Support for various audio-loading backends instead of always relying on SoundFile
### Feature request

Introduce an option to select from a variety of audio-loading backends rather than solely relying on the SoundFile library. For instance, if the ffmpeg library is installed, it can serve as a fallback loading option.

### Motivation

- The SoundFile library, used in [features/audio.py](https://github.com/huggingface/datasets/blob/649d5a3315f9e7666713b6affe318ee00c7163a0/src/datasets/features/audio.py#L185), supports only a [limited number of audio formats](https://pysoundfile.readthedocs.io/en/latest/index.html?highlight=supported#soundfile.available_formats). 
- However, current methods for creating audio datasets permit the inclusion of audio files in formats not supported by SoundFile. 
- As a result, developers may potentially create a dataset they cannot read back.

In my most recent project, I dealt with phone call recordings in `.amr` or `.gsm` formats and was genuinely surprised when I couldn't read the dataset I had just packaged a minute prior. Nonetheless, I can still accurately read these files using the librosa library, which employs the audioread library that internally leverages ffmpeg to read such files.

Example:
```python
audio_dataset_amr = Dataset.from_dict({"audio": ["audio_samples/audio.amr"]}).cast_column("audio", Audio())
audio_dataset_amr.save_to_disk("audio_dataset_amr")
audio_dataset_amr = Dataset.load_from_disk("audio_dataset_amr")
print(audio_dataset_amr[0])
```
Results in:
```
Traceback (most recent call last):
...
    raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7f316323e4d0>: Format not recognised.
```

While I acknowledge that support for these rare file types may not be a priority, I believe it's quite unfortunate that it's possible to create an unreadable dataset in this manner.

### Your contribution

I've created a [simple demo repository](https://github.com/BoringDonut/hf-datasets-ffmpeg-audio) that highlights the mentioned issue. It demonstrates how to create an .amr dataset that results in an error when attempting to read it just a few lines later.

Additionally, I've made a [fork with a rudimentary solution](https://github.com/BoringDonut/datasets/blob/fea73a8fbbc8876467c7e6422c9360546c6372d8/src/datasets/features/audio.py#L189) that utilizes ffmpeg to load files not supported by SoundFile.


Here you may see github actions fails to read `.amr` dataset using the version of the current dataset, but will work with the patched version:
- https://github.com/BoringDonut/hf-datasets-ffmpeg-audio/actions/runs/4773780420/jobs/8487063785
- https://github.com/BoringDonut/hf-datasets-ffmpeg-audio/actions/runs/4773780420/jobs/8487063829

As evident from the GitHub action above, this solution resolves the previously mentioned problem.

I'd be happy to create a proper pull request, provide runtime benchmarks and tests if you could offer some guidance on the following:
- Where should I incorporate the ffmpeg (or other backends) code? For example, should I create a new file or simply add a function within the Audio class?
- Is it feasible to pass the audio-loading function as an argument within the current architecture? This would be useful if I know in advance that I'll be reading files not supported by SoundFile.

A few more notes:
- In theory, it's possible to load audio using librosa/audioread since librosa is already expected to be installed. However, librosa [will soon discontinue audioread support](https://github.com/librosa/librosa/blob/aacb4c134002903ae56bbd4b4a330519a5abacc0/librosa/core/audio.py#L227). Moreover, using audioread on its own seems inconvenient because it requires a file [path as input](https://github.com/beetbox/audioread/blob/ff9535df934c48038af7be9617fdebb12078cc07/audioread/__init__.py#L108) and cannot work with bytes already loaded into memory or an open file descriptor (as mentioned in [librosa docs](https://librosa.org/doc/main/generated/librosa.load.html#librosa.load), only SoundFile backend supports an open file descriptor as an input).
Hi! 

You can use `set_transform`/`with_transform` to define a custom decoding for audio formats not supported by `soundfile`:
```python
audio_dataset_amr = Dataset.from_dict({"audio": ["audio_samples/audio.amr"]})

def decode_audio(batch):
    batch["audio"] = [read_ffmpeg(audio_path) for audio_path in batch["audio"]]
    return batch

audio_dataset_amr.set_transform(decode_amr) 
```

Supporting multiple backends is more work to maintain, but we could consider this if we get more requests such as this one. Could it be put somewhere as an example tip or something? Considering the number of times a custom decoding transform has been suggested as a solution, an example in the [docs](https://huggingface.co/docs/datasets/process#format-transform) would be nice.

cc @stevhliu 