nnet-get-egs-discriminative.cc
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// nnet2bin/nnet-get-egs-discriminative.cc
// Copyright 2012-2013 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 "hmm/transition-model.h"
#include "nnet2/nnet-example-functions.h"
#include "nnet2/am-nnet.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet2;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"Get examples of data for discriminative neural network training;\n"
"each one corresponds to part of a file, of variable (and configurable)\n"
"length.\n"
"\n"
"Usage: nnet-get-egs-discriminative [options] <model> "
"<features-rspecifier> <ali-rspecifier> <den-lat-rspecifier> "
"<training-examples-out>\n"
"\n"
"An example [where $feats expands to the actual features]:\n"
"nnet-get-egs-discriminative --acoustic-scale=0.1 \\\n"
" 1.mdl '$feats' 'ark,s,cs:gunzip -c ali.1.gz|' 'ark,s,cs:gunzip -c lat.1.gz|' ark:1.degs\n";
SplitDiscriminativeExampleConfig split_config;
ParseOptions po(usage);
split_config.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() != 5) {
po.PrintUsage();
exit(1);
}
std::string nnet_rxfilename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
ali_rspecifier = po.GetArg(3),
clat_rspecifier = po.GetArg(4),
examples_wspecifier = po.GetArg(5);
TransitionModel trans_model;
AmNnet am_nnet;
{
bool binary;
Input ki(nnet_rxfilename, &binary);
trans_model.Read(ki.Stream(), binary);
am_nnet.Read(ki.Stream(), binary);
}
int32 left_context = am_nnet.GetNnet().LeftContext(),
right_context = am_nnet.GetNnet().RightContext();
// Read in all the training files.
SequentialBaseFloatMatrixReader feat_reader(feature_rspecifier);
RandomAccessInt32VectorReader ali_reader(ali_rspecifier);
RandomAccessCompactLatticeReader clat_reader(clat_rspecifier);
DiscriminativeNnetExampleWriter example_writer(examples_wspecifier);
int32 num_done = 0, num_err = 0;
int64 examples_count = 0; // used in generating id's.
SplitExampleStats stats; // diagnostic.
for (; !feat_reader.Done(); feat_reader.Next()) {
std::string key = feat_reader.Key();
const Matrix<BaseFloat> &feats = feat_reader.Value();
if (!ali_reader.HasKey(key)) {
KALDI_WARN << "No pdf-level posterior for key " << key;
num_err++;
continue;
}
const std::vector<int32> &alignment = ali_reader.Value(key);
if (!clat_reader.HasKey(key)) {
KALDI_WARN << "No denominator lattice for key " << key;
num_err++;
continue;
}
CompactLattice clat = clat_reader.Value(key);
CreateSuperFinal(&clat); // make sure only one state has a final-prob (of One()).
if (clat.Properties(fst::kTopSorted, true) == 0) {
TopSort(&clat);
}
BaseFloat weight = 1.0;
DiscriminativeNnetExample eg;
if (!LatticeToDiscriminativeExample(alignment, feats, clat, weight,
left_context, right_context, &eg)) {
KALDI_WARN << "Error converting lattice to example.";
num_err++;
continue;
}
std::vector<DiscriminativeNnetExample> egs;
SplitDiscriminativeExample(split_config, trans_model, eg,
&egs, &stats);
KALDI_VLOG(2) << "Split lattice " << key << " into "
<< egs.size() << " pieces.";
for (size_t i = 0; i < egs.size(); i++) {
// Note: excised_egs will be of size 0 or 1.
std::vector<DiscriminativeNnetExample> excised_egs;
ExciseDiscriminativeExample(split_config, trans_model, egs[i],
&excised_egs, &stats);
for (size_t j = 0; j < excised_egs.size(); j++) {
std::ostringstream os;
os << (examples_count++);
std::string example_key = os.str();
example_writer.Write(example_key, excised_egs[j]);
}
}
num_done++;
}
if (num_done > 0) stats.Print();
KALDI_LOG << "Finished generating examples, "
<< "successfully processed " << num_done
<< " feature files, " << num_err << " had errors.";
return (num_done == 0 ? 1 : 0);
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}