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Trainer version 0.11.0 is released
I recently released a new version of the Python package for training models. As usual, the newest version is automatically used when...
Steve
Dec 19, 20241 min read
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NeuralAmpModelerPlugin v0.7.12 is released
The next version of NeuralAmpModelerPlugin is available! Download the installer from the users page. This release is all about signal...
Steve
Dec 6, 20246 min read
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Plug-in version 0.7.11 is released
I've released a new version of the NAM snapshot plug-in. You can download it on the users page . This release fixes a bug that users were...
Steve
Oct 30, 20241 min read
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TONEZONE3000: Training Made Simple
NAM is the fastest, most accurate--and now (I'd argue), easiest --way to make a neural model of your gear. TONEZONE3000 is a third-party...
Steve
Oct 29, 20242 min read
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NeuralAmpModelerCore v0.2.0 is released
There's a new version of the core DSP library for NAM: version 0.2.0 . This makes a few breaking changes but also cleans some things up...
Steve
Oct 20, 20242 min read
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Trainer version 0.10.0 is released
I've released a new version of the Python package for training models! As usual, the newest version is automatically used in the online...
Steve
Oct 7, 20242 min read
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NAM Plug-in version 0.7.10 is released
I've released a new version of the NAM snapshot plug-in. You can download it on the users page . Let's go through what's new:...
Steve
Sep 21, 20242 min read
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Trainer version 0.9.0 is released
I've just released a new version of the Python package used for training models! As usual, the newest version is automatically used in...
Steve
Jul 27, 20243 min read
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Artera DSP Releases GigFast Lite
I just got to sit down with the recently-released GigFast Lite from Artera DSP. It's a standalone application and AU plugin for macOS and...
Steve
Jul 5, 20241 min read
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NAM Trainer: Should "fit_cab" be always-on?
I'm contemplating simplifying the UI of the trainers. Currently, there's a checkbox that users should check if the gear they're modeling...
Steve
Jul 4, 20241 min read
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NAM Trainer v0.8.4 is released
I've just released a new version of the Python package used for training NAMs! As usual, the newest version is automatically used in the...
Steve
May 13, 20242 min read
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NAM Plugin version 0.7.9 is released
I've released a patch for the NAM snapshot plugin. Download it on the users page. The full changelog can be read on GitHub. Nothing much...
Steve
Apr 6, 20241 min read
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Towards a (good) CPU-efficient NAM
One of the most common problems people have with NAM is that the standard WaveNet architecture (the default in the simplified trainers...
Steve
Mar 29, 20242 min read
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Ugritone releases Ampenstein: Dual Rektifier
Over this last weekend, Ugritone have release their second amp sim for their Ampenstein plugin: Dual Rektifier! Ampenstein's plugin GUI...
Steve
Mar 19, 20241 min read
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The NAM trainer now has online documentation
At long last, I finally got the start of some documentation for NAM's trainer online. Have a look here: https://neural-amp-modeler.readth...
Steve
Mar 10, 20241 min read
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NAM Trainer v0.8.1 is released
Edit 2024-02-11: Released version 0.8.1 with bug fixes. I've just released a new version of the NAM trainer. As usual, the new version is...
Steve
Feb 10, 20242 min read
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NAM Plugin version 0.7.8 is released
Edit 2024-02-05: I've added a bullet point addressing the changes to the license and a link to the full changelog. I've released a patch...
Steve
Feb 4, 20241 min read
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Two notes Audio Engineering Announces GENOME
Yesterday at NAMM 2024, Two notes announced GENOME, a full-featured guitar and bass plugin. I'm particularly excited for its CODEX Amp...
Steve
Jan 26, 20242 min read
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ParametricOD v0.1.3 is released
I've release a new version of my parametric overdrive plugin. You can download it via the updated links on the Users page. Users who have...
Steve
Jan 24, 20241 min read
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The first publicly-available parametric Neural Amp Model
Today, I'm releasing ParametricOD, a plugin that uses NAM's parametric modeling capabilities to give you a model of my overdrive pedal...
Steve
Jan 20, 20244 min read
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