“2024-10-14”版本间的差异

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（7位用户的10个中间修订版本未显示）
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|Dong Wang
 
|Dong Wang
 
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* AI handbook high-education version, experiment booklet
 
* Check AI primary school handbook (1-20)
 
* Check AI primary school handbook (1-20)
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第18行： 第18行：
 
|Lantian Li
 
|Lantian Li
 
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* AI-Graph EN (20/50)
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* Prepare CSTR intro report
 
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第29行： 第30行：
 
|Ying Shi
 
|Ying Shi
 
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* Finish Text enroll keywords spotting code & document and deliver to Wei & Du
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* Cohort Overlap ASR code v0.0
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** code has finished and training has been done
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* Cohort Speech separation code v0.0
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** code has finished training is in progress
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* [https://z1et6d3xtb.feishu.cn/docx/OHjsdgVmhoXUGpxvh5tcaBN4nAh?from=from_copylink here]
 
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第76行： 第82行：
 
|Xiaolou Li
 
|Xiaolou Li
 
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* AV-HuBERT discrete unit training (wer: ↓1.5-3%)
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** rethink how to prove the advantage or disadvantage of discrete unit?
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* Dense connector experiments (in training)
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* Double check the data of existing 3000h data in CVS2
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* Paper reading (discrete unit, VTS)
 
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* Design a experiment to explain the performance of discrete unit
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* Finish data double check
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* Try to establish a simple VTS system based on our VSR system
 
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第87行： 第99行：
 
|Zehua Liu
 
|Zehua Liu
 
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*Av-Hubert as Encoder performe very bad(cer:80%)
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*Av-Hubert(Frozen) as Encoder performe very bad(cer:80%)[https://z1et6d3xtb.feishu.cn/docx/JBsidACDVojhCaxFQLbcCVbsnAc?from=from_copylink]
 
**after finetune maybe better ,but still bad
 
**after finetune maybe better ,but still bad
 
*Qwen-14B perform better(47%) than Qwen-7B(50%)
 
*Qwen-14B perform better(47%) than Qwen-7B(50%)
第93行： 第105行：
 
** maybe i will get result very soon
 
** maybe i will get result very soon
 
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*verify collected data with XiaoLou
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*finish VTS data Acceptance report
 
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第114行： 第127行：
 
|Wan Lin
 
|Wan Lin
 
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* NS
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** poster
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** data preparing and processing
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** adjust the training code
 
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第125行： 第141行：
 
|Tianhao Wang
 
|Tianhao Wang
 
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* CLIPSep exps for 2-mix and 5-mix [https://z1et6d3xtb.feishu.cn/docx/DnJgdwtNhotEpIxH7zfcksETnte]
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** 2-mix(whole vggsound, 300 classes): SDR-mix = -1.1748, SDR-separate = 5.0145
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** 5-mix(50 classes of vggsound): SDR-mix = -11.4529, SDR-separate = -0.4764
 
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第149行： 第167行：
 
|Zhenyu Zhou
 
|Zhenyu Zhou
 
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*Model quantization version2
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*Multi-talker mix data preparation
 
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第160行： 第179行：
 
|Junhui Chen
 
|Junhui Chen
 
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* Prepare vb2 data
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** Too many utterances for training (out of memory), thinking a smart way to divide them.
 
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2024年10月14日 (一) 11:02的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • AI handbook high-education version, experiment booklet
  • Check AI primary school handbook (1-20)
Lantian Li
  • AI-Graph EN (20/50)
  • Prepare CSTR intro report
Ying Shi
  • Finish Text enroll keywords spotting code & document and deliver to Wei & Du
  • Cohort Overlap ASR code v0.0
    • code has finished and training has been done
  • Cohort Speech separation code v0.0
    • code has finished training is in progress
  • here
Zhenghai You
  • Exploring the role of speaker encoder in TSE and generality of SPK-AUG[1]
Junming Yuan
  • MT-Hubert exp[2]:
    • codebook set + infoNCE ---> FC+softmax+CE / FC+sigmoid+BCE
      • To reduce the learning rate can work.
    • verified the feat-mask MT-Hubert with different lr
    • time-mask MT-Hubert verification (in progress)
Chen Chen
Xiaolou Li
  • AV-HuBERT discrete unit training (wer: ↓1.5-3%)
    • rethink how to prove the advantage or disadvantage of discrete unit?
  • Dense connector experiments (in training)
  • Double check the data of existing 3000h data in CVS2
  • Paper reading (discrete unit, VTS)
  • Design a experiment to explain the performance of discrete unit
  • Finish data double check
  • Try to establish a simple VTS system based on our VSR system
Zehua Liu
  • Av-Hubert(Frozen) as Encoder performe very bad(cer:80%)[3]
    • after finetune maybe better ,but still bad
  • Qwen-14B perform better(47%) than Qwen-7B(50%)
  • Finish In-Context-Learning code and is training
    • maybe i will get result very soon
  • verify collected data with XiaoLou
  • finish VTS data Acceptance report
Pengqi Li
  • Evaluate TAO and LayerCAM(verification) reliability.
    • Exploring the Consistency of TAO and LayerCAM Results on different models and datasets.
Wan Lin
  • NS
    • poster
    • data preparing and processing
    • adjust the training code
Tianhao Wang
  • CLIPSep exps for 2-mix and 5-mix [4]
    • 2-mix(whole vggsound, 300 classes): SDR-mix = -1.1748, SDR-separate = 5.0145
    • 5-mix(50 classes of vggsound): SDR-mix = -11.4529, SDR-separate = -0.4764
Xiaoxue Luo
  • Paper reading about sound separation
  • AudioSep reproduction
    • Training time is too long -> replace with a small dataset(in training)
Zhenyu Zhou
  • Model quantization version2
  • Multi-talker mix data preparation
Junhui Chen
  • Prepare vb2 data
    • Too many utterances for training (out of memory), thinking a smart way to divide them.
Jiaying Wang
Yu Zhang
  • SocioDojo Llama version
    • news integration is adjusted once every 12 hours
    • wikipedia & google search is banned
Wenqiang Du
  • Check the data from past training models and update the KWS model again（Model testing）
    • Chinese, Cantonese, Minnan, Haining and Uyghur
Yang Wei
  • Train text enroll KWS model with updated code (in progress)
Lily
Turi
  • Whisper model finetuning[5]
Yue Gu
  • revise the TASLP paper
  • read several papers about accent and prosody
Qi Qu
  • AED: classifiers retrained w/ new method (suppression on negative stimuli) and improvement attested.
取自“http://cslt.org/mediawiki/index.php?title=2024-10-14&oldid=42317”