nnet3-discriminative-train.cc
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// nnet3bin/nnet3-discriminative-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-discriminative-training.h"
#include "nnet3/am-nnet-simple.h"
#include "nnet3/nnet-utils.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet3;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"Train nnet3 neural network parameters with discriminative sequence objective \n"
"gradient descent. Minibatches are to be created by nnet3-discriminative-merge-egs in\n"
"the input pipeline. This training program is single-threaded (best to\n"
"use it with a GPU).\n"
"\n"
"Usage: nnet3-discriminative-train [options] <nnet-in> <discriminative-training-examples-in> <raw-nnet-out>\n"
"\n"
"nnet3-discriminative-train 1.mdl 'ark:nnet3-merge-egs 1.degs ark:-|' 2.raw\n";
bool binary_write = true;
std::string use_gpu = "yes";
bool dropout_test_mode = true;
NnetDiscriminativeOptions 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");
po.Register("dropout-test-mode", &dropout_test_mode,
"If true, set test-mode to true on any DropoutComponents and "
"DropoutMaskComponents.");
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 model_rxfilename = po.GetArg(1),
examples_rspecifier = po.GetArg(2),
model_wxfilename = po.GetArg(3);
TransitionModel tmodel;
AmNnetSimple am_nnet;
bool binary;
Input ki(model_rxfilename, &binary);
tmodel.Read(ki.Stream(), binary);
am_nnet.Read(ki.Stream(), binary);
Nnet nnet = am_nnet.GetNnet();
if (dropout_test_mode)
SetDropoutTestMode(true, &nnet);
const VectorBase<BaseFloat> &priors = am_nnet.Priors();
NnetDiscriminativeTrainer trainer(opts, tmodel, priors, &nnet);
SequentialNnetDiscriminativeExampleReader example_reader(examples_rspecifier);
for (; !example_reader.Done(); example_reader.Next())
trainer.Train(example_reader.Value());
bool ok = trainer.PrintTotalStats();
#if HAVE_CUDA==1
CuDevice::Instantiate().PrintProfile();
#endif
Output ko(model_wxfilename, binary_write);
nnet.Write(ko.Stream(), binary_write);
KALDI_LOG << "Wrote raw nnet model to " << model_wxfilename;
return (ok ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}