Sulak2020/github-issues-multirepo-datasets-Sulakshana huggingface dataset:
Title: .map() is not caching and ram goes OOM
State: open

### Describe the bug

Im trying to run a fairly simple map that is converting a dataset into numpy arrays. however, it just piles up on memory and doesnt write to disk. Ive tried multiple cache techniques such as specifying the cache dir, setting max mem, +++ but none seem to work. What am I missing here?

### Steps to reproduce the bug

```
from pydub import AudioSegment
import io
import base64
import numpy as np
import os
CACHE_PATH = "/mnt/extdisk/cache" # "/root/.cache/huggingface/"# 
os.environ["HF_HOME"] = CACHE_PATH
import datasets
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# Create a handler for Jupyter notebook
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)

#datasets.config.IN_MEMORY_MAX_SIZE= 1000#*(2**30) #50 gb
print(datasets.config.HF_CACHE_HOME)
print(datasets.config.HF_DATASETS_CACHE)
# Decode the base64 string into bytes
def convert_mp3_to_audio_segment(example):
    """
    example = ds['train'][0]
    """
    try:
        audio_data_bytes = base64.b64decode(example['audio'])
        # Use pydub to load the MP3 audio from the decoded bytes
        audio_segment = AudioSegment.from_file(io.BytesIO(audio_data_bytes), format="mp3")
        # Resample to 24_000
        audio_segment = audio_segment.set_frame_rate(24_000)
        audio = {'sampling_rate' : audio_segment.frame_rate,
        'array' : np.array(audio_segment.get_array_of_samples(), dtype="float")}
        del audio_segment
        duration = len(audio['array']) / audio['sampling_rate']
    except Exception as e:
        logger.warning(f"Failed to convert audio for {example['id']}. Error: {e}")
        audio = {'sampling_rate' : 0,
        'array' : np.array([]), duration : 0}
    return {'audio' : audio, 'duration' : duration}

ds = datasets.load_dataset("NbAiLab/nb_distil_speech_noconcat_stortinget", cache_dir=CACHE_PATH, keep_in_memory=False)

#%%
num_proc=32
ds_processed = (
    ds
    #.select(range(10))
    .map(convert_mp3_to_audio_segment, num_proc=num_proc, desc="Converting mp3 to audio segment") #, cache_file_name=f"{CACHE_PATH}/stortinget_audio" # , cache_file_name="test"
)
```

### Expected behavior

the map should write to disk

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.39
- Python version: 3.12.7
- `huggingface_hub` version: 0.26.3
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0

Comments: I have the same issue - any update on this?
Title: Remove upper bound for fsspec
State: open

### Describe the bug

As also raised by @cyyever in https://github.com/huggingface/datasets/pull/7296 and @NeilGirdhar in https://github.com/huggingface/datasets/commit/d5468836fe94e8be1ae093397dd43d4a2503b926#commitcomment-140952162 , `datasets` has a problematic version constraint on `fsspec`.

In our case this causes (unnecessary?) troubles due to a race condition bug in that version of the corresponding `gcsfs` plugin, that causes deadlocks: https://github.com/fsspec/gcsfs/pull/643

We just use a version override to ignore the constraint from `datasets`, but imho the version constraint could just be removed in the first place?

The last few PRs bumping the upper bound were basically uneventful:

* https://github.com/huggingface/datasets/pull/7219
* https://github.com/huggingface/datasets/pull/6921
* https://github.com/huggingface/datasets/pull/6747

### Steps to reproduce the bug

-

### Expected behavior

Installing `fsspec>=2024.10.0` along `datasets` should be possible without overwriting constraints.

### Environment info

All recent datasets versions

Comments: Unfortunately `fsspec` versioning allows breaking changes across version and there is no way we can keep it without constrains at the moment. It already broke `datasets` once in the past. Maybe one day once `fsspec` decides on a stable and future proof API but I don't think this will happen anytime soon

edit: bumped to 2024.10.0 in https://github.com/huggingface/datasets/pull/7352
Title: Introduce pdf support (#7318)
State: closed

First implementation of the Pdf feature to support pdfs (#7318) . Using [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library) as the default library to work with pdfs. 
@lhoestq and @AndreaFrancis 

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7325). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.Hi @AndreaFrancis and @lhoestq ! Thanks for looking at the code and for all the changes and suggestions. I have worked on all your suggestions. I need to work a bit more on the tests, but I created a first version and uploaded a simple pdf to use for testing. The pdf has a couple of pages some of which have images, figures, and tables (this is exactly the pdfs for which I think keeping the pdf format is very interesting because they have multiple types of content). Will try to finish the tests as soon as possible.(I updated the CI a bit, I took the liberty to update your branch and re-run it on your PR @yabramuvdi)
Title: Unexpected cache behaviour using load_dataset
State: closed

### Describe the bug

Following the (Cache management)[https://huggingface.co/docs/datasets/en/cache] docu and previous behaviour from datasets version 2.18.0, one is able to change the cache directory. Previously, all downloaded/extracted/etc files were found in this folder. As i have recently update to the latest version this is not the case anymore. Downloaded files are stored in `~/.cache/huggingface/hub`.
Providing the `cache_dir` argument in `load_dataset` the cache directory is created and there are some files but the bulk is still in `~/.cache/huggingface/hub`.

I believe this could be solved by adding the cache_dir argument [here](https://github.com/huggingface/datasets/blob/fdda5585ab18ea1292547f36c969d12c408ab842/src/datasets/utils/file_utils.py#L188)


### Steps to reproduce the bug

For example using https://huggingface.co/datasets/ashraq/esc50:
```python
from datasets import load_dataset
ds = load_dataset("ashraq/esc50", "default", cache_dir="~/custom/cache/path/esc50")
```


### Expected behavior

I would expect the bulk of files related to the dataset to be stored somewhere in `~/custom/cache/path/esc50`, but it seems they are in `~/.cache/huggingface/hub/datasets--ashraq--esc50`. 

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-5.14.0-503.15.1.el9_5.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.14
- `huggingface_hub` version: 0.26.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1

Comments: Hi ! Since `datasets` 3.x,  the `datasets` specific files are in `cache_dir=` and the HF files are cached using `huggingface_hub` and you can set its cache directory using the `HF_HOME` environment variable.

They are independent, for example you can delete the Hub cache (containing downloaded files) but still reload your cached datasets from the `datasets` cache (containing prepared datasets in Arrow format)
Title: ImportError: cannot import name 'set_caching_enabled' from 'datasets' 
State: open

### Describe the bug

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/runpy.py", line 187, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "/usr/local/lib/python3.10/runpy.py", line 110, in _get_module_details
    __import__(pkg_name)
  File "/home/Medusa/axolotl/src/axolotl/cli/__init__.py", line 23, in <module>
    from axolotl.train import TrainDatasetMeta
  File "/home/Medusa/axolotl/src/axolotl/train.py", line 23, in <module>
    from axolotl.utils.trainer import setup_trainer
  File "/home/Medusa/axolotl/src/axolotl/utils/trainer.py", line 13, in <module>
    from datasets import set_caching_enabled
ImportError: cannot import name 'set_caching_enabled' from 'datasets' (/usr/local/lib/python3.10/site-packages/datasets/__init__.py)

### Steps to reproduce the bug

1、axolotl
2、accelerate launch -m axolotl.cli.train examples/medusa/qwen_lora_stage1.yml

### Expected behavior

enable datasets

### Environment info

python3.10

Comments: pip install datasets==2.18.0Hi ! I think you need to update axolotl
Title: Introduce support for PDFs
State: open
Labels: name: enhancement
description: New feature or request

### Feature request

The idea (discussed in the Discord server with @lhoestq ) is to have a Pdf type like Image/Audio/Video. For example [Video](https://github.com/huggingface/datasets/blob/main/src/datasets/features/video.py) was recently added and contains how to decode a video file encoded in a dictionary like {"path": ..., "bytes": ...} as a VideoReader using decord. We want to do the same with pdf and get a [pypdfium2.PdfDocument](https://pypdfium2.readthedocs.io/en/stable/_modules/pypdfium2/_helpers/document.html#PdfDocument).

### Motivation

In many cases PDFs contain very valuable information beyond text (e.g. images, figures). Support for PDFs would help create datasets where all the information is preserved.

### Your contribution

I can start the implementation of the Pdf type :)

Comments: #self-assignAwesome ! Let me know if you have any question or if I can help :)

cc @AndreaFrancis as well for vizOther candidates libraries for the Pdf type: PyMuPDF pypdf and pdfplumber

EDIT: Pymupdf looks like a good choice when it comes to maturity + performance + versatility BUT the license is maybe an issue, and pypdf, pypdfium2 or pdfplumber are good options imoRelated to https://github.com/huggingface/datasets/issues/7058PyMuPDF is AGPL licensed, so we can't use it. I will move forward with [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library).Hi both! I have made a pull request with a first basic implementation of the Pdf feature. I followed closely what I saw on the Video and Image features. It is my first time contributing so any comments are very welcomed. I think it would be useful to outline together what additional things we can implement (e.g. enabling parsing of the pdf). Thanks :)
Title: More docs to from_dict to mention that the result lives in RAM
State: closed

following discussions at https://discuss.huggingface.co/t/how-to-load-this-simple-audio-data-set-and-use-dataset-map-without-memory-issues/17722/14

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7316). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Cannot create a dataset with relative audio path
State: open

### Describe the bug

Hello! I want to create a dataset of parquet files, with audios stored as separate .mp3 files. However, it says "No such file or directory" (see the reproducing code).

### Steps to reproduce the bug

Creating a dataset
```
from pathlib import Path
from datasets import Dataset, load_dataset, Audio

Path('my_dataset/audio').mkdir(parents=True, exist_ok=True)
Path('my_dataset/audio/file.mp3').touch(exist_ok=True)
Dataset.from_list(
    [{'audio': {'path': 'audio/file.mp3'}}]
).to_parquet('my_dataset/data.parquet')
```

Result:
```
# my_dataset
# ├── audio
# │   └── file.mp3
# └── data.parquet
```

Trying to load the dataset
```
dataset = (
    load_dataset('my_dataset', split='train')
    .cast_column('audio', Audio(sampling_rate=16_000))
)
dataset[0]

>>> FileNotFoundError: [Errno 2] No such file or directory: 'audio/file.mp3'
```

### Expected behavior

I expect the dataset to load correctly.

I've found 2 workarounds, but they are not very good:
1. I can specify an absolute path to the audio, however, when I move the folder or upload to HF it will stop working.
2. I can set `'path': 'file.mp3'`, and load with `load_dataset('my_dataset', data_dir='audio')` - it seems to work, but does this mean that anyone from Hugging Face who wants to use this dataset should also pass the `data_dir` argument, otherwise it won't work?

### Environment info

datasets 3.1.0, Ubuntu 24.04.1

Comments: Hello ! when you `cast_column` you need the paths to be absolute paths or relative paths to your working directory, not the original dataset directory.

Though I'd recommend structuring your dataset as an AudioFolder which automatically links a metadata.jsonl or csv to the audio files via relative paths **within** the dataset repository: https://huggingface.co/docs/datasets/v3.2.0/en/audio_load#audiofolder@lhoestq thank you, but there are two problems with using AudioFolder:
1. It is said that AudioFolder requires metadata.csv. However, my datset is too large and contains nested and np.ndarray fields, so I can't use csv.
2. It is said that I need to load the dataset with `load_dataset("audiofolder", ...)`. However, if possible, I want my dataset to be loaded as usual with `load_dataset(dataset_name)` after I upload if to HF.You can use metadata.jsonl if you have nested data :)

And actually if you have a dataset structured as an AudioFolder then `load_dataset(dataset_name)` does work after uploading to HFI have created an audio dataset. In my repo, I have explained the steps and structure. An example dataset is also available in the repo. https://github.com/pr0mila/ParquetToHuggingFace
Title: [Audio Features - DO NOT MERGE] PoC for adding an offset+sliced reading to audio file.
State: open

This is a proof of concept for #7310 . The idea is to enable the access to others column of the dataset row when loading an audio file into a table. This is to allow sliced reading. As stated in the issue, many people have very long audio files and use start and stop slicing in this audio file.

Right now, this code work as a PoC on my dataset. However, this is **just to illustrate** the idea. Many things are messed up, the first being that the shards have wildly varying sizes. 

Could be of interest to @lhoestq and @sanchit-gandhi ? 

Happy to test better ideas locally.
Title: How to get the original dataset name with username?
State: open
Labels: name: enhancement
description: New feature or request

### Feature request

The issue is related to ray data https://github.com/ray-project/ray/issues/49008 which it requires to check if the dataset is the original one just after `load_dataset` and parquet files are already available on hf hub.

The solution used now is to get the dataset name, config and split, then `load_dataset` again and check the fingerprint. But it's unable to get the correct dataset name if it contains username. So how to get the dataset name with username prefix, or is there another way to query if a dataset is the original one with parquet available?

@lhoestq 

### Motivation

https://github.com/ray-project/ray/issues/49008

### Your contribution

Would like to fix that.

Comments: Hi ! why not pass the dataset id to Ray and let it check the parquet files ? Or pass the parquet files lists directly ?I'm not sure why ray design an API like this to accept a `Dataset` object, so they need to verify the `Dataset` is the original one and use the `DatasetInfo` to query the huggingface hub. I'll advise the ray data team to use dataset id instead of dataset for the `HuggingFaceDatasource` API.
Title: Enable the Audio Feature to decode / read with an offset + duration
State: open
Labels: name: enhancement
description: New feature or request

### Feature request

For most large speech dataset, we do not wish to generate hundreds of millions of small audio samples. Instead, it is quite common to provide larger audio files with frame offset (soundfile start and stop arguments). We should be able to pass these arguments to Audio() (column ID corresponding in the dataset row).

### Motivation

I am currently generating a fairly big dataset to .parquet(). Unfortunately, it does not work because all existing functions load the whole .wav file corresponding to the row. All my attempts at bypassing this failed. We should be able to put in the Table only the bytes corresponding to what soundfile reads with an offset (and subset of the audio file). 

### Your contribution

I can totally test whatever code on my large dataset creation script. 

Comments: Hi ! What about having audio + start + duration columns and enable something like this ?

```python
for example in ds:
    array = example["audio"].read(start=example["start"], frames=example["duration"])
```Hi @lhoestq, this would work with a file-based dataset but would be terrible for a sharded one as it would duplicate the large audio file many times. Also, very long audio files are not embedded very well in the parquet file, even with large_binary(). It crashed a few times for me until I switched to one sample == one file :-(
Title: Faster parquet streaming + filters with predicate pushdown
State: closed

ParquetFragment.to_batches uses a buffered stream to read parquet data, which makes streaming faster (x2 on my laptop).

I also added the `filters` config parameter to support filtering with predicate pushdown, e.g.

```python
from datasets import load_dataset

filters = [('problem_source', '==', 'math')]
ds = load_dataset("nvidia/OpenMathInstruct-2", streaming=True, filters=filters)
first_example = next(iter(ds["train"]))
print(first_example["problem_source"])
# 'math'
```

cc @allisonwang-db this is a nice plus for usage in spark

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7309). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Creating new dataset from list loses information. (Audio Information Lost - either Datatype or Values).
State: open

### Describe the bug

When creating a dataset from a list of datapoints, information is lost of the individual items.

Specifically, when creating a dataset from a list of datapoints (from another dataset). Either the datatype is lost or the values are lost. See examples below. 

-> What is the best way to create a dataset from a list of datapoints?

---
e.g.:
**When running this code:**
```python
from datasets import load_dataset, Dataset
commonvoice_data = load_dataset("mozilla-foundation/common_voice_17_0", "it", split="test", streaming=True)
datapoint = next(iter(commonvoice_data))
out = [datapoint]
new_data = Dataset.from_list(out) #this loses datatype information
new_data2= Dataset.from_list(out,features=commonvoice_data.features) #this loses value information
```

**We get the following**:
---
1. `datapoint`: (the original datapoint)
```
'audio': {'path': 'it_test_0/common_voice_it_23606167.mp3', 'array': array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
       2.21619011e-05, 2.72628222e-05, 0.00000000e+00]), 'sampling_rate': 48000}
 ```
Original Dataset Features:
```
>>> commonvoice_data.features
'audio': Audio(sampling_rate=48000, mono=True, decode=True, id=None)
```
 - Here we see column "audio", has the proper values (both `path` & and `array`) and has the correct datatype (Audio).

 
 ----
 2. new_data[0]:
```
# Cannot be printed (as it prints the entire array).
```
New Dataset 1 Features:
```
>>> new_data.features
'audio': {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)}
```
 - Here we see that the column "audio", has the correct values, but is not the Audio datatype anymore.

---
3. new_data2[0]:
```
'audio': {'path': None, 'array': array([0., 0., 0., ..., 0., 0., 0.]), 'sampling_rate': 48000},
```
New Dataset 2 Features:
```
>>> new_data2.features
'audio': Audio(sampling_rate=48000, mono=True, decode=True, id=None),
```
 - Here we see that the column "audio", has the correct datatype, but all the array & path values were lost!



### Steps to reproduce the bug

## Run:
```python
from datasets import load_dataset, Dataset
commonvoice_data = load_dataset("mozilla-foundation/common_voice_17_0", "it", split="test", streaming=True)
datapoint = next(iter(commonvoice_data))
out = [datapoint]
new_data = Dataset.from_list(out) #this loses datatype information
new_data2= Dataset.from_list(out,features=commonvoice_data.features) #this loses value information
```





### Expected behavior

## Expected:
```datapoint == new_data[0]```

AND

```datapoint == new_data2[0]```

### Environment info

- `datasets` version: 3.1.0
- Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
Title:  Build Documentation Test Fails Due to "Bad Credentials" Error
State: open

### Describe the bug

The `Build documentation / build / build_main_documentation (push)` job is consistently failing during the "Syncing repository" step. The error occurs when attempting to determine the default branch name, resulting in "Bad credentials" errors.

### Steps to reproduce the bug

1. Trigger the `build_main_documentation` job.
2. Observe the logs during the "Syncing repository" step.

### Expected behavior

The workflow should be able to retrieve the default branch name without encountering credential issues.

### Environment info

```plaintext
Syncing repository: huggingface/notebooks
Getting Git version info
Temporarily overriding HOME='/home/runner/work/_temp/00e62748-9940-4a4f-bbbc-eb2cda6d7ed6' before making global git config changes
Adding repository directory to the temporary git global config as a safe directory
/usr/bin/git config --global --add safe.directory /home/runner/work/datasets/datasets/notebooks
Initializing the repository
Disabling automatic garbage collection
Setting up auth
Determining the default branch
  Retrieving the default branch name
  Bad credentials - https://docs.github.com/rest
  Waiting 20 seconds before trying again
  Retrieving the default branch name
  Bad credentials - https://docs.github.com/rest
  Waiting 19 seconds before trying again
  Retrieving the default branch name
  Error: Bad credentials - https://docs.github.com/rest
```

Comments: how were you able to fix this please?> how were you able to fix this please?

I was not able to fix this.