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Optimize the recognition of mixed Chinese and English characters in numbers. (#212)

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* Optimize the recognition of mixed Chinese and English characters in numbers.

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This commit is contained in:
Artrajz
2023-12-01 19:20:31 +08:00
committed by GitHub
parent b4d7bc582e
commit 2c50b7816b
2 changed files with 48 additions and 20 deletions
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35
tools/sentence.py
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@@ -25,12 +25,13 @@ def markup_language(text: str, target_languages: list = None) -> str:
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
if target_languages is not None:
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):
@@ -68,12 +69,13 @@ def split_by_language(text: str, target_languages: list = None) -> list:
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
if target_languages is not None:
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):
@@ -154,5 +156,14 @@ if __name__ == "__main__":
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"]))
text = "vits和Bert-VITS2是tts模型。花费3days.花费3天。Take 3 days"
print(split_by_language(text, ["zh", "ja", "en"]))
# output: [('vits', 'en'), ('和', 'ja'), ('Bert-VITS', 'en'), ('2是', 'zh'), ('tts', 'en'), ('模型。花费3', 'zh'), ('days.', 'en'), ('花费3天。', 'zh'), ('Take 3 days', 'en')]
print(split_by_language(text, ["zh", "en"]))
# output: [('vits', 'en'), ('和', 'zh'), ('Bert-VITS', 'en'), ('2是', 'zh'), ('tts', 'en'), ('模型。花费3', 'zh'), ('days.', 'en'), ('花费3天。', 'zh'), ('Take 3 days', 'en')]