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Update ctc-decoding on pretrained.py and conformer_ctc.rst (#78)

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Mingshuang Luo 2021-10-14 23:29:06 +08:00 committed by GitHub
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2 changed files with 34 additions and 25 deletions
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16
docs/source/recipes/librispeech/conformer_ctc.rst
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@ -429,7 +429,6 @@ After downloading, you will have the following files:
|-- README.md
|-- data
| |-- lang_bpe
| | |-- Linv.pt
| | |-- HLG.pt
| | |-- bpe.model
| | |-- tokens.txt
@ -447,10 +446,6 @@ After downloading, you will have the following files:
6 directories, 11 files
**File descriptions**:
- ``data/lang_bpe/Linv.pt``
It is the lexicon file, with word IDs as labels and token IDs as aux_labels.
- ``data/lang_bpe/HLG.pt``
It is the decoding graph.
@ -551,7 +546,7 @@ The command to run CTC decoding is:
$ cd egs/librispeech/ASR
$ ./conformer_ctc/pretrained.py \
--checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
--lang-dir ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe \
--bpe-model ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/bpe.model \
--method ctc-decoding \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
@ -595,7 +590,8 @@ The command to run HLG decoding is:
$ cd egs/librispeech/ASR
$ ./conformer_ctc/pretrained.py \
--checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
--lang-dir ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe \
--words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
--HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
@ -637,7 +633,8 @@ The command to run HLG decoding + LM rescoring is:
$ cd egs/librispeech/ASR
$ ./conformer_ctc/pretrained.py \
--checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
--lang-dir ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe \
--words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
--HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
--method whole-lattice-rescoring \
--G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
--ngram-lm-scale 0.8 \
@ -684,7 +681,8 @@ The command to run HLG decoding + LM rescoring + attention decoder rescoring is:
$ cd egs/librispeech/ASR
$ ./conformer_ctc/pretrained.py \
--checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
--lang-dir ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe \
--words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
--HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
--method attention-decoder \
--G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
--ngram-lm-scale 1.3 \

43
egs/librispeech/ASR/conformer_ctc/pretrained.py
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@ -36,7 +36,6 @@ from icefall.decode import (
rescore_with_attention_decoder,
rescore_with_whole_lattice,
)
from icefall.lexicon import Lexicon
from icefall.utils import AttributeDict, get_texts
@ -55,10 +54,27 @@ def get_parser():
)
parser.add_argument(
"--lang-dir",
"--words-file",
type=str,
required=True,
help="Path to lang dir.",
help="""Path to words.txt.
Used only when method is not ctc-decoding.
""",
)
parser.add_argument(
"--HLG",
type=str,
help="""Path to HLG.pt.
Used only when method is not ctc-decoding.
""",
)
parser.add_argument(
"--bpe-model",
type=str,
help="""Path to bpe.model.
Used only when method is ctc-decoding.
""",
)
parser.add_argument(
@ -287,17 +303,16 @@ def main():
if params.method == "ctc-decoding":
logging.info("Use CTC decoding")
lexicon = Lexicon(params.lang_dir)
max_token_id = max(lexicon.tokens)
bpe_model = spm.SentencePieceProcessor()
bpe_model.load(params.bpe_model)
max_token_id = bpe_model.get_piece_size() - 1
H = k2.ctc_topo(
max_token=max_token_id,
modified=False,
device=device,
)
bpe_model = spm.SentencePieceProcessor()
bpe_model.load(params.lang_dir + "/bpe.model")
lattice = get_lattice(
nnet_output=nnet_output,
decoding_graph=H,
@ -320,10 +335,8 @@ def main():
"whole-lattice-rescoring",
"attention-decoder",
]:
logging.info(f"Loading HLG from {params.lang_dir}/HLG.pt")
HLG = k2.Fsa.from_dict(
torch.load(params.lang_dir + "/HLG.pt", map_location="cpu")
)
logging.info(f"Loading HLG from {params.HLG}")
HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
HLG = HLG.to(device)
if not hasattr(HLG, "lm_scores"):
# For whole-lattice-rescoring and attention-decoder
@ -386,9 +399,7 @@ def main():
best_path = next(iter(best_path_dict.values()))
hyps = get_texts(best_path)
word_sym_table = k2.SymbolTable.from_file(
params.lang_dir + "/words.txt"
)
word_sym_table = k2.SymbolTable.from_file(params.words_file)
hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
else:
raise ValueError(f"Unsupported decoding method: {params.method}")