|
|
| （2位用户的2个中间修订版本未显示） |
| 第45行： |
第45行： |
| | |Ying Shi | | |Ying Shi |
| | || | | || |
| − | * | + | * Text enroll keywords spotting [https://z1et6d3xtb.feishu.cn/docx/LoFAdgVpgo3YlAx1oBAcc9uwn5b?from=from_copylink here] |
| | || | | || |
| | * | | * |
| 第193行： |
第193行： |
| | |Yu Zhang | | |Yu Zhang |
| | || | | || |
| − | * | + | * R2SAC result [https://z1et6d3xtb.feishu.cn/docx/Bs3gd4rk7oSsfaxBUYhc35Ssn2c], The results were not what we expected |
| | || | | || |
| − | * | + | * |
| | || | | || |
| | * | | * |
| 第259行： |
第259行： |
| | |Qi Qu | | |Qi Qu |
| | || | | || |
| − | * | + | * AED: |
| | + | ** Fixed some bugs while developing c/jni/python/go libs. |
| | + | ** Unit test. |
| | + | ** More positive/negative samples collected for classifier training. |
| | + | * KWS: |
| | + | ** Data collected and cleaned for the new Mandarin Chinese wordlist: 48 keywords, ~200 speakers, ~60k audio segments. |
| | + | ** Contextual keyword data (keyword embedded in contextual utterances) collected and annotated (and yet to be delivered). |
| | || | | || |
| − | * | + | * AED: |
| | + | ** Classifier to be trained. |
| | + | ** On-device integration test. |
| | + | * KWS: |
| | + | ** Test datasets to be delivered. |
| | || | | || |
| | * | | * |
| | |- | | |- |
| People |
This Week |
Next Week |
Task Tracking (DeadLine)
|
| Dong Wang
|
- ISCSLP 2 papers refinement
- AI Graph slildes checking (to chapt 23)
- Content design for medcine vocational education
- Paper review for NC
|
|
|
| Lantian Li
|
- GPU status [1]
- Rabbit05 will be ready in this week.
- Projects
- AED -> miniaturization
- TSE -> finish 1st phase delivery
- VSR -> start a new data collection phase
- Finance -> R^2 testing
- Papers
- NeuralScoring
- check ISCSLP paper
- AI graph
- Slides checking (18/50)
- High school handbook (2/40)
|
|
|
| Ying Shi
|
- Text enroll keywords spotting here
|
|
|
| Zhenghai You
|
- Finish huawei project first phase delivery
|
|
|
| Junming Yuan
|
- check the multi-lingual experiment again, the result in [2]
- the opposite trend appears on different English datasets
- our MT pretrained model show the better performance in 2-mixed test with multi-lingual
|
|
|
| Chen Chen
|
|
|
|
| Xiaolou Li
|
- Different length inference test
- MLLM paper reading and LLaMA-Factroy testing
- Report and Interview preparation
|
- Prepare dataset for LLaMA finetuning
- Try different PEFT method.
|
|
| Zehua Liu
|
- Friday Report
- HUAWEI Interview
- Training For VSP-LLM（443h）
|
|
|
| Pengqi Li
|
- Supervised learning of the ASP has been successfully trained[3].
|
|
|
| Wan Lin
|
- Neural Scoring
- Experiments(CN & layer_num & chunk_len)
- Paper revision
|
|
|
| Tianhao Wang
|
- Neural Scoring [4]
- parameter tuning for three genre CN fine-tuning (minDCF is weak)
- noisy training and testing with musan (minDCF is weak)
|
|
|
| Zhenyu Zhou
|
- Huawei Project Submission
|
|
|
| Junhui Chen
|
- Neural Scoring
- One transformer encoder layer exps (get good performance)
- paper refinement
|
|
|
| Jiaying Wang
|
- dptnet wsj 2mix（training）
- dptnet libri3ix （done）
- dptnet libri3mix cohort(training, seems overfit with poor performance)
- condition chain code preparing
|
|
|
| Yu Zhang
|
- R2SAC result [5], The results were not what we expected
|
|
|
| Wenqiang Du
|
- Training of some local dialect models [6]
|
|
|
| Yang Wei
|
- AIBabel
- Train Uyghur and Kazakh KWS model.
|
|
|
| Lily
|
- Thesis writing
- ISCSLP paper submission
- AIRadiance daily works
- Live broadcast
|
|
|
| Turi
|
- Updated the Data collection app to enable uploading in the background while users record.
- Prepared 60hrs of data to start experiment
- Tried running using wenet toolkit for few epochs(loss fluctuates)
|
|
|
| Yue Gu
|
- paper writing: finish 5.5 pages (5.5/9)
- find a bug which influence the real time factor (RTF), now test again
|
|
|
| Qi Qu
|
- AED:
- Fixed some bugs while developing c/jni/python/go libs.
- Unit test.
- More positive/negative samples collected for classifier training.
- KWS:
- Data collected and cleaned for the new Mandarin Chinese wordlist: 48 keywords, ~200 speakers, ~60k audio segments.
- Contextual keyword data (keyword embedded in contextual utterances) collected and annotated (and yet to be delivered).
|
- AED:
- Classifier to be trained.
- On-device integration test.
- KWS:
- Test datasets to be delivered.
|
|
