Speech quality assessment (SQA) is typically formulated as a score regression task based on subjective ratings, such as the Mean Opinion Score (MOS), which inherently suffer from inconsistent standards and limit cross-dataset training and evaluation. To address these limitations, we reformulate SQA as a preference-based comparison paradigm and construct MOS-Pref, a large-scale MOS-derived preference dataset. Building on MOS-Pref, we systematically implement and evaluate three reward modeling paradigms: scalar, semi-scalar, and generative reward models, alongside existing SQA approaches. Our experiments reveal three key findings: (1) scalar models achieve the strongest overall performance, consistently exceeding 74% accuracy; (2) score regression-based approaches generally underperform preference-based methods in both overall performance and generalization; and (3) all reward models struggle on pairs with very small MOS gap. Motivated by these observations, we propose a MOS-aware GRM design that incorporates MOS gap into the reward function during reinforcement learning. Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination. We hope this work fosters more rigorous and scalable research in SQA.

Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, while academic research remains largely non-reproducible due to the lack of publicly available training data, hindering fair comparison and progress. To this end, we release a fully open-source system for long-form song generation with fine-grained style conditioning, including a licensed synthetic dataset, training and evaluation pipelines, and Muse, an easy-to-deploy song generation model. The dataset consists of 116k fully licensed synthetic songs with automatically generated lyrics and style descriptions paired with audio synthesized by SunoV5. We train Muse via single-stage supervised finetuning of a Qwen-based language model extended with discrete audio tokens using MuCodec, without task-specific losses, auxiliary objectives, or additional architectural components. Our evaluations find that although Muse is trained with a modest data scale and model size, it achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality, while enabling controllable segment-level generation across different musical structures. All data, model weights, and training and evaluation pipelines will be publicly released, paving the way for continued progress in controllable long-form song generation research.