mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
348 lines
10 KiB
Bash
Executable File
348 lines
10 KiB
Bash
Executable File
#!/usr/bin/env bash
|
|
|
|
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
|
|
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
|
|
|
|
set -eou pipefail
|
|
|
|
nj=15
|
|
stage=0
|
|
stop_stage=100
|
|
|
|
# Split XL subset to a number of pieces (about 2000)
|
|
# This is to avoid OOM during feature extraction.
|
|
num_per_split=50
|
|
|
|
# We assume dl_dir (download dir) contains the following
|
|
# directories and files. If not, they will be downloaded
|
|
# by this script automatically.
|
|
#
|
|
# - $dl_dir/GigaSpeech
|
|
# You can find audio, dict, GigaSpeech.json inside it.
|
|
# You can apply for the download credentials by following
|
|
# https://github.com/SpeechColab/GigaSpeech#download
|
|
#
|
|
# - $dl_dir/lm
|
|
# This directory contains the language model downloaded from
|
|
# https://huggingface.co/wgb14/gigaspeech_lm
|
|
#
|
|
# - 3gram_pruned_1e7.arpa.gz
|
|
# - 4gram.arpa.gz
|
|
# - lexicon.txt
|
|
#
|
|
# - $dl_dir/musan
|
|
# This directory contains the following directories downloaded from
|
|
# http://www.openslr.org/17/
|
|
#
|
|
# - music
|
|
# - noise
|
|
# - speech
|
|
dl_dir=$PWD/download
|
|
|
|
. shared/parse_options.sh || exit 1
|
|
|
|
# vocab size for sentence piece models.
|
|
# It will generate data/lang_bpe_xxx,
|
|
# data/lang_bpe_yyy if the array contains xxx, yyy
|
|
vocab_sizes=(
|
|
500
|
|
)
|
|
|
|
# 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 -1 ] && [ $stop_stage -ge -1 ]; then
|
|
log "stage -1: Download LM"
|
|
# We assume that you have installed the git-lfs, if not, you could install it
|
|
# using: `sudo apt-get install git-lfs && git-lfs install`
|
|
[ ! -e $dl_dir/lm ] && mkdir -p $dl_dir/lm
|
|
git clone https://huggingface.co/wgb14/gigaspeech_lm $dl_dir/lm
|
|
gunzip -c $dl_dir/lm/3gram_pruned_1e7.arpa.gz > $dl_dir/lm/3gram_pruned_1e7.arpa
|
|
gunzip -c $dl_dir/lm/4gram.arpa.gz > $dl_dir/lm/4gram.arpa
|
|
fi
|
|
|
|
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
|
|
log "Stage 0: Download data"
|
|
|
|
[ ! -e $dl_dir/GigaSpeech ] && mkdir -p $dl_dir/GigaSpeech
|
|
|
|
# If you have pre-downloaded it to /path/to/GigaSpeech,
|
|
# you can create a symlink
|
|
#
|
|
# ln -sfv /path/to/GigaSpeech $dl_dir/GigaSpeech
|
|
#
|
|
if [ ! -d $dl_dir/GigaSpeech/audio ] && [ ! -f $dl_dir/GigaSpeech.json ]; then
|
|
# Check credentials.
|
|
if [ ! -f $dl_dir/password ]; then
|
|
echo -n "$0: Please apply for the download credentials by following"
|
|
echo -n "https://github.com/SpeechColab/GigaSpeech#download"
|
|
echo " and save it to $dl_dir/password."
|
|
exit 1;
|
|
fi
|
|
PASSWORD=`cat $dl_dir/password 2>/dev/null`
|
|
if [ -z "$PASSWORD" ]; then
|
|
echo "$0: Error, $dl_dir/password is empty."
|
|
exit 1;
|
|
fi
|
|
PASSWORD_MD5=`echo $PASSWORD | md5sum | cut -d ' ' -f 1`
|
|
if [[ $PASSWORD_MD5 != "dfbf0cde1a3ce23749d8d81e492741b8" ]]; then
|
|
echo "$0: Error, invalid $dl_dir/password."
|
|
exit 1;
|
|
fi
|
|
# Download XL, DEV and TEST sets by default.
|
|
lhotse download gigaspeech --subset XL \
|
|
--subset L \
|
|
--subset M \
|
|
--subset S \
|
|
--subset XS \
|
|
--subset DEV \
|
|
--subset TEST \
|
|
--host tsinghua \
|
|
$dl_dir/password $dl_dir/GigaSpeech
|
|
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 GigaSpeech manifest (may take 15 minutes)"
|
|
# We assume that you have downloaded the GigaSpeech corpus
|
|
# to $dl_dir/GigaSpeech
|
|
mkdir -p data/manifests
|
|
lhotse prepare gigaspeech --subset XL \
|
|
--subset L \
|
|
--subset M \
|
|
--subset S \
|
|
--subset XS \
|
|
--subset DEV \
|
|
--subset TEST \
|
|
-j $nj \
|
|
$dl_dir/GigaSpeech data/manifests
|
|
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 GigaSpeech manifest"
|
|
if [ ! -f data/fbank/.preprocess_complete ]; then
|
|
python3 ./local/preprocess_gigaspeech.py
|
|
touch data/fbank/.preprocess_complete
|
|
fi
|
|
fi
|
|
|
|
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
|
log "Stage 4: Compute features for L, M, S, XS, DEV and TEST subsets of GigaSpeech."
|
|
python3 ./local/compute_fbank_gigaspeech.py
|
|
fi
|
|
|
|
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
|
|
log "Stage 5: Split XL subset into pieces (may take 30 minutes)"
|
|
split_dir=data/fbank/XL_split
|
|
if [ ! -f $split_dir/.split_completed ]; then
|
|
lhotse split-lazy ./data/fbank/gigaspeech_cuts_XL_raw.jsonl.gz $split_dir $num_per_split
|
|
touch $split_dir/.split_completed
|
|
fi
|
|
fi
|
|
|
|
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
|
log "Stage 6: Compute features for XL"
|
|
num_splits=$(find data/fbank/XL_split -name "gigaspeech_cuts_XL_raw.*.jsonl.gz" | wc -l)
|
|
python3 ./local/compute_fbank_gigaspeech_splits.py \
|
|
--num-workers 20 \
|
|
--batch-duration 600 \
|
|
--num-splits $num_splits
|
|
fi
|
|
|
|
if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
|
|
log "Stage 7: Combine features for XL (may take 3 hours)"
|
|
if [ ! -f data/fbank/gigaspeech_cuts_XL.jsonl.gz ]; then
|
|
pieces=$(find data/fbank/XL_split -name "gigaspeech_cuts_XL.*.jsonl.gz")
|
|
lhotse combine $pieces data/fbank/gigaspeech_cuts_XL.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
|
|
|
|
if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
|
|
log "Stage 9: Prepare transcript_words.txt and words.txt"
|
|
lang_dir=data/lang_phone
|
|
mkdir -p $lang_dir
|
|
if [ ! -f $lang_dir/transcript_words.txt ]; then
|
|
gunzip -c "data/manifests/gigaspeech_supervisions_XL.jsonl.gz" \
|
|
| jq '.text' \
|
|
| sed 's/"//g' \
|
|
> $lang_dir/transcript_words.txt
|
|
|
|
# Delete utterances with garbage meta tags
|
|
garbage_utterance_tags="<SIL> <MUSIC> <NOISE> <OTHER>"
|
|
for tag in $garbage_utterance_tags; do
|
|
sed -i "/${tag}/d" $lang_dir/transcript_words.txt
|
|
done
|
|
|
|
# Delete punctuations in utterances
|
|
punctuation_tags="<COMMA> <EXCLAMATIONPOINT> <PERIOD> <QUESTIONMARK>"
|
|
for tag in $punctuation_tags; do
|
|
sed -i "s/${tag}//g" $lang_dir/transcript_words.txt
|
|
done
|
|
|
|
# Ensure space only appears once
|
|
sed -i 's/\t/ /g' $lang_dir/transcript_words.txt
|
|
sed -i 's/[ ][ ]*/ /g' $lang_dir/transcript_words.txt
|
|
fi
|
|
|
|
cat $lang_dir/transcript_words.txt | sed 's/ /\n/g' \
|
|
| sort -u | sed '/^$/d' > $lang_dir/words.txt
|
|
(echo '!SIL'; echo '<SPOKEN_NOISE>'; echo '<UNK>'; ) |
|
|
cat - $lang_dir/words.txt | sort | uniq | awk '
|
|
BEGIN {
|
|
print "<eps> 0";
|
|
}
|
|
{
|
|
if ($1 == "<s>") {
|
|
print "<s> is in the vocabulary!" | "cat 1>&2"
|
|
exit 1;
|
|
}
|
|
if ($1 == "</s>") {
|
|
print "</s> is in the vocabulary!" | "cat 1>&2"
|
|
exit 1;
|
|
}
|
|
printf("%s %d\n", $1, NR);
|
|
}
|
|
END {
|
|
printf("#0 %d\n", NR+1);
|
|
printf("<s> %d\n", NR+2);
|
|
printf("</s> %d\n", NR+3);
|
|
}' > $lang_dir/words || exit 1;
|
|
mv $lang_dir/words $lang_dir/words.txt
|
|
fi
|
|
|
|
if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
|
|
log "Stage 10: Prepare phone based lang"
|
|
lang_dir=data/lang_phone
|
|
mkdir -p $lang_dir
|
|
|
|
(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
|
|
cat - $dl_dir/lm/lexicon.txt |
|
|
sort | uniq > $lang_dir/lexicon.txt
|
|
|
|
if [ ! -f $lang_dir/L_disambig.pt ]; then
|
|
./local/prepare_lang.py --lang-dir $lang_dir
|
|
fi
|
|
fi
|
|
|
|
if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
|
|
log "Stage 11: Prepare BPE based lang"
|
|
|
|
for vocab_size in ${vocab_sizes[@]}; do
|
|
lang_dir=data/lang_bpe_${vocab_size}
|
|
mkdir -p $lang_dir
|
|
# We reuse words.txt from phone based lexicon
|
|
# so that the two can share G.pt later.
|
|
cp data/lang_phone/{words.txt,transcript_words.txt} $lang_dir
|
|
|
|
if [ ! -f $lang_dir/bpe.model ]; then
|
|
./local/train_bpe_model.py \
|
|
--lang-dir $lang_dir \
|
|
--vocab-size $vocab_size \
|
|
--transcript $lang_dir/transcript_words.txt
|
|
fi
|
|
|
|
if [ ! -f $lang_dir/L_disambig.pt ]; then
|
|
./local/prepare_lang_bpe.py --lang-dir $lang_dir
|
|
fi
|
|
done
|
|
fi
|
|
|
|
if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
|
|
log "Stage 12: Prepare bigram P"
|
|
|
|
for vocab_size in ${vocab_sizes[@]}; do
|
|
lang_dir=data/lang_bpe_${vocab_size}
|
|
|
|
if [ ! -f $lang_dir/transcript_tokens.txt ]; then
|
|
./local/convert_transcript_words_to_tokens.py \
|
|
--lexicon $lang_dir/lexicon.txt \
|
|
--transcript $lang_dir/transcript_words.txt \
|
|
--oov "<UNK>" \
|
|
> $lang_dir/transcript_tokens.txt
|
|
fi
|
|
|
|
if [ ! -f $lang_dir/P.arpa ]; then
|
|
./shared/make_kn_lm.py \
|
|
-ngram-order 2 \
|
|
-text $lang_dir/transcript_tokens.txt \
|
|
-lm $lang_dir/P.arpa
|
|
fi
|
|
|
|
if [ ! -f $lang_dir/P.fst.txt ]; then
|
|
python3 -m kaldilm \
|
|
--read-symbol-table="$lang_dir/tokens.txt" \
|
|
--disambig-symbol='#0' \
|
|
--max-order=2 \
|
|
$lang_dir/P.arpa > $lang_dir/P.fst.txt
|
|
fi
|
|
done
|
|
fi
|
|
|
|
if [ $stage -le 13 ] && [ $stop_stage -ge 13 ]; then
|
|
log "Stage 13: Prepare G"
|
|
# We assume you have installed kaldilm, if not, please install
|
|
# it using: pip install kaldilm
|
|
|
|
mkdir -p data/lm
|
|
|
|
if [ ! -f data/lm/G_3_gram.fst.txt ]; then
|
|
# It is used in building HLG
|
|
python3 -m kaldilm \
|
|
--read-symbol-table="data/lang_phone/words.txt" \
|
|
--disambig-symbol='#0' \
|
|
--max-order=3 \
|
|
$dl_dir/lm/3gram_pruned_1e7.arpa > data/lm/G_3_gram.fst.txt
|
|
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_phone/words.txt" \
|
|
--disambig-symbol='#0' \
|
|
--max-order=4 \
|
|
$dl_dir/lm/4gram.arpa > data/lm/G_4_gram.fst.txt
|
|
fi
|
|
fi
|
|
|
|
if [ $stage -le 14 ] && [ $stop_stage -ge 14 ]; then
|
|
log "Stage 14: Compile HLG"
|
|
./local/compile_hlg.py --lang-dir data/lang_phone
|
|
|
|
for vocab_size in ${vocab_sizes[@]}; do
|
|
lang_dir=data/lang_bpe_${vocab_size}
|
|
./local/compile_hlg.py --lang-dir $lang_dir
|
|
done
|
|
fi
|