Open Source Speech Synthesis Projects
Speech synthesis converts text or other inputs into spoken audio. It can make written content accessible through speech, provide voices for assistants and applications, and support narration, localization, and interactive dialogue. Modern systems use machine learning to produce natural-sounding speech, with variation in language coverage, speaking style, voice control, and generation speed. Some also support voice cloning, subject to consent and legal considerations.
Open source tools in this area include speech models, inference libraries, training and fine-tuning frameworks, and interfaces for generating or editing audio. When choosing one, consider its license, maintenance activity, supported languages, hardware and software requirements, audio quality, and integration options. These tools are useful to developers, researchers, accessibility teams, and creators who need to build or adapt speech features and want greater control over deployment and customization.
2 repositories · updated November 20, 2025
audio2photoreal: Synthesizing Photorealistic Codec Avatars from Audio
audio2photoreal is a powerful GitHub repository from Facebook Research that provides code and a dataset for generating photorealistic Codec Avatars driven solely from audio input. This project enables the synthesis of human embodiment in conversations, offering tools for training, testing, and running pretrained models to create lifelike digital representations. It represents a significant advancement in AI-driven computer graphics and virtual reality.
chatterbox-vllm: Accelerating Chatterbox TTS with vLLM for Enhanced Performance
chatterbox-vllm is a high-performance port of the Chatterbox Text-to-Speech (TTS) model to vLLM, designed to significantly improve generation speed and GPU memory efficiency. This personal project aims to provide a more efficient and easily integratable solution for speech synthesis, offering substantial speedups compared to the original implementation. While currently usable and demonstrating benchmark-topping throughput, it leverages internal vLLM APIs and hacky workarounds, with ongoing refactoring planned.