Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/349

Pre-trained Transducer-Stateless2 models for the WenetSpeech dataset with icefall.

The model was trained on the L subset of WenetSpeech with the scripts in icefall based on the latest version k2.

Training procedure

The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2 icefall: https://github.com/k2-fsa/icefall lhotse: https://github.com/lhotse-speech/lhotse

Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.

git clone https://github.com/k2-fsa/icefall
cd icefall

Preparing data.

cd egs/wenetspeech/ASR
bash ./prepare.sh

Training

export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./pruned_transducer_stateless2/train.py \
                  --world-size 8 \
                  --num-epochs 15 \
                  --start-epoch 0 \
                  --exp-dir pruned_transducer_stateless2/exp \
                  --lang-dir data/lang_char \
                  --max-duration 180 \
                  --valid-interval 3000 \
                  --model-warm-step 3000 \
                  --save-every-n 8000 \
                  --training-subset L

Evaluation results

The decoding results (WER%) on WenetSpeech(dev, test-net and test-meeting) are listed below, we got this result by averaging models from epoch 9 to 10. The WERs are

	dev	test-net	test-meeting	comment
greedy search	7.80	8.75	13.49	--epoch 10, --avg 2, --max-duration 100
modified beam search (beam size 4)	7.76	8.71	13.41	--epoch 10, --avg 2, --max-duration 100
fast beam search (1best)	7.94	8.74	13.80	--epoch 10, --avg 2, --max-duration 1500
fast beam search (nbest)	9.82	10.98	16.37	--epoch 10, --avg 2, --max-duration 600
fast beam search (nbest oracle)	6.88	7.18	11.77	--epoch 10, --avg 2, --max-duration 600
fast beam search (nbest LG)	14.94	16.14	22.93	--epoch 10, --avg 2, --max-duration 600