gmm-basis-fmllr-accs.cc
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// gmmbin/gmm-basis-fmllr-accs.cc
// Copyright 2012 Carnegie Mellon University (author: Yajie Miao)
// 2014 Guoguo Chen
// 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 <string>
using std::string;
#include <vector>
using std::vector;
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "gmm/am-diag-gmm.h"
#include "hmm/transition-model.h"
#include "transform/fmllr-diag-gmm.h"
#include "transform/basis-fmllr-diag-gmm.h"
#include "hmm/posterior.h"
namespace kaldi {
void AccumulateForUtterance(const Matrix<BaseFloat> &feats,
const Posterior &post,
const TransitionModel &trans_model,
const AmDiagGmm &am_gmm,
FmllrDiagGmmAccs *spk_stats) {
Posterior pdf_post;
ConvertPosteriorToPdfs(trans_model, post, &pdf_post);
for (size_t i = 0; i < post.size(); i++) {
for (size_t j = 0; j < pdf_post[i].size(); j++) {
int32 pdf_id = pdf_post[i][j].first;
spk_stats->AccumulateForGmm(am_gmm.GetPdf(pdf_id),
feats.Row(i),
pdf_post[i][j].second);
}
}
}
}
int main(int argc, char *argv[]) {
try {
typedef kaldi::int32 int32;
using namespace kaldi;
const char *usage =
"Accumulate gradient scatter from training set, either per utterance or \n"
"for the supplied set of speakers (spk2utt option). Reads posterior to accumulate \n"
"fMLLR stats for each speaker/utterance. Writes gradient scatter matrix.\n"
"Usage: gmm-basis-fmllr-accs [options] <model-in> <feature-rspecifier>"
"<post-rspecifier> <accs-wspecifier>\n";
bool binary_write = true;
string spk2utt_rspecifier;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("spk2utt", &spk2utt_rspecifier, "rspecifier for speaker to "
"utterance-list map");
po.Read(argc, argv);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
string
model_rxfilename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
post_rspecifier = po.GetArg(3),
accs_wspecifier = po.GetArg(4);
TransitionModel trans_model;
AmDiagGmm am_gmm;
{
bool binary;
Input ki(model_rxfilename, &binary);
trans_model.Read(ki.Stream(), binary);
am_gmm.Read(ki.Stream(), binary);
}
RandomAccessPosteriorReader post_reader(post_rspecifier);
BasisFmllrAccus basis_accs(am_gmm.Dim());
int32 num_done = 0, num_no_post = 0, num_other_error = 0;
if (spk2utt_rspecifier != "") { // per-speaker mode
SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
int32 num_spk = 0;
for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
FmllrDiagGmmAccs spk_stats(am_gmm.Dim());
string spk = spk2utt_reader.Key();
const vector<string> &uttlist = spk2utt_reader.Value();
for (size_t i = 0; i < uttlist.size(); i++) {
std::string utt = uttlist[i];
if (!feature_reader.HasKey(utt)) {
KALDI_WARN << "Did not find features for utterance " << utt;
num_other_error++;
continue;
}
if (!post_reader.HasKey(utt)) {
KALDI_WARN << "Did not find posteriors for utterance " << utt;
num_no_post++;
continue;
}
const Matrix<BaseFloat> &feats = feature_reader.Value(utt);
const Posterior &post = post_reader.Value(utt);
if (static_cast<int32>(post.size()) != feats.NumRows()) {
KALDI_WARN << "Posterior vector has wrong size " << (post.size())
<< " vs. " << (feats.NumRows());
num_other_error++;
continue;
}
AccumulateForUtterance(feats, post, trans_model, am_gmm, &spk_stats);
num_done++;
} // end looping over all utterances of this speaker
basis_accs.AccuGradientScatter(spk_stats);
num_spk++;
} // end looping over speakers
KALDI_LOG << "Accumulate statistics from " << num_spk << " speakers";
} else { // per-utterance mode
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
for (; !feature_reader.Done(); feature_reader.Next()) {
string utt = feature_reader.Key();
if (!post_reader.HasKey(utt)) {
KALDI_WARN << "Did not find posts for utterance "
<< utt;
num_no_post++;
continue;
}
const Matrix<BaseFloat> &feats = feature_reader.Value();
const Posterior &post = post_reader.Value(utt);
if (static_cast<int32>(post.size()) != feats.NumRows()) {
KALDI_WARN << "Posterior has wrong size " << (post.size())
<< " vs. " << (feats.NumRows());
num_other_error++;
continue;
}
// Accumulate stats for this utterance
FmllrDiagGmmAccs utt_stats(am_gmm.Dim());
AccumulateForUtterance(feats, post, trans_model, am_gmm, &utt_stats);
num_done++;
basis_accs.AccuGradientScatter(utt_stats);
} // end looping over utterances
}
// Write out accumulations
{
Output ko(accs_wspecifier, binary_write);
basis_accs.Write(ko.Stream(), binary_write);
}
KALDI_LOG << "Done " << num_done << " files, " << num_no_post
<< " with no posts, " << num_other_error << " with other errors.";
KALDI_LOG << "Written gradient scatter to " << accs_wspecifier;
return (num_done != 0 ? 0 : 1);
} catch(const std::exception& e) {
std::cerr << e.what();
return -1;
}
}