File size: 4,289 Bytes
f8a0cc5 |
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 |
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
"""
PARAMS
------
C: compression factor used to compress
"""
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
def spectral_de_normalize_torch(magnitudes):
output = dynamic_range_decompression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
def spectrogram_torch(y,
n_fft,
sampling_rate,
hop_size,
win_size,
center=False):
if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
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 = F.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)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
return spec
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
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(sampling_rate, n_fft, num_mels, fmin, fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
dtype=spec.dtype, device=spec.device)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)
return spec
def mel_spectrogram_torch(y,
n_fft,
num_mels,
sampling_rate,
hop_size,
win_size,
fmin,
fmax,
center=False):
if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
global mel_basis, hann_window
dtype_device = str(y.dtype) + '_' + str(y.device)
fmax_dtype_device = str(fmax) + '_' + dtype_device
wnsize_dtype_device = str(win_size) + '_' + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
dtype=y.dtype, device=y.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 = F.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)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)
return spec
|