Use conditional `@skipif`

tests/test_bgm_separation.py

@@ -6,9 +6,14 @@ from test_transcription import download_file, test_transcribe
 
 import gradio as gr
 import pytest
+import torch
 import os
 
 
+@pytest.mark.skipif(
+    not is_cuda_available(),
+    reason="Skipping because the test only works on GPU"
+)
 @pytest.mark.parametrize(
     "whisper_type,vad_filter,bgm_separation,diarization",
     [
@@ -26,7 +31,10 @@ def test_bgm_separation_pipeline(
     test_transcribe(whisper_type, vad_filter, bgm_separation, diarization)
 
 
-@pytest.mark.skip(reason="Too heavy to run in actions with all of other tests")
+@pytest.mark.skipif(
+    not is_cuda_available(),
+    reason="Skipping because the test only works on GPU"
+)
 @pytest.mark.parametrize(
     "whisper_type,vad_filter,bgm_separation,diarization",
     [

tests/test_config.py

@@ -1,6 +1,7 @@
 from modules.utils.paths import *
 
 import os
+import torch
 
 TEST_FILE_DOWNLOAD_URL = "https://github.com/jhj0517/whisper_flutter_new/raw/main/example/assets/jfk.wav"
 TEST_FILE_PATH = os.path.join(WEBUI_DIR, "tests", "jfk.wav")
@@ -11,3 +12,6 @@ TEST_NLLB_MODEL = "facebook/nllb-200-distilled-600M"
 TEST_SUBTITLE_SRT_PATH = os.path.join(WEBUI_DIR, "tests", "test_srt.srt")
 TEST_SUBTITLE_VTT_PATH = os.path.join(WEBUI_DIR, "tests", "test_vtt.vtt")
 
+
+def is_cuda_available():
+    return torch.cuda.is_available()