//-------------------------------------------------------------------------
// Get a tab with indexes of speakers with maximum likelihood (mdtm and etf only)
void getTarClientIdx(Config & config, XList & inputList, unsigned long nbLoc, unsigned long * tarTab)
{
XLine *linep;
unsigned long cpt=0;
unsigned long cpttab=0;
double minLLK=config.getParam("minLLK").toDouble();
double maxScore=minLLK;
unsigned long idxTar=0;
bool verbose=config.existsParam("verbose");
while((linep=inputList.getLine())!=NULL)
{
double score=linep->getElement(6).toDouble();
if (score>=maxScore)
{
maxScore=score; // this is the maximum score
idxTar=cpt; // index is just the line
if (verbose) {cout << "giving highest score to " << linep->getElement(1) << " "<<maxScore << endl;}
}
if (cpt%nbLoc==(nbLoc-1)) // when changing segment
{
tarTab[cpttab]=idxTar; // idx of the target goes in the tab
if (verbose) {cout << linep->getElement(1) << " max score: "<<maxScore <<"idx: "<<idxTar<<"cpt: "<<cpt<< endl;}
cpttab++;
maxScore=minLLK; //reset maxScore
}
cpt++;
}
}
//-------------------------------------------------------------------------
//
unsigned long getIndexOfMaxScore(XList & inputList,unsigned long scoreField, unsigned long segField, unsigned long & i, unsigned long nbListLines)
{
String seg=inputList.getLine(i).getElement(segField); //this is a new segment
long max_score=-200;
long score;
unsigned long maxIndex=0;
while (inputList.getLine(i).getElement(segField)==seg) // while same segment test
{
score= inputList.getLine(i).getElement(scoreField).toLong();
if (score >= max_score) {max_score=score; maxIndex=i;} // store max score and its index
i++;
if (i >= nbListLines) break; // break the loop if end of file
}
return maxIndex;
}
//-------------------------------------------------------------------------
// Produce a tab with mean and cov by segment without the maximum score(mdtm and etf only)
void getSegmentalMeanCovWithoutMax(Config & config, XList & inputList, unsigned long nbLoc, unsigned long * tarTab, double* meanTab, double * covTab)
{
XLine *linep;
unsigned long cpt=0;
unsigned long cpttab=0;
double minLLK=config.getParam("minLLK").toDouble();
double maxScore=minLLK;
double meanAcc=0.0;
double covAcc=0.0;
unsigned long idxTar=0;
bool verbose=config.existsParam("verbose");
while((linep=inputList.getLine())!=NULL)
{
double score=linep->getElement(6).toDouble();
if (score>=maxScore)
{
maxScore=score; // this is the maximum score
idxTar=cpt; // index is just the line
if (verbose) {cout << "giving highest score to " << linep->getElement(1) << " "<<maxScore << endl;}
}
meanAcc+=score; // Accumulate mean and cov
covAcc+=(score*score);
if (cpt%nbLoc==(nbLoc-1)) // when changing segment
{
tarTab[cpttab]=idxTar;
meanAcc-=maxScore; //remove max from Stats
covAcc-=(maxScore*maxScore);
meanTab[cpttab]=meanAcc;
covTab[cpttab]=covAcc;
if (verbose) {cout << linep->getElement(1) << " max score: "<<maxScore <<"idx: "<<idxTar<<"cpt: "<<cpt<< " meanA: "<<meanAcc<<" covA: "<<covAcc<<endl;}
cpttab++;
maxScore=minLLK;
meanAcc=0.0;
covAcc=0.0;
}
cpt++;
}
}
//-------------------------------------------------------------------------
// Produce a tab with mean and cov by segment (mdtm and etf only)
void getSegmentalMeanCov(XList & inputList, unsigned long nbLoc, double* meanTab, double * covTab)
{
XLine *linep;
unsigned long cpt=0;
unsigned long cpttab=0;
double meanAcc=0.0;
double covAcc=0.0;
while((linep=inputList.getLine())!=NULL)
{
double score=linep->getElement(6).toDouble();
meanAcc+=score; // Accumulate mean and cov
covAcc+=(score*score);
if (cpt%nbLoc==(nbLoc-1)) // when changing segment
{
meanTab[cpttab]=meanAcc;
covTab[cpttab]=covAcc;
cpttab++;
meanAcc=0.0;
covAcc=0.0;
}
cpt++;
}
}
//-----------------------------------------------------------------------------------------------------------------------------------------------------------
int IvExtractorEigenDecomposition(Config& config)
{
String inputWorldFilename = config.getParam("inputWorldFilename");
// label for selected frames - Only the frames associated with this label, in the label files, will be used
bool fixedLabelSelectedFrame=true;
String labelSelectedFrames;
if (config.existsParam("useIdForSelectedFrame")) // the ID of each speaker is used as labelSelectedFrame ?
fixedLabelSelectedFrame=(config.getParam("useIdForSelectedFrame").toBool()==false);
if (fixedLabelSelectedFrame) // the label is decided by the command line and is unique for the run
labelSelectedFrames=config.getParam("labelSelectedFrames");
try{
MixtureServer ms(config);
if (verbose) cout << "(IvExtractor) Approximate i-vector by using Eigen Decomposition"<<endl;
if (verbose) cout << "(IvExtractor) TotalVariability - Load world model [" << inputWorldFilename<<"]"<<endl;
MixtureGD& world = ms.loadMixtureGD(inputWorldFilename);
//Read the NDX file
String ndxFilename = config.getParam("targetIdList");
//Remove the first element of each line which is the model name
XList tmpFileList(ndxFilename);
XList fileList;
for(unsigned long ll=0;ll<tmpFileList.getLineCount();ll++){
fileList.addLine();
for(unsigned long i=1;i<tmpFileList.getLine()->getElementCount();i++){
fileList.getLine(fileList.getLineCount()-1).addElement(tmpFileList.getLine(ll).getElement(i));
}
}
//Create and initialise the accumulator
TVAcc tvAcc(fileList, config);
Matrix<double> Q(tvAcc.getRankT(),tvAcc.getRankT());
Matrix<double> D(tvAcc.getNDistrib(),tvAcc.getRankT());
if(config.existsParam("loadEigenDecompositionParam") && config.getParam("loadEigenDecompositionParam").toBool()){ // Load normalized T matrix and weighted Covariance matrix if pre-computed
//Load TotalVariability matrix
String normTFilename = config.getParam("totalVariabilityMatrix") + "_norm";
tvAcc.loadT(normTFilename, config);
//Load D and Q matrices
String dFilename = config.getParam("matrixFilesPath") + config.getParam("totalVariabilityMatrix") + "_EigDec_D" + config.getParam("loadMatrixFilesExtension");
D.load(dFilename,config);
String qFilename = config.getParam("matrixFilesPath") + config.getParam("totalVariabilityMatrix") + "_EigDec_Q" + config.getParam("loadMatrixFilesExtension");
Q.load(qFilename,config);
}
else{
//Load TotalVariability matrix
tvAcc.loadT(config.getParam("totalVariabilityMatrix"), config);
// Normalize matrix T
tvAcc.normTMatrix();
// Compute weighted co-variance matrix by using UBM weight coefficients
DoubleSquareMatrix W(tvAcc.getRankT());
W.setAllValues(0.0);
tvAcc.getWeightedCov(W,world.getTabWeight(),config);
// Eigen Decomposition of W to get Q
Matrix<double> tmpW(W);
Q.setAllValues(0.0);
tvAcc.computeEigenProblem(tmpW,Q,tvAcc.getRankT(),config);
// Compute D matrices (approximation of Tc'Tc matrices)
D.setAllValues(0.0);
tvAcc.approximateTcTc(D,Q,config);
}
//Load the statistics from files or compute statistics for all segments at once
if((config.existsParam("loadAccs")) && config.getParam("loadAccs").toBool()){ //load pre-computed statistics
cout<<" (IvExtractor) Load Accumulators"<<endl;
tvAcc.loadN(config);
tvAcc.loadF_X(config);
}
else{ //Compute statistics if they don't exists
tvAcc.computeAndAccumulateTVStat(config);
tvAcc.saveAccs(config);
}
// Then load the meanEstimate computed by minDiv if required
DoubleVector meanEstimate = tvAcc.getUbmMeans();
if(config.existsParam("minDivergence")&& config.getParam("minDivergence").toBool()){
String minDivName = config.getParam("matrixFilesPath") + config.getParam("meanEstimate") + config.getParam("loadMatrixFilesExtension");
Matrix<double> tmpMean(minDivName,config);
for(unsigned long i=0;i<meanEstimate.size();i++){
meanEstimate[i] = tmpMean(0,i);
}
}
//Update the mean Estimate
cout<<" (IvExtractor) Load Mean Estimate"<<endl;
tvAcc.loadMeanEstimate(meanEstimate);
//Substract mean from the statistics and normalize co-variance
tvAcc.normStatistics(config);
// Estimate I-Vectors
tvAcc.estimateWEigenDecomposition(D,Q,config);
cout<<"--------- save IV by File --------"<<endl;
tvAcc.saveWbyFile(config);
cout<<"--------- end of process --------"<<endl;
} // fin try
catch (Exception& e) {cout << e.toString().c_str() << endl;}
return 0;
}
//-----------------------------------------------------------------------------------------------------------------------------------------------------------
int IvExtractor(Config& config)
{
String inputWorldFilename = config.getParam("inputWorldFilename");
// label for selected frames - Only the frames associated with this label, in the label files, will be used
bool fixedLabelSelectedFrame=true;
String labelSelectedFrames;
if (config.existsParam("useIdForSelectedFrame")) // the ID of each speaker is used as labelSelectedFrame ?
fixedLabelSelectedFrame=(config.getParam("useIdForSelectedFrame").toBool()==false);
if (fixedLabelSelectedFrame) // the label is decided by the command line and is unique for the run
labelSelectedFrames=config.getParam("labelSelectedFrames");
try{
MixtureServer ms(config);
if (verbose) cout << "(IvExtractor) TotalVariability - Load world model [" << inputWorldFilename<<"]"<<endl;
MixtureGD& world = ms.loadMixtureGD(inputWorldFilename);
//Load the statistics from files or compute statistics for all segments at once
//Read the NDX file
String ndxFilename = config.getParam("targetIdList");
//Remove the first element of each line which is the model name
XList tmpFileList(ndxFilename);
XList fileList;
for(unsigned long ll=0;ll<tmpFileList.getLineCount();ll++){
fileList.addLine();
for(unsigned long i=1;i<tmpFileList.getLine()->getElementCount();i++){
fileList.getLine(fileList.getLineCount()-1).addElement(tmpFileList.getLine(ll).getElement(i));
}
}
//Create and initialise the accumulator
TVAcc tvAcc(fileList, config);
//Load TotalVariability matrix
tvAcc.loadT(config.getParam("totalVariabilityMatrix"), config);
//Statistics
if((config.existsParam("loadAccs")) && config.getParam("loadAccs").toBool()){ //load pre-computed statistics
cout<<" (IvExtractor) Load Accumulators"<<endl;
tvAcc.loadN(config);
tvAcc.loadF_X(config);
}
else{ //Compute statistics if they don't exists
tvAcc.computeAndAccumulateTVStat(config);
tvAcc.saveAccs(config);
}
// Then load the meanEstimate computed by minDiv if required
DoubleVector meanEstimate = tvAcc.getUbmMeans();
if(config.existsParam("minDivergence")&& config.getParam("minDivergence").toBool()){
String minDivName = config.getParam("matrixFilesPath") + config.getParam("meanEstimate") + config.getParam("loadMatrixFilesExtension");
Matrix<double> tmpMean(minDivName,config);
for(unsigned long i=0;i<meanEstimate.size();i++){
meanEstimate[i] = tmpMean(0,i);
}
}
//Update the mean Estimate
cout<<" (IvExtractor) Load Mean Estimate"<<endl;
tvAcc.loadMeanEstimate(meanEstimate);
//Substract mean from the statistics
tvAcc.substractM(config);
//Compute vEvT for each session
tvAcc.estimateTETt(config);
// Estimate I-Vectors
tvAcc.estimateW(config);
cout<<"--------- save IV by File --------"<<endl;
tvAcc.saveWbyFile(config);
cout<<"--------- end of process --------"<<endl;
} // fin try
catch (Exception& e) {cout << e.toString().c_str() << endl;}
return 0;
}
//-------------------------------------------------------------------------
// Retrieve info in a nist file providing the good fields
void retrieveNISTSegmentInfo(XList & inputList, String & gender, String & clientName, String & seg, unsigned long genderField, unsigned long nameField, unsigned long segField, unsigned long & i) {
if (inputList.getLine(i).getElement(genderField)=="F") {gender="f";} else {gender="m";}
clientName=inputList.getLine(i).getElement(nameField);
seg=inputList.getLine(i).getElement(segField);
}
void launchTurnDetectionProcess(Config & config){
String outputFilesPath=config.getParam("outputFilesPath");
String inputListFileName = config.getParam("listFileToSegment"); //file including the list of files to segment
XLine classToAnalyse; //Array of labels to analyze
classToAnalyse.reset();
if(verbose){
cout << "*********** Current Configuration ***************" << endl;
for(unsigned long i=0; i<config.getParamCount(); i++){
cout << config.getParamName(i) << " => " << config.getParamContent(i) << endl;
}
cout << "*************************************************" << endl;
}
try{
XList listLabel;
XList listFileName;
try{
listFileName.load(inputListFileName,config);
}
catch(FileNotFoundException& e){
cout<<"There is no files to segment !"<<endl;
exit(-1);
}
listFileName.rewind();
XLine *filep;
while ((filep=listFileName.getLine()) != NULL){ // For each stream of audio data (in several files in the same line)
const XLine & listFile=filep->getElements(); // One or several files, as several part of the same stream
MixtureServer ms(config);
StatServer ss(config, ms);
SegServer Resultat;
FeatureServer fs(config,listFile); // Reading the features (one or more files)
SegServer segmentsServer; // Create the segment server for managing the segments/clusters
LabelServer labelServer; // Create the lable server, for indexing the segments/clusters
initializeClusters(listFile,segmentsServer,labelServer,config); // Reading the segmentation files for each feature input file
verifyClusterFile(segmentsServer,fs,config); // Verify if the segments ending before the end of the feature files
String fileInit=listFile.getElement(0);
config.setParam("fileSize", String::valueOf(fs.getFeatureCountOfASource(fileInit)));
if(config.existsParam("fileRefPath")){
// assumption: all the segments in the segment server come from the same source file !!!
displayAllSegmentsFromRef(config, fileInit, fs.getFeatureCountOfASource(fileInit));
}
for(unsigned long icluster=0;icluster<segmentsServer.getClusterCount();icluster++){ // for each cluster
SegCluster& cluster=segmentsServer.getCluster(icluster);
SegServer segOutputServer;
TurnDetection(config,cluster,segOutputServer,ss,fs,ms,labelServer);
displayAllSegments(config,segOutputServer);
for(unsigned long i=0;i<segOutputServer.getSegCount();i++){
Seg& segment=segOutputServer.getSeg(i);
Resultat.createSeg(segment.begin(),segment.length(),segment.labelCode(),segment.string(),segment.sourceName());
}
}//for icluster
saveSegmentation(config,Resultat,fs,outputFilesPath,1);
}// while
} // end try
catch (Exception& e){
cout << e.toString().c_str() << endl;
}
}//launchTurnDetectionProcess
