Update README.md

README.md

@@ -32,4 +32,70 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+task_categories:
+- automatic-speech-recognition
+
+pretty_name: A
 ---
+
+# 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"])