Omnizart is an automatic music transcription (AMT) library that converts polyphonic audio into symbolic representations including pitched notes, drums, chords, vocal melody, and beat/downbeat. Built as a Python CLI tool with multiple subcommands (music, drum, chord, vocal, vocal-contour, beat, patch-cnn), it provides pre-trained TensorFlow models and supports custom training and feature extraction. The architecture follows a modular pattern with separate app, inference, features, labels, and prediction modules per task, all inheriting from common base classes (BaseTranscription, BaseDatasetLoader). While primarily an application library for end-to-end transcription, it has strong research components: it uses standard MIR datasets (MAESTRO, MusicNet, MedleyDB, etc.), implements state-of-the-art models (U-Net with ASPP, attention-based U-Net, PyramidNet, Chord Transformer), and includes custom feature extraction (CFP, CQT, chroma). The repository also bundles a JavaScript waveform playlist library for documentation demos. The codebase shows active maintenance (recent commits in 2026, CI testing up to Python 3.14) but has technical drift risks due to heavy reliance on TensorFlow 1.x compat layers, external dependencies (madmom, vamp), and known bugs in drum model training.
BeatNet is a Python library for real-time and offline music beat, downbeat, tempo, and meter tracking. It provides a complete system comprising a CRNN-based neural network for feature extraction, particle filtering for causal inference, and DBN for non-causal inference. The library supports four operational modes: streaming (microphone input), real-time (file processing), online (fast file processing), and offline (DBN-based). Beyond inference, it includes a complete training pipeline for training the CRNN model on custom datasets with data preparation tools, dataset handling, and validation procedures. The architecture is modular with clear separation between feature extraction, neural network modeling, and inference algorithms.