python markov_continuator.py
or
python markov_steerable.py
or
python exponential_families.py
This repo implements (parts of) several ideas from Sony CSL, led by Francois Pachet. See References for paper citations/links. Hear some example audio at this link.
Exponential families is in a semi-modifiable form (see constants at the top of the file) but takes a while to run the first time.
Continuator experiment is easier to modify (constants near the top) but is not very exciting.
Using the constraint experiments (steerable) will generate chord progressions, but needs more know-how to use. Here the modifications should happen at the bottom of the file.
This is not meant to be easy to use on new data, or out-of-the box usable without knowledge of the papers. The goal was for me to understand a series of interesting works, and hopefully provide some code that could be read/hacked up by other motivated people to understand the core ideas in these papers.
That said, feel free to fork and add whatever you want :D
An instance of a CMP object can be used by the .insert, and .branch methods. When creating these objects, one can specify the following:
order to calculate likelihoods over
max order, over which there cannot be any copying from the dataset (default None disables the check)
ptype, which is how the probability is calculated for longer history
constraints, passed as a dictionary
These constraints are requirements that each sequence returned from .branch must satisfy. "start", "end", and "position" arguments all define that a given place in the sequence must have one of the elements of the list in that position. The alldiff constraint specifies that every sequence element must be different.
Calling the .insert method with a sequence (list of tokens) will add it to the CMP object.
Finally when calling branch the two key arguments are the sequence to start from, and the length of the sequence to generate. This is the expensive call, .insert should be fast.
For example, this snippet approximates the setup from "Markov Constraints: Steerable Generation of Markov Sequences"
order = 1
m = CMP(order,
max_order=None,
ptype="fixed",
named_constraints={"not_contains": ["C7"],
"position": {8: ["F7"]},
"alldiff": True,
"end": ["G7"]})
m.insert(chord_seq1)
m.insert(chord_seq2)
m.insert(chord_seq3)
t = m.branch(["C7"], 15)
Adding constraints can greatly decrease runtime, due to reducing the search space. ptype "fixed" is faster to evaluate than the other options ("max", "avg") but gives different results. Changing the branch search type is not recommended, but could be useful in new problems
Best sounding results come from editing /etc/timidity/timidity.cfg
and using source /etc/timidity/fluidr3_gm.cfg instead of the default source /etc/timidity/freepats.cfg
This may require the fluid-soundfont package, which I installed with sudo apt-get install fluidsynth
To play midi files, do timidity whatever_sample.mid.
Use the helper function timidifyit.sh myfile.mid to convert mid files into wav
numpy
scipy
music21
pretty_midi
python 2.7 (3 may work, not tested)
timidity (optional)
The Continuator: Musical Interaction with Style F. Pachet https://www.csl.sony.fr/downloads/papers/uploads/pachet-02f.pdf
Finite-Length Markov Processes With Constraints F. Pachet, P. Roy, G. Barbieri https://www.csl.sony.fr/downloads/papers/2011/pachet-11b.pdf
Markov Constraints: Steerable Generation of Markov Sequences F. Pachet, P. Roy https://www.csl.sony.fr/downloads/papers/2011/pachet-09c.pdf
Avoiding Plagiarism in Markov Sequence Generation A. Papadopolous, P. Roy, F. Pachet https://www.csl.sony.fr/downloads/papers/2014/papadopoulos-14a.pdf
Enforcing Meter in Finite-Length Markov Sequences P. Roy, F. Pachet https://www.csl.sony.fr/downloads/papers/2013/roy-13a.pdf
Non-Conformant Harmonization: The Real Book in the Style of Take 6 F. Pachet, P. Roy https://www.csl.sony.fr/downloads/papers/2014/pachet-14a.pdf
Style Imitation and Chord Invention in Polyphonic Music with Exponential Families Gaetan Hadjeres, Jason Sakellariou, Francois Pachet https://arxiv.org/abs/1609.05152
The omnibook data came from here http://francoispachet.fr/texts/12BarBluesOmnibook.txt
All Bach data is pulled from Music21 (https://github.com/cuthbertLab/music21)