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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.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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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33
tools/classify_language.py
View File

@@ -1,8 +1,13 @@
import re
import regex as re
from config import config
try:
from config import config
LANGUAGE_IDENTIFICATION_LIBRARY = config.webui_config.language_identification_library
LANGUAGE_IDENTIFICATION_LIBRARY = (
config.webui_config.language_identification_library
)
except:
LANGUAGE_IDENTIFICATION_LIBRARY = "langid"
module = LANGUAGE_IDENTIFICATION_LIBRARY.lower()
@@ -155,11 +160,15 @@ def classify_zh_ja(text: str) -> str:
return "zh"
def split_alpha_nonalpha(text):
return re.split(
r"(?:(?<=[\u4e00-\u9fff])|(?<=[\u3040-\u30FF]))(?=[a-zA-Z])|(?<=[a-zA-Z])(?:(?=[\u4e00-\u9fff])|(?=[\u3040-\u30FF]))",
text,
)
def split_alpha_nonalpha(text, mode=1):
if mode == 1:
pattern = r"(?<=[\u4e00-\u9fff\u3040-\u30FF\d])(?=[\p{Latin}])|(?<=[\p{Latin}])(?=[\u4e00-\u9fff\u3040-\u30FF\d])"
elif mode == 2:
pattern = r"(?<=[\u4e00-\u9fff\u3040-\u30FF])(?=[\p{Latin}\d])|(?<=[\p{Latin}\d])(?=[\u4e00-\u9fff\u3040-\u30FF])"
else:
raise ValueError("Invalid mode. Supported modes are 1 and 2.")
return re.split(pattern, text)
if __name__ == "__main__":
@@ -170,3 +179,11 @@ if __name__ == "__main__":
text = "これはテストテキストです"
print(classify_language(text))
print(classify_zh_ja(text)) # "ja"
text = "vits和Bert-VITS2是tts模型。花费3days.花费3天。Take 3 days"
print(split_alpha_nonalpha(text, mode=1))
# output: ['vits', '和', 'Bert-VITS', '2是', 'tts', '模型。花费3', 'days.花费3天。Take 3 days']
print(split_alpha_nonalpha(text, mode=2))
# output: ['vits', '和', 'Bert-VITS2', '是', 'tts', '模型。花费', '3days.花费', '3', '天。Take 3 days']

35
tools/sentence.py
View File

@@ -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')]