“2024-11-04”版本间的差异

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第6行： 第6行：
 
|Dong Wang
 
|Dong Wang
 
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* AI Medical sector 2 chapters done
 
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第17行： 第17行：
 
|Lantian Li
 
|Lantian Li
 
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* Submit three papers supporting ICCIP 2024.
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* Go on designing 2025 AI daily posts
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* Attend CSTR 40th anniversary
 
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第28行： 第30行：
 
|Ying Shi
 
|Ying Shi
 
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* Stop strategy for Cohort Overlap ASR [https://z1et6d3xtb.feishu.cn/docx/KKVHd8EDWoTkjexNhG5cbAWunfb?from=from_copylink here]
 
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第39行： 第41行：
 
|Zhenghai You
 
|Zhenghai You
 
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* Huawei project (Unsuccessful IRA) [https://z1et6d3xtb.feishu.cn/docx/RnHLdHO0jobr8uxajiQcvZx6nyc]
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* Summarize SPK-AUG experiments[https://z1et6d3xtb.feishu.cn/docx/IiNhdn9xroVlomxdKmxc8B3Cnqe]
 
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第73行： 第76行：
 
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* Debug the Chinese VTS (in training already)
 
* Debug the Chinese VTS (in training already)
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* Process the data of CVS3
 
* Write the report of VTS project (main work)
 
* Write the report of VTS project (main work)
 
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第84行： 第88行：
 
|Zehua Liu
 
|Zehua Liu
 
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*In-Context-Learning（if sentence is very long，context seems fail）still finding reason
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** （context<30s）45.30% | 44.69% (context = 30s) | 46.02%(context = 120s)
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*Writing VTS project document
 
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第95行： 第101行：
 
|Pengqi Li
 
|Pengqi Li
 
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* New Process of consistency of TAO and LayerCAM.[https://z1et6d3xtb.feishu.cn/docx/VtlIdFxdRodp8Nx8oQjcVLC4nCd]
 
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第106行： 第112行：
 
|Wan Lin
 
|Wan Lin
 
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* NS:detection (edit code with Chen)
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** EER of the model decrease faster in the previous epochs
 
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第117行： 第124行：
 
|Tianhao Wang
 
|Tianhao Wang
 
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* investigating some new approach for target sound separation
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* prepare the code for LoRA tuned CLAP
 
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第138行： 第146行：
 
|Zhenyu Zhou
 
|Zhenyu Zhou
 
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*Reproduction：conditional TasNet [https://z1et6d3xtb.feishu.cn/docx/D2UQdxMBvojkF9xCXGfcFBLGned]
 
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第186行： 第194行：
 
|Wenqiang Du
 
|Wenqiang Du
 
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*Training of New Dialect Models(Yi language )
 
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第197行： 第205行：
 
|Yang Wei
 
|Yang Wei
 
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* Write text enroll KWS model document.
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* Prepare data and code for Aibabel data finetuning.
 
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2024年11月4日 (一) 10:58的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • AI Medical sector 2 chapters done
Lantian Li
  • Submit three papers supporting ICCIP 2024.
  • Go on designing 2025 AI daily posts
  • Attend CSTR 40th anniversary
Ying Shi
  • Stop strategy for Cohort Overlap ASR here
Zhenghai You
  • Huawei project (Unsuccessful IRA) [1]
  • Summarize SPK-AUG experiments[2]
Junming Yuan
  • paper reading
  • prepare to reproduce cocktail HuBERT (in progress)
Chen Chen
Xiaolou Li
  • Debug the Chinese VTS (in training already)
  • Process the data of CVS3
  • Write the report of VTS project (main work)
Zehua Liu
  • In-Context-Learning（if sentence is very long，context seems fail）still finding reason
    • （context<30s）45.30% | 44.69% (context = 30s) | 46.02%(context = 120s)
  • Writing VTS project document
Pengqi Li
  • New Process of consistency of TAO and LayerCAM.[3]
Wan Lin
  • NS:detection (edit code with Chen)
    • EER of the model decrease faster in the previous epochs
Tianhao Wang
  • investigating some new approach for target sound separation
  • prepare the code for LoRA tuned CLAP
Xiaoxue Luo
  • prepare the report
Zhenyu Zhou
  • Reproduction：conditional TasNet [4]
Junhui Chen
  • NS with frame-level detection loss
    • use silero-vad
    • Model is training, seems EER decrease faster.
Jiaying Wang
Yu Zhang
  • SocioDojo
    • with cash ratio risk aware, and change information sources, seems have a decent risk control over Nasdaq 100 index [5]
  • Some paper reading and report in RoyalFlush, get some idea (mainly about LLM for time series task)
Wenqiang Du
  • Training of New Dialect Models(Yi language )
Yang Wei
  • Write text enroll KWS model document.
  • Prepare data and code for Aibabel data finetuning.
Lily
Turi
  • LoRA finetuning (Result is not good)
  • Data cleaning
Yue Gu
  • read several paper about speech tokenizer. I want to design a encoder, which processes different size feature frame and construct several different codebooks, to extract personality from the varing speech speed. It is still in progress.
  • paper writing
Qi Qu
  • KWS:
    • Yi (Liangshan, Sichuan) dataset prepared for training; dataset to be annotated for testing.
    • Experiments on model quantization for NPU devices: i16 quantization arrives at a balance between accuracy and efficiency (~2ms per inference, compared to ~250ms for non-quantized); more calibration data needed for further confirmation.
    • Full-featured demo (recording + feature extraction + model inference) for NPU devices in development.
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