diff --git a/.gitignore b/.gitignore
index efd1a54..d0158c6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@
 checkpoint/
 checkpoint_bak/
 output/
+.DS_Store
 
 __pycache__/
 *.py[cod]
diff --git a/inference.py b/inference.py
index 598c6cb..4d721d0 100644
--- a/inference.py
+++ b/inference.py
@@ -494,7 +494,7 @@ class OmniInference:
                 if current_index == nums_generate:
                     current_index = 0
                     snac = get_snac(list_output, index, nums_generate)
-                    audio_stream = generate_audio_data(snac, self.snacmodel)
+                    audio_stream = generate_audio_data(snac, self.snacmodel, self.device)
                     yield audio_stream
 
             input_pos = input_pos.add_(1)
diff --git a/utils/snac_utils.py b/utils/snac_utils.py
index a2a4a6a..e66eccc 100644
--- a/utils/snac_utils.py
+++ b/utils/snac_utils.py
@@ -21,8 +21,8 @@ def layershift(input_id, layer, stride=4160, shift=152000):
     return input_id + shift + layer * stride
 
     
-def generate_audio_data(snac_tokens, snacmodel):
-    audio = reconstruct_tensors(snac_tokens)
+def generate_audio_data(snac_tokens, snacmodel, device=None):
+    audio = reconstruct_tensors(snac_tokens, device)
     with torch.inference_mode():
         audio_hat = snacmodel.decode(audio)
     audio_data = audio_hat.cpu().numpy().astype(np.float64) * 32768.0
@@ -55,9 +55,12 @@ def reconscruct_snac(output_list):
     return output
 
 
-def reconstruct_tensors(flattened_output):
+def reconstruct_tensors(flattened_output, device=None):
     """Reconstructs the list of tensors from the flattened output."""
 
+    if device is None:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
     def count_elements_between_hashes(lst):
         try:
             # Find the index of the first '#'
@@ -107,9 +110,9 @@ def reconstruct_tensors(flattened_output):
             tensor3.append(flattened_output[i + 6])
             tensor3.append(flattened_output[i + 7])
             codes = [
-                list_to_torch_tensor(tensor1).cuda(),
-                list_to_torch_tensor(tensor2).cuda(),
-                list_to_torch_tensor(tensor3).cuda(),
+                list_to_torch_tensor(tensor1).to(device),
+                list_to_torch_tensor(tensor2).to(device),
+                list_to_torch_tensor(tensor3).to(device),
             ]
 
     if n_tensors == 15:
@@ -133,10 +136,10 @@ def reconstruct_tensors(flattened_output):
             tensor4.append(flattened_output[i + 15])
 
             codes = [
-                list_to_torch_tensor(tensor1).cuda(),
-                list_to_torch_tensor(tensor2).cuda(),
-                list_to_torch_tensor(tensor3).cuda(),
-                list_to_torch_tensor(tensor4).cuda(),
+                list_to_torch_tensor(tensor1).to(device),
+                list_to_torch_tensor(tensor2).to(device),
+                list_to_torch_tensor(tensor3).to(device),
+                list_to_torch_tensor(tensor4).to(device),
             ]
 
     return codes