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第183行： |
| | |Wenqiang Du | | |Wenqiang Du |
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| − | * | + | * optimize primary school handbook（14/45） |
| | + | * Some of the company's work |
| | + | ** Training of New Dialect Models |
| | + | ** Project application |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- AIGraph high education version
- Prepare AIgraph Large Model version
- NMI paper publication staff
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| Lantian Li
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- AI-Graph EN (1/4)
- Huawei Project Proposal v1.0
- First Lesson on 24-fall AI Undergraduates
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| Ying Shi
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- Huawei project proposal
- Optimize the Text-enroll KWS code
- improve readability.
- remove redundant code.
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| Zhenghai You
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- Exploring the generality of spk aug on different data and structures[1]
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| Junming Yuan
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- double check mixed Hubert code：
- fix some bugs (time-mask.etc)
- feat-mask vs. time-mask: (Top-1 acc, EER): (27.98%, 23.17%) vs.(23.19%, 25.99%)
- softmax+CE --> sigmoid+BCE still have problem.
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| Xiaolou Li
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- Writing VTS documents
- Paper Reading & Preparing for Report
- Exp on LRS3
- LLM: LLaMA2 -> LLaMA3.1 (30h ↓0.4%)
- Grouping LLaMA2: (443h ↑0.5%, 30h ↓2.5%)
- Rethinking the method to inject information (ablation study first)
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| Zehua Liu
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| Pengqi Li
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| Wan Lin
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- VC2 pre-train; VB1+VC2 mix-tuning
- Data filter in VB1: 1.25% EER in vox1-o
- VB1 pre-train; VC2 fine-tuning
- VB1 pre-train: 2.61% EER in vox1-o
- VC2 fine-tuning: maybe couldn't reach better performance
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| Tianhao Wang
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- IS24 paper reading & weekly report
- sound separartion project proposal
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| Zhenyu Zhou
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- Model quantification phase document
- paper reading
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| Junhui Chen
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- Some vb1 filter exps with NS as Wan Lin marked.
- Prepare vb1 test data and code, ready for vb2 training.
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| Jiaying Wang
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| Yu Zhang
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- Dataset collection from THS
- Retraining R^2 SAC paper, with same env still failed (TCN ACC: 0.708, RECALL: 0.183), will check with Han this week
- Paper reading and some plan (report this Fri)
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| Wenqiang Du
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- optimize primary school handbook（14/45）
- Some of the company's work
- Training of New Dialect Models
- Project application
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| Yang Wei
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- Train text enroll KWS model with aishell and kespeech data.
- Prepare live broadcast
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| Lily
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- Prepare for holiday course（October 2nd、3rd） and online-course
- AI radiance's daily work
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| Turi
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- Trained conformer on Sagalee data excluding utterances containing digits
- Achieved 21.28% WER, 2.65 WER reduction
- Preparing KWS data from Sagalee dataset using MFA
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| Yue Gu
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- paper writing
- open the code
- prepare for the presentation
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| Qi Qu
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- KWS:
- Finding ideal thresholds for b0-models in predefined scenes: Chinese Mandarin, Cantonese, Uyghur and Kazakh.
- Finding ideal thresholds for b6-models with fixed b0-model thresholds.
- AED:
- Fixing parameters of Fbank feature extraction for CED and retraining classifiers.
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