Add some iteration method on a dataset column (specific for inference)
Thanks for the suggestion ! I agree it would be nice to have something directly in `datasets` to do something as simple as that

cc @albertvillanova @mariosasko @polinaeterna What do you think if we have something similar to pandas `Series` that wouldn't bring everything in memory when doing `dataset["audio"]` ? Currently it returns a list with all the decoded audio data in memory.

It would be a breaking change though, since `isinstance(dataset["audio"], list)` wouldn't work anymore, but we could implement a `Sequence` so that `dataset["audio"][0]` still works and only loads one item in memory.

Your alternative suggestion with `iterate` is also sensible, though maybe less satisfactory in terms of experience IMO I agree that current behavior (decoding all audio file sin the dataset when accessing `dataset["audio"]`) is not useful, IMHO. Indeed in our docs, we are constantly warning our collaborators not to do that.

Therefore I upvote for a "useful" behavior of `dataset["audio"]`. I don't think the breaking change is important in this case, as I guess no many people use it with its current behavior. Therefore, for me it seems reasonable to return a generator (instead of an in-memeory list) for "special" features, like Audio/Image.

@lhoestq on the other hand I don't understand your proposal about Pandas-like...  I recall I had the same idea while working on the `Image` feature, so I agree implementing something similar to `pd.Series` that lazily brings elements in memory would be beneficial. @lhoestq @mariosasko Could you please give a link to that new feature of `pandas.Series`? As far as I remember since I worked with pandas for more than 6 years, there was no lazy in-memory feature; it was everything in-memory; that was the reason why other frameworks were created, like Vaex or Dask, e.g.  Yea pandas doesn't do lazy loading. I was referring to pandas.Series to say that they have a dedicated class to represent a column ;)
**Is your feature request related to a problem? Please describe.**
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Currently, `dataset["audio"]` will load EVERY element in the dataset in RAM, which can be quite big for an audio dataset.
Having an iterator (or sequence) type of object, would make inference with `transformers` 's `pipeline` easier to use and not so memory hungry.

**Describe the solution you'd like**
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For a non breaking change:

```python
for audio in dataset.iterate("audio"):
    # {"array": np.array(...), "sampling_rate":...}
```

For a  breaking change solution (not necessary), changing the type of `dataset["audio"]` to a sequence type so that

```python
pipe = pipeline(model="...")
for out in pipe(dataset["audio"]):
    # {"text":....}
```
could work

**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.

```python
def iterate(dataset, key):
    for item in dataset:
        yield dataset[key]

for out in pipeline(iterate(dataset, "audio")):
    # {"array": ...}
```

This works but requires the helper function which feels slightly clunky.

**Additional context**
Add any other context about the feature request here.

The context is actually to showcase better integration between  `pipeline` and `datasets` in the Quicktour demo: https://github.com/huggingface/transformers/pull/16723/files

@lhoestq 
