nnet-combine-egs-discriminative.cc
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// nnet2bin/nnet-combine-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"
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 =
"Copy examples for discriminative neural network training,\n"
"and combine successive examples if their combined length will\n"
"be less than --max-length. This can help to improve efficiency\n"
"(--max-length corresponds to minibatch size)\n"
"\n"
"Usage: nnet-combine-egs-discriminative [options] <egs-rspecifier> <egs-wspecifier>\n"
"\n"
"e.g.\n"
"nnet-combine-egs-discriminative --max-length=512 ark:temp.1.degs ark:1.degs\n";
int32 max_length = 512;
int32 hard_max_length = 2048;
int32 batch_size = 250;
ParseOptions po(usage);
po.Register("max-length", &max_length, "Maximum length of example that we "
"will create when combining");
po.Register("batch-size", &batch_size, "Size of batch used when combinging "
"examples");
po.Register("hard-max-length", &hard_max_length, "Length of example beyond "
"which we will discard (very long examples may cause out of "
"memory errors)");
po.Read(argc, argv);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
KALDI_ASSERT(hard_max_length >= max_length);
KALDI_ASSERT(batch_size >= 1);
std::string examples_rspecifier = po.GetArg(1),
examples_wspecifier = po.GetArg(2);
SequentialDiscriminativeNnetExampleReader example_reader(
examples_rspecifier);
DiscriminativeNnetExampleWriter example_writer(
examples_wspecifier);
int64 num_read = 0, num_written = 0, num_discarded = 0;
while (!example_reader.Done()) {
std::vector<DiscriminativeNnetExample> buffer;
size_t size = batch_size;
buffer.reserve(size);
for (; !example_reader.Done() && buffer.size() < size;
example_reader.Next()) {
buffer.push_back(example_reader.Value());
num_read++;
}
std::vector<DiscriminativeNnetExample> combined;
CombineDiscriminativeExamples(max_length, buffer, &combined);
buffer.clear();
for (size_t i = 0; i < combined.size(); i++) {
const DiscriminativeNnetExample &eg = combined[i];
int32 num_frames = eg.input_frames.NumRows();
if (num_frames > hard_max_length) {
KALDI_WARN << "Discarding segment of length " << num_frames
<< " because it exceeds --hard-max-length="
<< hard_max_length;
num_discarded++;
} else {
std::ostringstream ostr;
ostr << (num_written++);
example_writer.Write(ostr.str(), eg);
}
}
}
KALDI_LOG << "Read " << num_read << " discriminative neural-network training"
<< " examples, wrote " << num_written << ", discarded "
<< num_discarded;
return (num_written == 0 ? 1 : 0);
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}