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// ivectorbin/ivector-extract-online.cc
// Copyright 2014 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 "ivector/ivector-extractor.h"
#include "util/kaldi-thread.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
try {
const char *usage =
"Extract iVectors for utterances, using a trained iVector extractor,
"
"and features and Gaussian-level posteriors. This version extracts an
"
"iVector every n frames (see the --ivector-period option), by including
"
"all frames up to that point in the utterance. This is designed to
"
"correspond with what will happen in a streaming decoding scenario;
"
"the iVectors would be used in neural net training. The iVectors are
"
"output as an archive of matrices, indexed by utterance-id; each row
"
"corresponds to an iVector.
"
"See also ivector-extract-online2
"
"
"
"Usage: ivector-extract-online [options] <model-in> <feature-rspecifier>"
"<posteriors-rspecifier> <ivector-wspecifier>
"
"e.g.:
"
" gmm-global-get-post 1.dubm '$feats' ark:- | \\
"
" ivector-extract-online --ivector-period=10 final.ie '$feats' ark,s,cs:- ark,t:ivectors.1.ark
";
ParseOptions po(usage);
int32 num_cg_iters = 15;
int32 ivector_period = 10;
BaseFloat max_count = 0.0;
g_num_threads = 8;
po.Register("num-cg-iters", &num_cg_iters,
"Number of iterations of conjugate gradient descent to perform "
"each time we re-estimate the iVector.");
po.Register("ivector-period", &ivector_period,
"Controls how frequently we re-estimate the iVector as we get "
"more data.");
po.Register("num-threads", &g_num_threads,
"Number of threads to use for computing derived variables "
"of iVector extractor, at process start-up.");
po.Register("max-count", &max_count,
"If >0, when the count of posteriors exceeds max-count we will "
"start using a stronger prior term. Can make iVectors from "
"longer than normal utterances look more 'typical'. Interpret "
"this value as a number of frames multiplied by your "
"posterior scale (so typically 0.1 times a number of frames).");
po.Read(argc, argv);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
std::string ivector_extractor_rxfilename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
posteriors_rspecifier = po.GetArg(3),
ivectors_wspecifier = po.GetArg(4);
IvectorExtractor extractor;
ReadKaldiObject(ivector_extractor_rxfilename, &extractor);
double tot_objf_impr = 0.0, tot_t = 0.0, tot_length = 0.0,
tot_length_utt_end = 0.0;
int32 num_done = 0, num_err = 0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
BaseFloatMatrixWriter ivector_writer(ivectors_wspecifier);
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string utt = feature_reader.Key();
if (!posteriors_reader.HasKey(utt)) {
KALDI_WARN << "No posteriors for utterance " << utt;
num_err++;
continue;
}
const Matrix<BaseFloat> &feats = feature_reader.Value();
const Posterior &posterior = posteriors_reader.Value(utt);
if (static_cast<int32>(posterior.size()) != feats.NumRows()) {
KALDI_WARN << "Size mismatch between posterior " << posterior.size()
<< " and features " << feats.NumRows() << " for utterance "
<< utt;
num_err++;
continue;
}
Matrix<BaseFloat> ivectors;
double objf_impr_per_frame;
objf_impr_per_frame = EstimateIvectorsOnline(feats, posterior, extractor,
ivector_period, num_cg_iters,
max_count, &ivectors);
BaseFloat offset = extractor.PriorOffset();
for (int32 i = 0 ; i < ivectors.NumRows(); i++)
ivectors(i, 0) -= offset;
double tot_post = TotalPosterior(posterior);
KALDI_VLOG(2) << "For utterance " << utt << " objf impr/frame is "
<< objf_impr_per_frame << " per frame, over "
<< tot_post << " frames (weighted).";
ivector_writer.Write(utt, ivectors);
tot_t += tot_post;
tot_objf_impr += objf_impr_per_frame * tot_post;
tot_length_utt_end += ivectors.Row(ivectors.NumRows() - 1).Norm(2.0) *
tot_post;
for (int32 i = 0; i < ivectors.NumRows(); i++)
tot_length += ivectors.Row(i).Norm(2.0) * tot_post / ivectors.NumRows();
num_done++;
}
KALDI_LOG << "Estimated iVectors for " << num_done << " files, " << num_err
<< " with errors.";
KALDI_LOG << "Average objective-function improvement was "
<< (tot_objf_impr / tot_t) << " per frame, over "
<< tot_t << " frames (weighted).";
KALDI_LOG << "Average iVector length was " << (tot_length / tot_t)
<< " and at utterance-end was " << (tot_length_utt_end / tot_t)
<< ", over " << tot_t << " frames (weighted); "
<< " expected length is " << sqrt(extractor.IvectorDim());
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}
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