Dragunflie-420/FMADataset

Free Music Archive (FMA) Dataset

Overview

This repository contains the Free Music Archive (FMA) dataset, curated and made available on Hugging Face by dragunflie-420. The FMA dataset is a large-scale, open-source dataset of music tracks, designed for music information retrieval and machine learning tasks.

Dataset Description

The Free Music Archive (FMA) is an open and easily accessible dataset consisting of full-length audio tracks with associated metadata. This particular version focuses on the "small" subset of the FMA, which includes:

• 8,000 tracks of 30 seconds each
• 8 balanced genres (Electronic, Experimental, Folk, Hip-Hop, Instrumental, International, Pop, Rock)
• Audio files in 128k MP3 format
• Comprehensive metadata for each track

Contents

This dataset provides:

1. Audio files: 30-second MP3 clips of music tracks
2. Metadata: Information about each track, including:
   • Track ID
   • Title
   • Artist
   • Genre
   • Additional features (e.g., acoustic features, music analysis data)

Data Files

To use this dataset, you need to manually download and place the following files in the repository:

1. fma_small.zip: Contains the audio files
2. fma_metadata.zip: Contains the metadata for the tracks

After downloading, extract these files and ensure the following directory structure:

fma_dataset/
├── fma_small/
│   ├── 000/
│   ├── 001/
│   └── ...
└── fma_metadata/
    ├── tracks.csv
    ├── genres.csv
    └── features.csv

Usage

To use this dataset in your Hugging Face projects:

from datasets import load_dataset

dataset = load_dataset("dragunflie-420/fma")

# Access the first example
first_example = dataset['train'][0]
print(first_example['title'], first_example['artist'], first_example['genre'])

# Play the audio (if in a notebook environment)
from IPython.display import Audio
Audio(first_example['audio']['array'], rate=first_example['audio']['sampling_rate'])

[... rest of the README content remains the same ...]

---

language: - en license: cc-by-4.0 pretty_name: Free Music Archive (FMA) Dataset size_categories: - 1K<n<10K source_datasets: - original task_categories: - audio-classification task_ids: - multi-class-classification

Free Music Archive (FMA) Dataset

Overview

This repository contains the Free Music Archive (FMA) dataset, curated and made available on Hugging Face by dragunflie-420. The FMA dataset is a large-scale, open-source dataset of music tracks, designed for music information retrieval and machine learning tasks.

[... rest of the README content remains the same ...] Free Music Archive (FMA) Dataset

Dataset Description

The Free Music Archive (FMA) is an open and easily accessible dataset consisting of full-length audio tracks with associated metadata. This particular version focuses on the "small" subset of the FMA, which includes:

• 8,000 tracks of 30 seconds each
• 8 balanced genres (Electronic, Experimental, Folk, Hip-Hop, Instrumental, International, Pop, Rock)
• Audio files in 128k MP3 format
• Comprehensive metadata for each track

Contents

This dataset provides:

1. Audio files: 30-second MP3 clips of music tracks
2. Metadata: Information about each track, including:
   • Track ID
   • Title
   • Artist
   • Genre
   • Additional features (e.g., acoustic features, music analysis data)

Usage

To use this dataset in your Hugging Face projects:

from datasets import load_dataset

dataset = load_dataset("dragunflie-420/fma")

# Access the first example
first_example = dataset['train'][0]
print(first_example['title'], first_example['artist'], first_example['genre'])

# Play the audio (if in a notebook environment)
from IPython.display import Audio
Audio(first_example['audio']['array'], rate=first_example['audio']['sampling_rate'])

Dataset Structure

Each example in the dataset contains:

• track_id: Unique identifier for the track
• title: Title of the track
• artist: Name of the artist
• genre: Top-level genre classification
• audio: Audio file in the format compatible with Hugging Face's Audio feature

Applications

This dataset is suitable for various music information retrieval and machine learning tasks, including:

• Music genre classification
• Artist identification
• Music recommendation systems
• Audio feature extraction and analysis
• Music generation and style transfer

Citation

If you use this dataset in your research, please cite the original FMA paper:

@inproceedings{defferrard2016fma,
  title={FMA: A Dataset for Music Analysis},
  author={Defferrard, Micha{\"e}l and Ben