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 =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