espnet2.asvspoof.loss.abs_loss.AbsASVSpoofLoss

source

class espnet2.asvspoof.loss.abs_loss.AbsASVSpoofLoss(*args, **kwargs)

Bases: Module, ABC

Base class for all ASV Spoofing loss modules.

This abstract base class defines the interface for ASV Spoofing loss functions. Subclasses must implement the forward and score methods to compute the loss and score, respectively.

name

A string representing the name of the loss function,

• Type: str

which will be used as a key in the reporter.

forward(ref, inf) → torch.Tensor

Computes the loss given reference and inferred values.

score(pred) → torch.Tensor

Computes the score based on the predictions.

• Raises:
  • NotImplementedError – If the forward or score methods are not
  • implemented in the subclass. –

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

To create a custom loss module, inherit from this class and implement the forward and score methods:

``

`

python class CustomASVSpoofLoss(AbsASVSpoofLoss):

@property def name(self) -> str:

return “custom_loss”

def forward(self, ref, inf) -> torch.Tensor: : # Implement custom loss computation return loss

def score(self, pred) -> torch.Tensor: : # Implement scoring mechanism return score

``

`

####### NOTE Ensure that the returned tensor from forward is of shape (batch).

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

abstract forward(ref, inf) → Tensor

Computes the forward pass of the ASV spoofing loss.

This method calculates the loss based on the reference and inference tensors provided. It is expected that the input tensors have compatible shapes for loss computation.

• Parameters:
  • ref (torch.Tensor) – A tensor containing the reference values (ground truth) for the ASV spoofing task. The shape should be (batch_size, …), where ‘…’ can represent additional dimensions depending on the specific implementation.
  • inf (torch.Tensor) – A tensor containing the inference values predicted by the model. The shape should match the shape of the ‘ref’ tensor.
• Returns: A tensor containing the computed loss value, which should be of shape (batch_size,). The value represents the loss for each sample in the batch.
• Return type: torch.Tensor
• Raises:
  • NotImplementedError – If the method is not overridden in a
  • subclass. –

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

>>> ref = torch.tensor([[0.0], [1.0]])
>>> inf = torch.tensor([[0.1], [0.9]])
>>> loss = model.forward(ref, inf)
>>> print(loss)  # Example output: tensor([0.01, 0.01])

####### NOTE This is an abstract method and should be implemented in subclasses of AbsASVSpoofLoss.

property name : str

Base class for all ASV Spoofing loss modules.

This abstract base class defines the structure and interface for ASV (Automatic Speaker Verification) spoofing loss functions. Subclasses should implement the forward and score methods to provide specific loss calculations and scoring mechanisms.

name

A string representing the name of the loss module. Subclasses must implement this property.

• Type: str

forward(ref, inf) → torch.Tensor

Computes the loss given reference and input tensors.

score(pred) → torch.Tensor

Evaluates the predictions and returns a score tensor.

• Raises:NotImplementedError – If name property, forward, or score methods are not implemented in a subclass.

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

>>> class MyASVSpoofLoss(AbsASVSpoofLoss):
...     @property
...     def name(self):
...         return "MyASVSpoofLoss"
...
...     def forward(self, ref, inf):
...         # Implementation of loss calculation
...         return torch.tensor(0.0)  # Example output
...
...     def score(self, pred):
...         # Implementation of scoring
...         return torch.tensor(1.0)  # Example score

>>> loss_module = MyASVSpoofLoss()
>>> print(loss_module.name)
MyASVSpoofLoss

abstract score(pred) → Tensor

Calculates the score based on the model’s predictions.

This method takes the model predictions as input and computes a score that reflects the performance of the ASV spoofing model. The specific scoring mechanism should be implemented in the derived classes.

• Parameters:pred (torch.Tensor) – A tensor containing the model predictions. The shape of the tensor should be (batch_size, num_classes).
• Returns: A tensor containing the calculated scores, with a shape of (batch_size,).
• Return type: torch.Tensor
• Raises:
  • NotImplementedError – If the method is not implemented in a
  • derived class. –

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

>>> model = MyASVSpoofLoss()  # Assume MyASVSpoofLoss implements this
>>> predictions = torch.tensor([[0.1, 0.9], [0.8, 0.2]])
>>> scores = model.score(predictions)
>>> print(scores)  # Output will depend on the specific implementation

####### NOTE The score computation can vary significantly depending on the derived class and the specific ASV spoofing loss method being implemented.