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fix README
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caillonantoine committed May 12, 2023
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[![RAVE x nn~](http://img.youtube.com/vi/dMZs04TzxUI/mqdefault.jpg)](https://www.youtube.com/watch?v=dMZs04TzxUI)


# Installation

Grab the [latest release of nn~](https://github.com/acids-ircam/nn_tilde/releases/latest) ! Be sure to download the correct version for your installation.
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The attribute list and values are again **model dependant** and should retrieved in the documentation of the model.

## Buffer configuration

Internally, `nn~` has a circular buffer mechanism that helps maintain a reasonable computational load. You can modify its size through the use of an additional integer after the method declaration, as shown below
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The attribute list and values are again **model dependant** and should retrieved in the documentation of the model.

## Multicanal (Max/MSP)

The Max/MSP release of `nn~` includes additional externals, namely `mc.nn~` and `mcs.nn~`, allowing the use of the multicanal abilities of Max 8+ to simplify the patching process with `nn~` and optionally decrease the computational load.
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![regular](assets/max_regular.png)

This patch can be improved both visually *and* computationally speaking by using `mc.nn~` and using *batch operations*
This patch can be improved both visually _and_ computationally speaking by using `mc.nn~` and using _batch operations_

![mc](assets/max_mc.png)


Using `mc.nn~` we build the multicanal signals **over the different batches**. In the example above, each multicanal signal will have 2 different canals. We also propose the `mcs.nn~` external that builds multicanal signals **over the different dimensions**, as shown in the example below

![mcs](assets/max_mcs.png)

In the example above, the two multicanals signals yielded by the `nn~ rave encode 2` object have 16 canals each, corresponding to the 16 latent dimensions. This can help patching, while keeping the batching abilities of `mc.nn~` by creating an explicit number of inlets / oulets corresponding to the number of examples we want to process in parallel.

To recap, the regular `nn~` operates on a single example, and has as many inlets / outlets as the model has inputs / outputs. The `mc.nn~` external is like `nn~`, but can process multiple examples *at the same time*. The `mcs.nn~` variant is a bit different, and can process mulitple examples at the same time, but will **have one inlet / outlet per examples**.
To recap, the regular `nn~` operates on a single example, and has as many inlets / outlets as the model has inputs / outputs. The `mc.nn~` external is like `nn~`, but can process multiple examples _at the same time_. The `mcs.nn~` variant is a bit different, and can process mulitple examples at the same time, but will **have one inlet / outlet per examples**.

# Build Instructions

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