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PaddleSpeech/examples/librispeech/asr1/local/test_wav.sh

68 lines
1.5 KiB

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