run.sh

#!/bin/bash
source path.sh
set -e

stage=0
stop_stage=100
conf_path=conf/conformer.yaml
avg_num=20

source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;

avg_ckpt=avg_${avg_num}
ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
echo "checkpoint name ${ckpt}"

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    # prepare data
    bash ./local/data.sh || exit -1
fi

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    # train model, all `ckpt` under `exp` dir
    CUDA_VISIBLE_DEVICES=0,1,2,3 ./local/train.sh ${conf_path}  ${ckpt}
fi

if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    # avg n best model
    avg.sh best exp/${ckpt}/checkpoints ${avg_num}
fi

if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    # test ckpt avg_n
    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
    # ctc alignment of test data
    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi

if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
    # export ckpt avg_n
    CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi

 # Optionally, you can add LM and test it with runtime.
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
    # train lm and build TLG
    ./local/tlg.sh --corpus aishell --lmtype srilm
 fi