Datasets:

zjt24
/

random50_v3

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
              tsfile.exceptions.FileOpenError: 28: 
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
              SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Random50 v3 (TsFile)

This dataset is an Apache TsFile conversion of the Hugging Face dataset iiyudana/random50_v3. The source dataset was created with LeRobot and contains ALOHA robot demonstrations for transferring a cube between arms.

Modalities: Time-series. The original repository also contains synchronized top-camera videos; videos are not included in this converted repository.

Source Dataset

Original dataset: iiyudana/random50_v3
License: apache-2.0
LeRobot codebase version: v2.1
Robot type: aloha
Task: Pick up the cube with the right arm and transfer it to the left arm.
Split: train (0:50)
Scale: 50 episodes, 20,000 frames, 1 task
Sampling rate: 50 fps
Source videos: 50 MP4 files from observation.images.top
Source data layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
Source video layout: videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4

Converted File

TsFile: data/random50_v3_train.tsfile
TsFile table: random50_v3_train
Converted rows: 20,000
Episodes: 50
Time precision: milliseconds
TAG columns: episode_index, task_index
File size: 2,195,882 bytes

All source episodes in the train split are merged into one TsFile. The source episode_index and task_index columns are retained as TAG columns, allowing queries to select an episode without creating synthetic tag aliases.

Schema

Time is computed as round(timestamp * 1000) in milliseconds and restarts in each episode. At 50 fps, consecutive source frames are approximately 20 ms apart. The source timestamp column is dropped because it is redundant with Time / 1000 seconds. No source rows are dropped.

TAG columns:

episode_index
task_index

FIELD columns:

frame_index
sample_index (renamed from source index)
observation_state_0 through observation_state_13 (FLOAT)
action_0 through action_13 (FLOAT)

The 14 elements in both observation.state and action use this source order:

left_waist
left_shoulder
left_elbow
left_forearm_roll
left_wrist_angle
left_wrist_rotate
left_gripper
right_waist
right_shoulder
right_elbow
right_forearm_roll
right_wrist_angle
right_wrist_rotate
right_gripper

Vector names preserve the full source feature name: . is replaced with _ and the element index is appended.

Video Policy

The source feature observation.images.top is not converted or uploaded. It is 480 x 640 RGB AV1 video at 50 fps. Use the original dataset for the synchronized videos: iiyudana/random50_v3/videos.

The numeric rows retain episode_index, frame_index, task_index, and sample_index, preserving their alignment with the original per-episode video.

Metadata

The source meta/ files are mirrored in this repository. meta/info.json is updated so data_path points to data/random50_v3_train.tsfile. Its tsfile_conversion object records the actual table name, source episode-file count, converted TsFile count, Time formula, TAG columns, row count, flattened features, renamed and dropped fields, and frame/video alignment. The converted total_videos value is 0; the original count of 50 is preserved as tsfile_conversion.source_video_count.

Validation

The converted file was validated with the project pipeline and read back with the TsFile Python SDK:

staged Parquet rows: 20,000
TsFile metadata rows: 20,000
TsFile query rows: 20,000
duplicate (episode_index, task_index, Time) rows: 0
TsFile size: 2,195,882 bytes

Usage

from tsfile import TsFileReader

path = "data/random50_v3_train.tsfile"
with TsFileReader(path) as reader:
    schemas = reader.get_all_table_schemas()
    table = schemas["random50_v3_train"]
    print([(column.get_column_name(), column.get_category())
           for column in table.get_columns()])

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Total file size:

2.38 MB