diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index fbf6426..cd41968 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -281,6 +281,27 @@ class AudioEmbedding(nn.Module):
 
 		return x
 
+# time-step embedding
+# for the NAR-len, since it probably most likely requires encoding the timestep
+class TimeEmbedding(nn.Module):
+	def __init__(
+		self,
+		d_model
+	):
+		super().__init__()
+		self.emb = SinusoidalEmbedding(d_model)
+		self.mlp = nn.Sequential(
+			nn.Linear(d_model, d_model*4),
+			nn.SiLU(),
+			nn.Linear(d_model*4, d_model),
+		)
+
+	def forward( self, t ):
+		t = self.emb(t)
+		t = self.mlp(t)
+
+		return t
+
 # per-level classification
 # it might actually be "better" in the long run to only have one output head like a traditional LM, and just de-stitch it here instead of doing modulus math and whatever like the HF/experimental impl
 class Classifiers(nn.Module):
@@ -545,6 +566,7 @@ class Base(nn.Module):
 		
 			# experimental NAR-only mode
 			self.len_emb = Embedding(11, d_model) if "len" in self.capabilities else None
+			self.time_emb = TimeEmbedding(d_model) if "len" in self.capabilities else None
 
 		if attention_backend == "auto":
 			attention_backend = "sdpa"
@@ -967,6 +989,7 @@ class Base(nn.Module):
 		tone_list: list[Tensor] | None = None,
 		len_list: list[Tensor] | None = None,
 		task_list: list[str] | None = None,
+		time_list: list[Tensor] | None = None,
 
 		quant_levels: int | list[int] | Tensor | None = None
 	):
@@ -1012,6 +1035,8 @@ class Base(nn.Module):
 						t = random.random()
 						p = math.cos(t * math.pi * 0.5)
 						dropout_mask = _dropout_mask( resps_list[i], p=p )
+						
+						inputs[i].append( ("timestep", torch.tensor(t, device=device) ) )
 						inputs[i].append( ("dropout_mask", dropout_mask ) )
 		
 			# Audio length prediction task
@@ -1108,16 +1133,14 @@ class Base(nn.Module):
 			task_type = "tts"
 			input_prom = None
 			dropout_mask = None
+			timestep = None
 			
 			# pre-iterate
 			for name, input in batch_input:
-				"""
-				if name == "prop":
-					proms = [ input ] if isinstance(input, torch.Tensor) else input
-					input_prom = torch.cat([ prom for prom in proms if isinstance(prom, torch.Tensor) ])
-				"""
 				if 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
@@ -1160,6 +1183,9 @@ class Base(nn.Module):
 							offset = 0,
 							quant_level = 0,
 						)
+
+						t_emb = self.time_emb( timestep )
+						embedding += t_emb
 					# 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......
@@ -1236,7 +1262,7 @@ class Base(nn.Module):
 					return 1
 
 				# a mask
-				if name == "dropout_mask":
+				if name in ["dropout_mask", "timestep"]:
 					return 0
 
 				# list of tokens
diff --git a/vall_e/models/nar.py b/vall_e/models/nar.py
index 30a3702..67700c2 100644
--- a/vall_e/models/nar.py
+++ b/vall_e/models/nar.py
@@ -298,6 +298,7 @@ class NAR(Base):
 						resps_list=resps_list,
 						lang_list=lang_list,
 						tone_list=tone_list,
+						time_list=[ timestep ],
 						quant_levels=quant_levels,
 					)
 					output = _super.forward(