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Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'preprocessing' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1539, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1419, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 976, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 794, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1570, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 835, in finalize
self._build_writer(self.schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 861, in _build_writer
self.pa_writer = pq.ParquetWriter(
~~~~~~~~~~~~~~~~^
self.stream,
^^^^^^^^^^^^
...<3 lines>...
**get_parquet_column_options(self._features, self._schema),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 1115, in __init__
self.writer = _parquet.ParquetWriter(
~~~~~~~~~~~~~~~~~~~~~~^
sink, schema,
^^^^^^^^^^^^^
...<20 lines>...
max_rows_per_page=max_rows_per_page,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**options)
^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2430, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'preprocessing' with no child field to Parquet. Consider adding a dummy child field.
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/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1401, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1579, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
low.mp4 video | high.mp4 video | json dict | __key__ string | __url__ string |
|---|---|---|---|---|
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"member": "final_output/low_cost_video/10-o/10-Scene-0016_part1.mp4",
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{
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"member": "final_output/low_cost_video/10-o/10-Scene-0019_part1.mp4",
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{
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... | cc_1dc5f94a8b525dbd6a6e | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"population": "baseline",
"low": {
"archive": "baseline/final_output.zip",
"member": "final_output/low_cost_video/10-o/10-Scene-0038_part1.mp4",
... | cc_7aad39dc8b8b94dbfd0c | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"member": "final_output/low_cost_video/10-o/10-Scene-0045_part1.mp4",
... | cc_5417b78c3bc3e5e4cce9 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"member": "final_output/low_cost_video/10-o/10-Scene-0072_part1.mp4",
... | cc_277a7aa3c5c2151a202e | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"archive": "baseline/final_output.zip",
"member": "final_output/low_cost_video/10-o/10-Scene-0077_part1.mp4",
... | cc_37e1cf26022584f7c055 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"member": "final_output/low_cost_video/10-o/10-Scene-0089_part1.mp4",
... | cc_8cbc636f1fce75c352c9 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"member": "final_output/low_cost_video/10-o/10-Scene-0092_part1.mp4",
... | cc_b0e10e990a04c266be68 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"member": "final_output/low_cost_video/10-o/10-Scene-0094_part1.mp4",
... | cc_0101629acf7b3490818d | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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{
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... | cc_b6c1aeef571a50af9072 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"member": "final_output/low_cost_video/10-o/10-Scene-0100_part2.mp4",
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{
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"member": "final_output/low_cost_video/10-o/10-Scene-0105_part1.mp4",
... | cc_1bd07cfc021c5e5e6660 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"member": "final_output/low_cost_video/10-o/10-Scene-0114_part1.mp4",
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{
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{
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... | cc_cbb37d3b379b27c0777f | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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... | cc_e5ddf30f427cdd2e5d4e | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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... | cc_40afb6bac513c153274a | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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... | cc_29c2a0bea21a5a8acd5c | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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{
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{
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... | cc_f47738c5e1dbb0f328c8 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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... | cc_4db4d011cb4909038963 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"member": "final_output/low_cost_video/10-o/10-Scene-0221_part3.mp4",
... | cc_fd2c8ca1a14184129a14 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"low": {
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"member": "final_output/low_cost_video/10-o/10-Scene-0222_part1.mp4",
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{
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"population": "baseline",
"low": {
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"member": "final_output/low_cost_video/10-o/10-Scene-0225_part1.mp4",
... | cc_03db1bfdcf649f2e46fe | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"sample_id": "10-Scene-0230_part1",
"source_group": "10-o",
"population": "baseline",
"low": {
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"member": "final_output/low_cost_video/10-o/10-Scene-0230_part1.mp4",
... | cc_fbbea972f48ad46fcafd | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"sample_id": "10-Scene-0231_part1",
"source_group": "10-o",
"population": "baseline",
"low": {
"archive": "baseline/final_output.zip",
"member": "final_output/low_cost_video/10-o/10-Scene-0231_part1.mp4",
... | cc_f9f02533fe07dc0080f3 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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"sample_id": "10-Scene-0242_part1",
"source_group": "10-o",
"population": "baseline",
"low": {
"archive": "baseline/final_output.zip",
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{
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{
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{
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... | cc_9d70928f1e23630dea15 | hf://datasets/Anna-SY/CineCraft-120K@2b18b33698053f1fe19fa012a48607999db5909b/data/baseline-000000.tar | ||
{
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{
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{
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{
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{
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{
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CineCraft-120K
This release is being prepared incrementally. It currently contains 30120 pairs in data/. This is not a claim that the entire source collection has been published.
Source inventory
- Existing paired archive: 102,881 low/high pairs.
- Additional uniquely matched low videos: 22,020.
- Unique candidate pairs: 124,901.
- Duplicate smoke clips held out: 2.
Source revisions and unresolved items are recorded in audit/. Videos are published as their original source bytes without resampling, cropping, resizing, or re-encoding. New low videos have 81 frames at 832x480; their raw high counterparts retain their original frame counts and resolutions.
Contents and validation
data/*.tar contains MP4 pairs joined by source directory and video ID, plus per-pair JSON metadata; metadata/ contains corresponding JSONL records. Source bytes are checked with ZIP CRC or repository SHA256, and every published video receives its own SHA256. ffprobe validates container/stream metadata; a deterministic 1% sample receives a full frame scan. Per-side dimensions, frames, FPS and durations are retained. Frame counts and timing may differ across a pair; temporal_metadata_match records this rather than implying frame alignment. These checks do not constitute a per-example aesthetic rating or proof of per-frame spatial/temporal alignment.
pilot/ is a separate packaging test and must not be combined with data/ as additional unique examples. Dataset splits remain unassigned until the original split mapping is recovered.
Prompts
The original source supplies high-to-low prompts for 102,881 existing pairs. They are stored as prompt_high_to_low. prompt_low_to_high is currently null; reverse instructions are not inferred or relabeled. The additional low-video ZIPs do not include prompt files.
Sources
- https://huggingface.co/datasets/yangxiaoda/low_high_cost
- https://huggingface.co/datasets/yangxiaoda/todelete3
- https://huggingface.co/datasets/zjq0455/zjq_videos/tree/main/high
No new data license is assigned by this preparation process.
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