mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-14 04:22:21 +00:00
167 lines
4.7 KiB
Bash
Executable File
167 lines
4.7 KiB
Bash
Executable File
#!/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=3
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stop_stage=3
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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/icmcasr
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# You can find data_icmcasr, resource_icmcasr inside it.
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# You can download them from https://www.openslr.org/33
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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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# ln -s /your/parent/path/to/ICMC-ASR $PWD/downloa
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dl_dir=$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_bbpe_xxx,
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# data/lang_bbpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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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".
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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 1: Prepare icmcasr manifest"
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# We assume that you have downloaded the icmcasr corpus
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# to $dl_dir/icmcasr
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if [ ! -f data/manifests/.icmcasr_manifests.done ]; then
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mkdir -p data/manifests
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for part in ihm sdm; do
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lhotse prepare icmcasr --mic ${part} $dl_dir/ICMC-ASR data/manifests
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done
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touch data/manifests/.icmcasr_manifests.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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if [ ! -f data/manifests/.musan_manifests.done ]; then
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mkdir -p data/manifests
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lhotse prepare musan $dl_dir/musan data/manifests
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touch data/manifests/.musan_manifests.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 fbank for icmcasr"
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if [ ! -f data/fbank/.icmcasr.done ]; then
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mkdir -p data/fbank
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./local/compute_fbank_icmcasr.py --perturb-speed True
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touch data/fbank/.icmcasr.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: Compute fbank for musan"
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if [ ! -f data/fbank/.msuan.done ]; then
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mkdir -p data/fbank
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./local/compute_fbank_musan.py
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touch data/fbank/.msuan.done
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fi
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fi
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lang_phone_dir=data/lang_phone
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Prepare phone based lang"
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mkdir -p $lang_phone_dir
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(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
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cat - $dl_dir/icmcasr/resource_icmcasr/lexicon.txt |
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sort | uniq > $lang_phone_dir/lexicon.txt
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./local/generate_unique_lexicon.py --lang-dir $lang_phone_dir
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if [ ! -f $lang_phone_dir/L_disambig.pt ]; then
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./local/prepare_lang.py --lang-dir $lang_phone_dir
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fi
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fi
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lang_char_dir=data/lang_char
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Prepare char based lang"
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mkdir -p $lang_char_dir
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# We reuse words.txt from phone based lexicon
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# so that the two can share G.pt later.
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# The transcripts in training set, generated in stage 5
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cp $lang_phone_dir/transcript_words.txt $lang_char_dir/transcript_words.txt
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cat $dl_dir/icmcasr/data_icmcasr/transcript/icmcasr_transcript_v0.8.txt |
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cut -d " " -f 2- > $lang_char_dir/text
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(echo '<eps> 0'; echo '!SIL 1'; echo '<SPOKEN_NOISE> 2'; echo '<UNK> 3';) \
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> $lang_char_dir/words.txt
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cat $lang_char_dir/text | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
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| awk '{print $1" "NR+3}' >> $lang_char_dir/words.txt
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num_lines=$(< $lang_char_dir/words.txt wc -l)
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(echo "#0 $num_lines"; echo "<s> $(($num_lines + 1))"; echo "</s> $(($num_lines + 2))";) \
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>> $lang_char_dir/words.txt
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if [ ! -f $lang_char_dir/L_disambig.pt ]; then
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./local/prepare_char.py --lang-dir $lang_char_dir
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fi
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if [ ! -f $lang_char_dir/HLG.fst ]; then
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./local/prepare_lang_fst.py --lang-dir $lang_phone_dir --ngram-G ./data/lm/G_3_gram.fst.txt
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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: Prepare Byte BPE based lang"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bbpe_${vocab_size}
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mkdir -p $lang_dir
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cp $lang_char_dir/words.txt $lang_dir
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cp $lang_char_dir/text $lang_dir
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if [ ! -f $lang_dir/bbpe.model ]; then
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./local/train_bbpe_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/text
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fi
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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang_bbpe.py --lang-dir $lang_dir
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fi
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done
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fi
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