o wOi*@sddlmZddlmZddlZddlmmZddlmZ ddl m Z  d0deej e fde de deej e ffd d Zd1dej fd d Zde dej fddZde dej dee e ffddZ d2de de dede fddZde de de fddZde de de fddZ   !d3de d"ej d#e d$e dedede fd%d&Zd4de de fd'd(Z   )  d5d*e d"ej d+e d,e d-ed#e d$e fd.d/ZdS)6)Tuple)UnionN) ComplexTensorsignal frame_length frame_stepreturncst|trt|j||}t|j||}t||St|ddfd|}tfddtd| dd|Dg}|d|fj g| 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.0irr ?/home/ubuntu/.local/lib/python3.10/site-packages/pytorch_wpe.py sz"signal_framing...N) isinstancersignal_framingrealimagFpadsumr sizeview)rrr pad_valuerrindicesr rrr s   "rcCs$|jd|jd}|j|d}|S)zCalculates power for `signal` Args: signal : Single frequency signal with shape (F, C, T). axis: reduce_mean axis Returns: Power with shape (F, T) dimrrmean)rr#powerr r r get_power&s r'cCs,|jd|jd}|jddjdd}|S)zCalculates power for `signal` Args: signal : Single frequency signal with shape (F, C, T). axis: reduce_mean axis Returns: Power with shape (F, ) r!rr"r r$)rr&r r rget_power_online6s r(Y inverse_powerc Cs|dks J||d|dks"J|d|df|\}}}t||dddd|||dddf}tj|dd}||dd||dddf}td ||f} | |||||} td ||d||ddff} | | 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 )rr.Nrr"zfdtk,fetl->fkdlezfdtk,fet->fked)r#rrFCreverseconjeinsumreshape) r)r*tapsdelayrCTPsi Psi_conj_normcorrelation_matrixcorrelation_vectorr r rget_correlationsFs&"r8绽|=r6r7epsc Cs|\}}}}|dddd||||}tj|d|j|jd}tddt | dD|j d d }|j|}|||7}| } t || dd } | ||||dddd} | S) a6Calculate (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 (ComplexTensor): (F, taps, C, C) rr!r r)dtypedevicecss|]}dVqdS)r Nr )r_r r r sz)get_filter_matrix_conj..r N)rpermute contiguousrtorcheyer<r=tupler r#shapeinverser+matmul transpose) r6r7r:rr0r2r>rCrEinv_correlation_matrixstacked_filter_conjfilter_matrix_conjr r rget_filter_matrix_conjns(      rLrKc Cs|d}tt|jt|j}t|D]6}td|dd|ddddf|ddddd|||ff}tj|||dfddd}||}q||S)zperform_filter_operation Args: Y : Complex-valued STFT signal of shape (F, C, T) filter Matrix (F, taps, C, C) rz fde,fdt->fetNrr modevalue) rrrB zeros_likerr r+r.r)r)rKr0r1r3 reverb_tailtau_minus_delaynewr r rperform_filter_operations     rTcsFdtjfddt|Ddd}td||f}|S)zperform_filter_operation_v2 Args: Y : Complex-valued STFT signal of shape (F, C, T) filter Matrix (F, taps, C, C) rc sDg|]}tjddddd|f|dfdddqS)Nrr rM)r+rrr3r)r1r rrs . z/perform_filter_operation_v2..rr"zfpde,pfdt->fet)rr+stackr r.)r)rKr0r1Y_tilderQr rUrperform_filter_operation_v2s rX r;Tr&r0r1c 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)rrrBclampr8rLrX) r)r&r0r1r:r*batch_freq_sizer6r7rKenhancedr r rwpe_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) )r0r1)r r'r^)r)r0r1 iterationsr]r>r&r r rwpes  r`Gz? input_bufferinv_cov filter_tapsalphacCs|d||dksJ||d}|dur:ttj|||jdjg|dd||||R}|durXttjg|dd|||Rd|ji}tj|dd| dfdd}|j g|dddR}|d t d | |f} t d ||f} t d | | f||} | | d } |t d| || f} | |} |t d| | f}| | |fS)ahOne step of online dereverberation. Args: input_buffer: (F, C, taps + delay + 1) power: Estimate for the current PSD (F, T) inv_cov: Current estimate of R^-1 filter_taps: Current estimate of filter taps (F, taps * C, taps) alpha (float): Smoothing factor taps (int): Number of filter taps delay (int): Delay in frames Returns: Dereverberated frame of shape (F, D) Updated estimate of R^-1 Updated estimate of the filter taps >>> frame_length = 512 >>> frame_shift = 128 >>> taps = 6 >>> delay = 3 >>> alpha = 0.999 >>> frequency_bins = frame_length // 2 + 1 >>> Q = None >>> G = None >>> unreverbed, Q, G = online_wpe_step(stft, get_power_online(stft), Q, G, ... alpha=alpha, taps=taps, delay=delay) rr r N)r<r<.r").rz...id,...i->...dz...ij,...j->...iz...i,...i->...).Nz...j,...jm,...i->...imz...i,...m->...im) rrrBrCr<expandzerosr+r,rr.r-)rbr&rcrdrer0r1r2windowpred nominator denominator kalman_gain inv_cov_k filter_taps_kr r ronline_wpe_stepsD"% &      ro)r)r )r9)rYr;r9T)rYr;r;)NNrarYr;)typingrrrBtorch.nn.functionalnn functionalrtorch_complex.functionalr+torch_complex.tensorrTensorintrr'r(r8floatrLrTrXboolr^r`ror r r rs        * '   &