Update GPT_SoVITS/text/cleaner.py

GPT_SoVITS/text/cleaner.py

@@ -1,75 +1,73 @@
-from text import japanese, cleaned_text_to_sequence, english,korean,cantonese
-print(japanese.__file__)
-import os
-if os.environ.get("version","v1")=="v1":
-    from text import chinese
-    from text.symbols import symbols
-else:
-    from text import chinese2 as chinese
-    from text.symbols2 import symbols
-print("THIS IS IN CLEANER.py")
-language_module_map = {"zh": chinese, "ja": japanese, "en": english, "ko": korean,"yue":cantonese}
-special = [
-    # ("%", "zh", "SP"),
-    ("￥", "zh", "SP2"),
-    ("^", "zh", "SP3"),
-    # ('@', 'zh', "SP4")#不搞鬼畜了，和第二版保持一致吧
-]
-
-
-def clean_text(text, language):
-    print('this is clean_text')
-    if(language not in language_module_map):
-        language="en"
-        text=" "
-    for special_s, special_l, target_symbol in special:
-        if special_s in text and language == special_l:
-            return clean_special(text, language, special_s, target_symbol)
-    language_module = language_module_map[language]
-    if hasattr(language_module,"text_normalize"):
-        norm_text = language_module.text_normalize(text)
-    else:
-        norm_text=text
-    if language == "zh" or language=="yue":##########
-        phones, word2ph = language_module.g2p(norm_text)
-        assert len(phones) == sum(word2ph)
-        assert len(norm_text) == len(word2ph)
-    elif language == "en":
-        phones = language_module.g2p(norm_text)
-        if len(phones) < 4:
-            phones = [','] * (4 - len(phones)) + phones
-        word2ph = None
-    else:
-        phones = language_module.g2p(norm_text)
-        word2ph = None
-
-    for ph in phones:
-        assert ph in symbols, ph
-    return phones, word2ph, norm_text
-
-
-def clean_special(text, language, special_s, target_symbol):
-    """
-    特殊静音段sp符号处理
-    """
-    text = text.replace(special_s, ",")
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones = language_module.g2p(norm_text)
-    new_ph = []
-    for ph in phones[0]:
-        assert ph in symbols
-        if ph == ",":
-            new_ph.append(target_symbol)
-        else:
-            new_ph.append(ph)
-    return new_ph, phones[1], norm_text
-
-
-def text_to_sequence(text, language):
-    phones = clean_text(text)
-    return cleaned_text_to_sequence(phones)
-
-
-if __name__ == "__main__":
-    print(clean_text("你好%啊啊啊额、还是到付红四方。", "zh"))
+from text import japanese, cleaned_text_to_sequence
+print(japanese.__file__)
+import os
+if os.environ.get("version","v1")=="v1":
+    from text.symbols import symbols
+else:
+    from text.symbols2 import symbols
+print("THIS IS IN CLEANER.py")
+language_module_map = { "ja": japanese}
+special = [
+    # ("%", "zh", "SP"),
+    ("￥", "zh", "SP2"),
+    ("^", "zh", "SP3"),
+    # ('@', 'zh', "SP4")#不搞鬼畜了，和第二版保持一致吧
+]
+
+
+def clean_text(text, language):
+    print('this is clean_text')
+    if(language not in language_module_map):
+        language="en"
+        text=" "
+    for special_s, special_l, target_symbol in special:
+        if special_s in text and language == special_l:
+            return clean_special(text, language, special_s, target_symbol)
+    language_module = language_module_map[language]
+    if hasattr(language_module,"text_normalize"):
+        norm_text = language_module.text_normalize(text)
+    else:
+        norm_text=text
+    if language == "zh" or language=="yue":##########
+        phones, word2ph = language_module.g2p(norm_text)
+        assert len(phones) == sum(word2ph)
+        assert len(norm_text) == len(word2ph)
+    elif language == "en":
+        phones = language_module.g2p(norm_text)
+        if len(phones) < 4:
+            phones = [','] * (4 - len(phones)) + phones
+        word2ph = None
+    else:
+        phones = language_module.g2p(norm_text)
+        word2ph = None
+
+    for ph in phones:
+        assert ph in symbols, ph
+    return phones, word2ph, norm_text
+
+
+def clean_special(text, language, special_s, target_symbol):
+    """
+    特殊静音段sp符号处理
+    """
+    text = text.replace(special_s, ",")
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    phones = language_module.g2p(norm_text)
+    new_ph = []
+    for ph in phones[0]:
+        assert ph in symbols
+        if ph == ",":
+            new_ph.append(target_symbol)
+        else:
+            new_ph.append(ph)
+    return new_ph, phones[1], norm_text
+
+
+def text_to_sequence(text, language):
+    phones = clean_text(text)
+    return cleaned_text_to_sequence(phones)
+
+
+if __name__ == "__main__":
+    print(clean_text("你好%啊啊啊额、还是到付红四方。", "zh"))