package edu.cmu.sphinx.linguist.acoustic.tiedstate.kaldi;
import java.util.Arrays;
import edu.cmu.sphinx.frontend.Data;
import edu.cmu.sphinx.frontend.FloatData;
import edu.cmu.sphinx.linguist.acoustic.tiedstate.MixtureComponent;
import edu.cmu.sphinx.linguist.acoustic.tiedstate.ScoreCachingSenone;
import edu.cmu.sphinx.util.LogMath;
/**
* Gaussian Mixture Model with diagonal covariances.
*
* @see DiagGmm class in Kaldi.
*/
@SuppressWarnings("serial")
public class DiagGmm extends ScoreCachingSenone {
private int id;
private float[] gconsts;
private float[] invVars;
private float[] meansInvVars;
/**
* Constructs new mixture model.
*
* @param id identifier of this GMM as defined in the model
* @param parser text format parser
*/
public DiagGmm(int id, KaldiTextParser parser) {
this.id = id;
parser.expectToken("<DiagGMM>");
parser.expectToken("<GCONSTS>");
gconsts = parser.getFloatArray();
parser.expectToken("<WEIGHTS>");
// Do not use weights as they are in gconsts.
parser.getFloatArray();
parser.expectToken("<MEANS_INVVARS>");
meansInvVars = parser.getFloatArray();
parser.expectToken("<INV_VARS>");
invVars = parser.getFloatArray();
parser.expectToken("</DiagGMM>");
}
/**
* Convenient method if 32-bit ID is required.
*
* Kaldi model uses 32-bit integer to store GMM id while Senone contract
* imposes long type. This method is present to avaoid type cast when
* working in the Kaldi domain.
*/
public int getId() {
return id;
}
@Override
public float calculateScore(Data data) {
float logTotal = LogMath.LOG_ZERO;
LogMath logMath = LogMath.getLogMath();
for (Float mixtureScore : calculateComponentScore(data))
logTotal = logMath.addAsLinear(logTotal, mixtureScore);
return logTotal;
}
public float[] calculateComponentScore(Data data) {
float[] features = FloatData.toFloatData(data).getValues();
int dim = meansInvVars.length / gconsts.length;
if (features.length != dim) {
String fmt = "feature vector must be of length %d, got %d";
String msg = String.format(fmt, dim, features.length);
throw new IllegalArgumentException(msg);
}
float[] likelihoods = Arrays.copyOf(gconsts, gconsts.length);
for (int i = 0; i < likelihoods.length; ++i) {
for (int j = 0; j < features.length; ++j) {
int k = i * features.length + j;
likelihoods[i] += meansInvVars[k] * features[j];
likelihoods[i] -= .5f * invVars[k] * features[j] * features[j];
}
likelihoods[i] = LogMath.getLogMath().lnToLog(likelihoods[i]);
}
return likelihoods;
}
public long getID() {
return id;
}
public void dump(String msg) {
System.out.format("%s DiagGmm: ID %d\n", msg, id);
}
public MixtureComponent[] getMixtureComponents() {
return null;
}
public float[] getLogMixtureWeights() {
return null;
}
}