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import numpy as np
from numba import njit, prange
from scipy.signal import firwin2
import torch
from .fx import Delay, FDN, module2coeffs
@njit
def rt_fdn(
x: np.ndarray,
delay_steps: np.ndarray,
firs: np.ndarray,
U: np.ndarray,
):
_, T = x.shape
M = delay_steps.shape[0]
order = firs.shape[1]
y = np.zeros_like(x)
buf_size = delay_steps.max() + order
delay_buf = np.zeros((M, buf_size), dtype=x.dtype)
read_pointer = 0
for t in range(T):
# out = delay_buf[(range(M), read_pointers)]
# for i in prange(M):
# out[i] = delay_buf[i, read_pointers[i]]
out = delay_buf[:, read_pointer]
y[:, t] = out
s = out * firs[:, 0]
# indexes = (read_pointers[:, None] - np.arange(1, order)) % buf_sizes[:, None]
# reg = np.take_along_axis(delay_buf, indexes, axis=1)
# s += firs[:, 1:] @ reg.T
# for j in prange(M):
# s[j] += firs[j, 1:] @ delay_buf[j, indexes[j]]
for i in prange(M):
for j in prange(1, order):
s[i] += firs[i, j] * delay_buf[i, (read_pointer - j) % buf_size]
# for i in prange(1, order):
# s += firs[:, i] * delay_buf[:, (read_pointer - i) % buf_size]
feedback = U @ s + x[:, t]
w_pointers = (read_pointer + delay_steps) % buf_size
# delay_buf[(range(M), w_pointers)] = s + B @ x[:, t]
for i in prange(M):
delay_buf[i, w_pointers[i]] = feedback[i]
read_pointer = (read_pointer + 1) % buf_size
return y
@njit
def rt_delay(
x: np.ndarray,
delay_step: int,
b0: float,
b1: float,
b2: float,
a1: float,
a2: float,
):
T = x.shape[0]
y = np.zeros((2, T), dtype=x.dtype)
buf_size = delay_step + 1
read_pointer = 0
delay_buf = np.zeros((2, buf_size), dtype=x.dtype)
bq_buf = np.zeros((2, 2), dtype=x.dtype)
for t in range(T):
out = delay_buf[:, read_pointer]
y[:, t] = out
s = bq_buf[:, 0] + b0 * out
bq_buf[:, 0] = bq_buf[:, 1] + b1 * out - a1 * s
bq_buf[:, 1] = b2 * out - a2 * s
w_pointer = (read_pointer + delay_step) % buf_size
# cross feeding because of ping-pong delay
delay_buf[0, w_pointer] = s[1] + x[t]
delay_buf[1, w_pointer] = s[0]
read_pointer = (read_pointer + 1) % buf_size
return y
class RealTimeDelay(Delay):
def forward(self, x):
assert x.size(1) == 1, x.size()
assert x.size(0) == 1, x.size()
with torch.no_grad():
delay_in_samples = round(self.sr * self.params.delay.item() * 0.001)
feedback = self.params.feedback.item()
if self.recursive_eq and self.eq is not None:
b0, b1, b2, a0, a1, a2 = [p.item() for p in module2coeffs(self.eq)]
b0, b1, b2, a1, a2 = b0 / a0, b1 / a0, b2 / a0, a1 / a0, a2 / a0
else:
b0, b1, b2, a1, a2 = 1.0, 0.0, 0.0, 0.0, 0.0
b0 = b0 * feedback
b1 = b1 * feedback
b2 = b2 * feedback
x_numpy = x.squeeze().cpu().numpy()
y_numpy = rt_delay(x_numpy, delay_in_samples, b0, b1, b2, a1, a2)
y = torch.from_numpy(y_numpy).unsqueeze(0).to(x.device) * self.params.gain
return self.odd_pan(y[:, :1]) + self.even_pan(y[:, 1:])
class RealTimeFDN(FDN):
def forward(self, x):
assert x.size(1) == 2, x.size()
assert x.size(0) == 1, x.size()
with torch.no_grad():
delays = self.delays if hasattr(self, "delays") else self.params.delays
c = self.params.c
b = self.params.b
gamma = self.params.gamma.clone()
if gamma.size(1) == 1:
gamma = gamma ** (delays / delays.min())
freqs = np.linspace(0, 1, gamma.size(0))
firs = np.apply_along_axis(
lambda x: firwin2(gamma.size(0) * 2 - 1, freqs, x, fs=2),
1,
gamma.cpu().numpy().T,
).astype(np.float32)
shifted_delays = delays - firs.shape[1] // 2
U = self.params.U
x = b @ x.squeeze()
y_numpy = rt_fdn(
x.cpu().numpy(),
# delays.cpu().numpy(),
shifted_delays.cpu().numpy(),
# firs.cpu().numpy(),
firs,
U.cpu().numpy(),
)
y = c @ torch.from_numpy(y_numpy).to(x.device)
y = y.unsqueeze(0)
if self.eq is not None:
y = self.eq(y)
return y
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