Feature Extraction
Transformers
Safetensors
sheetsage2
audio
music
music-transcription
midi
abc-notation
custom_code
Instructions to use m-a-p/SheetSage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-a-p/SheetSage2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="m-a-p/SheetSage2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("m-a-p/SheetSage2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Replace chord specialist with verified ChordFormer benchmarks
Verified signatureUse the released ChordFormer models on osu2017 (86.55%) and held-out Chords1217 folds (83.94%), rescored with the common SheetSage2 protocol. Explain the aggregation change in the five-fold footnote. Add exact benchmark results, per-track scores and hashes, and a portable frozen scorer. SheetSage2 scores and inference settings are unchanged.7cb7b16