nnet-am-init.cc
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// nnet2bin/nnet-am-init.cc
// Copyright 2012 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 "nnet2/am-nnet.h"
#include "hmm/transition-model.h"
#include "tree/context-dep.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet2;
typedef kaldi::int32 int32;
// TODO: specify in the usage message where the example scripts are.
const char *usage =
"Initialize the neural network acoustic model and its associated\n"
"transition-model, from a tree, a topology file, and a neural-net\n"
"without an associated acoustic model.\n"
"See example scripts to see how this works in practice.\n"
"\n"
"Usage: nnet-am-init [options] <tree-in> <topology-in> <raw-nnet-in> <nnet-am-out>\n"
"or: nnet-am-init [options] <transition-model-in> <raw-nnet-in> <nnet-am-out>\n"
"e.g.:\n"
" nnet-am-init tree topo \"nnet-init nnet.config - |\" 1.mdl\n";
bool binary_write = true;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Read(argc, argv);
if (po.NumArgs() != 3 && po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
std::string raw_nnet_rxfilename, nnet_wxfilename;
TransitionModel *trans_model = NULL;
if (po.NumArgs() == 4) {
std::string tree_rxfilename = po.GetArg(1),
topo_rxfilename = po.GetArg(2);
raw_nnet_rxfilename = po.GetArg(3);
nnet_wxfilename = po.GetArg(4);
ContextDependency ctx_dep;
ReadKaldiObject(tree_rxfilename, &ctx_dep);
HmmTopology topo;
ReadKaldiObject(topo_rxfilename, &topo);
// Construct the transition model from the tree and the topology file.
trans_model = new TransitionModel(ctx_dep, topo);
} else {
std::string trans_model_rxfilename = po.GetArg(1);
raw_nnet_rxfilename = po.GetArg(2);
nnet_wxfilename = po.GetArg(3);
trans_model = new TransitionModel();
ReadKaldiObject(trans_model_rxfilename, trans_model);
}
AmNnet am_nnet;
{
Nnet nnet;
bool binary;
Input ki(raw_nnet_rxfilename, &binary);
nnet.Read(ki.Stream(), binary);
am_nnet.Init(nnet);
}
if (am_nnet.NumPdfs() != trans_model->NumPdfs())
KALDI_ERR << "Mismatch in number of pdfs, neural net has "
<< am_nnet.NumPdfs() << ", transition model has "
<< trans_model->NumPdfs();
{
Output ko(nnet_wxfilename, binary_write);
trans_model->Write(ko.Stream(), binary_write);
am_nnet.Write(ko.Stream(), binary_write);
}
delete trans_model;
KALDI_LOG << "Initialized neural net and wrote it to " << nnet_wxfilename;
return 0;
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}