Improve model card: add paper/code links, license and sample usage (#1)

- Improve model card: add paper/code links, license and sample usage (e185044424134682546c6ea82f462242035fd129)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,11 +1,52 @@
 ---
-license: other
 library_name: pytorch
+license: apache-2.0
 pipeline_tag: audio-to-audio
 ---
 
 # HoliTok
 
-This repository hosts runtime-only HoliTok checkpoints for inference. The
-checkpoints are loaded by the HoliTok inference package through the public
-aliases `HoliTok-Base` and `HoliTok-Unite`.
+HoliTok is a continuous holistic speech tokenization model designed for unified generation-understanding modeling. It encodes 48 kHz speech into a compact 25 Hz sequence of 128-dimensional latents. It is designed to preserve signal-level fidelity, incorporate semantic information, and maintain strong latent learnability.
+
+- **Paper:** [HoliTok: A Continuous Holistic Tokenization with Robust Dual Capabilities of Speech Generation and Understanding](https://huggingface.co/papers/2605.29948)
+- **Code:** [GitHub Repository](https://github.com/bovod-sjtu/HoliTok)
+
+This repository hosts runtime-only HoliTok checkpoints for inference. The checkpoints are loaded by the HoliTok inference package through the public aliases `HoliTok-Base` and `HoliTok-Unite`.
+
+## Sample Usage
+
+You can use the HoliTok Python API for audio tokenization, reconstruction, and semantic feature extraction.
+
+```python
+import torch
+from holitok import HoliTok, SemanticModule
+
+model = HoliTok.from_pretrained("HoliTok-Unite", device="cuda:0")
+audio = torch.randn(1, 1, 48000, device="cuda:0")
+
+# [B, 2 * latent_dim, T], concat(mu, log_std)
+posterior = model.encode_posterior(audio)
+
+# [B, latent_dim, T]
+latents = model.posterior_mean(posterior)
+
+# [B, 1, samples]
+recon = model.decode(latents)
+
+semantic = SemanticModule.from_pretrained("HoliTok-Unite", device="cuda:0")
+features = semantic(latents.transpose(1, 2))  # [B, T, 1536]
+```
+
+## Citation
+
+```bibtex
+@misc{li2026holitokacoutinuousholistictokenization,
+      title={HoliTok:A Coutinuous Holistic Tokenization with Robust Dual Capabilities of Speech Generation and Understanding}, 
+      author={Bohan Li and Shi Lian and Hankun Wang and Yiwei Guo and Yu Xi and Zhihan Li and Da Zheng and Colin Zhang and Kai Yu},
+      year={2026},
+      eprint={2605.29948},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD},
+      url={https://arxiv.org/abs/2605.29948}, 
+}
+```