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| |Qi Qu | | |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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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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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
|
- 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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