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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"))
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