Upload folder using huggingface_hub

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.ckpt
+# C extensions
+*.so
+*.pt
+
+# Distribution / packaging
+.Python
+outputs/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+asset/*
+.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; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
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+# SageMath parsed files
+*.sage.py
+
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+.env
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+
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+/site
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+.mypy_cache/
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+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

ChatTTS/__init__.py

@@ -0,0 +1 @@
+from .core import Chat

ChatTTS/core.py

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+
+import os
+import logging
+from functools import partial
+from omegaconf import OmegaConf
+
+import torch
+from vocos import Vocos
+from .model.dvae import DVAE
+from .model.gpt import GPT_warpper
+from .utils.gpu_utils import select_device
+from .utils.infer_utils import count_invalid_characters, detect_language, apply_character_map, apply_half2full_map
+from .utils.io_utils import get_latest_modified_file
+from .infer.api import refine_text, infer_code
+
+from huggingface_hub import snapshot_download
+
+logging.basicConfig(level = logging.INFO)
+
+
+class Chat:
+    def __init__(self, ):
+        self.pretrain_models = {}
+        self.normalizer = {}
+        self.logger = logging.getLogger(__name__)
+        
+    def check_model(self, level = logging.INFO, use_decoder = False):
+        not_finish = False
+        check_list = ['vocos', 'gpt', 'tokenizer']
+        
+        if use_decoder:
+            check_list.append('decoder')
+        else:
+            check_list.append('dvae')
+            
+        for module in check_list:
+            if module not in self.pretrain_models:
+                self.logger.log(logging.WARNING, f'{module} not initialized.')
+                not_finish = True
+                
+        if not not_finish:
+            self.logger.log(level, f'All initialized.')
+            
+        return not not_finish
+        
+    def load_models(self, source='huggingface', force_redownload=False, local_path='<LOCAL_PATH>', **kwargs):
+        if source == 'huggingface':
+            hf_home = os.getenv('HF_HOME', os.path.expanduser("~/.cache/huggingface"))
+            try:
+                download_path = get_latest_modified_file(os.path.join(hf_home, 'hub/models--2Noise--ChatTTS/snapshots'))
+            except:
+                download_path = None
+            if download_path is None or force_redownload: 
+                self.logger.log(logging.INFO, f'Download from HF: https://huggingface.co/2Noise/ChatTTS')
+                download_path = snapshot_download(repo_id="2Noise/ChatTTS", allow_patterns=["*.pt", "*.yaml"])
+            else:
+                self.logger.log(logging.INFO, f'Load from cache: {download_path}')
+        elif source == 'local':
+            self.logger.log(logging.INFO, f'Load from local: {local_path}')
+            download_path = local_path
+
+        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)
+        
+    def _load(
+        self, 
+        vocos_config_path: str = None, 
+        vocos_ckpt_path: str = None,
+        dvae_config_path: str = None,
+        dvae_ckpt_path: str = None,
+        gpt_config_path: str = None,
+        gpt_ckpt_path: str = None,
+        decoder_config_path: str = None,
+        decoder_ckpt_path: str = None,
+        tokenizer_path: str = None,
+        device: str = None,
+        compile: bool = True,
+    ):
+        if not device:
+            device = select_device(4096)
+            self.logger.log(logging.INFO, f'use {device}')
+            
+        if vocos_config_path:
+            vocos = Vocos.from_hparams(vocos_config_path).to(device).eval()
+            assert vocos_ckpt_path, 'vocos_ckpt_path should not be None'
+            vocos.load_state_dict(torch.load(vocos_ckpt_path))
+            self.pretrain_models['vocos'] = vocos
+            self.logger.log(logging.INFO, 'vocos loaded.')
+        
+        if dvae_config_path:
+            cfg = OmegaConf.load(dvae_config_path)
+            dvae = DVAE(**cfg).to(device).eval()
+            assert dvae_ckpt_path, 'dvae_ckpt_path should not be None'
+            dvae.load_state_dict(torch.load(dvae_ckpt_path, map_location='cpu'))
+            self.pretrain_models['dvae'] = dvae
+            self.logger.log(logging.INFO, 'dvae loaded.')
+            
+        if gpt_config_path:
+            cfg = OmegaConf.load(gpt_config_path)
+            gpt = GPT_warpper(**cfg).to(device).eval()
+            assert gpt_ckpt_path, 'gpt_ckpt_path should not be None'
+            gpt.load_state_dict(torch.load(gpt_ckpt_path, map_location='cpu'))
+            if compile:
+                gpt.gpt.forward = torch.compile(gpt.gpt.forward,  backend='inductor', dynamic=True)
+            self.pretrain_models['gpt'] = gpt
+            spk_stat_path = os.path.join(os.path.dirname(gpt_ckpt_path), 'spk_stat.pt')
+            assert os.path.exists(spk_stat_path), f'Missing spk_stat.pt: {spk_stat_path}'
+            self.pretrain_models['spk_stat'] = torch.load(spk_stat_path).to(device)
+            self.logger.log(logging.INFO, 'gpt loaded.')
+            
+        if decoder_config_path:
+            cfg = OmegaConf.load(decoder_config_path)
+            decoder = DVAE(**cfg).to(device).eval()
+            assert decoder_ckpt_path, 'decoder_ckpt_path should not be None'
+            decoder.load_state_dict(torch.load(decoder_ckpt_path, map_location='cpu'))
+            self.pretrain_models['decoder'] = decoder
+            self.logger.log(logging.INFO, 'decoder loaded.')
+        
+        if tokenizer_path:
+            tokenizer = torch.load(tokenizer_path, map_location='cpu')
+            tokenizer.padding_side = 'left'
+            self.pretrain_models['tokenizer'] = tokenizer
+            self.logger.log(logging.INFO, 'tokenizer loaded.')
+            
+        self.check_model()
+    
+    def infer(
+        self, 
+        text, 
+        skip_refine_text=False, 
+        refine_text_only=False, 
+        params_refine_text={}, 
+        params_infer_code={'prompt':'[speed_5]'}, 
+        use_decoder=True,
+        do_text_normalization=True,
+        lang=None,
+    ):
+        
+        assert self.check_model(use_decoder=use_decoder)
+        
+        if not isinstance(text, list): 
+            text = [text]
+        
+        if do_text_normalization:
+            for i, t in enumerate(text):
+                _lang = detect_language(t) if lang is None else lang
+                self.init_normalizer(_lang)
+                text[i] = self.normalizer[_lang](t)
+                if _lang == 'zh':
+                    text[i] = apply_half2full_map(text[i])
+            
+        for i, t in enumerate(text):
+            invalid_characters = count_invalid_characters(t)
+            if len(invalid_characters):
+                self.logger.log(logging.WARNING, f'Invalid characters found! : {invalid_characters}')
+                text[i] = apply_character_map(t)
+                
+        if not skip_refine_text:
+            text_tokens = refine_text(self.pretrain_models, text, **params_refine_text)['ids']
+            text_tokens = [i[i < self.pretrain_models['tokenizer'].convert_tokens_to_ids('[break_0]')] for i in text_tokens]
+            text = self.pretrain_models['tokenizer'].batch_decode(text_tokens)
+            if refine_text_only:
+                return text
+            
+        text = [params_infer_code.get('prompt', '') + i for i in text]
+        params_infer_code.pop('prompt', '')
+        result = infer_code(self.pretrain_models, text, **params_infer_code, return_hidden=use_decoder)
+        
+        if use_decoder:
+            mel_spec = [self.pretrain_models['decoder'](i[None].permute(0,2,1)) for i in result['hiddens']]
+        else:
+            mel_spec = [self.pretrain_models['dvae'](i[None].permute(0,2,1)) for i in result['ids']]
+            
+        wav = [self.pretrain_models['vocos'].decode(i).cpu().numpy() for i in mel_spec]
+        
+        return wav
+    
+    def sample_random_speaker(self, ):
+        
+        dim = self.pretrain_models['gpt'].gpt.layers[0].mlp.gate_proj.in_features
+        std, mean = self.pretrain_models['spk_stat'].chunk(2)
+        return torch.randn(dim, device=std.device) * std + mean
+    
+    def init_normalizer(self, lang):
+        
+        if lang not in self.normalizer:
+            if lang == 'zh':
+                try:
+                    from tn.chinese.normalizer import Normalizer
+                except:
+                    self.logger.log(logging.WARNING, f'Package WeTextProcessing not found! \
+                        Run: conda install -c conda-forge pynini=2.1.5 && pip install WeTextProcessing')
+                self.normalizer[lang] = Normalizer().normalize
+            else:
+                try:
+                    from nemo_text_processing.text_normalization.normalize import Normalizer
+                except:
+                    self.logger.log(logging.WARNING, f'Package nemo_text_processing not found! \
+                        Run: conda install -c conda-forge pynini=2.1.5 && pip install nemo_text_processing')
+                self.normalizer[lang] = partial(Normalizer(input_case='cased', lang=lang).normalize, verbose=False, punct_post_process=True)
+

ChatTTS/experimental/llm.py

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+
+from openai import OpenAI
+ 
+prompt_dict = {
+    '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。"},
+              {"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。"},
+              {"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。"},],
+    'deepseek': [
+        {"role": "system", "content": "You are a helpful assistant"},
+        {"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。"},
+        {"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。"},],
+    'deepseek_TN': [
+        {"role": "system", "content": "You are a helpful assistant"},
+        {"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"},
+        {"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"},
+        {"role": "user", "content": "We paid $123 for this desk."},
+        {"role": "assistant", "content": "We paid one hundred and twenty three dollars for this desk."},
+        {"role": "user", "content": "详询请拨打010-724654"},
+        {"role": "assistant", "content": "详询请拨打零幺零，七二四六五四"},
+        {"role": "user", "content": "罗森宣布将于7月24日退市，在华门店超6000家！"},
+        {"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。"},
+        ],
+}          
+                
+class llm_api:
+    def __init__(self, api_key, base_url, model):
+        self.client =  OpenAI(
+            api_key = api_key,
+            base_url = base_url,
+        )
+        self.model = model
+    def call(self, user_question, temperature = 0.3, prompt_version='kimi', **kwargs):
+    
+        completion = self.client.chat.completions.create(
+            model = self.model,
+            messages = prompt_dict[prompt_version]+[{"role": "user", "content": user_question},],
+            temperature = temperature,
+            **kwargs
+        )
+        return completion.choices[0].message.content

ChatTTS/infer/api.py

@@ -0,0 +1,125 @@
+
+import torch
+import torch.nn.functional as F
+from transformers.generation import TopKLogitsWarper, TopPLogitsWarper
+from ..utils.infer_utils import CustomRepetitionPenaltyLogitsProcessorRepeat
+
+def infer_code(
+    models,
+    text, 
+    spk_emb = None,
+    top_P = 0.7, 
+    top_K = 20, 
+    temperature = 0.3, 
+    repetition_penalty = 1.05,
+    max_new_token = 2048,
+    **kwargs
+):
+    
+    device = next(models['gpt'].parameters()).device
+    
+    if not isinstance(text, list): 
+        text = [text]
+        
+    if not isinstance(temperature, list):
+        temperature = [temperature] * models['gpt'].num_vq
+    
+    if spk_emb is not None:
+        text = [f'[Stts][spk_emb]{i}[Ptts]' for i in text] 
+    else:
+        text = [f'[Stts][empty_spk]{i}[Ptts]' for i in text]
+    
+    text_token = models['tokenizer'](text, return_tensors='pt', add_special_tokens=False, padding=True).to(device)
+    input_ids = text_token['input_ids'][...,None].expand(-1, -1, models['gpt'].num_vq)
+    text_mask = torch.ones(text_token['input_ids'].shape, dtype=bool, device=device)
+    
+    inputs = {
+        'input_ids': input_ids,
+        'text_mask': text_mask,
+        'attention_mask': text_token['attention_mask'],
+    }
+
+    emb = models['gpt'].get_emb(**inputs)
+    if spk_emb is not None:
+        emb[inputs['input_ids'][..., 0] == models['tokenizer'].convert_tokens_to_ids('[spk_emb]')] = \
+            F.normalize(spk_emb.to(device).to(emb.dtype)[None].expand(len(text), -1), p=2.0, dim=1, eps=1e-12)  
+    
+    num_code = models['gpt'].emb_code[0].num_embeddings - 1
+    
+    LogitsWarpers = []
+    if top_P is not None:
+        LogitsWarpers.append(TopPLogitsWarper(top_P, min_tokens_to_keep=3))
+    if top_K is not None:
+        LogitsWarpers.append(TopKLogitsWarper(top_K, min_tokens_to_keep=3))
+        
+    LogitsProcessors = []
+    if repetition_penalty is not None and repetition_penalty != 1:
+        LogitsProcessors.append(CustomRepetitionPenaltyLogitsProcessorRepeat(\
+            repetition_penalty, num_code, 16))
+    
+    result = models['gpt'].generate(
+        emb, inputs['input_ids'], 
+        temperature = torch.tensor(temperature, device=device), 
+        attention_mask = inputs['attention_mask'],
+        LogitsWarpers = LogitsWarpers,
+        LogitsProcessors = LogitsProcessors,
+        eos_token = num_code, 
+        max_new_token = max_new_token, 
+        infer_text = False,
+        **kwargs
+    )
+    
+    return result
+
+
+def refine_text(
+    models, 
+    text,
+    top_P = 0.7, 
+    top_K = 20, 
+    temperature = 0.7, 
+    repetition_penalty = 1.0,
+    max_new_token = 384,
+    prompt = '',
+    **kwargs
+):
+    
+    device = next(models['gpt'].parameters()).device
+    
+    if not isinstance(text, list): 
+        text = [text]
+    
+    assert len(text), 'text should not be empty'
+
+    text = [f"[Sbreak]{i}[Pbreak]{prompt}" for i in text]
+    text_token = models['tokenizer'](text, return_tensors='pt', add_special_tokens=False, padding=True).to(device)
+    text_mask = torch.ones(text_token['input_ids'].shape, dtype=bool, device=device)
+
+    inputs = {
+        'input_ids': text_token['input_ids'][...,None].expand(-1, -1, models['gpt'].num_vq),
+        'text_mask': text_mask,
+        'attention_mask': text_token['attention_mask'],
+    }
+    
+    LogitsWarpers = []
+    if top_P is not None:
+        LogitsWarpers.append(TopPLogitsWarper(top_P, min_tokens_to_keep=3))
+    if top_K is not None:
+        LogitsWarpers.append(TopKLogitsWarper(top_K, min_tokens_to_keep=3))
+        
+    LogitsProcessors = []
+    if repetition_penalty is not None and repetition_penalty != 1:
+        LogitsProcessors.append(CustomRepetitionPenaltyLogitsProcessorRepeat(repetition_penalty, len(models['tokenizer']), 16))
+    
+    result = models['gpt'].generate(
+        models['gpt'].get_emb(**inputs), inputs['input_ids'], 
+        temperature = torch.tensor([temperature,], device=device), 
+        attention_mask = inputs['attention_mask'],
+        LogitsWarpers = LogitsWarpers,
+        LogitsProcessors = LogitsProcessors,
+        eos_token = torch.tensor(models['tokenizer'].convert_tokens_to_ids('[Ebreak]'), device=device)[None], 
+        max_new_token = max_new_token, 
+        infer_text = True,
+        **kwargs
+    )
+    return result

ChatTTS/model/dvae.py

@@ -0,0 +1,155 @@
+import math
+from einops import rearrange
+from vector_quantize_pytorch import GroupedResidualFSQ
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ConvNeXtBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        kernel, dilation,
+        layer_scale_init_value: float = 1e-6,
+    ):
+        # ConvNeXt Block copied from Vocos.
+        super().__init__()
+        self.dwconv = nn.Conv1d(dim, dim, 
+                                kernel_size=kernel, padding=dilation*(kernel//2), 
+                                dilation=dilation, groups=dim
+                            )  # depthwise conv
+        
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+        self.gamma = (
+            nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
+            if layer_scale_init_value > 0
+            else None
+        )
+
+    def forward(self, x: torch.Tensor, cond = None) -> torch.Tensor:
+        residual = x
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # (B, C, T) -> (B, T, C)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.transpose(1, 2)  # (B, T, C) -> (B, C, T)
+
+        x = residual + x
+        return x
+    
+
+
+class GFSQ(nn.Module):
+
+    def __init__(self, 
+            dim, levels, G, R, eps=1e-5, transpose = True
+        ):
+        super(GFSQ, self).__init__()
+        self.quantizer = GroupedResidualFSQ(
+            dim=dim,
+            levels=levels,
+            num_quantizers=R,
+            groups=G,
+        )
+        self.n_ind = math.prod(levels)
+        self.eps = eps
+        self.transpose = transpose
+        self.G = G
+        self.R = R
+        
+    def _embed(self, x):
+        if self.transpose:
+            x = x.transpose(1,2)
+        x = rearrange(
+            x, "b t (g r) -> g b t r", g = self.G, r = self.R,
+        )  
+        feat = self.quantizer.get_output_from_indices(x)
+        return feat.transpose(1,2) if self.transpose else feat
+        
+    def forward(self, x,):
+        if self.transpose:
+            x = x.transpose(1,2)
+        feat, ind = self.quantizer(x)
+        ind = rearrange(
+            ind, "g b t r ->b t (g r)",
+        )  
+        embed_onehot = F.one_hot(ind.long(), self.n_ind).to(x.dtype)
+        e_mean = torch.mean(embed_onehot, dim=[0,1])
+        e_mean = e_mean / (e_mean.sum(dim=1) + self.eps).unsqueeze(1)
+        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + self.eps), dim=1))
+        
+        return (
+            torch.zeros(perplexity.shape, dtype=x.dtype, device=x.device),
+            feat.transpose(1,2) if self.transpose else feat,
+            perplexity,
+            None,
+            ind.transpose(1,2) if self.transpose else ind,
+        )
+        
+class DVAEDecoder(nn.Module):
+    def __init__(self, idim, odim,
+                 n_layer = 12, bn_dim = 64, hidden = 256, 
+                 kernel = 7, dilation = 2, up = False
+                ):
+        super().__init__()
+        self.up = up
+        self.conv_in = nn.Sequential(
+            nn.Conv1d(idim, bn_dim, 3, 1, 1), nn.GELU(),
+            nn.Conv1d(bn_dim, hidden, 3, 1, 1)
+        )
+        self.decoder_block = nn.ModuleList([
+            ConvNeXtBlock(hidden, hidden* 4, kernel, dilation,)
+            for _ in range(n_layer)])
+        self.conv_out = nn.Conv1d(hidden, odim, kernel_size=1, bias=False)
+
+    def forward(self, input, conditioning=None):
+        # B, T, C
+        x = input.transpose(1, 2)
+        x = self.conv_in(x)
+        for f in self.decoder_block:
+            x = f(x, conditioning)
+        
+        x = self.conv_out(x)
+        return x.transpose(1, 2)
+    
+
+class DVAE(nn.Module):
+    def __init__(
+        self, decoder_config, vq_config, dim=512
+    ):
+        super().__init__()
+        self.register_buffer('coef', torch.randn(1, 100, 1))
+
+        self.decoder = DVAEDecoder(**decoder_config)
+        self.out_conv = nn.Conv1d(dim, 100, 3, 1, 1, bias=False)
+        if vq_config is not None:
+            self.vq_layer = GFSQ(**vq_config)
+        else:
+            self.vq_layer = None
+
+    def forward(self, inp):
+
+        if self.vq_layer is not None:
+            vq_feats = self.vq_layer._embed(inp)
+        else:
+            vq_feats = inp.detach().clone()
+            
+        temp = torch.chunk(vq_feats, 2, dim=1) # flatten trick :)
+        temp = torch.stack(temp, -1)
+        vq_feats = temp.reshape(*temp.shape[:2], -1)
+
+        vq_feats = vq_feats.transpose(1, 2)
+        dec_out = self.decoder(input=vq_feats)
+        dec_out = self.out_conv(dec_out.transpose(1, 2))
+        mel = dec_out * self.coef
+
+        return mel

ChatTTS/model/gpt.py

@@ -0,0 +1,265 @@
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import logging
+from tqdm import tqdm
+from einops import rearrange
+from transformers.cache_utils import Cache
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.utils.parametrize as P
+from torch.nn.utils.parametrizations import weight_norm
+from transformers import LlamaModel, LlamaConfig
+    
+    
+class LlamaMLP(nn.Module):
+    def __init__(self, hidden_size, intermediate_size):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = F.silu
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+    
+    
+class GPT_warpper(nn.Module):
+    def __init__(
+        self, 
+        gpt_config, 
+        num_audio_tokens,
+        num_text_tokens,
+        num_vq=4,
+        **kwargs,
+        ):
+        super().__init__()
+
+        self.logger = logging.getLogger(__name__)
+        self.gpt = self.build_model(gpt_config)
+        self.model_dim = self.gpt.config.hidden_size 
+
+        self.num_vq = num_vq
+        self.emb_code = nn.ModuleList([nn.Embedding(num_audio_tokens, self.model_dim) for i in range(self.num_vq)])
+        self.emb_text = nn.Embedding(num_text_tokens, self.model_dim)
+        self.head_text = weight_norm(nn.Linear(self.model_dim, num_text_tokens, bias=False), name='weight')
+        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)])
+
+    def build_model(self, config):
+        
+        configuration = LlamaConfig(**config)
+        model = LlamaModel(configuration)
+        del model.embed_tokens
+        
+        return model
+    
+    def get_emb(self, input_ids, text_mask, **kwargs):
+
+        emb_text = self.emb_text(input_ids[text_mask][:, 0])
+        
+        emb_code = [self.emb_code[i](input_ids[~text_mask][:, i]) for i in range(self.num_vq)]
+        emb_code = torch.stack(emb_code, 2).sum(2)
+        
+        emb = torch.zeros((input_ids.shape[:-1])+(emb_text.shape[-1],), device=emb_text.device, dtype=emb_text.dtype)
+        emb[text_mask] = emb_text
+        emb[~text_mask] = emb_code.to(emb.dtype)
+        
+        return emb
+    
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, **kwargs
+    ):
+        # With static cache, the `past_key_values` is None
+        # TODO joao: standardize interface for the different Cache classes and remove of this if
+        has_static_cache = False
+        if past_key_values is None:
+            past_key_values = getattr(self.gpt.layers[0].self_attn, "past_key_value", None)
+            has_static_cache = past_key_values is not None
+
+        past_length = 0
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
+                max_cache_length = (
+                    torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
+                    if past_key_values.get_max_length() is not None
+                    else None
+                )
+                cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
+            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {"input_ids": input_ids.contiguous()}
+
+        input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
+        else:
+            cache_position = cache_position[-input_length:]
+
+        if has_static_cache:
+            past_key_values = None
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+    
+    def generate(
+        self, 
+        emb, 
+        inputs_ids, 
+        temperature, 
+        eos_token, 
+        attention_mask = None,
+        max_new_token = 2048, 
+        min_new_token = 0,
+        LogitsWarpers = [],
+        LogitsProcessors = [],
+        infer_text=False,
+        return_attn=False,
+        return_hidden=False,
+    ):
+        
+        with torch.no_grad():   
+        
+            attentions = []
+            hiddens = []
+            
+            start_idx, end_idx = inputs_ids.shape[1], torch.zeros(inputs_ids.shape[0], device=inputs_ids.device, dtype=torch.long)
+            finish = torch.zeros(inputs_ids.shape[0], device=inputs_ids.device).bool()
+            
+            temperature = temperature[None].expand(inputs_ids.shape[0], -1)
+            temperature = rearrange(temperature, "b n -> (b n) 1")
+
+            attention_mask_cache = torch.ones((inputs_ids.shape[0], inputs_ids.shape[1]+max_new_token,), dtype=torch.bool, device=inputs_ids.device)
+            if attention_mask is not None:
+                attention_mask_cache[:, :attention_mask.shape[1]] = attention_mask
+            
+            for i in tqdm(range(max_new_token)):
+        
+                model_input = self.prepare_inputs_for_generation(inputs_ids, 
+                    outputs.past_key_values if i!=0 else None, 
+                    attention_mask_cache[:, :inputs_ids.shape[1]], use_cache=True)
+            
+                if i == 0:
+                    model_input['inputs_embeds'] = emb
+                else:
+                    if infer_text:
+                        model_input['inputs_embeds'] = self.emb_text(model_input['input_ids'][:,:,0])
+                    else:
+                        code_emb = [self.emb_code[i](model_input['input_ids'][:,:,i]) for i in range(self.num_vq)]
+                        model_input['inputs_embeds'] = torch.stack(code_emb, 3).sum(3)
+                
+                model_input['input_ids'] = None
+                outputs = self.gpt.forward(**model_input, output_attentions=return_attn)
+                attentions.append(outputs.attentions)
+                hidden_states = outputs[0] # 🐻
+                if return_hidden:
+                    hiddens.append(hidden_states[:, -1])
+
+                with P.cached():
+                    if infer_text:
+                        logits = self.head_text(hidden_states) 
+                    else:
+                        logits = torch.stack([self.head_code[i](hidden_states) for i in range(self.num_vq)], 3)
+        
+                logits = logits[:, -1].float()
+
+                if not infer_text:
+                    logits = rearrange(logits, "b c n -> (b n) c")
+                    logits_token = rearrange(inputs_ids[:, start_idx:], "b c n -> (b n) c")
+                else:
+                    logits_token = inputs_ids[:, start_idx:, 0]
+                    
+                logits = logits / temperature
+                
+                for logitsProcessors in LogitsProcessors:
+                    logits = logitsProcessors(logits_token, logits)
+                    
+                for logitsWarpers in LogitsWarpers:
+                    logits = logitsWarpers(logits_token, logits)
+                    
+                if i < min_new_token:
+                    logits[:, eos_token] = -torch.inf
+                
+                scores = F.softmax(logits, dim=-1)
+            
+                idx_next = torch.multinomial(scores, num_samples=1)
+                
+                if not infer_text:
+                    idx_next = rearrange(idx_next, "(b n) 1 -> b n", n=self.num_vq)
+                    finish = finish | (idx_next == eos_token).any(1)
+                    inputs_ids = torch.cat([inputs_ids, idx_next.unsqueeze(1)], 1)
+                else:
+                    finish = finish | (idx_next == eos_token).any(1)
+                    inputs_ids = torch.cat([inputs_ids, idx_next.unsqueeze(-1).expand(-1, -1, self.num_vq)], 1)
+
+                end_idx = end_idx + (~finish).int()
+            
+                if finish.all():
+                    break
+            
+            inputs_ids = [inputs_ids[idx, start_idx: start_idx+i] for idx, i in enumerate(end_idx.int())]
+            inputs_ids = [i[:, 0] for i in inputs_ids] if infer_text else inputs_ids
+            
+            if return_hidden:
+                hiddens = torch.stack(hiddens, 1)
+                hiddens = [hiddens[idx, :i] for idx, i in enumerate(end_idx.int())]
+                    
+            if not finish.all():
+                self.logger.warn(f'Incomplete result. hit max_new_token: {max_new_token}')    
+                   
+            return {
+                'ids': inputs_ids, 
+                'attentions': attentions,
+                'hiddens':hiddens,
+            }

ChatTTS/utils/gpu_utils.py

@@ -0,0 +1,23 @@
+
+import torch
+import logging
+
+def select_device(min_memory = 2048):
+    logger = logging.getLogger(__name__)
+    if torch.cuda.is_available():
+        available_gpus = []
+        for i in range(torch.cuda.device_count()):
+            props = torch.cuda.get_device_properties(i)
+            free_memory = props.total_memory - torch.cuda.memory_reserved(i)
+            available_gpus.append((i, free_memory))
+        selected_gpu, max_free_memory = max(available_gpus, key=lambda x: x[1])
+        device = torch.device(f'cuda:{selected_gpu}')
+        free_memory_mb = max_free_memory / (1024 * 1024)
+        if free_memory_mb < min_memory:
+            logger.log(logging.WARNING, f'GPU {selected_gpu} has {round(free_memory_mb, 2)} MB memory left.')
+            device = torch.device('cpu')
+    else:
+        logger.log(logging.WARNING, f'No GPU found, use CPU instead')
+        device = torch.device('cpu')
+    
+    return device

ChatTTS/utils/infer_utils.py

@@ -0,0 +1,141 @@
+
+import re
+import torch
+import torch.nn.functional as F
+
+    
+class CustomRepetitionPenaltyLogitsProcessorRepeat():
+
+    def __init__(self, penalty: float, max_input_ids, past_window):
+        if not isinstance(penalty, float) or not (penalty > 0):
+            raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
+
+        self.penalty = penalty
+        self.max_input_ids = max_input_ids
+        self.past_window = past_window
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        
+        input_ids = input_ids[:, -self.past_window:]
+        freq = F.one_hot(input_ids, scores.size(1)).sum(1)
+        freq[self.max_input_ids:] = 0
+        alpha = self.penalty**freq
+        scores = torch.where(scores < 0, scores*alpha, scores/alpha)
+
+        return scores
+    
+class CustomRepetitionPenaltyLogitsProcessor():
+
+    def __init__(self, penalty: float, max_input_ids, past_window):
+        if not isinstance(penalty, float) or not (penalty > 0):
+            raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
+
+        self.penalty = penalty
+        self.max_input_ids = max_input_ids
+        self.past_window = past_window
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        
+        input_ids = input_ids[:, -self.past_window:]
+        score = torch.gather(scores, 1, input_ids)
+        _score = score.detach().clone()
+        score = torch.where(score < 0, score * self.penalty, score / self.penalty)
+        score[input_ids>=self.max_input_ids] = _score[input_ids>=self.max_input_ids]
+        scores.scatter_(1, input_ids, score)
+        
+        return scores
+    
+def count_invalid_characters(s):
+    
+    s = re.sub(r'\[uv_break\]|\[laugh\]|\[lbreak\]', '', s)
+    pattern = re.compile(r'[^\u4e00-\u9fffA-Za-z，。、,\. ]')
+    non_alphabetic_chinese_chars = pattern.findall(s)
+    return set(non_alphabetic_chinese_chars)
+
+def detect_language(sentence):
+
+    chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]')
+    english_word_pattern = re.compile(r'\b[A-Za-z]+\b')
+
+    chinese_chars = chinese_char_pattern.findall(sentence)
+    english_words = english_word_pattern.findall(sentence)
+
+    if len(chinese_chars) > len(english_words):
+        return "zh"
+    else:
+        return "en"
+    
+    
+character_map = {
+    '：': '，',
+    '；': '，',
+    '！': '。',
+    '（': '，',
+    '）': '，',
+    '【': '，',
+    '】': '，',
+    '『': '，',
+    '』': '，',
+    '「': '，',
+    '」': '，',
+    '《': '，',
+    '》': '，',
+    '－': '，',
+    '‘': '',
+    '“': '',
+    '’': '',
+    '”': '',
+    ':': ',',
+    ';': ',',
+    '!': '.',
+    '(': ',',
+    ')': ',',
+    '[': ',',
+    ']': ',',
+    '>': ',',
+    '<': ',',
+    '-': ',',
+}
+
+halfwidth_2_fullwidth_map = {
+        '!': '！',
+        '"': '“',
+        "'": '‘',
+        '#': '＃',
+        '$': '＄',
+        '%': '％',
+        '&': '＆',
+        '(': '（',
+        ')': '）',
+        ',': '，',
+        '-': '－',
+        '*': '＊',
+        '+': '＋',
+        '.': '。',
+        '/': '／',
+        ':': '：',
+        ';': '；',
+        '<': '＜',
+        '=': '＝',
+        '>': '＞',
+        '?': '？',
+        '@': '＠',
+        '[': '［',
+        '\\': '＼',
+        ']': '］',
+        '^': '＾',
+        '_': '＿',
+        '`': '｀',
+        '{': '｛',
+        '|': '｜',
+        '}': '｝',
+        '~': '～'
+    }
+
+def apply_half2full_map(text):
+    translation_table = str.maketrans(halfwidth_2_fullwidth_map)
+    return text.translate(translation_table)
+
+def apply_character_map(text):
+    translation_table = str.maketrans(character_map)
+    return text.translate(translation_table)

ChatTTS/utils/io_utils.py

@@ -0,0 +1,14 @@
+
+import os
+import logging
+
+def get_latest_modified_file(directory):
+    logger = logging.getLogger(__name__)
+    
+    files = [os.path.join(directory, f) for f in os.listdir(directory)] 
+    if not files:
+        logger.log(logging.WARNING, f'No files found in the directory: {directory}')
+        return None
+    latest_file = max(files, key=os.path.getmtime)
+
+    return latest_file

LICENSE

@@ -0,0 +1,157 @@
+# Attribution-NonCommercial-NoDerivatives 4.0 International
+
+> *Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible.*
+>
+> ### Using Creative Commons Public Licenses
+>
+> Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses.
+>
+> * __Considerations for licensors:__ Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. [More considerations for licensors](http://wiki.creativecommons.org/Considerations_for_licensors_and_licensees#Considerations_for_licensors).
+>
+> * __Considerations for the public:__ By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor’s permission is not necessary for any reason–for example, because of any applicable exception or limitation to copyright–then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. Although not required by our licenses, you are encouraged to respect those requests where reasonable. [More considerations for the public](http://wiki.creativecommons.org/Considerations_for_licensors_and_licensees#Considerations_for_licensees).
+
+## Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License
+
+By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions.
+
+### Section 1 – Definitions.
+
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Pipfile

@@ -0,0 +1,19 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+omegaconf = "~=2.3.0"
+torch = "~=2.1.0"
+tqdm = "*"
+einops = "*"
+vector-quantize-pytorch = "*"
+transformers = "~=4.41.1"
+vocos = "*"
+ipython = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.12"

README.md

@@ -1,12 +1,138 @@
----
-title: ChatTTS
-emoji: 📈
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 4.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: ChatTTS
+app_file: webui.py
+sdk: gradio
+sdk_version: 4.42.0
+---
+# ChatTTS
+[**English**](./README.md) | [**中文简体**](./README_CN.md)
+
+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.
+
+For formal inquiries about model and roadmap, please contact us at **open-source@2noise.com**. You could join our QQ group: 808364215 for discussion. Adding github issues is always welcomed.
+
+---
+## Highlights
+1. **Conversational TTS**: ChatTTS is optimized for dialogue-based tasks, enabling natural and expressive speech synthesis. It supports multiple speakers, facilitating interactive conversations.
+2. **Fine-grained Control**: The model could predict and control fine-grained prosodic features, including laughter, pauses, and interjections. 
+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.
+
+For the detailed description of the model, you can refer to **[video on Bilibili](https://www.bilibili.com/video/BV1zn4y1o7iV)**
+
+---
+
+## Disclaimer
+
+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.
+
+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.
+
+
+---
+## Usage
+
+<h4>Basic usage</h4>
+
+```python
+import ChatTTS
+from IPython.display import Audio
+
+chat = ChatTTS.Chat()
+chat.load_models(compile=False) # Set to True for better performance
+
+texts = ["PUT YOUR TEXT HERE",]
+
+wavs = chat.infer(texts, )
+
+torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000)
+```
+
+<h4>Advanced usage</h4>
+
+```python
+###################################
+# Sample a speaker from Gaussian.
+
+rand_spk = chat.sample_random_speaker()
+
+params_infer_code = {
+  'spk_emb': rand_spk, # add sampled speaker 
+  'temperature': .3, # using custom temperature
+  'top_P': 0.7, # top P decode
+  'top_K': 20, # top K decode
+}
+
+###################################
+# For sentence level manual control.
+
+# use oral_(0-9), laugh_(0-2), break_(0-7) 
+# to generate special token in text to synthesize.
+params_refine_text = {
+  'prompt': '[oral_2][laugh_0][break_6]'
+} 
+
+wav = chat.infer(texts, params_refine_text=params_refine_text, params_infer_code=params_infer_code)
+
+###################################
+# For word level manual control.
+text = 'What is [uv_break]your favorite english food?[laugh][lbreak]'
+wav = chat.infer(text, skip_refine_text=True, params_refine_text=params_refine_text,  params_infer_code=params_infer_code)
+torchaudio.save("output2.wav", torch.from_numpy(wavs[0]), 24000)
+```
+
+<details open>
+  <summary><h4>Example: self introduction</h4></summary>
+
+```python
+inputs_en = """
+chat T T S is a text to speech model designed for dialogue applications. 
+[uv_break]it supports mixed language input [uv_break]and offers multi speaker 
+capabilities with precise control over prosodic elements [laugh]like like 
+[uv_break]laughter[laugh], [uv_break]pauses, [uv_break]and intonation. 
+[uv_break]it delivers natural and expressive speech,[uv_break]so please
+[uv_break] use the project responsibly at your own risk.[uv_break]
+""".replace('\n', '') # English is still experimental.
+
+params_refine_text = {
+  'prompt': '[oral_2][laugh_0][break_4]'
+} 
+# audio_array_cn = chat.infer(inputs_cn, params_refine_text=params_refine_text)
+audio_array_en = chat.infer(inputs_en, params_refine_text=params_refine_text)
+torchaudio.save("output3.wav", torch.from_numpy(audio_array_en[0]), 24000)
+```
+[male speaker](https://github.com/2noise/ChatTTS/assets/130631963/e0f51251-db7f-4d39-a0e9-3e095bb65de1)
+
+[female speaker](https://github.com/2noise/ChatTTS/assets/130631963/f5dcdd01-1091-47c5-8241-c4f6aaaa8bbd)
+</details>
+
+---
+## Roadmap
+- [x] Open-source the 40k hour base model and spk_stats file
+- [ ] Open-source VQ encoder and Lora training code
+- [ ] Streaming audio generation without refining the text*
+- [ ] Open-source the 40k hour version with multi-emotion control
+- [ ] ChatTTS.cpp maybe? (PR or new repo are welcomed.)
+ 
+----
+## FAQ
+
+##### How much VRAM do I need? How about infer speed?
+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.
+
+##### model stability is not good enough, with issues such as multi speakers or poor audio quality.
+
+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.
+
+##### Besides laughter, can we control anything else? Can we control other emotions?
+
+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.
+
+---
+## Acknowledgements
+- [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.
+- [fish-speech](https://github.com/fishaudio/fish-speech) reveals capability of GVQ as audio tokenizer for LLM modeling.
+- [vocos](https://github.com/gemelo-ai/vocos) which is used as a pretrained vocoder.
+
+---
+## Special Appreciation
+- [wlu-audio lab](https://audio.westlake.edu.cn/) for early algorithm experiments.

README_CN.md

@@ -0,0 +1,136 @@
+# ChatTTS
+[**English**](./README.md) | [**中文简体**](./README_CN.md)
+
+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.
+
+如需就模型进行正式商业咨询，请发送邮件至 **[email protected]**。对于中文用户，您可以加入我们的QQ群：808364215 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. 
+
+---
+## 亮点
+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。
+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。
+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。
+
+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)**
+
+---
+
+## 免责声明
+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。
+
+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。
+
+---
+## 用法
+
+<h4>基本用法</h4>
+
+```python
+import ChatTTS
+from IPython.display import Audio
+
+chat = ChatTTS.Chat()
+chat.load_models(compile=False) # 设置为True以获得更快速度
+
+texts = ["在这里输入你的文本",]
+
+wavs = chat.infer(texts, use_decoder=True)
+
+torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000)
+```
+
+<h4>进阶用法</h4>
+
+```python
+###################################
+# Sample a speaker from Gaussian.
+
+rand_spk = chat.sample_random_speaker()
+
+params_infer_code = {
+  'spk_emb': rand_spk, # add sampled speaker 
+  'temperature': .3, # using custom temperature
+  'top_P': 0.7, # top P decode
+  'top_K': 20, # top K decode
+}
+
+###################################
+# For sentence level manual control.
+
+# use oral_(0-9), laugh_(0-2), break_(0-7) 
+# to generate special token in text to synthesize.
+params_refine_text = {
+  'prompt': '[oral_2][laugh_0][break_6]'
+} 
+
+wav = chat.infer(texts, params_refine_text=params_refine_text, params_infer_code=params_infer_code)
+
+###################################
+# For word level manual control.
+# use_decoder=False to infer faster with a bit worse quality
+text = 'What is [uv_break]your favorite english food?[laugh][lbreak]'
+wav = chat.infer(text, skip_refine_text=True, params_infer_code=params_infer_code, use_decoder=False)
+
+torchaudio.save("output2.wav", torch.from_numpy(wavs[0]), 24000)
+```
+
+<details open>
+  <summary><h4>自我介绍样例</h4></summary>
+
+```python
+inputs_cn = """
+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。
+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]，
+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]。
+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]'
+""".replace('\n', '')
+
+params_refine_text = {
+  'prompt': '[oral_2][laugh_0][break_4]'
+} 
+audio_array_cn = chat.infer(inputs_cn, params_refine_text=params_refine_text)
+# audio_array_en = chat.infer(inputs_en, params_refine_text=params_refine_text)
+
+torchaudio.save("output3.wav", torch.from_numpy(audio_array_cn[0]), 24000)
+```
+[男说话人](https://github.com/2noise/ChatTTS/assets/130631963/bbfa3b83-2b67-4bb6-9315-64c992b63788)
+
+[女说话人](https://github.com/2noise/ChatTTS/assets/130631963/e061f230-0e05-45e6-8e4e-0189f2d260c4)
+</details>
+
+
+---
+## 计划路线
+- [x] 开源4w小时基础模型和spk_stats文件
+- [ ] 开源VQ encoder和Lora 训练代码
+- [ ] 在非refine text情况下, 流式生成音频*
+- [ ] 开源多情感可控的4w小时版本
+- [ ] ChatTTS.cpp maybe? (欢迎社区PR或独立的新repo)
+
+---
+## 常见问题
+
+##### 连不上HuggingFace
+请使用[modelscope](https://www.modelscope.cn/models/pzc163/chatTTS)的版本. 并设置cache的位置:
+```python
+chat.load_models(source='local', local_path='你的下载位置')
+```
+
+##### 我要多少显存? Infer的速度是怎么样的?
+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.
+
+##### 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.
+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.
+
+##### 除了笑声还能控制什么吗? 还能控制其他情感吗?
+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.
+
+---
+## 致谢
+- [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.
+- [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.
+- [vocos](https://github.com/gemelo-ai/vocos)作为模型中的vocoder.
+
+---
+## 特别致谢
+- [wlu-audio lab](https://audio.westlake.edu.cn/)为我们提供了早期算法试验的支持.

requirements.txt

@@ -0,0 +1,8 @@
+omegaconf~=2.3.0
+torch~=2.3.0
+tqdm
+einops
+vector_quantize_pytorch
+transformers~=4.41.1
+vocos
+IPython

webui.py

@@ -0,0 +1,106 @@
+import os
+import random
+import argparse
+
+import torch
+import gradio as gr
+import numpy as np
+
+import ChatTTS
+
+print("loading ChatTTS model...")
+chat = ChatTTS.Chat()
+chat.load_models()
+
+
+def generate_seed():
+    new_seed = random.randint(1, 100000000)
+    return {
+        "__type__": "update",
+        "value": new_seed
+        }
+
+
+def generate_audio(text, temperature, top_P, top_K, audio_seed_input, text_seed_input, refine_text_flag):
+
+    torch.manual_seed(audio_seed_input)
+    rand_spk = torch.randn(768)
+    params_infer_code = {
+        'spk_emb': rand_spk, 
+        'temperature': temperature,
+        'top_P': top_P,
+        'top_K': top_K,
+        }
+    params_refine_text = {'prompt': '[oral_2][laugh_0][break_6]'}
+    
+    torch.manual_seed(text_seed_input)
+
+    if refine_text_flag:
+        text = chat.infer(text, 
+                          skip_refine_text=False,
+                          refine_text_only=True,
+                          params_refine_text=params_refine_text,
+                          params_infer_code=params_infer_code
+                          )
+    
+    wav = chat.infer(text, 
+                     skip_refine_text=True, 
+                     params_refine_text=params_refine_text, 
+                     params_infer_code=params_infer_code
+                     )
+    
+    audio_data = np.array(wav[0]).flatten()
+    sample_rate = 24000
+    text_data = text[0] if isinstance(text, list) else text
+
+    return [(sample_rate, audio_data), text_data]
+
+
+def main():
+
+    with gr.Blocks() as demo:
+        gr.Markdown("# ChatTTS Webui")
+        gr.Markdown("ChatTTS Model: [2noise/ChatTTS](https://github.com/2noise/ChatTTS)")
+
+        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。"        
+        text_input = gr.Textbox(label="Input Text", lines=4, placeholder="Please Input Text...", value=default_text)
+
+        with gr.Row():
+            refine_text_checkbox = gr.Checkbox(label="Refine text", value=True)
+            temperature_slider = gr.Slider(minimum=0.00001, maximum=1.0, step=0.00001, value=0.3, label="Audio temperature")
+            top_p_slider = gr.Slider(minimum=0.1, maximum=0.9, step=0.05, value=0.7, label="top_P")
+            top_k_slider = gr.Slider(minimum=1, maximum=20, step=1, value=20, label="top_K")
+
+        with gr.Row():
+            audio_seed_input = gr.Number(value=42, label="Audio Seed")
+            generate_audio_seed = gr.Button("\U0001F3B2")
+            text_seed_input = gr.Number(value=42, label="Text Seed")
+            generate_text_seed = gr.Button("\U0001F3B2")
+
+        generate_button = gr.Button("Generate")
+        
+        text_output = gr.Textbox(label="Output Text", interactive=False)
+        audio_output = gr.Audio(label="Output Audio")
+
+        generate_audio_seed.click(generate_seed, 
+                                  inputs=[], 
+                                  outputs=audio_seed_input)
+        
+        generate_text_seed.click(generate_seed, 
+                                 inputs=[], 
+                                 outputs=text_seed_input)
+        
+        generate_button.click(generate_audio, 
+                              inputs=[text_input, temperature_slider, top_p_slider, top_k_slider, audio_seed_input, text_seed_input, refine_text_checkbox], 
+                              outputs=[audio_output, text_output])
+
+    parser = argparse.ArgumentParser(description='ChatTTS demo Launch')
+    parser.add_argument('--server_name', type=str, default='0.0.0.0', help='Server name')
+    parser.add_argument('--server_port', type=int, default=8000, help='Server port')
+    args = parser.parse_args()
+
+    demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True, share=True)
+
+
+if __name__ == '__main__':
+    main()