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README.md

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----
-license: mit
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+---
+license: mit
+---
+# Token-Based Audio Inpainting via Discrete Diffusion (AIDD)
+
+Pretrained model weights for **AIDD**, introduced in:
+
+**Token-Based Audio Inpainting via Discrete Diffusion**  
+ICLR 2026  
+https://arxiv.org/abs/2507.08333
+
+AIDD performs audio inpainting by applying diffusion in a discrete token space, enabling semantically coherent reconstruction of missing audio segments, including long gaps of up to 750 ms.
+
+---
+
+## Model
+
+The model operates on discrete audio tokens produced by a pretrained WavTokenizer and performs inpainting using a Diffusion Transformer (DiT) trained with a discrete diffusion objective. The training incorporates span-based masking to model structured missing regions and a derivative-based regularization loss that encourages smooth temporal dynamics in token embedding space. The model is designed for restoring missing segments in musical audio, including long gaps.
+
+---
+
+## Usage
+
+This repository provides **model weights only**.  
+For code, see the official GitHub repository:
+
+👉 https://github.com/iftachShoham/AIDD
+
+---
+
+## Data & Evaluation
+
+Trained and evaluated on **MusicNet** and **MAESTRO**, using FAD, LSD, ODG, and MOS metrics.  
+See the paper for full details.
+
+---
+
+## Acknowledgments
+
+Built upon  
+[Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution](https://github.com/louaaron/Score-Entropy-Discrete-Diffusion.git) and  
+[WavTokenizer: An Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling](https://github.com/jishengpeng/WavTokenizer.git).  
+We thank the authors for making their work publicly available.
+
+---
+
+## Citation
+
+```bibtex
+@article{dror2025token,
+  title={Token-based Audio Inpainting via Discrete Diffusion},
+  author={Dror, Tali and Shoham, Iftach and Buchris, Moshe and Gal, Oren and Permuter, Haim and Katz, Gilad and Nachmani, Eliya},
+  journal={arXiv preprint arXiv:2507.08333},
+  year={2025}
+}