/*
* 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.trainer;
import edu.cmu.sphinx.linguist.acoustic.AcousticModel;
import edu.cmu.sphinx.linguist.acoustic.UnitManager;
import edu.cmu.sphinx.linguist.acoustic.tiedstate.trainer.TrainerAcousticModel;
import edu.cmu.sphinx.linguist.acoustic.tiedstate.trainer.TrainerScore;
import edu.cmu.sphinx.util.Utilities;
import edu.cmu.sphinx.util.props.PropertyException;
import edu.cmu.sphinx.util.props.PropertySheet;
import edu.cmu.sphinx.util.props.S4Component;
import edu.cmu.sphinx.util.props.S4Boolean;
import edu.cmu.sphinx.util.props.S4ComponentList;
import java.io.IOException;
import java.util.List;
/** This is a dummy implementation of a TrainManager. */
public class SimpleTrainManager implements TrainManager {
@S4Component(type = ControlFile.class)
public static final String CONTROL_FILE = "control";
private ControlFile controlFile;
private boolean dumpMemoryInfo;
@S4Component(type = Learner.class)
public static final String LEARNER = "learner";
private Learner learner;
@S4Component(type = Learner.class)
public static final String INIT_LEARNER = "initLearner";
private Learner initLearner;
@S4Component(type = UnitManager.class)
public static final String UNIT_MANAGER = "unitManager";
private UnitManager unitManager;
@S4ComponentList(type = TrainerAcousticModel.class)
public static final String AM_COLLECTION = "models";
private List<? extends TrainerAcousticModel> acousticModels;
/**
* The property for the boolean property that controls whether or not the recognizer will display detailed
* memory information while it is running. The default value is <code>true</code>.
*/
@S4Boolean(defaultValue = false)
public final static String DUMP_MEMORY_INFO = "dumpMemoryInfo";
private int maxIteration;
private float minimumImprovement;
public void newProperties(PropertySheet ps) throws PropertyException {
dumpMemoryInfo = ps.getBoolean(DUMP_MEMORY_INFO);
learner = (Learner) ps.getComponent(LEARNER);
controlFile = (ControlFile) ps.getComponent(CONTROL_FILE);
initLearner = (Learner) ps.getComponent(INIT_LEARNER);
minimumImprovement = ps.getFloat(PROP_MINIMUM_IMPROVEMENT);
maxIteration = ps.getInt(PROP_MAXIMUM_ITERATION);
acousticModels = ps.getComponentList(AM_COLLECTION, TrainerAcousticModel.class);
unitManager = (UnitManager)ps.getComponent(UNIT_MANAGER);
}
/** Do the train. */
public void train() {
assert controlFile != null;
for (controlFile.startUtteranceIterator();
controlFile.hasMoreUtterances();) {
Utterance utterance = controlFile.nextUtterance();
System.out.println(utterance);
for (utterance.startTranscriptIterator();
utterance.hasMoreTranscripts();) {
System.out.println(utterance.nextTranscript());
}
}
}
/**
* Copy the model.
* <p/>
* This method copies to model set, possibly to a new location and new format. This is useful if one wants to
* convert from binary to ascii and vice versa, or from a directory structure to a JAR file. If only one model is
* used, then name can be null.
*
* @param context this TrainManager's context
* @throws IOException if an error occurs while loading the data
*/
public void copyModels(String context) throws IOException {
loadModels(context);
saveModels(context);
}
/**
* Saves the acoustic models.
*
* @param context the context of this TrainManager
* @throws IOException if an error occurs while loading the data
*/
public void saveModels(String context) throws IOException {
if (1 == acousticModels.size()) {
acousticModels.get(0).save(null);
} else {
for (AcousticModel model : acousticModels) {
if (model instanceof TrainerAcousticModel) {
TrainerAcousticModel tmodel =
(TrainerAcousticModel) model;
tmodel.save(model.getName());
}
}
}
}
/**
* Loads the acoustic models.
*
* @param context the context of this TrainManager
*/
private void loadModels(String context) throws IOException {
dumpMemoryInfo("TrainManager start");
for (TrainerAcousticModel model : acousticModels) {
model.load();
}
dumpMemoryInfo("acoustic model");
}
/**
* Initializes the acoustic models.
*
* @param context the context of this TrainManager
*/
public void initializeModels(String context) throws IOException {
TrainerScore score[];
dumpMemoryInfo("TrainManager start");
for (TrainerAcousticModel model : acousticModels) {
for (controlFile.startUtteranceIterator();
controlFile.hasMoreUtterances();) {
Utterance utterance = controlFile.nextUtterance();
initLearner.setUtterance(utterance);
while ((score = initLearner.getScore()) != null) {
assert score.length == 1;
model.accumulate(0, score);
}
}
// normalize() has a return value, but we can ignore it here.
model.normalize();
}
dumpMemoryInfo("acoustic model");
}
/**
* Trains context independent models. If the initialization stage was skipped, it loads models from files,
* automatically.
*
* @param context the context of this train manager.
* @throws IOException
*/
public void trainContextIndependentModels(String context)
throws IOException {
UtteranceGraph uttGraph;
TrainerScore[] score;
TrainerScore[] nextScore;
// If initialization was performed, then learner should not be
// null. Otherwise, we need to load the models.
if (learner == null) {
loadModels(context);
}
dumpMemoryInfo("TrainManager start");
for (TrainerAcousticModel model : acousticModels) {
float logLikelihood;
float lastLogLikelihood = Float.MAX_VALUE;
float relativeImprovement = 100.0f;
for (int iteration = 0;
(iteration < maxIteration) &&
(relativeImprovement > minimumImprovement);
iteration++) {
System.out.println("Iteration: " + iteration);
model.resetBuffers();
for (controlFile.startUtteranceIterator();
controlFile.hasMoreUtterances();) {
Utterance utterance = controlFile.nextUtterance();
uttGraph =
new UtteranceHMMGraph(context, utterance, model, unitManager);
learner.setUtterance(utterance);
learner.setGraph(uttGraph);
nextScore = null;
while ((score = learner.getScore()) != null) {
for (int i = 0; i < score.length; i++) {
if (i > 0) {
model.accumulate(i, score, nextScore);
} else {
model.accumulate(i, score);
}
}
nextScore = score;
}
model.updateLogLikelihood();
}
logLikelihood = model.normalize();
System.out.println("Loglikelihood: " + logLikelihood);
saveModels(context);
if (iteration > 0) {
if (lastLogLikelihood != 0) {
relativeImprovement =
(logLikelihood - lastLogLikelihood) /
lastLogLikelihood * 100.0f;
} else if (lastLogLikelihood == logLikelihood) {
relativeImprovement = 0;
}
System.out.println("Finished iteration: " + iteration +
" - Improvement: " +
relativeImprovement);
}
lastLogLikelihood = logLikelihood;
}
}
}
/**
* Conditional dumps out memory information
*
* @param what an additional info string
*/
private void dumpMemoryInfo(String what) {
if (dumpMemoryInfo) {
Utilities.dumpMemoryInfo(what);
}
}
}