diff --git a/scripts/application_packager/convert_to_onnx.py b/scripts/application_packager/convert_to_onnx.py
index a2401b7..dd050c4 100644
--- a/scripts/application_packager/convert_to_onnx.py
+++ b/scripts/application_packager/convert_to_onnx.py
@@ -21,7 +21,7 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
 
     in_channels = 2
     batch_size = 1
-    slice_length = int(1024 / dataset_cfg.num_slices)
+    slice_length = int(dataset_cfg.recording_length / dataset_cfg.num_slices)
     num_classes = len(dataset_cfg.modulation_types)
 
     model = RFClassifier(
@@ -42,7 +42,7 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
     model.eval()
 
     # Generate random sample data
-    base, ext = os.path.splitext(os.path.basename(ckpt_path))
+    base, _ = os.path.splitext(os.path.basename(ckpt_path))
     if fp16:
         output_path = os.path.join("onnx_files", f"{base}.onnx")
         sample_input = torch.from_numpy(
diff --git a/scripts/dataset_manager/split_dataset.py b/scripts/dataset_manager/split_dataset.py
index 0f820b1..f2f26f2 100644
--- a/scripts/dataset_manager/split_dataset.py
+++ b/scripts/dataset_manager/split_dataset.py
@@ -90,7 +90,7 @@ def split_recording(
     snippet_list = []
 
     for data, md in recording_list:
-        C, N = data.shape
+        _, N = data.shape
         L = N // num_snippets
         for i in range(num_snippets):
             start = i * L