text: import torch
  from torchaudio.models import Wav2Letter, _MelResNet
  
  from . import common_utils
  
  
  class TestWav2Letter(common_utils.TorchaudioTestCase):
  
      def test_waveform(self):
          batch_size = 2
          num_features = 1
          num_classes = 40
          input_length = 320
  
          model = Wav2Letter(num_classes=num_classes, num_features=num_features)
  
          x = torch.rand(batch_size, num_features, input_length)
          out = model(x)
  
          assert out.size() == (batch_size, num_classes, 2)
  
      def test_mfcc(self):
          batch_size = 2
          num_features = 13
          num_classes = 40
          input_length = 2
  
          model = Wav2Letter(num_classes=num_classes, input_type="mfcc", num_features=num_features)
  
          x = torch.rand(batch_size, num_features, input_length)
          out = model(x)
  
          assert out.size() == (batch_size, num_classes, 2)
  
  
  class TestMelResNet(common_utils.TorchaudioTestCase):
  
      def test_waveform(self):
  
          batch_size = 2
          num_features = 200
          input_dims = 100
          output_dims = 128
          res_blocks = 10
          hidden_dims = 128
          pad = 2
  
          model = _MelResNet(res_blocks, input_dims, hidden_dims, output_dims, pad)
  
          x = torch.rand(batch_size, input_dims, num_features)
          out = model(x)
  
          assert out.size() == (batch_size, output_dims, num_features - pad * 2)
  