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嘉渊 2023-05-28 23:46:01 +08:00
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12
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 . ./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:
@ -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 ... ... 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: At the end of each epoch, the evaluation metrics are calculated on the validation set, like follows:
```text ```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 ... ... [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_*/$dset`. The main two files are `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text, like follows: The inference results are saved in `${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 ```text
... ...

2
egs/librispeech_100h/conformer/run.sh
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@ -120,7 +120,7 @@ fi
# ASR Training Stage # ASR Training Stage
world_size=$gpu_num # run on one machine world_size=$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" echo "stage 4: ASR Training"
mkdir -p ${exp_dir}/exp/${model_dir} mkdir -p ${exp_dir}/exp/${model_dir}
mkdir -p ${exp_dir}/exp/${model_dir}/log mkdir -p ${exp_dir}/exp/${model_dir}/log