text: import torch
  import torchaudio
  from librosa.core import load
  from librosa.output import write_wav
  import numpy as np
  
  from spleeter.estimator import Estimator
  
  es = Estimator(2, './checkpoints/2stems/model')
  
  # load wav audio
  wav, sr = torchaudio.load_wav('./audio_example.mp3')
  
  # normalize audio 
  wav_torch = wav / (wav.max() + 1e-8)
  
  wavs = es.separate(wav_torch)
  for i in range(len(wavs)):
      fname = 'output/out_{}.wav'.format(i)
      print('Writing ',fname)
      write_wav(fname, np.asfortranarray(wavs[i].squeeze().numpy()), sr)
  