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update modelscope details

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Lizerui9926 2022-12-02 22:43:13 +08:00
parent 419533aa3a
commit 2326aefc39
10 changed files with 41 additions and 31 deletions
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9
egs_modelscope/aishell/paraformer/paraformer_large_finetune.sh
View File

@ -11,7 +11,7 @@ njob=4 # the number of jobs for each gpu
train_cmd=utils/run.pl
# general configuration
feats_dir="." #feature output dictionary, for large data
feats_dir="../DATA" #feature output dictionary, for large data
exp_dir="."
lang=zh
dumpdir=dump/fbank
@ -32,6 +32,7 @@ lfr_m=7
lfr_n=6
init_model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope during fine-tuning
model_revision="v1.0.3" # please do not modify the model revision
cmvn_file=init_model/${init_model_name}/am.mvn
seg_file=init_model/${init_model_name}/seg_dict
vocab=init_model/${init_model_name}/tokens.txt
@ -53,7 +54,7 @@ valid_set=dev
test_sets="dev test"
asr_config=conf/train_asr_paraformer_sanm_50e_16d_2048_512_lfr6.yaml
init_param="init_model/${init_model_name}/${init_model_name}"
init_param="init_model/${init_model_name}/model.pb"
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
@ -61,7 +62,7 @@ inference_asr_model=valid.acc.ave_10best.pth
. utils/parse_options.sh || exit 1;
# download model from modelscope
python modelscope_utils/download_model.py --model_name ${init_model_name}
python modelscope_utils/download_model.py --model_name ${init_model_name} --model_revision ${model_revision}
if [ ! -d ${HOME}/.cache/modelscope/hub/damo/${init_model_name} ]; then
echo "${HOME}/.cache/modelscope/hub/damo/${init_model_name} must exist"
@ -152,7 +153,7 @@ fi
world_size=$gpu_num # run on one machine
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# update asr train config.yaml
python modelscope_utils/update_config.py --modelscope_config init_model/${init_model_name}/asr_train_config.yaml --finetune_config ${asr_config} --output_config init_model/${init_model_name}/asr_finetune_config.yaml
python modelscope_utils/update_config.py --modelscope_config init_model/${init_model_name}/finetune.yaml --finetune_config ${asr_config} --output_config init_model/${init_model_name}/asr_finetune_config.yaml
finetune_config=init_model/${init_model_name}/asr_finetune_config.yaml
mkdir -p ${exp_dir}/exp/${model_dir}

2
egs_modelscope/aishell/paraformer/paraformer_large_infer.sh
View File

@ -8,6 +8,7 @@ ori_data=
data_dir=
exp_dir=
model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
model_revision="v1.0.3" # please do not modify the model revision
inference_nj=32
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
@ -61,6 +62,7 @@ modelscope_utils/modelscope_infer.sh \
--exp_dir ${exp_dir}/aishell \
--test_sets "${test_sets}" \
--model_name ${model_name} \
--model_revision ${model_revision} \
--inference_nj ${inference_nj} \
--gpuid_list ${gpuid_list} \
--njob ${njob} \

9
egs_modelscope/aishell2/paraformer/paraformer_large_finetune.sh
View File

@ -11,7 +11,7 @@ njob=4 # the number of jobs for each gpu
train_cmd=utils/run.pl
# general configuration
feats_dir="." #feature output dictionary, for large data
feats_dir="../DATA" #feature output dictionary, for large data
exp_dir="."
lang=zh
dumpdir=dump/fbank
@ -32,6 +32,7 @@ lfr_m=7
lfr_n=6
init_model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope during fine-tuning
model_revision="v1.0.3" # please do not modify the model revision
cmvn_file=init_model/${init_model_name}/am.mvn
seg_file=init_model/${init_model_name}/seg_dict
vocab=init_model/${init_model_name}/tokens.txt
@ -54,7 +55,7 @@ valid_set=dev_ios
test_sets="dev_ios test_android test_ios test_mic"
asr_config=conf/train_asr_paraformer_sanm_50e_16d_2048_512_lfr6.yaml
init_param="init_model/${init_model_name}/${init_model_name}"
init_param="init_model/${init_model_name}/model.pb"
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
@ -62,7 +63,7 @@ inference_asr_model=valid.acc.ave_10best.pth
. utils/parse_options.sh || exit 1;
# download model from modelscope
python modelscope_utils/download_model.py --model_name ${init_model_name}
python modelscope_utils/download_model.py --model_name ${init_model_name} --model_revision ${model_revision}
if [ ! -d ${HOME}/.cache/modelscope/hub/damo/${init_model_name} ]; then
echo "${HOME}/.cache/modelscope/hub/damo/${init_model_name} must exist"
@ -167,7 +168,7 @@ fi
world_size=$gpu_num # run on one machine
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# update asr train config.yaml
python modelscope_utils/update_config.py --modelscope_config init_model/${init_model_name}/asr_train_config.yaml --finetune_config ${asr_config} --output_config init_model/${init_model_name}/asr_finetune_config.yaml
python modelscope_utils/update_config.py --modelscope_config init_model/${init_model_name}/finetune.yaml --finetune_config ${asr_config} --output_config init_model/${init_model_name}/asr_finetune_config.yaml
finetune_config=init_model/${init_model_name}/asr_finetune_config.yaml
mkdir -p ${exp_dir}/exp/${model_dir}

3
egs_modelscope/aishell2/paraformer/paraformer_large_infer.sh
View File

@ -8,6 +8,7 @@ ori_data=
data_dir=
exp_dir=
model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
model_revision="v1.0.3" # please do not modify the model revision
inference_nj=32
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
@ -20,6 +21,7 @@ else
inference_nj=$njob
fi
# LM configs
use_lm=false
beam_size=1
lm_weight=0.0
@ -47,6 +49,7 @@ modelscope_utils/modelscope_infer.sh \
--exp_dir ${exp_dir}/aishell2 \
--test_sets "${test_sets}" \
--model_name ${model_name} \
--model_revision ${model_revision} \
--inference_nj ${inference_nj} \
--gpuid_list ${gpuid_list} \
--njob ${njob} \

9
egs_modelscope/common/modelscope_common_finetune.sh
View File

@ -11,7 +11,7 @@ njob=4 # the number of jobs for each gpu
train_cmd=utils/run.pl
# general configuration
feats_dir="." #feature output dictionary, for large data
feats_dir="../DATA" #feature output dictionary, for large data
exp_dir="."
lang=zh
dumpdir=dump/fbank
@ -32,6 +32,7 @@ lfr_m=7
lfr_n=6
init_model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope during fine-tuning
model_revision="v1.0.3" # please do not modify the model revision
cmvn_file=init_model/${init_model_name}/am.mvn
seg_file=init_model/${init_model_name}/seg_dict
vocab=init_model/${init_model_name}/tokens.txt
@ -53,7 +54,7 @@ valid_set=dev
test_sets="dev test"
asr_config=conf/train_asr_paraformer_sanm_50e_16d_2048_512_lfr6.yaml
init_param="init_model/${init_model_name}/${init_model_name}"
init_param="init_model/${init_model_name}/model.pb"
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
@ -61,7 +62,7 @@ inference_asr_model=valid.acc.ave_10best.pth
. utils/parse_options.sh || exit 1;
# download model from modelscope
python modelscope_utils/download_model.py --model_name ${init_model_name}
python modelscope_utils/download_model.py --model_name ${init_model_name} --model_revision ${model_revision}
if [ ! -d ${HOME}/.cache/modelscope/hub/damo/${init_model_name} ]; then
echo "${HOME}/.cache/modelscope/hub/damo/${init_model_name} must exist"
@ -158,7 +159,7 @@ fi
world_size=$gpu_num # run on one machine
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# update asr train config.yaml
python modelscope_utils/update_config.py --modelscope_config init_model/${init_model_name}/asr_train_config.yaml --finetune_config ${asr_config} --output_config init_model/${init_model_name}/asr_finetune_config.yaml
python modelscope_utils/update_config.py --modelscope_config init_model/${init_model_name}/finetune.yaml --finetune_config ${asr_config} --output_config init_model/${init_model_name}/asr_finetune_config.yaml
finetune_config=init_model/${init_model_name}/asr_finetune_config.yaml
mkdir -p ${exp_dir}/exp/${model_dir}

15
egs_modelscope/common/modelscope_common_infer.sh
View File

@ -5,6 +5,7 @@ set -u
set -o pipefail
model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope
model_revision="v1.0.3" # please do not modify the model revision
data_dir= # wav list, ${data_dir}/wav.scp
exp_dir="exp"
gpuid_list="0,1"
@ -29,7 +30,7 @@ beam_size=1
lm_weight=0.0
python modelscope_utils/download_model.py \
--model_name ${model_name}
--model_name ${model_name} --model_revision ${model_revision}
if [ -d ${exp_dir} ]; then
echo "${exp_dir} is already exists. if you want to decode again, please delete ${exp_dir} first."
@ -50,12 +51,9 @@ done
utils/split_scp.pl "${data_dir}/wav.scp" ${split_scps}
if "${use_lm}"; then
cp ${exp_dir}/${model_name}/decode_asr_transformer.yaml ${exp_dir}/${model_name}/decode_asr_transformer.yaml.back
cp ${exp_dir}/${model_name}/decode_asr_transformer_wav.yaml ${exp_dir}/${model_name}/decode_asr_transformer_wav.yaml.back
sed -i "s#beam_size: [0-9]*#beam_size: `echo $beam_size`#g" ${exp_dir}/${model_name}/decode_asr_transformer.yaml
sed -i "s#beam_size: [0-9]*#beam_size: `echo $beam_size`#g" ${exp_dir}/${model_name}/decode_asr_transformer_wav.yaml
sed -i "s#lm_weight: 0.[0-9]*#lm_weight: `echo $lm_weight`#g" ${exp_dir}/${model_name}/decode_asr_transformer.yaml
sed -i "s#lm_weight: 0.[0-9]*#lm_weight: `echo $lm_weight`#g" ${exp_dir}/${model_name}/decode_asr_transformer_wav.yaml
cp ${exp_dir}/${model_name}/decoding.yaml ${exp_dir}/${model_name}/decoding.yaml.back
sed -i "s#beam_size: [0-9]*#beam_size: `echo $beam_size`#g" ${exp_dir}/${model_name}/decoding.yaml
sed -i "s#lm_weight: 0.[0-9]*#lm_weight: `echo $lm_weight`#g" ${exp_dir}/${model_name}/decoding.yaml
fi
echo "Decoding started... log: '${_logdir}/asr_inference.*.log'"
@ -73,6 +71,5 @@ ${decode_cmd} --max-jobs-run "${inference_nj}" JOB=1:"${inference_nj}" "${_logdi
cat ${_logdir}/text.${i}
done | sort -k1 >${_dir}/text
mv ${exp_dir}/${model_name}/decode_asr_transformer.yaml.back ${exp_dir}/${model_name}/decode_asr_transformer.yaml
mv ${exp_dir}/${model_name}/decode_asr_transformer_wav.yaml.back ${exp_dir}/${model_name}/decode_asr_transformer_wav.yaml
mv ${exp_dir}/${model_name}/decoding.yaml.back ${exp_dir}/${model_name}/decoding.yaml

6
egs_modelscope/common/modelscope_utils/download_model.py
View File

@ -13,9 +13,13 @@ if __name__ == '__main__':
type=str,
default="speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
help="model name in modelscope")
parser.add_argument("--model_revision",
type=str,
default="v1.0.3",
help="model revision in modelscope")
args = parser.parse_args()
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/{}'.format(args.model_name),
model_revision='v1.0.0')
model_revision=args.model_revision)

15
egs_modelscope/common/modelscope_utils/modelscope_infer.sh
View File

@ -7,6 +7,7 @@ set -o pipefail
data_dir=
exp_dir=
model_name=
model_revision=
inference_nj=32
gpuid_list="0,1,2,3"
njob=32
@ -30,7 +31,7 @@ fi
# download model from modelscope
python modelscope_utils/download_model.py \
--model_name ${model_name}
--model_name ${model_name} --model_revision ${model_revision}
modelscope_dir=${HOME}/.cache/modelscope/hub/damo/${model_name}
@ -48,12 +49,9 @@ for dset in ${test_sets}; do
fi
if "${use_lm}"; then
cp ${modelscope_dir}/decode_asr_transformer.yaml ${modelscope_dir}/decode_asr_transformer.yaml.back
cp ${modelscope_dir}/decode_asr_transformer_wav.yaml ${modelscope_dir}/decode_asr_transformer_wav.yaml.back
sed -i "s#beam_size: [0-9]*#beam_size: `echo $beam_size`#g" ${modelscope_dir}/decode_asr_transformer.yaml
sed -i "s#beam_size: [0-9]*#beam_size: `echo $beam_size`#g" ${modelscope_dir}/decode_asr_transformer_wav.yaml
sed -i "s#lm_weight: 0.[0-9]*#lm_weight: `echo $lm_weight`#g" ${modelscope_dir}/decode_asr_transformer.yaml
sed -i "s#lm_weight: 0.[0-9]*#lm_weight: `echo $lm_weight`#g" ${modelscope_dir}/decode_asr_transformer_wav.yaml
cp ${modelscope_dir}/decoding.yaml ${modelscope_dir}/decoding.yaml.back
sed -i "s#beam_size: [0-9]*#beam_size: `echo $beam_size`#g" ${modelscope_dir}/decoding.yaml
sed -i "s#lm_weight: 0.[0-9]*#lm_weight: `echo $lm_weight`#g" ${modelscope_dir}/decoding.yaml
fi
for n in $(seq "${inference_nj}"); do
@ -85,6 +83,5 @@ for dset in ${test_sets}; do
done
if "${use_lm}"; then
mv ${modelscope_dir}/decode_asr_transformer.yaml.back ${modelscope_dir}/decode_asr_transformer.yaml
mv ${modelscope_dir}/decode_asr_transformer_wav.yaml.back ${modelscope_dir}/decode_asr_transformer_wav.yaml
mv ${modelscope_dir}/decoding.yaml.back ${modelscope_dir}/decoding.yaml
fi

2
egs_modelscope/speechio/paraformer/paraformer_large_infer.sh
View File

@ -8,6 +8,7 @@ ori_data=
data_dir=
exp_dir=
model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
model_revision="v1.0.3" # please do not modify the model revision
inference_nj=32
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
@ -46,6 +47,7 @@ modelscope_utils/modelscope_infer.sh \
--exp_dir ${exp_dir}/speechio \
--test_sets "${test_sets}" \
--model_name ${model_name} \
--model_revision ${model_revision} \
--inference_nj ${inference_nj} \
--gpuid_list ${gpuid_list} \
--njob ${njob} \

2
egs_modelscope/wenetspeech/paraformer/paraformer_large_infer.sh
View File

@ -8,6 +8,7 @@ ori_data=
data_dir=
exp_dir=
model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
model_revision="v1.0.3" # please do not modify the model revision
inference_nj=32
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
@ -46,6 +47,7 @@ modelscope_utils/modelscope_infer.sh \
--exp_dir ${exp_dir}/wenetspeech \
--test_sets "${test_sets}" \
--model_name ${model_name} \
--model_revision ${model_revision} \
--inference_nj ${inference_nj} \
--gpuid_list ${gpuid_list} \
--njob ${njob} \