code: def augment_data(
      noise_datasets, speech_datasets, wavs, targets, lens_targ
  ):
      """This method creates different types of augmented that are useful to train
      a VAD system. It creates signals with different types of transitions, such
      as speech=>speech, noise=>speech, speech=>noise. The new signals are
      concatenated with the original ones (wavs, targets) such that the output
      is already an augmented batch useful to train a VAD model.
  
  
      Arguments
      ---------
      noise_datasets: list
          List containing noise datasets. More precisely, we expect here the pointers
          to the object used in speechbrain for data augmentation
          (e.g, speechbrain.lobes.augment.EnvCorrupt).
      speech_datasets: list
          List containing noise datasets. More precisely, we expect here the pointers
          to the object used in speechbrain for data augmentation
          (e.g, speechbrain.lobes.augment.EnvCorrupt).
      wavs: torch.tensor
          The original waveform.
      targets: torch.tensor
          The original targets.
      lens_targ: torch.tensor
          The lenght of the original targets.
  
  
      Returns
      -------
      wavs: torch.tensor
          The output batch with the augmented signals
      target: torch.tensor
          The new targets corresponding to the augmented signals.
      lengths: torch.tensor
          relative lenghts of each element in the batch.
      """
      # Sample a noise sequence
      wav_samples_noise = get_samples_from_datasets(
          noise_datasets, wavs
      )
  
      # Sample a speech sequence
      wav_samples_speech = get_samples_from_datasets(
          speech_datasets, wavs
      )
  
      # Create chunk with noise=>speech transition
      (
          wav_noise_speech,
          target_noise_speech,
          lengths_noise_speech,
      ) = create_chunks(
          wav_samples_noise,
          wav_samples_speech,
          wav_samples_noise,
          speech1=False,
          speech2=True,
      )
  
      # Create chunk with speech=>noise transition
      (
          wav_speech_noise,
          target_speech_noise,
          lengths_speech_noise,
      ) = create_chunks(
          wav_samples_speech,
          wav_samples_noise,
          wav_samples_noise,
          speech1=True,
          speech2=False,
      )
  
      # Create chunk with speech=>speech transition
      wav_samples_speech2 = torch.roll(wav_samples_speech, 1, dims=-1)
      (
          wav_speech_speech,
          target_speech_speech,
          lengths_speech_speech,
      ) = create_chunks(
          wav_samples_speech,
          wav_samples_speech2,
          wav_samples_noise,
          speech1=True,
          speech2=True,
      )
  
      # Create chunk with noise=>noise transition
      wav_samples_noise2 = torch.roll(wav_samples_noise, 1, dims=-1)
      (
          wav_noise_noise,
          target_noise_noise,
          lengths_noise_noise,
      ) = create_chunks(
          wav_samples_noise,
          wav_samples_noise2,
          wav_samples_noise,
          speech1=False,
          speech2=False,
      )
  
      # Concatenate all the augmented data
      wavs = torch.cat(
          [
              wavs,
              wav_noise_speech,
              wav_speech_noise,
              wav_speech_speech,
              wav_noise_noise,
          ],
          dim=0,
      )
  
      # Concatenate targets
      targets = torch.cat(
          [
              targets,
              target_noise_speech,
              target_speech_noise,
              target_speech_speech,
              target_noise_noise,
          ],
          dim=0,
      )
  
      # Concatenate lengths
      lens = torch.cat(
          [
              lens_targ,
              lengths_noise_speech,
              lengths_speech_noise,
              lengths_speech_speech,
              lengths_noise_noise,
          ],
          dim=0,
      )
  
      # Assign random amplitude to the signals
      max_amp, _ = wavs.abs().max(1)
      wavs = wavs / max_amp.unsqueeze(1)
      wavs = wavs * torch.rand_like(max_amp).unsqueeze(1)
  
      return wavs, targets, lens
  
imports: import torch
  import torchaudio
  import random
label_desc: Use image node_id 5 for calling a global function with example usage: augment_data(noise_datasets, speech_datasets, wavs, targets, lens_targ) and returns: wavs,  targets,  lens