def get_texts(best_paths: k2.Fsa) -> List[List[int]]:
"""Extract the texts from the best-path FSAs.
Args:
best_paths: a k2.Fsa with best_paths.arcs.num_axes() == 3, i.e.
containing multiple FSAs, which is expected to be the result
of k2.shortest_path (otherwise the returned values won't
be meaningful). Must have the 'aux_labels' attribute, as
a ragged tensor.
Return:
Returns a list of lists of int, containing the label sequences we
decoded.
"""
# remove any 0's or -1's (there should be no 0's left but may be -1's.)
if isinstance(best_paths.aux_labels, k2.RaggedInt):
aux_labels = k2r.remove_values_leq(best_paths.aux_labels, 0)
aux_shape = k2r.compose_ragged_shapes(best_paths.arcs.shape(),
aux_labels.shape())
# remove the states and arcs axes.
aux_shape = k2r.remove_axis(aux_shape, 1)
aux_shape = k2r.remove_axis(aux_shape, 1)
aux_labels = k2.RaggedInt(aux_shape, aux_labels.values())
else:
# remove axis corresponding to states.
aux_shape = k2r.remove_axis(best_paths.arcs.shape(), 1)
aux_labels = k2.RaggedInt(aux_shape, best_paths.aux_labels)
# remove 0's and -1's.
aux_labels = k2r.remove_values_leq(aux_labels, 0)
assert aux_labels.num_axes() == 2
return k2r.to_list(aux_labels)
def get_texts(best_paths: k2.Fsa,
indices: Optional[torch.Tensor] = None) -> List[List[int]]:
"""Extract the texts from the best-path FSAs.
In the original order (before the permutation given by `indices`).
Args:
best_paths: a k2.Fsa with best_paths.arcs.num_axes() == 3, i.e.
containing multiple FSAs, which is expected to be the result
of k2.shortest_path (otherwise the returned values won't
be meaningful). Must have the 'aux_labels' attribute, as
a ragged tensor.
indices: possibly a torch.Tensor giving the permutation that we used
on the supervisions of this minibatch to put them in decreasing
order of num-frames. We'll apply the inverse permutation.
Doesn't have to be on the same device as `best_paths`
Return:
Returns a list of lists of int, containing the label sequences we
decoded.
"""
# remove any 0's or -1's (there should be no 0's left but may be -1's.)
if isinstance(best_paths.aux_labels, k2.RaggedInt):
aux_labels = k2r.remove_values_leq(best_paths.aux_labels, 0)
aux_shape = k2r.compose_ragged_shapes(best_paths.arcs.shape(),
aux_labels.shape())
# remove the states and arcs axes.
aux_shape = k2r.remove_axis(aux_shape, 1)
aux_shape = k2r.remove_axis(aux_shape, 1)
aux_labels = k2.RaggedInt(aux_shape, aux_labels.values())
else:
# remove axis corresponding to states.
aux_shape = k2r.remove_axis(best_paths.arcs.shape(), 1)
aux_labels = k2.RaggedInt(aux_shape, best_paths.aux_labels)
# remove 0's and -1's.
aux_labels = k2r.remove_values_leq(aux_labels, 0)
assert aux_labels.num_axes() == 2
aux_labels, _ = k2r.index(
aux_labels,
invert_permutation(indices).to(dtype=torch.int32,
device=best_paths.device),
)
return k2r.to_list(aux_labels)