diff --git a/Dockerfile b/Dockerfile index 74e282c4..80cd9f3a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -34,9 +34,6 @@ RUN if [ "$IMAGE_TYPE" != "elite" ]; then \ fi -# Copy the rest of the application -COPY . /workspace - # Copy the rest of the application COPY . /workspace diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 4befc0c4..f5eb38a5 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -5,6 +5,7 @@ import random import traceback from tqdm import tqdm + now_dir = os.getcwd() sys.path.append(now_dir) import ffmpeg @@ -26,6 +27,7 @@ from my_utils import load_audio from module.mel_processing import spectrogram_torch from TTS_infer_pack.text_segmentation_method import splits from TTS_infer_pack.TextPreprocessor import TextPreprocessor + i18n = I18nAuto() # configs/tts_infer.yaml @@ -49,7 +51,8 @@ custom: """ -def set_seed(seed:int): + +def set_seed(seed: int): seed = int(seed) seed = seed if seed != -1 else random.randrange(1 << 32) print(f"Set seed to {seed}") @@ -70,50 +73,52 @@ def set_seed(seed:int): except: pass return seed - + + class TTS_Config: - default_configs={ - "device": "cpu", - "is_half": False, - "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", - "vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth", - "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", - "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", - } - configs:dict = None - def __init__(self, configs: Union[dict, str]=None): - + default_configs = { + "device": "cpu", + "is_half": False, + "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", + "vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth", + "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", + "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", + "load_base": True, + } + configs: dict = None + + def __init__(self, configs: Union[dict, str] = None): + # 设置默认配置文件路径 - configs_base_path:str = "GPT_SoVITS/configs/" + configs_base_path: str = "GPT_SoVITS/configs/" os.makedirs(configs_base_path, exist_ok=True) - self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml") - + self.configs_path: str = os.path.join(configs_base_path, "tts_infer.yaml") + if configs in ["", None]: if not os.path.exists(self.configs_path): self.save_configs() print(f"Create default config file at {self.configs_path}") - configs:dict = {"default": deepcopy(self.default_configs)} - + configs: dict = {"default": deepcopy(self.default_configs)} + if isinstance(configs, str): self.configs_path = configs - configs:dict = self._load_configs(self.configs_path) - + configs: dict = self._load_configs(self.configs_path) + assert isinstance(configs, dict) - default_configs:dict = configs.get("default", None) + default_configs: dict = configs.get("default", None) if default_configs is not None: self.default_configs = default_configs - - self.configs:dict = configs.get("custom", deepcopy(self.default_configs)) - - + + self.configs: dict = configs.get("custom", deepcopy(self.default_configs)) + self.device = self.configs.get("device", torch.device("cpu")) self.is_half = self.configs.get("is_half", False) self.t2s_weights_path = self.configs.get("t2s_weights_path", None) self.vits_weights_path = self.configs.get("vits_weights_path", None) self.bert_base_path = self.configs.get("bert_base_path", None) self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None) + self.load_base = self.configs.get("load_base", True) - if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)): self.t2s_weights_path = self.default_configs['t2s_weights_path'] print(f"fall back to default t2s_weights_path: {self.t2s_weights_path}") @@ -127,34 +132,32 @@ class TTS_Config: self.cnhuhbert_base_path = self.default_configs['cnhuhbert_base_path'] print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}") self.update_configs() - - - self.max_sec = None - self.hz:int = 50 - self.semantic_frame_rate:str = "25hz" - self.segment_size:int = 20480 - self.filter_length:int = 2048 - self.sampling_rate:int = 32000 - self.hop_length:int = 640 - self.win_length:int = 2048 - self.n_speakers:int = 300 - - self.languages:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] - - def _load_configs(self, configs_path: str)->dict: + self.max_sec = None + self.hz: int = 50 + self.semantic_frame_rate: str = "25hz" + self.segment_size: int = 20480 + self.filter_length: int = 2048 + self.sampling_rate: int = 32000 + self.hop_length: int = 640 + self.win_length: int = 2048 + self.n_speakers: int = 300 + + self.languages: list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] + + def _load_configs(self, configs_path: str) -> dict: with open(configs_path, 'r') as f: configs = yaml.load(f, Loader=yaml.FullLoader) - + return configs - def save_configs(self, configs_path:str=None)->None: - configs={ - "default":self.default_configs, + def save_configs(self, configs_path: str = None) -> None: + configs = { + "default": self.default_configs, } if self.configs is not None: configs["custom"] = self.update_configs() - + if configs_path is None: configs_path = self.configs_path with open(configs_path, 'w') as f: @@ -162,15 +165,16 @@ class TTS_Config: def update_configs(self): self.config = { - "device" : str(self.device), - "is_half" : self.is_half, - "t2s_weights_path" : self.t2s_weights_path, - "vits_weights_path" : self.vits_weights_path, - "bert_base_path" : self.bert_base_path, + "device": str(self.device), + "is_half": self.is_half, + "t2s_weights_path": self.t2s_weights_path, + "vits_weights_path": self.vits_weights_path, + "bert_base_path": self.bert_base_path, "cnhuhbert_base_path": self.cnhuhbert_base_path, + "load_base": self.load_base, } return self.config - + def __str__(self): self.configs = self.update_configs() string = "TTS Config".center(100, '-') + '\n' @@ -178,75 +182,87 @@ class TTS_Config: string += f"{str(k).ljust(20)}: {str(v)}\n" string += "-" * 100 + '\n' return string - + def __repr__(self): return self.__str__() + def __hash__(self): + return hash(self.configs_path) + + def __eq__(self, other): + return isinstance(other, TTS_Config) and self.configs_path == other.configs_path + class TTS: + bert_tokenizer: AutoTokenizer = None + bert_model: AutoModelForMaskedLM = None + cnhuhbert_model: CNHubert = None + def __init__(self, configs: Union[dict, str, TTS_Config]): if isinstance(configs, TTS_Config): self.configs = configs else: - self.configs:TTS_Config = TTS_Config(configs) - - self.t2s_model:Text2SemanticLightningModule = None - self.vits_model:SynthesizerTrn = None - self.bert_tokenizer:AutoTokenizer = None - self.bert_model:AutoModelForMaskedLM = None - self.cnhuhbert_model:CNHubert = None - - self._init_models() - - self.text_preprocessor:TextPreprocessor = \ - TextPreprocessor(self.bert_model, - self.bert_tokenizer, - self.configs.device) - - - self.prompt_cache:dict = { - "ref_audio_path" : None, - "prompt_semantic": None, - "refer_spec" : None, - "prompt_text" : None, - "prompt_lang" : None, - "phones" : None, - "bert_features" : None, - "norm_text" : None, - } - - - self.stop_flag:bool = False - self.precision:torch.dtype = torch.float16 if self.configs.is_half else torch.float32 + self.configs: TTS_Config = TTS_Config(configs) - def _init_models(self,): + self.t2s_model: Text2SemanticLightningModule = None + self.vits_model: SynthesizerTrn = None + # self.bert_tokenizer:AutoTokenizer = None + # self.bert_model:AutoModelForMaskedLM = None + # self.cnhuhbert_model:CNHubert = None + + self._init_models() + + self.text_preprocessor: TextPreprocessor = \ + TextPreprocessor(TTS.bert_model, + TTS.bert_tokenizer, + self.configs.device) + + self.prompt_cache: dict = { + "ref_audio_path": None, + "prompt_semantic": None, + "refer_spec": None, + "prompt_text": None, + "prompt_lang": None, + "phones": None, + "bert_features": None, + "norm_text": None, + } + + self.stop_flag: bool = False + self.precision: torch.dtype = torch.float16 if self.configs.is_half else torch.float32 + + def _init_models(self): self.init_t2s_weights(self.configs.t2s_weights_path) self.init_vits_weights(self.configs.vits_weights_path) - self.init_bert_weights(self.configs.bert_base_path) - self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path) + if self.configs.load_base: + TTS.init_bert_weights(self.configs) + TTS.init_cnhuhbert_weights(self.configs) # self.enable_half_precision(self.configs.is_half) - - - - def init_cnhuhbert_weights(self, base_path: str): - print(f"Loading CNHuBERT weights from {base_path}") - self.cnhuhbert_model = CNHubert(base_path) - self.cnhuhbert_model=self.cnhuhbert_model.eval() - self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) - if self.configs.is_half and str(self.configs.device)!="cpu": - self.cnhuhbert_model = self.cnhuhbert_model.half() - - - - def init_bert_weights(self, base_path: str): - print(f"Loading BERT weights from {base_path}") - self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path) - self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path) - self.bert_model=self.bert_model.eval() - self.bert_model = self.bert_model.to(self.configs.device) - if self.configs.is_half and str(self.configs.device)!="cpu": - self.bert_model = self.bert_model.half() - + + @staticmethod + def init_base_models(configs: TTS_Config): + TTS.init_bert_weights(configs) + TTS.init_cnhuhbert_weights(configs) + + @staticmethod + def init_cnhuhbert_weights(configs: TTS_Config): + print(f"Loading CNHuBERT weights from {configs.cnhuhbert_base_path}") + TTS.cnhuhbert_model = CNHubert(configs.cnhuhbert_base_path) + TTS.cnhuhbert_model = TTS.cnhuhbert_model.eval() + TTS.cnhuhbert_model = TTS.cnhuhbert_model.to(configs.device) + if configs.is_half and str(configs.device) != "cpu": + TTS.cnhuhbert_model = TTS.cnhuhbert_model.half() + + @staticmethod + def init_bert_weights(configs: TTS_Config): + print(f"Loading BERT weights from {configs.bert_base_path}") + TTS.bert_tokenizer = AutoTokenizer.from_pretrained(configs.bert_base_path) + TTS.bert_model = AutoModelForMaskedLM.from_pretrained(configs.bert_base_path) + TTS.bert_model = TTS.bert_model.eval() + TTS.bert_model = TTS.bert_model.to(configs.device) + if configs.is_half and str(configs.device) != "cpu": + TTS.bert_model = TTS.bert_model.half() + def init_vits_weights(self, weights_path: str): print(f"Loading VITS weights from {weights_path}") self.configs.vits_weights_path = weights_path @@ -255,7 +271,7 @@ class TTS: hps = dict_s2["config"] self.configs.filter_length = hps["data"]["filter_length"] self.configs.segment_size = hps["train"]["segment_size"] - self.configs.sampling_rate = hps["data"]["sampling_rate"] + self.configs.sampling_rate = hps["data"]["sampling_rate"] self.configs.hop_length = hps["data"]["hop_length"] self.configs.win_length = hps["data"]["win_length"] self.configs.n_speakers = hps["data"]["n_speakers"] @@ -270,15 +286,14 @@ class TTS: # if ("pretrained" not in weights_path): if hasattr(vits_model, "enc_q"): del vits_model.enc_q - + vits_model = vits_model.to(self.configs.device) vits_model = vits_model.eval() vits_model.load_state_dict(dict_s2["weight"], strict=False) self.vits_model = vits_model - if self.configs.is_half and str(self.configs.device)!="cpu": + if self.configs.is_half and str(self.configs.device) != "cpu": self.vits_model = self.vits_model.half() - def init_t2s_weights(self, weights_path: str): print(f"Loading Text2Semantic weights from {weights_path}") self.configs.t2s_weights_path = weights_path @@ -292,9 +307,9 @@ class TTS: t2s_model = t2s_model.to(self.configs.device) t2s_model = t2s_model.eval() self.t2s_model = t2s_model - if self.configs.is_half and str(self.configs.device)!="cpu": + if self.configs.is_half and str(self.configs.device) != "cpu": self.t2s_model = self.t2s_model.half() - + def enable_half_precision(self, enable: bool = True): ''' To enable half precision for the TTS model. @@ -305,29 +320,29 @@ class TTS: if str(self.configs.device) == "cpu" and enable: print("Half precision is not supported on CPU.") return - + self.configs.is_half = enable self.precision = torch.float16 if enable else torch.float32 self.configs.save_configs() if enable: if self.t2s_model is not None: - self.t2s_model =self.t2s_model.half() + self.t2s_model = self.t2s_model.half() if self.vits_model is not None: self.vits_model = self.vits_model.half() - if self.bert_model is not None: - self.bert_model =self.bert_model.half() - if self.cnhuhbert_model is not None: - self.cnhuhbert_model = self.cnhuhbert_model.half() + if TTS.bert_model is not None: + TTS.bert_model = TTS.bert_model.half() + if TTS.cnhuhbert_model is not None: + TTS.cnhuhbert_model = TTS.cnhuhbert_model.half() else: if self.t2s_model is not None: self.t2s_model = self.t2s_model.float() if self.vits_model is not None: self.vits_model = self.vits_model.float() - if self.bert_model is not None: - self.bert_model = self.bert_model.float() - if self.cnhuhbert_model is not None: - self.cnhuhbert_model = self.cnhuhbert_model.float() - + if TTS.bert_model is not None: + TTS.bert_model = TTS.bert_model.float() + if TTS.cnhuhbert_model is not None: + TTS.cnhuhbert_model = TTS.cnhuhbert_model.float() + def set_device(self, device: torch.device): ''' To set the device for all models. @@ -340,12 +355,12 @@ class TTS: self.t2s_model = self.t2s_model.to(device) if self.vits_model is not None: self.vits_model = self.vits_model.to(device) - if self.bert_model is not None: - self.bert_model = self.bert_model.to(device) - if self.cnhuhbert_model is not None: - self.cnhuhbert_model = self.cnhuhbert_model.to(device) - - def set_ref_audio(self, ref_audio_path:str): + if TTS.bert_model is not None: + TTS.bert_model = TTS.bert_model.to(device) + if TTS.cnhuhbert_model is not None: + TTS.cnhuhbert_model = TTS.cnhuhbert_model.to(device) + + def set_ref_audio(self, ref_audio_path: str): ''' To set the reference audio for the TTS model, including the prompt_semantic and refer_spepc. @@ -354,7 +369,7 @@ class TTS: ''' self._set_prompt_semantic(ref_audio_path) self._set_ref_spec(ref_audio_path) - + def _set_ref_spec(self, ref_audio_path): audio = load_audio(ref_audio_path, int(self.configs.sampling_rate)) audio = torch.FloatTensor(audio) @@ -373,9 +388,8 @@ class TTS: spec = spec.half() # self.refer_spec = spec self.prompt_cache["refer_spec"] = spec - - - def _set_prompt_semantic(self, ref_wav_path:str): + + def _set_prompt_semantic(self, ref_wav_path: str): zero_wav = np.zeros( int(self.configs.sampling_rate * 0.3), dtype=np.float16 if self.configs.is_half else np.float32, @@ -399,16 +413,16 @@ class TTS: 1, 2 ) # .float() codes = self.vits_model.extract_latent(hubert_feature) - + prompt_semantic = codes[0, 0].to(self.configs.device) self.prompt_cache["prompt_semantic"] = prompt_semantic - - def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None): + + def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length: int = None): seq = sequences[0] ndim = seq.dim() if axis < 0: axis += ndim - dtype:torch.dtype = seq.dtype + dtype: torch.dtype = seq.dtype pad_value = torch.tensor(pad_value, dtype=dtype) seq_lengths = [seq.shape[axis] for seq in sequences] if max_length is None: @@ -423,16 +437,16 @@ class TTS: padded_sequences.append(padded_seq) batch = torch.stack(padded_sequences) return batch - - def to_batch(self, data:list, - prompt_data:dict=None, - batch_size:int=5, - threshold:float=0.75, - split_bucket:bool=True, - device:torch.device=torch.device("cpu"), - precision:torch.dtype=torch.float32, + + def to_batch(self, data: list, + prompt_data: dict = None, + batch_size: int = 5, + threshold: float = 0.75, + split_bucket: bool = True, + device: torch.device = torch.device("cpu"), + precision: torch.dtype = torch.float32, ): - _data:list = [] + _data: list = [] index_and_len_list = [] for idx, item in enumerate(data): norm_text_len = len(item["norm_text"]) @@ -441,33 +455,32 @@ class TTS: batch_index_list = [] if split_bucket: index_and_len_list.sort(key=lambda x: x[1]) - index_and_len_list = np.array(index_and_len_list, dtype=np.int64) - + index_and_len_list = np.array(index_and_len_list, dtype=np.int64) + batch_index_list_len = 0 pos = 0 - while pos =threshold) or (pos_end-pos==1): - batch_index=index_and_len_list[pos:pos_end, 0].tolist() + batch = index_and_len_list[pos:pos_end, 1].astype(np.float32) + score = batch[(pos_end - pos) // 2] / (batch.mean() + 1e-8) + if (score >= threshold) or (pos_end - pos == 1): + batch_index = index_and_len_list[pos:pos_end, 0].tolist() batch_index_list_len += len(batch_index) batch_index_list.append(batch_index) pos = pos_end break - pos_end=pos_end-1 - + pos_end = pos_end - 1 + assert batch_index_list_len == len(data) - + else: for i in range(len(data)): - if i%batch_size == 0: + if i % batch_size == 0: batch_index_list.append([]) batch_index_list[-1].append(i) - for batch_idx, index_list in enumerate(batch_index_list): item_list = [data[idx] for idx in index_list] phones_list = [] @@ -481,33 +494,32 @@ class TTS: phones_max_len = 0 for item in item_list: if prompt_data is not None: - all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\ - .to(dtype=precision, device=device) - all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]).to(device) + all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1) \ + .to(dtype=precision, device=device) + all_phones = torch.LongTensor(prompt_data["phones"] + item["phones"]).to(device) phones = torch.LongTensor(item["phones"]).to(device) # norm_text = prompt_data["norm_text"]+item["norm_text"] else: - all_bert_features = item["bert_features"]\ - .to(dtype=precision, device=device) + all_bert_features = item["bert_features"] \ + .to(dtype=precision, device=device) phones = torch.LongTensor(item["phones"]).to(device) all_phones = phones # norm_text = item["norm_text"] bert_max_len = max(bert_max_len, all_bert_features.shape[-1]) phones_max_len = max(phones_max_len, phones.shape[-1]) - + phones_list.append(phones) phones_len_list.append(phones.shape[-1]) all_phones_list.append(all_phones) all_phones_len_list.append(all_phones.shape[-1]) all_bert_features_list.append(all_bert_features) norm_text_batch.append(item["norm_text"]) - + phones_batch = phones_list all_phones_batch = all_phones_list all_bert_features_batch = all_bert_features_list - - + max_len = max(bert_max_len, phones_max_len) # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len) #### 直接对phones和bert_features进行pad。（padding策略会影响T2S模型生成的结果，但不直接影响复读概率。影响复读概率的主要因素是mask的策略） @@ -516,16 +528,16 @@ class TTS: # all_bert_features_batch = torch.zeros((len(all_bert_features_list), 1024, max_len), dtype=precision, device=device) # for idx, item in enumerate(all_bert_features_list): # all_bert_features_batch[idx, :, : item.shape[-1]] = item - + # #### 先对phones进行embedding、对bert_features进行project，再pad到相同长度，（padding策略会影响T2S模型生成的结果，但不直接影响复读概率。影响复读概率的主要因素是mask的策略） # all_phones_list = [self.t2s_model.model.ar_text_embedding(item.to(self.t2s_model.device)) for item in all_phones_list] # all_phones_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) for item in all_phones_list] # all_phones_batch = torch.stack(all_phones_list, dim=0) - + # all_bert_features_list = [self.t2s_model.model.bert_proj(item.to(self.t2s_model.device).transpose(0, 1)) for item in all_bert_features_list] # all_bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) for item in all_bert_features_list] # all_bert_features_batch = torch.stack(all_bert_features_list, dim=0) - + batch = { "phones": phones_batch, "phones_len": torch.LongTensor(phones_len_list).to(device), @@ -536,10 +548,10 @@ class TTS: "max_len": max_len, } _data.append(batch) - + return _data, batch_index_list - - def recovery_order(self, data:list, batch_index_list:list)->list: + + def recovery_order(self, data: list, batch_index_list: list) -> list: ''' Recovery the order of the audio according to the batch_index_list. @@ -551,20 +563,20 @@ class TTS: list (List[np.ndarray]): the data in the original order. ''' length = len(sum(batch_index_list, [])) - _data = [None]*length + _data = [None] * length for i, index_list in enumerate(batch_index_list): for j, index in enumerate(index_list): _data[index] = data[i][j] return _data - def stop(self,): + def stop(self, ): ''' Stop the inference process. ''' self.stop_flag = True - + @torch.no_grad() - def run(self, inputs:dict): + def run(self, inputs: dict): """ Text to speech inference. @@ -594,16 +606,16 @@ class TTS: tuple[int, np.ndarray]: sampling rate and audio data. """ ########## variables initialization ########### - self.stop_flag:bool = False - text:str = inputs.get("text", "") - text_lang:str = inputs.get("text_lang", "") - ref_audio_path:str = inputs.get("ref_audio_path", "") - prompt_text:str = inputs.get("prompt_text", "") - prompt_lang:str = inputs.get("prompt_lang", "") - top_k:int = inputs.get("top_k", 5) - top_p:float = inputs.get("top_p", 1) - temperature:float = inputs.get("temperature", 1) - text_split_method:str = inputs.get("text_split_method", "cut0") + self.stop_flag: bool = False + text: str = inputs.get("text", "") + text_lang: str = inputs.get("text_lang", "") + ref_audio_path: str = inputs.get("ref_audio_path", "") + prompt_text: str = inputs.get("prompt_text", "") + prompt_lang: str = inputs.get("prompt_lang", "") + top_k: int = inputs.get("top_k", 5) + top_p: float = inputs.get("top_p", 1) + temperature: float = inputs.get("temperature", 1) + text_split_method: str = inputs.get("text_split_method", "cut0") batch_size = inputs.get("batch_size", 1) batch_threshold = inputs.get("batch_threshold", 0.75) speed_factor = inputs.get("speed_factor", 1.0) @@ -632,7 +644,7 @@ class TTS: if split_bucket: print(i18n("分桶处理模式已开启")) - if fragment_interval<0.01: + if fragment_interval < 0.01: fragment_interval = 0.01 print(i18n("分段间隔过小，已自动设置为0.01")) @@ -645,8 +657,9 @@ class TTS: assert prompt_lang in self.configs.languages if ref_audio_path in [None, ""] and \ - ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)): - raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()") + ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)): + raise ValueError( + "ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()") ###### setting reference audio and prompt text preprocessing ######## t0 = ttime() @@ -662,48 +675,49 @@ class TTS: self.prompt_cache["prompt_lang"] = prompt_lang phones, bert_features, norm_text = \ self.text_preprocessor.segment_and_extract_feature_for_text( - prompt_text, - prompt_lang) + prompt_text, + prompt_lang) self.prompt_cache["phones"] = phones self.prompt_cache["bert_features"] = bert_features self.prompt_cache["norm_text"] = norm_text ###### text preprocessing ######## t1 = ttime() - data:list = None + data: list = None if not return_fragment: data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) if len(data) == 0: yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), - dtype=np.int16) + dtype=np.int16) return - batch_index_list:list = None - data, batch_index_list = self.to_batch(data, - prompt_data=self.prompt_cache if not no_prompt_text else None, - batch_size=batch_size, - threshold=batch_threshold, - split_bucket=split_bucket, - device=self.configs.device, - precision=self.precision - ) + batch_index_list: list = None + data, batch_index_list = self.to_batch(data, + prompt_data=self.prompt_cache if not no_prompt_text else None, + batch_size=batch_size, + threshold=batch_threshold, + split_bucket=split_bucket, + device=self.configs.device, + precision=self.precision + ) else: print(i18n("############ 切分文本 ############")) texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method) data = [] for i in range(len(texts)): - if i%batch_size == 0: + if i % batch_size == 0: data.append([]) data[-1].append(texts[i]) - + def make_batch(batch_texts): batch_data = [] print(i18n("############ 提取文本Bert特征 ############")) for text in tqdm(batch_texts): - phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang) + phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, + text_lang) if phones is None: continue - res={ + res = { "phones": phones, "bert_features": bert_features, "norm_text": norm_text, @@ -711,17 +725,16 @@ class TTS: batch_data.append(res) if len(batch_data) == 0: return None - batch, _ = self.to_batch(batch_data, - prompt_data=self.prompt_cache if not no_prompt_text else None, - batch_size=batch_size, - threshold=batch_threshold, - split_bucket=False, - device=self.configs.device, - precision=self.precision - ) + batch, _ = self.to_batch(batch_data, + prompt_data=self.prompt_cache if not no_prompt_text else None, + batch_size=batch_size, + threshold=batch_threshold, + split_bucket=False, + device=self.configs.device, + precision=self.precision + ) return batch[0] - t2 = ttime() try: print("############ 推理 ############") @@ -736,21 +749,21 @@ class TTS: if item is None: continue - batch_phones:List[torch.LongTensor] = item["phones"] + batch_phones: List[torch.LongTensor] = item["phones"] # batch_phones:torch.LongTensor = item["phones"] - batch_phones_len:torch.LongTensor = item["phones_len"] - all_phoneme_ids:torch.LongTensor = item["all_phones"] - all_phoneme_lens:torch.LongTensor = item["all_phones_len"] - all_bert_features:torch.LongTensor = item["all_bert_features"] - norm_text:str = item["norm_text"] + batch_phones_len: torch.LongTensor = item["phones_len"] + all_phoneme_ids: torch.LongTensor = item["all_phones"] + all_phoneme_lens: torch.LongTensor = item["all_phones_len"] + all_bert_features: torch.LongTensor = item["all_bert_features"] + norm_text: str = item["norm_text"] max_len = item["max_len"] print(i18n("前端处理后的文本(每句):"), norm_text) - if no_prompt_text : + if no_prompt_text: prompt = None else: - prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device) - + prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to( + self.configs.device) pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( all_phoneme_ids, @@ -768,14 +781,14 @@ class TTS: t4 = ttime() t_34 += t4 - t3 - refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\ - .to(dtype=self.precision, device=self.configs.device) + refer_audio_spec: torch.Tensor = self.prompt_cache["refer_spec"] \ + .to(dtype=self.precision, device=self.configs.device) batch_audio_fragment = [] # 这里要记得加 torch.no_grad() 不然速度慢一大截 # with torch.no_grad(): - + # ## vits并行推理 method 1 # pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] # pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device) @@ -792,15 +805,17 @@ class TTS: # ## vits并行推理 method 2 pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] upsample_rate = math.prod(self.vits_model.upsample_rates) - audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))] - audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))] + audio_frag_idx = [pred_semantic_list[i].shape[0] * 2 * upsample_rate for i in + range(0, len(pred_semantic_list))] + audio_frag_end_idx = [sum(audio_frag_idx[:i + 1]) for i in range(0, len(audio_frag_idx))] all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device) _batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device) _batch_audio_fragment = (self.vits_model.decode( - all_pred_semantic, _batch_phones, refer_audio_spec - ).detach()[0, 0, :]) + all_pred_semantic, _batch_phones, refer_audio_spec + ).detach()[0, 0, :]) audio_frag_end_idx.insert(0, 0) - batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))] + batch_audio_fragment = [_batch_audio_fragment[audio_frag_end_idx[i - 1]:audio_frag_end_idx[i]] for i in + range(1, len(audio_frag_end_idx))] # ## vits串行推理 # for i, idx in enumerate(idx_list): @@ -817,36 +832,36 @@ class TTS: t_45 += t5 - t4 if return_fragment: print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4)) - yield self.audio_postprocess([batch_audio_fragment], - self.configs.sampling_rate, - None, - speed_factor, - False, - fragment_interval - ) + yield self.audio_postprocess([batch_audio_fragment], + self.configs.sampling_rate, + None, + speed_factor, + False, + fragment_interval + ) else: audio.append(batch_audio_fragment) if self.stop_flag: yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), - dtype=np.int16) + dtype=np.int16) return if not return_fragment: print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45)) - yield self.audio_postprocess(audio, - self.configs.sampling_rate, - batch_index_list, - speed_factor, - split_bucket, - fragment_interval - ) + yield self.audio_postprocess(audio, + self.configs.sampling_rate, + batch_index_list, + speed_factor, + split_bucket, + fragment_interval + ) except Exception as e: traceback.print_exc() # 必须返回一个空音频, 否则会导致显存不释放。 yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), - dtype=np.int16) + dtype=np.int16) # 重置模型, 否则会导致显存释放不完全。 del self.t2s_model del self.vits_model @@ -857,60 +872,56 @@ class TTS: raise e finally: self.empty_cache() - + def empty_cache(self): - try: + try: if "cuda" in str(self.configs.device): torch.cuda.empty_cache() elif str(self.configs.device) == "mps": torch.mps.empty_cache() except: - pass - - def audio_postprocess(self, - audio:List[torch.Tensor], - sr:int, - batch_index_list:list=None, - speed_factor:float=1.0, - split_bucket:bool=True, - fragment_interval:float=0.3 - )->tuple[int, np.ndarray]: + pass + + def audio_postprocess(self, + audio: List[torch.Tensor], + sr: int, + batch_index_list: list = None, + speed_factor: float = 1.0, + split_bucket: bool = True, + fragment_interval: float = 0.3 + ) -> tuple[int, np.ndarray]: zero_wav = torch.zeros( - int(self.configs.sampling_rate * fragment_interval), - dtype=self.precision, - device=self.configs.device - ) - + int(self.configs.sampling_rate * fragment_interval), + dtype=self.precision, + device=self.configs.device + ) + for i, batch in enumerate(audio): for j, audio_fragment in enumerate(batch): - max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音 - if max_audio>1: audio_fragment/=max_audio - audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0) + max_audio = torch.abs(audio_fragment).max() # 简单防止16bit爆音 + if max_audio > 1: audio_fragment /= max_audio + audio_fragment: torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0) audio[i][j] = audio_fragment.cpu().numpy() - - + if split_bucket: audio = self.recovery_order(audio, batch_index_list) else: # audio = [item for batch in audio for item in batch] audio = sum(audio, []) - - + audio = np.concatenate(audio, 0) - audio = (audio * 32768).astype(np.int16) - + audio = (audio * 32768).astype(np.int16) + try: if speed_factor != 1.0: audio = speed_change(audio, speed=speed_factor, sr=int(sr)) except Exception as e: print(f"Failed to change speed of audio: \n{e}") - + return sr, audio - - - - -def speed_change(input_audio:np.ndarray, speed:float, sr:int): + + +def speed_change(input_audio: np.ndarray, speed: float, sr: int): # 将 NumPy 数组转换为原始 PCM 流 raw_audio = input_audio.astype(np.int16).tobytes() @@ -929,4 +940,4 @@ def speed_change(input_audio:np.ndarray, speed:float, sr:int): # 将管道输出解码为 NumPy 数组 processed_audio = np.frombuffer(out, np.int16) - return processed_audio \ No newline at end of file + return processed_audio diff --git a/GPT_SoVITS/configs/tts_infer.yaml b/GPT_SoVITS/configs/tts_infer.yaml index 5f56a4ec..56a07fc8 100644 --- a/GPT_SoVITS/configs/tts_infer.yaml +++ b/GPT_SoVITS/configs/tts_infer.yaml @@ -1,6 +1,7 @@ custom: bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + load_base: true device: cuda is_half: true t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt diff --git a/GPT_SoVITS/configs/voices/voice1.yaml b/GPT_SoVITS/configs/voices/voice1.yaml new file mode 100644 index 00000000..c1d4a2a1 --- /dev/null +++ b/GPT_SoVITS/configs/voices/voice1.yaml @@ -0,0 +1,15 @@ +custom: + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + device: cpu + is_half: false + load_base: false + t2s_weights_path: GPT_weights/voice1-e10.ckpt + vits_weights_path: SoVITS_weights/voice1_e8_s192.pth +default: + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + device: cpu + is_half: false + t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth diff --git a/GPT_SoVITS/configs/voices/voice2.yaml b/GPT_SoVITS/configs/voices/voice2.yaml new file mode 100644 index 00000000..c1d4a2a1 --- /dev/null +++ b/GPT_SoVITS/configs/voices/voice2.yaml @@ -0,0 +1,15 @@ +custom: + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + device: cpu + is_half: false + load_base: false + t2s_weights_path: GPT_weights/voice1-e10.ckpt + vits_weights_path: SoVITS_weights/voice1_e8_s192.pth +default: + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + device: cpu + is_half: false + t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth diff --git a/api_v3.py b/api_v3.py new file mode 100644 index 00000000..aad643a7 --- /dev/null +++ b/api_v3.py @@ -0,0 +1,470 @@ +""" +# WebAPI文档 (3.0) - 使用了缓存技术，初始化时使用LRU Cache TTS 实例，缓存加载模型的世界，达到减少切换不同语音时的推理时间 + +` python api_v2.py -a 127.0.0.1 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml ` + +## 执行参数: + `-a` - `绑定地址, 默认"127.0.0.1"` + `-p` - `绑定端口, 默认9880` + `-c` - `TTS配置文件路径, 默认"GPT_SoVITS/configs/tts_infer.yaml"` + +## 调用: + +### 推理 + +endpoint: `/tts` +GET: +``` +http://127.0.0.1:9880/tts?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_lang=zh&ref_audio_path=archive_jingyuan_1.wav&prompt_lang=zh&prompt_text=我是「罗浮」云骑将军景元。不必拘谨，「将军」只是一时的身份，你称呼我景元便可&text_split_method=cut5&batch_size=1&media_type=wav&streaming_mode=true +``` + +POST: +```json +{ + "text": "", # str.(required) text to be synthesized + "text_lang": "", # str.(required) language of the text to be synthesized + "ref_audio_path": "", # str.(required) reference audio path. + "prompt_text": "", # str.(optional) prompt text for the reference audio + "prompt_lang": "", # str.(required) language of the prompt text for the reference audio + "top_k": 5, # int.(optional) top k sampling + "top_p": 1, # float.(optional) top p sampling + "temperature": 1, # float.(optional) temperature for sampling + "text_split_method": "cut5", # str.(optional) text split method, see text_segmentation_method.py for details. + "batch_size": 1, # int.(optional) batch size for inference + "batch_threshold": 0.75, # float.(optional) threshold for batch splitting. + "split_bucket": true, # bool.(optional) whether to split the batch into multiple buckets. + "speed_factor":1.0, # float.(optional) control the speed of the synthesized audio. + "fragment_interval":0.3, # float.(optional) to control the interval of the audio fragment. + "seed": -1, # int.(optional) random seed for reproducibility. + "media_type": "wav", # str.(optional) media type of the output audio, support "wav", "raw", "ogg", "aac". + "streaming_mode": false, # bool.(optional) whether to return a streaming response. + "parallel_infer": True, # bool.(optional) whether to use parallel inference. + "repetition_penalty": 1.35, # float.(optional) repetition penalty for T2S model. + "tts_infer_yaml_path": “GPT_SoVITS/configs/tts_infer.yaml” # str.(optional) tts infer yaml path +} +``` + +RESP: +成功: 直接返回 wav 音频流， http code 200 +失败: 返回包含错误信息的 json, http code 400 + +### 命令控制 + +endpoint: `/control` + +command: +"restart": 重新运行 +"exit": 结束运行 + +GET: +``` +http://127.0.0.1:9880/control?command=restart +``` +POST: +```json +{ + "command": "restart" +} +``` + +RESP: 无 + + +### 切换GPT模型 + +endpoint: `/set_gpt_weights` + +GET: +``` +http://127.0.0.1:9880/set_gpt_weights?weights_path=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt +``` +RESP: +成功: 返回"success", http code 200 +失败: 返回包含错误信息的 json, http code 400 + + +### 切换Sovits模型 + +endpoint: `/set_sovits_weights` + +GET: +``` +http://127.0.0.1:9880/set_sovits_weights?weights_path=GPT_SoVITS/pretrained_models/s2G488k.pth +``` + +RESP: +成功: 返回"success", http code 200 +失败: 返回包含错误信息的 json, http code 400 + +""" +import os +import sys +import traceback +from typing import Generator + +import torch + +now_dir = os.getcwd() +sys.path.append(now_dir) +sys.path.append("%s/GPT_SoVITS" % (now_dir)) + +import argparse +import subprocess +import wave +import signal +import numpy as np +import soundfile as sf +from fastapi import Response +from fastapi.responses import JSONResponse +from fastapi import FastAPI +import uvicorn +from io import BytesIO +from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config +from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names +from fastapi.responses import StreamingResponse +from pydantic import BaseModel +from functools import lru_cache + +cut_method_names = get_cut_method_names() + +parser = argparse.ArgumentParser(description="GPT-SoVITS api") +parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0") +parser.add_argument("-p", "--port", type=int, default="9880", help="default: 9880") +args = parser.parse_args() +port = args.port +host = args.bind_addr +argv = sys.argv + +default_tts_config = TTS_Config() +TTS.init_base_models(default_tts_config) + +APP = FastAPI() + + +class TTS_Request(BaseModel): + text: str = None + text_lang: str = None + ref_audio_path: str = None + prompt_lang: str = None + prompt_text: str = "" + top_k: int = 5 + top_p: float = 1 + temperature: float = 1 + text_split_method: str = "cut5" + batch_size: int = 1 + batch_threshold: float = 0.75 + split_bucket: bool = True + speed_factor: float = 1.0 + fragment_interval: float = 0.3 + seed: int = -1 + media_type: str = "wav" + streaming_mode: bool = False + parallel_infer: bool = True + repetition_penalty: float = 1.35 + tts_infer_yaml_path: str = None + """推理时需要加载的声音模型的yaml配置文件路径，如：GPT_SoVITS/configs/tts_infer.yaml""" + + +@lru_cache(maxsize=10) +def get_tts_instance(tts_config: TTS_Config) -> TTS: + print(f"load tts config from {tts_config.configs_path}") + return TTS(tts_config) + + +def pack_ogg(io_buffer: BytesIO, data: np.ndarray, rate: int): + """modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files""" + with sf.SoundFile(io_buffer, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file: + audio_file.write(data) + return io_buffer + + +def pack_raw(io_buffer: BytesIO, data: np.ndarray, rate: int): + io_buffer.write(data.tobytes()) + return io_buffer + + +def pack_wav(io_buffer: BytesIO, data: np.ndarray, rate: int): + io_buffer = BytesIO() + sf.write(io_buffer, data, rate, format='wav') + return io_buffer + + +def pack_aac(io_buffer: BytesIO, data: np.ndarray, rate: int): + process = subprocess.Popen([ + 'ffmpeg', + '-f', 's16le', # 输入16位有符号小端整数PCM + '-ar', str(rate), # 设置采样率 + '-ac', '1', # 单声道 + '-i', 'pipe:0', # 从管道读取输入 + '-c:a', 'aac', # 音频编码器为AAC + '-b:a', '192k', # 比特率 + '-vn', # 不包含视频 + '-f', 'adts', # 输出AAC数据流格式 + 'pipe:1' # 将输出写入管道 + ], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + out, _ = process.communicate(input=data.tobytes()) + io_buffer.write(out) + return io_buffer + + +def pack_audio(io_buffer: BytesIO, data: np.ndarray, rate: int, media_type: str): + if media_type == "ogg": + io_buffer = pack_ogg(io_buffer, data, rate) + elif media_type == "aac": + io_buffer = pack_aac(io_buffer, data, rate) + elif media_type == "wav": + io_buffer = pack_wav(io_buffer, data, rate) + else: + io_buffer = pack_raw(io_buffer, data, rate) + io_buffer.seek(0) + return io_buffer + + +# from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py +def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000): + # This will create a wave header then append the frame input + # It should be first on a streaming wav file + # Other frames better should not have it (else you will hear some artifacts each chunk start) + wav_buf = BytesIO() + with wave.open(wav_buf, "wb") as vfout: + vfout.setnchannels(channels) + vfout.setsampwidth(sample_width) + vfout.setframerate(sample_rate) + vfout.writeframes(frame_input) + + wav_buf.seek(0) + return wav_buf.read() + + +def handle_control(command: str): + if command == "restart": + os.execl(sys.executable, sys.executable, *argv) + elif command == "exit": + os.kill(os.getpid(), signal.SIGTERM) + exit(0) + + +def check_params(req: dict, tts_config: TTS_Config): + text: str = req.get("text", "") + text_lang: str = req.get("text_lang", "") + ref_audio_path: str = req.get("ref_audio_path", "") + streaming_mode: bool = req.get("streaming_mode", False) + media_type: str = req.get("media_type", "wav") + prompt_lang: str = req.get("prompt_lang", "") + text_split_method: str = req.get("text_split_method", "cut5") + + if ref_audio_path in [None, ""]: + return JSONResponse(status_code=400, content={"message": "ref_audio_path is required"}) + if text in [None, ""]: + return JSONResponse(status_code=400, content={"message": "text is required"}) + if (text_lang in [None, ""]): + return JSONResponse(status_code=400, content={"message": "text_lang is required"}) + elif text_lang.lower() not in tts_config.languages: + return JSONResponse(status_code=400, content={"message": "text_lang is not supported"}) + if (prompt_lang in [None, ""]): + return JSONResponse(status_code=400, content={"message": "prompt_lang is required"}) + elif prompt_lang.lower() not in tts_config.languages: + return JSONResponse(status_code=400, content={"message": "prompt_lang is not supported"}) + if media_type not in ["wav", "raw", "ogg", "aac"]: + return JSONResponse(status_code=400, content={"message": "media_type is not supported"}) + elif media_type == "ogg" and not streaming_mode: + return JSONResponse(status_code=400, content={"message": "ogg format is not supported in non-streaming mode"}) + + if text_split_method not in cut_method_names: + return JSONResponse(status_code=400, + content={"message": f"text_split_method:{text_split_method} is not supported"}) + + return None + + +async def tts_handle(req: dict): + """ + Text to speech handler. + + Args: + req (dict): + { + "text": "", # str.(required) text to be synthesized + "text_lang: "", # str.(required) language of the text to be synthesized + "ref_audio_path": "", # str.(required) reference audio path + "prompt_text": "", # str.(optional) prompt text for the reference audio + "prompt_lang": "", # str.(required) language of the prompt text for the reference audio + "top_k": 5, # int. top k sampling + "top_p": 1, # float. top p sampling + "temperature": 1, # float. temperature for sampling + "text_split_method": "cut5", # str. text split method, see text_segmentation_method.py for details. + "batch_size": 1, # int. batch size for inference + "batch_threshold": 0.75, # float. threshold for batch splitting. + "split_bucket: True, # bool. whether to split the batch into multiple buckets. + "speed_factor":1.0, # float. control the speed of the synthesized audio. + "fragment_interval":0.3, # float. to control the interval of the audio fragment. + "seed": -1, # int. random seed for reproducibility. + "media_type": "wav", # str. media type of the output audio, support "wav", "raw", "ogg", "aac". + "streaming_mode": False, # bool. whether to return a streaming response. + "parallel_infer": True, # bool.(optional) whether to use parallel inference. + "repetition_penalty": 1.35 # float.(optional) repetition penalty for T2S model. + } + returns: + StreamingResponse: audio stream response. + """ + + streaming_mode = req.get("streaming_mode", False) + media_type = req.get("media_type", "wav") + tts_infer_yaml_path = req.get("tts_infer_yaml_path", "GPT_SoVITS/configs/tts_infer.yaml") + + tts_config = TTS_Config(tts_infer_yaml_path) + check_res = check_params(req, tts_config) + if check_res is not None: + return check_res + + if streaming_mode: + req["return_fragment"] = True + + try: + tts_instance = get_tts_instance(tts_config) + + move_to_gpu(tts_instance, tts_config) + + tts_generator = tts_instance.run(req) + + if streaming_mode: + def streaming_generator(tts_generator: Generator, media_type: str): + if media_type == "wav": + yield wave_header_chunk() + media_type = "raw" + for sr, chunk in tts_generator: + yield pack_audio(BytesIO(), chunk, sr, media_type).getvalue() + move_to_cpu(tts_instance) + + # _media_type = f"audio/{media_type}" if not (streaming_mode and media_type in ["wav", "raw"]) else f"audio/x-{media_type}" + return StreamingResponse(streaming_generator(tts_generator, media_type, ), media_type=f"audio/{media_type}") + + else: + sr, audio_data = next(tts_generator) + audio_data = pack_audio(BytesIO(), audio_data, sr, media_type).getvalue() + move_to_cpu(tts_instance) + return Response(audio_data, media_type=f"audio/{media_type}") + except Exception as e: + return JSONResponse(status_code=400, content={"message": f"tts failed", "Exception": str(e)}) + + +def move_to_cpu(tts): + cpu_device = torch.device('cpu') + tts.set_device(cpu_device) + print("Moved TTS models to CPU to save GPU memory.") + + +def move_to_gpu(tts: TTS, tts_config: TTS_Config): + tts.set_device(tts_config.device) + print("Moved TTS models back to GPU for performance.") + + +@APP.get("/control") +async def control(command: str = None): + if command is None: + return JSONResponse(status_code=400, content={"message": "command is required"}) + handle_control(command) + + +@APP.get("/tts") +async def tts_get_endpoint( + text: str = None, + text_lang: str = None, + ref_audio_path: str = None, + prompt_lang: str = None, + prompt_text: str = "", + top_k: int = 5, + top_p: float = 1, + temperature: float = 1, + text_split_method: str = "cut0", + batch_size: int = 1, + batch_threshold: float = 0.75, + split_bucket: bool = True, + speed_factor: float = 1.0, + fragment_interval: float = 0.3, + seed: int = -1, + media_type: str = "wav", + streaming_mode: bool = False, + parallel_infer: bool = True, + repetition_penalty: float = 1.35, + tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml" +): + req = { + "text": text, + "text_lang": text_lang.lower(), + "ref_audio_path": ref_audio_path, + "prompt_text": prompt_text, + "prompt_lang": prompt_lang.lower(), + "top_k": top_k, + "top_p": top_p, + "temperature": temperature, + "text_split_method": text_split_method, + "batch_size": int(batch_size), + "batch_threshold": float(batch_threshold), + "speed_factor": float(speed_factor), + "split_bucket": split_bucket, + "fragment_interval": fragment_interval, + "seed": seed, + "media_type": media_type, + "streaming_mode": streaming_mode, + "parallel_infer": parallel_infer, + "repetition_penalty": float(repetition_penalty), + "tts_infer_yaml_path": tts_infer_yaml_path + } + + return await tts_handle(req) + + +@APP.post("/tts") +async def tts_post_endpoint(request: TTS_Request): + req = request.dict() + return await tts_handle(req) + + +@APP.get("/set_refer_audio") +async def set_refer_audio(refer_audio_path: str = None, tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml"): + try: + tts_config = TTS_Config(tts_infer_yaml_path) + tts_instance = get_tts_instance(tts_config) + tts_instance.set_ref_audio(refer_audio_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": f"set refer audio failed", "Exception": str(e)}) + return JSONResponse(status_code=200, content={"message": "success"}) + + +@APP.get("/set_gpt_weights") +async def set_gpt_weights(weights_path: str = None, tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml"): + try: + if weights_path in ["", None]: + return JSONResponse(status_code=400, content={"message": "gpt weight path is required"}) + + tts_config = TTS_Config(tts_infer_yaml_path) + tts_instance = get_tts_instance(tts_config) + tts_instance.init_t2s_weights(weights_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": f"change gpt weight failed", "Exception": str(e)}) + + return JSONResponse(status_code=200, content={"message": "success"}) + + +@APP.get("/set_sovits_weights") +async def set_sovits_weights(weights_path: str = None, tts_infer_yaml_path: str = "GPT_SoVITS/configs/tts_infer.yaml"): + try: + if weights_path in ["", None]: + return JSONResponse(status_code=400, content={"message": "sovits weight path is required"}) + + tts_config = TTS_Config(tts_infer_yaml_path) + tts_instance = get_tts_instance(tts_config) + tts_instance.init_vits_weights(weights_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": f"change sovits weight failed", "Exception": str(e)}) + return JSONResponse(status_code=200, content={"message": "success"}) + + +if __name__ == "__main__": + try: + uvicorn.run(APP, host=host, port=port, workers=1) + except Exception as e: + traceback.print_exc() + os.kill(os.getpid(), signal.SIGTERM) + exit(0)