def generate_nbest_list(lats: Fsa, num_paths: int) -> Nbest:
'''Generate an n-best list from a lattice.
Args:
lats:
The decoding lattice from the first pass after LM rescoring.
lats is an FsaVec. It can be the return value of
:func:`whole_lattice_rescoring`
num_paths:
Size of n for n-best list. CAUTION: After removing paths
that represent the same token sequences, the number of paths
in different sequences may not be equal.
Return:
Return an Nbest object. Note the returned FSAs don't have epsilon
self-loops.
'''
assert len(lats.shape) == 3
# CAUTION: We use `phones` instead of `tokens` here because
# :func:`compile_HLG` uses `phones`
#
# Note: compile_HLG is from k2-fsa/snowfall
assert hasattr(lats, 'phones')
assert not hasattr(lats, 'tokens')
lats.tokens = lats.phones
# we use tokens instead of phones in the following code
# First, extract `num_paths` paths for each sequence.
# paths is a k2.RaggedTensor with axes [seq][path][arc_pos]
paths = k2.random_paths(lats, num_paths=num_paths, use_double_scores=True)
# token_seqs is a k2.RaggedTensor sharing the same shape as `paths`
# but it contains token IDs. Note that it also contains 0s and -1s.
# The last entry in each sublist is -1.
# Its axes are [seq][path][token_id]
token_seqs = k2.ragged.index(lats.tokens, paths)
# Remove epsilons (0s) and -1 from token_seqs
token_seqs = token_seqs.remove_values_leq(0)
# unique_token_seqs is still a k2.RaggedTensor with axes
# [seq][path]token_id].
# But then number of paths in each sequence may be different.
unique_token_seqs, _, _ = token_seqs.unique(need_num_repeats=False,
need_new2old_indexes=False)
seq_to_path_shape = unique_token_seqs.shape.get_layer(0)
# Remove the seq axis.
# Now unique_token_seqs has only two axes [path][token_id]
unique_token_seqs = unique_token_seqs.remove_axis(0)
token_fsas = k2.linear_fsa(unique_token_seqs)
return Nbest(fsa=token_fsas, shape=seq_to_path_shape)
def nbest_am_lm_scores(
lats: k2.Fsa,
num_paths: int,
device: str = "cuda",
batch_size: int = 500,
):
"""Compute am scores with word_seqs
Compatible with both ctc_decoding or TLG decoding.
"""
paths = k2.random_paths(lats, num_paths=num_paths, use_double_scores=True)
if isinstance(lats.aux_labels, torch.Tensor):
word_seqs = k2.ragged.index(lats.aux_labels.contiguous(), paths)
else:
# '_k2.RaggedInt' object has no attribute 'contiguous'
word_seqs = lats.aux_labels.index(paths)
word_seqs = word_seqs.remove_axis(word_seqs.num_axes - 2)
# With ctc_decoding, word_seqs stores token_ids.
# With TLG decoding, word_seqs stores word_ids.
word_seqs = word_seqs.remove_values_leq(0)
unique_word_seqs, num_repeats, new2old = word_seqs.unique(
need_num_repeats=True, need_new2old_indexes=True
)
seq_to_path_shape = unique_word_seqs.shape.get_layer(0)
path_to_seq_map = seq_to_path_shape.row_ids(1)
# used to split final computed tot_scores
seq_to_path_splits = seq_to_path_shape.row_splits(1)
unique_word_seqs = unique_word_seqs.remove_axis(0)
word_fsas = k2.linear_fsa(unique_word_seqs)
word_fsas_with_epsilon_loops = k2.add_epsilon_self_loops(word_fsas)
am_scores, lm_scores = compute_am_scores_and_lm_scores(
lats, word_fsas_with_epsilon_loops, path_to_seq_map, device, batch_size
)
token_seqs = k2.ragged.index(lats.labels.contiguous(), paths)
token_seqs = token_seqs.remove_axis(0)
token_ids, _ = token_seqs.index(new2old, axis=0)
token_ids = token_ids.tolist()
# Now remove repeated tokens and 0s and -1s.
token_ids = [remove_repeated_and_leq(tokens) for tokens in token_ids]
return am_scores, lm_scores, token_ids, new2old, path_to_seq_map, seq_to_path_splits
def rescore_with_n_best_list(lats: k2.Fsa, G: k2.Fsa,
num_paths: int) -> k2.Fsa:
'''Decode using n-best list with LM rescoring.
`lats` is a decoding lattice, which has 3 axes. This function first
extracts `num_paths` paths from `lats` for each sequence using
`k2.random_paths`. The `am_scores` of these paths are computed.
For each path, its `lm_scores` is computed using `G` (which is an LM).
The final `tot_scores` is the sum of `am_scores` and `lm_scores`.
The path with the greatest `tot_scores` within a sequence is used
as the decoding output.
Args:
lats:
An FsaVec. It can be the output of `k2.intersect_dense_pruned`.
G:
An FsaVec representing the language model (LM). Note that it
is an FsaVec, but it contains only one Fsa.
num_paths:
It is the size `n` in `n-best` list.
Returns:
An FsaVec representing the best decoding path for each sequence
in the lattice.
'''
device = lats.device
assert len(lats.shape) == 3
assert hasattr(lats, 'aux_labels')
assert hasattr(lats, 'lm_scores')
assert G.shape == (1, None, None)
assert G.device == device
assert hasattr(G, 'aux_labels') is False
# First, extract `num_paths` paths for each sequence.
# paths is a k2.RaggedInt with axes [seq][path][arc_pos]
paths = k2.random_paths(lats, num_paths=num_paths, use_double_scores=True)
# word_seqs is a k2.RaggedInt sharing the same shape as `paths`
# but it contains word IDs. Note that it also contains 0s and -1s.
# The last entry in each sublist is -1.
word_seqs = k2.index(lats.aux_labels, paths)
# Remove epsilons and -1 from word_seqs
word_seqs = k2.ragged.remove_values_leq(word_seqs, 0)
# Remove repeated sequences to avoid redundant computation later.
#
# unique_word_seqs is still a k2.RaggedInt with 3 axes [seq][path][word]
# except that there are no repeated paths with the same word_seq
# within a seq.
#
# num_repeats is also a k2.RaggedInt with 2 axes containing the
# multiplicities of each path.
# num_repeats.num_elements() == unique_word_seqs.num_elements()
#
# Since k2.ragged.unique_sequences will reorder paths within a seq,
# `new2old` is a 1-D torch.Tensor mapping from the output path index
# to the input path index.
# new2old.numel() == unique_word_seqs.num_elements()
unique_word_seqs, num_repeats, new2old = k2.ragged.unique_sequences(
word_seqs, need_num_repeats=True, need_new2old_indexes=True)
seq_to_path_shape = k2.ragged.get_layer(unique_word_seqs.shape(), 0)
# path_to_seq_map is a 1-D torch.Tensor.
# path_to_seq_map[i] is the seq to which the i-th path
# belongs.
path_to_seq_map = seq_to_path_shape.row_ids(1)
# Remove the seq axis.
# Now unique_word_seqs has only two axes [path][word]
unique_word_seqs = k2.ragged.remove_axis(unique_word_seqs, 0)
# word_fsas is an FsaVec with axes [path][state][arc]
word_fsas = k2.linear_fsa(unique_word_seqs)
word_fsas_with_epsilon_loops = k2.add_epsilon_self_loops(word_fsas)
am_scores = compute_am_scores(lats, word_fsas_with_epsilon_loops,
path_to_seq_map)
# Now compute lm_scores
b_to_a_map = torch.zeros_like(path_to_seq_map)
lm_path_lats = _intersect_device(G,
word_fsas_with_epsilon_loops,
b_to_a_map=b_to_a_map,
sorted_match_a=True)
lm_path_lats = k2.top_sort(k2.connect(lm_path_lats.to('cpu'))).to(device)
lm_scores = lm_path_lats.get_tot_scores(True, True)
tot_scores = am_scores + lm_scores
# Remember that we used `k2.ragged.unique_sequences` to remove repeated
# paths to avoid redundant computation in `k2.intersect_device`.
# Now we use `num_repeats` to correct the scores for each path.
#
# NOTE(fangjun): It is commented out as it leads to a worse WER
# tot_scores = tot_scores * num_repeats.values()
# TODO(fangjun): We may need to add `k2.RaggedDouble`
ragged_tot_scores = k2.RaggedFloat(seq_to_path_shape,
tot_scores.to(torch.float32))
argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
# Use k2.index here since argmax_indexes' dtype is torch.int32
best_path_indexes = k2.index(new2old, argmax_indexes)
paths = k2.ragged.remove_axis(paths, 0)
# best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
best_paths = k2.index(paths, best_path_indexes)
# labels is a k2.RaggedInt with 2 axes [path][phone_id]
# Note that it contains -1s.
labels = k2.index(lats.labels.contiguous(), best_paths)
labels = k2.ragged.remove_values_eq(labels, -1)
# lats.aux_labels is a k2.RaggedInt tensor with 2 axes, so
# aux_labels is also a k2.RaggedInt with 2 axes
aux_labels = k2.index(lats.aux_labels, best_paths.values())
best_path_fsas = k2.linear_fsa(labels)
best_path_fsas.aux_labels = aux_labels
return best_path_fsas
def nbest_decoding(lats: k2.Fsa, num_paths: int):
'''
(Ideas of this function are from Dan)
It implements something like CTC prefix beam search using n-best lists
The basic idea is to first extra n-best paths from the given lattice,
build a word seqs from these paths, and compute the total scores
of these sequences in the log-semiring. The one with the max score
is used as the decoding output.
'''
# First, extract `num_paths` paths for each sequence.
# paths is a k2.RaggedInt with axes [seq][path][arc_pos]
paths = k2.random_paths(lats, num_paths=num_paths, use_double_scores=True)
# word_seqs is a k2.RaggedInt sharing the same shape as `paths`
# but it contains word IDs. Note that it also contains 0s and -1s.
# The last entry in each sublist is -1.
word_seqs = k2.index(lats.aux_labels, paths)
# Note: the above operation supports also the case when
# lats.aux_labels is a ragged tensor. In that case,
# `remove_axis=True` is used inside the pybind11 binding code,
# so the resulting `word_seqs` still has 3 axes, like `paths`.
# The 3 axes are [seq][path][word]
# Remove epsilons and -1 from word_seqs
word_seqs = k2.ragged.remove_values_leq(word_seqs, 0)
# Remove repeated sequences to avoid redundant computation later.
#
# Since k2.ragged.unique_sequences will reorder paths within a seq,
# `new2old` is a 1-D torch.Tensor mapping from the output path index
# to the input path index.
# new2old.numel() == unique_word_seqs.num_elements()
unique_word_seqs, _, new2old = k2.ragged.unique_sequences(
word_seqs, need_num_repeats=False, need_new2old_indexes=True)
# Note: unique_word_seqs still has the same axes as word_seqs
seq_to_path_shape = k2.ragged.get_layer(unique_word_seqs.shape(), 0)
# path_to_seq_map is a 1-D torch.Tensor.
# path_to_seq_map[i] is the seq to which the i-th path
# belongs.
path_to_seq_map = seq_to_path_shape.row_ids(1)
# Remove the seq axis.
# Now unique_word_seqs has only two axes [path][word]
unique_word_seqs = k2.ragged.remove_axis(unique_word_seqs, 0)
# word_fsas is an FsaVec with axes [path][state][arc]
word_fsas = k2.linear_fsa(unique_word_seqs)
word_fsas_with_epsilon_loops = k2.add_epsilon_self_loops(word_fsas)
# lats has phone IDs as labels and word IDs as aux_labels.
# inv_lats has word IDs as labels and phone IDs as aux_labels
inv_lats = k2.invert(lats)
inv_lats = k2.arc_sort(inv_lats) # no-op if inv_lats is already arc-sorted
path_lats = k2.intersect_device(inv_lats,
word_fsas_with_epsilon_loops,
b_to_a_map=path_to_seq_map,
sorted_match_a=True)
# path_lats has word IDs as labels and phone IDs as aux_labels
path_lats = k2.top_sort(k2.connect(path_lats.to('cpu')).to(lats.device))
tot_scores = path_lats.get_tot_scores(True, True)
# RaggedFloat currently supports float32 only.
# We may bind Ragged<double> as RaggedDouble if needed.
ragged_tot_scores = k2.RaggedFloat(seq_to_path_shape,
tot_scores.to(torch.float32))
argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
# Since we invoked `k2.ragged.unique_sequences`, which reorders
# the index from `paths`, we use `new2old`
# here to convert argmax_indexes to the indexes into `paths`.
#
# Use k2.index here since argmax_indexes' dtype is torch.int32
best_path_indexes = k2.index(new2old, argmax_indexes)
paths_2axes = k2.ragged.remove_axis(paths, 0)
# best_paths is a k2.RaggedInt with 2 axes [path][arc_pos]
best_paths = k2.index(paths_2axes, best_path_indexes)
# labels is a k2.RaggedInt with 2 axes [path][phone_id]
# Note that it contains -1s.
labels = k2.index(lats.labels.contiguous(), best_paths)
labels = k2.ragged.remove_values_eq(labels, -1)
# lats.aux_labels is a k2.RaggedInt tensor with 2 axes, so
# aux_labels is also a k2.RaggedInt with 2 axes
aux_labels = k2.index(lats.aux_labels, best_paths.values())
best_path_fsas = k2.linear_fsa(labels)
best_path_fsas.aux_labels = aux_labels
return best_path_fsas
