luomingshuang/icefall_asr_aishell4_pruned_transducer_stateless5

Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/399

Pre-trained Transducer-Stateless5 models for the AISHELL4 dataset with icefall.

The model was trained on the far data of AISHELL4 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/aishell4/ASR
bash ./prepare.sh

• —Training

export CUDA_VISIBLE_DEVICES="0,1,2,3"
./pruned_transducer_stateless5/train.py \
                  --world-size 4 \
                  --num-epochs 30 \
                  --start-epoch 1 \
                  --exp-dir pruned_transducer_stateless5/exp \
                  --lang-dir data/lang_char \
                  --max-duration 220

Evaluation results

The decoding results (CER%) on AISHELL4(test) are listed below:

When use-averaged-model=False, the CERs are

When use-averaged-model=True, the CERs are