README.md

---
license: mit
tags:
- music
pretty_name: FMA rank
size_categories:
- 100K<n<1M
---

# What is FMA-rank?

FMA is a music dataset from the Free Music Archive, containing over 8000 hours of Creative Commons-licensed music from 107k tracks across 16k artists and 15k albums.
It was created in 2017 by [Defferrard et al.](https://arxiv.org/abs/1612.01840) in collaboration with [Free Music Archive](https://freemusicarchive.org/).

FMA contains a lot of good music, and a lot of bad music, so the question is: can we rank the samples in FMA?

FMA-rank is a CLAP-based statistical ranking of each sample in FMA. We calculate the log-likelihood of each sample in FMA belonging to an estimated gaussian in the CLAP latent space, using these values we can rank and filter FMA. In log-likelihood, higher values are better.

# Quickstart

Download any FMA split from the official github https://github.com/mdeff/fma. Extract the FMA folder from the downloaded zip and set the path to the folder in `fma_root_dir`.
Run the following code snippet to load and filter the FMA samples according to the given percentages. The code snippet will return a HF audio dataset.
```Python
from datasets import load_dataset, Dataset, Audio
import os

# provide location of fma folder
fma_root_dir = "/path/to/fma/folder"

# provide percentage of fma dataset to use
# for whole dataset, use start_percentage=0 and end_percentage=100
# for worst 20% of dataset, use start_percentage=0 and end_percentage=20
# for best 20% of dataset, use the following values:
start_percentage = 80
end_percentage = 100

# load fma_rank.csv from huggingface and sort from lowest to highest
csv_loaded = load_dataset("disco-eth/FMA-rank")
fma_item_list = csv_loaded["train"]
fma_sorted_list = sorted(fma_item_list, key=lambda d: d['CLAP-log-likelihood'])

def parse_fma_audio_folder(fma_root_dir):
    valid_fma_ids = []
    subfolders = os.listdir(fma_root_dir)
    for subfolder in subfolders:
        subfolder_path = os.path.join(fma_root_dir, subfolder)
        if os.path.isdir(subfolder_path):
            music_files = os.listdir(subfolder_path)
            for music_file in music_files:
                if ".mp3" not in music_file:
                    continue
                else:
                    fma_id = music_file.split('.')[0]
                    valid_fma_ids.append(fma_id)
    return valid_fma_ids

# select the existing files according to the provided fma folder
valid_fma_ids = parse_fma_audio_folder(fma_root_dir)
df_dict = {"id":[], "score": [], "audio": []}
for fma_item in fma_sorted_list:
    this_id = f"{fma_item['id']:06d}"
    if this_id in valid_fma_ids:
        df_dict["id"].append(this_id)
        df_dict["score"].append(fma_item["CLAP-log-likelihood"])
        df_dict["audio"].append(os.path.join(fma_root_dir, this_id[:3] , this_id+".mp3"))

# filter the fma dataset according to the percentage defined above
i_start = int(start_percentage * len(df_dict["id"]) / 100)
i_end = int(end_percentage * len(df_dict["id"]) / 100)
df_dict_filtered = {
    "id": df_dict["id"][i_start:i_end],
    "score": df_dict["score"][i_start:i_end],
    "audio": df_dict["audio"][i_start:i_end],
}

# get final dataset
audio_dataset = Dataset.from_dict(df_dict_filtered).cast_column("audio", Audio())

"""
Dataset({
    features: ['id', 'score', 'audio'],
    num_rows: 1599
})
"""
```