fixes, throw an exception when using NAR only model with non-unified position IDs, since for some reason it outputs garbage for the NAR

vall_e/data.py

@@ -808,7 +808,8 @@ class Dataset(_Dataset):
 			if key not in cfg.hdf5:
 				raise RuntimeError(f'Key of Path ({path}) not in HDF5: {key}')
 
-			metadata = cfg.hdf5[key].attrs
+			#metadata = cfg.hdf5[key].attrs
+			metadata = { f'{k}': f'{v}' for k, v in cfg.hdf5[key].attrs.items() }
 
 			text = cfg.hdf5[key]["text"][:]
 			resps = cfg.hdf5[key]["audio"][:, :]
@@ -915,7 +916,8 @@ class Dataset(_Dataset):
 			if key not in cfg.hdf5:
 				raise RuntimeError(f'Key of Path ({path}) not in HDF5: {key}')
 
-			metadata = cfg.hdf5[key].attrs
+			# I need to do some weird coersion to a normal dict because it'll bitch about Hdf5 objects not being pickleable in worker processes
+			metadata = { f'{k}': f'{v}' for k, v in cfg.hdf5[key].attrs.items() }
 
 			text = cfg.hdf5[key]["text"][:]
 			resps = cfg.hdf5[key]["audio"][:, :]

vall_e/models/ar_nar.py

@@ -536,8 +536,10 @@ def example_usage():
 	engines = Engines({"ar+nar": engine})
 	engines.setup()
 	
+	"""
 	if cfg.optimizations.model_offloading:
 		model = ml.offload_model( model, policy=cfg.optimizations.model_offloading )
+	"""
 
 	"""
 	torch.save( {

vall_e/models/base.py

@@ -438,6 +438,10 @@ class Base(nn.Module):
 		audio_embedding_mode = self.config.experimental.audio_embedding_mode if self.config is not None else ""
 		unified_position_ids = self.config.experimental.unified_position_ids if self.config is not None else True
 
+		# there seems to be a problem with the NAR-only model with non-unified position IDs.............
+		if "len" in self.capabilities and not unified_position_ids:
+			raise Exception("ERROR: model instability for NAR-only model when not using unified position IDs.")
+
 		self.unified_position_ids = unified_position_ids
 
 		self.text_emb = Embedding(n_text_tokens, d_model)
@@ -1081,11 +1085,12 @@ class Base(nn.Module):
 				if not isinstance(input, torch.Tensor):
 					return sum( [ i.shape[0] for i in input if isinstance(i, torch.Tensor) ] ) + 1
 
+				# ending input will not have a separator later
 				return input.shape[0] + (0 if name in ["resp", "len"] else 1)
 
 			for batch_index, batch_input in enumerate(inputs):
 				batch = torch.cat( [
-					torch.Tensor([*range(get_input_token_length(name, input))])
+					torch.Tensor([*range(get_input_token_length(name, input))]).to(dtype=torch.int32)
 					for name, input in batch_input if name != "task"
 				] )
 

vall_e/models/nar.py

@@ -390,7 +390,7 @@ def example_usage():
 	"""
 
 	model = NAR(**kwargs).to(device)
-	steps = 500 
+	steps = 250 
 
 	optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy"
 	scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else ""