app.py

import sys
sys.path.append('.')
import gradio as gr
import os
os.system('pip install -U torchtext==0.8.0')
os.system('pip install numpy --upgrade')
os.system('./separate_scripts/download_checkpoints.sh')

def inference(audio):
    os.system('./separate_scripts/separate_vocals.sh ' + audio.name + ' "sep_vocals.mp3"')
    os.system('./separate_scripts/separate_accompaniment.sh ' + audio.name + ' "sep_accompaniment.mp3"')
    return 'sep_vocals.mp3', 'sep_accompaniment.mp3'
title = "Music Source Separation"
description = "Gradio demo for Music Source Separation. To use it, simply add your audio, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://github.com/bytedance/music_source_separation'>Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation</a> | <a href='https://github.com/bytedance/music_source_separation'>Github Repo</a></p>"

gr.Interface(
    inference, 
    gr.inputs.Audio(type="file", label="Input"), 
    [gr.outputs.Audio(type="file", label="Vocals"),gr.outputs.Audio(type="file", label="Accompaniment")],
    title=title,
    description=description,
    article=article,
    enable_queue=True
    ).launch(debug=True)