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import torch
import torchaudio
import numpy as np
def gain(xs, min_dB=-12, max_dB=12):
gain_dB = (torch.rand(1) * (max_dB - min_dB)) + min_dB
gain_ln = 10 ** (gain_dB / 20)
for idx, x in enumerate(xs):
xs[idx] = x * gain_ln
return xs
def peaking_filter(xs, sr=44100, frequency=1000, width_q=0.707, gain_db=12):
# gain_db = ((torch.rand(1) * 6) + 6).numpy().squeeze()
# width_q = (torch.rand(1) * 4).numpy().squeeze()
# frequency = ((torch.rand(1) * 9960) + 40).numpy().squeeze()
# if torch.rand(1) > 0.5:
# gain_db = -gain_db
effects = [["equalizer", f"{frequency}", f"{width_q}", f"{gain_db}"]]
for idx, x in enumerate(xs):
y, sr = torchaudio.sox_effects.apply_effects_tensor(
x, sr, effects, channels_first=True
)
xs[idx] = y
return xs
def pitch_shift(xs, min_shift=-200, max_shift=200, sr=44100):
shift = min_shift + (torch.rand(1)).numpy().squeeze() * (max_shift - min_shift)
effects = [["pitch", f"{shift}"]]
for idx, x in enumerate(xs):
y, sr = torchaudio.sox_effects.apply_effects_tensor(
x, sr, effects, channels_first=True
)
xs[idx] = y
return xs
def time_stretch(xs, min_stretch=0.8, max_stretch=1.2, sr=44100):
stretch = min_stretch + (torch.rand(1)).numpy().squeeze() * (
max_stretch - min_stretch
)
effects = [["tempo", f"{stretch}"]]
for idx, x in enumerate(xs):
y, sr = torchaudio.sox_effects.apply_effects_tensor(
x, sr, effects, channels_first=True
)
xs[idx] = y
return xs
def frequency_corruption(xs, sr=44100):
effects = []
# apply a random number of peaking bands from 0 to 4s
bands = [[200, 2000], [800, 4000], [2000, 8000], [4000, int((sr // 2) * 0.9)]]
total_gain_db = 0.0
for band in bands:
if torch.rand(1).sum() > 0.2:
frequency = (torch.randint(band[0], band[1], [1])).numpy().squeeze()
width_q = ((torch.rand(1) * 10) + 0.1).numpy().squeeze()
gain_db = ((torch.rand(1) * 48)).numpy().squeeze()
if torch.rand(1).sum() > 0.5:
gain_db = -gain_db
total_gain_db += gain_db
if np.abs(total_gain_db) >= 24:
continue
cmd = ["equalizer", f"{frequency}", f"{width_q}", f"{gain_db}"]
effects.append(cmd)
# low shelf (bass)
if torch.rand(1).sum() > 0.2:
gain_db = ((torch.rand(1) * 24)).numpy().squeeze()
frequency = (torch.randint(20, 200, [1])).numpy().squeeze()
if torch.rand(1).sum() > 0.5:
gain_db = -gain_db
effects.append(["bass", f"{gain_db}", f"{frequency}"])
# high shelf (treble)
if torch.rand(1).sum() > 0.2:
gain_db = ((torch.rand(1) * 24)).numpy().squeeze()
frequency = (torch.randint(4000, int((sr // 2) * 0.9), [1])).numpy().squeeze()
if torch.rand(1).sum() > 0.5:
gain_db = -gain_db
effects.append(["treble", f"{gain_db}", f"{frequency}"])
for idx, x in enumerate(xs):
y, sr = torchaudio.sox_effects.apply_effects_tensor(
x.view(1, -1) * 10 ** (-48 / 20), sr, effects, channels_first=True
)
# apply gain back
y *= 10 ** (48 / 20)
xs[idx] = y
return xs
def dynamic_range_corruption(xs, sr=44100):
"""Apply an expander."""
attack = (torch.rand([1]).numpy()[0] * 0.05) + 0.001
release = (torch.rand([1]).numpy()[0] * 0.2) + attack
knee = (torch.rand([1]).numpy()[0] * 12) + 0.0
# design the compressor transfer function
start = -100.0
threshold = -(
(torch.rand([1]).numpy()[0] * 20) + 10
) # threshold from -30 to -10 dB
ratio = (torch.rand([1]).numpy()[0] * 4.0) + 1 # ratio from 1:1 to 5:1
# compute the transfer curve
point = -((-threshold / -ratio) + (-start / ratio) + -threshold)
# apply some makeup gain
makeup = torch.rand([1]).numpy()[0] * 6
effects = [
[
"compand",
f"{attack},{release}",
f"{knee}:{point},{start},{threshold},{threshold}",
f"{makeup}",
f"{start}",
]
]
for idx, x in enumerate(xs):
# if the input is clipping normalize it
if x.abs().max() >= 1.0:
x /= x.abs().max()
gain_db = -((torch.rand(1) * 24)).numpy().squeeze()
x *= 10 ** (gain_db / 20.0)
y, sr = torchaudio.sox_effects.apply_effects_tensor(
x.view(1, -1), sr, effects, channels_first=True
)
xs[idx] = y
return xs
def dynamic_range_compression(xs, sr=44100):
"""Apply a compressor."""
attack = (torch.rand([1]).numpy()[0] * 0.05) + 0.0005
release = (torch.rand([1]).numpy()[0] * 0.2) + attack
knee = (torch.rand([1]).numpy()[0] * 12) + 0.0
# design the compressor transfer function
start = -100.0
threshold = -((torch.rand([1]).numpy()[0] * 52) + 12)
# threshold from -64 to -12 dB
ratio = (torch.rand([1]).numpy()[0] * 10.0) + 1 # ratio from 1:1 to 10:1
# compute the transfer curve
point = threshold * (1 - (1 / ratio))
# apply some makeup gain
makeup = torch.rand([1]).numpy()[0] * 6
effects = [
[
"compand",
f"{attack},{release}",
f"{knee}:{start},{threshold},{threshold},0,{point}",
f"{makeup}",
f"{start}",
f"{attack}",
]
]
for idx, x in enumerate(xs):
y, sr = torchaudio.sox_effects.apply_effects_tensor(
x.view(1, -1), sr, effects, channels_first=True
)
xs[idx] = y
return xs
def lowpass_filter(xs, sr=44100, frequency=4000):
effects = [["lowpass", f"{frequency}"]]
for idx, x in enumerate(xs):
y, sr = torchaudio.sox_effects.apply_effects_tensor(
x, sr, effects, channels_first=True
)
xs[idx] = y
return xs
def apply(xs, sr, augmentations):
# iterate over augmentation dict
for aug, params in augmentations.items():
if aug == "gain":
xs = gain(xs, **params)
elif aug == "peak":
xs = peaking_filter(xs, **params)
elif aug == "lowpass":
xs = lowpass_filter(xs, **params)
elif aug == "pitch":
xs = pitch_shift(xs, **params)
elif aug == "tempo":
xs = time_stretch(xs, **params)
elif aug == "freq_corrupt":
xs = frequency_corruption(xs, **params)
else:
raise RuntimeError("Invalid augmentation: {aug}")
return xs
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