diff --git a/vall_e/data.py b/vall_e/data.py
index 4338250..eef2009 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -253,6 +253,10 @@ class Dataset(_Dataset):
 
 		return prom
 
+	@cached_property
+	def tasks(self):
+		return ["tts"] # "ns", "sr", "tse", "cse", "nse"
+
 	def __getitem__(self, index):
 		if hasattr(self, "sample_type") and self.sample_type == "speaker":
 			spkr_name = self.spkrs[index]
@@ -274,14 +278,17 @@ class Dataset(_Dataset):
 			text = torch.tensor([*map(self.phone_symmap.get, _get_phones(path))]).to(self.text_dtype)
 			resps = _load_quants(path)
 		
-		# I could probably do some logic to directly use the resps, but I'm putting my faith in python aliasing
-		proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
+		task = random.choice(self.tasks)
+		if task == "tts":
+			# I could probably do some logic to directly use the resps, but I'm putting my faith in python aliasing
+			proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
 
 		return dict(
 			index=index,
 			path=path,
 			spkr_name=spkr_name,
 			spkr_id=spkr_id,
+			task=task,
 			text=text,
 			proms=proms,
 			resps=resps,
@@ -360,26 +367,12 @@ def _load_dataset_paths():
 
 		paths[type].extend([ f"{key}/{child.attrs['id']}" for child in cfg.hdf5[key].values() ])
 
-		# files = data_dir.rglob("*.qnt.pt")
-		#paths[type].extend([ f'/{type}{_get_hdf5_path( str(file).replace(".qnt.pt", "") )}' for file in files ])
-
 	for data_dir in cfg.dataset.training:
 		get_paths( data_dir, "training" )
 
 	for data_dir in cfg.dataset.validation:
 		get_paths( data_dir, "validation" )
 
-	"""
-	def process( entity ):
-		if "id" in entity.attrs:
-			paths[entity.attrs['type']].append( f"{entity.attrs['speaker']}{entity.attrs['id']}" )
-			return
-
-		for child in entity.values():
-			process( child )
-	"""
-
-
 	for _, type in enumerate(paths):
 		dirs = paths[type]
 
@@ -398,6 +391,7 @@ def _load_dataset_paths():
 
 	return datasets["training"], datasets["validation"]
 
+# to-do: mirror the hdf5-based load function
 def _load_train_val_paths():
 	paths = []
 	train_paths = []
@@ -432,8 +426,10 @@ def _load_train_val_paths():
 	train_paths, val_paths = map(sorted, [train_paths, val_paths])
 
 	if len(train_paths) == 0:
-		raise RuntimeError(f"Failed to find any .qnt.pt file in {cfg.dataset.training}.")
-	# to get it to shut up
+		raise RuntimeError(f"Failed to find any .qnt.pt file in specified training dataset.")
+	
+	# to-do: allow setting aside a fixed portion of the training dataset for validation
+	# something like the last X percent of each speaker is set aside
 	if len(val_paths) == 0:
 		val_paths = [ train_paths[0] ]
 
diff --git a/vall_e/train.py b/vall_e/train.py
index b5e4499..3799b5e 100755
--- a/vall_e/train.py
+++ b/vall_e/train.py
@@ -95,6 +95,7 @@ def run_eval(engines, eval_name, dl):
 				stats['loss'].append(0)
 				print(str(e))
 	
+	processed = 0
 	for batch in tqdm(dl):
 		batch: dict = to_device(batch, cfg.device)
 
@@ -131,6 +132,10 @@ def run_eval(engines, eval_name, dl):
 
 				process( name, batch, resps_list )
 
+		processed += len(batch["text"])
+		if processed > cfg.evaluation.size:
+			break
+
 	stats = {k: sum(v) / len(v) for k, v in stats.items()}
 	engines_stats.update(flatten_dict({ name: stats }))