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58AILab/
wenet_efficient_conformer_librispeech_v2

Name: wenet_efficient_conformer_librispeech_v2
Availability: InStock

automatic-speech-recognition

apache-2.0

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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

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}

58AILab/wenet_efficient_conformer_librispeech_v2

Efficient Conformer v2 for non-streaming ASR

Results

Start to Use

58AILab/
wenet_efficient_conformer_librispeech_v2