The Engine
Performance. Quantized.Beyond Wrappers
C0vibe isn't just another API wrapper. It's a ground-up re-engineering of the audio transcription pipeline. We built a custom inference engine to achieve <200ms latency and 95% zero-edit rates.
The Streaming Pipeline
Optimization Layer
TensorRT Acceleration
We don't run raw PyTorch. Models are compiled to TensorRT engines, unlocking GPU-specific optimizations (kernel fusion, precision calibration) for 4x faster inference.
Speculative Decoding
Using a smaller "draft" model to predict tokens and a larger "target" model to verify them. Achieves 2-3x speedup without quality loss.
WebWorker Parallelization
Non-blocking main thread. Audio processing, VAD, and correction logic run in dedicated worker pools for buttery smooth UI.
Accuracy Engine
O(1) Dictionary Trie
Custom prefix tree implementation for instant (<0.1ms) lookup of 12,000+ specialized terms. Zero latency penalty for massive dictionaries.
Logit Biasing
We inject domain-specific terms (Medical, Legal, Code) directly into the model's beam search, forcing it to prefer correct terminology.
Cascade Correction
4-stage progressive quality check: Regex → Dictionary → Fast LLM → Deep LLM. Only uses heavier models when confidence is low.
Local Intelligence
Privacy isn't an afterthought. It's the architecture. C0vibe integrates llama.cpp to run state-of-the-art open models directly on your hardware.
