“2024-11-18”版本间的差异

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* Knock detection: output every knock's offset so that a shorter audio can be built to speed up human verification.
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* Text-enroll KWS: model i/o optimized; 2.5x faster than the first version.
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* KWS: Chongqing dialect train dataset (15 keywords, ~24.5k utterances).
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* Exp. using new FunASR model (SeACoParaformer) for cloud verification, which handles hotwords better.
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* Exp. using B0-based KWS model for local verification after detection from Chipintelli's chip.
 
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2024年11月18日 (一) 10:56的版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • 2nd round check for middle-school AI handbook
  • AI training for teachres of Tsinghua Middle School
Lantian Li
  • AI-Graph EN Chapter 3 Done
  • 2025 Daily Sign v1.0 Done
  • CSTR Report
Ying Shi
  • Test Google's product about sound separation
  • Correct the test results of the previous condition overlap asr model here
Zhenghai You
Junming Yuan
  • reproduce cocktail-Hubert
  • feat-mask MT-Hubert
    • change training strategy
  • result in [1]
Chen Chen
Xiaolou Li
  • 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...
Zehua Liu
  • VTS Documents Revise with Xiaolou
  • Iterative inference training
  • LLM Different context length[2]
Pengqi Li
  • Summarize recently work and report.
  • Mapping to IPA from diff language.
  • Write Paper.
Wan Lin
Tianhao Wang
  • organizing the exp plan and modify the code for In-context-Audio-Retrieval (in training)
Xiaoxue Luo
  • prepare the code for CED+AudioSep
  • participate in an AI competition with Wenqiang and Zhangyu
Zhenyu Zhou
  • Huawei project
  • read papers
  • code review(Design new ordering method)
Junhui Chen
Jiaying Wang
Yu Zhang
  • ICCIP 2024
  • Paper reading about LLM Market Simulation
Wenqiang Du
Yang Wei
Lily
Turi
Yue Gu
  • synthesis some audios for target speakers [3]
  • paper writing
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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