def __init__(self, log_probs: torch.Tensor,
supervision_segments: torch.Tensor) -> None:
'''Construct a DenseFsaVec from neural net log-softmax outputs.
Args:
log_probs:
A 3-D tensor of dtype ``torch.float32`` with shape ``(N, T, C)``,
where ``N`` is the number of sequences, ``T`` the maximum input
length, and ``C`` the number of output classes.
supervision_segments:
A 2-D **CPU** tensor of dtype ``torch.int32`` with 3 columns.
Each row contains information for a supervision segment. Column 0
is the ``sequence_index`` indicating which sequence this segment
comes from; column 1 specifies the ``start_frame`` of this segment
within the sequence; column 2 contains the ``duration`` of this
segment.
Note:
- ``0 < start_frame + duration <= T``
- ``0 <= start_frame < T``
- ``duration > 0``
'''
assert log_probs.ndim == 3
assert log_probs.dtype == torch.float32
assert supervision_segments.ndim == 2
assert supervision_segments.dtype == torch.int32
assert supervision_segments.device.type == 'cpu'
N, T, C = log_probs.shape
# Also, if a particular FSA has T frames of neural net output,
# we actually have T+1 potential indexes, 0 through T, so there is
# space for the terminating final-symbol on frame T. (On the last
# frame, the final symbol has logprob=0, the others have logprob=-inf).
placeholder = torch.tensor([0]) # this extra row is for the last frame
indexes = []
last_frame_indexes = []
cur = 0
for segment in supervision_segments:
segment_index, start_frame, duration = segment.tolist()
assert 0 <= segment_index < N
assert 0 <= start_frame < T
assert duration > 0
assert start_frame + duration <= T
offset = segment_index * T
indexes.append(
torch.arange(start_frame, start_frame + duration) + offset)
indexes.append(placeholder)
cur += duration
last_frame_indexes.append(cur)
cur += 1 # increment for the extra row
device = log_probs.device
indexes = torch.cat(indexes).to(device)
scores = torch.empty(cur, C + 1, dtype=log_probs.dtype, device=device)
scores[:, 1:] = log_probs.reshape(-1, C).index_select(0, indexes)
# `scores` contains -infinity in certain locations: in scores[j,0] where
# j is not the last row-index for a given FSA-index, and scores[j,k]
# where j is the last row-index for a given FSA-index and k>0.
# The remaining locations contain the neural net output, except
# scores[j,0] where j is the last row-index for a given FSA-index;
# this contains zero.
scores[:, 0] = np.NINF
scores[last_frame_indexes] = torch.tensor([0] + [np.NINF] * C,
device=device)
row_splits = torch.zeros(supervision_segments.size(0) + 1,
device='cpu',
dtype=torch.int32)
row_splits[1:] = torch.tensor(last_frame_indexes) + 1
row_splits = row_splits.to(device)
self.dense_fsa_vec = _k2.DenseFsaVec(scores, row_splits)
self.scores = scores # for back propagation
def __init__(self,
log_probs: torch.Tensor,
supervision_segments: torch.Tensor,
allow_truncate: int = 0) -> None:
'''Construct a DenseFsaVec from neural net log-softmax outputs.
Args:
log_probs:
A 3-D tensor of dtype `torch.float32` with shape `(N, T, C)`,
where `N` is the number of sequences, `T` the maximum input
length, and `C` the number of output classes.
supervision_segments:
A 2-D **CPU** tensor of dtype `torch.int32` with 3 columns.
Each row contains information for a supervision segment. Column 0
is the `sequence_index` indicating which sequence this segment
comes from; column 1 specifies the `start_frame` of this segment
within the sequence; column 2 contains the `duration` of this
segment.
Note:
- `0 < start_frame + duration <= T + allow_truncate`
- `0 <= start_frame < T`
- `duration > 0`
Caution:
The last column, i.e., the duration column, has to be sorted
in **decreasing** order. That is, the first supervision_segment
(the first row) has the largest duration.
allow_truncate:
If not zero, it truncates at most this number of frames from
duration in case start_frame + duration > T.
'''
assert log_probs.ndim == 3
assert log_probs.dtype == torch.float32
assert supervision_segments.ndim == 2
assert supervision_segments.dtype == torch.int32
assert supervision_segments.device.type == 'cpu'
assert allow_truncate >= 0
N, T, C = log_probs.shape
# Also, if a particular FSA has T frames of neural net output,
# we actually have T+1 potential indexes, 0 through T, so there is
# space for the terminating final-symbol on frame T. (On the last
# frame, the final symbol has logprob=0, the others have logprob=-inf).
placeholder = torch.tensor([0]) # this extra row is for the last frame
indexes = []
last_frame_indexes = []
cur = 0
for segment in supervision_segments.tolist():
segment_index, start_frame, duration = segment
assert 0 <= segment_index < N
assert 0 <= start_frame < T
assert duration > 0
assert start_frame + duration <= T + allow_truncate
offset = segment_index * T
end_frame = min(start_frame + duration, T) # exclusive
# update duration if it's too large
duration = end_frame - start_frame
indexes.append(torch.arange(start_frame, end_frame) + offset)
indexes.append(placeholder)
cur += duration # NOTE: the duration may be updated above
last_frame_indexes.append(cur)
cur += 1 # increment for the extra row
device = log_probs.device
indexes = torch.cat(indexes).to(device)
scores = torch.empty(cur, C + 1, dtype=log_probs.dtype, device=device)
scores[:, 1:] = log_probs.reshape(-1, C).index_select(0, indexes)
# `scores` contains -infinity in certain locations: in scores[j,0] where
# j is not the last row-index for a given FSA-index, and scores[j,k]
# where j is the last row-index for a given FSA-index and k>0.
# The remaining locations contain the neural net output, except
# scores[j,0] where j is the last row-index for a given FSA-index;
# this contains zero.
scores[:, 0] = np.NINF
scores[last_frame_indexes] = torch.tensor([0] + [np.NINF] * C,
device=device)
row_splits = torch.zeros(supervision_segments.size(0) + 1,
device='cpu',
dtype=torch.int32)
row_splits[1:] = torch.tensor(last_frame_indexes) + 1
# minus one to exclude the fake row [0, -inf, -inf, ...]
self._duration = row_splits[1:] - row_splits[:-1] - 1
row_splits = row_splits.to(device)
self.dense_fsa_vec = _k2.DenseFsaVec(scores, row_splits)
self.scores = scores # for back propagation
