title: [Librispeech] Add 'all' config
comments: Fix https://github.com/huggingface/datasets/issues/4179 _The documentation is not available anymore as the PR was closed or merged._ Just that I understand: With this change, simply doing `load_dataset("librispeech_asr")` is possible and returns the whole dataset?
  
  And to get the subsets, I do sth like:
  ```python
  ds = load_dataset("librispeech_asr")
  train_ds = ds["train"]
  dev_clean_ds = ds["dev-clean"]
  dev_other_ds = ds["dev-other"]
  test_clean_ds = ds["test-clean"]
  test_other_ds = ds["test-other"]
  ```
  ?
   > Just that I understand: With this change, simply doing `load_dataset("librispeech_asr")` is possible and returns the whole dataset?
  > 
  > And to get the subsets, I do sth like:
  > 
  > ```python
  > ds = load_dataset("librispeech_asr")
  > train_ds = ds["train"]
  > dev_clean_ds = ds["dev-clean"]
  > dev_other_ds = ds["dev-other"]
  > test_clean_ds = ds["test-clean"]
  > test_other_ds = ds["test-other"]
  > ```
  > 
  > ?
  
  You could do:
  
  
  ```python
  ds = load_dataset("librispeech_asr", "all")  # <- note that we have to pass a config
  train_ds = ds["train"]
  dev_clean_ds = ds["dev-clean"]
  dev_other_ds = ds["dev-other"]
  test_clean_ds = ds["test-clean"]
  test_other_ds = ds["test-other"]
  ``` So, `load_dataset("librispeech_asr")` is not possible, it must be `load_dataset("librispeech_asr", "all")`?
  
  Why is that?
  
  The docs say:
  ```
  name: `str` name, optional configuration for the dataset that affects the data generated on disk. Different
      `builder_config`s will have their own subdirectories and versions.
      If not provided, uses the first configuration in self.BUILDER_CONFIGS
  ```
  https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/src/datasets/builder.py#L228
  
  Or maybe you could just define `DEFAULT_CONFIG_NAME`?
   > If not provided, uses the first configuration in self.BUILDER_CONFIGS
  
  Oh crap this is outdated documentation. No it doesn't take the first config by default.
  
  EDIT: opened a PR to fix this: https://github.com/huggingface/datasets/pull/4186 > No it doesn't take the first config by default.
  
  But defining `DEFAULT_CONFIG_NAME` would work?
  
  So should we define `DEFAULT_CONFIG_NAME = "all"` here as well? I think this is a reasonable default config.
  
  Don't most datasets have some default config?
   > But defining DEFAULT_CONFIG_NAME would work?
  >
  > So should we define DEFAULT_CONFIG_NAME = "all" here as well? I think this is a reasonable default config.
  
  Yes that would work, and I also find it reasonable to do it :)
  
  > Don't most datasets have some default config?
  
  Most datasets only have one configuration, so the single configuration is the default one. Then other datasets gave several configurations, and whether they have a default one is decided case-by-case.
  
  e.g. `glue` is a benchmark and doesn't have a default task, one must choose which task of `glue` they want to use explicitely. Thanks a lot for the feedback! 
  
  Using `"all"` now as the default config. I changed the layout a bit so that there is not a single "train", but instead we have multiple "train.clean.100", "train.clean.360", "train.other.500". This way we don't even need to do filtering and it's also cleaner IMO.
  
  @albertz - you should now be able to do the following:
  
  ```python
  load_dataset("librispeech_asr")  # <- run this once to download, prepare dataset and cache everything
  
  # The following operations will be very fast since all the downloading and processing is already cached
  train_1 = load_dataset("librispeech_asr", split="train.clean.100")
  print(train_1)
  train_2 = load_dataset("librispeech_asr", split="train.clean.100+train.clean.360")
  print(train_2)
  train_full = load_dataset("librispeech_asr", split="train.clean.100+train.clean.360+train.other.500")
  print(train_full)
  dev_clean_ds = load_dataset("librispeech_asr", split="validation.clean")
  print(dev_clean_ds)
  dev_other_ds = load_dataset("librispeech_asr", split="validation.other")
  print(dev_other_ds)
  test_clean_ds = load_dataset("librispeech_asr", split="test.clean")
  print(test_clean_ds)
  test_other_ds = load_dataset("librispeech_asr", split="test.other")
  print(test_other_ds)
  ```
  
  
   Think this way we have the best of both worlds. Also @lhoestq, I think we could highlight better in the docs that it's possible to combine different splits. We do this actually quite a lot for speech. For Common Voice many people include "validation" in the training if the data is too small, e.g.: https://github.com/huggingface/transformers/blob/ff06b177917384137af2d9585697d2d76c40cdfc/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L147
  
  Should we maybe add a short section to the loading tutorial here: https://huggingface.co/docs/datasets/v2.1.0/en/loading#hugging-face-hub ? (Happy to do it) Is there any advantage or difference in calling `load_dataset` multiple times for each split? Or why not just call `load_dataset` once and then access each split?
  
  Note in our case, we cannot really use the caching mechanism because we have a recipe pipeline used by multiple users (and I think a common cache dir for all users might end up in problems) and we basically would use `load_dataset("librispeech_asr").save_to_disk(...)` and then later `load_from_disk(...)`. (See here: https://github.com/rwth-i6/i6_core/pull/253)
  
  So with `load_from_disk`, we cannot really provide the split this way, so we anyway would do sth like:
  ```python
  ds = datasets.load_from_disk(...)
  train = ds["train"]
  ```
  Or with your latest proposal, it would look like:
  ```python
  ds = datasets.load_from_disk(...)
  train_ds = datasets.concatenate_datasets(
    [ds["train.clean.100"], ds["train.clean.360"], ds["train.other.500"]])
  ```
  right?
   > Is there any advantage or difference in calling `load_dataset` multiple times for each split? Or why not just call `load_dataset` once and then access each split?
  > 
  > Note in our case, we cannot really use the caching mechanism because we have a recipe pipeline used by multiple users (and I think a common cache dir for all users might end up in problems) and we basically would use `load_dataset("librispeech_asr").save_to_disk(...)` and then later `load_from_disk(...)`. (See here: [rwth-i6/i6_core#253](https://github.com/rwth-i6/i6_core/pull/253))
  > 
  > So with `load_from_disk`, we cannot really provide the split this way, so we anyway would do sth like:
  > 
  > ```python
  > ds = datasets.load_from_disk(...)
  > train = ds["train"]
  > ```
  > 
  > Or with your latest proposal, it would look like:
  > 
  > ```python
  > ds = datasets.load_from_disk(...)
  > train_ds = datasets.concatenate_datasets(
  >   [ds["train.clean.100"], ds["train.clean.360"], ds["train.other.500"]])
  > ```
  > 
  > right?
  
  I see the use case! The only advantage by calling `datasets` multiple times is that one can easily "merge" splits with `"+"`, but yeah you can do the exact same with `concatenate`.
  
  @lhoestq what do you think is the best approach with `load_from_disk`? 
  
  @albertz, you could also define the `cache_dir` when doing `load_dataset(...)` which will then put all the relevant `arrow` files int the cache dir that you defined, e.g.:
  
  ```python
  load_dataset("librispeech_asr", cache_dir="/easy/to/access/directory")
  ``` @albertz, I took a read through https://github.com/rwth-i6/i6_core/pull/253 . 
  
  I think the best would be the following:
  
  1. Do `ds = load_dataset(..., cache_dir="/dir/that/is/easy/to/access")` <- having merged this PR, this will save all the original `.flac` files in the `cache_dir`
  2. Do `ds.save_to_disk("local/path")` this should then only save the `arrow.format` with a `path` string to the audio files which are located in `cache_dir` <- this won't require a lot of memory after https://github.com/huggingface/datasets/pull/4184#discussion_r854132740 is fixed and can be done for each person individually.
  3. `ds = datasets.load_from_disk("local/path")` can the be used. An object of `ds` will then have a `path` variable that links to the original audio files in the `cache_dir`. You can change these audio files then easily to `.mp3. You could do this with the `.map(...)` function, e.g. define a function that maps through all audio files, load them and then save them on disk afterward. @lhoestq - I think this one is good to go > @albertz, I took a read through [rwth-i6/i6_core#253](https://github.com/rwth-i6/i6_core/pull/253) .
  > 
  > I think the best would be the following:
  > 
  > 1. Do `ds = load_dataset(..., cache_dir="/dir/that/is/easy/to/access")` <- having merged this PR, this will save all the original `.flac` files in the `cache_dir`
  > 2. Do `ds.save_to_disk("local/path")` this should then only save the `arrow.format` with a `path` string to the audio files which are located in `cache_dir` <- this won't require a lot of memory after [[Librispeech] Add 'all' config #4184 (comment)](https://github.com/huggingface/datasets/pull/4184#discussion_r854132740) is fixed and can be done for each person individually.
  > 3. `ds = datasets.load_from_disk("local/path")` can the be used. An object of `ds` will then have a `path` variable that links to the original audio files in the `cache_dir`. You can change these audio files then easily to `.mp3. You could do this with the `.map(...)` function, e.g. define a function that maps through all audio files, load them and then save them on disk afterward.
  
  Oh, so you say that our current implementation in https://github.com/rwth-i6/i6_core/pull/253 is broken? Because our cache dir is just some temp directory which will be removed afterwards, and we just store what we get out of `save_to_disk`. I think it would be good to clarify that in the doc of `save_to_disk`, that this is not enough and can depend on files from the cache dir. (@dthulke)
  
  So, you say we anyway need to share the cache dir among users? But we would want to make sure that after the initial download and preparation of the data, this is set to readonly, because we want to make sure that other people will not modify the data in any way. Right?
  
  But then, we don't really need the `save_to_disk` and `load_from_disk` at all, right?
   @albertz 
  
  > Oh, so you say that our current implementation in https://github.com/rwth-i6/i6_core/pull/253 is broken? Because our cache dir is just some temp directory which will be removed afterwards, and we just store what we get out of save_to_disk. I think it would be good to clarify that in the doc of save_to_disk, that this is not enough and can depend on files from the cache dir. (@dthulke)
  
  Oh, I wasn't aware that audio files are handled this way. Then we should have the cache directory as an additional job output, so that we keep the audio files. 
  
  > So, you say we anyway need to share the cache dir among users?
  
  No, the cache dir can still be a directory in the job output folder. Then the audio paths in the corresponding dataset column correspond to the flac files in that directory. This way the "output" of the job is contained into the job directory and we don't write files to a global cache directory that is independent of the sisyphus graph.
  
  If we want to share the audio data between different users, we can just link to a central instance of the job (similar to how we do it with the `DownloadLibriSpeechCorpusJob`). @dthulke - that's a good point actually! So you can do both things:
  
  1. Convert all audio files to bytes. Bytes can be saved by `arrow` so in this case you can do `save_to_disk(...)`, but then you cannot really inspect the audio files locally as they'll just be saved within a large arrow file (this actually used to be the default case but we're changing this now). The problem of this is summarized here a bit:  https://github.com/huggingface/datasets/issues/3663 . You can still do this if you'd like, e.g. you could do:
  
  ```python
  ds = load_dataset("librispeech_asr")
  
  def read_file(batch):
      with open(batch["file"], "r") as f:
          batch["bytes"] = f.read()   
      return batch
  
  ds = ds.map(read_file)
  ds.save_to_disk("/path") <- the saved arrow object will now contain everything you need
  ```
  
  however this is not recommend - it's should be much easier to just save the path to the downloaded audio files.
  
  2. Not convert audio files to bytes, but just leave them in their original file format. Then only the path to the original files will be save in arrow. This will be the default case. This means that when you do `load_dataset(...)` both the orginal audio data and the arrow file will be saved in the `cache_dir` (which can be saved locally for every user or in a shared cache - we actually use a shared cache quite a bit at Hugging Face). When do you do `save_to_disk(...)` now only the `path` will be saved in `arrow` format (after this PR is merged, you'll see that the `arrow files should be very light weight` meaning that `save_to_disk(...)` can be done for every user, but has a dependency on the `cache_dir` (because the audio files live there).
  
  => Now what you could do as well would be to simply move all the audio files to the folder you want (the `save_to_disk(...)` folder) and then change the path of every sample to this folder (maybe with `map(...)`) and then this folder would be self contained. I do however think it's better to just specific a `cache_dir` and re-use `load_dataset(...)` every time instead of `load_from_disk` or `save_to_disk(...)`. Note that you can even pass the relevant cache files to `load_dataset(...)` here: https://huggingface.co/docs/datasets/v2.1.0/en/package_reference/loading_methods#datasets.load_dataset.data_files in which case you can be 100% sure that nothing is redownloaded. 
  
  We discussed storing audio files quite a bit, e.g. see: https://github.com/huggingface/datasets/issues/3663 and had (too many) changes around this topic recently, but we've come to the conclusion that the best is to leave the audio format in the format it was originally (`.flac` for Librispeech) so that the user can easily inspect it / understand the data. Arrow cannot save data is `.flac` so we'll just save a path to the original data. Curious to hear your guys opinion on this as well. So what I would suggest here is to do the following:
  
  1. Do `load_dataset(..., cache_dir=/a/read-only/folder)`
  2. 
  - Either just re-use `load_dataset(..., cache_dir=...)` which should always re-use the data in the `cache_dir` since the hash of the url matches - so there should never be any duplicated downloading 
  
  or 
  
  - If you want to store the files in MP3 locally, first convert the files to MP3 in the read-only folder, then take do `ds.save_to_disk(/some/path)` which will save the correct path to the read-only folder to MP3 and then you can easily re-use the small arrow dataset that is saved in `/some/path` > So what I would suggest here is to do the following:
  > 
  > 1. Do `load_dataset(..., cache_dir=/a/read-only/folder)`
  > 
  > * Either just re-use `load_dataset(..., cache_dir=...)` which should always re-use the data in the `cache_dir` since the hash of the url matches - so there should never be any duplicated downloading
  > 
  > or
  > 
  > * If you want to store the files in MP3 locally, first convert the files to MP3 in the read-only folder, then take do `ds.save_to_disk(/some/path)` which will save the correct path to the read-only folder to MP3 and then you can easily re-use the small arrow dataset that is saved in `/some/path`
  
  Also relevant here: https://github.com/huggingface/datasets/issues/3663 I also added some documentation about how `save_to_disk` handles audio files here: https://github.com/huggingface/datasets/pull/4193 > > So, you say we anyway need to share the cache dir among users?
  > 
  > No, the cache dir can still be a directory in the job output folder.
  
  @dthulke But this is what I mean. When we share the job output folder, it means we share the cache dir among users.
  
  I wonder if `load_dataset(..., cache_dir=job_output_cache_dir)` is always save to do then, that it really would not modify the `job_output_cache_dir`.
  
  We could enforce that by making the `job_output_cache_dir` read-only afterwards. We currently don't do this.
  
  @patrickvonplaten @dthulke But in any case, we actually prefer the data content to be inside the dataset (the arrow files). Lots of small files would be very problematic for our cache manager. We have one main copy of the data on NFS, but accessing the NFS directly by all computing nodes is not feasible, so the cache manager will have copies of the files on the nodes. So it means, whenever we access some file, we query the cache manager DB whether the file is already cached somewhere (some other computing node) and if so, it copies it from the other computing node and not from NFS. This works very well when there are not too many files (but the files can be big). So, we want to have only a few but big files. Even for NFS access this is much better.
  
  I also commented in #3663.
   Hey @albertz @dthulke,
  
  Thanks a lot for your input! 
  
  We've discussed quite a bit with @lhoestq and we think the best approach is the following:
  
  
  a)
  `load_dataset(...)` will not store both bytes and the files because this would mean that 3x the size of the dataset would often be needed (1. the compressed `tar.gz` file, 2. the extracted file b, 3. the raw bytes in arrow format). 
  
  For canonical datasets like librispeech and common voice I think we want to keep the dataset filenames because of i) no breaking changes and ii) reasons explained in #3663
  
  However it's also trivial to write your own datasetset downloading script of librispeech and just not extract the folder e.g. this line: https://huggingface.co/datasets/common_voice/blob/main/common_voice.py#L671
  
  And then it'll be allowed to save the bytes and the dataset will be self-contained out-of-the-box when using `load_dataset(...)`
  
  b) Now, one major problem that you guys uncovered is that `save_to_disk(...)` is currently not necessarily saving a dataset to be self-contained. We will change that asap. This means that after we've corrected this when you do download the canonical librispeech dataset the following will work:
  
  ```python
  ds = load_dataset("....")  # <- here we have a dependency on the filepathes
  ds[0]["audio"]["bytes"] # <- will not work
  
  ds.save_to_disk("/local/path")  # <- now we want to have a self-contained dataset in arrow format, so we load the files into bytes and save it in arrow format
  
  # now you can delete everything besides "/local/path"
  
  ds = load_from_disk("/local/path")  # <- this will work
  ```
  
  So either option a) where you define your own librispeech data downloading script (you guys could just sign up here: https://huggingface.co/join) and upload a dataset loading script in private mode so that no one can see it and you would always store the audio as bytes or b) where you first load then save to disk then delete cache would work. 
  
  Hope that fits in your vision :-)
  
  cc @lhoestq @mariosasko  @patrickvonplaten sounds like a good approach to me. For b) this could even be configurable with a parameter like `embed_external_files` as you have for `push_to_hub` (if people prefer to keep separate audio files).
   > However it's also trivial to write your own datasetset downloading script of librispeech and just not extract the folder
  
  I don't exactly understand. In all cases, we need to extract it to prepare the dataset, or not? No matter if we want to store the raw bytes inside the dataset or leaving them as local files. Just in the first case, we can safely delete the extracted files after the dataset preparation.
  
  > `save_to_disk(...)` is currently not necessarily saving a dataset to be self-contained. We will change that asap.
  
  For us, this sounds exactly like what we want.
  
  But regarding not introducing breaking changes, wouldn't this maybe also break some setups for users who don't expect this new behavior?
   @albertz I would suggest to move the discussion on implementation details on our side to the following issue: rwth-i6/i6_core/issues/257 I like the idea of adding `embed_external_files` and set it to True by default to `save_to_disk`.
  It's indeed a kind of breaking change since some users will get bigger Arrow files when updating the lib, but the advantages are nice:
  1. I like the idea of having it self contained, in case you want to delete your cache
  2. users also upload these Arrow files to cloud storage via the `fs` parameter, and in this case they would expect to upload a self-contained dataset
  3. consistency with `push_to_hub`
  
  If it sounds good to you I'll open an issue to discuss this and track the advancements
body: Add `"all"` config to Librispeech