mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-06 14:40:00 +08:00
323 lines
12 KiB
Python
323 lines
12 KiB
Python
import os
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import numpy as np
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import librosa,torch
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from feature_extractor import cnhubert
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from module.models import SynthesizerTrn
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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from text import cleaned_text_to_sequence
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from text.cleaner import clean_text
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from time import time as ttime
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from module.mel_processing import spectrogram_torch
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from my_utils import load_audio
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class DictToAttrRecursive(dict):
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def __init__(self, input_dict):
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super().__init__(input_dict)
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for key, value in input_dict.items():
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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self[key] = value
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setattr(self, key, value)
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def __getattr__(self, item):
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try:
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return self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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def __setattr__(self, key, value):
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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super(DictToAttrRecursive, self).__setitem__(key, value)
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super().__setattr__(key, value)
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def __delattr__(self, item):
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try:
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del self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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class Inference:
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def __init__(self, is_half, GPT_weight_root, SoVITS_weight_root):
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self.n_semantic = 1024
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self.model_loaded = False
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self.is_half = is_half
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self.GPT_weight_root = GPT_weight_root
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self.SoVITS_weight_root = SoVITS_weight_root
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def update_envs(self, gpt_path, sovits_path, cnhubert_base_path, bert_path):
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self.gpt_path = os.path.join(self.GPT_weight_root, gpt_path)
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self.sovits_path = os.path.join(self.SoVITS_weight_root, sovits_path)
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self.cnhubert_base_path = cnhubert_base_path
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self.bert_path = bert_path
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cnhubert.cnhubert_base_path=cnhubert_base_path
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yield self.load_model()
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def load_model(self, device='cuda'):
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try:
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# Load bert model
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self.device = device
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self.tokenizer = AutoTokenizer.from_pretrained(self.bert_path)
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self.bert_model = AutoModelForMaskedLM.from_pretrained(self.bert_path)
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if self.is_half == True:
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self.bert_model = self.bert_model.half().to(device)
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else:
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self.bert_model = self.bert_model.to(device)
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# Load ssl model
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dict_s1 = torch.load(self.gpt_path, map_location="cpu")
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self.config = dict_s1["config"]
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self.ssl_model = cnhubert.get_model()
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if self.is_half == True:
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self.ssl_model = self.ssl_model.half().to(device)
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else:
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self.ssl_model = self.ssl_model.to(device)
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dict_s2=torch.load(self.sovits_path,map_location="cpu")
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self.hps=dict_s2["config"]
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self.hps = DictToAttrRecursive(self.hps)
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self.hps.model.semantic_frame_rate = "25hz"
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# Load vq model
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self.vq_model = SynthesizerTrn(
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model
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)
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if self.is_half == True:
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self.vq_model = self.vq_model.half().to(device)
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else:
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self.vq_model = self.vq_model.to(device)
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self.vq_model.eval()
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self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
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# Load t2s model
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# t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
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self.t2s_model = Text2SemanticLightningModule(self.config, "ojbk", is_train=False)
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self.t2s_model.load_state_dict(dict_s1["weight"])
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if self.is_half == True:
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self.t2s_model = self.t2s_model.half()
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self.t2s_model = self.t2s_model.to(device)
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self.t2s_model.eval()
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total = sum([param.nelement() for param in self.t2s_model.parameters()])
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print("Number of parameter: %.2fM" % (total / 1e6))
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self.model_loaded = True
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return '模型加载成功'
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except Exception as e:
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return f'模型加载失败：{e}'
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def unload_model(self):
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if self.model_loaded:
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try:
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del self.bert_model, self.ssl_model, self.hps, self.vq_model, self.t2s_model
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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self.model_loaded = False
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yield '模型卸载成功'
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except Exception as e:
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yield f'模型卸载失败：{e}'
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else:
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yield '模型未加载'
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def get_bert_feature(self, text, word2ph):
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with torch.no_grad():
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inputs = self.tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(self.device) #####输入是long不用管精度问题，精度随bert_model
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res = self.bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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phone_level_feature = []
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for i in range(len(word2ph)):
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repeat_feature = res[i].repeat(word2ph[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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# if(is_half==True):phone_level_feature=phone_level_feature.half()
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return phone_level_feature.T
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def get_tts_wav(self, ref_wav_path, prompt_text, prompt_language, text, text_language):
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if not self.model_loaded:
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return
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hz = 50
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dict_language = {"中文": "zh", "英文": "en", "日文": "ja"}
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t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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prompt_language, text = prompt_language, text.strip("\n")
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with torch.no_grad():
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wav16k, sr = librosa.load(ref_wav_path, sr=16000) # 派蒙
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wav16k = torch.from_numpy(wav16k)
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if self.is_half == True:
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wav16k = wav16k.half().to(self.device)
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else:
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wav16k = wav16k.to(self.device)
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ssl_content = self.ssl_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.vq_model.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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t1 = ttime()
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prompt_language = dict_language[prompt_language]
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text_language = dict_language[text_language]
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phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
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phones1 = cleaned_text_to_sequence(phones1)
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texts = text.split("\n")
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audio_opt = []
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zero_wav = np.zeros(
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int(self.hps.data.sampling_rate * 0.3),
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dtype=np.float16 if self.is_half == True else np.float32,
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)
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for text in texts:
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# 解决输入目标文本的空行导致报错的问题
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if (len(text.strip()) == 0):
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continue
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phones2, word2ph2, norm_text2 = clean_text(text, text_language)
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phones2 = cleaned_text_to_sequence(phones2)
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if prompt_language == "zh":
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bert1 = self.get_bert_feature(norm_text1, word2ph1).to(self.device)
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else:
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bert1 = torch.zeros(
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(1024, len(phones1)),
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dtype=torch.float16 if self.is_half == True else torch.float32,
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).to(self.device)
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if text_language == "zh":
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bert2 = self.get_bert_feature(norm_text2, word2ph2).to(self.device)
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else:
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bert2 = torch.zeros((1024, len(phones2))).to(bert1)
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bert = torch.cat([bert1, bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(self.device).unsqueeze(0)
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bert = bert.to(self.device).unsqueeze(0)
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all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(self.device)
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prompt = prompt_semantic.unsqueeze(0).to(self.device)
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t2 = ttime()
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with torch.no_grad():
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# pred_semantic = t2s_model.model.infer(
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pred_semantic, idx = self.t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_len,
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prompt,
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bert,
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# prompt_phone_len=ph_offset,
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top_k=self.config["inference"]["top_k"],
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early_stop_num=hz * self.config["data"]["max_sec"]
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)
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t3 = ttime()
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# print(pred_semantic.shape,idx)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(
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0
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) # .unsqueeze(0)#mq要多unsqueeze一次
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refer = get_spepc(self.hps, ref_wav_path) # .to(device)
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if self.is_half == True:
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refer = refer.half().to(self.device)
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else:
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refer = refer.to(self.device)
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# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
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audio = (
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self.vq_model.decode(
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pred_semantic, torch.LongTensor(phones2).to(self.device).unsqueeze(0), refer
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)
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.detach()
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.cpu()
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.numpy()[0, 0]
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) ###试试重建不带上prompt部分
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audio_opt.append(audio)
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audio_opt.append(zero_wav)
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t4 = ttime()
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
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yield self.hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
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np.int16
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)
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def get_spepc(hps, filename):
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audio = load_audio(filename, int(hps.data.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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hps.data.filter_length,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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center=False,
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)
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return spec
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def split(todo_text):
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splits = { "，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } # 不考虑省略号
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todo_text = todo_text.replace("……", "。").replace("——", "，")
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if todo_text[-1] not in splits:
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todo_text += "。"
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i_split_head = i_split_tail = 0
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len_text = len(todo_text)
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todo_texts = []
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while 1:
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if i_split_head >= len_text:
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break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
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if todo_text[i_split_head] in splits:
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i_split_head += 1
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todo_texts.append(todo_text[i_split_tail:i_split_head])
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i_split_tail = i_split_head
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else:
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i_split_head += 1
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return todo_texts
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def cut1(inp):
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inp = inp.strip("\n")
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inps = split(inp)
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split_idx = list(range(0, len(inps), 5))
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split_idx[-1] = None
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if len(split_idx) > 1:
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opts = []
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for idx in range(len(split_idx) - 1):
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opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
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else:
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opts = [inp]
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return "\n".join(opts)
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def cut2(inp):
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inp = inp.strip("\n")
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inps = split(inp)
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if len(inps) < 2:
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return [inp]
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opts = []
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summ = 0
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tmp_str = ""
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for i in range(len(inps)):
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summ += len(inps[i])
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tmp_str += inps[i]
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if summ > 50:
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summ = 0
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opts.append(tmp_str)
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tmp_str = ""
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if tmp_str != "":
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opts.append(tmp_str)
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if len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
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opts[-2] = opts[-2] + opts[-1]
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opts = opts[:-1]
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return "\n".join(opts)
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def cut3(inp):
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inp = inp.strip("\n")
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return "\n".join(["%s。" % item for item in inp.strip("。").split("。")])
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