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d1adc25338
* add MMI to AIShell * fix MMI decode graph * export model * typo * fix code style * typo * fix data prepare to just use train text by uid * use a faster way to get the intersection of train and aishell_transcript_v0.8.txt * update AIShell result * update * typo
99 lines
3.3 KiB
Markdown
99 lines
3.3 KiB
Markdown
## Results
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### Aishell training results (Conformer-MMI)
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#### 2021-12-04
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(Pingfeng Luo): Result of <https://github.com/k2-fsa/icefall/pull/140>
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The tensorboard log for training is available at <https://tensorboard.dev/experiment/PSRYVbptRGynqpPRSykp1g>
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And pretrained model is available at <https://huggingface.co/pfluo/icefall_aishell_mmi_model>
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The best decoding results (CER) are listed below, we got this results by averaging models from epoch 61 to 85, and using `attention-decoder` decoder with num_paths equals to 100.
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||test|
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|--|--|
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|CER| 4.94% |
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||lm_scale|attention_scale|
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|--|--|--|
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|test|1.1|0.3|
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You can use the following commands to reproduce our results:
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```bash
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git clone https://github.com/k2-fsa/icefall
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cd icefall
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cd egs/aishell/ASR
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
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python conformer_mmi/train.py --bucketing-sampler True \
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--max-duration 200 \
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--start-epoch 0 \
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--num-epochs 90 \
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--world-size 8
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python conformer_mmi/decode.py --nbest-scale 0.5 \
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--epoch 85 \
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--avg 25 \
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--method attention-decoder \
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--max-duration 20 \
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--num-paths 100
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```
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### Aishell training results (Conformer-CTC)
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#### 2021-11-16
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(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/30
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Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc
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The best decoding results (CER) are listed below, we got this results by averaging models from epoch 60 to 84, and using `attention-decoder` decoder with num_paths equals to 100.
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||test|
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|CER| 4.26% |
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To get more unique paths, we scaled the lattice.scores with 0.5 (see https://github.com/k2-fsa/icefall/pull/10#discussion_r690951662 for more details), we searched the lm_score_scale and attention_score_scale for best results, the scales that produced the CER above are also listed below.
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||lm_scale|attention_scale|
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|--|--|--|
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|test|0.3|0.9|
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You can use the following commands to reproduce our results:
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```bash
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git clone https://github.com/k2-fsa/icefall
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cd icefall
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cd egs/aishell/ASR
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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python conformer_ctc/train.py --bucketing-sampler True \
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--max-duration 200 \
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--start-epoch 0 \
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--num-epochs 90 \
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--world-size 4
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python conformer_ctc/decode.py --nbest-scale 0.5 \
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--epoch 84 \
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--avg 25 \
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--method attention-decoder \
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--max-duration 20 \
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--num-paths 100
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```
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### Aishell training results (Tdnn-Lstm)
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#### 2021-09-13
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(Wei Kang): Result of phone based Tdnn-Lstm model, https://github.com/k2-fsa/icefall/pull/30
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Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc_lstm_ctc
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The best decoding results (CER) are listed below, we got this results by averaging models from epoch 19 to 8, and using `1best` decoding method.
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||test|
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|--|--|
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|CER| 10.16% |
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