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import math
import torch
import scipy.signal
import deepafx_st.processors.autodiff.signal
from deepafx_st.processors.processor import Processor
@torch.jit.script
def compressor(
x: torch.Tensor,
sample_rate: float,
threshold: torch.Tensor,
ratio: torch.Tensor,
attack_time: torch.Tensor,
release_time: torch.Tensor,
knee_dB: torch.Tensor,
makeup_gain_dB: torch.Tensor,
eps: float = 1e-8,
):
"""Note the `release` parameter is not used."""
# print(f"autodiff comp fs = {sample_rate}")
s = x.size() # should be one 1d
threshold = threshold.squeeze()
ratio = ratio.squeeze()
attack_time = attack_time.squeeze()
makeup_gain_dB = makeup_gain_dB.squeeze()
# uni-polar dB signal
# Turn the input signal into a uni-polar signal on the dB scale
x_G = 20 * torch.log10(torch.abs(x) + 1e-8) # x_uni casts type
# Ensure there are no values of negative infinity
x_G = torch.clamp(x_G, min=-96)
# Static characteristics with knee
y_G = torch.zeros(s).type_as(x)
ratio = ratio.view(-1)
threshold = threshold.view(-1)
attack_time = attack_time.view(-1)
release_time = release_time.view(-1)
knee_dB = knee_dB.view(-1)
makeup_gain_dB = makeup_gain_dB.view(-1)
# Below knee
idx = torch.where((2 * (x_G - threshold)) < -knee_dB)[0]
y_G[idx] = x_G[idx]
# At knee
idx = torch.where((2 * torch.abs(x_G - threshold)) <= knee_dB)[0]
y_G[idx] = x_G[idx] + (
(1 / ratio) * (((x_G[idx] - threshold + knee_dB) / 2) ** 2)
) / (2 * knee_dB)
# Above knee threshold
idx = torch.where((2 * (x_G - threshold)) > knee_dB)[0]
y_G[idx] = threshold + ((x_G[idx] - threshold) / ratio)
x_L = x_G - y_G
# design 1-pole butterworth lowpass
fc = 1.0 / (attack_time * sample_rate)
b, a = deepafx_st.processors.autodiff.signal.butter(fc)
# apply FIR approx of IIR filter
y_L = deepafx_st.processors.autodiff.signal.approx_iir_filter(b, a, x_L)
lin_y_L = torch.pow(10.0, -y_L / 20.0) # convert back to linear
y = lin_y_L * x # apply gain
# apply makeup gain
y *= torch.pow(10.0, makeup_gain_dB / 20.0)
return y
class Compressor(Processor):
def __init__(
self,
sample_rate,
max_threshold=0.0,
min_threshold=-80,
max_ratio=20.0,
min_ratio=1.0,
max_attack=0.1,
min_attack=0.0001,
max_release=1.0,
min_release=0.005,
max_knee=12.0,
min_knee=0.0,
max_mkgain=48.0,
min_mkgain=-48.0,
eps=1e-8,
):
""" """
super().__init__()
self.sample_rate = sample_rate
self.eps = eps
self.ports = [
{
"name": "Threshold",
"min": min_threshold,
"max": max_threshold,
"default": -12.0,
"units": "dB",
},
{
"name": "Ratio",
"min": min_ratio,
"max": max_ratio,
"default": 2.0,
"units": "",
},
{
"name": "Attack",
"min": min_attack,
"max": max_attack,
"default": 0.001,
"units": "s",
},
{
# this is a dummy parameter
"name": "Release (dummy)",
"min": min_release,
"max": max_release,
"default": 0.045,
"units": "s",
},
{
"name": "Knee",
"min": min_knee,
"max": max_knee,
"default": 6.0,
"units": "dB",
},
{
"name": "Makeup Gain",
"min": min_mkgain,
"max": max_mkgain,
"default": 0.0,
"units": "dB",
},
]
self.num_control_params = len(self.ports)
def forward(self, x, p, sample_rate=24000, **kwargs):
"""
Assume that parameters in p are normalized between 0 and 1.
x (tensor): Shape batch x 1 x samples
p (tensor): shape batch x params
"""
bs, ch, s = x.size()
inputs = torch.split(x, 1, 0)
params = torch.split(p, 1, 0)
y = [] # loop over batch dimension
for input, param in zip(inputs, params):
denorm_param = self.denormalize_params(param.view(-1))
y.append(compressor(input.view(-1), sample_rate, *denorm_param))
return torch.stack(y, dim=0).view(bs, 1, -1)
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