diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index 21a7e81..7c86d0b 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -511,6 +511,7 @@ class Base(nn.Module):
 		self.interleave = interleave
 		self.layerskip = layerskip
 		self.special_tasks = [ "len", "stt" ]
+		self.inject_timestep_embedding = False # results in bad output
 
 		self.text_emb = Embedding(n_text_tokens, d_model)
 		self.langs_emb = None
@@ -1026,6 +1027,12 @@ class Base(nn.Module):
 			# Sequence: <text><sep><rvq lvl><sep><prom><sep><resp>
 			# prom /may/ include <task> tokens inside to help guide things, per SpeechX
 			if f'<{task_type}>' in get_task_symmap() and task_type not in self.special_tasks:
+				# pick a random timestep
+				if "len" in self.capabilities and quant_level == 0:
+					timestep = random.random()
+				else:
+					timestep = 1.0
+
 				# insert the text prompt
 				if text_list is not None and text_list[i] is not None:
 					inputs[i].append( ( "text", text_list[i] ) )
@@ -1041,22 +1048,18 @@ class Base(nn.Module):
 				# insert tone token if we're trained for it
 				if "tone" in self.capabilities and tone_list is not None and tone_list[i] is not None:
 					inputs[i].append( ( "tone", tone_list[i] ) )
+				# insert timestep token
+				if "len" in self.capabilities and quant_level == 0:
+					# cosine schedule
+					dropout_mask = _dropout_mask( resps_list[i], p=math.cos(timestep * math.pi * 0.5) )
+					
+					# store timestep information
+					inputs[i].append( ("timestep", torch.tensor([timestep], device=device, dtype=self.time_emb.mlp[0].weight.dtype) ) )
+					# store dropout mask
+					inputs[i].append( ("dropout_mask", dropout_mask ) )
 				# insert the current output response
 				if resps_list is not None and resps_list[i] is not None:
 					inputs[i].append( ( "resp", resps_list[i] ) )
-
-					# store dropout mask
-					if "len" in self.capabilities and quant_level == 0:
-						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, dtype=self.time_emb.mlp[0].weight.dtype) ) )
-						inputs[i].append( ("dropout_mask", dropout_mask ) )
-					else:
-						# in the event it's needed (it makes shit sound worse)
-						#inputs[i].append( ("timestep", torch.tensor([1.0], device=device, dtype=self.time_emb.mlp[0].weight.dtype) ) )
-						...
 		
 			# Audio length prediction task
 			# Sequence: <text><sep><rvq lvl><prom><sep><len>
@@ -1618,12 +1621,12 @@ class Base(nn.Module):
 		position_ids = self.inputs_to_position_ids( inputs, mask=mask ) if not self.unified_position_ids else None
 
 		tasks = [ self.get_input(inputs, "task", at=i) for i in range( batch_size ) ]
-		"""
-		timesteps = [ self.get_input(inputs, "timestep", at=i) for i in range( batch_size ) ]
-		#timesteps = [ inputs[i][-1] if timestep is not None else None for i, timestep in enumerate(timesteps) ]
-		timesteps = [ self.time_emb(timestep) if timestep is not None else None for i, timestep in enumerate(timesteps) ]
-		"""
-		timesteps = []
+		
+		if self.inject_timestep_embedding:
+			timesteps = [ self.get_input(inputs, "timestep", at=i) for i in range( batch_size ) ]
+			timesteps = [ self.time_emb(timestep) if timestep is not None else None for i, timestep in enumerate(timesteps) ]
+		else:
+			timesteps = []
 
 		classifier_quant_levels = [ -1 if tasks[i] in self.special_tasks else l for i, l in enumerate( quant_levels ) ]