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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'title_text', 'title'})
This happened while the csv dataset builder was generating data using
hf://datasets/Granataa/MusicClassificator/train_balanced_title.csv (at revision 14e604b72e6ddaa4199e93e69fbd934053f12526)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id_track: int64
genre: string
mfccs: string
chroma: string
spec_contrast: string
zcr: string
tempo: string
beats: string
macro_genre: string
macro_genre_id: int64
title: string
title_text: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1637
to
{'id_track': Value(dtype='int64', id=None), 'genre': Value(dtype='string', id=None), 'mfccs': Value(dtype='string', id=None), 'chroma': Value(dtype='string', id=None), 'spec_contrast': Value(dtype='string', id=None), 'zcr': Value(dtype='string', id=None), 'tempo': Value(dtype='string', id=None), 'beats': Value(dtype='string', id=None), 'macro_genre': Value(dtype='string', id=None), 'macro_genre_id': Value(dtype='int64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1436, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1053, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'title_text', 'title'})
This happened while the csv dataset builder was generating data using
hf://datasets/Granataa/MusicClassificator/train_balanced_title.csv (at revision 14e604b72e6ddaa4199e93e69fbd934053f12526)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
id_track int64 | genre string | mfccs string | chroma string | spec_contrast string | zcr string | tempo string | beats string | macro_genre string | macro_genre_id int64 |
|---|---|---|---|---|---|---|---|---|---|
3,853 | Country | {'mean': [[-128.9213409423828, -113.4515609741211, -151.32363891601562, -186.380615234375, -137.75369262695312, -91.21424865722656, -111.2973403930664, -146.38864135742188, -126.6888656616211, -209.22975158691406, -102.82124328613281, -92.00021362304688, -144.4923858642578, -120.85223388671875, -109.41667938232422, -84... | {'mean': [[0.5585707426071167, 0.47463852167129517, 0.4397081136703491, 0.6031904220581055, 0.45275619626045227, 0.38341522216796875, 0.5037292242050171, 0.5964443683624268, 0.4663906395435333, 0.6091833710670471, 0.5559048056602478, 0.28847789764404297, 0.363405704498291, 0.35527071356773376, 0.3479849696159363, 0.520... | {'mean': [[14.003592798250743, 15.997993849815693, 18.65331061117474, 20.300438839117483, 15.927205652152901, 13.713492663856048, 15.697827484083566, 16.770155746696886, 18.025088081126416, 18.55785632748935, 15.237402208986008, 15.100538702390748, 16.14734006523605, 15.689007285631675, 17.28709923892313, 15.1240416812... | {'mean': [[0.031504991319444446, 0.04723849826388889, 0.024696180555555555, 0.03147243923611111, 0.03633355034722222, 0.05255533854166667, 0.04994574652777778, 0.03917643229166667, 0.04396701388888889, 0.024283854166666667, 0.04201388888888889, 0.07078450520833333, 0.037342664930555554, 0.03257378472222222, 0.034825303... | [102.27272727272727] | {'count': 149, 'interval_mean': 55.33108108108108, 'interval_std': 2.8341272394940336} | Folk/Country | 3 |
6,723 | Latin | "{'mean': [[-193.87762451171875, -198.0846405029297, -179.4471435546875, -148.01919555664062, -186.2(...TRUNCATED) | "{'mean': [[0.8053150773048401, 0.7667430639266968, 0.8132947087287903, 0.4785346984863281, 0.557271(...TRUNCATED) | "{'mean': [[12.569758361158453, 13.402338045629454, 12.243013873906625, 16.72761993152343, 16.040547(...TRUNCATED) | "{'mean': [[0.028304036458333334, 0.028824869791666666, 0.03341471354166667, 0.06565755208333333, 0.(...TRUNCATED) | [125.0] | {'count': 143, 'interval_mean': 44.79577464788732, 'interval_std': 1.4609409103665427} | Folk/Country | 3 |
633 | Hip-Hop | "{'mean': [[-352.8139953613281, -347.4449768066406, -351.31951904296875, -348.7555847167969, -334.47(...TRUNCATED) | "{'mean': [[0.13335181772708893, 0.17087611556053162, 0.19698363542556763, 0.6349459886550903, 0.686(...TRUNCATED) | "{'mean': [[19.84609270383944, 20.04765043670865, 19.060123012536508, 19.150291771724856, 20.0107722(...TRUNCATED) | "{'mean': [[0.016107855902777777, 0.01842990451388889, 0.016145833333333335, 0.007541232638888889, 0(...TRUNCATED) | [59.840425531914896] | {'count': 75, 'interval_mean': 93.75675675675676, 'interval_std': 1.5752375265835465} | Hip-Hop | 4 |
3,161 | Punk | "{'mean': [[-34.72438430786133, -34.763179779052734, -25.455245971679688, -6.964302062988281, -16.68(...TRUNCATED) | "{'mean': [[0.30329954624176025, 0.3737560212612152, 0.4441714286804199, 0.3814219534397125, 0.30996(...TRUNCATED) | "{'mean': [[16.9546160126131, 12.61268089778352, 12.534247202972473, 16.19922653302389, 15.049959165(...TRUNCATED) | "{'mean': [[0.04918619791666667, 0.04580078125, 0.04886610243055556, 0.05872395833333333, 0.06786024(...TRUNCATED) | [148.02631578947367] | {'count': 220, 'interval_mean': 38.10958904109589, 'interval_std': 1.5280779448516368} | Rock | 8 |
5,335 | Drum and Bass | "{'mean': [[-6.289465427398682, -33.59734344482422, -44.71078872680664, -34.78848648071289, -37.1757(...TRUNCATED) | "{'mean': [[0.5125671625137329, 0.6638219952583313, 0.7012325525283813, 0.45923206210136414, 0.62422(...TRUNCATED) | "{'mean': [[13.570074189926032, 17.312502366191147, 17.344843974874216, 13.946567659247126, 14.67176(...TRUNCATED) | "{'mean': [[0.06965603298611112, 0.07030707465277777, 0.051920572916666664, 0.06187608506944445, 0.0(...TRUNCATED) | [86.53846153846153] | {'count': 131, 'interval_mean': 64.14615384615385, 'interval_std': 3.213221576456918} | Ambient/Other | 0 |
1,495 | Classical | "{'mean': [[-375.1128845214844, -371.6091003417969, -381.27484130859375, -374.3807373046875, -371.32(...TRUNCATED) | "{'mean': [[0.07431800663471222, 0.24627771973609924, 0.2390177994966507, 0.8287324905395508, 0.5097(...TRUNCATED) | "{'mean': [[14.400876668244425, 11.303908454288447, 11.053906080914546, 10.744855272413353, 10.98350(...TRUNCATED) | "{'mean': [[0.014588758680555555, 0.01669921875, 0.011952039930555556, 0.013883463541666667, 0.01203(...TRUNCATED) | [165.44117647058823] | {'count': 222, 'interval_mean': 34.19457013574661, 'interval_std': 2.1782901303866296} | Classical | 1 |
3,387 | Punk | "{'mean': [[-91.89808654785156, -174.52244567871094, -2.842862606048584, 34.679805755615234, 50.7629(...TRUNCATED) | "{'mean': [[0.42484503984451294, 0.4987553358078003, 0.6514370441436768, 0.5558352470397949, 0.55323(...TRUNCATED) | "{'mean': [[11.282614010019488, 11.629464026375864, 15.68393144450246, 12.001261323946387, 11.785180(...TRUNCATED) | "{'mean': [[0.011534288194444445, 0.0023546006944444443, 0.029329427083333335, 0.04631076388888889, (...TRUNCATED) | [110.29411764705883] | {'count': 151, 'interval_mean': 50.28, 'interval_std': 3.8920774230051824} | Rock | 8 |
4,311 | Techno | "{'mean': [[-141.8767547607422, -143.7412872314453, -110.5738525390625, -85.53426361083984, -118.264(...TRUNCATED) | "{'mean': [[0.5980082750320435, 0.6039637923240662, 0.5622550845146179, 0.6278316974639893, 0.584451(...TRUNCATED) | "{'mean': [[12.913499327464299, 13.705227126932451, 13.463709301491189, 13.85688785833313, 13.170637(...TRUNCATED) | "{'mean': [[0.009700520833333334, 0.0080078125, 0.014984809027777778, 0.01677517361111111, 0.0102267(...TRUNCATED) | [152.02702702702703] | {'count': 227, 'interval_mean': 37.25221238938053, 'interval_std': 0.6803951669503062} | Electronic | 2 |
2,795 | Soul | "{'mean': [[-211.61512756347656, -217.81747436523438, -205.0585479736328, -222.05665588378906, -238.(...TRUNCATED) | "{'mean': [[0.5886014699935913, 0.760075032711029, 0.5031370520591736, 0.2642838656902313, 0.2991839(...TRUNCATED) | "{'mean': [[23.52123259382069, 25.758878308310265, 28.292863664867188, 28.31950796884962, 28.5674460(...TRUNCATED) | "{'mean': [[0.023003472222222224, 0.01577690972222222, 0.016981336805555556, 0.015158420138888889, 0(...TRUNCATED) | [119.68085106382979] | {'count': 178, 'interval_mean': 47.63841807909605, 'interval_std': 2.8826960437370257} | Pop | 6 |
354 | Rock | "{'mean': [[-159.2598419189453, -172.42160034179688, -176.91839599609375, -175.3537139892578, -161.5(...TRUNCATED) | "{'mean': [[0.5384091138839722, 0.41365572810173035, 0.541944682598114, 0.2708336412906647, 0.655677(...TRUNCATED) | "{'mean': [[13.609397065763755, 12.985612414622588, 14.426342911108794, 13.83138656017447, 15.037933(...TRUNCATED) | "{'mean': [[0.05565863715277778, 0.051318359375, 0.061181640625, 0.051171875, 0.04993489583333333, 0(...TRUNCATED) | [122.28260869565217] | {'count': 171, 'interval_mean': 45.923529411764704, 'interval_std': 0.6945412466172151} | Rock | 8 |
End of preview.
🎵 Music Genre Classification Dataset
Questo dataset è stato progettato per la classificazione automatica dei generi musicali a partire da feature audio estratte da brani YouTube. Include diverse versioni dello stesso insieme di dati per supportare esperimenti con bilanciamento, macro-categorie e test separati.
📁 Contenuto del dataset
| File | Descrizione |
|---|---|
Tracks1.csv |
Contiene metadati grezzi: ID del brano, titolo, artista e genere originale. Usato per costruire il dataset di feature. |
TrackFeature4.csv |
Dataset originale: ogni riga rappresenta un brano con le sue feature audio e il genere corrispondente. |
dataset_macro_generi.csv |
Versione del dataset in cui i 29 generi originali sono stati uniti in 9 macro-generi, per semplificare la classificazione. |
test_clean.csv |
Test set derivato da dataset_macro_generi.csv, prima dell'applicazione dell'oversampling. |
test_clean_title.csv |
Versione del test set con l'aggiunta del titolo del brano come feature testuale (title_text). |
train_balanced.csv |
Training set bilanciato tramite oversampling, con lo stesso numero di brani per ciascun macro-genere. |
train_balanced_title.csv |
Versione del training set bilanciato con l'aggiunta del titolo del brano (title_text). |
📊 Feature
Le feature audio sono state estratte con librosa da segmenti centrali di 90 secondi di ogni canzone, campionati a 90 frame al secondo. Per ogni secondo, sono state calcolate media e deviazione standard, producendo un insieme compatto ma informativo di statistiche audio.
🧪 Obiettivo
Il dataset è stato utilizzato per addestrare modelli di classificazione in grado di riconoscere il genere musicale di un brano, esplorando anche l’uso combinato di feature audio e testuali (titolo della canzone).
📎 Licenza
apache-2.0
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