Multilingual Transcription Engine — Apple Music Lyrics Operations
MULTILINGUAL TRANSCRIPTION ENGINE
Prototype · Lyrics Ops AIML & Tooling
Apple Music — Lyrics Operations

Multilingual Transcription Engine

Draft · Correct · Verify

A first draft for every track. An LLM corrects it. Editors verify what's uncertain.

listening across 5 tracks · 5 languages detecting…
Try it

Five tracks — or upload your own.

Raw ASR draft

unpunctuated · unedited

After LLM correction

awaiting run
Click "Run correction" to send this draft through the LLM correction stage.
How it works

A signal chain, not a black box.

1 · Vocal isolation

Vocals isolated from the mix before transcription.

Runs server-side

2 · ASR draft

A first-pass transcript, tuned for singing, not speech.

Runs server-side

3 · LLM correction

Homophones, word breaks, and mis-hears resolved in context.

● Live in this demo

4 · Confidence routing

Uncertain lines go to an editor. Certain lines move on.

● Live in this demo

5 · Feedback loop

Every correction retrains the next draft.

Runs server-side
Track credits — under the hood
ASR backboneWhisper large-v3 / wav2vec 2.0 XLS-R — server-side in production
Vocal isolationDemucs source separation — server-side in production
Correction modelClaude, live via API — the one stage this demo actually calls
Routing logicConfidence-threshold triage — live in this demo
Feedback loopApproved corrections re-enter training data — production only
Human roleVerify and edit — not transcribe from scratch
In production

From catalog gap to published lyric.

1 · Detect

New ingestion checked against the licensed-lyrics feed. Gaps outside Tier 1 auto-enqueue.

2 · Prioritize

Queued by release recency, streaming signal, and market coverage targets — not ingestion order.

3 · Route by confidence

High confidence goes to spot-check. Medium goes to standard review. Low goes to a specialist or holds.

4 · Editor acts

Approve confirms the draft. Edit corrects it. Reject escalates it — nothing publishes on a guess.

5 · Publish + feedback

Approved and edited lines sync to TTML and ship. Edits feed the next fine-tune.

Describes production operation — the demo above runs the signal-chain stages only.
The coverage gap

Human transcription doesn't scale past the top of the catalog.

Tier 1 · Global-language, high-catalog-value
92%
Already solved.
Tier 2 · Major non-English markets
61%
Headcount hasn't kept pace with growth.
Tier 3 · Long-tail, emerging-market, independent
18%
Where manual transcription doesn't reach.
Illustrative tiering for discussion purposes — not Apple Music catalog data.

The bottleneck was never talent. It was time.