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
File size: 518 Bytes
498fda8 027b3e2 498fda8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # 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.
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