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config.yaml

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+dependencies: 
+  pyannote.audio: 4.0.0
+
+pipeline:
+  name: pyannote.audio.pipelines.SpeakerDiarization
+  params:
+    clustering: VBxClustering
+    segmentation: $model/segmentation
+    segmentation_batch_size: 32
+    embedding: $model/embedding
+    embedding_batch_size: 32
+    embedding_exclude_overlap: true
+    plda: $model/plda
+    
+params:
+  clustering:
+    threshold: 0.6
+    Fa: 0.07
+    Fb: 0.8
+  segmentation:
+    min_duration_off: 0.0

embedding/README.md

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+Copied from https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM
+
+## License
+
+According to [this page](https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md):
+
+> The pretrained model in WeNet follows the license of it's corresponding dataset. For example, the pretrained model on VoxCeleb follows Creative Commons Attribution 4.0 International License., since it is used as license of the VoxCeleb dataset, see https://mm.kaist.ac.kr/datasets/voxceleb/.
+
+## Citation
+
+```bibtex
+@inproceedings{Wang2023,
+  title={Wespeaker: A research and production oriented speaker embedding learning toolkit},
+  author={Wang, Hongji and Liang, Chengdong and Wang, Shuai and Chen, Zhengyang and Zhang, Binbin and Xiang, Xu and Deng, Yanlei and Qian, Yanmin},
+  booktitle={ICASSP 2023, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={1--5},
+  year={2023},
+  organization={IEEE}
+}
+```

embedding/pytorch_model.bin

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+size 26646242

plda/README.md

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+PLDA model trained by [BUT Speech@FIT](https://speech.fit.vut.cz/) group.
+
+Thanks to [Jiangyu Han](https://github.com/jyhan03) and [Petr Pálka](https://github.com/Selesnyan) for the integration of VBx in pyannote.audio.

plda/plda.npz

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plda/xvec_transform.npz

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+size 134376

segmentation/pytorch_model.bin

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