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 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
import sys, os
import math
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"]
}

if sys.platform == "darwin" and torch.backends.mps.is_available():
    device = "mps"
    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
else:
    #device = "cuda"
    device = "cpu"

def generate_audio(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    speaker,
    language,
):
    audio_list = []
    with torch.no_grad():
        if language == 'Auto':
            language = "EN"
            if is_japanese(text):
                language = "JP"
            elif is_chinese(text):
                language = "ZH"
        print(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 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 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(
             f"少歌邦邦全员TTS,使用本模型请严格遵守法律法规!\现已支持日语bert推理<a href='https://huggingface.co/spaces/Mahiruoshi/MyGO_VIts-bert/'>上一版本模型</a>及V1.0版本模型\n 发布二创作品请注明项目和本模型作者<a href='https://space.bilibili.com/19874615/'>B站@Mahiroshi</a>及项目链接\n从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看使用说明</a>"
        )
        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"
                                    )
                                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")
                                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"
                                    )
                                    LongSentence = gr.Checkbox(value=True, label="Generate LongSentence")
                                    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],
                    )

print("推理页面已开启!")
app.launch()