decodable-am-sgmm2.cc
1.65 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
// sgmm2/decodable-am-sgmm2.cc
// Copyright 2009-2012 Saarland University; Lukas Burget;
// 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 <vector>
using std::vector;
#include "sgmm2/decodable-am-sgmm2.h"
namespace kaldi {
DecodableAmSgmm2::~DecodableAmSgmm2() {
if (delete_vars_) {
delete gselect_;
delete feature_matrix_;
delete spk_;
}
}
BaseFloat DecodableAmSgmm2::LogLikelihoodForPdf(int32 frame, int32 pdf_id) {
if (frame != cur_frame_) {
cur_frame_ = frame;
sgmm_cache_.NextFrame(); // it has a frame-index internally but it doesn't
// have to match up with our index here, it just needs to be unique.
SubVector<BaseFloat> data(*feature_matrix_, frame);
sgmm_.ComputePerFrameVars(data, (*gselect_)[frame], *spk_,
&per_frame_vars_);
}
return sgmm_.LogLikelihood(per_frame_vars_, pdf_id, &sgmm_cache_, spk_,
log_prune_);
}
} // namespace kaldi