class_docstr: Generate features for input to the speech pipeline.
  
  Arguments
  ---------
  deltas : bool (default: True)
      Whether or not to append derivatives and second derivatives
      to the features.
  context : bool (default: True)
      Whether or not to append forward and backward contexts to
      the features.
  requires_grad : bool (default: False)
      Whether to allow parameters (i.e. fbank centers and
      spreads) to update during training.
  sample_rate : int (default: 16000)
      Sampling rate for the input waveforms.
  f_min : int (default: 0)
      Lowest frequency for the Mel filters.
  f_max : int (default: None)
      Highest frequency for the Mel filters. Note that if f_max is not
      specified it will be set to sample_rate // 2.
  win_length : float (default: 25)
      Length (in ms) of the sliding window used to compute the STFT.
  hop_length : float (default: 10)
      Length (in ms) of the hop of the sliding window used to compute
      the STFT.
  n_fft : int (default: 400)
      Number of samples to use in each stft.
  n_mels : int (default: 23)
      Number of filters to use for creating filterbank.
  n_mfcc : int (default: 20)
      Number of output coefficients
  filter_shape : str (default 'triangular')
      Shape of the filters ('triangular', 'rectangular', 'gaussian').
  param_change_factor: bool (default 1.0)
      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 (default 0.0)
      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).
  left_frames : int (default 5)
      Number of frames of left context to add.
  right_frames : int (default 5)
      Number of frames of right context to add.
  
  Example
  -------
  >>> import torch
  >>> inputs = torch.randn([10, 16000])
  >>> feature_maker = MFCC()
  >>> feats = feature_maker(inputs)
  >>> feats.shape
  torch.Size([10, 101, 660])
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docstr: Returns a set of mfccs generated from the input waveforms.
  
  Arguments
  ---------
  wav : tensor
      A batch of audio signals to transform to features.
docstr_tok: ["Returns","a","set","of","mfccs","generated","from","the","input","waveforms",".","Arguments","--","--","--","--","-","wav",":","tensor","A","batch","of","audio","signals","to","transform","to","features","."]
code: def forward(self, wav):
      """Returns a set of mfccs generated from the input waveforms.
  
      Arguments
      ---------
      wav : tensor
          A batch of audio signals to transform to features.
      """
      STFT = self.compute_STFT(wav)
      mag = spectral_magnitude(STFT)
      fbanks = self.compute_fbanks(mag)
      mfccs = self.compute_dct(fbanks)
      if self.deltas:
          delta1 = self.compute_deltas(mfccs)
          delta2 = self.compute_deltas(delta1)
          mfccs = torch.cat([mfccs, delta1, delta2], dim=2)
      if self.context:
          mfccs = self.context_window(mfccs)
      return mfccs
  
file_path: speechbrain/speechbrain/lobes/features.py
imports: import torch
  from speechbrain.processing.features import STFT, spectral_magnitude, Filterbank, DCT, Deltas, ContextWindow
  from speechbrain.utils.autocast import fwd_default_precision
  from speechbrain.nnet.CNN import GaborConv1d
  from speechbrain.nnet.normalization import PCEN
  from speechbrain.nnet.pooling import GaussianLowpassPooling
label_desc: Use image node_id 2 for calling the MFCC obj's underlying member method code with example usage: obj.forward(wav) and returns: mfccs