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https://github.com/k2-fsa/icefall.git
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62c9dd9703
* prepare.sh: restore working directory after git lfs pull * set execute permisons on python scripts called by prepare.sh
160 lines
4.3 KiB
Bash
160 lines
4.3 KiB
Bash
#!/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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num_phones=39
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# Here we use num_phones=39 for modeling
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nj=15
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stage=-1
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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/timit
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# You can find data, train_data.csv, test_data.csv, etc, inside it.
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# You can download them from https://data.deepai.org/timit.zip
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#
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# - $dl_dir/lm
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# This directory contains the language model(LM) downloaded from
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# https://huggingface.co/luomingshuang/timit_lm, and the LM is based
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# on 39 phones. About how to get these LM files, you can know it
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# from https://github.com/luomingshuang/Train_LM_with_kaldilm.
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#
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# - lm_3_gram.arpa
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# - lm_4_gram.arpa
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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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splits_dir=$PWD/splits_dir
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. shared/parse_options.sh || exit 1
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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log() {
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# This function is from espnet
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local fname=${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 -1 ] && [ $stop_stage -ge -1 ]; then
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log "Stage -1: Download LM"
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# We assume that you have installed the git-lfs, if not, you could install it
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# using: `sudo apt-get install git-lfs && git-lfs install`
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[ ! -e $dl_dir/lm ] && mkdir -p $dl_dir/lm
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git clone https://huggingface.co/luomingshuang/timit_lm $dl_dir/lm
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pushd $dl_dir/lm
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git lfs pull
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popd
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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: Download data"
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# If you have pre-downloaded it to /path/to/timit,
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# you can create a symlink
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#
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# ln -sfv /path/to/timit $dl_dir/timit
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#
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if [ ! -d $dl_dir/timit ]; then
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lhotse download timit $dl_dir
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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 timit manifest"
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# We assume that you have downloaded the timit corpus
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# to $dl_dir/timit
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mkdir -p data/manifests
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lhotse prepare timit -p $num_phones -j $nj $dl_dir/timit/data 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 data/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 timit"
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mkdir -p data/fbank
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./local/compute_fbank_timit.py
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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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./local/prepare_lexicon.py \
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--manifests-dir data/manifests \
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--lang-dir $lang_dir
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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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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Prepare G"
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# We assume you have installed kaldilm, if not, please install
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# it using: pip install kaldilm
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mkdir -p data/lm
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if [ ! -f data/lm/G_3_gram.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$dl_dir/lm/lm_3_gram.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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--disambig-symbol='#0' \
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--max-order=4 \
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$dl_dir/lm/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 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Compile HLG"
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./local/compile_hlg.py --lang-dir data/lang_phone
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fi
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