mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
367 lines
11 KiB
Bash
Executable File
367 lines
11 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=0
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stop_stage=100
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# Split L subset 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/WenetSpeech
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# You can find audio, WenetSpeech.json inside it.
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# You can apply for the download credentials by following
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# https://github.com/wenet-e2e/WenetSpeech#download
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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dl_dir=$PWD/download
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lang_char_dir=data/lang_char
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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/WenetSpeech ] && mkdir -p $dl_dir/WenetSpeech
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# If you have pre-downloaded it to /path/to/WenetSpeech,
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# you can create a symlink
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#
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# ln -sfv /path/to/WenetSpeech $dl_dir/WenetSpeech
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#
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if [ ! -d $dl_dir/WenetSpeech/wenet_speech ] && [ ! -f $dl_dir/WenetSpeech/metadata/v1.list ]; then
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log "Stage 0: You should download WenetSpeech first"
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exit 1;
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fi
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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/musan
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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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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare WenetSpeech manifest"
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# We assume that you have downloaded the WenetSpeech corpus
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# to $dl_dir/WenetSpeech
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mkdir -p data/manifests
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lhotse prepare wenet-speech $dl_dir/WenetSpeech data/manifests -j $nj
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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: Preprocess WenetSpeech manifest"
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if [ ! -f data/fbank/.preprocess_complete ]; then
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python3 ./local/preprocess_wenetspeech.py --perturb-speed True
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touch data/fbank/.preprocess_complete
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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 features for DEV and TEST subsets of WenetSpeech (may take 2 minutes)"
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python3 ./local/compute_fbank_wenetspeech_dev_test.py
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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: Split S subset into ${num_splits} pieces"
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split_dir=data/fbank/S_split_${num_splits}
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if [ ! -f $split_dir/.split_completed ]; then
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lhotse split $num_splits ./data/fbank/cuts_S_raw.jsonl.gz $split_dir
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touch $split_dir/.split_completed
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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: Split M subset into ${num_splits} piece"
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split_dir=data/fbank/M_split_${num_splits}
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if [ ! -f $split_dir/.split_completed ]; then
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lhotse split $num_splits ./data/fbank/cuts_M_raw.jsonl.gz $split_dir
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touch $split_dir/.split_completed
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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: Split L subset into ${num_splits} pieces"
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split_dir=data/fbank/L_split_${num_splits}
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if [ ! -f $split_dir/.split_completed ]; then
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lhotse split $num_splits ./data/fbank/cuts_L_raw.jsonl.gz $split_dir
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touch $split_dir/.split_completed
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fi
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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: Compute features for S"
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python3 ./local/compute_fbank_wenetspeech_splits.py \
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--training-subset S \
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--num-workers 20 \
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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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fi
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if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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log "Stage 9: Compute features for M"
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python3 ./local/compute_fbank_wenetspeech_splits.py \
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--training-subset M \
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--num-workers 20 \
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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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fi
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if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
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log "Stage 10: Compute features for L"
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python3 ./local/compute_fbank_wenetspeech_splits.py \
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--training-subset L \
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--num-workers 20 \
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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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fi
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if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
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log "Stage 11: Combine features for S"
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if [ ! -f data/fbank/cuts_S.jsonl.gz ]; then
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pieces=$(find data/fbank/S_split_1000 -name "cuts_S.*.jsonl.gz")
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lhotse combine $pieces data/fbank/cuts_S.jsonl.gz
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fi
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fi
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if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
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log "Stage 12: Combine features for M"
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if [ ! -f data/fbank/cuts_M.jsonl.gz ]; then
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pieces=$(find data/fbank/M_split_1000 -name "cuts_M.*.jsonl.gz")
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lhotse combine $pieces data/fbank/cuts_M.jsonl.gz
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fi
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fi
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if [ $stage -le 13 ] && [ $stop_stage -ge 13 ]; then
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log "Stage 13: Combine features for L"
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if [ ! -f data/fbank/cuts_L.jsonl.gz ]; then
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pieces=$(find data/fbank/L_split_1000 -name "cuts_L.*.jsonl.gz")
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lhotse combine $pieces data/fbank/cuts_L.jsonl.gz
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fi
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fi
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if [ $stage -le 14 ] && [ $stop_stage -ge 14 ]; then
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log "Stage 14: Compute fbank for musan"
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mkdir -p data/fbank
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./local/compute_fbank_musan.py
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fi
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if [ $stage -le 15 ] && [ $stop_stage -ge 15 ]; then
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log "Stage 15: Prepare char based lang"
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mkdir -p $lang_char_dir
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if ! which jq; then
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echo "This script is intended to be used with jq but you have not installed jq
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Note: in Linux, you can install jq with the following command:
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1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
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2. chmod +x ./jq
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3. cp jq /usr/bin" && exit 1
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fi
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if [ ! -f $lang_char_dir/text ] || [ ! -s $lang_char_dir/text ]; then
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log "Prepare text."
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gunzip -c data/manifests/wenetspeech_supervisions_L.jsonl.gz \
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| jq '.text' | sed 's/"//g' \
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| ./local/text2token.py -t "char" > $lang_char_dir/text
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fi
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# The implementation of chinese word segmentation for text,
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# and it will take about 15 minutes.
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if [ ! -f $lang_char_dir/text_words_segmentation ]; then
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python3 ./local/text2segments.py \
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--num-process $nj \
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--input-file $lang_char_dir/text \
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--output-file $lang_char_dir/text_words_segmentation
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fi
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cat $lang_char_dir/text_words_segmentation | sed 's/ /\n/g' \
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| sort -u | sed '/^$/d' | uniq > $lang_char_dir/words_no_ids.txt
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if [ ! -f $lang_char_dir/words.txt ]; then
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python3 ./local/prepare_words.py \
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--input-file $lang_char_dir/words_no_ids.txt \
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--output-file $lang_char_dir/words.txt
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fi
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fi
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if [ $stage -le 16 ] && [ $stop_stage -ge 16 ]; then
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log "Stage 16: Prepare char based L_disambig.pt"
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if [ ! -f data/lang_char/L_disambig.pt ]; then
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python3 ./local/prepare_char.py \
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--lang-dir data/lang_char
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fi
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fi
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# If you don't want to use LG for decoding, the following steps are not necessary.
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if [ $stage -le 17 ] && [ $stop_stage -ge 17 ]; then
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log "Stage 17: Prepare G"
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# It will take about 20 minutes.
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# We assume you have installed kaldilm, if not, please install
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# it using: pip install kaldilm
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if [ ! -f $lang_char_dir/3-gram.unpruned.arpa ]; then
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python3 ./shared/make_kn_lm.py \
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-ngram-order 3 \
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-text $lang_char_dir/text_words_segmentation \
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-lm $lang_char_dir/3-gram.unpruned.arpa
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fi
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mkdir -p data/lm
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if [ ! -f data/lm/G_3_gram.fst.txt ]; then
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# It is used in building LG
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python3 -m kaldilm \
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--read-symbol-table="$lang_char_dir/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$lang_char_dir/3-gram.unpruned.arpa > data/lm/G_3_gram.fst.txt
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fi
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fi
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if [ $stage -le 18 ] && [ $stop_stage -ge 18 ]; then
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log "Stage 18: Compile LG"
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python ./local/compile_lg.py --lang-dir $lang_char_dir
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fi
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# prepare RNNLM data
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if [ $stage -le 19 ] && [ $stop_stage -ge 19 ]; then
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log "Stage 19: Prepare LM training data"
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log "Processing char based data"
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text_out_dir=data/lm_char
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mkdir -p $text_out_dir
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log "Genearating training text data"
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if [ ! -f $text_out_dir/lm_data.pt ]; then
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./local/prepare_char_lm_training_data.py \
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--lang-char data/lang_char \
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--lm-data $lang_char_dir/text_words_segmentation \
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--lm-archive $text_out_dir/lm_data.pt
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fi
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log "Generating DEV text data"
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# prepare validation text data
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if [ ! -f $text_out_dir/valid_text_words_segmentation ]; then
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valid_text=${text_out_dir}/
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gunzip -c data/manifests/wenetspeech_supervisions_DEV.jsonl.gz \
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| jq '.text' | sed 's/"//g' \
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| ./local/text2token.py -t "char" > $text_out_dir/valid_text
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python3 ./local/text2segments.py \
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--num-process $nj \
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--input-file $text_out_dir/valid_text \
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--output-file $text_out_dir/valid_text_words_segmentation
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fi
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./local/prepare_char_lm_training_data.py \
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--lang-char data/lang_char \
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--lm-data $text_out_dir/valid_text_words_segmentation \
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--lm-archive $text_out_dir/lm_data_valid.pt
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# prepare TEST text data
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if [ ! -f $text_out_dir/TEST_text_words_segmentation ]; then
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log "Prepare text for test set."
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for test_set in TEST_MEETING TEST_NET; do
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gunzip -c data/manifests/wenetspeech_supervisions_${test_set}.jsonl.gz \
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| jq '.text' | sed 's/"//g' \
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| ./local/text2token.py -t "char" > $text_out_dir/${test_set}_text
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python3 ./local/text2segments.py \
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--num-process $nj \
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--input-file $text_out_dir/${test_set}_text \
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--output-file $text_out_dir/${test_set}_text_words_segmentation
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done
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cat $text_out_dir/TEST_*_text_words_segmentation > $text_out_dir/test_text_words_segmentation
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fi
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./local/prepare_char_lm_training_data.py \
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--lang-char data/lang_char \
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--lm-data $text_out_dir/test_text_words_segmentation \
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--lm-archive $text_out_dir/lm_data_test.pt
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fi
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# sort RNNLM data
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if [ $stage -le 20 ] && [ $stop_stage -ge 20 ]; then
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text_out_dir=data/lm_char
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log "Sort lm data"
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./local/sort_lm_training_data.py \
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--in-lm-data $text_out_dir/lm_data.pt \
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--out-lm-data $text_out_dir/sorted_lm_data.pt \
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--out-statistics $text_out_dir/statistics.txt
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./local/sort_lm_training_data.py \
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--in-lm-data $text_out_dir/lm_data_valid.pt \
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--out-lm-data $text_out_dir/sorted_lm_data-valid.pt \
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--out-statistics $text_out_dir/statistics-valid.txt
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./local/sort_lm_training_data.py \
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--in-lm-data $text_out_dir/lm_data_test.pt \
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--out-lm-data $text_out_dir/sorted_lm_data-test.pt \
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--out-statistics $text_out_dir/statistics-test.txt
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fi
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export CUDA_VISIBLE_DEVICES="0,1"
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if [ $stage -le 21 ] && [ $stop_stage -ge 21 ]; then
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log "Stage 21: Train RNN LM model"
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python ../../../icefall/rnn_lm/train.py \
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--start-epoch 0 \
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--world-size 2 \
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--num-epochs 20 \
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--use-fp16 0 \
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--embedding-dim 2048 \
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--hidden-dim 2048 \
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--num-layers 2 \
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--batch-size 400 \
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--exp-dir rnnlm_char/exp \
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--lm-data data/lm_char/sorted_lm_data.pt \
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--lm-data-valid data/lm_char/sorted_lm_data-valid.pt \
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--vocab-size 5537 \
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--master-port 12340
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fi |