nightingale-karaoke

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

3,823 stars

Best use case

nightingale-karaoke is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using nightingale-karaoke should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/nightingale-karaoke/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/adisinghstudent/nightingale-karaoke/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/nightingale-karaoke/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How nightingale-karaoke Compares

Feature / Agentnightingale-karaokeStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Nightingale Karaoke Skill

> Skill by [ara.so](https://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](https://github.com/rzru/nightingale/releases) for your platform and run it.

**macOS only** — remove quarantine after extracting:
```bash
xattr -cr Nightingale.app
```

### Build from Source

**Prerequisites:**
- Rust 1.85+ (edition 2024)
- Linux additionally needs: `libasound2-dev libudev-dev libwayland-dev libxkbcommon-dev`

```bash
git clone https://github.com/rzru/nightingale
cd nightingale

# Development build
cargo build --release

# Run directly
./target/release/nightingale
```

### Release Packaging

```bash
# 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:
```bash
./nightingale --setup
```

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

---

## CLI Flags

| Flag | Description |
|---|---|
| `--setup` | Force re-run of the first-launch bootstrap (re-downloads vendor deps) |

---

## Keyboard & Gamepad Controls

### Navigation

| Action | Keyboard | Gamepad |
|---|---|---|
| Move | Arrow keys | D-pad / Left stick |
| Confirm | Enter | A (South) |
| Back | Escape | B (East) / Start |
| Switch panel | Tab | — |
| Search | Type to filter | — |

### Playback

| Action | Keyboard | Gamepad |
|---|---|---|
| Pause / Resume | Space | Start |
| Exit to menu | Escape | B (East) |
| Toggle guide vocals | G | — |
| Guide volume up/down | + / - | — |
| Cycle background | T | — |
| Cycle video flavor | F | — |
| Toggle microphone | M | — |
| Next microphone | N | — |
| Toggle fullscreen | F11 | — |

---

## Configuration

### Main Config

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

```json
{
  "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`:

```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:

```bash
# .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:

| Backend | Device | Notes |
|---|---|---|
| CUDA | NVIDIA GPU | Fastest; ~2–5 min/song |
| MPS | Apple Silicon | macOS; WhisperX alignment falls back to CPU |
| CPU | Any | Always 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)

```rust
// 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

```rust
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

```rust
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)

```rust
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

```rust
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

```bash
# Force re-bootstrap
./nightingale --setup

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

### Song Analysis Hangs or Errors

```bash
# 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

```bash
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

```bash
# 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:
```bash
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

| Platform | Target Triple |
|---|---|
| Linux x86_64 | `x86_64-unknown-linux-gnu` |
| Linux aarch64 | `aarch64-unknown-linux-gnu` |
| macOS ARM | `aarch64-apple-darwin` |
| macOS Intel | `x86_64-apple-darwin` |
| Windows x86_64 | `x86_64-pc-windows-msvc` |

Cross-compile with:
```bash
rustup target add aarch64-unknown-linux-gnu
cargo build --release --target aarch64-unknown-linux-gnu
```

---

## License

GPL-3.0-or-later. See [LICENSE](https://github.com/rzru/nightingale/blob/main/LICENSE).

Related Skills

---

3891
from openclaw/skills

name: article-factory-wechat

Content & Documentation

humanizer

3891
from openclaw/skills

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Content & Documentation

find-skills

3891
from openclaw/skills

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

General Utilities

tavily-search

3891
from openclaw/skills

Use Tavily API for real-time web search and content extraction. Use when: user needs real-time web search results, research, or current information from the web. Requires Tavily API key.

Data & Research

baidu-search

3891
from openclaw/skills

Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics.

Data & Research

agent-autonomy-kit

3891
from openclaw/skills

Stop waiting for prompts. Keep working.

Workflow & Productivity

Meeting Prep

3891
from openclaw/skills

Never walk into a meeting unprepared again. Your agent researches all attendees before calendar events—pulling LinkedIn profiles, recent company news, mutual connections, and conversation starters. Generates a briefing doc with talking points, icebreakers, and context so you show up informed and confident. Triggered automatically before meetings or on-demand. Configure research depth, advance timing, and output format. Walking into meetings blind is amateur hour—missed connections, generic small talk, zero leverage. Use when setting up meeting intelligence, researching specific attendees, generating pre-meeting briefs, or automating your prep workflow.

Workflow & Productivity

self-improvement

3891
from openclaw/skills

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

Agent Intelligence & Learning

botlearn-healthcheck

3891
from openclaw/skills

botlearn-healthcheck — BotLearn autonomous health inspector for OpenClaw instances across 5 domains (hardware, config, security, skills, autonomy); triggers on system check, health report, diagnostics, or scheduled heartbeat inspection.

DevOps & Infrastructure

linkedin-cli

3891
from openclaw/skills

A bird-like LinkedIn CLI for searching profiles, checking messages, and summarizing your feed using session cookies.

Content & Documentation

notebooklm

3891
from openclaw/skills

Google NotebookLM 非官方 Python API 的 OpenClaw Skill。支持内容生成(播客、视频、幻灯片、测验、思维导图等)、文档管理和研究自动化。当用户需要使用 NotebookLM 生成音频概述、视频、学习材料或管理知识库时触发。

Data & Research

小红书长图文发布 Skill

3891
from openclaw/skills

## 概述

Content & Documentation