test

语帆

2024-02-28 5f91acae0d8be4b3223bcb4732bad2796d654547

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file_dir="/nfs/yufan.yf/workspace/github/FunASR/examples/industrial_data_pretraining/lcbnet/exp/speech_lcbnet_contextual_asr-en-16k-bpe-vocab5002-pytorch"
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
inference_device="cuda"
 
if [ ${inference_device} == "cuda" ]; then
    nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
    else
        inference_batch_size=1
            CUDA_VISIBLE_DEVICES=""
                for JOB in $(seq ${nj}); do
                        CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
                            done
                            fi
 
                            inference_dir="outputs/test"
                            _logdir="${inference_dir}/logdir"
                            echo "inference_dir: ${inference_dir}"
 
                            # mkdir -p "${_logdir}"
                            # key_file1=${file_dir}/wav.scp
                            # key_file2=${file_dir}/ocr.txt
                            # split_scps1=
                            # split_scps2=
                            # for JOB in $(seq "${nj}"); do
                            #     split_scps1+=" ${_logdir}/wav.${JOB}.scp"
                            #     split_scps2+=" ${_logdir}/ocr.${JOB}.txt"
                            # done
                            # utils/split_scp.pl "${key_file1}" ${split_scps1}
                            # utils/split_scp.pl "${key_file2}" ${split_scps2}
 
                            # gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
                            # for JOB in $(seq ${nj}); do
                            #     {
                            #         id=$((JOB-1))
                            #         gpuid=${gpuid_list_array[$id]}
 
                            #         export CUDA_VISIBLE_DEVICES=${gpuid}
 
                            #         python -m funasr.bin.inference \
                            #         --config-path=${file_dir} \
                            #         --config-name="config.yaml" \
                            #         ++init_param=${file_dir}/model.pb \
                            #         ++tokenizer_conf.token_list=${file_dir}/tokens.txt \
                            #         ++input=[${_logdir}/wav.${JOB}.scp,${_logdir}/ocr.${JOB}.txt] \
                            #         +data_type='["kaldi_ark", "text"]' \
                            #         ++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
                            #         ++output_dir="${inference_dir}/${JOB}" \
                            #         ++device="${inference_device}" \
                            #         ++ncpu=1 \
                            #         ++disable_log=true  &> ${_logdir}/log.${JOB}.txt
 
                            #     }&
                            # done
                            # wait
 
 
                            #mkdir -p ${inference_dir}/1best_recog
                            for f in token; do
                                if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then
                                        for JOB in $(seq "${nj}"); do
                                                    cat "${inference_dir}/${JOB}/1best_recog/${f}"
                                                            done | sort -k1 >"${inference_dir}/1best_recog/${f}"
                                                                fi
                                                                done
 
                                                                echo "Computing WER ..."
                                                                echo "Computing WER ..."
                                                                #python utils/postprocess_text_zh.py ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
 
                                                                #cp  ${data_dir}/text ${inference_dir}/1best_recog/text.ref
                                                                #python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer
                                                                #tail -n 3 ${inference_dir}/1best_recog/text.cer