From ff274a5d4358eb34b5d023552350d32fb8bab57f Mon Sep 17 00:00:00 2001
From: aarora8 <aarora8@jhu.edu>
Date: Sun, 17 Nov 2019 15:17:19 -0500
Subject: [PATCH 1/2] 'Adding chime6 with paderborn gss'

---
 egs/chime5/s5b/local/nnet3/compare_wer.sh     |   1 +
 egs/chime5/s5b/local/nnet3/decode.sh          |   4 +-
 egs/chime5/s5b/local/run_recog.sh             |  62 ++-
 egs/chime5/s5b/local/run_wpe.sh               |   3 +-
 egs/chime6/s5_track1/RESULTS                  |  21 +
 egs/chime6/s5_track1/cmd.sh                   |  15 +
 egs/chime6/s5_track1/conf/beamformit.cfg      |  50 +++
 egs/chime6/s5_track1/conf/mfcc.conf           |   2 +
 egs/chime6/s5_track1/conf/mfcc_hires.conf     |  10 +
 egs/chime6/s5_track1/conf/online_cmvn.conf    |   1 +
 egs/chime6/s5_track1/conf/queue.conf          |  10 +
 .../s5_track1/local/chain/compare_wer.sh      | 131 ++++++
 .../local/chain/multi_condition/run_tdnn.sh   |   1 +
 .../chain/multi_condition/run_tdnn_lstm.sh    |   1 +
 .../tuning/run_cnn_tdnn_lstm_1a.sh            | 329 +++++++++++++++
 .../multi_condition/tuning/run_tdnn_1a.sh     | 286 +++++++++++++
 .../multi_condition/tuning/run_tdnn_1b.sh     | 311 +++++++++++++++
 .../tuning/run_tdnn_lstm_1a.sh                | 323 +++++++++++++++
 egs/chime6/s5_track1/local/chain/run_tdnn.sh  |   1 +
 .../chain/tuning/run_cnn_tdnn_lstm_1a.sh      | 304 ++++++++++++++
 .../local/chain/tuning/run_tdnn_1a.sh         | 270 +++++++++++++
 .../local/chain/tuning/run_tdnn_1b.sh         | 247 ++++++++++++
 .../local/chain/tuning/run_tdnn_lstm_1a.sh    | 297 ++++++++++++++
 egs/chime6/s5_track1/local/check_tools.sh     |  71 ++++
 .../s5_track1/local/copy_lat_dir_parallel.sh  |  97 +++++
 egs/chime6/s5_track1/local/distant_audio_list | 376 ++++++++++++++++++
 egs/chime6/s5_track1/local/extract_noises.py  |  83 ++++
 .../s5_track1/local/extract_vad_weights.sh    |  86 ++++
 egs/chime6/s5_track1/local/get_location.py    |  52 +++
 .../s5_track1/local/install_pb_chime5.sh      |  21 +
 egs/chime6/s5_track1/local/json2text.py       |  88 ++++
 egs/chime6/s5_track1/local/make_noise_list.py |  17 +
 .../s5_track1/local/nnet3/compare_wer.sh      | 132 ++++++
 egs/chime6/s5_track1/local/nnet3/decode.sh    | 164 ++++++++
 .../multi_condition/run_ivector_common.sh     | 194 +++++++++
 .../local/nnet3/run_ivector_common.sh         | 151 +++++++
 egs/chime6/s5_track1/local/prepare_data.sh    | 151 +++++++
 egs/chime6/s5_track1/local/prepare_dict.sh    | 124 ++++++
 egs/chime6/s5_track1/local/replace_uttid.py   |  20 +
 .../s5_track1/local/reverberate_lat_dir.sh    |  93 +++++
 egs/chime6/s5_track1/local/run_beamformit.sh  |  87 ++++
 egs/chime6/s5_track1/local/run_gss.sh         |  54 +++
 egs/chime6/s5_track1/local/run_recog.sh       | 192 +++++++++
 egs/chime6/s5_track1/local/run_wpe.py         |  59 +++
 egs/chime6/s5_track1/local/run_wpe.sh         |  86 ++++
 egs/chime6/s5_track1/local/score.sh           |   1 +
 .../s5_track1/local/score_for_submit.sh       | 123 ++++++
 egs/chime6/s5_track1/local/train_lms_srilm.sh | 261 ++++++++++++
 egs/chime6/s5_track1/local/wer_output_filter  |  25 ++
 egs/chime6/s5_track1/local/worn_audio_list    |  64 +++
 egs/chime6/s5_track1/path.sh                  |   7 +
 egs/chime6/s5_track1/run.sh                   | 329 +++++++++++++++
 egs/chime6/s5_track1/steps                    |   1 +
 egs/chime6/s5_track1/utils                    |   1 +
 54 files changed, 5853 insertions(+), 37 deletions(-)
 mode change 100755 => 100644 egs/chime5/s5b/local/nnet3/compare_wer.sh
 create mode 100644 egs/chime6/s5_track1/RESULTS
 create mode 100644 egs/chime6/s5_track1/cmd.sh
 create mode 100755 egs/chime6/s5_track1/conf/beamformit.cfg
 create mode 100644 egs/chime6/s5_track1/conf/mfcc.conf
 create mode 100644 egs/chime6/s5_track1/conf/mfcc_hires.conf
 create mode 100644 egs/chime6/s5_track1/conf/online_cmvn.conf
 create mode 100644 egs/chime6/s5_track1/conf/queue.conf
 create mode 100755 egs/chime6/s5_track1/local/chain/compare_wer.sh
 create mode 120000 egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn.sh
 create mode 120000 egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn_lstm.sh
 create mode 100755 egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_cnn_tdnn_lstm_1a.sh
 create mode 100755 egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1a.sh
 create mode 100755 egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1b.sh
 create mode 100755 egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
 create mode 120000 egs/chime6/s5_track1/local/chain/run_tdnn.sh
 create mode 100755 egs/chime6/s5_track1/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
 create mode 100755 egs/chime6/s5_track1/local/chain/tuning/run_tdnn_1a.sh
 create mode 100755 egs/chime6/s5_track1/local/chain/tuning/run_tdnn_1b.sh
 create mode 100755 egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lstm_1a.sh
 create mode 100755 egs/chime6/s5_track1/local/check_tools.sh
 create mode 100755 egs/chime6/s5_track1/local/copy_lat_dir_parallel.sh
 create mode 100644 egs/chime6/s5_track1/local/distant_audio_list
 create mode 100755 egs/chime6/s5_track1/local/extract_noises.py
 create mode 100755 egs/chime6/s5_track1/local/extract_vad_weights.sh
 create mode 100755 egs/chime6/s5_track1/local/get_location.py
 create mode 100755 egs/chime6/s5_track1/local/install_pb_chime5.sh
 create mode 100755 egs/chime6/s5_track1/local/json2text.py
 create mode 100755 egs/chime6/s5_track1/local/make_noise_list.py
 create mode 100755 egs/chime6/s5_track1/local/nnet3/compare_wer.sh
 create mode 100755 egs/chime6/s5_track1/local/nnet3/decode.sh
 create mode 100755 egs/chime6/s5_track1/local/nnet3/multi_condition/run_ivector_common.sh
 create mode 100755 egs/chime6/s5_track1/local/nnet3/run_ivector_common.sh
 create mode 100755 egs/chime6/s5_track1/local/prepare_data.sh
 create mode 100755 egs/chime6/s5_track1/local/prepare_dict.sh
 create mode 100755 egs/chime6/s5_track1/local/replace_uttid.py
 create mode 100755 egs/chime6/s5_track1/local/reverberate_lat_dir.sh
 create mode 100755 egs/chime6/s5_track1/local/run_beamformit.sh
 create mode 100755 egs/chime6/s5_track1/local/run_gss.sh
 create mode 100755 egs/chime6/s5_track1/local/run_recog.sh
 create mode 100755 egs/chime6/s5_track1/local/run_wpe.py
 create mode 100755 egs/chime6/s5_track1/local/run_wpe.sh
 create mode 120000 egs/chime6/s5_track1/local/score.sh
 create mode 100755 egs/chime6/s5_track1/local/score_for_submit.sh
 create mode 100755 egs/chime6/s5_track1/local/train_lms_srilm.sh
 create mode 100755 egs/chime6/s5_track1/local/wer_output_filter
 create mode 100644 egs/chime6/s5_track1/local/worn_audio_list
 create mode 100644 egs/chime6/s5_track1/path.sh
 create mode 100755 egs/chime6/s5_track1/run.sh
 create mode 120000 egs/chime6/s5_track1/steps
 create mode 120000 egs/chime6/s5_track1/utils

diff --git a/egs/chime5/s5b/local/nnet3/compare_wer.sh b/egs/chime5/s5b/local/nnet3/compare_wer.sh
old mode 100755
new mode 100644
index 095e85cc338..fa627acd27b
--- a/egs/chime5/s5b/local/nnet3/compare_wer.sh
+++ b/egs/chime5/s5b/local/nnet3/compare_wer.sh
@@ -130,3 +130,4 @@ done
 echo
 
 echo
+
diff --git a/egs/chime5/s5b/local/nnet3/decode.sh b/egs/chime5/s5b/local/nnet3/decode.sh
index 7af09f36a13..8fa54e0d4a6 100755
--- a/egs/chime5/s5b/local/nnet3/decode.sh
+++ b/egs/chime5/s5b/local/nnet3/decode.sh
@@ -35,6 +35,8 @@ post_decode_acwt=1.0 # important to change this when using chain models
 extra_left_context_initial=0
 extra_right_context_final=0
 
+graph_affix=
+
 score_opts="--min-lmwt 6 --max-lmwt 13"
 
 . ./cmd.sh
@@ -94,7 +96,7 @@ if [ $stage -le 2 ]; then
   fi
 fi
 
-decode_dir=$dir/decode_${data_set}${affix}
+decode_dir=$dir/decode${graph_affix}_${data_set}${affix}
 # generate the lattices
 if [ $stage -le 3 ]; then
   echo "Generating lattices, stage 1"
diff --git a/egs/chime5/s5b/local/run_recog.sh b/egs/chime5/s5b/local/run_recog.sh
index 5c74c9ff242..989a5f95d01 100755
--- a/egs/chime5/s5b/local/run_recog.sh
+++ b/egs/chime5/s5b/local/run_recog.sh
@@ -28,8 +28,8 @@ json_dir=${chime5_corpus}/transcriptions
 audio_dir=${chime5_corpus}/audio
 
 # training and test data
-train_set=train_worn_u100k
-test_sets="eval_${enhancement}_ref"
+train_set=train_worn_simu_u400k
+test_sets="eval_${enhancement}_dereverb_ref"
 
 # This script also needs the phonetisaurus g2p, srilm, beamformit
 ./local/check_tools.sh || exit 1
@@ -38,18 +38,27 @@ if [ $stage -le 4 ]; then
   # Beamforming using reference arrays
   # enhanced WAV directory
   enhandir=enhan
+  dereverb_dir=${PWD}/wav/wpe/
   for dset in eval; do
     for mictype in u01 u02 u03 u04 u05 u06; do
-      local/run_beamformit.sh --cmd "$train_cmd" \
+      local/run_wpe.sh --nj 4 --cmd "$train_cmd --mem 120G" \
 			      ${audio_dir}/${dset} \
+			      ${dereverb_dir}/${dset} \
+			      ${mictype}
+    done
+  done
+  for dset in dev eval; do
+    for mictype in u01 u02 u03 u04 u05 u06; do
+      local/run_beamformit.sh --cmd "$train_cmd" \
+			      ${dereverb_dir}/${dset} \
 			      ${enhandir}/${dset}_${enhancement}_${mictype} \
 			      ${mictype}
     done
   done
-  
+
   for dset in eval; do
     local/prepare_data.sh --mictype ref "$PWD/${enhandir}/${dset}_${enhancement}_u0*" \
-			  ${json_dir}/${dset} data/${dset}_${enhancement}_ref
+			  ${json_dir}/${dset} data/${dset}_${enhancement}_dereverb_ref
   done
 fi
 
@@ -92,28 +101,13 @@ if [ $stage -le 7 ]; then
   done
 fi
 
-if [ $stage -le 17 ]; then
-  nnet3_affix=_${train_set}_cleaned
-  for datadir in ${test_sets}; do
-    utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
-  done
-  for datadir in ${test_sets}; do
-    steps/make_mfcc.sh --nj 20 --mfcc-config conf/mfcc_hires.conf \
-      --cmd "$train_cmd" data/${datadir}_hires || exit 1;
-    steps/compute_cmvn_stats.sh data/${datadir}_hires || exit 1;
-    utils/fix_data_dir.sh data/${datadir}_hires || exit 1;
-  done
-  for data in $test_sets; do
-    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 20 \
-      data/${data}_hires exp/nnet3${nnet3_affix}/extractor \
-      exp/nnet3${nnet3_affix}/ivectors_${data}_hires
-  done
-fi
+nnet3_affix=_${train_set}_cleaned_rvb
+
+lm_suffix=
 
 if [ $stage -le 18 ]; then
   # First the options that are passed through to run_ivector_common.sh
   # (some of which are also used in this script directly).
-  lm_suffix=
 
   # The rest are configs specific to this script.  Most of the parameters
   # are just hardcoded at this level, in the commands below.
@@ -138,16 +132,14 @@ if [ $stage -le 18 ]; then
 
   for data in $test_sets; do
     (
-      steps/nnet3/decode.sh \
-          --acwt 1.0 --post-decode-acwt 10.0 \
-          --extra-left-context $chunk_left_context \
-          --extra-right-context $chunk_right_context \
-          --extra-left-context-initial 0 \
-          --extra-right-context-final 0 \
-          --frames-per-chunk $frames_per_chunk \
-          --nj 8 --cmd "$decode_cmd"  --num-threads 4 \
-          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
-          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+      local/nnet3/decode.sh --affix 2stage --pass2-decode-opts "--min-active 1000" \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --frames-per-chunk 150 --nj $decode_nj \
+        --ivector-dir exp/nnet3${nnet3_affix} \
+        --graph-affix ${lm_suffix} \
+        data/${data} data/lang${lm_suffix} \
+        $tree_dir/graph${lm_suffix} \
+        exp/chain${nnet3_affix}/tdnn1b_sp 
     ) || touch $dir/.error &
   done
   wait
@@ -159,6 +151,6 @@ if [ $stage -le 20 ]; then
   # please specify both dev and eval set directories so that the search parameters
   # (insertion penalty and language model weight) will be tuned using the dev set
   local/score_for_submit.sh \
-      --dev exp/chain_${train_set}_cleaned/tdnn1a_sp/decode_dev_${enhancement}_ref \
-      --eval exp/chain_${train_set}_cleaned/tdnn1a_sp/decode_eval_${enhancement}_ref
+      --dev exp/chain${nnet3_affix}/tdnn1b_sp/decode${lm_suffix}_dev_${enhancement}_dereverb_ref_2stage \
+      --eval exp/chain${nnet3_affix}/tdnn1b_sp/decode${lm_suffix}_eval_${enhancement}_dereverb_ref_2stage
 fi
diff --git a/egs/chime5/s5b/local/run_wpe.sh b/egs/chime5/s5b/local/run_wpe.sh
index 1c4b1c80291..ed512e69aae 100755
--- a/egs/chime5/s5b/local/run_wpe.sh
+++ b/egs/chime5/s5b/local/run_wpe.sh
@@ -33,7 +33,8 @@ set -o pipefail
 
 miniconda_dir=$HOME/miniconda3/
 if [ ! -d $miniconda_dir ]; then
-    echo "$miniconda_dir does not exist. Please run '../../../tools/extras/install_miniconda.sh' and '../../../tools/extras/install_wpe.sh';"
+    echo "$miniconda_dir does not exist. Please run '$KALDI_ROOT/tools/extras/install_miniconda.sh'."
+    exit 1
 fi
 
 # check if WPE is installed
diff --git a/egs/chime6/s5_track1/RESULTS b/egs/chime6/s5_track1/RESULTS
new file mode 100644
index 00000000000..00cc98401bc
--- /dev/null
+++ b/egs/chime6/s5_track1/RESULTS
@@ -0,0 +1,21 @@
+
+# tri2
+%WER 88.52 [ 52121 / 58881, 2023 ins, 30285 del, 19813 sub ] exp/tri2/decode_dev_gss/wer_17_0.5
+
+# tri3
+%WER 85.72 [ 50471 / 58881, 3079 ins, 23787 del, 23605 sub ] exp/tri3/decode_dev_gss/wer_17_0.5
+
+# nnet3 tdnn+chain
+%WER 41.21 [ 24267 / 58881, 2428 ins, 7606 del, 14233 sub ] exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn1b_sp/decode_dev_worn_2stage/wer_11_0.0
+%WER 62.30 [ 36684 / 58881, 3048 ins, 17964 del, 15672 sub ] exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn1b_sp/decode_dev_gss_2stage/wer_11_0.0
+
+# result with the challenge submission format (Nov 17, 2019)
+# after the fix of speaker ID across arrays
+==== development set ====
+session S02 room dining: #words 8288, #errors 5106, wer 61.60 %
+session S02 room kitchen: #words 12696, #errors 9177, wer 72.28 %
+session S02 room living: #words 15460, #errors 8388, wer 54.25 %
+session S09 room dining: #words 5766, #errors 3821, wer 66.26 %
+session S09 room kitchen: #words 8911, #errors 5594, wer 62.77 %
+session S09 room living: #words 7760, #errors 4598, wer 59.25 %
+overall: #words 58881, #errors 36684, wer 62.30 %
diff --git a/egs/chime6/s5_track1/cmd.sh b/egs/chime6/s5_track1/cmd.sh
new file mode 100644
index 00000000000..9702501f1a7
--- /dev/null
+++ b/egs/chime6/s5_track1/cmd.sh
@@ -0,0 +1,15 @@
+# you can change cmd.sh depending on what type of queue you are using.
+# If you have no queueing system and want to run on a local machine, you
+# can change all instances 'queue.pl' to run.pl (but be careful and run
+# commands one by one: most recipes will exhaust the memory on your
+# machine).  queue.pl works with GridEngine (qsub).  slurm.pl works
+# with slurm.  Different queues are configured differently, with different
+# queue names and different ways of specifying things like memory;
+# to account for these differences you can create and edit the file
+# conf/queue.conf to match your queue's configuration.  Search for
+# conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information,
+# or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl.
+
+export train_cmd="retry.pl queue.pl --mem 2G"
+export decode_cmd="queue.pl --mem 4G"
+
diff --git a/egs/chime6/s5_track1/conf/beamformit.cfg b/egs/chime6/s5_track1/conf/beamformit.cfg
new file mode 100755
index 00000000000..70fdd858651
--- /dev/null
+++ b/egs/chime6/s5_track1/conf/beamformit.cfg
@@ -0,0 +1,50 @@
+#BeamformIt sample configuration file for AMI data (http://groups.inf.ed.ac.uk/ami/download/)
+
+# scrolling size to compute the delays
+scroll_size = 250
+
+# cross correlation computation window size
+window_size = 500
+
+#amount of maximum points for the xcorrelation taken into account
+nbest_amount = 4
+
+#flag wether to apply an automatic noise thresholding 
+do_noise_threshold = 1
+
+#Percentage of frames with lower xcorr taken as noisy
+noise_percent = 10
+
+######## acoustic modelling parameters
+
+#transition probabilities weight for multichannel decoding
+trans_weight_multi = 25
+trans_weight_nbest = 25
+
+###
+
+#flag wether to print the feaures after setting them, or not
+print_features = 1
+
+#flag wether to use the bad frames in the sum process
+do_avoid_bad_frames = 1
+
+#flag to use the best channel (SNR) as a reference
+#defined from command line
+do_compute_reference = 1
+
+#flag wether to use a uem file or not(process all the file)
+do_use_uem_file = 0
+
+#flag wether to use an adaptative weights scheme or fixed weights
+do_adapt_weights = 1
+
+#flag wether to output the sph files or just run the system to create the auxiliary files
+do_write_sph_files = 1
+
+####directories where to store/retrieve info####
+#channels_file = ./cfg-files/channels
+
+#show needs to be passed as argument normally, here a default one is given just in case
+#show_id = Ttmp
+
diff --git a/egs/chime6/s5_track1/conf/mfcc.conf b/egs/chime6/s5_track1/conf/mfcc.conf
new file mode 100644
index 00000000000..32988403b00
--- /dev/null
+++ b/egs/chime6/s5_track1/conf/mfcc.conf
@@ -0,0 +1,2 @@
+--use-energy=false
+--sample-frequency=16000
diff --git a/egs/chime6/s5_track1/conf/mfcc_hires.conf b/egs/chime6/s5_track1/conf/mfcc_hires.conf
new file mode 100644
index 00000000000..fd64b62eb16
--- /dev/null
+++ b/egs/chime6/s5_track1/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 
+--num-mel-bins=40
+--num-ceps=40
+--low-freq=40
+--high-freq=-400
diff --git a/egs/chime6/s5_track1/conf/online_cmvn.conf b/egs/chime6/s5_track1/conf/online_cmvn.conf
new file mode 100644
index 00000000000..7748a4a4dd3
--- /dev/null
+++ b/egs/chime6/s5_track1/conf/online_cmvn.conf
@@ -0,0 +1 @@
+# configuration file for apply-cmvn-online, used in the script ../local/run_online_decoding.sh
diff --git a/egs/chime6/s5_track1/conf/queue.conf b/egs/chime6/s5_track1/conf/queue.conf
new file mode 100644
index 00000000000..73103195684
--- /dev/null
+++ b/egs/chime6/s5_track1/conf/queue.conf
@@ -0,0 +1,10 @@
+command qsub -v PATH -cwd -S /bin/bash -j y -l arch=*64*
+option mem=* -l mem_free=$0,ram_free=$0
+option mem=0          # Do not add anything to qsub_opts
+option num_threads=* -pe smp $0
+option num_threads=1  # Do not add anything to qsub_opts
+option max_jobs_run=* -tc $0
+default gpu=0
+option gpu=0 -q all.q -l hostname='!b19*'
+option gpu=* -l gpu=$0 -q g.q -l hostname='!b19*'
+
diff --git a/egs/chime6/s5_track1/local/chain/compare_wer.sh b/egs/chime6/s5_track1/local/chain/compare_wer.sh
new file mode 100755
index 00000000000..cd6be14ed88
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/compare_wer.sh
@@ -0,0 +1,131 @@
+#!/bin/bash
+
+# this script is used for comparing decoding results between systems.
+# e.g. local/chain/compare_wer.sh exp/chain/tdnn_{c,d}_sp
+# For use with discriminatively trained systems you specify the epochs after a colon:
+# for instance,
+# local/chain/compare_wer.sh exp/chain/tdnn_c_sp exp/chain/tdnn_c_sp_smbr:{1,2,3}
+
+
+if [ $# == 0 ]; then
+  echo "Usage: $0: [--looped] [--online] <dir1> [<dir2> ... ]"
+  echo "e.g.: $0 exp/chain/tdnn_{b,c}_sp"
+  echo "or (with epoch numbers for discriminative training):"
+  echo "$0 exp/chain/tdnn_b_sp_disc:{1,2,3}"
+  exit 1
+fi
+
+echo "# $0 $*"
+
+include_looped=false
+if [ "$1" == "--looped" ]; then
+  include_looped=true
+  shift
+fi
+include_online=false
+if [ "$1" == "--online" ]; then
+  include_online=true
+  shift
+fi
+
+
+used_epochs=false
+
+# this function set_names is used to separate the epoch-related parts of the name
+# [for discriminative training] and the regular parts of the name.
+# If called with a colon-free directory name, like:
+#  set_names exp/chain/tdnn_lstm1e_sp_bi_smbr
+# it will set dir=exp/chain/tdnn_lstm1e_sp_bi_smbr and epoch_infix=""
+# If called with something like:
+#  set_names exp/chain/tdnn_d_sp_smbr:3
+# it will set dir=exp/chain/tdnn_d_sp_smbr and epoch_infix="_epoch3"
+
+
+set_names() {
+  if [ $# != 1 ]; then
+    echo "compare_wer_general.sh: internal error"
+    exit 1  # exit the program
+  fi
+  dirname=$(echo $1 | cut -d: -f1)
+  epoch=$(echo $1 | cut -s -d: -f2)
+  if [ -z $epoch ]; then
+    epoch_infix=""
+  else
+    used_epochs=true
+    epoch_infix=_epoch${epoch}
+  fi
+}
+
+
+
+echo -n "# System               "
+for x in $*; do   printf "% 10s" " $(basename $x)";   done
+echo
+
+strings=(
+  "#WER dev_clean_2 (tgsmall) "
+  "#WER dev_clean_2 (tglarge) ")
+
+for n in 0 1; do
+   echo -n "${strings[$n]}"
+   for x in $*; do
+     set_names $x  # sets $dirname and $epoch_infix
+    decode_names=(tgsmall_dev_clean_2 tglarge_dev_clean_2)
+
+     wer=$(cat $dirname/decode_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+     printf "% 10s" $wer
+   done
+   echo
+   if $include_looped; then
+     echo -n "#             [looped:]    "
+     for x in $*; do
+       set_names $x  # sets $dirname and $epoch_infix
+       wer=$(cat $dirname/decode_looped_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+       printf "% 10s" $wer
+     done
+     echo
+   fi
+   if $include_online; then
+     echo -n "#             [online:]    "
+     for x in $*; do
+       set_names $x  # sets $dirname and $epoch_infix
+       wer=$(cat ${dirname}_online/decode_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+       printf "% 10s" $wer
+     done
+     echo
+   fi
+done
+
+
+if $used_epochs; then
+  exit 0;  # the diagnostics aren't comparable between regular and discriminatively trained systems.
+fi
+
+
+echo -n "# Final train prob     "
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_train.final.log | grep -v xent | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final valid prob     "
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_valid.final.log | grep -v xent | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final train prob (xent)"
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_train.final.log | grep -w xent | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final valid prob (xent)"
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_valid.final.log | grep -w xent | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn.sh b/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn.sh
new file mode 120000
index 00000000000..61f8f499182
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn.sh
@@ -0,0 +1 @@
+tuning/run_tdnn_1b.sh
\ No newline at end of file
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn_lstm.sh b/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn_lstm.sh
new file mode 120000
index 00000000000..8e647598556
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn_lstm.sh
@@ -0,0 +1 @@
+tuning/run_tdnn_lstm_1a.sh
\ No newline at end of file
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_cnn_tdnn_lstm_1a.sh b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_cnn_tdnn_lstm_1a.sh
new file mode 100755
index 00000000000..65d7caed24c
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_cnn_tdnn_lstm_1a.sh
@@ -0,0 +1,329 @@
+#!/bin/bash
+
+# Set -e here so that we catch if any executable fails immediately
+set -euo pipefail
+
+# First the options that are passed through to run_ivector_common.sh
+# (some of which are also used in this script directly).
+stage=0
+nj=96
+decode_nj=40
+train_set_clean=train_worn_cleaned
+train_set_noisy=train_u400k_cleaned
+combined_train_set=train_worn_u400k_cleaned
+test_sets="dev_worn"
+gmm=tri3_cleaned
+nnet3_affix=_train_worn_u400k_cleaned_rvb
+lm_suffix=
+noise_list=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=_rvb_1a   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+common_egs_dir=
+
+hidden_dim=1024
+cell_dim=1024
+projection_dim=256
+
+# training options
+num_epochs=2  # 2 works better than 4
+chunk_width=140,100,160
+chunk_left_context=40
+chunk_right_context=0
+dropout_schedule='0,0@0.20,0.3@0.50,0'
+xent_regularize=0.025
+label_delay=5
+
+# decode options
+extra_left_context=50
+extra_right_context=0
+
+# training options
+srand=0
+remove_egs=true
+
+#decode options
+test_online_decoding=false  # if true, it will run the last decoding stage.
+
+
+# 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 11" if you have already
+# run those things.
+local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
+                                  --train-set-clean $train_set_clean \
+                                  --train-set-noisy $train_set_noisy \
+                                  --combined-train-set $combined_train_set \
+                                  --noise-list "$noise_list" \
+                                  --test-sets "$test_sets" \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+dir=exp/chain${nnet3_affix}/cnn_tdnn_lstm${affix}_sp
+train_data_dir=data/${combined_train_set}_sp_rvb_hires
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${combined_train_set}_sp_rvb_hires
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+  data/${train_set_clean}_sp/feats.scp data/${train_set_noisy}_sp/feats.scp; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+lat_dir_clean=exp/chain${nnet3_affix}/${gmm}_${train_set_clean}_sp_lats
+lat_dir_noisy=exp/chain${nnet3_affix}/${gmm}_${train_set_noisy}_sp_lats
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${combined_train_set}_sp_rvb_lats
+
+if [ $stage -le 11 ]; then
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    data/${train_set_noisy}_sp data/lang $gmm_dir $lat_dir_noisy || exit 1
+  rm $lat_dir_noisy/fsts.*.gz 
+fi
+
+if [ $stage -le 12 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    data/${train_set_clean}_sp data/lang $gmm_dir $lat_dir_clean || exit 1
+  rm $lat_dir_clean/fsts.*.gz 
+fi
+
+if [ $stage -le 13 ]; then
+  local/reverberate_lat_dir.sh --cmd "$train_cmd" --num-data-reps 2 \
+    data/${combined_train_set}_sp_rvb_hires $lat_dir_noisy \
+    $lat_dir_clean $lat_dir || exit 1
+fi
+
+if [ $stage -le 14 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+  if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --context-opts "--context-width=2 --central-position=1" \
+    --cmd "$train_cmd" 3500 data/${train_set_clean}_sp \
+    $lang $lat_dir_clean $tree_dir
+fi
+
+if [ $stage -le 15 ]; 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)
+
+  lstm_opts="decay-time=40"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+  idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
+      
+  conv-relu-batchnorm-layer name=cnn1 input=idct height-in=40 height-out=20 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256 learning-rate-factor=0.333 max-change=0.25
+  conv-relu-batchnorm-layer name=cnn2 input=cnn1 height-in=20 height-out=20 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
+
+  relu-batchnorm-layer name=affine1 input=lda dim=512
+ 
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 input=cnn2 dim=1024
+  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1,affine1) dim=1024
+  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
+
+  # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
+  fast-lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
+  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
+  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
+  fast-lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
+  relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024
+  relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024
+  fast-lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
+
+  ## adding the layers for chain branch
+  output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  output-layer name=output-xent input=lstm3 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
+
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 16 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  mkdir -p $dir/egs
+  touch $dir/egs/.nodelete # keep egs around when that run dies.
+
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd="$train_cmd --mem 4G" \
+    --feat.online-ivector-dir=$train_ivector_dir \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient=0.1 \
+    --chain.l2-regularize=0.00005 \
+    --chain.apply-deriv-weights=false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.num-chunk-per-minibatch 32 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.max-param-change 2.0 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.srand=$srand \
+    --trainer.optimization.shrink-value 0.99 \
+    --trainer.optimization.num-jobs-initial=3 \
+    --trainer.optimization.num-jobs-final=16 \
+    --trainer.optimization.initial-effective-lrate=0.001 \
+    --trainer.optimization.final-effective-lrate=0.0001 \
+    --trainer.optimization.momentum=0.0 \
+    --trainer.deriv-truncate-margin 8 \
+    --egs.stage $get_egs_stage \
+    --egs.opts="--frames-overlap-per-eg 0" \
+    --egs.chunk-width=$chunk_width \
+    --egs.chunk-left-context=$chunk_left_context \
+    --egs.chunk-right-context=$chunk_right_context \
+    --egs.chunk-left-context-initial=0 \
+    --egs.chunk-right-context-final=0 \
+    --egs.dir="$common_egs_dir" \
+    --cleanup.remove-egs=$remove_egs \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 17 ]; then
+  # Note: it's not important to give mkgraph.sh the lang directory with the
+  # matched topology (since it gets the topology file from the model).
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+fi
+
+if [ -z "$extra_left_context" ]; then
+  extra_left_context=$chunk_left_context
+fi
+if [ -z "$extra_right_context" ]; then
+  extra_right_context=$chunk_right_context
+fi
+
+if [ $stage -le 18 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      steps/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --extra-left-context $extra_left_context \
+          --extra-right-context $extra_right_context \
+          --extra-left-context-initial 0 \
+          --extra-right-context-final 0 \
+          --frames-per-chunk $frames_per_chunk \
+          --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
+          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+# Not testing the 'looped' decoding separately, because for
+# TDNN systems it would give exactly the same results as the
+# normal decoding.
+
+if $test_online_decoding && [ $stage -le 19 ]; then
+  # note: if the features change (e.g. you add pitch features), you will have to
+  # change the options of the following command line.
+  steps/online/nnet3/prepare_online_decoding.sh \
+    --mfcc-config conf/mfcc_hires.conf \
+    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
+
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      nspk=$(wc -l <data/${data}_hires/spk2utt)
+      # note: we just give it "data/${data}" as it only uses the wav.scp, the
+      # feature type does not matter.
+      steps/online/nnet3/decode.sh \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --nj $decode_nj --cmd "$decode_cmd" \
+        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+
+exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1a.sh b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1a.sh
new file mode 100755
index 00000000000..421cd88da3a
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1a.sh
@@ -0,0 +1,286 @@
+#!/bin/bash
+
+# Set -e here so that we catch if any executable fails immediately
+set -euo pipefail
+
+# First the options that are passed through to run_ivector_common.sh
+# (some of which are also used in this script directly).
+stage=0
+nj=96
+decode_nj=40
+train_set_clean=train_worn_cleaned
+train_set_noisy=train_u400k_cleaned
+combined_train_set=train_worn_u400k_cleaned
+test_sets="dev_worn dev_beamformit_ref"
+gmm=tri3_cleaned
+nnet3_affix=_train_worn_u400k_cleaned_rvb
+lm_suffix=
+noise_list=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=_rvb_1a   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+common_egs_dir=
+hidden_dim=512
+# training options
+num_epochs=4
+chunk_width=140,100,160
+xent_regularize=0.1
+
+# training options
+srand=0
+remove_egs=true
+
+#decode options
+test_online_decoding=false  # if true, it will run the last decoding stage.
+
+# 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 11" if you have already
+# run those things.
+local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
+                                  --train-set-clean $train_set_clean \
+                                  --train-set-noisy $train_set_noisy \
+                                  --combined-train-set $combined_train_set \
+                                  --noise-list "$noise_list" \
+                                  --test-sets "$test_sets" \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
+train_data_dir=data/${combined_train_set}_sp_rvb_hires
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${combined_train_set}_sp_rvb_hires
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+  data/${train_set_clean}_sp/feats.scp data/${train_set_noisy}_sp/feats.scp; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+lat_dir_clean=exp/chain${nnet3_affix}/${gmm}_${train_set_clean}_sp_lats
+lat_dir_noisy=exp/chain${nnet3_affix}/${gmm}_${train_set_noisy}_sp_lats
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${combined_train_set}_sp_rvb_lats
+
+if [ $stage -le 11 ]; then
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    data/${train_set_noisy}_sp data/lang $gmm_dir $lat_dir_noisy || exit 1
+  rm $lat_dir_noisy/fsts.*.gz 
+fi
+
+if [ $stage -le 12 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    data/${train_set_clean}_sp data/lang $gmm_dir $lat_dir_clean || exit 1
+  rm $lat_dir_clean/fsts.*.gz 
+fi
+
+if [ $stage -le 13 ]; then
+  local/reverberate_lat_dir.sh --cmd "$train_cmd" --num-data-reps 2 \
+    data/${combined_train_set}_sp_rvb_hires $lat_dir_noisy \
+    $lat_dir_clean $lat_dir || exit 1
+fi
+
+if [ $stage -le 14 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+  if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --cmd "$train_cmd" 3500 data/${train_set_clean}_sp \
+    $lang $lat_dir_clean $tree_dir
+fi
+
+if [ $stage -le 15 ]; 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)
+  opts="l2-regularize=0.05"
+  output_opts="l2-regularize=0.01 bottleneck-dim=320"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 $opts dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn2 $opts dim=$hidden_dim input=Append(-1,0,1)
+  relu-batchnorm-layer name=tdnn3 $opts dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn4 $opts dim=$hidden_dim input=Append(-1,0,1)
+  relu-batchnorm-layer name=tdnn5 $opts dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn6 $opts dim=$hidden_dim input=Append(-3,0,3)
+  relu-batchnorm-layer name=tdnn7 $opts dim=$hidden_dim input=Append(-3,0,3)
+  relu-batchnorm-layer name=tdnn8 $opts dim=$hidden_dim input=Append(-6,-3,0)
+
+  ## adding the layers for chain branch
+  relu-batchnorm-layer name=prefinal-chain $opts dim=$hidden_dim target-rms=0.5
+  output-layer name=output include-log-softmax=false $output_opts dim=$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=tdnn8 $opts dim=$hidden_dim target-rms=0.5
+  output-layer name=output-xent $output_opts dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 16 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  mkdir -p $dir/egs
+  touch $dir/egs/.nodelete # keep egs around when that run dies.
+
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd="$train_cmd --mem 4G" \
+    --feat.online-ivector-dir=$train_ivector_dir \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient=0.1 \
+    --chain.l2-regularize=0.00005 \
+    --chain.apply-deriv-weights=false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.num-chunk-per-minibatch 256,128,64 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.max-param-change 2.0 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.srand=$srand \
+    --trainer.optimization.num-jobs-initial=3 \
+    --trainer.optimization.num-jobs-final=16 \
+    --trainer.optimization.initial-effective-lrate=0.001 \
+    --trainer.optimization.final-effective-lrate=0.0001 \
+    --trainer.optimization.momentum=0.0 \
+    --egs.stage $get_egs_stage \
+    --egs.opts="--frames-overlap-per-eg 0" \
+    --egs.chunk-width=$chunk_width \
+    --egs.dir="$common_egs_dir" \
+    --cleanup.remove-egs=$remove_egs \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 17 ]; then
+  # Note: it's not important to give mkgraph.sh the lang directory with the
+  # matched topology (since it gets the topology file from the model).
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+fi
+
+if [ $stage -le 18 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      steps/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --frames-per-chunk $frames_per_chunk \
+          --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
+          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+# Not testing the 'looped' decoding separately, because for
+# TDNN systems it would give exactly the same results as the
+# normal decoding.
+
+if $test_online_decoding && [ $stage -le 19 ]; then
+  # note: if the features change (e.g. you add pitch features), you will have to
+  # change the options of the following command line.
+  steps/online/nnet3/prepare_online_decoding.sh \
+    --mfcc-config conf/mfcc_hires.conf \
+    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
+
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      nspk=$(wc -l <data/${data}_hires/spk2utt)
+      # note: we just give it "data/${data}" as it only uses the wav.scp, the
+      # feature type does not matter.
+      steps/online/nnet3/decode.sh \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --nj $decode_nj --cmd "$decode_cmd" \
+        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+
+exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1b.sh b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1b.sh
new file mode 100755
index 00000000000..7ed480f284a
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1b.sh
@@ -0,0 +1,311 @@
+#!/bin/bash
+
+# 7q is as 7p but a modified topology with resnet-style skip connections, more layers,
+#  skinnier bottlenecks, removing the 3-way splicing and skip-layer splicing,
+#  and re-tuning the learning rate and l2 regularize.  The configs are
+#  standardized and substantially simplified.  There isn't any advantage in WER
+#  on this setup; the advantage of this style of config is that it also works
+#  well on smaller datasets, and we adopt this style here also for consistency.
+
+# local/chain/compare_wer_general.sh --rt03 tdnn7p_sp tdnn7q_sp
+# System                tdnn7p_sp tdnn7q_sp
+# WER on train_dev(tg)      11.80     11.79
+# WER on train_dev(fg)      10.77     10.84
+# WER on eval2000(tg)        14.4      14.3
+# WER on eval2000(fg)        13.0      12.9
+# WER on rt03(tg)            17.5      17.6
+# WER on rt03(fg)            15.3      15.2
+# Final train prob         -0.057    -0.058
+# Final valid prob         -0.069    -0.073
+# Final train prob (xent)        -0.886    -0.894
+# Final valid prob (xent)       -0.9005   -0.9106
+# Num-parameters               22865188  18702628
+
+
+# steps/info/chain_dir_info.pl exp/chain/tdnn7q_sp
+# exp/chain/tdnn7q_sp: num-iters=394 nj=3..16 num-params=18.7M dim=40+100->6034 combine=-0.058->-0.057 (over 8) xent:train/valid[261,393,final]=(-1.20,-0.897,-0.894/-1.20,-0.919,-0.911) logprob:train/valid[261,393,final]=(-0.090,-0.059,-0.058/-0.098,-0.073,-0.073)
+
+set -e
+
+# configs for 'chain'
+stage=0
+nj=96
+decode_nj=40
+train_set_clean=train_worn_cleaned
+train_set_noisy=train_u400k_cleaned
+combined_train_set=train_worn_u400k_cleaned
+test_sets="dev_worn dev_beamformit_ref"
+gmm=tri3_cleaned
+nnet3_affix=_train_worn_u400k_cleaned_rvb
+lm_suffix=
+noise_list=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=_rvb_1b   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+num_epochs=4
+common_egs_dir=
+# training options
+# training chunk-options
+chunk_width=140,100,160
+xent_regularize=0.1
+dropout_schedule='0,0@0.20,0.5@0.50,0'
+
+# training options
+srand=0
+remove_egs=true
+
+#decode options
+test_online_decoding=false  # if true, it will run the last decoding stage.
+
+# 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 11" if you have already
+# run those things.
+local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
+                                  --train-set-clean $train_set_clean \
+                                  --train-set-noisy $train_set_noisy \
+                                  --combined-train-set $combined_train_set \
+                                  --noise-list "$noise_list" \
+                                  --test-sets "$test_sets" \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
+train_data_dir=data/${combined_train_set}_sp_rvb_hires
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${combined_train_set}_sp_rvb_hires
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+  data/${train_set_clean}_sp/feats.scp data/${train_set_noisy}_sp/feats.scp; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+lat_dir_clean=exp/chain${nnet3_affix}/${gmm}_${train_set_clean}_sp_lats
+lat_dir_noisy=exp/chain${nnet3_affix}/${gmm}_${train_set_noisy}_sp_lats
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${combined_train_set}_sp_rvb_lats
+
+if [ $stage -le 11 ]; then
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    data/${train_set_noisy}_sp data/lang $gmm_dir $lat_dir_noisy || exit 1
+  rm $lat_dir_noisy/fsts.*.gz 
+fi
+
+if [ $stage -le 12 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    data/${train_set_clean}_sp data/lang $gmm_dir $lat_dir_clean || exit 1
+  rm $lat_dir_clean/fsts.*.gz 
+fi
+
+if [ $stage -le 13 ]; then
+  local/reverberate_lat_dir.sh --cmd "$train_cmd" --num-data-reps 2 \
+    data/${combined_train_set}_sp_rvb_hires $lat_dir_noisy \
+    $lat_dir_clean $lat_dir || exit 1
+fi
+
+if [ $stage -le 14 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+  if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --cmd "$train_cmd" 3500 data/${train_set_clean}_sp \
+    $lang $lat_dir_clean $tree_dir
+fi
+
+if [ $stage -le 15 ]; 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)
+  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 > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-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 16 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  mkdir -p $dir/egs
+  touch $dir/egs/.nodelete # keep egs around when that run dies.
+
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd="$train_cmd --mem 4G" \
+    --feat.online-ivector-dir=$train_ivector_dir \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.0 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.dropout-schedule "$dropout_schedule" \
+    --trainer.add-option="--optimization.memory-compression-level=2" \
+    --trainer.num-chunk-per-minibatch 64 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.max-param-change 2.0 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.num-jobs-initial 3 \
+    --trainer.optimization.num-jobs-final 16 \
+    --trainer.optimization.initial-effective-lrate 0.00025 \
+    --trainer.optimization.final-effective-lrate 0.000025 \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--frames-overlap-per-eg 0" \
+    --egs.chunk-width $chunk_width \
+    --egs.dir "$common_egs_dir" \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 17 ]; then
+  # Note: it's not important to give mkgraph.sh the lang directory with the
+  # matched topology (since it gets the topology file from the model).
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+fi
+
+if [ $stage -le 18 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      steps/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --frames-per-chunk $frames_per_chunk \
+          --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
+          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+# Not testing the 'looped' decoding separately, because for
+# TDNN systems it would give exactly the same results as the
+# normal decoding.
+
+if $test_online_decoding && [ $stage -le 19 ]; then
+  # note: if the features change (e.g. you add pitch features), you will have to
+  # change the options of the following command line.
+  steps/online/nnet3/prepare_online_decoding.sh \
+    --mfcc-config conf/mfcc_hires.conf \
+    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
+
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      nspk=$(wc -l <data/${data}_hires/spk2utt)
+      # note: we just give it "data/${data}" as it only uses the wav.scp, the
+      # feature type does not matter.
+      steps/online/nnet3/decode.sh \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --nj $decode_nj --cmd "$decode_cmd" \
+        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+
+exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
new file mode 100755
index 00000000000..cda306b0d06
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
@@ -0,0 +1,323 @@
+#!/bin/bash
+
+# Set -e here so that we catch if any executable fails immediately
+set -euo pipefail
+
+# First the options that are passed through to run_ivector_common.sh
+# (some of which are also used in this script directly).
+stage=0
+nj=96
+decode_nj=40
+train_set_clean=train_worn_cleaned
+train_set_noisy=train_u400k_cleaned
+combined_train_set=train_worn_u400k_cleaned
+test_sets="dev_worn"
+gmm=tri3_cleaned
+nnet3_affix=_train_worn_u400k_cleaned_rvb
+lm_suffix=
+noise_list=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=_rvb_1a   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+common_egs_dir=
+
+hidden_dim=1024
+cell_dim=1024
+projection_dim=256
+
+# training options
+num_epochs=2  # 2 works better than 4
+chunk_width=140,100,160
+chunk_left_context=40
+chunk_right_context=0
+dropout_schedule='0,0@0.20,0.3@0.50,0'
+xent_regularize=0.025
+label_delay=5
+
+# decode options
+extra_left_context=50
+extra_right_context=0
+
+# training options
+srand=0
+remove_egs=true
+
+#decode options
+test_online_decoding=false  # if true, it will run the last decoding stage.
+
+
+# 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 11" if you have already
+# run those things.
+local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
+                                  --train-set-clean $train_set_clean \
+                                  --train-set-noisy $train_set_noisy \
+                                  --combined-train-set $combined_train_set \
+                                  --noise-list "$noise_list" \
+                                  --test-sets "$test_sets" \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+dir=exp/chain${nnet3_affix}/tdnn_lstm${affix}_sp
+train_data_dir=data/${combined_train_set}_sp_rvb_hires
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${combined_train_set}_sp_rvb_hires
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+  data/${train_set_clean}_sp/feats.scp data/${train_set_noisy}_sp/feats.scp; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+lat_dir_clean=exp/chain${nnet3_affix}/${gmm}_${train_set_clean}_sp_lats
+lat_dir_noisy=exp/chain${nnet3_affix}/${gmm}_${train_set_noisy}_sp_lats
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${combined_train_set}_sp_rvb_lats
+
+if [ $stage -le 11 ]; then
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    data/${train_set_noisy}_sp data/lang $gmm_dir $lat_dir_noisy || exit 1
+  rm $lat_dir_noisy/fsts.*.gz 
+fi
+
+if [ $stage -le 12 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    data/${train_set_clean}_sp data/lang $gmm_dir $lat_dir_clean || exit 1
+  rm $lat_dir_clean/fsts.*.gz 
+fi
+
+if [ $stage -le 13 ]; then
+  local/reverberate_lat_dir.sh --cmd "$train_cmd" --num-data-reps 2 \
+    data/${combined_train_set}_sp_rvb_hires $lat_dir_noisy \
+    $lat_dir_clean $lat_dir || exit 1
+fi
+
+if [ $stage -le 14 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+  if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --context-opts "--context-width=2 --central-position=1" \
+    --cmd "$train_cmd" 3500 data/${train_set_clean}_sp \
+    $lang $lat_dir_clean $tree_dir
+fi
+
+if [ $stage -le 15 ]; 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)
+
+  lstm_opts="decay-time=40"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=$hidden_dim
+
+  fast-lstmp-layer name=lstm1 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=$hidden_dim
+  fast-lstmp-layer name=lstm2 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=$hidden_dim
+  fast-lstmp-layer name=lstm3 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=$hidden_dim
+  fast-lstmp-layer name=lstm4 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
+
+  ## adding the layers for chain branch
+  output-layer name=output input=lstm4 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  output-layer name=output-xent input=lstm4 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
+
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 16 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  mkdir -p $dir/egs
+  touch $dir/egs/.nodelete # keep egs around when that run dies.
+
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd="$train_cmd --mem 4G" \
+    --feat.online-ivector-dir=$train_ivector_dir \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient=0.1 \
+    --chain.l2-regularize=0.00005 \
+    --chain.apply-deriv-weights=false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.num-chunk-per-minibatch 64,32 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.max-param-change 2.0 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.srand=$srand \
+    --trainer.optimization.shrink-value 0.99 \
+    --trainer.optimization.num-jobs-initial=3 \
+    --trainer.optimization.num-jobs-final=16 \
+    --trainer.optimization.initial-effective-lrate=0.001 \
+    --trainer.optimization.final-effective-lrate=0.0001 \
+    --trainer.optimization.momentum=0.0 \
+    --trainer.deriv-truncate-margin 8 \
+    --egs.stage $get_egs_stage \
+    --egs.opts="--frames-overlap-per-eg 0" \
+    --egs.chunk-width=$chunk_width \
+    --egs.chunk-left-context=$chunk_left_context \
+    --egs.chunk-right-context=$chunk_right_context \
+    --egs.chunk-left-context-initial=0 \
+    --egs.chunk-right-context-final=0 \
+    --egs.dir="$common_egs_dir" \
+    --cleanup.remove-egs=$remove_egs \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 17 ]; then
+  # Note: it's not important to give mkgraph.sh the lang directory with the
+  # matched topology (since it gets the topology file from the model).
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+fi
+
+if [ -z "$extra_left_context" ]; then
+  extra_left_context=$chunk_left_context
+fi
+if [ -z "$extra_right_context" ]; then
+  extra_right_context=$chunk_right_context
+fi
+
+if [ $stage -le 18 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      steps/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --extra-left-context $extra_left_context \
+          --extra-right-context $extra_right_context \
+          --extra-left-context-initial 0 \
+          --extra-right-context-final 0 \
+          --frames-per-chunk $frames_per_chunk \
+          --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
+          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+# Not testing the 'looped' decoding separately, because for
+# TDNN systems it would give exactly the same results as the
+# normal decoding.
+
+if $test_online_decoding && [ $stage -le 19 ]; then
+  # note: if the features change (e.g. you add pitch features), you will have to
+  # change the options of the following command line.
+  steps/online/nnet3/prepare_online_decoding.sh \
+    --mfcc-config conf/mfcc_hires.conf \
+    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
+
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      nspk=$(wc -l <data/${data}_hires/spk2utt)
+      # note: we just give it "data/${data}" as it only uses the wav.scp, the
+      # feature type does not matter.
+      steps/online/nnet3/decode.sh \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --nj $decode_nj --cmd "$decode_cmd" \
+        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+
+exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/run_tdnn.sh b/egs/chime6/s5_track1/local/chain/run_tdnn.sh
new file mode 120000
index 00000000000..34499362831
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/run_tdnn.sh
@@ -0,0 +1 @@
+tuning/run_tdnn_1a.sh
\ No newline at end of file
diff --git a/egs/chime6/s5_track1/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh b/egs/chime6/s5_track1/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
new file mode 100755
index 00000000000..95e9d934bd3
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
@@ -0,0 +1,304 @@
+#!/bin/bash
+
+# Set -e here so that we catch if any executable fails immediately
+set -euo pipefail
+
+# First the options that are passed through to run_ivector_common.sh
+# (some of which are also used in this script directly).
+stage=0
+nj=96
+train_set=train_worn_u400k_cleaned
+test_sets="dev_beamformit_ref"
+gmm=tri3_cleaned
+nnet3_affix=_train_worn_u400k_cleaned
+lm_suffix=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=_1a   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+common_egs_dir=
+
+hidden_dim=1024
+cell_dim=1024
+projection_dim=256
+
+# training options
+num_epochs=2  # 2 works better than 4
+chunk_width=140,100,160
+chunk_left_context=40
+chunk_right_context=0
+dropout_schedule='0,0@0.20,0.3@0.50,0'
+xent_regularize=0.025
+label_delay=5
+
+# decode options
+extra_left_context=50
+extra_right_context=0
+
+# training options
+srand=0
+remove_egs=true
+
+#decode options
+test_online_decoding=false  # if true, it will run the last decoding stage.
+
+
+# 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 11" if you have already
+# run those things.
+local/nnet3/run_ivector_common.sh --stage $stage \
+                                  --train-set $train_set \
+				  --test-sets "$test_sets" \
+                                  --gmm $gmm \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+ali_dir=exp/${gmm}_ali_${train_set}_sp
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
+dir=exp/chain${nnet3_affix}/cnn_tdnn_lstm${affix}_sp
+train_data_dir=data/${train_set}_sp_hires
+lores_train_data_dir=data/${train_set}_sp
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+    $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+if [ $stage -le 11 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" ${lores_train_data_dir} \
+    data/lang $gmm_dir $lat_dir
+  rm $lat_dir/fsts.*.gz # save space
+fi
+
+if [ $stage -le 12 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+   if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --context-opts "--context-width=2 --central-position=1" \
+    --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
+    $lang $ali_dir $tree_dir
+fi
+
+if [ $stage -le 13 ]; 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)
+
+  lstm_opts="decay-time=40"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+  idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
+      
+  conv-relu-batchnorm-layer name=cnn1 input=idct height-in=40 height-out=20 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256 learning-rate-factor=0.333 max-change=0.25
+  conv-relu-batchnorm-layer name=cnn2 input=cnn1 height-in=20 height-out=20 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
+
+  relu-batchnorm-layer name=affine1 input=lda dim=512
+ 
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 input=cnn2 dim=1024
+  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1,affine1) dim=1024
+  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
+
+  # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
+  fast-lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
+  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
+  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
+  fast-lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
+  relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024
+  relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024
+  fast-lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
+
+  ## adding the layers for chain branch
+  output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  output-layer name=output-xent input=lstm3 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
+
+EOF
+
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 14 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  mkdir -p $dir/egs
+  touch $dir/egs/.nodelete # keep egs around when that run dies.
+
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd="$train_cmd --mem 4G" \
+    --feat.online-ivector-dir=$train_ivector_dir \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient=0.1 \
+    --chain.l2-regularize=0.00005 \
+    --chain.apply-deriv-weights=false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.num-chunk-per-minibatch 64,32 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.max-param-change 2.0 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.srand=$srand \
+    --trainer.optimization.shrink-value 0.99 \
+    --trainer.optimization.num-jobs-initial=3 \
+    --trainer.optimization.num-jobs-final=16 \
+    --trainer.optimization.initial-effective-lrate=0.001 \
+    --trainer.optimization.final-effective-lrate=0.0001 \
+    --trainer.optimization.momentum=0.0 \
+    --trainer.deriv-truncate-margin 8 \
+    --egs.stage $get_egs_stage \
+    --egs.opts="--frames-overlap-per-eg 0" \
+    --egs.chunk-width=$chunk_width \
+    --egs.chunk-left-context=$chunk_left_context \
+    --egs.chunk-right-context=$chunk_right_context \
+    --egs.chunk-left-context-initial=0 \
+    --egs.chunk-right-context-final=0 \
+    --egs.dir="$common_egs_dir" \
+    --cleanup.remove-egs=$remove_egs \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 15 ]; then
+  # Note: it's not important to give mkgraph.sh the lang directory with the
+  # matched topology (since it gets the topology file from the model).
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+fi
+
+if [ $stage -le 16 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      steps/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --extra-left-context $chunk_left_context \
+          --extra-right-context $chunk_right_context \
+          --extra-left-context-initial 0 \
+          --extra-right-context-final 0 \
+          --frames-per-chunk $frames_per_chunk \
+          --nj 8 --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
+          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+# Not testing the 'looped' decoding separately, because for
+# TDNN systems it would give exactly the same results as the
+# normal decoding.
+
+if $test_online_decoding && [ $stage -le 17 ]; then
+  # note: if the features change (e.g. you add pitch features), you will have to
+  # change the options of the following command line.
+  steps/online/nnet3/prepare_online_decoding.sh \
+    --mfcc-config conf/mfcc_hires.conf \
+    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
+
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      nspk=$(wc -l <data/${data}_hires/spk2utt)
+      # note: we just give it "data/${data}" as it only uses the wav.scp, the
+      # feature type does not matter.
+      steps/online/nnet3/decode.sh \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --nj 8 --cmd "$decode_cmd" \
+        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+
+exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_1a.sh b/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_1a.sh
new file mode 100755
index 00000000000..daad37e2cd7
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_1a.sh
@@ -0,0 +1,270 @@
+#!/bin/bash
+
+# Set -e here so that we catch if any executable fails immediately
+set -euo pipefail
+
+# First the options that are passed through to run_ivector_common.sh
+# (some of which are also used in this script directly).
+stage=0
+nj=96
+train_set=train_worn_u100k
+test_sets="dev_worn dev_beamformit_ref"
+gmm=tri3
+nnet3_affix=_train_worn_u100k
+lm_suffix=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=1a   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+# training options
+# training chunk-options
+chunk_width=140,100,160
+common_egs_dir=
+xent_regularize=0.1
+
+# training options
+srand=0
+remove_egs=true
+
+#decode options
+test_online_decoding=false  # if true, it will run the last decoding stage.
+
+# 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 11" if you have already
+# run those things.
+local/nnet3/run_ivector_common.sh --stage $stage \
+                                  --train-set $train_set \
+				  --test-sets "$test_sets" \
+                                  --gmm $gmm \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+ali_dir=exp/${gmm}_ali_${train_set}_sp
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
+dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
+train_data_dir=data/${train_set}_sp_hires
+lores_train_data_dir=data/${train_set}_sp
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+    $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+if [ $stage -le 11 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" ${lores_train_data_dir} \
+    data/lang $gmm_dir $lat_dir
+  rm $lat_dir/fsts.*.gz # save space
+fi
+
+if [ $stage -le 12 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+   if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --context-opts "--context-width=2 --central-position=1" \
+    --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
+    $lang $ali_dir $tree_dir
+fi
+
+
+if [ $stage -le 13 ]; 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)
+  opts="l2-regularize=0.05"
+  output_opts="l2-regularize=0.01 bottleneck-dim=320"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 $opts dim=512
+  relu-batchnorm-layer name=tdnn2 $opts dim=512 input=Append(-1,0,1)
+  relu-batchnorm-layer name=tdnn3 $opts dim=512
+  relu-batchnorm-layer name=tdnn4 $opts dim=512 input=Append(-1,0,1)
+  relu-batchnorm-layer name=tdnn5 $opts dim=512
+  relu-batchnorm-layer name=tdnn6 $opts dim=512 input=Append(-3,0,3)
+  relu-batchnorm-layer name=tdnn7 $opts dim=512 input=Append(-3,0,3)
+  relu-batchnorm-layer name=tdnn8 $opts dim=512 input=Append(-6,-3,0)
+
+  ## adding the layers for chain branch
+  relu-batchnorm-layer name=prefinal-chain $opts dim=512 target-rms=0.5
+  output-layer name=output include-log-softmax=false $output_opts dim=$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=tdnn8 $opts dim=512 target-rms=0.5
+  output-layer name=output-xent $output_opts dim=$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 14 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd="$decode_cmd" \
+    --feat.online-ivector-dir=$train_ivector_dir \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient=0.1 \
+    --chain.l2-regularize=0.00005 \
+    --chain.apply-deriv-weights=false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.srand=$srand \
+    --trainer.max-param-change=2.0 \
+    --trainer.num-epochs=10 \
+    --trainer.frames-per-iter=3000000 \
+    --trainer.optimization.num-jobs-initial=2 \
+    --trainer.optimization.num-jobs-final=4 \
+    --trainer.optimization.initial-effective-lrate=0.001 \
+    --trainer.optimization.final-effective-lrate=0.0001 \
+    --trainer.optimization.shrink-value=1.0 \
+    --trainer.num-chunk-per-minibatch=256,128,64 \
+    --trainer.optimization.momentum=0.0 \
+    --egs.chunk-width=$chunk_width \
+    --egs.chunk-left-context=$chunk_left_context \
+    --egs.chunk-right-context=$chunk_right_context \
+    --egs.chunk-left-context-initial=0 \
+    --egs.chunk-right-context-final=0 \
+    --egs.dir="$common_egs_dir" \
+    --egs.opts="--frames-overlap-per-eg 0" \
+    --cleanup.remove-egs=$remove_egs \
+    --use-gpu=true \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 15 ]; then
+  # Note: it's not important to give mkgraph.sh the lang directory with the
+  # matched topology (since it gets the topology file from the model).
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+fi
+
+if [ $stage -le 16 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      steps/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --frames-per-chunk $frames_per_chunk \
+          --nj 8 --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
+          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+# Not testing the 'looped' decoding separately, because for
+# TDNN systems it would give exactly the same results as the
+# normal decoding.
+
+if $test_online_decoding && [ $stage -le 17 ]; then
+  # note: if the features change (e.g. you add pitch features), you will have to
+  # change the options of the following command line.
+  steps/online/nnet3/prepare_online_decoding.sh \
+    --mfcc-config conf/mfcc_hires.conf \
+    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
+
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      nspk=$(wc -l <data/${data}_hires/spk2utt)
+      # note: we just give it "data/${data}" as it only uses the wav.scp, the
+      # feature type does not matter.
+      steps/online/nnet3/decode.sh \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --nj 8 --cmd "$decode_cmd" \
+        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+
+exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_1b.sh b/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_1b.sh
new file mode 100755
index 00000000000..a21fdccaeb9
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_1b.sh
@@ -0,0 +1,247 @@
+#!/bin/bash
+
+# This factorized TDNN (TDNN-F) script is adapted from SWBD recipe 7q.
+# It uses resnet-style skip connections.
+# For details, refer to the paper:
+# "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks", Daniel Povey, Gaofeng Cheng, Yiming Wang, Ke Li, Hainan Xu, Mahsa Yarmohamadi, Sanjeev Khudanpur, Interspeech 2018
+
+#%WER 62.30 [ 36684 / 58881, 3048 ins, 17964 del, 15672 sub ] exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn1b_sp/decode_dev_gss_2stage/wer_11_0.0
+#%WER 64.55 [ 38009 / 58881, 3266 ins, 19371 del, 15372 sub ] exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn1b_sp/decode_dev_gss/wer_11_0.0
+
+# steps/info/chain_dir_info.pl exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn_1b_sp
+# exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn1b_sp/: num-iters=429 nj=3..16 num-params=17.0M dim=40+100->2776 combine=-0.134->-0.133 (over 3) xent:train/valid[285,428,final]=(-2.37,-1.95,-1.95/-2.19,-1.90,-1.91) logprob:train/valid[285,428,final]=(-0.201,-0.125,-0.124/-0.198,-0.147,-0.148)
+
+set -e
+
+# configs for 'chain'
+stage=0
+nj=96
+train_set=train_worn_u400k
+test_sets="dev_worn dev_beamformit_ref"
+gmm=tri3
+nnet3_affix=_train_worn_u400k
+lm_suffix=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=1b   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+num_epochs=4
+common_egs_dir=
+# training options
+# training chunk-options
+chunk_width=140,100,160
+xent_regularize=0.1
+dropout_schedule='0,0@0.20,0.5@0.50,0'
+
+# training options
+srand=0
+remove_egs=true
+
+#decode options
+test_online_decoding=false  # if true, it will run the last decoding stage.
+
+# 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 11" if you have already
+# run those things.
+local/nnet3/run_ivector_common.sh --stage $stage \
+                                  --train-set $train_set \
+                                  --test-sets "$test_sets" \
+                                  --gmm $gmm \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
+dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
+train_data_dir=data/${train_set}_sp_hires
+lores_train_data_dir=data/${train_set}_sp
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+    $lores_train_data_dir/feats.scp; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+if [ $stage -le 11 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
+    ${lores_train_data_dir} \
+    data/lang $gmm_dir $lat_dir
+  rm $lat_dir/fsts.*.gz # save space
+fi
+
+if [ $stage -le 12 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+  if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
+    $lang $lat_dir $tree_dir
+fi
+
+if [ $stage -le 13 ]; 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)
+  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 > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-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 14 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  steps/nnet3/chain/train.py --stage $train_stage \
+    --cmd "$train_cmd --mem 4G" \
+    --feat.online-ivector-dir=$train_ivector_dir \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.0 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.dropout-schedule "$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" \
+    --egs.chunk-width $chunk_width \
+    --trainer.num-chunk-per-minibatch 64 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.num-jobs-initial 3 \
+    --trainer.optimization.num-jobs-final 16 \
+    --trainer.optimization.initial-effective-lrate 0.00025 \
+    --trainer.optimization.final-effective-lrate 0.000025 \
+    --trainer.max-param-change 2.0 \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir $dir  || exit 1;
+
+fi
+
+if [ $stage -le 15 ]; then
+  # Note: it's not important to give mkgraph.sh the lang directory with the
+  # matched topology (since it gets the topology file from the model).
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+fi
+
+if [ $stage -le 16 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      steps/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --frames-per-chunk $frames_per_chunk \
+          --nj 8 --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
+          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lstm_1a.sh
new file mode 100755
index 00000000000..e3d8e6ac4dc
--- /dev/null
+++ b/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lstm_1a.sh
@@ -0,0 +1,297 @@
+#!/bin/bash
+
+# Set -e here so that we catch if any executable fails immediately
+set -euo pipefail
+
+# First the options that are passed through to run_ivector_common.sh
+# (some of which are also used in this script directly).
+stage=0
+nj=96
+train_set=train_worn_u400k_cleaned
+test_sets="dev_worn dev_beamformit_ref"
+gmm=tri3_cleaned
+nnet3_affix=_train_worn_u400k_cleaned
+lm_suffix=
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=_1a   # affix for the TDNN directory name
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+
+common_egs_dir=
+
+hidden_dim=1024
+cell_dim=1024
+projection_dim=256
+
+# training options
+num_epochs=2  # 2 works better than 4
+chunk_width=140,100,160
+chunk_left_context=40
+chunk_right_context=0
+dropout_schedule='0,0@0.20,0.3@0.50,0'
+xent_regularize=0.025
+label_delay=5
+
+# decode options
+extra_left_context=50
+extra_right_context=0
+
+# training options
+srand=0
+remove_egs=true
+
+#decode options
+test_online_decoding=false  # if true, it will run the last decoding stage.
+
+
+# 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 11" if you have already
+# run those things.
+local/nnet3/run_ivector_common.sh --stage $stage \
+                                  --train-set $train_set \
+				  --test-sets "$test_sets" \
+                                  --gmm $gmm \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+# Problem: We have removed the "train_" prefix of our training set in
+# the alignment directory names! Bad!
+gmm_dir=exp/$gmm
+ali_dir=exp/${gmm}_ali_${train_set}_sp
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
+dir=exp/chain${nnet3_affix}/tdnn_lstm${affix}_sp
+train_data_dir=data/${train_set}_sp_hires
+lores_train_data_dir=data/${train_set}_sp
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+    $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 10 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+if [ $stage -le 11 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" ${lores_train_data_dir} \
+    data/lang $gmm_dir $lat_dir
+  rm $lat_dir/fsts.*.gz # save space
+fi
+
+if [ $stage -le 12 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+   if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor 3 \
+    --context-opts "--context-width=2 --central-position=1" \
+    --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
+    $lang $ali_dir $tree_dir
+fi
+
+if [ $stage -le 13 ]; 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)
+
+  lstm_opts="decay-time=40"
+
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-layer name=tdnn1 dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=$hidden_dim
+
+  fast-lstmp-layer name=lstm1 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=$hidden_dim
+  fast-lstmp-layer name=lstm2 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=$hidden_dim
+  fast-lstmp-layer name=lstm3 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
+  relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=$hidden_dim
+  relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=$hidden_dim
+  fast-lstmp-layer name=lstm4 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
+
+  ## adding the layers for chain branch
+  output-layer name=output input=lstm4 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' models... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  output-layer name=output-xent input=lstm4 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
+
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 14 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  mkdir -p $dir/egs
+  touch $dir/egs/.nodelete # keep egs around when that run dies.
+
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd="$train_cmd --mem 4G" \
+    --feat.online-ivector-dir=$train_ivector_dir \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient=0.1 \
+    --chain.l2-regularize=0.00005 \
+    --chain.apply-deriv-weights=false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.num-chunk-per-minibatch 64,32 \
+    --trainer.frames-per-iter 1500000 \
+    --trainer.max-param-change 2.0 \
+    --trainer.num-epochs $num_epochs \
+    --trainer.srand=$srand \
+    --trainer.optimization.shrink-value 0.99 \
+    --trainer.optimization.num-jobs-initial=3 \
+    --trainer.optimization.num-jobs-final=16 \
+    --trainer.optimization.initial-effective-lrate=0.001 \
+    --trainer.optimization.final-effective-lrate=0.0001 \
+    --trainer.optimization.momentum=0.0 \
+    --trainer.deriv-truncate-margin 8 \
+    --egs.stage $get_egs_stage \
+    --egs.opts="--frames-overlap-per-eg 0" \
+    --egs.chunk-width=$chunk_width \
+    --egs.chunk-left-context=$chunk_left_context \
+    --egs.chunk-right-context=$chunk_right_context \
+    --egs.chunk-left-context-initial=0 \
+    --egs.chunk-right-context-final=0 \
+    --egs.dir="$common_egs_dir" \
+    --cleanup.remove-egs=$remove_egs \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 15 ]; then
+  # Note: it's not important to give mkgraph.sh the lang directory with the
+  # matched topology (since it gets the topology file from the model).
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+fi
+
+if [ $stage -le 16 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      steps/nnet3/decode.sh \
+          --acwt 1.0 --post-decode-acwt 10.0 \
+          --extra-left-context $chunk_left_context \
+          --extra-right-context $chunk_right_context \
+          --extra-left-context-initial 0 \
+          --extra-right-context-final 0 \
+          --frames-per-chunk $frames_per_chunk \
+          --nj 8 --cmd "$decode_cmd"  --num-threads 4 \
+          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
+          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+# Not testing the 'looped' decoding separately, because for
+# TDNN systems it would give exactly the same results as the
+# normal decoding.
+
+if $test_online_decoding && [ $stage -le 17 ]; then
+  # note: if the features change (e.g. you add pitch features), you will have to
+  # change the options of the following command line.
+  steps/online/nnet3/prepare_online_decoding.sh \
+    --mfcc-config conf/mfcc_hires.conf \
+    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
+
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      nspk=$(wc -l <data/${data}_hires/spk2utt)
+      # note: we just give it "data/${data}" as it only uses the wav.scp, the
+      # feature type does not matter.
+      steps/online/nnet3/decode.sh \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --nj 8 --cmd "$decode_cmd" \
+        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+
+exit 0;
diff --git a/egs/chime6/s5_track1/local/check_tools.sh b/egs/chime6/s5_track1/local/check_tools.sh
new file mode 100755
index 00000000000..9c0f9290a75
--- /dev/null
+++ b/egs/chime6/s5_track1/local/check_tools.sh
@@ -0,0 +1,71 @@
+#!/bin/bash -u
+
+# Copyright 2015 (c) Johns Hopkins University (Jan Trmal <jtrmal@gmail.com>)
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#  http://www.apache.org/licenses/LICENSE-2.0
+#
+# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+# MERCHANTABLITY OR NON-INFRINGEMENT.
+# See the Apache 2 License for the specific language governing permissions and
+# limitations under the License.
+
+[ -f ./path.sh ] && . ./path.sh
+
+command -v uconv &>/dev/null \
+  || { echo  >&2 "uconv not found on PATH. You will have to install ICU4C"; exit 1; }
+
+command -v ngram &>/dev/null \
+  || { echo  >&2 "srilm not found on PATH. Please use the script $KALDI_ROOT/tools/extras/install_srilm.sh to install it"; exit 1; }
+
+if [  -z ${LIBLBFGS} ]; then
+  echo >&2  "SRILM is not compiled with the support of MaxEnt models."
+  echo >&2  "You should use the script in \$KALDI_ROOT/tools/install_srilm.sh"
+  echo >&2  "which will take care of compiling the SRILM with MaxEnt support"
+  exit 1;
+fi
+
+sox=`command -v sox 2>/dev/null` \
+  || { echo  >&2 "sox not found on PATH. Please install it manually (you will need version 14.4.0 and higher)."; exit 1; }
+
+# If sox is found on path, check if the version is correct
+if [ ! -z "$sox" ]; then
+  sox_version=`$sox --version 2>&1| head -1 | sed -e 's?.*: ??' -e 's?.* ??'`
+  if [[ ! $sox_version =~ v14.4.* ]]; then
+    echo "Unsupported sox version $sox_version found on path. You will need version v14.4.0 and higher."
+    exit 1
+  fi
+fi
+
+command -v phonetisaurus-align &>/dev/null \
+  || { echo  >&2 "Phonetisaurus not found on PATH. Please use the script $KALDI_ROOT/tools/extras/install_phonetisaurus.sh to install it"; exit 1; }
+
+command -v BeamformIt &>/dev/null \
+  || { echo  >&2 "BeamformIt not found on PATH. Please use the script $KALDI_ROOT/tools/extras/install_beamformit.sh to install it"; exit 1; }
+
+miniconda_dir=$HOME/miniconda3/
+if [ ! -d $miniconda_dir ]; then
+    echo "$miniconda_dir does not exist. Please run '../../../tools/extras/install_miniconda.sh'"
+fi
+
+# check if WPE is installed
+result=`$miniconda_dir/bin/python -c "\
+try:
+    import nara_wpe
+    print('1')
+except ImportError:
+    print('0')"`
+
+if [ "$result" == "1" ]; then
+    echo "WPE is installed"
+else
+    echo "WPE is not installed. Please run ../../../tools/extras/install_wpe.sh"
+    exit 1
+fi
+
+exit  0
diff --git a/egs/chime6/s5_track1/local/copy_lat_dir_parallel.sh b/egs/chime6/s5_track1/local/copy_lat_dir_parallel.sh
new file mode 100755
index 00000000000..82839604c9e
--- /dev/null
+++ b/egs/chime6/s5_track1/local/copy_lat_dir_parallel.sh
@@ -0,0 +1,97 @@
+#!/bin/bash
+
+cmd=queue.pl
+nj=40
+stage=0
+speed_perturb=true
+
+. ./path.sh
+. utils/parse_options.sh
+
+if [ $# -ne 4 ]; then
+  echo "Usage: $0 <utt-map> <data-dir> <src-lat-dir> <out-lat-dir>"
+  exit 1
+fi
+
+utt_map=$1
+data=$2
+srcdir=$3
+dir=$4
+
+mkdir -p $dir
+
+cp $srcdir/{phones.txt,tree,final.mdl} $dir || exit 1
+cp $srcdir/{final.alimdl,final.occs,splice_opts,cmvn_opts,delta_opts,final.mat,full.mat} 2>/dev/null || true
+
+nj_src=$(cat $srcdir/num_jobs) || exit 1
+
+if [ $stage -le 1 ]; then
+  $cmd JOB=1:$nj_src $dir/log/copy_lats_orig.JOB.log \
+    lattice-copy "ark:gunzip -c $srcdir/lat.JOB.gz |" \
+    ark,scp:$dir/lat_orig.JOB.ark,$dir/lat_orig.JOB.scp || exit 1
+fi
+
+for n in $(seq $nj_src); do
+  cat $dir/lat_orig.$n.scp
+done > $dir/lat_orig.scp || exit 1
+
+if $speed_perturb; then
+  for s in 0.9 1.1; do
+    awk -v s=$s '{print "sp"s"-"$1" sp"s"-"$2}' $utt_map
+  done | cat - $utt_map | sort -k1,1 > $dir/utt_map
+  utt_map=$dir/utt_map
+fi
+
+if [ $stage -le 2 ]; then
+  utils/filter_scp.pl -f 2 $dir/lat_orig.scp < $utt_map | \
+    utils/apply_map.pl -f 2 $dir/lat_orig.scp > \
+    $dir/lat.scp || exit 1
+
+  if [ ! -s $dir/lat.scp ]; then
+    echo "$0: $dir/lat.scp is empty. Something went wrong!"
+    exit 1
+  fi
+fi
+
+utils/split_data.sh $data $nj
+
+if [ $stage -le 3 ]; then
+  $cmd JOB=1:$nj $dir/log/copy_lats.JOB.log \
+    lattice-copy "scp:utils/filter_scp.pl $data/split$nj/JOB/utt2spk $dir/lat.scp |" \
+    "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1
+fi
+
+echo $nj > $dir/num_jobs
+
+if [ -f $srcdir/ali.1.gz ]; then
+  if [ $stage -le 4 ]; then
+    $cmd JOB=1:$nj_src $dir/log/copy_ali_orig.JOB.log \
+      copy-int-vector "ark:gunzip -c $srcdir/ali.JOB.gz |" \
+      ark,scp:$dir/ali_orig.JOB.ark,$dir/ali_orig.JOB.scp || exit 1
+  fi
+
+  for n in $(seq $nj_src); do
+    cat $dir/ali_orig.$n.scp
+  done > $dir/ali_orig.scp || exit 1
+
+  if [ $stage -le 5 ]; then
+    utils/filter_scp.pl -f 2 $dir/ali_orig.scp < $utt_map | \
+      utils/apply_map.pl -f 2 $dir/ali_orig.scp > \
+      $dir/ali.scp || exit 1
+  
+    if [ ! -s $dir/ali.scp ]; then
+      echo "$0: $dir/ali.scp is empty. Something went wrong!"
+      exit 1
+    fi
+  fi
+
+  utils/split_data.sh $data $nj
+
+  if [ $stage -le 6 ]; then
+    $cmd JOB=1:$nj $dir/log/copy_ali.JOB.log \
+      copy-int-vector "scp:utils/filter_scp.pl $data/split$nj/JOB/utt2spk $dir/ali.scp |" \
+      "ark:|gzip -c > $dir/ali.JOB.gz" || exit 1
+  fi
+fi
+
+rm $dir/lat_orig.*.{ark,scp} $dir/ali_orig.*.{ark,scp} 2>/dev/null || true
diff --git a/egs/chime6/s5_track1/local/distant_audio_list b/egs/chime6/s5_track1/local/distant_audio_list
new file mode 100644
index 00000000000..fc7aff15cd0
--- /dev/null
+++ b/egs/chime6/s5_track1/local/distant_audio_list
@@ -0,0 +1,376 @@
+S03_U01.CH1
+S03_U01.CH2
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+S24_U06.CH4
diff --git a/egs/chime6/s5_track1/local/extract_noises.py b/egs/chime6/s5_track1/local/extract_noises.py
new file mode 100755
index 00000000000..f7b7f752d9e
--- /dev/null
+++ b/egs/chime6/s5_track1/local/extract_noises.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+import logging
+import os
+import sys
+import scipy.io.wavfile as siw
+import math
+import numpy as np
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        """Extract noises from the corpus based on the non-speech regions.
+        e.g. {} /export/corpora4/CHiME5/audio/train/ \\
+                /export/corpora4/CHiME5/transcriptions/train/ \\
+                /export/b05/zhiqiw/noise/""".format(sys.argv[0]))
+
+    parser.add_argument("--segment-length", default=20)
+    parser.add_argument("audio_dir", help="""Location of the CHiME5 Audio files. e.g. /export/corpora4/CHiME5/audio/train/""")
+    parser.add_argument("trans_dir", help="""Location of the CHiME5 Transcriptions. e.g. /export/corpora4/CHiME5/transcriptions/train/""")
+    parser.add_argument("audio_list", help="""List of ids of the CHiME5 recordings from which noise is extracted. e.g. local/distant_audio_list""")
+    parser.add_argument("out_dir", help="Output directory to write noise files. e.g. /export/b05/zhiqiw/noise/")
+
+    args = parser.parse_args()
+    return args
+
+
+def Trans_time(time, fs):
+    units = time.split(':')
+    time_second = float(units[0]) * 3600 + float(units[1]) * 60 + float(units[2])
+    return int(time_second*fs)
+
+
+def Get_time(conf, tag, mic, fs):
+    for i in conf:
+        st = Trans_time(i['start_time'][mic], fs)
+        ed = Trans_time(i['end_time'][mic], fs)
+        tag[st:ed] = 0
+    return tag
+
+
+def write_noise(out_dir, seg, audio, sig, tag, fs, cnt):
+    sig_noise = sig[np.nonzero(tag)]
+    for i in range(math.floor(len(sig_noise)/(seg*fs))):
+        siw.write(out_dir +'/noise'+str(cnt)+'.wav', fs, sig_noise[i*seg*fs:(i+1)*seg*fs])
+        cnt += 1
+    return cnt
+
+
+def main():
+    args = get_args()
+
+    if not os.path.exists(args.out_dir):
+        os.makedirs(args.out_dir)
+
+    wav_list = open(args.audio_list).readlines()
+
+    cnt = 1
+    for i, audio in enumerate(wav_list):
+        parts = audio.strip().split('.')
+        if len(parts) == 2:
+            # Assuming distant mic with name like S03_U01.CH1
+            session, mic = parts[0].split('_')
+            channel = parts[1]
+            base_name = session + "_" + mic + "." + channel
+        else:
+            # Assuming close talk mic with name like S03_P09
+            session, mic = audio.strip().split('_')
+            base_name = session + "_" + mic
+        fs, sig = siw.read(args.audio_dir + "/" + base_name + '.wav')
+        tag = np.ones(len(sig))
+        if i == 0 or session != session_p:
+            with open(args.trans_dir + "/" + session + '.json') as f:
+                conf = json.load(f)
+        tag = Get_time(conf, tag, mic, fs)
+        cnt = write_noise(args.out_dir, args.segment_length, audio, sig, tag, fs, cnt)
+        session_p = session
+
+
+if __name__ == '__main__':
+    main()
diff --git a/egs/chime6/s5_track1/local/extract_vad_weights.sh b/egs/chime6/s5_track1/local/extract_vad_weights.sh
new file mode 100755
index 00000000000..250b021bd8f
--- /dev/null
+++ b/egs/chime6/s5_track1/local/extract_vad_weights.sh
@@ -0,0 +1,86 @@
+#!/bin/bash
+
+# Copyright 2016 Johns Hopkins University (Author: Daniel Povey, Vijayaditya Peddinti)
+#           2019 Vimal Manohar
+# Apache 2.0.
+
+# This script converts lattices available from a first pass decode into a per-frame weights file
+# The ctms generated from the lattices are filtered. Silence frames are assigned a low weight (e.g.0.00001)
+# and voiced frames have a weight of 1.
+
+set -e
+
+stage=1
+cmd=run.pl
+silence_weight=0.00001
+#end configuration section.
+
+. ./cmd.sh
+
+[ -f ./path.sh ] && . ./path.sh
+. utils/parse_options.sh || exit 1;
+if [ $# -ne 4 ]; then
+  echo "Usage: $0 [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <input-decode-dir> <output-wts-file-gzipped>"
+  echo " Options:"
+  echo "    --cmd (run.pl|queue.pl...)      # specify how to run the sub-processes."
+  exit 1;
+fi
+
+data_dir=$1
+lang=$2 # Note: may be graph directory not lang directory, but has the necessary stuff copied.
+decode_dir=$3
+output_wts_file_gz=$4
+
+if [ $stage -le 1 ]; then
+  echo "$0: generating CTM from input lattices"
+  steps/get_ctm_conf.sh --cmd "$cmd" \
+    --use-segments false \
+    $data_dir \
+    $lang \
+    $decode_dir
+fi
+
+if [ $stage -le 2 ]; then
+  name=`basename $data_dir`
+  # we just take the ctm from LMWT 10, it doesn't seem to affect the results a lot
+  ctm=$decode_dir/score_10/$name.ctm
+  echo "$0: generating weights file from ctm $ctm"
+
+  pad_frames=0  # this did not seem to be helpful but leaving it as an option.
+  feat-to-len scp:$data_dir/feats.scp ark,t:- >$decode_dir/utt.lengths
+  if [ ! -f $ctm ]; then  echo "$0: expected ctm to exist: $ctm"; exit 1; fi
+
+  cat $ctm | awk '$6 == 1.0 && $4 < 1.0' | \
+  grep -v -w mm | grep -v -w mhm | grep -v -F '[noise]' | \
+  grep -v -F '[laughter]' | grep -v -F '<unk>' | \
+  perl -e ' $lengths=shift @ARGV;  $pad_frames=shift @ARGV; $silence_weight=shift @ARGV;
+   $pad_frames >= 0 || die "bad pad-frames value $pad_frames";
+   open(L, "<$lengths") || die "opening lengths file";
+   @all_utts = ();
+   $utt2ref = { };
+   while (<L>) {
+     ($utt, $len) = split(" ", $_);
+     push @all_utts, $utt;
+     $array_ref = [ ];
+     for ($n = 0; $n < $len; $n++) { ${$array_ref}[$n] = $silence_weight; }
+     $utt2ref{$utt} = $array_ref;
+   }
+   while (<STDIN>) {
+     @A = split(" ", $_);
+     @A == 6 || die "bad ctm line $_";
+     $utt = $A[0]; $beg = $A[2]; $len = $A[3];
+     $beg_int = int($beg * 100) - $pad_frames;
+     $len_int = int($len * 100) + 2*$pad_frames;
+     $array_ref = $utt2ref{$utt};
+     !defined $array_ref  && die "No length info for utterance $utt";
+     for ($t = $beg_int; $t < $beg_int + $len_int; $t++) {
+       if ($t >= 0 && $t < @$array_ref) {
+         ${$array_ref}[$t] = 1;
+        }
+      }
+    }
+    foreach $utt (@all_utts) {  $array_ref = $utt2ref{$utt};
+      print $utt, " [ ", join(" ", @$array_ref), " ]\n";
+      } ' $decode_dir/utt.lengths $pad_frames $silence_weight | \
+        gzip -c > $output_wts_file_gz
+fi
diff --git a/egs/chime6/s5_track1/local/get_location.py b/egs/chime6/s5_track1/local/get_location.py
new file mode 100755
index 00000000000..5493e1b6012
--- /dev/null
+++ b/egs/chime6/s5_track1/local/get_location.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+import json
+from datetime import timedelta
+from glob import glob
+import sys, io
+
+
+output = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+location_dict = {}
+
+json_file_location= sys.argv[1] + '/*.json'
+json_files = glob(json_file_location)
+
+for file in json_files:
+    with open(file, 'r') as f:
+        session_dict = json.load(f)
+
+    for uttid in session_dict:
+        try:
+            ref=uttid['ref']
+            speaker_id = uttid['speaker']
+            location = uttid['location']
+            session_id=uttid['session_id']
+            words = uttid['words']
+
+            end_time_hh=str(uttid['end_time'][speaker_id])
+            time = end_time_hh.strip().split(':')
+            hrs, mins, secs = float(time[0]), float(time[1]), float(time[2])
+            end_time = timedelta(hours=hrs, minutes=mins, seconds=secs).total_seconds()
+            end_time = '{0:7.2f}'.format(end_time)
+            end_time = "".join(end_time.strip().split('.'))
+            end_time = int(end_time)
+            end_time = str(end_time).zfill(7)
+
+            start_time_hh = str(uttid['start_time'][speaker_id])
+            time = start_time_hh.strip().split(':')
+            hrs, mins, secs = float(time[0]), float(time[1]), float(time[2])
+            start_time = timedelta(hours=hrs, minutes=mins, seconds=secs).total_seconds()
+            start_time = '{0:7.2f}'.format(start_time)
+            start_time = "".join(start_time.strip().split('.'))
+            start_time = int(start_time)
+            start_time =str(start_time).zfill(7)
+
+            utt = "{0}_{1}-{2}-{3}".format(speaker_id, session_id, start_time, end_time)
+            location_dict[utt]=(location)
+        except:
+            continue
+
+for key in sorted(location_dict.keys()):
+    utt= "{0} {1}".format(key, location_dict[key])
+    output.write(utt+ '\n')
+
diff --git a/egs/chime6/s5_track1/local/install_pb_chime5.sh b/egs/chime6/s5_track1/local/install_pb_chime5.sh
new file mode 100755
index 00000000000..b907b5ee66e
--- /dev/null
+++ b/egs/chime6/s5_track1/local/install_pb_chime5.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Installs pb_chime5
+# miniconda should be installed in $HOME/miniconda3/ 
+
+miniconda_dir=$HOME/miniconda3/
+
+if [ ! -d $miniconda_dir ]; then
+    echo "$miniconda_dir does not exist. Please run 'tools/extras/install_miniconda.sh" && exit 1;
+fi
+
+git clone https://github.com/fgnt/pb_chime5.git
+cd pb_chime5
+# Download submodule dependencies  # https://stackoverflow.com/a/3796947/5766934
+git submodule init  
+git submodule update
+
+$miniconda_dir/bin/python -m pip install cython
+$miniconda_dir/bin/python -m pip install pymongo
+$miniconda_dir/bin/python -m pip install -e pb_bss/
+$miniconda_dir/bin/python -m pip install -e .
diff --git a/egs/chime6/s5_track1/local/json2text.py b/egs/chime6/s5_track1/local/json2text.py
new file mode 100755
index 00000000000..3de66064848
--- /dev/null
+++ b/egs/chime6/s5_track1/local/json2text.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+
+# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+import json
+import argparse
+import logging
+import sys
+
+
+def hms_to_seconds(hms):
+    hour = hms.split(':')[0]
+    minute = hms.split(':')[1]
+    second = hms.split(':')[2].split('.')[0]
+
+    # .xx (10 ms order)
+    ms10 = hms.split(':')[2].split('.')[1]
+
+    # total seconds
+    seconds = int(hour) * 3600 + int(minute) * 60 + int(second)
+
+    return '{:07d}'.format(int(str(seconds) + ms10))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('json', type=str, help='JSON transcription file')
+    parser.add_argument('--mictype', type=str,
+                        choices=['ref', 'worn', 'gss', 'u01', 'u02', 'u03', 'u04', 'u05', 'u06'],
+                        help='Type of microphones')
+    args = parser.parse_args()
+
+    # logging info
+    log_format = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s:%(message)s"
+    logging.basicConfig(level=logging.INFO, format=log_format)
+
+    logging.debug("reading %s", args.json)
+    with open(args.json, 'rt', encoding="utf-8") as f:
+        j = json.load(f)
+
+    for x in j:
+        if '[redacted]' not in x['words']:
+            session_id = x['session_id']
+            speaker_id = x['speaker']
+            if args.mictype == 'ref':
+                mictype = x['ref']
+            elif args.mictype == 'worn' or args.mictype == 'gss':
+                mictype = 'original'
+            else:
+                mictype = args.mictype.upper() # convert from u01 to U01
+
+            # add location tag for scoring (only for dev and eval sets)
+            if 'location' in x.keys():
+                location = x['location'].upper()
+            else:
+                location = 'NOLOCATION'
+
+            start_time = x['start_time'][mictype]
+            end_time = x['end_time'][mictype]
+
+            # remove meta chars and convert to lower
+            words = x['words'].replace('"', '')\
+                              .replace('.', '')\
+                              .replace('?', '')\
+                              .replace(',', '')\
+                              .replace(':', '')\
+                              .replace(';', '')\
+                              .replace('!', '').lower()
+
+            # remove multiple spaces
+            words = " ".join(words.split())
+
+            # convert to seconds, e.g., 1:10:05.55 -> 3600 + 600 + 5.55 = 4205.55
+            start_time = hms_to_seconds(start_time)
+            end_time = hms_to_seconds(end_time)
+
+            uttid = speaker_id + '_' + session_id
+            if not args.mictype in ['worn', 'gss']:
+                uttid += '_' + mictype
+
+            if args.mictype == 'gss':
+                uttid += '-' + start_time + '-' + end_time
+            else:
+                uttid += '_' + location + '-' + start_time + '-' + end_time
+
+            if end_time > start_time:
+                sys.stdout.buffer.write((uttid + ' ' + words + '\n').encode("utf-8"))
diff --git a/egs/chime6/s5_track1/local/make_noise_list.py b/egs/chime6/s5_track1/local/make_noise_list.py
new file mode 100755
index 00000000000..5aaf7fa4062
--- /dev/null
+++ b/egs/chime6/s5_track1/local/make_noise_list.py
@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+
+import glob
+import os
+import sys
+
+
+if len(sys.argv) != 2:
+    print ("Usage: {} <noises-dir>".format(sys.argv[0]))
+    raise SystemExit(1)
+
+
+for line in glob.glob("{}/*.wav".format(sys.argv[1])):
+    fname = os.path.basename(line.strip())
+
+    print ("--noise-id {} --noise-type point-source "
+           "--bg-fg-type foreground {}".format(fname, line.strip()))
diff --git a/egs/chime6/s5_track1/local/nnet3/compare_wer.sh b/egs/chime6/s5_track1/local/nnet3/compare_wer.sh
new file mode 100755
index 00000000000..095e85cc338
--- /dev/null
+++ b/egs/chime6/s5_track1/local/nnet3/compare_wer.sh
@@ -0,0 +1,132 @@
+#!/bin/bash
+
+# this script is used for comparing decoding results between systems.
+# e.g. local/chain/compare_wer.sh exp/chain/tdnn_{c,d}_sp
+# For use with discriminatively trained systems you specify the epochs after a colon:
+# for instance,
+# local/chain/compare_wer.sh exp/chain/tdnn_c_sp exp/chain/tdnn_c_sp_smbr:{1,2,3}
+
+
+if [ $# == 0 ]; then
+  echo "Usage: $0: [--looped] [--online] <dir1> [<dir2> ... ]"
+  echo "e.g.: $0 exp/chain/tdnn_{b,c}_sp"
+  echo "or (with epoch numbers for discriminative training):"
+  echo "$0 exp/chain/tdnn_b_sp_disc:{1,2,3}"
+  exit 1
+fi
+
+echo "# $0 $*"
+
+include_looped=false
+if [ "$1" == "--looped" ]; then
+  include_looped=true
+  shift
+fi
+include_online=false
+if [ "$1" == "--online" ]; then
+  include_online=true
+  shift
+fi
+
+
+used_epochs=false
+
+# this function set_names is used to separate the epoch-related parts of the name
+# [for discriminative training] and the regular parts of the name.
+# If called with a colon-free directory name, like:
+#  set_names exp/chain/tdnn_lstm1e_sp_bi_smbr
+# it will set dir=exp/chain/tdnn_lstm1e_sp_bi_smbr and epoch_infix=""
+# If called with something like:
+#  set_names exp/chain/tdnn_d_sp_smbr:3
+# it will set dir=exp/chain/tdnn_d_sp_smbr and epoch_infix="_epoch3"
+
+
+set_names() {
+  if [ $# != 1 ]; then
+    echo "compare_wer_general.sh: internal error"
+    exit 1  # exit the program
+  fi
+  dirname=$(echo $1 | cut -d: -f1)
+  epoch=$(echo $1 | cut -s -d: -f2)
+  if [ -z $epoch ]; then
+    epoch_infix=""
+  else
+    used_epochs=true
+    epoch_infix=_epoch${epoch}
+  fi
+}
+
+
+
+echo -n "# System               "
+for x in $*; do   printf "% 10s" " $(basename $x)";   done
+echo
+
+strings=(
+  "#WER dev_clean_2 (tgsmall) "
+  "#WER dev_clean_2 (tglarge) ")
+
+for n in 0 1; do
+   echo -n "${strings[$n]}"
+   for x in $*; do
+     set_names $x  # sets $dirname and $epoch_infix
+    decode_names=(tgsmall_dev_clean_2 tglarge_dev_clean_2)
+
+     wer=$(cat $dirname/decode_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+     printf "% 10s" $wer
+   done
+   echo
+   if $include_looped; then
+     echo -n "#             [looped:]    "
+     for x in $*; do
+       set_names $x  # sets $dirname and $epoch_infix
+       wer=$(cat $dirname/decode_looped_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+       printf "% 10s" $wer
+     done
+     echo
+   fi
+   if $include_online; then
+     echo -n "#             [online:]    "
+     for x in $*; do
+       set_names $x  # sets $dirname and $epoch_infix
+       wer=$(cat ${dirname}_online/decode_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+       printf "% 10s" $wer
+     done
+     echo
+   fi
+done
+
+
+if $used_epochs; then
+  exit 0;  # the diagnostics aren't comparable between regular and discriminatively trained systems.
+fi
+
+echo -n "# Final train prob     "
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_train.{final,combined}.log 2>/dev/null | grep log-like | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final valid prob     "
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_valid.{final,combined}.log 2>/dev/null | grep log-like | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final train acc      "
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_train.{final,combined}.log 2>/dev/null | grep accuracy | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final valid acc      "
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_valid.{final,combined}.log 2>/dev/null | grep accuracy | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo
diff --git a/egs/chime6/s5_track1/local/nnet3/decode.sh b/egs/chime6/s5_track1/local/nnet3/decode.sh
new file mode 100755
index 00000000000..8fa54e0d4a6
--- /dev/null
+++ b/egs/chime6/s5_track1/local/nnet3/decode.sh
@@ -0,0 +1,164 @@
+#!/bin/bash
+
+# Copyright 2016 Johns Hopkins University (Author: Daniel Povey, Vijayaditya Peddinti)
+#           2019 Vimal Manohar 
+# Apache 2.0.
+
+# This script does 2-stage decoding where the first stage is used to get 
+# reliable frames for i-vector extraction.
+
+set -e
+
+# general opts
+iter=
+stage=0
+nj=30
+affix=  # affix for decode directory
+
+# ivector opts
+max_count=75  # parameter for extract_ivectors.sh
+sub_speaker_frames=6000
+ivector_scale=0.75
+get_weights_from_ctm=true
+weights_file=   # use weights from this archive (must be compressed using gunzip)
+silence_weight=0.00001   # apply this weight to silence frames during i-vector extraction
+ivector_dir=exp/nnet3
+
+# decode opts
+pass2_decode_opts="--min-active 1000"
+lattice_beam=8
+extra_left_context=0 # change for (B)LSTM
+extra_right_context=0 # change for BLSTM
+frames_per_chunk=50 # change for (B)LSTM
+acwt=0.1 # important to change this when using chain models
+post_decode_acwt=1.0 # important to change this when using chain models
+extra_left_context_initial=0
+extra_right_context_final=0
+
+graph_affix=
+
+score_opts="--min-lmwt 6 --max-lmwt 13"
+
+. ./cmd.sh
+[ -f ./path.sh ] && . ./path.sh
+. utils/parse_options.sh || exit 1;
+
+if [ $# -ne 4 ]; then
+  echo "Usage: $0 [options] <data-dir> <lang-dir> <graph-dir> <model-dir>"
+  echo " Options:"
+  echo "    --stage (0|1|2)   # start scoring script from part-way through."
+  echo "e.g.:"
+  echo "$0 data/dev data/lang exp/tri5a/graph_pp exp/nnet3/tdnn"
+  exit 1;
+fi
+
+data=$1 # data directory 
+lang=$2 # data/lang
+graph=$3 #exp/tri5a/graph_pp
+dir=$4 # exp/nnet3/tdnn
+
+model_affix=`basename $dir`
+ivector_affix=${affix:+_$affix}_chain_${model_affix}${iter:+_iter$iter}
+affix=${affix:+_${affix}}${iter:+_iter${iter}}
+
+if [ $stage -le 1 ]; then
+  if [ ! -s ${data}_hires/feats.scp ]; then
+    utils/copy_data_dir.sh $data ${data}_hires
+    steps/make_mfcc.sh --mfcc-config conf/mfcc_hires.conf --nj $nj --cmd "$train_cmd" ${data}_hires
+    steps/compute_cmvn_stats.sh ${data}_hires
+    utils/fix_data_dir.sh ${data}_hires
+  fi
+fi
+
+data_set=$(basename $data)
+if [ $stage -le 2 ]; then
+  echo "Extracting i-vectors, stage 1"
+  steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj $nj \
+    --max-count $max_count \
+    ${data}_hires $ivector_dir/extractor \
+    $ivector_dir/ivectors_${data_set}${ivector_affix}_stage1;
+  # float comparisons are hard in bash
+  if [ `bc <<< "$ivector_scale != 1"` -eq 1 ]; then
+    ivector_scale_affix=_scale$ivector_scale
+  else
+    ivector_scale_affix=
+  fi
+
+  if [ ! -z "$ivector_scale_affix" ]; then
+    echo "$0: Scaling iVectors, stage 1"
+    srcdir=$ivector_dir/ivectors_${data_set}${ivector_affix}_stage1
+    outdir=$ivector_dir/ivectors_${data_set}${ivector_affix}${ivector_scale_affix}_stage1
+    mkdir -p $outdir
+    $train_cmd $outdir/log/scale_ivectors.log \
+      copy-matrix --scale=$ivector_scale scp:$srcdir/ivector_online.scp ark:- \| \
+      copy-feats --compress=true ark:-  ark,scp:$outdir/ivector_online.ark,$outdir/ivector_online.scp;
+    cp $srcdir/ivector_period $outdir/ivector_period
+  fi
+fi
+
+decode_dir=$dir/decode${graph_affix}_${data_set}${affix}
+# generate the lattices
+if [ $stage -le 3 ]; then
+  echo "Generating lattices, stage 1"
+  steps/nnet3/decode.sh --nj $nj --cmd "$decode_cmd" \
+    --acwt $acwt --post-decode-acwt $post_decode_acwt \
+    --extra-left-context $extra_left_context  \
+    --extra-right-context $extra_right_context  \
+    --extra-left-context-initial $extra_left_context_initial \
+    --extra-right-context-final $extra_right_context_final \
+    --frames-per-chunk "$frames_per_chunk" \
+    --online-ivector-dir $ivector_dir/ivectors_${data_set}${ivector_affix}${ivector_scale_affix}_stage1 \
+    --skip-scoring true ${iter:+--iter $iter} \
+    $graph ${data}_hires ${decode_dir}_stage1;
+fi
+
+if [ $stage -le 4 ]; then
+  if $get_weights_from_ctm; then
+    if [ ! -z $weights_file ]; then
+      echo "$0: Using provided vad weights file $weights_file"
+      ivector_extractor_weights=$weights_file
+    else
+      echo "$0 : Generating vad weights file"
+      ivector_extractor_weights=${decode_dir}_stage1/weights${affix}.gz
+      local/extract_vad_weights.sh --silence-weight $silence_weight \
+        --cmd "$decode_cmd" ${iter:+--iter $iter} \
+        ${data}_hires $lang \
+        ${decode_dir}_stage1 $ivector_extractor_weights
+    fi
+  else
+    # get weights from best path decoding
+    ivector_extractor_weights=${decode_dir}_stage1
+  fi
+fi
+
+if [ $stage -le 5 ]; then
+  echo "Extracting i-vectors, stage 2 with weights from $ivector_extractor_weights"
+  # this does offline decoding, except we estimate the iVectors per
+  # speaker, excluding silence (based on alignments from a DNN decoding), with a
+  # different script.  This is just to demonstrate that script.
+  # the --sub-speaker-frames is optional; if provided, it will divide each speaker
+  # up into "sub-speakers" of at least that many frames... can be useful if
+  # acoustic conditions drift over time within the speaker's data.
+  steps/online/nnet2/extract_ivectors.sh --cmd "$train_cmd" --nj $nj \
+    --silence-weight $silence_weight \
+    --sub-speaker-frames $sub_speaker_frames --max-count $max_count \
+    ${data}_hires $lang $ivector_dir/extractor \
+    $ivector_extractor_weights $ivector_dir/ivectors_${data_set}${ivector_affix};
+fi
+
+if [ $stage -le 6 ]; then
+  echo "Generating lattices, stage 2 with --acwt $acwt"
+  rm -f ${decode_dir}/.error
+  steps/nnet3/decode.sh --nj $nj --cmd "$decode_cmd" $pass2_decode_opts \
+      --acwt $acwt --post-decode-acwt $post_decode_acwt \
+      --extra-left-context $extra_left_context  \
+      --extra-right-context $extra_right_context  \
+      --extra-left-context-initial $extra_left_context_initial \
+      --extra-right-context-final $extra_right_context_final \
+      --frames-per-chunk "$frames_per_chunk" \
+      --skip-scoring false ${iter:+--iter $iter} --lattice-beam $lattice_beam \
+      --online-ivector-dir $ivector_dir/ivectors_${data_set}${ivector_affix} \
+     $graph ${data}_hires ${decode_dir} || touch ${decode_dir}/.error
+  [ -f ${decode_dir}/.error ] && echo "$0: Error decoding" && exit 1;
+fi
+exit 0
diff --git a/egs/chime6/s5_track1/local/nnet3/multi_condition/run_ivector_common.sh b/egs/chime6/s5_track1/local/nnet3/multi_condition/run_ivector_common.sh
new file mode 100755
index 00000000000..8f823f4306e
--- /dev/null
+++ b/egs/chime6/s5_track1/local/nnet3/multi_condition/run_ivector_common.sh
@@ -0,0 +1,194 @@
+#!/bin/bash
+
+set -euo pipefail
+
+# 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_clean=train_worn
+train_set_noisy=train_u400k
+combined_train_set=train_worn_u400k
+test_sets="dev_worn"
+nj=96
+include_clean=false
+
+noise_list=
+num_data_reps=2
+snrs="20:10:15:5:0"
+foreground_snrs="20:10:15:5:0"
+background_snrs="20:10:15:5:0"
+
+nnet3_affix=_train_worn_u400k_rvb
+
+. ./cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+if [ $stage -le 0 ]; then
+  # Perturb the original data. We need this 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_clean} data/${train_set_clean}_sp
+
+  utils/data/perturb_data_dir_speed_3way.sh data/${train_set_noisy} data/${train_set_noisy}_sp
+
+  for datadir in ${train_set_clean}_sp ${train_set_noisy}_sp $test_sets; do
+    utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
+  done
+  
+  for datadir in ${train_set_clean}_sp ${train_set_noisy}_sp; do
+    utils/data/perturb_data_dir_volume.sh data/${datadir}_hires
+  done
+fi
+ 
+
+if [ $stage -le 1 ]; then
+  for datadir in ${train_set_clean}_sp ${train_set_noisy}_sp; do
+    mfccdir=data/${datadir}/data
+    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
+      utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/mfcc/chime5-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage
+    fi
+
+    steps/make_mfcc.sh --nj 20 \
+      --cmd "$train_cmd" data/${datadir} || exit 1;
+    steps/compute_cmvn_stats.sh data/${datadir} || exit 1;
+    utils/fix_data_dir.sh data/${datadir} || exit 1;
+  done
+fi
+
+if [ $stage -le 2 ]; then
+  if [ ! -d RIRS_NOISES/ ]; then
+    # Download the package that includes the real RIRs, simulated RIRs, isotropic noises and point-source noises
+    wget --no-check-certificate http://www.openslr.org/resources/28/rirs_noises.zip
+    unzip rirs_noises.zip
+  fi
+
+  if [ -z "$noise_list" ]; then
+    noise_list=RIRS_NOISES/pointsource_noises/noise_list
+  fi
+
+  # This is the config for the system using simulated RIRs and point-source noises
+  rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/smallroom/rir_list")
+  rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/mediumroom/rir_list")
+  rvb_opts+=(--noise-set-parameters $noise_list)
+
+  python steps/data/reverberate_data_dir.py \
+    "${rvb_opts[@]}" \
+    --prefix "rev" \
+    --foreground-snrs $foreground_snrs \
+    --background-snrs $background_snrs \
+    --speech-rvb-probability 1 \
+    --pointsource-noise-addition-probability 1 \
+    --isotropic-noise-addition-probability 1 \
+    --num-replications $num_data_reps \
+    --max-noises-per-minute 1 \
+    --source-sampling-rate 16000 \
+    data/${train_set_clean}_sp_hires data/${train_set_clean}_sp_rvb_hires
+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"
+  for datadir in ${train_set_clean}_sp_rvb ${train_set_noisy}_sp ${test_sets}; do
+    mfccdir=data/${datadir}_hires/data
+    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
+      utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/mfcc/chime5-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage
+    fi
+  
+    steps/make_mfcc.sh --nj 20 --mfcc-config conf/mfcc_hires.conf \
+      --cmd "$train_cmd" data/${datadir}_hires || exit 1;
+    steps/compute_cmvn_stats.sh data/${datadir}_hires || exit 1;
+    utils/fix_data_dir.sh data/${datadir}_hires || exit 1;
+  done
+fi
+  
+temp_data_root=exp/nnet3${nnet3_affix}/diag_ubm
+
+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
+  optional_clean=
+  if $include_clean; then
+    optional_clean=data/${train_set_clean}_sp_hires
+  fi
+  utils/combine_data.sh data/${combined_train_set}_sp_rvb_hires \
+    ${optional_clean} \
+    data/${train_set_clean}_sp_rvb_hires data/${train_set_noisy}_sp_hires
+
+  num_utts_total=$(wc -l < data/${combined_train_set}_sp_rvb_hires/utt2spk)
+  num_utts=$[$num_utts_total/4]
+  utils/data/subset_data_dir.sh data/${combined_train_set}_sp_rvb_hires \
+     $num_utts ${temp_data_root}/${combined_train_set}_sp_rvb_hires_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 \
+      ${temp_data_root}/${combined_train_set}_sp_rvb_hires_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 \
+    --num-threads 8 \
+    data/${combined_train_set}_sp_rvb_hires 512 \
+    exp/nnet3${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 20 \
+    data/${combined_train_set}_sp_rvb_hires exp/nnet3${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.
+
+  for datadir in ${combined_train_set}_sp_rvb; do
+    ivectordir=exp/nnet3${nnet3_affix}/ivectors_${datadir}_hires
+    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $ivectordir/storage ]; then
+      utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/ivectors/chime5-$(date +'%m_%d_%H_%M')/s5/$ivectordir/storage $ivectordir/storage
+    fi
+
+    # having a larger number of speakers is helpful for generalization, and to
+    # handle per-utterance decoding well (iVector starts at zero).
+    temp_data_root=${ivectordir}
+    utils/data/modify_speaker_info.sh --utts-per-spk-max 2 \
+      data/${datadir}_hires ${temp_data_root}/${datadir}_hires_max2
+
+    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj ${nj} \
+      ${temp_data_root}/${datadir}_hires_max2 \
+      exp/nnet3${nnet3_affix}/extractor $ivectordir
+  done
+
+  # 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 20 \
+      data/${data}_hires exp/nnet3${nnet3_affix}/extractor \
+      exp/nnet3${nnet3_affix}/ivectors_${data}_hires
+  done
+fi
+
+exit 0
+
diff --git a/egs/chime6/s5_track1/local/nnet3/run_ivector_common.sh b/egs/chime6/s5_track1/local/nnet3/run_ivector_common.sh
new file mode 100755
index 00000000000..3910e1812a3
--- /dev/null
+++ b/egs/chime6/s5_track1/local/nnet3/run_ivector_common.sh
@@ -0,0 +1,151 @@
+#!/bin/bash
+
+set -euo pipefail
+
+# 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_worn_u100k
+test_sets="dev_worn dev_beamformit_ref"
+gmm=tri3
+nj=96
+
+nnet3_affix=_train_worn_u100k
+
+. ./cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+gmm_dir=exp/${gmm}
+ali_dir=exp/${gmm}_ali_${train_set}_sp
+
+for f in ${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 "$0: making MFCC features for low-resolution speed-perturbed data"
+  steps/make_mfcc.sh --cmd "$train_cmd" --nj 20 data/${train_set}_sp || exit 1;
+  steps/compute_cmvn_stats.sh data/${train_set}_sp || 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 ${nj} --cmd "$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 "$0: creating high-resolution MFCC features"
+  mfccdir=data/${train_set}_sp_hires/data
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
+    utils/create_split_dir.pl /export/b1{5,6,8,9}/$USER/kaldi-data/mfcc/chime5-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage
+  fi
+
+  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 ${test_sets}; do
+    steps/make_mfcc.sh --nj 20 --mfcc-config conf/mfcc_hires.conf \
+      --cmd "$train_cmd" data/${datadir}_hires || exit 1;
+    steps/compute_cmvn_stats.sh data/${datadir}_hires || exit 1;
+    utils/fix_data_dir.sh data/${datadir}_hires || 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/${train_set}_sp_hires/utt2spk)
+  num_utts=$[$num_utts_total/4]
+  utils/data/subset_data_dir.sh data/${train_set}_sp_hires \
+     $num_utts ${temp_data_root}/${train_set}_sp_hires_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_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 \
+    --num-threads 8 \
+    ${temp_data_root}/${train_set}_sp_hires_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 20 \
+     data/${train_set}_sp_hires exp/nnet3${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_hires
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $ivectordir/storage ]; then
+    utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/ivectors/chime5-$(date +'%m_%d_%H_%M')/s5/$ivectordir/storage $ivectordir/storage
+  fi
+
+
+  # having a larger number of speakers is helpful for generalization, and to
+  # handle per-utterance decoding well (iVector starts at zero).
+  temp_data_root=${ivectordir}
+  utils/data/modify_speaker_info.sh --utts-per-spk-max 2 \
+    data/${train_set}_sp_hires ${temp_data_root}/${train_set}_sp_hires_max2
+
+  steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj ${nj} \
+    ${temp_data_root}/${train_set}_sp_hires_max2 \
+    exp/nnet3${nnet3_affix}/extractor $ivectordir
+fi
+
+if [ $stage -le 7 ]; then
+  # 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 20 \
+      data/${data}_hires exp/nnet3${nnet3_affix}/extractor \
+      exp/nnet3${nnet3_affix}/ivectors_${data}_hires
+  done
+fi
+
+exit 0
diff --git a/egs/chime6/s5_track1/local/prepare_data.sh b/egs/chime6/s5_track1/local/prepare_data.sh
new file mode 100755
index 00000000000..ee0f9c37e43
--- /dev/null
+++ b/egs/chime6/s5_track1/local/prepare_data.sh
@@ -0,0 +1,151 @@
+#!/bin/bash
+#
+# Copyright  2017  Johns Hopkins University (Author: Shinji Watanabe, Yenda Trmal)
+# Apache 2.0
+
+# Begin configuration section.
+mictype=worn # worn, ref or others
+cleanup=true
+# End configuration section
+. ./utils/parse_options.sh  # accept options.. you can run this run.sh with the
+
+. ./path.sh
+
+echo >&2 "$0" "$@"
+if [ $# -ne 3 ] ; then
+  echo >&2 "$0" "$@"
+  echo >&2 "$0: Error: wrong number of arguments"
+  echo -e >&2 "Usage:\n  $0 [opts] <audio-dir> <json-transcript-dir> <output-dir>"
+  echo -e >&2 "eg:\n  $0 /corpora/chime5/audio/train /corpora/chime5/transcriptions/train data/train"
+  exit 1
+fi
+
+set -e -o pipefail
+
+adir=$1
+jdir=$2
+dir=$3
+
+json_count=$(find -L $jdir -name "*.json" | wc -l)
+wav_count=$(find -L $adir -name "*.wav" | wc -l)
+
+if [ "$json_count" -eq 0 ]; then
+  echo >&2 "We expect that the directory $jdir will contain json files."
+  echo >&2 "That implies you have supplied a wrong path to the data."
+  exit 1
+fi
+if [ "$wav_count" -eq 0 ]; then
+  echo >&2 "We expect that the directory $adir will contain wav files."
+  echo >&2 "That implies you have supplied a wrong path to the data."
+  exit 1
+fi
+
+echo "$0: Converting transcription to text"
+
+mkdir -p $dir
+
+for file in $jdir/*json; do
+  ./local/json2text.py --mictype $mictype $file
+done | \
+  sed -e "s/\[inaudible[- 0-9]*\]/[inaudible]/g" |\
+  sed -e 's/ - / /g' |\
+  sed -e 's/mm-/mm/g' > $dir/text.orig
+
+echo "$0: Creating datadir $dir for type=\"$mictype\""
+
+if [ $mictype == "worn" ]; then
+  # convert the filenames to wav.scp format, use the basename of the file
+  # as a the wav.scp key, add .L and .R for left and right channel
+  # i.e. each file will have two entries (left and right channel)
+  find -L $adir -name  "S[0-9]*_P[0-9]*.wav" | \
+    perl -ne '{
+      chomp;
+      $path = $_;
+      next unless $path;
+      @F = split "/", $path;
+      ($f = $F[@F-1]) =~ s/.wav//;
+      @F = split "_", $f;
+      print "${F[1]}_${F[0]}.L sox $path -t wav - remix 1 |\n";
+      print "${F[1]}_${F[0]}.R sox $path -t wav - remix 2 |\n";
+    }' | sort > $dir/wav.scp
+
+  # generate the transcripts for both left and right channel
+  # from the original transcript in the form
+  # P09_S03-0006072-0006147 gimme the baker
+  # create left and right channel transcript
+  # P09_S03.L-0006072-0006147 gimme the baker
+  # P09_S03.R-0006072-0006147 gimme the baker
+  sed -n 's/  *$//; h; s/-/\.L-/p; g; s/-/\.R-/p' $dir/text.orig | sort > $dir/text
+elif [ $mictype == "ref" ]; then
+  # fixed reference array
+
+  # first get a text, which will be used to extract reference arrays
+  perl -ne 's/-/.ENH-/;print;' $dir/text.orig | sort > $dir/text
+
+  find -L $adir | grep "\.wav" | sort > $dir/wav.flist
+  # following command provide the argument for grep to extract only reference arrays
+  grep `cut -f 1 -d"-" $dir/text | awk -F"_" '{print $2 "_" $3}' | sed -e "s/\.ENH//" | sort | uniq | sed -e "s/^/ -e /" | tr "\n" " "` $dir/wav.flist > $dir/wav.flist2
+  paste -d" " \
+	<(awk -F "/" '{print $NF}' $dir/wav.flist2 | sed -e "s/\.wav/.ENH/") \
+	$dir/wav.flist2 | sort > $dir/wav.scp
+elif [ $mictype == "gss" ]; then
+  find -L $adir -name  "P[0-9]*_S[0-9]*.wav" | \
+    perl -ne '{
+      chomp;
+      $path = $_;
+      next unless $path;
+      @F = split "/", $path;
+      ($f = $F[@F-1]) =~ s/.wav//;
+      $f =~ m/(P[0-9]*_S[0-9]*)_(\d+)-(\d+)/;
+      print "$1-$2-$3 $path\n";
+    }' | sort > $dir/wav.scp
+  
+  cat $dir/text.orig | sort > $dir/text
+else
+  # array mic case
+  # convert the filenames to wav.scp format, use the basename of the file
+  # as a the wav.scp key
+  find -L $adir -name "*.wav" -ipath "*${mictype}*" |\
+    perl -ne '$p=$_;chomp $_;@F=split "/";$F[$#F]=~s/\.wav//;print "$F[$#F] $p";' |\
+    sort -u > $dir/wav.scp
+
+  # convert the transcripts from
+  # P09_S03-0006072-0006147 gimme the baker
+  # to the per-channel transcripts
+  # P09_S03_U01_NOLOCATION.CH1-0006072-0006147 gimme the baker
+  # P09_S03_U01_NOLOCATION.CH2-0006072-0006147 gimme the baker
+  # P09_S03_U01_NOLOCATION.CH3-0006072-0006147 gimme the baker
+  # P09_S03_U01_NOLOCATION.CH4-0006072-0006147 gimme the baker
+  perl -ne '$l=$_;
+    for($i=1; $i<=4; $i++) {
+      ($x=$l)=~ s/-/.CH\Q$i\E-/;
+      print $x;}' $dir/text.orig | sort > $dir/text
+
+fi
+$cleanup && rm -f $dir/text.* $dir/wav.scp.* $dir/wav.flist
+
+# Prepare 'segments', 'utt2spk', 'spk2utt'
+if [ $mictype == "worn" ]; then
+  cut -d" " -f 1 $dir/text | \
+    awk -F"-" '{printf("%s %s %08.2f %08.2f\n", $0, $1, $2/100.0, $3/100.0)}' |\
+    sed -e "s/_[A-Z]*\././2" \
+    > $dir/segments
+elif [ $mictype == "ref" ]; then
+  cut -d" " -f 1 $dir/text | \
+    awk -F"-" '{printf("%s %s %08.2f %08.2f\n", $0, $1, $2/100.0, $3/100.0)}' |\
+    sed -e "s/_[A-Z]*\././2" |\
+    sed -e "s/ P.._/ /" > $dir/segments
+elif [ $mictype != "gss" ]; then
+  cut -d" " -f 1 $dir/text | \
+    awk -F"-" '{printf("%s %s %08.2f %08.2f\n", $0, $1, $2/100.0, $3/100.0)}' |\
+    sed -e "s/_[A-Z]*\././2" |\
+    sed -e 's/ P.._/ /' > $dir/segments
+fi
+
+cut -f 1 -d ' ' $dir/text | \
+  perl -ne 'chomp;$utt=$_;s/_.*//;print "$utt $_\n";' > $dir/utt2spk
+
+utils/utt2spk_to_spk2utt.pl $dir/utt2spk > $dir/spk2utt
+
+# Check that data dirs are okay!
+utils/validate_data_dir.sh --no-feats $dir || exit 1
diff --git a/egs/chime6/s5_track1/local/prepare_dict.sh b/egs/chime6/s5_track1/local/prepare_dict.sh
new file mode 100755
index 00000000000..09083d0e795
--- /dev/null
+++ b/egs/chime6/s5_track1/local/prepare_dict.sh
@@ -0,0 +1,124 @@
+#!/bin/bash
+# Copyright (c) 2018, Johns Hopkins University (Jan "Yenda" Trmal<jtrmal@gmail.com>)
+# License: Apache 2.0
+
+# Begin configuration section.
+# End configuration section
+. ./utils/parse_options.sh
+
+. ./path.sh
+
+set -e -o pipefail
+set -o nounset                              # Treat unset variables as an error
+
+
+# The parts of the output of this that will be needed are
+# [in data/local/dict/ ]
+# lexicon.txt
+# extra_questions.txt
+# nonsilence_phones.txt
+# optional_silence.txt
+# silence_phones.txt
+
+
+# check existing directories
+[ $# != 0 ] && echo "Usage: $0" && exit 1;
+
+dir=data/local/dict
+
+mkdir -p $dir
+echo "$0: Getting CMU dictionary"
+if [ ! -f $dir/cmudict.done ]; then
+  [ -d $dir/cmudict ] && rm -rf $dir/cmudict
+  svn co https://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict $dir/cmudict
+  touch $dir/cmudict.done
+fi
+
+# silence phones, one per line.
+for w in sil spn inaudible laughs noise; do
+  echo $w;
+done > $dir/silence_phones.txt
+echo sil > $dir/optional_silence.txt
+
+# For this setup we're discarding stress.
+cat $dir/cmudict/cmudict-0.7b.symbols | \
+  perl -ne 's:[0-9]::g; s:\r::; print lc($_)' | \
+  sort -u > $dir/nonsilence_phones.txt
+
+# An extra question will be added by including the silence phones in one class.
+paste -d ' ' -s $dir/silence_phones.txt > $dir/extra_questions.txt
+
+grep -v ';;;' $dir/cmudict/cmudict-0.7b |\
+  uconv -f latin1 -t utf-8 -x Any-Lower |\
+  perl -ne 's:(\S+)\(\d+\) :$1 :; s:  : :; print;' |\
+  perl -ne '@F = split " ",$_,2; $F[1] =~ s/[0-9]//g; print "$F[0] $F[1]";' \
+  > $dir/lexicon1_raw_nosil.txt || exit 1;
+
+# Add prons for laughter, noise, oov
+for w in `grep -v sil $dir/silence_phones.txt`; do
+  echo "[$w] $w"
+done | cat - $dir/lexicon1_raw_nosil.txt > $dir/lexicon2_raw.txt || exit 1;
+
+# we keep all words from the cmudict in the lexicon
+# might reduce OOV rate on dev and eval
+cat $dir/lexicon2_raw.txt  \
+   <( echo "mm m"
+      echo "<unk> spn"
+      echo "cuz k aa z"
+      echo "cuz k ah z"
+      echo "cuz k ao z"
+      echo "mmm m"; \
+      echo "hmm hh m"; \
+    ) | sort -u | sed 's/[\t ]/\t/' > $dir/iv_lexicon.txt
+
+
+cat data/train*/text  | \
+  awk '{for (n=2;n<=NF;n++){ count[$n]++; } } END { for(n in count) { print count[n], n; }}' | \
+  sort -nr > $dir/word_counts
+
+cat $dir/word_counts | awk '{print $2}' > $dir/word_list
+
+awk '{print $1}' $dir/iv_lexicon.txt | \
+  perl -e '($word_counts)=@ARGV;
+   open(W, "<$word_counts")||die "opening word-counts $word_counts";
+   while(<STDIN>) { chop; $seen{$_}=1; }
+   while(<W>) {
+     ($c,$w) = split;
+     if (!defined $seen{$w}) { print; }
+   } ' $dir/word_counts > $dir/oov_counts.txt
+
+echo "*Highest-count OOVs (including fragments) are:"
+head -n 10 $dir/oov_counts.txt
+echo "*Highest-count OOVs (excluding fragments) are:"
+grep -v -E '^-|-$' $dir/oov_counts.txt | head -n 10 || true
+
+echo "*Training a G2P and generating missing pronunciations"
+mkdir -p $dir/g2p/
+phonetisaurus-align --input=$dir/iv_lexicon.txt --ofile=$dir/g2p/aligned_lexicon.corpus
+ngram-count -order 4 -kn-modify-counts-at-end -ukndiscount\
+  -gt1min 0 -gt2min 0 -gt3min 0 -gt4min 0 \
+  -text $dir/g2p/aligned_lexicon.corpus -lm $dir/g2p/aligned_lexicon.arpa
+phonetisaurus-arpa2wfst --lm=$dir/g2p/aligned_lexicon.arpa --ofile=$dir/g2p/g2p.fst
+awk '{print $2}' $dir/oov_counts.txt > $dir/oov_words.txt
+phonetisaurus-apply --nbest 2 --model $dir/g2p/g2p.fst --thresh 5 --accumulate \
+  --word_list $dir/oov_words.txt > $dir/oov_lexicon.txt
+
+## The next section is again just for debug purposes
+## to show words for which the G2P failed
+cat $dir/oov_lexicon.txt $dir/iv_lexicon.txt | sort -u > $dir/lexicon.txt
+rm -f $dir/lexiconp.txt 2>/dev/null; # can confuse later script if this exists.
+awk '{print $1}' $dir/lexicon.txt | \
+  perl -e '($word_counts)=@ARGV;
+   open(W, "<$word_counts")||die "opening word-counts $word_counts";
+   while(<STDIN>) { chop; $seen{$_}=1; }
+   while(<W>) {
+     ($c,$w) = split;
+     if (!defined $seen{$w}) { print; }
+   } ' $dir/word_counts > $dir/oov_counts.g2p.txt
+
+echo "*Highest-count OOVs (including fragments) after G2P are:"
+head -n 10 $dir/oov_counts.g2p.txt
+
+utils/validate_dict_dir.pl $dir
+exit 0;
+
diff --git a/egs/chime6/s5_track1/local/replace_uttid.py b/egs/chime6/s5_track1/local/replace_uttid.py
new file mode 100755
index 00000000000..372b5b2e93e
--- /dev/null
+++ b/egs/chime6/s5_track1/local/replace_uttid.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+
+import sys, io
+output = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+
+def load_uttid_location(f):
+    locations = {}
+    for line in f:
+        parts=line.strip().split(' ')
+        uttid, loc = parts[0], parts[1]
+        locations[uttid] = loc
+    return locations
+
+locations = load_uttid_location(open(sys.argv[1],'r', encoding='utf8'))
+
+for line in open(sys.argv[2],'r', encoding='utf8'):
+    uttid, res = line.split(None, 1)
+    location = locations[uttid]
+    location_uttid = location +'_'+ str(uttid)
+    output.write(location_uttid + ' ' + res)
diff --git a/egs/chime6/s5_track1/local/reverberate_lat_dir.sh b/egs/chime6/s5_track1/local/reverberate_lat_dir.sh
new file mode 100755
index 00000000000..f601a37c0e1
--- /dev/null
+++ b/egs/chime6/s5_track1/local/reverberate_lat_dir.sh
@@ -0,0 +1,93 @@
+#!/bin/bash
+
+# Copyright 2018  Vimal Manohar
+# Apache 2.0
+
+num_data_reps=1
+cmd=run.pl
+nj=20
+include_clean=false
+
+. utils/parse_options.sh
+. ./path.sh
+
+if [ $# -ne 4 ]; then
+  echo "Usage: $0 <train-data-dir> <noisy-latdir> <clean-latdir> <output-latdir>"
+  exit 1
+fi
+
+train_data_dir=$1
+noisy_latdir=$2
+clean_latdir=$3
+dir=$4
+
+clean_nj=$(cat $clean_latdir/num_jobs)
+
+$cmd JOB=1:$clean_nj $dir/copy_clean_lattices.JOB.log \
+  lattice-copy "ark:gunzip -c $clean_latdir/lat.JOB.gz |" \
+  ark,scp:$dir/lats_clean.JOB.ark,$dir/lats_clean.JOB.scp || exit 1
+  
+for n in $(seq $clean_nj); do
+  cat $dir/lats_clean.$n.scp 
+done > $dir/lats_clean.scp
+
+for i in $(seq $num_data_reps); do
+  cat $dir/lats_clean.scp | awk -vi=$i '{print "rev"i"_"$0}'
+done > $dir/lats_rvb.scp
+
+noisy_nj=$(cat $noisy_latdir/num_jobs)
+$cmd JOB=1:$noisy_nj $dir/copy_noisy_lattices.JOB>log \
+  lattice-copy "ark:gunzip -c $noisy_latdir/lat.JOB.gz |" \
+  ark,scp:$dir/lats_noisy.JOB.ark,$dir/lats_noisy.JOB.scp || exit 1
+
+optional_clean=
+if $include_clean; then
+  optional_clean=$dir/lats_clean.scp
+fi
+
+for n in $(seq $noisy_nj); do
+  cat $dir/lats_noisy.$n.scp
+done | cat - $dir/lats_rvb.scp ${optional_clean} | sort -k1,1 > $dir/lats.scp
+
+utils/split_data.sh $train_data_dir $nj
+$cmd JOB=1:$nj $dir/copy_lattices.JOB.log \
+  lattice-copy "scp:utils/filter_scp.pl $train_data_dir/split$nj/JOB/utt2spk $dir/lats.scp |" \
+  "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1
+
+echo $nj > $dir/num_jobs
+
+if [ -f $clean_latdir/ali.1.gz ]; then
+  $cmd JOB=1:$clean_nj $dir/copy_clean_alignments.JOB.log \
+    copy-int-vector "ark:gunzip -c $clean_latdir/ali.JOB.gz |" \
+    ark,scp:$dir/ali_clean.JOB.ark,$dir/ali_clean.JOB.scp
+    
+  for n in $(seq $clean_nj); do
+    cat $dir/ali_clean.$n.scp 
+  done > $dir/ali_clean.scp
+
+  for i in $(seq $num_data_reps); do
+    cat $dir/ali_clean.scp | awk -vi=$i '{print "rev"i"_"$0}'
+  done > $dir/ali_rvb.scp
+  
+  optional_clean=
+  if $include_clean; then
+    optional_clean=$dir/ali_clean.scp
+  fi
+
+  $cmd JOB=1:$noisy_nj $dir/copy_noisy_alignments.JOB.log \
+    copy-int-vector "ark:gunzip -c $noisy_latdir/ali.JOB.gz |" \
+    ark,scp:$dir/ali_noisy.JOB.ark,$dir/ali_noisy.JOB.scp
+
+  for n in $(seq $noisy_nj); do
+    cat $dir/ali_noisy.$n.scp
+  done | cat - $dir/ali_rvb.scp $optional_clean | sort -k1,1 > $dir/ali.scp
+
+  utils/split_data.sh $train_data_dir $nj || exit 1
+  $cmd JOB=1:$nj $dir/copy_rvb_alignments.JOB.log \
+    copy-int-vector "scp:utils/filter_scp.pl $train_data_dir/split$nj/JOB/utt2spk $dir/ali.scp |" \
+    "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1
+fi
+
+cp $clean_latdir/{final.*,tree,*.mat,*opts,*.txt} $dir || true
+
+rm $dir/lats_{clean,noisy}.*.{ark,scp} $dir/ali_{clean,noisy}.*.{ark,scp} || true # save space
diff --git a/egs/chime6/s5_track1/local/run_beamformit.sh b/egs/chime6/s5_track1/local/run_beamformit.sh
new file mode 100755
index 00000000000..aa3badd90d8
--- /dev/null
+++ b/egs/chime6/s5_track1/local/run_beamformit.sh
@@ -0,0 +1,87 @@
+#!/bin/bash
+
+# Copyright 2015, Mitsubishi Electric Research Laboratories, MERL (Author: Shinji Watanabe)
+
+. ./cmd.sh
+. ./path.sh
+
+# Config:
+cmd=run.pl
+bmf="1 2 3 4"
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# != 3 ]; then
+   echo "Wrong #arguments ($#, expected 3)"
+   echo "Usage: local/run_beamformit.sh [options] <wav-in-dir> <wav-out-dir> <array-id>"
+   echo "main options (for others, see top of script file)"
+   echo "  --cmd <cmd>                              # Command to run in parallel with"
+   echo "  --bmf \"1 2 3 4\"                        # microphones used for beamforming"
+   exit 1;
+fi
+
+sdir=$1
+odir=$2
+array=$3
+expdir=exp/enhan/`echo $odir | awk -F '/' '{print $NF}'`_`echo $bmf | tr ' ' '_'`
+
+if ! command  -v BeamformIt &>/dev/null ; then
+  echo "Missing BeamformIt, run 'cd $KALDI_ROOT/tools/; ./extras/install_beamformit.sh; cd -;'" && exit 1
+fi
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+mkdir -p $odir
+mkdir -p $expdir/log
+
+echo "Will use the following channels: $bmf"
+# number of channels
+numch=`echo $bmf | tr ' ' '\n' | wc -l`
+echo "the number of channels: $numch"
+
+# wavfiles.list can be used as the name of the output files
+output_wavfiles=$expdir/wavfiles.list
+find -L ${sdir} | grep -i ${array} | awk -F "/" '{print $NF}' | sed -e "s/\.CH.\.wav//" | sort | uniq > $expdir/wavfiles.list
+
+# this is an input file list of the microphones
+# format: 1st_wav 2nd_wav ... nth_wav
+input_arrays=$expdir/channels_$numch
+for x in `cat $output_wavfiles`; do
+  echo -n "$x"
+  for ch in $bmf; do
+    echo -n " $x.CH$ch.wav"
+  done
+  echo ""
+done > $input_arrays
+
+# split the list for parallel processing
+# number of jobs are set by the number of WAV files
+nj=`wc -l $expdir/wavfiles.list | awk '{print $1}'`
+split_wavfiles=""
+for n in `seq $nj`; do
+  split_wavfiles="$split_wavfiles $output_wavfiles.$n"
+done
+utils/split_scp.pl $output_wavfiles $split_wavfiles || exit 1;
+
+echo -e "Beamforming\n"
+# making a shell script for each job
+for n in `seq $nj`; do
+cat << EOF > $expdir/log/beamform.$n.sh
+while read line; do
+  $BEAMFORMIT/BeamformIt -s \$line -c $input_arrays \
+    --config_file `pwd`/conf/beamformit.cfg \
+    --source_dir $sdir \
+    --result_dir $odir
+done < $output_wavfiles.$n
+EOF
+done
+
+chmod a+x $expdir/log/beamform.*.sh
+$cmd JOB=1:$nj $expdir/log/beamform.JOB.log \
+  $expdir/log/beamform.JOB.sh
+
+echo "`basename $0` Done."
diff --git a/egs/chime6/s5_track1/local/run_gss.sh b/egs/chime6/s5_track1/local/run_gss.sh
new file mode 100755
index 00000000000..92444bdbcd0
--- /dev/null
+++ b/egs/chime6/s5_track1/local/run_gss.sh
@@ -0,0 +1,54 @@
+#!/bin/bash
+
+# Copyright 2015, Mitsubishi Electric Research Laboratories, MERL (Author: Shinji Watanabe)
+
+. ./cmd.sh
+if [ -f ./path.sh ]; then . ./path.sh; fi
+
+# Config:
+cmd=run.pl
+nj=4
+use_multiarray=false
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# != 3 ]; then
+   echo "Wrong #arguments ($#, expected 3)"
+   echo "Usage: local/run_gss.sh [options] <session-id> <log-dir> <enhanced-dir>"
+   echo "main options (for others, see top of script file)"
+   echo "  --cmd <cmd>                              # Command to run in parallel with"
+   echo "  --bmf \"1 2 3 4\"                        # microphones used for beamforming"
+   exit 1;
+fi
+
+session_id=$1
+log_dir=$2
+enhanced_dir=$3
+
+if [ ! -d pb_chime5/ ]; then
+  echo "Missing pb_chime5, run 'local/install_pb_chime5'" 
+  exit 1
+fi
+
+miniconda_dir=$HOME/miniconda3/
+if [ ! -d $miniconda_dir/ ]; then
+  echo "$miniconda_dir/ does not exist. Please run '../../../tools/extras/install_miniconda.sh'"
+  exit 1
+fi
+
+enhanced_dir=$(utils/make_absolute.sh $enhanced_dir) || \
+  { echo "Could not make absolute '$enhanced_dir'" && exit 1; }
+
+if $use_multiarray; then
+  multiarray=True
+else
+  multiarray=False
+fi
+
+$cmd JOB=1:$nj $log_dir/log/enhance_${session_id}.JOB.log \
+  cd pb_chime5/ '&&' \
+  $miniconda_dir/bin/python -m pb_chime5.scripts.kaldi_run with \
+    storage_dir=$enhanced_dir \
+    session_id=$session_id \
+    job_id=JOB number_of_jobs=$nj \
+    multiarray=$multiarray || exit 1
diff --git a/egs/chime6/s5_track1/local/run_recog.sh b/egs/chime6/s5_track1/local/run_recog.sh
new file mode 100755
index 00000000000..9eaa870e8b8
--- /dev/null
+++ b/egs/chime6/s5_track1/local/run_recog.sh
@@ -0,0 +1,192 @@
+#!/bin/bash
+#
+# Based mostly on the TED-LIUM and Switchboard recipe
+#
+# Copyright  2017  Johns Hopkins University (Author: Shinji Watanabe and Yenda Trmal)
+# Apache 2.0
+#
+# This is a subset of run.sh to only perform recognition experiments with evaluation data
+
+# Begin configuration section.
+decode_nj=20
+stage=0
+use_multiarray=false
+enhancement=gss        # for a new enhancement method,
+                       # change this variable and stage 4
+# End configuration section
+. ./utils/parse_options.sh
+
+. ./cmd.sh
+. ./path.sh
+
+
+set -e # exit on error
+
+# chime5 main directory path
+# please change the path accordingly
+chime5_corpus=/export/corpora4/CHiME5
+json_dir=${chime5_corpus}/transcriptions
+audio_dir=${chime5_corpus}/audio
+
+# training and test data
+train_set=train_worn_simu_u400k
+test_sets="dev_${enhancement} eval_${enhancement}"
+
+# This script also needs the phonetisaurus g2p, srilm, beamformit
+./local/check_tools.sh || exit 1
+
+enhanced_dir=enhanced
+if $use_multiarray; then
+  enhanced_dir=${enhanced_dir}_multiarray
+  enhancement=${enhancement}_multiarray
+fi
+
+enhanced_dir=$(utils/make_absolute.sh $enhanced_dir) || exit 1
+
+if [ $stage -le 1 ]; then
+  # Guided Source Separation (GSS) from Paderbon Univerisity
+  # http://spandh.dcs.shef.ac.uk/chime_workshop/papers/CHiME_2018_paper_boeddecker.pdf
+  # @Article{PB2018CHiME5,
+  #   author    = {Boeddeker, Christoph and Heitkaemper, Jens and Schmalenstroeer, Joerg and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold},
+  #   title     = {{Front-End Processing for the CHiME-5 Dinner Party Scenario}},
+  #   year      = {2018},
+  #   booktitle = {CHiME5 Workshop},
+  # }
+
+  echo "$0:  enhance data..."
+  if [ ! -d pb_chime5/ ]; then
+    local/install_pb_chime5.sh
+  fi
+  
+  if [ ! -f pb_chime5/cache/chime5.json ]; then
+    (
+    cd pb_chime5
+    miniconda_dir=$HOME/miniconda3/
+    export PATH=$miniconda_dir/bin:$PATH
+    export CHIME5_DIR=$chime5_corpus
+    make cache/chime5.json
+    )
+  fi
+ 
+  for dset in dev eval; do
+    local/run_gss.sh \
+      --cmd "$train_cmd --max-jobs-run 30" --nj 160 \
+      --use-multiarray $use_multiarray \
+      ${dset} \
+      ${enhanced_dir} \
+      ${enhanced_dir} || exit 1
+  done
+
+
+  echo "$0: Prepare data..."
+  for dset in dev eval; do
+    local/prepare_data.sh --mictype gss ${enhanced_dir}/audio/${dset} \
+      ${json_dir}/${dset} data/${dset}_${enhancement} || exit 1
+  done
+
+fi
+
+
+if [ $stage -le 2 ]; then
+  # fix speaker ID issue (thanks to Dr. Naoyuki Kanda)
+  # add array ID to the speaker ID to avoid the use of other array information to meet regulations
+  # Before this fix
+  # $ head -n 2 data/eval_beamformit_ref_nosplit/utt2spk
+  # P01_S01_U02_KITCHEN.ENH-0000192-0001278 P01
+  # P01_S01_U02_KITCHEN.ENH-0001421-0001481 P01
+  # After this fix
+  # $ head -n 2 data/eval_beamformit_ref_nosplit_fix/utt2spk
+  # P01_S01_U02_KITCHEN.ENH-0000192-0001278 P01_U02
+  # P01_S01_U02_KITCHEN.ENH-0001421-0001481 P01_U02
+  echo "$0: fix data..."
+  for dset in ${test_sets}; do
+    utils/copy_data_dir.sh data/${dset} data/${dset}_nosplit
+    mkdir -p data/${dset}_nosplit_fix
+    for f in segments text wav.scp; do
+      if [ -f data/${dset}_nosplit/$f ]; then
+        cp data/${dset}_nosplit/$f data/${dset}_nosplit_fix
+      fi
+    done
+    awk -F "_" '{print $0 "_" $3}' data/${dset}_nosplit/utt2spk > data/${dset}_nosplit_fix/utt2spk
+    utils/utt2spk_to_spk2utt.pl data/${dset}_nosplit_fix/utt2spk > data/${dset}_nosplit_fix/spk2utt
+  done
+
+  # Split speakers up into 3-minute chunks.  This doesn't hurt adaptation, and
+  # lets us use more jobs for decoding etc.
+  for dset in ${test_sets}; do
+    utils/data/modify_speaker_info.sh --seconds-per-spk-max 180 data/${dset}_nosplit_fix data/${dset}
+  done
+fi
+
+
+if [ $stage -le 3 ]; then
+  # Now make MFCC features.
+  # mfccdir should be some place with a largish disk where you
+  # want to store MFCC features.
+  echo "$0: make features..."
+  mfccdir=mfcc
+  for x in ${test_sets}; do
+    steps/make_mfcc.sh --nj 20 --cmd "$train_cmd" \
+           data/$x exp/make_mfcc/$x $mfccdir
+    steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir
+    utils/fix_data_dir.sh data/$x
+  done
+fi
+
+
+nnet3_affix=_${train_set}_cleaned_rvb
+lm_suffix=
+
+if [ $stage -le 4 ]; then
+  # First the options that are passed through to run_ivector_common.sh
+  # (some of which are also used in this script directly).
+
+  # The rest are configs specific to this script.  Most of the parameters
+  # are just hardcoded at this level, in the commands below.
+  echo "$0: decode data..."
+  affix=1b   # affix for the TDNN directory name
+  tree_affix=
+  tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+  dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
+
+  # training options
+  # training chunk-options
+  chunk_width=140,100,160
+  # we don't need extra left/right context for TDNN systems.
+  chunk_left_context=0
+  chunk_right_context=0
+  
+  utils/mkgraph.sh \
+      --self-loop-scale 1.0 data/lang${lm_suffix}/ \
+      $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
+
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  rm $dir/.error 2>/dev/null || true
+
+  for data in $test_sets; do
+    (
+      local/nnet3/decode.sh --affix 2stage --pass2-decode-opts "--min-active 1000" \
+        --acwt 1.0 --post-decode-acwt 10.0 \
+        --frames-per-chunk 150 --nj $decode_nj \
+        --ivector-dir exp/nnet3${nnet3_affix} \
+        data/${data} data/lang${lm_suffix} \
+        $tree_dir/graph${lm_suffix} \
+        exp/chain${nnet3_affix}/tdnn${affix}_sp
+    ) || touch $dir/.error &
+  done
+  wait
+  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
+fi
+
+if [ $stage -le 5 ]; then
+  # final scoring to get the official challenge result
+  # please specify both dev and eval set directories so that the search parameters
+  # (insertion penalty and language model weight) will be tuned using the dev set
+  echo "$0: score data..."
+
+  local/get_location.py $json_dir/dev  > exp/chain_${train_set}_cleaned_rvb/tdnn1b_sp/decode_dev_${enhancement}_2stage/uttid_location
+  local/get_location.py $json_dir/eval > exp/chain_${train_set}_cleaned_rvb/tdnn1b_sp/decode_eval_${enhancement}_2stage/uttid_location
+  local/score_for_submit.sh \
+      --dev exp/chain${nnet3_affix}/tdnn1b_sp/decode${lm_suffix}_dev_${enhancement}_2stage \
+      --eval exp/chain${nnet3_affix}/tdnn1b_sp/decode${lm_suffix}_eval_${enhancement}_2stage
+fi
diff --git a/egs/chime6/s5_track1/local/run_wpe.py b/egs/chime6/s5_track1/local/run_wpe.py
new file mode 100755
index 00000000000..2f3818f9c42
--- /dev/null
+++ b/egs/chime6/s5_track1/local/run_wpe.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+# Copyright 2018 Johns Hopkins University (Author: Aswin Shanmugam Subramanian)
+# Apache 2.0
+# Works with both python2 and python3
+# This script assumes that WPE (nara_wpe) is installed locally using miniconda.
+# ../../../tools/extras/install_miniconda.sh and ../../../tools/extras/install_wpe.sh
+# needs to be run and this script needs to be launched run with that version of
+# python.
+# See local/run_wpe.sh for example.
+
+import numpy as np
+import soundfile as sf
+import time
+import os, errno
+from tqdm import tqdm
+import argparse
+
+from nara_wpe.wpe import wpe
+from nara_wpe.utils import stft, istft
+from nara_wpe import project_root
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--files', '-f', nargs='+')
+args = parser.parse_args()
+
+input_files = args.files[:len(args.files)//2]
+output_files = args.files[len(args.files)//2:]
+out_dir = os.path.dirname(output_files[0])
+try:
+    os.makedirs(out_dir)
+except OSError as e:
+    if e.errno != errno.EEXIST:
+        raise
+
+stft_options = dict(
+    size=512,
+    shift=128,
+    window_length=None,
+    fading=True,
+    pad=True,
+    symmetric_window=False
+)
+
+sampling_rate = 16000
+delay = 3
+iterations = 5
+taps = 10
+
+signal_list = [
+    sf.read(f)[0]
+    for f in input_files
+]
+y = np.stack(signal_list, axis=0)
+Y = stft(y, **stft_options).transpose(2, 0, 1)
+Z = wpe(Y, iterations=iterations, statistics_mode='full').transpose(1, 2, 0)
+z = istft(Z, size=stft_options['size'], shift=stft_options['shift'])
+
+for d in range(len(signal_list)):
+    sf.write(output_files[d], z[d,:], sampling_rate)
diff --git a/egs/chime6/s5_track1/local/run_wpe.sh b/egs/chime6/s5_track1/local/run_wpe.sh
new file mode 100755
index 00000000000..ed512e69aae
--- /dev/null
+++ b/egs/chime6/s5_track1/local/run_wpe.sh
@@ -0,0 +1,86 @@
+#!/bin/bash
+# Copyright 2018 Johns Hopkins University (Author: Aswin Shanmugam Subramanian)
+# Apache 2.0
+
+. ./cmd.sh
+. ./path.sh
+
+# Config:
+nj=4
+cmd=run.pl
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# != 3 ]; then
+   echo "Wrong #arguments ($#, expected 3)"
+   echo "Usage: local/run_wpe.sh [options] <wav-in-dir> <wav-out-dir> <array-id>"
+   echo "main options (for others, see top of script file)"
+   echo "  --cmd <cmd>                              # Command to run in parallel with"
+   echo "  --nj 50                        # number of jobs for parallel processing"
+   exit 1;
+fi
+
+sdir=$1
+odir=$2
+array=$3
+task=`basename $sdir`
+expdir=exp/wpe/${task}_${array}
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+miniconda_dir=$HOME/miniconda3/
+if [ ! -d $miniconda_dir ]; then
+    echo "$miniconda_dir does not exist. Please run '$KALDI_ROOT/tools/extras/install_miniconda.sh'."
+    exit 1
+fi
+
+# check if WPE is installed
+result=`$miniconda_dir/bin/python -c "\
+try:
+    import nara_wpe
+    print('1')
+except ImportError:
+    print('0')"`
+
+if [ "$result" == "1" ]; then
+    echo "WPE is installed"
+else
+    echo "WPE is not installed. Please run ../../../tools/extras/install_wpe.sh"
+    exit 1
+fi
+
+mkdir -p $odir
+mkdir -p $expdir/log
+
+# wavfiles.list can be used as the name of the output files
+output_wavfiles=$expdir/wavfiles.list
+find -L ${sdir} | grep -i ${array} > $expdir/channels_input
+cat $expdir/channels_input | awk -F '/' '{print $NF}' | sed "s@S@$odir\/S@g" > $expdir/channels_output
+paste -d" " $expdir/channels_input $expdir/channels_output > $output_wavfiles
+
+# split the list for parallel processing
+split_wavfiles=""
+for n in `seq $nj`; do
+  split_wavfiles="$split_wavfiles $output_wavfiles.$n"
+done
+utils/split_scp.pl $output_wavfiles $split_wavfiles || exit 1;
+
+echo -e "Dereverberation - $task - $array\n"
+# making a shell script for each job
+for n in `seq $nj`; do
+cat <<-EOF > $expdir/log/wpe.$n.sh
+while read line; do
+  $miniconda_dir/bin/python local/run_wpe.py \
+    --file \$line
+done < $output_wavfiles.$n
+EOF
+done
+
+chmod a+x $expdir/log/wpe.*.sh
+$cmd JOB=1:$nj $expdir/log/wpe.JOB.log \
+  $expdir/log/wpe.JOB.sh
+
+echo "`basename $0` Done."
diff --git a/egs/chime6/s5_track1/local/score.sh b/egs/chime6/s5_track1/local/score.sh
new file mode 120000
index 00000000000..6a200b42ed3
--- /dev/null
+++ b/egs/chime6/s5_track1/local/score.sh
@@ -0,0 +1 @@
+../steps/scoring/score_kaldi_wer.sh
\ No newline at end of file
diff --git a/egs/chime6/s5_track1/local/score_for_submit.sh b/egs/chime6/s5_track1/local/score_for_submit.sh
new file mode 100755
index 00000000000..e6376074c6e
--- /dev/null
+++ b/egs/chime6/s5_track1/local/score_for_submit.sh
@@ -0,0 +1,123 @@
+#!/bin/bash
+# Copyright 2012-2014  Johns Hopkins University (Author: Daniel Povey, Yenda Trmal)
+# Apache 2.0
+#
+# This script provides official CHiME-5 challenge submission scores per room and session.
+# It first calculates the best search parameter configurations by using the dev set
+# and also create the transcriptions for dev and eval sets to be submitted.
+# The default setup does not calculate scores of the evaluation set since
+# the evaluation transcription is not distributed (July 9 2018)
+
+cmd=run.pl
+dev=exp/chain_train_worn_u100k_cleaned/tdnn1a_sp/decode_dev_beamformit_ref
+eval=exp/chain_train_worn_u100k_cleaned/tdnn1a_sp/decode_eval_beamformit_ref
+do_eval=true
+
+echo "$0 $@"  # Print the command line for logging
+[ -f ./path.sh ] && . ./path.sh
+. parse_options.sh || exit 1;
+
+if [ $# -ne 0 ]; then
+    echo "Usage: $0 [--cmd (run.pl|queue.pl...)]"
+    echo "This script provides official CHiME-5 challenge submission scores"
+    echo " Options:"
+    echo "    --cmd (run.pl|queue.pl...)      # specify how to run the sub-processes."
+    echo "    --dev <dev-decode-dir>          # dev set decoding directory"
+    echo "    --eval <eval-decode-dir>        # eval set decoding directory"
+    exit 1;
+fi
+
+# get language model weight and word insertion penalty from the dev set
+best_lmwt=`cat $dev/scoring_kaldi/wer_details/lmwt`
+best_wip=`cat $dev/scoring_kaldi/wer_details/wip`
+
+echo "best LM weight: $best_lmwt"
+echo "insertion penalty weight: $best_wip"
+
+echo "==== development set ===="
+# development set
+# get the scoring result per utterance
+local/replace_uttid.py $dev/uttid_location $dev/scoring_kaldi/wer_details/per_utt > $dev/scoring_kaldi/wer_details/per_utt_loc
+score_result=$dev/scoring_kaldi/wer_details/per_utt_loc
+
+for session in S02 S09; do
+    for room in dining kitchen living; do
+	# get nerror
+	nerr=`grep "\#csid" $score_result | grep $room | grep $session | awk '{sum+=$4+$5+$6} END {print sum}'`
+	# get nwords from references (NF-2 means to exclude utterance id and " ref ")
+	nwrd=`grep "\#csid" $score_result | grep $room | grep $session | awk '{sum+=$3+$4+$6} END {print sum}'`
+	# compute wer with scale=2
+	wer=`echo "scale=2; 100 * $nerr / $nwrd" | bc`
+	
+	# report the results
+	echo -n "session $session "
+	echo -n "room $room: "
+	echo -n "#words $nwrd, "
+	echo -n "#errors $nerr, "
+	echo "wer $wer %"
+    done
+done
+echo -n "overall: "
+# get nerror
+nerr=`grep "\#csid" $score_result | awk '{sum+=$4+$5+$6} END {print sum}'`
+# get nwords from references (NF-2 means to exclude utterance id and " ref ")
+nwrd=`grep "\#csid" $score_result | awk '{sum+=$3+$4+$6} END {print sum}'`
+# compute wer with scale=2
+wer=`echo "scale=2; 100 * $nerr / $nwrd" | bc`
+echo -n "#words $nwrd, "
+echo -n "#errors $nerr, "
+echo "wer $wer %"
+
+echo "==== evaluation set ===="
+# evaluation set
+# get the scoring result per utterance. Copied from local/score.sh
+mkdir -p $eval/scoring_kaldi/wer_details_devbest
+$cmd $eval/scoring_kaldi/log/stats1.log \
+     cat $eval/scoring_kaldi/penalty_$best_wip/$best_lmwt.txt \| \
+     align-text --special-symbol="'***'" ark:$eval/scoring_kaldi/test_filt.txt ark:- ark,t:- \|  \
+     utils/scoring/wer_per_utt_details.pl --special-symbol "'***'" \> $eval/scoring_kaldi/wer_details_devbest/per_utt
+
+local/replace_uttid.py $eval/uttid_location $eval/scoring_kaldi/wer_details_devbest/per_utt > $eval/scoring_kaldi/wer_details_devbest/per_utt_loc
+score_result=$eval/scoring_kaldi/wer_details_devbest/per_utt_loc
+for session in S01 S21; do
+    for room in dining kitchen living; do
+	if $do_eval; then
+	    # get nerror
+	    nerr=`grep "\#csid" $score_result | grep $room | grep $session | awk '{sum+=$4+$5+$6} END {print sum}'`
+	    # get nwords from references (NF-2 means to exclude utterance id and " ref ")
+	    nwrd=`grep "\#csid" $score_result | grep $room | grep $session | awk '{sum+=$3+$4+$6} END {print sum}'`
+	    # compute wer with scale=2
+	    wer=`echo "scale=2; 100 * $nerr / $nwrd" | bc`
+	
+	    # report the results
+	    echo -n "session $session "
+	    echo -n "room $room: "
+	    echo -n "#words $nwrd, "
+	    echo -n "#errors $nerr, "
+	    echo "wer $wer %"
+	fi
+    done
+done
+if $do_eval; then
+    # get nerror
+    nerr=`grep "\#csid" $score_result | awk '{sum+=$4+$5+$6} END {print sum}'`
+    # get nwords from references (NF-2 means to exclude utterance id and " ref ")
+    nwrd=`grep "\#csid" $score_result | awk '{sum+=$3+$4+$6} END {print sum}'`
+    # compute wer with scale=2
+    wer=`echo "scale=2; 100 * $nerr / $nwrd" | bc`
+    echo -n "overall: "
+    echo -n "#words $nwrd, "
+    echo -n "#errors $nerr, "
+    echo "wer $wer %"
+else
+    echo "skip evaluation scoring"
+    echo ""
+    echo "==== when you submit your result to the CHiME-5 challenge ===="
+    echo "Please rename your recognition results of "
+    echo "$dev/scoring_kaldi/penalty_$best_wip/$best_lmwt.txt"
+    echo "$eval/scoring_kaldi/penalty_$best_wip/$best_lmwt.txt"
+    echo "with {dev,eval}_<last name>_<affiliation>.txt, e.g., dev_watanabe_jhu.txt and eval_watanabe_jhu.txt, "
+    echo "and submit both of them as your final challenge result"
+    echo "=================================================================="    
+fi
+
diff --git a/egs/chime6/s5_track1/local/train_lms_srilm.sh b/egs/chime6/s5_track1/local/train_lms_srilm.sh
new file mode 100755
index 00000000000..5a1d56d24b3
--- /dev/null
+++ b/egs/chime6/s5_track1/local/train_lms_srilm.sh
@@ -0,0 +1,261 @@
+#!/bin/bash
+# Copyright (c) 2017  Johns Hopkins University (Author: Yenda Trmal, Shinji Watanabe)
+# Apache 2.0
+
+export LC_ALL=C
+
+# Begin configuration section.
+words_file=
+train_text=
+dev_text=
+oov_symbol="<UNK>"
+# End configuration section
+
+echo "$0 $@"
+
+[ -f path.sh ]  && . ./path.sh
+. ./utils/parse_options.sh || exit 1
+
+echo "-------------------------------------"
+echo "Building an SRILM language model     "
+echo "-------------------------------------"
+
+if [ $# -ne 2 ] ; then
+  echo "Incorrect number of parameters. "
+  echo "Script has to be called like this:"
+  echo "  $0 [switches] <datadir> <tgtdir>"
+  echo "For example: "
+  echo "  $0 data data/srilm"
+  echo "The allowed switches are: "
+  echo "    words_file=<word_file|>        word list file -- data/lang/words.txt by default"
+  echo "    train_text=<train_text|>       data/train/text is used in case when not specified"
+  echo "    dev_text=<dev_text|>           last 10 % of the train text is used by default"
+  echo "    oov_symbol=<unk_sumbol|<UNK>>  symbol to use for oov modeling -- <UNK> by default"
+  exit 1
+fi
+
+datadir=$1
+tgtdir=$2
+
+##End of configuration
+loc=`which ngram-count`;
+if [ -z $loc ]; then
+  echo >&2 "You appear to not have SRILM tools installed, either on your path,"
+  echo >&2 "Use the script \$KALDI_ROOT/tools/install_srilm.sh to install it."
+  exit 1
+fi
+
+# Prepare the destination directory
+mkdir -p $tgtdir
+
+for f in $words_file $train_text $dev_text; do
+  [ ! -s $f ] && echo "No such file $f" && exit 1;
+done
+
+[ -z $words_file ] && words_file=$datadir/lang/words.txt
+if [ ! -z "$train_text" ] && [ -z "$dev_text" ] ; then
+  nr=`cat  $train_text | wc -l`
+  nr_dev=$(($nr / 10 ))
+  nr_train=$(( $nr - $nr_dev ))
+  orig_train_text=$train_text
+  head -n $nr_train $train_text > $tgtdir/train_text
+  tail -n $nr_dev $train_text > $tgtdir/dev_text
+
+  train_text=$tgtdir/train_text
+  dev_text=$tgtdir/dev_text
+  echo "Using words file: $words_file"
+  echo "Using train text: 9/10 of $orig_train_text"
+  echo "Using dev text  : 1/10 of $orig_train_text"
+elif [ ! -z "$train_text" ] && [ ! -z "$dev_text" ] ; then
+  echo "Using words file: $words_file"
+  echo "Using train text: $train_text"
+  echo "Using dev text  : $dev_text"
+  train_text=$train_text
+  dev_text=$dev_text
+else
+  train_text=$datadir/train/text
+  dev_text=$datadir/dev2h/text
+  echo "Using words file: $words_file"
+  echo "Using train text: $train_text"
+  echo "Using dev text  : $dev_text"
+
+fi
+
+[ ! -f $words_file ] && echo >&2 "File $words_file must exist!" && exit 1
+[ ! -f $train_text ] && echo >&2 "File $train_text must exist!" && exit 1
+[ ! -f $dev_text ] && echo >&2 "File $dev_text must exist!" && exit 1
+
+
+# Extract the word list from the training dictionary; exclude special symbols
+sort $words_file | awk '{print $1}' | grep -v '\#0' | grep -v '<eps>' | grep -v -F "$oov_symbol" > $tgtdir/vocab
+if (($?)); then
+  echo "Failed to create vocab from $words_file"
+  exit 1
+else
+  # wc vocab # doesn't work due to some encoding issues
+  echo vocab contains `cat $tgtdir/vocab | perl -ne 'BEGIN{$l=$w=0;}{split; $w+=$#_; $w++; $l++;}END{print "$l lines, $w words\n";}'`
+fi
+
+# Kaldi transcript files contain Utterance_ID as the first word; remove it
+# We also have to avoid skewing the LM by incorporating  the same sentences
+# from different channels
+sed -e "s/\.CH.//" -e "s/_.\-./_/" -e "s/NOLOCATION\(\.[LR]\)*-//" -e "s/U[0-9][0-9]_//" $train_text | sort -u | \
+  perl -ane 'print join(" ", @F[1..$#F]) . "\n" if @F > 1' > $tgtdir/train.txt
+if (($?)); then
+    echo "Failed to create $tgtdir/train.txt from $train_text"
+    exit 1
+else
+    echo "Removed first word (uid) from every line of $train_text"
+    # wc text.train train.txt # doesn't work due to some encoding issues
+    echo $train_text contains `cat $train_text | perl -ane 'BEGIN{$w=$s=0;}{$w+=@F; $w--; $s++;}END{print "$w words, $s sentences\n";}'`
+    echo train.txt contains `cat $tgtdir/train.txt | perl -ane 'BEGIN{$w=$s=0;}{$w+=@F; $s++;}END{print "$w words, $s sentences\n";}'`
+fi
+
+# Kaldi transcript files contain Utterance_ID as the first word; remove it
+sed -e "s/\.CH.//" -e "s/_.\-./_/" $dev_text | sort -u | \
+  perl -ane 'print join(" ", @F[1..$#F]) . "\n" if @F > 1' > $tgtdir/dev.txt
+if (($?)); then
+    echo "Failed to create $tgtdir/dev.txt from $dev_text"
+    exit 1
+else
+    echo "Removed first word (uid) from every line of $dev_text"
+    # wc text.train train.txt # doesn't work due to some encoding issues
+    echo $dev_text contains `cat $dev_text | perl -ane 'BEGIN{$w=$s=0;}{$w+=@F; $w--; $s++;}END{print "$w words, $s sentences\n";}'`
+    echo $tgtdir/dev.txt contains `cat $tgtdir/dev.txt | perl -ane 'BEGIN{$w=$s=0;}{$w+=@F;  $s++;}END{print "$w words, $s sentences\n";}'`
+fi
+
+
+echo "-------------------"
+echo "Good-Turing 3grams"
+echo "-------------------"
+ngram-count -lm $tgtdir/3gram.gt011.gz -gt1min 0 -gt2min 1 -gt3min 1 -order 3 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.gt012.gz -gt1min 0 -gt2min 1 -gt3min 2 -order 3 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.gt022.gz -gt1min 0 -gt2min 2 -gt3min 2 -order 3 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.gt023.gz -gt1min 0 -gt2min 2 -gt3min 3 -order 3 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+
+echo "-------------------"
+echo "Kneser-Ney 3grams"
+echo "-------------------"
+ngram-count -lm $tgtdir/3gram.kn011.gz -kndiscount1 -gt1min 0 \
+  -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 1 -order 3 -interpolate \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.kn012.gz -kndiscount1 -gt1min 0 \
+  -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 2 -order 3 -interpolate \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.kn022.gz -kndiscount1 -gt1min 0 \
+  -kndiscount2 -gt2min 2 -kndiscount3 -gt3min 2 -order 3 -interpolate \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.kn023.gz -kndiscount1 -gt1min 0 \
+  -kndiscount2 -gt2min 2 -kndiscount3 -gt3min 3 -order 3 -interpolate \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.kn111.gz -kndiscount1 -gt1min 1 \
+  -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 1 -order 3 -interpolate \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.kn112.gz -kndiscount1 -gt1min 1 \
+  -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 2 -order 3 -interpolate \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.kn122.gz -kndiscount1 -gt1min 1 \
+  -kndiscount2 -gt2min 2 -kndiscount3 -gt3min 2 -order 3 -interpolate \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/3gram.kn123.gz -kndiscount1 -gt1min 1 \
+  -kndiscount2 -gt2min 2 -kndiscount3 -gt3min 3 -order 3 -interpolate \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+
+
+echo "-------------------"
+echo "Good-Turing 4grams"
+echo "-------------------"
+ngram-count -lm $tgtdir/4gram.gt0111.gz \
+  -gt1min 0 -gt2min 1 -gt3min 1 -gt4min 1 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.gt0112.gz \
+  -gt1min 0 -gt2min 1 -gt3min 1 -gt4min 2 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.gt0122.gz \
+  -gt1min 0 -gt2min 1 -gt3min 2 -gt4min 2 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.gt0123.gz \
+  -gt1min 0 -gt2min 1 -gt3min 2 -gt4min 3 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.gt0113.gz \
+  -gt1min 0 -gt2min 1 -gt3min 1 -gt4min 3 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.gt0222.gz \
+  -gt1min 0 -gt2min 2 -gt3min 2 -gt4min 2 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.gt0223.gz \
+  -gt1min 0 -gt2min 2 -gt3min 2 -gt4min 3 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+
+echo "-------------------"
+echo "Kneser-Ney 4grams"
+echo "-------------------"
+ngram-count -lm $tgtdir/4gram.kn0111.gz \
+  -kndiscount1 -gt1min 0 -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 1 -kndiscount4 -gt4min 1 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.kn0112.gz \
+  -kndiscount1 -gt1min 0 -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 1 -kndiscount4 -gt4min 2 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.kn0113.gz \
+  -kndiscount1 -gt1min 0 -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 1 -kndiscount4 -gt4min 3 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.kn0122.gz \
+  -kndiscount1 -gt1min 0 -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 2 -kndiscount4 -gt4min 2 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.kn0123.gz \
+  -kndiscount1 -gt1min 0 -kndiscount2 -gt2min 1 -kndiscount3 -gt3min 2 -kndiscount4 -gt4min 3 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.kn0222.gz \
+  -kndiscount1 -gt1min 0 -kndiscount2 -gt2min 2 -kndiscount3 -gt3min 2 -kndiscount4 -gt4min 2 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+ngram-count -lm $tgtdir/4gram.kn0223.gz \
+  -kndiscount1 -gt1min 0 -kndiscount2 -gt2min 2 -kndiscount3 -gt3min 2 -kndiscount4 -gt4min 3 -order 4 \
+  -text $tgtdir/train.txt -vocab $tgtdir/vocab -unk -sort -map-unk "$oov_symbol"
+
+if [ ! -z ${LIBLBFGS} ]; then
+  #please note that if the switch -map-unk "$oov_symbol" is used with -maxent-convert-to-arpa, ngram-count will segfault
+  #instead of that, we simply output the model in the maxent format and convert it using the "ngram"
+  echo "-------------------"
+  echo "Maxent 3grams"
+  echo "-------------------"
+  sed 's/'${oov_symbol}'/<unk>/g' $tgtdir/train.txt | \
+    ngram-count -lm - -order 3 -text - -vocab $tgtdir/vocab -unk -sort -maxent -maxent-convert-to-arpa|\
+    ngram -lm - -order 3 -unk -map-unk "$oov_symbol" -prune-lowprobs -write-lm - |\
+    sed 's/<unk>/'${oov_symbol}'/g' | gzip -c > $tgtdir/3gram.me.gz || exit 1
+
+  echo "-------------------"
+  echo "Maxent 4grams"
+  echo "-------------------"
+  sed 's/'${oov_symbol}'/<unk>/g' $tgtdir/train.txt | \
+    ngram-count -lm - -order 4 -text - -vocab $tgtdir/vocab -unk -sort -maxent -maxent-convert-to-arpa|\
+    ngram -lm - -order 4 -unk -map-unk "$oov_symbol" -prune-lowprobs -write-lm - |\
+    sed 's/<unk>/'${oov_symbol}'/g' | gzip -c > $tgtdir/4gram.me.gz || exit 1
+else
+  echo >&2  "SRILM is not compiled with the support of MaxEnt models."
+  echo >&2  "You should use the script in \$KALDI_ROOT/tools/install_srilm.sh"
+  echo >&2  "which will take care of compiling the SRILM with MaxEnt support"
+  exit 1;
+fi
+
+
+echo "--------------------"
+echo "Computing perplexity"
+echo "--------------------"
+(
+  for f in $tgtdir/3gram* ; do ( echo $f; ngram -order 3 -lm $f -unk -map-unk "$oov_symbol" -prune-lowprobs -ppl $tgtdir/dev.txt ) | paste -s -d ' ' ; done
+  for f in $tgtdir/4gram* ; do ( echo $f; ngram -order 4 -lm $f -unk -map-unk "$oov_symbol" -prune-lowprobs -ppl $tgtdir/dev.txt ) | paste -s -d ' ' ; done
+)  | sort  -r -n -k 15,15g | column -t | tee $tgtdir/perplexities.txt
+
+echo "The perlexity scores report is stored in $tgtdir/perplexities.txt "
+echo ""
+
+for best_ngram in {3,4}gram ; do
+  outlm=best_${best_ngram}.gz
+  lmfilename=$(grep "${best_ngram}" $tgtdir/perplexities.txt | head -n 1 | cut -f 1 -d ' ')
+  echo "$outlm -> $lmfilename"
+  (cd $tgtdir; rm -f $outlm; ln -sf $(basename $lmfilename) $outlm )
+done
diff --git a/egs/chime6/s5_track1/local/wer_output_filter b/egs/chime6/s5_track1/local/wer_output_filter
new file mode 100755
index 00000000000..6f4b6400716
--- /dev/null
+++ b/egs/chime6/s5_track1/local/wer_output_filter
@@ -0,0 +1,25 @@
+#!/bin/bash
+# Copyright (c) 2017  Johns Hopkins University (Author: Yenda Trmal <jtrmal@gmail.com>)
+# Apache 2.0
+
+
+## Filter for scoring of the STT results. Convert everything to lowercase
+## and add some ad-hoc fixes for the hesitations
+
+perl -e '
+   while(<STDIN>) {
+     @A  = split(" ", $_);
+     $id = shift @A; print "$id ";
+     foreach $a (@A) {
+       print lc($a) . " " unless $a =~ /\[.*\]/;
+     }
+     print "\n";
+    }' | \
+sed -e '
+    s/\<mhm\>/hmm/g;
+    s/\<mm\>/hmm/g;
+    s/\<mmm\>/hmm/g;
+'
+
+#| uconv -f  utf-8  -t utf-8 -x Latin-ASCII
+
diff --git a/egs/chime6/s5_track1/local/worn_audio_list b/egs/chime6/s5_track1/local/worn_audio_list
new file mode 100644
index 00000000000..fc7a44ad77d
--- /dev/null
+++ b/egs/chime6/s5_track1/local/worn_audio_list
@@ -0,0 +1,64 @@
+/export/corpora4/CHiME5/audio/train/S03_P09.wav
+/export/corpora4/CHiME5/audio/train/S03_P10.wav
+/export/corpora4/CHiME5/audio/train/S03_P11.wav
+/export/corpora4/CHiME5/audio/train/S03_P12.wav
+/export/corpora4/CHiME5/audio/train/S04_P09.wav
+/export/corpora4/CHiME5/audio/train/S04_P10.wav
+/export/corpora4/CHiME5/audio/train/S04_P11.wav
+/export/corpora4/CHiME5/audio/train/S04_P12.wav
+/export/corpora4/CHiME5/audio/train/S05_P13.wav
+/export/corpora4/CHiME5/audio/train/S05_P14.wav
+/export/corpora4/CHiME5/audio/train/S05_P15.wav
+/export/corpora4/CHiME5/audio/train/S05_P16.wav
+/export/corpora4/CHiME5/audio/train/S06_P13.wav
+/export/corpora4/CHiME5/audio/train/S06_P14.wav
+/export/corpora4/CHiME5/audio/train/S06_P15.wav
+/export/corpora4/CHiME5/audio/train/S06_P16.wav
+/export/corpora4/CHiME5/audio/train/S07_P17.wav
+/export/corpora4/CHiME5/audio/train/S07_P18.wav
+/export/corpora4/CHiME5/audio/train/S07_P19.wav
+/export/corpora4/CHiME5/audio/train/S07_P20.wav
+/export/corpora4/CHiME5/audio/train/S08_P21.wav
+/export/corpora4/CHiME5/audio/train/S08_P22.wav
+/export/corpora4/CHiME5/audio/train/S08_P23.wav
+/export/corpora4/CHiME5/audio/train/S08_P24.wav
+/export/corpora4/CHiME5/audio/train/S12_P33.wav
+/export/corpora4/CHiME5/audio/train/S12_P34.wav
+/export/corpora4/CHiME5/audio/train/S12_P35.wav
+/export/corpora4/CHiME5/audio/train/S12_P36.wav
+/export/corpora4/CHiME5/audio/train/S13_P33.wav
+/export/corpora4/CHiME5/audio/train/S13_P34.wav
+/export/corpora4/CHiME5/audio/train/S13_P35.wav
+/export/corpora4/CHiME5/audio/train/S13_P36.wav
+/export/corpora4/CHiME5/audio/train/S16_P21.wav
+/export/corpora4/CHiME5/audio/train/S16_P22.wav
+/export/corpora4/CHiME5/audio/train/S16_P23.wav
+/export/corpora4/CHiME5/audio/train/S16_P24.wav
+/export/corpora4/CHiME5/audio/train/S17_P17.wav
+/export/corpora4/CHiME5/audio/train/S17_P18.wav
+/export/corpora4/CHiME5/audio/train/S17_P19.wav
+/export/corpora4/CHiME5/audio/train/S17_P20.wav
+/export/corpora4/CHiME5/audio/train/S18_P41.wav
+/export/corpora4/CHiME5/audio/train/S18_P42.wav
+/export/corpora4/CHiME5/audio/train/S18_P43.wav
+/export/corpora4/CHiME5/audio/train/S18_P44.wav
+/export/corpora4/CHiME5/audio/train/S19_P49.wav
+/export/corpora4/CHiME5/audio/train/S19_P50.wav
+/export/corpora4/CHiME5/audio/train/S19_P51.wav
+/export/corpora4/CHiME5/audio/train/S19_P52.wav
+/export/corpora4/CHiME5/audio/train/S20_P49.wav
+/export/corpora4/CHiME5/audio/train/S20_P50.wav
+/export/corpora4/CHiME5/audio/train/S20_P51.wav
+/export/corpora4/CHiME5/audio/train/S20_P52.wav
+/export/corpora4/CHiME5/audio/train/S22_P41.wav
+/export/corpora4/CHiME5/audio/train/S22_P42.wav
+/export/corpora4/CHiME5/audio/train/S22_P43.wav
+/export/corpora4/CHiME5/audio/train/S22_P44.wav
+/export/corpora4/CHiME5/audio/train/S23_P53.wav
+/export/corpora4/CHiME5/audio/train/S23_P54.wav
+/export/corpora4/CHiME5/audio/train/S23_P55.wav
+/export/corpora4/CHiME5/audio/train/S23_P56.wav
+/export/corpora4/CHiME5/audio/train/S24_P53.wav
+/export/corpora4/CHiME5/audio/train/S24_P54.wav
+/export/corpora4/CHiME5/audio/train/S24_P55.wav
+/export/corpora4/CHiME5/audio/train/S24_P56.wav
diff --git a/egs/chime6/s5_track1/path.sh b/egs/chime6/s5_track1/path.sh
new file mode 100644
index 00000000000..fb1c0489386
--- /dev/null
+++ b/egs/chime6/s5_track1/path.sh
@@ -0,0 +1,7 @@
+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
+. $KALDI_ROOT/tools/config/common_path.sh
+export LC_ALL=C
+
diff --git a/egs/chime6/s5_track1/run.sh b/egs/chime6/s5_track1/run.sh
new file mode 100755
index 00000000000..b25e7a2101f
--- /dev/null
+++ b/egs/chime6/s5_track1/run.sh
@@ -0,0 +1,329 @@
+#!/bin/bash
+#
+# Based mostly on the TED-LIUM and Switchboard recipe
+#
+# Copyright  2017  Johns Hopkins University (Author: Shinji Watanabe and Yenda Trmal)
+# Apache 2.0
+#
+
+# Begin configuration section.
+nj=96
+decode_nj=20
+stage=0
+nnet_stage=-10
+num_data_reps=4
+snrs="20:10:15:5:0"
+foreground_snrs="20:10:15:5:0"
+background_snrs="20:10:15:5:0"
+use_multiarray=false
+enhancement=gss        # for a new enhancement method,
+                       # change this variable and stage 4
+# End configuration section
+. ./utils/parse_options.sh
+
+. ./cmd.sh
+. ./path.sh
+
+
+set -e # exit on error
+
+# chime5 main directory path
+# please change the path accordingly
+chime5_corpus=/export/corpora4/CHiME5
+json_dir=${chime5_corpus}/transcriptions
+audio_dir=${chime5_corpus}/audio
+
+# training and test data
+train_set=train_worn_simu_u400k
+test_sets="dev_${enhancement}" #"dev_worn dev_addition_dereverb_ref"
+#test_sets="dev_${enhancement}_ref" #"dev_worn dev_addition_dereverb_ref"
+
+# This script also needs the phonetisaurus g2p, srilm, beamformit
+./local/check_tools.sh || exit 1
+
+if [ $stage -le 1 ]; then
+  echo "$0:  prepare data..."
+  # skip u03 as they are missing
+  for mictype in worn u01 u02 u04 u05 u06; do
+    local/prepare_data.sh --mictype ${mictype} \
+			  ${audio_dir}/train ${json_dir}/train data/train_${mictype}
+  done
+  for dataset in dev; do
+    for mictype in worn; do
+      local/prepare_data.sh --mictype ${mictype} \
+			    ${audio_dir}/${dataset} ${json_dir}/${dataset} \
+			    data/${dataset}_${mictype}
+    done
+  done
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0:  train lm ..."
+  local/prepare_dict.sh
+
+  utils/prepare_lang.sh \
+    data/local/dict "<unk>" data/local/lang data/lang
+
+  local/train_lms_srilm.sh \
+    --train-text data/train_worn/text --dev-text data/dev_worn/text \
+    --oov-symbol "<unk>" --words-file data/lang/words.txt \
+    data/ data/srilm
+fi
+
+LM=data/srilm/best_3gram.gz
+if [ $stage -le 3 ]; then
+  # Compiles G for chime5 trigram LM
+  echo "$0:  prepare lang..."
+  utils/format_lm.sh \
+		data/lang $LM data/local/dict/lexicon.txt data/lang
+
+fi
+  
+enhanced_dir=enhanced
+if $use_multiarray; then
+  enhanced_dir=${enhanced_dir}_multiarray
+  enhancement=${enhancement}_multiarray
+fi
+
+enhanced_dir=$(utils/make_absolute.sh $enhanced_dir) || exit 1
+
+if [ $stage -le 4 ]; then
+  echo "$0:  enhance data..."
+  # Guided Source Separation (GSS) from Paderbon Univerisity
+  # http://spandh.dcs.shef.ac.uk/chime_workshop/papers/CHiME_2018_paper_boeddecker.pdf
+  # @Article{PB2018CHiME5,
+  #   author    = {Boeddeker, Christoph and Heitkaemper, Jens and Schmalenstroeer, Joerg and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold},
+  #   title     = {{Front-End Processing for the CHiME-5 Dinner Party Scenario}},
+  #   year      = {2018},
+  #   booktitle = {CHiME5 Workshop},
+  # }
+
+  if [ ! -d pb_chime5/ ]; then
+    local/install_pb_chime5.sh
+  fi
+  
+  if [ ! -f pb_chime5/cache/chime5.json ]; then
+    (
+    cd pb_chime5
+    miniconda_dir=$HOME/miniconda3/
+    export PATH=$miniconda_dir/bin:$PATH
+    export CHIME5_DIR=$chime5_corpus
+    make cache/chime5.json
+    )
+  fi
+
+  for dset in dev eval; do
+    local/run_gss.sh \
+      --cmd "$train_cmd --max-jobs-run 30" --nj 160 \
+      --use-multiarray $use_multiarray \
+      ${dset} \
+      ${enhanced_dir} \
+      ${enhanced_dir} || exit 1
+  done
+
+  for dset in dev eval; do
+    local/prepare_data.sh --mictype gss ${enhanced_dir}/audio/${dset} \
+      ${json_dir}/${dset} data/${dset}_${enhancement} || exit 1
+  done
+fi
+
+if [ $stage -le 5 ]; then
+  # remove possibly bad sessions (P11_S03, P52_S19, P53_S24, P54_S24)
+  # see http://spandh.dcs.shef.ac.uk/chime_challenge/data.html for more details
+  utils/copy_data_dir.sh data/train_worn data/train_worn_org # back up
+  grep -v -e "^P11_S03" -e "^P52_S19" -e "^P53_S24" -e "^P54_S24" data/train_worn_org/text > data/train_worn/text
+  utils/fix_data_dir.sh data/train_worn
+fi
+
+if [ $stage -le 6 ]; then
+  local/extract_noises.py $chime5_corpus/audio/train $chime5_corpus/transcriptions/train \
+    local/distant_audio_list distant_noises
+  local/make_noise_list.py distant_noises > distant_noise_list
+
+  noise_list=distant_noise_list
+  
+  if [ ! -d RIRS_NOISES/ ]; then
+    # Download the package that includes the real RIRs, simulated RIRs, isotropic noises and point-source noises
+    wget --no-check-certificate http://www.openslr.org/resources/28/rirs_noises.zip
+    unzip rirs_noises.zip
+  fi
+
+  # This is the config for the system using simulated RIRs and point-source noises
+  rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/smallroom/rir_list")
+  rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/mediumroom/rir_list")
+  rvb_opts+=(--noise-set-parameters $noise_list)
+
+  steps/data/reverberate_data_dir.py \
+    "${rvb_opts[@]}" \
+    --prefix "rev" \
+    --foreground-snrs $foreground_snrs \
+    --background-snrs $background_snrs \
+    --speech-rvb-probability 1 \
+    --pointsource-noise-addition-probability 1 \
+    --isotropic-noise-addition-probability 1 \
+    --num-replications $num_data_reps \
+    --max-noises-per-minute 1 \
+    --source-sampling-rate 16000 \
+    data/train_worn data/train_worn_rvb
+fi
+
+if [ $stage -le 7 ]; then
+  # combine mix array and worn mics
+  # randomly extract first 100k utterances from all mics
+  # if you want to include more training data, you can increase the number of array mic utterances
+  utils/combine_data.sh data/train_uall data/train_u01 data/train_u02 data/train_u04 data/train_u05 data/train_u06
+  utils/subset_data_dir.sh data/train_uall 400000 data/train_u400k
+  utils/combine_data.sh data/${train_set} data/train_worn data/train_worn_rvb data/train_u400k
+
+  # only use left channel for worn mic recognition
+  # you can use both left and right channels for training
+  for dset in train dev; do
+    utils/copy_data_dir.sh data/${dset}_worn data/${dset}_worn_stereo
+    grep "\.L-" data/${dset}_worn_stereo/text > data/${dset}_worn/text
+    utils/fix_data_dir.sh data/${dset}_worn
+  done
+fi
+
+if [ $stage -le 8 ]; then
+  # fix speaker ID issue (thanks to Dr. Naoyuki Kanda)
+  # add array ID to the speaker ID to avoid the use of other array information to meet regulations
+  # Before this fix
+  # $ head -n 2 data/eval_beamformit_ref_nosplit/utt2spk
+  # P01_S01_U02_KITCHEN.ENH-0000192-0001278 P01
+  # P01_S01_U02_KITCHEN.ENH-0001421-0001481 P01
+  # After this fix
+  # $ head -n 2 data/eval_beamformit_ref_nosplit_fix/utt2spk
+  # P01_S01_U02_KITCHEN.ENH-0000192-0001278 P01_U02
+  # P01_S01_U02_KITCHEN.ENH-0001421-0001481 P01_U02
+  for dset in ${test_sets}; do
+    utils/copy_data_dir.sh data/${dset} data/${dset}_nosplit
+    mkdir -p data/${dset}_nosplit_fix
+    for f in segments text wav.scp; do
+      if [ -f data/${dset}_nosplit/$f ]; then
+        cp data/${dset}_nosplit/$f data/${dset}_nosplit_fix
+      fi
+    done
+    awk -F "_" '{print $0 "_" $3}' data/${dset}_nosplit/utt2spk > data/${dset}_nosplit_fix/utt2spk
+    utils/utt2spk_to_spk2utt.pl data/${dset}_nosplit_fix/utt2spk > data/${dset}_nosplit_fix/spk2utt
+  done
+
+  # Split speakers up into 3-minute chunks.  This doesn't hurt adaptation, and
+  # lets us use more jobs for decoding etc.
+  for dset in ${train_set} dev_worn; do
+    utils/copy_data_dir.sh data/${dset} data/${dset}_nosplit
+    utils/data/modify_speaker_info.sh --seconds-per-spk-max 180 data/${dset}_nosplit data/${dset}
+  done
+  for dset in ${test_sets}; do
+    utils/data/modify_speaker_info.sh --seconds-per-spk-max 180 data/${dset}_nosplit_fix data/${dset}
+  done
+fi
+
+if [ $stage -le 9 ]; then
+  # Now make MFCC features.
+  # mfccdir should be some place with a largish disk where you
+  # want to store MFCC features.
+  echo "$0:  make features..."
+  mfccdir=mfcc
+  for x in ${train_set} ${test_sets}; do
+    steps/make_mfcc.sh --nj 20 --cmd "$train_cmd" \
+		       data/$x exp/make_mfcc/$x $mfccdir
+    steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir
+    utils/fix_data_dir.sh data/$x
+  done
+fi
+
+if [ $stage -le 10 ]; then
+  # make a subset for monophone training
+  utils/subset_data_dir.sh --shortest data/${train_set} 100000 data/${train_set}_100kshort
+  utils/subset_data_dir.sh data/${train_set}_100kshort 30000 data/${train_set}_30kshort
+fi
+
+if [ $stage -le 11 ]; then
+  # Starting basic training on MFCC features
+  steps/train_mono.sh --nj $nj --cmd "$train_cmd" \
+		      data/${train_set}_30kshort data/lang exp/mono
+fi
+
+if [ $stage -le 12 ]; then
+  steps/align_si.sh --nj $nj --cmd "$train_cmd" \
+		    data/${train_set} data/lang exp/mono exp/mono_ali
+
+  steps/train_deltas.sh --cmd "$train_cmd" \
+			2500 30000 data/${train_set} data/lang exp/mono_ali exp/tri1
+fi
+
+if [ $stage -le 13 ]; then
+  steps/align_si.sh --nj $nj --cmd "$train_cmd" \
+		    data/${train_set} data/lang exp/tri1 exp/tri1_ali
+
+  steps/train_lda_mllt.sh --cmd "$train_cmd" \
+			  4000 50000 data/${train_set} data/lang exp/tri1_ali exp/tri2
+fi
+
+if [ $stage -le 14 ]; then
+  utils/mkgraph.sh data/lang exp/tri2 exp/tri2/graph
+  for dset in ${test_sets}; do
+    steps/decode.sh --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
+		    exp/tri2/graph data/${dset} exp/tri2/decode_${dset} &
+  done
+  wait
+fi
+
+if [ $stage -le 15 ]; then
+  steps/align_si.sh --nj $nj --cmd "$train_cmd" \
+		    data/${train_set} data/lang exp/tri2 exp/tri2_ali
+
+  steps/train_sat.sh --cmd "$train_cmd" \
+		     5000 100000 data/${train_set} data/lang exp/tri2_ali exp/tri3
+fi
+
+if [ $stage -le 16 ]; then
+  utils/mkgraph.sh data/lang exp/tri3 exp/tri3/graph
+  for dset in ${test_sets}; do
+    steps/decode_fmllr.sh --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
+			  exp/tri3/graph data/${dset} exp/tri3/decode_${dset} &
+  done
+  wait
+fi
+
+if [ $stage -le 17 ]; then
+  # The following script cleans the data and produces cleaned data
+  steps/cleanup/clean_and_segment_data.sh --nj ${nj} --cmd "$train_cmd" \
+    --segmentation-opts "--min-segment-length 0.3 --min-new-segment-length 0.6" \
+    data/${train_set} data/lang exp/tri3 exp/tri3_cleaned data/${train_set}_cleaned
+fi
+
+if [ $stage -le 18 ]; then
+  # chain TDNN
+  local/chain/tuning/run_tdnn_1b.sh --nj ${nj} \
+    --stage $nnet_stage \
+    --train-set ${train_set}_cleaned \
+    --test-sets "$test_sets" \
+    --gmm tri3_cleaned --nnet3-affix _${train_set}_cleaned_rvb
+fi
+
+if [ $stage -le 19 ]; then
+  # 2-stage decoding
+  for test_set in $test_sets; do
+    local/nnet3/decode.sh --affix 2stage --pass2-decode-opts "--min-active 1000" \
+      --acwt 1.0 --post-decode-acwt 10.0 \
+      --frames-per-chunk 150 --nj $decode_nj \
+      --ivector-dir exp/nnet3_${train_set}_cleaned_rvb \
+      data/${test_set} data/lang_chain \
+      exp/chain_${train_set}_cleaned_rvb/tree_sp/graph \
+      exp/chain_${train_set}_cleaned_rvb/tdnn1b_sp 
+  done
+fi
+
+if [ $stage -le 20 ]; then
+  # final scoring to get the official challenge result
+  # please specify both dev and eval set directories so that the search parameters
+  # (insertion penalty and language model weight) will be tuned using the dev set
+
+  local/get_location.py $json_dir/dev > exp/chain_${train_set}_cleaned_rvb/tdnn1b_sp/decode_dev_${enhancement}_2stage/uttid_location
+  local/get_location.py $json_dir/eval > exp/chain_${train_set}_cleaned_rvb/tdnn1b_sp/decode_eval_${enhancement}_2stage/uttid_location
+
+  local/score_for_submit.sh \
+      --dev exp/chain_${train_set}_cleaned_rvb/tdnn1b_sp/decode_dev_${enhancement}_2stage \
+      --eval exp/chain_${train_set}_cleaned_rvb/tdnn1b_sp/decode_eval_${enhancement}_2stage
+fi
diff --git a/egs/chime6/s5_track1/steps b/egs/chime6/s5_track1/steps
new file mode 120000
index 00000000000..1b186770dd1
--- /dev/null
+++ b/egs/chime6/s5_track1/steps
@@ -0,0 +1 @@
+../../wsj/s5/steps/
\ No newline at end of file
diff --git a/egs/chime6/s5_track1/utils b/egs/chime6/s5_track1/utils
new file mode 120000
index 00000000000..a3279dc8679
--- /dev/null
+++ b/egs/chime6/s5_track1/utils
@@ -0,0 +1 @@
+../../wsj/s5/utils/
\ No newline at end of file

From 30f3556851921eb2d8e955d09a319a7998b229fb Mon Sep 17 00:00:00 2001
From: aarora8 <aarora8@jhu.edu>
Date: Mon, 18 Nov 2019 12:11:37 -0500
Subject: [PATCH 2/2] modification from review: adding comments, removing
 multicondition scripts, worn_audio_list

---
 .../local/chain/multi_condition/run_tdnn.sh   |   1 -
 .../chain/multi_condition/run_tdnn_lstm.sh    |   1 -
 .../tuning/run_cnn_tdnn_lstm_1a.sh            | 329 ------------------
 .../multi_condition/tuning/run_tdnn_1a.sh     | 286 ---------------
 .../multi_condition/tuning/run_tdnn_1b.sh     | 311 -----------------
 .../tuning/run_tdnn_lstm_1a.sh                | 323 -----------------
 egs/chime6/s5_track1/local/chain/run_tdnn.sh  |   2 +-
 .../chain/tuning/run_cnn_tdnn_lstm_1a.sh      | 304 ----------------
 .../local/chain/tuning/run_tdnn_lstm_1a.sh    | 297 ----------------
 .../multi_condition/run_ivector_common.sh     | 194 -----------
 egs/chime6/s5_track1/local/worn_audio_list    |  64 ----
 egs/chime6/s5_track1/run.sh                   |  37 ++
 12 files changed, 38 insertions(+), 2111 deletions(-)
 delete mode 120000 egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn.sh
 delete mode 120000 egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn_lstm.sh
 delete mode 100755 egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_cnn_tdnn_lstm_1a.sh
 delete mode 100755 egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1a.sh
 delete mode 100755 egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1b.sh
 delete mode 100755 egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
 delete mode 100755 egs/chime6/s5_track1/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
 delete mode 100755 egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lstm_1a.sh
 delete mode 100755 egs/chime6/s5_track1/local/nnet3/multi_condition/run_ivector_common.sh
 delete mode 100644 egs/chime6/s5_track1/local/worn_audio_list

diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn.sh b/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn.sh
deleted file mode 120000
index 61f8f499182..00000000000
--- a/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn.sh
+++ /dev/null
@@ -1 +0,0 @@
-tuning/run_tdnn_1b.sh
\ No newline at end of file
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn_lstm.sh b/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn_lstm.sh
deleted file mode 120000
index 8e647598556..00000000000
--- a/egs/chime6/s5_track1/local/chain/multi_condition/run_tdnn_lstm.sh
+++ /dev/null
@@ -1 +0,0 @@
-tuning/run_tdnn_lstm_1a.sh
\ No newline at end of file
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_cnn_tdnn_lstm_1a.sh b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_cnn_tdnn_lstm_1a.sh
deleted file mode 100755
index 65d7caed24c..00000000000
--- a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_cnn_tdnn_lstm_1a.sh
+++ /dev/null
@@ -1,329 +0,0 @@
-#!/bin/bash
-
-# Set -e here so that we catch if any executable fails immediately
-set -euo pipefail
-
-# First the options that are passed through to run_ivector_common.sh
-# (some of which are also used in this script directly).
-stage=0
-nj=96
-decode_nj=40
-train_set_clean=train_worn_cleaned
-train_set_noisy=train_u400k_cleaned
-combined_train_set=train_worn_u400k_cleaned
-test_sets="dev_worn"
-gmm=tri3_cleaned
-nnet3_affix=_train_worn_u400k_cleaned_rvb
-lm_suffix=
-noise_list=
-
-# The rest are configs specific to this script.  Most of the parameters
-# are just hardcoded at this level, in the commands below.
-affix=_rvb_1a   # affix for the TDNN directory name
-tree_affix=
-train_stage=-10
-get_egs_stage=-10
-decode_iter=
-
-common_egs_dir=
-
-hidden_dim=1024
-cell_dim=1024
-projection_dim=256
-
-# training options
-num_epochs=2  # 2 works better than 4
-chunk_width=140,100,160
-chunk_left_context=40
-chunk_right_context=0
-dropout_schedule='0,0@0.20,0.3@0.50,0'
-xent_regularize=0.025
-label_delay=5
-
-# decode options
-extra_left_context=50
-extra_right_context=0
-
-# training options
-srand=0
-remove_egs=true
-
-#decode options
-test_online_decoding=false  # if true, it will run the last decoding stage.
-
-
-# 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 11" if you have already
-# run those things.
-local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
-                                  --train-set-clean $train_set_clean \
-                                  --train-set-noisy $train_set_noisy \
-                                  --combined-train-set $combined_train_set \
-                                  --noise-list "$noise_list" \
-                                  --test-sets "$test_sets" \
-                                  --nnet3-affix "$nnet3_affix" || exit 1;
-
-# Problem: We have removed the "train_" prefix of our training set in
-# the alignment directory names! Bad!
-gmm_dir=exp/$gmm
-tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
-lang=data/lang_chain
-dir=exp/chain${nnet3_affix}/cnn_tdnn_lstm${affix}_sp
-train_data_dir=data/${combined_train_set}_sp_rvb_hires
-train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${combined_train_set}_sp_rvb_hires
-
-for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
-  data/${train_set_clean}_sp/feats.scp data/${train_set_noisy}_sp/feats.scp; do
-  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
-done
-
-if [ $stage -le 10 ]; then
-  echo "$0: creating lang directory $lang with chain-type topology"
-  # Create a version of the lang/ directory that has one state per phone in the
-  # topo file. [note, it really has two states.. the first one is only repeated
-  # once, the second one has zero or more repeats.]
-  if [ -d $lang ]; then
-    if [ $lang/L.fst -nt data/lang/L.fst ]; then
-      echo "$0: $lang already exists, not overwriting it; continuing"
-    else
-      echo "$0: $lang already exists and seems to be older than data/lang..."
-      echo " ... not sure what to do.  Exiting."
-      exit 1;
-    fi
-  else
-    cp -r data/lang $lang
-    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
-    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
-    # Use our special topology... note that later on may have to tune this
-    # topology.
-    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
-  fi
-fi
-
-lat_dir_clean=exp/chain${nnet3_affix}/${gmm}_${train_set_clean}_sp_lats
-lat_dir_noisy=exp/chain${nnet3_affix}/${gmm}_${train_set_noisy}_sp_lats
-lat_dir=exp/chain${nnet3_affix}/${gmm}_${combined_train_set}_sp_rvb_lats
-
-if [ $stage -le 11 ]; then
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
-    data/${train_set_noisy}_sp data/lang $gmm_dir $lat_dir_noisy || exit 1
-  rm $lat_dir_noisy/fsts.*.gz 
-fi
-
-if [ $stage -le 12 ]; then
-  # Get the alignments as lattices (gives the chain training more freedom).
-  # use the same num-jobs as the alignments
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
-    data/${train_set_clean}_sp data/lang $gmm_dir $lat_dir_clean || exit 1
-  rm $lat_dir_clean/fsts.*.gz 
-fi
-
-if [ $stage -le 13 ]; then
-  local/reverberate_lat_dir.sh --cmd "$train_cmd" --num-data-reps 2 \
-    data/${combined_train_set}_sp_rvb_hires $lat_dir_noisy \
-    $lat_dir_clean $lat_dir || exit 1
-fi
-
-if [ $stage -le 14 ]; then
-  # Build a tree using our new topology.  We know we have alignments for the
-  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
-  # those.  The num-leaves is always somewhat less than the num-leaves from
-  # the GMM baseline.
-  if [ -f $tree_dir/final.mdl ]; then
-     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
-     exit 1;
-  fi
-  steps/nnet3/chain/build_tree.sh \
-    --frame-subsampling-factor 3 \
-    --context-opts "--context-width=2 --central-position=1" \
-    --cmd "$train_cmd" 3500 data/${train_set_clean}_sp \
-    $lang $lat_dir_clean $tree_dir
-fi
-
-if [ $stage -le 15 ]; 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)
-
-  lstm_opts="decay-time=40"
-
-  mkdir -p $dir/configs
-  cat <<EOF > $dir/configs/network.xconfig
-  input dim=100 name=ivector
-  input dim=40 name=input
-
-  # please note that it is important to have input layer with the name=input
-  # as the layer immediately preceding the fixed-affine-layer to enable
-  # the use of short notation for the descriptor
-  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
-  idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
-      
-  conv-relu-batchnorm-layer name=cnn1 input=idct height-in=40 height-out=20 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256 learning-rate-factor=0.333 max-change=0.25
-  conv-relu-batchnorm-layer name=cnn2 input=cnn1 height-in=20 height-out=20 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
-
-  relu-batchnorm-layer name=affine1 input=lda dim=512
- 
-  # the first splicing is moved before the lda layer, so no splicing here
-  relu-batchnorm-layer name=tdnn1 input=cnn2 dim=1024
-  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1,affine1) dim=1024
-  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
-
-  # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
-  fast-lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
-  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
-  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
-  fast-lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
-  relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024
-  relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024
-  fast-lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
-
-  ## adding the layers for chain branch
-  output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
-
-  # adding the layers for xent branch
-  # This block prints the configs for a separate output that will be
-  # trained with a cross-entropy objective in the 'chain' models... this
-  # has the effect of regularizing the hidden parts of the model.  we use
-  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
-  # 0.5 / args.xent_regularize is suitable as it means the xent
-  # final-layer learns at a rate independent of the regularization
-  # constant; and the 0.5 was tuned so as to make the relative progress
-  # similar in the xent and regular final layers.
-  output-layer name=output-xent input=lstm3 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
-
-EOF
-  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
-fi
-
-if [ $stage -le 16 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
-  mkdir -p $dir/egs
-  touch $dir/egs/.nodelete # keep egs around when that run dies.
-
-  steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$train_cmd --mem 4G" \
-    --feat.online-ivector-dir=$train_ivector_dir \
-    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
-    --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--num-extra-lm-states=2000" \
-    --trainer.dropout-schedule $dropout_schedule \
-    --trainer.num-chunk-per-minibatch 32 \
-    --trainer.frames-per-iter 1500000 \
-    --trainer.max-param-change 2.0 \
-    --trainer.num-epochs $num_epochs \
-    --trainer.srand=$srand \
-    --trainer.optimization.shrink-value 0.99 \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.momentum=0.0 \
-    --trainer.deriv-truncate-margin 8 \
-    --egs.stage $get_egs_stage \
-    --egs.opts="--frames-overlap-per-eg 0" \
-    --egs.chunk-width=$chunk_width \
-    --egs.chunk-left-context=$chunk_left_context \
-    --egs.chunk-right-context=$chunk_right_context \
-    --egs.chunk-left-context-initial=0 \
-    --egs.chunk-right-context-final=0 \
-    --egs.dir="$common_egs_dir" \
-    --cleanup.remove-egs=$remove_egs \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
-    --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
-fi
-
-if [ $stage -le 17 ]; then
-  # Note: it's not important to give mkgraph.sh the lang directory with the
-  # matched topology (since it gets the topology file from the model).
-  utils/mkgraph.sh \
-    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
-    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
-fi
-
-if [ -z "$extra_left_context" ]; then
-  extra_left_context=$chunk_left_context
-fi
-if [ -z "$extra_right_context" ]; then
-  extra_right_context=$chunk_right_context
-fi
-
-if [ $stage -le 18 ]; then
-  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      steps/nnet3/decode.sh \
-          --acwt 1.0 --post-decode-acwt 10.0 \
-          --extra-left-context $extra_left_context \
-          --extra-right-context $extra_right_context \
-          --extra-left-context-initial 0 \
-          --extra-right-context-final 0 \
-          --frames-per-chunk $frames_per_chunk \
-          --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
-          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
-          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-# Not testing the 'looped' decoding separately, because for
-# TDNN systems it would give exactly the same results as the
-# normal decoding.
-
-if $test_online_decoding && [ $stage -le 19 ]; then
-  # note: if the features change (e.g. you add pitch features), you will have to
-  # change the options of the following command line.
-  steps/online/nnet3/prepare_online_decoding.sh \
-    --mfcc-config conf/mfcc_hires.conf \
-    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
-
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      nspk=$(wc -l <data/${data}_hires/spk2utt)
-      # note: we just give it "data/${data}" as it only uses the wav.scp, the
-      # feature type does not matter.
-      steps/online/nnet3/decode.sh \
-        --acwt 1.0 --post-decode-acwt 10.0 \
-        --nj $decode_nj --cmd "$decode_cmd" \
-        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-
-exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1a.sh b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1a.sh
deleted file mode 100755
index 421cd88da3a..00000000000
--- a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1a.sh
+++ /dev/null
@@ -1,286 +0,0 @@
-#!/bin/bash
-
-# Set -e here so that we catch if any executable fails immediately
-set -euo pipefail
-
-# First the options that are passed through to run_ivector_common.sh
-# (some of which are also used in this script directly).
-stage=0
-nj=96
-decode_nj=40
-train_set_clean=train_worn_cleaned
-train_set_noisy=train_u400k_cleaned
-combined_train_set=train_worn_u400k_cleaned
-test_sets="dev_worn dev_beamformit_ref"
-gmm=tri3_cleaned
-nnet3_affix=_train_worn_u400k_cleaned_rvb
-lm_suffix=
-noise_list=
-
-# The rest are configs specific to this script.  Most of the parameters
-# are just hardcoded at this level, in the commands below.
-affix=_rvb_1a   # affix for the TDNN directory name
-tree_affix=
-train_stage=-10
-get_egs_stage=-10
-decode_iter=
-
-common_egs_dir=
-hidden_dim=512
-# training options
-num_epochs=4
-chunk_width=140,100,160
-xent_regularize=0.1
-
-# training options
-srand=0
-remove_egs=true
-
-#decode options
-test_online_decoding=false  # if true, it will run the last decoding stage.
-
-# 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 11" if you have already
-# run those things.
-local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
-                                  --train-set-clean $train_set_clean \
-                                  --train-set-noisy $train_set_noisy \
-                                  --combined-train-set $combined_train_set \
-                                  --noise-list "$noise_list" \
-                                  --test-sets "$test_sets" \
-                                  --nnet3-affix "$nnet3_affix" || exit 1;
-
-# Problem: We have removed the "train_" prefix of our training set in
-# the alignment directory names! Bad!
-gmm_dir=exp/$gmm
-tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
-lang=data/lang_chain
-dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
-train_data_dir=data/${combined_train_set}_sp_rvb_hires
-train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${combined_train_set}_sp_rvb_hires
-
-for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
-  data/${train_set_clean}_sp/feats.scp data/${train_set_noisy}_sp/feats.scp; do
-  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
-done
-
-if [ $stage -le 10 ]; then
-  echo "$0: creating lang directory $lang with chain-type topology"
-  # Create a version of the lang/ directory that has one state per phone in the
-  # topo file. [note, it really has two states.. the first one is only repeated
-  # once, the second one has zero or more repeats.]
-  if [ -d $lang ]; then
-    if [ $lang/L.fst -nt data/lang/L.fst ]; then
-      echo "$0: $lang already exists, not overwriting it; continuing"
-    else
-      echo "$0: $lang already exists and seems to be older than data/lang..."
-      echo " ... not sure what to do.  Exiting."
-      exit 1;
-    fi
-  else
-    cp -r data/lang $lang
-    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
-    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
-    # Use our special topology... note that later on may have to tune this
-    # topology.
-    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
-  fi
-fi
-
-lat_dir_clean=exp/chain${nnet3_affix}/${gmm}_${train_set_clean}_sp_lats
-lat_dir_noisy=exp/chain${nnet3_affix}/${gmm}_${train_set_noisy}_sp_lats
-lat_dir=exp/chain${nnet3_affix}/${gmm}_${combined_train_set}_sp_rvb_lats
-
-if [ $stage -le 11 ]; then
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
-    data/${train_set_noisy}_sp data/lang $gmm_dir $lat_dir_noisy || exit 1
-  rm $lat_dir_noisy/fsts.*.gz 
-fi
-
-if [ $stage -le 12 ]; then
-  # Get the alignments as lattices (gives the chain training more freedom).
-  # use the same num-jobs as the alignments
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
-    data/${train_set_clean}_sp data/lang $gmm_dir $lat_dir_clean || exit 1
-  rm $lat_dir_clean/fsts.*.gz 
-fi
-
-if [ $stage -le 13 ]; then
-  local/reverberate_lat_dir.sh --cmd "$train_cmd" --num-data-reps 2 \
-    data/${combined_train_set}_sp_rvb_hires $lat_dir_noisy \
-    $lat_dir_clean $lat_dir || exit 1
-fi
-
-if [ $stage -le 14 ]; then
-  # Build a tree using our new topology.  We know we have alignments for the
-  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
-  # those.  The num-leaves is always somewhat less than the num-leaves from
-  # the GMM baseline.
-  if [ -f $tree_dir/final.mdl ]; then
-     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
-     exit 1;
-  fi
-  steps/nnet3/chain/build_tree.sh \
-    --frame-subsampling-factor 3 \
-    --cmd "$train_cmd" 3500 data/${train_set_clean}_sp \
-    $lang $lat_dir_clean $tree_dir
-fi
-
-if [ $stage -le 15 ]; 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)
-  opts="l2-regularize=0.05"
-  output_opts="l2-regularize=0.01 bottleneck-dim=320"
-
-  mkdir -p $dir/configs
-  cat <<EOF > $dir/configs/network.xconfig
-  input dim=100 name=ivector
-  input dim=40 name=input
-
-  # please note that it is important to have input layer with the name=input
-  # as the layer immediately preceding the fixed-affine-layer to enable
-  # the use of short notation for the descriptor
-  fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
-
-  # the first splicing is moved before the lda layer, so no splicing here
-  relu-batchnorm-layer name=tdnn1 $opts dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn2 $opts dim=$hidden_dim input=Append(-1,0,1)
-  relu-batchnorm-layer name=tdnn3 $opts dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn4 $opts dim=$hidden_dim input=Append(-1,0,1)
-  relu-batchnorm-layer name=tdnn5 $opts dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn6 $opts dim=$hidden_dim input=Append(-3,0,3)
-  relu-batchnorm-layer name=tdnn7 $opts dim=$hidden_dim input=Append(-3,0,3)
-  relu-batchnorm-layer name=tdnn8 $opts dim=$hidden_dim input=Append(-6,-3,0)
-
-  ## adding the layers for chain branch
-  relu-batchnorm-layer name=prefinal-chain $opts dim=$hidden_dim target-rms=0.5
-  output-layer name=output include-log-softmax=false $output_opts dim=$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=tdnn8 $opts dim=$hidden_dim target-rms=0.5
-  output-layer name=output-xent $output_opts dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
-EOF
-  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
-fi
-
-if [ $stage -le 16 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
-  mkdir -p $dir/egs
-  touch $dir/egs/.nodelete # keep egs around when that run dies.
-
-  steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$train_cmd --mem 4G" \
-    --feat.online-ivector-dir=$train_ivector_dir \
-    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
-    --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--num-extra-lm-states=2000" \
-    --trainer.num-chunk-per-minibatch 256,128,64 \
-    --trainer.frames-per-iter 1500000 \
-    --trainer.max-param-change 2.0 \
-    --trainer.num-epochs $num_epochs \
-    --trainer.srand=$srand \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.momentum=0.0 \
-    --egs.stage $get_egs_stage \
-    --egs.opts="--frames-overlap-per-eg 0" \
-    --egs.chunk-width=$chunk_width \
-    --egs.dir="$common_egs_dir" \
-    --cleanup.remove-egs=$remove_egs \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
-    --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
-fi
-
-if [ $stage -le 17 ]; then
-  # Note: it's not important to give mkgraph.sh the lang directory with the
-  # matched topology (since it gets the topology file from the model).
-  utils/mkgraph.sh \
-    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
-    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
-fi
-
-if [ $stage -le 18 ]; then
-  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      steps/nnet3/decode.sh \
-          --acwt 1.0 --post-decode-acwt 10.0 \
-          --frames-per-chunk $frames_per_chunk \
-          --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
-          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
-          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-# Not testing the 'looped' decoding separately, because for
-# TDNN systems it would give exactly the same results as the
-# normal decoding.
-
-if $test_online_decoding && [ $stage -le 19 ]; then
-  # note: if the features change (e.g. you add pitch features), you will have to
-  # change the options of the following command line.
-  steps/online/nnet3/prepare_online_decoding.sh \
-    --mfcc-config conf/mfcc_hires.conf \
-    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
-
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      nspk=$(wc -l <data/${data}_hires/spk2utt)
-      # note: we just give it "data/${data}" as it only uses the wav.scp, the
-      # feature type does not matter.
-      steps/online/nnet3/decode.sh \
-        --acwt 1.0 --post-decode-acwt 10.0 \
-        --nj $decode_nj --cmd "$decode_cmd" \
-        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-
-exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1b.sh b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1b.sh
deleted file mode 100755
index 7ed480f284a..00000000000
--- a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_1b.sh
+++ /dev/null
@@ -1,311 +0,0 @@
-#!/bin/bash
-
-# 7q is as 7p but a modified topology with resnet-style skip connections, more layers,
-#  skinnier bottlenecks, removing the 3-way splicing and skip-layer splicing,
-#  and re-tuning the learning rate and l2 regularize.  The configs are
-#  standardized and substantially simplified.  There isn't any advantage in WER
-#  on this setup; the advantage of this style of config is that it also works
-#  well on smaller datasets, and we adopt this style here also for consistency.
-
-# local/chain/compare_wer_general.sh --rt03 tdnn7p_sp tdnn7q_sp
-# System                tdnn7p_sp tdnn7q_sp
-# WER on train_dev(tg)      11.80     11.79
-# WER on train_dev(fg)      10.77     10.84
-# WER on eval2000(tg)        14.4      14.3
-# WER on eval2000(fg)        13.0      12.9
-# WER on rt03(tg)            17.5      17.6
-# WER on rt03(fg)            15.3      15.2
-# Final train prob         -0.057    -0.058
-# Final valid prob         -0.069    -0.073
-# Final train prob (xent)        -0.886    -0.894
-# Final valid prob (xent)       -0.9005   -0.9106
-# Num-parameters               22865188  18702628
-
-
-# steps/info/chain_dir_info.pl exp/chain/tdnn7q_sp
-# exp/chain/tdnn7q_sp: num-iters=394 nj=3..16 num-params=18.7M dim=40+100->6034 combine=-0.058->-0.057 (over 8) xent:train/valid[261,393,final]=(-1.20,-0.897,-0.894/-1.20,-0.919,-0.911) logprob:train/valid[261,393,final]=(-0.090,-0.059,-0.058/-0.098,-0.073,-0.073)
-
-set -e
-
-# configs for 'chain'
-stage=0
-nj=96
-decode_nj=40
-train_set_clean=train_worn_cleaned
-train_set_noisy=train_u400k_cleaned
-combined_train_set=train_worn_u400k_cleaned
-test_sets="dev_worn dev_beamformit_ref"
-gmm=tri3_cleaned
-nnet3_affix=_train_worn_u400k_cleaned_rvb
-lm_suffix=
-noise_list=
-
-# The rest are configs specific to this script.  Most of the parameters
-# are just hardcoded at this level, in the commands below.
-affix=_rvb_1b   # affix for the TDNN directory name
-tree_affix=
-train_stage=-10
-get_egs_stage=-10
-decode_iter=
-
-num_epochs=4
-common_egs_dir=
-# training options
-# training chunk-options
-chunk_width=140,100,160
-xent_regularize=0.1
-dropout_schedule='0,0@0.20,0.5@0.50,0'
-
-# training options
-srand=0
-remove_egs=true
-
-#decode options
-test_online_decoding=false  # if true, it will run the last decoding stage.
-
-# 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 11" if you have already
-# run those things.
-local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
-                                  --train-set-clean $train_set_clean \
-                                  --train-set-noisy $train_set_noisy \
-                                  --combined-train-set $combined_train_set \
-                                  --noise-list "$noise_list" \
-                                  --test-sets "$test_sets" \
-                                  --nnet3-affix "$nnet3_affix" || exit 1;
-
-# Problem: We have removed the "train_" prefix of our training set in
-# the alignment directory names! Bad!
-gmm_dir=exp/$gmm
-tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
-lang=data/lang_chain
-dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
-train_data_dir=data/${combined_train_set}_sp_rvb_hires
-train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${combined_train_set}_sp_rvb_hires
-
-for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
-  data/${train_set_clean}_sp/feats.scp data/${train_set_noisy}_sp/feats.scp; do
-  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
-done
-
-if [ $stage -le 10 ]; then
-  echo "$0: creating lang directory $lang with chain-type topology"
-  # Create a version of the lang/ directory that has one state per phone in the
-  # topo file. [note, it really has two states.. the first one is only repeated
-  # once, the second one has zero or more repeats.]
-  if [ -d $lang ]; then
-    if [ $lang/L.fst -nt data/lang/L.fst ]; then
-      echo "$0: $lang already exists, not overwriting it; continuing"
-    else
-      echo "$0: $lang already exists and seems to be older than data/lang..."
-      echo " ... not sure what to do.  Exiting."
-      exit 1;
-    fi
-  else
-    cp -r data/lang $lang
-    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
-    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
-    # Use our special topology... note that later on may have to tune this
-    # topology.
-    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
-  fi
-fi
-
-lat_dir_clean=exp/chain${nnet3_affix}/${gmm}_${train_set_clean}_sp_lats
-lat_dir_noisy=exp/chain${nnet3_affix}/${gmm}_${train_set_noisy}_sp_lats
-lat_dir=exp/chain${nnet3_affix}/${gmm}_${combined_train_set}_sp_rvb_lats
-
-if [ $stage -le 11 ]; then
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
-    data/${train_set_noisy}_sp data/lang $gmm_dir $lat_dir_noisy || exit 1
-  rm $lat_dir_noisy/fsts.*.gz 
-fi
-
-if [ $stage -le 12 ]; then
-  # Get the alignments as lattices (gives the chain training more freedom).
-  # use the same num-jobs as the alignments
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
-    data/${train_set_clean}_sp data/lang $gmm_dir $lat_dir_clean || exit 1
-  rm $lat_dir_clean/fsts.*.gz 
-fi
-
-if [ $stage -le 13 ]; then
-  local/reverberate_lat_dir.sh --cmd "$train_cmd" --num-data-reps 2 \
-    data/${combined_train_set}_sp_rvb_hires $lat_dir_noisy \
-    $lat_dir_clean $lat_dir || exit 1
-fi
-
-if [ $stage -le 14 ]; then
-  # Build a tree using our new topology.  We know we have alignments for the
-  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
-  # those.  The num-leaves is always somewhat less than the num-leaves from
-  # the GMM baseline.
-  if [ -f $tree_dir/final.mdl ]; then
-     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
-     exit 1;
-  fi
-  steps/nnet3/chain/build_tree.sh \
-    --frame-subsampling-factor 3 \
-    --cmd "$train_cmd" 3500 data/${train_set_clean}_sp \
-    $lang $lat_dir_clean $tree_dir
-fi
-
-if [ $stage -le 15 ]; 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)
-  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 > $dir/configs/network.xconfig
-  input dim=100 name=ivector
-  input dim=40 name=input
-
-  # please note that it is important to have input layer with the name=input
-  # as the layer immediately preceding the fixed-affine-layer to enable
-  # the use of short notation for the descriptor
-  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
-
-  # the first splicing is moved before the lda layer, so no splicing here
-  relu-batchnorm-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 16 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
-  mkdir -p $dir/egs
-  touch $dir/egs/.nodelete # keep egs around when that run dies.
-
-  steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$train_cmd --mem 4G" \
-    --feat.online-ivector-dir=$train_ivector_dir \
-    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
-    --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient 0.1 \
-    --chain.l2-regularize 0.0 \
-    --chain.apply-deriv-weights false \
-    --chain.lm-opts="--num-extra-lm-states=2000" \
-    --trainer.dropout-schedule "$dropout_schedule" \
-    --trainer.add-option="--optimization.memory-compression-level=2" \
-    --trainer.num-chunk-per-minibatch 64 \
-    --trainer.frames-per-iter 1500000 \
-    --trainer.max-param-change 2.0 \
-    --trainer.num-epochs $num_epochs \
-    --trainer.optimization.num-jobs-initial 3 \
-    --trainer.optimization.num-jobs-final 16 \
-    --trainer.optimization.initial-effective-lrate 0.00025 \
-    --trainer.optimization.final-effective-lrate 0.000025 \
-    --egs.stage $get_egs_stage \
-    --egs.opts "--frames-overlap-per-eg 0" \
-    --egs.chunk-width $chunk_width \
-    --egs.dir "$common_egs_dir" \
-    --cleanup.remove-egs $remove_egs \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
-    --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
-fi
-
-if [ $stage -le 17 ]; then
-  # Note: it's not important to give mkgraph.sh the lang directory with the
-  # matched topology (since it gets the topology file from the model).
-  utils/mkgraph.sh \
-    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
-    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
-fi
-
-if [ $stage -le 18 ]; then
-  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      steps/nnet3/decode.sh \
-          --acwt 1.0 --post-decode-acwt 10.0 \
-          --frames-per-chunk $frames_per_chunk \
-          --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
-          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
-          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-# Not testing the 'looped' decoding separately, because for
-# TDNN systems it would give exactly the same results as the
-# normal decoding.
-
-if $test_online_decoding && [ $stage -le 19 ]; then
-  # note: if the features change (e.g. you add pitch features), you will have to
-  # change the options of the following command line.
-  steps/online/nnet3/prepare_online_decoding.sh \
-    --mfcc-config conf/mfcc_hires.conf \
-    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
-
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      nspk=$(wc -l <data/${data}_hires/spk2utt)
-      # note: we just give it "data/${data}" as it only uses the wav.scp, the
-      # feature type does not matter.
-      steps/online/nnet3/decode.sh \
-        --acwt 1.0 --post-decode-acwt 10.0 \
-        --nj $decode_nj --cmd "$decode_cmd" \
-        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-
-exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh b/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
deleted file mode 100755
index cda306b0d06..00000000000
--- a/egs/chime6/s5_track1/local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh
+++ /dev/null
@@ -1,323 +0,0 @@
-#!/bin/bash
-
-# Set -e here so that we catch if any executable fails immediately
-set -euo pipefail
-
-# First the options that are passed through to run_ivector_common.sh
-# (some of which are also used in this script directly).
-stage=0
-nj=96
-decode_nj=40
-train_set_clean=train_worn_cleaned
-train_set_noisy=train_u400k_cleaned
-combined_train_set=train_worn_u400k_cleaned
-test_sets="dev_worn"
-gmm=tri3_cleaned
-nnet3_affix=_train_worn_u400k_cleaned_rvb
-lm_suffix=
-noise_list=
-
-# The rest are configs specific to this script.  Most of the parameters
-# are just hardcoded at this level, in the commands below.
-affix=_rvb_1a   # affix for the TDNN directory name
-tree_affix=
-train_stage=-10
-get_egs_stage=-10
-decode_iter=
-
-common_egs_dir=
-
-hidden_dim=1024
-cell_dim=1024
-projection_dim=256
-
-# training options
-num_epochs=2  # 2 works better than 4
-chunk_width=140,100,160
-chunk_left_context=40
-chunk_right_context=0
-dropout_schedule='0,0@0.20,0.3@0.50,0'
-xent_regularize=0.025
-label_delay=5
-
-# decode options
-extra_left_context=50
-extra_right_context=0
-
-# training options
-srand=0
-remove_egs=true
-
-#decode options
-test_online_decoding=false  # if true, it will run the last decoding stage.
-
-
-# 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 11" if you have already
-# run those things.
-local/nnet3/multi_condition/run_ivector_common.sh --stage $stage \
-                                  --train-set-clean $train_set_clean \
-                                  --train-set-noisy $train_set_noisy \
-                                  --combined-train-set $combined_train_set \
-                                  --noise-list "$noise_list" \
-                                  --test-sets "$test_sets" \
-                                  --nnet3-affix "$nnet3_affix" || exit 1;
-
-# Problem: We have removed the "train_" prefix of our training set in
-# the alignment directory names! Bad!
-gmm_dir=exp/$gmm
-tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
-lang=data/lang_chain
-dir=exp/chain${nnet3_affix}/tdnn_lstm${affix}_sp
-train_data_dir=data/${combined_train_set}_sp_rvb_hires
-train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${combined_train_set}_sp_rvb_hires
-
-for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
-  data/${train_set_clean}_sp/feats.scp data/${train_set_noisy}_sp/feats.scp; do
-  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
-done
-
-if [ $stage -le 10 ]; then
-  echo "$0: creating lang directory $lang with chain-type topology"
-  # Create a version of the lang/ directory that has one state per phone in the
-  # topo file. [note, it really has two states.. the first one is only repeated
-  # once, the second one has zero or more repeats.]
-  if [ -d $lang ]; then
-    if [ $lang/L.fst -nt data/lang/L.fst ]; then
-      echo "$0: $lang already exists, not overwriting it; continuing"
-    else
-      echo "$0: $lang already exists and seems to be older than data/lang..."
-      echo " ... not sure what to do.  Exiting."
-      exit 1;
-    fi
-  else
-    cp -r data/lang $lang
-    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
-    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
-    # Use our special topology... note that later on may have to tune this
-    # topology.
-    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
-  fi
-fi
-
-lat_dir_clean=exp/chain${nnet3_affix}/${gmm}_${train_set_clean}_sp_lats
-lat_dir_noisy=exp/chain${nnet3_affix}/${gmm}_${train_set_noisy}_sp_lats
-lat_dir=exp/chain${nnet3_affix}/${gmm}_${combined_train_set}_sp_rvb_lats
-
-if [ $stage -le 11 ]; then
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
-    data/${train_set_noisy}_sp data/lang $gmm_dir $lat_dir_noisy || exit 1
-  rm $lat_dir_noisy/fsts.*.gz 
-fi
-
-if [ $stage -le 12 ]; then
-  # Get the alignments as lattices (gives the chain training more freedom).
-  # use the same num-jobs as the alignments
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" --generate-ali-from-lats true \
-    data/${train_set_clean}_sp data/lang $gmm_dir $lat_dir_clean || exit 1
-  rm $lat_dir_clean/fsts.*.gz 
-fi
-
-if [ $stage -le 13 ]; then
-  local/reverberate_lat_dir.sh --cmd "$train_cmd" --num-data-reps 2 \
-    data/${combined_train_set}_sp_rvb_hires $lat_dir_noisy \
-    $lat_dir_clean $lat_dir || exit 1
-fi
-
-if [ $stage -le 14 ]; then
-  # Build a tree using our new topology.  We know we have alignments for the
-  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
-  # those.  The num-leaves is always somewhat less than the num-leaves from
-  # the GMM baseline.
-  if [ -f $tree_dir/final.mdl ]; then
-     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
-     exit 1;
-  fi
-  steps/nnet3/chain/build_tree.sh \
-    --frame-subsampling-factor 3 \
-    --context-opts "--context-width=2 --central-position=1" \
-    --cmd "$train_cmd" 3500 data/${train_set_clean}_sp \
-    $lang $lat_dir_clean $tree_dir
-fi
-
-if [ $stage -le 15 ]; 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)
-
-  lstm_opts="decay-time=40"
-
-  mkdir -p $dir/configs
-  cat <<EOF > $dir/configs/network.xconfig
-  input dim=100 name=ivector
-  input dim=40 name=input
-
-  # please note that it is important to have input layer with the name=input
-  # as the layer immediately preceding the fixed-affine-layer to enable
-  # the use of short notation for the descriptor
-  fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
-
-  # the first splicing is moved before the lda layer, so no splicing here
-  relu-batchnorm-layer name=tdnn1 dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=$hidden_dim
-
-  fast-lstmp-layer name=lstm1 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=$hidden_dim
-  fast-lstmp-layer name=lstm2 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=$hidden_dim
-  fast-lstmp-layer name=lstm3 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=$hidden_dim
-  fast-lstmp-layer name=lstm4 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
-
-  ## adding the layers for chain branch
-  output-layer name=output input=lstm4 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
-
-  # adding the layers for xent branch
-  # This block prints the configs for a separate output that will be
-  # trained with a cross-entropy objective in the 'chain' models... this
-  # has the effect of regularizing the hidden parts of the model.  we use
-  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
-  # 0.5 / args.xent_regularize is suitable as it means the xent
-  # final-layer learns at a rate independent of the regularization
-  # constant; and the 0.5 was tuned so as to make the relative progress
-  # similar in the xent and regular final layers.
-  output-layer name=output-xent input=lstm4 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
-
-EOF
-  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
-fi
-
-if [ $stage -le 16 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
-  mkdir -p $dir/egs
-  touch $dir/egs/.nodelete # keep egs around when that run dies.
-
-  steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$train_cmd --mem 4G" \
-    --feat.online-ivector-dir=$train_ivector_dir \
-    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
-    --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--num-extra-lm-states=2000" \
-    --trainer.dropout-schedule $dropout_schedule \
-    --trainer.num-chunk-per-minibatch 64,32 \
-    --trainer.frames-per-iter 1500000 \
-    --trainer.max-param-change 2.0 \
-    --trainer.num-epochs $num_epochs \
-    --trainer.srand=$srand \
-    --trainer.optimization.shrink-value 0.99 \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.momentum=0.0 \
-    --trainer.deriv-truncate-margin 8 \
-    --egs.stage $get_egs_stage \
-    --egs.opts="--frames-overlap-per-eg 0" \
-    --egs.chunk-width=$chunk_width \
-    --egs.chunk-left-context=$chunk_left_context \
-    --egs.chunk-right-context=$chunk_right_context \
-    --egs.chunk-left-context-initial=0 \
-    --egs.chunk-right-context-final=0 \
-    --egs.dir="$common_egs_dir" \
-    --cleanup.remove-egs=$remove_egs \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
-    --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
-fi
-
-if [ $stage -le 17 ]; then
-  # Note: it's not important to give mkgraph.sh the lang directory with the
-  # matched topology (since it gets the topology file from the model).
-  utils/mkgraph.sh \
-    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
-    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
-fi
-
-if [ -z "$extra_left_context" ]; then
-  extra_left_context=$chunk_left_context
-fi
-if [ -z "$extra_right_context" ]; then
-  extra_right_context=$chunk_right_context
-fi
-
-if [ $stage -le 18 ]; then
-  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      steps/nnet3/decode.sh \
-          --acwt 1.0 --post-decode-acwt 10.0 \
-          --extra-left-context $extra_left_context \
-          --extra-right-context $extra_right_context \
-          --extra-left-context-initial 0 \
-          --extra-right-context-final 0 \
-          --frames-per-chunk $frames_per_chunk \
-          --nj $decode_nj --cmd "$decode_cmd"  --num-threads 4 \
-          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
-          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-# Not testing the 'looped' decoding separately, because for
-# TDNN systems it would give exactly the same results as the
-# normal decoding.
-
-if $test_online_decoding && [ $stage -le 19 ]; then
-  # note: if the features change (e.g. you add pitch features), you will have to
-  # change the options of the following command line.
-  steps/online/nnet3/prepare_online_decoding.sh \
-    --mfcc-config conf/mfcc_hires.conf \
-    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
-
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      nspk=$(wc -l <data/${data}_hires/spk2utt)
-      # note: we just give it "data/${data}" as it only uses the wav.scp, the
-      # feature type does not matter.
-      steps/online/nnet3/decode.sh \
-        --acwt 1.0 --post-decode-acwt 10.0 \
-        --nj $decode_nj --cmd "$decode_cmd" \
-        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-
-exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/run_tdnn.sh b/egs/chime6/s5_track1/local/chain/run_tdnn.sh
index 34499362831..61f8f499182 120000
--- a/egs/chime6/s5_track1/local/chain/run_tdnn.sh
+++ b/egs/chime6/s5_track1/local/chain/run_tdnn.sh
@@ -1 +1 @@
-tuning/run_tdnn_1a.sh
\ No newline at end of file
+tuning/run_tdnn_1b.sh
\ No newline at end of file
diff --git a/egs/chime6/s5_track1/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh b/egs/chime6/s5_track1/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
deleted file mode 100755
index 95e9d934bd3..00000000000
--- a/egs/chime6/s5_track1/local/chain/tuning/run_cnn_tdnn_lstm_1a.sh
+++ /dev/null
@@ -1,304 +0,0 @@
-#!/bin/bash
-
-# Set -e here so that we catch if any executable fails immediately
-set -euo pipefail
-
-# First the options that are passed through to run_ivector_common.sh
-# (some of which are also used in this script directly).
-stage=0
-nj=96
-train_set=train_worn_u400k_cleaned
-test_sets="dev_beamformit_ref"
-gmm=tri3_cleaned
-nnet3_affix=_train_worn_u400k_cleaned
-lm_suffix=
-
-# The rest are configs specific to this script.  Most of the parameters
-# are just hardcoded at this level, in the commands below.
-affix=_1a   # affix for the TDNN directory name
-tree_affix=
-train_stage=-10
-get_egs_stage=-10
-decode_iter=
-
-common_egs_dir=
-
-hidden_dim=1024
-cell_dim=1024
-projection_dim=256
-
-# training options
-num_epochs=2  # 2 works better than 4
-chunk_width=140,100,160
-chunk_left_context=40
-chunk_right_context=0
-dropout_schedule='0,0@0.20,0.3@0.50,0'
-xent_regularize=0.025
-label_delay=5
-
-# decode options
-extra_left_context=50
-extra_right_context=0
-
-# training options
-srand=0
-remove_egs=true
-
-#decode options
-test_online_decoding=false  # if true, it will run the last decoding stage.
-
-
-# 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 11" if you have already
-# run those things.
-local/nnet3/run_ivector_common.sh --stage $stage \
-                                  --train-set $train_set \
-				  --test-sets "$test_sets" \
-                                  --gmm $gmm \
-                                  --nnet3-affix "$nnet3_affix" || exit 1;
-
-# Problem: We have removed the "train_" prefix of our training set in
-# the alignment directory names! Bad!
-gmm_dir=exp/$gmm
-ali_dir=exp/${gmm}_ali_${train_set}_sp
-tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
-lang=data/lang_chain
-lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
-dir=exp/chain${nnet3_affix}/cnn_tdnn_lstm${affix}_sp
-train_data_dir=data/${train_set}_sp_hires
-lores_train_data_dir=data/${train_set}_sp
-train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
-
-for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
-    $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
-  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
-done
-
-if [ $stage -le 10 ]; then
-  echo "$0: creating lang directory $lang with chain-type topology"
-  # Create a version of the lang/ directory that has one state per phone in the
-  # topo file. [note, it really has two states.. the first one is only repeated
-  # once, the second one has zero or more repeats.]
-  if [ -d $lang ]; then
-    if [ $lang/L.fst -nt data/lang/L.fst ]; then
-      echo "$0: $lang already exists, not overwriting it; continuing"
-    else
-      echo "$0: $lang already exists and seems to be older than data/lang..."
-      echo " ... not sure what to do.  Exiting."
-      exit 1;
-    fi
-  else
-    cp -r data/lang $lang
-    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
-    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
-    # Use our special topology... note that later on may have to tune this
-    # topology.
-    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
-  fi
-fi
-
-if [ $stage -le 11 ]; then
-  # Get the alignments as lattices (gives the chain training more freedom).
-  # use the same num-jobs as the alignments
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" ${lores_train_data_dir} \
-    data/lang $gmm_dir $lat_dir
-  rm $lat_dir/fsts.*.gz # save space
-fi
-
-if [ $stage -le 12 ]; then
-  # Build a tree using our new topology.  We know we have alignments for the
-  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
-  # those.  The num-leaves is always somewhat less than the num-leaves from
-  # the GMM baseline.
-   if [ -f $tree_dir/final.mdl ]; then
-     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
-     exit 1;
-  fi
-  steps/nnet3/chain/build_tree.sh \
-    --frame-subsampling-factor 3 \
-    --context-opts "--context-width=2 --central-position=1" \
-    --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
-    $lang $ali_dir $tree_dir
-fi
-
-if [ $stage -le 13 ]; 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)
-
-  lstm_opts="decay-time=40"
-
-  mkdir -p $dir/configs
-  cat <<EOF > $dir/configs/network.xconfig
-  input dim=100 name=ivector
-  input dim=40 name=input
-
-  # please note that it is important to have input layer with the name=input
-  # as the layer immediately preceding the fixed-affine-layer to enable
-  # the use of short notation for the descriptor
-  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
-  idct-layer name=idct input=input dim=40 cepstral-lifter=22 affine-transform-file=$dir/configs/idct.mat
-      
-  conv-relu-batchnorm-layer name=cnn1 input=idct height-in=40 height-out=20 height-subsample-out=2 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=256 learning-rate-factor=0.333 max-change=0.25
-  conv-relu-batchnorm-layer name=cnn2 input=cnn1 height-in=20 height-out=20 time-offsets=-1,0,1 height-offsets=-1,0,1 num-filters-out=128
-
-  relu-batchnorm-layer name=affine1 input=lda dim=512
- 
-  # the first splicing is moved before the lda layer, so no splicing here
-  relu-batchnorm-layer name=tdnn1 input=cnn2 dim=1024
-  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1,affine1) dim=1024
-  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
-
-  # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
-  fast-lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
-  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
-  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
-  fast-lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
-  relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024
-  relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024
-  fast-lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 $lstm_opts
-
-  ## adding the layers for chain branch
-  output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
-
-  # adding the layers for xent branch
-  # This block prints the configs for a separate output that will be
-  # trained with a cross-entropy objective in the 'chain' models... this
-  # has the effect of regularizing the hidden parts of the model.  we use
-  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
-  # 0.5 / args.xent_regularize is suitable as it means the xent
-  # final-layer learns at a rate independent of the regularization
-  # constant; and the 0.5 was tuned so as to make the relative progress
-  # similar in the xent and regular final layers.
-  output-layer name=output-xent input=lstm3 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
-
-EOF
-
-  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
-fi
-
-if [ $stage -le 14 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
-  mkdir -p $dir/egs
-  touch $dir/egs/.nodelete # keep egs around when that run dies.
-
-  steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$train_cmd --mem 4G" \
-    --feat.online-ivector-dir=$train_ivector_dir \
-    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
-    --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--num-extra-lm-states=2000" \
-    --trainer.dropout-schedule $dropout_schedule \
-    --trainer.num-chunk-per-minibatch 64,32 \
-    --trainer.frames-per-iter 1500000 \
-    --trainer.max-param-change 2.0 \
-    --trainer.num-epochs $num_epochs \
-    --trainer.srand=$srand \
-    --trainer.optimization.shrink-value 0.99 \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.momentum=0.0 \
-    --trainer.deriv-truncate-margin 8 \
-    --egs.stage $get_egs_stage \
-    --egs.opts="--frames-overlap-per-eg 0" \
-    --egs.chunk-width=$chunk_width \
-    --egs.chunk-left-context=$chunk_left_context \
-    --egs.chunk-right-context=$chunk_right_context \
-    --egs.chunk-left-context-initial=0 \
-    --egs.chunk-right-context-final=0 \
-    --egs.dir="$common_egs_dir" \
-    --cleanup.remove-egs=$remove_egs \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
-    --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
-fi
-
-if [ $stage -le 15 ]; then
-  # Note: it's not important to give mkgraph.sh the lang directory with the
-  # matched topology (since it gets the topology file from the model).
-  utils/mkgraph.sh \
-    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
-    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
-fi
-
-if [ $stage -le 16 ]; then
-  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      steps/nnet3/decode.sh \
-          --acwt 1.0 --post-decode-acwt 10.0 \
-          --extra-left-context $chunk_left_context \
-          --extra-right-context $chunk_right_context \
-          --extra-left-context-initial 0 \
-          --extra-right-context-final 0 \
-          --frames-per-chunk $frames_per_chunk \
-          --nj 8 --cmd "$decode_cmd"  --num-threads 4 \
-          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
-          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-# Not testing the 'looped' decoding separately, because for
-# TDNN systems it would give exactly the same results as the
-# normal decoding.
-
-if $test_online_decoding && [ $stage -le 17 ]; then
-  # note: if the features change (e.g. you add pitch features), you will have to
-  # change the options of the following command line.
-  steps/online/nnet3/prepare_online_decoding.sh \
-    --mfcc-config conf/mfcc_hires.conf \
-    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
-
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      nspk=$(wc -l <data/${data}_hires/spk2utt)
-      # note: we just give it "data/${data}" as it only uses the wav.scp, the
-      # feature type does not matter.
-      steps/online/nnet3/decode.sh \
-        --acwt 1.0 --post-decode-acwt 10.0 \
-        --nj 8 --cmd "$decode_cmd" \
-        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-
-exit 0;
diff --git a/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lstm_1a.sh b/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lstm_1a.sh
deleted file mode 100755
index e3d8e6ac4dc..00000000000
--- a/egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lstm_1a.sh
+++ /dev/null
@@ -1,297 +0,0 @@
-#!/bin/bash
-
-# Set -e here so that we catch if any executable fails immediately
-set -euo pipefail
-
-# First the options that are passed through to run_ivector_common.sh
-# (some of which are also used in this script directly).
-stage=0
-nj=96
-train_set=train_worn_u400k_cleaned
-test_sets="dev_worn dev_beamformit_ref"
-gmm=tri3_cleaned
-nnet3_affix=_train_worn_u400k_cleaned
-lm_suffix=
-
-# The rest are configs specific to this script.  Most of the parameters
-# are just hardcoded at this level, in the commands below.
-affix=_1a   # affix for the TDNN directory name
-tree_affix=
-train_stage=-10
-get_egs_stage=-10
-decode_iter=
-
-common_egs_dir=
-
-hidden_dim=1024
-cell_dim=1024
-projection_dim=256
-
-# training options
-num_epochs=2  # 2 works better than 4
-chunk_width=140,100,160
-chunk_left_context=40
-chunk_right_context=0
-dropout_schedule='0,0@0.20,0.3@0.50,0'
-xent_regularize=0.025
-label_delay=5
-
-# decode options
-extra_left_context=50
-extra_right_context=0
-
-# training options
-srand=0
-remove_egs=true
-
-#decode options
-test_online_decoding=false  # if true, it will run the last decoding stage.
-
-
-# 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 11" if you have already
-# run those things.
-local/nnet3/run_ivector_common.sh --stage $stage \
-                                  --train-set $train_set \
-				  --test-sets "$test_sets" \
-                                  --gmm $gmm \
-                                  --nnet3-affix "$nnet3_affix" || exit 1;
-
-# Problem: We have removed the "train_" prefix of our training set in
-# the alignment directory names! Bad!
-gmm_dir=exp/$gmm
-ali_dir=exp/${gmm}_ali_${train_set}_sp
-tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
-lang=data/lang_chain
-lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
-dir=exp/chain${nnet3_affix}/tdnn_lstm${affix}_sp
-train_data_dir=data/${train_set}_sp_hires
-lores_train_data_dir=data/${train_set}_sp
-train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
-
-for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
-    $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
-  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
-done
-
-if [ $stage -le 10 ]; then
-  echo "$0: creating lang directory $lang with chain-type topology"
-  # Create a version of the lang/ directory that has one state per phone in the
-  # topo file. [note, it really has two states.. the first one is only repeated
-  # once, the second one has zero or more repeats.]
-  if [ -d $lang ]; then
-    if [ $lang/L.fst -nt data/lang/L.fst ]; then
-      echo "$0: $lang already exists, not overwriting it; continuing"
-    else
-      echo "$0: $lang already exists and seems to be older than data/lang..."
-      echo " ... not sure what to do.  Exiting."
-      exit 1;
-    fi
-  else
-    cp -r data/lang $lang
-    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
-    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
-    # Use our special topology... note that later on may have to tune this
-    # topology.
-    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
-  fi
-fi
-
-if [ $stage -le 11 ]; then
-  # Get the alignments as lattices (gives the chain training more freedom).
-  # use the same num-jobs as the alignments
-  steps/align_fmllr_lats.sh --nj ${nj} --cmd "$train_cmd" ${lores_train_data_dir} \
-    data/lang $gmm_dir $lat_dir
-  rm $lat_dir/fsts.*.gz # save space
-fi
-
-if [ $stage -le 12 ]; then
-  # Build a tree using our new topology.  We know we have alignments for the
-  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
-  # those.  The num-leaves is always somewhat less than the num-leaves from
-  # the GMM baseline.
-   if [ -f $tree_dir/final.mdl ]; then
-     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
-     exit 1;
-  fi
-  steps/nnet3/chain/build_tree.sh \
-    --frame-subsampling-factor 3 \
-    --context-opts "--context-width=2 --central-position=1" \
-    --cmd "$train_cmd" 3500 ${lores_train_data_dir} \
-    $lang $ali_dir $tree_dir
-fi
-
-if [ $stage -le 13 ]; 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)
-
-  lstm_opts="decay-time=40"
-
-  mkdir -p $dir/configs
-  cat <<EOF > $dir/configs/network.xconfig
-  input dim=100 name=ivector
-  input dim=40 name=input
-
-  # please note that it is important to have input layer with the name=input
-  # as the layer immediately preceding the fixed-affine-layer to enable
-  # the use of short notation for the descriptor
-  fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
-
-  # the first splicing is moved before the lda layer, so no splicing here
-  relu-batchnorm-layer name=tdnn1 dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn2 input=Append(-1,0,1) dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn3 input=Append(-1,0,1) dim=$hidden_dim
-
-  fast-lstmp-layer name=lstm1 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn4 input=Append(-3,0,3) dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn5 input=Append(-3,0,3) dim=$hidden_dim
-  fast-lstmp-layer name=lstm2 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn6 input=Append(-3,0,3) dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn7 input=Append(-3,0,3) dim=$hidden_dim
-  fast-lstmp-layer name=lstm3 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
-  relu-batchnorm-layer name=tdnn8 input=Append(-3,0,3) dim=$hidden_dim
-  relu-batchnorm-layer name=tdnn9 input=Append(-3,0,3) dim=$hidden_dim
-  fast-lstmp-layer name=lstm4 cell-dim=$cell_dim recurrent-projection-dim=$projection_dim non-recurrent-projection-dim=$projection_dim delay=-3 dropout-proportion=0.0 $lstm_opts
-
-  ## adding the layers for chain branch
-  output-layer name=output input=lstm4 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
-
-  # adding the layers for xent branch
-  # This block prints the configs for a separate output that will be
-  # trained with a cross-entropy objective in the 'chain' models... this
-  # has the effect of regularizing the hidden parts of the model.  we use
-  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
-  # 0.5 / args.xent_regularize is suitable as it means the xent
-  # final-layer learns at a rate independent of the regularization
-  # constant; and the 0.5 was tuned so as to make the relative progress
-  # similar in the xent and regular final layers.
-  output-layer name=output-xent input=lstm4 output-delay=$label_delay dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
-
-EOF
-  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
-fi
-
-if [ $stage -le 14 ]; then
-  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
-    utils/create_split_dir.pl \
-     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/chime5-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
-  fi
-
-  mkdir -p $dir/egs
-  touch $dir/egs/.nodelete # keep egs around when that run dies.
-
-  steps/nnet3/chain/train.py --stage=$train_stage \
-    --cmd="$train_cmd --mem 4G" \
-    --feat.online-ivector-dir=$train_ivector_dir \
-    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
-    --chain.xent-regularize $xent_regularize \
-    --chain.leaky-hmm-coefficient=0.1 \
-    --chain.l2-regularize=0.00005 \
-    --chain.apply-deriv-weights=false \
-    --chain.lm-opts="--num-extra-lm-states=2000" \
-    --trainer.dropout-schedule $dropout_schedule \
-    --trainer.num-chunk-per-minibatch 64,32 \
-    --trainer.frames-per-iter 1500000 \
-    --trainer.max-param-change 2.0 \
-    --trainer.num-epochs $num_epochs \
-    --trainer.srand=$srand \
-    --trainer.optimization.shrink-value 0.99 \
-    --trainer.optimization.num-jobs-initial=3 \
-    --trainer.optimization.num-jobs-final=16 \
-    --trainer.optimization.initial-effective-lrate=0.001 \
-    --trainer.optimization.final-effective-lrate=0.0001 \
-    --trainer.optimization.momentum=0.0 \
-    --trainer.deriv-truncate-margin 8 \
-    --egs.stage $get_egs_stage \
-    --egs.opts="--frames-overlap-per-eg 0" \
-    --egs.chunk-width=$chunk_width \
-    --egs.chunk-left-context=$chunk_left_context \
-    --egs.chunk-right-context=$chunk_right_context \
-    --egs.chunk-left-context-initial=0 \
-    --egs.chunk-right-context-final=0 \
-    --egs.dir="$common_egs_dir" \
-    --cleanup.remove-egs=$remove_egs \
-    --feat-dir=$train_data_dir \
-    --tree-dir=$tree_dir \
-    --lat-dir=$lat_dir \
-    --dir=$dir  || exit 1;
-fi
-
-if [ $stage -le 15 ]; then
-  # Note: it's not important to give mkgraph.sh the lang directory with the
-  # matched topology (since it gets the topology file from the model).
-  utils/mkgraph.sh \
-    --self-loop-scale 1.0 data/lang${lm_suffix}/ \
-    $tree_dir $tree_dir/graph${lm_suffix} || exit 1;
-fi
-
-if [ $stage -le 16 ]; then
-  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      steps/nnet3/decode.sh \
-          --acwt 1.0 --post-decode-acwt 10.0 \
-          --extra-left-context $chunk_left_context \
-          --extra-right-context $chunk_right_context \
-          --extra-left-context-initial 0 \
-          --extra-right-context-final 0 \
-          --frames-per-chunk $frames_per_chunk \
-          --nj 8 --cmd "$decode_cmd"  --num-threads 4 \
-          --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
-          $tree_dir/graph${lm_suffix} data/${data}_hires ${dir}/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-# Not testing the 'looped' decoding separately, because for
-# TDNN systems it would give exactly the same results as the
-# normal decoding.
-
-if $test_online_decoding && [ $stage -le 17 ]; then
-  # note: if the features change (e.g. you add pitch features), you will have to
-  # change the options of the following command line.
-  steps/online/nnet3/prepare_online_decoding.sh \
-    --mfcc-config conf/mfcc_hires.conf \
-    $lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
-
-  rm $dir/.error 2>/dev/null || true
-
-  for data in $test_sets; do
-    (
-      nspk=$(wc -l <data/${data}_hires/spk2utt)
-      # note: we just give it "data/${data}" as it only uses the wav.scp, the
-      # feature type does not matter.
-      steps/online/nnet3/decode.sh \
-        --acwt 1.0 --post-decode-acwt 10.0 \
-        --nj 8 --cmd "$decode_cmd" \
-        $tree_dir/graph${lm_suffix} data/${data} ${dir}_online/decode${lm_suffix}_${data} || exit 1
-    ) || touch $dir/.error &
-  done
-  wait
-  [ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
-fi
-
-
-exit 0;
diff --git a/egs/chime6/s5_track1/local/nnet3/multi_condition/run_ivector_common.sh b/egs/chime6/s5_track1/local/nnet3/multi_condition/run_ivector_common.sh
deleted file mode 100755
index 8f823f4306e..00000000000
--- a/egs/chime6/s5_track1/local/nnet3/multi_condition/run_ivector_common.sh
+++ /dev/null
@@ -1,194 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-# 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_clean=train_worn
-train_set_noisy=train_u400k
-combined_train_set=train_worn_u400k
-test_sets="dev_worn"
-nj=96
-include_clean=false
-
-noise_list=
-num_data_reps=2
-snrs="20:10:15:5:0"
-foreground_snrs="20:10:15:5:0"
-background_snrs="20:10:15:5:0"
-
-nnet3_affix=_train_worn_u400k_rvb
-
-. ./cmd.sh
-. ./path.sh
-. utils/parse_options.sh
-
-if [ $stage -le 0 ]; then
-  # Perturb the original data. We need this 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_clean} data/${train_set_clean}_sp
-
-  utils/data/perturb_data_dir_speed_3way.sh data/${train_set_noisy} data/${train_set_noisy}_sp
-
-  for datadir in ${train_set_clean}_sp ${train_set_noisy}_sp $test_sets; do
-    utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
-  done
-  
-  for datadir in ${train_set_clean}_sp ${train_set_noisy}_sp; do
-    utils/data/perturb_data_dir_volume.sh data/${datadir}_hires
-  done
-fi
- 
-
-if [ $stage -le 1 ]; then
-  for datadir in ${train_set_clean}_sp ${train_set_noisy}_sp; do
-    mfccdir=data/${datadir}/data
-    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
-      utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/mfcc/chime5-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage
-    fi
-
-    steps/make_mfcc.sh --nj 20 \
-      --cmd "$train_cmd" data/${datadir} || exit 1;
-    steps/compute_cmvn_stats.sh data/${datadir} || exit 1;
-    utils/fix_data_dir.sh data/${datadir} || exit 1;
-  done
-fi
-
-if [ $stage -le 2 ]; then
-  if [ ! -d RIRS_NOISES/ ]; then
-    # Download the package that includes the real RIRs, simulated RIRs, isotropic noises and point-source noises
-    wget --no-check-certificate http://www.openslr.org/resources/28/rirs_noises.zip
-    unzip rirs_noises.zip
-  fi
-
-  if [ -z "$noise_list" ]; then
-    noise_list=RIRS_NOISES/pointsource_noises/noise_list
-  fi
-
-  # This is the config for the system using simulated RIRs and point-source noises
-  rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/smallroom/rir_list")
-  rvb_opts+=(--rir-set-parameters "0.5, RIRS_NOISES/simulated_rirs/mediumroom/rir_list")
-  rvb_opts+=(--noise-set-parameters $noise_list)
-
-  python steps/data/reverberate_data_dir.py \
-    "${rvb_opts[@]}" \
-    --prefix "rev" \
-    --foreground-snrs $foreground_snrs \
-    --background-snrs $background_snrs \
-    --speech-rvb-probability 1 \
-    --pointsource-noise-addition-probability 1 \
-    --isotropic-noise-addition-probability 1 \
-    --num-replications $num_data_reps \
-    --max-noises-per-minute 1 \
-    --source-sampling-rate 16000 \
-    data/${train_set_clean}_sp_hires data/${train_set_clean}_sp_rvb_hires
-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"
-  for datadir in ${train_set_clean}_sp_rvb ${train_set_noisy}_sp ${test_sets}; do
-    mfccdir=data/${datadir}_hires/data
-    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
-      utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/mfcc/chime5-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage
-    fi
-  
-    steps/make_mfcc.sh --nj 20 --mfcc-config conf/mfcc_hires.conf \
-      --cmd "$train_cmd" data/${datadir}_hires || exit 1;
-    steps/compute_cmvn_stats.sh data/${datadir}_hires || exit 1;
-    utils/fix_data_dir.sh data/${datadir}_hires || exit 1;
-  done
-fi
-  
-temp_data_root=exp/nnet3${nnet3_affix}/diag_ubm
-
-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
-  optional_clean=
-  if $include_clean; then
-    optional_clean=data/${train_set_clean}_sp_hires
-  fi
-  utils/combine_data.sh data/${combined_train_set}_sp_rvb_hires \
-    ${optional_clean} \
-    data/${train_set_clean}_sp_rvb_hires data/${train_set_noisy}_sp_hires
-
-  num_utts_total=$(wc -l < data/${combined_train_set}_sp_rvb_hires/utt2spk)
-  num_utts=$[$num_utts_total/4]
-  utils/data/subset_data_dir.sh data/${combined_train_set}_sp_rvb_hires \
-     $num_utts ${temp_data_root}/${combined_train_set}_sp_rvb_hires_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 \
-      ${temp_data_root}/${combined_train_set}_sp_rvb_hires_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 \
-    --num-threads 8 \
-    data/${combined_train_set}_sp_rvb_hires 512 \
-    exp/nnet3${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 20 \
-    data/${combined_train_set}_sp_rvb_hires exp/nnet3${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.
-
-  for datadir in ${combined_train_set}_sp_rvb; do
-    ivectordir=exp/nnet3${nnet3_affix}/ivectors_${datadir}_hires
-    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $ivectordir/storage ]; then
-      utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/ivectors/chime5-$(date +'%m_%d_%H_%M')/s5/$ivectordir/storage $ivectordir/storage
-    fi
-
-    # having a larger number of speakers is helpful for generalization, and to
-    # handle per-utterance decoding well (iVector starts at zero).
-    temp_data_root=${ivectordir}
-    utils/data/modify_speaker_info.sh --utts-per-spk-max 2 \
-      data/${datadir}_hires ${temp_data_root}/${datadir}_hires_max2
-
-    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj ${nj} \
-      ${temp_data_root}/${datadir}_hires_max2 \
-      exp/nnet3${nnet3_affix}/extractor $ivectordir
-  done
-
-  # 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 20 \
-      data/${data}_hires exp/nnet3${nnet3_affix}/extractor \
-      exp/nnet3${nnet3_affix}/ivectors_${data}_hires
-  done
-fi
-
-exit 0
-
diff --git a/egs/chime6/s5_track1/local/worn_audio_list b/egs/chime6/s5_track1/local/worn_audio_list
deleted file mode 100644
index fc7a44ad77d..00000000000
--- a/egs/chime6/s5_track1/local/worn_audio_list
+++ /dev/null
@@ -1,64 +0,0 @@
-/export/corpora4/CHiME5/audio/train/S03_P09.wav
-/export/corpora4/CHiME5/audio/train/S03_P10.wav
-/export/corpora4/CHiME5/audio/train/S03_P11.wav
-/export/corpora4/CHiME5/audio/train/S03_P12.wav
-/export/corpora4/CHiME5/audio/train/S04_P09.wav
-/export/corpora4/CHiME5/audio/train/S04_P10.wav
-/export/corpora4/CHiME5/audio/train/S04_P11.wav
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diff --git a/egs/chime6/s5_track1/run.sh b/egs/chime6/s5_track1/run.sh
index b25e7a2101f..042bf5a1db0 100755
--- a/egs/chime6/s5_track1/run.sh
+++ b/egs/chime6/s5_track1/run.sh
@@ -41,6 +41,11 @@ test_sets="dev_${enhancement}" #"dev_worn dev_addition_dereverb_ref"
 # This script also needs the phonetisaurus g2p, srilm, beamformit
 ./local/check_tools.sh || exit 1
 
+
+###########################################################################
+# We prepare dict and lang in stages 1 to 3.
+###########################################################################
+
 if [ $stage -le 1 ]; then
   echo "$0:  prepare data..."
   # skip u03 as they are missing
@@ -87,6 +92,11 @@ fi
 
 enhanced_dir=$(utils/make_absolute.sh $enhanced_dir) || exit 1
 
+#########################################################################################
+# In stage 4, we perform GSS based enhacement for the dev and test sets. multiarray = false 
+#can take around 15 hrs for dev and eval data.
+#########################################################################################
+
 if [ $stage -le 4 ]; then
   echo "$0:  enhance data..."
   # Guided Source Separation (GSS) from Paderbon Univerisity
@@ -127,6 +137,12 @@ if [ $stage -le 4 ]; then
   done
 fi
 
+#########################################################################################
+# In stages 5 to 7, we augment and fix train data for our training purpose. point source
+# noises are extracted from chime corpus. Here we use 400k utterances from array microphones,
+# its augmentation and all the worn set utterances in train.
+#########################################################################################
+
 if [ $stage -le 5 ]; then
   # remove possibly bad sessions (P11_S03, P52_S19, P53_S24, P54_S24)
   # see http://spandh.dcs.shef.ac.uk/chime_challenge/data.html for more details
@@ -218,6 +234,10 @@ if [ $stage -le 8 ]; then
   done
 fi
 
+##################################################################################
+# Now make MFCC features. We use 40-dim "hires" MFCCs for all our systems.
+##################################################################################
+
 if [ $stage -le 9 ]; then
   # Now make MFCC features.
   # mfccdir should be some place with a largish disk where you
@@ -238,6 +258,11 @@ if [ $stage -le 10 ]; then
   utils/subset_data_dir.sh data/${train_set}_100kshort 30000 data/${train_set}_30kshort
 fi
 
+###################################################################################
+# Stages 11 to 15 train monophone and triphone models. They will be used for
+# generating lattices for training the chain model
+###################################################################################
+
 if [ $stage -le 11 ]; then
   # Starting basic training on MFCC features
   steps/train_mono.sh --nj $nj --cmd "$train_cmd" \
@@ -293,6 +318,10 @@ if [ $stage -le 17 ]; then
     data/${train_set} data/lang exp/tri3 exp/tri3_cleaned data/${train_set}_cleaned
 fi
 
+##########################################################################
+# CHAIN MODEL TRAINING
+##########################################################################
+
 if [ $stage -le 18 ]; then
   # chain TDNN
   local/chain/tuning/run_tdnn_1b.sh --nj ${nj} \
@@ -302,6 +331,10 @@ if [ $stage -le 18 ]; then
     --gmm tri3_cleaned --nnet3-affix _${train_set}_cleaned_rvb
 fi
 
+##########################################################################
+# DECODING: we perform 2 stage decoding. 
+##########################################################################
+
 if [ $stage -le 19 ]; then
   # 2-stage decoding
   for test_set in $test_sets; do
@@ -315,6 +348,10 @@ if [ $stage -le 19 ]; then
   done
 fi
 
+##########################################################################
+# Scoring: here we obtian wer per session per location and overall WER
+##########################################################################
+
 if [ $stage -le 20 ]; then
   # final scoring to get the official challenge result
   # please specify both dev and eval set directories so that the search parameters