Merge pull request #5 from alibaba-damo-academy/dev

zhifu gao

2022-12-02 fd5e9fa3dc8156f2712667fcbd74f44635cfde3c

Merge pull request #5 from alibaba-damo-academy/dev

update modelscope details

 egs_modelscope/aishell/paraformer/paraformer_large_finetune.sh

@@ -11,7 +11,7 @@
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_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 @@
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 @@
. 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 @@
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}

 egs_modelscope/aishell/paraformer/paraformer_large_infer.sh

@@ -8,6 +8,7 @@
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 @@
        --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} \

 egs_modelscope/aishell2/paraformer/paraformer_large_finetune.sh

@@ -11,7 +11,7 @@
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_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 @@
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 @@
. 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 @@
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}

 egs_modelscope/aishell2/paraformer/paraformer_large_infer.sh

@@ -8,6 +8,7 @@
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 $gpuid_list | awk -F "," '{print NF}')
@@ -20,6 +21,7 @@
    inference_nj=$njob
fi

# LM configs
use_lm=false
beam_size=1
lm_weight=0.0
@@ -47,6 +49,7 @@
        --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} \

 egs_modelscope/common/modelscope_common_finetune.sh

@@ -11,7 +11,7 @@
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_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 @@
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 @@
. 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 @@
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}

 egs_modelscope/common/modelscope_common_infer.sh

@@ -5,6 +5,7 @@
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 @@
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 ${exp_dir} first."
@@ -50,12 +51,9 @@
utils/split_scp.pl "${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 @@
        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


 egs_modelscope/common/modelscope_utils/download_model.py

@@ -13,9 +13,13 @@
                        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)

 egs_modelscope/common/modelscope_utils/modelscope_infer.sh

@@ -7,6 +7,7 @@
data_dir=
exp_dir=
model_name=
model_revision=
inference_nj=32
gpuid_list="0,1,2,3"
njob=32
@@ -30,7 +31,7 @@

# 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 @@
    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 @@
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

 egs_modelscope/speechio/paraformer/paraformer_large_infer.sh

@@ -8,6 +8,7 @@
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 @@
        --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} \

 egs_modelscope/wenetspeech/paraformer/paraformer_large_infer.sh

@@ -8,6 +8,7 @@
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 @@
        --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} \