[TESTS] ASR pipeline (#33925)

* fix whisper translation

* correct slow_unfinished_sequence test

* make fixup

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -1212,7 +1212,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
         speech_recognizer_2 = AutomaticSpeechRecognitionPipeline(
             model=model, tokenizer=tokenizer, feature_extractor=feature_extractor
         )
-        output_2 = speech_recognizer_2(ds[0]["audio"])
+        output_2 = speech_recognizer_2(ds[40]["audio"])
         self.assertEqual(output, output_2)
 
         # either use generate_kwargs or set the model's generation_config
@@ -1224,7 +1224,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
             feature_extractor=feature_extractor,
             generate_kwargs={"task": "transcribe", "language": "<|it|>"},
         )
-        output_3 = speech_translator(ds[0]["audio"])
+        output_3 = speech_translator(ds[40]["audio"])
         self.assertEqual(output_3, {"text": " Un uomo ha detto all'universo, Sir, esiste."})
 
     @slow
@@ -1896,15 +1896,15 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
             model="vasista22/whisper-hindi-large-v2",
             device=torch_device,
         )
-        # Original model wasn't trained with timestamps and has incorrect generation config
-        pipe.model.generation_config = GenerationConfig.from_pretrained("openai/whisper-large-v2")
 
         # the audio is 4 seconds long
         audio = hf_hub_download("Narsil/asr_dummy", filename="hindi.ogg", repo_type="dataset")
 
+        # Original model wasn't trained with timestamps and has incorrect generation config
         out = pipe(
             audio,
             return_timestamps=True,
+            generate_kwargs={"generation_config": GenerationConfig.from_pretrained("openai/whisper-large-v2")},
         )
         self.assertEqual(
             out,