run_tdnn_1b.sh
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#!/bin/bash
# 1b is like 1a, but using a different splicing setup; the difference
# is like the difference from
# egs/tedlium/s5_r2/local/nnet3/tuning/run_tdnn{c->b}.sh
# There seems to be no consistent difference.
# run_tdnn_1a.sh is the standard "tdnn" system, built in nnet3 with xconfigs.
# local/nnet3/compare_wer.sh exp/nnet3/tdnn1a_sp exp/nnet3/tdnn1b_sp
# System tdnn1a_sp tdnn1b_sp
#WER dev93 (tgpr) 9.18 9.12
#WER dev93 (tg) 8.59 8.51
#WER dev93 (big-dict,tgpr) 6.45 6.19
#WER dev93 (big-dict,fg) 5.83 5.78
#WER eval92 (tgpr) 6.15 6.33
#WER eval92 (tg) 5.55 5.74
#WER eval92 (big-dict,tgpr) 3.58 3.62
#WER eval92 (big-dict,fg) 2.98 3.10
# Final train prob -0.7200 -0.6035
# Final valid prob -0.8834 -0.7578
# Final train acc 0.7762 0.8015
# Final valid acc 0.7301 0.7607
set -e -o pipefail -u
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0
nj=30
train_set=train_si284
test_sets="test_dev93 test_eval92"
gmm=tri4b # this is the source gmm-dir that we'll use for alignments; it
# should have alignments for the specified training data.
num_threads_ubm=32
nnet3_affix= # affix for exp dirs, e.g. it was _cleaned in tedlium.
tdnn_affix=1b #affix for TDNN directory e.g. "1a" or "1b", in case we change the configuration.
# Options which are not passed through to run_ivector_common.sh
train_stage=-10
remove_egs=true
srand=0
reporting_email=
# set common_egs_dir to use previously dumped egs.
common_egs_dir=
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
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.
EOF
fi
local/nnet3/run_ivector_common.sh --stage $stage --nj $nj \
--train-set $train_set --gmm $gmm \
--num-threads-ubm $num_threads_ubm \
--nnet3-affix "$nnet3_affix"
gmm_dir=exp/${gmm}
ali_dir=exp/${gmm}_ali_${train_set}_sp
dir=exp/nnet3${nnet3_affix}/tdnn${tdnn_affix}_sp
train_data_dir=data/${train_set}_sp_hires
train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
for f in $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
$gmm_dir/{graph_tgpr,graph_bd_tgpr}/HCLG.fst \
$ali_dir/ali.1.gz $gmm_dir/final.mdl; do
[ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
done
if [ $stage -le 12 ]; then
mkdir -p $dir
echo "$0: creating neural net configs using the xconfig parser";
num_targets=$(tree-info $gmm_dir/tree |grep num-pdfs|awk '{print $2}')
mkdir -p $dir/configs
cat <<EOF > $dir/configs/network.xconfig
input dim=100 name=ivector
input dim=40 name=input
# please note that it is important to have input layer with the name=input
# as the layer immediately preceding the fixed-affine-layer to enable
# the use of short notation for the descriptor
fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
# the first splicing is moved before the lda layer, so no splicing here
relu-renorm-layer name=tdnn1 dim=750
relu-renorm-layer name=tdnn2 dim=750 input=Append(-1,2)
relu-renorm-layer name=tdnn3 dim=750 input=Append(-3,3)
relu-renorm-layer name=tdnn4 dim=750 input=Append(-7,2)
relu-renorm-layer name=tdnn5 dim=750 input=Append(-3,3)
relu-renorm-layer name=tdnn6 dim=750
output-layer name=output dim=$num_targets max-change=1.5
EOF
steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
fi
if [ $stage -le 13 ]; then
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
utils/create_split_dir.pl \
/export/b0{3,4,5,6}/$USER/kaldi-data/egs/tedlium-$(date +'%m_%d_%H_%M')/s5_r2/$dir/egs/storage $dir/egs/storage
fi
steps/nnet3/train_dnn.py --stage=$train_stage \
--cmd="$decode_cmd" \
--feat.online-ivector-dir=$train_ivector_dir \
--feat.cmvn-opts="--norm-means=false --norm-vars=false" \
--trainer.srand=$srand \
--trainer.max-param-change=2.0 \
--trainer.num-epochs=3 \
--trainer.samples-per-iter=400000 \
--trainer.optimization.num-jobs-initial=2 \
--trainer.optimization.num-jobs-final=10 \
--trainer.optimization.initial-effective-lrate=0.0015 \
--trainer.optimization.final-effective-lrate=0.00015 \
--trainer.optimization.minibatch-size=256,128 \
--egs.dir="$common_egs_dir" \
--cleanup.remove-egs=$remove_egs \
--use-gpu=true \
--feat-dir=$train_data_dir \
--ali-dir=$ali_dir \
--lang=data/lang \
--reporting.email="$reporting_email" \
--dir=$dir || exit 1;
fi
if [ $stage -le 14 ]; then
# note: for TDNNs, looped decoding gives exactly the same results
# as regular decoding, so there is no point in testing it separately.
# We use regular decoding because it supports multi-threaded (we just
# didn't create the binary for that, for looped decoding, so far).
rm $dir/.error || true 2>/dev/null
for data in $test_sets; do
(
data_affix=$(echo $data | sed s/test_//)
nj=$(wc -l <data/${data}_hires/spk2utt)
for lmtype in tgpr bd_tgpr; do
graph_dir=$gmm_dir/graph_${lmtype}
steps/nnet3/decode.sh --nj $nj --cmd "$decode_cmd" --num-threads 4 \
--online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
${graph_dir} data/${data}_hires ${dir}/decode_${lmtype}_${data_affix} || exit 1
done
steps/lmrescore.sh --cmd "$decode_cmd" data/lang_test_{tgpr,tg} \
data/${data}_hires ${dir}/decode_{tgpr,tg}_${data_affix} || exit 1
steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
data/lang_test_bd_{tgpr,fgconst} \
data/${data}_hires ${dir}/decode_${lmtype}_${data_affix}{,_fg} || exit 1
) || touch $dir/.error &
done
wait
[ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
fi
exit 0;