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| 第54行： |
第54行： |
| | |Junming Yuan | | |Junming Yuan |
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| − | * Grade 8 AI practice book(done!) | + | * Grade 8 AI practice handbook(done!) |
| − | * SUPERB benchmark(Speech processing Universal Performance Benchmark) | + | * Further evaluating our MT-HuBERT model on other speech downstream tasks. |
| − | ** Source Separation downstream task(SI-SDRi at 50K training steps): | + | ** Based on SUPERB benchmark(Speech processing Universal Performance Benchmark) |
| − | *** MT-HuBERT: 10.77, HuBERT-BASE: 9.84
| + | *** Firstly focused on Source Separation downstream task(still in training) |
| | + | *** Intermediate results(SI-SDRi at 50K training steps): MT-HuBERT: 10.77, HuBERT-BASE: 9.84. |
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| 第90行： |
第91行： |
| | |Pengqi Li | | |Pengqi Li |
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| − | * | + | * Science popularization activities in Changzhi, Shanxi, and returned to the lab on tomorrow. |
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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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- Review AI Book — College Edition (12/53).
- Project matters: HUAWEI SS/AutoBGM; FYT A/V GenreDetect.
- Prepare materials for my professional title evaluation.
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| Ying Shi
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| Zhenghai You
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| Junming Yuan
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- Grade 8 AI practice handbook(done!)
- Further evaluating our MT-HuBERT model on other speech downstream tasks.
- Based on SUPERB benchmark(Speech processing Universal Performance Benchmark)
- Firstly focused on Source Separation downstream task(still in training)
- Intermediate results(SI-SDRi at 50K training steps): MT-HuBERT: 10.77, HuBERT-BASE: 9.84.
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| Xiaolou Li
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| Zehua Liu
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| Pengqi Li
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- Science popularization activities in Changzhi, Shanxi, and returned to the lab on tomorrow.
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| Wan Lin
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| Tianhao Wang
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| Xiaoxue Luo
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- read some papers on speech separation of unknown number of speakers
- Apply EDA(encoder-decoder based attractor calculation) method to speech separation
- Environment Configuration(done)
- Familiar with the code and make some adjustments to it(in progress)
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| Junhui Chen
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- LLM:
- 2 different reflection paper reading
- Baseline self-reflection metric collection — code completed, data collection in progress.
- Integrating new reflection methods (e.g. beamsearch-based reflection) into the current pipeline in collaboration with @Zhang Yu — work in progress.
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| Jiaying Wang
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- construct loudness training data: librimix with different loudness source
- loudness order exp[1]:
- chain based structure: 2mix 11.92(150 epoch)
- convtasnet structure: still training, 60epoch achieve 12.29
- ctc order exp:
- only use ctc for order, chain based structure: 10.65
- a speculation: Compared with semantic information (provided by CTC), acoustic-oriented information may be more suitable as a basis for separation.
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- try to use speaker info as order
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| Yu Zhang
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- LLM:
- Baseline self-reflection metric collection — code completed, data collection in progress.
- Integrating new reflection methods (e.g. beamsearch-based reflection) into the current pipeline in collaboration with @chenjunhui — work in progress.
- AED:
- Added humming test and analysis for Huawei.
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| Wenqiang Du
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| Yang Wei
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| Yue Gu
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- polish the structure of my thesis and continue find jobs
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| Qi Qu
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