mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
73 lines
4.2 KiB
Bash
73 lines
4.2 KiB
Bash
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
|