mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
atsr
This commit is contained in:
语帆
parent
52cec21672
commit
1d7ba1be1a
1
.gitignore
vendored
1
.gitignore
vendored
@ -25,4 +25,5 @@ outputs*
|
||||
emotion2vec*
|
||||
GPT-SoVITS*
|
||||
examples/*/*/outputs
|
||||
examples/*/*/exp
|
||||
cmd_read
|
||||
|
||||
71
examples/industrial_data_pretraining/lcbnet/demo2.sh
Executable file
71
examples/industrial_data_pretraining/lcbnet/demo2.sh
Executable file
@ -0,0 +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="0,1,2,3,4,5,6,7"
|
||||
inference_device="cuda"
|
||||
test_set="dev_wav"
|
||||
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/slidespeech_dev_beamsearch_wav"
|
||||
_logdir="${inference_dir}/logdir"
|
||||
echo "inference_dir: ${inference_dir}"
|
||||
|
||||
mkdir -p "${_logdir}"
|
||||
key_file1=${file_dir}/${test_set}/wav.scp
|
||||
key_file2=${file_dir}/${test_set}/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='["sound", "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 JOB in $(seq "${nj}"); do
|
||||
cat "${inference_dir}/${JOB}/1best_recog/token" >> "${inference_dir}/1best_recog/token"
|
||||
done
|
||||
|
||||
echo "Computing WER ..."
|
||||
sed -e 's/ /\t/' -e 's/ //g' -e 's/▁/ /g' -e 's/\t /\t/' ${inference_dir}/1best_recog/token > ${inference_dir}/1best_recog/token.proc
|
||||
cp ${file_dir}/${test_set}/text ${inference_dir}/1best_recog/token.ref
|
||||
cp ${file_dir}/${test_set}/ocr.list ${inference_dir}/1best_recog/ocr.list
|
||||
python utils/compute_wer.py ${inference_dir}/1best_recog/token.ref ${inference_dir}/1best_recog/token.proc ${inference_dir}/1best_recog/token.cer
|
||||
tail -n 3 ${inference_dir}/1best_recog/token.cer
|
||||
|
||||
./run_bwer_recall.sh ${inference_dir}/1best_recog/
|
||||
tail -n 6 ${inference_dir}/1best_recog/BWER-UWER.results |head -n 5
|
||||
71
examples/industrial_data_pretraining/lcbnet/demo2_tmp.sh
Executable file
71
examples/industrial_data_pretraining/lcbnet/demo2_tmp.sh
Executable file
@ -0,0 +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="0,1,2,3,4,5,6,7"
|
||||
inference_device="cuda"
|
||||
test_set="test_wav"
|
||||
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/slidespeech_test_beamsearch_wav"
|
||||
_logdir="${inference_dir}/logdir"
|
||||
echo "inference_dir: ${inference_dir}"
|
||||
|
||||
mkdir -p "${_logdir}"
|
||||
key_file1=${file_dir}/${test_set}/wav.scp
|
||||
key_file2=${file_dir}/${test_set}/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='["sound", "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 JOB in $(seq "${nj}"); do
|
||||
cat "${inference_dir}/${JOB}/1best_recog/token" >> "${inference_dir}/1best_recog/token"
|
||||
done
|
||||
|
||||
echo "Computing WER ..."
|
||||
sed -e 's/ /\t/' -e 's/ //g' -e 's/▁/ /g' -e 's/\t /\t/' ${inference_dir}/1best_recog/token > ${inference_dir}/1best_recog/token.proc
|
||||
cp ${file_dir}/${test_set}/text ${inference_dir}/1best_recog/token.ref
|
||||
cp ${file_dir}/${test_set}/ocr.list ${inference_dir}/1best_recog/ocr.list
|
||||
python utils/compute_wer.py ${inference_dir}/1best_recog/token.ref ${inference_dir}/1best_recog/token.proc ${inference_dir}/1best_recog/token.cer
|
||||
tail -n 3 ${inference_dir}/1best_recog/token.cer
|
||||
|
||||
./run_bwer_recall.sh ${inference_dir}/1best_recog/
|
||||
tail -n 6 ${inference_dir}/1best_recog/BWER-UWER.results |head -n 5
|
||||
@ -6,8 +6,13 @@ python -m funasr.bin.inference \
|
||||
--config-name="config.yaml" \
|
||||
++init_param=${file_dir}/model.pb \
|
||||
++tokenizer_conf.token_list=${file_dir}/tokens.txt \
|
||||
++input=[${file_dir}/dev/wav.scp,${file_dir}/dev/ocr.txt] \
|
||||
+data_type='["kaldi_ark", "text"]' \
|
||||
+input=["${file_dir}/example/asr_example.wav","${file_dir}/example/ocr.txt"] \
|
||||
+data_type='["sound","text"]' \
|
||||
++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
|
||||
++output_dir="./outputs/debug" \
|
||||
++device="cpu" \
|
||||
|
||||
#++input=["/nfs/yufan.yf/workspace/espnet/egs2/youtube_ppt/asr/dump/raw/dev_oracle_v1_new/data/format.1/YTB+--tMoLpQI-w+00322.wav"] \
|
||||
#+data_type='["sound"]' \
|
||||
#++input=["/nfs/yufan.yf/workspace/espnet/egs2/youtube_ppt/asr/dump/raw/dev_oracle_v1_new/data/format.1/YTB+--tMoLpQI-w+00322.wav","/nfs/yufan.yf/workspace/github/FunASR/examples/industrial_data_pretraining/lcbnet/exp/speech_lcbnet_contextual_asr-en-16k-bpe-vocab5002-pytorch/example/ocr2.txt"] \
|
||||
#+data_type='["sound","text"]' \
|
||||
|
||||
15
examples/industrial_data_pretraining/lcbnet/demo_pdb2.sh
Executable file
15
examples/industrial_data_pretraining/lcbnet/demo_pdb2.sh
Executable file
@ -0,0 +1,15 @@
|
||||
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="" \
|
||||
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}/dev_wav/wav.scp,${file_dir}/dev_wav/ocr.txt] \
|
||||
+data_type='["sound", "text"]' \
|
||||
++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
|
||||
++output_dir="./outputs/debug" \
|
||||
++device="cpu" \
|
||||
|
||||
#++input=[${file_dir}/dev_wav/wav.scp,${file_dir}/dev_wav/ocr.txt] \
|
||||
71
examples/industrial_data_pretraining/lcbnet/demo_tmp1.sh
Executable file
71
examples/industrial_data_pretraining/lcbnet/demo_tmp1.sh
Executable file
@ -0,0 +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="0,1,2,3,4,5,6,7"
|
||||
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/slidespeech_test_beamsearch_new"
|
||||
_logdir="${inference_dir}/logdir"
|
||||
echo "inference_dir: ${inference_dir}"
|
||||
|
||||
mkdir -p "${_logdir}"
|
||||
key_file1=${file_dir}/test/wav.scp
|
||||
key_file2=${file_dir}/test/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 JOB in $(seq "${nj}"); do
|
||||
cat "${inference_dir}/${JOB}/1best_recog/token" >> "${inference_dir}/1best_recog/token"
|
||||
done
|
||||
|
||||
echo "Computing WER ..."
|
||||
sed -e 's/ /\t/' -e 's/ //g' -e 's/▁/ /g' -e 's/\t /\t/' ${inference_dir}/1best_recog/token > ${inference_dir}/1best_recog/token.proc
|
||||
cp ${file_dir}/test/text ${inference_dir}/1best_recog/token.ref
|
||||
cp ${file_dir}/test/ocr.list ${inference_dir}/1best_recog/ocr.list
|
||||
python utils/compute_wer.py ${inference_dir}/1best_recog/token.ref ${inference_dir}/1best_recog/token.proc ${inference_dir}/1best_recog/token.cer
|
||||
tail -n 3 ${inference_dir}/1best_recog/token.cer
|
||||
|
||||
./run_bwer_recall.sh ${inference_dir}/1best_recog/
|
||||
tail -n 6 ${inference_dir}/1best_recog/BWER-UWER.results |head -n 5
|
||||
Loading…
Reference in New Issue
Block a user