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#!/bin/bash
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set -e
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. ./path.sh || exit 1;
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. ./cmd.sh || exit 1;
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gpus=0,1,2,3
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stage=1
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stop_stage=4
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conf_path=conf/transformer_mtl_noam.yaml
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ips= #xx.xx.xx.xx,xx.xx.xx.xx
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decode_conf_path=conf/tuning/decode.yaml
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ckpt_path= # paddle.98 # (finetune from FAT-ST pretrained model)
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avg_num=5
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data_path=./TED_EnZh # path to unzipped data
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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avg_ckpt=avg_${avg_num}
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ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
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echo "checkpoint name ${ckpt}"
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# prepare data
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bash ./local/data.sh --data_dir ${data_path} || exit -1
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# train model, all `ckpt` under `exp` dir
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if [ -n "${ckpt_path}" ]; then
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echo "Finetune from Pretrained Model" ${ckpt_path}
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./local/download_pretrain.sh || exit -1
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fi
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt} "${ckpt_path}" ${ips}
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# avg n best model
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avg.sh best exp/${ckpt}/checkpoints ${avg_num}
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# test ckpt avg_n
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CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
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fi
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