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89 lines
2.1 KiB
Markdown
89 lines
2.1 KiB
Markdown
# Results
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### MGB2 BPE training results (Conformer-CTC) (after 3 epochs)
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#### 2022-06-04
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The best WER, as of 2022-06-04, for the MGB2 test dataset is below
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Using whole lattice HLG decoding + n-gram LM rescoring + attention decoder rescoring
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| | dev | test |
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|-----|------------|------------|
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| WER | 25.32 | 23.53 |
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Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
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| ngram_lm_scale | attention_scale |
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|----------------|-----------------|
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| 0.1 | - |
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Using n-best (n=0.5) HLG decoding + n-gram LM rescoring + attention decoder rescoring:
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| | dev | test |
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|-----|------------|------------|
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| WER | 27.87 | 26.12 |
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Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
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| ngram_lm_scale | attention_scale |
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|----------------|-----------------|
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| 0.01 | 0.3 |
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To reproduce the above result, use the following commands for training:
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# Note: the model was trained on V-100 32GB GPU
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```
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cd egs/mgb2/ASR
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. ./path.sh
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1"
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./conformer_ctc/train.py \
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--lang-dir data/lang_bpe_5000 \
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--att-rate 0.8 \
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--lr-factor 10 \
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--max-duration \
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--concatenate-cuts 0 \
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--world-size 2 \
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--bucketing-sampler 1 \
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--max-duration 100 \
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--start-epoch 0 \
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--num-epochs 30
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```
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and the following command for nbest decoding
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```
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./conformer_ctc/decode.py \
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--lang-dir data/lang_bpe_5000 \
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--max-duration 30 \
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--concatenate-cuts 0 \
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--bucketing-sampler 1 \
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--num-paths 1000 \
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--epoch 2 \
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--avg 2 \
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--method attention-decoder \
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--nbest-scale 0.5
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```
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and the following command for whole-lattice decoding
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```
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./conformer_ctc/decode.py \
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--lang-dir data/lang_bpe_5000 \
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--max-duration 30 \
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--concatenate-cuts 0 \
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--bucketing-sampler 1 \
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--num-paths 1000 \
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--epoch 2 \
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--avg 2 \
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--method whole-lattice-rescoring
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```
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You can find the pre-trained model by visiting
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<comming soon>
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The tensorboard log for training is available at
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<https://tensorboard.dev/experiment/zy6FnumCQlmiO7BPsdCmEg/#scalars> |