dataset map function could not be hash properly
State: closed

### Describe the bug

I follow the [blog post](https://huggingface.co/blog/fine-tune-whisper#building-a-demo) to finetune a Cantonese transcribe model.

When using map function to prepare dataset, following warning pop out:

`common_voice = common_voice.map(prepare_dataset, 
                                remove_columns=common_voice.column_names["train"], num_proc=1)`

> Parameter 'function'=<function prepare_dataset at 0x000001D1D9D79A60> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.

I read https://github.com/huggingface/datasets/issues/4521 and https://github.com/huggingface/datasets/issues/3178 but cannot solve the issue.

### Steps to reproduce the bug

```python
from datasets import load_dataset, DatasetDict

common_voice = DatasetDict()
common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK", 
                                     split="train+validation")
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK", 
                                    split="test")

common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"])

from transformers import WhisperFeatureExtractor, WhisperTokenizer, WhisperProcessor

feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="chinese", task="transcribe")
processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="chinese", task="transcribe")

from datasets import Audio
common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))

def prepare_dataset(batch):
    # load and resample audio data from 48 to 16kHz
    audio = batch["audio"]

    # compute log-Mel input features from input audio array 
    batch["input_features"] = feature_extractor(audio["array"], 
                                                sampling_rate=audio["sampling_rate"]).input_features[0]

    # encode target text to label ids 
    batch["labels"] = tokenizer(batch["sentence"]).input_ids
    return batch

common_voice = common_voice.map(prepare_dataset, 
                                remove_columns=common_voice.column_names["train"], num_proc=1)
```

### Expected behavior

Should be no warning shown.

### Environment info

- `datasets` version: 2.7.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
- dill version: 0.3.4
- multiprocess version: 0.70.12.2 

Hi ! On macos I tried with
- py 3.9.11
- datasets 2.8.0
- transformers 4.25.1
- dill 0.3.4

and I was able to hash `prepare_dataset` correctly:
```python
from datasets.fingerprint import Hasher
Hasher.hash(prepare_dataset)
```

What version of transformers do you have ? Can you try to call `Hasher.hash` on the the tokenizer and the feature extractor to see which one can't be hashed ?

Thanks for your prompt reply.

I update datasets version to 2.8.0 and the warning is gong.