docstr: Forward computations from the waveform batches to the output probabilities.
docstr_tok: ["Forward","computations","from","the","waveform","batches","to","the","output","probabilities","."]
code: def compute_forward(self, batch, stage):
      """Forward computations from the waveform batches to the output probabilities."""
      batch = batch.to(self.device)
      wavs, wav_lens = batch.sig
      wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
  
      # Add augmentation if specified
      if stage == sb.Stage.TRAIN:
          if hasattr(self.hparams, "SpeedPerturb"):
              wavs = self.hparams.speed_perturb(wavs, wav_lens)
  
      # Forward pass
      feats = self.modules.wav2vec2(wavs, wav_lens)
  
      # Add feature augmentation if specified.
      if stage == sb.Stage.TRAIN and hasattr(
          self.hparams, "fea_augment"
      ):
          feats, fea_lens = self.hparams.fea_augment(feats, wav_lens)
  
      x = self.modules.enc(feats)
      logits = self.modules.ctc_lin(x)
      p_ctc = self.hparams.log_softmax(logits)
  
      return p_ctc, wav_lens
  
file_path: speechbrain/recipes/AISHELL-1/ASR/CTC/train_with_wav2vec.py
imports: import sys
  import torch
  import logging
  import speechbrain
  from speechbrain.utils.distributed import run_on_main
  from hyperpyyaml import load_hyperpyyaml
label_desc: Use image node_id 1 for calling the ASR obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: p_ctc,  wav_lens