docstr: Computes the forward pass.
  
  Arguments
  ---------
  batch : torch.Tensor or tensors
      An element from the dataloader, including inputs for processing.
  stage : Stage
      The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
  
  Returns
  -------
  (torch.Tensor or Tensors, list of float or None, list of str or None)
      The outputs after all processing is complete.
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code: def compute_forward(self, batch, stage):
      """Computes the forward pass.
  
      Arguments
      ---------
      batch : torch.Tensor or tensors
          An element from the dataloader, including inputs for processing.
      stage : Stage
          The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
  
      Returns
      -------
      (torch.Tensor or Tensors, list of float or None, list of str or None)
          The outputs after all processing is complete.
      """
      batch = batch.to(self.device)
      wavs, wav_lens = batch.src_sig
      tokens_bos, _ = batch.code_bos
  
      # Use default padding value for wav2vec2
      wavs[wavs == self.hparams.pad_index] = 0.0
  
      # compute features
      enc_out = self.modules.wav2vec2(wavs, wav_lens)
  
      # dimensionality reduction
      enc_out = self.modules.enc(enc_out)
  
      if isinstance(self.modules.transformer, DistributedDataParallel):
          dec_out = (
              self.modules.transformer.module.forward_mt_decoder_only(
                  enc_out, tokens_bos, pad_idx=self.hparams.pad_index
              )
          )
      else:
          dec_out = self.modules.transformer.forward_mt_decoder_only(
              enc_out, tokens_bos, pad_idx=self.hparams.pad_index
          )
  
      # logits and softmax
      pred = self.modules.seq_lin(dec_out)
      p_seq = self.hparams.log_softmax(pred)
  
      hyps = None
      wavs = None
      transcripts = None
      if stage != sb.Stage.TRAIN:
          if (
              stage == sb.Stage.TEST
              or self.hparams.epoch_counter.current
              % self.hparams.evaluation_interval
              == 0
          ):
              ids = batch.id
              tgt_text = batch.tgt_text
  
              search = (
                  self.hparams.valid_search
                  if stage == sb.Stage.VALID
                  else self.hparams.test_search
              )
              hyps, _ = search(enc_out.detach(), wav_lens)
  
              # generate speech and transcriptions
              wavs = []
              for hyp in hyps:
                  if len(hyp) > 3:
                      code = torch.LongTensor(hyp)
                      wav = self.test_vocoder.decode_unit(code)
                      wavs.append(wav.squeeze(0))
              if wavs:
                  wavs, wav_lens = sb.utils.data_utils.batch_pad_right(
                      wavs
                  )
                  transcripts, _ = self.test_asr.transcribe_batch(
                      wavs, wav_lens
                  )
                  transcripts = [
                      transcript.lower() for transcript in transcripts
                  ]
  
                  self.bleu_metric.append(ids, transcripts, [tgt_text])
  
      return (
          p_seq,
          wavs,
          transcripts,
      )
  
imports: import sys
  import torch
  import logging
  import pathlib
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  from speechbrain.inference.vocoders import UnitHIFIGAN
  from speechbrain.inference.ASR import EncoderDecoderASR
  import tqdm
  import torchaudio
  import numpy
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 1 for calling the S2UT obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: p_seq,  wavs,  transcripts