Create README.md

README.md

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+---
+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}}
+}
+```