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DSing/sing_300x30x2/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/README.md

@@ -0,0 +1,9 @@
+This is the baseline Kaldi recipe for unaccompanied karaoke singing using DSing corpus.
+The DSing corpus is derived from the Smule Sing! 300x30x2 karaoke corpus [1].
+It possesses three training sets: DSing1 with 15 hours of GB accent, 
+DSing3 with 45 hours of GB+US+AU accents, and DSing30 with mix of accents from 30 countries.  
+It was prepare as a speech recognition corpus by Gerardo Roa Dabike.
+The recipe is in s5/
+
+
+[1] Smule Sing! 300x30x2 Dataset, “https://ccrma.stanford.edu/damp/” accessed September 2018.

DSing/sing_300x30x2/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/cmd.sh

@@ -0,0 +1,24 @@
+#!/bin/bash
+
+# "queue.pl" uses qsub.  The options to it are
+# options to qsub.  If you have GridEngine installed,
+# change this to a queue you have access to.
+# Otherwise, use "run.pl", which will run jobs locally
+# (make sure your --num-jobs options are no more than
+# the number of cpus on your machine.
+
+# This cmd.sh file is to be used on Sharc in RSE queue
+
+export train_cmd="run.pl"
+export decode_cmd="run.pl"
+
+
+if [[ "$HOSTNAME" == *"sharc"* ]]; then
+    export train_cmd="queue.pl --mem 6G"
+    export decode_cmd="queue.pl --mem 8G"
+fi
+if [[ "$HOSTNAME" == *"bessemer"* ]]; then
+    export train_cmd="slurm.pl --mem 6G"
+    export decode_cmd="slurm.pl --mem 8G"
+
+fi

DSing/sing_300x30x2/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/conf/decode.config

@@ -0,0 +1,3 @@
+#first_beam=10.0
+beam=18.0           # Was 13.0
+lattice_beam=10.0    # Was 6.0

DSing/sing_300x30x2/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/conf/mfcc.conf

@@ -0,0 +1,6 @@
+--use-energy=false   # only non-default option.
+--sample-frequency=16000 #  sampled at 16kHz
+--allow_downsample=true 
+#--num-ceps=23
+#--low-freq=20 # the default.
+#--frame-length=25 # the default is 25

DSing/sing_300x30x2/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/conf/mfcc_hires.conf

@@ -0,0 +1,11 @@
+# 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/sing_300x30x2/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/sing_300x30x2/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/sing_300x30x2/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/sing_300x30x2/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/sing_300x30x2/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

DSing/sing_300x30x2/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/path.sh

@@ -0,0 +1,8 @@
+export KALDI_ROOT=`pwd`/../../..
+[ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
+export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
+[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1
+. $KALDI_ROOT/tools/config/common_path.sh
+export LC_ALL=C
+source setup_env.sh
+

DSing/sing_300x30x2/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/run.sh

@@ -0,0 +1,226 @@
+#!/bin/bash
+#
+# Recipe for the Dsing baseline
+# Based mostly on the Librispeech recipe
+#
+# Copyright 2019  Gerardo Roa
+#                 University of Sheffield
+# Apache 2.0
+
+# Begin configuration section
+
+nj=40
+stage=0
+dsing=1   #  Set:  1  for DSing1
+          #        3  for DSing3
+          #        30 for DSing30
+
+# For TDNN-F only
+decode_nj=1
+
+# End configuration section
+. ./utils/parse_options.sh
+
+. ./path.sh
+. ./cmd.sh
+
+set -e # exit on error
+
+# Sing! 300x30x2 corpus path
+# please change the path accordingly
+sing_corpus=/fastdata/acp13gr/DAMP300x30x20/sing_300x30x2
+
+
+echo "Using steps and utils from WSJ recipe"
+[[ ! -L "wav" ]] && ln -s $sing_corpus wav
+[[ ! -L "steps" ]] && ln -s $KALDI_ROOT/egs/wsj/s5/steps
+[[ ! -L "utils" ]] && ln -s $KALDI_ROOT/egs/wsj/s5/utils
+
+
+trainset=train${dsing}
+devset="dev"
+testset="test"
+
+# This script also needs the phonetisaurus g2p, srilm, sox
+./local/check_tools.sh || exit 1
+
+
+echo; echo "===== Starting at  $(date +"%D_%T") ====="; echo
+
+
+if [ $stage -le 1 ]; then
+    mkdir -p data/local/dict
+    cp conf/corpus.txt  data/local/corpus.txt  # Corpus.txt for language model
+
+    for datadir in $devset $testset $trainset; do
+        python local/prepare_data.py data/ wav/ conf/${datadir}.json $datadir
+    done
+
+    # Selecting the top 25000 words by frequency
+    # Is expected that the final size will be larger as
+    # it use all the words with the same frequency avoiding an arbitrary cut-off
+    local/prepare_dict.sh --words 26000
+
+    utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang data/lang
+
+    local/train_lms_srilm.sh \
+        --train-text data/local/corpus.txt \
+        --dev_text data/dev/text \
+        --oov-symbol "<UNK>" --words-file data/lang/words.txt \
+        data/ data/srilm
+
+    # Compiles G for DSing Preconstructed trigram LM
+    utils/format_lm.sh  data/lang data/srilm/best_3gram.gz data/local/dict/lexicon.txt data/lang_3G
+    utils/format_lm.sh  data/lang data/srilm/best_4gram.gz data/local/dict/lexicon.txt data/lang_4G
+fi
+
+# Features Extraction
+if [[ $stage -le 2 ]]; then
+
+  echo
+  echo "============================="
+  echo "---- MFCC FEATURES EXTRACTION ----"
+  echo "=====  $(date +"%D_%T") ====="
+
+  for datadir in $trainset $devset $testset; do
+    echo; echo "---- $datadir"
+    utils/fix_data_dir.sh data/$datadir
+    steps/make_mfcc.sh --cmd "$train_cmd" --nj $nj data/${datadir} exp/make_mfcc/${datadir} mfcc
+    steps/compute_cmvn_stats.sh data/${datadir}
+    utils/fix_data_dir.sh data/$datadir
+  done
+fi
+
+if [[ $stage -le 3 ]]; then
+
+    echo
+    echo "============================="
+    echo "-------- Train GMM ----------"
+    echo
+
+    echo
+    echo "Mono"
+    echo "=====  $(date +"%D_%T") ====="
+
+    # Monophone
+    steps/train_mono.sh --nj $nj --cmd "$train_cmd"  \
+          data/${trainset} data/lang exp/mono
+
+    steps/align_si.sh --nj $nj --cmd "$train_cmd" \
+      data/${trainset} data/lang exp/mono exp/mono_ali
+    
+    echo
+    echo "Tri 1 - delta-based triphones"
+    echo "=====  $(date +"%D_%T") ====="
+    
+    # Tri1      
+
+
+    steps/train_deltas.sh  --cmd "$train_cmd" 2000 15000 \
+      data/${trainset} data/lang exp/mono_ali exp/tri1
+
+    steps/align_si.sh --nj $nj --cmd "$train_cmd"  \
+     data/${trainset} data/lang exp/tri1 exp/tri1_ali
+      
+    echo
+    echo "Tri 2 - LDA-MLLT triphones"
+    echo "=====  $(date +"%D_%T") ====="
+    # Tri2  
+
+
+    steps/train_lda_mllt.sh --cmd "$train_cmd" 2500 20000 \
+      data/${trainset} data/lang exp/tri1_ali exp/tri2b
+
+    steps/align_si.sh --nj $nj --cmd "$train_cmd"  \
+      data/${trainset} data/lang exp/tri2b exp/tri2b_ali
+      
+    echo
+    echo "Tri 3 - SAT triphones"
+    echo "=====  $(date +"%D_%T") ====="
+   
+    # Tri3 SAT
+
+
+    steps/train_sat.sh --cmd "$train_cmd" 3000 25000 \
+      data/${trainset} data/lang exp/tri2b_ali exp/tri3b
+
+    utils/mkgraph.sh data/lang_3G exp/tri3b exp/tri3b/graph
+   
+    echo
+    echo "------ End Train GMM --------"
+    echo "=====  $(date +"%D_%T") ====="
+fi
+
+
+if [[ $stage -le 4 ]]; then
+    echo
+    echo "============================="
+    echo "------- Decode TRI3B --------"
+    echo "=====  $(date +"%D_%T") ====="
+    echo
+
+
+    
+    echo; echo "--------decode ${devset}"; echo
+    steps/decode_fmllr.sh --config conf/decode.config --nj $nj --cmd "$decode_cmd" \
+      --scoring-opts "--min-lmwt 10 --max-lmwt 20" --num-threads 4  \
+      exp/tri3b/graph data/${devset} exp/tri3b/decode_${devset}
+
+
+    # Scoring test model with the best
+    lmwt=$(cat exp/tri3b/decode_${devset}/scoring_kaldi/wer_details/lmwt)
+    wip=$(cat exp/tri3b/decode_${devset}/scoring_kaldi/wer_details/wip)
+    echo; echo "--------decode ${testset}"
+    echo "Using [lmwt=$lmwt, wip=$wip] to score"; echo
+    steps/decode_fmllr.sh --config conf/decode.config --nj $nj --cmd "$decode_cmd" \
+      --scoring-opts "--min_lmwt $lmwt --max_lmwt $lmwt --word_ins_penalty $wip" --num-threads 4  \
+      exp/tri3b/graph data/${testset} exp/tri3b/decode_${testset}
+
+
+fi
+
+# Produce clean data
+if [[ $stage -le 5 ]]; then
+    echo
+    echo "============================="
+    echo "------- Cleanup Tri3b -------"
+    echo "=====  $(date +"%D_%T") ====="
+    echo
+
+    steps/cleanup/clean_and_segment_data.sh --nj $nj --cmd "$train_cmd"  \
+    --segmentation-opts "--min-segment-length 0.3 --min-new-segment-length 0.6" \
+      data/${trainset} data/lang exp/tri3b exp/tri3b_cleaned \
+      data/${trainset}_cleaned
+fi
+
+if [[ $stage -le 6 ]]; then
+    echo
+    echo "=================="
+    echo "----- TDNN-F -----"
+    echo "=====  $(date +"%D_%T") ====="
+    echo
+
+    local/chain/run_tdnn_1d.sh --nj $nj --decode_nj $decode_nj \
+    --train_set ${trainset}_cleaned --test_sets "$devset $testset" \
+    --gmm tri3b_cleaned --nnet3-affix _${trainset}_cleaned
+fi
+
+
+if [[ $stage -le 7 ]]; then
+    echo
+    echo "============================="
+    echo "------- FINAL SCORES --------"
+    echo "=====  $(date +"%D_%T") ====="
+    echo
+
+    for x in `find exp/* -name "best_wer"`; do
+        cat $x | grep -v ".si"
+    done
+fi
+
+echo
+echo "=====  $(date +"%D_%T") ====="
+echo "===== PROCESS ENDED ====="
+echo
+
+exit 1

DSing/sing_300x30x2/Kaldi-Dsing-task/DSing Kaldi Recipe/dsing/s5/setup_env.sh

@@ -0,0 +1,28 @@
+# setup_env.sh
+
+# Sharc HPC
+if [[ "$HOSTNAME" == *"sharc"* ]]; then
+    module load apps/python/conda
+    module load libs/CUDA/9.0.176/binary
+    module load libs/cudnn/7.3.1.20/binary-cuda-9.0.176
+    module load dev/gcc/5.4
+    module load libs/icu/58.2/gcc-4.9.4
+    PATH=$PATH:/usr/local/packages/libs/icu/58.2/gcc-4.9.4/bin
+    module load libs/intel-mkl/2019.3/binary
+
+    # Add SOX PATH
+    LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/acp13gr/apps/sox/lib
+    PATH=$PATH:/home/acp13gr/apps/sox/bin
+    source activate py27
+fi
+if [[ "$HOSTNAME" == *"bessemer"* ]]; then
+    module load Anaconda3/5.3.0
+    source activate py27
+    module load fosscuda/2019a
+    module load imkl/2019.1.144-iimpi-2019a
+
+    # Sox PATH
+    LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/apps/sox/lib
+    PATH=$PATH:$HOME/apps/sox/bin
+fi
+

DSing/sing_300x30x2/Kaldi-Dsing-task/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
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DSing/sing_300x30x2/Kaldi-Dsing-task/README.md

@@ -0,0 +1,28 @@
+# DSing ASR task: Resources and Baseline for an unaccompanied singing ASR.
+
+In this repository, you will find the scripts used to construct the DSing ASR-oriented dataset
+and the baseline system constructed on Kaldi.  
+
+Cite:
+```
+@inproceedings{Roa_Dabike-Barker_2019,  
+  author = {Roa Dabike, Gerardo and Barker, Jon}  
+  title = {{Automatic Lyric Transcription from Karaoke Vocal Tracks: Resources and a Baseline System}},  
+  year = 2019,  
+  booktitle = {Proceedings of the 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019)}  
+}
+```
+
+## 1- DSing dataset
+
+DSing is an ASR-oriented dataset constructed from the [Smule Sing!300x30x2](https://ccrma.stanford.edu/damp/) dataset (**Sing!**).
+This repository provides the scripts to transform **Sing!** to the DSing ASR task. 
+
+## 2- Initial steps
+
+The first step before running any of the scripts is to obtain access to **Sing!** dataset.
+For more details, go to [DAMP repository](https://ccrma.stanford.edu/damp/).   
+
+## 3- Extract DSing dataset using pre-segmented data.
+
+In directory **[DSing preconstructed](DSing preconstructed)** you can find the DSing dataset segmentation.

DSing/sing_300x30x2/PT/PTArrangementMeta/235546191_2197388.txt

@@ -0,0 +1,6 @@
+Arrangement ID: 235546191_2197388
+Arrangement title: K.O.
+Arrangement has lyrics: True
+Arrangement has PitchTrack: False
+Arrangement tags: pablo, vittar, pablovittar, ko, KO
+Arrangement artist: Pablo Vittar

DSing/sing_300x30x2/PT/PTArrangementMeta/3771663_3771663.txt

@@ -0,0 +1,6 @@
+Arrangement ID: 3771663_3771663
+Arrangement title: Que Sorte a Nossa
+Arrangement has lyrics: True
+Arrangement has PitchTrack: True
+Arrangement tags: 2010s, brasil, brasilian, brazil, brazillian, pop, portuguese, sertanejo
+Arrangement artist: Matheus & Kauan

DSing/sing_300x30x2/PT/PTArrangementMeta/416273850_387685.txt

@@ -0,0 +1,6 @@
+Arrangement ID: 416273850_387685
+Arrangement title: Ó gente da minha terra
+Arrangement has lyrics: True
+Arrangement has PitchTrack: True
+Arrangement tags: Mariza, gente, da, minha, terra, portuguesa, fado
+Arrangement artist: Mariza

DSing/sing_300x30x2/PT/PTArrangementMeta/447282422_402184.txt

@@ -0,0 +1,6 @@
+Arrangement ID: 447282422_402184
+Arrangement title: Amar Não É Pecado
+Arrangement has lyrics: True
+Arrangement has PitchTrack: True
+Arrangement tags: 
+Arrangement artist: Luan Santana

DSing/sing_300x30x2/PT/PTArrangementMeta/584358163_1439214.txt

@@ -0,0 +1,6 @@
+Arrangement ID: 584358163_1439214
+Arrangement title: Tumbalatum
+Arrangement has lyrics: True
+Arrangement has PitchTrack: False
+Arrangement tags: 
+Arrangement artist: MC Kevinho