“2024-04-22”版本间的差异

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|Lantian Li
 
|Lantian Li
 
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* GPU status [https://z1et6d3xtb.feishu.cn/wiki/XGcGwRK5viJmpRkjH9AczIhynCh]
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* Projects (AED delivery, TSE plan)
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* ASIP-BUPT (NeuralScoring, CohortSS)
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* Welcome to Rabbit04
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* BlockChain Course
 
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|Chen Chen
 
|Chen Chen
 
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* read papers
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* vii group [https://z1et6d3xtb.feishu.cn/docx/CNzFdnE0toiDtDxNkq2cetngndf?from=from_copylink]
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** Structure have good news
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** Strategy need to be checked
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** Data collection need more effort
 
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|Xiaolou Li
 
|Xiaolou Li
 
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* Experiment on E-Branchformer and Resnet3D + E-Branchformer(last exp on this)
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* Paper Reading
 
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|Wan Lin
 
|Wan Lin
 
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* Graduation paper revision (already submitted)
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* NS
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** transfer to wespeaker-toolkit(fix bug)
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** all-pairs training & mix training
 
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* Started ASR Data Collection
 
* Started ASR Data Collection
** 6.3K collected so far
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** 6.5K collected so far
 
* Course work
 
* Course work
 
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|Qi Qu
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* Web service:
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** EfficientNetB6-based KWS
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* KWS model training:
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** FA collected and processed (~40k)
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** Appended to training dataset
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* Other:
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** FunASR w/ hotwords enabled as cloud verification
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* Test:
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** KWS models: B0/B6; alone and combined as two-phased processing
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** KWS model + FunASR w/ hotwords
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** Different trigger strategies
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* KWS model training:
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** Collect FA in large scale
 
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2024年4月22日 (一) 11:11的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • NSFC XAI proposal
  • AI Graph PPT recheck
  • Course preparation
Lantian Li
  • GPU status [1]
  • Projects (AED delivery, TSE plan)
  • ASIP-BUPT (NeuralScoring, CohortSS)
  • Welcome to Rabbit04
  • BlockChain Course
Ying Shi
  • Finish SPL paper
  • Discuss about next DI-TING
  • Restart Cohort ASR
  • group work
Zhenghai You
  • Paper reading report
  • Prepare extreme data for Huawei project( Crawling Chinese karaoke song, then mixed with rock, punk music data as TSE task's noise )
Junming Yuan
  • Paper reading report prepared
  • FA data analysis[2]
  • AI Graph slides refinement
  • NSFC check
Chen Chen
  • read papers
  • vii group [3]
    • Structure have good news
    • Strategy need to be checked
    • Data collection need more effort
Xiaolou Li
  • Experiment on E-Branchformer and Resnet3D + E-Branchformer(last exp on this)
  • Paper Reading
Zehua Liu
  • papper reading
  • cropsize exp
  • AKVSR code(still doing)
Pengqi Li
  • ICASSP papers reading[4]
  • Start Experiment(PID) on Timit(Extend workshop paper)
  • Leave of Absence(Family matters)
Wan Lin
  • Graduation paper revision (already submitted)
  • NS
    • transfer to wespeaker-toolkit(fix bug)
    • all-pairs training & mix training
Tianhao Wang
  • EA-ASP exps [5]
    • target and nontarget training data ratio (1:1 to 2:8)
    • totally mix training data
  • spex+ code modification
Zhenyu Zhou
  • ICASSP2024 poster
Junhui Chen
  • Graduation paper
Jiaying Wang
  • cohort gender-aware verification
    • need regenerate training, validation and test data
Yu Zhang
  • AutoML
    • learners: [xgboost lgbm xgb_limitdepth rf]
    • use last 30 days metrics to predict the return value
  • RL continuous learning related paper reading
Wenqiang Du
  • EfficientNet-B6 kws model has been trained[6]
  • Large hard negative data collection has been complete 60%
    • (about 5000h collect 5000 FA)
Yang Wei
  • Evaluate mispronunciation detection system with detection cost function
  • Try to estimate best DCF threshold without test set
Lily
  • Overview for thesis
  • AIgraph100 course material
Turi
  • Started ASR Data Collection
    • 6.5K collected so far
  • Course work
Yue Gu
  • Parallel lattice construction
  • Method reproduction, a work in ICASSP2024 related to my contextual ASR
  • Paper reading (50%)
Qi Qu
  • Web service:
    • EfficientNetB6-based KWS
  • KWS model training:
    • FA collected and processed (~40k)
    • Appended to training dataset
  • Other:
    • FunASR w/ hotwords enabled as cloud verification
  • Test:
    • KWS models: B0/B6; alone and combined as two-phased processing
    • KWS model + FunASR w/ hotwords
    • Different trigger strategies
  • KWS model training:
    • Collect FA in large scale