diff --git a/nnunetv2/inference/predict_from_raw_data.py b/nnunetv2/inference/predict_from_raw_data.py
index 14056d139..0710c44b4 100644
--- a/nnunetv2/inference/predict_from_raw_data.py
+++ b/nnunetv2/inference/predict_from_raw_data.py
@@ -469,7 +469,7 @@ def predict_logits_from_preprocessed_data(self, data: torch.Tensor) -> torch.Ten
                     self.network._orig_mod.load_state_dict(params)
 
                 # why not leave prediction on device if perform_everything_on_device? Because this may cause the
-                # second iteration to crash due to OOM. Grabbing tha twith try except cause way more bloated code than
+                # second iteration to crash due to OOM. Grabbing that with try except cause way more bloated code than
                 # this actually saves computation time
                 if prediction is None:
                     prediction = self.predict_sliding_window_return_logits(data).to('cpu')
diff --git a/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py b/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py
index 45948daac..97abdde0a 100644
--- a/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py
+++ b/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py
@@ -1241,7 +1241,7 @@ def perform_actual_validation(self, save_probabilities: bool = False):
                                  self.dataset_json),
                             )
                         ))
-                # if we don't barrier from time to time we will get nccl timeouts for large datsets. Yuck.
+                # if we don't barrier from time to time we will get nccl timeouts for large datasets. Yuck.
                 if self.is_ddp and i < last_barrier_at_idx and (i + 1) % 20 == 0:
                     dist.barrier()