Instructions to use Kyudan/eDIAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kyudan/eDIAR with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Kyudan/eDIAR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download NOTICE from Kyudan/eDIAR: direct link, hf CLI and curl.
- Browser
- Download file 736 Bytes
-
https://huggingface.co/Kyudan/eDIAR/resolve/main/NOTICE
- Command line
-
hf download hf://Kyudan/eDIAR/NOTICE
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curl -L -o NOTICE https://huggingface.co/Kyudan/eDIAR/resolve/main/NOTICE
736 Bytes
| This repository contains code only; it stores no model weights. | |
| Third-party code included (with its own license): | |
| - emeans_diar/faiss_e_means/ e-means from facebookresearch/faiss contrib/e_means (commit 7767c6396381c6586aba873fda72eec8d6819b7b), MIT license, (c) Meta Platforms, Inc. and affiliates. | |
| - emeans_diar/nemotron_ref/ Nemotron-3 Diarization modeling/processing code, Apache License 2.0, (c) The HuggingFace Inc. team. | |
| - emeans_diar/wespeaker_resnet.py, pooling_layers.py from WeSpeaker, Apache License 2.0, (c) Shuai Wang et al. | |
| Models used at run time, downloaded from their own repositories and subject to their own licenses: | |
| - nvidia/Nemotron-3-Diarization (openmdw-1.1) | |
| - Wespeaker/wespeaker-voxceleb-resnet34-LM | |