MusicLang Predict, your controllable music copilot.
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☞ You want to generate music that you can export to your favourite DAW in MIDI ?
☞ You want to control the chord progression of the generated music ?
☞ You need to run it fast on your laptop without a gpu ?
MusicLang is the contraction of “Music” & “language”: we bring advanced controllability features over music generation by manipulating symbolic music.
- Quickstart 🚀
- Examples 🎹
- What's coming next at musiclang? 👀
- How does MusicLang work? 🔬
- Contributing & spread the word 🤝
- License ⚖️
Quickstart 🚀
Try it quickly 📙
Go to our ♾Colab, or to our 🤗HuggingFace space, we have a lot of cool examples, from generating creative musical ideas to continuing a song with a specified chord progression.
Install MusicLang ♫
Install the musiclang-predict package :
pip install musiclang_predictExamples 🎹
1. Generate your first music 🕺
Open your favourite notebook and start generating music in a few lines :
from musiclang_predict import MusicLangPredictor
nb_tokens = 1024
temperature = 0.9 # Don't go over 1.0, at your own risks !
top_p = 1.0 # <=1.0, Usually 1 best to get not too much repetitive music
seed = 16 # change here to change result, or set to 0 to unset seed
ml = MusicLangPredictor('musiclang/musiclang-v2') # Only available model for now
score = ml.predict(
nb_tokens=nb_tokens, # 1024 tokens ~ 25s of music (depending of the number of instruments generated)
temperature=temperature,
topp=top_p,
rng_seed=seed # change here to change result, or set to 0 to unset seed
)
score.to_midi('test.mid') # Open that file in your favourite DAW, score editor or even in VLC2. Controlling chord progression generation 🪩
You had a specific harmony in mind, right ? MusicLang allows fine control over the chord progression of the generated music. Just specify it as a string like below, choose a time signature and let the magic happen.
from musiclang_predict import MusicLangPredictor
# Control the chord progression
# Chord qualities available : M, m, 7, m7b5, sus2, sus4, m7, M7, dim, dim0.
# You can also specify the bass if it belongs to the chord (eg : Bm/D)
chord_progression = "Am CM Dm E7 Am" # 1 chord = 1 bar
time_signature = (4, 4) # 4/4 time signature, don't be too crazy here
nb_tokens = 1024
temperature = 0.8
top_p = 1.0
seed = 42
ml = MusicLangPredictor('musiclang/musiclang-v2')
score = ml.predict_chords(
chord_progression,
time_signature=time_signature,
temperature=temperature,
topp=top_p,
rng_seed=seed # set to 0 to unset seed
)
score.to_midi('test.mid', tempo=120, time_signature=(4, 4))Disclaimer : The chord progression is not guaranteed to be exactly the same as the one you specified. It's a generative model after all. This may occur more frequently when using an exotic chord progression or setting a high temperature.
3. Generation from an existing music 💃
What if I want to use MusicLang from an existing music ? Don't worry, we got you covered. You can use your music as a template to generate new music. Let's continue with some Bach music and explore a chord progression he might have used:
from musiclang_predict import MusicLangPredictor
from musiclang_predict import corpus
song_name = 'bach_847' # corpus.list_corpus() to get the list of available songs
chord_progression = "Cm C7/E Fm F#dim G7 Cm"
nb_tokens = 1024
temperature = 0.8
top_p = 1.0
seed = 3666
ml = MusicLangPredictor('musiclang/musiclang-v2')
score = ml.predict_chords(
chord_progression,
score=corpus.get_midi_path_from_corpus(song_name),
time_signature=(4, 4),
nb_tokens=1024,
prompt_chord_range=(0,4),
temperature=temperature,
topp=top_p,
rng_seed=seed # set to 0 to unset seed
)
score.to_midi('test.mid', tempo=110, time_signature=(4, 4))What's coming next at MusicLang? 👀
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;
- And more to come! 😎
How does MusicLang work? 🔬
If you want to learn more about how we are moving toward symbolic music generation, go to our technical blog. The tokenization, the model are described in great details:
1. Annotate chords and scales progression of MIDIs using MusicLang analysis
2. The MusicLang tokenizer : Toward controllable symbolic music generation
3. Examples of sound made with MusicLang ❤️
We are using a LLAMA2 architecture (many thanks to Andrej Karpathy's awesome llama2.c), trained on a large dataset of midi files (The CC0 licensed LAKH). 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 & spread the word 🤝
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.
Whether you're contributing code or just saying hello, we'd love to hear about the work you are creating with MusicLang. Here's how you can reach out to us:
- Join our Discord to ask your questions and get support
- Follow us on Linkedin
- Add your star on GitHub or HuggingFace
License ⚖️
MusicLang Predict (This package) is licensed under the GPL-3.0 License. However please note that specific licenses applies to our models. If you would like to use the model in your commercial product, please contact us. We are looking forward to hearing from you !
The MusicLang base language package on which the model rely (musiclang package) is licensed under the BSD 3-Clause License.