speaker_loss

(s3prl.nn.speaker_loss)

Speaker verification loss

Authors:

• Haibin Wu 2022

softmax

class s3prl.nn.speaker_loss.softmax(input_size: int, output_size: int)

Bases: Module

The standard softmax loss in an unified interface for all speaker-related softmax losses

property input_size

property output_size

forward(x: Tensor, label: LongTensor)

Parameters:

• x (torch.Tensor) – (batch_size, input_size)

• label (torch.LongTensor) – (batch_size, )

Returns:

loss (torch.float) logit (torch.Tensor): (batch_size, )

call_super_init: bool = False

dump_patches: bool = False

training: bool

amsoftmax

class s3prl.nn.speaker_loss.amsoftmax(input_size: int, output_size: int, margin: float = 0.2, scale: float = 30)

Bases: Module

AMSoftmax

Parameters:

• input_size (int) – The input feature size

• output_size (int) – The output feature size

• margin (float) – Hyperparameter denotes the margin to the decision boundry

• scale (float) – Hyperparameter that scales the cosine value

property input_size

property output_size

forward(x: Tensor, label: LongTensor)

Parameters:

• x (torch.Tensor) – (batch_size, input_size)

• label (torch.LongTensor) – (batch_size, )

Returns:

loss (torch.float) logit (torch.Tensor): (batch_size, )

call_super_init: bool = False

dump_patches: bool = False

training: bool