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Build Instructions (MAX/MSP)

macOS

Pre-built binaries are available in the release section of this repo.

  • Download the latest libtorch (CPU) here and unzip it to a known directory
  • Run the following commands:
git clone https://github.com/acids-ircam/nn_tilde --recursive
cd nn_tilde
mkdir build
cd build
cmake ../src/ -DCMAKE_PREFIX_PATH=/path/to/libtorch -DCMAKE_BUILD_TYPE=Release
make
  • Copy the produced .mxo external inside ~/Documents/Max 8/Packages/nn_tilde/externals/

Windows

  • Download Libtorch (CPU) and dependencies here and unzip to a known directory.
  • Install Visual Studio and the C++ tools
  • Run the following commands:
git clone https://github.com/acids-ircam/nn_tilde --recurse-submodules
cd nn_tilde
mkdir build
cd build
cmake . -S ..\src  -DCMAKE_BUILD_TYPE:STRING=Release -G "<generator name of your Visual Studio version>" -A x64  -DTorch_DIR="<unzipped libtorch directory>\share\cmake\Torch"
cmake --build . --config Release

Use cmake --help to find the generator name for your Visual Studio version

  • Copy all the DLLs from <unzipped libtorch directory>\lib to the Max 7/8 installation directory, next to Max.exe

Build Instructions (Pd)

macOS

  1. clone nn_tilde ( make sure you clone submodules and nested submodules) $ git submodule update --init --recursive
  2. install libtorch via homebrew: brew install libtorch
  3. place this in the nn_tilde directory and make executable: chmod +x build-nn_tilde-macos-brew.sh
  4. run: ./build-nn_tilde-macos-brew.sh

notes:

  • this will build for the current arch, so should provide native builds for both x86_64 (Intel) and arm64 (M1) systems
  • binary external is placed in root dir
  • there doesn't appear to be a help file, so check the Max docs and/or video tutorials on youtube, ex. https://www.youtube.com/watch?v=dMZs04TzxUI
  • the built external will most likely not be portable to other systems as it relies on finding the libtorch dylibs installed by homebrew

Raspberry Pi

While nn~ can be compiled and used on Raspberry Pi, you may have to consider using lighter deep learning models. We currently only support 64bit OS.

Install nn~ for PureData using

curl -s https://raw.githubusercontent.com/acids-ircam/nn_tilde/master/install/raspberrypi.sh | bash

Models

Some models can be downloaded from: https://caillonantoine.github.io/NIME_workshop/

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  • Max 48.8%
  • C++ 39.6%
  • CMake 7.1%
  • Shell 4.5%