v0.1.0

• Standalone recognition: first experimental release, with a Python image/PDF command and API plus a dependency-free Java recognition core.
• Outputs: JSON recognition results and MIDI previews, with original examples and geometry regression tests.
• Model and training: independent float16 TFLite weights, a PyTorch checkpoint, exporter arrays and training scripts.
• Validation: 180 Java tests, 9 Python tests, exact eight-note PNG/PDF model-to-MIDI smoke checks, a synthetic generator audit, a two-step training smoke run and a fresh TFLite re-export. The re-export reproduces the bundled float16 model byte-for-byte, and the installed wheel passed the same image/PDF checks.

The wheel includes the core JAR and model. Requires Python 3.10/3.11 and Java 17+. Use the inference and optional pdf extras. Individual model/JAR downloads and a model archive support other integrations. SHA256SUMS covers all binary assets.

Recognition is experimental. Review recognized notes and timing. Text OCR is supplied by the caller, and initial meter and tempo are explicit arguments. MIDI is a preview rather than a complete expressive performance. These checks are not a general transcription-accuracy score. Original code and independent model weights are Apache-2.0 licensed, permitting commercial and closed-source use subject to the license and notices. See the model card and licensing guide.