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import torch
from deepafx_st.processors.autodiff.compressor import Compressor
from deepafx_st.processors.autodiff.peq import ParametricEQ
from deepafx_st.processors.autodiff.fir import FIRFilter
class AutodiffChannel(torch.nn.Module):
def __init__(self, sample_rate):
super().__init__()
self.peq = ParametricEQ(sample_rate)
self.comp = Compressor(sample_rate)
self.ports = [self.peq.ports, self.comp.ports]
self.num_control_params = (
self.peq.num_control_params + self.comp.num_control_params
)
def forward(self, x, p, sample_rate=24000, **kwargs):
# split params between EQ and Comp.
p_peq = p[:, : self.peq.num_control_params]
p_comp = p[:, self.peq.num_control_params :]
y = self.peq(x, p_peq, sample_rate)
y = self.comp(y, p_comp, sample_rate)
return y
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