code: def mfcc(wav_path):
      
  
      (rate, sig) = wav.read(wav_path)
      feat = python_speech_features.mfcc(sig, rate, appendEnergy=True)
      delta_feat = python_speech_features.delta(feat, 2)
      all_feats = [feat, delta_feat]
      all_feats = np.array(all_feats)
      # Make time the first dimension for easy length normalization padding later.
      all_feats = np.swapaxes(all_feats, 0, 1)
      all_feats = np.swapaxes(all_feats, 1, 2)
  
      feat_fn = wav_path[:-3] + "mfcc13_d.npy"
      np.save(feat_fn, all_feats)
docs: Grabs MFCC features with energy and derivates.