wehub-resource-sync
103 lines
3.2 KiB
Bash
Executable File
103 lines
3.2 KiB
Bash
Executable File
#!/usr/bin/env bash
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. ./cmd.sh
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. ./path.sh
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stage=-1
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stop_stage=100
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. utils/parse_options.sh
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# Data preparation
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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data_url=www.openslr.org/resources/31
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lm_url=www.openslr.org/resources/11
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database=corpus
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mkdir -p $database
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for part in dev-clean-2 train-clean-5; do
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local/download_and_untar.sh $database $data_url $part
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done
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local/download_lm.sh $lm_url $database data/local/lm
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local/data_prep.sh $database/LibriSpeech/train-clean-5 data/train
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local/data_prep.sh $database/LibriSpeech/dev-clean-2 data/test
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fi
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# Dictionary formatting
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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local/prepare_dict.sh data/local/lm data/local/dict
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utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang data/lang
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fi
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# Extract MFCC features
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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for task in train; do
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steps/make_mfcc.sh --cmd "$train_cmd" --nj 10 data/$task exp/make_mfcc/$task $mfcc
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steps/compute_cmvn_stats.sh data/$task exp/make_mfcc/$task $mfcc
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done
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fi
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# Train GMM models
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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steps/train_mono.sh --nj 10 --cmd "$train_cmd" \
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data/train data/lang exp/mono
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steps/align_si.sh --nj 10 --cmd "$train_cmd" \
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data/train data/lang exp/mono exp/mono_ali
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steps/train_lda_mllt.sh --cmd "$train_cmd" \
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2000 10000 data/train data/lang exp/mono_ali exp/tri1
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steps/align_si.sh --nj 10 --cmd "$train_cmd" \
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data/train data/lang exp/tri1 exp/tri1_ali
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steps/train_lda_mllt.sh --cmd "$train_cmd" \
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2500 15000 data/train data/lang exp/tri1_ali exp/tri2
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steps/align_si.sh --nj 10 --cmd "$train_cmd" \
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data/train data/lang exp/tri2 exp/tri2_ali
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steps/train_lda_mllt.sh --cmd "$train_cmd" \
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2500 20000 data/train data/lang exp/tri2_ali exp/tri3
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steps/align_si.sh --nj 10 --cmd "$train_cmd" \
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data/train data/lang exp/tri3 exp/tri3_ali
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fi
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# Train TDNN model
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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local/chain/run_tdnn.sh
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fi
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# Decode
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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utils/format_lm.sh data/lang data/local/lm/lm_tgsmall.arpa.gz data/local/dict/lexicon.txt data/lang_test
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utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test exp/chain/tdnn exp/chain/tdnn/graph
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utils/build_const_arpa_lm.sh data/local/lm/lm_tgmed.arpa.gz \
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data/lang data/lang_test_rescore
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for task in test; do
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steps/make_mfcc.sh --cmd "$train_cmd" --nj 10 data/$task exp/make_mfcc/$task $mfcc
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steps/compute_cmvn_stats.sh data/$task exp/make_mfcc/$task $mfcc
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steps/online/nnet2/extract_ivectors_online.sh --nj 10 \
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data/${task} exp/chain/extractor \
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exp/chain/ivectors_${task}
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steps/nnet3/decode.sh --cmd $decode_cmd --num-threads 10 --nj 1 \
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--beam 13.0 --max-active 7000 --lattice-beam 4.0 \
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--online-ivector-dir exp/chain/ivectors_${task} \
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--acwt 1.0 --post-decode-acwt 10.0 \
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exp/chain/tdnn/graph data/${task} exp/chain/tdnn/decode_${task}
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steps/lmrescore_const_arpa.sh data/lang_test data/lang_test_rescore \
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data/${task} exp/chain/tdnn/decode_${task} exp/chain/tdnn/decode_${task}_rescore
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done
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bash RESULTS
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fi
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