---
license: cc-by-4.0
library_name: amtflow
tags:
- audio
- music
- instrument-recognition
- music-transcription
datasets:
- openmic-2018
---

# AMTFlow

Model assets for [AMTFlow](https://github.com/vaclisinc/AMTFlow), a modular
instrument-conditioned automatic music transcription pipeline (instrument tagging →
instrument-family routing → MuScriptor). AMTFlow downloads these files on first use;
you do not need to fetch them by hand.

## `heads/openmic/` — instrument-tagging heads

One classification head per frozen audio encoder, trained on
[OpenMIC-2018](https://zenodo.org/records/1432913) (20 instrument classes). AMTFlow's
`probe` tagger runs the encoder, applies the head to one layer's time-averaged
features, and marks an instrument family present when any of its classes reaches that
class's threshold. Several taggers, including audio-language models, can be combined
by union or majority vote.

| head | encoder | layer | OpenMIC test mAP | macro-F1 |
|---|---|---:|---:|---:|
| `openmic/af3-whisper-mlp` | [nvidia/audio-flamingo-3-hf](https://huggingface.co/nvidia/audio-flamingo-3-hf) audio tower | 31 | 0.861 ± 0.001 | 0.793 |
| `openmic/afnext-whisper-mlp` | [nvidia/audio-flamingo-next-hf](https://huggingface.co/nvidia/audio-flamingo-next-hf) audio tower | 29 | 0.860 ± 0.003 | 0.788 |
| `openmic/matpac-music-mlp` | [auriankelen/matpac_music](https://huggingface.co/auriankelen/matpac_music) | 10 | 0.860 ± 0.002 | 0.792 |
| `openmic/matpac-general-mlp` | [auriankelen/matpac_general_audio](https://huggingface.co/auriankelen/matpac_general_audio) | 10 | 0.858 ± 0.002 | 0.787 |
| `openmic/pupum2d-mlp` | [spellbrush/PupuM2D](https://huggingface.co/spellbrush/PupuM2D) (large) | 19 | 0.858 ± 0.001 | 0.790 |
| `openmic/muq-mlp` | [OpenMuQ/MuQ-large-msd-iter](https://huggingface.co/OpenMuQ/MuQ-large-msd-iter) | 2 | 0.832 ± 0.001 | 0.764 |
| `openmic/qwen3-omni-aut-mlp` | [Qwen/Qwen3-Omni-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct) AuT encoder | 23 | 0.821 ± 0.001 | 0.753 |
| `openmic/mert-mlp` | [m-a-p/MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) | 9 | 0.818 ± 0.002 | 0.740 |

Test mAP is the mean over five probe seeds with a 95% t-interval over seeds (the file
is the best-validation seed). For reference, the MATPAC++ authors' released OpenMIC
probe scores 0.861 on the same test clips.

**Protocol.** Official `split01` test partition (5,085 clips); the official train
partition split 80/20 by track (seed 0) into train/validation. Labels: aggregated
relevance ≥ 0.5 is positive; unobserved (clip, class) labels are masked out. Every
encoder layer is averaged over the 10 s clip and z-scored on train; the head is an MLP
(512 hidden, ReLU, dropout 0.2) trained with masked BCE; layer and learning rate are
selected on validation mAP. Per-class thresholds are calibrated on validation by
F-beta (β = 2, precision ≥ 0.25), i.e. recall-oriented, because the transcriber
cannot produce an instrument outside its conditioning set.

**Files.** `<encoder>-mlp.safetensors` holds the head weights and the train-set
feature mean/std; its metadata (`amtflow` key, JSON) holds the encoder, layer, class
names, thresholds and calibration details. `index.json` lists every head with its
test scores and SHA-256.

## License

The heads are released under CC BY 4.0 (OpenMIC-2018 is CC BY 4.0). A head is only
usable together with its encoder, and the encoder's own license applies: MATPAC++
(Apache-2.0), PupuM2D (MIT), Qwen3-Omni (Apache-2.0), MERT and MuQ (CC BY-NC 4.0),
Audio Flamingo 3 / Next (NVIDIA non-commercial). MuScriptor, the transcriber AMTFlow
uses, is CC BY-NC 4.0.

## Citation

AMTFlow is the transcription pipeline of the
[MIDI-REPA paper](https://arxiv.org/abs/2610.20967). If you use it, please cite:

```bibtex
@misc{yu2026midirepa,
      title={MIDI-REPA: Improving Text-to-Music Generation with Symbolic Representation Alignment},
      author={Song-Ze Yu and Fang-Duo Tsai and Yi-Hsuan Yang},
      year={2026},
      eprint={2610.20967},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2610.20967},
}
```