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--- |
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tags: |
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- pyannote |
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- audio |
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- voice-activity-detection |
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datasets: |
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- dihard |
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license: mit |
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inference: false |
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--- |
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## Example pyannote-audio Voice Activity Detection model |
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### `pyannote.audio.models.segmentation.PyanNet` |
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♻️ Imported from https://github.com/pyannote/pyannote-audio-hub |
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This model was trained by @hbredin. |
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### Demo: How to use in pyannote-audio |
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```python |
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from pyannote.audio.core.inference import Inference |
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model = Inference('julien-c/voice-activity-detection', device='cuda') |
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model({ |
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"audio": "TheBigBangTheory.wav" |
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}) |
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``` |
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### Citing pyannote-audio |
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```BibTex |
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@inproceedings{Bredin2020, |
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Title = {{pyannote.audio: neural building blocks for speaker diarization}}, |
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Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe}, |
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Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing}, |
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Address = {Barcelona, Spain}, |
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Month = {May}, |
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Year = {2020}, |
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} |
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``` |
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or |
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```bibtex |
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@inproceedings{Lavechin2020, |
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author = {Marvin Lavechin and Marie-Philippe Gill and Ruben Bousbib and Herv\'{e} Bredin and Leibny Paola Garcia-Perera}, |
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title = {{End-to-end Domain-Adversarial Voice Activity Detection}}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/1910.10655}, |
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} |
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``` |
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