def rescore_with_whole_lattice(lats: k2.Fsa,
G_with_epsilon_loops: k2.Fsa) -> k2.Fsa:
'''Use whole lattice to rescore.
Args:
lats:
An FsaVec It can be the output of `k2.intersect_dense_pruned`.
G_with_epsilon_loops:
An FsaVec representing the language model (LM). Note that it
is an FsaVec, but it contains only one Fsa.
'''
assert len(lats.shape) == 3
assert hasattr(lats, 'lm_scores')
assert G_with_epsilon_loops.shape == (1, None, None)
device = lats.device
lats.scores = lats.scores - lats.lm_scores
# Now, lats.scores contains only am_scores
# inverted_lats has word IDs as labels.
# Its aux_labels are phone IDs, which is a ragged tensor k2.RaggedInt
inverted_lats = k2.invert(lats)
num_seqs = lats.shape[0]
inverted_lats_with_epsilon_loops = k2.add_epsilon_self_loops(inverted_lats)
b_to_a_map = torch.zeros(num_seqs, device=device, dtype=torch.int32)
try:
rescoring_lats = k2.intersect_device(G_with_epsilon_loops,
inverted_lats_with_epsilon_loops,
b_to_a_map,
sorted_match_a=True)
except RuntimeError as e:
print(f'Caught exception:\n{e}\n')
print(f'Number of FSAs: {inverted_lats.shape[0]}')
print('num_arcs before pruning: ',
inverted_lats_with_epsilon_loops.arcs.num_elements())
# NOTE(fangjun): The choice of the threshold 0.01 is arbitrary here
# to avoid OOM. We may need to fine tune it.
inverted_lats = k2.prune_on_arc_post(inverted_lats, 0.001, True)
inverted_lats_with_epsilon_loops = k2.add_epsilon_self_loops(
inverted_lats)
print('num_arcs after pruning: ',
inverted_lats_with_epsilon_loops.arcs.num_elements())
rescoring_lats = k2.intersect_device(G_with_epsilon_loops,
inverted_lats_with_epsilon_loops,
b_to_a_map,
sorted_match_a=True)
rescoring_lats = k2.top_sort(k2.connect(
rescoring_lats.to('cpu'))).to(device)
inverted_rescoring_lats = k2.invert(rescoring_lats)
# inverted rescoring_lats has phone IDs as labels
# and word IDs as aux_labels.
inverted_rescoring_lats = k2.remove_epsilon_self_loops(
inverted_rescoring_lats)
best_paths = k2.shortest_path(inverted_rescoring_lats,
use_double_scores=True)
return best_paths
def rescore_with_whole_lattice(lats: k2.Fsa, G_with_epsilon_loops: k2.Fsa,
lm_scale_list: List[float]
) -> Dict[str, k2.Fsa]:
'''Use whole lattice to rescore.
Args:
lats:
An FsaVec It can be the output of `k2.intersect_dense_pruned`.
G_with_epsilon_loops:
An FsaVec representing the language model (LM). Note that it
is an FsaVec, but it contains only one Fsa.
lm_scale_list:
A list containing lm_scale values.
Returns:
A dict of FsaVec, whose key is a lm_scale and the value represents the
best decoding path for each sequence in the lattice.
'''
assert len(lats.shape) == 3
assert hasattr(lats, 'lm_scores')
assert G_with_epsilon_loops.shape == (1, None, None)
device = lats.device
lats.scores = lats.scores - lats.lm_scores
# We will use lm_scores from G, so remove lats.lm_scores here
del lats.lm_scores
assert hasattr(lats, 'lm_scores') is False
# lats.scores = scores / lm_scale
# Now, lats.scores contains only am_scores
# inverted_lats has word IDs as labels.
# Its aux_labels are phone IDs, which is a ragged tensor k2.RaggedInt
inverted_lats = k2.invert(lats)
num_seqs = lats.shape[0]
b_to_a_map = torch.zeros(num_seqs, device=device, dtype=torch.int32)
try:
rescoring_lats = k2.intersect_device(G_with_epsilon_loops,
inverted_lats,
b_to_a_map,
sorted_match_a=True)
except RuntimeError as e:
print(f'Caught exception:\n{e}\n')
print(f'Number of FSAs: {inverted_lats.shape[0]}')
print('num_arcs before pruning: ', inverted_lats.arcs.num_elements())
# NOTE(fangjun): The choice of the threshold 0.01 is arbitrary here
# to avoid OOM. We may need to fine tune it.
inverted_lats = k2.prune_on_arc_post(inverted_lats, 0.001, True)
print('num_arcs after pruning: ', inverted_lats.arcs.num_elements())
rescoring_lats = k2.intersect_device(G_with_epsilon_loops,
inverted_lats,
b_to_a_map,
sorted_match_a=True)
rescoring_lats = k2.top_sort(k2.connect(rescoring_lats.to('cpu')).to(device))
# inv_lats has phone IDs as labels
# and word IDs as aux_labels.
inv_lats = k2.invert(rescoring_lats)
ans = dict()
#
# The following implements
# scores = (scores - lm_scores)/lm_scale + lm_scores
# = scores/lm_scale + lm_scores*(1 - 1/lm_scale)
#
saved_scores = inv_lats.scores.clone()
for lm_scale in lm_scale_list:
am_scores = saved_scores - inv_lats.lm_scores
am_scores /= lm_scale
inv_lats.scores = am_scores + inv_lats.lm_scores
best_paths = k2.shortest_path(inv_lats, use_double_scores=True)
key = f'lm_scale_{lm_scale}'
ans[key] = best_paths
return ans