Librispeech documentation, clarification on format
State: open | Author: albertz (NONE)

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?

Comments: (@patrickvonplaten )

Also cc @lhoestq here

The documentation in the code is definitely outdated - thanks for letting me know, I'll remove it in https://github.com/huggingface/datasets/pull/4184 .

You're exactly right `audio` `array` already decodes the audio file to the correct waveform. This is done on the fly, which is also why one should **not** do `ds["audio"]["array"][0]` as this will decode all dataset samples, but instead `ds[0]["audio"]["array"]` see: https://huggingface.co/docs/datasets/audio_process#audio-datasets



So, again to clarify: On disk, only the raw flac file content is stored? Is this also the case after `save_to_disk`?

And is it simple to also store it re-encoded as ogg or mp3 instead?


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 ? 

> 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.


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.


`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.