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| |Yang Wei | | |Yang Wei |
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− | * | + | * AIBabel KWS |
| + | ** Prepare negative test data (from cn-celeb, aishell-4) |
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People |
This Week |
Next Week |
Task Tracking (DeadLine)
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Dong Wang
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- AIGraph slides done
- Check for thermal face recognition paper
- Quick check for Guyue's paper
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Lantian Li
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- GPU status [1]
- AI graph
- Slides checking (50/50)
- High school handbook (12/40)
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- High school handbook (20/40)
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Ying Shi
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- Finish training U-Net based text-enroll keyword spotting
- continue work on cohort conditional-chain group work
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Zhenghai You
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- Complete the Reproduce of IRA[2]
- Design a new TSE structure using U-NET and G&L Transformer (idea form sepreformer)
- Write the work content for Huawei's first phase as ICCIP2024
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Junming Yuan
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- Reimplementation of the Hubert baseline:
- fix some bugs
- the base model for the 1st iteration is finished on hawk02.
- the base model for the 2nd iteration need to migrate to dragon03(in progress)
- Beginner's Guide for pretraining Hubert with fairseq:[3]
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Chen Chen
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Xiaolou Li
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- LRS-30h PALR2 (4 epoch result)
- VSR: 29.74%
- Refinement: 29.45%
- Calibration Test [4]
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Zehua Liu
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- LRS3-30h: VSP-LLM - cluster(WER : 28.11%) < VSP-LLM (WER : 29.1%)
- LRS3-30h: VSP-LLM - cluster + adaptive_mask(WER : 27.75%) < VSP-LLM (WER : 29.1%)
- LRS3-30h: In-Context-learning (still training)
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Pengqi Li
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- Trianed a new attention pooling with condition(Analysis ongoing).[5]
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Wan Lin
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- Neural Scoring
- First draft of paper finished [6]
- Supplement experimental results
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Tianhao Wang
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- SoundFilter weekly report
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Zhenyu Zhou
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Junhui Chen
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- Neural Scoring:
- Paper Writing (1st Ver. finished with LW)
- Supplement the experiments
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Jiaying Wang
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- paper reading(report today)
- live broadcast
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Yu Zhang
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Wenqiang Du
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Yang Wei
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- AIBabel KWS
- Prepare negative test data (from cn-celeb, aishell-4)
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Lily
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- Prepare for high shcool summer trip class(last Sunday)
- Accident & get sick
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Turi
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Yue Gu
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- complete and revise the DPR paper
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Qi Qu
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- AED:
- AudioSet data prepared.
- Positive samples of "cries" collected and to be annotated.
- KWS:
- B6-based service optimized with memory consumption considerably reduced (~600MB v.s. formerly ~2GB).
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