“CN-Celeb”版本间的差异
来自cslt Wiki
| （相同用户的2个中间修订版本未显示） | |||
| 第38行： | 第38行： | ||
primaryClass={eess.AS} | primaryClass={eess.AS} | ||
} | } | ||
| + | |||
| + | @misc{li2020cn, | ||
| + | title={CN-Celeb: multi-genre speaker recognition}, | ||
| + | author={Lantian Li and Ruiqi Liu and Jiawen Kang and Yue Fan and Hao Cui and Yunqi Cai and Ravichander Vipperla and Thomas Fang Zheng and Dong Wang}, | ||
| + | year={2020}, | ||
| + | eprint={2012.12468}, | ||
| + | archivePrefix={arXiv}, | ||
| + | primaryClass={eess.AS} | ||
| + | } | ||
</pre> | </pre> | ||
===Source Code=== | ===Source Code=== | ||
* Collection Pipeline: [https://github.com/celebrity-audio-collection/videoprocess celebrity-audio-collection] | * Collection Pipeline: [https://github.com/celebrity-audio-collection/videoprocess celebrity-audio-collection] | ||
| − | * Baseline Systems: [https://github.com/ | + | * Baseline Systems: [https://github.com/csltstu/kaldi/tree/cnceleb/egs/cnceleb kaldi-cn-celeb] |
===Download=== | ===Download=== | ||
| − | |||
| − | |||
| − | |||
* Public (recommended) | * Public (recommended) | ||
OpenSLR: http://www.openslr.org/82/ | OpenSLR: http://www.openslr.org/82/ | ||
| + | |||
| + | * Local (not recommended) | ||
| + | CSLT@Tsinghua: http://cslt.riit.tsinghua.edu.cn/~data/CN-Celeb/ | ||
===Future Plans=== | ===Future Plans=== | ||
2021年1月6日 (三) 10:06的最后版本
目录
Introduction
- CN-Celeb, a large-scale Chinese celebrities dataset published by Center for Speech and Language Technology (CSLT) at Tsinghua University.
Members
- Current：Dong Wang, Yunqi Cai, Lantian Li, Yue Fan, Jiawen Kang
- History：Ziya Zhou, Kaicheng Li, Haolin Chen, Sitong Cheng, Pengyuan Zhang
Description
- Collect audio data of 1,000 Chinese celebrities.
- Automatically clip videos through a pipeline including face detection, face recognition, speaker validation and speaker diarization.
- Create a benchmark database for speaker recognition community.
Basic Methods
- Environments: Tensorflow, PyTorch, Keras, MxNet
- Face detection and tracking: RetinaFace and ArcFace models.
- Active speaker verification: SyncNet model.
- Speaker diarization: UIS-RNN model.
- Double check by speaker recognition: VGG model.
- Input: pictures and videos of POIs (Persons of Interest).
- Output: well-labelled videos of POIs (Persons of Interest).
Reports
Publications
@misc{fan2019cnceleb,
title={CN-CELEB: a challenging Chinese speaker recognition dataset},
author={Yue Fan and Jiawen Kang and Lantian Li and Kaicheng Li and Haolin Chen and Sitong Cheng and Pengyuan Zhang and Ziya Zhou and Yunqi Cai and Dong Wang},
year={2019},
eprint={1911.01799},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
@misc{li2020cn,
title={CN-Celeb: multi-genre speaker recognition},
author={Lantian Li and Ruiqi Liu and Jiawen Kang and Yue Fan and Hao Cui and Yunqi Cai and Ravichander Vipperla and Thomas Fang Zheng and Dong Wang},
year={2020},
eprint={2012.12468},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
Source Code
- Collection Pipeline: celebrity-audio-collection
- Baseline Systems: kaldi-cn-celeb
Download
- Public (recommended)
OpenSLR: http://www.openslr.org/82/
- Local (not recommended)
CSLT@Tsinghua: http://cslt.riit.tsinghua.edu.cn/~data/CN-Celeb/
Future Plans
- Augment the database to 10,000 people.
- Build a model between SyncNet and Speaker_Diarization based on LSTM, which can learn the relationship of them.
License
- All the resources contained in the database are free for research institutes and individuals.
- No commerical usage is permitted.
References
- Deng et al., "RetinaFace: Single-stage Dense Face Localisation in the Wild", 2019. [1]
- Deng et al., "ArcFace: Additive Angular Margin Loss for Deep Face Recognition", 2018, [2]
- Wang et al., "CosFace: Large Margin Cosine Loss for Deep Face Recognition", 2018, [3]
- Liu et al., "SphereFace: Deep Hypersphere Embedding for Face Recognition", 2017[4]
- Zhong et al., "GhostVLAD for set-based face recognition", 2018. [5]
- Chung et al., "Out of time: automated lip sync in the wild", 2016.[6]
- Xie et al., "Utterance-level Aggregation For Speaker Recognition In The Wild", 2019. [7]
- Zhang1 et al., "Fully Supervised Speaker Diarization", 2018. [8]
