A music theory binary and library for Rust / JS (via WASM) (capability playground).
Windows:
$ iwr https://github.com/twitchax/kord/releases/latest/download/kord_x86_64-pc-windows-gnu.zip
$ Expand-Archive kord_x86_64-pc-windows-gnu.zip -DestinationPath C:\Users\%USERNAME%\AppData\Local\Programs\kordMac OS (Apple Silicon):
$ curl -LO https://github.com/twitchax/kord/releases/latest/download/kord_aarch64-apple-darwin.zip
$ unzip kord_aarch64-apple-darwin.zip -d /usr/local/bin
$ chmod a+x /usr/local/bin/kordLinux:
$ curl -LO https://github.com/twitchax/kord/releases/latest/download/kord_x86_64-unknown-linux-gnu.zip
$ unzip kord_x86_64-unknown-linux-gnu.zip -d /usr/local/bin
$ chmod a+x /usr/local/bin/kordCargo:
$ cargo install kordNPM:
$ npm install --save kordwebThis has a reduced capability set (no audio input / output), but works well for some of the core use cases.
$ wasmer install twitchax/kordAlternatively, you can use wasmer run.
$ wasmer run twitchax/kord -- describe Am7$ kord -h
A tool to easily explore music theory principles.
Usage: kord.exe [COMMAND]
Commands:
describe Describes a chord
play Describes and plays a chord
loop Loops on a set of chord changes, while simultaneously outputting the descriptions
guess Attempt to guess the chord from a set of notes (ordered by simplicity)
analyze Set of commands to analyze audio data
ml Set of commands to train and infer with ML
help Print this message or the help of the given subcommand(s)
Options:
-h, --help Print help information
-V, --version Print version information$ kord describe Cmaj7
Cmaj7
major 7, ionian, first mode of major scale
C, D, E, F, G, A, B
C, E, G, B$ kord play Bb7#9#11
B♭7(♯9)(♯11)
dominant sharp 9, altered, altered dominant, super locrian, diminished whole tone, seventh mode of a melodic minor scale, melodic minor up a half step
B♭, C♭, D♭, E𝄫, F♭, G♭, A♭
B♭, D, F, A♭, C♯, E$ kord loop -b 120 "Em7b5@3^2" "A7b13@3!" "D-maj7@3^2" "G7@3" "Cmaj7@3^2"$ kord guess C F# D# A
Cdim
fully diminished (whole first), diminished seventh, whole/half/whole diminished
C, D, E♭, F, G♭, A♭, B𝄫, B
C, E♭, G♭, B𝄫
Cm(♭5)(add6)
minor
C, D, E♭, F, G, A♭, B♭
C, E♭, G♭, A$ kord guess C G Bb F#5 F
C7(♯11)(sus4)
dominant sharp 11, lydian dominant, lyxian, major with sharp four and flat seven
C, D, E, F♯, G, A, B♭
C, F, G, B♭, F♯
Cm7(♯11)(sus4)
minor 7, dorian, second mode of major scale, major with flat third and flat seven
C, D, E♭, F, G, A, B♭
C, F, G, B♭, F♯$ kord guess E3 C4 Eb4 F#4 A#4 D5 D4
Cm9(♭5)(add2)/E
half diminished, locrian, minor seven flat five, seventh mode of major scale, major scale starting one half step up
C, D, E♭, F, G♭, A♭, B♭
E, C, D, E♭, G♭, B♭, DUsing the deterministic algorithm only:
$ kord analyze mic
Notes: C3 E3 G3
C@3
major
C, D, E, F, G, A, B
C, E, GUsing the ML algorithm:
$ kord ml infer mic
Notes: C3 E3 G3
C@3
major
C, D, E, F, G, A, B
C, E, GAdd this to your Cargo.toml:
[dependencies]
kord = "*" #choose a versionuse klib::known_chord::KnownChord;
use klib::modifier::Degree;
use klib::note::*;
use klib::chord::*;
// Check to see what _kind_ of chord this is.
assert_eq!(Chord::new(C).augmented().seven().known_chord(), KnownChord::AugmentedDominant(Degree::Seven));use crate::klib::base::Parsable;
use klib::note::*;
use klib::chord::*;
// Parse a chord from a string, and inspect the scale.
assert_eq!(Chord::parse("Cm7b5").unwrap().scale(), vec![C, D, EFlat, F, GFlat, AFlat, BFlat]);use klib::note::*;
use klib::chord::*;
// From a note, create a chord, and look at the chord tones.
assert_eq!(C.into_chord().augmented().major7().chord(), vec![C, E, GSharp, B]);The npm package is available here.
First, load the module as you would any other ES module.
import init, { KordNote, KordChord } from 'kordweb/klib.js';
// Run `init` once.
await init();Then, you can use the library similarly as you would in Rust.
// Create a note.
const note = KordNote.parse('C4');
note.name(); // C4
note.octave(); // 4
// Create a chord.
const chord = KordChord.parse('C7#9');
chord.name(); // C7(♯9)
chord.chordString(); // C4 E4 G4 Bb5 D#5
// Easy chaining.
KordChord.parse('C7b9').withOctave(2).chord().map(n => n.name()); // [ 'C2', 'D♭2', 'E2', 'G2', 'B♭2' ]
// Build chords.
KordChord.parse('C').minor().seven().chord().map(n => n.name()); // [ 'C4', 'Eb4', 'G4', 'Bb4' ]The library and binary both support various feature flags. Of most important note are:
default = ["cli", "analyze", "audio"]cli: enables the CLI features, and can be removed if only compiling the library.analyze = ["analyze_mic", "analyze_file"]: enables theanalyzesubcommand, which allows for analyzing audio data (and the underlying library features).analyze_mic: enables theanalyze micsubcommand, which allows for analyzing audio from a microphone (and the underlying library features).analyze_file: enables theanalyze filesubcommand, which allows for analyzing audio from a file (and the underlying library features).analyze_file_mp3: enables the features to analyze mp3 files.analyze_file_aac: enables the features to analyze aac files.analyze_file_alac: enables the features to analyze alac files.
ml = ["ml_train", "ml_infer"]: enables themlsubcommand, which allows for training and inferring with ML (and the underlying library features).ml_train: enables theml trainsubcommand, which allows for training ML models (and the underlying library features).ml_infer: enables theml infersubcommand, which allows for inferring with ML models (and the underlying library features).-
NOTE: Adding the
analyze_micfeature flag will enable theml infer micsubcommand, which allows for inferring with ML models from a microphone. -
NOTE: Adding the
analyze_filefeature flag will enable theml infer filesubcommand, which allows for inferring with ML models from a file.
-
ml_gpu: enables the features to use a GPU for ML training.
wasm: enables the features to compile to wasm.plot: enables the features to plot data.
cargo testMIT