WebAudioFont is a browser-based sample synthesis library that enables playback of SoundFont instruments (GeneralUserGS, FluidR3, etc.) via the Web Audio API. It provides a comprehensive set of classes: `WebAudioFontPlayer` for scheduling notes, chords, and strums; `WebAudioFontLoader` for dynamic loading of instrument presets from an external GitHub Pages repository; `WebAudioFontChannel` for a 10-band equalizer; `WebAudioFontReverberator` for convolution reverb with a built-in impulse response; and `WebAudioFontTicker` for requestAnimationFrame-based sequencing. The library is written in TypeScript, compiled into a single bundled file (`WebAudioFontPlayer.js`), and includes numerous interactive HTML examples (piano, drums, MIDI player, custom samples) that demonstrate real-world usage. Architecturally, the library is a monolith with well-defined modules but no formal test suite. It relies on an external data repository for instrument sound data, which is fetched as JavaScript files defining global variables. The core player uses a queue-based scheduling system with envelope reuse and zone/preset selection. The channel and reverberator classes form a modular audio graph that can be connected to the audio context destination. The library is licensed under GPL-3.0 and is actively maintained by a single developer, with recent commits and a responsive issue tracker. However, the external data dependency and lack of testing pose moderate risks for long-term stability.
Spleeter is a Python library and CLI tool for music source separation, providing pretrained models to decompose audio tracks into stems (e.g., vocals, drums, bass, piano). The codebase is structured around a core Separator class backed by TensorFlow estimation, with abstract audio adapters (default FFMPEG), model providers (GitHub releases), and configuration loading from embedded or file-based JSON. The architecture supports both inference and training, with a well-defined pipeline for waveform loading, STFT computation, U-Net-based separation, and output saving. The library is designed for production use with multiprocessing support and Docker deployment. However, it relies heavily on deprecated TensorFlow 1.x APIs (tf.compat.v1, tf.estimator), indicating technical drift. The code is well-organized with clear separation of concerns, reusable components (audio adapter, model provider, utilities), and comprehensive test coverage.