“2013-04-19”版本间的差异
来自cslt Wiki
（→400 hour DNN training） |
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===400 hour DNN training=== | ===400 hour DNN training=== | ||
{| class="wikitable" | {| class="wikitable" | ||
| − | !Test Set!! Tencent Baseline!! bMMI!! fMMI !! BN !! Hybrid | + | !Test Set!! Tencent Baseline!! bMMI!! fMMI !! BN(with fMMI) !! Hybrid |
|- | |- | ||
| − | |1900||8.4 || 7.65 || 7.35||6.57 | + | |1900||8.4 || 7.65 || 7.35||6.57 || 7.27 |
|- | |- | ||
| − | |2044|| 22.4 ||24.44|| 24.03||21.77 | + | |2044|| 22.4 ||24.44|| 24.03||21.77 || 20.24 |
|- | |- | ||
| − | |online1||35.6 ||34.66||34.33||31.44 | + | |online1||35.6 ||34.66||34.33||31.44 || 30.53 |
|- | |- | ||
| − | |online2||29.6 ||27.23||26.80||24.10 | + | |online2||29.6 ||27.23||26.80||24.10 || 23.89 |
|- | |- | ||
| − | |map||24.5|| 27.54||27.69||23.79 | + | |map||24.5|| 27.54||27.69||23.79 || 22.46 |
|- | |- | ||
| − | |notepad||16|| 19.81||21.75||15.81 | + | |notepad||16|| 19.81||21.75||15.81 || 12.74 |
|- | |- | ||
| − | |general||36|| 38.52||38.90||33.61 | + | |general||36|| 38.52||38.90||33.61 || 31.55 |
|- | |- | ||
| − | |speedup||26.8||27.88||26.81||22.82 | + | |speedup||26.8||27.88||26.81||22.82 || 22.00 |
|- | |- | ||
|} | |} | ||
2013年4月26日 (五) 05:31的最后版本
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(with fMMI) | Hybrid |
|---|---|---|---|---|---|
| 1900 | 8.4 | 7.65 | 7.35 | 6.57 | 7.27 |
| 2044 | 22.4 | 24.44 | 24.03 | 21.77 | 20.24 |
| online1 | 35.6 | 34.66 | 34.33 | 31.44 | 30.53 |
| online2 | 29.6 | 27.23 | 26.80 | 24.10 | 23.89 |
| map | 24.5 | 27.54 | 27.69 | 23.79 | 22.46 |
| notepad | 16 | 19.81 | 21.75 | 15.81 | 12.74 |
| general | 36 | 38.52 | 38.90 | 33.61 | 31.55 |
| speedup | 26.8 | 27.88 | 26.81 | 22.82 | 22.00 |
- 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.
