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update AIShell result

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PingFeng Luo 2021-12-04 10:42:31 +08:00
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@ -4,19 +4,19 @@
#### 2021-12-01 #### 2021-12-01
(Pingfeng Luo): Result of <https://github.com/k2-fsa/icefall/pull/137> (Pingfeng Luo): Result of <https://github.com/k2-fsa/icefall/pull/137>
The tensorboard log for training is available at <https://tensorboard.dev/experiment/dyp3vWE9RE6SkqBAgLJjUw/> The tensorboard log for training is available at <https://tensorboard.dev/experiment/PSRYVbptRGynqpPRSykp1g>
And pretrained model is available at <https://huggingface.co/pfluo/icefall_aishell_model> And pretrained model is available at <https://huggingface.co/pfluo/icefall_aishell_mmi_model>
The best decoding results (CER) are listed below, we got this results by averaging models from epoch 20 to 49, and using `attention-decoder` decoder with num_paths equals to 100. 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.
||test| ||test|
|--|--| |--|--|
|CER| 5.12% | |CER| 4.94% |
||lm_scale|attention_scale| ||lm_scale|attention_scale|
|--|--|--| |--|--|--|
|test|1.5|0.5| |test|1.1|0.3|
You can use the following commands to reproduce our results: You can use the following commands to reproduce our results:
@ -31,12 +31,12 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
python conformer_mmi/train.py --bucketing-sampler True \ python conformer_mmi/train.py --bucketing-sampler True \
--max-duration 200 \ --max-duration 200 \
--start-epoch 0 \ --start-epoch 0 \
--num-epochs 50 \ --num-epochs 90 \
--world-size 8 --world-size 8
python conformer_mmi/decode.py --nbest-scale 0.5 \ python conformer_mmi/decode.py --nbest-scale 0.5 \
--epoch 49 \ --epoch 85 \
--avg 20 \ --avg 25 \
--method attention-decoder \ --method attention-decoder \
--max-duration 20 \ --max-duration 20 \
--num-paths 100 --num-paths 100