gmm-acc-stats-twofeats.cc
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// gmmbin/gmm-acc-stats-twofeats.cc
// Copyright 2009-2011 Microsoft Corporation
// 2014 Guoguo Chen
// 2014 Johns Hopkins University (author: Daniel Povey)
// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "gmm/am-diag-gmm.h"
#include "hmm/transition-model.h"
#include "gmm/mle-am-diag-gmm.h"
#include "hmm/posterior.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
try {
const char *usage =
"Accumulate stats for GMM training, computing posteriors with one set of features\n"
"but accumulating statistics with another.\n"
"First features are used to get posteriors, second to accumulate stats\n"
"Usage: gmm-acc-stats-twofeats [options] <model-in> <feature1-rspecifier> <feature2-rspecifier> <posteriors-rspecifier> <stats-out>\n"
"e.g.: \n"
" gmm-acc-stats-twofeats 1.mdl 1.ali scp:train.scp scp:train_new.scp ark:1.ali 1.acc\n";
ParseOptions po(usage);
bool binary = true;
po.Register("binary", &binary, "Write output in binary mode");
po.Read(argc, argv);
if (po.NumArgs() != 5) {
po.PrintUsage();
exit(1);
}
std::string model_filename = po.GetArg(1),
feature1_rspecifier = po.GetArg(2),
feature2_rspecifier = po.GetArg(3),
posteriors_rspecifier = po.GetArg(4),
accs_wxfilename = po.GetArg(5);
using namespace kaldi;
typedef kaldi::int32 int32;
AmDiagGmm am_gmm;
TransitionModel trans_model;
{
bool binary;
Input ki(model_filename, &binary);
trans_model.Read(ki.Stream(), binary);
am_gmm.Read(ki.Stream(), binary);
}
Vector<double> transition_accs;
trans_model.InitStats(&transition_accs);
int32 new_dim = 0;
AccumAmDiagGmm gmm_accs;
// will initialize once we know new_dim.
double tot_like = 0.0;
double tot_t = 0.0;
SequentialBaseFloatMatrixReader feature1_reader(feature1_rspecifier);
RandomAccessBaseFloatMatrixReader feature2_reader(feature2_rspecifier);
RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
int32 num_done = 0, num_no2ndfeats = 0, num_no_posterior = 0, num_other_error = 0;
for (; !feature1_reader.Done(); feature1_reader.Next()) {
std::string key = feature1_reader.Key();
if (!feature2_reader.HasKey(key)) {
KALDI_WARN << "For utterance " << key << ", second features not present.";
num_no2ndfeats ++;
} else if (!posteriors_reader.HasKey(key)) {
num_no_posterior++;
} else {
const Matrix<BaseFloat> &mat1 = feature1_reader.Value();
const Matrix<BaseFloat> &mat2 = feature2_reader.Value(key);
KALDI_ASSERT(mat1.NumRows() == mat2.NumRows());
if (new_dim == 0) {
new_dim = mat2.NumCols();
gmm_accs.Init(am_gmm, new_dim, kGmmAll);
}
const Posterior &posterior = posteriors_reader.Value(key);
if (posterior.size() != mat1.NumRows()) {
KALDI_WARN << "Posteriors has wrong size "<< (posterior.size()) << " vs. "<< (mat1.NumRows());
num_other_error++;
continue;
}
if (mat1.NumRows() != mat2.NumRows()) {
KALDI_WARN << "Features have mismatched numbers of frames "
<< mat1.NumRows() << " vs. " << mat2.NumRows();
num_other_error++;
continue;
}
num_done++;
BaseFloat tot_like_this_file = 0.0,
tot_weight_this_file = 0.0;
Posterior pdf_posterior;
ConvertPosteriorToPdfs(trans_model, posterior, &pdf_posterior);
for (size_t i = 0; i < posterior.size(); i++) {
// Accumulates for GMM.
for (size_t j = 0; j <pdf_posterior[i].size(); j++) {
int32 pdf_id = pdf_posterior[i][j].first;
BaseFloat weight = pdf_posterior[i][j].second;
tot_like_this_file += weight *
gmm_accs.AccumulateForGmmTwofeats(am_gmm,
mat1.Row(i),
mat2.Row(i),
pdf_id,
weight);
tot_weight_this_file += weight;
}
// Accumulates for transitions.
for (size_t j = 0; j < posterior[i].size(); j++) {
int32 tid = posterior[i][j].first;
BaseFloat weight = posterior[i][j].second;
trans_model.Accumulate(weight, tid, &transition_accs);
}
}
KALDI_LOG << "Average like for this file is "
<< (tot_like_this_file/tot_weight_this_file) << " over "
<< tot_weight_this_file <<" frames.";
tot_like += tot_like_this_file;
tot_t += tot_weight_this_file;
if (num_done % 10 == 0)
KALDI_LOG << "Avg like per frame so far is " << (tot_like/tot_t);
}
}
KALDI_LOG << "Done " << num_done << " files, " << num_no_posterior
<< " with no posteriors, " << num_no2ndfeats
<< " with no second features, " << num_other_error
<< " with other errors.";
KALDI_LOG << "Overall avg like per frame (Gaussian only) = "
<< (tot_like/tot_t) << " over " << tot_t << " frames.";
{
Output ko(accs_wxfilename, binary);
transition_accs.Write(ko.Stream(), binary);
gmm_accs.Write(ko.Stream(), binary);
}
KALDI_LOG << "Written accs.";
if (num_done != 0) return 0;
else return 1;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}