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Update ctc-decoding on pretrained.py and conformer_ctc.rst

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Mingshuang Luo 2021-10-14 18:27:15 +08:00
parent f3fd2792ae
commit 40db9985ec
2 changed files with 37 additions and 20 deletions
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11
docs/source/recipes/librispeech/conformer_ctc.rst
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@ -551,7 +551,7 @@ 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 +595,8 @@ 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 +638,8 @@ 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 +686,8 @@ 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 \

46
egs/librispeech/ASR/conformer_ctc/pretrained.py
View File

@ -20,6 +20,7 @@
import argparse
import logging
import math
import os
from typing import List
import k2
@ -36,7 +37,6 @@ 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 +55,21 @@ def get_parser():
)
parser.add_argument(
"--lang-dir",
"--words-file",
type=str,
required=True,
help="Path to lang dir.",
help="Path to words.txt",
)
parser.add_argument(
"--HLG",
type=str,
help="Path to HLG.pt.",
)
parser.add_argument(
"--bpe-model",
type=str,
help="Path to bpe.model.",
)
parser.add_argument(
@ -287,17 +298,19 @@ def main():
if params.method == "ctc-decoding":
logging.info("Use CTC decoding")
lexicon = Lexicon(params.lang_dir)
max_token_id = max(lexicon.tokens)
if not os.path.exists(params.bpe_model):
raise ValueError("The path to bpe.model is required!")
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 +333,13 @@ 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")
)
if not os.path.exists(params.HLG):
raise ValueError("The path to HLG.pt is required!")
if not os.path.exists(params.words_file):
raise ValueError("The path to words.txt is required!")
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 +402,7 @@ 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}")