mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-26 18:24:18 +00:00
302 lines
8.7 KiB
Bash
Executable File
302 lines
8.7 KiB
Bash
Executable File
#!/usr/bin/env bash
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set -eou pipefail
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nj=15
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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/GigaSpeech
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# You can find audio, dict, GigaSpeech.json inside it.
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# You can apply for the download credentials by following
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# https://github.com/SpeechColab/GigaSpeech#download
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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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# 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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[ ! -e $dl_dir/GigaSpeech ] && mkdir -p $dl_dir/GigaSpeech
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# If you have pre-downloaded it to /path/to/GigaSpeech,
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# you can create a symlink
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#
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# ln -sfv /path/to/GigaSpeech $dl_dir/GigaSpeech
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#
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if [ ! -d $dl_dir/GigaSpeech/audio ] && [ ! -f $dl_dir/GigaSpeech.json ]; then
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# Check credentials.
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if [ ! -f $dl_dir/password ]; then
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echo -n "$0: Please apply for the download credentials by following"
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echo -n "https://github.com/SpeechColab/GigaSpeech#dataset-download"
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echo " and save it to $dl_dir/password."
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exit 1;
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fi
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PASSWORD=`cat $dl_dir/password 2>/dev/null`
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if [ -z "$PASSWORD" ]; then
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echo "$0: Error, $dl_dir/password is empty."
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exit 1;
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fi
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PASSWORD_MD5=`echo $PASSWORD | md5sum | cut -d ' ' -f 1`
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if [[ $PASSWORD_MD5 != "dfbf0cde1a3ce23749d8d81e492741b8" ]]; then
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echo "$0: Error, invalid $dl_dir/password."
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exit 1;
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fi
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# Download XL, DEV and TEST sets by default.
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lhotse download gigaspeech --subset auto --host tsinghua \
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$dl_dir/password $dl_dir/GigaSpeech
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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/
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#
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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 GigaSpeech manifest"
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# We assume that you have downloaded the GigaSpeech corpus
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# to $dl_dir/GigaSpeech
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mkdir -p data/manifests
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lhotse prepare gigaspeech --subset auto -j $nj \
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$dl_dir/GigaSpeech data/manifests
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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 manifest"
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# We assume that you have downloaded the musan corpus
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# to $dl_dir/musan
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mkdir -p data/manifests
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lhotse prepare musan $dl_dir/musan data/manifests
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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 GigaSpeech"
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mkdir -p data/fbank
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# We assume you have a GPU card and implement CUDA extraction here.
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# Since without CUDA it would take too much time to compute feats
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# for L or XL subset, we recommend --precomputed-features False.
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#
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# We assume you have install kaldifeat, if not, please install
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# it using: pip install kaldifeat
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./local/compute_fbank_gigaspeech.py --precomputed-features True \
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--num-workers 4 --batch-duration 600.0 \
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--context-window 0.0 --context-direction center
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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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mkdir -p data/fbank
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./local/compute_fbank_musan.py
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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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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 $lang_dir/L_disambig.pt ]; then
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# ./local/prepare_lang.py --lang-dir $lang_dir
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# fi
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if [ ! -f $lang_dir/transcript_words.txt ]; then
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gunzip -c "data/manifests/gigaspeech_supervisions_XL.jsonl.gz" \
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| jq '.text' \
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| sed 's/"//g' \
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> $lang_dir/transcript_words.txt
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# Delete utterances with garbage meta tags
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garbage_utterance_tags="<SIL> <MUSIC> <NOISE> <OTHER>"
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for tag in $garbage_utterance_tags; do
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sed -i "/${tag}/d" $lang_dir/transcript_words.txt
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done
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# Delete punctuations in utterances
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punctuation_tags="<COMMA> <EXCLAMATIONPOINT> <PERIOD> <QUESTIONMARK>"
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for tag in $punctuation_tags; do
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sed -i "s/${tag}//g" $lang_dir/transcript_words.txt
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done
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# Ensure space only appears once
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sed -i 's/\t/ /g' $lang_dir/transcript_words.txt
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sed -i 's/[ ][ ]*/ /g' $lang_dir/transcript_words.txt
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fi
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cat $lang_dir/transcript_words.txt | sed 's/ /\n/g' \
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| sort -u | sed '/^$/d' > $lang_dir/words.txt
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(echo '!SIL'; echo '<SPOKEN_NOISE>'; echo '<UNK>'; ) |
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cat - $lang_dir/words.txt | sort | uniq | awk '
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BEGIN {
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print "<eps> 0";
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}
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{
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if ($1 == "<s>") {
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print "<s> is in the vocabulary!" | "cat 1>&2"
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exit 1;
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}
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if ($1 == "</s>") {
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print "</s> is in the vocabulary!" | "cat 1>&2"
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exit 1;
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}
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printf("%s %d\n", $1, NR);
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}
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END {
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printf("#0 %d\n", NR+1);
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printf("<s> %d\n", NR+2);
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printf("</s> %d\n", NR+3);
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}' > $lang_dir/words || exit 1;
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mv $lang_dir/words $lang_dir/words.txt
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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,transcript_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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gunzip -c "data/manifests/gigaspeech_supervisions_XL.jsonl.gz" \
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| jq '.text' \
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| sed 's/"//g' \
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> $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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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 P"
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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 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/3-gram.arpa ]; then
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./shared/make_kn_lm.py \
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-ngram-order 3 \
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-text "data/lang_phone/transcript_words.txt" \
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-lm data/lm/3-gram.arpa
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fi
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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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data/lm/3-gram.arpa > data/lm/G_3_gram.fst.txt
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fi
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if [ ! -f data/lm/4-gram.arpa ]; then
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./shared/make_kn_lm.py \
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-ngram-order 4 \
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-text "data/lang_phone/transcript_words.txt" \
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-lm data/lm/4-gram.arpa
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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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data/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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log "Stage 9: Compile HLG"
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# ./local/compile_hlg.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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done
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fi
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