BUT-FIT
/

diarizen-wavlm-large-s80-md

@@ -18,7 +18,7 @@ This hub features the pre-trained model by [DiariZen](https://github.com/BUTSpee
 Then structured pruning at 80% sparsity is applied. After pruning, the number of parameters in WavLM Large is reduced from **316.6M to 63.3M**, and the computational cost (MACs) decreases from **17.8G to 3.8G** per second. When loading this model, please ensure **non-commercial** usage, in accordance with the CC BY-NC 4.0 license.
-## Note:
 This is the original version of the repository. It is optimized for cases where no more than 3 speakers overlap at any given time. For scenarios with high speaker overlap (up to 4 simultaneous speakers), the [new repository](https://huggingface.co/BUT-FIT/diarizen-wavlm-large-s80-md-v2) is recommended as it provides significantly better performance under such conditions.

 Then structured pruning at 80% sparsity is applied. After pruning, the number of parameters in WavLM Large is reduced from **316.6M to 63.3M**, and the computational cost (MACs) decreases from **17.8G to 3.8G** per second. When loading this model, please ensure **non-commercial** usage, in accordance with the CC BY-NC 4.0 license.
+## Note
 This is the original version of the repository. It is optimized for cases where no more than 3 speakers overlap at any given time. For scenarios with high speaker overlap (up to 4 simultaneous speakers), the [new repository](https://huggingface.co/BUT-FIT/diarizen-wavlm-large-s80-md-v2) is recommended as it provides significantly better performance under such conditions.