mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-06 03:57:44 +08:00
554 lines
23 KiB
Python
554 lines
23 KiB
Python
import os, sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import os
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from typing import Generator, List, Union
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import numpy as np
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import torch
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import yaml
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from feature_extractor.cnhubert import CNHubert
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from module.models import SynthesizerTrn
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import librosa
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from time import time as ttime
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from tools.i18n.i18n import I18nAuto
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from my_utils import load_audio
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from module.mel_processing import spectrogram_torch
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from .text_segmentation_method import splits
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from .TextPreprocessor import TextPreprocessor
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i18n = I18nAuto()
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# tts_infer.yaml
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"""
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default:
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device: cpu
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is_half: false
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
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vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
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custom:
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device: cuda
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is_half: true
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
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vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
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"""
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class TTS_Config:
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def __init__(self, configs: Union[dict, str]):
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configs_base_path:str = "GPT_SoVITS/configs/"
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os.makedirs(configs_base_path, exist_ok=True)
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self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml")
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if isinstance(configs, str):
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self.configs_path = configs
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configs:dict = self._load_configs(configs)
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# assert isinstance(configs, dict)
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self.default_configs:dict = configs.get("default", None)
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if self.default_configs is None:
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self.default_configs={
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"device": "cpu",
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"is_half": False,
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"t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
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"vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth",
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"cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
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"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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}
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self.configs:dict = configs.get("custom", self.default_configs)
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self.device = self.configs.get("device")
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self.is_half = self.configs.get("is_half")
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self.t2s_weights_path = self.configs.get("t2s_weights_path")
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self.vits_weights_path = self.configs.get("vits_weights_path")
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self.bert_base_path = self.configs.get("bert_base_path")
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self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path")
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self.max_sec = None
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self.hz:int = 50
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self.semantic_frame_rate:str = "25hz"
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self.segment_size:int = 20480
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self.filter_length:int = 2048
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self.sampling_rate:int = 32000
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self.hop_length:int = 640
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self.win_length:int = 2048
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self.n_speakers:int = 300
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self.langauges:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"]
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def _load_configs(self, configs_path: str)->dict:
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with open(configs_path, 'r') as f:
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configs = yaml.load(f, Loader=yaml.FullLoader)
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return configs
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def save_configs(self, configs_path:str=None)->None:
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configs={
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"default": {
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"device": "cpu",
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"is_half": False,
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"t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
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"vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth",
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"cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
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"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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},
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"custom": {
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"device": str(self.device),
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"is_half": self.is_half,
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"t2s_weights_path": self.t2s_weights_path,
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"vits_weights_path": self.vits_weights_path,
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"bert_base_path": self.bert_base_path,
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"cnhuhbert_base_path": self.cnhuhbert_base_path
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}
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}
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if configs_path is None:
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configs_path = self.configs_path
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with open(configs_path, 'w') as f:
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yaml.dump(configs, f)
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class TTS:
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def __init__(self, configs: Union[dict, str, TTS_Config]):
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if isinstance(configs, TTS_Config):
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self.configs = configs
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else:
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self.configs:TTS_Config = TTS_Config(configs)
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self.t2s_model:Text2SemanticLightningModule = None
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self.vits_model:SynthesizerTrn = None
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self.bert_tokenizer:AutoTokenizer = None
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self.bert_model:AutoModelForMaskedLM = None
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self.cnhuhbert_model:CNHubert = None
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self._init_models()
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self.text_preprocessor:TextPreprocessor = \
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TextPreprocessor(self.bert_model,
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self.bert_tokenizer,
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self.configs.device)
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self.prompt_cache:dict = {
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"ref_audio_path":None,
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"prompt_semantic":None,
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"refer_spepc":None,
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"prompt_text":None,
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"prompt_lang":None,
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"phones":None,
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"bert_features":None,
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"norm_text":None,
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}
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def _init_models(self,):
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self.init_t2s_weights(self.configs.t2s_weights_path)
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self.init_vits_weights(self.configs.vits_weights_path)
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self.init_bert_weights(self.configs.bert_base_path)
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self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path)
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def init_cnhuhbert_weights(self, base_path: str):
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self.cnhuhbert_model = CNHubert(base_path)
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self.cnhuhbert_model.eval()
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if self.configs.is_half == True:
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self.cnhuhbert_model = self.cnhuhbert_model.half()
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self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device)
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def init_bert_weights(self, base_path: str):
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self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path)
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self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path)
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if self.configs.is_half:
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self.bert_model = self.bert_model.half()
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self.bert_model = self.bert_model.to(self.configs.device)
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def init_vits_weights(self, weights_path: str):
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self.configs.vits_weights_path = weights_path
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self.configs.save_configs()
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dict_s2 = torch.load(weights_path, map_location=self.configs.device)
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hps = dict_s2["config"]
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self.configs.filter_length = hps["data"]["filter_length"]
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self.configs.segment_size = hps["train"]["segment_size"]
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self.configs.sampling_rate = hps["data"]["sampling_rate"]
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self.configs.hop_length = hps["data"]["hop_length"]
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self.configs.win_length = hps["data"]["win_length"]
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self.configs.n_speakers = hps["data"]["n_speakers"]
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self.configs.semantic_frame_rate = "25hz"
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kwargs = hps["model"]
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vits_model = SynthesizerTrn(
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self.configs.filter_length // 2 + 1,
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self.configs.segment_size // self.configs.hop_length,
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n_speakers=self.configs.n_speakers,
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**kwargs
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)
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# if ("pretrained" not in weights_path):
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if hasattr(vits_model, "enc_q"):
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del vits_model.enc_q
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if self.configs.is_half:
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vits_model = vits_model.half()
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vits_model = vits_model.to(self.configs.device)
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vits_model.eval()
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vits_model.load_state_dict(dict_s2["weight"], strict=False)
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self.vits_model = vits_model
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def init_t2s_weights(self, weights_path: str):
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self.configs.t2s_weights_path = weights_path
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self.configs.save_configs()
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self.configs.hz = 50
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dict_s1 = torch.load(weights_path, map_location=self.configs.device)
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config = dict_s1["config"]
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self.configs.max_sec = config["data"]["max_sec"]
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
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t2s_model.load_state_dict(dict_s1["weight"])
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if self.configs.is_half:
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t2s_model = t2s_model.half()
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t2s_model = t2s_model.to(self.configs.device)
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t2s_model.eval()
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self.t2s_model = t2s_model
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def set_ref_audio(self, ref_audio_path:str):
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self._set_prompt_semantic(ref_audio_path)
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self._set_ref_spepc(ref_audio_path)
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def _set_ref_spepc(self, ref_audio_path):
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audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
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audio = torch.FloatTensor(audio)
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audio_norm = audio
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(
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audio_norm,
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self.configs.filter_length,
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self.configs.sampling_rate,
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self.configs.hop_length,
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self.configs.win_length,
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center=False,
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)
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spec = spec.to(self.configs.device)
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if self.configs.is_half:
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spec = spec.half()
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# self.refer_spepc = spec
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self.prompt_cache["refer_spepc"] = spec
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def _set_prompt_semantic(self, ref_wav_path:str):
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zero_wav = np.zeros(
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int(self.configs.sampling_rate * 0.3),
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dtype=np.float16 if self.configs.is_half else np.float32,
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)
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with torch.no_grad():
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wav16k, sr = librosa.load(ref_wav_path, sr=16000)
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if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
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raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
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wav16k = torch.from_numpy(wav16k)
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zero_wav_torch = torch.from_numpy(zero_wav)
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wav16k = wav16k.to(self.configs.device)
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zero_wav_torch = zero_wav_torch.to(self.configs.device)
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if self.configs.is_half:
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wav16k = wav16k.half()
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zero_wav_torch = zero_wav_torch.half()
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wav16k = torch.cat([wav16k, zero_wav_torch])
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hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[
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"last_hidden_state"
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].transpose(
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1, 2
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) # .float()
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codes = self.vits_model.extract_latent(hubert_feature)
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prompt_semantic = codes[0, 0].to(self.configs.device)
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self.prompt_cache["prompt_semantic"] = prompt_semantic
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def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None):
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seq = sequences[0]
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ndim = seq.dim()
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if axis < 0:
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axis += ndim
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dtype:torch.dtype = seq.dtype
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pad_value = torch.tensor(pad_value, dtype=dtype)
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seq_lengths = [seq.shape[axis] for seq in sequences]
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if max_length is None:
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max_length = max(seq_lengths)
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else:
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max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length
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padded_sequences = []
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for seq, length in zip(sequences, seq_lengths):
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padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1)
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padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value)
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padded_sequences.append(padded_seq)
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batch = torch.stack(padded_sequences)
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return batch
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def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75):
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_data:list = []
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index_and_len_list = []
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for idx, item in enumerate(data):
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norm_text_len = len(item["norm_text"])
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index_and_len_list.append([idx, norm_text_len])
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index_and_len_list.sort(key=lambda x: x[1])
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# index_and_len_batch_list = [index_and_len_list[idx:min(idx+batch_size,len(index_and_len_list))] for idx in range(0,len(index_and_len_list),batch_size)]
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index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
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# for batch_idx, index_and_len_batch in enumerate(index_and_len_batch_list):
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batch_index_list = []
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batch_index_list_len = 0
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pos = 0
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while pos <index_and_len_list.shape[0]:
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# batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
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pos_end = min(pos+batch_size,index_and_len_list.shape[0])
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while pos < pos_end:
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batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
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score=batch[(pos_end-pos)//2]/batch.mean()
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if (score>=threshold) or (pos_end-pos==1):
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batch_index=index_and_len_list[pos:pos_end, 0].tolist()
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batch_index_list_len += len(batch_index)
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batch_index_list.append(batch_index)
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pos = pos_end
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break
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pos_end=pos_end-1
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assert batch_index_list_len == len(data)
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for batch_idx, index_list in enumerate(batch_index_list):
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item_list = [data[idx] for idx in index_list]
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phones_list = []
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# bert_features_list = []
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all_phones_list = []
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all_bert_features_list = []
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norm_text_batch = []
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bert_max_len = 0
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phones_max_len = 0
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for item in item_list:
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if prompt_data is not None:
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all_bert_features = torch.cat([prompt_data["bert_features"].clone(), item["bert_features"]], 1)
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all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"])
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phones = torch.LongTensor(item["phones"])
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# norm_text = prompt_data["norm_text"]+item["norm_text"]
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else:
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all_bert_features = item["bert_features"]
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phones = torch.LongTensor(item["phones"])
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all_phones = phones.clone()
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# norm_text = item["norm_text"]
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bert_max_len = max(bert_max_len, all_bert_features.shape[-1])
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phones_max_len = max(phones_max_len, phones.shape[-1])
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phones_list.append(phones)
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all_phones_list.append(all_phones)
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all_bert_features_list.append(all_bert_features)
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norm_text_batch.append(item["norm_text"])
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# phones_batch = phones_list
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max_len = max(bert_max_len, phones_max_len)
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phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
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all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
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all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, max_len)
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all_bert_features_batch.zero_()
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for idx, item in enumerate(all_bert_features_list):
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if item != None:
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all_bert_features_batch[idx, :, : item.shape[-1]] = item
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batch = {
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"phones": phones_batch,
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"all_phones": all_phones_batch,
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"all_bert_features": all_bert_features_batch,
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"norm_text": norm_text_batch
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}
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_data.append(batch)
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return _data, batch_index_list
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def recovery_order(self, data:list, batch_index_list:list)->list:
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lenght = len(sum(batch_index_list, []))
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_data = [None]*lenght
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for i, index_list in enumerate(batch_index_list):
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for j, index in enumerate(index_list):
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_data[index] = data[i][j]
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return _data
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def run(self, inputs:dict):
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"""
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Text to speech inference.
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Args:
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inputs (dict):
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{
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"text": "",
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"text_lang: "",
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"ref_audio_path": "",
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"prompt_text": "",
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"prompt_lang": "",
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"top_k": 5,
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"top_p": 0.9,
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"temperature": 0.6,
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"text_split_method": "",
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"batch_size": 1,
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"batch_threshold": 0.75
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}
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returns:
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tulpe[int, np.ndarray]: sampling rate and audio data.
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"""
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text:str = inputs.get("text", "")
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text_lang:str = inputs.get("text_lang", "")
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ref_audio_path:str = inputs.get("ref_audio_path", "")
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prompt_text:str = inputs.get("prompt_text", "")
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prompt_lang:str = inputs.get("prompt_lang", "")
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top_k:int = inputs.get("top_k", 20)
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top_p:float = inputs.get("top_p", 0.9)
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temperature:float = inputs.get("temperature", 0.6)
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text_split_method:str = inputs.get("text_split_method", "")
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batch_size = inputs.get("batch_size", 1)
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batch_threshold = inputs.get("batch_threshold", 0.75)
|
||
|
||
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()")
|
||
|
||
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
|
||
|
||
zero_wav = np.zeros(
|
||
int(self.configs.sampling_rate * 0.3),
|
||
dtype=np.float16 if self.configs.is_half else np.float32,
|
||
)
|
||
|
||
|
||
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
|
||
audio = []
|
||
t1 = ttime()
|
||
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)
|
||
t2 = ttime()
|
||
zero_wav = torch.zeros(
|
||
int(self.configs.sampling_rate * 0.3),
|
||
dtype=torch.float16 if self.configs.is_half else torch.float32,
|
||
device=self.configs.device
|
||
)
|
||
|
||
t_34 = 0.0
|
||
t_45 = 0.0
|
||
for item in data:
|
||
t3 = ttime()
|
||
batch_phones = item["phones"]
|
||
all_phoneme_ids = item["all_phones"]
|
||
all_bert_features = item["all_bert_features"]
|
||
norm_text = item["norm_text"]
|
||
|
||
# phones = phones.to(self.configs.device)
|
||
all_phoneme_ids = all_phoneme_ids.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()
|
||
# all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]*all_phoneme_ids.shape[0], device=self.configs.device)
|
||
|
||
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"].clone().repeat(all_phoneme_ids.shape[0], 1).to(self.configs.device)
|
||
|
||
with torch.no_grad():
|
||
# pred_semantic = t2s_model.model.infer(
|
||
pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
|
||
all_phoneme_ids,
|
||
None,
|
||
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"].clone().to(self.configs.device)
|
||
if self.configs.is_half:
|
||
refer_audio_spepc = refer_audio_spepc.half()
|
||
|
||
## 直接对batch进行decode 生成的音频会有问题
|
||
# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
|
||
# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
|
||
# batch_phones = batch_phones.to(self.configs.device)
|
||
# batch_audio_fragment =(self.vits_model.decode(
|
||
# pred_semantic, batch_phones, refer_audio_spepc
|
||
# ).detach()[:, 0, :])
|
||
# max_audio=torch.abs(batch_audio_fragment).max()#简单防止16bit爆音
|
||
# if max_audio>1: batch_audio_fragment/=max_audio
|
||
# batch_audio_fragment = batch_audio_fragment.cpu().numpy()
|
||
|
||
## 改成串行处理
|
||
batch_audio_fragment = []
|
||
for i, idx in enumerate(idx_list):
|
||
phones = batch_phones[i].clone().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, :])
|
||
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.cat([audio_fragment, zero_wav], dim=0)
|
||
batch_audio_fragment.append(
|
||
audio_fragment.cpu().numpy()
|
||
) ###试试重建不带上prompt部分
|
||
|
||
audio.append(batch_audio_fragment)
|
||
# audio.append(zero_wav)
|
||
t5 = ttime()
|
||
t_45 += t5 - t4
|
||
|
||
audio = self.recovery_order(audio, batch_index_list)
|
||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
|
||
yield self.configs.sampling_rate, (np.concatenate(audio, 0) * 32768).astype(
|
||
np.int16
|
||
)
|
||
|