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// nnetbin/cmvn-to-nnet.cc
// Copyright 2012-2016 Brno University of Technology
// 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 "nnet/nnet-nnet.h"
#include "nnet/nnet-various.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet1;
typedef kaldi::int32 int32;
const char *usage =
"Convert cmvn-stats into <AddShift> and <Rescale> components.
"
"Usage: cmvn-to-nnet [options] <transf-in> <nnet-out>
"
"e.g.:
"
" cmvn-to-nnet --binary=false transf.mat nnet.mdl
";
bool binary_write = false;
float std_dev = 1.0;
float var_floor = 1e-10;
float learn_rate_coef = 0.0;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("std-dev", &std_dev, "Standard deviation of the output.");
po.Register("var-floor", &var_floor,
"Floor the variance, so the factors in <Rescale> are bounded.");
po.Register("learn-rate-coef", &learn_rate_coef,
"Initialize learning-rate coefficient to a value.");
po.Read(argc, argv);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
std::string cmvn_stats_rxfilename = po.GetArg(1),
model_out_filename = po.GetArg(2);
// read the matrix,
Matrix<double> cmvn_stats;
{
bool binary_read;
Input ki(cmvn_stats_rxfilename, &binary_read);
cmvn_stats.Read(ki.Stream(), binary_read);
}
KALDI_ASSERT(cmvn_stats.NumRows() == 2);
KALDI_ASSERT(cmvn_stats.NumCols() > 1);
int32 num_dims = cmvn_stats.NumCols() - 1;
double frame_count = cmvn_stats(0, cmvn_stats.NumCols() - 1);
// buffers for shift and scale
Vector<BaseFloat> shift(num_dims);
Vector<BaseFloat> scale(num_dims);
// compute the shift and scale per each dimension
for (int32 d = 0; d < num_dims; d++) {
BaseFloat mean = cmvn_stats(0, d) / frame_count;
BaseFloat var = cmvn_stats(1, d) / frame_count - mean * mean;
if (var <= var_floor) {
KALDI_WARN << "Very small variance " << var
<< " flooring to " << var_floor;
var = var_floor;
}
shift(d) = -mean;
scale(d) = std_dev / sqrt(var);
}
// create empty nnet,
Nnet nnet;
// append shift component to nnet,
{
AddShift shift_component(shift.Dim(), shift.Dim());
shift_component.SetParams(shift);
shift_component.SetLearnRateCoef(learn_rate_coef);
nnet.AppendComponent(shift_component);
}
// append scale component to nnet,
{
Rescale scale_component(scale.Dim(), scale.Dim());
scale_component.SetParams(scale);
scale_component.SetLearnRateCoef(learn_rate_coef);
nnet.AppendComponent(scale_component);
}
// write the nnet,
{
Output ko(model_out_filename, binary_write);
nnet.Write(ko.Stream(), binary_write);
KALDI_LOG << "Written cmvn in 'nnet1' model to: " << model_out_filename;
}
return 0;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}
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