icefall_asr_timit_tdnn_ligru_ctc

Pre-trained TDNN-LiGRU-CTC models for the TIMIT dataset with icefall.

The model was trained on full TIMIT with the scripts in icefall.
See (https://github.com/k2-fsa/icefall/tree/master/egs/timit/ASR/tdnn_ligru_ctc) for more details of this model.

How to use

See (https://github.com/k2-fsa/icefall/blob/master/egs/timit/ASR/tdnn_ligru_ctc/Pre-trained.md)

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/timit/ASR
bash ./prepare.sh

• Training

export CUDA_VISIBLE_DEVICES="0"
python tdnn_ligru_ctc/train.py --bucketing-sampler True \
                                --concatenate-cuts False \
                                --max-duration 200 \
                                --world-size 1

Evaluation results

The best decoding results (PER, equals to WER) on TIMIT TEST are listed below, we got this result by averaging models from epoch 9 to 25, the lm_scale is 0.1, the decoding method is whole-lattice-rescoring.