README.md

Egyptian Arabic ASR Clean 72 h

Dataset Summary

This corpus contains ≈72 h of Egyptian‑Arabic speech aligned to text.
Audio has been resampled to 16 kHz mono WAV, transcripts are normalised Arabic (no diacritics, Tatweel, digits verbalised), and the data are split 80 / 10 / 10 into train / validation / test.

Supported Tasks and Leaderboards

Task	Tags	Notes
Automatic Speech Recognition	asr, speech-recognition	Primary use‑case
Forced Alignment / VAD	alignment, vad	Clips ≤ 25 s

Languages

The dataset is predominantly Egyptian Arabic (ar‑EG).
~85 % of recorded hours are male speakers; speaker IDs are unavailable.

Dataset Structure

Data Fields

Field	Type	Description
audio	Audio	Pointer to WAV @ 16 kHz
text	string	Normalised Arabic transcript
duration	float	Seconds (post‑resample)
dataset_source	string	One‑letter code A–D

Splits

Split	Hours
train	≈57.6
validation	≈7.2
test	≈7.2

Source Data

Code	Raw Hours	Description
A	~465	Long clips, heavy overlap
B	~65	Similar to A, shorter
C	~5	Dual‑channel conversations
D	~2.5	YouTube excerpts
Other	<5	Minor sources

Only ≈12 % of the original 570 h survived the cleaning pipeline.

Data Collection and Processing

1. Format Unification – convert all audio to 16 kHz WAV.
2. Deduplication – drop exact audio/text duplicates; remove nulls.
3. Metadata Pruning – retain only core fields.
4. Text Normalisation – strip diacritics, Tatweel, punctuation, Latin letters; verbalise digits; fix common glyph errors; run CAMeL‑Tools morphology checks.
5. Alignment Diagnostics – compute chars/s and words/s; flag extreme values.
6. Duration Filtering – keep clips 0.5–25 s.
7. Shuffle & Split – 80 / 10 / 10 random split, uploaded as datasets.DatasetDict.

Usage Example

from datasets import load_dataset

ds = load_dataset("your-username/egyptian_arabic_asr_clean72h")
print(ds["train"][0]["audio"].sampling_rate)  # 16000
print(ds["train"][0]["text"])