diff --git a/audio.py b/audio.py
index 32b20c449..32ab5fabe 100644
--- a/audio.py
+++ b/audio.py
@@ -97,7 +97,7 @@ def _linear_to_mel(spectogram):
 
 def _build_mel_basis():
     assert hp.fmax <= hp.sample_rate // 2
-    return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels,
+    return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels,
                                fmin=hp.fmin, fmax=hp.fmax)
 
 def _amp_to_db(x):
diff --git a/inference.py b/inference.py
index 90692521e..7fe0ac53e 100644
--- a/inference.py
+++ b/inference.py
@@ -159,21 +159,29 @@ def datagen(frames, mels):
 
 def _load(checkpoint_path):
 	if device == 'cuda':
-		checkpoint = torch.load(checkpoint_path)
+		checkpoint = torch.load(checkpoint_path, weights_only=False)
 	else:
 		checkpoint = torch.load(checkpoint_path,
-								map_location=lambda storage, loc: storage)
+								map_location='cpu',
+								weights_only=False)
 	return checkpoint
 
 def load_model(path):
-	model = Wav2Lip()
 	print("Load checkpoint from: {}".format(path))
 	checkpoint = _load(path)
-	s = checkpoint["state_dict"]
-	new_s = {}
-	for k, v in s.items():
-		new_s[k.replace('module.', '')] = v
-	model.load_state_dict(new_s)
+	
+	# Check if it's a TorchScript model
+	if isinstance(checkpoint, torch.jit.ScriptModule):
+		print("Detected TorchScript model, loading directly...")
+		model = checkpoint
+	else:
+		# Regular checkpoint with state_dict
+		model = Wav2Lip()
+		s = checkpoint["state_dict"]
+		new_s = {}
+		for k, v in s.items():
+			new_s[k.replace('module.', '')] = v
+		model.load_state_dict(new_s)
 
 	model = model.to(device)
 	return model.eval()
@@ -252,6 +260,9 @@ def main():
 			model = load_model(args.checkpoint_path)
 			print ("Model loaded")
 
+			# Ensure temp directory exists
+			os.makedirs('temp', exist_ok=True)
+
 			frame_h, frame_w = full_frames[0].shape[:-1]
 			out = cv2.VideoWriter('temp/result.avi', 
 									cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))