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GTZAN Genre Dataset

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Visualization of the GTZAN Genre dataset in the Deep Lake UI

GTZAN dataset

What is GTZAN Genre Dataset?

In 2002, G. Tzanetakis and P. Cook presented their well-known article on genre classification, “Musical genre classification of audio signals”, published in IEEE Transactions on Audio and Speech Processing.

GTZAN Genre Dataset represents a total of 1000 audio tracks with a 30-second duration contained in the dataset. The dataset is divided into a total of 10 genres, each with 100 tracks. All the tracks are 22050Hz Mono 16-bit audio files in .wav format.

Download GTZAN genre Dataset in Python

Instead of downloading the GTZAN dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load GTZAN genre Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/gtzan-genre")

GTZAN genre Dataset Structure

GTZAN genre Data Fields
  • audio: a tensor containing audio.
  • genre: a class label tensor to classify audio into 10 classes
GTZAN genre Data Splits
  • GTZAN genre contains a single split with 10000 audio tracks

How to use GTZAN genre Dataset with PyTorch and TensorFlow in Python

Train a model on GTZAN genre dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line data loader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
Train a model on the GTZAN genre dataset with TensorFlow in Python
				
					dataloader = ds.tensorflow()

Additional Information about GTZAN genre Dataset

Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

Citation Information
				
					@ONLINE {kaggle-diabetic-retinopathy,
    author = "Kaggle and EyePacs",
    title  = "Kaggle Diabetic Retinopathy Detection",
    month  = "jul",
    year   = "2015",
    url    = "https://www.kaggle.com/c/diabetic-retinopathy-detection/data"
}

GTZAN genreDataset FAQs

What is the GTZAN Genre Collection dataset for Python?

The GTZAN genre collection dataset consists of 1000 audio files each being 30 seconds in duration. The dataset contains 10 classes that represent 10 music genres. The music genres include blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, and rock. Each class contains 100 audio tracks that are in .wav format.

What is the GTZAN Genre collection dataset used for?

The GTZAN Genre collection dataset is used as a benchmark dataset in the field of machine learning. The GTZAN Genre collection dataset is used for music classification into different genres. This dataset was used in the popular paper “Musical genre classification of audio signals” by G. Tzanetakis and P. Cook.

How to download the GTZAN Genre dataset in Python?

With the open-source package Activeloop Deep Lake you can load the GTZAN Genre dataset fast with one line of code in Python. See detailed instructions on how to load the GTZAN Genre dataset training subset in Python.

How can I use the GTZAN Genre dataset in PyTorch or TensorFlow?

Using the open-source package Activeloop Deep Lake you can stream the GTZAN Genre dataset while training a model in PyTorch or TensorFlow with one line of code. See detailed instructions on how to train a model on the GTZAN Genre dataset with PyTorch in Python or train a model on the GTZAN Genre dataset with TensorFlow in Python.

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