gmm-global-gselect-to-post.cc
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// gmmbin/gmm-global-gselect-to-post.cc
// Copyright 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/posterior.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"Given features and Gaussian-selection (gselect) information for\n"
"a diagonal-covariance GMM, output per-frame posteriors for the selected\n"
"indices. Also supports pruning the posteriors if they are below\n"
"a stated threshold, (and renormalizing the rest to sum to one)\n"
"See also: gmm-gselect, fgmm-gselect, gmm-global-get-post,\n"
" fgmm-global-gselect-to-post\n"
"\n"
"Usage: gmm-global-gselect-to-post [options] <model-in> <feature-rspecifier> "
"<gselect-rspecifier> <post-wspecifier>\n"
"e.g.: gmm-global-gselect-to-post 1.dubm ark:- 'ark:gunzip -c 1.gselect|' ark:-\n";
ParseOptions po(usage);
BaseFloat min_post = 0.0;
po.Register("min-post", &min_post, "If nonzero, posteriors below this "
"threshold will be pruned away and the rest will be renormalized "
"to sum to one.");
po.Read(argc, argv);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
std::string model_rxfilename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
gselect_rspecifier = po.GetArg(3),
post_wspecifier = po.GetArg(4);
DiagGmm gmm;
ReadKaldiObject(model_rxfilename, &gmm);
double tot_loglike = 0.0, tot_frames = 0.0;
int64 tot_posts = 0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessInt32VectorVectorReader gselect_reader(gselect_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> &mat = feature_reader.Value();
int32 num_frames = mat.NumRows();
// typedef std::vector<std::vector<std::pair<int32, BaseFloat> > > Posterior;
Posterior post(num_frames);
if (!gselect_reader.HasKey(utt)) {
KALDI_WARN << "No gselect information for utterance " << utt;
num_err++;
continue;
}
const std::vector<std::vector<int32> > &gselect(gselect_reader.Value(utt));
if (static_cast<int32>(gselect.size()) != num_frames) {
KALDI_WARN << "gselect information for utterance " << utt
<< " has wrong size " << gselect.size() << " vs. "
<< num_frames;
num_err++;
continue;
}
double this_tot_loglike = 0;
bool utt_ok = true;
for (int32 t = 0; t < num_frames; t++) {
SubVector<BaseFloat> frame(mat, t);
const std::vector<int32> &this_gselect = gselect[t];
KALDI_ASSERT(!gselect[t].empty());
Vector<BaseFloat> loglikes;
gmm.LogLikelihoodsPreselect(frame, this_gselect, &loglikes);
this_tot_loglike += loglikes.ApplySoftMax();
// now "loglikes" contains posteriors.
if (fabs(loglikes.Sum() - 1.0) > 0.01) {
utt_ok = false;
} else {
if (min_post != 0.0) {
int32 max_index = 0; // in case all pruned away...
loglikes.Max(&max_index);
for (int32 i = 0; i < loglikes.Dim(); i++)
if (loglikes(i) < min_post)
loglikes(i) = 0.0;
BaseFloat sum = loglikes.Sum();
if (sum == 0.0) {
loglikes(max_index) = 1.0;
} else {
loglikes.Scale(1.0 / sum);
}
}
for (int32 i = 0; i < loglikes.Dim(); i++) {
if (loglikes(i) != 0.0) {
post[t].push_back(std::make_pair(this_gselect[i], loglikes(i)));
tot_posts++;
}
}
KALDI_ASSERT(!post[t].empty());
}
}
if (!utt_ok) {
KALDI_WARN << "Skipping utterance " << utt
<< " because bad posterior-sum encountered (NaN?)";
num_err++;
} else {
post_writer.Write(utt, post);
num_done++;
KALDI_VLOG(2) << "Like/frame for utt " << utt << " was "
<< (this_tot_loglike/num_frames) << " per frame over "
<< num_frames << " frames.";
tot_loglike += this_tot_loglike;
tot_frames += num_frames;
}
}
KALDI_LOG << "Done " << num_done << " files; " << num_err << " had errors.";
KALDI_LOG << "Overall loglike per frame is " << (tot_loglike / tot_frames)
<< " with " << (tot_posts / tot_frames) << " entries per frame, "
<< " over " << tot_frames << " frames";
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}