Adjust text normlization

GPT_SoVITS/inference_webui.py

@@ -245,7 +245,7 @@ def get_phones_and_bert(text,language):
             formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
         else:
             # 因无法区别中日文汉字,以用户输入为准
-            formattext = re.sub('[a-zA-Z]', '', text)
+            formattext = text
         while "  " in formattext:
             formattext = formattext.replace("  ", " ")
         phones, word2ph, norm_text = clean_text_inf(formattext, language)
@@ -286,7 +286,7 @@ def get_phones_and_bert(text,language):
             bert_list.append(bert)
         bert = torch.cat(bert_list, dim=1)
         phones = sum(phones_list, [])
-        norm_text = ' '.join(norm_text_list)
+        norm_text = ''.join(norm_text_list)
 
     return phones,bert.to(dtype),norm_text
 
@@ -375,6 +375,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
         if (text[-1] not in splits): text += "。" if text_language != "en" else "."
         print(i18n("实际输入的目标文本(每句):"), text)
         phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
+        print(i18n("前端处理后的文本(每句):"), norm_text2)
         if not ref_free:
             bert = torch.cat([bert1, bert2], 1)
             all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)

GPT_SoVITS/text/chinese.py

@@ -34,6 +34,8 @@ rep_map = {
     "$": ".",
     "/": ",",
     "—": "-",
+    "~": "…",
+    "～":"…",
 }
 
 tone_modifier = ToneSandhi()

GPT_SoVITS/text/zh_normalization/num.py

@@ -172,6 +172,21 @@ def replace_range(match) -> str:
     return result
 
 
+# ~至表达式
+RE_TO_RANGE = re.compile(
+    r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)[~]((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)')
+
+def replace_to_range(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    result = match.group(0).replace('~', '至')
+    return result
+
+
 def _get_value(value_string: str, use_zero: bool=True) -> List[str]:
     stripped = value_string.lstrip('0')
     if len(stripped) == 0:

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)