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src/gmm/decodable-am-diag-gmm.cc
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// gmm/decodable-am-diag-gmm.cc
// Copyright 2009-2011 Saarland University; Lukas Burget
// 2013 Johns Hopkins Universith (author: Daniel Povey)
// 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 <vector>
using std::vector;
#include "gmm/decodable-am-diag-gmm.h"
namespace kaldi {
BaseFloat DecodableAmDiagGmmUnmapped::LogLikelihoodZeroBased(
int32 frame, int32 state) {
KALDI_ASSERT(static_cast<size_t>(frame) <
static_cast<size_t>(NumFramesReady()));
KALDI_ASSERT(static_cast<size_t>(state) < static_cast<size_t>(NumIndices()) &&
"Likely graph/model mismatch, e.g. using wrong HCLG.fst");
if (log_like_cache_[state].hit_time == frame) {
return log_like_cache_[state].log_like; // return cached value, if found
}
if (frame != previous_frame_) { // cache the squared stats.
data_squared_.CopyFromVec(feature_matrix_.Row(frame));
data_squared_.ApplyPow(2.0);
previous_frame_ = frame;
}
const DiagGmm &pdf = acoustic_model_.GetPdf(state);
const VectorBase<BaseFloat> &data = feature_matrix_.Row(frame);
// check if everything is in order
if (pdf.Dim() != data.Dim()) {
KALDI_ERR << "Dim mismatch: data dim = " << data.Dim()
<< " vs. model dim = " << pdf.Dim();
}
if (!pdf.valid_gconsts()) {
KALDI_ERR << "State " << (state) << ": Must call ComputeGconsts() "
"before computing likelihood.";
}
Vector<BaseFloat> loglikes(pdf.gconsts()); // need to recreate for each pdf
// loglikes += means * inv(vars) * data.
loglikes.AddMatVec(1.0, pdf.means_invvars(), kNoTrans, data, 1.0);
// loglikes += -0.5 * inv(vars) * data_sq.
loglikes.AddMatVec(-0.5, pdf.inv_vars(), kNoTrans, data_squared_, 1.0);
BaseFloat log_sum = loglikes.LogSumExp(log_sum_exp_prune_);
if (KALDI_ISNAN(log_sum) || KALDI_ISINF(log_sum))
KALDI_ERR << "Invalid answer (overflow or invalid variances/features?)";
log_like_cache_[state].log_like = log_sum;
log_like_cache_[state].hit_time = frame;
return log_sum;
}
void DecodableAmDiagGmmUnmapped::ResetLogLikeCache() {
if (static_cast<int32>(log_like_cache_.size()) != acoustic_model_.NumPdfs()) {
log_like_cache_.resize(acoustic_model_.NumPdfs());
}
vector<LikelihoodCacheRecord>::iterator it = log_like_cache_.begin(),
end = log_like_cache_.end();
for (; it != end; ++it) { it->hit_time = -1; }
}
} // namespace kaldi
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