private void createBaseGrammar(
List<GrammarNode> wordGrammarNodes, GrammarNode branchNode, GrammarNode finalNode) {
GrammarNode currNode = branchNode;
ListIterator<GrammarNode> iter = wordGrammarNodes.listIterator();
while (iter.hasNext()) {
GrammarNode nextNode = iter.next();
currNode.add(nextNode, logMath.getLogOne());
currNode = nextNode;
}
currNode.add(finalNode, logMath.getLogOne());
}
private void addSelfLoops(List<GrammarNode> wordGrammarNodes) {
ListIterator<GrammarNode> iter = wordGrammarNodes.listIterator();
while (iter.hasNext()) {
GrammarNode currNode = iter.next();
currNode.add(currNode, logMath.linearToLog(selfLoopProbability));
}
}
/**
* Creates a result
*
* @param activeList the active list associated with this result
* @param resultList the result list associated with this result
* @param frameNumber the frame number for this result.
* @param isFinal if true, the result is a final result. This means that the last frame in the
* speech segment has been decoded.
*/
public Result(ActiveList activeList, List<Token> resultList, int frameNumber, boolean isFinal) {
this.activeList = activeList;
this.resultList = resultList;
this.currentFrameNumber = frameNumber;
this.isFinal = isFinal;
logMath = LogMath.getInstance();
}
/** @return the (linearly scaled) mixture weights of the component densities */
public float[] getComponentWeights() {
float[] mixWeights = new float[getMixtureComponents().length];
for (int i = 0; i < mixWeights.length; i++) {
mixWeights[i] = (float) logMath.logToLinear(logMixtureWeights[i]);
}
return mixWeights;
}
public float calculateScore(Data feature) {
if (feature instanceof DoubleData) {
System.err.println("DoubleData conversion required on mixture level!");
}
float[] featureVector = FloatData.toFloatData(feature).getValues();
float logTotal = LogMath.getLogZero();
for (int i = 0; i < mixtureComponents.length; i++) {
// In linear form, this would be:
//
// Total += Mixture[i].score * MixtureWeight[i]
logTotal =
logMath.addAsLinear(
logTotal, mixtureComponents[i].getScore(featureVector) + logMixtureWeights[i]);
}
return logTotal;
}
private void addBackwardJumps(
List<GrammarNode> wordGrammarNodes, GrammarNode branchNode, GrammarNode finalNode2) {
GrammarNode currNode;
for (int i = 0; i < wordGrammarNodes.size(); i++) {
currNode = wordGrammarNodes.get(i);
for (int j = Math.max(i - numAllowedWordJumps - 1, 0); j < i - 1; j++) {
GrammarNode jumpToNode = wordGrammarNodes.get(j);
currNode.add(jumpToNode, logMath.linearToLog(backwardTransitionProbability));
}
}
}
private void addForwardJumps(
List<GrammarNode> wordGrammarNodes, GrammarNode branchNode, GrammarNode finalNode) {
GrammarNode currNode = branchNode;
for (int i = -1; i < wordGrammarNodes.size(); i++) {
if (i > -1) {
currNode = wordGrammarNodes.get(i);
}
for (int j = i + 2; j < Math.min(wordGrammarNodes.size(), i + numAllowedWordJumps + 1); j++) {
GrammarNode jumpNode = wordGrammarNodes.get(j);
currNode.add(jumpNode, logMath.linearToLog(forwardJumpProbability));
}
}
for (int i = wordGrammarNodes.size() - numAllowedWordJumps - 1;
i < wordGrammarNodes.size() - 1;
i++) {
int j = wordGrammarNodes.size();
currNode = wordGrammarNodes.get(i);
currNode.add(
finalNode, logMath.linearToLog((float) forwardJumpProbability * Math.pow(Math.E, j - i)));
}
}
/**
* Calculates probability p = w[1]*p[1] + w[2]*p[2] + ... (in log domain)
*
* @see
* edu.cmu.sphinx.linguist.language.ngram.LanguageModel#getProbability(edu.cmu.sphinx.linguist.WordSequence)
*/
public float getProbability(WordSequence wordSequence) {
float prob = 0;
for (int i = 0; i < numberOfLanguageModels; i++) {
float p = weights[i] + (languageModels.get(i)).getProbability(wordSequence);
if (i == 0) {
prob = p;
} else {
prob = logMath.addAsLinear(prob, p);
}
}
return prob;
}
/** Tests whether working with different types transformations works properly. */
@Test
public void testUnivariateMeanTransformation() {
float mean = 20;
float var = 0.001f;
MixtureComponent gaussian =
new MixtureComponent(
new float[] {mean},
new float[][] {{2}},
new float[] {5},
new float[] {var},
null,
null);
Assert.assertTrue(
LogMath.getLogMath().logToLinear(gaussian.getScore(new float[] {2 * mean + 5})) > 10);
}
InterpolatedLanguageModel(LogMath logMath, List<LanguageModel> languageModels, float[] floats) {
this.languageModels = languageModels;
this.numberOfLanguageModels = languageModels.size();
this.weights = new float[floats.length];
float weightSum = 0;
for (int i = 0; i < floats.length; i++) {
weightSum += floats[i];
this.weights[i] = logMath.linearToLog(floats[i]);
}
if (weightSum < 1.0 - EPSILON || weightSum > 1.0 + EPSILON) {
throw new PropertyException(
InterpolatedLanguageModel.class.getName(),
PROP_LANGUAGE_MODEL_WEIGHTS,
"Weights do not sum to 1.0");
}
}
public void newProperties(PropertySheet ps) throws PropertyException {
logMath = LogMath.getLogMath();
logger = ps.getLogger();
unitManager = (UnitManager) ps.getComponent(PROP_UNIT_MANAGER);
hmmManager = new HMMManager();
contextIndependentUnits = new LinkedHashMap<String, Unit>();
phoneList = new LinkedHashMap<String, Integer>();
meanTransformationMatrixPool = createDummyMatrixPool("meanTransformationMatrix");
meanTransformationVectorPool = createDummyVectorPool("meanTransformationMatrix");
varianceTransformationMatrixPool = createDummyMatrixPool("varianceTransformationMatrix");
varianceTransformationVectorPool = createDummyVectorPool("varianceTransformationMatrix");
String modelName = ps.getString(MODEL_NAME);
String location = ps.getString(LOCATION);
String phone = ps.getString(PHONE_LIST);
String dataDir = ps.getString(DATA_DIR);
logger.info("Creating Sphinx3 acoustic model: " + modelName);
logger.info(" Path : " + location);
logger.info(" phonelist : " + phone);
logger.info(" dataDir : " + dataDir);
// load the HMM model file
boolean useCDUnits = ps.getBoolean(PROP_USE_CD_UNITS);
assert !useCDUnits;
try {
loadPhoneList(
ps,
useCDUnits,
StreamFactory.getInputStream(location, phone),
location + File.separator + phone);
} catch (StreamCorruptedException sce) {
printPhoneListHelp();
} catch (IOException e) {
e.printStackTrace();
}
}
/**
* Adds transition matrix to the transition matrices pool
*
* @param pool the pool to add matrix to
* @param hmmId current HMM's id
* @param numEmittingStates number of states in current HMM
* @param floor the transition probability floor
* @param skip if true, states can be skipped
* @throws IOException if an error occurs while loading the data
*/
private void addModelToTransitionMatrixPool(
Pool<float[][]> pool, int hmmId, int numEmittingStates, float floor, boolean skip)
throws IOException {
assert pool != null;
// Add one to account for the last, non-emitting, state
int numStates = numEmittingStates + 1;
float[][] tmat = new float[numStates][numStates];
for (int j = 0; j < numStates; j++) {
for (int k = 0; k < numStates; k++) {
// Just to be sure...
tmat[j][k] = 0.0f;
// the last row is just zeros, so we just do
// the first (numStates - 1) rows
// The value assigned could be anything, provided
// we normalize it.
if (j < numStates - 1) {
// Usual case: state can transition to itself
// or the next state.
if (k == j || k == j + 1) {
tmat[j][k] = floor;
}
// If we can skip, we can also transition to
// the next state
if (skip) {
if (k == j + 2) {
tmat[j][k] = floor;
}
}
}
}
normalize(tmat[j]);
logMath.linearToLog(tmat[j]);
}
pool.put(hmmId, tmat);
}
/**
* Compute the density values of a sampled interval with an univariate <code>MixtureComponent
* </code> and compare values with the precomputed-computed ones.
*/
@Test
public void testUnivariateDensity() {
float minX = 10;
float maxX = 30;
float resolution = 0.1f;
float mean = 20;
float var = 3;
MixtureComponent gaussian = new MixtureComponent(new float[] {mean}, new float[] {var});
for (float curX = minX; curX <= maxX; curX += resolution) {
double gauLogScore = gaussian.getScore(new FloatData(new float[] {curX}, 16000, 0));
double manualScore =
(1 / sqrt(var * 2 * PI)) * exp((-0.5 / var) * (curX - mean) * (curX - mean));
double gauScore = LogMath.getLogMath().logToLinear((float) gauLogScore);
Assert.assertEquals(manualScore, gauScore, 1E-5);
}
}
public void newProperties(PropertySheet ps) throws PropertyException {
if (allocated) {
throw new RuntimeException("Can't change properties after allocation");
}
logMath = (LogMath) ps.getComponent(PROP_LOG_MATH);
languageModels = ps.getComponentList(PROP_LANGUAGE_MODELS, LanguageModel.class);
numberOfLanguageModels = languageModels.size();
// read weights as a String List.
List<String> items = ps.getStringList(PROP_LANGUAGE_MODEL_WEIGHTS);
if (items.size() != numberOfLanguageModels) {
throw new RuntimeException("Number of weights not equal to number of language models");
}
// convert Strings to floats and assign weights.
float[] floats = new float[items.size()];
weights = new float[floats.length];
float weightSum = 0;
for (int i = 0; i < items.size(); i++) {
try {
floats[i] = Float.parseFloat(items.get(i));
weightSum += floats[i];
weights[i] = logMath.linearToLog(floats[i]);
} catch (NumberFormatException e) {
throw new PropertyException(
InterpolatedLanguageModel.class.getName(),
PROP_LANGUAGE_MODEL_WEIGHTS,
"Float value expected from the property list. But found:" + items.get(i));
}
}
if (weightSum < 1.0 - EPSILON || weightSum > 1.0 + EPSILON) {
throw new PropertyException(
InterpolatedLanguageModel.class.getName(),
PROP_LANGUAGE_MODEL_WEIGHTS,
"Weights do not sum to 1.0");
}
}
@Override
protected GrammarNode createGrammar() throws IOException {
logger.info("Creating Grammar");
initialNode = createGrammarNode(Dictionary.SILENCE_SPELLING);
finalNode = createGrammarNode(Dictionary.SILENCE_SPELLING);
finalNode.setFinalNode(true);
final GrammarNode branchNode = createGrammarNode(false);
final List<GrammarNode> wordGrammarNodes = new ArrayList<GrammarNode>();
final int end = tokens.size();
logger.info("Creating Grammar nodes");
for (final String word : tokens.subList(0, end)) {
final GrammarNode wordNode = createGrammarNode(word.toLowerCase());
wordGrammarNodes.add(wordNode);
}
logger.info("Done creating grammar node");
// now connect all the GrammarNodes together
initialNode.add(branchNode, LogMath.getLogOne());
createBaseGrammar(wordGrammarNodes, branchNode, finalNode);
if (modelDeletions) {
addForwardJumps(wordGrammarNodes, branchNode, finalNode);
}
if (modelBackwardJumps) {
addBackwardJumps(wordGrammarNodes, branchNode, finalNode);
}
if (modelRepetitions) {
addSelfLoops(wordGrammarNodes);
}
logger.info("Done making Grammar");
// initialNode.dumpDot("./graph.dot");
return initialNode;
}
/**
* Adds model to the mixture weights
*
* @param pool the pool to add models to
* @param stateID vector containing state ids for hmm
* @param numStreams the number of streams
* @param numGaussiansPerState the number of Gaussians per state
* @param floor the minimum mixture weight allowed
* @throws IOException if an error occurs while loading the data
*/
private void addModelToMixtureWeightPool(
Pool<float[]> pool, int[] stateID, int numStreams, int numGaussiansPerState, float floor)
throws IOException {
int numStates = stateID.length;
assert pool != null;
int numInPool = pool.getFeature(NUM_SENONES, 0);
pool.setFeature(NUM_SENONES, numStates + numInPool);
numInPool = pool.getFeature(NUM_STREAMS, -1);
if (numInPool == -1) {
pool.setFeature(NUM_STREAMS, numStreams);
} else {
assert numInPool == numStreams;
}
numInPool = pool.getFeature(NUM_GAUSSIANS_PER_STATE, -1);
if (numInPool == -1) {
pool.setFeature(NUM_GAUSSIANS_PER_STATE, numGaussiansPerState);
} else {
assert numInPool == numGaussiansPerState;
}
// TODO: allow any number for numStreams
assert numStreams == 1;
for (int i = 0; i < numStates; i++) {
int state = stateID[i];
float[] logMixtureWeight = new float[numGaussiansPerState];
// Initialize the weights with the same value, e.g. floor
floorData(logMixtureWeight, floor);
// Normalize, so the numbers are not all too low
normalize(logMixtureWeight);
logMath.linearToLog(logMixtureWeight);
pool.put(state, logMixtureWeight);
}
}
@BeforeSuite
public void setupLogMath() {
LogMath.setUseTable(true);
}
