“2024-07-22”版本间的差异

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第46行: 第46行:
 
|Zhenghai You
 
|Zhenghai You
 
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*
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* Reproduce DPRNN-IRA[https://z1et6d3xtb.feishu.cn/docx/EcNmdXC2Uo7Egdx5UvWcvaFgnr2]
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* TSE Project: Targetless model with tSDR and tMSE Loss
 
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第78行: 第79行:
 
|Xiaolou Li
 
|Xiaolou Li
 
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*
+
* LRS-30h PALR2 (2 epoch result, still training)
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** VSR:            30.56%
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** Refinement: 30.40%
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* Paper reading
 
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第90行: 第94行:
 
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*LRS3-30h ICL (CER:30.3 > 29.1)
 
*LRS3-30h ICL (CER:30.3 > 29.1)
*LRS3-30h image adaptive mask (CER: 27.70 < 29.1)
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*LRS3-30h image adaptive mask code
 
*Papper reading[https://z1et6d3xtb.feishu.cn/docx/JBsidACDVojhCaxFQLbcCVbsnAc?from=from_copylink] and Maybe Q-former can be useful
 
*Papper reading[https://z1et6d3xtb.feishu.cn/docx/JBsidACDVojhCaxFQLbcCVbsnAc?from=from_copylink] and Maybe Q-former can be useful
 
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第113行: 第117行:
 
|Wan Lin
 
|Wan Lin
 
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*
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* NS paper writing [https://z1et6d3xtb.feishu.cn/docx/WNYJddKBBo6dHtxcszZcuBbBnXc?from=from_copylink]
 
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第124行: 第128行:
 
|Tianhao Wang
 
|Tianhao Wang
 
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* New task investigation: Sound Filter
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* Neural Scoring: CN-Celeb exps refresh the results
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* project: data washing and generator toolkit
 
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第135行: 第141行:
 
|Zhenyu Zhou
 
|Zhenyu Zhou
 
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*
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*TSE stream test[https://z1et6d3xtb.feishu.cn/docx/AVKSdXxJooSgfXxbYW1c1lNrnab]
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*Multi-speaker SS Paper reading
 
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第146行: 第153行:
 
|Junhui Chen
 
|Junhui Chen
 
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*
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* NS paper writing
 
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第157行: 第164行:
 
|Jiaying Wang
 
|Jiaying Wang
 
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*
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* change to chain-based methods
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** reproducing sequence to multi-sequence code
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** read related papers
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*
 
*
第168行: 第178行:
 
|Yu Zhang
 
|Yu Zhang
 
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*
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* AED:
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** retraining model without dilation convolution
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** assist Huawei in model quantization and engineering
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* Finance:
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** retraining model with data totally align with JunWang
 
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第179行: 第193行:
 
|Wenqiang Du
 
|Wenqiang Du
 
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*
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* Complete the company's model upgrade with Weiyang
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** cn + minnanyu + Uyghur
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** cn + Uyghur + Kazakh
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* primary school handbook (12/46)
 
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第190行: 第207行:
 
|Yang Wei
 
|Yang Wei
 
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*
+
* AIBabel KWS
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** Train a new version of Chinese, Uyghur, Kazakh model. [https://z1et6d3xtb.feishu.cn/docx/UWfJd11gCofyaexMkmJcJ9f7nTc]
 
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第228行: 第246行:
 
|Qi Qu
 
|Qi Qu
 
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*  
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* KWS:
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** Documents: data preparation, test method.
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** Datasets prepared: 1. Mandarin Chinese mild-accent (20 keywords, 14 persons); 2. FAs from production (150k, from Jul. 1 to Jul. 15).
 +
* AED:
 +
** AudioSet as negative samples.
 
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2024年7月22日 (一) 11:01的最后版本

  • Finish AIGraph slides check
People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Paper review for ISCSLP
  • AIGraph slides check (37/50)
Lantian Li
  • GPU status [1]
  • Review several chain-based separation papers
  • AI graph
    • Slides checking (23/50)
    • High school handbook (11/40)
  • Submit GTV
  • High school handbook (20/40)
Ying Shi
Zhenghai You
  • Reproduce DPRNN-IRA[2]
  • TSE Project: Targetless model with tSDR and tMSE Loss
Junming Yuan
Chen Chen
Xiaolou Li
  • LRS-30h PALR2 (2 epoch result, still training)
    • VSR: 30.56%
    • Refinement: 30.40%
  • Paper reading
Zehua Liu
  • LRS3-30h ICL (CER:30.3 > 29.1)
  • LRS3-30h image adaptive mask code
  • Papper reading[3] and Maybe Q-former can be useful
Pengqi Li
Wan Lin
  • NS paper writing [4]
Tianhao Wang
  • New task investigation: Sound Filter
  • Neural Scoring: CN-Celeb exps refresh the results
  • project: data washing and generator toolkit
Zhenyu Zhou
  • TSE stream test[5]
  • Multi-speaker SS Paper reading
Junhui Chen
  • NS paper writing
Jiaying Wang
  • change to chain-based methods
    • reproducing sequence to multi-sequence code
    • read related papers
Yu Zhang
  • AED:
    • retraining model without dilation convolution
    • assist Huawei in model quantization and engineering
  • Finance:
    • retraining model with data totally align with JunWang
Wenqiang Du
  • Complete the company's model upgrade with Weiyang
    • cn + minnanyu + Uyghur
    • cn + Uyghur + Kazakh
  • primary school handbook (12/46)
Yang Wei
  • AIBabel KWS
    • Train a new version of Chinese, Uyghur, Kazakh model. [6]
Lily
Turi
  • Completed data collection
  • Trained conformer on full data (100h) for 200 epochs. WER 23.9% on 2hrs of test data.
Yue Gu
  • complete the introduction、related work and 1/3 results of the journal paper (8/9).
Qi Qu
  • KWS:
    • Documents: data preparation, test method.
    • Datasets prepared: 1. Mandarin Chinese mild-accent (20 keywords, 14 persons); 2. FAs from production (150k, from Jul. 1 to Jul. 15).
  • AED:
    • AudioSet as negative samples.