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| 第38行： |
第38行： |
| | * Review several NC paper | | * Review several NC paper |
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| − | * | + | * Text enroll Keyword Spotting |
| | + | ** Employ Homo loss |
| | + | * Cohort Conditional Chain Overlap ASR |
| | + | ** Check the SID performance of the current speaker embedding model on the training dataset |
| | + | ** Reproduce the previous Cohort-SOT methods on the training dataset |
| | + | * Finish the SPL response |
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| | * | | * |
| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- A trial talk in Retired Professor Association of THU.
- Check Aigraph slides
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| Lantian Li
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| Ying Shi
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- Text enroll Keyword Spotting
- Training with sufficient data augmentation [failed]
- Employ Homo loss
- Cohort Conditional Chain Overlap ASR
- Fix the bug about the positional embedding of Condition, the performance is still not good
- Review several NC paper
|
- Text enroll Keyword Spotting
- Cohort Conditional Chain Overlap ASR
- Check the SID performance of the current speaker embedding model on the training dataset
- Reproduce the previous Cohort-SOT methods on the training dataset
- Finish the SPL response
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|
| Zhenghai You
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- Complete the project deliverables
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| Junming Yuan
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- find the bug in SSL model finetuning experiment with multi-lingual
- double check result in [1]
- The results need check again.
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| Chen Chen
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| Xiaolou Li
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| Zehua Liu
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- VSP-LLM Reproduce（LRS3(30h) wer:36.32 > wer: 29.2）[2]
- still need work on the code
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| Pengqi Li
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| Wan Lin
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| Tianhao Wang
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- Neural Scoring [3]:
- vox: vox1-e, vox1-h test [4]
- cn: three genres fine-tuning: resnet and ns, 2s and 4s
- weekly report
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| Zhenyu Zhou
|
- Huawei Project Submission
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| Junhui Chen
|
- Neural Scoring [5]:
- vox: vox1-e, vox1-h test [6]
- vox: one transformer encoder layer training
- cn: three genres fine-tuning: resnet and ns, 2s and 4s
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| Jiaying Wang
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- Preliminary validation: cohort works[7]
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| Yu Zhang
|
- Finance
- Data Collection (2015 - 2019 HS300 stocks)
- AED
- 8k 2s CNN model training and Window inference code
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| Wenqiang Du
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- Quantified the kws model of Aibabel
- Training dialect models for AIbabel（ Uyghur language）
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- Train a joint model for Chinese, Uyghur, and Kazakh languages
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|
| Yang Wei
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- AIbabel
- Learn to train and test KWS model
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| Lily
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- Assisted with design of AI courses for primary, high school
- Some chores about 'AIradiance'
- Prepare application for Beijing Science and Technology Progress Award
- Live broadcast
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| Turi
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- Thesis Proposal Defense
- Data Collection
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|
|
| Yue Gu
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- Prepare the live content
- writing paper
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| Qi Qu
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- AED:
- CED + Linear: c/jni/python lib development and test.
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- AED:
- CED: Linear to be trained on data.
- On-device demo.
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