class_docstr: Class that manages the training loop. See speechbrain.core.Brain.
class_docstr_tok: ["Class","that","manages","the","training","loop",".","See","speechbrain.core.Brain","."]
docstr: Returns corresponding log-spectral features of the input waveforms.
  
  Arguments
  ---------
  wavs : torch.Tensor
      The batch of waveforms to convert to log-spectral features.
docstr_tok: ["Returns","corresponding","log-spectral","features","of","the","input","waveforms",".","Arguments","--","--","--","--","-","wavs",":","torch.Tensor","The","batch","of","waveforms","to","convert","to","log-spectral","features","."]
code: def compute_feats(self, wavs):
      """Returns corresponding log-spectral features of the input waveforms.
  
      Arguments
      ---------
      wavs : torch.Tensor
          The batch of waveforms to convert to log-spectral features.
      """
  
      # Log-spectral features
      feats = self.hparams.compute_STFT(wavs)
      feats = sb.processing.features.spectral_magnitude(
          feats, power=0.5
      )
  
      # Log1p reduces the emphasis on small differences
      feats = torch.log1p(feats)
  
      return feats
  
file_path: speechbrain/templates/enhancement/train.py
imports: import sys
  import torch
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  from mini_librispeech_prepare import prepare_mini_librispeech
label_desc: Use image node_id 2 for calling the SEBrain obj's underlying member method code with example usage: obj.compute_feats(wavs) and returns: feats