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| 第99行： |
第99行： |
| | |Pengqi Li | | |Pengqi Li |
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| − | * | + | * New Process of consistency of TAO and LayerCAM.[https://z1et6d3xtb.feishu.cn/docx/VtlIdFxdRodp8Nx8oQjcVLC4nCd] |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- AI Medical sector 2 chapters done
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| Lantian Li
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- Submit three papers supporting ICCIP 2024.
- Go on designing 2025 AI daily posts
- Attend CSTR 40th anniversary
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| Ying Shi
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- Stop strategy for Cohort Overlap ASR here
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| Zhenghai You
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| Junming Yuan
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- paper reading
- prepare to reproduce cocktail HuBERT (in progress)
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| Chen Chen
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| Xiaolou Li
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- Debug the Chinese VTS (in training already)
- Write the report of VTS project (main work)
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| Zehua Liu
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- In-Context-Learning（if sentence is very long，context seems fail）still finding reason
- （context<30s）45.30% | 44.69% (context = 30s) | 46.02%(context = 120s)
- Writing VTS project document
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| Pengqi Li
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- New Process of consistency of TAO and LayerCAM.[1]
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| Wan Lin
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| Tianhao Wang
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- investigating some new approach for target sound separation
- prepare the code for LoRA tuned CLAP
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| Xiaoxue Luo
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| Zhenyu Zhou
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| Junhui Chen
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- NS with frame-level detection loss
- use silero-vad
- Model is training, seems EER decrease faster.
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| Jiaying Wang
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| Yu Zhang
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- SocioDojo
- with cash ratio risk aware, and change information sources, seems have a decent risk control over Nasdaq 100 index [2]
- Some paper reading and report in RoyalFlush, get some idea (mainly about LLM for time series task)
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| Wenqiang Du
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- Training of New Dialect Models(Yi language )
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| Yang Wei
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| Lily
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| Turi
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- LoRA finetuning (Result is not good)
- Data cleaning
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| Yue Gu
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- read several paper about speech tokenizer. I want to design a encoder, which processes different size feature frame and construct several different codebooks, to extract personality from the varing speech speed. It is still in progress.
- paper writing
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| Qi Qu
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- KWS:
- Yi (Liangshan, Sichuan) dataset prepared for training; dataset to be annotated for testing.
- Experiments on model quantization for NPU devices: i16 quantization arrives at a balance between accuracy and efficiency (~2ms per inference, compared to ~250ms for non-quantized); more calibration data needed for further confirmation.
- Full-featured demo (recording + feature extraction + model inference) for NPU devices in development.
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