├── ChatTTS ├── __init__.py ├── utils │ ├── io_utils.py │ ├── gpu_utils.py │ └── infer_utils.py ├── experimental │ └── llm.py ├── infer │ └── api.py ├── model │ ├── dvae.py │ └── gpt.py └── core.py ├── requirements.txt ├── .gitignore ├── webui.py ├── README_CN.md ├── README.md └── LICENSE /ChatTTS/__init__.py: -------------------------------------------------------------------------------- 1 | from .core import Chat -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | omegaconf~=2.3.0 2 | torch~=2.1.0 3 | tqdm 4 | einops 5 | vector_quantize_pytorch 6 | transformers~=4.41.1 7 | vocos 8 | IPython -------------------------------------------------------------------------------- /ChatTTS/utils/io_utils.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | import logging 4 | 5 | def get_latest_modified_file(directory): 6 | logger = logging.getLogger(__name__) 7 | 8 | files = [os.path.join(directory, f) for f in os.listdir(directory)] 9 | if not files: 10 | logger.log(logging.WARNING, f'No files found in the directory: {directory}') 11 | return None 12 | latest_file = max(files, key=os.path.getmtime) 13 | 14 | return latest_file -------------------------------------------------------------------------------- /ChatTTS/utils/gpu_utils.py: -------------------------------------------------------------------------------- 1 | 2 | import torch 3 | import logging 4 | 5 | def select_device(min_memory = 2048): 6 | logger = logging.getLogger(__name__) 7 | if torch.cuda.is_available(): 8 | available_gpus = [] 9 | for i in range(torch.cuda.device_count()): 10 | props = torch.cuda.get_device_properties(i) 11 | free_memory = props.total_memory - torch.cuda.memory_reserved(i) 12 | available_gpus.append((i, free_memory)) 13 | selected_gpu, max_free_memory = max(available_gpus, key=lambda x: x[1]) 14 | device = torch.device(f'cuda:{selected_gpu}') 15 | free_memory_mb = max_free_memory / (1024 * 1024) 16 | if free_memory_mb < min_memory: 17 | logger.log(logging.WARNING, f'GPU {selected_gpu} has {round(free_memory_mb, 2)} MB memory left.') 18 | device = torch.device('cpu') 19 | else: 20 | logger.log(logging.WARNING, f'No GPU found, use CPU instead') 21 | device = torch.device('cpu') 22 | 23 | return device 24 | -------------------------------------------------------------------------------- /ChatTTS/experimental/llm.py: -------------------------------------------------------------------------------- 1 | 2 | from openai import OpenAI 3 | 4 | prompt_dict = { 5 | 'kimi': [ {"role": "system", "content": "你是 Kimi，由 Moonshot AI 提供的人工智能助手，你更擅长中文和英文的对话。"}, 6 | {"role": "user", "content": "你好，请注意你现在生成的文字要按照人日常生活的口吻，你的回复将会后续用TTS模型转为语音，并且请把回答控制在100字以内。并且标点符号仅包含逗号和句号，将数字等转为文字回答。"}, 7 | {"role": "assistant", "content": "好的，我现在生成的文字将按照人日常生活的口吻， 并且我会把回答控制在一百字以内, 标点符号仅包含逗号和句号，将阿拉伯数字等转为中文文字回答。下面请开始对话。"},], 8 | 'deepseek': [ 9 | {"role": "system", "content": "You are a helpful assistant"}, 10 | {"role": "user", "content": "你好，请注意你现在生成的文字要按照人日常生活的口吻，你的回复将会后续用TTS模型转为语音，并且请把回答控制在100字以内。并且标点符号仅包含逗号和句号，将数字等转为文字回答。"}, 11 | {"role": "assistant", "content": "好的，我现在生成的文字将按照人日常生活的口吻， 并且我会把回答控制在一百字以内, 标点符号仅包含逗号和句号，将阿拉伯数字等转为中文文字回答。下面请开始对话。"},], 12 | 'deepseek_TN': [ 13 | {"role": "system", "content": "You are a helpful assistant"}, 14 | {"role": "user", "content": "你好，现在我们在处理TTS的文本输入，下面将会给你输入一段文本，请你将其中的阿拉伯数字等等转为文字表达，并且输出的文本里仅包含逗号和句号这两个标点符号"}, 15 | {"role": "assistant", "content": "好的，我现在对TTS的文本输入进行处理。这一般叫做text normalization。下面请输入"}, 16 | {"role": "user", "content": "We paid $123 for this desk."}, 17 | {"role": "assistant", "content": "We paid one hundred and twenty three dollars for this desk."}, 18 | {"role": "user", "content": "详询请拨打010-724654"}, 19 | {"role": "assistant", "content": "详询请拨打零幺零，七二四六五四"}, 20 | {"role": "user", "content": "罗森宣布将于7月24日退市，在华门店超6000家！"}, 21 | {"role": "assistant", "content": "罗森宣布将于七月二十四日退市，在华门店超过六千家。"}, 22 | ], 23 | } 24 | 25 | class llm_api: 26 | def __init__(self, api_key, base_url, model): 27 | self.client = OpenAI( 28 | api_key = api_key, 29 | base_url = base_url, 30 | ) 31 | self.model = model 32 | def call(self, user_question, temperature = 0.3, prompt_version='kimi', **kwargs): 33 | 34 | completion = self.client.chat.completions.create( 35 | model = self.model, 36 | messages = prompt_dict[prompt_version]+[{"role": "user", "content": user_question},], 37 | temperature = temperature, 38 | **kwargs 39 | ) 40 | return completion.choices[0].message.content 41 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | *.ckpt 6 | # C extensions 7 | *.so 8 | *.pt 9 | 10 | # Distribution / packaging 11 | .Python 12 | outputs/ 13 | build/ 14 | develop-eggs/ 15 | dist/ 16 | downloads/ 17 | eggs/ 18 | .eggs/ 19 | lib/ 20 | lib64/ 21 | parts/ 22 | sdist/ 23 | var/ 24 | wheels/ 25 | share/python-wheels/ 26 | *.egg-info/ 27 | asset/* 28 | .installed.cfg 29 | *.egg 30 | MANIFEST 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .nox/ 46 | .coverage 47 | .coverage.* 48 | .cache 49 | nosetests.xml 50 | coverage.xml 51 | *.cover 52 | *.py,cover 53 | .hypothesis/ 54 | .pytest_cache/ 55 | cover/ 56 | 57 | # Translations 58 | *.mo 59 | *.pot 60 | 61 | # Django stuff: 62 | *.log 63 | local_settings.py 64 | db.sqlite3 65 | db.sqlite3-journal 66 | 67 | # Flask stuff: 68 | instance/ 69 | .webassets-cache 70 | 71 | # Scrapy stuff: 72 | .scrapy 73 | 74 | # Sphinx documentation 75 | docs/_build/ 76 | 77 | # PyBuilder 78 | .pybuilder/ 79 | target/ 80 | 81 | # Jupyter Notebook 82 | .ipynb_checkpoints 83 | 84 | # IPython 85 | profile_default/ 86 | ipython_config.py 87 | 88 | # pyenv 89 | # For a library or package, you might want to ignore these files since the code is 90 | # intended to run in multiple environments; otherwise, check them in: 91 | # .python-version 92 | 93 | # pipenv 94 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 95 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 96 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 97 | # install all needed dependencies. 98 | #Pipfile.lock 99 | 100 | # poetry 101 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 102 | # This is especially recommended for binary packages to ensure reproducibility, and is more 103 | # commonly ignored for libraries. 104 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 105 | #poetry.lock 106 | 107 | # pdm 108 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 109 | #pdm.lock 110 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 111 | # in version control. 112 | # https://pdm.fming.dev/#use-with-ide 113 | .pdm.toml 114 | 115 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 116 | __pypackages__/ 117 | 118 | # Celery stuff 119 | celerybeat-schedule 120 | celerybeat.pid 121 | 122 | # SageMath parsed files 123 | *.sage.py 124 | 125 | # Environments 126 | .env 127 | .venv 128 | env/ 129 | venv/ 130 | ENV/ 131 | env.bak/ 132 | venv.bak/ 133 | 134 | # Spyder project settings 135 | .spyderproject 136 | .spyproject 137 | 138 | # Rope project settings 139 | .ropeproject 140 | 141 | # mkdocs documentation 142 | /site 143 | 144 | # mypy 145 | .mypy_cache/ 146 | .dmypy.json 147 | dmypy.json 148 | 149 | # Pyre type checker 150 | .pyre/ 151 | 152 | # pytype static type analyzer 153 | .pytype/ 154 | 155 | # Cython debug symbols 156 | cython_debug/ 157 | 158 | # PyCharm 159 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 160 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 161 | # and can be added to the global gitignore or merged into this file. For a more nuclear 162 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 163 | #.idea/ 164 | -------------------------------------------------------------------------------- /ChatTTS/utils/infer_utils.py: -------------------------------------------------------------------------------- 1 | 2 | import re 3 | import torch 4 | import torch.nn.functional as F 5 | 6 | 7 | class CustomRepetitionPenaltyLogitsProcessorRepeat(): 8 | 9 | def __init__(self, penalty: float, max_input_ids, past_window): 10 | if not isinstance(penalty, float) or not (penalty > 0): 11 | raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}") 12 | 13 | self.penalty = penalty 14 | self.max_input_ids = max_input_ids 15 | self.past_window = past_window 16 | 17 | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: 18 | 19 | input_ids = input_ids[:, -self.past_window:] 20 | freq = F.one_hot(input_ids, scores.size(1)).sum(1) 21 | freq[self.max_input_ids:] = 0 22 | alpha = self.penalty**freq 23 | scores = torch.where(scores < 0, scores*alpha, scores/alpha) 24 | 25 | return scores 26 | 27 | class CustomRepetitionPenaltyLogitsProcessor(): 28 | 29 | def __init__(self, penalty: float, max_input_ids, past_window): 30 | if not isinstance(penalty, float) or not (penalty > 0): 31 | raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}") 32 | 33 | self.penalty = penalty 34 | self.max_input_ids = max_input_ids 35 | self.past_window = past_window 36 | 37 | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: 38 | 39 | input_ids = input_ids[:, -self.past_window:] 40 | score = torch.gather(scores, 1, input_ids) 41 | _score = score.detach().clone() 42 | score = torch.where(score < 0, score * self.penalty, score / self.penalty) 43 | score[input_ids>=self.max_input_ids] = _score[input_ids>=self.max_input_ids] 44 | scores.scatter_(1, input_ids, score) 45 | 46 | return scores 47 | 48 | def count_invalid_characters(s): 49 | 50 | s = re.sub(r'\[uv_break\]|\[laugh\]|\[lbreak\]', '', s) 51 | pattern = re.compile(r'[^\u4e00-\u9fffA-Za-z，。、,\. ]') 52 | non_alphabetic_chinese_chars = pattern.findall(s) 53 | return set(non_alphabetic_chinese_chars) 54 | 55 | def detect_language(sentence): 56 | 57 | chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]') 58 | english_word_pattern = re.compile(r'\b[A-Za-z]+\b') 59 | 60 | chinese_chars = chinese_char_pattern.findall(sentence) 61 | english_words = english_word_pattern.findall(sentence) 62 | 63 | if len(chinese_chars) > len(english_words): 64 | return "zh" 65 | else: 66 | return "en" 67 | 68 | 69 | character_map = { 70 | '：': '，', 71 | '；': '，', 72 | '！': '。', 73 | '（': '，', 74 | '）': '，', 75 | '【': '，', 76 | '】': '，', 77 | '『': '，', 78 | '』': '，', 79 | '「': '，', 80 | '」': '，', 81 | '《': '，', 82 | '》': '，', 83 | '－': '，', 84 | '‘': '', 85 | '“': '', 86 | '’': '', 87 | '”': '', 88 | ':': ',', 89 | ';': ',', 90 | '!': '.', 91 | '(': ',', 92 | ')': ',', 93 | '[': ',', 94 | ']': ',', 95 | '>': ',', 96 | '<': ',', 97 | '-': ',', 98 | } 99 | 100 | halfwidth_2_fullwidth_map = { 101 | '!': '！', 102 | '"': '“', 103 | "'": '‘', 104 | '#': '＃', 105 | '$': '＄', 106 | '%': '％', 107 | '&': '＆', 108 | '(': '（', 109 | ')': '）', 110 | ',': '，', 111 | '-': '－', 112 | '*': '＊', 113 | '+': '＋', 114 | '.': '。', 115 | '/': '／', 116 | ':': '：', 117 | ';': '；', 118 | '<': '＜', 119 | '=': '＝', 120 | '>': '＞', 121 | '?': '？', 122 | '@': '＠', 123 | # '[': '［', 124 | '\\': '＼', 125 | # ']': '］', 126 | '^': '＾', 127 | # '_': '＿', 128 | '`': '｀', 129 | '{': '｛', 130 | '|': '｜', 131 | '}': '｝', 132 | '~': '～' 133 | } 134 | 135 | def apply_half2full_map(text): 136 | translation_table = str.maketrans(halfwidth_2_fullwidth_map) 137 | return text.translate(translation_table) 138 | 139 | def apply_character_map(text): 140 | translation_table = str.maketrans(character_map) 141 | return text.translate(translation_table) -------------------------------------------------------------------------------- /webui.py: -------------------------------------------------------------------------------- 1 | import os 2 | import random 3 | import argparse 4 | 5 | import torch 6 | import gradio as gr 7 | import numpy as np 8 | 9 | import ChatTTS 10 | 11 | 12 | def generate_seed(): 13 | new_seed = random.randint(1, 100000000) 14 | return { 15 | "__type__": "update", 16 | "value": new_seed 17 | } 18 | 19 | 20 | def generate_audio(text, temperature, top_P, top_K, audio_seed_input, text_seed_input, refine_text_flag): 21 | 22 | torch.manual_seed(audio_seed_input) 23 | rand_spk = chat.sample_random_speaker() 24 | params_infer_code = { 25 | 'spk_emb': rand_spk, 26 | 'temperature': temperature, 27 | 'top_P': top_P, 28 | 'top_K': top_K, 29 | } 30 | params_refine_text = {'prompt': '[oral_2][laugh_0][break_6]'} 31 | 32 | torch.manual_seed(text_seed_input) 33 | 34 | if refine_text_flag: 35 | text = chat.infer(text, 36 | skip_refine_text=False, 37 | refine_text_only=True, 38 | params_refine_text=params_refine_text, 39 | params_infer_code=params_infer_code 40 | ) 41 | 42 | wav = chat.infer(text, 43 | skip_refine_text=True, 44 | params_refine_text=params_refine_text, 45 | params_infer_code=params_infer_code 46 | ) 47 | 48 | audio_data = np.array(wav[0]).flatten() 49 | sample_rate = 24000 50 | text_data = text[0] if isinstance(text, list) else text 51 | 52 | return [(sample_rate, audio_data), text_data] 53 | 54 | 55 | def main(): 56 | 57 | with gr.Blocks() as demo: 58 | gr.Markdown("# ChatTTS Webui") 59 | gr.Markdown("ChatTTS Model: [2noise/ChatTTS](https://github.com/2noise/ChatTTS)") 60 | 61 | default_text = "四川美食确实以辣闻名，但也有不辣的选择。比如甜水面、赖汤圆、蛋烘糕、叶儿粑等，这些小吃口味温和，甜而不腻，也很受欢迎。" 62 | text_input = gr.Textbox(label="Input Text", lines=4, placeholder="Please Input Text...", value=default_text) 63 | 64 | with gr.Row(): 65 | refine_text_checkbox = gr.Checkbox(label="Refine text", value=True) 66 | temperature_slider = gr.Slider(minimum=0.00001, maximum=1.0, step=0.00001, value=0.3, label="Audio temperature") 67 | top_p_slider = gr.Slider(minimum=0.1, maximum=0.9, step=0.05, value=0.7, label="top_P") 68 | top_k_slider = gr.Slider(minimum=1, maximum=20, step=1, value=20, label="top_K") 69 | 70 | with gr.Row(): 71 | audio_seed_input = gr.Number(value=2, label="Audio Seed") 72 | generate_audio_seed = gr.Button("\U0001F3B2") 73 | text_seed_input = gr.Number(value=42, label="Text Seed") 74 | generate_text_seed = gr.Button("\U0001F3B2") 75 | 76 | generate_button = gr.Button("Generate") 77 | 78 | text_output = gr.Textbox(label="Output Text", interactive=False) 79 | audio_output = gr.Audio(label="Output Audio") 80 | 81 | generate_audio_seed.click(generate_seed, 82 | inputs=[], 83 | outputs=audio_seed_input) 84 | 85 | generate_text_seed.click(generate_seed, 86 | inputs=[], 87 | outputs=text_seed_input) 88 | 89 | generate_button.click(generate_audio, 90 | inputs=[text_input, temperature_slider, top_p_slider, top_k_slider, audio_seed_input, text_seed_input, refine_text_checkbox], 91 | outputs=[audio_output, text_output]) 92 | 93 | parser = argparse.ArgumentParser(description='ChatTTS demo Launch') 94 | parser.add_argument('--server_name', type=str, default='0.0.0.0', help='Server name') 95 | parser.add_argument('--server_port', type=int, default=8080, help='Server port') 96 | parser.add_argument('--local_path', type=str, default=None, help='the local_path if need') 97 | args = parser.parse_args() 98 | 99 | print("loading ChatTTS model...") 100 | global chat 101 | chat = ChatTTS.Chat() 102 | 103 | if args.local_path == None: 104 | chat.load_models() 105 | else: 106 | print('local model path:', args.local_path) 107 | chat.load_models('local', local_path=args.local_path) 108 | 109 | demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True) 110 | 111 | 112 | if __name__ == '__main__': 113 | main() -------------------------------------------------------------------------------- /README_CN.md: -------------------------------------------------------------------------------- 1 | # ChatTTS 2 | [**English**](./README.md) | [**中文简体**](./README_CN.md) 3 | 4 | ChatTTS是专门为对话场景设计的文本转语音模型，例如LLM助手对话任务。它支持英文和中文两种语言。最大的模型使用了10万小时以上的中英文数据进行训练。在HuggingFace中开源的版本为4万小时训练且未SFT的版本. 5 | 6 | 如需就模型进行正式商业咨询，请发送邮件至 **open-source@2noise.com**。对于中文用户，您可以加入我们的QQ群：~~808364215 (已满)~~ 230696694 (二群) 进行讨论。同时欢迎在GitHub上提出问题。如果遇到无法使用 **[HuggingFace](https://huggingface.co/2Noise/ChatTTS)** 的情况,可以在 [modelscope](https://www.modelscope.cn/models/pzc163/chatTTS) 上进行下载. 7 | 8 | --- 9 | ## 亮点 10 | 1. **对话式 TTS**: ChatTTS针对对话式任务进行了优化，实现了自然流畅的语音合成，同时支持多说话人。 11 | 2. **细粒度控制**: 该模型能够预测和控制细粒度的韵律特征，包括笑声、停顿和插入词等。 12 | 3. **更好的韵律**: ChatTTS在韵律方面超越了大部分开源TTS模型。同时提供预训练模型，支持进一步的研究。 13 | 14 | 对于模型的具体介绍, 可以参考B站的 **[宣传视频](https://www.bilibili.com/video/BV1zn4y1o7iV)** 15 | 16 | --- 17 | 18 | ## 免责声明 19 | 本文件中的信息仅供学术交流使用。其目的是用于教育和研究，不得用于任何商业或法律目的。作者不保证信息的准确性、完整性或可靠性。本文件中使用的信息和数据，仅用于学术研究目的。这些数据来自公开可用的来源，作者不对数据的所有权或版权提出任何主张。 20 | 21 | ChatTTS是一个强大的文本转语音系统。然而，负责任地和符合伦理地利用这项技术是非常重要的。为了限制ChatTTS的使用，我们在4w小时模型的训练过程中添加了少量额外的高频噪音，并用mp3格式尽可能压低了音质，以防不法分子用于潜在的犯罪可能。同时我们在内部训练了检测模型，并计划在未来开放。 22 | 23 | --- 24 | ## 用法 25 | 26 |

基本用法

27 | 28 | ```python 29 | import ChatTTS 30 | from IPython.display import Audio 31 | 32 | chat = ChatTTS.Chat() 33 | chat.load_models(compile=False) # 设置为True以获得更快速度 34 | 35 | texts = ["在这里输入你的文本",] 36 | 37 | wavs = chat.infer(texts, use_decoder=True) 38 | 39 | torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000) 40 | ``` 41 | 42 |

进阶用法

43 | 44 | ```python 45 | ################################### 46 | # Sample a speaker from Gaussian. 47 | 48 | rand_spk = chat.sample_random_speaker() 49 | 50 | params_infer_code = { 51 | 'spk_emb': rand_spk, # add sampled speaker 52 | 'temperature': .3, # using custom temperature 53 | 'top_P': 0.7, # top P decode 54 | 'top_K': 20, # top K decode 55 | } 56 | 57 | ################################### 58 | # For sentence level manual control. 59 | 60 | # use oral_(0-9), laugh_(0-2), break_(0-7) 61 | # to generate special token in text to synthesize. 62 | params_refine_text = { 63 | 'prompt': '[oral_2][laugh_0][break_6]' 64 | } 65 | 66 | wav = chat.infer(texts, params_refine_text=params_refine_text, params_infer_code=params_infer_code) 67 | 68 | ################################### 69 | # For word level manual control. 70 | # use_decoder=False to infer faster with a bit worse quality 71 | text = 'What is [uv_break]your favorite english food?[laugh][lbreak]' 72 | wav = chat.infer(text, skip_refine_text=True, params_infer_code=params_infer_code, use_decoder=False) 73 | 74 | torchaudio.save("output2.wav", torch.from_numpy(wavs[0]), 24000) 75 | ``` 76 | 77 |
78 |

自我介绍样例

 79 | 80 | ```python 81 | inputs_cn = """ 82 | chat T T S 是一款强大的对话式文本转语音模型。它有中英混读和多说话人的能力。 83 | chat T T S 不仅能够生成自然流畅的语音，还能控制[laugh]笑声啊[laugh]， 84 | 停顿啊[uv_break]语气词啊等副语言现象[uv_break]。这个韵律超越了许多开源模型[uv_break]。 85 | 请注意，chat T T S 的使用应遵守法律和伦理准则，避免滥用的安全风险。[uv_break]' 86 | """.replace('\n', '') 87 | 88 | params_refine_text = { 89 | 'prompt': '[oral_2][laugh_0][break_4]' 90 | } 91 | audio_array_cn = chat.infer(inputs_cn, params_refine_text=params_refine_text) 92 | # audio_array_en = chat.infer(inputs_en, params_refine_text=params_refine_text) 93 | 94 | torchaudio.save("output3.wav", torch.from_numpy(audio_array_cn[0]), 24000) 95 | ``` 96 | [男说话人](https://github.com/2noise/ChatTTS/assets/130631963/bbfa3b83-2b67-4bb6-9315-64c992b63788) 97 | 98 | [女说话人](https://github.com/2noise/ChatTTS/assets/130631963/e061f230-0e05-45e6-8e4e-0189f2d260c4) 99 |
100 | 101 | 102 | --- 103 | ## 计划路线 104 | - [x] 开源4w小时基础模型和spk_stats文件 105 | - [ ] 开源VQ encoder和Lora 训练代码 106 | - [ ] 在非refine text情况下, 流式生成音频* 107 | - [ ] 开源多情感可控的4w小时版本 108 | - [ ] ChatTTS.cpp maybe? (欢迎社区PR或独立的新repo) 109 | 110 | --- 111 | ## 常见问题 112 | 113 | ##### 连不上HuggingFace 114 | 请使用[modelscope](https://www.modelscope.cn/models/pzc163/chatTTS)的版本. 并设置cache的位置: 115 | ```python 116 | chat.load_models(source='local', local_path='你的下载位置') 117 | ``` 118 | 119 | ##### 我要多少显存? Infer的速度是怎么样的? 120 | 对于30s的音频, 至少需要4G的显存. 对于4090, 1s生成约7个字所对应的音频. RTF约0.3. 121 | 122 | ##### 模型稳定性似乎不够好, 会出现其他说话人或音质很差的现象. 123 | 这是自回归模型通常都会出现的问题. 说话人可能会在中间变化, 可能会采样到音质非常差的结果, 这通常难以避免. 可以多采样几次来找到合适的结果. 124 | 125 | ##### 除了笑声还能控制什么吗? 还能控制其他情感吗? 126 | 在现在放出的模型版本中, 只有[laugh]和[uv_break], [lbreak]作为字级别的控制单元. 在未来的版本中我们可能会开源其他情感控制的版本. 127 | 128 | --- 129 | ## 致谢 130 | - [bark](https://github.com/suno-ai/bark),[XTTSv2](https://github.com/coqui-ai/TTS)和[valle](https://arxiv.org/abs/2301.02111)展示了自回归任务用于TTS任务的可能性. 131 | - [fish-speech](https://github.com/fishaudio/fish-speech)一个优秀的自回归TTS模型, 揭示了GVQ用于LLM任务的可能性. 132 | - [vocos](https://github.com/gemelo-ai/vocos)作为模型中的vocoder. 133 | 134 | --- 135 | ## 特别致谢 136 | - [wlu-audio lab](https://audio.westlake.edu.cn/)为我们提供了早期算法试验的支持. 137 | -------------------------------------------------------------------------------- /ChatTTS/infer/api.py: -------------------------------------------------------------------------------- 1 | 2 | import torch 3 | import torch.nn.functional as F 4 | from transformers.generation import TopKLogitsWarper, TopPLogitsWarper 5 | from ..utils.infer_utils import CustomRepetitionPenaltyLogitsProcessorRepeat 6 | 7 | def infer_code( 8 | models, 9 | text, 10 | spk_emb = None, 11 | top_P = 0.7, 12 | top_K = 20, 13 | temperature = 0.3, 14 | repetition_penalty = 1.05, 15 | max_new_token = 2048, 16 | **kwargs 17 | ): 18 | 19 | device = next(models['gpt'].parameters()).device 20 | 21 | if not isinstance(text, list): 22 | text = [text] 23 | 24 | if not isinstance(temperature, list): 25 | temperature = [temperature] * models['gpt'].num_vq 26 | 27 | if spk_emb is not None: 28 | text = [f'[Stts][spk_emb]{i}[Ptts]' for i in text] 29 | else: 30 | text = [f'[Stts][empty_spk]{i}[Ptts]' for i in text] 31 | 32 | text_token = models['tokenizer'](text, return_tensors='pt', add_special_tokens=False, padding=True).to(device) 33 | input_ids = text_token['input_ids'][...,None].expand(-1, -1, models['gpt'].num_vq) 34 | text_mask = torch.ones(text_token['input_ids'].shape, dtype=bool, device=device) 35 | 36 | inputs = { 37 | 'input_ids': input_ids, 38 | 'text_mask': text_mask, 39 | 'attention_mask': text_token['attention_mask'], 40 | } 41 | 42 | emb = models['gpt'].get_emb(**inputs) 43 | if spk_emb is not None: 44 | emb[inputs['input_ids'][..., 0] == models['tokenizer'].convert_tokens_to_ids('[spk_emb]')] = \ 45 | F.normalize(spk_emb.to(device).to(emb.dtype)[None].expand(len(text), -1), p=2.0, dim=1, eps=1e-12) 46 | 47 | num_code = models['gpt'].emb_code[0].num_embeddings - 1 48 | 49 | LogitsWarpers = [] 50 | if top_P is not None: 51 | LogitsWarpers.append(TopPLogitsWarper(top_P, min_tokens_to_keep=3)) 52 | if top_K is not None: 53 | LogitsWarpers.append(TopKLogitsWarper(top_K, min_tokens_to_keep=3)) 54 | 55 | LogitsProcessors = [] 56 | if repetition_penalty is not None and repetition_penalty != 1: 57 | LogitsProcessors.append(CustomRepetitionPenaltyLogitsProcessorRepeat(\ 58 | repetition_penalty, num_code, 16)) 59 | 60 | result = models['gpt'].generate( 61 | emb, inputs['input_ids'], 62 | temperature = torch.tensor(temperature, device=device), 63 | attention_mask = inputs['attention_mask'], 64 | LogitsWarpers = LogitsWarpers, 65 | LogitsProcessors = LogitsProcessors, 66 | eos_token = num_code, 67 | max_new_token = max_new_token, 68 | infer_text = False, 69 | **kwargs 70 | ) 71 | 72 | return result 73 | 74 | 75 | def refine_text( 76 | models, 77 | text, 78 | top_P = 0.7, 79 | top_K = 20, 80 | temperature = 0.7, 81 | repetition_penalty = 1.0, 82 | max_new_token = 384, 83 | prompt = '', 84 | **kwargs 85 | ): 86 | 87 | device = next(models['gpt'].parameters()).device 88 | 89 | if not isinstance(text, list): 90 | text = [text] 91 | 92 | assert len(text), 'text should not be empty' 93 | 94 | text = [f"[Sbreak]{i}[Pbreak]{prompt}" for i in text] 95 | text_token = models['tokenizer'](text, return_tensors='pt', add_special_tokens=False, padding=True).to(device) 96 | text_mask = torch.ones(text_token['input_ids'].shape, dtype=bool, device=device) 97 | 98 | inputs = { 99 | 'input_ids': text_token['input_ids'][...,None].expand(-1, -1, models['gpt'].num_vq), 100 | 'text_mask': text_mask, 101 | 'attention_mask': text_token['attention_mask'], 102 | } 103 | 104 | LogitsWarpers = [] 105 | if top_P is not None: 106 | LogitsWarpers.append(TopPLogitsWarper(top_P, min_tokens_to_keep=3)) 107 | if top_K is not None: 108 | LogitsWarpers.append(TopKLogitsWarper(top_K, min_tokens_to_keep=3)) 109 | 110 | LogitsProcessors = [] 111 | if repetition_penalty is not None and repetition_penalty != 1: 112 | LogitsProcessors.append(CustomRepetitionPenaltyLogitsProcessorRepeat(repetition_penalty, len(models['tokenizer']), 16)) 113 | 114 | result = models['gpt'].generate( 115 | models['gpt'].get_emb(**inputs), inputs['input_ids'], 116 | temperature = torch.tensor([temperature,], device=device), 117 | attention_mask = inputs['attention_mask'], 118 | LogitsWarpers = LogitsWarpers, 119 | LogitsProcessors = LogitsProcessors, 120 | eos_token = torch.tensor(models['tokenizer'].convert_tokens_to_ids('[Ebreak]'), device=device)[None], 121 | max_new_token = max_new_token, 122 | infer_text = True, 123 | **kwargs 124 | ) 125 | return result -------------------------------------------------------------------------------- /ChatTTS/model/dvae.py: -------------------------------------------------------------------------------- 1 | import math 2 | from einops import rearrange 3 | from vector_quantize_pytorch import GroupedResidualFSQ 4 | 5 | import torch 6 | import torch.nn as nn 7 | import torch.nn.functional as F 8 | 9 | class ConvNeXtBlock(nn.Module): 10 | def __init__( 11 | self, 12 | dim: int, 13 | intermediate_dim: int, 14 | kernel, dilation, 15 | layer_scale_init_value: float = 1e-6, 16 | ): 17 | # ConvNeXt Block copied from Vocos. 18 | super().__init__() 19 | self.dwconv = nn.Conv1d(dim, dim, 20 | kernel_size=kernel, padding=dilation*(kernel//2), 21 | dilation=dilation, groups=dim 22 | ) # depthwise conv 23 | 24 | self.norm = nn.LayerNorm(dim, eps=1e-6) 25 | self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers 26 | self.act = nn.GELU() 27 | self.pwconv2 = nn.Linear(intermediate_dim, dim) 28 | self.gamma = ( 29 | nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True) 30 | if layer_scale_init_value > 0 31 | else None 32 | ) 33 | 34 | def forward(self, x: torch.Tensor, cond = None) -> torch.Tensor: 35 | residual = x 36 | x = self.dwconv(x) 37 | x = x.transpose(1, 2) # (B, C, T) -> (B, T, C) 38 | x = self.norm(x) 39 | x = self.pwconv1(x) 40 | x = self.act(x) 41 | x = self.pwconv2(x) 42 | if self.gamma is not None: 43 | x = self.gamma * x 44 | x = x.transpose(1, 2) # (B, T, C) -> (B, C, T) 45 | 46 | x = residual + x 47 | return x 48 | 49 | 50 | 51 | class GFSQ(nn.Module): 52 | 53 | def __init__(self, 54 | dim, levels, G, R, eps=1e-5, transpose = True 55 | ): 56 | super(GFSQ, self).__init__() 57 | self.quantizer = GroupedResidualFSQ( 58 | dim=dim, 59 | levels=levels, 60 | num_quantizers=R, 61 | groups=G, 62 | ) 63 | self.n_ind = math.prod(levels) 64 | self.eps = eps 65 | self.transpose = transpose 66 | self.G = G 67 | self.R = R 68 | 69 | def _embed(self, x): 70 | if self.transpose: 71 | x = x.transpose(1,2) 72 | x = rearrange( 73 | x, "b t (g r) -> g b t r", g = self.G, r = self.R, 74 | ) 75 | feat = self.quantizer.get_output_from_indices(x) 76 | return feat.transpose(1,2) if self.transpose else feat 77 | 78 | def forward(self, x,): 79 | if self.transpose: 80 | x = x.transpose(1,2) 81 | feat, ind = self.quantizer(x) 82 | ind = rearrange( 83 | ind, "g b t r ->b t (g r)", 84 | ) 85 | embed_onehot = F.one_hot(ind.long(), self.n_ind).to(x.dtype) 86 | e_mean = torch.mean(embed_onehot, dim=[0,1]) 87 | e_mean = e_mean / (e_mean.sum(dim=1) + self.eps).unsqueeze(1) 88 | perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + self.eps), dim=1)) 89 | 90 | return ( 91 | torch.zeros(perplexity.shape, dtype=x.dtype, device=x.device), 92 | feat.transpose(1,2) if self.transpose else feat, 93 | perplexity, 94 | None, 95 | ind.transpose(1,2) if self.transpose else ind, 96 | ) 97 | 98 | class DVAEDecoder(nn.Module): 99 | def __init__(self, idim, odim, 100 | n_layer = 12, bn_dim = 64, hidden = 256, 101 | kernel = 7, dilation = 2, up = False 102 | ): 103 | super().__init__() 104 | self.up = up 105 | self.conv_in = nn.Sequential( 106 | nn.Conv1d(idim, bn_dim, 3, 1, 1), nn.GELU(), 107 | nn.Conv1d(bn_dim, hidden, 3, 1, 1) 108 | ) 109 | self.decoder_block = nn.ModuleList([ 110 | ConvNeXtBlock(hidden, hidden* 4, kernel, dilation,) 111 | for _ in range(n_layer)]) 112 | self.conv_out = nn.Conv1d(hidden, odim, kernel_size=1, bias=False) 113 | 114 | def forward(self, input, conditioning=None): 115 | # B, T, C 116 | x = input.transpose(1, 2) 117 | x = self.conv_in(x) 118 | for f in self.decoder_block: 119 | x = f(x, conditioning) 120 | 121 | x = self.conv_out(x) 122 | return x.transpose(1, 2) 123 | 124 | 125 | class DVAE(nn.Module): 126 | def __init__( 127 | self, decoder_config, vq_config, dim=512 128 | ): 129 | super().__init__() 130 | self.register_buffer('coef', torch.randn(1, 100, 1)) 131 | 132 | self.decoder = DVAEDecoder(**decoder_config) 133 | self.out_conv = nn.Conv1d(dim, 100, 3, 1, 1, bias=False) 134 | if vq_config is not None: 135 | self.vq_layer = GFSQ(**vq_config) 136 | else: 137 | self.vq_layer = None 138 | 139 | def forward(self, inp): 140 | 141 | if self.vq_layer is not None: 142 | vq_feats = self.vq_layer._embed(inp) 143 | else: 144 | vq_feats = inp.detach().clone() 145 | 146 | temp = torch.chunk(vq_feats, 2, dim=1) # flatten trick :) 147 | temp = torch.stack(temp, -1) 148 | vq_feats = temp.reshape(*temp.shape[:2], -1) 149 | 150 | vq_feats = vq_feats.transpose(1, 2) 151 | dec_out = self.decoder(input=vq_feats) 152 | dec_out = self.out_conv(dec_out.transpose(1, 2)) 153 | mel = dec_out * self.coef 154 | 155 | return mel 156 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ChatTTS 2 | [**English**](./README.md) | [**中文简体**](./README_CN.md) 3 | 4 | ChatTTS is a text-to-speech model designed specifically for dialogue scenario such as LLM assistant. It supports both English and Chinese languages. Our model is trained with 100,000+ hours composed of chinese and english. The open-source version on **[HuggingFace](https://huggingface.co/2Noise/ChatTTS)** is a 40,000 hours pre trained model without SFT. 5 | 6 | For formal inquiries about model and roadmap, please contact us at **open-source@2noise.com**. You could join our QQ group: ~~808364215 (Full)~~ 230696694 (Group 2) for discussion. Adding github issues is always welcomed. 7 | 8 | --- 9 | ## Highlights 10 | 1. **Conversational TTS**: ChatTTS is optimized for dialogue-based tasks, enabling natural and expressive speech synthesis. It supports multiple speakers, facilitating interactive conversations. 11 | 2. **Fine-grained Control**: The model could predict and control fine-grained prosodic features, including laughter, pauses, and interjections. 12 | 3. **Better Prosody**: ChatTTS surpasses most of open-source TTS models in terms of prosody. We provide pretrained models to support further research and development. 13 | 14 | For the detailed description of the model, you can refer to **[video on Bilibili](https://www.bilibili.com/video/BV1zn4y1o7iV)** 15 | 16 | --- 17 | 18 | ## Disclaimer 19 | 20 | This repo is for academic purposes only. It is intended for educational and research use, and should not be used for any commercial or legal purposes. The authors do not guarantee the accuracy, completeness, or reliability of the information. The information and data used in this repo, are for academic and research purposes only. The data obtained from publicly available sources, and the authors do not claim any ownership or copyright over the data. 21 | 22 | ChatTTS is a powerful text-to-speech system. However, it is very important to utilize this technology responsibly and ethically. To limit the use of ChatTTS, we added a small amount of high-frequency noise during the training of the 40,000-hour model, and compressed the audio quality as much as possible using MP3 format, to prevent malicious actors from potentially using it for criminal purposes. At the same time, we have internally trained a detection model and plan to open-source it in the future. 23 | 24 | 25 | --- 26 | ## Usage 27 | 28 |

Basic usage

29 | 30 | ```python 31 | import ChatTTS 32 | from IPython.display import Audio 33 | 34 | chat = ChatTTS.Chat() 35 | chat.load_models(compile=False) # Set to True for better performance 36 | 37 | texts = ["PUT YOUR TEXT HERE",] 38 | 39 | wavs = chat.infer(texts, ) 40 | 41 | torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000) 42 | ``` 43 | 44 |

Advanced usage

45 | 46 | ```python 47 | ################################### 48 | # Sample a speaker from Gaussian. 49 | 50 | rand_spk = chat.sample_random_speaker() 51 | 52 | params_infer_code = { 53 | 'spk_emb': rand_spk, # add sampled speaker 54 | 'temperature': .3, # using custom temperature 55 | 'top_P': 0.7, # top P decode 56 | 'top_K': 20, # top K decode 57 | } 58 | 59 | ################################### 60 | # For sentence level manual control. 61 | 62 | # use oral_(0-9), laugh_(0-2), break_(0-7) 63 | # to generate special token in text to synthesize. 64 | params_refine_text = { 65 | 'prompt': '[oral_2][laugh_0][break_6]' 66 | } 67 | 68 | wav = chat.infer(texts, params_refine_text=params_refine_text, params_infer_code=params_infer_code) 69 | 70 | ################################### 71 | # For word level manual control. 72 | text = 'What is [uv_break]your favorite english food?[laugh][lbreak]' 73 | wav = chat.infer(text, skip_refine_text=True, params_refine_text=params_refine_text, params_infer_code=params_infer_code) 74 | torchaudio.save("output2.wav", torch.from_numpy(wavs[0]), 24000) 75 | ``` 76 | 77 |
78 |

Example: self introduction

 79 | 80 | ```python 81 | inputs_en = """ 82 | chat T T S is a text to speech model designed for dialogue applications. 83 | [uv_break]it supports mixed language input [uv_break]and offers multi speaker 84 | capabilities with precise control over prosodic elements [laugh]like like 85 | [uv_break]laughter[laugh], [uv_break]pauses, [uv_break]and intonation. 86 | [uv_break]it delivers natural and expressive speech,[uv_break]so please 87 | [uv_break] use the project responsibly at your own risk.[uv_break] 88 | """.replace('\n', '') # English is still experimental. 89 | 90 | params_refine_text = { 91 | 'prompt': '[oral_2][laugh_0][break_4]' 92 | } 93 | # audio_array_cn = chat.infer(inputs_cn, params_refine_text=params_refine_text) 94 | audio_array_en = chat.infer(inputs_en, params_refine_text=params_refine_text) 95 | torchaudio.save("output3.wav", torch.from_numpy(audio_array_en[0]), 24000) 96 | ``` 97 | [male speaker](https://github.com/2noise/ChatTTS/assets/130631963/e0f51251-db7f-4d39-a0e9-3e095bb65de1) 98 | 99 | [female speaker](https://github.com/2noise/ChatTTS/assets/130631963/f5dcdd01-1091-47c5-8241-c4f6aaaa8bbd) 100 |
101 | 102 | --- 103 | ## Roadmap 104 | - [x] Open-source the 40k hour base model and spk_stats file 105 | - [ ] Open-source VQ encoder and Lora training code 106 | - [ ] Streaming audio generation without refining the text* 107 | - [ ] Open-source the 40k hour version with multi-emotion control 108 | - [ ] ChatTTS.cpp maybe? (PR or new repo are welcomed.) 109 | 110 | ---- 111 | ## FAQ 112 | 113 | ##### How much VRAM do I need? How about infer speed? 114 | For a 30-second audio clip, at least 4GB of GPU memory is required. For the 4090 GPU, it can generate audio corresponding to approximately 7 semantic tokens per second. The Real-Time Factor (RTF) is around 0.3. 115 | 116 | ##### model stability is not good enough, with issues such as multi speakers or poor audio quality. 117 | 118 | This is a problem that typically occurs with autoregressive models(for bark and valle). It's generally difficult to avoid. One can try multiple samples to find a suitable result. 119 | 120 | ##### Besides laughter, can we control anything else? Can we control other emotions? 121 | 122 | In the current released model, the only token-level control units are [laugh], [uv_break], and [lbreak]. In future versions, we may open-source models with additional emotional control capabilities. 123 | 124 | --- 125 | ## Acknowledgements 126 | - [bark](https://github.com/suno-ai/bark), [XTTSv2](https://github.com/coqui-ai/TTS) and [valle](https://arxiv.org/abs/2301.02111) demostrate a remarkable TTS result by a autoregressive-style system. 127 | - [fish-speech](https://github.com/fishaudio/fish-speech) reveals capability of GVQ as audio tokenizer for LLM modeling. 128 | - [vocos](https://github.com/gemelo-ai/vocos) which is used as a pretrained vocoder. 129 | 130 | --- 131 | ## Special Appreciation 132 | - [wlu-audio lab](https://audio.westlake.edu.cn/) for early algorithm experiments. 133 | -------------------------------------------------------------------------------- /ChatTTS/core.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | import logging 4 | from functools import partial 5 | from omegaconf import OmegaConf 6 | 7 | import torch 8 | from vocos import Vocos 9 | from .model.dvae import DVAE 10 | from .model.gpt import GPT_warpper 11 | from .utils.gpu_utils import select_device 12 | from .utils.infer_utils import count_invalid_characters, detect_language, apply_character_map, apply_half2full_map 13 | from .utils.io_utils import get_latest_modified_file 14 | from .infer.api import refine_text, infer_code 15 | 16 | from huggingface_hub import snapshot_download 17 | 18 | logging.basicConfig(level = logging.INFO) 19 | 20 | 21 | class Chat: 22 | def __init__(self, ): 23 | self.pretrain_models = {} 24 | self.normalizer = {} 25 | self.logger = logging.getLogger(__name__) 26 | 27 | def check_model(self, level = logging.INFO, use_decoder = False): 28 | not_finish = False 29 | check_list = ['vocos', 'gpt', 'tokenizer'] 30 | 31 | if use_decoder: 32 | check_list.append('decoder') 33 | else: 34 | check_list.append('dvae') 35 | 36 | for module in check_list: 37 | if module not in self.pretrain_models: 38 | self.logger.log(logging.WARNING, f'{module} not initialized.') 39 | not_finish = True 40 | 41 | if not not_finish: 42 | self.logger.log(level, f'All initialized.') 43 | 44 | return not not_finish 45 | 46 | def load_models(self, source='huggingface', force_redownload=False, local_path='', **kwargs): 47 | if source == 'huggingface': 48 | hf_home = os.getenv('HF_HOME', os.path.expanduser("~/.cache/huggingface")) 49 | try: 50 | download_path = get_latest_modified_file(os.path.join(hf_home, 'hub/models--2Noise--ChatTTS/snapshots')) 51 | except: 52 | download_path = None 53 | if download_path is None or force_redownload: 54 | self.logger.log(logging.INFO, f'Download from HF: https://huggingface.co/2Noise/ChatTTS') 55 | download_path = snapshot_download(repo_id="2Noise/ChatTTS", allow_patterns=["*.pt", "*.yaml"]) 56 | else: 57 | self.logger.log(logging.INFO, f'Load from cache: {download_path}') 58 | elif source == 'local': 59 | self.logger.log(logging.INFO, f'Load from local: {local_path}') 60 | download_path = local_path 61 | 62 | self._load(**{k: os.path.join(download_path, v) for k, v in OmegaConf.load(os.path.join(download_path, 'config', 'path.yaml')).items()}, **kwargs) 63 | 64 | def _load( 65 | self, 66 | vocos_config_path: str = None, 67 | vocos_ckpt_path: str = None, 68 | dvae_config_path: str = None, 69 | dvae_ckpt_path: str = None, 70 | gpt_config_path: str = None, 71 | gpt_ckpt_path: str = None, 72 | decoder_config_path: str = None, 73 | decoder_ckpt_path: str = None, 74 | tokenizer_path: str = None, 75 | device: str = None, 76 | compile: bool = True, 77 | ): 78 | if not device: 79 | device = select_device(4096) 80 | self.logger.log(logging.INFO, f'use {device}') 81 | 82 | if vocos_config_path: 83 | vocos = Vocos.from_hparams(vocos_config_path).to(device).eval() 84 | assert vocos_ckpt_path, 'vocos_ckpt_path should not be None' 85 | vocos.load_state_dict(torch.load(vocos_ckpt_path)) 86 | self.pretrain_models['vocos'] = vocos 87 | self.logger.log(logging.INFO, 'vocos loaded.') 88 | 89 | if dvae_config_path: 90 | cfg = OmegaConf.load(dvae_config_path) 91 | dvae = DVAE(**cfg).to(device).eval() 92 | assert dvae_ckpt_path, 'dvae_ckpt_path should not be None' 93 | dvae.load_state_dict(torch.load(dvae_ckpt_path, map_location='cpu')) 94 | self.pretrain_models['dvae'] = dvae 95 | self.logger.log(logging.INFO, 'dvae loaded.') 96 | 97 | if gpt_config_path: 98 | cfg = OmegaConf.load(gpt_config_path) 99 | gpt = GPT_warpper(**cfg).to(device).eval() 100 | assert gpt_ckpt_path, 'gpt_ckpt_path should not be None' 101 | gpt.load_state_dict(torch.load(gpt_ckpt_path, map_location='cpu')) 102 | if compile and 'cuda' in str(device): 103 | gpt.gpt.forward = torch.compile(gpt.gpt.forward, backend='inductor', dynamic=True) 104 | self.pretrain_models['gpt'] = gpt 105 | spk_stat_path = os.path.join(os.path.dirname(gpt_ckpt_path), 'spk_stat.pt') 106 | assert os.path.exists(spk_stat_path), f'Missing spk_stat.pt: {spk_stat_path}' 107 | self.pretrain_models['spk_stat'] = torch.load(spk_stat_path).to(device) 108 | self.logger.log(logging.INFO, 'gpt loaded.') 109 | 110 | if decoder_config_path: 111 | cfg = OmegaConf.load(decoder_config_path) 112 | decoder = DVAE(**cfg).to(device).eval() 113 | assert decoder_ckpt_path, 'decoder_ckpt_path should not be None' 114 | decoder.load_state_dict(torch.load(decoder_ckpt_path, map_location='cpu')) 115 | self.pretrain_models['decoder'] = decoder 116 | self.logger.log(logging.INFO, 'decoder loaded.') 117 | 118 | if tokenizer_path: 119 | tokenizer = torch.load(tokenizer_path, map_location='cpu') 120 | tokenizer.padding_side = 'left' 121 | self.pretrain_models['tokenizer'] = tokenizer 122 | self.logger.log(logging.INFO, 'tokenizer loaded.') 123 | 124 | self.check_model() 125 | 126 | def infer( 127 | self, 128 | text, 129 | skip_refine_text=False, 130 | refine_text_only=False, 131 | params_refine_text={}, 132 | params_infer_code={'prompt':'[speed_5]'}, 133 | use_decoder=True, 134 | do_text_normalization=True, 135 | lang=None, 136 | ): 137 | 138 | assert self.check_model(use_decoder=use_decoder) 139 | 140 | if not isinstance(text, list): 141 | text = [text] 142 | 143 | if do_text_normalization: 144 | for i, t in enumerate(text): 145 | _lang = detect_language(t) if lang is None else lang 146 | self.init_normalizer(_lang) 147 | text[i] = self.normalizer[_lang](t) 148 | if _lang == 'zh': 149 | text[i] = apply_half2full_map(text[i]) 150 | 151 | for i, t in enumerate(text): 152 | invalid_characters = count_invalid_characters(t) 153 | if len(invalid_characters): 154 | self.logger.log(logging.WARNING, f'Invalid characters found! : {invalid_characters}') 155 | text[i] = apply_character_map(t) 156 | 157 | if not skip_refine_text: 158 | text_tokens = refine_text(self.pretrain_models, text, **params_refine_text)['ids'] 159 | text_tokens = [i[i < self.pretrain_models['tokenizer'].convert_tokens_to_ids('[break_0]')] for i in text_tokens] 160 | text = self.pretrain_models['tokenizer'].batch_decode(text_tokens) 161 | if refine_text_only: 162 | return text 163 | 164 | text = [params_infer_code.get('prompt', '') + i for i in text] 165 | params_infer_code.pop('prompt', '') 166 | result = infer_code(self.pretrain_models, text, **params_infer_code, return_hidden=use_decoder) 167 | 168 | if use_decoder: 169 | mel_spec = [self.pretrain_models['decoder'](i[None].permute(0,2,1)) for i in result['hiddens']] 170 | else: 171 | mel_spec = [self.pretrain_models['dvae'](i[None].permute(0,2,1)) for i in result['ids']] 172 | 173 | wav = [self.pretrain_models['vocos'].decode(i).cpu().numpy() for i in mel_spec] 174 | 175 | return wav 176 | 177 | def sample_random_speaker(self, ): 178 | 179 | dim = self.pretrain_models['gpt'].gpt.layers[0].mlp.gate_proj.in_features 180 | std, mean = self.pretrain_models['spk_stat'].chunk(2) 181 | return torch.randn(dim, device=std.device) * std + mean 182 | 183 | def init_normalizer(self, lang): 184 | 185 | if lang not in self.normalizer: 186 | if lang == 'zh': 187 | try: 188 | from tn.chinese.normalizer import Normalizer 189 | except: 190 | self.logger.log(logging.WARNING, f'Package WeTextProcessing not found! \ 191 | Run: conda install -c conda-forge pynini=2.1.5 && pip install WeTextProcessing') 192 | self.normalizer[lang] = Normalizer().normalize 193 | else: 194 | try: 195 | from nemo_text_processing.text_normalization.normalize import Normalizer 196 | except: 197 | self.logger.log(logging.WARNING, f'Package nemo_text_processing not found! \ 198 | Run: conda install -c conda-forge pynini=2.1.5 && pip install nemo_text_processing') 199 | self.normalizer[lang] = partial(Normalizer(input_case='cased', lang=lang).normalize, verbose=False, punct_post_process=True) 200 | 201 | -------------------------------------------------------------------------------- /ChatTTS/model/gpt.py: -------------------------------------------------------------------------------- 1 | import os 2 | os.environ["TOKENIZERS_PARALLELISM"] = "false" 3 | 4 | import logging 5 | from tqdm import tqdm 6 | from einops import rearrange 7 | from transformers.cache_utils import Cache 8 | 9 | import torch 10 | import torch.nn as nn 11 | import torch.nn.functional as F 12 | import torch.nn.utils.parametrize as P 13 | from torch.nn.utils.parametrizations import weight_norm 14 | from transformers import LlamaModel, LlamaConfig 15 | 16 | 17 | class LlamaMLP(nn.Module): 18 | def __init__(self, hidden_size, intermediate_size): 19 | super().__init__() 20 | self.hidden_size = hidden_size 21 | self.intermediate_size = intermediate_size 22 | self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) 23 | self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) 24 | self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) 25 | self.act_fn = F.silu 26 | 27 | def forward(self, x): 28 | down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) 29 | return down_proj 30 | 31 | 32 | class GPT_warpper(nn.Module): 33 | def __init__( 34 | self, 35 | gpt_config, 36 | num_audio_tokens, 37 | num_text_tokens, 38 | num_vq=4, 39 | **kwargs, 40 | ): 41 | super().__init__() 42 | 43 | self.logger = logging.getLogger(__name__) 44 | self.gpt = self.build_model(gpt_config) 45 | self.model_dim = self.gpt.config.hidden_size 46 | 47 | self.num_vq = num_vq 48 | self.emb_code = nn.ModuleList([nn.Embedding(num_audio_tokens, self.model_dim) for i in range(self.num_vq)]) 49 | self.emb_text = nn.Embedding(num_text_tokens, self.model_dim) 50 | self.head_text = weight_norm(nn.Linear(self.model_dim, num_text_tokens, bias=False), name='weight') 51 | self.head_code = nn.ModuleList([weight_norm(nn.Linear(self.model_dim, num_audio_tokens, bias=False), name='weight') for i in range(self.num_vq)]) 52 | 53 | def build_model(self, config): 54 | 55 | configuration = LlamaConfig(**config) 56 | model = LlamaModel(configuration) 57 | del model.embed_tokens 58 | 59 | return model 60 | 61 | def get_emb(self, input_ids, text_mask, **kwargs): 62 | 63 | emb_text = self.emb_text(input_ids[text_mask][:, 0]) 64 | 65 | emb_code = [self.emb_code[i](input_ids[~text_mask][:, i]) for i in range(self.num_vq)] 66 | emb_code = torch.stack(emb_code, 2).sum(2) 67 | 68 | emb = torch.zeros((input_ids.shape[:-1])+(emb_text.shape[-1],), device=emb_text.device, dtype=emb_text.dtype) 69 | emb[text_mask] = emb_text 70 | emb[~text_mask] = emb_code.to(emb.dtype) 71 | 72 | return emb 73 | 74 | def prepare_inputs_for_generation( 75 | self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, **kwargs 76 | ): 77 | # With static cache, the `past_key_values` is None 78 | # TODO joao: standardize interface for the different Cache classes and remove of this if 79 | has_static_cache = False 80 | if past_key_values is None: 81 | past_key_values = getattr(self.gpt.layers[0].self_attn, "past_key_value", None) 82 | has_static_cache = past_key_values is not None 83 | 84 | past_length = 0 85 | if past_key_values is not None: 86 | if isinstance(past_key_values, Cache): 87 | past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length() 88 | max_cache_length = ( 89 | torch.tensor(past_key_values.get_max_length(), device=input_ids.device) 90 | if past_key_values.get_max_length() is not None 91 | else None 92 | ) 93 | cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length) 94 | # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects 95 | else: 96 | cache_length = past_length = past_key_values[0][0].shape[2] 97 | max_cache_length = None 98 | 99 | # Keep only the unprocessed tokens: 100 | # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where 101 | # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as 102 | # input) 103 | if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: 104 | input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] 105 | # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard 106 | # input_ids based on the past_length. 107 | elif past_length < input_ids.shape[1]: 108 | input_ids = input_ids[:, past_length:] 109 | # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. 110 | 111 | # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. 112 | if ( 113 | max_cache_length is not None 114 | and attention_mask is not None 115 | and cache_length + input_ids.shape[1] > max_cache_length 116 | ): 117 | attention_mask = attention_mask[:, -max_cache_length:] 118 | 119 | position_ids = kwargs.get("position_ids", None) 120 | if attention_mask is not None and position_ids is None: 121 | # create position_ids on the fly for batch generation 122 | position_ids = attention_mask.long().cumsum(-1) - 1 123 | position_ids.masked_fill_(attention_mask == 0, 1) 124 | if past_key_values: 125 | position_ids = position_ids[:, -input_ids.shape[1] :] 126 | 127 | # if `inputs_embeds` are passed, we only want to use them in the 1st generation step 128 | if inputs_embeds is not None and past_key_values is None: 129 | model_inputs = {"inputs_embeds": inputs_embeds} 130 | else: 131 | # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise 132 | # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114 133 | # TODO: use `next_tokens` directly instead. 134 | model_inputs = {"input_ids": input_ids.contiguous()} 135 | 136 | input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1] 137 | if cache_position is None: 138 | cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device) 139 | else: 140 | cache_position = cache_position[-input_length:] 141 | 142 | if has_static_cache: 143 | past_key_values = None 144 | 145 | model_inputs.update( 146 | { 147 | "position_ids": position_ids, 148 | "cache_position": cache_position, 149 | "past_key_values": past_key_values, 150 | "use_cache": kwargs.get("use_cache"), 151 | "attention_mask": attention_mask, 152 | } 153 | ) 154 | return model_inputs 155 | 156 | def generate( 157 | self, 158 | emb, 159 | inputs_ids, 160 | temperature, 161 | eos_token, 162 | attention_mask = None, 163 | max_new_token = 2048, 164 | min_new_token = 0, 165 | LogitsWarpers = [], 166 | LogitsProcessors = [], 167 | infer_text=False, 168 | return_attn=False, 169 | return_hidden=False, 170 | ): 171 | 172 | with torch.no_grad(): 173 | 174 | attentions = [] 175 | hiddens = [] 176 | 177 | start_idx, end_idx = inputs_ids.shape[1], torch.zeros(inputs_ids.shape[0], device=inputs_ids.device, dtype=torch.long) 178 | finish = torch.zeros(inputs_ids.shape[0], device=inputs_ids.device).bool() 179 | 180 | temperature = temperature[None].expand(inputs_ids.shape[0], -1) 181 | temperature = rearrange(temperature, "b n -> (b n) 1") 182 | 183 | attention_mask_cache = torch.ones((inputs_ids.shape[0], inputs_ids.shape[1]+max_new_token,), dtype=torch.bool, device=inputs_ids.device) 184 | if attention_mask is not None: 185 | attention_mask_cache[:, :attention_mask.shape[1]] = attention_mask 186 | 187 | for i in tqdm(range(max_new_token)): 188 | 189 | model_input = self.prepare_inputs_for_generation(inputs_ids, 190 | outputs.past_key_values if i!=0 else None, 191 | attention_mask_cache[:, :inputs_ids.shape[1]], use_cache=True) 192 | 193 | if i == 0: 194 | model_input['inputs_embeds'] = emb 195 | else: 196 | if infer_text: 197 | model_input['inputs_embeds'] = self.emb_text(model_input['input_ids'][:,:,0]) 198 | else: 199 | code_emb = [self.emb_code[i](model_input['input_ids'][:,:,i]) for i in range(self.num_vq)] 200 | model_input['inputs_embeds'] = torch.stack(code_emb, 3).sum(3) 201 | 202 | model_input['input_ids'] = None 203 | outputs = self.gpt.forward(**model_input, output_attentions=return_attn) 204 | attentions.append(outputs.attentions) 205 | hidden_states = outputs[0] # 🐻 206 | if return_hidden: 207 | hiddens.append(hidden_states[:, -1]) 208 | 209 | with P.cached(): 210 | if infer_text: 211 | logits = self.head_text(hidden_states) 212 | else: 213 | logits = torch.stack([self.head_code[i](hidden_states) for i in range(self.num_vq)], 3) 214 | 215 | logits = logits[:, -1].float() 216 | 217 | if not infer_text: 218 | logits = rearrange(logits, "b c n -> (b n) c") 219 | logits_token = rearrange(inputs_ids[:, start_idx:], "b c n -> (b n) c") 220 | else: 221 | logits_token = inputs_ids[:, start_idx:, 0] 222 | 223 | logits = logits / temperature 224 | 225 | for logitsProcessors in LogitsProcessors: 226 | logits = logitsProcessors(logits_token, logits) 227 | 228 | for logitsWarpers in LogitsWarpers: 229 | logits = logitsWarpers(logits_token, logits) 230 | 231 | if i < min_new_token: 232 | logits[:, eos_token] = -torch.inf 233 | 234 | scores = F.softmax(logits, dim=-1) 235 | 236 | idx_next = torch.multinomial(scores, num_samples=1) 237 | 238 | if not infer_text: 239 | idx_next = rearrange(idx_next, "(b n) 1 -> b n", n=self.num_vq) 240 | finish = finish | (idx_next == eos_token).any(1) 241 | inputs_ids = torch.cat([inputs_ids, idx_next.unsqueeze(1)], 1) 242 | else: 243 | finish = finish | (idx_next == eos_token).any(1) 244 | inputs_ids = torch.cat([inputs_ids, idx_next.unsqueeze(-1).expand(-1, -1, self.num_vq)], 1) 245 | 246 | end_idx = end_idx + (~finish).int() 247 | 248 | if finish.all(): 249 | break 250 | 251 | inputs_ids = [inputs_ids[idx, start_idx: start_idx+i] for idx, i in enumerate(end_idx.int())] 252 | inputs_ids = [i[:, 0] for i in inputs_ids] if infer_text else inputs_ids 253 | 254 | if return_hidden: 255 | hiddens = torch.stack(hiddens, 1) 256 | hiddens = [hiddens[idx, :i] for idx, i in enumerate(end_idx.int())] 257 | 258 | if not finish.all(): 259 | self.logger.warn(f'Incomplete result. hit max_new_token: {max_new_token}') 260 | 261 | return { 262 | 'ids': inputs_ids, 263 | 'attentions': attentions, 264 | 'hiddens':hiddens, 265 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Attribution-NonCommercial 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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