DDSynth-RL checkpoints

Official checkpoints for DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning, accepted at ISMIR 2026.

[Paper] · [Demo] · [Code]

Files

File	Model	Selection
ar_dexed_best_id.pt	Autoregressive	Best in-domain Dexed validation checkpoint
ar_dexed_best_ood.pt	Autoregressive	Best OOD NSynth validation checkpoint
fm_dexed_best_id.pt	Flow matching	Checkpoint used for the in-domain demo; selected on OOD validation
fm_dexed_best_ood.pt	Flow matching	Best OOD NSynth validation checkpoint
dd_dexed_best_id.pt	Discrete diffusion	Best in-domain Dexed validation checkpoint
dd_dexed_best_ood.pt	Discrete diffusion	Best OOD NSynth validation checkpoint
ddsynth_rl_multi_reward.pt	DDSynth-RL	GRPO with the multi-metric audio reward
ddsynth_rl_clap_crepe.pt	DDSynth-RL	GRPO continued with CLAP and CREPE rewards

The demo uses separate checkpoints for in-domain and OOD evaluation of the supervised baselines. The exact selection metric for every file is recorded in manifest.json; both released flow-matching checkpoints were selected on OOD validation. Each GRPO checkpoint is used for both evaluation domains.

These are inference release checkpoints: model weights, model configuration, and training step are preserved exactly, while optimizer and runtime states are omitted. manifest.json records release and source-checkpoint SHA-256 digests, file sizes, model classes, training steps, and selection criteria. SHA256SUMS can be used for download verification.

Download

python -m pip install -U huggingface_hub
hf download MINNE-WU/DDSynth-RL --local-dir checkpoints/ddsynth-rl

Evaluation

From the DDSynth-RL code repository:

bash scripts/test.sh checkpoints/ddsynth-rl/ddsynth_rl_multi_reward.pt \
  --in-domain-h5 dataset/dexed/test.h5 \
  --ood-audio-root dataset/nsynth/test/audio

Dexed must be installed separately at synth/Dexed.vst3 for render-based evaluation.

Data

The processed Dexed dataset is not hosted in this repository. Source presets and the upstream four-way preset augmentation are available from SPINVAE-2. DDSynth-RL additionally renders every preset variation under four sampled MIDI note, velocity, and duration conditions. See the code repository's DATA.md for artifact statistics and the current reproduction boundary.

Citation

@inproceedings{wu2026ddsynthrl,
  title     = {DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning},
  author    = {Wu, Tristan and Chin, Daniel and Zhang, Junan and Jiang, Junyan and Jing, Yansen and Xia, Gus},
  booktitle = {Proceedings of the 27th International Society for Music Information Retrieval Conference},
  year      = {2026},
  address   = {Abu Dhabi, UAE}
}

License

The checkpoints are released under CC BY 4.0. The DDSynth-RL code is released separately under Apache-2.0.