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src/gmmbin/gmm-global-acc-stats.cc
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// gmmbin/gmm-global-acc-stats.cc
// Copyright 2009-2011 Microsoft Corporation; Saarland University
// 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/model-common.h"
#include "gmm/full-gmm.h"
#include "gmm/diag-gmm.h"
#include "gmm/mle-full-gmm.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
const char *usage =
"Accumulate stats for training a diagonal-covariance GMM.
"
"Usage: gmm-global-acc-stats [options] <model-in> <feature-rspecifier> "
"<stats-out>
"
"e.g.: gmm-global-acc-stats 1.mdl scp:train.scp 1.acc
";
ParseOptions po(usage);
bool binary = true;
std::string update_flags_str = "mvw";
std::string gselect_rspecifier, weights_rspecifier;
po.Register("binary", &binary, "Write output in binary mode");
po.Register("update-flags", &update_flags_str, "Which GMM parameters will be "
"updated: subset of mvw.");
po.Register("gselect", &gselect_rspecifier, "rspecifier for gselect objects "
"to limit the #Gaussians accessed on each frame.");
po.Register("weights", &weights_rspecifier, "rspecifier for a vector of floats "
"for each utterance, that's a per-frame weight.");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string model_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
accs_wxfilename = po.GetArg(3);
DiagGmm gmm;
{
bool binary_read;
Input ki(model_filename, &binary_read);
gmm.Read(ki.Stream(), binary_read);
}
AccumDiagGmm gmm_accs;
gmm_accs.Resize(gmm, StringToGmmFlags(update_flags_str));
double tot_like = 0.0, tot_weight = 0.0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
RandomAccessBaseFloatVectorReader weights_reader(weights_rspecifier);
int32 num_done = 0, num_err = 0;
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string key = feature_reader.Key();
const Matrix<BaseFloat> &mat = feature_reader.Value();
int32 file_frames = mat.NumRows();
BaseFloat file_like = 0.0,
file_weight = 0.0; // total of weights of frames (will each be 1 unless
// --weights option supplied.
Vector<BaseFloat> weights;
if (weights_rspecifier != "") { // We have per-frame weighting.
if (!weights_reader.HasKey(key)) {
KALDI_WARN << "No per-frame weights available for utterance " << key;
num_err++;
continue;
}
weights = weights_reader.Value(key);
if (weights.Dim() != file_frames) {
KALDI_WARN << "Weights for utterance " << key << " have wrong dim "
<< weights.Dim() << " vs. " << file_frames;
num_err++;
continue;
}
}
if (gselect_rspecifier != "") {
if (!gselect_reader.HasKey(key)) {
KALDI_WARN << "No gselect information for utterance " << key;
num_err++;
continue;
}
const std::vector<std::vector<int32> > &gselect =
gselect_reader.Value(key);
if (gselect.size() != static_cast<size_t>(file_frames)) {
KALDI_WARN << "gselect information for utterance " << key
<< " has wrong size " << gselect.size() << " vs. "
<< file_frames;
num_err++;
continue;
}
for (int32 i = 0; i < file_frames; i++) {
BaseFloat weight = (weights.Dim() != 0) ? weights(i) : 1.0;
if (weight == 0.0) continue;
file_weight += weight;
SubVector<BaseFloat> data(mat, i);
const std::vector<int32> &this_gselect = gselect[i];
int32 gselect_size = this_gselect.size();
KALDI_ASSERT(gselect_size > 0);
Vector<BaseFloat> loglikes;
gmm.LogLikelihoodsPreselect(data, this_gselect, &loglikes);
file_like += weight * loglikes.ApplySoftMax();
loglikes.Scale(weight);
for (int32 j = 0; j < loglikes.Dim(); j++)
gmm_accs.AccumulateForComponent(data, this_gselect[j], loglikes(j));
}
} else { // no gselect..
for (int32 i = 0; i < file_frames; i++) {
BaseFloat weight = (weights.Dim() != 0) ? weights(i) : 1.0;
if (weight == 0.0) continue;
file_weight += weight;
file_like += weight *
gmm_accs.AccumulateFromDiag(gmm, mat.Row(i), weight);
}
}
KALDI_VLOG(2) << "File '" << key << "': Average likelihood = "
<< (file_like/file_weight) << " over "
<< file_weight <<" frames.";
tot_like += file_like;
tot_weight += file_weight;
num_done++;
}
KALDI_LOG << "Done " << num_done << " files; "
<< num_err << " with errors.";
KALDI_LOG << "Overall likelihood per "
<< "frame = " << (tot_like/tot_weight) << " over " << tot_weight
<< " (weighted) frames.";
WriteKaldiObject(gmm_accs, accs_wxfilename, binary);
KALDI_LOG << "Written accs to " << accs_wxfilename;
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}
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