RNNTLoss

class torchaudio.transforms.RNNTLoss(blank: int = -1, clamp: float = -1.0, reduction: str = 'mean', fused_log_softmax: bool = True)

Compute the RNN Transducer loss from Sequence Transduction with Recurrent Neural Networks [Graves, 2012].

The RNN Transducer loss extends the CTC loss by defining a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output dependencies.

Parameters:

• blank (int, optional) – blank label (Default: -1)

• clamp (float, optional) – clamp for gradients (Default: -1)

• reduction (string, optional) – Specifies the reduction to apply to the output: "none" | "mean" | "sum". (Default: "mean")

• fused_log_softmax (bool) – set to False if calling log_softmax outside of loss (Default: True)

Example

>>> # Hypothetical values
>>> logits = torch.tensor([[[[0.1, 0.6, 0.1, 0.1, 0.1],
>>>                          [0.1, 0.1, 0.6, 0.1, 0.1],
>>>                          [0.1, 0.1, 0.2, 0.8, 0.1]],
>>>                         [[0.1, 0.6, 0.1, 0.1, 0.1],
>>>                          [0.1, 0.1, 0.2, 0.1, 0.1],
>>>                          [0.7, 0.1, 0.2, 0.1, 0.1]]]],
>>>                       dtype=torch.float32,
>>>                       requires_grad=True)
>>> targets = torch.tensor([[1, 2]], dtype=torch.int)
>>> logit_lengths = torch.tensor([2], dtype=torch.int)
>>> target_lengths = torch.tensor([2], dtype=torch.int)
>>> transform = transforms.RNNTLoss(blank=0)
>>> loss = transform(logits, targets, logit_lengths, target_lengths)
>>> loss.backward()

forward(logits: Tensor, targets: Tensor, logit_lengths: Tensor, target_lengths: Tensor)

Parameters:

• logits (Tensor) – Tensor of dimension (batch, max seq length, max target length + 1, class) containing output from joiner

• targets (Tensor) – Tensor of dimension (batch, max target length) containing targets with zero padded

• logit_lengths (Tensor) – Tensor of dimension (batch) containing lengths of each sequence from encoder

• target_lengths (Tensor) – Tensor of dimension (batch) containing lengths of targets for each sequence

Returns:

Loss with the reduction option applied. If reduction is "none", then size (batch), otherwise scalar.

Return type:

Tensor