“2024-03-18”版本间的差异

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* Design/Discussion AI popular science
 
* Design/Discussion AI popular science
 
* Conjecture for minmum loss training
 
* Conjecture for minmum loss training
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|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97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:
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** Focus on model structure of VSR Benchmark
 
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*Finish IS24  
 
*Finish IS24  
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*VSR work continues
 
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|Pengqi Li
 
|Pengqi Li
 
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* Extending workshop paper
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** Finish slide for workshop paper.
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** make plan, investigate, prepare dataset for extending paper.
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** Rethink how to design a method that can globally PID
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* Team Working[https://z1et6d3xtb.feishu.cn/docx/T3U2dTs5poiIgtxtM2Sc0QennWe]
 
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第130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166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177行： 第187行：
 
|Jiaying Wang
 
|Jiaying Wang
 
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* weekly report
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* PIT baseline: ConTasNet (finish tonight)
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* test whether the separation target is the closer one to the cohort embedding: the rate is around 0.5
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** confused about the efficiency of cohort
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** Further experiment:TasNet with minimal loss
 
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第213行： 第227行：
 
|Yang Wei
 
|Yang Wei
 
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* Read training code of Paraformer model, in order to get intermediate data
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* Prepare Huilan product training, and deal with problems of ASR and TTS service
 
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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
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