From e702cad2fb38d8458d57b8ee7639e35ef84f0967 Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期三, 28 二月 2024 19:36:19 +0800
Subject: [PATCH] test
---
examples/industrial_data_pretraining/lcbnet/demo_nj.sh | 72 ++++++++++++++++++++++++++++++++++++
1 files changed, 72 insertions(+), 0 deletions(-)
diff --git a/examples/industrial_data_pretraining/lcbnet/demo_nj.sh b/examples/industrial_data_pretraining/lcbnet/demo_nj.sh
new file mode 100644
index 0000000..d9f42a0
--- /dev/null
+++ b/examples/industrial_data_pretraining/lcbnet/demo_nj.sh
@@ -0,0 +1,72 @@
+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
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