Transformers
PyTorch
Safetensors
English
pyannet
speaker-diarization
speaker-segmentation
Generated from Trainer
Instructions to use JSWOOK/pyannote_3_fine_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JSWOOK/pyannote_3_fine_tuning with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JSWOOK/pyannote_3_fine_tuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.yaml from JSWOOK/pyannote_3_fine_tuning: direct link, hf CLI and curl.
- Browser
- Download file 468 Bytes
-
https://huggingface.co/JSWOOK/pyannote_3_fine_tuning/resolve/main/config.yaml
- Command line
-
hf download hf://JSWOOK/pyannote_3_fine_tuning/config.yaml
-
curl -L -o config.yaml https://huggingface.co/JSWOOK/pyannote_3_fine_tuning/resolve/main/config.yaml
468 Bytes
| version: 3.0.0 | |
| pipeline: | |
| name: pyannote.audio.pipelines.SpeakerDiarization | |
| params: | |
| clustering: AgglomerativeClustering | |
| embedding: hbredin/wespeaker-voxceleb-resnet34-LM | |
| embedding_batch_size: 16 | |
| embedding_exclude_overlap: true | |
| segmentation: pyannote/segmentation-3.0 | |
| segmentation_batch_size: 32 | |
| params: | |
| clustering: | |
| method: centroid | |
| min_cluster_size: 12 | |
| threshold: 0.7045654963945799 | |
| segmentation: | |
| min_duration_off: 0.0 | |