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src/bin/logprob-to-post.cc
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// bin/logprob-to-post.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 "gmm/am-diag-gmm.h"
#include "hmm/transition-model.h"
#include "hmm/hmm-utils.h"
#include "hmm/posterior.h"
/* Convert a matrix of log-probabilities
to something of type Posterior, i.e. for each utterance, a
vector<vector<pair<int32, BaseFloat> > >, which is a sparse representation
of the probabilities.
To avoid getting very tiny values making it non-sparse, we support
thresholding, and this can either be done as a simple threshold, or (the
default) a pseudo-random thing where you preserve the expectation, e.g.
if the threshold is 0.01 and the value is 0.001, it will be zero with
probability 0.9 and 0.01 with probability 0.1.
*/
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
try {
const char *usage =
"Convert a matrix of log-probabilities (e.g. from nnet-logprob) to posteriors
"
"Usage: logprob-to-post [options] <logprob-matrix-rspecifier> <posteriors-wspecifier>
"
"e.g.:
"
" nnet-logprob [args] | logprob-to-post ark:- ark:1.post
"
"Caution: in this particular example, the output would be posteriors of pdf-ids,
"
"rather than transition-ids (c.f. post-to-pdf-post)
";
ParseOptions po(usage);
BaseFloat min_post = 0.01;
bool random_prune = true; // preserve expectations.
po.Register("min-post", &min_post, "Minimum posterior we will output (smaller "
"ones are pruned). Also see --random-prune");
po.Register("random-prune", &random_prune, "If true, prune posteriors with a "
"randomized method that preserves expectations.");
po.Read(argc, argv);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
std::string logprob_rspecifier = po.GetArg(1);
std::string posteriors_wspecifier = po.GetArg(2);
int32 num_done = 0;
SequentialBaseFloatMatrixReader logprob_reader(logprob_rspecifier);
PosteriorWriter posterior_writer(posteriors_wspecifier);
for (; !logprob_reader.Done(); logprob_reader.Next()) {
num_done++;
const Matrix<BaseFloat> &logprobs = logprob_reader.Value();
// Posterior is vector<vector<pair<int32, BaseFloat> > >
Posterior post(logprobs.NumRows());
for (int32 i = 0; i < logprobs.NumRows(); i++) {
SubVector<BaseFloat> row(logprobs, i);
for (int32 j = 0; j < row.Dim(); j++) {
BaseFloat p = Exp(row(j));
if (p >= min_post) {
post[i].push_back(std::make_pair(j, p));
} else if (random_prune && (p / min_post) >= RandUniform()) {
post[i].push_back(std::make_pair(j, min_post));
}
}
}
posterior_writer.Write(logprob_reader.Key(), post);
}
KALDI_LOG << "Converted " << num_done << " log-prob matrices to posteriors.";
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}
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