gmm-fmpe-acc-stats.cc
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// gmmbin/gmm-fmpe-acc-stats.cc
// Copyright 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 "transform/fmpe.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
using kaldi::int32;
try {
const char *usage =
"Accumulate stats for fMPE training, using GMM model. Note: this could\n"
"be done using gmm-get-feat-deriv and fmpe-acc-stats (but you'd be computing\n"
"the features twice). Features input should be pre-fMPE features.\n"
"\n"
"Usage: gmm-fmpe-acc-stats [options] <model-in> <fmpe-in> <feature-rspecifier> "
"<gselect-rspecifier> <posteriors-rspecifier> <fmpe-stats-out>\n"
"e.g.: \n"
" gmm-fmpe-acc-stats --model-derivative 1.accs 1.mdl 1.fmpe \"$feats\" ark:1.gselect ark:1.post 1.fmpe_stats\n";
ParseOptions po(usage);
bool binary = true;
std::string model_derivative_rxfilename;
po.Register("binary", &binary, "If true, write stats in binary mode.");
po.Register("model-derivative", &model_derivative_rxfilename,
"GMM-accs file containing model derivative [note: contains no transition stats]. Used for indirect differential. Warning: this will only work correctly in the case of MMI/BMMI objective function, with non-canceled stats.");
po.Read(argc, argv);
if (po.NumArgs() != 6) {
po.PrintUsage();
exit(1);
}
std::string model_rxfilename = po.GetArg(1),
fmpe_rxfilename = po.GetArg(2),
feature_rspecifier = po.GetArg(3),
gselect_rspecifier = po.GetArg(4),
posteriors_rspecifier = po.GetArg(5),
stats_wxfilename = po.GetArg(6);
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);
}
Fmpe fmpe;
ReadKaldiObject(fmpe_rxfilename, &fmpe);
bool have_indirect = (model_derivative_rxfilename != "");
AccumAmDiagGmm model_derivative;
if (have_indirect)
ReadKaldiObject(model_derivative_rxfilename, &model_derivative);
FmpeStats fmpe_stats(fmpe);
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
BaseFloat tot_like = 0.0; // tot like weighted by posterior.
int32 num_frames = 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++;
KALDI_WARN << "No posteriors for utterance " << key;
continue;
}
const Matrix<BaseFloat> &feat_in = feature_reader.Value();
const Posterior &posterior = posteriors_reader.Value(key);
if (static_cast<int32>(posterior.size()) != feat_in.NumRows()) {
KALDI_WARN << "Posterior vector has wrong size " <<
(posterior.size()) << " vs. "<< (feat_in.NumRows());
num_err++;
continue;
}
if (!gselect_reader.HasKey(key)) {
KALDI_WARN << "No gselect information for key " << key;
num_err++;
continue;
}
const std::vector<std::vector<int32> > &gselect =
gselect_reader.Value(key);
if (static_cast<int32>(gselect.size()) != feat_in.NumRows()) {
KALDI_WARN << "gselect information has wrong size";
num_err++;
continue;
}
num_done++;
Matrix<BaseFloat> fmpe_feat(feat_in.NumRows(), feat_in.NumCols());
fmpe.ComputeFeatures(feat_in, gselect, &fmpe_feat);
fmpe_feat.AddMat(1.0, feat_in);
Matrix<BaseFloat> direct_deriv, indirect_deriv;
tot_like += ComputeAmGmmFeatureDeriv(am_gmm, trans_model, posterior,
fmpe_feat, &direct_deriv,
(have_indirect ? &model_derivative : NULL),
(have_indirect ? &indirect_deriv : NULL));
num_frames += feat_in.NumRows();
fmpe.AccStats(feat_in, gselect, direct_deriv,
(have_indirect ? &indirect_deriv : NULL), &fmpe_stats);
if (num_done % 100 == 0)
KALDI_LOG << "Processed " << num_done << " utterances.";
}
KALDI_LOG << "Done " << num_done << " files, " << num_err
<< " with errors.";
KALDI_LOG << "Overall weighted acoustic likelihood per frame is "
<< (tot_like/num_frames) << " over " << num_frames << " frames.";
Output ko(stats_wxfilename, binary);
fmpe_stats.Write(ko.Stream(), binary);
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}