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Refactor prepare.sh in librispeech (#1493)
* Refactor prepare.sh in librispeech, break it into three parts, prepare.sh (basic, minimal requirement for transducer), prepare_lm.sh (ngram & nnlm staff), prepare_mmi.sh (for MMI training).
This commit is contained in:
Wei Kang
GitHub
parent
4ed88d9484
commit
711d6bc462
@ -1526,7 +1526,7 @@ done
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You may also decode using LODR + LM shallow fusion. This decoding method is proposed in <https://arxiv.org/pdf/2203.16776.pdf>.
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It subtracts the internal language model score during shallow fusion, which is approximated by a bi-gram model. The bi-gram can be
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generated by `generate-lm.sh`, or you may download it from <https://huggingface.co/marcoyang/librispeech_bigram>.
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generated by `prepare_lm.sh` at stage 4, or you may download it from <https://huggingface.co/marcoyang/librispeech_bigram>.
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The decoding command is as follows:
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@ -1,20 +0,0 @@
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#!/usr/bin/env bash
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lang_dir=data/lang_bpe_500
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for ngram in 2 3 4 5; do
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if [ ! -f $lang_dir/${ngram}gram.arpa ]; then
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./shared/make_kn_lm.py \
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-ngram-order ${ngram} \
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-text $lang_dir/transcript_tokens.txt \
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-lm $lang_dir/${ngram}gram.arpa
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fi
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if [ ! -f $lang_dir/${ngram}gram.fst.txt ]; then
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python3 -m kaldilm \
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--read-symbol-table="$lang_dir/tokens.txt" \
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--disambig-symbol='#0' \
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--max-order=${ngram} \
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$lang_dir/${ngram}gram.arpa > $lang_dir/${ngram}gram.fst.txt
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fi
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done
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@ -28,6 +28,7 @@
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import argparse
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import shutil
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from pathlib import Path
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from typing import Dict
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import sentencepiece as spm
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@ -57,6 +58,18 @@ def get_args():
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return parser.parse_args()
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def generate_tokens(lang_dir: Path):
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"""
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Generate the tokens.txt from a bpe model.
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"""
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sp = spm.SentencePieceProcessor()
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sp.load(str(lang_dir / "bpe.model"))
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token2id: Dict[str, int] = {sp.id_to_piece(i): i for i in range(sp.vocab_size())}
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with open(lang_dir / "tokens.txt", "w", encoding="utf-8") as f:
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for sym, i in token2id.items():
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f.write(f"{sym} {i}\n")
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def main():
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args = get_args()
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vocab_size = args.vocab_size
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@ -95,6 +108,8 @@ def main():
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shutil.copyfile(model_file, f"{lang_dir}/bpe.model")
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generate_tokens(lang_dir)
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if __name__ == "__main__":
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main()
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@ -6,8 +6,21 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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set -eou pipefail
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nj=15
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stage=-1
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stop_stage=100
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# run step 0 to step 5 by default
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stage=0
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stop_stage=5
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# Note: This script just prepare the minimal requirements that needed by a
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# transducer training with bpe units.
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#
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# If you want to use ngram or nnlm, please continue running prepare_lm.sh after
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# you succeed running this script.
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#
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# This script also contains the steps to generate phone based units, but they
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# will not run automatically, you can generate the phone based units by
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# bash prepare.sh --stage -1 --stop-stage -1
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# bash prepare.sh --stage 6 --stop-stage 6
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# We assume dl_dir (download dir) contains the following
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# directories and files. If not, they will be downloaded
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@ -17,6 +30,18 @@ stop_stage=100
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# You can find BOOKS.TXT, test-clean, train-clean-360, etc, inside it.
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# You can download them from https://www.openslr.org/12
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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#
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# lm directory is not necessary for transducer training with bpe units, but it
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# is needed by phone based modeling, you can download it by running
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# bash prepare.sh --stage -1 --stop-stage -1
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# then you can see the following files in the directory.
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# - $dl_dir/lm
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# This directory contains the following files downloaded from
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# http://www.openslr.org/resources/11
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@ -28,14 +53,7 @@ stop_stage=100
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# - librispeech-vocab.txt
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# - librispeech-lexicon.txt
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# - librispeech-lm-norm.txt.gz
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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dl_dir=$PWD/download
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. shared/parse_options.sh || exit 1
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@ -60,6 +78,8 @@ log() {
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "Running prepare.sh"
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log "dl_dir: $dl_dir"
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if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
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@ -159,13 +179,49 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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fi
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Prepare phone based lang"
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log "Stage 5: Prepare BPE based lang"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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mkdir -p $lang_dir
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if [ ! -f $lang_dir/transcript_words.txt ]; then
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log "Generate data for BPE training"
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files=$(
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find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
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)
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for f in ${files[@]}; do
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cat $f | cut -d " " -f 2-
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done > $lang_dir/transcript_words.txt
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fi
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if [ ! -f $lang_dir/bpe.model ]; then
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./local/train_bpe_model.py \
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--lang-dir $lang_dir \
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--vocab-size $vocab_size \
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--transcript $lang_dir/transcript_words.txt
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fi
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done
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Prepare phone based lang"
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lang_dir=data/lang_phone
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mkdir -p $lang_dir
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(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
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cat - $dl_dir/lm/librispeech-lexicon.txt |
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sort | uniq > $lang_dir/lexicon.txt
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if [ ! -f $dl_dir/lm/librispeech-lexicon.txt ]; then
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log "No lexicon file in $dl_dir/lm, please run :"
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log "prepare.sh --stage -1 --stop-stage -1"
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exit -1
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fi
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if [ ! -f $lang_dir/lexicon.txt ]; then
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(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
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cat - $dl_dir/lm/librispeech-lexicon.txt |
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sort | uniq > $lang_dir/lexicon.txt
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fi
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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang.py --lang-dir $lang_dir
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@ -187,253 +243,3 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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$lang_dir/L_disambig.fst
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fi
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Prepare BPE based lang"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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mkdir -p $lang_dir
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# We reuse words.txt from phone based lexicon
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# so that the two can share G.pt later.
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cp data/lang_phone/words.txt $lang_dir
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if [ ! -f $lang_dir/transcript_words.txt ]; then
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log "Generate data for BPE training"
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files=$(
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find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
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)
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for f in ${files[@]}; do
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cat $f | cut -d " " -f 2-
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done > $lang_dir/transcript_words.txt
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fi
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if [ ! -f $lang_dir/bpe.model ]; then
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./local/train_bpe_model.py \
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--lang-dir $lang_dir \
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--vocab-size $vocab_size \
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--transcript $lang_dir/transcript_words.txt
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fi
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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang_bpe.py --lang-dir $lang_dir
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log "Validating $lang_dir/lexicon.txt"
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./local/validate_bpe_lexicon.py \
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--lexicon $lang_dir/lexicon.txt \
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--bpe-model $lang_dir/bpe.model
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fi
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if [ ! -f $lang_dir/L.fst ]; then
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log "Converting L.pt to L.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$lang_dir/L.pt \
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$lang_dir/L.fst
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fi
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if [ ! -f $lang_dir/L_disambig.fst ]; then
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log "Converting L_disambig.pt to L_disambig.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$lang_dir/L_disambig.pt \
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$lang_dir/L_disambig.fst
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fi
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done
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Prepare bigram token-level P for MMI training"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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if [ ! -f $lang_dir/transcript_tokens.txt ]; then
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./local/convert_transcript_words_to_tokens.py \
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--lexicon $lang_dir/lexicon.txt \
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--transcript $lang_dir/transcript_words.txt \
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--oov "<UNK>" \
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> $lang_dir/transcript_tokens.txt
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fi
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if [ ! -f $lang_dir/P.arpa ]; then
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./shared/make_kn_lm.py \
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-ngram-order 2 \
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-text $lang_dir/transcript_tokens.txt \
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-lm $lang_dir/P.arpa
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fi
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if [ ! -f $lang_dir/P.fst.txt ]; then
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python3 -m kaldilm \
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--read-symbol-table="$lang_dir/tokens.txt" \
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--disambig-symbol='#0' \
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--max-order=2 \
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$lang_dir/P.arpa > $lang_dir/P.fst.txt
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fi
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done
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fi
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if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
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log "Stage 8: Prepare G"
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# We assume you have installed kaldilm, if not, please install
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# it using: pip install kaldilm
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mkdir -p data/lm
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if [ ! -f data/lm/G_3_gram.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$dl_dir/lm/3-gram.pruned.1e-7.arpa > data/lm/G_3_gram.fst.txt
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fi
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if [ ! -f data/lm/G_4_gram.fst.txt ]; then
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# It is used for LM rescoring
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python3 -m kaldilm \
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=4 \
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$dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
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fi
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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if [ ! -f $lang_dir/HL.fst ]; then
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./local/prepare_lang_fst.py \
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--lang-dir $lang_dir \
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--ngram-G ./data/lm/G_3_gram.fst.txt
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fi
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done
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fi
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if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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log "Stage 9: Compile HLG"
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./local/compile_hlg.py --lang-dir data/lang_phone
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# Note If ./local/compile_hlg.py throws OOM,
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# please switch to the following command
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#
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# ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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./local/compile_hlg.py --lang-dir $lang_dir
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# Note If ./local/compile_hlg.py throws OOM,
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# please switch to the following command
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#
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# ./local/compile_hlg_using_openfst.py --lang-dir $lang_dir
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done
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fi
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# Compile LG for RNN-T fast_beam_search decoding
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if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
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log "Stage 10: Compile LG"
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./local/compile_lg.py --lang-dir data/lang_phone
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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./local/compile_lg.py --lang-dir $lang_dir
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done
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fi
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if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
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log "Stage 11: Generate LM training data"
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for vocab_size in ${vocab_sizes[@]}; do
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log "Processing vocab_size == ${vocab_size}"
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lang_dir=data/lang_bpe_${vocab_size}
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out_dir=data/lm_training_bpe_${vocab_size}
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mkdir -p $out_dir
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./local/prepare_lm_training_data.py \
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--bpe-model $lang_dir/bpe.model \
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--lm-data $dl_dir/lm/librispeech-lm-norm.txt \
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--lm-archive $out_dir/lm_data.pt
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done
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fi
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if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
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log "Stage 12: Generate LM validation data"
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for vocab_size in ${vocab_sizes[@]}; do
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log "Processing vocab_size == ${vocab_size}"
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out_dir=data/lm_training_bpe_${vocab_size}
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mkdir -p $out_dir
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if [ ! -f $out_dir/valid.txt ]; then
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files=$(
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find "$dl_dir/LibriSpeech/dev-clean" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/dev-other" -name "*.trans.txt"
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)
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for f in ${files[@]}; do
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cat $f | cut -d " " -f 2-
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done > $out_dir/valid.txt
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fi
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lang_dir=data/lang_bpe_${vocab_size}
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./local/prepare_lm_training_data.py \
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--bpe-model $lang_dir/bpe.model \
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--lm-data $out_dir/valid.txt \
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--lm-archive $out_dir/lm_data-valid.pt
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done
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fi
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if [ $stage -le 13 ] && [ $stop_stage -ge 13 ]; then
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log "Stage 13: Generate LM test data"
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for vocab_size in ${vocab_sizes[@]}; do
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log "Processing vocab_size == ${vocab_size}"
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out_dir=data/lm_training_bpe_${vocab_size}
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mkdir -p $out_dir
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if [ ! -f $out_dir/test.txt ]; then
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files=$(
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find "$dl_dir/LibriSpeech/test-clean" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/test-other" -name "*.trans.txt"
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)
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for f in ${files[@]}; do
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cat $f | cut -d " " -f 2-
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done > $out_dir/test.txt
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fi
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lang_dir=data/lang_bpe_${vocab_size}
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./local/prepare_lm_training_data.py \
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--bpe-model $lang_dir/bpe.model \
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--lm-data $out_dir/test.txt \
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--lm-archive $out_dir/lm_data-test.pt
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done
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fi
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if [ $stage -le 14 ] && [ $stop_stage -ge 14 ]; then
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log "Stage 14: Sort LM training data"
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# Sort LM training data by sentence length in descending order
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# for ease of training.
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#
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# Sentence length equals to the number of BPE tokens
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# in a sentence.
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for vocab_size in ${vocab_sizes[@]}; do
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out_dir=data/lm_training_bpe_${vocab_size}
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mkdir -p $out_dir
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./local/sort_lm_training_data.py \
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--in-lm-data $out_dir/lm_data.pt \
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--out-lm-data $out_dir/sorted_lm_data.pt \
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--out-statistics $out_dir/statistics.txt
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||||
|
||||
./local/sort_lm_training_data.py \
|
||||
--in-lm-data $out_dir/lm_data-valid.pt \
|
||||
--out-lm-data $out_dir/sorted_lm_data-valid.pt \
|
||||
--out-statistics $out_dir/statistics-valid.txt
|
||||
|
||||
./local/sort_lm_training_data.py \
|
||||
--in-lm-data $out_dir/lm_data-test.pt \
|
||||
--out-lm-data $out_dir/sorted_lm_data-test.pt \
|
||||
--out-statistics $out_dir/statistics-test.txt
|
||||
done
|
||||
fi
|
||||
|
||||
262
egs/librispeech/ASR/prepare_lm.sh
Executable file
262
egs/librispeech/ASR/prepare_lm.sh
Executable file
@ -0,0 +1,262 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
|
||||
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
|
||||
|
||||
set -eou pipefail
|
||||
|
||||
# This script generate Ngram LM / NNLM and related files that needed by decoding.
|
||||
|
||||
# We assume dl_dir (download dir) contains the following
|
||||
# directories and files. If not, they will be downloaded
|
||||
# by this script automatically.
|
||||
#
|
||||
# - $dl_dir/lm
|
||||
# This directory contains the following files downloaded from
|
||||
# http://www.openslr.org/resources/11
|
||||
#
|
||||
# - 3-gram.pruned.1e-7.arpa.gz
|
||||
# - 3-gram.pruned.1e-7.arpa
|
||||
# - 4-gram.arpa.gz
|
||||
# - 4-gram.arpa
|
||||
# - librispeech-vocab.txt
|
||||
# - librispeech-lexicon.txt
|
||||
# - librispeech-lm-norm.txt.gz
|
||||
#
|
||||
|
||||
. prepare.sh --stage -1 --stop-stage 6 || exit 1
|
||||
|
||||
log "Running prepare_lm.sh"
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
|
||||
log "Stage 0: Prepare BPE based lexicon."
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
# We reuse words.txt from phone based lexicon
|
||||
# so that the two can share G.pt later.
|
||||
cp data/lang_phone/words.txt $lang_dir
|
||||
|
||||
if [ ! -f $lang_dir/L_disambig.pt ]; then
|
||||
./local/prepare_lang_bpe.py --lang-dir $lang_dir
|
||||
|
||||
log "Validating $lang_dir/lexicon.txt"
|
||||
./local/validate_bpe_lexicon.py \
|
||||
--lexicon $lang_dir/lexicon.txt \
|
||||
--bpe-model $lang_dir/bpe.model
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/L.fst ]; then
|
||||
log "Converting L.pt to L.fst"
|
||||
./shared/convert-k2-to-openfst.py \
|
||||
--olabels aux_labels \
|
||||
$lang_dir/L.pt \
|
||||
$lang_dir/L.fst
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/L_disambig.fst ]; then
|
||||
log "Converting L_disambig.pt to L_disambig.fst"
|
||||
./shared/convert-k2-to-openfst.py \
|
||||
--olabels aux_labels \
|
||||
$lang_dir/L_disambig.pt \
|
||||
$lang_dir/L_disambig.fst
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
||||
log "Stage 1: Prepare word level G"
|
||||
# We assume you have installed kaldilm, if not, please install
|
||||
# it using: pip install kaldilm
|
||||
|
||||
mkdir -p data/lm
|
||||
if [ ! -f data/lm/G_3_gram.fst.txt ]; then
|
||||
# It is used in building HLG
|
||||
python3 -m kaldilm \
|
||||
--read-symbol-table="data/lang_phone/words.txt" \
|
||||
--disambig-symbol='#0' \
|
||||
--max-order=3 \
|
||||
$dl_dir/lm/3-gram.pruned.1e-7.arpa > data/lm/G_3_gram.fst.txt
|
||||
fi
|
||||
|
||||
if [ ! -f data/lm/G_4_gram.fst.txt ]; then
|
||||
# It is used for LM rescoring
|
||||
python3 -m kaldilm \
|
||||
--read-symbol-table="data/lang_phone/words.txt" \
|
||||
--disambig-symbol='#0' \
|
||||
--max-order=4 \
|
||||
$dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
|
||||
fi
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
|
||||
if [ ! -f $lang_dir/HL.fst ]; then
|
||||
./local/prepare_lang_fst.py \
|
||||
--lang-dir $lang_dir \
|
||||
--ngram-G ./data/lm/G_3_gram.fst.txt
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
|
||||
log "Stage 2: Compile HLG"
|
||||
./local/compile_hlg.py --lang-dir data/lang_phone
|
||||
|
||||
# Note If ./local/compile_hlg.py throws OOM,
|
||||
# please switch to the following command
|
||||
#
|
||||
# ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
./local/compile_hlg.py --lang-dir $lang_dir
|
||||
|
||||
# Note If ./local/compile_hlg.py throws OOM,
|
||||
# please switch to the following command
|
||||
#
|
||||
# ./local/compile_hlg_using_openfst.py --lang-dir $lang_dir
|
||||
done
|
||||
fi
|
||||
|
||||
# Compile LG for RNN-T fast_beam_search decoding
|
||||
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
|
||||
log "Stage 3: Compile LG"
|
||||
./local/compile_lg.py --lang-dir data/lang_phone
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
./local/compile_lg.py --lang-dir $lang_dir
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
||||
log "Stage 4: Prepare token level ngram G"
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
|
||||
if [ ! -f $lang_dir/transcript_tokens.txt ]; then
|
||||
./local/convert_transcript_words_to_tokens.py \
|
||||
--lexicon $lang_dir/lexicon.txt \
|
||||
--transcript $lang_dir/transcript_words.txt \
|
||||
--oov "<UNK>" \
|
||||
> $lang_dir/transcript_tokens.txt
|
||||
fi
|
||||
|
||||
for ngram in 2 3 4 5; do
|
||||
if [ ! -f $lang_dir/${ngram}gram.arpa ]; then
|
||||
./shared/make_kn_lm.py \
|
||||
-ngram-order ${ngram} \
|
||||
-text $lang_dir/transcript_tokens.txt \
|
||||
-lm $lang_dir/${ngram}gram.arpa
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/${ngram}gram.fst.txt ]; then
|
||||
python3 -m kaldilm \
|
||||
--read-symbol-table="$lang_dir/tokens.txt" \
|
||||
--disambig-symbol='#0' \
|
||||
--max-order=${ngram} \
|
||||
$lang_dir/${ngram}gram.arpa > $lang_dir/${ngram}gram.fst.txt
|
||||
fi
|
||||
done
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
|
||||
log "Stage 5: Generate NNLM training data"
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
log "Processing vocab_size == ${vocab_size}"
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
out_dir=data/lm_training_bpe_${vocab_size}
|
||||
mkdir -p $out_dir
|
||||
|
||||
./local/prepare_lm_training_data.py \
|
||||
--bpe-model $lang_dir/bpe.model \
|
||||
--lm-data $dl_dir/lm/librispeech-lm-norm.txt \
|
||||
--lm-archive $out_dir/lm_data.pt
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
||||
log "Stage 6: Generate NNLM validation data"
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
log "Processing vocab_size == ${vocab_size}"
|
||||
out_dir=data/lm_training_bpe_${vocab_size}
|
||||
mkdir -p $out_dir
|
||||
|
||||
if [ ! -f $out_dir/valid.txt ]; then
|
||||
files=$(
|
||||
find "$dl_dir/LibriSpeech/dev-clean" -name "*.trans.txt"
|
||||
find "$dl_dir/LibriSpeech/dev-other" -name "*.trans.txt"
|
||||
)
|
||||
for f in ${files[@]}; do
|
||||
cat $f | cut -d " " -f 2-
|
||||
done > $out_dir/valid.txt
|
||||
fi
|
||||
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
./local/prepare_lm_training_data.py \
|
||||
--bpe-model $lang_dir/bpe.model \
|
||||
--lm-data $out_dir/valid.txt \
|
||||
--lm-archive $out_dir/lm_data-valid.pt
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
|
||||
log "Stage 7: Generate NNLM test data"
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
log "Processing vocab_size == ${vocab_size}"
|
||||
out_dir=data/lm_training_bpe_${vocab_size}
|
||||
mkdir -p $out_dir
|
||||
|
||||
if [ ! -f $out_dir/test.txt ]; then
|
||||
files=$(
|
||||
find "$dl_dir/LibriSpeech/test-clean" -name "*.trans.txt"
|
||||
find "$dl_dir/LibriSpeech/test-other" -name "*.trans.txt"
|
||||
)
|
||||
for f in ${files[@]}; do
|
||||
cat $f | cut -d " " -f 2-
|
||||
done > $out_dir/test.txt
|
||||
fi
|
||||
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
./local/prepare_lm_training_data.py \
|
||||
--bpe-model $lang_dir/bpe.model \
|
||||
--lm-data $out_dir/test.txt \
|
||||
--lm-archive $out_dir/lm_data-test.pt
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
|
||||
log "Stage 8: Sort NNLM training data"
|
||||
# Sort LM training data by sentence length in descending order
|
||||
# for ease of training.
|
||||
#
|
||||
# Sentence length equals to the number of BPE tokens
|
||||
# in a sentence.
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
out_dir=data/lm_training_bpe_${vocab_size}
|
||||
mkdir -p $out_dir
|
||||
./local/sort_lm_training_data.py \
|
||||
--in-lm-data $out_dir/lm_data.pt \
|
||||
--out-lm-data $out_dir/sorted_lm_data.pt \
|
||||
--out-statistics $out_dir/statistics.txt
|
||||
|
||||
./local/sort_lm_training_data.py \
|
||||
--in-lm-data $out_dir/lm_data-valid.pt \
|
||||
--out-lm-data $out_dir/sorted_lm_data-valid.pt \
|
||||
--out-statistics $out_dir/statistics-valid.txt
|
||||
|
||||
./local/sort_lm_training_data.py \
|
||||
--in-lm-data $out_dir/lm_data-test.pt \
|
||||
--out-lm-data $out_dir/sorted_lm_data-test.pt \
|
||||
--out-statistics $out_dir/statistics-test.txt
|
||||
done
|
||||
fi
|
||||
45
egs/librispeech/ASR/prepare_mmi.sh
Executable file
45
egs/librispeech/ASR/prepare_mmi.sh
Executable file
@ -0,0 +1,45 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
|
||||
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
|
||||
|
||||
set -eou pipefail
|
||||
|
||||
|
||||
. prepare.sh --stage -1 --stop-stage 6 || exit 1
|
||||
|
||||
log "Running prepare_mmi.sh"
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
|
||||
log "Stage 0: Prepare bigram token-level P for MMI training"
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
|
||||
if [ ! -f $lang_dir/transcript_tokens.txt ]; then
|
||||
./local/convert_transcript_words_to_tokens.py \
|
||||
--lexicon $lang_dir/lexicon.txt \
|
||||
--transcript $lang_dir/transcript_words.txt \
|
||||
--oov "<UNK>" \
|
||||
> $lang_dir/transcript_tokens.txt
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/P.arpa ]; then
|
||||
./shared/make_kn_lm.py \
|
||||
-ngram-order 2 \
|
||||
-text $lang_dir/transcript_tokens.txt \
|
||||
-lm $lang_dir/P.arpa
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/P.fst.txt ]; then
|
||||
python3 -m kaldilm \
|
||||
--read-symbol-table="$lang_dir/tokens.txt" \
|
||||
--disambig-symbol='#0' \
|
||||
--max-order=2 \
|
||||
$lang_dir/P.arpa > $lang_dir/P.fst.txt
|
||||
fi
|
||||
done
|
||||
fi
|
||||
Loading…
x
Reference in New Issue
Block a user