idZddlmZmZddlZddlmZddlmZddl m Z Gdd ejjZ dd ejd ed eed eeedededeefdZy)zH Decomposition of a signal over frequency bands in the waveform domain. )OptionalSequenceN)mel_frequencies)LowPassFilters) simple_reprcxeZdZdZ d dedeedeeedededeef fd Z d Z e d Z d Z xZS) 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_bandscutoffspadzerosfftct||du|duzdk7r td||_||_| t |nd|_||_||_||_ |8| td|dk\std|dt|dzd|dz dd}nt|d |zkDr td t|dkDr&t|Dcgc]}||z  c}||| |_yd|_ycc}w) 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 r rrrc __class__s a/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/julius/bands.pyrzSplitBands.__init__2s  tO4 0A 5Z[ [& )0)<W $   ? !NOOa< #I'RS!TUU%gk1kAoFqLG7|cK// !TUU w "Gr,r signalr r r rrrc Lt||||||j||S)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)r<r r r rrrs r split_bandsr?ms- Q:k7GS% E H H PQW XXr,r1)r6typingrrr#corerrrutilsrnnModuler Tensorr7r8r9r?r,r rGs& !#\G\G~TXGK8< Y Y5 Y8C= Y!(5/2 Y@D Y Y'/~ Yr,