text: import torch
  from torch import nn
  import torch.nn.functional as F
  from torchvision import transforms as T
  from torchaudio import transforms
  
  class SpecAug(nn.Module):
      def __init__(self, freq_len=1, time_len=10, freq_mask=27, time_mask=100):
          super(SpecAug, self).__init__()
  
          self.freq_len = freq_len
          self.time_len = time_len
  
          self.freq_mask = freq_mask
          self.time_mask = time_mask
  
          self.freq_aug = transforms.FrequencyMasking(freq_mask_param=self.freq_len)
          self.time_aug = transforms.TimeMasking(time_mask_param=self.time_len)
      
      def __call__(self, x):
          x = self.freq_aug(x, mask_value=self.freq_mask)
          x = self.time_aug(x, mask_value=self.time_mask)
  
          return x
  
  train_audio_transforms = T.Compose([transforms.MelSpectrogram(sample_rate=16000, n_mels=128, normalized=True), SpecAug()])
  valid_audio_transforms = transforms.MelSpectrogram(sample_rate=16000, n_mels=128, normalized=True)