models/TTS/ace_step/language_segmentation/LangSegment.py

"""
This file bundles language identification functions.

Modifications (fork): Copyright (c) 2021, Adrien Barbaresi.

Original code: Copyright (c) 2011 Marco Lui <saffsd@gmail.com>.
Based on research by Marco Lui and Tim Baldwin.

See LICENSE file for more info.
https://github.com/adbar/py3langid

Projects:
https://github.com/juntaosun/LangSegment

LICENSE:
py3langid - Language Identifier
BSD 3-Clause License

Modifications (fork): Copyright (c) 2021, Adrien Barbaresi.

Original code: Copyright (c) 2011 Marco Lui <saffsd@gmail.com>.
Based on research by Marco Lui and Tim Baldwin.

All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are
permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER ``AS IS'' AND ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""

import os
import re
import sys
import numpy as np
from collections import Counter
from collections import defaultdict

# import langid
# import py3langid as langid
# pip install py3langid==0.2.2

# 启用语言预测概率归一化，概率预测的分数。因此，实现重新规范化 产生 0-1 范围内的输出。
# langid disables probability normalization by default. For command-line usages of , it can be enabled by passing the flag.
# For probability normalization in library use, the user must instantiate their own . An example of such usage is as follows:
try:
    from py3langid.langid import LanguageIdentifier, MODEL_FILE
except Exception:
    LanguageIdentifier = None
    MODEL_FILE = None


class _FallbackLanguageIdentifier:
    def classify(self, text):
        # Default to English when py3langid is unavailable.
        return ("en", 1.0)

from .utils.num import num2str

# -----------------------------------
# 更新日志：新版本分词更加精准。
# Changelog: The new version of the word segmentation is more accurate.
# チェンジログ:新しいバージョンの単語セグメンテーションはより正確です。
# Changelog: 분할이라는 단어의 새로운 버전이 더 정확합니다.
# -----------------------------------


# Word segmentation function:
# automatically identify and split the words (Chinese/English/Japanese/Korean) in the article or sentence according to different languages,
# making it more suitable for TTS processing.
# This code is designed for front-end text multi-lingual mixed annotation distinction, multi-language mixed training and inference of various TTS projects.
# This processing result is mainly for (Chinese = zh, Japanese = ja, English = en, Korean = ko), and can actually support up to 97 different language mixing processing.

# ===========================================================================================================
# 分かち書き機能:文章や文章の中の例えば（中国語/英語/日本語/韓国語）を、異なる言語で自動的に認識して分割し、TTS処理により適したものにします。
# このコードは、さまざまなTTSプロジェクトのフロントエンドテキストの多言語混合注釈区別、多言語混合トレーニング、および推論のために特別に作成されています。
# ===========================================================================================================
# (1)自動分詞:「韓国語では何を読むのですかあなたの体育の先生は誰ですか?今回の発表会では、iPhone 15シリーズの4機種が登場しました」
# （2）手动分词:“あなたの名前は<ja>佐々木ですか?<ja>ですか?”
# この処理結果は主に（中国語=ja、日本語=ja、英語=en、韓国語=ko）を対象としており、実際には最大97の異なる言語の混合処理をサポートできます。
# ===========================================================================================================

# ===========================================================================================================
# 단어 분할 기능: 기사 또는 문장에서 단어(중국어/영어/일본어/한국어)를 다른 언어에 따라 자동으로 식별하고 분할하여 TTS 처리에 더 적합합니다.
# 이 코드는 프런트 엔드 텍스트 다국어 혼합 주석 분화, 다국어 혼합 교육 및 다양한 TTS 프로젝트의 추론을 위해 설계되었습니다.
# ===========================================================================================================
# (1) 자동 단어 분할: "한국어로 무엇을 읽습니까? 스포츠 씨? 이 컨퍼런스는 4개의 iPhone 15 시리즈 모델을 제공합니다."
# (2) 수동 참여: "이름이 <ja>Saki입니까? <ja>?"
# 이 처리 결과는 주로 (중국어 = zh, 일본어 = ja, 영어 = en, 한국어 = ko)를 위한 것이며 실제로 혼합 처리를 위해 최대 97개의 언어를 지원합니다.
# ===========================================================================================================

# ===========================================================================================================
# 分词功能：将文章或句子里的例如（中/英/日/韩），按不同语言自动识别并拆分，让它更适合TTS处理。
# 本代码专为各种 TTS 项目的前端文本多语种混合标注区分，多语言混合训练和推理而编写。
# ===========================================================================================================
# （1）自动分词：“韩语中的오빠读什么呢？あなたの体育の先生は誰ですか? 此次发布会带来了四款iPhone 15系列机型”
# （2）手动分词：“你的名字叫<ja>佐々木？<ja>吗？”
# 本处理结果主要针对（中文=zh , 日文=ja , 英文=en , 韩语=ko）, 实际上可支持多达 97 种不同的语言混合处理。
# ===========================================================================================================


# 手动分词标签规范：<语言标签>文本内容</语言标签>
# 수동 단어 분할 태그 사양: <언어 태그> 텍스트 내용</언어 태그>
# Manual word segmentation tag specification: <language tags> text content </language tags>
# 手動分詞タグ仕様:<言語タグ>テキスト内容</言語タグ>
# ===========================================================================================================
# For manual word segmentation, labels need to appear in pairs, such as:
# 如需手动分词，标签需要成对出现，例如：“<ja>佐々木<ja>”  或者  “<ja>佐々木</ja>”
# 错误示范：“你的名字叫<ja>佐々木。” 此句子中出现的单个<ja>标签将被忽略，不会处理。
# Error demonstration: "Your name is <ja>佐々木。" Single <ja> tags that appear in this sentence will be ignored and will not be processed.
# ===========================================================================================================


# ===========================================================================================================
# 语音合成标记语言 SSML , 这里只支持它的标签（非 XML）Speech Synthesis Markup Language SSML, only its tags are supported here (not XML)
# 想支持更多的 SSML 标签？欢迎 PR！ Want to support more SSML tags? PRs are welcome!
# 说明：除了中文以外，它也可改造成支持多语种 SSML ，不仅仅是中文。
# Note: In addition to Chinese, it can also be modified to support multi-language SSML, not just Chinese.
# ===========================================================================================================
# 中文实现：Chinese implementation:
# 【SSML】<number>=中文大写数字读法（单字）
# 【SSML】<telephone>=数字转成中文电话号码大写汉字（单字）
# 【SSML】<currency>=按金额发音。
# 【SSML】<date>=按日期发音。支持 2024年08月24, 2024/8/24, 2024-08, 08-24, 24 等输入。
# ===========================================================================================================
class LangSSML:

    def __init__(self):
        # 纯数字
        self._zh_numerals_number = {
            "0": "零",
            "1": "一",
            "2": "二",
            "3": "三",
            "4": "四",
            "5": "五",
            "6": "六",
            "7": "七",
            "8": "八",
            "9": "九",
        }

    # 将2024/8/24, 2024-08, 08-24, 24 标准化“年月日”
    # Standardize 2024/8/24, 2024-08, 08-24, 24 to "year-month-day"
    def _format_chinese_data(self, date_str: str):
        # 处理日期格式
        input_date = date_str
        if date_str is None or date_str.strip() == "":
            return ""
        date_str = re.sub(r"[\/\._|年|月]", "-", date_str)
        date_str = re.sub(r"日", r"", date_str)
        date_arrs = date_str.split(" ")
        if len(date_arrs) == 1 and ":" in date_arrs[0]:
            time_str = date_arrs[0]
            date_arrs = []
        else:
            time_str = date_arrs[1] if len(date_arrs) >= 2 else ""

        def nonZero(num, cn, func=None):
            if func is not None:
                num = func(num)
            return f"{num}{cn}" if num is not None and num != "" and num != "0" else ""

        f_number = self.to_chinese_number
        f_currency = self.to_chinese_currency
        # year, month, day
        year_month_day = ""
        if len(date_arrs) > 0:
            year, month, day = "", "", ""
            parts = date_arrs[0].split("-")
            if len(parts) == 3:  # 格式为 YYYY-MM-DD
                year, month, day = parts
            elif len(parts) == 2:  # 格式为 MM-DD 或 YYYY-MM
                if len(parts[0]) == 4:  # 年-月
                    year, month = parts
                else:
                    month, day = parts  # 月-日
            elif len(parts[0]) > 0:  # 仅有月-日或年
                if len(parts[0]) == 4:
                    year = parts[0]
                else:
                    day = parts[0]
            year, month, day = (
                nonZero(year, "年", f_number),
                nonZero(month, "月", f_currency),
                nonZero(day, "日", f_currency),
            )
            year_month_day = re.sub(r"([年|月|日])+", r"\1", f"{year}{month}{day}")
        # hours, minutes, seconds
        time_str = re.sub(r"[\/\.\-：_]", ":", time_str)
        time_arrs = time_str.split(":")
        hours, minutes, seconds = "", "", ""
        if len(time_arrs) == 3:  # H/M/S
            hours, minutes, seconds = time_arrs
        elif len(time_arrs) == 2:  # H/M
            hours, minutes = time_arrs
        elif len(time_arrs[0]) > 0:
            hours = f"{time_arrs[0]}点"  # H
        if len(time_arrs) > 1:
            hours, minutes, seconds = (
                nonZero(hours, "点", f_currency),
                nonZero(minutes, "分", f_currency),
                nonZero(seconds, "秒", f_currency),
            )
        hours_minutes_seconds = re.sub(
            r"([点|分|秒])+", r"\1", f"{hours}{minutes}{seconds}"
        )
        output_date = f"{year_month_day}{hours_minutes_seconds}"
        return output_date

    # 【SSML】number=中文大写数字读法（单字）
    # Chinese Numbers(single word)
    def to_chinese_number(self, num: str):
        pattern = r"(\d+)"
        zh_numerals = self._zh_numerals_number
        arrs = re.split(pattern, num)
        output = ""
        for item in arrs:
            if re.match(pattern, item):
                output += "".join(
                    zh_numerals[digit] if digit in zh_numerals else ""
                    for digit in str(item)
                )
            else:
                output += item
        output = output.replace(".", "点")
        return output

    # 【SSML】telephone=数字转成中文电话号码大写汉字（单字）
    # Convert numbers to Chinese phone numbers in uppercase Chinese characters(single word)
    def to_chinese_telephone(self, num: str):
        output = self.to_chinese_number(num.replace("+86", ""))  # zh +86
        output = output.replace("一", "幺")
        return output

    # 【SSML】currency=按金额发音。
    # Digital processing from GPT_SoVITS num.py （thanks）
    def to_chinese_currency(self, num: str):
        pattern = r"(\d+)"
        arrs = re.split(pattern, num)
        output = ""
        for item in arrs:
            if re.match(pattern, item):
                output += num2str(item)
            else:
                output += item
        output = output.replace(".", "点")
        return output

    # 【SSML】date=按日期发音。支持 2024年08月24, 2024/8/24, 2024-08, 08-24, 24 等输入。
    def to_chinese_date(self, num: str):
        chinese_date = self._format_chinese_data(num)
        return chinese_date


class LangSegment:

    def __init__(self):

        if LanguageIdentifier is None or MODEL_FILE is None:
            self.langid = _FallbackLanguageIdentifier()
        else:
            try:
                self.langid = LanguageIdentifier.from_pickled_model(
                    MODEL_FILE, norm_probs=True
                )
            except Exception:
                self.langid = _FallbackLanguageIdentifier()

        self._text_cache = None
        self._text_lasts = None
        self._text_langs = None
        self._lang_count = None
        self._lang_eos = None

        # 可自定义语言匹配标签：カスタマイズ可能な言語対応タグ:사용자 지정 가능한 언어 일치 태그:
        # Customizable language matching tags: These are supported，이 표현들은 모두 지지합니다
        # <zh>你好<zh> , <ja>佐々木</ja> , <en>OK<en> , <ko>오빠</ko> 这些写法均支持
        self.SYMBOLS_PATTERN = r"(<([a-zA-Z|-]*)>(.*?)<\/*[a-zA-Z|-]*>)"

        # 语言过滤组功能, 可以指定保留语言。不在过滤组中的语言将被清除。您可随心搭配TTS语音合成所支持的语言。
        # 언어 필터 그룹 기능을 사용하면 예약된 언어를 지정할 수 있습니다. 필터 그룹에 없는 언어는 지워집니다. TTS 텍스트에서 지원하는 언어를 원하는 대로 일치시킬 수 있습니다.
        # 言語フィルターグループ機能では、予約言語を指定できます。フィルターグループに含まれていない言語はクリアされます。TTS音声合成がサポートする言語を自由に組み合わせることができます。
        # The language filter group function allows you to specify reserved languages.
        # Languages not in the filter group will be cleared. You can match the languages supported by TTS Text To Speech as you like.
        # 排名越前，优先级越高，The higher the ranking, the higher the priority，ランキングが上位になるほど、優先度が高くなります。

        # 系统默认过滤器。System default filter。(ISO 639-1 codes given)
        # ----------------------------------------------------------------------------------------------------------------------------------
        # "zh"中文=Chinese ,"en"英语=English ,"ja"日语=Japanese ,"ko"韩语=Korean ,"fr"法语=French ,"vi"越南语=Vietnamese , "ru"俄语=Russian
        # "th"泰语=Thai
        # ----------------------------------------------------------------------------------------------------------------------------------
        self.DEFAULT_FILTERS = ["zh", "ja", "ko", "en"]

        # 用户可自定义过滤器。User-defined filters
        self.Langfilters = self.DEFAULT_FILTERS[:]  # 创建副本

        # 合并文本
        self.isLangMerge = True

        # 试验性支持：您可自定义添加："fr"法语 , "vi"越南语。Experimental: You can customize to add: "fr" French, "vi" Vietnamese.
        # 请使用API启用：self.setfilters(["zh", "en", "ja", "ko", "fr", "vi" , "ru" , "th"]) # 您可自定义添加，如："fr"法语 , "vi"越南语。

        # 预览版功能，自动启用或禁用，无需设置
        # Preview feature, automatically enabled or disabled, no settings required
        self.EnablePreview = False

        # 除此以外，它支持简写过滤器，只需按不同语种任意组合即可。
        # In addition to that, it supports abbreviation filters, allowing for any combination of different languages.
        # 示例：您可以任意指定多种组合，进行过滤
        # Example: You can specify any combination to filter

        # 中/日语言优先级阀值（评分范围为 0 ~ 1）:评分低于设定阀值 <0.89 时，启用 filters 中的优先级。\n
        # 중/일본어 우선 순위 임계값(점수 범위 0-1): 점수가 설정된 임계값 <0.89보다 낮을 때 필터에서 우선 순위를 활성화합니다.
        # 中国語/日本語の優先度しきい値（スコア範囲0〜1）:スコアが設定されたしきい値<0.89未満の場合、フィルターの優先度が有効になります。\n
        # Chinese and Japanese language priority threshold (score range is 0 ~ 1): The default threshold is 0.89.  \n
        # Only the common characters between Chinese and Japanese are processed with confidence and priority. \n
        self.LangPriorityThreshold = 0.89

        # Langfilters = ["zh"]              # 按中文识别
        # Langfilters = ["en"]              # 按英文识别
        # Langfilters = ["ja"]              # 按日文识别
        # Langfilters = ["ko"]              # 按韩文识别
        # Langfilters = ["zh_ja"]           # 中日混合识别
        # Langfilters = ["zh_en"]           # 中英混合识别
        # Langfilters = ["ja_en"]           # 日英混合识别
        # Langfilters = ["zh_ko"]           # 中韩混合识别
        # Langfilters = ["ja_ko"]           # 日韩混合识别
        # Langfilters = ["en_ko"]           # 英韩混合识别
        # Langfilters = ["zh_ja_en"]        # 中日英混合识别
        # Langfilters = ["zh_ja_en_ko"]     # 中日英韩混合识别

        # 更多过滤组合，请您随意。。。For more filter combinations, please feel free to......
        # より多くのフィルターの組み合わせ、お気軽に。。。더 많은 필터 조합을 원하시면 자유롭게 해주세요. .....

        # 可选保留：支持中文数字拼音格式，更方便前端实现拼音音素修改和推理，默认关闭 False 。
        # 开启后 True ，括号内的数字拼音格式均保留，并识别输出为："zh"中文。
        self.keepPinyin = False

        # DEFINITION
        self.PARSE_TAG = re.compile(r"(⑥\$*\d+[\d]{6,}⑥)")

        self.LangSSML = LangSSML()

    def _clears(self):
        self._text_cache = None
        self._text_lasts = None
        self._text_langs = None
        self._text_waits = None
        self._lang_count = None
        self._lang_eos = None

    def _is_english_word(self, word):
        return bool(re.match(r"^[a-zA-Z]+$", word))

    def _is_chinese(self, word):
        for char in word:
            if "\u4e00" <= char <= "\u9fff":
                return True
        return False

    def _is_japanese_kana(self, word):
        pattern = re.compile(r"[\u3040-\u309F\u30A0-\u30FF]+")
        matches = pattern.findall(word)
        return len(matches) > 0

    def _insert_english_uppercase(self, word):
        modified_text = re.sub(r"(?<!\b)([A-Z])", r" \1", word)
        modified_text = modified_text.strip("-")
        return modified_text + " "

    def _split_camel_case(self, word):
        return re.sub(r"(?<!^)(?=[A-Z])", " ", word)

    def _statistics(self, language, text):
        # Language word statistics:
        # Chinese characters usually occupy double bytes
        if self._lang_count is None or not isinstance(self._lang_count, defaultdict):
            self._lang_count = defaultdict(int)
        lang_count = self._lang_count
        if not "|" in language:
            lang_count[language] += (
                int(len(text) * 2) if language == "zh" else len(text)
            )
        self._lang_count = lang_count

    def _clear_text_number(self, text):
        if text == "\n":
            return text, False  # Keep Line Breaks
        clear_text = re.sub(r"([^\w\s]+)", "", re.sub(r"\n+", "", text)).strip()
        is_number = len(re.sub(re.compile(r"(\d+)"), "", clear_text)) == 0
        return clear_text, is_number

    def _saveData(self, words, language: str, text: str, score: float, symbol=None):
        # Pre-detection
        clear_text, is_number = self._clear_text_number(text)
        # Merge the same language and save the results
        preData = words[-1] if len(words) > 0 else None
        if symbol is not None:
            pass
        elif preData is not None and preData["symbol"] is None:
            if len(clear_text) == 0:
                language = preData["lang"]
            elif is_number == True:
                language = preData["lang"]
            _, pre_is_number = self._clear_text_number(preData["text"])
            if preData["lang"] == language:
                self._statistics(preData["lang"], text)
                text = preData["text"] + text
                preData["text"] = text
                return preData
            elif pre_is_number == True:
                text = f'{preData["text"]}{text}'
                words.pop()
        elif is_number == True:
            priority_language = self._get_filters_string()[:2]
            if priority_language in "ja-zh-en-ko-fr-vi":
                language = priority_language
        data = {"lang": language, "text": text, "score": score, "symbol": symbol}
        filters = self.Langfilters
        if (
            filters is None
            or len(filters) == 0
            or "?" in language
            or language in filters
            or language in filters[0]
            or filters[0] == "*"
            or filters[0] in "alls-mixs-autos"
        ):
            words.append(data)
            self._statistics(data["lang"], data["text"])
        return data

    def _addwords(self, words, language, text, score, symbol=None):
        if text == "\n":
            pass  # Keep Line Breaks
        elif text is None or len(text.strip()) == 0:
            return True
        if language is None:
            language = ""
        language = language.lower()
        if language == "en":
            text = self._insert_english_uppercase(text)
        # text = re.sub(r'[(（）)]', ',' , text) # Keep it.
        text_waits = self._text_waits
        ispre_waits = len(text_waits) > 0
        preResult = text_waits.pop() if ispre_waits else None
        if preResult is None:
            preResult = words[-1] if len(words) > 0 else None
        if preResult and ("|" in preResult["lang"]):
            pre_lang = preResult["lang"]
            if language in pre_lang:
                preResult["lang"] = language = language.split("|")[0]
            else:
                preResult["lang"] = pre_lang.split("|")[0]
            if ispre_waits:
                preResult = self._saveData(
                    words,
                    preResult["lang"],
                    preResult["text"],
                    preResult["score"],
                    preResult["symbol"],
                )
        pre_lang = preResult["lang"] if preResult else None
        if ("|" in language) and (
            pre_lang and not pre_lang in language and not "…" in language
        ):
            language = language.split("|")[0]
        if "|" in language:
            self._text_waits.append(
                {"lang": language, "text": text, "score": score, "symbol": symbol}
            )
        else:
            self._saveData(words, language, text, score, symbol)
        return False

    def _get_prev_data(self, words):
        data = words[-1] if words and len(words) > 0 else None
        if data:
            return (data["lang"], data["text"])
        return (None, "")

    def _match_ending(self, input, index):
        if input is None or len(input) == 0:
            return False, None
        input = re.sub(r"\s+", "", input)
        if len(input) == 0 or abs(index) > len(input):
            return False, None
        ending_pattern = re.compile(r'([「」“”‘’"\':：。.！!?．？])')
        return ending_pattern.match(input[index]), input[index]

    def _cleans_text(self, cleans_text):
        cleans_text = re.sub(r"(.*?)([^\w]+)", r"\1 ", cleans_text)
        cleans_text = re.sub(r"(.)\1+", r"\1", cleans_text)
        return cleans_text.strip()

    def _mean_processing(self, text: str):
        if text is None or (text.strip()) == "":
            return None, 0.0
        arrs = self._split_camel_case(text).split(" ")
        langs = []
        for t in arrs:
            if len(t.strip()) <= 3:
                continue
            language, score = self.langid.classify(t)
            langs.append({"lang": language})
        if len(langs) == 0:
            return None, 0.0
        return Counter([item["lang"] for item in langs]).most_common(1)[0][0], 1.0

    def _lang_classify(self, cleans_text):
        language, score = self.langid.classify(cleans_text)
        # fix: Huggingface is np.float32
        if (
            score is not None
            and isinstance(score, np.generic)
            and hasattr(score, "item")
        ):
            score = score.item()
        score = round(score, 3)
        return language, score

    def _get_filters_string(self):
        filters = self.Langfilters
        return "-".join(filters).lower().strip() if filters is not None else ""

    def _parse_language(self, words, segment):
        LANG_JA = "ja"
        LANG_ZH = "zh"
        LANG_ZH_JA = f"{LANG_ZH}|{LANG_JA}"
        LANG_JA_ZH = f"{LANG_JA}|{LANG_ZH}"
        language = LANG_ZH
        regex_pattern = re.compile(r"([^\w\s]+)")
        lines = regex_pattern.split(segment)
        lines_max = len(lines)
        LANG_EOS = self._lang_eos
        for index, text in enumerate(lines):
            if len(text) == 0:
                continue
            EOS = index >= (lines_max - 1)
            nextId = index + 1
            nextText = lines[nextId] if not EOS else ""
            nextPunc = (
                len(re.sub(regex_pattern, "", re.sub(r"\n+", "", nextText)).strip())
                == 0
            )
            textPunc = (
                len(re.sub(regex_pattern, "", re.sub(r"\n+", "", text)).strip()) == 0
            )
            if not EOS and (
                textPunc == True or (len(nextText.strip()) >= 0 and nextPunc == True)
            ):
                lines[nextId] = f"{text}{nextText}"
                continue
            number_tags = re.compile(r"(⑥\d{6,}⑥)")
            cleans_text = re.sub(number_tags, "", text)
            cleans_text = re.sub(r"\d+", "", cleans_text)
            cleans_text = self._cleans_text(cleans_text)
            # fix:Langid's recognition of short sentences is inaccurate, and it is spliced longer.
            if not EOS and len(cleans_text) <= 2:
                lines[nextId] = f"{text}{nextText}"
                continue
            language, score = self._lang_classify(cleans_text)
            prev_language, prev_text = self._get_prev_data(words)
            if language != LANG_ZH and all(
                "\u4e00" <= c <= "\u9fff" for c in re.sub(r"\s", "", cleans_text)
            ):
                language, score = LANG_ZH, 1
            if len(cleans_text) <= 5 and self._is_chinese(cleans_text):
                filters_string = self._get_filters_string()
                if score < self.LangPriorityThreshold and len(filters_string) > 0:
                    index_ja, index_zh = filters_string.find(
                        LANG_JA
                    ), filters_string.find(LANG_ZH)
                    if index_ja != -1 and index_ja < index_zh:
                        language = LANG_JA
                    elif index_zh != -1 and index_zh < index_ja:
                        language = LANG_ZH
                if self._is_japanese_kana(cleans_text):
                    language = LANG_JA
                elif len(cleans_text) > 2 and score > 0.90:
                    pass
                elif EOS and LANG_EOS:
                    language = LANG_ZH if len(cleans_text) <= 1 else language
                else:
                    LANG_UNKNOWN = (
                        LANG_ZH_JA
                        if language == LANG_ZH
                        or (len(cleans_text) <= 2 and prev_language == LANG_ZH)
                        else LANG_JA_ZH
                    )
                    match_end, match_char = self._match_ending(text, -1)
                    referen = (
                        prev_language in LANG_UNKNOWN or LANG_UNKNOWN in prev_language
                        if prev_language
                        else False
                    )
                    if match_char in "。.":
                        language = (
                            prev_language if referen and len(words) > 0 else language
                        )
                    else:
                        language = f"{LANG_UNKNOWN}|…"
            text, *_ = re.subn(number_tags, self._restore_number, text)
            self._addwords(words, language, text, score)

    # ----------------------------------------------------------
    # 【SSML】中文数字处理：Chinese Number Processing (SSML support)
    # 这里默认都是中文，用于处理 SSML 中文标签。当然可以支持任意语言，例如：
    # The default here is Chinese, which is used to process SSML Chinese tags. Of course, any language can be supported, for example:
    # 中文电话号码：<telephone>1234567</telephone>
    # 中文数字号码：<number>1234567</number>
    def _process_symbol_SSML(self, words, data):
        tag, match = data
        language = SSML = match[1]
        text = match[2]
        score = 1.0
        if SSML == "telephone":
            # 中文-电话号码
            language = "zh"
            text = self.LangSSML.to_chinese_telephone(text)
        elif SSML == "number":
            # 中文-数字读法
            language = "zh"
            text = self.LangSSML.to_chinese_number(text)
        elif SSML == "currency":
            # 中文-按金额发音
            language = "zh"
            text = self.LangSSML.to_chinese_currency(text)
        elif SSML == "date":
            # 中文-按金额发音
            language = "zh"
            text = self.LangSSML.to_chinese_date(text)
        self._addwords(words, language, text, score, SSML)

    # ----------------------------------------------------------
    def _restore_number(self, matche):
        value = matche.group(0)
        text_cache = self._text_cache
        if value in text_cache:
            process, data = text_cache[value]
            tag, match = data
            value = match
        return value

    def _pattern_symbols(self, item, text):
        if text is None:
            return text
        tag, pattern, process = item
        matches = pattern.findall(text)
        if len(matches) == 1 and "".join(matches[0]) == text:
            return text
        for i, match in enumerate(matches):
            key = f"⑥{tag}{i:06d}⑥"
            text = re.sub(pattern, key, text, count=1)
            self._text_cache[key] = (process, (tag, match))
        return text

    def _process_symbol(self, words, data):
        tag, match = data
        language = match[1]
        text = match[2]
        score = 1.0
        filters = self._get_filters_string()
        if language not in filters:
            self._process_symbol_SSML(words, data)
        else:
            self._addwords(words, language, text, score, True)

    def _process_english(self, words, data):
        tag, match = data
        text = match[0]
        filters = self._get_filters_string()
        priority_language = filters[:2]
        # Preview feature, other language segmentation processing
        enablePreview = self.EnablePreview
        if enablePreview == True:
            # Experimental: Other language support
            regex_pattern = re.compile(r"(.*?[。.?？!！]+[\n]{,1})")
            lines = regex_pattern.split(text)
            for index, text in enumerate(lines):
                if len(text.strip()) == 0:
                    continue
                cleans_text = self._cleans_text(text)
                language, score = self._lang_classify(cleans_text)
                if language not in filters:
                    language, score = self._mean_processing(cleans_text)
                if language is None or score <= 0.0:
                    continue
                elif language in filters:
                    pass  # pass
                elif score >= 0.95:
                    continue  # High score, but not in the filter, excluded.
                elif score <= 0.15 and filters[:2] == "fr":
                    language = priority_language
                else:
                    language = "en"
                self._addwords(words, language, text, score)
        else:
            # Default is English
            language, score = "en", 1.0
            self._addwords(words, language, text, score)

    def _process_Russian(self, words, data):
        tag, match = data
        text = match[0]
        language = "ru"
        score = 1.0
        self._addwords(words, language, text, score)

    def _process_Thai(self, words, data):
        tag, match = data
        text = match[0]
        language = "th"
        score = 1.0
        self._addwords(words, language, text, score)

    def _process_korean(self, words, data):
        tag, match = data
        text = match[0]
        language = "ko"
        score = 1.0
        self._addwords(words, language, text, score)

    def _process_quotes(self, words, data):
        tag, match = data
        text = "".join(match)
        childs = self.PARSE_TAG.findall(text)
        if len(childs) > 0:
            self._process_tags(words, text, False)
        else:
            cleans_text = self._cleans_text(match[1])
            if len(cleans_text) <= 5:
                self._parse_language(words, text)
            else:
                language, score = self._lang_classify(cleans_text)
                self._addwords(words, language, text, score)

    def _process_pinyin(self, words, data):
        tag, match = data
        text = match
        language = "zh"
        score = 1.0
        self._addwords(words, language, text, score)

    def _process_number(self, words, data):  # "$0" process only
        """
        Numbers alone cannot accurately identify language.
        Because numbers are universal in all languages.
        So it won't be executed here, just for testing.
        """
        tag, match = data
        language = words[0]["lang"] if len(words) > 0 else "zh"
        text = match
        score = 0.0
        self._addwords(words, language, text, score)

    def _process_tags(self, words, text, root_tag):
        text_cache = self._text_cache
        segments = re.split(self.PARSE_TAG, text)
        segments_len = len(segments) - 1
        for index, text in enumerate(segments):
            if root_tag:
                self._lang_eos = index >= segments_len
            if self.PARSE_TAG.match(text):
                process, data = text_cache[text]
                if process:
                    process(words, data)
            else:
                self._parse_language(words, text)
        return words

    def _merge_results(self, words):
        new_word = []
        for index, cur_data in enumerate(words):
            if "symbol" in cur_data:
                del cur_data["symbol"]
            if index == 0:
                new_word.append(cur_data)
            else:
                pre_data = new_word[-1]
                if cur_data["lang"] == pre_data["lang"]:
                    pre_data["text"] = f'{pre_data["text"]}{cur_data["text"]}'
                else:
                    new_word.append(cur_data)
        return new_word

    def _parse_symbols(self, text):
        TAG_NUM = "00"  # "00" => default channels , "$0" => testing channel
        TAG_S1, TAG_S2, TAG_P1, TAG_P2, TAG_EN, TAG_KO, TAG_RU, TAG_TH = (
            "$1",
            "$2",
            "$3",
            "$4",
            "$5",
            "$6",
            "$7",
            "$8",
        )
        TAG_BASE = re.compile(rf'(([【《（(“‘"\']*[LANGUAGE]+[\W\s]*)+)')
        # Get custom language filter
        filters = self.Langfilters
        filters = filters if filters is not None else ""
        # =======================================================================================================
        # Experimental: Other language support.Thử nghiệm: Hỗ trợ ngôn ngữ khác.Expérimental : prise en charge d’autres langues.
        # 相关语言字符如有缺失，熟悉相关语言的朋友，可以提交把缺失的发音符号补全。
        # If relevant language characters are missing, friends who are familiar with the relevant languages can submit a submission to complete the missing pronunciation symbols.
        # S'il manque des caractères linguistiques pertinents, les amis qui connaissent les langues concernées peuvent soumettre une soumission pour compléter les symboles de prononciation manquants.
        # Nếu thiếu ký tự ngôn ngữ liên quan, những người bạn quen thuộc với ngôn ngữ liên quan có thể gửi bài để hoàn thành các ký hiệu phát âm còn thiếu.
        # -------------------------------------------------------------------------------------------------------
        # Preview feature, other language support
        enablePreview = self.EnablePreview
        if "fr" in filters or "vi" in filters:
            enablePreview = True
        self.EnablePreview = enablePreview
        # 实验性：法语字符支持。Prise en charge des caractères français
        RE_FR = "" if not enablePreview else "àáâãäåæçèéêëìíîïðñòóôõöùúûüýþÿ"
        # 实验性：越南语字符支持。Hỗ trợ ký tự tiếng Việt
        RE_VI = (
            ""
            if not enablePreview
            else "đơưăáàảãạắằẳẵặấầẩẫậéèẻẽẹếềểễệíìỉĩịóòỏõọốồổỗộớờởỡợúùủũụứừửữựôâêơưỷỹ"
        )
        # -------------------------------------------------------------------------------------------------------
        # Basic options:
        process_list = [
            (
                TAG_S1,
                re.compile(self.SYMBOLS_PATTERN),
                self._process_symbol,
            ),  # Symbol Tag
            (
                TAG_KO,
                re.compile(re.sub(r"LANGUAGE", f"\uac00-\ud7a3", TAG_BASE.pattern)),
                self._process_korean,
            ),  # Korean words
            (
                TAG_TH,
                re.compile(re.sub(r"LANGUAGE", f"\u0e00-\u0e7f", TAG_BASE.pattern)),
                self._process_Thai,
            ),  # Thai words support.
            (
                TAG_RU,
                re.compile(re.sub(r"LANGUAGE", f"А-Яа-яЁё", TAG_BASE.pattern)),
                self._process_Russian,
            ),  # Russian words support.
            (
                TAG_NUM,
                re.compile(r"(\W*\d+\W+\d*\W*\d*)"),
                self._process_number,
            ),  # Number words, Universal in all languages, Ignore it.
            (
                TAG_EN,
                re.compile(
                    re.sub(r"LANGUAGE", f"a-zA-Z{RE_FR}{RE_VI}", TAG_BASE.pattern)
                ),
                self._process_english,
            ),  # English words + Other language support.
            (
                TAG_P1,
                re.compile(r'(["\'])(.*?)(\1)'),
                self._process_quotes,
            ),  # Regular quotes
            (
                TAG_P2,
                re.compile(
                    r"([\n]*[【《（(“‘])([^【《（(“‘’”)）》】]{3,})([’”)）》】][\W\s]*[\n]{,1})"
                ),
                self._process_quotes,
            ),  # Special quotes, There are left and right.
        ]
        # Extended options: Default False
        if self.keepPinyin == True:
            process_list.insert(
                1,
                (
                    TAG_S2,
                    re.compile(r"([\(（{](?:\s*\w*\d\w*\s*)+[}）\)])"),
                    self._process_pinyin,
                ),  # Chinese Pinyin Tag.
            )
        # -------------------------------------------------------------------------------------------------------
        words = []
        lines = re.findall(r".*\n*", re.sub(self.PARSE_TAG, "", text))
        for index, text in enumerate(lines):
            if len(text.strip()) == 0:
                continue
            self._lang_eos = False
            self._text_cache = {}
            for item in process_list:
                text = self._pattern_symbols(item, text)
            cur_word = self._process_tags([], text, True)
            if len(cur_word) == 0:
                continue
            cur_data = cur_word[0] if len(cur_word) > 0 else None
            pre_data = words[-1] if len(words) > 0 else None
            if (
                cur_data
                and pre_data
                and cur_data["lang"] == pre_data["lang"]
                and cur_data["symbol"] == False
                and pre_data["symbol"]
            ):
                cur_data["text"] = f'{pre_data["text"]}{cur_data["text"]}'
                words.pop()
            words += cur_word
        if self.isLangMerge == True:
            words = self._merge_results(words)
        lang_count = self._lang_count
        if lang_count and len(lang_count) > 0:
            lang_count = dict(
                sorted(lang_count.items(), key=lambda x: x[1], reverse=True)
            )
            lang_count = list(lang_count.items())
            self._lang_count = lang_count
        return words

    def setfilters(self, filters):
        # 当过滤器更改时，清除缓存
        # 필터가 변경되면 캐시를 지웁니다.
        # フィルタが変更されると、キャッシュがクリアされます
        # When the filter changes, clear the cache
        if self.Langfilters != filters:
            self._clears()
            self.Langfilters = filters

    def getfilters(self):
        return self.Langfilters

    def setPriorityThreshold(self, threshold: float):
        self.LangPriorityThreshold = threshold

    def getPriorityThreshold(self):
        return self.LangPriorityThreshold

    def getCounts(self):
        lang_count = self._lang_count
        if lang_count is not None:
            return lang_count
        text_langs = self._text_langs
        if text_langs is None or len(text_langs) == 0:
            return [("zh", 0)]
        lang_counts = defaultdict(int)
        for d in text_langs:
            lang_counts[d["lang"]] += (
                int(len(d["text"]) * 2) if d["lang"] == "zh" else len(d["text"])
            )
        lang_counts = dict(
            sorted(lang_counts.items(), key=lambda x: x[1], reverse=True)
        )
        lang_counts = list(lang_counts.items())
        self._lang_count = lang_counts
        return lang_counts

    def getTexts(self, text: str):
        if text is None or len(text.strip()) == 0:
            self._clears()
            return []
        # lasts
        text_langs = self._text_langs
        if self._text_lasts == text and text_langs is not None:
            return text_langs
        # parse
        self._text_waits = []
        self._lang_count = None
        self._text_lasts = text
        text = self._parse_symbols(text)
        self._text_langs = text
        return text

    def classify(self, text: str):
        return self.getTexts(text)


def printList(langlist):
    """
    功能：打印数组结果
    기능: 어레이 결과 인쇄
    機能:配列結果を印刷
    Function: Print array results
    """
    print("\n===================【打印结果】===================")
    if langlist is None or len(langlist) == 0:
        print("无内容结果,No content result")
        return
    for line in langlist:
        print(line)
    pass


def main():

    # -----------------------------------
    # 更新日志：新版本分词更加精准。
    # Changelog: The new version of the word segmentation is more accurate.
    # チェンジログ:新しいバージョンの単語セグメンテーションはより正確です。
    # Changelog: 분할이라는 단어의 새로운 버전이 더 정확합니다.
    # -----------------------------------

    # 输入示例1：（包含日文，中文）Input Example 1: (including Japanese, Chinese)
    # text = "“昨日は雨が降った，音楽、映画。。。”你今天学习日语了吗？春は桜の季節です。语种分词是语音合成必不可少的环节。言語分詞は音声合成に欠かせない環節である！"

    # 输入示例2：（包含日文，中文）Input Example 1: (including Japanese, Chinese)
    # text = "欢迎来玩。東京，は日本の首都です。欢迎来玩.  太好了!"

    # 输入示例3：（包含日文，中文）Input Example 1: (including Japanese, Chinese)
    # text = "明日、私たちは海辺にバカンスに行きます。你会说日语吗：“中国語、話せますか” 你的日语真好啊！"

    # 输入示例4：（包含日文，中文，韩语，英文）Input Example 4: (including Japanese, Chinese, Korean, English)
    # text = "你的名字叫<ja>佐々木？<ja>吗？韩语中的안녕 오빠读什么呢？あなたの体育の先生は誰ですか? 此次发布会带来了四款iPhone 15系列机型和三款Apple Watch等一系列新品，这次的iPad Air采用了LCD屏幕"

    # 试验性支持："fr"法语 , "vi"越南语 , "ru"俄语 , "th"泰语。Experimental: Other language support.
    langsegment = LangSegment()
    langsegment.setfilters(["fr", "vi", "ja", "zh", "ko", "en", "ru", "th"])
    text = """
我喜欢在雨天里听音乐。
I enjoy listening to music on rainy days.
雨の日に音楽を聴くのが好きです。
비 오는 날에 음악을 듣는 것을 즐깁니다。
J'aime écouter de la musique les jours de pluie.
Tôi thích nghe nhạc vào những ngày mưa.
Мне нравится слушать музыку в дождливую погоду.
ฉันชอบฟังเพลงในวันที่ฝนตก
"""

    # 进行分词：（接入TTS项目仅需一行代码调用）Segmentation: (Only one line of code is required to access the TTS project)
    langlist = langsegment.getTexts(text)
    printList(langlist)

    # 语种统计:Language statistics:
    print("\n===================【语种统计】===================")
    # 获取所有语种数组结果，根据内容字数降序排列
    # Get the array results in all languages, sorted in descending order according to the number of content words
    langCounts = langsegment.getCounts()
    print(langCounts, "\n")

    # 根据结果获取内容的主要语种 (语言，字数含标点)
    # Get the main language of content based on the results (language, word count including punctuation)
    lang, count = langCounts[0]
    print(f"输入内容的主要语言为 = {lang} ，字数 = {count}")
    print("==================================================\n")

    # 分词输出：lang=语言，text=内容。Word output: lang = language, text = content
    # ===================【打印结果】===================
    # {'lang': 'zh', 'text': '你的名字叫'}
    # {'lang': 'ja', 'text': '佐々木？'}
    # {'lang': 'zh', 'text': '吗？韩语中的'}
    # {'lang': 'ko', 'text': '안녕 오빠'}
    # {'lang': 'zh', 'text': '读什么呢？'}
    # {'lang': 'ja', 'text': 'あなたの体育の先生は誰ですか?'}
    # {'lang': 'zh', 'text': ' 此次发布会带来了四款'}
    # {'lang': 'en', 'text': 'i Phone  '}
    # {'lang': 'zh', 'text': '15系列机型和三款'}
    # {'lang': 'en', 'text': 'Apple Watch '}
    # {'lang': 'zh', 'text': '等一系列新品，这次的'}
    # {'lang': 'en', 'text': 'i Pad Air '}
    # {'lang': 'zh', 'text': '采用了'}
    # {'lang': 'en', 'text': 'L C D '}
    # {'lang': 'zh', 'text': '屏幕'}
    # ===================【语种统计】===================

    # ===================【语种统计】===================
    # [('zh', 51), ('ja', 19), ('en', 18), ('ko', 5)]

    # 输入内容的主要语言为 = zh ，字数 = 51
    # ==================================================
    # The main language of the input content is = zh, word count = 51


if __name__ == "__main__":
    main()