mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-09-05 15:14:18 +00:00
406 lines
13 KiB
Bash
Executable File
406 lines
13 KiB
Bash
Executable File
#!/usr/bin/env bash
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export PYTHONPATH=$PYTHONPATH:/mnt/samsung-t7/yuekai/asr/icefall
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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=15
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stage=-1
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stop_stage=11
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perturb_speed=true
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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/aishell
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# You can find data_aishell, resource_aishell inside it.
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# You can download them from https://www.openslr.org/33
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#
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# - $dl_dir/lm
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# This directory contains the language model downloaded from
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# https://huggingface.co/pkufool/aishell_lm
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#
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# - 3-gram.unpruned.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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. 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_bbpe_xxx,
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# data/lang_bbpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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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/aishell,
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# you can create a symlink
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#
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# ln -sfv /path/to/aishell $dl_dir/aishell
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#
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# The directory structure is
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# aishell/
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# |-- data_aishell
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# | |-- transcript
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# | `-- wav
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# `-- resource_aishell
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# |-- lexicon.txt
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# `-- speaker.info
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if [ ! -d $dl_dir/aishell/data_aishell/wav/train ]; then
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lhotse download aishell $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/musan
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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 aishell manifest"
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# We assume that you have downloaded the aishell corpus
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# to $dl_dir/aishell
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if [ ! -f data/manifests/.aishell_manifests.done ]; then
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mkdir -p data/manifests
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lhotse prepare aishell $dl_dir/aishell data/manifests
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touch data/manifests/.aishell_manifests.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 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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# if [ ! -f data/manifests/.musan_manifests.done ]; then
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# log "It may take 6 minutes"
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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_manifests.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 aishell"
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if [ ! -f data/fbank/.aishell.done ]; then
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mkdir -p data/fbank
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./local/compute_fbank_aishell.py --perturb-speed ${perturb_speed}
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touch data/fbank/.aishell.done
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fi
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fi
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# if [ $stage -le 30 ] && [ $stop_stage -ge 30 ]; then
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# log "Stage 30: Compute whisper fbank for aishell"
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# if [ ! -f data/fbank/.aishell.done ]; then
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# mkdir -p data/fbank
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# ./local/compute_whisper_fbank_aishell.py --perturb-speed True
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# touch data/fbank/.aishell.done
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# fi
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# fi
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if [ $stage -le 300 ] && [ $stop_stage -ge 300 ]; then
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log "Stage 30: Compute whisper fbank for aishell"
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if [ ! -f data/fbank/.aishell.done ]; then
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mkdir -p data/fbank
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./local/compute_whisper_fbank_aishell.py --perturb-speed True --num-mel-bins 128
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touch data/fbank/.aishell.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 [ ! -f data/fbank/.msuan.done ]; then
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# mkdir -p data/fbank
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# ./local/compute_fbank_musan.py
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# touch data/fbank/.msuan.done
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# fi
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# fi
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# if [ $stage -le 40 ] && [ $stop_stage -ge 40 ]; then
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# log "Stage 4: Compute fbank for musan"
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# if [ ! -f data/fbank/.msuan.done ]; then
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# mkdir -p data/fbank
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# ./local/compute_whisper_fbank_musan.py
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# touch data/fbank/.msuan.done
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# fi
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# fi
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# lang_phone_dir=data/lang_phone
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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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# mkdir -p $lang_phone_dir
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# (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
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# cat - $dl_dir/aishell/resource_aishell/lexicon.txt |
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# sort | uniq > $lang_phone_dir/lexicon.txt
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# ./local/generate_unique_lexicon.py --lang-dir $lang_phone_dir
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# if [ ! -f $lang_phone_dir/L_disambig.pt ]; then
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# ./local/prepare_lang.py --lang-dir $lang_phone_dir
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# fi
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# Train a bigram P for MMI training
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if [ ! -f $lang_phone_dir/transcript_words.txt ]; then
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log "Generate data to train phone based bigram P"
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aishell_text=$dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
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aishell_train_uid=$dl_dir/aishell/data_aishell/transcript/aishell_train_uid
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find $dl_dir/aishell/data_aishell/wav/train -name "*.wav" | sed 's/\.wav//g' | awk -F '/' '{print $NF}' > $aishell_train_uid
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awk 'NR==FNR{uid[$1]=$1} NR!=FNR{if($1 in uid) print $0}' $aishell_train_uid $aishell_text |
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cut -d " " -f 2- > $lang_phone_dir/transcript_words.txt
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fi
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# if [ ! -f $lang_phone_dir/transcript_tokens.txt ]; then
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# ./local/convert_transcript_words_to_tokens.py \
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# --lexicon $lang_phone_dir/uniq_lexicon.txt \
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# --transcript $lang_phone_dir/transcript_words.txt \
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# --oov "<UNK>" \
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# > $lang_phone_dir/transcript_tokens.txt
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# fi
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# if [ ! -f $lang_phone_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_phone_dir/transcript_tokens.txt \
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# -lm $lang_phone_dir/P.arpa
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# fi
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# if [ ! -f $lang_phone_dir/P.fst.txt ]; then
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# python3 -m kaldilm \
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# --read-symbol-table="$lang_phone_dir/tokens.txt" \
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# --disambig-symbol='#0' \
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# --max-order=2 \
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# $lang_phone_dir/P.arpa > $lang_phone_dir/P.fst.txt
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# fi
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# fi
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# lang_char_dir=data/lang_char
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# if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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# log "Stage 6: Prepare char based lang"
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# mkdir -p $lang_char_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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# # The transcripts in training set, generated in stage 5
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# cp $lang_phone_dir/transcript_words.txt $lang_char_dir/transcript_words.txt
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# cat $dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt |
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# cut -d " " -f 2- > $lang_char_dir/text
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# (echo '<eps> 0'; echo '!SIL 1'; echo '<SPOKEN_NOISE> 2'; echo '<UNK> 3';) \
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# > $lang_char_dir/words.txt
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# cat $lang_char_dir/text | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
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# | awk '{print $1" "NR+3}' >> $lang_char_dir/words.txt
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# num_lines=$(< $lang_char_dir/words.txt wc -l)
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# (echo "#0 $num_lines"; echo "<s> $(($num_lines + 1))"; echo "</s> $(($num_lines + 2))";) \
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# >> $lang_char_dir/words.txt
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# if [ ! -f $lang_char_dir/L_disambig.pt ]; then
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# ./local/prepare_char.py --lang-dir $lang_char_dir
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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: Prepare Byte BPE based lang"
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# for vocab_size in ${vocab_sizes[@]}; do
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# lang_dir=data/lang_bbpe_${vocab_size}
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# mkdir -p $lang_dir
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# cp $lang_char_dir/words.txt $lang_dir
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# cp $lang_char_dir/text $lang_dir
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# if [ ! -f $lang_dir/bbpe.model ]; then
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# ./local/train_bbpe_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/text
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# fi
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# if [ ! -f $lang_dir/L_disambig.pt ]; then
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# ./local/prepare_lang_bbpe.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 8 ] && [ $stop_stage -ge 8 ]; then
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# log "Stage 8: Prepare G"
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# mkdir -p data/lm
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# # Train LM on transcripts
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# if [ ! -f data/lm/3-gram.unpruned.arpa ]; then
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# python3 ./shared/make_kn_lm.py \
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# -ngram-order 3 \
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# -text $lang_char_dir/transcript_words.txt \
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# -lm data/lm/3-gram.unpruned.arpa
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# fi
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# We assume you have installed kaldilm, if not, please install
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# it using: pip install kaldilm
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if [ ! -f data/lm/G_3_gram_char.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="$lang_phone_dir/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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data/lm/3-gram.unpruned.arpa > data/lm/G_3_gram_phone.fst.txt
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python3 -m kaldilm \
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--read-symbol-table="$lang_char_dir/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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data/lm/3-gram.unpruned.arpa > data/lm/G_3_gram_char.fst.txt
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fi
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if [ ! -f $lang_char_dir/HLG.fst ]; then
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./local/prepare_lang_fst.py \
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--lang-dir $lang_char_dir \
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--ngram-G ./data/lm/G_3_gram_char.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 LG & HLG"
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# ./local/compile_hlg.py --lang-dir $lang_phone_dir --lm G_3_gram_phone
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# ./local/compile_hlg.py --lang-dir $lang_char_dir --lm G_3_gram_char
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# for vocab_size in ${vocab_sizes[@]}; do
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# lang_dir=data/lang_bbpe_${vocab_size}
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# ./local/compile_hlg.py --lang-dir $lang_dir --lm G_3_gram_char
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# done
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# ./local/compile_lg.py --lang-dir $lang_phone_dir --lm G_3_gram_phone
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# ./local/compile_lg.py --lang-dir $lang_char_dir --lm G_3_gram_char
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# for vocab_size in ${vocab_sizes[@]}; do
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# lang_dir=data/lang_bbpe_${vocab_size}
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# ./local/compile_lg.py --lang-dir $lang_dir --lm G_3_gram_char
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# done
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# fi
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# if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
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# log "Stage 10: Generate LM training data"
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# log "Processing char based data"
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# out_dir=data/lm_training_char
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# mkdir -p $out_dir $dl_dir/lm
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# if [ ! -f $dl_dir/lm/aishell-train-word.txt ]; then
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# cp $lang_phone_dir/transcript_words.txt $dl_dir/lm/aishell-train-word.txt
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# fi
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# # training words
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# ./local/prepare_char_lm_training_data.py \
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# --lang-char data/lang_char \
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# --lm-data $dl_dir/lm/aishell-train-word.txt \
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# --lm-archive $out_dir/lm_data.pt
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# # valid words
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# if [ ! -f $dl_dir/lm/aishell-valid-word.txt ]; then
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# aishell_text=$dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
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# aishell_valid_uid=$dl_dir/aishell/data_aishell/transcript/aishell_valid_uid
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# find $dl_dir/aishell/data_aishell/wav/dev -name "*.wav" | sed 's/\.wav//g' | awk -F '/' '{print $NF}' > $aishell_valid_uid
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# awk 'NR==FNR{uid[$1]=$1} NR!=FNR{if($1 in uid) print $0}' $aishell_valid_uid $aishell_text |
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# cut -d " " -f 2- > $dl_dir/lm/aishell-valid-word.txt
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# fi
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# ./local/prepare_char_lm_training_data.py \
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# --lang-char data/lang_char \
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# --lm-data $dl_dir/lm/aishell-valid-word.txt \
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# --lm-archive $out_dir/lm_data_valid.pt
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# # test words
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# if [ ! -f $dl_dir/lm/aishell-test-word.txt ]; then
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# aishell_text=$dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
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# aishell_test_uid=$dl_dir/aishell/data_aishell/transcript/aishell_test_uid
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# find $dl_dir/aishell/data_aishell/wav/test -name "*.wav" | sed 's/\.wav//g' | awk -F '/' '{print $NF}' > $aishell_test_uid
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# awk 'NR==FNR{uid[$1]=$1} NR!=FNR{if($1 in uid) print $0}' $aishell_test_uid $aishell_text |
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# cut -d " " -f 2- > $dl_dir/lm/aishell-test-word.txt
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# fi
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# ./local/prepare_char_lm_training_data.py \
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# --lang-char data/lang_char \
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# --lm-data $dl_dir/lm/aishell-test-word.txt \
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# --lm-archive $out_dir/lm_data_test.pt
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# fi
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# if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
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# log "Stage 11: Sort LM training data"
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# # Sort LM training data by sentence length in descending order
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# # for ease of training.
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# #
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# # Sentence length equals to the number of tokens
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# # in a sentence.
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# out_dir=data/lm_training_char
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# mkdir -p $out_dir
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# ln -snf ../../../librispeech/ASR/local/sort_lm_training_data.py local/
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# ./local/sort_lm_training_data.py \
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# --in-lm-data $out_dir/lm_data.pt \
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# --out-lm-data $out_dir/sorted_lm_data.pt \
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# --out-statistics $out_dir/statistics.txt
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# ./local/sort_lm_training_data.py \
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# --in-lm-data $out_dir/lm_data_valid.pt \
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# --out-lm-data $out_dir/sorted_lm_data-valid.pt \
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# --out-statistics $out_dir/statistics-valid.txt
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# ./local/sort_lm_training_data.py \
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# --in-lm-data $out_dir/lm_data_test.pt \
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# --out-lm-data $out_dir/sorted_lm_data-test.pt \
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# --out-statistics $out_dir/statistics-test.txt
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# fi
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# if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
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# log "Stage 11: Train RNN LM model"
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# python ../../../icefall/rnn_lm/train.py \
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# --start-epoch 0 \
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# --world-size 1 \
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# --num-epochs 20 \
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# --use-fp16 0 \
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# --embedding-dim 512 \
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# --hidden-dim 512 \
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# --num-layers 2 \
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# --batch-size 400 \
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# --exp-dir rnnlm_char/exp \
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# --lm-data $out_dir/sorted_lm_data.pt \
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# --lm-data-valid $out_dir/sorted_lm_data-valid.pt \
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# --vocab-size 4336 \
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# --master-port 12345
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# fi
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