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GPT_SoVITS/text/cleaner.py

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+from text import chinese, japanese, cleaned_text_to_sequence, symbols, english
+
+language_module_map = {
+    'zh': chinese,
+    "ja": japanese,
+    'en': english
+}
+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):
+    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]
+    norm_text = language_module.text_normalize(text)
+    if(language=="zh"):
+        phones, word2ph = language_module.g2p(norm_text)
+        assert len(phones) == sum(word2ph)
+        assert len(norm_text) == len(word2ph)
+    else:
+        phones = language_module.g2p(norm_text)
+        word2ph=None
+
+    for ph in phones:
+        assert ph in symbols
+    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:
+        assert ph in symbols
+        if ph == ',':
+            new_ph.append(target_symbol)
+        else:
+            new_ph.append(ph)
+    return new_ph
+
+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'))
+
+