# 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"])