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Ubenwa/ecapa-voxceleb-ft2-cryceleb
ecapa-voxceleb-ft2-cryceleb is a machine learning model from Ubenwa. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-sa-4.0.
This model is SpeechBrain ECAPA-TDNN fine-tuned on CryCeleb dataset.
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From the Hugging Face model README
This model is SpeechBrain ECAPA-TDNN fine-tuned on CryCeleb dataset.
Training can be reproduced using SpeechBrain recipe available on Github.
It can be used as a baseline for CryCeleb2023 challenge.
@article{ubenwa2023cryceleb,
title={CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds},
author={David Budaghyan and Arsenii Gorin and Cem Subakan and Charles C. Onu},
year={2023},
journal={preprint arXiv:2305.00969},
}
@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
author = {Brecht Desplanques and
Jenthe Thienpondt and
Kris Demuynck},
editor = {Helen Meng and
Bo Xu and
Thomas Fang Zheng},
title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
in {TDNN} Based Speaker Verification},
booktitle = {Interspeech 2020},
pages = {3830--3834},
publisher = {{ISCA}},
year = {2020},
}
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}