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
| # PLDA model | |
| The files in this directory: | |
| - `plda.npz` | |
| - `xvec_transform.npz` | |
| contain a PLDA model and its associated transform trained by BUT Speech@FIT | |
| using the VoxCeleb2 development data. | |
| These files are licensed under the Creative Commons Attribution 4.0 | |
| International License (CC BY 4.0), which permits commercial use. | |
| See [`LICENSE`](https://huggingface.co/BUT-FIT/diarizen-wavlm-large-s80-md-v2/blob/main/plda/LICENSE) for the license terms. | |
| Copyright (c) Brno University of Technology / BUT Speech@FIT. | |