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from typing import Literal, Optional, Tuple, Union
import librosa
import torch
from torch import Tensor, nn
from torch.nn import functional
class TorchSTFT(nn.Module):
r"""Some of the audio processing funtions using Torch for faster batch processing.
Args:
n_fft (int): FFT window size for STFT.
hop_length (int): number of frames between STFT columns.
win_length (int, optional): STFT window length.
pad_wav (bool, optional): If True pad the audio with (n_fft - hop_length) / 2). Defaults to False.
window (str, optional): The name of a function to create a window tensor that is applied/multiplied to each frame/window. Defaults to "hann_window"
sample_rate (int, optional): target audio sampling rate. Defaults to None.
mel_fmin (int, optional): minimum filter frequency for computing melspectrograms. Defaults to None.
mel_fmax (int, optional): maximum filter frequency for computing melspectrograms. Defaults to None.
n_mels (int, optional): number of melspectrogram dimensions. Defaults to None.
use_mel (bool, optional): If True compute the melspectrograms otherwise. Defaults to False.
do_amp_to_db_linear (bool, optional): enable/disable amplitude to dB conversion of linear spectrograms. Defaults to False.
spec_gain (float, optional): gain applied when converting amplitude to DB. Defaults to 1.0.
power (float, optional): Exponent for the magnitude spectrogram, e.g., 1 for energy, 2 for power, etc. Defaults to None.
use_htk (bool, optional): Use HTK formula in mel filter instead of Slaney.
mel_norm (None, 'slaney', or number, optional): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization).
If numeric, use `librosa.util.normalize` to normalize each filter by to unit l_p norm.
See `librosa.util.normalize` for a full description of supported norm values (including `+-np.inf`).
Otherwise, leave all the triangles aiming for a peak value of 1.0. Defaults to "slaney".
"""
def __init__(
self,
n_fft: int,
hop_length: int,
win_length: int,
pad_wav: bool = False,
window: str = "hann_window",
sample_rate: int = 22050,
mel_fmin: int = 0,
mel_fmax: Optional[int] = None,
n_mels: int = 80,
use_mel: bool = False,
do_amp_to_db:bool = False,
spec_gain: float = 1.0,
power: Optional[float] = None,
use_htk: bool = False,
mel_norm: Optional[Union[Literal["slaney"], float]] = "slaney",
normalized: bool = False,
):
super().__init__()
self.n_fft = n_fft
self.hop_length = hop_length
self.win_length = win_length
self.pad_wav = pad_wav
self.sample_rate = sample_rate
self.mel_fmin = mel_fmin
self.mel_fmax = mel_fmax
self.n_mels = n_mels
self.use_mel = use_mel
self.do_amp_to_db = do_amp_to_db
self.spec_gain = spec_gain
self.power = power
self.use_htk = use_htk
self.window = nn.Parameter(getattr(torch, window)(win_length), requires_grad=False)
self.normalized = normalized
self.mel_norm: Optional[Union[Literal["slaney"], float]] = mel_norm
self.mel_basis = None
if use_mel:
self._build_mel_basis()
def __call__(self, x: Tensor):
"""Compute spectrogram frames by torch based stft.
Args:
x (Tensor): input waveform
Returns:
Tensor: spectrogram frames.
Shapes:
x: [B x T] or [:math:`[B, 1, T]`]
"""
if x.ndim == 2:
x = x.unsqueeze(1)
if self.pad_wav:
padding = int((self.n_fft - self.hop_length) / 2)
x = torch.nn.functional.pad(x, (padding, padding), mode="reflect")
# B x D x T x 2
o = torch.stft(
x.squeeze(1),
self.n_fft,
self.hop_length,
self.win_length,
self.window,
center=True,
pad_mode="reflect", # compatible with audio.py
normalized=self.normalized,
onesided=True,
return_complex=False,
)
M = o[:, :, :, 0]
P = o[:, :, :, 1]
S = torch.sqrt(torch.clamp(M**2 + P**2, min=1e-8))
if self.power is not None:
S = S**self.power
if self.use_mel and self.mel_basis is not None:
S = torch.matmul(self.mel_basis.to(x), S)
if self.do_amp_to_db:
S = self._amp_to_db(S, spec_gain=self.spec_gain)
return S
def _build_mel_basis(self):
r"""Builds the mel basis for the spectrogram transformation.
This method is called during initialization if use_mel is set to True.
"""
mel_basis = librosa.filters.mel(
sr=self.sample_rate,
n_fft=self.n_fft,
n_mels=self.n_mels,
fmin=self.mel_fmin,
fmax=self.mel_fmax,
htk=self.use_htk,
norm=self.mel_norm,
)
self.mel_basis = torch.from_numpy(mel_basis).float()
@staticmethod
def _amp_to_db(x: Tensor, spec_gain: float = 1.0) -> Tensor:
r"""Converts amplitude to decibels.
Args:
x (Tensor): The amplitude tensor to convert.
spec_gain (float, optional): The gain applied when converting. Defaults to 1.0.
Returns:
Tensor: The converted tensor in decibels.
"""
return torch.log(torch.clamp(x, min=1e-5) * spec_gain)
@staticmethod
def _db_to_amp(x: Tensor, spec_gain: float = 1.0) -> Tensor:
r"""Converts decibels to amplitude.
Args:
x (Tensor): The decibel tensor to convert.
spec_gain (float, optional): The gain applied when converting. Defaults to 1.0.
Returns:
Tensor: The converted tensor in amplitude.
"""
return torch.exp(x) / spec_gain
class STFTLoss(nn.Module):
r"""STFT loss. Input generate and real waveforms are converted
to spectrograms compared with L1 and Spectral convergence losses.
It is from ParallelWaveGAN paper https://arxiv.org/pdf/1910.11480.pdf
Attributes:
n_fft (int): The FFT size.
hop_length (int): The hop (stride) size.
win_length (int): The window size.
stft (TorchSTFT): The STFT function.
Methods:
forward(y_hat: Tensor, y: Tensor)
Compute the STFT loss.
"""
def __init__(self, n_fft: int, hop_length: int, win_length: int):
r"""Constructs all the necessary attributes for the STFTLoss object.
Args:
n_fft (int): The FFT size.
hop_length (int): The hop (stride) size.
win_length (int): The window size.
"""
super().__init__()
self.n_fft = n_fft
self.hop_length = hop_length
self.win_length = win_length
self.stft = TorchSTFT(n_fft, hop_length, win_length)
def forward(self, y_hat: Tensor, y: Tensor):
r"""Compute the STFT loss.
Args:
y_hat (Tensor): The generated waveforms.
y (Tensor): The real waveforms.
Returns:
loss_mag (Tensor): The magnitude loss.
loss_sc (Tensor): The spectral convergence loss.
"""
y_hat_M = self.stft(y_hat)
y_M = self.stft(y)
# magnitude loss
loss_mag = functional.l1_loss(torch.log(y_M), torch.log(y_hat_M))
# spectral convergence loss
loss_sc = torch.norm(y_M - y_hat_M, p="fro") / torch.norm(y_M, p="fro")
return loss_mag, loss_sc
class MultiScaleSTFTLoss(nn.Module):
"""Multi-scale STFT loss. Input generate and real waveforms are converted
to spectrograms compared with L1 and Spectral convergence losses.
It is from ParallelWaveGAN paper https://arxiv.org/pdf/1910.11480.pdf
Attributes:
loss_funcs (torch.nn.ModuleList): A list of STFTLoss modules for different scales.
Methods:
forward(y_hat: Tensor, y: Tensor) -> Tuple[Tensor, Tensor]
Compute the multi-scale STFT loss.
"""
def __init__(
self,
n_ffts: Tuple[int, int, int] = (1024, 2048, 512),
hop_lengths: Tuple[int, int, int] = (120, 240, 50),
win_lengths: Tuple[int, int, int] = (600, 1200, 240),
):
r"""Initialize the MultiScaleSTFTLoss module.
Args:
n_ffts (Tuple[int, int, int], optional): The FFT sizes for the STFTLoss modules. Defaults to (1024, 2048, 512).
hop_lengths (Tuple[int, int, int], optional): The hop lengths for the STFTLoss modules. Defaults to (120, 240, 50).
win_lengths (Tuple[int, int, int], optional): The window lengths for the STFTLoss modules. Defaults to (600, 1200, 240).
"""
super().__init__()
self.loss_funcs = torch.nn.ModuleList()
for n_fft, hop_length, win_length in zip(n_ffts, hop_lengths, win_lengths):
self.loss_funcs.append(STFTLoss(n_fft, hop_length, win_length))
def forward(self, y_hat: Tensor, y: Tensor):
r"""Compute the multi-scale STFT loss.
Args:
y_hat (Tensor): The generated waveforms.
y (Tensor): The real waveforms.
Returns:
Tuple[Tensor, Tensor]: The magnitude and spectral convergence losses.
"""
N = len(self.loss_funcs)
loss_sc = 0
loss_mag = 0
for f in self.loss_funcs:
lm, lsc = f(y_hat, y)
loss_mag += lm
loss_sc += lsc
loss_sc /= N
loss_mag /= N
return loss_mag, loss_sc
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