Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,283 Bytes
4efe6b5 |
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 |
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,
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
|