“2024-12-16”版本间的差异

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* Prepare 2025 Calendar
 
* Prepare 2025 Calendar
 
* fix bug of data collection server(often in trouble recently)
 
* fix bug of data collection server(often in trouble recently)
 +
* Finished 433h training, but not infer
 
* bad cold
 
* bad cold
 
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2024年12月16日 (一) 10:59的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • High-school AI handbook recheck
  • Talk on Hebei Education conference
  • Paper review for TASLP


Lantian Li
  • Prepare 2025 Calendar (4th round, thx Xue/Lou)
  • Review BUPT Master Viva Report (Done)
  • Apply BUPT Innovation Scholar (Failed... Fxxk)
  • Submit NSFC Annual Report (Done)
  • Prepare some materials for another rubbish project...
  • Go on AI-Graph EN Chapter 4 (37/50)
  • Two HUAWEI projects delivery
Ying Shi
  • Revise the HUAWEI Project Proposal
    • more test about Google App and write test report
  • Prepare for the Interview
  • Back to conditional chain-overlap asr model
Zhenghai You
  • Huawei Stage II Model Training and Experimental Testing
  • Revise Topicreport
Junming Yuan
Xiaolou Li
  • Modify Proposal
  • Prepare 2025 Calendar
  • fix bug of data collection server(often in trouble recently)
  • Finished 433h training, but not infer
  • bad cold
Zehua Liu
  • Start-term Document Revise
  • Some Corruption Exp(still training)
  • Upload GA data to server
Pengqi Li
  • paper reading
  • Check the primary school version(8/10)
  • Make Proposal for IS25 Special Session of XAI
Wan Lin
  • NS
    • Solve the problem of slow training
    • Results [1]
Tianhao Wang
  • Revise Start-term Document
  • paper reading
Xiaoxue Luo
  • polish the 2025 calendar
  • prepare a course presentation
Zhenyu Zhou
  • Submit interim report
  • Some personal matters
Junhui Chen
  • prepare for final exam
Jiaying Wang
Yu Zhang
  • Huawei AED Delivery Package
  • Revise Topicreport
Wenqiang Du
  • update Exhibition scene KWS model [2]
  • Tencent AI Course Development（2/35）
    • 2 Sample Courses（80%）
Yang Wei
  • Correct the performance of text enroll kws model (with cross attention) on simple testset.(fnr 15% -> 2%)
  • Experiment on text enroll kws model (no cross attention)
Lily
Turi
  • Evaluated ASR model using CER (6.08%) & Thesis writing
  • Application for University
Yue Gu
Qi Qu
  • NPU confirmed working for both Zeus-KWS and CED models on both mr536 and v851se platforms: dynamic fixed point int16 quantization.
  • KWS training data processed for Yichang (Hubei) dialect: 6 keywords.
  • Adapt Text-Enroll-KWS model(s) to mr536 NPU.
取自“http://cslt.org/mediawiki/index.php?title=2024-12-16&oldid=42644”