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| from gpt_sovits.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): | |
| 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] | |
| 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[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")) | |