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.

Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. However, most systems fail to model temporally varying attributes of songs, severely limiting fine-grained control over musical structure and dynamics. To address this, we propose Segtune, a Diffusion Transformer-based framework enabling structured and fine-grained controllability by allowing users or large language models (LLMs) to specify local musical descriptions aligned to song segments. These segment prompts are temporally broadcast to corresponding time windows, while global prompts ensure stylistic coherence. To support precise lyric-to-music alignment, we introduce an LLM-based duration predictor that autoregressively generates sentence-level timestamps in LyRiCs format. We further construct a large-scale data pipeline for high-quality song collection with aligned lyrics and prompts, and propose new metrics to evaluate segment alignment and vocal consistency. Experiments demonstrate that Segtune outperforms existing baselines in both musicality and controllability. Visit our demo page for codes and more generated songs.