#cli #music #theory #jazz #chords

nightly bin+lib kord

A tool to easily explore music theory principles

18 unstable releases (6 breaking)

0.7.1 Dec 21, 2023
0.6.1 Jun 16, 2023
0.5.16 Apr 7, 2023
0.5.3 Mar 24, 2023
0.1.4 Dec 22, 2022

#232 in Command line utilities

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186 downloads per month

MIT license

4MB
5.5K SLoC

Build and Test codecov Version Crates.io GitHub all releases npm Documentation Rust License:MIT

kord

A music theory binary and library for Rust / JS (via WASM) (capability playground).

Binary Usage

Install

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\kord

Mac 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/kord

Linux:

$ 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/kord

Cargo:

$ cargo install kord

NPM:

$ npm install --save kordweb

Wasmer

This has a reduced capability set (no audio input / output), but works well for some of the core use cases.

$ wasmer install twitchax/kord

Alternatively, you can use wasmer run.

$ wasmer run twitchax/kord -- describe Am7

Help Docs

$ 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

Describe A Chord

$ kord describe Cmaj7

Cmaj7
   major 7, ionian, first mode of major scale
   C, D, E, F, G, A, B
   C, E, G, B

Play A Chord

$ 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

Loop Through Chord Changes

$ kord loop -b 120 "Em7b5@3^2" "A7b13@3!" "D-maj7@3^2" "G7@3" "Cmaj7@3^2"

Guess A Chord

$ 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♭, D

Guess Notes / Chord From Audio

Using the deterministic algorithm only:

$ kord analyze mic

Notes: C3 E3 G3
C@3
   major
   C, D, E, F, G, A, B
   C, E, G

Using the ML algorithm:

$ kord ml infer mic

Notes: C3 E3 G3
C@3
   major
   C, D, E, F, G, A, B
   C, E, G

Library Usage

Add this to your Cargo.toml:

[dependencies]
kord = "*" #choose a version

Examples

use 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]);

JS Usage

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' ]

Feature Flags

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 the analyze subcommand, which allows for analyzing audio data (and the underlying library features).
    • analyze_mic: enables the analyze mic subcommand, which allows for analyzing audio from a microphone (and the underlying library features).
    • analyze_file: enables the analyze file subcommand, 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 the ml subcommand, which allows for training and inferring with ML (and the underlying library features).
    • ml_train: enables the ml train subcommand, which allows for training ML models (and the underlying library features).
    • ml_infer: enables the ml infer subcommand, which allows for inferring with ML models (and the underlying library features).
      • NOTE: Adding the analyze_mic feature flag will enable the ml infer mic subcommand, which allows for inferring with ML models from a microphone.

      • NOTE: Adding the analyze_file feature flag will enable the ml infer file subcommand, 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.

Test

cargo test

License

MIT

Dependencies

~2–50MB
~769K SLoC