/**
* Creates a pool with a single zero vector in it.
*
* @param name the name of the pool
* @return the pool with the vector
*/
private Pool<float[]> createDummyVectorPool(String name) {
logger.info("creating dummy vector pool " + name);
Pool<float[]> pool = new Pool<float[]>(name);
float[] vector = new float[vectorLength];
for (int i = 0; i < vectorLength; i++) {
vector[i] = 0.0f;
}
pool.put(0, vector);
return pool;
}
/**
* Creates a pool with a single identity matrix in it.
*
* @param name the name of the pool
* @return the pool with the matrix
*/
private Pool<float[][]> createDummyMatrixPool(String name) {
Pool<float[][]> pool = new Pool<float[][]>(name);
float[][] matrix = new float[vectorLength][vectorLength];
logger.info("creating dummy matrix pool " + name);
for (int i = 0; i < vectorLength; i++) {
for (int j = 0; j < vectorLength; j++) {
if (i == j) {
matrix[i][j] = 1.0F;
} else {
matrix[i][j] = 0.0F;
}
}
}
pool.put(0, matrix);
return pool;
}
/**
* Adds a model to the senone pool.
*
* @param pool the senone pool
* @param stateID vector with senone ID for an HMM
* @param distFloor the lowest allowed score
* @param varianceFloor the lowest allowed variance
* @return the senone pool
*/
private void addModelToSenonePool(
Pool<Senone> pool, int[] stateID, float distFloor, float varianceFloor) {
assert pool != null;
// int numMixtureWeights = mixtureWeightsPool.size();
/*
int numMeans = meansPool.size();
int numVariances = variancePool.size();
int numSenones = mixtureWeightsPool.getFeature(NUM_SENONES, 0);
int whichGaussian = 0;
logger.fine("NG " + numGaussiansPerSenone);
logger.fine("NS " + numSenones);
logger.fine("NMIX " + numMixtureWeights);
logger.fine("NMNS " + numMeans);
logger.fine("NMNS " + numVariances);
assert numMixtureWeights == numSenones;
assert numVariances == numSenones * numGaussiansPerSenone;
assert numMeans == numSenones * numGaussiansPerSenone;
*/
int numGaussiansPerSenone = mixtureWeightsPool.getFeature(NUM_GAUSSIANS_PER_STATE, 0);
assert numGaussiansPerSenone > 0;
for (int state : stateID) {
MixtureComponent[] mixtureComponents = new MixtureComponent[numGaussiansPerSenone];
for (int j = 0; j < numGaussiansPerSenone; j++) {
int whichGaussian = state * numGaussiansPerSenone + j;
mixtureComponents[j] =
new MixtureComponent(
meansPool.get(whichGaussian),
meanTransformationMatrixPool.get(0),
meanTransformationVectorPool.get(0),
variancePool.get(whichGaussian),
varianceTransformationMatrixPool.get(0),
varianceTransformationVectorPool.get(0),
distFloor,
varianceFloor);
}
Senone senone = new GaussianMixture(mixtureWeightsPool.get(state), mixtureComponents, state);
pool.put(state, senone);
}
}
/**
* 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);
}
/**
* 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);
}
}
/**
* Adds a set of density arrays to a given pool.
*
* @param pool the pool to add densities to
* @param stateID a vector with the senone id of the states in a model
* @param numStreams the number of streams
* @param numGaussiansPerState the number of Gaussians per state
* @throws IOException if an error occurs while loading the data
*/
private void addModelToDensityPool(
Pool<float[]> pool, int[] stateID, int numStreams, int numGaussiansPerState)
throws IOException {
assert pool != null;
assert stateID != null;
int numStates = stateID.length;
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: numStreams should be any number > 0, but for now....
assert numStreams == 1;
for (int i = 0; i < numStates; i++) {
int state = stateID[i];
for (int j = 0; j < numGaussiansPerState; j++) {
// We're creating densities here, so it's ok if values
// are all zero.
float[] density = new float[vectorLength];
int id = state * numGaussiansPerState + j;
pool.put(id, density);
}
}
}
