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| from copy import deepcopy | |
| import math | |
| import os, sys | |
| import random | |
| import traceback | |
| from tqdm import tqdm | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| import ffmpeg | |
| import os | |
| from typing import Generator, List, Union | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| import yaml | |
| from transformers import AutoModelForMaskedLM, AutoTokenizer | |
| from AR.models.t2s_lightning_module import Text2SemanticLightningModule | |
| from feature_extractor.cnhubert import CNHubert | |
| from module.models import SynthesizerTrn | |
| import librosa | |
| from time import time as ttime | |
| from tools.i18n.i18n import I18nAuto | |
| 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 | |
| """ | |
| default: | |
| device: cpu | |
| is_half: false | |
| bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large | |
| cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base | |
| t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt | |
| vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth | |
| flash_attn_enabled: true | |
| custom: | |
| device: cuda | |
| is_half: true | |
| bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large | |
| cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base | |
| t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt | |
| vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth | |
| flash_attn_enabled: true | |
| """ | |
| def set_seed(seed:int): | |
| seed = int(seed) | |
| seed = seed if seed != -1 else random.randrange(1 << 32) | |
| print(f"Set seed to {seed}") | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| try: | |
| if torch.cuda.is_available(): | |
| torch.cuda.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| # torch.backends.cudnn.deterministic = True | |
| # torch.backends.cudnn.benchmark = False | |
| # torch.backends.cudnn.enabled = True | |
| 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", | |
| "flash_attn_enabled": 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/" | |
| os.makedirs(configs_base_path, exist_ok=True) | |
| 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)} | |
| if isinstance(configs, str): | |
| self.configs_path = configs | |
| configs:dict = self._load_configs(self.configs_path) | |
| assert isinstance(configs, dict) | |
| 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.device = self.configs.get("device", torch.device("cpu")) | |
| self.is_half = self.configs.get("is_half", False) | |
| self.flash_attn_enabled = self.configs.get("flash_attn_enabled", True) | |
| 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) | |
| 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}") | |
| if (self.vits_weights_path in [None, ""]) or (not os.path.exists(self.vits_weights_path)): | |
| self.vits_weights_path = self.default_configs['vits_weights_path'] | |
| print(f"fall back to default vits_weights_path: {self.vits_weights_path}") | |
| if (self.bert_base_path in [None, ""]) or (not os.path.exists(self.bert_base_path)): | |
| self.bert_base_path = self.default_configs['bert_base_path'] | |
| print(f"fall back to default bert_base_path: {self.bert_base_path}") | |
| if (self.cnhuhbert_base_path in [None, ""]) or (not os.path.exists(self.cnhuhbert_base_path)): | |
| 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.langauges:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] | |
| # print(self) | |
| 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, | |
| } | |
| 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: | |
| yaml.dump(configs, f) | |
| 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, | |
| "cnhuhbert_base_path": self.cnhuhbert_base_path, | |
| "flash_attn_enabled" : self.flash_attn_enabled | |
| } | |
| return self.config | |
| def __str__(self): | |
| self.configs = self.update_configs() | |
| string = "TTS Config".center(100, '-') + '\n' | |
| for k, v in self.configs.items(): | |
| string += f"{str(k).ljust(20)}: {str(v)}\n" | |
| string += "-" * 100 + '\n' | |
| return string | |
| def __repr__(self): | |
| return self.__str__() | |
| class TTS: | |
| 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_spepc":None, | |
| "prompt_text":None, | |
| "prompt_lang":None, | |
| "phones":None, | |
| "bert_features":None, | |
| "norm_text":None, | |
| } | |
| self.stop_flag:bool = False | |
| self.precison: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) | |
| # 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() | |
| def init_vits_weights(self, weights_path: str): | |
| print(f"Loading VITS weights from {weights_path}") | |
| self.configs.vits_weights_path = weights_path | |
| self.configs.save_configs() | |
| dict_s2 = torch.load(weights_path, map_location=self.configs.device) | |
| 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.hop_length = hps["data"]["hop_length"] | |
| self.configs.win_length = hps["data"]["win_length"] | |
| self.configs.n_speakers = hps["data"]["n_speakers"] | |
| self.configs.semantic_frame_rate = "25hz" | |
| kwargs = hps["model"] | |
| vits_model = SynthesizerTrn( | |
| self.configs.filter_length // 2 + 1, | |
| self.configs.segment_size // self.configs.hop_length, | |
| n_speakers=self.configs.n_speakers, | |
| **kwargs | |
| ) | |
| # 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": | |
| 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 | |
| self.configs.save_configs() | |
| self.configs.hz = 50 | |
| dict_s1 = torch.load(weights_path, map_location=self.configs.device) | |
| config = dict_s1["config"] | |
| self.configs.max_sec = config["data"]["max_sec"] | |
| t2s_model = Text2SemanticLightningModule(config, "****", is_train=False, | |
| flash_attn_enabled=self.configs.flash_attn_enabled) | |
| t2s_model.load_state_dict(dict_s1["weight"]) | |
| 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": | |
| self.t2s_model = self.t2s_model.half() | |
| def enable_half_precision(self, enable: bool = True): | |
| ''' | |
| To enable half precision for the TTS model. | |
| Args: | |
| enable: bool, whether to enable half precision. | |
| ''' | |
| if str(self.configs.device) == "cpu" and enable: | |
| print("Half precision is not supported on CPU.") | |
| return | |
| self.configs.is_half = enable | |
| self.precison = 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() | |
| 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() | |
| 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() | |
| def set_device(self, device: torch.device): | |
| ''' | |
| To set the device for all models. | |
| Args: | |
| device: torch.device, the device to use for all models. | |
| ''' | |
| self.configs.device = device | |
| self.configs.save_configs() | |
| if self.t2s_model is not None: | |
| 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): | |
| ''' | |
| To set the reference audio for the TTS model, | |
| including the prompt_semantic and refer_spepc. | |
| Args: | |
| ref_audio_path: str, the path of the reference audio. | |
| ''' | |
| self._set_prompt_semantic(ref_audio_path) | |
| self._set_ref_spepc(ref_audio_path) | |
| def _set_ref_spepc(self, ref_audio_path): | |
| audio = load_audio(ref_audio_path, int(self.configs.sampling_rate)) | |
| audio = torch.FloatTensor(audio) | |
| audio_norm = audio | |
| audio_norm = audio_norm.unsqueeze(0) | |
| spec = spectrogram_torch( | |
| audio_norm, | |
| self.configs.filter_length, | |
| self.configs.sampling_rate, | |
| self.configs.hop_length, | |
| self.configs.win_length, | |
| center=False, | |
| ) | |
| spec = spec.to(self.configs.device) | |
| if self.configs.is_half: | |
| spec = spec.half() | |
| # self.refer_spepc = spec | |
| self.prompt_cache["refer_spepc"] = spec | |
| 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, | |
| ) | |
| with torch.no_grad(): | |
| wav16k, sr = librosa.load(ref_wav_path, sr=16000) | |
| if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): | |
| raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) | |
| wav16k = torch.from_numpy(wav16k) | |
| zero_wav_torch = torch.from_numpy(zero_wav) | |
| wav16k = wav16k.to(self.configs.device) | |
| zero_wav_torch = zero_wav_torch.to(self.configs.device) | |
| if self.configs.is_half: | |
| wav16k = wav16k.half() | |
| zero_wav_torch = zero_wav_torch.half() | |
| wav16k = torch.cat([wav16k, zero_wav_torch]) | |
| hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[ | |
| "last_hidden_state" | |
| ].transpose( | |
| 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): | |
| seq = sequences[0] | |
| ndim = seq.dim() | |
| if axis < 0: | |
| axis += ndim | |
| 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: | |
| max_length = max(seq_lengths) | |
| else: | |
| max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length | |
| padded_sequences = [] | |
| for seq, length in zip(sequences, seq_lengths): | |
| padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1) | |
| padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value) | |
| 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): | |
| _data:list = [] | |
| index_and_len_list = [] | |
| for idx, item in enumerate(data): | |
| norm_text_len = len(item["norm_text"]) | |
| index_and_len_list.append([idx, norm_text_len]) | |
| 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) | |
| batch_index_list_len = 0 | |
| pos = 0 | |
| while pos <index_and_len_list.shape[0]: | |
| # batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))]) | |
| pos_end = min(pos+batch_size,index_and_len_list.shape[0]) | |
| while pos < pos_end: | |
| 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 | |
| assert batch_index_list_len == len(data) | |
| else: | |
| for i in range(len(data)): | |
| 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 = [] | |
| phones_len_list = [] | |
| # bert_features_list = [] | |
| all_phones_list = [] | |
| all_phones_len_list = [] | |
| all_bert_features_list = [] | |
| norm_text_batch = [] | |
| bert_max_len = 0 | |
| 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=self.precison) | |
| all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]) | |
| phones = torch.LongTensor(item["phones"]) | |
| # norm_text = prompt_data["norm_text"]+item["norm_text"] | |
| else: | |
| all_bert_features = item["bert_features"]\ | |
| .to(dtype=self.precison) | |
| phones = torch.LongTensor(item["phones"]) | |
| 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 | |
| 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) | |
| all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len) | |
| # all_bert_features_batch = all_bert_features_list | |
| all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=self.precison) | |
| for idx, item in enumerate(all_bert_features_list): | |
| all_bert_features_batch[idx, :, : item.shape[-1]] = item | |
| batch = { | |
| "phones": phones_batch, | |
| "phones_len": torch.LongTensor(phones_len_list), | |
| "all_phones": all_phones_batch, | |
| "all_phones_len": torch.LongTensor(all_phones_len_list), | |
| "all_bert_features": all_bert_features_batch, | |
| "norm_text": norm_text_batch | |
| } | |
| _data.append(batch) | |
| return _data, batch_index_list | |
| def recovery_order(self, data:list, batch_index_list:list)->list: | |
| ''' | |
| Recovery the order of the audio according to the batch_index_list. | |
| Args: | |
| data (List[list(np.ndarray)]): the out of order audio . | |
| batch_index_list (List[list[int]]): the batch index list. | |
| Returns: | |
| list (List[np.ndarray]): the data in the original order. | |
| ''' | |
| lenght = len(sum(batch_index_list, [])) | |
| _data = [None]*lenght | |
| 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,): | |
| ''' | |
| Stop the inference process. | |
| ''' | |
| self.stop_flag = True | |
| def run(self, inputs:dict): | |
| """ | |
| Text to speech inference. | |
| Args: | |
| inputs (dict): | |
| { | |
| "text": "", # str. text to be synthesized | |
| "text_lang: "", # str. language of the text to be synthesized | |
| "ref_audio_path": "", # str. reference audio path | |
| "prompt_text": "", # str. prompt text for the reference audio | |
| "prompt_lang": "", # str. 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": "", # str. text split method, see text_segmentaion_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. | |
| "return_fragment": False, # bool. step by step return the audio fragment. | |
| "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. | |
| } | |
| returns: | |
| tulpe[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", "") | |
| batch_size = inputs.get("batch_size", 1) | |
| batch_threshold = inputs.get("batch_threshold", 0.75) | |
| speed_factor = inputs.get("speed_factor", 1.0) | |
| split_bucket = inputs.get("split_bucket", True) | |
| return_fragment = inputs.get("return_fragment", False) | |
| fragment_interval = inputs.get("fragment_interval", 0.3) | |
| seed = inputs.get("seed", -1) | |
| seed = -1 if seed in ["", None] else seed | |
| set_seed(seed) | |
| if return_fragment: | |
| # split_bucket = False | |
| print(i18n("分段返回模式已开启")) | |
| if split_bucket: | |
| split_bucket = False | |
| print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理")) | |
| if split_bucket: | |
| print(i18n("分桶处理模式已开启")) | |
| 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")) | |
| no_prompt_text = False | |
| if prompt_text in [None, ""]: | |
| no_prompt_text = True | |
| assert text_lang in self.configs.langauges | |
| if not no_prompt_text: | |
| assert prompt_lang in self.configs.langauges | |
| if ref_audio_path in [None, ""] and \ | |
| ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spepc"] 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() | |
| if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]): | |
| self.set_ref_audio(ref_audio_path) | |
| if not no_prompt_text: | |
| prompt_text = prompt_text.strip("\n") | |
| if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "." | |
| print(i18n("实际输入的参考文本:"), prompt_text) | |
| if self.prompt_cache["prompt_text"] != prompt_text: | |
| self.prompt_cache["prompt_text"] = prompt_text | |
| 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) | |
| 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 | |
| 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) | |
| 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 | |
| ) | |
| 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: | |
| 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) | |
| if phones is None: | |
| continue | |
| res={ | |
| "phones": phones, | |
| "bert_features": bert_features, | |
| "norm_text": norm_text, | |
| } | |
| 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 | |
| ) | |
| return batch[0] | |
| t2 = ttime() | |
| try: | |
| print("############ 推理 ############") | |
| ###### inference ###### | |
| t_34 = 0.0 | |
| t_45 = 0.0 | |
| audio = [] | |
| for item in data: | |
| t3 = ttime() | |
| if return_fragment: | |
| item = make_batch(item) | |
| if item is None: | |
| continue | |
| batch_phones = item["phones"] | |
| batch_phones_len = item["phones_len"] | |
| all_phoneme_ids = item["all_phones"] | |
| all_phoneme_lens = item["all_phones_len"] | |
| all_bert_features = item["all_bert_features"] | |
| norm_text = item["norm_text"] | |
| # batch_phones = batch_phones.to(self.configs.device) | |
| batch_phones_len = batch_phones_len.to(self.configs.device) | |
| all_phoneme_ids = all_phoneme_ids.to(self.configs.device) | |
| all_phoneme_lens = all_phoneme_lens.to(self.configs.device) | |
| all_bert_features = all_bert_features.to(self.configs.device) | |
| if self.configs.is_half: | |
| all_bert_features = all_bert_features.half() | |
| print(i18n("前端处理后的文本(每句):"), norm_text) | |
| if no_prompt_text : | |
| prompt = None | |
| else: | |
| prompt = self.prompt_cache["prompt_semantic"].expand(all_phoneme_ids.shape[0], -1).to(self.configs.device) | |
| with torch.no_grad(): | |
| pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( | |
| all_phoneme_ids, | |
| all_phoneme_lens, | |
| prompt, | |
| all_bert_features, | |
| # prompt_phone_len=ph_offset, | |
| top_k=top_k, | |
| top_p=top_p, | |
| temperature=temperature, | |
| early_stop_num=self.configs.hz * self.configs.max_sec, | |
| ) | |
| t4 = ttime() | |
| t_34 += t4 - t3 | |
| refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"]\ | |
| .to(dtype=self.precison, device=self.configs.device) | |
| batch_audio_fragment = [] | |
| # ## 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) | |
| # pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0) | |
| # max_len = 0 | |
| # for i in range(0, len(batch_phones)): | |
| # max_len = max(max_len, batch_phones[i].shape[-1]) | |
| # batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len) | |
| # batch_phones = batch_phones.to(self.configs.device) | |
| # batch_audio_fragment = (self.vits_model.batched_decode( | |
| # pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc | |
| # )) | |
| # ## 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))] | |
| 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_spepc | |
| ).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))] | |
| # ## vits串行推理 | |
| # for i, idx in enumerate(idx_list): | |
| # phones = batch_phones[i].unsqueeze(0).to(self.configs.device) | |
| # _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次 | |
| # audio_fragment =(self.vits_model.decode( | |
| # _pred_semantic, phones, refer_audio_spepc | |
| # ).detach()[0, 0, :]) | |
| # batch_audio_fragment.append( | |
| # audio_fragment | |
| # ) ###试试重建不带上prompt部分 | |
| t5 = ttime() | |
| 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 | |
| ) | |
| 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) | |
| 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 | |
| ) | |
| 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) | |
| # 重置模型, 否则会导致显存释放不完全。 | |
| del self.t2s_model | |
| del self.vits_model | |
| self.t2s_model = None | |
| self.vits_model = None | |
| self.init_t2s_weights(self.configs.t2s_weights_path) | |
| self.init_vits_weights(self.configs.vits_weights_path) | |
| finally: | |
| self.empty_cache() | |
| def empty_cache(self): | |
| 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]: | |
| zero_wav = torch.zeros( | |
| int(self.configs.sampling_rate * fragment_interval), | |
| dtype=self.precison, | |
| 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) | |
| 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) | |
| 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): | |
| # 将 NumPy 数组转换为原始 PCM 流 | |
| raw_audio = input_audio.astype(np.int16).tobytes() | |
| # 设置 ffmpeg 输入流 | |
| input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1) | |
| # 变速处理 | |
| output_stream = input_stream.filter('atempo', speed) | |
| # 输出流到管道 | |
| out, _ = ( | |
| output_stream.output('pipe:', format='s16le', acodec='pcm_s16le') | |
| .run(input=raw_audio, capture_stdout=True, capture_stderr=True) | |
| ) | |
| # 将管道输出解码为 NumPy 数组 | |
| processed_audio = np.frombuffer(out, np.int16) | |
| return processed_audio |