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| # File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details. | |
| # Author: adefossez, 2020 | |
| """ | |
| Decomposition of a signal over frequency bands in the waveform domain. | |
| """ | |
| from typing import Optional, Sequence | |
| import torch | |
| from .core import mel_frequencies | |
| from .lowpass import LowPassFilters | |
| from .utils import simple_repr | |
| class SplitBands(torch.nn.Module): | |
| """ | |
| 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] | |
| """ | |
| def __init__(self, sample_rate: float, n_bands: Optional[int] = None, | |
| cutoffs: Optional[Sequence[float]] = None, pad: bool = True, | |
| zeros: float = 8, fft: Optional[bool] = None): | |
| super().__init__() | |
| if (cutoffs is None) + (n_bands is None) != 1: | |
| raise ValueError("You must provide either n_bands, or cutoffs, but not boths.") | |
| self.sample_rate = sample_rate | |
| self.n_bands = n_bands | |
| self._cutoffs = list(cutoffs) if cutoffs is not None else None | |
| self.pad = pad | |
| self.zeros = zeros | |
| self.fft = fft | |
| if cutoffs is None: | |
| if n_bands is None: | |
| raise ValueError("You must provide one of n_bands or cutoffs.") | |
| if not n_bands >= 1: | |
| raise ValueError(f"n_bands must be greater than one (got {n_bands})") | |
| cutoffs = mel_frequencies(n_bands + 1, 0, sample_rate / 2)[1:-1] | |
| else: | |
| if max(cutoffs) > 0.5 * sample_rate: | |
| raise ValueError("A cutoff above sample_rate/2 does not make sense.") | |
| if len(cutoffs) > 0: | |
| self.lowpass = LowPassFilters( | |
| [c / sample_rate for c in cutoffs], pad=pad, zeros=zeros, fft=fft) | |
| else: | |
| # Here I cannot make both TorchScript and MyPy happy. | |
| # I miss the good old times, before all this madness was created. | |
| self.lowpass = None # type: ignore | |
| def forward(self, input): | |
| if self.lowpass is None: | |
| return input[None] | |
| lows = self.lowpass(input) | |
| low = lows[0] | |
| bands = [low] | |
| for low_and_band in lows[1:]: | |
| # Get a bandpass filter by substracting lowpasses | |
| band = low_and_band - low | |
| bands.append(band) | |
| low = low_and_band | |
| # Last band is whatever is left in the signal | |
| bands.append(input - low) | |
| return torch.stack(bands) | |
| def cutoffs(self): | |
| if self._cutoffs is not None: | |
| return self._cutoffs | |
| elif self.lowpass is not None: | |
| return [c * self.sample_rate for c in self.lowpass.cutoffs] | |
| else: | |
| return [] | |
| def __repr__(self): | |
| return simple_repr(self, overrides={"cutoffs": self._cutoffs}) | |
| def split_bands(signal: torch.Tensor, sample_rate: float, n_bands: Optional[int] = None, | |
| cutoffs: Optional[Sequence[float]] = None, pad: bool = True, | |
| zeros: float = 8, fft: Optional[bool] = None): | |
| """ | |
| 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] | |
| """ | |
| return SplitBands(sample_rate, n_bands, cutoffs, pad, zeros, fft).to(signal)(signal) | |