CODA: Cascaded Online Discontinuity-Aware Alignment for Real-Time Image-Based Score Following
Paper • 2607.21899 • Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
json_reader = JsonReader(
path_or_buf,
...<16 lines>...
engine=engine,
)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
self.data = self._preprocess_data(data)
~~~~~~~~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
data = data.read()
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
out = read(*args, **kwargs)
File "<frozen codecs>", line 325, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa7 in position 14: invalid start byte
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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, 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: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CODA Repeat-Aware Jump Benchmark
This is the official repeat-aware jump evaluation benchmark for CODA: Cascaded Online Discontinuity-Aware Alignment for Real-Time Score Following. It is derived from the 94-piece MSMD test split distributed with the CYOLO score-following dataset.
This release is an evaluation benchmark. It is not a training split.
Subsets
| Subset | Pieces | Construction |
|---|---|---|
repeat |
66 | Fixed, manually annotated written-repeat performance orders |
random |
28 | Three deterministic synthetic jumps per non-repeat piece, using seed 42 and stable per-piece seeds |
The two configurations contain 94 unique pieces and have no cross-subset
overlap. The fixed repeat annotations are provided in
repeat_annotations.json.
Files
The versioned ZIP contains an evaluator-compatible pair of files for every piece.
.wavcontains the original synthesized performance audio..npzcontains the score representation, augmented playback sequence, and jump metadata consumed by CODA.metadata.csvrecords piece identifiers, subset information, jump counts, generation seeds, and hashes.manifest.jsonrecords the exact piece lists, generation parameters, per-piece random seeds, and SHA-256 hashes for all 188 data files.
The complete evaluator-compatible release is also available as
CODA_JumpBench_v1.0.0.zip.
Download and evaluation
Download the complete archive with the Hugging Face CLI.
hf download ValleyC/CODA-JumpBench CODA_JumpBench_v1.0.0.zip \
--repo-type dataset --local-dir data
unzip data/CODA_JumpBench_v1.0.0.zip -d data/msmd
The resulting directories are data/msmd/msmd_test_jump/repeat and
data/msmd/msmd_test_jump/random. They can be passed directly to CODA's
scripts/evaluate.py or scripts/evaluate_batch.py with --break_mode.
The repository can also be downloaded programmatically.
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="ValleyC/CODA-JumpBench",
repo_type="dataset",
local_dir="data/CODA-JumpBench",
)
Release integrity
- Archive size: 183,908,305 bytes
- Archive SHA-256:
91f1acaffd29391065fee44efb021bcad0f69004a53c7a35b628ee2b6a5364b9 - Annotation SHA-256:
7652fb8c823eba9b2e85bc28722d9e25d2e9f4694d876ad4ae95291456dc8f97
The release was generated twice from clean output directories. Both runs produced byte-identical manifests and file hashes. The final audit verified 66 repeat pieces, 28 random pieces, zero overlap, 94 WAV/NPZ pairs, valid audio, finite sequence fields, and every archived file against the manifest.
Generation
The benchmark can be regenerated from the base MSMD test set using the CODA repository.
python scripts/generate_repeat_test.py \
--input_dir data/msmd/msmd_test \
--output_dir data/msmd/msmd_test_jump \
--annotations data/repeat_annotations.json \
--seed 42 \
--clean_output
Provenance and license
The source data are based on MSMD and the preprocessed CYOLO score-following dataset. Both referenced Zenodo records identify the data license as Creative Commons Attribution 4.0. This derived benchmark is released under CC BY 4.0. Please attribute MSMD, CYOLO, and CODA when using it. CODA's MIT license applies to the software repository, not to these dataset files.
Citation
@inproceedings{yang2026coda,
title={{CODA}: Cascaded Online Discontinuity-Aware Alignment for Real-Time Score Following},
author={Yang, Yining and Chen, Ruogu and Han, Jie},
booktitle={Proceedings of the 27th International Society for Music Information Retrieval Conference (ISMIR)},
address={Abu Dhabi, UAE},
year={2026}
}
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