app.py

import argparse
import os
from pathlib import Path

import logging
import re_matching

logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

logging.basicConfig(
    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
)

logger = logging.getLogger(__name__)
import shutil
from scipy.io.wavfile import write
import librosa
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm

import gradio as gr

import utils
from config import config

import torch
import commons
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
import utils

from models import SynthesizerTrn
from text.symbols import symbols
import sys
import re

import random
import hashlib

from fugashi import Tagger
import jaconv
import unidic
import subprocess

import requests

from ebooklib import epub
import PyPDF2
from PyPDF2 import PdfReader
from bs4 import BeautifulSoup
import jieba 
import romajitable

webBase = {
    'pyopenjtalk-V2.3-Katakana': 'https://mahiruoshi-mygo-vits-bert.hf.space/',
    'fugashi-V2.3-Katakana': 'https://mahiruoshi-mygo-vits-bert.hf.space/',
}

languages = [ "Auto", "ZH", "JP"]
modelPaths = []
modes = ['pyopenjtalk-V2.3','fugashi-V2.3']
sentence_modes = ['sentence','paragraph']

net_g = None

device = (
        "cuda:0"
        if torch.cuda.is_available()
        else (
            "mps"
            if sys.platform == "darwin" and torch.backends.mps.is_available()
            else "cpu"
        )
    )

#device = "cpu"
BandList = {
        "PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
        "Afterglow":["蘭","モカ","ひまり","巴","つぐみ"],
        "HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"],
        "PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"],
        "Roselia":["友希那","紗夜","リサ","燐子","あこ"],
        "RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"],
        "Morfonica":["ましろ","瑠唯","つくし","七深","透子"],
        "MyGo":["燈","愛音","そよ","立希","楽奈"],
        "AveMujica":["祥子","睦","海鈴","にゃむ","初華"],
        "圣翔音乐学园":["華戀","光","香子","雙葉","真晝","純那","克洛迪娜","真矢","奈奈"],
        "凛明馆女子学校":["珠緒","壘","文","悠悠子","一愛"],
        "弗隆提亚艺术学校":["艾露","艾露露","菈樂菲","司","靜羽"],
        "西克菲尔特音乐学院":["晶","未知留","八千代","栞","美帆"]
}

#翻译

def translate(Sentence: str, to_Language: str = "jp", from_Language: str = ""):
    """
    :param Sentence: 待翻译语句
    :param from_Language: 待翻译语句语言
    :param to_Language: 目标语言
    :return: 翻译后语句 出错时返回None

    常见语言代码：中文 zh 英语 en 日语 jp
    """
    appid = "20231117001883321"
    key = "lMQbvZHeJveDceLof2wf"
    if appid == "" or key == "":
        return "请开发者在config.yml中配置app_key与secret_key"
    url = "https://fanyi-api.baidu.com/api/trans/vip/translate"
    texts = Sentence.splitlines()
    outTexts = []
    for t in texts:
        if t != "":
            # 签名计算 参考文档 https://api.fanyi.baidu.com/product/113
            salt = str(random.randint(1, 100000))
            signString = appid + t + salt + key
            hs = hashlib.md5()
            hs.update(signString.encode("utf-8"))
            signString = hs.hexdigest()
            if from_Language == "":
                from_Language = "auto"
            headers = {"Content-Type": "application/x-www-form-urlencoded"}
            payload = {
                "q": t,
                "from": from_Language,
                "to": to_Language,
                "appid": appid,
                "salt": salt,
                "sign": signString,
            }
            # 发送请求
            try:
                response = requests.post(
                    url=url, data=payload, headers=headers, timeout=3
                )
                response = response.json()
                if "trans_result" in response.keys():
                    result = response["trans_result"][0]
                    if "dst" in result.keys():
                        dst = result["dst"]
                        outTexts.append(dst)
            except Exception:
                return Sentence
        else:
            outTexts.append(t)
    return "\n".join(outTexts)

#文本清洗工具
def is_japanese(string):
        for ch in string:
            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
                return True
        return False

def is_chinese(string):
    for ch in string:
        if '\u4e00' <= ch <= '\u9fff':
            return True
    return False

def is_single_language(sentence):
    # 检查句子是否为单一语言
    contains_chinese = re.search(r'[\u4e00-\u9fff]', sentence) is not None
    contains_japanese = re.search(r'[\u3040-\u30ff\u31f0-\u31ff]', sentence) is not None
    contains_english = re.search(r'[a-zA-Z]', sentence) is not None
    language_count = sum([contains_chinese, contains_japanese, contains_english])
    return language_count == 1

def merge_scattered_parts(sentences):
    """合并零散的部分到相邻的句子中，并确保单一语言性"""
    merged_sentences = []
    buffer_sentence = ""

    for sentence in sentences:
        # 检查是否是单一语言或者太短（可能是标点或单个词）
        if is_single_language(sentence) and len(sentence) > 1:
            # 如果缓冲区有内容，先将缓冲区的内容添加到列表
            if buffer_sentence:
                merged_sentences.append(buffer_sentence)
                buffer_sentence = ""
            merged_sentences.append(sentence)
        else:
            # 如果是零散的部分，将其添加到缓冲区
            buffer_sentence += sentence

    # 确保最后的缓冲区内容被添加
    if buffer_sentence:
        merged_sentences.append(buffer_sentence)

    return merged_sentences

def is_only_punctuation(s):
    """检查字符串是否只包含标点符号"""
    # 此处列出中文、日文、英文常见标点符号
    punctuation_pattern = re.compile(r'^[\s。*；，：“”（）、！？《》\u3000\.,;:"\'?!()]+$')
    return punctuation_pattern.match(s) is not None

def split_mixed_language(sentence):
    # 分割混合语言句子
    # 逐字符检查，分割不同语言部分
    sub_sentences = []
    current_language = None
    current_part = ""

    for char in sentence:
        if re.match(r'[\u4e00-\u9fff]', char):  # Chinese character
            if current_language != 'chinese':
                if current_part:
                    sub_sentences.append(current_part)
                current_part = char
                current_language = 'chinese'
            else:
                current_part += char
        elif re.match(r'[\u3040-\u30ff\u31f0-\u31ff]', char):  # Japanese character
            if current_language != 'japanese':
                if current_part:
                    sub_sentences.append(current_part)
                current_part = char
                current_language = 'japanese'
            else:
                current_part += char
        elif re.match(r'[a-zA-Z]', char):  # English character
            if current_language != 'english':
                if current_part:
                    sub_sentences.append(current_part)
                current_part = char
                current_language = 'english'
            else:
                current_part += char
        else:
            current_part += char  # For punctuation and other characters
    
    if current_part:
        sub_sentences.append(current_part)

    return sub_sentences

def replace_quotes(text):
    # 替换中文、日文引号为英文引号
    text = re.sub(r'[“”‘’『』「」（）()]', '"', text)
    return text

def remove_numeric_annotations(text):
    # 定义用于匹配数字注释的正则表达式
    # 包括 “”、【】和〔〕包裹的数字
    pattern = r'“\d+”|【\d+】|〔\d+〕'
    # 使用正则表达式替换掉这些注释
    cleaned_text = re.sub(pattern, '', text)
    return cleaned_text

def merge_adjacent_japanese(sentences):
    """合并相邻且都只包含日语的句子"""
    merged_sentences = []
    i = 0
    while i < len(sentences):
        current_sentence = sentences[i]
        if i + 1 < len(sentences) and is_japanese(current_sentence) and is_japanese(sentences[i + 1]):
            # 当前句子和下一句都是日语，合并它们
            while i + 1 < len(sentences) and is_japanese(sentences[i + 1]):
                current_sentence += sentences[i + 1]
                i += 1
        merged_sentences.append(current_sentence)
        i += 1
    return merged_sentences

def extrac(text):
    text = replace_quotes(remove_numeric_annotations(text))  # 替换引号
    text = re.sub("<[^>]*>", "", text)  # 移除 HTML 标签
    # 使用换行符和标点符号进行初步分割
    preliminary_sentences = re.split(r'([\n。；！？\.\?!])', text)
    final_sentences = []

    preliminary_sentences = re.split(r'([\n。；！？\.\?!])', text)

    for piece in preliminary_sentences:
        if is_single_language(piece):
            final_sentences.append(piece)
        else:
            sub_sentences = split_mixed_language(piece)
            final_sentences.extend(sub_sentences)
    
    # 处理长句子，使用jieba进行分词
    split_sentences = []
    for sentence in final_sentences:
        split_sentences.extend(split_long_sentences(sentence))

    # 合并相邻的日语句子
    merged_japanese_sentences = merge_adjacent_japanese(split_sentences)

    # 剔除只包含标点符号的元素
    clean_sentences = [s for s in merged_japanese_sentences if not is_only_punctuation(s)]

    # 移除空字符串并去除多余引号
    return [s.replace('"','').strip() for s in clean_sentences if s]



  # 移除空字符串

def is_mixed_language(sentence):
    contains_chinese = re.search(r'[\u4e00-\u9fff]', sentence) is not None
    contains_japanese = re.search(r'[\u3040-\u30ff\u31f0-\u31ff]', sentence) is not None
    contains_english = re.search(r'[a-zA-Z]', sentence) is not None
    languages_count = sum([contains_chinese, contains_japanese, contains_english])
    return languages_count > 1

def split_mixed_language(sentence):
    # 分割混合语言句子
    sub_sentences = re.split(r'(?<=[。！？\.\?!])(?=")|(?<=")(?=[\u4e00-\u9fff\u3040-\u30ff\u31f0-\u31ff]|[a-zA-Z])', sentence)
    return [s.strip() for s in sub_sentences if s.strip()]

def seconds_to_ass_time(seconds):
    """将秒数转换为ASS时间格式"""
    hours = int(seconds / 3600)
    minutes = int((seconds % 3600) / 60)
    seconds = int(seconds) % 60
    milliseconds = int((seconds - int(seconds)) * 1000)
    return "{:01d}:{:02d}:{:02d}.{:02d}".format(hours, minutes, seconds, int(milliseconds / 10))

def extract_text_from_epub(file_path):
    book = epub.read_epub(file_path)
    content = []
    for item in book.items:
        if isinstance(item, epub.EpubHtml):
            soup = BeautifulSoup(item.content, 'html.parser')
            content.append(soup.get_text())
    return '\n'.join(content)

def extract_text_from_pdf(file_path):
    with open(file_path, 'rb') as file:
        reader = PdfReader(file)
        content = [page.extract_text() for page in reader.pages]
    return '\n'.join(content)

def remove_annotations(text):
    # 移除方括号、尖括号和中文方括号中的内容
    text = re.sub(r'\[.*?\]', '', text)
    text = re.sub(r'\<.*?\>', '', text)
    text = re.sub(r'&#8203;``【oaicite:1】``&#8203;', '', text)
    return text

def extract_text_from_file(inputFile):
    file_extension = os.path.splitext(inputFile)[1].lower()
    if file_extension == ".epub":
        return extract_text_from_epub(inputFile)
    elif file_extension == ".pdf":
        return extract_text_from_pdf(inputFile)
    elif file_extension == ".txt":
        with open(inputFile, 'r', encoding='utf-8') as f:
            return f.read()
    else:
        raise ValueError(f"Unsupported file format: {file_extension}")

def split_by_punctuation(sentence):
    """按照中文次级标点符号分割句子"""
    # 常见的中文次级分隔符号：逗号、分号等
    parts = re.split(r'([，,；;])', sentence)
    # 将标点符号与前面的词语合并，避免单独标点符号成为一个部分
    merged_parts = []
    for part in parts:
        if part and not part in '，,；;':
            merged_parts.append(part)
        elif merged_parts:
            merged_parts[-1] += part
    return merged_parts

def split_long_sentences(sentence, max_length=30):
    """如果中文句子太长，先按标点分割，必要时使用jieba进行分词并分割"""
    if len(sentence) > max_length and is_chinese(sentence):
        # 首先尝试按照次级标点符号分割
        preliminary_parts = split_by_punctuation(sentence)
        new_sentences = []

        for part in preliminary_parts:
            # 如果部分仍然太长，使用jieba进行分词
            if len(part) > max_length:
                words = jieba.lcut(part)
                current_sentence = ""
                for word in words:
                    if len(current_sentence) + len(word) > max_length:
                        new_sentences.append(current_sentence)
                        current_sentence = word
                    else:
                        current_sentence += word
                if current_sentence:
                    new_sentences.append(current_sentence)
            else:
                new_sentences.append(part)

        return new_sentences
    return [sentence]  # 如果句子不长或不是中文，直接返回

def extract_and_convert(text):

    # 使用正则表达式找出所有英文单词
    english_parts = re.findall(r'\b[A-Za-z]+\b', text)  # \b为单词边界标识
    
    # 对每个英文单词进行片假名转换
    kana_parts = ['\n{}\n'.format(romajitable.to_kana(word).katakana) for word in english_parts]

    # 替换原文本中的英文部分
    for eng, kana in zip(english_parts, kana_parts):
        text = text.replace(eng, kana, 1)  # 限制每次只替换一个实例
    
    return text
# 推理工具
def download_unidic():
    try:
        Tagger()
        print("Tagger launch successfully.")
    except Exception as e:
        print("UNIDIC dictionary not found, downloading...")
        subprocess.run([sys.executable, "-m", "unidic", "download"])
        print("Download completed.")

def kanji_to_hiragana(text):
    global tagger
    output = ""

    # 更新正则表达式以更准确地区分文本和标点符号
    segments = re.findall(r'[一-龥ぁ-んァ-ン\w]+|[^\一-龥ぁ-んァ-ン\w\s]', text, re.UNICODE)

    for segment in segments:
        if re.match(r'[一-龥ぁ-んァ-ン\w]+', segment):
            # 如果是单词或汉字，转换为平假名
            for word in tagger(segment):
                kana = word.feature.kana or word.surface
                hiragana = jaconv.kata2hira(kana)  # 将片假名转换为平假名
                output += hiragana
        else:
            # 如果是标点符号，保持不变
            output += segment

    return output

def get_net_g(model_path: str,  device: str, hps):
    net_g = SynthesizerTrn(
        len(symbols),
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model,
    ).to(device)
    _ = net_g.eval()
    _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
    return net_g

def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
    style_text = None if style_text == "" else style_text
    norm_text, phone, tone, word2ph = clean_text(text, language_str)
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)

    if hps.data.add_blank:
        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
    bert_ori = get_bert(
        norm_text, word2ph, language_str, device, style_text, style_weight
    )
    del word2ph
    assert bert_ori.shape[-1] == len(phone), phone

    if language_str == "ZH":
        bert = bert_ori
        ja_bert = torch.randn(1024, len(phone))
        en_bert = torch.randn(1024, len(phone))
    elif language_str == "JP":
        bert = torch.randn(1024, len(phone))
        ja_bert = bert_ori
        en_bert = torch.randn(1024, len(phone))
    elif language_str == "EN":
        bert = torch.randn(1024, len(phone))
        ja_bert = torch.randn(1024, len(phone))
        en_bert = bert_ori
    else:
        raise ValueError("language_str should be ZH, JP or EN")

    assert bert.shape[-1] == len(
        phone
    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"

    phone = torch.LongTensor(phone)
    tone = torch.LongTensor(tone)
    language = torch.LongTensor(language)
    return bert, ja_bert, en_bert, phone, tone, language

def infer(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    style_text=None,
    style_weight=0.7,
    language = "Auto",
    mode = 'pyopenjtalk-V2.3',
    skip_start=False,
    skip_end=False,
):
    if style_text == None:
        style_text = ""
        style_weight=0,
    if mode == 'fugashi-V2.3':
        text = kanji_to_hiragana(text) if is_japanese(text) else text
    if language == "JP":
        text = translate(text,"jp")
    if language == "ZH":
        text = translate(text,"zh")
    if language == "Auto":
        language= 'JP' if is_japanese(text) else 'ZH'
    #print(f'{text}:{sdp_ratio}:{noise_scale}:{noise_scale_w}:{length_scale}:{length_scale}:{sid}:{language}:{mode}:{skip_start}:{skip_end}')            
    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
        text,
        language,
        hps,
        device,
        style_text=style_text,
        style_weight=style_weight,
    )
    if skip_start:
        phones = phones[3:]
        tones = tones[3:]
        lang_ids = lang_ids[3:]
        bert = bert[:, 3:]
        ja_bert = ja_bert[:, 3:]
        en_bert = en_bert[:, 3:]
    if skip_end:
        phones = phones[:-2]
        tones = tones[:-2]
        lang_ids = lang_ids[:-2]
        bert = bert[:, :-2]
        ja_bert = ja_bert[:, :-2]
        en_bert = en_bert[:, :-2]
    with torch.no_grad():
        x_tst = phones.to(device).unsqueeze(0)
        tones = tones.to(device).unsqueeze(0)
        lang_ids = lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        ja_bert = ja_bert.to(device).unsqueeze(0)
        en_bert = en_bert.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        # emo = emo.to(device).unsqueeze(0)
        del phones
        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
        audio = (
            net_g.infer(
                x_tst,
                x_tst_lengths,
                speakers,
                tones,
                lang_ids,
                bert,
                ja_bert,
                en_bert,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )[0][0, 0]
            .data.cpu()
            .float()
            .numpy()
        )
        del (
            x_tst,
            tones,
            lang_ids,
            bert,
            x_tst_lengths,
            speakers,
            ja_bert,
            en_bert,
        )  # , emo
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
    print("Success.")
    return audio

def loadmodel(model):
    _ = net_g.eval()
    _ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True)
    return "success"

def generate_audio_and_srt_for_group(
    group,
    outputPath,
    group_index,
    sampling_rate,
    speaker,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    speakerList,
    silenceTime,
    language,
    mode,
    skip_start,
    skip_end,
    style_text,
    style_weight,
    ):
    audio_fin = []
    ass_entries = []
    start_time = 0
    #speaker = random.choice(cara_list)
    ass_header = """[Script Info]
        ; 我没意见
        Title: Audiobook
        ScriptType: v4.00+
        WrapStyle: 0
        PlayResX: 640
        PlayResY: 360
        ScaledBorderAndShadow: yes
        [V4+ Styles]
        Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
        Style: Default,Arial,20,&H00FFFFFF,&H000000FF,&H00000000,&H00000000,0,0,0,0,100,100,0,0,1,1,1,2,10,10,10,1
        [Events]
        Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
        """

    for sentence in group:
        try:
            if len(sentence) > 1:
                FakeSpeaker = sentence.split("|")[0]
                print(FakeSpeaker)
                SpeakersList = re.split('\n', speakerList)
                if FakeSpeaker in list(hps.data.spk2id.keys()):
                    speaker = FakeSpeaker
                for i in SpeakersList:
                    if FakeSpeaker == i.split("|")[1]:
                        speaker = i.split("|")[0]
                if sentence != '\n':
                    text = (remove_annotations(sentence.split("|")[-1]).replace(" ","")+"。").replace("，。","。")
                    if mode == 'pyopenjtalk-V2.3' or mode == 'fugashi-V2.3':
                        #print(f'{text}:{sdp_ratio}:{noise_scale}:{noise_scale_w}:{length_scale}:{length_scale}:{speaker}:{language}:{mode}:{skip_start}:{skip_end}')
                        audio = infer(
                            text,
                            sdp_ratio,
                            noise_scale,
                            noise_scale_w,
                            length_scale,
                            speaker,
                            style_text,
                            style_weight,
                            language,
                            mode,
                            skip_start,
                            skip_end,
                        )
                    silence_frames = int(silenceTime * 44010) if is_chinese(sentence) else int(silenceTime * 44010)
                    silence_data = np.zeros((silence_frames,), dtype=audio.dtype)
                    audio_fin.append(audio)
                    audio_fin.append(silence_data)
                    duration = len(audio) / sampling_rate
                    print(duration)
                    end_time = start_time + duration + silenceTime
                    ass_entries.append("Dialogue: 0,{},{},".format(seconds_to_ass_time(start_time), seconds_to_ass_time(end_time)) + "Default,,0,0,0,,{}".format(sentence.replace("|","：")))
                    start_time = end_time
        except:
            pass
    wav_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.wav')
    ass_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.ass')
    write(wav_filename, sampling_rate, gr.processing_utils.convert_to_16_bit_wav(np.concatenate(audio_fin)))

    with open(ass_filename, 'w', encoding='utf-8') as f:
        f.write(ass_header + '\n'.join(ass_entries))
    return (hps.data.sampling_rate, gr.processing_utils.convert_to_16_bit_wav(np.concatenate(audio_fin)))

def generate_audio(
    inputFile,
    groupSize,
    filepath,
    silenceTime,
    speakerList,
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    style_text=None,
    style_weight=0.7,
    language = "Auto",
    mode = 'pyopenjtalk-V2.3',
    sentence_mode = 'sentence',
    skip_start=False,
    skip_end=False,
):
    if inputFile:
        text = extract_text_from_file(inputFile.name)
        sentence_mode = 'paragraph'
    if mode == 'pyopenjtalk-V2.3' or mode == 'fugashi-V2.3':
        if sentence_mode == 'sentence':
            audio = infer(
                        text,
                        sdp_ratio,
                        noise_scale,
                        noise_scale_w,
                        length_scale,
                        sid,
                        style_text,
                        style_weight,
                        language,
                        mode,
                        skip_start,
                        skip_end,
                    )
            return (hps.data.sampling_rate,gr.processing_utils.convert_to_16_bit_wav(audio))
        if sentence_mode == 'paragraph':
            GROUP_SIZE = groupSize
            directory_path = filepath if torch.cuda.is_available() else "books"
            if os.path.exists(directory_path):
                shutil.rmtree(directory_path)
            os.makedirs(directory_path)
            if language == 'Auto':
                sentences = extrac(extract_and_convert(text))
            else:
                sentences = extrac(text)
            for i in range(0, len(sentences), GROUP_SIZE):
                group = sentences[i:i+GROUP_SIZE]
                if speakerList == "":
                    speakerList = "无"
                result = generate_audio_and_srt_for_group(
                    group,
                    directory_path,
                    i//GROUP_SIZE + 1,
                    44100,
                    sid,
                    sdp_ratio,
                    noise_scale,
                    noise_scale_w,
                    length_scale,
                    speakerList,
                    silenceTime,
                    language,
                    mode,
                    skip_start,
                    skip_end,
                    style_text,
                    style_weight,       
                    )
                if not torch.cuda.is_available():
                    return result
            return result
    #url = f'{webBase[mode]}?text={text}&speaker={sid}&sdp_ratio={sdp_ratio}&noise_scale={noise_scale}&noise_scale_w={noise_scale_w}&length_scale={length_scale}&language={language}&skip_start={skip_start}&skip_end={skip_end}'
    #print(url)
    #res = requests.get(url)
    #改用post
    res = requests.post(webBase[mode], json = {
        "groupSize": groupSize,
        "filepath": filepath,
        "silenceTime": silenceTime,
        "speakerList": speakerList,
        "text": text,
        "speaker": sid,
        "sdp_ratio": sdp_ratio,
        "noise_scale": noise_scale,
        "noise_scale_w": noise_scale_w,
        "length_scale": length_scale,
        "language": language,
        "skip_start": skip_start,
        "skip_end": skip_end,
        "mode": mode,
        "sentence_mode": sentence_mode,
        "style_text": style_text,
        "style_weight": style_weight
    })
    audio = res.content
    with open('output.wav', 'wb') as code:
        code.write(audio)
    file_path = "output.wav"
    return file_path

if __name__ == "__main__":
    download_unidic()
    tagger = Tagger()
    for dirpath, dirnames, filenames in os.walk('Data/BangDream/models/'):
        for filename in filenames:
            modelPaths.append(os.path.join(dirpath, filename))
    hps = utils.get_hparams_from_file('Data/BangDream/config.json')
    net_g = get_net_g(
        model_path=modelPaths[-1], device=device, hps=hps
    )
    speaker_ids = hps.data.spk2id
    speakers = list(speaker_ids.keys())
    with gr.Blocks() as app:
        gr.Markdown(value="""
            ([Bert-Vits2](https://github.com/Stardust-minus/Bert-VITS2) V2.3)少歌邦邦全员在线语音合成\n
            [好玩的](http://love.soyorin.top/)\n
            该界面的真实链接(国内可用): https://mahiruoshi-bangdream-bert-vits2.hf.space/\n
            API: https://mahiruoshi-bert-vits2-api.hf.space/ \n
            调用方式: https://mahiruoshi-bert-vits2-api.hf.space/?text={{speakText}}&speaker=chosen_speaker\n
            推荐搭配[Legado开源阅读](https://github.com/gedoor/legado)或[聊天bot](https://github.com/Paraworks/BangDreamAi)使用\n
            二创请标注作者：B站@Mahiroshi: https://space.bilibili.com/19874615\n
            训练数据集归属：BangDream及少歌手游,提取自BestDori,[数据集获取流程](https://nijigaku.top/2023/09/29/Bestbushiroad%E8%AE%A1%E5%88%92-vits-%E9%9F%B3%E9%A2%91%E6%8A%93%E5%8F%96%E5%8F%8A%E6%95%B0%E6%8D%AE%E9%9B%86%E5%AF%B9%E9%BD%90/)\n
            BangDream数据集下载[链接](https://huggingface.co/spaces/Mahiruoshi/BangDream-Bert-VITS2/blob/main/%E7%88%AC%E8%99%AB/SortPathUrl.txt)\n
            ！！！注意：huggingface容器仅用作展示，建议在右上角更多选项中克隆本项目或Docker运行app.py/server.py,环境参考requirements.txt\n""")
        for band in BandList:
            with gr.TabItem(band):
                for name in BandList[band]:
                    with gr.TabItem(name):
                        with gr.Row():
                            with gr.Column():
                                with gr.Row():
                                    gr.Markdown(
                                        '<div align="center">'
                                        f'<img style="width:auto;height:400px;" src="https://mahiruoshi-bangdream-bert-vits2.hf.space/file/image/{name}.png">' 
                                        '</div>'
                                    )
                                with gr.Accordion(label="参数设定", open=False):
                                    sdp_ratio = gr.Slider(
                                    minimum=0, maximum=1, value=0.5, step=0.01, label="SDP/DP混合比"
                                    )
                                    noise_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.6, step=0.01, label="Noise:感情调节"
                                    )
                                    noise_scale_w = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.667, step=0.01, label="Noise_W:音素长度"
                                    )
                                    skip_start = gr.Checkbox(label="skip_start")
                                    skip_end = gr.Checkbox(label="skip_end")
                                    speaker = gr.Dropdown(
                                        choices=speakers, value=name, label="说话人"
                                    )
                                length_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
                                    )
                                language = gr.Dropdown(
                                        choices=languages, value="Auto", label="语言选择,若不选自动则会将输入语言翻译为日语或中文"
                                    )
                                mode = gr.Dropdown(
                                        choices=modes, value="pyopenjtalk-V2.3", label="TTS模式，合成少歌角色需要切换成 pyopenjtalk-V2.3-Katakana "
                                    )
                                sentence_mode = gr.Dropdown(
                                        choices=sentence_modes, value="paragraph", label="文本合成模式"
                                    )
                                with gr.Accordion(label="扩展选项", open=False):
                                    inputFile = gr.UploadButton(label="txt文件输入")
                                    speakerList = gr.TextArea(
                                        label="角色对应表,如果你记不住角色名可以这样,左边是你想要在每一句话合成中用到的speaker(见角色清单)右边是你上传文本时分隔符左边设置的说话人:{ChoseSpeakerFromConfigList}|{SeakerInUploadText}",
                                        value = "ましろ|真白\n七深|七深\n透子|透子\nつくし|筑紫\n瑠唯|瑠唯\nそよ|素世\n祥子|祥子",
                                    )
                                    groupSize = gr.Slider(
                                        minimum=10, maximum=1000 if  torch.cuda.is_available() else 50,value = 50, step=1, label="单个音频文件包含的最大句子数"
                                    )
                                    filepath = gr.TextArea(
                                        label="本地合成时的音频存储文件夹(会清空文件夹,别把C盘删了)",
                                        value = "D:/audiobook/book1",
                                    )
                                    silenceTime = gr.Slider(
                                        minimum=0, maximum=1, value=0.5, step=0.01, label="句子的间隔"
                                    )
                                    modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
                                    btnMod = gr.Button("载入模型")
                                    statusa = gr.TextArea(label = "模型加载状态")
                                    btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa])
                            with gr.Column():
                                text = gr.TextArea(
                                        label="文本输入,可用'|'分割说话人和文本,注意换行",
                                        info="输入纯日语或者中文",
                                        #placeholder=f"{name}|你觉得你是职业歌手吗\n真白|我觉得我是",
                                        value=f"{name}|你觉得你是职业歌手吗\n真白|我觉得我是" 
                                    )
                                style_text = gr.Textbox(
                                    label="情感辅助文本",
                                    info="语言保持跟主文本一致,文本可以参考训练集:https://huggingface.co/spaces/Mahiruoshi/BangDream-Bert-VITS2/blob/main/filelists/Mygo.list)",
                                    placeholder="使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
                        "**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）"
                                                        )
                                style_weight = gr.Slider(
                                        minimum=0,
                                        maximum=1,
                                        value=0.7,
                                        step=0.1,
                                        label="Weight",
                                        info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本",
                                    )
                                btn = gr.Button("点击生成", variant="primary")
                                audio_output = gr.Audio(label="Output Audio")
                                btntran = gr.Button("快速中翻日")
                                translateResult = gr.TextArea(label="使用百度翻译",placeholder="从这里复制翻译后的文本")
                                btntran.click(translate, inputs=[text], outputs = [translateResult])
                    btn.click(
                        generate_audio,
                        inputs=[
                            inputFile,
                            groupSize,
                            filepath,
                            silenceTime,
                            speakerList,
                            text,
                            sdp_ratio,
                            noise_scale,
                            noise_scale_w,
                            length_scale,
                            speaker,
                            style_text,
                            style_weight,
                            language,
                            mode,
                            sentence_mode,
                            skip_start,
                            skip_end
                        ],
                        outputs=[audio_output],
                    )
    print("推理页面已开启!")
    app.launch()