File size: 1,122 Bytes
aab5975 2749826 aab5975 2749826 aab5975 |
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 |
from torchaudio import transforms as T
import torch
import torch.nn as nn
MEAN, STD = 0.5347, 0.0772 # Xeno-Canto stats
SR = 16000
NFFT = 1024
HOPLEN = 320
NMELS = 128
FMIN = 50
FMAX = 8000
class Normalization(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return (x - x.min()) / (x.max() - x.min())
class Standardization(torch.nn.Module):
def __init__(self, mean, std):
super().__init__()
self.mean = mean
self.std = std
def forward(self, x):
return (x - self.mean) / self.std
class MelSpectrogramProcessor:
def __init__(self, sample_rate=SR, n_mels=NMELS, n_fft=NFFT, hop_length=HOPLEN, f_min=FMIN, f_max=FMAX, device='cpu'):
self.transform = nn.Sequential(
T.MelSpectrogram(sample_rate=sample_rate, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length, f_min=f_min, f_max=f_max),
T.AmplitudeToDB(),
Normalization(),
Standardization(mean=MEAN, std=STD),
).to(device)
def process(self, waveform):
return self.transform(waveform) |