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(6位用户的6个中间修订版本未显示) |
第9行: |
第9行: |
| * Design/Discussion AI popular science | | * Design/Discussion AI popular science |
| * Conjecture for minmum loss training | | * Conjecture for minmum loss training |
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| * | | * |
第20行: |
第19行: |
| |Lantian Li | | |Lantian Li |
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− | * | + | * GPU status [https://z1et6d3xtb.feishu.cn/wiki/XGcGwRK5viJmpRkjH9AczIhynCh] |
| + | * INTERSPEECH 2024 |
| + | * ASIP-BUPT (CohortTSE, SE-Adapter, SpeakerAug, NeuralScoring) |
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| * | | * |
第97行: |
第98行: |
| |Xiaolou Li | | |Xiaolou Li |
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− | * | + | * Finish INTERSPEECH2024 paper |
| + | * review code of cnvsrc |
| + | * Next step: |
| + | ** Focus on model structure of VSR Benchmark |
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| * | | * |
第109行: |
第113行: |
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| *Finish IS24 | | *Finish IS24 |
− | VSR work continues
| + | *VSR work continues |
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第120行: |
第124行: |
| |Pengqi Li | | |Pengqi Li |
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− | * | + | * Extending workshop paper |
| + | ** Finish slide for workshop paper. |
| + | ** make plan, investigate, prepare dataset for extending paper. |
| + | ** Rethink how to design a method that can globally PID |
| + | * Team Working[https://z1et6d3xtb.feishu.cn/docx/T3U2dTs5poiIgtxtM2Sc0QennWe] |
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| * | | * |
第131行: |
第139行: |
| |Wan Lin | | |Wan Lin |
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− | * | + | * Neural scoring [https://z1et6d3xtb.feishu.cn/docx/TQvWdk8LVo9ONaxQ5Qac9A2Dn3d?from=from_copylink] |
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| * | | * |
第167行: |
第175行: |
| |Junhui Chen | | |Junhui Chen |
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− | * | + | * Neural scoring |
| + | * Interim report |
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第218行: |
第227行: |
| |Yang Wei | | |Yang Wei |
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− | * | + | * Read training code of Paraformer model, in order to get intermediate data |
| + | * Prepare Huilan product training, and deal with problems of ASR and TTS service |
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| * | | * |
People |
This Week |
Next Week |
Task Tracking (DeadLine)
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Dong Wang
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- Interspeech 2024 paper refinement
- Design/Discussion AI popular science
- Conjecture for minmum loss training
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Lantian Li
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- GPU status [1]
- INTERSPEECH 2024
- ASIP-BUPT (CohortTSE, SE-Adapter, SpeakerAug, NeuralScoring)
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Ying Shi
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- Finish INTERSPEECH paper
- Investigate random order SOT for multi-talker ASR task
- 3-mix 0s offset test condition
- DOM-SOT 20.51
- PIT-SOT 23.26
- random-order SOT 26.20
- group work
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Zhenghai You
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- Weekly report
- Some evaluations about TSE speaker encoder
- Huawei project (Phase 1st)
- Some doubts about the paper due to the latest testing in minimum loss
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- Change the speakerbeam speaker encoder to frequency domain
- Train a SID with a speakerbeam structure
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Junming Yuan
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- Finish INTERSPEECH paper
- Make the plan for the large vocabulary pretraining task.
- Focus on the experimental details of the few-shot paper from Google.
- Try to address the 3 questions:
- How to change MT pretraining model structure?
- How to train three strictly comparable pretraining models based on MT, Hubert, and wav2vec?
- Why does Hubert+MT perform significantly better?
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Chen Chen
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- Finish IS24 paper
- Some documents for VTS X project
- Proposal for next stage work on VSR/VTS
- Focus on two task: 1) CNCVS2 dataset 2) Mandarin VSR Benchmark [2] on CNCVS1&2&CNVSRC
- Aim at a solid benchmark with data/code/model
- Perhaps a long journal paper
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- Conditional entropy analysis of VTS task
- MFA is done
- TODOs: feature/embedding extracting, clustering, discrete conditional entropy calculating
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Xiaolou Li
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- Finish INTERSPEECH2024 paper
- review code of cnvsrc
- Next step:
- Focus on model structure of VSR Benchmark
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Zehua Liu
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- Finish IS24
- VSR work continues
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Pengqi Li
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- Extending workshop paper
- Finish slide for workshop paper.
- make plan, investigate, prepare dataset for extending paper.
- Rethink how to design a method that can globally PID
- Team Working[3]
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Wan Lin
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Tianhao Wang
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- Finish INTERSPEECH paper
- Code reorganization
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Zhenyu Zhou
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- InterSpeech2024 submission
- Code reorganization
- Neuro scoring reviewing
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Junhui Chen
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- Neural scoring
- Interim report
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Jiaying Wang
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- weekly report
- PIT baseline: ConTasNet (finish tonight)
- test whether the separation target is the closer one to the cohort embedding: the rate is around 0.5
- confused about the efficiency of cohort
- Further experiment:TasNet with minimal loss
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Yu Zhang
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- Portfolio backtesting report
- stock trade API
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Wenqiang Du
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- Aibabel
- Control Uyghur KWS model FA,but not get a good performance yet.
- Continue test and update CN KWS model
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Yang Wei
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- Read training code of Paraformer model, in order to get intermediate data
- Prepare Huilan product training, and deal with problems of ASR and TTS service
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Lily
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- Paper reading
- Prepare for overview paper
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Turi
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- Data collection app[5]
- Course works
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