File size: 2,048 Bytes
fb97c91 e9c54b6 fb97c91 e9c54b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
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
## Aishell-1 Results
* Feature info:
* using fbank feature, cmvn, speed perturb, dither
* Training info:
* [train_u2++_efficonformer_v2.yaml](https://github.com/wenet-e2e/wenet/blob/main/examples/aishell/s0/conf/train_u2%2B%2B_efficonformer_v2.yaml)
* 8 gpu, batch size 16, acc_grad 1, 200 epochs
* lr 0.001, warmup_steps 25000
* Model info:
* Model Params: 49,354,651
* 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
| decoding mode | full | 18 | 16 |
|------------------------|------|------|------|
| attention decoder | 4.87 | 5.03 | 5.07 |
| ctc prefix beam search | 4.97 | 5.18 | 5.20 |
| attention rescoring | 4.56 | 4.75 | 4.77 |
## Start to Use
Install **WeNet** follow: https://wenet.org.cn/wenet/install.html#install-for-training
Decode
```sh
cd wenet/examples/aishell/s0
dir=exp/wenet_efficient_conformer_aishell_v2/
ctc_weight=0.5
reverse_weight=0.3
decoding_chunk_size=-1
mode="attention_rescoring"
test_dir=$dir/test_${mode}
mkdir -p $test_dir
# Decode
nohup python wenet/bin/recognize.py --gpu 0 \
--mode $mode \
--config $dir/train.yaml \
--data_type "raw" \
--test_data data/test/data.list \
--checkpoint $dir/final.pt \
--beam_size 10 \
--batch_size 1 \
--penalty 0.0 \
--dict $dir/words.txt \
--ctc_weight $ctc_weight \
--reverse_weight $reverse_weight \
--result_file $test_dir/text \
${decoding_chunk_size:+--decoding_chunk_size $decoding_chunk_size} > logs/decode_aishell.log &
# CER
python tools/compute-cer.py --char=1 --v=1 \
data/test/text $test_dir/text > $test_dir/cer.txt
```
|