espnet2.gan_codec.abs_gan_codec.AbsGANCodec

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class espnet2.gan_codec.abs_gan_codec.AbsGANCodec(*args, **kwargs)

Bases: ABC, Module

GAN-based Neural Codec model abstract class.

This abstract class serves as a base for implementing GAN-based neural codecs. It defines the core methods that any subclass must implement to function as a GAN codec, including methods for encoding and decoding waveforms, as well as obtaining meta information about the codec.

None

• Parameters:None
• Returns: None
• Yields: None
• Raises:
  • NotImplementedError – If any of the abstract methods are called directly
  • on this class. –

############# Examples

To create a custom GAN codec, subclass AbsGANCodec and implement the required abstract methods as follows:

``

`

python class MyGANCodec(AbsGANCodec):

def meta_info(self) -> Dict[str, Any]: : return

def forward(self, forward_generator,

*

args,

**

kwargs) -> Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor], int]]: : # Implementation for forward pass pass

def encode(self,

*

args,

**

kwargs) -> torch.Tensor: : # Implementation for encoding pass

def decode(self,

*

args,

**

kwargs) -> torch.Tensor: : # Implementation for decoding pass

``

`

####### NOTE This class is designed to be subclassed, and should not be instantiated directly.

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

abstract decode(*args, **kwargs) → Tensor

Return decoded waveform from codecs.

This method is responsible for converting the encoded codecs back into a waveform. The exact implementation will depend on the specific codec derived from this abstract class. This function is expected to handle any necessary transformations and return the resulting waveform as a tensor.

• Parameters:
  • *args – Positional arguments that may be required by the specific implementation.
  • **kwargs – Keyword arguments that may be required by the specific implementation.
• Returns: The decoded waveform represented as a tensor.
• Return type: torch.Tensor
• Raises:NotImplementedError – If the method is not implemented in the subclass.

############# Examples

Example of usage in a subclass implementation

class MyCodec(AbsGANCodec):

def decode(self,

*

args,

**

kwargs) -> torch.Tensor: : # Custom decoding logic here return decoded_waveform_tensor

codec = MyCodec() waveform = codec.decode(encoded_codecs)

abstract encode(*args, **kwargs) → Tensor

Encode the input waveform into a latent representation using the codec.

This method processes the input waveform and generates an encoded representation that can be used for further tasks such as decoding or other operations within the GAN framework.

• Parameters:
  • *args – Variable length argument list that can include the input waveform and any additional parameters required for encoding.
  • **kwargs – Arbitrary keyword arguments that may be needed for encoding, such as configuration settings.
• Returns: A tensor representing the encoded latent representation of the input waveform.
• Return type: torch.Tensor
• Raises:
  • NotImplementedError – If the method is not implemented in a
  • subclass. –

############# Examples

Example usage of the encode method

codec = MyCustomGANCodec() # Assuming MyCustomGANCodec inherits from AbsGANCodec waveform = torch.randn(1, 16000) # Example waveform tensor encoded = codec.encode(waveform) print(encoded.shape) # Output the shape of the encoded representation

####### NOTE The actual implementation of this method should define how the encoding is performed based on the specific codec design.

abstract forward(forward_generator, *args, **kwargs) → Dict[str, Tensor | Dict[str, Tensor] | int]

Return generator or discriminator loss.

This method processes the input through the forward generator and computes the loss associated with the generator or discriminator.

• Parameters:
  • forward_generator – The generator or discriminator function that will process the input.
  • *args – Variable length argument list that can include additional parameters required by the forward generator.
  • **kwargs – Arbitrary keyword arguments that can be passed to the forward generator for customization.
• Returns: A dictionary containing the loss values. The dictionary can include tensors representing generator or discriminator losses, additional metrics, or an integer status code.
• Raises:NotImplementedError – If this method is called directly from an instance of AbsGANCodec without being overridden in a subclass.

############# Examples

>>> codec = MyGANCodec()
>>> loss = codec.forward(generator, input_data)
>>> print(loss)

####### NOTE This method is intended to be overridden by subclasses of AbsGANCodec to provide specific loss computations based on their architecture.

abstract meta_info() → Dict[str, Any]

Return meta information of the codec.

This method is expected to return a dictionary containing meta information about the GAN-based codec, such as version, authorship, or any other relevant details that describe the codec’s characteristics.

• Returns: A dictionary containing meta information about the codec.
• Return type: Dict[str, Any]

############# Examples

>>> codec = MyGANCodec()  # MyGANCodec should be a subclass of AbsGANCodec
>>> info = codec.meta_info()
>>> print(info)
{'version': '1.0', 'author': 'Jiatong Shi', 'description': 'A GAN-based codec'}