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Support pure ctc decoding requiring neither a lexicon nor an n-gram LM (#58)

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* Rename lattice_score_scale to nbest_scale.

* Support pure CTC decoding requiring neither a lexicion nor an n-gram LM.

* Fix style issues.

* Fix a typo.

* Minor fixes.
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Fangjun Kuang 2021-09-26 14:21:49 +08:00 committed by GitHub
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commit 707d7017a7
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10 changed files with 136 additions and 60 deletions
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6
docs/source/recipes/librispeech/conformer_ctc.rst
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@ -299,9 +299,9 @@ The commonly used options are:
.. code-block::
$ cd egs/librispeech/ASR
$ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --lattice-score-scale 0.5
$ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --nbest-scale 0.5
- ``--lattice-score-scale``
- ``--nbest-scale``
It is used to scale down lattice scores so that there are more unique
paths for rescoring.
@ -577,7 +577,7 @@ The command to run HLG decoding + LM rescoring + attention decoder rescoring is:
--G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
--ngram-lm-scale 1.3 \
--attention-decoder-scale 1.2 \
--lattice-score-scale 0.5 \
--nbest-scale 0.5 \
--num-paths 100 \
--sos-id 1 \
--eos-id 1 \

2
egs/librispeech/ASR/RESULTS.md
View File

@ -40,7 +40,7 @@ python conformer_ctc/train.py --bucketing-sampler True \
--full-libri True \
--world-size 4
python conformer_ctc/decode.py --lattice-score-scale 0.5 \
python conformer_ctc/decode.py --nbest-scale 0.5 \
--epoch 34 \
--avg 20 \
--method attention-decoder \

107
egs/librispeech/ASR/conformer_ctc/decode.py
View File

@ -23,6 +23,7 @@ from pathlib import Path
from typing import Dict, List, Optional, Tuple
import k2
import sentencepiece as spm
import torch
import torch.nn as nn
from asr_datamodule import LibriSpeechAsrDataModule
@ -77,6 +78,9 @@ def get_parser():
default="attention-decoder",
help="""Decoding method.
Supported values are:
- (0) ctc-decoding. Use CTC decoding. It uses a sentence piece
model, i.e., lang_dir/bpe.model, to convert word pieces to words.
It needs neither a lexicon nor an n-gram LM.
- (1) 1best. Extract the best path from the decoding lattice as the
decoding result.
- (2) nbest. Extract n paths from the decoding lattice; the path
@ -106,7 +110,7 @@ def get_parser():
)
parser.add_argument(
"--lattice-score-scale",
"--nbest-scale",
type=float,
default=0.5,
help="""The scale to be applied to `lattice.scores`.
@ -128,14 +132,26 @@ def get_parser():
""",
)
parser.add_argument(
"--exp-dir",
type=str,
default="conformer_ctc/exp",
help="The experiment dir",
)
parser.add_argument(
"--lang-dir",
type=str,
default="data/lang_bpe",
help="The lang dir",
)
return parser
def get_params() -> AttributeDict:
params = AttributeDict(
{
"exp_dir": Path("conformer_ctc/exp"),
"lang_dir": Path("data/lang_bpe"),
"lm_dir": Path("data/lm"),
# parameters for conformer
"subsampling_factor": 4,
@ -159,13 +175,15 @@ def get_params() -> AttributeDict:
def decode_one_batch(
params: AttributeDict,
model: nn.Module,
HLG: k2.Fsa,
HLG: Optional[k2.Fsa],
H: Optional[k2.Fsa],
bpe_model: Optional[spm.SentencePieceProcessor],
batch: dict,
word_table: k2.SymbolTable,
sos_id: int,
eos_id: int,
G: Optional[k2.Fsa] = None,
) -> Dict[str, List[List[int]]]:
) -> Dict[str, List[List[str]]]:
"""Decode one batch and return the result in a dict. The dict has the
following format:
@ -190,7 +208,11 @@ def decode_one_batch(
model:
The neural model.
HLG:
The decoding graph.
The decoding graph. Used only when params.method is NOT ctc-decoding.
H:
The ctc topo. Used only when params.method is ctc-decoding.
bpe_model:
The BPE model. Used only when params.method is ctc-decoding.
batch:
It is the return value from iterating
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
@ -209,7 +231,10 @@ def decode_one_batch(
Return the decoding result. See above description for the format of
the returned dict.
"""
if HLG is not None:
device = HLG.device
else:
device = H.device
feature = batch["inputs"]
assert feature.ndim == 3
feature = feature.to(device)
@ -229,9 +254,17 @@ def decode_one_batch(
1,
).to(torch.int32)
if H is None:
assert HLG is not None
decoding_graph = HLG
else:
assert HLG is None
assert bpe_model is not None
decoding_graph = H
lattice = get_lattice(
nnet_output=nnet_output,
HLG=HLG,
decoding_graph=decoding_graph,
supervision_segments=supervision_segments,
search_beam=params.search_beam,
output_beam=params.output_beam,
@ -240,6 +273,24 @@ def decode_one_batch(
subsampling_factor=params.subsampling_factor,
)
if params.method == "ctc-decoding":
best_path = one_best_decoding(
lattice=lattice, use_double_scores=params.use_double_scores
)
# Note: `best_path.aux_labels` contains token IDs, not word IDs
# since we are using H, not HLG here.
#
# token_ids is a lit-of-list of IDs
token_ids = get_texts(best_path)
# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
hyps = bpe_model.decode(token_ids)
# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
hyps = [s.split() for s in hyps]
key = "ctc-decoding"
return {key: hyps}
if params.method == "nbest-oracle":
# Note: You can also pass rescored lattices to it.
# We choose the HLG decoded lattice for speed reasons
@ -250,12 +301,12 @@ def decode_one_batch(
num_paths=params.num_paths,
ref_texts=supervisions["text"],
word_table=word_table,
lattice_score_scale=params.lattice_score_scale,
nbest_scale=params.nbest_scale,
oov="<UNK>",
)
hyps = get_texts(best_path)
hyps = [[word_table[i] for i in ids] for ids in hyps]
key = f"oracle_{params.num_paths}_lattice_score_scale_{params.lattice_score_scale}" # noqa
key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}" # noqa
return {key: hyps}
if params.method in ["1best", "nbest"]:
@ -269,9 +320,9 @@ def decode_one_batch(
lattice=lattice,
num_paths=params.num_paths,
use_double_scores=params.use_double_scores,
lattice_score_scale=params.lattice_score_scale,
nbest_scale=params.nbest_scale,
)
key = f"no_rescore-scale-{params.lattice_score_scale}-{params.num_paths}" # noqa
key = f"no_rescore-nbest-scale-{params.nbest_scale}-{params.num_paths}" # noqa
hyps = get_texts(best_path)
hyps = [[word_table[i] for i in ids] for ids in hyps]
@ -293,7 +344,7 @@ def decode_one_batch(
G=G,
num_paths=params.num_paths,
lm_scale_list=lm_scale_list,
lattice_score_scale=params.lattice_score_scale,
nbest_scale=params.nbest_scale,
)
elif params.method == "whole-lattice-rescoring":
best_path_dict = rescore_with_whole_lattice(
@ -319,7 +370,7 @@ def decode_one_batch(
memory_key_padding_mask=memory_key_padding_mask,
sos_id=sos_id,
eos_id=eos_id,
lattice_score_scale=params.lattice_score_scale,
nbest_scale=params.nbest_scale,
)
else:
assert False, f"Unsupported decoding method: {params.method}"
@ -340,12 +391,14 @@ def decode_dataset(
dl: torch.utils.data.DataLoader,
params: AttributeDict,
model: nn.Module,
HLG: k2.Fsa,
HLG: Optional[k2.Fsa],
H: Optional[k2.Fsa],
bpe_model: Optional[spm.SentencePieceProcessor],
word_table: k2.SymbolTable,
sos_id: int,
eos_id: int,
G: Optional[k2.Fsa] = None,
) -> Dict[str, List[Tuple[List[int], List[int]]]]:
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
"""Decode dataset.
Args:
@ -356,7 +409,11 @@ def decode_dataset(
model:
The neural model.
HLG:
The decoding graph.
The decoding graph. Used only when params.method is NOT ctc-decoding.
H:
The ctc topo. Used only when params.method is ctc-decoding.
bpe_model:
The BPE model. Used only when params.method is ctc-decoding.
word_table:
It is the word symbol table.
sos_id:
@ -391,6 +448,8 @@ def decode_dataset(
params=params,
model=model,
HLG=HLG,
H=H,
bpe_model=bpe_model,
batch=batch,
word_table=word_table,
G=G,
@ -469,6 +528,8 @@ def main():
parser = get_parser()
LibriSpeechAsrDataModule.add_arguments(parser)
args = parser.parse_args()
args.exp_dir = Path(args.exp_dir)
args.lang_dir = Path(args.lang_dir)
params = get_params()
params.update(vars(args))
@ -496,6 +557,18 @@ def main():
sos_id = graph_compiler.sos_id
eos_id = graph_compiler.eos_id
if params.method == "ctc-decoding":
HLG = None
H = k2.ctc_topo(
max_token=max_token_id,
modified=False,
device=device,
)
bpe_model = spm.SentencePieceProcessor()
bpe_model.load(str(params.lang_dir / "bpe.model"))
else:
H = None
bpe_model = None
HLG = k2.Fsa.from_dict(
torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
)
@ -593,6 +666,8 @@ def main():
params=params,
model=model,
HLG=HLG,
H=H,
bpe_model=bpe_model,
word_table=lexicon.word_table,
G=G,
sos_id=sos_id,

6
egs/librispeech/ASR/conformer_ctc/pretrained.py
View File

@ -125,7 +125,7 @@ def get_parser():
)
parser.add_argument(
"--lattice-score-scale",
"--nbest-scale",
type=float,
default=0.5,
help="""
@ -301,7 +301,7 @@ def main():
lattice = get_lattice(
nnet_output=nnet_output,
HLG=HLG,
decoding_graph=HLG,
supervision_segments=supervision_segments,
search_beam=params.search_beam,
output_beam=params.output_beam,
@ -336,7 +336,7 @@ def main():
memory_key_padding_mask=memory_key_padding_mask,
sos_id=params.sos_id,
eos_id=params.eos_id,
lattice_score_scale=params.lattice_score_scale,
nbest_scale=params.nbest_scale,
ngram_lm_scale=params.ngram_lm_scale,
attention_scale=params.attention_decoder_scale,
)

12
egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
View File

@ -97,7 +97,7 @@ def get_parser():
)
parser.add_argument(
"--lattice-score-scale",
"--nbest-scale",
type=float,
default=0.5,
help="""The scale to be applied to `lattice.scores`.
@ -146,7 +146,7 @@ def decode_one_batch(
batch: dict,
lexicon: Lexicon,
G: Optional[k2.Fsa] = None,
) -> Dict[str, List[List[int]]]:
) -> Dict[str, List[List[str]]]:
"""Decode one batch and return the result in a dict. The dict has the
following format:
@ -210,7 +210,7 @@ def decode_one_batch(
lattice = get_lattice(
nnet_output=nnet_output,
HLG=HLG,
decoding_graph=HLG,
supervision_segments=supervision_segments,
search_beam=params.search_beam,
output_beam=params.output_beam,
@ -229,7 +229,7 @@ def decode_one_batch(
lattice=lattice,
num_paths=params.num_paths,
use_double_scores=params.use_double_scores,
lattice_score_scale=params.lattice_score_scale,
nbest_scale=params.nbest_scale,
)
key = f"no_rescore-{params.num_paths}"
hyps = get_texts(best_path)
@ -248,7 +248,7 @@ def decode_one_batch(
G=G,
num_paths=params.num_paths,
lm_scale_list=lm_scale_list,
lattice_score_scale=params.lattice_score_scale,
nbest_scale=params.nbest_scale,
)
else:
best_path_dict = rescore_with_whole_lattice(
@ -272,7 +272,7 @@ def decode_dataset(
HLG: k2.Fsa,
lexicon: Lexicon,
G: Optional[k2.Fsa] = None,
) -> Dict[str, List[Tuple[List[int], List[int]]]]:
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
"""Decode dataset.
Args:

2
egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py
View File

@ -232,7 +232,7 @@ def main():
lattice = get_lattice(
nnet_output=nnet_output,
HLG=HLG,
decoding_graph=HLG,
supervision_segments=supervision_segments,
search_beam=params.search_beam,
output_beam=params.output_beam,

2
egs/yesno/ASR/tdnn/decode.py
View File

@ -124,7 +124,7 @@ def decode_one_batch(
lattice = get_lattice(
nnet_output=nnet_output,
HLG=HLG,
decoding_graph=HLG,
supervision_segments=supervision_segments,
search_beam=params.search_beam,
output_beam=params.output_beam,

2
egs/yesno/ASR/tdnn/pretrained.py
View File

@ -175,7 +175,7 @@ def main():
lattice = get_lattice(
nnet_output=nnet_output,
HLG=HLG,
decoding_graph=HLG,
supervision_segments=supervision_segments,
search_beam=params.search_beam,
output_beam=params.output_beam,

39
icefall/decode.py
View File

@ -66,7 +66,7 @@ def _intersect_device(
def get_lattice(
nnet_output: torch.Tensor,
HLG: k2.Fsa,
decoding_graph: k2.Fsa,
supervision_segments: torch.Tensor,
search_beam: float,
output_beam: float,
@ -79,8 +79,9 @@ def get_lattice(
Args:
nnet_output:
It is the output of a neural model of shape `(N, T, C)`.
HLG:
An Fsa, the decoding graph. See also `compile_HLG.py`.
decoding_graph:
An Fsa, the decoding graph. It can be either an HLG
(see `compile_HLG.py`) or an H (see `k2.ctc_topo`).
supervision_segments:
A 2-D **CPU** tensor of dtype `torch.int32` with 3 columns.
Each row contains information for a supervision segment. Column 0
@ -117,7 +118,7 @@ def get_lattice(
)
lattice = k2.intersect_dense_pruned(
HLG,
decoding_graph,
dense_fsa_vec,
search_beam=search_beam,
output_beam=output_beam,
@ -180,7 +181,7 @@ class Nbest(object):
lattice: k2.Fsa,
num_paths: int,
use_double_scores: bool = True,
lattice_score_scale: float = 0.5,
nbest_scale: float = 0.5,
) -> "Nbest":
"""Construct an Nbest object by **sampling** `num_paths` from a lattice.
@ -206,7 +207,7 @@ class Nbest(object):
Return an Nbest instance.
"""
saved_scores = lattice.scores.clone()
lattice.scores *= lattice_score_scale
lattice.scores *= nbest_scale
# path is a ragged tensor with dtype torch.int32.
# It has three axes [utt][path][arc_pos]
path = k2.random_paths(
@ -446,7 +447,7 @@ def nbest_decoding(
lattice: k2.Fsa,
num_paths: int,
use_double_scores: bool = True,
lattice_score_scale: float = 1.0,
nbest_scale: float = 1.0,
) -> k2.Fsa:
"""It implements something like CTC prefix beam search using n-best lists.
@ -474,7 +475,7 @@ def nbest_decoding(
use_double_scores:
True to use double precision floating point in the computation.
False to use single precision.
lattice_score_scale:
nbest_scale:
It's the scale applied to the `lattice.scores`. A smaller value
leads to more unique paths at the risk of missing the correct path.
Returns:
@ -484,7 +485,7 @@ def nbest_decoding(
lattice=lattice,
num_paths=num_paths,
use_double_scores=use_double_scores,
lattice_score_scale=lattice_score_scale,
nbest_scale=nbest_scale,
)
# nbest.fsa.scores contains 0s
@ -505,7 +506,7 @@ def nbest_oracle(
ref_texts: List[str],
word_table: k2.SymbolTable,
use_double_scores: bool = True,
lattice_score_scale: float = 0.5,
nbest_scale: float = 0.5,
oov: str = "<UNK>",
) -> Dict[str, List[List[int]]]:
"""Select the best hypothesis given a lattice and a reference transcript.
@ -517,7 +518,7 @@ def nbest_oracle(
The decoding result returned from this function is the best result that
we can obtain using n-best decoding with all kinds of rescoring techniques.
This function is useful to tune the value of `lattice_score_scale`.
This function is useful to tune the value of `nbest_scale`.
Args:
lattice:
@ -533,7 +534,7 @@ def nbest_oracle(
use_double_scores:
True to use double precision for computation. False to use
single precision.
lattice_score_scale:
nbest_scale:
It's the scale applied to the lattice.scores. A smaller value
yields more unique paths.
oov:
@ -549,7 +550,7 @@ def nbest_oracle(
lattice=lattice,
num_paths=num_paths,
use_double_scores=use_double_scores,
lattice_score_scale=lattice_score_scale,
nbest_scale=nbest_scale,
)
hyps = nbest.build_levenshtein_graphs()
@ -590,7 +591,7 @@ def rescore_with_n_best_list(
G: k2.Fsa,
num_paths: int,
lm_scale_list: List[float],
lattice_score_scale: float = 1.0,
nbest_scale: float = 1.0,
use_double_scores: bool = True,
) -> Dict[str, k2.Fsa]:
"""Rescore an n-best list with an n-gram LM.
@ -607,7 +608,7 @@ def rescore_with_n_best_list(
Size of nbest list.
lm_scale_list:
A list of float representing LM score scales.
lattice_score_scale:
nbest_scale:
Scale to be applied to ``lattice.score`` when sampling paths
using ``k2.random_paths``.
use_double_scores:
@ -631,7 +632,7 @@ def rescore_with_n_best_list(
lattice=lattice,
num_paths=num_paths,
use_double_scores=use_double_scores,
lattice_score_scale=lattice_score_scale,
nbest_scale=nbest_scale,
)
# nbest.fsa.scores are all 0s at this point
@ -769,7 +770,7 @@ def rescore_with_attention_decoder(
memory_key_padding_mask: Optional[torch.Tensor],
sos_id: int,
eos_id: int,
lattice_score_scale: float = 1.0,
nbest_scale: float = 1.0,
ngram_lm_scale: Optional[float] = None,
attention_scale: Optional[float] = None,
use_double_scores: bool = True,
@ -796,7 +797,7 @@ def rescore_with_attention_decoder(
The token ID for SOS.
eos_id:
The token ID for EOS.
lattice_score_scale:
nbest_scale:
It's the scale applied to `lattice.scores`. A smaller value
leads to more unique paths at the risk of missing the correct path.
ngram_lm_scale:
@ -812,7 +813,7 @@ def rescore_with_attention_decoder(
lattice=lattice,
num_paths=num_paths,
use_double_scores=use_double_scores,
lattice_score_scale=lattice_score_scale,
nbest_scale=nbest_scale,
)
# nbest.fsa.scores are all 0s at this point

2
test/test_decode.py
View File

@ -43,7 +43,7 @@ def test_nbest_from_lattice():
lattice=lattice,
num_paths=10,
use_double_scores=True,
lattice_score_scale=0.5,
nbest_scale=0.5,
)
# each lattice has only 4 distinct paths that have different word sequences:
# 10->30