CRASSHHFY

CRASSHHFY is a neural drum sampler plugin that uses a diffusion network to generate drum samples. Besides unconditional generation, the plugin also supports priming ("Drumify") and inpainting ("Variation"). Also, it has a classifier that can classify the drum samples into 3 categories: kick, snare, and hihat.

It is based on the paper CRASH: A Diffusion Network for Drum Sound Synthesis by Simon Rouard and Gaëtan Hadjeres and their original implementation here.

We have also added a classifier to the plugin which can classify the drum samples into 3 categories: kick, snare and hihat.

The 2 models used were exporting using ONNX and then converted to ORT format using ONNX Runtime.

CRASSSHFY is available as a Standalone, AU, or VST plugin for Mac only. For other platforms, you can build from source.

Building from Source

To build from source, you must have CMake installed.

Clone the repo

git clone https://github.com/calgoheen/crasshhfy.git --recurse-submodules

Download the models

https://drive.google.com/file/d/1XIOoqXdSG2--scESA7fcVpZMYwt7tc_m/view?usp=share_link

Copy the files in the downloaded directory to ort-builder

Directory structure should look like

- crasshhfy
    - ort-builder
      - include
        - libs
            - macos-arm64_x86_64
                - onnxruntime.a
        - model
            - classifier.ort.c
            - classifier.ort.h
            - crash.ort.c
            - crash.ort.h

Build the plugin

cmake -Bbuild -DCMAKE_BUILD_TYPE=Release
cmake --build build

Compiling the models

If you'd like to export the models yourself, follow the steps below. The models are exported to ONNX format and then converted to ORT format using their tools.

Clone CRASH

git clone https://github.com/simonrouard/CRASH.git

Export the models

Install requirements

python3 -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt
pip install onnx

Then copy the export.py script from this repo to that one. Then run python export.py

This will create crash.onnx and classifier.onnx

Build models

1. Create a directory to store the models inside ort-builder

   mkdir ort-builder/onnx-models

2. Then copy crash.onnx and classifier.onnx to ort-builder/onnx-models

cd ort-builder
git clone https://github.com/microsoft/onnxruntime.git
python3 -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt
pip install onnx
python -m onnxruntime.tools.convert_onnx_models_to_ort onnx-models --enable_type_reduction
python -m bin2c -o ./model/crash.ort onnx_models/crash.ort
python -m bin2c -o ./model/crash.ort onnx_models/classifier.ort

4. Update the build script (in our case ./build-mac.sh) such that model.required_operators_and_types.config becomes required_operators_and_types.config (2 places)

5. Run the build script (./build-mac.sh)

6. Ensure output looks the same as in "Download the models"

Credits

• Simon Rouard and Gaëtan Hadjeres for the paper and their original implementation
• Oli Larkin for his ort-builder repo, tutorial, and examples
• Sample rate converter designed by Aleksey Vaneev of Voxengo