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import torch
import auraloss
import resampy
import torchaudio
from pesq import pesq
import pyloudnorm as pyln
def crest_factor(x):
"""Compute the crest factor of waveform."""
peak, _ = x.abs().max(dim=-1)
rms = torch.sqrt((x ** 2).mean(dim=-1))
return 20 * torch.log(peak / rms.clamp(1e-8))
def rms_energy(x):
rms = torch.sqrt((x ** 2).mean(dim=-1))
return 20 * torch.log(rms.clamp(1e-8))
def spectral_centroid(x):
"""Compute the crest factor of waveform.
See: https://gist.github.com/endolith/359724
"""
spectrum = torch.fft.rfft(x).abs()
normalized_spectrum = spectrum / spectrum.sum()
normalized_frequencies = torch.linspace(0, 1, spectrum.shape[-1])
spectral_centroid = torch.sum(normalized_frequencies * normalized_spectrum)
return spectral_centroid
def loudness(x, sample_rate):
"""Compute the loudness in dB LUFS of waveform."""
meter = pyln.Meter(sample_rate)
# add stereo dim if needed
if x.shape[0] < 2:
x = x.repeat(2, 1)
return torch.tensor(meter.integrated_loudness(x.permute(1, 0).numpy()))
class MelSpectralDistance(torch.nn.Module):
def __init__(self, sample_rate, length=65536):
super().__init__()
self.error = auraloss.freq.MelSTFTLoss(
sample_rate,
fft_size=length,
hop_size=length,
win_length=length,
w_sc=0,
w_log_mag=1,
w_lin_mag=1,
n_mels=128,
scale_invariance=False,
)
# I think scale invariance may not work well,
# since aspects of the phase may be considered?
def forward(self, input, target):
return self.error(input, target)
class PESQ(torch.nn.Module):
def __init__(self, sample_rate):
super().__init__()
self.sample_rate = sample_rate
def forward(self, input, target):
if self.sample_rate != 16000:
target = resampy.resample(
target.view(-1).numpy(),
self.sample_rate,
16000,
)
input = resampy.resample(
input.view(-1).numpy(),
self.sample_rate,
16000,
)
return pesq(
16000,
target,
input,
"wb",
)
class CrestFactorError(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
return torch.nn.functional.l1_loss(
crest_factor(input),
crest_factor(target),
).item()
class RMSEnergyError(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
return torch.nn.functional.l1_loss(
rms_energy(input),
rms_energy(target),
).item()
class SpectralCentroidError(torch.nn.Module):
def __init__(self, sample_rate, n_fft=2048, hop_length=512):
super().__init__()
self.spectral_centroid = torchaudio.transforms.SpectralCentroid(
sample_rate,
n_fft=n_fft,
hop_length=hop_length,
)
def forward(self, input, target):
return torch.nn.functional.l1_loss(
self.spectral_centroid(input + 1e-16).mean(),
self.spectral_centroid(target + 1e-16).mean(),
).item()
class LoudnessError(torch.nn.Module):
def __init__(self, sample_rate: int, peak_normalize: bool = False):
super().__init__()
self.sample_rate = sample_rate
self.peak_normalize = peak_normalize
def forward(self, input, target):
if self.peak_normalize:
# peak normalize
x = input / input.abs().max()
y = target / target.abs().max()
else:
x = input
y = target
return torch.nn.functional.l1_loss(
loudness(x.view(1, -1), self.sample_rate),
loudness(y.view(1, -1), self.sample_rate),
).item()
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