diff --git a/frechet_audio_distance/fad.py b/frechet_audio_distance/fad.py
index 3095b67..1a38f02 100644
--- a/frechet_audio_distance/fad.py
+++ b/frechet_audio_distance/fad.py
@@ -84,8 +84,17 @@ def __init__(
         self.submodel_name = submodel_name
         self.sample_rate = sample_rate
         self.channels = channels
-        self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
         self.verbose = verbose
+        self.device = torch.device(
+            'cuda') if torch.cuda.is_available() else torch.device('mps') if torch.backends.mps.is_available() else torch.device('cpu')
+        if self.device == torch.device('mps') and self.model_name == "clap":
+            if self.verbose:
+                print("[Frechet Audio Distance] CLAP does not support MPS device yet, because:")
+                print("[Frechet Audio Distance] The operator 'aten::upsample_bicubic2d.out' is not currently implemented for the MPS device.")
+                print("[Frechet Audio Distance] Using CPU device instead.")
+            self.device = torch.device('cpu')
+        if self.verbose:
+            print("[Frechet Audio Distance] Using device: {}".format(self.device))
         self.audio_load_worker = audio_load_worker
         self.enable_fusion = enable_fusion
         if ckpt_dir is not None:
@@ -112,6 +121,7 @@ def __get_model(self, model_name="vggish", use_pca=False, use_activation=False):
                 self.model.postprocess = False
             if not use_activation:
                 self.model.embeddings = nn.Sequential(*list(self.model.embeddings.children())[:-1])
+            self.model.device = self.device
         # pann
         elif model_name == "pann":
             # Kong et al., "PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition", IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020)
@@ -218,6 +228,7 @@ def __get_model(self, model_name="vggish", use_pca=False, use_activation=False):
             # these models use 32 residual quantizers
             self.model.set_target_bandwidth(24.0)
 
+        self.model.to(self.device)
         self.model.eval()
 
     def get_embeddings(self, x, sr):
@@ -234,7 +245,7 @@ def get_embeddings(self, x, sr):
                     embd = self.model.forward(audio, sr)
                 elif self.model_name == "pann":
                     with torch.no_grad():
-                        audio = torch.tensor(audio).float().unsqueeze(0)
+                        audio = torch.tensor(audio).float().unsqueeze(0).to(self.device)
                         out = self.model(audio, None)
                         embd = out['embedding'].data[0]
                 elif self.model_name == "clap":
@@ -242,13 +253,15 @@ def get_embeddings(self, x, sr):
                     embd = self.model.get_audio_embedding_from_data(audio, use_tensor=True)
                 elif self.model_name == "encodec":
                     # add two dimensions
-                    audio = torch.tensor(audio).float().unsqueeze(0).unsqueeze(0)
+                    audio = torch.tensor(
+                        audio).float().unsqueeze(0).unsqueeze(0).to(self.device)
                     # if SAMPLE_RATE is 48000, we need to make audio stereo
                     if self.model.sample_rate == 48000:
                         if audio.shape[-1] != 2:
-                            print(
-                                "[Frechet Audio Distance] Audio is mono, converting to stereo for 48khz model..."
-                            )
+                            if self.verbose:
+                                print(
+                                    "[Frechet Audio Distance] Audio is mono, converting to stereo for 48khz model..."
+                                )
                             audio = torch.cat((audio, audio), dim=1)
                         else:
                             # transpose to (batch, channels, samples)
@@ -272,7 +285,7 @@ def get_embeddings(self, x, sr):
                             embd.shape
                         )
                     )
-                if self.device == torch.device("cuda"):
+                if embd.device != torch.device("cpu"):
                     embd = embd.cpu()
                 
                 if torch.is_tensor(embd):