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import numpy as np
import torch
import torch.nn.functional as F
from scipy import signal as sig
# adapted from
# https://github.com/kan-bayashi/ParallelWaveGAN/tree/master/parallel_wavegan
class PQMF(torch.nn.Module):
def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
super().__init__()
self.N = N
self.taps = taps
self.cutoff = cutoff
self.beta = beta
QMF = sig.firwin(taps + 1, cutoff, window=("kaiser", beta))
H = np.zeros((N, len(QMF)))
G = np.zeros((N, len(QMF)))
for k in range(N):
constant_factor = (
(2 * k + 1) * (np.pi / (2 * N)) * (np.arange(taps + 1) - ((taps - 1) / 2))
) # TODO: (taps - 1) -> taps
phase = (-1) ** k * np.pi / 4
H[k] = 2 * QMF * np.cos(constant_factor + phase)
G[k] = 2 * QMF * np.cos(constant_factor - phase)
H = torch.from_numpy(H[:, None, :]).float()
G = torch.from_numpy(G[None, :, :]).float()
self.register_buffer("H", H)
self.register_buffer("G", G)
updown_filter = torch.zeros((N, N, N)).float()
for k in range(N):
updown_filter[k, k, 0] = 1.0
self.register_buffer("updown_filter", updown_filter)
self.N = N
self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
def forward(self, x):
return self.analysis(x)
def analysis(self, x):
return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N)
def synthesis(self, x):
x = F.conv_transpose1d(x, self.updown_filter * self.N, stride=self.N)
x = F.conv1d(x, self.G, padding=self.taps // 2)
return x
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