/** * Clustering linear. * * @param threshold the threshold * @param clusterSet the cluster set * @param featureSet the feature set * @param parameter the parameter * @return the cluster set * @throws Exception the exception */ public ClusterSet clusteringLinear( double threshold, ClusterSet clusterSet, AudioFeatureSet featureSet, Parameter parameter) throws Exception { String mask = parameter.getParameterSegmentationOutputFile().getMask(); String oldMethod = parameter.getParameterClustering().getMethodAsString(); double oldThreshold = parameter.getParameterClustering().getThreshold(); String oldModelKind = parameter.getParameterModel().getModelKindAsString(); int oldNumberOfComponent = parameter.getParameterModel().getNumberOfComponents(); parameter.getParameterModel().setModelKind("FULL"); parameter.getParameterModel().setNumberOfComponents(1); parameter.getParameterClustering().setMethod("l"); parameter.getParameterClustering().setThreshold(threshold); ClusterSet clustersLClust = MClust.make(featureSet, clusterSet, parameter, null); if (parameter.getParameterDiarization().isSaveAllStep()) { parameter.getParameterSegmentationOutputFile().setMask(mask + ".l.seg"); MainTools.writeClusterSet(parameter, clustersLClust, false); } parameter.getParameterSegmentation().setMethod(oldMethod); parameter.getParameterModel().setNumberOfComponents(oldNumberOfComponent); parameter.getParameterModel().setModelKind(oldModelKind); parameter.getParameterClustering().setThreshold(oldThreshold); parameter.getParameterSegmentationOutputFile().setMask(mask); return clustersLClust; }
/** * Clustering. * * @param threshold the threshold * @param clusterSet the cluster set * @param featureSet the feature set * @param parameter the parameter * @return the cluster set * @throws Exception the exception */ public ClusterSet clustering( double threshold, ClusterSet clusterSet, AudioFeatureSet featureSet, Parameter parameter) throws Exception { String mask = parameter.getParameterSegmentationOutputFile().getMask(); String oldMethod = parameter.getParameterClustering().getMethodAsString(); double oldThreshold = parameter.getParameterClustering().getThreshold(); String oldModelKind = parameter.getParameterModel().getModelKindAsString(); int oldNumberOfComponent = parameter.getParameterModel().getNumberOfComponents(); // --- begin NEW v 1.14 / 4.16 / 4.18 / 4.20--- parameter.getParameterClustering().setMethod("h"); // parameter.getParameterClustering().setMethod("sr"); // --- end NEW v 1.14 --- parameter.getParameterClustering().setThreshold(threshold); logger.finer( "method:" + parameter.getParameterClustering().getMethod() + " thr:" + parameter.getParameterClustering().getThreshold()); parameter.getParameterModel().setModelKind("FULL"); parameter.getParameterModel().setNumberOfComponents(1); ClusterSet clustersHClust = MClust.make(featureSet, clusterSet, parameter, null); if (parameter.getParameterDiarization().isSaveAllStep()) { parameter.getParameterSegmentationOutputFile().setMask(mask + ".h.seg"); MainTools.writeClusterSet(parameter, clustersHClust, false); } parameter.getParameterSegmentation().setMethod(oldMethod); parameter.getParameterModel().setNumberOfComponents(oldNumberOfComponent); parameter.getParameterModel().setModelKind(oldModelKind); parameter.getParameterClustering().setThreshold(oldThreshold); parameter.getParameterSegmentationOutputFile().setMask(mask); return clustersHClust; }
/** * Tun ester2 speaker clr clustering. * * @param referenceClusterSet the reference cluster set * @param uemClusterSet the uem cluster set * @param partialKey the partial key * @param method the method * @param clusterSetBase the cluster set base * @param clusterSet the cluster set * @param featureSet the feature set * @param parameter the parameter * @return the diarization result list * @throws Exception the exception */ public DiarizationResultList tunEster2SpeakerCLRClustering( ClusterSet referenceClusterSet, ClusterSet uemClusterSet, String partialKey, String method, ClusterSet clusterSetBase, ClusterSet clusterSet, AudioFeatureSet featureSet, Parameter parameter) throws Exception { String oldSpeechDetectorMethod = parameter.getParameterInputFeature().getSpeechMethodAsString(); double oldSpeechDetectorThreshold = parameter.getParameterInputFeature().getSpeechThreshold(); String oldModelKind = parameter.getParameterModel().getModelKindAsString(); int oldNumberOfComponent = parameter.getParameterModel().getNumberOfComponents(); String oldMethod = parameter.getParameterClustering().getMethodAsString(); double oldThreshold = parameter.getParameterClustering().getThreshold(); String oldEMControl = parameter.getParameterEM().getEMControl(); int oldNTop = parameter.getParameterTopGaussian().getScoreNTop(); boolean oldSaveAll = parameter.getParameterDiarization().isSaveAllStep(); DiarizationResultList localResult = new DiarizationResultList(cMin, cMax, mult); DiarizationError computeError = new DiarizationError(referenceClusterSet, uemClusterSet); double prevScore = cMin; // ---- Begin NEW v 1.13 --- parameter.getParameterInputFeature().setSpeechMethod("E"); parameter.getParameterInputFeature().setSpeechThreshold(0.1); // ---- End NEW v 1.13 --- String FeatureFormat = "featureSetTransformation"; String dir = "ester2"; InputStream ubmInputStream = getClass().getResourceAsStream(dir + "/ubm.gmm"); GMMArrayList ubmVect = MainTools.readGMMContainer(ubmInputStream, parameter.getParameterModel()); GMM ubm = ubmVect.get(0); /* * int nbCep = 16; logger.info("---> nbCep:"+nbCep); FeatureSet featureSet2 = loadFeature(parameter, clusterSet, "audio16kHz2sphinx,1:3:2:0:0:0,"+nbCep+",1:1:300:4"); logger.info("---> nbCep:"+nbCep); //logger.fine("*** nbFeaturesNorm:" + * clusterSetBase.getLength()); */ // A tester AudioFeatureSet featureSet2 = loadFeature(featureSet, parameter, clusterSet, FeatureFormat + ",1:3:2:0:0:0,13,1:1:300:4"); // v5.14 // FeatureSet featureSet2 = loadFeature(featureSet, parameter, clusterSet, FeatureFormat // + ",1:3:2:0:0:0,13,1:1:0:0"); parameter.getParameterModel().setModelKind("DIAG"); parameter.getParameterModel().setNumberOfComponents(ubm.getNbOfComponents()); parameter.getParameterClustering().setMethod(method); // ---- Begin NEW v 1.19 --- // parameter.getParameterEM().setEMControl("1,1,0.01"); // parameter.getParameterClustering().setThreshold(0); // ---- End NEW v 1.19 --- parameter.getParameterClustering().setThreshold(cMax); parameter.getParameterEM().setEMControl("1,5,0.01"); parameter.getParameterTopGaussian().setScoreNTop(5); parameter.getParameterDiarization().setSaveAllStep(false); CLRHClustering clustering = new CLRHClustering(clusterSet, featureSet2, parameter, ubm); // int nbCluster = clusterSet.clusterGetSize(); // logger.info("initialise clustering CLR clusterSet:"+clusterSet); clustering.initialize(); double score = clustering.getScoreOfCandidatesForMerging(); DiarizationResult error = computeError.scoreOfMatchedSpeakers(clustering.getClusterSet()); double errorRate = error.getErrorRate(); localResult.setResult(prevScore, score, error); // prevScore = Math.max(score, prevScore); logger.fine( "first " + parameter.show + " key=" + partialKey + " clrScore=" + score + " clrErrorRate=" + errorRate + " clrSize=" + clustering.getSize() + "/" + referenceClusterSet.clusterGetSize()); while ((score < cMax) && (clustering.getSize() > 1)) { localResult.setResult(prevScore, score, error); prevScore = Math.max(score, prevScore); clustering.mergeCandidates(); // -- start V5.16 -- // logger.info("--> Decoding"); // ClusterSet decodeClusterSet = MDecode.make(featureSet2, clustering.getClusterSet(), // clustering.getGmmList(), parameter); // logger.info("--> Clustering"); // featureSet2 = loadFeature(featureSet, parameter, decodeClusterSet, FeatureFormat // + ",1:3:2:0:0:0,13,1:1:300:4"); // clustering = new CLRHClustering(decodeClusterSet, featureSet2, parameter, ubm); // clustering.initialize(); // -- end V5.16 -- score = clustering.getScoreOfCandidatesForMerging(); error = computeError.scoreOfMatchedSpeakers(clustering.getClusterSet()); errorRate = error.getErrorRate(); // localResult.setResult(prevScore, score, error); // prevScore = Math.max(score, prevScore); logger.fine( parameter.show + " key=" + partialKey + " clrScore=" + score + " clrErrorRate=" + errorRate + " clrSize=" + clustering.getSize() + "/" + referenceClusterSet.clusterGetSize()); } localResult.setResult(prevScore, score, error); localResult.setResult(score, cMax, error); logger.finer(parameter.show + " key=" + partialKey + " resultat du fichier"); localResult.log("partial result: " + parameter.show + " " + partialKey); clustering.reset(); parameter.getParameterModel().setNumberOfComponents(oldNumberOfComponent); parameter.getParameterModel().setModelKind(oldModelKind); parameter.getParameterClustering().setMethod(oldMethod); parameter.getParameterClustering().setThreshold(oldThreshold); parameter.getParameterEM().setEMControl(oldEMControl); parameter.getParameterTopGaussian().setScoreNTop(oldNTop); parameter.getParameterInputFeature().setSpeechMethod(oldSpeechDetectorMethod); parameter.getParameterInputFeature().setSpeechThreshold(oldSpeechDetectorThreshold); parameter.getParameterDiarization().setSaveAllStep(oldSaveAll); return localResult; }
/** * Speaker clustering. * * @param threshold the threshold * @param method the method * @param clusterSetBase the cluster set base * @param clustersSet the clusters set * @param featureSet the feature set * @param parameter the parameter * @return the cluster set * @throws Exception the exception */ public ClusterSet speakerClustering( double threshold, String method, ClusterSet clusterSetBase, ClusterSet clustersSet, AudioFeatureSet featureSet, Parameter parameter) throws Exception { String mask = parameter.getParameterSegmentationOutputFile().getMask(); String oldMethod = parameter.getParameterClustering().getMethodAsString(); double oldThreshold = parameter.getParameterClustering().getThreshold(); String oldEMControl = parameter.getParameterEM().getEMControl(); int oldNTop = parameter.getParameterTopGaussian().getScoreNTop(); String oldSpeechDetectorMethod = parameter.getParameterInputFeature().getSpeechMethodAsString(); double oldSpeechDetectorThreshold = parameter.getParameterInputFeature().getSpeechThreshold(); // ** bottom up hierarchical classification using GMMs // ** one for each cluster, trained by MAP adaptation of a UBM composed of the fusion of // 4x128GMM // ** the feature normalization use feature mapping technique, after the cluster frames are // centered and reduced String dir = "ester2"; InputStream ubmInputStream = getClass().getResourceAsStream(dir + "/ubm.gmm"); GMMArrayList ubmVect = MainTools.readGMMContainer(ubmInputStream, parameter.getParameterModel()); GMM ubm = ubmVect.get(0); // int nbCep = ubm.getDimension() + 1; String FeatureFormat = "featureSetTransformation"; parameter.getParameterInputFeature().setSpeechMethod("E"); parameter.getParameterInputFeature().setSpeechThreshold(0.1); AudioFeatureSet featureSet2 = loadFeature( featureSet, parameter, clustersSet, FeatureFormat + ",1:3:2:0:0:0,13,1:1:300:4"); parameter.getParameterClustering().setMethod(method); parameter.getParameterClustering().setThreshold(threshold); parameter.getParameterEM().setEMControl("1,5,0.01"); parameter.getParameterTopGaussian().setScoreNTop(5); // ---- Begin NEW v 1.13 --- // if (parameter.parameterSpeechDetector.useSpeechDetection() == true) { // MSpeechDetector.EnergyThresholdMethod(clustersSet, featureSet, parameter); // } // ---- End NEW v 1.13 --- boolean saveAll = parameter.getParameterDiarization().isSaveAllStep(); parameter.getParameterDiarization().setSaveAllStep(false); ClusterSet clustersCLR = MClust.make(featureSet2, clustersSet, parameter, ubm); parameter.getParameterDiarization().setSaveAllStep(saveAll); parameter.getParameterSegmentationOutputFile().setMask(mask); if (parameter.getParameterDiarization().isSaveAllStep()) { parameter.getParameterSegmentationOutputFile().setMask(mask + ".c.seg"); MainTools.writeClusterSet(parameter, clustersCLR, false); } parameter.getParameterSegmentationOutputFile().setMask(mask); parameter.getParameterClustering().setMethod(oldMethod); parameter.getParameterClustering().setThreshold(oldThreshold); parameter.getParameterEM().setEMControl(oldEMControl); parameter.getParameterTopGaussian().setScoreNTop(oldNTop); parameter.getParameterInputFeature().setSpeechMethod(oldSpeechDetectorMethod); parameter.getParameterInputFeature().setSpeechThreshold(oldSpeechDetectorThreshold); return clustersCLR; }