C-STAR-database approach
来自cslt Wiki
CN-Celeb
- A large-scale Chinese celebrities dataset collected `in the wild'.
- Members：Dong Wang, Yunqi Cai, Lantian Li, Yue Fan, Jiawen Kang
- Historical Members：Ziya Zhou, Kaicheng Li, Haolin Chen, Sitong Cheng, Pengyuan Zhang
Target
- Collect audio data of 1,000 Chinese celebrities.
- Automatically clip videoes through a pipeline including face detection, face recognition, speaker validation and speaker diarization.
- Create a database.
未来计划
- 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.
基本方法
- Tensorflow, PyTorch, Keras, MxNet 实现
- 检测、识别人脸的RetinaFace和ArcFace模型，说话人识别的SyncNet模型，Speaker Diarization的UIS-RNN模型
- 输入为目标主人公的视频、目标主人公的面部图片
- 输出为该视频中主人公声音片段的时间标签
项目GitHub地址
项目报告
参考文献
- 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. link
- Chung et al., "Out of time: automated lip sync in the wild", 2016.link
- Xie et al., "UTTERANCE-LEVEL AGGREGATION FOR SPEAKER RECOGNITION IN THE WILD", 2019. link
- Zhang1 et al., "FULLY SUPERVISED SPEAKER DIARIZATION", 2018. link
