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Add GruNonlinearityComponent(by Dan) and OutputGruNonlinearityCompone…
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…nt; moving aroun some sources in nnet3 to avoid very large files

rename nnet-combined-component.{h,cc} and str case

Update get_saturation.pl for fast gru version. Get matched resutls
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@LvHang
LvHang committed Nov 14, 2018
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25 changes: 15 additions & 10 deletions egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1a.sh
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# This is based on TDNN_LSTM_1b, but using the NormOPGRU to replace the LSTMP,
# and adding chunk-{left,right}-context-initial=0
# For the details of OPGRU structure, please check the paper
# "Output-Gate Projected Gated Recurrent Unit for Speech Recognition"
# by Gaofeng Cheng et al,
# http://www.danielpovey.com/files/2018_interspeech_opgru.pdf

# Different from the vanilla OPGRU, Norm-OPGRU adds batchnorm in its output (forward direction)
# and renorm in its recurrence. Experiments show that the TDNN-NormOPGRU could achieve similar
# results than TDNN-LSTMP and BLSTMP in both large or small data sets (80 ~ 2300 Hrs).

# ./local/chain/compare_wer_general.sh --looped tdnn_lstm_1e_sp tdnn_opgru_1a_sp
# System tdnn_lstm_1e_sp tdnn_opgru_1a_sp
# WER on train_dev(tg) 12.81 12.39
# [looped:] 12.93 12.32
# WER on train_dev(fg) 11.92 11.39
# [looped:] 12.07 11.35
# WER on train_dev(tg) 12.81 12.31
# [looped:] 12.93 12.26
# WER on train_dev(fg) 11.92 11.60
# [looped:] 12.07 11.65
# WER on eval2000(tg) 15.6 15.1
# [looped:] 16.0 15.1
# WER on eval2000(fg) 14.1 13.6
# WER on eval2000(fg) 14.1 13.5
# [looped:] 14.5 13.5
# Final train prob -0.065 -0.066
# Final valid prob -0.087 -0.085
# Final train prob (xent) -0.918 -0.889
# Final valid prob (xent) -1.0309 -0.9837
# Final train prob -0.065 -0.068
# Final valid prob -0.087 -0.091
# Final train prob (xent) -0.918 -0.879
# Final valid prob (xent) -1.0309 -0.9667



set -e

# configs for 'chain'
stage=12
stage=0
train_stage=-10
get_egs_stage=-10
speed_perturb=true
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315 changes: 315 additions & 0 deletions egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1b.sh
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#!/bin/bash
# Apache 2.0

# This is based on TDNN_OPGRU_1A, but using the FastNormOPGRU to replace the NormPGRU.
# For the details of OPGRU structure, please check the paper
# "Output-Gate Projected Gated Recurrent Unit for Speech Recognition"
# by Gaofeng Cheng et al,
# http://www.danielpovey.com/files/2018_interspeech_opgru.pdf

# Different from the vanilla OPGRU, Norm-OPGRU adds batchnorm in its output (forward direction)
# and renorm in its recurrence. Experiments show that the TDNN-NormOPGRU could achieve similar
# results than TDNN-LSTMP and BLSTMP in both large or small data sets (80 ~ 2300 Hrs).

# ./local/chain/compare_wer_general.sh --looped tdnn_opgru_1a_sp tdnn_opgru_1b_sp
# System tdnn_opgru_1a_sp tdnn_opgru_1b_sp
# WER on train_dev(tg) 12.31 12.41
# [looped:] 12.26 12.38
# WER on train_dev(fg) 11.49 11.60
# [looped:] 11.43 11.65
# WER on eval2000(tg) 14.9 15.1
# [looped:] 15.0 15.1
# WER on eval2000(fg) 13.5 13.7
# [looped:] 13.5 13.7
# Final train prob -0.068 -0.070
# Final valid prob -0.091 -0.092
# Final train prob (xent) -0.879 -0.889
# Final valid prob (xent) -0.9667 -0.9723



set -e

# configs for 'chain'
stage=0
train_stage=-10
get_egs_stage=-10
speed_perturb=true
dir=exp/chain/tdnn_opgru_1b # Note: _sp will get added to this if $speed_perturb == true.
decode_iter=
decode_dir_affix=

# training options
leftmost_questions_truncate=-1
chunk_width=150
chunk_left_context=40
chunk_right_context=0
xent_regularize=0.025
self_repair_scale=0.00001
label_delay=5
dropout_schedule='0,0@0.20,0.2@0.50,0'
# decode options
extra_left_context=50
extra_right_context=0
frames_per_chunk=
test_online_decoding=

remove_egs=false
common_egs_dir=

affix=
# End configuration section.
echo "$0 $@" # Print the command line for logging

. ./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

# The iVector-extraction and feature-dumping parts are the same as the standard
# nnet3 setup, and you can skip them by setting "--stage 8" if you have already
# run those things.

suffix=
if [ "$speed_perturb" == "true" ]; then
suffix=_sp
fi

dir=$dir${affix:+_$affix}
dir=${dir}$suffix
train_set=train_nodup$suffix
ali_dir=exp/tri4_ali_nodup$suffix
treedir=exp/chain/tri5_7d_tree$suffix
lang=data/lang_chain_2y


# if we are using the speed-perturbed data we need to generate
# alignments for it.
local/nnet3/run_ivector_common.sh --stage $stage \
--speed-perturb $speed_perturb \
--generate-alignments $speed_perturb || exit 1;


if [ $stage -le 9 ]; then
# Get the alignments as lattices (gives the CTC training more freedom).
# use the same num-jobs as the alignments
nj=$(cat exp/tri4_ali_nodup$suffix/num_jobs) || exit 1;
steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \
data/lang exp/tri4 exp/tri4_lats_nodup$suffix
rm exp/tri4_lats_nodup$suffix/fsts.*.gz # save space
fi


if [ $stage -le 10 ]; then
# Create a version of the lang/ directory that has one state per phone in the
# topo file. [note, it really has two states.. the first one is only repeated
# once, the second one has zero or more repeats.]
rm -rf $lang
cp -r data/lang $lang
silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
# Use our special topology... note that later on may have to tune this
# topology.
steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
fi

if [ $stage -le 11 ]; then
# Build a tree using our new topology.
steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
--leftmost-questions-truncate $leftmost_questions_truncate \
--context-opts "--context-width=2 --central-position=1" \
--cmd "$train_cmd" 7000 data/$train_set $lang $ali_dir $treedir
fi

if [ $stage -le 12 ]; then
echo "$0: creating neural net configs using the xconfig parser";

num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
gru_opts="dropout-per-frame=true dropout-proportion=0.0 gru-nonlinearity-options=\"max-change=0.75\""

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-batchnorm-layer name=tdnn1 dim=1024
relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=1024
relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
# check steps/libs/nnet3/xconfig/gru.py for the other options and defaults
fast-norm-opgru-layer name=opgru1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $gru_opts
relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
fast-norm-opgru-layer name=opgru2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $gru_opts
relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
fast-norm-opgru-layer name=opgru3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $gru_opts
## adding the layers for chain branch
output-layer name=output input=opgru3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
# adding the layers for xent branch
# This block prints the configs for a separate output that will be
# trained with a cross-entropy objective in the 'chain' models... this
# has the effect of regularizing the hidden parts of the model. we use
# 0.5 / args.xent_regularize as the learning rate factor- the factor of
# 0.5 / args.xent_regularize is suitable as it means the xent
# final-layer learns at a rate independent of the regularization
# constant; and the 0.5 was tuned so as to make the relative progress
# similar in the xent and regular final layers.
output-layer name=output-xent input=opgru3 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor 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 $dir/egs/storage ]; then
utils/create_split_dir.pl \
/export/b0{5,6,7,8}/$USER/kaldi-data/egs/swbd-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
fi

steps/nnet3/chain/train.py --stage $train_stage \
--cmd "$decode_cmd" \
--feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \
--feat.cmvn-opts "--norm-means=false --norm-vars=false" \
--chain.xent-regularize $xent_regularize \
--chain.leaky-hmm-coefficient 0.1 \
--chain.l2-regularize 0.00005 \
--chain.apply-deriv-weights false \
--chain.lm-opts="--num-extra-lm-states=2000" \
--trainer.num-chunk-per-minibatch 64 \
--trainer.frames-per-iter 1200000 \
--trainer.max-param-change 2.0 \
--trainer.num-epochs 4 \
--trainer.optimization.shrink-value 0.99 \
--trainer.optimization.num-jobs-initial 3 \
--trainer.optimization.num-jobs-final 16 \
--trainer.optimization.initial-effective-lrate 0.001 \
--trainer.optimization.final-effective-lrate 0.0001 \
--trainer.optimization.momentum 0.0 \
--trainer.deriv-truncate-margin 8 \
--egs.stage $get_egs_stage \
--egs.opts "--frames-overlap-per-eg 0" \
--egs.chunk-width $chunk_width \
--egs.chunk-left-context $chunk_left_context \
--egs.chunk-right-context $chunk_right_context \
--trainer.dropout-schedule $dropout_schedule \
--egs.chunk-left-context-initial 0 \
--egs.chunk-right-context-final 0 \
--egs.dir "$common_egs_dir" \
--cleanup.remove-egs $remove_egs \
--feat-dir data/${train_set}_hires \
--tree-dir $treedir \
--lat-dir exp/tri4_lats_nodup$suffix \
--dir $dir || exit 1;
fi

if [ $stage -le 14 ]; then
# Note: it might appear that this $lang directory is mismatched, and it is as
# far as the 'topo' is concerned, but this script doesn't read the 'topo' from
# the lang directory.
utils/mkgraph.sh --self-loop-scale 1.0 data/lang_sw1_tg $dir $dir/graph_sw1_tg
fi

decode_suff=sw1_tg
graph_dir=$dir/graph_sw1_tg
if [ $stage -le 15 ]; then
[ -z $extra_left_context ] && extra_left_context=$chunk_left_context;
[ -z $extra_right_context ] && extra_right_context=$chunk_right_context;
[ -z $frames_per_chunk ] && frames_per_chunk=$chunk_width;
iter_opts=
if [ ! -z $decode_iter ]; then
iter_opts=" --iter $decode_iter "
fi
for decode_set in train_dev eval2000; do
(
steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
--nj 50 --cmd "$decode_cmd" $iter_opts \
--extra-left-context $extra_left_context \
--extra-right-context $extra_right_context \
--extra-left-context-initial 0 \
--extra-right-context-final 0 \
--frames-per-chunk "$frames_per_chunk" \
--online-ivector-dir exp/nnet3/ivectors_${decode_set} \
$graph_dir data/${decode_set}_hires \
$dir/decode_${decode_set}${decode_dir_affix:+_$decode_dir_affix}_${decode_suff} || exit 1;
if $has_fisher; then
steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
data/lang_sw1_{tg,fsh_fg} data/${decode_set}_hires \
$dir/decode_${decode_set}${decode_dir_affix:+_$decode_dir_affix}_sw1_{tg,fsh_fg} || exit 1;
fi
) &
done
fi

if $test_online_decoding && [ $stage -le 16 ]; then
# note: if the features change (e.g. you add pitch features), you will have to
# change the options of the following command line.
steps/online/nnet3/prepare_online_decoding.sh \
--mfcc-config conf/mfcc_hires.conf \
$lang exp/nnet3/extractor $dir ${dir}_online

rm $dir/.error 2>/dev/null || true
for decode_set in train_dev eval2000; do
(
# note: we just give it "$decode_set" as it only uses the wav.scp, the
# feature type does not matter.
steps/online/nnet3/decode.sh --nj 50 --cmd "$decode_cmd" $iter_opts \
--acwt 1.0 --post-decode-acwt 10.0 \
$graph_dir data/${decode_set}_hires \
${dir}_online/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_tg || exit 1;
if $has_fisher; then
steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
data/lang_sw1_{tg,fsh_fg} data/${decode_set}_hires \
${dir}_online/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_{tg,fsh_fg} || exit 1;
fi
) || touch $dir/.error &
done
wait
if [ -f $dir/.error ]; then
echo "$0: something went wrong in online decoding"
exit 1
fi
fi

if [ $stage -le 17 ]; then
rm $dir/.error 2>/dev/null || true
for decode_set in train_dev eval2000; do
(
steps/nnet3/decode_looped.sh \
--acwt 1.0 --post-decode-acwt 10.0 \
--nj 50 --cmd "$decode_cmd" $iter_opts \
--online-ivector-dir exp/nnet3/ivectors_${decode_set} \
$graph_dir data/${decode_set}_hires \
$dir/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_tg_looped || exit 1;
if $has_fisher; then
steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" \
data/lang_sw1_{tg,fsh_fg} data/${decode_set}_hires \
$dir/decode_${decode_set}${decode_iter:+_$decode_iter}_sw1_{tg,fsh_fg}_looped || exit 1;
fi
) &
done
wait
if [ -f $dir/.error ]; then
echo "$0: something went wrong in looped decoding"
exit 1
fi
fi

wait;
exit 0;
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