# -*- coding: utf-8 -*- import os import re import LangSegment from text import chinese 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") if "_CUDA_VISIBLE_DEVICES" in os.environ: os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] opt_dir = os.environ.get("opt_dir") bert_pretrained_dir = os.environ.get("bert_pretrained_dir") import torch is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() version = os.environ.get('version', None) import sys, numpy as np, traceback, pdb import os.path from glob import glob from tqdm import tqdm from text.cleaner import clean_text from transformers import AutoModelForMaskedLM, AutoTokenizer import numpy as np from tools.my_utils import clean_path # 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) tmp_path="%s%s.pth"%(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) if torch.cuda.is_available(): device = "cuda:0" # elif torch.backends.mps.is_available(): # device = "mps" else: device = "cpu" if os.path.exists(bert_pretrained_dir):... else:raise FileNotFoundError(bert_pretrained_dir) 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 get_bert_inf(phones:list, word2ph:list, norm_text:str, language:str): language=language.replace("all_","") if language == "zh": feature = get_bert_feature(norm_text, word2ph).to(device) else: feature = torch.zeros( (1024, len(phones)), dtype=torch.float32, ).to(device) return feature def get_phones_and_bert(text:str, language:str, version:str, final:bool=False): if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}: language = language.replace("all_","") if language == "en": LangSegment.setfilters(["en"]) formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) else: # 因无法区别中日韩文汉字,以用户输入为准 formattext = text while " " in formattext: formattext = formattext.replace(" ", " ") if language == "zh": if re.search(r'[A-Za-z]', formattext): formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext) formattext = chinese.mix_text_normalize(formattext) return get_phones_and_bert(formattext,"zh",version) else: phones, word2ph, norm_text = clean_text(formattext, language, version) bert = get_bert_feature(norm_text, word2ph).to(device) elif language == "yue" and re.search(r'[A-Za-z]', formattext): formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext) formattext = chinese.mix_text_normalize(formattext) return get_phones_and_bert(formattext,"yue",version) else: phones, word2ph, norm_text = clean_text(formattext, language, version) bert = torch.zeros( (1024, len(phones)), dtype=torch.float32, ).to(device) elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}: textlist=[] langlist=[] LangSegment.setfilters(["zh","ja","en","ko"]) if language == "auto": for tmp in LangSegment.getTexts(text): langlist.append(tmp["lang"]) textlist.append(tmp["text"]) elif language == "auto_yue": for tmp in LangSegment.getTexts(text): if tmp["lang"] == "zh": tmp["lang"] = "yue" langlist.append(tmp["lang"]) textlist.append(tmp["text"]) else: for tmp in LangSegment.getTexts(text): if tmp["lang"] == "en": langlist.append(tmp["lang"]) else: # 因无法区别中日韩文汉字,以用户输入为准 langlist.append(language) textlist.append(tmp["text"]) # print(textlist) # print(langlist) phones_list = [] bert_list = [] norm_text_list = [] for i in range(len(textlist)): lang = langlist[i] phones, word2ph, norm_text = clean_text(textlist[i], lang, version) bert = get_bert_inf(phones, word2ph, norm_text, lang) phones_list.append(phones) norm_text_list.append(norm_text) bert_list.append(bert) bert = torch.cat(bert_list, dim=1) phones = sum(phones_list, []) norm_text = ''.join(norm_text_list) return phones, bert, norm_text def process(data, res): for name, text, lan in data: try: name=clean_path(name) name = os.path.basename(name) print(name) phones, bert_feature, norm_text = get_phones_and_bert( text.replace("%", "-").replace("￥", ","), lan, 'v2' ) path_bert = "%s/%s.pt" % (bert_dir, name) if os.path.exists(path_bert) == False and lan == "zh": 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, None, 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", "zh": "zh", "JP": "ja", "jp": "ja", "JA": "ja", "ja": "ja", "EN": "en", "en": "en", "En": "en", "KO": "ko", "Ko": "ko", "ko": "ko", "yue": "yue", "YUE": "yue", "Yue": "yue", } for line in lines[int(i_part) :: int(all_parts)]: try: wav_name, spk_name, language, text = line.split("|") # todo.append([name,text,"zh"]) if language in language_v1_to_language_v2.keys(): todo.append( [wav_name, text, language_v1_to_language_v2.get(language, language)] ) else: print(f"\033[33m[Waring] The {language = } of {wav_name} is not supported for training.\033[0m") 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")