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egs/callhome_egyptian/s5/local/nnet3/run_ivector_common.sh
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#!/bin/bash
# Inherited from the WSJ nnet3 recipe, modified for use with ECA
# this script is called from scripts like run_ms.sh; it does the common stages
# of the build, such as feature extraction.
# This is actually the same as local/online/run_nnet2_common.sh, except
# for the directory names.
mfccdir=mfcc
stage=1
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if [ $stage -le 1 ]; then
for datadir in train dev test sup h5; do
utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
steps/make_mfcc.sh --nj 40 --mfcc-config conf/mfcc_hires.conf \
--cmd "$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 --first data/train 7388 data/train_small || exit 1
utils/subset_data_dir.sh --first data/train_hires 7388 data/train_small_hires || exit 1
fi
if [ $stage -le 2 ]; then
# We need to build a small system just because we need the LDA+MLLT transform
# to train the diag-UBM on top of. We align the si84 data for this purpose.
steps/align_fmllr.sh --nj 40 --cmd "$train_cmd" \
data/train_small data/lang exp/tri5a exp/nnet3/tri5a_ali_small
fi
if [ $stage -le 3 ]; then
# Train a small system just for its LDA+MLLT transform. We use --num-iters 13
# because after we get the transform (12th iter is the last), any further
# training is pointless.
steps/train_lda_mllt.sh --cmd "$train_cmd" --num-iters 13 \
--realign-iters "" \
--splice-opts "--left-context=3 --right-context=3" \
5000 10000 data/train_small_hires data/lang \
exp/nnet3/tri5a_ali_small exp/nnet3/tri5b
fi
if [ $stage -le 4 ]; then
mkdir -p exp/nnet3
steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 \
--num-frames 400000 data/train_small_hires 256 exp/nnet3/tri5b exp/nnet3/diag_ubm
fi
if [ $stage -le 5 ]; then
# even though $nj is just 10, each job uses multiple processes and threads.
steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
data/train_hires exp/nnet3/diag_ubm exp/nnet3/extractor || exit 1;
fi
if [ $stage -le 6 ]; then
# We extract iVectors on all the train_si284 data, which will be what we
# train the system on.
# having a larger number of speakers is helpful for generalization, and to
# handle per-utterance decoding well (iVector starts at zero).
steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 data/train_hires \
data/train_hires_max2
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 30 \
data/train_hires_max2 exp/nnet3/extractor exp/nnet3/ivectors_train || exit 1;
fi
if [ $stage -le 7 ]; then
rm exp/nnet3/.error 2>/dev/null
for data in dev test sup h5; do
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 8 \
data/${data}_hires exp/nnet3/extractor exp/nnet3/ivectors_${data} || touch exp/nnet3/.error &
done
wait
[ -f exp/nnet3/.error ] && echo "$0: error extracting iVectors." && exit 1;
fi
exit 0;
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