(waveforms, params)
return mel_fbanks
params:
     waveforms: float32 tensor with shape [max_len]
params:
     waveforms: float32 tensor with shape [max_len]
params : waveforms : float32 tensor with shape [ max_len ]
def extract_logfbank_with_delta(waveforms, params):
  '''
   params:
     waveforms: float32 tensor with shape [max_len]
  '''
  p = params
  mel_fbanks = compute_mel_filterbank_features(
      waveforms,
      sample_rate=p.audio_sample_rate,
      preemphasis=p.audio_preemphasis,
      frame_length=p.audio_frame_length,
      frame_step=p.audio_frame_step,
      lower_edge_hertz=p.audio_lower_edge_hertz,
      upper_edge_hertz=p.audio_upper_edge_hertz,
      num_mel_bins=p.audio_num_mel_bins,
      apply_mask=False)

  if p.audio_add_delta_deltas:
    mel_fbanks = delta_delta(mel_fbanks)
  else:
    mel_fbanks = tf.expand_dims(mel_fbanks, axis=-1)
  # shape: [nframes, nbins, nchannels]
  return mel_fbanks
def extract_logfbank_with_delta ( waveforms , params ) : p = params mel_fbanks = compute_mel_filterbank_features ( waveforms , sample_rate = p . audio_sample_rate , preemphasis = p . audio_preemphasis , frame_length = p . audio_frame_length , frame_step = p . audio_frame_step , lower_edge_hertz = p . audio_lower_edge_hertz , upper_edge_hertz = p . audio_upper_edge_hertz , num_mel_bins = p . audio_num_mel_bins , apply_mask = False ) if p . audio_add_delta_deltas : mel_fbanks = delta_delta ( mel_fbanks ) else : mel_fbanks = tf . expand_dims ( mel_fbanks , axis = - 1 ) # shape: [nframes, nbins, nchannels] return mel_fbanks