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| （3位用户的4个中间修订版本未显示） |
| 第82行： |
第82行： |
| | |Xiaolou Li | | |Xiaolou Li |
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| − | * | + | * 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) |
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| − | * | + | * 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 |
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| | * | | * |
| 第121行： |
第127行： |
| | |Wan Lin | | |Wan Lin |
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| − | * | + | * NS |
| | + | ** poster |
| | + | ** data preparing and processing |
| | + | ** adjust the training code |
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| | * | | * |
| 第132行： |
第141行： |
| | |Tianhao Wang | | |Tianhao Wang |
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| − | * CLIPSep exps for 2-mix and 5-mix | + | * CLIPSep exps for 2-mix and 5-mix [https://z1et6d3xtb.feishu.cn/docx/DnJgdwtNhotEpIxH7zfcksETnte] |
| | ** 2-mix(whole vggsound, 300 classes): SDR-mix = -1.1748, SDR-separate = 5.0145 | | ** 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 | | ** 5-mix(50 classes of vggsound): SDR-mix = -11.4529, SDR-separate = -0.4764 |
| 第170行： |
第179行： |
| | |Junhui Chen | | |Junhui Chen |
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| − | * | + | * Prepare vb2 data |
| | + | ** Too many utterances for training (out of memory), thinking a smart way to divide them. |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- AI handbook high-education version, experiment booklet
- Check AI primary school handbook (1-20)
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| Lantian Li
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- AI-Graph EN (20/50)
- Prepare CSTR intro report
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| Ying Shi
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- 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
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| Zhenghai You
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- Exploring the role of speaker encoder in TSE and generality of SPK-AUG[1]
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| Junming Yuan
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- 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)
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| Chen Chen
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| 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
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- verify collected data with XiaoLou
- finish VTS data Acceptance report
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| Pengqi Li
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- Evaluate TAO and LayerCAM(verification) reliability.
- Exploring the Consistency of TAO and LayerCAM Results on different models and datasets.
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| Wan Lin
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- NS
- poster
- data preparing and processing
- adjust the training code
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|
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| 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
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| Xiaoxue Luo
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- Paper reading about sound separation
- AudioSep reproduction
- Training time is too long -> replace with a small dataset(in training)
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| Zhenyu Zhou
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- Model quantization version2
- Multi-talker mix data preparation
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| Junhui Chen
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- Prepare vb2 data
- Too many utterances for training (out of memory), thinking a smart way to divide them.
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| Jiaying Wang
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| Yu Zhang
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- SocioDojo Llama version
- news integration is adjusted once every 12 hours
- wikipedia & google search is banned
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| Wenqiang Du
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- Check the data from past training models and update the KWS model again（Model testing）
- Chinese, Cantonese, Minnan, Haining and Uyghur
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| Yang Wei
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- Train text enroll KWS model with updated code (in progress)
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| Lily
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| Turi
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- Whisper model finetuning[5]
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| Yue Gu
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- revise the TASLP paper
- read several papers about accent and prosody
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| Qi Qu
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- AED: classifiers retrained w/ new method (suppression on negative stimuli) and improvement attested.
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