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src/ivectorbin/ivector-compute-plda.cc
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// ivectorbin/ivector-compute-plda.cc
// Copyright 2013 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 "ivector/plda.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
try {
const char *usage =
"Computes a Plda object (for Probabilistic Linear Discriminant Analysis)
"
"from a set of iVectors. Uses speaker information from a spk2utt file
"
"to compute within and between class variances.
"
"
"
"Usage: ivector-compute-plda [options] <spk2utt-rspecifier> <ivector-rspecifier> "
"<plda-out>
"
"e.g.:
"
" ivector-compute-plda ark:spk2utt ark,s,cs:ivectors.ark plda
";
ParseOptions po(usage);
bool binary = true;
PldaEstimationConfig plda_config;
plda_config.Register(&po);
po.Register("binary", &binary, "Write output in binary mode");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string spk2utt_rspecifier = po.GetArg(1),
ivector_rspecifier = po.GetArg(2),
plda_wxfilename = po.GetArg(3);
int64 num_spk_done = 0, num_spk_err = 0,
num_utt_done = 0, num_utt_err = 0;
SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
RandomAccessBaseFloatVectorReader ivector_reader(ivector_rspecifier);
PldaStats plda_stats;
for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
std::string spk = spk2utt_reader.Key();
const std::vector<std::string> &uttlist = spk2utt_reader.Value();
if (uttlist.empty()) {
KALDI_ERR << "Speaker with no utterances.";
}
std::vector<Vector<BaseFloat> > ivectors;
ivectors.reserve(uttlist.size());
for (size_t i = 0; i < uttlist.size(); i++) {
std::string utt = uttlist[i];
if (!ivector_reader.HasKey(utt)) {
KALDI_WARN << "No iVector present in input for utterance " << utt;
num_utt_err++;
} else {
ivectors.resize(ivectors.size() + 1);
ivectors.back() = ivector_reader.Value(utt);
num_utt_done++;
}
}
if (ivectors.size() == 0) {
KALDI_WARN << "Not producing output for speaker " << spk
<< " since no utterances had iVectors";
num_spk_err++;
} else {
Matrix<double> ivector_mat(ivectors.size(), ivectors[0].Dim());
for (size_t i = 0; i < ivectors.size(); i++)
ivector_mat.Row(i).CopyFromVec(ivectors[i]);
double weight = 1.0; // The code supports weighting but
// we don't support this at the command-line
// level yet.
plda_stats.AddSamples(weight, ivector_mat);
num_spk_done++;
}
}
if (num_utt_done <= plda_stats.Dim())
KALDI_ERR << "Number of training iVectors is not greater than their "
<< "dimension, unable to estimate PLDA.";
KALDI_LOG << "Accumulated stats from " << num_spk_done << " speakers ("
<< num_spk_err << " with no utterances), consisting of "
<< num_utt_done << " utterances (" << num_utt_err
<< " absent from input).";
if (num_spk_done == 0)
KALDI_ERR << "No stats accumulated, unable to estimate PLDA.";
if (num_spk_done == num_utt_done)
KALDI_ERR << "No speakers with multiple utterances, "
<< "unable to estimate PLDA.";
plda_stats.Sort();
PldaEstimator plda_estimator(plda_stats);
Plda plda;
plda_estimator.Estimate(plda_config, &plda);
WriteKaldiObject(plda, plda_wxfilename, binary);
return (num_spk_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}
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