app.py

# flake8: noqa: E402

import sys, os
import logging

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 datetime
import numpy as np
import torch
import argparse
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
import gradio as gr
import webbrowser
import re

net_g = None
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","ミッシェル","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(初华和喵梦没法用)":["祥子","睦","海鈴","初華","にゃむ"],
}

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

def is_japanese(string):
        for ch in string:
            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
                return True
        return False

def extrac(text):
    text = re.sub("<[^>]*>","",text)
    result_list = re.split(r'\n', text)
    final_list = []
    for i in result_list:
        i = i.replace('\n','').replace(' ','')
        #Current length of single sentence: 20 
        if len(i)>1:
            if len(i) > 20:
                try:
                    cur_list = re.split(r'。|！', i)
                    for i in cur_list:
                        if len(i)>1:
                            final_list.append(i+'。')
                except:
                    pass
            else:
                final_list.append(i)
            '''
        final_list.append(i)
        '''
    final_list = [x for x in final_list if x != '']
    print(final_list)
    return final_list

def get_text(text, language_str, hps):
    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 = get_bert(norm_text, word2ph, language_str, device)
    del word2ph
    assert bert.shape[-1] == len(phone), phone

    if language_str == "ZH":
        bert = bert
        ja_bert = torch.zeros(768, len(phone))
    elif language_str == "JA":
        ja_bert = bert
        bert = torch.zeros(1024, len(phone))
    else:
        bert = torch.zeros(1024, len(phone))
        ja_bert = torch.zeros(768, len(phone))

    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, phone, tone, language


def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
    global net_g
    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
    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)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        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,
                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
        return audio


def tts_fn(
    text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,LongSentence
):
    if not LongSentence:
        with torch.no_grad():
            audio = infer(
                text,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
                sid=speaker,
                language= "JP" if is_japanese(text) else "ZH",
            )
            torch.cuda.empty_cache()
        return (hps.data.sampling_rate, audio)
    else:
        audiopath = 'voice.wav'
        a = ['【','[','(','（']
        b = ['】',']',')','）']
        for i in a:
            text = text.replace(i,'<')
        for i in b:
            text = text.replace(i,'>')
        final_list = extrac(text.replace('“','').replace('”',''))
        audio_fin = []
        for sentence in final_list:
            with torch.no_grad():
                audio = infer(
                    sentence,
                    sdp_ratio=sdp_ratio,
                    noise_scale=noise_scale,
                    noise_scale_w=noise_scale_w,
                    length_scale=length_scale,
                    sid=speaker,
                    language= "JP" if is_japanese(text) else "ZH",
                )
                print(sentence)
            audio_fin.append(audio)
        return (hps.data.sampling_rate, np.concatenate(audio_fin))


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-m", "--model", default="./logs/BangDream/G_6000.pth", help="path of your model"
    )
    parser.add_argument(
        "-c",
        "--config",
        default="./logs/BangDream/config.json",
        help="path of your config file",
    )
    parser.add_argument(
        "--share", default=True, help="make link public", action="store_true"
    )
    parser.add_argument(
        "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
    )

    args = parser.parse_args()
    if args.debug:
        logger.info("Enable DEBUG-LEVEL log")
        logging.basicConfig(level=logging.DEBUG)
    hps = utils.get_hparams_from_file(args.config)

    device = (
        "cuda:0"
        if torch.cuda.is_available()
        else (
            "mps"
            if sys.platform == "darwin" and torch.backends.mps.is_available()
            else "cpu"
        )
    )
    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(args.model, net_g, None, skip_optimizer=True)

    speaker_ids = hps.data.spk2id
    speakers = list(speaker_ids.keys())
    languages = ["ZH", "JP"]
    with gr.Blocks() as app:
        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>'
                                    )
                                LongSentence = gr.Checkbox(value=True, label="Generate LongSentence")
                            with gr.Column():
                                text = gr.TextArea(
                                    label="Text",
                                    placeholder="Input Text Here",
                                    value="有个人躺在地上，哀嚎。\n有个人睡着了。\n睡在盒子里。\n我要把它打开。\n看看他的梦，是什么。",
                                )                                
                                btn = gr.Button("Generate!", variant="primary")
                                audio_output = gr.Audio(label="Output Audio")
                                with gr.Accordion(label="Setting", open=False):
                                    sdp_ratio = gr.Slider(
                                    minimum=0, maximum=1, value=0.2, step=0.01, label="SDP Ratio"
                                    )
                                    noise_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.6, step=0.01, label="Noise Scale"
                                    )
                                    noise_scale_w = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.8, step=0.01, label="Noise Scale W"
                                    )
                                    length_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=1, step=0.01, label="Length Scale"
                                    )
                                    speaker = gr.Dropdown(
                                        choices=speakers, value=name, label="Speaker"
                                    )
                    btn.click(
                        tts_fn,
                        inputs=[
                            text,
                            speaker,
                            sdp_ratio,
                            noise_scale,
                            noise_scale_w,
                            length_scale,
                            LongSentence,
                        ],
                        outputs=[ audio_output],
                    )

    app.launch()