#!/usr/bin/env bash
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set -e
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set -u
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set -o pipefail
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model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope
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model_revision="v1.0.4" # please do not modify the model revision
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data_dir= # wav list, ${data_dir}/wav.scp
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exp_dir="exp"
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gpuid_list="0"
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ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
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njob=1
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gpu_inference=true
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decode_cmd=utils/run.pl
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. utils/parse_options.sh
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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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# LM configs
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use_lm=false
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beam_size=1
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lm_weight=0.0
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python modelscope_utils/download_model.py \
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--model_name ${model_name} --model_revision ${model_revision}
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if [ -d ${exp_dir} ]; then
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echo "${exp_dir} is already exists. if you want to decode again, please delete ${exp_dir} first."
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exit 1
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else
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mkdir -p ${exp_dir}/${model_name}
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cp ${HOME}/.cache/modelscope/hub/damo/${model_name}/* ${exp_dir}/${model_name}/. -r
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_dir=${exp_dir}/decode_asr
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_logdir=${_dir}/logdir
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mkdir -p "${_dir}"
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mkdir -p "${_logdir}"
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fi
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for n in $(seq "${inference_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 "${data_dir}/wav.scp" ${split_scps}
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if "${use_lm}"; then
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cp ${exp_dir}/${model_name}/decoding.yaml ${exp_dir}/${model_name}/decoding.yaml.back
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sed -i "s#beam_size: [0-9]*#beam_size: `echo $beam_size`#g" ${exp_dir}/${model_name}/decoding.yaml
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sed -i "s#lm_weight: 0.[0-9]*#lm_weight: `echo $lm_weight`#g" ${exp_dir}/${model_name}/decoding.yaml
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fi
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echo "Decoding started... log: '${_logdir}/asr_inference.*.log'"
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# shellcheck disable=SC2086
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${decode_cmd} --max-jobs-run "${inference_nj}" JOB=1:"${inference_nj}" "${_logdir}"/asr_inference.JOB.log \
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python -m funasr.bin.modelscope_infer \
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--local_model_path ${exp_dir}/${model_name} \
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--wav_list ${_logdir}/keys.JOB.scp \
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--output_file ${_logdir}/text.JOB \
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--gpuid_list ${gpuid_list} \
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--njob ${njob} \
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--ngpu ${_ngpu} \
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for i in $(seq ${inference_nj}); do
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cat ${_logdir}/text.${i}
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done | sort -k1 >${_dir}/text
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if "${use_lm}"; then
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mv ${exp_dir}/${model_name}/decoding.yaml.back ${exp_dir}/${model_name}/decoding.yaml
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fi
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