PyArrow Dataset error when calling `load_dataset`
## Describe the bug

I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error:

```sh
File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main
  raw_datasets = load_dataset(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset
  builder_instance.download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
  self._download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
  self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split
  for key, table in logging.tqdm(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__
  for obj in iterable:
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
  for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1309, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```

## Steps to reproduce the bug

I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`.

When creating the dataset, I do something like this:

```python
import json
from datasets import Dataset, Audio

# manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text
for line in manifest_lines:
    line = line.strip()
    if line:
        line_dict = json.loads(line)
        manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}")
        manifest_dict["duration"].append(line_dict["duration"])
        manifest_dict["transcription"].append(line_dict["text"])

# Create a HF dataset
dataset = Dataset.from_dict(manifest_dict).cast_column(
    "audio", Audio(sampling_rate=16_000),
)

# From the docs for saving to disk
# https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk
def read_audio_file(example):
    with open(example["audio"]["path"], "rb") as f:
        return {"audio": {"bytes": f.read()}}

dataset = dataset.map(read_audio_file, num_proc=70)
dataset.save_to_disk(f"/audio-data/hf/{artifact_name}")
dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True)
```

Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace.
I am able to load the dataset fine if I use `load_from_disk()`.

## Expected results

`load_dataset()` should behave just like `load_from_disk()` and not cause any errors.

## Actual results

See above

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I am using the `huggingface/transformers-pytorch-gpu:latest` image
- `datasets` version: 2.3.0
- Platform: Docker/Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 8.0.0

Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.

To achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.

Do you have a minimum reproducible example that we can share with the Arrow team for further debugging ? > If you manage to end up with only one chunk per parquet file it should workaround this issue.

Yup, I did not encounter this bug when I was testing my script with a slice of <1000 samples for my dataset.

> Do you have a minimum reproducible example...

Not sure if I can get more minimal than the script I shared above. Are you asking for a sample json file?
Just generate a random manifest list, I can add that to the above script if that's what you mean?
 Actually this is probably linked to this open issue: https://issues.apache.org/jira/browse/ARROW-5030.

setting `max_shard_size="2GB"` should do the job (or `max_shard_size="1GB"` if you want to be on the safe side, especially given that there can be some variance in the shard sizes if the dataset is not evenly distributed)