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license: apache-2.0
language:
- en
- zh
metrics:
- cer
pipeline_tag: automatic-speech-recognition
---
# Efficient Conformer v2 for non-streaming ASR
**Specification**: https://github.com/wenet-e2e/wenet/pull/1636
## Results
* Feature info:
* using fbank feature, cmvn, speed perturb, dither
* Training info:
* train_u2++_efficonformer_v2.yaml
* 8 gpu, batch size 16, acc_grad 1, 120 epochs
* lr 0.001, warmup_steps 35000
* Model info:
* Model Params: 50,341,278
* Downsample rate: 1/2 (conv2d2) * 1/4 (efficonformer block)
* encoder_dim 256, output_size 256, head 8, linear_units 2048
* num_blocks 12, cnn_module_kernel 15, group_size 3
* Decoding info:
* ctc_weight 0.5, reverse_weight 0.3, average_num 20
test clean
| decoding mode | full | 18 | 16 |
|------------------------|------|------|------|
| attention decoder | 3.49 | 3.71 | 3.72 |
| ctc_greedy_search | 3.49 | 3.74 | 3.77 |
| ctc prefix beam search | 3.47 | 3.72 | 3.74 |
| attention rescoring | 3.12 | 3.38 | 3.36 |
test other
| decoding mode | full | 18 | 16 |
|------------------------|------|------|------|
| attention decoder | 8.15 | 9.05 | 9.03 |
| ctc_greedy_search | 8.73 | 9.82 | 9.83 |
| ctc prefix beam search | 8.70 | 9.81 | 9.79 |
| attention rescoring | 8.05 | 9.08 | 9.10 |
## Start to Use
Install **WeNet** follow: https://wenet.org.cn/wenet/install.html#install-for-training
Decode
```sh
cd examples/librispeech/s0
cp exp/wenet_efficient_conformer_librispeech_v2/decode.sh ./
cp exp/wenet_efficient_conformer_librispeech_v2/wer.sh ./
dir=exp/wenet_efficient_conformer_librispeech_v2
decoding_chunk_size=-1
. ./decode.sh ${dir} 20 ${decoding_chunk_size}
# WER
. ./wer.sh test_clean wenet_efficient_conformer_librispeech_v2 ${decoding_chunk_size}
. ./wer.sh test_other wenet_efficient_conformer_librispeech_v2 ${decoding_chunk_size}
```
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