gmm-est.cc
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// gmmbin/gmm-est.cc
// Copyright 2009-2011 Microsoft Corporation
// 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/mle-am-diag-gmm.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
typedef kaldi::int32 int32;
const char *usage =
"Do Maximum Likelihood re-estimation of GMM-based acoustic model\n"
"Usage: gmm-est [options] <model-in> <stats-in> <model-out>\n"
"e.g.: gmm-est 1.mdl 1.acc 2.mdl\n";
bool binary_write = true;
MleTransitionUpdateConfig tcfg;
MleDiagGmmOptions gmm_opts;
int32 mixup = 0;
int32 mixdown = 0;
BaseFloat perturb_factor = 0.01;
BaseFloat power = 0.2;
BaseFloat min_count = 20.0;
std::string update_flags_str = "mvwt";
std::string occs_out_filename;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("mix-up", &mixup, "Increase number of mixture components to "
"this overall target.");
po.Register("min-count", &min_count,
"Minimum per-Gaussian count enforced while mixing up and down.");
po.Register("mix-down", &mixdown, "If nonzero, merge mixture components to this "
"target.");
po.Register("power", &power, "If mixing up, power to allocate Gaussians to"
" states.");
po.Register("update-flags", &update_flags_str, "Which GMM parameters to "
"update: subset of mvwt.");
po.Register("perturb-factor", &perturb_factor, "While mixing up, perturb "
"means by standard deviation times this factor.");
po.Register("write-occs", &occs_out_filename, "File to write pdf "
"occupation counts to.");
tcfg.Register(&po);
gmm_opts.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
kaldi::GmmFlagsType update_flags =
StringToGmmFlags(update_flags_str);
std::string model_in_filename = po.GetArg(1),
stats_filename = po.GetArg(2),
model_out_filename = po.GetArg(3);
AmDiagGmm am_gmm;
TransitionModel trans_model;
{
bool binary_read;
Input ki(model_in_filename, &binary_read);
trans_model.Read(ki.Stream(), binary_read);
am_gmm.Read(ki.Stream(), binary_read);
}
Vector<double> transition_accs;
AccumAmDiagGmm gmm_accs;
{
bool binary;
Input ki(stats_filename, &binary);
transition_accs.Read(ki.Stream(), binary);
gmm_accs.Read(ki.Stream(), binary, true); // true == add; doesn't matter here.
}
if (update_flags & kGmmTransitions) { // Update transition model.
BaseFloat objf_impr, count;
trans_model.MleUpdate(transition_accs, tcfg, &objf_impr, &count);
KALDI_LOG << "Transition model update: Overall " << (objf_impr/count)
<< " log-like improvement per frame over " << (count)
<< " frames.";
}
{ // Update GMMs.
BaseFloat objf_impr, count;
BaseFloat tot_like = gmm_accs.TotLogLike(),
tot_t = gmm_accs.TotCount();
MleAmDiagGmmUpdate(gmm_opts, gmm_accs, update_flags, &am_gmm,
&objf_impr, &count);
KALDI_LOG << "GMM update: Overall " << (objf_impr/count)
<< " objective function improvement per frame over "
<< count << " frames";
KALDI_LOG << "GMM update: Overall avg like per frame = "
<< (tot_like/tot_t) << " over " << tot_t << " frames.";
}
if (mixup != 0 || mixdown != 0 || !occs_out_filename.empty()) {
// get pdf occupation counts
Vector<BaseFloat> pdf_occs;
pdf_occs.Resize(gmm_accs.NumAccs());
for (int i = 0; i < gmm_accs.NumAccs(); i++)
pdf_occs(i) = gmm_accs.GetAcc(i).occupancy().Sum();
if (mixdown != 0)
am_gmm.MergeByCount(pdf_occs, mixdown, power, min_count);
if (mixup != 0)
am_gmm.SplitByCount(pdf_occs, mixup, perturb_factor,
power, min_count);
if (!occs_out_filename.empty()) {
bool binary = false;
WriteKaldiObject(pdf_occs, occs_out_filename, binary);
}
}
{
Output ko(model_out_filename, binary_write);
trans_model.Write(ko.Stream(), binary_write);
am_gmm.Write(ko.Stream(), binary_write);
}
KALDI_LOG << "Written model to " << model_out_filename;
return 0;
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}