“2025-03-03”版本间的差异

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第6行： 第6行：
 
|Dong Wang
 
|Dong Wang
 
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* Three slides for AIGE to gov and enterprise.
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|Lantian Li
 
|Lantian Li
 
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* Proofread of the high-school book (Done)
 
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第42行： 第41行：
 
|Zhenghai You
 
|Zhenghai You
 
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* Training TSE model for with content enrollment（for Huawei & CSSC(中船) projects）
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* Reading papers about refiner
 
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第66行： 第66行：
 
|Xiaolou Li
 
|Xiaolou Li
 
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* VSR training (1500h) cnvsrc-single valid 300 CER: 36.14% (not converged)
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* Finish pre-processing 4000h data
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* get ASR transcript for 4000h data
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* Writing NSFC document
 
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|Wan Lin
 
|Wan Lin
 
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* try some methods for clean performance（no improvement）
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* try some adjustment for clean performance（no improvement）
 
* supply experiments for other tests
 
* supply experiments for other tests
 
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第173行： 第176行：
 
|Yu Zhang
 
|Yu Zhang
 
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* AED:
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** Split AED model into two smaller model to detect the human voice in noisy environments and in clean environments separately.
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** Trying smaller model (under 200K)
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* Multi Agent Investment
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** try index enhancement trading, no obvious excess return
 
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* try do portfolio investment on some selected big company
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* add the debate topic about the logical consistency inside investment decisions.
 
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第198行： 第206行：
 
|Yang Wei
 
|Yang Wei
 
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* Tuning text enroll kws model for dialect data with linear layer. (recall: 65%->85%->94%)
 
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第208行： 第216行：
 
|Turi
 
|Turi
 
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* Thesis writing
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* Result with LM[https://z1et6d3xtb.feishu.cn/docx/JvDsd8zR4oMwnyxQEQdckpMjn7m?from=from_copylink]
 
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第226行： 第235行：
 
|Qi Qu
 
|Qi Qu
 
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* Applying pre-prod eval routine on text-enroll KWS models: the ideal thresholds for each keyword vary significantly.
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* Applying pre-prod eval routine on text-enroll KWS models: the ideal thresholds for each keyword vary significantly. [https://b30lttjm7l.feishu.cn/docx/BepsdxzYloNlLNxHgGncSUXVnee?from=from_copylink]
 
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2025年3月3日 (一) 11:00的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Three slides for AIGE to gov and enterprise.
Lantian Li
  • Proofread of the high-school book (Done)
Ying Shi
  • Prepare Ascend Sever environment
  • training Conditional Chain overlap ASR model with Hierachical-Transformer here
Zhenghai You
  • Training TSE model for with content enrollment（for Huawei & CSSC(中船) projects）
  • Reading papers about refiner
Junming Yuan
  • Finish MPC-HuBERT pretrain.
  • Double check the related experimental code.
    • MT-HuBERT(in progress) & Cocktail-HuBERT need re-pretrain.
    • The results of other baseline in here
Xiaolou Li
  • VSR training (1500h) cnvsrc-single valid 300 CER: 36.14% (not converged)
  • Finish pre-processing 4000h data
  • get ASR transcript for 4000h data
  • Writing NSFC document
Zehua Liu
  • Paper Reading and Sharing in last Friday
  • Writing Vision Language Model code
  • Writing NSFC document
Pengqi Li
  • Prepare the AI course for Tsinghua University Junior High School.
  • Using t-SNE to visualize the factorized content vector.
    • Next step is to color(speaker information importance or not) each point.
Wan Lin
  • try some adjustment for clean performance（no improvement）
  • supply experiments for other tests
Tianhao Wang
  • sound separation: 2-mix and 3-mix model training
  • weekly report
  • subset data training
Xiaoxue Luo
  • generation of multi-mix audio data and did some test experiments.
  • read papers
Zhenyu Zhou
  • finish graduation thesis
Junhui Chen
  • Reproducing speaker diarization method for NS (debugging...)
  • read paper
Jiaying Wang
  • debug ctc loss part[1]
Yu Zhang
  • AED:
    • Split AED model into two smaller model to detect the human voice in noisy environments and in clean environments separately.
    • Trying smaller model (under 200K)
  • Multi Agent Investment
    • try index enhancement trading, no obvious excess return
  • try do portfolio investment on some selected big company
  • add the debate topic about the logical consistency inside investment decisions.
Wenqiang Du
  • Primary handbook's PPT (24/44)
  • Continue to check Primary and middle handbook(Completed this week)
  • Speech cloning sample for the company
Yang Wei
  • Tuning text enroll kws model for dialect data with linear layer. (recall: 65%->85%->94%)
Turi
  • Thesis writing
  • Result with LM[2]
Yue Gu
  • finish some exps, but nothing is improved.
  • finish a proposal，I will present it recently
Qi Qu
  • Applying pre-prod eval routine on text-enroll KWS models: the ideal thresholds for each keyword vary significantly. [3]
取自“http://cslt.org/mediawiki/index.php?title=2025-03-03&oldid=42877”