]ibSrSSKrSSKJrJr SSKrSSKJr SSK J r SSK J r SSK Jr "S S \RR 5r"S S \RR 5rSS \R&S\\S\S\S\S\\4 SjjrSS \R&S\S\S\S\S\\4 Sjjrg)z$ FIR windowed sinc lowpass filters. N)SequenceOptional) functional)sinc) fft_conv1d simple_reprc d^\rSrSrSrS S\\S\S\S\S\ \4 U4Sjjjr S r S r S r U=r$) LowPassFiltersa Bank of low pass filters. Note that a high pass or band pass filter can easily be implemented by substracting a same signal processed with low pass filters with different frequencies (see `julius.bands.SplitBands` for instance). This uses a windowed sinc filter, very similar to the one used in `julius.resample`. However, because we do not change the sample rate here, this filter can be much more efficiently implemented using the FFT convolution from `julius.fftconv`. Args: cutoffs (list[float]): list of cutoff frequencies, in [0, 0.5] expressed as `f/f_s` where f_s is the samplerate and `f` is the cutoff frequency. The upper limit is 0.5, because a signal sampled at `f_s` contains only frequencies under `f_s / 2`. stride (int): how much to decimate the output. Keep in mind that decimation of the output is only acceptable if the cutoff frequency is under `1/ (2 * stride)` of the original sampling rate. pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`, the output will have the same length as the input. zeros (float): Number of zero crossings to keep. Controls the receptive field of the Finite Impulse Response filter. For lowpass filters with low cutoff frequency, e.g. 40Hz at 44.1kHz, it is a bad idea to set this to a high value. This is likely appropriate for most use. Lower values will result in a faster filter, but with a slower attenuation around the cutoff frequency. fft (bool or None): if True, uses `julius.fftconv` rather than PyTorch convolutions. If False, uses PyTorch convolutions. If None, either one will be chosen automatically depending on the effective filter size. ..warning:: All the filters will use the same filter size, aligned on the lowest frequency provided. If you combine a lot of filters with very diverse frequencies, it might be more efficient to split them over multiple modules with similar frequencies. ..note:: A lowpass with a cutoff frequency of 0 is defined as the null function by convention here. This allows for a highpass with a cutoff of 0 to be equal to identity, as defined in `julius.filters.HighPassFilters`. Shape: - Input: `[*, T]` - Output: `[F, *, T']`, with `T'=T` if `pad` is True and `stride` is 1, and `F` is the numer of cutoff frequencies. >>> lowpass = LowPassFilters([1/4]) >>> x = torch.randn(4, 12, 21, 1024) >>> list(lowpass(x).shape) [1, 4, 12, 21, 1024] cutoffsstridepadzerosfftc >[T U]5 [U5Ul[ UR5S:a [ S5e[ UR5S:a [ S5eX lX0lX@l [U[ URVs/sH ofS:dM UPM sn5- S- 5Ul UcURS:nXPl [R"SUR-S-SS 9n[R"UR*URS-5n/n UHnn U S:Xa[R "U5n O=SU -U-[#SU -[$R&-U-5-n XR)5-n U R+U 5 Mp UR-S [R."U 5SS2S45 gs snf) Nrz(Minimum cutoff must be larger than zero.g?z'A cutoff above 0.5 does not make sense. rF)periodicfilters)super__init__listrmin ValueErrormaxrrrint half_sizertorch hann_windowarange zeros_likermathpisumappendregister_bufferstack) selfrrrrrcwindowtimercutofffilter_ __class__s K/mnt/rpi/tmp/demucs-venv-sys/lib/python3.13/site-packages/julius/lowpass.pyrLowPassFilters.__init__Hs G} t|| q GH H t|| s "FG G  UST\\)K\U!\)K%LLqPQ ;..2%C""1t~~#5#9EJ||T^^OT^^a-?@F{**40f*v-QZ$''5ID5P0QQ;;=( NN7 # Y G(>> lowpass = LowPassFilter(1/4, stride=2) >>> x = torch.randn(4, 124) >>> list(lowpass(x).shape) [4, 62] r.rrrrcH>[TU]5 [U/X#XE5UlgrE)rrr _lowpasses)r*r.rrrrr0s r1rLowPassFilter.__init__s  (&6KrCc4URRS$Nr)rZrrFs r1r.LowPassFilter.cutoffs&&q))rCc.URR$rE)rZrrFs r1rLowPassFilter.strides%%%rCc.URR$rE)rZrrFs r1rLowPassFilter.pad"""rCc.URR$rE)rZrrFs r1rLowPassFilter.zeross$$$rCc.URR$rE)rZrrFs r1rLowPassFilter.fftrcrCc*URU5S$r]rZ)r*r?s r1rALowPassFilter.forwardsu%a((rCc[U5$rEr rFs r1rGLowPassFilter.__repr__rIrCrirJ)rLrMrNrOrPrQrrRrrpropertyr.rrrrrArGrSrTrUs@r1rWrWvs DH9=LuLcLDLL(0LL **&&##%%##)!!rCrWr?rrrrrcF[XX4U5RU5"U5$)zS Functional version of `LowPassFilters`, refer to this class for more information. )r to)r?rrrrrs r1lowpass_filtersrps# '3s ; > >u Ee LLrCr.c$[X/X#XE5S$)zw Same as `lowpass_filters` but with a single cutoff frequency. Output will not have a dimension inserted in the front. r)rp)r?r.rrrrs r1lowpass_filterrrs 5(F DQ GGrCrJ)rPr$typingrrr torch.nnrr9corerfftconvrutilsr nnModuler rWTensorrQrrRrprrrCr1r|s % $a!UXX__a!H,!EHHOO,!`26<@M5<<M8E?MM*.M M+3D>M15;?H%,,HHH)-HH*24.HrC