raise error if training set is < 100

setup.py

@@ -19,7 +19,7 @@ args, unknown_args = parser.parse_known_args()
 # Remove our arguments from argv so that setuptools doesn't see them
 sys.argv = [sys.argv[0]] + unknown_args
 
-version = '0.0.2'
+version = '0.0.3'
 
 # Adapted from https://github.com/pytorch/pytorch
 cwd = os.path.dirname(os.path.abspath(__file__))

utils/generic_utils.py

@@ -75,6 +75,7 @@ def split_dataset(items):
     is_multi_speaker = len(set(speakers)) > 1
     eval_split_size = 500 if len(items) * 0.01 > 500 else int(
         len(items) * 0.01)
+    assert len(eval_split_size) > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
     np.random.seed(0)
     np.random.shuffle(items)
     if is_multi_speaker: