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Commit d54b85be authored by Yajie Miao's avatar Yajie Miao
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Minor changes, due to modification in transform/basis-fmllr-diag-gmm

git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@1094 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
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// gmmbin/gmm-est-basis-fmllr.cc
// Copyright 2012 Carnegie Mellon University (author: Yajie Miao)
// 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"
namespace kaldi {
void AccumulateForUtterance(const Matrix<BaseFloat> &feats,
const Posterior &post,
const TransitionModel &trans_model,
const AmDiagGmm &am_gmm,
FmllrDiagGmmAccs *spk_stats) {
for (size_t i = 0; i < post.size(); i++) {
for (size_t j = 0; j < post[i].size(); j++) {
int32 pdf_id = trans_model.TransitionIdToPdf(post[i][j].first);
spk_stats->AccumulateForGmm(am_gmm.GetPdf(pdf_id),
feats.Row(i),
post[i][j].second);
}
}
}
}
int main(int argc, char *argv[]) {
try {
typedef kaldi::int32 int32;
using namespace kaldi;
const char *usage =
"Perform basis fMLLR adaptation in testing stage, either per utterance or\n"
"for the supplied set of speakers (spk2utt option). Reads posterior to\n"
"accumulate fMLLR stats for each speaker/utterance. Writes to a table of\n"
"matrices.\n"
"Usage: gmm-est-basis-fmllr [options] <model-in> <basis-rspecifier> <feature-rspecifier> "
"<post-rspecifier> <transform-wspecifier>\n";
ParseOptions po(usage);
BasisFmllrOptions basis_fmllr_opts;
string spk2utt_rspecifier;
string weights_out_filename;
po.Register("spk2utt", &spk2utt_rspecifier, "Rspecifier for speaker to "
"utterance-list map");
po.Register("write-weights", &weights_out_filename, "File to write base "
"weights to.");
basis_fmllr_opts.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() != 5) {
po.PrintUsage();
exit(1);
}
string
model_rxfilename = po.GetArg(1),
basis_rspecifier = po.GetArg(2),
feature_rspecifier = po.GetArg(3),
post_rspecifier = po.GetArg(4),
trans_wspecifier = po.GetArg(5);
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);
}
BasisFmllrEstimate basis_est(am_gmm.Dim());
{
bool binary;
Input ki(basis_rspecifier, &binary);
basis_est.ReadBasis(ki.Stream(), binary, false);
}
RandomAccessPosteriorReader post_reader(post_rspecifier);
double tot_impr = 0.0, tot_t = 0.0;
BaseFloatMatrixWriter transform_writer(trans_wspecifier);
BaseFloatVectorWriter weights_writer;
if (!weights_out_filename.empty()) {
weights_writer.Open(weights_out_filename);
}
int32 num_done = 0, num_no_post = 0, num_other_error = 0;
if (spk2utt_rspecifier != "") { // per-speaker adaptation
SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);
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 the current speaker
double impr, spk_tot_t;
{
// Compute the transform and write it out.
Matrix<BaseFloat> transform(am_gmm.Dim(), am_gmm.Dim() + 1);
transform.SetUnit();
Vector<BaseFloat> weights(am_gmm.Dim() * (am_gmm.Dim() + 1)); // size will be adjusted
impr = basis_est.BasisFmllrCoefficients(spk_stats, &transform,
&weights, basis_fmllr_opts);
spk_tot_t = spk_stats.beta_;
transform_writer.Write(spk, transform);
// Optionally write out the base weights
if (!weights_out_filename.empty() && weights.Dim() > 0)
weights_writer.Write(spk, weights);
}
KALDI_LOG << "For speaker " << spk << ", auxf-impr from Basis fMLLR is "
<< (impr / spk_tot_t) << ", over " << spk_tot_t << " frames.";
tot_impr += impr;
tot_t += spk_tot_t;
} // end looping over speakers
} else { // per-utterance adaptation
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;
}
FmllrDiagGmmAccs spk_stats(am_gmm.Dim());
AccumulateForUtterance(feats, post, trans_model, am_gmm, &spk_stats);
num_done++;
BaseFloat impr, utt_tot_t;
{ // Compute the transform and write it out.
Matrix<BaseFloat> transform(am_gmm.Dim(), am_gmm.Dim()+1);
transform.SetUnit();
Vector<BaseFloat> weights(am_gmm.Dim() * (am_gmm.Dim() + 1)); // size will be adjusted
impr = basis_est.BasisFmllrCoefficients(spk_stats, &transform,
&weights, basis_fmllr_opts);
utt_tot_t = spk_stats.beta_;
transform_writer.Write(utt, transform);
// Optionally write out the base weights
if (!weights_out_filename.empty() && weights.Dim() > 0)
weights_writer.Write(utt, weights);
}
KALDI_LOG << "For utterance " << utt << ", auxf-impr from Basis fMLLR is "
<< (impr / utt_tot_t) << ", over " << utt_tot_t << " frames.";
tot_impr += impr;
tot_t += utt_tot_t;
} // end looping over all the utterances
}
KALDI_LOG << "Done " << num_done << " files, " << num_no_post
<< " with no posts, " << num_other_error << " with other errors.";
KALDI_LOG << "Overall fMLLR auxf-impr per frame is "
<< (tot_impr / tot_t) << " over " << tot_t << " frames.";
return (num_done != 0 ? 0 : 1);
} catch(const std::exception& e) {
std::cerr << e.what();
return -1;
}
}
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