gmm-gselect.cc
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// gmmbin/gmm-gselect.cc
// Copyright 2009-2011 Saarland University; Microsoft Corporation
// 2013 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/diag-gmm.h"
#include "hmm/transition-model.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using std::vector;
typedef kaldi::int32 int32;
const char *usage =
"Precompute Gaussian indices for pruning\n"
" (e.g. in training UBMs, SGMMs, tied-mixture systems)\n"
" For each frame, gives a list of the n best Gaussian indices,\n"
" sorted from best to worst.\n"
"See also: gmm-global-get-post, fgmm-global-gselect-to-post,\n"
"copy-gselect, fgmm-gselect\n"
"Usage: gmm-gselect [options] <model-in> <feature-rspecifier> <gselect-wspecifier>\n"
"The --gselect option (which takes an rspecifier) limits selection to a subset\n"
"of indices:\n"
"e.g.: gmm-gselect \"--gselect=ark:gunzip -c bigger.gselect.gz|\" --n=20 1.gmm \"ark:feature-command |\" \"ark,t:|gzip -c >gselect.1.gz\"\n";
ParseOptions po(usage);
int32 num_gselect = 50;
std::string gselect_rspecifier;
std::string likelihood_wspecifier;
po.Register("n", &num_gselect, "Number of Gaussians to keep per frame\n");
po.Register("write-likes", &likelihood_wspecifier, "rspecifier for likelihoods per "
"utterance");
po.Register("gselect", &gselect_rspecifier, "rspecifier for gselect objects "
"to limit the search to");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string model_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
gselect_wspecifier = po.GetArg(3);
DiagGmm gmm;
ReadKaldiObject(model_filename, &gmm);
KALDI_ASSERT(num_gselect > 0);
int32 num_gauss = gmm.NumGauss();
if (num_gselect > num_gauss) {
KALDI_WARN << "You asked for " << num_gselect << " Gaussians but GMM "
<< "only has " << num_gauss << ", returning this many. "
<< "Note: this means the Gaussian selection is pointless.";
num_gselect = num_gauss;
}
double tot_like = 0.0;
kaldi::int64 tot_t = 0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
Int32VectorVectorWriter gselect_writer(gselect_wspecifier);
RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier); // may be ""
BaseFloatWriter likelihood_writer(likelihood_wspecifier); // may be ""
int32 num_done = 0, num_err = 0;
for (; !feature_reader.Done(); feature_reader.Next()) {
int32 tot_t_this_file = 0; double tot_like_this_file = 0;
std::string utt = feature_reader.Key();
const Matrix<BaseFloat> &mat = feature_reader.Value();
vector<vector<int32> > gselect(mat.NumRows());
tot_t_this_file += mat.NumRows();
if(gselect_rspecifier != "") { // Limit Gaussians to preselected group...
if (!gselect_reader.HasKey(utt)) {
KALDI_WARN << "No gselect information for utterance " << utt;
num_err++;
continue;
}
const vector<vector<int32> > &preselect = gselect_reader.Value(utt);
if (preselect.size() != static_cast<size_t>(mat.NumRows())) {
KALDI_WARN << "Input gselect for utterance " << utt << " has wrong size "
<< preselect.size() << " vs. " << mat.NumRows();
num_err++;
continue;
}
for (int32 i = 0; i < mat.NumRows(); i++)
tot_like_this_file +=
gmm.GaussianSelectionPreselect(mat.Row(i), preselect[i],
num_gselect, &(gselect[i]));
} else { // No "preselect" [i.e. no existing gselect]: simple case.
tot_like_this_file =
gmm.GaussianSelection(mat, num_gselect, &gselect);
}
gselect_writer.Write(utt, gselect);
if (num_done % 10 == 0)
KALDI_LOG << "For " << num_done << "'th file, average UBM likelihood over "
<< tot_t_this_file << " frames is "
<< (tot_like_this_file/tot_t_this_file);
tot_t += tot_t_this_file;
tot_like += tot_like_this_file;
if(likelihood_wspecifier != "")
likelihood_writer.Write(utt, tot_like_this_file);
num_done++;
}
KALDI_LOG << "Done " << num_done << " files, " << num_err
<< " with errors, average UBM log-likelihood is "
<< (tot_like/tot_t) << " over " << tot_t << " frames.";
if (num_done != 0) return 0;
else return 1;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}