mirror of
https://github.com/k2-fsa/icefall.git
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1b2e99d374
* add the pruned_transducer_stateless7_streaming recipe for commonvoice * fix the symlinks * Update RESULTS.md
305 lines
8.9 KiB
Bash
Executable File
305 lines
8.9 KiB
Bash
Executable File
#!/usr/bin/env bash
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set -eou pipefail
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nj=16
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stage=-1
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stop_stage=100
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# Split data/${lang}set to this number of pieces
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# This is to avoid OOM during feature extraction.
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num_splits=1000
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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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# - $dl_dir/$release/$lang
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# This directory contains the following files downloaded from
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# https://mozilla-common-voice-datasets.s3.dualstack.us-west-2.amazonaws.com/${release}/${release}-${lang}.tar.gz
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#
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# - clips
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# - dev.tsv
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# - invalidated.tsv
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# - other.tsv
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# - reported.tsv
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# - test.tsv
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# - train.tsv
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# - validated.tsv
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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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release=cv-corpus-12.0-2022-12-07
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lang=fr
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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/${lang}/lang_bpe_xxx,
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# data/${lang}/lang_bpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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# 5000
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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/${lang}".
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# You can safely remove "data/${lang}" and rerun this script to regenerate it.
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mkdir -p data/${lang}
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log() {
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# This function is from espnet
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local fname=${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 ! command -v ffmpeg &> /dev/null; then
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echo "This dataset requires ffmpeg"
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echo "Please install ffmpeg first"
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echo ""
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echo " sudo apt-get install ffmpeg"
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exit 1
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fi
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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# If you have pre-downloaded it to /path/to/$release,
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# you can create a symlink
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#
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# ln -sfv /path/to/$release $dl_dir/$release
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#
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if [ ! -d $dl_dir/$release/$lang/clips ]; then
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lhotse download commonvoice --languages $lang --release $release $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 CommonVoice manifest"
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# We assume that you have downloaded the CommonVoice corpus
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# to $dl_dir/$release
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mkdir -p data/${lang}/manifests
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if [ ! -e data/${lang}/manifests/.cv-${lang}.done ]; then
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lhotse prepare commonvoice --language $lang -j $nj $dl_dir/$release data/${lang}/manifests
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touch data/${lang}/manifests/.cv-${lang}.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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mkdir -p data/manifests
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if [ ! -e data/manifests/.musan.done ]; then
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lhotse prepare musan $dl_dir/musan data/manifests
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touch data/manifests/.musan.done
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Preprocess CommonVoice manifest"
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if [ ! -e data/${lang}/fbank/.preprocess_complete ]; then
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./local/preprocess_commonvoice.py --language $lang
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touch data/${lang}/fbank/.preprocess_complete
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fi
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Compute fbank for dev and test subsets of CommonVoice"
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mkdir -p data/${lang}/fbank
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if [ ! -e data/${lang}/fbank/.cv-${lang}_dev_test.done ]; then
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./local/compute_fbank_commonvoice_dev_test.py --language $lang
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touch data/${lang}/fbank/.cv-${lang}_dev_test.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: Split train subset into ${num_splits} pieces"
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split_dir=data/${lang}/fbank/cv-${lang}_train_split_${num_splits}
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if [ ! -e $split_dir/.cv-${lang}_train_split.done ]; then
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lhotse split $num_splits ./data/${lang}/fbank/cv-${lang}_cuts_train_raw.jsonl.gz $split_dir
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touch $split_dir/.cv-${lang}_train_split.done
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fi
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Compute features for train subset of CommonVoice"
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if [ ! -e data/${lang}/fbank/.cv-${lang}_train.done ]; then
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./local/compute_fbank_commonvoice_splits.py \
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--num-workers $nj \
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--batch-duration 200 \
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--start 0 \
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--num-splits $num_splits \
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--language $lang
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touch data/${lang}/fbank/.cv-${lang}_train.done
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fi
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Combine features for train"
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if [ ! -f data/${lang}/fbank/cv-${lang}_cuts_train.jsonl.gz ]; then
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pieces=$(find data/${lang}/fbank/cv-${lang}_train_split_${num_splits} -name "cv-${lang}_cuts_train.*.jsonl.gz")
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lhotse combine $pieces data/${lang}/fbank/cv-${lang}_cuts_train.jsonl.gz
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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: Compute fbank for musan"
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mkdir -p data/fbank
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if [ ! -e data/fbank/.musan.done ]; then
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./local/compute_fbank_musan.py
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touch data/fbank/.musan.done
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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: Prepare BPE based lang"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/${lang}/lang_bpe_${vocab_size}
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mkdir -p $lang_dir
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if [ ! -f $lang_dir/transcript_words.txt ]; then
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log "Generate data for BPE training"
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file=$(
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find "data/${lang}/fbank/cv-${lang}_cuts_train.jsonl.gz"
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)
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gunzip -c ${file} | awk -F '"' '{print $30}' > $lang_dir/transcript_words.txt
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# Ensure space only appears once
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sed -i 's/\t/ /g' $lang_dir/transcript_words.txt
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sed -i 's/[ ][ ]*/ /g' $lang_dir/transcript_words.txt
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fi
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if [ ! -f $lang_dir/words.txt ]; then
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cat $lang_dir/transcript_words.txt | sed 's/ /\n/g' \
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| sort -u | sed '/^$/d' > $lang_dir/words.txt
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(echo '!SIL'; echo '<SPOKEN_NOISE>'; echo '<UNK>'; ) |
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cat - $lang_dir/words.txt | sort | uniq | awk '
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BEGIN {
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print "<eps> 0";
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}
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{
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if ($1 == "<s>") {
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print "<s> is in the vocabulary!" | "cat 1>&2"
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exit 1;
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}
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if ($1 == "</s>") {
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print "</s> is in the vocabulary!" | "cat 1>&2"
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exit 1;
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}
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printf("%s %d\n", $1, NR);
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}
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END {
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printf("#0 %d\n", NR+1);
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printf("<s> %d\n", NR+2);
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printf("</s> %d\n", NR+3);
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}' > $lang_dir/words || exit 1;
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mv $lang_dir/words $lang_dir/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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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: 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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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/${lang}/lang_bpe_${vocab_size}
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mkdir -p $lang_dir/lm
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#3-gram used in building HLG, 4-gram used for LM rescoring
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for ngram in 3 4; do
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if [ ! -f $lang_dir/lm/${ngram}gram.arpa ]; then
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./shared/make_kn_lm.py \
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-ngram-order ${ngram} \
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-text $lang_dir/transcript_words.txt \
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-lm $lang_dir/lm/${ngram}gram.arpa
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fi
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if [ ! -f $lang_dir/lm/${ngram}gram.fst.txt ]; then
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python3 -m kaldilm \
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--read-symbol-table="$lang_dir/words.txt" \
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--disambig-symbol='#0' \
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--max-order=${ngram} \
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$lang_dir/lm/${ngram}gram.arpa > $lang_dir/lm/G_${ngram}_gram.fst.txt
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fi
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done
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done
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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: Compile HLG"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/${lang}/lang_bpe_${vocab_size}
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./local/compile_hlg.py --lang-dir $lang_dir
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# Note If ./local/compile_hlg.py throws OOM,
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# please switch to the following command
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#
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# ./local/compile_hlg_using_openfst.py --lang-dir $lang_dir
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done
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fi
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# Compile LG for RNN-T fast_beam_search decoding
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if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
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log "Stage 12: Compile LG"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/${lang}/lang_bpe_${vocab_size}
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./local/compile_lg.py --lang-dir $lang_dir
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done
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fi
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