app.py

# flake8: noqa: E402
import os
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 warnings
warnings.filterwarnings("ignore", category=UserWarning, module="gradio.blocks")

import shutil

from datetime import datetime
import re
import torch
import utils
from infer import infer, latest_version, get_net_g
import gradio as gr
import numpy as np
from tools.sentence import extrac, is_japanese, is_chinese, seconds_to_ass_time, extract_text_from_file, remove_annotations
import sys
import math

from scipy.io.wavfile import write

from tools.translate import translate

import random

net_g = None

cara_list = ["ひまり","たえ","彩","日菜","美咲","ましろ","燐子","香子","珠緒","たえ"]

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&AveMujica(Part)":["燈","愛音","そよ","立希","楽奈","祥子","睦","海鈴"],
        "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"]
}

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

def generate_audio(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    speaker,
    language,
):
    if len(text) < 2:
        return
    with torch.no_grad():
        if language == 'Auto':
            language = "EN"
            if is_japanese(text):
                language = "JP"
            elif is_chinese(text):
                language = "ZH"
        current_time = datetime.now()
        print(str(current_time)+':'+str(speaker)+":"+ text+":"+language)
        audio = infer(
            text,
            sdp_ratio=sdp_ratio,
            noise_scale=noise_scale,
            noise_scale_w=noise_scale_w,
            length_scale=length_scale,
            sid=speaker,
            language=language,
            hps=hps,
            net_g=net_g,
            device=device,
        )
    return gr.processing_utils.convert_to_16_bit_wav(audio)

def tts_fn(
    text: str,
    speaker,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    language,
    LongSentence,
):
    if not LongSentence:
        with torch.no_grad():
            audio = generate_audio(
                text,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
                speaker=speaker,
                language= language,
            )
            torch.cuda.empty_cache()
        return (hps.data.sampling_rate, audio)
    else:

        final_list = extrac(text)
        audio_fin = []
        for sentence in final_list:
            if len(sentence) > 1:
                with torch.no_grad():
                    audio = generate_audio(
                        sentence,
                        sdp_ratio=sdp_ratio,
                        noise_scale=noise_scale,
                        noise_scale_w=noise_scale_w,
                        length_scale=length_scale,
                        speaker=speaker,
                        language= language,
                    )
                silence_frames = int(math.log(len(sentence)+1, 1000) * 44010) if is_chinese(sentence) else int(math.log(len(sentence)+1, 3000) * 44010)
                silence_data = np.zeros((silence_frames,), dtype=audio.dtype)
                audio_fin.append(audio)
                audio_fin.append(silence_data)
        return (hps.data.sampling_rate, np.concatenate(audio_fin))

def generate_audio_and_srt_for_group(group, outputPath, group_index, sampling_rate, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime):
    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:
            FakeSpeaker = sentence.split("|")[0]
            print(FakeSpeaker)
            SpeakersList = re.split('\n', spealerList)
            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':
                audio = generate_audio(remove_annotations(sentence.split("|")[-1]).replace(" ",""), speaker=speaker, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, language='Auto')
                silence_frames = 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
                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, 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, np.concatenate(audio_fin))

def audiobook(inputFile, groupsize, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime,filepath):
    directory_path = filepath if torch.cuda.is_available() else "books"
 
    if os.path.exists(directory_path):
        shutil.rmtree(directory_path)

    os.makedirs(directory_path)
    text = extract_text_from_file(inputFile.name)
    sentences = extrac(text)
    GROUP_SIZE = groupsize
    for i in range(0, len(sentences), GROUP_SIZE):
        group = sentences[i:i+GROUP_SIZE]
        if spealerList == "":
            spealerList = "无"
        result = generate_audio_and_srt_for_group(group,directory_path, i//GROUP_SIZE + 1, 44100, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime)
        if not torch.cuda.is_available():
            return result
    return result

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

if __name__ == "__main__":
    hps = utils.get_hparams_from_file('Data/BangDream/config.json')
    version = hps.version if hasattr(hps, "version") else latest_version
    net_g = get_net_g(
        model_path='Data/BangDream/models/G_10000.pth', version=version, device=device, hps=hps
    )
    speaker_ids = hps.data.spk2id
    speakers = list(speaker_ids.keys())
    languages = [ "Auto", "ZH", "JP"]
    modelPaths = []
    for dirpath, dirnames, filenames in os.walk("Data/BangDream/models/"):
        for filename in filenames:
            modelPaths.append(os.path.join(dirpath, filename))
    with gr.Blocks() as app:
        gr.Markdown(value="""
            少歌邦邦全员在线语音合成（Bert-Vits2）\n
            作者：B站@Mahiroshi https://space.bilibili.com/19874615\n
            声音归属：BangDream及少歌手游\n
            Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n
            使用参考: https://nijigaku.top/2023/10/03/BangDreamTTS\n
            数据集制作: https://huggingface.co/spaces/Mahiruoshi/BangDream-Bert-VITS2/tree/main/%E7%88%AC%E8%99%AB
            服务器启动示例: https://huggingface.co/spaces/Mahiruoshi/BangDream-Bert-VITS2/blob/main/server.py\n
            使用本模型请严格遵守法律法规!禁止生成任何有损声优或者企划的内容!!!!!\n
            このモデルを使用する際は法律法規を厳守してください！声優や企画に損害を与える内容の生成は禁止です!!!!!\n
            Please strictly follow the laws in your country and regulations when using this model! It is prohibited to generate any content that is harmful to others!!!!!\n
            发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\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="file/image/{name}.png">' 
                                        '</div>'
                                    )
                                length_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
                                    )
                                LongSentence = gr.Checkbox(value=True, label="自动拆分句子")
                                with gr.Accordion(label="切换模型", open=False):
                                    modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
                                    btnMod = gr.Button("载入模型")
                                    statusa = gr.TextArea()
                                    btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa])
                            with gr.Column():
                                text = gr.TextArea(
                                    label="输入纯日语或者中文",
                                    placeholder="输入纯日语或者中文",
                                    value="有个人躺在地上，哀嚎......\n有个人睡着了，睡在盒子里。\n我要把它打开，看看他的梦是什么。",
                                )                                
                                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("从这复制翻译后的文本")
                                btntran.click(translate, inputs=[text], outputs = [translateResult])
                                with gr.Accordion(label="其它参数设定", open=False):
                                    sdp_ratio = gr.Slider(
                                    minimum=0, maximum=1, value=0.2, 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="感情调节"
                                    )
                                    noise_scale_w = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
                                    )
                                    language = gr.Dropdown(
                                    choices=languages, value=languages[0], label="选择语言(默认自动)"
                                    )
                                    speaker = gr.Dropdown(
                                        choices=speakers, value=name, label="说话人"
                                    )
                    btn.click(
                        tts_fn,
                        inputs=[
                            text,
                            speaker,
                            sdp_ratio,
                            noise_scale,
                            noise_scale_w,
                            length_scale,
                            language,
                            LongSentence,
                        ],
                        outputs=[audio_output],
                    )

        with gr.Tab('拓展功能'):
            with gr.Row():
                with gr.Column():
                    gr.Markdown(
                                    f"从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看自制galgame使用说明\n</a>"
                                )
                    inputFile = gr.UploadButton(label="上传txt(可设置角色对应表)、epub或mobi文件")
                    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字数"
                    )
                    silenceTime = gr.Slider(
                    minimum=0, maximum=1, value=0.5, step=0.1, label="句子的间隔"
                    )
                    filepath = gr.TextArea(
                                        label="本地合成时的音频存储文件夹(会清空文件夹警告)",
                                        value = "D:/audiobook/book1",
                    )
                    spealerList = gr.TextArea(
                                        label="角色对应表(example)",
                                        placeholder="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:{ChoseSpeakerFromConfigList1}|{SeakerInUploadText1}\n{ChoseSpeakerFromConfigList2}|{SeakerInUploadText2}\n{ChoseSpeakerFromConfigList3}|{SeakerInUploadText3}\n",
                                        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",
                    )                  
                    speaker = gr.Dropdown(
                        choices=speakers, value = "ましろ", label="选择默认说话人"
                    )
                with gr.Column():
                    sdp_ratio = gr.Slider(
                    minimum=0, maximum=1, value=0.2, 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="感情调节"
                    )
                    noise_scale_w = gr.Slider(
                        minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
                    )
                    length_scale = gr.Slider(
                        minimum=0.1, maximum=2, value=1, step=0.01, label="生成长度"
                    )
                    LastAudioOutput = gr.Audio(label="当使用cuda时才能在本地文件夹浏览全部文件")
                    btn2 = gr.Button("点击生成", variant="primary")
                btn2.click(
                    audiobook,
                    inputs=[
                        inputFile,
                        groupSize,
                        speaker,
                        sdp_ratio,
                        noise_scale,
                        noise_scale_w,
                        length_scale,
                        spealerList,
                        silenceTime,
                        filepath
                    ],
                    outputs=[LastAudioOutput],
                )
print("推理页面已开启!")
app.launch()