SpeechRecognitionModel(
  # First convolutional layer
  (cnn): Conv2d(1, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  # 5 blocks of convolutional layers with residual connections
  (res_cnn): Sequential(
    (0): ResidualCNN(
      (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (dropout): Dropout(p=0.2, inplace=False)
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (1): ResidualCNN(
      (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (dropout): Dropout(p=0.2, inplace=False)
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (2): ResidualCNN(
      (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (dropout): Dropout(p=0.2, inplace=False)
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (3): ResidualCNN(
      (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (dropout): Dropout(p=0.2, inplace=False)
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (4): ResidualCNN(
      (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (dropout): Dropout(p=0.2, inplace=False)
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
  )
  # Single linear layer to tie CNN and RNN parts together
  (fc): Linear(in_features=2048, out_features=512, bias=True)
  # 5 blocks of recurrent layers
  (rnn): Sequential(
    (0): RNN(
      (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (gru): GRU(512, 512, batch_first=True, bidirectional=True)
      (dropout): Dropout(p=0.2, inplace=False)
    )
    (1): RNN(
      (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (gru): GRU(1024, 512, bidirectional=True)
      (dropout): Dropout(p=0.2, inplace=False)
    )
    (2): RNN(
      (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (gru): GRU(1024, 512, bidirectional=True)
      (dropout): Dropout(p=0.2, inplace=False)
    )
    (3): RNN(
      (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (gru): GRU(1024, 512, bidirectional=True)
      (dropout): Dropout(p=0.2, inplace=False)
    )
    (4): RNN(
      (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      (relu): ReLU()
      (gru): GRU(1024, 512, bidirectional=True)
      (dropout): Dropout(p=0.2, inplace=False)
    )
  )
  # Two fully connected layers to convert the output of RNN layers to a probability 
  # distribution for each vertical feature vector and each character
  (dense): Sequential(
    (0): Linear(in_features=1024, out_features=512, bias=True)
    (1): ReLU()
    (2): Dropout(p=0.2, inplace=False)
    (3): Linear(in_features=512, out_features=29, bias=True)
  )
)