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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):
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),
)
def process(self, waveform):
return self.transform(waveform) |