mirror of
https://github.com/k2-fsa/icefall.git
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Add People's Speech to multidataset
This commit is contained in:
Yifan Yang
parent
7307440c53
commit
5d59f48193
@ -1,117 +0,0 @@
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#!/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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dl_dir=$PWD/download
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release=cv-corpus-13.0-2023-03-09
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lang=en
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. shared/parse_options.sh || exit 1
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# All files generated by this script are saved in "data/${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 [ $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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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 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Preprocess CommonVoice manifest"
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if [ ! -e data/${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 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Compute fbank for dev and test subsets of CommonVoice"
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mkdir -p data/${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 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: 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 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Compute features for train subset of CommonVoice"
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if [ ! -e data/${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 600 \
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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 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Combine features for train"
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if [ ! -f data/${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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@ -1,151 +0,0 @@
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#!/usr/bin/env bash
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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=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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# - $dl_dir/GigaSpeech
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# You can find audio, dict, GigaSpeech.json inside it.
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# You can apply for the download credentials by following
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# https://github.com/SpeechColab/GigaSpeech#download
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# Number of hours for GigaSpeech subsets
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# XL 10k hours
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# L 2.5k hours
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# M 1k hours
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# S 250 hours
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# XS 10 hours
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# DEV 12 hours
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# Test 40 hours
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# Split XL subset to this number of pieces
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# This is to avoid OOM during feature extraction.
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num_splits=2000
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# We use lazy split from lhotse.
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# The XL subset (10k hours) contains 37956 cuts without speed perturbing.
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# We want to split it into 2000 splits, so each split
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# contains about 37956 / 2000 = 19 cuts. As a result, there will be 1998 splits.
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chunk_size=19 # number of cuts in each split. The last split may contain fewer cuts.
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dl_dir=$PWD/download
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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 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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[ ! -e $dl_dir/GigaSpeech ] && mkdir -p $dl_dir/GigaSpeech
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# If you have pre-downloaded it to /path/to/GigaSpeech,
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# you can create a symlink
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#
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# ln -sfv /path/to/GigaSpeech $dl_dir/GigaSpeech
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#
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if [ ! -d $dl_dir/GigaSpeech/audio ] && [ ! -f $dl_dir/GigaSpeech.json ]; then
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# Check credentials.
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if [ ! -f $dl_dir/password ]; then
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echo -n "$0: Please apply for the download credentials by following"
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echo -n "https://github.com/SpeechColab/GigaSpeech#dataset-download"
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echo " and save it to $dl_dir/password."
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exit 1;
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fi
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PASSWORD=`cat $dl_dir/password 2>/dev/null`
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if [ -z "$PASSWORD" ]; then
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echo "$0: Error, $dl_dir/password is empty."
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exit 1;
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fi
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PASSWORD_MD5=`echo $PASSWORD | md5sum | cut -d ' ' -f 1`
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if [[ $PASSWORD_MD5 != "dfbf0cde1a3ce23749d8d81e492741b8" ]]; then
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echo "$0: Error, invalid $dl_dir/password."
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exit 1;
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fi
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# Download XL, DEV and TEST sets by default.
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lhotse download gigaspeech \
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--subset XL \
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--subset L \
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--subset M \
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--subset S \
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--subset XS \
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--subset DEV \
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--subset TEST \
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--host tsinghua \
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$dl_dir/password $dl_dir/GigaSpeech
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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 GigaSpeech manifest (may take 30 minutes)"
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# We assume that you have downloaded the GigaSpeech corpus
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# to $dl_dir/GigaSpeech
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if [ ! -f data/manifests/.gigaspeech.done ]; then
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mkdir -p data/manifests
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lhotse prepare gigaspeech \
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--subset XL \
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--subset L \
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--subset M \
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--subset S \
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--subset XS \
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--subset DEV \
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--subset TEST \
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-j $nj \
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$dl_dir/GigaSpeech data/manifests
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touch data/manifests/.gigaspeech.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: Preprocess GigaSpeech manifest"
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if [ ! -f data/fbank/.gigaspeech_preprocess.done ]; then
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log "It may take 2 hours for this stage"
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./local/preprocess_gigaspeech.py
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touch data/fbank/.gigaspeech_preprocess.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 features for DEV and TEST subsets of GigaSpeech (may take 2 minutes)"
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if [ ! -f data/fbank/.gigaspeech_dev_test.done ]; then
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./local/compute_fbank_gigaspeech_dev_test.py
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touch data/fbank/.gigaspeech_dev_test.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: Split XL subset into ${num_splits} pieces"
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split_dir=data/fbank/gigaspeech_XL_split_${num_splits}
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if [ ! -f $split_dir/.gigaspeech_XL_split.done ]; then
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lhotse split-lazy ./data/fbank/gigaspeech_cuts_XL_raw.jsonl.gz $split_dir $chunk_size
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touch $split_dir/.gigaspeech_XL_split.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: Compute features for XL"
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# Note: The script supports --start and --stop options.
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# You can use several machines to compute the features in parallel.
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if [ ! -f data/fbank/.gigaspeech_XL.done ]; then
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./local/compute_fbank_gigaspeech_splits.py \
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--num-workers $nj \
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--batch-duration 600 \
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--num-splits $num_splits
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touch data/fbank/.gigaspeech_XL.done
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fi
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fi
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@ -281,28 +281,7 @@ if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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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: Compile HLG"
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./local/compile_hlg.py --lang-dir data/lang_phone
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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 data/lang_phone
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${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 9 ] && [ $stop_stage -ge 9 ]; then
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log "Stage 9: Compile LG"
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log "Stage 8: Compile LG"
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./local/compile_lg.py --lang-dir data/lang_phone
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for vocab_size in ${vocab_sizes[@]}; do
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@ -311,23 +290,51 @@ if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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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 the other datasets"
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if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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log "Stage 9: Prepare the other datasets"
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# GigaSpeech
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if [[ "${multidataset[@]}" =~ "gigaspeech" ]]; then
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if [[ "${multidataset[@]}" =~ "gigaspeech" ]] && [ ! -f data/fbank/.gigaspeech.done ]; then
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log "Dataset: GigaSpeech"
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./prepare_giga_speech.sh
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cd data/fbank
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if [ -f ../../../../gigaspeech/ASR/data/fbank/XL_split/.split_completed ]; then
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ln -svf $(realpath ../../../../gigaspeech/ASR/data/fbank/XL_split) .
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else
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log "Abort! Please run gigaspeech prepare.sh --stage 5 --stop-stage 6"
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exit 1
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fi
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touch .gigaspeech.done
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cd ../..
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fi
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# CommonVoice
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if [[ "${multidataset[@]}" =~ "commonvoice" ]]; then
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if [[ "${multidataset[@]}" =~ "commonvoice" ]] && [ ! -f data/fbank/.commonvoice.done ]; then
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log "Dataset: CommonVoice"
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./prepare_common_voice.sh
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cd data/fbank
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if [ -f ../../../../commonvoice/ASR/data/en/fbank/.cv-en_train.done ]; then
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ln -svf $(realpath ../../../../commonvoice/ASR/data/en/fbank/cv-en_train_split_1000) .
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ln -svf $(realpath ../../../../commonvoice/ASR/data/en/fbank/cv-en_cuts_train.jsonl.gz) .
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else
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log "Abort! Please run commonvoice prepare.sh --stage 5 --stop-stage 6"
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exit 1
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fi
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touch .commonvoice.done
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cd ../..
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fi
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# People's Speech
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if [[ "${multidataset[@]}" =~ "peoples_speech" ]]; then
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if [[ "${multidataset[@]}" =~ "peoples_speech" ]] && [ ! -f data/fbank/.peoples_speech.done ]; then
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log "Dataset: People's Speech"
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./prepare_peoples_speech.sh
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cd data/fbank
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if [ -f ../../../../peoples_speech/ASR/data/fbank/.peoples_speech_train.done ]; then
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ln -svf $(realpath ../../../../peoples_speech/ASR/data/fbank/peoples_speech_train_split) .
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else
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log "Abort! Please run commonvoice prepare.sh --stage 5 --stop-stage 6"
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exit 1
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fi
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touch .peoples_speech.done
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cd ../..
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fi
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fi
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@ -1,127 +0,0 @@
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#!/usr/bin/env bash
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set -eou pipefail
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nj=32
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stage=-1
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stop_stage=100
|
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|
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# Split data/set to a number of pieces
|
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# This is to avoid OOM during feature extraction.
|
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num_per_split=4000
|
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|
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# We assume dl_dir (download dir) contains the following
|
||||
# directories and files. If not, they will be downloaded
|
||||
# by this script automatically.
|
||||
#
|
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# - $dl_dir/peoples_speech
|
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# This directory contains the following files downloaded from
|
||||
# https://huggingface.co/datasets/MLCommons/peoples_speech
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#
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# - test
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# - train
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# - validation
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dl_dir=$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_bpe_xxx,
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# data/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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|
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# 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]}) $*"
|
||||
}
|
||||
|
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log "dl_dir: $dl_dir"
|
||||
|
||||
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
|
||||
log "Stage 0: Download data"
|
||||
|
||||
# If you have pre-downloaded it to /path/to/peoples_speech,
|
||||
# you can create a symlink
|
||||
#
|
||||
# ln -sfv /path/to/peoples_speech $dl_dir/peoples_speech
|
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#
|
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if [ ! -d $dl_dir/peoples_speech/train ]; then
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git lfs install
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git clone https://huggingface.co/datasets/MLCommons/peoples_speech
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fi
|
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fi
|
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|
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
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log "Stage 1: Prepare People's Speech manifest"
|
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# We assume that you have downloaded the People's Speech corpus
|
||||
# to $dl_dir/peoples_speech
|
||||
mkdir -p data/manifests
|
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if [ ! -e data/manifests/.peoples_speech.done ]; then
|
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lhotse prepare peoples-speech -j $nj $dl_dir/peoples_speech data/manifests
|
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touch data/manifests/.peoples_speech.done
|
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fi
|
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fi
|
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|
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
|
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log "Stage 2: Preprocess People's Speech manifest"
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mkdir -p data/fbank
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if [ ! -e data/fbank/.preprocess_complete ]; then
|
||||
./local/preprocess_peoples_speech.py
|
||||
touch data/fbank/.preprocess_complete
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
|
||||
log "Stage 3: Compute fbank for valid and test subsets of People's Speech"
|
||||
if [ ! -e data/fbank/.peoples_speech_valid_test.done ]; then
|
||||
./local/compute_fbank_peoples_speech_valid_test.py
|
||||
touch data/fbank/.peoples_speech_valid_test.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
||||
log "Stage 4: Split train subset into pieces"
|
||||
split_dir=data/fbank/peoples_speech_train_split
|
||||
if [ ! -e $split_dir/.peoples_speech_dirty_split.done ]; then
|
||||
lhotse split-lazy ./data/fbank/peoples_speech_cuts_dirty_raw.jsonl.gz $split_dir $num_per_split
|
||||
touch $split_dir/.peoples_speech_dirty_split.done
|
||||
fi
|
||||
|
||||
if [ ! -e $split_dir/.peoples_speech_dirty_sa_split.done ]; then
|
||||
lhotse split-lazy ./data/fbank/peoples_speech_cuts_dirty_sa_raw.jsonl.gz $split_dir $num_per_split
|
||||
touch $split_dir/.peoples_speech_dirty_sa_split.done
|
||||
fi
|
||||
|
||||
if [ ! -e $split_dir/.peoples_speech_clean_split.done ]; then
|
||||
lhotse split-lazy ./data/fbank/peoples_speech_cuts_clean_raw.jsonl.gz $split_dir $num_per_split
|
||||
touch $split_dir/.peoples_speech_clean_split.done
|
||||
fi
|
||||
|
||||
if [ ! -e $split_dir/.peoples_speech_clean_sa_split.done ]; then
|
||||
lhotse split-lazy ./data/fbank/peoples_speech_cuts_clean_sa_raw.jsonl.gz $split_dir $num_per_split
|
||||
touch $split_dir/.peoples_speech_clean_sa_split.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
|
||||
log "Stage 5: Compute features for train subset of People's Speech"
|
||||
if [ ! -e data/fbank/.peoples_speech_train.done ]; then
|
||||
./local/compute_fbank_peoples_speech_splits.py \
|
||||
--num-workers $nj \
|
||||
--batch-duration 600 \
|
||||
--start 0 \
|
||||
--num-splits 2000
|
||||
touch data/fbank/.peoples_speech_train.done
|
||||
fi
|
||||
fi
|
||||
@ -25,26 +25,21 @@ from lhotse import CutSet, load_manifest_lazy
|
||||
|
||||
|
||||
class MultiDataset:
|
||||
def __init__(self, manifest_dir: str, cv_manifest_dir: str):
|
||||
def __init__(self, manifest_dir: str):
|
||||
"""
|
||||
Args:
|
||||
manifest_dir:
|
||||
It is expected to contain the following files:
|
||||
|
||||
- librispeech_cuts_train-all-shuf.jsonl.gz
|
||||
- gigaspeech_XL_split_2000/gigaspeech_cuts_XL.*.jsonl.gz
|
||||
- XL_split_2000/cuts_XL.*.jsonl.gz
|
||||
- peoples_speech_train_split/peoples_speech_cuts_dirty.*.jsonl.gz
|
||||
- peoples_speech_train_split/peoples_speech_cuts_dirty_sa.*.jsonl.gz
|
||||
- peoples_speech_train_split/peoples_speech_cuts_clean.*.jsonl.gz
|
||||
- peoples_speech_train_split/peoples_speech_cuts_clean_sa.*.jsonl.gz
|
||||
|
||||
cv_manifest_dir:
|
||||
It is expected to contain the following files:
|
||||
|
||||
- cv-en_cuts_train.jsonl.gz
|
||||
"""
|
||||
self.manifest_dir = Path(manifest_dir)
|
||||
self.cv_manifest_dir = Path(cv_manifest_dir)
|
||||
|
||||
def train_cuts(self) -> CutSet:
|
||||
logging.info("About to get multidataset train cuts")
|
||||
@ -57,10 +52,10 @@ class MultiDataset:
|
||||
|
||||
# GigaSpeech
|
||||
filenames = glob.glob(
|
||||
f"{self.manifest_dir}/gigaspeech_XL_split_2000/gigaspeech_cuts_XL.*.jsonl.gz"
|
||||
f"{self.manifest_dir}/XL_split_2000/cuts_XL.*.jsonl.gz"
|
||||
)
|
||||
|
||||
pattern = re.compile(r"gigaspeech_cuts_XL.([0-9]+).jsonl.gz")
|
||||
pattern = re.compile(r"cuts_XL.([0-9]+).jsonl.gz")
|
||||
idx_filenames = ((int(pattern.search(f).group(1)), f) for f in filenames)
|
||||
idx_filenames = sorted(idx_filenames, key=lambda x: x[0])
|
||||
|
||||
@ -75,7 +70,7 @@ class MultiDataset:
|
||||
# CommonVoice
|
||||
logging.info(f"Loading CommonVoice in lazy mode")
|
||||
commonvoice_cuts = load_manifest_lazy(
|
||||
self.cv_manifest_dir / f"cv-en_cuts_train.jsonl.gz"
|
||||
self.manifest_dir / f"cv-en_cuts_train.jsonl.gz"
|
||||
)
|
||||
|
||||
# People's Speech
|
||||
|
||||
@ -1043,7 +1043,7 @@ def run(rank, world_size, args):
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
if params.use_multidataset:
|
||||
multidataset = MultiDataset(params.manifest_dir, params.cv_manifest_dir)
|
||||
multidataset = MultiDataset(params.manifest_dir)
|
||||
train_cuts = multidataset.train_cuts()
|
||||
else:
|
||||
if params.mini_libri:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user