o i@sddlmZmZddlZddlmmZddlmZ ddl m Z ddl mZddlmZmZmZe eje dkZ d'deejefded ed eejeffd d Zd(d ejfddZdeejefdejd eeejefeejefffddZ d)deejefdeejefded eejeffddZdeejefdeejefd eejeffddZ    d*deejefd ejd!ed"ededed eejeffd#d$Z d+deejefd eejeffd%d&ZdS),)TupleUnionN)parse) ComplexTensor)einsummatmulreversez1.9.0signal frame_length frame_stepreturncst|trt|j||}t|j||}t||Str7t|r7t|j||}t|j||}t||St |ddfd|}t fddt d| dd|Dg}|d|fjg| dddR}|S) zExpands signal into frames of frame_length. Args: signal : (B * F, D, T) Returns: torch.Tensor: (B * F, D, T, W) rconstantcsg|] }tt||qS)listrange.0ir rJ/home/ubuntu/.local/lib/python3.10/site-packages/espnet2/enh/layers/wpe.py *sz"signal_framing...N) isinstancersignal_framingrealimagis_torch_1_9_plustorch is_complexcomplexFpadsumrsizeview)r r r pad_valuerrindicesrrrrs"    ,rcCs$|jd|jd}|j|d}|S)zCalculates power for `signal` Args: signal : Single frequency signal with shape (F, C, T). axis: reduce_mean axis Returns: Power with shape (F, T) dim)rrmean)r r+powerrrr get_power5s r.Y inverse_powerc Cs|dks J||d|dks"J|d|df|\}}}t||dddd|||dddf}t|dd}||dd||dddf}td ||} | |||||} td ||d||ddf} | | fS) aCalculates weighted correlations of a window of length taps Args: Y : Complex-valued STFT signal with shape (F, C, T) inverse_power : Weighting factor with shape (F, T) taps (int): Lenghts of correlation window delay (int): Delay for the weighting factor Returns: Correlation matrix of shape (F, taps*C, taps*C) Correlation vector of shape (F, taps, C, C) r)rr )r r .Nrr*zfdtk,fetl->fkdlezfdtk,fet->fked)r+r$rrconjrreshape) r/r0tapsdelayr!CTPsi Psi_conj_normcorrelation_matrixcorrelation_vectorrrrget_correlationsEs,  $ r;绽|=r9r:epsc Cs|\}}}}|dddd||||}tj|d|j|jd}tddt | dD|j d d }|j|}|||7}| } t || dd } | ||||dddd} | S) aDCalculate (conjugate) filter matrix based on correlations for one freq. Args: correlation_matrix : Correlation matrix (F, taps * C, taps * C) correlation_vector : Correlation vector (F, taps, C, C) eps: Returns: filter_matrix_conj (torch.complex/ComplexTensor): (F, taps, C, C) rr)r r)dtypedevicecss|]}dVqdS)r Nr)r_rrr sz)get_filter_matrix_conj..r(N)r$permute contiguousr%reyer?r@tuplerr+shapeinverser transpose) r9r:r=r!r3r5rArErGinv_correlation_matrixstacked_filter_conjfilter_matrix_conjrrrget_filter_matrix_conjns(     rMrLcsttr t}tjntrtrt}tjntd d|j fddt |Ddd}| d||f}|S)zperform_filter_operation Args: Y : Complex-valued STFT signal of shape (F, C, T) filter Matrix (F, taps, C, C) z?Please update your PyTorch version to 1.9+ for complex support.rc sBg|]}ddddd|f|dfdddqS)Nrr)modevaluerrr6r/r4pad_funcrrrs4z,perform_filter_operation..rr*zfpde,pfdt->fet) rrFCr"rrrr! ValueErrorr$stackrr)r/rLr3r4complex_moduleY_tilde reverb_tailrrPrperform_filter_operations$  rX r>Tr-r3r4c Cs|dd|ddksJ|dd}|jdg|ddR}|d|d}|r?dtj||d}n|}t||||\}}t||} t|| ||} | jg||ddR} | S)aBWPE for one iteration Args: Y: Complex valued STFT signal with shape (..., C, T) power: : (..., T) taps: Number of filter taps delay: Delay as a guard interval, such that X does not become zero. eps: inverse_power (bool): Returns: enhanced: (..., C, T) Nr(rr )min)r$r%rclampr;rMrX) r/r-r3r4r=r0batch_freq_sizer9r:rLenhancedrrrwpe_one_iterations$  r^cCs.|}t|D]}t|}t||||d}q|S)zWPE Args: Y: Complex valued STFT signal with shape (F, C, T) taps: Number of filter taps delay: Delay as a guard interval, such that X does not become zero. iterations: Returns: enhanced: (F, C, T) )r3r4)rr.r^)r/r3r4 iterationsr]rAr-rrrwpes  r`)r)r()r<)rYr>r<T)rYr>r>) typingrrrtorch.nn.functionalnn functionalr!torch_complex.functionalrRpackaging.versionrVtorch_complex.tensorr espnet2.enh.layers.complex_utilsrrr __version__rTensorintrr.r;floatrMrXboolr^r`rrrrs      #  ,    -    '   )