[egs] swbd/chain : added blstm script using fast-LSTM; added BLSTM+TDNN script. (#1497)

Author: vijayaditya

egs/swbd/s5c/local/chain/run_blstm.sh

@@ -1 +1 @@
-tuning/run_blstm_6j.sh
+tuning/run_blstm_6k.sh

egs/swbd/s5c/local/chain/run_tdnn_blstm.sh

@@ -0,0 +1 @@
+tuning/run_tdnn_blstm_1a.sh

egs/swbd/s5c/local/chain/tuning/run_blstm_6k.sh

@@ -0,0 +1,240 @@
+#!/bin/bash
+
+# 6k is same as 6j, but with the fast lstm layers
+
+# local/chain/compare_wer_general.sh blstm_6j_sp blstm_6k_sp
+# System blstm_6j_sp blstm_6k_sp
+# WER on train_dev(tg) 13.80 13.25
+# WER on train_dev(fg) 12.64 12.27
+# WER on eval2000(tg) 15.6 15.7
+# WER on eval2000(fg) 14.2 14.5
+# Final train prob -0.055 -0.052
+# Final valid prob -0.077 -0.080
+# Final train prob (xent) -0.777 -0.743
+# Final valid prob (xent) -0.9126 -0.8816
+
+set -e
+
+# configs for 'chain'
+stage=12
+train_stage=-10
+get_egs_stage=-10
+speed_perturb=true
+dir=exp/chain/blstm_6k # 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=40
+xent_regularize=0.025
+self_repair_scale=0.00001
+label_delay=0
+
+# decode options
+extra_left_context=50
+extra_right_context=50
+frames_per_chunk=
+
+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}
+if [ $label_delay -gt 0 ]; then dir=${dir}_ld$label_delay; fi
+dir=${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}')
+ [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
+ learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+
+ lstm_opts="decay-time=20"
+
+ mkdir -p $dir/configs
+ cat <<EOF > $dir/configs/network.xconfig
+ input dim=100 name=ivector
+ input dim=40 name=input
+
+ # please note that it is important to have input layer with the name=input
+ # as the layer immediately preceding the fixed-affine-layer to enable
+ # the use of short notation for the descriptor
+ fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+ # the first splicing is moved before the lda layer, so no splicing here
+
+ # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
+ fast-lstmp-layer name=blstm1-forward input=lda cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts
+ fast-lstmp-layer name=blstm1-backward input=lda cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=3 $lstm_opts
+
+ fast-lstmp-layer name=blstm2-forward input=Append(blstm1-forward, blstm1-backward) cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts
+ fast-lstmp-layer name=blstm2-backward input=Append(blstm1-forward, blstm1-backward) cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=3 $lstm_opts
+
+ fast-lstmp-layer name=blstm3-forward input=Append(blstm2-forward, blstm2-backward) cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 $lstm_opts
+ fast-lstmp-layer name=blstm3-backward input=Append(blstm2-forward, blstm2-backward) cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=3 $lstm_opts
+
+ ## adding the layers for chain branch
+ output-layer name=output input=Append(blstm3-forward, blstm3-backward) output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
+
+ # adding the layers for xent branch
+ # This block prints the configs for a separate output that will be
+ # trained with a cross-entropy objective in the 'chain' models... this
+ # has the effect of regularizing the hidden parts of the model. we use
+ # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+ # 0.5 / args.xent_regularize is suitable as it means the xent
+ # final-layer learns at a rate independent of the regularization
+ # constant; and the 0.5 was tuned so as to make the relative progress
+ # similar in the xent and regular final layers.
+ output-layer name=output-xent input=Append(blstm3-forward, blstm3-backward) output-delay=$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 $chunk_right_context \
+ --egs.dir "$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 $extra_right_context \
+ --frames-per-chunk "$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
+wait;
+exit 0;