Updated README to clarify: code under MIT, weights under CC BY-NC 4.0

README.md

@@ -1,5 +1,5 @@
 ---
-license: mit
+license: cc-by-nc-4.0
 library_name: transformers
 pipeline_tag: voice-activity-detection
 tags:
@@ -14,7 +14,7 @@ tags:
 ---
 
 ## Overview
-This hub features the pre-trained model by [DiariZen](https://github.com/BUTSpeechFIT/DiariZen) as described in [BUT System for the MLC-SLM Challenge](https://huggingface.co/papers/2506.13414). The EEND component is built upon WavLM-Large and Conformer layers. The model was pre-trained on far-field, single-channel audio from a diverse set of public datasets, including AMI, AISHELL-4, AliMeeting, NOTSOFAR-1, MSDWild, DIHARD3, RAMC, and VoxConverse. Then structured pruning at 80% sparsity is applied. Finally, the pruned model is fine-tuned with [MLC-SLM](https://www.nexdata.ai/competition/mlc-slm) data. 
+This hub features the pre-trained model by [DiariZen](https://github.com/BUTSpeechFIT/DiariZen) as described in [BUT System for the MLC-SLM Challenge](https://huggingface.co/papers/2506.13414). The EEND component is built upon WavLM-Large and Conformer layers. The model was pre-trained on far-field, single-channel audio from a diverse set of public datasets, including AMI, AISHELL-4, AliMeeting, NOTSOFAR-1, MSDWild, DIHARD3, RAMC, and VoxConverse. Then structured pruning at 80% sparsity is applied. Finally, the pruned model is fine-tuned with [MLC-SLM](https://www.nexdata.ai/competition/mlc-slm) data. When loading this model, please ensure **non-commercial** usage, in accordance with the CC BY-NC 4.0 license.
 
 
 ## Usage
@@ -59,4 +59,20 @@ DER evaluation of [Pyannote baseline](https://github.com/mubingshen/MLC-SLM-Base
 | Spanish            | 12.92    | 10.82    |
 | Thai               | 10.90    | 10.62    |
 | Vietnamese         | 14.64    | 12.69    |
-| **Average**        | **16.44**| **12.71**|
+| **Average**        | **16.44**| **12.71**|
+
+## Citation
+If you found this work helpful, please consider citing:
+```
+@article{polok2025but,
+  title={BUT System for the MLC-SLM Challenge},
+  author={Polok, Alexander and Han, Jiangyu and Klement, Dominik and Cornell, Samuele and {\v{C}}ernock{\`y}, Jan and Burget, Luk{\'a}{\v{s}}},
+  journal={arXiv preprint arXiv:2506.13414},
+  year={2025}
+}
+```
+
+## License
+- **Source code**: MIT (see the [project’s GitHub repository](https://github.com/BUTSpeechFIT/DiariZen)).
+- **Model weights**: CC BY-NC 4.0 (non-commercial).
+- Rationale: some training datasets are research-only or non-commercial, so the released weights cannot be used commercially.