class_docstr: computes filter bank (FBANK) features given spectral magnitudes.
  
  Arguments
  ---------
  n_mels : float
      Number of Mel filters used to average the spectrogram.
  log_mel : bool
      If True, it computes the log of the FBANKs.
  filter_shape : str
      Shape of the filters ('triangular', 'rectangular', 'gaussian').
  f_min : int
      Lowest frequency for the Mel filters.
  f_max : int
      Highest frequency for the Mel filters.
  n_fft : int
      Number of fft points of the STFT. It defines the frequency resolution
      (n_fft should be<= than win_len).
  sample_rate : int
      Sample rate of the input audio signal (e.g, 16000)
  power_spectrogram : float
      Exponent used for spectrogram computation.
  amin : float
      Minimum amplitude (used for numerical stability).
  ref_value : float
      Reference value used for the dB scale.
  top_db : float
      Minimum negative cut-off in decibels.
  freeze : bool
      If False, it the central frequency and the band of each filter are
      added into nn.parameters. If True, the standard frozen features
      are computed.
  param_change_factor: bool
      If freeze=False, this parameter affects the speed at which the filter
      parameters (i.e., central_freqs and bands) can be changed.  When high
      (e.g., param_change_factor=1) the filters change a lot during training.
      When low (e.g. param_change_factor=0.1) the filter parameters are more
      stable during training
  param_rand_factor: float
      This parameter can be used to randomly change the filter parameters
      (i.e, central frequencies and bands) during training.  It is thus a
      sort of regularization. param_rand_factor=0 does not affect, while
      param_rand_factor=0.15 allows random variations within +-15% of the
      standard values of the filter parameters (e.g., if the central freq
      is 100 Hz, we can randomly change it from 85 Hz to 115 Hz).
  
  Example
  -------
  >>> import torch
  >>> compute_fbanks = Filterbank()
  >>> inputs = torch.randn([10, 101, 201])
  >>> features = compute_fbanks(inputs)
  >>> features.shape
  torch.Size([10, 101, 40])
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docstr: Returns hz-frequency value corresponding to the input
  mel-frequency value.
  
  Arguments
  ---------
  x : float
      The frequency point in the mel-scale.
docstr_tok: ["Returns","hz-frequency","value","corresponding","to","the","input","mel-frequency","value",".","Arguments","--","--","--","--","-","x",":","float","The","frequency","point","in","the","mel-scale","."]
code: def _to_hz(mel):
      """Returns hz-frequency value corresponding to the input
      mel-frequency value.
  
      Arguments
      ---------
      x : float
          The frequency point in the mel-scale.
      """
      return 700 * (10 ** (mel / 2595) - 1)
  
imports: import math
  import torch
  import logging
  from speechbrain.utils.checkpoints import mark_as_saver, mark_as_loader, mark_as_transfer, register_checkpoint_hooks
  from speechbrain.dataio.dataio import length_to_mask
label_desc: Use image node_id 4 for calling the Filterbank obj's underlying member method code with example usage: obj._to_hz(mel) and returns: unknown