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231
egs/tedlium3/ASR/2
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231
egs/tedlium3/ASR/2
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#!/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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stage=0
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stop_stage=100
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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/tedlium3
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# You can find data, doc, legacy, LM, etc, inside it.
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# You can download them from https://www.openslr.org/51
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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dl_dir=/DB
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. shared/parse_options.sh || exit 1
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# vocab size for sentence piece models.
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# It will generate data/lang_bpe_xxx,
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# data/lang_bpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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5000
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2000
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1000
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500
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)
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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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 "dl_dir: $dl_dir"
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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# If you have pre-downloaded it to /path/to/tedlium3,
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# you can create a symlink
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#
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# ln -sfv /path/to/tedlium3 $dl_dir/tedlium3
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#
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if [ ! -d $dl_dir/tedlium3 ]; then
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lhotse download tedlium $dl_dir
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mv $dl_dir/TEDLIUM_release-3 $dl_dir/tedlium3
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fi
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# Download big and small 4 gram lanuage models
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if [ ! -d $dl_dir/lm ]; then
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wget --continue http://kaldi-asr.org/models/5/4gram_small.arpa.gz -P $dl_dir/lm
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wget --continue http://kaldi-asr.org/models/5/4gram_big.arpa.gz -P $dl_dir/lm
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gzip -d $dl_dir/lm/4gram_small.arpa.gz $dl_dir/lm/4gram_big.arpa.gz
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fi
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# If you have pre-downloaded it to /path/to/musan,
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# you can create a symlink
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#
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#ln -sfv /path/to/musan $dl_dir/musan
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if [ ! -d $dl_dir/musan ]; then
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lhotse download musan $dl_dir
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fi
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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 tedlium3 manifests"
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if [ ! -f data/manifests/.tedlium3.done ]; then
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# We assume that you have downloaded the tedlium3 corpus
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# to $dl_dir/tedlium3
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mkdir -p data/manifests
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lhotse prepare tedlium $dl_dir/tedlium3 data/manifests
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touch data/manifests/.tedlium3.done
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fi
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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: Prepare musan manifests"
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# We assume that you have downloaded the musan corpus
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# to data/musan
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if [ ! -e data/manifests/.musan.done ]; then
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mkdir -p data/manifests
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lhotse prepare musan $dl_dir/musan data/manifests
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touch data/manifests/.musan.done
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Compute fbank for tedlium3"
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if [ ! -e data/fbank/.tedlium3.done ]; then
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mkdir -p data/fbank
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python3 ./local/compute_fbank_tedlium.py
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gunzip -c data/fbank/tedlium_cuts_train.jsonl.gz | shuf | \
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gzip -c > data/fbank/tedlium_cuts_train-shuf.jsonl.gz
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mv data/fbank/tedlium_cuts_train-shuf.jsonl.gz \
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data/fbank/tedlium_cuts_train.jsonl.gz
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touch data/fbank/.tedlium3.done
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fi
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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: Compute fbank for musan"
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if [ ! -e data/fbank/.musan.done ]; then
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mkdir -p data/fbank
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python3 ./local/compute_fbank_musan.py
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touch data/fbank/.musan.done
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fi
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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 BPE train data and set of words"
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lang_dir=data/lang
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mkdir -p $lang_dir
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if [ ! -f $lang_dir/train.txt ]; then
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gunzip -c $dl_dir/tedlium3/LM/*.en.gz | sed 's: <\/s>::g' > $lang_dir/train_orig.txt
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./local/prepare_transcripts.py \
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--input-text-path $lang_dir/train_orig.txt \
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--output-text-path $lang_dir/train.txt
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fi
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if [ ! -f $lang_dir/words.txt ]; then
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awk '{print $1}' $dl_dir/tedlium3/TEDLIUM.152k.dic |
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sed 's:([0-9])::g' | sort | uniq > $lang_dir/words_orig.txt
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./local/prepare_words.py --lang-dir $lang_dir
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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/words.txt $lang_dir
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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 data/lang/train.txt
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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 --oov "<unk>"
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fi
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done
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Prepare G"
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# We assume you have install kaldilm, if not, please install
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# it using: pip install kaldilm
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mkdir -p data/lm
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if [ ! -f data/lm/G_4_gram_small.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="data/lang/words.txt" \
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--disambig-symbol='#0' \
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--max-order=4 \
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--max-arpa-warnings=-1 \
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$dl_dir/lm/4gram_small.arpa > data/lm/G_4_gram_small.fst.txt
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fi
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if [ ! -f data/lm/G_4_gram_big.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/words.txt" \
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--disambig-symbol='#0' \
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--max-order=4 \
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--max-arpa-warnings=-1 \
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$dl_dir/lm/4gram_big.arpa > data/lm/G_4_gram_big.fst.txt
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fi
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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: Compile HLG"
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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/HLG.pt ]; then
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./local/compile_hlg.py \
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--lang-dir $lang_dir \
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--lm G_4_gram_small
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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: Split cuts by speaker id"
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gzip -d data/fbank/tedlium_cuts_test.jsonl.gz
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i=0
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for spk in $dl_dir/tedlium3/legacy/test/sph/*; do
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spk_id=${spk#*sph\/}
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spk_id=${spk_id%.sph}
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echo $spk_id
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cat data/fbank/tedlium_cuts_test.jsonl | grep speaker\":\ \"$spk_id\" > data/fbank/tedlium_cuts_test_$i.jsonl
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gzip data/fbank/tedlium_cuts_test_$i.jsonl
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i=`expr $i + 1`
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done
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gzip data/fbank/tedlium_cuts_test.jsonl
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#cat data/fbank/tedlium_cuts_test.jsonl.gz | grep
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fi
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