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DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/conf/mfcc_hires.conf

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+# config for high-resolution MFCC features, intended for neural network training
+# Note: we keep all cepstra, so it has the same info as filterbank features,
+# but MFCC is more easily compressible (because less correlated) which is why 
+# we prefer this method.
+--use-energy=false   # use average of log energy, not energy.
+--num-mel-bins=40     # similar to Google's setup.
+--num-ceps=40     # there is no dimensionality reduction.
+--low-freq=20     # low cutoff frequency for mel bins... this is high-bandwidth data, so
+                  # there might be some information at the low end.
+--high-freq=7600 # high cutoff frequently, relative to Nyquist of 8000 (=7600)
+--allow_downsample=true

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/conf/online_cmvn.conf

@@ -0,0 +1 @@
+# configuration file for apply-cmvn-online, used in the script ../local/run_online_decoding.sh

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/conf/queue.conf

@@ -0,0 +1,9 @@
+command qsub -v PATH -cwd -S /bin/bash -j y
+option mem=* -l rmem=$0 -j y
+option mem=0          # Do not add anything to qsub_opts
+option num_threads=* -pe smp $0
+option num_threads=1  # Do not add anything to qsub_opts
+option max_jobs_run=* -tc $0
+default gpu=0
+option gpu=0
+option gpu=* -l gpu=$0

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/chain/compare_wer.sh

@@ -0,0 +1,131 @@
+#!/bin/bash
+
+# this script is used for comparing decoding results between systems.
+# e.g. local/chain/compare_wer.sh exp/chain/tdnn_{c,d}_sp
+# For use with discriminatively trained systems you specify the epochs after a colon:
+# for instance,
+# local/chain/compare_wer.sh exp/chain/tdnn_c_sp exp/chain/tdnn_c_sp_smbr:{1,2,3}
+
+
+if [ $# == 0 ]; then
+  echo "Usage: $0: [--looped] [--online] <dir1> [<dir2> ... ]"
+  echo "e.g.: $0 exp/chain/tdnn_{b,c}_sp"
+  echo "or (with epoch numbers for discriminative training):"
+  echo "$0 exp/chain/tdnn_b_sp_disc:{1,2,3}"
+  exit 1
+fi
+
+echo "# $0 $*"
+
+include_looped=false
+if [ "$1" == "--looped" ]; then
+  include_looped=true
+  shift
+fi
+include_online=false
+if [ "$1" == "--online" ]; then
+  include_online=true
+  shift
+fi
+
+
+used_epochs=false
+
+# this function set_names is used to separate the epoch-related parts of the name
+# [for discriminative training] and the regular parts of the name.
+# If called with a colon-free directory name, like:
+#  set_names exp/chain/tdnn_lstm1e_sp_bi_smbr
+# it will set dir=exp/chain/tdnn_lstm1e_sp_bi_smbr and epoch_infix=""
+# If called with something like:
+#  set_names exp/chain/tdnn_d_sp_smbr:3
+# it will set dir=exp/chain/tdnn_d_sp_smbr and epoch_infix="_epoch3"
+
+
+set_names() {
+  if [ $# != 1 ]; then
+    echo "compare_wer_general.sh: internal error"
+    exit 1  # exit the program
+  fi
+  dirname=$(echo $1 | cut -d: -f1)
+  epoch=$(echo $1 | cut -s -d: -f2)
+  if [ -z $epoch ]; then
+    epoch_infix=""
+  else
+    used_epochs=true
+    epoch_infix=_epoch${epoch}
+  fi
+}
+
+
+
+echo -n "# System               "
+for x in $*; do   printf "% 10s" " $(basename $x)";   done
+echo
+
+strings=(
+  "#WER dev_clean_2 (tgsmall) "
+  "#WER dev_clean_2 (tglarge) ")
+
+for n in 0 1; do
+   echo -n "${strings[$n]}"
+   for x in $*; do
+     set_names $x  # sets $dirname and $epoch_infix
+    decode_names=(tgsmall_dev_clean_2 tglarge_dev_clean_2)
+
+     wer=$(cat $dirname/decode_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+     printf "% 10s" $wer
+   done
+   echo
+   if $include_looped; then
+     echo -n "#             [looped:]    "
+     for x in $*; do
+       set_names $x  # sets $dirname and $epoch_infix
+       wer=$(cat $dirname/decode_looped_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+       printf "% 10s" $wer
+     done
+     echo
+   fi
+   if $include_online; then
+     echo -n "#             [online:]    "
+     for x in $*; do
+       set_names $x  # sets $dirname and $epoch_infix
+       wer=$(cat ${dirname}_online/decode_${decode_names[$n]}/wer_* | utils/best_wer.sh | awk '{print $2}')
+       printf "% 10s" $wer
+     done
+     echo
+   fi
+done
+
+
+if $used_epochs; then
+  exit 0;  # the diagnostics aren't comparable between regular and discriminatively trained systems.
+fi
+
+
+echo -n "# Final train prob     "
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_train.final.log | grep -v xent | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final valid prob     "
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_valid.final.log | grep -v xent | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final train prob (xent)"
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_train.final.log | grep -w xent | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo
+
+echo -n "# Final valid prob (xent)"
+for x in $*; do
+  prob=$(grep Overall $x/log/compute_prob_valid.final.log | grep -w xent | awk '{printf("%.4f", $8)}')
+  printf "% 10s" $prob
+done
+echo

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/chain/run_chain_common.sh

@@ -0,0 +1,82 @@
+#!/bin/bash
+
+# this script has common stages shared across librispeech chain recipes.
+# It generates a new topology in a new lang directory, gets the alignments as
+# lattices, and builds a tree for the new topology
+set -e
+
+stage=11
+
+# input directory names. These options are actually compulsory, and they have
+# been named for convenience
+gmm_dir=
+ali_dir=
+lores_train_data_dir=
+
+num_leaves=6000
+
+# output directory names. They are also compulsory.
+lang=
+lat_dir=
+tree_dir=
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+[ -z $lang ] && echo "Set --lang, this specifies the new lang directory which will have the new topology" && exit 1;
+[ -z $lat_dir ] && echo "Set --lat-dir, this specifies the experiment directory to store lattice" && exit 1;
+[ -z $tree_dir ] && echo "Set --tree-dir, this specifies the directory to store new tree " && exit 1;
+
+for f in $gmm_dir/final.mdl $ali_dir/ali.1.gz $lores_train_data_dir/feats.scp; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+if [ $stage -le 11 ]; then
+  echo "$0: creating lang directory with one state per phone."
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/lang/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/lang $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+if [ $stage -le 12 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  nj=$(cat ${ali_dir}/num_jobs) || exit 1;
+  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" ${lores_train_data_dir} \
+    $lang $gmm_dir $lat_dir
+  rm $lat_dir/fsts.*.gz # save space
+fi
+
+if [ $stage -le 13 ]; then
+  # Build a tree using our new topology. We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.
+  if [ -f $tree_dir/final.mdl ]; then
+    echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+    exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
+      --context-opts "--context-width=2 --central-position=1" \
+      --cmd "$train_cmd" $num_leaves ${lores_train_data_dir} $lang $ali_dir $tree_dir
+fi
+
+exit 0;

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/chain/run_tdnn_1d.sh

@@ -0,0 +1,230 @@
+#!/bin/bash
+set -e
+
+# 1d is as 1c but a recipe based on the newer, more compact configs, and with
+#   various configuration changes; it also includes dropout (although I'm not
+#   sure whether dropout was actually helpful, that needs to be tested).
+# configs for 'chain'
+stage=0
+decode_nj=50
+train_set=train
+test_sets="test dev"
+gmm=tri6b_cleaned
+nnet3_affix=_cleaned
+
+# The rest are configs specific to this script.  Most of the parameters
+# are just hardcoded at this level, in the commands below.
+affix=1d
+tree_affix=
+train_stage=-10
+get_egs_stage=-10
+decode_iter=
+nj=
+
+# TDNN options
+frames_per_eg=150,110,100
+remove_egs=true
+common_egs_dir=
+xent_regularize=0.1
+dropout_schedule='0,0@0.20,0.5@0.50,0'
+
+test_online_decoding=true  # if true, it will run the last decoding stage.
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+# The iVector-extraction and feature-dumping parts are the same as the standard
+# nnet3 setup, and you can skip them by setting "--stage 11" if you have already
+# run those things.
+
+local/nnet3/run_ivector_common.sh --stage $stage \
+                                  --train_set $train_set \
+				                  --test_sets "$test_sets" \
+                                  --gmm $gmm \
+                                  --nj $nj \
+                                  --num-threads-ubm 6  \
+                                  --nnet3-affix "$nnet3_affix" || exit 1;
+
+gmm_dir=exp/$gmm
+ali_dir=exp/${gmm}_ali_${train_set}_sp
+tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
+lang=data/lang_chain
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
+dir=exp/chain${nnet3_affix}/tdnn${affix:+_$affix}_sp
+train_data_dir=data/${train_set}_sp_hires
+lores_train_data_dir=data/${train_set}_sp
+train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
+
+# if we are using the speed-perturbed data we need to generate
+# alignments for it.
+
+
+for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
+    $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+# Please take this as a reference on how to specify all the options of
+# local/chain/run_chain_common.sh
+local/chain/run_chain_common.sh --stage $stage \
+                                --gmm-dir $gmm_dir \
+                                --ali-dir $ali_dir \
+                                --lores-train-data-dir ${lores_train_data_dir} \
+                                --lang $lang \
+                                --lat-dir $lat_dir \
+                                --num-leaves 7000 \
+                                --tree-dir $tree_dir || exit 1;
+
+if [ $stage -le 14 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
+  learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
+  affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim=true dropout-per-dim-continuous=true"
+  tdnnf_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.75"
+  linear_opts="l2-regularize=0.008 orthonormal-constraint=-1.0"
+  prefinal_opts="l2-regularize=0.008"
+  output_opts="l2-regularize=0.002"
+  layer_dim=1536
+  bottleneck_dim=160
+
+  mkdir -p $dir/configs
+
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=100 name=ivector
+  input dim=40 name=input
+
+  # please note that it is important to have input layer with the name=input
+  # as the layer immediately preceding the fixed-affine-layer to enable
+  # the use of short notation for the descriptor
+  fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
+
+  # the first splicing is moved before the lda layer, so no splicing here
+  relu-batchnorm-dropout-layer name=tdnn1 $affine_opts dim=$layer_dim
+  tdnnf-layer name=tdnnf2 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=1
+  tdnnf-layer name=tdnnf3 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=1
+  tdnnf-layer name=tdnnf4 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=1
+  tdnnf-layer name=tdnnf5 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=0
+  tdnnf-layer name=tdnnf6 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf7 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf8 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf9 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf10 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf11 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf12 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf13 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf14 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf15 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf16 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  tdnnf-layer name=tdnnf17 $tdnnf_opts dim=$layer_dim bottleneck-dim=$bottleneck_dim time-stride=3
+  linear-component name=prefinal-l dim=256 $linear_opts
+
+  prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=$layer_dim small-dim=256
+  output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
+
+  prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=$layer_dim small-dim=256
+  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+if [ $stage -le 15 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b{09,10,11,12}/$USER/kaldi-data/egs/lyirics-$(date +'%m_%d_%H_%M')/s5c/$dir/egs/storage $dir/egs/storage
+  fi
+
+  steps/nnet3/chain/train.py --stage $train_stage \
+    --cmd "$decode_cmd" \
+    --feat.online-ivector-dir $train_ivector_dir \
+    --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize 0.0 \
+    --chain.apply-deriv-weights false \
+    --chain.lm-opts="--num-extra-lm-states=2000" \
+    --egs.dir "$common_egs_dir" \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--frames-overlap-per-eg 0 --constrained false" \
+    --egs.chunk-width $frames_per_eg \
+    --trainer.dropout-schedule $dropout_schedule \
+    --trainer.add-option="--optimization.memory-compression-level=2" \
+    --trainer.num-chunk-per-minibatch 64 \
+    --trainer.frames-per-iter 2500000 \
+    --trainer.num-epochs 4 \
+    --trainer.optimization.num-jobs-initial 6 \
+    --trainer.optimization.num-jobs-final 16 \
+    --trainer.optimization.initial-effective-lrate 0.00015 \
+    --trainer.optimization.final-effective-lrate 0.000015 \
+    --trainer.max-param-change 2.0 \
+    --cleanup.remove-egs $remove_egs \
+    --feat-dir $train_data_dir \
+    --tree-dir $tree_dir \
+    --lat-dir $lat_dir \
+    --dir $dir \
+    --use-gpu=wait || exit 1;
+
+    # --trainer.optimization.num-jobs-initial 3  
+    # --trainer.optimization.num-jobs-final 16 
+    # --trainer.num-chunk-per-minibatch 64 \
+
+fi
+
+
+
+graph_dir=$dir/graph_3G
+if [ $stage -le 16 ]; then
+  # Note: it might appear that this $lang directory is mismatched, and it is as
+  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
+  # the lang directory.
+  utils/mkgraph.sh --self-loop-scale 1.0 --remove-oov data/lang_3G $dir $graph_dir
+  # remove <UNK> from the graph, and convert back to const-FST.
+  fstrmsymbols --apply-to-output=true --remove-arcs=true "echo 3|" $graph_dir/HCLG.fst - | \
+    fstconvert --fst_type=const > $graph_dir/temp.fst
+  mv $graph_dir/temp.fst $graph_dir/HCLG.fst
+fi
+
+iter_opts=
+if [ ! -z $decode_iter ]; then
+  iter_opts=" --iter $decode_iter "
+fi
+if [ $stage -le 17 ]; then
+  rm $dir/.error 2>/dev/null || true
+  for decode_set in $test_sets; do
+      (
+      steps/nnet3/decode.sh \
+        --acwt 1.0  \
+        --post-decode-acwt 10.0 \
+        --nj $decode_nj \
+        --cmd "$decode_cmd" $iter_opts \
+        --online-ivector-dir \
+        exp/nnet3${nnet3_affix}/ivectors_${decode_set}_hires \
+        $graph_dir \
+        data/${decode_set}_hires \
+        $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_3G || exit 1
+        
+      steps/lmrescore.sh --cmd "$decode_cmd" --self-loop-scale 1.0 data/lang_{3G,4G} \
+          data/${decode_set}_hires $dir/decode_${decode_set}${decode_iter:+_$decode_iter}_{3G,4G} || exit 1
+
+      ) || touch $dir/.error &
+  done
+  wait
+  if [ -f $dir/.error ]; then
+    echo "$0: something went wrong in decoding"
+    exit 1
+  fi
+fi
+
+exit 0;

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/check_tools.sh

@@ -0,0 +1,50 @@
+#!/bin/bash -u
+
+# Copyright 2015 (c) Johns Hopkins University (Jan Trmal <jtrmal@gmail.com>)
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#  http://www.apache.org/licenses/LICENSE-2.0
+#
+# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+# MERCHANTABLITY OR NON-INFRINGEMENT.
+# See the Apache 2 License for the specific language governing permissions and
+# limitations under the License.
+
+[ -f ./path.sh ] && . ./path.sh
+
+command -v uconv &>/dev/null \
+  || { echo  >&2 "uconv not found on PATH. You will have to install ICU4C"; exit 1; }
+
+command -v ngram &>/dev/null \
+  || { echo  >&2 "srilm not found on PATH. Please use the script $KALDI_ROOT/tools/extras/install_srilm.sh to install it"; exit 1; }
+
+if [  -z ${LIBLBFGS} ]; then
+  echo >&2  "SRILM is not compiled with the support of MaxEnt models."
+  echo >&2  "You should use the script in \$KALDI_ROOT/tools/install_srilm.sh"
+  echo >&2  "which will take care of compiling the SRILM with MaxEnt support"
+  exit 1;
+fi
+
+sox=`command -v sox 2>/dev/null` \
+  || { echo  >&2 "sox not found on PATH. Please install it manually (you will need version 14.4.0 and higher)."; exit 1; }
+
+# If sox is found on path, check if the version is correct
+if [ ! -z "$sox" ]; then
+  sox_version=`$sox --version 2>&1| head -1 | sed -e 's?.*: ??' -e 's?.* ??'`
+  if [[ ! $sox_version =~ v14.4.* ]]; then
+    echo "Unsupported sox version $sox_version found on path. You will need version v14.4.0 and higher."
+    exit 1
+  fi
+fi
+
+command -v phonetisaurus-align &>/dev/null \
+  || { echo  >&2 "Phonetisaurus not found on PATH. Please use the script $KALDI_ROOT/tools/extras/install_phonetisaurus.sh to install it"; exit 1; }
+
+exit  0
+
+

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/nnet3/run_ivector_common.sh

@@ -0,0 +1,188 @@
+#!/bin/bash
+
+set -e -o pipefail
+
+# This script is called from scripts like local/nnet3/run_tdnn.sh and
+# local/chain/run_tdnn.sh (and may eventually be called by more scripts).  It
+# contains the common feature preparation and iVector-related parts of the
+# script.  See those scripts for examples of usage.
+
+
+stage=1
+nj=39
+train_set=train_cleaned       # you might set this to e.g. train.
+test_sets="test dev"
+gmm=tri3b_cleaned             # This specifies a GMM-dir from the features of the type you're training the system on;
+                              # it should contain alignments for 'train_set'.
+
+num_threads_ubm=32
+nnet3_affix=_train_cleaned    # affix for exp/nnet3 directory to put iVector stuff in (e.g.
+                              # in the tedlium recip it's _cleaned).
+echo "$0 $@" # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. utils/parse_options.sh
+
+gmm_dir=exp/${gmm}
+ali_dir=exp/${gmm}_ali_${train_set}_sp
+
+for f in data/${train_set}/feats.scp ${gmm_dir}/final.mdl; do
+  if [ ! -f $f ]; then
+    echo "$0: expected file $f to exist"
+    exit 1
+  fi
+done
+
+if [ $stage -le 2 ] && [ -f data/${train_set}_sp_hires/feats.scp ]; then
+  echo "$0: data/${train_set}_sp_hires/feats.scp already exists."
+  echo " ... Please either remove it, or rerun this script with stage > 2."
+  exit 1
+fi
+
+
+if [ $stage -le 1 ]; then
+  echo "$0: preparing directory for speed-perturbed data"
+  utils/data/perturb_data_dir_speed_3way.sh data/${train_set} data/${train_set}_sp
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0: creating high-resolution MFCC features"
+
+  # this shows how you can split across multiple file-systems.  we'll split the
+  # MFCC dir across multiple locations.  You might want to be careful here, if you
+  # have multiple copies of Kaldi checked out and run the same recipe, not to let
+  # them overwrite each other.
+  mfccdir=data/${train_set}_sp_hires/data
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
+    utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/egs/wsj-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage
+  fi
+
+  for datadir in ${train_set}_sp ${test_sets}; do
+    utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
+  done
+
+  # do volume-perturbation on the training data prior to extracting hires
+  # features; this helps make trained nnets more invariant to test data volume.
+  utils/data/perturb_data_dir_volume.sh data/${train_set}_sp_hires
+
+  for datadir in ${train_set}_sp ${test_sets}; do
+    steps/make_mfcc.sh --nj $nj --mfcc-config conf/mfcc_hires.conf \
+      --cmd "$train_cmd" data/${datadir}_hires
+    steps/compute_cmvn_stats.sh data/${datadir}_hires
+    utils/fix_data_dir.sh data/${datadir}_hires
+  done
+fi
+
+if [ $stage -le 3 ]; then
+  echo "$0: computing a subset of data to train the diagonal UBM."
+
+  mkdir -p exp/nnet3${nnet3_affix}/diag_ubm
+  temp_data_root=exp/nnet3${nnet3_affix}/diag_ubm
+
+  # train a diagonal UBM using a subset of about a quarter of the data
+  num_utts_total=$(wc -l <data/${train_set}_sp_hires/utt2spk)
+  num_utts=$[$num_utts_total/4]
+  utils/data/subset_data_dir.sh data/${train_set}_sp_hires \
+      $num_utts ${temp_data_root}/${train_set}_sp_hires_subset
+
+  echo "$0: computing a PCA transform from the hires data."
+  steps/online/nnet2/get_pca_transform.sh --cmd "$train_cmd" \
+      --splice-opts "--left-context=3 --right-context=3" \
+      --max-utts 10000 --subsample 2 \
+       ${temp_data_root}/${train_set}_sp_hires_subset \
+       exp/nnet3${nnet3_affix}/pca_transform
+
+  echo "$0: training the diagonal UBM."
+  # Use 512 Gaussians in the UBM.
+  steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 \
+    --num-frames 700000 \
+    --num-threads $num_threads_ubm \
+    ${temp_data_root}/${train_set}_sp_hires_subset 512 \
+    exp/nnet3${nnet3_affix}/pca_transform exp/nnet3${nnet3_affix}/diag_ubm
+
+fi
+
+if [ $stage -le 4 ]; then
+  # Train the iVector extractor.  Use all of the speed-perturbed data since iVector extractors
+  # can be sensitive to the amount of data.  The script defaults to an iVector dimension of
+  # 100.
+  echo "$0: training the iVector extractor"
+  # in steps/online/nnet2/train_ivector_extractor.sh calculate nj_full as
+  # num_threads * num_processes
+  steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
+    --num_threads 1 --num_processes 1 \
+    data/${train_set}_sp_hires exp/nnet3${nnet3_affix}/diag_ubm exp/nnet3${nnet3_affix}/extractor || exit 1;
+fi
+
+if [ $stage -le 5 ]; then
+  # note, we don't encode the 'max2' in the name of the ivectordir even though
+  # that's the data we extract the ivectors from, as it's still going to be
+  # valid for the non-'max2' data; the utterance list is the same.
+  ivectordir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $ivectordir/storage ]; then
+    utils/create_split_dir.pl /export/b0{5,6,7,8}/$USER/kaldi-data/egs/wsj-$(date +'%m_%d_%H_%M')/s5/$ivectordir/storage $ivectordir/storage
+  fi
+
+  # We now extract iVectors on the speed-perturbed training data .  With
+  # --utts-per-spk-max 2, the script pairs the utterances into twos, and treats
+  # each of these pairs as one speaker; this gives more diversity in iVectors..
+  # Note that these are extracted 'online' (they vary within the utterance).
+
+  # Having a larger number of speakers is helpful for generalization, and to
+  # handle per-utterance decoding well (the iVector starts at zero at the beginning
+  # of each pseudo-speaker).
+  temp_data_root=${ivectordir}
+  utils/data/modify_speaker_info.sh --utts-per-spk-max 2 \
+    data/${train_set}_sp_hires ${temp_data_root}/${train_set}_sp_hires_max2
+
+  steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj $nj \
+    ${temp_data_root}/${train_set}_sp_hires_max2 \
+    exp/nnet3${nnet3_affix}/extractor $ivectordir
+
+  # Also extract iVectors for the test data, but in this case we don't need the speed
+  # perturbation (sp).
+  for data in ${test_sets}; do
+    nspk=$(wc -l <data/${data}_hires/spk2utt)
+    steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj "${nspk}" \
+      data/${data}_hires exp/nnet3${nnet3_affix}/extractor \
+      exp/nnet3${nnet3_affix}/ivectors_${data}_hires
+  done
+fi
+
+if [ -f data/${train_set}_sp/feats.scp ] && [ $stage -le 8 ]; then
+  echo "$0: $feats already exists.  Refusing to overwrite the features "
+  echo " to avoid wasting time.  Please remove the file and continue if you really mean this."
+  exit 1;
+fi
+
+
+if [ $stage -le 6 ]; then
+  echo "$0: preparing directory for low-resolution speed-perturbed data (for alignment)"
+  utils/data/perturb_data_dir_speed_3way.sh \
+    data/${train_set} data/${train_set}_sp
+fi
+
+if [ $stage -le 7 ]; then
+  echo "$0: making MFCC features for low-resolution speed-perturbed data (needed for alignments)"
+  steps/make_mfcc.sh --nj $nj \
+    --cmd "$train_cmd" data/${train_set}_sp
+  steps/compute_cmvn_stats.sh data/${train_set}_sp
+  echo "$0: fixing input data-dir to remove nonexistent features, in case some "
+  echo ".. speed-perturbed segments were too short."
+  utils/fix_data_dir.sh data/${train_set}_sp
+fi
+
+if [ $stage -le 8 ]; then
+  if [ -f $ali_dir/ali.1.gz ]; then
+    echo "$0: alignments in $ali_dir appear to already exist.  Please either remove them "
+    echo " ... or use a later --stage option."
+    exit 1
+  fi
+  echo "$0: aligning with the perturbed low-resolution data"
+  steps/align_fmllr.sh --nj $nj --cmd "$train_cmd" \
+    data/${train_set}_sp data/lang $gmm_dir $ali_dir
+fi
+
+
+exit 0;

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/prepare_data.py

@@ -0,0 +1,130 @@
+import json
+import argparse
+from os.path import join, exists, isfile
+from os import makedirs, listdir
+import re
+import hashlib
+
+
+class DataSet:
+    def __init__(self, name, workspace):
+        self.segments = []
+        self.spk2gender = []
+        self.text = []
+        self.utt2spk = []
+        self.wavscp = []
+        self.workspace = join(workspace, name)
+
+    def add_utterance(self, utt, recording):
+
+        text = utt["text"]
+        arrangement, performance, country, gender, user = recording[:-4].split("-")
+
+        # the following mapping is necessary for errors in gender in country IN
+        insensitive_none = re.compile(re.escape('none'), re.IGNORECASE)
+
+        gender = insensitive_none.sub('', utt["gender"])
+        spk = "{}{}".format(insensitive_none.sub('', gender).upper(), insensitive_none.sub('', user))
+
+        rec_id = recording[:-4]
+        utt_id = "{}-{}-{}-{}-{}-{:03}".format(spk, arrangement, performance, country, gender.upper(), utt["index"])
+
+        start = utt["start"]
+        end = utt["end"]
+
+        wavpath = join(country, "{}{}".format(country, "Vocals"), recording)
+
+        self._add_segment(utt_id, rec_id, start, end)
+        self._add_spk2gender(spk, gender)
+        self._add_text(utt_id, text)
+        self._add_utt2spk(utt_id, spk)
+        self._add_wavscp(rec_id, wavpath)
+
+    def _add_segment(self, rec_id, utt_id, start, end):
+        self.segments.append("{} {} {:.3f} {:.3f}".format(rec_id, utt_id, start, end))
+
+    def _add_spk2gender(self, spk, gender):
+        self.spk2gender.append("{} {}".format(spk, gender))
+
+    def _add_text(self, utt_id, text):
+        self.text.append("{} {}".format(utt_id, text))
+
+    def _add_utt2spk(self, utt_id, spk):
+        self.utt2spk.append("{} {}".format(utt_id, spk))
+
+    def _add_wavscp(self, rec_id, wavpath):
+        self.wavscp.append("{} sox wav/{} -G -t wav -r 16000 -c 1 - remix 1 | ".format(rec_id, wavpath))
+
+    def list2file(self, outfile, list_data):
+        list_data = list(set(list_data))
+        with open(outfile, "w") as f:
+            for line in list_data:
+                f.write("{}\n".format(line))
+
+    def save(self):
+        if not exists(self.workspace):
+            makedirs(self.workspace)
+        self.list2file(join(self.workspace, "spk2gender"), sorted(self.spk2gender))
+        self.list2file(join(self.workspace, "text"), sorted(self.text))
+        self.list2file(join(self.workspace, "wav.scp"), sorted(self.wavscp))
+        self.list2file(join(self.workspace, "utt2spk"), sorted(self.utt2spk))
+        self.list2file(join(self.workspace, "segments"), sorted(self.segments))
+
+
+def read_json(filepath):
+    try:  # Read the json
+        with open(filepath) as data_file:
+            data = json.load(data_file)
+    except json.decoder.JSONDecodeError:  # Json has an extra first line. Error when was created
+        data = []
+
+    return data
+
+
+def map_rec2chec(db_path, countries):
+    """
+    Method read all the original audio tracks and create a dict  {<checksum>: <recording>}
+    :param db_path: string, path to root of DAMP Sing!
+    :return: dict
+    """
+    rec2chec = {}
+    for country in countries:
+        recordings = [f for f in listdir(join(db_path, country, country + "Vocals")) if f.endswith(".m4a")]
+        for record in recordings:
+            rec2chec[hashlib.md5(open(join(db_path, country, country + "Vocals", record), 'rb').read()).hexdigest()] = record
+
+    return rec2chec
+
+
+def main(args):
+    db_path = args.db_path
+    workspace = args.workspace
+    utts_path = args.utterances
+    dset = args.dset
+
+    countries = ["GB"]
+    countries += ["US", "AU"] if dset in ["train3", "train30"] else []
+    countries += ['AE', 'AR', 'BR', 'CL', 'CN', 'DE', 'ES', 'FR', 'HU',
+                  'ID', 'IN', 'IQ', 'IR', 'IT', 'JP', 'KR', 'MX', 'MY',
+                  'NO', 'PH', 'PT', 'RU', 'SA', 'SG', 'TH', 'VN', 'ZA'] if dset in ["train30"] else []
+
+    performances = map_rec2chec(db_path, countries)
+    utterances = read_json(utts_path)
+    dataset = DataSet(dset, workspace)
+
+    for utt in utterances:
+        dataset.add_utterance(utt, performances[utt["wavfile"]])
+
+    dataset.save()
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("workspace", type=str, help="Path where the output files will be saved")
+    parser.add_argument("db_path", type=str, help="Path to DAMP 300x30x2 database")
+    parser.add_argument("utterances", type=str, help="Path to utterance details in json format",
+                        default="metadata.json")
+    parser.add_argument("dset", type=str, help="Name of the dataset")
+
+    args = parser.parse_args()
+    main(args)

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/prepare_dict.sh

@@ -0,0 +1,154 @@
+#!/bin/bash
+
+#adapted from ami and chime5 dict preparation script
+#Author: Gerardo Roa
+
+# Begin configuration section.
+words=5000
+# End configuration section
+
+echo "$0 $@"  # Print the command line for logging
+
+if [ -f path.sh ]; then . ./path.sh; fi
+. utils/parse_options.sh || exit 1;
+
+# 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
+
+mkdir -p data
+
+
+
+dir=data/local/dict
+mkdir -p $dir
+
+echo "$0: Getting CMU dictionary"
+if [ ! -f $dir/cmudict.done ]; then
+  [ -d $dir/cmudict ] && rm -rf $dir/cmudict
+  svn co https://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict $dir/cmudict
+  touch $dir/cmudict.done
+fi
+
+echo "$0: Preparing files in $dir"
+# Silence phones
+for w in SIL SPN; do echo $w; done > $dir/silence_phones.txt
+echo SIL > $dir/optional_silence.txt
+
+
+# For this setup we're discarding stress.
+cat $dir/cmudict/cmudict-0.7b.symbols | \
+  perl -ne 's:[0-9]::g; s:\r::; print lc($_)' | \
+  tr a-z A-Z | \
+  sort -u > $dir/nonsilence_phones.txt
+
+# An extra question will be added by including the silence phones in one class.
+paste -d ' ' -s $dir/silence_phones.txt > $dir/extra_questions.txt
+
+
+grep -v ';;;' $dir/cmudict/cmudict-0.7b |\
+  uconv -f latin1 -t utf-8 -x Any-Lower |\
+  perl -ne 's:(\S+)\(\d+\) :$1 :; s:  : :; print;' |\
+  perl -ne '@F = split " ",$_,2; $F[1] =~ s/[0-9]//g; print "$F[0] $F[1]";' \
+  > $dir/lexicon1_raw_nosil.txt || exit 1;
+
+
+# Add prons for laughter, noise, oov
+for w in `grep -v sil $dir/silence_phones.txt`; do
+  echo "[$w] $w"
+done | cat - $dir/lexicon1_raw_nosil.txt > $dir/lexicon2_raw.txt || exit 1;
+
+
+# we keep all words from the cmudict in the lexicon
+# might reduce OOV rate on dev and test
+cat $dir/lexicon2_raw.txt  \
+   <( echo "mm m"
+      echo "<unk> spn" \
+    )  | sed 's/[\t ]/\t/' | tr a-z A-Z  | sort -u > $dir/iv_lexicon.txt
+
+
+cat data/local/corpus.txt  | \
+  awk '{for (n=1;n<=NF;n++){ count[$n]++; } } END { for(n in count) { print count[n], n; }}' | \
+  sort -nr > $dir/word_counts_b
+
+
+# Select the N numbers of words increasingly in order to select all the words with same count
+
+vocab_size=0
+start_line=3  #  first two are <s> and </s>
+touch $dir/word_list
+
+while [ "$vocab_size" -le "$words" ]; do
+    current_count=`sed "${start_line}q;d" $dir/word_counts_b | awk '{print $1}'`
+    cat $dir/word_counts_b | grep "^$current_count " | awk '{print $2}' >> $dir/word_list
+    vocab_size=`cat $dir/word_list | wc -l`
+    start_line=$((vocab_size + 1 ))
+done
+
+
+head -n $vocab_size $dir/word_counts_b > $dir/word_counts
+sort -u $dir/word_list > $dir/word_list_sorted
+
+
+awk '{print $1}' $dir/iv_lexicon.txt | \
+  perl -e '($word_counts)=@ARGV;
+   open(W, "<$word_counts")||die "opening word-counts $word_counts";
+   while(<STDIN>) { chop; $seen{$_}=1; }
+   while(<W>) {
+     ($c,$w) = split;
+     if (!defined $seen{$w}) { print; }
+   } ' $dir/word_counts > $dir/oov_counts.txt
+
+
+echo "*Highest-count OOVs (including fragments) are:"
+head -n 10 $dir/oov_counts.txt
+echo "*Highest-count OOVs (excluding fragments) are:"
+grep -v -E '^-|-$' $dir/oov_counts.txt | head -n 10 || true
+
+
+echo "*Training a G2P and generating missing pronunciations"
+mkdir -p $dir/g2p/
+
+if [ -e $dir/g2p/g2p.fst ]
+then
+    echo "$0: Phonetisaurus exist. $dir/g2p/g2p.fst will be used"
+else
+    phonetisaurus-align --input=$dir/iv_lexicon.txt --ofile=$dir/g2p/aligned_lexicon.corpus
+    ngram-count -order 4 -kn-modify-counts-at-end -ukndiscount\
+      -gt1min 0 -gt2min 0 -gt3min 0 -gt4min 0 \
+      -text $dir/g2p/aligned_lexicon.corpus -lm $dir/g2p/aligned_lexicon.arpa
+    phonetisaurus-arpa2wfst --lm=$dir/g2p/aligned_lexicon.arpa --ofile=$dir/g2p/g2p.fst
+fi
+
+awk '{print $2}' $dir/oov_counts.txt > $dir/oov_words.txt
+phonetisaurus-apply --nbest 2 --model $dir/g2p/g2p.fst --thresh 5 --accumulate \
+  --word_list $dir/oov_words.txt > $dir/oov_lexicon.txt
+
+
+## We join pronunciation with the selected words to create lexicon.txt
+cat $dir/oov_lexicon.txt $dir/iv_lexicon.txt | sort -u > $dir/lexicon1_plus_g2p.txt
+join $dir/lexicon1_plus_g2p.txt $dir/word_list_sorted > $dir/lexicon.txt
+
+echo "<UNK> SPN" >> $dir/lexicon.txt
+
+## The next section is again just for debug purposes
+## to show words for which the G2P failed
+rm -f $dir/lexiconp.txt 2>null; # can confuse later script if this exists.
+awk '{print $1}' $dir/lexicon.txt | \
+  perl -e '($word_counts)=@ARGV;
+   open(W, "<$word_counts")||die "opening word-counts $word_counts";
+   while(<STDIN>) { chop; $seen{$_}=1; }
+   while(<W>) {
+     ($c,$w) = split;
+     if (!defined $seen{$w}) { print; }
+   } ' $dir/word_counts > $dir/oov_counts.g2p.txt
+
+echo "*Highest-count OOVs (including fragments) after G2P are:"
+head -n 10 $dir/oov_counts.g2p.txt
+
+utils/validate_dict_dir.pl $dir
+exit 0;

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/score.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+steps/scoring/score_kaldi_wer.sh "$@"

DSing/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/local/train_lms_srilm.sh

@@ -0,0 +1,167 @@
+#!/bin/bash
+# Copyright (c) 2017  Johns Hopkins University (Author: Yenda Trmal, Shinji Watanabe)
+# Apache 2.0
+
+export LC_ALL=C
+
+# Begin configuration section.
+words_file=
+train_text=
+dev_text=
+oov_symbol="<UNK>"
+# End configuration section
+
+echo "$0 $@"
+
+[ -f path.sh ]  && . ./path.sh
+. ./utils/parse_options.sh || exit 1
+
+echo "-------------------------------------"
+echo "Building an SRILM language model     "
+echo "-------------------------------------"
+
+if [ $# -ne 2 ] ; then
+  echo "Incorrect number of parameters. "
+  echo "Script has to be called like this:"
+  echo "  $0 [switches] <datadir> <tgtdir>"
+  echo "For example: "
+  echo "  $0 data data/srilm"
+  echo "The allowed switches are: "
+  echo "    words_file=<word_file|>        word list file -- data/lang/words.txt by default"
+  echo "    train_text=<train_text|>       data/train/text is used in case when not specified"
+  echo "    dev_text=<dev_text|>           last 10 % of the train text is used by default"
+  echo "    oov_symbol=<unk_sumbol|<UNK>>  symbol to use for oov modeling -- <UNK> by default"
+  exit 1
+fi
+
+datadir=$1
+tgtdir=$2
+
+##End of configuration
+loc=`which ngram-count`;
+if [ -z $loc ]; then
+  echo >&2 "You appear to not have SRILM tools installed, either on your path,"
+  echo >&2 "Use the script \$KALDI_ROOT/tools/install_srilm.sh to install it."
+  exit 1
+fi
+
+# Prepare the destination directory
+mkdir -p $tgtdir
+
+for f in $words_file $train_text $dev_text; do
+  [ ! -s $f ] && echo "No such file $f" && exit 1;
+done
+
+[ -z $words_file ] && words_file=$datadir/lang/words.txt
+if [ ! -z "$train_text" ] && [ -z "$dev_text" ] ; then
+  nr=`cat  $train_text | wc -l`
+  nr_dev=$(($nr / 10 ))
+  nr_train=$(( $nr - $nr_dev ))
+  orig_train_text=$train_text
+  head -n $nr_train $train_text > $tgtdir/train_text
+  tail -n $nr_dev $train_text > $tgtdir/dev_text
+
+  train_text=$tgtdir/train_text
+  dev_text=$tgtdir/dev_text
+  echo "Using words file: $words_file"
+  echo "Using train text: 9/10 of $orig_train_text"
+  echo "Using dev text  : 1/10 of $orig_train_text"
+elif [ ! -z "$train_text" ] && [ ! -z "$dev_text" ] ; then
+  echo "Using words file: $words_file"
+  echo "Using train text: $train_text"
+  echo "Using dev text  : $dev_text"
+  train_text=$train_text
+  dev_text=$dev_text
+else
+  train_text=$datadir/train/text
+  dev_text=$datadir/dev2h/text
+  echo "Using words file: $words_file"
+  echo "Using train text: $train_text"
+  echo "Using dev text  : $dev_text"
+
+fi
+
+[ ! -f $words_file ] && echo >&2 "File $words_file must exist!" && exit 1
+[ ! -f $train_text ] && echo >&2 "File $train_text must exist!" && exit 1
+[ ! -f $dev_text ] && echo >&2 "File $dev_text must exist!" && exit 1
+
+
+# Extract the word list from the training dictionary; exclude special symbols
+sort $words_file | awk '{print $1}' | grep -v '\#0' | grep -v '<eps>' | grep -v -F "$oov_symbol" > $tgtdir/vocab
+if (($?)); then
+  echo "Failed to create vocab from $words_file"
+  exit 1
+else
+  # wc vocab # doesn't work due to some encoding issues
+  echo vocab contains `cat $tgtdir/vocab | perl -ne 'BEGIN{$l=$w=0;}{split; $w+=$#_; $w++; $l++;}END{print "$l lines, $w words\n";}'`
+fi
+
+# corpus file has <s> <\s> tag; remove it
+sed -e 's/^\w*\ *//' -e 's/ \+[^ ]\+$//' $train_text | sort -u | \
+  perl -ane 'print join(" ", @F[1..$#F]) . "\n" if @F > 1' > $tgtdir/train.txt
+if (($?)); then
+    echo "Failed to create $tgtdir/train.txt from $train_text"
+    exit 1
+else
+    echo "Removed first and last word (<s> <\s> tags) from every line of $train_text"
+    # wc text.train train.txt # doesn't work due to some encoding issues
+    echo $train_text contains `cat $train_text | perl -ane 'BEGIN{$w=$s=0;}{$w+=@F; $w--; $s++;}END{print "$w words, $s sentences\n";}'`
+    echo train.txt contains `cat $tgtdir/train.txt | perl -ane 'BEGIN{$w=$s=0;}{$w+=@F; $s++;}END{print "$w words, $s sentences\n";}'`
+fi
+
+# data/dev/text
+cat $dev_text | cut -d ' ' -f 2- > $tgtdir/dev.txt
+if (($?)); then
+    echo "Failed to create $tgtdir/dev.txt from $dev_text"
+    exit 1
+else
+    echo "Removed first word (uid) from every line of $dev_text"
+    # wc text.train train.txt # doesn't work due to some encoding issues
+    echo $dev_text contains `cat $dev_text | perl -ane 'BEGIN{$w=$s=0;}{$w+=@F; $w--; $s++;}END{print "$w words, $s sentences\n";}'`
+    echo $tgtdir/dev.txt contains `cat $tgtdir/dev.txt | perl -ane 'BEGIN{$w=$s=0;}{$w+=@F;  $s++;}END{print "$w words, $s sentences\n";}'`
+fi
+
+
+if [ ! -z ${LIBLBFGS} ]; then
+  #please note that if the switch -map-unk "$oov_symbol" is used with -maxent-convert-to-arpa, ngram-count will segfault
+  #instead of that, we simply output the model in the maxent format and convert it using the "ngram"
+  echo "-------------------"
+  echo "Maxent 3grams"
+  echo "-------------------"
+  sed 's/'${oov_symbol}'/<unk>/g' $tgtdir/train.txt | \
+    ngram-count -lm - -order 3 -text - -vocab $tgtdir/vocab -unk -sort -maxent -maxent-convert-to-arpa|\
+    ngram -lm - -order 3 -unk -map-unk "$oov_symbol" -prune-lowprobs -write-lm - |\
+    sed 's/<unk>/'${oov_symbol}'/g' | gzip -c > $tgtdir/3gram.me.gz || exit 1
+
+  echo "-------------------"
+  echo "Maxent 4grams"
+  echo "-------------------"
+  sed 's/'${oov_symbol}'/<unk>/g' $tgtdir/train.txt | \
+    ngram-count -lm - -order 4 -text - -vocab $tgtdir/vocab -unk -sort -maxent -maxent-convert-to-arpa|\
+    ngram -lm - -order 4 -unk -map-unk "$oov_symbol" -prune-lowprobs -write-lm - |\
+    sed 's/<unk>/'${oov_symbol}'/g' | gzip -c > $tgtdir/4gram.me.gz || exit 1
+else
+  echo >&2  "SRILM is not compiled with the support of MaxEnt models."
+  echo >&2  "You should use the script in \$KALDI_ROOT/tools/install_srilm.sh"
+  echo >&2  "which will take care of compiling the SRILM with MaxEnt support"
+  exit 1;
+fi
+
+
+echo "--------------------"
+echo "Computing perplexity"
+echo "--------------------"
+(
+  for f in $tgtdir/3gram* ; do ( echo $f; ngram -order 3 -lm $f -unk -map-unk "$oov_symbol" -prune-lowprobs -ppl $tgtdir/dev.txt ) | paste -s -d ' ' ; done
+  for f in $tgtdir/4gram* ; do ( echo $f; ngram -order 4 -lm $f -unk -map-unk "$oov_symbol" -prune-lowprobs -ppl $tgtdir/dev.txt ) | paste -s -d ' ' ; done
+)  | sort  -r -n -k 15,15g | column -t | tee $tgtdir/perplexities.txt
+
+echo "The perlexity scores report is stored in $tgtdir/perplexities.txt "
+echo ""
+
+for best_ngram in {3,4}gram ; do
+  outlm=best_${best_ngram}.gz
+  lmfilename=$(grep "${best_ngram}" $tgtdir/perplexities.txt | head -n 1 | cut -f 1 -d ' ')
+  echo "$outlm -> $lmfilename"
+  (cd $tgtdir; rm -f $outlm; ln -sf $(basename $lmfilename) $outlm )
+done

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