164 lines
5.9 KiB
Markdown
164 lines
5.9 KiB
Markdown
---
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library_name: transformers
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tags: []
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---
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MusicLang : Controllable Symbolic Music Generation
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========================================================
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🎶 <b> You want to generate music that you can export to your favourite DAW in MIDI ?</b>
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🎛️ <b> You want to control the chord progression of the generated music ? </b>
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🚀 <b> You need to run it fast on your laptop without a gpu ?</b>
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Here is MusicLang Predict, your controllable music copilot.
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I just want to try !
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--------------------
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[](https://colab.research.google.com/drive/1MA2mek826c05BjbWk2nRkVv2rW7kIU_S?usp=sharing)
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Go to our Colab, we have a lot of cool examples. From generating creative musical ideas to continuing a song with a specified chord progression.
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I am more serious about it
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--------------------------
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Install the musiclang-predict package :
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```bash
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pip install musiclang_predict
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```
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Then open your favourite notebook and start generating music in a few lines :
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```python
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from musiclang_predict import MusicLangPredictor
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nb_tokens = 1024
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temperature = 0.9 # Don't go over 1.0, at your own risks !
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top_p = 1.0 # <=1.0, Usually 1 best to get not too much repetitive music
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seed = 16 # change here to change result, or set to 0 to unset seed
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ml = MusicLangPredictor('musiclang/musiclang-v2') # Only available model for now
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score = ml.predict(
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nb_tokens=nb_tokens, # 1024 tokens ~ 25s of music (depending of the number of instruments generated)
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temperature=temperature,
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topp=top_p,
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rng_seed=seed # change here to change result, or set to 0 to unset seed
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)
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score.to_midi('test.mid') # Open that file in your favourite DAW, score editor or even in VLC
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```
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You were talking about controlling the chord progression ?
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----------------------------------------------------------
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You had a specific harmony in mind am I right ?
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That's why we allow a fine control over the chord progression of the generated music.
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Just specify it as a string like below, choose a time signature and let the magic happen.
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```python
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from musiclang_predict import MusicLangPredictor
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# Control the chord progression
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# Chord qualities available : M, m, 7, m7b5, sus2, sus4, m7, M7, dim, dim0.
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# You can also specify the bass if it belongs to the chord (eg : Bm/D)
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chord_progression = "Am CM Dm E7 Am" # 1 chord = 1 bar
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time_signature = (4, 4) # 4/4 time signature, don't be too crazy here
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nb_tokens = 1024
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temperature = 0.8
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top_p = 1.0
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seed = 42
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ml = MusicLangPredictor('musiclang/musiclang-v2')
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score = ml.predict_chords(
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chord_progression,
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time_signature=time_signature,
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temperature=temperature,
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topp=top_p,
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rng_seed=seed # set to 0 to unset seed
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)
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score.to_midi('test.mid', tempo=120, time_signature=(4, 4))
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```
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Disclaimer : The chord progression is not guaranteed to be exactly the same as the one you specified. It's a generative model after all.
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Usually it will happen when you use an exotic chord progression and if you set a high temperature.
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That's cool but I have my music to plug in ...
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------------------------------------------------
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Don't worry, we got you covered. You can use your music as a template to generate new music.
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Let's continue some Bach music with a chord progression he could have used :
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```python
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from musiclang_predict import MusicLangPredictor
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from musiclang_predict import corpus
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song_name = 'bach_847' # corpus.list_corpus() to get the list of available songs
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chord_progression = "Cm C7/E Fm F#dim G7 Cm"
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nb_tokens = 1024
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temperature = 0.8
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top_p = 1.0
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seed = 3666
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ml = MusicLangPredictor('musiclang/musiclang-v2')
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score = ml.predict_chords(
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chord_progression,
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score=corpus.get_midi_path_from_corpus(song_name),
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time_signature=(4, 4),
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nb_tokens=1024,
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prompt_chord_range=(0,4),
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temperature=temperature,
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topp=top_p,
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rng_seed=seed # set to 0 to unset seed
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)
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score.to_midi('test.mid', tempo=110, time_signature=(4, 4))
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```
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What's coming next ?
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---------------------
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We are working on a lot of cool features, some are already encoded in the model :
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- A control over the instruments used in each bar and their properties (note density, pitch range, average velocity)
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- Some performances improvements over the inference C script
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- A faster distilled model for real-time generation that can be embedded in plugins or mobile applications
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- An integration into a DAW as a plugin
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- Some specialized smaller models depending on our user's needs
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How does that work ?
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---------------------
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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/).
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The tokenization, the model are described in great details.
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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/)).
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We heavily rely on preprocessing the midi files to get an enriched tokenization that describe chords & scale for each bar.
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The is also helpful for normalizing melodies relative to the current chord/scale.
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Contributing & Contact us
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-------------------------
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We are looking for contributors to help us improve the model, the tokenization, the performances and the documentation.
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If you are interested in this project, open an issue, a pull request, or even [contact us directly](https://www.musiclang.io/contact).
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License
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-------
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Specific licenses applies to our models. If you would like to use the model in your product, please
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[contact us](https://www.musiclang.io/contact). We are looking forward to hearing from you !
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MusicLang Predict is licensed under the GPL-3.0 License.
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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.
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