pyramidi is a very experimental package to generate / manipulate midi
data from R. Be aware that a lot of the code I’ve written some years ago
hurts my eyes when I look at it now :) Midi data is read into
dataframes, using the python package
miditapyr under the hood (which
itself uses the excellent mido). The
notes’ midi information (one line per note_on
/note_off
midi event)
is translated into a wide format (one line per note). This format
facilitates some manipulations of the notes’ data and also plotting them
in piano roll plots. Finally, the modified dataframes can be written
back to midi files (again using miditapyr).
Thus, you can manipulate all the intermediate dataframes and create midi files from R. However, you need to make sure yourself that the midi files you write can be understood by your softsynth. The data is not yet validated by pyramidi, but mido (also used to write midi files) already catches some of the possible inconsistencies.
If you’re new to midi, mido’s documentation might be a good start.
The midi data can now be
- played live in the R console OR generate a sound file and a html audio
player when knitting rmarkdown documents thanks to the excellent R
packages fluidsynth (see the
documentation of the
play()
method in theMidiFramer
class and its helper functionplayer()
which use fluidsynth::midi_convert()
to synthesize midi to wav files (needs fluidsynth installed, but if I understand correctly R will do that for you)
You can install pyramidi from R-universe with:
install.packages('pyramidi', repos = c('https://urswilke.r-universe.dev', 'https://cloud.r-project.org'))
The python package miditapyr also needs to be installed in your python environment used by reticulate.
pip install miditapyr
But if everything works as I believe it should, miditapyr is automatically installed if you install pyramidi, as soon as you access the module for the first time.
Otherwise, you can also install it in your reticulate python environment with the included helper function:
pyramidi::install_miditapyr()
I’m not sure if that works on windows too. Perhaps there you have to manually configure your reticulate environment.
We can create a MidiFramer
object by passing the file path to the
constructor method
(new()
).
library(pyramidi)
library(dplyr)
midi_file_string <- system.file("extdata", "test_midi_file.mid", package = "pyramidi")
mfr <- MidiFramer$new(midi_file_string)
The object contains the midi data in various dataframe formats and an
interface to the miditapyr
miditapyr.MidiFrames
object mfr$mf
. You can write the midi file resulting of the
MidiFramer
object to disk:
mfr$mf$write_file("/path/to/your/midifile.mid")
In the MidiFramer
object, we can modify mfr$df_notes_wide
, the notes
in note-wise wide format (note_on
& note_off
events in the same
line). Thus we don’t need to worry which midi events belong together
Let’s look at a small example. We’ll define a function to replace every note with a random midi note between 60 & 71::
mod <- function(dfn, seed) {
n_notes <- sum(!is.na(dfn$note))
dfn %>% mutate(note = ifelse(
!is.na(note),
sample(60:71, n_notes, TRUE),
note
))
}
When we call the update_notes_wide()
method, the midi data in mfr
is
updated:
mfr$update_notes_wide(mod)
Thus, we can now save the modifications to a midi file:
mfr$mf$write_file("mod_test_midi_file.mid")
See the vignette("pyramidi", package = "pyramidi")
to see how you can
synthesize the midi data to wav, convert to mp3 if you want, and then
embed a player in your rmarkdown html document with
mfr$play("mod_test_midi_file.mp3")
*The player only appears in the docs.
Even if that sound is very weird, I was very happy not having to listen to the package midi file over and over again. :)
You can find the complete online documentation of the package here.
- See the
vignette("pyramidi")
for a brief usage introduction how to manipulate midi data. - The
vignette("compose")
shows a more extended example how to compose music and generate midi files from scratch. vignette("package_workflow")
shows in detail the structure of theMidiFramer
class and the functions of the pyramidi package.vignette("functions_usage")
illustrates the low-level functions of the pyramidi package, thatMidiFramer
objects use under the hood.
To see examples where pyramidi is used for midi mangling in R dataframes etc. (amongst plenty other of his awesome writings about music), please check out Matt Crump’s blog and his package midiblender!
- The tabr package is a massive music notation, transcription and analysis program allowing to create musical scores (using Lilypond). It allows to read midi files (wrapping tuneR; see below) and also to export them (also using Lilypond).
- The gm package also allows to create and show musical scores using musescore. It also allows to export the music to audio (also using musescore) and to embed the players in html documents.
- The noon package wraps node.js libraries and can be used to read live midi input port data. I wrote a small blog post how reading a midi port can also be done in R with mido. Interestingly, the node.js libraries and mido rely on a the same C++ library RtMidi.
- The tuneR package can also
read in midi data. See the
vignette("tuner")
, for an example how you can transform the tuner format into the pyramidi format.
This package stands on the shoulders of giants. A big thank you to the authors of the following libraries!
Package | Version | Citation |
---|---|---|
base | 4.3.2 | R Core Team (2023a) |
details | 0.3.0 | Sidi (2022) |
DiagrammeR | 1.0.11 | Iannone and Roy (2024) |
fluidsynth | 1.0.0 | Ooms (2024) |
glue | 1.7.0 | Hester and Bryan (2024) |
grateful | 0.2.6 | Rodriguez-Sanchez and Jackson (2023) |
htmltools | 0.5.7 | Cheng et al. (2023) |
knitr | 1.45 | Xie (2014); Xie (2015); Xie (2023) |
pichor | 0.0.0.9030 | Andersen (2024) |
pkgdown | 2.0.7 | Wickham, Hesselberth, and Salmon (2022) |
R6 | 2.5.1 | Chang (2021) |
reticulate | 1.35.0 | Ushey, Allaire, and Tang (2024) |
rmarkdown | 2.25 | Xie, Allaire, and Grolemund (2018); Xie, Dervieux, and Riederer (2020); Allaire et al. (2023) |
spelling | 2.2.1 | Ooms and Hester (2023) |
testthat | 3.2.1 | Wickham (2011) |
tidyverse | 2.0.0 | Wickham et al. (2019) |
tools | 4.3.2 | R Core Team (2023b) |
tuneR | 1.4.6 | Ligges et al. (2023) |
usethis | 2.2.2 | Wickham et al. (2023) |
zeallot | 0.1.0 | Teetor (2018) |
Allaire, JJ, Yihui Xie, Christophe Dervieux, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, et al. 2023. rmarkdown: Dynamic Documents for r. https://github.com/rstudio/rmarkdown.
Andersen, Mikkel Meyer. 2024. pichor: Piano Chords in r.
Chang, Winston. 2021. R6: Encapsulated Classes with Reference Semantics. https://CRAN.R-project.org/package=R6.
Cheng, Joe, Carson Sievert, Barret Schloerke, Winston Chang, Yihui Xie, and Jeff Allen. 2023. htmltools: Tools for HTML. https://CRAN.R-project.org/package=htmltools.
Hester, Jim, and Jennifer Bryan. 2024. glue: Interpreted String Literals. https://CRAN.R-project.org/package=glue.
Iannone, Richard, and Olivier Roy. 2024. DiagrammeR: Graph/Network Visualization. https://CRAN.R-project.org/package=DiagrammeR.
Ligges, Uwe, Sebastian Krey, Olaf Mersmann, and Sarah Schnackenberg. 2023. tuneR: Analysis of Music and Speech. https://CRAN.R-project.org/package=tuneR.
Ooms, Jeroen. 2024. fluidsynth: Read and Play Digital Music (MIDI). [https://docs.ropensci.org/fluidsynth/ https://ropensci.r-universe.dev/fluidsynth](https://docs.ropensci.org/fluidsynth/ https://ropensci.r-universe.dev/fluidsynth).
Ooms, Jeroen, and Jim Hester. 2023. spelling: Tools for Spell Checking in r. https://CRAN.R-project.org/package=spelling.
R Core Team. 2023a. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
———. 2023b. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Rodriguez-Sanchez, Francisco, and Connor P. Jackson. 2023. grateful: Facilitate Citation of r Packages. https://pakillo.github.io/grateful/.
Sidi, Jonathan. 2022. details: Create Details HTML Tag for Markdown and Package Documentation. https://CRAN.R-project.org/package=details.
Teetor, Nathan. 2018. zeallot: Multiple, Unpacking, and Destructuring Assignment. https://CRAN.R-project.org/package=zeallot.
Ushey, Kevin, JJ Allaire, and Yuan Tang. 2024. reticulate: Interface to “Python”. https://CRAN.R-project.org/package=reticulate.
Wickham, Hadley. 2011. “testthat: Get Started with Testing.” The R Journal 3: 5–10. https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Wickham, Hadley, Jennifer Bryan, Malcolm Barrett, and Andy Teucher. 2023. usethis: Automate Package and Project Setup. https://CRAN.R-project.org/package=usethis.
Wickham, Hadley, Jay Hesselberth, and Maëlle Salmon. 2022. pkgdown: Make Static HTML Documentation for a Package. https://CRAN.R-project.org/package=pkgdown.
Xie, Yihui. 2014. “knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC.
———. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.
———. 2023. knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.
Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown.
Xie, Yihui, Christophe Dervieux, and Emily Riederer. 2020. R Markdown Cookbook. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown-cookbook.