Add TensorFlow Whisper model for audio classification

### Feature request The PR https://github.com/huggingface/transformers/pull/21754 adds the PyTorch version of `WhisperForAudioClassification`. It would be great to add the TensorFlow equivalent. ### Motivation Whisper is an encoder-decoder model for speech recognition. However, we can repurpose the model for other speech tasks, such as _audio classification_. Audio classification is the task of mapping from an input speech sequence to a single class prediction. For more details, refer to the task page on the Hub: https://huggingface.co/tasks/audio-classification For audio classification, we only require a _single_ model output. Thus, we do not need the auto-regressive generation capacities of the Whisper decoder (which is used to generate a _sequence_ of text tokens during speech recognition). Instead, we can just use the Whisper encoder to get hidden states, and add a classification head on top to make class label predictions. This is analogous to using a Wav2Vec2 model for audio classification: the Wav2Vec2 encoder is used to get hidden states, and a classification head added on top to make class label predictions. The PR https://github.com/huggingface/transformers/pull/21754 adds the PyTorch version of `WhisperForAudioClassification`. It required adding a projection layer and classification layer on top of the `WhisperEncoder`. For more details, refer directly to the pull request. It would be great to add the TensorFlow equivalent of this model for cross-framework support. The most difficult part of this PR will be getting the model tester to work. You can see from the PyTorch PR that we require a standalone tester for the audio classification model. This is because the original Whisper model is an encoder-decoder model, but the audio classification model is an encoder-only model. Thus, we require different testing logic. ### Your contribution Opening this one up to the community! If you're interested in tackling this, free to drop a comment in this thread and op…