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import torch
import torchaudio
import scipy.signal
import numpy as np
import pyloudnorm as pyln
import matplotlib.pyplot as plt
from deepafx_st.processors.dsp.compressor import compressor
from tqdm import tqdm
class BaselineEQ(torch.nn.Module):
def __init__(
self,
ntaps: int = 63,
n_fft: int = 65536,
sample_rate: float = 44100,
):
super().__init__()
self.ntaps = ntaps
self.n_fft = n_fft
self.sample_rate = sample_rate
# compute the target spectrum
# print("Computing target spectrum...")
# self.target_spec, self.sm_target_spec = self.analyze_speech_dataset(filepaths)
# self.plot_spectrum(self.target_spec, filename="targetEQ")
# self.plot_spectrum(self.sm_target_spec, filename="targetEQsm")
def forward(self, x, y):
bs, ch, s = x.size()
x = x.view(bs * ch, -1)
y = y.view(bs * ch, -1)
in_spec = self.get_average_spectrum(x)
ref_spec = self.get_average_spectrum(y)
sm_in_spec = self.smooth_spectrum(in_spec)
sm_ref_spec = self.smooth_spectrum(ref_spec)
# self.plot_spectrum(in_spec, filename="inSpec")
# self.plot_spectrum(sm_in_spec, filename="inSpecsm")
# design inverse FIR filter to match target EQ
freqs = np.linspace(0, 1.0, num=(self.n_fft // 2) + 1)
response = sm_ref_spec / sm_in_spec
response[-1] = 0.0 # zero gain at nyquist
b = scipy.signal.firwin2(
self.ntaps,
freqs * (self.sample_rate / 2),
response,
fs=self.sample_rate,
)
# scale the coefficients for less intense filter
# clearb *= 0.5
# apply the filter
x_filt = scipy.signal.lfilter(b, [1.0], x.numpy())
x_filt = torch.tensor(x_filt.astype("float32"))
if False:
# plot the filter response
w, h = scipy.signal.freqz(b, fs=self.sample_rate, worN=response.shape[-1])
fig, ax1 = plt.subplots()
ax1.set_title("Digital filter frequency response")
ax1.plot(w, 20 * np.log10(abs(h + 1e-8)))
ax1.plot(w, 20 * np.log10(abs(response + 1e-8)))
ax1.set_xscale("log")
ax1.set_ylim([-12, 12])
plt.grid(c="lightgray")
plt.savefig(f"inverse.png")
x_filt_avg_spec = self.get_average_spectrum(x_filt)
sm_x_filt_avg_spec = self.smooth_spectrum(x_filt_avg_spec)
y_avg_spec = self.get_average_spectrum(y)
sm_y_avg_spec = self.smooth_spectrum(y_avg_spec)
compare = torch.stack(
[
torch.tensor(sm_in_spec),
torch.tensor(sm_x_filt_avg_spec),
torch.tensor(sm_ref_spec),
torch.tensor(sm_y_avg_spec),
]
)
self.plot_multi_spectrum(
compare,
legend=["in", "out", "target curve", "actual target"],
filename="outSpec",
)
return x_filt
def analyze_speech_dataset(self, filepaths, peak=-3.0):
avg_spec = []
for filepath in tqdm(filepaths, ncols=80):
x, sr = torchaudio.load(filepath)
x /= x.abs().max()
x *= 10 ** (peak / 20.0)
avg_spec.append(self.get_average_spectrum(x))
avg_specs = torch.stack(avg_spec)
avg_spec = avg_specs.mean(dim=0).numpy()
avg_spec_std = avg_specs.std(dim=0).numpy()
# self.plot_multi_spectrum(avg_specs, filename="allTargetEQs")
# self.plot_spectrum_stats(avg_spec, avg_spec_std, filename="targetEQstats")
sm_avg_spec = self.smooth_spectrum(avg_spec)
return avg_spec, sm_avg_spec
def smooth_spectrum(self, H):
# apply Savgol filter for smoothed target curve
return scipy.signal.savgol_filter(H, 1025, 2)
def get_average_spectrum(self, x):
# x = x[:, : self.n_fft]
X = torch.stft(x, self.n_fft, return_complex=True, normalized=True)
# fft_size = self.next_power_of_2(x.shape[-1])
# X = torch.fft.rfft(x, n=fft_size)
X = X.abs() # convert to magnitude
X = X.mean(dim=-1).view(-1) # average across frames
return X
@staticmethod
def next_power_of_2(x):
return 1 if x == 0 else int(2 ** np.ceil(np.log2(x)))
def plot_multi_spectrum(self, Hs, legend=[], filename=None):
bin_width = (self.sample_rate / 2) / (self.n_fft // 2)
freqs = np.arange(0, (self.sample_rate / 2) + bin_width, step=bin_width)
fig, ax1 = plt.subplots()
for H in Hs:
ax1.plot(
freqs,
20 * np.log10(abs(H) + 1e-8),
)
plt.legend(legend)
# avg_spec = Hs.mean(dim=0).numpy()
# ax1.plot(freqs, 20 * np.log10(avg_spec), color="k", linewidth=2)
ax1.set_xscale("log")
ax1.set_ylim([-80, 0])
plt.grid(c="lightgray")
if filename is not None:
plt.savefig(f"{filename}.png")
def plot_spectrum_stats(self, H_mean, H_std, filename=None):
bin_width = (self.sample_rate / 2) / (self.n_fft // 2)
freqs = np.arange(0, (self.sample_rate / 2) + bin_width, step=bin_width)
fig, ax1 = plt.subplots()
ax1.plot(freqs, 20 * np.log10(H_mean))
ax1.plot(
freqs,
(20 * np.log10(H_mean)) + (20 * np.log10(H_std)),
linestyle="--",
color="k",
)
ax1.plot(
freqs,
(20 * np.log10(H_mean)) - (20 * np.log10(H_std)),
linestyle="--",
color="k",
)
ax1.set_xscale("log")
ax1.set_ylim([-80, 0])
plt.grid(c="lightgray")
if filename is not None:
plt.savefig(f"{filename}.png")
def plot_spectrum(self, H, legend=[], filename=None):
bin_width = (self.sample_rate / 2) / (self.n_fft // 2)
freqs = np.arange(0, (self.sample_rate / 2) + bin_width, step=bin_width)
fig, ax1 = plt.subplots()
ax1.plot(freqs, 20 * np.log10(H))
ax1.set_xscale("log")
ax1.set_ylim([-80, 0])
plt.grid(c="lightgray")
plt.legend(legend)
if filename is not None:
plt.savefig(f"{filename}.png")
class BaslineComp(torch.nn.Module):
def __init__(
self,
sample_rate: float = 44100,
):
super().__init__()
self.sample_rate = sample_rate
self.meter = pyln.Meter(sample_rate)
def forward(self, x, y):
x_lufs = self.meter.integrated_loudness(x.view(-1).numpy())
y_lufs = self.meter.integrated_loudness(y.view(-1).numpy())
delta_lufs = y_lufs - x_lufs
threshold = 0.0
x_comp = x
x_comp_new = x
while delta_lufs > 0.5 and threshold > -80.0:
x_comp = x_comp_new # use the last setting
x_comp_new = compressor(
x.view(-1).numpy(),
self.sample_rate,
threshold=threshold,
ratio=3,
attack_time=0.001,
release_time=0.05,
knee_dB=6.0,
makeup_gain_dB=0.0,
)
x_comp_new = torch.tensor(x_comp_new)
x_comp_new /= x_comp_new.abs().max()
x_comp_new *= 10 ** (-12.0 / 20)
x_lufs = self.meter.integrated_loudness(x_comp_new.view(-1).numpy())
delta_lufs = y_lufs - x_lufs
threshold -= 0.5
return x_comp.view(1, 1, -1)
class BaselineEQAndComp(torch.nn.Module):
def __init__(
self,
ntaps=63,
n_fft=65536,
sample_rate=44100,
block_size=1024,
plugin_config=None,
):
super().__init__()
self.eq = BaselineEQ(ntaps, n_fft, sample_rate)
self.comp = BaslineComp(sample_rate)
def forward(self, x, y):
with torch.inference_mode():
x /= x.abs().max()
y /= y.abs().max()
x *= 10 ** (-12.0 / 20)
y *= 10 ** (-12.0 / 20)
x = self.eq(x, y)
x /= x.abs().max()
y /= y.abs().max()
x *= 10 ** (-12.0 / 20)
y *= 10 ** (-12.0 / 20)
x = self.comp(x, y)
x /= x.abs().max()
x *= 10 ** (-12.0 / 20)
return x
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