test

语帆

2024-02-28 2d71d8f679894ab49374b10784547db001bba7be

 examples/industrial_data_pretraining/lcbnet/demo_nj.sh

@@ -1,13 +1,71 @@
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"
inference_device="cuda"

#CUDA_VISIBLE_DEVICES="" \
python -m funasr.bin.inference \
--config-path=${file_dir} \
--config-name="config.yaml" \
++init_param=${file_dir}/model.pb \
++tokenizer_conf.token_list=${file_dir}/tokens.txt \
++input=[${file_dir}/wav.scp,${file_dir}/ocr.txt] \
+data_type='["kaldi_ark", "text"]' \
++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
++output_dir="./outputs/debug" \
++device="cpu" \
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 ${inference_dir}/1best_recog
for f in token score text; do
    if [ -f "${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
python utils/postprocess_text_zh.py  ${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