Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/MusicLang/musiclang_predict
AI Prediction api of the MusicLang package
https://github.com/MusicLang/musiclang_predict
Last synced: 2 days ago
JSON representation
AI Prediction api of the MusicLang package
- Host: GitHub
- URL: https://github.com/MusicLang/musiclang_predict
- Owner: MusicLang
- License: gpl-3.0
- Created: 2023-11-23T14:57:08.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-03-25T09:00:58.000Z (8 months ago)
- Last Synced: 2024-10-29T01:07:30.448Z (16 days ago)
- Language: Python
- Size: 258 KB
- Stars: 267
- Watchers: 6
- Forks: 16
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
MusicLang Predict, your controllable music copilot.
🤗 HuggingFace |
Discord |
Follow us!
☞ 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.
Table of contents
- Quickstart 🚀
- Try it quickly 📙
- Install MusicLang ♫
- Examples 🎹
- 1. Generate your first music 🕺
- 2. Controlling chord progression generation 🪩
- 3. Generation from an existing music 💃
- What's coming next at musiclang? 👀
- How does MusicLang work? 🔬
- 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 ❤️
- Contributing & spread the word 🤝
- License ⚖️
Quickstart 🚀
Try it quickly 📙
[](https://colab.research.google.com/drive/1MA2mek826c05BjbWk2nRkVv2rW7kIU_S?usp=sharing)
[](https://huggingface.co/spaces/musiclang/musiclang-predict)
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 :
```bash
pip install musiclang_predict
```
Examples 🎹
1. Generate your first music 🕺
Open your favourite notebook and start generating music in a few lines :
```python
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 VLC
```
2. 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.
```python
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:
```python
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](https://musiclang.github.io/). 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](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 & 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](https://www.musiclang.io/contact).
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](https://discord.gg/2g7eA5vP) to ask your questions and get support
* Follow us on [Linkedin](https://www.linkedin.com/company/musiclang/)
* Add your star on [GitHub](https://github.com/musiclang/musiclang_predict?tab=readme-ov-file) or [HuggingFace](https://huggingface.co/musiclang/musiclang-4k)
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](https://www.musiclang.io/contact). We are looking forward to hearing from you !
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.