| --- |
| license: cc-by-nc-4.0 |
| library_name: pytorch |
| pipeline_tag: text-to-audio |
| base_model: AndreasXi/MeanAudio |
| tags: |
| - text-to-music |
| - music-generation |
| - flow-matching |
| - rlhf |
| - preference-alignment |
| --- |
| |
| # TTM-HumanPref: G-Clef FluxAudio-S checkpoint (ICME 2026 ATTM Grand Challenge) |
|
|
| Final submitted checkpoint of **Team G-Clef** for the Efficiency track of the [ICME 2026 Academic Text-to-Music Grand Challenge](https://ntu-musicailab.github.io/ICME26-ATTM-Grand-Challenge/). |
|
|
| A ~120M-parameter FluxAudio-S rectified-flow model, trained from scratch on the challenge's CC-licensed instrumental MTG-Jamendo subset, then refined with human-preference reward conditioning, expert iteration, and a short CRPO preference fine-tune. |
|
|
| - **Paper:** [Improving Text-to-Music Generation with Human Preference Rewards](https://arxiv.org/abs/2606.21670) |
| - **Code:** [github.com/yonghyunk1m/TTM-HumanPref](https://github.com/yonghyunk1m/TTM-HumanPref) |
| - **Demo:** [TTM-HumanPref Space](https://huggingface.co/spaces/yonghyunk1m/TTM-HumanPref) |
|
|
| ## Files |
|
|
| | File | Size | Description | |
| |---|---|---| |
| | `gclef_crpoA_fluxaudio_s.pt` | 483 MB | Final CRPO-A checkpoint, used for both submissions | |
|
|
| SHA-256: `0cfb8295fbd695a99c7a2599363a465fdc28a94f39c8d43faf7487461e5e15f2` |
|
|
| This file is identical to the asset of the GitHub release [`v1.0-icme2026`](https://github.com/yonghyunk1m/TTM-HumanPref/releases/tag/v1.0-icme2026). |
|
|
| ## Usage |
|
|
| Inference runs through the code repository, on top of the official challenge baseline. See the [GitHub README](https://github.com/yonghyunk1m/TTM-HumanPref) for setup. |
|
|
| ```bash |
| python run.py download-weights # fetches this checkpoint and verifies its SHA-256 |
| python run.py reproduce --seed 42 # Submission 1 |
| python run.py reproduce --seed 55 # Submission 2 |
| ``` |
|
|
| Both submissions come from this single checkpoint and differ only in the generation seed. Inference uses joint classifier-free guidance on text and reward, 25 Euler steps, and the prompt prefix `"high quality instrumental music, "`. |
|
|
| ## Intended use and limitations |
|
|
| Research and non-commercial use. The model generates instrumental music only (~10 s, 44.1 kHz) and was trained on a limited instrumental subset of MTG-Jamendo, so its stylistic coverage reflects that data. |
|
|
| ## License |
|
|
| The model weights are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (see `LICENSE`). The code in the GitHub repository is MIT. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{kim2026ttmhumanpref, |
| title = {Improving Text-to-Music Generation with Human Preference Rewards}, |
| author = {Kim, Yonghyun and Lee, Junwon and Xia, Haiwen and Ma, Yinghao and Donahue, Chris}, |
| year = {2026}, |
| eprint = {2606.21670}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.SD}, |
| url = {https://arxiv.org/abs/2606.21670} |
| } |
| ``` |
|
|
