Usage

defssnt_loss_mem(log_probs:Tensor,targets:Tensor,source_lengths:Tensor,target_lengths:Tensor,emit_logits:Optional[Tensor]=None,emit_probs:Optional[Tensor]=None,neg_inf:float=-1e4,reduction="none",fastemit_lambda=0):"""The memory efficient implementation concatenates along the targets    dimension to reduce wasted computation on padding positions.    N is the minibatch size    T is the maximum number of output labels    S is the maximum number of input frames    V is the vocabulary of labels.    T_flat is the summation of lengths of all output labels    Assuming the original tensor is of (N, T, ...), then it should be reduced to    (T_flat, ...). This can be obtained by using a target mask.    For example:        >>> target_mask = targets.ne(pad)   # (B, T)        >>> targets = targets[target_mask]  # (T_flat,)        >>> log_probs = log_probs[target_mask]  # (T_flat, S, V)    Args:        log_probs (Tensor): (T_flat, S, V) Word prediction log-probs, should be output of log_softmax.        targets (Tensor): (T_flat,) target labels for all samples in the minibatch.        source_lengths (Tensor): (N,) Length of the source frames for each sample in the minibatch.        target_lengths (Tensor): (N,) Length of the target labels for each sample in the minibatch.        emit_logits, emit_probs (Tensor, optional): (T_flat, S) Emission logits (before sigmoid) or            probs (after sigmoid). If both are provided, logits is used.        neg_inf (float, optional): The constant representing -inf used for masking.            Default: -1e4        reduction (string, optional): Specifies reduction. suppoerts mean / sum.            Default: None.        fastemit_lambda (float, optional): Scale the emission gradient of emission paths to            encourage low latency. https://arxiv.org/pdf/2010.11148.pdf            Default: 0    """

Minimal example

python example.py

Note

Reference