metadata
dataset_info:
features:
- name: analysis_sample_rate
dtype: int32
- name: artist_7digitalid
dtype: int32
- name: artist_familiarity
dtype: float64
- name: artist_hotttnesss
dtype: float64
- name: artist_id
dtype: string
- name: artist_latitude
dtype: float64
- name: artist_location
dtype: string
- name: artist_longitude
dtype: float64
- name: artist_mbid
dtype: string
- name: artist_mbtags
sequence: binary
- name: artist_mbtags_count
sequence: int64
- name: artist_name
dtype: string
- name: artist_playmeid
dtype: int32
- name: artist_terms
sequence: binary
- name: artist_terms_freq
sequence: float64
- name: artist_terms_weight
sequence: float64
- name: audio_md5
dtype: string
- name: bars_confidence
sequence: float64
- name: bars_start
sequence: float64
- name: beats_confidence
sequence: float64
- name: beats_start
sequence: float64
- name: danceability
dtype: float64
- name: duration
dtype: float64
- name: end_of_fade_in
dtype: float64
- name: energy
dtype: float64
- name: key
dtype: int32
- name: key_confidence
dtype: float64
- name: loudness
dtype: float64
- name: mode
dtype: int32
- name: mode_confidence
dtype: float64
- name: num_songs
dtype: int64
- name: release
dtype: string
- name: release_7digitalid
dtype: int32
- name: sections_confidence
sequence: float64
- name: sections_start
sequence: float64
- name: segments_confidence
sequence: float64
- name: segments_loudness_max
sequence: float64
- name: segments_loudness_max_time
sequence: float64
- name: segments_loudness_start
sequence: float64
- name: segments_pitches
sequence:
sequence: float64
- name: segments_start
sequence: float64
- name: segments_timbre
sequence:
sequence: float64
- name: similar_artists
sequence: binary
- name: song_hotttnesss
dtype: float64
- name: song_id
dtype: string
- name: start_of_fade_out
dtype: float64
- name: tatums_confidence
sequence: float64
- name: tatums_start
sequence: float64
- name: tempo
dtype: float64
- name: time_signature
dtype: int32
- name: time_signature_confidence
dtype: float64
- name: title
dtype: string
- name: track_7digitalid
dtype: int32
- name: track_id
dtype: string
- name: year
dtype: int32
splits:
- name: train
num_bytes: 2365768621
num_examples: 10000
download_size: 1041881893
dataset_size: 2365768621
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Million Song Subset (Processed Version)
Overview
This dataset is a structured extraction of the Million Song Subset, derived from HDF5 files into a tabular format for easier accessibility and analysis.
Source
- Original dataset: Million Song Dataset (LabROSA, Columbia University & The Echo Nest)
- Subset used: Million Song Subset (10,000 songs)
- URL: http://millionsongdataset.com
Processing Steps
- Extraction: Used
hdf5_getters.pyto retrieve all available fields. - Parallel Processing: Optimized extraction with
ProcessPoolExecutorfor speed. - Conversion: Structured into a Pandas DataFrame.
- Storage: Saved as a Parquet file for efficient usage.
Format
- Columns: Contains all available attributes from the original dataset, including artist metadata, song features, and audio analysis.
- File Format: Parquet (optimized for efficient querying & storage).
Usage
- Load the dataset with Datasets:
from datasets import load_dataset ds = load_dataset("trojblue/million-song-subset") - Explore and analyze various musical attributes easily.
License
- Original License: Refer to the Million Song Dataset license
- Processed Version: Shared for research and non-commercial purposes.
For more details, visit the Million Song Dataset website.
Appendix: Processing Code
The dataset was converted using the following snippet:
import os
import unibox as ub
import pandas as pd
import numpy as np
import h5py
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor
# https://github.com/tbertinmahieux/MSongsDB/blob/0c276e289606d5bd6f3991f713e7e9b1d4384e44/PythonSrc/hdf5_getters.py
import hdf5_getters
# Define dataset path
dataset_path = "/lv0/yada/dataproc5/data/MillionSongSubset"
# Function to extract all available fields from an HDF5 file
def extract_song_data(file_path):
"""Extracts all available fields from an HDF5 song file using hdf5_getters."""
song_data = {}
try:
with hdf5_getters.open_h5_file_read(file_path) as h5:
# Get all getter functions from hdf5_getters
getters = [func for func in dir(hdf5_getters) if func.startswith("get_")]
for getter in getters:
try:
# Dynamically call each getter function
value = getattr(hdf5_getters, getter)(h5)
# Optimize conversions
if isinstance(value, np.ndarray):
value = value.tolist()
elif isinstance(value, bytes):
value = value.decode()
# Store in dictionary with a cleaned-up key name
song_data[getter[4:]] = value
except Exception:
continue # Skip errors but don't slow down
except Exception as e:
print(f"Error processing {file_path}: {e}")
return song_data
# Function to process multiple files in parallel
def process_files_in_parallel(h5_files, num_workers=8):
"""Processes multiple .h5 files in parallel."""
all_songs = []
with ProcessPoolExecutor(max_workers=num_workers) as executor:
for song_data in tqdm(executor.map(extract_song_data, h5_files), total=len(h5_files)):
if song_data:
all_songs.append(song_data)
return all_songs
# Find all .h5 files
h5_files = [os.path.join(root, file) for root, _, files in os.walk(dataset_path) for file in files if file.endswith(".h5")]
# Process files in parallel
all_songs = process_files_in_parallel(h5_files, num_workers=24)
# Convert to Pandas DataFrame
df = pd.DataFrame(all_songs)
ub.saves(df, "hf://trojblue/million-song-subset", private=False)