MuScriptor: an open model that turns real songs into sheet music

2 months ago

Hand a machine a full band recording, and get back the actual notes. Every instrument, written out as a score. That's MuScriptor, from a Kyutai, Mirelo, and IRCAM team, and the weights are open.

Ask

Ask about this presentation

Answers are generated from this presentation.

Chapters

  1. 0:00MuScriptor: an open model that turns real songs into sheet music
  2. 0:13The errors vanish as it trains
  3. 0:32It won on data and training, not a new architecture
  4. 0:55More than double the best open model on the full-mix metric
  5. 1:17The honest read: it wins on notes, not yet on timing
  6. 1:43Try it now: paste a song, get the notes
  7. 1:57The open bar for real-world transcription just moved
Show transcript

MuScriptor: an open model that turns real songs into sheet music

From Kyutai, Mirelo AI and IRCAM; audio to MIDI at 103M, 307M or 1.4B.
Kyutai · Mirelo AI · IRCAM CC BY-NC 4.0 · gated audio → MIDI 103M / 307M / 1.4B
arXiv 2607.08168 · Rouard, Krause, Roebel, Simon-Gabriel, Défossez · July 2026

Hand a machine a full band recording, and get back the actual notes. Every instrument, written out as a score. That's MuScriptor, from a Kyutai, Mirelo, and IRCAM team, and the weights are open.

The errors vanish as it trains

one guitar part, one real song · blue = correct · green = missed · red = wrong
MuScriptor paper · Figure 1

You've seen apps turn speech into text. This does it for a whole band. Here's one guitar part from a real song. Up top, the last open model. It misses notes, shown in green, and gets others wrong, in red. Watch them turn blue, correct, as MuScriptor adds real data, then reinforcement learning. The errors basically vanish.

It won on data and training, not a new architecture

pretrain
DSynth
1.45M synthetic scores
→ +~20 Multi
fine-tune
DReal
170k real recordings
11k hours · classical → metal
→ +~6 Multi
post-train
DRL
300 curated tracks
GRPO-like RL
The network itself is a plain decoder-only transformer with instrument-presence conditioning.
MuScriptor paper · §3–4

Here's the surprise. It's not a cleverer network, it's a plain transformer. They pretrained on a million and a half synthetic scores, then fine tuned on a hundred and seventy thousand real recordings. Eleven thousand hours, classical to heavy metal. That real audio pass adds about twenty points. A final round of reinforcement learning adds six more. Data, not architecture.

More than double the best open model on the full-mix metric

Multi F1 right note + right instrument
MuScriptor 48.2 2.2× the open baseline
YourMT3+ 21.9
Frame right note, right time
MuScriptor 73.3
YourMT3+ 45.54
Onset note starts
MuScriptor 60.4
YourMT3+ 32.52
Offset note ends
MuScriptor 49.0
YourMT3+ 17.79
Drums percussion notes
MuScriptor 50.2
YourMT3+ 41.4
F1 ×100 · authors' held-out DTest · baseline YourMT3+ (authors' re-run) · paper Table 1

So how good is it. The benchmark is the authors' own held-out set, scored against the previous open model, YourMT3 plus. The headline metric asks one thing. Did it get the right note, and the right instrument. MuScriptor scores forty eight point two. YourMT3 plus, twenty one point nine. More than double. Frame accuracy, seventy three versus forty six.

The honest read: it wins on notes, not yet on timing

That crown is on the authors' own test set. On public sets it wins pitch content but often loses exact timing — Bach10 onset 43.1 vs 59.8.
48.2 Multi = under half the notes at a strict ±50 ms; overlapping notes drop it to 42.0.
For builders: the weights are CC BY-NC 4.0 — non-commercial and gated.
paper Table 2 · §4.2.3 · repo README

Now the honest part. That crown is on the team's own test set. On outside datasets it still wins on pitch content, but it often loses on exact timing. On Bach ten, its onset score is forty three, below YourMT3 plus at sixty. And forty eight point two Multi means under half the notes land, at a strict fifty millisecond window. A step change from unusable, not perfect. And for builders, the weights are non-commercial, and gated.

Try it now: paste a song, get the notes

drums electric bass distorted guitar clean guitar voice note colors here = per-instrument
muscriptor.kyutai.org · github.com/muscriptor/muscriptor · huggingface.co/MuScriptor (gated) · arXiv 2607.08168 · CC BY-NC 4.0

You can try it yourself. There's a live web demo, inference code you run with one command, and three model sizes on Hugging Face. This is the demo transcribing a real rock track, each instrument drawn in its own color.

The open bar for real-world transcription just moved

And it moved on data and training, not a new architecture.
muscriptor.kyutai.org · arXiv 2607.08168 · CC BY-NC 4.0

The open bar for turning real recordings into scores just moved. And it moved on data and training, not a new architecture. The thing to watch now is whether the timing gap closes.

Up next

2:58

Apodex 1.1 mini · the same file, run alone and run as a team

@ai-ml-today · 1 month ago

3:09

Ornith-1.5 · an open model trained on problems it wrote for itself

@ai-ml-today · 1 month ago

3:08

MOSS-VL · an open 11B video model trained to decide, after every frame, whether to speak or stay silent

@ai-ml-today · 1 month ago

3:29

Qwen3.8-27B · a 27B open model anyone can download averages 84.3 across real desktop jobs on Qwen’s own run, and loses the widest row on its own card by nine points

@ai-ml-today · 1 month ago

3:15

MiLMMT-46-v1.0 · Xiaomi finished training an open translator on sentences that had no translation attached, and the scores it now leads on are produced by the two models that graded it

@ai-ml-today · 1 month ago

3:31

BDH-CQ — a 150M-parameter system that learns a puzzle rule from the worked examples in front of it and does its thinking as numbers, never as words

@ai-ml-today · 2 months ago

3:29

Muse Glimmer 30B, Meta’s open agent model, and the second tiny model that guesses sixteen words ahead so it runs fast on one consumer graphics card

@ai-ml-today · 2 months ago

3:05

JoyAI-Video-Edit, JD’s open 16B model that edits a video while the video is still playing, at about 30 frames a second on one Nvidia B200

@ai-ml-today · 2 months ago

3:26

Inkling-Small, Thinking Machines’ 12B-active / 276B open mixture-of-experts model that out-reasons its own 3.5× bigger flagship

@ai-ml-today · 2 months ago

3:42

Maple-Preview, DeepGrove’s 20-billion-parameter ternary reasoning model that runs on a Mac mini

@ai-ml-today · 2 months ago

3:16

Hunyuan3D-Buffalo, Tencent’s one model that builds, understands, and edits 3D from words

@ai-ml-today · 2 months ago

3:28

MiniMax H3, an open model that makes a video and its soundtrack in one pass

@ai-ml-today · 2 months ago