gmm-acc-stats2.cc
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// gmmbin/gmm-acc-stats2.cc
// Copyright 2009-2012 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"
#include "hmm/posterior.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
try {
const char *usage =
"Accumulate stats for GMM training (from posteriors)\n"
"This version writes two accumulators (e.g. num and den),\n"
"and puts the positive accumulators in num, negative in den\n"
"Usage: gmm-acc-stats2 [options] <model> <feature-rspecifier>"
"<posteriors-rspecifier> <num-stats-out> <den-stats-out>\n"
"e.g.:\n"
"gmm-acc-stats 1.mdl \"$feats\" ark:1.post 1.num_acc 1.den_acc\n";
ParseOptions po(usage);
bool binary = true;
std::string update_flags_str = "mvwt"; // note: t is ignored, we acc
// transition stats regardless.
po.Register("binary", &binary, "Write stats in binary mode");
po.Register("update-flags", &update_flags_str, "Which GMM parameters to "
"update: subset of mvwt.");
po.Read(argc, argv);
if (po.NumArgs() != 5) {
po.PrintUsage();
exit(1);
}
std::string model_rxfilename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
posteriors_rspecifier = po.GetArg(3),
num_accs_wxfilename = po.GetArg(4),
den_accs_wxfilename = po.GetArg(5);
AmDiagGmm am_gmm;
TransitionModel trans_model;
{
bool binary;
Input ki(model_rxfilename, &binary);
trans_model.Read(ki.Stream(), binary);
am_gmm.Read(ki.Stream(), binary);
}
Vector<double> num_trans_accs, den_trans_accs;
trans_model.InitStats(&num_trans_accs);
trans_model.InitStats(&den_trans_accs);
AccumAmDiagGmm num_gmm_accs, den_gmm_accs;
num_gmm_accs.Init(am_gmm, StringToGmmFlags(update_flags_str));
den_gmm_accs.Init(am_gmm, StringToGmmFlags(update_flags_str));
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
BaseFloat tot_like = 0.0, tot_weight = 0.0;
// tot_like is total weighted likelihood (note: weighted
// by both +ve and -ve numbers)
// tot_t is total weight in posteriors (will often be about zero).
int64 tot_frames = 0.0;
int32 num_done = 0, num_err = 0;
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string key = feature_reader.Key();
if (!posteriors_reader.HasKey(key)) {
num_err++;
} 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_err++;
continue;
}
BaseFloat tot_like_this_file = 0.0, tot_weight_this_file = 0.0;
num_done++;
for (size_t i = 0; i < posterior.size(); i++) {
for (size_t j = 0; j < posterior[i].size(); j++) {
int32 tid = posterior[i][j].first,
pdf_id = trans_model.TransitionIdToPdf(tid);
BaseFloat weight = posterior[i][j].second;
trans_model.Accumulate(fabs(weight), tid,
(weight > 0.0 ?
&num_trans_accs : &den_trans_accs));
tot_like_this_file +=
(weight > 0.0 ? &num_gmm_accs : &den_gmm_accs) ->
AccumulateForGmm(am_gmm, mat.Row(i), pdf_id, fabs(weight)) * weight;
tot_weight_this_file += weight;
}
}
tot_like += tot_like_this_file;
tot_weight += tot_weight_this_file;
tot_frames += static_cast<int32>(posterior.size());
}
}
KALDI_LOG << "Done " << num_done << " files, " << num_err
<< " had errors.";
KALDI_LOG << "Overall weighted acoustic likelihood per frame was "
<< (tot_like/tot_frames) << " over " << tot_frames << " frames;"
<< " average weight per frame was " << (tot_weight / tot_frames);
{
Output ko(num_accs_wxfilename, binary);
num_trans_accs.Write(ko.Stream(), binary);
num_gmm_accs.Write(ko.Stream(), binary);
}
{
Output ko(den_accs_wxfilename, binary);
den_trans_accs.Write(ko.Stream(), binary);
den_gmm_accs.Write(ko.Stream(), binary);
}
KALDI_LOG << "Written accs.";
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}