diff --git a/egs/aishell/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/aishell/s5/local/chain/tuning/run_tdnn_1a.sh
index a0b183e3c5a..b38fa4d9c7a 100755
--- a/egs/aishell/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/aishell/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -90,7 +90,7 @@ if [ $stage -le 10 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/aishell/s5/local/chain/tuning/run_tdnn_2a.sh b/egs/aishell/s5/local/chain/tuning/run_tdnn_2a.sh
index 2ebe2a3092b..6b7223785d9 100755
--- a/egs/aishell/s5/local/chain/tuning/run_tdnn_2a.sh
+++ b/egs/aishell/s5/local/chain/tuning/run_tdnn_2a.sh
@@ -92,7 +92,7 @@ if [ $stage -le 10 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/aishell2/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/aishell2/s5/local/chain/tuning/run_tdnn_1a.sh
index 459bd64eeb5..86c9becac5b 100755
--- a/egs/aishell2/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/aishell2/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -103,7 +103,7 @@ fi
 if [ $stage -le 10 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
   num_targets=$(tree-info $treedir/tree | grep num-pdfs | awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.002"
   linear_opts="orthonormal-constraint=1.0"
   output_opts="l2-regularize=0.0005 bottleneck-dim=256"
diff --git a/egs/aishell2/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/aishell2/s5/local/chain/tuning/run_tdnn_1b.sh
index ba2a4344349..d8560e63909 100755
--- a/egs/aishell2/s5/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/aishell2/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -150,7 +150,7 @@ if [ $stage -le 10 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
   feat_dim=$(feat-to-dim scp:data/${train_set}_hires/feats.scp -)
   num_targets=$(tree-info $treedir/tree | grep num-pdfs | awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.002"
   linear_opts="orthonormal-constraint=1.0"
   output_opts="l2-regularize=0.0005 bottleneck-dim=256"
diff --git a/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_1a.sh b/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_1a.sh
index 1fc641f1166..4d260e3c517 100755
--- a/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_1a.sh
+++ b/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_1a.sh
@@ -220,7 +220,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   affine_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
   linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
diff --git a/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh b/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
index a8494420b0d..3546b6a7ced 100755
--- a/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
@@ -211,7 +211,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1b.sh b/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1b.sh
index a12e7efa7b9..1a839b045bd 100755
--- a/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1b.sh
+++ b/egs/ami/s5b/local/chain/multi_condition/tuning/run_tdnn_lstm_1b.sh
@@ -235,7 +235,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   tdnn_opts="l2-regularize=0.006"
   lstm_opts="l2-regularize=0.0025 decay-time=20 dropout-proportion=0.0"
   output_opts="l2-regularize=0.001"
diff --git a/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh b/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
index 16d1f4044f5..d926c1dc6d7 100644
--- a/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
@@ -184,7 +184,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1b.sh b/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1b.sh
index 83e6a95582f..d9cd1c356e8 100644
--- a/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1b.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1b.sh
@@ -176,7 +176,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20 dropout-proportion=0"
 
diff --git a/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1c.sh b/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1c.sh
index 387b4bfcc88..a0805b4f9f1 100755
--- a/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1c.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_cnn_tdnn_lstm_1c.sh
@@ -185,7 +185,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=40"
   
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_1b.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_1b.sh
index 57108dbddae..997357b80a9 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_1b.sh
@@ -164,7 +164,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_1c.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_1c.sh
index f87e1a12d36..4d062e65429 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_1c.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_1c.sh
@@ -151,7 +151,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_1d.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_1d.sh
index eb84a1cd876..387570388d0 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_1d.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_1d.sh
@@ -163,7 +163,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_1e.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_1e.sh
index e6592b667dc..0436b08cdc0 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_1e.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_1e.sh
@@ -161,7 +161,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_1f.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_1f.sh
index 8bf2b73dada..4ca526d63b8 100644
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_1f.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_1f.sh
@@ -165,7 +165,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_1g.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_1g.sh
index dfb6dfedee7..baed760bb68 100644
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_1g.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_1g.sh
@@ -166,7 +166,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_1h.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_1h.sh
index 3e26a8b38bd..e721a858c0a 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_1h.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_1h.sh
@@ -167,7 +167,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_1i.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_1i.sh
index 1931127c86d..de40cb2d1a4 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_1i.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_1i.sh
@@ -168,7 +168,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.02"
   output_opts="l2-regularize=0.004"
 
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1a.sh
index d63712f1f0f..4f580b88f6b 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -171,7 +171,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1b.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1b.sh
index a53785f45c2..904a079d7de 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1b.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1b.sh
@@ -173,7 +173,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1c.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1c.sh
index 76a9f735c5f..511e520465a 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1c.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1c.sh
@@ -172,7 +172,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1d.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1d.sh
index 8cc1a4e15fa..bd81b7df4eb 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1d.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1d.sh
@@ -172,7 +172,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1e.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1e.sh
index accfd158a9d..50903e78b6d 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1e.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1e.sh
@@ -174,7 +174,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1f.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1f.sh
index 2b275e4e27d..f6c53001498 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1f.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1f.sh
@@ -173,7 +173,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1g.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1g.sh
index 1c90af38c4c..79fd9ef3fb5 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1g.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1g.sh
@@ -174,7 +174,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1h.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1h.sh
index fb4b6a475e2..e58a7f89e03 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1h.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1h.sh
@@ -171,7 +171,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1i.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1i.sh
index 92636b4c17e..13f894f5a48 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1i.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1i.sh
@@ -174,7 +174,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1j.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1j.sh
index 89fd8ce2915..48b31832e8c 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1j.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1j.sh
@@ -181,7 +181,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1k.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1k.sh
index b8d947d8e92..e675bc494bb 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1k.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1k.sh
@@ -177,7 +177,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1l.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1l.sh
index 74c0f5a6ead..2d019398274 100644
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1l.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1l.sh
@@ -224,7 +224,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1m.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1m.sh
index b0e7af0618d..9e5b971bbe2 100644
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1m.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1m.sh
@@ -226,7 +226,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20 dropout-proportion=0.0"
 
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1n.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1n.sh
index bee4d997b01..9575c3cf686 100644
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1n.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1n.sh
@@ -178,7 +178,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1o.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1o.sh
index 1e4111adc6a..a7f2625c181 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1o.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1o.sh
@@ -182,7 +182,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   tdnn_opts="l2-regularize=0.025"
   lstm_opts="l2-regularize=0.01"
   output_opts="l2-regularize=0.004"
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_bs_1a.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_bs_1a.sh
index b672a44e572..ca920869b30 100755
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_bs_1a.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_bs_1a.sh
@@ -180,7 +180,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   tdnn_opts="l2-regularize=0.003"
   lstm_opts="l2-regularize=0.005"
   output_opts="l2-regularize=0.001"
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1a.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1a.sh
index f68c4203767..53dbd5238db 100644
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1a.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1a.sh
@@ -178,7 +178,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0"
 
   mkdir -p $dir/configs
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1b.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1b.sh
index ac4266ca162..dafef668e60 100644
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1b.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1b.sh
@@ -177,7 +177,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0"
 
   mkdir -p $dir/configs
diff --git a/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1c.sh b/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1c.sh
index 74b21f10c33..677946d0b9a 100644
--- a/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1c.sh
+++ b/egs/ami/s5b/local/chain/tuning/run_tdnn_opgru_1c.sh
@@ -176,7 +176,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0"
 
   mkdir -p $dir/configs
diff --git a/egs/aspire/s5/local/chain/tuning/run_blstm_7b.sh b/egs/aspire/s5/local/chain/tuning/run_blstm_7b.sh
index 8ff59d83ed0..bd13010c791 100755
--- a/egs/aspire/s5/local/chain/tuning/run_blstm_7b.sh
+++ b/egs/aspire/s5/local/chain/tuning/run_blstm_7b.sh
@@ -138,7 +138,7 @@ if [ $stage -le 11 ]; then
 
   num_targets=$(tree-info $treedir/tree | grep num-pdfs | awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/aspire/s5/local/chain/tuning/run_tdnn_7b.sh b/egs/aspire/s5/local/chain/tuning/run_tdnn_7b.sh
index 201f61dc64b..d6292fbadb3 100755
--- a/egs/aspire/s5/local/chain/tuning/run_tdnn_7b.sh
+++ b/egs/aspire/s5/local/chain/tuning/run_tdnn_7b.sh
@@ -136,7 +136,7 @@ if [ $stage -le 11 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/aspire/s5/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/aspire/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
index 63d3a7ca988..e6aa37a7543 100755
--- a/egs/aspire/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/aspire/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -151,7 +151,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=40"
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn.sh
index 4f485edf7da..7b4535f8c5e 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn.sh
@@ -128,7 +128,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm.sh
index 72f7a3c32dd..5fc14dda826 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm.sh
@@ -129,7 +129,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20"
   label_delay=5
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab1.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab1.sh
index be0c2cc4b9b..8c7de5d18d4 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab1.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab1.sh
@@ -127,7 +127,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20"
   label_delay=5
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab2.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab2.sh
index 8f21a239794..0b3e70b5a04 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab2.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab2.sh
@@ -127,7 +127,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20"
   label_delay=5
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab3.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab3.sh
index 7898d172242..45f2907645e 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab3.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab3.sh
@@ -128,7 +128,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20"
   label_delay=5
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab4.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab4.sh
index 49462573245..0d92aff5c28 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab4.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab4.sh
@@ -128,7 +128,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20"
   label_delay=5
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab5.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab5.sh
index c888d985f5e..4129c00dcb4 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab5.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab5.sh
@@ -128,7 +128,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20"
   label_delay=5
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab6.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab6.sh
index e9a045e113a..1cfa50c1aa1 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab6.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab6.sh
@@ -128,7 +128,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20"
   label_delay=5
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab7.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab7.sh
index ce192a91665..ba8ac1e0373 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab7.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab7.sh
@@ -129,7 +129,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20 dropout-proportion=0.0"
   label_delay=5
 
diff --git a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab8.sh b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab8.sh
index 3fc0ef2206c..5de285e080e 100755
--- a/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab8.sh
+++ b/egs/babel/s5d/local/chain/tuning/run_tdnn_lstm_bab8.sh
@@ -129,7 +129,7 @@ if [ $stage -le 17 ]; then
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20 dropout-proportion=0.0 "
   label_delay=5
 
diff --git a/egs/bentham/v1/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/bentham/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
index 6bac5a22398..ec530ef1ce4 100755
--- a/egs/bentham/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/bentham/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
@@ -139,7 +139,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/chime4/s5_1ch/local/chain/tuning/run_tdnn_1a.sh b/egs/chime4/s5_1ch/local/chain/tuning/run_tdnn_1a.sh
index d5ad3629cee..3f8b7c60090 100755
--- a/egs/chime4/s5_1ch/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/chime4/s5_1ch/local/chain/tuning/run_tdnn_1a.sh
@@ -217,7 +217,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.01"
   output_opts="l2-regularize=0.005"
 
diff --git a/egs/chime4/s5_1ch/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/chime4/s5_1ch/local/chain/tuning/run_tdnn_lstm_1a.sh
index f5c8973ab67..8b4e93cd05b 100755
--- a/egs/chime4/s5_1ch/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/chime4/s5_1ch/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -180,7 +180,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/chime4/s5_1ch/local/chime4_calc_wers_looped.sh b/egs/chime4/s5_1ch/local/chime4_calc_wers_looped.sh
index 9fe4a20f43a..84bb2cb8dbd 100755
--- a/egs/chime4/s5_1ch/local/chime4_calc_wers_looped.sh
+++ b/egs/chime4/s5_1ch/local/chime4_calc_wers_looped.sh
@@ -82,4 +82,4 @@ for e_d in $tasks; do
       | utils/int2sym.pl -f 2- $graph_dir/words.txt \
       | sed s:\<UNK\>::g
   done
-done
\ No newline at end of file
+done
diff --git a/egs/chime4/s5_1ch/local/run_lmrescore_tdnn_lstm.sh b/egs/chime4/s5_1ch/local/run_lmrescore_tdnn_lstm.sh
index 7173dcea78b..0bea4dd7102 100755
--- a/egs/chime4/s5_1ch/local/run_lmrescore_tdnn_lstm.sh
+++ b/egs/chime4/s5_1ch/local/run_lmrescore_tdnn_lstm.sh
@@ -165,4 +165,4 @@ if [ $stage -le 4 ]; then
   local/chime4_calc_wers_looped.sh $dir ${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest} $dir/graph_tgpr_5k \
       > $dir/best_wer_looped_${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest}.result
   head -n 15 $dir/best_wer_looped_${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest}.result
-fi
\ No newline at end of file
+fi
diff --git a/egs/chime5/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/chime5/s5/local/chain/tuning/run_tdnn_1a.sh
index 45a7fd84bd6..5418ecf2b4f 100755
--- a/egs/chime5/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/chime5/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -133,7 +133,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.05"
   output_opts="l2-regularize=0.01 bottleneck-dim=320"
 
diff --git a/egs/commonvoice/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/commonvoice/s5/local/chain/tuning/run_tdnn_1a.sh
index 635e3de1076..d4acd0fed4b 100755
--- a/egs/commonvoice/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/commonvoice/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -141,7 +141,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/csj/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/csj/s5/local/chain/tuning/run_tdnn_1a.sh
index a463db77066..75ceb80e3e0 100755
--- a/egs/csj/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/csj/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -133,7 +133,7 @@ if [ $stage -le 12 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/csj/s5/local/nnet/run_dnn_tandem_uc.sh b/egs/csj/s5/local/nnet/run_dnn_tandem_uc.sh
index 4677ff473cb..297aed1f486 100755
--- a/egs/csj/s5/local/nnet/run_dnn_tandem_uc.sh
+++ b/egs/csj/s5/local/nnet/run_dnn_tandem_uc.sh
@@ -280,4 +280,4 @@ exit 0
 %WER 14.88 [ 2557 / 17189, 556 ins, 359 del, 1642 sub ] exp/tandem2uc-tri4/decode_eval3_csj/wer_20_0.5
 %WER 17.03 [ 2927 / 17189, 592 ins, 417 del, 1918 sub ] exp/tandem2uc-tri4/decode_eval3_csj.si/wer_20_1.0
 %WER 13.44 [ 2311 / 17189, 430 ins, 340 del, 1541 sub ] exp/tandem2uc-tri4_mmi_b0.1/decode_eval3_csj/wer_20_1.0
-EOF
\ No newline at end of file
+EOF
diff --git a/egs/fisher_callhome_spanish/s5/local/chain/run_tdnn_1g.sh b/egs/fisher_callhome_spanish/s5/local/chain/run_tdnn_1g.sh
index c487f1bd222..7f407552c2e 100755
--- a/egs/fisher_callhome_spanish/s5/local/chain/run_tdnn_1g.sh
+++ b/egs/fisher_callhome_spanish/s5/local/chain/run_tdnn_1g.sh
@@ -156,7 +156,7 @@ if [ $stage -le 19 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   tdnn_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
   linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
diff --git a/egs/fisher_english/s5/local/chain/run_tdnn.sh b/egs/fisher_english/s5/local/chain/run_tdnn.sh
index 14174e617c4..1fd0f1fdf3a 100755
--- a/egs/fisher_english/s5/local/chain/run_tdnn.sh
+++ b/egs/fisher_english/s5/local/chain/run_tdnn.sh
@@ -112,7 +112,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_100k_semisupervised_1a.sh b/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_100k_semisupervised_1a.sh
index e95de232304..b76efc4f1de 100644
--- a/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_100k_semisupervised_1a.sh
+++ b/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_100k_semisupervised_1a.sh
@@ -231,7 +231,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_1a.sh b/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_1a.sh
index e76df666e8a..b1c133942ef 100755
--- a/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_1a.sh
+++ b/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_1a.sh
@@ -142,7 +142,7 @@ if [ $stage -le 13 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_50k_semisupervised_1a.sh b/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_50k_semisupervised_1a.sh
index 2d5b2f8480e..53aac8c08ea 100755
--- a/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_50k_semisupervised_1a.sh
+++ b/egs/fisher_english/s5/local/semisup/chain/tuning/run_tdnn_50k_semisupervised_1a.sh
@@ -250,7 +250,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/fisher_swbd/s5/local/chain/run_blstm_6j.sh b/egs/fisher_swbd/s5/local/chain/run_blstm_6j.sh
index cbf0ef6cb6c..c12f604f26b 100755
--- a/egs/fisher_swbd/s5/local/chain/run_blstm_6j.sh
+++ b/egs/fisher_swbd/s5/local/chain/run_blstm_6j.sh
@@ -133,7 +133,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/fisher_swbd/s5/local/chain/run_tdnn_7c.sh b/egs/fisher_swbd/s5/local/chain/run_tdnn_7c.sh
index 12b3187a5fa..efcd1eced4a 100644
--- a/egs/fisher_swbd/s5/local/chain/run_tdnn_7c.sh
+++ b/egs/fisher_swbd/s5/local/chain/run_tdnn_7c.sh
@@ -129,7 +129,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/fisher_swbd/s5/local/chain/run_tdnn_7d.sh b/egs/fisher_swbd/s5/local/chain/run_tdnn_7d.sh
index 7d640c3262a..e4a555abfdd 100644
--- a/egs/fisher_swbd/s5/local/chain/run_tdnn_7d.sh
+++ b/egs/fisher_swbd/s5/local/chain/run_tdnn_7d.sh
@@ -134,7 +134,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.002"
   linear_opts="orthonormal-constraint=1.0"
   output_opts="l2-regularize=0.0005 bottleneck-dim=256"
diff --git a/egs/fisher_swbd/s5/local/chain/run_tdnn_lstm_1a.sh b/egs/fisher_swbd/s5/local/chain/run_tdnn_lstm_1a.sh
index 07e88b59ddc..5650cedca28 100755
--- a/egs/fisher_swbd/s5/local/chain/run_tdnn_lstm_1a.sh
+++ b/egs/fisher_swbd/s5/local/chain/run_tdnn_lstm_1a.sh
@@ -142,7 +142,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20"
 
   mkdir -p $dir/configs
diff --git a/egs/fisher_swbd/s5/local/chain/run_tdnn_lstm_1b.sh b/egs/fisher_swbd/s5/local/chain/run_tdnn_lstm_1b.sh
index c9d50d1f7bd..f3cc869e6de 100755
--- a/egs/fisher_swbd/s5/local/chain/run_tdnn_lstm_1b.sh
+++ b/egs/fisher_swbd/s5/local/chain/run_tdnn_lstm_1b.sh
@@ -151,7 +151,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20 dropout-proportion=0.0"
 
   mkdir -p $dir/configs
diff --git a/egs/fisher_swbd/s5/local/chain/run_tdnn_opgru_1a.sh b/egs/fisher_swbd/s5/local/chain/run_tdnn_opgru_1a.sh
index 1cce08abeee..059a81e15fc 100755
--- a/egs/fisher_swbd/s5/local/chain/run_tdnn_opgru_1a.sh
+++ b/egs/fisher_swbd/s5/local/chain/run_tdnn_opgru_1a.sh
@@ -148,7 +148,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0 "
 
   mkdir -p $dir/configs
diff --git a/egs/fisher_swbd/s5/local/chain/run_tdnn_opgru_1b.sh b/egs/fisher_swbd/s5/local/chain/run_tdnn_opgru_1b.sh
index 2334c6a1bc1..d86b699d6f6 100755
--- a/egs/fisher_swbd/s5/local/chain/run_tdnn_opgru_1b.sh
+++ b/egs/fisher_swbd/s5/local/chain/run_tdnn_opgru_1b.sh
@@ -149,7 +149,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0 "
 
   mkdir -p $dir/configs
diff --git a/egs/formosa/README.txt b/egs/formosa/README.txt
new file mode 100644
index 00000000000..3b9d78dad92
--- /dev/null
+++ b/egs/formosa/README.txt
@@ -0,0 +1,22 @@
+### Welcome to the demo recipe of the Formosa Speech in the Wild (FSW) Project ###
+
+The language habits of Taiwanese people are different from other Mandarin speakers (both accents and cultures) [1]. Especially Tainwaese use tranditional Chinese characters, i.e., 繁體中文). To address this issue, a Taiwanese speech corpus collection project "Formosa Speech in the Wild (FSW)" was initiated in 2017 to improve the development of Taiwanese-specific speech recognition techniques.
+
+FSW corpus will be a large-scale database of real-Life/multi-gene Taiwanese Spontaneous speech collected and transcribed from various sources (radio, TV, open courses, etc.). To demostrate that this database is a reasonable data resource for Taiwanese spontaneous speech recognition research, a baseline recipe is provied here for everybody, especially students, to develop their own systems easily and quickly.
+
+This recipe is based on the "NER-Trs-Vol1" corpus (about 150 hours broadcast radio speech selected from FSW). For more details, please visit: 
+* Formosa Speech in the Wild (FSW) project (https://sites.google.com/speech.ntut.edu.tw/fsw)
+
+If you want to apply the NER-Trs-Vol1 corpus, please contact Yuan-Fu Liao (廖元甫) via "yfliao@mail.ntut.edu.tw". This corpus is only for non-commercial research/education use and will be distributed via our GitLab server in https://speech.nchc.org.tw.
+
+Any bug, errors, comments or suggestions are very welcomed.
+
+Yuan-Fu Liao (廖元甫)
+Associate Professor
+Department of electronic Engineering,
+National Taipei University of Technology
+http://www.ntut.edu.tw/~yfliao
+yfliao@mail.ntut.edu.tw
+
+............
+[1] The languages of Taiwan consist of several varieties of languages under families of the Austronesian languages and the Sino-Tibetan languages. Taiwanese Mandarin, Hokkien, Hakka and Formosan languages are used by 83.5%, 81.9%, 6.6% and 1.4% of the population respectively (2010). Given the prevalent use of Taiwanese Hokkien, the Mandarin spoken in Taiwan has been to a great extent influenced by it.
diff --git a/egs/formosa/s5/RESULTS b/egs/formosa/s5/RESULTS
new file mode 100644
index 00000000000..b047e5cefe4
--- /dev/null
+++ b/egs/formosa/s5/RESULTS
@@ -0,0 +1,43 @@
+#
+# Reference results
+#
+# Experimental settings:
+#
+# training set:	show CS, BG, DA, QG, SR, SY and WK,	in total 18977 utt., 1,088,948 words
+# test set:	show JZ, GJ, KX and YX,			in total  2112 utt.,   135,972 words
+# eval set:     show JX, TD and WJ,                     in total  2222 utt.,   104,648 words
+#
+# lexicon: 274,036 words
+# phones (IPA):  196 (tonal)
+#
+
+# WER: test
+
+%WER 61.32 [ 83373 / 135972, 5458 ins, 19156 del, 58759 sub ] exp/mono/decode_test/wer_11_0.0
+%WER 41.00 [ 55742 / 135972, 6725 ins, 12763 del, 36254 sub ] exp/tri1/decode_test/wer_15_0.0
+%WER 40.41 [ 54948 / 135972, 7366 ins, 11505 del, 36077 sub ] exp/tri2/decode_test/wer_14_0.0
+%WER 38.67 [ 52574 / 135972, 6855 ins, 11250 del, 34469 sub ] exp/tri3a/decode_test/wer_15_0.0
+%WER 35.70 [ 48546 / 135972, 7197 ins,  9717 del, 31632 sub ] exp/tri4a/decode_test/wer_17_0.0
+%WER 32.11 [ 43661 / 135972, 6112 ins, 10185 del, 27364 sub ] exp/tri5a/decode_test/wer_17_0.5
+%WER 31.36 [ 42639 / 135972, 6846 ins, 8860 del, 26933 sub ] exp/tri5a_cleaned/decode_test/wer_17_0.5
+%WER 24.43 [ 33218 / 135972, 5524 ins,  7583 del, 20111 sub ] exp/nnet3/tdnn_sp/decode_test/wer_12_0.0
+%WER 23.95 [ 32568 / 135972, 4457 ins, 10271 del, 17840 sub ] exp/chain/tdnn_1a_sp/decode_test/wer_10_0.0
+%WER 23.54 [ 32006 / 135972, 4717 ins, 8644 del, 18645 sub ] exp/chain/tdnn_1b_sp/decode_test/wer_10_0.0
+%WER 20.64 [ 28067 / 135972, 4434 ins, 7946 del, 15687 sub ] exp/chain/tdnn_1c_sp/decode_test/wer_11_0.0
+%WER 20.98 [ 28527 / 135972, 4706 ins, 7816 del, 16005 sub ] exp/chain/tdnn_1d_sp/decode_test/wer_10_0.0
+
+# CER: test
+
+%WER 54.09 [ 116688 / 215718, 4747 ins, 24510 del, 87431 sub ] exp/mono/decode_test/cer_10_0.0
+%WER 32.61 [  70336 / 215718, 5866 ins, 16282 del, 48188 sub ] exp/tri1/decode_test/cer_13_0.0
+%WER 32.10 [  69238 / 215718, 6186 ins, 15772 del, 47280 sub ] exp/tri2/decode_test/cer_13_0.0
+%WER 30.40 [  65583 / 215718, 6729 ins, 13115 del, 45739 sub ] exp/tri3a/decode_test/cer_12_0.0
+%WER 27.53 [  59389 / 215718, 6311 ins, 13008 del, 40070 sub ] exp/tri4a/decode_test/cer_15_0.0
+%WER 24.21 [  52232 / 215718, 6425 ins, 11543 del, 34264 sub ] exp/tri5a/decode_test/cer_15_0.0
+%WER 23.41 [ 50492 / 215718, 6645 ins, 10997 del, 32850 sub ] exp/tri5a_cleaned/decode_test/cer_17_0.0
+%WER 17.07 [  36829 / 215718, 4734 ins,  9938 del, 22157 sub ] exp/nnet3/tdnn_sp/decode_test/cer_12_0.0
+%WER 16.83 [ 36305 / 215718, 4772 ins, 10810 del, 20723 sub ] exp/chain/tdnn_1a_sp/decode_test/cer_9_0.0
+%WER 16.44 [ 35459 / 215718, 4216 ins, 11278 del, 19965 sub ] exp/chain/tdnn_1b_sp/decode_test/cer_10_0.0
+%WER 13.72 [ 29605 / 215718, 4678 ins, 8066 del, 16861 sub ] exp/chain/tdnn_1c_sp/decode_test/cer_10_0.0
+%WER 14.08 [ 30364 / 215718, 5182 ins, 7588 del, 17594 sub ] exp/chain/tdnn_1d_sp/decode_test/cer_9_0.0
+
diff --git a/egs/formosa/s5/cmd.sh b/egs/formosa/s5/cmd.sh
new file mode 100755
index 00000000000..66ae9090820
--- /dev/null
+++ b/egs/formosa/s5/cmd.sh
@@ -0,0 +1,27 @@
+# "queue.pl" uses qsub.  The options to it are
+# options to qsub.  If you have GridEngine installed,
+# change this to a queue you have access to.
+# Otherwise, use "run.pl", which will run jobs locally
+# (make sure your --num-jobs options are no more than
+# the number of cpus on your machine.
+
+# Run locally:
+#export train_cmd=run.pl
+#export decode_cmd=run.pl
+
+# JHU cluster (or most clusters using GridEngine, with a suitable
+# conf/queue.conf).
+export train_cmd="queue.pl"
+export decode_cmd="queue.pl --mem 4G"
+
+host=$(hostname -f)
+if [ ${host#*.} == "fit.vutbr.cz" ]; then
+  queue_conf=$HOME/queue_conf/default.conf # see example /homes/kazi/iveselyk/queue_conf/default.conf,
+  export train_cmd="queue.pl --config $queue_conf --mem 2G --matylda 0.2"
+  export decode_cmd="queue.pl --config $queue_conf --mem 3G --matylda 0.1"
+  export cuda_cmd="queue.pl --config $queue_conf --gpu 1 --mem 10G --tmp 40G"
+elif [ ${host#*.} == "cm.cluster" ]; then
+  # MARCC bluecrab cluster:
+  export train_cmd="slurm.pl --time 4:00:00 "
+  export decode_cmd="slurm.pl --mem 4G --time 4:00:00 "
+fi
diff --git a/egs/formosa/s5/conf/decode.config b/egs/formosa/s5/conf/decode.config
new file mode 100644
index 00000000000..d91f86183af
--- /dev/null
+++ b/egs/formosa/s5/conf/decode.config
@@ -0,0 +1,5 @@
+beam=11.0 # beam for decoding.  Was 13.0 in the scripts.
+first_beam=8.0 # beam for 1st-pass decoding in SAT.
+
+
+
diff --git a/egs/formosa/s5/conf/mfcc.conf b/egs/formosa/s5/conf/mfcc.conf
new file mode 100644
index 00000000000..a1aa3d6c158
--- /dev/null
+++ b/egs/formosa/s5/conf/mfcc.conf
@@ -0,0 +1,2 @@
+--use-energy=false   # only non-default option.
+--sample-frequency=16000
diff --git a/egs/formosa/s5/conf/mfcc_hires.conf b/egs/formosa/s5/conf/mfcc_hires.conf
new file mode 100644
index 00000000000..ca067e77b37
--- /dev/null
+++ b/egs/formosa/s5/conf/mfcc_hires.conf
@@ -0,0 +1,10 @@
+# config for high-resolution MFCC features, intended for neural network training.
+# Note: we keep all cepstra, so it has the same info as filterbank features,
+# but MFCC is more easily compressible (because less correlated) which is why
+# we prefer this method.
+--use-energy=false   # use average of log energy, not energy.
+--sample-frequency=16000 #  Switchboard is sampled at 8kHz
+--num-mel-bins=40     # similar to Google's setup.
+--num-ceps=40     # there is no dimensionality reduction.
+--low-freq=40    # low cutoff frequency for mel bins
+--high-freq=-200 # high cutoff frequently, relative to Nyquist of 8000 (=3800)
diff --git a/egs/formosa/s5/conf/online_cmvn.conf b/egs/formosa/s5/conf/online_cmvn.conf
new file mode 100644
index 00000000000..591367e7ae9
--- /dev/null
+++ b/egs/formosa/s5/conf/online_cmvn.conf
@@ -0,0 +1 @@
+# configuration file for apply-cmvn-online, used when invoking online2-wav-nnet3-latgen-faster.
diff --git a/egs/formosa/s5/conf/pitch.conf b/egs/formosa/s5/conf/pitch.conf
new file mode 100644
index 00000000000..e959a19d5b8
--- /dev/null
+++ b/egs/formosa/s5/conf/pitch.conf
@@ -0,0 +1 @@
+--sample-frequency=16000
diff --git a/egs/formosa/s5/local/chain/run_tdnn.sh b/egs/formosa/s5/local/chain/run_tdnn.sh
new file mode 120000
index 00000000000..e1adaa9346d
--- /dev/null
+++ b/egs/formosa/s5/local/chain/run_tdnn.sh
@@ -0,0 +1 @@
+tuning/run_tdnn_1d.sh
\ No newline at end of file
diff --git a/egs/formosa/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/formosa/s5/local/chain/tuning/run_tdnn_1a.sh
new file mode 100755
index 00000000000..66c5ad3335f
--- /dev/null
+++ b/egs/formosa/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -0,0 +1,181 @@
+#!/bin/bash
+
+# This script is based on run_tdnn_7h.sh in swbd chain recipe.
+
+set -e
+
+# configs for 'chain'
+affix=1a
+stage=0
+train_stage=-10
+get_egs_stage=-10
+dir=exp/chain/tdnn  # Note: _sp will get added to this
+decode_iter=
+
+# training options
+num_epochs=4
+initial_effective_lrate=0.001
+final_effective_lrate=0.0001
+max_param_change=2.0
+final_layer_normalize_target=0.5
+num_jobs_initial=2
+num_jobs_final=12
+minibatch_size=128
+frames_per_eg=150,110,90
+remove_egs=false
+common_egs_dir=
+xent_regularize=0.1
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+# The iVector-extraction and feature-dumping parts are the same as the standard
+# nnet3 setup, and you can skip them by setting "--stage 8" if you have already
+# run those things.
+
+dir=${dir}${affix:+_$affix}_sp
+train_set=train_sp
+ali_dir=exp/tri5a_sp_ali
+treedir=exp/chain/tri6_7d_tree_sp
+lang=data/lang_chain
+
+
+# if we are using the speed-perturbed data we need to generate
+# alignments for it.
+local/nnet3/run_ivector_common.sh --stage $stage || exit 1;
+
+if [ $stage -le 7 ]; then
+  # Get the alignments as lattices (gives the LF-MMI training more freedom).
+  # use the same num-jobs as the alignments
+  nj=$(cat $ali_dir/num_jobs) || exit 1;
+  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \
+    data/lang exp/tri5a exp/tri5a_sp_lats
+  rm exp/tri5a_sp_lats/fsts.*.gz # save space
+fi
+
+if [ $stage -le 8 ]; then
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  rm -rf $lang
+  cp -r data/lang $lang
+  silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+  # Use our special topology... note that later on may have to tune this
+  # topology.
+  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+fi
+
+if [ $stage -le 9 ]; then
+  # Build a tree using our new topology. This is the critically different
+  # step compared with other recipes.
+  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
+      --context-opts "--context-width=2 --central-position=1" \
+      --cmd "$train_cmd" 5000 data/$train_set $lang $ali_dir $treedir
+fi
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=43 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 dim=625
+  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=625
+  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=625
+  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=625
+  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=625
+  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=625
+
+  ## adding the layers for chain branch
+  relu-batchnorm-layer name=prefinal-chain input=tdnn6 dim=625 target-rms=0.5
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  relu-batchnorm-layer name=prefinal-xent input=tdnn6 dim=625 target-rms=0.5
+  output-layer name=output-xent dim=$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 11 ]; then
+  steps/nnet3/chain/train.py --stage $train_stage \
+    --cmd "$decode_cmd" \
+    --feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.00005 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --egs.dir "$common_egs_dir" \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--frames-overlap-per-eg 0" \
+    --egs.chunk-width $frames_per_eg \
+    --trainer.num-chunk-per-minibatch $minibatch_size \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.num-jobs-initial $num_jobs_initial \
+    --trainer.optimization.num-jobs-final $num_jobs_final \
+    --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
+    --trainer.optimization.final-effective-lrate $final_effective_lrate \
+    --trainer.max-param-change $max_param_change \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir data/${train_set}_hires \
+    --tree-dir $treedir \
+    --lat-dir exp/tri5a_sp_lats \
+    --use-gpu wait \
+    --dir $dir  || exit 1;
+fi
+
+if [ $stage -le 12 ]; then
+  # Note: it might appear that this $lang directory is mismatched, and it is as
+  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
+  # the lang directory.
+  utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test $dir $dir/graph
+fi
+
+graph_dir=$dir/graph
+if [ $stage -le 13 ]; then
+  for test_set in test eval; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj 10 --cmd "$decode_cmd" \
+      --online-ivector-dir exp/nnet3/ivectors_$test_set \
+      $graph_dir data/${test_set}_hires $dir/decode_${test_set} || exit 1;
+  done
+  wait;
+fi
+
+exit 0;
diff --git a/egs/formosa/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/formosa/s5/local/chain/tuning/run_tdnn_1b.sh
new file mode 100755
index 00000000000..1981bb0530d
--- /dev/null
+++ b/egs/formosa/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -0,0 +1,188 @@
+#!/bin/bash
+
+# This script shows improvement arising from data cleaning.
+
+# CER:
+# %WER 16.83 [ 36305 / 215718, 4772 ins, 10810 del, 20723 sub ] exp/chain/tdnn_1a_sp/decode_test/cer_9_0.0
+# %WER 16.44 [ 35459 / 215718, 4216 ins, 11278 del, 19965 sub ] exp/chain/tdnn_1b_sp/decode_test/cer_10_0.0
+
+# steps/info/chain_dir_info.pl exp/chain/tdnn_1b_sp
+# exp/chain/tdnn_1b_sp: num-iters=133 nj=2..12 num-params=12.5M dim=43+100->4528 combine=-0.073->-0.073 (over 2) xent:train/valid[87,132,final]=(-1.05,-0.964,-0.963/-1.10,-1.06,-1.05) logprob:train/valid[87,132,final]=(-0.079,-0.065,-0.065/-0.094,-0.092,-0.092)
+
+set -e
+
+# configs for 'chain'
+affix=1b
+nnet3_affix=_1b
+stage=0
+train_stage=-10
+get_egs_stage=-10
+dir=exp/chain/tdnn  # Note: _sp will get added to this
+decode_iter=
+
+# training options
+num_epochs=4
+initial_effective_lrate=0.001
+final_effective_lrate=0.0001
+max_param_change=2.0
+final_layer_normalize_target=0.5
+num_jobs_initial=2
+num_jobs_final=12
+minibatch_size=128
+frames_per_eg=150,110,90
+remove_egs=false
+common_egs_dir=
+xent_regularize=0.1
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+# The iVector-extraction and feature-dumping parts are the same as the standard
+# nnet3 setup, and you can skip them by setting "--stage 8" if you have already
+# run those things.
+
+dir=${dir}${affix:+_$affix}_sp
+train_set=train_cleaned_sp
+ali_dir=exp/tri5a_cleaned_sp_ali
+treedir=exp/chain/tri6a_cleaned_tree_sp
+lang=data/lang_chain
+
+
+# if we are using the speed-perturbed data we need to generate
+# alignments for it.
+local/nnet3/run_ivector_common.sh --stage $stage --train-set train_cleaned --gmm tri5a_cleaned --nnet3-affix $nnet3_affix
+
+if [ $stage -le 7 ]; then
+  # Get the alignments as lattices (gives the LF-MMI training more freedom).
+  # use the same num-jobs as the alignments
+  nj=$(cat $ali_dir/num_jobs) || exit 1;
+  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \
+    data/lang exp/tri5a exp/tri5a_sp_lats
+  rm exp/tri5a_sp_lats/fsts.*.gz # save space
+fi
+
+if [ $stage -le 8 ]; then
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  rm -rf $lang
+  cp -r data/lang $lang
+  silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+  # Use our special topology... note that later on may have to tune this
+  # topology.
+  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+fi
+
+if [ $stage -le 9 ]; then
+  # Build a tree using our new topology. This is the critically different
+  # step compared with other recipes.
+  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
+      --context-opts "--context-width=2 --central-position=1" \
+      --cmd "$train_cmd" 5000 data/$train_set $lang $ali_dir $treedir
+fi
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=43 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 dim=625
+  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=625
+  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=625
+  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=625
+  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=625
+  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=625
+
+  ## adding the layers for chain branch
+  relu-batchnorm-layer name=prefinal-chain input=tdnn6 dim=625 target-rms=0.5
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  relu-batchnorm-layer name=prefinal-xent input=tdnn6 dim=625 target-rms=0.5
+  output-layer name=output-xent dim=$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 11 ]; then
+  steps/nnet3/chain/train.py --stage $train_stage \
+    --cmd "$decode_cmd" \
+    --feat.online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${train_set} \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.00005 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --egs.dir "$common_egs_dir" \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--frames-overlap-per-eg 0" \
+    --egs.chunk-width $frames_per_eg \
+    --trainer.num-chunk-per-minibatch $minibatch_size \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.num-jobs-initial $num_jobs_initial \
+    --trainer.optimization.num-jobs-final $num_jobs_final \
+    --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
+    --trainer.optimization.final-effective-lrate $final_effective_lrate \
+    --trainer.max-param-change $max_param_change \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir data/${train_set}_hires \
+    --tree-dir $treedir \
+    --lat-dir exp/tri5a_sp_lats \
+    --use-gpu wait \
+    --dir $dir  || exit 1;
+fi
+
+if [ $stage -le 12 ]; then
+  # Note: it might appear that this $lang directory is mismatched, and it is as
+  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
+  # the lang directory.
+  utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test $dir $dir/graph
+fi
+
+graph_dir=$dir/graph
+if [ $stage -le 13 ]; then
+  for test_set in test eval; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj 10 --cmd "$decode_cmd" \
+      --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_$test_set \
+      $graph_dir data/${test_set}_hires $dir/decode_${test_set} || exit 1;
+  done
+  wait;
+fi
+exit 0;
diff --git a/egs/formosa/s5/local/chain/tuning/run_tdnn_1c.sh b/egs/formosa/s5/local/chain/tuning/run_tdnn_1c.sh
new file mode 100755
index 00000000000..6fa10344cfc
--- /dev/null
+++ b/egs/formosa/s5/local/chain/tuning/run_tdnn_1c.sh
@@ -0,0 +1,191 @@
+#!/bin/bash
+
+# CER:
+# %WER 16.44 [ 35459 / 215718, 4216 ins, 11278 del, 19965 sub ] exp/chain/tdnn_1b_sp/decode_test/cer_10_0.0
+# %WER 13.72 [ 29605 / 215718, 4678 ins, 8066 del, 16861 sub ] exp/chain/tdnn_1c_sp/decode_test/cer_10_0.0
+
+# steps/info/chain_dir_info.pl exp/chain/tdnn_1c_sp
+# exp/chain/tdnn_1c_sp: num-iters=147 nj=3..16 num-params=17.9M dim=43+100->4528 combine=-0.041->-0.041 (over 2) xent:train/valid[97,146,final]=(-0.845,-0.625,-0.618/-0.901,-0.710,-0.703) logprob:train/valid[97,146,final]=(-0.064,-0.040,-0.039/-0.072,-0.058,-0.057)
+
+set -e
+
+# configs for 'chain'
+affix=1c
+nnet3_affix=_1b
+stage=0
+train_stage=-10
+get_egs_stage=-10
+dir=exp/chain/tdnn  # Note: _sp will get added to this
+decode_iter=
+
+# training options
+num_epochs=6
+initial_effective_lrate=0.00025
+final_effective_lrate=0.000025
+max_param_change=2.0
+final_layer_normalize_target=0.5
+num_jobs_initial=3
+num_jobs_final=16
+minibatch_size=64
+frames_per_eg=150,110,90
+remove_egs=false
+common_egs_dir=
+xent_regularize=0.1
+dropout_schedule='0,0@0.20,0.5@0.50,0'
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+# The iVector-extraction and feature-dumping parts are the same as the standard
+# nnet3 setup, and you can skip them by setting "--stage 8" if you have already
+# run those things.
+
+dir=${dir}${affix:+_$affix}_sp
+train_set=train_cleaned_sp
+ali_dir=exp/tri5a_cleaned_sp_ali
+treedir=exp/chain/tri6a_cleaned_tree_sp
+lang=data/lang_chain
+
+
+# if we are using the speed-perturbed data we need to generate
+# alignments for it.
+local/nnet3/run_ivector_common.sh --stage $stage --train-set train_cleaned --gmm tri5a_cleaned --nnet3-affix $nnet3_affix
+
+if [ $stage -le 7 ]; then
+  # Get the alignments as lattices (gives the LF-MMI training more freedom).
+  # use the same num-jobs as the alignments
+  nj=$(cat $ali_dir/num_jobs) || exit 1;
+  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \
+    data/lang exp/tri5a exp/tri5a_sp_lats
+  rm exp/tri5a_sp_lats/fsts.*.gz # save space
+fi
+
+if [ $stage -le 8 ]; then
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  rm -rf $lang
+  cp -r data/lang $lang
+  silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+  # Use our special topology... note that later on may have to tune this
+  # topology.
+  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+fi
+
+if [ $stage -le 9 ]; then
+  # Build a tree using our new topology. This is the critically different
+  # step compared with other recipes.
+  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
+      --context-opts "--context-width=2 --central-position=1" \
+      --cmd "$train_cmd" 5000 data/$train_set $lang $ali_dir $treedir
+fi
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
+  affine_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
+  tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
+  linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
+  prefinal_opts="l2-regularize=0.01"
+  output_opts="l2-regularize=0.002"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=43 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-dropout-layer name=tdnn1 $affine_opts dim=1536
+  tdnnf-layer name=tdnnf2 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
+  tdnnf-layer name=tdnnf3 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
+  tdnnf-layer name=tdnnf4 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
+  tdnnf-layer name=tdnnf5 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=0
+  tdnnf-layer name=tdnnf6 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf7 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf9 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf10 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf11 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf12 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf13 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf14 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf15 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  linear-component name=prefinal-l dim=256 $linear_opts
+  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
+  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
+  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
+  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 11 ]; then
+  steps/nnet3/chain/train.py --stage $train_stage \
+    --cmd "$decode_cmd" \
+    --feat.online-ivector-dir exp/nnet3$nnet3_affix/ivectors_${train_set} \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.0 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.add-option="--optimization.memory-compression-level=2" \
+    --egs.dir "$common_egs_dir" \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--frames-overlap-per-eg 0 --constrained false" \
+    --egs.chunk-width $frames_per_eg \
+    --trainer.num-chunk-per-minibatch $minibatch_size \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.num-jobs-initial $num_jobs_initial \
+    --trainer.optimization.num-jobs-final $num_jobs_final \
+    --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
+    --trainer.optimization.final-effective-lrate $final_effective_lrate \
+    --trainer.max-param-change $max_param_change \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir data/${train_set}_hires \
+    --tree-dir $treedir \
+    --lat-dir exp/tri5a_sp_lats \
+    --use-gpu wait \
+    --dir $dir  || exit 1;
+fi
+
+if [ $stage -le 12 ]; then
+  # Note: it might appear that this $lang directory is mismatched, and it is as
+  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
+  # the lang directory.
+  utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test $dir $dir/graph
+fi
+
+graph_dir=$dir/graph
+if [ $stage -le 13 ]; then
+  for test_set in test eval; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj 10 --cmd "$decode_cmd" \
+      --online-ivector-dir exp/nnet3${nnet3_affix:+_$nnet3_affix}/ivectors_$test_set \
+      $graph_dir data/${test_set}_hires $dir/decode_${test_set} || exit 1;
+  done
+  wait;
+fi
+
+exit 0;
diff --git a/egs/formosa/s5/local/chain/tuning/run_tdnn_1d.sh b/egs/formosa/s5/local/chain/tuning/run_tdnn_1d.sh
new file mode 100755
index 00000000000..1f4b7e12850
--- /dev/null
+++ b/egs/formosa/s5/local/chain/tuning/run_tdnn_1d.sh
@@ -0,0 +1,190 @@
+#!/bin/bash
+
+# CER:
+# 1a: %WER 16.83 [ 36305 / 215718, 4772 ins, 10810 del, 20723 sub ] exp/chain/tdnn_1a_sp/decode_test/cer_9_0.0
+# 1d: %WER 14.08 [ 30364 / 215718, 5182 ins, 7588 del, 17594 sub ] exp/chain/tdnn_1d_sp/decode_test/cer_9_0.0
+
+# steps/info/chain_dir_info.pl exp/chain/tdnn_1d_sp
+# exp/chain/tdnn_1d_sp: num-iters=157 nj=3..16 num-params=18.6M dim=43+100->5792 combine=-0.050->-0.050 (over 1) xent:train/valid[103,156,final]=(-0.977,-0.735,-0.725/-0.953,-0.772,-0.768) logprob:train/valid[103,156,final]=(-0.077,-0.052,-0.052/-0.079,-0.065,-0.066)
+
+set -e
+
+# configs for 'chain'
+affix=1d
+stage=0
+train_stage=-10
+get_egs_stage=-10
+dir=exp/chain/tdnn  # Note: _sp will get added to this
+decode_iter=
+
+# training options
+num_epochs=6
+initial_effective_lrate=0.00025
+final_effective_lrate=0.000025
+max_param_change=2.0
+final_layer_normalize_target=0.5
+num_jobs_initial=3
+num_jobs_final=16
+minibatch_size=64
+frames_per_eg=150,110,90
+remove_egs=false
+common_egs_dir=
+xent_regularize=0.1
+dropout_schedule='0,0@0.20,0.5@0.50,0'
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+# The iVector-extraction and feature-dumping parts are the same as the standard
+# nnet3 setup, and you can skip them by setting "--stage 8" if you have already
+# run those things.
+
+dir=${dir}${affix:+_$affix}_sp
+train_set=train_sp
+ali_dir=exp/tri5a_sp_ali
+treedir=exp/chain/tri6a_tree_sp
+lang=data/lang_chain
+
+
+# if we are using the speed-perturbed data we need to generate
+# alignments for it.
+local/nnet3/run_ivector_common.sh --stage $stage --train-set train --gmm tri5a ${nnet3_affix:+ --nnet3-affix $nnet3_affix}
+
+if [ $stage -le 7 ]; then
+  # Get the alignments as lattices (gives the LF-MMI training more freedom).
+  # use the same num-jobs as the alignments
+  nj=$(cat $ali_dir/num_jobs) || exit 1;
+  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/$train_set \
+    data/lang exp/tri5a exp/tri5a_sp_lats
+  rm exp/tri5a_sp_lats/fsts.*.gz # save space
+fi
+
+if [ $stage -le 8 ]; then
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  rm -rf $lang
+  cp -r data/lang $lang
+  silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+  # Use our special topology... note that later on may have to tune this
+  # topology.
+  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+fi
+
+if [ $stage -le 9 ]; then
+  # Build a tree using our new topology. This is the critically different
+  # step compared with other recipes.
+  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
+      --context-opts "--context-width=2 --central-position=1" \
+      --cmd "$train_cmd" 7000 data/$train_set $lang $ali_dir $treedir
+fi
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
+  affine_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
+  tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
+  linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
+  prefinal_opts="l2-regularize=0.01"
+  output_opts="l2-regularize=0.002"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=43 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-dropout-layer name=tdnn1 $affine_opts dim=1536
+  tdnnf-layer name=tdnnf2 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
+  tdnnf-layer name=tdnnf3 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
+  tdnnf-layer name=tdnnf4 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=1
+  tdnnf-layer name=tdnnf5 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=0
+  tdnnf-layer name=tdnnf6 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf7 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf9 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf10 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf11 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf12 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf13 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf14 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  tdnnf-layer name=tdnnf15 $tdnnf_opts dim=1536 bottleneck-dim=160 time-stride=3
+  linear-component name=prefinal-l dim=256 $linear_opts
+  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
+  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
+  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=1536 small-dim=256
+  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 11 ]; then
+  steps/nnet3/chain/train.py --stage $train_stage \
+    --cmd "$decode_cmd" \
+    --feat.online-ivector-dir exp/nnet3$nnet3_affix/ivectors_${train_set} \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.0 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.add-option="--optimization.memory-compression-level=2" \
+    --egs.dir "$common_egs_dir" \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--frames-overlap-per-eg 0 --constrained false" \
+    --egs.chunk-width $frames_per_eg \
+    --trainer.num-chunk-per-minibatch $minibatch_size \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.num-jobs-initial $num_jobs_initial \
+    --trainer.optimization.num-jobs-final $num_jobs_final \
+    --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
+    --trainer.optimization.final-effective-lrate $final_effective_lrate \
+    --trainer.max-param-change $max_param_change \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir data/${train_set}_hires \
+    --tree-dir $treedir \
+    --lat-dir exp/tri5a_sp_lats \
+    --use-gpu wait \
+    --dir $dir  || exit 1;
+fi
+
+if [ $stage -le 12 ]; then
+  # Note: it might appear that this $lang directory is mismatched, and it is as
+  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
+  # the lang directory.
+  utils/mkgraph.sh --self-loop-scale 1.0 data/lang_test $dir $dir/graph
+fi
+
+graph_dir=$dir/graph
+if [ $stage -le 13 ]; then
+  for test_set in test eval; do
+    steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+      --nj 10 --cmd "$decode_cmd" \
+      --online-ivector-dir exp/nnet3${nnet3_affix:+_$nnet3_affix}/ivectors_$test_set \
+      $graph_dir data/${test_set}_hires $dir/decode_${test_set} || exit 1;
+  done
+  wait;
+fi
+
+exit 0;
diff --git a/egs/formosa/s5/local/nnet3/run_ivector_common.sh b/egs/formosa/s5/local/nnet3/run_ivector_common.sh
new file mode 100755
index 00000000000..723589ddd2e
--- /dev/null
+++ b/egs/formosa/s5/local/nnet3/run_ivector_common.sh
@@ -0,0 +1,145 @@
+#!/bin/bash
+
+set -euo pipefail
+
+# This script is modified based on mini_librispeech/s5/local/nnet3/run_ivector_common.sh
+
+# This script is called from local/nnet3/run_tdnn.sh and
+# local/chain/run_tdnn.sh (and may eventually be called by more
+# scripts).  It contains the common feature preparation and
+# iVector-related parts of the script.  See those scripts for examples
+# of usage.
+
+stage=0
+train_set=train
+test_sets="test eval"
+gmm=tri5a
+
+nnet3_affix=
+
+. ./cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+gmm_dir=exp/${gmm}
+ali_dir=exp/${gmm}_sp_ali
+
+for f in data/${train_set}/feats.scp ${gmm_dir}/final.mdl; do
+  if [ ! -f $f ]; then
+    echo "$0: expected file $f to exist"
+    exit 1
+  fi
+done
+
+if [ $stage -le 1 ]; then
+  # Although the nnet will be trained by high resolution data, we still have to
+  # perturb the normal data to get the alignment _sp stands for speed-perturbed
+  echo "$0: preparing directory for low-resolution speed-perturbed data (for alignment)"
+  utils/data/perturb_data_dir_speed_3way.sh data/${train_set} data/${train_set}_sp
+  echo "$0: making MFCC features for low-resolution speed-perturbed data"
+  steps/make_mfcc_pitch.sh --cmd "$train_cmd" --nj 70 data/${train_set}_sp \
+    exp/make_mfcc/${train_set}_sp mfcc_perturbed || exit 1;
+  steps/compute_cmvn_stats.sh data/${train_set}_sp \
+    exp/make_mfcc/${train_set}_sp mfcc_perturbed || exit 1;
+  utils/fix_data_dir.sh data/${train_set}_sp
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0: aligning with the perturbed low-resolution data"
+  steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
+    data/${train_set}_sp data/lang $gmm_dir $ali_dir || exit 1
+fi
+
+if [ $stage -le 3 ]; then
+  # Create high-resolution MFCC features (with 40 cepstra instead of 13).
+  # this shows how you can split across multiple file-systems.
+  echo "$0: creating high-resolution MFCC features"
+  mfccdir=mfcc_perturbed_hires
+
+  for datadir in ${train_set}_sp ${test_sets}; do
+    utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
+  done
+
+  # do volume-perturbation on the training data prior to extracting hires
+  # features; this helps make trained nnets more invariant to test data volume.
+  utils/data/perturb_data_dir_volume.sh data/${train_set}_sp_hires || exit 1;
+
+  for datadir in ${train_set}_sp ${test_sets}; do
+    steps/make_mfcc_pitch.sh --nj 10 --mfcc-config conf/mfcc_hires.conf \
+      --cmd "$train_cmd" data/${datadir}_hires exp/make_hires/$datadir $mfccdir || exit 1;
+    steps/compute_cmvn_stats.sh data/${datadir}_hires exp/make_hires/$datadir $mfccdir || exit 1;
+    utils/fix_data_dir.sh data/${datadir}_hires || exit 1;
+    # create MFCC data dir without pitch to extract iVector
+    utils/data/limit_feature_dim.sh 0:39 data/${datadir}_hires data/${datadir}_hires_nopitch || exit 1;
+    steps/compute_cmvn_stats.sh data/${datadir}_hires_nopitch exp/make_hires/$datadir $mfccdir || exit 1;
+  done
+fi
+
+if [ $stage -le 4 ]; then
+  echo "$0: computing a subset of data to train the diagonal UBM."
+  # We'll use about a quarter of the data.
+  mkdir -p exp/nnet3${nnet3_affix}/diag_ubm
+  temp_data_root=exp/nnet3${nnet3_affix}/diag_ubm
+
+  num_utts_total=$(wc -l <data/${train_set}_sp_hires_nopitch/utt2spk)
+  num_utts=$[$num_utts_total/4]
+  utils/data/subset_data_dir.sh data/${train_set}_sp_hires_nopitch \
+     $num_utts ${temp_data_root}/${train_set}_sp_hires_nopitch_subset
+
+  echo "$0: computing a PCA transform from the hires data."
+  steps/online/nnet2/get_pca_transform.sh --cmd "$train_cmd" \
+      --splice-opts "--left-context=3 --right-context=3" \
+      --max-utts 10000 --subsample 2 \
+       ${temp_data_root}/${train_set}_sp_hires_nopitch_subset \
+       exp/nnet3${nnet3_affix}/pca_transform
+
+  echo "$0: training the diagonal UBM."
+  # Use 512 Gaussians in the UBM.
+  steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 \
+    --num-frames 700000 \
+    ${temp_data_root}/${train_set}_sp_hires_nopitch_subset 512 \
+    exp/nnet3${nnet3_affix}/pca_transform exp/nnet3${nnet3_affix}/diag_ubm
+fi
+
+if [ $stage -le 5 ]; then
+  # Train the iVector extractor.  Use all of the speed-perturbed data since iVector extractors
+  # can be sensitive to the amount of data.  The script defaults to an iVector dimension of
+  # 100.
+  echo "$0: training the iVector extractor"
+  steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
+     data/${train_set}_sp_hires_nopitch exp/nnet3${nnet3_affix}/diag_ubm \
+     exp/nnet3${nnet3_affix}/extractor || exit 1;
+fi
+
+if [ $stage -le 6 ]; then
+  # We extract iVectors on the speed-perturbed training data after combining
+  # short segments, which will be what we train the system on.  With
+  # --utts-per-spk-max 2, the script pairs the utterances into twos, and treats
+  # each of these pairs as one speaker; this gives more diversity in iVectors..
+  # Note that these are extracted 'online'.
+
+  # note, we don't encode the 'max2' in the name of the ivectordir even though
+  # that's the data we extract the ivectors from, as it's still going to be
+  # valid for the non-'max2' data, the utterance list is the same.
+
+  ivectordir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp
+
+  # having a larger number of speakers is helpful for generalization, and to
+  # handle per-utterance decoding well (iVector starts at zero).
+  temp_data_root=${ivectordir}
+  utils/data/modify_speaker_info.sh --utts-per-spk-max 2 \
+    data/${train_set}_sp_hires_nopitch ${temp_data_root}/${train_set}_sp_hires_nopitch_max2
+  steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 30 \
+    ${temp_data_root}/${train_set}_sp_hires_nopitch_max2 \
+    exp/nnet3${nnet3_affix}/extractor $ivectordir
+
+  # Also extract iVectors for the test data, but in this case we don't need the speed
+  # perturbation (sp).
+  for data in $test_sets; do
+    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 8 \
+      data/${data}_hires_nopitch exp/nnet3${nnet3_affix}/extractor \
+      exp/nnet3${nnet3_affix}/ivectors_${data}
+  done
+fi
+
+exit 0
diff --git a/egs/formosa/s5/local/nnet3/run_tdnn.sh b/egs/formosa/s5/local/nnet3/run_tdnn.sh
new file mode 100755
index 00000000000..a41d990a9b2
--- /dev/null
+++ b/egs/formosa/s5/local/nnet3/run_tdnn.sh
@@ -0,0 +1,113 @@
+#!/bin/bash
+
+# This script is based on swbd/s5c/local/nnet3/run_tdnn.sh
+
+# this is the standard "tdnn" system, built in nnet3; it's what we use to
+# call multi-splice.
+
+# At this script level we don't support not running on GPU, as it would be painfully slow.
+# If you want to run without GPU you'd have to call train_tdnn.sh with --gpu false,
+# --num-threads 16 and --minibatch-size 128.
+set -e
+
+stage=0
+train_stage=-10
+affix=
+common_egs_dir=
+
+# training options
+initial_effective_lrate=0.0015
+final_effective_lrate=0.00015
+num_epochs=4
+num_jobs_initial=2
+num_jobs_final=8
+remove_egs=false
+
+# feature options
+use_ivectors=true
+
+# End configuration section.
+
+. ./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
+
+dir=exp/nnet3/tdnn_sp${affix:+_$affix}
+gmm_dir=exp/tri5a
+train_set=train_sp
+ali_dir=${gmm_dir}_sp_ali
+graph_dir=$gmm_dir/graph
+
+local/nnet3/run_ivector_common.sh --stage $stage || exit 1;
+
+if [ $stage -le 7 ]; then
+  echo "$0: creating neural net configs";
+
+  num_targets=$(tree-info $ali_dir/tree |grep num-pdfs|awk '{print $2}')
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=43 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 dim=850
+  relu-batchnorm-layer name=tdnn2 dim=850 input=Append(-1,0,2)
+  relu-batchnorm-layer name=tdnn3 dim=850 input=Append(-3,0,3)
+  relu-batchnorm-layer name=tdnn4 dim=850 input=Append(-7,0,2)
+  relu-batchnorm-layer name=tdnn5 dim=850 input=Append(-3,0,3)
+  relu-batchnorm-layer name=tdnn6 dim=850
+  output-layer name=output input=tdnn6 dim=$num_targets max-change=1.5
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 8 ]; then
+  steps/nnet3/train_dnn.py --stage=$train_stage \
+    --cmd="$decode_cmd" \
+    --feat.online-ivector-dir exp/nnet3/ivectors_${train_set} \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.num-jobs-initial $num_jobs_initial \
+    --trainer.optimization.num-jobs-final $num_jobs_final \
+    --trainer.optimization.initial-effective-lrate $initial_effective_lrate \
+    --trainer.optimization.final-effective-lrate $final_effective_lrate \
+    --egs.dir "$common_egs_dir" \
+    --cleanup.remove-egs $remove_egs \
+    --cleanup.preserve-model-interval 500 \
+    --use-gpu wait \
+    --feat-dir=data/${train_set}_hires \
+    --ali-dir $ali_dir \
+    --lang data/lang \
+    --reporting.email="$reporting_email" \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 9 ]; then
+  # this version of the decoding treats each utterance separately
+  # without carrying forward speaker information.
+
+  for decode_set in test eval; do
+    num_jobs=`cat data/${decode_set}_hires/utt2spk|cut -d' ' -f2|sort -u|wc -l`
+    decode_dir=${dir}/decode_$decode_set
+    steps/nnet3/decode.sh --nj $num_jobs --cmd "$decode_cmd" \
+       --online-ivector-dir exp/nnet3/ivectors_${decode_set} \
+       $graph_dir data/${decode_set}_hires $decode_dir || exit 1;
+  done
+  wait;
+fi
+
+exit 0;
diff --git a/egs/formosa/s5/local/prepare_data.sh b/egs/formosa/s5/local/prepare_data.sh
new file mode 100755
index 00000000000..68f342e1549
--- /dev/null
+++ b/egs/formosa/s5/local/prepare_data.sh
@@ -0,0 +1,60 @@
+#!/bin/bash
+# Copyright 2015-2016  Sarah Flora Juan
+# Copyright 2016  Johns Hopkins University (Author: Yenda Trmal)
+# Copyright 2018  Yuan-Fu Liao, National Taipei University of Technology
+#                 AsusTek Computer Inc. (Author: Alex Hung)
+
+# Apache 2.0
+
+set -e -o pipefail
+
+train_dir=NER-Trs-Vol1/Train
+eval_dir=NER-Trs-Vol1-Eval
+eval_key_dir=NER-Trs-Vol1-Eval-Key
+
+. ./path.sh
+. parse_options.sh
+
+for x in $train_dir $eval_dir; do
+  if [ ! -d "$x" ] ; then
+    echo >&2 "The directory $x does not exist"
+  fi
+done
+
+if [ -z "$(command -v dos2unix 2>/dev/null)" ]; then
+    echo "dos2unix not found on PATH. Please install it manually."
+    exit 1;
+fi
+
+# have to remvoe previous files to avoid filtering speakers according to cmvn.scp and feats.scp
+rm -rf   data/all data/train data/test data/eval data/local/train
+mkdir -p data/all data/train data/test data/eval data/local/train
+
+
+# make utt2spk, wav.scp and text
+find $train_dir -name *.wav -exec sh -c 'x={}; y=$(basename -s .wav $x); printf "%s %s\n"     $y $y' \; | dos2unix > data/all/utt2spk
+find $train_dir -name *.wav -exec sh -c 'x={}; y=$(basename -s .wav $x); printf "%s %s\n"     $y $x' \; | dos2unix > data/all/wav.scp
+find $train_dir -name *.txt -exec sh -c 'x={}; y=$(basename -s .txt $x); printf "%s " $y; cat $x'    \; | dos2unix > data/all/text
+
+# fix_data_dir.sh fixes common mistakes (unsorted entries in wav.scp,
+# duplicate entries and so on). Also, it regenerates the spk2utt from
+# utt2spk
+utils/fix_data_dir.sh data/all
+
+echo "Preparing train and test data"
+# test set: JZ, GJ, KX, YX
+grep -E "(JZ|GJ|KX|YX)_" data/all/utt2spk | awk '{print $1}' > data/all/cv.spk
+utils/subset_data_dir_tr_cv.sh --cv-spk-list data/all/cv.spk data/all data/train data/test
+
+# for LM training
+echo "cp data/train/text data/local/train/text for language model training"
+cat data/train/text | awk '{$1=""}1;' | awk '{$1=$1}1;' > data/local/train/text
+
+# preparing EVAL set.
+find $eval_dir     -name *.wav -exec sh -c 'x={}; y=$(basename -s .wav $x); printf "%s %s\n"     $y $y' \; | dos2unix > data/eval/utt2spk
+find $eval_dir     -name *.wav -exec sh -c 'x={}; y=$(basename -s .wav $x); printf "%s %s\n"     $y $x' \; | dos2unix > data/eval/wav.scp
+find $eval_key_dir -name *.txt -exec sh -c 'x={}; y=$(basename -s .txt $x); printf "%s " $y; cat $x'    \; | dos2unix > data/eval/text
+utils/fix_data_dir.sh data/eval
+
+echo "Data preparation completed."
+exit 0;
diff --git a/egs/formosa/s5/local/prepare_dict.sh b/egs/formosa/s5/local/prepare_dict.sh
new file mode 100755
index 00000000000..4e580f5f6e8
--- /dev/null
+++ b/egs/formosa/s5/local/prepare_dict.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+# Copyright 2015-2016  Sarah Flora Juan
+# Copyright 2016  Johns Hopkins University (Author: Yenda Trmal)
+# Copyright 2018  Yuan-Fu Liao, National Taipei University of Technology
+# Apache 2.0
+
+source_dir=NER-Trs-Vol1/Language
+dict_dir=data/local/dict
+rm -rf $dict_dir
+mkdir -p $dict_dir
+
+#
+#
+#
+rm -f $dict_dir/lexicon.txt
+touch $dict_dir/lexicon.txt
+cat $source_dir/lexicon.txt > $dict_dir/lexicon.txt
+echo "<SIL> SIL"	>> $dict_dir/lexicon.txt
+
+#
+# define silence phone
+#
+rm -f $dict_dir/silence_phones.txt
+touch $dict_dir/silence_phones.txt
+
+echo "SIL"	> $dict_dir/silence_phones.txt
+
+#
+# find nonsilence phones
+#
+rm -f $dict_dir/nonsilence_phones.txt
+touch $dict_dir/nonsilence_phones.txt
+
+cat $source_dir/lexicon.txt | grep -v -F -f $dict_dir/silence_phones.txt | \
+    perl -ane 'print join("\n", @F[1..$#F]) . "\n"; '  | \
+    sort -u > $dict_dir/nonsilence_phones.txt
+
+#
+# add optional silence phones
+#
+
+rm -f $dict_dir/optional_silence.txt
+touch $dict_dir/optional_silence.txt
+echo "SIL"	> $dict_dir/optional_silence.txt
+
+#
+# extra questions
+#
+rm -f $dict_dir/extra_questions.txt
+touch $dict_dir/extra_questions.txt
+cat $dict_dir/silence_phones.txt    | awk '{printf("%s ", $1);} END{printf "\n";}'  > $dict_dir/extra_questions.txt || exit 1;
+cat $dict_dir/nonsilence_phones.txt | awk '{printf("%s ", $1);} END{printf "\n";}' >> $dict_dir/extra_questions.txt || exit 1;
+
+echo "Dictionary preparation succeeded"
+exit 0;
diff --git a/egs/formosa/s5/local/prepare_lm.sh b/egs/formosa/s5/local/prepare_lm.sh
new file mode 100755
index 00000000000..59fe1529658
--- /dev/null
+++ b/egs/formosa/s5/local/prepare_lm.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+# Copyright 2015-2016  Sarah Flora Juan
+# Copyright 2016  Johns Hopkins University (Author: Yenda Trmal)
+# Apache 2.0
+
+set -e -o pipefail
+
+# To create G.fst from ARPA language model
+. ./path.sh || die "path.sh expected";
+
+local/train_lms_srilm.sh --train-text data/train/text data/ data/srilm
+
+#nl -nrz -w10  corpus/LM/iban-bp-2012.txt | utils/shuffle_list.pl > data/local/external_text
+local/train_lms_srilm.sh --train-text data/local/external_text data/ data/srilm_external
+
+# let's do ngram interpolation of the previous two LMs
+# the lm.gz is always symlink to the model with the best perplexity, so we use that
+
+mkdir -p data/srilm_interp
+for w in 0.9 0.8 0.7 0.6 0.5; do
+    ngram -lm data/srilm/lm.gz  -mix-lm data/srilm_external/lm.gz \
+          -lambda $w -write-lm data/srilm_interp/lm.${w}.gz
+    echo -n "data/srilm_interp/lm.${w}.gz "
+    ngram -lm data/srilm_interp/lm.${w}.gz -ppl data/srilm/dev.txt | paste -s -
+done | sort  -k15,15g  > data/srilm_interp/perplexities.txt
+
+# for basic decoding, let's use only a trigram LM
+[ -d data/lang_test/ ] && rm -rf data/lang_test
+cp -R data/lang data/lang_test
+lm=$(cat data/srilm/perplexities.txt | grep 3gram | head -n1 | awk '{print $1}')
+local/arpa2G.sh $lm data/lang_test data/lang_test
+
+# for decoding using bigger LM let's find which interpolated gave the most improvement
+[ -d data/lang_big ] && rm -rf data/lang_big
+cp -R data/lang data/lang_big
+lm=$(cat data/srilm_interp/perplexities.txt | head -n1 | awk '{print $1}')
+local/arpa2G.sh $lm data/lang_big data/lang_big
+
+# for really big lm, we should only decode using small LM
+# and resocre using the big lm
+utils/build_const_arpa_lm.sh $lm data/lang_big data/lang_big
+exit 0;
diff --git a/egs/formosa/s5/local/run_cleanup_segmentation.sh b/egs/formosa/s5/local/run_cleanup_segmentation.sh
new file mode 100755
index 00000000000..b72cd89b4d1
--- /dev/null
+++ b/egs/formosa/s5/local/run_cleanup_segmentation.sh
@@ -0,0 +1,66 @@
+#!/bin/bash
+
+# Copyright   2016  Vimal Manohar
+#             2016  Johns Hopkins University (author: Daniel Povey)
+#             2017  Nagendra Kumar Goel
+#             2019  AsusTek Computer Inc. (author: Alex Hung)
+# Apache 2.0
+
+# This script demonstrates how to re-segment training data selecting only the
+# "good" audio that matches the transcripts.
+# The basic idea is to decode with an existing in-domain acoustic model, and a
+# biased language model built from the reference, and then work out the
+# segmentation from a ctm like file.
+
+# For nnet3 and chain results after cleanup, see the scripts in
+# local/nnet3/run_tdnn.sh and local/chain/run_tdnn.sh
+
+# GMM Results for speaker-independent (SI) and speaker adaptive training (SAT) systems on dev and test sets
+# [will add these later].
+
+set -e
+set -o pipefail
+set -u
+
+stage=0
+cleanup_stage=0
+data=data/train
+cleanup_affix=cleaned
+srcdir=exp/tri5a
+langdir=data/lang_test
+nj=20
+decode_nj=20
+decode_num_threads=1
+
+. ./cmd.sh
+if [ -f ./path.sh ]; then . ./path.sh; fi
+. utils/parse_options.sh
+
+cleaned_data=${data}_${cleanup_affix}
+
+dir=${srcdir}_${cleanup_affix}_work
+cleaned_dir=${srcdir}_${cleanup_affix}
+
+if [ $stage -le 1 ]; then
+  # This does the actual data cleanup.
+  steps/cleanup/clean_and_segment_data.sh --stage $cleanup_stage \
+    --nj $nj --cmd "$train_cmd" \
+    $data $langdir $srcdir $dir $cleaned_data
+fi
+
+if [ $stage -le 2 ]; then
+  steps/align_fmllr.sh --nj $nj --cmd "$train_cmd" \
+    $cleaned_data $langdir $srcdir ${srcdir}_ali_${cleanup_affix}
+fi
+
+if [ $stage -le 3 ]; then
+  steps/train_sat.sh --cmd "$train_cmd" \
+    3500 100000 $cleaned_data $langdir ${srcdir}_ali_${cleanup_affix} ${cleaned_dir}
+fi
+
+utils/data/get_utt2dur.sh data/train_cleaned
+ori_avg_dur=$(awk 'BEGIN{total=0}{total += $2}END{printf("%.2f", total/NR)}' ${data}/utt2dur)
+new_avg_dur=$(awk 'BEGIN{total=0}{total += $2}END{printf("%.2f", total/NR)}' ${cleaned_data}/utt2dur)
+echo "average duration was reduced from ${ori_avg_dur}s to ${new_avg_dur}s."
+# average duration was reduced from 21.68s to 10.97s.
+exit 0;
diff --git a/egs/formosa/s5/local/score.sh b/egs/formosa/s5/local/score.sh
new file mode 100755
index 00000000000..a9786169973
--- /dev/null
+++ b/egs/formosa/s5/local/score.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+set -e -o pipefail
+set -x
+steps/score_kaldi.sh "$@"
+steps/scoring/score_kaldi_cer.sh --stage 2 "$@"
+
+echo "$0: Done"
diff --git a/egs/formosa/s5/local/train_lms.sh b/egs/formosa/s5/local/train_lms.sh
new file mode 100755
index 00000000000..efc5b92c573
--- /dev/null
+++ b/egs/formosa/s5/local/train_lms.sh
@@ -0,0 +1,63 @@
+#!/bin/bash
+
+
+# To be run from one directory above this script.
+. ./path.sh
+
+text=data/local/train/text
+lexicon=data/local/dict/lexicon.txt
+
+for f in "$text" "$lexicon"; do
+  [ ! -f $x ] && echo "$0: No such file $f" && exit 1;
+done
+
+# This script takes no arguments.  It assumes you have already run
+# aishell_data_prep.sh.
+# It takes as input the files
+# data/local/train/text
+# data/local/dict/lexicon.txt
+dir=data/local/lm
+mkdir -p $dir
+
+kaldi_lm=`which train_lm.sh`
+if [ -z $kaldi_lm ]; then
+  echo "$0: train_lm.sh is not found. That might mean it's not installed"
+  echo "$0: or it is not added to PATH"
+  echo "$0: Use the script tools/extra/install_kaldi_lm.sh to install it"
+  exit 1
+fi
+
+cleantext=$dir/text.no_oov
+
+cat $text | awk -v lex=$lexicon 'BEGIN{while((getline<lex) >0){ seen[$1]=1; } }
+  {for(n=1; n<=NF;n++) {  if (seen[$n]) { printf("%s ", $n); } else {printf("<SIL> ");} } printf("\n");}' \
+  > $cleantext || exit 1;
+
+cat $cleantext | awk '{for(n=2;n<=NF;n++) print $n; }' | sort | uniq -c | \
+   sort -nr > $dir/word.counts || exit 1;
+
+# Get counts from acoustic training transcripts, and add  one-count
+# for each word in the lexicon (but not silence, we don't want it
+# in the LM-- we'll add it optionally later).
+cat $cleantext | awk '{for(n=2;n<=NF;n++) print $n; }' | \
+  cat - <(grep -w -v '!SIL' $lexicon | awk '{print $1}') | \
+   sort | uniq -c | sort -nr > $dir/unigram.counts || exit 1;
+
+# note: we probably won't really make use of <SIL> as there aren't any OOVs
+cat $dir/unigram.counts  | awk '{print $2}' | get_word_map.pl "<s>" "</s>" "<SIL>" > $dir/word_map \
+   || exit 1;
+
+# note: ignore 1st field of train.txt, it's the utterance-id.
+cat $cleantext | awk -v wmap=$dir/word_map 'BEGIN{while((getline<wmap)>0)map[$1]=$2;}
+  { for(n=2;n<=NF;n++) { printf map[$n]; if(n<NF){ printf " "; } else { print ""; }}}' | gzip -c >$dir/train.gz \
+   || exit 1;
+
+train_lm.sh --arpa --lmtype 3gram-mincount $dir || exit 1;
+
+# LM is small enough that we don't need to prune it (only about 0.7M N-grams).
+# Perplexity over 128254.000000 words is 90.446690
+
+# note: output is
+# data/local/lm/3gram-mincount/lm_unpruned.gz
+
+exit 0;
diff --git a/egs/formosa/s5/local/wer_hyp_filter b/egs/formosa/s5/local/wer_hyp_filter
new file mode 100755
index 00000000000..519d92ee80d
--- /dev/null
+++ b/egs/formosa/s5/local/wer_hyp_filter
@@ -0,0 +1,19 @@
+#!/usr/bin/env perl
+
+@filters=('<SIL>');
+
+foreach $w (@filters) {
+  $bad{$w} = 1;
+}
+
+while(<STDIN>) {
+  @A  = split(" ", $_);
+  $id = shift @A;
+  print "$id ";
+  foreach $a (@A) {
+    if (!defined $bad{$a}) {
+      print "$a ";
+    }
+  }
+  print "\n";
+}
diff --git a/egs/formosa/s5/local/wer_output_filter b/egs/formosa/s5/local/wer_output_filter
new file mode 100755
index 00000000000..06a99a43e34
--- /dev/null
+++ b/egs/formosa/s5/local/wer_output_filter
@@ -0,0 +1,25 @@
+#!/usr/bin/env perl
+# Copyright 2012-2014  Johns Hopkins University (Author: Yenda Trmal)
+# Apache 2.0
+use utf8;
+
+use open qw(:encoding(utf8));
+binmode STDIN, ":utf8";
+binmode STDOUT, ":utf8";
+binmode STDERR, ":utf8";
+
+while (<>) {
+  @F = split " ";
+  print $F[0] . " "; 
+  foreach $s (@F[1..$#F]) {
+    if (($s =~ /\[.*\]/) || ($s =~ /\<.*\>/) || ($s =~ "<SIL>")) {
+      print "";
+    } else {
+      print "$s"
+    }
+    print " ";
+  }
+  print "\n";
+}
+
+
diff --git a/egs/formosa/s5/local/wer_ref_filter b/egs/formosa/s5/local/wer_ref_filter
new file mode 100755
index 00000000000..519d92ee80d
--- /dev/null
+++ b/egs/formosa/s5/local/wer_ref_filter
@@ -0,0 +1,19 @@
+#!/usr/bin/env perl
+
+@filters=('<SIL>');
+
+foreach $w (@filters) {
+  $bad{$w} = 1;
+}
+
+while(<STDIN>) {
+  @A  = split(" ", $_);
+  $id = shift @A;
+  print "$id ";
+  foreach $a (@A) {
+    if (!defined $bad{$a}) {
+      print "$a ";
+    }
+  }
+  print "\n";
+}
diff --git a/egs/formosa/s5/path.sh b/egs/formosa/s5/path.sh
new file mode 100755
index 00000000000..2d17b17a84a
--- /dev/null
+++ b/egs/formosa/s5/path.sh
@@ -0,0 +1,6 @@
+export KALDI_ROOT=`pwd`/../../..
+[ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
+export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
+[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1
+. $KALDI_ROOT/tools/config/common_path.sh
+export LC_ALL=C
diff --git a/egs/formosa/s5/run.sh b/egs/formosa/s5/run.sh
new file mode 100755
index 00000000000..a4d0f2dcd1d
--- /dev/null
+++ b/egs/formosa/s5/run.sh
@@ -0,0 +1,217 @@
+#!/bin/bash
+#
+# Copyright 2018, Yuan-Fu Liao, National Taipei University of Technology, yfliao@mail.ntut.edu.tw
+#
+# Before you run this recipe, please apply, download and put or make a link of the corpus under this folder (folder name: "NER-Trs-Vol1").
+# For more detail, please check:
+# 1. Formosa Speech in the Wild (FSW) project (https://sites.google.com/speech.ntut.edu.tw/fsw/home/corpus)
+# 2. Formosa Speech Recognition Challenge (FSW) 2018 (https://sites.google.com/speech.ntut.edu.tw/fsw/home/challenge)
+stage=-2
+num_jobs=20
+
+train_dir=NER-Trs-Vol1/Train
+eval_dir=NER-Trs-Vol1-Eval
+eval_key_dir=NER-Trs-Vol1-Eval-Key
+
+# shell options
+set -eo pipefail
+
+. ./cmd.sh
+. ./utils/parse_options.sh
+
+# configure number of jobs running in parallel, you should adjust these numbers according to your machines
+# data preparation
+if [ $stage -le -2 ]; then
+  # Lexicon Preparation,
+  echo "$0: Lexicon Preparation"
+  local/prepare_dict.sh || exit 1;
+
+  # Data Preparation
+  echo "$0: Data Preparation"
+  local/prepare_data.sh --train-dir $train_dir --eval-dir $eval_dir --eval-key-dir $eval_key_dir || exit 1;
+
+  # Phone Sets, questions, L compilation
+  echo "$0: Phone Sets, questions, L compilation Preparation"
+  rm -rf data/lang
+  utils/prepare_lang.sh --position-dependent-phones false data/local/dict \
+      "<SIL>" data/local/lang data/lang || exit 1;
+
+  # LM training
+  echo "$0: LM training"
+  rm -rf data/local/lm/3gram-mincount
+  local/train_lms.sh || exit 1;
+
+  # G compilation, check LG composition
+  echo "$0: G compilation, check LG composition"
+  utils/format_lm.sh data/lang data/local/lm/3gram-mincount/lm_unpruned.gz \
+      data/local/dict/lexicon.txt data/lang_test || exit 1;
+
+fi
+
+# Now make MFCC plus pitch features.
+# mfccdir should be some place with a largish disk where you
+# want to store MFCC features.
+mfccdir=mfcc
+
+# mfcc
+if [ $stage -le -1 ]; then
+  echo "$0: making mfccs"
+  for x in train test eval; do
+    steps/make_mfcc_pitch.sh --cmd "$train_cmd" --nj $num_jobs data/$x exp/make_mfcc/$x $mfccdir || exit 1;
+    steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
+    utils/fix_data_dir.sh data/$x || exit 1;
+  done
+fi
+
+# mono
+if [ $stage -le 0 ]; then
+  echo "$0: train mono model"
+  # Make some small data subsets for early system-build stages.
+  echo "$0: make training subsets"
+  utils/subset_data_dir.sh --shortest data/train 3000 data/train_mono
+
+  # train mono
+  steps/train_mono.sh --boost-silence 1.25 --cmd "$train_cmd" --nj $num_jobs \
+    data/train_mono data/lang exp/mono || exit 1;
+
+  # Get alignments from monophone system.
+  steps/align_si.sh --boost-silence 1.25 --cmd "$train_cmd" --nj $num_jobs \
+    data/train data/lang exp/mono exp/mono_ali || exit 1;
+
+  # Monophone decoding
+  (
+  utils/mkgraph.sh data/lang_test exp/mono exp/mono/graph || exit 1;
+  steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj $num_jobs \
+    exp/mono/graph data/test exp/mono/decode_test
+  )&
+fi
+
+# tri1
+if [ $stage -le 1 ]; then
+  echo "$0: train tri1 model"
+  # train tri1 [first triphone pass]
+  steps/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \
+   2500 20000 data/train data/lang exp/mono_ali exp/tri1 || exit 1;
+
+  # align tri1
+  steps/align_si.sh --cmd "$train_cmd" --nj $num_jobs \
+    data/train data/lang exp/tri1 exp/tri1_ali || exit 1;
+
+  # decode tri1
+  (
+  utils/mkgraph.sh data/lang_test exp/tri1 exp/tri1/graph || exit 1;
+  steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj $num_jobs \
+    exp/tri1/graph data/test exp/tri1/decode_test
+  )&
+fi
+
+# tri2
+if [ $stage -le 2 ]; then
+  echo "$0: train tri2 model"
+  # train tri2 [delta+delta-deltas]
+  steps/train_deltas.sh --cmd "$train_cmd" \
+   2500 20000 data/train data/lang exp/tri1_ali exp/tri2 || exit 1;
+
+  # align tri2b
+  steps/align_si.sh --cmd "$train_cmd" --nj $num_jobs \
+    data/train data/lang exp/tri2 exp/tri2_ali || exit 1;
+
+  # decode tri2
+  (
+  utils/mkgraph.sh data/lang_test exp/tri2 exp/tri2/graph
+  steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj $num_jobs \
+    exp/tri2/graph data/test exp/tri2/decode_test
+  )&
+fi
+
+# tri3a
+if [ $stage -le 3 ]; then
+  echo "$-: train tri3 model"
+  # Train tri3a, which is LDA+MLLT,
+  steps/train_lda_mllt.sh --cmd "$train_cmd" \
+   2500 20000 data/train data/lang exp/tri2_ali exp/tri3a || exit 1;
+
+  # decode tri3a
+  (
+  utils/mkgraph.sh data/lang_test exp/tri3a exp/tri3a/graph || exit 1;
+  steps/decode.sh --cmd "$decode_cmd" --nj $num_jobs --config conf/decode.config \
+    exp/tri3a/graph data/test exp/tri3a/decode_test
+  )&
+fi
+
+# tri4
+if [ $stage -le 4 ]; then
+  echo "$0: train tri4 model"
+  # From now, we start building a more serious system (with SAT), and we'll
+  # do the alignment with fMLLR.
+  steps/align_fmllr.sh --cmd "$train_cmd" --nj $num_jobs \
+    data/train data/lang exp/tri3a exp/tri3a_ali || exit 1;
+
+  steps/train_sat.sh --cmd "$train_cmd" \
+    2500 20000 data/train data/lang exp/tri3a_ali exp/tri4a || exit 1;
+
+  # align tri4a
+  steps/align_fmllr.sh  --cmd "$train_cmd" --nj $num_jobs \
+    data/train data/lang exp/tri4a exp/tri4a_ali
+
+  # decode tri4a
+  (
+  utils/mkgraph.sh data/lang_test exp/tri4a exp/tri4a/graph
+  steps/decode_fmllr.sh --cmd "$decode_cmd" --nj $num_jobs --config conf/decode.config \
+    exp/tri4a/graph data/test exp/tri4a/decode_test
+  )&
+fi
+
+# tri5
+if [ $stage -le 5 ]; then
+  echo "$0: train tri5 model"
+  # Building a larger SAT system.
+  steps/train_sat.sh --cmd "$train_cmd" \
+    3500 100000 data/train data/lang exp/tri4a_ali exp/tri5a || exit 1;
+
+  # align tri5a
+  steps/align_fmllr.sh --cmd "$train_cmd" --nj $num_jobs \
+    data/train data/lang exp/tri5a exp/tri5a_ali || exit 1;
+
+  # decode tri5
+  (
+  utils/mkgraph.sh data/lang_test exp/tri5a exp/tri5a/graph || exit 1;
+  steps/decode_fmllr.sh --cmd "$decode_cmd" --nj $num_jobs --config conf/decode.config \
+     exp/tri5a/graph data/test exp/tri5a/decode_test || exit 1;
+  )&
+fi
+
+# nnet3 tdnn models
+# commented out by default, since the chain model is usually faster and better
+#if [ $stage -le 6 ]; then
+  # echo "$0: train nnet3 model"
+  # local/nnet3/run_tdnn.sh
+#fi
+
+# chain model
+if [ $stage -le 7 ]; then
+  # The iVector-extraction and feature-dumping parts coulb be skipped by setting "--train_stage 7"
+  echo "$0: train chain model"
+  local/chain/run_tdnn.sh
+fi
+
+# getting results (see RESULTS file)
+if [ $stage -le 8 ]; then
+  echo "$0: extract the results"
+  for test_set in test eval; do
+  echo "WER: $test_set"
+  for x in exp/*/decode_${test_set}*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done 2>/dev/null
+  for x in exp/*/*/decode_${test_set}*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done 2>/dev/null
+  echo
+
+  echo "CER: $test_set"
+  for x in exp/*/decode_${test_set}*; do [ -d $x ] && grep WER $x/cer_* | utils/best_wer.sh; done 2>/dev/null
+  for x in exp/*/*/decode_${test_set}*; do [ -d $x ] && grep WER $x/cer_* | utils/best_wer.sh; done 2>/dev/null
+  echo
+  done
+fi
+
+# finish
+echo "$0: all done"
+
+exit 0;
diff --git a/egs/formosa/s5/steps b/egs/formosa/s5/steps
new file mode 120000
index 00000000000..6e99bf5b5ad
--- /dev/null
+++ b/egs/formosa/s5/steps
@@ -0,0 +1 @@
+../../wsj/s5/steps
\ No newline at end of file
diff --git a/egs/formosa/s5/utils b/egs/formosa/s5/utils
new file mode 120000
index 00000000000..b240885218f
--- /dev/null
+++ b/egs/formosa/s5/utils
@@ -0,0 +1 @@
+../../wsj/s5/utils
\ No newline at end of file
diff --git a/egs/gale_arabic/s5/local/gale_format_data.sh b/egs/gale_arabic/s5/local/gale_format_data.sh
index 85a946a58d9..053323dc194 100755
--- a/egs/gale_arabic/s5/local/gale_format_data.sh
+++ b/egs/gale_arabic/s5/local/gale_format_data.sh
@@ -57,4 +57,4 @@ fsttablecompose data/lang/L_disambig.fst data/lang_test/G.fst | \
 
 echo gale_format_data  succeeded.
 
-exit 0
\ No newline at end of file
+exit 0
diff --git a/egs/gale_arabic/s5/local/gale_train_lms.sh b/egs/gale_arabic/s5/local/gale_train_lms.sh
index 1b5d4665a19..8f8e715390f 100755
--- a/egs/gale_arabic/s5/local/gale_train_lms.sh
+++ b/egs/gale_arabic/s5/local/gale_train_lms.sh
@@ -113,4 +113,4 @@ fi
 
 echo train lm succeeded
 
-exit 0
\ No newline at end of file
+exit 0
diff --git a/egs/gale_arabic/s5b/local/chain/tuning/run_tdnn_1a.sh b/egs/gale_arabic/s5b/local/chain/tuning/run_tdnn_1a.sh
index a3ccfda04ac..bf2e45c9914 100755
--- a/egs/gale_arabic/s5b/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/gale_arabic/s5b/local/chain/tuning/run_tdnn_1a.sh
@@ -108,7 +108,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   affine_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
   linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
diff --git a/egs/gale_arabic/s5b/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/gale_arabic/s5b/local/chain/tuning/run_tdnn_lstm_1a.sh
index 604f32a1de4..deebafc95e4 100755
--- a/egs/gale_arabic/s5b/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/gale_arabic/s5b/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -120,7 +120,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/gp/s1/local/gp_convert_audio.sh b/egs/gp/s1/local/gp_convert_audio.sh
index a7c2d7285c4..b3db909c9b6 100755
--- a/egs/gp/s1/local/gp_convert_audio.sh
+++ b/egs/gp/s1/local/gp_convert_audio.sh
@@ -108,4 +108,4 @@ done < "$INLIST"
   echo "sox: error converting following $nsoxerr file(s):" >&2
 [ -f "$soxerr" ] && cat "$soxerr" >&2
 
-exit 0;
\ No newline at end of file
+exit 0;
diff --git a/egs/gp/s1/utils/mkgraph.sh b/egs/gp/s1/utils/mkgraph.sh
index 2e45296593b..3aba742832d 100755
--- a/egs/gp/s1/utils/mkgraph.sh
+++ b/egs/gp/s1/utils/mkgraph.sh
@@ -131,4 +131,4 @@ cp $lang/silphones.csl $dir/
 # to make const fst:
 # fstconvert --fst_type=const $dir/HCLG.fst $dir/HCLG_c.fst
 
-echo "Finished making decoding graphs in $dir"
\ No newline at end of file
+echo "Finished making decoding graphs in $dir"
diff --git a/egs/heroico/s5/local/chain/tuning/run_cnn_tdnn_1a.sh b/egs/heroico/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
index 1112f0ec08b..361879b4142 100755
--- a/egs/heroico/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
+++ b/egs/heroico/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
@@ -149,7 +149,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   cnn_opts="l2-regularize=0.03"
   ivector_layer_opts="l2-regularize=0.03"
diff --git a/egs/heroico/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/heroico/s5/local/chain/tuning/run_tdnn_1a.sh
index 6dde42bef79..290bd4c7970 100755
--- a/egs/heroico/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/heroico/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -150,7 +150,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.01"
   output_opts="l2-regularize=0.0025"
 
diff --git a/egs/heroico/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/heroico/s5/local/chain/tuning/run_tdnn_1b.sh
index d255d85327f..cfb4dc1f697 100755
--- a/egs/heroico/s5/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/heroico/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -151,7 +151,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   affine_opts="l2-regularize=0.03 dropout-proportion=0.0 dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.03 dropout-proportion=0.0 bypass-scale=0.66"
   linear_opts="l2-regularize=0.03 orthonormal-constraint=-1.0"
diff --git a/egs/hkust/s5/local/chain/tuning/run_tdnn_2a.sh b/egs/hkust/s5/local/chain/tuning/run_tdnn_2a.sh
index f771387785c..c62b776de2b 100755
--- a/egs/hkust/s5/local/chain/tuning/run_tdnn_2a.sh
+++ b/egs/hkust/s5/local/chain/tuning/run_tdnn_2a.sh
@@ -109,7 +109,7 @@ if [ $stage -le 12 ]; then
   ivector_dim=$(feat-to-dim scp:exp/nnet3/ivectors_${train_set}/ivector_online.scp -)
   feat_dim=$(feat-to-dim scp:data/${train_set}_hires/feats.scp -)
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.004 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   linear_opts="orthonormal-constraint=-1.0 l2-regularize=0.004"
   output_opts="l2-regularize=0.002"
diff --git a/egs/hub4_spanish/s5/local/chain/tuning/run_cnn_tdnn_1a.sh b/egs/hub4_spanish/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
index 81915fec5a6..d1b657a2d74 100755
--- a/egs/hub4_spanish/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
+++ b/egs/hub4_spanish/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
@@ -147,7 +147,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   cnn_opts="l2-regularize=0.03"
   ivector_affine_opts="l2-regularize=0.03"
diff --git a/egs/hub4_spanish/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/hub4_spanish/s5/local/chain/tuning/run_tdnn_1a.sh
index 23a55f93023..40bbbe1ae79 100755
--- a/egs/hub4_spanish/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/hub4_spanish/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -136,7 +136,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/hub4_spanish/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/hub4_spanish/s5/local/chain/tuning/run_tdnn_1b.sh
index 724bb1e0794..a498d8157f3 100755
--- a/egs/hub4_spanish/s5/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/hub4_spanish/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -147,7 +147,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   tdnn_opts="l2-regularize=0.03 dropout-proportion=0.0 dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.03 dropout-proportion=0.0 bypass-scale=0.66"
diff --git a/egs/iam/v1/local/chain/tuning/run_cnn_1a.sh b/egs/iam/v1/local/chain/tuning/run_cnn_1a.sh
index 1253bbe5aa3..ef1273f3961 100755
--- a/egs/iam/v1/local/chain/tuning/run_cnn_1a.sh
+++ b/egs/iam/v1/local/chain/tuning/run_cnn_1a.sh
@@ -128,7 +128,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(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
diff --git a/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1a.sh b/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1a.sh
index a8d7f6c6091..bbcc55aa2b0 100755
--- a/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1a.sh
+++ b/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1a.sh
@@ -125,7 +125,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(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"
   common3="height-offsets=-1,0,1 num-filters-out=70"
diff --git a/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1b.sh b/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1b.sh
index f5dbb93e7b7..401ffa14e19 100755
--- a/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1b.sh
+++ b/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1b.sh
@@ -124,7 +124,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(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"
diff --git a/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1c.sh b/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1c.sh
index 1dd83c5078f..17209b9204f 100755
--- a/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1c.sh
+++ b/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1c.sh
@@ -122,7 +122,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1d.sh b/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1d.sh
index 3979b3d2da0..89a40ed2a13 100755
--- a/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1d.sh
+++ b/egs/iam/v1/local/chain/tuning/run_cnn_chainali_1d.sh
@@ -127,7 +127,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
index f95f6a90ca1..703d404159a 100755
--- a/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
@@ -121,7 +121,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1b.sh b/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
index 81700ce2180..905c4661477 100755
--- a/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
+++ b/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
@@ -117,7 +117,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1c.sh b/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1c.sh
index 047d673db17..26b1aca0929 100755
--- a/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1c.sh
+++ b/egs/iam/v1/local/chain/tuning/run_cnn_e2eali_1c.sh
@@ -119,7 +119,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1a.sh
index a80bb02290b..9a01688ba35 100755
--- a/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1a.sh
@@ -135,7 +135,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1b.sh b/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1b.sh
index 6615c4669d6..28aa246f334 100755
--- a/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1b.sh
+++ b/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1b.sh
@@ -137,7 +137,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1c.sh b/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1c.sh
index f44c073635e..f158317950a 100755
--- a/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1c.sh
+++ b/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1c.sh
@@ -139,7 +139,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1d.sh b/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1d.sh
index e7d9246fb89..1c44057454a 100755
--- a/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1d.sh
+++ b/egs/iam/v2/local/chain/tuning/run_cnn_e2eali_1d.sh
@@ -137,7 +137,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/iban/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/iban/s5/local/chain/tuning/run_tdnn_1a.sh
index d320f49d3aa..10650a18269 100755
--- a/egs/iban/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/iban/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -136,7 +136,7 @@ if [ $stage -le 12 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.08 dropout-per-dim-continuous=true"
   output_opts="l2-regularize=0.02 bottleneck-dim=256"
 
diff --git a/egs/iban/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/iban/s5/local/chain/tuning/run_tdnn_1b.sh
index 56f5255288c..db62e6f8a55 100755
--- a/egs/iban/s5/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/iban/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -136,7 +136,7 @@ if [ $stage -le 12 ]; 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/$xent_regularize" | python) 
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python) 
   opts="l2-regularize=0.08 dropout-per-dim=true dropout-per-dim-continuous=true"
   linear_opts="orthonormal-constraint=-1.0"
   output_opts="l2-regularize=0.04"
diff --git a/egs/ifnenit/v1/local/chain/run_cnn_1a.sh b/egs/ifnenit/v1/local/chain/run_cnn_1a.sh
index b0e147d157b..b0ecd547741 100755
--- a/egs/ifnenit/v1/local/chain/run_cnn_1a.sh
+++ b/egs/ifnenit/v1/local/chain/run_cnn_1a.sh
@@ -123,7 +123,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   common1="required-time-offsets=0 height-offsets=-2,-1,0,1,2 num-filters-out=36"
   common2="required-time-offsets=0 height-offsets=-2,-1,0,1,2 num-filters-out=70"
   mkdir -p $dir/configs
diff --git a/egs/ifnenit/v1/local/chain/run_cnn_chainali_1a.sh b/egs/ifnenit/v1/local/chain/run_cnn_chainali_1a.sh
index b1f33b41a0c..7f3132d657e 100755
--- a/egs/ifnenit/v1/local/chain/run_cnn_chainali_1a.sh
+++ b/egs/ifnenit/v1/local/chain/run_cnn_chainali_1a.sh
@@ -128,7 +128,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(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"
diff --git a/egs/librispeech/s5/local/chain/tuning/run_cnn_tdnn_1a.sh b/egs/librispeech/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
index 2a60587fc35..8ebca6fd650 100755
--- a/egs/librispeech/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
+++ b/egs/librispeech/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
@@ -112,7 +112,7 @@ if [ $stage -le 14 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.01"
   ivector_affine_opts="l2-regularize=0.0"
   affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
diff --git a/egs/librispeech/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/librispeech/s5/local/chain/tuning/run_tdnn_1b.sh
index 7129827fe19..57f50df761d 100755
--- a/egs/librispeech/s5/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/librispeech/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -122,7 +122,7 @@ if [ $stage -le 14 ]; then
   # create the config files for nnet initialization
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/librispeech/s5/local/chain/tuning/run_tdnn_1c.sh b/egs/librispeech/s5/local/chain/tuning/run_tdnn_1c.sh
index 29ebe62ddde..3970fa8c4d9 100755
--- a/egs/librispeech/s5/local/chain/tuning/run_tdnn_1c.sh
+++ b/egs/librispeech/s5/local/chain/tuning/run_tdnn_1c.sh
@@ -112,7 +112,7 @@ if [ $stage -le 14 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.002"
   linear_opts="orthonormal-constraint=1.0"
   output_opts="l2-regularize=0.0005 bottleneck-dim=256"
diff --git a/egs/librispeech/s5/local/chain/tuning/run_tdnn_1d.sh b/egs/librispeech/s5/local/chain/tuning/run_tdnn_1d.sh
index 81b621ef86f..5c488362e59 100755
--- a/egs/librispeech/s5/local/chain/tuning/run_tdnn_1d.sh
+++ b/egs/librispeech/s5/local/chain/tuning/run_tdnn_1d.sh
@@ -207,7 +207,7 @@ if [ $stage -le 14 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.75"
   linear_opts="l2-regularize=0.008 orthonormal-constraint=-1.0"
diff --git a/egs/librispeech/s5/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/librispeech/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
index 812bf5e7fc5..4277f769119 100755
--- a/egs/librispeech/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/librispeech/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -85,7 +85,7 @@ if [ $stage -le 12 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   opts="l2-regularize=0.002"
   linear_opts="orthonormal-constraint=1.0"
diff --git a/egs/librispeech/s5/local/chain/tuning/run_tdnn_lstm_1b.sh b/egs/librispeech/s5/local/chain/tuning/run_tdnn_lstm_1b.sh
index d9f20fae011..383cc533270 100755
--- a/egs/librispeech/s5/local/chain/tuning/run_tdnn_lstm_1b.sh
+++ b/egs/librispeech/s5/local/chain/tuning/run_tdnn_lstm_1b.sh
@@ -120,7 +120,7 @@ if [ $stage -le 12 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   opts="l2-regularize=0.002"
   linear_opts="orthonormal-constraint=1.0"
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
index d449805be1d..892ee441516 100755
--- a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_1a.sh
+++ b/egs/madcat_ar/v1/local/chain/tuning/run_cnn_1a.sh
@@ -115,7 +115,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(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
diff --git a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_chainali_1a.sh b/egs/madcat_ar/v1/local/chain/tuning/run_cnn_chainali_1a.sh
index 23c4d5c2036..7ca7c652fd2 100755
--- a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_chainali_1a.sh
+++ b/egs/madcat_ar/v1/local/chain/tuning/run_cnn_chainali_1a.sh
@@ -112,7 +112,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(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"
diff --git a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
index ee84ea0d83f..a8bc1836ffe 100755
--- a/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
@@ -116,7 +116,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(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"
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
index c6052b76e7f..0828e051dcc 100755
--- 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
@@ -129,7 +129,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(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"
diff --git a/egs/madcat_ar/v1/local/tl/chain/run_cnn_e2eali.sh b/egs/madcat_ar/v1/local/tl/chain/run_cnn_e2eali.sh
index e0cca104f50..ccbb7119674 100755
--- a/egs/madcat_ar/v1/local/tl/chain/run_cnn_e2eali.sh
+++ b/egs/madcat_ar/v1/local/tl/chain/run_cnn_e2eali.sh
@@ -124,7 +124,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/madcat_zh/v1/local/chain/tuning/run_cnn_1a.sh b/egs/madcat_zh/v1/local/chain/tuning/run_cnn_1a.sh
index d17b3e3c9c5..164d62a7ad9 100755
--- a/egs/madcat_zh/v1/local/chain/tuning/run_cnn_1a.sh
+++ b/egs/madcat_zh/v1/local/chain/tuning/run_cnn_1a.sh
@@ -122,7 +122,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(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
diff --git a/egs/madcat_zh/v1/local/chain/tuning/run_cnn_chainali_1a.sh b/egs/madcat_zh/v1/local/chain/tuning/run_cnn_chainali_1a.sh
index d53949dd3de..be51bdcc3d1 100755
--- a/egs/madcat_zh/v1/local/chain/tuning/run_cnn_chainali_1a.sh
+++ b/egs/madcat_zh/v1/local/chain/tuning/run_cnn_chainali_1a.sh
@@ -119,7 +119,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(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"
   common3="height-offsets=-1,0,1 num-filters-out=70"
diff --git a/egs/madcat_zh/v1/local/chain/tuning/run_cnn_chainali_1b.sh b/egs/madcat_zh/v1/local/chain/tuning/run_cnn_chainali_1b.sh
index 5a3b85422f6..aa61620a92f 100755
--- a/egs/madcat_zh/v1/local/chain/tuning/run_cnn_chainali_1b.sh
+++ b/egs/madcat_zh/v1/local/chain/tuning/run_cnn_chainali_1b.sh
@@ -123,7 +123,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(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"
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_cnn_tdnn_1a.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
index 0b86ace2de1..c8f2503b578 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
@@ -144,7 +144,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   cnn_opts="l2-regularize=0.03"
   ivector_affine_opts="l2-regularize=0.03"
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1a.sh
index 642c20ec191..da16297c9dd 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -147,7 +147,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1b.sh
index 110b7b87415..3d0c2d63902 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -154,7 +154,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1c.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1c.sh
index fe6f1b50f9e..081af8fe2f8 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1c.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1c.sh
@@ -150,7 +150,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1d.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1d.sh
index 225b36f909c..04df38d4da3 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1d.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1d.sh
@@ -150,7 +150,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1e.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1e.sh
index 565387003ff..cdf9bb584f4 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1e.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1e.sh
@@ -148,7 +148,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.05"
   output_opts="l2-regularize=0.01"
 
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1f.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1f.sh
index 9cc6d93022a..d1385ff2be5 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1f.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1f.sh
@@ -156,7 +156,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.05"
   output_opts="l2-regularize=0.02 bottleneck-dim=192"
 
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1g.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1g.sh
index e234b847aa7..ad51780e191 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1g.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1g.sh
@@ -155,7 +155,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.05 dropout-per-dim-continuous=true"
   output_opts="l2-regularize=0.02 bottleneck-dim=192"
 
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1g20.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1g20.sh
index 18540806028..dbfe5c5a07a 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1g20.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1g20.sh
@@ -168,7 +168,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.05 dropout-per-dim-continuous=true"
   output_opts="l2-regularize=0.02 bottleneck-dim=192"
 
diff --git a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1h.sh b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1h.sh
index 776247f5ea3..cc4123e2755 100755
--- a/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1h.sh
+++ b/egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1h.sh
@@ -151,7 +151,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   tdnn_opts="l2-regularize=0.03 dropout-proportion=0.0 dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.03 dropout-proportion=0.0 bypass-scale=0.66"
diff --git a/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1a.sh b/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1a.sh
index de858973c98..c2f90df4b5c 100755
--- a/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1a.sh
@@ -99,7 +99,7 @@ if [ $stage -le 10 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $ali_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1b.sh b/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1b.sh
index ba4ecc268df..2b3c2844972 100755
--- a/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1b.sh
+++ b/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1b.sh
@@ -102,7 +102,7 @@ if [ $stage -le 10 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $ali_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="decay-time=20 delay=-3 dropout-proportion=0.0"
 
   mkdir -p $dir/configs
diff --git a/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1c.sh b/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1c.sh
index 74df56b0537..5118cb0f8bd 100755
--- a/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1c.sh
+++ b/egs/mini_librispeech/s5/local/nnet3/tuning/run_tdnn_lstm_1c.sh
@@ -100,7 +100,7 @@ if [ $stage -le 10 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $ali_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   tdnn_opts="l2-regularize=0.05"
   lstm_opts="l2-regularize=0.01 decay-time=20 delay=-3 dropout-proportion=0.0"
   output_opts="l2-regularize=0.01"
diff --git a/egs/multi_en/s5/local/chain/tuning/run_tdnn_5b.sh b/egs/multi_en/s5/local/chain/tuning/run_tdnn_5b.sh
index 9f8c49387b1..96f5fdac8f3 100755
--- a/egs/multi_en/s5/local/chain/tuning/run_tdnn_5b.sh
+++ b/egs/multi_en/s5/local/chain/tuning/run_tdnn_5b.sh
@@ -132,7 +132,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.0015 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   linear_opts="l2-regularize=0.0015 orthonormal-constraint=-1.0"
   output_opts="l2-regularize=0.001"
diff --git a/egs/multi_en/s5/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/multi_en/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
index 5793fef0fc2..62266334962 100755
--- a/egs/multi_en/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/multi_en/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -155,7 +155,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   lstm_opts="dropout-proportion=0.0 decay-time=40"
 
   relu_dim=1024
diff --git a/egs/multi_en/s5/local/chain/tuning/run_tdnn_opgru_1a.sh b/egs/multi_en/s5/local/chain/tuning/run_tdnn_opgru_1a.sh
index 98e7c2ed6c1..79cd3eb3014 100755
--- a/egs/multi_en/s5/local/chain/tuning/run_tdnn_opgru_1a.sh
+++ b/egs/multi_en/s5/local/chain/tuning/run_tdnn_opgru_1a.sh
@@ -150,7 +150,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0 "
 
   mkdir -p $dir/configs
diff --git a/egs/multi_en/s5/local/chain/tuning/run_tdnn_opgru_1b.sh b/egs/multi_en/s5/local/chain/tuning/run_tdnn_opgru_1b.sh
index 8b1f34b15a6..a7170af9431 100755
--- a/egs/multi_en/s5/local/chain/tuning/run_tdnn_opgru_1b.sh
+++ b/egs/multi_en/s5/local/chain/tuning/run_tdnn_opgru_1b.sh
@@ -146,7 +146,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0 "
 
   mkdir -p $dir/configs
diff --git a/egs/reverb/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/reverb/s5/local/chain/tuning/run_tdnn_1a.sh
index 61cc8b97d41..c8b4997161e 100755
--- a/egs/reverb/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/reverb/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -133,7 +133,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.05"
   output_opts="l2-regularize=0.01 bottleneck-dim=320"
 
diff --git a/egs/reverb/s5/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/reverb/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
index 9369e00a7ba..4723400c76b 100755
--- a/egs/reverb/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/reverb/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -141,7 +141,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=40"
 
diff --git a/egs/rimes/v1/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/rimes/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
index 4eb3e5e1e76..33eb9dcb98c 100755
--- a/egs/rimes/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/rimes/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
@@ -151,7 +151,7 @@ if [ $stage -le 5 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/rm/s5/local/chain/tuning/run_tdnn_wsj_rm_1a.sh b/egs/rm/s5/local/chain/tuning/run_tdnn_wsj_rm_1a.sh
index 6b6c08e779a..2fd2556c19b 100755
--- a/egs/rm/s5/local/chain/tuning/run_tdnn_wsj_rm_1a.sh
+++ b/egs/rm/s5/local/chain/tuning/run_tdnn_wsj_rm_1a.sh
@@ -130,7 +130,7 @@ if [ $stage -le 7 ]; then
   echo " generating new layers, that are specific to rm. These layers ";
   echo " are added to the transferred part of the wsj network.";
   num_targets=$(tree-info --print-args=false $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   mkdir -p $dir
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/rm/s5/local/run_vtln2.sh b/egs/rm/s5/local/run_vtln2.sh
index 6437032ca61..b87030d2e3d 100755
--- a/egs/rm/s5/local/run_vtln2.sh
+++ b/egs/rm/s5/local/run_vtln2.sh
@@ -59,4 +59,4 @@ steps/compute_cmvn_stats.sh data/test_vtln exp/make_mfcc/test_vtln $featdir
 # %WER 3.13 [ 392 / 12533, 59 ins, 64 del, 269 sub ] exp/tri3b/decode.si/wer_3
 # %WER 10.36 [ 1298 / 12533, 147 ins, 192 del, 959 sub ] exp/tri3b/decode_ug/wer_12
 # %WER 13.48 [ 1689 / 12533, 159 ins, 277 del, 1253 sub ] exp/tri3b/decode_ug.si/wer_13
-# a04:s5: 
\ No newline at end of file
+# a04:s5: 
diff --git a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1a.sh b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1a.sh
index ec6b8941955..47557f93696 100755
--- a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1a.sh
+++ b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1a.sh
@@ -152,7 +152,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1b.sh b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1b.sh
index 53aa92710e8..7afa1b7f902 100755
--- a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1b.sh
+++ b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1b.sh
@@ -153,7 +153,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1c.sh b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1c.sh
index 83c2f3607f0..e69e499e152 100755
--- a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1c.sh
+++ b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1c.sh
@@ -151,7 +151,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1d.sh b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1d.sh
index 2665ea91ff8..86e0352828c 100755
--- a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1d.sh
+++ b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1d.sh
@@ -164,7 +164,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1e.sh b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1e.sh
index 80f67d34ba9..313f899a471 100755
--- a/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1e.sh
+++ b/egs/sprakbanken/s5/local/chain/tuning/run_lstm_1e.sh
@@ -152,7 +152,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/sprakbanken/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/sprakbanken/s5/local/chain/tuning/run_tdnn_1b.sh
index e242660a10e..600f27ddf86 100755
--- a/egs/sprakbanken/s5/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/sprakbanken/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -135,7 +135,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/sprakbanken/s5/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/sprakbanken/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
index 86dc4b75a24..cedc448464a 100755
--- a/egs/sprakbanken/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/sprakbanken/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -145,7 +145,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/sprakbanken/s5/local/norm_dk/write_punct.sh b/egs/sprakbanken/s5/local/norm_dk/write_punct.sh
index 57726bd44cb..3b8decaf376 100755
--- a/egs/sprakbanken/s5/local/norm_dk/write_punct.sh
+++ b/egs/sprakbanken/s5/local/norm_dk/write_punct.sh
@@ -22,4 +22,4 @@ perl -pe 's/([\n ])\;([ \n])/\1SEMIKOLON\2/g' | \
 perl -pe 's/([\n ])_NL_([ \n])/\1NY LINJE\2/g' | \
 perl -pe 's/([\n ])_NS_([ \n])/\1NYT AFSNIT\2/g' | \
 
-tr -s ' '
\ No newline at end of file
+tr -s ' '
diff --git a/egs/swbd/s5c/local/chain/multi_condition/run_tdnn_7k.sh b/egs/swbd/s5c/local/chain/multi_condition/run_tdnn_7k.sh
index 6792332da56..20dcab8eb50 100755
--- a/egs/swbd/s5c/local/chain/multi_condition/run_tdnn_7k.sh
+++ b/egs/swbd/s5c/local/chain/multi_condition/run_tdnn_7k.sh
@@ -152,7 +152,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_blstm_6j.sh b/egs/swbd/s5c/local/chain/tuning/run_blstm_6j.sh
index ae7c97e7d08..acdae844b65 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_blstm_6j.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_blstm_6j.sh
@@ -120,7 +120,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_blstm_6k.sh b/egs/swbd/s5c/local/chain/tuning/run_blstm_6k.sh
index 90d672b9ae9..bbd8cb63697 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_blstm_6k.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_blstm_6k.sh
@@ -116,7 +116,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_blstm_6l.sh b/egs/swbd/s5c/local/chain/tuning/run_blstm_6l.sh
index 68daf81ab01..16f2ea211d0 100644
--- a/egs/swbd/s5c/local/chain/tuning/run_blstm_6l.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_blstm_6l.sh
@@ -125,7 +125,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20 dropout-proportion=0.0"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_blstm_6m.sh b/egs/swbd/s5c/local/chain/tuning/run_blstm_6m.sh
index 4668aac9ebc..09f7d72434c 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_blstm_6m.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_blstm_6m.sh
@@ -124,7 +124,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_blstm_6n.sh b/egs/swbd/s5c/local/chain/tuning/run_blstm_6n.sh
index 22316d56ed2..8e44d0bc114 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_blstm_6n.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_blstm_6n.sh
@@ -123,7 +123,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_blstm_6o.sh b/egs/swbd/s5c/local/chain/tuning/run_blstm_6o.sh
index ad2ac4bf043..6a836e81b09 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_blstm_6o.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_blstm_6o.sh
@@ -125,7 +125,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_cnn_tdnn_1a.sh b/egs/swbd/s5c/local/chain/tuning/run_cnn_tdnn_1a.sh
index 174925315a0..d1a61360f85 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_cnn_tdnn_1a.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_cnn_tdnn_1a.sh
@@ -112,7 +112,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   cnn_opts="l2-regularize=0.01"
   ivector_affine_opts="l2-regularize=0.01"
diff --git a/egs/swbd/s5c/local/chain/tuning/run_lstm_6j.sh b/egs/swbd/s5c/local/chain/tuning/run_lstm_6j.sh
index e432435a551..48db81f586f 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_lstm_6j.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_lstm_6j.sh
@@ -119,7 +119,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_lstm_6k.sh b/egs/swbd/s5c/local/chain/tuning/run_lstm_6k.sh
index b9b7152dcbe..021eab09506 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_lstm_6k.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_lstm_6k.sh
@@ -121,7 +121,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_lstm_6l.sh b/egs/swbd/s5c/local/chain/tuning/run_lstm_6l.sh
index 12564c4faae..f219167f9ec 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_lstm_6l.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_lstm_6l.sh
@@ -131,7 +131,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7g.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7g.sh
index fa6518a9ad9..0623d26a9e4 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7g.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7g.sh
@@ -117,7 +117,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7h.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7h.sh
index 9dfaa1d4509..dbbe3c1e6fd 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7h.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7h.sh
@@ -120,7 +120,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7i.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7i.sh
index c5b5633d94c..2a8a658bf6b 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7i.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7i.sh
@@ -113,7 +113,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7j.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7j.sh
index 793b40f7fe3..a9eba36ddaa 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7j.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7j.sh
@@ -112,7 +112,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7k.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7k.sh
index bd47ed61f23..8e0b290cf87 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7k.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7k.sh
@@ -114,7 +114,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7l.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7l.sh
index f7681a743e1..bb9ddf209d6 100644
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7l.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7l.sh
@@ -112,7 +112,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7m.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7m.sh
index 03b1ee3c97f..97f92c14f1f 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7m.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7m.sh
@@ -122,7 +122,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7m25l.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7m25l.sh
index 0fa7353edb2..d9fe106e5d7 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7m25l.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7m25l.sh
@@ -452,7 +452,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.002 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   linear_opts="orthonormal-constraint=1.0"
   output_opts="l2-regularize=0.0005"
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7n.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7n.sh
index cf4855db611..99e43443f99 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7n.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7n.sh
@@ -119,7 +119,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.002"
   linear_opts="orthonormal-constraint=1.0"
   output_opts="l2-regularize=0.0005 bottleneck-dim=256"
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7o.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7o.sh
index fb47b1e88ad..44ca3b3d279 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7o.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7o.sh
@@ -126,7 +126,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.004 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   linear_opts="orthonormal-constraint=-1.0 l2-regularize=0.004"
   output_opts="l2-regularize=0.002"
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7p.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7p.sh
index 096ed9c54fd..d19a4ef4c0b 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7p.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7p.sh
@@ -114,7 +114,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.004 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   linear_opts="orthonormal-constraint=-1.0 l2-regularize=0.004"
   output_opts="l2-regularize=0.002"
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7q.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7q.sh
index 8eab54a9dc2..cea0891d5d7 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_7q.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_7q.sh
@@ -118,7 +118,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   affine_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
   linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_attention_1a.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_attention_1a.sh
index 3ce4fa68397..d4febd61e94 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_attention_1a.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_attention_1a.sh
@@ -122,7 +122,7 @@ fi
 if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1a.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1a.sh
index 7854bac44c5..4414147bf0e 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1a.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1a.sh
@@ -120,7 +120,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1b.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1b.sh
index 3929cdc432e..cd9d4dc6f2b 100644
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1b.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1b.sh
@@ -122,7 +122,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20 dropout-proportion=0.0"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1c.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1c.sh
index 311fe15d895..18b660b4080 100644
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1c.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1c.sh
@@ -119,7 +119,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1d.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1d.sh
index 4894e492542..be615e0e361 100644
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1d.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_blstm_1d.sh
@@ -112,7 +112,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20 dropout-proportion=0.0"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1a.sh
index 32234ff009c..43855e6f7ce 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -118,7 +118,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1b.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1b.sh
index 1d305186fc2..5c82ed0eb11 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1b.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1b.sh
@@ -114,7 +114,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1c.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1c.sh
index e2492ee277b..c3df0bf2b2c 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1c.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1c.sh
@@ -121,7 +121,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1d.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1d.sh
index 2028e20ff00..3d353387239 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1d.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1d.sh
@@ -140,7 +140,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1e.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1e.sh
index bf3eddb90ae..2a2d508ecdd 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1e.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1e.sh
@@ -135,7 +135,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1f.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1f.sh
index e500ee0a9a8..5af5463b372 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1f.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1f.sh
@@ -152,7 +152,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1g.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1g.sh
index 9b3a5d29957..28105a587ec 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1g.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1g.sh
@@ -134,7 +134,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=15"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1h.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1h.sh
index ca578195323..d6e81f2d8eb 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1h.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1h.sh
@@ -131,7 +131,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1i.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1i.sh
index a0848cc8894..060d98c9d05 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1i.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1i.sh
@@ -152,7 +152,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1j.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1j.sh
index 84258624447..9bd39a262c5 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1j.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1j.sh
@@ -119,7 +119,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1k.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1k.sh
index 258f067cf2b..ccd6138da6e 100644
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1k.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1k.sh
@@ -129,7 +129,7 @@ if [ $stage -le 12 ]; then
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
   [ -z $num_targets ] && { echo "$0: error getting num-targets"; exit 1; }
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=20"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1l.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1l.sh
index 0a518572201..f702033377a 100644
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1l.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1l.sh
@@ -120,7 +120,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1m.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1m.sh
index 3a2b34792f3..b43577bd76c 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1m.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1m.sh
@@ -128,7 +128,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   lstm_opts="decay-time=40"
 
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1n.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1n.sh
index 34fcf731639..5bb6e7da152 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1n.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_lstm_1n.sh
@@ -125,7 +125,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   opts="l2-regularize=0.002"
   linear_opts="orthonormal-constraint=1.0"
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1a.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1a.sh
index 18d3f81ffde..4db38d74508 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1a.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1a.sh
@@ -134,7 +134,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0"
 
   mkdir -p $dir/configs
diff --git a/egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1b.sh b/egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1b.sh
index 579008b5658..7e9dec67068 100755
--- a/egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1b.sh
+++ b/egs/swbd/s5c/local/chain/tuning/run_tdnn_opgru_1b.sh
@@ -132,7 +132,7 @@ if [ $stage -le 12 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $treedir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0 gru-nonlinearity-options=\"max-change=0.75\""
 
   mkdir -p $dir/configs
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_blstm_1a.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_blstm_1a.sh
index 5e60ee1178c..2ac8c09dad1 100644
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_blstm_1a.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_blstm_1a.sh
@@ -139,7 +139,7 @@ if [ $stage -le 17 ]; then
   
   lstm_opts="decay-time=20"
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1a.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1a.sh
index ec6b8941955..47557f93696 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1a.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1a.sh
@@ -152,7 +152,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1b.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1b.sh
index 53aa92710e8..7afa1b7f902 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1b.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1b.sh
@@ -153,7 +153,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1c.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1c.sh
index 83c2f3607f0..e69e499e152 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1c.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1c.sh
@@ -151,7 +151,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1d.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1d.sh
index 2665ea91ff8..86e0352828c 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1d.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1d.sh
@@ -164,7 +164,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1e.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1e.sh
index f768c7659d7..0fdb2b3b63e 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1e.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_lstm_1e.sh
@@ -154,7 +154,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1b.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1b.sh
index 3384b085114..492d3efb804 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1b.sh
@@ -143,7 +143,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1c.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1c.sh
index 5dd838a15e3..01768c3875f 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1c.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1c.sh
@@ -160,7 +160,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1d.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1d.sh
index 4f86691b752..bb5007f4c9f 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1d.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1d.sh
@@ -151,7 +151,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1e.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1e.sh
index e32c08562c6..1476ed1fd40 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1e.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1e.sh
@@ -143,7 +143,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1f.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1f.sh
index 2eab0285828..47f939fea1c 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1f.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1f.sh
@@ -141,7 +141,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1g.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1g.sh
index 64ce1f02fdd..f02025674e8 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1g.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_1g.sh
@@ -142,7 +142,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.66"
   linear_opts="l2-regularize=0.008 orthonormal-constraint=-1.0"
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1a.sh
index 8f0be130e27..b03da27e760 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -156,7 +156,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1b.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1b.sh
index fef021c6482..e896a7867b3 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1b.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1b.sh
@@ -169,7 +169,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1c.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1c.sh
index d05ae15dfec..00f72fab796 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1c.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1c.sh
@@ -160,7 +160,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1d.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1d.sh
index 29d8e69b04c..80a9ed1c4d0 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1d.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1d.sh
@@ -165,7 +165,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1e.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1e.sh
index db3fde91656..031978f878a 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1e.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1e.sh
@@ -213,7 +213,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1f.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1f.sh
index f6a1d49890d..c60b8f7fefc 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1f.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1f.sh
@@ -167,7 +167,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1g.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1g.sh
index ff2c302fdf6..2d2048a6869 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1g.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1g.sh
@@ -170,7 +170,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1h.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1h.sh
index d4cb5e85657..a074e128270 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1h.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1h.sh
@@ -168,7 +168,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1i.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1i.sh
index 40b1bf7f54a..3bfe175806f 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1i.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1i.sh
@@ -189,7 +189,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1j.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1j.sh
index 838f49f977f..acbef783823 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1j.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1j.sh
@@ -186,7 +186,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1k.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1k.sh
index b1abfdcf525..173be863608 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1k.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1k.sh
@@ -184,7 +184,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   # note: the value of the dropout-proportion is not important, as it's
   # controlled by the dropout schedule; what's important is that we set it.
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1l.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1l.sh
index ef151d72875..94955d0472c 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1l.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1l.sh
@@ -174,7 +174,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   # note: the value of the dropout-proportion is not important, as it's
   # controlled by the dropout schedule; what's important is that we set it.
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1m.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1m.sh
index c2aac3f6e20..efd3bc98725 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1m.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1m.sh
@@ -174,7 +174,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   # note: the value of the dropout-proportion is not important, as it's
   # controlled by the dropout schedule; what's important is that we set it.
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1n.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1n.sh
index ed6cb66957d..c0559e8d389 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1n.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1n.sh
@@ -185,7 +185,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   # note: the value of the dropout-proportion is not important, as it's
   # controlled by the dropout schedule; what's important is that we set it.
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1o.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1o.sh
index 8a4b7468058..5a6dbaef8af 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1o.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1o.sh
@@ -189,7 +189,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   # note: the value of the dropout-proportion is not important, as it's
   # controlled by the dropout schedule; what's important is that we set it.
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1r.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1r.sh
index 8f80a6885ca..dd38d56759f 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1r.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1r.sh
@@ -187,7 +187,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   tdnn_opts='ng-affine-options="update-period=1"'
   lstmp_opts='ng-affine-options="update-period=1" decay-time=20'
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1s.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1s.sh
index ef1c7fc196f..1378d2d176d 100644
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1s.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1s.sh
@@ -151,7 +151,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1t.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1t.sh
index 19479de41aa..3c4882ec2c6 100644
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1t.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1t.sh
@@ -152,7 +152,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1u.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1u.sh
index 85c0e4a0661..23ea14ae151 100644
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1u.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1u.sh
@@ -145,7 +145,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1v.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1v.sh
index e0431a83ceb..7c44d963504 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1v.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_1v.sh
@@ -149,7 +149,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_1a.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_1a.sh
index e1543c0120f..042ef346578 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_1a.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_1a.sh
@@ -159,7 +159,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_bs_1a.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_bs_1a.sh
index d08a7ad5e86..905e1845183 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_bs_1a.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_bs_1a.sh
@@ -163,7 +163,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_bs_1b.sh b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_bs_1b.sh
index d256150484b..7bd96e7d82c 100755
--- a/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_bs_1b.sh
+++ b/egs/tedlium/s5_r2/local/chain/tuning/run_tdnn_lstm_attention_bs_1b.sh
@@ -150,7 +150,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r3/local/chain/tuning/run_tdnn_1a.sh b/egs/tedlium/s5_r3/local/chain/tuning/run_tdnn_1a.sh
index 40cdcb5b5ff..1204ff6ce4c 100755
--- a/egs/tedlium/s5_r3/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/tedlium/s5_r3/local/chain/tuning/run_tdnn_1a.sh
@@ -143,7 +143,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r3/local/chain/tuning/run_tdnn_1b.sh b/egs/tedlium/s5_r3/local/chain/tuning/run_tdnn_1b.sh
index 9144508e62b..f06ba3fa195 100755
--- a/egs/tedlium/s5_r3/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/tedlium/s5_r3/local/chain/tuning/run_tdnn_1b.sh
@@ -148,7 +148,7 @@ if [ $stage -le 17 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/tedlium/s5_r3/local/ted_download_lm.sh b/egs/tedlium/s5_r3/local/ted_download_lm.sh
index ad833555b5f..6118876a0ab 100755
--- a/egs/tedlium/s5_r3/local/ted_download_lm.sh
+++ b/egs/tedlium/s5_r3/local/ted_download_lm.sh
@@ -13,4 +13,4 @@ echo "$0: downloading Tedlium 4 gram language models (it won't re-download if it
 wget --continue http://kaldi-asr.org/models/5/4gram_small.arpa.gz -P data/local/local_lm/data/arpa || exit 1
 wget --continue http://kaldi-asr.org/models/5/4gram_big.arpa.gz -P data/local/local_lm/data/arpa || exit 1
 
-exit 0
\ No newline at end of file
+exit 0
diff --git a/egs/tunisian_msa/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/tunisian_msa/s5/local/chain/tuning/run_tdnn_1a.sh
index a2662584549..ab68ba6fb68 100755
--- a/egs/tunisian_msa/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/tunisian_msa/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -142,7 +142,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   affine_opts="l2-regularize=0.03 dropout-proportion=0.0 dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.03 dropout-proportion=0.0 bypass-scale=0.66"
   linear_opts="l2-regularize=0.03 orthonormal-constraint=-1.0"
diff --git a/egs/uw3/v1/local/chain/run_cnn_1a.sh b/egs/uw3/v1/local/chain/run_cnn_1a.sh
index 582bfc90105..e3548609da7 100755
--- a/egs/uw3/v1/local/chain/run_cnn_1a.sh
+++ b/egs/uw3/v1/local/chain/run_cnn_1a.sh
@@ -130,7 +130,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   common1="required-time-offsets=0 height-offsets=-2,-1,0,1,2 num-filters-out=12"
 
   mkdir -p $dir/configs
diff --git a/egs/vystadial_cz/s5b/local/chain/tuning/run_tdnn_1a.sh b/egs/vystadial_cz/s5b/local/chain/tuning/run_tdnn_1a.sh
index 496ee5e84ca..844ccf80677 100755
--- a/egs/vystadial_cz/s5b/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/vystadial_cz/s5b/local/chain/tuning/run_tdnn_1a.sh
@@ -148,7 +148,7 @@ if [ $stage -le 13 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.05 dropout-per-dim-continuous=true"
   output_opts="l2-regularize=0.02 bottleneck-dim=192"
 
diff --git a/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1a.sh b/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
index ceca428f5c1..e656b67e529 100755
--- a/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1a.sh
@@ -167,7 +167,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1b.sh b/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1b.sh
index a3a747ed743..9db76e94430 100755
--- a/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1b.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1b.sh
@@ -170,7 +170,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1c.sh b/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1c.sh
index dc47681593f..36ec5bb61af 100755
--- a/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1c.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_cnn_tdnn_1c.sh
@@ -155,7 +155,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.01"
   ivector_affine_opts="l2-regularize=0.01"
   tdnn_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim-continuous=true"
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_1a.sh
index 10a9c608811..8d44db6f917 100755
--- a/egs/wsj/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -183,7 +183,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_1b.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_1b.sh
index a2bb7e93388..544b9b04a0a 100755
--- a/egs/wsj/s5/local/chain/tuning/run_tdnn_1b.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_1b.sh
@@ -158,7 +158,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_1c.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_1c.sh
index 7dc30ecf8fe..b268ed7feda 100755
--- a/egs/wsj/s5/local/chain/tuning/run_tdnn_1c.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_1c.sh
@@ -159,7 +159,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_1d.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_1d.sh
index 603e0f064b9..d1a7f9d0663 100755
--- a/egs/wsj/s5/local/chain/tuning/run_tdnn_1d.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_1d.sh
@@ -159,7 +159,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_1e.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_1e.sh
index 9808e274d83..e20069fbfa1 100755
--- a/egs/wsj/s5/local/chain/tuning/run_tdnn_1e.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_1e.sh
@@ -167,7 +167,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.01"
   output_opts="l2-regularize=0.0025"
 
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_1f.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_1f.sh
index e3d13ac1f65..86df0779841 100755
--- a/egs/wsj/s5/local/chain/tuning/run_tdnn_1f.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_1f.sh
@@ -161,7 +161,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   opts="l2-regularize=0.01"
   output_opts="l2-regularize=0.005 bottleneck-dim=320"
 
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
index 4b752a55a4b..6e4f220c1f2 100755
--- a/egs/wsj/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -181,7 +181,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
 
   mkdir -p $dir/configs
   cat <<EOF > $dir/configs/network.xconfig
diff --git a/egs/wsj/s5/local/chain/tuning/run_tdnn_lstm_1b.sh b/egs/wsj/s5/local/chain/tuning/run_tdnn_lstm_1b.sh
index 51fefb9ca88..2d113e58a93 100755
--- a/egs/wsj/s5/local/chain/tuning/run_tdnn_lstm_1b.sh
+++ b/egs/wsj/s5/local/chain/tuning/run_tdnn_lstm_1b.sh
@@ -473,7 +473,7 @@ if [ $stage -le 15 ]; then
   echo "$0: creating neural net configs using the xconfig parser";
 
   num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
-  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   tdnn_opts="l2-regularize=0.01"
   output_opts="l2-regularize=0.005 bottleneck-dim=256"
   lstm_opts="l2-regularize=0.005 self-scale=2.0"
diff --git a/egs/wsj/s5/steps/cleanup/internal/segment_ctm_edits.py b/egs/wsj/s5/steps/cleanup/internal/segment_ctm_edits.py
index e571fefb84c..2ea8f5f6070 100755
--- a/egs/wsj/s5/steps/cleanup/internal/segment_ctm_edits.py
+++ b/egs/wsj/s5/steps/cleanup/internal/segment_ctm_edits.py
@@ -70,7 +70,7 @@
                     help="""Minimum duration of silence or non-scored word
                     to be considered a viable split point when
                     truncating based on junk proportion.""")
-parser.add_argument("--max-deleted-words-kept-when-merging", type = str, default = 1,
+parser.add_argument("--max-deleted-words-kept-when-merging", type = int, default = 1,
                     help = "When merging segments that are found to be overlapping or "
                     "adjacent after all other processing, keep in the transcript the "
                     "reference words that were deleted between the segments [if any] "
diff --git a/egs/yomdle_fa/v1/local/chain/run_cnn_e2eali_1b.sh b/egs/yomdle_fa/v1/local/chain/run_cnn_e2eali_1b.sh
index e7c125d16de..700b57d9fce 100755
--- a/egs/yomdle_fa/v1/local/chain/run_cnn_e2eali_1b.sh
+++ b/egs/yomdle_fa/v1/local/chain/run_cnn_e2eali_1b.sh
@@ -131,7 +131,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/yomdle_korean/v1/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/yomdle_korean/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
index c43d7c669c1..03333f6d229 100755
--- a/egs/yomdle_korean/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/yomdle_korean/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
@@ -127,7 +127,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/yomdle_korean/v1/local/chain/tuning/run_cnn_e2eali_1b.sh b/egs/yomdle_korean/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
index 8fca9235f46..fd9cdc8921d 100755
--- a/egs/yomdle_korean/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
+++ b/egs/yomdle_korean/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
@@ -124,7 +124,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/yomdle_korean/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1a.sh b/egs/yomdle_korean/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1a.sh
index 654880fcf59..f6b2c1bac42 100755
--- a/egs/yomdle_korean/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1a.sh
+++ b/egs/yomdle_korean/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1a.sh
@@ -143,7 +143,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/yomdle_korean/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1b.sh b/egs/yomdle_korean/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1b.sh
index eb688151665..8185fa2645d 100755
--- a/egs/yomdle_korean/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1b.sh
+++ b/egs/yomdle_korean/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1b.sh
@@ -142,7 +142,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/yomdle_russian/v1/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/yomdle_russian/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
index 7301db33d85..cd582472993 100755
--- a/egs/yomdle_russian/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/yomdle_russian/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
@@ -119,7 +119,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/yomdle_tamil/v1/local/chain/tuning/run_cnn_e2eali_1a.sh b/egs/yomdle_tamil/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
index c43d7c669c1..03333f6d229 100755
--- a/egs/yomdle_tamil/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
+++ b/egs/yomdle_tamil/v1/local/chain/tuning/run_cnn_e2eali_1a.sh
@@ -127,7 +127,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/yomdle_tamil/v1/local/chain/tuning/run_cnn_e2eali_1b.sh b/egs/yomdle_tamil/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
index 9a12a5a9e1e..fb15ce10dde 100755
--- a/egs/yomdle_tamil/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
+++ b/egs/yomdle_tamil/v1/local/chain/tuning/run_cnn_e2eali_1b.sh
@@ -125,7 +125,7 @@ if [ $stage -le 4 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/yomdle_tamil/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1a.sh b/egs/yomdle_tamil/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1a.sh
index 654880fcf59..f6b2c1bac42 100755
--- a/egs/yomdle_tamil/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1a.sh
+++ b/egs/yomdle_tamil/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1a.sh
@@ -143,7 +143,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/yomdle_tamil/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1b.sh b/egs/yomdle_tamil/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1b.sh
index 08641f6a38a..17d59642b05 100755
--- a/egs/yomdle_tamil/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1b.sh
+++ b/egs/yomdle_tamil/v1/local/semisup/chain/run_cnn_chainali_semisupervised_1b.sh
@@ -142,7 +142,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.03 dropout-proportion=0.0"
   tdnn_opts="l2-regularize=0.03"
   output_opts="l2-regularize=0.04"
diff --git a/egs/yomdle_zh/v1/local/chain/run_cnn_e2eali_1b.sh b/egs/yomdle_zh/v1/local/chain/run_cnn_e2eali_1b.sh
index 4183aa74587..0a4e00d7aed 100755
--- a/egs/yomdle_zh/v1/local/chain/run_cnn_e2eali_1b.sh
+++ b/egs/yomdle_zh/v1/local/chain/run_cnn_e2eali_1b.sh
@@ -130,7 +130,7 @@ 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   cnn_opts="l2-regularize=0.075"
   tdnn_opts="l2-regularize=0.075"
   output_opts="l2-regularize=0.1"
diff --git a/egs/zeroth_korean/s5/local/chain/tuning/run_tdnn_1a.sh b/egs/zeroth_korean/s5/local/chain/tuning/run_tdnn_1a.sh
index 55e046dd55a..14b9a8d6c8e 100755
--- a/egs/zeroth_korean/s5/local/chain/tuning/run_tdnn_1a.sh
+++ b/egs/zeroth_korean/s5/local/chain/tuning/run_tdnn_1a.sh
@@ -156,7 +156,7 @@ if [ $stage -le 11 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   tdnn_opts="l2-regularize=0.01 dropout-proportion=0.0 dropout-per-dim-continuous=true"
   tdnnf_opts="l2-regularize=0.01 dropout-proportion=0.0 bypass-scale=0.66"
   linear_opts="l2-regularize=0.01 orthonormal-constraint=-1.0"
diff --git a/egs/zeroth_korean/s5/local/chain/tuning/run_tdnn_opgru_1a.sh b/egs/zeroth_korean/s5/local/chain/tuning/run_tdnn_opgru_1a.sh
index 44110888519..28b36243ba3 100755
--- a/egs/zeroth_korean/s5/local/chain/tuning/run_tdnn_opgru_1a.sh
+++ b/egs/zeroth_korean/s5/local/chain/tuning/run_tdnn_opgru_1a.sh
@@ -158,7 +158,7 @@ if [ $stage -le 11 ]; 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/$xent_regularize" | python)
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
   gru_opts="dropout-per-frame=true dropout-proportion=0.0"
 
   mkdir -p $dir/configs
diff --git a/scripts/rnnlm/choose_features.py b/scripts/rnnlm/choose_features.py
index c6621e04494..595c1d85bc1 100755
--- a/scripts/rnnlm/choose_features.py
+++ b/scripts/rnnlm/choose_features.py
@@ -10,12 +10,8 @@
 from collections import defaultdict
 sys.stdout = open(1, 'w', encoding='utf-8', closefd=False)
 
-# because this script splits inside words, we cannot use latin-1; we actually need to know what 
-# what the encoding is.  By default we make this utf-8; to handle encodings that are not compatible
-# with utf-8 (e.g. gbk), we'll eventually have to make the encoding an option to this script.
-
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="This script chooses the sparse feature representation of words. "
                                              "To be more specific, it chooses the set of features-- you compute "
@@ -92,7 +88,7 @@ def read_vocab(vocab_file):
     vocab = {}
     with open(vocab_file, 'r', encoding="utf-8") as f:
         for line in f:
-            fields = re.split(tab_or_space, line)
+            fields = line.split()
             assert len(fields) == 2
             if fields[0] in vocab:
                 sys.exit(sys.argv[0] + ": duplicated word({0}) in vocab: {1}"
@@ -121,7 +117,7 @@ def read_unigram_probs(unigram_probs_file):
     unigram_probs = []
     with open(unigram_probs_file, 'r', encoding="utf-8") as f:
         for line in f:
-            fields = re.split(tab_or_space, line)
+            fields = line.split()
             assert len(fields) == 2
             idx = int(fields[0])
             if idx >= len(unigram_probs):
diff --git a/scripts/rnnlm/get_best_model.py b/scripts/rnnlm/get_best_model.py
index 333ed8dbfc7..ed266346e06 100755
--- a/scripts/rnnlm/get_best_model.py
+++ b/scripts/rnnlm/get_best_model.py
@@ -21,7 +21,7 @@
 
 num_iters = None
 try:
-    with open(args.rnnlm_dir + "/info.txt", encoding="latin-1") as f:
+    with open(args.rnnlm_dir + "/info.txt", encoding="utf-8") as f:
         for line in f:
             a = line.split("=")
             if a[0] == "num_iters":
@@ -40,7 +40,7 @@
 for i in range(1, num_iters):
     this_logfile = "{0}/log/compute_prob.{1}.log".format(args.rnnlm_dir, i)
     try:
-        f = open(this_logfile, 'r', encoding='latin-1')
+        f = open(this_logfile, 'r', encoding='utf-8')
     except:
         sys.exit(sys.argv[0] + ": could not open log-file {0}".format(this_logfile))
     this_objf = -1000
diff --git a/scripts/rnnlm/get_embedding_dim.py b/scripts/rnnlm/get_embedding_dim.py
index 63eaf307498..1d516e0edf5 100755
--- a/scripts/rnnlm/get_embedding_dim.py
+++ b/scripts/rnnlm/get_embedding_dim.py
@@ -45,7 +45,7 @@
 left_context=0
 right_context=0
 for line in out_lines:
-    line = line.decode('latin-1')
+    line = line.decode('utf-8')
     m = re.search(r'input-node name=input dim=(\d+)', line)
     if m is not None:
         try:
diff --git a/scripts/rnnlm/get_special_symbol_opts.py b/scripts/rnnlm/get_special_symbol_opts.py
index 4310b116ad7..7ee0ca54c9a 100755
--- a/scripts/rnnlm/get_special_symbol_opts.py
+++ b/scripts/rnnlm/get_special_symbol_opts.py
@@ -9,7 +9,7 @@
 import sys
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="This script checks whether the special symbols "
                                  "appear in words.txt with expected values, if not, it will "
@@ -28,9 +28,9 @@
 
 lower_ids = {}
 upper_ids = {}
-input_stream = io.TextIOWrapper(sys.stdin.buffer, encoding='latin-1')
+input_stream = io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8')
 for line in input_stream:
-    fields = re.split(tab_or_space, line)
+    fields = line.split()
     assert(len(fields) == 2)
     sym = fields[0]
     if sym in special_symbols:
diff --git a/scripts/rnnlm/get_unigram_probs.py b/scripts/rnnlm/get_unigram_probs.py
index ab3f9bb382f..e3189b26a92 100755
--- a/scripts/rnnlm/get_unigram_probs.py
+++ b/scripts/rnnlm/get_unigram_probs.py
@@ -8,7 +8,7 @@
 import sys
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="This script gets the unigram probabilities of words.",
                                  epilog="E.g. " + sys.argv[0] + " --vocab-file=data/rnnlm/vocab/words.txt "
@@ -77,10 +77,10 @@ def get_all_data_sources_except_dev(text_dir):
 #                    value is a tuple (repeated_times_per_epoch, weight)
 def read_data_weights(weights_file, data_sources):
     data_weights = {}
-    with open(weights_file, 'r', encoding="latin-1") as f:
+    with open(weights_file, 'r', encoding="utf-8") as f:
         for line in f:
             try:
-                fields = re.split(tab_or_space, line)
+                fields = line.split()
                 assert len(fields) == 3
                 if fields[0] in data_weights:
                     raise Exception("duplicated data source({0}) specified in "
@@ -102,9 +102,9 @@ def read_data_weights(weights_file, data_sources):
 # return the vocab, which is a dict mapping the word to a integer id.
 def read_vocab(vocab_file):
     vocab = {}
-    with open(vocab_file, 'r', encoding="latin-1") as f:
+    with open(vocab_file, 'r', encoding="utf-8") as f:
         for line in f:
-            fields = re.split(tab_or_space, line)
+            fields = line.split()
             assert len(fields) == 2
             if fields[0] in vocab:
                 sys.exit(sys.argv[0] + ": duplicated word({0}) in vocab: {1}"
@@ -131,9 +131,9 @@ def get_counts(data_sources, data_weights, vocab):
         if weight == 0.0:
             continue
 
-        with open(counts_file, 'r', encoding="latin-1") as f:
+        with open(counts_file, 'r', encoding="utf-8") as f:
             for line in f:
-                fields = re.split(tab_or_space, line)
+                fields = line.split()
                 if len(fields) != 2: print("Warning, should be 2 cols:", fields, line, file=sys.stderr);
                 assert(len(fields) == 2)
                 word = fields[0]
diff --git a/scripts/rnnlm/get_vocab.py b/scripts/rnnlm/get_vocab.py
index 1502e915f9c..baafcb3a131 100755
--- a/scripts/rnnlm/get_vocab.py
+++ b/scripts/rnnlm/get_vocab.py
@@ -6,10 +6,10 @@
 import os
 import argparse
 import sys
-sys.stdout = open(1, 'w', encoding='latin-1', closefd=False)
+sys.stdout = open(1, 'w', encoding='utf-8', closefd=False)
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="This script get a vocab from unigram counts "
                                  "of words produced by get_unigram_counts.sh",
@@ -28,10 +28,10 @@
 # Add the count for every word in counts_file
 # the result is written into word_counts
 def add_counts(word_counts, counts_file):
-    with open(counts_file, 'r', encoding="latin-1") as f:
+    with open(counts_file, 'r', encoding="utf-8") as f:
         for line in f:
             line = line.strip(" \t\r\n")
-            word_and_count = re.split(tab_or_space, line)
+            word_and_count = line.split()
             assert len(word_and_count) == 2
             if word_and_count[0] in word_counts:
                 word_counts[word_and_count[0]] += int(word_and_count[1])
diff --git a/scripts/rnnlm/get_word_features.py b/scripts/rnnlm/get_word_features.py
index aeb7a3ec6ae..cdcc0a77734 100755
--- a/scripts/rnnlm/get_word_features.py
+++ b/scripts/rnnlm/get_word_features.py
@@ -10,7 +10,7 @@
 from collections import defaultdict
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="This script turns the words into the sparse feature representation, "
                                              "using features from rnnlm/choose_features.py.",
@@ -41,9 +41,9 @@
 # return the vocab, which is a dict mapping the word to a integer id.
 def read_vocab(vocab_file):
     vocab = {}
-    with open(vocab_file, 'r', encoding="latin-1") as f:
+    with open(vocab_file, 'r', encoding="utf-8") as f:
         for line in f:
-            fields = re.split(tab_or_space, line)
+            fields = line.split()
             assert len(fields) == 2
             if fields[0] in vocab:
                 sys.exit(sys.argv[0] + ": duplicated word({0}) in vocab: {1}"
@@ -62,9 +62,9 @@ def read_vocab(vocab_file):
 # return a list of unigram_probs, indexed by word id
 def read_unigram_probs(unigram_probs_file):
     unigram_probs = []
-    with open(unigram_probs_file, 'r', encoding="latin-1") as f:
+    with open(unigram_probs_file, 'r', encoding="utf-8") as f:
         for line in f:
-            fields = re.split(tab_or_space, line)
+            fields = line.split()
             assert len(fields) == 2
             idx = int(fields[0])
             if idx >= len(unigram_probs):
@@ -103,9 +103,9 @@ def read_features(features_file):
     feats['min_ngram_order'] = 10000
     feats['max_ngram_order'] = -1
 
-    with open(features_file, 'r', encoding="latin-1") as f:
+    with open(features_file, 'r', encoding="utf-8") as f:
         for line in f:
-            fields = re.split(tab_or_space, line)
+            fields = line.split()
             assert(len(fields) in [3, 4, 5])
 
             feat_id = int(fields[0])
diff --git a/scripts/rnnlm/prepare_split_data.py b/scripts/rnnlm/prepare_split_data.py
index cceac48313e..427f043df98 100755
--- a/scripts/rnnlm/prepare_split_data.py
+++ b/scripts/rnnlm/prepare_split_data.py
@@ -9,7 +9,7 @@
 import sys
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="This script prepares files containing integerized text, "
                                  "for consumption by nnet3-get-egs.",
@@ -66,10 +66,10 @@ def get_all_data_sources_except_dev(text_dir):
 #                    value is a tuple (repeated_times_per_epoch, weight)
 def read_data_weights(weights_file, data_sources):
     data_weights = {}
-    with open(weights_file, 'r', encoding="latin-1") as f:
+    with open(weights_file, 'r', encoding="utf-8") as f:
         for line in f:
             try:
-                fields = re.split(tab_or_space, line)
+                fields = line.split()
                 assert len(fields) == 3
                 if fields[0] in data_weights:
                     raise Exception("duplicated data source({0}) specified in "
@@ -97,7 +97,7 @@ def distribute_to_outputs(source_filename, weight, output_filehandles):
     num_outputs = len(output_filehandles)
     n = 0
     try:
-        f = open(source_filename, 'r', encoding="latin-1")
+        f = open(source_filename, 'r', encoding="utf-8")
     except Exception as e:
         sys.exit(sys.argv[0] + ": failed to open file {0} for reading: {1} ".format(
             source_filename, str(e)))
@@ -124,7 +124,7 @@ def distribute_to_outputs(source_filename, weight, output_filehandles):
     os.makedirs(args.split_dir +  "/info")
 
 # set up the 'num_splits' file, which contains an integer.
-with open("{0}/info/num_splits".format(args.split_dir), 'w', encoding="latin-1") as f:
+with open("{0}/info/num_splits".format(args.split_dir), 'w', encoding="utf-8") as f:
     print(args.num_splits, file=f)
 
 # e.g. set temp_files = [ 'foo/1.tmp', 'foo/2.tmp', ..., 'foo/5.tmp' ]
@@ -136,7 +136,7 @@ def distribute_to_outputs(source_filename, weight, output_filehandles):
 temp_filehandles = []
 for fname in temp_files:
     try:
-        temp_filehandles.append(open(fname, 'w', encoding="latin-1"))
+        temp_filehandles.append(open(fname, 'w', encoding="utf-8"))
     except Exception as e:
         sys.exit(sys.argv[0] + ": failed to open file: " + str(e) +
                  ".. if this is a max-open-filehandles limitation, you may "
diff --git a/scripts/rnnlm/rnnlm_cleanup.py b/scripts/rnnlm/rnnlm_cleanup.py
index 40cbee7a496..6a304f7f4cb 100644
--- a/scripts/rnnlm/rnnlm_cleanup.py
+++ b/scripts/rnnlm/rnnlm_cleanup.py
@@ -69,7 +69,7 @@ def get_compute_prob_info(log_file):
     compute_prob_done = False
     # roughly based on code in get_best_model.py
     try:
-        f = open(log_file, "r", encoding="latin-1")
+        f = open(log_file, "r", encoding="utf-8")
     except:
         print(script_name + ": warning: compute_prob log not found for iteration " +
               str(iter) + ". Skipping",
diff --git a/scripts/rnnlm/show_word_features.py b/scripts/rnnlm/show_word_features.py
index 89b134adaf9..4335caed5d8 100755
--- a/scripts/rnnlm/show_word_features.py
+++ b/scripts/rnnlm/show_word_features.py
@@ -7,15 +7,10 @@
 import argparse
 import sys
 
-# The use of latin-1 encoding does not preclude reading utf-8.  latin-1 encoding
-# means "treat words as sequences of bytes", and it is compatible with utf-8
-# encoding as well as other encodings such as gbk, as long as the spaces are
-# also spaces in ascii (which we check).  It is basically how we emulate the
-# behavior of python before python3.
-sys.stdout = open(1, 'w', encoding='latin-1', closefd=False)
+sys.stdout = open(1, 'w', encoding='utf-8', closefd=False)
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="This script turns the word features to a human readable format.",
                                  epilog="E.g. " + sys.argv[0] + "exp/rnnlm/word_feats.txt exp/rnnlm/features.txt "
@@ -36,9 +31,9 @@
 def read_feature_type_and_key(features_file):
     feat_types = {}
 
-    with open(features_file, 'r', encoding="latin-1") as f:
+    with open(features_file, 'r', encoding="utf-8") as f:
         for line in f:
-            fields = re.split(tab_or_space, line)
+            fields = line.split()
             assert(len(fields) in [2, 3, 4])
 
             feat_id = int(fields[0])
@@ -53,9 +48,9 @@ def read_feature_type_and_key(features_file):
 feat_type_and_key = read_feature_type_and_key(args.features_file)
 
 num_word_feats = 0
-with open(args.word_features_file, 'r', encoding="latin-1") as f:
+with open(args.word_features_file, 'r', encoding="utf-8") as f:
     for line in f:
-        fields = re.split(tab_or_space, line)
+        fields = line.split()
         assert len(fields) % 2 == 1
 
         print(int(fields[0]), end='\t')
diff --git a/scripts/rnnlm/validate_features.py b/scripts/rnnlm/validate_features.py
index 2a077da4758..e67f03207bb 100755
--- a/scripts/rnnlm/validate_features.py
+++ b/scripts/rnnlm/validate_features.py
@@ -8,7 +8,7 @@
 import sys
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="Validates features file, produced by rnnlm/choose_features.py.",
                                  epilog="E.g. " + sys.argv[0] + " exp/rnnlm/features.txt",
@@ -24,7 +24,7 @@
 if not os.path.isfile(args.features_file):
     sys.exit(sys.argv[0] + ": Expected file {0} to exist".format(args.features_file))
 
-with open(args.features_file, 'r', encoding="latin-1") as f:
+with open(args.features_file, 'r', encoding="utf-8") as f:
     has_unigram = False
     has_length = False
     idx = 0
@@ -33,7 +33,7 @@
     final_feats = {}
     word_feats = {}
     for line in f:
-        fields = re.split(tab_or_space, line)
+        fields = line.split()
         assert(len(fields) in [3, 4, 5])
 
         assert idx == int(fields[0])
diff --git a/scripts/rnnlm/validate_text_dir.py b/scripts/rnnlm/validate_text_dir.py
index 903e720bdf4..1f250d4c2f8 100755
--- a/scripts/rnnlm/validate_text_dir.py
+++ b/scripts/rnnlm/validate_text_dir.py
@@ -8,7 +8,7 @@
 import sys
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="Validates data directory containing text "
                                  "files from one or more data sources, including dev.txt.",
@@ -40,7 +40,7 @@
 
 
 def check_text_file(text_file):
-    with open(text_file, 'r', encoding="latin-1") as f:
+    with open(text_file, 'r', encoding="utf-8") as f:
         found_nonempty_line = False
         lineno = 0
         if args.allow_internal_eos == 'true':
@@ -54,7 +54,7 @@ def check_text_file(text_file):
             lineno += 1
             if args.spot_check == 'true' and lineno > 10:
                 break
-            words = re.split(tab_or_space, line)
+            words = line.split()
             if len(words) != 0:
                 found_nonempty_line = True
                 for word in words:
@@ -76,9 +76,9 @@ def check_text_file(text_file):
     # with some kind of utterance-id
     first_field_set = set()
     other_fields_set = set()
-    with open(text_file, 'r', encoding="latin-1") as f:
+    with open(text_file, 'r', encoding="utf-8") as f:
         for line in f:
-            array = re.split(tab_or_space, line)
+            array = line.split()
             if len(array) > 0:
                 first_word = array[0]
                 if first_word in first_field_set or first_word in other_fields_set:
diff --git a/scripts/rnnlm/validate_word_features.py b/scripts/rnnlm/validate_word_features.py
index 205b934ae1b..372286d8d12 100755
--- a/scripts/rnnlm/validate_word_features.py
+++ b/scripts/rnnlm/validate_word_features.py
@@ -8,7 +8,7 @@
 import sys
 
 import re
-tab_or_space = re.compile('[ \t]+')
+
 
 parser = argparse.ArgumentParser(description="Validates word features file, produced by rnnlm/get_word_features.py.",
                                  epilog="E.g. " + sys.argv[0] + " --features-file=exp/rnnlm/features.txt "
@@ -28,9 +28,9 @@
 unigram_feat_id = -1
 length_feat_id = -1
 max_feat_id = -1
-with open(args.features_file, 'r', encoding="latin-1") as f:
+with open(args.features_file, 'r', encoding="utf-8") as f:
     for line in f:
-        fields = re.split(tab_or_space, line)
+        fields = line.split()
         assert(len(fields) in [3, 4, 5])
 
         feat_id = int(fields[0])
@@ -52,9 +52,9 @@
         if feat_id > max_feat_id:
             max_feat_id = feat_id
 
-with open(args.word_features_file, 'r', encoding="latin-1") as f:
+with open(args.word_features_file, 'r', encoding="utf-8") as f:
     for line in f:
-        fields = re.split(tab_or_space, line)
+        fields = line.split()
         assert len(fields) > 0 and len(fields) % 2 == 1
         word_id = int(fields[0])