Adjust text normlization

GPT_SoVITS/text/zh_normalization/text_normlization.py

@@ -33,6 +33,7 @@ from .num import RE_NUMBER
 from .num import RE_PERCENTAGE
 from .num import RE_POSITIVE_QUANTIFIERS
 from .num import RE_RANGE
+from .num import RE_TO_RANGE
 from .num import replace_default_num
 from .num import replace_frac
 from .num import replace_negative_num
@@ -40,6 +41,7 @@ from .num import replace_number
 from .num import replace_percentage
 from .num import replace_positive_quantifier
 from .num import replace_range
+from .num import replace_to_range
 from .phonecode import RE_MOBILE_PHONE
 from .phonecode import RE_NATIONAL_UNIFORM_NUMBER
 from .phonecode import RE_TELEPHONE
@@ -65,7 +67,7 @@ class TextNormalizer():
         if lang == "zh":
             text = text.replace(" ", "")
             # 过滤掉特殊字符
-            text = re.sub(r'[——《》【】<=>{}()（）#&@“”^_|…\\]', '', text)
+            text = re.sub(r'[——《》【】<=>{}()（）#&@“”^_|\\]', '', text)
         text = self.SENTENCE_SPLITOR.sub(r'\1\n', text)
         text = text.strip()
         sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
@@ -73,8 +75,8 @@ class TextNormalizer():
 
     def _post_replace(self, sentence: str) -> str:
         sentence = sentence.replace('/', '每')
-        sentence = sentence.replace('~', '至')
-        sentence = sentence.replace('～', '至')
+        # sentence = sentence.replace('~', '至')
+        # sentence = sentence.replace('～', '至')
         sentence = sentence.replace('①', '一')
         sentence = sentence.replace('②', '二')
         sentence = sentence.replace('③', '三')
@@ -128,6 +130,8 @@ class TextNormalizer():
         sentence = RE_TIME_RANGE.sub(replace_time, sentence)
         sentence = RE_TIME.sub(replace_time, sentence)
 
+        # 处理~波浪号作为至的替换
+        sentence = RE_TO_RANGE.sub(replace_to_range, sentence)
         sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
         sentence = replace_measure(sentence)
         sentence = RE_FRAC.sub(replace_frac, sentence)