,j%SrSSKrSSKJr SSKrSSKJr SSKJ r SSK J r "SS \RR5rSS \RS \S \S \S\S\\S\4SjjrSrSSjrSSjrg)a  Differentiable, Pytorch based resampling. Implementation of Julius O. Smith algorithm for resampling. See https://ccrma.stanford.edu/~jos/resample/ for details. This implementation is specially optimized for when new_sr / old_sr is a fraction with a small numerator and denominator when removing the gcd (e.g. new_sr = 700, old_sr = 500). Very similar to [bmcfee/resampy](https://github.com/bmcfee/resampy) except this implementation is optimized for the case mentioned before, while resampy is slower but more general. N)Optional) functional)sinc simple_reprc ^\rSrSrSrSS\S\S\S\4U4SjjjrSrSS \ RS \ \S \ 4S jjr S rSrU=r$) ResampleFracz7 Resampling from the sample rate `old_sr` to `new_sr`. old_srnew_srzerosrolloffc>[TU]5 [U[5(a[U[5(d [ S5e[ R "X5nX-UlX%-UlX0l X@l UR5 g)a  Args: old_sr (int): sample rate of the input signal x. new_sr (int): sample rate of the output. zeros (int): number of zero crossing to keep in the sinc filter. rolloff (float): use a lowpass filter that is `rolloff * new_sr / 2`, to ensure sufficient margin due to the imperfection of the FIR filter used. Lowering this value will reduce anti-aliasing, but will reduce some of the highest frequencies. Shape: - Input: `[*, T]` - Output: `[*, T']` with `T' = int(new_sr * T / old_sr) .. caution:: After dividing `old_sr` and `new_sr` by their GCD, both should be small for this implementation to be fast. >>> import torch >>> resample = ResampleFrac(4, 5) >>> x = torch.randn(1000) >>> print(len(resample(x))) 1250 z$old_sr and new_sr should be integersN) super__init__ isinstanceint ValueErrormathgcdr r rr _init_kernels)selfr r rrr __class__s ڝC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\julius/resample.pyrResampleFrac.__init__sk6 &#&&j.E.ECD Dhhv&m m    cURUR:Xag/n[URUR5nX R-n[R "UR UR-U- 5Ul[R"UR*URUR-5R5n[UR5HnU*UR- X0R- -U-nURUR *UR 5nU[R-n[R"XPR - S- 5S-n[U5U-nUR!UR#55 UR%U5 M UR'S[R("U5R+URSS55 g)Nkernelr)r r minrrceilr_widthtorcharangefloatrangeclamp_picosrdiv_sumappendregister_bufferstackview)rkernelssridxitwindowr s rrResampleFrac._init_kernelsCs_ ;;$++ %  dkk * ll&ii T[[ 82 => llDKK<t{{)BCIIKt{{#ADKK#kk/1R7A$**djj1A LAYYq|A~.1F!Wv%F KK % NN6 "$ Xu{{7';'@'@aQS'TUrx output_lengthfullcjURUR:XaU$URnURSnURSU5n[R "USS2S4UR UR UR-4SS9n[R"XRURS9nURSS5R[USS5S/-5n[R"URU-UR- 5n[R"U5R5n [R"U5R5n Uc U(aU OU n OAUS:dX):a[!S U 35e[R""U5n U(a [!S 5eUS SU 24$) a Resample x. Args: x (Tensor): signal to resample, time should be the last dimension output_length (None or int): This can be set to the desired output length (last dimension). Allowed values are between 0 and ceil(length * new_sr / old_sr). When None (default) is specified, the floored output length will be used. In order to select the largest possible size, use the `full` argument. full (bool): return the longest possible output from the input. This can be useful if you chain resampling operations, and want to give the `output_length` only for the last one, while passing `full=True` to all the other ones. r!N replicate)mode)striderrrz$output_length must be between 0 and z0You cannot pass both full=True and output_length.)r r shapereshapeFpadr$conv1dr transposelistr% as_tensorr#longfloorrtensor) rr9r:r;r@lengthysyfloat_output_lengthmax_output_lengthdefault_output_lengthapplied_output_lengths rforwardResampleFrac.forwardrsp ;;$++ %H IIb& ! EE!AtG*t{{DKK$++,EF[ Y XXaT[[ 9 LLA  & &tE#2J'72$'> ?#oodkkF.BT[[.PQ!JJ':;@@B % ,? @ E E G  9=$5CX ! Q -"CCDUCVWX X$)LL$? ! !STT,,,,--rc[U5$)Nr)rs r__repr__ResampleFrac.__repr__s 4  r)r$r r rr)= ףp=?)NF)__name__ __module__ __qualname____firstlineno____doc__rr'rrr%TensorrboolrRrU__static_attributes__ __classcell__)rs@rr r sd$s$C$$5$$L-V^#.#.hsm#.RV#.J!!rr r9r r rrr:r;cF[XX45RU5"XU5$)a: Functional version of `ResampleFrac`, refer to its documentation for more information. ..warning:: If you call repeatidly this functions with the same sample rates, then the resampling kernel will be recomputed everytime. For best performance, you should use and cache an instance of `ResampleFrac`. )r to)r9r r rrr:r;s r resample_fracrds$  7 : :1 =aPT UUrc[R"SU-S-SS9nUSSS2n[R"U*S-US- SU-5nU[R-n[ U5U-R SSS5nU$)NrF)periodicr?r!)r% hann_windowlinspacerr*rr1)rwinwinoddr6r s r_kernel_upsample2_downsample2rms|   AIME :C AYF v|US[!e)sABw G L L Ze ZUY ZC QH"%A 66 5 " rcURSS-S:wa[R"US5nUSSSS24nUSSSS24nURGtpE[U5R U5nU[R "UR SSU5XaS9SSS24R "/UQUP76-nUR "/UQSP76RS 5$) a} Downsample x by a factor of two. The output length is half of the input, ceiled. Args: x (Tensor): signal to downsample, time should be the last dimension zeros (int): number of zero crossing to keep in the sinc filter. This function is kept only for reference, you should use the more generic `resample_frac` one. This function does not perform anti-aliasing filtering. r!rr)rr.Nrrorh)r@rBrCrmrcrDr1mul)r9rxevenxoddrrrsr rts r _downsample2rzs wwr{Q! EE!V  c3Q3hKE S!$Q$Yrs   $!588??!F5:DI VU\\ V3 V V V,1 V!)# V=A V"")r