]iSrSSKJrJr SSKrSSKJr SSKJr SSK J r "SS \RR5r SS \RS \S \\S \\\S\S\S\\4Sjjrg)zH Decomposition of a signal over frequency bands in the waveform domain. )OptionalSequenceN)mel_frequencies)LowPassFilters) simple_reprc^\rSrSrSrSS\S\\S\\\S\ S\S\\ 4 U4S jjjr S r \ S 5r S rS rU=r$) SplitBandsa Decomposes a signal over the given frequency bands in the waveform domain using a cascade of low pass filters as implemented by `julius.lowpass.LowPassFilters`. You can either specify explicitely the frequency cutoffs, or just the number of bands, in which case the frequency cutoffs will be spread out evenly in mel scale. Args: sample_rate (float): Sample rate of the input signal in Hz. n_bands (int or None): number of bands, when not giving them explictely with `cutoffs`. In that case, the cutoff frequencies will be evenly spaced in mel-space. cutoffs (list[float] or None): list of frequency cutoffs in Hz. pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`, the output will have the same length as the input. zeros (float): Number of zero crossings to keep. See `LowPassFilters` for more informations. fft (bool or None): See `LowPassFilters` for more info. ..note:: The sum of all the bands will always be the input signal. ..warning:: Unlike `julius.lowpass.LowPassFilters`, the cutoffs frequencies must be provided in Hz along with the sample rate. Shape: - Input: `[*, T]` - Output: `[B, *, T']`, with `T'=T` if `pad` is True. If `n_bands` was provided, `B = n_bands` otherwise `B = len(cutoffs) + 1` >>> bands = SplitBands(sample_rate=128, n_bands=10) >>> x = torch.randn(6, 4, 1024) >>> list(bands(x).shape) [10, 6, 4, 1024] sample_raten_bandscutoffspadzerosfftc>[TU]5 USLUSL-S:wa [S5eXlX lUb [ U5OSUlX@lXPlX`l Uc:Uc [S5eUS:d[SUS35e[US-SUS- 5SSnO[U5S U-:a [S 5e[U5S:a$[UVs/sHowU- PM snXEUS 9UlgSUlgs snf) Nrz;You must provide either n_bands, or cutoffs, but not boths.z+You must provide one of n_bands or cutoffs.z&n_bands must be greater than one (got )rg?z1A cutoff above sample_rate/2 does not make sense.)rrr)super__init__ ValueErrorr r list_cutoffsrrrrmaxlenrlowpass) selfr r rrrrc __class__s I/mnt/rpi/tmp/demucs-venv-sys/lib/python3.13/site-packages/julius/bands.pyrSplitBands.__init__2s   tO4 0A 5Z[ [& )0)<W $   ? !NOOa< #I'RS!TUU%gk1kAoFqLG7|cK// !TUU w "GGr/r signalr r rrrrcF[XX4XV5RU5"U5$)z Functional version of `SplitBands`, refer to this class for more information. >>> x = torch.randn(6, 4, 1024) >>> list(split_bands(x, sample_rate=64, cutoffs=[12, 24]).shape) [3, 6, 4, 1024] )r to)rCr r rrrrs r! split_bandsrFms$ kG% E H H PQW XXr/r6)r<typingrrr%corerrrutilsrnnModuler Tensorr=r>r?rFr/r!rNs& !#\G\G~TXGK8< Y Y5 Y8C= Y!(5/2 Y@D Y Y'/~ Yr/