From 2e6a7625e4e97c2e1549915759209114a86c8122 Mon Sep 17 00:00:00 2001
From: mrq <mrq@ecker.tech>
Date: Sun, 5 Jan 2025 12:47:03 -0600
Subject: [PATCH] experimental

---
 data/text_tokenizer.json   | 8696 ++++++++++++++++++++++++++++++++++++
 scripts/train_tokenizer.py |   49 +-
 vall_e/config.py           |   21 +-
 vall_e/data.py             |   13 +
 vall_e/inference.py        |    2 +-
 vall_e/models/ar_nar.py    |    4 +
 vall_e/models/base.py      |  131 +-
 vall_e/train.py            |    1 +
 8 files changed, 8870 insertions(+), 47 deletions(-)
 create mode 100644 data/text_tokenizer.json

diff --git a/data/text_tokenizer.json b/data/text_tokenizer.json
new file mode 100644
index 0000000..469ced4
--- /dev/null
+++ b/data/text_tokenizer.json
@@ -0,0 +1,8696 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "<unk>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
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+      "content": "<bos>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
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+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
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+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
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+  "post_processor": {
+    "type": "TemplateProcessing",
+    "single": [
+      {
+        "SpecialToken": {
+          "id": "<bos>",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      },
+      {
+        "SpecialToken": {
+          "id": "<eos>",
+          "type_id": 0
+        }
+      }
+    ],
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+          "type_id": 0
+        }
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+      {
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+        }
+      }
+    ],
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+          1
+        ],
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+        ]
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+        ],
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+        ]
+      }
+    }
+  },
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+  },
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+      "윙": 7997,
+      "유": 7998,
+      "육": 7999,
+      "윤": 8000,
+      "율": 8001,
+      "윳": 8002,
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+      "윷": 8004,
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+      "통": 8365,
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+      "팰": 8412,
+      "팸": 8413,
+      "팻": 8414,
+      "팽": 8415,
+      "퍼": 8416,
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+      "펙": 8425,
+      "펜": 8426,
+      "펠": 8427,
+      "펨": 8428,
+      "펩": 8429,
+      "펫": 8430,
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+      "펴": 8432,
+      "편": 8433,
+      "펼": 8434,
+      "폄": 8435,
+      "폅": 8436,
+      "폈": 8437,
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+      "포": 8440,
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+      "푼": 8450,
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+      "풉": 8453,
+      "풋": 8454,
+      "풍": 8455,
+      "퓨": 8456,
+      "퓰": 8457,
+      "퓸": 8458,
+      "프": 8459,
+      "픈": 8460,
+      "플": 8461,
+      "픔": 8462,
+      "픕": 8463,
+      "피": 8464,
+      "픽": 8465,
+      "핀": 8466,
+      "필": 8467,
+      "핌": 8468,
+      "핍": 8469,
+      "핏": 8470,
+      "핑": 8471,
+      "하": 8472,
+      "학": 8473,
+      "한": 8474,
+      "할": 8475,
+      "핥": 8476,
+      "함": 8477,
+      "합": 8478,
+      "핫": 8479,
+      "항": 8480,
+      "핳": 8481,
+      "해": 8482,
+      "핵": 8483,
+      "핸": 8484,
+      "햄": 8485,
+      "햅": 8486,
+      "햇": 8487,
+      "했": 8488,
+      "행": 8489,
+      "향": 8490,
+      "허": 8491,
+      "헉": 8492,
+      "헌": 8493,
+      "헐": 8494,
+      "험": 8495,
+      "헛": 8496,
+      "헝": 8497,
+      "헤": 8498,
+      "헥": 8499,
+      "헨": 8500,
+      "헬": 8501,
+      "헴": 8502,
+      "헵": 8503,
+      "헷": 8504,
+      "헹": 8505,
+      "헿": 8506,
+      "혀": 8507,
+      "혁": 8508,
+      "현": 8509,
+      "혈": 8510,
+      "혐": 8511,
+      "협": 8512,
+      "혓": 8513,
+      "혔": 8514,
+      "형": 8515,
+      "혜": 8516,
+      "호": 8517,
+      "혹": 8518,
+      "혼": 8519,
+      "홀": 8520,
+      "홈": 8521,
+      "홉": 8522,
+      "홍": 8523,
+      "화": 8524,
+      "확": 8525,
+      "환": 8526,
+      "활": 8527,
+      "황": 8528,
+      "횃": 8529,
+      "회": 8530,
+      "획": 8531,
+      "횐": 8532,
+      "횟": 8533,
+      "횡": 8534,
+      "효": 8535,
+      "횹": 8536,
+      "후": 8537,
+      "훅": 8538,
+      "훈": 8539,
+      "훌": 8540,
+      "훑": 8541,
+      "훔": 8542,
+      "훗": 8543,
+      "훨": 8544,
+      "훼": 8545,
+      "훽": 8546,
+      "휑": 8547,
+      "휘": 8548,
+      "휙": 8549,
+      "휠": 8550,
+      "휨": 8551,
+      "휩": 8552,
+      "휴": 8553,
+      "흉": 8554,
+      "흐": 8555,
+      "흑": 8556,
+      "흔": 8557,
+      "흘": 8558,
+      "흙": 8559,
+      "흠": 8560,
+      "흡": 8561,
+      "흣": 8562,
+      "흥": 8563,
+      "흩": 8564,
+      "흫": 8565,
+      "희": 8566,
+      "흰": 8567,
+      "히": 8568,
+      "힌": 8569,
+      "힐": 8570,
+      "힘": 8571,
+      "힙": 8572,
+      "힛": 8573,
+      "𫚕": 8574
+    },
+    "merges": []
+  }
+}
\ No newline at end of file
diff --git a/scripts/train_tokenizer.py b/scripts/train_tokenizer.py
index b2f6c9b..bb1e5a5 100644
--- a/scripts/train_tokenizer.py
+++ b/scripts/train_tokenizer.py
@@ -16,17 +16,55 @@ from tokenizers.trainers import BpeTrainer
 from tokenizers.pre_tokenizers import Whitespace
 from tokenizers.processors import TemplateProcessing
 
-input_metadata = "training/data"
+from vall_e.config import cfg
+from vall_e.utils.io import json_read
+from vall_e.emb.g2p import coerce_to_hiragana
 
-output_file = Path("./training/tokenizer_training_data.json")
+input_metadata = "training/metadata/"
+
+output_file = Path("./training/tokenizer_pretraining_data.json")
 tokenizer_data = []
 
 def pad(num, zeroes):
 	return str(num).zfill(zeroes+1)
 
+def add( dir, type="training", audios=True, texts=True ):
+	name = str(dir)
+	name = name.replace(str(cfg.data_dir), "")
+	speaker_name = name
+	"""
+	if "LibriTTS-R" in speaker_name:
+		speaker_name = speaker_name.replace("LibriTTS-R", "LibriVox")
+	"""
+
+	metadata_path = cfg.metadata_dir / f'{speaker_name}.json'
+	metadata = json_read( metadata_path, default={} )
+
+	for k, entry in metadata.items():
+		if "text" not in entry:
+			continue
+
+		language = entry.get('language','auto')
+		text = entry['text']
+		tokenizer_data.append( text )
+
 if output_file.exists():
 	tokenizer_data = json.loads(open(str(output_file), "r", encoding="utf-8").read())
 else:
+	# training
+	for data_dir in tqdm(sorted(cfg.dataset.training), desc="Processing Training"):
+		try:
+			add( data_dir, type="training" )
+		except Exception as e:
+			pass
+
+	# validation
+	for data_dir in tqdm(sorted(cfg.dataset.validation), desc='Processing Validation'):
+		try:
+			add( data_dir, type="validation" )
+		except Exception as e:
+			pass
+	"""
 	for dataset_name in os.listdir(f'./{input_metadata}/'):
 		if not os.path.isdir(f'./{input_metadata}/{dataset_name}/'):
 			continue
@@ -42,17 +80,18 @@ else:
 				metadata_path = Path(f'./{input_metadata}/{dataset_name}/{speaker_id}/{id}')
 				metadata = json.loads(open(metadata_path, "r", encoding="utf-8").read())
 
-				if "phonemes" not in metadata:
+				if "text" not in metadata:
 					continue
 
-				tokenizer_data.append( f'{"".join(metadata["phonemes"])}' )
+				tokenizer_data.append( f'{"".join(metadata["text"])}' )
 
 	open(output_file, 'w', encoding='utf-8').write(json.dumps(tokenizer_data))
+	"""
 
 unk_token = "<unk>"
 spl_tokens = [unk_token, "<bos>", "</eos>", "<mask>", "<space>"]
 
-trainer = BpeTrainer(special_tokens = spl_tokens, vocab_size = 256)
+trainer = BpeTrainer(special_tokens = spl_tokens, vocab_size = 32768, max_token_length=1, min_frequency=len(tokenizer_data))
 tokenizer = Tokenizer(BPE(unk_token = unk_token))
 tokenizer.pre_tokenizer = Whitespace() # takes 2 hours to process without this, we'll just manually add spaces as a token
 tokenizer.post_processor = TemplateProcessing(
diff --git a/vall_e/config.py b/vall_e/config.py
index 6c11fef..8e72771 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -267,7 +267,8 @@ class ModelExperimentalSettings:
 	ignore_inputs_for_loss: bool = True # only calculate the loss on the outputs since thats what matters, as the inputs that do have loss calculated upon affects the loss for the entire sequence
 
 	noncausal_masks: bool = False # to correct an oversight with Llama always using causal masks......
-	classifiers_bias: bool = True # ugh
+	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
 
 	# classifier-free guidance training settings
 	cfg_cond_dropout_p: float = 0.0 # 0.2 # probability to drop out text and audio during training
@@ -785,6 +786,9 @@ class Config(BaseConfig):
 	
 	tokenizer: str | None = None # tokenizer class
 	tokenizer_path: str = "./tokenizer.json" # tokenizer path
+	
+	text_tokenizer: str | None = None # tokenizer class
+	text_tokenizer_path: str = "./text_tokenizer.json" # tokenizer path
 
 	sample_rate: int = 24_000 # sample rate the model expects
 	audio_backend: str = "vocos" # audio backend to use "encodec" | "vocos" | "dac""
@@ -1053,6 +1057,21 @@ class Config(BaseConfig):
 				raise Exception(f'Tokenizer path not found: {tokenizer_path}')
 
 			self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=str(tokenizer_path))
+		
+		if self.tokenizer == "naive":
+			...
+		else:
+			from transformers import PreTrainedTokenizerFast
+
+			text_tokenizer_path = self.rel_path / self.text_tokenizer_path
+			# deduce path if a local copy is not provided
+			if not text_tokenizer_path.exists():
+				text_tokenizer_path = Path("./data/") / self.text_tokenizer_path
+			
+			if not self.silent_errors and not text_tokenizer_path.exists():
+				raise Exception(f'Tokenizer path not found: {text_tokenizer_path}')
+
+			self.text_tokenizer = PreTrainedTokenizerFast(tokenizer_file=str(text_tokenizer_path))
 
 
 # Preserves the old behavior
diff --git a/vall_e/data.py b/vall_e/data.py
index d59b81d..cb96e2f 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -642,6 +642,11 @@ def tokenize( phones ):
 		phones = "".join( phones )
 	return cfg.tokenizer.encode( phones )
 
+def text_tokenize( text ):
+	if isinstance( text, list ):
+		text = "".join( text )
+	return cfg.text_tokenizer.encode( text )
+
 def get_lang_symmap():
 	return {
 		"en": 0,
@@ -677,6 +682,9 @@ def get_task_symmap():
 		"len": 0, # fake
 		"nse": 6, # fake
 		"cse": 6, # fake
+		
+		"phn": 0, # fake
+		"un-phn": 0, # fake
 	}
 
 def _replace_file_extension(path, suffix):
@@ -1299,6 +1307,8 @@ class Dataset(_Dataset):
 			text_string = metadata["text"] if "text" in metadata else None
 
 		lang = self.get_language(spkr_group) if not lang else lang.lower()
+
+		raw_text = torch.tensor(text_tokenize(text_string)).to(torch.int16) if text_string else None
 		
 		if not tone:
 			tone = "neutral"
@@ -1388,6 +1398,8 @@ class Dataset(_Dataset):
 		elif task == "len":
 			proms = self.sample_prompts(spkr_name, reference=path)
 
+		elif task in ["phn", "un-phn"]:
+			proms = []
 		# noise suppression (<text>? <resp+noise> => <resp>)
 		# speech removal (<text>?<resp+noise> => <noise>)
 		elif task == "ns" or task == "sr":
@@ -1532,6 +1544,7 @@ class Dataset(_Dataset):
 			text=text,
 			proms=proms,
 			resps=resps,
+			raw_text=raw_text,
 			
 			metadata=metadata,
 		)
diff --git a/vall_e/inference.py b/vall_e/inference.py
index d774caf..ea205ac 100755
--- a/vall_e/inference.py
+++ b/vall_e/inference.py
@@ -473,7 +473,7 @@ class TTS():
 				)
 				if model_len is not None:
 					# skip calculating len_list if possible
-					if task in ["ns, sr"]:
+					if task in ["ns", "sr"]:
 						len_list = [ prom[1].shape[0] ]
 					elif vc_utterance is not None:
 						len_list = [ vc_utterance.shape[0] ]
diff --git a/vall_e/models/ar_nar.py b/vall_e/models/ar_nar.py
index 4b57846..0c8391d 100644
--- a/vall_e/models/ar_nar.py
+++ b/vall_e/models/ar_nar.py
@@ -48,6 +48,7 @@ class AR_NAR(Base):
 		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:
@@ -194,6 +195,7 @@ class AR_NAR(Base):
 			lang_list=lang_list,
 			tone_list=tone_list,
 			task_list=task_list,
+			raw_text_list=raw_text_list,
 			time_list=timesteps,
 
 			quant_levels=quant_levels,
@@ -822,6 +824,7 @@ class AR_NAR(Base):
 		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,
 
 		training: bool | None = None,
 
@@ -860,6 +863,7 @@ class AR_NAR(Base):
 				lang_list=lang_list,
 				tone_list=tone_list,
 				len_list=len_list,
+				raw_text_list=raw_text_list,
 			)
 
 		# is NAR
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index 61b01af..a77f29c 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -47,12 +47,14 @@ from ..utils.pattern import DelayedPatternProvider, VALLEPattern
 """
 
 summed_embeddings_task = [ "stt" ]
-special_tasks = [ "len", "stt" ]
+special_tasks = [ "len", "stt", "phn", "un-phn" ]
 non_tokened_names = ["task", "dropout_mask", "classifier_level"]
 task_outputs = {
 	"tts": "resp",
 	"stt": "text",
 	"len": "len",
+	"phn": "text",
+	"un-phn": "raw_text",
 }
 
 # yuck
@@ -187,7 +189,7 @@ class MultiEmbedding(nn.Module):
 class AudioEmbedding_Old(nn.Module):
 	def __init__(
 		self,
-		l_tokens: int, # list of number of tokens (needed because AR resps includes stop token)
+		l_embedding_tokens: int, # list of number of tokens (needed because AR resps includes stop token)
 		token_dim: int, # dimensionality of the embedding
 		levels: int | None = None, # number of RVQ-bins (I don't remember the specifics)
 	):
@@ -195,7 +197,7 @@ class AudioEmbedding_Old(nn.Module):
 		# array of embeddings
 		#   proms are [0, resp_levels]
 		#   resp are split to where [0] is for the AR, and [1:] are reserved for NAR
-		self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for n_tokens in l_tokens])
+		self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for n_tokens in l_embedding_tokens])
 		# weight influencer for the influence for each level (desu this should be really useless because the weights in the embedding themselves should factor this)
 		self.weight = nn.ParameterList([nn.Parameter( torch.tensor([1]) ) for i in range(levels)]) if levels is not None else None
 
@@ -217,20 +219,20 @@ class AudioEmbedding_Old(nn.Module):
 class AudioEmbedding(nn.Module):
 	def __init__(
 		self,
-		l_tokens: list[int], # list of number of tokens (needed because AR resps includes stop token)
+		l_embedding_tokens: list[int], # list of number of tokens (needed because AR resps includes stop token)
 		token_dim: int, # dimensionality of the embedding
 		sums: bool = True, # whether to sum all previous layers of embeddings to factor in other RVQ bin levels (I do not know which way is better)
-		l_names: list[str] = [], # names to map to indices
+		l_embedding_names: list[str] = [], # names to map to indices
 	):
 		super().__init__()
 		# array of embeddings
 		#   proms are [0, resp_levels]
 		#   resp are split to where [0] is for the AR, and [1:] are reserved for NAR
-		self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for n_tokens in l_tokens])
+		self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for n_tokens in l_embedding_tokens])
 		# further experimentation is needed to see if this actually is useful
 		self.sums = sums
 		# 
-		self.names = l_names
+		self.names = l_embedding_names
 
 	def forward(self, xi: Tensor, offset: int | None = None, quant_level: int | None = None, name: str | None = None, sums = None ) -> Tensor:
 		if sums is None:
@@ -278,14 +280,14 @@ class TimeEmbedding(nn.Module):
 class Classifiers(nn.Module):
 	def __init__(
 		self,
-		l_tokens: list[int], # list of number of tokens (needed because AR resps includes stop token)
+		l_embedding_tokens: list[int], # list of number of tokens (needed because AR resps includes stop token)
 		token_dim: int, # dimensionality of the embedding
-		l_names: list[str] | None = None, # list of names to map to each classifier,
+		l_embedding_names: list[str] | None = None, # list of names to map to each classifier,
 		bias: bool = True,
 	):
 		super().__init__()
-		self.proj = nn.ModuleList([nn.Linear(token_dim, n_tokens, bias=bias) for n_tokens in l_tokens])
-		self.names = l_names
+		self.proj = nn.ModuleList([nn.Linear(token_dim, n_tokens, bias=bias) for n_tokens in l_embedding_tokens])
+		self.names = l_embedding_names
 
 	def indices(
 		self,
@@ -326,7 +328,7 @@ class Classifiers(nn.Module):
 class Metrics(nn.Module):
 	def __init__(
 		self,
-		l_tokens: int | list[int],
+		l_embedding_tokens: int | list[int],
 		top_k = 10,
 		average="micro",
 		multidim_average="global",
@@ -339,14 +341,14 @@ class Metrics(nn.Module):
 			average=average,
 			multidim_average=multidim_average,
 			ignore_index=ignore_index,
-		) for n_tokens in l_tokens ])
+		) for n_tokens in l_embedding_tokens ])
 		self.precision = nn.ModuleList([ MulticlassPrecision(
 			n_tokens,
 			top_k=top_k,
 			average=average,
 			multidim_average=multidim_average,
 			ignore_index=ignore_index,
-		) for n_tokens in l_tokens ])
+		) for n_tokens in l_embedding_tokens ])
 
 	def calc_accuracy( self, inputs, targets, classifier_levels ):
 		return sum( [ self.accuracy[l]( input[:, :self.accuracy[l].num_classes], target ) for target, input, l in zip( targets, inputs, classifier_levels ) ] ) / len( inputs )
@@ -413,6 +415,7 @@ class Base(nn.Module):
 		
 		n_text_tokens: int = 256,
 		n_audio_tokens: int = 1024,
+		n_raw_text_tokens: int = 8575,
 
 		d_model: int = 512,
 		n_heads: int = 8,
@@ -434,6 +437,7 @@ class Base(nn.Module):
 
 		self.n_text_tokens = n_text_tokens
 		self.n_audio_tokens = n_audio_tokens
+		self.n_raw_text_tokens = n_raw_text_tokens
 
 		self.d_model = d_model
 		self.n_heads = n_heads
@@ -477,6 +481,7 @@ class Base(nn.Module):
 		interleave = self.config.experimental.interleave if self.config is not None else False
 		noncausal_masks = self.config.experimental.noncausal_masks if self.config is not None else False
 		classifiers_bias = self.config.experimental.classifiers_bias if self.config is not None else False
+		max_position_embeddings = self.config.experimental.max_position_embeddings if self.config is not None else (75 * 60 * 5)
 		
 		masking_ratio = self.config.experimental.masking_ratio if self.config is not None else False
 		ignore_inputs_for_loss = self.config.experimental.ignore_inputs_for_loss if self.config is not None else False
@@ -493,35 +498,44 @@ class Base(nn.Module):
 		# pure AR
 		if "nar" not in self.capabilities:
 			n_resp_tokens = n_audio_tokens + 1
-			l_tokens = [n_resp_tokens] * self.n_resp_levels
-			resp_l_names = [f'AR:{i}:{i}' for i in range( self.n_resp_levels )]
-			classifier_l_tokens = [n_resp_tokens] * self.n_resp_levels
+			l_embedding_tokens = [n_resp_tokens] * self.n_resp_levels
+			l_embedding_names = [f'AR:{i}:{i}' for i in range( self.n_resp_levels )]
+			l_classifier_tokens = [n_resp_tokens] * self.n_resp_levels
 		# NAR-len model
 		elif "len" in self.capabilities:
 			# +1 to include the stop or mask token
 			n_resp_tokens = n_audio_tokens + ( 1 if self.causal_size > 0 else 0 )
 			if "ar" in self.capabilities:
-				l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1) + [n_resp_tokens]
-				classifier_l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1) + [n_resp_tokens - 1]
-				resp_l_names = ['AR:0:0'] + [f'NAR:{i}:{i+1}' for i in range( self.n_resp_levels - 1 )] + ['NAR:0:0']
+				l_embedding_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1) + [n_resp_tokens]
+				l_classifier_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1) + [n_resp_tokens - 1]
+				l_embedding_names = ['AR:0:0'] + [f'NAR:{i}:{i+1}' for i in range( self.n_resp_levels - 1 )] + ['NAR:0:0']
 			else:
-				l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
-				classifier_l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
-				resp_l_names = ['NAR:0:0'] + [f'NAR:{i}:{i+1}' for i in range( self.n_resp_levels - 1 )]
+				l_embedding_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
+				l_classifier_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
+				l_embedding_names = ['NAR:0:0'] + [f'NAR:{i}:{i+1}' for i in range( self.n_resp_levels - 1 )]
 		# AR+NAR model
 		else:
 			# +1 to include the stop or mask token
 			n_resp_tokens = n_audio_tokens + ( 1 if self.causal_size > 0 else 0 )
-			l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
-			resp_l_names = ['AR:0:0'] + [f'NAR:{i}:{i+1}' for i in range( self.n_resp_levels - 1 )]
-			classifier_l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
+			l_embedding_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
+			l_embedding_names = ['AR:0:0'] + [f'NAR:{i}:{i+1}' for i in range( self.n_resp_levels - 1 )]
+			l_classifier_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
+		
+		l_classifier_names = l_embedding_names
 
-		classifier_l_tokens += [ n_text_tokens ]
-		classifier_l_names = resp_l_names + [ "stt" ]
+		# STT
+		l_classifier_names += [ "stt" ]
+		l_classifier_tokens += [ n_text_tokens ]
 
+		# LEN
 		if "len" in self.capabilities:
-			classifier_l_tokens += [ 11 ]
-			classifier_l_names += ["len"]
+			l_classifier_tokens += [ 11 ]
+			l_classifier_names += ["len"]
+
+		# TEXT => PHN / PHN => TEXT
+		if self.version >= 6:
+			l_classifier_tokens += [ n_raw_text_tokens ]
+			l_classifier_names = l_embedding_names + [ "raw_text" ]
 
 		n_vocab = 17702 if not split_classifiers else n_resp_tokens + 1
 
@@ -541,6 +555,7 @@ class Base(nn.Module):
 		"""
 
 		self.text_emb = Embedding(n_text_tokens, d_model)
+		self.raw_text_emb = None
 		self.langs_emb = None
 		self.tones_emb = None
 		self.tasks_emb = None
@@ -563,7 +578,7 @@ class Base(nn.Module):
 			)
 			# [1024 + STOP] + [1024] * 8
 			self.resps_emb = AudioEmbedding_Old(
-				l_tokens, d_model,
+				l_embedding_tokens, d_model,
 				levels=self.n_resp_levels if self.version > 3 else None,
 			)
 		else:
@@ -572,9 +587,9 @@ class Base(nn.Module):
 				sums=audio_embedding_sums == "prom" or audio_embedding_sums == True,
 			)
 			self.resps_emb = AudioEmbedding(
-				l_tokens, d_model,
+				l_embedding_tokens, d_model,
 				sums=audio_embedding_sums == "resp" or audio_embedding_sums == True,
-				l_names=resp_l_names,
+				l_embedding_names=l_embedding_names,
 			)
 
 		if self.version >= 3:
@@ -597,6 +612,9 @@ class Base(nn.Module):
 			self.len_emb = Embedding(11, d_model)
 			self.time_emb = None # TimeEmbedding(d_model) # if not masking_ratio else None
 
+		if self.version >= 6:
+			self.raw_text_emb = Embedding(self.n_raw_text_tokens, d_model)
+
 		if attention_backend == "auto":
 			attention_backend = "sdpa"
 			"""
@@ -635,7 +653,7 @@ class Base(nn.Module):
 				self.model = MistralModel(MistralConfig(
 					vocab_size=n_vocab,
 					hidden_size=d_model,
-					max_position_embeddings=75 * 60 * 5, # max-length of 60 seconds
+					max_position_embeddings=max_position_embeddings,
 					intermediate_size=d_model*4,
 					num_hidden_layers=n_layers,
 					num_attention_heads=n_heads,
@@ -651,7 +669,7 @@ class Base(nn.Module):
 				self.model = MixtralModel(MixtralConfig(
 					vocab_size =n_resp_tokens,
 					hidden_size=d_model,
-					max_position_embeddings=75 * 60 * 5, # max-length of 60 seconds
+					max_position_embeddings=max_position_embeddings,
 					intermediate_size=d_model*4,
 					num_hidden_layers=n_layers,
 					num_attention_heads=n_heads,
@@ -681,7 +699,7 @@ class Base(nn.Module):
 				config = LlamaConfig(
 					vocab_size=n_vocab,
 					hidden_size=d_model,
-					max_position_embeddings=75 * 60 * 5, # max-length of 60 seconds
+					max_position_embeddings=max_position_embeddings,
 					intermediate_size=d_model*4,
 					num_hidden_layers=n_layers,
 					num_attention_heads=n_heads,
@@ -703,7 +721,7 @@ class Base(nn.Module):
 				self.model = MixtralModel(MixtralConfig(
 					vocab_size =n_resp_tokens,
 					hidden_size=d_model,
-					max_position_embeddings=75 * 60 * 5, # max-length of 60 seconds
+					max_position_embeddings=max_position_embeddings,
 					intermediate_size=d_model*4,
 					num_hidden_layers=n_layers,
 					num_attention_heads=n_heads,
@@ -800,8 +818,8 @@ class Base(nn.Module):
 			self.metrics = None
 		else:
 			self.classifier = None
-			self.classifiers = Classifiers( classifier_l_tokens, d_model, l_names=classifier_l_names, bias=classifiers_bias )
-			self.metrics = Metrics( classifier_l_tokens )
+			self.classifiers = Classifiers( l_classifier_tokens, d_model, l_embedding_names=l_classifier_names, bias=classifiers_bias )
+			self.metrics = Metrics( l_classifier_tokens )
 
 			"""
 			if tie_classifier_to_embedding:
@@ -928,6 +946,7 @@ class Base(nn.Module):
 		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
 	):
@@ -1042,6 +1061,34 @@ class Base(nn.Module):
 					inputs[i].append( ( "text", text_list[i] ) )
 
 				inputs[i].append( ("classifier_level", "stt") )
+			# Text phonemizing task
+			# Sequence: <raw_text><sep><lang><sep><phonemes>
+			elif task_type == "phn":
+				# 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] ) )
+				# 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] ) )
+				# insert the text prompt
+				if text_list is not None and text_list[i] is not None:
+					inputs[i].append( ( "text", text_list[i] ) )
+
+				inputs[i].append( ("classifier_level", "stt") )
+			# Text de-phonemizing task
+			# Sequence: <raw_text><sep><lang><sep><phonemes>
+			elif task_type == "un-phn":
+				# insert the text prompt
+				if text_list is not None and text_list[i] is not None:
+					inputs[i].append( ( "text", 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] ) )
+				# 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] ) )
+				
+				inputs[i].append( ("classifier_level", "raw_text") )
 			else:
 				raise Exception(f'Unrecognized task: {task_type}')
 		return inputs
@@ -1149,6 +1196,10 @@ class Base(nn.Module):
 				elif name == "text":
 					embedding = self.text_emb( input )
 
+					device = embedding.device
+				elif name == "raw_text":
+					embedding = self.raw_text_emb( input )
+
 					device = embedding.device
 				elif name == "quant_level" and self.rvq_l_emb is not None:
 					embedding = self.rvq_l_emb( input )
@@ -1628,7 +1679,7 @@ class Base(nn.Module):
 		# needs to be done here as we still have our raw inputs
 		position_ids = self.inputs_to_position_ids( inputs, mask=mask ) if not self.unified_position_ids else None
 		classifier_levels = self.get_input( inputs, name="classifier_level" )
-		casual_levels = [ "AR:0:0", "stt", "len" ]
+		casual_levels = [ "AR:0:0", "stt", "len", "phn" ]
 
 		# right now limit to new versions because I need to retrain the model for noncausal masks...
 		is_causal = [ l in casual_levels for l in classifier_levels ] if self.noncausal_masks else [ True for l in classifier_levels ]
diff --git a/vall_e/train.py b/vall_e/train.py
index 69175ca..a8fb017 100755
--- a/vall_e/train.py
+++ b/vall_e/train.py
@@ -38,6 +38,7 @@ def train_feeder(engine, batch, teacher=None):
 			lang_list=batch["lang"],
 			tone_list=batch["tone"],
 			task_list=batch["task"],
+			raw_text_list=batch["raw_text"],
 
 			training=True,
 		)