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Remove library trait; move things in playlist.rs

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Polochon-street 2022-04-02 19:30:43 +02:00
parent 3a4bac5a34
commit d2124a3b2c
6 changed files with 361 additions and 929 deletions
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3
CHANGELOG.md
View file

@ -1,6 +1,9 @@
#Changelog
## bliss 0.5.0
* Remove the unusued Library trait, and extract a few useful functions from
there (`analyze_paths`, `closest_to_album_group`.
* Rename `distance` module to `playlist`.
* Remove all traces of the "analyse" word vs "analyze" to make the codebase
more coherent.
* Rename `Song::new` to `Song::from_path`.

10
examples/playlist.rs
View file

@ -1,9 +1,9 @@
#[cfg(feature = "serde")]
use anyhow::Result;
#[cfg(feature = "serde")]
use bliss_audio::distance::{closest_to_first_song, dedup_playlist, euclidean_distance};
use bliss_audio::playlist::{closest_to_first_song, dedup_playlist, euclidean_distance};
#[cfg(feature = "serde")]
use bliss_audio::{library::analyze_paths_streaming, Song};
use bliss_audio::{analyze_paths, Song};
#[cfg(feature = "serde")]
use clap::{App, Arg};
#[cfg(feature = "serde")]
@ -66,16 +66,16 @@ fn main() -> Result<()> {
.map(|x| x.to_string_lossy().to_string())
.collect::<Vec<String>>();
let rx = analyze_paths_streaming(
let song_iterator = analyze_paths(
paths
.iter()
.filter(|p| !analyzed_paths.contains(&PathBuf::from(p)))
.map(|p| p.to_owned())
.collect(),
)?;
);
let first_song = Song::from_path(file)?;
let mut analyzed_songs = vec![first_song.to_owned()];
for (path, result) in rx.iter() {
for (path, result) in song_iterator {
match result {
Ok(song) => analyzed_songs.push(song),
Err(e) => println!("error analyzing {}: {}", path, e),

161
src/lib.rs
View file

@ -7,19 +7,14 @@
//! other metadata fields (album, genre...).
//! Analyzing a song is as simple as running `Song::from_path("/path/to/song")`.
//!
//! The [analysis](Song::analysis) field of each song is an array of f32, which makes the
//! comparison between songs easy, by just using euclidean distance (see
//! [distance](Song::distance) for instance).
//! The [analysis](Song::analysis) field of each song is an array of f32, which
//! makes the comparison between songs easy, by just using e.g. euclidean
//! distance (see [distance](Song::distance) for instance).
//!
//! Once several songs have been analyzed, making a playlist from one Song
//! is as easy as computing distances between that song and the rest, and ordering
//! the songs by distance, ascending.
//!
//! It is also convenient to make plug-ins for existing audio players.
//! It should be as easy as implementing the necessary traits for [Library].
//! A reference implementation for the MPD player is available
//! [here](https://github.com/Polochon-street/blissify-rs)
//!
//! # Examples
//!
//! ## Analyze & compute the distance between two songs
@ -65,9 +60,8 @@
#![warn(missing_docs)]
#![warn(rustdoc::missing_doc_code_examples)]
mod chroma;
pub mod distance;
pub mod library;
mod misc;
pub mod playlist;
mod song;
mod temporal;
mod timbral;
@ -78,9 +72,11 @@ extern crate num_cpus;
#[cfg(feature = "serde")]
#[macro_use]
extern crate serde;
use log::info;
use std::sync::mpsc;
use std::thread;
use thiserror::Error;
pub use library::Library;
pub use song::{Analysis, AnalysisIndex, Song, NUMBER_FEATURES};
const CHANNELS: u16 = 1;
@ -94,54 +90,73 @@ pub const FEATURES_VERSION: u16 = 1;
/// Umbrella type for bliss error types
pub enum BlissError {
#[error("error happened while decoding file – {0}")]
/// An error happened while decoding an (audio) file
/// An error happened while decoding an (audio) file.
DecodingError(String),
#[error("error happened while analyzing file – {0}")]
/// An error happened during the analysis of the samples by bliss
/// An error happened during the analysis of the song's samples by bliss.
AnalysisError(String),
#[error("error happened with the music library provider - {0}")]
/// An error happened with the music library provider.
/// Useful to report errors when you implement the [Library] trait.
/// Useful to report errors when you implement bliss for an audio player.
ProviderError(String),
}
/// bliss error type
pub type BlissResult<T> = Result<T, BlissError>;
/// Simple function to bulk analyze a set of songs represented by their
/// absolute paths.
/// Analyze songs in `paths`, and return the analyzed [Song] objects through an
/// [mpsc::IntoIter]
///
/// When making an extension for an audio player, prefer
/// implementing the `Library` trait.
#[doc(hidden)]
pub fn bulk_analyze(paths: Vec<String>) -> Vec<BlissResult<Song>> {
let mut songs = Vec::with_capacity(paths.len());
/// Returns an iterator, whose items are a tuple made of
/// the song path (to display to the user in case the analysis failed),
/// and a Result<Song>.
///
/// * Example:
/// ```no_run
/// use bliss_audio::{analyze_paths, BlissResult};
///
/// fn main() -> BlissResult<()> {
/// let paths = vec![String::from("/path/to/song1"), String::from("/path/to/song2")];
/// for (path, result) in analyze_paths(paths) {
/// match result {
/// Ok(song) => println!("Do something with analyzed song {} with title {:?}", song.path.display(), song.title),
/// Err(e) => println!("Song at {} could not be analyzed. Failed with: {}", path, e),
/// }
/// }
/// Ok(())
/// }
/// ```
pub fn analyze_paths(paths: Vec<String>) -> mpsc::IntoIter<(String, BlissResult<Song>)> {
let num_cpus = num_cpus::get();
crossbeam::scope(|s| {
let mut handles = Vec::with_capacity(paths.len() / num_cpus);
let mut chunk_number = paths.len() / num_cpus;
if chunk_number == 0 {
chunk_number = paths.len();
}
for chunk in paths.chunks(chunk_number) {
handles.push(s.spawn(move |_| {
let mut result = Vec::with_capacity(chunk.len());
for path in chunk {
let song = Song::from_path(&path);
result.push(song);
}
result
}));
}
#[allow(clippy::type_complexity)]
let (tx, rx): (
mpsc::Sender<(String, BlissResult<Song>)>,
mpsc::Receiver<(String, BlissResult<Song>)>,
) = mpsc::channel();
if paths.is_empty() {
return rx.into_iter();
}
let mut handles = Vec::new();
let mut chunk_length = paths.len() / num_cpus;
if chunk_length == 0 {
chunk_length = paths.len();
}
for handle in handles {
songs.extend(handle.join().unwrap());
}
})
.unwrap();
for chunk in paths.chunks(chunk_length) {
let tx_thread = tx.clone();
let owned_chunk = chunk.to_owned();
let child = thread::spawn(move || {
for path in owned_chunk {
info!("Analyzing file '{}'", path);
let song = Song::from_path(&path);
tx_thread.send((path.to_string(), song)).unwrap();
}
});
handles.push(child);
}
songs
rx.into_iter()
}
#[cfg(test)]
@ -161,52 +176,28 @@ mod tests {
}
#[test]
fn test_bulk_analyze() {
let results = bulk_analyze(vec![
String::from("data/s16_mono_22_5kHz.flac"),
String::from("data/s16_mono_22_5kHz.flac"),
String::from("nonexistent"),
String::from("data/s16_stereo_22_5kHz.flac"),
String::from("nonexistent"),
String::from("nonexistent"),
String::from("nonexistent"),
String::from("nonexistent"),
String::from("nonexistent"),
String::from("nonexistent"),
String::from("nonexistent"),
]);
let mut errored_songs: Vec<String> = results
.iter()
.filter_map(|x| x.as_ref().err().map(|x| x.to_string()))
.collect();
errored_songs.sort_by(|a, b| a.cmp(b));
let mut analyzed_songs: Vec<String> = results
.iter()
.filter_map(|x| {
x.as_ref()
.ok()
.map(|x| x.path.to_str().unwrap().to_string())
fn test_analyze_paths() {
let paths = vec![
String::from("./data/s16_mono_22_5kHz.flac"),
String::from("./data/white_noise.flac"),
String::from("definitely-not-existing.foo"),
String::from("not-existing.foo"),
];
let mut results = analyze_paths(paths)
.map(|x| match &x.1 {
Ok(s) => (true, s.path.to_string_lossy().to_string()),
Err(_) => (false, x.0.to_owned()),
})
.collect();
analyzed_songs.sort_by(|a, b| a.cmp(b));
.collect::<Vec<_>>();
results.sort();
assert_eq!(
results,
vec![
String::from(
"error happened while decoding file – while opening format: ffmpeg::Error(2: No such file or directory)."
);
8
(false, String::from("definitely-not-existing.foo")),
(false, String::from("not-existing.foo")),
(true, String::from("./data/s16_mono_22_5kHz.flac")),
(true, String::from("./data/white_noise.flac")),
],
errored_songs
);
assert_eq!(
vec![
String::from("data/s16_mono_22_5kHz.flac"),
String::from("data/s16_mono_22_5kHz.flac"),
String::from("data/s16_stereo_22_5kHz.flac"),
],
analyzed_songs,
);
}
}

829
src/library.rs
View file

@ -1,829 +0,0 @@
//! Module containing the Library trait, useful to get started to implement
//! a plug-in for an audio player.
//!
//! Looking at the [reference implementation for
//! MPD](https://github.com/Polochon-street/blissify-rs) could also be useful.
#[cfg(doc)]
use crate::distance;
use crate::distance::{closest_to_first_song, euclidean_distance, DistanceMetric};
use crate::{BlissError, BlissResult, Song};
use log::{debug, error, info};
use ndarray::{Array, Array2, Axis};
use noisy_float::prelude::n32;
use std::collections::HashMap;
use std::sync::mpsc;
use std::sync::mpsc::{Receiver, Sender};
use std::thread;
/// Library trait to make creating plug-ins for existing audio players easier.
pub trait Library {
/// Return the absolute path of all the songs in an
/// audio player's music library.
fn get_songs_paths(&self) -> BlissResult<Vec<String>>;
/// Store an analyzed Song object in some (cold) storage, e.g.
/// a database, a file...
fn store_song(&mut self, song: &Song) -> BlissResult<()>;
/// Log and / or store that an error happened while trying to decode and
/// analyze a song.
fn store_error_song(&mut self, song_path: String, error: BlissError) -> BlissResult<()>;
/// Retrieve a list of all the stored Songs.
///
/// This should work only after having run `analyze_library` at least
/// once.
fn get_stored_songs(&self) -> BlissResult<Vec<Song>>;
/// Return a list of `number_albums` albums that are similar
/// to `album`, discarding songs that don't belong to an album.
///
/// # Arguments
///
/// * `album` - The album the playlist will be built from.
/// * `number_albums` - The number of albums to queue.
///
/// # Returns
///
/// A vector of songs, including `first_song`, that you
/// most likely want to plug in your audio player by using something like
/// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
fn playlist_from_songs_album(
&self,
first_album: &str,
playlist_length: usize,
) -> BlissResult<Vec<Song>> {
let songs = self.get_stored_songs()?;
let mut albums_analysis: HashMap<&str, Array2<f32>> = HashMap::new();
let mut albums = Vec::new();
for song in &songs {
if let Some(album) = &song.album {
if let Some(analysis) = albums_analysis.get_mut(album as &str) {
analysis
.push_row(song.analysis.as_arr1().view())
.map_err(|e| {
BlissError::ProviderError(format!("while computing distances: {}", e))
})?;
} else {
let mut array = Array::zeros((1, song.analysis.as_arr1().len()));
array.assign(&song.analysis.as_arr1());
albums_analysis.insert(album, array);
}
}
}
let mut first_analysis = None;
for (album, analysis) in albums_analysis.iter() {
let mean_analysis = analysis
.mean_axis(Axis(0))
.ok_or_else(|| BlissError::ProviderError(String::from("Mean of empty slice")))?;
let album = album.to_owned();
albums.push((album, mean_analysis.to_owned()));
if album == first_album {
first_analysis = Some(mean_analysis);
}
}
if first_analysis.is_none() {
return Err(BlissError::ProviderError(format!(
"Could not find album \"{}\".",
first_album
)));
}
albums.sort_by_key(|(_, analysis)| {
n32(euclidean_distance(
first_analysis.as_ref().unwrap(),
analysis,
))
});
let albums = albums.get(..playlist_length).unwrap_or(&albums);
let mut playlist = Vec::new();
for (album, _) in albums {
let mut al = songs
.iter()
.filter(|s| s.album.is_some() && s.album.as_ref().unwrap() == &album.to_string())
.map(|s| s.to_owned())
.collect::<Vec<Song>>();
al.sort_by(|s1, s2| {
let track_number1 = s1
.track_number
.to_owned()
.unwrap_or_else(|| String::from(""));
let track_number2 = s2
.track_number
.to_owned()
.unwrap_or_else(|| String::from(""));
if let Ok(x) = track_number1.parse::<i32>() {
if let Ok(y) = track_number2.parse::<i32>() {
return x.cmp(&y);
}
}
s1.track_number.cmp(&s2.track_number)
});
playlist.extend_from_slice(&al);
}
Ok(playlist)
}
/// Return a list of `playlist_length` songs that are similar
/// to ``first_song``, deduplicating identical songs.
///
/// # Arguments
///
/// * `first_song` - The song the playlist will be built from.
/// * `playlist_length` - The playlist length. If there are not enough
/// songs in the library, it will be truncated to the size of the library.
///
/// # Returns
///
/// A vector of `playlist_length` songs, including `first_song`, that you
/// most likely want to plug in your audio player by using something like
/// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
// TODO return an iterator and not a Vec
fn playlist_from_song(
&self,
first_song: Song,
playlist_length: usize,
) -> BlissResult<Vec<Song>> {
let playlist = self.playlist_from_song_custom(
first_song,
playlist_length,
euclidean_distance,
closest_to_first_song,
)?;
debug!(
"Playlist created: {}",
playlist
.iter()
.map(|s| format!("{:?}", &s))
.collect::<Vec<String>>()
.join("\n"),
);
Ok(playlist)
}
/// Return a list of songs that are similar to ``first_song``, using a
/// custom distance metric and deduplicating indentical songs.
///
/// # Arguments
///
/// * `first_song` - The song the playlist will be built from.
/// * `playlist_length` - The playlist length. If there are not enough
/// songs in the library, it will be truncated to the size of the library.
/// * `distance` - a user-supplied valid distance metric, either taken
/// from the [distance](distance) module, or made from scratch.
///
/// # Returns
///
/// A vector of `playlist_length` Songs, including `first_song`, that you
/// most likely want to plug in your audio player by using something like
/// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
///
/// # Custom distance example
///
/// ```
/// use ndarray::Array1;
///
/// fn manhattan_distance(a: &Array1<f32>, b: &Array1<f32>) -> f32 {
/// (a - b).mapv(|x| x.abs()).sum()
/// }
/// ```
fn playlist_from_song_custom_distance(
&self,
first_song: Song,
playlist_length: usize,
distance: impl DistanceMetric,
) -> BlissResult<Vec<Song>> {
let playlist = self.playlist_from_song_custom(
first_song,
playlist_length,
distance,
closest_to_first_song,
)?;
debug!(
"Playlist created: {}",
playlist
.iter()
.map(|s| format!("{:?}", &s))
.collect::<Vec<String>>()
.join("\n"),
);
Ok(playlist)
}
/// Return a playlist of songs, starting with `first_song`, sorted using
/// the custom `sort` function, and the custom `distance` metric.
///
/// # Arguments
///
/// * `first_song` - The song the playlist will be built from.
/// * `playlist_length` - The playlist length. If there are not enough
/// songs in the library, it will be truncated to the size of the library.
/// * `distance` - a user-supplied valid distance metric, either taken
/// from the [distance](distance) module, or made from scratch.
/// * `sort` - a user-supplied sorting function that uses the `distance`
/// metric, either taken from the [distance module](distance), or made
/// from scratch.
///
/// # Returns
///
/// A vector of `playlist_length` Songs, including `first_song`, that you
/// most likely want to plug in your audio player by using something like
/// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
fn playlist_from_song_custom<F, G>(
&self,
first_song: Song,
playlist_length: usize,
distance: G,
mut sort: F,
) -> BlissResult<Vec<Song>>
where
F: FnMut(&Song, &mut Vec<Song>, G),
G: DistanceMetric,
{
let mut songs = self.get_stored_songs()?;
sort(&first_song, &mut songs, distance);
Ok(songs
.into_iter()
.take(playlist_length)
.collect::<Vec<Song>>())
}
/// Analyze and store songs in `paths`, using `store_song` and
/// `store_error_song` implementations.
///
/// note: this is mostly useful for updating a song library. for the first
/// run, you probably want to use `analyze_library`.
fn analyze_paths(&mut self, paths: Vec<String>) -> BlissResult<()> {
if paths.is_empty() {
return Ok(());
}
let num_cpus = num_cpus::get();
#[allow(clippy::type_complexity)]
let (tx, rx): (
Sender<(String, BlissResult<Song>)>,
Receiver<(String, BlissResult<Song>)>,
) = mpsc::channel();
let mut handles = Vec::new();
let mut chunk_length = paths.len() / num_cpus;
if chunk_length == 0 {
chunk_length = paths.len();
}
for chunk in paths.chunks(chunk_length) {
let tx_thread = tx.clone();
let owned_chunk = chunk.to_owned();
let child = thread::spawn(move || {
for path in owned_chunk {
info!("Analyzing file '{}'", path);
let song = Song::from_path(&path);
tx_thread.send((path.to_string(), song)).unwrap();
}
drop(tx_thread);
});
handles.push(child);
}
drop(tx);
for (path, song) in rx.iter() {
// A storage fail should just warn the user, but not abort the whole process
match song {
Ok(song) => {
self.store_song(&song).unwrap_or_else(|e| {
error!("Error while storing song '{}': {}", song.path.display(), e)
});
info!(
"Analyzed and stored song '{}' successfully.",
song.path.display()
)
}
Err(e) => {
self.store_error_song(path.to_string(), e.to_owned())
.unwrap_or_else(|e| {
error!("Error while storing errored song '{}': {}", path, e)
});
error!(
"Analysis of song '{}': {} failed. Error has been stored.",
path, e
)
}
}
}
for child in handles {
child
.join()
.map_err(|_| BlissError::AnalysisError("in analysis".to_string()))?;
}
Ok(())
}
/// Analyzes a song library, using `get_songs_paths`, `store_song` and
/// `store_error_song` implementations.
fn analyze_library(&mut self) -> BlissResult<()> {
let paths = self
.get_songs_paths()
.map_err(|e| BlissError::ProviderError(e.to_string()))?;
self.analyze_paths(paths)?;
Ok(())
}
/// Analyze an entire library using `get_songs_paths`, but instead of
/// storing songs using [store_song](Library::store_song)
/// and [store_error_song](Library::store_error_song).
///
/// Returns an iterable [Receiver], whose items are a tuple made of
/// the song path (to display to the user in case the analysis failed),
/// and a Result<Song>.
fn analyze_library_streaming(&mut self) -> BlissResult<Receiver<(String, BlissResult<Song>)>> {
let paths = self
.get_songs_paths()
.map_err(|e| BlissError::ProviderError(e.to_string()))?;
analyze_paths_streaming(paths)
}
}
/// Analyze songs in `paths`, and return the analyzed [Song] objects through a
/// [Receiver].
///
/// Returns an iterable [Receiver], whose items are a tuple made of
/// the song path (to display to the user in case the analysis failed),
/// and a Result<Song>.
///
/// Note: this is mostly useful for updating a song library, while displaying
/// status to the user (since you have access to each song object). For the
/// first run, you probably want to use `analyze_library`.
///
/// * Example:
/// ```no_run
/// use bliss_audio::{library::analyze_paths_streaming, BlissResult};
///
/// fn main() -> BlissResult<()> {
/// let paths = vec![String::from("/path/to/song1"), String::from("/path/to/song2")];
/// let rx = analyze_paths_streaming(paths)?;
/// for (path, result) in rx.iter() {
/// match result {
/// Ok(song) => println!("Do something with analyzed song {} with title {:?}", song.path.display(), song.title),
/// Err(e) => println!("Song at {} could not be analyzed. Failed with: {}", path, e),
/// }
/// }
/// Ok(())
/// }
/// ```
pub fn analyze_paths_streaming(
paths: Vec<String>,
) -> BlissResult<Receiver<(String, BlissResult<Song>)>> {
let num_cpus = num_cpus::get();
#[allow(clippy::type_complexity)]
let (tx, rx): (
Sender<(String, BlissResult<Song>)>,
Receiver<(String, BlissResult<Song>)>,
) = mpsc::channel();
if paths.is_empty() {
return Ok(rx);
}
let mut handles = Vec::new();
let mut chunk_length = paths.len() / num_cpus;
if chunk_length == 0 {
chunk_length = paths.len();
}
for chunk in paths.chunks(chunk_length) {
let tx_thread = tx.clone();
let owned_chunk = chunk.to_owned();
let child = thread::spawn(move || {
for path in owned_chunk {
info!("Analyzing file '{}'", path);
let song = Song::from_path(&path);
tx_thread.send((path.to_string(), song)).unwrap();
}
});
handles.push(child);
}
Ok(rx)
}
#[cfg(test)]
mod test {
use super::*;
use crate::song::Analysis;
use ndarray::Array1;
use std::path::Path;
#[derive(Default)]
struct TestLibrary {
internal_storage: Vec<Song>,
failed_files: Vec<(String, String)>,
}
impl Library for TestLibrary {
fn get_songs_paths(&self) -> BlissResult<Vec<String>> {
Ok(vec![
String::from("./data/white_noise.flac"),
String::from("./data/s16_mono_22_5kHz.flac"),
String::from("not-existing.foo"),
String::from("definitely-not-existing.foo"),
])
}
fn store_song(&mut self, song: &Song) -> BlissResult<()> {
self.internal_storage.push(song.to_owned());
Ok(())
}
fn store_error_song(&mut self, song_path: String, error: BlissError) -> BlissResult<()> {
self.failed_files.push((song_path, error.to_string()));
Ok(())
}
fn get_stored_songs(&self) -> BlissResult<Vec<Song>> {
Ok(self.internal_storage.to_owned())
}
}
#[derive(Default)]
struct FailingLibrary;
impl Library for FailingLibrary {
fn get_songs_paths(&self) -> BlissResult<Vec<String>> {
Err(BlissError::ProviderError(String::from(
"Could not get songs path",
)))
}
fn store_song(&mut self, _: &Song) -> BlissResult<()> {
Ok(())
}
fn get_stored_songs(&self) -> BlissResult<Vec<Song>> {
Err(BlissError::ProviderError(String::from(
"Could not get stored songs",
)))
}
fn store_error_song(&mut self, _: String, _: BlissError) -> BlissResult<()> {
Ok(())
}
}
#[derive(Default)]
struct FailingStorage;
impl Library for FailingStorage {
fn get_songs_paths(&self) -> BlissResult<Vec<String>> {
Ok(vec![
String::from("./data/white_noise.flac"),
String::from("./data/s16_mono_22_5kHz.flac"),
String::from("not-existing.foo"),
String::from("definitely-not-existing.foo"),
])
}
fn store_song(&mut self, song: &Song) -> BlissResult<()> {
Err(BlissError::ProviderError(format!(
"Could not store song {}",
song.path.display()
)))
}
fn get_stored_songs(&self) -> BlissResult<Vec<Song>> {
Ok(vec![])
}
fn store_error_song(&mut self, song_path: String, error: BlissError) -> BlissResult<()> {
Err(BlissError::ProviderError(format!(
"Could not store errored song: {}, with error: {}",
song_path, error
)))
}
}
#[test]
fn test_analyze_library_fail() {
let mut test_library = FailingLibrary {};
assert_eq!(
test_library.analyze_library(),
Err(BlissError::ProviderError(String::from(
"error happened with the music library provider - Could not get songs path"
))),
);
}
#[test]
fn test_playlist_from_song_fail() {
let test_library = FailingLibrary {};
let song = Song {
path: Path::new("path-to-first").to_path_buf(),
analysis: Analysis::new([0.; 20]),
..Default::default()
};
assert_eq!(
test_library.playlist_from_song(song, 10),
Err(BlissError::ProviderError(String::from(
"Could not get stored songs"
))),
);
}
#[test]
fn test_analyze_library_fail_storage() {
let mut test_library = FailingStorage {};
// A storage fail should just warn the user, but not abort the whole process
assert!(test_library.analyze_library().is_ok())
}
#[test]
fn test_analyze_library_streaming() {
let mut test_library = TestLibrary {
internal_storage: vec![],
failed_files: vec![],
};
let rx = test_library.analyze_library_streaming().unwrap();
let mut result = rx.iter().collect::<Vec<(String, BlissResult<Song>)>>();
result.sort_by_key(|k| k.0.to_owned());
let expected = result
.iter()
.map(|x| match &x.1 {
Ok(s) => (true, s.path.to_string_lossy().to_string()),
Err(_) => (false, x.0.to_owned()),
})
.collect::<Vec<(bool, String)>>();
assert_eq!(
vec![
(true, String::from("./data/s16_mono_22_5kHz.flac")),
(true, String::from("./data/white_noise.flac")),
(false, String::from("definitely-not-existing.foo")),
(false, String::from("not-existing.foo")),
],
expected,
);
}
#[test]
fn test_analyze_library() {
let mut test_library = TestLibrary {
internal_storage: vec![],
failed_files: vec![],
};
test_library.analyze_library().unwrap();
let mut failed_files = test_library
.failed_files
.iter()
.map(|x| x.0.to_owned())
.collect::<Vec<String>>();
failed_files.sort();
assert_eq!(
failed_files,
vec![
String::from("definitely-not-existing.foo"),
String::from("not-existing.foo"),
],
);
let mut songs = test_library
.internal_storage
.iter()
.map(|x| x.path.to_str().unwrap().to_string())
.collect::<Vec<String>>();
songs.sort();
assert_eq!(
songs,
vec![
String::from("./data/s16_mono_22_5kHz.flac"),
String::from("./data/white_noise.flac"),
],
);
}
#[test]
fn test_playlist_from_album() {
let mut test_library = TestLibrary::default();
let first_song = Song {
path: Path::new("path-to-first").to_path_buf(),
analysis: Analysis::new([0.; 20]),
album: Some(String::from("Album")),
track_number: Some(String::from("01")),
..Default::default()
};
let second_song = Song {
path: Path::new("path-to-second").to_path_buf(),
analysis: Analysis::new([0.1; 20]),
album: Some(String::from("Another Album")),
track_number: Some(String::from("10")),
..Default::default()
};
let third_song = Song {
path: Path::new("path-to-third").to_path_buf(),
analysis: Analysis::new([10.; 20]),
album: Some(String::from("Album")),
track_number: Some(String::from("02")),
..Default::default()
};
let fourth_song = Song {
path: Path::new("path-to-fourth").to_path_buf(),
analysis: Analysis::new([20.; 20]),
album: Some(String::from("Another Album")),
track_number: Some(String::from("01")),
..Default::default()
};
let fifth_song = Song {
path: Path::new("path-to-fifth").to_path_buf(),
analysis: Analysis::new([20.; 20]),
album: None,
..Default::default()
};
test_library.internal_storage = vec![
first_song.to_owned(),
fourth_song.to_owned(),
third_song.to_owned(),
second_song.to_owned(),
fifth_song.to_owned(),
];
assert_eq!(
vec![first_song, third_song, fourth_song, second_song],
test_library.playlist_from_songs_album("Album", 3).unwrap()
);
}
#[test]
fn test_playlist_from_song() {
let mut test_library = TestLibrary::default();
let first_song = Song {
path: Path::new("path-to-first").to_path_buf(),
analysis: Analysis::new([0.; 20]),
..Default::default()
};
let second_song = Song {
path: Path::new("path-to-second").to_path_buf(),
analysis: Analysis::new([0.1; 20]),
..Default::default()
};
let third_song = Song {
path: Path::new("path-to-third").to_path_buf(),
analysis: Analysis::new([10.; 20]),
..Default::default()
};
let fourth_song = Song {
path: Path::new("path-to-fourth").to_path_buf(),
analysis: Analysis::new([20.; 20]),
..Default::default()
};
test_library.internal_storage = vec![
first_song.to_owned(),
fourth_song.to_owned(),
third_song.to_owned(),
second_song.to_owned(),
];
assert_eq!(
vec![first_song.to_owned(), second_song, third_song],
test_library.playlist_from_song(first_song, 3).unwrap()
);
}
#[test]
fn test_playlist_from_song_too_little_songs() {
let mut test_library = TestLibrary::default();
let first_song = Song {
path: Path::new("path-to-first").to_path_buf(),
analysis: Analysis::new([0.; 20]),
..Default::default()
};
let second_song = Song {
path: Path::new("path-to-second").to_path_buf(),
analysis: Analysis::new([0.1; 20]),
..Default::default()
};
let third_song = Song {
path: Path::new("path-to-third").to_path_buf(),
analysis: Analysis::new([10.; 20]),
..Default::default()
};
test_library.internal_storage = vec![
first_song.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
];
assert_eq!(
vec![first_song.to_owned(), second_song, third_song],
test_library.playlist_from_song(first_song, 200).unwrap()
);
}
#[test]
fn test_analyze_empty_path() {
let mut test_library = TestLibrary::default();
assert!(test_library.analyze_paths(vec![]).is_ok());
}
fn custom_distance(a: &Array1<f32>, b: &Array1<f32>) -> f32 {
if a == b {
return 0.;
}
1. / (a.first().unwrap() - b.first().unwrap()).abs()
}
#[test]
fn test_playlist_from_song_custom_distance() {
let mut test_library = TestLibrary::default();
let first_song = Song {
path: Path::new("path-to-first").to_path_buf(),
analysis: Analysis::new([0.; 20]),
..Default::default()
};
let second_song = Song {
path: Path::new("path-to-second").to_path_buf(),
analysis: Analysis::new([0.1; 20]),
..Default::default()
};
let third_song = Song {
path: Path::new("path-to-third").to_path_buf(),
analysis: Analysis::new([10.; 20]),
..Default::default()
};
let fourth_song = Song {
path: Path::new("path-to-fourth").to_path_buf(),
analysis: Analysis::new([20.; 20]),
..Default::default()
};
test_library.internal_storage = vec![
first_song.to_owned(),
fourth_song.to_owned(),
third_song.to_owned(),
second_song.to_owned(),
];
assert_eq!(
vec![first_song.to_owned(), fourth_song, third_song],
test_library
.playlist_from_song_custom_distance(first_song, 3, custom_distance)
.unwrap()
);
}
fn custom_sort(_: &Song, songs: &mut Vec<Song>, _: impl DistanceMetric) {
songs.sort_by_key(|song| song.path.to_owned());
}
#[test]
fn test_playlist_from_song_custom() {
let mut test_library = TestLibrary::default();
let first_song = Song {
path: Path::new("path-to-first").to_path_buf(),
analysis: Analysis::new([0.; 20]),
..Default::default()
};
let second_song = Song {
path: Path::new("path-to-second").to_path_buf(),
analysis: Analysis::new([0.1; 20]),
..Default::default()
};
let third_song = Song {
path: Path::new("path-to-third").to_path_buf(),
analysis: Analysis::new([10.; 20]),
..Default::default()
};
let fourth_song = Song {
path: Path::new("path-to-fourth").to_path_buf(),
analysis: Analysis::new([20.; 20]),
..Default::default()
};
test_library.internal_storage = vec![
first_song.to_owned(),
fourth_song.to_owned(),
third_song.to_owned(),
second_song.to_owned(),
];
assert_eq!(
vec![first_song.to_owned(), fourth_song, second_song],
test_library
.playlist_from_song_custom(first_song, 3, custom_distance, custom_sort)
.unwrap()
);
}
}

166
src/distance.rs → src/playlist.rs
View file

@ -1,19 +1,17 @@
//! Module containing various distance metric functions.
//! Module containing various functions to build playlists, as well as various
//! distance metrics.
//!
//! All of these functions are intended to be used with the
//! All of the distance functions are intended to be used with the
//! [custom_distance](Song::custom_distance) method, or with
//! [playlist_from_songs_custom_distance](Library::playlist_from_song_custom_distance).
//!
//! They will yield different styles of playlists, so don't hesitate to
//! experiment with them if the default (euclidean distance for now) doesn't
//! suit you.
#[cfg(doc)]
use crate::Library;
use crate::Song;
use crate::NUMBER_FEATURES;
use ndarray::{Array, Array1};
use crate::{BlissError, BlissResult, Song, NUMBER_FEATURES};
use ndarray::{Array, Array1, Array2, Axis};
use ndarray_stats::QuantileExt;
use noisy_float::prelude::*;
use std::collections::HashMap;
/// Convenience trait for user-defined distance metrics.
pub trait DistanceMetric: Fn(&Array1<f32>, &Array1<f32>) -> f32 {}
@ -117,6 +115,92 @@ pub fn dedup_playlist_custom_distance(
});
}
/// Return a list of albums in a `pool` of songs that are similar to
/// songs in `group`, discarding songs that don't belong to an album.
/// It basically makes an "album" playlist from the `pool` of songs.
///
/// Songs from `group` would usually just be songs from an album, but not
/// necessarily - they are discarded from `pool` no matter what.
///
/// # Arguments
///
/// * `group` - A small group of songs, e.g. an album.
/// * `pool` - A pool of songs to find similar songs in, e.g. a user's song
/// library.
///
/// # Returns
///
/// A vector of songs, including `group` at the beginning, that you
/// most likely want to plug in your audio player by using something like
/// `ret.map(|song| song.path.to_owned()).collect::<Vec<String>>()`.
pub fn closest_album_to_group(group: Vec<Song>, pool: Vec<Song>) -> BlissResult<Vec<Song>> {
let mut albums_analysis: HashMap<&str, Array2<f32>> = HashMap::new();
let mut albums = Vec::new();
// Remove songs from the group from the pool.
let pool = pool
.into_iter()
.filter(|s| !group.contains(s))
.collect::<Vec<_>>();
for song in &pool {
if let Some(album) = &song.album {
if let Some(analysis) = albums_analysis.get_mut(album as &str) {
analysis
.push_row(song.analysis.as_arr1().view())
.map_err(|e| {
BlissError::ProviderError(format!("while computing distances: {}", e))
})?;
} else {
let mut array = Array::zeros((1, song.analysis.as_arr1().len()));
array.assign(&song.analysis.as_arr1());
albums_analysis.insert(album, array);
}
}
}
let mut group_analysis = Array::zeros((group.len(), NUMBER_FEATURES));
for (song, mut column) in group.iter().zip(group_analysis.axis_iter_mut(Axis(0))) {
column.assign(&song.analysis.as_arr1());
}
let first_analysis = group_analysis
.mean_axis(Axis(0))
.ok_or_else(|| BlissError::ProviderError(String::from("Mean of empty slice")))?;
for (album, analysis) in albums_analysis.iter() {
let mean_analysis = analysis
.mean_axis(Axis(0))
.ok_or_else(|| BlissError::ProviderError(String::from("Mean of empty slice")))?;
let album = album.to_owned();
albums.push((album, mean_analysis.to_owned()));
}
albums.sort_by_key(|(_, analysis)| n32(euclidean_distance(&first_analysis, analysis)));
let mut playlist = group;
for (album, _) in albums {
let mut al = pool
.iter()
.filter(|s| s.album.is_some() && s.album.as_ref().unwrap() == &album.to_string())
.map(|s| s.to_owned())
.collect::<Vec<Song>>();
al.sort_by(|s1, s2| {
let track_number1 = s1
.track_number
.to_owned()
.unwrap_or_else(|| String::from(""));
let track_number2 = s2
.track_number
.to_owned()
.unwrap_or_else(|| String::from(""));
if let Ok(x) = track_number1.parse::<i32>() {
if let Ok(y) = track_number2.parse::<i32>() {
return x.cmp(&y);
}
}
s1.track_number.cmp(&s2.track_number)
});
playlist.extend_from_slice(&al);
}
Ok(playlist)
}
#[cfg(test)]
mod test {
use super::*;
@ -227,7 +311,7 @@ mod test {
vec![
first_song.to_owned(),
second_song.to_owned(),
fourth_song.to_owned()
fourth_song.to_owned(),
]
);
}
@ -389,4 +473,68 @@ mod test {
assert_eq!(cosine_distance(&a, &b), 0.);
assert_eq!(cosine_distance(&a, &b), 0.);
}
#[test]
fn test_closest_to_group() {
let first_song = Song {
path: Path::new("path-to-first").to_path_buf(),
analysis: Analysis::new([0.; 20]),
album: Some(String::from("Album")),
artist: Some(String::from("Artist")),
track_number: Some(String::from("01")),
..Default::default()
};
let second_song = Song {
path: Path::new("path-to-second").to_path_buf(),
analysis: Analysis::new([0.1; 20]),
album: Some(String::from("Another Album")),
artist: Some(String::from("Artist")),
track_number: Some(String::from("10")),
..Default::default()
};
let third_song = Song {
path: Path::new("path-to-third").to_path_buf(),
analysis: Analysis::new([10.; 20]),
album: Some(String::from("Album")),
artist: Some(String::from("Another Artist")),
track_number: Some(String::from("02")),
..Default::default()
};
let fourth_song = Song {
path: Path::new("path-to-fourth").to_path_buf(),
analysis: Analysis::new([20.; 20]),
album: Some(String::from("Another Album")),
artist: Some(String::from("Another Artist")),
track_number: Some(String::from("01")),
..Default::default()
};
let fifth_song = Song {
path: Path::new("path-to-fifth").to_path_buf(),
analysis: Analysis::new([40.; 20]),
artist: Some(String::from("Third Artist")),
album: None,
..Default::default()
};
let pool = vec![
first_song.to_owned(),
fourth_song.to_owned(),
third_song.to_owned(),
second_song.to_owned(),
fifth_song.to_owned(),
];
let group = vec![first_song.to_owned(), third_song.to_owned()];
assert_eq!(
vec![
first_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
second_song.to_owned()
],
closest_album_to_group(group, pool.to_owned()).unwrap(),
);
}
}

121
src/song.rs
View file

@ -13,8 +13,10 @@ extern crate ndarray;
extern crate ndarray_npy;
use crate::chroma::ChromaDesc;
use crate::distance::{euclidean_distance, DistanceMetric};
use crate::misc::LoudnessDesc;
#[cfg(doc)]
use crate::playlist;
use crate::playlist::{closest_to_first_song, dedup_playlist, euclidean_distance, DistanceMetric};
use crate::temporal::BPMDesc;
use crate::timbral::{SpectralDesc, ZeroCrossingRateDesc};
use crate::{BlissError, BlissResult, SAMPLE_RATE};
@ -228,6 +230,44 @@ impl Song {
self.analysis.custom_distance(&other.analysis, distance)
}
/// Orders songs in `pool` by proximity to `self`, using the distance
/// metric `distance` to compute the order.
/// Basically return a playlist from songs in `pool`, starting
/// from `self`, using `distance` (some distance metrics can
/// be found in the [playlist] module).
///
/// Note that contrary to [Song::closest_from_pool], `self` is NOT added
/// to the beginning of the returned vector.
///
/// No deduplication is ran either; if you're looking for something easy
/// that works "out of the box", use [Song::closest_from_pool].
pub fn closest_from_pool_custom(
&self,
pool: Vec<Self>,
distance: impl DistanceMetric,
) -> Vec<Self> {
let mut pool = pool;
closest_to_first_song(self, &mut pool, distance);
pool
}
/// Order songs in `pool` by proximity to `self`.
/// Convenience method to return a playlist from songs in `pool`,
/// starting from `self`.
///
/// The distance is already chosen, deduplication is ran, and the first song
/// is added to the top of the playlist, to make everything easier.
///
/// If you want more control over which distance metric is chosen,
/// run deduplication manually, etc, use [Song::closest_from_pool_custom].
pub fn closest_from_pool(&self, pool: Vec<Self>) -> Vec<Self> {
let mut playlist = vec![self.to_owned()];
playlist.extend_from_slice(&pool);
closest_to_first_song(self, &mut playlist, euclidean_distance);
dedup_playlist(&mut playlist, None);
playlist
}
/// Returns a decoded [Song] given a file path, or an error if the song
/// could not be analyzed for some reason.
///
@ -848,6 +888,7 @@ mod tests {
fn dummy_distance(_: &Array1<f32>, _: &Array1<f32>) -> f32 {
0.
}
#[test]
fn test_custom_distance() {
let mut a = Song::default();
@ -865,6 +906,84 @@ mod tests {
]);
assert_eq!(a.custom_distance(&b, dummy_distance), 0.);
}
#[test]
fn test_closest_from_pool() {
let song = Song {
path: Path::new("path-to-first").to_path_buf(),
analysis: Analysis::new([
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
]),
..Default::default()
};
let first_song_dupe = Song {
path: Path::new("path-to-dupe").to_path_buf(),
analysis: Analysis::new([
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
]),
..Default::default()
};
let second_song = Song {
path: Path::new("path-to-second").to_path_buf(),
analysis: Analysis::new([
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1.9, 1., 1., 1.,
]),
..Default::default()
};
let third_song = Song {
path: Path::new("path-to-third").to_path_buf(),
analysis: Analysis::new([
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.5, 1., 1., 1.,
]),
..Default::default()
};
let fourth_song = Song {
path: Path::new("path-to-fourth").to_path_buf(),
analysis: Analysis::new([
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 1., 1., 1.,
]),
..Default::default()
};
let fifth_song = Song {
path: Path::new("path-to-fifth").to_path_buf(),
analysis: Analysis::new([
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 1., 1., 1.,
]),
..Default::default()
};
let songs = vec![
song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
let playlist = song.closest_from_pool(songs.to_owned());
assert_eq!(
playlist,
vec![
song.to_owned(),
second_song.to_owned(),
fourth_song.to_owned(),
third_song.to_owned(),
],
);
let playlist = song.closest_from_pool_custom(songs, euclidean_distance);
assert_eq!(
playlist,
vec![
song,
first_song_dupe,
second_song,
fourth_song,
fifth_song,
third_song
],
);
}
}
#[cfg(all(feature = "bench", test))]