espnet2.asvspoof.decoder.abs_decoder.AbsDecoder

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class espnet2.asvspoof.decoder.abs_decoder.AbsDecoder(*args, **kwargs)

Bases: Module, ABC

Abstract base class for decoders in the ESPnet ASVSpoof framework.

This class serves as a blueprint for all decoder implementations in the ESPnet ASVSpoof project. It inherits from PyTorch’s nn.Module and requires subclasses to implement the forward method, which defines the computation performed at every call.

None

• Parameters:
  • input (torch.Tensor) – The input tensor representing the features to be decoded. Its shape should be compatible with the decoder’s requirements.
  • ilens (torch.Tensor) – A tensor containing the lengths of the input sequences. This is used to manage variable-length inputs.
• Returns: A tuple containing two tensors: : - The first tensor represents the output of the decoder.
  • The second tensor represents the output lengths.
• Return type: Tuple[torch.Tensor, torch.Tensor]
• Yields: None
• Raises:NotImplementedError – If the forward method is called directly on the abstract class without being overridden by a subclass.

####### Examples

Here is an example of how to implement a subclass of AbsDecoder:

``

`

python class MyDecoder(AbsDecoder):

def forward(self, input: torch.Tensor, ilens: torch.Tensor) -> : Tuple[torch.Tensor, torch.Tensor]:

Custom decoding logic goes here

output = … # process input output_lengths = … # compute output lengths return output, output_lengths

``

`

NOTE

Subclasses must provide an implementation of the forward method to be instantiated.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

abstract forward(input: Tensor, ilens: Tensor) → Tuple[Tensor, Tensor]

Computes the forward pass of the decoder model.

This method takes an input tensor and its corresponding lengths, processes the input through the decoder, and returns the output tensor along with the updated lengths.

• Parameters:
  • input (torch.Tensor) – A tensor containing the input data to the decoder. The shape should be (batch_size, sequence_length, feature_dimension).
  • ilens (torch.Tensor) – A tensor containing the lengths of each input sequence in the batch. The shape should be (batch_size,).
• Returns: A tuple containing: : - output (torch.Tensor): The output tensor from the decoder, with <br/> shape (batch_size, output_sequence_length, output_dimension).
  • olens (torch.Tensor): A tensor containing the lengths of each output sequence in the batch. The shape will be (batch_size,).
• Return type: Tuple[torch.Tensor, torch.Tensor]
• Raises:
  • NotImplementedError – If this method is called directly on the
  • AbsDecoder class**,** as it is intended to be overridden in derived –
  • classes. –

####### Examples

>>> decoder = MyDecoder()  # MyDecoder should inherit from AbsDecoder
>>> input_tensor = torch.randn(32, 10, 256)  # Example input
>>> input_lengths = torch.tensor([10] * 32)  # All sequences are of length 10
>>> output, output_lengths = decoder(input_tensor, input_lengths)
>>> print(output.shape)  # Output shape may vary depending on implementation

NOTE

This method must be implemented in subclasses of AbsDecoder to define the specific behavior of the decoder.