Spaces:
Sleeping
Sleeping
File size: 5,200 Bytes
9b2107c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
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
|