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PaddleSpeech/examples/librispeech/s0/run.sh

40 lines
1.1 KiB

#!/bin/bash
set -e
source path.sh
stage=0
stop_stage=100
conf_path=conf/deepspeech2.yaml
avg_num=30
model_type=offline
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=0,1,2,3,4,5,6,7 ./local/train.sh ${conf_path} ${ckpt} ${model_type}
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=7 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
fi