README.md

metadata

license: other
task_categories:
  - audio-classification
pretty_name: DaCoS Dataset
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train/audio/*.tar
tags:
  - audio
  - music
  - cover-song-identification
  - dacos
  - webdataset

Yougen/datacos_dataset

DaCoS (Da Cover Song) audio dataset, packed as WebDataset tar shards. Each row in the original output.csv is one recording; rows sharing the same clique are alternative versions ("covers") of the same underlying song.

Layout

data/
  train/
    metadata.csv
    audio/
      train-000.tar
      train-001.tar
      ...

Shard counts:

• train: 90 tar shard(s)

Inside each tar, every sample is a pair sharing a unique key:

<key>.mp3       # raw MP3 bytes
<key>.json      # {"id":..., "rel_path":..., "wav_format":"mp3",
                #   "duration":..., "label_str":"<clique>", "label":<int>,
                #   "clique":..., "version":..., "title":...,
                #   "performer":..., "video_id":...}

metadata.csv columns: key, shard, id, rel_path, wav_format, duration, label_str, label, clique, version, title, performer, video_id

Loading

from datasets import load_dataset

ds = load_dataset("Yougen/datacos_dataset")
print(ds)
print(ds["train"][0])
# sample keys: 'mp3' (decoded audio), 'json' (metadata), '__key__', '__url__'

For streaming (no full download needed):

ds = load_dataset("Yougen/datacos_dataset", streaming=True)
for example in ds["train"]:
    print(example["__key__"], example["json"]["clique"], example["json"]["title"])
    break

HuggingFace's webdataset builder will automatically pair <key>.mp3 with <key>.json inside every tar and decode the audio.