mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
c3f6f28116
* init commit * Create README.md * handle code switching cases * misc. fixes * added manifest statistics * init commit for the zipformer recipe * added scripts for exporting model * added RESULTS.md * added scripts for streaming related stuff * doc str fixed
309 lines
8.8 KiB
Bash
Executable File
309 lines
8.8 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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stage=-1
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stop_stage=100
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perturb_speed=true
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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/mdcc
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# |-- README.md
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# |-- audio/
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# |-- clip_info_rthk.csv
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# |-- cnt_asr_metadata_full.csv
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# |-- cnt_asr_test_metadata.csv
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# |-- cnt_asr_train_metadata.csv
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# |-- cnt_asr_valid_metadata.csv
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# |-- data_statistic.py
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# |-- length
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# |-- podcast_447_2021.csv
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# |-- test.txt
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# |-- transcription/
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# `-- words_length
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# You can download them from:
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# https://drive.google.com/file/d/1epfYMMhXdBKA6nxPgUugb2Uj4DllSxkn/view?usp=drive_link
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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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. 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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# If you have pre-downloaded it to /path/to/mdcc,
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# you can create a symlink
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#
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# ln -sfv /path/to/mdcc $dl_dir/mdcc
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#
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# The directory structure is
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# mdcc/
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# |-- README.md
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# |-- audio/
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# |-- clip_info_rthk.csv
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# |-- cnt_asr_metadata_full.csv
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# |-- cnt_asr_test_metadata.csv
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# |-- cnt_asr_train_metadata.csv
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# |-- cnt_asr_valid_metadata.csv
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# |-- data_statistic.py
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# |-- length
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# |-- podcast_447_2021.csv
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# |-- test.txt
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# |-- transcription/
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# `-- words_length
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if [ ! -d $dl_dir/mdcc/audio ]; then
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lhotse download mdcc $dl_dir
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# this will download and unzip dataset.zip to $dl_dir/
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mv $dl_dir/dataset $dl_dir/mdcc
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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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#
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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 MDCC manifest"
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# We assume that you have downloaded the MDCC corpus
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# to $dl_dir/mdcc
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if [ ! -f data/manifests/.mdcc_manifests.done ]; then
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log "Might take 40 minutes to traverse the directory."
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mkdir -p data/manifests
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lhotse prepare mdcc $dl_dir/mdcc data/manifests
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touch data/manifests/.mdcc_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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log "It may take 6 minutes"
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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 MDCC"
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if [ ! -f data/fbank/.mdcc.done ]; then
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mkdir -p data/fbank
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./local/compute_fbank_mdcc.py --perturb-speed ${perturb_speed}
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touch data/fbank/.mdcc.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_char_dir=data/lang_char
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Prepare char based lang"
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mkdir -p $lang_char_dir
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# Prepare text.
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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
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if [ ! -f $lang_char_dir/text ]; then
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gunzip -c data/manifests/mdcc_supervisions_train.jsonl.gz \
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|jq '.text' | sed 's/"//g' | ./local/text2token.py -t "char" \
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> $lang_char_dir/train_text
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cat $lang_char_dir/train_text > $lang_char_dir/text
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gunzip -c data/manifests/mdcc_supervisions_test.jsonl.gz \
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|jq '.text' | sed 's/"//g' | ./local/text2token.py -t "char" \
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> $lang_char_dir/valid_text
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cat $lang_char_dir/valid_text >> $lang_char_dir/text
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gunzip -c data/manifests/mdcc_supervisions_valid.jsonl.gz \
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|jq '.text' | sed 's/"//g' | ./local/text2token.py -t "char" \
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> $lang_char_dir/test_text
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cat $lang_char_dir/test_text >> $lang_char_dir/text
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fi
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if [ ! -f $lang_char_dir/text_words_segmentation ]; then
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./local/preprocess_mdcc.py --input-file $lang_char_dir/text \
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--output-dir $lang_char_dir
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mv $lang_char_dir/text $lang_char_dir/_text
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cp $lang_char_dir/text_words_segmentation $lang_char_dir/text
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fi
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if [ ! -f $lang_char_dir/tokens.txt ]; then
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./local/prepare_char.py --lang-dir $lang_char_dir
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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: Prepare G"
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mkdir -p data/lm
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# Train LM on transcripts
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if [ ! -f data/lm/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 $lang_char_dir/text_words_segmentation \
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-lm data/lm/3-gram.unpruned.arpa
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fi
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# We assume you have installed kaldilm, if not, please install
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# it using: pip install kaldilm
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if [ ! -f data/lm/G_3_gram_char.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="$lang_char_dir/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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data/lm/3-gram.unpruned.arpa > data/lm/G_3_gram_char.fst.txt
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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 \
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--lang-dir $lang_char_dir \
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--ngram-G ./data/lm/G_3_gram_char.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: Compile LG & HLG"
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./local/compile_hlg.py --lang-dir $lang_char_dir --lm G_3_gram_char
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./local/compile_lg.py --lang-dir $lang_char_dir --lm G_3_gram_char
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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: Generate LM training data"
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log "Processing char based data"
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out_dir=data/lm_training_char
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mkdir -p $out_dir $dl_dir/lm
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if [ ! -f $dl_dir/lm/mdcc-train-word.txt ]; then
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./local/text2segments.py --input-file $lang_char_dir/train_text \
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--output-file $dl_dir/lm/mdcc-train-word.txt
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fi
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# training words
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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 $dl_dir/lm/mdcc-train-word.txt \
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--lm-archive $out_dir/lm_data.pt
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# valid words
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if [ ! -f $dl_dir/lm/mdcc-valid-word.txt ]; then
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./local/text2segments.py --input-file $lang_char_dir/valid_text \
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--output-file $dl_dir/lm/mdcc-valid-word.txt
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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 $dl_dir/lm/mdcc-valid-word.txt \
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--lm-archive $out_dir/lm_data_valid.pt
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# test words
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if [ ! -f $dl_dir/lm/mdcc-test-word.txt ]; then
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./local/text2segments.py --input-file $lang_char_dir/test_text \
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--output-file $dl_dir/lm/mdcc-test-word.txt
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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 $dl_dir/lm/mdcc-test-word.txt \
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--lm-archive $out_dir/lm_data_test.pt
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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: Sort LM training data"
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# Sort LM training data by sentence length in descending order
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# for ease of training.
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#
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# Sentence length equals to the number of tokens
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# in a sentence.
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out_dir=data/lm_training_char
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mkdir -p $out_dir
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ln -snf ../../../librispeech/ASR/local/sort_lm_training_data.py local/
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./local/sort_lm_training_data.py \
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--in-lm-data $out_dir/lm_data.pt \
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--out-lm-data $out_dir/sorted_lm_data.pt \
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--out-statistics $out_dir/statistics.txt
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./local/sort_lm_training_data.py \
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--in-lm-data $out_dir/lm_data_valid.pt \
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--out-lm-data $out_dir/sorted_lm_data-valid.pt \
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--out-statistics $out_dir/statistics-valid.txt
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./local/sort_lm_training_data.py \
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--in-lm-data $out_dir/lm_data_test.pt \
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--out-lm-data $out_dir/sorted_lm_data-test.pt \
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--out-statistics $out_dir/statistics-test.txt
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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: 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 1 \
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--num-epochs 20 \
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--use-fp16 0 \
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--embedding-dim 512 \
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--hidden-dim 512 \
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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 $out_dir/sorted_lm_data.pt \
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--lm-data-valid $out_dir/sorted_lm_data-valid.pt \
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--vocab-size 4336 \
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--master-port 12345
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fi
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