diff --git a/codes/trainer/ExtensibleTrainer.py b/codes/trainer/ExtensibleTrainer.py
index a1c26053..4f0c8b16 100644
--- a/codes/trainer/ExtensibleTrainer.py
+++ b/codes/trainer/ExtensibleTrainer.py
@@ -145,7 +145,7 @@ class ExtensibleTrainer(BaseModel):
                     if opt_get(opt, ['ddp_static_graph'], False):
                         dnet._set_static_graph()
             else:
-                dnet = DataParallel(anet, device_ids=opt['gpu_ids'])
+                dnet = DataParallel(anet, device_ids=[torch.cuda.current_device()])
             if self.is_train:
                 dnet.train()
             else:
diff --git a/codes/trainer/eval/eval_wer.py b/codes/trainer/eval/eval_wer.py
index b38ff8ae..1d89b399 100644
--- a/codes/trainer/eval/eval_wer.py
+++ b/codes/trainer/eval/eval_wer.py
@@ -100,10 +100,9 @@ if __name__ == '__main__':
         'text_seq_key': 'padded_text',
         'text_seq_lengths_key': 'text_lengths',
     }
-    model = Wav2VecWrapper(vocab_size=148, basis_model='facebook/wav2vec2-large-960h', freeze_transformer=True, checkpointing_enabled=False)
-    model.w2v = Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-large-960h')
-    weights = torch.load('X:\\dlas\\experiments\\train_wav2vec_mass_large\\models\\0_wav2vec.pth')
-    #model.load_state_dict(weights)
+    model = Wav2VecWrapper(vocab_size=148, basis_model='facebook/wav2vec2-large-robust-ft-libri-960h', freeze_transformer=True, checkpointing_enabled=False)
+    weights = torch.load('X:\\dlas\\experiments/train_wav2vec_mass_diverse_initial_annealing_large_pt/models/7000_wav2vec.pth')
+    model.load_state_dict(weights)
     model = model.cuda()
-    eval = WerEvaluator(model, opt_eval, env, detokenizer_fn=fb_detokenize)
+    eval = WerEvaluator(model, opt_eval, env)
     print(eval.perform_eval())
\ No newline at end of file