diff --git a/codes/models/gpt_voice/gpt_asr_hf2.py b/codes/models/gpt_voice/gpt_asr_hf2.py
index 46c1d83d..246cde02 100644
--- a/codes/models/gpt_voice/gpt_asr_hf2.py
+++ b/codes/models/gpt_voice/gpt_asr_hf2.py
@@ -297,17 +297,11 @@ class GptAsrHf2(nn.Module):
         self.inference_model.store_mel_emb(mel_emb)
 
         # "fake_inputs" are stand-ins for the MEL frames, which will be injected with the prep_inputs function above.
-        if cond_text is None:
-            fake_inputs = torch.full((mel_emb.shape[0],mel_emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
-            fake_inputs[:,-1] = self.start_token
-        else:
-            cond_used = 10
-            fake_inputs = torch.full((mel_emb.shape[0],mel_emb.shape[1]+1+cond_used,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
-            fake_inputs[:,-1-cond_used] = self.start_token
-            fake_inputs[:, -cond_used:] = cond_text[:, :cond_used]
+        fake_inputs = torch.full((mel_emb.shape[0],mel_emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
+        fake_inputs[:,-1] = self.start_token
         gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.start_token, pad_token_id=0, eos_token_id=0,
                                             max_length=self.max_symbols_per_phrase+mel_emb.shape[1], temperature=temperature, num_beams=num_beams, use_cache=True)
-        return gen[:, mel_emb.shape[1]:]
+        return gen[:, mel_emb.shape[1]+1:]
 
 
 @register_model
diff --git a/codes/scripts/audio/asr_eval.py b/codes/scripts/audio/asr_eval.py
index 7aa9976c..a16045a7 100644
--- a/codes/scripts/audio/asr_eval.py
+++ b/codes/scripts/audio/asr_eval.py
@@ -18,19 +18,25 @@ import numpy as np
 from scipy.io import wavfile
 
 
-def forward_pass(model, data, output_dir, opt, b):
+def forward_pass(model, data, output_dir, opt, macro_b, dataset):
     with torch.no_grad():
         model.feed_data(data, 0)
         model.test()
 
-    if 'real_text' in opt['eval'].keys():
-        real = data[opt['eval']['real_text']][0]
-        print(f'{b} Real text: "{real}"')
-
     gt_key = opt['eval']['gen_text']
     txts = []
     for b in range(model.eval_state[gt_key][0].shape[0]):
-        txts.append(sequence_to_text(model.eval_state[opt['eval']['gen_text']][0][b]))
+        if 'real_text' in opt['eval'].keys():
+            real = data[opt['eval']['real_text']][b]
+            print(f'{macro_b} {b} Real text: "{real}"')
+
+        codes = model.eval_state[opt['eval']['gen_text']][0][b].cpu()
+        if hasattr(dataset, 'tokenizer'):
+            text = dataset.tokenizer.decode(codes.numpy())
+            text = text.replace(' $$$', '')
+            txts.append(text)
+        else:
+            txts.append(sequence_to_text(codes))
     return txts
 
 
@@ -57,30 +63,26 @@ if __name__ == "__main__":
     logger = logging.getLogger('base')
     logger.info(option.dict2str(opt))
 
-    test_loaders = []
-    for phase, dataset_opt in sorted(opt['datasets'].items()):
-        test_set, collate_fn = create_dataset(dataset_opt, return_collate=True)
-        test_loader = create_dataloader(test_set, dataset_opt, collate_fn=collate_fn)
-        logger.info('Number of test texts in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
-        test_loaders.append(test_loader)
+    dataset_opt = opt['datasets']['val']
+    test_set, collate_fn = create_dataset(dataset_opt, return_collate=True)
+    test_loader = create_dataloader(test_set, dataset_opt, collate_fn=collate_fn)
+    logger.info('Number of test texts in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
 
     model = ExtensibleTrainer(opt)
 
     batch = 0
     output = open('results.tsv', 'w')
-    for test_loader in test_loaders:
-        dataset_dir = opt['path']['results_root']
-        util.mkdir(dataset_dir)
+    dataset_dir = opt['path']['results_root']
+    util.mkdir(dataset_dir)
 
-        tq = tqdm(test_loader)
-        for data in tq:
-            #if data['clip'].shape[-1] > opt['networks']['asr_gen']['kwargs']['max_mel_frames']*255:
-            #    continue
-            preds = forward_pass(model, data, dataset_dir, opt, batch)
-            for b, pred in enumerate(preds):
-                pred = pred.replace('_', '')
-                output.write(f'{pred}\t{os.path.basename(data["filenames"][b])}\n')
-                print(pred)
-                batch += 1
-            output.flush()
+    for data in tqdm(test_loader):
+        #if data['clip'].shape[-1] > opt['networks']['asr_gen']['kwargs']['max_mel_frames']*255:
+        #    continue
+        preds = forward_pass(model, data, dataset_dir, opt, batch, test_set)
+        for b, pred in enumerate(preds):
+            pred = pred.replace('_', '')
+            output.write(f'{pred}\t{os.path.basename(data["filenames"][b])}\n')
+            print(pred)
+            batch += 1
+        output.flush()