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dohe0342
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egs/tedlium2/ASR/.prepare.sh.swp
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BIN
egs/tedlium2/ASR/.prepare.sh.swp
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@ -5,7 +5,6 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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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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@ -13,7 +12,7 @@ stop_stage=100
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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/tedlium2
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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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@ -24,7 +23,7 @@ stop_stage=100
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# - music
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# - noise
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# - speech
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dl_dir=/DB/LibriSpeech_tar
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dl_dir=/home/work/workspace/tedlium3
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. shared/parse_options.sh || exit 1
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@ -58,10 +57,17 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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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/tedlium2 ]; then
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# lhotse download tedlium $dl_dir
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# mv $dl_dir/TEDLIUM_release-2 $dl_dir/tedlium2
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#fi
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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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@ -74,13 +80,13 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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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 tedlium2 manifests"
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if [ ! -f data/manifests/.tedlium2.done ]; 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/tedlium2 data/manifests
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touch data/manifests/.tedlium2.done
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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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@ -96,12 +102,19 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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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 tedlium2"
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log "Stage 3: Compute fbank for tedlium3"
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if [ ! -e data/fbank/.tedlium2.done ]; then
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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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touch data/fbank/.tedlium2.done
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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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@ -115,28 +128,24 @@ 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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lang_dir=data/lang_phone
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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.text ]; then
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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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--lang-dir $lang_dir \
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--manifests-dir data/manifests
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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/lexicon_words.txt ]; then
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./local/prepare_lexicon.py \
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--lang-dir $lang_dir \
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--manifests-dir data/manifests
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fi
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if [ ! -f $lang_dir/words.txt ]; then
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(echo '!SIL SIL'; echo '<UNK> <UNK>'; ) |
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cat - $lang_dir/lexicon_words.txt |
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sort | uniq > $lang_dir/lexicon.txt
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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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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang.py --lang-dir $lang_dir
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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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@ -148,92 +157,56 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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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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cat data/lang_phone/train.text |
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cut -d " " -f 2- > $lang_dir/transcript_words.txt
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# remove the <unk> for transcript_words.txt
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sed -i 's/ <unk>//g' $lang_dir/transcript_words.txt
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sed -i 's/<unk> //g' $lang_dir/transcript_words.txt
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sed -i 's/<unk>//g' $lang_dir/transcript_words.txt
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fi
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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 $lang_dir/transcript_words.txt
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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
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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 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 ] && [ $stop_stage -ge 8 ]; then
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log "Stage 8: Prepare G"
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log "Stage 7: Prepare G"
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# We assume you have install kaldilm, if not, please install
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# it using: pip install kaldilm
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mkdir -p data/lm
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if [ ! -f data/lm/G_3_gram.fst.txt ]; then
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if [ ! -f data/lm/G_4_gram_small.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$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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--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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$dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
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--max-arpa-warnings=-1 \
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$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 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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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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./local/compile_hlg.py --lang-dir $lang_dir
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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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