The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: BadZipFile
Message: zipfiles that span multiple disks are not supported
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 165, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1664, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1621, in dataset_module_factory
return HubDatasetModuleFactoryWithoutScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1025, in get_module
module_name, default_builder_kwargs = infer_module_for_data_files(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 594, in infer_module_for_data_files
split_modules = {
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 595, in <dictcomp>
split: infer_module_for_data_files_list(data_files_list, download_config=download_config)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 536, in infer_module_for_data_files_list
return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 564, in infer_module_for_data_files_list_in_archives
for f in xglob(extracted, recursive=True, download_config=download_config)[
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1013, in xglob
fs, *_ = url_to_fs(urlpath, **storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 395, in url_to_fs
fs = filesystem(protocol, **inkwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 293, in filesystem
return cls(**storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 80, in __call__
obj = super().__call__(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 62, in __init__
self.zip = zipfile.ZipFile(
File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
self._RealGetContents()
File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
endrec = _EndRecData(fp)
File "/usr/local/lib/python3.9/zipfile.py", line 286, in _EndRecData
return _EndRecData64(fpin, -sizeEndCentDir, endrec)
File "/usr/local/lib/python3.9/zipfile.py", line 232, in _EndRecData64
raise BadZipFile("zipfiles that span multiple disks are not supported")
zipfile.BadZipFile: zipfiles that span multiple disks are not supportedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for Test-Audio of CMI-Bench
CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
🔗 Paper (arXiv) 🧪 Evaluation Toolkit 📊 License: CC BY-NC 4.0
Dataset Summary
The CMI-Bench/test-audio dataset provides the complete test split audio files used in the CMI-Bench benchmark. CMI-Bench evaluates the instruction-following capabilities of audio-text large language models (LLMs) on a wide range of Music Information Retrieval (MIR) tasks.
This collection includes test set audio excerpts from 20 public MIR datasets, covering tasks such as genre classification, emotion tagging, music captioning, lyrics transcription, pitch/key estimation, and beat tracking.
⚠️ This release includes only test set audio files for evaluation purposes, in line with usage terms of the source datasets.
unzip dataset:
zip -s0 test_data.zip --out test_data_full.zip
unzip test_data_full.zip
or you can use 7z to unzip the dataset:
7z x test_data.zip
Supported Tasks and Sources
| Task | Source Dataset(s) |
|---|---|
| Genre Classification | MTG-Genre, GTZAN |
| Emotion Tagging | MTG-Emotion |
| Emotion Regression | EMO |
| Instrument Classification | MTG-Instrument, Nsynth |
| Music Tagging | MagnaTagATune, MTG-Top50 |
| Pitch Estimation | Nsynth-Pitch |
| Key Detection | GiantSteps |
| Lyrics Transcription | DSing |
| Music Captioning | SDD, MusicCaps |
| Melody Extraction | MedleyDB v2 |
| (Down)Beat Tracking | GTZAN-Rhythm, Ballroom |
| Vocal Technique | VocalSet |
| Performance Technique | GuZheng99 |
Intended Use
This dataset is intended for research purposes only, especially for evaluating audio-text models on instruction-following tasks in music understanding.
Please refer to the CMI-Bench GitHub repository for scripts and metrics to run standardized evaluations.
License
This dataset is distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). You are free to share and adapt the data for non-commercial purposes, with proper attribution.
Source data is aggregated from multiple public datasets that may carry their own licenses. Please ensure compliance with any additional dataset-specific terms.
Citation
Please cite the following paper if you use this dataset:
@misc{ma2025cmibenchcomprehensivebenchmarkevaluating,
title={CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following},
author={Yinghao Ma and Siyou Li and Juntao Yu and Emmanouil Benetos and Akira Maezawa},
year={2025},
eprint={2506.12285},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2506.12285},
}
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