30c43b7f69
* Add aishell recipe * Remove unnecessary code and update docs * adapt to k2 v1.7, add docs and results * Update conformer ctc model * Update docs, pretrained.py & results * Fix code style * Fix code style * Fix code style * Minor fix * Minor fix * Fix pretrained.py * Update pretrained model & corresponding docs
1.9 KiB
Results
Aishell training results (Conformer-CTC)
2021-11-16
(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/30
Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc
The best decoding results (CER) are listed below, we got this results by averaging models from epoch 60 to 84, and using attention-decoder decoder with num_paths equals to 100.
| test | |
|---|---|
| CER | 4.26% |
To get more unique paths, we scaled the lattice.scores with 0.5 (see https://github.com/k2-fsa/icefall/pull/10#discussion_r690951662 for more details), we searched the lm_score_scale and attention_score_scale for best results, the scales that produced the CER above are also listed below.
| lm_scale | attention_scale | |
|---|---|---|
| test | 0.3 | 0.9 |
You can use the following commands to reproduce our results:
git clone https://github.com/k2-fsa/icefall
cd icefall
cd egs/aishell/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
python conformer_ctc/train.py --bucketing-sampler True \
--max-duration 200 \
--start-epoch 0 \
--num-epoch 90 \
--world-size 4
python conformer_ctc/decode.py --nbest-scale 0.5 \
--epoch 84 \
--avg 25 \
--method attention-decoder \
--max-duration 20 \
--num-paths 100
Aishell training results (Tdnn-Lstm)
2021-09-13
(Wei Kang): Result of phone based Tdnn-Lstm model, https://github.com/k2-fsa/icefall/pull/30
Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc_lstm_ctc
The best decoding results (CER) are listed below, we got this results by averaging models from epoch 19 to 8, and using 1best decoding method.
| test | |
|---|---|
| CER | 10.16% |