People |
This Week |
Next Week |
Task Tracking (DeadLine)
|
Dong Wang
|
- AI handbook refinment for the college version.
|
|
|
Lantian Li
|
- Submit of the AI-Graph EN version
- Review Master theses
|
|
|
Ying Shi
|
- thesis
- some school-related stuff
|
|
|
Zhenghai You
|
- Complete the training and experimentation of the TSE-IRA model[1] for Huawei, and will write the report
|
|
|
Junming Yuan
|
- prepare report
- double-check middle-school AI handbook(1/7)
|
- write paper
- double-check middle-school AI handbook
- AI practice handbook design of primary school and middle school
|
|
Xiaolou Li
|
- Writing NSFC document
- Find solutions for the low training speed with hybrid data disk
|
|
|
Zehua Liu
|
- Writing NFSC document
- Reading VTS paper
|
|
|
Pengqi Li
|
- Prepare the AI course for Tsinghua University middle School.
- Add references to the middle handbook(Done)
- Check middle handbook(1/3)
|
|
|
Wan Lin
|
- train ns(multi-enroll) for voxblink1+voxceleb2: perform better than before, but still worse than vc2-only
- retrain multi-scenario ns(multi-enroll) model: consistently better than before now [2]
- run ablation experiments: w/o multi-enroll/multi-head/two encoder
|
|
|
Tianhao Wang
|
- sound sep subset training & testing
- huawei interview
- professor Guo's project investigation
|
|
|
Xiaoxue Luo
|
- Sound separation
- train AudioSep on subset data and evaluate it with our testing dataset
- read a paper about USS, try to test this model using our test set firstly(adjusting code in progress)
|
|
|
Zhenyu Zhou
|
|
|
|
Junhui Chen
|
- Finished diarization test in vox-o
|
|
|
Jiaying Wang
|
|
|
|
Yu Zhang
|
|
|
|
Wenqiang Du
|
- Check Primary handbook V3.1
- Check middle handbook
- We have Dragon05 (A6000 * 6)
|
|
|
Yang Wei
|
- Text enroll kws adaptation experiment on cross lingual data. (not work. trying it with more complex adaptation layer)
|
|
|
Turi
|
- Prepared ppt and poster for ICASSP2025
- Did presentation video and submitted with poster
|
|
|
Yue Gu
|
- synthesize 100h for each target spk and use KL loss as the regular term, the CER of target speakers reduce 10%.
|
|
|
Qi Qu
|
- Android demo of Text-enroll KWS model.
|
|
|