| # Licensing |
|
|
| This project is released under a **dual non-commercial license**: |
|
|
| - [`LICENSE.NSCL-A2SB`](LICENSE.NSCL-A2SB) — NVIDIA Source Code License for A2SB (the upstream license we inherit) |
| - [`LICENSE.PolyForm-NC`](LICENSE.PolyForm-NC) — PolyForm Noncommercial 1.0.0 (our additional terms) |
|
|
| You must comply with **both** licenses when using this work. |
|
|
| ## What you can do |
|
|
| - Use it for **research, evaluation, or any other non-commercial purpose** |
| - Modify it, study it, share modifications under the same terms |
| - Cite, reproduce, and discuss it in academic or technical writing |
|
|
| ## What you cannot do |
|
|
| - **Use it commercially.** This includes: |
| - Selling the model, the code, or access to either |
| - Including it in a paid product or paid service |
| - Running it as part of a monetized API, SaaS, or hosted offering |
| - Internal use within an organization for revenue-generating activities |
| - Use NVIDIA's name, logos, or trademarks in any derivative |
|
|
| If you want commercial use, you'd need a separate commercial license from NVIDIA |
| (for the A2SB-derived parts) and from us (for the rest). We won't pretend |
| otherwise. |
|
|
| ## Why two licenses |
|
|
| The model and parts of the codebase are derivative works of NVIDIA's A2SB |
| release, which is governed by the NVIDIA Source Code License for A2SB. That |
| license restricts use to non-commercial purposes and we cannot relax that |
| restriction. |
|
|
| PolyForm Noncommercial 1.0.0 layers on top with stricter, software-specific |
| terms — source-availability requirements, patent retaliation, clean |
| disclaimers — that the NVIDIA license itself doesn't impose. Both licenses |
| restrict use to non-commercial purposes, so they stack without contradiction. |
|
|
| ## What's covered by what |
|
|
| | Component | Why it's NSCL-A2SB-bound | Also under PolyForm-NC | |
| |---|---|---| |
| | `training/locutius_train/network.py` | Verbatim port of NVIDIA's `networks.py` | yes | |
| | `training/locutius_train/diffusion.py` | Verbatim port of NVIDIA's `diffusion.py` | yes | |
| | Trained model checkpoints | Derivative of NVIDIA-released A2SB weights | yes | |
| | `corruption-chain/` (the synthesis/profiling package) | Original work, no NVIDIA code | yes | |
| | `training/main.py`, `config.py`, `data.py`, `trainer.py`, `restore.py`, `representation.py`, `utils.py`, `checkpoints.py` | Original work | yes | |
| | Profiles (`corruption-chain/profiles/*.json`) | Original measurements | yes | |
| | Documentation (`README.md`, `PLAN.md`, etc.) | Original | yes | |
|
|
| For the A2SB-derived portions, NVIDIA's copyright headers must remain intact |
| when you redistribute. We retain them in the source files. |
|
|
| ## Practical examples |
|
|
| | Use case | Allowed? | |
| |---|---| |
| | Training your own variant on personal music for personal listening | ✅ | |
| | Publishing a research paper that uses this model | ✅ (cite it) | |
| | Sharing the code/model on GitHub or HuggingFace under the same dual license | ✅ | |
| | Using restored audio you made with the model in a commercial release | ❌ (the *output* is a derivative of the model under NSCL §3.3) | |
| | Selling a "festival audio cleanup" service powered by this model | ❌ | |
| | A streaming platform integrating this to clean audience-recorded uploads | ❌ | |
| | Internal tool inside a record label to triage live recordings before commercial release | ❌ (revenue-adjacent) | |
| | University course using it as a teaching example | ✅ | |
|
|
| ## Not legal advice |
|
|
| The licenses themselves are the controlling documents — this file just |
| summarizes our reading. We are not lawyers. If you plan to do anything |
| non-trivial with this work, talk to one. |
|
|
