diff --git a/lmms_eval/__main__.py b/lmms_eval/__main__.py
index 7c89535a..e43b588f 100755
--- a/lmms_eval/__main__.py
+++ b/lmms_eval/__main__.py
@@ -264,6 +264,7 @@ def parse_eval_args() -> argparse.Namespace:
         action="store_true",
         help="Sets trust_remote_code to True to execute code to create HF Datasets from the Hub",
     )
+    parser.add_argument("--process_with_media", action="store_true", help="Whether you will process you dataset with audio, image. By default set to False" "In case some benchmarks need to be processed with media, set this flag to True.")
     args = parser.parse_args()
     return args
 
diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py
index 937b0fcb..032acc93 100755
--- a/lmms_eval/api/task.py
+++ b/lmms_eval/api/task.py
@@ -29,7 +29,7 @@
 import datasets
 import numpy as np
 from accelerate import Accelerator
-from datasets import DownloadConfig, Image, Sequence
+from datasets import Audio, DownloadConfig, Image, Sequence
 from huggingface_hub import snapshot_download
 from loguru import logger as eval_logger
 from PIL import ImageFile
@@ -430,9 +430,10 @@ def build_all_requests(
         if cache_requests and (not cached_instances or rewrite_requests_cache) and limit is not None:
             limit = None
 
-        doc_id_docs = list(self.doc_iterator(rank=rank, limit=limit, world_size=world_size))
+        doc_id_docs = self.doc_iterator(rank=rank, limit=limit, world_size=world_size)
+        doc_iterator_for_counting = itertools.islice(range(len(self.test_docs())), rank, limit, world_size) if self.has_test_docs() else itertools.islice(range(len(self.validation_docs())), rank, limit, world_size)
 
-        num_docs = len(doc_id_docs)
+        num_docs = sum(1 for _ in doc_iterator_for_counting)
 
         for doc_id, doc in tqdm(
             doc_id_docs,
@@ -1064,6 +1065,8 @@ def concat_tar_parts(tar_parts, output_tar):
                     remove_cols.append(feature)
                 elif isinstance(features[feature], Sequence) and isinstance(features[feature].feature, Image):
                     remove_cols.append(feature)
+                elif isinstance(features[feature], Audio):
+                    remove_cols.append(feature)
             for remove_col in remove_cols:
                 self.dataset_no_image[doc_name] = self.dataset_no_image[doc_name].remove_columns(remove_col)
 
@@ -1093,10 +1096,27 @@ def validation_docs(self) -> datasets.Dataset:
         if self.has_validation_docs():
             return self.dataset[self.config.validation_split]
 
+    def validation_docs_no_media(self) -> datasets.Dataset:
+        if self.has_validation_docs():
+            return self.dataset_no_image[self.config.validation_split]
+
     def test_docs(self) -> datasets.Dataset:
         if self.has_test_docs():
             return self.dataset[self.config.test_split]
 
+    def test_docs_no_media(self) -> datasets.Dataset:
+        if self.has_test_docs():
+            return self.dataset_no_image[self.config.test_split]
+
+    @property
+    def eval_docs_no_media(self) -> Union[datasets.Dataset, List[dict]]:
+        if self.has_test_docs():
+            return self.test_docs_no_media()
+        elif self.has_validation_docs():
+            return self.validation_docs_no_media()
+        else:
+            raise ValueError(f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!")
+
     def fewshot_docs(self):
         if self.config.fewshot_split is not None:
             return self.dataset[self.config.fewshot_split]
diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py
index c6460e9d..11e6c1c5 100755
--- a/lmms_eval/evaluator.py
+++ b/lmms_eval/evaluator.py
@@ -483,7 +483,10 @@ def evaluate(
             instances.sort(key=lambda x: x.idx)
         # iterate over different filters used
         for filter_key in task.instances[0].filtered_resps.keys():
-            doc_iterator = task.doc_iterator(rank=RANK, limit=limit, world_size=WORLD_SIZE)
+            if not cli_args.process_with_media:
+                doc_iterator = create_iterator(enumerate(task.eval_docs_no_media), rank=RANK, limit=int(limit) if limit else None, world_size=WORLD_SIZE)
+            else:
+                doc_iterator = task.doc_iterator(rank=RANK, limit=limit, world_size=WORLD_SIZE)
             doc_iterator_for_counting = itertools.islice(range(len(task.test_docs())), RANK, limit, WORLD_SIZE) if task.has_test_docs() else itertools.islice(range(len(task.validation_docs())), RANK, limit, WORLD_SIZE)
             total_docs = sum(1 for _ in doc_iterator_for_counting)
             pbar = tqdm(total=total_docs, desc=f"Postprocessing", disable=(RANK != 0))