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egs/rm/s5/local/online/run_nnet2_common.sh
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#!/bin/bash
# This script extracts mfcc features using mfcc_config and trains ubm model and
# ivector extractor and extracts ivector for train and test.
. ./cmd.sh
stage=1
nnet_affix=_online
extractor=exp/nnet2${nnet_affix}/extractor
ivector_dim=50
mfcc_config=conf/mfcc_hires.conf
use_ivector=true # If false, it skips training ivector extractor and
# ivector extraction stages.
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if $use_gpu; then
if ! cuda-compiled; then
cat <<EOF && exit 1
This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
If you want to use GPUs (and have them), go to src/, and configure and make on a machine
where "nvcc" is installed. Otherwise, call this script with --use-gpu false
EOF
fi
parallel_opts="--gpu 1"
num_threads=1
minibatch_size=512
else
# Use 4 nnet jobs just like run_4d_gpu.sh so the results should be
# almost the same, but this may be a little bit slow.
num_threads=16
minibatch_size=128
parallel_opts="--num-threads $num_threads"
dir=exp/nnet2${nnet_affix}/nnet
fi
train_set=train
test_set=test
if [ $stage -le 0 ]; then
echo "$0: creating high-resolution MFCC features."
mfccdir=data/${train_set}_hires/data
for datadir in $train_set test; do
utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
steps/make_mfcc.sh --nj 30 --mfcc-config $mfcc_config \
--cmd "$train_cmd" data/${datadir}_hires || exit 1;
steps/compute_cmvn_stats.sh data/${datadir}_hires
utils/fix_data_dir.sh data/${datadir}_hires
done
train_set=${train_set}_hires
test_set=${test_set}_hires
fi
if [ ! -f $extractor/final.ie ] && [ $ivector_dim -gt 0 ]; then
if [ $stage -le 1 ]; then
mkdir -p exp/nnet2${nnet_affix}
steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 40 --num-frames 200000 \
data/${train_set} 256 exp/tri3b exp/nnet2${nnet_affix}/diag_ubm
fi
if [ $stage -le 2 ]; then
# use a smaller iVector dim (50) than the default (100) because RM has a very
# small amount of data.
steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
--ivector-dim $ivector_dim \
data/${train_set} exp/nnet2${nnet_affix}/diag_ubm $extractor || exit 1;
fi
fi
if [ $stage -le 3 ] && [ $ivector_dim -gt 0 ]; then
# having a larger number of speakers is helpful for generalization, and to
# handle per-utterance decoding well (iVector starts at zero).
steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 data/${train_set} data/${train_set}_max2
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 40 \
data/${train_set}_max2 $extractor exp/nnet2${nnet_affix}/ivectors || exit 1;
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 10 \
data/${test_set} $extractor exp/nnet2${nnet_affix}/ivectors_test || exit 1;
fi
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