diff --git a/nam/models/_base.py b/nam/models/_base.py
index e77e797a..40ce84af 100644
--- a/nam/models/_base.py
+++ b/nam/models/_base.py
@@ -54,6 +54,18 @@ def _metadata_loudness_x(cls) -> torch.Tensor:
             )
         )
 
+    @property
+    def device(self) -> Optional[torch.device]:
+        """
+        Helpful property, where the parameters of the model live.
+        """
+        # We can do this because the models are tiny and I don't expect a NAM to be on
+        # multiple devices
+        try:
+            return next(self.parameters()).device
+        except StopIteration:
+            return None
+
     @property
     def sample_rate(self) -> Optional[float]:
         return self._sample_rate.item() if self._has_sample_rate else None
@@ -81,7 +93,7 @@ def _metadata_loudness(self, gain: float = 1.0, db: bool = True) -> float:
 
         :param gain: Multiplies input signal
         """
-        x = self._metadata_loudness_x()
+        x = self._metadata_loudness_x().to(self.device)
         y = self._at_nominal_settings(gain * x)
         loudness = torch.sqrt(torch.mean(torch.square(y)))
         if db:
diff --git a/nam/models/wavenet.py b/nam/models/wavenet.py
index e7015c20..b48e41e2 100644
--- a/nam/models/wavenet.py
+++ b/nam/models/wavenet.py
@@ -318,7 +318,7 @@ def export_weights(self) -> np.ndarray:
         weights = torch.cat([layer.export_weights() for layer in self._layers])
         if self._head is not None:
             weights = torch.cat([weights, self._head.export_weights()])
-        weights = torch.cat([weights, torch.Tensor([self._head_scale])])
+        weights = torch.cat([weights.cpu(), torch.Tensor([self._head_scale])])
         return weights.detach().cpu().numpy()
 
     def import_weights(self, weights: torch.Tensor):