update

嘉渊

2023-04-24 df5f263e5fe3d7961b1aeb3589012400a9905a8f

 egs/aishell/paraformer/run.sh

@@ -3,8 +3,8 @@
. ./path.sh || exit 1;

# machines configuration
CUDA_VISIBLE_DEVICES="0,1"
gpu_num=2
CUDA_VISIBLE_DEVICES="0"
gpu_num=1
count=1
gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decoding
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
@@ -13,15 +13,15 @@
infer_cmd=utils/run.pl

# general configuration
feats_dir="../DATA" #feature output dictionary
feats_dir="/nfs/wangjiaming.wjm/Funasr_data/aishell-1-fix-cmvn" #feature output dictionary
exp_dir="."
lang=zh
dumpdir=dump/fbank
feats_type=fbank
token_type=char
scp=feats.scp
type=kaldi_ark
stage=0
scp=wav.scp
type=sound
stage=3
stop_stage=4

# feature configuration
@@ -52,7 +52,7 @@
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"

inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb

# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES  # set gpus for decoding, the same as training stage by default
@@ -161,7 +161,7 @@
            rank=$i
            local_rank=$i
            gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
            asr_train_paraformer.py \
            train.py \
                --gpu_id $gpu_id \
                --use_preprocessor true \
                --token_type char \