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| （5位用户的6个中间修订版本未显示） |
| 第66行： |
第66行： |
| | |Xiaolou Li | | |Xiaolou Li |
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| − | * | + | * AVHuBERT unit exp |
| | + | ** dc connector (↑0.8% than discrete unit) |
| | + | ** concat feature and embedding (↑2% than discrete unit, ↓0.3% than baseline) |
| | + | * CVS3 quality check (30h totally) [https://z1et6d3xtb.feishu.cn/drive/folder/HGHbfyCJRlLYzUdSlEicOEztnYc] |
| | + | * This work is help by Zehua, Linwan, Tianhao |
| | + | * MLLM system with audio output design |
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| | * | | * |
| 第104行： |
第109行： |
| | |Wan Lin | | |Wan Lin |
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| − | * | + | * help VSR data verification |
| | + | * experiment in voxblink2 [https://z1et6d3xtb.feishu.cn/docx/MxBNdPbLao0tsoxkBVCcUgUoneh?from=from_copylink] |
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| | * | | * |
| 第153行： |
第159行： |
| | |Junhui Chen | | |Junhui Chen |
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| − | * | + | * Experiments for NS |
| | + | * Look for speaker detection model with Resnet34 for frame label |
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| | * | | * |
| 第175行： |
第182行： |
| | |Yu Zhang | | |Yu Zhang |
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| − | * | + | * SocioDojo Llama 3.1 8B investment task |
| | + | ** acc return is about 10% below nasdaq 100 index |
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| − | * | + | * add more professional information source, such as WSJ (current is Tweets Trending, which is too entertainment-oriented) |
| | + | * control the BUY/SELL amount of Actuator (current investments ratio is too high) |
| | + | * reproduce other Multi Agent investment pipeline such as FinAgent or FinRobot |
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| | * | | * |
| 第197行： |
第207行： |
| | |Yang Wei | | |Yang Wei |
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| − | * | + | * Train text enroll KWS model and test with Aibabel dialect data. |
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| | * | | * |
| 第227行： |
第237行： |
| | |Yue Gu | | |Yue Gu |
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| − | * | + | * write the cover letter |
| | + | * design a new speaker adaptation framework |
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| | * | | * |
| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- Primary School AI hand book (20-30)
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| Lantian Li
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- AI-Graph EN (25/50)
- Complete CSTR intro report （11.18)
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| Ying Shi
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- Cohort-Overlap ASR
- condition on real decode result
- Design stop criterion
- Cohort-Speech separation
- several configs for Dual-path model
- group work
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| Zhenghai You
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| Junming Yuan
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- The result of feat-mask/time-mask MT-HuBERT [1]
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| Xiaolou Li
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- AVHuBERT unit exp
- dc connector (↑0.8% than discrete unit)
- concat feature and embedding (↑2% than discrete unit, ↓0.3% than baseline)
- CVS3 quality check (30h totally) [2]
- This work is help by Zehua, Linwan, Tianhao
- MLLM system with audio output design
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| Zehua Liu
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- Verify VSR data
- Finish Data Verification Report
- ICL work(CER: 47.87% < CER: 51.08%)
- Time Mask matters[3]
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| Pengqi Li
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- Complete the final report of the doctoral innovation project(School)
- Exploring the Consistency of TAO and LayerCAM Results on different models and datasets.
- Conclusion and hypothesis[4]
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| Wan Lin
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- help VSR data verification
- experiment in voxblink2 [5]
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| Tianhao Wang
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- adjust the code of AudioSep (CLAP) to support multi-mix and audio-query (in training)
- some project testing
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| Xiaoxue Luo
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- AudioSep reproduction
- evaluate the performance of AudioSep
- comparative experiment between AudioSep and baseline system(CLIPSep)
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| Zhenyu Zhou
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- conditional chain 2-mix results reproduction（sisidr: 10.714 -> 15.6）
- model quantization finial version submission
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| Junhui Chen
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- Experiments for NS
- Look for speaker detection model with Resnet34 for frame label
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| Jiaying Wang
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| Yu Zhang
|
- SocioDojo Llama 3.1 8B investment task
- acc return is about 10% below nasdaq 100 index
|
- add more professional information source, such as WSJ (current is Tweets Trending, which is too entertainment-oriented)
- control the BUY/SELL amount of Actuator (current investments ratio is too high)
- reproduce other Multi Agent investment pipeline such as FinAgent or FinRobot
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| Wenqiang Du
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- Participated in an AI competition
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| Yang Wei
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- Train text enroll KWS model and test with Aibabel dialect data.
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| Lily
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| Turi
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- Whisper finetuning on sagalee
- with encoder frozen, whisper-large-v3 (20.5 WER)
- Finetuning LLM
- Finetuned Qwen2.5-0.5B on conversation dataset translated from English to Oromo
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| Yue Gu
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- write the cover letter
- design a new speaker adaptation framework
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| Qi Qu
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- AED:
- New CED-based classifiers deployed onto devices, yielding acceptable performance.
- KWS:
- Quantization and format conversion of production models for deployment on embedded device w/ NPU. Default quantization mode leads to unacceptable loss of precision. Will try hybrid quantization.
- Text-enrollment KWS: some dynamic dimensions misinterpreted as constant duration exportation to ONNX.
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