#!/usr/bin/env bash
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. ./path.sh || exit 1;
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# machines configuration
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CUDA_VISIBLE_DEVICES="0,1"
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gpu_num=2
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
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njob=1
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# general configuration
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feats_dir="../DATA" #feature output dictionary
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exp_dir="."
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lang=zh
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token_type=char
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stage=0
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stop_stage=5
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# feature configuration
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nj=64
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# data
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raw_data=../raw_data
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data_url=www.openslr.org/resources/33
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# exp tag
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tag="exp1"
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. utils/parse_options.sh || exit 1;
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# Set bash to 'debug' mode, it will exit on :
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# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
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set -e
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set -u
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set -o pipefail
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train_set=train
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valid_set=dev
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test_sets="dev test"
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asr_config=conf/train_asr_paraformer_conformer_12e_6d_2048_256.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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#inference_config=conf/decode_asr_transformer_noctc_1best.yaml
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#inference_asr_model=valid.acc.ave_10best.pb
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## you can set gpu num for decoding here
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#gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
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#ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
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#
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#if ${gpu_inference}; then
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# inference_nj=$[${ngpu}*${njob}]
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# _ngpu=1
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#else
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# inference_nj=$njob
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# _ngpu=0
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#fi
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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echo "stage -1: Data Download"
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local/download_and_untar.sh ${raw_data} ${data_url} data_aishell
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local/download_and_untar.sh ${raw_data} ${data_url} resource_aishell
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "stage 0: Data preparation"
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# Data preparation
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local/aishell_data_prep.sh ${raw_data}/data_aishell/wav ${raw_data}/data_aishell/transcript ${feats_dir}
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for x in train dev test; do
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cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
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paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | tr -d " ") \
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> ${feats_dir}/data/${x}/text
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utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
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mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
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python funasr/datasets/audio_datasets/scp2jsonl.py \
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++scp_file_list='["${feats_dir}/data/${x}/wav.scp", "${feats_dir}/data/${x}/text"]' \
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++data_type_list='["source", "target"]' \
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++jsonl_file_out=${feats_dir}/data/${x}/audio_datasets.jsonl
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done
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Feature and CMVN Generation"
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# utils/compute_cmvn.sh --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$asr_config" --scale 1.0
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python funasr/bin/compute_audio_cmvn.py \
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--config-path "/Users/zhifu/funasr1.0/examples/aishell/conf" \
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--config-name "train_asr_paraformer_conformer_12e_6d_2048_256.yaml" \
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++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
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++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \
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++dataset_conf.num_workers=$nj
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fi
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token_list=${feats_dir}/data/${lang}_token_list/$token_type/tokens.txt
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echo "dictionary: ${token_list}"
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: Dictionary Preparation"
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mkdir -p ${feats_dir}/data/${lang}_token_list/$token_type/
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echo "make a dictionary"
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echo "<blank>" > ${token_list}
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echo "<s>" >> ${token_list}
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echo "</s>" >> ${token_list}
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utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/$train_set/text | cut -f 2- -d" " | tr " " "\n" \
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| sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
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echo "<unk>" >> ${token_list}
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fi
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# LM Training Stage
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: LM Training"
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fi
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# ASR Training Stage
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "stage 4: ASR Training"
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torchrun \
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--nnodes 1 \
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--nproc_per_node ${gpu_num} \
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funasr/bin/train.py \
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--config-path "/Users/zhifu/funasr1.0/examples/aishell/conf" \
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--config-name "train_asr_paraformer_conformer_12e_6d_2048_256.yaml" \
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++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
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++cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
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++token_list="${token_list}" \
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++output_dir="${exp_dir}/exp/${model_dir}"
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fi
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#
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## Testing Stage
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#if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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# echo "stage 5: Inference"
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# for dset in ${test_sets}; do
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# asr_exp=${exp_dir}/exp/${model_dir}
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# inference_tag="$(basename "${inference_config}" .yaml)"
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# _dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
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# _logdir="${_dir}/logdir"
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# if [ -d ${_dir} ]; then
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# echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
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# exit 0
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# fi
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# mkdir -p "${_logdir}"
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# _data="${feats_dir}/data/${dset}"
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# key_file=${_data}/${scp}
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# num_scp_file="$(<${key_file} wc -l)"
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# _nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
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# split_scps=
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# for n in $(seq "${_nj}"); do
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# split_scps+=" ${_logdir}/keys.${n}.scp"
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# done
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# # shellcheck disable=SC2086
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# utils/split_scp.pl "${key_file}" ${split_scps}
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# _opts=
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# if [ -n "${inference_config}" ]; then
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# _opts+="--config ${inference_config} "
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# fi
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# ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
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# python -m funasr.bin.asr_inference_launch \
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# --batch_size 1 \
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# --ngpu "${_ngpu}" \
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# --njob ${njob} \
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# --gpuid_list ${gpuid_list} \
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# --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
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# --cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.mvn \
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# --key_file "${_logdir}"/keys.JOB.scp \
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# --asr_train_config "${asr_exp}"/config.yaml \
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# --asr_model_file "${asr_exp}"/"${inference_asr_model}" \
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# --output_dir "${_logdir}"/output.JOB \
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# --mode paraformer \
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# ${_opts}
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#
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# for f in token token_int score text; do
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# if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
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# for i in $(seq "${_nj}"); do
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# cat "${_logdir}/output.${i}/1best_recog/${f}"
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# done | sort -k1 >"${_dir}/${f}"
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# fi
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# done
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# python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
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# python utils/proce_text.py ${_data}/text ${_data}/text.proc
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# python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
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# tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
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# cat ${_dir}/text.cer.txt
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# done
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#fi
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#
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## Prepare files for ModelScope fine-tuning and inference
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#if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
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# echo "stage 6: ModelScope Preparation"
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# cp ${feats_dir}/data/${train_set}/cmvn/am.mvn ${exp_dir}/exp/${model_dir}/am.mvn
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# vocab_size=$(cat ${token_list} | wc -l)
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# python utils/gen_modelscope_configuration.py \
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# --am_model_name $inference_asr_model \
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# --mode paraformer \
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# --model_name paraformer \
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# --dataset aishell \
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# --output_dir $exp_dir/exp/$model_dir \
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# --vocab_size $vocab_size \
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# --nat _nat \
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# --tag $tag
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#fi
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