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egs/fisher_swbd/s5/local/online/run_nnet2_common.sh
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#!/bin/bash
# Make the features, build the iVector extractor
. ./cmd.sh
stage=1
set -e
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
mkdir -p exp/nnet2_online
if [ $stage -le 1 ]; then
# 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 $mfccdir/storage ]; then
date=$(date +'%m_%d_%H_%M')
utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/fisher_english-$date/s5/$mfccdir/storage $mfccdir/storage
fi
for datadir in train_nodup eval2000 rt03; do
utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
steps/make_mfcc.sh --nj 70 --mfcc-config conf/mfcc_hires.conf \
--cmd "$train_cmd" 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
utils/subset_data_dir.sh data/train_nodup_hires 30000 data/train_nodup_hires_30k
utils/subset_data_dir.sh data/train_nodup_hires 100000 data/train_nodup_hires_100k
utils/copy_data_dir.sh data/train_nodup_hires_30k data/train_nodup_nonhires_30k
utils/copy_data_dir.sh data/train_nodup_hires_100k data/train_nodup_nonhires_100k
utils/filter_scp.pl data/train_nodup_nonhires_30k/feats.scp data/train_nodup/feats.scp > data/train_nodup_nonhires_30k/feats.scp.new
mv data/train_nodup_nonhires_30k/feats.scp.new data/train_nodup_nonhires_30k/feats.scp
steps/compute_cmvn_stats.sh data/train_nodup_nonhires_30k exp/make_mfcc/train_nodup_nonhires_30k $mfccdir
utils/filter_scp.pl data/train_nodup_nonhires_100k/feats.scp data/train_nodup/feats.scp > data/train_nodup_nonhires_100k/feats.scp.new
mv data/train_nodup_nonhires_100k/feats.scp.new data/train_nodup_nonhires_100k/feats.scp
steps/compute_cmvn_stats.sh data/train_nodup_nonhires_100k exp/make_mfcc/train_nodup_nonhires_100k $mfccdir
fi
if [ $stage -le 2 ]; then
# We need to build a small system just because we need the LDA+MLLT transform
# to train the diag-UBM on top of. We use --num-iters 13 because after we get
# the transform (12th iter is the last), any further training is pointless.
mkdir -p exp/tri4a_new_ali
steps/align_fmllr.sh --nj 60 --cmd "$train_cmd" \
data/train_nodup_nonhires_100k data/lang exp/tri4a exp/tri4a_new_ali || exit 1;
steps/train_lda_mllt.sh --cmd "$train_cmd" --num-iters 13 \
--splice-opts "--left-context=3 --right-context=3" \
5000 10000 data/train_nodup_hires_100k data/lang exp/tri4a_new_ali exp/nnet2_online/tri5a
fi
if [ $stage -le 3 ]; then
# To train a diagonal UBM we don't need very much data, so use the smallest
# subset. the input directory exp/nnet2_online/tri5a is only needed for
# the splice-opts and the LDA transform.
steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 --num-frames 400000 \
data/train_nodup_hires_30k 512 exp/nnet2_online/tri5a exp/nnet2_online/diag_ubm
fi
if [ $stage -le 4 ]; then
# iVector extractors can in general be sensitive to the amount of data, but
# this one has a fairly small dim (defaults to 100) so we don't use all of it,
# we use just the 100k subset (about one sixteenth of the data).
steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
data/train_nodup_hires_100k exp/nnet2_online/diag_ubm exp/nnet2_online/extractor || exit 1;
fi
if [ $stage -le 5 ]; then
ivectordir=exp/nnet2_online/ivectors_train
if [[ $(hostname -f) == *.clsp.jhu.edu ]]; then # this shows how you can split across multiple file-systems.
utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/fisher_english/s5/$ivectordir/storage $ivectordir/storage
fi
# having a larger number of speakers is helpful for generalization, and to
# handle per-utterance decoding well (iVector starts at zero).
steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 data/train_nodup_hires data/train_nodup_hires_max2
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 60 \
data/train_nodup_hires_max2 exp/nnet2_online/extractor $ivectordir || exit 1;
fi
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