#!/usr/bin/env bash set -eou pipefail nj=15 stage=10 stop_stage=12 # Split L subset to this number of pieces # This is to avoid OOM during feature extraction. num_splits=1000 # We assume dl_dir (download dir) contains the following # directories and files. If not, they will be downloaded # by this script automatically. # # - $dl_dir/WenetSpeech # You can find audio, WenetSpeech.json inside it. # You can apply for the download credentials by following # https://github.com/wenet-e2e/WenetSpeech#download # # - $dl_dir/musan # This directory contains the following directories downloaded from # http://www.openslr.org/17/ # # - music # - noise # - speech dl_dir=$PWD/download . shared/parse_options.sh || exit 1 # All files generated by this script are saved in "data". # You can safely remove "data" and rerun this script to regenerate it. mkdir -p data log() { # This function is from espnet local fname=${BASH_SOURCE[1]##*/} echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" } log "dl_dir: $dl_dir" if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then log "Stage 0: Download data" [ ! -e $dl_dir/WenetSpeech ] && mkdir -p $dl_dir/WenetSpeech # If you have pre-downloaded it to /path/to/WenetSpeech, # you can create a symlink # # ln -sfv /path/to/WenetSpeech $dl_dir/WenetSpeech # if [ ! -d $dl_dir/WenetSpeech/audio ] && [ ! -f $dl_dir/WenetSpeech.json ]; then log "Stage 0: should download WenetSpeech first" exit 1; fi # If you have pre-downloaded it to /path/to/musan, # you can create a symlink # # ln -sfv /path/to/musan $dl_dir/ # if [ ! -d $dl_dir/musan ]; then lhotse download musan $dl_dir fi fi if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then log "Stage 1: Prepare WenetSpeech manifest (may take 15 minutes)" # We assume that you have downloaded the WenetSpeech corpus # to $dl_dir/WenetSpeech mkdir -p data/manifests lhotse prepare wenet-speech $dl_dir/WenetSpeech data/manifests -j $nj fi if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then log "Stage 2: Prepare musan manifest" # We assume that you have downloaded the musan corpus # to $dl_dir/musan mkdir -p data/manifests lhotse prepare musan $dl_dir/musan data/manifests fi if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then log "State 3: Preprocess WenetSpeech manifest" if [ ! -f data/fbank/.preprocess_complete ]; then python3 ./local/preprocess_wenetspeech.py touch data/fbank/.preprocess_complete fi fi if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then log "Stage 4: Compute features for DEV and TEST subsets of WenetSpeech (may take 2 minutes)" python3 ./local/compute_fbank_wenetspeech_dev_test.py fi if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then log "Stage 5: Split L subset into ${num_splits} pieces (may take 30 minutes)" split_dir=data/fbank/L_split_${num_splits} if [ ! -f $split_dir/.split_completed ]; then lhotse split $num_splits ./data/fbank/cuts_L_raw.jsonl.gz $split_dir touch $split_dir/.split_completed fi fi if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then log "Stage 6: Compute features for L" python3 ./local/compute_fbank_wenetspeech_splits.py \ --num-workers 20 \ --batch-duration 600 \ --start 1000 \ --num-splits $num_splits fi if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then log "Stage 7: Combine features for L" if [ ! -f data/fbank/cuts_L.jsonl.gz ]; then pieces=$(find data/fbank/L_split_${num_splits} -name "cuts_L.*.jsonl.gz") lhotse combine $pieces data/fbank/cuts_L.jsonl.gz fi fi if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then log "Stage 8: Compute fbank for musan" mkdir -p data/fbank ./local/compute_fbank_musan.py fi lang_char_dir=data/lang_char if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then log "Stage 9: Prepare char based lang" mkdir -p $lang_char_dir gunzip -c data/manifests/supervisions_L.jsonl.gz \ | jq '.text' | sed 's/"//g' \ | ./local/text2token.py -t "char" > $lang_char_dir/text cat $lang_char_dir/text | sed 's/ /\n/g' \ | sort -u | sed '/^$/d' > $lang_char_dir/words.txt (echo ''; echo ''; echo ''; ) | cat - $lang_char_dir/words.txt | sort | uniq | awk ' BEGIN { print " 0"; } { if ($1 == "") { print " is in the vocabulary!" | "cat 1>&2" exit 1; } if ($1 == "") { print " is in the vocabulary!" | "cat 1>&2" exit 1; } printf("%s %d\n", $1, NR); } END { printf("#0 %d\n", NR+1); printf(" %d\n", NR+2); printf(" %d\n", NR+3); }' > $lang_char_dir/words || exit 1; mv $lang_char_dir/words $lang_char_dir/words.txt fi if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then if [ ! -f $lang_char_dir/L_disambig.pt ]; then ./local/prepare_char.py fi fi if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then log "Stage 11: Prepare G" # We assume you have install kaldilm, if not, please install # it using: pip install kaldilm mkdir -p data/lm if [ ! -f data/lm/3-gram.arpa ]; then ./shared/make_kn_lm.py \ -ngram-order 3 \ -text "data/lang_char/text" \ -lm data/lm/3-gram.arpa fi if [ ! -f data/lm/G_3_gram.fst.txt ]; then # It is used in building HLG python3 -m kaldilm \ --read-symbol-table="data/lang_char/words.txt" \ --disambig-symbol='#0' \ --max-order=3 \ data/lm/3-gram.arpa > data/lm/G_3_gram.fst.txt fi if [ ! -f data/lm/4-gram.arpa ]; then ./shared/make_kn_lm.py \ -ngram-order 4 \ -text "data/lang_char/text" \ -lm data/lm/4-gram.arpa fi if [ ! -f data/lm/G_4_gram.fst.txt ]; then # It is used for LM rescoring python3 -m kaldilm \ --read-symbol-table="data/lang_char/words.txt" \ --disambig-symbol='#0' \ --max-order=4 \ data/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt fi fi if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then log "Stage 12: Compile HLG" ./local/compile_hlg.py --lang-dir $lang_char_dir fi