“2024-08-19”版本间的差异

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（9位用户的17个中间修订版本未显示）
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* AI primary (middle-school) 1-6
 
* AI primary (middle-school) 1-6
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|Lantian Li
 
|Lantian Li
 
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* GPU status [https://z1et6d3xtb.feishu.cn/wiki/XGcGwRK5viJmpRkjH9AczIhynCh]
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* AI primary
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** High school handbook (30/40)
 
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* High school handbook (40/40)
 
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|Zhenghai You
 
|Zhenghai You
 
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* Continue the work of speaker augument and Ex-Former in TSE[https://z1et6d3xtb.feishu.cn/docx/ZbtsdTGuQo4IXnxuxHXcpvBynoe]
 
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|Junming Yuan
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|Jiaying Wang
 
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* Verified two parameters in Hubert pretraining config file that were confused with the original paper.[https://z1et6d3xtb.feishu.cn/docx/PaATdHi26oEc0Pxovd4cSyp0nQ2]
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* reproduce conditional chain code
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** Confirmed that in the second iteration of pretraining, features should be extracted from the 6-th layer of the transformer, not the 9-th layer.
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** on both libri and wsj: training loss hard to reduce (around -3) and the corresponding test sisdr is positive
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*** in 175k step, result of 6-th layer: 71.55/9.39, result of 9-th layer: 37.31/16.72
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* rewriting the code (preserving the original model)
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** Basically confirmed the setting of the parameter 'untie_final_proj' for the two iterations of pretraining.
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|Chen Chen
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|Junming Yuan
 
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* Verified two parameters in Hubert pretraining config file that were confused with the original paper.[https://z1et6d3xtb.feishu.cn/docx/PaATdHi26oEc0Pxovd4cSyp0nQ2]
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** Confirmed that in the second iteration of pretraining, features should be extracted from the 6-th layer of the transformer, not the 9-th layer.
 +
*** in 175k step, result of 6-th layer: 71.55/9.39, result of 9-th layer: 37.31/16.72
 +
** Basically confirmed the setting of the parameter 'untie_final_proj' for the two iterations of pretraining.
 
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* Investigating Extremely Short-Utterance in speaker recognition[https://z1et6d3xtb.feishu.cn/docx/JBGhdZDJUoSDwGx3wbFcDk9NnLb]
 
* Investigating Extremely Short-Utterance in speaker recognition[https://z1et6d3xtb.feishu.cn/docx/JBGhdZDJUoSDwGx3wbFcDk9NnLb]
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|Wan Lin
 
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* VoxBlink1
 
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** Data processing
 
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** Baseline(ResNet34) training and NS training [https://z1et6d3xtb.feishu.cn/docx/BywjdkGvNou12sxQ4dAcxYa9noh?from=from_copylink]
 
 
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|Tianhao Wang
 
|Tianhao Wang
 
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* reproducing CLIPSep on two datasets: MUSIC and VGGSound [https://z1et6d3xtb.feishu.cn/docx/HyLGduUu2oixxJxaFdwcDaIsnhd]
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** MUSIC: Text query: 10.06 SDR, Image query: 12.13 SDR
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** VGGSound: Text query: 2.78 SDR, Image query: 5.01 SDR
 
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|Zhenyu Zhou
 
|Zhenyu Zhou
 
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*conditional chain reproduce
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*model quantify[https://z1et6d3xtb.feishu.cn/docx/S9ChdyH7go490txZ2ZHcNjXTn2b]
 
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|Junhui Chen
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|Wan Lin
 
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* VoxBlink1
 
* VoxBlink1
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|Jiaying Wang
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|Junhui Chen
 
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* VoxBlink1
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** Data processing
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** Baseline(ResNet34 ASP) training and NS training [https://z1et6d3xtb.feishu.cn/docx/BywjdkGvNou12sxQ4dAcxYa9noh#Ro69dyERUoN0HvxOGMWcOJjrnuf]
 
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|Yu Zhang
 
|Yu Zhang
 
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* AED engineering problem assist
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* paper reading (will report this FRI)
 
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|Wenqiang Du
 
|Wenqiang Du
 
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*Complete Primary school handbook draft (45 + 8)
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* Modify the format, expression, and distribution of knowledge points in the draft（40%）
 
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|Yang Wei
 
|Yang Wei
 
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* Test KWS model on data v1.2.(https://z1et6d3xtb.feishu.cn/docx/Jkv2dnZVRo12eKx65tLc1vctnfb)
 
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|Turi
 
|Turi
 
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* Traveled back home and took rest
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* Now writing dataset paper.
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**Done with Intro, Literature review, Data collection sections. Experiment, Result and Conclusion sections remaining.
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* Wasn't able to do more experiment on dataset from Ethiopia due to poor network.
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2024年8月19日 (一) 10:59的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • AI primary (middle-school) 1-6
Lantian Li
  • GPU status [1]
  • AI primary
    • High school handbook (30/40)
  • High school handbook (40/40)
Ying Shi
Zhenghai You
  • Continue the work of speaker augument and Ex-Former in TSE[2]
Jiaying Wang
  • reproduce conditional chain code
    • on both libri and wsj: training loss hard to reduce (around -3) and the corresponding test sisdr is positive
  • rewriting the code (preserving the original model)
Junming Yuan
  • Verified two parameters in Hubert pretraining config file that were confused with the original paper.[3]
    • Confirmed that in the second iteration of pretraining, features should be extracted from the 6-th layer of the transformer, not the 9-th layer.
      • in 175k step, result of 6-th layer: 71.55/9.39, result of 9-th layer: 37.31/16.72
    • Basically confirmed the setting of the parameter 'untie_final_proj' for the two iterations of pretraining.
Xiaolou Li
Zehua Liu
Pengqi Li
  • Investigating Extremely Short-Utterance in speaker recognition[4]
Tianhao Wang
  • reproducing CLIPSep on two datasets: MUSIC and VGGSound [5]
    • MUSIC: Text query: 10.06 SDR, Image query: 12.13 SDR
    • VGGSound: Text query: 2.78 SDR, Image query: 5.01 SDR
Zhenyu Zhou
  • conditional chain reproduce
  • model quantify[6]
Wan Lin
  • VoxBlink1
    • Data processing
    • Baseline(ResNet34) training and NS training [7]
Junhui Chen
  • VoxBlink1
    • Data processing
    • Baseline(ResNet34 ASP) training and NS training [8]
Yu Zhang
  • AED engineering problem assist
  • paper reading (will report this FRI)
Wenqiang Du
  • Complete Primary school handbook draft (45 + 8)
  • Modify the format, expression, and distribution of knowledge points in the draft（40%）
Yang Wei
Lily
Turi
  • Traveled back home and took rest
  • Now writing dataset paper.
    • Done with Intro, Literature review, Data collection sections. Experiment, Result and Conclusion sections remaining.
  • Wasn't able to do more experiment on dataset from Ethiopia due to poor network.
Yue Gu
  • modify the introduction
  • complete the interspeech poster, and open source the paper code
  • rest for two days, next I will focus on my new work
Qi Qu
  • Inactive due to absence.
  • KWS:
    • zh48 test dataset to be updated: ~30 speakers in 3 locations.
    • yue10 (Cantonese 10 keywords) train dataset to be updated: ~120 speakers verified, more to come.
    • Try to find suitable keyword-wise thresholds based on Recall ~ FA relation.
取自“http://cslt.org/mediawiki/index.php?title=2024-08-19&oldid=42147”