A music theory binary and library for Rust (capability playground).
Windows:
powershell
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):
bash
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:
bash
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:
bash
$ cargo install kord
```bash $ 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 ```
```bash $ kord describe Cmaj7
Cmaj7 major 7, ionian, first mode of major scale C, D, E, F, G, A, B C, E, G, B ```
```bash $ 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 ```
bash
$ kord loop -b 120 "Em7b5@3^2" "A7b13@3!" "D-maj7@3^2" "G7@3" "Cmaj7@3^2"
bash
$ 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
bash
$ 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♯
bash
$ 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
Using the deterministic algorithm only:
```bash $ kord analyze mic
Notes: C3 E3 G3 C@3 major C, D, E, F, G, A, B C, E, G ```
Using the ML algorithm:
```bash $ kord ml infer mic
Notes: C3 E3 G3 C@3 major C, D, E, F, G, A, B C, E, G ```
Add this to your Cargo.toml:
toml
[dependencies]
kord = "*" #choose a version
```rust use klib::known_chord::KnownChord; use klib::modifier::Degree; use klib::note::; use klib::chord::;
// Check to see what kind of chord this is. asserteq!(Chord::new(C).augmented().seven().knownchord(), KnownChord::AugmentedDominant(Degree::Seven)); ```
```rust 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]); ```
```rust use klib::note::; use klib::chord::;
// From a note, create a chord, and look at the chord tones. asserteq!(C.intochord().augmented().major7().chord(), vec![C, E, GSharp, B]); ```
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.
bash
cargo test
MIT