64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
from typing import Optional
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import numpy as np
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import torch
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from scipy.signal import get_window
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from torch import nn
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class ConvFFT(nn.Module):
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def __init__(
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self,
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window_len: int,
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nfft: Optional[int] = None,
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window: str = "hamming",
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):
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self.window_len = window_len
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self.nfft = nfft if nfft else np.int(2 ** np.ceil(np.log2(window_len)))
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self.window = get_window(window, window_len)
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@property
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def init_kernel(self):
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fourier_basis = np.fft.rfft(np.eye(self.nfft))[: self.window_len]
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real, imag = np.real(fourier_basis), np.imag(fourier_basis)
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kernel = np.concatenate([real, imag], 1).T
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kernel *= self.window
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return torch.from_numpy(kernel)
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class ConvSTFT(ConvFFT):
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def __init__(
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self,
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window_len: int,
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hop_size: Optional[int] = None,
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nfft: Optional[int] = None,
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window: str = "hamming",
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):
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super(self, ConvSTFT).__init__(
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window_len=window_len, nfft=nfft, window=window
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)
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self.hop_size = hop_size if hop_size else window_len // 2
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self.register_buffer("weight", self.init_kernel)
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def forward(self, input):
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pass
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class ConviSTFT(ConvFFT):
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def __init__(
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self,
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window_len: int,
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hop_size: Optional[int] = None,
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nfft: Optional[int] = None,
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window: str = "hamming",
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):
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super(self, ConvSTFT).__init__(
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window_len=window_len, nfft=nfft, window=window
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)
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self.hop_size = hop_size if hop_size else window_len // 2
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self.register_buffer("weight", self.init_kernel)
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def forward(self, input):
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pass
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