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mel_spectrogram.py

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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, 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)