| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- Revise AI textbook of the colleage version
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| Lantian Li
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- Submit the high school textbook
- Proofreading of the EN book (3/4)
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| Ying Shi
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- Compare Ascend and Nvidia
- Performance: Clean ASR task 20epochs WER 6.91% : 7.02% (Ascend vs Nvidia)
- Speed: Nvidia is one time faster than Ascend
- Start think about my thesis
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| Zhenghai You
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- Training IRA TSE for noisy enroll situation[1]
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| Junming Yuan
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- Pretraining work:
- MT-HuBERT & Cocktail-HuBERT will be finished next week.
- Get a set of comparable finetuning results(15/5/3-shot) for each pretrain model at the 400K training step.[2]
- Check and add reference for AI junior high school handbook(1/2).(Done)
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| Xiaolou Li
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- Writing NFSC document
- VSR training (1500 h) already have some result
- cnvsrc-single valid 300: 29.47%
- cnvsrc-multi valid: 31.60%
- webVideo valid: 15.54%
- Finished producing pseudo-label for CVS3(4000h)
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| Zehua Liu
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- Writing NFSC document
- Lora finetune VLM(both Encoder and LLM Decoder) result seem not very well(maybe need parameeter adjustment)
- Pretrained VSR Encoder + VLM(Decoder) seems better than Normal LM
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- Design VTS architecture and implement it
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| Pengqi Li
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- Prepare the AI course for Tsinghua University Junior High School.
- Add references to the handbook(junior high school version 1/2)(Done).
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| Wan Lin
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- Supply NS experiments [3]
- Help xiaochen reproduce the diarization SV method
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| Tianhao Wang
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- 3-mix training: CLAPSep baseline: SDR=5.560; Ours: SDR=6.574.
- subset data training (in progress)
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| Xiaoxue Luo
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| Zhenyu Zhou
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| Junhui Chen
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- speaker diarization baseline for NS (mix test: baseline EER 15.972% -> 12.983%) others still testing...
- make ppt about scaling law on speaker volume.
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| Jiaying Wang
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| Yu Zhang
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- Multi Agent Investment
- use Top 31 stocks in 11 sector to do portfolio for better correlation with input news (no excess return)
- analysis the trading decision
- Huawei AED
- smallest model to keep AUC excess 0.9
- split inference into two phase (Phase 1: Human Voice vs None Human Voice, Phase 2: Speech vs Other Human Voice) with two smaller model
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| Wenqiang Du
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- Check Primary handbook V3.0（Done）
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| Yang Wei
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- Adapt text enroll kws model with synthesized dialect data.(recall: 83% -> 94%)[4]
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| Turi
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- Finetuned Llama3 on Oromo text (pretrain)
- Experiment to use it as LM for ASR failed, 100%+ WER
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| Yue Gu
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- a 0.4% CER reduction has achieved for one spk, but no improvement was discovered on other spks. I'm still do some exps.
- restart the synthetic-data related exps, try to fill the gap between synthetic data and real data on the output distribution of model.
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| Qi Qu
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- Technical investigation on Visual Event Detection.
- Experiment on annotating and auditing audio with Audio LLM: insufficient VRAM; poor I/O in CPU/GPU hybrid mode.
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