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egs/librispeech/s5/local/online_pitch/run_nnet2_common.sh
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#!/bin/bash # this script contains some common (shared) parts of the run_nnet*.sh scripts. . ./cmd.sh stage=0 set -e . ./cmd.sh . ./path.sh . ./utils/parse_options.sh if [ $stage -le 1 ]; then # Create high-resolution MFCC features (with 40 cepstra instead of 13). # this shows how you can split across multiple file-systems. we'll split the # MFCC dir across multiple locations. You might want to be careful here, if you # have multiple copies of Kaldi checked out and run the same recipe, not to let # them overwrite each other. mfccdir=mfcc if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/librispeech-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage fi for datadir in train_960 dev_clean dev_other; do utils/copy_data_dir.sh data/$datadir data/${datadir}_hiresp steps/make_mfcc_pitch_online.sh --nj 150 --mfcc-config conf/mfcc_hires.conf \ --online-pitch-config conf/online_pitch.conf \ --cmd "$train_cmd" data/${datadir}_hiresp exp/make_hiresp/$datadir $mfccdir || exit 1; steps/compute_cmvn_stats.sh data/${datadir}_hiresp exp/make_hiresp/$datadir $mfccdir || exit 1; # dump plain MFCC features by selecting MFCC-only part steps/select_feats.sh 0-39 data/${datadir}_hiresp data/${datadir}_hires exp/make_hires/$datadir $mfccdir || exit 1; steps/compute_cmvn_stats.sh data/${datadir}_hires exp/make_hires/$datadir $mfccdir || exit 1; done # now create some data subsets. # mixed is the clean+other data. # 30k is 1/10 of the data (around 100 hours), 60k is 1/5th of it (around 200 hours). utils/subset_data_dir.sh data/train_960_hires 30000 data/train_mixed_hires_30k utils/subset_data_dir.sh data/train_960_hires 60000 data/train_mixed_hires_60k fi # The stages where we build the iVector extractor are the same as the # non-pitch system, because the features given to the iVector extractor don't use pitch. steps/online/run_nnet2_common.sh --stage 2 || exit 1; exit 0; |