A new method called Bayesian factorized adaptation enables high-performance multilingual ASR models to handle code-switching without degrading monolingual performance. It integrates switching-relevant knowledge efficiently using minimal synthetic data, reducing transcription errors by 32.87% and overall WER by 5.31%.