Librispeech documentation, clarification on format
 https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/datasets/librispeech_asr/librispeech_asr.py#L53

> Note that in order to limit the required storage for preparing this dataset, the audio
> is stored in the .flac format and is not converted to a float32 array. To convert, the audio
> file to a float32 array, please make use of the `.map()` function as follows:
>
> ```python
> import soundfile as sf
> def map_to_array(batch):
>     speech_array, _ = sf.read(batch["file"])
>     batch["speech"] = speech_array
>     return batch
> dataset = dataset.map(map_to_array, remove_columns=["file"])
> ```

Is this still true?

In my case, `ds["train.100"]` returns:
```
Dataset({
    features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],
    num_rows: 28539
})
```
and taking the first instance yields:
```
{'file': '374-180298-0000.flac',
 'audio': {'path': '374-180298-0000.flac',
  'array': array([ 7.01904297e-04,  7.32421875e-04,  7.32421875e-04, ...,
         -2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
  'sampling_rate': 16000},
 'text': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED',
 'speaker_id': 374,
 'chapter_id': 180298,
 'id': '374-180298-0000'}
```

The `audio` `array` seems to be already decoded. So such convert/decode code as mentioned in the doc is wrong?

But I wonder, is it actually stored as flac on disk, and the decoding is done on-the-fly? Or was it decoded already during the preparation and is stored as raw samples on disk?

Note that I also used `datasets.load_dataset("librispeech_asr", "clean").save_to_disk(...)` and then `datasets.load_from_disk(...)` in this example. Does this change anything on how it is stored on disk?

A small related question: Actually I would prefer to even store it as mp3 or ogg on disk. Is this easy to convert?
 Hey,

Sorry yeah I was just about to look into this! We actually had an outdated version of Librispeech ASR that didn't save any files, but instead converted the audio files to a byte string, then was then decoded on-the-fly. This however is not very user-friendly so we recently decided to instead show the full path of the audio files with the `path` parameter.

I'm currently changing this for Librispeech here: https://github.com/huggingface/datasets/pull/4184 .
You should be able to see the audio file in the original `flac` format under `path` then. I don't think it's a good idea to convert to MP3 out-of-the-box, but we could maybe think about some kind of convert function for audio datasets cc @lhoestq ?


Librispeech documentation, clarification on format
 https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/datasets/librispeech_asr/librispeech_asr.py#L53

> Note that in order to limit the required storage for preparing this dataset, the audio
> is stored in the .flac format and is not converted to a float32 array. To convert, the audio
> file to a float32 array, please make use of the `.map()` function as follows:
>
> ```python
> import soundfile as sf
> def map_to_array(batch):
>     speech_array, _ = sf.read(batch["file"])
>     batch["speech"] = speech_array
>     return batch
> dataset = dataset.map(map_to_array, remove_columns=["file"])
> ```

Is this still true?

In my case, `ds["train.100"]` returns:
```
Dataset({
    features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],
    num_rows: 28539
})
```
and taking the first instance yields:
```
{'file': '374-180298-0000.flac',
 'audio': {'path': '374-180298-0000.flac',
  'array': array([ 7.01904297e-04,  7.32421875e-04,  7.32421875e-04, ...,
         -2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
  'sampling_rate': 16000},
 'text': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED',
 'speaker_id': 374,
 'chapter_id': 180298,
 'id': '374-180298-0000'}
```

The `audio` `array` seems to be already decoded. So such convert/decode code as mentioned in the doc is wrong?

But I wonder, is it actually stored as flac on disk, and the decoding is done on-the-fly? Or was it decoded already during the preparation and is stored as raw samples on disk?

Note that I also used `datasets.load_dataset("librispeech_asr", "clean").save_to_disk(...)` and then `datasets.load_from_disk(...)` in this example. Does this change anything on how it is stored on disk?

A small related question: Actually I would prefer to even store it as mp3 or ogg on disk. Is this easy to convert?
 > I don't think it's a good idea to convert to MP3 out-of-the-box, but we could maybe think about some kind of convert function for audio datasets cc @lhoestq ?

Sure, I would expect that `load_dataset("librispeech_asr")` would give you the original (not re-encoded) data (flac or already decoded). So such re-encoding logic would be some separate generic function. So I could do sth like `dataset.reencode_as_ogg(**ogg_encode_opts).save_to_disk(...)` or so.


Librispeech documentation, clarification on format
 https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/datasets/librispeech_asr/librispeech_asr.py#L53

> Note that in order to limit the required storage for preparing this dataset, the audio
> is stored in the .flac format and is not converted to a float32 array. To convert, the audio
> file to a float32 array, please make use of the `.map()` function as follows:
>
> ```python
> import soundfile as sf
> def map_to_array(batch):
>     speech_array, _ = sf.read(batch["file"])
>     batch["speech"] = speech_array
>     return batch
> dataset = dataset.map(map_to_array, remove_columns=["file"])
> ```

Is this still true?

In my case, `ds["train.100"]` returns:
```
Dataset({
    features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],
    num_rows: 28539
})
```
and taking the first instance yields:
```
{'file': '374-180298-0000.flac',
 'audio': {'path': '374-180298-0000.flac',
  'array': array([ 7.01904297e-04,  7.32421875e-04,  7.32421875e-04, ...,
         -2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
  'sampling_rate': 16000},
 'text': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED',
 'speaker_id': 374,
 'chapter_id': 180298,
 'id': '374-180298-0000'}
```

The `audio` `array` seems to be already decoded. So such convert/decode code as mentioned in the doc is wrong?

But I wonder, is it actually stored as flac on disk, and the decoding is done on-the-fly? Or was it decoded already during the preparation and is stored as raw samples on disk?

Note that I also used `datasets.load_dataset("librispeech_asr", "clean").save_to_disk(...)` and then `datasets.load_from_disk(...)` in this example. Does this change anything on how it is stored on disk?

A small related question: Actually I would prefer to even store it as mp3 or ogg on disk. Is this easy to convert?
 A follow-up question: I wonder whether a Parquet dataset is maybe more what we actually want to have? (Following also my comment here: https://github.com/huggingface/datasets/pull/4184#issuecomment-1105045491.) Because I think we actually would prefer to embed the data content in the dataset.

So, instead of `save_to_disk`/`load_from_disk`, we would use `to_parquet`,`from_parquet`? Is there any downside? Are arrow files more efficient?

Related is also the doc update in #4193.


Librispeech documentation, clarification on format
 https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/datasets/librispeech_asr/librispeech_asr.py#L53

> Note that in order to limit the required storage for preparing this dataset, the audio
> is stored in the .flac format and is not converted to a float32 array. To convert, the audio
> file to a float32 array, please make use of the `.map()` function as follows:
>
> ```python
> import soundfile as sf
> def map_to_array(batch):
>     speech_array, _ = sf.read(batch["file"])
>     batch["speech"] = speech_array
>     return batch
> dataset = dataset.map(map_to_array, remove_columns=["file"])
> ```

Is this still true?

In my case, `ds["train.100"]` returns:
```
Dataset({
    features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],
    num_rows: 28539
})
```
and taking the first instance yields:
```
{'file': '374-180298-0000.flac',
 'audio': {'path': '374-180298-0000.flac',
  'array': array([ 7.01904297e-04,  7.32421875e-04,  7.32421875e-04, ...,
         -2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
  'sampling_rate': 16000},
 'text': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED',
 'speaker_id': 374,
 'chapter_id': 180298,
 'id': '374-180298-0000'}
```

The `audio` `array` seems to be already decoded. So such convert/decode code as mentioned in the doc is wrong?

But I wonder, is it actually stored as flac on disk, and the decoding is done on-the-fly? Or was it decoded already during the preparation and is stored as raw samples on disk?

Note that I also used `datasets.load_dataset("librispeech_asr", "clean").save_to_disk(...)` and then `datasets.load_from_disk(...)` in this example. Does this change anything on how it is stored on disk?

A small related question: Actually I would prefer to even store it as mp3 or ogg on disk. Is this easy to convert?
 `save_to_disk` saves the dataset as an Arrow file, which is the format we use to load a dataset using memory mapping. This way the dataset does not fill your RAM, but is read from your disk instead.

Therefore you can directly reload a dataset saved with `save_to_disk` using `load_from_disk`.

Parquet files are used for cold storage: to use memory mapping on a Parquet dataset, you first have to convert it to Arrow. We use Parquet to reduce the I/O when pushing/downloading data from the Hugging face Hub. When you load a Parquet file from the Hub, it is converted to Arrow on the fly during the download.


Zenodo.org download is not responding

Zenodo.org download is not responding
 ## Describe the bug
Source download_url from zenodo.org does not respond.
`_DOWNLOAD_URL = "https://zenodo.org/record/2787612/files/SICK.zip?download=1"`
Other datasets also use zenodo.org to store data and they cannot be downloaded as well.

It would be better to actually use more reliable way to store original data like s3 bucket.

## Steps to reproduce the bug
```python
load_dataset("sick")
```

## Expected results
Dataset should be downloaded.

## Actual results
ConnectionError: Couldn't reach https://zenodo.org/record/2787612/files/SICK.zip?download=1 (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out. (read timeout=100)")))

## Environment info
- `datasets` version: 2.1.0
- Platform: Darwin-21.4.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 7.0.0
- Pandas version: 1.3.5

 Hi @dkajtoch, please note that at HuggingFace we are not hosting this dataset: we are just using a script to download their data file and create a dataset from it.

It was the dataset owners decision to host their data at Zenodo. You can see this on their website: https://marcobaroni.org/composes/sick.html

And yes, you are right: Zenodo is currently having some incidents and people are reporting problems from it.

On the other hand, we could contact the data owners and propose them to host their data at our Hugging Face Hub.

@julien-c I guess so.


Zenodo.org download is not responding

Zenodo.org download is not responding
 ## Describe the bug
Source download_url from zenodo.org does not respond.
`_DOWNLOAD_URL = "https://zenodo.org/record/2787612/files/SICK.zip?download=1"`
Other datasets also use zenodo.org to store data and they cannot be downloaded as well.

It would be better to actually use more reliable way to store original data like s3 bucket.

## Steps to reproduce the bug
```python
load_dataset("sick")
```

## Expected results
Dataset should be downloaded.

## Actual results
ConnectionError: Couldn't reach https://zenodo.org/record/2787612/files/SICK.zip?download=1 (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out. (read timeout=100)")))

## Environment info
- `datasets` version: 2.1.0
- Platform: Darwin-21.4.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 7.0.0
- Pandas version: 1.3.5

 Thanks @albertvillanova. I know that the problem lies in the source data. I just wanted to point out that these kind of problems are unavoidable without having one place where data sources are cached. Websites may go down or data sources may move. Having a copy in Hugging Face Hub would be a great solution.


Zenodo.org download is not responding

Zenodo.org download is not responding
 ## Describe the bug
Source download_url from zenodo.org does not respond.
`_DOWNLOAD_URL = "https://zenodo.org/record/2787612/files/SICK.zip?download=1"`
Other datasets also use zenodo.org to store data and they cannot be downloaded as well.

It would be better to actually use more reliable way to store original data like s3 bucket.

## Steps to reproduce the bug
```python
load_dataset("sick")
```

## Expected results
Dataset should be downloaded.

## Actual results
ConnectionError: Couldn't reach https://zenodo.org/record/2787612/files/SICK.zip?download=1 (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out. (read timeout=100)")))

## Environment info
- `datasets` version: 2.1.0
- Platform: Darwin-21.4.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 7.0.0
- Pandas version: 1.3.5

 Definitely, @dkajtoch! But we have to ask permission to the data owners. And many dataset licenses directly forbid data redistribution: in those cases we are not allowed to host their data on our Hub.


FLEURS

FLEURS
 ## Dataset viewer issue for '*name of the dataset*'

https://huggingface.co/datasets/google/fleurs

```
Status code:   400
Exception:     NotImplementedError
Message:       Extraction protocol for TAR archives like 'https://storage.googleapis.com/xtreme_translations/FLEURS/af_za.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```

Am I the one who added this dataset ? Yes

Can I fix this somehow in the script? @lhoestq @severo

 Yes, you just have to use `dl_manager.iter_archive` instead of `dl_manager.download_and_extract`.

That's because `download_and_extract` doesn't support TAR archives in streaming mode.


FLEURS

FLEURS
 ## Dataset viewer issue for '*name of the dataset*'

https://huggingface.co/datasets/google/fleurs

```
Status code:   400
Exception:     NotImplementedError
Message:       Extraction protocol for TAR archives like 'https://storage.googleapis.com/xtreme_translations/FLEURS/af_za.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```

Am I the one who added this dataset ? Yes

Can I fix this somehow in the script? @lhoestq @severo

 Tried to make it streamable, but I don't think it's really possible. @lhoestq @polinaeterna maybe you guys can check:
https://huggingface.co/datasets/google/fleurs/commit/dcf80160cd77977490a8d32b370c027107f2407b

real quick.

I think the problem is that we cannot ensure that the metadata file is found before the audio. Or is this possible somehow @lhoestq ?


FLEURS

FLEURS
 ## Dataset viewer issue for '*name of the dataset*'

https://huggingface.co/datasets/google/fleurs

```
Status code:   400
Exception:     NotImplementedError
Message:       Extraction protocol for TAR archives like 'https://storage.googleapis.com/xtreme_translations/FLEURS/af_za.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```

Am I the one who added this dataset ? Yes

Can I fix this somehow in the script? @lhoestq @severo

 @patrickvonplaten I think the metadata file should be found first because the audio files are contained in a folder next to the metadata files (just as in common voice), so the metadata files should be "on top of the list"   as they are closer to the root in the directories hierarchy


FLEURS

FLEURS
 ## Dataset viewer issue for '*name of the dataset*'

https://huggingface.co/datasets/google/fleurs

```
Status code:   400
Exception:     NotImplementedError
Message:       Extraction protocol for TAR archives like 'https://storage.googleapis.com/xtreme_translations/FLEURS/af_za.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```

Am I the one who added this dataset ? Yes

Can I fix this somehow in the script? @lhoestq @severo

 The order of the files is determined when the TAR archive is created, depending on the commands the creator ran.
If the metadata file is not at the beginning of the file, that makes streaming completely inefficient. In this case the TAR archive needs to be recreated in an appropriate order.


FLEURS

FLEURS
 ## Dataset viewer issue for '*name of the dataset*'

https://huggingface.co/datasets/google/fleurs

```
Status code:   400
Exception:     NotImplementedError
Message:       Extraction protocol for TAR archives like 'https://storage.googleapis.com/xtreme_translations/FLEURS/af_za.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```

Am I the one who added this dataset ? Yes

Can I fix this somehow in the script? @lhoestq @severo

 Actually we could maybe just host the metadata file ourselves and then stream the audio data only. Don't think that this would be a problem for the FLEURS authors (I can ask them :-))


Add some iteration method on a dataset column (specific for inference)
 **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.

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**
A clear and concise description of what you want to happen.

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

 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


Add some iteration method on a dataset column (specific for inference)
 **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.

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**
A clear and concise description of what you want to happen.

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

 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...


Add some iteration method on a dataset column (specific for inference)
 **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.

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**
A clear and concise description of what you want to happen.

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

 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.


Add some iteration method on a dataset column (specific for inference)
 **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.

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**
A clear and concise description of what you want to happen.

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

 @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.


Add some iteration method on a dataset column (specific for inference)
 **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.

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**
A clear and concise description of what you want to happen.

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

 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 ;)


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 Another thing, but maybe this should be a separate issue: As I see from the code, it would try to use up to 16 simultaneous downloads? This is problematic for Librispeech or anything on OpenSLR. On [the homepage](https://www.openslr.org/), it says:

> If you want to download things from this site, please download them one at a time, and please don't use any fancy software-- just download things from your browser or use 'wget'. We have a firewall rule to drop connections from hosts with more than 5 simultaneous connections, and certain types of download software may activate this rule.

Related: https://github.com/tensorflow/datasets/issues/3885


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 Sorry maybe the docs haven't been super clear here. By `split` we mean one of `train.500`, `train.360`, `train.100`, `validation`, `test`. For Librispeech, you'll have to specific a config (either `other` or `clean`) though:

```py
datasets.load_dataset("librispeech_asr", "clean")
```

should work and give you all splits (being "train", "test", ...) for the clean config of the dataset.


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 If you need both `"clean"` and `"other"` I think you'll have to do concatenate them as follows:

```py
from datasets import concatenate_datasets, load_dataset

other = load_dataset("librispeech_asr", "other")
clean = load_dataset("librispeech_asr", "clean")

librispeech = concatenate_datasets([other, clean])
```

See https://huggingface.co/docs/datasets/v2.1.0/en/process#concatenate


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 Downloading one split would be:

```py
from datasets import load_dataset

other = load_dataset("librispeech_asr", "other", split="train.500")
```


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 Ah thanks. But wouldn't it be easier/nicer (and more canonical) to just make it in a way that simply `load_dataset("librispeech_asr")` works?


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 Pinging @lhoestq here, think this could make sense! Not sure however how the dictionary would then look like


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 Would it make sense to have `clean` as the default config ?

Also I think `load_dataset("librispeech_asr")` should have raised you an error that says that you need to specify a config

I also opened a PR to improve the doc: https://github.com/huggingface/datasets/pull/4183


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 > Would it make sense to have `clean` as the default config ?

I think a user would expect that the default would give you the full dataset.

> Also I think `load_dataset("librispeech_asr")` should have raised you an error that says that you need to specify a config

It does raise an error, but this error confused me because I did not understand why I needed a config, or why I could not simply download the whole dataset, which is what people usually do with Librispeech.


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 +1 for @albertz. Also think lots of people download the whole dataset (`"clean"` + `"other"`) for Librispeech.

Think there are also some people though who:
- a) Don't have the memory to store the whole dataset
- b) Just want to evaluate on one of the two configs


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 Ok ! Adding the "all" configuration would do the job then, thanks ! In the "all" configuration we can merge all the train.xxx splits into one "train" split, or keep them separate depending on what's the most practical to use (probably put everything in "train" no ?)


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 I'm not too familiar with how to work with HuggingFace datasets, but people often do some curriculum learning scheme, where they start with train.100, later go over to train.100 + train.360, and then later use the whole train (960h). It would be good if this is easily possible.


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 Hey @albertz,

opened a PR here. Think by adding the "subdataset" class to each split "train", "dev", "other" as shown here: https://github.com/huggingface/datasets/pull/4184/files#r853272727 it should be easily possible (e.g. with the filter function https://huggingface.co/docs/datasets/v2.1.0/en/package_reference/main_classes#datasets.Dataset.filter )


Dataset librispeech_asr fails to load

Dataset librispeech_asr fails to load
 ## Describe the bug
The dataset librispeech_asr (standard Librispeech) fails to load.

## Steps to reproduce the bug
```python
datasets.load_dataset("librispeech_asr")
```

## Expected results
It should download and prepare the whole dataset (all subsets).

In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other).
However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want.

Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training.

## Actual results
```
...
  File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators
    line: archive_path = dl_manager.download(_DL_URLS[self.config.name])
    locals:
      archive_path = <not found>
      dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>
      dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>>
      _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'...
      self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310>
      self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None)
      self.config.name = <local> 'default', len = 7
KeyError: 'default'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31
- Python version: 3.9.9
- PyArrow version: 6.0.1
- Pandas version: 1.4.2

 But also since everything is cached one could also just do:

```python
load_dataset("librispeech", "clean", "train.100")
load_dataset("librispeech", "clean", "train.100+train.360")
load_dataset("librispeech" "all", "train")
```


Timit_asr dataset cannot be previewed recently
 ## Dataset viewer issue for '*timit_asr*'

**Link:** *https://huggingface.co/datasets/timit_asr*

Issue: The timit-asr dataset cannot be previewed recently.

Am I the one who added this dataset ? Yes-No
No
 Thanks for reporting. The bug has already been detected, and we hope to fix it soon.


Timit_asr dataset cannot be previewed recently
 ## Dataset viewer issue for '*timit_asr*'

**Link:** *https://huggingface.co/datasets/timit_asr*

Issue: The timit-asr dataset cannot be previewed recently.

Am I the one who added this dataset ? Yes-No
No
 TIMIT is now a dataset that requires manual download, see #4145

Therefore it might take a bit more time to fix it


Timit_asr dataset cannot be previewed recently
 ## Dataset viewer issue for '*timit_asr*'

**Link:** *https://huggingface.co/datasets/timit_asr*

Issue: The timit-asr dataset cannot be previewed recently.

Am I the one who added this dataset ? Yes-No
No
 > TIMIT is now a dataset that requires manual download, see #4145
>
> Therefore it might take a bit more time to fix it

Thank you for your quickly response. Exactly, I also found the manual download issue in the morning. But when I used *list_datasets()* to check the available datasets, *'timit_asr'* is still in the list. So I am a little bit confused. If *'timit_asr'* need to be manually downloaded, does that mean we can **not** automatically download it **any more** in the future?


Timit_asr dataset cannot be previewed recently
 ## Dataset viewer issue for '*timit_asr*'

**Link:** *https://huggingface.co/datasets/timit_asr*

Issue: The timit-asr dataset cannot be previewed recently.

Am I the one who added this dataset ? Yes-No
No
 Yes exactly. If you try to load the dataset it will ask you to download it manually first, and to pass the downloaded and extracted data like `load_dataset("timir_asr", data_dir="path/to/extracted/data")`

The URL we were using was coming from a host that doesn't have the permission to redistribute the data, and the dataset owners (LDC) notified us about it.


Optional Content Warning for Datasets

Optional Content Warning for Datasets
 **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.

We now have hate speech datasets on the hub, like this one: https://huggingface.co/datasets/HannahRoseKirk/HatemojiBuild

I'm wondering if there is an option to select a content warning message that appears before the dataset preview? Otherwise, people immediately see hate speech when clicking on this dataset.

**Describe the solution you'd like**
A clear and concise description of what you want to happen.

Implementation of a content warning message that separates users from the dataset preview until they click out of the warning.

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

Possibly just a way to remove the dataset preview completely? I think I like the content warning option better, though.

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

 Hi! You can use the `extra_gated_prompt` YAML field in a dataset card for displaying custom messages/warnings that the user must accept before gaining access to the actual dataset. This option also keeps the viewer hidden until the user agrees to terms.


ArrayND error in pyarrow 5

ArrayND error in pyarrow 5
 As found in https://github.com/huggingface/datasets/pull/3903, The ArrayND features fail on pyarrow 5:
```python
import pyarrow as pa
from datasets import Array2D
from datasets.table import cast_array_to_feature

arr = pa.array([[[0]]])
feature_type = Array2D(shape=(1, 1), dtype="int64")
cast_array_to_feature(arr, feature_type)
```
raises
```python
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-8-04610f9fa78c> in <module>
----> 1 cast_array_to_feature(pa.array([[[0]]]), Array2D(shape=(1, 1), dtype="int32"))

~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
   1672             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
   1673         else:
-> 1674             return func(array, *args, **kwargs)
   1675
   1676     return wrapper

~/Desktop/hf/datasets/src/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
   1806         return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
   1807     elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1808         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
   1809     raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
   1810

~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
   1672             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
   1673         else:
-> 1674             return func(array, *args, **kwargs)
   1675
   1676     return wrapper

~/Desktop/hf/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_number_to_str)
   1705         array = array.storage
   1706     if isinstance(pa_type, pa.ExtensionType):
-> 1707         return pa_type.wrap_array(array)
   1708     elif pa.types.is_struct(array.type):
   1709         if pa.types.is_struct(pa_type) and (

AttributeError: 'Array2DExtensionType' object has no attribute 'wrap_array'
```

The thing is that `cast_array_to_feature` is called when writing an Arrow file, so creating an Arrow dataset using any ArrayND type currently fails.

`wrap_array` has been added in pyarrow 6, so we can either bump the required pyarrow version or fix this for pyarrow 5
 Where do we bump the required pyarrow version? Any inputs on how I fix this issue?


ArrayND error in pyarrow 5

ArrayND error in pyarrow 5
 As found in https://github.com/huggingface/datasets/pull/3903, The ArrayND features fail on pyarrow 5:
```python
import pyarrow as pa
from datasets import Array2D
from datasets.table import cast_array_to_feature

arr = pa.array([[[0]]])
feature_type = Array2D(shape=(1, 1), dtype="int64")
cast_array_to_feature(arr, feature_type)
```
raises
```python
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-8-04610f9fa78c> in <module>
----> 1 cast_array_to_feature(pa.array([[[0]]]), Array2D(shape=(1, 1), dtype="int32"))

~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
   1672             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
   1673         else:
-> 1674             return func(array, *args, **kwargs)
   1675
   1676     return wrapper

~/Desktop/hf/datasets/src/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
   1806         return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
   1807     elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1808         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
   1809     raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
   1810

~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
   1672             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
   1673         else:
-> 1674             return func(array, *args, **kwargs)
   1675
   1676     return wrapper

~/Desktop/hf/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_number_to_str)
   1705         array = array.storage
   1706     if isinstance(pa_type, pa.ExtensionType):
-> 1707         return pa_type.wrap_array(array)
   1708     elif pa.types.is_struct(array.type):
   1709         if pa.types.is_struct(pa_type) and (

AttributeError: 'Array2DExtensionType' object has no attribute 'wrap_array'
```

The thing is that `cast_array_to_feature` is called when writing an Arrow file, so creating an Arrow dataset using any ArrayND type currently fails.

`wrap_array` has been added in pyarrow 6, so we can either bump the required pyarrow version or fix this for pyarrow 5
 We need to bump it in `setup.py` as well as update some CI job to use pyarrow 6 instead of 5 in `.circleci/config.yaml` and `.github/workflows/benchmarks.yaml`


load_dataset for winoground returning decoding error
 ## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.

## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```

## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json

with open('examples.jsonl', 'r') as f:
    examples = f.read().split('\n')

# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
 'caption_1': 'someone is doing it over',
 'collapsed_tag': 'Relation',
 'id': 399,
 'image_0': 'ex_399_img_0',
 'image_1': 'ex_399_img_1',
 'num_main_preds': 1,
 'secondary_tag': 'Morpheme-Level',
 'tag': 'Scope, Preposition'}

```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.

## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0

 I thought I had fixed it with this after some helpful hints from @severo
```python
import datasets
token = 'hf_XXXXX'
dataset = datasets.load_dataset(
    'facebook/winoground',
    name='facebook--winoground',
    split='train',
    streaming=True,
    use_auth_token=token,
)
```
but I found out that wasn't the case
```python
[x for x in dataset]
...
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/facebook/winoground/resolve/a86a60456fbbd242e9a744199071a6bd3e7fd9de/examples.jsonl')
```


load_dataset for winoground returning decoding error
 ## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.

## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```

## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json

with open('examples.jsonl', 'r') as f:
    examples = f.read().split('\n')

# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
 'caption_1': 'someone is doing it over',
 'collapsed_tag': 'Relation',
 'id': 399,
 'image_0': 'ex_399_img_0',
 'image_1': 'ex_399_img_1',
 'num_main_preds': 1,
 'secondary_tag': 'Morpheme-Level',
 'tag': 'Scope, Preposition'}

```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.

## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0

 Hi ! This dataset structure (image + labels in a JSON file) is not supported yet, though we're adding support for this in  in #4069

The following structure will be supported soon:
```
metadata.json
images/
     image0.png
     image1.png
    ...
```
Where `metadata.json` is a JSON Lines file with labels or other metadata, and each line must have a "file_name" field with the name of the image file.

For the moment are only supported:
- JSON files only
- image files only

Since this dataset is a mix of the two, at the moment it fails trying to read the images as JSON.

Therefore to be able to load this dataset we need to wait for the new structure to be supported (very soon ^^), or add a dataset script in the repository that reads both the JSON and the images cc @TristanThrush


load_dataset for winoground returning decoding error
 ## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.

## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```

## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json

with open('examples.jsonl', 'r') as f:
    examples = f.read().split('\n')

# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
 'caption_1': 'someone is doing it over',
 'collapsed_tag': 'Relation',
 'id': 399,
 'image_0': 'ex_399_img_0',
 'image_1': 'ex_399_img_1',
 'num_main_preds': 1,
 'secondary_tag': 'Morpheme-Level',
 'tag': 'Scope, Preposition'}

```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.

## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0

 We'll also investigate the issue with the streaming download manager in https://github.com/huggingface/datasets/issues/4139 ;) thanks for reporting


load_dataset for winoground returning decoding error
 ## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.

## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```

## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json

with open('examples.jsonl', 'r') as f:
    examples = f.read().split('\n')

# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
 'caption_1': 'someone is doing it over',
 'collapsed_tag': 'Relation',
 'id': 399,
 'image_0': 'ex_399_img_0',
 'image_1': 'ex_399_img_1',
 'num_main_preds': 1,
 'secondary_tag': 'Morpheme-Level',
 'tag': 'Scope, Preposition'}

```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.

## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0

 In the meantime, anyone can always download the images.zip and examples.jsonl files directly from huggingface.co - let me know if anyone has issues with that.


load_dataset for winoground returning decoding error
 ## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.

## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```

## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json

with open('examples.jsonl', 'r') as f:
    examples = f.read().split('\n')

# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
 'caption_1': 'someone is doing it over',
 'collapsed_tag': 'Relation',
 'id': 399,
 'image_0': 'ex_399_img_0',
 'image_1': 'ex_399_img_1',
 'num_main_preds': 1,
 'secondary_tag': 'Morpheme-Level',
 'tag': 'Scope, Preposition'}

```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.

## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0

 I mirrored the files at https://huggingface.co/datasets/facebook/winoground in a folder on my local machine `winground`
and when I tried
```python
import datasets
ds = datasets.load_from_disk('./winoground')
```
I get the following error
```python
--------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Input In [2], in <cell line: 1>()
----> 1 ds = datasets.load_from_disk('./winoground')

File ~/.local/lib/python3.8/site-packages/datasets/load.py:1759, in load_from_disk(dataset_path, fs, keep_in_memory)
   1757     return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)
   1758 else:
-> 1759     raise FileNotFoundError(
   1760         f"Directory {dataset_path} is neither a dataset directory nor a dataset dict directory."
   1761     )

FileNotFoundError: Directory ./winoground is neither a dataset directory nor a dataset dict directory.
```
so still some work to be done on the backend imo.


load_dataset for winoground returning decoding error
 ## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.

## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```

## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json

with open('examples.jsonl', 'r') as f:
    examples = f.read().split('\n')

# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
 'caption_1': 'someone is doing it over',
 'collapsed_tag': 'Relation',
 'id': 399,
 'image_0': 'ex_399_img_0',
 'image_1': 'ex_399_img_1',
 'num_main_preds': 1,
 'secondary_tag': 'Morpheme-Level',
 'tag': 'Scope, Preposition'}

```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.

## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0

 Note that `load_from_disk` is the function that reloads an Arrow dataset saved with `my_dataset.save_to_disk`.

Once we do support images with metadata you'll be able to use `load_dataset("facebook/winoground")` directly (or `load_dataset("./winoground")` of you've cloned the winoground repository locally).


load_dataset for winoground returning decoding error
 ## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.

## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```

## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json

with open('examples.jsonl', 'r') as f:
    examples = f.read().split('\n')

# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
 'caption_1': 'someone is doing it over',
 'collapsed_tag': 'Relation',
 'id': 399,
 'image_0': 'ex_399_img_0',
 'image_1': 'ex_399_img_1',
 'num_main_preds': 1,
 'secondary_tag': 'Morpheme-Level',
 'tag': 'Scope, Preposition'}

```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.

## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0

 Apologies for the delay. I added a custom dataset loading script for winoground. It should work now, with an auth token:

`examples = load_dataset('facebook/winoground', use_auth_token=<your auth token>)`

Let me know if there are any issues


load_dataset for winoground returning decoding error
 ## Describe the bug
I am trying to use datasets to load winoground and I'm getting a JSON decoding error.

## Steps to reproduce the bug
```python
from datasets import load_dataset
token = 'hf_XXXXX' # my HF access token
datasets = load_dataset('facebook/winoground', use_auth_token=token)
```

## Expected results
I downloaded images.zip and examples.jsonl manually. I was expecting to have some trouble decoding json so I didn't use jsonlines but instead was able to get a complete set of 400 examples by doing
```python
import json

with open('examples.jsonl', 'r') as f:
    examples = f.read().split('\n')

# Thinking this would error if the JSON is not utf-8 encoded
json_data = [json.loads(x) for x in examples]
print(json_data[-1])
```
and I see
```python
{'caption_0': 'someone is overdoing it',
 'caption_1': 'someone is doing it over',
 'collapsed_tag': 'Relation',
 'id': 399,
 'image_0': 'ex_399_img_0',
 'image_1': 'ex_399_img_1',
 'num_main_preds': 1,
 'secondary_tag': 'Morpheme-Level',
 'tag': 'Scope, Preposition'}

```
so I'm not sure what's going on here honestly. The file `examples.jsonl` doesn't have non-UTF-8 encoded text.

## Actual results
During the split operation after downloading, datasets encounters an error in the JSON ([trace](https://gist.github.com/odellus/e55d390ca203386bf551f38e0c63a46b) abbreviated for brevity).
```
datasets/packaged_modules/json/json.py:144 in Json._generate_tables(self, files)
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: Linux-5.13.0-39-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0

 Adding the dataset loading script definitely didn't take as long as I thought it would 😅


SAMSum dataset viewer not working

SAMSum dataset viewer not working
 ## Dataset viewer issue for '*name of the dataset*'

**Link:** *link to the dataset viewer page*

*short description of the issue*

Am I the one who added this dataset ? Yes-No

 Currently, only the datasets that can be streamed support the dataset viewer. Maybe @lhoestq @albertvillanova or @mariosasko could give more details about why the dataset cannot be streamed.


SAMSum dataset viewer not working

SAMSum dataset viewer not working
 ## Dataset viewer issue for '*name of the dataset*'

**Link:** *link to the dataset viewer page*

*short description of the issue*

Am I the one who added this dataset ? Yes-No

 It looks like the host (https://arxiv.org) doesn't allow HTTP Range requests, which is what we use to stream data.

This can be fix if we host the data ourselves, which is ok since the dataset is under CC BY-NC-ND 4.0


Unable to download `Wikepedia` 20220301.en version
 ## Describe the bug

Unable to download `Wikepedia` dataset, 20220301.en version

## Steps to reproduce the bug
```python
!pip install apache_beam mwparserfromhell
dataset_wikipedia = load_dataset("wikipedia", "20220301.en")
```

## Actual results
```
ValueError: BuilderConfig 20220301.en not found.
Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
 Hi! We've recently updated the Wikipedia script, so these changes are only available on master and can be fetched as follows:
```python
dataset_wikipedia = load_dataset("wikipedia", "20220301.en", revision="master")
```