diff --git a/egs/madcat_ar/v1/cmd.sh b/egs/madcat_ar/v1/cmd.sh
index 3c8eb9f93a5..6080a8bab68 100644
--- a/egs/madcat_ar/v1/cmd.sh
+++ b/egs/madcat_ar/v1/cmd.sh
@@ -10,4 +10,4 @@
 # conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information,
 # or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl.
 
-export cmd="queue.pl"
+export cmd="retry.pl queue.pl"
diff --git a/egs/madcat_ar/v1/local/chain/compare_wer.sh b/egs/madcat_ar/v1/local/chain/compare_wer.sh
index 7f04061dafb..9f00308658a 100755
--- a/egs/madcat_ar/v1/local/chain/compare_wer.sh
+++ b/egs/madcat_ar/v1/local/chain/compare_wer.sh
@@ -22,28 +22,28 @@ echo
 
 echo -n "# WER                        "
 for x in $*; do
-  wer=$(cat $x/decode_test/scoring_kaldi/best_wer | awk '{print $2}')
+  wer=$(cat $x/decode_test.5k/scoring_kaldi/best_wer | awk '{print $2}')
   printf "% 10s" $wer
 done
 echo
 
 echo -n "# WER (rescored)             "
 for x in $*; do
-  wer=$(cat $x/decode_test_rescored/scoring_kaldi/best_wer | awk '{print $2}')
+  wer=$(cat $x/decode_test.5k_rescored/scoring_kaldi/best_wer | awk '{print $2}')
   printf "% 10s" $wer
 done
 echo
 
 echo -n "# CER                        "
 for x in $*; do
-  cer=$(cat $x/decode_test/scoring_kaldi/best_cer | awk '{print $2}')
+  cer=$(cat $x/decode_test.5k/scoring_kaldi/best_cer | awk '{print $2}')
   printf "% 10s" $cer
 done
 echo
 
 echo -n "# CER (rescored)             "
 for x in $*; do
-  cer=$(cat $x/decode_test_rescored/scoring_kaldi/best_cer | awk '{print $2}')
+  cer=$(cat $x/decode_test.5k_rescored/scoring_kaldi/best_cer | awk '{print $2}')
   printf "% 10s" $cer
 done
 echo
diff --git a/egs/madcat_ar/v1/local/chain/run_cnn.sh b/egs/madcat_ar/v1/local/chain/run_cnn.sh
deleted file mode 120000
index df6f0a468c1..00000000000
--- a/egs/madcat_ar/v1/local/chain/run_cnn.sh
+++ /dev/null
@@ -1 +0,0 @@
-tuning/run_cnn_1a.sh
\ No newline at end of file
diff --git a/egs/madcat_ar/v1/local/chain/run_cnn_chainali.sh b/egs/madcat_ar/v1/local/chain/run_cnn_chainali.sh
deleted file mode 120000
index a864819f542..00000000000
--- a/egs/madcat_ar/v1/local/chain/run_cnn_chainali.sh
+++ /dev/null
@@ -1 +0,0 @@
-tuning/run_cnn_chainali_1a.sh
\ No newline at end of file
diff --git a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_chainali_1a.sh b/egs/madcat_ar/v1/local/chain/run_cnn_chainali_1a.sh
similarity index 52%
rename from egs/madcat_ar/v1/local/chain/tuning/run_cnn_chainali_1a.sh
rename to egs/madcat_ar/v1/local/chain/run_cnn_chainali_1a.sh
index 23c4d5c2036..b870f3f5179 100755
--- a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_chainali_1a.sh
+++ b/egs/madcat_ar/v1/local/chain/run_cnn_chainali_1a.sh
@@ -1,32 +1,17 @@
 #!/bin/bash
-
 set -e -o pipefail
-
 stage=0
-
 nj=70
 train_set=train
-gmm=tri3        # this is the source gmm-dir that we'll use for alignments; it
-                # should have alignments for the specified training data.
-nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
-affix=_1a  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
-ali=tri3_ali
-chain_model_dir=exp/chain${nnet3_affix}/cnn_1a
-common_egs_dir=
-reporting_email=
-lats_affix=
-# chain options
 train_stage=-10
-xent_regularize=0.1
 chunk_width=340,300,200,100
 num_leaves=500
 tdnn_dim=450
-remove_egs=false
-lang_decode=data/lang
+lang_decode=data/lang_test
 lang_rescore=data/lang_rescore_6g
+dropout_schedule='0,0@0.20,0.2@0.50,0'
 # End configuration section.
 echo "$0 $@"  # Print the command line for logging
-
 . ./cmd.sh
 . ./path.sh
 . ./utils/parse_options.sh
@@ -39,21 +24,19 @@ where "nvcc" is installed.
 EOF
 fi
 
-gmm_dir=exp/${gmm}
-ali_dir=exp/${ali}
-lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_lats_chain_$lats_affix
-gmm_lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_lats
-dir=exp/chain${nnet3_affix}/cnn_chainali${affix}
+affix=_1a_${train_set}
+chain_model_dir=exp/chain/cnn_e2eali${affix}
+ali_dir=exp/chain/e2eali_$train_set
+lat_dir=exp/chain/chainali_${train_set}_lats
+dir=exp/chain/cnn_chainali${affix}
 train_data_dir=data/${train_set}
-tree_dir=exp/chain${nnet3_affix}/tree_chain_$lats_affix
-
+tree_dir=exp/chain/tree_chainali_${train_set}
+xent_regularize=0.1
 # the 'lang' directory is created by this script.
 # If you create such a directory with a non-standard topology
 # you should probably name it differently.
 lang=data/lang_chain
-for f in $train_data_dir/feats.scp \
-    $train_data_dir/feats.scp $gmm_dir/final.mdl \
-    $ali_dir/ali.1.gz $gmm_dir/final.mdl; do
+for f in $train_data_dir/feats.scp; do
   [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
 done
 
@@ -88,7 +71,7 @@ if [ $stage -le 2 ]; then
                             --acoustic-scale 1.0 \
                             --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
                             ${train_data_dir} data/lang $chain_model_dir $lat_dir
-  cp $gmm_lat_dir/splice_opts $lat_dir/splice_opts
+  cp exp/chain/e2eali_${train_set}_lats/splice_opts $lat_dir/splice_opts
 fi
 
 if [ $stage -le 3 ]; then
@@ -102,16 +85,15 @@ if [ $stage -le 3 ]; then
   fi
   steps/nnet3/chain/build_tree.sh \
     --frame-subsampling-factor 4 \
+    --alignment-subsampling-factor 1 \
     --context-opts "--context-width=2 --central-position=1" \
     --cmd "$cmd" $num_leaves $train_data_dir \
     $lang $ali_dir $tree_dir
 fi
 
 if [ $stage -le 4 ]; then
-  mkdir -p $dir
   echo "$0: creating neural net configs using the xconfig parser";
-
-  num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
+  num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
   common1="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
   common2="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
@@ -119,71 +101,58 @@ if [ $stage -le 4 ]; then
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
   input dim=40 name=input
-  conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
-  conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
-  conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
-  conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
-  conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
-  relu-batchnorm-layer name=tdnn1 input=Append(-4,0,4) dim=$tdnn_dim
-  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim
-  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim
-  ## adding the layers for chain branch
+  conv-relu-batchnorm-dropout-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1 dropout-proportion=0.0
+  conv-relu-batchnorm-dropout-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2 dropout-proportion=0.0
+  conv-relu-batchnorm-dropout-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-dropout-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-dropout-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
+  conv-relu-batchnorm-dropout-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
+  conv-relu-batchnorm-dropout-layer name=cnn7 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
+  relu-batchnorm-dropout-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim dropout-proportion=0.0
+  relu-batchnorm-dropout-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim dropout-proportion=0.0
+  relu-batchnorm-dropout-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim dropout-proportion=0.0
   relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5
   output-layer name=output include-log-softmax=false dim=$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' mod?els... 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=tdnn3 dim=$tdnn_dim target-rms=0.5
   output-layer name=output-xent dim=$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
 
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs
+fi
 
 if [ $stage -le 5 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
+  # no need to store the egs in a shared storage because we always
+  # remove them. Anyway, it takes only 5 minutes to generate them.
   steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$cmd" \
+    --cmd "$cmd" \
     --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.00005 \
+    --chain.apply-deriv-weights true \
+    --egs.dir "$common_egs_dir" \
     --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000" \
-    --chain.frame-subsampling-factor=4 \
-    --chain.alignment-subsampling-factor=1 \
-    --trainer.srand=0 \
-    --trainer.max-param-change=2.0 \
-    --trainer.num-epochs=4 \
-    --trainer.frames-per-iter=2000000 \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.shrink-value=1.0 \
-    --trainer.num-chunk-per-minibatch=64,32 \
-    --egs.chunk-width=$chunk_width \
-    --egs.dir="$common_egs_dir" \
-    --egs.opts="--frames-overlap-per-eg 0" \
-    --cleanup.remove-egs=$remove_egs \
-    --use-gpu=true \
-    --reporting.email="$reporting_email" \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
+    --chain.frame-subsampling-factor 4 \
+    --chain.alignment-subsampling-factor 1 \
+    --trainer.num-chunk-per-minibatch 16,8 \
+    --trainer.frames-per-iter 500000 \
+    --trainer.num-epochs 4 \
+    --trainer.optimization.momentum 0 \
+    --trainer.optimization.num-jobs-initial 2 \
+    --trainer.optimization.num-jobs-final 4 \
+    --trainer.optimization.initial-effective-lrate 0.001 \
+    --trainer.optimization.final-effective-lrate 0.0001 \
+    --trainer.optimization.shrink-value 1.0 \
+    --trainer.max-param-change 2.0 \
+    --trainer.dropout-schedule $dropout_schedule \
+    --cleanup.remove-egs false \
+    --feat-dir data/${train_set} \
+    --tree-dir $tree_dir \
     --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
+    --chain.left-tolerance 1 \
+    --chain.right-tolerance 1 \
+    --egs.chunk-width=$chunk_width \
+    --egs.opts="--frames-overlap-per-eg 0 --constrained false" \
+    --dir $dir  || exit 1;
 fi
 
 if [ $stage -le 6 ]; then
@@ -200,14 +169,14 @@ fi
 
 if [ $stage -le 7 ]; then
   frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
-    --frames-per-chunk $frames_per_chunk \
-    --nj $nj --cmd "$cmd" \
-    $dir/graph data/test $dir/decode_test || exit 1;
-
+  for decode_set in test.5k; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj $nj --cmd "$cmd" \
+      $dir/graph data/$decode_set $dir/decode_$decode_set || exit 1;
+  done
   steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
-                                data/test $dir/decode_test{,_rescored} || exit 1
+                                data/$decode_set $dir/decode_${decode_set}{,_rescored} || exit 1
 fi
 
-echo "$0 Done. Date: $(date). Results:"
+echo "Done. Date: $(date). Results:"
 local/chain/compare_wer.sh $dir
diff --git a/egs/madcat_ar/v1/local/chain/run_cnn_chainali_oracle_1a.sh b/egs/madcat_ar/v1/local/chain/run_cnn_chainali_oracle_1a.sh
new file mode 100755
index 00000000000..1518409baa3
--- /dev/null
+++ b/egs/madcat_ar/v1/local/chain/run_cnn_chainali_oracle_1a.sh
@@ -0,0 +1,209 @@
+#!/bin/bash
+set -e -o pipefail
+stage=0
+nj=70
+train_set=train
+train_stage=-10
+chunk_width=340,300,200,100
+num_leaves=500
+tdnn_dim=450
+lang_decode=data/lang_test
+lang_rescore=data/lang_rescore_6g
+dropout_schedule='0,0@0.20,0.2@0.50,0'
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+affix=_1a_oracle_${train_set}
+chain_model_dir=exp/chain/cnn_chainali_1a_train_sup
+#ali_dir=exp/chain/chainali_$train_set
+lat_dir=exp/chain/chainali_${train_set}_lats
+dir=exp/chain/cnn_chainali${affix}
+train_data_dir=data/${train_set}
+#use chainali tree
+tree_dir=exp/chain/tree_chainali_${train_set}
+tree_dir=exp/chain/tree_chainali_train_sup
+# the 'lang' directory is created by this script.
+# If you create such a directory with a non-standard topology
+# you should probably name it differently.
+lang=data/lang_chain
+xent_regularize=0.1
+lm_weights=3,2  # Weights on phone counts from supervised, unsupervised data for denominator FST creation
+for f in $train_data_dir/feats.scp; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+
+if [ $stage -le 1 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # 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 $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang 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 $lang
+    silphonelist=$(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
+fi
+
+if [ $stage -le 2 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/nnet3/align_lats.sh --nj $nj --cmd "$cmd" \
+                            --acoustic-scale 1.0 \
+                            --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
+                            ${train_data_dir} data/lang $chain_model_dir $lat_dir
+  cp exp/chain/chainali_train_sup_lats/splice_opts $lat_dir/splice_opts
+fi
+
+#if [ $stage -le 3 ]; 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.  The num-leaves is always somewhat less than the num-leaves from
+#  # the GMM baseline.
+#   if [ -f $tree_dir/final.mdl ]; then
+#     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+#     exit 1;
+#  fi
+#  steps/nnet3/chain/build_tree.sh \
+#    --frame-subsampling-factor 4 \
+#    --alignment-subsampling-factor 1 \
+#    --context-opts "--context-width=2 --central-position=1" \
+#    --cmd "$cmd" $num_leaves $train_data_dir \
+#    $lang $ali_dir $tree_dir
+#fi
+
+# Get best path alignment and lattice posterior of best path alignment to be
+if [ $stage -le 4 ]; then
+  steps/best_path_weights.sh --cmd "${cmd}" --acwt 0.1 \
+    data/train_unsup_unique \
+    $lat_dir \
+    $chain_model_dir/best_path_train_unsup_unique
+fi
+
+# Train denominator FST using phone alignments from
+# supervised and unsupervised data
+if [ $stage -le 5 ]; then
+  steps/nnet3/chain/make_weighted_den_fst.sh --num-repeats $lm_weights --cmd "$cmd" \
+    --lm_opts '--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000' \
+    $tree_dir $chain_model_dir/best_path_train_unsup_unique \
+    $dir
+fi
+
+if [ $stage -le 6 ]; 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)
+  common1="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
+  common2="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
+  common3="required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=40 name=input
+  conv-relu-batchnorm-dropout-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1 dropout-proportion=0.0
+  conv-relu-batchnorm-dropout-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2 dropout-proportion=0.0
+  conv-relu-batchnorm-dropout-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-dropout-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-dropout-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
+  conv-relu-batchnorm-dropout-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
+  conv-relu-batchnorm-dropout-layer name=cnn7 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
+  relu-batchnorm-dropout-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim dropout-proportion=0.0
+  relu-batchnorm-dropout-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim dropout-proportion=0.0
+  relu-batchnorm-dropout-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim dropout-proportion=0.0
+  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
+  relu-batchnorm-layer name=prefinal-xent input=tdnn3 dim=$tdnn_dim target-rms=0.5
+  output-layer name=output-xent dim=$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 [ $train_stage -le -4 ]; then
+  # This is to skip stages of den-fst creation, which was already done.
+  train_stage=-4
+fi
+
+if [ $stage -le 7 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd "$cmd" \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.00005 \
+    --chain.apply-deriv-weights true \
+    --egs.dir "$common_egs_dir" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.frame-subsampling-factor 4 \
+    --chain.alignment-subsampling-factor 1 \
+    --trainer.num-chunk-per-minibatch 32,16 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs 4 \
+    --trainer.optimization.momentum 0 \
+    --trainer.optimization.num-jobs-initial 5 \
+    --trainer.optimization.num-jobs-final 8 \
+    --trainer.optimization.initial-effective-lrate 0.001 \
+    --trainer.optimization.final-effective-lrate 0.0001 \
+    --trainer.optimization.shrink-value 1.0 \
+    --trainer.max-param-change 2.0 \
+    --trainer.dropout-schedule $dropout_schedule \
+    --cleanup.remove-egs false \
+    --feat-dir data/${train_set} \
+    --tree-dir $tree_dir \
+    --lat-dir=$lat_dir \
+    --chain.left-tolerance 1 \
+    --chain.right-tolerance 1 \
+    --egs.chunk-width=$chunk_width \
+    --egs.opts="--frames-overlap-per-eg 0 --constrained false" \
+    --dir $dir  || exit 1;
+fi
+
+if [ $stage -le 8 ]; then
+  # The reason we are using data/lang here, instead of $lang, is just to
+  # emphasize that it's not actually important to give mkgraph.sh the
+  # lang directory with the matched topology (since it gets the
+  # topology file from the model).  So you could give it a different
+  # lang directory, one that contained a wordlist and LM of your choice,
+  # as long as phones.txt was compatible.
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 $lang_decode \
+    $dir $dir/graph || exit 1;
+fi
+
+if [ $stage -le 9 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  for decode_set in test.5k; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj $nj --cmd "$cmd" \
+      $dir/graph data/$decode_set $dir/decode_$decode_set || exit 1;
+  done
+  steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
+                                data/$decode_set $dir/decode_${decode_set}{,_rescored} || exit 1
+fi
+
+echo "Done. Date: $(date). Results:"
+local/chain/compare_wer.sh $dir
diff --git a/egs/madcat_ar/v1/local/chain/run_cnn_chainali_semisupervised_1a.sh b/egs/madcat_ar/v1/local/chain/run_cnn_chainali_semisupervised_1a.sh
new file mode 100755
index 00000000000..87455fa5a42
--- /dev/null
+++ b/egs/madcat_ar/v1/local/chain/run_cnn_chainali_semisupervised_1a.sh
@@ -0,0 +1,284 @@
+#!/bin/bash
+
+set -e -o pipefail
+stage=0   # Start from -1 for supervised seed system training
+train_stage=-100
+nj=70
+# dir=${exp_root}/chain${chain_affix}/tdnn${tdnn_affix}
+exp_root=exp/semisup_100k
+chain_affix=    # affix for chain dir
+tdnn_affix=_semisup.uncon  # affix for semi-supervised chain system
+
+# Datasets-Expects supervised_set and unsupervised_set
+supervised_set=train_sup
+unsupervised_set=train_unsup
+# Input seed system
+sup_chain_dir=exp/chain/cnn_chainali_1b  # supervised chain system
+sup_lat_dir=exp/chain/chainali_train_sup_lats  # Seed model options
+sup_tree_dir=exp/chain/tree_e2eali_train_sup  # tree directory for supervised chain system
+
+# Semi-supervised options
+supervision_weights=1.0,1.0   # Weights for supervised, unsupervised data egs.
+                              # Can be used to scale down the effect of unsupervised data
+                              # by using a smaller scale for it e.g. 1.0,0.3
+lm_weights=3,2  # Weights on phone counts from supervised, unsupervised data for denominator FST creation
+
+sup_egs_dir=   # Supply this to skip supervised egs creation
+unsup_egs_dir=  # Supply this to skip unsupervised egs creation
+unsup_egs_opts=  # Extra options to pass to unsupervised egs creation
+# Neural network opts
+xent_regularize=0.1
+tdnn_dim=450
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+lang_decode=data/lang_test
+lang_rescore=data/lang_rescore_6g
+dropout_schedule='0,0@0.20,0.2@0.50,0'
+dir=$exp_root/chain$chain_affix/tdnn$tdnn_affix
+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
+
+graphdir=$sup_chain_dir/graph_unsup
+unsupervised_set=train_unsup_unique
+for f in data/$supervised_set/feats.scp \
+  data/$unsupervised_set/feats.scp \
+  $sup_lat_dir/lat.1.gz $sup_tree_dir/ali.1.gz \
+  $lang_decode/G.fst; do
+  if [ ! -f $f ]; then
+    echo "$0: Could not find file $f"
+    exit 1
+  fi
+done
+
+if [ ! -f $graphdir/HCLG.fst ]; then
+  utils/mkgraph.sh --self-loop-scale 1.0 data/lang_decode_unsup $sup_chain_dir $graphdir
+fi
+
+# Decode unsupervised data and write lattices in non-compact
+if [ $stage -le 4 ]; then
+  steps/nnet3/decode_semisup.sh --num-threads 4 --nj 45 --cmd "$cmd" --beam 15 \
+            --frames-per-chunk 340 \
+            --acwt 1.0 --post-decode-acwt 10.0 --write-compact false \
+            --scoring-opts "--min-lmwt 10 --max-lmwt 10" --word-determinize false \
+            $graphdir data/$unsupervised_set $sup_chain_dir/decode_${unsupervised_set}
+fi
+
+# Get best path alignment and lattice posterior of best path alignment to be
+if [ $stage -le 8 ]; then
+  steps/best_path_weights.sh --cmd "${cmd}" --acwt 0.1 \
+    data/$unsupervised_set \
+    $sup_chain_dir/decode_$unsupervised_set \
+    $sup_chain_dir/best_path_$unsupervised_set
+fi
+
+frame_subsampling_factor=4
+if [ -f $sup_chain_dir/frame_subsampling_factor ]; then
+  frame_subsampling_factor=$(cat $sup_chain_dir/frame_subsampling_factor)
+fi
+cmvn_opts=$(cat $sup_chain_dir/cmvn_opts) || exit 1
+
+diff $sup_tree_dir/tree $sup_chain_dir/tree || { echo "$0: $sup_tree_dir/tree and $sup_chain_dir/tree differ"; exit 1; }
+
+# Train denominator FST using phone alignments from
+# supervised and unsupervised data
+if [ $stage -le 10 ]; then
+  steps/nnet3/chain/make_weighted_den_fst.sh --num-repeats $lm_weights --cmd "$cmd" \
+    --lm_opts '--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000' \
+    $sup_tree_dir $sup_chain_dir/best_path_$unsupervised_set \
+    $dir
+fi
+
+if [ $stage -le 11 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+  num_targets=$(tree-info $sup_tree_dir/tree |grep num-pdfs|awk '{print $2}')
+  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  common1="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
+  common2="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
+  common3="required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=40 name=input
+  conv-relu-batchnorm-dropout-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1 dropout-proportion=0.0
+  conv-relu-batchnorm-dropout-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2 dropout-proportion=0.0
+  conv-relu-batchnorm-dropout-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-dropout-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-dropout-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
+  conv-relu-batchnorm-dropout-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
+  conv-relu-batchnorm-dropout-layer name=cnn7 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
+  relu-batchnorm-dropout-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim dropout-proportion=0.0
+  relu-batchnorm-dropout-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim dropout-proportion=0.0
+  relu-batchnorm-dropout-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim dropout-proportion=0.0
+  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
+  relu-batchnorm-layer name=prefinal-xent input=tdnn3 dim=$tdnn_dim target-rms=0.5
+  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
+
+  # We use separate outputs for supervised and unsupervised data
+  # so we can properly track the train and valid objectives.
+  output name=output-0 input=output.affine
+  output name=output-1 input=output.affine
+  output name=output-0-xent input=output-xent.log-softmax
+  output name=output-1-xent input=output-xent.log-softmax
+EOF
+
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+# Get values for $model_left_context, $model_right_context
+. $dir/configs/vars
+
+left_context=$model_left_context
+right_context=$model_right_context
+
+egs_left_context=$(perl -e "print int($left_context + $frame_subsampling_factor / 2)")
+egs_right_context=$(perl -e "print int($right_context + $frame_subsampling_factor / 2)")
+
+if [ -z "$sup_egs_dir" ]; then
+  sup_egs_dir=$dir/egs_$supervised_set
+  frames_per_eg=$(cat $sup_chain_dir/egs/info/frames_per_eg)
+
+  if [ $stage -le 12 ]; then
+    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $sup_egs_dir/storage ]; then
+      utils/create_split_dir.pl \
+       /export/b0{5,6,7}/$USER/kaldi-data/egs/fisher_english-$(date +'%m_%d_%H_%M')/s5c/$sup_egs_dir/storage $sup_egs_dir/storage
+    fi
+    mkdir -p $sup_egs_dir/
+    touch $sup_egs_dir/.nodelete # keep egs around when that run dies.
+
+    echo "$0: generating egs from the supervised data"
+    steps/nnet3/chain/get_egs.sh --cmd "$cmd" \
+               --left-tolerance 1 --right-tolerance 1 \
+               --left-context $egs_left_context --right-context $egs_right_context \
+               --frame-subsampling-factor $frame_subsampling_factor \
+               --alignment-subsampling-factor 1 \
+               --frames-overlap-per-eg 0 --constrained false \
+               --frames-per-eg $frames_per_eg \
+               --frames-per-iter 1500000 \
+               --cmvn-opts "$cmvn_opts" \
+               --generate-egs-scp true \
+               data/${supervised_set} $dir \
+               $sup_lat_dir $sup_egs_dir
+  fi
+else
+  frames_per_eg=$(cat $sup_egs_dir/info/frames_per_eg)
+fi
+
+unsup_frames_per_eg=340,300,200,100  # Using a frames-per-eg of 150 for unsupervised data
+                         # was found to be better than allowing smaller chunks
+                         # (160,140,110,80) like for supervised system
+lattice_lm_scale=0.5  # lm-scale for using the weights from unsupervised lattices when
+                      # creating numerator supervision
+lattice_prune_beam=4.0  # beam for pruning the lattices prior to getting egs
+                        # for unsupervised data
+tolerance=2   # frame-tolerance for chain training
+
+unsup_lat_dir=$sup_chain_dir/decode_$unsupervised_set
+if [ -z "$unsup_egs_dir" ]; then
+  unsup_egs_dir=$dir/egs_$unsupervised_set
+
+  if [ $stage -le 13 ]; then
+    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $unsup_egs_dir/storage ]; then
+      utils/create_split_dir.pl \
+       /export/b0{5,6,7}/$USER/kaldi-data/egs/fisher_english-$(date +'%m_%d_%H_%M')/s5c/$unsup_egs_dir/storage $unsup_egs_dir/storage
+    fi
+    mkdir -p $unsup_egs_dir
+    touch $unsup_egs_dir/.nodelete # keep egs around when that run dies.
+
+    echo "$0: generating egs from the unsupervised data"
+    steps/nnet3/chain/get_egs.sh \
+      --cmd "$cmd" --alignment-subsampling-factor 1 \
+      --left-tolerance $tolerance --right-tolerance $tolerance \
+      --left-context $egs_left_context --right-context $egs_right_context \
+      --frames-per-eg $unsup_frames_per_eg --frames-per-iter 1500000 \
+      --frame-subsampling-factor $frame_subsampling_factor \
+      --cmvn-opts "$cmvn_opts" --lattice-lm-scale $lattice_lm_scale \
+      --lattice-prune-beam "$lattice_prune_beam" \
+      --deriv-weights-scp $sup_chain_dir/best_path_$unsupervised_set/weights.scp \
+      --generate-egs-scp true $unsup_egs_opts \
+      data/$unsupervised_set $dir \
+      $unsup_lat_dir $unsup_egs_dir
+  fi
+fi
+
+comb_egs_dir=$dir/comb_egs
+if [ $stage -le 14 ]; then
+  steps/nnet3/chain/multilingual/combine_egs.sh --cmd "$cmd" \
+    --block-size 64 \
+    --lang2weight $supervision_weights 2 \
+    $sup_egs_dir $unsup_egs_dir $comb_egs_dir
+  touch $comb_egs_dir/.nodelete # keep egs around when that run dies.
+fi
+
+if [ $train_stage -le -4 ]; then
+  # This is to skip stages of den-fst creation, which was already done.
+  train_stage=-4
+fi
+
+chunk_width=340,300,200,100
+if [ $stage -le 15 ]; then
+  steps/nnet3/chain/train.py --stage $train_stage \
+    --egs.dir "$comb_egs_dir" \
+    --egs.chunk-width=$chunk_width \
+    --cmd "$cmd" \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.00005 \
+    --chain.apply-deriv-weights=true \
+    --chain.frame-subsampling-factor=$frame_subsampling_factor \
+    --chain.alignment-subsampling-factor=1 \
+    --chain.left-tolerance 1 \
+    --chain.right-tolerance 1 \
+    --chain.xent-regularize $xent_regularize \
+    --trainer.optimization.shrink-value=1.0 \
+    --trainer.num-chunk-per-minibatch=32,16 \
+    --trainer.optimization.momentum=0.0 \
+    --trainer.frames-per-iter=1500000 \
+    --trainer.max-param-change=2.0 \
+    --trainer.num-epochs 4 \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.optimization.num-jobs-initial 5 \
+    --trainer.optimization.num-jobs-final 8 \
+    --trainer.optimization.initial-effective-lrate 0.001 \
+    --trainer.optimization.final-effective-lrate 0.0001 \
+    --egs.opts="--frames-overlap-per-eg 0 --constrained false" \
+    --cleanup.remove-egs false \
+    --feat-dir data/$supervised_set \
+    --tree-dir $sup_tree_dir \
+    --lat-dir $sup_lat_dir \
+    --dir $dir || exit 1;
+fi
+
+if [ $stage -le 17 ]; then
+  # The reason we are using data/lang here, instead of $lang, is just to
+  # emphasize that it's not actually important to give mkgraph.sh the
+  # lang directory with the matched topology (since it gets the
+  # topology file from the model).  So you could give it a different
+  # lang directory, one that contained a wordlist and LM of your choice,
+  # as long as phones.txt was compatible.
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 $lang_decode \
+    $dir $dir/graph || exit 1;
+fi
+
+if [ $stage -le 18 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  for decode_set in test.5k; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj $nj --cmd "$cmd" \
+      $dir/graph data/$decode_set $dir/decode_$decode_set || exit 1;
+  done
+  steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
+                                data/$decode_set $dir/decode_${decode_set}{,_rescored} || exit 1
+fi
+
+echo "Done. Date: $(date). Results:"
+local/chain/compare_wer.sh $dir
diff --git a/egs/madcat_ar/v1/local/chain/run_cnn_e2eali.sh b/egs/madcat_ar/v1/local/chain/run_cnn_e2eali.sh
deleted file mode 120000
index fcf59f917c1..00000000000
--- a/egs/madcat_ar/v1/local/chain/run_cnn_e2eali.sh
+++ /dev/null
@@ -1 +0,0 @@
-tuning/run_cnn_e2eali_1b.sh
\ No newline at end of file
diff --git a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/madcat_ar/v1/local/chain/run_cnn_e2eali_1a.sh
similarity index 58%
rename from egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
rename to egs/madcat_ar/v1/local/chain/run_cnn_e2eali_1a.sh
index ee84ea0d83f..15d7d19f517 100755
--- a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/madcat_ar/v1/local/chain/run_cnn_e2eali_1a.sh
@@ -1,41 +1,20 @@
 #!/bin/bash
-
-# e2eali_1a is the same as chainali_1c but uses the e2e chain model to get the
-# lattice alignments and to build a tree
-
 set -e -o pipefail
-
 stage=0
-
 nj=70
 train_set=train
-nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
-affix=_1a  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
-e2echain_model_dir=exp/chain/e2e_cnn_1a
-common_egs_dir=
-reporting_email=
-
-# chain options
 train_stage=-10
-xent_regularize=0.1
-frame_subsampling_factor=4
 chunk_width=340,300,200,100
 num_leaves=500
 tdnn_dim=450
-# training options
-srand=0
-remove_egs=true
-lang_decode=data/lang
+lang_decode=data/lang_test
 lang_rescore=data/lang_rescore_6g
 # 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
@@ -44,12 +23,14 @@ where "nvcc" is installed.
 EOF
 fi
 
-ali_dir=exp/chain/e2e_ali_train
-lat_dir=exp/chain${nnet3_affix}/e2e_${train_set}_lats
-dir=exp/chain${nnet3_affix}/cnn_e2eali${affix}
+affix=_1a_${train_set}
+e2echain_model_dir=exp/chain/flatstart_cnn_1a_$train_set
+ali_dir=exp/chain/flatstartali_$train_set
+lat_dir=exp/chain/e2eali_${train_set}_lats
+dir=exp/chain/cnn_e2eali${affix}
 train_data_dir=data/${train_set}
-tree_dir=exp/chain${nnet3_affix}/tree_e2e
-
+tree_dir=exp/chain/tree_e2eali_${train_set}
+xent_regularize=0.1
 # the 'lang' directory is created by this script.
 # If you create such a directory with a non-standard topology
 # you should probably name it differently.
@@ -103,98 +84,72 @@ if [ $stage -le 3 ]; then
   fi
 
   steps/nnet3/chain/build_tree.sh \
-    --frame-subsampling-factor $frame_subsampling_factor \
+    --frame-subsampling-factor 4 \
     --alignment-subsampling-factor 1 \
     --context-opts "--context-width=2 --central-position=1" \
     --cmd "$cmd" $num_leaves ${train_data_dir} \
     $lang $ali_dir $tree_dir
 fi
 
-
 if [ $stage -le 4 ]; then
-  mkdir -p $dir
   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)
-  common1="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
-  common2="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
-  common3="required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
+  common1="height-offsets=-2,-1,0,1,2 num-filters-out=36"
+  common2="height-offsets=-2,-1,0,1,2 num-filters-out=70"
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
   input dim=40 name=input
-
   conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
   conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
   conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
-  conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
-  conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
-  relu-batchnorm-layer name=tdnn1 input=Append(-4,0,4) dim=$tdnn_dim
+  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
+  relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim
   relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim
   relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim
-
+  relu-batchnorm-layer name=tdnn4 input=Append(-4,0,4) dim=$tdnn_dim
   ## adding the layers for chain branch
-  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5
-  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
-
-  # adding the layers for xent branch
-  # This block prints the configs for a separate output that will be
-  # trained with a cross-entropy objective in the 'chain' mod?els... this
-  # has the effect of regularizing the hidden parts of the model.  we use
-  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
-  # 0.5 / args.xent_regularize is suitable as it means the xent
-  # final-layer learns at a rate independent of the regularization
-  # constant; and the 0.5 was tuned so as to make the relative progress
-  # similar in the xent and regular final layers.
+  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $output_opts
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $output_opts
   relu-batchnorm-layer name=prefinal-xent input=tdnn3 dim=$tdnn_dim 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
 
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs
+fi
 
 if [ $stage -le 5 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
+  # no need to store the egs in a shared storage because we always
+  # remove them. Anyway, it takes only 5 minutes to generate them.
   steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$cmd" \
+    --cmd "$cmd" \
     --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.00005 \
+    --chain.apply-deriv-weights true \
     --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000" \
-    --chain.frame-subsampling-factor=$frame_subsampling_factor \
-    --chain.alignment-subsampling-factor=1 \
-    --chain.left-tolerance 3 \
-    --chain.right-tolerance 3 \
-    --trainer.srand=$srand \
-    --trainer.max-param-change=2.0 \
-    --trainer.num-epochs=4 \
-    --trainer.frames-per-iter=2000000 \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.shrink-value=1.0 \
-    --trainer.num-chunk-per-minibatch=64,32 \
-    --trainer.optimization.momentum=0.0 \
-    --trainer.add-option="--optimization.memory-compression-level=2" \
-    --egs.chunk-width=$chunk_width \
-    --egs.dir="$common_egs_dir" \
-    --egs.opts="--frames-overlap-per-eg 0" \
-    --cleanup.remove-egs=$remove_egs \
-    --use-gpu=true \
-    --reporting.email="$reporting_email" \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
+    --egs.dir "$common_egs_dir" \
+    --chain.frame-subsampling-factor 4 \
+    --chain.alignment-subsampling-factor 1 \
+    --trainer.num-chunk-per-minibatch 16,8 \
+    --trainer.frames-per-iter 500000 \
+    --trainer.num-epochs 4 \
+    --trainer.optimization.momentum 0 \
+    --trainer.optimization.num-jobs-initial 2 \
+    --trainer.optimization.num-jobs-final 4 \
+    --trainer.optimization.initial-effective-lrate 0.001 \
+    --trainer.optimization.final-effective-lrate 0.0001 \
+    --trainer.optimization.shrink-value 1.0 \
+    --trainer.max-param-change 2.0 \
+    --cleanup.remove-egs false \
+    --feat-dir data/${train_set} \
+    --tree-dir $tree_dir \
     --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
+    --chain.left-tolerance 1 \
+    --chain.right-tolerance 1 \
+    --egs.chunk-width=$chunk_width \
+    --egs.opts="--frames-overlap-per-eg 0 --constrained false" \
+    --dir $dir  || exit 1;
 fi
 
 if [ $stage -le 6 ]; then
@@ -204,7 +159,6 @@ if [ $stage -le 6 ]; then
   # topology file from the model).  So you could give it a different
   # lang directory, one that contained a wordlist and LM of your choice,
   # as long as phones.txt was compatible.
-
   utils/mkgraph.sh \
     --self-loop-scale 1.0 $lang_decode \
     $dir $dir/graph || exit 1;
@@ -212,13 +166,13 @@ fi
 
 if [ $stage -le 7 ]; then
   frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
-    --frames-per-chunk $frames_per_chunk \
-    --nj $nj --cmd "$cmd" \
-    $dir/graph data/test $dir/decode_test || exit 1;
-
+  for decode_set in test.5k; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj $nj --cmd "$cmd" \
+      $dir/graph data/$decode_set $dir/decode_$decode_set || exit 1;
+  done
   steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
-                                data/test $dir/decode_test{,_rescored} || exit 1
+                                data/$decode_set $dir/decode_${decode_set}{,_rescored} || exit 1
 fi
 
 echo "Done. Date: $(date). Results:"
diff --git a/egs/madcat_ar/v1/local/chain/run_e2e_cnn.sh b/egs/madcat_ar/v1/local/chain/run_e2e_cnn.sh
deleted file mode 120000
index d26ba0182ce..00000000000
--- a/egs/madcat_ar/v1/local/chain/run_e2e_cnn.sh
+++ /dev/null
@@ -1 +0,0 @@
-tuning/run_e2e_cnn_1a.sh
\ No newline at end of file
diff --git a/egs/madcat_ar/v1/local/chain/tuning/run_e2e_cnn_1a.sh b/egs/madcat_ar/v1/local/chain/run_flatstart_cnn_1a.sh
similarity index 61%
rename from egs/madcat_ar/v1/local/chain/tuning/run_e2e_cnn_1a.sh
rename to egs/madcat_ar/v1/local/chain/run_flatstart_cnn_1a.sh
index 3caf8ae4494..00a68e3ae26 100755
--- a/egs/madcat_ar/v1/local/chain/tuning/run_e2e_cnn_1a.sh
+++ b/egs/madcat_ar/v1/local/chain/run_flatstart_cnn_1a.sh
@@ -1,35 +1,20 @@
 #!/bin/bash
 # Copyright    2017  Hossein Hadian
-
-# This script does end2end chain training (i.e. from scratch)
-# ./local/chain/compare_wer.sh exp/chain/e2e_cnn_1a/
-# System                      e2e_cnn_1a     e2e_cnn_1a (with extra corpus text)
-# WER                              9.47            5.73
-# WER (rescored)                   8.05            5.67
-# CER                              2.45            1.45
-# CER (rescored)                   2.10            1.42
-# Final train prob              -0.0934         -0.0934
-# Final valid prob              -0.0746         -0.0746
-# Final train prob (xent)
-# Final valid prob (xent)
-# Parameters                      2.94M           2.94M
-
-# steps/info/chain_dir_info.pl exp/chain/e2e_cnn_1a/
-# exp/chain/e2e_cnn_1a/: num-iters=98 nj=6..16 num-params=2.9M dim=40->330 combine=-0.071->-0.070 (over 5) logprob:train/valid[64,97,final]=(-0.089,-0.084,-0.093/-0.075,-0.073,-0.075)
 set -e
 
 # configs for 'chain'
 stage=0
 train_stage=-10
 get_egs_stage=-10
-affix=1a
+nj=70
 
 # training options
 tdnn_dim=450
-minibatch_size=150=128,64/300=128,64/600=64,32/1200=32,16
+minibatch_size=150=32,16/300=16,8/600=8,4/1200=4,2
 common_egs_dir=
-cmvn_opts="--norm-means=false --norm-vars=false"
 train_set=train
+lang_decode=data/lang_test
+lang_rescore=data/lang_rescore_6g
 # End configuration section.
 echo "$0 $@"  # Print the command line for logging
 
@@ -45,9 +30,10 @@ where "nvcc" is installed.
 EOF
 fi
 
-lang=data/lang_e2e
-treedir=exp/chain/e2e_monotree  # it's actually just a trivial tree (no tree building)
-dir=exp/chain/e2e_cnn_${affix}
+affix=1a_$train_set
+lang=data/lang_flatstart
+treedir=exp/chain/flatstart_monotree  # it's actually just a trivial tree (no tree building)
+dir=exp/chain/flatstart_cnn_${affix}
 
 if [ $stage -le 0 ]; then
   # Create a version of the lang/ directory that has one state per phone in the
@@ -78,56 +64,78 @@ fi
 if [ $stage -le 2 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
   num_targets=$(tree-info $treedir/tree | grep num-pdfs | awk '{print $2}')
-  common1="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
-  common2="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
-  common3="required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
+  common1="height-offsets=-2,-1,0,1,2 num-filters-out=36"
+  common2="height-offsets=-2,-1,0,1,2 num-filters-out=70"
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
   input dim=40 name=input
   conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
   conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
   conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
-  conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
-  conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
-  relu-batchnorm-layer name=tdnn1 input=Append(-4,0,4) dim=$tdnn_dim
+  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
+  relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim
   relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim
   relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim
+  relu-batchnorm-layer name=tdnn4 input=Append(-4,0,4) dim=$tdnn_dim
   ## adding the layers for chain branch
-  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5
-  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
+  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $output_opts
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $output_opts
 EOF
 
   steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs
 fi
 
 if [ $stage -le 3 ]; then
+  # no need to store the egs in a shared storage because we always
+  # remove them. Anyway, it takes only 5 minutes to generate them.
   steps/nnet3/chain/e2e/train_e2e.py --stage $train_stage \
     --cmd "$cmd" \
-    --feat.cmvn-opts "$cmvn_opts" \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
     --chain.leaky-hmm-coefficient 0.1 \
     --chain.l2-regularize 0.00005 \
-    --chain.apply-deriv-weights false \
+    --chain.apply-deriv-weights true \
     --egs.dir "$common_egs_dir" \
     --egs.stage $get_egs_stage \
     --egs.opts "--num_egs_diagnostic 100 --num_utts_subset 400" \
     --chain.frame-subsampling-factor 4 \
     --chain.alignment-subsampling-factor 4 \
-    --chain.lm-opts="--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000" \
-    --trainer.add-option="--optimization.memory-compression-level=2" \
     --trainer.num-chunk-per-minibatch $minibatch_size \
-    --trainer.frames-per-iter 2000000 \
-    --trainer.num-epochs 2 \
+    --trainer.frames-per-iter 500000 \
+    --trainer.num-epochs 4 \
     --trainer.optimization.momentum 0 \
-    --trainer.optimization.num-jobs-initial 6 \
-    --trainer.optimization.num-jobs-final 16 \
+    --trainer.optimization.num-jobs-initial 2 \
+    --trainer.optimization.num-jobs-final 4 \
     --trainer.optimization.initial-effective-lrate 0.001 \
     --trainer.optimization.final-effective-lrate 0.0001 \
     --trainer.optimization.shrink-value 1.0 \
     --trainer.max-param-change 2.0 \
-    --cleanup.remove-egs true \
+    --cleanup.remove-egs false \
     --feat-dir data/${train_set} \
     --tree-dir $treedir \
     --dir $dir  || exit 1;
 fi
+
+if [ $stage -le 4 ]; then
+  # The reason we are using data/lang here, instead of $lang, is just to
+  # emphasize that it's not actually important to give mkgraph.sh the
+  # lang directory with the matched topology (since it gets the
+  # topology file from the model).  So you could give it a different
+  # lang directory, one that contained a wordlist and LM of your choice,
+  # as long as phones.txt was compatible.
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 $lang_decode \
+    $dir $dir/graph || exit 1;
+fi
+
+if [ $stage -le 5 ]; then
+  for decode_set in test.5k; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj $nj --cmd "$cmd" \
+      $dir/graph data/$decode_set $dir/decode_$decode_set || exit 1;
+  done
+  steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
+                                data/$decode_set $dir/decode_${decode_set}{,_rescored} || exit 1
+fi
+
+echo "Done. Date: $(date). Results:"
+local/chain/compare_wer.sh $dir
diff --git a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_1a.sh b/egs/madcat_ar/v1/local/chain/tuning/run_cnn_1a.sh
deleted file mode 100755
index d449805be1d..00000000000
--- a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_1a.sh
+++ /dev/null
@@ -1,213 +0,0 @@
-#!/bin/bash
-
-# Copyright    2017 Hossein Hadian
-#              2017 Chun Chieh Chang
-#              2017 Ashish Arora
-
-set -e -o pipefail
-
-stage=0
-
-nj=70
-train_set=train
-gmm=tri3        # this is the source gmm-dir that we'll use for alignments; it
-                # should have alignments for the specified training data.
-nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
-affix=_1a  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
-ali=tri3_ali
-common_egs_dir=
-reporting_email=
-
-# chain options
-train_stage=-10
-xent_regularize=0.1
-# training chunk-options
-chunk_width=340,300,200,100
-num_leaves=500
-tdnn_dim=450
-remove_egs=false
-lang_decode=data/lang
-lang_rescore=data/lang_rescore_6g
-# End configuration section.
-echo "$0 $@"  # Print the command line for logging
-
-. ./cmd.sh
-. ./path.sh
-. ./utils/parse_options.sh
-
-if ! cuda-compiled; then
-  cat <<EOF && exit 1
-This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
-If you want to use GPUs (and have them), go to src/, and configure and make on a machine
-where "nvcc" is installed.
-EOF
-fi
-
-gmm_dir=exp/${gmm}
-ali_dir=exp/${ali}
-lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_lats
-dir=exp/chain${nnet3_affix}/cnn${affix}
-train_data_dir=data/${train_set}
-tree_dir=exp/chain${nnet3_affix}/tree
-
-# the 'lang' directory is created by this script.
-# If you create such a directory with a non-standard topology
-# you should probably name it differently.
-lang=data/lang_chain
-
-for f in $train_data_dir/feats.scp \
-    $train_data_dir/feats.scp $gmm_dir/final.mdl \
-    $ali_dir/ali.1.gz $gmm_dir/final.mdl; do
-  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
-done
-
-
-if [ $stage -le 1 ]; then
-  echo "$0: creating lang directory $lang with chain-type topology"
-  # 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 $lang ]; then
-    if [ $lang/L.fst -nt data/lang/L.fst ]; then
-      echo "$0: $lang already exists, not overwriting it; continuing"
-    else
-      echo "$0: $lang 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 $lang
-    silphonelist=$(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
-fi
-
-if [ $stage -le 2 ]; 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 $nj --cmd "$cmd" ${train_data_dir} \
-    data/lang $gmm_dir $lat_dir
-  rm $lat_dir/fsts.*.gz # save space
-fi
-
-if [ $stage -le 3 ]; 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.  The num-leaves is always somewhat less than the num-leaves from
-  # the GMM baseline.
-   if [ -f $tree_dir/final.mdl ]; then
-     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
-     exit 1;
-  fi
-  steps/nnet3/chain/build_tree.sh \
-    --frame-subsampling-factor 4 \
-    --context-opts "--context-width=2 --central-position=1" \
-    --cmd "$cmd" $num_leaves $train_data_dir \
-    $lang $ali_dir $tree_dir
-fi
-
-
-if [ $stage -le 4 ]; then
-  mkdir -p $dir
-  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)
-  common1="height-offsets=-2,-1,0,1,2 num-filters-out=36"
-  common2="height-offsets=-2,-1,0,1,2 num-filters-out=70"
-  mkdir -p $dir/configs
-  cat <<EOF > $dir/configs/network.xconfig
-  input dim=40 name=input
-  conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
-  conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
-  conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
-  relu-batchnorm-layer name=tdnn1 input=Append(-4,0,4) dim=$tdnn_dim
-  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim
-  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim
-  relu-batchnorm-layer name=tdnn4 input=Append(-4,0,4) dim=$tdnn_dim
-  ## adding the layers for chain branch
-  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5
-  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
-  # adding the layers for xent branch
-  # This block prints the configs for a separate output that will be
-  # trained with a cross-entropy objective in the 'chain' mod?els... this
-  # has the effect of regularizing the hidden parts of the model.  we use
-  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
-  # 0.5 / args.xent_regularize is suitable as it means the xent
-  # final-layer learns at a rate independent of the regularization
-  # constant; and the 0.5 was tuned so as to make the relative progress
-  # similar in the xent and regular final layers.
-  relu-batchnorm-layer name=prefinal-xent input=tdnn4 dim=$tdnn_dim 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 5 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
-  steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$cmd" \
-    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
-    --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000" \
-    --chain.frame-subsampling-factor=4 \
-    --chain.alignment-subsampling-factor=4 \
-    --trainer.srand=0 \
-    --trainer.max-param-change=2.0 \
-    --trainer.num-epochs=4 \
-    --trainer.frames-per-iter=2000000 \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.shrink-value=1.0 \
-    --trainer.num-chunk-per-minibatch=64,32 \
-    --egs.chunk-width=$chunk_width \
-    --egs.dir="$common_egs_dir" \
-    --egs.opts="--frames-overlap-per-eg 0" \
-    --cleanup.remove-egs=$remove_egs \
-    --use-gpu=true \
-    --reporting.email="$reporting_email" \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
-    --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
-fi
-
-if [ $stage -le 6 ]; then
-  # The reason we are using data/lang here, instead of $lang, is just to
-  # emphasize that it's not actually important to give mkgraph.sh the
-  # lang directory with the matched topology (since it gets the
-  # topology file from the model).  So you could give it a different
-  # lang directory, one that contained a wordlist and LM of your choice,
-  # as long as phones.txt was compatible.
-  utils/mkgraph.sh \
-    --self-loop-scale 1.0 $lang_decode \
-    $dir $dir/graph || exit 1;
-fi
-
-if [ $stage -le 7 ]; then
-  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
-    --frames-per-chunk $frames_per_chunk \
-    --nj $nj --cmd "$cmd" \
-    $dir/graph data/test $dir/decode_test || exit 1;
-
-  steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
-                                data/test $dir/decode_test{,_rescored} || exit 1
-fi
-
-echo "$0 Done. Date: $(date). Results:"
-local/chain/compare_wer.sh $dir
diff --git a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1b.sh b/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
deleted file mode 100755
index c6052b76e7f..00000000000
--- a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
+++ /dev/null
@@ -1,238 +0,0 @@
-#!/bin/bash
-
-# e2eali_1b is the same as chainali_1a but uses the e2e chain model to get the
-# lattice alignments and to build a tree
-
-# local/chain/compare_wer.sh exp/chain/exp/chain/cnn_e2eali_1b
-# System                      cnn_e2eali_1b
-# WER                             10.78
-# CER                              2.99
-# Final train prob              -0.0587
-# Final valid prob              -0.0609
-# Final train prob (xent)       -0.4471
-# Final valid prob (xent)       -0.4653
-# Parameters                      3.37M
-
-# steps/info/chain_dir_info.pl exp/chain/cnn_e2eali_1b
-#exp/chain/cnn_e2eali_1b: num-iters=179 nj=8..16 num-params=3.4M dim=40->416 combine=-0.058->-0.058 (over 3) xent:train/valid[118,178,final]=(-0.463,-0.445,-0.447/-0.477,-0.462,-0.465) logprob:train/valid[118,178,final]=(-0.062,-0.059,-0.059/-0.063,-0.061,-0.061)
-
-set -e -o pipefail
-
-stage=0
-
-nj=70
-train_set=train
-nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
-affix=_1b  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
-e2echain_model_dir=exp/chain/e2e_cnn_1a
-common_egs_dir=
-reporting_email=
-
-# chain options
-train_stage=-10
-xent_regularize=0.1
-frame_subsampling_factor=4
-chunk_width=340,300,200,100
-num_leaves=500
-tdnn_dim=450
-# training options
-srand=0
-remove_egs=true
-lang_decode=data/lang
-lang_rescore=data/lang_rescore_6g
-# End configuration section.
-echo "$0 $@"  # Print the command line for logging
-
-
-. ./cmd.sh
-. ./path.sh
-. ./utils/parse_options.sh
-
-
-if ! cuda-compiled; then
-  cat <<EOF && exit 1
-This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
-If you want to use GPUs (and have them), go to src/, and configure and make on a machine
-where "nvcc" is installed.
-EOF
-fi
-
-ali_dir=exp/chain/e2e_ali_train
-lat_dir=exp/chain${nnet3_affix}/e2e_${train_set}_lats
-dir=exp/chain${nnet3_affix}/cnn_e2eali${affix}
-train_data_dir=data/${train_set}
-tree_dir=exp/chain${nnet3_affix}/tree_e2e
-
-# the 'lang' directory is created by this script.
-# If you create such a directory with a non-standard topology
-# you should probably name it differently.
-lang=data/lang_chain
-for f in $train_data_dir/feats.scp $ali_dir/ali.1.gz $ali_dir/final.mdl; do
-  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
-done
-
-
-if [ $stage -le 1 ]; then
-  echo "$0: creating lang directory $lang with chain-type topology"
-  # 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 $lang ]; then
-    if [ $lang/L.fst -nt data/lang/L.fst ]; then
-      echo "$0: $lang already exists, not overwriting it; continuing"
-    else
-      echo "$0: $lang 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 $lang
-    silphonelist=$(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
-fi
-
-if [ $stage -le 2 ]; then
-  # Get the alignments as lattices (gives the chain training more freedom).
-  # use the same num-jobs as the alignments
-  steps/nnet3/align_lats.sh --nj $nj --cmd "$cmd" \
-                            --acoustic-scale 1.0 \
-                            --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
-                            ${train_data_dir} data/lang $e2echain_model_dir $lat_dir
-  echo "" >$lat_dir/splice_opts
-fi
-
-if [ $stage -le 3 ]; 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.  The num-leaves is always somewhat less than the num-leaves from
-  # the GMM baseline.
-  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 $frame_subsampling_factor \
-    --alignment-subsampling-factor 1 \
-    --context-opts "--context-width=2 --central-position=1" \
-    --cmd "$cmd" $num_leaves ${train_data_dir} \
-    $lang $ali_dir $tree_dir
-fi
-
-
-if [ $stage -le 4 ]; then
-  mkdir -p $dir
-  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)
-  common1="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
-  common2="required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
-  common3="required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
-  mkdir -p $dir/configs
-  cat <<EOF > $dir/configs/network.xconfig
-  input dim=40 name=input
-
-  conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
-  conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
-  conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
-  conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
-  conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
-  conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-4,0,4 $common3
-  relu-batchnorm-layer name=tdnn1 input=Append(-4,0,4) dim=$tdnn_dim
-  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim
-  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim
-
-  ## adding the layers for chain branch
-  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5
-  output-layer name=output include-log-softmax=false dim=$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' mod?els... 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=tdnn3 dim=$tdnn_dim target-rms=0.5
-  output-layer name=output-xent dim=$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 5 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
-  steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$cmd" \
-    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
-    --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000" \
-    --chain.frame-subsampling-factor=$frame_subsampling_factor \
-    --chain.alignment-subsampling-factor=1 \
-    --chain.left-tolerance 3 \
-    --chain.right-tolerance 3 \
-    --trainer.srand=$srand \
-    --trainer.max-param-change=2.0 \
-    --trainer.num-epochs=4 \
-    --trainer.frames-per-iter=2000000 \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.shrink-value=1.0 \
-    --trainer.num-chunk-per-minibatch=64,32 \
-    --trainer.optimization.momentum=0.0 \
-    --trainer.add-option="--optimization.memory-compression-level=2" \
-    --egs.chunk-width=$chunk_width \
-    --egs.dir="$common_egs_dir" \
-    --egs.opts="--frames-overlap-per-eg 0 --constrained false" \
-    --cleanup.remove-egs=$remove_egs \
-    --use-gpu=true \
-    --reporting.email="$reporting_email" \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
-    --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
-fi
-
-if [ $stage -le 6 ]; then
-  # The reason we are using data/lang here, instead of $lang, is just to
-  # emphasize that it's not actually important to give mkgraph.sh the
-  # lang directory with the matched topology (since it gets the
-  # topology file from the model).  So you could give it a different
-  # lang directory, one that contained a wordlist and LM of your choice,
-  # as long as phones.txt was compatible.
-
-  utils/mkgraph.sh \
-    --self-loop-scale 1.0 $lang_decode \
-    $dir $dir/graph || exit 1;
-fi
-
-if [ $stage -le 7 ]; then
-  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
-    --frames-per-chunk $frames_per_chunk \
-    --nj $nj --cmd "$cmd" \
-    $dir/graph data/test $dir/decode_test || exit 1;
-
-  steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
-                                data/test $dir/decode_test{,_rescored} || exit 1
-fi
-
-echo "Done. Date: $(date). Results:"
-local/chain/compare_wer.sh $dir
diff --git a/egs/madcat_ar/v1/local/get_unique_utterances.py b/egs/madcat_ar/v1/local/get_unique_utterances.py
new file mode 100755
index 00000000000..f0cd268e026
--- /dev/null
+++ b/egs/madcat_ar/v1/local/get_unique_utterances.py
@@ -0,0 +1,40 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import numpy as np
+import sys
+import re
+import io
+
+parser = argparse.ArgumentParser(description="""Removes dev/test set lines
+                                                corpus from stdin, and writes it to stdout.""")
+parser.add_argument('text_file_path', type=str,
+                    help='transcription location.')
+args = parser.parse_args()
+
+def read_utterances(text_file_path):
+    utterance_dict = dict()
+    with open(text_file_path, 'r', encoding="utf8") as in_file:
+        for line in in_file:
+            words = line.strip().split()
+            transcript = ' '.join(words[1:])
+            utterance_dict[words[0]] = transcript
+    return utterance_dict
+
+def get_unique_utterances(utterance_dict):
+    unique_utt_transcription_dict = dict()
+    for utt_id, transcript in utterance_dict.items():
+        if transcript not in list(unique_utt_transcription_dict.values()):
+            unique_utt_transcription_dict[utt_id] = transcript
+    return unique_utt_transcription_dict
+
+### main ###
+utterance_dict = read_utterances(args.text_file_path)
+unique_utt_transcription_dict = get_unique_utterances(utterance_dict)
+
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf8")
+for k, v in unique_utt_transcription_dict.items():
+    sys.stdout.write(k + "\n")
+    #print('{}'.format(k))
+    #print('{} {}'.format(k, v))
diff --git a/egs/madcat_ar/v1/local/remove_sup_utts_from_unsup.py b/egs/madcat_ar/v1/local/remove_sup_utts_from_unsup.py
new file mode 100755
index 00000000000..e997dd7f0f0
--- /dev/null
+++ b/egs/madcat_ar/v1/local/remove_sup_utts_from_unsup.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import numpy as np
+import sys
+import re
+import io
+
+parser = argparse.ArgumentParser(description="""Removes dev/test set lines
+                                                from the LOB corpus. Reads the
+                                                corpus from stdin, and writes it to stdout.""")
+parser.add_argument('sup_text_path', type=str,
+                    help='dev transcription location.')
+parser.add_argument('unsup_text_path', type=str,
+                    help='test transcription location.')
+args = parser.parse_args()
+
+def read_utterances(text_file_path):
+    utterance_dict = dict()
+    with open(text_file_path, 'r', encoding="utf8") as in_file:
+        for line in in_file:
+            words = line.strip().split()
+            transcript = ' '.join(words[1:])
+            utterance_dict[words[0]] = transcript
+    return utterance_dict
+
+def get_unique_utterances():
+    unique_utt_transcription_dict = dict()
+    for utt_id, transcript in unsup_utterance_dict.items():
+        if transcript not in list(sup_utterance_dict.values()):
+            unique_utt_transcription_dict[utt_id] = transcript
+    return unique_utt_transcription_dict
+
+### main ###
+sup_utterance_dict = read_utterances(args.sup_text_path)
+unsup_utterance_dict = read_utterances(args.unsup_text_path)
+unique_utt_transcription_dict = get_unique_utterances()
+
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf8")
+for k, v in unique_utt_transcription_dict.items():
+    sys.stdout.write(k + "\n")
+    #print('{}'.format(k))
+    #print('{} {}'.format(k, v))
diff --git a/egs/madcat_ar/v1/local/train_lm.sh b/egs/madcat_ar/v1/local/train_lm.sh
index 903b288a834..9a0c8271f5a 100755
--- a/egs/madcat_ar/v1/local/train_lm.sh
+++ b/egs/madcat_ar/v1/local/train_lm.sh
@@ -64,12 +64,6 @@ if [ $stage -le 0 ]; then
   # we can later fold the dev data into this.
   cat data/train/text | cut -d " " -f 2- >  ${dir}/data/text/train.txt
 
-  if [ -d "data/local/gigawordcorpus/arb_gw_5/data" ]; then
-    cat data/local/gigawordcorpus/arb_gw_5/data/nhr_arb_combined.txt | \
-      utils/lang/bpe/prepend_words.py | utils/lang/bpe/apply_bpe.py -c data/local/bpe.txt \
-      | sed 's/@@//g' > ${dir}/data/text/corpus_text.txt
-  fi
-
   # for reporting perplexities, we'll use the "real" dev set.
   # (the validation data is used as ${dir}/data/text/dev.txt to work
   # out interpolation weights.)
@@ -78,7 +72,7 @@ if [ $stage -le 0 ]; then
   cut -d " " -f 2-  < data/test/text  > ${dir}/data/real_dev_set.txt
 
   # get the wordlist from MADCAT text
-  cat ${dir}/data/text/{train,corpus_text}.txt | tr '[:space:]' '[\n*]' | grep -v "^\s*$" | sort | uniq -c | sort -bnr > ${dir}/data/word_count
+  cat ${dir}/data/text/train.txt | tr '[:space:]' '[\n*]' | grep -v "^\s*$" | sort | uniq -c | sort -bnr > ${dir}/data/word_count
   cat ${dir}/data/word_count | awk '{print $2}' > ${dir}/data/wordlist
 fi
 
@@ -89,7 +83,7 @@ if [ $stage -le 1 ]; then
   # Note: if you have more than one order, use a certain amount of words as the
   # vocab and want to restrict max memory for 'sort',
   echo "$0: training the unpruned LM"
-  min_counts='corpus_text=2 train=1'
+  min_counts='train=1'
   wordlist=${dir}/data/wordlist
 
   lm_name="`basename ${wordlist}`_${order}"
@@ -109,22 +103,11 @@ fi
 
 if [ $stage -le 2 ]; then
   echo "$0: pruning the LM (to larger size)"
-  # Using 20 million n-grams for a big LM for rescoring purposes.
-  size=20000000
+  # Using 1 million n-grams for a big LM for rescoring purposes.
+  size=1000000
   prune_lm_dir.py --target-num-ngrams=$size --initial-threshold=0.02 ${unpruned_lm_dir} ${dir}/data/lm_${order}_prune_big
 
   get_data_prob.py ${dir}/data/real_dev_set.txt ${dir}/data/lm_${order}_prune_big 2>&1 | grep -F '[perplexity'
   mkdir -p ${dir}/data/arpa
   format_arpa_lm.py ${dir}/data/lm_${order}_prune_big | gzip -c > ${dir}/data/arpa/${order}gram_big.arpa.gz
 fi
-
-if [ $stage -le 3 ]; then
-  echo "$0: pruning the LM (to smaller size)"
-  # Using 10 million n-grams for a smaller LM for graph building.  Prune from the
-  # bigger-pruned LM, it'll be faster.
-  size=10000000
-  prune_lm_dir.py --target-num-ngrams=$size ${dir}/data/lm_${order}_prune_big ${dir}/data/lm_${order}_prune_small
-
-  get_data_prob.py ${dir}/data/real_dev_set.txt ${dir}/data/lm_${order}_prune_small 2>&1 | grep -F '[perplexity'
-  format_arpa_lm.py ${dir}/data/lm_${order}_prune_small | gzip -c > ${dir}/data/arpa/${order}gram_small.arpa.gz
-fi
diff --git a/egs/madcat_ar/v1/local/train_lm.unsup.sh b/egs/madcat_ar/v1/local/train_lm.unsup.sh
new file mode 100755
index 00000000000..3b7f6a9b7ce
--- /dev/null
+++ b/egs/madcat_ar/v1/local/train_lm.unsup.sh
@@ -0,0 +1,98 @@
+#!/bin/bash
+
+# Copyright 2016  Vincent Nguyen
+#           2016  Johns Hopkins University (author: Daniel Povey)
+#           2017  Ashish Arora
+#           2017  Hossein Hadian
+# Apache 2.0
+#
+# This script trains a LM on the training transcriptions.
+# It is based on the example scripts distributed with PocoLM
+
+# It will check if pocolm is installed and if not will proceed with installation
+
+set -e
+stage=0
+dir=data/local/local_lm
+order=6
+echo "$0 $@"  # Print the command line for logging
+. ./utils/parse_options.sh || exit 1;
+
+lm_dir=${dir}/data
+
+
+mkdir -p $dir
+. ./path.sh || exit 1; # for KALDI_ROOT
+export PATH=$KALDI_ROOT/tools/pocolm/scripts:$PATH
+( # First make sure the pocolm toolkit is installed.
+ cd $KALDI_ROOT/tools || exit 1;
+ if [ -d pocolm ]; then
+   echo Not installing the pocolm toolkit since it is already there.
+ else
+   echo "$0: Please install the PocoLM toolkit with: "
+   echo " cd ../../../tools; extras/install_pocolm.sh; cd -"
+   exit 1;
+ fi
+) || exit 1;
+
+bypass_metaparam_optim_opt=
+# If you want to bypass the metaparameter optimization steps with specific metaparameters
+# un-comment the following line, and change the numbers to some appropriate values.
+# You can find the values from output log of train_lm.py.
+# These example numbers of metaparameters is for 4-gram model (with min-counts)
+# running with train_lm.py.
+# The dev perplexity should be close to the non-bypassed model.
+# Note: to use these example parameters, you may need to remove the .done files
+# to make sure the make_lm_dir.py be called and tain only 3-gram model
+#for order in 3; do
+#rm -f ${lm_dir}/${num_word}_${order}.pocolm/.done
+if [ $stage -le 0 ]; then
+  mkdir -p ${dir}/data
+  mkdir -p ${dir}/data/text
+
+  echo "$0: Getting the Data sources"
+
+  rm ${dir}/data/text/* 2>/dev/null || true
+
+  # use the validation data as the dev set.
+  # Note: the name 'dev' is treated specially by pocolm, it automatically
+  # becomes the dev set.
+  cat data/dev/text | cut -d " " -f 2-  > ${dir}/data/text/dev.txt
+
+  cat data/train_LM/text | cut -d " " -f 2-  > ${dir}/data/text/train.txt
+  # for reporting perplexities, we'll use the "real" dev set.
+  # (the validation data is used as ${dir}/data/text/dev.txt to work
+  # out interpolation weights.)
+  # note, we can't put it in ${dir}/data/text/, because then pocolm would use
+  # it as one of the data sources.
+  cut -d " " -f 2-  < data/test/text  > ${dir}/data/real_dev_set.txt
+
+  # get the wordlist from MADCAT text
+  cat ${dir}/data/text/train.txt | tr '[:space:]' '[\n*]' | grep -v "^\s*$" | sort | uniq -c | sort -bnr > ${dir}/data/word_count
+  cat ${dir}/data/word_count | awk '{print $2}' > ${dir}/data/wordlist
+fi
+
+if [ $stage -le 1 ]; then
+  # decide on the vocabulary.
+  # Note: you'd use --wordlist if you had a previously determined word-list
+  # that you wanted to use.
+  # Note: if you have more than one order, use a certain amount of words as the
+  # vocab and want to restrict max memory for 'sort',
+  echo "$0: training the unpruned LM"
+  min_counts='train=1'
+  wordlist=${dir}/data/wordlist
+
+  lm_name="`basename ${wordlist}`_${order}"
+  if [ -n "${min_counts}" ]; then
+    lm_name+="_`echo ${min_counts} | tr -s "[:blank:]" "_" | tr "=" "-"`"
+  fi
+  unpruned_lm_dir=${lm_dir}/${lm_name}.pocolm
+  train_lm.py  --wordlist=${wordlist} --num-splits=20 --warm-start-ratio=20 \
+               --limit-unk-history=true \
+               ${bypass_metaparam_optim_opt} \
+               ${dir}/data/text ${order} ${lm_dir}/work ${unpruned_lm_dir}
+
+  get_data_prob.py ${dir}/data/real_dev_set.txt ${unpruned_lm_dir} 2>&1 | grep -F '[perplexity'
+  mkdir -p ${dir}/data/arpa
+  format_arpa_lm.py ${unpruned_lm_dir} | gzip -c > ${dir}/data/arpa/${order}gram_unpruned.train80k.arpa.gz
+fi
diff --git a/egs/madcat_ar/v1/run.sh b/egs/madcat_ar/v1/run.sh
deleted file mode 100755
index 01bfdbed543..00000000000
--- a/egs/madcat_ar/v1/run.sh
+++ /dev/null
@@ -1,161 +0,0 @@
-#!/bin/bash
-
-# Copyright      2017  Chun Chieh Chang
-#                2017  Ashish Arora
-#                2017  Hossein Hadian
-
-set -e
-stage=0
-nj=70
-decode_gmm=false
-# download_dir{1,2,3} points to the database path on the JHU grid. If you have not
-# already downloaded the database you can set it to a local directory
-# This corpus can be purchased here:
-# https://catalog.ldc.upenn.edu/{LDC2012T15,LDC2013T09/,LDC2013T15/}
-download_dir1=/export/corpora/LDC/LDC2012T15/data
-download_dir2=/export/corpora/LDC/LDC2013T09/data
-download_dir3=/export/corpora/LDC/LDC2013T15/data
-writing_condition1=/export/corpora/LDC/LDC2012T15/docs/writing_conditions.tab
-writing_condition2=/export/corpora/LDC/LDC2013T09/docs/writing_conditions.tab
-writing_condition3=/export/corpora/LDC/LDC2013T15/docs/writing_conditions.tab
-data_splits_dir=data/download/data_splits
-images_scp_dir=data/local
-overwrite=false
-subset=false
-augment=false
-use_extra_corpus_text=true
-. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
-           ## This relates to the queue.
-. ./path.sh
-. ./utils/parse_options.sh  # e.g. this parses the above options
-                            # if supplied.
-./local/check_tools.sh
-mkdir -p data/{train,test,dev}/data
-mkdir -p data/local/{train,test,dev}
-
-if [ $stage -le 0 ]; then
-  if [ -f data/train/text ] && ! $overwrite; then
-    echo "$0: Not processing, probably script have run from wrong stage"
-    echo "Exiting with status 1 to avoid data corruption"
-    exit 1;
-  fi
-  local/prepare_data.sh --data_splits $data_splits_dir --download_dir1 $download_dir1 \
-                         --download_dir2 $download_dir2 --download_dir3 $download_dir3 \
-                         --use_extra_corpus_text $use_extra_corpus_text
-
-  for set in test train dev; do
-    data_split_file=$data_splits_dir/madcat.$set.raw.lineid
-    local/extract_lines.sh --nj $nj --cmd $cmd --data_split_file $data_split_file \
-        --download_dir1 $download_dir1 --download_dir2 $download_dir2 \
-        --download_dir3 $download_dir3 --writing_condition1 $writing_condition1 \
-        --writing_condition2 $writing_condition2 --writing_condition3 $writing_condition3 \
-        --data data/local/$set --subset $subset --augment $augment || exit 1
-  done
-
-  echo "$0: Processing data..."
-  for set in dev train test; do
-    local/process_data.py $download_dir1 $download_dir2 $download_dir3 \
-      $data_splits_dir/madcat.$set.raw.lineid data/$set $images_scp_dir/$set/images.scp \
-      $writing_condition1 $writing_condition2 $writing_condition3 --augment $augment --subset $subset
-    image/fix_data_dir.sh data/${set}
-  done
-fi
-
-
-if [ $stage -le 1 ]; then
-  for dataset in test train; do
-    local/extract_features.sh --nj $nj --cmd $cmd --feat-dim 40 data/$dataset
-    steps/compute_cmvn_stats.sh data/$dataset || exit 1;
-  done
-  utils/fix_data_dir.sh data/train
-fi
-
-if [ $stage -le 2 ]; then
-  echo "$0: Preparing BPE..."
-  cut -d' ' -f2- data/train/text | utils/lang/bpe/reverse.py | \
-    utils/lang/bpe/prepend_words.py | \
-    utils/lang/bpe/learn_bpe.py -s 700 > data/local/bpe.txt
-
-  for set in test train dev; do
-    cut -d' ' -f1 data/$set/text > data/$set/ids
-    cut -d' ' -f2- data/$set/text | utils/lang/bpe/reverse.py | \
-      utils/lang/bpe/prepend_words.py | \
-      utils/lang/bpe/apply_bpe.py -c data/local/bpe.txt \
-      | sed 's/@@//g' > data/$set/bpe_text
-
-    mv data/$set/text data/$set/text.old
-    paste -d' ' data/$set/ids data/$set/bpe_text > data/$set/text
-    rm -f data/$set/bpe_text data/$set/ids
-  done
-
-  echo "$0:Preparing dictionary and lang..."
-  local/prepare_dict.sh
-  utils/prepare_lang.sh --num-sil-states 4 --num-nonsil-states 8 --sil-prob 0.0 --position-dependent-phones false \
-                        data/local/dict "<sil>" data/lang/temp data/lang
-  utils/lang/bpe/add_final_optional_silence.sh --final-sil-prob 0.5 data/lang
-fi
-
-if [ $stage -le 3 ]; then
-  echo "$0: Estimating a language model for decoding..."
-  local/train_lm.sh
-  utils/format_lm.sh data/lang data/local/local_lm/data/arpa/6gram_small.arpa.gz \
-                     data/local/dict/lexicon.txt data/lang
-  utils/build_const_arpa_lm.sh data/local/local_lm/data/arpa/6gram_unpruned.arpa.gz \
-                               data/lang data/lang_rescore_6g
-fi
-
-if [ $stage -le 4 ]; then
-  steps/train_mono.sh --nj $nj --cmd $cmd --totgauss 10000 data/train \
-    data/lang exp/mono
-fi
-
-if [ $stage -le 5 ] && $decode_gmm; then
-  utils/mkgraph.sh --mono data/lang exp/mono exp/mono/graph
-
-  steps/decode.sh --nj $nj --cmd $cmd exp/mono/graph data/test \
-    exp/mono/decode_test
-fi
-
-if [ $stage -le 6 ]; then
-  steps/align_si.sh --nj $nj --cmd $cmd data/train data/lang \
-    exp/mono exp/mono_ali
-
-  steps/train_deltas.sh --cmd $cmd 500 20000 data/train data/lang \
-    exp/mono_ali exp/tri
-fi
-
-if [ $stage -le 7 ] && $decode_gmm; then
-  utils/mkgraph.sh data/lang exp/tri exp/tri/graph
-
-  steps/decode.sh --nj $nj --cmd $cmd exp/tri/graph data/test \
-    exp/tri/decode_test
-fi
-
-if [ $stage -le 8 ]; then
-  steps/align_si.sh --nj $nj --cmd $cmd data/train data/lang \
-    exp/tri exp/tri_ali
-
-  steps/train_lda_mllt.sh --cmd $cmd \
-    --splice-opts "--left-context=3 --right-context=3" 500 20000 \
-    data/train data/lang exp/tri_ali exp/tri3
-fi
-
-if [ $stage -le 9 ] && $decode_gmm; then
-  utils/mkgraph.sh data/lang exp/tri3 exp/tri3/graph
-
-  steps/decode.sh --nj $nj --cmd $cmd exp/tri3/graph \
-    data/test exp/tri3/decode_test
-fi
-
-if [ $stage -le 10 ]; then
-  steps/align_fmllr.sh --nj $nj --cmd $cmd --use-graphs true \
-    data/train data/lang exp/tri3 exp/tri3_ali
-fi
-
-if [ $stage -le 11 ]; then
-  local/chain/run_cnn.sh
-fi
-
-if [ $stage -le 12 ]; then
-  local/chain/run_cnn_chainali.sh --stage 2
-fi
diff --git a/egs/madcat_ar/v1/run_end2end.sh b/egs/madcat_ar/v1/run_end2end.sh
index 62f4eeb7c71..1b2943cb804 100755
--- a/egs/madcat_ar/v1/run_end2end.sh
+++ b/egs/madcat_ar/v1/run_end2end.sh
@@ -66,13 +66,16 @@ if [ $stage -le 1 ]; then
   image/get_image2num_frames.py data/train
   image/get_allowed_lengths.py --frame-subsampling-factor 4 10 data/train
 
-  for set in test dev train; do
+  image/get_image2num_frames.py data/dev
+  image/get_allowed_lengths.py --frame-subsampling-factor 4 10 data/dev
+
+  for set in dev; do
     echo "$0: Extracting features and calling compute_cmvn_stats for dataset:  $set. $(date)"
     local/extract_features.sh --nj $nj --cmd $cmd --feat-dim 40 data/$set
     steps/compute_cmvn_stats.sh data/$set || exit 1;
   done
   echo "$0: Fixing data directory for train dataset $(date)."
-  utils/fix_data_dir.sh data/train
+  utils/fix_data_dir.sh data/dev
 fi
 
 if [ $stage -le 2 ]; then
@@ -92,7 +95,9 @@ if [ $stage -le 2 ]; then
     paste -d' ' data/$set/ids data/$set/bpe_text > data/$set/text
     rm -f data/$set/bpe_text data/$set/ids
   done
+fi
 
+if [ $stage -le 3 ]; then
   echo "$0:Preparing dictionary and lang..."
   local/prepare_dict.sh
   utils/prepare_lang.sh --num-sil-states 4 --num-nonsil-states 8 --sil-prob 0.0 --position-dependent-phones false \
@@ -100,34 +105,96 @@ if [ $stage -le 2 ]; then
   utils/lang/bpe/add_final_optional_silence.sh --final-sil-prob 0.5 data/lang
 fi
 
-if [ $stage -le 3 ]; then
-  echo "$0: Calling the flat-start chain recipe... $(date)."
-  local/chain/run_e2e_cnn.sh
-fi
-
-lang_decode=data/lang
-lang_rescore=data/lang_rescore_6g
-decode_e2e=true
+# training language model
+lang_decode=data/lang_test
 if [ $stage -le 4 ]; then
   echo "$0: Estimating a language model for decoding..."
   local/train_lm.sh
   utils/format_lm.sh data/lang data/local/local_lm/data/arpa/6gram_big.arpa.gz \
                      data/local/dict/lexicon.txt $lang_decode
   utils/build_const_arpa_lm.sh data/local/local_lm/data/arpa/6gram_unpruned.arpa.gz \
-                               data/lang $lang_rescore
+                               data/lang data/lang_rescore_6g
+fi
+
+if [ $stage -le 5 ]; then
+  echo "$0:Preparing supervised and unsupervised data..."
+  #local/get_unique_utterances.py data/train/text.old > data/train/uttlist.full
+  #head -40000 data/train/uttlist.full > data/train/uttlist.40k
+  #utils/subset_data_dir.sh --utt-list data/train/uttlist.40k data/train data/train_unsup
+  #tail +40000 data/train/uttlist.full > data/train/uttlist.tail.80k
+  #utils/subset_data_dir.sh --utt-list data/train/uttlist.tail.80k data/train data/train_LM
+  #utils/subset_data_dir.sh data/test 5000 data/test_5k
+
+  utils/subset_data_dir.sh data/dev 8000 data/train_sup8k
+  local/get_unique_utterances.py data/train_sup8k/text.old > data/train_sup8k/uttlist
+  utils/subset_data_dir.sh --utt-list data/train_sup8k/uttlist data/train_sup8k data/train_sup
+  local/remove_sup_utts_from_unsup.py data/train_sup/text.old data/train_unsup/text.old > data/local/unsup_uttlist
+  utils/subset_data_dir.sh --utt-list data/local/unsup_uttlist data/train_unsup data/train_unsup_unique
+  cp data/train/allowed_lengths.txt data/train_unsup_unique/allowed_lengths.txt
+  cp data/dev/allowed_lengths.txt data/train_sup/allowed_lengths.txt
+fi
+
+#if [ $stage -le 6 ]; then
+#  echo "$0: Estimating a language model for decoding..."
+#  local/train_lm.unsup.sh
+#  utils/format_lm.sh data/lang data/local/local_lm/data/arpa/6gram_unpruned.train80k.arpa.gz \
+#                     data/local/dict/lexicon.txt data/lang_decode_unsup 
+#fi
+
+if [ $stage -le 7 ]; then
+  utils/combine_data.sh data/semisup \
+    data/train_sup data/train_unsup_unique || exit 1
+fi
+
+train_set=train_sup
+# training flat-start system
+if [ $stage -le 8 ]; then
+  echo "$0: Calling the flat-start chain recipe... $(date)."
+  local/chain/run_flatstart_cnn_1a.sh --train-set train_sup
 fi
 
-if [ $stage -le 5 ] && $decode_e2e; then
-  echo "$0: $(date) stage 5: decoding end2end setup..."
-  utils/mkgraph.sh --self-loop-scale 1.0 $lang_decode \
-    exp/chain/e2e_cnn_1a/ exp/chain/e2e_cnn_1a/graph || exit 1;
+# alignments are used in tree
+if [ $stage -le 9 ]; then
+  echo "$0: Aligning the training data using the e2e chain model..."
+  steps/nnet3/align.sh --nj 50 --cmd "$cmd" \
+                       --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0 --acoustic-scale=1.0' \
+                       data/train_sup data/lang exp/chain/flatstart_cnn_1a_$train_set exp/chain/flatstartali_$train_set
+fi
 
-  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 --nj $nj --cmd "$cmd" \
-    exp/chain/e2e_cnn_1a/graph data/test exp/chain/e2e_cnn_1a/decode_test || exit 1;
+# training e2eali system
+if [ $stage -le 10 ]; then
+  echo "$(date) stage 5: Building a tree and training a regular chain model using the e2e alignments..."
+  local/chain/run_cnn_e2eali_1a.sh --train-set train_sup
+fi
 
-  steps/lmrescore_const_arpa.sh --cmd "$cmd" $lang_decode $lang_rescore \
-    data/test exp/chain/e2e_cnn_1a/decode_test{,_rescored} || exit 1
+if [ $stage -le 11 ]; then
+  echo "$0: Aligning the training data using the e2e chain model..."
+  steps/nnet3/align.sh --nj 50 --cmd "$cmd" \
+                       --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0 --acoustic-scale=1.0' \
+                       data/train_sup data/lang exp/chain/cnn_e2eali_1a_$train_set exp/chain/e2eali_$train_set
+fi
+
+# training chainali baseline system (larger network)
+if [ $stage -le 12 ]; then
+  echo "$0: chain model using the chainali alignments..."
+  local/chain/run_cnn_chainali_1a.sh --stage 2 --train-set train_sup
+fi
+
+# training semi-supervised system
+if [ $stage -le 13 ]; then
+  local/chain/run_cnn_chainali_semisupervised_1a.sh \
+    --supervised-set train_sup \
+    --unsupervised-set train_unsup_unique \
+    --sup-chain-dir exp/chain/cnn_chainali_1a_$train_set \
+    --sup-lat-dir exp/chain/chainali_${train_set}_lats \
+    --sup-tree-dir exp/chain/tree_chainali_${train_set} \
+    --tdnn-affix _1a_tol1_beam4 \
+    --exp-root exp/semisup || exit 1
+fi
 
-  echo "$0: Done. Date: $(date). Results:"
-  local/chain/compare_wer.sh exp/chain/e2e_cnn_1a/
+# training oracle system
+train_set=semisup
+if [ $stage -le 14 ]; then
+  echo "$(date) stage 5: Building a tree and training a regular chain model using the e2e alignments..."
+  local/chain/run_cnn_chainali_oracle_1a.sh --train-set semisup --stage 2
 fi
diff --git a/egs/wsj/s5/steps/nnet3/chain/get_egs.sh b/egs/wsj/s5/steps/nnet3/chain/get_egs.sh
index 9996820d6d3..504fc929198 100755
--- a/egs/wsj/s5/steps/nnet3/chain/get_egs.sh
+++ b/egs/wsj/s5/steps/nnet3/chain/get_egs.sh
@@ -75,7 +75,7 @@ lattice_prune_beam=         # If supplied, the lattices will be pruned to this b
                             # before being used to get supervisions.
 acwt=0.1   # For pruning
 deriv_weights_scp=
-generate_egs_scp=false
+generate_egs_scp=true
 
 echo "$0 $@"  # Print the command line for logging
 
diff --git a/egs/wsj/s5/utils/combine_data.sh b/egs/wsj/s5/utils/combine_data.sh
index a43cf9d77f3..a689fa2b98d 100755
--- a/egs/wsj/s5/utils/combine_data.sh
+++ b/egs/wsj/s5/utils/combine_data.sh
@@ -94,7 +94,7 @@ else
   echo "$0 [info]: not combining segments as it does not exist"
 fi
 
-for file in utt2spk utt2lang utt2dur reco2dur feats.scp text cmvn.scp vad.scp reco2file_and_channel wav.scp spk2gender $extra_files; do
+for file in utt2spk utt2lang utt2dur reco2dur feats.scp text text.old cmvn.scp vad.scp reco2file_and_channel wav.scp spk2gender $extra_files; do
   exists_somewhere=false
   absent_somewhere=false
   for d in $*; do
diff --git a/egs/wsj/s5/utils/subset_data_dir.sh b/egs/wsj/s5/utils/subset_data_dir.sh
index 93ee0971b88..526bfd7aa2a 100755
--- a/egs/wsj/s5/utils/subset_data_dir.sh
+++ b/egs/wsj/s5/utils/subset_data_dir.sh
@@ -111,9 +111,11 @@ function do_filtering {
   [ -f $srcdir/utt2num_frames ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/utt2num_frames >$destdir/utt2num_frames
   [ -f $srcdir/utt2uniq ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/utt2uniq >$destdir/utt2uniq
   [ -f $srcdir/wav.scp ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/wav.scp >$destdir/wav.scp
+  [ -f $srcdir/images.scp ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/images.scp >$destdir/images.scp
   [ -f $srcdir/spk2warp ] && utils/filter_scp.pl $destdir/spk2utt <$srcdir/spk2warp >$destdir/spk2warp
   [ -f $srcdir/utt2warp ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/utt2warp >$destdir/utt2warp
   [ -f $srcdir/text ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/text >$destdir/text
+  [ -f $srcdir/text.old ] && utils/filter_scp.pl $destdir/utt2spk <$srcdir/text.old >$destdir/text.old
   [ -f $srcdir/spk2gender ] && utils/filter_scp.pl $destdir/spk2utt <$srcdir/spk2gender >$destdir/spk2gender
   [ -f $srcdir/cmvn.scp ] && utils/filter_scp.pl $destdir/spk2utt <$srcdir/cmvn.scp >$destdir/cmvn.scp
   if [ -f $srcdir/segments ]; then