app.py

import os
os.system('pip install gradio==2.3.0a0')
os.system('pip freeze')
import sys
sys.path.append('.')
import gradio as gr
os.system('pip install -U torchtext==0.8.0')
#os.system('python setup.py install --install-dir .')
from scipy.io import wavfile

os.system('chmod a+x ./separate_scripts/*.sh')
os.system('chmod a+x ./scripts/*.sh')
os.system('chmod a+x ./scripts/*/*.sh')
os.system('./separate_scripts/download_checkpoints.sh')

def inference(audio):
    # read the file and get the sample rate and data
    rate, data = wavfile.read(audio.name) 
    
    # save the result
    wavfile.write('foo_left.wav', rate, data)
    os.system("""python bytesep/inference.py --config_yaml=./scripts/4_train/musdb18/configs/vocals-accompaniment,resunet_subbandtime.yaml --checkpoint_path=./downloaded_checkpoints/resunet143_subbtandtime_vocals_8.8dB_350k_steps.pth --audio_path=foo_left.wav --output_path=sep_vocals.mp3""")
    #os.system('./separate_scripts/separate_vocals.sh ' + audio.name + ' "sep_vocals.mp3"')
    os.system("""python bytesep/inference.py --config_yaml=./scripts/4_train/musdb18/configs/accompaniment-vocals,resunet_subbandtime.yaml --checkpoint_path=./downloaded_checkpoints/resunet143_subbtandtime_accompaniment_16.4dB_350k_steps.pth --audio_path=foo_left.wav --output_path=sep_accompaniment.mp3""")
    #os.system('./separate_scripts/separate_accompaniment.sh ' + audio.name + ' "sep_accompaniment.mp3"')
    #os.system('python separate_scripts/separate.py --audio_path=' +audio.name+' --source_type="accompaniment"')
    #os.system('python separate_scripts/separate.py --audio_path=' +audio.name+' --source_type="vocals"')
    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. Currently supports .wav files. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.05418'>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>"

examples = [['example.wav']]
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,
    examples=examples
    ).launch(debug=True)