예제 #1
0
  /**
   * 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;
  }
예제 #2
0
  /**
   * Tun ester2 diarization.
   *
   * @param parameter the parameter
   * @param clusterSet the cluster set
   * @return the tree map
   * @throws DiarizationException the diarization exception
   * @throws Exception the exception
   */
  public TreeMap<String, DiarizationResultList> tunEster2Diarization(
      Parameter parameter, ClusterSet clusterSet) throws DiarizationException, Exception {

    TreeMap<String, DiarizationResultList> result = new TreeMap<String, DiarizationResultList>();

    // double paramThr = parameter.getParameterClustering().getThreshold();
    lMin = parameter.getParameterDiarization().getThreshold("l");
    lMax = parameter.getParameterDiarization().getMaxThreshold("l");
    hMin = parameter.getParameterDiarization().getThreshold("h");
    hMax = parameter.getParameterDiarization().getMaxThreshold("h");
    dMin = parameter.getParameterDiarization().getThreshold("d");
    dMax = parameter.getParameterDiarization().getMaxThreshold("d");
    cMin = parameter.getParameterDiarization().getThreshold("c");
    cMax = parameter.getParameterDiarization().getMaxThreshold("c");

    String featureDesc = parameter.getParameterInputFeature().getFeaturesDescriptorAsString();

    AudioFeatureSet featureSet = null;
    ClusterSet clustersSegInit = null;

    if (parameter.getParameterDiarization().isLoadInputSegmentation() == false) {
      featureSet = loadFeature(parameter, clusterSet, featureDesc);
      featureSet.setCurrentShow(parameter.show);
      int nbFeatures = featureSet.getNumberOfFeatures();
      clusterSet.getFirstCluster().firstSegment().setLength(nbFeatures);
      clustersSegInit = sanityCheck(clusterSet, featureSet, parameter);
    } else {
      featureSet = loadFeature(parameter, clusterSet, featureDesc);
      featureSet.setCurrentShow(parameter.show);
      clustersSegInit = sanityCheck(clusterSet, featureSet, parameter);
      featureSet = loadFeature(parameter, clustersSegInit, featureDesc);
      featureSet.setCurrentShow(parameter.show);
    }

    // seg IRIT
    // ClusterSet clustersSegSave = clustersSegInit;
    // seg IRIT
    ClusterSet referenceClusterSet = MainTools.readTheSecondClusterSet(parameter);
    ClusterSet uemClusterSet = MainTools.readThe3rdClusterSet(parameter);

    if (parameter.getParameterDiarization().isLastStepOnly()) {
      String key = "l=" + lMin + " h=" + hMin + " d=" + dMin;
      DiarizationResultList values = null;
      if (parameter.getParameterDiarization().isCEClustering() == false) {
        logger.warning(" nothing to do isCEClustering == false");
      } else {
        values =
            tunEster2SpeakerCLRClustering(
                referenceClusterSet,
                uemClusterSet,
                key,
                "ce",
                clusterSet,
                clusterSet,
                featureSet,
                parameter);
      }
      result.put(key, values);
      return result;
    }

    ClusterSet clustersSegSave =
        segmentation("GLR", "FULL", clustersSegInit, featureSet, parameter);
    for (double l = lMin; l <= lMax; l += 0.5) {
      ClusterSet clustersSeg = clustersSegSave.clone();
      logger.finest("clustering l=" + l);
      ClusterSet clustersLClust = clusteringLinear(l, clustersSeg, featureSet, parameter);
      // ---- Begin NEW v 1.14 ---
      for (double h = hMin; h <= hMax; h += 0.5) {
        // for (double h = hMin; h <= hMax; h += 0.2) {
        // ---- end NEW v 1.14 ---
        // if (h > l) {
        ClusterSet clustersHClust = clustering(h, clustersLClust, featureSet, parameter);
        for (double d = dMin; d <= dMax; d += 50) {
          ClusterSet clustersDClust = decode(8, d, clustersHClust, featureSet, parameter);
          // double error = DiarizationError.scoreOfMatchedSpeakers(referenceClusterSet,
          // clustersDClust);
          ClusterSet clustersSplitClust =
              speech(
                  "10,10,50", clusterSet, clustersSegInit, clustersDClust, featureSet, parameter);
          ClusterSet clustersGender = gender(clusterSet, clustersSplitClust, featureSet, parameter);

          String key = "l=" + l + " h=" + h + " d=" + d;
          DiarizationResultList values = null;
          if (parameter.getParameterDiarization().isCEClustering() == false) {
            values = new DiarizationResultList(0, 0, 1);
            DiarizationError computeError =
                new DiarizationError(referenceClusterSet, uemClusterSet);
            DiarizationResult error = computeError.scoreOfMatchedSpeakers(clustersGender);
            values.setResult(0, 0, error);
            logger.finer(parameter.show + " key=" + key + " resultat du fichier");
            values.log("partial result: " + parameter.show + " " + key);
          } else {
            // V4.19 = CLUST_H_BIC_GMM_MAP
            // values = tunEster2SpeakerCLRClustering(referenceClusterSet, key, "bicgmmmap",
            // clustersGender, clustersGender, featureSet, parameter);
            // V5.16 = ce_d
            // values = tunEster2SpeakerCLRClustering(referenceClusterSet, key, "ce_d",
            // clustersGender, clustersGender, featureSet, parameter);
            values =
                tunEster2SpeakerCLRClustering(
                    referenceClusterSet,
                    uemClusterSet,
                    key,
                    "ce",
                    clustersGender,
                    clustersGender,
                    featureSet,
                    parameter);
          }
          if (result.containsKey(key)) {
            result.get(key).addResultArray(values);
          } else {
            result.put(key, values);
          }
        }
        // }
      }
    }
    return result;
  }