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src/nnet2bin/nnet-train-discriminative-simple.cc
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// nnet2bin/nnet-train-discriminative-simple.cc
// Copyright 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/am-nnet.h"
#include "nnet2/nnet-compute-discriminative.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 =
"Train the neural network parameters with a discriminative objective
"
"function (MMI, SMBR or MPFE). This uses training examples prepared with
"
"nnet-get-egs-discriminative
"
"
"
"Usage: nnet-train-discriminative-simple [options] <model-in> <training-examples-in> <model-out>
"
"e.g.:
"
"nnet-train-discriminative-simple 1.nnet ark:1.degs 2.nnet
";
bool binary_write = true;
std::string use_gpu = "yes";
NnetDiscriminativeUpdateOptions update_opts;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("use-gpu", &use_gpu,
"yes|no|optional|wait, only has effect if compiled with CUDA");
update_opts.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
#if HAVE_CUDA==1
CuDevice::Instantiate().SelectGpuId(use_gpu);
#endif
std::string nnet_rxfilename = po.GetArg(1),
examples_rspecifier = po.GetArg(2),
nnet_wxfilename = po.GetArg(3);
int64 num_examples = 0;
{
TransitionModel trans_model;
AmNnet am_nnet;
{
bool binary_read;
Input ki(nnet_rxfilename, &binary_read);
trans_model.Read(ki.Stream(), binary_read);
am_nnet.Read(ki.Stream(), binary_read);
}
NnetDiscriminativeStats stats;
SequentialDiscriminativeNnetExampleReader example_reader(examples_rspecifier);
for (; !example_reader.Done(); example_reader.Next(), num_examples++) {
NnetDiscriminativeUpdate(am_nnet, trans_model, update_opts,
example_reader.Value(),
&(am_nnet.GetNnet()), &stats);
if (num_examples % 10 == 0 && num_examples != 0) { // each example might be 500 frames.
if (GetVerboseLevel() >= 2) {
stats.Print(update_opts.criterion);
}
}
}
stats.Print(update_opts.criterion);
{
Output ko(nnet_wxfilename, binary_write);
trans_model.Write(ko.Stream(), binary_write);
am_nnet.Write(ko.Stream(), binary_write);
}
}
#if HAVE_CUDA==1
CuDevice::Instantiate().PrintProfile();
#endif
KALDI_LOG << "Finished training, processed " << num_examples
<< " training examples. Wrote model to "
<< nnet_wxfilename;
return (num_examples == 0 ? 1 : 0);
} catch(const std::exception &e) {
std::cerr << e.what() << '
';
return -1;
}
}
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