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Zipformer update result (#1296)

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* update Zipformer results
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Zengwei Yao 2023-10-09 23:15:22 +08:00 committed by GitHub
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6
README.md
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@ -120,9 +120,9 @@ We provide a Colab notebook to run a pre-trained transducer conformer + stateles
| Encoder | Params | test-clean | test-other |
|-----------------|--------|------------|------------|
| zipformer | 65.5M | 2.21 | 4.91 |
| zipformer-small | 23.2M | 2.46 | 5.83 |
| zipformer-large | 148.4M | 2.11 | 4.77 |
| zipformer | 65.5M | 2.21 | 4.79 |
| zipformer-small | 23.2M | 2.42 | 5.73 |
| zipformer-large | 148.4M | 2.06 | 4.63 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.

34
egs/librispeech/ASR/RESULTS.md
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@ -75,7 +75,7 @@ See <https://github.com/k2-fsa/icefall/pull/1058> for more details.
##### normal-scaled model, number of model parameters: 65549011, i.e., 65.55 M
The tensorboard log can be found at
<https://tensorboard.dev/experiment/cBaoIabCQxSDsyZM7FzqZA/>
<https://tensorboard.dev/experiment/R2DT9Ju4QiadC4e2ioKh5A/>
You can find a pretrained model, training logs, decoding logs, and decoding results at:
<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15>
@ -90,18 +90,20 @@ You can use <https://github.com/k2-fsa/sherpa> to deploy it.
| greedy_search | 2.23 | 4.96 | --epoch 40 --avg 16 |
| modified_beam_search | 2.21 | 4.91 | --epoch 40 --avg 16 |
| fast_beam_search | 2.24 | 4.93 | --epoch 40 --avg 16 |
| greedy_search | 2.22 | 4.87 | --epoch 50 --avg 25 |
| modified_beam_search | 2.21 | 4.79 | --epoch 50 --avg 25 |
| fast_beam_search | 2.21 | 4.82 | --epoch 50 --avg 25 |
| modified_beam_search_shallow_fusion | 2.01 | 4.37 | --epoch 40 --avg 16 --beam-size 12 --lm-scale 0.3 |
| modified_beam_search_LODR | 1.94 | 4.17 | --epoch 40 --avg 16 --beam-size 12 --lm-scale 0.52 --LODR-scale -0.26 |
| modified_beam_search_rescore | 2.04 | 4.39 | --epoch 40 --avg 16 --beam-size 12 |
| modified_beam_search_rescore_LODR | 2.01 | 4.33 | --epoch 40 --avg 16 --beam-size 12 |
The training command is:
```bash
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./zipformer/train.py \
--world-size 4 \
--num-epochs 40 \
--num-epochs 50 \
--start-epoch 1 \
--use-fp16 1 \
--exp-dir zipformer/exp \
@ -115,8 +117,8 @@ The decoding command is:
export CUDA_VISIBLE_DEVICES="0"
for m in greedy_search modified_beam_search fast_beam_search; do
./zipformer/decode.py \
--epoch 30 \
--avg 9 \
--epoch 50 \
--avg 25 \
--use-averaged-model 1 \
--exp-dir ./zipformer/exp \
--max-duration 600 \
@ -129,7 +131,7 @@ To decode with external language models, please refer to the documentation [here
##### small-scaled model, number of model parameters: 23285615, i.e., 23.3 M
The tensorboard log can be found at
<https://tensorboard.dev/experiment/53P4tL22TpO0UdiL0kPaLg/>
<https://tensorboard.dev/experiment/M9C8cYPWSN2MVBYaBIX3EQ/>
You can find a pretrained model, training logs, decoding logs, and decoding results at:
<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-small-2023-05-16>
@ -144,13 +146,16 @@ You can use <https://github.com/k2-fsa/sherpa> to deploy it.
| greedy_search | 2.49 | 5.91 | --epoch 40 --avg 13 |
| modified_beam_search | 2.46 | 5.83 | --epoch 40 --avg 13 |
| fast_beam_search | 2.46 | 5.87 | --epoch 40 --avg 13 |
| greedy_search | 2.46 | 5.86 | --epoch 50 --avg 23 |
| modified_beam_search | 2.42 | 5.73 | --epoch 50 --avg 23 |
| fast_beam_search | 2.46 | 5.78 | --epoch 50 --avg 23 |
The training command is:
```bash
export CUDA_VISIBLE_DEVICES="0,1"
./zipformer/train.py \
--world-size 2 \
--num-epochs 40 \
--num-epochs 50 \
--start-epoch 1 \
--use-fp16 1 \
--exp-dir zipformer/exp-small \
@ -169,8 +174,8 @@ The decoding command is:
export CUDA_VISIBLE_DEVICES="0"
for m in greedy_search modified_beam_search fast_beam_search; do
./zipformer/decode.py \
--epoch 40 \
--avg 13 \
--epoch 50 \
--avg 23 \
--exp-dir zipformer/exp-small \
--max-duration 600 \
--causal 0 \
@ -185,7 +190,7 @@ done
##### large-scaled model, number of model parameters: 148439574, i.e., 148.4 M
The tensorboard log can be found at
<https://tensorboard.dev/experiment/HJ74wWYpQAGSzETkmQnrmQ/>
<https://tensorboard.dev/experiment/C5ZPE5u1So2ZwhYLKW0FVg/>
You can find a pretrained model, training logs, decoding logs, and decoding results at:
<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-large-2023-05-16>
@ -200,13 +205,16 @@ You can use <https://github.com/k2-fsa/sherpa> to deploy it.
| greedy_search | 2.12 | 4.8 | --epoch 40 --avg 13 |
| modified_beam_search | 2.11 | 4.7 | --epoch 40 --avg 13 |
| fast_beam_search | 2.13 | 4.78 | --epoch 40 --avg 13 |
| greedy_search | 2.08 | 4.69 | --epoch 50 --avg 30 |
| modified_beam_search | 2.06 | 4.63 | --epoch 50 --avg 30 |
| fast_beam_search | 2.09 | 4.68 | --epoch 50 --avg 30 |
The training command is:
```bash
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./zipformer/train.py \
--world-size 4 \
--num-epochs 40 \
--num-epochs 50 \
--start-epoch 1 \
--use-fp16 1 \
--exp-dir zipformer/exp-large \
@ -224,8 +232,8 @@ The decoding command is:
export CUDA_VISIBLE_DEVICES="0"
for m in greedy_search modified_beam_search fast_beam_search; do
./zipformer/decode.py \
--epoch 40 \
--avg 16 \
--epoch 50 \
--avg 30 \
--exp-dir zipformer/exp-large \
--max-duration 600 \
--causal 0 \