Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,187 Bytes
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import torch
import numpy as np
from scipy.signal import freqz
from typing import Iterable
from functools import reduce
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]
jsonparse2hydra = lambda d: (
(
{"_target_": d["class_path"]}
| (
{k: jsonparse2hydra(v) for k, v in d["init_args"].items()}
if "init_args" in d
else {}
)
if "class_path" in d
else {k: jsonparse2hydra(v) for k, v in d.items()}
)
if isinstance(d, dict)
else (list(map(jsonparse2hydra, d)) if isinstance(d, list) else d)
)
remove_window_fn = lambda d: (
{k: remove_window_fn(v) for k, v in d.items() if k != "window_fn"}
if isinstance(d, dict)
else (list(map(remove_window_fn, d)) if isinstance(d, list) else d)
)
def chain_functions(*functions):
return lambda *initial_args: reduce(
lambda xs, f: f(*xs) if isinstance(xs, tuple) else f(xs),
functions,
initial_args,
)
|