amtflow is an open-weight model from Vaclis, released under Creative Commons Attribution 4.0. Its published files total 45.1 MB.
Model assets for 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.
Model Card
By Vaclis, published under cc-by-4.0, revision 1e9418469645.
Model assets for 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. One classification head per frozen audio encoder, trained on OpenMIC-2018 (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. Test mAP is the mean over five probe seeds with a 95% t-interval over…
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Model assets for 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 (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 audio tower | 31 | 0.861 ± 0.001 | 0.793 |
openmic/afnext-whisper-mlp |
nvidia/audio-flamingo-next-hf audio tower | 29 | 0.860 ± 0.003 | 0.788 |
openmic/matpac-music-mlp |
auriankelen/matpac_music | 10 | 0.860 ± 0.002 | 0.792 |
openmic/matpac-general-mlp |
auriankelen/matpac_general_audio | 10 | 0.858 ± 0.002 | 0.787 |
openmic/pupum2d-mlp |
spellbrush/PupuM2D (large) | 19 | 0.858 ± 0.001 | 0.790 |
openmic/muq-mlp |
OpenMuQ/MuQ-large-msd-iter | 2 | 0.832 ± 0.001 | 0.764 |
openmic/qwen3-omni-aut-mlp |
Qwen/Qwen3-Omni-30B-A3B-Instruct AuT encoder | 23 | 0.821 ± 0.001 | 0.753 |
openmic/mert-mlp |
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. If you use it, please cite:
@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},
}
Identity and Version
- Repository
- vaclis/amtflow
- Publisher
- Vaclis
- Task
- Not stated by the source
- Modality
- Other
- Library
- amtflow
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- 1e94184696459f43efa6b87785c771d3e1653914
- First published
- 2026-09-29
- Last updated
- 2026-10-16
Files and Weights
11 files, 45.1 MB in total. The weights are 8 files totalling 45.1 MB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| heads/openmic/af3-whisper-mlp.safetensors | Weights | 2.7 MB | a90b7c832ee3 |
| heads/openmic/afnext-whisper-mlp.safetensors | Weights | 2.7 MB | 9c73f445d079 |
| heads/openmic/matpac-general-mlp.safetensors | Weights | 7.9 MB | 2b02dfaa07b3 |
| heads/openmic/matpac-music-mlp.safetensors | Weights | 7.9 MB | 45368f57df0b |
| heads/openmic/mert-mlp.safetensors | Weights | 2.2 MB | 0e2f7079312e |
| heads/openmic/muq-mlp.safetensors | Weights | 2.2 MB | 491768d02912 |
| heads/openmic/pupum2d-mlp.safetensors | Weights | 16.9 MB | 910b619fa5b2 |
| heads/openmic/qwen3-omni-aut-mlp.safetensors | Weights | 2.7 MB | d5b7ccbb0916 |
| heads/openmic/index.json | Configuration | 8.4 KB | — |
| README.md | Documentation | 4.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- cc-by-4.0
- Access
- Open weights, no gate
- Download size
- 45.1 MB
Released by Vaclis through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2610.20967
- Trained on (disclosed) openmic-2018
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 45.1 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About amtflow
Can I use amtflow commercially?
Yes. amtflow is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated.