acc-lda.cc
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// bin/acc-lda.cc
// Copyright 2009-2011 Microsoft Corporation, Go-Vivace Inc.
// 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 "base/kaldi-common.h"
#include "util/common-utils.h"
#include "hmm/transition-model.h"
#include "hmm/posterior.h"
#include "transform/lda-estimate.h"
/** @brief Accumulate LDA statistics based on pdf-ids. Inputs are the
source models, that serve as the input (and may potentially contain
the current transformation), the un-transformed features and state
posterior probabilities */
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
try {
const char *usage =
"Accumulate LDA statistics based on pdf-ids.\n"
"Usage: acc-lda [options] <transition-gmm/model> <features-rspecifier> <posteriors-rspecifier> <lda-acc-out>\n"
"Typical usage:\n"
" ali-to-post ark:1.ali ark:- | acc-lda 1.mdl \"ark:splice-feats scp:train.scp|\" ark:- ldaacc.1\n";
bool binary = true;
BaseFloat rand_prune = 0.0;
ParseOptions po(usage);
po.Register("binary", &binary, "Write accumulators in binary mode.");
po.Register("rand-prune", &rand_prune,
"Randomized pruning threshold for posteriors");
po.Read(argc, argv);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
std::string model_rxfilename = po.GetArg(1);
std::string features_rspecifier = po.GetArg(2);
std::string posteriors_rspecifier = po.GetArg(3);
std::string acc_wxfilename = po.GetArg(4);
TransitionModel trans_model;
{
bool binary_read;
Input ki(model_rxfilename, &binary_read);
trans_model.Read(ki.Stream(), binary_read);
// discard rest of file.
}
LdaEstimate lda;
SequentialBaseFloatMatrixReader feature_reader(features_rspecifier);
RandomAccessPosteriorReader posterior_reader(posteriors_rspecifier);
int32 num_done = 0, num_fail = 0;
for (;!feature_reader.Done(); feature_reader.Next()) {
std::string utt = feature_reader.Key();
if (!posterior_reader.HasKey(utt)) {
KALDI_WARN << "No posteriors for utterance " << utt;
num_fail++;
continue;
}
const Posterior &post (posterior_reader.Value(utt));
const Matrix<BaseFloat> &feats(feature_reader.Value());
if (lda.Dim() == 0)
lda.Init(trans_model.NumPdfs(), feats.NumCols());
if (feats.NumRows() != static_cast<int32>(post.size())) {
KALDI_WARN << "Posterior vs. feats size mismatch "
<< post.size() << " vs. " << feats.NumRows();
num_fail++;
continue;
}
if (lda.Dim() != 0 && lda.Dim() != feats.NumCols()) {
KALDI_WARN << "Feature dimension mismatch " << lda.Dim()
<< " vs. " << feats.NumCols();
num_fail++;
continue;
}
Posterior pdf_post;
ConvertPosteriorToPdfs(trans_model, post, &pdf_post);
for (int32 i = 0; i < feats.NumRows(); i++) {
SubVector<BaseFloat> feat(feats, i);
for (size_t j = 0; j < pdf_post[i].size(); j++) {
int32 pdf_id = pdf_post[i][j].first;
BaseFloat weight = RandPrune(pdf_post[i][j].second, rand_prune);
if (weight != 0.0) {
lda.Accumulate(feat, pdf_id, weight);
}
}
}
num_done++;
if (num_done % 100 == 0)
KALDI_LOG << "Done " << num_done << " utterances.";
}
KALDI_LOG << "Done " << num_done << " files, failed for "
<< num_fail;
Output ko(acc_wxfilename, binary);
lda.Write(ko.Stream(), binary);
KALDI_LOG << "Written statistics.";
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}