diff --git a/vall_e/config.py b/vall_e/config.py
index f9ced76..4bbc306 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -343,7 +343,6 @@ class DeepSpeed:
 	inferencing: bool = False
 	
 	amp: bool = False
-	fp16: bool = False
 
 	config: dict = field(default_factory=lambda: {}) # to pass through deepspeed config
 
@@ -373,7 +372,7 @@ class DeepSpeed:
 			autotune_params['exps_dir'] = str( cfg.relpath / "autotune" / "exps_" )
 
 		# DeepSpeed fp16 is incompatible with its AMP
-		if cfg.trainer.weight_dtype.lower() == "float16" and self.fp16:
+		if cfg.trainer.weight_dtype.lower() == "float16":
 			self.amp = False
 
 		# disable local AMP
@@ -393,7 +392,7 @@ class DeepSpeed:
 			} if not cfg.hyperparameters.torch_scheduler else None,
 			"gradient_clipping": cfg.hyperparameters.gradient_clipping,
 			"fp16": {
-				"enabled": cfg.trainer.weight_dtype.lower() == "float16" and self.fp16,
+				"enabled": cfg.trainer.weight_dtype.lower() == "float16",
 				"auto_cast": True, # ???
 			},
 			"bf16": {
diff --git a/vall_e/data.py b/vall_e/data.py
index bb28d90..5257abf 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -91,14 +91,12 @@ def _load_paths_from_metadata(dataset_name, type="training", validate=False):
 
 	_fn = _get_hdf5_paths if cfg.dataset.use_hdf5 else _get_paths_of_extensions
 
-	def _validate( entry ):
-		phones = entry['phones'] if "phones" in entry else 0
-		duration = entry['duration'] if "duration" in entry else 0
-		if type not in _total_durations:
-			_total_durations[type] = 0
-		_total_durations[type] += duration
+	def key( id ):
+		if not cfg.dataset.use_hdf5:
+			return data_dir / id
+
+		return f"/{type}/{_get_hdf5_path(data_dir)}/{id}" 
 
-		return cfg.dataset.min_duration <= duration and duration <= cfg.dataset.max_duration and cfg.dataset.min_phones <= phones and phones <= cfg.dataset.max_phones
 
 	metadata_path = cfg.metadata_dir / f'{dataset_name}.json'
 	metadata = {}
@@ -110,13 +108,24 @@ def _load_paths_from_metadata(dataset_name, type="training", validate=False):
 		return _fn( data_dir, type if cfg.dataset.use_hdf5 else _get_quant_extension(), validate )
 
 
-	def key( dir, id ):
-		if not cfg.dataset.use_hdf5:
-			return data_dir / id
+	def _validate( id ):
+		entry = metadata[id]
 
-		return f"/{type}/{_get_hdf5_path(data_dir)}/{id}" 
+		phones = entry['phones'] if "phones" in entry else 0
+		duration = entry['duration'] if "duration" in entry else 0
+		if type not in _total_durations:
+			_total_durations[type] = 0
+		
+		_total_durations[type] += duration
+		
+		if cfg.dataset.use_hdf5:
+			k = key( id )
+			if k not in cfg.hdf5 or "audio" not in cfg.hdf5[k] or "text" not in cfg.hdf5[k]:
+				return False
 
-	return [ key(dir, id) for id in metadata.keys() if not validate or _validate(metadata[id]) ]
+		return cfg.dataset.min_duration <= duration and duration <= cfg.dataset.max_duration and cfg.dataset.min_phones <= phones and phones <= cfg.dataset.max_phones
+
+	return [ key(id) for id in metadata.keys() if not validate or _validate(id) ]
 
 
 def _get_hdf5_path(path):
@@ -377,7 +386,7 @@ class Dataset(_Dataset):
 				key = _get_hdf5_path(path)
 
 				if "audio" not in cfg.hdf5[key]:
-					_logger.warning("MISSING AUDIO:", key)
+					_logger.warning(f'MISSING AUDIO: {key}')
 					continue
 
 				qnt = torch.from_numpy(cfg.hdf5[key]["audio"][:, :]).to(torch.int16)