Datasets:
Tasks:
Audio Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K<n<10K
License:
File size: 4,982 Bytes
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---
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](https://huggingface.co/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:
```python
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](https://huggingface.co/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:
```python
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 |