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src/gmmbin/gmm-acc-mllt.cc
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// gmmbin/gmm-acc-mllt.cc
// Copyright 2009-2011 Microsoft Corporation
// 2014 Guoguo Chen
// 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/am-diag-gmm.h"
#include "hmm/transition-model.h"
#include "transform/mllt.h"
#include "hmm/posterior.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
try {
const char *usage =
"Accumulate MLLT (global STC) statistics
"
"Usage: gmm-acc-mllt [options] <model-in> <feature-rspecifier> <posteriors-rspecifier> <stats-out>
"
"e.g.:
"
" gmm-acc-mllt 1.mdl scp:train.scp ark:1.post 1.macc
";
ParseOptions po(usage);
bool binary = true;
BaseFloat rand_prune = 0.25;
po.Register("binary", &binary, "Write output in binary mode");
po.Register("rand-prune", &rand_prune, "Randomized pruning parameter to speed up "
"accumulation (larger -> more pruning. May exceed one).");
po.Read(argc, argv);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
std::string model_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
posteriors_rspecifier = po.GetArg(3),
accs_wxfilename = po.GetArg(4);
using namespace kaldi;
typedef kaldi::int32 int32;
AmDiagGmm am_gmm;
TransitionModel trans_model;
{
bool binary;
Input ki(model_filename, &binary);
trans_model.Read(ki.Stream(), binary);
am_gmm.Read(ki.Stream(), binary);
}
MlltAccs mllt_accs(am_gmm.Dim(), rand_prune);
double tot_like = 0.0;
double tot_t = 0.0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
int32 num_done = 0, num_no_posterior = 0, num_other_error = 0;
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string key = feature_reader.Key();
if (!posteriors_reader.HasKey(key)) {
num_no_posterior++;
} else {
const Matrix<BaseFloat> &mat = feature_reader.Value();
const Posterior &posterior = posteriors_reader.Value(key);
if (static_cast<int32>(posterior.size()) != mat.NumRows()) {
KALDI_WARN << "Posterior vector has wrong size "<< (posterior.size()) << " vs. "<< (mat.NumRows());
num_other_error++;
continue;
}
num_done++;
BaseFloat tot_like_this_file = 0.0, tot_weight = 0.0;
Posterior pdf_posterior;
ConvertPosteriorToPdfs(trans_model, posterior, &pdf_posterior);
for (size_t i = 0; i < posterior.size(); i++) {
for (size_t j = 0; j < pdf_posterior[i].size(); j++) {
int32 pdf_id = pdf_posterior[i][j].first;
BaseFloat weight = pdf_posterior[i][j].second;
tot_like_this_file += mllt_accs.AccumulateFromGmm(am_gmm.GetPdf(pdf_id),
mat.Row(i),
weight) * weight;
tot_weight += weight;
}
}
KALDI_LOG << "Average like for this file is "
<< (tot_like_this_file/tot_weight) << " over "
<< tot_weight << " frames.";
tot_like += tot_like_this_file;
tot_t += tot_weight;
if (num_done % 10 == 0)
KALDI_LOG << "Avg like per frame so far is "
<< (tot_like/tot_t);
}
}
KALDI_LOG << "Done " << num_done << " files, " << num_no_posterior
<< " with no posteriors, " << num_other_error
<< " with other errors.";
KALDI_LOG << "Overall avg like per frame (Gaussian only) = "
<< (tot_like/tot_t) << " over " << tot_t << " frames.";
WriteKaldiObject(mllt_accs, accs_wxfilename, binary);
KALDI_LOG << "Written accs.";
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}
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