tools/classify_language.py

import regex as re

try:
    from config import config

    LANGUAGE_IDENTIFICATION_LIBRARY = (
        config.webui_config.language_identification_library
    )
except:
    LANGUAGE_IDENTIFICATION_LIBRARY = "langid"

module = LANGUAGE_IDENTIFICATION_LIBRARY.lower()

langid_languages = [
    "af",
    "am",
    "an",
    "ar",
    "as",
    "az",
    "be",
    "bg",
    "bn",
    "br",
    "bs",
    "ca",
    "cs",
    "cy",
    "da",
    "de",
    "dz",
    "el",
    "en",
    "eo",
    "es",
    "et",
    "eu",
    "fa",
    "fi",
    "fo",
    "fr",
    "ga",
    "gl",
    "gu",
    "he",
    "hi",
    "hr",
    "ht",
    "hu",
    "hy",
    "id",
    "is",
    "it",
    "ja",
    "jv",
    "ka",
    "kk",
    "km",
    "kn",
    "ko",
    "ku",
    "ky",
    "la",
    "lb",
    "lo",
    "lt",
    "lv",
    "mg",
    "mk",
    "ml",
    "mn",
    "mr",
    "ms",
    "mt",
    "nb",
    "ne",
    "nl",
    "nn",
    "no",
    "oc",
    "or",
    "pa",
    "pl",
    "ps",
    "pt",
    "qu",
    "ro",
    "ru",
    "rw",
    "se",
    "si",
    "sk",
    "sl",
    "sq",
    "sr",
    "sv",
    "sw",
    "ta",
    "te",
    "th",
    "tl",
    "tr",
    "ug",
    "uk",
    "ur",
    "vi",
    "vo",
    "wa",
    "xh",
    "zh",
    "zu",
]


def classify_language(text: str, target_languages: list = None) -> str:
    if module == "fastlid" or module == "fasttext":
        from fastlid import fastlid, supported_langs

        classifier = fastlid
        if target_languages != None:
            target_languages = [
                lang for lang in target_languages if lang in supported_langs
            ]
            fastlid.set_languages = target_languages
    elif module == "langid":
        import langid

        classifier = langid.classify
        if target_languages != None:
            target_languages = [
                lang for lang in target_languages if lang in langid_languages
            ]
            langid.set_languages(target_languages)
    else:
        raise ValueError(f"Wrong module {module}")

    lang = classifier(text)[0]

    return lang


def classify_zh_ja(text: str) -> str:
    for idx, char in enumerate(text):
        unicode_val = ord(char)

        # 检测日语字符
        if 0x3040 <= unicode_val <= 0x309F or 0x30A0 <= unicode_val <= 0x30FF:
            return "ja"

        # 检测汉字字符
        if 0x4E00 <= unicode_val <= 0x9FFF:
            # 检查周围的字符
            next_char = text[idx + 1] if idx + 1 < len(text) else None

            if next_char and (
                0x3040 <= ord(next_char) <= 0x309F or 0x30A0 <= ord(next_char) <= 0x30FF
            ):
                return "ja"

    return "zh"


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__":
    text = "这是一个测试文本"
    print(classify_language(text))
    print(classify_zh_ja(text))  # "zh"

    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']