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clean prepare.sh
This commit is contained in:
sathvik udupa
parent
3e4179bebb
commit
c5115fc460
@ -6,36 +6,15 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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set -eou pipefail
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nj=60
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stage=-1
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stage=6
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stop_stage=9
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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/LibriSpeech
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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/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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# - $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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# - $dl_dir/hi-en
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dl_dir=$PWD/download
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espnet_path=/home/wtc7/espnet/egs2/MUCS/asr1/data/hi-en/
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@ -43,13 +22,8 @@ espnet_path=/home/wtc7/espnet/egs2/MUCS/asr1/data/hi-en/
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# vocab size for sentence piece models.
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# It will generate data/lang_bpe_xxx,
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# data/lang_bpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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# 5000
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# 2000
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# 1000
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200
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)
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# data/lang_bpe_yyy
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vocab_size=400
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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@ -68,7 +42,7 @@ if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
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mkdir -p $dl_dir/lm
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if [ ! -e $dl_dir/lm/.done ]; then
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./local/prepare_lm_files.py --out-dir=$dl_dir/lm --data-path=$espnet_path --mode="train"
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# touch $dl_dir/lm/.done
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touch $dl_dir/lm/.done
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fi
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fi
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@ -78,11 +52,11 @@ fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare MUCS manifest"
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# We assume that you have downloaded the LibriSpeech corpus
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# to $dl_dir/LibriSpeech
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# We assume that you have downloaded the MUCS corpus
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# to $dl_dir/
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mkdir -p data/manifests
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if [ ! -e data/manifests/.mucs.done ]; then
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# lhotse prepare mucs -j $nj $dl_dir/hi-en data/manifests
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# generate lhotse manifests from kaldi style files
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./local/prepare_manifest.py "$espnet_path" $nj data/manifests
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touch data/manifests/.mucs.done
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@ -94,7 +68,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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mkdir -p data/fbank
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if [ ! -e data/fbank/.mucs.done ]; then
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./local/compute_fbank_mucs.py
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# touch data/fbank/.mucs.done
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touch data/fbank/.mucs.done
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fi
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# exit
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@ -110,7 +84,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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python3 ./local/validate_manifest.py \
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data/fbank/mucs_cuts_${part}.jsonl.gz
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done
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# touch data/fbank/.mucs-validated.done
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touch data/fbank/.mucs-validated.done
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fi
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fi
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@ -150,7 +124,6 @@ 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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@ -193,23 +166,14 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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$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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log "Stage 7: Train LM from training data"
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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/lm_3.arpa ]; then
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./shared/make_kn_lm.py \
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-ngram-order 3 \
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@ -224,14 +188,6 @@ if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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-lm $lang_dir/lm_4.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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@ -246,7 +202,7 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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data/lang_bpe_200/lm_3.arpa > data/lm/G_3_gram.fst.txt
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data/lang_bpe_${vocab_size}/lm_3.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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@ -255,17 +211,9 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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data/lang_bpe_200/lm_4.arpa > data/lm/G_4_gram.fst.txt
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data/lang_bpe_${vocab_size}/lm_4.arpa > data/lm/G_4_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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fi
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if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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@ -277,120 +225,8 @@ if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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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 \
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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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