Audio Dataset

audio_toolset/mel_dataset.py

@@ -0,0 +1,30 @@
+from pathlib import Path
+
+import numpy as np
+from torch.utils.data import Dataset
+
+
+class TorchMelDataset(Dataset):
+    def __init__(self, identifier, mel_path, segment_len, hop_len, label, padding=0, transform=None):
+        self.padding = padding
+        self.path = next(iter(Path(mel_path).glob(f'{identifier}_*')))
+        self.segment_len = segment_len
+        self.m, self.n = str(self.path).split('_')[-2:]  # get spectrogram dimensions
+        self.n = int(self.n.split('.', 1)[0])  # remove .npy
+        self.m, self.n = (int(i) for i in (self.m, self.n))
+        self.offsets = list(range(0, self.n - segment_len, hop_len))
+        self.label = label
+        self.transform = transform
+
+    def __getitem__(self, item):
+        start = self.offsets[item]
+        mel_spec = np.load(str(self.path), allow_pickle=True)
+        if self.padding > 0:
+            mel_spec = np.pad(mel_spec, pad_width=[(0, 0), (self.padding // 2, self.padding // 2)], mode='mean')
+        snippet = mel_spec[:, start: start + self.segment_len]
+        if self.transform:
+            snippet = self.transform(snippet)
+        return snippet, self.label
+
+    def __len__(self):
+        return len(self.offsets)