Despite advancements in controllable text-to-music (TTM) generation, existing models struggle with cross-domain voice cloning for singing due to inadequate modeling of speech-derived vocal characteristics, inherently limiting precise timbre transfer. To achieve precise and controllable music generation conditioned on the timbre of reference speech, we propose S2M-Inject, a cross-domain framework driven by reference speech. Incorporating a fixed timbre embedding strategy, S2M-Inject injects speaker representations into an instruction-guided generation pipeline. This effectively preserves target vocal attributes while enabling comprehensive natural-language control over lyrics, genre, instrumentation, rhythm, melody, and mood. Extensive experiments demonstrate that the framework achieves effective cross-domain voice cloning, significantly enhancing vocal similarity while maintaining high-fidelity audio quality in both Chinese and English tasks. This work extends voice cloning into the musical domain, offering a rigorous paradigm for personalized music generation. Audio samples are available at https://anonymous.4open.science/w/S2M-Inject-975.