features=$1
#the MFCC corresponds to sphinx 12 MFCC + Energy
# sphinx=the mfcc was computed by the sphinx tools
# 1: static coefficients are present in the file
# 1: energy coefficient is present in the file
# 0: delta coefficients are not present in the file
# 0: delta energy coefficient is not present in the file
# 0: delta delta coefficients are not present in the file
# 0: delta delta energy coefficient is not present in the file
# 13: total size of a feature vector in the mfcc file
# 0:0:0: no feature normalization
fDesc="audio2sphinx,1:1:0:0:0:0,13,0:0:0"

#this variable is use in CLR/NCLR clustering and gender detection
#the MFCC corresponds to sphinx 12 MFCC + E
# sphinx=the mfcc is computed by sphinx tools
# 1: static coefficients are present in the file
# 3: energy coefficient is present in the file but will not be used
# 2: delta coefficients are not present in the file and will be computed on the fly
# 0: delta energy coefficient is not present in the file
# 0: delta delta coefficients are not present in the file
# 0: delta delta energy coefficient is not present in the file
# 13: size of a feature vector in the mfcc file
# 1:1:300:4: the MFCC are wrapped (feature warping using a sliding windows of 300 features),
#                   next the features are centered and reduced: mean and variance are computed by segment
fDescCLR="audio2sphinx,1:3:2:0:0:0,13,1:1:300:4"

show=`basename $1 .sph`
show=`basename $show .wav`

echo $show

#need JVM 1.6
java=java

datadir=${show}

ubm=./ubm.gmm
pmsgmm=./sms.gmms
sgmm=./s.gmms
ggmm=./gender.gmms

LOCALCLASSPATH=./LIUM_SpkDiarization.jar

echo "#####################################################"
echo "#   $show"
echo "#####################################################"

mkdir ./$datadir >& /dev/null

$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MSegInit  –trace —help –fInputMask=$features –fInputDesc=$fDesc –sInputMask=$uem –sOutputMask=./$datadir/%s.i.seg  $show

#Speech/Music/Silence segmentation
iseg=./$datadir/$show.i.seg
pmsseg=./$datadir/$show.pms.seg
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MDecode –trace —help  –fInputDesc=audio2sphinx,1:3:2:0:0:0,13,0:0:0 –fInputMask=$features –sInputMask=$iseg –sOutputMask=$pmsseg –dPenality=10,10,50 –tInputMask=$pmsgmm $show

#GLR based segmentation, make small segments
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MSeg   –kind=FULL –sMethod=GLR –trace —help –fInputMask=$features –fInputDesc=$fDesc –sInputMask=./$datadir/%s.i.seg –sOutputMask=./$datadir/%s.s.seg  $show

# linear clustering
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MClust   –trace —help –fInputMask=$features –fInputDesc=$fDesc –sInputMask=./$datadir/%s.s.seg –sOutputMask=./$datadir/%s.l.seg –cMethod=l –cThr=2 $show
 
h=3
# hierarchical clustering
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MClust   –trace —help –fInputMask=$features –fInputDesc=$fDesc –sInputMask=./$datadir/%s.l.seg –sOutputMask=./$datadir/%s.h.$h.seg –cMethod=h –cThr=$h $show
 
# initialize GMM
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MTrainInit   —help –nbComp=8 –kind=DIAG –fInputMask=$features –fInputDesc=$fDesc –sInputMask=./$datadir/%s.h.$h.seg –tOutputMask=./$datadir/%s.init.gmms $show
 
# EM computation
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MTrainEM   —help  –nbComp=8 –kind=DIAG –fInputMask=$features –fInputDesc=$fDesc –sInputMask=./$datadir/%s.h.$h.seg –tOutputMask=./$datadir/%s.gmms  –tInputMask=./$datadir/%s.init.gmms  $show
 
#Viterbi decoding
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MDecode   –trace —help –fInputMask=${features} –fInputDesc=$fDesc –sInputMask=./$datadir/%s.h.$h.seg –sOutputMask=./$datadir/%s.d.$h.seg –dPenality=250  –tInputMask=$datadir/%s.gmms $show
 
#Adjust segment boundaries
adjseg=./$datadir/$show.adj.$h.seg
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.tools.SAdjSeg —help  –trace –fInputMask=$features –fInputDesc=audio2sphinx,1:1:0:0:0:0,13,0:0:0 –sInputMask=./$datadir/%s.d.$h.seg –sOutputMask=$adjseg $show
 
#filter spk segmentation according pms segmentation
fltseg=./$datadir/$show.flt.$h.seg
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.tools.SFilter —help  –fInputDesc=audio2sphinx,1:3:2:0:0:0,13,0:0:0 –fInputMask=$features –fltSegMinLenSpeech=150 –fltSegMinLenSil=25 –sFilterClusterName=j –fltSegPadding=25 –sFilterMask=$pmsseg –sInputMask=$adjseg –sOutputMask=$fltseg $show

#Split segment longer than 20s
splseg=./$datadir/$show.spl.$h.seg
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.tools.SSplitSeg —help  –sFilterMask=$pmsseg –sFilterClusterName=iS,iT,j –sInputMask=$fltseg –sOutputMask=$splseg –fInputMask=$features –fInputDesc=audio2sphinx,1:3:2:0:0:0,13,0:0:0 –tInputMask=$sgmm $show

#——————————————————————————-
#Set gender and bandwith
gseg=./$datadir/$show.g.$h.seg
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MScore —help  –sGender –sByCluster –fInputDesc=$fDescCLR –fInputMask=$features –sInputMask=$splseg –sOutputMask=$gseg –tInputMask=$ggmm $show

#CLR clustering
# Features contain static and delta and are centered and reduced (–fdesc)
c=1.7
spkseg=./$datadir/$show.c.$h.seg
$java -Xmx2048m -classpath "$LOCALCLASSPATH" fr.lium.spkDiarization.programs.MClust  —help –trace –fInputMask=$features –fInputDesc=$fDescCLR –sInputMask=$gseg –sOutputMask=./$datadir/%s.c.$h.seg –cMethod=ce –cThr=$c –tInputMask=$ubm –emCtrl=1,5,0.01 –sTop=5,$ubm –tOutputMask=./$show/$show.c.gmm $show