Multiverse Product Brief
Video walkthrough: overview of this doc, overall opportunity, and flushed out v1 of the product. If you prefer reading it in Google Doc form, here's the original Google Doc for the spec.
“Somewhere, your song is already finished. Go find it.”
Multiverse is a songwriting sketchpad that turns a lyric into dozens of fully sung versions in seconds, so writers can build on the ones they love, change the words or the length, and hear their song come off the page while the idea is still fresh, which is what gets a song finished.
The Multiverse launch video, 1:08. Sound on.
Idea & product framework
I chose the idea using a framework built around what DiffRhythm can do today and where it's headed:
Take advantage of the model's breakthroughs
DiffRhythm is about 50x faster than SongLM, so it's also cheap, and it generates a full-length song without us having to combine lyrics, vocals and instruments. That favors fast, inexpensive experimentation. If a song took 5 minutes and the experience got a lot worse, that’s a good proxy that we’ve found an idea that fits the model.
Work today and turn the limitations into strengths
You need to come in with your own lyrics and a reference sound, the words aren't always intelligible, and it doesn't consistently follow a chorus or melody. Writers already come in with lyrics, and sometimes a riff they've recorded, and they know their own words well enough to judge a rough take.
Show latent demand
People are already trying to use DiffRhythm this way. On the DiffRhythm Hacker News thread:
An amateur writer said they want tools that "help me enact MY vision," and that "the most exciting possibilities of this is enabling talented writers with day jobs, not one click song writing"Hacker News
A professional in the same thread said that “at best, these tools are for getting ideas rather than creating output that will be used directly”
Have a large market with recurring propensity to pay
Songwriters already pay to hear their songs: $250–900 per studio demo (Underground Treehouse charges $600 to turn a work tape and lyrics into one), plus subscriptions to tools like Hookpad, Band-in-a-Box and ACE Studio (a reported $30–60M run rate). With 2M paying Suno subscribers and ElevenLabs' new lyrics-first Composer, the willingness to pay is proven.
Get better as the model gets better
As editing, inpainting and new languages improve, the product improves and the market expands into adjacent use cases.
Multiverse does well on all five. Writers can generate tens or hundreds of variations, keep exploring the ones they like, and get a strong direction for the song before they polish it. See Appendix for ideas we considered but ruled out due to not fitting the framework.
Ideas we ruled outAudience
We're starting with songwriters, specifically the ones who write lyrics first.
Why them
They already come in with what the model needs: lyrics, a structure and a feel in mind. They're also the right audience for its limitations, since they know their own words and want a sketch, not a release-quality track.
They already pay
ASCAP alone has over 1.1 million songwriter and publisher members and paid out $1.76B in 2025 (ASCAP). Writers spend a few hundred dollars per demo today, and the Underground Treehouse in Nashville charges $600 to turn a work tape and lyrics into one (Underground Treehouse).
We learn from experts first and go downstream
Every song they keep, every line they rewrite and every version they build on tells us what good sounds like. That gives us strong edit data and ground truth to make the product work for less experienced writers.
Where it goes next
The same product works for people with nearly the same job, like musical-theatre writers, poets and agencies mocking up a jingle for a pitch. Marketers come second, since most of what they need is instrumental. Once songwriting works, we'd test new use cases like workout and worship songs, and finished jingles as word accuracy improves.
User experience
Current songwriter pain points
UX inspiration
Exploration tools
Multiverse borrows from tools like Lovable and FLORA, where you try lots of directions quickly and keep building on the ones you like, instead of trying to get one perfect output.
Music editors
The lanes come from how producers compare recordings today. In Logic Pro and Ableton Live, each take of a vocal sits in its own lane, so you can hear the same moment across takes and keep the best parts. Multiverse does the same with whole songs: one lane per version, one column per section.
Multiverse user journey
Recorded from the working prototype. Pick a step to see it.
Safeguards for quality
Word check
Whisper checks every song against the lyrics, and we keep making songs until enough are clean.
Timing check
Timings are set automatically, and writers get a warning when a line is too fast to sing.
Success metrics & evals
Weekly active writers who export or share a song
This is a weekly active user count that only includes high-quality use: a writer counts when they export or share a song made from their own lyrics that week. That’s the clearest signal they made something worth using, and making more songs doesn’t move it. We report it next to the percentage of all active writers, and it should predict revenue, since writers who regularly leave with a usable song are the ones who keep paying.
Other north stars we consideredSupporting metrics
Evals
See the latest run at app.makemultiverse.com/evals with full details, or read more in the appendix:
Core behaviors the product depends on, which must pass before anything ships. For example, songs come out clean in English and Chinese, lines land on their timing, and the model doesn't invent lyrics.
Known limits of today's model, rerun on every new checkpoint to see whether it's closing the gap. For example, singing in Korean and Spanish, and a chorus that sounds the same each time it returns.
The same checks on every real song. Songs with timing misses, dead air, the wrong length, the wrong language or invented lyrics go to a review list. New model versions get a small share of songs before full rollout.
Go-to-market, pricing and legal are in the appendix: Go-to-market Pricing Legal
Research direction & next steps
I'd prioritize research in three buckets: improving the quality and reliability of today's workflow, building for latent demand, and expanding the TAM.
Quality and reliability of today's workflow
- Word accuracy: Lost words are probably one of the top reasons a song isn't kept. On GitHub, one user calls DiffRhythm "really very good quality" but says "it often fails to follow the lyrics" (GitHub), and about half our songs come out clean today.
- Train on more lyric-heavy songs: The model drops words most on fast, crowded lines, so more training songs with dense lyrics should help.
- Tie each lyric line more tightly to the vocal: Generate the song in chunks, so each part of the lyric lines up with the stretch of audio where it's sung.
- Song structure: Listeners on the DiffRhythm Hacker News thread hear no recognizable chorus, and the paper names long-range consistency as a weakness.
- Teach the model what sections are: Label verses and choruses in training, so it learns a chorus is meant to come back the same way.
- Let later parts look back at earlier ones: When the chorus returns, the model should hear how it sounded the first time.
- Melody: Every new song has a new tune, and writers can't steer it. Taking a hummed or played melody as an input would let writers keep the tune they hear in their head.
- Learning from what writers keep: Every song a writer keeps, skips, rewrites or exports tells us what good sounds like. Training the model on those preferences improves all three of the above, and Multiverse collects that data from expert writers by default.
Latent demand
- Section editing and inpainting: Writers already crop the best part of a song and regenerate around it (Hacker News). Fixing one part without remaking the whole song is how they actually revise. Writers ask for:
- Fix one line: Re-sing a line the model dropped or slurred, and keep the rest.
- Replace a section: Try a new chorus or bridge while the verses stay the same.
- Extend a song: Add a verse or an outro to a song they already like.
- Agentic song editing: With the strong trend in coding agents, writers could describe edits in plain words, like "use song 3's verse with song 7's chorus" or "fix the line it dropped," and an agent does the cutting, regenerating and joining. Line timings give it exact cut points, and fixed seeds keep the tune aligned between versions.
Expanding the TAM & personalization
- Your own voice and style: Recording your voice, and a memory of your taste and past songs, so every round sounds more like you, will likely improve retention and northstar metrics. Suno is heading the same way, with features for your own voice, your preferences and your own music.
- More languages: The model only pronounces English and Chinese today. Korean and Spanish open up markets like K-pop, and we already track them as moonshot evals.
- Licensed fine-tuning for labels and catalogs: Once the model is trained on licensed data, labels can fine-tune on their own catalog. This is where monetization grows beyond individual writers.
Appendix / FAQs
How did you use AI?
I used Claude Code for nearly everything, with Wispr Flow for dictation: an orchestrator agent plus subagents for the write-up, the promo video, the landing page, brainstorming and the RunPod GPU deployment. The ideas, framework and product decisions are mine; Claude helped me research, structure, build and tighten them.
What other ideas did you consider but not pursue, and why?
- Language learning: The words have to be correct, and a learner wouldn't know when they aren't. I prototyped this first and dropped it.
- Kids' singing device: It's a big market (Tonies made €630M in 2025), but it doesn't use the speed, since a bedtime song can be made ahead of time. Unclear words are also a dealbreaker, because you can't guarantee what a kid hears.
- Fine-tuning for labels: This is the model Thinking Machines and Black Forest Labs use for text and images, but in music the buyer is the rights holder. The paper doesn’t say where its training songs came from or show it can capture a specific singer’s voice, so labels can’t use it yet. It’s a likely later revenue line once the model is trained on licensed data.
- Affirmation and gift songs: Both fit the model, but the markets are small or one-off.
What other northstar metrics did you consider?
- Songs made: Rewards burning credits. More songs can mean the model is failing and writers are retrying.
- Weekly active writers: Says nothing about whether writers got anything useful.
- Songs kept: Keeping costs nothing, so it inflates easily, and it counts songs, not people.
- Exported or shared songs per week: Close, but a few heavy users can carry it.
- Paying writers: The strongest proof of value, but too slow for a weekly metric.
What are the regression evals?
- Same seed, same song: Two songs made with one seed match, at the exact length asked for.
- Clean songs: At least 7 of 20 songs clean, in English and in Chinese. About half are clean today. This bar catches a drop to 25% most of the time without failing a healthy model.
- Lines on time: 90% of lines start within 3 s of their timing, and none more than 5 s off.
- No dead air: No silent stretch of 6 s or more mid-song.
- Gaps stay instrumental: 2 words or fewer heard where no lyric is written.
- No invented lyrics: Under 5% of heard words are outside the lyric, and there's no made-up run of 4+ words. Today's misses are short soundalikes the transcriber misheard.
- Speed: A song in under 8 s, and a round of four in under 30 s, on a warm GPU.
What are the moonshot evals?
- Korean and Spanish: Clean rate and language match. Near zero today, since the model only pronounces English and Chinese.
- Consistent chorus: Listeners judge whether a returning chorus keeps its tune. An automatic score replaces listening once it agrees with them on 85% of pairs.
What's the go-to-market strategy?
- Recruit from songwriting communities with a free "hear your lyrics 12 ways" offer:
- r/Songwriting: about 988K subscribers
- r/WeAreTheMusicMakers: about 3.7M members
- r/lyricists: smaller, but the closest fit for lyric-first writers
- Partner with songwriting organizations to reach writers who already pitch songs:
- Onboard each writer one by one: Watch how early writers use it, what they keep, and what they do with their songs.
- Launch big for FAWM: February Album Writing Month challenges writers to write 14 songs in 28 days. We'd launch there with a free promotion.
How do we handle pricing?
- Paid from day one: We assume writers pay from launch, like Suno, which pairs a free tier with paid plans and has reached 2M paid subscribers.
- What a song costs us:
- We host DiffRhythm on a 48 GB GPU on RunPod at about $1.75 to $1.90 an hour.
- A 1:35 song takes about 5.5 seconds, roughly $0.003 of GPU time, plus about $0.005 to run Whisper on it. That's under a cent per song.
- The real cost is idle time: keeping the GPU warm costs about $1,350 a month whether or not anyone is making songs, which roughly 135 Writer subscribers cover.
- Plans:
- Free: 50 songs to start, plus 50 for each writer who joins from your share link. We expect writers to be activated within about 20 songs (two or three rounds to find a song they like, then one build-on), so 50 leaves room for a second project, at about $0.40 per new writer.
- Writer, $10 a month: 400 songs, a 68% margin even if every song is used.
- Studio, $30 a month: 1,500 songs plus longer songs, a 60% margin even if every song is used.
- What's free and what's paid: Sharing stays free because it drives growth. Exporting and commercial use are paid, like Suno. We'd test the free allowance against conversion.
What are the legal considerations?
- We need the right licenses before we charge. DiffRhythm (Apache 2.0) and Whisper (MIT) allow paid use, and DiffRhythm's audio decoder from Stability AI allows it under $1M a year in revenue.
- MuQ-MuLan is the blocker. The model that turns a sound or description into DiffRhythm's style input is non-commercial (CC BY-NC 4.0). We need a commercial license from its authors, or we retrain that step on a model that allows paid use.