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第84行: |
| |Zehua Liu | | |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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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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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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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 [1]
- 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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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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