code: def fbank_feat(
      powspec,
      sr=8000,
      feature_size=40,
      nfft=512,
      lowfreq=0,
      highfreq=None,
  ):
      """powspec: [audio_channels, spectrogram_length, spectrogram_feat_dim]
      return : [auido_chnnels, nframe, nfbank]
      """
      del nfft
  
      true_fn = lambda: tf.expand_dims(powspec, 0)
      false_fn = lambda: powspec
      powspec = tf.cond(
          tf.equal(tf.rank(powspec), 2), true_fn, false_fn
      )
  
      feat = py_x_ops.fbank(
          powspec,
          sr,
          filterbank_channel_count=feature_size,
          lower_frequency_limit=lowfreq,
          upper_frequency_limit=highfreq,
      )
      return feat
  
file_path: delta/delta/data/feat/speech_ops.py
imports: import numpy
  from absl import logging
  import delta.compat
  from tensorflow.python.ops import gen_audio_ops
  from delta import utils
  from delta.utils.hparam import HParams
  from core.ops import py_x_ops
label_desc: Use image node_id 4 for calling a global function with example usage: fbank_feat(powspec, sr, feature_size, nfft, lowfreq, highfreq) and returns: feat