Nomen Audio
Nomen Audio is a standalone windows program that intelligently generates filename and metadata information for professional (and otherwise) audio assets.
Core trick
Runs CLAP zero-shot classification and ClapCap captioning locally to propose UCS-compliant filenames and metadata for audio files.
Description
Nomen Audio is a standalone windows program that intelligently generates filename and metadata information for professional (and otherwise) audio assets. The system reads all embedded metadata in an asset (iXML, BEXT, RIFF INFO chunks) and enables the user to edit them directly in-place and/or add addtional metadata fields. An ML pipeline that uses open source models (MS-CLAP zero-shot classification + ClapCap captioning) provides a first-pass suggestion for each file's metadata, adhering to industry conventions. Suggestions can be reviewed and adjusted individually, or accepted and saved in bulk across an entire batch. Nothing touches disk until the user explicitly saves. There's some additional features here and there. Everything runs completely offline.
README excerpt
<p align="center"> <img src="frontend/src-tauri/icons/icon.png" width="80" alt="Nomen Audio icon" /> </p> # Nomen Audio Windows desktop tool for sound designers to rename and re-tag audio assets to the [UCS](https://universalcategorysystem.com/) (Universal Category System) standard. See a video demonstration [here](https://youtu.be/ReToNGbxtVU). ## What It Does Professional audio libraries often differ in metadata and naming convention across different libraries and projects. As a sound designer working with many audio libraries, this means that finding the right sounds can be very time consuming and tedious, especially if assets are poorly named or if certain metadata simply doesn't exist. The [Universal Category System (UCS)](https://universalcategorysystem.com/) helps solve this issue, but applying it to existing libraries is a very manual, file-by-file process. Nomen Audio addresses this issue by providing an automated way to standardize audio assets to the UCS convention. The application imports audio assets (`.wav`) and their embedded metadata (iXML, BEXT, RIFF INFO chunks), and a machine learning backed pipeline (CLAP for retrieval, a small local LLM for the final pick) infers each file's UCS category, subcategory, and description. Additional metadata fields are also generated, and the user may review every suggestion, edit fields freely in-place, or write additional metadata tags. Nothing touches disk until the user explicitly saves - the assets are then atomic