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| 第106行： |
第106行： |
| | * Incorporate a "learn-not-to-listen" mechanism into MT-HuBERT and retrain the backbone. (in progress) | | * Incorporate a "learn-not-to-listen" mechanism into MT-HuBERT and retrain the backbone. (in progress) |
| | ** At 425K steps, PR(PER%): 8.18%, ASR(WER%): 9.32%, SD(DER%): 5.05% | | ** At 425K steps, PR(PER%): 8.18%, ASR(WER%): 9.32%, SD(DER%): 5.05% |
| − | * prepare the slides of the seminar
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| | * middle school AI textbook checking(picture, table) | | * middle school AI textbook checking(picture, table) |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- Recheck AI textbooks for primary and middle schools.
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| Lantian Li
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| Wenqiang Du
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- Statistics the accounts for three companies, our laboratory and AIGE.
- Check AI textbooks
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| Yang Wei
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| Ying Shi
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- Thesis
- Huawei multi-talker ASR project(Mandarin 2-mix test with FunASR-Nano-800M)
- ground-truth WER 3.89%
- separated WER 12.11%
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| Yue Gu
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- write my Phd thesis
- the seminar
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| Lily
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- Participated in reviewing the AI handbooks (primary/middle/high school)
- Compiled course materials for the AI Handbook (Senior Edition)
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| Pengqi Li
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- Writing the Experiments section.
- Focus on hard-to-explain results by formulating hypotheses and conducting deeper analysis.
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| Junming Yuan
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- Aug-MT-HuBERT(failed)
- Continued pre-training for 600K steps. there is still no improvement observed on clean-speech tasks.
- Aug-MT-HuBERT has more substitution errors in clean ASR adaptation.
- Incorporate a "learn-not-to-listen" mechanism into MT-HuBERT and retrain the backbone. (in progress)
- At 425K steps, PR(PER%): 8.18%, ASR(WER%): 9.32%, SD(DER%): 5.05%
- middle school AI textbook checking(picture, table)
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| Yu Zhang
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- GPU Util: [1]
- LLM
- Rewrite the graph connectivity of Swarm MMLU (the original topology does not align with our expectations).
- Add context segmentation logic to the ECS computation, so that we can trace which preceding node a given text segment originates from.
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| Junhui Chen
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- LLM
- Get swarm crosswords experiment with metric detection working
- Implement multi-process of LLM instances in experiments
- p.s.: try multi-threading, fails because a single Python interpreter only initializes one CUDA context, causing resource contention between different instances.
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| Jiaying Wang
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| Xiaoxue Luo
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- 2-5mix multi_head separation model for Huawei project [2]
- prepare 2mix speech separation results for ASR test
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| Bochao Hu
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- read some papers
- refactor P2S code, using ms-swift and adding n-best seq to input.
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| Hongcheng Zhang
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- Complete the training of short audio descriptions for AudioSet-SL in WavCaps
- METEOR: 0.3373， ROUGE-L: 0.3121， BLEU-4 : 0.0769， CIDEr : 0.6207
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| Weiman Sun
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- Label audioset vedio dataset
- Analyze the representation of MT-LLM inference and review relevant literature
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