code: def compute_log_fbank(
      x: paddle.Tensor,
      sample_rate: int = 16000,
      n_fft: int = 400,
      hop_length: int = 160,
      win_length: int = 400,
      n_mels: int = 80,
      window: str = "hamming",
      center: bool = True,
      pad_mode: str = "constant",
      f_min: float = 0.0,
      f_max: float = None,
      top_db: float = 80.0,
  ):
      if f_max is None:
          f_max = sample_rate / 2
  
      spect = Spectrogram(
          n_fft=n_fft,
          hop_length=hop_length,
          win_length=win_length,
          window=window,
          center=center,
          pad_mode=pad_mode,
      )(x)
  
      fbank_matrix = compute_fbank_matrix(
          sample_rate=sample_rate,
          n_fft=n_fft,
          n_mels=n_mels,
          f_min=f_min,
          f_max=f_max,
      )
      fbank = paddle.matmul(fbank_matrix, spect)
      log_fbank = power_to_db(fbank, top_db=top_db).transpose([0, 2, 1])
      return log_fbank
  
file_path: PaddleHub/modules/audio/speaker_recognition/ecapa_tdnn_voxceleb/feature.py
imports: import paddle
  import paddleaudio
  from paddleaudio.features.spectrum import hz_to_mel
  from paddleaudio.features.spectrum import mel_to_hz
  from paddleaudio.features.spectrum import power_to_db
  from paddleaudio.features.spectrum import Spectrogram
  from paddleaudio.features.window import get_window
label_desc: Use image node_id 2 for calling a global function with example usage: compute_log_fbank(x, sample_rate, n_fft, hop_length, win_length, n_mels, window, center, pad_mode, f_min, f_max, top_db) and returns: log_fbank