espnet2.s2st.losses.abs_loss.AbsS2STLoss
espnet2.s2st.losses.abs_loss.AbsS2STLoss
class espnet2.s2st.losses.abs_loss.AbsS2STLoss(*args, **kwargs)
Bases: Module, ABC
Base class for all S2ST (Speech-to-Speech Translation) loss modules.
This class serves as an abstract base class for implementing various loss functions in the context of speech-to-speech translation. It defines the structure and contract for all derived loss classes, ensuring they implement the required methods.
name
The name of the loss function, to be implemented by derived classes.
- Type: str
forward()
Computes the loss for the given input tensors. This method must be implemented by any subclass.
- Raises:NotImplementedError – If the derived class does not implement the forward method or the name property.
####### Examples
To create a custom loss, subclass this class and implement the name property and forward method:
```python class CustomLoss(AbsS2STLoss):
@property def name(self) -> str:
return “custom_loss”
def forward(self, inputs: torch.Tensor) -> torch.Tensor: : # Implement the loss calculation return torch.mean(inputs)
```Initialize internal Module state, shared by both nn.Module and ScriptModule.
abstract forward() → Tensor
Computes the forward pass for the S2ST loss module.
This method must be implemented by subclasses of AbsS2STLoss. It defines the computation that takes place during the forward pass of the loss calculation. The output tensor should have the shape of (batch).
- Returns: A tensor representing the computed loss for the batch.
- Return type: torch.Tensor
- Raises:NotImplementedError – If the method is not overridden in a subclass.
####### Examples
class CustomLoss(AbsS2STLoss): : def forward(self) -> torch.Tensor: : # Example implementation return torch.tensor([1.0, 2.0, 3.0])
loss = CustomLoss() result = loss.forward() print(result) # Output: tensor([1.0, 2.0, 3.0])
property name : str