gmm-est-regtree-fmllr.cc
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// gmmbin/gmm-est-regtree-fmllr.cc
// Copyright 2009-2011 Saarland University; Microsoft Corporation
// 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 "hmm/posterior.h"
#include "transform/regtree-fmllr-diag-gmm.h"
int main(int argc, char *argv[]) {
try {
typedef kaldi::int32 int32;
using namespace kaldi;
const char *usage =
"Compute FMLLR transforms per-utterance (default) or per-speaker for "
"the supplied set of speakers (spk2utt option). Note: writes RegtreeFmllrDiagGmm objects\n"
"Usage: gmm-est-regtree-fmllr [options] <model-in> <feature-rspecifier> "
"<posteriors-rspecifier> <regression-tree> <transforms-wspecifier>\n";
ParseOptions po(usage);
string spk2utt_rspecifier;
bool binary = true;
po.Register("spk2utt", &spk2utt_rspecifier, "rspecifier for speaker to "
"utterance-list map");
po.Register("binary", &binary, "Write output in binary mode");
// register other modules
RegtreeFmllrOptions opts;
opts.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() != 5) {
po.PrintUsage();
exit(1);
}
string model_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
posteriors_rspecifier = po.GetArg(3),
regtree_filename = po.GetArg(4),
xforms_wspecifier = po.GetArg(5);
RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
RegtreeFmllrDiagGmmWriter fmllr_writer(xforms_wspecifier);
AmDiagGmm am_gmm;
TransitionModel trans_model;
{
bool binary;
Input ki(model_filename, &binary);
trans_model.Read(ki.Stream(), binary);
am_gmm.Read(ki.Stream(), binary);
}
RegressionTree regtree;
{
bool binary;
Input in(regtree_filename, &binary);
regtree.Read(in.Stream(), binary, am_gmm);
}
RegtreeFmllrDiagGmm fmllr_xforms;
RegtreeFmllrDiagGmmAccs fmllr_accs;
fmllr_accs.Init(regtree.NumBaseclasses(), am_gmm.Dim());
double tot_like = 0.0, tot_t = 0;
int32 num_done = 0, num_no_posterior = 0, num_other_error = 0;
double tot_objf_impr = 0.0, tot_t_objf = 0.0;
if (spk2utt_rspecifier != "") { // per-speaker adaptation
SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
string spk = spk2utt_reader.Key();
fmllr_accs.SetZero();
const vector<string> &uttlist = spk2utt_reader.Value();
for (vector<string>::const_iterator utt_itr = uttlist.begin(),
itr_end = uttlist.end(); utt_itr != itr_end; ++utt_itr) {
if (!feature_reader.HasKey(*utt_itr)) {
KALDI_WARN << "Did not find features for utterance " << *utt_itr;
continue;
}
if (!posteriors_reader.HasKey(*utt_itr)) {
KALDI_WARN << "Did not find posteriors for utterance "
<< *utt_itr;
num_no_posterior++;
continue;
}
const Matrix<BaseFloat> &feats = feature_reader.Value(*utt_itr);
const Posterior &posterior = posteriors_reader.Value(*utt_itr);
if (static_cast<int32>(posterior.size()) != feats.NumRows()) {
KALDI_WARN << "Posteriors has wrong size " << (posterior.size())
<< " vs. " << (feats.NumRows());
num_other_error++;
continue;
}
BaseFloat file_like = 0.0, file_t = 0.0;
Posterior pdf_posterior;
ConvertPosteriorToPdfs(trans_model, posterior, &pdf_posterior);
for (size_t i = 0; i < posterior.size(); i++) {
for (size_t j = 0; j < pdf_posterior[i].size(); j++) {
int32 pdf_id = pdf_posterior[i][j].first;
BaseFloat prob = pdf_posterior[i][j].second;
file_like += fmllr_accs.AccumulateForGmm(regtree, am_gmm,
feats.Row(i), pdf_id,
prob);
file_t += prob;
}
}
KALDI_VLOG(2) << "Average like for this file is " << (file_like/file_t)
<< " over " << file_t << " frames.";
tot_like += file_like;
tot_t += file_t;
num_done++;
if (num_done % 10 == 0)
KALDI_VLOG(1) << "Avg like per frame so far is "
<< (tot_like / tot_t);
} // end looping over all utterances of the current speaker
BaseFloat objf_impr, t;
fmllr_accs.Update(regtree, opts, &fmllr_xforms, &objf_impr, &t);
KALDI_LOG << "fMLLR objf improvement for speaker " << spk << " is "
<< (objf_impr/(t+1.0e-10)) << " per frame over " << t
<< " frames.";
tot_objf_impr += objf_impr;
tot_t_objf += t;
fmllr_writer.Write(spk, fmllr_xforms);
} // end looping over speakers
} else { // per-utterance adaptation
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
for (; !feature_reader.Done(); feature_reader.Next()) {
string key = feature_reader.Key();
if (!posteriors_reader.HasKey(key)) {
KALDI_WARN << "Did not find posteriors for utterance "
<< key;
num_no_posterior++;
continue;
}
const Matrix<BaseFloat> &feats = feature_reader.Value();
const Posterior &posterior = posteriors_reader.Value(key);
if (static_cast<int32>(posterior.size()) != feats.NumRows()) {
KALDI_WARN << "Posteriors has wrong size " << (posterior.size())
<< " vs. " << (feats.NumRows());
num_other_error++;
continue;
}
num_done++;
BaseFloat file_like = 0.0, file_t = 0.0;
fmllr_accs.SetZero();
Posterior pdf_posterior;
ConvertPosteriorToPdfs(trans_model, posterior, &pdf_posterior);
for (size_t i = 0; i < posterior.size(); i++) {
for (size_t j = 0; j < pdf_posterior[i].size(); j++) {
int32 pdf_id = pdf_posterior[i][j].first;
BaseFloat prob = pdf_posterior[i][j].second;
file_like += fmllr_accs.AccumulateForGmm(regtree, am_gmm,
feats.Row(i), pdf_id,
prob);
file_t += prob;
}
}
KALDI_VLOG(2) << "Average like for this file is " << (file_like/file_t)
<< " over " << file_t << " frames.";
tot_like += file_like;
tot_t += file_t;
if (num_done % 10 == 0)
KALDI_VLOG(1) << "Avg like per frame so far is "
<< (tot_like / tot_t);
BaseFloat objf_impr, t;
fmllr_accs.Update(regtree, opts, &fmllr_xforms, &objf_impr, &t);
KALDI_LOG << "fMLLR objf improvement for utterance " << key << " is "
<< (objf_impr/(t+1.0e-10)) << " per frame over " << t
<< " frames.";
tot_objf_impr += objf_impr;
tot_t_objf += t;
fmllr_writer.Write(feature_reader.Key(), fmllr_xforms);
}
}
KALDI_LOG << "Done " << num_done << " files, " << num_no_posterior
<< " with no posteriors, " << num_other_error
<< " with other errors.";
KALDI_LOG << "Overall objf improvement from MLLR is " << (tot_objf_impr/tot_t_objf)
<< " per frame " << " over " << tot_t_objf << " frames.";
KALDI_LOG << "Overall acoustic likelihood was " << (tot_like/tot_t)
<< " over " << tot_t << " frames.";
return 0;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}