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第47行: |
第47行: |
| |Zhenghai You | | |Zhenghai You |
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− | * Exploring the role of speaker encoder in TSE | + | * Exploring the role of speaker encoder in TSE[https://z1et6d3xtb.feishu.cn/docx/GHF8doRjDo50ihxGUPpcsZgLncb] |
| ** Joint traing Spk Enc have better separation effect, but the EER is poor | | ** Joint traing Spk Enc have better separation effect, but the EER is poor |
| ** Pretrain & Freeing Spk Enc EER well, but SI-SDR is poor | | ** Pretrain & Freeing Spk Enc EER well, but SI-SDR is poor |
第156行: |
第156行: |
| |Zhenyu Zhou | | |Zhenyu Zhou |
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− | * | + | * Model Quantization document submit |
| + | * Review conditional chain code |
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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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- AI-Graph handbook v0.1
- AI-Graph EN (12/50)
- Huawei TiDing 3.0 - Model Quantization
- BUPT/AI-Radiance trivial things
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Ying Shi
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- Add 4 kinds of negative sampling strategies Optimized Text-enroll KWS code
- (deletion, substitution, insertion, and shuffle) and verify them to ensure no bugs.
- Find that new negative sampling will increase the difficulty of training which indicates that only depending on positional embedding is not enough.
- Reproduce conditional chain overlap asr (Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals)
- According to Jiaying's work the code released by the published paper can not work
- Write dominance-based conditional chain overlap asr by myself (in progress)
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Zhenghai You
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- Exploring the role of speaker encoder in TSE[1]
- Joint traing Spk Enc have better separation effect, but the EER is poor
- Pretrain & Freeing Spk Enc EER well, but SI-SDR is poor
- Further explore the different impacts of using spk aug on different tasks
- The generality of SPK-AUG
- Refactored DPRNN-TSE results are reliable and have been accelerated from 87 hours to 32 hours
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Junming Yuan
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Chen Chen
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Xiaolou Li
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- Use MFA on LRS3 to cut it into small segments
- Use discrete embedding of avhubert in vsp-llm training (Still training)
- Some idea of align video feature and LLM (Dense Connector, CL methods)
- Handover the data collection and get familiar with the process
- Data Collection: 3138 h (need to re-check, DDL: 10.15)
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Zehua Liu
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Pengqi Li
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Wan Lin
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- Voxblink1 model training and testing [2]
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Tianhao Wang
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- AudioSep reproduction
- problem: LAION CLAP needs 48kHz audio so the data needs to be up-resample
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Xiaoxue Luo
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- AI-Graph High school handbook(v0.1)
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Zhenyu Zhou
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- Model Quantization document submit
- Review conditional chain code
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Junhui Chen
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Jiaying Wang
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Yu Zhang
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- Fri Report
- Change SocioDojo Agent from ChatGPT-3.5-Turbo to Llama-3.1-8B (still working)
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Wenqiang Du
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- Check primary school handbook(43/45)
- Release chinese and haining KWS model
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Yang Wei
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Lily
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Turi
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- Segmented audios in dataset into individual words.
- Paper reading
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Yue Gu
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- Almost complete the revisions of my journal paper
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Qi Qu
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- KWS
- Testing zh48 models on dataset of Mandarin Chinese w/ Guangdong accent: recall drops significantly.
- AED
- Evaluating third-party solution of baby crying detection.
- Misc.
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