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+# 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)
+
+## 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