mirror of
https://github.com/k2-fsa/icefall.git
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116 lines
3.1 KiB
Bash
116 lines
3.1 KiB
Bash
#!/usr/bin/env bash
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# Copyright 2024 Johns Hopkins University (Amir Hussein)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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set -eou pipefail
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nj=20
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stage=-1
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stop_stage=6
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# We assume dl_dir (download dir) contains the following
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# directories and files.
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#
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# - $dl_dir/mgb2
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#
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# You can download the data from
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#
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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#
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dl_dir=download
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. shared/parse_options.sh || exit 1
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# vocab size for sentence piece models.
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# It will generate data/lang_bpe_xxx,
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# data/lang_bpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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4000
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)
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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log() {
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# This function is from espnet
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local fname=${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 -1 ] && [ $stop_stage -ge -1 ]; then
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log "Stage 0: Download data"
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# Note: Download SEAME from https://catalog.ldc.upenn.edu/LDC2015S04
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#
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# downlaod the splits https://github.com/zengzp0912/SEAME-dev-set.git to $dl_dir
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#
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# If you have pre-downloaded it to /path/to/seame,
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# you can create a symlink
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#
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# ln -sfv /path/to/seame $dl_dir/seame
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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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fbank=data_seame/fbank
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manifests=data_seame/manifests
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mkdir -p $manifests
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if [ $stage -le 0] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Prepare seame manifest"
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# We assume that you have downloaded the corpus
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# to $dl_dir/seame
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lhotse prepare seame $dl_dir/seame $dl_dir/SEAME-dev-set data/manifests
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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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lhotse prepare musan $dl_dir/musan data/manifests
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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 features"
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mkdir -p ${fbank}
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python local/compute_fbank_gpu_seame.py
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gunzip -c $fbank/cuts_train.jsonl.gz | shuf | gzip -c > ${fbank}/cuts_train_shuf.jsonl.gz
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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: Prepare BPE based lang"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data_seame/lang_bpe_${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 text for BPE training from data_seame/fbank/cuts_train_shuf.jsonl.gz"
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python local/prepare_transcripts.py --cut ${fbank}/cuts_train_shuf.jsonl.gz --langdir ${lang_dir}
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fi
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source data_seame/manifests/token.man.1
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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 $lang_dir/transcript_words.txt \
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--predef-symbols "$bpe_nlsyms"
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done
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fi
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