Make audio classification pipeline spec-compliant and add test (#33730)

* Make audio classification pipeline spec-compliant and add test

* Check that test actually running in CI

* Try a different pipeline for the CI

* Move the test so it gets triggered

* Move it again, this time into task_tests!

* make fixup

* indentation fix

* comment

* Move everything from testing_utils to test_pipeline_mixin

* Add output testing too

* revert small diff with main

* make fixup

* Clarify comment

* Update tests/pipelines/test_pipelines_audio_classification.py

Co-authored-by: Lucain <lucainp@gmail.com>

* Update tests/test_pipeline_mixin.py

Co-authored-by: Lucain <lucainp@gmail.com>

* Rename function and js_args -> hub_args

* Cleanup the spec recursion

* Check keys for all outputs

---------

Co-authored-by: Lucain <lucainp@gmail.com>

tests/pipelines/test_pipelines_audio_classification.py

@@ -13,8 +13,10 @@
 # limitations under the License.
 
 import unittest
+from dataclasses import fields
 
 import numpy as np
+from huggingface_hub import AudioClassificationOutputElement
 
 from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
 from transformers.pipelines import AudioClassificationPipeline, pipeline
@@ -66,6 +68,11 @@ class AudioClassificationPipelineTests(unittest.TestCase):
 
         self.run_torchaudio(audio_classifier)
 
+        spec_output_keys = {field.name for field in fields(AudioClassificationOutputElement)}
+        for single_output in output:
+            output_keys = set(single_output.keys())
+            self.assertEqual(spec_output_keys, output_keys, msg="Pipeline output keys do not match HF Hub spec!")
+
     @require_torchaudio
     def run_torchaudio(self, audio_classifier):
         import datasets