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