app.py

import os
import gradio as gr
from scipy.io.wavfile import write
import spaces
import numpy as np
import librosa

def resample_audio(audio, target_sample_rate):
    current_sample_rate, audio_data = audio
    audio_data = audio_data.astype(np.float32)
    if current_sample_rate != target_sample_rate:
        resampled_audio_data = librosa.resample(audio_data, orig_sr=current_sample_rate, target_sr=target_sample_rate)
        return target_sample_rate, resampled_audio_data
    return audio

@spaces.GPU
def inference(audio):
  os.system("pwd")
  os.makedirs("out", exist_ok=True)
  target_sample_rate = 44100
  audio = resample_audio(audio, target_sample_rate)
  write('test.wav', audio[0], audio[1])
  os.system("python3 -m demucs.separate -n mdx_extra_q --two-stems=vocals test.wav -o out")
  return "./out/mdx_extra_q/test/vocals.wav","./out/mdx_extra_q/test/no_vocals.wav"
    
title = "음성 분리"

demo = gr.Interface(
    inference, 
    gr.Audio(type="numpy", label="Input"),
    [gr.Audio(type="filepath", label="음성"),gr.Audio(type="filepath", label="배경음")],
    title=title,
    )
demo.queue(max_size=1)
demo.launch(debug=True)