tweaks

vall_e/config.py

@@ -948,6 +948,7 @@ class Config(BaseConfig):
 			if not isinstance( model, dict ):
 				continue
 
+			# to-do: prune unused keys in here too automatically
 			if "experimental" not in model or not model["experimental"]:
 				model["experimental"] = {}
 
@@ -962,6 +963,9 @@ class Config(BaseConfig):
 			if "p_rvq_levels" in model["experimental"]:
 				model["experimental"]["rvq_levels_p"] = model["experimental"]["p_rvq_levels"]
 				del model["experimental"]["p_rvq_levels"]
+			
+			if "p_len_train" in model["experimental"]:
+				del model["experimental"]["p_len_train"]
 
 		self.models = [ Model(**model) if isinstance(model, dict) else model for model in self.models ]
 		self.loras = [ LoRA(**lora)  if isinstance(lora, dict) else lora for lora in self.loras ]

vall_e/models/ar_nar.py

@@ -275,9 +275,9 @@ class AR_NAR(Base):
 			_super = super()
 			# to-do: allow for batch processing (it should probably work batched anyways)
 			def demask_sampling( batch_index, seq_len ):
-				# overrides
+				# overrides, to be user-controllable soonTM
 				max_steps = 10
-				temperature = 0.3
+				temperature = 1.0
 				cfg_strength = 1.0
 				sampling_repetition_penalty = 1.0 # force rep pen off, because this caused false positives due to how rep pen was being naively applied......
 				sampling_top_p = 0.9 # a lot of demasking samplers use a top-k of seq_len * 0.9