antonlyap/bliss-rs
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Merge branch 'master' into github-mirror-master

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This commit is contained in:
Anton Liaposhchenko 2024-02-16 17:57:14 +02:00
commit 6d9489d3a6
26 changed files with 504 additions and 6706 deletions
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80
.github/workflows/rust.yml vendored
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@ -1,80 +0,0 @@
name: Rust
on:
push:
branches: [ master ]
pull_request:
branches: [ master ]
env:
CARGO_TERM_COLOR: always
jobs:
build-test-lint-linux:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
with:
submodules: recursive
- uses: actions-rs/toolchain@v1
with:
toolchain: nightly-2023-02-16
override: false
- name: Packages
run: sudo apt-get update && sudo apt-get install build-essential yasm libavutil-dev libavcodec-dev libavformat-dev libavfilter-dev libavfilter-dev libavdevice-dev libswresample-dev libfftw3-dev ffmpeg
- name: Check format
run: cargo fmt -- --check
- name: Lint
run: cargo clippy --examples --features=serde,library -- -D warnings
- name: Build
run: cargo build --verbose
- name: Run tests
run: cargo test --verbose
- name: Run library tests
run: cargo test --verbose --features=library
- name: Run example tests
run: cargo test --verbose --examples
- name: Build benches
run: cargo +nightly-2023-02-16 bench --verbose --features=bench --no-run
- name: Build examples
run: cargo build --examples --verbose --features=serde,library
build-test-lint-windows:
name: Windows - build, test and lint
runs-on: windows-latest
strategy:
matrix:
include:
- ffmpeg_version: latest
ffmpeg_download_url: https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-full-shared.7z
fail-fast: false
env:
FFMPEG_DOWNLOAD_URL: ${{ matrix.ffmpeg_download_url }}
steps:
- uses: actions/checkout@v2
- name: Install dependencies
run: |
$VCINSTALLDIR = $(& "${env:ProgramFiles(x86)}\Microsoft Visual Studio\Installer\vswhere.exe" -latest -property installationPath)
Add-Content $env:GITHUB_ENV "LIBCLANG_PATH=${VCINSTALLDIR}\VC\Tools\LLVM\x64\bin`n"
Invoke-WebRequest "${env:FFMPEG_DOWNLOAD_URL}" -OutFile ffmpeg-release-full-shared.7z
7z x ffmpeg-release-full-shared.7z
mkdir ffmpeg
mv ffmpeg-*/* ffmpeg/
Add-Content $env:GITHUB_ENV "FFMPEG_DIR=${pwd}\ffmpeg`n"
Add-Content $env:GITHUB_PATH "${pwd}\ffmpeg\bin`n"
- name: Set up Rust
uses: actions-rs/toolchain@v1
with:
toolchain: stable
override: true
components: rustfmt, clippy
- name: Lint
run: cargo clippy --examples --features=serde -- -D warnings
- name: Check format
run: cargo fmt -- --check
- name: Build
run: cargo build --examples
- name: Test
run: cargo test --examples --features=serde

4
.gitignore vendored
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@ -1 +1,5 @@
target
node_modules
index.node
index-*.node
bliss-rs-bliss-rs-*.tgz

929
Cargo.lock generated
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File diff suppressed because it is too large Load Diff

57
Cargo.toml
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@ -1,5 +1,5 @@
[package]
name = "bliss-audio"
name = "bliss-rs"
version = "0.6.11"
build = "build.rs"
authors = ["Polochon-street <polochonstreet@gmx.fr>"]
@ -10,38 +10,21 @@ homepage = "https://lelele.io/bliss.html"
repository = "https://github.com/Polochon-street/bliss-rs"
keywords = ["audio", "analysis", "MIR", "playlist", "similarity"]
readme = "README.md"
exclude = ["data/"]
exclude = ["data/", "index.node"]
[lib]
crate-type = ["rlib", "cdylib"]
[package.metadata.docs.rs]
features = ["bliss-audio-aubio-rs/rustdoc", "library"]
no-default-features = true
[features]
default = ["bliss-audio-aubio-rs/static"]
# Build ffmpeg instead of using the host's.
build-ffmpeg = ["ffmpeg-next/build"]
ffmpeg-static = ["ffmpeg-next/static"]
# Build for raspberry pis
rpi = ["ffmpeg-next/rpi"]
# Use if you get "No prebuilt bindings. Try use `bindgen` feature"
update-aubio-bindings = ["bliss-audio-aubio-rs/bindgen"]
# Use if you want to build python bindings with maturin.
python-bindings = ["bliss-audio-aubio-rs/fftw3"]
# Enable the benchmarks with `cargo +nightly bench --features=bench`
bench = []
library = [
"serde", "dep:rusqlite", "dep:dirs", "dep:tempdir",
"dep:anyhow", "dep:serde_ini", "dep:serde_json",
"dep:indicatif",
]
serde = ["dep:serde"]
[dependencies]
# Until https://github.com/aubio/aubio/issues/336 is somehow solved
# Hopefully we'll be able to use the official aubio-rs at some point.
bliss-audio-aubio-rs = "0.2.1"
ffmpeg-next = "6.1.1"
ffmpeg-sys-next = { version = "6.1.0", default-features = false }
bliss-audio-aubio-rs = { version = "0.2.1", features = ["static"] }
crossbeam = "0.8.2"
ffmpeg-next = { version = "6.1.1", features = ["static"] }
log = "0.4.17"
# `rayon` is used only by `par_mapv_inplace` in chroma.rs.
# TODO: is the speed gain that substantial?
@ -53,17 +36,16 @@ rustfft = "6.1.0"
thiserror = "1.0.40"
strum = "0.24.1"
strum_macros = "0.24.3"
rcue = "0.1.3"
# Deps for the library feature
serde = { version = "1.0", optional = true, features = ["derive"] }
serde_json = { version = "1.0.59", optional = true }
serde_ini = { version = "0.2.0", optional = true }
tempdir = { version = "0.3.7", optional = true }
rusqlite = { version = "0.28.0", optional = true }
dirs = { version = "5.0.0", optional = true }
anyhow = { version = "1.0.58", optional = true }
indicatif = { version = "0.17.0", optional = true }
[dependencies.neon]
version = "1.0.0-alpha.4"
default-features = false
features = ["napi-6", "channel-api", "promise-api", "try-catch-api"]
[dev-dependencies]
ndarray-npy = { version = "0.8.1", default-features = false }
@ -73,16 +55,3 @@ anyhow = "1.0.45"
clap = "2.33.3"
pretty_assertions = "1.3.0"
serde_json = "1.0.59"
[[example]]
name = "library"
required-features = ["library"]
[[example]]
name = "library_extra_info"
required-features = ["library"]
[[example]]
name = "playlist"
required-features = ["serde"]

20
Dockerfile Normal file
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@ -0,0 +1,20 @@
FROM node:20-slim
RUN apt-get update
RUN apt-get install -yqq gnupg dirmngr apt-transport-https software-properties-common
RUN gpg -K && gpg --no-default-keyring \
--keyring /usr/share/keyrings/deb-multimedia.gpg \
--keyserver keyserver.ubuntu.com \
--recv-keys 5C808C2B65558117
RUN echo "deb [signed-by=/usr/share/keyrings/deb-multimedia.gpg] https://www.deb-multimedia.org $(lsb_release -sc) main non-free" \
| tee /etc/apt/sources.list.d/deb-multimedia.list
RUN apt-get update
RUN apt-get install -yqq wget build-essential yasm libavutil-dev libavcodec-dev libavformat-dev libavfilter-dev libavfilter-dev libavdevice-dev libswresample-dev libfftw3-dev libclang-dev ffmpeg
WORKDIR /opt/rust
RUN wget https://sh.rustup.rs -O rustup-init.sh
RUN chmod +x rustup-init.sh
RUN ./rustup-init.sh -y -t x86_64-unknown-linux-gnu x86_64-unknown-linux-musl aarch64-unknown-linux-gnu aarch64-unknown-linux-musl

21
README.md
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@ -2,7 +2,26 @@
[![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/)
# bliss music analyzer - Rust version
# Fork notice
This repo is a fork of [bliss-rs](https://github.com/Polochon-street/bliss-rs) with bindings for Node.js (using N-API and Neon).
## Example usage:
The package is published to the Gitea registry: https://gitea.antonlyap.pp.ua/antonlyap/-/packages/npm/@bliss-rs%2Fbliss-rs/1.0.0
```ts
import { analyze, analyzeSync } from '@bliss-rs/bliss-rs';
await analyze("/path/to/track.mp3") // returns Uint8Array
```
## Return value
The output of `bliss-rs` consists of single-precision floats, currently 20 of them. This fork contains code to convert it into an array of 80 bytes in little endian order. An additional version (also comes from `bliss-rs`, currently equal to `1`) is prepended at the start (16-bit unsigned little-endian integer). Therefore, the total output size is 82 bytes.
### Usage
The output (without the version) is meant to be converted back into floats and used to calculate the [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance#Higher_dimensions) between two songs. Other distance algorithms are being worked on by the Bliss team.
---
# (Original README) 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.

2
examples/analyze.rs
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@ -1,4 +1,4 @@
use bliss_audio::Song;
use bliss_rs::bliss_lib::Song;
use std::env;
/**

19
examples/distance.rs
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@ -1,4 +1,4 @@
use bliss_audio::Song;
use bliss_rs::bliss_lib::Song;
use std::env;
/**
@ -13,14 +13,21 @@ fn main() -> Result<(), String> {
let first_path = paths.next().ok_or("Help: ./distance <song1> <song2>")?;
let second_path = paths.next().ok_or("Help: ./distance <song1> <song2>")?;
let song1 = Song::from_path(first_path).map_err(|x| x.to_string())?;
let song2 = Song::from_path(second_path).map_err(|x| x.to_string())?;
let song1 = Song::from_path(&first_path).map_err(|x| x.to_string())?;
let song2 = Song::from_path(&second_path).map_err(|x| x.to_string())?;
let mut distance_squared: f64 = 0.0;
let analysis1 = song1.analysis.as_bytes();
let analysis2 = song2.analysis.as_bytes();
for (i, feature1) in analysis1.iter().enumerate() {
distance_squared += (feature1 - analysis2[i]).pow(2) as f64;
}
println!(
"d({:?}, {:?}) = {}",
song1.path,
song2.path,
song1.distance(&song2)
&first_path,
&second_path,
distance_squared.sqrt(),
);
Ok(())
}

204
examples/library.rs
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@ -1,204 +0,0 @@
/// Basic example of how one would combine bliss with an "audio player",
/// through [Library].
///
/// For simplicity's sake, this example recursively gets songs from a folder
/// to emulate an audio player library, without handling CUE files.
use anyhow::Result;
use bliss_audio::library::{AppConfigTrait, BaseConfig, Library};
use clap::{App, Arg, SubCommand};
use glob::glob;
use serde::{Deserialize, Serialize};
use std::fs;
use std::num::NonZeroUsize;
use std::path::{Path, PathBuf};
#[derive(Serialize, Deserialize, Clone, Debug)]
// A config structure, that will be serialized as a
// JSON file upon Library creation.
pub struct Config {
#[serde(flatten)]
// The base configuration, containing both the config file
// path, as well as the database path.
pub base_config: BaseConfig,
// An extra field, to store the music library path. Any number
// of arbitrary fields (even Serializable structures) can
// of course be added.
pub music_library_path: PathBuf,
}
impl Config {
pub fn new(
music_library_path: PathBuf,
config_path: Option<PathBuf>,
database_path: Option<PathBuf>,
number_cores: Option<NonZeroUsize>,
) -> Result<Self> {
let base_config = BaseConfig::new(config_path, database_path, number_cores)?;
Ok(Self {
base_config,
music_library_path,
})
}
}
// The AppConfigTrait must know how to access the base config.
impl AppConfigTrait for Config {
fn base_config(&self) -> &BaseConfig {
&self.base_config
}
fn base_config_mut(&mut self) -> &mut BaseConfig {
&mut self.base_config
}
}
// A trait allowing to implement methods for the Library,
// useful if you don't need to store extra information in fields.
// Otherwise, doing
// ```
// struct CustomLibrary {
// library: Library<Config>,
// extra_field: ...,
// }
// ```
// and implementing functions for that struct would be the way to go.
// That's what the [reference](https://github.com/Polochon-street/blissify-rs)
// implementation does.
trait CustomLibrary {
fn song_paths(&self) -> Result<Vec<String>>;
}
impl CustomLibrary for Library<Config> {
/// Get all songs in the player library
fn song_paths(&self) -> Result<Vec<String>> {
let music_path = &self.config.music_library_path;
let pattern = Path::new(&music_path).join("**").join("*");
Ok(glob(&pattern.to_string_lossy())?
.map(|e| fs::canonicalize(e.unwrap()).unwrap())
.filter(|e| match mime_guess::from_path(e).first() {
Some(m) => m.type_() == "audio",
None => false,
})
.map(|x| x.to_string_lossy().to_string())
.collect::<Vec<String>>())
}
}
// A simple example of what a CLI-app would look.
//
// Note that `Library::new` is used only on init, and subsequent
// commands use `Library::from_path`.
fn main() -> Result<()> {
let matches = App::new("library-example")
.version(env!("CARGO_PKG_VERSION"))
.author("Polochon_street")
.about("Example binary implementing bliss for an audio player.")
.subcommand(
SubCommand::with_name("init")
.about(
"Initialize a Library, both storing the config and analyzing folders
containing songs.",
)
.arg(
Arg::with_name("FOLDER")
.help("A folder containing the music library to analyze.")
.required(true),
)
.arg(
Arg::with_name("database-path")
.short("d")
.long("database-path")
.help(
"Optional path where to store the database file containing
the songs' analysis. Defaults to XDG_DATA_HOME/bliss-rs/bliss.db.",
)
.takes_value(true),
)
.arg(
Arg::with_name("config-path")
.short("c")
.long("config-path")
.help(
"Optional path where to store the config file containing
the library setup. Defaults to XDG_DATA_HOME/bliss-rs/config.json.",
)
.takes_value(true),
),
)
.subcommand(
SubCommand::with_name("update")
.about(
"Update a Library's songs, trying to analyze failed songs,
as well as songs not in the library.",
)
.arg(
Arg::with_name("config-path")
.short("c")
.long("config-path")
.help(
"Optional path where to load the config file containing
the library setup. Defaults to XDG_DATA_HOME/bliss-rs/config.json.",
)
.takes_value(true),
),
)
.subcommand(
SubCommand::with_name("playlist")
.about(
"Make a playlist, starting with the song at SONG_PATH, returning
the songs' paths.",
)
.arg(Arg::with_name("SONG_PATH").takes_value(true))
.arg(
Arg::with_name("config-path")
.short("c")
.long("config-path")
.help(
"Optional path where to load the config file containing
the library setup. Defaults to XDG_DATA_HOME/bliss-rs/config.json.",
)
.takes_value(true),
)
.arg(
Arg::with_name("playlist-length")
.short("l")
.long("playlist-length")
.help("Optional playlist length. Defaults to 20.")
.takes_value(true),
),
)
.get_matches();
if let Some(sub_m) = matches.subcommand_matches("init") {
let folder = PathBuf::from(sub_m.value_of("FOLDER").unwrap());
let config_path = sub_m.value_of("config-path").map(PathBuf::from);
let database_path = sub_m.value_of("database-path").map(PathBuf::from);
let config = Config::new(folder, config_path, database_path, None)?;
let mut library = Library::new(config)?;
library.analyze_paths(library.song_paths()?, true)?;
} else if let Some(sub_m) = matches.subcommand_matches("update") {
let config_path = sub_m.value_of("config-path").map(PathBuf::from);
let mut library: Library<Config> = Library::from_config_path(config_path)?;
library.update_library(library.song_paths()?, true, true)?;
} else if let Some(sub_m) = matches.subcommand_matches("playlist") {
let song_path = sub_m.value_of("SONG_PATH").unwrap();
let config_path = sub_m.value_of("config-path").map(PathBuf::from);
let playlist_length = sub_m
.value_of("playlist-length")
.unwrap_or("20")
.parse::<usize>()?;
let library: Library<Config> = Library::from_config_path(config_path)?;
let songs = library.playlist_from::<()>(song_path, playlist_length)?;
let song_paths = songs
.into_iter()
.map(|s| s.bliss_song.path.to_string_lossy().to_string())
.collect::<Vec<String>>();
for song in song_paths {
println!("{song:?}");
}
}
Ok(())
}

227
examples/library_extra_info.rs
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@ -1,227 +0,0 @@
/// Basic example of how one would combine bliss with an "audio player",
/// through [Library], showing how to put extra info in the database for
/// each song.
///
/// For simplicity's sake, this example recursively gets songs from a folder
/// to emulate an audio player library, without handling CUE files.
use anyhow::Result;
use bliss_audio::library::{AppConfigTrait, BaseConfig, Library};
use clap::{App, Arg, SubCommand};
use glob::glob;
use serde::{Deserialize, Serialize};
use std::fs;
use std::num::NonZeroUsize;
use std::path::{Path, PathBuf};
#[derive(Serialize, Deserialize, Clone, Debug)]
/// A config structure, that will be serialized as a
/// JSON file upon Library creation.
pub struct Config {
#[serde(flatten)]
/// The base configuration, containing both the config file
/// path, as well as the database path.
pub base_config: BaseConfig,
/// An extra field, to store the music library path. Any number
/// of arbitrary fields (even Serializable structures) can
/// of course be added.
pub music_library_path: PathBuf,
}
impl Config {
pub fn new(
music_library_path: PathBuf,
config_path: Option<PathBuf>,
database_path: Option<PathBuf>,
number_cores: Option<NonZeroUsize>,
) -> Result<Self> {
let base_config = BaseConfig::new(config_path, database_path, number_cores)?;
Ok(Self {
base_config,
music_library_path,
})
}
}
// The AppConfigTrait must know how to access the base config.
impl AppConfigTrait for Config {
fn base_config(&self) -> &BaseConfig {
&self.base_config
}
fn base_config_mut(&mut self) -> &mut BaseConfig {
&mut self.base_config
}
}
// A trait allowing to implement methods for the Library,
// useful if you don't need to store extra information in fields.
// Otherwise, doing
// ```
// struct CustomLibrary {
// library: Library<Config>,
// extra_field: ...,
// }
// ```
// and implementing functions for that struct would be the way to go.
// That's what the [reference](https://github.com/Polochon-street/blissify-rs)
// implementation does.
trait CustomLibrary {
fn song_paths_info(&self) -> Result<Vec<(String, ExtraInfo)>>;
}
impl CustomLibrary for Library<Config> {
/// Get all songs in the player library, along with the extra info
/// one would want to store along with each song.
fn song_paths_info(&self) -> Result<Vec<(String, ExtraInfo)>> {
let music_path = &self.config.music_library_path;
let pattern = Path::new(&music_path).join("**").join("*");
Ok(glob(&pattern.to_string_lossy())?
.map(|e| fs::canonicalize(e.unwrap()).unwrap())
.filter_map(|e| {
mime_guess::from_path(&e).first().map(|m| {
(
e.to_string_lossy().to_string(),
ExtraInfo {
extension: e.extension().map(|e| e.to_string_lossy().to_string()),
file_name: e.file_name().map(|e| e.to_string_lossy().to_string()),
mime_type: format!("{}/{}", m.type_(), m.subtype()),
},
)
})
})
.collect::<Vec<(String, ExtraInfo)>>())
}
}
#[derive(Deserialize, Serialize, Debug, PartialEq, Clone, Default)]
// An (somewhat simple) example of what extra metadata one would put, along
// with song analysis data.
struct ExtraInfo {
extension: Option<String>,
file_name: Option<String>,
mime_type: String,
}
// A simple example of what a CLI-app would look.
//
// Note that `Library::new` is used only on init, and subsequent
// commands use `Library::from_path`.
fn main() -> Result<()> {
let matches = App::new("library-example")
.version(env!("CARGO_PKG_VERSION"))
.author("Polochon_street")
.about("Example binary implementing bliss for an audio player.")
.subcommand(
SubCommand::with_name("init")
.about(
"Initialize a Library, both storing the config and analyzing folders
containing songs.",
)
.arg(
Arg::with_name("FOLDER")
.help("A folder containing the music library to analyze.")
.required(true),
)
.arg(
Arg::with_name("database-path")
.short("d")
.long("database-path")
.help(
"Optional path where to store the database file containing
the songs' analysis. Defaults to XDG_DATA_HOME/bliss-rs/bliss.db.",
)
.takes_value(true),
)
.arg(
Arg::with_name("config-path")
.short("c")
.long("config-path")
.help(
"Optional path where to store the config file containing
the library setup. Defaults to XDG_DATA_HOME/bliss-rs/config.json.",
)
.takes_value(true),
),
)
.subcommand(
SubCommand::with_name("update")
.about(
"Update a Library's songs, trying to analyze failed songs,
as well as songs not in the library.",
)
.arg(
Arg::with_name("config-path")
.short("c")
.long("config-path")
.help(
"Optional path where to load the config file containing
the library setup. Defaults to XDG_DATA_HOME/bliss-rs/config.json.",
)
.takes_value(true),
),
)
.subcommand(
SubCommand::with_name("playlist")
.about(
"Make a playlist, starting with the song at SONG_PATH, returning
the songs' paths.",
)
.arg(Arg::with_name("SONG_PATH").takes_value(true))
.arg(
Arg::with_name("config-path")
.short("c")
.long("config-path")
.help(
"Optional path where to load the config file containing
the library setup. Defaults to XDG_DATA_HOME/bliss-rs/config.json.",
)
.takes_value(true),
)
.arg(
Arg::with_name("playlist-length")
.short("l")
.long("playlist-length")
.help("Optional playlist length. Defaults to 20.")
.takes_value(true),
),
)
.get_matches();
if let Some(sub_m) = matches.subcommand_matches("init") {
let folder = PathBuf::from(sub_m.value_of("FOLDER").unwrap());
let config_path = sub_m.value_of("config-path").map(PathBuf::from);
let database_path = sub_m.value_of("database-path").map(PathBuf::from);
let config = Config::new(folder, config_path, database_path, None)?;
let mut library = Library::new(config)?;
library.analyze_paths_extra_info(library.song_paths_info()?, true)?;
} else if let Some(sub_m) = matches.subcommand_matches("update") {
let config_path = sub_m.value_of("config-path").map(PathBuf::from);
let mut library: Library<Config> = Library::from_config_path(config_path)?;
library.update_library_extra_info(library.song_paths_info()?, true, true)?;
} else if let Some(sub_m) = matches.subcommand_matches("playlist") {
let song_path = sub_m.value_of("SONG_PATH").unwrap();
let config_path = sub_m.value_of("config-path").map(PathBuf::from);
let playlist_length = sub_m
.value_of("playlist-length")
.unwrap_or("20")
.parse::<usize>()?;
let library: Library<Config> = Library::from_config_path(config_path)?;
let songs = library.playlist_from::<ExtraInfo>(song_path, playlist_length)?;
let playlist = songs
.into_iter()
.map(|s| {
(
s.bliss_song.path.to_string_lossy().to_string(),
s.extra_info.mime_type,
)
})
.collect::<Vec<(String, String)>>();
for (path, mime_type) in playlist {
println!("{path} <{mime_type}>");
}
}
Ok(())
}

95
examples/playlist.rs
View File

@ -1,95 +0,0 @@
use anyhow::Result;
use bliss_audio::playlist::{closest_to_first_song, dedup_playlist, euclidean_distance};
use bliss_audio::{analyze_paths, Song};
use clap::{App, Arg};
use glob::glob;
use std::env;
use std::fs;
use std::io::BufReader;
use std::path::{Path, PathBuf};
/* Analyzes a folder recursively, and make a playlist out of the file
* provided by the user. */
// How to use: ./playlist [-o file.m3u] [-a analysis.json] <folder> <file to start the playlist from>
fn main() -> Result<()> {
let matches = App::new("playlist")
.version(env!("CARGO_PKG_VERSION"))
.author("Polochon_street")
.about("Analyze a folder and make a playlist from a target song")
.arg(Arg::with_name("output-playlist").short("o").long("output-playlist")
.value_name("PLAYLIST.M3U")
.help("Outputs the playlist to a file.")
.takes_value(true))
.arg(Arg::with_name("analysis-file").short("a").long("analysis-file")
.value_name("ANALYSIS.JSON")
.help("Use the songs that have been analyzed in <analysis-file>, and appends newly analyzed songs to it. Defaults to /tmp/analysis.json.")
.takes_value(true))
.arg(Arg::with_name("FOLDER").help("Folders containing some songs.").required(true))
.arg(Arg::with_name("FIRST-SONG").help("Song to start from (can be outside of FOLDER).").required(true))
.get_matches();
let folder = matches.value_of("FOLDER").unwrap();
let file = fs::canonicalize(matches.value_of("FIRST-SONG").unwrap())?;
let pattern = Path::new(folder).join("**").join("*");
let mut songs: Vec<Song> = Vec::new();
let analysis_path = matches
.value_of("analysis-file")
.unwrap_or("/tmp/analysis.json");
let analysis_file = fs::File::open(analysis_path);
if let Ok(f) = analysis_file {
let reader = BufReader::new(f);
songs = serde_json::from_reader(reader)?;
}
let analyzed_paths = songs
.iter()
.map(|s| s.path.to_owned())
.collect::<Vec<PathBuf>>();
let paths = glob(&pattern.to_string_lossy())?
.map(|e| fs::canonicalize(e.unwrap()).unwrap())
.filter(|e| match mime_guess::from_path(e).first() {
Some(m) => m.type_() == "audio",
None => false,
})
.map(|x| x.to_string_lossy().to_string())
.collect::<Vec<String>>();
let song_iterator = analyze_paths(
paths
.iter()
.filter(|p| !analyzed_paths.contains(&PathBuf::from(p)))
.map(|p| p.to_owned())
.collect::<Vec<String>>(),
);
let first_song = Song::from_path(file)?;
let mut analyzed_songs = vec![first_song.to_owned()];
for (path, result) in song_iterator {
match result {
Ok(song) => analyzed_songs.push(song),
Err(e) => println!("error analyzing {}: {}", path.display(), e),
};
}
analyzed_songs.extend_from_slice(&songs);
let serialized = serde_json::to_string(&analyzed_songs).unwrap();
let mut songs_to_chose_from: Vec<_> = analyzed_songs
.into_iter()
.filter(|x| x == &first_song || paths.contains(&x.path.to_string_lossy().to_string()))
.collect();
closest_to_first_song(&first_song, &mut songs_to_chose_from, euclidean_distance);
dedup_playlist(&mut songs_to_chose_from, None);
fs::write(analysis_path, serialized)?;
let playlist = songs_to_chose_from
.iter()
.map(|s| s.path.to_string_lossy().to_string())
.collect::<Vec<String>>()
.join("\n");
if let Some(m) = matches.value_of("output-playlist") {
fs::write(m, playlist)?;
} else {
println!("{playlist}");
}
Ok(())
}

2
index.d.ts vendored Normal file
View File

@ -0,0 +1,2 @@
export function analyzeSync(path: string): Uint8Array;
export function analyze(path: string): Promise<Uint8Array>;

13
index.js Normal file
View File

@ -0,0 +1,13 @@
try {
module.exports = require('./index.node');
} catch {
const isLinux = process.platform === 'linux';
if (isLinux && process.arch === 'x64') {
module.exports = require('./index-x86_64-unknown-linux-gnu.node');
} else if (isLinux && process.arch === 'arm64') {
module.exports = require('./index-aarch64-unknown-linux-gnu.node');
} else {
throw new Error('Bliss: unsupported architecture');
}
}

42
package-lock.json generated Normal file
View File

@ -0,0 +1,42 @@
{
"name": "bliss-rs",
"version": "1.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "bliss-rs",
"version": "1.0.0",
"license": "ISC",
"dependencies": {
"cargo-cp-artifact": "^0.1.8"
},
"devDependencies": {
"@types/node": "^20.10.5"
}
},
"node_modules/@types/node": {
"version": "20.10.5",
"resolved": "https://registry.npmjs.org/@types/node/-/node-20.10.5.tgz",
"integrity": "sha512-nNPsNE65wjMxEKI93yOP+NPGGBJz/PoN3kZsVLee0XMiJolxSekEVD8wRwBUBqkwc7UWop0edW50yrCQW4CyRw==",
"dev": true,
"dependencies": {
"undici-types": "~5.26.4"
}
},
"node_modules/cargo-cp-artifact": {
"version": "0.1.8",
"resolved": "https://registry.npmjs.org/cargo-cp-artifact/-/cargo-cp-artifact-0.1.8.tgz",
"integrity": "sha512-3j4DaoTrsCD1MRkTF2Soacii0Nx7UHCce0EwUf4fHnggwiE4fbmF2AbnfzayR36DF8KGadfh7M/Yfy625kgPlA==",
"bin": {
"cargo-cp-artifact": "bin/cargo-cp-artifact.js"
}
},
"node_modules/undici-types": {
"version": "5.26.5",
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
"integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
"dev": true
}
}
}

23
package.json Normal file
View File

@ -0,0 +1,23 @@
{
"name": "@bliss-rs/bliss-rs",
"version": "0.0.4",
"description": "A fork of the bliss-rs library with Node.js bindings",
"main": "index.js",
"types": "index.d.ts",
"directories": {
"example": "examples"
},
"files": ["index.js", "index.d.ts", "index-*.node"],
"scripts": {
"test": "echo \"Error: no test specified\" && exit 1",
"build": "cargo-cp-artifact -nc index.node -- cargo build --message-format=json-render-diagnostics"
},
"author": "antonlyap",
"license": "GPL",
"dependencies": {
"cargo-cp-artifact": "^0.1.8"
},
"devDependencies": {
"@types/node": "^20.10.5"
}
}

82
src/bliss_lib.rs Normal file
View File

@ -0,0 +1,82 @@
//! # bliss audio library
//!
//! bliss is a library for making "smart" audio playlists.
//!
//! The core of the library is the [Song] object, which relates to a
//! specific analyzed song and contains its path, title, analysis, and
//! 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 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.
//!
//! # Examples
//!
//! ### Analyze & compute the distance between two songs
//! ```no_run
//! use bliss_audio::{BlissResult, Song};
//!
//! fn main() -> BlissResult<()> {
//! let song1 = Song::from_path("/path/to/song1")?;
//! let song2 = Song::from_path("/path/to/song2")?;
//!
//! println!("Distance between song1 and song2 is {}", song1.distance(&song2));
//! Ok(())
//! }
//! ```
#![cfg_attr(feature = "bench", feature(test))]
#![warn(missing_docs)]
use thiserror::Error;
pub use crate::song::{Analysis, AnalysisIndex, Song, NUMBER_FEATURES};
/// Target channels for ffmpeg
pub const CHANNELS: u16 = 1;
/// Target sample rate for ffmpeg
pub const SAMPLE_RATE: u32 = 22050;
/// Stores the current version of bliss-rs' features.
/// It is bumped every time one or more feature is added, updated or removed,
/// so plug-ins can rescan libraries when there is a major change.
pub const FEATURES_VERSION: u16 = 1;
#[derive(Error, Clone, Debug, PartialEq, Eq)]
/// Umbrella type for bliss error types
pub enum BlissError {
#[error("error happened while decoding file – {0}")]
/// 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 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 bliss for an audio player.
ProviderError(String),
}
/// bliss error type
pub type BlissResult<T> = Result<T, BlissError>;
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_send_song() {
fn assert_send<T: Send>() {}
assert_send::<Song>();
}
#[test]
fn test_sync_song() {
fn assert_sync<T: Send>() {}
assert_sync::<Song>();
}
}

14
src/chroma.rs
View File

@ -7,7 +7,7 @@ extern crate noisy_float;
use crate::utils::stft;
use crate::utils::{hz_to_octs_inplace, Normalize};
use crate::{BlissError, BlissResult};
use crate::bliss_lib::{BlissError, BlissResult};
use ndarray::{arr1, arr2, concatenate, s, Array, Array1, Array2, Axis, Zip};
use ndarray_stats::interpolate::Midpoint;
use ndarray_stats::QuantileExt;
@ -365,7 +365,7 @@ fn chroma_stft(
mod test {
use super::*;
use crate::utils::stft;
use crate::{Song, SAMPLE_RATE};
use crate::bliss_lib::{Song, SAMPLE_RATE};
use ndarray::{arr1, arr2, Array2};
use ndarray_npy::ReadNpyExt;
use std::fs::File;
@ -437,7 +437,7 @@ mod test {
fn test_chroma_desc() {
let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let mut chroma_desc = ChromaDesc::new(SAMPLE_RATE, 12);
chroma_desc.do_(&song.sample_array).unwrap();
chroma_desc.do_(&song).unwrap();
let expected_values = vec![
-0.35661936,
-0.63578653,
@ -457,9 +457,7 @@ mod test {
#[test]
fn test_chroma_stft_decode() {
let signal = Song::decode(Path::new("data/s16_mono_22_5kHz.flac"))
.unwrap()
.sample_array;
let signal = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let mut stft = stft(&signal, 8192, 2205);
let file = File::open("data/chroma.npy").unwrap();
@ -490,9 +488,7 @@ mod test {
#[test]
fn test_estimate_tuning_decode() {
let signal = Song::decode(Path::new("data/s16_mono_22_5kHz.flac"))
.unwrap()
.sample_array;
let signal = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let stft = stft(&signal, 8192, 2205);
let tuning = estimate_tuning(22050, &stft, 8192, 0.01, 12).unwrap();

339
src/cue.rs
View File

@ -1,339 +0,0 @@
//! CUE-handling module.
//!
//! Using [BlissCue::songs_from_path] is most likely what you want.
use crate::{Analysis, BlissError, BlissResult, Song, FEATURES_VERSION, SAMPLE_RATE};
use rcue::cue::{Cue, Track};
use rcue::parser::parse_from_file;
use std::path::{Path, PathBuf};
use std::time::Duration;
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Default, Debug, PartialEq, Eq, Clone)]
/// A struct populated when the corresponding [Song] has been extracted from an
/// audio file split with the help of a CUE sheet.
pub struct CueInfo {
/// The path of the original CUE sheet, e.g. `/path/to/album_name.cue`.
pub cue_path: PathBuf,
/// The path of the audio file the song was extracted from, e.g.
/// `/path/to/album_name.wav`. Used because one CUE sheet can refer to
/// several audio files.
pub audio_file_path: PathBuf,
}
/// A struct to handle CUEs with bliss.
/// Use either [analyze_paths](crate::analyze_paths) with CUE files or
/// [songs_from_path](BlissCue::songs_from_path) to return a list of [Song]s
/// from CUE files.
pub struct BlissCue {
cue: Cue,
cue_path: PathBuf,
}
#[allow(missing_docs)]
#[derive(Default, Debug, PartialEq, Clone)]
struct BlissCueFile {
sample_array: Vec<f32>,
album: Option<String>,
artist: Option<String>,
genre: Option<String>,
tracks: Vec<Track>,
cue_path: PathBuf,
audio_file_path: PathBuf,
}
impl BlissCue {
/// Analyze songs from a CUE file, extracting individual [Song] objects
/// for each individual song.
///
/// Each returned [Song] has a populated [cue_info](Song::cue_info) object, that can be
/// be used to retrieve which CUE sheet was used to extract it, as well
/// as the corresponding audio file.
pub fn songs_from_path<P: AsRef<Path>>(path: P) -> BlissResult<Vec<BlissResult<Song>>> {
let cue = BlissCue::from_path(&path)?;
let cue_files = cue.files();
let mut songs = Vec::new();
for cue_file in cue_files.into_iter() {
match cue_file {
Ok(f) => {
if !f.sample_array.is_empty() {
songs.extend_from_slice(&f.get_songs());
} else {
songs.push(Err(BlissError::DecodingError(
"empty audio file associated to CUE sheet".into(),
)));
}
}
Err(e) => songs.push(Err(e)),
}
}
Ok(songs)
}
// Extract a BlissCue from a given path.
fn from_path<P: AsRef<Path>>(path: P) -> BlissResult<Self> {
let cue = parse_from_file(&path.as_ref().to_string_lossy(), false).map_err(|e| {
BlissError::DecodingError(format!(
"when opening CUE file '{:?}': {:?}",
path.as_ref(),
e
))
})?;
Ok(BlissCue {
cue,
cue_path: path.as_ref().to_owned(),
})
}
// List all BlissCueFile from a BlissCue.
fn files(&self) -> Vec<BlissResult<BlissCueFile>> {
let mut cue_files = Vec::new();
for cue_file in self.cue.files.iter() {
let audio_file_path = match &self.cue_path.parent() {
Some(parent) => parent.join(Path::new(&cue_file.file)),
None => PathBuf::from(cue_file.file.to_owned()),
};
let genre = self
.cue
.comments
.iter()
.find(|(c, _)| c == "GENRE")
.map(|(_, v)| v.to_owned());
let raw_song = Song::decode(Path::new(&audio_file_path));
if let Ok(song) = raw_song {
let bliss_cue_file = BlissCueFile {
sample_array: song.sample_array,
genre,
artist: self.cue.performer.to_owned(),
album: self.cue.title.to_owned(),
tracks: cue_file.tracks.to_owned(),
audio_file_path,
cue_path: self.cue_path.to_owned(),
};
cue_files.push(Ok(bliss_cue_file))
} else {
cue_files.push(Err(raw_song.unwrap_err()));
}
}
cue_files
}
}
impl BlissCueFile {
fn create_song(
&self,
analysis: BlissResult<Analysis>,
current_track: &Track,
duration: Duration,
index: usize,
) -> BlissResult<Song> {
if let Ok(a) = analysis {
let song = Song {
path: PathBuf::from(format!(
"{}/CUE_TRACK{:03}",
self.cue_path.to_string_lossy(),
index,
)),
album: self.album.to_owned(),
artist: current_track.performer.to_owned(),
album_artist: self.artist.to_owned(),
analysis: a,
duration,
genre: self.genre.to_owned(),
title: current_track.title.to_owned(),
track_number: Some(current_track.no.to_owned()),
features_version: FEATURES_VERSION,
cue_info: Some(CueInfo {
cue_path: self.cue_path.to_owned(),
audio_file_path: self.audio_file_path.to_owned(),
}),
};
Ok(song)
} else {
Err(analysis.unwrap_err())
}
}
// Get all songs from a BlissCueFile, using Song::analyze, each song being
// located using the sample_array and the timestamp delimiter.
fn get_songs(&self) -> Vec<BlissResult<Song>> {
let mut songs = Vec::new();
for (index, tuple) in (self.tracks[..]).windows(2).enumerate() {
let (current_track, next_track) = (tuple[0].to_owned(), tuple[1].to_owned());
if let Some((_, start_current)) = current_track.indices.first() {
if let Some((_, end_current)) = next_track.indices.first() {
let start_current = (start_current.as_secs_f32() * SAMPLE_RATE as f32) as usize;
let end_current = (end_current.as_secs_f32() * SAMPLE_RATE as f32) as usize;
let duration = Duration::from_secs_f32(
(end_current - start_current) as f32 / SAMPLE_RATE as f32,
);
let analysis = Song::analyze(&self.sample_array[start_current..end_current]);
let song = self.create_song(analysis, &current_track, duration, index + 1);
songs.push(song);
}
}
}
// Take care of the last track, since the windows iterator doesn't.
if let Some(last_track) = self.tracks.last() {
if let Some((_, start_current)) = last_track.indices.first() {
let start_current = (start_current.as_secs_f32() * SAMPLE_RATE as f32) as usize;
let duration = Duration::from_secs_f32(
(self.sample_array.len() - start_current) as f32 / SAMPLE_RATE as f32,
);
let analysis = Song::analyze(&self.sample_array[start_current..]);
let song = self.create_song(analysis, last_track, duration, self.tracks.len());
songs.push(song);
}
}
songs
}
}
#[cfg(test)]
mod tests {
use super::*;
use pretty_assertions::assert_eq;
#[test]
fn test_empty_cue() {
let songs = BlissCue::songs_from_path("data/empty.cue").unwrap();
let error = songs[0].to_owned().unwrap_err();
assert_eq!(
error,
BlissError::DecodingError("empty audio file associated to CUE sheet".to_string())
);
}
#[test]
fn test_cue_analysis() {
let songs = BlissCue::songs_from_path("data/testcue.cue").unwrap();
let expected = vec![
Ok(Song {
path: Path::new("data/testcue.cue/CUE_TRACK001").to_path_buf(),
analysis: Analysis {
internal_analysis: [
0.38463724,
-0.85219246,
-0.761946,
-0.8904667,
-0.63892543,
-0.73945934,
-0.8004017,
-0.8237293,
0.33865356,
0.32481194,
-0.35692245,
-0.6355889,
-0.29584837,
0.06431806,
0.21875131,
-0.58104205,
-0.9466792,
-0.94811195,
-0.9820919,
-0.9596871,
],
},
album: Some(String::from("Album for CUE test")),
artist: Some(String::from("David TMX")),
title: Some(String::from("Renaissance")),
genre: Some(String::from("Random")),
track_number: Some(String::from("01")),
features_version: FEATURES_VERSION,
album_artist: Some(String::from("Polochon_street")),
duration: Duration::from_secs_f32(11.066666603),
cue_info: Some(CueInfo {
cue_path: PathBuf::from("data/testcue.cue"),
audio_file_path: PathBuf::from("data/testcue.flac"),
}),
..Default::default()
}),
Ok(Song {
path: Path::new("data/testcue.cue/CUE_TRACK002").to_path_buf(),
analysis: Analysis {
internal_analysis: [
0.18622077,
-0.5989029,
-0.5554645,
-0.6343865,
-0.24163479,
-0.25766593,
-0.40616858,
-0.23334873,
0.76875293,
0.7785741,
-0.5075115,
-0.5272629,
-0.56706166,
-0.568486,
-0.5639081,
-0.5706943,
-0.96501005,
-0.96501285,
-0.9649896,
-0.96498996,
],
},
features_version: FEATURES_VERSION,
album: Some(String::from("Album for CUE test")),
artist: Some(String::from("Polochon_street")),
title: Some(String::from("Piano")),
genre: Some(String::from("Random")),
track_number: Some(String::from("02")),
album_artist: Some(String::from("Polochon_street")),
duration: Duration::from_secs_f64(5.853333473),
cue_info: Some(CueInfo {
cue_path: PathBuf::from("data/testcue.cue"),
audio_file_path: PathBuf::from("data/testcue.flac"),
}),
..Default::default()
}),
Ok(Song {
path: Path::new("data/testcue.cue/CUE_TRACK003").to_path_buf(),
analysis: Analysis {
internal_analysis: [
0.0024261475,
0.9874661,
0.97330654,
-0.9724426,
0.99678576,
-0.9961549,
-0.9840142,
-0.9269961,
0.7498772,
0.22429907,
-0.8355152,
-0.9977258,
-0.9977849,
-0.997785,
-0.99778515,
-0.997785,
-0.99999976,
-0.99999976,
-0.99999976,
-0.99999976,
],
},
album: Some(String::from("Album for CUE test")),
artist: Some(String::from("Polochon_street")),
title: Some(String::from("Tone")),
genre: Some(String::from("Random")),
track_number: Some(String::from("03")),
features_version: FEATURES_VERSION,
album_artist: Some(String::from("Polochon_street")),
duration: Duration::from_secs_f32(5.586666584),
cue_info: Some(CueInfo {
cue_path: PathBuf::from("data/testcue.cue"),
audio_file_path: PathBuf::from("data/testcue.flac"),
}),
..Default::default()
}),
Err(BlissError::DecodingError(String::from(
"while opening format for file 'data/not-existing.wav': \
ffmpeg::Error(2: No such file or directory).",
))),
];
assert_eq!(expected, songs);
}
}

367
src/lib.rs
View File

@ -1,332 +1,67 @@
//! # bliss audio library
//!
//! bliss is a library for making "smart" audio playlists.
//!
//! The core of the library is the [Song] object, which relates to a
//! specific analyzed song and contains its path, title, analysis, and
//! 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 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.
//!
//! If you want to implement a bliss plugin for an already existing audio
//! player, the [Library] struct is a collection of goodies that should prove
//! useful (it contains utilities to store analyzed songs in a self-contained
//! database file, to make playlists directly from the database, etc).
//! [blissify](https://github.com/Polochon-street/blissify-rs/) for both
//! an example of how the [Library] struct works, and a real-life demo of bliss
//! implemented for [MPD](https://www.musicpd.org/).
//!
//! # Examples
//!
//! ### Analyze & compute the distance between two songs
//! ```no_run
//! use bliss_audio::{BlissResult, Song};
//!
//! fn main() -> BlissResult<()> {
//! let song1 = Song::from_path("/path/to/song1")?;
//! let song2 = Song::from_path("/path/to/song2")?;
//!
//! println!("Distance between song1 and song2 is {}", song1.distance(&song2));
//! Ok(())
//! }
//! ```
//!
//! ### Make a playlist from a song, discarding failed songs
//! ```no_run
//! use bliss_audio::{
//! analyze_paths,
//! playlist::{closest_to_first_song, euclidean_distance},
//! BlissResult, Song,
//! };
//!
//! fn main() -> BlissResult<()> {
//! let paths = vec!["/path/to/song1", "/path/to/song2", "/path/to/song3"];
//! let mut songs: Vec<Song> = analyze_paths(&paths).filter_map(|(_, s)| s.ok()).collect();
//!
//! // Assuming there is a first song
//! let first_song = songs.first().unwrap().to_owned();
//!
//! closest_to_first_song(&first_song, &mut songs, euclidean_distance);
//!
//! println!("Playlist is:");
//! for song in songs {
//! println!("{}", song.path.display());
//! }
//! Ok(())
//! }
//! ```
#![cfg_attr(feature = "bench", feature(test))]
#![warn(missing_docs)]
pub mod bliss_lib;
mod chroma;
pub mod cue;
#[cfg(feature = "library")]
pub mod library;
mod misc;
pub mod playlist;
mod song;
mod misc;
mod temporal;
mod timbral;
mod utils;
#[cfg(feature = "serde")]
#[macro_use]
extern crate serde;
use crate::cue::BlissCue;
use log::info;
use std::num::NonZeroUsize;
use std::path::{Path, PathBuf};
use std::sync::mpsc;
use std::thread;
use thiserror::Error;
use neon::{prelude::*, types::buffer::TypedArray};
use song::Song;
use bliss_lib::BlissResult;
pub use song::{Analysis, AnalysisIndex, Song, NUMBER_FEATURES};
const CHANNELS: u16 = 1;
const SAMPLE_RATE: u32 = 22050;
/// Stores the current version of bliss-rs' features.
/// It is bumped every time one or more feature is added, updated or removed,
/// so plug-ins can rescan libraries when there is a major change.
pub const FEATURES_VERSION: u16 = 1;
#[derive(Error, Clone, Debug, PartialEq, Eq)]
/// Umbrella type for bliss error types
pub enum BlissError {
#[error("error happened while decoding file – {0}")]
/// 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 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 bliss for an audio player.
ProviderError(String),
#[neon::main]
fn main(mut cx: ModuleContext) -> NeonResult<()> {
cx.export_function("analyzeSync", analyze)?;
cx.export_function("analyze", analyze_async)?;
Ok(())
}
/// bliss error type
pub type BlissResult<T> = Result<T, BlissError>;
#[allow(deprecated)]
fn analyze_async(mut cx: FunctionContext) -> JsResult<JsPromise> {
let path = cx.argument::<JsString>(0)?.value(&mut cx);
let promise = cx.task(move || {
analyze_raw(&path)
}).promise(|mut cx, result| {
result
.map(|(version_bytes, analysis_bytes)| {
let mut buffer_handle = JsUint8Array::new(
&mut cx,
analysis_bytes.len() + version_bytes.len(),
).unwrap();
let buffer = buffer_handle.as_mut_slice(&mut cx);
/// Analyze songs in `paths`, and return the analyzed [Song] objects through an
/// [mpsc::IntoIter].
///
/// 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>.
///
/// # Note
///
/// This function also works with CUE files - it finds the audio files
/// mentionned in the CUE sheet, and then runs the analysis on each song
/// defined by it, returning a proper [Song] object for each one of them.
///
/// Make sure that you don't submit both the audio file along with the CUE
/// sheet if your library uses them, otherwise the audio file will be
/// analyzed as one, single, long song. For instance, with a CUE sheet named
/// `cue-file.cue` with the corresponding audio files `album-1.wav` and
/// `album-2.wav` defined in the CUE sheet, you would just pass `cue-file.cue`
/// to `analyze_paths`, and it will return [Song]s from both files, with
/// more information about which file it is extracted from in the
/// [cue info field](Song::cue_info).
///
/// # 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.display(), e),
/// }
/// }
/// Ok(())
/// }
/// ```
pub fn analyze_paths<P: Into<PathBuf>, F: IntoIterator<Item = P>>(
paths: F,
) -> mpsc::IntoIter<(PathBuf, BlissResult<Song>)> {
let cores = thread::available_parallelism().unwrap_or(NonZeroUsize::new(1).unwrap());
analyze_paths_with_cores(paths, cores)
}
/// Analyze songs in `paths`, and return the analyzed [Song] objects through an
/// [mpsc::IntoIter]. `number_cores` sets the number of cores the analysis
/// will use, capped by your system's capacity. Most of the time, you want to
/// use the simpler `analyze_paths` functions, which autodetects the number
/// of cores in your system.
///
/// Return 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>.
///
/// # Note
///
/// This function also works with CUE files - it finds the audio files
/// mentionned in the CUE sheet, and then runs the analysis on each song
/// defined by it, returning a proper [Song] object for each one of them.
///
/// Make sure that you don't submit both the audio file along with the CUE
/// sheet if your library uses them, otherwise the audio file will be
/// analyzed as one, single, long song. For instance, with a CUE sheet named
/// `cue-file.cue` with the corresponding audio files `album-1.wav` and
/// `album-2.wav` defined in the CUE sheet, you would just pass `cue-file.cue`
/// to `analyze_paths`, and it will return [Song]s from both files, with
/// more information about which file it is extracted from in the
/// [cue info field](Song::cue_info).
///
/// # 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.display(), e),
/// }
/// }
/// Ok(())
/// }
/// ```
pub fn analyze_paths_with_cores<P: Into<PathBuf>, F: IntoIterator<Item = P>>(
paths: F,
number_cores: NonZeroUsize,
) -> mpsc::IntoIter<(PathBuf, BlissResult<Song>)> {
let mut cores = thread::available_parallelism().unwrap_or(NonZeroUsize::new(1).unwrap());
if cores > number_cores {
cores = number_cores;
}
let paths: Vec<PathBuf> = paths.into_iter().map(|p| p.into()).collect();
#[allow(clippy::type_complexity)]
let (tx, rx): (
mpsc::Sender<(PathBuf, BlissResult<Song>)>,
mpsc::Receiver<(PathBuf, BlissResult<Song>)>,
) = mpsc::channel();
if paths.is_empty() {
return rx.into_iter();
}
let mut handles = Vec::new();
let mut chunk_length = paths.len() / cores;
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);
if let Some(extension) = Path::new(&path).extension() {
let extension = extension.to_string_lossy().to_lowercase();
if extension == "cue" {
match BlissCue::songs_from_path(&path) {
Ok(songs) => {
for song in songs {
tx_thread.send((path.to_owned(), song)).unwrap();
}
}
Err(e) => tx_thread.send((path.to_owned(), Err(e))).unwrap(),
};
continue;
}
}
let song = Song::from_path(&path);
tx_thread.send((path.to_owned(), song)).unwrap();
}
buffer[0..version_bytes.len()].copy_from_slice(&version_bytes);
buffer[version_bytes.len()..].copy_from_slice(&analysis_bytes);
buffer_handle
})
.or_else(|e| cx.throw_error(e.to_string()))
});
handles.push(child);
Ok(promise)
}
rx.into_iter()
/// Returns a Uint8Array, with the first 2 bytes being the version (16-bit unsigned little endian)
/// and the rest (currently 80 bytes) being the analysis data in little endian
fn analyze(mut cx: FunctionContext) -> JsResult<JsUint8Array> {
let path = cx.argument::<JsString>(0)?.value(&mut cx);
let (version_bytes, analysis_bytes) = analyze_raw(&path)
.or_else(|e| cx.throw_error(e.to_string()))?;
let mut buffer_handle = JsUint8Array::new(
&mut cx,
analysis_bytes.len() + version_bytes.len(),
)?;
let buffer = buffer_handle.as_mut_slice(&mut cx);
buffer[0..version_bytes.len()].copy_from_slice(&version_bytes);
buffer[version_bytes.len()..].copy_from_slice(&analysis_bytes);
Ok(buffer_handle)
}
#[cfg(test)]
mod tests {
use super::*;
#[cfg(test)]
use pretty_assertions::assert_eq;
#[test]
fn test_send_song() {
fn assert_send<T: Send>() {}
assert_send::<Song>();
}
#[test]
fn test_sync_song() {
fn assert_sync<T: Send>() {}
assert_sync::<Song>();
}
#[test]
fn test_analyze_paths() {
let paths = vec![
"./data/s16_mono_22_5kHz.flac",
"./data/testcue.cue",
"./data/white_noise.mp3",
"definitely-not-existing.foo",
"not-existing.foo",
];
let mut results = analyze_paths(&paths)
.map(|x| match &x.1 {
Ok(s) => (true, s.path.to_owned(), None),
Err(e) => (false, x.0.to_owned(), Some(e.to_string())),
})
.collect::<Vec<_>>();
results.sort();
let expected_results = vec![
(
false,
PathBuf::from("./data/testcue.cue"),
Some(String::from(
"error happened while decoding file – while \
opening format for file './data/not-existing.wav': \
ffmpeg::Error(2: No such file or directory).",
)),
),
(
false,
PathBuf::from("definitely-not-existing.foo"),
Some(String::from(
"error happened while decoding file – while \
opening format for file 'definitely-not-existing\
.foo': ffmpeg::Error(2: No such file or directory).",
)),
),
(
false,
PathBuf::from("not-existing.foo"),
Some(String::from(
"error happened while decoding file – \
while opening format for file 'not-existing.foo': \
ffmpeg::Error(2: No such file or directory).",
)),
),
(true, PathBuf::from("./data/s16_mono_22_5kHz.flac"), None),
(true, PathBuf::from("./data/testcue.cue/CUE_TRACK001"), None),
(true, PathBuf::from("./data/testcue.cue/CUE_TRACK002"), None),
(true, PathBuf::from("./data/testcue.cue/CUE_TRACK003"), None),
(true, PathBuf::from("./data/white_noise.mp3"), None),
];
assert_eq!(results, expected_results);
let mut results = analyze_paths_with_cores(&paths, NonZeroUsize::new(1).unwrap())
.map(|x| match &x.1 {
Ok(s) => (true, s.path.to_owned(), None),
Err(e) => (false, x.0.to_owned(), Some(e.to_string())),
})
.collect::<Vec<_>>();
results.sort();
assert_eq!(results, expected_results);
}
fn analyze_raw(path: &str) -> BlissResult<([u8; 2], [u8; 80])> {
let song = Song::from_path(path)?;
let version_bytes = song.features_version.to_le_bytes();
let analysis_bytes = song.analysis.as_bytes();
Ok((version_bytes, analysis_bytes))
}

3220
src/library.rs
View File

File diff suppressed because it is too large Load Diff

4
src/misc.rs
View File

@ -63,14 +63,14 @@ impl Normalize for LoudnessDesc {
#[cfg(test)]
mod tests {
use super::*;
use crate::Song;
use crate::bliss_lib::Song;
use std::path::Path;
#[test]
fn test_loudness() {
let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let mut loudness_desc = LoudnessDesc::default();
for chunk in song.sample_array.chunks_exact(LoudnessDesc::WINDOW_SIZE) {
for chunk in song.chunks_exact(LoudnessDesc::WINDOW_SIZE) {
loudness_desc.do_(&chunk);
}
let expected_values = vec![0.271263, 0.2577181];

984
src/playlist.rs
View File

@ -1,984 +0,0 @@
//! Module containing various functions to build playlists, as well as various
//! distance metrics.
//!
//! All of the distance functions are intended to be used with the
//! [custom_distance](Song::custom_distance) method, or with
//!
//! 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.
// TODO on the `by_key` functions: maybe Fn(&T) -> &Song is enough? Compared
// to -> Song
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 {}
impl<F> DistanceMetric for F where F: Fn(&Array1<f32>, &Array1<f32>) -> f32 {}
/// Return the [euclidean
/// distance](https://en.wikipedia.org/wiki/Euclidean_distance#Higher_dimensions)
/// between two vectors.
pub fn euclidean_distance(a: &Array1<f32>, b: &Array1<f32>) -> f32 {
// Could be any square symmetric positive semi-definite matrix;
// just no metric learning has been done yet.
// See https://lelele.io/thesis.pdf chapter 4.
let m = Array::eye(NUMBER_FEATURES);
(a - b).dot(&m).dot(&(a - b)).sqrt()
}
/// Return the [cosine
/// distance](https://en.wikipedia.org/wiki/Cosine_similarity#Angular_distance_and_similarity)
/// between two vectors.
pub fn cosine_distance(a: &Array1<f32>, b: &Array1<f32>) -> f32 {
let similarity = a.dot(b) / (a.dot(a).sqrt() * b.dot(b).sqrt());
1. - similarity
}
/// Sort `songs` in place by putting songs close to `first_song` first
/// using the `distance` metric.
pub fn closest_to_first_song(
first_song: &Song,
#[allow(clippy::ptr_arg)] songs: &mut Vec<Song>,
distance: impl DistanceMetric,
) {
songs.sort_by_cached_key(|song| n32(first_song.custom_distance(song, &distance)));
}
/// Sort `songs` in place by putting songs close to `first_song` first
/// using the `distance` metric.
///
/// Sort songs with a key extraction function, useful for when you have a
/// structure like `CustomSong { bliss_song: Song, something_else: bool }`
pub fn closest_to_first_song_by_key<F, T>(
first_song: &T,
#[allow(clippy::ptr_arg)] songs: &mut Vec<T>,
distance: impl DistanceMetric,
key_fn: F,
) where
F: Fn(&T) -> Song,
{
let first_song = key_fn(first_song);
songs.sort_by_cached_key(|song| n32(first_song.custom_distance(&key_fn(song), &distance)));
}
/// Sort `songs` in place using the `distance` metric and ordering by
/// the smallest distance between each song.
///
/// If the generated playlist is `[song1, song2, song3, song4]`, it means
/// song2 is closest to song1, song3 is closest to song2, and song4 is closest
/// to song3.
///
/// Note that this has a tendency to go from one style to the other very fast,
/// and it can be slow on big libraries.
pub fn song_to_song(first_song: &Song, songs: &mut Vec<Song>, distance: impl DistanceMetric) {
let mut new_songs = Vec::with_capacity(songs.len());
let mut song = first_song.to_owned();
while !songs.is_empty() {
let distances: Array1<f32> =
Array::from_shape_fn(songs.len(), |i| song.custom_distance(&songs[i], &distance));
let idx = distances.argmin().unwrap();
song = songs[idx].to_owned();
new_songs.push(song.to_owned());
songs.retain(|s| s != &song);
}
*songs = new_songs;
}
/// Sort `songs` in place using the `distance` metric and ordering by
/// the smallest distance between each song.
///
/// If the generated playlist is `[song1, song2, song3, song4]`, it means
/// song2 is closest to song1, song3 is closest to song2, and song4 is closest
/// to song3.
///
/// Note that this has a tendency to go from one style to the other very fast,
/// and it can be slow on big libraries.
///
/// Sort songs with a key extraction function, useful for when you have a
/// structure like `CustomSong { bliss_song: Song, something_else: bool }`
// TODO: maybe Clone is not needed?
pub fn song_to_song_by_key<F, T: std::cmp::PartialEq + Clone>(
first_song: &T,
songs: &mut Vec<T>,
distance: impl DistanceMetric,
key_fn: F,
) where
F: Fn(&T) -> Song,
{
let mut new_songs: Vec<T> = Vec::with_capacity(songs.len());
let mut bliss_song = key_fn(&first_song.to_owned());
while !songs.is_empty() {
let distances: Array1<f32> = Array::from_shape_fn(songs.len(), |i| {
bliss_song.custom_distance(&key_fn(&songs[i]), &distance)
});
let idx = distances.argmin().unwrap();
let song = songs[idx].to_owned();
bliss_song = key_fn(&songs[idx]).to_owned();
new_songs.push(song.to_owned());
songs.retain(|s| s != &song);
}
*songs = new_songs;
}
/// Remove duplicate songs from a playlist, in place.
///
/// Two songs are considered duplicates if they either have the same,
/// non-empty title and artist name, or if they are close enough in terms
/// of distance.
///
/// # Arguments
///
/// * `songs`: The playlist to remove duplicates from.
/// * `distance_threshold`: The distance threshold under which two songs are
/// considered identical. If `None`, a default value of 0.05 will be used.
pub fn dedup_playlist(songs: &mut Vec<Song>, distance_threshold: Option<f32>) {
dedup_playlist_custom_distance(songs, distance_threshold, euclidean_distance);
}
/// Remove duplicate songs from a playlist, in place.
///
/// Two songs are considered duplicates if they either have the same,
/// non-empty title and artist name, or if they are close enough in terms
/// of distance.
///
/// Dedup songs with a key extraction function, useful for when you have a
/// structure like `CustomSong { bliss_song: Song, something_else: bool }` you
/// want to deduplicate.
///
/// # Arguments
///
/// * `songs`: The playlist to remove duplicates from.
/// * `distance_threshold`: The distance threshold under which two songs are
/// considered identical. If `None`, a default value of 0.05 will be used.
/// * `key_fn`: A function used to retrieve the bliss [Song] from `T`.
pub fn dedup_playlist_by_key<T, F>(songs: &mut Vec<T>, distance_threshold: Option<f32>, key_fn: F)
where
F: Fn(&T) -> Song,
{
dedup_playlist_custom_distance_by_key(songs, distance_threshold, euclidean_distance, key_fn);
}
/// Remove duplicate songs from a playlist, in place, using a custom distance
/// metric.
///
/// Two songs are considered duplicates if they either have the same,
/// non-empty title and artist name, or if they are close enough in terms
/// of distance.
///
/// # Arguments
///
/// * `songs`: The playlist to remove duplicates from.
/// * `distance_threshold`: The distance threshold under which two songs are
/// considered identical. If `None`, a default value of 0.05 will be used.
/// * `distance`: A custom distance metric.
pub fn dedup_playlist_custom_distance(
songs: &mut Vec<Song>,
distance_threshold: Option<f32>,
distance: impl DistanceMetric,
) {
songs.dedup_by(|s1, s2| {
n32(s1.custom_distance(s2, &distance)) < distance_threshold.unwrap_or(0.05)
|| (s1.title.is_some()
&& s2.title.is_some()
&& s1.artist.is_some()
&& s2.artist.is_some()
&& s1.title == s2.title
&& s1.artist == s2.artist)
});
}
/// Remove duplicate songs from a playlist, in place, using a custom distance
/// metric.
///
/// Two songs are considered duplicates if they either have the same,
/// non-empty title and artist name, or if they are close enough in terms
/// of distance.
///
/// Dedup songs with a key extraction function, useful for when you have a
/// structure like `CustomSong { bliss_song: Song, something_else: bool }`
/// you want to deduplicate.
///
/// # Arguments
///
/// * `songs`: The playlist to remove duplicates from.
/// * `distance_threshold`: The distance threshold under which two songs are
/// considered identical. If `None`, a default value of 0.05 will be used.
/// * `distance`: A custom distance metric.
/// * `key_fn`: A function used to retrieve the bliss [Song] from `T`.
pub fn dedup_playlist_custom_distance_by_key<F, T>(
songs: &mut Vec<T>,
distance_threshold: Option<f32>,
distance: impl DistanceMetric,
key_fn: F,
) where
F: Fn(&T) -> Song,
{
songs.dedup_by(|s1, s2| {
let s1 = key_fn(s1);
let s2 = key_fn(s2);
n32(s1.custom_distance(&s2, &distance)) < distance_threshold.unwrap_or(0.05)
|| (s1.title.is_some()
&& s2.title.is_some()
&& s1.artist.is_some()
&& s2.artist.is_some()
&& s1.title == s2.title
&& s1.artist == s2.artist)
});
}
/// 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.
///
/// `group` should be ordered by track number.
///
/// 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)
}
/// 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.
///
/// `group` should be ordered by track number.
///
/// Songs from `group` would usually just be songs from an album, but not
/// necessarily - they are discarded from `pool` no matter what.
///
/// Order songs with a key extraction function, useful for when you have a
/// structure like `CustomSong { bliss_song: Song, something_else: bool }`
/// you want to order.
///
/// # 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.
/// * `key_fn`: A function used to retrieve the bliss [Song] from `T`.
///
/// # Returns
///
/// A vector of T, 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>>()`.
// TODO: maybe Clone is not needed?
pub fn closest_album_to_group_by_key<T: PartialEq + Clone, F>(
group: Vec<T>,
pool: Vec<T>,
key_fn: F,
) -> BlissResult<Vec<T>>
where
F: Fn(&T) -> Song,
{
let mut albums_analysis: HashMap<String, 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 {
let song = key_fn(song);
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.to_owned(), 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))) {
let song = key_fn(song);
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| {
let s = key_fn(s);
s.album.is_some() && s.album.as_ref().unwrap() == &album.to_string()
})
.map(|s| s.to_owned())
.collect::<Vec<T>>();
al.sort_by(|s1, s2| {
let s1 = key_fn(s1);
let s2 = key_fn(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::*;
use crate::Analysis;
use ndarray::arr1;
use std::path::Path;
#[derive(Debug, Clone, PartialEq)]
struct CustomSong {
something: bool,
bliss_song: Song,
}
#[test]
fn test_dedup_playlist_custom_distance() {
let first_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.,
]),
title: Some(String::from("dupe-title")),
artist: Some(String::from("dupe-artist")),
..Default::default()
};
let third_song = Song {
path: Path::new("path-to-third").to_path_buf(),
title: Some(String::from("dupe-title")),
artist: Some(String::from("dupe-artist")),
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(),
artist: Some(String::from("no-dupe-artist")),
title: Some(String::from("dupe-title")),
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-fourth").to_path_buf(),
analysis: Analysis::new([
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0.001, 1., 1., 1.,
]),
..Default::default()
};
let mut playlist = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
dedup_playlist_custom_distance(&mut playlist, None, euclidean_distance);
assert_eq!(
playlist,
vec![
first_song.to_owned(),
second_song.to_owned(),
fourth_song.to_owned(),
],
);
let mut playlist = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
dedup_playlist_custom_distance(&mut playlist, Some(20.), cosine_distance);
assert_eq!(playlist, vec![first_song.to_owned()]);
let mut playlist = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
dedup_playlist(&mut playlist, Some(20.));
assert_eq!(playlist, vec![first_song.to_owned()]);
let mut playlist = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
dedup_playlist(&mut playlist, None);
assert_eq!(
playlist,
vec![
first_song.to_owned(),
second_song.to_owned(),
fourth_song.to_owned(),
]
);
let first_song = CustomSong {
bliss_song: first_song,
something: true,
};
let second_song = CustomSong {
bliss_song: second_song,
something: true,
};
let first_song_dupe = CustomSong {
bliss_song: first_song_dupe,
something: true,
};
let third_song = CustomSong {
bliss_song: third_song,
something: true,
};
let fourth_song = CustomSong {
bliss_song: fourth_song,
something: true,
};
let fifth_song = CustomSong {
bliss_song: fifth_song,
something: true,
};
let mut playlist = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
dedup_playlist_custom_distance_by_key(&mut playlist, None, euclidean_distance, |s| {
s.bliss_song.to_owned()
});
assert_eq!(
playlist,
vec![
first_song.to_owned(),
second_song.to_owned(),
fourth_song.to_owned(),
],
);
let mut playlist = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
dedup_playlist_custom_distance_by_key(&mut playlist, Some(20.), cosine_distance, |s| {
s.bliss_song.to_owned()
});
assert_eq!(playlist, vec![first_song.to_owned()]);
let mut playlist = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
dedup_playlist_by_key(&mut playlist, Some(20.), |s| s.bliss_song.to_owned());
assert_eq!(playlist, vec![first_song.to_owned()]);
let mut playlist = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
dedup_playlist_by_key(&mut playlist, None, |s| s.bliss_song.to_owned());
assert_eq!(
playlist,
vec![
first_song.to_owned(),
second_song.to_owned(),
fourth_song.to_owned(),
]
);
}
#[test]
fn test_song_to_song() {
let first_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 mut songs = vec![
first_song.to_owned(),
third_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
fourth_song.to_owned(),
];
song_to_song(&first_song, &mut songs, euclidean_distance);
assert_eq!(
songs,
vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
],
);
let first_song = CustomSong {
bliss_song: first_song,
something: true,
};
let second_song = CustomSong {
bliss_song: second_song,
something: true,
};
let first_song_dupe = CustomSong {
bliss_song: first_song_dupe,
something: true,
};
let third_song = CustomSong {
bliss_song: third_song,
something: true,
};
let fourth_song = CustomSong {
bliss_song: fourth_song,
something: true,
};
let mut songs: Vec<CustomSong> = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
second_song.to_owned(),
];
song_to_song_by_key(&first_song, &mut songs, euclidean_distance, |s| {
s.bliss_song.to_owned()
});
assert_eq!(
songs,
vec![
first_song,
first_song_dupe,
second_song,
third_song,
fourth_song,
],
);
}
#[test]
fn test_sort_closest_to_first_song() {
let first_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 mut songs = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
closest_to_first_song(&first_song, &mut songs, euclidean_distance);
let first_song = CustomSong {
bliss_song: first_song,
something: true,
};
let second_song = CustomSong {
bliss_song: second_song,
something: true,
};
let first_song_dupe = CustomSong {
bliss_song: first_song_dupe,
something: true,
};
let third_song = CustomSong {
bliss_song: third_song,
something: true,
};
let fourth_song = CustomSong {
bliss_song: fourth_song,
something: true,
};
let fifth_song = CustomSong {
bliss_song: fifth_song,
something: true,
};
let mut songs: Vec<CustomSong> = vec![
first_song.to_owned(),
first_song_dupe.to_owned(),
second_song.to_owned(),
third_song.to_owned(),
fourth_song.to_owned(),
fifth_song.to_owned(),
];
closest_to_first_song_by_key(&first_song, &mut songs, euclidean_distance, |s| {
s.bliss_song.to_owned()
});
assert_eq!(
songs,
vec![
first_song,
first_song_dupe,
second_song,
fourth_song,
fifth_song,
third_song
],
);
}
#[test]
fn test_euclidean_distance() {
let a = arr1(&[
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,
]);
let b = arr1(&[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,
]);
assert_eq!(euclidean_distance(&a, &b), 4.242640687119285);
let a = arr1(&[0.5; 20]);
let b = arr1(&[0.5; 20]);
assert_eq!(euclidean_distance(&a, &b), 0.);
assert_eq!(euclidean_distance(&a, &b), 0.);
}
#[test]
fn test_cosine_distance() {
let a = arr1(&[
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,
]);
let b = arr1(&[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,
]);
assert_eq!(cosine_distance(&a, &b), 0.7705842661294382);
let a = arr1(&[0.5; 20]);
let b = arr1(&[0.5; 20]);
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(),
);
let first_song = CustomSong {
bliss_song: first_song,
something: true,
};
let second_song = CustomSong {
bliss_song: second_song,
something: true,
};
let third_song = CustomSong {
bliss_song: third_song,
something: true,
};
let fourth_song = CustomSong {
bliss_song: fourth_song,
something: true,
};
let fifth_song = CustomSong {
bliss_song: fifth_song,
something: true,
};
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_by_key(group, pool.to_owned(), |s| s.bliss_song.to_owned())
.unwrap(),
);
}
}

424
src/song.rs
View File

@ -11,15 +11,11 @@ extern crate ffmpeg_next as ffmpeg;
extern crate ndarray;
use crate::chroma::ChromaDesc;
use crate::cue::CueInfo;
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};
use crate::{CHANNELS, FEATURES_VERSION};
use crate::bliss_lib::{BlissError, BlissResult, SAMPLE_RATE};
use crate::bliss_lib::{CHANNELS, FEATURES_VERSION};
use ::log::warn;
use core::ops::Index;
use ffmpeg_next::codec::threading::{Config, Type as ThreadingType};
@ -35,11 +31,9 @@ use ndarray::{arr1, Array1};
use std::convert::TryInto;
use std::fmt;
use std::path::Path;
use std::path::PathBuf;
use std::sync::mpsc;
use std::sync::mpsc::Receiver;
use std::thread;
use std::time::Duration;
use strum::{EnumCount, IntoEnumIterator};
use strum_macros::{EnumCount, EnumIter};
@ -48,35 +42,12 @@ use strum_macros::{EnumCount, EnumIter};
/// Simple object used to represent a Song, with its path, analysis, and
/// other metadata (artist, genre...)
pub struct Song {
/// Song's provided file path
pub path: PathBuf,
/// Song's artist, read from the metadata
pub artist: Option<String>,
/// Song's title, read from the metadata
pub title: Option<String>,
/// Song's album name, read from the metadata
pub album: Option<String>,
/// Song's album's artist name, read from the metadata
pub album_artist: Option<String>,
/// Song's tracked number, read from the metadata
/// TODO normalize this into an integer
pub track_number: Option<String>,
/// Song's genre, read from the metadata (`""` if empty)
pub genre: Option<String>,
/// bliss analysis results
pub analysis: Analysis,
/// The song's duration
pub duration: Duration,
/// Version of the features the song was analyzed with.
/// A simple integer that is bumped every time a breaking change
/// is introduced in the features.
pub features_version: u16,
/// Populated only if the song was extracted from a larger audio file,
/// through the use of a CUE sheet.
/// By default, such a song's path would be
/// `path/to/cue_file.wav/CUE_TRACK00<track_number>`. Using this field,
/// you can change `song.path` to fit your needs.
pub cue_info: Option<CueInfo>,
}
#[derive(Debug, EnumIter, EnumCount)]
@ -189,95 +160,36 @@ impl Analysis {
self.internal_analysis.to_vec()
}
/// Compute distance between two analysis using a user-provided distance
/// metric. You most likely want to use `song.custom_distance` directly
/// rather than this function.
///
/// For this function to be integrated properly with the rest
/// of bliss' parts, it should be a valid distance metric, i.e.:
/// 1. For X, Y real vectors, d(X, Y) = 0 ⇔ X = Y
/// 2. For X, Y real vectors, d(X, Y) >= 0
/// 3. For X, Y real vectors, d(X, Y) = d(Y, X)
/// 4. For X, Y, Z real vectors d(X, Y) ≤ d(X + Z) + d(Z, Y)
///
/// Note that almost all distance metrics you will find obey these
/// properties, so don't sweat it too much.
pub fn custom_distance(&self, other: &Self, distance: impl DistanceMetric) -> f32 {
distance(&self.as_arr1(), &other.as_arr1())
/// Returns a little endian byte array representing the analysis' features.
pub fn as_bytes(&self) -> [u8; 80] {
let mut result = [0; 80];
for (i, float) in self.internal_analysis.iter().enumerate() {
let [a, b, c, d] = float.to_le_bytes();
result[4*i] = a;
result[4*i + 1] = b;
result[4*i + 2] = c;
result[4*i + 3] = d;
}
result
}
/// Creates an Analysis object from a little endian byte array
pub fn from_bytes(bytes: &[u8]) -> Option<Self> {
let floats = bytes
.chunks(4)
.map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
.collect::<Vec<_>>();
if floats.len() != NUMBER_FEATURES {
return None;
}
match floats.try_into() {
Ok(arr) => Some(Analysis { internal_analysis: arr }),
Err(_) => None,
}
}
}
impl Song {
#[allow(dead_code)]
/// Compute the distance between the current song and any given
/// Song.
///
/// The smaller the number, the closer the songs; usually more useful
/// if compared between several songs
/// (e.g. if song1.distance(song2) < song1.distance(song3), then song1 is
/// closer to song2 than it is to song3.
///
/// Currently uses the euclidean distance, but this can change in an
/// upcoming release if another metric performs better.
pub fn distance(&self, other: &Self) -> f32 {
self.analysis
.custom_distance(&other.analysis, euclidean_distance)
}
/// Compute distance between two songs using a user-provided distance
/// metric.
///
/// For this function to be integrated properly with the rest
/// of bliss' parts, it should be a valid distance metric, i.e.:
/// 1. For X, Y real vectors, d(X, Y) = 0 ⇔ X = Y
/// 2. For X, Y real vectors, d(X, Y) >= 0
/// 3. For X, Y real vectors, d(X, Y) = d(Y, X)
/// 4. For X, Y, Z real vectors d(X, Y) ≤ d(X + Z) + d(Z, Y)
///
/// Note that almost all distance metrics you will find obey these
/// properties, so don't sweat it too much.
pub fn custom_distance(&self, other: &Self, distance: impl DistanceMetric) -> f32 {
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.
///
@ -295,20 +207,11 @@ impl Song {
/// decoding ([DecodingError](BlissError::DecodingError)) or an analysis
/// ([AnalysisError](BlissError::AnalysisError)) error.
pub fn from_path<P: AsRef<Path>>(path: P) -> BlissResult<Self> {
let raw_song = Song::decode(path.as_ref())?;
let samples = Song::decode(path.as_ref())?;
Ok(Song {
path: raw_song.path,
artist: raw_song.artist,
album_artist: raw_song.album_artist,
title: raw_song.title,
album: raw_song.album,
track_number: raw_song.track_number,
genre: raw_song.genre,
duration: raw_song.duration,
analysis: Song::analyze(&raw_song.sample_array)?,
analysis: Song::analyze(&samples)?,
features_version: FEATURES_VERSION,
cue_info: None,
})
}
@ -430,7 +333,7 @@ impl Song {
})
}
pub(crate) fn decode(path: &Path) -> BlissResult<InternalSong> {
pub(crate) fn decode(path: &Path) -> BlissResult<Vec<f32>> {
ffmpeg::init().map_err(|e| {
BlissError::DecodingError(format!(
"ffmpeg init error while decoding file '{}': {:?}.",
@ -439,10 +342,7 @@ impl Song {
))
})?;
log::set_level(Level::Quiet);
let mut song = InternalSong {
path: path.into(),
..Default::default()
};
let mut ictx = ffmpeg::format::input(&path).map_err(|e| {
BlissError::DecodingError(format!(
"while opening format for file '{}': {:?}.",
@ -468,8 +368,7 @@ impl Song {
context.set_threading(Config {
kind: ThreadingType::Frame,
count: 0,
#[cfg(not(feature = "ffmpeg_6_0"))]
safe: true,
// safe: true,
});
let decoder = context.decoder().audio().map_err(|e| {
BlissError::DecodingError(format!(
@ -493,42 +392,7 @@ impl Song {
(decoder, input.index(), expected_sample_number)
};
let sample_array: Vec<f32> = Vec::with_capacity(expected_sample_number as usize);
if let Some(title) = ictx.metadata().get("title") {
song.title = match title {
"" => None,
t => Some(t.to_string()),
};
};
if let Some(artist) = ictx.metadata().get("artist") {
song.artist = match artist {
"" => None,
a => Some(a.to_string()),
};
};
if let Some(album) = ictx.metadata().get("album") {
song.album = match album {
"" => None,
a => Some(a.to_string()),
};
};
if let Some(genre) = ictx.metadata().get("genre") {
song.genre = match genre {
"" => None,
g => Some(g.to_string()),
};
};
if let Some(track_number) = ictx.metadata().get("track") {
song.track_number = match track_number {
"" => None,
t => Some(t.to_string()),
};
};
if let Some(album_artist) = ictx.metadata().get("album_artist") {
song.album_artist = match album_artist {
"" => None,
t => Some(t.to_string()),
};
};
let (empty_in_channel_layout, in_channel_layout) = {
if decoder.channel_layout() == ChannelLayout::empty() {
(true, ChannelLayout::default(decoder.channels().into()))
@ -569,8 +433,7 @@ impl Song {
path.display()
);
drop(tx);
song.sample_array = child.join().unwrap()?;
return Ok(song);
return Ok(child.join().unwrap()?);
}
Err(e) => warn!("error while decoding file '{}': {}", path.display(), e),
};
@ -608,8 +471,7 @@ impl Song {
path.display()
);
drop(tx);
song.sample_array = child.join().unwrap()?;
return Ok(song);
return Ok(child.join().unwrap()?);
}
Err(e) => warn!("error while decoding {}: {}", path.display(), e),
};
@ -631,26 +493,10 @@ impl Song {
}
drop(tx);
song.sample_array = child.join().unwrap()?;
let duration_seconds = song.sample_array.len() as f32 / SAMPLE_RATE as f32;
song.duration = Duration::from_nanos((duration_seconds * 1e9_f32).round() as u64);
Ok(song)
Ok(child.join().unwrap()?)
}
}
#[derive(Default, Debug)]
pub(crate) struct InternalSong {
pub path: PathBuf,
pub artist: Option<String>,
pub album_artist: Option<String>,
pub title: Option<String>,
pub album: Option<String>,
pub track_number: Option<String>,
pub genre: Option<String>,
pub duration: Duration,
pub sample_array: Vec<f32>,
}
fn resample_frame(
rx: Receiver<Audio>,
in_codec_format: Sample,
@ -796,42 +642,15 @@ mod tests {
}
fn _test_decode(path: &Path, expected_hash: u32) {
let song = Song::decode(path).unwrap();
let samples = Song::decode(path).unwrap();
let mut hasher = RollingAdler32::new();
for sample in song.sample_array.iter() {
for sample in samples.iter() {
hasher.update_buffer(&sample.to_le_bytes());
}
assert_eq!(expected_hash, hasher.hash());
}
#[test]
fn test_tags() {
let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
assert_eq!(song.artist, Some(String::from("David TMX")));
assert_eq!(
song.album_artist,
Some(String::from("David TMX - Album Artist"))
);
assert_eq!(song.title, Some(String::from("Renaissance")));
assert_eq!(song.album, Some(String::from("Renaissance")));
assert_eq!(song.track_number, Some(String::from("02")));
assert_eq!(song.genre, Some(String::from("Pop")));
// Test that there is less than 10ms of difference between what
// the song advertises and what we compute.
assert!((song.duration.as_millis() as f32 - 11070.).abs() < 10.);
}
#[test]
fn test_empty_tags() {
let song = Song::decode(Path::new("data/no_tags.flac")).unwrap();
assert_eq!(song.artist, None);
assert_eq!(song.title, None);
assert_eq!(song.album, None);
assert_eq!(song.track_number, None);
assert_eq!(song.genre, None);
}
#[test]
fn test_resample_multi() {
let path = Path::new("data/s32_stereo_44_1_kHz.flac");
@ -875,54 +694,23 @@ mod tests {
#[test]
fn test_decode_right_capacity_vec() {
let path = Path::new("data/s16_mono_22_5kHz.flac");
let song = Song::decode(&path).unwrap();
let sample_array = song.sample_array;
let samples = Song::decode(&path).unwrap();
assert_eq!(
sample_array.len() + SAMPLE_RATE as usize,
sample_array.capacity()
samples.len() + SAMPLE_RATE as usize,
samples.capacity()
);
let path = Path::new("data/s32_stereo_44_1_kHz.flac");
let song = Song::decode(&path).unwrap();
let sample_array = song.sample_array;
let samples = Song::decode(&path).unwrap();
assert_eq!(
sample_array.len() + SAMPLE_RATE as usize,
sample_array.capacity()
samples.len() + SAMPLE_RATE as usize,
samples.capacity()
);
let path = Path::new("data/capacity_fix.ogg");
let song = Song::decode(&path).unwrap();
let sample_array = song.sample_array;
assert!(sample_array.len() as f32 / sample_array.capacity() as f32 > 0.90);
assert!(sample_array.len() as f32 / (sample_array.capacity() as f32) < 1.);
}
#[test]
fn test_analysis_distance() {
let mut a = Song::default();
a.analysis = Analysis::new([
0.16391512, 0.11326739, 0.96868552, 0.8353934, 0.49867523, 0.76532606, 0.63448005,
0.82506196, 0.71457147, 0.62395476, 0.69680329, 0.9855766, 0.41369333, 0.13900452,
0.68001012, 0.11029723, 0.97192943, 0.57727861, 0.07994821, 0.88993185,
]);
let mut b = Song::default();
b.analysis = Analysis::new([
0.5075758, 0.36440256, 0.28888011, 0.43032829, 0.62387977, 0.61894916, 0.99676086,
0.11913155, 0.00640396, 0.15943407, 0.33829514, 0.34947174, 0.82927523, 0.18987604,
0.54437275, 0.22076826, 0.91232151, 0.29233168, 0.32846024, 0.04522147,
]);
assert_eq!(a.distance(&b), 1.9469079)
}
#[test]
fn test_analysis_distance_indiscernible() {
let mut a = Song::default();
a.analysis = Analysis::new([
1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
20.,
]);
assert_eq!(a.distance(&a), 0.)
let samples = Song::decode(&path).unwrap();
assert!(samples.len() as f32 / samples.capacity() as f32 > 0.90);
assert!(samples.len() as f32 / (samples.capacity() as f32) < 1.);
}
#[test]
@ -973,120 +761,4 @@ mod tests {
format!("{:?}", song.analysis),
);
}
fn dummy_distance(_: &Array1<f32>, _: &Array1<f32>) -> f32 {
0.
}
#[test]
fn test_custom_distance() {
let mut a = Song::default();
a.analysis = Analysis::new([
0.16391512, 0.11326739, 0.96868552, 0.8353934, 0.49867523, 0.76532606, 0.63448005,
0.82506196, 0.71457147, 0.62395476, 0.69680329, 0.9855766, 0.41369333, 0.13900452,
0.68001012, 0.11029723, 0.97192943, 0.57727861, 0.07994821, 0.88993185,
]);
let mut b = Song::default();
b.analysis = Analysis::new([
0.5075758, 0.36440256, 0.28888011, 0.43032829, 0.62387977, 0.61894916, 0.99676086,
0.11913155, 0.00640396, 0.15943407, 0.33829514, 0.34947174, 0.82927523, 0.18987604,
0.54437275, 0.22076826, 0.91232151, 0.29233168, 0.32846024, 0.04522147,
]);
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))]
mod bench {
extern crate test;
use crate::Song;
use std::path::Path;
use test::Bencher;
#[bench]
fn bench_resample_multi(b: &mut Bencher) {
let path = Path::new("./data/s32_stereo_44_1_kHz.flac");
b.iter(|| {
Song::decode(&path).unwrap();
});
}
}

6
src/temporal.rs
View File

@ -4,7 +4,7 @@
//! of a given Song.
use crate::utils::Normalize;
use crate::{BlissError, BlissResult};
use crate::bliss_lib::{BlissError, BlissResult};
use bliss_audio_aubio_rs::{OnsetMode, Tempo};
use log::warn;
use ndarray::arr1;
@ -94,14 +94,14 @@ impl Normalize for BPMDesc {
#[cfg(test)]
mod tests {
use super::*;
use crate::{Song, SAMPLE_RATE};
use crate::bliss_lib::{Song, SAMPLE_RATE};
use std::path::Path;
#[test]
fn test_tempo_real() {
let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let mut tempo_desc = BPMDesc::new(SAMPLE_RATE).unwrap();
for chunk in song.sample_array.chunks_exact(BPMDesc::HOP_SIZE) {
for chunk in song.chunks_exact(BPMDesc::HOP_SIZE) {
tempo_desc.do_(&chunk).unwrap();
}
assert!(0.01 > (0.378605 - tempo_desc.get_value()).abs());

18
src/timbral.rs
View File

@ -9,7 +9,7 @@ use bliss_audio_aubio_rs::{bin_to_freq, PVoc, SpecDesc, SpecShape};
use ndarray::{arr1, Axis};
use super::utils::{geometric_mean, mean, number_crossings, Normalize};
use crate::{BlissError, BlissResult, SAMPLE_RATE};
use crate::bliss_lib::{BlissError, BlissResult, SAMPLE_RATE};
/**
* General object holding all the spectral descriptor.
@ -258,7 +258,7 @@ impl Normalize for ZeroCrossingRateDesc {
#[cfg(test)]
mod tests {
use super::*;
use crate::Song;
use crate::bliss_lib::Song;
use std::path::Path;
#[test]
@ -283,7 +283,7 @@ mod tests {
fn test_zcr() {
let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let mut zcr_desc = ZeroCrossingRateDesc::default();
for chunk in song.sample_array.chunks_exact(SpectralDesc::HOP_SIZE) {
for chunk in song.chunks_exact(SpectralDesc::HOP_SIZE) {
zcr_desc.do_(&chunk);
}
assert!(0.001 > (-0.85036 - zcr_desc.get_value()).abs());
@ -305,7 +305,7 @@ mod tests {
let song = Song::decode(Path::new("data/white_noise.mp3")).unwrap();
let mut spectral_desc = SpectralDesc::new(22050).unwrap();
for chunk in song.sample_array.chunks_exact(SpectralDesc::HOP_SIZE) {
for chunk in song.chunks_exact(SpectralDesc::HOP_SIZE) {
spectral_desc.do_(&chunk).unwrap();
}
println!("{:?}", spectral_desc.get_flatness());
@ -323,7 +323,7 @@ mod tests {
fn test_spectral_flatness() {
let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let mut spectral_desc = SpectralDesc::new(SAMPLE_RATE).unwrap();
for chunk in song.sample_array.chunks_exact(SpectralDesc::HOP_SIZE) {
for chunk in song.chunks_exact(SpectralDesc::HOP_SIZE) {
spectral_desc.do_(&chunk).unwrap();
}
// Spectral flatness mean value computed here with phase vocoder before normalization: 0.111949615
@ -353,7 +353,7 @@ mod tests {
let song = Song::decode(Path::new("data/tone_11080Hz.flac")).unwrap();
let mut spectral_desc = SpectralDesc::new(SAMPLE_RATE).unwrap();
for chunk in song.sample_array.chunks_exact(SpectralDesc::HOP_SIZE) {
for chunk in song.chunks_exact(SpectralDesc::HOP_SIZE) {
spectral_desc.do_(&chunk).unwrap();
}
let expected_values = vec![0.9967681, -0.99615175];
@ -369,7 +369,7 @@ mod tests {
fn test_spectral_roll_off() {
let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let mut spectral_desc = SpectralDesc::new(SAMPLE_RATE).unwrap();
for chunk in song.sample_array.chunks_exact(SpectralDesc::HOP_SIZE) {
for chunk in song.chunks_exact(SpectralDesc::HOP_SIZE) {
spectral_desc.do_(&chunk).unwrap();
}
let expected_values = vec![-0.6326486, -0.7260933];
@ -387,7 +387,7 @@ mod tests {
fn test_spectral_centroid() {
let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap();
let mut spectral_desc = SpectralDesc::new(SAMPLE_RATE).unwrap();
for chunk in song.sample_array.chunks_exact(SpectralDesc::HOP_SIZE) {
for chunk in song.chunks_exact(SpectralDesc::HOP_SIZE) {
spectral_desc.do_(&chunk).unwrap();
}
// Spectral centroid mean value computed here with phase vocoder before normalization: 1354.2273
@ -416,7 +416,7 @@ mod tests {
}
let song = Song::decode(Path::new("data/tone_11080Hz.flac")).unwrap();
let mut spectral_desc = SpectralDesc::new(SAMPLE_RATE).unwrap();
for chunk in song.sample_array.chunks_exact(SpectralDesc::HOP_SIZE) {
for chunk in song.chunks_exact(SpectralDesc::HOP_SIZE) {
spectral_desc.do_(&chunk).unwrap();
}
let expected_values = vec![0.97266, -0.9609926];

4
src/utils.rs
View File

@ -165,7 +165,7 @@ pub(crate) fn convolve(input: &Array1<f64>, kernel: &Array1<f64>) -> Array1<f64>
#[cfg(test)]
mod tests {
use super::*;
use crate::Song;
use crate::bliss_lib::Song;
use ndarray::Array2;
use ndarray::{arr1, Array};
use ndarray_npy::ReadNpyExt;
@ -498,7 +498,7 @@ mod tests {
let song = Song::decode(Path::new("data/piano.flac")).unwrap();
let stft = stft(&song.sample_array, 2048, 512);
let stft = stft(&song, 2048, 512);
assert!(!stft.is_empty() && !expected_stft.is_empty());
for (expected, actual) in expected_stft.iter().zip(stft.iter()) {