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--- |
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license: mit |
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tags: |
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- music |
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pretty_name: FMA rank |
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size_categories: |
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- 100K<n<1M |
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--- |
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# What is FMA-rank? |
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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. |
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It was created in 2017 by [Defferrard et al.](https://arxiv.org/abs/1612.01840) in collaboration with [Free Music Archive](https://freemusicarchive.org/). |
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FMA contains a lot of good music, and a lot of bad music, so the question is: can we rank the samples in FMA? |
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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. |
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# Quickstart |
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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`. |
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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. |
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```Python |
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from datasets import load_dataset, Dataset, Audio |
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import os |
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# provide location of fma folder |
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fma_root_dir = "/path/to/fma/folder" |
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# provide percentage of fma dataset to use |
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# for whole dataset, use start_percentage=0 and end_percentage=100 |
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# for worst 20% of dataset, use start_percentage=0 and end_percentage=20 |
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# for best 20% of dataset, use the following values: |
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start_percentage = 80 |
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end_percentage = 100 |
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# load fma_rank.csv from huggingface and sort from lowest to highest |
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csv_loaded = load_dataset("DISCOX/FMA-rank") |
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fma_item_list = csv_loaded["train"] |
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fma_sorted_list = sorted(fma_item_list, key=lambda d: d['CLAP-log-likelihood']) |
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def parse_fma_audio_folder(fma_root_dir): |
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valid_fma_ids = [] |
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subfolders = os.listdir(fma_root_dir) |
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for subfolder in subfolders: |
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subfolder_path = os.path.join(fma_root_dir, subfolder) |
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if os.path.isdir(subfolder_path): |
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music_files = os.listdir(subfolder_path) |
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for music_file in music_files: |
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if ".mp3" not in music_file: |
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continue |
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else: |
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fma_id = music_file.split('.')[0] |
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valid_fma_ids.append(fma_id) |
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return valid_fma_ids |
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# select the existing files according to the provided fma folder |
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valid_fma_ids = parse_fma_audio_folder(fma_root_dir) |
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df_dict = {"id":[], "score": [], "audio": []} |
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for fma_item in fma_sorted_list: |
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this_id = f"{fma_item['id']:06d}" |
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if this_id in valid_fma_ids: |
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df_dict["id"].append(this_id) |
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df_dict["score"].append(fma_item["CLAP-log-likelihood"]) |
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df_dict["audio"].append(os.path.join(fma_root_dir, this_id[:3] , this_id+".mp3")) |
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# filter the fma dataset according to the percentage defined above |
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i_start = int(start_percentage * len(df_dict["id"]) / 100) |
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i_end = int(end_percentage * len(df_dict["id"]) / 100) |
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df_dict_filtered = { |
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"id": df_dict["id"][i_start:i_end], |
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"score": df_dict["score"][i_start:i_end], |
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"audio": df_dict["audio"][i_start:i_end], |
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} |
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# get final dataset |
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audio_dataset = Dataset.from_dict(df_dict_filtered).cast_column("audio", Audio()) |
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""" |
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Dataset({ |
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features: ['id', 'score', 'audio'], |
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num_rows: 1599 |
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}) |
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""" |
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``` |
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