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import librosa
import torch
from torch import nn
class TorchSTFT(nn.Module): # pylint: disable=abstract-method
"""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,
hop_length,
win_length,
pad_wav=False,
window="hann_window",
sample_rate=None,
mel_fmin=0,
mel_fmax=None,
n_mels=80,
use_mel=False,
do_amp_to_db=False,
spec_gain=1.0,
power=None,
use_htk=False,
mel_norm="slaney",
normalized=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.mel_norm = mel_norm
self.window = nn.Parameter(getattr(torch, window)(win_length), requires_grad=False)
self.mel_basis = None
self.normalized = normalized
if use_mel:
self._build_mel_basis()
def __call__(self, x):
"""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:
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):
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, spec_gain=1.0):
return torch.log(torch.clamp(x, min=1e-5) * spec_gain)
@staticmethod
def _db_to_amp(x, spec_gain=1.0):
return torch.exp(x) / spec_gain
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