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--- |
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license: |
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- cc-by-4.0 |
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size_categories: |
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ar: |
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- n==1k |
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task_categories: |
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- automatic-speech-recognition |
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task_ids: [] |
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pretty_name: MASC dataset |
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extra_gated_prompt: >- |
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By clicking on “Access repository” below, you also agree to not attempt to |
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determine the identity of speakers in the MASC dataset. |
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language: |
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- ar |
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--- |
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# Dataset Card for Common Voice Corpus 11.0 |
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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Languages](#languages) |
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- [How to use](#how-to-use) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Additional Information](#additional-information) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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- **Homepage:** https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus |
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- **Paper:** https://ieeexplore.ieee.org/document/10022652 |
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### Dataset Summary |
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MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels. |
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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. |
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### Supported Tasks |
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- Automatics Speach Recognition |
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### Languages |
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``` |
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Arabic |
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``` |
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## How to use |
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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. |
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```python |
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from datasets import load_dataset |
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masc = load_dataset("pain/MASC", split="train") |
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``` |
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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. |
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```python |
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from datasets import load_dataset |
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masc = load_dataset("pain/MASC", split="train", streaming=True) |
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print(next(iter(masc))) |
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``` |
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*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). |
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### Local |
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```python |
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from datasets import load_dataset |
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from torch.utils.data.sampler import BatchSampler, RandomSampler |
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masc = load_dataset("pain/MASC", split="train") |
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batch_sampler = BatchSampler(RandomSampler(masc), batch_size=32, drop_last=False) |
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dataloader = DataLoader(masc, batch_sampler=batch_sampler) |
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``` |
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### Streaming |
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```python |
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from datasets import load_dataset |
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from torch.utils.data import DataLoader |
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masc = load_dataset("pain/MASC", split="train") |
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dataloader = DataLoader(masc, batch_size=32) |
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``` |
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To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). |
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### Example scripts |
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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). |
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## Dataset Structure |
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### Data Instances |
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A typical data point comprises the `path` to the audio file and its `sentence`. |
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```python |
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{'video_id': 'OGqz9G-JO0E', 'start': 770.6, 'end': 781.835, 'duration': 11.24, |
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'text': 'اللهم من ارادنا وبلادنا وبلاد المسلمين بسوء اللهم فاشغله في نفسه ورد كيده في نحره واجعل تدبيره تدميره يا رب العالمين', |
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'type': 'c', 'file_path': '87edeceb-5349-4210-89ad-8c3e91e54062_OGqz9G-JO0E.wav', |
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'audio': {'path': None, |
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'array': array([ |
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0.05938721, |
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0.0539856, |
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0.03460693, ..., |
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0.00393677, |
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0.01745605, |
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0.03045654 |
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]), 'sampling_rate': 16000 |
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} |
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} |
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``` |
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### Data Fields |
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`video_id` (`string`): An id for the video that the voice has been created from |
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`start` (`float64`): The start of the audio's chunk |
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`end` (`float64`): The end of the audio's chunk |
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`duration` (`float64`): The duration of the chunk |
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`text` (`string`): The text of the chunk |
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`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]`. |
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`type` (`string`): It refers to the data set type, either clean or noisy where "c: clean and n: noisy" |
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'file_path' (`string`): A path for the audio chunk |
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"audio" ("audio"): Audio for the chunk |
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### Data Splits |
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The speech material has been subdivided into portions for train, dev, test. |
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The dataset splits has clean and noisy data that can be determined by type field. |
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### Citation Information |
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``` |
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@INPROCEEDINGS{10022652, |
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author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha}, |
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booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)}, |
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title={MASC: Massive Arabic Speech Corpus}, |
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year={2023}, |
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volume={}, |
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number={}, |
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pages={1006-1013}, |
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doi={10.1109/SLT54892.2023.10022652}} |
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} |
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``` |
