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TuneJury/
tunejury

Name: tunejury
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TuneJury — instance-level pairwise reward model for text-to-music

TuneJury is a 2.8M-parameter MLP head over frozen LAION-CLAP-Music + MERT-v1-330M embeddings, trained with a shared-weight pairwise-logistic objective on ~17.5K human A vs. B preferences from four open sources. It scores a single audio clip (with an optional text prompt) as one preference scalar.

Paper: https://arxiv.org/abs/2606.17006
Project Page: https://yonghyunk1m.github.io/TuneJury/
Code: https://github.com/yonghyunk1m/TuneJury
Demo: https://huggingface.co/spaces/TuneJury/tune-jury-demo
Scope: trained and evaluated on instrumental music. For prompt formats unlike the live-arena style it was trained on (e.g. post-hoc captions), an empty prompt can be the safer default (paper §4.2). Anchor calibration adapts to newly released generators without retraining.

Checkpoints

File	Encoder / mix	License
`tunejury.pt`	CLAP+MERT, 4-dataset (primary)	CC-BY-NC 4.0
`tunejury_muq_leave_MA.pt`	MuQ-MuLan-large encoder-swap	CC-BY-NC 4.0
`A1_clap_audio_only.pt`	CLAP-audio-only	Apache-2.0
`tunejury_leave_*.pt`	leave-one / leave-two-out (fair-eval)	CC-BY-NC 4.0

Quick scoring

Install the package (also pulls the LAION-CLAP encoder ~2.2 GB on first use; needs ffmpeg and libsndfile):

pip install git+https://github.com/yonghyunk1m/TuneJury

python

from huggingface_hub import hf_hub_download
from tunejury.score import Scorer

sc = Scorer.from_pretrained(hf_hub_download("TuneJury/tunejury", "tunejury.pt"))
print(sc.score("clip.wav", ""))   # "" -> 512-d zero text vector (paper §3/§4.2 empty-prompt)
print(sc.score("clip.wav", "a calm lo-fi piano loop"))   # a prompt uses the text branch

License

The released head is CC-BY-NC 4.0, tracking the strictest upstream weight license (MERT-v1-330M). Frozen encoders at inference: LAION-CLAP-Music (CC0 1.0), MERT-v1-330M (CC-BY-NC 4.0), MuQ-MuLan-large (CC-BY-NC 4.0). The A1_clap_audio_only head is released under Apache-2.0.

Citation

bibtex

@misc{tunejury2026,
  title         = {TuneJury: An Open Metric for Improving Music Generation Preference Alignment},
  author        = {Kim, Yonghyun and Lee, Junwon and Xia, Haiwen and
                   Ma, Yinghao and Koo, Junghyun and Saito, Koichi and
                   Mitsufuji, Yuki and Donahue, Chris},
  year          = {2026},
  eprint        = {2606.17006},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SD},
  url           = {https://arxiv.org/abs/2606.17006},
}