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

`audio-classification`.

src/transformers/pipelines/__init__.py

@@ -449,6 +449,13 @@ def pipeline(
     load_tokenizer = type(model_config) in TOKENIZER_MAPPING or model_config.tokenizer_class is not None
     load_feature_extractor = type(model_config) in FEATURE_EXTRACTOR_MAPPING or feature_extractor is not None
 
+    if task in {"audio-classification"}:
+        # Audio classification will never require a tokenizer.
+        # the model on the other hand might have a tokenizer, but
+        # the files could be missing from the hub, instead of failing
+        # on such repos, we just force to not load it.
+        load_tokenizer = False
+
     if load_tokenizer:
         # Try to infer tokenizer from model or config name (if provided as str)
         if tokenizer is None:

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)