The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<ytid: string, timestampType: string, start: double, end: double, video_length: double>
to
{'ytid': Value('string'), 'timestampType': Value('string'), 'start': Value('float64'), 'end': Value('float64')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 478, 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 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<ytid: string, timestampType: string, start: double, end: double, video_length: double>
to
{'ytid': Value('string'), 'timestampType': Value('string'), 'start': Value('float64'), 'end': Value('float64')}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.
MulTTiPop: A Multitrack Transcription Dataset for Pop Music
Nathan Pruyne, Benjamin Stoler, William Chen, Chien-yu Huang, Shinji Watanabe, Chris Donahue
🎼Preview the Data: https://gclef-cmu.org/multtipop
📄Read the Paper: ARXIV LINK
MulTTiPop is a dataset of segments of pop music, sourced from TheoryTab, and aligned multitrack MIDI transcriptions, sourced from the Lakh MIDI Dataset. MulTTiPop contains 572 total segments and approximately 3.5 hours of total data.
We present MulTTiPop as pairs of transformed, aligned multitrack MIDI files and associated metadata files with song information. The provided metadata also contains YouTube video IDs and timestamps within the video for the corresponding audio.
We make 2 key recommendations for the use of MulTTiPop:
- Researchers should only obtain the relevant segments of the original audio.
- Researchers should only use MulTTiPop for model evaluation, not training.
Splits
MulTTiPop is split in an approximately 3:7 ratio between dev and test splits. The dev split is intended for development, and the test split for model evaluation. As MulTTiPop is not intended for model training, we do not provide a train or validation split.
Uses
MulTTiPop is intended for use for the evaluation of multitrack automatic music transcription (AMT) systems.
Direct Use
For the evaluation of AMT systems such as MT3 and YourMT3+
Out-of-Scope Use
MulTTiPop is not intended for model training due to both its size and lack of audio licensing.
Dataset Structure
Each folder in the dev and test splits contain two files: an aligned MIDI file (aligned.mid) and a metadata file (meta.json).
Metadata Fields
"id": Internal dataset ID"artist": Artist of the song"name": Title of the song"section": Section of the song, as labeled on TheoryTab"tempo": Tempo of the song, as labeled on TheoryTab"meter": Meter (beats per measure) of the song, as labeled on TheoryTab"key": Key of the song, as labeled on TheoryTab"youtube": YouTube video associated with the song. Contains the following subfields:"id": YouTube video ID (video can be accessed athttps://youtube.com/watch?v=VIDEO_ID)"start": Start time of the video segment, in seconds"end": End time of the video segment, in seconds
"chosen_method": Starting anchor beat selection method for audio-MIDI alignment. See our paper for more details."chosen_beat_idx": Starting beat number in the LMD MIDI file"chosen_beat_time": Starting time in the LMD MIDI file in seconds"chosen_lmd_midi_filename": Filename in the LMD MIDI dataset"chosen_candidate": Additional information used for audio-MIDI alignment"lmd_beats": Timestamps of beats within the LMD MIDI, used for audio-MIDI alignment"audio_beats": Timestamps of beats within the audio, used for audio-MIDI alignment"aligned_midi_checksum": SHA-256 checksum of the associatedaligned.midfile"lmd_midi_checksum": SHA-256 checksum of the original LMD MIDI file"split_name":devortest"year": Publication year of the song, according to Claude Sonnet"audio_length": Total length of the associated audio
Source Data
MulTTiPop segments are sourced from TheoryTab.
Multitrack MIDI is adapted from the Lakh MIDI Dataset.
Bias, Risks, and Limitations
MulTTiPop is an evaluation dataset for automatic music transcription models on multitrack pop music. It is intended to provide a representative sample of songs that we hypothesize users of AMT systems would want to transcribe. MulTTiPop is not intended for the training of AMT systems or other machine learning models, especially generative models, both because of the small size of the dataset and due to it being comprised of copyrighted songs.
While MulTTiPop is intended to give a representative sample of music that users may wish to transcribe, there is a strong Western bias in our data preparation and presentation. The transcriptions we provide are based on Western harmony and notation. By sourcing our annotations of interest from HookTheory, an American company with a primarily Western audience, the songs included in MulTTiPop overwhelmingly represent the transcription interests of a Western, not global audience. This is reflected in the contents of the data too, as the artists included are predominantly American and are popular in the American pop music landscape. Because of these limitations, we recommend that model results on MulTTiPop should be considered largely a reflection of their performance on music in the American Western popular music canon, and not representative of performance on music as a whole.
Citation
If you use MulTTiPop in your research, please cite our ArXiv paper:
bibtex for paper
License and Attribution
MulTTiPop is distributed with a CC-BY 4.0 License.
The aligned MIDI files provided in MulTTiPop are adapted from the Lakh MIDI Dataset by Colin Raffel, which is distributed with a CC-BY 4.0 License.
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