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  • Neural Amp Modeler | Highly-accurate free and open-source amp modeling . . .
    Neural Amp Modeler is a free and open-source technology for modeling guitar amplifiers and pedals using deep learning Get started making music with NAM, contribute to the code, or build your own products using state of the art modeling
  • Users | Neural Amp Modeler
    Share Share your models with other NAM users and find others' models on TONE3000, a community-organized online library Join other users to learn more about NAM, ask questions, and share models and music you've made
  • Builders | Neural Amp Modeler
    The past decade has seen amazing strides in digital modeling for guitar amplifiers Today's best models are virtually indistinguishable from the real thing Neural Amp Modeler is the start of the next generation of digital modeling, where perfection is table stakes To support this, NAM's is available under open-source licenses Support your product vision with state-of-the-art modeling
  • The History of NAM - Neural Amp Modeler
    First, The TONEX appeared to be more accurate than Neural Capture (though I hadn't directly compared the two); second, NAM was still better Others have also replicated my findings about this "hierarchy" of modeling As of my writing this, to my knowledge, NAM is the state of the art for modeling accuracy
  • The first publicly-available parametric Neural Amp Model
    Since NAM was first introduced in 2019, there have been a lot of data-driven modeling products that have come onto the scene in the guitar space Many (Kemper's profiling, Neural DSP's Neural Capture, TONEX's Tone Modeling, Headrush's Smart amp pedal cloning, and Tonocracy's ToneSnap) have focused on emulating the tone of the gear at a single "snapshot", leading to the impression that neural
  • The code | Neural Amp Modeler
    Open-source NAM has three separate code repositories All of them are available under permissive open-source licenses and open to contributions
  • 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 website that puts the standard snapshot training process in a streamlined, easy-to-use website Like the GUI and Colab trainers I've developed, users can reamp a standardized "sweep" signal through their gear, then upload the recording and follow the steps to
  • NAM Trainer v0. 8. 1 is released - Neural Amp Modeler
    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 used automatically in the online trainer,and folks training locally on their own computers can update their install from the terminal by typingpip install --upgrade neural-amp-modelerFull release notes are on GitHub What's new?Improved Colab trainer UXI've
  • NAM-to-Image: NUXs Amp Academy Stomp releases integration to load . . .
    The latest in the ecosystem of NAM-integrating products is NUX's Amp Academy Stomp, a "3-footswitch modeler" featuring a variety of options for creating a signal chain But, since this is the NAM blog, I'm going to focus on Image, NUX's proprietary data-driven modeling technology
  • NAM Plugin version 0. 7. 6 is released - Neural Amp Modeler
    Update 2023-12-14: I've just released v0 7 7, which fixes a bug with this version that was causing crashes for some users The download links have been updated After a long hiatus, I've finally got a new version of the NAM plugin for you! As always, you can download the installer from the users page What's newSupport for all DAW sample ratesThis is the big one and I didn't want to make a





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