“2024-03-18”版本间的差异

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第9行： 第9行：
 
* Design/Discussion AI popular science
 
* Design/Discussion AI popular science
 
* Conjecture for minmum loss training
 
* Conjecture for minmum loss training
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第20行： 第19行：
 
|Lantian Li
 
|Lantian Li
 
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* GPU status [https://z1et6d3xtb.feishu.cn/wiki/XGcGwRK5viJmpRkjH9AczIhynCh]
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* INTERSPEECH 2024
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* ASIP-BUPT (CohortTSE, SE-Adapter, SpeakerAug, NeuralScoring)
 
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第48行： 第49行：
 
|Zhenghai You
 
|Zhenghai You
 
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* Weekly report
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* Some evaluations about TSE speaker encoder
 +
* Huawei project (Phase 1st)
 +
* Some doubts about the paper due to the latest testing in minimum loss
 
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*  
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* Change the speakerbeam speaker encoder to frequency domain
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* Train a SID with a speakerbeam structure
 
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第58行： 第63行：
 
|Junming Yuan
 
|Junming Yuan
 
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* Finish INTERSPEECH paper
 
* Make the plan for the large vocabulary pretraining task.
 
* Make the plan for the large vocabulary pretraining task.
 
** Focus on the experimental details of the few-shot paper from Google.
 
** Focus on the experimental details of the few-shot paper from Google.
第74行： 第80行：
 
|Chen Chen
 
|Chen Chen
 
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*  
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* Finish IS24 paper
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* Some documents for VTS X project
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* Proposal for next stage work on VSR/VTS
 +
** Focus on two task: 1) CNCVS2 dataset 2) Mandarin VSR Benchmark [https://z1et6d3xtb.feishu.cn/docx/PUNcdn0mZoYciuxhNogcu1TMnQd?from=from_copylink] on CNCVS1&2&CNVSRC
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** Aim at a solid benchmark with data/code/model
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** Perhaps a long journal paper
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* Conditional entropy analysis of VTS task
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** MFA is done
 +
** TODOs: feature/embedding extracting, clustering, discrete conditional entropy calculating
 
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第85行： 第98行：
 
|Xiaolou Li
 
|Xiaolou Li
 
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* Finish INTERSPEECH2024 paper
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* review code of cnvsrc
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* Next step:
 +
** Focus on model structure of VSR Benchmark
 
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第96行： 第112行：
 
|Zehua Liu
 
|Zehua Liu
 
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*Finish IS24
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*VSR work continues
 
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第107行： 第124行：
 
|Pengqi Li
 
|Pengqi Li
 
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*  
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* Extending workshop paper
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** Finish slide for workshop paper.
 +
** make plan, investigate, prepare dataset for extending paper.
 +
** Rethink how to design a method that can globally PID
 +
* Team Working[https://z1et6d3xtb.feishu.cn/docx/T3U2dTs5poiIgtxtM2Sc0QennWe]
 
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第118行： 第139行：
 
|Wan Lin
 
|Wan Lin
 
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* Neural scoring [https://z1et6d3xtb.feishu.cn/docx/TQvWdk8LVo9ONaxQ5Qac9A2Dn3d?from=from_copylink]
 
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第129行： 第150行：
 
|Tianhao Wang
 
|Tianhao Wang
 
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* Finish INTERSPEECH paper
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* Code reorganization
 
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第153行： 第175行：
 
|Junhui Chen
 
|Junhui Chen
 
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* Neural scoring
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* Interim report
 
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第164行： 第187行：
 
|Jiaying Wang
 
|Jiaying Wang
 
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−
*  
+
* weekly report
 +
* PIT baseline: ConTasNet (finish tonight)
 +
* test whether the separation target is the closer one to the cohort embedding: the rate is around 0.5
 +
** confused about the efficiency of cohort
 +
** Further experiment:TasNet with minimal loss
 
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*  
 
*  
第175行： 第202行：
 
|Yu Zhang
 
|Yu Zhang
 
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* Portfolio backtesting report
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* stock trade API
 
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*
 
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第199行： 第227行：
 
|Yang Wei
 
|Yang Wei
 
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*  
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* Read training code of Paraformer model, in order to get intermediate data
 +
* Prepare Huilan product training, and deal with problems of ASR and TTS service
 
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*
 
*
第209行： 第238行：
 
|Lily
 
|Lily
 
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* Paper reading
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* Prepare for overview paper
 
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*
 
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2024年3月18日 (一) 11:39的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Interspeech 2024 paper refinement
  • Design/Discussion AI popular science
  • Conjecture for minmum loss training
Lantian Li
  • GPU status [1]
  • INTERSPEECH 2024
  • ASIP-BUPT (CohortTSE, SE-Adapter, SpeakerAug, NeuralScoring)
Ying Shi
  • Finish INTERSPEECH paper
  • Investigate random order SOT for multi-talker ASR task
  • 3-mix 0s offset test condition
    • DOM-SOT 20.51
    • PIT-SOT 23.26
    • random-order SOT 26.20
  • group work
Zhenghai You
  • Weekly report
  • Some evaluations about TSE speaker encoder
  • Huawei project (Phase 1st)
  • Some doubts about the paper due to the latest testing in minimum loss
  • Change the speakerbeam speaker encoder to frequency domain
  • Train a SID with a speakerbeam structure
Junming Yuan
  • Finish INTERSPEECH paper
  • Make the plan for the large vocabulary pretraining task.
    • Focus on the experimental details of the few-shot paper from Google.
    • Try to address the 3 questions:
      • How to change MT pretraining model structure?
      • How to train three strictly comparable pretraining models based on MT, Hubert, and wav2vec?
      • Why does Hubert+MT perform significantly better?
Chen Chen
  • Finish IS24 paper
  • Some documents for VTS X project
  • Proposal for next stage work on VSR/VTS
    • Focus on two task: 1) CNCVS2 dataset 2) Mandarin VSR Benchmark [2] on CNCVS1&2&CNVSRC
    • Aim at a solid benchmark with data/code/model
    • Perhaps a long journal paper
  • Conditional entropy analysis of VTS task
    • MFA is done
    • TODOs: feature/embedding extracting, clustering, discrete conditional entropy calculating
Xiaolou Li
  • Finish INTERSPEECH2024 paper
  • review code of cnvsrc
  • Next step:
    • Focus on model structure of VSR Benchmark
Zehua Liu
  • Finish IS24
  • VSR work continues
Pengqi Li
  • Extending workshop paper
    • Finish slide for workshop paper.
    • make plan, investigate, prepare dataset for extending paper.
    • Rethink how to design a method that can globally PID
  • Team Working[3]
Wan Lin
  • Neural scoring [4]
Tianhao Wang
  • Finish INTERSPEECH paper
  • Code reorganization
Zhenyu Zhou
  • InterSpeech2024 submission
  • Code reorganization
  • Neuro scoring reviewing
Junhui Chen
  • Neural scoring
  • Interim report
Jiaying Wang
  • weekly report
  • PIT baseline: ConTasNet (finish tonight)
  • test whether the separation target is the closer one to the cohort embedding: the rate is around 0.5
    • confused about the efficiency of cohort
    • Further experiment:TasNet with minimal loss
Yu Zhang
  • Portfolio backtesting report
  • stock trade API
Wenqiang Du
  • Aibabel
    • Control Uyghur KWS model FA,but not get a good performance yet.
    • Continue test and update CN KWS model
Yang Wei
  • Read training code of Paraformer model, in order to get intermediate data
  • Prepare Huilan product training, and deal with problems of ASR and TTS service
Lily
  • Paper reading
  • Prepare for overview paper
Turi
  • Data collection app[5]
  • Course works
取自“http://index.cslt.org/mediawiki/index.php?title=2024-03-18&oldid=41483”