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Most existing speech enhancement operate directly in the acoustic space, where noise and speech components are inherently entangled, leading to artifacts such as high-frequency attenuation and background holes. In this paper, we propose Seed-Enh, a generative speech enhancement framework that performs enhancement in decoupled semantic and timbre spaces. Our framework processes noisy speech in three stages: (1) semantic space processing using a frozen Whisper encoder to extract noise-robust semantic representations, (2) timbre space processing using CAM++ embeddings and context learning, and (3) space fusion via flow matching. Experiments demonstrate that Seed-Enh achieves the highest overall quality scores compared to state-of-the-art baselines, effectively suppressing noise while restoring harmonic structures and high-frequency details. Beyond speech enhancement, Seed-Enh also enables zero-shot voice conversion, significantly outperforming Seed-VC when handling noisy inputs. The demo and pretrained model are available at https://github.com/shangqwe123/Seed-Enh.