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import logging
import regex as re
from tools.classify_language import classify_language, split_alpha_nonalpha
def check_is_none(item) -> bool:
"""none -> True, not none -> False"""
return (
item is None
or (isinstance(item, str) and str(item).isspace())
or str(item) == ""
)
def markup_language(text: str, target_languages: list = None) -> str:
pattern = (
r"[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`"
r"\！？。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」"
r"『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+"
)
sentences = re.split(pattern, text)
pre_lang = ""
p = 0
sorted_target_languages = sorted(target_languages)
if sorted_target_languages in [["en", "zh"], ["en", "ja"], ["en", "ja", "zh"]]:
new_sentences = []
for sentence in sentences:
new_sentences.extend(split_alpha_nonalpha(sentence))
sentences = new_sentences
for sentence in sentences:
if check_is_none(sentence):
continue
lang = classify_language(sentence, target_languages)
if pre_lang == "":
text = text[:p] + text[p:].replace(
sentence, f"[{lang.upper()}]{sentence}", 1
)
p += len(f"[{lang.upper()}]")
elif pre_lang != lang:
text = text[:p] + text[p:].replace(
sentence, f"[{pre_lang.upper()}][{lang.upper()}]{sentence}", 1
)
p += len(f"[{pre_lang.upper()}][{lang.upper()}]")
pre_lang = lang
p += text[p:].index(sentence) + len(sentence)
text += f"[{pre_lang.upper()}]"
return text
def split_by_language(text: str, target_languages: list = None) -> list:
pattern = (
r"[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`"
r"\！？\。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」"
r"『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+"
)
sentences = re.split(pattern, text)
pre_lang = ""
start = 0
end = 0
sentences_list = []
sorted_target_languages = sorted(target_languages)
if sorted_target_languages in [["en", "zh"], ["en", "ja"], ["en", "ja", "zh"]]:
new_sentences = []
for sentence in sentences:
new_sentences.extend(split_alpha_nonalpha(sentence))
sentences = new_sentences
for sentence in sentences:
if check_is_none(sentence):
continue
lang = classify_language(sentence, target_languages)
end += text[end:].index(sentence)
if pre_lang != "" and pre_lang != lang:
sentences_list.append((text[start:end], pre_lang))
start = end
end += len(sentence)
pre_lang = lang
sentences_list.append((text[start:], pre_lang))
return sentences_list
def sentence_split(text: str, max: int) -> list:
pattern = r"[!(),—+\-.:;?？。，、；：]+"
sentences = re.split(pattern, text)
discarded_chars = re.findall(pattern, text)
sentences_list, count, p = [], 0, 0
# 按被分割的符号遍历
for i, discarded_chars in enumerate(discarded_chars):
count += len(sentences[i]) + len(discarded_chars)
if count >= max:
sentences_list.append(text[p : p + count].strip())
p += count
count = 0
# 加入最后剩余的文本
if p < len(text):
sentences_list.append(text[p:])
return sentences_list
def sentence_split_and_markup(text, max=50, lang="auto", speaker_lang=None):
# 如果该speaker只支持一种语言
if speaker_lang is not None and len(speaker_lang) == 1:
if lang.upper() not in ["AUTO", "MIX"] and lang.lower() != speaker_lang[0]:
logging.debug(
f'lang "{lang}" is not in speaker_lang {speaker_lang},automatically set lang={speaker_lang[0]}'
)
lang = speaker_lang[0]
sentences_list = []
if lang.upper() != "MIX":
if max <= 0:
sentences_list.append(
markup_language(text, speaker_lang)
if lang.upper() == "AUTO"
else f"[{lang.upper()}]{text}[{lang.upper()}]"
)
else:
for i in sentence_split(text, max):
if check_is_none(i):
continue
sentences_list.append(
markup_language(i, speaker_lang)
if lang.upper() == "AUTO"
else f"[{lang.upper()}]{i}[{lang.upper()}]"
)
else:
sentences_list.append(text)
for i in sentences_list:
logging.debug(i)
return sentences_list
if __name__ == "__main__":
text = "这几天心里颇不宁静。今晚在院子里坐着乘凉，忽然想起日日走过的荷塘，在这满月的光里，总该另有一番样子吧。月亮渐渐地升高了，墙外马路上孩子们的欢笑，已经听不见了；妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。我悄悄地披了大衫，带上门出去。"
print(markup_language(text, target_languages=None))
print(sentence_split(text, max=50))
print(sentence_split_and_markup(text, max=50, lang="auto", speaker_lang=None))
text = "你好，这是一段用来测试自动标注的文本。こんにちは,これは自動ラベリングのテスト用テキストです.Hello, this is a piece of text to test autotagging.你好！今天我们要介绍VITS项目，其重点是使用了GAN Duration predictor和transformer flow,并且接入了Bert模型来提升韵律。Bert embedding会在稍后介绍。"
print(split_by_language(text, ["zh", "ja", "en"]))
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