Multi-track music generation requires tokens that preserve acoustic fidelity, support sequence modeling, and maintain cross-track structure. Reconstruction-oriented codecs retain acoustic detail but are difficult to model, while semantic tokenizers may sacrifice fidelity or cross-track alignment. We present DuoTok, a source-aware dual-track music tokenizer for vocal-accompaniment generation based on staged disentanglement. DuoTok first learns a semantic audio representation through self-supervised pretraining, then shapes source-aware structure using feature replacement noise and multi-task supervision: spectral reconstruction, music source separation regularization, and an ASR head for lyric alignment. It freezes the encoder and learns hard-routed codebooks for vocals and accompaniment, while a diffusion decoder restores fine acoustic detail from discrete tokens. Across public benchmarks, DuoTok achieves a favorable predictability-fidelity trade-off at ultra-low bitrate. Under held-constant dual-track language modeling, it improves both unconditional vocal-accompaniment modeling and vocal-conditioned accompaniment prediction. Controlled diagnostics show larger predictability costs under cross-track corruption and greater gains from longer temporal context, supporting stronger use of cross-track and temporal structure rather than merely easier local prediction. DuoTok also maintains competitive reconstruction quality and preserves control-relevant musical attributes in its discrete space. These results suggest that tokenizer design is a core modeling problem for multi-track music generation, beyond compression alone.

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