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第235行： |
| | |Qi Qu | | |Qi Qu |
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| − | * | + | * AED: |
| | + | ** New CED-based classifiers deployed onto devices, yielding acceptable performance. |
| | + | * KWS: |
| | + | ** Quantization and format conversion of production models for deployment on embedded device w/ NPU. Default quantization mode leads to unacceptable loss of precision. Will try hybrid quantization. |
| | + | ** Text-enrollment KWS: some dynamic dimensions misinterpreted as constant duration exportation to ONNX. |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- Primary School AI hand book (20-30)
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| Lantian Li
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- AI-Graph EN (25/50)
- Complete CSTR intro report （11.18)
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| Ying Shi
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- Cohort-Overlap ASR
- condition on real decode result
- Design stop criterion
- Cohort-Speech separation
- several configs for Dual-path model
- group work
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| Zhenghai You
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| Junming Yuan
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- The result of feat-mask/time-mask MT-HuBERT [1]
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| Xiaolou Li
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| Zehua Liu
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- Verify VSR data
- Finish Data Verification Report
- ICL work(CER: 47.87% < CER: 51.08%)
- Time Mask matters[2]
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| Pengqi Li
|
- Complete the final report of the doctoral innovation project(School)
- Exploring the Consistency of TAO and LayerCAM Results on different models and datasets.
- Conclusion and hypothesis[3]
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| Wan Lin
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| Tianhao Wang
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- adjust the code of AudioSep (CLAP) to support multi-mix and audio-query (in training)
- some project testing
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| Xiaoxue Luo
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- AudioSep reproduction
- evaluate the performance of AudioSep
- comparative experiment between AudioSep and baseline system(CLIPSep)
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| Zhenyu Zhou
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| Junhui Chen
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| Jiaying Wang
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| Yu Zhang
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| Wenqiang Du
|
- Participated in an AI competition
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| Yang Wei
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| Lily
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| Turi
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- Whisper finetuning on sagalee
- with encoder frozen, whisper-large-v3 (20.5 WER)
- Finetuning LLM
- Finetuned Qwen2.5-0.5B on conversation dataset translated from English to Oromo
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| Yue Gu
|
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| Qi Qu
|
- AED:
- New CED-based classifiers deployed onto devices, yielding acceptable performance.
- KWS:
- Quantization and format conversion of production models for deployment on embedded device w/ NPU. Default quantization mode leads to unacceptable loss of precision. Will try hybrid quantization.
- Text-enrollment KWS: some dynamic dimensions misinterpreted as constant duration exportation to ONNX.
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