gmm-acc-stats-ali.cc
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// gmmbin/gmm-acc-stats-ali.cc
// Copyright 2009-2012 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 "gmm/am-diag-gmm.h"
#include "hmm/transition-model.h"
#include "gmm/mle-am-diag-gmm.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
try {
const char *usage =
"Accumulate stats for GMM training.\n"
"Usage: gmm-acc-stats-ali [options] <model-in> <feature-rspecifier> "
"<alignments-rspecifier> <stats-out>\n"
"e.g.:\n gmm-acc-stats-ali 1.mdl scp:train.scp ark:1.ali 1.acc\n";
ParseOptions po(usage);
bool binary = true;
po.Register("binary", &binary, "Write output in binary mode");
po.Read(argc, argv);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
std::string model_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
alignments_rspecifier = po.GetArg(3),
accs_wxfilename = po.GetArg(4);
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);
}
Vector<double> transition_accs;
trans_model.InitStats(&transition_accs);
AccumAmDiagGmm gmm_accs;
gmm_accs.Init(am_gmm, kGmmAll);
double tot_like = 0.0;
kaldi::int64 tot_t = 0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessInt32VectorReader alignments_reader(alignments_rspecifier);
int32 num_done = 0, num_err = 0;
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string key = feature_reader.Key();
if (!alignments_reader.HasKey(key)) {
KALDI_WARN << "No alignment for utterance " << key;
num_err++;
} else {
const Matrix<BaseFloat> &mat = feature_reader.Value();
const std::vector<int32> &alignment = alignments_reader.Value(key);
if (alignment.size() != mat.NumRows()) {
KALDI_WARN << "Alignments has wrong size " << (alignment.size())
<< " vs. " << (mat.NumRows());
num_err++;
continue;
}
num_done++;
BaseFloat tot_like_this_file = 0.0;
for (size_t i = 0; i < alignment.size(); i++) {
int32 tid = alignment[i], // transition identifier.
pdf_id = trans_model.TransitionIdToPdf(tid);
trans_model.Accumulate(1.0, tid, &transition_accs);
tot_like_this_file += gmm_accs.AccumulateForGmm(am_gmm, mat.Row(i),
pdf_id, 1.0);
}
tot_like += tot_like_this_file;
tot_t += alignment.size();
if (num_done % 50 == 0) {
KALDI_LOG << "Processed " << num_done << " utterances; for utterance "
<< key << " avg. like is "
<< (tot_like_this_file/alignment.size())
<< " over " << alignment.size() <<" frames.";
}
}
}
KALDI_LOG << "Done " << num_done << " files, " << num_err
<< " with errors.";
KALDI_LOG << "Overall avg like per frame (Gaussian only) = "
<< (tot_like/tot_t) << " over " << tot_t << " frames.";
{
Output ko(accs_wxfilename, binary);
transition_accs.Write(ko.Stream(), binary);
gmm_accs.Write(ko.Stream(), binary);
}
KALDI_LOG << "Written accs.";
if (num_done != 0)
return 0;
else
return 1;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}