public override void initializeModels(string context)
{
this.dumpMemoryInfo("TrainManager start");
Iterator iterator = this.acousticModels.iterator();
while (iterator.hasNext())
{
TrainerAcousticModel trainerAcousticModel = (TrainerAcousticModel)iterator.next();
this.controlFile.startUtteranceIterator();
while (this.controlFile.hasMoreUtterances())
{
Utterance utterance = this.controlFile.nextUtterance();
this.initLearner.setUtterance(utterance);
TrainerScore[] score;
while ((score = this.initLearner.getScore()) != null)
{
if (!SimpleTrainManager.assertionsDisabled && score.Length != 1)
{
throw new AssertionError();
}
trainerAcousticModel.accumulate(0, score);
}
}
trainerAcousticModel.normalize();
}
this.dumpMemoryInfo("acoustic model");
}
private void loadModels(string text)
{
this.dumpMemoryInfo("TrainManager start");
Iterator iterator = this.acousticModels.iterator();
while (iterator.hasNext())
{
TrainerAcousticModel trainerAcousticModel = (TrainerAcousticModel)iterator.next();
trainerAcousticModel.load();
}
this.dumpMemoryInfo("acoustic model");
}
public override void saveModels(string context)
{
if (1 == this.acousticModels.size())
{
((TrainerAcousticModel)this.acousticModels.get(0)).save(null);
}
else
{
Iterator iterator = this.acousticModels.iterator();
while (iterator.hasNext())
{
AcousticModel acousticModel = (AcousticModel)iterator.next();
if (acousticModel is TrainerAcousticModel)
{
TrainerAcousticModel trainerAcousticModel = (TrainerAcousticModel)acousticModel;
trainerAcousticModel.save(acousticModel.getName());
}
}
}
}
public override void trainContextIndependentModels(string context)
{
if (this.learner == null)
{
this.loadModels(context);
}
this.dumpMemoryInfo("TrainManager start");
Iterator iterator = this.acousticModels.iterator();
while (iterator.hasNext())
{
TrainerAcousticModel trainerAcousticModel = (TrainerAcousticModel)iterator.next();
float num = float.MaxValue;
float num2 = 100f;
int num3 = 0;
while (num3 < this.maxIteration && num2 > this.minimumImprovement)
{
[email protected](new StringBuilder().append("Iteration: ").append(num3).toString());
trainerAcousticModel.resetBuffers();
this.controlFile.startUtteranceIterator();
while (this.controlFile.hasMoreUtterances())
{
Utterance utterance = this.controlFile.nextUtterance();
UtteranceHMMGraph graph = new UtteranceHMMGraph(context, utterance, trainerAcousticModel, this.unitManager);
this.learner.setUtterance(utterance);
this.learner.setGraph(graph);
TrainerScore[] nextTrainerScore = null;
TrainerScore[] score;
while ((score = this.learner.getScore()) != null)
{
for (int i = 0; i < score.Length; i++)
{
if (i > 0)
{
trainerAcousticModel.accumulate(i, score, nextTrainerScore);
}
else
{
trainerAcousticModel.accumulate(i, score);
}
}
nextTrainerScore = score;
}
trainerAcousticModel.updateLogLikelihood();
}
float num4 = trainerAcousticModel.normalize();
[email protected](new StringBuilder().append("Loglikelihood: ").append(num4).toString());
this.saveModels(context);
if (num3 > 0)
{
if (num != 0f)
{
num2 = (num4 - num) / num * 100f;
}
else if (num == num4)
{
num2 = 0f;
}
[email protected](new StringBuilder().append("Finished iteration: ").append(num3).append(" - Improvement: ").append(num2).toString());
}
num = num4;
num3++;
}
}
}
