bliss-rs is the Rust improvement of bliss, a library used to make playlists by analyzing songs, and computing distance between them.
Like bliss, it eases the creation of « intelligent » playlists and/or continuous play, à la Spotify/Grooveshark Radio, as well as easing creating plug-ins for existing audio players.
For now (and if you're looking for an easy-to use smooth play experience), blissify implements bliss for MPD.
There are also python bindings.
Note 1: the features bliss-rs outputs is not compatible with the ones used by C-bliss, since it uses different, more accurate values, based on actual literature. It is also faster.
For simple analysis / distance computing, a look at examples/distance.rs and
examples/analyse.rs.
Ready to use examples:
``` use bliss_audio::{BlissError, Song};
fn main() -> Result<(), BlissError> { let song1 = Song::new("/path/to/song1")?; let song2 = Song::new("/path/to/song2")?;
println!("Distance between song1 and song2 is {}", song1.distance(&song2));
Ok(())
} ```
``` use blissaudio::{BlissError, Song}; use noisyfloat::prelude::n32;
fn main() -> Result<(), BlissError> {
let paths = vec!["/path/to/song1", "/path/to/song2", "/path/to/song3"];
let mut songs: Vec
// Assuming there is a first song
let first_song = songs.first().unwrap().to_owned();
songs.sort_by_cached_key(|song| n32(first_song.distance(&song)));
println!(
"Playlist is: {:?}",
songs
.iter()
.map(|song| &song.path)
.collect::<Vec<&String>>()
);
Ok(())
} ```
Instead of reinventing ways to fetch a user library, play songs, etc, and embed that into bliss, it is easier to look at the Library trait.
By implementing a few functions to get songs from a media library, and store the resulting analysis, you get access to functions to analyze an entire library (with multithreading), and to make playlists easily.
See blissify for a reference implementation.