mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
265 lines
7.7 KiB
Bash
Executable File
265 lines
7.7 KiB
Bash
Executable File
#!/usr/bin/env bash
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# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
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export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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set -eou pipefail
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# This script generates Ngram LM / NNLM and related files needed by decoding.
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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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# by this script automatically.
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#
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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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#
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# - 3-gram.pruned.1e-7.arpa.gz
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# - 3-gram.pruned.1e-7.arpa
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# - 4-gram.arpa.gz
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# - 4-gram.arpa
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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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. prepare.sh --stage -1 --stop-stage 6 || exit 1
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log "Running prepare_lm.sh"
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stage=0
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stop_stage=100
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. shared/parse_options.sh || exit 1
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Prepare BPE based lexicon."
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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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# 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/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 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare word level G"
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# We assume you have installed kaldilm, if not, please install
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# it using: pip install kaldilm
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mkdir -p data/lm
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if [ ! -f data/lm/G_3_gram.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$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 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: 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 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: 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 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Prepare token level ngram G"
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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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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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done
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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: Generate NNLM 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 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Generate NNLM 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 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Generate NNLM 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 8 ] && [ $stop_stage -ge 8 ]; then
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log "Stage 8: Sort NNLM 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 \
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--in-lm-data $out_dir/lm_data-valid.pt \
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--out-lm-data $out_dir/sorted_lm_data-valid.pt \
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--out-statistics $out_dir/statistics-valid.txt
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./local/sort_lm_training_data.py \
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--in-lm-data $out_dir/lm_data-test.pt \
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--out-lm-data $out_dir/sorted_lm_data-test.pt \
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--out-statistics $out_dir/statistics-test.txt
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done
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fi
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