scrapeURmusic
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Build a music dataset from
eleven open sources.

Song metadata, time-synced lyrics and full-length audio — collected into one flat folder and exported to Excel, SQL, PostgreSQL and JSON. No API keys, nothing to sign up for.

Open the console See how it works
Every bar is one track. Height is its duration, colour is whether full audio came back.

Console

Set the size of the run, choose what goes in it, then start.

Tracks to collect
new tracks

Release years 1992 – 2026
Genres

What to collect
Monitor
Idle

Nothing running yet.

Local
0
Intl
0
Full audio
0
Skipped
0
Failed
0

Every step — sources queried, tracks saved, failures — shows up here once a run starts.

Collected tracks

Everything the current run has written to disk.

# Artist Title Year Region Audio Lyrics File
No tracks yet — start a run in the console above.

How a run works

Four stages, in order. Each one hands its output to the next.

  1. 1

    Discover

    Chart and metadata sources are queried for the seed artists in the genres you picked. Slow sources are skipped once there are enough candidates.

  2. 2

    Filter

    Candidates are merged across sources, deduplicated four ways — normalised key, ISRC, fuzzy title, then audio hash — and balanced between local and international.

  3. 3

    Fetch

    Full-length audio via yt-dlp, time-synced lyrics from LRCLIB, and release metadata backfilled from Wikipedia. Every file is verified before it counts.

  4. 4

    Export

    One flat output folder plus combined exports: Excel, generic SQL, a PostgreSQL dump and JSON.

Sources

Every one is open. Anything that demanded a key, token or OAuth was removed from the project rather than left switched off.