Make ASR pipeline compliant with Hub spec + add tests (#33769)

* Remove max_new_tokens arg

* Add ASR pipeline to testing

* make fixup

* Factor the output test out into a util

* Full error reporting

* Full error reporting

* Update src/transformers/pipelines/automatic_speech_recognition.py

Co-authored-by: Lysandre Debut <hi@lysand.re>

* Small comment

---------

Co-authored-by: Lysandre Debut <hi@lysand.re>

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -18,7 +18,7 @@ import unittest
 import numpy as np
 import pytest
 from datasets import Audio, load_dataset
-from huggingface_hub import hf_hub_download, snapshot_download
+from huggingface_hub import AutomaticSpeechRecognitionOutput, hf_hub_download, snapshot_download
 
 from transformers import (
     MODEL_FOR_CTC_MAPPING,
@@ -36,6 +36,7 @@ from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
 from transformers.pipelines.audio_utils import chunk_bytes_iter
 from transformers.pipelines.automatic_speech_recognition import _find_timestamp_sequence, chunk_iter
 from transformers.testing_utils import (
+    compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     is_torch_available,
     nested_simplify,
@@ -86,6 +87,8 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
         outputs = speech_recognizer(audio)
         self.assertEqual(outputs, {"text": ANY(str)})
 
+        compare_pipeline_output_to_hub_spec(outputs, AutomaticSpeechRecognitionOutput)
+
         # Striding
         audio = {"raw": audio, "stride": (0, 4000), "sampling_rate": speech_recognizer.feature_extractor.sampling_rate}
         if speech_recognizer.type == "ctc":