sgmm2-post-to-gpost.cc
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// sgmm2bin/sgmm2-post-to-gpost.cc
// Copyright 2009-2012 Saarland University Microsoft Corporation
// Johns Hopkins University (Author: Daniel Povey)
// 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 "sgmm2/am-sgmm2.h"
#include "hmm/transition-model.h"
#include "sgmm2/estimate-am-sgmm2.h"
#include "hmm/posterior.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
try {
const char *usage =
"Convert posteriors to Gaussian-level posteriors for SGMM training.\n"
"Usage: sgmm2-post-to-gpost [options] <model-in> <feature-rspecifier> "
"<posteriors-rspecifier> <gpost-wspecifier>\n"
"e.g.: sgmm2-post-to-gpost 1.mdl 1.ali scp:train.scp 'ark:ali-to-post ark:1.ali ark:-|' ark:-";
ParseOptions po(usage);
std::string gselect_rspecifier, spkvecs_rspecifier, utt2spk_rspecifier;
po.Register("gselect", &gselect_rspecifier, "Precomputed Gaussian indices (rspecifier)");
po.Register("spk-vecs", &spkvecs_rspecifier, "Speaker vectors (rspecifier)");
po.Register("utt2spk", &utt2spk_rspecifier,
"rspecifier for utterance to speaker map");
po.Read(argc, argv);
if (po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
if (gselect_rspecifier == "")
KALDI_ERR << "--gselect option is required";
std::string model_filename = po.GetArg(1),
feature_rspecifier = po.GetArg(2),
posteriors_rspecifier = po.GetArg(3),
gpost_wspecifier = po.GetArg(4);
using namespace kaldi;
typedef kaldi::int32 int32;
AmSgmm2 am_sgmm;
TransitionModel trans_model;
{
bool binary;
Input ki(model_filename, &binary);
trans_model.Read(ki.Stream(), binary);
am_sgmm.Read(ki.Stream(), binary);
}
double tot_like = 0.0;
kaldi::int64 tot_t = 0;
SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
RandomAccessBaseFloatVectorReaderMapped spkvecs_reader(spkvecs_rspecifier,
utt2spk_rspecifier);
Sgmm2PerFrameDerivedVars per_frame_vars;
Sgmm2GauPostWriter gpost_writer(gpost_wspecifier);
int32 num_done = 0, num_err = 0;
for (; !feature_reader.Done(); feature_reader.Next()) {
const Matrix<BaseFloat> &mat = feature_reader.Value();
std::string utt = feature_reader.Key();
if (!posteriors_reader.HasKey(utt)
|| posteriors_reader.Value(utt).size() != mat.NumRows()) {
KALDI_WARN << "No posteriors available for utterance " << utt
<< " (or wrong size)";
num_err++;
continue;
}
Posterior posterior = posteriors_reader.Value(utt);
if (!gselect_reader.HasKey(utt) ||
gselect_reader.Value(utt).size() != mat.NumRows()) {
KALDI_WARN << "No Gaussian-selection info available for utterance "
<< utt << " (or wrong size)";
num_err++;
continue;
}
const std::vector<std::vector<int32> > &gselect =
gselect_reader.Value(utt);
Sgmm2PerSpkDerivedVars spk_vars;
if (spkvecs_reader.IsOpen()) {
if (spkvecs_reader.HasKey(utt)) {
spk_vars.SetSpeakerVector(spkvecs_reader.Value(utt));
am_sgmm.ComputePerSpkDerivedVars(&spk_vars);
} else {
KALDI_WARN << "Cannot find speaker vector for " << utt;
num_err++;
continue;
}
} // else spk_vars is "empty"
num_done++;
BaseFloat tot_like_this_file = 0.0, tot_weight = 0.0;
Sgmm2GauPost gpost(posterior.size()); // posterior.size() == T.
SortPosteriorByPdfs(trans_model, &posterior);
int32 prev_pdf_id = -1;
BaseFloat prev_like = 0;
Matrix<BaseFloat> prev_posterior;
for (size_t i = 0; i < posterior.size(); i++) {
am_sgmm.ComputePerFrameVars(mat.Row(i), gselect[i],
spk_vars, &per_frame_vars);
gpost[i].gselect = gselect[i];
gpost[i].tids.resize(posterior[i].size());
gpost[i].posteriors.resize(posterior[i].size());
prev_pdf_id = -1; // Only cache for the same frame.
for (size_t j = 0; j < posterior[i].size(); j++) {
int32 tid = posterior[i][j].first, // transition identifier.
pdf_id = trans_model.TransitionIdToPdf(tid);
BaseFloat weight = posterior[i][j].second;
gpost[i].tids[j] = tid;
if (pdf_id != prev_pdf_id) {
// First time see this pdf-id for this frame, update the cached
// variables.
prev_pdf_id = pdf_id;
prev_like = am_sgmm.ComponentPosteriors(per_frame_vars, pdf_id,
&spk_vars,
&prev_posterior);
}
gpost[i].posteriors[j] = prev_posterior;
tot_like_this_file += prev_like * weight;
tot_weight += weight;
gpost[i].posteriors[j].Scale(weight);
}
}
KALDI_VLOG(2) << "Average like for this file is "
<< (tot_like_this_file/posterior.size()) << " over "
<< posterior.size() <<" frames.";
tot_like += tot_like_this_file;
tot_t += posterior.size();
if (num_done % 10 == 0)
KALDI_LOG << "Avg like per frame so far is "
<< (tot_like/tot_t);
gpost_writer.Write(utt, gpost);
}
KALDI_LOG << "Overall like per frame (Gaussian only) = "
<< (tot_like/tot_t) << " over " << tot_t << " frames.";
KALDI_LOG << "Done " << num_done << " files, " << num_err
<< " with errors.";
return (num_done != 0 ? 0 : 1);
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}