| # 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 | |
| ```python | |
| 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"]) |