nnet-subset-egs.cc
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// nnet2bin/nnet-subset-egs.cc
// Copyright 2012 Johns Hopkins University (author: Daniel Povey)
// Copyright 2014 Vimal Manohar
// 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"
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 =
"Creates a random subset of the input examples, of a specified size.\n"
"Uses no more memory than the size of the subset.\n"
"\n"
"Usage: nnet-subset-egs [options] <egs-rspecifier> [<egs-wspecifier2> ...]\n"
"\n"
"e.g.\n"
"nnet-subset-egs [args] ark:- | nnet-subset-egs --n=1000 ark:- ark:subset.egs\n";
int32 srand_seed = 0;
int32 n = 1000;
bool randomize_order = true;
ParseOptions po(usage);
po.Register("srand", &srand_seed, "Seed for random number generator ");
po.Register("n", &n, "Number of examples to output");
po.Register("randomize-order", &randomize_order, "If true, randomize the order "
"of the output");
po.Read(argc, argv);
srand(srand_seed);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
std::string examples_rspecifier = po.GetArg(1),
examples_wspecifier = po.GetArg(2);
std::vector<std::pair<std::string, NnetExample> > egs;
egs.reserve(n);
SequentialNnetExampleReader example_reader(examples_rspecifier);
int64 num_read = 0;
for (; !example_reader.Done(); example_reader.Next()) {
num_read++;
if (num_read <= n) {
egs.resize(egs.size() + 1);
egs.back().first = example_reader.Key();
egs.back().second = example_reader.Value();
} else {
BaseFloat keep_prob = n / static_cast<BaseFloat>(num_read);
if (WithProb(keep_prob)) { // With probability "keep_prob"
int32 index = RandInt(0, n-1);
egs[index].first = example_reader.Key();
egs[index].second = example_reader.Value();
}
}
}
if (randomize_order)
std::random_shuffle(egs.begin(), egs.end());
NnetExampleWriter writer(examples_wspecifier);
for (size_t i = 0; i < egs.size(); i++) {
writer.Write(egs[i].first, egs[i].second);
}
KALDI_LOG << "Selected a subset of " << egs.size() << " out of " << num_read
<< " neural-network training examples ";
return (num_read != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}