gmm-acc-mllt-global.cc
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// gmmbin/gmm-acc-mllt-global.cc
// Copyright 2009-2011 Microsoft Corporation
// 2014 Guoguo Chen
// 2014 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/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: this version is for where there is\n"
"one global GMM (e.g. a UBM)\n"
"Usage: gmm-acc-mllt-global [options] <gmm-in> <feature-rspecifier> <stats-out>\n"
"e.g.: \n"
" gmm-acc-mllt-global 1.dubm scp:feats.scp 1.macc\n";
ParseOptions po(usage);
bool binary = true;
BaseFloat rand_prune = 0.25;
std::string gselect_rspecifier;
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.Register("gselect", &gselect_rspecifier, "Rspecifier for Gaussian selection "
"information");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string gmm_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
accs_wxfilename = po.GetArg(3);
using namespace kaldi;
typedef kaldi::int32 int32;
DiagGmm gmm;
ReadKaldiObject(gmm_filename, &gmm);
MlltAccs mllt_accs(gmm.Dim(), rand_prune);
double tot_like = 0.0;
double tot_t = 0.0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
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();
num_done++;
BaseFloat tot_like_this_file = 0.0, tot_weight = 0.0;
if (gselect_rspecifier == "") {
for (int32 i = 0; i < mat.NumRows(); i++) {
tot_like_this_file += mllt_accs.AccumulateFromGmm(gmm, mat.Row(i), 1.0);
tot_weight += 1.0;
}
} else {
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()) != mat.NumRows()) {
KALDI_WARN << "Gselect information has wrong size for utterance "
<< utt << ", " << gselect.size() << " vs. "
<< mat.NumRows();
num_err++;
continue;
}
for (int32 i = 0; i < mat.NumRows(); i++) {
tot_like_this_file += mllt_accs.AccumulateFromGmmPreselect(
gmm, gselect[i], mat.Row(i), 1.0);
tot_weight += 1.0;
}
}
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. ";
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;
}
}