We're excited to announce the launch of the ASR (Automatic Speech Recognition) Project, a dedicated initiative to develop a custom Arabic speech recognition model optimized specifically for Abserny.
Why a Custom Model?
While Abserny v1.0 uses Vosk for speech recognition with excellent results, we've identified several areas where a custom model could provide significant improvements:
Improved Accuracy
- Better recognition of our specific trigger words
- Support for various Arabic dialects and accents
- Reduced false positives in noisy environments
- Higher confidence scores for correct detections
Reduced Latency
- Smaller model size for faster loading
- Optimized inference for our specific use case
- Lower computational requirements
- Better real-time performance
Better Resource Efficiency
- Lower CPU usage during continuous listening
- Reduced memory footprint
- Optimized for edge devices and mobile
Project Scope
The ASR project is a comprehensive effort that includes:
Dataset Creation
We're building a custom dataset specifically for our trigger words:
- 10,000+ recordings of Arabic trigger words
- Multiple speakers representing different demographics
- Various acoustic conditions (quiet, noisy, reverberant)
- Different recording devices and quality levels
Model Development
The technical approach includes:
- Deep learning architecture selection and testing
- Custom training pipeline development
- Hyperparameter optimization
- Model compression and quantization
Integration Planning
The model will be designed for seamless integration:
- Drop-in replacement for current Vosk implementation
- Backwards compatible configuration
- Optional fallback to Vosk
- Easy model updates and improvements
Development Timeline
Phase 1: Foundation (Current - Q1 2024)
- Dataset collection and preparation
- Model architecture research and selection
- Training infrastructure setup
- Initial baseline model training
Phase 2: Training (Q2 2024)
- Full dataset training
- Model evaluation and benchmarking
- Optimization and fine-tuning
- Performance comparison with Vosk
Phase 3: Integration (Q3 2024)
- Integration with Abserny Core
- End-to-end testing
- User acceptance testing
- Documentation
Phase 4: Release (Q4 2024)
- Beta release to testers
- Feedback collection and improvements
- Production release with Abserny v1.1
Technical Approach
Our initial research points to a hybrid approach:
Architecture
- Feature extraction using MFCC or mel-spectrograms
- LSTM-based acoustic model for temporal patterns
- Attention mechanisms for better context
- CTC loss for sequence-to-sequence learning
Optimization
- Model quantization for smaller size
- Pruning unnecessary connections
- Knowledge distillation from larger models
- TensorFlow Lite conversion for mobile
How You Can Help
This is a community-driven project and we welcome contributions:
Voice Contributions
Help us build a diverse dataset:
- Record the trigger words in your voice
- Record in different environments
- Contribute recordings from different dialects
We'll provide detailed recording guidelines and a simple submission process.
Technical Contributions
- Model architecture suggestions
- Training pipeline improvements
- Evaluation metrics and benchmarks
- Documentation
Expected Impact
Once integrated, the custom ASR model will:
- Improve trigger word recognition accuracy by an estimated 15-20%
- Reduce voice activation latency by 30-40%
- Decrease CPU usage during listening by ~25%
- Enable better mobile performance
- Support future expansion to more trigger words
Stay Updated
Follow our progress:
We're excited about this initiative and believe it will significantly enhance the Abserny experience for all users.