diff --git a/docs/academic_recipe/asr_recipe.md b/docs/academic_recipe/asr_recipe.md
index 5a11dc5ee..9e19c61ef 100644
--- a/docs/academic_recipe/asr_recipe.md
+++ b/docs/academic_recipe/asr_recipe.md
@@ -12,7 +12,7 @@ cd egs/aishell/paraformer
 Then you can directly start the recipe as follows:
 ```sh
 conda activate funasr
-. ./run.sh --CUDA_VISIBLE_DEVICES="0,1" --gpu_num=2
+bash run.sh --CUDA_VISIBLE_DEVICES "0,1" --gpu_num 2
 ```
 
 The training log files are saved in `${exp_dir}/exp/${model_dir}/log/train.log.*`， which can be viewed using the following command:
@@ -264,4 +264,4 @@ Users can use ModelScope for inference and fine-tuning based on a trained academ
 
 ### Decoding by CPU or GPU
 
-We support CPU and GPU decoding. For CPU decoding, set `gpu_inference=false` and `njob` to specific the total number of CPU jobs. For GPU decoding, first set `gpu_inference=true`. Then set `gpuid_list` to specific which GPUs for decoding and `njob` to specific the number of decoding jobs on each GPU.
\ No newline at end of file
+We support CPU and GPU decoding. For CPU decoding, set `gpu_inference=false` and `njob` to specific the total number of CPU jobs. For GPU decoding, first set `gpu_inference=true`. Then set `gpuid_list` to specific which GPUs for decoding and `njob` to specific the number of decoding jobs on each GPU.