luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5

Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/428

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

The model was trained on the far data of TAL_CSASR 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/tal_csasr/ASR
bash ./prepare.sh

• Training

export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5"
./pruned_transducer_stateless5/train.py \
                  --world-size 6 \
                  --num-epochs 30 \
                  --start-epoch 1 \
                  --exp-dir pruned_transducer_stateless5/exp \
                  --lang-dir data/lang_char \
                  --max-duration 90

Evaluation results

The decoding results (CER%) on TAL_CSASR(dev and test) are listed below:

decoding-method	epoch(iter)	avg	dev	test
greedy_search	30	24	7.49	7.58
modified_beam_search	30	24	7.33	7.38
fast_beam_search	30	24	7.32	7.42
greedy_search(use-averaged-model=True)	30	24	7.30	7.39
modified_beam_search(use-averaged-model=True)	30	24	7.15	7.22
fast_beam_search(use-averaged-model=True)	30	24	7.18	7.27
greedy_search	348000	30	7.46	7.54
modified_beam_search	348000	30	7.24	7.36
fast_beam_search	348000	30	7.25	7.39

The results (CER(%) and WER(%)) for Chinese CER and English WER respectivly (zh: Chinese, en: English):

decoding-method	epoch(iter)	avg	dev	dev_zh	dev_en	test	test_zh	test_en
greedy_search(use-averaged-model=True)	30	24	7.30	6.48	19.19	7.39	6.66	19.13
modified_beam_search(use-averaged-model=True)	30	24	7.15	6.35	18.95	7.22	6.50	18.70
fast_beam_search(use-averaged-model=True)	30	24	7.18	6.39	18.90	7.27	6.55	18.77