Worked on enhancing Chinese tokenization robustness in the Blaizzy/mlx-audio repository, focusing on improving compatibility for NLP and ASR pipelines. Developed a feature in Python that introduced multi-character token splitting, aligning VoxCPM2 tokenization with the tokenizer’s expected behavior. This adjustment improved preprocessing reliability and reduced tokenization errors, resulting in more consistent model input for Chinese text. The work leveraged skills in machine learning, natural language processing, and unit testing to ensure quality and maintainability. These changes laid the foundation for broader multilingual support, streamlining future expansions and improving data quality for both model training and inference in downstream applications.