| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- Double check English version of high-scholl handbook.
- Check English version of middle-school handbook.
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| Lantian Li
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- Final review of my MLA book (6/10)
- MoE daily work
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| Wenqiang Du
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- Complete recording high school AI courses（14/14）
- Creat PPT for high school AI course (3/14)
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| Yang Wei
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| Ying Shi
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- Thesis
- Some stuff about HUAWEI project
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| Yue Gu
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- Revise the Phd thesis structure
- Seminar. In an anonymous vote of 20 people, 75% chose to freely discuss.
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| Lily
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- Check English version of middle-scholl handbook
- Check English version of high-scholl handbook
- Organized course materials production （小初高分册）
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| Pengqi Li
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- Drafting the method part of paper.
- Identified bugs in the reproduction code. Re-ran experiments and confirmed that conclusions remain consistent.
- Assisting with the revision of the middle school handbook.
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| Junming Yuan
|
- Aug-MT-HuBERT:
- Based on last week’s best configuration, Continued pre-training for 300K steps.
- No improvement was observed on clean-speech tasks.
- After inspection, found that the pre-training inherited a low lr from the previous model after 1.6M steps.
- After increase the lr and retraining for 200K steps, there was still no improvement on clean-speech tasks.
- 200K steps, PR(PER): 8.14, ASR(WER): 8.93
- SS Adaptation:
- following the SA-WavLM strategy, further evaluated MT-HuBERT under low-resource settings (10% and 1% of the training data).
- 10% data: Cocktail(11.29) > MT-HuBERT(11.07) > WavLM(10.81)
- 1% data: Cocktail(8.56) > MT-HuBERT(8.43) > WavLM(8.11)
- Draft Paper writing(EN version almost done)
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| Yu Zhang
|
- GPU Util: [1]
- Finish final exam
- Writing code to analyze LLM Swarm metrics, mainly looking at how ECS/PKS correlate with the optimized edge probability.
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| Junhui Chen
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- finish final exam
- debug code about swarm minicrossword test
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| Xiaolou Li
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| Jiaying Wang
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- spk model training(130/300):reconstruct data, now training data is aligned with separation data
- recall@k of 130 epoch: 2mix recall@2=0.9799, 3mix recall@3=0.9101, 4mix recall@4=0.8247
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| Tianhao Wang
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| Xiaoxue Luo
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- 2-5mix multi_head separation model for Huawei project
- write code for the multi-speaker and multi-sound events separation task，complete data preparation and feature extraction
- adjust the model structure to three heads(speech, music and others), the model is still in training, and current val_sisdr is 11.38
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| Bochao Hu
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- finish final exam
- debug and train vsr E2E model, still in training
- resently results [2]
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| Hongcheng Zhang
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- finish final exam
- debug for asu-llm code and train the task audio caption with WavCaps(1/20 epoches)
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