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68 lines
1.5 KiB
68 lines
1.5 KiB
#!/bin/bash
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if [ $# != 4 ];then
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echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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config_path=$1
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decode_config_path=$2
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ckpt_prefix=$3
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audio_file=$4
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mkdir -p data
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wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/
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if [ $? -ne 0 ]; then
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exit 1
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fi
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if [ ! -f ${audio_file} ]; then
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echo "Plase input the right audio_file path"
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exit 1
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fi
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# bpemode (unigram or bpe)
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nbpe=5000
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bpemode=unigram
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bpeprefix="data/bpe_${bpemode}_${nbpe}"
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bpemodel=${bpeprefix}.model
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chunk_mode=false
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if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
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chunk_mode=true
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fi
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# download language model
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#bash local/download_lm_ch.sh
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#if [ $? -ne 0 ]; then
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# exit 1
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#fi
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for type in attention_rescoring; do
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echo "decoding ${type}"
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batch_size=1
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output_dir=${ckpt_prefix}
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mkdir -p ${output_dir}
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python3 -u ${BIN_DIR}/test_wav.py \
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--ngpu ${ngpu} \
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--config ${config_path} \
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--decode_cfg ${decode_config_path} \
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--result_file ${output_dir}/${type}.rsl \
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--checkpoint_path ${ckpt_prefix} \
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--opts decode.decoding_method ${type} \
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--opts decode.decode_batch_size ${batch_size} \
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--audio_file ${audio_file}
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#score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel}.model --wer true ${expdir}/${decode_dir} ${dict}
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if [ $? -ne 0 ]; then
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echo "Failed in evaluation!"
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exit 1
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fi
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done
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exit 0
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