Datasets:
language:
- ar
tags:
- arabic
- egypt
- egyptian
- ASR
- automatic speech recognition
pretty_name: 'Egyptian Arabic dialect automatic speech recognition '
size_categories:
- 1K<n<10K
task_categories:
- automatic-speech-recognition
Egyptian Arabic dialect automatic speech recognition
Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
Dataset Summary
This dataset was collected, cleaned and adjusted for huggingface hub and ready to be used for whisper finetunning/training.
The MGB-3 is using 16 hours multi-genre data collected from different YouTube channels. The 16 hours have been manually transcribed. The chosen Arabic dialect for this year is Egyptian. Given that dialectal Arabic has no orthographic rules, each program has been transcribed by four different transcribers using this transcription guidelines.
Supported Tasks and Leaderboards
ASR: automatic speech recognition
Languages
Arabic - Egyptian dialect
Data Fields
- audio: sampled in 16000HZ and have a max duration of 30 sec (ideal for whispear and others ASR models)
- sentence: the transcription in Egyptian Arabic
Dataset Creation
The youtube videos that are still avalible (some of them got deleted/ made private) were downloaded and synced with the provided transcription. Then the 12 min of each youtube video were cut down into 30 sec segments. the resulting dataset was uploaded to huggingface.
Egyptian broadcast data collected from YouTube.This year, we collected about 80 programs from different YouTube channels. The first 12 minutes from each program has been transcribed and released. This sums up to roughly 16 hours in total
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
Speech Recognition Challenge in the Wild: Arabic MGB-3