From 44e55e2c603b8d3cadc6014019e4e706343f0e41 Mon Sep 17 00:00:00 2001
From: speech_asr <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 14 二月 2023 19:38:31 +0800
Subject: [PATCH] update docs
---
docs/get_started.md | 11 +++++------
1 files changed, 5 insertions(+), 6 deletions(-)
diff --git a/docs/get_started.md b/docs/get_started.md
index b8b9996..d77ee93 100644
--- a/docs/get_started.md
+++ b/docs/get_started.md
@@ -75,7 +75,7 @@
* `<unk>`: indicates the out-of-vocabulary token
## Stage 3: Training
-This stage achieves the training of the specified model. To start training, you should manually set `exp_dir`, `CUDA_VISIBLE_DEVICES` and `gpu_num`, which have already been explained above. By default, the best `$keep_nbest_models` checkpoints on validation dataset will be averaged to generate a better model and adopted for decoding.
+This stage achieves the training of the specified model. To start training, users should manually set `exp_dir`, `CUDA_VISIBLE_DEVICES` and `gpu_num`, which have already been explained above. By default, the best `$keep_nbest_models` checkpoints on validation dataset will be averaged to generate a better model and adopted for decoding.
* DDP Training
@@ -83,20 +83,19 @@
* DataLoader
-[comment]: <> (We support two types of DataLoaders for small and large datasets, respectively. By default, the small DataLoader is used and you can set `dataset_type=large` to enable large DataLoader. For small DataLoader, )
-We support an optional iterable-style DataLoader based on [Pytorch Iterable-style DataPipes](https://pytorch.org/data/beta/torchdata.datapipes.iter.html) for large dataset and you can set `dataset_type=large` to enable it.
+We support an optional iterable-style DataLoader based on [Pytorch Iterable-style DataPipes](https://pytorch.org/data/beta/torchdata.datapipes.iter.html) for large dataset and users can set `dataset_type=large` to enable it.
* Configuration
-The parameters of the training, including model, optimization, dataset, etc., are specified by a YAML file in `conf` directory. Also, you can directly specify the parameters in `run.sh` recipe. Please avoid to specify the same parameters in both the YAML file and the recipe.
+The parameters of the training, including model, optimization, dataset, etc., can be set by a YAML file in `conf` directory. Also, users can directly set the parameters in `run.sh` recipe. Please avoid to set the same parameters in both the YAML file and the recipe.
* Training Steps
-We support two parameters to specify the training steps, namely `max_epoch` and `max_update`. `max_epoch` indicates the total training epochs while `max_update` indicates the total training steps. If these two parameters are specified at the same time, once the training reaches any one of the two parameters, the training will be stopped.
+We support two parameters to specify the training steps, namely `max_epoch` and `max_update`. `max_epoch` indicates the total training epochs while `max_update` indicates the total training steps. If these two parameters are specified at the same time, once the training reaches any one of these two parameters, the training will be stopped.
* Tensorboard
-You can use tensorboard to observe the loss, learning rate, etc. Please run the following command:
+Users can use tensorboard to observe the loss, learning rate, etc. Please run the following command:
```
tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train
```
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