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[egs] CNN+TDNN+LSTM experiments on AMI (#1685)
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@GaofengCheng @danpovey
GaofengCheng authored and danpovey committed Jun 14, 2017
1 parent d46890d commit 08dbc18
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8 changes: 8 additions & 0 deletions egs/ami/s5b/local/chain/compare_wer_general.sh
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Expand Up @@ -26,28 +26,36 @@ echo

echo -n "Final train prob "
for x in $*; do
if [[ "${x}" != *online* ]]; then
prob=$(grep Overall exp/$mic/chain_cleaned/${x}/log/compute_prob_train.final.log | grep -v xent | awk '{print $8}')
printf "% 10s" $prob
fi
done
echo

echo -n "Final valid prob "
for x in $*; do
if [[ "${x}" != *online* ]]; then
prob=$(grep Overall exp/$mic/chain_cleaned/${x}/log/compute_prob_valid.final.log | grep -v xent | awk '{print $8}')
printf "% 10s" $prob
fi
done
echo

echo -n "Final train prob (xent) "
for x in $*; do
if [[ "${x}" != *online* ]]; then
prob=$(grep Overall exp/$mic/chain_cleaned/${x}/log/compute_prob_train.final.log | grep -w xent | awk '{print $8}')
printf "% 10s" $prob
fi
done
echo

echo -n "Final valid prob (xent) "
for x in $*; do
if [[ "${x}" != *online* ]]; then
prob=$(grep Overall exp/$mic/chain_cleaned/${x}/log/compute_prob_valid.final.log | grep -w xent | awk '{print $8}')
printf "% 10s" $prob
fi
done
echo
340 changes: 340 additions & 0 deletions egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
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#!/bin/bash

# cnn_tdnn_lstm_1a is based on tdnn_lstm_1j, but adding the cnn front end, and
# replacing all renorm in tdnn layers with batchnorm

# SDM
# Results with flags : --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned \
# ./local/chain/compare_wer_general.sh sdm1 tdnn_lstm1j_sp_bi_ihmali_ld5 cnn_tdnn_lstm1a_sp_bi_ihmali_ld5 cnn_tdnn_lstm1a_sp_bi_ihmali_ld5_online
# System tdnn_lstm1j_sp_bi_ihmali_ld5 cnn_tdnn_lstm1a_sp_bi_ihmali_ld5 cnn_tdnn_lstm1a_sp_bi_ihmali_ld5_online
# WER on dev 36.9 35.9 35.9
# WER on eval 40.5 39.2 39.2
# Final train prob -0.108141 -0.0937906
# Final valid prob -0.257468 -0.263064
# Final train prob (xent) -1.38179 -1.24004
# Final valid prob (xent) -2.13095 -2.08523

# IHM
# System tdnn_lstm1j_sp_bi_ld5 cnn_tdnn_lstm1a_sp_bi_ld5 cnn_tdnn_lstm1a_sp_bi_ld5_online
# WER on dev 20.8 20.4 20.3
# WER on eval 20.3 19.9 19.9
# Final train prob -0.0439145 -0.0420102
# Final valid prob -0.10673 -0.104629
# Final train prob (xent) -0.683776 -0.640218
# Final valid prob (xent) -1.05254 -1.01591

# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).

stage=15
mic=ihm
nj=30
min_seg_len=1.55
use_ihm_ali=false
train_set=train_cleaned
gmm=tri3_cleaned # the gmm for the target data
ihm_gmm=tri3 # the gmm for the IHM system (if --use-ihm-ali true).
num_threads_ubm=32
nnet3_affix=_cleaned # cleanup affix for nnet3 and chain dirs, e.g. _cleaned
num_epochs=4

chunk_width=150
chunk_left_context=40
chunk_right_context=0
label_delay=5
# The rest are configs specific to this script. Most of the parameters
# are just hardcoded at this level, in the commands below.
train_stage=-10
tree_affix= # affix for tree directory, e.g. "a" or "b", in case we change the configuration.
tlstm_affix=1a #affix for TDNN-LSTM directory, e.g. "a" or "b", in case we change the configuration.
common_egs_dir= # you can set this to use previously dumped egs.


# decode options
extra_left_context=50
frames_per_chunk=
test_online_decoding=true
# 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


local/nnet3/run_ivector_common.sh --stage $stage \
--mic $mic \
--nj $nj \
--min-seg-len $min_seg_len \
--train-set $train_set \
--gmm $gmm \
--num-threads-ubm $num_threads_ubm \
--nnet3-affix "$nnet3_affix"

# Note: the first stage of the following script is stage 8.
local/nnet3/prepare_lores_feats.sh --stage $stage \
--mic $mic \
--nj $nj \
--min-seg-len $min_seg_len \
--use-ihm-ali $use_ihm_ali \
--train-set $train_set

if $use_ihm_ali; then
gmm_dir=exp/ihm/${ihm_gmm}
ali_dir=exp/${mic}/${ihm_gmm}_ali_${train_set}_sp_comb_ihmdata
lores_train_data_dir=data/$mic/${train_set}_ihmdata_sp_comb
tree_dir=exp/$mic/chain${nnet3_affix}/tree_bi${tree_affix}_ihmdata
lat_dir=exp/$mic/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats_ihmdata
dir=exp/$mic/chain${nnet3_affix}/cnn_tdnn_lstm${tlstm_affix}_sp_bi_ihmali
# note: the distinction between when we use the 'ihmdata' suffix versus
# 'ihmali' is pretty arbitrary.
else
gmm_dir=exp/${mic}/$gmm
ali_dir=exp/${mic}/${gmm}_ali_${train_set}_sp_comb
lores_train_data_dir=data/$mic/${train_set}_sp_comb
tree_dir=exp/$mic/chain${nnet3_affix}/tree_bi${tree_affix}
lat_dir=exp/$mic/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats
dir=exp/$mic/chain${nnet3_affix}/cnn_tdnn_lstm${tlstm_affix}_sp_bi
fi

if [ $label_delay -gt 0 ]; then dir=${dir}_ld$label_delay; fi

train_data_dir=data/$mic/${train_set}_sp_hires_comb
train_ivector_dir=exp/$mic/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires_comb
final_lm=`cat data/local/lm/final_lm`
LM=$final_lm.pr1-7


for f in $gmm_dir/final.mdl $lores_train_data_dir/feats.scp \
$train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp; do
[ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
done


if [ $stage -le 11 ]; then
if [ -f $ali_dir/ali.1.gz ]; then
echo "$0: alignments in $ali_dir appear to already exist. Please either remove them "
echo " ... or use a later --stage option."
exit 1
fi
echo "$0: aligning perturbed, short-segment-combined ${maybe_ihm}data"
steps/align_fmllr.sh --nj $nj --cmd "$train_cmd" \
${lores_train_data_dir} data/lang $gmm_dir $ali_dir
fi

[ ! -f $ali_dir/ali.1.gz ] && echo "$0: expected $ali_dir/ali.1.gz to exist" && exit 1

if [ $stage -le 12 ]; then
echo "$0: creating lang directory with one state per phone."
# 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.]
if [ -d data/lang_chain ]; then
if [ data/lang_chain/L.fst -nt data/lang/L.fst ]; then
echo "$0: data/lang_chain already exists, not overwriting it; continuing"
else
echo "$0: data/lang_chain already exists and seems to be older than data/lang..."
echo " ... not sure what to do. Exiting."
exit 1;
fi
else
cp -r data/lang data/lang_chain
silphonelist=$(cat data/lang_chain/phones/silence.csl) || exit 1;
nonsilphonelist=$(cat data/lang_chain/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 >data/lang_chain/topo
fi
fi

if [ $stage -le 13 ]; then
# Get the alignments as lattices (gives the chain training more freedom).
# use the same num-jobs as the alignments
steps/align_fmllr_lats.sh --nj 100 --cmd "$train_cmd" ${lores_train_data_dir} \
data/lang $gmm_dir $lat_dir
rm $lat_dir/fsts.*.gz # save space
fi

if [ $stage -le 14 ]; then
# Build a tree using our new topology. We know we have alignments for the
# speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
# those.
if [ -f $tree_dir/final.mdl ]; then
echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
exit 1;
fi
steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
--context-opts "--context-width=2 --central-position=1" \
--leftmost-questions-truncate -1 \
--cmd "$train_cmd" 4200 ${lores_train_data_dir} data/lang_chain $ali_dir $tree_dir
fi

xent_regularize=0.1

if [ $stage -le 15 ]; then
echo "$0: creating neural net configs using the xconfig parser";

num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)

lstm_opts="decay-time=20"

mkdir -p $dir/configs
cat <<EOF > $dir/configs/network.xconfig
input dim=100 name=ivector
input dim=40 name=input
# please note that it is important to have input layer with the name=input
# as the layer immediately preceding the fixed-affine-layer to enable
# the use of short notation for the descriptor
fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
conv-relu-batchnorm-layer name=cnn1 input=idct height-in=40 height-out=20 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256 learning-rate-factor=0.333 max-change=0.25
conv-relu-batchnorm-layer name=cnn2 input=cnn1 height-in=20 height-out=20 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
relu-batchnorm-layer name=affine1 input=lda dim=512
# the first splicing is moved before the lda layer, so no splicing here
relu-batchnorm-layer name=tdnn1 input=cnn2 dim=1024
relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1,affine1) dim=1024
relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
# check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
fast-lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_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
relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
fast-lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts
relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024
relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024
fast-lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts
## adding the layers for chain branch
output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
# adding the layers for xent branch
# This block prints the configs for a separate output that will be
# trained with a cross-entropy objective in the 'chain' models... this
# has the effect of regularizing the hidden parts of the model. we use
# 0.5 / args.xent_regularize as the learning rate factor- the factor of
# 0.5 / args.xent_regularize is suitable as it means the xent
# final-layer learns at a rate independent of the regularization
# constant; and the 0.5 was tuned so as to make the relative progress
# similar in the xent and regular final layers.
output-layer name=output-xent input=lstm3 output-delay=$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 16 ]; 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/ami-$(date +'%m_%d_%H_%M')/s5b/$dir/egs/storage $dir/egs/storage
fi

steps/nnet3/chain/train.py --stage $train_stage \
--cmd "$decode_cmd" \
--feat.online-ivector-dir $train_ivector_dir \
--feat.cmvn-opts "--norm-means=false --norm-vars=false" \
--chain.xent-regularize $xent_regularize \
--chain.leaky-hmm-coefficient 0.1 \
--chain.l2-regularize 0.00005 \
--chain.apply-deriv-weights false \
--chain.lm-opts="--num-extra-lm-states=2000" \
--egs.dir "$common_egs_dir" \
--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 \
--egs.chunk-left-context-initial 0 \
--egs.chunk-right-context-final 0 \
--trainer.num-chunk-per-minibatch 64,32 \
--trainer.frames-per-iter 1500000 \
--trainer.num-epochs $num_epochs \
--trainer.optimization.shrink-value 0.99 \
--trainer.optimization.num-jobs-initial 2 \
--trainer.optimization.num-jobs-final 12 \
--trainer.optimization.initial-effective-lrate 0.001 \
--trainer.optimization.final-effective-lrate 0.0001 \
--trainer.max-param-change 2.0 \
--trainer.deriv-truncate-margin 8 \
--cleanup.remove-egs true \
--feat-dir $train_data_dir \
--tree-dir $tree_dir \
--lat-dir $lat_dir \
--dir $dir
fi


graph_dir=$dir/graph_${LM}
if [ $stage -le 17 ]; then
# Note: it might appear that this data/lang_chain directory is mismatched, and it is as
# far as the 'topo' is concerned, but this script doesn't read the 'topo' from
# the lang directory.
utils/mkgraph.sh --self-loop-scale 1.0 data/lang_${LM} $dir $graph_dir
fi

if [ $stage -le 18 ]; then
rm $dir/.error 2>/dev/null || true

[ -z $extra_left_context ] && extra_left_context=$chunk_left_context;
[ -z $frames_per_chunk ] && frames_per_chunk=$chunk_width;

for decode_set in dev eval; do
(
steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
--nj $nj --cmd "$decode_cmd" \
--extra-left-context $extra_left_context \
--frames-per-chunk "$frames_per_chunk" \
--extra-left-context-initial 0 \
--extra-right-context-final 0 \
--online-ivector-dir exp/$mic/nnet3${nnet3_affix}/ivectors_${decode_set}_hires \
--scoring-opts "--min-lmwt 5 " \
$graph_dir data/$mic/${decode_set}_hires $dir/decode_${decode_set} || exit 1;
) || touch $dir/.error &
done
wait
if [ -f $dir/.error ]; then
echo "$0: something went wrong in decoding"
exit 1
fi
fi

if $test_online_decoding && [ $stage -le 19 ]; then
steps/online/nnet3/prepare_online_decoding.sh \
--mfcc-config conf/mfcc_hires.conf \
data/lang_${LM} exp/$mic/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online

rm $dir/.error 2>/dev/null || true

for decode_set in dev eval; do
(
nspk=$(wc -l <data/$mic/${decode_set}_hires/spk2utt)
# note: we just give it "data/${data}" as it only uses the wav.scp, the
# feature type does not matter.
steps/online/nnet3/decode.sh \
--nj $nspk --cmd "$decode_cmd" \
--acwt 1.0 --post-decode-acwt 10.0 \
--extra-left-context-initial 0 \
--scoring-opts "--min-lmwt 5 " \
$graph_dir data/$mic/${decode_set}_hires ${dir}_online/decode_${decode_set}_online
) || touch ${dir}_online/.error &
done
wait
[ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
fi

exit 0
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