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src/chainbin/nnet3-chain-train.cc
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// nnet3bin/nnet3-chain-train.cc
// Copyright 2015 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 "nnet3/nnet-chain-training.h"
#include "cudamatrix/cu-allocator.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet3;
using namespace kaldi::chain;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"Train nnet3+chain neural network parameters with backprop and stochastic
"
"gradient descent. Minibatches are to be created by nnet3-chain-merge-egs in
"
"the input pipeline. This training program is single-threaded (best to
"
"use it with a GPU).
"
"
"
"Usage: nnet3-chain-train [options] <raw-nnet-in> <denominator-fst-in> <chain-training-examples-in> <raw-nnet-out>
"
"
"
"nnet3-chain-train 1.raw den.fst 'ark:nnet3-merge-egs 1.cegs ark:-|' 2.raw
";
int32 srand_seed = 0;
bool binary_write = true;
std::string use_gpu = "yes";
NnetChainTrainingOptions opts;
ParseOptions po(usage);
po.Register("srand", &srand_seed, "Seed for random number generator ");
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");
opts.Register(&po);
RegisterCuAllocatorOptions(&po);
po.Read(argc, argv);
srand(srand_seed);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
#if HAVE_CUDA==1
CuDevice::Instantiate().SelectGpuId(use_gpu);
#endif
std::string nnet_rxfilename = po.GetArg(1),
den_fst_rxfilename = po.GetArg(2),
examples_rspecifier = po.GetArg(3),
nnet_wxfilename = po.GetArg(4);
Nnet nnet;
ReadKaldiObject(nnet_rxfilename, &nnet);
bool ok;
{
fst::StdVectorFst den_fst;
ReadFstKaldi(den_fst_rxfilename, &den_fst);
NnetChainTrainer trainer(opts, den_fst, &nnet);
SequentialNnetChainExampleReader example_reader(examples_rspecifier);
for (; !example_reader.Done(); example_reader.Next())
trainer.Train(example_reader.Value());
ok = trainer.PrintTotalStats();
}
#if HAVE_CUDA==1
CuDevice::Instantiate().PrintProfile();
#endif
WriteKaldiObject(nnet, nnet_wxfilename, binary_write);
KALDI_LOG << "Wrote raw model to " << nnet_wxfilename;
return (ok ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what() << '
';
return -1;
}
}
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