diff --git a/extract_f0_print.py b/extract_f0_print.py
index 063e322326..2feab32011 100644
--- a/extract_f0_print.py
+++ b/extract_f0_print.py
@@ -241,7 +241,8 @@ def compute_f0(self, path, f0_method, crepe_hop_length):
                 from rmvpe import RMVPE
 
                 print("loading rmvpe model")
-                self.model_rmvpe = RMVPE("rmvpe.pt", is_half=False, device="cuda:0")
+                _dev = "cuda:0" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
+                self.model_rmvpe = RMVPE("rmvpe.pt", is_half=False, device=_dev)
             f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
         elif f0_method == "dio":
             f0, t = pyworld.dio(
diff --git a/extract_feature_print.py b/extract_feature_print.py
index 09d87c052d..ff24e66e9b 100644
--- a/extract_feature_print.py
+++ b/extract_feature_print.py
@@ -15,6 +15,14 @@
     os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
     version = sys.argv[6]
 import torch
+# Patch torch.load for PyTorch 2.6+ (fairseq calls it with weights_only=True by default)
+_orig_torch_load = torch.load
+def _patched_torch_load(*args, **kwargs):
+    if "weights_only" not in kwargs:
+        kwargs["weights_only"] = False
+    return _orig_torch_load(*args, **kwargs)
+torch.load = _patched_torch_load
+
 import torch.nn.functional as F
 import soundfile as sf
 import numpy as np