gmm-global-get-post.cc
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// gmmbin/gmm-global-get-post.cc
// Copyright 2009-2011 Saarland University; Microsoft Corporation
// 2013-2014 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/diag-gmm.h"
#include "hmm/posterior.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using std::vector;
typedef kaldi::int32 int32;
const char *usage =
"Precompute Gaussian indices and convert immediately to top-n\n"
"posteriors (useful in iVector extraction with diagonal UBMs)\n"
"See also: gmm-gselect, fgmm-gselect, fgmm-global-gselect-to-post\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"
"Usage: gmm-global-get-post [options] <model-in> <feature-rspecifier> <post-wspecifier>\n"
"e.g.: gmm-global-get-post --n=20 1.gmm \"ark:feature-command |\" \"ark,t:|gzip -c >post.1.gz\"\n";
ParseOptions po(usage);
int32 num_post = 50;
BaseFloat min_post = 0.0;
po.Register("n", &num_post, "Number of Gaussians to keep per frame\n");
po.Register("min-post", &min_post, "Minimum posterior we will output "
"before pruning and renormalizing (e.g. 0.01)");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string model_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
post_wspecifier = po.GetArg(3);
DiagGmm gmm;
ReadKaldiObject(model_filename, &gmm);
KALDI_ASSERT(num_post > 0);
KALDI_ASSERT(min_post < 1.0);
int32 num_gauss = gmm.NumGauss();
if (num_post > num_gauss) {
KALDI_WARN << "You asked for " << num_post << " Gaussians but GMM "
<< "only has " << num_gauss << ", returning this many. ";
num_post = num_gauss;
}
double tot_like = 0.0;
kaldi::int64 tot_t = 0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
PosteriorWriter post_writer(post_wspecifier);
int32 num_done = 0, num_err = 0;
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string utt = feature_reader.Key();
const Matrix<BaseFloat> &feats = feature_reader.Value();
int32 T = feats.NumRows();
if (T == 0) {
KALDI_WARN << "Empty features for utterance " << utt;
num_err++;
continue;
}
if (feats.NumCols() != gmm.Dim()) {
KALDI_WARN << "Dimension mismatch for utterance " << utt
<< ": got " << feats.NumCols() << ", expected " << gmm.Dim();
num_err++;
continue;
}
vector<vector<int32> > gselect(T);
Matrix<BaseFloat> loglikes;
gmm.LogLikelihoods(feats, &loglikes);
Posterior post(T);
double log_like_this_file = 0.0;
for (int32 t = 0; t < T; t++) {
log_like_this_file +=
VectorToPosteriorEntry(loglikes.Row(t), num_post,
min_post, &(post[t]));
}
KALDI_VLOG(1) << "Processed utterance " << utt << ", average likelihood "
<< (log_like_this_file / T) << " over " << T << " frames";
tot_like += log_like_this_file;
tot_t += T;
post_writer.Write(utt, post);
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;
}
}