Yaxin
/
live-digital-avatar-gptsovits
25
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix: prevent concurrent access to BERT model with thread lock (#2165)

Browse Source 
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>
main
lishq 4 months ago committed by GitHub
parent b0e465eb72
commit fef65d40fe
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 67 additions and 64 deletions
Show Stats Download Patch File Download Diff File

131
GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
Unescape Escape View File

@ -1,5 +1,6 @@
import os, sys
import threading
from tqdm import tqdm
now_dir = os.getcwd()
@ -54,6 +55,7 @@ class TextPreprocessor:
self.bert_model = bert_model
self.tokenizer = tokenizer
self.device = device
self.bert_lock = threading.RLock()
def preprocess(self, text:str, lang:str, text_split_method:str, version:str="v2")->List[Dict]:
print(f'############ {i18n("切分文本")} ############')
@ -117,70 +119,71 @@ class TextPreprocessor:
return self.get_phones_and_bert(text, language, version)
def get_phones_and_bert(self, 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_","")
formattext = text
while " " in formattext:
formattext = formattext.replace(" ", " ")
if language == "all_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 self.get_phones_and_bert(formattext,"zh",version)
else:
phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
bert = self.get_bert_feature(norm_text, word2ph).to(self.device)
elif language == "all_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 self.get_phones_and_bert(formattext,"yue",version)
else:
phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
bert = torch.zeros(
(1024, len(phones)),
dtype=torch.float32,
).to(self.device)
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
textlist=[]
langlist=[]
if language == "auto":
for tmp in LangSegmenter.getTexts(text):
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
elif language == "auto_yue":
for tmp in LangSegmenter.getTexts(text):
if tmp["lang"] == "zh":
tmp["lang"] = "yue"
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
else:
for tmp in LangSegmenter.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 = self.clean_text_inf(textlist[i], lang, version)
bert = self.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)
if not final and len(phones) < 6:
return self.get_phones_and_bert("." + text,language,version,final=True)
return phones, bert, norm_text
with self.bert_lock:
if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
# language = language.replace("all_","")
formattext = text
while " " in formattext:
formattext = formattext.replace(" ", " ")
if language == "all_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 self.get_phones_and_bert(formattext,"zh",version)
else:
phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
bert = self.get_bert_feature(norm_text, word2ph).to(self.device)
elif language == "all_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 self.get_phones_and_bert(formattext,"yue",version)
else:
phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
bert = torch.zeros(
(1024, len(phones)),
dtype=torch.float32,
).to(self.device)
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
textlist=[]
langlist=[]
if language == "auto":
for tmp in LangSegmenter.getTexts(text):
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
elif language == "auto_yue":
for tmp in LangSegmenter.getTexts(text):
if tmp["lang"] == "zh":
tmp["lang"] = "yue"
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
else:
for tmp in LangSegmenter.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 = self.clean_text_inf(textlist[i], lang, version)
bert = self.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)
if not final and len(phones) < 6:
return self.get_phones_and_bert("." + text,language,version,final=True)
return phones, bert, norm_text
def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor:

Write Preview
Loading…
Cancel
Save