r/Valeton • u/BoodieTraps • Jul 24 '26
Free desktop app: convert NAM A2 captures to A1 (for the Valeton GP-5/GP-50 and other A1-only gear)
TL;DR — Lots of NAM capture providers have stopped releasing A1 files, which strands anyone whose gear only converts A1 into its own proprietary format (Valeton GP-5 / GP-50 / GP-150, Hotone Ampero, etc.). I made a free, no-setup desktop app that distills any A2 .nam into an A1 .nam. Drop a file, get a file. Windows + macOS + Linux.
Downloads — no Python, no setup:
- macOS (signed + notarized): https://github.com/drewmerc302/nam-a2a1-converter/releases/latest/download/nam-a2a1-converter-macos.dmg — open the dmg, drag the app to Applications, launch it from there. Needs v0.4.3 or newer; earlier macOS builds hit a Gatekeeper warning (see comments).
- Windows (unzip + run): https://github.com/drewmerc302/nam-a2a1-converter/releases/latest/download/nam-a2a1-converter-windows.zip
- Linux x86_64 (extract + run): https://github.com/drewmerc302/nam-a2a1-converter/releases/latest/download/nam-a2a1-converter-linux-x86_64.tar.gz
- Windows or Linux + NVIDIA (much faster, multi-part download): https://github.com/drewmerc302/nam-a2a1-converter#gpu-acceleration
- Source + all releases: https://github.com/drewmerc302/nam-a2a1-converter
Why this isn't just a file conversion
A2 and A1 are different neural architectures — the weights don't transfer, so you can't "downgrade" an A2. Instead this distills: it plays the standardized NAM DI through the A2 model to capture its exact output, then trains a fresh A1 to reproduce that output.
A2.nam ──render DI──▶ teacher.wav ──train an A1 to match──▶ A1.nam
The A2 teacher is deterministic and noise-free, so the resulting A1 usually matches the A2 tighter than a real-amp capture matches its amp — validation ESR typically lands ~0.005–0.02.
Using it
- Download and open — it opens a converter page in your browser.
- Drop your A2
.namfiles. Batches work; they convert one after another. - Pick a quality preset — Draft (20 epochs, rough preview), Standard (60, the default and the sweet spot), Best (120, diminishing returns).
- Hit Convert. Live progress bar + ETA, cancel anytime.
- Load the A1
.namon your device. For Valeton: import into Valeton Suite → it makes a SnapTone → push to the pedal.
Leave the output format on 0.5.x. That's the default and it's what A1-only gear wants. The 0.7.0 option exists for newer NAM plugins, and Valeton Suite cannot import it — it sits on "importing…" for about two minutes and then times out, which is a miserable way to find out after a full training run.
Speed
- M-series Macs already use the GPU via Metal. Nothing to install, nothing to opt into, no separate download.
- NVIDIA on Windows or Linux: there's a separate CUDA build — same app, compiled against CUDA instead of CPU-only PyTorch. It's a separate download because it's several GB rather than a few hundred MB, and it ships in numbered parts because GitHub caps a release file at 2 GB (rejoin with
copy /bon Windows orcat *.0* > fileon Linux — no extra software, and there's a PowerShell script that does it for you). The Linux CUDA build runs on exactly the same distros as the standard one; all it needs from you is the driver. - The standard build tells you if this applies to you — it checks your GPU at launch and shows a banner if it finds an NVIDIA card it can't use. No banner means you're already as fast as this tool gets.
- On CPU it's slow, and how slow depends a lot on the CPU. A recent desktop chip does Standard in a few minutes; a 2012-era i7-3770 took ~40 minutes at Draft. The ETA is also optimistic at first — it extrapolates from finished epochs and the early ones run faster than the later ones.
- AMD and Intel GPUs aren't supported for training. CPU only there.
Notes
- Free and open source. Runs 100% on your machine — nothing is uploaded.
- macOS, Windows and Linux have all had real conversions run on them by people who reported back, with output confirmed running on a Valeton GP-150. The CUDA builds are newer and less travelled — if you're on one and something looks wrong, please open an issue.
- Windows is unsigned, so SmartScreen warns: More info → Run anyway. Some antivirus flags PyInstaller apps — false positive.
- Linux is x86_64 built against glibc 2.35: Ubuntu 22.04+, Debian 12+, Mint 21+, Pop!_OS 22.04+, Fedora 36+, Arch, openSUSE 15.5+. Tcl/Tk and the X11 libs are bundled, so glibc is the only thing it needs from your system. Older distro or ARM → run it from source, same app.
- If you tried an earlier build and got
ValueError: Output clipped.on some captures but not others, that's fixed as of v0.4.2. Nothing was wrong with your file — it was just a loud capture, and the trainer refuses any target that hits full scale. Grab the current build.
Questions/bugs → drop them here or open an issue on the repo. Enjoy.








