this embedding class definitely works, and migrating from the previous embedding weights seems to work.

vall_e/config.py

@@ -160,11 +160,12 @@ class Model:
 	resp_levels: int = 1
 	prom_levels: int = 8
 	tasks: int = 8 # ["tts", "ns", "sr", "tse", "cse", "nse"] and leaves two more for anything else I want (like "svc")
-	arch_type: str = "transformer"
+	arch_type: str = "retnet"
 	training: bool = True
 	interleave: bool = False
 	frozen_params: list[str] = field(default_factory=lambda: [])
 	p_ar_nar: float = 0.5
+	version: int = 1
 
 	@property
 	def full_name(self):

vall_e/data.py

@@ -279,7 +279,7 @@ class Dataset(_Dataset):
 
 		# shuffle it up a bit
 		prom_length = 0
-		trim_length = int(cfg.dataset.prompt_duration * 75) + random.randint(-16, 16)
+		trim_length = int(cfg.dataset.prompt_duration * 75) + random.randint(-75, 75)
 
 		for _ in range(cfg.dataset.max_prompts):
 			path = random.choice(choices)

vall_e/models/ar.py

@@ -57,6 +57,12 @@ class AR(Base):
 	def monolithic(self) -> bool:
 		return False
 
+	@property
+	def version(self) -> int:
+		if hasattr(self, "config") and self.config:
+			return self.config.version
+		return cfg.models.ar.version
+
 	def _prune(self, l: Tensor):
 		indices = (l == self.stop_token).nonzero()
 		if len(indices) == 0:

vall_e/models/ar_nar.py

@@ -54,6 +54,12 @@ class AR_NAR(Base):
 	def monolithic(self) -> bool:
 		return True
 
+	@property
+	def version(self) -> int:
+		if hasattr(self, "config") and self.config:
+			return self.config.version
+		return cfg.models.ar_nar.version
+
 	def _prune(self, l: Tensor):
 		indices = (l == self.stop_token).nonzero()
 		if len(indices) == 0:
@@ -208,7 +214,7 @@ def example_usage():
 	]
 	proms_list = [
 		#x8(torch.tensor([1, 2, 3], device=device)),
-		qnt.to(device),
+		qnt[:75*3, :].to(device),
 	]
 	resps_list = [
 		qnt.to(device),
@@ -233,12 +239,16 @@ def example_usage():
 	"""
 
 	model = AR_NAR(**kwargs).to(device)
+	#steps = 500
+	#optimizer = ml.Prodigy(model.parameters(), lr=1.0)
 	steps = 500
-	optimizer = ml.Prodigy(model.parameters(), lr=1.0)
+	optimizer = ml.AdamW(model.parameters(), lr=1.0e-4)
 	engine = Engine(model=model, optimizer=optimizer)
 
 	print(f"AR+NAR parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
 
+	print([ name for name, _ in model.named_parameters()])
+	
 	@torch.inference_mode()
 	def sample( name, steps=600 ):
 		engine.eval()

vall_e/models/base.py

@@ -166,49 +166,30 @@ class MultiEmbedding(nn.Embedding):
 
 		return x_list
 
-		"""
-		w_ar, w_nar = self.weight[:1], self.weight[1:]
-		p_ar_list, p_nar_list = [], []
-
-		for i, xi in enumerate(x_list):
-			if quant_levels is None or quant_levels[i] == 0:
-				w padded_x_list, = w_ar, p_ar_list
-			else:
-				w, padded_x_list = w_nar, p_nar_list
-
-			# pad resp/prom tensor to fit weight
-			xi = F.one_hot(xi.to(torch.int64), num_classes=self.n_tokens)  # t l' k
-			xi = F.pad(xi, (0, 0, 0, w.shape[0] - xi.shape[1]))  # t l k
-			padded_x_list.append(xi.to(w))
-
-		# batch list => batch tensor
-		x_ar_list = einsum("l k d, n l k -> n d", w_ar, torch.cat(p_ar_list)) if len(p_ar_list) > 0 else []
-		x_nar_list = einsum("l k d, n l k -> n d", w_nar, torch.cat(p_nar_list)) if len(p_nar_list) > 0 else []
-
-		x_list = x.split([*map(len, x_list)])
-		"""
-
-"""
 # Embedding that sums each RVQ-bin level within a given input acoustic prompt
-class PromEmbedding(nn.Module):
+class AudioEmbedding(nn.Module):
-	def __init__(self, n_levels, n_tokens, token_dim):
-		super().__init__()
-		self.n_levels = n_levels
-		self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for _ in range(self.n_levels)])
-	
-	def forward(self, x_list: list[Tensor] ) -> list[Tensor]:
-		return [ sum([ self.embeddings[k](xi[:, k]) for k in range(xi.shape[-1]) ]) for i, xi in enumerate(x_list) ]
-
-# Embedding that selects which embedding based on a quant_level tensor for a given batch
-class RespEmbedding(nn.Module):
 	def __init__(self, n_levels, n_tokens, token_dim):
 		super().__init__()
 		self.n_levels = n_levels
+		# would it be better to have embeddings[1:] reduced to 1024 tokens to attend to, so it's *not* factoring in the stop token?
 		self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for _ in range(self.n_levels)])
 	
 	def forward(self, x_list: list[Tensor], quant_levels: Tensor | None = None ) -> list[Tensor]:
-		return [ self.embeddings[min(self.n_levels, quant_levels[i]) if quant_levels is not None else 0](xi)[:, 0, :] for i, xi in enumerate(x_list) ]
+		res_list = []
-"""
+
+		for i, xi in enumerate(x_list):
+			# prom
+			if quant_levels is None and xi.shape[-1] > 1:
+				x = sum( [ self.embeddings[k]( xi[:, k] ) for k in range(xi.shape[-1]) ] )
+			# AR resp
+			elif quant_levels is None or quant_levels[i] == 0:
+				x = self.embeddings[0]( xi[:, 0] )
+			# NAR resp
+			else:
+				x = sum( [ self.embeddings[k+1]( xi[:, k] ) for k in range(xi.shape[-1]) ] )
+			res_list.append(x)
+
+		return res_list
 
 class Base(nn.Module):
 	@property
@@ -252,8 +233,8 @@ class Base(nn.Module):
 		return False
 
 	@property
-	def n_embeddings(self) -> int:
+	def version(self) -> int:
-		return 2 if self.monolithic else 1
+		return 1
 
 	@property
 	def stop_token(self):
@@ -298,8 +279,12 @@ class Base(nn.Module):
 
 		self.text_emb = Embedding(n_tokens, d_model)
 
-		self.proms_emb = MultiEmbedding(self.n_prom_levels, n_prom_tokens, d_model) #, monolithic=self.monolithic)		
+		if self.version == 1: # legacy
+			self.proms_emb = MultiEmbedding(self.n_prom_levels, n_prom_tokens, d_model)
 			self.resps_emb = MultiEmbedding(self.n_resp_levels, n_resp_tokens, d_model, monolithic=self.monolithic)
+		else:
+			self.proms_emb = AudioEmbedding(self.n_prom_levels, n_prom_tokens, d_model)
+			self.resps_emb = AudioEmbedding(self.n_resp_levels, n_resp_tokens, d_model)
 
 		self.sep = nn.Parameter(torch.randn(d_model))
 

vall_e/models/nar.py

@@ -39,6 +39,12 @@ class NAR(Base):
 	def n_tasks(self) -> int:
 		return cfg.models.tasks
 
+	@property
+	def version(self) -> int:
+		if hasattr(self, "config") and self.config:
+			return self.config.version
+		return cfg.models.nar.version
+
 	@property
 	def recurrent_chunk_size(self) -> int:
 		return 0