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widget: |
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- text: CHORD_CHANGE |
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example_title: Predict chord progression |
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--- |
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MusicLang Chord Predictor model |
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=============================== |
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MusicLang Chord Predictor is a model for creating original chord scale progressions in the musiclang format with generative AI model. |
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It can be used for different use cases : |
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- Predict a chord progression from scratch (a fixed number of chords) |
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- Continue a chord progression (using a MusicLang prompt) |
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If you are only looking to generate chord progressions in an easily readable format, consider using [our text chord predictor](https://huggingface.co/musiclang/text-chord-predictor) |
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To make the prediction we have an inference package available here : [MusicLang Predict](https://github.com/MusicLang/musiclang_predict) |
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which is based on the musiclang language : [MusicLang](https://github.com/MusicLang/musiclang). |
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Installation |
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------------ |
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Install the musiclang-predict package with pip : |
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```bash |
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pip install musiclang-predict |
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``` |
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How to use ? |
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------------ |
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1. Generate a 4 chords progression in few lines : |
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```python |
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from musiclang_predict import predict_chords, MusicLangTokenizer |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from musiclang.library import * |
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# Load model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained('musiclang/musiclang-chord-v2-4k') |
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tokenizer = AutoTokenizer.from_pretrained('musiclang/musiclang-chord-v2-4k') |
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soundtrack = predict_chords(model, tokenizer, nb_chords=4, temperature=1.0) |
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# Give the chord a simple voicing (closed position chord) |
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soundtrack = soundtrack(b0, b1, b2, b3) |
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# Save it to midi |
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soundtrack.to_midi('song.mid', tempo=120, time_signature=(4, 4)) |
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``` |
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2. Use a prompt |
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```python |
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from musiclang_predict import predict_chords, MusicLangTokenizer |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from musiclang.library import * |
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prompt = (I % I.M) + (V % I.M)['6'].o(-1) |
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# Load model and tokenizer |
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model = GPT2LMHeadModel.from_pretrained('musiclang/musiclang-chord-v2-4k') |
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tokenizer = AutoTokenizer.from_pretrained('musiclang/musiclang-chord-v2-4k') |
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soundtrack = predict_chords(model, tokenizer, nb_chords=4, prompt=prompt) |
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# Give the chord a simple voicing (closed position chord) |
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soundtrack = soundtrack(b0, b1, b2, b3) |
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# Save it to midi |
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soundtrack.to_midi('song.mid', tempo=120, time_signature=(4, 4)) |
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``` |
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Contact us |
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---------- |
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If you want to help shape the future of open source music generation, |
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please contact [us](mailto:fgardin.pro@gmail.com) |
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License |
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======== |
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This model is free to use for research and open source purpose only. Please credit me (Florian GARDIN) and musiclang if you do so. |
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If you would like to use this in a commercial product please contact [us](florian.gardin@musiclang.io) to discuss licensing terms and potential integration in your product. I am looking forward to hearing about your project ! |
