mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
260 lines
7.2 KiB
Bash
Executable File
260 lines
7.2 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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nj=60
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stage=-1
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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. download them from https://www.openslr.org/resources/104/
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#
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# - $dl_dir/hi-en
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dl_dir=$PWD/download
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mkdir -p $dl_dir
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raw_data_path="/data/Database/MUCS/"
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dataset="hi-en" #hin-en or bn-en
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datadir="data_"$dataset
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raw_kaldi_files_path=$dl_dir/$dataset/
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. shared/parse_options.sh || exit 1
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# vocab size for sentence piece models.
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vocab_size=400
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mkdir -p $datadir
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log() {
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# This function is from espnet
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local fname=${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 -1 ] && [ $stop_stage -ge -1 ]; then
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log "Stage -1: prepare data files"
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mkdir -p $dl_dir/$dataset
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for x in train dev test train_all; do
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if [ -d "$dl_dir/$dataset/$x" ]; then rm -Rf $dl_dir/$dataset/$x; fi
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done
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mkdir -p $dl_dir/$dataset/{train,test,dev}
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cp -r $raw_data_path/$dataset/"train"/"transcripts"/* $dl_dir/$dataset/"train"
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cp -r $raw_data_path/$dataset/"test"/"transcripts"/* $dl_dir/$dataset/"test"
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for x in train test
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do
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cp $dl_dir/$dataset/$x/"wav.scp" $dl_dir/$dataset/$x/"wav.scp_old"
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cat $dl_dir/$dataset/$x/"wav.scp" | cut -d' ' -f1 > $dl_dir/$dataset/$x/wav_ids
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cat $dl_dir/$dataset/$x/"wav.scp" | cut -d' ' -f2 | awk -v var="$raw_data_path/$dataset/$x/" '{print var$1}' > $dl_dir/$dataset/$x/wav_ids_with_fullpath
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paste -d' ' $dl_dir/$dataset/$x/wav_ids $dl_dir/$dataset/$x/wav_ids_with_fullpath > $dl_dir/$dataset/$x/"wav.scp"
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rm $dl_dir/$dataset/$x/wav_ids
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rm $dl_dir/$dataset/$x/wav_ids_with_fullpath
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done
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./local/subset_data_dir.sh --first $dl_dir/$dataset/"train" 1000 $dl_dir/$dataset/"dev"
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total=$(wc -l $dl_dir/$dataset/"train"/"text" | cut -d' ' -f1)
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count=$(expr $total - 1000)
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./local/subset_data_dir.sh --first $dl_dir/$dataset/"train" $count $dl_dir/$dataset/"train_reduced"
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mv $dl_dir/$dataset/"train" $dl_dir/$dataset/"train_all"
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mv $dl_dir/$dataset/"train_reduced" $dl_dir/$dataset/"train"
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fi
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: prepare LM files"
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mkdir -p $raw_kaldi_files_path/lm
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if [ ! -e $raw_kaldi_files_path/lm/.done ]; then
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./local/prepare_lm_files.py --out-dir=$dl_dir/lm --data-path=$raw_kaldi_files_path --mode="train"
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touch $raw_kaldi_files_path/lm/.done
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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 MUCS manifest"
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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 $datadir/manifests
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if [ ! -e $datadir/manifests/.mucs.done ]; then
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# generate lhotse manifests from kaldi style files
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./local/prepare_manifest.py "$raw_kaldi_files_path" $nj $datadir/manifests
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touch $datadir/manifests/.mucs.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 mucs"
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mkdir -p $datadir/fbank
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if [ ! -e $datadir/fbank/.mucs.done ]; then
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./local/compute_fbank_mucs.py --manifestpath $datadir/manifests/ --fbankpath $datadir/fbank
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touch $datadir/fbank/.mucs.done
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fi
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# exit
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if [ ! -e $datadir/fbank/.mucs-validated.done ]; then
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log "Validating $datadir/fbank for mucs"
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parts=(
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train
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test
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dev
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)
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for part in ${parts[@]}; do
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python3 ./local/validate_manifest.py \
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$datadir/fbank/mucs_cuts_${part}.jsonl.gz
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done
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touch $datadir/fbank/.mucs-validated.done
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fi
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fi
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Prepare phone based lang"
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lang_dir=$datadir/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/mucs_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 $lang_dir/L.fst ]; then
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log "Converting L.pt to L.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$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/disambig_L.fst
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fi
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Prepare BPE based lang"
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lang_dir=$datadir/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 $datadir/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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cp download/lm/mucs_vocab_text.txt $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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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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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Train LM from training data"
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lang_dir=$datadir/lang_bpe_${vocab_size}
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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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-text $lang_dir/transcript_words.txt \
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-lm $lang_dir/lm_3.arpa
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fi
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if [ ! -f $lang_dir/lm_4.arpa ]; then
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./shared/make_kn_lm.py \
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-ngram-order 4 \
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-text $lang_dir/transcript_words.txt \
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-lm $lang_dir/lm_4.arpa
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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: 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 $datadir/lm
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if [ ! -f $datadir/lm/G_3_gram.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="$datadir/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$datadir/lang_bpe_${vocab_size}/lm_3.arpa > $datadir/lm/G_3_gram.fst.txt
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fi
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if [ ! -f $datadir/lm/G_4_gram.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="$datadir/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$datadir/lang_bpe_${vocab_size}/lm_4.arpa > $datadir/lm/G_4_gram.fst.txt
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fi
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fi
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if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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log "Stage 9: Compile HLG"
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lang_dir=$datadir/lang_bpe_${vocab_size}
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./local/compile_hlg.py --lang-dir $lang_dir
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fi
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