Web Developer Makes Kokoro TTS 50-100x Faster Using LLM Optimization
A web developer utilized an LLM to automatically hill-climb and distill the Kokoro TTS model, achieving a 50 to 100x speedup. This demonstrates a powerful practical application where non-ML specialists can leverage LLMs for complex automated model optimization on consumer hardware. The training process took about a day running on a local consumer graphics card, an RTX 4070 Ti Super, without requiring deep machine learning expertise.
## BACKGROUND
Model distillation is the process of training a smaller student model to imitate the performance of a larger teacher model to reduce computational costs. Kokoro TTS is an open-source text-to-speech model known for its efficiency, while hill-climbing is an iterative optimization algorithm that continuously makes incremental improvements to reach a better solution.