code: def extract_logfbank_with_delta(waveforms, params):
      """extract logfbank with delta detla"""
      p = params
      # waveforms = tf.expand_dims(waveforms, 0) # add batch_size dim
      mel_fbanks = compute_mel_filterbank_features(
          waveforms,
          sample_rate=p.audio_sample_rate,
          dither=p.audio_dither,
          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 = add_delta_deltas(mel_fbanks)
      # shape: [batch, nframes, nbins, nchannels]
      return mel_fbanks
  
file_path: delta/delta/data/feat/tf_speech_feature.py
imports: import functools
  import numpy
  import delta.compat
  import scipy.signal
  from delta import utils
  from delta.utils.hparam import HParams
label_desc: Use image node_id 5 for calling a global function with example usage: extract_logfbank_with_delta(waveforms, params) and returns: mel_fbanks