Merge pull request #1000 from alibaba-damo-academy/dev_lhn
update github io
| | |
|---|
| | | 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: |
|---|
| | |
|---|
| | | |
|---|
| | | ### 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. |
|---|
