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egs/rm/s5/local/online/run_nnet2_wsj_joint_disc.sh
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#!/bin/bash
# this script is discriminative training after multi-language training (as
# run_nnet2_gale_combined_disc1.sh), but the discriminative training is
# multi-language too.
# some of the stages are the same as run_nnet2_gale_combined_disc1.sh,
# and we didn't repeat them (we used the --stage option, it defaults to 4).
# This script is to be run after run_nnet2_gale_combined.sh.
# It's discriminative training, using just the BOLT data.
# note, the _filt data has some bad conversations removed, that
# weren't aligning.
train_stage=-10
stage=0
# dir is the base directory, as in ./run_nnet2_wsj_joint.sh
dir=exp/nnet2_online_wsj/nnet_ms_a
criterion=smbr
drop_frames=false # only relevant for MMI.
learning_rate=0.00005
data_wsj=../../wsj/s5/data/train_si284_max2
data_rm=data/train_max2
lang_wsj=../../wsj/s5/data/lang
lang_rm=data/lang
num_epochs=4
set -e
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if [ $stage -le 1 ]; then
nj=30
sub_split=100
num_threads=6
steps/online/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G --num-threads $num_threads" \
--nj $nj --sub-split $sub_split --num-threads "$num_threads" \
$data_wsj $lang_wsj ${dir}_wsj_online ${dir}_wsj_denlats
fi
if [ $stage -le 2 ]; then
# hardcode no-GPU for alignment, although you could use GPU [you wouldn't
# get excellent GPU utilization though.]
nj=200
use_gpu=no
gpu_opts=
steps/online/nnet2/align.sh --cmd "$decode_cmd $gpu_opts" --use-gpu "$use_gpu" \
--nj $nj $data_wsj $lang_wsj ${dir}_wsj_online ${dir}_wsj_ali
fi
if [ $stage -le 3 ]; then
if [[ $(hostname -f) == *.clsp.jhu.edu ]]; then
date=$(date +'%m_%d_%H_%M')
utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/egs/rm-$date/s5/${dir}_wsj_degs/storage ${dir}_wsj_degs/storage
fi
steps/online/nnet2/get_egs_discriminative2.sh \
--cmd "$decode_cmd --max-jobs-run 10" \
--criterion $criterion --drop-frames $drop_frames \
$data_wsj $lang_wsj ${dir}_wsj_{ali,denlats,online,degs}
fi
if [ $stage -le 4 ]; then
nj=30
sub_split=100
num_threads=6
steps/online/nnet2/make_denlats.sh \
--cmd "$decode_cmd --mem 1G --num-threads $num_threads" \
--nj $nj --sub-split $sub_split --num-threads "$num_threads" \
$data_rm $lang_rm ${dir}_rm_online ${dir}_rm_denlats
fi
if [ $stage -le 5 ]; then
# hardcode no-GPU for alignment, although you could use GPU [you wouldn't
# get excellent GPU utilization though.]
nj=200
use_gpu=no
gpu_opts=
steps/online/nnet2/align.sh --cmd "$decode_cmd $gpu_opts" --use-gpu "$use_gpu" \
--nj $nj $data_rm $lang_rm ${dir}_rm_online ${dir}_rm_ali
fi
if [ $stage -le 6 ]; then
if [[ $(hostname -f) == *.clsp.jhu.edu ]]; then
date=$(date +'%m_%d_%H_%M')
utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/egs/rm-$date/s5/${dir}_rm_degs/storage ${dir}_rm_degs/storage
fi
steps/online/nnet2/get_egs_discriminative2.sh \
--cmd "$decode_cmd --max-jobs-run 10" \
--criterion $criterion --drop-frames $drop_frames \
$data_rm $lang_rm ${dir}_rm_{ali,denlats,online,degs}
fi
if [ $stage -le 7 ]; then
steps/nnet2/train_discriminative_multilang2.sh --cmd "$decode_cmd --gpu 1" --stage $train_stage \
--learning-rate $learning_rate --num-jobs-nnet "4 1" \
--criterion $criterion --drop-frames $drop_frames \
--num-epochs $num_epochs --num-threads 1 \
${dir}_wsj_degs ${dir}_rm_degs ${dir}_${criterion}_${learning_rate}
fi
if [ $stage -le 8 ]; then
discdir=${dir}_${criterion}_${learning_rate}/1 # RM is directory number 1.
ln -sf $(utils/make_absolute.sh ${dir}_rm_online/conf) $discdir/conf
# ... so it acts like an online-decoding directory.
for epoch in $(seq 0 $num_epochs); do
steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
--iter epoch$epoch exp/tri3b/graph data/test $discdir/decode_epoch$epoch &
steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
--iter epoch$epoch exp/tri3b/graph_ug data/test $discdir/decode_ug_epoch$epoch &
done
wait
fi
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