question about the rnnt loss arguments
songtaoshi opened this issue · comments
log_probs (torch.FloatTensor): Input tensor with shape (N, T, U, V)
where N is the minibatch size, T is the maximum number of
input frames, U is the maximum number of output labels and V is
the vocabulary of labels (including the blank).
labels (torch.IntTensor): Tensor with shape (N, U-1) representing the
reference labels for all samples in the minibatch.
Hi, I am confused about the labels, why the shape should be U-1,
<eos>
should not be included in the labels ?
@1ytic
and also I see the training code, the LM input ys is the same as the target ys.
This should not be +text as input; text+ as output?
If I remember correctly, U includes "empty" output, very similar to the first element in the scoring matrix when you align two sequences, for example like this https://en.wikipedia.org/wiki/Smith–Waterman_algorithm
log_probs (torch.FloatTensor): Input tensor with shape (N, T, U, V) where N is the minibatch size, T is the maximum number of input frames, U is the maximum number of output labels and V is the vocabulary of labels (including the blank). labels (torch.IntTensor): Tensor with shape (N, U-1) representing the reference labels for all samples in the minibatch.Hi, I am confused about the labels, why the shape should be U-1,
<eos>
should not be included in the labels ?
@1ytic
I have the same doubt, do you understand it? Why the shape of labels be U-1?
I guess, U = len() + len(labels), len() = 1. shouldn't in the labels, but in the encoder logits