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| 第101行： |
第101行： |
| | |Wan Lin | | |Wan Lin |
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| − | * | + | * NS: detection |
| | + | ** clean: 1.479% EER vs. 1.239% EER |
| | + | ** multi: in training |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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| Lantian Li
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- Complete all the script for the 2025 AI calendar
- AI-Graph EN (32/50)
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| Ying Shi
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| Zhenghai You
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- Huawei project with IRA-TSE[1]
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| Junming Yuan
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- re-check some details from Cocktail HuBERT paper and prepared the code.
- pseudo-label preparation finished.
- paper reading
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| Xiaolou Li
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- Finish VTS documents with Zehua
- Process the CVS3 data
- Inherit the AV-HuBERT training code and debug
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| Zehua Liu
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- Finish 2 VTS documents with Xiaolou
- Financial Document
- Technical Document
- Paper Reading on last Friday
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| Pengqi Li
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| Wan Lin
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- NS: detection
- clean: 1.479% EER vs. 1.239% EER
- multi: in training
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| Tianhao Wang
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- ablation study about some new approach for sound separation [2]
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| Xiaoxue Luo
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- paper reading to investigate some new approach for sound separation
- retrain AudioSep with a DPRNN block(AudioSep-DP)
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| Zhenyu Zhou
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- Attemp to add silence loss during training（seems like useless）
- Conditional Chain 2-5 mix results（still some bugs，the acc of speaker number is poor）[3]
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| Junhui Chen
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| Jiaying Wang
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| Yu Zhang
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- SocioDojo
- Single stock (TSLA) investment (still running)
- Investigate some Text guided LLM centric time-series forecaster and reproduce some of them (Time-LLM LLM-Process, AutoTimes), and some toy experiment about how prompt prefix influence the forecast result
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| Wenqiang Du
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- Training of New language Models(Cantonese)
- Prepare the PPT for the competition
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| Yang Wei
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- Train text enroll KWS model with 7000h data
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| Lily
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| Turi
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- kws data preparation and checking some implementations
- Paper Reading about kws
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| Yue Gu
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- use CosyVoice model to synthesize the target speaker utterance, which is employed as the supplement for target speaker adaptation. The adaptation exp is running.
- icassp 2025 paper review
- paper writing
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| Qi Qu
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- KWS:
- Yi (Liangshan, Sichuan) test dataset annotated and finalized. Optimal thresholds for predefined scenes. Cloud model service deployed.
- Quantization for NPU with more calibration data (6k): mean_loss=1.3e-4, max_loss=6.2e-2.
- NPU demo: feature extraction + model inference.
- Text-enroll method: android demo benchmark.
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