MCPInstall

nightingale-karaoke

ML-powered Karaoke app in Rust using Bevy, WhisperX, and Demucs for stem separation, lyrics transcription, and pitch scoring.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/reason-machines/trending-skills --skill nightingale-karaoke
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads nightingale-karaoke automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Nightingale Karaoke Skill

Skill by ara.so — Daily 2026 Skills collection.

Nightingale is a self-contained, ML-powered karaoke application written in Rust (Bevy engine). It scans a local music folder, separates vocals from instrumentals (UVR Karaoke model or Demucs), transcribes lyrics with word-level timestamps (WhisperX), and plays back with synchronized highlighting, real-time pitch scoring, player profiles, and GPU shader / video backgrounds. Everything — ffmpeg, Python, PyTorch, ML models — is bootstrapped automatically on first launch.


Installation

Pre-built Binary (Recommended)

Download the latest release from the Releases page for your platform and run it.

macOS only — remove quarantine after extracting:

xattr -cr Nightingale.app

Build from Source

Prerequisites:

  • Rust 1.85+ (edition 2024)
  • Linux additionally needs: libasound2-dev libudev-dev libwayland-dev libxkbcommon-dev
git clone https://github.com/rzru/nightingale
cd nightingale

# Development build
cargo build --release

# Run directly
./target/release/nightingale

Release Packaging

# Linux / macOS
scripts/make-release.sh

# Windows (PowerShell)
powershell -ExecutionPolicy Bypass -File scripts/make-release.ps1

Outputs a .tar.gz (Linux/macOS) or .zip (Windows) ready for distribution.


First Launch / Bootstrap

On first run, Nightingale downloads and configures:

  • ffmpeg binary
  • uv (Python package manager)
  • Python 3.10 via uv
  • PyTorch + WhisperX + audio-separator in a virtual environment
  • UVR Karaoke ONNX model and WhisperX large-v3 model

This takes 2–10 minutes depending on network speed. A progress screen is shown in-app.

To force re-bootstrap at any time:

./nightingale --setup

Bootstrap completion is marked by ~/.nightingale/vendor/.ready.


CLI Flags

FlagDescription
--setupForce re-run of the first-launch bootstrap (re-downloads vendor deps)

Keyboard & Gamepad Controls

Navigation

ActionKeyboardGamepad
MoveArrow keysD-pad / Left stick
ConfirmEnterA (South)
BackEscapeB (East) / Start
Switch panelTab—
SearchType to filter—

Playback

ActionKeyboardGamepad
Pause / ResumeSpaceStart
Exit to menuEscapeB (East)
Toggle guide vocalsG—
Guide volume up/down+ / -—
Cycle backgroundT—
Cycle video flavorF—
Toggle microphoneM—
Next microphoneN—
Toggle fullscreenF11—

Configuration

Main Config

Located at ~/.nightingale/config.json. Edit directly or via in-app settings.

{
  "music_folder": "/home/user/Music",
  "separator": "uvr",
  "guide_vocal_volume": 0.3,
  "background_theme": "plasma",
  "video_flavor": "nature",
  "default_profile": "Alice"
}

separator options: "uvr" (default, preserves backing vocals) | "demucs"

background_theme options: "plasma", "aurora", "waves", "nebula", "starfield", "video", "source_video"

video_flavor options: "nature", "underwater", "space", "city", "countryside"

Profiles

Located at ~/.nightingale/profiles.json:

{
  "profiles": [
    {
      "name": "Alice",
      "scores": {
        "blake3_hash_of_song": {
          "stars": 4,
          "score": 87250,
          "played_at": "2026-03-18T21:00:00Z"
        }
      }
    }
  ]
}

Pixabay Video Backgrounds (Dev)

API key is embedded in release builds. For local development, create .env at project root:

# .env
PIXABAY_API_KEY=$PIXABAY_API_KEY

The release script (make-release.sh) sources .env automatically.


Data Storage Layout

~/.nightingale/
├── cache/              # Per-song stems, transcripts, lyrics (keyed by blake3 hash)
├── config.json         # App settings
├── profiles.json       # Player profiles and per-song scores
├── videos/             # Pre-downloaded Pixabay video backgrounds
├── sounds/             # Sound effects
├── vendor/
│   ├── ffmpeg          # ffmpeg binary
│   ├── uv              # uv binary
│   ├── python/         # Python 3.10
│   ├── venv/           # ML virtualenv (WhisperX, Demucs, audio-separator)
│   ├── analyzer/       # Python analyzer scripts
│   └── .ready          # Bootstrap completion marker
└── models/
    ├── torch/          # Demucs model weights
    ├── huggingface/    # WhisperX large-v3 weights
    └── audio_separator/ # UVR Karaoke ONNX model

Cache keys are blake3 hashes of the source file — re-analysis only triggers if the file changes or is manually invalidated.


Supported File Formats

Audio: .mp3, .flac, .ogg, .wav, .m4a, .aac, .wma

Video: .mp4, .mkv, .avi, .webm, .mov, .m4v

Video files: audio track is extracted, vocals separated, original video plays as background automatically.


Hardware Acceleration

PyTorch backend is auto-detected:

BackendDeviceNotes
CUDANVIDIA GPUFastest; ~2–5 min/song
MPSApple SiliconmacOS; WhisperX alignment falls back to CPU
CPUAnyAlways works; ~10–20 min/song

UVR Karaoke model uses ONNX Runtime with CUDA (NVIDIA) or CoreML (Apple Silicon) automatically.


Processing Pipeline

Audio/Video file
       │
       ▼
 UVR Karaoke (ONNX) or Demucs (PyTorch)
       │  vocals.ogg + instrumental.ogg
       ▼
 LRCLIB API  ──▶  Synced lyrics fetch (if available)
       │
       ▼
 WhisperX large-v3  ──▶  Transcription + word-level timestamps
       │
       ▼
 Bevy App (Rust)
   - Plays instrumental audio
   - Synchronized word highlighting
   - Real-time pitch detection & scoring
   - GPU shader / video backgrounds
   - Scoreboards per profile

Code Patterns

Adding a New Background Theme (Bevy System)

// In your Bevy plugin, register a new background variant
use bevy::prelude::*;

#[derive(Component)]
pub struct MyCustomBackground;

pub fn spawn_custom_background(mut commands: Commands) {
    commands.spawn((
        MyCustomBackground,
        // ... your background components
    ));
}

pub struct CustomBackgroundPlugin;

impl Plugin for CustomBackgroundPlugin {
    fn build(&self, app: &mut App) {
        app.add_systems(OnEnter(AppState::Playing), spawn_custom_background);
    }
}

Extending Config Deserialization

use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NightingaleConfig {
    pub music_folder: String,
    #[serde(default = "default_separator")]
    pub separator: StemSeparator,
    #[serde(default = "default_guide_volume")]
    pub guide_vocal_volume: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize, Default)]
#[serde(rename_all = "lowercase")]
pub enum StemSeparator {
    #[default]
    Uvr,
    Demucs,
}

fn default_guide_volume() -> f32 { 0.3 }
fn default_separator() -> StemSeparator { StemSeparator::Uvr }

// Load config
fn load_config() -> NightingaleConfig {
    let path = dirs::home_dir()
        .unwrap()
        .join(".nightingale/config.json");
    let raw = std::fs::read_to_string(&path).unwrap_or_default();
    serde_json::from_str(&raw).unwrap_or_default()
}

Triggering Re-analysis Programmatically

use std::fs;
use std::path::PathBuf;

/// Remove cached stems/transcript for a song to force re-analysis
fn invalidate_song_cache(song_hash: &str) {
    let cache_dir = dirs::home_dir()
        .unwrap()
        .join(".nightingale/cache")
        .join(song_hash);

    if cache_dir.exists() {
        fs::remove_dir_all(&cache_dir)
            .expect("Failed to remove cache directory");
        println!("Cache invalidated for {}", song_hash);
    }
}

Computing a Song's Blake3 Hash (for Cache Lookup)

use blake3::Hasher;
use std::fs::File;
use std::io::{BufReader, Read};

fn hash_file(path: &std::path::Path) -> String {
    let file = File::open(path).expect("Cannot open file");
    let mut reader = BufReader::new(file);
    let mut hasher = Hasher::new();
    let mut buf = [0u8; 65536];
    loop {
        let n = reader.read(&mut buf).unwrap();
        if n == 0 { break; }
        hasher.update(&buf[..n]);
    }
    hasher.finalize().to_hex().to_string()
}

Profile Score Update Pattern

use serde::{Deserialize, Serialize};
use std::collections::HashMap;

#[derive(Debug, Serialize, Deserialize)]
pub struct SongScore {
    pub stars: u8,
    pub score: u32,
    pub played_at: String,
}

#[derive(Debug, Serialize, Deserialize)]
pub struct Profile {
    pub name: String,
    pub scores: HashMap<String, SongScore>, // key = blake3 hash
}

fn update_score(profile: &mut Profile, song_hash: &str, stars: u8, score: u32) {
    profile.scores.insert(song_hash.to_string(), SongScore {
        stars,
        score,
        played_at: chrono::Utc::now().to_rfc3339(),
    });
}

Troubleshooting

Bootstrap Fails / Stuck on Setup Screen

# Force re-bootstrap
./nightingale --setup

# Or manually remove the vendor directory and restart
rm -rf ~/.nightingale/vendor
./nightingale

Song Analysis Hangs or Errors

# Check the analyzer venv is healthy
~/.nightingale/vendor/venv/bin/python -c "import whisperx; print('ok')"

# Re-bootstrap if broken
./nightingale --setup

macOS "App is damaged" Error

xattr -cr Nightingale.app

GPU Not Being Used

  • NVIDIA: Ensure CUDA drivers are installed and nvidia-smi shows your GPU.
  • Apple Silicon: MPS is used automatically on macOS with Apple Silicon; WhisperX alignment falls back to CPU (normal behavior).
  • Check ~/.nightingale/vendor/venv — if PyTorch installed the CPU-only build, re-bootstrap after installing CUDA drivers.

Cache Corruption / Wrong Lyrics

# Find the blake3 hash of your file (build a small tool or use b3sum)
b3sum /path/to/song.mp3

# Remove that song's cache
rm -rf ~/.nightingale/cache/<hash>

Then re-open the song in Nightingale to re-analyze.

Audio Playback Issues (Linux)

Ensure ALSA/PulseAudio/PipeWire is running. Install missing deps:

sudo apt install libasound2-dev libudev-dev libwayland-dev libxkbcommon-dev

Video Backgrounds Not Loading

Video backgrounds are pre-downloaded during setup via the Pixabay API. For development builds, ensure .env contains a valid PIXABAY_API_KEY. If videos are missing in a release build, run --setup to re-trigger the download.


Platform Targets

PlatformTarget Triple
Linux x86_64x86_64-unknown-linux-gnu
Linux aarch64aarch64-unknown-linux-gnu
macOS ARMaarch64-apple-darwin
macOS Intelx86_64-apple-darwin
Windows x86_64x86_64-pc-windows-msvc

Cross-compile with:

rustup target add aarch64-unknown-linux-gnu
cargo build --release --target aarch64-unknown-linux-gnu

License

GPL-3.0-or-later. See LICENSE.

FAQ

What does the nightingale-karaoke skill do?

ML-powered Karaoke app in Rust using Bevy, WhisperX, and Demucs for stem separation, lyrics transcription, and pitch scoring.

How do I install nightingale-karaoke?

Run npx skills add https://github.com/reason-machines/trending-skills --skill nightingale-karaoke in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nightingale-karaoke/ (personal) or .claude/skills/nightingale-karaoke/ (project).

Which AI agents can use nightingale-karaoke?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is nightingale-karaoke safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains nightingale-karaoke?

It is published by reason-machines in reason-machines/trending-skills. It has 1,263 recorded installs.

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