
Developed the BatonVoice subsystem for the Tencent/digitalhuman repository, delivering a modular core architecture for multi-mode text-to-speech with a Gradio-based user interface and integrated audio feature extraction. The work focused on scalable backend development in Python, leveraging API design and deep learning to support unified, fine-grained control across TTS modes. External TTS dependencies were integrated using Git submodules, streamlining build processes and maintenance. Comprehensive documentation and media assets were overhauled to improve onboarding and cross-team collaboration. Additionally, prosodic feature output in the Gemini client was simplified, enhancing readability and downstream processing while strengthening long-term maintainability and production readiness.
In September 2025, the Tencent/digitalhuman BatonVoice work established a solid foundation for multi-mode TTS with a scalable architecture and improved developer experience. Key platform improvements include a modular BatonVoice core architecture with a Gradio-based multi-mode UI, integrated external TTS dependencies via submodules, and a comprehensive documentation/media assets refresh that aligns resources for onboarding and cross-team usage. A simplification of prosodic feature output in the Gemini client further enhances readability and downstream processing. These efforts collectively reduce time-to-value for new TTS experiments, improve build stability, and strengthen long-term maintainability across the BatonVoice subsystem.
In September 2025, the Tencent/digitalhuman BatonVoice work established a solid foundation for multi-mode TTS with a scalable architecture and improved developer experience. Key platform improvements include a modular BatonVoice core architecture with a Gradio-based multi-mode UI, integrated external TTS dependencies via submodules, and a comprehensive documentation/media assets refresh that aligns resources for onboarding and cross-team usage. A simplification of prosodic feature output in the Gemini client further enhances readability and downstream processing. These efforts collectively reduce time-to-value for new TTS experiments, improve build stability, and strengthen long-term maintainability across the BatonVoice subsystem.

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