docstr: Given an input batch it computes the binary probability.
  In training phase, we create on-the-fly augmentation data.
docstr_tok: ["Given","an","input","batch","it","computes","the","binary","probability",".","In","training","phase",",","we","create","on-the-fly","augmentation","data","."]
code: def compute_forward(self, batch, stage):
      """Given an input batch it computes the binary probability.
      In training phase, we create on-the-fly augmentation data.
      """
      batch = batch.to(self.device)
      wavs, lens = batch.signal
      targets, lens_targ = batch.target
      self.targets = targets
  
      if stage == sb.Stage.TRAIN:
          wavs, targets, lens = augment_data(
              self.noise_datasets,
              self.speech_datasets,
              wavs,
              targets,
              lens_targ,
          )
          self.lens = lens
          self.targets = targets
  
      # From wav input to output binary prediciton
      feats = self.hparams.compute_features(wavs)
      feats = self.modules.mean_var_norm(feats, lens)
      feats = feats.detach()
      outputs = self.modules.cnn(feats)
  
      outputs = outputs.reshape(
          outputs.shape[0],
          outputs.shape[1],
          outputs.shape[2] * outputs.shape[3],
      )
  
      outputs, h = self.modules.rnn(outputs)
      outputs = self.modules.dnn(outputs)
      return outputs, lens
  
file_path: speechbrain/recipes/LibriParty/VAD/train.py
imports: import sys
  import torch
  import logging
  import numpy
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
  from data_augment import augment_data
label_desc: Use image node_id 1 for calling the VADBrain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: outputs,  lens