/** * 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); } } }
/** * 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 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); } }