lol

vall_e/config.py

@@ -884,18 +884,29 @@ class Config(BaseConfig):
 		if not isinstance( path, Path ):
 			path = Path(path)
 
-		# do not glob
+		# do not glob if no wildcard to glob
 		if "*" not in str(path):
 			return [ path ]
-		
-		metadata_parent = cfg.metadata_dir / path.parent
-		data_parent = cfg.data_dir / path.parent
-		
-		if metadata_parent.exists():
-			return [ path.parent / child.stem for child in Path(metadata_parent).glob(path.name) ]
 
+		dir = path.parent
+		name = path.name
+		
+		metadata_parent = cfg.metadata_dir / dir
+		data_parent = cfg.data_dir / dir
+		
+		res = []
+		# grab any paths from metadata folder (since this is for HDF5)
+		if metadata_parent.exists():
+			res = [ path.parent / child.stem for child in Path(metadata_parent).glob(name) ]
+			# return if found anything
+			if res:
+				return res
+		# grab anything from the data folder (if no metadata exists)
 		if data_parent.exists():
-			return [ path.parent / child.name for child in Path(data_parent).glob(path.name) ]
+			res = [ path.parent / child.name for child in Path(data_parent).glob(name) ]
+			# return if found anything
+			if res:
+				return res
 		
 		# return an empty list
 		if self.silent_errors:

vall_e/data.py

@@ -633,8 +633,6 @@ def _load_paths_from_metadata(group_name, type="training", validate=False):
 		phones = entry['phones'] if "phones" in entry else 0
 		duration = entry['duration'] if "duration" in entry else 0
 
-		#print( id, duration )
-
 		# add to duration bucket
 		k = key(id, entry)
 		if type not in _durations_map:
@@ -1079,7 +1077,7 @@ class Dataset(_Dataset):
 	def sample_prompts(self, spkr_name, reference, should_trim=True):
 		# return no prompt if explicitly requested for who knows why
 		# or if there's no other speakers to sample from (Emilia has a lot of singleton speakers, but I still want to make use of them)
-		if not cfg.dataset.prompt_duration_range or cfg.dataset.prompt_duration_range[-1] == 0 or len(self.paths_by_spkr_name[key]) <= 1:
+		if not cfg.dataset.prompt_duration_range or cfg.dataset.prompt_duration_range[-1] == 0 or len(self.paths_by_spkr_name[spkr_name]) <= 1:
 			return None
 
 		prom_list = []
@@ -1686,7 +1684,11 @@ def create_dataset_hdf5( skip_existing=True ):
 		metadata_path = Path(f"{metadata_root}/{speaker_name}.json")
 		metadata_path.parents[0].mkdir(parents=True, exist_ok=True)
 
-		metadata = json_read(metadata_path, default={})
+		try:
+			metadata = json_read(metadata_path, default={})
+		except Exception as e:
+			print(metadata_path, e)
+			return
 
 		if not os.path.isdir(f'{root}/{name}/'):
 			return

vall_e/demo.py

@@ -302,8 +302,8 @@ def main():
 		num = args.dataset_samples if args.dataset_samples else length
 
 		for i in trange( num, desc="Sampling dataset for samples" ):
-			index = i if not cfg.dataset.sample_shuffle else random.randint( 0, len( dataloader.dataset ) )
+			index = i if not cfg.dataset.sample_shuffle else random.randint( 0, len( dataloader.dataset ) - 1 )
-			batch = dataloader.dataset[i]
+			batch = dataloader.dataset[index]
 
 			if args.dataset_dir_name_prefix:
 				dir = args.demo_dir / args.dataset_dir_name / f'{args.dataset_dir_name_prefix}_{i}'

vall_e/models/ar_nar.py

@@ -961,6 +961,11 @@ def example_usage():
 			learning_rate = 1.0e-4
 
 		optimizer = ml.SGD
+	elif optimizer == "apollo":
+		if learning_rate is None:
+			learning_rate = 0.01
+
+		optimizer = ml.Apollo
 	else:
 		raise ValueError(f"Unrecognized optimizer: {optimizer}")