mirror of
https://github.com/k2-fsa/icefall.git
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d67a49afe4
* Add Common Voice for multidataset * Add prepare_multidataset.sh * Add dataset mixing * Update prepare_multidataset.sh * Update prepare_giga_speech.sh * update comments * Add split and shuffle mechanism * Add multi-dataset train * Fix for deleting * Fix for modifying * Add comments * Change type for perturb_speed * Fix for style check * Small fix * Add filter * Remove warning
118 lines
3.7 KiB
Bash
Executable File
118 lines
3.7 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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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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