Adding last missing audio features in LeRobotDataset

src/lerobot/datasets/lerobot_dataset.py

@@ -621,6 +621,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         revision: str | None = None,
         force_cache_sync: bool = False,
         download_videos: bool = True,
+        download_audio: bool = True,
         video_backend: str | None = None,
         audio_backend: str | None = None,
         batch_encoding_size: int = 1,
@@ -752,9 +753,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
             download_videos (bool, optional): Flag to download the videos. Note that when set to True but the
                 video files are already present on local disk, they won't be downloaded again. Defaults to
                 True.
+            download_audio (bool, optional): Flag to download the audio. Defaults to True.
             video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec when available int the platform; otherwise, defaults to 'pyav'.
                 You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision.
-            audio_backend (str | None, optional): Audio backend to use for decoding audio. Defaults to 'ffmpeg'.
+            audio_backend (str | None, optional): Audio backend to use for decoding audio. Defaults to 'ffmpeg' decoder used by 'torchaudio'.
             batch_encoding_size (int, optional): Number of episodes to accumulate before batch encoding videos.
                 Set to 1 for immediate encoding (default), or higher for batched encoding. Defaults to 1.
             vcodec (str, optional): Video codec for encoding videos during recording. Options: 'h264', 'hevc',
@@ -847,6 +849,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         license: str | None = "apache-2.0",
         tag_version: bool = True,
         push_videos: bool = True,
+        push_audio: bool = True,
         private: bool = False,
         allow_patterns: list[str] | str | None = None,
         upload_large_folder: bool = False,
@@ -855,6 +858,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         ignore_patterns = ["images/"]
         if not push_videos:
             ignore_patterns.append("videos/")
+        if not push_audio:
+            ignore_patterns.append("audio/")
 
         hub_api = HfApi()
         hub_api.create_repo(
@@ -909,7 +914,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             ignore_patterns=ignore_patterns,
         )
 
-    def download(self, download_videos: bool = True) -> None:
+    def download(self, download_videos: bool = True, download_audio: bool = True) -> None:
         """Downloads the dataset from the given 'repo_id' at the provided version. If 'episodes' is given, this
         will only download those episodes (selected by their episode_index). If 'episodes' is None, the whole
         dataset will be downloaded. Thanks to the behavior of snapshot_download, if the files are already present
@@ -917,8 +922,12 @@ class LeRobotDataset(torch.utils.data.Dataset):
         """
         # TODO(rcadene, aliberts): implement faster transfer
         # https://huggingface.co/docs/huggingface_hub/en/guides/download#faster-downloads
-        ignore_patterns = None if download_videos else "videos/"
         files = None
+        ignore_patterns = []
+        if not download_videos:
+            ignore_patterns.append("videos/")
+        if not download_audio:
+            ignore_patterns.append("audio/")
         if self.episodes is not None:
             files = self.get_episodes_file_paths()
         self.pull_from_repo(allow_patterns=files, ignore_patterns=ignore_patterns)
@@ -933,6 +942,15 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 for ep_idx in episodes
             ]
             fpaths += video_files
+
+        if len(self.meta.audio_keys) > 0:
+            audio_files = [
+                str(self.meta.get_compressed_audio_file_path(ep_idx, audio_key))
+                for audio_key in self.meta.audio_keys
+                for ep_idx in episodes
+            ]
+            fpaths += audio_files
+
         # episodes are stored in the same files, so we return unique paths only
         fpaths = list(set(fpaths))
         return fpaths