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Dataset Card for GTZAN

Dataset Summary

GTZAN is a dataset for musical genre classification of audio signals. The dataset consists of 1,000 audio tracks, each of 30 seconds long. It contains 10 genres, each represented by 100 tracks. The tracks are all 22,050Hz Mono 16-bit audio files in WAV format. The genres are: blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, and rock.



Dataset Structure

GTZAN is distributed as a single dataset without a predefined training and test split. The information below refers to the single train split that is assigned by default.

Data Instances

An example of GTZAN looks as follows:

    "file": "/path/to/cache/genres/blues/blues.00000.wav",
    "audio": {
        "path": "/path/to/cache/genres/blues/blues.00000.wav",
        "array": array(
        "sampling_rate": 22050,
    "genre": 0,

Data Fields

The types associated with each of the data fields is as follows:

  • file: a string feature.
  • audio: an Audio feature containing the path of the sound file, the decoded waveform in the array field, and the sampling_rate.
  • genre: a ClassLabel feature.

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]


Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

author    = "Tzanetakis, George and Essl, Georg and Cook, Perry",
title     = "Automatic Musical Genre Classification Of Audio Signals",
url       = "",
publisher = "The International Society for Music Information Retrieval",
year      = "2001"


Thanks to @lewtun for adding this dataset.

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