“2026-01-26”版本间的差异

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(以“{| class="wikitable" !People !! This Week !! Next Week !! Task Tracking (<font color="red">DeadLine</font>) |- |- |Dong Wang || * || * || * |- |- |Lantian Li || *...”为内容创建页面)
 
 
(15位用户的22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 middle school hand book / English version almost completed.
 
||
 
||
 
*
 
*
第17行: 第17行:
 
|Lantian Li
 
|Lantian Li
 
||
 
||
*
+
* Recheck AI textbooks for senior middle school.
 +
* MoE daily work (busy week)
 
||
 
||
*
+
* Go on the final review of my MLA book (6/10)
 
||
 
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*
 
*
第28行: 第29行:
 
|Wenqiang Du
 
|Wenqiang Du
 
||
 
||
*
+
* Project work about AIbabel
 +
** Upgraded online model
 +
** New demand technology survey
 
||
 
||
 
*
 
*
第39行: 第42行:
 
|Yang Wei
 
|Yang Wei
 
||
 
||
*
+
* Analyze uy/ch_child data annotation and exp result.
 +
* Test the av speech separation model with a dataset with more speakers.
 
||
 
||
 
*
 
*
第50行: 第54行:
 
|Ying Shi
 
|Ying Shi
 
||
 
||
*
+
* Thesis
 +
* Huawei project
 
||
 
||
 
*  
 
*  
第59行: 第64行:
  
 
|-
 
|-
|Pengqi Li
+
|Yue Gu
 
||
 
||
*
+
* write my thesis
 
||
 
||
 
*
 
*
第70行: 第75行:
  
 
|-
 
|-
|Junming Yuan
+
|Lily
 
||
 
||
*
+
* Organized course materials production (小初高分册)
 +
* Participated in reviewing the AI handbooks
 +
* Miyun 3-high school AI report
 
||
 
||
 
*
 
*
第81行: 第88行:
  
 
|-
 
|-
|Yu Zhang
+
|Pengqi Li
 
||
 
||
*
+
* Experimental analysis revealed the unreliability of the insertion and deletion metrics adapted from face verification.[https://z1et6d3xtb.feishu.cn/docx/BZxUdDC1YoOSXwxQCrycW9Ymnqf]
 +
* Proposed and implemented a novel framework for assessing insertion and deletion.(still test)
 
||
 
||
 
*
 
*
第92行: 第100行:
  
 
|-
 
|-
|Junhui Chen
+
|Junming Yuan
 
||
 
||
*
+
* learn-not-to-listen MT-HuBERT
 +
** PR(PER%), LNTL-MT-HuBERT: 7.24%, Clean-HuBERT: 5.41%, WavLM: 4.84%
 +
** ASR(WER%), LNTL-MT-HuBERT: 8.43%, Clean-HuBERT: 6.42%, WavLM: 6.21%
 +
** SD(DER%), LNTL-MT-HuBERT: 4.31%, Cocktail-HuBERT: 4.07%, MT-HuBERT: 3.93%
 +
* revise ICASSP final paper
 
||
 
||
 
*
 
*
第103行: 第115行:
  
 
|-
 
|-
|Xiaolou Li
+
|Yu Zhang
 +
||
 +
* GPU Util: [https://z1et6d3xtb.feishu.cn/wiki/XX4NwX3tJiBDcgkMi0hcFUtInHh]
 +
* LLM
 +
** Completed the code for Swarm MMLU metric-based optimization together with @chenjunhui
 +
** Still working on modifying the Swarm topology code (for more complex swarm topology)
 
||
 
||
 
*
 
*
||
 
*
 
 
||
 
||
 
*
 
*
第114行: 第129行:
  
 
|-
 
|-
|Jiaying Wang
+
|Junhui Chen
 
||
 
||
*
+
* (caught a cold)
 +
* MMLU
 +
** difference in the ECS distribution between good nodes and bad nodes.
 +
** metrics loss helps select high-quality nodes and eliminate low-quality ones. [https://z1et6d3xtb.feishu.cn/wiki/WIbewlbcwihGoCkChpZcwZpEnnh?from=from_copylink]
 +
 
 
||
 
||
 
*
 
*
第125行: 第144行:
  
 
|-
 
|-
|Tianhao Wang
+
|Jiaying Wang
 
||
 
||
*
+
* spk order separation:2-mix,3-mix training finished,testing; 4-mix are training
 +
* loudness order verification done
 +
* content order verification 1/3, will be finished these days
 
||
 
||
 
*
 
*
第138行: 第159行:
 
|Xiaoxue Luo
 
|Xiaoxue Luo
 
||
 
||
*
+
* prepare 2mix speech separation results for ASR test
 +
* 2-5mix multi_head separation model for Huawei project
 +
** data: remove data that the target audio is mixed audio(input equals output, model does not learn)
 +
** loss: add MSE_loss to the original SISDR_loss to control the energy amplitude of output audio
 
||
 
||
 
*
 
*
第147行: 第171行:
  
 
|-
 
|-
|Yue Gu
+
|Bochao Hu
 
||
 
||
*
+
* read papers
 +
* prepare the weekly report
 
||
 
||
 
*
 
*
第158行: 第183行:
  
 
|-
 
|-
|Lily
+
|Hongcheng Zhang
 
||
 
||
*
+
*made zero-shot on clotho and Audiocaps
 +
**result:ROUGE-L(0.2931,0.3998),CIDEr(0.0986,0.2312),SPIDEr(0.0800,0.1762),(clotho,Audiocaps)
 
||
 
||
 
*
 
*
第169行: 第195行:
  
 
|-
 
|-
|Bochao Hu
+
|Weiman Sun
 
||
 
||
*
+
*Ran a completely opposite ASR task on the same data
||
+
**Consistent with last week's result
*
+
*Analyze different models
||
+
*
+
|-
+
 
+
 
+
|-
+
|Hongcheng Zhang
+
||
+
*
+
 
||
 
||
 
*
 
*

2026年2月2日 (一) 09:48的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • AI middle school hand book / English version almost completed.
Lantian Li
  • Recheck AI textbooks for senior middle school.
  • MoE daily work (busy week)
  • Go on the final review of my MLA book (6/10)
Wenqiang Du
  • Project work about AIbabel
    • Upgraded online model
    • New demand technology survey
Yang Wei
  • Analyze uy/ch_child data annotation and exp result.
  • Test the av speech separation model with a dataset with more speakers.
Ying Shi
  • Thesis
  • Huawei project
Yue Gu
  • write my thesis
Lily
  • Organized course materials production (小初高分册)
  • Participated in reviewing the AI handbooks
  • Miyun 3-high school AI report
Pengqi Li
  • Experimental analysis revealed the unreliability of the insertion and deletion metrics adapted from face verification.[1]
  • Proposed and implemented a novel framework for assessing insertion and deletion.(still test)
Junming Yuan
  • learn-not-to-listen MT-HuBERT
    • PR(PER%), LNTL-MT-HuBERT: 7.24%, Clean-HuBERT: 5.41%, WavLM: 4.84%
    • ASR(WER%), LNTL-MT-HuBERT: 8.43%, Clean-HuBERT: 6.42%, WavLM: 6.21%
    • SD(DER%), LNTL-MT-HuBERT: 4.31%, Cocktail-HuBERT: 4.07%, MT-HuBERT: 3.93%
  • revise ICASSP final paper
Yu Zhang
  • GPU Util: [2]
  • LLM
    • Completed the code for Swarm MMLU metric-based optimization together with @chenjunhui
    • Still working on modifying the Swarm topology code (for more complex swarm topology)
Junhui Chen
  • (caught a cold)
  • MMLU
    • difference in the ECS distribution between good nodes and bad nodes.
    • metrics loss helps select high-quality nodes and eliminate low-quality ones. [3]
Jiaying Wang
  • spk order separation:2-mix,3-mix training finished,testing; 4-mix are training
  • loudness order verification done
  • content order verification 1/3, will be finished these days
Xiaoxue Luo
  • prepare 2mix speech separation results for ASR test
  • 2-5mix multi_head separation model for Huawei project
    • data: remove data that the target audio is mixed audio(input equals output, model does not learn)
    • loss: add MSE_loss to the original SISDR_loss to control the energy amplitude of output audio
Bochao Hu
  • read papers
  • prepare the weekly report
Hongcheng Zhang
  • made zero-shot on clotho and Audiocaps
    • result:ROUGE-L(0.2931,0.3998),CIDEr(0.0986,0.2312),SPIDEr(0.0800,0.1762),(clotho,Audiocaps)
Weiman Sun
  • Ran a completely opposite ASR task on the same data
    • Consistent with last week's result
  • Analyze different models