dataset = datasets.load_dataset("superb", name="asr", split="test")
KeyDataset (only pt) will simply return the item in the dict returned by the dataset item
as we're not interested in the target part of the dataset.
for out in tqdm(pipe(KeyDataset(dataset, "file"))):
    print(out)
    # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
    # {"text": ....}
    # ....
```
For ease of use, a generator is also possible:
```python
from transformers import pipeline
pipe = pipeline("text-classification")
def data():
    while True:
        # This could come from a dataset, a database, a queue or HTTP request
        # in a server
        # Caveat: because this is iterative, you cannot use num_workers > 1 variable
        # to use multiple threads to preprocess data. You can still have 1 thread that
