diff --git a/utils/batch_generator.py b/utils/batch_generator.py
index 008e5c6..9e8d419 100644
--- a/utils/batch_generator.py
+++ b/utils/batch_generator.py
@@ -104,16 +104,6 @@ class SequenceGenerator:
         self.batch_size = batch_size
         self.shuffle = shuffle
 
-        self.index_list = []
-        for sequence_index in range(len(data)):
-            start_indices = np.expand_dims(
-                np.arange(len(data[sequence_index]) - sequence_size, dtype=np.uint8),
-                axis=-1)
-            start_indices = np.insert(start_indices, 0, sequence_index, axis=1)
-            self.index_list.append(start_indices)
-        self.index_list = np.concatenate(self.index_list, axis=0)
-        self.step_per_epoch = math.ceil(len(self.index_list) / batch_size)
-
         if not preload:
             self.data_processor = data_processor
             self.label_processor = label_processor
@@ -122,25 +112,52 @@ class SequenceGenerator:
         else:
             self.data_processor = None
             self.label_processor = None
+            save_path = save
+            if save is not None:
+                if '.' not in os.path.basename(save_path):
+                    save_path += '.hdf5'
+                if not os.path.exists(os.path.dirname(save_path)):
+                    os.makedirs(os.path.dirname(save_path))
 
-            if save and os.path.exists(save + '_data.npy'):
-                self.data = np.load(save + '_data.npy', allow_pickle=True)
-                self.label = np.load(save + '_label.npy', allow_pickle=True)
+            if save and os.path.exists(save_path):
+                with h5py.File(save_path, 'r') as h5_file:
+                    data_len = np.asarray(h5_file['data_len'])
+                    self.data = []
+                    self.label = []
+                    for sequence_index in range(data_len):
+                        self.data.append(np.asarray(h5_file[f'data_{sequence_index}']))
+                        self.label.append(np.asarray(h5_file[f'label_{sequence_index}']))
+                    self.data = np.asarray(self.data)
+                    self.label = np.asarray(self.label)
             else:
                 if data_processor:
                     self.data = np.asarray(
-                        [np.asarray([data_processor(entry) for entry in serie]) for serie in data])
+                        [np.asarray([data_processor(entry) for entry in serie]) for serie in data],
+                        dtype=np.object if len(data) > 1 else None)
                 else:
                     self.data = data
                 if label_processor:
                     self.label = np.asarray(
-                        [np.asarray([label_processor(entry) for entry in serie]) for serie in label])
+                        [np.asarray([label_processor(entry) for entry in serie]) for serie in label],
+                        dtype=np.object if len(label) > 1 else None)
                 else:
                     self.label = label
+                if save:
+                    with h5py.File(save_path, 'w') as h5_file:
+                        h5_file.create_dataset(f'data_len', data=len(self.data))
+                        for sequence_index in range(len(self.data)):
+                            h5_file.create_dataset(f'data_{sequence_index}', data=self.data[sequence_index])
+                            h5_file.create_dataset(f'label_{sequence_index}', data=self.label[sequence_index])
 
-            if save:
-                np.save(save + '_data.npy', self.data, allow_pickle=True)
-                np.save(save + '_label.npy', self.label, allow_pickle=True)
+        self.index_list = []
+        for sequence_index in range(len(self.data)):
+            start_indices = np.expand_dims(
+                np.arange(len(self.data[sequence_index]) - sequence_size, dtype=np.uint32),
+                axis=-1)
+            start_indices = np.insert(start_indices, 0, sequence_index, axis=1)
+            self.index_list.append(start_indices)
+        self.index_list = np.concatenate(self.index_list, axis=0)
+        self.step_per_epoch = math.ceil(len(self.index_list) / batch_size)
 
         self.epoch = 0
         self.global_step = -1