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tools/__init__.py

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+"""
+工具包
+"""

tools/classify_language.py

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+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\s])(?=[\p{Latin}])|(?<=[\p{Latin}\s])(?=[\u4e00-\u9fff\u3040-\u30FF\d])"
+    elif mode == 2:
+        pattern = r"(?<=[\u4e00-\u9fff\u3040-\u30FF\s])(?=[\p{Latin}\d])|(?<=[\p{Latin}\d\s])(?=[\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']
+
+    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']
+
+    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=2))
+    # output: ['vits ', '和 ', 'Bert-VITS2 ', '是 ', 'tts ', '模型。花费', '3days.花费', '3', '天。Take ', '3 ', 'days']

tools/gen_phones.py

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+"""
+文本转拼音
+"""
+import commons
+from text import cleaned_text_to_sequence
+from text.cleaner import clean_text
+
+
+def gen_phones(text, language_str, add_blank, style_text=None, style_weight=0.7):
+    style_text = None if style_text == "" else style_text
+    # 在此处实现当前版本的get_text
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+    phone = commons.intersperse(phone, 0)
+    tone = commons.intersperse(tone, 0)
+    language = commons.intersperse(language, 0)
+    for i in range(len(word2ph)):
+        word2ph[i] = word2ph[i] * 2
+    word2ph[0] += 1
+    result = "{}|{}|{}|{}".format(norm_text, phone, tone, word2ph)
+    return result

tools/log.py

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+"""
+logger封装
+"""
+from loguru import logger
+import sys
+
+
+# 移除所有默认的处理器
+logger.remove()
+
+# 自定义格式并添加到标准输出
+log_format = (
+    "<g>{time:MM-DD HH:mm:ss}</g> <lvl>{level:<9}</lvl>| {file}:{line} | {message}"
+)
+
+logger.add(sys.stdout, format=log_format, backtrace=True, diagnose=True)

tools/sentence.py

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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
+
+    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):
+            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 = []
+
+    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):
+            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"]))
+
+    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')]
+
+    text = "vits 和 Bert-VITS2 是 tts 模型。花费 3 days. 花费 3天。Take 3 days"
+    print(split_by_language(text, ["zh", "en"]))
+    # output: [('vits ', 'en'), ('和 ', 'zh'), ('Bert-VITS2 ', 'en'), ('是 ', 'zh'), ('tts ', 'en'), ('模型。花费 ', 'zh'), ('3 days. ', 'en'), ('花费 3天。', 'zh'), ('Take 3 days', 'en')]