nn.Conv1d(mult, mult // 2, kernel_size=7, stride=1)) ) r_kernel = r if r >= 5 else 5 self.trans_upsample = nn.Sequential(nn.LeakyReLU(0.2), nn.utils.weight_norm(nn.ConvTranspose1d(mult, mult // 2, kernel_size=r_kernel * 2, stride=r, padding=r_kernel - r // 2, output_padding=r % 2) )) def forward(self, x): x = torch.sin(x) + x out1 = self.upsample(x) out2 = self.trans_upsample(x) return out1 + out2 class Downsample(nn.Module): def __init__(self, mult, r): super(Downsample, self).__init__() self.r = r r_kernel = r if r >= 5 else 5 self.trans_downsample = nn.Sequential(nn.LeakyReLU(0.2), nn.utils.weight_norm(nn.Conv1d(mult, mult * 2, kernel_size=r_kernel * 2, stride=r, padding=r_kernel - r // 2) )) def forward(self, x): out = self.trans_downsample(x) return out def weights_init(m): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(0.0, 0.02) elif classname.find("BatchNorm2d") != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) def weights_zero_init(m): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.fill_(0.0) m.bias.data.fill_(0.0) def WNConv1d(*args, **kwargs): return weight_norm(nn.Conv1d(*args, **kwargs)) def WNConvTranspose1d(*args, **kwargs): return weight_norm(nn.ConvTranspose1d(*args, **kwargs)) class Audio2Mel(nn.Module): def __init__( self, hop_length=300, sampling_rate=24000, n_mel_channels=80, mel_fmin=0., mel_fmax=None, frame_size=0.05, device='cpu' ): super().__init__() ############################################## # FFT Parameters # ############################################## self.n_fft = int(np.power(2., np.ceil(np.log(sampling_rate * frame_size) / np.log(2)))) window = torch.hann_window(int(sampling_rate * frame_size)).float() mel_basis = librosa_mel_fn( sampling_rate, self.n_fft, n_mel_channels, mel_fmin, mel_fmax ) # Mel filter (by librosa) mel_basis = torch.from_numpy(mel_basis).float() self.register_buffer("mel_basis", mel_basis) self.register_buffer("window", window) self.hop_length = hop_length self.win_length = int(sampling_rate * frame_size) self.sampling_rate = sampling_rate self.n_mel_channels = n_mel_channels def forward(self, audio): fft = torch.stft( audio.squeeze(1), n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window=self.window, center=True, ) real_part, imag_part = fft.unbind(-1) magnitude = torch.sqrt(torch.clamp(real_part ** 2 + imag_part ** 2, min=1e-5)) mel_output = torch.matmul(self.mel_basis, magnitude) log_mel_spec = 20 * torch.log10(torch.clamp(mel_output, min=1e-5)) - 20 norm_mel = (log_mel_spec + 115.) / 115. mel_comp = torch.clamp(norm_mel * 8. - 4., -4., 4.) return mel_comp class ResnetBlock(nn.Module): def __init__(self, dim, dilation=1, dim_in=None): super().__init__() if dim_in is None: dim_in = dim self.block = nn.Sequential( nn.LeakyReLU(0.2), nn.ReflectionPad1d(dilation), WNConv1d(dim_in, dim, kernel_size=3, dilation=dilation), nn.LeakyReLU(0.2), WNConv1d(dim, dim, kernel_size=1), ) self.shortcut = WNConv1d(dim_in, dim, kernel_size=1) def forward(self, x): return self.shortcut(x) + self.block(x) ''' 参照hifigan（https://arxiv.org/pdf/2010.05646.pdf）v2结构 多尺度主要是kernel_size不同，3组并行卷积模块，每个卷积模块内部采用不同的串行dilation size，且中间交叉正常无dilation卷积层 ''' class ResBlockMRFV2(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): super(ResBlockMRFV2, self).__init__() self.convs1 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2]))) ]) self.convs1.apply(init_weights) self.convs2 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))) ]) self.convs2.apply(init_weights) def forward(self, x): for c1, c2 in zip(self.convs1, self.convs2): xt = F.leaky_relu(x, 0.2) xt = c1(xt) xt = F.leaky_relu(xt, 0.2) xt = c2(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs1: remove_weight_norm(l) for l in self.convs2: remove_weight_norm(l) class ResBlockMRFV2Inter(torch.nn.Module): def __init__(self, channels, kernel_size=3): super(ResBlockMRFV2Inter, self).__init__() self.block1 = ResBlockMRFV2(channels) self.block2 = ResBlockMRFV2(channels, 7) self.block3 = ResBlockMRFV2(channels, 11) def forward(self, x): xs = self.block1(x) xs += self.block2(x) xs += self.block3(x) x = xs / 3 return x class Generator(nn.Module): def __init__(self, input_size_, ngf, n_residual_layers, num_band, args, ratios=[5, 5, 4, 3], onnx_export=False, device='cpu'): super().__init__() self.hop_length = args.frame_shift self.args = args self.onnx_export = onnx_export # ------------- Define upsample layers ---------------- mult = int(2 ** len(ratios)) model_up = [] input_size = input_size_ model_up += [ nn.ReflectionPad1d(3), WNConv1d(input_size, mult * ngf, kernel_size=7, padding=0), ] # Upsample to raw audio scale for i, r in enumerate(ratios): model_up += [Upsample(mult * ngf, r)] model_up += [ResBlockMRFV2Inter(mult * ngf // 2)] mult //= 2 model_up += [ nn.LeakyReLU(0.2), nn.ReflectionPad1d(3), WNConv1d(ngf, num_band, kernel_size=7, padding=0), nn.Tanh(), ] if not args.use_tanh: model_up[-1] = nn.Conv1d(num_band, num_band, 1) model_up[-2].apply(weights_zero_init) self.model_up = nn.Sequential(*model_up) self.apply(weights_init) def forward(self, mel, step=None): # mel input: (batch_size, seq_num, 80) if self.onnx_export: mel = mel.transpose(1, 2) # on onnx, for engineering, mel input: (batch_size, 80, seq_num) # Between Down and up x = mel # Upsample pipline cnt_after_upsample = 0 for i, m in enumerate(self.model_up): x = m(x) if type(m) == Upsample: cnt_after_upsample += 1 return x ================================================ FILE: tts/modules/wavvae/decoder/seanet_encoder.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List import torch from torch import nn from tts.modules.wavvae.encoder.common_modules.seanet import SEANetEncoder class Encoder(nn.Module): def __init__( self, dowmsamples: List[int] = [6, 5, 5, 4, 2], ): super().__init__() # breakpoint() self.frame_rate = 25 # not use self.encoder = SEANetEncoder(causal=False, n_residual_layers=1, norm='weight_norm', pad_mode='reflect', lstm=2, dimension=512, channels=1, n_filters=32, ratios=dowmsamples, activation='ELU', kernel_size=7, residual_kernel_size=3, last_kernel_size=7, dilation_base=2, true_skip=False, compress=2) def forward(self, audio: torch.Tensor): audio = audio.unsqueeze(1) # audio(16,24000) emb = self.encoder(audio) return emb ================================================ FILE: tts/modules/wavvae/decoder/wavvae_v3.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import torch from torch import nn import torch.nn.functional as F from tts.modules.wavvae.decoder.seanet_encoder import Encoder from tts.modules.wavvae.decoder.diag_gaussian import DiagonalGaussianDistribution from tts.modules.wavvae.decoder.hifigan_modules import Generator, Upsample class WavVAE_V3(nn.Module): def __init__(self, hparams=None): super().__init__() self.encoder = Encoder(dowmsamples=[6, 5, 4, 4, 2]) self.proj_to_z = nn.Linear(512, 64) self.proj_to_decoder = nn.Linear(32, 320) config_path = hparams['melgan_config'] args = argparse.Namespace() args.__dict__.update(config_path) self.latent_upsampler = Upsample(320, 4) self.decoder = Generator( input_size_=160, ngf=128, n_residual_layers=4, num_band=1, args=args, ratios=[5,4,4,3]) ''' encode waveform into 25 hz latent representation ''' def encode_latent(self, audio): posterior = self.encode(audio) latent = posterior.sample().permute(0, 2, 1) # (b,t,latent_channel) return latent def encode(self, audio): x = self.encoder(audio).permute(0, 2, 1) x = self.proj_to_z(x).permute(0, 2, 1) poseterior = DiagonalGaussianDistribution(x) return poseterior def decode(self, latent): latent = self.proj_to_decoder(latent).permute(0, 2, 1) return self.decoder(self.latent_upsampler(latent)) def forward(self, audio): posterior = self.encode(audio) latent = posterior.sample().permute(0, 2, 1) # (b, t, latent_channel) recon_wav = self.decode(latent) return recon_wav, posterior ================================================ FILE: tts/modules/wavvae/encoder/common_modules/conv.py ================================================ # MIT License # Copyright (c) Meta Platforms, Inc. and affiliates. # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Copyright (c) [2023] [Meta Platforms, Inc. and affiliates.] # Copyright (c) [2025] [Ziyue Jiang] # SPDX-License-Identifier: MIT # This file has been modified by Ziyue Jiang on 2025/03/19 # Original file was released under MIT, with the full license text # available at https://github.com/facebookresearch/encodec/blob/gh-pages/LICENSE. # This modified file is released under the same license. """Convolutional layers wrappers and utilities.""" import math import typing as tp import warnings import einops import torch from torch import nn from torch.nn import functional as F from torch.nn.utils import spectral_norm, weight_norm CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm', 'time_layer_norm', 'layer_norm', 'time_group_norm']) def apply_parametrization_norm(module: nn.Module, norm: str = 'none') -> nn.Module: assert norm in CONV_NORMALIZATIONS if norm == 'weight_norm': return weight_norm(module) elif norm == 'spectral_norm': return spectral_norm(module) else: return module def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs) -> nn.Module: assert norm in CONV_NORMALIZATIONS if norm == 'layer_norm': assert isinstance(module, nn.modules.conv._ConvNd) return ConvLayerNorm(module.out_channels, **norm_kwargs) elif norm == 'time_group_norm': if causal: raise ValueError("GroupNorm doesn't support causal evaluation.") assert isinstance(module, nn.modules.conv._ConvNd) return nn.GroupNorm(1, module.out_channels, **norm_kwargs) else: return nn.Identity() def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0) -> int: length = x.shape[-1] n_frames = (length - kernel_size + padding_total) / stride + 1 ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total) return ideal_length - length def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'zero', value: float = 0.): length = x.shape[-1] padding_left, padding_right = paddings assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right) if mode == 'reflect': max_pad = max(padding_left, padding_right) extra_pad = 0 if length <= max_pad: extra_pad = max_pad - length + 1 x = F.pad(x, (0, extra_pad)) padded = F.pad(x, paddings, mode, value) end = padded.shape[-1] - extra_pad return padded[..., :end] else: return F.pad(x, paddings, mode, value) class ConvLayerNorm(nn.LayerNorm): def __init__(self, normalized_shape: tp.Union[int, tp.List[int], torch.Size], **kwargs): super().__init__(normalized_shape, **kwargs) def forward(self, x): x = einops.rearrange(x, 'b ... t -> b t ...') x = super().forward(x) x = einops.rearrange(x, 'b t ... -> b ... t') return class NormConv1d(nn.Module): def __init__(self, *args, causal: bool = False, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): super().__init__() self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm) self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs) self.norm_type = norm def forward(self, x): x = self.conv(x) x = self.norm(x) return x class SConv1d(nn.Module): def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, dilation: int = 1, groups: int = 1, bias: bool = True, causal: bool = False, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, pad_mode: str = 'reflect'): super().__init__() # warn user on unusual setup between dilation and stride if stride > 1 and dilation > 1: warnings.warn('SConv1d has been initialized with stride > 1 and dilation > 1' f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).') self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride, dilation=dilation, groups=groups, bias=bias, causal=causal, norm=norm, norm_kwargs=norm_kwargs) self.causal = causal self.pad_mode = pad_mode def forward(self, x): B, C, T = x.shape kernel_size = self.conv.conv.kernel_size[0] stride = self.conv.conv.stride[0] dilation = self.conv.conv.dilation[0] kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations padding_total = kernel_size - stride extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total) if self.causal: # Left padding for causal x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode) else: # Asymmetric padding required for odd strides padding_right = padding_total // 2 padding_left = padding_total - padding_right x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode) return self.conv(x) ================================================ FILE: tts/modules/wavvae/encoder/common_modules/lstm.py ================================================ # MIT License # Copyright (c) Meta Platforms, Inc. and affiliates. # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Copyright (c) [2023] [Meta Platforms, Inc. and affiliates.] # Copyright (c) [2025] [Ziyue Jiang] # SPDX-License-Identifier: MIT # This file has been modified by Ziyue Jiang on 2025/03/19 # Original file was released under MIT, with the full license text # available at https://github.com/facebookresearch/encodec/blob/gh-pages/LICENSE. # This modified file is released under the same license. """LSTM layers module.""" from torch import nn class SLSTM(nn.Module): """ LSTM without worrying about the hidden state, nor the layout of the data. Expects input as convolutional layout. """ def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True): super().__init__() self.skip = skip self.lstm = nn.LSTM(dimension, dimension, num_layers) # 修改transpose顺序 def forward(self, x): x1 = x.permute(2, 0, 1) y, _ = self.lstm(x1) y = y.permute(1, 2, 0) if self.skip: y = y + x return y ================================================ FILE: tts/modules/wavvae/encoder/common_modules/seanet.py ================================================ # MIT License # Copyright (c) Meta Platforms, Inc. and affiliates. # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Copyright (c) [2023] [Meta Platforms, Inc. and affiliates.] # Copyright (c) [2025] [Ziyue Jiang] # SPDX-License-Identifier: MIT # This file has been modified by Ziyue Jiang on 2025/03/19 # Original file was released under MIT, with the full license text # available at https://github.com/facebookresearch/encodec/blob/gh-pages/LICENSE. # This modified file is released under the same license. """Encodec SEANet-based encoder and decoder implementation.""" import typing as tp import numpy as np import torch.nn as nn from .conv import SConv1d from .lstm import SLSTM class SEANetResnetBlock(nn.Module): def __init__(self, dim: int, kernel_sizes: tp.List[int] = [3, 1], dilations: tp.List[int] = [1, 1], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, causal: bool = False, pad_mode: str = 'reflect', compress: int = 2, true_skip: bool = True): super().__init__() assert len(kernel_sizes) == len(dilations), 'Number of kernel sizes should match number of dilations' act = getattr(nn, activation) hidden = dim // compress block = [] for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)): in_chs = dim if i == 0 else hidden out_chs = dim if i == len(kernel_sizes) - 1 else hidden block += [ act(**activation_params), SConv1d(in_chs, out_chs, kernel_size=kernel_size, dilation=dilation, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode), ] self.block = nn.Sequential(*block) self.shortcut: nn.Module if true_skip: self.shortcut = nn.Identity() else: self.shortcut = SConv1d(dim, dim, kernel_size=1, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) def forward(self, x): return self.shortcut(x) + self.block(x) class SEANetEncoder(nn.Module): def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 1, ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, pad_mode: str = 'reflect', true_skip: bool = False, compress: int = 2, lstm: int = 2): super().__init__() self.channels = channels self.dimension = dimension self.n_filters = n_filters self.ratios = list(reversed(ratios)) del ratios self.n_residual_layers = n_residual_layers self.hop_length = np.prod(self.ratios) act = getattr(nn, activation) mult = 1 model: tp.List[nn.Module] = [ SConv1d(channels, mult * n_filters, kernel_size, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) ] # Downsample to raw audio scale for i, ratio in enumerate(self.ratios): # Add residual layers for j in range(n_residual_layers): model += [ SEANetResnetBlock(mult * n_filters, kernel_sizes=[residual_kernel_size, 1], dilations=[dilation_base ** j, 1], norm=norm, norm_params=norm_params, activation=activation, activation_params=activation_params, causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)] # Add downsampling layers model += [ act(**activation_params), SConv1d(mult * n_filters, mult * n_filters * 2, kernel_size=ratio * 2, stride=ratio, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode), ] mult *= 2 if lstm: model += [SLSTM(mult * n_filters, num_layers=lstm)] model += [ act(**activation_params), SConv1d(mult * n_filters, dimension, last_kernel_size, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) ] self.model = nn.Sequential(*model) def forward(self, x): return self.model(x) ================================================ FILE: tts/utils/audio_utils/align.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch def mel2token_to_dur(mel2token, T_txt=None, max_dur=None): is_torch = isinstance(mel2token, torch.Tensor) has_batch_dim = True if not is_torch: mel2token = torch.LongTensor(mel2token) if T_txt is None: T_txt = mel2token.max() if len(mel2token.shape) == 1: mel2token = mel2token[None, ...] has_batch_dim = False B, _ = mel2token.shape dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token)) dur = dur[:, 1:] if max_dur is not None: dur = dur.clamp(max=max_dur) if not is_torch: dur = dur.numpy() if not has_batch_dim: dur = dur[0] return dur ================================================ FILE: tts/utils/audio_utils/io.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import io import os import subprocess import numpy as np from scipy.io import wavfile import pyloudnorm as pyln from pydub import AudioSegment def to_wav_bytes(wav, sr, norm=False): wav = wav.astype(float) if norm: meter = pyln.Meter(sr) # create BS.1770 meter loudness = meter.integrated_loudness(wav) wav = pyln.normalize.loudness(wav, loudness, -18.0) if np.abs(wav).max() >= 1: wav = wav / np.abs(wav).max() * 0.95 wav = wav * 32767 bytes_io = io.BytesIO() wavfile.write(bytes_io, sr, wav.astype(np.int16)) return bytes_io.getvalue() def save_wav(wav_bytes, path): with open(path[:-4] + '.wav', 'wb') as file: file.write(wav_bytes) if path[-4:] == '.mp3': to_mp3(path[:-4]) def to_mp3(out_path): if out_path[-4:] == '.wav': out_path = out_path[:-4] subprocess.check_call( f'ffmpeg -threads 1 -loglevel error -i "{out_path}.wav" -vn -b:a 192k -y -hide_banner -async 1 "{out_path}.mp3"', shell=True, stdin=subprocess.PIPE) subprocess.check_call(f'rm -f "{out_path}.wav"', shell=True) def convert_to_wav(wav_path): # Check if the file exists if not os.path.exists(wav_path): print(f"The file '{wav_path}' does not exist.") return # Check if the file already has a .wav extension if not wav_path.endswith(".wav"): # Define the output path with a .wav extension out_path = os.path.splitext(wav_path)[0] + ".wav" # Load the audio file using pydub and convert it to WAV audio = AudioSegment.from_file(wav_path) audio.export(out_path, format="wav") print(f"Converted '{wav_path}' to '{out_path}'") def convert_to_wav_bytes(audio_binary): # Load the audio binary using pydub and convert it to WAV audio = AudioSegment.from_file(io.BytesIO(audio_binary)) wav_bytes = io.BytesIO() audio.export(wav_bytes, format="wav") wav_bytes.seek(0) return wav_bytes ''' Smoothly combine audio segments using crossfade transitions." ''' def combine_audio_segments(segments, crossfade_duration=0.16, sr=24000): window_length = int(sr * crossfade_duration) hanning_window = np.hanning(2 * window_length) # Combine for i, segment in enumerate(segments): if i == 0: combined_audio = segment else: overlap = combined_audio[-window_length:] * hanning_window[window_length:] + segment[:window_length] * hanning_window[:window_length] combined_audio = np.concatenate( [combined_audio[:-window_length], overlap, segment[window_length:]] ) return combined_audio ================================================ FILE: tts/utils/audio_utils/plot.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import torch LINE_COLORS = ['w', 'r', 'orange', 'k', 'cyan', 'm', 'b', 'lime', 'g', 'brown', 'navy'] def spec_to_figure(spec, vmin=None, vmax=None, title='', f0s=None, dur_info=None, figsize=(12, 6)): if isinstance(spec, torch.Tensor): spec = spec.cpu().numpy() H = spec.shape[1] // 2 fig = plt.figure(figsize=figsize) plt.title(title) plt.pcolor(spec.T, vmin=vmin, vmax=vmax) if dur_info is not None: assert isinstance(dur_info, dict) txt = dur_info['txt'] dur_gt = dur_info['dur_gt'] if isinstance(dur_gt, torch.Tensor): dur_gt = dur_gt.cpu().numpy() dur_gt = np.cumsum(dur_gt).astype(int) for i in range(len(dur_gt)): shift = (i % 8) + 1 plt.text(dur_gt[i], shift * 4, txt[i]) plt.vlines(dur_gt[i], 0, H // 2, colors='b') # blue is gt plt.xlim(0, dur_gt[-1]) if 'dur_pred' in dur_info: dur_pred = dur_info['dur_pred'] if isinstance(dur_pred, torch.Tensor): dur_pred = dur_pred.cpu().numpy() dur_pred = np.cumsum(dur_pred).astype(int) for i in range(len(dur_pred)): shift = (i % 8) + 1 plt.text(dur_pred[i], H + shift * 4, txt[i]) plt.vlines(dur_pred[i], H, H * 1.5, colors='r') # red is pred plt.xlim(0, max(dur_gt[-1], dur_pred[-1])) if f0s is not None: ax = plt.gca() ax2 = ax.twinx() # ax.set_xticks() if not isinstance(f0s, dict): f0s = {'f0': f0s} for i, (k, f0) in enumerate(f0s.items()): if f0 is not None: if isinstance(f0, torch.Tensor): f0 = f0.cpu().numpy() ax2.plot( np.arange(len(f0)) + 0.5, f0, label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.5) ax2.set_ylim(0, 1000) ax2.legend() return fig def align_to_figure(align, dur_info): if isinstance(align, torch.Tensor): align = align.cpu().numpy() H = align.shape[1] fig = plt.figure(figsize=(12, 6)) plt.pcolor(align.T, vmin=0, vmax=1) if dur_info is not None: assert isinstance(dur_info, dict) txt = dur_info['txt'] dur_gt = dur_info['dur_gt'] if isinstance(dur_gt, torch.Tensor): dur_gt = dur_gt.cpu().numpy() dur_gt = np.cumsum(dur_gt).astype(int) // 2 for i in range(len(dur_gt)): plt.text(dur_gt[i], i, txt[i], color='red') plt.vlines(dur_gt[i], 0, H, colors='b') # blue is gt # plt.xlim(0, dur_gt[-1]) return fig ================================================ FILE: tts/utils/commons/ckpt_utils.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import glob import os import re import subprocess import traceback import torch from torch.nn.parallel import DistributedDataParallel import torch.distributed as dist @contextlib.contextmanager def dist_load(path): if not dist.is_initialized() or dist.get_world_size() == 1 or os.path.realpath(path).startswith('/dev/shm'): yield path else: from tts.utils.commons.hparams import hparams from tts.utils.commons.trainer import LOCAL_RANK tmpdir = '/dev/shm' assert len(os.path.basename(path)) > 0 shm_ckpt_path = f'{tmpdir}/{hparams["exp_name"]}/{os.path.basename(path)}' if LOCAL_RANK == 0: subprocess.check_call( f'mkdir -p {os.path.dirname(shm_ckpt_path)}; ' f'cp -Lr {path} {shm_ckpt_path}', shell=True) dist.barrier() yield shm_ckpt_path dist.barrier() if LOCAL_RANK == 0: subprocess.check_call(f'rm -rf {shm_ckpt_path}', shell=True) def torch_load_dist(path, map_location='cpu'): with dist_load(path) as tmp_path: checkpoint = torch.load(tmp_path, map_location=map_location) return checkpoint def get_last_checkpoint(work_dir, steps=None): checkpoint = None last_ckpt_path = None ckpt_paths = get_all_ckpts(work_dir, steps) if len(ckpt_paths) > 0: last_ckpt_path = ckpt_paths[0] checkpoint = torch_load_dist(last_ckpt_path, map_location='cpu') return checkpoint, last_ckpt_path def get_all_ckpts(work_dir, steps=None): if steps is None or steps == 0: ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_*.ckpt' else: ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_{steps}.ckpt' return sorted(glob.glob(ckpt_path_pattern), key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0])) def load_ckpt(cur_model, ckpt_base_dir, model_name='model', force=True, strict=True, silent=False, load_opt=False, opts=None, steps=None, checkpoint=None, ckpt_path='', delete_unmatch=True): if checkpoint is None: if os.path.isfile(ckpt_base_dir): base_dir = os.path.dirname(ckpt_base_dir) ckpt_path = ckpt_base_dir checkpoint = torch_load_dist(ckpt_base_dir, map_location='cpu') else: base_dir = ckpt_base_dir if load_opt: checkpoint, ckpt_path = get_last_checkpoint(ckpt_base_dir, steps) else: ckpt_path = f'{ckpt_base_dir}/model_only_last.ckpt' if os.path.exists(ckpt_path): checkpoint = torch_load_dist(ckpt_path, map_location='cpu') else: checkpoint, ckpt_path = get_last_checkpoint(ckpt_base_dir, steps) if checkpoint is not None: state_dict_all = { k.replace('module.', '').replace('_orig_mod.', ''): v for k, v in checkpoint["state_dict"].items()} if not isinstance(cur_model, list): cur_models = [cur_model] model_names = [model_name] else: cur_models = cur_model model_names = model_name for model_name, cur_model in zip(model_names, cur_models): if isinstance(cur_model, DistributedDataParallel): cur_model = cur_model.module device = next(cur_model.parameters()).device if '.' not in model_name: state_dict = state_dict_all[model_name] else: base_model_name = model_name.split('.')[0] rest_model_name = model_name[len(base_model_name) + 1:] state_dict = { k[len(rest_model_name) + 1:]: v for k, v in state_dict_all[base_model_name].items() if k.startswith(f'{rest_model_name}.')} state_dict = {k.replace('module.', '').replace('_orig_mod.', ''): v for k, v in state_dict.items()} if not strict and delete_unmatch: try: cur_model.load_state_dict(state_dict, strict=True) if not silent: print(f"| loaded '{model_name}' from '{ckpt_path}' with strict=True.") except: cur_model_state_dict = cur_model.state_dict() cur_model_state_dict = {k.replace('module.', '').replace('_orig_mod.', ''): v for k, v in cur_model_state_dict.items()} unmatched_keys = [] for key, param in state_dict.items(): if key in cur_model_state_dict: new_param = cur_model_state_dict[key] if new_param.shape != param.shape: unmatched_keys.append(key) print("| Unmatched keys: ", key, "cur model: ", new_param.shape, "ckpt model: ", param.shape) for key in unmatched_keys: del state_dict[key] load_results = cur_model.load_state_dict(state_dict, strict=strict) cur_model.to(device) if not silent: print(f"| loaded '{model_name}' from '{ckpt_path}'.") missing_keys, unexpected_keys = load_results.missing_keys, load_results.unexpected_keys print(f"| Missing keys: {len(missing_keys)}, Unexpected keys: {len(unexpected_keys)}") if load_opt: optimizer_states = checkpoint['optimizer_states'] assert len(opts) == len(optimizer_states) for optimizer, opt_state in zip(opts, optimizer_states): opt_state = {k.replace('_orig_mod.', ''): v for k, v in opt_state.items()} if optimizer is None: return try: optimizer.load_state_dict(opt_state) for i, state in enumerate(optimizer.state.values()): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.to(device) except ValueError: print(f"| WARMING: optimizer {optimizer} parameters not match !!!") return checkpoint.get('global_step', 0) else: e_msg = f"| ckpt not found in {base_dir}." if force: assert False, e_msg else: print(e_msg) def load_with_size_mismatch(model, state_dict, prefix=""): current_model_dict = model.state_dict() cm_keys = current_model_dict.keys() mismatch_keys = {k.replace(prefix, "") for k, v in state_dict.items() if k.replace(prefix, "") in cm_keys and v.size() != current_model_dict[k.replace(prefix, "")].size()} new_state_dict = {k.replace(prefix, ""): v for k, v in state_dict.items() if k.replace(prefix, "") in cm_keys and v.size() == current_model_dict[k.replace(prefix, "")].size()} missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False) print(f"| mismatch keys: ", mismatch_keys) if len(missing_keys) > 0: print(f"| missing_keys in dit: {missing_keys}") if len(unexpected_keys) > 0: print(f"| unexpected_keys in dit: {unexpected_keys}") ================================================ FILE: tts/utils/commons/hparams.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os import re import ast import yaml global_print_hparams = True hparams = {} class Args: def __init__(self, **kwargs): for k, v in kwargs.items(): self.__setattr__(k, v) def override_config(old_config: dict, new_config: dict): if new_config.get('__replace', False): old_config.clear() for k, v in new_config.items(): if isinstance(v, dict) and k in old_config: override_config(old_config[k], new_config[k]) else: old_config[k] = v def traverse_dict(d, func, ctx): for k in list(d.keys()): v = d[k] if isinstance(v, dict): traverse_dict(v, func, ctx) else: d[k] = func(v, ctx) def parse_config(v, context=None): if context is None: context = {} if isinstance(v, str): if v.startswith('^'): return load_config(v[1:], [], set()) match = re.match(r"\${(.*)}", v) if match: expression = match.group(1) return ast.literal_eval(expression, {}, context) return v def remove_meta_key(d): for k in list(d.keys()): v = d[k] if isinstance(v, dict): remove_meta_key(v) else: if k[:2] == '__': del d[k] def load_config(config_fn, config_chains, loaded_configs): # deep first inheritance and avoid the second visit of one node if not os.path.exists(config_fn): print(f"| WARN: {config_fn} not exist.", ) return {} with open(config_fn) as f: hparams_ = yaml.safe_load(f) loaded_configs.add(config_fn) if 'base_config' in hparams_: ret_hparams = {} if not isinstance(hparams_['base_config'], list): hparams_['base_config'] = [hparams_['base_config']] for c in hparams_['base_config']: if c.startswith('.'): c = f'{os.path.dirname(config_fn)}/{c}' c = os.path.normpath(c) if c not in loaded_configs: override_config(ret_hparams, load_config(c, config_chains, loaded_configs)) override_config(ret_hparams, hparams_) else: ret_hparams = hparams_ config_chains.append(config_fn) return ret_hparams def set_hparams(config='', exp_name='', hparams_str='', print_hparams=True, global_hparams=True): if config == '' and exp_name == '': parser = argparse.ArgumentParser(description='') parser.add_argument('--config', type=str, default='', help='location of the data corpus') parser.add_argument('--exp_name', type=str, default='', help='exp_name') parser.add_argument('-hp', '--hparams', type=str, default='', help='location of the data corpus') parser.add_argument('--infer', action='store_true', help='infer') parser.add_argument('--validate', action='store_true', help='validate') parser.add_argument('--reset', action='store_true', help='reset hparams') parser.add_argument('--remove', action='store_true', help='remove old ckpt') parser.add_argument('--debug', action='store_true', help='debug') parser.add_argument('--start_rank', type=int, default=-1, help='the start rank id for DDP, keep 0 when single-machine multi-GPU') parser.add_argument('--world_size', type=int, default=-1, help='the total number of GPU used across all machines, keep -1 for single-machine multi-GPU') parser.add_argument('--init_method', type=str, default='tcp', help='method to init ddp, use tcp or file') parser.add_argument('--master_addr', type=str, default='', help='') parser.add_argument('--ddp_dir', type=str, default='', help='') args, unknown = parser.parse_known_args() if print_hparams: print("| set_hparams Unknow hparams: ", unknown) else: args = Args(config=config, exp_name=exp_name, hparams=hparams_str, infer=False, validate=False, reset=False, debug=False, remove=False, start_rank=-1, world_size=-1, init_method='tcp', ddp_dir='', master_addr='') global hparams assert args.config != '' or args.exp_name != '' if args.config != '': assert os.path.exists(args.config), f"{args.config} not exists" saved_hparams = {} args_work_dir = '' if args.exp_name != '': args_work_dir = f'{args.exp_name}' ckpt_config_path = f'{args_work_dir}/config.yaml' if os.path.exists(ckpt_config_path): with open(ckpt_config_path) as f: saved_hparams_ = yaml.safe_load(f) if saved_hparams_ is not None: saved_hparams.update(saved_hparams_) hparams_ = {} config_chains = [] if args.config != '': hparams_.update(load_config(args.config, config_chains, set())) if len(config_chains) > 1 and print_hparams: print('| Hparams chains: ', config_chains) if not args.reset: hparams_.update(saved_hparams) traverse_dict(hparams_, parse_config, hparams_) hparams_['work_dir'] = args_work_dir # Support config overriding in command line. Support list type config overriding. # Examples: --hparams="a=1,b.c=2,d=[1 1 1]" if args.hparams != "": for new_hparam in args.hparams.split(","): k, v = new_hparam.split("=") v = v.strip("\'\" ") config_node = hparams_ for k_ in k.split(".")[:-1]: config_node = config_node[k_] k = k.split(".")[-1] if k in config_node: if v in ['True', 'False'] or type(config_node[k]) in [bool, list, dict]: if type(config_node[k]) == list: v = v.replace(" ", ",").replace('^', "\"") if '|' in v: tp = type(config_node[k][0]) if len(config_node[k]) else str config_node[k] = [tp(x) for x in v.split("|") if x != ''] continue config_node[k] = ast.literal_eval(v) else: config_node[k] = type(config_node[k])(v) else: config_node[k] = v try: config_node[k] = float(v) except: pass try: config_node[k] = int(v) except: pass if v.lower() in ['false', 'true']: config_node[k] = v.lower() == 'true' if args_work_dir != '' and not args.infer: os.makedirs(hparams_['work_dir'], exist_ok=True) hparams_['infer'] = args.infer hparams_['debug'] = args.debug hparams_['validate'] = args.validate hparams_['exp_name'] = args.exp_name hparams_['start_rank'] = args.start_rank # useful for multi-machine training hparams_['world_size'] = args.world_size hparams_['init_method'] = args.init_method hparams_['ddp_dir'] = args.ddp_dir hparams_['master_addr'] = args.master_addr remove_meta_key(hparams_) global global_print_hparams if global_hparams: hparams.clear() hparams.update(hparams_) if print_hparams and global_print_hparams and global_hparams: print('| Hparams: ', json.dumps(hparams_, indent=2, sort_keys=True)) # for i, (k, v) in enumerate(sorted(hparams_.items())): # print(f"\033[;33;m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "") global_print_hparams = False return hparams_ ================================================ FILE: tts/utils/text_utils/dict.json ================================================ {"phone": ["C0a", "C0ai", "C0air", "C0an", "C0ang", "C0angr", "C0anr", "C0ao", "C0aor", "C0ar", "C0b", "C0c", "C0ch", "C0d", "C0e", "C0ei", "C0eir", "C0en", "C0eng", "C0engr", "C0enr", "C0er", "C0f", "C0g", "C0h", "C0i", "C0ia", "C0ian", "C0iang", "C0iangr", "C0ianr", "C0iao", "C0iaor", "C0iar", "C0ie", "C0ier", "C0ii", "C0iii", "C0iiir", "C0iir", "C0in", "C0ing", "C0ingr", "C0inr", "C0io", "C0iong", "C0iongr", "C0iou", "C0iour", "C0ir", "C0j", "C0k", "C0l", "C0m", "C0n", "C0ng", "C0o", "C0ong", "C0ongr", "C0or", "C0ou", "C0our", "C0p", "C0q", "C0r", "C0s", "C0sh", "C0t", "C0u", "C0ua", "C0uai", "C0uair", "C0uan", "C0uang", "C0uangr", "C0uanr", "C0uar", "C0uei", "C0ueir", "C0uen", "C0ueng", "C0uengr", "C0uenr", "C0uo", "C0uor", "C0ur", "C0v", "C0van", "C0vanr", "C0ve", "C0ver", "C0vn", "C0vnr", "C0vr", "C0x", "C0z", "C0zh", "C0＿", "E0aa", "E0ae", "E0ah", "E0ao", "E0aw", "E0ax", "E0ay", "E0b", "E0ch", "E0d", "E0dh", "E0eh", "E0ehr", "E0er", "E0ey", "E0f", "E0g", "E0hh", "E0ih", "E0iy", "E0iyr", "E0jh", "E0k", "E0l", "E0m", "E0n", "E0ng", "E0oh", "E0ow", "E0oy", "E0p", "E0r", "E0s", "E0sh", "E0t", "E0th", "E0uh", "E0uw", "E0uwr", "E0v", "E0w", "E0y", "E0z", "E0zh", "sil", "…", "、", "。", "《", "》", "【", "】", "！", "＂", "＃", "＄", "％", "＇", "＇＇", "（", "）", "＊", "，", "：", "；", "？", "＼", "＾", "＿", "｀", "｛", "｝", "～"], "tone": ["0", "1", "10", "11", "12", "13", "15", "17", "2", "3", "4", "5", "6", "7", "8", "9"], "wordCategory": ["0", "B", "E", "M", "S"], "prosody": ["0", "1", "2", "3", "4"], "focus": ["0", "1"], "intonation": ["0", "1", "2"], "phraseAccent": ["0", "H-", "L-"], "boundaryTone": ["0", "H%", "L%"], "accentType": ["!H*", "0", "H*", "L*", "L*+H", "L+H*"]} ================================================ FILE: tts/utils/text_utils/ph_tone_convert.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn.functional as F def map_phone_to_tokendict(item, pad_bos_eos=True): # Merge Chinese phone and tone (Original dict ends at 173, i.e., ph_dict_size=173). 146~173 is punctuations. phone = item['txt_token'].clone() merged_phone = item['txt_token'].clone() tone_tmp = item['tone'].clone() # In tone_dict, tone_1 is 4, tone_2 is 11, tone_3 is 12, tone_4 is 13, tone_5 is 14, tone_6 is 15 tone_tmp[tone_tmp==4] = 1 tone_tmp[tone_tmp==11] = 2 tone_tmp[tone_tmp==12] = 3 tone_tmp[tone_tmp==13] = 4 tone_tmp[tone_tmp==14] = 5 tone_tmp[tone_tmp==15] = 6 # Chinese phones lie in 3~100 in the phone_dict, we map them to 200~788 ch_phone_idx = (phone >= 3) & (phone <= 100) merged_phone[ch_phone_idx] = (merged_phone[ch_phone_idx] - 3) * 6 + 200 + tone_tmp[ch_phone_idx] if pad_bos_eos: merged_phone = F.pad(merged_phone, (1, 0), mode='constant', value=798) merged_phone = F.pad(merged_phone, (0, 1), mode='constant', value=799) return merged_phone def split_ph_timestamp(ph_timestamp): ''' Input: ph_timestamp, shape [T] ''' # Map the timestamp of each phone back to its original frame-level lengths ph_timestamp[ph_timestamp >= 800] -= 800 ph_list = [] tone_list = [] dur_list = [] cur_timestamp = 0 for idx, item in enumerate(ph_timestamp): if idx % 2 == 0: # Map Chinese phones back to its original phone_dict if (200 <= item <= 788): ph = (item - 200 - 1) // 6 + 3 tone = (item - 200 - 1) % 6 + 1 if tone == 1: tone = 4 else: tone = tone + 9 # Set English tone to '3' else: ph = item tone = 3 ph_list.append(ph) tone_list.append(tone) else: dur_list.append((item - cur_timestamp)) cur_timestamp = item assert len(ph_list) == len(dur_list), f"{len(ph_list)}, {len(dur_list)}" ph_seq, tone_seq, dur_seq = torch.LongTensor(ph_list), torch.LongTensor(tone_list), torch.LongTensor(dur_list) return ph_seq, tone_seq, dur_seq, ph_timestamp[-1] def split_ph(ph_seq): ''' Input: ph_timestamp, shape [T] ''' ph_list = [] tone_list = [] for idx, item in enumerate(ph_seq): # Map Chinese phones back to its original phone_dict if (200 <= item <= 788): ph = (item - 200 - 1) // 6 + 3 tone = (item - 200 - 1) % 6 + 1 if tone == 1: tone = 4 else: tone = tone + 9 # Set English tone to '3' else: ph = item tone = 3 ph_list.append(ph) tone_list.append(tone) assert len(ph_list) == len(tone_list) ph_seq, tone_seq = torch.LongTensor(ph_list), torch.LongTensor(tone_list) return ph_seq, tone_seq ================================================ FILE: tts/utils/text_utils/split_text.py ================================================ # -*- coding: utf-8 -*- # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re def chunk_text_chinese(text, limit=60): # 中文字符匹配 chinese_pattern = re.compile(r'[\u4e00-\u9fff]') # 标点符号匹配 punctuation = r"，。！？；：,.!?;:" result = [] # 存储断句结果 current_chunk = [] # 当前片段 chinese_count = 0 # 中文字符计数 i = 0 while i < len(text): char = text[i] current_chunk.append(char) if chinese_pattern.match(char): chinese_count += 1 if chinese_count >= limit: # 达到限制字符数 # 从当前位置往前找最近的标点符号 for j in range(len(current_chunk) - 1, -1, -1): if current_chunk[j] in punctuation: result.append(''.join(current_chunk[:j + 1])) current_chunk = current_chunk[j + 1:] chinese_count = sum(1 for c in current_chunk if chinese_pattern.match(c)) break else: # 如果前面没有标点符号，则继续找后面的标点符号 for k in range(i + 1, len(text)): if text[k] in punctuation: result.append(''.join(current_chunk)+text[i+1:k+1]) current_chunk = [] chinese_count = 0 i = k break i+=1 # 添加最后剩余的部分 if current_chunk: result.append(''.join(current_chunk)) return result def chunk_text_english(text, max_chars=130): """ Splits the input text into chunks, each with a maximum number of characters. Args: text (str): The text to be split. max_chars (int): The maximum number of characters per chunk. Returns: List[str]: A list of text chunks. """ chunks = [] current_chunk = "" # Split the text into sentences based on punctuation followed by whitespace sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text) for sentence in sentences: if len(current_chunk.encode("utf-8")) + len(sentence.encode("utf-8")) <= max_chars: current_chunk += sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence else: if current_chunk: chunks.append(current_chunk.strip()) current_chunk = sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence if current_chunk: chunks.append(current_chunk.strip()) return chunks def chunk_text_chinesev2(text, limit=60, look_ahead_limit=30): """ 将中文文本分成多个块，优先确保每个块以句号、感叹号或问号结尾， 其次考虑逗号等其他标点符号，避免在无标点处断句 参数: text: 要分块的文本 limit: 每个块的中文字符数限制 look_ahead_limit: 向后查找的最大字符数限制 返回: 分块后的文本列表 """ # 中文字符匹配 chinese_pattern = re.compile(r'[\u4e00-\u9fff]') # 分级定义标点符号（优先级从高到低） primary_end_marks = "。.！!？?" # 首选：句号、感叹号、问号 secondary_end_marks = "，,；;：" # 次选：逗号、分号、冒号 tertiary_end_marks = "、…—-~～" # 再次：顿号、省略号、破折号等 result = [] # 存储断句结果 current_chunk = [] # 当前片段 chinese_count = 0 # 中文字符计数 i = 0 while i < len(text): char = text[i] current_chunk.append(char) if chinese_pattern.match(char): chinese_count += 1 if chinese_count >= limit: # 达到字符数限制，需要寻找断句点 found_end = False # 依次尝试不同优先级的断句策略 # 1. 向后查找首选标点 for k in range(1, min(look_ahead_limit, len(text) - i)): next_char = text[i + k] if next_char in primary_end_marks: result.append(''.join(current_chunk) + text[i+1:i+k+1]) current_chunk = [] chinese_count = 0 i = i + k found_end = True break if not found_end: # 2. 向前查找首选标点 for j in range(len(current_chunk) - 1, -1, -1): if current_chunk[j] in primary_end_marks: result.append(''.join(current_chunk[:j + 1])) current_chunk = current_chunk[j + 1:] chinese_count = sum(1 for c in current_chunk if chinese_pattern.match(c)) found_end = True break if not found_end: # 3. 向后查找次选标点 for k in range(1, min(look_ahead_limit, len(text) - i)): next_char = text[i + k] if next_char in secondary_end_marks: result.append(''.join(current_chunk) + text[i+1:i+k+1]) current_chunk = [] chinese_count = 0 i = i + k found_end = True break if not found_end: # 4. 向前查找次选标点 for j in range(len(current_chunk) - 1, -1, -1): if current_chunk[j] in secondary_end_marks: result.append(''.join(current_chunk[:j + 1])) current_chunk = current_chunk[j + 1:] chinese_count = sum(1 for c in current_chunk if chinese_pattern.match(c)) found_end = True break if not found_end: # 5. 向后查找三级标点 for k in range(1, min(look_ahead_limit, len(text) - i)): next_char = text[i + k] if next_char in tertiary_end_marks: result.append(''.join(current_chunk) + text[i+1:i+k+1]) current_chunk = [] chinese_count = 0 i = i + k found_end = True break if not found_end: # 6. 向前查找三级标点 for j in range(len(current_chunk) - 1, -1, -1): if current_chunk[j] in tertiary_end_marks: result.append(''.join(current_chunk[:j + 1])) current_chunk = current_chunk[j + 1:] chinese_count = sum(1 for c in current_chunk if chinese_pattern.match(c)) found_end = True break if not found_end: # 万不得已，在此处断句（这种情况很少见，因为汉语文本中通常会有标点） result.append(''.join(current_chunk)) current_chunk = [] chinese_count = 0 i += 1 # 添加最后剩余的部分 if current_chunk: result.append(''.join(current_chunk)) # 英文标点替换为中文标点 punctuation_map = { '.': '。', ',': '，', '!': '！', '?': '？', ';': '；', ':': '：' } for i in range(len(result)): for eng_punc, cn_punc in punctuation_map.items(): result[i] = result[i].replace(eng_punc, cn_punc) return result if __name__ == '__main__': print(chunk_text_chinese("哇塞！家人们，你们太好运了。我居然发现了一个宝藏零食大礼包，简直适合所有人的口味！有香辣的，让你舌尖跳舞；有盐焗的，咸香可口；还有五香的，香气四溢。就连怀孕的姐妹都吃得津津有味！整整三十包啊！什么手撕蟹柳、辣子鸡、嫩豆干、手撕素肉、鹌鹑蛋、小肉枣肠、猪肉腐、魔芋、魔芋丝等等，应有尽有。香辣土豆爽辣过瘾，各种素肉嚼劲十足，鹌鹑蛋营养美味，真的太多太多啦，...家人们，现在价格太划算了，赶紧下单。")) print(chunk_text_english("Washington CNN When President Donald Trump declared in the House Chamber this week that executives at the nation’s top automakers were “so excited” about their prospects amid his new tariff regime, it did not entirely reflect the conversation he’d held with them earlier that day.")) text = "欢迎收听《TED Talks Daily》，在这里，我们每天为您带来新思想，激发您的好奇心。我是您的主持人，Elise Hugh。当我们去看医生时，医生会评估我们的身体健康状况，检查我们的生命体征，可能还会关注我们的胆固醇水平，确保我们整体处于健康状态。医生可能还会通过一系列问题来检查我们的心理健康。然而，人际交往专家Casley Killam指出，我们在理解健康时忽略了一个关键指标，那就是我们的社会健康。在2024年的演讲中，她解释了为什么人际关系如此重要，以及忽视它可能带来的代价。几年前，我认识的一位女士，我们暂且称她为Maya，在短时间内经历了许多重大变化。她结婚了，和丈夫因工作搬到了一个陌生的城市，在那里她谁也不认识。她开始了一份在家办公的新工作，同时还要应对父亲新确诊的痴呆症。为了应对这些变化带来的压力，Maya加倍关注自己的身心健康。她几乎每天都锻炼，吃健康的食物，每周去看一次心理医生。这些措施确实有帮助，她的身体变得更加强壮，心理也更具韧性，但效果有限。她仍然感到困扰，经常在半夜失眠，白天感到注意力不集中，缺乏动力。Maya做了医生通常建议我们做的所有事情来保持身心健康，但似乎还缺少些什么。如果我告诉你，Maya所缺少的东西，也是全球数十亿人所缺少的，甚至可能也是你所缺少的呢？如果我告诉你，缺乏它会削弱我们为保持健康所做的其他努力，甚至可能缩短你的寿命呢？我研究这个问题已经超过十年，我发现，我们传统上对健康的理解是不完整的。通过将健康主要视为身体和心理的健康，我们忽略了我认为是我们这个时代最大的挑战和机遇——社会健康。身体健康关乎我们的身体，心理健康关乎我们的思想，而社会健康则关乎我们的人际关系。如果你以前没有听说过这个词，那是因为它还没有进入主流词汇，但它同样重要。Maya在她的新家还没有归属感。她不再亲自见到她的家人、朋友或同事，她经常一连几周只和丈夫共度时光。她的故事告诉我们，如果我们只照顾身体和心理，而不关注人际关系，我们就无法完全健康，无法真正茁壮成长。与Maya类似，全球有数亿人连续几周不与任何朋友或家人交谈。全球范围内，有四分之一的人感到孤独。20%的成年人觉得他们没有任何人可以求助。想想看，你遇到的每五个人中，可能有一个人觉得自己孤立无援。这不仅令人心碎，也是一场公共卫生危机。" for res in chunk_text_chinesev2(text): print(res) ================================================ FILE: tts/utils/text_utils/text_encoder.py ================================================ # Copyright 2025 ByteDance and/or its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import re import six from six.moves import range # pylint: disable=redefined-builtin PAD = "" EOS = "" UNK = "" SEG = "|" PUNCS = '!,.?;:' RESERVED_TOKENS = [PAD, EOS, UNK] NUM_RESERVED_TOKENS = len(RESERVED_TOKENS) PAD_ID = RESERVED_TOKENS.index(PAD) # Normally 0 EOS_ID = RESERVED_TOKENS.index(EOS) # Normally 1 UNK_ID = RESERVED_TOKENS.index(UNK) # Normally 2 if six.PY2: RESERVED_TOKENS_BYTES = RESERVED_TOKENS else: RESERVED_TOKENS_BYTES = [bytes(PAD, "ascii"), bytes(EOS, "ascii")] # Regular expression for unescaping token strings. # '\u' is converted to '_' # '\\' is converted to '\' # '\213;' is converted to unichr(213) _UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);") _ESCAPE_CHARS = set(u"\\_u;0123456789") def strip_ids(ids, ids_to_strip): """Strip ids_to_strip from the end ids.""" ids = list(ids) while ids and ids[-1] in ids_to_strip: ids.pop() return ids class TextEncoder(object): """Base class for converting from ints to/from human readable strings.""" def __init__(self, num_reserved_ids=NUM_RESERVED_TOKENS): self._num_reserved_ids = num_reserved_ids @property def num_reserved_ids(self): return self._num_reserved_ids def encode(self, s): """Transform a human-readable string into a sequence of int ids. The ids should be in the range [num_reserved_ids, vocab_size). Ids [0, num_reserved_ids) are reserved. EOS is not appended. Args: s: human-readable string to be converted. Returns: ids: list of integers """ return [int(w) + self._num_reserved_ids for w in s.split()] def decode(self, ids, strip_extraneous=False): """Transform a sequence of int ids into a human-readable string. EOS is not expected in ids. Args: ids: list of integers to be converted. strip_extraneous: bool, whether to strip off extraneous tokens (EOS and PAD). Returns: s: human-readable string. """ if strip_extraneous: ids = strip_ids(ids, list(range(self._num_reserved_ids or 0))) return " ".join(self.decode_list(ids)) def decode_list(self, ids): """Transform a sequence of int ids into a their string versions. This method supports transforming individual input/output ids to their string versions so that sequence to/from text conversions can be visualized in a human readable format. Args: ids: list of integers to be converted. Returns: strs: list of human-readable string. """ decoded_ids = [] for id_ in ids: if 0 <= id_ < self._num_reserved_ids: decoded_ids.append(RESERVED_TOKENS[int(id_)]) else: decoded_ids.append(id_ - self._num_reserved_ids) return [str(d) for d in decoded_ids] @property def vocab_size(self): raise NotImplementedError() class TokenTextEncoder(TextEncoder): """Encoder based on a user-supplied vocabulary (file or list).""" def __init__(self, vocab_filename, reverse=False, vocab_list=None, replace_oov=None, num_reserved_ids=NUM_RESERVED_TOKENS): """Initialize from a file or list, one token per line. Handling of reserved tokens works as follows: - When initializing from a list, we add reserved tokens to the vocab. - When initializing from a file, we do not add reserved tokens to the vocab. - When saving vocab files, we save reserved tokens to the file. Args: vocab_filename: If not None, the full filename to read vocab from. If this is not None, then vocab_list should be None. reverse: Boolean indicating if tokens should be reversed during encoding and decoding. vocab_list: If not None, a list of elements of the vocabulary. If this is not None, then vocab_filename should be None. replace_oov: If not None, every out-of-vocabulary token seen when encoding will be replaced by this string (which must be in vocab). num_reserved_ids: Number of IDs to save for reserved tokens like . """ super(TokenTextEncoder, self).__init__(num_reserved_ids=num_reserved_ids) self._reverse = reverse self._replace_oov = replace_oov if vocab_filename: self._init_vocab_from_file(vocab_filename) else: assert vocab_list is not None self._init_vocab_from_list(vocab_list) self.pad_index = self.token_to_id[PAD] self.eos_index = self.token_to_id[EOS] self.unk_index = self.token_to_id[UNK] self.seg_index = self.token_to_id[SEG] if SEG in self.token_to_id else self.eos_index def encode(self, s): """Converts a space-separated string of tokens to a list of ids.""" if isinstance(s, str): sentence = s tokens = sentence.strip().split() else: tokens = s if self._replace_oov is not None: tokens = [t if t in self.token_to_id else self._replace_oov for t in tokens] ret = [self.token_to_id[tok] for tok in tokens] return ret[::-1] if self._reverse else ret def decode(self, ids, strip_eos=False, strip_padding=False): if strip_padding and self.pad() in list(ids): pad_pos = list(ids).index(self.pad()) ids = ids[:pad_pos] if strip_eos and self.eos() in list(ids): eos_pos = list(ids).index(self.eos()) ids = ids[:eos_pos] return " ".join(self.decode_list(ids)) def decode_list(self, ids): seq = reversed(ids) if self._reverse else ids return [self._safe_id_to_token(i) for i in seq] @property def vocab_size(self): return len(self.id_to_token) def __len__(self): return self.vocab_size def _safe_id_to_token(self, idx): return self.id_to_token.get(idx, "ID_%d" % idx) def _init_vocab_from_file(self, filename): """Load vocab from a file. Args: filename: The file to load vocabulary from. """ with open(filename) as f: tokens = [token.strip() for token in f.readlines()] def token_gen(): for token in tokens: yield token self._init_vocab(token_gen(), add_reserved_tokens=False) def _init_vocab_from_list(self, vocab_list): """Initialize tokens from a list of tokens. It is ok if reserved tokens appear in the vocab list. They will be removed. The set of tokens in vocab_list should be unique. Args: vocab_list: A list of tokens. """ def token_gen(): for token in vocab_list: if token not in RESERVED_TOKENS: yield token self._init_vocab(token_gen()) def _init_vocab(self, token_generator, add_reserved_tokens=True): """Initialize vocabulary with tokens from token_generator.""" self.id_to_token = {} non_reserved_start_index = 0 if add_reserved_tokens: self.id_to_token.update(enumerate(RESERVED_TOKENS)) non_reserved_start_index = len(RESERVED_TOKENS) self.id_to_token.update( enumerate(token_generator, start=non_reserved_start_index)) # _token_to_id is the reverse of _id_to_token self.token_to_id = dict((v, k) for k, v in six.iteritems(self.id_to_token)) def pad(self): return self.pad_index def eos(self): return self.eos_index def unk(self): return self.unk_index def seg(self): return self.seg_index def store_to_file(self, filename): """Write vocab file to disk. Vocab files have one token per line. The file ends in a newline. Reserved tokens are written to the vocab file as well. Args: filename: Full path of the file to store the vocab to. """ with open(filename, "w") as f: for i in range(len(self.id_to_token)): f.write(self.id_to_token[i] + "\n") def sil_phonemes(self): return [p for p in self.id_to_token.values() if is_sil_phoneme(p)] def build_token_encoder(token_list_file): token_list = json.load(open(token_list_file)) return TokenTextEncoder(None, vocab_list=token_list, replace_oov='') def is_sil_phoneme(p): return p == '' or not p[0].isalpha() or p == 'sil' or p == 'sp' or p == 'XX' ================================================ FILE: workflow-examples/单人语音.json ================================================ 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