“2024-10-28”版本间的差异

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（15位用户的20个中间修订版本未显示）
第6行： 第6行：
 
|Dong Wang
 
|Dong Wang
 
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* AI primary book done
 
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第17行： 第17行：
 
|Lantian Li
 
|Lantian Li
 
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* AI-Graph EN (1-20 finalized)
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* Design 2025 Daily Posts
 
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第28行： 第29行：
 
|Ying Shi
 
|Ying Shi
 
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* revise the code about cohort-overlap asr [the training is in progress]
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** Support arbitrary source mixing training
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** Use the real hypothesis as condition by Token error rate
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** Design stop criterion
 
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第39行： 第43行：
 
|Zhenghai You
 
|Zhenghai You
 
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*
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* Introduce more hard samples to improve model performance[https://z1et6d3xtb.feishu.cn/docx/CURxdy3tEorxkrxtjjqcdMaYnJg]
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** SPK-AUG with same length: There is an improvement, but the SI-SDR decreases when hard sample rate increases
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** Design more hard samples
 
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第72行： 第78行：
 
|Xiaolou Li
 
|Xiaolou Li
 
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*
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* VTS with LLM structure design and baseline code writing [https://z1et6d3xtb.feishu.cn/docx/ZBnOdEMxgo8bs5xrkb1cPZnCnQg?from=from_copylink]
 
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第97行： 第103行：
 
|Pengqi Li
 
|Pengqi Li
 
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*Consistency of TAO and LayerCAM
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** Change TAO from input to final conv layer and obtain more consistency.(Aishell:0.93 in any model)
 
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第108行： 第115行：
 
|Wan Lin
 
|Wan Lin
 
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* NS: downsampling is not useful [https://z1et6d3xtb.feishu.cn/docx/MxBNdPbLao0tsoxkBVCcUgUoneh?from=from_copylink]
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* share speaker meeting in Friday
 
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第119行： 第127行：
 
|Tianhao Wang
 
|Tianhao Wang
 
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* AudioSep (CLAP) 5-mix exps:
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* AudioSep (CLAP) 5-mix exps[https://z1et6d3xtb.feishu.cn/docx/DlR8dZRdEoZIwIxTOFvcQdbGnqg]:
 
** text-query: SDR=4.978, SI-SDR=1.972
 
** text-query: SDR=4.978, SI-SDR=1.972
 
** audio-query: SDR=6.907, SI-SDR=5.058
 
** audio-query: SDR=6.907, SI-SDR=5.058
第146行： 第154行：
 
|Zhenyu Zhou
 
|Zhenyu Zhou
 
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*
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*reproduce 5-mix speech Separation results：
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**pit：2-mix：16.04 ；5-mix：6.87
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**conditional：5-mix：5.38（40 epoch）
 
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第157行： 第167行：
 
|Junhui Chen
 
|Junhui Chen
 
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* NS：speaker detection （method survey & debug）
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* get sick
 
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第179行： 第190行：
 
|Yu Zhang
 
|Yu Zhang
 
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* SocioDojo (still worse than Nasdaq100 baseline)
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** Change information sources, from the perspective of the report generated by LLM, more new information sources will be referenced.
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** Prompt Actuator to consider current cash ratio before investing (with out this, the asset ratio goes up to 100%, which leads to high risks, still running)
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* Read some papers about integrating time series into LLM
 
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第190行： 第204行：
 
|Wenqiang Du
 
|Wenqiang Du
 
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* Prepare data,code and environment for Pro.Mijiti
 
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第201行： 第215行：
 
|Yang Wei
 
|Yang Wei
 
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* Train text enroll KWS model with Aibabel training data. Not work.
 
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第221行： 第235行：
 
|Turi
 
|Turi
 
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*
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* Whisper-largev3 finetuning
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** Freezing 20 layers of encoder achieved 9.75 WER. Vanilla finetuning 8.02 WER
 
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第229行： 第244行：
 
|Yue Gu
 
|Yue Gu
 
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*
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* seek suggestions from other authors. Many suggestions are conflicting, so I'm try to figure out the reasons and fix these issues.
 
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第238行： 第253行：
 
|Qi Qu
 
|Qi Qu
 
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*  
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* KWS:
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** Text-enroll models exported to ONNX.
 +
** C/JNI libs built based on ONNX models and ready for on-device test.
 
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2024年10月28日 (一) 10:59的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • AI primary book done
Lantian Li
  • AI-Graph EN (1-20 finalized)
  • Design 2025 Daily Posts
Ying Shi
  • revise the code about cohort-overlap asr [the training is in progress]
    • Support arbitrary source mixing training
    • Use the real hypothesis as condition by Token error rate
    • Design stop criterion
Zhenghai You
  • Introduce more hard samples to improve model performance[1]
    • SPK-AUG with same length: There is an improvement, but the SI-SDR decreases when hard sample rate increases
    • Design more hard samples
Junming Yuan
  • The result of time-mask MT-HuBERT [2]
    • A sad news
Chen Chen
Xiaolou Li
  • VTS with LLM structure design and baseline code writing [3]
Zehua Liu
  • Reading Papper about In-Context-Learning in ASR
  • Training model with Adaptive Time Mask
  • Try In-Context-Learning with only previous sentence[4]
  • VTS Project Report starts
Pengqi Li
  • Consistency of TAO and LayerCAM
    • Change TAO from input to final conv layer and obtain more consistency.(Aishell:0.93 in any model)
Wan Lin
  • NS: downsampling is not useful [5]
  • share speaker meeting in Friday
Tianhao Wang
  • AudioSep (CLAP) 5-mix exps[6]:
    • text-query: SDR=4.978, SI-SDR=1.972
    • audio-query: SDR=6.907, SI-SDR=5.058
    • This results with the loudness limitation
  • AudioSep (CLAP) without loudness limitation
  • Project things
Xiaoxue Luo
  • Comparative experiment between AudioSep and baseline system(CLIPSep)
  • Prepare the report
Zhenyu Zhou
  • reproduce 5-mix speech Separation results：
    • pit：2-mix：16.04 ；5-mix：6.87
    • conditional：5-mix：5.38（40 epoch）
Junhui Chen
  • NS：speaker detection （method survey & debug）
  • get sick
Jiaying Wang
Yu Zhang
  • SocioDojo (still worse than Nasdaq100 baseline)
    • Change information sources, from the perspective of the report generated by LLM, more new information sources will be referenced.
    • Prompt Actuator to consider current cash ratio before investing (with out this, the asset ratio goes up to 100%, which leads to high risks, still running)
  • Read some papers about integrating time series into LLM
Wenqiang Du
  • Prepare data,code and environment for Pro.Mijiti
Yang Wei
  • Train text enroll KWS model with Aibabel training data. Not work.
Lily
Turi
  • Whisper-largev3 finetuning
    • Freezing 20 layers of encoder achieved 9.75 WER. Vanilla finetuning 8.02 WER
Yue Gu
  • seek suggestions from other authors. Many suggestions are conflicting, so I'm try to figure out the reasons and fix these issues.
Qi Qu
  • KWS:
    • Text-enroll models exported to ONNX.
    • C/JNI libs built based on ONNX models and ready for on-device test.
取自“http://index.cslt.org/mediawiki/index.php?title=2024-10-28&oldid=42378”