“2024-11-25”版本间的差异

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第33行： 第33行：
 
|Ying Shi
 
|Ying Shi
 
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* Design cohort- conditional chain multi-talker ASR with round-RNN
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** WER result :  round-1 32.15% , round-2: 69.69% round-3: 92.33%
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** For 500 utterances sub-test set: Only 28% of the sentences have a recognition order that matches the cosine distance.
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* Prepare for Huawei's interview.
 
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第44行： 第47行：
 
|Zhenghai You
 
|Zhenghai You
 
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* Huawei TSE(Train models that better fit the scene)[https://z1et6d3xtb.feishu.cn/docx/AArOdQEQPoFcshxD5OfcB9SLnFg]
 
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第54行： 第57行：
 
|Junming Yuan
 
|Junming Yuan
 
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* Comparable results between Clean-HuBERT, Cocktail-HuBERT, and MT-HuBERT[https://z1et6d3xtb.feishu.cn/docx/YhJadT52mokvPQxfV3qcmtlwnkb]
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** Bad news: Cocktail-HuBERT > Clean-HuBERT > MT-HuBERT
 
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第76行： 第80行：
 
|Xiaolou Li
 
|Xiaolou Li
 
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* Data process
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** CVS3 1/4 already cut from original video, waiting for pre-process
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** Copying pre-processed GongAn video data from gonganbu
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* VSR Contrastive Loss Exp
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** Inspired by paper [https://arxiv.org/abs/2408.11813]
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** Main idea: For better align visual feature to LLM input, calculate cos similarity of target and video feature, set the biggest as the positive pair.
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** Result: Under training
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* Paper Reading
 
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第88行： 第99行：
 
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*Rebutall writing
 
*Rebutall writing
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*Iterative training
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*Iterative training and inference
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**Baseline(45.99%) < Iter-1(45.53%) < Iter-2(45.00%) < Iter-3(44.85%)  
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**Iter-1(45.53%) < Iter-2(45.00%) < Iter-3(44.85%)  
 
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第100行： 第111行：
 
|Pengqi Li
 
|Pengqi Li
 
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* Begin writing paper about importance of phonemes analysis work.
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* Reading a doctoral thesis about speaker explainability[https://theses.hal.science/tel-04634215v1/file/These_BEN_AMOR.pdf].
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第111行： 第124行：
 
|Wan Lin
 
|Wan Lin
 
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* NS: all transformer
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** 6k spk: EER 2.6%
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** 20k spk: EER 2.3%
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** 20k spk+multi-enroll: EER 1.9%
 
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第121行： 第137行：
 
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|Tianhao Wang
 
|Tianhao Wang
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* Experiments about query embedding conditional approach:
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** SDR: FiLM (7.492) > self-attention (6.573)
 
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|Xiaoxue Luo
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* training of the USS(CED+AudioSep) model
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** adjust the audio format to meet the needs of the model(in training)
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* production of 2025 Daily Sign( March )
 
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第133行： 第163行：
 
|Zhenyu Zhou
 
|Zhenyu Zhou
 
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*Speaker identity based conditional chain proposal[https://z1et6d3xtb.feishu.cn/docx/MzZ8d3cDWokCzCx0MmDcRJDFnke]
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*prepare Interim Report
 
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第144行： 第175行：
 
|Junhui Chen
 
|Junhui Chen
 
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* Read paper (ICCIP keynote speak paper and some other)
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* NS
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** Some tests about transformer feature extractor
 
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第193行： 第226行：
 
|Yang Wei
 
|Yang Wei
 
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* Fix some bugs about keyword sampling in text enroll kws training code.
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* Add spec augmentation for text enroll kws training.
 
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第222行： 第256行：
 
|Yue Gu
 
|Yue Gu
 
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* Synthesis about 1h data for each target speaker, then using these data to train the adapter module.[https://z1et6d3xtb.feishu.cn/wiki/VPZfwx53ei2zkgkSvPtcCiDSnVh?from=from_copylink]
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* writing taslp paper
 
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2024年11月25日 (一) 11:04的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • 2nd round of check for AI handbook middle school.
  • Deal with pictures in AI handbook (primary & middle).
  • Start to check AI handbook high school.
  • Check AI book for Tianjin medical school.
Lantian Li
  • Complete my CSTR Report
  • Go on AI-Graph EN Chapter 4
  • Polish 2025 Daily Sign
Ying Shi
  • Design cohort- conditional chain multi-talker ASR with round-RNN
    • WER result : round-1 32.15% , round-2: 69.69% round-3: 92.33%
    • For 500 utterances sub-test set: Only 28% of the sentences have a recognition order that matches the cosine distance.
  • Prepare for Huawei's interview.
Zhenghai You
  • Huawei TSE(Train models that better fit the scene)[1]
Junming Yuan
  • Comparable results between Clean-HuBERT, Cocktail-HuBERT, and MT-HuBERT[2]
    • Bad news: Cocktail-HuBERT > Clean-HuBERT > MT-HuBERT
Chen Chen
Xiaolou Li
  • Data process
    • CVS3 1/4 already cut from original video, waiting for pre-process
    • Copying pre-processed GongAn video data from gonganbu
  • VSR Contrastive Loss Exp
    • Inspired by paper [3]
    • Main idea: For better align visual feature to LLM input, calculate cos similarity of target and video feature, set the biggest as the positive pair.
    • Result: Under training
  • Paper Reading
Zehua Liu
  • Rebutall writing
  • Iterative training and inference
    • Iter-1(45.53%) < Iter-2(45.00%) < Iter-3(44.85%)
Pengqi Li
  • Begin writing paper about importance of phonemes analysis work.
  • Reading a doctoral thesis about speaker explainability[4].
Wan Lin
  • NS: all transformer
    • 6k spk: EER 2.6%
    • 20k spk: EER 2.3%
    • 20k spk+multi-enroll: EER 1.9%
Tianhao Wang
  • Experiments about query embedding conditional approach:
    • SDR: FiLM (7.492) > self-attention (6.573)
Xiaoxue Luo
  • training of the USS(CED+AudioSep) model
    • adjust the audio format to meet the needs of the model(in training)
  • production of 2025 Daily Sign( March )
Zhenyu Zhou
  • Speaker identity based conditional chain proposal[5]
  • prepare Interim Report
Junhui Chen
  • Read paper (ICCIP keynote speak paper and some other)
  • NS
    • Some tests about transformer feature extractor
Jiaying Wang
Yu Zhang
  • Huawei AED
    • data aug & human annotated dataset [6]
  • Finance
    • Paper reading, reproduce local Llama version of StockAgent [7] (a LLM based market simulation framework)
Wenqiang Du
  • Training of New language Models(HeNan)[8]
  • Training of New language Models(ChongQing)[9]
Yang Wei
  • Fix some bugs about keyword sampling in text enroll kws training code.
  • Add spec augmentation for text enroll kws training.
Lily
Turi
  • Paper reading
  • ICASSP 2025 rebuttal
Yue Gu
  • Synthesis about 1h data for each target speaker, then using these data to train the adapter module.[10]
  • writing taslp paper
Qi Qu
  • Finding ideal thresholds and deploying cloud services for KWS models: `zh48_guangdong` and `zh48_haining20`.
  • Located and fixed a bug in FunASR which may lead to segmentation fault. Built service with extended gRPC protocol.
  • Analysis of some AED (cries and slaps) FAs.
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