diff --git a/research/audio/wavenet/train.py b/research/audio/wavenet/train.py
index 80b13acfee58519cfd0d7b5abac51c02753da835..f982c5cbc4486b7415e0c013b7c950d461a5ac06 100644
--- a/research/audio/wavenet/train.py
+++ b/research/audio/wavenet/train.py
@@ -167,7 +167,8 @@ if __name__ == '__main__':
         optimizer = Adam(weights, learning_rate=lr, loss_scale=1024.)
         train_net = TrainOneStepCell(loss_net, optimizer)
 
-    summary_collector = SummaryCollector(summary_dir='summary_dir/device_{}'.format(device_id), collect_freq=1)
+    if target != 'CPU':
+        summary_collector = SummaryCollector(summary_dir='summary_dir/device_{}'.format(device_id), collect_freq=1)
     model = Model(train_net)
     lr_cb = Monitor(lr)
     callback_list = [lr_cb]
@@ -179,7 +180,8 @@ if __name__ == '__main__':
     config_ck = CheckpointConfig(save_checkpoint_steps=step_size_per_epoch, keep_checkpoint_max=hparams.nepochs)
     ckpt_cb = ModelCheckpoint(prefix='wavenet', directory=ckpt_path, config=config_ck)
     callback_list.append(ckpt_cb)
-    callback_list.append(summary_collector)
+    if target != 'CPU':
+        callback_list.append(summary_collector)
     if target == 'Ascend' and resume_epoch is not None:
         model.train(hparams.nepochs - resume_epoch, data_loaders, callbacks=callback_list, dataset_sink_mode=False)
     else: