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| （某位用户的一个中间修订版本未显示） |
| 第15行： |
第15行： |
| | |- | | |- |
| | |Zhiyong Zhang || | | |Zhiyong Zhang || |
| − | * Contract with DataTang for 1000h data purchasing and other cooperation and exchange; | + | * Finished scripts for duration and acoustic model training of Merline. |
| − | * Exchange with THS team for the Third project;
| + | |
| − | * Start The TTS normalization of Merlin part.
| + | |
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| − | * To finish the Merline experiments. | + | * To finish the residual jobs for wav generation of Merlin |
| | || | | || |
| | * To re-produce the TTS using KALDI. | | * To re-produce the TTS using KALDI. |
| 第26行： |
第24行： |
| | |- | | |- |
| | |Yang Wei || | | |Yang Wei || |
| − | * Crawl 1G data from Internet
| |
| − | ||
| |
| | * Crawl 6G data from Internet | | * Crawl 6G data from Internet |
| | + | || |
| | + | * Crawl more data from Internet |
| | || | | || |
| | * Crawl more data from Internet | | * Crawl more data from Internet |
| People |
Last Week |
This Week |
Next Week |
Tast Tracing(DeadLine)
|
| Mengyuan Zhao |
- Negotiation with tonghuashun.
- Train 500h AM for roobo.
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- KT for tongfang.
- nnet-forward for embedded asr.
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- nnet-forward for embedded asr.
|
|
| Zhiyong Zhang |
- Finished scripts for duration and acoustic model training of Merline.
|
- To finish the residual jobs for wav generation of Merlin
|
- To re-produce the TTS using KALDI.
|
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| Yang Wei |
- Crawl 6G data from Internet
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- Crawl more data from Internet
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- Crawl more data from Internet
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- TongFang Janpanese 300G text lm traing(Deadline-20170407)
|
| Dong Wang |
- draft contract with Xinsong
- discuss potential opportunity speaker recognition
- re-arrange company-lab relation
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- re-arrange company-lab relation
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|
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| Mingrui Ruan |
- TTS schedule
- review other schedule
|
- Haitian agreement
- cooperation with Shujutang
- review other schedule
- about FreeNeb
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|
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| Zhenlong Han |
- In the roobo project to training a model used 760h.
- Finish crawler documentation
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- Improve the acoustic model documentation.
- Write how to run acoustic model documentation.
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- Train roobo model random linear classification and test WER.
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