app_multi.py

from typing import Union

from argparse import ArgumentParser
from pathlib import Path
import subprocess
import librosa
import os
import time
import random
import yt_dlp

from search import get_youtube, download_random
import soundfile

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from moviepy.editor import *
from moviepy.video.io.VideoFileClip import VideoFileClip

import asyncio
import json
import hashlib
from os import path, getenv
from pydub import AudioSegment

import gradio as gr

import torch

import edge_tts

from datetime import datetime
from scipy.io.wavfile import write

import config
import util
from infer_pack.models import (
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono
)
from vc_infer_pipeline import VC

# music search

def auto_search(name):
    save_music_path = '/tmp/downloaded'
    if not os.path.exists(save_music_path):
        os.makedirs(save_music_path)

    config = {'logfilepath': 'musicdl.log', save_music_path: save_music_path, 'search_size_per_source': 5,
              'proxies': {}}
    save_path = os.path.join(save_music_path, name + '.mp3')
    # youtube
    get_youtube(name, os.path.join(save_music_path, name))
    # task1 = threading.Thread(
    #     target=get_youtube,
    #     args=(name, os.path.join(save_music_path, name))
    # )
    # task1.start()
    # task2 = threading.Thread(
    #     target=download_random,
    #     args=(name, config, save_path)
    # )
    # task2.start()
    # task1.join(timeout=20)
    # task2.join(timeout=10)

    if not os.path.exists(save_path):
        return "Not Found", None
    signal, sampling_rate = soundfile.read(save_path, dtype=np.int16)
    # signal, sampling_rate = open_audio(save_path)

    return (sampling_rate, signal)

    
# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21  # noqa
in_hf_space = getenv('SYSTEM') == 'spaces'

high_quality = True

# Argument parsing
arg_parser = ArgumentParser()
arg_parser.add_argument(
    '--hubert',
    default=getenv('RVC_HUBERT', 'hubert_base.pt'),
    help='path to hubert base model (default: hubert_base.pt)'
)
arg_parser.add_argument(
    '--config',
    default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
    help='path to config file (default: multi_config.json)'
)
arg_parser.add_argument(
    '--api',
    action='store_true',
    help='enable api endpoint'
)
arg_parser.add_argument(
    '--cache-examples',
    action='store_true',
    help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified'  # noqa
)
args = arg_parser.parse_args()

app_css = '''
#model_info img {
    max-width: 100px;
    max-height: 100px;
    float: right;
}

#model_info p {
    margin: unset;
}
'''

app = gr.Blocks(
    theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
    css=app_css,
    analytics_enabled=False
)

# Load hubert model
hubert_model = util.load_hubert_model(config.device, args.hubert)
hubert_model.eval()

# Load models
multi_cfg = json.load(open(args.config, 'r'))
loaded_models = []

for model_name in multi_cfg.get('models'):
    print(f'Loading model: {model_name}')

    # Load model info
    model_info = json.load(
        open(path.join('model', model_name, 'config.json'), 'r')
    )

    # Load RVC checkpoint
    cpt = torch.load(
        path.join('model', model_name, model_info['model']),
        map_location='cpu'
    )
    tgt_sr = cpt['config'][-1]
    cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0]  # n_spk

    if_f0 = cpt.get('f0', 1)
    net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
    if if_f0 == 1:
        net_g = SynthesizerTrnMs768NSFsid(
            *cpt['config'],
            is_half=util.is_half(config.device)
        )
    else:
        net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])

    del net_g.enc_q

    # According to original code, this thing seems necessary.
    print(net_g.load_state_dict(cpt['weight'], strict=False))

    net_g.eval().to(config.device)
    net_g = net_g.half() if util.is_half(config.device) else net_g.float()

    vc = VC(tgt_sr, config)
    
    loaded_models.append(dict(
        name=model_name,
        metadata=model_info,
        vc=vc,
        net_g=net_g,
        if_f0=if_f0,
        target_sr=tgt_sr
    ))
        
print(f'Models loaded: {len(loaded_models)}')

# Edge TTS speakers
tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())  # noqa

# Make MV
def make_bars_image(height_values, index, new_height):
    
    # Define the size of the image
    width = 512  
    height = new_height
    
    # Create a new image with a transparent background
    image = Image.new('RGBA', (width, height), color=(0, 0, 0, 0))
    
    # Get the image drawing context
    draw = ImageDraw.Draw(image)
    
    # Define the rectangle width and spacing
    rect_width = 2
    spacing = 2
    
    # Define the list of height values for the rectangles
    #height_values = [20, 40, 60, 80, 100, 80, 60, 40]
    num_bars = len(height_values)
    # Calculate the total width of the rectangles and the spacing
    total_width = num_bars * rect_width + (num_bars - 1) * spacing
    
    # Calculate the starting position for the first rectangle
    start_x = int((width - total_width) / 2)
    # Define the buffer size
    buffer_size = 80
    # Draw the rectangles from left to right
    x = start_x
    for i, height in enumerate(height_values):
        
        # Define the rectangle coordinates
        y0 = buffer_size
        y1 = height + buffer_size
        x0 = x
        x1 = x + rect_width

        # Draw the rectangle
        draw.rectangle([x0, y0, x1, y1], fill='white')  
        
        # Move to the next rectangle position
        if i < num_bars - 1:
            x += rect_width + spacing
        

    # Rotate the image by 180 degrees
    image = image.rotate(180)
    
    # Mirror the image
    image = image.transpose(Image.FLIP_LEFT_RIGHT)
    
    # Save the image
    image.save('audio_bars_'+ str(index) + '.png')

    return 'audio_bars_'+ str(index) + '.png'

def db_to_height(db_value):
    # Scale the dB value to a range between 0 and 1
    scaled_value = (db_value + 80) / 80
    
    # Convert the scaled value to a height between 0 and 100
    height = scaled_value * 50
    
    return height

def infer(title, audio_in, image_in):
    # Load the audio file
    audio_path = audio_in
    audio_data, sr = librosa.load(audio_path)

    # Get the duration in seconds
    duration = librosa.get_duration(y=audio_data, sr=sr)
    
    # Extract the audio data for the desired time
    start_time = 0 # start time in seconds
    end_time = duration # end time in seconds
    
    start_index = int(start_time * sr)
    end_index = int(end_time * sr)
    
    audio_data = audio_data[start_index:end_index]
    
    # Compute the short-time Fourier transform
    hop_length = 512

    
    stft = librosa.stft(audio_data, hop_length=hop_length)
    spectrogram = librosa.amplitude_to_db(np.abs(stft), ref=np.max)

    # Get the frequency values
    freqs = librosa.fft_frequencies(sr=sr, n_fft=stft.shape[0])

    # Select the indices of the frequency values that correspond to the desired frequencies
    n_freqs = 114
    freq_indices = np.linspace(0, len(freqs) - 1, n_freqs, dtype=int)
    
    # Extract the dB values for the desired frequencies
    db_values = []
    for i in range(spectrogram.shape[1]):
        db_values.append(list(zip(freqs[freq_indices], spectrogram[freq_indices, i])))
    
    # Print the dB values for the first time frame
    print(db_values[0])

    proportional_values = []

    for frame in db_values:
        proportional_frame = [db_to_height(db) for f, db in frame]
        proportional_values.append(proportional_frame)

    print(proportional_values[0])
    print("AUDIO CHUNK: " + str(len(proportional_values)))

    # Open the background image
    background_image = Image.open(image_in)
    
    # Resize the image while keeping its aspect ratio
    bg_width, bg_height = background_image.size
    aspect_ratio = bg_width / bg_height
    new_width = 512
    new_height = int(new_width / aspect_ratio)
    resized_bg = background_image.resize((new_width, new_height))

    # Apply black cache for better visibility of the white text
    bg_cache = Image.open('black_cache.png')
    resized_bg.paste(bg_cache, (0, resized_bg.height - bg_cache.height), mask=bg_cache)

    # Create a new ImageDraw object
    draw = ImageDraw.Draw(resized_bg)
    
    # Define the text to be added
    text = title
    font = ImageFont.truetype("NotoSansSC-Regular.otf", 16)
    text_color = (255, 255, 255) # white color
    
    # Calculate the position of the text
    text_width, text_height = draw.textsize(text, font=font)
    x = 30
    y = new_height - 70
    
    # Draw the text on the image
    draw.text((x, y), text, fill=text_color, font=font)

    # Save the resized image
    resized_bg.save('resized_background.jpg')
    
    generated_frames = []
    for i, frame in enumerate(proportional_values): 
        bars_img = make_bars_image(frame, i, new_height)
        bars_img = Image.open(bars_img)
        # Paste the audio bars image on top of the background image
        fresh_bg = Image.open('resized_background.jpg')
        fresh_bg.paste(bars_img, (0, 0), mask=bars_img)
        # Save the image
        fresh_bg.save('audio_bars_with_bg' + str(i) + '.jpg')
        generated_frames.append('audio_bars_with_bg' + str(i) + '.jpg')
    print(generated_frames)

    # Create a video clip from the images
    clip = ImageSequenceClip(generated_frames, fps=len(generated_frames)/(end_time-start_time))
    audio_clip = AudioFileClip(audio_in)
    clip = clip.set_audio(audio_clip)
    # Set the output codec
    codec = 'libx264'
    audio_codec = 'aac'
    # Save the video to a file
    clip.write_videofile("my_video.mp4", codec=codec, audio_codec=audio_codec)

    retimed_clip = VideoFileClip("my_video.mp4")

    # Set the desired frame rate
    new_fps = 25
    
    # Create a new clip with the new frame rate
    new_clip = retimed_clip.set_fps(new_fps)
    
    # Save the new clip as a new video file
    new_clip.write_videofile("my_video_retimed.mp4", codec=codec, audio_codec=audio_codec)

    return "my_video_retimed.mp4"

# mix vocal and non-vocal
def mix(audio1, audio2):
  sound1 = AudioSegment.from_file(audio1)
  sound2 = AudioSegment.from_file(audio2)
  length = len(sound1)
  mixed = sound1[:length].overlay(sound2)

  mixed.export("song.wav", format="wav")

  return "song.wav"


import requests
import yt_dlp
headers = {
    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4302.0 Safari/537.36"
}
import re

pattern = r'//www\.bilibili\.com/video[^"]*'

def find_first_appearance_with_neighborhood(text, pattern):
    match = re.search(pattern, text)
    
    if match:
        return match.group()
    else:
        return None

def search_bilibili(keyword):
    req = requests.get("https://search.bilibili.com/all?keyword={}&duration=1&tids=3&page=1".format(keyword), headers=headers).text

    video_link = "https:" + find_first_appearance_with_neighborhood(req, pattern)

    return video_link


# Bilibili
def youtube_downloader(
    song_name,
    start_time,
    end_time,
    is_full_song,
    output_filename="track.wav",
    num_attempts=5,
    url_base="",
    quiet=False,
    force=True,
):
    video_identifier = search_bilibili(song_name)
    if is_full_song:
        ydl_opts = {
            'noplaylist': True,
            'format': 'bestaudio/best',
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': 'wav',
            }],
            "outtmpl": 'dl_audio/youtube_audio',
        }
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.download([video_identifier])
        audio_path = "dl_audio/youtube_audio.wav"
        return audio_path
    
    else:
        output_path = Path(output_filename)
        if output_path.exists():
            if not force:
                return output_path
            else:
                output_path.unlink()
    
        quiet = "--quiet --no-warnings" if quiet else ""
        command = f"""
            yt-dlp {quiet} -x --audio-format wav -f bestaudio -o "{output_filename}" --download-sections "*{start_time}-{end_time}" "{url_base}{video_identifier}"  # noqa: E501
        """.strip()
    
        attempts = 0
        while True:
            try:
                _ = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
            except subprocess.CalledProcessError:
                attempts += 1
                if attempts == num_attempts:
                    return None
            else:
                break
    
        if output_path.exists():
            return output_path
        else:
            return None

def audio_separated(audio_input, progress=gr.Progress()):
    # start progress
    progress(progress=0, desc="Starting...")
    time.sleep(0.1)

    # check file input
    if audio_input is None:
        # show progress
        for i in progress.tqdm(range(100), desc="Please wait..."):
            time.sleep(0.01)
            
        return (None, None, 'Please input audio.')

    # create filename
    filename = str(random.randint(10000,99999))+datetime.now().strftime("%d%m%Y%H%M%S")
    
    # progress
    progress(progress=0.10, desc="Please wait...")
    
    # make dir output
    os.makedirs("output", exist_ok=True)
    
    # progress
    progress(progress=0.20, desc="Please wait...")
    
    # write
    if high_quality:
        write(filename+".wav", audio_input[0], audio_input[1])
    else:
        write(filename+".mp3", audio_input[0], audio_input[1])
        
    # progress
    progress(progress=0.50, desc="Please wait...")

    # demucs process
    if high_quality:
        command_demucs = "python3 -m demucs --two-stems=vocals -d cpu "+filename+".wav -o output"
    else:
        command_demucs = "python3 -m demucs --two-stems=vocals --mp3 --mp3-bitrate 128 -d cpu "+filename+".mp3 -o output"
    
    os.system(command_demucs)
    
    # progress
    progress(progress=0.70, desc="Please wait...")
    
    # remove file audio
    if high_quality:
        command_delete = "rm -v ./"+filename+".wav"
    else:
        command_delete = "rm -v ./"+filename+".mp3"
    
    os.system(command_delete)
    
    # progress
    progress(progress=0.80, desc="Please wait...")
    
    # progress
    for i in progress.tqdm(range(80,100), desc="Please wait..."):
        time.sleep(0.1)

    if high_quality:
        return "./output/htdemucs/"+filename+"/vocals.wav","./output/htdemucs/"+filename+"/no_vocals.wav","Successfully..."
    else:
        return "./output/htdemucs/"+filename+"/vocals.mp3","./output/htdemucs/"+filename+"/no_vocals.mp3","Successfully..."

        
# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118  # noqa
def vc_func(
    input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    if input_audio is None:
        return (None, 'Please provide input audio.')

    if model_index is None:
        return (None, 'Please select a model.')

    model = loaded_models[model_index]

    # Reference: so-vits
    (audio_samp, audio_npy) = input_audio

    # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
    # Can be change well, we will see
    if (audio_npy.shape[0] / audio_samp) > 600 and in_hf_space:
        return (None, 'Input audio is longer than 600 secs.')

    # Bloody hell: https://stackoverflow.com/questions/26921836/
    if audio_npy.dtype != np.float32:  # :thonk:
        audio_npy = (
            audio_npy / np.iinfo(audio_npy.dtype).max
        ).astype(np.float32)

    if len(audio_npy.shape) > 1:
        audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))

    if audio_samp != 16000:
        audio_npy = librosa.resample(
            audio_npy,
            orig_sr=audio_samp,
            target_sr=16000
        )

    pitch_int = int(pitch_adjust)

    resample = (
        0 if resample_option == 'Disable resampling'
        else int(resample_option)
    )

    times = [0, 0, 0]

    checksum = hashlib.sha512()
    checksum.update(audio_npy.tobytes())

    output_audio = model['vc'].pipeline(
        hubert_model,
        model['net_g'],
        model['metadata'].get('speaker_id', 0),
        audio_npy,
        checksum.hexdigest(),
        times,
        pitch_int,
        f0_method,
        path.join('model', model['name'], model['metadata']['feat_index']),
        feat_ratio,
        model['if_f0'],
        filter_radius,
        model['target_sr'],
        resample,
        rms_mix_rate,
        'v2'
    )

    out_sr = (
        resample if resample >= 16000 and model['target_sr'] != resample
        else model['target_sr']
    )

    print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
    return ((out_sr, output_audio), 'Success')


async def edge_tts_vc_func(
    input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    if input_text is None:
        return (None, 'Please provide TTS text.')

    if tts_speaker is None:
        return (None, 'Please select TTS speaker.')

    if model_index is None:
        return (None, 'Please select a model.')

    speaker = tts_speakers_list[tts_speaker]['ShortName']
    (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
    return vc_func(
        (tts_sr, tts_np),
        model_index,
        pitch_adjust,
        f0_method,
        feat_ratio,
        filter_radius,
        rms_mix_rate,
        resample_option
    )


def update_model_info(model_index):
    if model_index is None:
        return str(
            '### Model info\n'
            'Please select a model from dropdown above.'
        )

    model = loaded_models[model_index]
    model_icon = model['metadata'].get('icon', '')

    return str(
        '### Model info\n'
        '![model icon]({icon})'
        '**{name}**\n\n'
        'Author: {author}\n\n'
        'Source: {source}\n\n'
        '{note}'
    ).format(
        name=model['metadata'].get('name'),
        author=model['metadata'].get('author', 'Anonymous'),
        source=model['metadata'].get('source', 'Unknown'),
        note=model['metadata'].get('note', ''),
        icon=(
            model_icon
            if model_icon.startswith(('http://', 'https://'))
            else '/file/model/%s/%s' % (model['name'], model_icon)
        )
    )


def _example_vc(
    input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    (audio, message) = vc_func(
        input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
        filter_radius, rms_mix_rate, resample_option
    )
    return (
        audio,
        message,
        update_model_info(model_index)
    )


async def _example_edge_tts(
    input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    (audio, message) = await edge_tts_vc_func(
        input_text, model_index, tts_speaker, pitch_adjust, f0_method,
        feat_ratio, filter_radius, rms_mix_rate, resample_option
    )
    return (
        audio,
        message,
        update_model_info(model_index)
    )


with app:
    gr.HTML("<center>"
            "<h1>🥳🎶🎡 - AI歌手+RVC最新算法</h1>"
            "</center>")
    gr.Markdown("### <center>🌊 - 轻松上传音乐，一键生成歌曲，AI歌手准备就绪；Powered by [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)</center>")
    gr.Markdown("### <center>更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>")

    with gr.Tab("🤗 - 轻松提取音乐"):
        with gr.Row():
            with gr.Column():
                ydl_url_input  = gr.Textbox(label="通过歌曲名搜索", placeholder = "小幸运")
                with gr.Row():
                    start = gr.Number(value=0, label="起始时间 (秒)")
                    end = gr.Number(value=15, label="结束时间 (秒)")
                    check_full = gr.Checkbox(label="是否上传整首歌曲", info="若勾选则不需要填写起止时间", value=True)
                    search_name = gr.Dropdown(label="通过歌曲名搜索", info="选一首您喜欢的歌曲吧", visible=False, choices=["周杰伦晴天","周杰伦兰亭序","周杰伦七里香","周杰伦花海","周杰伦反方向的钟","周杰伦一路向北","周杰伦稻香","周杰伦明明就","周杰伦爱在西元前","孙燕姿逆光","陈奕迅富士山下","许嵩有何不可","薛之谦其实","邓紫棋光年之外","李荣浩年少有为"])
                    vc_search = gr.Button("用歌曲名来搜索吧", visible=False)

                ydl_url_submit = gr.Button("提取声音文件吧", variant="primary")
                as_audio_submit = gr.Button("去除背景音吧", variant="primary")
            with gr.Column():
                ydl_audio_output = gr.Audio(label="歌曲原声")
                as_audio_input  = ydl_audio_output
                as_audio_vocals    = gr.Audio(label="歌曲人声部分")
                as_audio_no_vocals = gr.Audio(label="歌曲伴奏部分", type="filepath")
                as_audio_message   = gr.Textbox(label="Message", visible=False)

    vc_search.click(auto_search, [search_name], [ydl_audio_output])
    ydl_url_submit.click(fn=youtube_downloader, inputs=[ydl_url_input, start, end, check_full], outputs=[ydl_audio_output])
    as_audio_submit.click(fn=audio_separated, inputs=[as_audio_input], outputs=[as_audio_vocals, as_audio_no_vocals, as_audio_message], show_progress=True, queue=True)
                    
    with gr.Row():
        with gr.Tab('🎶 - 歌声转换'):
            with gr.Row():
                with gr.Column():
                    input_audio = as_audio_vocals
                    vc_convert_btn = gr.Button('进行歌声转换吧！', variant='primary')
                    full_song = gr.Button("加入歌曲伴奏吧！", variant="primary")
                    new_song = gr.Audio(label="AI歌手+伴奏", type="filepath")
    
                    pitch_adjust = gr.Slider(
                        label='变调(默认为0；+2为升高两个key)',
                        minimum=-12,
                        maximum=12,
                        step=1,
                        value=0
                    )
                    f0_method = gr.Radio(
                        label='人声提取方法(pm时间更短；rmvpe效果更好)',
                        choices=['pm', 'rmvpe'],
                        value='pm',
                        interactive=True
                    )
    
                    with gr.Accordion('更多设置', open=False):
                        feat_ratio = gr.Slider(
                            label='Feature ratio',
                            minimum=0,
                            maximum=1,
                            step=0.1,
                            value=0.6,
                            visible=False
                        )
                        filter_radius = gr.Slider(
                            label='Filter radius',
                            minimum=0,
                            maximum=7,
                            step=1,
                            value=3,
                            visible=False
                        )
                        rms_mix_rate = gr.Slider(
                            label='Volume envelope mix rate',
                            minimum=0,
                            maximum=1,
                            step=0.1,
                            value=1,
                            visible=False                
                        )
                        resample_rate = gr.Dropdown(
                            [
                                'Disable resampling',
                                '16000',
                                '22050',
                                '44100',
                                '48000'
                            ],
                            label='是否更新采样率(默认为否)',
                            value='Disable resampling'
                        )
    
                with gr.Column():
                    # Model select
                    model_index = gr.Dropdown(
                        [
                            '%s - %s' % (
                                m['metadata'].get('source', 'Unknown'),
                                m['metadata'].get('name')
                            )
                            for m in loaded_models
                        ],
                        label='请选择您的AI歌手(必选)',
                        type='index'
                    )
        
                    # Model info
                    with gr.Box():
                        model_info = gr.Markdown(
                            '### AI歌手信息\n'
                            'Please select a model from dropdown above.',
                            elem_id='model_info'
                        )
        
                    output_audio = gr.Audio(label='AI歌手(无伴奏)', type="filepath")
                    output_msg = gr.Textbox(label='Output message', visible=False)

        vc_convert_btn.click(
            vc_func,
            [
                input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
                filter_radius, rms_mix_rate, resample_rate
            ],
            [output_audio, output_msg],
            api_name='audio_conversion'
        )
        
        full_song.click(fn=mix, inputs=[output_audio, as_audio_no_vocals], outputs=[new_song])

        model_index.change(
            update_model_info,
            inputs=[model_index],
            outputs=[model_info],
            show_progress=False,
            queue=False
        )   

        with gr.Tab("📺 - 音乐视频"):
            with gr.Row():
                with gr.Column():
                    inp1 = gr.Textbox(label="为视频配上精彩的文案吧(选填)")
                    inp2 = new_song
                    inp3 = gr.Image(source='upload', type='filepath', label="上传一张背景图片吧")
                    btn = gr.Button("生成您的专属音乐视频吧", variant="primary")
              
                with gr.Column():
                    out1 = gr.Video(label='您的专属音乐视频').style(width=512)
                    
        btn.click(fn=infer, inputs=[inp1, inp2, inp3], outputs=[out1])
 
    gr.Markdown("### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>")
    gr.Markdown("<center>🧸 - 如何使用此程序：填写视频网址和视频起止时间后，依次点击“提取声音文件吧”、“去除背景音吧”、“进行歌声转换吧！”、“加入歌曲伴奏吧！”四个按键即可。</center>")
    gr.HTML('''
        <div class="footer">
                    <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
                    </p>
        </div>
    ''')

app.queue(
    concurrency_count=1,
    max_size=20,
    api_open=args.api
).launch(show_error=True)