diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 3c3e28d..62930f2 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -26,8 +26,6 @@ jobs:
             libhdf5-dev \
             libfftw3-dev \
             libgsl-dev \
-            libframel-dev \
-            libmetaio-dev \
             pkg-config
 
       - name: Install package and dev dependencies
diff --git a/src/deepextractor/data/__init__.py b/src/deepextractor/data/__init__.py
new file mode 100644
index 0000000..9e90206
--- /dev/null
+++ b/src/deepextractor/data/__init__.py
@@ -0,0 +1,3 @@
+from deepextractor.data.datasets import SpectrogramDataset, TimeSeriesDataset
+
+__all__ = ["SpectrogramDataset", "TimeSeriesDataset"]
diff --git a/src/deepextractor/data/datasets.py b/src/deepextractor/data/datasets.py
new file mode 100644
index 0000000..2e45b63
--- /dev/null
+++ b/src/deepextractor/data/datasets.py
@@ -0,0 +1,52 @@
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+class TimeSeriesDataset(Dataset):
+    """Dataset for 1-D time-series arrays stored as .npy files.
+
+    Each array is expected to have shape ``(N, L)`` where *N* is the number
+    of samples and *L* is the signal length.  Items are returned as
+    ``(1, L)`` float32 tensors (channel dimension added).
+    """
+
+    def __init__(self, input_npy, target_npy, transform=None):
+        self.inputs = np.load(input_npy)
+        self.targets = np.load(target_npy)
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.inputs)
+
+    def __getitem__(self, idx):
+        x = torch.from_numpy(self.inputs[idx]).float().unsqueeze(0)
+        y = torch.from_numpy(self.targets[idx]).float().unsqueeze(0)
+        if self.transform:
+            x = self.transform(x)
+            y = self.transform(y)
+        return x, y
+
+
+class SpectrogramDataset(Dataset):
+    """Dataset for 2-D spectrogram arrays stored as .npy files.
+
+    Each array is expected to have shape ``(N, H, W)``.  Items are returned
+    as ``(1, H, W)`` float32 tensors (channel dimension added).
+    """
+
+    def __init__(self, input_npy, target_npy, transform=None):
+        self.inputs = np.load(input_npy)
+        self.targets = np.load(target_npy)
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.inputs)
+
+    def __getitem__(self, idx):
+        x = torch.from_numpy(self.inputs[idx]).float().unsqueeze(0)
+        y = torch.from_numpy(self.targets[idx]).float().unsqueeze(0)
+        if self.transform:
+            x = self.transform(x)
+            y = self.transform(y)
+        return x, y