update repo

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
+. ./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:
@@ -26,16 +26,16 @@ Users can observe the training loss, prediction accuracy and other training info
 ... 1epoch:train:801-850batch:850num_updates: ... loss_ctc=107.890, loss_att=87.832, acc=0.029, loss_pre=1.702 ...
 ```
 
-Also, users can use tensorboard to observe these training information by the following command:
-```sh
-tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train
-```
-
 At the end of each epoch, the evaluation metrics are calculated on the validation set, like follows:
 ```text
 ... [valid] loss_ctc=99.914, cer_ctc=1.000, loss_att=80.512, acc=0.029, cer=0.971, wer=1.000, loss_pre=1.952, loss=88.285 ...
 ```
 
+Also, users can use tensorboard to observe these training information by the following command:
+```sh
+tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train
+```
+
 The inference results are saved in `${exp_dir}/exp/${model_dir}/decode_asr_*/$dset`. The main two files are `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text, like follows:
 ```text
 ...

egs/librispeech_100h/conformer/run.sh

@@ -120,7 +120,7 @@ fi
 
 # ASR Training Stage
 world_size=$gpu_num  # run on one machine
-if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4; then
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     echo "stage 4: ASR Training"
     mkdir -p ${exp_dir}/exp/${model_dir}
     mkdir -p ${exp_dir}/exp/${model_dir}/log