diff --git a/wenet/dataset/datapipes.py b/wenet/dataset/datapipes.py
index 54127a821..f7bd75bd4 100644
--- a/wenet/dataset/datapipes.py
+++ b/wenet/dataset/datapipes.py
@@ -15,10 +15,11 @@
 import collections
 from collections.abc import Callable
 import copy
+from os import PathLike
 import sys
 import tarfile
 import logging
-from typing import List, Optional
+from typing import List, Optional, Union
 import numpy as np
 import torch
 from torch.utils.data import IterDataPipe, functional_datapipe
@@ -26,6 +27,7 @@
 from torch.utils.data.datapipes.iter import Mapper
 from torch.utils.data.datapipes.iter.sharding import (
     SHARDING_PRIORITIES, ShardingFilterIterDataPipe)
+from torch.utils.data.datapipes.iter.utils import IterableWrapperIterDataPipe
 from torch.utils.data.datapipes.utils.common import _check_unpickable_fn
 
 from wenet.dataset.processor import parse_url
@@ -430,18 +432,38 @@ def __iter__(self):
 class WenetRawDatasetSource(IterDataPipe):
 
     def __init__(self,
-                 filenames: str,
+                 filenames: Union[str, List[PathLike]],
                  prefetch: int = 500,
                  partition: bool = True,
                  shuffle: bool = False,
                  shuffle_size: int = 10000,
-                 cycle: int = 1) -> None:
+                 cycle: int = 1,
+                 shard_by_files=False) -> None:
         super().__init__()
-        self.dp = TextLineDataPipe(filenames)
-        if shuffle:
-            self.dp = self.dp.shuffle(buffer_size=shuffle_size)
-        self.dp = self.dp.repeat(cycle).prefetch(prefetch)
-        self.dp = self.dp.shard(partition)
+        if shard_by_files:
+            # shard by files
+            info = torch.utils.data.get_worker_info()
+            if info is not None:
+                assert isinstance(filenames, List)
+                n_workers_per_device = info.num_workers
+                world_size = torch.distributed.get_world_size()
+                assert n_workers_per_device * world_size > len(
+                    filenames) * cycle
+            dp = IterableWrapperIterDataPipe(filenames)
+            # 0 shard many jsonl files
+            dp = dp.shuffle().repeat(cycle).shard(partition)
+            # 1 read one json file
+            self.dp = TextLineDataPipe(dp)
+            if shuffle:
+                self.dp = self.dp.shuffle(buffer_size=shuffle_size)
+            self.dp = self.dp.prefetch(prefetch)
+        else:
+            # shard line by line
+            self.dp = TextLineDataPipe(filenames)
+            if shuffle:
+                self.dp = self.dp.shuffle(buffer_size=shuffle_size)
+            self.dp = self.dp.repeat(cycle).prefetch(prefetch)
+            self.dp = self.dp.shard(partition)
 
     def __iter__(self):
         for d in self.dp: