mirror of
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112 lines
4.4 KiB
Markdown
112 lines
4.4 KiB
Markdown
# Results
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## zipformer (zipformer + pruned stateless transducer)
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See <https://github.com/k2-fsa/icefall/pull/1261> for more details.
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[zipformer](./zipformer)
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### Non-streaming
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#### Training on normalized text, i.e. Upper case without punctuation
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##### normal-scaled model, number of model parameters: 65805511, i.e., 65.81 M
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You can find a pretrained model, training logs at:
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<https://www.modelscope.cn/models/pkufool/icefall-asr-zipformer-libriheavy-20230926/summary>
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Note: The repository above contains three models trained on different subset of libriheavy exp(large set), exp_medium_subset(medium set),
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exp_small_subset(small set).
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Results of models:
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| training set | decoding method | librispeech clean | librispeech other | libriheavy clean | libriheavy other | comment |
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|---------------|---------------------|-------------------|-------------------|------------------|------------------|--------------------|
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| small | greedy search | 4.19 | 9.99 | 4.75 | 10.25 |--epoch 90 --avg 20 |
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| small | modified beam search| 4.05 | 9.89 | 4.68 | 10.01 |--epoch 90 --avg 20 |
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| medium | greedy search | 2.39 | 4.85 | 2.90 | 6.6 |--epoch 60 --avg 20 |
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| medium | modified beam search| 2.35 | 4.82 | 2.90 | 6.57 |--epoch 60 --avg 20 |
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| large | greedy search | 1.67 | 3.32 | 2.24 | 5.61 |--epoch 16 --avg 3 |
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| large | modified beam search| 1.62 | 3.36 | 2.20 | 5.57 |--epoch 16 --avg 3 |
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The training command is:
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```bash
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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python ./zipformer/train.py \
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--world-size 4 \
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--master-port 12365 \
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--exp-dir zipformer/exp \
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--num-epochs 60 \ # 16 for large; 90 for small
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--lr-hours 15000 \ # 20000 for large; 5000 for small
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--use-fp16 1 \
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--start-epoch 1 \
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--bpe-model data/lang_bpe_500/bpe.model \
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--max-duration 1000 \
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--subset medium
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```
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The decoding command is:
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```bash
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export CUDA_VISIBLE_DEVICES="0"
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for m in greedy_search modified_beam_search; do
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./zipformer/decode.py \
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--epoch 16 \
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--avg 3 \
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--exp-dir zipformer/exp \
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--max-duration 1000 \
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--causal 0 \
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--decoding-method $m
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done
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```
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#### Training on full formatted text, i.e. with casing and punctuation
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##### normal-scaled model, number of model parameters: 66074067 , i.e., 66M
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You can find a pretrained model, training logs at:
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<https://www.modelscope.cn/models/pkufool/icefall-asr-zipformer-libriheavy-punc-20230830/summary>
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Note: The repository above contains three models trained on different subset of libriheavy exp(large set), exp_medium_subset(medium set),
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exp_small_subset(small set).
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Results of models:
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| training set | decoding method | libriheavy clean (WER) | libriheavy other (WER) | libriheavy clean (CER) | libriheavy other (CER) | comment |
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|---------------|---------------------|-------------------|-------------------|------------------|------------------|--------------------|
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| small | modified beam search| 13.04 | 19.54 | 4.51 | 7.90 |--epoch 88 --avg 41 |
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| medium | modified beam search| 9.84 | 13.39 | 3.02 | 5.10 |--epoch 50 --avg 15 |
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| large | modified beam search| 7.76 | 11.32 | 2.41 | 4.22 |--epoch 16 --avg 2 |
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The training command is:
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```bash
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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python ./zipformer/train.py \
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--world-size 4 \
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--master-port 12365 \
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--exp-dir zipformer/exp \
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--num-epochs 60 \ # 16 for large; 90 for small
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--lr-hours 15000 \ # 20000 for large; 10000 for small
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--use-fp16 1 \
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--train-with-punctuation 1 \
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--start-epoch 1 \
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--bpe-model data/lang_punc_bpe_756/bpe.model \
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--max-duration 1000 \
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--subset medium
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```
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The decoding command is:
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```bash
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export CUDA_VISIBLE_DEVICES="0"
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for m in greedy_search modified_beam_search; do
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./zipformer/decode.py \
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--epoch 16 \
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--avg 3 \
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--exp-dir zipformer/exp \
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--max-duration 1000 \
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--causal 0 \
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--decoding-method $m
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done
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```
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