--- a
+++ b/mmaction/datasets/audio_feature_dataset.py
@@ -0,0 +1,71 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import torch
+
+from .base import BaseDataset
+from .builder import DATASETS
+
+
+@DATASETS.register_module()
+class AudioFeatureDataset(BaseDataset):
+    """Audio feature dataset for video recognition. Reads the features
+    extracted off-line. Annotation file can be that of the rawframe dataset,
+    or:
+
+    .. code-block:: txt
+
+        some/directory-1.npy 163 1
+        some/directory-2.npy 122 1
+        some/directory-3.npy 258 2
+        some/directory-4.npy 234 2
+        some/directory-5.npy 295 3
+        some/directory-6.npy 121 3
+
+    Args:
+        ann_file (str): Path to the annotation file.
+        pipeline (list[dict | callable]): A sequence of data transforms.
+        suffix (str): The suffix of the audio feature file. Default: '.npy'.
+        kwargs (dict): Other keyword args for `BaseDataset`.
+    """
+
+    def __init__(self, ann_file, pipeline, suffix='.npy', **kwargs):
+        self.suffix = suffix
+        super().__init__(ann_file, pipeline, modality='Audio', **kwargs)
+
+    def load_annotations(self):
+        """Load annotation file to get video information."""
+        if self.ann_file.endswith('.json'):
+            return self.load_json_annotations()
+        video_infos = []
+        with open(self.ann_file, 'r') as fin:
+            for line in fin:
+                line_split = line.strip().split()
+                video_info = {}
+                idx = 0
+                filename = line_split[idx]
+                if self.data_prefix is not None:
+                    if not filename.endswith(self.suffix):
+                        filename = osp.join(self.data_prefix,
+                                            filename) + self.suffix
+                    else:
+                        filename = osp.join(self.data_prefix, filename)
+                video_info['audio_path'] = filename
+                idx += 1
+                # idx for total_frames
+                video_info['total_frames'] = int(line_split[idx])
+                idx += 1
+                # idx for label[s]
+                label = [int(x) for x in line_split[idx:]]
+                assert label, f'missing label in line: {line}'
+                if self.multi_class:
+                    assert self.num_classes is not None
+                    onehot = torch.zeros(self.num_classes)
+                    onehot[label] = 1.0
+                    video_info['label'] = onehot
+                else:
+                    assert len(label) == 1
+                    video_info['label'] = label[0]
+                video_infos.append(video_info)
+
+        return video_infos