diff --git a/vall_e/config.py b/vall_e/config.py
index 8e72771..d487d4d 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -248,11 +248,6 @@ class ModelExperimentalSettings:
 	rvq_level_range: list = field(default_factory=lambda: []) # some cringe to try and limit the RVQ training range for LoRAs, isn't necesary
 	unified_position_ids: bool = True # False will generate position IDs partitioned for each section
 	tie_classifier_to_embedding: bool = False # Ties the classifier output to their respective embeddings, this does not seem to do anything good in testing
-	
-	# performs token dropout to compensate for errors
-	token_dropout_error: float = 0.0 # probability to nudge a token by ±1
-	token_dropout_rate: float = 0.0 # probability to randomly set a token to a special dropout value
-	token_dropout_rvq_levels: list = field(default_factory=lambda: [1,8]) # determines which levels to do dropout, by default do not do dropout on RVQ level 0
 
 	causal_size: int = 1 # experimental setting to see if I can just do parallel decoding in chunks instead of one-at-a-time without resorting to exotic solutions
 	# VALL-E 2's approach of "combining token embeddings to group them" sounds terribad for a shared AR/NAR model
@@ -270,11 +265,19 @@ class ModelExperimentalSettings:
 	classifiers_bias: bool = True # base LLaMAs do not bias the output heads, but my existing weights do
 	max_position_embeddings: int = 70 * 65 * 5 # 5 minutes of audio
 
+	# these technically should be as hyperparameters
+	# performs token dropout to compensate for errors
+	token_dropout_error: float = 0.0 # probability to nudge a token by ±1
+	token_dropout_rate: float = 0.0 # probability to randomly set a token to a special dropout value
+	token_dropout_rvq_levels: list = field(default_factory=lambda: [1,8]) # determines which levels to do dropout, by default do not do dropout on RVQ level 0
+	# these technically should be as hyperparameters
 	# classifier-free guidance training settings
 	cfg_cond_dropout_p: float = 0.0 # 0.2 # probability to drop out text and audio during training
 	cfg_text_dropout_p: float = 0.0 # 0.0  # probability to drop out input audio prompt during training
 	cfg_prom_dropout_p: float = 0.0 # 0.3  # probability to drop out input audio prompt during training
 
+	use_raw_text_p: float = 0.0 # probability to use raw text as the input prompt instead
+
 	# failed experiment
 	layerskip: bool = False # layerskip compatible model (or training for)
 	#layerskip_rvq_levels: list = field(default_factory=lambda: []) # RVQ levels to train / inference layerskip for (to-do: implement, see if it matters)
diff --git a/vall_e/inference.py b/vall_e/inference.py
index ea205ac..a43383d 100755
--- a/vall_e/inference.py
+++ b/vall_e/inference.py
@@ -23,7 +23,7 @@ from .config import cfg, Config
 from .models import get_models
 from .models.lora import enable_lora
 from .engines import load_engines, deepspeed_available
-from .data import get_phone_symmap, get_lang_symmap, tokenize, sentence_split
+from .data import get_phone_symmap, get_lang_symmap, tokenize, text_tokenize, sentence_split
 from .models import download_model, DEFAULT_MODEL_PATH
 
 if deepspeed_available:
@@ -412,7 +412,7 @@ class TTS():
 				model = model_ar if model_ar is not None else model_nar
 				if model is not None:
 					text_list = model(
-						text_list=None, proms_list=[resp], lang_list=[lang], resps_list=[resp], task_list=["stt"],
+						text_list=None, proms_list=[resp], lang_list=[lang], resps_list=[resp], task_list=[task],
 						disable_tqdm=not use_tqdm,
 						use_lora=use_lora,
 						**sampling_kwargs,
@@ -423,6 +423,35 @@ class TTS():
 				text_list = [ cfg.tokenizer.decode( text ).replace("   ", "_").replace(" ", "").replace("_", " ") for text in text_list ]
 
 			return text_list[0]
+		elif task in ["phn", "un-phn"]:
+			lang = self.encode_lang( language )
+			lang = to_device(lang, device=self.device, dtype=torch.uint8)
+			
+			with torch.autocast(self.device, dtype=dtype, enabled=amp):
+				model = model_ar if model_ar is not None else model_nar
+				if task == "phn":
+					text_list = None
+					raw_text_list = [ torch.tensor( text_tokenize( text ), device=self.device, dtype=torch.int16) ]
+					output_tokenizer = cfg.tokenizer
+				else:
+					text_list = [ torch.tensor( tokenize( text ), device=self.device, dtype=torch.int16) ]
+					raw_text_list = None
+					output_tokenizer = cfg.text_tokenizer
+
+				if model is not None:
+					text_list = model(
+						text_list=text_list, raw_text_list=raw_text_list, lang_list=[lang], task_list=[task],
+						disable_tqdm=not use_tqdm,
+						use_lora=use_lora,
+						**sampling_kwargs,
+					)
+				else:
+					raise Exception("!")
+				
+				text_list = [ output_tokenizer.decode( text ).replace("   ", "_").replace(" ", "").replace("_", " ") for text in text_list ]
+
+			return text_list[0]
+
 
 		# stuff for rolling context
 		prefix_context = None
diff --git a/vall_e/models/ar_nar.py b/vall_e/models/ar_nar.py
index 0c8391d..4c4bbc1 100644
--- a/vall_e/models/ar_nar.py
+++ b/vall_e/models/ar_nar.py
@@ -29,7 +29,7 @@ from ..utils import get_devices, setup_logging, timer, clamp, convert_kwargs
 from .lora import enable_lora
 from ..samplers import cfg_logits
 
-text_task = [ "stt" ]
+text_task = [ "stt", "phn", "un-phn" ]
 
 class AR_NAR(Base):
 	# yikes
@@ -40,23 +40,28 @@ class AR_NAR(Base):
 	# a lot of this could be delegated back to the dataloader, but it's just easier to keep the task of the dataloader to provide sufficient data, and the model to process the data for training
 	def forward_train(
 		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
-		resps_list: list[Tensor],
-		
 		task_list: list[Tensor] | None = None,
+		
+		text_list: list[Tensor] | None = None,
+		proms_list: list[Tensor] | None = None,
+		resps_list: list[Tensor] | None = None,
+		
 		lang_list: list[Tensor] | None = None,
 		tone_list: list[Tensor] | None = None,
 		len_list: list[Tensor] | None = None,
 		raw_text_list: list[Tensor] | None = None,
 	):
 		# deduce batch_size
-		if text_list is not None:
-			default_task = "tts"
+		if text_list:
 			device = text_list[0].device
 			batch_size = len(text_list)
-		else:
-			default_task = "stt"
+		elif raw_text_list:
+			device = raw_text_list[0].device
+			batch_size = len(raw_text_list)
+		elif proms_list:
+			device = proms_list[0].device
+			batch_size = len(proms_list)
+		elif resps_list:
 			device = resps_list[0].device
 			batch_size = len(resps_list)
 
@@ -161,10 +166,7 @@ class AR_NAR(Base):
 			# only apply stop token for RVQ level 0
 			if quant_level <= 0 and timesteps[i] is None:
 				# append stop tokens for AR
-				if task in text_task:
-					#text_list[i] = torch.cat([ resps, text_stop_sequence ])
-					...
-				else:
+				if task not in text_task:
 					resps_list[i] = torch.cat([ resps, audio_stop_sequence ])
 
 			if task == "len":
@@ -174,6 +176,7 @@ class AR_NAR(Base):
 			if task not in text_task + ["len"]:
 				drop_text = False
 				drop_audio = False
+				swap_text = False
 
 				if random.random() < cfg_prom_dropout_p:
 					drop_audio = True
@@ -181,6 +184,9 @@ class AR_NAR(Base):
 				if random.random() < cfg_cond_dropout_p:
 					drop_audio = True
 					drop_text = True
+				
+				if random.random() < use_raw_text_p and raw_text_list[i] is not None:
+					swap_text = True
 
 				if drop_text:
 					text_list[i] = text_start_stop_sequence
@@ -188,6 +194,9 @@ class AR_NAR(Base):
 				if drop_audio:
 					proms_list[i] = None
 
+				if swap_text and not drop_text:
+					text_list[i] = None
+
 		inputs = self.inputs(
 			text_list=text_list,
 			proms_list=proms_list,
@@ -209,14 +218,16 @@ class AR_NAR(Base):
 	def forward_nar_masked(
 		self,
 
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
+		task_list: list[Tensor] | None = None,
+		
+		text_list: list[Tensor] | None = None,
+		proms_list: list[Tensor] | None = None,
 		resps_list: list[Tensor] | None = None,
 		
-		task_list: list[Tensor] | None = None,
 		lang_list: list[Tensor] | None = None,
 		tone_list: list[Tensor] | None = None,
 		len_list: list[Tensor] | None = None,
+		raw_text_list: list[Tensor] | None = None,
 
 		disable_tqdm=False,
 		use_lora=None,
@@ -420,14 +431,17 @@ class AR_NAR(Base):
 
 	def forward_nar(
 		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
+		task_list: list[Tensor] | None = None,
+		
+		text_list: list[Tensor] | None = None,
+		proms_list: list[Tensor] | None = None,
 		resps_list: list[Tensor] | None = None,
 		
-		task_list: list[Tensor] | None = None,
 		lang_list: list[Tensor] | None = None,
 		tone_list: list[Tensor] | None = None,
 		len_list: list[Tensor] | None = None,
+		
+		raw_text_list: list[Tensor] | None = None,
 
 		disable_tqdm=False,
 		use_lora=None,
@@ -447,12 +461,16 @@ class AR_NAR(Base):
 			)
 		
 		# deduce batch_size
-		if text_list is not None:
-			default_task = "tts"
+		if text_list:
 			device = text_list[0].device
 			batch_size = len(text_list)
-		else:
-			default_task = "stt"
+		elif raw_text_list:
+			device = raw_text_list[0].device
+			batch_size = len(raw_text_list)
+		elif proms_list:
+			device = proms_list[0].device
+			batch_size = len(proms_list)
+		elif resps_list:
 			device = resps_list[0].device
 			batch_size = len(resps_list)
 		
@@ -534,25 +552,31 @@ class AR_NAR(Base):
 	def forward_ar(
 		self,
 
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
+		task_list: list[Tensor],
+
+		text_list: list[Tensor] | None = None,
+		raw_text_list: list[Tensor] | None = None,
+		proms_list: list[Tensor] | None = None,
 		resps_list: list[Tensor] | None = None,
-		
-		task_list: list[Tensor] | None = None,
 		lang_list: list[Tensor] | None = None,
 		tone_list: list[Tensor] | None = None,
 		len_list: list[Tensor] | None = None,
+
 		disable_tqdm=False,
 		use_lora=None,
 		**sampling_kwargs,
 	):
 		# deduce batch_size
-		if text_list is not None:
-			default_task = "tts"
+		if text_list:
 			device = text_list[0].device
 			batch_size = len(text_list)
-		else:
-			default_task = "stt"
+		elif raw_text_list:
+			device = raw_text_list[0].device
+			batch_size = len(raw_text_list)
+		elif proms_list:
+			device = proms_list[0].device
+			batch_size = len(proms_list)
+		elif resps_list:
 			device = resps_list[0].device
 			batch_size = len(resps_list)
 
@@ -590,13 +614,17 @@ class AR_NAR(Base):
 				len_list = sequence_list
 
 				inputs = self.inputs(
+					task_list=task_list,
+					
 					text_list=text_list,
 					proms_list=proms_list,
 					resps_list=resps_list,
+					
 					lang_list=lang_list,
 					tone_list=tone_list,
 					len_list=len_list,
-					task_list=task_list,
+					raw_text_list=raw_text_list,
+					
 					quant_levels=quant_levels,
 				)
 
@@ -627,7 +655,6 @@ class AR_NAR(Base):
 			# convert tokens into int
 			return [ int("".join([ str(token.item()) for token in r if token != stop_token ])) for r in sequence_list ]
 
-		# STT
 		start_slice = [ 0 for _ in range(batch_size) ]
 		sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in range(batch_size) ]
 		stopped = torch.zeros(batch_size, device=device).bool()
@@ -684,19 +711,29 @@ class AR_NAR(Base):
 		# get next in sequence
 		iterator = trange(max_duration // max(1, self.causal_size), desc="AR", disable=disable_tqdm)
 		for n in iterator:
-			# it would technically be faster to just append the new token's embedding to the inputs, but there's a VERY small performance gain from doing it, so it's not worth it
-			text_list = [ sequence_list[i] if task in text_task else text_list[i] for i, task in enumerate(task_list) ]
-			resps_list = [ sequence_list[i] if task not in text_task else resps_list[i] for i, task in enumerate(task_list) ]
+			if batch_size == 1 and task_list[0] in ["phn", "un-phn"]:
+				text_list = [ sequence_list[i] if task in ["phn"] else text_list[i] for i, task in enumerate(task_list) ]
+				raw_text_list = [ sequence_list[i] if task in ["un-phn"] else raw_text_list[i] for i, task in enumerate(task_list) ]
+			else:
+				text_list = [ sequence_list[i] if task in text_task else text_list[i] for i, task in enumerate(task_list) ]
+				resps_list = [ sequence_list[i] if task not in text_task else resps_list[i] for i, task in enumerate(task_list) ]
+
+			print( text_list, raw_text_list )
+
 			quant_levels = [ 0 for _ in range( max( batch_size, beam_width ) ) ]
 
 			inputs = self.inputs(
+				task_list=task_list,
+				
 				text_list=text_list,
 				proms_list=proms_list,
 				resps_list=resps_list,
+				
 				lang_list=lang_list,
 				tone_list=tone_list,
 				len_list=len_list,
-				task_list=task_list,
+				raw_text_list=raw_text_list,
+				
 				quant_levels=quant_levels,
 			)
 
@@ -816,11 +853,12 @@ class AR_NAR(Base):
 
 	def forward(
 		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
+		task_list: list[Tensor] | None = None,
+
+		text_list: list[Tensor] | None = None,
+		proms_list: list[Tensor] | None = None,
 		resps_list: list[Tensor] | None = None,
 		
-		task_list: list[Tensor] | None = None,
 		lang_list: list[Tensor] | None = None,
 		tone_list: list[Tensor] | None = None,
 		len_list: list[Tensor] | None = None,
@@ -833,19 +871,20 @@ class AR_NAR(Base):
 		**sampling_kwargs,
 	):
 		# deduce batch_size
-		if text_list is not None:
-			default_task = "tts"
+		# deduce batch_size
+		if text_list:
 			device = text_list[0].device
 			batch_size = len(text_list)
-		else:
-			default_task = "stt"
+		elif raw_text_list:
+			device = raw_text_list[0].device
+			batch_size = len(raw_text_list)
+		elif proms_list:
+			device = proms_list[0].device
+			batch_size = len(proms_list)
+		elif resps_list:
 			device = resps_list[0].device
 			batch_size = len(resps_list)
 
-		# generate task list if not provided
-		if task_list is None:
-			task_list = [ default_task for _ in range(batch_size) ]
-
 		# implicitly set for training
 		if training is None and text_list is not None and resps_list is not None:
 			n_levels_set = {r.shape[-1] for r in resps_list}
@@ -856,10 +895,12 @@ class AR_NAR(Base):
 		# is training
 		if training:
 			return self.forward_train(
+				task_list=task_list,
+				
 				text_list=text_list,
 				proms_list=proms_list,
 				resps_list=resps_list,
-				task_list=task_list,
+				
 				lang_list=lang_list,
 				tone_list=tone_list,
 				len_list=len_list,
@@ -869,13 +910,17 @@ class AR_NAR(Base):
 		# is NAR
 		if (len_list is not None or resps_list is not None) and text_list is not None:
 			return self.forward_nar(
+				task_list=task_list,
+
 				text_list=text_list,
 				proms_list=proms_list,
 				resps_list=resps_list,
-				task_list=task_list,
+				
 				lang_list=lang_list,
 				tone_list=tone_list,
 				len_list=len_list,
+				raw_text_list=raw_text_list,
+
 				disable_tqdm=disable_tqdm,
 				use_lora=use_lora,
 				**sampling_kwargs,
@@ -883,13 +928,17 @@ class AR_NAR(Base):
 
 		# is AR
 		return self.forward_ar(
+			task_list=task_list,
+			
 			text_list=text_list,
 			proms_list=proms_list,
 			resps_list=resps_list,
-			task_list=task_list,
+			
 			lang_list=lang_list,
 			tone_list=tone_list,
 			len_list=len_list,
+			raw_text_list=raw_text_list,
+
 			disable_tqdm=disable_tqdm,
 			use_lora=use_lora,
 			**sampling_kwargs,
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index a77f29c..3f183e9 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -937,21 +937,32 @@ class Base(nn.Module):
 	# takes a bunch of separate lists and parses them into an ordered array of tuples to guide input sequence creation
 	def inputs(
 		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
-		resps_list: list[Tensor],
+		text_list: list[Tensor] | None = None,
+		raw_text_list: list[Tensor] | None = None,
+
+		proms_list: list[Tensor] | None = None,
+		resps_list: list[Tensor] | None = None,
 
 		lang_list: list[Tensor] | None = None,
 		tone_list: list[Tensor] | None = None,
 		len_list: list[Tensor] | None = None,
 		task_list: list[str] | None = None,
 		time_list: list[Tensor] | None = None,
-		raw_text_list: list[Tensor] | None = None,
 
 		quant_levels: int | list[int] | Tensor | None = None
 	):
-		device = text_list[0].device
-		batch_size = len(text_list)
+		if text_list:
+			device = text_list[0].device
+			batch_size = len(text_list)
+		elif raw_text_list:
+			device = raw_text_list[0].device
+			batch_size = len(raw_text_list)
+		elif proms_list:
+			device = proms_list[0].device
+			batch_size = len(proms_list)
+		elif resps_list:
+			device = resps_list[0].device
+			batch_size = len(resps_list)
 
 		inputs = [ [] for _ in range(batch_size) ]
 		for i in range(batch_size):
@@ -973,6 +984,8 @@ class Base(nn.Module):
 				# insert the text prompt
 				if text_list is not None and text_list[i] is not None:
 					inputs[i].append( ( "text", text_list[i] ) )
+				elif raw_text_list is not None and raw_text_list[i] is not None:
+					inputs[i].append( ( "raw_text", raw_text_list[i] ) )
 				# insert lang token if we're trained for it
 				if "lang" in self.capabilities and lang_list is not None and lang_list[i] is not None:
 					inputs[i].append( ( "lang", lang_list[i] ) )
@@ -1022,6 +1035,8 @@ class Base(nn.Module):
 				# insert the text prompt
 				if text_list is not None and text_list[i] is not None:
 					inputs[i].append( ( "text", text_list[i] ) )
+				elif raw_text_list is not None and raw_text_list[i] is not None:
+					inputs[i].append( ( "raw_text", raw_text_list[i] ) )
 				# insert lang token if we're trained for it
 				if "lang" in self.capabilities and lang_list is not None and lang_list[i] is not None:
 					inputs[i].append( ( "lang", lang_list[i] ) )
@@ -1070,6 +1085,8 @@ class Base(nn.Module):
 				# insert lang token if we're trained for it
 				if "lang" in self.capabilities and lang_list is not None and lang_list[i] is not None:
 					inputs[i].append( ( "lang", lang_list[i] ) )
+				if self.rvq_l_emb is not None and not self.interleave:
+					inputs[i].append( ( "quant_level", torch.tensor([ quant_level ], device=device, dtype=torch.int16) ) )
 				# insert the text prompt
 				if text_list is not None and text_list[i] is not None:
 					inputs[i].append( ( "text", text_list[i] ) )
@@ -1084,6 +1101,8 @@ class Base(nn.Module):
 				# insert lang token if we're trained for it
 				if "lang" in self.capabilities and lang_list is not None and lang_list[i] is not None:
 					inputs[i].append( ( "lang", lang_list[i] ) )
+				if self.rvq_l_emb is not None and not self.interleave:
+					inputs[i].append( ( "quant_level", torch.tensor([ quant_level ], device=device, dtype=torch.int16) ) )
 				# insert the text prompt
 				if raw_text_list is not None and raw_text_list[i] is not None:
 					inputs[i].append( ( "raw_text", raw_text_list[i] ) )
@@ -1197,7 +1216,7 @@ class Base(nn.Module):
 					embedding = self.text_emb( input )
 
 					device = embedding.device
-				elif name == "raw_text":
+				elif name == "raw_text" and self.raw_text_emb is not None:
 					embedding = self.raw_text_emb( input )
 
 					device = embedding.device
@@ -1643,6 +1662,10 @@ class Base(nn.Module):
 		if quant_levels is None:
 			quant_levels = [ x.item() for x in self.get_input( inputs, "quant_level" ) ]
 
+		# inputs don't have quant levels added, pure AR
+		if len(quant_levels) != len(inputs):
+			quant_levels = [ 0 for _ in range(len(inputs)) ]
+
 		x_list = self.inputs_to_embeddings( inputs, quant_levels )
 		
 		x, mask = list_to_tensor(x_list)
@@ -1652,10 +1675,6 @@ class Base(nn.Module):
 		device = x.device
 		batch_size = len(x_list)
 
-		# pure AR
-		if quant_levels is None:
-			quant_levels = [ 0 for _ in range(batch_size) ]
-
 		# we only need hidden states if we're training with layerskip
 		if self.layerskip and training:
 			output_hidden_states = True