Pushing a large dataset on the hub consistently hangs
### Describe the bug

Once I have locally built a large dataset that I want to push to hub, I use the recommended approach of .push_to_hub to get the dataset on the hub, and after pushing a few shards, it consistently hangs. This has happened over 40 times over the past week, and despite my best efforts to try and catch this happening and kill a process and restart, it seems to be extremely time wasting -- so I came to you to report this and to seek help. 

I already tried installing hf_transfer, but it doesn't support Byte file uploads so I uninstalled it.

### Reproduction

```python
import multiprocessing as mp
import pathlib
from math import ceil

import datasets
import numpy as np
from tqdm.auto import tqdm

from tali.data.data import select_subtitles_between_timestamps
from tali.utils import load_json

tali_dataset_dir = "/data/"

if __name__ == "__main__":
    full_dataset = datasets.load_dataset(
        "Antreas/TALI", num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir
    )

    def data_generator(set_name, percentage: float = 1.0):
        dataset = full_dataset[set_name]

        for item in tqdm(dataset):
            video_list = item["youtube_content_video"]
            video_list = np.random.choice(
                video_list, int(ceil(len(video_list) * percentage))
            )
            if len(video_list) == 0:
                continue
            captions = item["youtube_subtitle_text"]
            captions = select_subtitles_between_timestamps(
                subtitle_dict=load_json(
                    captions.replace(
                        "/data/",
                        tali_dataset_dir,
                    )
                ),
                starting_timestamp=0,
                ending_timestamp=100000000,
            )

            for video_path in video_list:
                temp_path = video_path.replace("/data/", tali_dataset_dir)
                video_path_actual: pathlib.Path = pathlib.Path(temp_path)

                if video_path_actual.exists():
                    item["youtube_content_video"] = open(video_path_actual, "rb").read()
                    item["youtube_subtitle_text"] = captions
                    yield item

    train_generator = lambda: data_generator("train", percentage=0.1)
    val_generator = lambda: data_generator("val")
    test_generator = lambda: data_generator("test")

    train_data = datasets.Dataset.from_generator(
        train_generator,
        num_proc=mp.cpu_count(),
        writer_batch_size=5000,
        cache_dir=tali_dataset_dir,
    )

    val_data = datasets.Dataset.from_generator(
        val_generator,
        writer_batch_size=5000,
        num_proc=mp.cpu_count(),
        cache_dir=tali_dataset_dir,
    )

    test_data = datasets.Dataset.from_generator(
        test_generator,
        writer_batch_size=5000,
        num_proc=mp.cpu_count(),
        cache_dir=tali_dataset_dir,
    )

    dataset = datasets.DatasetDict(
        {
            "train": train_data,
            "val": val_data,
            "test": test_data,
        }
    )
    succesful_competion = False
    while not succesful_competion:
        try:
            dataset.push_to_hub(repo_id="Antreas/TALI-small", max_shard_size="5GB")
            succesful_competion = True
        except Exception as e:
            print(e)
```

### Logs

```shell
Pushing dataset shards to the dataset hub:  33%|██████████████████████████████████████▎                                                                            | 7/21 [24:33<49:06, 210.45s/it]
Error while uploading 'data/val-00007-of-00021-6b216a984af1a4c8.parquet' to the Hub.                                                                                                               
Pushing split train to the Hub.                                                                                                                                                                    
Resuming upload of the dataset shards.                                                                                                                                                             
Pushing dataset shards to the dataset hub: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 46/46 [42:10<00:00, 55.01s/it]
Pushing split val to the Hub.                                                                                                                                                                      
Resuming upload of the dataset shards.                                                                                                                                                             
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  1.55ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.51s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.39ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:30<00:00, 30.19s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.28ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:24<00:00, 24.08s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.42ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.97s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.49ba/s]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.54ba/s^
Upload 1 LFS files:   0%|                                                                                                                                                    | 0/1 [04:42<?, ?it/s]
Pushing dataset shards to the dataset hub:  52%|████████████████████████████████████████████████████████████▏                                                      | 11/21 [17:23<15:48, 94.82s/it]

That's where it got stuck
```


### System info

```shell
- huggingface_hub version: 0.15.1
- Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.35
- Python version: 3.10.11
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /root/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: Antreas
- Configured git credential helpers: store
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.1.0.dev20230606+cu121
- Jinja2: 3.1.2
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.5.0
- hf_transfer: N/A
- gradio: N/A
- numpy: 1.24.3
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets
- HF_TOKEN_PATH: /root/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```
