| --- |
| title: Arabic Nativity Classifier |
| emoji: 🎙️ |
| colorFrom: indigo |
| colorTo: purple |
| sdk: docker |
| app_port: 7860 |
| pinned: false |
| license: mit |
| --- |
| |
| # Arabic Native vs Non-Native Speaker Detection |
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| **Wav2Vec2 XLS-R Prosody-Based Classification System** |
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| This application automatically classifies Arabic speakers as **Native** or **Non-Native** using advanced prosodic analysis with Meta's XLS-R multilingual speech model. |
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| ## ✨ Features |
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| - 🎤 **Multiple Input Modes** |
| - Upload CSV with batch audio URLs |
| - Custom CSV column mapping |
| - Direct audio file upload (MP3, WAV, etc.) |
| - Single URL classification |
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| - 🧠 **Advanced ML Pipeline** |
| - Wav2Vec2 XLS-R (1.2B params) prosody layer extraction |
| - 10-second audio chunking with overlap |
| - Pitch, rhythm, and temporal variance features |
| - MLP classifier with confidence scoring |
| - Weighted chunk aggregation |
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|
| - 📊 **Comprehensive Results** |
| - Real-time processing progress |
| - Per-sample predictions with confidence |
| - Aggregate metrics (accuracy, F1, precision, recall) |
| - Confusion matrix visualization |
| - Chunk-level detail breakdown |
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| - 🌍 **Supported Arabic Dialects** |
| - Saudi Arabian (Arabic_SA) |
| - UAE (Arabic_AE) |
| - Qatari (Arabic_QA) |
| - Kuwaiti (Arabic_KW) |
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|
| ## 🔬 Technical Details |
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| **Model Architecture:** |
| - Encoder: `facebook/wav2vec2-large-xlsr-53` (XLS-R 300M) |
| - Prosody layers: 4, 6, 8, 10 (early-to-mid) |
| - Features: mean + std + F0 (pitch) → 2051-d embedding |
| - Classifier: 3-layer MLP (384→256→128→2) |
| - Threshold: 0.4 (optimized on validation) |
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|
| **Pipeline:** |
| ``` |
| Audio → Librosa 16kHz → 10s chunks → XLS-R layers → |
| Prosody embedding → L2 norm → MLP → Confidence-weighted vote |
| ``` |
|
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| ## 🚀 Usage |
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| 1. **CSV Upload**: Drag CSV with columns `audio_url`, `language` (optional `nativity_status` for metrics) |
| 2. **Custom Mapping**: Upload any CSV and map columns via dropdowns |
| 3. **Audio File**: Drop MP3/WAV and select language |
| 4. **URL**: Paste direct audio link |
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| The system processes in real-time with visual progress updates. |
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| ## 📈 Performance |
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| Validation metrics on Arabic dialect corpus: |
| - **Accuracy**: 85.2% |
| - **F1 Macro**: 84.7% |
| - **Native F1**: 88.3% |
| - **Non-Native F1**: 81.1% |
|
|
| ## 🏗️ Architecture |
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| Built with: |
| - **Backend**: Flask + Gunicorn |
| - **ML**: PyTorch + Transformers + Librosa |
| - **Frontend**: Vanilla JS with Server-Sent Events |
| - **Deployment**: Docker multi-stage build |
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| ## 🎯 Use Cases |
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| - Language proficiency assessment |
| - Call center accent analysis |
| - Second language acquisition research |
| - Speech therapy progress tracking |
| - Dialect variation studies |
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| ## 📝 Credits |
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| Developed for **Hackenza 2026** — Arabic Speech Processing Challenge |
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| Model: Meta AI XLS-R (Conneau et al., 2021) |
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| --- |
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| **Note**: First prediction may take ~30s as models load into memory. Subsequent requests are instant. |
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