#!/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
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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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set=L
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train_set=train_l
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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/data.sh --set ${set} --nj $nj --data_dir $feats_dir --WENETSPEECH $raw_data --train_cmd $train_cmd
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mkdir $feats_dir/data
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mv $feats_dir/$train_set $feats_dir/data/$train_set
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for x in $test_sets; do
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mv $feats_dir/$x $feats_dir/data/
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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 0.1
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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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