Enables video processing tasks such as resizing, audio extraction, and metadata inspection using FFmpeg, with base64 input/output for stateless and portable operation.
Enables local, evidence-led analysis of a master recording into inspectable stems, timing, beats, harmony, lyrics, MIDI and sonic evidence, then finds complete-bar loopable sections for Shorts and other short-form video. Results stay traceable through source hashes, manifests, JSON/TSV/NPZ artifacts and portable Sonic Visualiser sessions, and it can run as a CLI, Python library, web service, container or MCP plugin.
A portable, Dockerized Python tool that implements Model Context Protocol for audio transcription using Whisper models, featuring both CLI and web UI interfaces for converting audio files to JSON transcriptions.