update repo

egs/wenetspeech/conformer/run.sh

@@ -10,4 +10,61 @@ gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decodin
 # for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
 njob=5
 train_cmd=utils/run.pl
-infer_cmd=utils/run.pl
+infer_cmd=utils/run.pl
+
+# general configuration
+feats_dir="../DATA" #feature output dictionary
+exp_dir="."
+lang=zh
+token_type=char
+type=sound
+scp=wav.scp
+speed_perturb="0.9 1.0 1.1"
+stage=0
+stop_stage=5
+
+# feature configuration
+feats_dim=80
+nj=64
+
+# data
+raw_data=../raw_data
+data_url=www.openslr.org/resources/33
+
+# exp tag
+tag="exp1"
+
+. utils/parse_options.sh || exit 1;
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+train_set=train
+valid_set=dev
+test_sets="dev test_net test_meeting"
+
+asr_config=conf/train_asr_conformer.yaml
+model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
+
+inference_config=conf/decode_asr_transformer_5beam.yaml
+inference_asr_model=valid.acc.ave_10best.pb
+
+# you can set gpu num for decoding here
+gpuid_list=$CUDA_VISIBLE_DEVICES  # set gpus for decoding, the same as training stage by default
+ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
+
+if ${gpu_inference}; then
+    inference_nj=$[${ngpu}*${njob}]
+    _ngpu=1
+else
+    inference_nj=$njob
+    _ngpu=0
+fi
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+    echo "For downloading data, please refer to https://github.com/wenet-e2e/WenetSpeech."
+    exit 0;
+fi