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https://github.com/k2-fsa/icefall.git
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process and compute fbank features for S and M subset
This commit is contained in:
luomingshuang
parent
c6b2c3b038
commit
18a1e959f7
@ -5,6 +5,7 @@ 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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use_whole_text=True
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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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@ -98,7 +99,25 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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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 L subset into ${num_splits} pieces (may take 30 minutes)"
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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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@ -106,39 +125,80 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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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: Compute features for L"
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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 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Combine features for L"
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if [ ! -f data/fbank/cuts_L_50.jsonl.gz ]; then
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pieces=$(find data/fbank/L_split_50 -name "cuts_L.*.jsonl.gz")
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lhotse combine $pieces data/fbank/cuts_L_50.jsonl.gz
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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 8 ] && [ $stop_stage -ge 8 ]; then
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log "Stage 8: Compute fbank for musan"
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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 9 ] && [ $stop_stage -ge 9 ]; then
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log "Stage 9: Prepare char based lang"
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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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lang_char_dir=data/lang_char
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mkdir -p $lang_char_dir
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# Prepare text.
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if [ ! -f $lang_char_dir/text ]; then
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gunzip -c data/manifests/supervisions_L.jsonl.gz \
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| jq '.text' | sed 's/"//g' \
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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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# if use the whole text to generate the text, you can use
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# the following command:
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# grep "\"text\":" $dl_dir/WenetSpeech/WenetSpeech.json |
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# sed -e 's/["text:\t ]*//g' > $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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@ -159,16 +219,16 @@ if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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fi
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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: Prepare char based L_disambig.pt"
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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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python ./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 [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
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log "Stage 11: Prepare pinyin based L_disambig.pt"
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if [ $stage -le 17 ] && [ $stop_stage -ge 17 ]; then
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log "Stage 17: Prepare pinyin based L_disambig.pt"
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lang_pinyin_dir=data/lang_pinyin
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mkdir -p $lang_pinyin_dir
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