“2013-04-19”版本间的差异
来自cslt Wiki
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|speedup||26.8||27.88||26.81||22.82 | |speedup||26.8||27.88||26.81||22.82 | ||
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:*Tencent baseline is with 700h online data+ 700h 863 data, HLDA+MPE, 88k lexicon | :*Tencent baseline is with 700h online data+ 700h 863 data, HLDA+MPE, 88k lexicon | ||
:*Our results are with 400 hour AM, 88k LM. ML+bMMI | :*Our results are with 400 hour AM, 88k LM. ML+bMMI | ||
2013年4月25日 (四) 04:22的版本
目录
Data sharing
- AM/lexicon/LM are shared.
- LM count files are still in transfering.
DNN progress
400 hour DNN training
| Test Set | Tencent Baseline | bMMI | fMMI | BN | Hybrid |
|---|---|---|---|---|---|
| 1900 | 8.4 | 7.65 | 7.35 | 6.57 | |
| 2044 | 22.4 | 24.44 | 24.03 | 21.77 | |
| online1 | 35.6 | 34.66 | 34.33 | 31.44 | |
| online2 | 29.6 | 27.23 | 26.80 | 24.10 | |
| map | 24.5 | 27.54 | 27.69 | 23.79 | |
| notepad | 16 | 19.81 | 21.75 | 15.81 | |
| general | 36 | 38.52 | 38.90 | 33.61 | |
| speedup | 26.8 | 27.88 | 26.81 | 22.82 |
- Tencent baseline is with 700h online data+ 700h 863 data, HLDA+MPE, 88k lexicon
- Our results are with 400 hour AM, 88k LM. ML+bMMI
Tencent test result
- AM: 70h training data(2 day, 15 machines, 10 threads)
- LM: 88k LM
- Test case: general
- gmmi-bmmi: 38.7%
- dnn-1: 28% 11 frame window, phone-based tree
- dnn-2: 34% 9 frame window, state-based tree
GPU & CPU merge
- Invesigate the possibility to merge GPU and CPU code. Try to find out an easier way. (1 week)
L-1 sparse initial training
- Start to investigating.
Kaldi/HTK merge
- HTK2Kaldi: the tool with Kaldi does not work.
- Kaldi2HTK: done with implementation. Testing?
Embedded progress
- Some large performance (speed) degradation with the embedded platform(1/60).
- Planning for sparse DNN.
- QA LM training, still failed. Mengyuan need more work on this.
