% Train GMM for speaker identification

tInit=clock;
addMyPath;
sidOpt=sidOptSet;

if ~exist('gaussianNumBestIndex')
	gaussianNumBestIndex=5;
end

gaussianNumBest=sidOpt.gaussianNums(gaussianNumBestIndex);		% gaussianNumBestIndex is obtained from goUtterRrWrtGaussianNum.m
fprintf('gaussianNumBest=%d\n', gaussianNumBest);
load output\mat\speakerSet.mat
if sidOpt.maxSpeakerNum<length(speakerSet1)
	speakerSet1(sidOpt.maxSpeakerNum+1:end)=[];
	speakerSet2(sidOpt.maxSpeakerNum+1:end)=[];
end

needRetraining=0;
if ~needRetraining
	load output\mat\gmmParamWrtGaussianNum
end

% ====== GMM training on speakerSet1
speakerNum=length(speakerSet1);
for i=1:speakerNum
	fprintf('%d/%d: GMM training for speaker %s\n', i, speakerNum, speakerSet1(i).name);
	if needRetraining
		mfcc=[speakerSet1(i).sentence.mfcc];
		gmmTrainParam=gmmTrainParamSet;
		gmmTrainParam.dispOpt=sidOpt.gmmTrainParam.dispOpt;
		gmmTrainParam.useKmeans=sidOpt.gmmTrainParam.useKmeans;		% Use K-means or not
		gmmTrainParam.maxIteration=sidOpt.gmmTrainParam.maxIteration;
		if sidOpt.useIntGmm
			gmmTrainParam.minVariance=100;
		end
		[gmm(i).gmmParam, gmm(i).logProb] = gmmTrain(mfcc, [gaussianNumBest, sidOpt.covType], gmmTrainParam);
	else
		gmm(i).gmmParam=gmmData(gaussianNumBestIndex).gmm(i).gmmParam;
	end
	gmm(i).name=speakerSet1(i).name;
	% ====== Save gmm files for C programs
	gmmFile=['output/gmm/', gmm(i).name, '.gmm'];
	fprintf('\tWriting %s\n', gmmFile);
%	if sidOpt.useIntGmm
%		gmmWrite(gmm(i), gmmFileInt, 1, 2^15);		% If sidOpt.useIntGmm==1, write GMM file in integer format
%	else
		gmmWrite(gmm(i), gmmFile);
%	end
end
fprintf('Saving mat/gmmParam.mat...\n');
save output\mat\gmmParam gmm
fprintf('%s ===> %g seconds\n', mfilename, etime(clock, tInit));