fix for splitting out valid dataset when dataset is directly passed i…

…nto soundstream trainer

audiolm_pytorch/trainer.py

@@ -276,25 +276,24 @@ def __init__(
                 seq_len_multiple_of = soundstream.seq_len_multiple_of
             )
 
-            # split for validation
-
-            if valid_frac > 0:
-                train_size = int((1 - valid_frac) * len(self.ds))
-                valid_size = len(self.ds) - train_size
-                dataset, val_dataset = random_split(self.ds, [train_size, valid_size], generator = torch.Generator().manual_seed(random_split_seed))
-                self.print(f'training with dataset of {len(self.ds)} samples and validating with randomly splitted {len(self.valid_ds)} samples')
-            else:
-                val_dataset = dataset
-                self.print(f'training with shared training and valid dataset of {len(self.ds)} samples')
-
             assert len(dataset) >= batch_size, 'dataset must have sufficient samples for training'
-            assert len(val_dataset) >= batch_size, f'validation dataset must have sufficient number of samples (currently {len(self.valid_ds)}) for training'
 
         if exists(dataset):
             assert not exists(train_dataloader)
             assert not exists(val_dataloader)
 
-            val_dataset = default(val_dataset, dataset)
+            # maybe split for validation
+
+            if valid_frac > 0:
+                train_size = int((1 - valid_frac) * len(dataset))
+                valid_size = len(dataset) - train_size
+                dataset, val_dataset = random_split(dataset, [train_size, valid_size], generator = torch.Generator().manual_seed(random_split_seed))
+                self.print(f'training with dataset of {len(dataset)} samples and validating with randomly splitted {len(val_dataset)} samples')
+            else:
+                val_dataset = dataset
+                self.print(f'training with shared training and valid dataset of {len(dataset)} samples')
+
+            assert len(val_dataset) >= batch_size, f'validation dataset must have sufficient number of samples (currently {len(val_dataset)}) for training'
 
             train_dataloader = get_dataloader(dataset, batch_size = batch_size, num_workers = dl_num_workers, shuffle = True, drop_last = dataloader_drop_last)
             val_dataloader = get_dataloader(val_dataset, batch_size = batch_size, num_workers = dl_num_workers, shuffle = True, drop_last = dataloader_drop_last)

audiolm_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.8.6'
+__version__ = '1.8.7'