- torchaudio.functional.rnnt_loss(logits: Tensor, targets: Tensor, logit_lengths: Tensor, target_lengths: Tensor, blank: int = -1, clamp: float = -1, reduction: str = 'mean', fused_log_softmax: bool = True)[source]¶
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.
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
blank (int, optional) – blank label (Default:
clamp (float, optional) – clamp for gradients (Default:
reduction (string, optional) – Specifies the reduction to apply to the output:
fused_log_softmax (bool) – set to False if calling log_softmax outside of loss (Default:
Loss with the reduction option applied. If
"none", then size (batch), otherwise scalar.
- Return type: