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src/gmmbin/gmm-get-stats-deriv.cc
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// gmmbin/gmm-get-stats-deriv.cc
// Copyright 2012 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 "tree/context-dep.h"
#include "hmm/transition-model.h"
#include "gmm/indirect-diff-diag-gmm.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
typedef kaldi::int32 int32;
MleDiagGmmOptions gmm_opts;
const char *usage =
"Get statistics derivative for GMM models
"
"(used in fMPE/fMMI feature-space discriminative training)
"
"Usage: gmm-get-stats-deriv [options] <model-in> <num-stats-in>"
" <den-stats-in> <ml-stats-in> <deriv-out>
"
"e.g. (for fMMI/fBMMI): gmm-get-stats-deriv 1.mdl 1.acc 2.mdl
";
bool binary_write = true;
MleDiagGmmOptions opts; // Not passed to command-line-- just a mechanism to
// ensure our options have the same default values as those ones.
BaseFloat min_variance = opts.min_variance;
BaseFloat min_gaussian_occupancy = opts.min_gaussian_occupancy;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("min-variance", &min_variance,
"Variance floor (absolute variance).");
po.Register("min-gaussian-occupancy", &min_gaussian_occupancy,
"Minimum occupancy to update a Gaussian.");
po.Read(argc, argv);
if (po.NumArgs() != 5) {
po.PrintUsage();
exit(1);
}
std::string model_rxfilename = po.GetArg(1),
num_stats_rxfilename = po.GetArg(2),
den_stats_rxfilename = po.GetArg(3),
ml_stats_rxfilename = po.GetArg(4),
deriv_wxfilename = po.GetArg(5);
AmDiagGmm am_gmm;
TransitionModel trans_model;
{
bool binary_read;
Input ki(model_rxfilename, &binary_read);
trans_model.Read(ki.Stream(), binary_read);
am_gmm.Read(ki.Stream(), binary_read);
}
Vector<double> transition_accs; // Reuse this for all transition accs we
// read, as it's not needed.
AccumAmDiagGmm num_stats, den_stats, ml_stats;
{
bool binary_read;
Input ki(num_stats_rxfilename, &binary_read);
transition_accs.Read(ki.Stream(), binary_read);
num_stats.Read(ki.Stream(), binary_read, false);
}
{
bool binary_read;
Input ki(den_stats_rxfilename, &binary_read);
transition_accs.Read(ki.Stream(), binary_read);
den_stats.Read(ki.Stream(), binary_read, false);
}
{
bool binary_read;
Input ki(ml_stats_rxfilename, &binary_read);
transition_accs.Read(ki.Stream(), binary_read);
ml_stats.Read(ki.Stream(), binary_read, false);
}
AccumAmDiagGmm model_deriv; // Use GMM accumulators to represent
// derivative of discriminative objective function w.r.t.
// accumulated stats.
GetStatsDerivative(am_gmm, num_stats, den_stats, ml_stats,
min_variance, min_gaussian_occupancy,
&model_deriv);
WriteKaldiObject(model_deriv, deriv_wxfilename, binary_write);
KALDI_LOG << "Computed model derivative and wrote it to "
<< deriv_wxfilename;
return 0;
} catch(const std::exception &e) {
std::cerr << e.what() << '
';
return -1;
}
}
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