Update README.md

README.md

@@ -1,3 +1,85 @@
 ---
 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("DISCOX/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
+})
+"""
+```