Datasets:

itsuzef
/

HTMOneShotLoopClassification

Error code:   FeaturesError
Exception:    ValueError
Message:      Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/itsuzef/HTMOneShotLoopClassification@28336f7905315e882590c388d6c967b48ae1430d/metadata_schema.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 186, in _generate_tables
                  raise ValueError(
              ValueError: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/itsuzef/HTMOneShotLoopClassification@28336f7905315e882590c388d6c967b48ae1430d/metadata_schema.json.

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YAML Metadata Warning: The task_categories "multi-class-classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Dataset Card for HTMOneShotLoopClassification

Dataset Summary

HTMOneShotLoopClassification is a dataset of 5,561 electronic music samples (one-shots and loops) from house, tech house, and minimal techno genres. Each sample is labeled with one of 8 stem categories and includes 55 extracted audio features for machine learning applications.

Supported Tasks and Leaderboards

Audio Classification: Multi-class classification into 8 stem categories
Baseline Performance: 85.41% accuracy using SVM with RBF kernel

Languages

Audio samples (no spoken language)
Metadata in English

Dataset Structure

Data Instances

Each instance contains:

Filename: Original audio file name
Stem Label: One of 8 categories (kick, snare, hihat, bass, synth, vocal, percussion, fx)
Category: Sample type (one-shots or loops)
55 Audio Features: Extracted using librosa

Data Fields

Field	Type	Description
filename	string	Original audio filename
stem	categorical	Target label (8 categories)
category	categorical	one-shots or loops
duration	float	Audio duration in seconds
sample_rate	integer	Sample rate (typically 44100 Hz)
rms	float	Root mean square energy
zcr	float	Zero crossing rate
spectral_centroid	float	Spectral centroid in Hz
...	...	(See metadata_schema.json for all 55 features)

Data Splits

Recommended splits:

Train: 80% (4,450 samples)
Validation: 10% (556 samples)
Test: 10% (556 samples)

Dataset Creation

Curation Rationale

The dataset was created to address the lack of curated electronic music sample datasets with:

High-quality professional samples
Consistent feature extraction
Genre-specific focus (house/tech house/minimal techno)
Both one-shots and loops

Source Data

Professional electronic music sample packs
Curated from house, tech house, and minimal techno collections
Quality-controlled and deduplicated

Annotations

Annotation process: Automatic categorization based on folder name patterns, with manual review for ambiguous cases
Annotators: Domain experts
Annotation guidelines: Based on standard electronic music production categories

Considerations for Using the Data

Social Impact

This dataset enables research in:

Automated music production tools
Audio classification systems
Electronic music analysis

Discussion of Biases

Genre Bias: Focused on house, tech house, and minimal techno (not all electronic music)
Class Balance: Good balance achieved (FX: 561, all categories within 80-130% of average)
Key Coverage: Not all categories have musical key information (only bass and synth have 100% coverage)

Other Known Limitations

Dataset size: 5,561 samples (moderate size)
Limited to specific sub-genres of electronic music
Some categories have low key coverage (important for harmonic mixing applications)

Additional Information

Dataset Curators

Youssef Hemimy

Licensing Information

Dataset License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)

License Scope: This license applies to the CSV dataset containing extracted audio features and metadata, documentation, and scripts. You are free to use, share, and adapt the dataset for any purpose, including commercial use, provided you give appropriate credit.

Original Audio Files: The original audio files used to generate this dataset are NOT included and are NOT covered by this license. The audio files were obtained from commercial sample packs under their respective licenses, which typically prohibit redistribution. Users must obtain the original audio files separately if needed.

Citation Information

x @dataset{hemimy2025htmoneshotloop, title={HTMOneShotLoopClassification: A Dataset of House, Tech House, and Minimal Techno Samples for Stem Category Classification}, author={Hemimy, Youssef}, year={2025}, publisher={Zenodo}, doi={10.5281/zenodo.17872191} }### Contributions

Contributions and feedback are welcome. Please see the repository for contribution guidelines.