| | |
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| | | infer_cmd=utils/run.pl |
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| | | |
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| | | # general configuration |
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| | | feats_dir="../DATA" #feature output dictionary, for large data |
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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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| | | dumpdir=dump/fbank |
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| | |
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| | | model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}" |
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| | | |
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| | | inference_config=conf/decode_asr_transformer_noctc_1best.yaml |
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| | | inference_asr_model=valid.acc.ave_10best.pth |
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| | | inference_asr_model=valid.acc.ave_10best.pb |
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| | | |
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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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| | |
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| | | fi |
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| | | ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \ |
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| | | python -m funasr.bin.asr_inference_launch \ |
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| | | --batch_size 100 \ |
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| | | --batch_size 1 \ |
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| | | --ngpu "${_ngpu}" \ |
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| | | --njob ${njob} \ |
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| | | --gpuid_list ${gpuid_list} \ |
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| | |
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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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| | | fi |
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