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src/gmmbin/gmm-global-get-frame-likes.cc
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// gmmbin/gmm-global-get-frame-likes.cc
// Copyright 2009-2011 Microsoft Corporation; Saarland University
// 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/model-common.h"
#include "gmm/full-gmm.h"
#include "gmm/diag-gmm.h"
#include "gmm/mle-full-gmm.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
const char *usage =
"Print out per-frame log-likelihoods for each utterance, as an archive
"
"of vectors of floats. If --average=true, prints out the average per-frame
"
"log-likelihood for each utterance, as a single float.
"
"Usage: gmm-global-get-frame-likes [options] <model-in> <feature-rspecifier> "
"<likes-out-wspecifier>
"
"e.g.: gmm-global-get-frame-likes 1.mdl scp:train.scp ark:1.likes
";
ParseOptions po(usage);
bool average = false;
std::string gselect_rspecifier;
po.Register("gselect", &gselect_rspecifier, "rspecifier for gselect objects "
"to limit the #Gaussians accessed on each frame.");
po.Register("average", &average, "If true, print out the average per-frame "
"log-likelihood as a single float per utterance.");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string model_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
likes_wspecifier = po.GetArg(3);
DiagGmm gmm;
{
bool binary_read;
Input ki(model_filename, &binary_read);
gmm.Read(ki.Stream(), binary_read);
}
double tot_like = 0.0, tot_frames = 0.0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
BaseFloatVectorWriter likes_writer(average ? "" : likes_wspecifier);
BaseFloatWriter average_likes_writer(average ? likes_wspecifier : "");
int32 num_done = 0, num_err = 0;
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string key = feature_reader.Key();
const Matrix<BaseFloat> &mat = feature_reader.Value();
int32 file_frames = mat.NumRows();
Vector<BaseFloat> likes(file_frames);
if (gselect_rspecifier != "") {
if (!gselect_reader.HasKey(key)) {
KALDI_WARN << "No gselect information for utterance " << key;
num_err++;
continue;
}
const std::vector<std::vector<int32> > &gselect =
gselect_reader.Value(key);
if (gselect.size() != static_cast<size_t>(file_frames)) {
KALDI_WARN << "gselect information for utterance " << key
<< " has wrong size " << gselect.size() << " vs. "
<< file_frames;
num_err++;
continue;
}
for (int32 i = 0; i < file_frames; i++) {
SubVector<BaseFloat> data(mat, i);
const std::vector<int32> &this_gselect = gselect[i];
int32 gselect_size = this_gselect.size();
KALDI_ASSERT(gselect_size > 0);
Vector<BaseFloat> loglikes;
gmm.LogLikelihoodsPreselect(data, this_gselect, &loglikes);
likes(i) = loglikes.LogSumExp();
}
} else { // no gselect..
for (int32 i = 0; i < file_frames; i++)
likes(i) = gmm.LogLikelihood(mat.Row(i));
}
tot_like += likes.Sum();
tot_frames += file_frames;
if (average)
average_likes_writer.Write(key, likes.Sum() / file_frames);
else
likes_writer.Write(key, likes);
num_done++;
}
KALDI_LOG << "Done " << num_done << " files; " << num_err
<< " with errors.";
KALDI_LOG << "Overall likelihood per "
<< "frame = " << (tot_like/tot_frames) << " over " << tot_frames
<< " frames.";
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}
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