diff --git a/docs/tutorial/README.md b/docs/tutorial/README.md
index 74febcddf..933b611a5 100644
--- a/docs/tutorial/README.md
+++ b/docs/tutorial/README.md
@@ -211,7 +211,7 @@ Full code ref to [finetune.sh](https://github.com/alibaba-damo-academy/FunASR/bl
 ### Detailed Parameter Description:
 
 ```shell
-funasr/bin/train.py \
+funasr/bin/train_ds.py \
 ++model="${model_name_or_model_dir}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
@@ -252,7 +252,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
 gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 
 torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
 ```
 --nnodes represents the total number of participating nodes, while --nproc_per_node indicates the number of processes running on each node.
 
@@ -264,7 +264,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
 gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 
 torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
 ```
 On the worker node (assuming the IP is 192.168.1.2), you need to ensure that the MASTER_ADDR and MASTER_PORT environment variables are set to match those of the master node, and then run the same command:
 
@@ -273,7 +273,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
 gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 
 torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
 ```
 
 --nnodes indicates the total number of nodes participating in the training, --node_rank represents the ID of the current node, and --nproc_per_node specifies the number of processes running on each node (usually corresponds to the number of GPUs).
@@ -321,6 +321,28 @@ jsonl2scp \
 ++jsonl_file_in="../../../data/list/train.jsonl"
 ```
 
+
+#### Large-Scale Data Training  
+When dealing with large datasets (e.g., 50,000 hours or more), memory issues may arise, especially in multi-GPU experiments. To address this, split the JSONL files into slices, write them into a TXT file (one slice per line), and set `data_split_num`. For example:  
+```shell  
+train_data="/root/data/list/data.list"  
+
+funasr/bin/train_ds.py \  
+++train_data_set_list="${train_data}" \  
+++dataset_conf.data_split_num=256  
+```  
+**Details:**  
+- `data.list`: A plain text file listing the split JSONL files. For example, the content of `data.list` might be:  
+  ```bash  
+  data/list/train.0.jsonl  
+  data/list/train.1.jsonl  
+  ...  
+  ```  
+- `data_split_num`: Specifies the number of slice groups. For instance, if `data.list` contains 512 lines and `data_split_num=256`, the data will be divided into 256 groups, each containing 2 JSONL files. This ensures that only 2 JSONL files are loaded for training at a time, reducing memory usage during training. Note: Groups are created sequentially.  
+
+**Recommendation:**  
+If the dataset is extremely large and contains heterogeneous data types, perform **data balancing** during splitting to ensure uniformity across groups.
+
 #### Training log
 
 ##### log.txt
diff --git a/docs/tutorial/README_zh.md b/docs/tutorial/README_zh.md
index 5bf15f0a4..6cbd9c38e 100644
--- a/docs/tutorial/README_zh.md
+++ b/docs/tutorial/README_zh.md
@@ -213,7 +213,7 @@ bash finetune.sh
 ### 详细参数介绍
 
 ```shell
-funasr/bin/train.py \
+funasr/bin/train_ds.py \
 ++model="${model_name_or_model_dir}" \
 ++train_data_set_list="${train_data}" \
 ++valid_data_set_list="${val_data}" \
@@ -258,7 +258,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
 gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 
 torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
 ```
 --nnodes 表示参与的节点总数，--nproc_per_node 表示每个节点上运行的进程数
 
@@ -270,7 +270,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
 gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 
 torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
 ```
 在从节点上（假设IP为192.168.1.2），你需要确保MASTER_ADDR和MASTER_PORT环境变量与主节点设置的一致，并运行同样的命令：
 ```shell
@@ -278,7 +278,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
 gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 
 torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
 ```
 
 --nnodes 表示参与的节点总数，--node_rank 表示当前节点id，--nproc_per_node 表示每个节点上运行的进程数（通常为gpu个数）
@@ -331,6 +331,25 @@ jsonl2scp \
 ++jsonl_file_in="../../../data/list/train.jsonl"
 ```
 
+#### 大数据训练
+如果数据量很大，例如5万小时以上，这时候容易遇到内存不足的问题，特别是多gpu实验，这时候需要对jsonl文件进行切分成slice，然后写到txt里面，一行一个slice，然后设置`data_split_num`，例如：
+```shell
+train_data="/root/data/list/data.list"
+
+funasr/bin/train_ds.py \
+++train_data_set_list="${train_data}" \
+++dataset_conf.data_split_num=256
+```
+其中：
+`data.list`：为纯文本，内容是切割后的jsonl文件，例如，`data.list`的内容为：
+```bash
+data/list/train.0.jsonl
+data/list/train.1.jsonl
+...
+```
+`data_split_num`：表示切分slice分组个数，例如，data.list中共512行，data_split_num=256，表示分成256组，每组有2个jsonl文件，这样每次只load 2个jsonl数据进行训练，从而降低训练过程中内存使用。注意是按照顺序分组。
+如果是，非常大，并且数据类型差异比较大，建议切分时候进行数据均衡。
+
 #### 查看训练日志
 
 ##### 查看实验log