actually have beam_width in the webUI work

vall_e/config.py

@@ -427,11 +427,8 @@ class Evaluation:
 	batch_size: int = 64 # number of samples per batch during eval / val
 	frequency: int = 250 # do eval / val every X iterations
 	size: int = 64 # number of samples to generate during eval / val
-
+	ar_kwargs: dict = field(default_factory=lambda: {}) # inferencing kwargs
-	steps: int = 500
+	nar_kwargs: dict = field(default_factory=lambda: {}) # inferencing kwargs
-	ar_temperature: float = 0.0 # AR temp for inferencing
-	nar_temperature: float = 0.0 # NAR temp for inferencing
-	nar_levels: int = 0 # maximum NAR levels to use for inferencing
 
 @dataclass()
 class DeepSpeed:

vall_e/demo.py

@@ -68,7 +68,7 @@ def main():
 	parser.add_argument("--top-p", type=float, default=1.0)
 	parser.add_argument("--top-k", type=int, default=0)
 	parser.add_argument("--min-p", type=float, default=0.0)
-	parser.add_argument("--repetition-penalty", type=float, default=1.0)
+	parser.add_argument("--repetition-penalty", type=float, default=1.125)
 	parser.add_argument("--repetition-penalty-decay", type=float, default=0.0)
 	parser.add_argument("--length-penalty", type=float, default=0.0)
 	parser.add_argument("--beam-width", type=int, default=0)
@@ -122,7 +122,9 @@ def main():
 		comparison_kwargs["titles"] = ["LoRA", "No LoRA"]
 
 		comparison_kwargs["disabled"]["use_lora"] = True
+		comparison_kwargs["disabled"]["ar_temp"] = 0.0
 		comparison_kwargs["enabled"]["use_lora"] = False
+		comparison_kwargs["enabled"]["ar_temp"] = 0.95
 	elif args.comparison == "entropix-sampling":
 		comparison_kwargs["suffix"] = "entropix_sampling"
 		comparison_kwargs["titles"] = ["Without Entropix", "With Entropix"]	

vall_e/models/ar_nar.py

@@ -375,6 +375,8 @@ class AR_NAR(Base):
 					text_list = text_list * sampling_beam_width
 					proms_list = proms_list * sampling_beam_width
 					sequence_list = sequence_list * sampling_beam_width
+					task_list = task_list * sampling_beam_width
+					start_slice = start_slice * sampling_beam_width
 					stopped = torch.zeros(batch_size, device=device).bool()
 
 				scores = [ scores[i] + score for i, score in enumerate(scores) ]
@@ -399,7 +401,8 @@ class AR_NAR(Base):
 		# pick the best scoring candidate
 		# desu this is always going to be candidate 0
 		if sampling_beam_width:
-			sequence_list = [ sequence_list[0] ]
+			sequence_list = sequence_list[:1]
+			task_list = task_list[:1]
 
 		# remove stop token
 		sequence_list = [self._prune(r, audio_stop_token if task_list[i] not in text_task else text_stop_token) for i, r in enumerate(sequence_list)]

vall_e/samplers.py

@@ -11,13 +11,16 @@ from dataclasses import asdict, dataclass, field
 # Simple filter to modify a token's probability if it shows up in the past
 # `one_time` will only apply the penalty once
 # `decay` is a factor that will exponentially apply to how far away it is
-def reptition_penalize( logits, previous, factor=1.0, decay=0.0, one_time=False ):
+def reptition_penalize( logits, previous, factor=1.0, decay=0.0, one_time=False, limit=75 ):
 	if factor == 1.0 or previous is None:
 		return logits
 
 	unique = set()
 	priors = reversed(previous)
 	for distance, token in enumerate(priors):
+		# rep-pen range
+		if limit and distance >= limit:
+			continue
 		# skip if we're only applying the decay once
 		if one_time and token in unique:
 			continue

vall_e/train.py

@@ -133,7 +133,7 @@ def run_eval(engines, eval_name, dl, args=None):
 			engine = engines[name]
 
 
-			kwargs = dict(
+			base_kwargs = dict(
 				text_list=batch["text"],
 				proms_list=batch["proms"],
 				lang_list=batch["lang"],
@@ -141,19 +141,23 @@ def run_eval(engines, eval_name, dl, args=None):
 			)
 
 			if engine.hyper_config.experimental.hf:
-				resps_list = engine( **kwargs )
+				resps_list = engine( **base_kwargs )
 			elif "len" in engine.hyper_config.capabilities:
-				len_list = engine( **kwargs, max_steps=10 ) # don't need more than that
+				len_list = engine( **base_kwargs, max_steps=10 ) # don't need more than that
 				len_list = [ min( l, cfg.evaluation.steps ) for l in len_list ]
-				resps_list = engine( **kwargs, len_list=len_list, max_levels=cfg.evaluation.nar_levels )
+				
+				kwargs = base_kwargs | cfg.evaluation.nar_kwargs
+				resps_list = engine( **kwargs, len_list=len_list )
 			else:
 				if "ar" in engine.hyper_config.capabilities:
-					resps_list = engine( **kwargs, max_steps=cfg.evaluation.steps, sampling_temperature=cfg.evaluation.ar_temperature)
+					kwargs = base_kwargs | cfg.evaluation.ar_kwargs
+					resps_list = engine( **kwargs )
 				else:
 					resps_list = [ resp[:, 0] for resp in batch["resps"] ]
 
 				if "nar" in engine.hyper_config.capabilities:
-					resps_list = engine( **kwargs, resps_list=resps_list, sampling_temperature=cfg.evaluation.nar_temperature, max_levels=cfg.evaluation.nar_levels )
+					kwargs = base_kwargs | cfg.evaluation.nar_kwargs
+					resps_list = engine( **kwargs, resps_list=resps_list )
 
 			process( name, batch, resps_list )
 

vall_e/webui.py

@@ -203,6 +203,7 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
 			top_p=args.top_p,
 			top_k=args.top_k,
 			min_p=args.min_p,
+			beam_width=args.beam_width,
 			repetition_penalty=args.repetition_penalty,
 			repetition_penalty_decay=args.repetition_penalty_decay,
 			length_penalty=args.length_penalty,