mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 04:22:46 +08:00
118 lines
4.4 KiB
Python
118 lines
4.4 KiB
Python
# -*- coding: utf-8 -*-
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import os
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inp_text= os.environ.get("inp_text")
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inp_wav_dir= os.environ.get("inp_wav_dir")
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exp_name= os.environ.get("exp_name")
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i_part= os.environ.get("i_part")
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all_parts= os.environ.get("all_parts")
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os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
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opt_dir= os.environ.get("opt_dir")
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bert_pretrained_dir= os.environ.get("bert_pretrained_dir")
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is_half=eval(os.environ.get("is_half","True"))
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import sys,numpy as np,traceback,pdb
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import os.path
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from glob import glob
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from tqdm import tqdm
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from text.cleaner import clean_text
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import torch
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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# inp_text=sys.argv[1]
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# inp_wav_dir=sys.argv[2]
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# exp_name=sys.argv[3]
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# i_part=sys.argv[4]
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# all_parts=sys.argv[5]
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# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]#i_gpu
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# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
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# bert_pretrained_dir="/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large"
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from time import time as ttime
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import shutil
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def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
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dir=os.path.dirname(path)
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name=os.path.basename(path)
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tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
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torch.save(fea,tmp_path)
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shutil.move(tmp_path,"%s/%s"%(dir,name))
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txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
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if(os.path.exists(txt_path)==False):
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bert_dir="%s/3-bert"%(opt_dir)
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os.makedirs(opt_dir,exist_ok=True)
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os.makedirs(bert_dir,exist_ok=True)
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device="cuda:0"
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tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
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bert_model=AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
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if (is_half == True):
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bert_model = bert_model.half().to(device)
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else:
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bert_model = bert_model.to(device)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = 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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return phone_level_feature.T
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def process(data,res):
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for name,text,lan in data:
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try:
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name=os.path.basename(name)
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phones, word2ph, norm_text=clean_text(text.replace("%", '-').replace('￥', ','),lan)
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path_bert="%s/%s.pt"%(bert_dir,name)
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if (os.path.exists(path_bert) == False and lan == "zh"):
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bert_feature = get_bert_feature(norm_text, word2ph)
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assert bert_feature.shape[-1] == len(phones)
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# torch.save(bert_feature, path_bert)
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my_save(bert_feature, path_bert)
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phones = " ".join(phones)
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# res.append([name,phones])
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res.append([name,phones, word2ph, norm_text])
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except:
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print(name, text, traceback.format_exc())
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todo=[]
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res=[]
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with open(inp_text,"r",encoding="utf8")as f:
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lines=f.read().strip("\n").split("\n")
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language_v1_to_language_v2={
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"ZH":"zh",
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"zh":"zh",
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"JP":"ja",
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"jp":"ja",
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"JA":"ja",
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"ja":"ja",
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"EN":"en",
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"en":"en",
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"En":"en",
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}
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for line in lines[int(i_part)::int(all_parts)]:
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try:
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wav_name,spk_name,language,text=line.split("|")
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# todo.append([name,text,"zh"])
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todo.append([wav_name,text,language_v1_to_language_v2.get(language,language)])
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except:
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print(line,traceback.format_exc())
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process(todo,res)
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opt=[]
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for name,phones, word2ph, norm_text in res:
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opt.append("%s\t%s\t%s\t%s"%(name,phones, word2ph, norm_text))
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with open(txt_path,"w",encoding="utf8")as f:
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f.write("\n".join(opt)+"\n")
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