def cat(srcs: List[Fsa]) -> Fsa:
'''Concatenate a list of FsaVec into a single FsaVec.
CAUTION:
Only common tensor attributes are kept in the output FsaVec.
For non-tensor attributes, only one copy is kept in the output
FsaVec. We choose the first copy of the FsaVec that has the
lowest index in `srcs`.
Args:
srcs:
A list of FsaVec. Each element MUST be an FsaVec.
Returns:
Return a single FsaVec concatenated from the input FsaVecs.
'''
for src in srcs:
assert len(src.shape) == 3, f'Expect an FsaVec. Given: {src.shape}'
src_ragged_arcs = [fsa.arcs for fsa in srcs]
ans_ragged_arcs = _k2.cat(src_ragged_arcs, axis=0)
out_fsa = Fsa(ans_ragged_arcs)
common_tensor_attributes = (set(dict(src.named_tensor_attr()).keys())
for src in srcs)
common_tensor_attributes = set.intersection(
*list(common_tensor_attributes))
for name in common_tensor_attributes:
# We assume that the type of the attributes among
# FsaVecs are the same if they share the same name.
values = [getattr(src, name) for src in srcs]
if isinstance(values[0], torch.Tensor):
# NOTE: We assume the shape of elements in values
# differ only in shape[0].
value = torch.cat(values)
else:
assert isinstance(values[0], k2.RaggedInt)
value = _k2.cat(values, axis=0)
setattr(out_fsa, name, value)
for src in srcs:
for name, value in src.named_non_tensor_attr():
if not hasattr(out_fsa, name):
setattr(out_fsa, name, value)
return out_fsa
def cat(srcs: List[_k2.RaggedInt], axis=0) -> _k2.RaggedInt:
'''Concatenate a list of :class:`_k2.RaggedInt` along a given axis.
Args:
srcs:
The input.
axis:
It can be either 0 or 1.
Returns:
A single ragged tensor.
'''
assert axis in (0, 1)
return _k2.cat(srcs, axis)