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| 第17行： |
第17行： |
| | |Lantian Li | | |Lantian Li |
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| − | * | + | * AI-Graph EN (1-20 finalized) |
| | + | * Design 2025 Daily Posts |
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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 EN (1-20 finalized)
- Design 2025 Daily Posts
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| Ying Shi
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- revise the code about cohort-overlap asr [the training is in progress]
- Support arbitrary source mixing training
- Use the real hypothesis by Token error rate
- Design stop criterion
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| Zhenghai You
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- Introduce more hard samples to improve model performance[1]
- SPK-AUG with same length: There is an improvement, but the SI-SDR decreases when hard sample rate increases
- Design more hard samples
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| Junming Yuan
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- The result of time-mask MT-HuBERT [2]
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| Chen Chen
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| Xiaolou Li
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- VTS with LLM structure design and baseline code writing [3]
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| Zehua Liu
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- Reading Papper about In-Context-Learning in ASR
- Training model with Adaptive Time Mask
- Try In-Context-Learning with only previous sentence[4]
- VTS Project Report starts
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| Pengqi Li
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- Consistency of TAO and LayerCAM
- Change TAO from input to final conv layer and obtain more consistency.(Aishell:0.93 in any model)
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| Wan Lin
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- NS: downsampling is not useful [5]
- share speaker meeting in Friday
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| Tianhao Wang
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- AudioSep (CLAP) 5-mix exps:
- text-query: SDR=4.978, SI-SDR=1.972
- audio-query: SDR=6.907, SI-SDR=5.058
- This results with the loudness limitation
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- AudioSep (CLAP) without loudness limitation
- Project things
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| Xiaoxue Luo
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- Comparative experiment between AudioSep and baseline system(CLIPSep)
- Prepare the report
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| Zhenyu Zhou
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- reproduce 5-mix speech Separation results：
- pit：2-mix：16.04 ；5-mix：6.87
- conditional：5-mix：5.38（40 epoch）
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| Junhui Chen
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- NS：speaker detection （method survey & debug）
- get sick
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| Jiaying Wang
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| Yu Zhang
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- SocioDojo (still worse than Nasdaq100 baseline)
- Change information sources, from the perspective of the report generated by LLM, more new information sources will be referenced.
- Prompt Actuator to consider current cash ratio before investing (with out this, the asset ratio goes up to 100%, which leads to high risks, still running)
- Read some papers about integrating time series into LLM
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| Wenqiang Du
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- Prepare data,code and environment for Pro.Mijiti
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| Yang Wei
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- Train text enroll KWS model with Aibabel training data. Not work.
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| Lily
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| Turi
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- Whisper-largev3 finetuning
- Freezing 20 layers of encoder achieved 9.75 WER. Vanilla finetuning 8.02 WER
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| Yue Gu
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- seek suggestions from other authors. Many suggestions are conflicting, so I'm try to figure out the reasons and fix these issues.
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| Qi Qu
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- KWS:
- Text-enroll models exported to ONNX.
- C/JNI libs built based on ONNX models and ready for on-device test.
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