Avoid pipeline test failing related to Hub call (#37170)

* cls

* cls

* cls

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/pipelines/test_pipelines_audio_classification.py

@@ -14,6 +14,7 @@
 
 import unittest
 
+import datasets
 import numpy as np
 from huggingface_hub import AudioClassificationOutputElement
 
@@ -24,6 +25,7 @@ from transformers import (
 )
 from transformers.pipelines import AudioClassificationPipeline, pipeline
 from transformers.testing_utils import (
+    _run_pipeline_tests,
     compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     nested_simplify,
@@ -45,6 +47,9 @@ class AudioClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     tf_model_mapping = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
 
+    if _run_pipeline_tests:
+        _dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+
     def get_test_pipeline(
         self,
         model,
@@ -94,11 +99,8 @@ class AudioClassificationPipelineTests(unittest.TestCase):
 
     @require_torchaudio
     def run_torchaudio(self, audio_classifier):
-        import datasets
-
         # test with a local file
-        dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-        audio = dataset[0]["audio"]["array"]
+        audio = self._dataset[0]["audio"]["array"]
         output = audio_classifier(audio)
         self.assertEqual(
             output,
@@ -168,8 +170,6 @@ class AudioClassificationPipelineTests(unittest.TestCase):
     @require_torch
     @slow
     def test_large_model_pt(self):
-        import datasets
-
         model = "superb/wav2vec2-base-superb-ks"
 
         audio_classifier = pipeline("audio-classification", model=model)