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fix: prevent concurrent access to BERT model with thread lock (#2165)
Added thread lock to protect get_phones_and_bert method from potential race conditions during concurrent access. This addresses issue #1844 where multiple threads accessing the BERT model simultaneously could cause data inconsistency or crashes. Co-authored-by: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com>
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lishq
GitHub
parent
b0e465eb72
commit
fef65d40fe
@ -1,5 +1,6 @@
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import os, sys
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import threading
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from tqdm import tqdm
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now_dir = os.getcwd()
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@ -54,6 +55,7 @@ class TextPreprocessor:
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self.bert_model = bert_model
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self.tokenizer = tokenizer
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self.device = device
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self.bert_lock = threading.RLock()
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def preprocess(self, text:str, lang:str, text_split_method:str, version:str="v2")->List[Dict]:
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print(f'############ {i18n("切分文本")} ############')
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@ -117,70 +119,71 @@ class TextPreprocessor:
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return self.get_phones_and_bert(text, language, version)
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def get_phones_and_bert(self, text:str, language:str, version:str, final:bool=False):
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if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
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# language = language.replace("all_","")
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formattext = text
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while " " in formattext:
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formattext = formattext.replace(" ", " ")
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if language == "all_zh":
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if re.search(r'[A-Za-z]', formattext):
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formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
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formattext = chinese.mix_text_normalize(formattext)
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return self.get_phones_and_bert(formattext,"zh",version)
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else:
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phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
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bert = self.get_bert_feature(norm_text, word2ph).to(self.device)
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elif language == "all_yue" and re.search(r'[A-Za-z]', formattext):
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formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
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formattext = chinese.mix_text_normalize(formattext)
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return self.get_phones_and_bert(formattext,"yue",version)
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else:
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phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
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bert = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float32,
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).to(self.device)
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elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
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textlist=[]
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langlist=[]
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if language == "auto":
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for tmp in LangSegmenter.getTexts(text):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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elif language == "auto_yue":
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for tmp in LangSegmenter.getTexts(text):
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if tmp["lang"] == "zh":
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tmp["lang"] = "yue"
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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else:
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for tmp in LangSegmenter.getTexts(text):
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if tmp["lang"] == "en":
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langlist.append(tmp["lang"])
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else:
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# 因无法区别中日韩文汉字,以用户输入为准
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langlist.append(language)
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textlist.append(tmp["text"])
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# print(textlist)
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# print(langlist)
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phones_list = []
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bert_list = []
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norm_text_list = []
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for i in range(len(textlist)):
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lang = langlist[i]
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phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang, version)
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bert = self.get_bert_inf(phones, word2ph, norm_text, lang)
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phones_list.append(phones)
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norm_text_list.append(norm_text)
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bert_list.append(bert)
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bert = torch.cat(bert_list, dim=1)
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phones = sum(phones_list, [])
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norm_text = ''.join(norm_text_list)
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with self.bert_lock:
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if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
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# language = language.replace("all_","")
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formattext = text
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while " " in formattext:
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formattext = formattext.replace(" ", " ")
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if language == "all_zh":
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if re.search(r'[A-Za-z]', formattext):
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formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
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formattext = chinese.mix_text_normalize(formattext)
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return self.get_phones_and_bert(formattext,"zh",version)
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else:
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phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
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bert = self.get_bert_feature(norm_text, word2ph).to(self.device)
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elif language == "all_yue" and re.search(r'[A-Za-z]', formattext):
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formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
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formattext = chinese.mix_text_normalize(formattext)
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return self.get_phones_and_bert(formattext,"yue",version)
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else:
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phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
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bert = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float32,
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).to(self.device)
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elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
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textlist=[]
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langlist=[]
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if language == "auto":
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for tmp in LangSegmenter.getTexts(text):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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elif language == "auto_yue":
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for tmp in LangSegmenter.getTexts(text):
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if tmp["lang"] == "zh":
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tmp["lang"] = "yue"
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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else:
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for tmp in LangSegmenter.getTexts(text):
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if tmp["lang"] == "en":
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langlist.append(tmp["lang"])
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else:
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# 因无法区别中日韩文汉字,以用户输入为准
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langlist.append(language)
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textlist.append(tmp["text"])
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# print(textlist)
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# print(langlist)
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phones_list = []
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bert_list = []
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norm_text_list = []
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for i in range(len(textlist)):
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lang = langlist[i]
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phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang, version)
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bert = self.get_bert_inf(phones, word2ph, norm_text, lang)
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phones_list.append(phones)
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norm_text_list.append(norm_text)
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bert_list.append(bert)
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bert = torch.cat(bert_list, dim=1)
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phones = sum(phones_list, [])
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norm_text = ''.join(norm_text_list)
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if not final and len(phones) < 6:
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return self.get_phones_and_bert("." + text,language,version,final=True)
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if not final and len(phones) < 6:
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return self.get_phones_and_bert("." + text,language,version,final=True)
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return phones, bert, norm_text
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return phones, bert, norm_text
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def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor:
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