code: def fbank(wav_path, flat=True):
      
  
      (rate, sig) = wav.read(wav_path)
      if len(sig) == 0:
          logger.warning("Empty wav: {}".format(wav_path))
      fbank_feat = python_speech_features.logfbank(sig, rate, nfilt=40)
      energy = extract_energy(rate, sig)
      feat = np.hstack([energy, fbank_feat])
      delta_feat = python_speech_features.delta(feat, 2)
      delta_delta_feat = python_speech_features.delta(delta_feat, 2)
      all_feats = [feat, delta_feat, delta_delta_feat]
      if not flat:
          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)
      else:
          all_feats = np.concatenate(all_feats, axis=1)
  
      # Log Mel Filterbank, with delta, and double delta
      feat_fn = wav_path[:-3] + "fbank.npy"
      np.save(feat_fn, all_feats)
docs: Currently grabs log Mel filterbank, deltas and double deltas.