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Update Documentation (#2032)

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* add exception handling

* Fill in the missing content

* remove GB code

* Simplify i18n text and remove trailing spaces

* ignore v3 model dir

* Update Changelog

* Fix encoding
This commit is contained in:
StaryLan
2025-02-12 11:27:35 +08:00
committed by GitHub
parent 6e2b49186c
commit 15cbd1b673
23 changed files with 667 additions and 165 deletions
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33
GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
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@@ -49,18 +49,18 @@ def merge_short_text_in_array(texts:str, threshold:int) -> list:
class TextPreprocessor:
def __init__(self, bert_model:AutoModelForMaskedLM,
def __init__(self, bert_model:AutoModelForMaskedLM,
tokenizer:AutoTokenizer, device:torch.device):
self.bert_model = bert_model
self.tokenizer = tokenizer
self.device = device
def preprocess(self, text:str, lang:str, text_split_method:str, version:str="v2")->List[Dict]:
print(i18n("############ 切分文本 ############"))
print(f'############ {i18n("切分文本")} ############')
text = self.replace_consecutive_punctuation(text)
texts = self.pre_seg_text(text, lang, text_split_method)
result = []
print(i18n("############ 提取文本Bert特征 ############"))
print(f'############ {i18n("提取文本Bert特征")} ############')
for text in tqdm(texts):
phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang, version)
if phones is None or norm_text=="":
@@ -77,14 +77,14 @@ class TextPreprocessor:
text = text.strip("\n")
if len(text) == 0:
return []
if (text[0] not in splits and len(get_first(text)) < 4):
if (text[0] not in splits and len(get_first(text)) < 4):
text = "。" + text if lang != "en" else "." + text
print(i18n("实际输入的目标文本:"))
print(text)
seg_method = get_seg_method(text_split_method)
text = seg_method(text)
while "\n\n" in text:
text = text.replace("\n\n", "\n")
@@ -93,29 +93,29 @@ class TextPreprocessor:
_texts = merge_short_text_in_array(_texts, 5)
texts = []
for text in _texts:
# 解决输入目标文本的空行导致报错的问题
if (len(text.strip()) == 0):
continue
if not re.sub("\W+", "", text):
if not re.sub("\W+", "", text):
# 检测一下，如果是纯符号，就跳过。
continue
if (text[-1] not in splits): text += "。" if lang != "en" else "."
# 解决句子过长导致Bert报错的问题
if (len(text) > 510):
texts.extend(split_big_text(text))
else:
texts.append(text)
print(i18n("实际输入的目标文本(切句后):"))
print(texts)
return texts
def segment_and_extract_feature_for_text(self, text:str, language:str, version:str="v1")->Tuple[list, torch.Tensor, str]:
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_","")
@@ -203,7 +203,7 @@ class TextPreprocessor:
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
return phone_level_feature.T
def clean_text_inf(self, text:str, language:str, version:str="v2"):
phones, word2ph, norm_text = clean_text(text, language, version)
phones = cleaned_text_to_sequence(phones, version)
@@ -232,13 +232,10 @@ class TextPreprocessor:
else:
_text.append(text)
return _text
def replace_consecutive_punctuation(self,text):
punctuations = ''.join(re.escape(p) for p in punctuation)
pattern = f'([{punctuations}])([{punctuations}])+'
result = re.sub(pattern, r'\1', text)
return result