├── models ├── asset │ └── 在此放置你的模型.txt ├── decoder.yaml ├── config │ ├── decoder.yaml │ ├── dvae.yaml │ ├── path.yaml │ ├── gpt.yaml │ └── vocos.yaml ├── dvae.yaml ├── path.yaml ├── gpt.yaml └── vocos.yaml ├── ChatTTS ├── __init__.py ├── __pycache__ │ ├── core.cpython-39.pyc │ └── __init__.cpython-39.pyc ├── infer │ ├── __pycache__ │ │ └── api.cpython-39.pyc │ └── api.py ├── model │ ├── __pycache__ │ │ ├── gpt.cpython-39.pyc │ │ └── dvae.cpython-39.pyc │ ├── dvae.py │ └── gpt.py ├── utils │ ├── __pycache__ │ │ ├── io_utils.cpython-39.pyc │ │ ├── gpu_utils.cpython-39.pyc │ │ └── infer_utils.cpython-39.pyc │ ├── io_utils.py │ ├── gpu_utils.py │ └── infer_utils.py ├── experimental │ └── llm.py └── core.py ├── screenshot.png ├── .gitattributes ├── outputs ├── 20240601005412 - 4077.wav └── 20240601010526 - 3921.wav ├── requirements.txt ├── README.md ├── utils.py ├── run_webui.py ├── LICENSE └── sampled_speaker ├── jidong.csv ├── suiji.csv ├── dandan.csv ├── niuniu.csv └── wenrou_nansheng.csv /models/asset/在此放置你的模型.txt: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ChatTTS/__init__.py: -------------------------------------------------------------------------------- 1 | from .core import Chat -------------------------------------------------------------------------------- /screenshot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/screenshot.png -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /outputs/20240601005412 - 4077.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/outputs/20240601005412 - 4077.wav -------------------------------------------------------------------------------- /outputs/20240601010526 - 3921.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/outputs/20240601010526 - 3921.wav -------------------------------------------------------------------------------- /ChatTTS/__pycache__/core.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/ChatTTS/__pycache__/core.cpython-39.pyc -------------------------------------------------------------------------------- /ChatTTS/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/ChatTTS/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /ChatTTS/infer/__pycache__/api.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/ChatTTS/infer/__pycache__/api.cpython-39.pyc -------------------------------------------------------------------------------- /ChatTTS/model/__pycache__/gpt.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/ChatTTS/model/__pycache__/gpt.cpython-39.pyc -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | omegaconf~=2.3.0 2 | torch~=2.0 3 | tqdm 4 | einops 5 | vector_quantize_pytorch 6 | transformers~=4.41.1 7 | vocos 8 | pandas -------------------------------------------------------------------------------- /ChatTTS/model/__pycache__/dvae.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/ChatTTS/model/__pycache__/dvae.cpython-39.pyc -------------------------------------------------------------------------------- /ChatTTS/utils/__pycache__/io_utils.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/ChatTTS/utils/__pycache__/io_utils.cpython-39.pyc -------------------------------------------------------------------------------- /ChatTTS/utils/__pycache__/gpu_utils.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/ChatTTS/utils/__pycache__/gpu_utils.cpython-39.pyc -------------------------------------------------------------------------------- /ChatTTS/utils/__pycache__/infer_utils.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/craii/ChatTTS_WebUI/HEAD/ChatTTS/utils/__pycache__/infer_utils.cpython-39.pyc -------------------------------------------------------------------------------- /models/decoder.yaml: -------------------------------------------------------------------------------- 1 | 2 | 3 | dim: 384 4 | 5 | decoder_config: 6 | idim: ${dim} 7 | odim: ${dim} 8 | hidden: 512 9 | n_layer: 12 10 | bn_dim: 128 11 | 12 | vq_config: null 13 | -------------------------------------------------------------------------------- /models/config/decoder.yaml: -------------------------------------------------------------------------------- 1 | 2 | 3 | dim: 384 4 | 5 | decoder_config: 6 | idim: ${dim} 7 | odim: ${dim} 8 | hidden: 512 9 | n_layer: 12 10 | bn_dim: 128 11 | 12 | vq_config: null 13 | -------------------------------------------------------------------------------- /models/dvae.yaml: -------------------------------------------------------------------------------- 1 | 2 | 3 | dim: 512 4 | decoder_config: 5 | idim: ${dim} 6 | odim: ${dim} 7 | n_layer: 12 8 | bn_dim: 128 9 | 10 | vq_config: 11 | dim: 1024 12 | levels: [5,5,5,5] 13 | G: 2 14 | R: 2 15 | -------------------------------------------------------------------------------- /models/config/dvae.yaml: -------------------------------------------------------------------------------- 1 | 2 | 3 | dim: 512 4 | decoder_config: 5 | idim: ${dim} 6 | odim: ${dim} 7 | n_layer: 12 8 | bn_dim: 128 9 | 10 | vq_config: 11 | dim: 1024 12 | levels: [5,5,5,5] 13 | G: 2 14 | R: 2 15 | -------------------------------------------------------------------------------- /models/path.yaml: -------------------------------------------------------------------------------- 1 | 2 | 3 | vocos_config_path: config/vocos.yaml 4 | vocos_ckpt_path: asset/Vocos.pt 5 | dvae_config_path: config/dvae.yaml 6 | dvae_ckpt_path: asset/DVAE.pt 7 | gpt_config_path: config/gpt.yaml 8 | gpt_ckpt_path: asset/GPT.pt 9 | decoder_config_path: config/decoder.yaml 10 | decoder_ckpt_path: asset/Decoder.pt 11 | tokenizer_path: asset/tokenizer.pt 12 | -------------------------------------------------------------------------------- /models/config/path.yaml: -------------------------------------------------------------------------------- 1 | 2 | 3 | vocos_config_path: config/vocos.yaml 4 | vocos_ckpt_path: asset/Vocos.pt 5 | dvae_config_path: config/dvae.yaml 6 | dvae_ckpt_path: asset/DVAE.pt 7 | gpt_config_path: config/gpt.yaml 8 | gpt_ckpt_path: asset/GPT.pt 9 | decoder_config_path: config/decoder.yaml 10 | decoder_ckpt_path: asset/Decoder.pt 11 | tokenizer_path: asset/tokenizer.pt 12 | -------------------------------------------------------------------------------- /models/gpt.yaml: -------------------------------------------------------------------------------- 1 | 2 | 3 | num_audio_tokens: 626 4 | num_text_tokens: 21178 5 | 6 | gpt_config: 7 | hidden_size: 768 8 | intermediate_size: 3072 9 | num_attention_heads: 12 10 | num_hidden_layers: 20 11 | use_cache: False 12 | max_position_embeddings: 4096 13 | # attn_implementation: flash_attention_2 14 | 15 | spk_emb_dim: 192 16 | spk_KL: False 17 | num_audio_tokens: 626 18 | num_text_tokens: null 19 | num_vq: 4 20 | 21 | -------------------------------------------------------------------------------- /models/config/gpt.yaml: -------------------------------------------------------------------------------- 1 | 2 | 3 | num_audio_tokens: 626 4 | num_text_tokens: 21178 5 | 6 | gpt_config: 7 | hidden_size: 768 8 | intermediate_size: 3072 9 | num_attention_heads: 12 10 | num_hidden_layers: 20 11 | use_cache: False 12 | max_position_embeddings: 4096 13 | # attn_implementation: flash_attention_2 14 | 15 | spk_emb_dim: 192 16 | spk_KL: False 17 | num_audio_tokens: 626 18 | num_text_tokens: null 19 | num_vq: 4 20 | 21 | -------------------------------------------------------------------------------- /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 -------------------------------------------------------------------------------- /models/vocos.yaml: -------------------------------------------------------------------------------- 1 | feature_extractor: 2 | class_path: vocos.feature_extractors.MelSpectrogramFeatures 3 | init_args: 4 | sample_rate: 24000 5 | n_fft: 1024 6 | hop_length: 256 7 | n_mels: 100 8 | padding: center 9 | 10 | backbone: 11 | class_path: vocos.models.VocosBackbone 12 | init_args: 13 | input_channels: 100 14 | dim: 512 15 | intermediate_dim: 1536 16 | num_layers: 8 17 | 18 | head: 19 | class_path: vocos.heads.ISTFTHead 20 | init_args: 21 | dim: 512 22 | n_fft: 1024 23 | hop_length: 256 24 | padding: center -------------------------------------------------------------------------------- /models/config/vocos.yaml: -------------------------------------------------------------------------------- 1 | feature_extractor: 2 | class_path: vocos.feature_extractors.MelSpectrogramFeatures 3 | init_args: 4 | sample_rate: 24000 5 | n_fft: 1024 6 | hop_length: 256 7 | n_mels: 100 8 | padding: center 9 | 10 | backbone: 11 | class_path: vocos.models.VocosBackbone 12 | init_args: 13 | input_channels: 100 14 | dim: 512 15 | intermediate_dim: 1536 16 | num_layers: 8 17 | 18 | head: 19 | class_path: vocos.heads.ISTFTHead 20 | init_args: 21 | dim: 512 22 | n_fft: 1024 23 | hop_length: 256 24 | padding: center -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## 简介 2 | 项目基于： 3 | 1. [ChatTTS](https://github.com/2noise/ChatTTS) 4 | 2. [yuhaolove/ChatTTS-WebUI/](https://github.com/yuhaolove/ChatTTS-WebUI/tree/main/webui) 5 | 6 | output文件夹保存有样本输出音频 7 | sampled_speaker文件夹保存有样本音色 8 | 9 | ![](https://github.com/craii/ChatTTS_WebUI/blob/main/screenshot.png) 10 | 11 | ## 使用方法 12 | 1. 参考[ChatTTS](https://github.com/2noise/ChatTTS) 的安装方法； 13 | 2. 将[pt模型](https://huggingface.co/2Noise/ChatTTS/tree/main)放置到 models/asset 文件夹下； 14 | 3. 运行 run_webui.py 15 | 16 | ## 带有虚拟环境的版本 17 | 我用夸克网盘分享了「ChatTTS_with_win_venv.zip」，点击链接即可保存。打开「夸克APP」，无需下载在线播放视频，畅享原画5倍速，支持电视投屏。 18 | 链接：https://pan.quark.cn/s/2a62d4e752ee 19 | 提取码：sdPP 20 | -------------------------------------------------------------------------------- /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/utils/infer_utils.py: -------------------------------------------------------------------------------- 1 | 2 | import torch 3 | import torch.nn.functional as F 4 | 5 | 6 | class CustomRepetitionPenaltyLogitsProcessorRepeat(): 7 | 8 | def __init__(self, penalty: float, max_input_ids, past_window): 9 | if not isinstance(penalty, float) or not (penalty > 0): 10 | raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}") 11 | 12 | self.penalty = penalty 13 | self.max_input_ids = max_input_ids 14 | self.past_window = past_window 15 | 16 | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: 17 | 18 | input_ids = input_ids[:, -self.past_window:] 19 | freq = F.one_hot(input_ids, scores.size(1)).sum(1) 20 | freq[self.max_input_ids:] = 0 21 | alpha = self.penalty**freq 22 | scores = torch.where(scores < 0, scores*alpha, scores/alpha) 23 | 24 | return scores 25 | 26 | class CustomRepetitionPenaltyLogitsProcessor(): 27 | 28 | def __init__(self, penalty: float, max_input_ids, past_window): 29 | if not isinstance(penalty, float) or not (penalty > 0): 30 | raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}") 31 | 32 | self.penalty = penalty 33 | self.max_input_ids = max_input_ids 34 | self.past_window = past_window 35 | 36 | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: 37 | 38 | input_ids = input_ids[:, -self.past_window:] 39 | score = torch.gather(scores, 1, input_ids) 40 | _score = score.detach().clone() 41 | score = torch.where(score < 0, score * self.penalty, score / self.penalty) 42 | score[input_ids>=self.max_input_ids] = _score[input_ids>=self.max_input_ids] 43 | scores.scatter_(1, input_ids, score) 44 | 45 | return scores -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | from pathlib import Path 4 | 5 | import torch 6 | import pandas as pd 7 | 8 | 9 | sys.path.append(f"{Path(__file__).resolve().parent}") 10 | 11 | 12 | def check_speaker_dir() -> list: 13 | msg = ["空(随机)"] 14 | 15 | current_dir = f"{Path(__file__).resolve().parent}" 16 | if not os.path.exists(f"{current_dir}/sampled_speaker"): 17 | os.makedirs(f"{current_dir}/sampled_speaker") 18 | 19 | if f"{current_dir}/sampled_speaker" not in sys.path: 20 | sys.path.append(f"{current_dir}/sampled_speaker") 21 | 22 | files = [file for file in os.listdir(f"{current_dir}/sampled_speaker") if file.endswith("csv")] 23 | msg.extend(list(map(lambda x: Path(x).resolve().stem, files))) 24 | return msg 25 | 26 | 27 | def generate_speaker_tensor(mean: float = 0.0, std: float = 15.247) -> torch.Tensor: 28 | return torch.normal(mean, std, size=(768,)) 29 | 30 | 31 | def generate_speaker_tensor_a() -> torch.Tensor: 32 | std, mean = torch.load(f'{Path(__file__).resolve().parent}/models/asset/spk_stat.pt').chunk(2) 33 | rand_spk = torch.randn(768) * std + mean 34 | return rand_spk 35 | 36 | 37 | def save_speaker_tensor_to_csv(speaker_name: str, tensor: torch.Tensor) -> str: 38 | msg = "succeed" 39 | speaker_path = f"{Path(__file__).resolve().parent}/sampled_speaker" 40 | try: 41 | df = pd.DataFrame({"speaker": [float(i) for i in tensor]}) 42 | df.to_csv(f"{speaker_path}/{speaker_name}.csv", index=False, header=False) 43 | except Exception as e: 44 | print(f"存储 speaker_tensor 时发生错误：{e}") 45 | msg = "fail" 46 | finally: 47 | return msg 48 | 49 | 50 | def load_speaker_tensor_from_csv(speaker_name: str) -> torch.Tensor: 51 | speaker_path = f"{Path(__file__).resolve().parent}/sampled_speaker" 52 | d_s = pd.read_csv(f"{speaker_path}/{speaker_name}.csv", header=None).iloc[:, 0] 53 | _speaker_tensor = torch.tensor(d_s.values) 54 | return _speaker_tensor 55 | 56 | 57 | if __name__ in "__main__": 58 | # y = check_speaker_dir() 59 | # print(y) 60 | # speaker_tensor = generate_speaker_tensor() 61 | # speaker_tensor = generate_speaker_tensor_a() 62 | # print(speaker_tensor) 63 | # sm = save_speaker_tensor_to_csv("niuniu", speaker_tensor) 64 | # print(sm) 65 | # lm = load_speaker_tensor_from_csv("niuniu") 66 | # print(lm) 67 | pass 68 | -------------------------------------------------------------------------------- /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}[uv_break][Ptts]' for i in text] 29 | else: 30 | text = [f'[Stts][empty_spk]{i}[uv_break][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 | -------------------------------------------------------------------------------- /run_webui.py: -------------------------------------------------------------------------------- 1 | import gradio as gr 2 | import numpy as np 3 | import pandas as pd 4 | import soundfile as sf 5 | 6 | import sys 7 | import os 8 | import random 9 | import datetime 10 | import torch 11 | 12 | from typing import Optional 13 | from utils import (check_speaker_dir, 14 | save_speaker_tensor_to_csv, 15 | generate_speaker_tensor_a, 16 | generate_speaker_tensor, 17 | load_speaker_tensor_from_csv) 18 | 19 | sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../ChatTTS'))) 20 | 21 | import ChatTTS 22 | 23 | chat = ChatTTS.Chat() 24 | 25 | # load models from local path or snapshot 26 | CURRENT_SPEAKER = None 27 | 28 | required_files = [ 29 | 'models/asset/Decoder.pt', 30 | 'models/asset/DVAE.pt', 31 | 'models/asset/GPT.pt', 32 | 'models/asset/spk_stat.pt', 33 | 'models/asset/tokenizer.pt', 34 | 'models/asset/Vocos.pt', 35 | 'models/config/decoder.yaml', 36 | 'models/config/dvae.yaml', 37 | 'models/config/gpt.yaml', 38 | 'models/config/path.yaml', 39 | 'models/config/vocos.yaml' 40 | ] 41 | 42 | # 检查所有文件是否存在 43 | all_files_exist = all(os.path.exists(file_path) for file_path in required_files) 44 | 45 | if all_files_exist: 46 | print('Load models from local path.') 47 | chat.load_models(source='local', local_path='models') 48 | else: 49 | print('Load models from snapshot.') 50 | chat.load_models() 51 | 52 | 53 | def text_to_speech(text: str, 54 | speaker: Optional[str] = None, 55 | temperature: float = .3, 56 | top_P: float = 0.7, 57 | top_K: float = 20, 58 | sample_method: str = "基于模型(spk_stat.pt)采样") -> str: 59 | """ 60 | :param text: 61 | :param speaker: 62 | :param temperature: 63 | :param top_P: 64 | :param top_K: 65 | :return: 66 | """ 67 | sampler = {"随机采样": generate_speaker_tensor, 68 | "基于模型(spk_stat.pt)采样": generate_speaker_tensor_a}[sample_method] 69 | 70 | if speaker not in (None, "空(随机)"): 71 | rand_spk = load_speaker_tensor_from_csv(speaker) 72 | else: 73 | # std, mean = torch.load('./models/asset/spk_stat.pt').chunk(2) 74 | # rand_spk = torch.randn(768) * std + mean 75 | rand_spk = sampler() 76 | 77 | global CURRENT_SPEAKER 78 | CURRENT_SPEAKER = rand_spk 79 | 80 | params_infer_code = { 81 | 'spk_emb': rand_spk, # add sampled speaker 82 | 'temperature': temperature, # using custom temperature 83 | 'top_P': top_P, # top P decode 84 | 'top_K': top_K, # top K decode 85 | } 86 | 87 | print(text, speaker, temperature, top_P, top_K, sampler.__name__) 88 | 89 | wavs = chat.infer([text], params_infer_code=params_infer_code, use_decoder=True) 90 | audio_data = np.array(wavs[0]) 91 | if audio_data.ndim == 1: 92 | audio_data = np.expand_dims(audio_data, axis=0) 93 | if not os.path.exists('outputs'): 94 | os.makedirs('outputs') 95 | output_file = f'outputs/{datetime.datetime.now().strftime("%Y%m%d%H%M%S")} - {random.randint(1000, 9999)}.wav' 96 | sf.write(output_file, audio_data.T, 24000) 97 | return output_file 98 | 99 | 100 | def save_voice(name: str) -> str: 101 | global CURRENT_SPEAKER 102 | voice = CURRENT_SPEAKER 103 | try: 104 | save_speaker_tensor_to_csv(speaker_name=name, tensor=voice) 105 | return f"音色 {name} 保存成功" 106 | except Exception as e: 107 | return f"音色保存失败:{e}" 108 | 109 | 110 | # examples 111 | examples = [ 112 | ["你先去做，哪怕做成屎一样，在慢慢改[laugh]，不要整天犹犹豫豫[uv_break]，一个粗糙的开始，就是最好的开始，什么也别管，先去做，然后你就会发现，用不了多久，你几十万就没了[laugh]"], 113 | ["生活就像一盒巧克力，你永远不知道你会得到什么。"], 114 | ["每一天都是新的开始，每一个梦想都值得被追寻。"], 115 | ["教授正直播，忽然一阵香风飘过，一女子突然闯入，搂着教授猛亲，教授猛然一惊，他回过神来，想起自己正在直播，骤然转过身去，脸色陡然变红，看着这尴尬的场面，真想猝然而亡！"] 116 | ] 117 | 118 | choices = check_speaker_dir() 119 | 120 | # create a block 121 | block = gr.Blocks(css="footer.svelte-mpyp5e {display: none !important;}", title='文本转语音').queue() 122 | 123 | with block: 124 | with gr.Row(): 125 | gr.Markdown("## ChatTTS-WebUI ") 126 | 127 | with gr.Row(): 128 | gr.Markdown( 129 | """ 130 | ### 说明 131 | - 输入一段文本，点击“生成”按钮。 132 | - 程序会生成对应的语音文件并显示在右侧。 133 | - 你可以下载生成的音频文件。 134 | - 也可以选择一些示例文本进行测试。 135 | """ 136 | ) 137 | 138 | with gr.Row(): 139 | with gr.Column(): 140 | input_text = gr.Textbox(label='输入文本', lines=2, placeholder='请输入文本...') 141 | example = gr.Examples( 142 | label="示例文本", 143 | inputs=input_text, 144 | examples=examples, 145 | examples_per_page=3, 146 | ) 147 | check_box = gr.Dropdown(label="选择音色", value=choices[0], choices=choices, interactive=True) 148 | input_temperature = gr.Slider(label="temperature", minimum=0.1, maximum=1.0, value=0.3, interactive=True) 149 | input_top_P = gr.Slider(label="top_P", minimum=0.0, maximum=1.0, value=0.7, interactive=True) 150 | input_top_K = gr.Slider(label="top_K", minimum=0.0, maximum=100.0, value=20.0, interactive=True) 151 | sample_method = gr.Dropdown(label="音色为空时的随机方法", value="随机采样", choices=["随机采样", "基于模型(spk_stat.pt)采样"], interactive=True) 152 | 153 | with gr.Column(): 154 | output_audio = gr.Audio(label='生成的音频', type='filepath', show_download_button=True) 155 | with gr.Row(): 156 | speaker_name = gr.Textbox(label='将当前音色保存', lines=1, placeholder='请输入音色名称...') 157 | save_button = gr.Button(value="保存音色") 158 | save_result = gr.TextArea(label="音色保存结果(重启服务后可用)", ) 159 | 160 | 161 | with gr.Column(): 162 | run_button = gr.Button(value="生成") 163 | 164 | run_button.click(fn=text_to_speech, inputs=[input_text, check_box, input_temperature, input_top_P, input_top_K, sample_method], outputs=output_audio) 165 | save_button.click(fn=save_voice, inputs=[speaker_name], outputs=save_result) 166 | 167 | # launch 168 | block.launch(server_name='127.0.0.1', server_port=9527, share=True) -------------------------------------------------------------------------------- /ChatTTS/core.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | import logging 4 | from omegaconf import OmegaConf 5 | 6 | import torch 7 | from vocos import Vocos 8 | from .model.dvae import DVAE 9 | from .model.gpt import GPT_warpper 10 | from .utils.gpu_utils import select_device 11 | from .utils.io_utils import get_latest_modified_file 12 | from .infer.api import refine_text, infer_code 13 | 14 | from huggingface_hub import snapshot_download 15 | 16 | logging.basicConfig(level = logging.INFO) 17 | 18 | 19 | class Chat: 20 | def __init__(self, ): 21 | self.pretrain_models = {} 22 | self.logger = logging.getLogger(__name__) 23 | 24 | def check_model(self, level = logging.INFO, use_decoder = False): 25 | not_finish = False 26 | check_list = ['vocos', 'gpt', 'tokenizer'] 27 | # check_list = ['vocos', 'gpt', 'tokenizer'] 28 | 29 | if use_decoder: 30 | check_list.append('decoder') 31 | else: 32 | check_list.append('dvae') 33 | 34 | for module in check_list: 35 | if module not in self.pretrain_models: 36 | self.logger.log(logging.WARNING, f'{module} not initialized.') 37 | not_finish = True 38 | 39 | if not not_finish: 40 | self.logger.log(level, f'All initialized.') 41 | 42 | return not not_finish 43 | 44 | def load_models(self, source='huggingface', force_redownload=False, local_path=''): 45 | if source == 'huggingface': 46 | hf_home = os.getenv('HF_HOME', os.path.expanduser("~/.cache/huggingface")) 47 | try: 48 | download_path = get_latest_modified_file(os.path.join(hf_home, 'hub/models--2Noise--ChatTTS/snapshots')) 49 | except: 50 | download_path = None 51 | if download_path is None or force_redownload: 52 | self.logger.log(logging.INFO, f'Download from HF: https://huggingface.co/2Noise/ChatTTS') 53 | download_path = snapshot_download(repo_id="2Noise/ChatTTS", allow_patterns=["*.pt", "*.yaml"]) 54 | else: 55 | self.logger.log(logging.INFO, f'Load from cache: {download_path}') 56 | self._load(**{k: os.path.join(download_path, v) for k, v in OmegaConf.load(os.path.join(download_path, 'config', 'path.yaml')).items()}) 57 | elif source == 'local': 58 | self.logger.log(logging.INFO, f'Load from local: {local_path}') 59 | self._load(**{k: os.path.join(local_path, v) for k, v in OmegaConf.load(os.path.join(local_path, 'config', 'path.yaml')).items()}) 60 | 61 | def _load( 62 | self, 63 | vocos_config_path: str = None, 64 | vocos_ckpt_path: str = None, 65 | dvae_config_path: str = None, 66 | dvae_ckpt_path: str = None, 67 | gpt_config_path: str = None, 68 | gpt_ckpt_path: str = None, 69 | decoder_config_path: str = None, 70 | decoder_ckpt_path: str = None, 71 | tokenizer_path: str = None, 72 | device: str = None 73 | ): 74 | if not device: 75 | device = select_device(4096) 76 | self.logger.log(logging.INFO, f'use {device}') 77 | 78 | if vocos_config_path: 79 | vocos = Vocos.from_hparams(vocos_config_path).to(device).eval() 80 | assert vocos_ckpt_path, 'vocos_ckpt_path should not be None' 81 | vocos.load_state_dict(torch.load(vocos_ckpt_path)) 82 | self.pretrain_models['vocos'] = vocos 83 | self.logger.log(logging.INFO, 'vocos loaded.') 84 | 85 | if dvae_config_path: 86 | cfg = OmegaConf.load(dvae_config_path) 87 | dvae = DVAE(**cfg).to(device).eval() 88 | assert dvae_ckpt_path, 'dvae_ckpt_path should not be None' 89 | dvae.load_state_dict(torch.load(dvae_ckpt_path, map_location='cpu')) 90 | self.pretrain_models['dvae'] = dvae 91 | self.logger.log(logging.INFO, 'dvae loaded.') 92 | 93 | if gpt_config_path: 94 | cfg = OmegaConf.load(gpt_config_path) 95 | gpt = GPT_warpper(**cfg).to(device).eval() 96 | assert gpt_ckpt_path, 'gpt_ckpt_path should not be None' 97 | gpt.load_state_dict(torch.load(gpt_ckpt_path, map_location='cpu')) 98 | self.pretrain_models['gpt'] = gpt 99 | spk_stat_path = os.path.join(os.path.dirname(gpt_ckpt_path), 'spk_stat.pt') 100 | assert os.path.exists(spk_stat_path), f'Missing spk_stat.pt: {spk_stat_path}' 101 | self.pretrain_models['spk_stat'] = torch.load(spk_stat_path).to(device) 102 | self.logger.log(logging.INFO, 'gpt loaded.') 103 | 104 | if decoder_config_path: 105 | cfg = OmegaConf.load(decoder_config_path) 106 | decoder = DVAE(**cfg).to(device).eval() 107 | assert decoder_ckpt_path, 'decoder_ckpt_path should not be None' 108 | decoder.load_state_dict(torch.load(decoder_ckpt_path, map_location='cpu')) 109 | self.pretrain_models['decoder'] = decoder 110 | self.logger.log(logging.INFO, 'decoder loaded.') 111 | 112 | if tokenizer_path: 113 | tokenizer = torch.load(tokenizer_path, map_location='cpu') 114 | tokenizer.padding_side = 'left' 115 | self.pretrain_models['tokenizer'] = tokenizer 116 | self.logger.log(logging.INFO, 'tokenizer loaded.') 117 | 118 | self.check_model() 119 | 120 | def infer( 121 | self, 122 | text, 123 | skip_refine_text=False, 124 | refine_text_only=False, 125 | params_refine_text={}, 126 | params_infer_code={}, 127 | use_decoder=False 128 | ): 129 | 130 | assert self.check_model(use_decoder=use_decoder) 131 | 132 | if not skip_refine_text: 133 | text_tokens = refine_text(self.pretrain_models, text, **params_refine_text)['ids'] 134 | text_tokens = [i[i < self.pretrain_models['tokenizer'].convert_tokens_to_ids('[break_0]')] for i in text_tokens] 135 | text = self.pretrain_models['tokenizer'].batch_decode(text_tokens) 136 | if refine_text_only: 137 | return text 138 | 139 | text = [params_infer_code.get('prompt', '') + i for i in text] 140 | params_infer_code.pop('prompt', '') 141 | result = infer_code(self.pretrain_models, text, **params_infer_code, return_hidden=use_decoder) 142 | 143 | if use_decoder: 144 | mel_spec = [self.pretrain_models['decoder'](i[None].permute(0,2,1)) for i in result['hiddens']] 145 | else: 146 | mel_spec = [self.pretrain_models['dvae'](i[None].permute(0,2,1)) for i in result['ids']] 147 | 148 | wav = [self.pretrain_models['vocos'].decode(i).cpu().numpy() for i in mel_spec] 149 | 150 | return wav 151 | 152 | def sample_random_speaker(self, ): 153 | 154 | dim = self.pretrain_models['gpt'].gpt.layers[0].mlp.gate_proj.in_features 155 | std, mean = self.pretrain_models['spk_stat'].chunk(2) 156 | return torch.randn(dim, device=std.device) * std + mean 157 | 158 | 159 | -------------------------------------------------------------------------------- /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 | # 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For the avoidance of doubt, this paragraph does not form part of the public licenses. 156 | > 157 | > Creative Commons may be contacted at [creativecommons.org](http://creativecommons.org). 158 | -------------------------------------------------------------------------------- /sampled_speaker/jidong.csv: -------------------------------------------------------------------------------- 1 | 3.7936673164367676 2 | 3.728569984436035 3 | 12.650328636169434 4 | 5.563755989074707 5 | 1.5919339656829834 6 | 3.4696643352508545 7 | -1.5330392122268677 8 | 2.7658567428588867 9 | 1.6120041608810425 10 | -14.178129196166992 11 | 0.35902827978134155 12 | -0.9380728006362915 13 | 0.9394299983978271 14 | 0.30636969208717346 15 | -0.12373754382133484 16 | 3.1166787147521973 17 | 2.130946397781372 18 | 0.20748019218444824 19 | 0.07943785190582275 20 | -2.158203125 21 | -11.458989143371582 22 | -0.7983209490776062 23 | 2.7497193813323975 24 | -9.029943466186523 25 | -1.1959748268127441 26 | 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