Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding
### Describe the bug

I am working with a dataset I am trying to publish. 

The path is Antreas/TALI. 

It's a fairly large dataset, and contains images, video, audio and text. 

I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process. 

With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths. 

Find the script I am using below:

```python

import multiprocessing as mp
import pathlib
from typing import Optional

import datasets
from rich import print
from tqdm import tqdm


def download_dataset_via_hub(
    dataset_name: str,
    dataset_download_path: pathlib.Path,
    num_download_workers: int = mp.cpu_count(),
):
    import huggingface_hub as hf_hub

    download_folder = hf_hub.snapshot_download(
        repo_id=dataset_name,
        repo_type="dataset",
        cache_dir=dataset_download_path,
        resume_download=True,
        max_workers=num_download_workers,
        ignore_patterns=[],
    )

    return pathlib.Path(download_folder) / "data"


def load_dataset_via_hub(
    dataset_download_path: pathlib.Path,
    num_download_workers: int = mp.cpu_count(),
    dataset_name: Optional[str] = None,
):
    from dataclasses import dataclass, field

    from datasets import ClassLabel, Features, Image, Sequence, Value

    dataset_path = download_dataset_via_hub(
        dataset_download_path=dataset_download_path,
        num_download_workers=num_download_workers,
        dataset_name=dataset_name,
    )
    # Building a list of file paths for validation set

    train_files = [
        file.as_posix()
        for file in pathlib.Path(dataset_path).glob("*.parquet")
        if "train" in file.as_posix()
    ]
    val_files = [
        file.as_posix()
        for file in pathlib.Path(dataset_path).glob("*.parquet")
        if "val" in file.as_posix()
    ]
    test_files = [
        file.as_posix()
        for file in pathlib.Path(dataset_path).glob("*.parquet")
        if "test" in file.as_posix()
    ]
    print(
        f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set"
    )
    data_files = {
        "test": test_files,
        "val": val_files,
        "train": train_files,
    }

    features = Features(
        {
            "image": Image(
                decode=True
            ),  # Set `decode=True` if you want to decode the images, otherwise `decode=False`
            "image_url": Value("string"),
            "item_idx": Value("int64"),
            "wit_features": Sequence(
                {
                    "attribution_passes_lang_id": Value("bool"),
                    "caption_alt_text_description": Value("string"),
                    "caption_reference_description": Value("string"),
                    "caption_title_and_reference_description": Value("string"),
                    "context_page_description": Value("string"),
                    "context_section_description": Value("string"),
                    "hierarchical_section_title": Value("string"),
                    "is_main_image": Value("bool"),
                    "language": Value("string"),
                    "page_changed_recently": Value("bool"),
                    "page_title": Value("string"),
                    "page_url": Value("string"),
                    "section_title": Value("string"),
                }
            ),
            "wit_idx": Value("int64"),
            "youtube_title_text": Value("string"),
            "youtube_description_text": Value("string"),
            "youtube_video_content": Value("binary"),
            "youtube_video_starting_time": Value("string"),
            "youtube_subtitle_text": Value("string"),
            "youtube_video_size": Value("int64"),
            "youtube_video_file_path": Value("string"),
        }
    )

    dataset = datasets.load_dataset(
        "parquet" if dataset_name is None else dataset_name,
        data_files=data_files,
        features=features,
        num_proc=1,
        cache_dir=dataset_download_path / "cache",
    )
    return dataset


if __name__ == "__main__":
    dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/")
    dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[
        "test"
    ]

    for sample in tqdm(dataset):
        print(list(sample.keys()))
```

Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start! 

### Steps to reproduce the bug

1. Run the code I provided to get a sense of how fast snapshot + manual is
2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP. 
3. You should now have an appreciation of how long these things take.



### Expected behavior

The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower. 

### Environment info

- `datasets` version: 2.14.5
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.13
- Huggingface_hub version: 0.17.3
- PyArrow version: 13.0.0
- Pandas version: 2.1.1