bliss-rs/README.md

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A modified version of the bliss-audio to remove ffmpeg and replace it with Rust's symphonia library.
[![crate](https://img.shields.io/crates/v/bliss-audio.svg)](https://crates.io/crates/bliss-audio)
[![build](https://github.com/Polochon-street/bliss-rs/workflows/Rust/badge.svg)](https://github.com/Polochon-street/bliss-rs/actions)
[![doc](https://docs.rs/bliss-audio/badge.svg)](https://docs.rs/bliss-audio/)
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# bliss music analyzer - Rust version
bliss-rs is the Rust improvement of [bliss](https://github.com/Polochon-street/bliss), a
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
play, à la Spotify/Grooveshark Radio, as well as easing creating plug-ins for
existing audio players. For instance, you can use it to make calm playlists
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),
[blissify](https://crates.io/crates/blissify) implements bliss for
[MPD](https://www.musicpd.org/).
There are also [python](https://pypi.org/project/bliss-audio/) 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](https://lelele.io/thesis.pdf). It is also faster.
## Examples
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For simple analysis / distance computing, take a look at `examples/distance.rs` and
`examples/analyze.rs`.
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If you simply want to try out making playlists from a folder containing songs,
[this example](https://github.com/Polochon-street/bliss-rs/blob/master/examples/playlist.rs)
contains all you need. Usage:
cargo run --features=serde --release --example=playlist /path/to/folder /path/to/first/song
Don't forget the `--release` flag!
By default, it outputs the playlist to stdout, but you can use `-o <path>`
to output it to a specific path.
To avoid having to analyze the entire folder
several times, it also stores the analysis in `/tmp/analysis.json`. You can customize
this behavior by using `-a <path>` to store this file in a specific place.
Ready to use code examples:
### Compute the distance between two songs
```
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use bliss_audio::{BlissError, Song};
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fn main() -> Result<(), BlissError> {
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let song1 = Song::from_path("/path/to/song1")?;
let song2 = Song::from_path("/path/to/song2")?;
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println!("Distance between song1 and song2 is {}", song1.distance(&song2));
Ok(())
}
```
### Make a playlist from a song
```
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use bliss_audio::{BlissError, Song};
use noisy_float::prelude::n32;
fn main() -> Result<(), BlissError> {
let paths = vec!["/path/to/song1", "/path/to/song2", "/path/to/song3"];
let mut songs: Vec<Song> = paths
.iter()
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.map(|path| Song::from_path(path))
.collect::<Result<Vec<Song>, BlissError>>()?;
// Assuming there is a first song
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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(())
}
```
## Further use
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](https://docs.rs/bliss-audio/latest/bliss_audio/library/index.html) module.
It implements common analysis functions, and allows analyzed songs to be put
in a sqlite database seamlessly.
See [blissify](https://crates.io/crates/blissify) for a reference
implementation.
## Cross-compilation
To cross-compile bliss-rs from linux to x86_64 windows, install the
`x86_64-pc-windows-gnu` target via:
rustup target add x86_64-pc-windows-gnu
Make sure you have `x86_64-w64-mingw32-gcc` installed on your computer.
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Then run:
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cargo build --target x86_64-pc-windows-gnu --release
Will produce a `.rlib` library file. If you want to generate a shared `.dll`
library, add:
[lib]
crate-type = ["cdylib"]
to `Cargo.toml` before compiling, and if you want to generate a `.lib` static
library, add:
[lib]
crate-type = ["staticlib"]
You can of course test the examples yourself by compiling them as .exe:
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cargo build --target x86_64-pc-windows-gnu --release --examples
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## Acknowledgements
* This library relies heavily on [aubio](https://aubio.org/)'s
[Rust bindings](https://crates.io/crates/aubio-rs) for the spectral /
timbral analysis, so a big thanks to both the creators and contributors
of librosa, and to @katyo for making aubio bindings for Rust.
* The first part of the chroma extraction is basically a rewrite of
[librosa](https://librosa.org/doc/latest/index.html)'s
[chroma feature extraction](https://librosa.org/doc/latest/generated/librosa.feature.chroma_stft.html?highlight=chroma#librosa.feature.chroma_stftfrom)
from python to Rust, with just as little features as needed. Thanks
to both creators and contributors as well.
* Finally, a big thanks to
[Christof Weiss](https://www.audiolabs-erlangen.de/fau/assistant/weiss)
for pointing me in the right direction for the chroma feature summarization,
which are basically also a rewrite from Python to Rust of some of the
awesome notebooks by AudioLabs Erlangen, that you can find
[here](https://www.audiolabs-erlangen.de/resources/MIR/FMP/C0/C0.html).