README.md

metadata

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

Wav2Vec2 XLS-R Prosody-Based Classification System

This application automatically classifies Arabic speakers as Native or Non-Native using advanced prosodic analysis with Meta's XLS-R multilingual speech model.

✨ Features

• 🎤 Multiple Input Modes

  • Upload CSV with batch audio URLs
  • Custom CSV column mapping
  • Direct audio file upload (MP3, WAV, etc.)
  • Single URL classification

• 🧠 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

• 📊 Comprehensive Results

  • Real-time processing progress
  • Per-sample predictions with confidence
  • Aggregate metrics (accuracy, F1, precision, recall)
  • Confusion matrix visualization
  • Chunk-level detail breakdown

• 🌍 Supported Arabic Dialects

  • Saudi Arabian (Arabic_SA)
  • UAE (Arabic_AE)
  • Qatari (Arabic_QA)
  • Kuwaiti (Arabic_KW)

🔬 Technical Details

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)

Pipeline:

Audio → Librosa 16kHz → 10s chunks → XLS-R layers → 
Prosody embedding → L2 norm → MLP → Confidence-weighted vote

🚀 Usage

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

The system processes in real-time with visual progress updates.

📈 Performance

Validation metrics on Arabic dialect corpus:

• Accuracy: 85.2%
• F1 Macro: 84.7%
• Native F1: 88.3%
• Non-Native F1: 81.1%

🏗️ Architecture

Built with:

• Backend: Flask + Gunicorn
• ML: PyTorch + Transformers + Librosa
• Frontend: Vanilla JS with Server-Sent Events
• Deployment: Docker multi-stage build

🎯 Use Cases

• Language proficiency assessment
• Call center accent analysis
• Second language acquisition research
• Speech therapy progress tracking
• Dialect variation studies

📝 Credits

Developed for Hackenza 2026 — Arabic Speech Processing Challenge

Model: Meta AI XLS-R (Conneau et al., 2021)

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Note: First prediction may take ~30s as models load into memory. Subsequent requests are instant.