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59 lines
1.9 KiB
Markdown
59 lines
1.9 KiB
Markdown
# Results
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## zipformer (zipformer + pruned stateless transducer)
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See <https://github.com/k2-fsa/icefall/pull/1746> for more details.
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[zipformer](./zipformer)
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### Non-streaming
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#### normal-scaled model, number of model parameters: 65549011, i.e., 65.55 M
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You can find a pretrained model, training logs, decoding logs, and decoding results at:
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<https://huggingface.co/zrjin/icefall-asr-libritts-zipformer-2024-10-20>
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You can use <https://github.com/k2-fsa/sherpa> to deploy it.
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| decoding method | test-clean | test-other | comment |
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|----------------------|------------|------------|--------------------|
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| greedy_search | 2.83 | 5.91 | --epoch 30 --avg 5 |
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| modified_beam_search | 2.80 | 5.87 | --epoch 30 --avg 5 |
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| fast_beam_search | 2.87 | 5.86 | --epoch 30 --avg 5 |
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| greedy_search | 2.76 | 5.68 | --epoch 40 --avg 16|
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| modified_beam_search | 2.74 | 5.66 | --epoch 40 --avg 16|
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| fast_beam_search | 2.75 | 5.67 | --epoch 40 --avg 16|
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| greedy_search | 2.74 | 5.67 | --epoch 50 --avg 30|
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| modified_beam_search | 2.73 | 5.58 | --epoch 50 --avg 30|
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| fast_beam_search | 2.78 | 5.61 | --epoch 50 --avg 30|
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The training command is:
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```bash
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export CUDA_VISIBLE_DEVICES="0,1"
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./zipformer/train.py \
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--world-size 2 \
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--num-epochs 50 \
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--start-epoch 1 \
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--use-fp16 1 \
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--exp-dir zipformer/exp \
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--causal 0 \
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--full-libri 1 \
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--max-duration 3600
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```
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This was used on 2 Nvidia A800 GPUs, you'll need to adjust the `CUDA_VISIBLE_DEVICES`, `--world-size` and `--max-duration` according to your hardware.
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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 fast_beam_search; do
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./zipformer/decode.py \
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--epoch 50 \
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--avg 30 \
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--use-averaged-model 1 \
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--exp-dir ./zipformer/exp \
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--max-duration 600 \
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--decoding-method $m
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done
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```
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