Voice Activity Detection
Transformers
PyTorch
speaker
speaker-diarization
meeting
wavlm
wespeaker
diarizen
pyannote
pyannote-audio-pipeline
Instructions to use BUT-FIT/diarizen-wavlm-large-s80-md-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/diarizen-wavlm-large-s80-md-v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BUT-FIT/diarizen-wavlm-large-s80-md-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| Creative Commons Attribution 4.0 International (CC BY 4.0) | |
| The following files in this directory are licensed under the Creative Commons | |
| Attribution 4.0 International License: | |
| - plda.npz | |
| - xvec_transform.npz | |
| This license permits sharing and adaptation of these files for any purpose, | |
| including commercial use, provided that appropriate attribution is given. | |
| License: | |
| https://creativecommons.org/licenses/by/4.0/ | |
| Full legal code: | |
| https://creativecommons.org/licenses/by/4.0/legalcode | |
| Copyright (c) Brno University of Technology / BUT Speech@FIT. | |