#!/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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ori_data=
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data_dir=
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exp_dir=
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model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
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model_revision="v1.0.4" # please do not modify the model revision
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inference_nj=32
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gpuid_list="0" # set gpus, e.g., gpuid_list="0,1"
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ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
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njob=1 # the number of jobs for each gpu
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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if ${gpu_inference}; then
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inference_nj=$[${ngpu}*${njob}]
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else
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inference_nj=$njob
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fi
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# LM configs
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use_lm=false
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beam_size=1
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lm_weight=0.0
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test_sets="dev test"
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. utils/parse_options.sh
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aishell_audio_dir=$ori_data/data_aishell/wav
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aishell_text=$ori_data/data_aishell/transcript/aishell_transcript_v0.8.txt
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dev_dir=${data_dir}/aishell/dev
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test_dir=${data_dir}/aishell/test
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tmp_dir=${data_dir}/aishell/tmp
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mkdir -p ${dev_dir}
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mkdir -p ${test_dir}
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mkdir -p ${tmp_dir}
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find $aishell_audio_dir -iname "*.wav" > $tmp_dir/wav.flist
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grep -i "wav/dev" $tmp_dir/wav.flist > $dev_dir/wav.flist || exit 1;
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grep -i "wav/test" $tmp_dir/wav.flist > $test_dir/wav.flist || exit 1;
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rm -r $tmp_dir
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for dir in $dev_dir $test_dir; do
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sed -e 's/\.wav//' $dir/wav.flist | awk -F '/' '{print $NF}' > $dir/utt.list
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paste -d' ' $dir/utt.list $dir/wav.flist > $dir/wav.scp_all
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utils/filter_scp.pl -f 1 $dir/utt.list $aishell_text > $dir/transcripts.txt
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awk '{print $1}' $dir/transcripts.txt > $dir/utt.list
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utils/filter_scp.pl -f 1 $dir/utt.list $dir/wav.scp_all | sort -u > $dir/wav.scp
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sort -u $dir/transcripts.txt > $dir/text
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done
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mkdir -p ${exp_dir}/aishell
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modelscope_utils/modelscope_infer.sh \
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--data_dir ${data_dir}/aishell \
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--exp_dir ${exp_dir}/aishell \
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--test_sets "${test_sets}" \
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--model_name ${model_name} \
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--model_revision ${model_revision} \
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--inference_nj ${inference_nj} \
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--gpuid_list ${gpuid_list} \
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--njob ${njob} \
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--gpu_inference ${gpu_inference} \
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--use_lm ${use_lm} \
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--beam_size ${beam_size} \
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--lm_weight ${lm_weight}
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