117 lines
4.6 KiB
Markdown
117 lines
4.6 KiB
Markdown
[](https://crates.io/crates/bliss-audio)
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[](https://github.com/Polochon-street/bliss-rs/actions)
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[](https://docs.rs/bliss-audio/)
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# bliss music analyser - Rust version
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bliss-rs is the Rust improvement of [bliss](https://github.com/Polochon-street/bliss), a
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library used to make playlists by analyzing songs, and computing distance between them.
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Like bliss, it eases the creation of « intelligent » playlists and/or continuous
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play, à la Spotify/Grooveshark Radio, as well as easing creating plug-ins for
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existing audio players. For instance, you can use it to make calm playlists
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to help you sleeping, fast playlists to get you started during the day, etc.
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For now (and if you're looking for an easy-to use smooth play experience),
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[blissify](https://crates.io/crates/blissify) implements bliss for
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[MPD](https://www.musicpd.org/).
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There are also [python](https://pypi.org/project/bliss-audio/) bindings.
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Note 1: the features bliss-rs outputs is not compatible with the ones
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used by C-bliss, since it uses
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different, more accurate values, based on
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[actual literature](https://lelele.io/thesis.pdf). It is also faster.
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## Examples
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For simple analysis / distance computing, take a look at `examples/distance.rs` and
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`examples/analyse.rs`.
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If you simply want to try out making playlists from a folder containing songs,
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[this example](https://github.com/Polochon-street/bliss-rs/blob/master/examples/playlist.rs)
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contains all you need. Usage:
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cargo run --features=serde --release --example=playlist /path/to/folder /path/to/first/song
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Don't forget the `--release` flag!
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By default, it outputs the playlist to stdout, but you can use `-o <path>`
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to output it to a specific path.
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To avoid having to analyze the entire folder
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several times, it also stores the analysis in `/tmp/analysis.json`. You can customize
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this behavior by using `-a <path>` to store this file in a specific place.
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Ready to use code examples:
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### Compute the distance between two songs
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```
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use bliss_audio::{BlissError, Song};
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fn main() -> Result<(), BlissError> {
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let song1 = Song::new("/path/to/song1")?;
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let song2 = Song::new("/path/to/song2")?;
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println!("Distance between song1 and song2 is {}", song1.distance(&song2));
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Ok(())
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}
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```
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### Make a playlist from a song
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```
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use bliss_audio::{BlissError, Song};
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use noisy_float::prelude::n32;
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fn main() -> Result<(), BlissError> {
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let paths = vec!["/path/to/song1", "/path/to/song2", "/path/to/song3"];
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let mut songs: Vec<Song> = paths
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.iter()
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.map(|path| Song::new(path))
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.collect::<Result<Vec<Song>, BlissError>>()?;
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// Assuming there is a first song
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let first_song = songs.first().unwrap().to_owned();
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songs.sort_by_cached_key(|song| n32(first_song.distance(&song)));
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println!(
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"Playlist is: {:?}",
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songs
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.iter()
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.map(|song| &song.path)
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.collect::<Vec<&String>>()
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);
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Ok(())
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}
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```
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## Further use
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Instead of reinventing ways to fetch a user library, play songs, etc,
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and embed that into bliss, it is easier to look at the
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[Library](https://docs.rs/bliss-audio/0.4.1/bliss_audio/library/trait.Library.html)
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trait.
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By implementing a few functions to get songs from a media library, and store
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the resulting analysis, you get access to functions to analyze an entire
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library (with multithreading), and to make playlists easily.
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See [blissify](https://crates.io/crates/blissify) for a reference
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implementation.
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## Acknowledgements
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* This library relies heavily on [aubio](https://aubio.org/)'s
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[Rust bindings](https://crates.io/crates/aubio-rs) for the spectral /
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timbral analysis, so a big thanks to both the creators and contributors
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of librosa, and to @katyo for making aubio bindings for Rust.
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* The first part of the chroma extraction is basically a rewrite of
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[librosa](https://librosa.org/doc/latest/index.html)'s
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[chroma feature extraction](https://librosa.org/doc/latest/generated/librosa.feature.chroma_stft.html?highlight=chroma#librosa.feature.chroma_stftfrom)
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from python to Rust, with just as little features as needed. Thanks
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to both creators and contributors as well.
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* Finally, a big thanks to
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[Christof Weiss](https://www.audiolabs-erlangen.de/fau/assistant/weiss)
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for pointing me in the right direction for the chroma feature summarization,
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which are basically also a rewrite from Python to Rust of some of the
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awesome notebooks by AudioLabs Erlangen, that you can find
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[here](https://www.audiolabs-erlangen.de/resources/MIR/FMP/C0/C0.html).
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