docstr: Forward computations from the waveform batches to the enhanced output.
docstr_tok: ["Forward","computations","from","the","waveform","batches","to","the","enhanced","output","."]
code: def compute_forward(self, batch, stage):
      """Forward computations from the waveform batches to the enhanced output."""
      batch = batch.to(self.device)
      noisy_wavs, lens = batch.noisy_sig
      noisy_feats = self.compute_feats(noisy_wavs)
  
      # mask with "signal approximation (SA)"
      mask = self.modules.model(noisy_feats)
      mask = torch.squeeze(mask, 2)
      predict_spec = torch.mul(mask, noisy_feats)
  
      # Also return predicted wav
      predict_wav = self.hparams.resynth(
          torch.expm1(predict_spec), noisy_wavs
      )
  
      return predict_spec, predict_wav
  
file_path: speechbrain/recipes/Voicebank/enhance/spectral_mask/train.py
imports: import os
  import sys
  import torch
  import torchaudio
  import speechbrain
  from pesq import pesq
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import MetricStats
  from speechbrain.processing.features import spectral_magnitude
  from speechbrain.nnet.loss.stoi_loss import stoi_loss
  from speechbrain.utils.distributed import run_on_main
label_desc: Use image node_id 1 for calling the SEBrain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: predict_spec,  predict_wav