diff --git a/vall_e/data.py b/vall_e/data.py
index ab1ffdb..1a643c7 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -534,7 +534,7 @@ _durations_map = {}
 def _get_duration_map( type="training" ):
 	return _durations_map[type] if type in _durations_map else {}
 
-def _load_paths(dataset, type="training", silent=False, dataset_hash_key=None):
+def _load_paths(dataset, type="training", silent=not is_global_leader(), dataset_hash_key=None):
 	if not dataset_hash_key:
 		dataset_hash_key = cfg.dataset.hash_key(sorted(dataset))
 
@@ -750,11 +750,11 @@ class Dataset(_Dataset):
 				self.duration_buckets[bucket] = []
 			self.duration_buckets[bucket].append( ( Path(path), duration ) )
 
-		# ensure they're ordered
-		self.duration_buckets = dict(sorted(self.duration_buckets.items()))
-
 		# sort by duration
 		if self.sampler_order == "duration":
+			# ensure they're ordered
+			self.duration_buckets = dict(sorted(self.duration_buckets.items()))
+
 			flattened = {}
 			# sort and interleave
 			for bucket in self.duration_buckets:
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index f9f495b..905e639 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -1117,10 +1117,12 @@ class Base(nn.Module):
 			
 			# pre-iterate
 			for name, input in batch_input:
-				if name == "dropout_mask":
-					dropout_mask = input
-				elif name == "classifier_level":
+				if name == "classifier_level":
 					classifier_level = input
+				elif name == "dropout_mask":
+					dropout_mask = input
+				elif name == "timestep":
+					timestep = input
 
 			for name, input in batch_input:
 				# technically can provide a map for input_name => embedding, but some embedding requires additional processing
@@ -1169,6 +1171,15 @@ class Base(nn.Module):
 							#quant_level = 0,
 							name = classifier_level,
 						)
+					# NAR-len
+					elif classifier_level == "NAR:0:0":
+						embedding = self.resps_emb(
+							# if masked use masked token, else original token
+							input if input.dim() == 1 else input[:, 0],
+							#offset = -1 if self.masking_separate_embeddings else 0, # pick last
+							#quant_level = 0,
+							name = classifier_level,
+						)
 					# cheat-y way to handle performing STT across all levels
 					elif task_type in summed_embeddings_task:
 						# we do a manual sum because I trained it to use the AR embeddings + NAR embeddings for STT......
@@ -1215,7 +1226,7 @@ class Base(nn.Module):
 									continue
 								
 								embedding[i] = self.dropout_token
-				elif name == "timestep" and self.time_emb is not None:
+				elif name == "timestep" and self.time_emb is not None and False:
 					embedding = self.time_emb( input )
 				elif name == "len" and self.len_emb is not None:
 					embedding = self.len_emb( input )