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[egs] ami : Added tdnn_lstm recipe with fast-lstmp layer. Added tdnn_…
…lstm recipe with -1 delay at lowest lstm layer (kaldi-asr#1505) swbd : Added tdnn_lstm recipe with delay -1 at the lowest lstm layer
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tuning/run_tdnn_lstm_1i.sh | ||
tuning/run_tdnn_lstm_1j.sh |
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#!/bin/bash | ||
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# 1j is same as 1i but with changes related to fast-lstmp layer | ||
# changed num-chunk-per-minibatch to be variable | ||
# added extra_left_context_initial=0 | ||
# and extra_right_context_final=0 | ||
# These changes are similar to those between swbd's run_tdnn_lstm_1{c,d}.sh | ||
# recipes | ||
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# Results with flags : --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned \ | ||
#System tdnn_lstm1i_sp_bi_ihmali_ld5 tdnn_lstm1j_sp_bi_ihmali_ld5 | ||
#WER on dev 37.6 37.3 | ||
#WER on eval 40.9 40.4 | ||
#Final train prob -0.114135 -0.118532 | ||
#Final valid prob -0.245208 -0.245593 | ||
#Final train prob (xent) -1.47648 -1.48337 | ||
#Final valid prob (xent) -2.16365 -2.11097 | ||
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# steps/info/chain_dir_info.pl exp/sdm1/chain_cleaned/tdnn_lstm1i_sp_bi_ihmali_ld5/ exp/sdm1/chain_cleaned/tdnn_lstm1j_sp_bi_ihmali_ld5/ | ||
# exp/sdm1/chain_cleaned/tdnn_lstm1i_sp_bi_ihmali_ld5/: num-iters=87 nj=2..12 num-params=43.4M dim=40+100->3770 combine=-0.142->-0.131 xent:train/valid[57,86,final]=(-1.78,-1.48,-1.48/-2.22,-2.17,-2.16) logprob:train/valid[57,86,final]=(-0.157,-0.117,-0.114/-0.243,-0.249,-0.245) | ||
# exp/sdm1/chain_cleaned/tdnn_lstm1j_sp_bi_ihmali_ld5/: num-iters=87 nj=2..12 num-params=43.4M dim=40+100->3770 combine=-0.139->-0.130 xent:train/valid[57,86,final]=(-1.82,-1.50,-1.48/-2.18,-2.12,-2.11) logprob:train/valid[57,86,final]=(-0.165,-0.121,-0.119/-0.240,-0.247,-0.246) | ||
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set -e -o pipefail | ||
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# First the options that are passed through to run_ivector_common.sh | ||
# (some of which are also used in this script directly). | ||
stage=0 | ||
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 | ||
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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=1j #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. | ||
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# decode options | ||
extra_left_context=50 | ||
frames_per_chunk= | ||
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# End configuration section. | ||
echo "$0 $@" # Print the command line for logging | ||
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. ./cmd.sh | ||
. ./path.sh | ||
. ./utils/parse_options.sh | ||
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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 | ||
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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" | ||
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# 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 | ||
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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}/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${nnet3_affix}/tdnn_lstm${tlstm_affix}_sp_bi | ||
fi | ||
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if [ $label_delay -gt 0 ]; then dir=${dir}_ld$label_delay; fi | ||
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train_data_dir=data/$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 | ||
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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 | ||
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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 | ||
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[ ! -f $ali_dir/ali.1.gz ] && echo "$0: expected $ali_dir/ali.1.gz to exist" && exit 1 | ||
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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 | ||
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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 | ||
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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 | ||
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xent_regularize=0.1 | ||
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if [ $stage -le 15 ]; then | ||
echo "$0: creating neural net configs using the xconfig parser"; | ||
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num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}') | ||
learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python) | ||
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lstm_opts="decay-time=20" | ||
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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 | ||
# the first splicing is moved before the lda layer, so no splicing here | ||
relu-renorm-layer name=tdnn1 dim=1024 | ||
relu-renorm-layer name=tdnn2 input=Append(-1,0,1) dim=1024 | ||
relu-renorm-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-renorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024 | ||
relu-renorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024 | ||
relu-renorm-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-renorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024 | ||
relu-renorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024 | ||
relu-renorm-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 | ||
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steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/ | ||
fi | ||
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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 | ||
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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 4 \ | ||
--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 $dir | ||
fi | ||
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graph_dir=$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 | ||
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if [ $stage -le 18 ]; then | ||
rm $dir/.error 2>/dev/null || true | ||
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[ -z $extra_left_context ] && extra_left_context=$chunk_left_context; | ||
[ -z $frames_per_chunk ] && frames_per_chunk=$chunk_width; | ||
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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 | ||
exit 0 |
Oops, something went wrong.