import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
Dynamic range compression using log10.
Args:
x (torch.Tensor): Input tensor.
C (float, optional): Scaling factor. Defaults to 1.
clip_val (float, optional): Minimum value for clamping. Defaults to 1e-5.
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
"""
Dynamic range decompression using exp.
Args:
x (torch.Tensor): Input tensor.
C (float, optional): Scaling factor. Defaults to 1.
"""
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
"""
Spectral normalization using dynamic range compression.
Args:
magnitudes (torch.Tensor): Magnitude spectrogram.
"""
return dynamic_range_compression_torch(magnitudes)
def spectral_de_normalize_torch(magnitudes):
"""
Spectral de-normalization using dynamic range decompression.
Args:
magnitudes (torch.Tensor): Normalized spectrogram.
"""
return dynamic_range_decompression_torch(magnitudes)
mel_basis = {}
hann_window = {}
def spectrogram_torch(y, n_fft, hop_size, win_size, center=False):
"""
Compute the spectrogram of a signal using STFT.
Args:
y (torch.Tensor): Input signal.
n_fft (int): FFT window size.
hop_size (int): Hop size between frames.
win_size (int): Window size.
center (bool, optional): Whether to center the window. Defaults to False.
"""
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
dtype=y.dtype, device=y.device
)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft=n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
return spec
def spec_to_mel_torch(spec, n_fft, num_mels, sample_rate, fmin, fmax):
"""
Convert a spectrogram to a mel-spectrogram.
Args:
spec (torch.Tensor): Magnitude spectrogram.
n_fft (int): FFT window size.
num_mels (int): Number of mel frequency bins.
sample_rate (int): Sampling rate of the audio signal.
fmin (float): Minimum frequency.
fmax (float): Maximum frequency.
"""
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(
sr=sample_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
dtype=spec.dtype, device=spec.device
)
melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
melspec = spectral_normalize_torch(melspec)
return melspec
def mel_spectrogram_torch(
y, n_fft, num_mels, sample_rate, hop_size, win_size, fmin, fmax, center=False
):
"""
Compute the mel-spectrogram of a signal.
Args:
y (torch.Tensor): Input signal.
n_fft (int): FFT window size.
num_mels (int): Number of mel frequency bins.
sample_rate (int): Sampling rate of the audio signal.
hop_size (int): Hop size between frames.
win_size (int): Window size.
fmin (float): Minimum frequency.
fmax (float): Maximum frequency.
center (bool, optional): Whether to center the window. Defaults to False.
"""
spec = spectrogram_torch(y, n_fft, hop_size, win_size, center)
melspec = spec_to_mel_torch(spec, n_fft, num_mels, sample_rate, fmin, fmax)
return melspec
def compute_window_length(n_mels: int, sample_rate: int):
f_min = 0
f_max = sample_rate / 2
window_length_seconds = 8 * n_mels / (f_max - f_min)
window_length = int(window_length_seconds * sample_rate)
return 2 ** (window_length.bit_length() - 1)
class MultiScaleMelSpectrogramLoss(torch.nn.Module):
def __init__(
self,
sample_rate: int = 24000,
n_mels: list[int] = [5, 10, 20, 40, 80, 160, 320, 480],
loss_fn=torch.nn.L1Loss(),
):
super().__init__()
self.sample_rate = sample_rate
self.loss_fn = loss_fn
self.log_base = torch.log(torch.tensor(10.0))
self.stft_params: list[tuple] = []
self.hann_window: dict[int, torch.Tensor] = {}
self.mel_banks: dict[int, torch.Tensor] = {}
self.stft_params = [
(mel, compute_window_length(mel, sample_rate), self.sample_rate // 100)
for mel in n_mels
]
def mel_spectrogram(
self,
wav: torch.Tensor,
n_mels: int,
window_length: int,
hop_length: int,
):
# IDs for caching
dtype_device = str(wav.dtype) + "_" + str(wav.device)
win_dtype_device = str(window_length) + "_" + dtype_device
mel_dtype_device = str(n_mels) + "_" + dtype_device
# caching hann window
if win_dtype_device not in self.hann_window:
self.hann_window[win_dtype_device] = torch.hann_window(
window_length, device=wav.device, dtype=torch.float32
)
wav = wav.squeeze(1) # -> torch(B, T)
stft = torch.stft(
wav.float(),
n_fft=window_length,
hop_length=hop_length,
window=self.hann_window[win_dtype_device],
return_complex=True,
) # -> torch (B, window_length // 2 + 1, (T - window_length)/hop_length + 1)
magnitude = torch.sqrt(stft.real.pow(2) + stft.imag.pow(2) + 1e-6)
# caching mel filter
if mel_dtype_device not in self.mel_banks:
self.mel_banks[mel_dtype_device] = torch.from_numpy(
librosa_mel_fn(
sr=self.sample_rate,
n_mels=n_mels,
n_fft=window_length,
fmin=0,
fmax=None,
)
).to(device=wav.device, dtype=torch.float32)
mel_spectrogram = torch.matmul(
self.mel_banks[mel_dtype_device], magnitude
) # torch(B, n_mels, stft.frames)
return mel_spectrogram
def forward(
self, real: torch.Tensor, fake: torch.Tensor
): # real: torch(B, 1, T) , fake: torch(B, 1, T)
loss = 0.0
for p in self.stft_params:
real_mels = self.mel_spectrogram(real, *p)
fake_mels = self.mel_spectrogram(fake, *p)
real_logmels = torch.log(real_mels.clamp(min=1e-5)) / self.log_base
fake_logmels = torch.log(fake_mels.clamp(min=1e-5)) / self.log_base
loss += self.loss_fn(real_logmels, fake_logmels)
return loss