/*
* Copyright 1999-2002 Carnegie Mellon University.
* Portions Copyright 2002 Sun Microsystems, Inc.
* Portions Copyright 2002 Mitsubishi Electric Research Laboratories.
* All Rights Reserved. Use is subject to license terms.
*
* See the file "license.terms" for information on usage and
* redistribution of this file, and for a DISCLAIMER OF ALL
* WARRANTIES.
*
*/
package edu.cmu.sphinx.linguist.acoustic.tiedstate.trainer;
import edu.cmu.sphinx.decoder.adaptation.ClusteredDensityFileData;
import edu.cmu.sphinx.decoder.adaptation.Transform;
import edu.cmu.sphinx.linguist.acoustic.HMM;
import edu.cmu.sphinx.linguist.acoustic.HMMPosition;
import edu.cmu.sphinx.linguist.acoustic.Unit;
import edu.cmu.sphinx.linguist.acoustic.UnitManager;
import edu.cmu.sphinx.linguist.acoustic.tiedstate.*;
import static edu.cmu.sphinx.linguist.acoustic.tiedstate.Pool.Feature.*;
import edu.cmu.sphinx.util.ExtendedStreamTokenizer;
import edu.cmu.sphinx.util.LogMath;
import edu.cmu.sphinx.util.StreamFactory;
import edu.cmu.sphinx.util.props.*;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.io.StreamCorruptedException;
import java.util.LinkedHashMap;
import java.util.Map;
import java.util.Properties;
import java.util.logging.Level;
import java.util.logging.Logger;
/**
* an acoustic model loader that initializes models
* <p/>
* Mixture weights and transition probabilities are maintained in logMath log base,
*/
public class ModelInitializerLoader implements Loader {
private final static String SILENCE_CIPHONE = "SIL";
public final static String MODEL_VERSION = "0.3";
private final static int CONTEXT_SIZE = 1;
private Pool<float[]> meansPool;
private Pool<float[]> variancePool;
private Pool<float[][]> matrixPool;
private Pool<float[][]> meanTransformationMatrixPool;
private Pool<float[]> meanTransformationVectorPool;
private Pool<float[][]> varianceTransformationMatrixPool;
private Pool<float[]> varianceTransformationVectorPool;
private Pool<float[]> mixtureWeightsPool;
private Pool<Senone> senonePool;
private int vectorLength = 39;
private Map<String, Unit> contextIndependentUnits;
private Map<String, Integer> phoneList;
private HMMManager hmmManager;
@S4String(defaultValue="model")
public static final String MODEL_NAME = "modelName";
@S4String(defaultValue = ".")
public static final String LOCATION = "location";
@S4String(defaultValue = "phonelist")
public static final String PHONE_LIST = "phones";
@S4String(defaultValue = "data")
public static final String DATA_DIR = "dataDir";
@S4String(defaultValue = "model.props")
public static final String PROP_FILE = "propsFile";
@S4Component(type = UnitManager.class)
public final static String PROP_UNIT_MANAGER = "unitManager";
private UnitManager unitManager;
@S4Boolean(defaultValue = false)
public final static String PROP_USE_CD_UNITS = "useCDUnits";
@S4Double(defaultValue = 0.0001f)
public final static String PROP_VARIANCE_FLOOR = "varianceFloor";
/** Mixture component score floor. */
@S4Double(defaultValue = 0.0)
public final static String PROP_MC_FLOOR = "MixtureComponentScoreFloor";
/** Mixture weight floor. */
@S4Double(defaultValue = 1e-7f)
public final static String PROP_MW_FLOOR = "mixtureWeightFloor";
private LogMath logMath;
/** The logger for this class */
private Logger logger;
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();
}
}
/** Prints out a help message with format of phone list. */
private void printPhoneListHelp() {
System.out.println("The format for the phone list file is:");
System.out.println("\tversion 0.1");
System.out.println("\tsame_sized_models yes");
System.out.println("\tn_state 3");
System.out.println("\ttmat_skip (no|yes)");
System.out.println("\tAA");
System.out.println("\tAE");
System.out.println("\tAH");
System.out.println("\t...");
System.out.println("Or:");
System.out.println("\tversion 0.1");
System.out.println("\tsame_sized_models no");
System.out.println("\ttmat_skip (no|yes)");
System.out.println("\tAA 5");
System.out.println("\tAE 3");
System.out.println("\tAH 4");
System.out.println("\t...");
}
public Map<String, Unit> getContextIndependentUnits() {
return contextIndependentUnits;
}
/**
* 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 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);
}
}
}
/**
* If a data point is below 'floor' make it equal to floor.
*
* @param data the data to floor
* @param floor the floored value
*/
private void floorData(float[] data, float floor) {
for (int i = 0; i < data.length; i++) {
if (data[i] < floor) {
data[i] = floor;
}
}
}
/**
* Normalize the given data.
*
* @param data the data to normalize
*/
private void normalize(float[] data) {
float sum = 0;
for (float val : data) {
sum += val;
}
if (sum != 0.0f) {
// Invert, so we multiply instead of dividing inside the loop
sum = 1.0f / sum;
for (int i = 0; i < data.length; i++) {
data[i] = data[i] * sum;
}
}
}
/**
* Loads the phone list, which possibly contains the sizes (number of states) of models.
*
* @param ps
* @param useCDUnits if true, uses context dependent units
* @param inputStream the open input stream to use
* @param path the path to a density file @throws FileNotFoundException if a file cannot be found
* @throws IOException if an error occurs while loading the data
*/
private void loadPhoneList(PropertySheet ps, boolean useCDUnits, InputStream inputStream, String path)
throws IOException {
int numState = 0;
// TODO: this should be flexible, but we're hardwiring for now
int numStreams = 1;
// Since we're initializing, we start simple.
int numGaussiansPerState = 1;
ExtendedStreamTokenizer est = new ExtendedStreamTokenizer(inputStream, '#', false);
// Initialize the pools we'll need.
meansPool = new Pool<float[]>("means");
variancePool = new Pool<float[]>("variances");
mixtureWeightsPool = new Pool<float[]>("mixtureweights");
matrixPool = new Pool<float[][]>("transitionmatrices");
senonePool = new Pool<Senone>("senones");
float distFloor = ps.getFloat(PROP_MC_FLOOR);
float mixtureWeightFloor = ps.getFloat(PROP_MW_FLOOR);
float transitionProbabilityFloor = 0;
float varianceFloor = ps.getFloat(PROP_VARIANCE_FLOOR);
logger.info("Loading phone list file from: ");
logger.info(path);
// At this point, we only accept version 0.1
String version = "0.1";
est.expectString("version");
est.expectString(version);
est.expectString("same_sized_models");
boolean sameSizedModels = est.getString().equals("yes");
if (sameSizedModels) {
est.expectString("n_state");
numState = est.getInt("numBase");
}
// for this phone list version, let's assume left-to-right
// models, with optional state skip.
est.expectString("tmat_skip");
boolean tmatSkip = est.getString().equals("yes");
// Load the phones with sizes
// stateIndex contains the absolute state index, that is, a
// unique index in the senone pool.
for (int stateIndex = 0, unitCount = 0; ;) {
String phone = est.getString();
if (est.isEOF()) {
break;
}
int size = numState;
if (!sameSizedModels) {
size = est.getInt("ModelSize");
}
phoneList.put(phone, size);
logger.fine("Phone: " + phone + " size: " + size);
int[] stid = new int[size];
String position = "-";
for (int j = 0; j < size; j++, stateIndex++) {
stid[j] = stateIndex;
}
Unit unit = unitManager.getUnit(phone, phone.equals(SILENCE_CIPHONE));
contextIndependentUnits.put(unit.getName(), unit);
if (logger.isLoggable(Level.FINE)) {
logger.fine("Loaded " + unit + " with " + size + " states");
}
// Means
addModelToDensityPool(meansPool, stid, numStreams, numGaussiansPerState);
// Variances
addModelToDensityPool(variancePool, stid, numStreams, numGaussiansPerState);
// Mixture weights
addModelToMixtureWeightPool(mixtureWeightsPool, stid, numStreams, numGaussiansPerState, mixtureWeightFloor);
// Transition matrix
addModelToTransitionMatrixPool(matrixPool, unitCount, stid.length, transitionProbabilityFloor, tmatSkip);
// After creating all pools, we create the senone pool.
addModelToSenonePool(senonePool, stid, distFloor, varianceFloor);
// With the senone pool in place, we go through all units, and
// create the HMMs.
// Create tmat
float[][] transitionMatrix = matrixPool.get(unitCount);
SenoneSequence ss = getSenoneSequence(stid);
HMM hmm = new SenoneHMM(unit, ss, transitionMatrix, HMMPosition.lookup(position));
hmmManager.put(hmm);
unitCount++;
}
// If we want to use this code to load sizes/create models for
// CD units, we need to find another way of establishing the
// number of CI models, instead of just reading until the end
// of file.
est.close();
}
/**
* Gets the senone sequence representing the given senones.
*
* @param stateid is the array of senone state ids
* @return the senone sequence associated with the states
*/
private SenoneSequence getSenoneSequence(int[] stateid) {
Senone[] senones = new Senone[stateid.length];
for (int i = 0; i < stateid.length; i++) {
senones[i] = senonePool.get(stateid[i]);
}
// TODO: Is there any advantage in trying to pool these?
return new SenoneSequence(senones);
}
/**
* 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 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);
}
/**
* 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;
}
/**
* 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;
}
public void load() throws IOException {
}
public Pool<float[]> getMeansPool() {
return meansPool;
}
public Pool<float[][]> getMeansTransformationMatrixPool() {
return meanTransformationMatrixPool;
}
public Pool<float[]> getMeansTransformationVectorPool() {
return meanTransformationVectorPool;
}
public Pool<float[]> getVariancePool() {
return variancePool;
}
public Pool<float[][]> getVarianceTransformationMatrixPool() {
return varianceTransformationMatrixPool;
}
public Pool<float[]> getVarianceTransformationVectorPool() {
return varianceTransformationVectorPool;
}
public Pool<float[]> getMixtureWeightPool() {
return mixtureWeightsPool;
}
public Pool<float[][]> getTransitionMatrixPool() {
return matrixPool;
}
public float[][] getTransformMatrix() {
return null;
}
public Pool<Senone> getSenonePool() {
return senonePool;
}
public int getLeftContextSize() {
return CONTEXT_SIZE;
}
public int getRightContextSize() {
return CONTEXT_SIZE;
}
public HMMManager getHMMManager() {
return hmmManager;
}
public void logInfo() {
logger.info("Sphinx3Loader");
meansPool.logInfo(logger);
variancePool.logInfo(logger);
matrixPool.logInfo(logger);
senonePool.logInfo(logger);
meanTransformationMatrixPool.logInfo(logger);
meanTransformationVectorPool.logInfo(logger);
varianceTransformationMatrixPool.logInfo(logger);
varianceTransformationVectorPool.logInfo(logger);
mixtureWeightsPool.logInfo(logger);
senonePool.logInfo(logger);
logger.info("Context Independent Unit Entries: " + contextIndependentUnits.size());
hmmManager.logInfo(logger);
}
public Properties getProperties() {
return new Properties();
}
@Override
public void update(Transform transform, ClusteredDensityFileData clusters) {
// TODO Not implemented yet
}
}