Darwin-TTS: We Gave a TTS Model 3% of an LLM's Brain — It Started Showing Emotion

Darwin-TTS is an innovative approach that blends a small percentage of a large language model (LLM)'s weights into a text-to-speech (TTS) model, enabling it to express emotions without any additional training or data. This method, demonstrated with the Darwin-TTS-1.7B-Cross model, leverages the architectural compatibility between Qwen3 LLM and Qwen3-TTS models to transfer emotional semantics by blending their feed-forward network (FFN) weights at low ratios, such as 3%. The result is a TTS model that can convey emotions in speech, a capability traditionally requiring extensive training. This cross-modal technique offers a lightweight and cost-effective alternative to end-to-end multimodal training, showcasing potential applications beyond text and speech, including image and video generation. The research highlights the importance of architecture matching and low blending ratios for successful integration and suggests further exploration of bidirectional weight transfers between modalities.