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src/sgmm2bin/sgmm2-gselect.cc
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// sgmm2bin/sgmm2-gselect.cc
// Copyright 2009-2012 Saarland University Microsoft Corporation
// 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 "sgmm2/am-sgmm2.h"
#include "hmm/transition-model.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
const char *usage =
"Precompute Gaussian indices for SGMM training "
"Usage: sgmm2-gselect [options] <model-in> <feature-rspecifier> <gselect-wspecifier>
"
"e.g.: sgmm2-gselect 1.sgmm \"ark:feature-command |\" ark:1.gs
"
"Note: you can do the same thing by combining the programs sgmm2-write-ubm, fgmm-global-to-gmm,
"
"gmm-gselect and fgmm-gselect
";
ParseOptions po(usage);
kaldi::Sgmm2GselectConfig sgmm_opts;
std::string preselect_rspecifier;
std::string likelihood_wspecifier;
po.Register("write-likes", &likelihood_wspecifier, "Wspecifier for likelihoods per "
"utterance");
sgmm_opts.Register(&po);
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);
using namespace kaldi;
typedef kaldi::int32 int32;
AmSgmm2 am_sgmm;
{
bool binary;
Input ki(model_filename, &binary);
TransitionModel trans_model;
trans_model.Read(ki.Stream(), binary);
am_sgmm.Read(ki.Stream(), binary);
}
double tot_like = 0.0;
kaldi::int64 tot_t = 0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
Int32VectorVectorWriter gselect_writer(gselect_wspecifier);
BaseFloatWriter likelihood_writer(likelihood_wspecifier);
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();
std::vector<std::vector<int32> > gselect_vec(mat.NumRows());
tot_t_this_file += mat.NumRows();
for (int32 i = 0; i < mat.NumRows(); i++)
tot_like_this_file += am_sgmm.GaussianSelection(sgmm_opts, mat.Row(i), &(gselect_vec[i]));
gselect_writer.Write(utt, gselect_vec);
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;
}
}
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