| import numpy as np |
| import torch |
| from scipy.signal import freqz |
| from typing import Iterable |
|
|
| from modules import fx |
| from modules.functional import ( |
| highpass_biquad_coef, |
| lowpass_biquad_coef, |
| highshelf_biquad_coef, |
| lowshelf_biquad_coef, |
| equalizer_biquad_coef, |
| ) |
|
|
|
|
| def get_log_mags_from_eq(eq: Iterable, worN=1024, sr=44100): |
| get_ba = lambda xs: torch.cat([x.view(1) for x in xs]).view(2, 3) |
|
|
| def f(biquad): |
| params = biquad.params |
| match type(biquad): |
| case fx.HighPass: |
| coeffs = highpass_biquad_coef(sr, params.freq, params.Q) |
| case fx.LowPass: |
| coeffs = lowpass_biquad_coef(sr, params.freq, params.Q) |
| case fx.HighShelf: |
| coeffs = highshelf_biquad_coef(sr, params.freq, params.gain, biquad.Q) |
| case fx.LowShelf: |
| coeffs = lowshelf_biquad_coef(sr, params.freq, params.gain, biquad.Q) |
| case fx.Peak: |
| coeffs = equalizer_biquad_coef(sr, params.freq, params.gain, params.Q) |
| case _: |
| raise ValueError(biquad) |
|
|
| b, a = get_ba(coeffs) |
| w, h = freqz(b.numpy(), a.numpy(), worN, fs=sr) |
| log_h = 20 * np.log10(np.abs(h) + 1e-10) |
| return w, log_h |
|
|
| log_mags = list(map(f, eq)) |
| return log_mags[0][0], [x for _, x in log_mags] |
|
|
