mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-10 02:22:17 +00:00
114 lines
3.7 KiB
Bash
114 lines
3.7 KiB
Bash
#!/usr/bin/env bash
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# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
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export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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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/ReazonSpeech
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# You can find FLAC files in this directory.
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# You can download them from https://huggingface.co/datasets/reazon-research/reazonspeech
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#
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# - $dl_dir/dataset.json
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# The metadata of the ReazonSpeech dataset.
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dl_dir=$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 "Running prepare.sh"
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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/ReazonSpeech,
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# you can create a symlink
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#
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# ln -sfv /path/to/ReazonSpeech $dl_dir/ReazonSpeech
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#
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if [ ! -d $dl_dir/ReazonSpeech/downloads ]; then
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# Download small-v1 by default.
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lhotse download reazonspeech --subset small-v1 $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 ReazonSpeech manifest"
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# We assume that you have downloaded the ReazonSpeech corpus
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# to $dl_dir/ReazonSpeech
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mkdir -p data/manifests
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if [ ! -e data/manifests/.reazonspeech.done ]; then
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lhotse prepare reazonspeech -j $nj $dl_dir/ReazonSpeech data/manifests
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touch data/manifests/.reazonspeech.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: Compute ReazonSpeech fbank"
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if [ ! -e data/manifests/.reazonspeech-validated.done ]; then
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python local/compute_fbank_reazonspeech.py --manifest-dir data/manifests
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python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_train.jsonl.gz
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python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_dev.jsonl.gz
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python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_test.jsonl.gz
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touch data/manifests/.reazonspeech-validated.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: Prepare ReazonSpeech lang_char"
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python local/prepare_lang_char.py data/manifests/reazonspeech_cuts_train.jsonl.gz
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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: Show manifest statistics"
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python local/display_manifest_statistics.py --manifest-dir data/manifests > data/manifests/manifest_statistics.txt
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cat data/manifests/manifest_statistics.txt
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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: Prepare BPE based lang"
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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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mkdir -p $lang_dir
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if [ ! -f $lang_dir/transcript_words.txt ]; then
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log "Generate data for BPE training"
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files=$(
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find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
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)
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for f in ${files[@]}; do
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cat $f | cut -d " " -f 2-
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done > $lang_dir/transcript_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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done
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fi |