diff --git a/vall_e/data.py b/vall_e/data.py
index 0b2ab4a..f151bcb 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -47,7 +47,6 @@ def fold_inputs(
 	audio_tokens = 1024,
 	audio_rvq_levels = cfg.model.max_levels,
 	quant_levels = None,
-	experimental = False
 ):
 	def _create_mask(l, device):
 		seq = torch.arange(max(l), device=device).unsqueeze(0)  # (1 t)
diff --git a/vall_e/models/experimental.py b/vall_e/models/experimental.py
index e712184..ac499c0 100644
--- a/vall_e/models/experimental.py
+++ b/vall_e/models/experimental.py
@@ -172,6 +172,9 @@ class Model(LlmArchClass):
 			if "do_sample" in kwargs:
 				kwargs.pop("do_sample")
 
+			if "min_length" in kwargs:
+				kwargs.pop("min_length")
+
 		return super().generate(*args, **kwargs)
 
 	def forward(
@@ -359,7 +362,7 @@ def example_usage():
 	@torch.inference_mode()
 	def sample( name, steps=cfg.model.max_levels*cfg.dataset.frames_per_second*6 ):
 		engine.eval()
-		target_length = 0
+		batch_size = len(text_list)
 		resp_list = None
 		if cfg.model.interleave:
 			input_ids, attention_mask = fold_inputs(text_list=text_list, prom_list=prom_list)
@@ -368,12 +371,14 @@ def example_usage():
 			unfolded = unfold_outputs( output )
 			resp_list = unfolded["resp_list"]
 		else:
-			resp_list = [ [] for _ in range(len(text_list)) ]
+			resp_list = [ [] for _ in range(batch_size) ]
 			for l in range(cfg.model.max_levels):
-				quant_levels = [ [ l ] for _ in range(len(text_list)) ]
+				quant_levels = [ l for _ in range(batch_size) ]
 
-				input_ids, attention_mask = fold_inputs(text_list=text_list, prom_list=prom_list, resp_list=resp_list, quant_levels=quant_levels, experimental=True)
-				min_length = len(input_ids[0]) + 1 
+				input_ids, attention_mask = fold_inputs(text_list=text_list, prom_list=prom_list, resp_list=resp_list, quant_levels=quant_levels)
+				min_length = 1 
+				for batch in input_ids:
+					min_length = max( min_length, batch.shape[0] + 1 )
 
 				output = model.generate(
 					input_ids=input_ids,
diff --git a/vall_e/train.py b/vall_e/train.py
index 0087933..64a5a01 100755
--- a/vall_e/train.py
+++ b/vall_e/train.py
@@ -135,7 +135,7 @@ def run_eval(engines, eval_name, dl):
 						input_ids, attention_mask = fold_inputs(text_list=batch["text"], prom_list=batch["proms"], resp_list=resps_list, quant_levels=quant_levels, experimental=True)
 						min_length = 1 
 						for batch in input_ids:
-							min_length = max( min_length, batch.shape[0] )
+							min_length = max( min_length, batch.shape[0] + 1 )
 
 						output = model.generate(
 							input_ids=input_ids,