diff --git a/gui_v1.py b/gui_v1.py
index fa57881..4331336 100644
--- a/gui_v1.py
+++ b/gui_v1.py
@@ -530,7 +530,7 @@ if __name__ == "__main__":
                         self.gui_config.sg_hostapi = self.hostapis[0]
                     self.window["sg_hostapi"].Update(values=self.hostapis)
                     self.window["sg_hostapi"].Update(value=self.gui_config.sg_hostapi)
-                    if self.gui_config.sg_input_device not in self.input_devices:
+                    if self.gui_config.sg_input_device not in self.input_devices and len(self.input_devices) > 0:
                         self.gui_config.sg_input_device = self.input_devices[0]
                     self.window["sg_input_device"].Update(values=self.input_devices)
                     self.window["sg_input_device"].Update(
diff --git a/tools/rvc_for_realtime.py b/tools/rvc_for_realtime.py
index 1f89cc6..f0c6bb9 100644
--- a/tools/rvc_for_realtime.py
+++ b/tools/rvc_for_realtime.py
@@ -372,15 +372,17 @@ class RVC:
             if hasattr(self, "index") and self.index_rate != 0:
                 npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
                 score, ix = self.index.search(npy, k=8)
-                weight = np.square(1 / score)
-                weight /= weight.sum(axis=1, keepdims=True)
-                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-                if self.config.is_half:
-                    npy = npy.astype("float16")
-                feats[0][skip_head // 2 :] = (
-                    torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
-                    + (1 - self.index_rate) * feats[0][skip_head // 2 :]
-                )
+                if (ix >= 0).all():
+                    weight = np.square(1 / score)
+                    weight /= weight.sum(axis=1, keepdims=True)
+                    npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
+                    if self.config.is_half:
+                        npy = npy.astype("float16")
+                    feats[0][skip_head // 2 :] = (
+                        torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
+                        + (1 - self.index_rate) * feats[0][skip_head // 2 :]
+                    )
+                else: printt("Invalid index. You MUST use added_xxxx.index but not trained_xxxx.index!")
             else:
                 printt("Index search FAILED or disabled")
         except: