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We are working on a lot of cool features, some are already encoded in the model :
- A control over the instruments used in each bar and their properties (note density, pitch range, average velocity)
- Some performances improvements over the inference C script
- A faster distilled model for real-time generation that can be embedded in plugins or mobile applications
- An integration into a DAW as a plugin
- Some specialized smaller models depending on our user's needs
How does that work ?
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If you want to learn more about how we are moving toward symbolic music generation, go to our [technical blog](https://musiclang.github.io/).
The tokenization, the model are described in great details.
We are using a LLAMA2 architecture (many thanks to Andrej Karpathy awesome [llama2.c](https://github.com/karpathy/llama2.c)), trained on a large dataset of midi files (The CC0 licensed [LAKH](https://colinraffel.com/projects/lmd/)).
We heavily rely on preprocessing the midi files to get an enriched tokenization that describe chords & scale for each bar.
The is also helpful for normalizing melodies relative to the current chord/scale.
Contributing & Contact us
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We are looking for contributors to help us improve the model, the tokenization, the performances and the documentation.
If you are interested in this project, open an issue, a pull request, or even [contact us directly](https://www.musiclang.io/contact).
License
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Specific licenses applies to our models. If you would like to use the model in your product, please
[contact us](https://www.musiclang.io/contact). We are looking forward to hearing from you !
MusicLang Predict is licensed under the GPL-3.0 License.
The MusicLang base language package on which the model rely ([musiclang package](https://github.com/musiclang/musiclang)) is licensed under the BSD 3-Clause License.