Thee-Unruly/Swahili-ASR-Solution

Build an offline, edge-device optimized Kiswahili ASR model for 200+ million speakers across East and Central Africa. # Kiswahili ASR Solution: Implementation Strategy --- ## Challenge Overview This project aims to develop an **offline, edge-device optimized Kiswahili Automatic Speech Recognition (ASR) model** capable of serving over 200 million speakers across East and Central Africa. **Key Constraints:** * **Single NVIDIA T4 GPU** (≤16 GB RAM) * **Real-time inference capability** * **Offline operation** (no cloud dependency) * **Open-source data and tools only** * **Bonus Target:** <4GB memory footprint for mobile deployment --- ## Solution Architecture ### 1. Base Model Selection * **Primary Approach:** Fine-tuned **Whisper-small** (244M parameters) for its proven multilingual capability and efficient architecture. * **Alternative:** **Wav2Vec2-base + CTC decoder** for potentially greater efficiency on edge devices. ### 2. Dataset Strategy * **Primary Dataset:** **Mozilla Common Voice Swahili** (100+ hours of labeled speech from native speakers). * **Data Augmentation:** * Speed perturbation (0.9x, 1.0x, 1.1x) * Noise addition (SNR: 10-20 dB) * SpecAugment * Time stretching * Pitch shifting (±2 semitones) * **Data Preprocessing:** * Audio normalization to 16kHz mono * Voice Activity Detection (VAD) for trimming * Text normalization for Kiswahili orthography * Handling code-switching (Swahili-English) ### 3. Model Optimization for Edge Deployment * **Quantization Strategy:** * Post-training quantization (INT8) * Dynamic quantization for inference * Knowledge distillation from a larger model * **Pruning Approach:** * Magnitude-based pruning (30-50% sparsity) * Structured pruning for hardware efficiency * Gradual pruning during fine-tuning * **Model Compression:** * ONNX Runtime optimization * TensorRT conversion for GPU inference * Mobile-specific optimizations (NNAPI, Core ML) ### 4. Training Strategy * **Phase 1: Transfer Learning** * Fine-tune pre-trained Whisper on Common Voice * Learning rate: 1e-5 with cosine annealing * Batch size: 16-32 (memory dependent) * M …