diff --git a/nn_fingerprint.py b/nn_fingerprint.py
index d71f6d7..46e6c7d 100644
--- a/nn_fingerprint.py
+++ b/nn_fingerprint.py
@@ -67,7 +67,13 @@ class Model:
         log.info('Model is loaded.')
 
     def __call__(self, *args, **kwargs):
-        return self.model(*args, **kwargs)
+        new_args = []
+        new_kwargs = {}
+        for x in new_args:
+            x = x.to(self.device)
+        for k, v in kwargs.items():
+            new_kwargs[k] = v.to(self.device)
+        return self.model(*new_args, **new_kwargs)
 
 
 @register(output_schema=['vecs'])
@@ -103,8 +109,6 @@ class NNFingerprint(NNOperator):
 
     def __call__(self, data: Union[str, List[AudioFrame]]) -> numpy.ndarray:
         audio_tensors = self.preprocess(data)
-        if audio_tensors.device != self.device:
-            audio_tensors = audio_tensors.to(self.device)
         # print(audio_tensors.shape)
         features = self.model(audio_tensors)
         outs = features.detach().cpu().numpy()
@@ -141,7 +145,7 @@ class NNFingerprint(NNOperator):
         wav = preprocess_wav(audio,
                              segment_size=int(self.params['sample_rate'] * self.params['segment_size']),
                              hop_size=int(self.params['sample_rate'] * self.params['hop_size']),
-                             frame_shift_mul=self.params['frame_shift_mul']).to(self.device)
+                             frame_shift_mul=self.params['frame_shift_mul'])
         wav = wav.to(torch.float32)
         mel = MelSpec(sample_rate=self.params['sample_rate'],
                       window_length=self.params['window_length'],
@@ -151,7 +155,7 @@ class NNFingerprint(NNOperator):
                       n_mels=self.params['n_mels'],
                       naf_mode=self.params['naf_mode'],
                       mel_log=self.params['mel_log'],
-                      spec_norm=self.params['spec_norm']).to(self.device)
+                      spec_norm=self.params['spec_norm'])
         wav = mel(wav)
         return wav