“2024-03-18”版本间的差异

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（18位用户的30个中间修订版本未显示）
第6行： 第6行：
 
|Dong Wang
 
|Dong Wang
 
||  
 
||  
−
*  
+
* Interspeech 2024 paper refinement
 +
* Design/Discussion AI popular science
 +
* Conjecture for minmum loss training
 
||
 
||
 
*
 
*
第17行： 第19行：
 
|Lantian Li
 
|Lantian Li
 
||
 
||
−
*  
+
* GPU status [https://z1et6d3xtb.feishu.cn/wiki/XGcGwRK5viJmpRkjH9AczIhynCh]
 +
* INTERSPEECH 2024
 +
* ASIP-BUPT (CohortTSE, SE-Adapter, SpeakerAug, NeuralScoring)
 
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||
 
*  
 
*  
第28行： 第32行：
 
|Ying Shi
 
|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
 +
* [https://z1et6d3xtb.feishu.cn/docx/Dx2pdEMOroS9w4xX6C2cUPn0nOd?from=from_copylink group work]
 
||
 
||
 
*  
 
*  
第39行： 第49行：
 
|Zhenghai You
 
|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
 
||
 
||
 
*   
 
*   
第49行： 第63行：
 
|Junming Yuan
 
|Junming Yuan
 
||  
 
||  
−
* Make the plan for the large vocabulary pretraining task
+
* Finish INTERSPEECH paper
 +
* 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.
 
** Try to address the 3 questions:
 
** Try to address the 3 questions:
−
**** How to change MT pretraining model structure?
+
*** How to change MT pretraining model structure?
−
**** How to train three strictly comparable pretraining models based on MT, Hubert, and wav2vec?
+
*** How to train three strictly comparable pretraining models based on MT, Hubert, and wav2vec?
−
**** Why does Hubert+MT perform significantly better?
+
*** Why does Hubert+MT perform significantly better?
 
||
 
||
 
*
 
*
第65行： 第80行：
 
|Chen Chen
 
|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 [https://z1et6d3xtb.feishu.cn/docx/PUNcdn0mZoYciuxhNogcu1TMnQd?from=from_copylink] 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
 
||
 
||
 
*  
 
*  
−
||
 
−
* 
 
 
|-
 
|-
  
第76行： 第98行：
 
|Xiaolou Li
 
|Xiaolou Li
 
||  
 
||  
−
*  
+
* Finish INTERSPEECH2024 paper
 +
* review code of cnvsrc
 +
* Next step:
 +
** Focus on model structure of VSR Benchmark
 
||
 
||
 
*  
 
*  
第87行： 第112行：
 
|Zehua Liu
 
|Zehua Liu
 
||  
 
||  
−
*
+
*Finish IS24
 +
*VSR work continues
 
||
 
||
 
*  
 
*  
第98行： 第124行：
 
|Pengqi Li
 
|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[https://z1et6d3xtb.feishu.cn/docx/T3U2dTs5poiIgtxtM2Sc0QennWe]
 
||
 
||
 
*
 
*
第109行： 第139行：
 
|Wan Lin
 
|Wan Lin
 
||  
 
||  
−
*  
+
* Neural scoring [https://z1et6d3xtb.feishu.cn/docx/TQvWdk8LVo9ONaxQ5Qac9A2Dn3d?from=from_copylink]
 
||
 
||
 
*
 
*
第120行： 第150行：
 
|Tianhao Wang
 
|Tianhao Wang
 
||  
 
||  
−
*  
+
* Finish INTERSPEECH paper
 +
* Code reorganization
 
||
 
||
 
*  
 
*  
第131行： 第162行：
 
|Zhenyu Zhou
 
|Zhenyu Zhou
 
||  
 
||  
−
*
+
*InterSpeech2024 submission
 +
*Code reorganization
 +
*Neuro scoring reviewing
 
||
 
||
 
*
 
*
第142行： 第175行：
 
|Junhui Chen
 
|Junhui Chen
 
||
 
||
−
*  
+
* Neural scoring
 +
* Interim report
 
||
 
||
 
*
 
*
第153行： 第187行：
 
|Jiaying Wang
 
|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
 
||
 
||
 
*  
 
*  
第164行： 第202行：
 
|Yu Zhang
 
|Yu Zhang
 
||
 
||
−
*  
+
* Portfolio backtesting report
 +
* stock trade API
 
||
 
||
 
*
 
*
第175行： 第214行：
 
|Wenqiang Du
 
|Wenqiang Du
 
||  
 
||  
−
*  
+
* Aibabel
 +
** Control Uyghur KWS model  FA,but not get a good performance yet.
 +
** Continue test and update CN  KWS model
 
||
 
||
 
*
 
*
第186行： 第227行：
 
|Yang Wei
 
|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
 
||
 
||
 
*
 
*
第196行： 第238行：
 
|Lily
 
|Lily
 
||
 
||
−
*  
+
* Paper reading
 +
* Prepare for overview paper
 +
||
 +
*
 +
||
 +
* 
 +
|-
 +
 
 +
 
 +
|-
 +
|Turi
 +
||
 +
* Data collection app[https://uestc.feishu.cn/docx/YBikdXfqpobawFxVtticv6L1nkh?from=from_copylink]
 +
* Course works
 
||
 
||
 
*
 
*

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”