Enabling automatic loading of tokenizer with pipeline for (#13376)

`audio-classification`.

tests/test_pipelines_audio_classification.py

@@ -16,7 +16,7 @@ import unittest
 
 import numpy as np
 
-from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, PreTrainedTokenizer
+from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
 from transformers.pipelines import AudioClassificationPipeline, pipeline
 from transformers.testing_utils import (
     is_pipeline_test,
@@ -77,9 +77,7 @@ class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
     def test_small_model_pt(self):
         model = "anton-l/wav2vec2-random-tiny-classifier"
 
-        # hack: dummy tokenizer is required to prevent pipeline from failing
-        tokenizer = PreTrainedTokenizer()
-        audio_classifier = pipeline("audio-classification", model=model, tokenizer=tokenizer)
+        audio_classifier = pipeline("audio-classification", model=model)
 
         audio = np.ones((8000,))
         output = audio_classifier(audio, top_k=4)
@@ -101,9 +99,7 @@ class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
 
         model = "superb/wav2vec2-base-superb-ks"
 
-        # hack: dummy tokenizer is required to prevent pipeline from failing
-        tokenizer = PreTrainedTokenizer()
-        audio_classifier = pipeline("audio-classification", model=model, tokenizer=tokenizer)
+        audio_classifier = pipeline("audio-classification", model=model)
         dataset = datasets.load_dataset("anton-l/superb_dummy", "ks", split="test")
 
         audio = np.array(dataset[3]["speech"], dtype=np.float32)