Add dataset template.

dataset.py

@@ -0,0 +1,46 @@
+import torch
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+import torchaudio
+import os
+import sys
+
+
+class VoiceBankDemandDataset(Dataset):
+    def __init__(self, data_dir, hop_length=128):
+        self.clean_path = self.get_clean(data_dir)
+        self.noisy_path = self.get_noisy(data_dir)
+        self.hop_length = hop_length
+
+    def get_clean(self, root):
+        raise NotImplementedError
+
+    def get_noisy(self, root):
+        raise NotImplementedError
+
+    def padding(self, x):
+        len_x = x.size(-1)
+        pad_len = self.hop_length - len_x % self.hop_length
+        x = F.pad(x, (0, pad_len))
+        return x
+
+    def normalize(self, x):
+        return 2 * (x - x.min()) / (x.max() - x.min()) - 1
+
+    def __len__(self):
+        return len(self.noisy_path)
+
+    def __getitem__(self, idx):
+        clean = torchaudio.load(self.clean_path[idx])[0]
+        noisy = torchaudio.load(self.noisy_path[idx])[0]
+
+        noisy = self.normalize(noisy)
+        length = clean.size(-1)
+        clean.squeeze_(0)
+        noisy.squeeze_(0)
+        start = torch.randint(0, length - 16384 - 1, (1, ))
+        end = start + 16384
+        clean = clean[start:end]
+        noisy = noisy[start:end]
+
+        return noisy, clean