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Merge pull request #1000 from alibaba-damo-academy/dev_lhn

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hnluo 2023-10-11 16:17:30 +08:00 committed by GitHub
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docs/academic_recipe/asr_recipe.md
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@ -12,7 +12,7 @@ cd egs/aishell/paraformer
Then you can directly start the recipe as follows: Then you can directly start the recipe as follows:
```sh ```sh
conda activate funasr 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: 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 ### 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. 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.