Kaldi 5.2 (the batch-norm + convolutional PR).

egs/ami/s5b/RESULTS_ihm

@@ -54,24 +54,20 @@
 %WER 22.4 | 12643 89977 | 80.3 12.5 7.2 2.7 22.4 53.6 | -0.503 | exp/ihm/nnet3_cleaned/lstm_bidirectional_sp/decode_eval/ascore_10/eval_hires.ctm.filt.sys
 
 ############################################
-# cleanup + chain TDNN model.
+# cleanup + chain TDNN model 
 # local/chain/run_tdnn.sh --mic ihm --stage 4 &
-# for d in exp/ihm/chain_cleaned/tdnn1d_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
-%WER 21.7 | 13098 94488 | 81.1 10.4 8.4 2.8 21.7 54.4 | 0.096 | exp/ihm/chain_cleaned/tdnn1d_sp_bi/decode_dev/ascore_10/dev_hires.ctm.filt.sys
-%WER 22.1 | 12643 89979 | 80.5 12.1 7.4 2.6 22.1 52.8 | 0.185 | exp/ihm/chain_cleaned/tdnn1d_sp_bi/decode_eval/ascore_10/eval_hires.ctm.filt.sys
+# for d in exp/ihm/chain_cleaned/tdnn1e_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
+%WER 21.4 | 13098 94487 | 81.4 10.1 8.5 2.8 21.4 53.7 | 0.090 | exp/ihm/chain_cleaned/tdnn1e_batch_sp_bi/decode_dev/ascore_10/dev_hires.ctm.filt.sys
+%WER 21.5 | 12643 89977 | 81.0 11.8 7.2 2.5 21.5 52.4 | 0.168 | exp/ihm/chain_cleaned/tdnn1e_batch_sp_bi/decode_eval/ascore_10/eval_hires.ctm.filt.sys
 
-# cleanup + chain TDNN model. Uses LDA instead of PCA for ivector features.
-# local/chain/tuning/run_tdnn_1b.sh --mic ihm --stage 4 &
-# for d in exp/ihm/chain_cleaned/tdnn1b_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
-%WER 22.0 | 13098 94488 | 80.8 10.2 9.0 2.8 22.0 54.7 | 0.102 | exp/ihm/chain_cleaned/tdnn1b_sp_bi/decode_dev/ascore_10/dev_hires.ctm.filt.sys
-%WER 22.2 | 12643 89968 | 80.3 12.1 7.6 2.6 22.2 52.9 | 0.170 | exp/ihm/chain_cleaned/tdnn1b_sp_bi/decode_eval/ascore_10/eval_hires.ctm.filt.sys
 
 # local/chain/run_tdnn.sh --mic ihm --train-set train --gmm tri3 --nnet3-affix "" --stage 4
 # chain TDNN model without cleanup [note: cleanup helps very little on this IHM data.]
 # for d in exp/ihm/chain/tdnn1d_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
 %WER 21.8 | 13098 94484 | 80.7 9.7 9.6 2.5 21.8 54.2 | 0.114 | exp/ihm/chain/tdnn1d_sp_bi/decode_dev/ascore_10/dev_hires.ctm.filt.sys
 %WER 22.1 | 12643 89965 | 80.2 11.5 8.3 2.3 22.1 52.5 | 0.203 | exp/ihm/chain/tdnn1d_sp_bi/decode_eval/ascore_10/eval_hires.ctm.filt.sy
 
+
 # local/chain/multi_condition/run_tdnn.sh --mic ihm
 # cleanup + chain TDNN model + IHM reverberated data
 # for d in exp/ihm/chain_cleaned_rvb/tdnn_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done

egs/ami/s5b/RESULTS_mdm

@@ -54,20 +54,13 @@
 %WER 41.6 | 13964 89980 | 62.7 23.1 14.2 4.3 41.6 65.6 | 0.649 | exp/mdm8/nnet3/tdnn_sp_ihmali/decode_eval/ascore_12/eval_hires_o4.ctm.filt.sys
 
 
-################
-
-# local/chain/run_tdnn.sh --mic mdm8 --stage 11 &
-# cleanup + chain TDNN model, alignments from mdm8 data itself.
-# for d in exp/mdm8/chain_cleaned/tdnn_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
-%WER 37.9 | 14471 94512 | 65.9 17.4 16.6 3.8 37.9 67.4 | 0.625 | exp/mdm8/chain_cleaned/tdnn_sp_bi/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
-%WER 41.3 | 13696 89959 | 62.0 18.6 19.4 3.3 41.3 67.2 | 0.591 | exp/mdm8/chain_cleaned/tdnn_sp_bi/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
-
-
+############################################
 # cleanup + chain TDNN model, alignments from IHM data (IHM alignments help).
 # local/chain/run_tdnn.sh --mic mdm8 --use-ihm-ali true --stage 12 &
-# for d in exp/mdm8/chain_cleaned/tdnn1d_sp_bi_ihmali/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
-%WER 36.4 | 15140 94513 | 67.3 17.5 15.2 3.6 36.4 63.2 | 0.613 | exp/mdm8/chain_cleaned/tdnn1d_sp_bi_ihmali/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
-%WER 39.7 | 13835 89969 | 63.2 18.4 18.4 3.0 39.7 65.7 | 0.584 | exp/mdm8/chain_cleaned/tdnn1d_sp_bi_ihmali/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
+# for d in exp/mdm8/chain_cleaned/tdnn1e_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
+%WER 36.0 | 14597 94517 | 67.8 17.7 14.5 3.8 36.0 64.9 | 0.623 | exp/mdm8/chain_cleaned/tdnn1e_sp_bi_ihmali/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
+%WER 39.3 | 13872 89973 | 63.9 19.0 17.1 3.2 39.3 65.1 | 0.594 | exp/mdm8/chain_cleaned/tdnn1e_sp_bi_ihmali/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
+
 
 # local/chain/run_tdnn.sh --use-ihm-ali true --mic mdm8 --train-set train --gmm tri3 --nnet3-affix "" --stage 12 &
 # chain TDNN model-- no cleanup, but IHM alignments.
@@ -76,6 +69,14 @@
 %WER 36.9 | 15282 94502 | 67.1 18.5 14.4 4.1 36.9 62.5 | 0.635 | exp/mdm8/chain/tdnn1d_sp_bi_ihmali/decode_dev/ascore_8/dev_hires_o4.ctm.filt.sys
 %WER 40.2 | 13729 89992 | 63.3 19.8 17.0 3.5 40.2 66.4 | 0.608 | exp/mdm8/chain/tdnn1d_sp_bi_ihmali/decode_eval/ascore_8/eval_hires_o4.ctm.filt.sys
 
+
+# local/chain/run_tdnn.sh --mic mdm8 --stage 11 &
+# cleanup + chain TDNN model, alignments from mdm8 data itself.
+# for d in exp/mdm8/chain_cleaned/tdnn_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
+%WER 37.9 | 14471 94512 | 65.9 17.4 16.6 3.8 37.9 67.4 | 0.625 | exp/mdm8/chain_cleaned/tdnn_sp_bi/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
+%WER 41.3 | 13696 89959 | 62.0 18.6 19.4 3.3 41.3 67.2 | 0.591 | exp/mdm8/chain_cleaned/tdnn_sp_bi/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
+
+
 # local/chain/multi_condition/run_tdnn.sh --mic mdm8 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
 # cleanup + chain TDNN model, MDM original + IHM reverberated data, alignments from IHM data
 # for d in exp/mdm8/chain_cleaned_rvb/tdnn_sp_rvb_bi_ihmali/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done

egs/ami/s5b/RESULTS_sdm

@@ -52,33 +52,29 @@
 %WER 37.9 | 15953 94512 | 66.7 22.0 11.3 4.7 37.9 58.9 | 0.734 | exp/sdm1/nnet3_cleaned/lstm_bidirectional_sp_ihmali/decode_dev/ascore_12/dev_hires_o4.ctm.filt.sys
 %WER 41.2 | 13271 89635 | 62.9 23.8 13.2 4.2 41.2 67.8 | 0.722 | exp/sdm1/nnet3_cleaned/lstm_bidirectional_sp_ihmali/decode_eval/ascore_11/eval_hires_o4.ctm.filt.sys
 
-# =========================
 
+############################################
+# cleanup + chain TDNN model, alignments from IHM data (IHM alignments help)
+# local/chain/run_tdnn.sh --mic sdm1 --use-ihm-ali true --stage 12 &
+# for d in exp/sdm1/chain_cleaned/tdnn1e_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
+%WER 39.1 | 14457 94509 | 64.6 19.7 15.7 3.7 39.1 66.5 | 0.585 | exp/sdm1/chain_cleaned/tdnn1e_sp_bi_ihmali/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
+%WER 43.2 | 13551 89981 | 60.3 20.9 18.8 3.5 43.2 67.1 | 0.554 | exp/sdm1/chain_cleaned/tdnn1e_sp_bi_ihmali/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
 
-# local/chain/run_tdnn.sh --mic sdm1 --stage 12 &
 
 # cleanup + chain TDNN model, alignments from sdm1 data itself.
 # for d in exp/sdm1/chain_cleaned/tdnn_sp_bi/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
 %WER 41.6 | 14357 94500 | 62.0 19.2 18.8 3.6 41.6 68.4 | 0.592 | exp/sdm1/chain_cleaned/tdnn_sp_bi/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
 %WER 45.4 | 12886 89960 | 58.1 21.0 20.9 3.5 45.4 71.9 | 0.558 | exp/sdm1/chain_cleaned/tdnn_sp_bi/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
 
 
-
-# cleanup + chain TDNN model, alignments from IHM data (IHM alignments help).
-# local/chain/run_tdnn.sh --mic sdm1 --use-ihm-ali true --stage 12 &
-# cleanup + chain TDNN model, cleaned data and alignments from ihm data.
-# for d in exp/sdm1/chain_cleaned/tdnn1d_sp_bi_ihmali/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
-%WER 39.5 | 14280 94503 | 64.0 19.3 16.7 3.5 39.5 67.7 | 0.582 | exp/sdm1/chain_cleaned/tdnn1d_sp_bi_ihmali/decode_dev/ascore_9/dev_hires_o4.ctm.filt.sys
-%WER 43.9 | 13566 89961 | 59.3 20.9 19.9 3.1 43.9 67.9 | 0.547 | exp/sdm1/chain_cleaned/tdnn1d_sp_bi_ihmali/decode_eval/ascore_9/eval_hires_o4.ctm.filt.sys
-
-
 # no-cleanup + chain TDNN model, IHM alignments.
 # A bit worse than with cleanup [+0.3, +0.4].
 # local/chain/run_tdnn.sh --use-ihm-ali true --mic sdm1 --train-set train --gmm tri3 --nnet3-affix "" --stage 12
 # for d in exp/sdm1/chain/tdnn1d_sp_bi_ihmali/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done
 %WER 39.8 | 15384 94535 | 64.4 21.0 14.6 4.2 39.8 62.8 | 0.610 | exp/sdm1/chain/tdnn1d_sp_bi_ihmali/decode_dev/ascore_8/dev_hires_o4.ctm.filt.sys
 %WER 44.3 | 14046 90002 | 59.6 23.1 17.3 3.9 44.3 65.6 | 0.571 | exp/sdm1/chain/tdnn1d_sp_bi_ihmali/decode_eval/ascore_8/eval_hires_o4.ctm.filt.sys
 
+
 # local/chain/multi_condition/run_tdnn.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned 
 # cleanup + chain TDNN model, SDM original + IHM reverberated data, alignments from ihm data.
 # for d in exp/sdm1/chain_cleaned_rvb/tdnn_sp_rvb_bi_ihmali/decode_*; do grep Sum $d/*sc*/*ys | utils/best_wer.sh; done

egs/ami/s5b/local/chain/run_tdnn.sh

@@ -1 +1 @@
-tuning/run_tdnn_1d.sh
+tuning/run_tdnn_1e.sh

egs/ami/s5b/local/chain/tuning/run_tdnn_1e.sh

@@ -0,0 +1,266 @@
+#!/bin/bash
+
+# same as 1b but uses batchnorm components instead of renorm
+
+# Results on 03/27/2017:
+# local/chain/compare_wer_general.sh ihm tdnn1b_sp_bi tdnn1e_sp_bi
+# System                   tdnn1b_sp_bi tdnn1e_sp_bi
+# WER on dev               21.9         21.4
+# WER on eval              22.2         21.5
+# Final train prob        -0.0906771   -0.0857669
+# Final valid prob        -0.126942    -0.124401
+# Final train prob (xent) -1.4427      -1.37837
+# Final valid prob (xent) -1.60284     -1.5634
+
+set -e -o pipefail
+# 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
+ivector_transform_type=pca
+nnet3_affix=_cleaned  # cleanup affix for nnet3 and chain dirs, e.g. _cleaned
+
+# 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.
+tdnn_affix=1e  #affix for TDNN directory, e.g. "a" or "b", in case we change the configuration.
+common_egs_dir=  # you can set this to use previously dumped egs.
+
+# 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 $num_threads_ubm \
+                                  --ivector-transform-type "$ivector_transform_type" \
+                                  --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}/tdnn${tdnn_affix}_sp_bi_ihmali
+  # note: the distinction between when we use the 'ihmdata' suffix versus
+  # 'ihmali' is pretty arbitrary.
+else
+  gmm_dir=exp/${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${nnet3_affix}/tdnn${tdnn_affix}_sp_bi
+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)
+
+  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-batchnorm-layer name=tdnn1 dim=450
+  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=450
+  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=450
+  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=450
+  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=450
+  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=450
+  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=450
+
+  ## adding the layers for chain branch
+  relu-batchnorm-layer name=prefinal-chain input=tdnn7 dim=450 target-rms=0.5
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  relu-batchnorm-layer name=prefinal-xent input=tdnn7 dim=450 target-rms=0.5
+  output-layer name=output-xent dim=$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 150 \
+    --trainer.num-chunk-per-minibatch 128 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs 4 \
+    --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 \
+    --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
+  for decode_set in dev eval; do
+      (
+      steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+          --nj $nj --cmd "$decode_cmd" \
+          --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