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Compute the Nbest oracle WER for RNN-T decoding.

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This commit is contained in:
Fangjun Kuang 2022-04-24 15:10:30 +08:00
parent e9f0975868
commit b1c3705fbe
2 changed files with 229 additions and 18 deletions
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173
egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
View File

@ -22,11 +22,11 @@ import k2
import torch
from model import Transducer
from icefall.decode import one_best_decoding
from icefall.decode import Nbest, one_best_decoding
from icefall.utils import get_texts
def fast_beam_search(
def fast_beam_search_one_best(
model: Transducer,
decoding_graph: k2.Fsa,
encoder_out: torch.Tensor,
@ -37,6 +37,9 @@ def fast_beam_search(
) -> List[List[int]]:
"""It limits the maximum number of symbols per frame to 1.
A lattice is first obtained using modified beam search, and then
the shortest path within the lattice is used as the final output.
Args:
model:
An instance of `Transducer`.
@ -56,6 +59,153 @@ def fast_beam_search(
Returns:
Return the decoded result.
"""
lattice = fast_beam_search(
model=model,
decoding_graph=decoding_graph,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=beam,
max_states=max_states,
max_contexts=max_contexts,
)
best_path = one_best_decoding(lattice)
hyps = get_texts(best_path)
return hyps
def fast_beam_search_nbest_oracle(
model: Transducer,
decoding_graph: k2.Fsa,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
beam: float,
max_states: int,
max_contexts: int,
num_paths: int,
ref_texts: List[List[int]],
use_double_scores: bool = True,
nbest_scale: float = 0.5,
) -> List[List[int]]:
"""It limits the maximum number of symbols per frame to 1.
A lattice is first obtained using modified beam search, and then
we select `num_paths` linear paths from the lattice. The path
that has the minimum edit distance with the given reference transcript
is used as the output.
This is the best result we can achieve for any nbest based rescoring
methods.
Args:
model:
An instance of `Transducer`.
decoding_graph:
Decoding graph used for decoding, may be a TrivialGraph or a HLG.
encoder_out:
A tensor of shape (N, T, C) from the encoder.
encoder_out_lens:
A tensor of shape (N,) containing the number of frames in `encoder_out`
before padding.
beam:
Beam value, similar to the beam used in Kaldi..
max_states:
Max states per stream per frame.
max_contexts:
Max contexts pre stream per frame.
num_paths:
Number of paths to extract from the decoded lattice.
ref_texts:
A list-of-list of integers containing the reference transcripts.
If the decoding_graph is a trivial_graph, the integer ID is the
BPE token ID.
use_double_scores:
True to use double precision for computation. False to use
single precision.
nbest_scale:
It's the scale applied to the lattice.scores. A smaller value
yields more unique paths.
Returns:
Return the decoded result.
"""
lattice = fast_beam_search(
model=model,
decoding_graph=decoding_graph,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=beam,
max_states=max_states,
max_contexts=max_contexts,
)
nbest = Nbest.from_lattice(
lattice=lattice,
num_paths=num_paths,
use_double_scores=use_double_scores,
nbest_scale=nbest_scale,
)
# We assume the labels of nbest.fsa are token IDs and the aux_labels
# are word IDs.
word_fsa = k2.invert(nbest.fsa)
word_ids = get_texts(word_fsa, return_ragged=True)
hyps = k2.levenshtein_graph(word_ids)
refs = k2.levenshtein_graph(ref_texts, device=hyps.device)
levenshtein_alignment = k2.levenshtein_alignment(
refs=refs,
hyps=hyps,
hyp_to_ref_map=nbest.shape.row_ids(1),
sorted_match_ref=True,
)
tot_scores = levenshtein_alignment.get_tot_scores(
use_double_scores=False, log_semiring=False
)
ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
max_indexes = ragged_tot_scores.argmax()
best_path = k2.index_fsa(nbest.fsa, max_indexes)
hyps = get_texts(best_path)
return hyps
def fast_beam_search(
model: Transducer,
decoding_graph: k2.Fsa,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
beam: float,
max_states: int,
max_contexts: int,
) -> k2.Fsa:
"""It limits the maximum number of symbols per frame to 1.
Args:
model:
An instance of `Transducer`.
decoding_graph:
Decoding graph used for decoding, may be a TrivialGraph or a HLG.
encoder_out:
A tensor of shape (N, T, C) from the encoder.
encoder_out_lens:
A tensor of shape (N,) containing the number of frames in `encoder_out`
before padding.
beam:
Beam value, similar to the beam used in Kaldi..
max_states:
Max states per stream per frame.
max_contexts:
Max contexts pre stream per frame.
Returns:
Return an FsaVec with axes [utt][state][arc] containing the decoded
lattice. Note: When the input graph is a TrivialGraph, the returned
lattice is actually an acceptor.
"""
assert encoder_out.ndim == 3
context_size = model.decoder.context_size
@ -104,9 +254,7 @@ def fast_beam_search(
decoding_streams.terminate_and_flush_to_streams()
lattice = decoding_streams.format_output(encoder_out_lens.tolist())
best_path = one_best_decoding(lattice)
hyps = get_texts(best_path)
return hyps
return lattice
def greedy_search(
@ -131,6 +279,7 @@ def greedy_search(
blank_id = model.decoder.blank_id
context_size = model.decoder.context_size
unk_id = getattr(model, "unk_id", blank_id)
device = model.device
@ -171,7 +320,7 @@ def greedy_search(
# logits is (1, 1, 1, vocab_size)
y = logits.argmax().item()
if y != blank_id:
if y not in (blank_id, unk_id):
hyp.append(y)
decoder_input = torch.tensor(
[hyp[-context_size:]], device=device
@ -212,6 +361,7 @@ def greedy_search_batch(
T = encoder_out.size(1)
blank_id = model.decoder.blank_id
unk_id = getattr(model, "unk_id", blank_id)
context_size = model.decoder.context_size
hyps = [[blank_id] * context_size for _ in range(batch_size)]
@ -240,7 +390,7 @@ def greedy_search_batch(
y = logits.argmax(dim=1).tolist()
emitted = False
for i, v in enumerate(y):
if v != blank_id:
if v not in (blank_id, unk_id):
hyps[i].append(v)
emitted = True
if emitted:
@ -433,6 +583,7 @@ def modified_beam_search(
T = encoder_out.size(1)
blank_id = model.decoder.blank_id
unk_id = getattr(model, "unk_id", blank_id)
context_size = model.decoder.context_size
device = model.device
B = [HypothesisList() for _ in range(batch_size)]
@ -515,7 +666,7 @@ def modified_beam_search(
new_ys = hyp.ys[:]
new_token = topk_token_indexes[k]
if new_token != blank_id:
if new_token not in (blank_id, unk_id):
new_ys.append(new_token)
new_log_prob = topk_log_probs[k]
@ -556,6 +707,7 @@ def _deprecated_modified_beam_search(
# support only batch_size == 1 for now
assert encoder_out.size(0) == 1, encoder_out.size(0)
blank_id = model.decoder.blank_id
unk_id = getattr(model, "unk_id", blank_id)
context_size = model.decoder.context_size
device = model.device
@ -626,7 +778,7 @@ def _deprecated_modified_beam_search(
hyp = A[topk_hyp_indexes[i]]
new_ys = hyp.ys[:]
new_token = topk_token_indexes[i]
if new_token != blank_id:
if new_token not in (blank_id, unk_id):
new_ys.append(new_token)
new_log_prob = topk_log_probs[i]
new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
@ -663,6 +815,7 @@ def beam_search(
# support only batch_size == 1 for now
assert encoder_out.size(0) == 1, encoder_out.size(0)
blank_id = model.decoder.blank_id
unk_id = getattr(model, "unk_id", blank_id)
context_size = model.decoder.context_size
device = model.device
@ -748,7 +901,7 @@ def beam_search(
# Second, process other non-blank labels
values, indices = log_prob.topk(beam + 1)
for i, v in zip(indices.tolist(), values.tolist()):
if i == blank_id:
if i in (blank_id, unk_id):
continue
new_ys = y_star.ys + [i]
new_log_prob = y_star.log_prob + v

74
egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
View File

@ -69,7 +69,8 @@ import torch.nn as nn
from asr_datamodule import AsrDataModule
from beam_search import (
beam_search,
fast_beam_search,
fast_beam_search_nbest_oracle,
fast_beam_search_one_best,
greedy_search,
greedy_search_batch,
modified_beam_search,
@ -145,6 +146,7 @@ def get_parser():
- beam_search
- modified_beam_search
- fast_beam_search
- fast_beam_search_nbest_oracle
""",
)
@ -164,7 +166,8 @@ def get_parser():
help="""A floating point value to calculate the cutoff score during beam
search (i.e., `cutoff = max-score - beam`), which is the same as the
`beam` in Kaldi.
Used only when --decoding-method is fast_beam_search""",
Used only when --decoding-method is
fast_beam_search or fast_beam_search_nbest_oracle""",
)
parser.add_argument(
@ -172,7 +175,7 @@ def get_parser():
type=int,
default=4,
help="""Used only when --decoding-method is
fast_beam_search""",
fast_beam_search or fast_beam_search_nbest_oracle""",
)
parser.add_argument(
@ -180,7 +183,7 @@ def get_parser():
type=int,
default=8,
help="""Used only when --decoding-method is
fast_beam_search""",
fast_beam_search or fast_beam_search_nbest_oracle""",
)
parser.add_argument(
@ -198,6 +201,23 @@ def get_parser():
Used only when --decoding_method is greedy_search""",
)
parser.add_argument(
"--num-paths",
type=int,
default=100,
help="""Number of paths for computed nbest oracle WER
when the decoding method is fast_beam_search_nbest_oracle.
""",
)
parser.add_argument(
"--nbest-scale",
type=float,
default=0.5,
help="""Scale applied to lattice scores when computing nbest paths.
Used only when the decoding_method is fast_beam_search_nbest_oracle.
""",
)
return parser
@ -231,7 +251,8 @@ def decode_one_batch(
for the format of the `batch`.
decoding_graph:
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
only when --decoding_method is fast_beam_search.
only when --decoding_method is
fast_beam_search or fast_beam_search_nbest_oracle.
Returns:
Return the decoding result. See above description for the format of
the returned dict.
@ -252,7 +273,7 @@ def decode_one_batch(
hyps = []
if params.decoding_method == "fast_beam_search":
hyp_tokens = fast_beam_search(
hyp_tokens = fast_beam_search_one_best(
model=model,
decoding_graph=decoding_graph,
encoder_out=encoder_out,
@ -263,6 +284,21 @@ def decode_one_batch(
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif params.decoding_method == "fast_beam_search_nbest_oracle":
hyp_tokens = fast_beam_search_nbest_oracle(
model=model,
decoding_graph=decoding_graph,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=params.beam,
max_contexts=params.max_contexts,
max_states=params.max_states,
num_paths=params.num_paths,
ref_texts=sp.encode(supervisions["text"]),
nbest_scale=params.nbest_scale,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif (
params.decoding_method == "greedy_search"
and params.max_sym_per_frame == 1
@ -316,6 +352,16 @@ def decode_one_batch(
f"max_states_{params.max_states}"
): hyps
}
elif params.decoding_method == "fast_beam_search_nbest_oracle":
return {
(
f"beam_{params.beam}_"
f"max_contexts_{params.max_contexts}_"
f"max_states_{params.max_states}_"
f"num_paths_{params.num_paths}_"
f"nbest_scale_{params.nbest_scale}"
): hyps
}
else:
return {f"beam_size_{params.beam_size}": hyps}
@ -450,15 +496,22 @@ def main():
"greedy_search",
"beam_search",
"fast_beam_search",
"fast_beam_search_nbest_oracle",
"modified_beam_search",
)
params.res_dir = params.exp_dir / params.decoding_method
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
if "fast_beam_search" in params.decoding_method:
if params.decoding_method == "fast_beam_search":
params.suffix += f"-beam-{params.beam}"
params.suffix += f"-max-contexts-{params.max_contexts}"
params.suffix += f"-max-states-{params.max_states}"
elif params.decoding_method == "fast_beam_search":
params.suffix += f"-beam-{params.beam}"
params.suffix += f"-max-contexts-{params.max_contexts}"
params.suffix += f"-max-states-{params.max_states}"
params.suffix += f"-num-paths-{params.num_paths}"
params.suffix += f"-nbest-scale-{params.nbest_scale}"
elif "beam_search" in params.decoding_method:
params.suffix += f"-beam-{params.beam_size}"
else:
@ -479,6 +532,7 @@ def main():
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.unk_id()
params.vocab_size = sp.get_piece_size()
logging.info(params)
@ -506,8 +560,12 @@ def main():
model.to(device)
model.eval()
model.device = device
model.unk_id = params.unk_id
if params.decoding_method == "fast_beam_search":
if params.decoding_method in (
"fast_beam_search",
"fast_beam_search_nbest_oracle",
):
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
else:
decoding_graph = None