diff --git a/vall_e/data.py b/vall_e/data.py
index cb96e2f..c3584f4 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -1024,6 +1024,8 @@ class Dataset(_Dataset):
 
 	@cached_property
 	def tasks(self):
+		if not self.training:
+			return ["tts"]
 		return cfg.dataset.tasks_list # ["tts", "tts", "ns", "sr", "tse", "tts", "tts"] # , "cse", "nse"
 
 	def save_state_dict(self, path = None):
diff --git a/vall_e/models/ar_nar.py b/vall_e/models/ar_nar.py
index 2d15623..4c206e9 100644
--- a/vall_e/models/ar_nar.py
+++ b/vall_e/models/ar_nar.py
@@ -719,8 +719,6 @@ class AR_NAR(Base):
 				text_list = [ sequence_list[i] if task in text_task else text_list[i] for i, task in enumerate(task_list) ]
 				resps_list = [ sequence_list[i] if task not in text_task else resps_list[i] for i, task in enumerate(task_list) ]
 
-			print( text_list, raw_text_list )
-
 			quant_levels = [ 0 for _ in range( max( batch_size, beam_width ) ) ]
 
 			inputs = self.inputs(
diff --git a/vall_e/train.py b/vall_e/train.py
index 0a28ebe..20cf753 100755
--- a/vall_e/train.py
+++ b/vall_e/train.py
@@ -29,7 +29,7 @@ mel_stft_loss = auraloss.freq.MelSTFTLoss(cfg.sample_rate, device="cpu")
 def train_feeder(engine, batch, teacher=None):
 	engine.tokens_processed += sum([ text.shape[0] for text in batch["text"] ])
 	engine.tokens_processed += sum([ resps.shape[0] for resps in batch["resps"] ])
-	
+
 	with torch.autocast("cuda", dtype=cfg.trainer.dtype, enabled=cfg.trainer.amp):
 		batch_size = len(batch["text"])
 		engine.current_batch_size = batch_size
@@ -184,6 +184,8 @@ def run_eval(engines, eval_name, dl, args=None):
 				# has_stt = True
 				batch["task"][i] = "tts"
 				batch["proms"][i] = batch["resps"][i][:75*3, :]
+			elif task != "tts":
+				batch["task"][i] = "tts"
 
 		# random prompts requested
 		if args and args.eval_random_text_prompts and eval_name == "subtrain":