Move audio top_k tests to the right file and add slow decorator

tests/pipelines/test_pipelines_audio_classification.py

@@ -188,3 +188,60 @@ def test_large_model_pt(self):
     @unittest.skip(reason="Audio classification is not implemented for TF")
     def test_small_model_tf(self):
         pass
+
+    @require_torch
+    @slow
+    def test_top_k_none_returns_all_labels(self):
+        model_name = "superb/wav2vec2-base-superb-ks"  # model with more than 5 labels
+        classification_pipeline = pipeline(
+            "audio-classification",
+            model=model_name,
+            top_k=None,
+        )
+
+        # Create dummy input
+        sampling_rate = 16000
+        signal = np.zeros((sampling_rate,), dtype=np.float32)
+
+        result = classification_pipeline(signal)
+        num_labels = classification_pipeline.model.config.num_labels
+
+        self.assertEqual(len(result), num_labels, "Should return all labels when top_k is None")
+
+    @require_torch
+    @slow
+    def test_top_k_none_with_few_labels(self):
+        model_name = "superb/hubert-base-superb-er"  # model with fewer labels
+        classification_pipeline = pipeline(
+            "audio-classification",
+            model=model_name,
+            top_k=None,
+        )
+
+        # Create dummy input
+        sampling_rate = 16000
+        signal = np.zeros((sampling_rate,), dtype=np.float32)
+
+        result = classification_pipeline(signal)
+        num_labels = classification_pipeline.model.config.num_labels
+
+        self.assertEqual(len(result), num_labels, "Should handle models with fewer labels correctly")
+
+    @require_torch
+    @slow
+    def test_top_k_greater_than_labels(self):
+        model_name = "superb/hubert-base-superb-er"
+        classification_pipeline = pipeline(
+            "audio-classification",
+            model=model_name,
+            top_k=100,  # intentionally large number
+        )
+
+        # Create dummy input
+        sampling_rate = 16000
+        signal = np.zeros((sampling_rate,), dtype=np.float32)
+
+        result = classification_pipeline(signal)
+        num_labels = classification_pipeline.model.config.num_labels
+
+        self.assertEqual(len(result), num_labels, "Should cap top_k to number of labels")