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| 第202行： |
第202行： |
| | |Wenqiang Du | | |Wenqiang Du |
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| − | * | + | * ICCIP2024 |
| | + | * Participated in an AI competition |
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| 第213行： |
第216行： |
| | |Yang Wei | | |Yang Wei |
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| − | * | + | * Do text enroll kws model experiment on small data set.(https://z1et6d3xtb.feishu.cn/docx/WLLLd2zQvoiE78xfMhpcHejdnnd) |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- 2nd round check for middle-school AI handbook
- AI training for teachres of Tsinghua Middle School
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| Lantian Li
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- AI-Graph EN Chapter 3 Done
- 2025 Daily Sign v1.0 Done
- CSTR Report
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| Ying Shi
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- Test Google's product about sound separation
- Correct the test results of the previous condition overlap asr model here
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| Zhenghai You
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| Junming Yuan
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- reproduce cocktail-Hubert
- feat-mask MT-Hubert
- result in [1]
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| Chen Chen
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| Xiaolou Li
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- Finally finish the VTS report
- Data preparation
- CVS3 process 1/4
- take over webVideo from SUN CHANG and preprocess it through auto-avsr pipline
- Code preparation
- Finish the Conformer/CTC pretraining code
- Still debuging AVHuBERT pretraining code
- Paper reading...
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| Zehua Liu
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- VTS Documents Revise with Xiaolou
- Iterative inference training
- LLM Different context length[2]
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| Pengqi Li
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- Summarize recently work and report.
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- Mapping to IPA from diff language.
- Write Paper.
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| Wan Lin
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| Tianhao Wang
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- organizing the exp plan and modify the code for In-context-Audio-Retrieval (in training)
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| Xiaoxue Luo
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- prepare the code for CED+AudioSep
- participate in an AI competition with Wenqiang and Zhangyu
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| Zhenyu Zhou
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- Huawei project
- read papers
- code review(Design new ordering method)
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| Junhui Chen
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| Jiaying Wang
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| Yu Zhang
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- ICCIP 2024
- Paper reading about LLM Market Simulation
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| Wenqiang Du
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- ICCIP2024
- Participated in an AI competition
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| Yang Wei
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| Lily
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| Turi
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| Yue Gu
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- synthesis some audios for target speakers [3]
- paper writing
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| Qi Qu
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- Knock detection: output every knock's offset so that a shorter audio can be built to speed up human verification.
- Text-enroll KWS: model i/o optimized; 2.5x faster than the first version.
- KWS: Chongqing dialect train dataset (15 keywords, ~24.5k utterances).
- Exp. using new FunASR model (SeACoParaformer) for cloud verification, which handles hotwords better.
- Exp. using B0-based KWS model for local verification after detection from Chipintelli's chip.
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