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../matrix/sp-matrix.h1.0 / Sigma_i_ml(0, 0) != 0.0up eigenvalues.For Gaussian component : Too low count diagonal: Floored covariance eigenvalues. over frames for i = frames, numerical problemj2 < NumPdfs()**Overall objf impr for c is , over frames.spk_space_dim != 0nonzero. zero. Sgmm2PdfModel: J1 = , J2 = , D = , S = , T = , I = Y: no. gamma_.size() != 0Y: yes, R_.size() == 0Z, R: no. Z: yes, R: yes, gamma_s: yes, S: no. S: yes, y: no. y: yes, a: no. a: yes. a: yes, gamma: no. gamma: yes. gamma: yes, gamma_c_ not set up.gamma_c_.size() == num_pdfs_gamma_c: yes, vs. t: no. !U_.empty()t: yes, U: no. U_.size() == num_gaussians_t_.NumRows() != 0 R_.size() != 0gamma_i.Sum() > 0.0 -- maybe first iter?Scatter of vectors v is : SigmaRenormalized subspace.num_substates > 0model.SpkSpaceDim() != 0Unexpected token '' in model file Before iteration col_covrow_covUpdating phone vectors**Overall auxf impr for v is y_s_.Dim() != 0, auxfs are -> Backtracked v_s per frame, over Updating weight projections[unknown]wpredicted_impr >= -1.0e-05, i = is per frame over For iteration , updating w gives , actual not increase**Overall objf impr for w is For component : not updating M due to very small count (=).M, is M is Computing the prior first < not updating.N because count is too small **Overall objf impr for N is and m = because auxf changed by [vs. predicted:] v times [not updating]Auxf impr for j = m = uCount is small for gaussian , not updating u_i., impr/frame is **Overall objf impr for u is static_cast(i) < static_cast(num_rows_)j1 < NumGroups() && m < NumSubstatesForGroup(j1) && i < NumGauss()mean_out->Dim() == FeatureDim()Not renormalizing N, since there are no counts.S_means.size() == static_cast(accs.num_gaussians_)static_cast(r) < static_cast(this->num_rows_)Updating variances: zero counts. Setting floor to unit.Covariance flooring matrix is poorly conditioned. Fixed Assuming you want to build a diagonal system since cov_diag_ratio is large: making diagonal covFloor. for covariance matrix estimation. Setting to objf impr from variance update =**Overall objf impr for variance update = Updating within-class covariance matrix i = Updating substate mixture weightsZero or negative weight, flooringIn destructor of MleAmSgmm2Accs: detected that you forgot to call CommitStatsForSpk()num_pdfs_ == model.NumPdfs() && num_pdfs_ > 0num_groups_ == model.NumGroups() && num_groups_ > 0num_gaussians_ == model.NumGauss() && num_gaussians_ > 0feature_dim_ == model.FeatureDim() && feature_dim_ > 0phn_space_dim_ == model.PhoneSpaceDim() && phn_space_dim_ > 0spk_space_dim_ == model.SpkSpaceDim()Y_.size() == static_cast(num_gaussians_)Y_[i].NumRows() == feature_dim_ && Y_[i].NumCols() == phn_space_dim_gamma_s_.Dim() == num_gaussians_Z_.size() == static_cast(num_gaussians_)R_.size() == static_cast(num_gaussians_)Z_[i].NumRows() == feature_dim_ && Z_[i].NumCols() == spk_space_dim_R_[i].NumRows() == spk_space_dim_S_.size() == static_cast(num_gaussians_)S_[i].NumRows() == feature_dim_y_.size() == static_cast(num_groups_)y_[j1].NumRows() == model.NumSubstatesForGroup(j1)y_[j1].NumCols() == phn_space_dim_a_.size() == static_cast(num_groups_)a_[j1].NumRows() == model.NumSubstatesForGroup(j1) && a_[j1].NumCols() == num_gaussians_gamma_.size() == static_cast(num_groups_)gamma_[j1].NumRows() == model.NumSubstatesForGroup(j1) && gamma_[j1].NumCols() == num_gaussians_gamma_c_.size() == num_pdfs_ && "gamma_ set up but not gamma_c_."gamma_c_[j2].Dim() == model.NumSubstatesForPdf(j2)Counts from gamma and gamma_c differ t_.NumRows() == num_gaussians_ && t_.NumCols() == spk_space_dim_U_[i].NumRows() == spk_space_dim_Subspace GMM model properties: Not renormalizing v because scatter is not positive definiteNote on the next diagnostic: the first number is generally not that meaningful as it relates to the static offsetH_sm_proj (Significance of dims in vector space.. note)accs.a_.size() == accs.num_groups_Inconsistency between a and gamma stats. [BAD!] sub-states with zero "a" (and presumably gamma) stats.1.0 / (*S_means)[i](0, 0) != 0.0Cannot set up accumulators for speaker projections because speaker subspace has not been set upmodel.HasSpeakerDependentWeights() && "remove the flag \"u\" if you don't have u set up."options_.map_M_prior_iters > 0 of updating prior over M, log like per dimension modeled is Computing column covariances for M: limited singular values, max condition is Computing row covariances for M: limited Backtracking in speaker vector update, on iter times in speaker-vector update.*Objf impr for speaker vector is Predicted objf impr for w, iter = predicted per-frame like impr Not updating weights as not increasing auxf and probably due to numerical issues (since small change).Halving step size for weights as likelihood did Objf impr for projection M for i = Overall objective function improvement for model projections Prior smoothing parameter: Tau = Updating speaker vectors, count is Speaker subspace dim is zero or no stats accumulatedNot updating speaker basis for i = Objf impr for spk projection N for i = Backtracking computation of v_jm for j = Updating speaker weight projections u, for Gaussian index frames, scaling back to not exceed Objf impr for spk weight-projection u for i = *Overall auxf improvement, combining all parameters, is ***Overall data likelihood is ;` &     c4G`+<   T:HE+)84kp  I  &    gWM$N}{':Rg h   w  "4|  &   c4 0   00,1,1112#2$$%%'2((L3*+3,.3//4/304,8888 2             O%-L+ddN=`x(|I&+$02$ 2  + & 2  N Q C& CX0 =`x(|)/}(P   % )/.}\.)/5}*??9"%h )/};!   0 ` kQ0+[7Q0'v[pWjJA9y LGB1   5 .fun $&&O&(&&%  %& )%z&/)&&)& !&"&"&%R  9# ;w         E.>@*u:ZQn;  / @ z t$!     :AG!/:!72! 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5.LC266.LC267.LC0.LC129.LC130.LC131.LC154.LC155.LC156.LC157.LC158.LC159.LC171.LC196_ZN5kaldi15KaldiFatalErrorD5Ev_ZN5kaldi23UpdatePhoneVectorsClassD5Ev_ZN5kaldi12UpdateWClassD5Ev_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi23UpdatePhoneVectorsClassEEEEEEED5Ev_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi12UpdateWClassEEEEEEED5Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED5Ev_ZNSt6vectorIN5kaldi6MatrixIdEESaIS2_EED5Ev_ZNSt6vectorIN5kaldi8SpMatrixIdEESaIS2_EED5Ev_ZNSt6vectorISt6threadSaIS0_EED5Ev_ZNK5kaldi15KaldiFatalError4whatEv_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi12UpdateWClassEEEEEEE6_M_runEv_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi23UpdatePhoneVectorsClassEEEEEEE6_M_runEv_ZN5kaldi15KaldiFatalErrorD2Ev_GLOBAL_OFFSET_TABLE__ZTVN5kaldi15KaldiFatalErrorE_ZNSt13runtime_errorD2Ev_ZN5kaldi15KaldiFatalErrorD1Ev_ZN5kaldi15KaldiFatalErrorD0Ev_ZdlPv_ZN5kaldi23UpdatePhoneVectorsClassD2Ev_ZTVN5kaldi23UpdatePhoneVectorsClassE_ZN5kaldi15MultiThreadableD2Ev_ZN5kaldi23UpdatePhoneVectorsClassD1Ev_ZN5kaldi23UpdatePhoneVectorsClassD0Ev_ZN5kaldi12UpdateWClassD2EvDW.ref.__gxx_personality_v0_ZTVN5kaldi12UpdateWClassE_ZN5kaldi10MatrixBaseIdE6AddMatEdRKS1_NS_19MatrixTransposeTypeE_ZN5kaldi6MatrixIdE7DestroyEv_ZN5kaldi12UpdateWClassD1Ev_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi23UpdatePhoneVectorsClassEEEEEEED2Ev_ZTVNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi23UpdatePhoneVectorsClassEEEEEEEE_ZNSt6thread6_StateD2Ev_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi23UpdatePhoneVectorsClassEEEEEEED1Ev_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi23UpdatePhoneVectorsClassEEEEEEED0Ev_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi12UpdateWClassEEEEEEED2Ev_ZTVNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi12UpdateWClassEEEEEEEE_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi12UpdateWClassEEEEEEED1Ev_ZNSt6thread11_State_implINS_8_InvokerISt5tupleIJSt17reference_wrapperIN5kaldi12UpdateWClassEEEEEEED0Ev_ZN5kaldi12UpdateWClassD0Ev_ZN5kaldi13MessageLogger11LogAndThrowaSERKS0__ZNK5kaldi13MessageLogger10LogMessageEv__cxa_allocate_exception_ZNKSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEE3strEv_ZNSt13runtime_errorC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTIN5kaldi15KaldiFatalErrorE__cxa_throw__cxa_free_exception_Unwind_Resume_ZN5kaldi14MleAmSgmm2Accs24AccumulateFromPosteriorsERKNS_7AmSgmm2ERKNS_24Sgmm2PerFrameDerivedVarsERKNS_6MatrixIfEEiPNS_22Sgmm2PerSpkDerivedVarsE_ZN5kaldi6VectorIfE6ResizeEiNS_16MatrixResizeTypeE_ZNK5kaldi7AmSgmm29Pdf2GroupEi_ZN5kaldi19KaldiAssertFailure_EPKcS1_iS1__ZN5kaldi10VectorBaseIdE6AddVecIfEEvdRKNS0_IT_EE_ZN5kaldi10MatrixBaseIdE9AddVecVecIfEEvdRKNS_10VectorBaseIT_EES7__ZN5kaldi10VectorBaseIfE9AddMatVecEfRKNS_10MatrixBaseIfEENS_19MatrixTransposeTypeERKS1_f_ZN5kaldi6VectorIfE7DestroyEv_ZN5kaldi10VectorBaseIfE11CopyFromVecERKS1__ZN5kaldi10VectorBaseIfE6AddVecIfEEvfRKNS0_IT_EE_ZN5kaldi4RandEPNS_11RandomStateE_ZNK5kaldi7AmSgmm27GetDjmsEiiPNS_22Sgmm2PerSpkDerivedVarsE_ZN5kaldi8SpMatrixIdE7AddVec2IfEEvdRKNS_10VectorBaseIT_EE__stack_chk_fail_ZN5kaldi14MleAmSgmm2Accs10AccumulateERKNS_7AmSgmm2ERKNS_24Sgmm2PerFrameDerivedVarsEifPNS_22Sgmm2PerSpkDerivedVarsE_ZNK5kaldi7AmSgmm219ComponentPosteriorsERKNS_24Sgmm2PerFrameDerivedVarsEiPNS_22Sgmm2PerSpkDerivedVarsEPNS_6MatrixIfEE_ZN5kaldi10MatrixBaseIfE5ScaleEf_ZN5kaldi6MatrixIfE7DestroyEv_ZN5kaldi14MleAmSgmm2Accs17CommitStatsForSpkERKNS_7AmSgmm2ERKNS_22Sgmm2PerSpkDerivedVarsE_ZN5kaldi10VectorBaseIfE11CopyFromVecIdEEvRKNS0_IT_EE_ZNK5kaldi10VectorBaseIfE6IsZeroEf_ZN5kaldi6VectorIdE7DestroyEv_ZN5kaldi6VectorIdE6ResizeEiNS_16MatrixResizeTypeE_ZN5kaldi10VectorBaseIdE11CopyFromVecIfEEvRKNS0_IT_EE_ZN5kaldi8SpMatrixIdE7AddVec2IdEEvdRKNS_10VectorBaseIT_EE_ZN5kaldi10VectorBaseIfE9AddVecVecEfRKS1_S3_f_ZN5kaldi10VectorBaseIdE7SetZeroEv_ZNK5kaldi14MleAmSgmm2Accs19GetStateOccupanciesEPNS_6VectorIfEE_ZNK5kaldi10VectorBaseIdE3SumEv_ZN5kaldi17MleAmSgmm2Updater15UpdateWGetStatsERKNS_14MleAmSgmm2AccsERKNS_7AmSgmm2ERKNS_6MatrixIdEERKSt6vectorIS8_SaIS8_EEPS8_SG_Pdii_ZN5kaldi12PackedMatrixIdE6ResizeEiNS_16MatrixResizeTypeE_ZN5kaldi6MatrixIdE6ResizeEiiNS_16MatrixResizeTypeENS_16MatrixStrideTypeE_ZN5kaldi6MatrixIdEC1IfEERKNS_10MatrixBaseIT_EENS_19MatrixTransposeTypeE_ZN5kaldi10MatrixBaseIdE9AddMatMatEdRKS1_NS_19MatrixTransposeTypeES3_S4_d_ZN5kaldi12PackedMatrixIdE7DestroyEv_ZNK5kaldi10VectorBaseIdE9LogSumExpEd_ZN5kaldi10VectorBaseIdE3AddEd_ZN5kaldi6VecVecIdEET_RKNS_10VectorBaseIS1_EES5__ZN5kaldi10VectorBaseIdE8ApplyExpEv_ZN5kaldi12PackedMatrixIdE7SetZeroEv_ZN5kaldi10VectorBaseIdE14CopyFromPackedIdEEvRKNS_12PackedMatrixIT_EE_ZN5kaldi12UpdateWClassclEv_ZN5kaldi17MleAmSgmm2Updater12RenormalizeNERKNS_14MleAmSgmm2AccsERKN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