Support cloud storage in load_dataset
Or for example an archive on GitHub releases!  Before I added support for JXL (locally only, PR still pending) I was considering hosting my files on GitHub instead... +1 to this. I would like to use 'audiofolder' with a data_dir that's on S3, for example. I don't want to upload my dataset to the Hub, but I would find all the fingerprinting/caching features useful. Adding to the conversation, Dask also uses `fsspec` for this feature.

[Dask: How to connect to remote data](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html)

Happy to help on this feature :D  +1 to this feature request since I think it also tackles my use-case. I am collaborating with a team, working with a loading script which takes some time to generate the dataset artifacts. It would be very handy to use this as a cloud cache to avoid duplicating the effort. 

Currently we could use `builder.download_and_prepare(path_to_cloud_storage, storage_options, ...)`  to cache the artifacts to cloud storage, but then `builder.as_dataset()` yields `NotImplementedError: Loading a dataset cached in SomeCloudFileSystem is not supported` Makes sense ! If you want to load locally a dataset that you download_and_prepared on a cloud storage, you would use `load_dataset(path_to_cloud_storage)` indeed. It would download the data from the cloud storage, cache them locally, and return a `Dataset`. It seems currently the `cached_path` function handles all URLs by `get_from_cache` that only supports `ftp` and `http(s)` here:
https://github.com/huggingface/datasets/blob/b5672a956d5de864e6f5550e493527d962d6ae55/src/datasets/utils/file_utils.py#L181

I guess one can add another condition that handles `s3://` or `gs://` URLs via `fsspec` here. I could use this functionality, so I put together a PR using @kyamagu's suggestion to use `fsspec` in `datasets.utils.file_utils`

https://github.com/huggingface/datasets/pull/5580
Would be nice to be able to do
```python
data_files=["s3://..."]
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
or even
```python
load_dataset("gs://...")
```

The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.

This has been requested several times already. Some users want to use their data from private cloud storage to train models

related:

https://github.com/huggingface/datasets/issues/3490
https://github.com/huggingface/datasets/issues/5244
[forum](https://discuss.huggingface.co/t/how-to-use-s3-path-with-load-dataset-with-streaming-true/25739/2)