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tools/ASR_2ch_track/RESULTS

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+# CHiME-4 2ch track results
+# The result is based on Hori et al, "The MERL/SRI system for the 3rd CHiME challenge using beamforming,
+# robust feature extraction, and advanced speech recognition," in Proc. ASRU'15,
+# and please refer the paper if you think the baseline useful.
+# Note that the following result is different from that in the paper since we don't include
+# SRI's robust features and system combination
+
+GMM noisy multi-condition with beamformit
+exp/tri3b_tr05_multi_noisy/best_wer_beamformit_2mics.result
+-------------------
+best overall dt05 WER 17.69% (language model weight = 11)
+-------------------
+dt05_simu WER: 19.15% (Average), 16.14% (BUS), 23.55% (CAFE), 15.49% (PEDESTRIAN), 21.42% (STREET)
+-------------------
+dt05_real WER: 16.22% (Average), 20.12% (BUS), 16.25% (CAFE), 12.35% (PEDESTRIAN), 16.18% (STREET)
+-------------------
+
+DNN sMBR
+exp/tri4a_dnn_tr05_multi_noisy_smbr_i1lats/best_wer_beamformit_2mics.result
+-------------------
+best overall dt05 WER 11.63% (language model weight = 11)
+ (Number of iterations = 4)
+-------------------
+dt05_simu WER: 12.36% (Average), 10.66% (BUS), 15.55% (CAFE), 9.87% (PEDESTRIAN), 13.36% (STREET)
+-------------------
+dt05_real WER: 10.90% (Average), 13.62% (BUS), 10.63% (CAFE), 7.69% (PEDESTRIAN), 11.65% (STREET)
+-------------------
+
+5-gram rescoring
+exp/tri4a_dnn_tr05_multi_noisy_smbr_lmrescore/best_wer_beamformit_2mics_5gkn_5k.result
+-------------------
+best overall dt05 WER 10.17% (language model weight = 11)
+-------------------
+dt05_simu WER: 10.72% (Average), 9.37% (BUS), 13.70% (CAFE), 8.07% (PEDESTRIAN), 11.73% (STREET)
+-------------------
+dt05_real WER: 9.63% (Average), 11.93% (BUS), 9.75% (CAFE), 6.46% (PEDESTRIAN), 10.37% (STREET)
+-------------------
+
+RNNLM
+exp/tri4a_dnn_tr05_multi_noisy_smbr_lmrescore/best_wer_beamformit_2mics_rnnlm_5k_h300_w0.5_n100.result
+-------------------
+best overall dt05 WER 8.86% (language model weight = 12)
+-------------------
+dt05_simu WER: 9.50% (Average), 8.19% (BUS), 12.15% (CAFE), 7.12% (PEDESTRIAN), 10.55% (STREET)
+-------------------
+dt05_real WER: 8.23% (Average), 10.90% (BUS), 7.96% (CAFE), 5.22% (PEDESTRIAN), 8.82% (STREET)
+-------------------
+
+

tools/ASR_2ch_track/cmd.sh

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+# 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="queue.pl --mem 2G"
+#export decode_cmd="queue.pl --mem 4G"
+#export mkgraph_cmd="queue.pl --mem 8G"
+
+# run it locally...
+export train_cmd=run.pl
+export decode_cmd=run.pl
+export cuda_cmd=run.pl
+export mkgraph_cmd=run.pl

tools/ASR_2ch_track/conf/chime4.cfg

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+#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
+

tools/ASR_2ch_track/conf/decode_dnn.config

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+beam=18.0 # beam for decoding.  Was 13.0 in the scripts.
+lattice_beam=10.0 # this has most effect on size of the lattices.

tools/ASR_2ch_track/conf/fbank.conf

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+# No non-default options for now.
+--window-type=hamming # disable Dans window, use the standard
+--use-energy=false    # only fbank outputs
+--sample-frequency=16000 # Cantonese is sampled at 8kHz
+
+--low-freq=64         # typical setup from Frantisek Grezl
+--high-freq=8000
+--dither=1
+
+--num-mel-bins=40     # 8kHz so we use 15 bins
+--htk-compat=true     # try to make it compatible with HTK

tools/ASR_2ch_track/conf/mfcc.conf

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+--use-energy=false   # only non-default option.

tools/ASR_2ch_track/local/chime4_calc_wers.sh

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+#!/bin/bash
+
+#  Copyright  2015  Mitsubishi Electric Research Laboratories (Author: Shinji Watanabe)
+#  Apache 2.0.
+
+set -e
+
+# Config:
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# -ne 3 ]; then
+  printf "\nUSAGE: %s <training experiment directory> <enhancement method> <graph_dir>\n\n" `basename $0`
+  printf "%s exp/tri3b_tr05_sr_noisy noisy exp/tri4a_dnn_tr05_sr_noisy/graph_tgpr_5k\n\n" `basename $0`
+  exit 1;
+fi
+
+echo "$0 $@"  # Print the command line for logging
+
+. path.sh
+
+dir=$1
+enhan=$2
+graph_dir=$3
+
+echo "compute dt05 WER for each location"
+echo ""
+mkdir -p $dir/log
+for a in `find $dir/decode_tgpr_5k_dt05_real_$enhan/ | grep "\/wer_" | awk -F'[/]' '{print $NF}' | sort`; do
+    echo -n "$a "
+    if [ -e $dir/decode_tgpr_5k_dt05_simu_$enhan ]; then
+	cat $dir/decode_tgpr_5k_dt05_{real,simu}_$enhan/$a | grep WER | awk '{err+=$4} {wrd+=$6} END{printf("%.2f\n",err/wrd*100)}'
+    else
+	cat $dir/decode_tgpr_5k_dt05_real_$enhan/$a | grep WER | awk '{err+=$4} {wrd+=$6} END{printf("%.2f\n",err/wrd*100)}'
+    fi
+done | sort -n -k 2 | head -n 1 > $dir/log/best_wer_$enhan
+
+lmw=`cut -f 1 -d" " $dir/log/best_wer_$enhan | cut -f 2 -d"_"`
+echo "-------------------"
+printf "best overall dt05 WER %s" `cut -f 2 -d" " $dir/log/best_wer_$enhan`
+echo -n "%"
+printf " (language model weight = %s)\n" $lmw
+echo "-------------------"
+if $eval_flag; then
+  tasks="dt05 et05"
+else
+  tasks="dt05"
+fi
+for e_d in $tasks; do
+  for task in simu real; do
+    rdir=$dir/decode_tgpr_5k_${e_d}_${task}_$enhan
+    if [ -e $rdir ]; then
+      for a in _BUS _CAF _PED _STR; do
+	grep $a $rdir/scoring/test_filt.txt \
+	  > $rdir/scoring/test_filt_$a.txt
+	cat $rdir/scoring/$lmw.tra \
+	  | utils/int2sym.pl -f 2- $graph_dir/words.txt \
+	  | sed s:\<UNK\>::g \
+	  | compute-wer --text --mode=present ark:$rdir/scoring/test_filt_$a.txt ark,p:- \
+	  1> $rdir/${a}_wer_$lmw 2> /dev/null
+      done
+    echo -n "${e_d}_${task} WER: `grep WER $rdir/wer_$lmw | cut -f 2 -d" "`% (Average), "
+    echo -n "`grep WER $rdir/_BUS_wer_$lmw | cut -f 2 -d" "`% (BUS), "
+    echo -n "`grep WER $rdir/_CAF_wer_$lmw | cut -f 2 -d" "`% (CAFE), "
+    echo -n "`grep WER $rdir/_PED_wer_$lmw | cut -f 2 -d" "`% (PEDESTRIAN), "
+    echo -n "`grep WER $rdir/_STR_wer_$lmw | cut -f 2 -d" "`% (STREET)"
+    echo ""
+    echo "-------------------"
+    fi
+  done
+done
+echo ""
+
+for e_d in $tasks; do
+  echo "-----------------------------"
+  echo "1-best transcription for $e_d"
+  echo "-----------------------------"
+  for task in simu real; do
+    rdir=$dir/decode_tgpr_5k_${e_d}_${task}_$enhan
+    cat $rdir/scoring/$lmw.tra \
+      | utils/int2sym.pl -f 2- $graph_dir/words.txt \
+      | sed s:\<UNK\>::g
+  done
+done

tools/ASR_2ch_track/local/chime4_calc_wers_smbr.sh

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+#!/bin/bash
+
+#  Copyright  2015  Mitsubishi Electric Research Laboratories (Author: Shinji Watanabe)
+#  Apache 2.0.
+
+set -e
+
+# Config:
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# -ne 3 ]; then
+  printf "\nUSAGE: %s <training experiment directory> <enhancement method> <graph_dir>\n\n" `basename $0`
+  printf "%s exp/tri3b_tr05_sr_noisy noisy exp/tri4a_dnn_tr05_sr_noisy/graph_tgpr_5k\n\n" `basename $0`
+  exit 1;
+fi
+
+echo "$0 $@"  # Print the command line for logging
+
+. path.sh
+
+dir=$1
+enhan=$2
+graph_dir=$3
+
+echo "compute WER for each location"
+echo ""
+mkdir -p $dir/log
+# collect scores
+for x in `find $dir/ -type d -name "*_it*" | awk -F "_it" '{print $NF}' | sort | uniq`; do
+    for y in `find $dir/*_${enhan}_it*/ | grep "\/wer_" | awk -F'[/]' '{print $NF}' | sort | uniq`; do
+	echo -n "${x}_$y "
+	cat $dir/decode_tgpr_5k_dt05_{real,simu}_${enhan}_it$x/$y | grep WER | awk '{err+=$4} {wrd+=$6} END{printf("%.2f\n",err/wrd*100)}'
+    done
+done | sort -n -k 2 | head -n 1 > $dir/log/best_wer_$enhan
+
+lmw=`cut -f 1 -d" " $dir/log/best_wer_$enhan | awk -F'[_]' '{print $NF}'`
+it=`cut -f 1 -d" " $dir/log/best_wer_$enhan | awk -F'[_]' '{print $1}'`
+echo "-------------------"
+printf "best overall dt05 WER %s" `cut -f 2 -d" " $dir/log/best_wer_$enhan`
+echo -n "%"
+printf " (language model weight = %s)\n" $lmw
+printf " (Number of iterations = %s)\n" $it
+echo "-------------------"
+if $eval_flag; then
+  tasks="dt05 et05"
+else
+  tasks="dt05"
+fi
+for e_d in $tasks; do
+  for task in simu real; do
+    rdir=$dir/decode_tgpr_5k_${e_d}_${task}_${enhan}_it$it
+    for a in _BUS _CAF _PED _STR; do
+      grep $a $rdir/scoring/test_filt.txt \
+	> $rdir/scoring/test_filt_$a.txt
+      cat $rdir/scoring/$lmw.tra \
+	| utils/int2sym.pl -f 2- $graph_dir/words.txt \
+	| sed s:\<UNK\>::g \
+	| compute-wer --text --mode=present ark:$rdir/scoring/test_filt_$a.txt ark,p:- \
+	1> $rdir/${a}_wer_$lmw 2> /dev/null
+    done
+    echo -n "${e_d}_${task} WER: `grep WER $rdir/wer_$lmw | cut -f 2 -d" "`% (Average), "
+    echo -n "`grep WER $rdir/_BUS_wer_$lmw | cut -f 2 -d" "`% (BUS), "
+    echo -n "`grep WER $rdir/_CAF_wer_$lmw | cut -f 2 -d" "`% (CAFE), "
+    echo -n "`grep WER $rdir/_PED_wer_$lmw | cut -f 2 -d" "`% (PEDESTRIAN), "
+    echo -n "`grep WER $rdir/_STR_wer_$lmw | cut -f 2 -d" "`% (STREET)"
+    echo ""
+    echo "-------------------"
+  done
+done
+
+for e_d in $tasks; do
+  echo "-----------------------------"
+  echo "1-best transcription for $e_d"
+  echo "-----------------------------"
+  for task in simu real; do
+    rdir=$dir/decode_tgpr_5k_${e_d}_${task}_${enhan}_it$it
+    cat $rdir/scoring/$lmw.tra \
+      | utils/int2sym.pl -f 2- $graph_dir/words.txt \
+      | sed s:\<UNK\>::g
+  done
+done

tools/ASR_2ch_track/local/chime4_train_lms.sh

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+#!/bin/bash
+
+# Modified from the script for CHiME3 baseline
+# Copyright 2015, Mitsubishi Electric Research Laboratories, MERL (Author: Takaaki Hori)
+
+# Config:
+order=5 # n-gram order
+
+. utils/parse_options.sh || exit 1;
+
+. ./path.sh
+
+if [ $# -ne 1 ]; then
+  printf "\nUSAGE: %s <Chime4 root directory>\n\n" `basename $0`
+  echo "Please specifies a Chime4 root directory"
+  echo "If you use kaldi scripts distributed in the Chime4 data,"
+  echo "It would be `pwd`/../.."
+  exit 1;
+fi
+
+# check data directories
+chime4_data=$1
+wsj0_data=$chime4_data/data/WSJ0 # directory of WSJ0 in Chime4. You can also specify your WSJ0 corpus directory
+if [ ! -d $chime4_data ]; then
+  echo "$chime4_data does not exist. Please specify chime4 data root correctly" && exit 1
+fi
+if [ ! -d $wsj0_data ]; then
+  echo "$wsj0_data does not exist. Please specify WSJ0 corpus directory" && exit 1
+fi
+lm_train=$wsj0_data/wsj0/doc/lng_modl/lm_train/np_data
+
+# check whether run_init is executed
+if [ ! -d data/lang ]; then
+  echo "error, execute local/run_init.sh, first"
+  exit 1;
+fi
+
+# lm directories
+dir=data/local/local_lm
+srcdir=data/local/nist_lm
+mkdir -p $dir
+
+# check srilm ngram
+! which ngram-count \
+  && echo "SRILM tools not installed, which are required for LM training" && exit 1;
+
+# extract 5k vocabulary from a baseline language model
+srclm=$srcdir/lm_tgpr_5k.arpa.gz
+if [ -f $srclm ]; then
+  echo "Getting vocabulary from a baseline language model";
+  gunzip -c $srclm | awk 'BEGIN{unig=0}{
+    if(unig==0){
+      if($1=="\\1-grams:"){unig=1}}
+    else {
+      if ($1 != "") {
+        if ($1=="\\2-grams:" || $1=="\\end\\") {exit}
+        else {print $2}}
+    }}' > $dir/vocab_5k.txt
+else
+  echo "Language model $srclm does not exist" && exit 1;
+fi
+
+# collect training data from WSJ0
+touch $dir/train.gz
+if [ `du -m $dir/train.gz | cut -f 1` -eq 63 ]; then
+  echo "Not getting training data again [already exists]";
+else
+  echo "Collecting training data from $lm_train";
+  gunzip -c $lm_train/{87,88,89}/*.z \
+   | awk -v voc=$dir/vocab_5k.txt '
+   BEGIN{ while((getline<voc)>0) { invoc[$1]=1; }}
+   /^</{next}{
+     for (x=1;x<=NF;x++) {
+       w=toupper($x);
+       if (invoc[w]) { printf("%s ",w); } else { printf("<UNK> "); }
+     }
+     printf("\n");
+   }' | gzip -c > $dir/train.gz
+fi
+
+# get validation data from Chime4 dev set
+touch $dir/valid.gz
+if [ `du -k $dir/valid.gz | cut -f 1` -eq 68 ]; then
+  echo "Not getting validation data again [already exists]";
+else
+  echo "Collecting validation data from $chime4_data/data/transcriptions";
+  cut -d" " -f2- $chime4_data/data/transcriptions/dt05_real.trn_all \
+                 $chime4_data/data/transcriptions/dt05_simu.trn_all \
+      |gzip -c > $dir/valid.gz
+fi
+
+# train a large n-gram language model
+lm_suffix=${order}gkn_5k
+if [ -f $dir/lm_${lm_suffix}.arpa.gz ]; then
+  echo "A $order-gram language model aready exists and is not constructed again"
+  echo "To reconstruct, remove $dir/$dir/lm_${lm_suffix}.arpa.gz first"
+else
+  echo "Training a $order-gram language model"
+  ngram-count -text $dir/train.gz -order $order \
+              -vocab $dir/vocab_5k.txt -unk -map-unk "<UNK>" \
+              -gt2min 1 -gt3min 1 -gt4min 2 -gt5min 2 \
+              -interpolate -kndiscount \
+              -lm $dir/lm_${lm_suffix}.arpa.gz
+fi
+echo "Checking validation perplexity of $order-gram language model"
+ngram -order $order -ppl $dir/valid.gz -lm $dir/lm_${lm_suffix}.arpa.gz
+# e.g. 5-gram perplexity:
+# file data/local/local_lm/valid.txt: 3280 sentences, 54239 words, 3 OOVs
+# 0 zeroprobs, logprob= -96775.5 ppl= 48.1486 ppl1= 60.8611
+
+# Next, create the corresponding FST and lang_test_* directory.
+echo "Preparing language models for test"
+tmpdir=data/local/lm_tmp
+lexicon=data/local/lang_tmp/lexiconp.txt
+mkdir -p $tmpdir
+
+test=data/lang_test_${lm_suffix}
+mkdir -p $test
+for f in phones.txt words.txt phones.txt L.fst L_disambig.fst \
+   phones; do
+  cp -r data/lang/$f $test
+done
+gunzip -c $dir/lm_${lm_suffix}.arpa.gz | \
+ utils/find_arpa_oovs.pl $test/words.txt  > $tmpdir/oovs_${lm_suffix}.txt
+
+# grep -v '<s> <s>' because the LM seems to have some strange and useless
+# stuff in it with multiple <s>'s in the history.  Encountered some other similar
+# things in a LM from Geoff.  Removing all "illegal" combinations of <s> and </s>,
+# which are supposed to occur only at being/end of utt.  These can cause
+# determinization failures of CLG [ends up being epsilon cycles].
+gunzip -c $dir/lm_${lm_suffix}.arpa.gz | \
+  grep -v '<s> <s>' | \
+  grep -v '</s> <s>' | \
+  grep -v '</s> </s>' | \
+  arpa2fst - | fstprint | \
+  utils/remove_oovs.pl $tmpdir/oovs_${lm_suffix}.txt | \
+  utils/eps2disambig.pl | utils/s2eps.pl | fstcompile --isymbols=$test/words.txt \
+    --osymbols=$test/words.txt  --keep_isymbols=false --keep_osymbols=false | \
+   fstrmepsilon | fstarcsort --sort_type=ilabel > $test/G.fst
+fstisstochastic $test/G.fst
+# The output is like:
+# 9.14233e-05 -0.259833
+# we do expect the first of these 2 numbers to be close to zero (the second is
+# nonzero because the backoff weights make the states sum to >1).
+# Because of the <s> fiasco for these particular LMs, the first number is not
+# as close to zero as it could be.
+
+# Everything below is only for diagnostic.
+# Checking that G has no cycles with empty words on them (e.g. <s>, </s>);
+# this might cause determinization failure of CLG.
+# #0 is treated as an empty word.
+mkdir -p $tmpdir/g
+awk '{if(NF==1){ printf("0 0 %s %s\n", $1,$1); }} END{print "0 0 #0 #0"; print "0";}' \
+    < "$lexicon"  >$tmpdir/g/select_empty.fst.txt
+fstcompile --isymbols=$test/words.txt --osymbols=$test/words.txt $tmpdir/g/select_empty.fst.txt | \
+ fstarcsort --sort_type=olabel | fstcompose - $test/G.fst > $tmpdir/g/empty_words.fst
+fstinfo $tmpdir/g/empty_words.fst | grep cyclic | grep -w 'y' &&
+  echo "Language model has cycles with empty words" && exit 1
+rm -r $tmpdir/g
+
+echo "Succeeded in preparing a large ${order}-gram LM"
+rm -r $tmpdir
+

tools/ASR_2ch_track/local/chime4_train_rnnlms.sh

@@ -0,0 +1,111 @@
+#!/bin/bash
+
+# Copyright 2015, Mitsubishi Electric Research Laboratories, MERL (Author: Takaaki Hori)
+
+# Config:
+hidden=300 # Num-hidden units
+class=200 # Num-classes
+rnnlm_ver=rnnlm-0.3e # version of RNNLM to use
+threads=1 # for RNNLM-HS
+bptt=4 # length of BPTT unfolding in RNNLM
+bptt_block=10 # length of BPTT unfolding in RNNLM
+
+. utils/parse_options.sh || exit 1;
+
+. ./path.sh
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+if [ $# -ne 1 ]; then
+  printf "\nUSAGE: %s <Chime4 root directory>\n\n" `basename $0`
+  echo "Please specifies a Chime4 root directory"
+  echo "If you use kaldi scripts distributed in the Chime4 data,"
+  echo "It would be `pwd`/../.."
+  exit 1;
+fi
+
+# check data directories
+chime4_data=$1
+wsj0_data=$chime4_data/data/WSJ0 # directory of WSJ0 in Chime4. You can also specify your WSJ0 corpus directory
+if [ ! -d $chime4_data ]; then
+  echo "$chime4_data does not exist. Please specify chime4 data root correctly" && exit 1
+fi
+if [ ! -d $wsj0_data ]; then
+  echo "$wsj0_data does not exist. Please specify WSJ0 corpus directory" && exit 1
+fi
+lm_train=$wsj0_data/wsj0/doc/lng_modl/lm_train/np_data
+
+# lm directories
+dir=data/local/local_lm
+srcdir=data/local/nist_lm
+mkdir -p $dir
+
+# extract 5k vocabulary from a baseline language model
+srclm=$srcdir/lm_tgpr_5k.arpa.gz
+if [ -f $srclm ]; then
+  echo "Getting vocabulary from a baseline language model";
+  gunzip -c $srclm | awk 'BEGIN{unig=0}{
+    if(unig==0){
+      if($1=="\\1-grams:"){unig=1}}
+    else {
+      if ($1 != "") {
+        if ($1=="\\2-grams:" || $1=="\\end\\") {exit}
+        else {print $2}}
+    }}' | sed "s/<UNK>/<RNN_UNK>/" > $dir/vocab_5k.rnn
+else
+  echo "Language model $srclm does not exist" && exit 1;
+fi
+
+# collect training data from WSJ0
+touch $dir/train.rnn
+if [ `du -m $dir/train.rnn | cut -f 1` -eq 223 ]; then
+  echo "Not getting training data again [already exists]";
+else
+  echo "Collecting training data from $lm_train";
+  gunzip -c $lm_train/{87,88,89}/*.z \
+   | awk -v voc=$dir/vocab_5k.rnn '
+   BEGIN{ while((getline<voc)>0) { invoc[$1]=1; }}
+   /^</{next}{
+     for (x=1;x<=NF;x++) {
+       w=toupper($x);
+       if (invoc[w]) { printf("%s ",w); } else { printf("<RNN_UNK> "); }
+     }
+     printf("\n");
+   }' > $dir/train.rnn
+fi
+
+# get validation data from Chime4 dev set
+touch $dir/valid.rnn
+if [ `cat $dir/valid.rnn | wc -w` -eq 54239 ]; then
+  echo "Not getting validation data again [already exists]";
+else
+  echo "Collecting validation data from $chime4_data/data/transcriptions";
+  cut -d" " -f2- $chime4_data/data/transcriptions/dt05_real.trn_all \
+                 $chime4_data/data/transcriptions/dt05_simu.trn_all \
+      > $dir/valid.rnn
+fi
+
+# RNN language model traing
+$KALDI_ROOT/tools/extras/check_for_rnnlm.sh "$rnnlm_ver" || exit 1
+
+# train a RNN language model
+rnnmodel=$dir/rnnlm_5k_h${hidden}_bptt${bptt}
+if [ -f $rnnmodel ]; then
+  echo "A RNN language model aready exists and is not constructed again"
+  echo "To reconstruct, remove $rnnmodel first"
+else
+  echo "Training a RNN language model with $rnnlm_ver"
+  echo "(runtime log is written to $dir/rnnlm.log)"
+  $train_cmd $dir/rnnlm.log \
+   $KALDI_ROOT/tools/$rnnlm_ver/rnnlm -train $dir/train.rnn -valid $dir/valid.rnn \
+        -rnnlm $rnnmodel -hidden $hidden -class $class \
+        -rand-seed 1 -independent -debug 1 -bptt $bptt -bptt-block $bptt_block || exit 1;
+fi
+
+# store in a RNNLM directory with necessary files
+rnndir=data/lang_test_rnnlm_5k_h${hidden}
+mkdir -p $rnndir
+cp $rnnmodel $rnndir/rnnlm
+grep -v -e "<s>" -e "</s>" $dir/vocab_5k.rnn > $rnndir/wordlist.rnn
+touch $rnndir/unk.probs # make an empty file because we don't know unk-word probs.
+

tools/ASR_2ch_track/local/clean_chime4_format_data.sh

@@ -0,0 +1,85 @@
+#!/bin/bash
+
+# Copyright 2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# This script takes data prepared in a corpus-dependent way
+# in data/local/, and converts it into the "canonical" form,
+# in various subdirectories of data/, e.g. data/lang, data/lang_test_ug,
+# data/train_si84, etc.
+
+# Modified from the script for Chime2 baseline
+# Shinji Watanabe 02/13/2015
+
+. ./path.sh || exit 1;
+
+echo "Preparing train and test data"
+srcdir=data/local/data
+lmdir=data/local/nist_lm
+tmpdir=data/local/lm_tmp
+lexicon=data/local/lang_tmp/lexiconp.txt
+mkdir -p $tmpdir
+
+for x in et05_orig_clean dt05_orig_clean tr05_orig_clean; do
+  mkdir -p data/$x
+  cp $srcdir/${x}_wav.scp data/$x/wav.scp || exit 1;
+  cp $srcdir/$x.txt data/$x/text || exit 1;
+  cp $srcdir/$x.spk2utt data/$x/spk2utt || exit 1;
+  cp $srcdir/$x.utt2spk data/$x/utt2spk || exit 1;
+  utils/filter_scp.pl data/$x/spk2utt $srcdir/spk2gender > data/$x/spk2gender || exit 1;
+done
+
+
+# Next, for each type of language model, create the corresponding FST
+# and the corresponding lang_test_* directory.
+
+echo Preparing language models for test
+
+for lm_suffix in tgpr_5k; do
+  test=data/lang_test_${lm_suffix}
+  mkdir -p $test
+  for f in phones.txt words.txt phones.txt L.fst L_disambig.fst \
+     phones; do
+    cp -r data/lang/$f $test
+  done
+  gunzip -c $lmdir/lm_${lm_suffix}.arpa.gz | \
+   utils/find_arpa_oovs.pl $test/words.txt  > $tmpdir/oovs_${lm_suffix}.txt
+
+  # grep -v '<s> <s>' because the LM seems to have some strange and useless
+  # stuff in it with multiple <s>'s in the history.  Encountered some other similar
+  # things in a LM from Geoff.  Removing all "illegal" combinations of <s> and </s>,
+  # which are supposed to occur only at being/end of utt.  These can cause
+  # determinization failures of CLG [ends up being epsilon cycles].
+  gunzip -c $lmdir/lm_${lm_suffix}.arpa.gz | \
+    grep -v '<s> <s>' | \
+    grep -v '</s> <s>' | \
+    grep -v '</s> </s>' | \
+    arpa2fst - | fstprint | \
+    utils/remove_oovs.pl $tmpdir/oovs_${lm_suffix}.txt | \
+    utils/eps2disambig.pl | utils/s2eps.pl | fstcompile --isymbols=$test/words.txt \
+      --osymbols=$test/words.txt  --keep_isymbols=false --keep_osymbols=false | \
+     fstrmepsilon | fstarcsort --sort_type=ilabel > $test/G.fst
+  fstisstochastic $test/G.fst
+ # The output is like:
+ # 9.14233e-05 -0.259833
+ # we do expect the first of these 2 numbers to be close to zero (the second is
+ # nonzero because the backoff weights make the states sum to >1).
+ # Because of the <s> fiasco for these particular LMs, the first number is not
+ # as close to zero as it could be.
+
+  # Everything below is only for diagnostic.
+  # Checking that G has no cycles with empty words on them (e.g. <s>, </s>);
+  # this might cause determinization failure of CLG.
+  # #0 is treated as an empty word.
+  mkdir -p $tmpdir/g
+  awk '{if(NF==1){ printf("0 0 %s %s\n", $1,$1); }} END{print "0 0 #0 #0"; print "0";}' \
+    < "$lexicon"  >$tmpdir/g/select_empty.fst.txt
+  fstcompile --isymbols=$test/words.txt --osymbols=$test/words.txt $tmpdir/g/select_empty.fst.txt | \
+   fstarcsort --sort_type=olabel | fstcompose - $test/G.fst > $tmpdir/g/empty_words.fst
+  fstinfo $tmpdir/g/empty_words.fst | grep cyclic | grep -w 'y' &&
+    echo "Language model has cycles with empty words" && exit 1
+  rm -r $tmpdir/g
+done
+
+echo "Succeeded in formatting data."
+rm -r $tmpdir

tools/ASR_2ch_track/local/clean_wsj0_data_prep.sh

@@ -0,0 +1,152 @@
+#!/bin/bash
+set -e
+
+# Copyright 2009-2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0.
+
+# This is modified from the script in standard Kaldi recipe to account
+# for the way the WSJ data is structured on the Edinburgh systems.
+# - Arnab Ghoshal, 29/05/12
+
+# Modified from the script for CHiME2 baseline
+# Shinji Watanabe 02/13/2015
+
+if [ $# -ne 1 ]; then
+  printf "\nUSAGE: %s <original WSJ0 corpus-directory>\n\n" `basename $0`
+  echo "The argument should be a the top-level WSJ corpus directory."
+  echo "It is assumed that there will be a 'wsj0' and a 'wsj1' subdirectory"
+  echo "within the top-level corpus directory."
+  exit 1;
+fi
+
+wsj0=$1
+
+dir=`pwd`/data/local/data
+lmdir=`pwd`/data/local/nist_lm
+mkdir -p $dir $lmdir
+local=`pwd`/local
+utils=`pwd`/utils
+
+. ./path.sh # Needed for KALDI_ROOT
+sph2pipe=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe
+if [ ! -x $sph2pipe ]; then
+  echo "Could not find (or execute) the sph2pipe program at $sph2pipe";
+  exit 1;
+fi
+
+if [ -z $IRSTLM ] ; then
+  export IRSTLM=$KALDI_ROOT/tools/irstlm/
+fi
+export PATH=${PATH}:$IRSTLM/bin
+if ! command -v prune-lm >/dev/null 2>&1 ; then
+  echo "$0: Error: the IRSTLM is not available or compiled" >&2
+  echo "$0: Error: We used to install it by default, but." >&2
+  echo "$0: Error: this is no longer the case." >&2
+  echo "$0: Error: To install it, go to $KALDI_ROOT/tools" >&2
+  echo "$0: Error: and run extras/install_irstlm.sh" >&2
+  exit 1
+fi
+
+cd $dir
+
+# This version for SI-84
+cat $wsj0/wsj0/doc/indices/train/tr_s_wv1.ndx \
+  | $local/cstr_ndx2flist.pl $wsj0 | sort -u > tr05_orig_clean.flist
+
+# Now for the test sets.
+# $wsj0/wsj1/doc/indices/readme.doc
+# describes all the different test sets.
+# Note: each test-set seems to come in multiple versions depending
+# on different vocabulary sizes, verbalized vs. non-verbalized
+# pronunciations, etc.  We use the largest vocab and non-verbalized
+# pronunciations.
+# The most normal one seems to be the "baseline 60k test set", which
+# is h1_p0.
+
+# Nov'92 (330 utts, 5k vocab)
+cat $wsj0/wsj0/doc/indices/test/nvp/si_et_05.ndx | \
+  $local/cstr_ndx2flist.pl $wsj0 | sort > et05_orig_clean.flist
+
+# Note: the ???'s below match WSJ and SI_DT, or wsj and si_dt.
+# Sometimes this gets copied from the CD's with upcasing, don't know
+# why (could be older versions of the disks).
+find $wsj0/wsj0/si_dt_05 -print | grep -i ".wv1" | sort > dt05_orig_clean.flist
+
+# Finding the transcript files:
+find -L $wsj0 -iname '*.dot' > dot_files.flist
+
+# Convert the transcripts into our format (no normalization yet)
+# adding suffix to utt_id
+# 0 for clean condition
+for x in tr05_orig_clean et05_orig_clean dt05_orig_clean; do
+  $local/flist2scp.pl $x.flist | sort > ${x}_sph_tmp.scp
+  cat ${x}_sph_tmp.scp | awk '{print $1}' \
+    | $local/find_transcripts.pl dot_files.flist > ${x}_tmp.trans1
+  cat ${x}_sph_tmp.scp | awk '{printf("%s %s\n", $1, $2);}' > ${x}_sph.scp
+  cat ${x}_tmp.trans1 | awk '{printf("%s ", $1); for(i=2;i<=NF;i++) printf("%s ", $i); printf("\n");}' > ${x}.trans1
+done
+
+# Do some basic normalization steps.  At this point we don't remove OOVs--
+# that will be done inside the training scripts, as we'd like to make the
+# data-preparation stage independent of the specific lexicon used.
+noiseword="<NOISE>";
+for x in tr05_orig_clean et05_orig_clean dt05_orig_clean; do
+  cat $x.trans1 | $local/normalize_transcript.pl $noiseword \
+    | sort > $x.txt || exit 1;
+done
+
+# Create scp's with wav's. (the wv1 in the distribution is not really wav, it is sph.)
+for x in tr05_orig_clean et05_orig_clean dt05_orig_clean; do
+  awk '{printf("%s '$sph2pipe' -f wav %s |\n", $1, $2);}' < ${x}_sph.scp \
+    > ${x}_wav.scp
+done
+
+# Make the utt2spk and spk2utt files.
+for x in tr05_orig_clean et05_orig_clean dt05_orig_clean; do
+  cat ${x}_sph.scp | awk '{print $1}' \
+    | perl -ane 'chop; m:^...:; print "$_ $&\n";' > $x.utt2spk
+  cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
+done
+
+#in case we want to limit lm's on most frequent words, copy lm training word frequency list
+cp $wsj0/wsj0/doc/lng_modl/vocab/wfl_64.lst $lmdir
+chmod u+w $lmdir/*.lst # had weird permissions on source.
+
+# The 5K vocab language model without verbalized pronunciations.
+# This is used for 3rd CHiME challenge
+# trigram would be: !only closed vocabulary here!
+cp $wsj0/wsj0/doc/lng_modl/base_lm/tcb05cnp.z $lmdir/lm_tg_5k.arpa.gz || exit 1;
+chmod u+rw $lmdir/lm_tg_5k.arpa.gz
+gunzip $lmdir/lm_tg_5k.arpa.gz
+tail -n 4328839 $lmdir/lm_tg_5k.arpa | gzip -c -f > $lmdir/lm_tg_5k.arpa.gz
+rm $lmdir/lm_tg_5k.arpa
+
+prune-lm --threshold=1e-7 $lmdir/lm_tg_5k.arpa.gz $lmdir/lm_tgpr_5k.arpa || exit 1;
+gzip -f $lmdir/lm_tgpr_5k.arpa || exit 1;
+
+
+if [ ! -f wsj0-train-spkrinfo.txt ] || [ `cat wsj0-train-spkrinfo.txt | wc -l` -ne 134 ]; then
+  rm -f wsj0-train-spkrinfo.txt
+  wget http://www.ldc.upenn.edu/Catalog/docs/LDC93S6A/wsj0-train-spkrinfo.txt \
+    || ( echo "Getting wsj0-train-spkrinfo.txt from backup location" && \
+         wget --no-check-certificate https://sourceforge.net/projects/kaldi/files/wsj0-train-spkrinfo.txt );
+fi
+
+if [ ! -f wsj0-train-spkrinfo.txt ]; then
+  echo "Could not get the spkrinfo.txt file from LDC website (moved)?"
+  echo "This is possibly omitted from the training disks; couldn't find it."
+  echo "Everything else may have worked; we just may be missing gender info"
+  echo "which is only needed for VTLN-related diagnostics anyway."
+  exit 1
+fi
+# Note: wsj0-train-spkrinfo.txt doesn't seem to be on the disks but the
+# LDC put it on the web.  Perhaps it was accidentally omitted from the
+# disks.
+
+cat $wsj0/wsj0/doc/spkrinfo.txt \
+    ./wsj0-train-spkrinfo.txt  | \
+    perl -ane 'tr/A-Z/a-z/; m/^;/ || print;' | \
+    awk '{print $1, $2}' | grep -v -- -- | sort | uniq > spk2gender
+
+
+echo "Data preparation succeeded"

tools/ASR_2ch_track/local/cstr_ndx2flist.pl

@@ -0,0 +1,54 @@
+#!/usr/bin/env perl
+
+# Copyright 2010-2011 Microsoft Corporation
+
+# 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.
+
+# This is modified from the script in standard Kaldi recipe to account
+# for the way the WSJ data is structured on the Edinburgh systems. 
+# - Arnab Ghoshal, 12/1/12
+
+# This program takes as its standard input an .ndx file from the WSJ corpus that looks
+# like this:
+#;; File: tr_s_wv1.ndx, updated 04/26/94
+#;;
+#;; Index for WSJ0 SI-short Sennheiser training data
+#;; Data is read WSJ sentences, Sennheiser mic.
+#;; Contains 84 speakers X (~100 utts per speaker MIT/SRI and ~50 utts 
+#;; per speaker TI) = 7236 utts
+#;;
+#11_1_1:wsj0/si_tr_s/01i/01ic0201.wv1
+#11_1_1:wsj0/si_tr_s/01i/01ic0202.wv1
+#11_1_1:wsj0/si_tr_s/01i/01ic0203.wv1
+
+# and as command-line argument it takes the names of the WSJ disk locations, e.g.:
+# /group/corpora/public/wsjcam0/data on DICE machines.
+# It outputs a list of absolute pathnames.
+
+$wsj_dir = $ARGV[0];
+
+while(<STDIN>){
+  if(m/^;/){ next; } # Comment.  Ignore it.
+  else {
+    m/^([0-9_]+):\s*(\S+)$/  || die "Could not parse line $_";
+    $filename = $2; # as a subdirectory of the distributed disk.
+    if ($filename !~ m/\.wv1$/) { $filename .= ".wv1"; }
+    $filename = "$wsj_dir/$filename";
+    if (-e $filename) {
+      print "$filename\n";
+    } else {
+      print STDERR "File $filename found in the index but not on disk\n";
+    }
+  }
+}

tools/ASR_2ch_track/local/find_noisy_transcripts.pl

@@ -0,0 +1,65 @@
+#!/usr/bin/env perl
+# Copyright 2010-2011 Microsoft Corporation
+
+# 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.
+
+
+
+# This program takes on its standard input a list of utterance
+# id's, one for each line. (e.g. 4k0c030a is a an utterance id).
+# It takes as
+# Extracts from the dot files the transcripts for a given
+# dataset (represented by a file list).
+# 
+
+@ARGV == 1 || die "find_transcripts.pl dot_files_flist < utterance_ids > transcripts";
+$dot_flist = shift @ARGV;
+
+open(L, "<$dot_flist") || die "Opening file list of dot files: $dot_flist\n";
+while(<L>){
+    chop;
+    m:\S+/(\w{6})00.dot: || die "Bad line in dot file list: $_";
+    $spk = $1;
+    $spk2dot{$spk} = $_;
+}
+
+
+
+while(<STDIN>){ 
+    chop;
+    $uttid_orig = $_;
+    $uttid = substr $uttid_orig, 0, 8; 
+    $uttid =~ m:(\w{6})\w\w: || die "Bad utterance id $_";
+    $spk = $1;
+    if($spk ne $curspk) {
+        %utt2trans = { }; # Don't keep all the transcripts in memory...
+        $curspk = $spk;
+        $dotfile = $spk2dot{$spk};
+        defined $dotfile || die "No dot file for speaker $spk\n";
+        open(F, "<$dotfile") || die "Error opening dot file $dotfile\n";
+        while(<F>) {
+            $_ =~ m:(.+)\((\w{8})\)\s*$: || die "Bad line $_ in dot file $dotfile (line $.)\n";
+            $trans = $1;
+            $utt = $2;
+            $utt2trans{$utt} = $trans;
+        }
+    }
+    if(!defined $utt2trans{$uttid}) {
+        print STDERR "No transcript for utterance $uttid (current dot file is $dotfile)\n";
+    } else {
+        print "$uttid_orig $utt2trans{$uttid}\n";
+    }
+}
+
+

tools/ASR_2ch_track/local/find_transcripts.pl

@@ -0,0 +1,64 @@
+#!/usr/bin/env perl
+# Copyright 2010-2011 Microsoft Corporation
+
+# 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.
+
+
+
+# This program takes on its standard input a list of utterance
+# id's, one for each line. (e.g. 4k0c030a is a an utterance id).
+# It takes as
+# Extracts from the dot files the transcripts for a given
+# dataset (represented by a file list).
+# 
+
+@ARGV == 1 || die "find_transcripts.pl dot_files_flist < utterance_ids > transcripts";
+$dot_flist = shift @ARGV;
+
+open(L, "<$dot_flist") || die "Opening file list of dot files: $dot_flist\n";
+while(<L>){
+    chop;
+    m:\S+/(\w{6})00.dot: || die "Bad line in dot file list: $_";
+    $spk = $1;
+    $spk2dot{$spk} = $_;
+}
+
+
+
+while(<STDIN>){ 
+    chop;
+    $uttid = $_;
+    $uttid =~ m:(\w{6})\w\w: || die "Bad utterance id $_";
+    $spk = $1;
+    if($spk ne $curspk) {
+        %utt2trans = { }; # Don't keep all the transcripts in memory...
+        $curspk = $spk;
+        $dotfile = $spk2dot{$spk};
+        defined $dotfile || die "No dot file for speaker $spk\n";
+        open(F, "<$dotfile") || die "Error opening dot file $dotfile\n";
+        while(<F>) {
+            $_ =~ m:(.+)\((\w{8})\)\s*$: || die "Bad line $_ in dot file $dotfile (line $.)\n";
+            $trans = $1;
+            $utt = $2;
+            $utt2trans{$utt} = $trans;
+        }
+    }
+    if(!defined $utt2trans{$uttid}) {
+        print STDERR "No transcript for utterance $uttid (current dot file is $dotfile)\n";
+    } else {
+        print "$uttid $utt2trans{$uttid}\n";
+    }
+}
+
+

tools/ASR_2ch_track/local/flist2scp.pl

@@ -0,0 +1,31 @@
+#!/usr/bin/env perl
+# Copyright 2010-2011 Microsoft Corporation
+
+# 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.
+
+
+# takes in a file list with lines like
+# /mnt/matylda2/data/WSJ1/13-16.1/wsj1/si_dt_20/4k0/4k0c030a.wv1
+# and outputs an scp in kaldi format with lines like
+# 4k0c030a /mnt/matylda2/data/WSJ1/13-16.1/wsj1/si_dt_20/4k0/4k0c030a.wv1
+# (the first thing is the utterance-id, which is the same as the basename of the file.
+
+
+while(<>){
+    m:^\S+/(\w+)\.[wW][vV]1$: || die "Bad line $_";
+    $id = $1;
+    $id =~ tr/A-Z/a-z/;  # Necessary because of weirdness on disk 13-16.1 (uppercase filenames)
+    print "$id $_";
+}
+

tools/ASR_2ch_track/local/normalize_transcript.pl

@@ -0,0 +1,59 @@
+#!/usr/bin/env perl
+# Copyright 2010-2011 Microsoft Corporation
+
+# 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.
+
+
+# This takes data from the standard input that's unnormalized transcripts in the format
+# 4k2c0308 Of course there isn\'t any guarantee the company will keep its hot hand [misc_noise] 
+# 4k2c030a [loud_breath] And new hardware such as the set of personal computers I\. B\. M\. introduced last week can lead to unexpected changes in the software business [door_slam] 
+# and outputs normalized transcripts.
+# c.f. /mnt/matylda2/data/WSJ0/11-10.1/wsj0/transcrp/doc/dot_spec.doc
+
+@ARGV == 1 ||  die "usage: normalize_transcript.pl noise_word < transcript > transcript2";
+$noise_word = shift @ARGV;
+
+while(<STDIN>) {
+    $_ =~ m:^(\S+) (.+): || die "bad line $_";
+    $utt = $1;
+    $trans = $2;
+    print "$utt";
+    foreach $w (split (" ",$trans)) {
+        $w =~ tr:a-z:A-Z:; # Upcase everything to match the CMU dictionary. .
+        $w =~ s:\\::g;      # Remove backslashes.  We don't need the quoting.
+        $w =~ s:^\%PERCENT$:PERCENT:; # Normalization for Nov'93 test transcripts.
+        $w =~ s:^\.POINT$:POINT:; # Normalization for Nov'93 test transcripts.
+        if($w =~ m:^\[\<\w+\]$:  || # E.g. [<door_slam], this means a door slammed in the preceding word. Delete.
+           $w =~ m:^\[\w+\>\]$:  ||  # E.g. [door_slam>], this means a door slammed in the next word.  Delete.
+           $w =~ m:\[\w+/\]$: ||  # E.g. [phone_ring/], which indicates the start of this phenomenon.
+           $w =~ m:\[\/\w+]$: ||  # E.g. [/phone_ring], which indicates the end of this phenomenon.
+           $w eq "~" ||        # This is used to indicate truncation of an utterance.  Not a word.
+           $w eq ".") {      # "." is used to indicate a pause.  Silence is optional anyway so not much 
+                             # point including this in the transcript.
+            next; # we won't print this word.
+        } elsif($w =~ m:\[\w+\]:) { # Other noises, e.g. [loud_breath].
+            print " $noise_word";
+        } elsif($w =~ m:^\<([\w\']+)\>$:) {
+            # e.g. replace <and> with and.  (the <> means verbal deletion of a word).. but it's pronounced.
+            print " $1";
+        } elsif($w eq "--DASH") {
+            print " -DASH";  # This is a common issue; the CMU dictionary has it as -DASH.
+#        } elsif($w =~ m:(.+)\-DASH$:) { # E.g. INCORPORATED-DASH... seems the DASH gets combined with previous word
+#            print " $1 -DASH";
+        } else {
+            print " $w";
+        }
+    }
+    print "\n";
+}

tools/ASR_2ch_track/local/real_enhan_chime4_data_prep.sh

@@ -0,0 +1,103 @@
+#!/bin/bash
+set -e
+
+# Copyright 2009-2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0.
+
+# This is modified from the script in standard Kaldi recipe to account
+# for the way the WSJ data is structured on the Edinburgh systems.
+# - Arnab Ghoshal, 29/05/12
+
+# Modified from the script for CHiME2 baseline
+# Shinji Watanabe 02/13/2015
+
+# Config:
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# -ne 2 ]; then
+  printf "\nUSAGE: %s <enhancement-name> <enhanced-speech-directory>\n\n" `basename $0`
+  echo "The argument should be a the directory that only contains enhanced speech data."
+  exit 1;
+fi
+
+echo "$0 $@"  # Print the command line for logging
+
+enhan=$1
+audio_dir=$2
+
+dir=`pwd`/data/local/data
+mkdir -p $dir
+local=`pwd`/local
+utils=`pwd`/utils
+odir=`pwd`/data
+
+. ./path.sh # Needed for KALDI_ROOT
+export PATH=$PATH:$KALDI_ROOT/tools/irstlm/bin
+sph2pipe=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe
+if [ ! -x $sph2pipe ]; then
+  echo "Could not find (or execute) the sph2pipe program at $sph2pipe";
+  exit 1;
+fi
+
+if $eval_flag; then
+list_set="tr05_real_$enhan dt05_real_$enhan et05_real_$enhan"
+else
+list_set="tr05_real_$enhan dt05_real_$enhan"
+fi
+
+cd $dir
+
+find $audio_dir/ -name '*.wav' | grep 'tr05_bus_real\|tr05_caf_real\|tr05_ped_real\|tr05_str_real' | sort -u > tr05_real_$enhan.flist
+find $audio_dir/ -name '*.wav' | grep 'dt05_bus_real\|dt05_caf_real\|dt05_ped_real\|dt05_str_real' | sort -u > dt05_real_$enhan.flist
+if $eval_flag; then
+find $audio_dir/ -name '*.wav' | grep 'et05_bus_real\|et05_caf_real\|et05_ped_real\|et05_str_real' | sort -u > et05_real_$enhan.flist
+fi
+
+# make a scp file from file list
+for x in $list_set; do
+    cat $x.flist | awk -F'[/]' '{print $NF}'| sed -e 's/\.wav/_REAL/' > ${x}_wav.ids
+    paste -d" " ${x}_wav.ids $x.flist | sort -k 1 > ${x}_wav.scp
+done
+
+#make a transcription from dot
+cat tr05_real.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF "_REAL"}'> tr05_real_$enhan.ids
+cat tr05_real.dot | sed -e 's/(.*)//' > tr05_real_$enhan.txt
+paste -d" " tr05_real_$enhan.ids tr05_real_$enhan.txt | sort -k 1 > tr05_real_$enhan.trans1
+cat dt05_real.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF "_REAL"}'> dt05_real_$enhan.ids
+cat dt05_real.dot | sed -e 's/(.*)//' > dt05_real_$enhan.txt
+paste -d" " dt05_real_$enhan.ids dt05_real_$enhan.txt | sort -k 1 > dt05_real_$enhan.trans1
+if $eval_flag; then
+cat et05_real.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF "_REAL"}'> et05_real_$enhan.ids
+cat et05_real.dot | sed -e 's/(.*)//' > et05_real_$enhan.txt
+paste -d" " et05_real_$enhan.ids et05_real_$enhan.txt | sort -k 1 > et05_real_$enhan.trans1
+fi
+
+# Do some basic normalization steps.  At this point we don't remove OOVs--
+# that will be done inside the training scripts, as we'd like to make the
+# data-preparation stage independent of the specific lexicon used.
+noiseword="<NOISE>";
+for x in $list_set;do
+  cat $x.trans1 | $local/normalize_transcript.pl $noiseword \
+    | sort > $x.txt || exit 1;
+done
+
+# Make the utt2spk and spk2utt files.
+for x in $list_set; do
+  cat ${x}_wav.scp | awk -F'_' '{print $1}' > $x.spk
+  cat ${x}_wav.scp | awk '{print $1}' > $x.utt
+  paste -d" " $x.utt $x.spk > $x.utt2spk
+  cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
+done
+
+# copying data to data/...
+for x in $list_set; do
+  mkdir -p $odir/$x
+  cp ${x}_wav.scp $odir/$x/wav.scp || exit 1;
+  cp ${x}.txt     $odir/$x/text    || exit 1;
+  cp ${x}.spk2utt $odir/$x/spk2utt || exit 1;
+  cp ${x}.utt2spk $odir/$x/utt2spk || exit 1;
+done
+
+echo "Data preparation succeeded"

tools/ASR_2ch_track/local/real_noisy_chime4_data_prep.sh

@@ -0,0 +1,114 @@
+#!/bin/bash
+set -e
+
+# Copyright 2009-2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0.
+
+# This is modified from the script in standard Kaldi recipe to account
+# for the way the WSJ data is structured on the Edinburgh systems.
+# - Arnab Ghoshal, 29/05/12
+
+# Modified from the script for CHiME2 baseline
+# Shinji Watanabe 02/13/2015
+
+# Config:
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# -ne 1 ]; then
+  printf "\nUSAGE: %s <corpus-directory>\n\n" `basename $0`
+  echo "The argument should be a the top-level Chime4 directory."
+  echo "It is assumed that there will be a 'data' subdirectory"
+  echo "within the top-level corpus directory."
+  exit 1;
+fi
+
+echo "$0 $@"  # Print the command line for logging
+
+audio_dir=$1/data/audio/16kHz/isolated
+trans_dir=$1/data/transcriptions
+
+echo "extract 5th channel (CH5.wav, the center bottom edge in the front of the tablet) for noisy data"
+
+dir=`pwd`/data/local/data
+lmdir=`pwd`/data/local/nist_lm
+mkdir -p $dir $lmdir
+local=`pwd`/local
+utils=`pwd`/utils
+
+. ./path.sh # Needed for KALDI_ROOT
+export PATH=$PATH:$KALDI_ROOT/tools/irstlm/bin
+sph2pipe=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe
+if [ ! -x $sph2pipe ]; then
+  echo "Could not find (or execute) the sph2pipe program at $sph2pipe";
+  exit 1;
+fi
+
+if $eval_flag; then
+list_set="tr05_real_noisy dt05_real_noisy et05_real_noisy"
+else
+list_set="tr05_real_noisy dt05_real_noisy"
+fi
+
+cd $dir
+
+find $audio_dir -name '*CH5.wav' | grep 'tr05_bus_real\|tr05_caf_real\|tr05_ped_real\|tr05_str_real' | sort -u > tr05_real_noisy.flist
+find $audio_dir -name '*CH5.wav' | grep 'dt05_bus_real\|dt05_caf_real\|dt05_ped_real\|dt05_str_real' | sort -u > dt05_real_noisy.flist
+if $eval_flag; then
+find $audio_dir -name '*CH5.wav' | grep 'et05_bus_real\|et05_caf_real\|et05_ped_real\|et05_str_real' | sort -u > et05_real_noisy.flist
+fi
+
+# make a dot format from json annotation files
+cp $trans_dir/tr05_real.dot_all tr05_real.dot
+cp $trans_dir/dt05_real.dot_all dt05_real.dot
+if $eval_flag; then
+cp $trans_dir/et05_real.dot_all et05_real.dot
+fi
+
+# make a scp file from file list
+for x in $list_set; do
+    cat $x.flist | awk -F'[/]' '{print $NF}'| sed -e 's/\.wav/_REAL/' > ${x}_wav.ids
+    paste -d" " ${x}_wav.ids $x.flist | sort -k 1 > ${x}_wav.scp
+done
+
+#make a transcription from dot
+cat tr05_real.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF ".CH5_REAL"}'> tr05_real_noisy.ids
+cat tr05_real.dot | sed -e 's/(.*)//' > tr05_real_noisy.txt
+paste -d" " tr05_real_noisy.ids tr05_real_noisy.txt | sort -k 1 > tr05_real_noisy.trans1
+cat dt05_real.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF ".CH5_REAL"}'> dt05_real_noisy.ids
+cat dt05_real.dot | sed -e 's/(.*)//' > dt05_real_noisy.txt
+paste -d" " dt05_real_noisy.ids dt05_real_noisy.txt | sort -k 1 > dt05_real_noisy.trans1
+if $eval_flag; then
+cat et05_real.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF ".CH5_REAL"}'> et05_real_noisy.ids
+cat et05_real.dot | sed -e 's/(.*)//' > et05_real_noisy.txt
+paste -d" " et05_real_noisy.ids et05_real_noisy.txt | sort -k 1 > et05_real_noisy.trans1
+fi
+
+# Do some basic normalization steps.  At this point we don't remove OOVs--
+# that will be done inside the training scripts, as we'd like to make the
+# data-preparation stage independent of the specific lexicon used.
+noiseword="<NOISE>";
+for x in $list_set;do
+  cat $x.trans1 | $local/normalize_transcript.pl $noiseword \
+    | sort > $x.txt || exit 1;
+done
+
+# Make the utt2spk and spk2utt files.
+for x in $list_set; do
+  cat ${x}_wav.scp | awk -F'_' '{print $1}' > $x.spk
+  cat ${x}_wav.scp | awk '{print $1}' > $x.utt
+  paste -d" " $x.utt $x.spk > $x.utt2spk
+  cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
+done
+
+# copying data to data/...
+for x in $list_set; do
+  mkdir -p ../../$x
+  cp ${x}_wav.scp ../../$x/wav.scp || exit 1;
+  cp ${x}.txt     ../../$x/text    || exit 1;
+  cp ${x}.spk2utt ../../$x/spk2utt || exit 1;
+  cp ${x}.utt2spk ../../$x/utt2spk || exit 1;
+done
+
+echo "Data preparation succeeded"

tools/ASR_2ch_track/local/run_beamform_2ch_track.sh

@@ -0,0 +1,83 @@
+#!/bin/bash
+
+# Copyright 2015, Mitsubishi Electric Research Laboratories, MERL (Author: Shinji Watanabe)
+
+. ./cmd.sh
+. ./path.sh
+
+# Config:
+nj=10
+cmd=run.pl
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# != 2 ]; then
+   echo "Wrong #arguments ($#, expected 3)"
+   echo "Usage: local/run_beamform_2ch_track.sh [options] <wav-in-dir> <wav-out-dir>"
+   echo "main options (for others, see top of script file)"
+   echo "  --nj <nj>                                # number of parallel jobs"
+   echo "  --cmd <cmd>                              # Command to run in parallel with"
+   exit 1;
+fi
+
+sdir=$1
+odir=$2
+
+wdir=data/beamforming_2ch_track
+
+if [ -z $BEAMFORMIT ] ; then
+  export BEAMFORMIT=$KALDI_ROOT/tools/BeamformIt
+fi
+export PATH=${PATH}:$BEAMFORMIT
+! hash BeamformIt && echo "Missing BeamformIt, run 'cd ../../../tools/; make beamformit;'" && exit 1
+
+# 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 $wdir/log
+
+allwavs=`find $sdir/ | grep "\.wav" | tr ' ' '\n' | awk -F '/' '{print $(NF-1)"/"$NF}'`
+
+# wavfiles.list can be used as the name of the output files
+output_wavfiles=$wdir/wavfiles.list
+echo $allwavs | tr ' ' '\n' | awk -F '.' '{print $1}' | sort | uniq > $output_wavfiles
+
+# channel list
+input_arrays=$wdir/channels
+echo $allwavs | tr ' ' '\n' | sort | awk 'NR%2==1' > $wdir/channels.1st
+echo $allwavs | tr ' ' '\n' | sort | awk 'NR%2==0' > $wdir/channels.2nd
+paste -d" " $output_wavfiles $wdir/channels.1st $wdir/channels.2nd > $input_arrays
+
+# 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 "Beamforming\n"
+# making a shell script for each job
+for n in `seq $nj`; do
+cat << EOF > $wdir/log/beamform.$n.sh
+while read line; do
+  $BEAMFORMIT/BeamformIt -s \$line -c $input_arrays \
+    --config_file `pwd`/conf/chime4.cfg \
+    --source_dir $sdir \
+    --result_dir $odir
+done < $output_wavfiles.$n
+EOF
+done
+# making a subdirectory for the output wav files
+for x in `awk -F '/' '{print $1}' $output_wavfiles | sort | uniq`; do
+  mkdir -p $odir/$x
+done
+
+chmod a+x $wdir/log/beamform.*.sh
+$cmd JOB=1:$nj $wdir/log/beamform.JOB.log \
+  $wdir/log/beamform.JOB.sh
+
+echo "`basename $0` Done."

tools/ASR_2ch_track/local/run_beamform_6ch_track.sh

@@ -0,0 +1,100 @@
+#!/bin/bash
+
+# Copyright 2015, Mitsubishi Electric Research Laboratories, MERL (Author: Shinji Watanabe)
+
+. ./cmd.sh
+. ./path.sh
+
+# Config:
+nj=10
+cmd=run.pl
+bmf="1 3 4 5 6"
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# != 2 ]; then
+   echo "Wrong #arguments ($#, expected 2)"
+   echo "Usage: local/run_beamform_6ch_track.sh [options] <wav-in-dir> <wav-out-dir>"
+   echo "main options (for others, see top of script file)"
+   echo "  --nj <nj>                                # number of parallel jobs"
+   echo "  --cmd <cmd>                              # Command to run in parallel with"
+   echo "  --bmf \"1 3 4 5 6\"                      # microphones used for beamforming (2th mic is omitted in default)"
+   exit 1;
+fi
+
+sdir=$1
+odir=$2
+wdir=data/beamforming_`echo $bmf | tr ' ' '_'`
+
+if [ -z $BEAMFORMIT ] ; then
+  export BEAMFORMIT=$KALDI_ROOT/tools/BeamformIt
+fi
+export PATH=${PATH}:$BEAMFORMIT
+! hash BeamformIt && echo "Missing BeamformIt, run 'cd ../../../tools/; make beamformit;'" && exit 1
+
+# 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 $wdir/log
+
+echo "Will use the following channels: $bmf"
+# number of channels
+numch=`echo $bmf | tr ' ' '\n' | wc -l`
+echo "the number of channels: $numch"
+
+# wavfiles.list can be used as the name of the output files
+# we only process dev and eval waves
+output_wavfiles=$wdir/wavfiles.list
+if $eval_flag; then
+  find $sdir/{dt,et}*{simu,real}/ | grep CH1.wav \
+    | awk -F '/' '{print $(NF-1) "/" $NF}' | sed -e "s/\.CH1\.wav//" | sort > $output_wavfiles
+else
+  find $sdir/dt*{simu,real}/ | grep CH1.wav \
+    | awk -F '/' '{print $(NF-1) "/" $NF}' | sed -e "s/\.CH1\.wav//" | sort > $output_wavfiles
+fi
+
+# this is an input file list of the microphones
+# format: 1st_wav 2nd_wav ... nth_wav
+input_arrays=$wdir/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
+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 > $wdir/log/beamform.$n.sh
+while read line; do
+  $BEAMFORMIT/BeamformIt -s \$line -c $input_arrays \
+    --config_file `pwd`/conf/chime4.cfg \
+    --source_dir $sdir \
+    --result_dir $odir
+done < $output_wavfiles.$n
+EOF
+done
+# making a subdirectory for the output wav files
+for x in `awk -F '/' '{print $1}' $output_wavfiles | sort | uniq`; do
+  mkdir -p $odir/$x
+done
+
+chmod a+x $wdir/log/beamform.*.sh
+$cmd JOB=1:$nj $wdir/log/beamform.JOB.log \
+  $wdir/log/beamform.JOB.sh
+
+echo "`basename $0` Done."

tools/ASR_2ch_track/local/run_dnn.sh

@@ -0,0 +1,230 @@
+#!/bin/bash
+
+# Copyright 2016 University of Sheffield (Jon Barker, Ricard Marxer)
+#                Inria (Emmanuel Vincent)
+#                Mitsubishi Electric Research Labs (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+# This script is made from the kaldi recipe of the 2nd CHiME Challenge Track 2
+# made by Chao Weng
+
+. ./path.sh
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+# Config:
+nj=30
+stage=0 # resume training with --stage=N
+train=noisy
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+# This is a shell script, but it's recommended that you run the commands one by
+# one by copying and pasting into the shell.
+
+if [ $# -ne 1 ]; then
+  printf "\nUSAGE: %s <enhancement method>\n\n" `basename $0`
+  echo "First argument specifies a unique name for different enhancement method"
+  exit 1;
+fi
+
+# set enhanced data
+enhan=$1
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+# check whether run_init is executed
+if [ ! -d data/lang ]; then
+  echo "error, execute local/run_init.sh, first"
+  exit 1;
+fi
+
+# check whether run_init is executed
+if [ ! -d exp/tri3b_tr05_multi_${train} ]; then
+  echo "error, execute local/run_init.sh, first"
+  exit 1;
+fi
+
+# get alignments
+if [ $stage -le 0 ]; then
+  steps/align_fmllr.sh --nj $nj --cmd "$train_cmd" \
+    data/tr05_multi_${train} data/lang exp/tri3b_tr05_multi_${train} exp/tri3b_tr05_multi_${train}_ali
+  steps/align_fmllr.sh --nj 4 --cmd "$train_cmd" \
+    data/dt05_multi_$enhan data/lang exp/tri3b_tr05_multi_${train} exp/tri3b_tr05_multi_${train}_ali_dt05
+fi
+
+# make fmllr feature for training multi = simu + real
+gmmdir=exp/tri3b_tr05_multi_${train}_ali
+data_fmllr=data-fmllr-tri3b
+mkdir -p $data_fmllr
+fmllrdir=fmllr-tri3b/${train}
+if [ $stage -le 1 ]; then
+  for x in tr05_real_${train} tr05_simu_${train}; do
+    steps/nnet/make_fmllr_feats.sh --nj 4 --cmd "$train_cmd" \
+      --transform-dir $gmmdir \
+      $data_fmllr/$x data/$x $gmmdir exp/make_fmllr_tri3b/$x $fmllrdir
+  done
+fi
+
+# make fmllr feature for dev and eval
+gmmdir=exp/tri3b_tr05_multi_${train}
+fmllrdir=fmllr-tri3b/$enhan
+if [ $stage -le 2 ]; then
+  if $eval_flag; then
+    tasks="dt05_real_$enhan dt05_simu_$enhan et05_real_$enhan et05_simu_$enhan"
+  else
+    tasks="dt05_real_$enhan dt05_simu_$enhan"
+  fi
+  for x in $tasks; do
+    steps/nnet/make_fmllr_feats.sh --nj 4 --cmd "$train_cmd" \
+      --transform-dir $gmmdir/decode_tgpr_5k_$x \
+      $data_fmllr/$x data/$x $gmmdir exp/make_fmllr_tri3b/$x $fmllrdir
+  done
+fi
+
+# make mixed training set from real and simulation enhanced data
+# multi = simu + real
+if [ $stage -le 3 ]; then
+  utils/combine_data.sh $data_fmllr/tr05_multi_${train} $data_fmllr/tr05_simu_${train} $data_fmllr/tr05_real_${train}
+  utils/combine_data.sh $data_fmllr/dt05_multi_$enhan $data_fmllr/dt05_simu_$enhan $data_fmllr/dt05_real_$enhan
+  if $eval_flag; then
+  utils/combine_data.sh $data_fmllr/et05_multi_$enhan $data_fmllr/et05_simu_$enhan $data_fmllr/et05_real_$enhan
+  fi
+fi
+
+# pre-train dnn
+dir=exp/tri4a_dnn_pretrain_tr05_multi_${train}
+if [ $stage -le 4 ]; then
+  $cuda_cmd $dir/_pretrain_dbn.log \
+    steps/nnet/pretrain_dbn.sh --nn-depth 7 --rbm-iter 3 $data_fmllr/tr05_multi_${train} $dir
+fi
+
+# train dnn
+dir=exp/tri4a_dnn_tr05_multi_${train}
+ali=exp/tri3b_tr05_multi_${train}_ali
+ali_dev=exp/tri3b_tr05_multi_${train}_ali_dt05
+feature_transform=exp/tri4a_dnn_pretrain_tr05_multi_${train}/final.feature_transform
+dbn=exp/tri4a_dnn_pretrain_tr05_multi_${train}/7.dbn
+if [ $stage -le 5 ]; then
+  $cuda_cmd $dir/_train_nnet.log \
+    steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
+    $data_fmllr/tr05_multi_${train} $data_fmllr/dt05_multi_$enhan data/lang $ali $ali_dev $dir
+fi
+
+# decode enhanced speech
+if [ $stage -le 6 ]; then
+  utils/mkgraph.sh data/lang_test_tgpr_5k $dir $dir/graph_tgpr_5k
+  steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --acwt 0.10 --config conf/decode_dnn.config \
+    $dir/graph_tgpr_5k $data_fmllr/dt05_real_$enhan $dir/decode_tgpr_5k_dt05_real_$enhan &
+  steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --acwt 0.10 --config conf/decode_dnn.config \
+    $dir/graph_tgpr_5k $data_fmllr/dt05_simu_$enhan $dir/decode_tgpr_5k_dt05_simu_$enhan &
+  if $eval_flag; then
+  steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --acwt 0.10 --config conf/decode_dnn.config \
+    $dir/graph_tgpr_5k $data_fmllr/et05_real_$enhan $dir/decode_tgpr_5k_et05_real_$enhan &
+  steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --acwt 0.10 --config conf/decode_dnn.config \
+    $dir/graph_tgpr_5k $data_fmllr/et05_simu_$enhan $dir/decode_tgpr_5k_et05_simu_$enhan &
+  fi
+  wait;
+fi
+
+# Sequence training using sMBR criterion, we do Stochastic-GD
+# with per-utterance updates. We use usually good acwt 0.1
+# Lattices are re-generated after 1st epoch, to get faster convergence.
+dir=exp/tri4a_dnn_tr05_multi_${train}_smbr
+srcdir=exp/tri4a_dnn_tr05_multi_${train}
+acwt=0.1
+
+# First we generate lattices and alignments:
+# gawk must be installed to perform awk -v FS="/" '{ print gensub(".gz","","",$NF)" gunzip -c "$0" |"; }' in
+# steps/nnet/make_denlats.sh
+if [ $stage -le 7 ]; then
+  steps/nnet/align.sh --nj $nj --cmd "$train_cmd" \
+    $data_fmllr/tr05_multi_${train} data/lang $srcdir ${srcdir}_ali
+  steps/nnet/make_denlats.sh --nj $nj --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
+    $data_fmllr/tr05_multi_${train} data/lang $srcdir ${srcdir}_denlats
+fi
+
+# Re-train the DNN by 1 iteration of sMBR
+if [ $stage -le 8 ]; then
+  steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 1 --acwt $acwt --do-smbr true \
+    $data_fmllr/tr05_multi_${train} data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir
+fi
+
+# Decode (reuse HCLG graph)
+if [ $stage -le 9 ]; then
+  for ITER in 1; do
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/dt05_real_${enhan} $dir/decode_tgpr_5k_dt05_real_${enhan}_it${ITER} &
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/dt05_simu_${enhan} $dir/decode_tgpr_5k_dt05_simu_${enhan}_it${ITER} &
+    if $eval_flag; then
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/et05_real_${enhan} $dir/decode_tgpr_5k_et05_real_${enhan}_it${ITER} &
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/et05_simu_${enhan} $dir/decode_tgpr_5k_et05_simu_${enhan}_it${ITER} &
+    fi
+  done
+fi
+
+# Re-generate lattices, run 4 more sMBR iterations
+dir=exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats
+srcdir=exp/tri4a_dnn_tr05_multi_${train}_smbr
+acwt=0.1
+
+# Generate lattices and alignments:
+if [ $stage -le 10 ]; then
+  steps/nnet/align.sh --nj $nj --cmd "$train_cmd" \
+    $data_fmllr/tr05_multi_${train} data/lang $srcdir ${srcdir}_ali
+  steps/nnet/make_denlats.sh --nj $nj --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
+    $data_fmllr/tr05_multi_${train} data/lang $srcdir ${srcdir}_denlats
+fi
+
+# Re-train the DNN by 4 iterations of sMBR
+if [ $stage -le 11 ]; then
+  steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 4 --acwt $acwt --do-smbr true \
+    $data_fmllr/tr05_multi_${train} data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
+fi
+
+# Decode (reuse HCLG graph)
+if [ $stage -le 12 ]; then
+  for ITER in 1 2 3 4; do
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/dt05_real_${enhan} $dir/decode_tgpr_5k_dt05_real_${enhan}_it${ITER} &
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/dt05_simu_${enhan} $dir/decode_tgpr_5k_dt05_simu_${enhan}_it${ITER} &
+    if $eval_flag; then
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/et05_real_${enhan} $dir/decode_tgpr_5k_et05_real_${enhan}_it${ITER} &
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/et05_simu_${enhan} $dir/decode_tgpr_5k_et05_simu_${enhan}_it${ITER} &
+    fi
+  done
+  wait
+fi
+
+# scoring
+if [ $stage -le 13 ]; then
+  # decoded results of enhanced speech using DNN AMs trained with enhanced data
+  local/chime4_calc_wers.sh exp/tri4a_dnn_tr05_multi_${train} $enhan exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k \
+    > exp/tri4a_dnn_tr05_multi_${train}/best_wer_$enhan.result
+  head -n 15 exp/tri4a_dnn_tr05_multi_${train}/best_wer_$enhan.result
+  # decoded results of enhanced speech using sequence-training DNN
+  ./local/chime4_calc_wers_smbr.sh exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats ${enhan} exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k \
+    > exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats/best_wer_${enhan}.result
+  head -n 15 exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats/best_wer_${enhan}.result
+fi
+
+echo "`basename $0` Done."

tools/ASR_2ch_track/local/run_dnn_recog.sh

@@ -0,0 +1,143 @@
+#!/bin/bash
+
+# Copyright 2016 University of Sheffield (Jon Barker, Ricard Marxer)
+#                Inria (Emmanuel Vincent)
+#                Mitsubishi Electric Research Labs (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+# This script is made from the kaldi recipe of the 2nd CHiME Challenge Track 2
+# made by Chao Weng
+
+. ./path.sh
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+# Config:
+nj=30
+stage=0 # resume training with --stage=N
+train=noisy
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+# This is a shell script, but it's recommended that you run the commands one by
+# one by copying and pasting into the shell.
+
+if [ $# -ne 2 ]; then
+  printf "\nUSAGE: %s <enhancement method> <model dir>\n\n" `basename $0`
+  echo "First argument specifies a unique name for different enhancement method"
+  echo "Second argument specifies acoustic and language model directory"
+  exit 1;
+fi
+
+# set enhanced data
+enhan=$1
+# set model directory
+mdir=$2
+
+# 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
+
+# check data/loca/data
+if [ ! -d $mdir/data/local/data ]; then
+  echo "error, set $mdir correctly"
+  exit 1;
+elif [ ! -d data/local/data ]; then
+  echo "copy $mdir/data/local/data"
+  mkdir -p data/local
+  cp -r $mdir/data/local/data data/local/
+fi
+
+# check gmm model
+if [ ! -d $mdir/exp/tri3b_tr05_multi_${train} ]; then
+  echo "error, set $mdir correctly"
+  exit 1;
+elif [ ! -d exp/tri3b_tr05_multi_${train} ]; then
+  echo "copy $mdir/exp/tri3b_tr05_multi_${train}"
+  mkdir -p exp
+  cp -r $mdir/exp/tri3b_tr05_multi_${train} exp/
+fi
+
+# check dnn graph
+if [ ! -d $mdir/exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k ]; then
+  echo "error, set $mdir correctly"
+  exit 1;
+elif [ ! -d exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k ]; then
+  echo "copy $mdir/exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k"
+  mkdir -p exp/tri4a_dnn_tr05_multi_${train}
+  cp -r $mdir/exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k exp/tri4a_dnn_tr05_multi_${train}/
+fi
+
+# check dnn smbr model
+if [ ! -d $mdir/exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats ]; then
+  echo "error, set $mdir correctly"
+  exit 1;
+elif [ ! -d exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats ]; then
+  echo "copy $mdir/exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats"
+  mkdir -p exp
+  cp -r $mdir/exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats exp/
+fi
+
+# make fmllr feature for dev and eval
+gmmdir=exp/tri3b_tr05_multi_${train}
+data_fmllr=data-fmllr-tri3b
+mkdir -p $data_fmllr
+fmllrdir=fmllr-tri3b/$enhan
+if [ $stage -le 4 ]; then
+  if $eval_flag; then
+    tasks="dt05_real_$enhan dt05_simu_$enhan et05_real_$enhan et05_simu_$enhan"
+  else
+    tasks="dt05_real_$enhan dt05_simu_$enhan"
+  fi
+  for x in $tasks; do
+    steps/nnet/make_fmllr_feats.sh --nj 4 --cmd "$train_cmd" \
+      --transform-dir $gmmdir/decode_tgpr_5k_$x \
+      $data_fmllr/$x data/$x $gmmdir exp/make_fmllr_tri3b/$x $fmllrdir
+  done
+fi
+
+# make mixed training set from real and simulation enhanced data
+# multi = simu + real
+if [ $stage -le 5 ]; then
+  utils/combine_data.sh $data_fmllr/dt05_multi_$enhan $data_fmllr/dt05_simu_$enhan $data_fmllr/dt05_real_$enhan
+  if $eval_flag; then
+  utils/combine_data.sh $data_fmllr/et05_multi_$enhan $data_fmllr/et05_simu_$enhan $data_fmllr/et05_real_$enhan
+  fi
+fi
+
+# Re-generate lattices, run 4 more sMBR iterations
+dir=exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats
+acwt=0.1
+
+# Decode (reuse HCLG graph)
+if [ $stage -le 6 ]; then
+  for ITER in 1 2 3 4; do
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/dt05_real_${enhan} $dir/decode_tgpr_5k_dt05_real_${enhan}_it${ITER} &
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/dt05_simu_${enhan} $dir/decode_tgpr_5k_dt05_simu_${enhan}_it${ITER} &
+    if $eval_flag; then
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/et05_real_${enhan} $dir/decode_tgpr_5k_et05_real_${enhan}_it${ITER} &
+    steps/nnet/decode.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" --config conf/decode_dnn.config \
+      --nnet $dir/${ITER}.nnet --acwt $acwt \
+      exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k $data_fmllr/et05_simu_${enhan} $dir/decode_tgpr_5k_et05_simu_${enhan}_it${ITER} &
+    fi
+    wait
+  done
+fi
+
+# scoring
+if [ $stage -le 7 ]; then
+  # decoded results of enhanced speech using sequence-training DNN
+  ./local/chime4_calc_wers_smbr.sh $dir ${enhan} exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k > $dir/best_wer_${enhan}.result
+  head -n 15 $dir/best_wer_${enhan}.result
+fi
+
+echo "`basename $0` Done."

tools/ASR_2ch_track/local/run_gmm.sh

@@ -0,0 +1,186 @@
+#!/bin/bash
+
+# Copyright 2016 University of Sheffield (Jon Barker, Ricard Marxer)
+#                Inria (Emmanuel Vincent)
+#                Mitsubishi Electric Research Labs (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+# This script is made from the kaldi recipe of the 2nd CHiME Challenge Track 2
+# made by Chao Weng
+
+. ./path.sh
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+# Config:
+nj=30
+stage=0 # resume training with --stage=N
+train=noisy # noisy data multi-condition training
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+# This is a shell script, but it's recommended that you run the commands one by
+# one by copying and pasting into the shell.
+
+if [ $# -ne 3 ]; then
+  printf "\nUSAGE: %s <enhancement method> <enhanced speech directory> <chime4 root directory>\n\n" `basename $0`
+  echo "First argument specifies a unique name for different enhancement method"
+  echo "Second argument specifies the directory of enhanced wav files"
+  echo "Third argument specifies the CHiME4 root directory"
+  exit 1;
+fi
+
+# set enhanced data
+enhan=$1
+enhan_data=$2
+# set chime4 data
+chime4_data=$3
+
+# 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
+
+# check whether run_init is executed
+if [ ! -d data/lang ]; then
+  echo "error, execute local/run_init.sh, first"
+  exit 1;
+fi
+
+#######################
+#### training #########
+if [ $stage -le 1 ]; then
+  # process for distant talking speech for real and simulation data
+  local/real_noisy_chime4_data_prep.sh $chime4_data
+  local/simu_noisy_chime4_data_prep.sh $chime4_data
+fi
+
+# Now make MFCC features for clean, close, and noisy data
+# mfccdir should be some place with a largish disk where you
+# want to store MFCC features.
+mfccdir=mfcc
+if [ $stage -le 2 ]; then
+  if $eval_flag; then
+    tasks="tr05_real_${train} dt05_real_${train} tr05_simu_${train} dt05_simu_${train} et05_real_${train} et05_simu_${train}"
+  else
+    tasks="tr05_real_${train} dt05_real_${train} tr05_simu_${train} dt05_simu_${train}"
+  fi
+  for x in $tasks; do
+    steps/make_mfcc.sh --nj 8 --cmd "$train_cmd" \
+      data/$x exp/make_mfcc/$x $mfccdir
+    steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir
+  done
+fi
+
+# make mixed training set from real and simulation training data
+# multi = simu + real
+if [ $stage -le 3 ]; then
+  utils/combine_data.sh data/tr05_multi_${train} data/tr05_simu_${train} data/tr05_real_${train}
+  utils/combine_data.sh data/dt05_multi_${train} data/dt05_simu_${train} data/dt05_real_${train}
+  if $eval_flag; then
+  utils/combine_data.sh data/et05_multi_${train} data/et05_simu_${train} data/et05_real_${train}
+  fi
+fi
+
+# training models for noisy data
+if [ $stage -le 4 ]; then
+  nspk=`wc -l data/tr05_multi_${train}/spk2utt | awk '{print $1}'`
+  if [ $nj -gt $nspk ]; then
+    nj2=$nspk
+  else
+    nj2=$nj
+  fi
+  # training monophone model
+  steps/train_mono.sh --boost-silence 1.25 --nj $nj2 --cmd "$train_cmd" \
+    data/tr05_multi_${train} data/lang exp/mono0a_tr05_multi_${train}
+  steps/align_si.sh --boost-silence 1.25 --nj $nj2 --cmd "$train_cmd" \
+    data/tr05_multi_${train} data/lang exp/mono0a_tr05_multi_${train} exp/mono0a_ali_tr05_multi_${train}
+
+  # training triphone model with lad mllt features
+  steps/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \
+    2000 10000 data/tr05_multi_${train} data/lang exp/mono0a_ali_tr05_multi_${train} exp/tri1_tr05_multi_${train}
+  steps/align_si.sh --nj $nj2 --cmd "$train_cmd" \
+    data/tr05_multi_${train} data/lang exp/tri1_tr05_multi_${train} exp/tri1_ali_tr05_multi_${train}
+
+  steps/train_lda_mllt.sh --cmd "$train_cmd" \
+    --splice-opts "--left-context=3 --right-context=3" \
+    2500 15000 data/tr05_multi_${train} data/lang exp/tri1_ali_tr05_multi_${train} exp/tri2b_tr05_multi_${train}
+  steps/align_si.sh  --nj $nj2 --cmd "$train_cmd" \
+    --use-graphs true data/tr05_multi_${train} data/lang exp/tri2b_tr05_multi_${train} exp/tri2b_ali_tr05_multi_${train}
+
+  steps/train_sat.sh --cmd "$train_cmd" \
+    2500 15000 data/tr05_multi_${train} data/lang exp/tri2b_ali_tr05_multi_${train} exp/tri3b_tr05_multi_${train}
+  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri3b_tr05_multi_${train} exp/tri3b_tr05_multi_${train}/graph_tgpr_5k
+fi
+#### training done ####
+#######################
+
+
+#####################
+#### tsting #########
+# process for enhanced data
+if [ $stage -le 5 ]; then
+  if [ ! -d data/dt05_real_$enhan ] || [ ! -d data/et05_real_$enhan ]; then
+    local/real_enhan_chime4_data_prep.sh $enhan $enhan_data
+    local/simu_enhan_chime4_data_prep.sh $enhan $enhan_data
+  fi
+fi
+
+# Now make MFCC features for enhanced data
+# mfccdir should be some place with a largish disk where you
+# want to store MFCC features.
+mfccdir=mfcc/$enhan
+if [ $stage -le 6 ]; then
+  if $eval_flag; then
+    tasks="dt05_real_$enhan dt05_simu_$enhan et05_real_$enhan et05_simu_$enhan"
+  else
+    tasks="dt05_real_$enhan dt05_simu_$enhan"
+  fi
+  for x in $tasks; do
+    if [ ! -e data/$x/feats.scp ]; then
+      steps/make_mfcc.sh --nj 8 --cmd "$train_cmd" \
+	data/$x exp/make_mfcc/$x $mfccdir
+      steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir
+    fi
+  done
+fi
+
+# make mixed training set from real and simulation enhanced data
+# multi = simu + real
+if [ $stage -le 7 ]; then
+  if [ ! -d data/dt05_multi_$enhan ]; then
+    utils/combine_data.sh data/dt05_multi_$enhan data/dt05_simu_$enhan data/dt05_real_$enhan
+    if $eval_flag; then
+    utils/combine_data.sh data/et05_multi_$enhan data/et05_simu_$enhan data/et05_real_$enhan
+    fi
+  fi
+fi
+
+# decode enhanced speech using AMs trained with enhanced data
+if [ $stage -le 8 ]; then
+  steps/decode_fmllr.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" \
+    exp/tri3b_tr05_multi_${train}/graph_tgpr_5k data/dt05_real_$enhan exp/tri3b_tr05_multi_${train}/decode_tgpr_5k_dt05_real_$enhan &
+  steps/decode_fmllr.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" \
+    exp/tri3b_tr05_multi_${train}/graph_tgpr_5k data/dt05_simu_$enhan exp/tri3b_tr05_multi_${train}/decode_tgpr_5k_dt05_simu_$enhan &
+  if $eval_flag; then
+  steps/decode_fmllr.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" \
+    exp/tri3b_tr05_multi_${train}/graph_tgpr_5k data/et05_real_$enhan exp/tri3b_tr05_multi_${train}/decode_tgpr_5k_et05_real_$enhan &
+  steps/decode_fmllr.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" \
+    exp/tri3b_tr05_multi_${train}/graph_tgpr_5k data/et05_simu_$enhan exp/tri3b_tr05_multi_${train}/decode_tgpr_5k_et05_simu_$enhan &
+  fi
+  wait;
+fi
+
+# scoring
+if [ $stage -le 9 ]; then
+  # decoded results of enhanced speech using AMs trained with enhanced data
+  local/chime4_calc_wers.sh exp/tri3b_tr05_multi_${train} $enhan exp/tri3b_tr05_multi_${train}/graph_tgpr_5k \
+    > exp/tri3b_tr05_multi_${train}/best_wer_$enhan.result
+  head -n 15 exp/tri3b_tr05_multi_${train}/best_wer_$enhan.result
+fi
+#### tsting done ####
+#####################
+
+echo "`basename $0` Done."

tools/ASR_2ch_track/local/run_gmm_recog.sh

@@ -0,0 +1,127 @@
+#!/bin/bash
+
+# Copyright 2016 University of Sheffield (Jon Barker, Ricard Marxer)
+#                Inria (Emmanuel Vincent)
+#                Mitsubishi Electric Research Labs (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+# This script is made from the kaldi recipe of the 2nd CHiME Challenge Track 2
+# made by Chao Weng
+
+. ./path.sh
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+# Config:
+nj=30
+stage=0 # resume training with --stage=N
+train=noisy
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+# This is a shell script, but it's recommended that you run the commands one by
+# one by copying and pasting into the shell.
+
+if [ $# -ne 3 ]; then
+  printf "\nUSAGE: %s <enhancement method> <enhanced speech directory> <model dir>\n\n" `basename $0`
+  echo "First argument specifies a unique name for different enhancement method"
+  echo "Second argument specifies the directory of enhanced wav files"
+  echo "Third argument specifies acoustic and language model directory"
+  exit 1;
+fi
+
+# set enhanced data
+enhan=$1
+enhan_data=$2
+# set model directory
+mdir=$3
+
+# 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
+
+# check data/loca/data
+if [ ! -d $mdir/data/local/data ]; then
+  echo "error, set $mdir correctly"
+  exit 1;
+elif [ ! -d data/local/data ]; then
+  echo "copy $mdir/data/local/data"
+  mkdir -p data/local
+  cp -r $mdir/data/local/data data/local/
+fi
+
+# check gmm model
+if [ ! -d $mdir/exp/tri3b_tr05_multi_${train} ]; then
+  echo "error, set $mdir correctly"
+  exit 1;
+elif [ ! -d exp/tri3b_tr05_multi_${train} ]; then
+  echo "copy $mdir/exp/tri3b_tr05_multi_${train}"
+  mkdir -p exp
+  cp -r $mdir/exp/tri3b_tr05_multi_${train} exp/
+fi
+
+# process for enhanced data
+if [ $stage -le 0 ]; then
+  if [ ! -d data/dt05_real_$enhan ] || [ ! -d data/et05_real_$enhan ]; then
+    local/real_enhan_chime4_data_prep.sh $enhan $enhan_data
+    local/simu_enhan_chime4_data_prep.sh $enhan $enhan_data
+  fi
+fi
+
+# Now make MFCC features for enhanced data
+# mfccdir should be some place with a largish disk where you
+# want to store MFCC features.
+mfccdir=mfcc/$enhan
+if [ $stage -le 1 ]; then
+  if $eval_flag; then
+    tasks="dt05_real_$enhan dt05_simu_$enhan et05_real_$enhan et05_simu_$enhan"
+  else
+    tasks="dt05_real_$enhan dt05_simu_$enhan"
+  fi
+  for x in $tasks; do
+    if [ ! -e data/$x/feats.scp ]; then
+      steps/make_mfcc.sh --nj 8 --cmd "$train_cmd" \
+	data/$x exp/make_mfcc/$x $mfccdir
+      steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir
+    fi
+  done
+fi
+
+# make mixed training set from real and simulation enhanced data
+# multi = simu + real
+if [ $stage -le 2 ]; then
+  if [ ! -d data/dt05_multi_$enhan ]; then
+    utils/combine_data.sh data/dt05_multi_$enhan data/dt05_simu_$enhan data/dt05_real_$enhan
+    if $eval_flag; then
+    utils/combine_data.sh data/et05_multi_$enhan data/et05_simu_$enhan data/et05_real_$enhan
+    fi
+  fi
+fi
+
+# decode enhanced speech using AMs trained with enhanced data
+if [ $stage -le 3 ]; then
+  steps/decode_fmllr.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" \
+    exp/tri3b_tr05_multi_${train}/graph_tgpr_5k data/dt05_real_$enhan exp/tri3b_tr05_multi_${train}/decode_tgpr_5k_dt05_real_$enhan &
+  steps/decode_fmllr.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" \
+    exp/tri3b_tr05_multi_${train}/graph_tgpr_5k data/dt05_simu_$enhan exp/tri3b_tr05_multi_${train}/decode_tgpr_5k_dt05_simu_$enhan &
+  if $eval_flag; then
+  steps/decode_fmllr.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" \
+    exp/tri3b_tr05_multi_${train}/graph_tgpr_5k data/et05_real_$enhan exp/tri3b_tr05_multi_${train}/decode_tgpr_5k_et05_real_$enhan &
+  steps/decode_fmllr.sh --nj 4 --num-threads 3 --cmd "$decode_cmd" \
+    exp/tri3b_tr05_multi_${train}/graph_tgpr_5k data/et05_simu_$enhan exp/tri3b_tr05_multi_${train}/decode_tgpr_5k_et05_simu_$enhan &
+  fi
+  wait;
+fi
+
+# scoring
+if [ $stage -le 4 ]; then
+  # decoded results of enhanced speech using AMs trained with enhanced data
+  local/chime4_calc_wers.sh exp/tri3b_tr05_multi_${train} $enhan exp/tri3b_tr05_multi_${train}/graph_tgpr_5k \
+    > exp/tri3b_tr05_multi_${train}/best_wer_$enhan.result
+  head -n 15 exp/tri3b_tr05_multi_${train}/best_wer_$enhan.result
+fi
+
+echo "`basename $0` Done."

tools/ASR_2ch_track/local/run_init.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+# Copyright 2016 University of Sheffield (Jon Barker, Ricard Marxer)
+#                Inria (Emmanuel Vincent)
+#                Mitsubishi Electric Research Labs (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+# Config:
+nj=30
+stage=0 # resume training with --stage=N
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+# This script is made from the kaldi recipe of the 2nd CHiME Challenge Track 2
+# made by Chao Weng
+
+. ./path.sh
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+if [ $# -ne 1 ]; then
+  printf "\nUSAGE: %s <Chime4 root directory>\n\n" `basename $0`
+  echo "Please specifies a CHiME4 root directory"
+  echo "If you use scripts distributed in the CHiME4 package,"
+  echo "It would be `pwd`/../.."
+  exit 1;
+fi
+
+# This is a shell script, but it's recommended that you run the commands one by
+# one by copying and pasting into the shell.
+
+# clean data
+chime4_data=$1
+wsj0_data=$chime4_data/data/WSJ0 # directory of WSJ0 in Chime4. You can also specify your WSJ0 corpus directory
+
+# 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
+
+if [ $stage -le 0 ]; then
+  # process for clean speech and making LMs etc. from original WSJ0
+  # note that training on clean data means original WSJ0 data only (no booth data)
+  local/clean_wsj0_data_prep.sh $wsj0_data
+  local/wsj_prepare_dict.sh
+  utils/prepare_lang.sh data/local/dict "<SPOKEN_NOISE>" data/local/lang_tmp data/lang
+  local/clean_chime4_format_data.sh
+fi
+
+echo "`basename $0` Done."

tools/ASR_2ch_track/local/run_lmrescore.sh

@@ -0,0 +1,136 @@
+#!/bin/bash
+
+# Copyright 2015 University of Sheffield (Jon Barker, Ricard Marxer)
+#                Inria (Emmanuel Vincent)
+#                Mitsubishi Electric Research Labs (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+# Copyright 2015, Mitsubishi Electric Research Laboratories, MERL (Author: Takaaki Hori)
+
+nj=12
+stage=1
+order=5
+hidden=300
+rnnweight=0.5
+nbest=100
+train=noisy
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+. ./path.sh
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+# This is a shell script, but it's recommended that you run the commands one by
+# one by copying and pasting into the shell.
+
+if [ $# -ne 2 ]; then
+  printf "\nUSAGE: %s <Chime4 root directory> <enhancement method>\n\n" `basename $0`
+  echo "First argument specifies a root directory of Chime4 data"
+  echo "Second argument specifies a unique name for different enhancement method"
+  exit 1;
+fi
+
+# set language models
+lm_suffix=${order}gkn_5k
+rnnlm_suffix=rnnlm_5k_h${hidden}
+
+# data root
+chime4_data=$1
+# enhan data
+enhan=$2
+
+# check data
+if [ ! -d $chime4_data ]; then
+  echo "$chime4_data does not exist. Please specify chime4 data root correctly" && exit 1
+fi
+
+# check whether run_dnn is executed
+srcdir=exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats
+if [ ! -d $srcdir ]; then
+  echo "error, execute local/run_dnn.sh, first"
+  exit 1;
+fi
+
+# train a high-order n-gram language model
+if [ $stage -le 1 ]; then
+  local/chime4_train_lms.sh $chime4_data || exit 1;
+fi
+
+# train a RNN language model
+if [ $stage -le 2 ]; then
+  local/chime4_train_rnnlms.sh $chime4_data || exit 1;
+fi
+
+# preparation
+dir=exp/tri4a_dnn_tr05_multi_${train}_smbr_lmrescore
+mkdir -p $dir
+# make a symbolic link to graph info
+if [ ! -e $dir/graph_tgpr_5k ]; then
+  if [ ! -e exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k ]; then
+    echo "graph is missing, execute local/run_dnn.sh, correctly"
+    exit 1;
+  fi
+  pushd . ; cd $dir
+  ln -s ../tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k .
+  popd
+fi
+
+# rescore lattices by a high-order N-gram
+if [ $stage -le 3 ]; then
+  # check the best iteration
+  if [ ! -f $srcdir/log/best_wer_$enhan ]; then
+    echo "error, execute local/run_dnn.sh, first"
+    exit 1;
+  fi
+  it=`cut -f 1 -d" " $srcdir/log/best_wer_$enhan | awk -F'[_]' '{print $1}'`
+  # rescore lattices
+  if $eval_flag; then
+    tasks="dt05_simu dt05_real et05_simu et05_real"
+  else
+    tasks="dt05_simu dt05_real"
+  fi
+  for t in $tasks; do
+    steps/lmrescore.sh --mode 3 \
+      data/lang_test_tgpr_5k \
+      data/lang_test_${lm_suffix} \
+      data-fmllr-tri3b/${t}_$enhan \
+      $srcdir/decode_tgpr_5k_${t}_${enhan}_it$it \
+      $dir/decode_tgpr_5k_${t}_${enhan}_${lm_suffix}
+  done
+  # rescored results by high-order n-gram LM
+  mkdir -p $dir/log
+  local/chime4_calc_wers.sh $dir ${enhan}_${lm_suffix} $dir/graph_tgpr_5k \
+      > $dir/best_wer_${enhan}_${lm_suffix}.result
+  head -n 15 $dir/best_wer_${enhan}_${lm_suffix}.result
+fi
+
+# N-best rescoring using a RNNLM
+if [ $stage -le 4 ]; then
+  # check the best lmw
+  if [ ! -f $dir/log/best_wer_${enhan}_${lm_suffix} ]; then
+    echo "error, rescoring with a high-order n-gram seems to be failed"
+    exit 1;
+  fi
+  lmw=`cut -f 1 -d" " $dir/log/best_wer_${enhan}_${lm_suffix} | awk -F'[_]' '{print $NF}'`
+  # rescore n-best list for all sets
+  if $eval_flag; then
+    tasks="dt05_simu dt05_real et05_simu et05_real"
+  else
+    tasks="dt05_simu dt05_real"
+  fi
+  for t in $tasks; do
+    steps/rnnlmrescore.sh --inv-acwt $lmw --N $nbest --use-phi true \
+      $rnnweight \
+      data/lang_test_${lm_suffix} \
+      data/lang_test_${rnnlm_suffix} \
+      data-fmllr-tri3b/${t}_$enhan \
+      $dir/decode_tgpr_5k_${t}_${enhan}_${lm_suffix} \
+      $dir/decode_tgpr_5k_${t}_${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest}
+  done
+  # calc wers for RNNLM results
+  local/chime4_calc_wers.sh $dir ${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest} $dir/graph_tgpr_5k \
+      > $dir/best_wer_${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest}.result
+  head -n 15 $dir/best_wer_${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest}.result
+fi

tools/ASR_2ch_track/local/run_lmrescore_recog.sh

@@ -0,0 +1,121 @@
+#!/bin/bash
+
+# Copyright 2015 University of Sheffield (Jon Barker, Ricard Marxer)
+#                Inria (Emmanuel Vincent)
+#                Mitsubishi Electric Research Labs (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+# Copyright 2015, Mitsubishi Electric Research Laboratories, MERL (Author: Takaaki Hori)
+
+nj=12
+stage=1
+order=5
+hidden=300
+rnnweight=0.5
+nbest=100
+train=noisy
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+. ./path.sh
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+
+# This is a shell script, but it's recommended that you run the commands one by
+# one by copying and pasting into the shell.
+
+if [ $# -ne 2 ]; then
+  printf "\nUSAGE: %s <enhancement method> <model dir>\n\n" `basename $0`
+  echo "First argument specifies a unique name for different enhancement method"
+  echo "Second argument specifies acoustic and language model directory"
+  exit 1;
+fi
+
+# set language models
+lm_suffix=${order}gkn_5k
+rnnlm_suffix=rnnlm_5k_h${hidden}
+
+# enhan data
+enhan=$1
+# set model directory
+mdir=$2
+srcdir=exp/tri4a_dnn_tr05_multi_${train}_smbr_i1lats
+
+# check language models
+if [ ! -d $mdir/data/lang ]; then
+  echo "error, set $mdir correctly"
+  exit 1;
+fi
+
+# preparation
+dir=exp/tri4a_dnn_tr05_multi_${train}_smbr_lmrescore
+mkdir -p $dir
+# make a symbolic link to graph info
+if [ ! -e $dir/graph_tgpr_5k ]; then
+  if [ ! -e exp/tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k ]; then
+    echo "graph is missing, execute local/run_dnn.sh, correctly"
+    exit 1;
+  fi
+  pushd . ; cd $dir
+  ln -s ../tri4a_dnn_tr05_multi_${train}/graph_tgpr_5k .
+  popd
+fi
+
+# rescore lattices by a high-order N-gram
+if [ $stage -le 3 ]; then
+  # check the best iteration
+  if [ ! -f $srcdir/log/best_wer_$enhan ]; then
+    echo "$0: error $srcdir/log/best_wer_$enhan not found. execute local/run_dnn.sh, first"
+    exit 1;
+  fi
+  it=`cut -f 1 -d" " $srcdir/log/best_wer_$enhan | awk -F'[_]' '{print $1}'`
+  # rescore lattices
+  if $eval_flag; then
+    tasks="dt05_simu dt05_real et05_simu et05_real"
+  else
+    tasks="dt05_simu dt05_real"
+  fi
+  for t in $tasks; do
+    steps/lmrescore.sh --mode 3 \
+      $mdir/data/lang_test_tgpr_5k \
+      $mdir/data/lang_test_${lm_suffix} \
+      data-fmllr-tri3b/${t}_$enhan \
+      $srcdir/decode_tgpr_5k_${t}_${enhan}_it$it \
+      $dir/decode_tgpr_5k_${t}_${enhan}_${lm_suffix}
+  done
+  # rescored results by high-order n-gram LM
+  mkdir -p $dir/log
+  local/chime4_calc_wers.sh $dir ${enhan}_${lm_suffix} $dir/graph_tgpr_5k \
+      > $dir/best_wer_${enhan}_${lm_suffix}.result
+  head -n 15 $dir/best_wer_${enhan}_${lm_suffix}.result
+fi
+
+# N-best rescoring using a RNNLM
+if [ $stage -le 4 ]; then
+  # check the best lmw
+  if [ ! -f $dir/log/best_wer_${enhan}_${lm_suffix} ]; then
+    echo "error, rescoring with a high-order n-gram seems to be failed"
+    exit 1;
+  fi
+  lmw=`cut -f 1 -d" " $dir/log/best_wer_${enhan}_${lm_suffix} | awk -F'[_]' '{print $NF}'`
+  # rescore n-best list for all sets
+  if $eval_flag; then
+    tasks="dt05_simu dt05_real et05_simu et05_real"
+  else
+    tasks="dt05_simu dt05_real"
+  fi
+  for t in $tasks; do
+    steps/rnnlmrescore.sh --inv-acwt $lmw --N $nbest --use-phi true \
+      $rnnweight \
+      $mdir/data/lang_test_${lm_suffix} \
+      $mdir/data/lang_test_${rnnlm_suffix} \
+      data-fmllr-tri3b/${t}_$enhan \
+      $dir/decode_tgpr_5k_${t}_${enhan}_${lm_suffix} \
+      $dir/decode_tgpr_5k_${t}_${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest}
+  done
+  # calc wers for RNNLM results
+  local/chime4_calc_wers.sh $dir ${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest} $dir/graph_tgpr_5k \
+      > $dir/best_wer_${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest}.result
+  head -n 15 $dir/best_wer_${enhan}_${rnnlm_suffix}_w${rnnweight}_n${nbest}.result
+fi

tools/ASR_2ch_track/local/score.sh

@@ -0,0 +1,67 @@
+#!/bin/bash
+# Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+[ -f ./path.sh ] && . ./path.sh
+
+# begin configuration section.
+cmd=run.pl
+stage=0
+decode_mbr=true
+reverse=false
+word_ins_penalty=0.0
+min_lmwt=5
+max_lmwt=20
+#end configuration section.
+
+[ -f ./path.sh ] && . ./path.sh
+. parse_options.sh || exit 1;
+
+if [ $# -ne 3 ]; then
+  echo "Usage: local/score.sh [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <decode-dir>"
+  echo " Options:"
+  echo "    --cmd (run.pl|queue.pl...)      # specify how to run the sub-processes."
+  echo "    --stage (0|1|2)                 # start scoring script from part-way through."
+  echo "    --decode_mbr (true/false)       # maximum bayes risk decoding (confusion network)."
+  echo "    --min_lmwt <int>                # minumum LM-weight for lattice rescoring "
+  echo "    --max_lmwt <int>                # maximum LM-weight for lattice rescoring "
+  echo "    --reverse (true/false)          # score with time reversed features "
+  exit 1;
+fi
+
+data=$1
+lang_or_graph=$2
+dir=$3
+
+symtab=$lang_or_graph/words.txt
+
+for f in $symtab $dir/lat.1.gz $data/text; do
+  [ ! -f $f ] && echo "score.sh: no such file $f" && exit 1;
+done
+
+mkdir -p $dir/scoring/log
+
+cat $data/text | sed 's:<NOISE>::g' | sed 's:<SPOKEN_NOISE>::g' > $dir/scoring/test_filt.txt
+
+$cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/best_path.LMWT.log \
+  lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
+  lattice-add-penalty --word-ins-penalty=$word_ins_penalty ark:- ark:- \| \
+  lattice-best-path --word-symbol-table=$symtab \
+    ark:- ark,t:$dir/scoring/LMWT.tra || exit 1;
+
+if $reverse; then
+  for lmwt in `seq $min_lmwt $max_lmwt`; do
+    mv $dir/scoring/$lmwt.tra $dir/scoring/$lmwt.tra.orig
+    awk '{ printf("%s ",$1); for(i=NF; i>1; i--){ printf("%s ",$i); } printf("\n"); }' \
+       <$dir/scoring/$lmwt.tra.orig >$dir/scoring/$lmwt.tra
+  done
+fi
+
+# Note: the double level of quoting for the sed command
+$cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/score.LMWT.log \
+   cat $dir/scoring/LMWT.tra \| \
+    utils/int2sym.pl -f 2- $symtab \| sed 's:\<UNK\>::g' \| \
+    compute-wer --text --mode=present \
+     ark:$dir/scoring/test_filt.txt  ark,p:- ">&" $dir/wer_LMWT || exit 1;
+
+exit 0;

tools/ASR_2ch_track/local/simu_enhan_chime4_data_prep.sh

@@ -0,0 +1,112 @@
+#!/bin/bash
+set -e
+
+# Copyright 2009-2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0.
+
+# This is modified from the script in standard Kaldi recipe to account
+# for the way the WSJ data is structured on the Edinburgh systems.
+# - Arnab Ghoshal, 29/05/12
+
+# Modified from the script for CHiME2 baseline
+# Shinji Watanabe 02/13/2015
+
+# Config:
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# -ne 2 ]; then
+  printf "\nUSAGE: %s <enhancement-name> <enhanced-speech-directory>\n\n" `basename $0`
+  echo "The argument should be a the directory that only contains enhanced speech data."
+  exit 1;
+fi
+
+echo "$0 $@"  # Print the command line for logging
+
+enhan=$1
+audio_dir=$2
+
+dir=`pwd`/data/local/data
+mkdir -p $dir
+local=`pwd`/local
+utils=`pwd`/utils
+odir=`pwd`/data
+
+. ./path.sh # Needed for KALDI_ROOT
+export PATH=$PATH:$KALDI_ROOT/tools/irstlm/bin
+sph2pipe=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe
+if [ ! -x $sph2pipe ]; then
+  echo "Could not find (or execute) the sph2pipe program at $sph2pipe";
+  exit 1;
+fi
+
+if $eval_flag; then
+list_set="tr05_simu_$enhan dt05_simu_$enhan et05_simu_$enhan"
+else
+list_set="tr05_simu_$enhan dt05_simu_$enhan"
+fi
+
+cd $dir
+
+find $audio_dir/ -name '*.wav' | grep 'tr05_bus_simu\|tr05_caf_simu\|tr05_ped_simu\|tr05_str_simu' | sort -u > tr05_simu_$enhan.flist
+find $audio_dir/ -name '*.wav' | grep 'dt05_bus_simu\|dt05_caf_simu\|dt05_ped_simu\|dt05_str_simu' | sort -u > dt05_simu_$enhan.flist
+if $eval_flag; then
+find $audio_dir/ -name '*.wav' | grep 'et05_bus_simu\|et05_caf_simu\|et05_ped_simu\|et05_str_simu' | sort -u > et05_simu_$enhan.flist
+fi
+
+# make a scp file from file list
+for x in $list_set; do
+    cat $x.flist | awk -F'[/]' '{print $NF}'| sed -e 's/\.wav/_SIMU/' > ${x}_wav.ids
+    paste -d" " ${x}_wav.ids $x.flist | sort -k 1 > ${x}_wav.scp
+done
+
+# make a transcription from dot
+# simulation training data extract dot file from original WSJ0 data
+# since it is generated from these data
+if [ ! -e dot_files.flist ]; then
+  echo "Could not find $dir/dot_files.flist files, first run local/clean_wsj0_data_prep.sh";
+  exit 1;
+fi
+cat tr05_simu_${enhan}_wav.scp | awk -F'[_]' '{print $2}' | tr '[A-Z]' '[a-z]' \
+    | $local/find_noisy_transcripts.pl dot_files.flist | cut -f 2- -d" " > tr05_simu_$enhan.txt
+cat tr05_simu_${enhan}_wav.scp | cut -f 1 -d" " > tr05_simu_$enhan.ids
+paste -d" " tr05_simu_$enhan.ids tr05_simu_$enhan.txt | sort -k 1 > tr05_simu_$enhan.trans1
+# dt05 and et05 simulation data are generated from the CHiME4 booth recording
+# and we use CHiME4 dot files
+cat dt05_simu.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF "_SIMU"}'> dt05_simu_$enhan.ids
+cat dt05_simu.dot | sed -e 's/(.*)//' > dt05_simu_$enhan.txt
+paste -d" " dt05_simu_$enhan.ids dt05_simu_$enhan.txt | sort -k 1 > dt05_simu_$enhan.trans1
+if $eval_flag; then
+cat et05_simu.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF "_SIMU"}'> et05_simu_$enhan.ids
+cat et05_simu.dot | sed -e 's/(.*)//' > et05_simu_$enhan.txt
+paste -d" " et05_simu_$enhan.ids et05_simu_$enhan.txt | sort -k 1 > et05_simu_$enhan.trans1
+fi
+
+# Do some basic normalization steps.  At this point we don't remove OOVs--
+# that will be done inside the training scripts, as we'd like to make the
+# data-preparation stage independent of the specific lexicon used.
+noiseword="<NOISE>";
+for x in $list_set;do
+  cat $x.trans1 | $local/normalize_transcript.pl $noiseword \
+    | sort > $x.txt || exit 1;
+done
+
+# Make the utt2spk and spk2utt files.
+for x in $list_set; do
+  cat ${x}_wav.scp | awk -F'_' '{print $1}' > $x.spk
+  cat ${x}_wav.scp | awk '{print $1}' > $x.utt
+  paste -d" " $x.utt $x.spk > $x.utt2spk
+  cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
+done
+
+# copying data to data/...
+for x in $list_set; do
+  mkdir -p $odir/$x
+  cp ${x}_wav.scp $odir/$x/wav.scp || exit 1;
+  cp ${x}.txt     $odir/$x/text    || exit 1;
+  cp ${x}.spk2utt $odir/$x/spk2utt || exit 1;
+  cp ${x}.utt2spk $odir/$x/utt2spk || exit 1;
+done
+
+echo "Data preparation succeeded"

tools/ASR_2ch_track/local/simu_noisy_chime4_data_prep.sh

@@ -0,0 +1,122 @@
+#!/bin/bash
+set -e
+
+# Copyright 2009-2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0.
+
+# This is modified from the script in standard Kaldi recipe to account
+# for the way the WSJ data is structured on the Edinburgh systems.
+# - Arnab Ghoshal, 29/05/12
+
+# Modified from the script for CHiME2 baseline
+# Shinji Watanabe 02/13/2015
+
+# Config:
+eval_flag=true # make it true when the evaluation data are released
+
+. utils/parse_options.sh || exit 1;
+
+if [ $# -ne 1 ]; then
+  printf "\nUSAGE: %s <corpus-directory>\n\n" `basename $0`
+  echo "The argument should be a the top-level Chime4 directory."
+  echo "It is assumed that there will be a 'data' subdirectory"
+  echo "within the top-level corpus directory."
+  exit 1;
+fi
+
+echo "$0 $@"  # Print the command line for logging
+
+audio_dir=$1/data/audio/16kHz/isolated
+trans_dir=$1/data/transcriptions
+
+echo "extract 5th channel (CH5.wav, the center bottom edge in the front of the tablet) for noisy data"
+
+dir=`pwd`/data/local/data
+lmdir=`pwd`/data/local/nist_lm
+mkdir -p $dir $lmdir
+local=`pwd`/local
+utils=`pwd`/utils
+
+. ./path.sh # Needed for KALDI_ROOT
+export PATH=$PATH:$KALDI_ROOT/tools/irstlm/bin
+sph2pipe=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe
+if [ ! -x $sph2pipe ]; then
+  echo "Could not find (or execute) the sph2pipe program at $sph2pipe";
+  exit 1;
+fi
+
+if $eval_flag; then
+list_set="tr05_simu_noisy dt05_simu_noisy et05_simu_noisy"
+else
+list_set="tr05_simu_noisy dt05_simu_noisy"
+fi
+
+cd $dir
+
+find $audio_dir -name '*CH5.wav' | grep 'tr05_bus_simu\|tr05_caf_simu\|tr05_ped_simu\|tr05_str_simu' | sort -u > tr05_simu_noisy.flist
+find $audio_dir -name '*CH5.wav' | grep 'dt05_bus_simu\|dt05_caf_simu\|dt05_ped_simu\|dt05_str_simu' | sort -u > dt05_simu_noisy.flist
+if $eval_flag; then
+find $audio_dir -name '*CH5.wav' | grep 'et05_bus_simu\|et05_caf_simu\|et05_ped_simu\|et05_str_simu' | sort -u > et05_simu_noisy.flist
+fi
+
+# make a dot format from json annotation files
+cp $trans_dir/dt05_simu.dot_all dt05_simu.dot
+if $eval_flag; then
+cp $trans_dir/et05_simu.dot_all et05_simu.dot
+fi
+
+# make a scp file from file list
+for x in $list_set; do
+    cat $x.flist | awk -F'[/]' '{print $NF}'| sed -e 's/\.wav/_SIMU/' > ${x}_wav.ids
+    paste -d" " ${x}_wav.ids $x.flist | sort -k 1 > ${x}_wav.scp
+done
+
+# make a transcription from dot
+# simulation training data extract dot file from original WSJ0 data
+# since it is generated from these data
+if [ ! -e dot_files.flist ]; then
+  echo "Could not find $dir/dot_files.flist files, first run local/clean_wsj0_data_prep.sh";
+  exit 1;
+fi
+cat tr05_simu_noisy_wav.scp | awk -F'[_]' '{print $2}' | tr '[A-Z]' '[a-z]' \
+    | $local/find_noisy_transcripts.pl dot_files.flist | cut -f 2- -d" " > tr05_simu_noisy.txt
+cat tr05_simu_noisy_wav.scp | cut -f 1 -d" " > tr05_simu_noisy.ids
+paste -d" " tr05_simu_noisy.ids tr05_simu_noisy.txt | sort -k 1 > tr05_simu_noisy.trans1
+# dt05 and et05 simulation data are generated from the CHiME4 booth recording
+# and we use CHiME4 dot files
+cat dt05_simu.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF ".CH5_SIMU"}'> dt05_simu_noisy.ids
+cat dt05_simu.dot | sed -e 's/(.*)//' > dt05_simu_noisy.txt
+paste -d" " dt05_simu_noisy.ids dt05_simu_noisy.txt | sort -k 1 > dt05_simu_noisy.trans1
+if $eval_flag; then
+cat et05_simu.dot | sed -e 's/(\(.*\))/\1/' | awk '{print $NF ".CH5_SIMU"}'> et05_simu_noisy.ids
+cat et05_simu.dot | sed -e 's/(.*)//' > et05_simu_noisy.txt
+paste -d" " et05_simu_noisy.ids et05_simu_noisy.txt | sort -k 1 > et05_simu_noisy.trans1
+fi
+
+# Do some basic normalization steps.  At this point we don't remove OOVs--
+# that will be done inside the training scripts, as we'd like to make the
+# data-preparation stage independent of the specific lexicon used.
+noiseword="<NOISE>";
+for x in $list_set;do
+  cat $x.trans1 | $local/normalize_transcript.pl $noiseword \
+    | sort > $x.txt || exit 1;
+done
+
+# Make the utt2spk and spk2utt files.
+for x in $list_set; do
+  cat ${x}_wav.scp | awk -F'_' '{print $1}' > $x.spk
+  cat ${x}_wav.scp | awk '{print $1}' > $x.utt
+  paste -d" " $x.utt $x.spk > $x.utt2spk
+  cat $x.utt2spk | $utils/utt2spk_to_spk2utt.pl > $x.spk2utt || exit 1;
+done
+
+# copying data to data/...
+for x in $list_set; do
+  mkdir -p ../../$x
+  cp ${x}_wav.scp ../../$x/wav.scp || exit 1;
+  cp ${x}.txt     ../../$x/text    || exit 1;
+  cp ${x}.spk2utt ../../$x/spk2utt || exit 1;
+  cp ${x}.utt2spk ../../$x/utt2spk || exit 1;
+done
+
+echo "Data preparation succeeded"

tools/ASR_2ch_track/local/wsj_prepare_dict.sh

@@ -0,0 +1,86 @@
+#!/bin/bash
+
+# Copyright 2010-2012 Microsoft Corporation  
+#           2012-2014 Johns Hopkins University (Author: Daniel Povey)
+
+# 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.
+
+# Call this script from one level above, e.g. from the s3/ directory.  It puts
+# its output in data/local/.
+
+# 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
+
+# run this from ../
+dir=data/local/dict
+mkdir -p $dir
+
+
+# (1) Get the CMU dictionary
+svn co  https://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict \
+  $dir/cmudict || exit 1;
+
+# can add -r 10966 for strict compatibility.
+
+
+#(2) Dictionary preparation:
+
+
+# Make phones symbol-table (adding in silence and verbal and non-verbal noises at this point).
+# We are adding suffixes _B, _E, _S for beginning, ending, and singleton phones.
+
+# silence phones, one per line.
+(echo SIL; echo SPN; echo NSN) > $dir/silence_phones.txt
+echo SIL > $dir/optional_silence.txt
+
+# nonsilence phones; on each line is a list of phones that correspond
+# really to the same base phone.
+cat $dir/cmudict/cmudict.0.7a.symbols | perl -ane 's:\r::; print;' | \
+ perl -e 'while(<>){
+  chop; m:^([^\d]+)(\d*)$: || die "Bad phone $_"; 
+  $phones_of{$1} .= "$_ "; }
+  foreach $list (values %phones_of) {print $list . "\n"; } ' \
+  | sort > $dir/nonsilence_phones.txt || exit 1;
+
+# A few extra questions that will be added to those obtained by automatically clustering
+# the "real" phones.  These ask about stress; there's also one for silence.
+cat $dir/silence_phones.txt| awk '{printf("%s ", $1);} END{printf "\n";}' > $dir/extra_questions.txt || exit 1;
+cat $dir/nonsilence_phones.txt | perl -e 'while(<>){ foreach $p (split(" ", $_)) {
+  $p =~ m:^([^\d]+)(\d*)$: || die "Bad phone $_"; $q{$2} .= "$p "; } } foreach $l (values %q) {print "$l\n";}' \
+ >> $dir/extra_questions.txt || exit 1;
+
+grep -v ';;;' $dir/cmudict/cmudict.0.7a | \
+ perl -ane 'if(!m:^;;;:){ s:(\S+)\(\d+\) :$1 :; print; }' \
+  > $dir/lexicon1_raw_nosil.txt || exit 1;
+
+# Add to cmudict the silences, noises etc.
+
+# the sort | uniq is to remove a duplicated pron from cmudict.
+(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; echo '<NOISE> NSN'; ) | \
+ cat - $dir/lexicon1_raw_nosil.txt | sort | uniq > $dir/lexicon2_raw.txt || exit 1;
+
+
+# lexicon.txt is without the _B, _E, _S, _I markers.
+# This is the input to wsj_format_data.sh
+cp $dir/lexicon2_raw.txt $dir/lexicon.txt
+
+rm $dir/lexiconp.txt 2>/dev/null
+
+echo "Dictionary preparation succeeded"
+

tools/ASR_2ch_track/path.sh

@@ -0,0 +1,10 @@
+if [ -z $KALDI_ROOT ] ; then
+  echo "Environemnt variable KALDI_ROOT is unset. Setting it to default /data1/tools/kaldi"
+  export KALDI_ROOT=/data1/tools/kaldi
+fi
+
+[ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
+export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/tools/irstlm/bin/:$KALDI_ROOT/tools/kaldi_lm/:$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

tools/ASR_2ch_track/run.sh

@@ -0,0 +1,93 @@
+#!/bin/bash
+
+# Kaldi ASR baseline for the CHiME-4 Challenge (2ch track: 2 channel track)
+#
+# Copyright 2016 University of Sheffield (Jon Barker, Ricard Marxer)
+#                Inria (Emmanuel Vincent)
+#                Mitsubishi Electric Research Labs (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+. ./path.sh
+. ./cmd.sh
+
+# Config:
+stage=0 # resume training with --stage=N
+flatstart=false
+
+. utils/parse_options.sh || exit 1;
+
+# 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
+
+#####check data and model paths################
+# Set a main root directory of the CHiME4 data
+# If you use scripts distributed in the CHiME4 package,
+chime4_data=`pwd`/../..
+# Otherwise, please specify it, e.g.,
+# chime4_data=/db/laputa1/data/original/public/CHiME4
+if [ ! -d $chime4_data ]; then
+  echo "$chime4_data does not exist. Please specify chime4 data root correctly" && exit 1
+fi
+# Set a model directory for the CHiME4 data.
+modeldir=$chime4_data/tools/ASR_models
+for d in $modeldir $modeldir/data/{lang,lang_test_tgpr_5k,lang_test_5gkn_5k,lang_test_rnnlm_5k_h300,local} \
+  $modeldir/exp/{tri3b_tr05_multi_noisy,tri4a_dnn_tr05_multi_noisy,tri4a_dnn_tr05_multi_noisy_smbr_i1lats}; do
+  [ ! -d ] && echo "$0: no such directory $d. specify models correctly or execute './run.sh --flatstart true' first" && exit 1;
+done
+#####check data and model paths finished#######
+
+
+#####main program start################
+# You can execute run_init.sh only "once"
+# This creates 3-gram LM, FSTs, and basic task files
+if [ $stage -le 0 ] && $flatstart; then
+  local/run_init.sh $chime4_data
+fi
+
+# Using Beamformit
+# See Hori et al, "The MERL/SRI system for the 3rd CHiME challenge using beamforming,
+# robust feature extraction, and advanced speech recognition," in Proc. ASRU'15
+# note that beamformed wav files are generated in the following directory
+enhancement_method=beamformit_2mics
+enhancement_data=`pwd`/enhan/$enhancement_method
+if [ $stage -le 1 ]; then
+  local/run_beamform_2ch_track.sh --cmd "$train_cmd" --nj 20 $chime4_data/data/audio/16kHz/isolated_2ch_track $enhancement_data
+fi
+
+# GMM based ASR experiment without "retraining"
+# Please set a directory of your speech enhancement method.
+# run_gmm_recog.sh can be done every time when you change a speech enhancement technique.
+# The directory structure and audio files must follow the attached baseline enhancement directory
+if [ $stage -le 2 ]; then
+  if $flatstart; then
+    local/run_gmm.sh $enhancement_method $enhancement_data $chime4_data
+  else
+    local/run_gmm_recog.sh $enhancement_method $enhancement_data $modeldir
+  fi
+fi
+
+# DNN based ASR experiment
+# Since it takes time to evaluate DNN, we make the GMM and DNN scripts separately.
+# You may execute it after you would have promising results using GMM-based ASR experiments
+if [ $stage -le 3 ]; then
+  if $flatstart; then
+    local/run_dnn.sh $enhancement_method
+  else
+    local/run_dnn_recog.sh $enhancement_method $modeldir
+  fi
+fi
+
+# LM-rescoring experiment with 5-gram and RNN LMs
+# It takes a few days to train a RNNLM.
+if [ $stage -le 4 ]; then
+  if $flatstart; then
+    local/run_lmrescore.sh $chime4_data $enhancement_method
+  else
+    local/run_lmrescore_recog.sh $enhancement_method $modeldir
+  fi
+fi
+
+echo "Done."

tools/ASR_2ch_track/steps/align_basis_fmllr.sh

@@ -0,0 +1,153 @@
+#!/bin/bash
+# Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
+# Copyright 2013  GoVivace Inc (Author: Nagendra Goel)
+# Apache 2.0
+
+# Computes training alignments; assumes features are (LDA+MLLT or delta+delta-delta)
+# + fMLLR (probably with SAT models).
+# It first computes an alignment with the final.alimdl (or the final.mdl if final.alimdl
+# is not present), then does 2 iterations of fMLLR estimation.
+
+# If you supply the --use-graphs option, it will use the training
+# graphs from the source directory (where the model is).  In this
+# case the number of jobs must match the source directory.
+
+
+# Begin configuration section.  
+stage=0
+nj=4
+cmd=run.pl
+use_graphs=false
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
+beam=10
+retry_beam=40
+boost_silence=1.5 # factor by which to boost silence during alignment.
+fmllr_update_type=full
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: steps/align_fmllr.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
+   echo "e.g.:  steps/align_fmllr.sh data/train data/lang exp/tri1 exp/tri1_ali"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   echo "  --fmllr-update-type (full|diag|offset|none)      # default full."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+graphdir=$dir
+
+oov=`cat $lang/oov.int` || exit 1;
+silphonelist=`cat $lang/phones/silence.csl` || exit 1;
+sdata=$data/split$nj
+
+mkdir -p $dir/log
+echo $nj > $dir/num_jobs
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+cp $srcdir/final.occs $dir;
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+delta_opts=`cat $srcdir/delta_opts 2>/dev/null`
+cp $srcdir/delta_opts $dir 2>/dev/null
+
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type"
+
+case $feat_type in
+  delta) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |";;
+  lda) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+    cp $srcdir/final.mat $dir    
+   ;;
+  *) echo "Invalid feature type $feat_type" && exit 1;
+esac
+
+## Set up model and alignment model.
+mdl=$srcdir/final.mdl
+if [ -f $srcdir/final.alimdl ]; then
+  alimdl=$srcdir/final.alimdl
+else
+  alimdl=$srcdir/final.mdl
+fi
+[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
+alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/boost_phones.csl` $alimdl - |"
+mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
+
+## Work out where we're getting the graphs from.
+if $use_graphs; then
+  [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
+    echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
+  [ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
+  graphdir=$srcdir
+else
+  graphdir=$dir
+  if [ $stage -le 0 ]; then
+    echo "$0: compiling training graphs"
+    tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";   
+    $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log  \
+      compile-train-graphs $dir/tree $dir/final.mdl  $lang/L.fst "$tra" \
+        "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
+  fi
+fi
+
+
+if [ $stage -le 1 ]; then
+  echo "$0: aligning data in $data using $alimdl and speaker-independent features."
+  $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$alimdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0: computing fMLLR transforms"
+  if [ "$alimdl" != "$mdl" ]; then
+    $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
+      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+      gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
+      gmm-est-basis-fmllr-gpost --fmllr-min-count=22  --num-iters=10 \
+        --size-scale=0.2 --step-size-iters=3 \
+        --write-weights=ark:$dir/pre_wgt.JOB \
+        $mdl $srcdir/fmllr.basis "$sifeats"  ark,s,cs:- \
+        ark:$dir/trans.JOB || exit 1;
+#  else
+#    $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
+#      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+#      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+#      gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
+#      --spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
+#      ark,s,cs:- ark:$dir/trans.JOB || exit 1;
+  fi
+fi
+
+feats="$sifeats transform-feats ark:$dir/pre_trans.JOB ark:- ark:- |"
+
+if [ $stage -le 3 ]; then
+  echo "$0: doing final alignment."
+  $cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+fi
+
+#rm $dir/pre_ali.*.gz
+
+echo "$0: done aligning data."
+
+utils/summarize_warnings.pl $dir/log
+
+exit 0;

tools/ASR_2ch_track/steps/align_fmllr.sh

@@ -0,0 +1,153 @@
+#!/bin/bash
+# Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# Computes training alignments; assumes features are (LDA+MLLT or delta+delta-delta)
+# + fMLLR (probably with SAT models).
+# It first computes an alignment with the final.alimdl (or the final.mdl if final.alimdl
+# is not present), then does 2 iterations of fMLLR estimation.
+
+# If you supply the --use-graphs option, it will use the training
+# graphs from the source directory (where the model is).  In this
+# case the number of jobs must match the source directory.
+
+
+# Begin configuration section.  
+stage=0
+nj=4
+cmd=run.pl
+use_graphs=false
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
+beam=10
+retry_beam=40
+careful=false
+boost_silence=1.0 # factor by which to boost silence during alignment.
+fmllr_update_type=full
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: steps/align_fmllr.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
+   echo "e.g.:  steps/align_fmllr.sh data/train data/lang exp/tri1 exp/tri1_ali"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   echo "  --fmllr-update-type (full|diag|offset|none)      # default full."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+oov=`cat $lang/oov.int` || exit 1;
+silphonelist=`cat $lang/phones/silence.csl` || exit 1;
+sdata=$data/split$nj
+
+mkdir -p $dir/log
+echo $nj > $dir/num_jobs
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+cp $srcdir/final.alimdl $dir 2>/dev/null
+cp $srcdir/final.occs $dir;
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+delta_opts=`cat $srcdir/delta_opts 2>/dev/null`
+cp $srcdir/delta_opts $dir 2>/dev/null
+
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type"
+
+case $feat_type in
+  delta) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |";;
+  lda) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+    cp $srcdir/final.mat $dir
+    cp $srcdir/full.mat $dir 2>/dev/null
+   ;;
+  *) echo "Invalid feature type $feat_type" && exit 1;
+esac
+
+## Set up model and alignment model.
+mdl=$srcdir/final.mdl
+if [ -f $srcdir/final.alimdl ]; then
+  alimdl=$srcdir/final.alimdl
+else
+  alimdl=$srcdir/final.mdl
+fi
+[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
+alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $alimdl - |"
+mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
+
+
+## Work out where we're getting the graphs from.
+if $use_graphs; then
+  [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
+    echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
+  [ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
+  graphdir=$srcdir
+else
+  graphdir=$dir
+  if [ $stage -le 0 ]; then
+    echo "$0: compiling training graphs"
+    tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";   
+    $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log  \
+      compile-train-graphs $dir/tree $dir/final.mdl  $lang/L.fst "$tra" \
+        "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
+  fi
+fi
+
+
+if [ $stage -le 1 ]; then
+  echo "$0: aligning data in $data using $alimdl and speaker-independent features."
+  $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$alimdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0: computing fMLLR transforms"
+  if [ "$alimdl" != "$mdl" ]; then
+    $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
+      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+      gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
+      gmm-est-fmllr-gpost --fmllr-update-type=$fmllr_update_type \
+      --spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
+      ark,s,cs:- ark:$dir/trans.JOB || exit 1;
+  else
+    $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
+      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+      gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
+      --spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
+      ark,s,cs:- ark:$dir/trans.JOB || exit 1;
+  fi
+fi
+
+feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
+
+if [ $stage -le 3 ]; then
+  echo "$0: doing final alignment."
+  $cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+fi
+
+rm $dir/pre_ali.*.gz
+
+echo "$0: done aligning data."
+
+utils/summarize_warnings.pl $dir/log
+
+exit 0;

tools/ASR_2ch_track/steps/align_fmllr_lats.sh

@@ -0,0 +1,160 @@
+#!/bin/bash
+#
+# Copyright 2012-2015  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# Version of align_fmllr.sh that generates lattices (lat.*.gz) with
+# alignments of alternative pronunciations in them.  Mainly intended
+# as a precursor to CTC training for now.
+
+# Begin configuration section.  
+stage=0
+nj=4
+cmd=run.pl
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --self-loop-scale=0.1"
+acoustic_scale=0.1
+beam=10
+retry_beam=40
+final_beam=20  # For the lattice-generation phase there is no retry-beam.  This
+               # is a limitation of gmm-latgen-faster.  We just use an
+               # intermediate beam.  We'll lose a little data and it will be
+               # slightly slower.  (however, the min-active of 200 that
+               # gmm-latgen-faster defaults to may help.)
+boost_silence=1.0 # factor by which to boost silence during alignment.
+fmllr_update_type=full
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: steps/align_fmllr_lats.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
+   echo "e.g.:  steps/align_fmllr_lats.sh data/train data/lang exp/tri1 exp/tri1_lats"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   echo "  --fmllr-update-type (full|diag|offset|none)      # default full."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+oov=`cat $lang/oov.int` || exit 1;
+silphonelist=`cat $lang/phones/silence.csl` || exit 1;
+sdata=$data/split$nj
+
+mkdir -p $dir/log
+echo $nj > $dir/num_jobs
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+cp $srcdir/final.alimdl $dir 2>/dev/null
+cp $srcdir/final.occs $dir;
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+delta_opts=`cat $srcdir/delta_opts 2>/dev/null`
+cp $srcdir/delta_opts $dir 2>/dev/null
+
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type"
+
+case $feat_type in
+  delta) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |";;
+  lda) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+    cp $srcdir/final.mat $dir
+    cp $srcdir/full.mat $dir 2>/dev/null
+   ;;
+  *) echo "Invalid feature type $feat_type" && exit 1;
+esac
+
+## Set up model and alignment model.
+mdl=$srcdir/final.mdl
+if [ -f $srcdir/final.alimdl ]; then
+  alimdl=$srcdir/final.alimdl
+else
+  alimdl=$srcdir/final.mdl
+fi
+[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
+alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $alimdl - |"
+mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
+
+
+## because gmm-latgen-faster doesn't support adding the transition-probs to the
+## graph itself, we need to bake them into the compiled graphs.  This means we can't reuse previously compiled graphs,
+## because the other scripts write them without transition probs.
+if [ $stage -le 0 ]; then
+  echo "$0: compiling training graphs"
+  tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";   
+  $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log  \
+    compile-train-graphs $scale_opts $dir/tree $dir/final.mdl  $lang/L.fst "$tra" \
+    "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
+fi
+
+
+if [ $stage -le 1 ]; then
+  # Note: we need to set --transition-scale=0.0 --self-loop-scale=0.0 because,
+  # as explained above, we compiled the transition probs into the training
+  # graphs.
+  echo "$0: aligning data in $data using $alimdl and speaker-independent features."
+  $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+    gmm-align-compiled --transition-scale=0.0 --self-loop-scale=0.0 --acoustic-scale=$acoustic_scale \
+        --beam=$beam --retry-beam=$retry_beam "$alimdl_cmd" \
+    "ark:gunzip -c $dir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0: computing fMLLR transforms"
+  if [ "$alimdl" != "$mdl" ]; then
+    $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
+      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+      gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
+      gmm-est-fmllr-gpost --fmllr-update-type=$fmllr_update_type \
+      --spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
+      ark,s,cs:- ark:$dir/trans.JOB || exit 1;
+  else
+    $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
+      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+      gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \
+      --spk2utt=ark:$sdata/JOB/spk2utt $mdl "$sifeats" \
+      ark,s,cs:- ark:$dir/trans.JOB || exit 1;
+  fi
+fi
+
+feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
+
+if [ $stage -le 3 ]; then
+  # Warning: gmm-latgen-faster doesn't support a retry-beam so you may get more
+  # alignment errors (however, it does have a default min-active=200 so this
+  # will tend to reduce alignment errors).
+  # --allow_partial=false makes sure we reach the end of the decoding graph.  
+  # --word-determinize=false makes sure we retain the alternative pronunciations of
+  #   words (including alternatives regarding optional silences).
+  #  --lattice-beam=$beam keeps all the alternatives that were within the beam,
+  #    it means we do no pruning of the lattice (lattices from a training transcription
+  #    will be small anyway).
+  echo "$0: generating lattices containing alternate pronunciations."
+  $cmd JOB=1:$nj $dir/log/generate_lattices.JOB.log \
+    gmm-latgen-faster --acoustic-scale=$acoustic_scale --beam=$final_beam \
+        --lattice-beam=$final_beam --allow-partial=false --word-determinize=false \
+      "$mdl_cmd" "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \
+      "ark:|gzip -c >$dir/lat.JOB.gz" || exit 1;
+fi
+
+rm $dir/pre_ali.*.gz
+
+echo "$0: done generating lattices from training transcripts."
+
+utils/summarize_warnings.pl $dir/log
+
+exit 0;

tools/ASR_2ch_track/steps/align_lvtln.sh

@@ -0,0 +1,196 @@
+#!/bin/bash
+
+# Copyright 2014  Vimal Manohar
+
+# Computes training alignments; assumes features are (LDA+MLLT or delta+delta-delta)
+# Will ignore fMLLR.
+# Will estimate VTLN warping factors
+# as a by product, which can be used to extract VTLN-warped features.
+
+# Begin configuration section
+stage=0
+nj=4
+cmd=run.pl
+use_graphs=false
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
+beam=10.0
+retry_beam=40
+boost_silence=1.0 # factor by which to boost silence during alignment.
+logdet_scale=1.0
+cleanup=false
+
+# End configuration section
+echo "$0 $@"  # Print the command line for logging
+
+[ -f ./path.sh ] && . ./path.sh; # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "Wrong #arguments ($#, expected 4)"
+   echo "Usage: steps/align_lvtln.sh [options] <data-dir> <lang-dir> <src-dir>  <align-dir>"
+   echo " e.g.: steps/align_lvtln.sh data/train data/lang exp/tri2c exp/tri2c_ali"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                   # config containing options"
+   echo "  --nj <nj>                                # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --cmd <cmd>                              # Command to run in parallel with"
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+oov=`cat $lang/oov.int` || exit 1;
+silphonelist=`cat $lang/phones/silence.csl` || exit 1;
+sdata=$data/split$nj
+
+if [ -f $data/spk2warp ]; then
+  echo "$0: file $data/spk2warp exists.  This script expects non-VTLN features"
+  exit 1;
+fi
+
+mkdir -p $dir/log
+echo $nj > $dir/num_jobs
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl,final.lvtln} $dir || exit 1;
+cp $srcdir/final.occs $dir;
+
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+
+## Set up the unadapted features "$sifeats"
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type";
+case $feat_type in
+  delta) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
+  lda) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+    cp $srcdir/final.mat $srcdir/full.mat $dir    
+    ;;
+  *) echo "Invalid feature type $feat_type" && exit 1;
+esac
+
+## Set up model and alignment model.
+mdl=$srcdir/final.mdl
+if [ -f $srcdir/final.alimdl ]; then
+  alimdl=$srcdir/final.alimdl
+else
+  alimdl=$srcdir/final.mdl
+fi
+[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
+alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $alimdl - |"
+mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
+
+## Work out where we're getting the graphs from.
+if $use_graphs; then
+  [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
+    echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
+  [ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
+  graphdir=$srcdir
+else
+  graphdir=$dir
+  if [ $stage -le 0 ]; then
+    echo "$0: compiling training graphs"
+    tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";   
+    $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log  \
+      compile-train-graphs $dir/tree $dir/final.mdl  $lang/L.fst "$tra" \
+        "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
+  fi
+fi
+
+if [ $stage -le 1 ]; then
+  echo "$0: aligning data in $data using $alimdl and speaker-independent features."
+  $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$alimdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
+fi
+
+if [ -f $data/segments ]; then
+  subset_utts="ark:extract-segments scp:$sdata/JOB/wav.scp $sdata/JOB/segments ark:- |"
+else
+  echo "$0 [info]: no segments file exists: using wav.scp directly."
+  subset_utts="ark:wav-copy scp:$sdata/JOB/wav.scp ark:- |"
+fi
+
+## Get the first-pass LVTLN transforms
+if [ $stage -le 2 ]; then
+  echo "$0: computing first-pass LVTLN transforms."
+  $cmd JOB=1:$nj $dir/log/lvtln_pass1.JOB.log \
+    ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+    weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+    gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
+    gmm-est-lvtln-trans --verbose=1 --spk2utt=ark:$sdata/JOB/spk2utt --logdet-scale=$logdet_scale \
+    $mdl $dir/final.lvtln "$sifeats" ark,s,cs:- ark:$dir/trans_pass1.JOB \
+    ark,t:$dir/warp_pass1.JOB || exit 1;
+fi
+##
+
+feats1="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans_pass1.JOB ark:- ark:- |"
+
+## Do a second pass of estimating the LVTLN transform.
+
+if [ $stage -le 3 ]; then
+  echo "$0: realigning with transformed features"
+  $cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats1" "ark:|gzip -c >$dir/ali_pass2.JOB.gz" || exit 1;
+fi
+
+if [ $stage -le 4 ]; then
+  echo "$0: re-estimating LVTLN transforms"
+  $cmd JOB=1:$nj $dir/log/lvtln_pass1.JOB.log \
+    ali-to-post "ark:gunzip -c $dir/ali_pass2.JOB.gz|" ark:- \| \
+    weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+    gmm-post-to-gpost $alimdl "$feats1" ark:- ark:- \| \
+    gmm-est-lvtln-trans --verbose=1 --spk2utt=ark:$sdata/JOB/spk2utt --logdet-scale=$logdet_scale \
+    $mdl $dir/final.lvtln "$sifeats" ark,s,cs:- ark:$dir/trans.JOB \
+    ark,t:$dir/warp.JOB || exit 1;
+fi
+
+feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |"
+
+if [ $stage -le 5 ]; then
+  # This second alignment does not affect the transforms.
+  echo "$0: realigning with the second-pass LVTLN transforms"
+  $cmd JOB=1:$nj $dir/log/align.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+fi
+
+if [ -f $dir/warp.1 ]; then
+  for j in $(seq $nj); do cat $dir/warp_pass1.$j; done > $dir/0.warp || exit 1;
+  for j in $(seq $nj); do cat $dir/warp.$j; done > $dir/final.warp || exit 1;
+  ns1=$(cat $dir/0.warp | wc -l)
+  ns2=$(cat $dir/final.warp | wc -l)
+  ! [ "$ns1" == "$ns2" ] && echo "$0: Number of speakers differ pass1 vs pass2, $ns1 != $ns2" && exit 1;
+
+  paste $dir/0.warp $dir/final.warp | awk '{x=$2 - $4; if ((x>0?x:-x) > 0.010001) { print $1, $2, $4; }}' > $dir/warp_changed
+  nc=$(cat $dir/warp_changed | wc -l)
+  echo "$0: For $nc speakers out of $ns1, warp changed pass1 vs pass2 by >0.01, see $dir/warp_changed for details"
+fi
+
+if true; then # Diagnostics
+  if [ -f $data/spk2gender ]; then 
+    # To make it easier to eyeball the male and female speakers' warps
+    # separately, separate them out.
+    for g in m f; do # means: for gender in male female
+      cat $dir/final.warp | \
+        utils/filter_scp.pl <(grep -w $g $data/spk2gender | awk '{print $1}') > $dir/final.warp.$g
+      echo -n "The last few warp factors for gender $g are: "
+      tail -n 10 $dir/final.warp.$g | awk '{printf("%s ", $2);}'; 
+      echo
+    done
+  fi
+fi
+
+if $cleanup; then
+  rm $dir/pre_ali.*.gz $dir/ali_pass?.*.gz $dir/trans_pass1.* $dir/warp_pass1.* $dir/warp.*
+fi
+
+exit 0;
+

tools/ASR_2ch_track/steps/align_raw_fmllr.sh

@@ -0,0 +1,146 @@
+#!/bin/bash
+# Copyright 2012-2013  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# Computes training alignments; assumes features are (LDA+MLLT or delta+delta-delta)
+# + fMLLR (probably with SAT models).
+# It first computes an alignment with the final.alimdl (or the final.mdl if final.alimdl
+# is not present), then does 2 iterations of fMLLR estimation.
+
+# If you supply the --use-graphs option, it will use the training
+# graphs from the source directory (where the model is).  In this
+# case the number of jobs must match the source directory.
+
+
+# Begin configuration section.  
+stage=0
+nj=4
+cmd=run.pl
+use_graphs=false
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
+beam=10
+retry_beam=40
+boost_silence=1.0 # factor by which to boost silence during alignment.
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: steps/align_fmllr.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
+   echo "e.g.:  steps/align_fmllr.sh data/train data/lang exp/tri1 exp/tri1_ali"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+oov=`cat $lang/oov.int` || exit 1;
+silphonelist=`cat $lang/phones/silence.csl` || exit 1;
+sdata=$data/split$nj
+
+mkdir -p $dir/log
+echo $nj > $dir/num_jobs
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+cp $srcdir/final.occs $dir;
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+
+if [[ ! -f $srcdir/final.mat || ! -f $srcdir/full.mat ]]; then
+  echo "$0: we require final.mat and full.mat in the source directory $srcdir"
+fi
+
+full_lda_mat="get-full-lda-mat --print-args=false $srcdir/final.mat $srcdir/full.mat -|"
+cp $srcdir/full.mat $srcdir/final.mat $dir 
+
+raw_dim=$(feat-to-dim scp:$data/feats.scp -) || exit 1;
+! [ "$raw_dim" -gt 0 ] && echo "raw feature dim not set" && exit 1;
+
+splicedfeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- |"
+sifeats="$splicedfeats transform-feats $srcdir/final.mat ark:- ark:- |"
+
+## Set up model and alignment model.
+mdl=$srcdir/final.mdl
+if [ -f $srcdir/final.alimdl ]; then
+  alimdl=$srcdir/final.alimdl
+else
+  alimdl=$srcdir/final.mdl
+fi
+[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
+alimdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $alimdl - |"
+mdl_cmd="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $mdl - |"
+
+
+## Work out where we're getting the graphs from.
+if $use_graphs; then
+  [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
+    echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
+  [ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
+  graphdir=$srcdir
+else
+  graphdir=$dir
+  if [ $stage -le 0 ]; then
+    echo "$0: compiling training graphs"
+    tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";   
+    $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log  \
+      compile-train-graphs $dir/tree $dir/final.mdl  $lang/L.fst "$tra" \
+        "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
+  fi
+fi
+
+
+if [ $stage -le 1 ]; then
+  echo "$0: aligning data in $data using $alimdl and speaker-independent features."
+  $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$alimdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$sifeats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0: computing fMLLR transforms"
+  if [ "$alimdl" != "$mdl" ]; then
+    $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
+      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+      gmm-post-to-gpost $alimdl "$sifeats" ark:- ark:- \| \
+      gmm-est-fmllr-raw-gpost --raw-feat-dim=$raw_dim --spk2utt=ark:$sdata/JOB/spk2utt \
+       $mdl "$full_lda_mat" "$splicedfeats" ark,s,cs:- ark:$dir/raw_trans.JOB || exit 1;
+  else
+    $cmd JOB=1:$nj $dir/log/fmllr.JOB.log \
+      ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+      weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+      gmm-est-fmllr-raw --raw-feat-dim=$raw_dim --spk2utt=ark:$sdata/JOB/spk2utt $mdl "$full_lda_mat" \
+       "$splicedfeats" ark,s,cs:- ark:$dir/raw_trans.JOB || exit 1;
+  fi
+fi
+
+feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/raw_trans.JOB ark:- ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+
+if [ $stage -le 3 ]; then
+  echo "$0: doing final alignment."
+  $cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam "$mdl_cmd" \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+fi
+
+rm $dir/pre_ali.*.gz
+
+echo "$0: done aligning data."
+
+utils/summarize_warnings.pl $dir/log
+
+exit 0;

tools/ASR_2ch_track/steps/align_sgmm.sh

@@ -0,0 +1,195 @@
+#!/bin/bash
+# Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# Computes training alignments and (if needed) speaker-vectors, given an 
+# SGMM system.  If the system is built on top of SAT, you should supply
+# transforms with the --transform-dir option.
+
+# If you supply the --use-graphs option, it will use the training
+# graphs from the source directory.
+
+# Begin configuration section.  
+stage=0
+nj=4
+cmd=run.pl
+use_graphs=false # use graphs from srcdir
+use_gselect=false # use gselect info from srcdir [regardless, we use
+   # Gaussian-selection info, we might have to compute it though.]
+gselect=15  # Number of Gaussian-selection indices for SGMMs.
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
+beam=10
+retry_beam=40
+transform_dir=  # directory to find fMLLR transforms in.
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: steps/align_sgmm.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
+   echo "e.g.:  steps/align_sgmm.sh --transform-dir exp/tri3b data/train data/lang \\"
+   echo "           exp/sgmm4a exp/sgmm5a_ali"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --transform-dir <transform-dir>                  # directory to find fMLLR transforms"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+oov=`cat $lang/oov.int` || exit 1;
+silphonelist=`cat $lang/phones/silence.csl` || exit 1;
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+sdata=$data/split$nj
+
+mkdir -p $dir/log
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+echo $nj > $dir/num_jobs
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+[ -f $srcdir/final.alimdl ] && cp $srcdir/final.alimdl $dir
+cp $srcdir/final.occs $dir;
+
+## Set up features.
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type"
+
+case $feat_type in
+  delta) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
+  lda) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+    cp $srcdir/final.mat $dir    
+   ;;
+  *) echo "Invalid feature type $feat_type" && exit 1;
+esac
+if [ ! -z "$transform_dir" ]; then
+  echo "$0: using transforms from $transform_dir"
+  [ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
+  [ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
+    && echo "$0: #jobs mismatch with transform-dir." && exit 1;
+  feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
+elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
+  echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
+  echo "  but you are not providing the --transform-dir option during alignment."
+fi
+##
+
+## Set up model and alignment model.
+mdl=$srcdir/final.mdl
+if [ -f $srcdir/final.alimdl ]; then
+  alimdl=$srcdir/final.alimdl
+else
+  alimdl=$srcdir/final.mdl
+fi
+[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
+
+## Work out where we're getting the graphs from.
+if $use_graphs; then
+  [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
+    echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
+  [ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
+  graphdir=$srcdir
+  ln.pl $srcdir/fsts.*.gz $dir
+else
+  graphdir=$dir
+  if [ $stage -le 0 ]; then
+    echo "$0: compiling training graphs"
+    tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";   
+    $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log  \
+      compile-train-graphs $dir/tree $dir/final.mdl  $lang/L.fst "$tra" \
+        "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
+  fi
+fi
+
+## Work out where we're getting the Gaussian-selection info from
+if $use_gselect; then
+  [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
+    echo "$0: you specified --use-gselect true, but #jobs mismatch." && exit 1;
+  [ ! -f $srcdir/gselect.1.gz ] && echo "No gselect info in $srcdir" && exit 1;
+  graphdir=$srcdir
+  gselect_opt="--gselect=ark,s,cs:gunzip -c $srcdir/gselect.JOB.gz|"
+  ln.pl $srcdir/gselect.*.gz $dir
+else
+  graphdir=$dir
+  if [ $stage -le 1 ]; then
+    echo "$0: computing Gaussian-selection info"
+    # Note: doesn't matter whether we use $alimdl or $mdl, they will
+    # have the same gselect info.
+    $cmd JOB=1:$nj $dir/log/gselect.JOB.log \
+      sgmm-gselect --full-gmm-nbest=$gselect $alimdl \
+      "$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
+  fi
+  gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
+fi
+
+
+if [ $alimdl == $mdl ]; then 
+  # Speaker-independent decoding-- just one pass.  Not normal.
+  T=`sgmm-info $mdl | grep 'speaker vector space' | awk '{print $NF}'` || exit 1;
+  [ "$T" -ne 0 ] && echo "No alignment model, yet speaker vector space nonempty" && exit 1;
+
+  if [ $stage -le 2 ]; then
+    echo "$0: aligning data in $data using model $mdl (no speaker-vectors)"
+    $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+      sgmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam $alimdl \
+      "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+  fi
+  echo "$0: done aligning data."
+  exit 0;
+fi
+
+# Continue with system with speaker vectors.
+if [ $stage -le 2 ]; then
+  echo "$0: aligning data in $data using model $alimdl"
+  $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+    sgmm-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam $alimdl \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
+fi
+
+if [ $stage -le 3 ]; then
+  echo "$0: computing speaker vectors (1st pass)"
+  $cmd JOB=1:$nj $dir/log/spk_vecs1.JOB.log \
+    ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+    weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+    sgmm-post-to-gpost "$gselect_opt" $alimdl "$feats" ark:- ark:- \| \
+    sgmm-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
+     $mdl "$feats" ark,s,cs:- ark:$dir/pre_vecs.JOB || exit 1;
+fi
+
+if [ $stage -le 4 ]; then
+  echo "$0: computing speaker vectors (2nd pass)"
+  $cmd JOB=1:$nj $dir/log/spk_vecs2.JOB.log \
+    ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+    weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+    sgmm-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
+     --spk-vecs=ark:$dir/pre_vecs.JOB $mdl "$feats" ark,s,cs:- ark:$dir/vecs.JOB || exit 1;
+  rm $dir/pre_vecs.*
+fi
+
+if [ $stage -le 5 ]; then
+  echo "$0: doing final alignment."
+  $cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
+    sgmm-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam \
+     --utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
+     $mdl "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+fi
+
+rm $dir/pre_ali.*.gz
+
+echo "$0: done aligning data."
+
+utils/summarize_warnings.pl $dir/log
+
+exit 0;

tools/ASR_2ch_track/steps/align_sgmm2.sh

@@ -0,0 +1,195 @@
+#!/bin/bash
+# Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# Computes training alignments and (if needed) speaker-vectors, given an 
+# SGMM system.  If the system is built on top of SAT, you should supply
+# transforms with the --transform-dir option.
+
+# If you supply the --use-graphs option, it will use the training
+# graphs from the source directory.
+
+# Begin configuration section.  
+stage=0
+nj=4
+cmd=run.pl
+use_graphs=false # use graphs from srcdir
+use_gselect=false # use gselect info from srcdir [regardless, we use
+   # Gaussian-selection info, we might have to compute it though.]
+gselect=15  # Number of Gaussian-selection indices for SGMMs.
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
+beam=10
+retry_beam=40
+transform_dir=  # directory to find fMLLR transforms in.
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: steps/align_sgmm.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
+   echo "e.g.:  steps/align_sgmm.sh --transform-dir exp/tri3b data/train data/lang \\"
+   echo "           exp/sgmm4a exp/sgmm5a_ali"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --transform-dir <transform-dir>                  # directory to find fMLLR transforms"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+oov=`cat $lang/oov.int` || exit 1;
+silphonelist=`cat $lang/phones/silence.csl` || exit 1;
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+sdata=$data/split$nj
+
+mkdir -p $dir/log
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+echo $nj > $dir/num_jobs
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+[ -f $srcdir/final.alimdl ] && cp $srcdir/final.alimdl $dir
+cp $srcdir/final.occs $dir;
+
+## Set up features.
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type"
+
+case $feat_type in
+  delta) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
+  lda) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+    cp $srcdir/final.mat $dir    
+   ;;
+  *) echo "Invalid feature type $feat_type" && exit 1;
+esac
+if [ ! -z "$transform_dir" ]; then
+  echo "$0: using transforms from $transform_dir"
+  [ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
+  [ "$nj" -ne "`cat $transform_dir/num_jobs`" ] \
+    && echo "$0: #jobs mismatch with transform-dir." && exit 1;
+  feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$transform_dir/trans.JOB ark:- ark:- |"
+elif grep 'transform-feats --utt2spk' $srcdir/log/acc.0.1.log 2>/dev/null; then
+  echo "$0: **WARNING**: you seem to be using an SGMM system trained with transforms,"
+  echo "  but you are not providing the --transform-dir option during alignment."
+fi
+##
+
+## Set up model and alignment model.
+mdl=$srcdir/final.mdl
+if [ -f $srcdir/final.alimdl ]; then
+  alimdl=$srcdir/final.alimdl
+else
+  alimdl=$srcdir/final.mdl
+fi
+[ ! -f $mdl ] && echo "$0: no such model $mdl" && exit 1;
+
+## Work out where we're getting the graphs from.
+if $use_graphs; then
+  [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
+    echo "$0: you specified --use-graphs true, but #jobs mismatch." && exit 1;
+  [ ! -f $srcdir/fsts.1.gz ] && echo "No graphs in $srcdir" && exit 1;
+  graphdir=$srcdir
+  ln.pl $srcdir/fsts.*.gz $dir
+else
+  graphdir=$dir
+  if [ $stage -le 0 ]; then
+    echo "$0: compiling training graphs"
+    tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";   
+    $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log  \
+      compile-train-graphs $dir/tree $dir/final.mdl  $lang/L.fst "$tra" \
+        "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
+  fi
+fi
+
+## Work out where we're getting the Gaussian-selection info from
+if $use_gselect; then
+  [ "$nj" != "`cat $srcdir/num_jobs`" ] && \
+    echo "$0: you specified --use-gselect true, but #jobs mismatch." && exit 1;
+  [ ! -f $srcdir/gselect.1.gz ] && echo "No gselect info in $srcdir" && exit 1;
+  graphdir=$srcdir
+  gselect_opt="--gselect=ark,s,cs:gunzip -c $srcdir/gselect.JOB.gz|"
+  ln.pl $srcdir/gselect.*.gz $dir
+else
+  graphdir=$dir
+  if [ $stage -le 1 ]; then
+    echo "$0: computing Gaussian-selection info"
+    # Note: doesn't matter whether we use $alimdl or $mdl, they will
+    # have the same gselect info.
+    $cmd JOB=1:$nj $dir/log/gselect.JOB.log \
+      sgmm2-gselect --full-gmm-nbest=$gselect $alimdl \
+      "$feats" "ark:|gzip -c >$dir/gselect.JOB.gz" || exit 1;
+  fi
+  gselect_opt="--gselect=ark,s,cs:gunzip -c $dir/gselect.JOB.gz|"
+fi
+
+
+if [ $alimdl == $mdl ]; then 
+  # Speaker-independent decoding-- just one pass.  Not normal.
+  T=`sgmm2-info $mdl | grep 'speaker vector space' | awk '{print $NF}'` || exit 1;
+  [ "$T" -ne 0 ] && echo "No alignment model, yet speaker vector space nonempty" && exit 1;
+
+  if [ $stage -le 2 ]; then
+    echo "$0: aligning data in $data using model $mdl (no speaker-vectors)"
+    $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+      sgmm2-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam $alimdl \
+      "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+  fi
+  echo "$0: done aligning data."
+  exit 0;
+fi
+
+# Continue with system with speaker vectors.
+if [ $stage -le 2 ]; then
+  echo "$0: aligning data in $data using model $alimdl"
+  $cmd JOB=1:$nj $dir/log/align_pass1.JOB.log \
+    sgmm2-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam $alimdl \
+    "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/pre_ali.JOB.gz" || exit 1;
+fi
+
+if [ $stage -le 3 ]; then
+  echo "$0: computing speaker vectors (1st pass)"
+  $cmd JOB=1:$nj $dir/log/spk_vecs1.JOB.log \
+    ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+    weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+    sgmm2-post-to-gpost "$gselect_opt" $alimdl "$feats" ark:- ark:- \| \
+    sgmm2-est-spkvecs-gpost --spk2utt=ark:$sdata/JOB/spk2utt \
+     $mdl "$feats" ark,s,cs:- ark:$dir/pre_vecs.JOB || exit 1;
+fi
+
+if [ $stage -le 4 ]; then
+  echo "$0: computing speaker vectors (2nd pass)"
+  $cmd JOB=1:$nj $dir/log/spk_vecs2.JOB.log \
+    ali-to-post "ark:gunzip -c $dir/pre_ali.JOB.gz|" ark:- \| \
+    weight-silence-post 0.0 $silphonelist $alimdl ark:- ark:- \| \
+    sgmm2-est-spkvecs --spk2utt=ark:$sdata/JOB/spk2utt "$gselect_opt" \
+     --spk-vecs=ark:$dir/pre_vecs.JOB $mdl "$feats" ark,s,cs:- ark:$dir/vecs.JOB || exit 1;
+  rm $dir/pre_vecs.*
+fi
+
+if [ $stage -le 5 ]; then
+  echo "$0: doing final alignment."
+  $cmd JOB=1:$nj $dir/log/align_pass2.JOB.log \
+    sgmm2-align-compiled $scale_opts "$gselect_opt" --beam=$beam --retry-beam=$retry_beam \
+     --utt2spk=ark:$sdata/JOB/utt2spk --spk-vecs=ark:$dir/vecs.JOB \
+     $mdl "ark:gunzip -c $graphdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+fi
+
+rm $dir/pre_ali.*.gz
+
+echo "$0: done aligning data."
+
+utils/summarize_warnings.pl $dir/log
+
+exit 0;

tools/ASR_2ch_track/steps/align_si.sh

@@ -0,0 +1,101 @@
+#!/bin/bash
+# Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# Computes training alignments using a model with delta or
+# LDA+MLLT features.
+
+# If you supply the "--use-graphs true" option, it will use the training
+# graphs from the source directory (where the model is).  In this
+# case the number of jobs must match with the source directory.
+
+
+# Begin configuration section.  
+nj=4
+cmd=run.pl
+use_graphs=false
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
+beam=10
+retry_beam=40
+careful=false
+boost_silence=1.0 # Factor by which to boost silence during alignment.
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: steps/align_si.sh <data-dir> <lang-dir> <src-dir> <align-dir>"
+   echo "e.g.:  steps/align_si.sh data/train data/lang exp/tri1 exp/tri1_ali"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+
+for f in $data/text $lang/oov.int $srcdir/tree $srcdir/final.mdl; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1;
+done
+
+oov=`cat $lang/oov.int` || exit 1;
+mkdir -p $dir/log
+echo $nj > $dir/num_jobs
+sdata=$data/split$nj
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+delta_opts=`cat $srcdir/delta_opts 2>/dev/null`
+cp $srcdir/delta_opts $dir 2>/dev/null
+
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+cp $srcdir/final.occs $dir;
+
+
+
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type"
+
+case $feat_type in
+  delta) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |";;
+  lda) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+    cp $srcdir/final.mat $srcdir/full.mat $dir    
+   ;;
+  *) echo "$0: invalid feature type $feat_type" && exit 1;
+esac
+
+echo "$0: aligning data in $data using model from $srcdir, putting alignments in $dir"
+
+mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/final.mdl - |"
+
+if $use_graphs; then 
+  [ $nj != "`cat $srcdir/num_jobs`" ] && echo "$0: mismatch in num-jobs" && exit 1;
+  [ ! -f $srcdir/fsts.1.gz ] && echo "$0: no such file $srcdir/fsts.1.gz" && exit 1;
+
+  $cmd JOB=1:$nj $dir/log/align.JOB.log \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" \
+      "ark:gunzip -c $srcdir/fsts.JOB.gz|" "$feats" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+else
+  tra="ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|";
+  # We could just use gmm-align in the next line, but it's less efficient as it compiles the
+  # training graphs one by one.
+  $cmd JOB=1:$nj $dir/log/align.JOB.log \
+    compile-train-graphs $dir/tree $dir/final.mdl  $lang/L.fst "$tra" ark:- \| \
+    gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" ark:- \
+      "$feats" "ark,t:|gzip -c >$dir/ali.JOB.gz" || exit 1;
+fi
+
+echo "$0: done aligning data."

tools/ASR_2ch_track/steps/append_feats.sh

@@ -0,0 +1,85 @@
+#!/bin/bash
+
+# Copyright 2014  Brno University of Technology (Author: Karel Vesely)
+# Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+# This script appends the features in two or more data directories.
+
+# To be run from .. (one directory up from here)
+# see ../run.sh for example
+
+# Begin configuration section.
+cmd=run.pl
+nj=4
+length_tolerance=10 # length tolerance in frames (trim to shortest)
+compress=true
+# End configuration section.
+
+echo "$0 $@"  # Print the command line for logging
+
+if [ -f path.sh ]; then . ./path.sh; fi
+. parse_options.sh || exit 1;
+
+if [ $# -lt 5 ]; then
+   echo "usage: $0 [options] <src-data-dir1> <src-data-dir2> [<src-data-dirN>] <dest-data-dir> <log-dir> <path-to-storage-dir>";
+   echo "e.g.: $0 data/train_mfcc data/train_bottleneck data/train_combined exp/append_mfcc_plp mfcc"
+   echo "options: "
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   exit 1;
+fi
+
+data_src_arr=(${@:1:$(($#-3))}) #array of source data-dirs
+data=${@: -3: 1}
+logdir=${@: -2: 1}
+ark_dir=${@: -1: 1} #last arg.
+
+data_src_first=${data_src_arr[0]} # get 1st src dir
+
+# make $ark_dir an absolute pathname.
+ark_dir=`perl -e '($dir,$pwd)= @ARGV; if($dir!~m:^/:) { $dir = "$pwd/$dir"; } print $dir; ' $ark_dir ${PWD}`
+
+for data_src in ${data_src_arr[@]}; do
+  utils/split_data.sh $data_src $nj || exit 1;
+done
+
+mkdir -p $ark_dir $logdir
+
+mkdir -p $data
+cp $data_src_first/* $data/ 2>/dev/null # so we get the other files, such as utt2spk.
+rm $data/cmvn.scp 2>/dev/null
+rm $data/feats.scp 2>/dev/null
+
+# use "name" as part of name of the archive.
+name=`basename $data`
+
+# get list of source scp's for pasting
+data_src_args=
+for data_src in ${data_src_arr[@]}; do
+  data_src_args="$data_src_args scp:$data_src/split$nj/JOB/feats.scp"
+done
+
+for n in $(seq $nj); do
+  # the next command does nothing unless $arkdir/storage/ exists, see
+  # utils/create_data_link.pl for more info.
+  utils/create_data_link.pl $arkdir/pasted_$name.$n.ark
+done
+
+$cmd JOB=1:$nj $logdir/append.JOB.log \
+   paste-feats --length-tolerance=$length_tolerance $data_src_args ark:- \| \
+   copy-feats --compress=$compress ark:- \
+    ark,scp:$ark_dir/pasted_$name.JOB.ark,$ark_dir/pasted_$name.JOB.scp || exit 1;
+
+# concatenate the .scp files together.
+for ((n=1; n<=nj; n++)); do
+  cat $ark_dir/pasted_$name.$n.scp >> $data/feats.scp || exit 1;
+done > $data/feats.scp || exit 1;
+
+
+nf=`cat $data/feats.scp | wc -l`
+nu=`cat $data/utt2spk | wc -l`
+if [ $nf -ne $nu ]; then
+  echo "It seems not all of the feature files were successfully processed ($nf != $nu);"
+  echo "consider using utils/fix_data_dir.sh $data"
+fi
+
+echo "Succeeded pasting features for $name into $data"

tools/ASR_2ch_track/steps/cleanup/combine_short_segments.py

@@ -0,0 +1,312 @@
+#!/usr/bin/env python
+
+# Copyright 2016 Vijayaditya Peddinti
+# Apache 2.0
+
+import argparse
+import sys
+import os
+import subprocess
+import errno
+import copy
+import shutil
+
+def GetArgs():
+    # we add compulsary arguments as named arguments for readability
+    parser = argparse.ArgumentParser(description="""
+    This script concatenates segments in the input_data_dir to ensure that"""
+    " the segments in the output_data_dir have a specified minimum length.",
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument("--minimum-duration", type=float, required = True,
+                        help="Minimum duration of the segments in the output directory")
+    parser.add_argument("--input-data-dir", type=str, required = True)
+    parser.add_argument("--output-data-dir", type=str, required = True)
+
+    print(' '.join(sys.argv))
+    args = parser.parse_args()
+    return args
+
+def RunKaldiCommand(command, wait = True):
+    """ Runs commands frequently seen in Kaldi scripts. These are usually a
+        sequence of commands connected by pipes, so we use shell=True """
+    p = subprocess.Popen(command, shell = True,
+                         stdout = subprocess.PIPE,
+                         stderr = subprocess.PIPE)
+
+    if wait:
+        [stdout, stderr] = p.communicate()
+        if p.returncode is not 0:
+            raise Exception("There was an error while running the command {0}\n".format(command)+"-"*10+"\n"+stderr)
+        return stdout, stderr
+    else:
+        return p
+
+def MakeDir(dir):
+    try:
+        os.mkdir(dir)
+    except OSError as exc:
+        if exc.errno != errno.EEXIST:
+            raise exc
+        raise Exception("Directory {0} already exists".format(dir))
+        pass
+
+def CheckFiles(input_data_dir):
+    for file_name in ['spk2utt', 'text', 'utt2spk', 'feats.scp']:
+        file_name = '{0}/{1}'.format(input_data_dir, file_name)
+        if not os.path.exists(file_name):
+            raise Exception("There is no such file {0}".format(file_name))
+
+def ParseFileToDict(file, assert2fields = False, value_processor = None):
+    if value_processor is None:
+        value_processor = lambda x: x[0]
+
+    dict = {}
+    for line in open(file, 'r'):
+        parts = line.split()
+        if assert2fields:
+            assert(len(parts) == 2)
+
+        dict[parts[0]] = value_processor(parts[1:])
+    return dict
+
+def WriteDictToFile(dict, file_name):
+    file = open(file_name, 'w')
+    keys = dict.keys()
+    keys.sort()
+    for key in keys:
+        value = dict[key]
+        if type(value) in [list, tuple] :
+            if type(value) is tuple:
+                value = list(value)
+            value.sort()
+            value = ' '.join(value)
+        file.write('{0}\t{1}\n'.format(key, value))
+    file.close()
+
+
+def ParseDataDirInfo(data_dir):
+    data_dir_file = lambda file_name: '{0}/{1}'.format(data_dir, file_name)
+
+    utt2spk = ParseFileToDict(data_dir_file('utt2spk'))
+    spk2utt = ParseFileToDict(data_dir_file('spk2utt'), value_processor = lambda x: x)
+    text = ParseFileToDict(data_dir_file('text'), value_processor = lambda x: " ".join(x))
+    # we want to assert feats.scp has just 2 fields, as we don't know how
+    # to process it otherwise
+    feat = ParseFileToDict(data_dir_file('feats.scp'), assert2fields = True)
+    utt2dur = ParseFileToDict(data_dir_file('utt2dur'), value_processor = lambda x: float(x[0]))
+    utt2uniq = None
+    if os.path.exists(data_dir_file('utt2uniq')):
+        utt2uniq = ParseFileToDict(data_dir_file('utt2uniq'))
+    return utt2spk, spk2utt, text, feat, utt2dur, utt2uniq
+
+
+def GetCombinedUttIndexRange(utt_index, utts, utt_durs, minimum_duration):
+    # We want the minimum number of concatenations
+    # to reach the minimum_duration. If two concatenations satisfy
+    # the minimum duration constraint we choose the shorter one.
+    left_index = utt_index - 1
+    right_index = utt_index + 1
+    num_remaining_segments = len(utts) - 1
+    cur_utt_dur = utt_durs[utts[utt_index]]
+
+    while num_remaining_segments > 0:
+
+        left_utt_dur = 0
+        if left_index >= 0:
+            left_utt_dur = utt_durs[utts[left_index]]
+        right_utt_dur = 0
+        if right_index <= len(utts) - 1:
+            right_utt_dur = utt_durs[utts[right_index]]
+
+        right_combined_utt_dur = cur_utt_dur + right_utt_dur
+        left_combined_utt_dur = cur_utt_dur + left_utt_dur
+        left_right_combined_utt_dur = cur_utt_dur + left_utt_dur + right_utt_dur
+
+        combine_left_exit = False
+        combine_right_exit = False
+        if right_combined_utt_dur >= minimum_duration:
+            if left_combined_utt_dur >= minimum_duration:
+                if left_combined_utt_dur <= right_combined_utt_dur:
+                    combine_left_exit = True
+                else:
+                    combine_right_exit = True
+            else:
+                combine_right_exit = True
+        elif left_combined_utt_dur >= minimum_duration:
+            combine_left_exit = True
+        elif left_right_combined_utt_dur >= minimum_duration :
+            combine_left_exit = True
+            combine_right_exit = True
+
+        if combine_left_exit and combine_right_exit:
+            cur_utt_dur = left_right_combined_utt_dur
+            break
+        elif combine_left_exit:
+            cur_utt_dur = left_combined_utt_dur
+            # move back the right_index as we don't need to combine it
+            right_index = right_index - 1
+            break
+        elif combine_right_exit:
+            cur_utt_dur = right_combined_utt_dur
+            # move back the left_index as we don't need to combine it
+            left_index = left_index + 1
+            break
+
+        # couldn't satisfy minimum duration requirement so continue search
+        if left_index >= 0:
+            num_remaining_segments = num_remaining_segments - 1
+        if right_index <= len(utts) - 1:
+            num_remaining_segments = num_remaining_segments - 1
+
+        left_index = left_index - 1
+        right_index = right_index + 1
+
+        cur_utt_dur = left_right_combined_utt_dur
+    left_index = max(0, left_index)
+    right_index = min(len(utts)-1, right_index)
+    return left_index, right_index, cur_utt_dur
+
+
+def WriteCombinedDirFiles(output_dir, utt2spk, spk2utt, text, feat, utt2dur, utt2uniq):
+    out_dir_file = lambda file_name: '{0}/{1}'.format(output_dir, file_name)
+    total_combined_utt_list = []
+    for speaker in spk2utt.keys():
+        utts = spk2utt[speaker]
+        for utt in utts:
+            if type(utt) is tuple:
+                #this is a combined utt
+                total_combined_utt_list.append((speaker, utt))
+
+    for speaker, combined_utt_tuple in total_combined_utt_list:
+        combined_utt_list = list(combined_utt_tuple)
+        combined_utt_list.sort()
+        new_utt_name = "-".join(combined_utt_list)+'-appended'
+
+        # updating the utt2spk dict
+        for utt in combined_utt_list:
+            spk_name = utt2spk.pop(utt)
+        utt2spk[new_utt_name] = spk_name
+
+        # updating the spk2utt dict
+        spk2utt[speaker].remove(combined_utt_tuple)
+        spk2utt[speaker].append(new_utt_name)
+
+        # updating the text dict
+        combined_text = []
+        for utt in combined_utt_list:
+            combined_text.append(text.pop(utt))
+        text[new_utt_name] = ' '.join(combined_text)
+
+        # updating the feat dict
+        combined_feat = []
+        for utt in combined_utt_list:
+            combined_feat.append(feat.pop(utt))
+        feat_command = "concat-feats --print-args=false {feats} - |".format(feats = " ".join(combined_feat))
+        feat[new_utt_name] = feat_command
+
+        # updating utt2dur
+        combined_dur = 0
+        for utt in combined_utt_list:
+            combined_dur += utt2dur.pop(utt)
+        utt2dur[new_utt_name] = combined_dur
+
+        # updating utt2uniq
+        if utt2uniq is not None:
+            combined_uniqs = []
+            for utt in combined_utt_list:
+                combined_uniqs.append(utt2uniq.pop(utt))
+            # utt2uniq file is used to map perturbed data to original unperturbed
+            # versions so that the training cross validation sets can avoid overlap
+            # of data however if perturbation changes the length of the utterance
+            # (e.g. speed perturbation) the utterance combinations in each
+            # perturbation of the original recording can be very different. So there
+            # is no good way to find the utt2uniq mappinng so that we can avoid
+            # overlap.
+            utt2uniq[new_utt_name] = combined_uniqs[0]
+
+
+    WriteDictToFile(utt2spk, out_dir_file('utt2spk'))
+    WriteDictToFile(spk2utt, out_dir_file('spk2utt'))
+    WriteDictToFile(feat, out_dir_file('feats.scp'))
+    WriteDictToFile(text, out_dir_file('text'))
+    if utt2uniq is not None:
+        WriteDictToFile(utt2uniq, out_dir_file('utt2uniq'))
+    WriteDictToFile(utt2dur, out_dir_file('utt2dur'))
+
+
+def CombineSegments(input_dir, output_dir, minimum_duration):
+    utt2spk, spk2utt, text, feat, utt2dur, utt2uniq = ParseDataDirInfo(input_dir)
+    total_combined_utt_list = []
+
+    # copy the duration dictionary so that we can modify it
+    utt_durs = copy.deepcopy(utt2dur)
+    speakers = spk2utt.keys()
+    speakers.sort()
+    for speaker in speakers:
+
+        utts = spk2utt[speaker] # this is an assignment of the reference
+        # In WriteCombinedDirFiles the values of spk2utt will have the list
+        # of combined utts which will be used as reference
+
+        # we make an assumption that the sorted uttlist corresponds
+        # to contiguous segments. This is true only if utt naming
+        # is done according to accepted conventions
+        # this is an easily violatable assumption. Have to think of a better
+        # way to do this.
+        utts.sort()
+        utt_index = 0
+        while utt_index < len(utts):
+            if utt_durs[utts[utt_index]] < minimum_duration:
+                left_index, right_index, cur_utt_dur = GetCombinedUttIndexRange(utt_index, utts, utt_durs, minimum_duration)
+                if not cur_utt_dur >= minimum_duration:
+                    # this is a rare occurrence, better make the user aware of this
+                    # situation and let them deal with it
+                    raise Exception('Speaker {0} does not have enough utterances to satisfy the minimum duration constraint'.format(speaker))
+
+                combined_duration = 0
+                combined_utts = []
+                # update the utts_dur dictionary
+                for utt in utts[left_index:right_index + 1]:
+                    combined_duration += utt_durs.pop(utt)
+                    if type(utt) is tuple:
+                        for item in utt:
+                            combined_utts.append(item)
+                    else:
+                        combined_utts.append(utt)
+                combined_utts = tuple(combined_utts) # converting to immutable type to use as dictionary key
+                assert(cur_utt_dur == combined_duration)
+
+                # now modify the utts list
+                combined_indices = range(left_index, right_index + 1)
+                # start popping from the largest index so that the lower
+                # indexes are valid
+                for i in combined_indices[::-1]:
+                    utts.pop(i)
+                utts.insert(left_index, combined_utts)
+                utt_durs[combined_utts] = combined_duration
+                utt_index = left_index
+            utt_index = utt_index + 1
+    WriteCombinedDirFiles(output_dir, utt2spk, spk2utt, text, feat, utt2dur, utt2uniq)
+
+def Main():
+    args = GetArgs()
+
+    CheckFiles(args.input_data_dir)
+    MakeDir(args.output_data_dir)
+    feat_lengths = {}
+    segments_file = '{0}/segments'.format(args.input_data_dir)
+
+    RunKaldiCommand("utils/data/get_utt2dur.sh {0}".format(args.input_data_dir))
+
+    CombineSegments(args.input_data_dir, args.output_data_dir, args.minimum_duration)
+
+    RunKaldiCommand("utils/utt2spk_to_spk2utt.pl {od}/utt2spk > {od}/spk2utt".format(od = args.output_data_dir))
+    if os.path.exists('{0}/cmvn.scp'.format(args.input_data_dir)):
+        shutil.copy('{0}/cmvn.scp'.format(args.input_data_dir), args.output_data_dir)
+
+    RunKaldiCommand("utils/fix_data_dir.sh {0}".format(args.output_data_dir))
+if __name__ == "__main__":
+    Main()
+
+

tools/ASR_2ch_track/steps/cleanup/create_segments_from_ctm.pl

@@ -0,0 +1,471 @@
+#!/usr/bin/env perl
+
+# Copyright 2014  Guoguo Chen; 2015 Nagendra Kumar Goel
+# Apache 2.0
+
+use strict;
+use warnings;
+use Getopt::Long;
+
+# $SIG{__WARN__} = sub { $DB::single = 1 };
+
+my $Usage = <<'EOU';
+This script creates the segments file and text file for a data directory with
+new segmentation. It takes a ctm file and an "alignment" file. The ctm file
+corresponds to the audio that we want to make segmentations for, and is created
+by decoding the audio using existing in-domain models. The "alignment" file is
+generated by the binary align-text, and is Levenshtein alignment between the
+original transcript and the decoded output.
+
+Internally, the script first tries to find silence regions (gaps in the CTM).
+If a silence region is found, and the neighboring words are free of errors
+according to the alignment file, then this silence region will be taken as
+a split point, and new segment will be created. If the new segment we are going
+to output is too long (longer than --max-seg-length), the script will split
+the long segments into smaller pieces with length roughly --max-seg-length.
+If you are going to use --wer-cutoff to filter out segments with high WER, make
+sure you set it to a reasonable value. If the value you set is higher than the
+WER from your alignment file, then most of the segments will be filtered out.
+
+Usage: steps/cleanup/create_segments_from_ctm.pl [options] \
+                              <ctm> <aligned.txt> <segments> <text>
+ e.g.: steps/cleanup/create_segments_from_ctm.pl \
+          train_si284_split.ctm train_si284_split.aligned.txt \
+          data/train_si284_reseg/segments data/train_si284_reseg/text
+
+Allowed options:
+  --max-seg-length  : Maximum length of new segments (default = 10.0)
+  --min-seg-length  : Minimum length of new segments (default = 2.0)
+  --min-sil-length  : Minimum length of silence as split point (default = 0.5)
+  --separator       : Separator for aligned pairs (default = ";")
+  --special-symbol  : Special symbol to aligned with inserted or deleted words
+                      (default = "<***>")
+  --wer-cutoff      : Ignore segments with WER higher than the specified value.
+                      -1 means no segment will be ignored. (default = -1)
+  --use-silence-midpoints : Set to 1 if you want to use silence midpoints
+                      instead of min_sil_length for silence overhang.(default 0)
+  --force-correct-boundary-words : Set to zero if the segments will not be
+                      required to have boundary words to be correct. Default 1
+  --aligned-ctm-filename : If set, the intermediate aligned ctm
+                      is saved to this file
+EOU
+
+my $max_seg_length = 10.0;
+my $min_seg_length = 2.0;
+my $min_sil_length = 0.5;
+my $separator = ";";
+my $special_symbol = "<***>";
+my $wer_cutoff = -1;
+my $use_silence_midpoints = 0;
+my $force_correct_boundary_words = 1;
+my $aligned_ctm_filename = "";
+GetOptions(
+  'wer-cutoff=f' => \$wer_cutoff,
+  'max-seg-length=f' => \$max_seg_length,
+  'min-seg-length=f' => \$min_seg_length,
+  'min-sil-length=f' => \$min_sil_length,
+  'use-silence-midpoints=f' => \$use_silence_midpoints,
+  'force-correct-boundary-words=f' => \$force_correct_boundary_words,
+  'aligned-ctm-filename=s' => \$aligned_ctm_filename,
+  'separator=s'      => \$separator,
+  'special-symbol=s' => \$special_symbol);
+
+if (@ARGV != 4) {
+  die $Usage;
+}
+
+my ($ctm_in, $align_in, $segments_out, $text_out) = @ARGV;
+
+open(CI, "<$ctm_in") || die "Error: fail to open $ctm_in\n";
+open(AI, "<$align_in") || die "Error: fail to open $align_in\n";
+open(my $SO, ">$segments_out") || die "Error: fail to open $segments_out\n";
+open(my $TO, ">$text_out") || die "Error: fail to open $text_out\n";
+my $ACT= undef;
+if ($aligned_ctm_filename ne "") {
+    open($ACT, ">$aligned_ctm_filename");
+}
+# Prints the current segment to file.
+sub PrintSegment {
+  my ($aligned_ctm, $wav_id, $min_sil_length, $min_seg_length,
+      $seg_start_index, $seg_end_index, $seg_count, $SO, $TO) = @_;
+
+  if ($seg_start_index > $seg_end_index) {
+    return -1;
+  }
+
+  # Removes the surrounding silence.
+  while ($seg_start_index < scalar(@{$aligned_ctm}) &&
+         $aligned_ctm->[$seg_start_index]->[0] eq "<eps>") {
+    $seg_start_index += 1;
+  }
+  while ($seg_end_index >= 0 &&
+         $aligned_ctm->[$seg_end_index]->[0] eq "<eps>") {
+    $seg_end_index -= 1;
+  }
+  if ($seg_start_index > $seg_end_index) {
+    return -1;
+  }
+
+  # Filters out segments with high WER.
+  if ($wer_cutoff != -1) {
+    my $num_errors = 0; my $num_words = 0;
+    for (my $i = $seg_start_index; $i <= $seg_end_index; $i += 1) {
+      if ($aligned_ctm->[$i]->[0] ne "<eps>") {
+        $num_words += 1;
+      }
+      $num_errors += $aligned_ctm->[$i]->[3];
+    }
+    if ($num_errors / $num_words > $wer_cutoff || $num_words < 1) {
+      return -1;
+    }
+  }
+
+  # Works out the surrounding silence.
+  my $index = $seg_start_index - 1;
+  while ($index >= 0 && $aligned_ctm->[$index]->[0] eq
+         "<eps>" && $aligned_ctm->[$index]->[3] == 0) {
+    $index -= 1;
+  }
+  my $left_of_segment_has_deletion = "false";
+  $left_of_segment_has_deletion = "true"
+      if ($index > 0 && $aligned_ctm->[$index-1]->[0] ne "<eps>"
+          && $aligned_ctm->[$index-1]->[3] == 0);
+
+  my $pad_start_sil = ($aligned_ctm->[$seg_start_index]->[1] -
+                       $aligned_ctm->[$index + 1]->[1]) / 2.0;
+  if (($left_of_segment_has_deletion eq "true") || !$use_silence_midpoints) {
+      if ($pad_start_sil > $min_sil_length / 2.0) {
+          $pad_start_sil = $min_sil_length / 2.0;
+      }
+  }
+  my $right_of_segment_has_deletion = "false";
+  $index = $seg_end_index + 1;
+  while ($index < scalar(@{$aligned_ctm}) &&
+         $aligned_ctm->[$index]->[0] eq "<eps>" &&
+         $aligned_ctm->[$index]->[3] == 0) {
+    $index += 1;
+  }
+  $right_of_segment_has_deletion = "true"
+      if ($index < scalar(@{$aligned_ctm})-1 && $aligned_ctm->[$index+1]->[0] ne
+          "<eps>" && $aligned_ctm->[$index - 1]->[3] > 0);
+  my $pad_end_sil = ($aligned_ctm->[$index - 1]->[1] +
+                     $aligned_ctm->[$index - 1]->[2] -
+                     $aligned_ctm->[$seg_end_index]->[1] -
+                     $aligned_ctm->[$seg_end_index]->[2]) / 2.0;
+  if (($right_of_segment_has_deletion eq "true") || !$use_silence_midpoints) {
+      if ($pad_end_sil > $min_sil_length / 2.0) {
+          $pad_end_sil = $min_sil_length / 2.0;
+      }
+  }
+
+  my $seg_start = $aligned_ctm->[$seg_start_index]->[1] - $pad_start_sil;
+  my $seg_end = $aligned_ctm->[$seg_end_index]->[1] +
+                $aligned_ctm->[$seg_end_index]->[2] + $pad_end_sil;
+  if ($seg_end - $seg_start < $min_seg_length) {
+      return -1;
+  }
+
+  $seg_start = sprintf("%.2f", $seg_start);
+  $seg_end = sprintf("%.2f", $seg_end);
+  my $seg_id = $wav_id . "_" . sprintf("%05d", $seg_count);
+  print $SO "$seg_id $wav_id $seg_start $seg_end\n";
+
+  print $TO "$seg_id ";
+  for (my $x = $seg_start_index; $x <= $seg_end_index; $x += 1) {
+    if ($aligned_ctm->[$x]->[0] ne "<eps>") {
+      print $TO "$aligned_ctm->[$x]->[0] ";
+    }
+  }
+  print $TO "\n";
+  return 0;
+}
+
+# Computes split point.
+sub GetSplitPoint {
+  my ($aligned_ctm, $seg_start_index, $seg_end_index, $max_seg_length) = @_;
+
+  # Scan in the reversed order so we can maximize the length.
+  my $split_point = $seg_start_index;
+  for (my $x = $seg_end_index; $x > $seg_start_index; $x -= 1) {
+    my $current_seg_length = $aligned_ctm->[$x]->[1] +
+                             $aligned_ctm->[$x]->[2] -
+                             $aligned_ctm->[$seg_start_index]->[1];
+    if ($current_seg_length <= $max_seg_length) {
+      $split_point = $x;
+      last;
+    }
+  }
+  return $split_point;
+}
+
+# Computes segment length without surrounding silence.
+sub GetSegmentLengthNoSil {
+  my ($aligned_ctm, $seg_start_index, $seg_end_index) = @_;
+  while ($seg_start_index < scalar(@{$aligned_ctm}) &&
+         $aligned_ctm->[$seg_start_index]->[0] eq "<eps>") {
+    $seg_start_index += 1;
+  }
+  while ($seg_end_index >= 0 &&
+         $aligned_ctm->[$seg_end_index]->[0] eq "<eps>") {
+    $seg_end_index -= 1;
+  }
+  if ($seg_start_index > $seg_end_index) {
+    return 0;
+  }
+  my $current_seg_length = $aligned_ctm->[$seg_end_index]->[1] +
+                           $aligned_ctm->[$seg_end_index]->[2] -
+                           $aligned_ctm->[$seg_start_index]->[1];
+  return $current_seg_length;
+}
+
+# Force splits long segments.
+sub SplitLongSegment {
+  my ($aligned_ctm, $wav_id, $max_seg_length, $min_sil_length,
+      $seg_start_index, $seg_end_index, $current_seg_count, $SO, $TO) = @_;
+  # If the segment is too long, we manually split it. We make sure that the
+  # resulting segments are at least ($max_seg_length / 2) seconds long.
+  my $current_seg_length = $aligned_ctm->[$seg_end_index]->[1] +
+                           $aligned_ctm->[$seg_end_index]->[2] -
+                           $aligned_ctm->[$seg_start_index]->[1];
+  my $current_seg_index = $seg_start_index;
+  my $aligned_ctm_size = keys($aligned_ctm);    
+  while ($current_seg_length > 1.5 * $max_seg_length && $current_seg_index < $aligned_ctm_size-1) {
+    my $split_point = GetSplitPoint($aligned_ctm, $current_seg_index,
+                                    $seg_end_index, $max_seg_length);
+    my $ans = PrintSegment($aligned_ctm, $wav_id, $min_sil_length,
+                           $min_seg_length, $current_seg_index, $split_point,
+                           $current_seg_count, $SO, $TO);
+    $current_seg_count += 1 if ($ans != -1);
+    $current_seg_index = $split_point + 1;
+    $current_seg_length = $aligned_ctm->[$seg_end_index]->[1] +
+                          $aligned_ctm->[$seg_end_index]->[2] -
+                          $aligned_ctm->[$current_seg_index]->[1];
+  }
+
+  if ($current_seg_index eq $aligned_ctm_size-1) {
+      my $ans = PrintSegment($aligned_ctm, $wav_id, $min_sil_length,
+                             $min_seg_length, $current_seg_index, $current_seg_index,
+                             $current_seg_count, $SO, $TO);
+      $current_seg_count += 1 if ($ans != -1);
+      return ($current_seg_count, $current_seg_index);
+  }
+
+  if ($current_seg_length > $max_seg_length) {
+    my $split_point = GetSplitPoint($aligned_ctm, $current_seg_index,
+                                    $seg_end_index,
+                                    $current_seg_length / 2.0 + 0.01);
+    my $ans = PrintSegment($aligned_ctm, $wav_id, $min_sil_length,
+                           $min_seg_length, $current_seg_index, $split_point,
+                           $current_seg_count, $SO, $TO);
+    $current_seg_count += 1 if ($ans != -1);
+    $current_seg_index = $split_point + 1;
+  }
+
+  my $split_point = GetSplitPoint($aligned_ctm, $current_seg_index,
+                                  $seg_end_index, $max_seg_length + 0.01);
+  my $ans = PrintSegment($aligned_ctm, $wav_id, $min_sil_length,
+                         $min_seg_length, $current_seg_index, $split_point,
+                         $current_seg_count, $SO, $TO);
+  $current_seg_count += 1 if ($ans != -1);
+  $current_seg_index = $split_point + 1;
+
+  return ($current_seg_count, $current_seg_index);
+}
+
+# Processes each wav file.
+sub ProcessWav {
+  my ($max_seg_length, $min_seg_length, $min_sil_length, $special_symbol,
+      $current_ctm, $current_align, $SO, $TO, $ACT) = @_;
+
+  my $wav_id = $current_ctm->[0]->[0];
+  my $channel_id = $current_ctm->[0]->[1];
+  defined($wav_id) || die "Error: empty wav section\n";
+
+  # First, we have to align the ctm file to the Levenshtein alignment.
+  # @aligned_ctm is a list of the following:
+  # [word, start_time, duration, num_errors]
+  my $ctm_index = 0;
+  my @aligned_ctm = ();
+  foreach my $entry (@{$current_align}) {
+    my $ref_word = $entry->[0];
+    my $hyp_word = $entry->[1];
+    if ($hyp_word eq $special_symbol) {
+      # Case 1: deletion, $hyp does not correspond to a word in the ctm file.
+      my $start = 0.0; my $dur = 0.0;
+      if (defined($aligned_ctm[-1])) {
+        $start = $aligned_ctm[-1]->[1] + $aligned_ctm[-1]->[2];
+      }
+      push(@aligned_ctm, [$ref_word, $start, $dur, 1]);
+    } else {
+      # Case 2: non-deletion, now $hyp corresponds to a word in ctm file.
+      while ($current_ctm->[$ctm_index]->[4] eq "<eps>") {
+        # Case 2.1: ctm contains silence at the corresponding place.
+        push(@aligned_ctm, ["<eps>", $current_ctm->[$ctm_index]->[2],
+                             $current_ctm->[$ctm_index]->[3], 0]);
+        $ctm_index += 1;
+      }
+      my $ctm_word = $current_ctm->[$ctm_index]->[4];
+      $hyp_word eq $ctm_word ||
+        die "Error: got word $hyp_word in alignment but $ctm_word in ctm\n";
+      my $start = $current_ctm->[$ctm_index]->[2];
+      my $dur = $current_ctm->[$ctm_index]->[3];
+      if ($ref_word ne $ctm_word) {
+        if ($ref_word eq $special_symbol) {
+          # Case 2.2: insertion, we propagate the duration and error to the
+          #           previous one.
+          if (defined($aligned_ctm[-1])) {
+            $aligned_ctm[-1]->[2] += $dur;
+            $aligned_ctm[-1]->[3] += 1;
+          } else {
+            push(@aligned_ctm, ["<eps>", $start, $dur, 1]);
+          } 
+        } else {
+          # Case 2.3: substitution.
+          push(@aligned_ctm, [$ref_word, $start, $dur, 1]);
+        }
+      } else {
+        # Case 2.4: correct.
+        push(@aligned_ctm, [$ref_word, $start, $dur, 0]);
+      }
+      $ctm_index += 1;
+    }
+  }
+
+  # Save the aligned CTM if needed
+  if(defined($ACT)){
+    for (my $i = 0; $i <= $#aligned_ctm; $i++) {
+      print $ACT "$wav_id $channel_id $aligned_ctm[$i][1] $aligned_ctm[$i][2] ";
+      print $ACT "$aligned_ctm[$i][0] $aligned_ctm[$i][3]\n";
+    }
+  }
+
+  # Second, we create segments from @align_ctm, using simple greedy method.
+  my $current_seg_index = 0;
+  my $current_seg_count = 0;
+  for (my $x = 0; $x < @aligned_ctm; $x += 1) {
+    my $lcorrect = "true"; my $rcorrect = "true";
+    $lcorrect = "false" if ($x > 0 && $aligned_ctm[$x - 1]->[3] > 0);
+    $rcorrect = "false" if ($x < @aligned_ctm - 1 &&
+                            $aligned_ctm[$x + 1]->[3] > 0);
+
+    my $current_seg_length = GetSegmentLengthNoSil(\@aligned_ctm,
+                                                   $current_seg_index, $x);
+
+    # We split the audio, if the silence is longer than the requested silence
+    # length, and if there are no alignment error around it. We also make sure
+    # that segment contains actual words, instead of pure silence.
+    if ($aligned_ctm[$x]->[0] eq "<eps>" &&
+        $aligned_ctm[$x]->[2] >= $min_sil_length
+       && (($force_correct_boundary_words && $lcorrect eq "true" &&
+            $rcorrect eq "true") || !$force_correct_boundary_words)) {
+      if ($current_seg_length <= $max_seg_length &&
+          $current_seg_length >= $min_seg_length) {
+        my $ans = PrintSegment(\@aligned_ctm, $wav_id, $min_sil_length,
+                               $min_seg_length, $current_seg_index, $x,
+                               $current_seg_count, $SO, $TO);
+        $current_seg_count += 1 if ($ans != -1);
+        $current_seg_index = $x + 1;
+      } elsif ($current_seg_length > $max_seg_length) {
+        ($current_seg_count, $current_seg_index)
+          = SplitLongSegment(\@aligned_ctm, $wav_id, $max_seg_length,
+                             $min_sil_length, $current_seg_index, $x,
+                             $current_seg_count, $SO, $TO);
+      }
+    }
+  }
+
+  # Last segment.
+  if ($current_seg_index <= @aligned_ctm - 1) {
+    SplitLongSegment(\@aligned_ctm, $wav_id, $max_seg_length, $min_sil_length,
+                     $current_seg_index, @aligned_ctm - 1,
+                     $current_seg_count, $SO, $TO);
+  }
+}
+
+# Insert <eps> as silence so the down stream process will be easier. Example:
+#
+# Input ctm:
+# 011 A 3.39 0.23 SELL
+# 011 A 3.62 0.18 OFF
+# 011 A 3.83 0.45 ASSETS
+#
+# Output ctm:
+# 011 A 3.39 0.23 SELL
+# 011 A 3.62 0.18 OFF
+# 011 A 3.80 0.03 <eps>
+# 011 A 3.83 0.45 ASSETS
+sub InsertSilence {
+  my ($ctm_in, $ctm_out) = @_;
+  for (my $x = 1; $x < @{$ctm_in}; $x += 1) {
+    push(@{$ctm_out}, $ctm_in->[$x - 1]);
+
+    my $new_start = sprintf("%.2f",
+                            $ctm_in->[$x - 1]->[2] + $ctm_in->[$x - 1]->[3]);
+    if ($new_start < $ctm_in->[$x]->[2]) {
+      my $new_dur = sprintf("%.2f", $ctm_in->[$x]->[2] - $new_start);
+      push(@{$ctm_out}, [$ctm_in->[$x - 1]->[0], $ctm_in->[$x - 1]->[1],
+                         $new_start, $new_dur, "<eps>"]);
+    }
+  }
+  push(@{$ctm_out}, $ctm_in->[@{$ctm_in} - 1]);
+}
+
+# Reads the alignment.
+my %aligned = ();
+while (<AI>) {
+  chomp;
+  my @col = split;
+  @col >= 2 || die "Error: bad line $_\n";
+  my $wav = shift @col;
+  my @pairs = split(" $separator ", join(" ", @col));
+  for (my $x = 0; $x < @pairs; $x += 1) {
+    my @col1 = split(" ", $pairs[$x]);
+    @col1 == 2 || die "Error: bad pair $pairs[$x]\n";
+    $pairs[$x] = \@col1;
+  }
+  ! defined($aligned{$wav}) || die "Error: $wav has already been processed\n";
+  $aligned{$wav} = \@pairs;
+}
+
+# Reads the ctm file and creates the segmentation.
+my $previous_wav_id = "";
+my $previous_channel_id = "";
+my @current_wav = ();
+while (<CI>) {
+  chomp;
+  my @col = split;
+  @col >= 5 || die "Error: bad line $_\n";
+  if ($previous_wav_id eq $col[0] && $previous_channel_id eq $col[1]) {
+    push(@current_wav, \@col);
+  } else {
+    if (@current_wav > 0) {
+      defined($aligned{$previous_wav_id}) ||
+        die "Error: no alignment info for $previous_wav_id\n";
+      my @current_wav_silence = ();
+      InsertSilence(\@current_wav, \@current_wav_silence);
+      ProcessWav($max_seg_length, $min_seg_length, $min_sil_length,
+                 $special_symbol, \@current_wav_silence,
+                 $aligned{$previous_wav_id}, $SO, $TO, $ACT);
+    }
+    @current_wav = ();
+    push(@current_wav, \@col);
+    $previous_wav_id = $col[0];
+    $previous_channel_id = $col[1];
+  }
+}
+
+# The last wav file.
+if (@current_wav > 0) {
+  defined($aligned{$previous_wav_id}) ||
+    die "Error: no alignment info for $previous_wav_id\n";
+  my @current_wav_silence = ();
+  InsertSilence(\@current_wav, \@current_wav_silence);
+  ProcessWav($max_seg_length, $min_seg_length, $min_sil_length, $special_symbol,
+             \@current_wav_silence, $aligned{$previous_wav_id}, $SO, $TO, $ACT);
+}
+
+close(CI);
+close(AI);
+close($SO);
+close($TO);
+close($ACT) if defined($ACT);

tools/ASR_2ch_track/steps/cleanup/debug_lexicon.sh

@@ -0,0 +1,200 @@
+#!/bin/bash
+# Copyright 2014  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# this script gets some stats that will help you debug the lexicon.
+
+# Begin configuration section.
+stage=1
+remove_stress=false
+nj=10  # number of jobs for various decoding-type things that we run.
+cmd=run.pl
+alidir=
+# End configuration section
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 5 ]; then
+   echo "usage: $0 <data-dir> <lang-dir> <src-dir> <src-dict> <dir>"
+   echo "e.g.: $0 data/train data/lang exp/tri4b data/local/dict/lexicon.txt exp/debug_lexicon"
+   echo "main options (for others, see top of script file)"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --cmd <cmd>                                      # command to run jobs, e.g. run.pl,queue.pl"
+   echo "  --stage <stage>                                  # use to control partial reruns."
+   echo "  --remove-stress <true|false>                     # if true, remove stress before printing analysis"
+   echo "                                                   # note: if you change this, you only have to rerun"
+   echo "                                                   # from stage 10."
+   echo "  --alidir <alignment-dir>                         # if supplied, training-data alignments and transforms"
+   echo "                                                   # are obtained from here instead of being generated."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+src=$3
+srcdict=$4
+dir=$5
+
+set -e
+
+for f in $data/feats.scp $lang/phones.txt $src/final.mdl $srcdict; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1;
+done
+
+if [ -z $alidir ]; then
+  alidir=${src}_ali_$(basename $data)
+  if [ $stage -le 1 ]; then
+    steps/align_fmllr.sh --cmd "$cmd" --nj $nj $data $lang $src $alidir
+  fi
+fi
+
+phone_lang=data/$(basename $lang)_phone_bg
+
+if [ $stage -le 2 ]; then
+  utils/make_phone_bigram_lang.sh $lang $alidir $phone_lang
+fi
+
+if [ $stage -le 3 ]; then
+  utils/mkgraph.sh $phone_lang $src $src/graph_phone_bg
+fi
+
+if [ $stage -le 4 ]; then
+  steps/decode_si.sh --skip-scoring true \
+    --cmd "$cmd" --nj $nj --transform-dir $alidir \
+    --acwt 0.25 --beam 10.0 --lattice-beam 5.0 --max-active 2500 \
+    $src/graph_phone_bg $data $src/decode_$(basename $data)_phone_bg
+fi
+
+if [ $stage -le 5 ]; then
+  steps/get_train_ctm.sh --print-silence true --use-segments false \
+     --cmd "$cmd" $data $lang $alidir
+fi
+
+if [ $stage -le 6 ]; then
+  steps/get_ctm.sh --use-segments false --cmd "$cmd" --min-lmwt 3 --max-lmwt 8 \
+     $data $phone_lang $src/decode_$(basename $data)_phone_bg
+fi
+
+if [ $stage -le 7 ]; then
+  mkdir -p $dir
+  # lmwt=4 corresponds to the scale we decoded at.
+  cp $src/decode_$(basename $data)_phone_bg/score_4/$(basename $data).ctm $dir/phone.ctm
+
+  cp $alidir/ctm $dir/word.ctm
+fi
+
+if [ $stage -le 8 ]; then
+# we'll use 'sort' to do most of the heavy lifting when processing the data.
+# suppose word.ctm has an entry like
+# sw02054 A 213.32 0.24 and
+# we'll convert it into two entries like this, with the start and end separately:
+# sw02054-A 0021332 START and
+# sw02054-A 0021356 END and
+#
+# and suppose phone.ctm has lines like
+# sw02054 A 213.09 0.24 sil
+# sw02054 A 213.33 0.13 ae_B
+# we'll convert them into lines where the time is derived the midpoint of the phone, like
+# sw02054 A 0021321 PHONE sil
+# sw02054 A 0021340 PHONE ae_B
+# and then we'll remove the optional-silence phones and, if needed, the word-boundary markers from
+# the phones, to get just
+# sw02054 A 0021340 PHONE ae
+# then after sorting and merge-sorting the two ctm files we can easily
+# work out for each word, what the phones were during that time.
+
+  grep -v '<eps>' $phone_lang/phones.txt | awk '{print $1, $1}' | \
+    sed 's/_B$//' | sed 's/_I$//' | sed 's/_E$//' | sed 's/_S$//' >$dir/phone_map.txt
+
+  cat $dir/phone.ctm | utils/apply_map.pl -f 5 $dir/phone_map.txt > $dir/phone_text.ctm > $dir/phone_mapped.ctm
+
+  export LC_ALL=C
+
+  cat $dir/word.ctm | awk '{printf("%s-%s %09d START %s\n", $1, $2, 100*$3, $5); printf("%s-%s %09d END %s\n", $1, $2, 100*($3+$4), $5);}' | \
+     sort >$dir/word_processed.ctm
+
+  cat $dir/phone_mapped.ctm | awk '{printf("%s-%s %09d PHONE %s\n", $1, $2, 100*($3+(0.5*$4)), $5);}' | \
+     sort >$dir/phone_processed.ctm
+
+  # merge-sort both ctm's
+  sort -m $dir/word_processed.ctm $dir/phone_processed.ctm > $dir/combined.ctm
+
+fi
+
+if [ $stage -le 9 ]; then
+  awk '{print $3, $4}' $dir/combined.ctm | \
+     perl -e ' while (<>) { chop; @A = split(" ", $_); ($a,$b) = @A;
+     if ($a eq "START") { $cur_word = $b; @phones = (); }
+     if ($a eq "END") { print $cur_word, " ", join(" ", @phones), "\n"; }
+     if ($a eq "PHONE") { push @phones, $b; }} ' | sort | uniq -c | sort -nr > $dir/prons.txt
+fi
+
+if [ $stage -le 10 ]; then
+  if $remove_stress; then
+    perl -e 'while(<>) { @A=split(" ", $_); for ($n=1;$n<@A;$n++) { $A[$n] =~ s/[0-9]$//; } print join(" ", @A) . "\n"; } ' \
+      <$srcdict >$dir/lexicon.txt
+  else
+    cp $srcdict $dir/lexicon.txt
+  fi
+  silphone=$(cat $phone_lang/phones/optional_silence.txt)
+  echo "<eps> $silphone" >> $dir/lexicon.txt
+
+  awk '{count[$2] += $1;} END {for (w in count){print w, count[w];}}' \
+      <$dir/prons.txt >$dir/counts.txt
+
+
+
+  cat $dir/prons.txt | \
+    if $remove_stress; then
+      perl -e 'while(<>) { @A=split(" ", $_); for ($n=1;$n<@A;$n++) { $A[$n] =~ s/[0-9]$//; } print join(" ", @A) . "\n"; } '
+    else
+      cat
+    fi | perl -e '
+     print ";; <count-of-this-pron> <rank-of-this-pron> <frequency-of-this-pron> CORRECT|INCORRECT <word> <pron>\n";
+     open(D, "<$ARGV[0]") || die "opening dict file $ARGV[0]";
+     # create a hash of all reference pronuncations, and for each word, record
+     # a list of the prons, separated by " | ".
+     while (<D>) {
+        @A = split(" ", $_); $is_pron{join(" ",@A)} = 1;
+        $w = shift @A;
+        if (!defined $prons{$w}) { $prons{$w} = join(" ", @A); }
+        else { $prons{$w} = $prons{$w} . " | " . join(" ", @A); }
+     }
+     open(C, "<$ARGV[1]") || die "opening counts file $ARGV[1];";
+     while (<C>) { @A = split(" ", $_); $word_count{$A[0]} = $A[1]; }
+     while (<STDIN>) { @A = split(" ", $_);
+       $count = shift @A; $word = $A[0]; $freq = sprintf("%0.2f", $count / $word_count{$word});
+       $rank = ++$wcount{$word}; # 1 if top observed pron of word, 2 if second...
+       $str = (defined $is_pron{join(" ", @A)} ? "CORRECT" : "INCORRECT");
+       shift @A;
+       print "$count $rank $freq $str $word \"" . join(" ", @A) . "\", ref = \"$prons{$word}\"\n";
+     } ' $dir/lexicon.txt $dir/counts.txt  >$dir/pron_info.txt
+
+  grep -v '^;;' $dir/pron_info.txt | \
+     awk '{ word=$5; count=$1; if (tot[word] == 0) { first_line[word] = $0; }
+            corr[word] += ($4 == "CORRECT" ? count : 0); tot[word] += count; }
+          END {for (w in tot) { printf("%s\t%s\t%s\t\t%s\n", tot[w], w, (corr[w]/tot[w]), first_line[w]); }} ' \
+     | sort -k1 -nr | cat <( echo ';; <total-count-of-word> <word> <correct-proportion>      <first-corresponding-line-in-pron_info.txt>') - \
+      > $dir/word_info.txt
+fi
+
+if [ $stage -le 11 ]; then
+  echo "$0: some of the more interesting stuff in $dir/pron_info.txt follows."
+  echo "# grep -w INCORRECT $dir/pron_info.txt  | grep -w 1 | head -n 20"
+
+  grep -w INCORRECT $dir/pron_info.txt  | grep -w 1 | head -n 20
+
+  echo "$0: here are some other interesting things.."
+  echo "# grep -w INCORRECT $dir/pron_info.txt  | grep -w 1 | awk '\$3 > 0.4 && \$1 > 10' | head -n 20"
+  grep -w INCORRECT $dir/pron_info.txt  | grep -w 1 | awk '$3 > 0.4 && $1 > 10' | head -n 20
+
+  echo "$0: here are some high-frequency words whose reference pronunciations rarely show up."
+  echo "# awk '\$3 < 0.1' $dir/word_info.txt  | head -n 20"
+  awk '$3 < 0.1 || $1 == ";;"' $dir/word_info.txt  | head -n 20
+
+
+fi
+

tools/ASR_2ch_track/steps/cleanup/decode_segmentation.sh

@@ -0,0 +1,133 @@
+#!/bin/bash
+
+# Copyright 2014  Guoguo Chen, 2015 GoVivace Inc. (Nagendra Goel) 
+# Apache 2.0
+
+# Begin configuration section.  
+transform_dir=   # this option won't normally be used, but it can be used if you
+                 # want to supply existing fMLLR transforms when decoding.
+iter=
+model= # You can specify the model to use (e.g. if you want to use the .alimdl)
+stage=0
+nj=4
+cmd=run.pl
+max_active=7000
+beam=13.0
+lattice_beam=6.0
+acwt=0.083333 # note: only really affects pruning (scoring is on lattices).
+num_threads=1 # if >1, will use gmm-latgen-faster-parallel
+parallel_opts=  # ignored now.
+scoring_opts=
+# note: there are no more min-lmwt and max-lmwt options, instead use
+# e.g. --scoring-opts "--min-lmwt 1 --max-lmwt 20"
+skip_scoring=false
+# End configuration section.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f ./path.sh ] && . ./path.sh; # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 3 ]; then
+   echo "This is a special decoding script for segmentation where we use one"
+   echo "decoding graph for each segment. We assume a file HCLG.fsts.scp exists"
+   echo "which is the scp file of the graphs for each segment."
+   echo ""
+   echo "Usage: $0 [options] <graph-dir> <data-dir> <decode-dir>"
+   echo " e.g.: $0 exp/tri2b/graph_train_si284_split \\"
+   echo "             data/train_si284_split exp/tri2b/decode_train_si284_split"
+   echo ""
+   echo "where <decode-dir> is assumed to be a sub-directory of the directory"
+   echo "where the model is."
+   echo ""
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --iter <iter>                                    # Iteration of model to test."
+   echo "  --model <model>                                  # which model to use (e.g. to"
+   echo "                                                   # specify the final.alimdl)"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   echo "  --transform-dir <trans-dir>                      # dir to find fMLLR transforms "
+   echo "  --acwt <float>                                   # acoustic scale used for lattice generation "
+   echo "  --scoring-opts <string>                          # options to local/score.sh"
+   echo "  --num-threads <n>                                # number of threads to use, default 1."
+   exit 1;
+fi
+
+
+graphdir=$1
+data=$2
+dir=$3
+srcdir=`dirname $dir`; # The model directory is one level up from decoding directory.
+sdata=$data/split$nj;
+
+mkdir -p $dir/log
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+echo $nj > $dir/num_jobs
+
+if [ -z "$model" ]; then # if --model <mdl> was not specified on the command line...
+  if [ -z $iter ]; then model=$srcdir/final.mdl; 
+  else model=$srcdir/$iter.mdl; fi
+fi
+
+for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $model $graphdir/HCLG.fsts.scp; do
+  [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
+done
+
+# Figures out the proper HCLG.
+sort -k1,1 -u < $graphdir/HCLG.fsts.scp > $graphdir/HCLG.fsts.scp.sorted
+mv $graphdir/HCLG.fsts.scp.sorted $graphdir/HCLG.fsts.scp
+for x in `seq 1 $nj`; do
+  cat $graphdir/HCLG.fsts.scp |\
+    utils/filter_scp.pl -f 1 $sdata/$x/feats.scp > $sdata/$x/graphs.scp
+  num_feats=`cat $sdata/$x/feats.scp | wc -l`
+  num_graphs=`cat $sdata/$x/graphs.scp | wc -l`
+  if [ $num_graphs -ne $num_feats ]; then
+    echo "$0: number of graphs is not consistent with number of features."
+    exit 1
+  fi
+done
+HCLG="scp:$sdata/JOB/graphs.scp"
+
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type";
+
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+delta_opts=`cat $srcdir/delta_opts 2>/dev/null`
+
+thread_string=
+[ $num_threads -gt 1 ] && thread_string="-parallel --num-threads=$num_threads" 
+
+case $feat_type in
+  delta) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |";;
+  lda) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
+  *) echo "Invalid feature type $feat_type" && exit 1;
+esac
+if [ ! -z "$transform_dir" ]; then # add transforms to features...
+  echo "Using fMLLR transforms from $transform_dir"
+  [ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
+  [ "`cat $transform_dir/num_jobs`" -ne $nj ] && \
+     echo "Mismatch in number of jobs with $transform_dir";
+  feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
+fi
+
+if [ $stage -le 0 ]; then
+  if [ -f "$graphdir/num_pdfs" ]; then
+    [ "`cat $graphdir/num_pdfs`" -eq `am-info --print-args=false $model | grep pdfs | awk '{print $NF}'` ] || \
+      { echo "Mismatch in number of pdfs with $model"; exit 1; }
+  fi
+  $cmd --num-threads $num_threads JOB=1:$nj $dir/log/decode.JOB.log \
+    gmm-latgen-faster$thread_string --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
+    --acoustic-scale=$acwt --allow-partial=true --word-symbol-table=$graphdir/words.txt \
+    $model "$HCLG" "$feats" "ark:|gzip -c > $dir/lat.JOB.gz" || exit 1;
+fi
+
+if ! $skip_scoring ; then
+  [ ! -x local/score.sh ] && \
+    echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
+  local/score.sh --cmd "$cmd" $scoring_opts $data $graphdir $dir ||
+    { echo "$0: Scoring failedr. (ignore by '--skip-scoring true')"; exit 1; }
+fi
+
+exit 0;

tools/ASR_2ch_track/steps/cleanup/find_bad_utts.sh

@@ -0,0 +1,193 @@
+#!/bin/bash
+# Copyright 2012-2014  Johns Hopkins University (Author: Daniel Povey)
+# Apache 2.0
+
+# Computes training alignments using a model with delta or
+# LDA+MLLT features.  This version, rather than just using the
+# text to align, computes mini-language models (unigram) from the text
+# and a few common words in the LM.
+
+# Begin configuration section.
+nj=4
+cmd=run.pl
+use_graphs=false
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --self-loop-scale=0.1"
+acoustic_scale=0.1
+beam=15.0
+lattice_beam=8.0
+max_active=750
+transform_dir=  # directory to find fMLLR transforms in.
+top_n_words=100 # Number of common words that we compile into each graph (most frequent
+                # in $lang/text.
+stage=-1
+cleanup=true
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: $0 <data-dir> <lang-dir> <src-dir> <dir>"
+   echo "e.g.:  $0 data/train data/lang exp/tri1 exp/tri1_debug"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+for f in $data/text $lang/oov.int $srcdir/tree $srcdir/final.mdl \
+    $lang/L_disambig.fst $lang/phones/disambig.int; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1;
+done
+
+oov=`cat $lang/oov.int` || exit 1;
+mkdir -p $dir/log
+echo $nj > $dir/num_jobs
+sdata=$data/split$nj
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+cp $srcdir/final.occs $dir;
+
+
+if [ $stage -le 0 ]; then
+  utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt <$data/text | \
+    awk '{for(x=2;x<=NF;x++) print $x;}' | sort | uniq -c | \
+    sort -rn > $dir/word_counts.int || exit 1;
+  num_words=$(awk '{x+=$1} END{print x}' < $dir/word_counts.int) || exit 1;
+  # print top-n words with their unigram probabilities.
+
+  head -n $top_n_words $dir/word_counts.int | awk -v tot=$num_words '{print $1/tot, $2;}' >$dir/top_words.int
+  utils/int2sym.pl -f 2 $lang/words.txt <$dir/top_words.int >$dir/top_words.txt
+fi
+
+if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
+echo "$0: feature type is $feat_type"
+
+case $feat_type in
+  delta) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas ark:- ark:- |";;
+  lda) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |"
+    cp $srcdir/final.mat $srcdir/full.mat $dir
+   ;;
+  *) echo "$0: invalid feature type $feat_type" && exit 1;
+esac
+if [ -z "$transform_dir" ] && [ -f $srcdir/trans.1 ]; then
+  transform_dir=$srcdir
+fi
+if [ ! -z "$transform_dir" ]; then
+  echo "$0: using transforms from $transform_dir"
+  [ ! -f $transform_dir/trans.1 ] && echo "$0: no such file $transform_dir/trans.1" && exit 1;
+  nj_orig=$(cat $transform_dir/num_jobs)
+  if [ $nj -ne $nj_orig ]; then
+    # Copy the transforms into an archive with an index.
+    for n in $(seq $nj_orig); do cat $transform_dir/trans.$n; done | \
+      copy-feats ark:- ark,scp:$dir/trans.ark,$dir/trans.scp || exit 1;
+    feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk scp:$dir/trans.scp ark:- ark:- |"
+  else
+    # number of jobs matches with alignment dir.
+    feats="$feats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$transform_dir/trans.JOB ark:- ark:- |"
+  fi
+elif [ -f $srcdir/final.alimdl ]; then
+  echo "$0: **WARNING**: you seem to be using an fMLLR system as input,"
+  echo "  but you are not providing the --transform-dir option during alignment."
+fi
+
+
+if [ $stage -le 1 ]; then
+  echo "$0: decoding $data using utterance-specific decoding graphs using model from $srcdir, output in $dir"
+
+  rm $dir/edits.*.txt $dir/aligned_ref.*.txt 2>/dev/null
+
+  $cmd JOB=1:$nj $dir/log/decode.JOB.log \
+    utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text \| \
+    steps/cleanup/make_utterance_fsts.pl $dir/top_words.int \| \
+    compile-train-graphs-fsts $scale_opts --read-disambig-syms=$lang/phones/disambig.int \
+     $dir/tree $dir/final.mdl $lang/L_disambig.fst ark:- ark:- \| \
+    gmm-latgen-faster --acoustic-scale=$acoustic_scale --beam=$beam \
+      --max-active=$max_active --lattice-beam=$lattice_beam \
+      --word-symbol-table=$lang/words.txt \
+     $dir/final.mdl ark:- "$feats" ark:- \| \
+    lattice-oracle ark:- "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|" \
+      ark,t:- ark,t:$dir/edits.JOB.txt \| \
+    utils/int2sym.pl -f 2- $lang/words.txt '>' $dir/aligned_ref.JOB.txt || exit 1;
+fi
+
+
+if [ $stage -le 2 ]; then
+  if [ -f $dir/edits.1.txt ]; then
+    # the awk commands below are to ensure that partially-written files don't confuse us.
+    for x in $(seq $nj); do cat $dir/edits.$x.txt; done | awk '{if(NF==2){print;}}' > $dir/edits.txt
+    for x in $(seq $nj); do cat $dir/aligned_ref.$x.txt; done | awk '{if(NF>=1){print;}}' > $dir/aligned_ref.txt
+  else
+    echo "$0: warning: no file $dir/edits.1.txt, using previously concatenated file if present."
+  fi
+
+  # in case any utterances failed to align, get filtered copy of $data/text
+  utils/filter_scp.pl $dir/edits.txt < $data/text  > $dir/text
+  cat $dir/text | awk '{print $1, (NF-1);}' > $dir/length.txt
+
+  n1=$(wc -l < $dir/edits.txt)
+  n2=$(wc -l < $dir/aligned_ref.txt)
+  n3=$(wc -l < $dir/text)
+  n4=$(wc -l < $dir/length.txt)
+  if [ $n1 -ne $n2 ] || [ $n2 -ne $n3 ] || [ $n3 -ne $n4 ]; then
+    echo "$0: mismatch in lengths of files:"
+    wc $dir/edits.txt $dir/aligned_ref.txt $dir/text $dir/length.txt
+    exit 1;
+  fi
+
+  # note: the format of all_info.txt is:
+  # <utterance-id>   <number of errors>  <reference-length>  <decoded-output>   <reference>
+  # with the fields separated by tabs, e.g.
+  # adg04_sr009_trn 1 	12	 SHOW THE GRIDLEY+S TRACK IN BRIGHT ORANGE WITH HORNE+S IN DIM RED AT	 SHOW THE GRIDLEY+S TRACK IN BRIGHT ORANGE WITH HORNE+S IN DIM RED
+
+  paste $dir/edits.txt \
+      <(awk '{print $2}' $dir/length.txt) \
+      <(awk '{$1="";print;}' <$dir/aligned_ref.txt) \
+      <(awk '{$1="";print;}' <$dir/text) > $dir/all_info.txt
+
+  sort -nr -k2 $dir/all_info.txt > $dir/all_info.sorted.txt
+
+  if $cleanup; then
+    rm $dir/edits.*.txt $dir/aligned_ref.*.txt
+  fi
+
+fi
+
+if [ $stage -le 3 ]; then
+  ###
+  # These stats might help people figure out what is wrong with the data
+  # a)human-friendly and machine-parsable alignment in the file per_utt_details.txt
+  # b)evaluation of per-speaker performance to possibly find speakers with
+  #   distinctive accents/speech disorders and similar
+  # c)Global analysis on (Ins/Del/Sub) operation, which might be used to figure
+  #   out if there is systematic issue with lexicon, pronunciation or phonetic confusability
+
+  mkdir -p $dir/analysis
+  align-text --special-symbol="***"  ark:$dir/text ark:$dir/aligned_ref.txt  ark,t:- | \
+    utils/scoring/wer_per_utt_details.pl --special-symbol "***" > $dir/analysis/per_utt_details.txt
+
+  cat $dir/analysis/per_utt_details.txt | \
+    utils/scoring/wer_per_spk_details.pl $data/utt2spk > $dir/analysis/per_spk_details.txt
+
+  cat $dir/analysis/per_utt_details.txt | \
+    utils/scoring/wer_ops_details.pl --special-symbol "***" | \
+    sort -i -b -k1,1 -k4,4nr -k2,2 -k3,3 > $dir/analysis/ops_details.txt
+
+fi
+

tools/ASR_2ch_track/steps/cleanup/find_bad_utts_nnet.sh

@@ -0,0 +1,162 @@
+#!/bin/bash
+# Copyright 2012-2014  Johns Hopkins University (Author: Daniel Povey)
+#           2016       Ilya Platonov      
+# Apache 2.0
+#
+# Tweaked version of find_bad_utts.sh to work with nnet2 baseline models. 
+#
+# Begin configuration section.  
+nj=32
+cmd=run.pl
+use_graphs=false
+# Begin configuration.
+scale_opts="--transition-scale=1.0 --self-loop-scale=0.1"
+acoustic_scale=0.1
+beam=15.0
+lattice_beam=8.0
+max_active=750
+transform_dir=  # directory to find fMLLR transforms in.
+top_n_words=100 # Number of common words that we compile into each graph (most frequent
+                # in $lang/text.
+stage=-1
+cleanup=true
+# End configuration options.
+
+echo "$0 $@"  # Print the command line for logging
+
+[ -f path.sh ] && . ./path.sh # source the path.
+. parse_options.sh || exit 1;
+
+if [ $# != 4 ]; then
+   echo "usage: $0 <data-dir> <lang-dir> <src-dir> <dir>"
+   echo "e.g.:  $0 data/train data/lang exp/tri1 exp/tri1_debug"
+   echo "main options (for others, see top of script file)"
+   echo "  --config <config-file>                           # config containing options"
+   echo "  --nj <nj>                                        # number of parallel jobs"
+   echo "  --use-graphs true                                # use graphs in src-dir"
+   echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
+   exit 1;
+fi
+
+data=$1
+lang=$2
+srcdir=$3
+dir=$4
+
+for f in $data/text $lang/oov.int $srcdir/tree $srcdir/final.mdl \
+    $lang/L_disambig.fst $lang/phones/disambig.int; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1;
+done
+
+oov=`cat $lang/oov.int` || exit 1;
+mkdir -p $dir/log
+echo $nj > $dir/num_jobs
+sdata=$data/split$nj
+splice_opts=`cat $srcdir/splice_opts 2>/dev/null` # frame-splicing options.
+cp $srcdir/splice_opts $dir 2>/dev/null # frame-splicing options.
+cmvn_opts=`cat $srcdir/cmvn_opts 2>/dev/null`
+cp $srcdir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option.
+
+[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1;
+
+cp $srcdir/{tree,final.mdl} $dir || exit 1;
+
+
+if [ $stage -le 0 ]; then
+  utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt <$data/text | \
+    awk '{for(x=2;x<=NF;x++) print $x;}' | sort | uniq -c | \
+    sort -rn > $dir/word_counts.int || exit 1;
+  num_words=$(awk '{x+=$1} END{print x}' < $dir/word_counts.int) || exit 1;
+  # print top-n words with their unigram probabilities.
+
+  head -n $top_n_words $dir/word_counts.int | awk -v tot=$num_words '{print $1/tot, $2;}' >$dir/top_words.int
+  utils/int2sym.pl -f 2 $lang/words.txt <$dir/top_words.int >$dir/top_words.txt
+fi
+
+echo "$0: feature type is raw"
+
+feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- |";
+
+if [ $stage -le 1 ]; then
+  echo "$0: decoding $data using utterance-specific decoding graphs using model from $srcdir, output in $dir"
+
+  rm $dir/edits.*.txt $dir/aligned_ref.*.txt 2>/dev/null
+
+  $cmd JOB=1:$nj $dir/log/decode.JOB.log \
+    utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text \| \
+    steps/cleanup/make_utterance_fsts.pl $dir/top_words.int \| \
+    compile-train-graphs-fsts $scale_opts --read-disambig-syms=$lang/phones/disambig.int \
+     $dir/tree $dir/final.mdl $lang/L_disambig.fst ark:- ark:- \| \
+    nnet-latgen-faster --acoustic-scale=$acoustic_scale --beam=$beam \
+      --max-active=$max_active --lattice-beam=$lattice_beam \
+      --word-symbol-table=$lang/words.txt \
+     $dir/final.mdl ark:- "$feats" ark:- \| \
+    lattice-oracle ark:- "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt $sdata/JOB/text|" \
+      ark,t:- ark,t:$dir/edits.JOB.txt \| \
+    utils/int2sym.pl -f 2- $lang/words.txt '>' $dir/aligned_ref.JOB.txt || exit 1;
+fi
+
+
+if [ $stage -le 2 ]; then
+  if [ -f $dir/edits.1.txt ]; then
+    # the awk commands below are to ensure that partially-written files don't confuse us.
+    for x in $(seq $nj); do cat $dir/edits.$x.txt; done | awk '{if(NF==2){print;}}' > $dir/edits.txt
+    for x in $(seq $nj); do cat $dir/aligned_ref.$x.txt; done | awk '{if(NF>=1){print;}}' > $dir/aligned_ref.txt
+  else
+    echo "$0: warning: no file $dir/edits.1.txt, using previously concatenated file if present."
+  fi
+
+  # in case any utterances failed to align, get filtered copy of $data/text
+  utils/filter_scp.pl $dir/edits.txt < $data/text  > $dir/text
+  cat $dir/text | awk '{print $1, (NF-1);}' > $dir/length.txt
+
+  n1=$(wc -l < $dir/edits.txt)
+  n2=$(wc -l < $dir/aligned_ref.txt)
+  n3=$(wc -l < $dir/text)
+  n4=$(wc -l < $dir/length.txt)
+  if [ $n1 -ne $n2 ] || [ $n2 -ne $n3 ] || [ $n3 -ne $n4 ]; then
+    echo "$0: mismatch in lengths of files:"
+    wc $dir/edits.txt $dir/aligned_ref.txt $dir/text $dir/length.txt
+    exit 1;
+  fi
+
+  # note: the format of all_info.txt is:
+  # <utterance-id>   <number of errors>  <reference-length>  <decoded-output>   <reference>
+  # with the fields separated by tabs, e.g.
+  # adg04_sr009_trn 1 	12	 SHOW THE GRIDLEY+S TRACK IN BRIGHT ORANGE WITH HORNE+S IN DIM RED AT	 SHOW THE GRIDLEY+S TRACK IN BRIGHT ORANGE WITH HORNE+S IN DIM RED
+  
+  paste $dir/edits.txt \
+      <(awk '{print $2}' $dir/length.txt) \
+      <(awk '{$1="";print;}' <$dir/aligned_ref.txt) \
+      <(awk '{$1="";print;}' <$dir/text) > $dir/all_info.txt
+
+  sort -nr -k2 $dir/all_info.txt > $dir/all_info.sorted.txt
+
+  if $cleanup; then
+    rm $dir/edits.*.txt $dir/aligned_ref.*.txt
+  fi
+
+fi
+
+if [ $stage -le 3 ]; then
+  ###
+  # These stats migh help people figure out what is wrong with the data
+  # a)human-friendly and machine-parsable alignment in the file per_utt_details.txt
+  # b)evaluation of per-speaker performance to possibly find speakers with 
+  #   distinctive accents/speech disorders and similar
+  # c)Global analysis on (Ins/Del/Sub) operation, which might be used to figure
+  #   out if there is systematic issue with lexicon, pronunciation or phonetic confusability
+
+  mkdir -p $dir/analysis
+  align-text --special-symbol="***"  ark:$dir/text ark:$dir/aligned_ref.txt  ark,t:- | \
+    utils/scoring/wer_per_utt_details.pl --special-symbol "***" > $dir/analysis/per_utt_details.txt
+
+  cat $dir/analysis/per_utt_details.txt | \
+    utils/scoring/wer_per_spk_details.pl $data/utt2spk > $dir/analysis/per_spk_details.txt
+
+  cat $dir/analysis/per_utt_details.txt | \
+    utils/scoring/wer_ops_details.pl --special-symbol "***" | \
+    sort -i -b -k1,1 -k4,4nr -k2,2 -k3,3 > $dir/analysis/ops_details.txt
+
+fi
+

tools/ASR_2ch_track/steps/cleanup/make_segmentation_data_dir.sh

@@ -0,0 +1,206 @@
+#!/bin/bash
+
+# Copyright 2014  Guoguo Chen
+# Apache 2.0
+
+# Begin configuration section.
+max_seg_length=10
+min_seg_length=2
+min_sil_length=0.5
+time_precision=0.05
+special_symbol="<***>"
+separator=";"
+wer_cutoff=-1
+# End configuration section.
+
+set -e
+
+echo "$0 $@"
+
+[ -f ./path.sh ] && . ./path.sh
+. parse_options.sh || exit 1;
+
+if [ $# -ne 3 ]; then
+  echo "This script takes the ctm file that corresponds to the data directory"
+  echo "created by steps/cleanup/split_long_utterance.sh, works out a new"
+  echo "segmentation and creates a new data directory for the new segmentation."
+  echo ""
+  echo "Usage: $0 [options] <ctm-file> <old-data-dir> <new-data-dir>"
+  echo " e.g.: $0 train_si284_split.ctm \\"
+  echo "                          data/train_si284_split data/train_si284_reseg"
+  echo "Options:"
+  echo "    --wer-cutoff            # ignore segments with WER higher than the"
+  echo "                            # specified value. -1 means no segment will"
+  echo "                            # be ignored."
+  echo "    --max-seg-length        # maximum length of new segments"
+  echo "    --min-seg-length        # minimum length of new segments"
+  echo "    --min-sil-length        # minimum length of silence as split point"
+  echo "    --time-precision        # precision for determining \"same time\""
+  echo "    --special-symbol        # special symbol to be aligned with"
+  echo "                            # inserted or deleted words"
+  echo "    --separator             # separator for aligned pairs"
+  exit 1;
+fi
+
+ctm=$1
+old_data_dir=$2
+new_data_dir=$3
+
+for f in $ctm $old_data_dir/text.orig $old_data_dir/utt2spk \
+  $old_data_dir/wav.scp $old_data_dir/segments; do
+  if [ ! -f $f ]; then
+    echo "$0: expected $f to exist"
+    exit 1;
+  fi
+done
+
+mkdir -p $new_data_dir/tmp/
+cp -f $old_data_dir/wav.scp $new_data_dir
+[ -f old_data_dir/spk2gender ] &&  cp -f $old_data_dir/spk2gender $new_data_dir
+
+# Removes the overlapping region (in utils/split_long_utterance.sh we create
+# the segmentation with overlapping region).
+#
+# Note that for each audio file, we expect its segments have been sorted in time
+# ascending order (if we ignore the overlap).
+cat $ctm | perl -e '
+  $precision = $ARGV[0];
+  @ctm = ();
+  %processed_ids = ();
+  $previous_id = "";
+  while (<STDIN>) {
+    chomp;
+    my @current = split;
+    @current >= 5 || die "Error: bad line $_\n";
+    $id = join("_", ($current[0], $current[1]));
+    @previous = @{$ctm[-1]};
+
+    # Start of a new audio file.
+    if ($previous_id ne $id) {
+      # Prints existing information.
+      if (@ctm > 0) {
+        foreach $line (@ctm) {
+          print "$line->[0] $line->[1] $line->[2] $line->[3] $line->[4]\n";
+        }
+      }
+
+      # Checks if the ctm file is sorted.
+      if (defined($processed_ids{$id})) {
+        die "Error: \"$current[0] $current[1]\" has already been processed\n";
+      } else {
+        $processed_ids{$id} = 1;
+      }
+
+      @ctm = ();
+      push(@ctm, \@current);
+      $previous_id = $id;
+      next;
+    }
+
+    $new_start = sprintf("%.2f", $previous[2] + $previous[3]);
+
+    if ($new_start > $current[2]) {
+      # Case 2: scans for a splice point.
+      $index = -1;
+      while (defined($ctm[$index])
+             && $ctm[$index]->[2] + $ctm[$index]->[3] > $current[2]) {
+        if ($ctm[$index]->[4] eq $current[4]
+            && abs($ctm[$index]->[2] - $current[2]) < $precision
+            && abs($ctm[$index]->[3] - $current[3]) < $precision) {
+          pop @ctm for 2..abs($index);
+          last;
+        } else {
+          $index -= 1;
+        }
+      }
+    } else {
+      push(@ctm, \@current);
+    }
+  }
+
+  if (@ctm > 0) {
+    foreach $line (@ctm) {
+      print "$line->[0] $line->[1] $line->[2] $line->[3] $line->[4]\n";
+    }
+  }' $time_precision > $new_data_dir/tmp/ctm
+
+# Creates a text file from the ctm, which will be used in Levenshtein alignment.
+# Note that we remove <eps> in the text file.
+cat $new_data_dir/tmp/ctm | perl -e '
+  $previous_wav = "";
+  $previous_channel = "";
+  $text = "";
+  while (<STDIN>) {
+    chomp;
+    @col = split;
+    @col >= 5 || die "Error: bad line $_\n";
+    if ($previous_wav eq $col[0]) {
+      $previous_channel eq $col[1] ||
+        die "Error: more than one channels detected\n";
+      if ($col[4] ne "<eps>") {
+        $text .= " $col[4]";
+      }
+    } else {
+      if ($text ne "") {
+        print "$previous_wav $text\n";
+      }
+      $text = $col[4];
+      $previous_wav = $col[0];
+      $previous_channel = $col[1];
+    }
+  }
+  if ($text ne "") {
+    print "$previous_wav $text\n";
+  }' > $new_data_dir/tmp/text
+
+# Computes the Levenshtein alignment.
+align-text --special-symbol=$special_symbol --separator=$separator \
+  ark:$old_data_dir/text.orig ark:$new_data_dir/tmp/text \
+  ark,t:$new_data_dir/tmp/aligned.txt
+
+# Creates new segmentation.
+steps/cleanup/create_segments_from_ctm.pl \
+  --max-seg-length $max_seg_length --min-seg-length $min_seg_length \
+  --min-sil-length $min_sil_length \
+  --separator $separator --special-symbol $special_symbol \
+  --wer-cutoff $wer_cutoff \
+  $new_data_dir/tmp/ctm $new_data_dir/tmp/aligned.txt \
+  $new_data_dir/segments $new_data_dir/text
+
+# Now creates the new utt2spk and spk2utt file.
+cat $old_data_dir/utt2spk | perl -e '
+  ($old_seg_file, $new_seg_file, $utt2spk_file_out) = @ARGV;
+  open(OS, "<$old_seg_file") || die "Error: fail to open $old_seg_file\n";
+  open(NS, "<$new_seg_file") || die "Error: fail to open $new_seg_file\n";
+  open(UO, ">$utt2spk_file_out") ||
+    die "Error: fail to open $utt2spk_file_out\n";
+  while (<STDIN>) {
+    chomp;
+    @col = split;
+    @col == 2 || die "Error: bad line $_\n";
+    $utt2spk{$col[0]} = $col[1];
+  }
+  while (<OS>) {
+    chomp;
+    @col = split;
+    @col == 4 || die "Error: bad line $_\n";
+    if (defined($wav2spk{$col[1]})) {
+      $wav2spk{$col[1]} == $utt2spk{$col[0]} ||
+        die "Error: multiple speakers detected for wav file $col[1]\n";
+    } else {
+      $wav2spk{$col[1]} = $utt2spk{$col[0]};
+    }
+  }
+  while (<NS>) {
+    chomp;
+    @col = split;
+    @col == 4 || die "Error: bad line $_\n";
+    defined($wav2spk{$col[1]}) ||
+      die "Error: could not find speaker for wav file $col[1]\n";
+    print UO "$col[0] $wav2spk{$col[1]}\n";
+  } ' $old_data_dir/segments $new_data_dir/segments $new_data_dir/utt2spk
+utils/utt2spk_to_spk2utt.pl $new_data_dir/utt2spk > $new_data_dir/spk2utt
+
+utils/fix_data_dir.sh $new_data_dir
+
+exit 0;