docstr: Forward computations from the waveform batches to the w2v2 loss.
docstr_tok: ["Forward","computations","from","the","waveform","batches","to","the","w2v2","loss","."]
code: def compute_forward(self, batch, stage):
      """Forward computations from the waveform batches to the w2v2 loss."""
  
      batch = batch.to(self.device)
      wavs, wav_lens = batch.sig
      wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
  
      # Forward on w2v2 and take the loss.
      # It has to be on train mode even for eval. Otherwise it would deactivate
      # the loss computation ...
      out, mask = self.modules.wav2vec2(wavs, wav_lens)
      loss = out.loss
  
      if stage != sb.Stage.TRAIN:
          return loss, out, mask
  
      return loss
  
file_path: speechbrain/recipes/CommonVoice/self-supervised-learning/wav2vec2/train_hf_wav2vec2.py
imports: import sys
  import torch
  import logging
  import speechbrain
  import torchaudio
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
label_desc: Use image node_id 1 for calling the W2VBrain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: loss, loss,  out,  mask