The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
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.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 306, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NoteGrab training data
Two million score-addressed note targets from 100,696 synthetic mixtures, plus eight recorded-scene shards. A mixture may be reused for several target notes. Each index row identifies the mixture, requested note, and waveform data.
| Path | Contents |
|---|---|
| synthetic/ | Synthetic audio, training-row index, timing and sampling metadata. |
| recorded/ | Recorded scenes, isolated note targets and their indexes. |
| example/ | One unpacked mixture, isolated target, reference sound and note labels. |
| manifest.json | File inventory, compression information and training path mappings. |
Download and restore the package from the NoteSep repository:
uv run notesep download-data --repo-id ben2002chou/notesep-training-data --output data/notegrab
Setup verifies the files, decompresses audio shards, and writes path-map.json for training. The compressed download is about 446 GB. Restored data occupies about 2.39 TB; allow space for both during setup. Audio values are preserved exactly. The training guide in the code repository gives the training command.
Start with example/ to listen to a training row. The larger files are indexed containers for efficient training, rather than directories of individual WAVs. ASO uses separate source-complete scenes and locally generated NoteGrab estimates.
synthetic/sampling-parent.json selects the rows for the original 30,000-update
parent run. synthetic/sampling.json selects rows for the later continuation.
The intermediate 250-update stage uses synthetic/sampling-adaptation.json
and the instrument-library labels in synthetic/render-methods.npz. All three
row selections refer to the same audio and row index.
Sources and licenses
See SOURCES.md for instrument, score and recording credits, transformations and source-specific terms. Original notices are included in licenses/ and source information in sources/. Noncommercial and share-alike terms apply to the corresponding source material.
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