audio_utils improvements
# What does this PR do?

Recently the `audio_utils.py` file was added to Transformers to provide shared functions for audio processing such as STFT. This PR aims to clean up the code and make the API more robust.


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- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer: @stas00, Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

_The documentation is not available anymore as the PR was closed or merged._<|||||>I cleaned up `hertz_to_mel` and `mel_to_hertz` a bit:

- more consistent doc comments
- both support single float inputs as well as numpy arrays
- simplified the formulas so it's not literally the same as the librosa code but also doesn't do pointless calculations

Since I think this implementation was based on librosa, we should also give them credit.<|||||>I rewrote `power_to_db` and added `amplitude_to_db`. They still work like the librosa versions but with argument names that make more sense to me.<|||||>Changed `get_mel_filter_banks` into `mel_filter_bank`. Mostly renamed arguments and variables and cleaned up the doc comments, so that the naming is more in line with the rest of Transformers, e.g. `num_frequency_bins` instead of `nb_frequency_bins`.
<|||||>Pushed significant changes to the `stft` code.

- Removed `fram_wave`; this is really an implementation detail that should happen inside the STFT.

- The new `stft` gives the same results as librosa and torchaudio for the same options. It's 25% faster than the previous implementation, mostly due to using `rfft` instead of `fft` (since the input is always real-only, not complex).

- librosa is still faster since they use a bunch of tricks under the hood to avoid memory copies etc; we can slowly work towards matching this speed (not super important to do this immediately since the new `stft` is already faster than what we had before)

- No batching yet.

I will be replacing the other hand-rolled STFTs with this soon (also in this PR). 

None of the changes I made are set in stone — feel free to discuss things like the argument names, the shapes of the returned tensors, and so on.
<|||||>Replaced the hand-rolled STFT in the different models with the one from `audio_utils`:

- CLAP
- M-CTC-T
- SpeechT5
- TVLT
- Whisper

Did not do `audio_spectrogram_transformer` and `speech_to_text`. These use `ta_kaldi.fbank`, which is simple enough and faster than `audio_utils`. If we want to get completely rid of torchaudio we could also replace these.

<|||||>@sanchit-gandhi @ArthurZucker I think this is ready for review now. Feel free to look at this with a critical eye! 

The STFT code is currently written for ease of understanding and flexibility, not speed, although it does outperform the previous methods we were using.
<|||||>@sanchit-gandhi @ArthurZucker Are you OK with the PR in its current state? Then I can ask a core maintainer for a final review.<|||||>Took a second look through and the changes LGTM @hollance!<|||||>If everyone's happy with it, feel free to merge (I don't have rights).
