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src/sgmm2/am-sgmm2.cc
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// sgmm2/am-sgmm2.cc
// Copyright 2009-2011 Microsoft Corporation; Lukas Burget;
// Saarland University (Author: Arnab Ghoshal);
// Ondrej Glembek; Yanmin Qian;
// Copyright 2012-2013 Johns Hopkins University (Author: Daniel Povey)
// Liang Lu; Arnab Ghoshal
// 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 <functional>
#include "sgmm2/am-sgmm2.h"
#include "util/kaldi-thread.h"
namespace kaldi {
using std::vector;
// This function needs to be added because std::generate is complaining
// about RandGauss(), which takes an optional arguments.
static inline float _RandGauss()
{
return RandGauss();
}
void Sgmm2LikelihoodCache::NextFrame() {
t++;
if (t == 0) {
t++; // skip over zero; zero is used to invalidate frames.
for (size_t i = 0; i < substate_cache.size(); i++)
substate_cache[i].t = 0;
for (size_t i = 0; i < pdf_cache.size(); i++)
pdf_cache[i].t = 0;
}
}
void AmSgmm2::ComputeGammaI(const Vector<BaseFloat> &state_occupancies,
Vector<BaseFloat> *gamma_i) const {
KALDI_ASSERT(state_occupancies.Dim() == NumPdfs());
Vector<BaseFloat> w_jm(NumGauss());
gamma_i->Resize(NumGauss());
for (int32 j1 = 0; j1 < NumGroups(); j1++) {
int32 M = NumSubstatesForGroup(j1);
const std::vector<int32> &pdfs = group2pdf_[j1];
Vector<BaseFloat> substate_weight(M); // total weight for each substate.
for (size_t i = 0; i < pdfs.size(); i++) {
int32 j2 = pdfs[i];
substate_weight.AddVec(state_occupancies(j2), c_[j2]);
}
for (int32 m = 0; m < M; m++) {
w_jm.AddMatVec(1.0, w_, kNoTrans, v_[j1].Row(m), 0.0);
w_jm.ApplySoftMax();
gamma_i->AddVec(substate_weight(m), w_jm);
}
}
}
void AmSgmm2::ComputePdfMappings() {
if (pdf2group_.empty()) {
KALDI_WARN << "ComputePdfMappings(): no pdf2group_ map, assuming you "
"are reading in old model.";
KALDI_ASSERT(v_.size() != 0);
pdf2group_.resize(v_.size());
for (int32 j2 = 0; j2 < static_cast<int32>(pdf2group_.size()); j2++)
pdf2group_[j2] = j2;
}
group2pdf_.clear();
for (int32 j2 = 0; j2 < static_cast<int32>(pdf2group_.size()); j2++) {
int32 j1 = pdf2group_[j2];
if (group2pdf_.size() <= j1) group2pdf_.resize(j1+1);
group2pdf_[j1].push_back(j2);
}
}
void AmSgmm2::Read(std::istream &in_stream, bool binary) {
{ // We want this to work even if the object was previously
// populated, so we clear the items that are more likely
// to cause problems.
pdf2group_.clear();
group2pdf_.clear();
u_.Resize(0,0);
w_jmi_.clear();
v_.clear();
}
// removing anything that was in the object before.
int32 num_pdfs = -1, feat_dim, num_gauss;
std::string token;
ExpectToken(in_stream, binary, "<SGMM>");
ExpectToken(in_stream, binary, "<NUMSTATES>");
ReadBasicType(in_stream, binary, &num_pdfs);
ExpectToken(in_stream, binary, "<DIMENSION>");
ReadBasicType(in_stream, binary, &feat_dim);
ExpectToken(in_stream, binary, "<NUMGAUSS>");
ReadBasicType(in_stream, binary, &num_gauss);
KALDI_ASSERT(num_pdfs > 0 && feat_dim > 0);
ReadToken(in_stream, binary, &token);
while (token != "</SGMM>") {
if (token == "<PDF2GROUP>") {
ReadIntegerVector(in_stream, binary, &pdf2group_);
ComputePdfMappings();
} else if (token == "<WEIGHTIDX2GAUSS>") { // TEMP! Will remove.
std::vector<int32> garbage;
ReadIntegerVector(in_stream, binary, &garbage);
} else if (token == "<DIAG_UBM>") {
diag_ubm_.Read(in_stream, binary);
} else if (token == "<FULL_UBM>") {
full_ubm_.Read(in_stream, binary);
} else if (token == "<SigmaInv>") {
SigmaInv_.resize(num_gauss);
for (int32 i = 0; i < num_gauss; i++) {
SigmaInv_[i].Read(in_stream, binary);
}
} else if (token == "<M>") {
M_.resize(num_gauss);
for (int32 i = 0; i < num_gauss; i++) {
M_[i].Read(in_stream, binary);
}
} else if (token == "<N>") {
N_.resize(num_gauss);
for (int32 i = 0; i < num_gauss; i++) {
N_[i].Read(in_stream, binary);
}
} else if (token == "<w>") {
w_.Read(in_stream, binary);
} else if (token == "<u>") {
u_.Read(in_stream, binary);
} else if (token == "<v>") {
int32 num_groups = group2pdf_.size();
if (num_groups == 0) {
KALDI_WARN << "Reading old model with new code (should still work)";
num_groups = num_pdfs;
}
v_.resize(num_groups);
for (int32 j1 = 0; j1 < num_groups; j1++) {
v_[j1].Read(in_stream, binary);
}
} else if (token == "<c>") {
c_.resize(num_pdfs);
for (int32 j2 = 0; j2 < num_pdfs; j2++) {
c_[j2].Read(in_stream, binary);
}
} else if (token == "<n>") {
int32 num_groups = group2pdf_.size();
if (num_groups == 0) num_groups = num_pdfs;
n_.resize(num_groups);
for (int32 j1 = 0; j1 < num_groups; j1++) {
n_[j1].Read(in_stream, binary);
}
// The following are the Gaussian prior parameters for MAP adaptation of M
// They may be moved to somewhere else eventually.
} else if (token == "<M_Prior>") {
ExpectToken(in_stream, binary, "<NUMGaussians>");
ReadBasicType(in_stream, binary, &num_gauss);
M_prior_.resize(num_gauss);
for (int32 i = 0; i < num_gauss; i++) {
M_prior_[i].Read(in_stream, binary);
}
} else if (token == "<Row_Cov_Inv>") {
row_cov_inv_.Read(in_stream, binary);
} else if (token == "<Col_Cov_Inv>") {
col_cov_inv_.Read(in_stream, binary);
} else {
KALDI_ERR << "Unexpected token '" << token << "' in model file ";
}
ReadToken(in_stream, binary, &token);
}
if (pdf2group_.empty())
ComputePdfMappings(); // sets up group2pdf_, and pdf2group_ if reading
// old model.
if (n_.empty())
ComputeNormalizers();
if (HasSpeakerDependentWeights())
ComputeWeights();
}
int32 AmSgmm2::Pdf2Group(int32 j2) const {
KALDI_ASSERT(static_cast<size_t>(j2) < pdf2group_.size());
int32 j1 = pdf2group_[j2];
return j1;
}
void AmSgmm2::Write(std::ostream &out_stream,
bool binary,
SgmmWriteFlagsType write_params) const {
int32 num_pdfs = NumPdfs(),
feat_dim = FeatureDim(),
num_gauss = NumGauss();
WriteToken(out_stream, binary, "<SGMM>");
if (!binary) out_stream << "
";
WriteToken(out_stream, binary, "<NUMSTATES>");
WriteBasicType(out_stream, binary, num_pdfs);
WriteToken(out_stream, binary, "<DIMENSION>");
WriteBasicType(out_stream, binary, feat_dim);
WriteToken(out_stream, binary, "<NUMGAUSS>");
WriteBasicType(out_stream, binary, num_gauss);
if (!binary) out_stream << "
";
if (write_params & kSgmmBackgroundGmms) {
WriteToken(out_stream, binary, "<DIAG_UBM>");
diag_ubm_.Write(out_stream, binary);
WriteToken(out_stream, binary, "<FULL_UBM>");
full_ubm_.Write(out_stream, binary);
}
if (write_params & kSgmmGlobalParams) {
WriteToken(out_stream, binary, "<SigmaInv>");
if (!binary) out_stream << "
";
for (int32 i = 0; i < num_gauss; i++) {
SigmaInv_[i].Write(out_stream, binary);
}
WriteToken(out_stream, binary, "<M>");
if (!binary) out_stream << "
";
for (int32 i = 0; i < num_gauss; i++) {
M_[i].Write(out_stream, binary);
}
if (N_.size() != 0) {
WriteToken(out_stream, binary, "<N>");
if (!binary) out_stream << "
";
for (int32 i = 0; i < num_gauss; i++) {
N_[i].Write(out_stream, binary);
}
}
WriteToken(out_stream, binary, "<w>");
w_.Write(out_stream, binary);
WriteToken(out_stream, binary, "<u>");
u_.Write(out_stream, binary);
}
if (write_params & kSgmmStateParams) {
WriteToken(out_stream, binary, "<PDF2GROUP>");
WriteIntegerVector(out_stream, binary, pdf2group_);
WriteToken(out_stream, binary, "<v>");
for (int32 j1 = 0; j1 < NumGroups(); j1++) {
v_[j1].Write(out_stream, binary);
}
WriteToken(out_stream, binary, "<c>");
for (int32 j2 = 0; j2 < num_pdfs; j2++) {
c_[j2].Write(out_stream, binary);
}
}
if (write_params & kSgmmNormalizers) {
WriteToken(out_stream, binary, "<n>");
if (n_.empty())
KALDI_WARN << "Not writing normalizers since they are not present.";
else
for (int32 j1 = 0; j1 < NumGroups(); j1++)
n_[j1].Write(out_stream, binary);
}
WriteToken(out_stream, binary, "</SGMM>");
}
void AmSgmm2::Check(bool show_properties) {
int32 J1 = NumGroups(),
J2 = NumPdfs(),
num_gauss = NumGauss(),
feat_dim = FeatureDim(),
phn_dim = PhoneSpaceDim(),
spk_dim = SpkSpaceDim();
if (show_properties)
KALDI_LOG << "AmSgmm2: #pdfs = " << J2 << ", #pdf-groups = "
<< J1 << ", #Gaussians = "
<< num_gauss << ", feature dim = " << feat_dim
<< ", phone-space dim =" << phn_dim
<< ", speaker-space dim =" << spk_dim;
KALDI_ASSERT(J1 > 0 && num_gauss > 0 && feat_dim > 0 && phn_dim > 0
&& J2 > 0 && J2 >= J1);
std::ostringstream debug_str;
// First check the diagonal-covariance UBM.
KALDI_ASSERT(diag_ubm_.NumGauss() == num_gauss);
KALDI_ASSERT(diag_ubm_.Dim() == feat_dim);
// Check the full-covariance UBM.
KALDI_ASSERT(full_ubm_.NumGauss() == num_gauss);
KALDI_ASSERT(full_ubm_.Dim() == feat_dim);
// Check the globally-shared covariance matrices.
KALDI_ASSERT(SigmaInv_.size() == static_cast<size_t>(num_gauss));
for (int32 i = 0; i < num_gauss; i++) {
KALDI_ASSERT(SigmaInv_[i].NumRows() == feat_dim &&
SigmaInv_[i](0, 0) > 0.0); // or it wouldn't be +ve definite.
}
if (spk_dim != 0) {
KALDI_ASSERT(N_.size() == static_cast<size_t>(num_gauss));
for (int32 i = 0; i < num_gauss; i++)
KALDI_ASSERT(N_[i].NumRows() == feat_dim && N_[i].NumCols() == spk_dim);
if (u_.NumRows() == 0) {
debug_str << "Speaker-weight projections: no.";
} else {
KALDI_ASSERT(u_.NumRows() == num_gauss && u_.NumCols() == spk_dim);
debug_str << "Speaker-weight projections: yes.";
}
} else {
KALDI_ASSERT(N_.size() == 0 && u_.NumRows() == 0);
}
KALDI_ASSERT(M_.size() == static_cast<size_t>(num_gauss));
for (int32 i = 0; i < num_gauss; i++) {
KALDI_ASSERT(M_[i].NumRows() == feat_dim && M_[i].NumCols() == phn_dim);
}
KALDI_ASSERT(w_.NumRows() == num_gauss && w_.NumCols() == phn_dim);
{ // check v, c.
KALDI_ASSERT(v_.size() == static_cast<size_t>(J1) &&
c_.size() == static_cast<size_t>(J2));
int32 nSubstatesTot = 0;
for (int32 j1 = 0; j1 < J1; j1++) {
int32 M_j = NumSubstatesForGroup(j1);
nSubstatesTot += M_j;
KALDI_ASSERT(M_j > 0 && v_[j1].NumRows() == M_j &&
v_[j1].NumCols() == phn_dim);
}
debug_str << "Substates: "<< (nSubstatesTot) << ". ";
int32 nSubstateWeights = 0;
for (int32 j2 = 0; j2 < J2; j2++) {
int32 j1 = Pdf2Group(j2);
int32 M = NumSubstatesForPdf(j2);
KALDI_ASSERT(M == NumSubstatesForGroup(j1));
nSubstateWeights += M;
}
KALDI_ASSERT(nSubstateWeights >= nSubstatesTot);
debug_str << "SubstateWeights: "<< (nSubstateWeights) << ". ";
}
// check normalizers.
if (n_.size() == 0) {
debug_str << "Normalizers: no. ";
} else {
debug_str << "Normalizers: yes. ";
KALDI_ASSERT(n_.size() == static_cast<size_t>(J1));
for (int32 j1 = 0; j1 < J1; j1++) {
KALDI_ASSERT(n_[j1].NumRows() == num_gauss &&
n_[j1].NumCols() == NumSubstatesForGroup(j1));
}
}
// check w_jmi_.
if (w_jmi_.size() == 0) {
debug_str << "Computed weights: no. ";
} else {
debug_str << "Computed weights: yes. ";
KALDI_ASSERT(w_jmi_.size() == static_cast<size_t>(J1));
for (int32 j1 = 0; j1 < J1; j1++) {
KALDI_ASSERT(w_jmi_[j1].NumRows() == NumSubstatesForGroup(j1) &&
w_jmi_[j1].NumCols() == num_gauss);
}
}
if (show_properties)
KALDI_LOG << "Subspace GMM model properties: " << debug_str.str();
}
void AmSgmm2::InitializeFromFullGmm(const FullGmm &full_gmm,
const std::vector<int32> &pdf2group,
int32 phn_subspace_dim,
int32 spk_subspace_dim,
bool speaker_dependent_weights,
BaseFloat self_weight) {
pdf2group_ = pdf2group;
ComputePdfMappings();
full_ubm_.CopyFromFullGmm(full_gmm);
diag_ubm_.CopyFromFullGmm(full_gmm);
if (phn_subspace_dim < 1 || phn_subspace_dim > full_gmm.Dim() + 1) {
KALDI_WARN << "Initial phone-subspace dimension must be >= 1, value is "
<< phn_subspace_dim << "; setting to " << full_gmm.Dim() + 1;
phn_subspace_dim = full_gmm.Dim() + 1;
}
KALDI_ASSERT(spk_subspace_dim >= 0);
w_.Resize(0, 0);
N_.clear();
c_.clear();
v_.clear();
SigmaInv_.clear();
KALDI_LOG << "Initializing model";
Matrix<BaseFloat> norm_xform;
ComputeFeatureNormalizingTransform(full_gmm, &norm_xform);
InitializeMw(phn_subspace_dim, norm_xform);
if (spk_subspace_dim > 0)
InitializeNu(spk_subspace_dim, norm_xform, speaker_dependent_weights);
InitializeVecsAndSubstateWeights(self_weight);
KALDI_LOG << "Initializing variances";
InitializeCovars();
}
void AmSgmm2::CopyFromSgmm2(const AmSgmm2 &other,
bool copy_normalizers,
bool copy_weights) {
KALDI_LOG << "Copying AmSgmm2";
pdf2group_ = other.pdf2group_;
group2pdf_ = other.group2pdf_;
// Copy background GMMs
diag_ubm_.CopyFromDiagGmm(other.diag_ubm_);
full_ubm_.CopyFromFullGmm(other.full_ubm_);
// Copy global params
SigmaInv_ = other.SigmaInv_;
M_ = other.M_;
w_ = other.w_;
N_ = other.N_;
u_ = other.u_;
// Copy state-specific params, but only copy normalizers if requested.
v_ = other.v_;
c_ = other.c_;
if (copy_normalizers) n_ = other.n_;
if (copy_weights) w_jmi_ = other.w_jmi_;
KALDI_LOG << "Done.";
}
void AmSgmm2::ComputePerFrameVars(const VectorBase<BaseFloat> &data,
const std::vector<int32> &gselect,
const Sgmm2PerSpkDerivedVars &spk_vars,
Sgmm2PerFrameDerivedVars *per_frame_vars) const {
KALDI_ASSERT(!n_.empty() && "ComputeNormalizers() must be called.");
per_frame_vars->Resize(gselect.size(), FeatureDim(), PhoneSpaceDim());
per_frame_vars->gselect = gselect;
per_frame_vars->xt.CopyFromVec(data);
for (int32 ki = 0, last = gselect.size(); ki < last; ki++) {
int32 i = gselect[ki];
per_frame_vars->xti.Row(ki).CopyFromVec(per_frame_vars->xt);
if (spk_vars.v_s.Dim() != 0)
per_frame_vars->xti.Row(ki).AddVec(-1.0, spk_vars.o_s.Row(i));
}
Vector<BaseFloat> SigmaInv_xt(FeatureDim());
bool speaker_dep_weights =
(spk_vars.v_s.Dim() != 0 && HasSpeakerDependentWeights());
for (int32 ki = 0, last = gselect.size(); ki < last; ki++) {
int32 i = gselect[ki];
BaseFloat ssgmm_term = (speaker_dep_weights ? spk_vars.log_b_is(i) : 0.0);
SigmaInv_xt.AddSpVec(1.0, SigmaInv_[i], per_frame_vars->xti.Row(ki), 0.0);
// Eq (35): z_{i}(t) = M_{i}^{T} \Sigma_{i}^{-1} x_{i}(t)
per_frame_vars->zti.Row(ki).AddMatVec(1.0, M_[i], kTrans, SigmaInv_xt, 0.0);
// Eq.(36): n_{i}(t) = -0.5 x_{i}^{T} \Sigma_{i}^{-1} x_{i}(t)
per_frame_vars->nti(ki) = -0.5 * VecVec(per_frame_vars->xti.Row(ki),
SigmaInv_xt) + ssgmm_term;
}
}
// inline
void AmSgmm2::ComponentLogLikes(const Sgmm2PerFrameDerivedVars &per_frame_vars,
int32 j1,
Sgmm2PerSpkDerivedVars *spk_vars,
Matrix<BaseFloat> *loglikes) const {
const vector<int32> &gselect = per_frame_vars.gselect;
int32 num_gselect = gselect.size(), num_substates = v_[j1].NumRows();
// Eq.(37): log p(x(t), m, i|j) [indexed by j, ki]
// Although the extra memory allocation of storing this as a
// matrix might seem unnecessary, we save time in the LogSumExp()
// via more effective pruning.
loglikes->Resize(num_gselect, num_substates);
bool speaker_dep_weights =
(spk_vars->v_s.Dim() != 0 && HasSpeakerDependentWeights());
if (speaker_dep_weights) {
KALDI_ASSERT(static_cast<int32>(spk_vars->log_d_jms.size()) == NumGroups());
KALDI_ASSERT(static_cast<int32>(w_jmi_.size()) == NumGroups() ||
"You need to call ComputeWeights().");
}
for (int32 ki = 0; ki < num_gselect; ki++) {
SubVector<BaseFloat> logp_xi(*loglikes, ki);
int32 i = gselect[ki];
// for all substates, compute z_{i}^T v_{jm}
logp_xi.AddMatVec(1.0, v_[j1], kNoTrans, per_frame_vars.zti.Row(ki), 0.0);
logp_xi.AddVec(1.0, n_[j1].Row(i)); // for all substates, add n_{jim}
logp_xi.Add(per_frame_vars.nti(ki)); // for all substates, add n_{i}(t)
}
if (speaker_dep_weights) { // [SSGMM]
Vector<BaseFloat> &log_d = spk_vars->log_d_jms[j1];
if (log_d.Dim() == 0) { // have not yet cached this quantity.
log_d.Resize(num_substates);
log_d.AddMatVec(1.0, w_jmi_[j1], kNoTrans, spk_vars->b_is, 0.0);
log_d.ApplyLog();
}
loglikes->AddVecToRows(-1.0, log_d); // [SSGMM] this is the term
// - log d_{jm}^{(s)} in the likelihood function [eq. 25 in
// the techreport]
}
}
BaseFloat AmSgmm2::LogLikelihood(const Sgmm2PerFrameDerivedVars &per_frame_vars,
int32 j2,
Sgmm2LikelihoodCache *cache,
Sgmm2PerSpkDerivedVars *spk_vars,
BaseFloat log_prune) const {
int32 t = cache->t; // not a real time; used to uniquely identify frames.
// Forgo asserts here, as this is frequently called.
// We'll probably get a segfault if an error is made.
Sgmm2LikelihoodCache::PdfCacheElement &pdf_cache =
cache->pdf_cache[j2];
#ifdef KALDI_PARANOID
bool random_test = (Rand() % 1000 == 1); // to check that the user is
// calling Next() on the cache, as they should.
#else
bool random_test = false; // compiler will ignore test branches.
#endif
if (pdf_cache.t == t) {
if (!random_test) return pdf_cache.log_like;
} else {
random_test = false;
}
// if random_test == true at this point, it was already cached, and we will
// verify that we return the same value as the cached one.
pdf_cache.t = t;
int32 j1 = pdf2group_[j2];
Sgmm2LikelihoodCache::SubstateCacheElement &substate_cache =
cache->substate_cache[j1];
if (substate_cache.t != t) { // Need to compute sub-state likelihoods.
substate_cache.t = t;
Matrix<BaseFloat> loglikes; // indexed [gselect-index][substate-index]
ComponentLogLikes(per_frame_vars, j1, spk_vars, &loglikes);
BaseFloat max = loglikes.Max(); // use this to keep things in good numerical range.
loglikes.Add(-max);
loglikes.ApplyExp();
substate_cache.remaining_log_like = max;
int32 num_substates = loglikes.NumCols();
substate_cache.likes.Resize(num_substates); // zeroes it.
substate_cache.likes.AddRowSumMat(1.0, loglikes); // add likelihoods [not in log!] for
// each column [i.e. summing over the rows], so we get the sum for
// each substate index. You have to multiply by exp(remaining_log_like)
// to get a real likelihood.
}
BaseFloat log_like = substate_cache.remaining_log_like
+ Log(VecVec(substate_cache.likes, c_[j2]));
if (random_test)
KALDI_ASSERT(ApproxEqual(pdf_cache.log_like, log_like));
pdf_cache.log_like = log_like;
KALDI_ASSERT(log_like == log_like && log_like - log_like == 0); // check
// that it's not NaN or infinity.
return log_like;
}
BaseFloat
AmSgmm2::ComponentPosteriors(const Sgmm2PerFrameDerivedVars &per_frame_vars,
int32 j2,
Sgmm2PerSpkDerivedVars *spk_vars,
Matrix<BaseFloat> *post) const {
KALDI_ASSERT(j2 < NumPdfs() && post != NULL);
int32 j1 = pdf2group_[j2];
ComponentLogLikes(per_frame_vars, j1, spk_vars, post); // now
// post is a matrix of log-likelihoods indexed by [gaussian-selection index]
// [sub-state index]. It doesn't include the sub-state weights,
// though.
BaseFloat loglike = post->Max();
post->Add(-loglike); // get it to nicer numeric range.
post->ApplyExp(); // so we're dealing with likelihoods (with an arbitrary offset
// "loglike" removed to make it in a nice numeric range)
post->MulColsVec(c_[j2]); // include the sub-state weights.
BaseFloat tot_like = post->Sum();
KALDI_ASSERT(tot_like != 0.0); // note: not valid to have zero weights.
loglike += Log(tot_like);
post->Scale(1.0 / tot_like); // so "post" now sums to one, and "loglike"
// contains the correct log-likelihood of the data given the pdf.
return loglike;
}
void AmSgmm2::SplitSubstatesInGroup(const Vector<BaseFloat> &pdf_occupancies,
const Sgmm2SplitSubstatesConfig &opts,
const SpMatrix<BaseFloat> &sqrt_H_sm,
int32 j1,
int32 tgt_M) {
const std::vector<int32> &pdfs = group2pdf_[j1];
int32 phn_dim = PhoneSpaceDim(), cur_M = NumSubstatesForGroup(j1),
num_pdfs_for_group = pdfs.size();
Vector<BaseFloat> rand_vec(phn_dim), v_shift(phn_dim);
KALDI_ASSERT(tgt_M >= cur_M);
if (cur_M == tgt_M) return;
// Resize v[j1] to fit new substates
{
Matrix<BaseFloat> tmp_v_j(v_[j1]);
v_[j1].Resize(tgt_M, phn_dim);
v_[j1].Range(0, cur_M, 0, phn_dim).CopyFromMat(tmp_v_j);
}
// we'll use a temporary matrix for the c quantities.
Matrix<BaseFloat> c_j(num_pdfs_for_group, tgt_M);
for (int32 i = 0; i < num_pdfs_for_group; i++) {
int32 j2 = pdfs[i];
c_j.Row(i).Range(0, cur_M).CopyFromVec(c_[j2]);
}
// Keep splitting substates until obtaining the desired number
for (; cur_M < tgt_M; cur_M++) {
int32 split_m; // substate to split.
{
Vector<BaseFloat> substate_count(tgt_M);
substate_count.AddRowSumMat(1.0, c_j);
BaseFloat *data = substate_count.Data();
split_m = std::max_element(data, data+cur_M) - data;
}
for (int32 i = 0; i < num_pdfs_for_group; i++) { // divide count of split
// substate. [extended for SCTM]
// c_{jkm} := c_{jmk}' := c_{jkm} / 2
c_j(i, split_m) = c_j(i, cur_M) = c_j(i, split_m) / 2;
}
// v_{jkm} := +/- split_perturb * H_k^{(sm)}^{-0.5} * rand_vec
std::generate(rand_vec.Data(), rand_vec.Data() + rand_vec.Dim(),
_RandGauss);
v_shift.AddSpVec(opts.perturb_factor, sqrt_H_sm, rand_vec, 0.0);
v_[j1].Row(cur_M).CopyFromVec(v_[j1].Row(split_m));
v_[j1].Row(cur_M).AddVec(1.0, v_shift);
v_[j1].Row(split_m).AddVec(-1.0, v_shift);
}
// copy the temporary matrix for the c_ (sub-state weight)
// quantities back to the place it belongs.
for (int32 i = 0; i < num_pdfs_for_group; i++) {
int32 j2 = pdfs[i];
c_[j2].Resize(tgt_M);
c_[j2].CopyFromVec(c_j.Row(i));
}
}
void AmSgmm2::SplitSubstates(const Vector<BaseFloat> &pdf_occupancies,
const Sgmm2SplitSubstatesConfig &opts) {
KALDI_ASSERT(pdf_occupancies.Dim() == NumPdfs());
int32 J1 = NumGroups(), J2 = NumPdfs();
Vector<BaseFloat> group_occupancies(J1);
for (int32 j2 = 0; j2 < J2; j2++)
group_occupancies(Pdf2Group(j2)) += pdf_occupancies(j2);
vector<int32> tgt_num_substates;
GetSplitTargets(group_occupancies, opts.split_substates,
opts.power, opts.min_count, &tgt_num_substates);
int32 tot_num_substates_old = 0, tot_num_substates_new = 0;
vector< SpMatrix<BaseFloat> > H_i;
SpMatrix<BaseFloat> sqrt_H_sm;
ComputeH(&H_i); // set up that array.
ComputeHsmFromModel(H_i, pdf_occupancies, &sqrt_H_sm, opts.max_cond);
H_i.clear();
sqrt_H_sm.ApplyPow(-0.5);
for (int32 j1 = 0; j1 < J1; j1++) {
int32 cur_M = NumSubstatesForGroup(j1),
tgt_M = tgt_num_substates[j1];
tot_num_substates_old += cur_M;
tot_num_substates_new += std::max(cur_M, tgt_M);
if (cur_M < tgt_M)
SplitSubstatesInGroup(pdf_occupancies, opts, sqrt_H_sm, j1, tgt_M);
}
if (tot_num_substates_old == tot_num_substates_new) {
KALDI_LOG << "Not splitting substates; current #substates is "
<< tot_num_substates_old << " and target is "
<< opts.split_substates;
} else {
KALDI_LOG << "Getting rid of normalizers as they will no longer be valid";
n_.clear();
KALDI_LOG << "Split " << tot_num_substates_old << " substates to "
<< tot_num_substates_new;
}
}
void AmSgmm2::IncreasePhoneSpaceDim(int32 target_dim,
const Matrix<BaseFloat> &norm_xform) {
KALDI_ASSERT(!M_.empty());
int32 initial_dim = PhoneSpaceDim(),
feat_dim = FeatureDim();
KALDI_ASSERT(norm_xform.NumRows() == feat_dim);
if (target_dim < initial_dim)
KALDI_ERR << "You asked to increase phn dim to a value lower than the "
<< " current dimension, " << target_dim << " < " << initial_dim;
if (target_dim > initial_dim + feat_dim) {
KALDI_WARN << "Cannot increase phone subspace dimensionality from "
<< initial_dim << " to " << target_dim << ", increasing to "
<< initial_dim + feat_dim;
target_dim = initial_dim + feat_dim;
}
if (initial_dim < target_dim) {
Matrix<BaseFloat> tmp_M(feat_dim, initial_dim);
for (int32 i = 0; i < NumGauss(); i++) {
tmp_M.CopyFromMat(M_[i]);
M_[i].Resize(feat_dim, target_dim);
M_[i].Range(0, feat_dim, 0, tmp_M.NumCols()).CopyFromMat(tmp_M);
M_[i].Range(0, feat_dim, tmp_M.NumCols(),
target_dim - tmp_M.NumCols()).CopyFromMat(norm_xform.Range(0,
feat_dim, 0, target_dim-tmp_M.NumCols()));
}
Matrix<BaseFloat> tmp_w = w_;
w_.Resize(tmp_w.NumRows(), target_dim);
w_.Range(0, tmp_w.NumRows(), 0, tmp_w.NumCols()).CopyFromMat(tmp_w);
for (int32 j1 = 0; j1 < NumGroups(); j1++) {
// Resize phonetic-subspce vectors.
Matrix<BaseFloat> tmp_v_j = v_[j1];
v_[j1].Resize(tmp_v_j.NumRows(), target_dim);
v_[j1].Range(0, tmp_v_j.NumRows(), 0, tmp_v_j.NumCols()).CopyFromMat(
tmp_v_j);
}
KALDI_LOG << "Phone subspace dimensionality increased from " <<
initial_dim << " to " << target_dim;
} else {
KALDI_LOG << "Phone subspace dimensionality unchanged, since target " <<
"dimension (" << target_dim << ") <= initial dimansion (" <<
initial_dim << ")";
}
}
void AmSgmm2::IncreaseSpkSpaceDim(int32 target_dim,
const Matrix<BaseFloat> &norm_xform,
bool speaker_dependent_weights) {
int32 initial_dim = SpkSpaceDim(),
feat_dim = FeatureDim();
KALDI_ASSERT(norm_xform.NumRows() == feat_dim);
if (N_.size() == 0)
N_.resize(NumGauss());
if (target_dim < initial_dim)
KALDI_ERR << "You asked to increase spk dim to a value lower than the "
<< " current dimension, " << target_dim << " < " << initial_dim;
if (target_dim > initial_dim + feat_dim) {
KALDI_WARN << "Cannot increase speaker subspace dimensionality from "
<< initial_dim << " to " << target_dim << ", increasing to "
<< initial_dim + feat_dim;
target_dim = initial_dim + feat_dim;
}
if (initial_dim < target_dim) {
int32 dim_change = target_dim - initial_dim;
Matrix<BaseFloat> tmp_N((initial_dim != 0) ? feat_dim : 0,
initial_dim);
for (int32 i = 0; i < NumGauss(); i++) {
if (initial_dim != 0) tmp_N.CopyFromMat(N_[i]);
N_[i].Resize(feat_dim, target_dim);
if (initial_dim != 0) {
N_[i].Range(0, feat_dim, 0, tmp_N.NumCols()).CopyFromMat(tmp_N);
}
N_[i].Range(0, feat_dim, tmp_N.NumCols(), dim_change).CopyFromMat(
norm_xform.Range(0, feat_dim, 0, dim_change));
}
// if we already have speaker-dependent weights or we are increasing
// spk-dim from zero and are asked to add them...
if (u_.NumRows() != 0 || (initial_dim == 0 && speaker_dependent_weights))
u_.Resize(NumGauss(), target_dim, kCopyData); // extend dim of u_i's
KALDI_LOG << "Speaker subspace dimensionality increased from " <<
initial_dim << " to " << target_dim;
if (initial_dim == 0 && speaker_dependent_weights)
KALDI_LOG << "Added parameters u for speaker-dependent weights.";
} else {
KALDI_LOG << "Speaker subspace dimensionality unchanged, since target " <<
"dimension (" << target_dim << ") <= initial dimansion (" <<
initial_dim << ")";
}
}
void AmSgmm2::ComputeWeights() {
int32 J1 = NumGroups();
w_jmi_.resize(J1);
int32 i = NumGauss();
for (int32 j1 = 0; j1 < J1; j1++) {
int32 M = NumSubstatesForGroup(j1);
w_jmi_[j1].Resize(M, i);
w_jmi_[j1].AddMatMat(1.0, v_[j1], kNoTrans, w_, kTrans, 0.0);
// now w_jmi_ contains un-normalized log weights.
for (int32 m = 0; m < M; m++)
w_jmi_[j1].Row(m).ApplySoftMax(); // get the actual weights.
}
}
void AmSgmm2::ComputeDerivedVars() {
if (n_.empty()) ComputeNormalizers();
if (diag_ubm_.NumGauss() != full_ubm_.NumGauss()
|| diag_ubm_.Dim() != full_ubm_.Dim()) {
diag_ubm_.CopyFromFullGmm(full_ubm_);
}
if (w_jmi_.empty() && HasSpeakerDependentWeights())
ComputeWeights();
}
class ComputeNormalizersClass: public MultiThreadable { // For multi-threaded.
public:
ComputeNormalizersClass(AmSgmm2 *am_sgmm,
int32 *entropy_count_ptr,
double *entropy_sum_ptr):
am_sgmm_(am_sgmm), entropy_count_ptr_(entropy_count_ptr),
entropy_sum_ptr_(entropy_sum_ptr), entropy_count_(0),
entropy_sum_(0.0) { }
ComputeNormalizersClass(const ComputeNormalizersClass &other):
MultiThreadable(other),
am_sgmm_(other.am_sgmm_), entropy_count_ptr_(other.entropy_count_ptr_),
entropy_sum_ptr_(other.entropy_sum_ptr_), entropy_count_(0),
entropy_sum_(0.0) { }
~ComputeNormalizersClass() {
*entropy_count_ptr_ += entropy_count_;
*entropy_sum_ptr_ += entropy_sum_;
}
inline void operator() () {
// Note: give them local copy of the sums we're computing,
// which will be propagated to original pointer in the destructor.
am_sgmm_->ComputeNormalizersInternal(num_threads_, thread_id_,
&entropy_count_,
&entropy_sum_);
}
private:
ComputeNormalizersClass() { } // Disallow empty constructor.
AmSgmm2 *am_sgmm_;
int32 *entropy_count_ptr_;
double *entropy_sum_ptr_;
int32 entropy_count_;
double entropy_sum_;
};
void AmSgmm2::ComputeNormalizers() {
KALDI_LOG << "Computing normalizers";
n_.resize(NumPdfs());
int32 entropy_count = 0;
double entropy_sum = 0.0;
ComputeNormalizersClass c(this, &entropy_count, &entropy_sum);
RunMultiThreaded(c);
KALDI_LOG << "Entropy of weights in substates is "
<< (entropy_sum / entropy_count) << " over " << entropy_count
<< " substates, equivalent to perplexity of "
<< (Exp(entropy_sum /entropy_count));
KALDI_LOG << "Done computing normalizers";
}
void AmSgmm2::ComputeNormalizersInternal(int32 num_threads, int32 thread,
int32 *entropy_count,
double *entropy_sum) {
BaseFloat DLog2pi = FeatureDim() * Log(2 * M_PI);
Vector<BaseFloat> log_det_Sigma(NumGauss());
for (int32 i = 0; i < NumGauss(); i++) {
try {
log_det_Sigma(i) = - SigmaInv_[i].LogPosDefDet();
} catch(...) {
if (thread == 0) // just for one thread, print errors [else, duplicates]
KALDI_WARN << "Covariance is not positive definite, setting to unit";
SigmaInv_[i].SetUnit();
log_det_Sigma(i) = 0.0;
}
}
int32 J1 = NumGroups();
int block_size = (NumPdfs() + num_threads-1) / num_threads;
int j_start = thread * block_size, j_end = std::min(J1, j_start + block_size);
int32 I = NumGauss();
for (int32 j1 = j_start; j1 < j_end; j1++) {
int32 M = NumSubstatesForGroup(j1);
Matrix<BaseFloat> log_w_jm(M, I);
n_[j1].Resize(I, M);
Matrix<BaseFloat> mu_jmi(M, FeatureDim());
Matrix<BaseFloat> SigmaInv_mu(M, FeatureDim());
// (in logs): w_jm = softmax([w_{k1}^T ... w_{kD}^T] * v_{jkm}) eq.(7)
log_w_jm.AddMatMat(1.0, v_[j1], kNoTrans, w_, kTrans, 0.0);
for (int32 m = 0; m < M; m++) {
log_w_jm.Row(m).Add(-1.0 * log_w_jm.Row(m).LogSumExp());
{ // DIAGNOSTIC CODE
(*entropy_count)++;
for (int32 i = 0; i < NumGauss(); i++) {
(*entropy_sum) -= log_w_jm(m, i) * Exp(log_w_jm(m, i));
}
}
}
for (int32 i = 0; i < I; i++) {
// mu_jmi = M_{i} * v_{jm}
mu_jmi.AddMatMat(1.0, v_[j1], kNoTrans, M_[i], kTrans, 0.0);
SigmaInv_mu.AddMatSp(1.0, mu_jmi, kNoTrans, SigmaInv_[i], 0.0);
for (int32 m = 0; m < M; m++) {
// mu_{jmi} * \Sigma_{i}^{-1} * mu_{jmi}
BaseFloat mu_SigmaInv_mu = VecVec(mu_jmi.Row(m), SigmaInv_mu.Row(m));
// Previously had:
// BaseFloat logc = log(c_[j](m));
// but because of STCM aspect, we can't include the sub-state mixture weights
// at this point [included later on.]
// eq.(31)
n_[j1](i, m) = log_w_jm(m, i) - 0.5 * (log_det_Sigma(i) + DLog2pi
+ mu_SigmaInv_mu);
{ // Mainly diagnostic code. Not necessary.
BaseFloat tmp = n_[j1](i, m);
if (!KALDI_ISFINITE(tmp)) { // NaN or inf
KALDI_LOG << "Warning: normalizer for j1 = " << j1 << ", m = " << m
<< ", i = " << i << " is infinite or NaN " << tmp << "= "
<< log_w_jm(m, i) << "+"
<< (-0.5 * log_det_Sigma(i)) << "+" << (-0.5 * DLog2pi)
<< "+" << (mu_SigmaInv_mu) << ", setting to finite.";
n_[j1](i, m) = -1.0e+40; // future work(arnab): get rid of magic number
}
}
}
}
}
}
BaseFloat AmSgmm2::GetDjms(int32 j1, int32 m,
Sgmm2PerSpkDerivedVars *spk_vars) const {
// This relates to SSGMMs (speaker-dependent weights).
if (spk_vars->log_d_jms.empty()) return -1; // this would be
// because we don't have speaker-dependent weights ("u" not set up).
KALDI_ASSERT(!w_jmi_.empty() && "You need to call ComputeWeights() on SGMM.");
Vector<BaseFloat> &log_d = spk_vars->log_d_jms[j1];
if (log_d.Dim() == 0) {
log_d.Resize(NumSubstatesForGroup(j1));
log_d.AddMatVec(1.0, w_jmi_[j1], kNoTrans, spk_vars->b_is, 0.0);
log_d.ApplyLog();
}
return Exp(log_d(m));
}
void AmSgmm2::ComputeFmllrPreXform(const Vector<BaseFloat> &state_occs,
Matrix<BaseFloat> *xform,
Matrix<BaseFloat> *inv_xform,
Vector<BaseFloat> *diag_mean_scatter) const {
int32 num_pdfs = NumPdfs(),
num_gauss = NumGauss(),
dim = FeatureDim();
KALDI_ASSERT(state_occs.Dim() == num_pdfs);
BaseFloat total_occ = state_occs.Sum();
// Degenerate case: unlikely to ever happen.
if (total_occ == 0) {
KALDI_WARN << "Zero probability (computing transform). Using unit "
<< "pre-transform";
xform->Resize(dim, dim + 1, kUndefined);
xform->SetUnit();
inv_xform->Resize(dim, dim + 1, kUndefined);
inv_xform->SetUnit();
diag_mean_scatter->Resize(dim, kSetZero);
return;
}
// Convert state occupancies to posteriors; Eq. (B.1)
Vector<BaseFloat> state_posteriors(state_occs);
state_posteriors.Scale(1/total_occ);
Vector<BaseFloat> mu_jmi(dim), global_mean(dim);
SpMatrix<BaseFloat> within_class_covar(dim), between_class_covar(dim);
Vector<BaseFloat> gauss_weight(num_gauss); // weights for within-class vars.
Vector<BaseFloat> w_jm(num_gauss);
for (int32 j1 = 0; j1 < NumGroups(); j1++) {
const std::vector<int32> &pdfs = group2pdf_[j1];
int32 M = NumSubstatesForGroup(j1);
Vector<BaseFloat> substate_weight(M); // total weight for each substate.
for (size_t i = 0; i < pdfs.size(); i++) {
int32 j2 = pdfs[i];
substate_weight.AddVec(state_posteriors(j2), c_[j2]);
}
for (int32 m = 0; m < M; m++) {
BaseFloat this_substate_weight = substate_weight(m);
// Eq. (7): w_jm = softmax([w_{1}^T ... w_{D}^T] * v_{jm})
w_jm.AddMatVec(1.0, w_, kNoTrans, v_[j1].Row(m), 0.0);
w_jm.ApplySoftMax();
for (int32 i = 0; i < num_gauss; i++) {
BaseFloat weight = this_substate_weight * w_jm(i);
mu_jmi.AddMatVec(1.0, M_[i], kNoTrans, v_[j1].Row(m), 0.0); // Eq. (6)
// Eq. (B.3): \mu_avg = \sum_{jmi} p(j) c_{jm} w_{jmi} \mu_{jmi}
global_mean.AddVec(weight, mu_jmi);
// \Sigma_B = \sum_{jmi} p(j) c_{jm} w_{jmi} \mu_{jmi} \mu_{jmi}^T
between_class_covar.AddVec2(weight, mu_jmi); // Eq. (B.4)
gauss_weight(i) += weight;
}
}
}
between_class_covar.AddVec2(-1.0, global_mean); // Eq. (B.4)
for (int32 i = 0; i < num_gauss; i++) {
SpMatrix<BaseFloat> Sigma(SigmaInv_[i]);
Sigma.InvertDouble();
// Eq. (B.2): \Sigma_W = \sum_{jmi} p(j) c_{jm} w_{jmi} \Sigma_i
within_class_covar.AddSp(gauss_weight(i), Sigma);
}
TpMatrix<BaseFloat> tmpL(dim);
Matrix<BaseFloat> tmpLInvFull(dim, dim);
tmpL.Cholesky(within_class_covar); // \Sigma_W = L L^T
tmpL.InvertDouble(); // L^{-1}
tmpLInvFull.CopyFromTp(tmpL); // get as full matrix.
// B := L^{-1} * \Sigma_B * L^{-T}
SpMatrix<BaseFloat> tmpB(dim);
tmpB.AddMat2Sp(1.0, tmpLInvFull, kNoTrans, between_class_covar, 0.0);
Matrix<BaseFloat> U(dim, dim);
diag_mean_scatter->Resize(dim);
xform->Resize(dim, dim + 1);
inv_xform->Resize(dim, dim + 1);
tmpB.Eig(diag_mean_scatter, &U); // Eq. (B.5): B = U D V^T
int32 n;
diag_mean_scatter->ApplyFloor(1.0e-04, &n);
if (n != 0)
KALDI_WARN << "Floored " << n << " elements of the mean-scatter matrix.";
// Eq. (B.6): A_{pre} = U^T * L^{-1}
SubMatrix<BaseFloat> Apre(*xform, 0, dim, 0, dim);
Apre.AddMatMat(1.0, U, kTrans, tmpLInvFull, kNoTrans, 0.0);
#ifdef KALDI_PARANOID
{
SpMatrix<BaseFloat> tmp(dim);
tmp.AddMat2Sp(1.0, Apre, kNoTrans, within_class_covar, 0.0);
KALDI_ASSERT(tmp.IsUnit(0.01));
}
{
SpMatrix<BaseFloat> tmp(dim);
tmp.AddMat2Sp(1.0, Apre, kNoTrans, between_class_covar, 0.0);
KALDI_ASSERT(tmp.IsDiagonal(0.01));
}
#endif
// Eq. (B.7): b_{pre} = - A_{pre} \mu_{avg}
Vector<BaseFloat> b_pre(dim);
b_pre.AddMatVec(-1.0, Apre, kNoTrans, global_mean, 0.0);
for (int32 r = 0; r < dim; r++) {
xform->Row(r)(dim) = b_pre(r); // W_{pre} = [ A_{pre}, b_{pre} ]
}
// Eq. (B.8) & (B.9): W_{inv} = [ A_{pre}^{-1}, \mu_{avg} ]
inv_xform->CopyFromMat(*xform);
inv_xform->Range(0, dim, 0, dim).InvertDouble();
for (int32 r = 0; r < dim; r++)
inv_xform->Row(r)(dim) = global_mean(r);
} // End of ComputePreXform()
template<typename Real>
void AmSgmm2::GetNtransSigmaInv(vector< Matrix<Real> > *out) const {
KALDI_ASSERT(SpkSpaceDim() > 0 &&
"Cannot compute N^{T} \\Sigma_{i}^{-1} without speaker projections.");
out->resize(NumGauss());
Matrix<Real> tmpcov(FeatureDim(), FeatureDim());
Matrix<Real> tmp_n(FeatureDim(), SpkSpaceDim());
for (int32 i = 0; i < NumGauss(); i++) {
tmpcov.CopyFromSp(SigmaInv_[i]);
tmp_n.CopyFromMat(N_[i]);
(*out)[i].Resize(SpkSpaceDim(), FeatureDim());
(*out)[i].AddMatMat(1.0, tmp_n, kTrans, tmpcov, kNoTrans, 0.0);
}
}
// Instantiate the above template.
template
void AmSgmm2::GetNtransSigmaInv(vector< Matrix<float> > *out) const;
template
void AmSgmm2::GetNtransSigmaInv(vector< Matrix<double> > *out) const;
///////////////////////////////////////////////////////////////////////////////
template<class Real>
void AmSgmm2::ComputeH(std::vector< SpMatrix<Real> > *H_i) const {
KALDI_ASSERT(NumGauss() != 0);
(*H_i).resize(NumGauss());
SpMatrix<BaseFloat> H_i_tmp(PhoneSpaceDim());
for (int32 i = 0; i < NumGauss(); i++) {
(*H_i)[i].Resize(PhoneSpaceDim());
H_i_tmp.AddMat2Sp(1.0, M_[i], kTrans, SigmaInv_[i], 0.0);
(*H_i)[i].CopyFromSp(H_i_tmp);
}
}
// Instantiate the template.
template
void AmSgmm2::ComputeH(std::vector< SpMatrix<float> > *H_i) const;
template
void AmSgmm2::ComputeH(std::vector< SpMatrix<double> > *H_i) const;
// Initializes the matrices M_{i} and w_i
void AmSgmm2::InitializeMw(int32 phn_subspace_dim,
const Matrix<BaseFloat> &norm_xform) {
int32 ddim = full_ubm_.Dim();
KALDI_ASSERT(phn_subspace_dim <= ddim + 1);
KALDI_ASSERT(phn_subspace_dim <= norm_xform.NumCols() + 1);
KALDI_ASSERT(ddim <= norm_xform.NumRows());
Vector<BaseFloat> mean(ddim);
int32 num_gauss = full_ubm_.NumGauss();
w_.Resize(num_gauss, phn_subspace_dim);
M_.resize(num_gauss);
for (int32 i = 0; i < num_gauss; i++) {
full_ubm_.GetComponentMean(i, &mean);
Matrix<BaseFloat> &thisM(M_[i]);
thisM.Resize(ddim, phn_subspace_dim);
// Eq. (27): M_{i} = [ \bar{\mu}_{i} (J)_{1:D, 1:(S-1)}]
thisM.CopyColFromVec(mean, 0);
int32 nonrandom_dim = std::min(phn_subspace_dim - 1, ddim),
random_dim = phn_subspace_dim - 1 - nonrandom_dim;
thisM.Range(0, ddim, 1, nonrandom_dim).CopyFromMat(
norm_xform.Range(0, ddim, 0, nonrandom_dim), kNoTrans);
// The following extension to the original paper allows us to
// initialize the model with a larger dimension of phone-subspace vector.
if (random_dim > 0)
thisM.Range(0, ddim, nonrandom_dim + 1, random_dim).SetRandn();
}
}
// Initializes the matrices N_i, and [if speaker_dependent_weights==true] u_i.
void AmSgmm2::InitializeNu(int32 spk_subspace_dim,
const Matrix<BaseFloat> &norm_xform,
bool speaker_dependent_weights) {
int32 ddim = full_ubm_.Dim();
int32 num_gauss = full_ubm_.NumGauss();
N_.resize(num_gauss);
for (int32 i = 0; i < num_gauss; i++) {
N_[i].Resize(ddim, spk_subspace_dim);
// Eq. (28): N_{i} = [ (J)_{1:D, 1:T)}]
int32 nonrandom_dim = std::min(spk_subspace_dim, ddim),
random_dim = spk_subspace_dim - nonrandom_dim;
N_[i].Range(0, ddim, 0, nonrandom_dim).
CopyFromMat(norm_xform.Range(0, ddim, 0, nonrandom_dim), kNoTrans);
// The following extension to the original paper allows us to
// initialize the model with a larger dimension of speaker-subspace vector.
if (random_dim > 0)
N_[i].Range(0, ddim, nonrandom_dim, random_dim).SetRandn();
}
if (speaker_dependent_weights) {
u_.Resize(num_gauss, spk_subspace_dim); // will set to zero.
} else {
u_.Resize(0, 0);
}
}
void AmSgmm2::CopyGlobalsInitVecs(const AmSgmm2 &other,
const std::vector<int32> &pdf2group,
BaseFloat self_weight) {
KALDI_LOG << "Initializing model";
pdf2group_ = pdf2group;
ComputePdfMappings();
// Copy background GMMs
diag_ubm_.CopyFromDiagGmm(other.diag_ubm_);
full_ubm_.CopyFromFullGmm(other.full_ubm_);
// Copy global params
SigmaInv_ = other.SigmaInv_;
M_ = other.M_;
w_ = other.w_;
u_ = other.u_;
N_ = other.N_;
InitializeVecsAndSubstateWeights(self_weight);
}
// Initializes the vectors v_{j1,m} and substate weights c_{j2,m}.
void AmSgmm2::InitializeVecsAndSubstateWeights(BaseFloat self_weight) {
int32 J1 = NumGroups(), J2 = NumPdfs();
KALDI_ASSERT(J1 > 0 && J2 >= J1);
int32 phn_subspace_dim = PhoneSpaceDim();
KALDI_ASSERT(phn_subspace_dim > 0 && "Initialize M and w first.");
v_.resize(J1);
if (self_weight == 1.0) {
for (int32 j1 = 0; j1 < J1; j1++) {
v_[j1].Resize(1, phn_subspace_dim);
v_[j1](0, 0) = 1.0; // Eq. (26): v_{j1} = [1 0 0 ... 0]
}
c_.resize(J2);
for (int32 j2 = 0; j2 < J2; j2++) {
c_[j2].Resize(1);
c_[j2](0) = 1.0; // Eq. (25): c_{j1} = 1.0
}
} else {
for (int32 j1 = 0; j1 < J1; j1++) {
int32 npdfs = group2pdf_[j1].size();
v_[j1].Resize(npdfs, phn_subspace_dim);
for (int32 m = 0; m < npdfs; m++)
v_[j1](m, 0) = 1.0; // Eq. (26): v_{j1} = [1 0 0 ... 0]
}
c_.resize(J2);
for (int32 j2 = 0; j2 < J2; j2++) {
int32 j1 = pdf2group_[j2], npdfs = group2pdf_[j1].size();
c_[j2].Resize(npdfs);
if (npdfs == 1) c_[j2].Set(1.0);
else {
// note: just avoid NaNs if npdfs-1... value won't matter.
double other_weight = (1.0 - self_weight) / std::max((1-npdfs), 1);
c_[j2].Set(other_weight);
for (int32 k = 0; k < npdfs; k++)
if(group2pdf_[j1][k] == j2) c_[j2](k) = self_weight;
}
}
}
}
// Initializes the within-class vars Sigma_{ki}
void AmSgmm2::InitializeCovars() {
std::vector< SpMatrix<BaseFloat> > &inv_covars(full_ubm_.inv_covars());
int32 num_gauss = full_ubm_.NumGauss();
int32 dim = full_ubm_.Dim();
SigmaInv_.resize(num_gauss);
for (int32 i = 0; i < num_gauss; i++) {
SigmaInv_[i].Resize(dim);
SigmaInv_[i].CopyFromSp(inv_covars[i]);
}
}
// Compute the "smoothing" matrix H^{(sm)} from expected counts given the model.
void AmSgmm2::ComputeHsmFromModel(
const std::vector< SpMatrix<BaseFloat> > &H,
const Vector<BaseFloat> &state_occupancies,
SpMatrix<BaseFloat> *H_sm,
BaseFloat max_cond) const {
int32 num_gauss = NumGauss();
BaseFloat tot_sum = 0.0;
KALDI_ASSERT(state_occupancies.Dim() == NumPdfs());
Vector<BaseFloat> w_jm(num_gauss);
H_sm->Resize(PhoneSpaceDim());
H_sm->SetZero();
Vector<BaseFloat> gamma_i;
ComputeGammaI(state_occupancies, &gamma_i);
BaseFloat sum = 0.0;
for (int32 i = 0; i < num_gauss; i++) {
if (gamma_i(i) > 0) {
H_sm->AddSp(gamma_i(i), H[i]);
sum += gamma_i(i);
}
}
if (sum == 0.0) {
KALDI_WARN << "Sum of counts is zero. ";
// set to unit matrix--arbitrary non-singular matrix.. won't ever matter.
H_sm->SetUnit();
} else {
H_sm->Scale(1.0 / sum);
int32 tmp = H_sm->LimitCondDouble(max_cond);
if (tmp > 0) {
KALDI_WARN << "Limited " << (tmp) << " eigenvalues of H_sm";
}
}
tot_sum += sum;
KALDI_LOG << "total count is " << tot_sum;
}
void ComputeFeatureNormalizingTransform(const FullGmm &gmm, Matrix<BaseFloat> *xform) {
int32 dim = gmm.Dim();
int32 num_gauss = gmm.NumGauss();
SpMatrix<BaseFloat> within_class_covar(dim);
SpMatrix<BaseFloat> between_class_covar(dim);
Vector<BaseFloat> global_mean(dim);
// Accumulate LDA statistics from the GMM parameters.
{
BaseFloat total_weight = 0.0;
Vector<BaseFloat> tmp_weight(num_gauss);
Matrix<BaseFloat> tmp_means;
std::vector< SpMatrix<BaseFloat> > tmp_covars;
tmp_weight.CopyFromVec(gmm.weights());
gmm.GetCovarsAndMeans(&tmp_covars, &tmp_means);
for (int32 i = 0; i < num_gauss; i++) {
BaseFloat w_i = tmp_weight(i);
total_weight += w_i;
within_class_covar.AddSp(w_i, tmp_covars[i]);
between_class_covar.AddVec2(w_i, tmp_means.Row(i));
global_mean.AddVec(w_i, tmp_means.Row(i));
}
KALDI_ASSERT(total_weight > 0);
if (fabs(total_weight - 1.0) > 0.001) {
KALDI_WARN << "Total weight across the GMMs is " << (total_weight)
<< ", renormalizing.";
global_mean.Scale(1.0 / total_weight);
within_class_covar.Scale(1.0 / total_weight);
between_class_covar.Scale(1.0 / total_weight);
}
between_class_covar.AddVec2(-1.0, global_mean);
}
TpMatrix<BaseFloat> chol(dim);
chol.Cholesky(within_class_covar); // Sigma_W = L L^T
TpMatrix<BaseFloat> chol_inv(chol);
chol_inv.InvertDouble();
Matrix<BaseFloat> chol_full(dim, dim);
chol_full.CopyFromTp(chol_inv);
SpMatrix<BaseFloat> LBL(dim);
// LBL = L^{-1} \Sigma_B L^{-T}
LBL.AddMat2Sp(1.0, chol_full, kNoTrans, between_class_covar, 0.0);
Vector<BaseFloat> Dvec(dim);
Matrix<BaseFloat> U(dim, dim);
LBL.Eig(&Dvec, &U);
SortSvd(&Dvec, &U);
xform->Resize(dim, dim);
chol_full.CopyFromTp(chol);
// T := L U, eq (23)
xform->AddMatMat(1.0, chol_full, kNoTrans, U, kNoTrans, 0.0);
#ifdef KALDI_PARANOID
Matrix<BaseFloat> inv_xform(*xform);
inv_xform.InvertDouble();
{ // Check that T*within_class_covar*T' = I.
Matrix<BaseFloat> wc_covar_full(dim, dim), tmp(dim, dim);
wc_covar_full.CopyFromSp(within_class_covar);
tmp.AddMatMat(1.0, inv_xform, kNoTrans, wc_covar_full, kNoTrans, 0.0);
wc_covar_full.AddMatMat(1.0, tmp, kNoTrans, inv_xform, kTrans, 0.0);
KALDI_ASSERT(wc_covar_full.IsUnit(0.01));
}
{ // Check that T*between_class_covar*T' = diagonal.
Matrix<BaseFloat> bc_covar_full(dim, dim), tmp(dim, dim);
bc_covar_full.CopyFromSp(between_class_covar);
tmp.AddMatMat(1.0, inv_xform, kNoTrans, bc_covar_full, kNoTrans, 0.0);
bc_covar_full.AddMatMat(1.0, tmp, kNoTrans, inv_xform, kTrans, 0.0);
KALDI_ASSERT(bc_covar_full.IsDiagonal(0.01));
}
#endif
}
void AmSgmm2::ComputePerSpkDerivedVars(Sgmm2PerSpkDerivedVars *vars) const {
KALDI_ASSERT(vars != NULL);
if (vars->v_s.Dim() != 0) {
KALDI_ASSERT(vars->v_s.Dim() == SpkSpaceDim());
vars->o_s.Resize(NumGauss(), FeatureDim());
int32 num_gauss = NumGauss();
// first compute the o_i^{(s)} quantities.
for (int32 i = 0; i < num_gauss; i++) {
// Eqn. (32): o_i^{(s)} = N_i v^{(s)}
vars->o_s.Row(i).AddMatVec(1.0, N_[i], kNoTrans, vars->v_s, 0.0);
}
// the rest relates to the SSGMM. We only need to to this
// if we're using speaker-dependent weights.
if (HasSpeakerDependentWeights()) {
vars->log_d_jms.clear();
vars->log_d_jms.resize(NumGroups());
vars->log_b_is.Resize(NumGauss());
vars->log_b_is.AddMatVec(1.0, u_, kNoTrans, vars->v_s, 0.0);
vars->b_is.Resize(NumGauss());
vars->b_is.CopyFromVec(vars->log_b_is);
vars->b_is.ApplyExp();
for (int32 i = 0; i < vars->b_is.Dim(); i++) {
if (vars->b_is(i) - vars->b_is(i) != 0.0) { // NaN.
vars->b_is(i) = 1.0;
KALDI_WARN << "Set NaN in b_is to 1.0";
}
}
} else {
vars->b_is.Resize(0);
vars->log_b_is.Resize(0);
vars->log_d_jms.resize(0);
}
} else {
vars->Clear(); // make sure everything is cleared.
}
}
BaseFloat AmSgmm2::GaussianSelection(const Sgmm2GselectConfig &config,
const VectorBase<BaseFloat> &data,
std::vector<int32> *gselect) const {
KALDI_ASSERT(diag_ubm_.NumGauss() != 0 &&
diag_ubm_.NumGauss() == full_ubm_.NumGauss() &&
diag_ubm_.Dim() == data.Dim());
KALDI_ASSERT(config.diag_gmm_nbest > 0 && config.full_gmm_nbest > 0 &&
config.full_gmm_nbest < config.diag_gmm_nbest);
int32 num_gauss = diag_ubm_.NumGauss();
std::vector< std::pair<BaseFloat, int32> > pruned_pairs;
if (config.diag_gmm_nbest < num_gauss) { Vector<BaseFloat> loglikes(num_gauss);
diag_ubm_.LogLikelihoods(data, &loglikes);
Vector<BaseFloat> loglikes_copy(loglikes);
BaseFloat *ptr = loglikes_copy.Data();
std::nth_element(ptr, ptr+num_gauss-config.diag_gmm_nbest, ptr+num_gauss);
BaseFloat thresh = ptr[num_gauss-config.diag_gmm_nbest];
for (int32 g = 0; g < num_gauss; g++)
if (loglikes(g) >= thresh) // met threshold for diagonal phase.
pruned_pairs.push_back(
std::make_pair(full_ubm_.ComponentLogLikelihood(data, g), g));
} else {
Vector<BaseFloat> loglikes(num_gauss);
full_ubm_.LogLikelihoods(data, &loglikes);
for (int32 g = 0; g < num_gauss; g++)
pruned_pairs.push_back(std::make_pair(loglikes(g), g));
}
KALDI_ASSERT(!pruned_pairs.empty());
if (pruned_pairs.size() > static_cast<size_t>(config.full_gmm_nbest)) {
std::nth_element(pruned_pairs.begin(),
pruned_pairs.end() - config.full_gmm_nbest,
pruned_pairs.end());
pruned_pairs.erase(pruned_pairs.begin(),
pruned_pairs.end() - config.full_gmm_nbest);
}
Vector<BaseFloat> loglikes_tmp(pruned_pairs.size()); // for return value.
KALDI_ASSERT(gselect != NULL);
gselect->resize(pruned_pairs.size());
// Make sure pruned Gaussians appear from best to worst.
std::sort(pruned_pairs.begin(), pruned_pairs.end(),
std::greater< std::pair<BaseFloat, int32> >());
for (size_t i = 0; i < pruned_pairs.size(); i++) {
loglikes_tmp(i) = pruned_pairs[i].first;
(*gselect)[i] = pruned_pairs[i].second;
}
return loglikes_tmp.LogSumExp();
}
void Sgmm2GauPost::Write(std::ostream &os, bool binary) const {
WriteToken(os, binary, "<Sgmm2GauPost>");
int32 T = this->size();
WriteBasicType(os, binary, T);
for (int32 t = 0; t < T; t++) {
WriteToken(os, binary, "<gselect>");
WriteIntegerVector(os, binary, (*this)[t].gselect);
WriteToken(os, binary, "<tids>");
WriteIntegerVector(os, binary, (*this)[t].tids);
KALDI_ASSERT((*this)[t].tids.size() == (*this)[t].posteriors.size());
for (size_t i = 0; i < (*this)[t].posteriors.size(); i++) {
(*this)[t].posteriors[i].Write(os, binary);
}
}
WriteToken(os, binary, "</Sgmm2GauPost>");
}
void Sgmm2GauPost::Read(std::istream &is, bool binary) {
ExpectToken(is, binary, "<Sgmm2GauPost>");
int32 T;
ReadBasicType(is, binary, &T);
KALDI_ASSERT(T >= 0);
this->resize(T);
for (int32 t = 0; t < T; t++) {
ExpectToken(is, binary, "<gselect>");
ReadIntegerVector(is, binary, &((*this)[t].gselect));
ExpectToken(is, binary, "<tids>");
ReadIntegerVector(is, binary, &((*this)[t].tids));
size_t sz = (*this)[t].tids.size();
(*this)[t].posteriors.resize(sz);
for (size_t i = 0; i < sz; i++)
(*this)[t].posteriors[i].Read(is, binary);
}
ExpectToken(is, binary, "</Sgmm2GauPost>");
}
} // namespace kaldi
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