---
license:
- cc-by-4.0
size_categories:
  ar:
  - n==1k
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: MASC dataset
extra_gated_prompt: >-
  By clicking on “Access repository” below, you also agree to not attempt to
  determine the identity of speakers in the MASC dataset.
language:
- ar
---

# Dataset Card for Common Voice Corpus 11.0

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Languages](#languages)
  - [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Additional Information](#additional-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus
- **Paper:** https://ieeexplore.ieee.org/document/10022652

### Dataset Summary

MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels.
The dataset is multi-regional, multi-genre, and multi-dialect intended to advance the research and development of Arabic speech technology with a special emphasis on Arabic speech recognition.


### Supported Tasks

- Automatics Speach Recognition

### Languages

```
Arabic
```

## How to use

The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. 

```python
from datasets import load_dataset

masc = load_dataset("pain/MASC", split="train")
```

Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset

masc = load_dataset("pain/MASC", split="train", streaming=True)

print(next(iter(masc)))
```

*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).

### Local

```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler

masc = load_dataset("pain/MASC", split="train")
batch_sampler = BatchSampler(RandomSampler(masc), batch_size=32, drop_last=False)
dataloader = DataLoader(masc, batch_sampler=batch_sampler)
```

### Streaming

```python
from datasets import load_dataset
from torch.utils.data import DataLoader

masc = load_dataset("pain/MASC", split="train")
dataloader = DataLoader(masc, batch_size=32)
```

To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).

### Example scripts

Train your own CTC or Seq2Seq Automatic Speech Recognition models on MASC with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).

## Dataset Structure

### Data Instances

A typical data point comprises the `path` to the audio file and its `sentence`. 

```python
{'video_id': 'OGqz9G-JO0E', 'start': 770.6, 'end': 781.835, 'duration': 11.24,
'text': 'اللهم من ارادنا وبلادنا وبلاد المسلمين بسوء اللهم فاشغله في نفسه ورد كيده في نحره واجعل تدبيره تدميره يا رب العالمين',
'type': 'c', 'file_path': '87edeceb-5349-4210-89ad-8c3e91e54062_OGqz9G-JO0E.wav',
'audio': {'path': None,
'array': array([
            0.05938721,
            0.0539856,
            0.03460693, ...,
            0.00393677,
            0.01745605,
            0.03045654
        ]), 'sampling_rate': 16000
    }
}
```

### Data Fields

`video_id` (`string`): An id for the video that the voice has been created from

`start` (`float64`): The start of the audio's chunk

`end` (`float64`): The end of the audio's chunk

`duration` (`float64`): The duration of the chunk

`text` (`string`): The text of the chunk

`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.

`type` (`string`): It refers to the data set type, either clean or noisy where "c: clean and n: noisy"

'file_path' (`string`): A path for the audio chunk

"audio" ("audio"): Audio for the chunk

### Data Splits

The speech material has been subdivided into portions for train, dev, test.

The dataset splits has clean and noisy data that can be determined by type field.




### Citation Information

```
@INPROCEEDINGS{10022652,
  author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
  booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)}, 
  title={MASC: Massive Arabic Speech Corpus}, 
  year={2023},
  volume={},
  number={},
  pages={1006-1013},
  doi={10.1109/SLT54892.2023.10022652}}
}
```