NeuralNote

NeuralNote is the audio plugin that brings state-of-the-art Audio to MIDI conversion into your favorite Digital Audio Workstation.

• Works with any tonal instrument (voice included)
• Supports polyphonic transcription
• Supports pitch bend detection
• Lightweight and very fast transcription
• Allows to adjust the parameters while listening to the transcription
• Allows to scale and time quantize transcribed MIDI directly in the plugin

Install NeuralNote

Download the latest release for your platform here (Windows and macOS ( Universal) supported)!

Installers are available for both Windows and Mac, including Standalone, VST3, and AU (Mac only) versions. The installers allow users to select which format(s) they want to install. On macOS, the code is signed, while on Windows, it is not. This means you may need to take a few additional steps to use NeuralNote on Windows.

Usage

NeuralNote comes as a simple AudioFX plugin (VST3/AU/Standalone app) to be applied on the track to transcribe.

The workflow is very simple:

• Gather some audio
  • Click record. Works when recording for real or when playing the track in a DAW.
  • Or drop an audio file on the plugin. (.wav, .aiff, .flac, .mp3 and .ogg (vorbis) supported)
• The MIDI transcription instantly appears in the piano roll section.
• Listen to the result by clicking the play button.
  • Play with the different settings to adjust the transcription, even while listening to it
  • Individually adjust the level of the source audio and of the synthesized transcription
• Once you're satisfied, export the MIDI transcription with a simple drag and drop from the plugin to a MIDI track.

Watch our presentation video for the Neural Audio Plugin competition here.

NeuralNote uses internally the model from Spotify's basic-pitch. See their blogpost and paper for more information. In NeuralNote, basic-pitch is run using RTNeural for the CNN part and ONNXRuntime for the feature part (Constant-Q transform calculation + Harmonic Stacking). As part of this project, we contributed to RTNeural to add 2D convolution support.

Build from source

Requirements are: git, cmake, and your OS's preferred compiler suite.

Use this when cloning:

git clone --recurse-submodules --shallow-submodules https://github.com/DamRsn/NeuralNote

The following OS-specific build scripts have to be executed at least once before being able to use the project as a normal CMake project. The script downloads onnxruntime static library (that we created with ort-builder) before calling CMake.

macOS

$ ./build.sh

Windows

Due to a known issue, if you're not using Visual Studio 2022 (MSVC version: 19.35.x, check cl output), then you'll need to manually build onnxruntime.lib like so:

1. Ensure you have Python installed; if not, download at https://www.python.org/downloads/windows/ (this does not currently work with Python 3.11, prefer Python 3.10).

2. Execute each of the following lines in a command prompt:

git clone --depth 1 --recurse-submodules --shallow-submodules https://github.com/tiborvass/libonnxruntime-neuralnote ThirdParty\onnxruntime
cd ThirdParty\onnxruntime
python3 -m venv venv
.\venv\Scripts\activate.bat
pip install -r requirements.txt
.\convert-model-to-ort.bat model.onnx
.\build-win.bat model.required_operators_and_types.with_runtime_opt.config
copy model.with_runtime_opt.ort ..\..\Lib\ModelData\features_model.ort
cd ..\..

Now you can get back to building NeuralNote as follows:

> .\build.bat

IDEs

Once the build script has been executed at least once, you can load this project in your favorite IDE (CLion/Visual Studio/VSCode/etc) and click 'build' for one of the targets.

Reuse code from NeuralNote’s transcription engine

All the code to perform the transcription is in Lib/Model and all the model weights are in Lib/ModelData/. Feel free to use only this part of the code in your own project! We'll try to isolate it more from the rest of the repo in the future and make it a library.

The code to generate the files in Lib/ModelData/ is not currently available as it required a lot of manual operations. But here's a description of the process we followed to create those files:

• features_model.onnx was generated by converting a keras model containing only the CQT + Harmonic Stacking part of the full basic-pitch graph using tf2onnx (with manually added weights for batch normalization).
• the .json files containing the weights of the basic-pitch cnn were generated from the tensorflow-js model available in the basic-pitch-ts repository, then converted to onnx with tf2onnx. Finally, the weights were gathered manually to .npy thanks to Netron and finally applied to a split keras model created with basic-pitch code.

The original basic-pitch CNN was split in 4 sequential models wired together, so they can be run with RTNeural.

Roadmap

• Improve stability
• UX improvements (zoom in/out, play/pause with spacebar, etc.)
• Add tooltips
• Make internal synth support pitch bends
• Linux support

Bug reports and feature requests

If you have any request/suggestion concerning the plugin or encounter a bug, please file a GitHub issue.

Contributing

Contributions are most welcome! If you want to add some features to the plugin or simply improve the documentation, please open a PR!

License

NeuralNote software and code is published under the Apache-2.0 license. See the license file.

Third Party libraries used and license

Here's a list of all the third party libraries used in NeuralNote and the license under which they are used.

• JUCE (JUCE Starter)
• RTNeural (BSD-3-Clause license)
• ONNXRuntime (MIT License)
• ort-builder (MIT License)
• basic-pitch (Apache-2.0 license)
• basic-pitch-ts (Apache-2.0 license)
• minimp3 (CC0-1.0 license)

Could NeuralNote transcribe audio in real-time?

Unfortunately no and this for a few reasons:

• Basic Pitch uses the Constant-Q transform (CQT) as input feature. The CQT requires really long audio chunks (> 1s) to get amplitudes for the lowest frequency bins. This makes the latency too high to have real-time transcription.
• The basic pitch CNN has an additional latency of approximately 120ms.
• The note events creation algorithm processes the posteriorgrams backward (from future to past) and is hence non-causal.

But if you have ideas please share!

Credits

NeuralNote was developed by Damien Ronssin and Tibor Vass. The plugin user interface was designed by Perrine Morel.

Contributors

Many thanks to the contributors!

• jatinchowdhury18: File browser.
• trirpi More scale options in SCALE QUANTIZE.