mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
75 lines
1.7 KiB
Bash
75 lines
1.7 KiB
Bash
#!/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="0,1,2,3,4,5,6,7"
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gpu_num=8
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count=1
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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=5
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train_cmd=utils/run.pl
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infer_cmd=utils/run.pl
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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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type=sound
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scp=wav.scp
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speed_perturb="0.9 1.0 1.1"
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stage=0
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stop_stage=5
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# feature configuration
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feats_dim=80
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nj=64
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# data
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raw_data=/nfs/zhifu.gzf/wenetspeech_proc/audio_seg
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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_net test_meeting"
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asr_config=conf/train_asr_conformer.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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inference_config=conf/decode_asr_transformer_5beam.yaml
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inference_asr_model=valid.acc.ave_10best.pb
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# you can set gpu num for decoding here
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gpuid_list=$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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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 "For downloading data, please refer to https://github.com/wenet-e2e/WenetSpeech."
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exit 0;
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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/wenetspeech_data_prep.sh $raw_data $feats_dir
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fi |