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 |   13 ++++++-------
 1 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/docs/get_started.md b/docs/get_started.md
index c62a610..d77ee93 100644
--- a/docs/get_started.md
+++ b/docs/get_started.md
@@ -56,7 +56,7 @@
 These two files have two columns. The first column is wav ids and the second column is the corresponding speech feature shape and text length.
 
 ## Stage 2: Dictionary Preparation
-This stage prepares a dictionary, which is used as a mapping between label characters and integer indices during ASR training. The output dictionary file is saved as `$feats_dir/data/$lang_toekn_list/$token_type/tokens.txt`. Here we show an example of `tokens.txt` as follows:
+This stage processes the dictionary, which is used as a mapping between label characters and integer indices during ASR training. The processed dictionary file is saved as `$feats_dir/data/$lang_toekn_list/$token_type/tokens.txt`. An example of `tokens.txt` is as follows:
 * `tokens.txt`
 ```
 <blank>
@@ -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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