Standardize expected input shapes for audio models
### Feature request

Hi, it seems some audio models expect `(batch_size, feature_size, n_frames)` (e.g. whisper) while others expect `(batch_size, sequence_length)` (e.g. wavlm, wav2vec2). Could this be standardized? It seems like the naming follows `input_features` vs `input_values` (but wav2vec2 feature extractor returns input_values!)

It seems to be the difference between models taking raw audio as an input vs MEL-spectrogram / STFT.

### Motivation

I ask because I am wondering if for models handling stereo I should pass inputs as `(batch_size, 2, n_frames)`. And if so, why not be able to pass `(batch_size, 1, n_frames)` for mono? Is not raw audio a feature as well?

Related https://github.com/huggingface/transformers/issues/16564 

Preliminar internal discussion at https://huggingface.slack.com/archives/C02G13FEMDH/p1669637602417809

### Your contribution

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It's not possible to standardize this across all models, since some models naturally work on raw audio while others use various types of spectrograms.

However, I feel that we should make the FeatureExtractors more flexible in the type of inputs they accept. 

For example, right now, the Wav2Vec2FeatureExtractor expects the input to be mono and 16 kHz. It would be nicer if you could pass in a tensor of shape `(batch_size, num_channels, num_samples)` and an arbitrary sampling rate. 

If the model requires mono input, the FeatureExtractor can automatically downmix stereo to mono. If the sampling rate does not match what the model expects, the FeatureExtractor can automatically resample the data. It's more convenient to do this in the FeatureExtractor than having the user do this themselves.

This is analogous to what the ImageProcessors do in vision models: if the image is not the expected size, it will be resized (= resampled). If the number of color channels is wrong, the ImageProcessor fixes this. 

These operations are optional, so if the user already has a pipeline where they put the data in the correct format, they can choose to skip the resampling / downmixing stages.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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