ultrastar-score 0.2.0

Score UltraStar karaoke files against vocal audio using Vocaluxe pitch detection and USDX scoring

ultrastar-score

Score UltraStar karaoke song files against vocal audio tracks using real game algorithms.

• Pitch detection: Vocaluxe ptAKF C++ extension (AKF/AMDF hybrid autocorrelation)
• Scoring: USDX-compatible (10000-point scale with line bonus)
• Difficulty levels: Easy (±2 ST), Medium (±1 ST), Hard (exact match), or custom tolerance

Installation

Pre-built wheels are available for all major platforms (no compiler needed):

OS	Architectures
Windows	x86_64, ARM64
Linux	x86_64, aarch64
macOS	x86_64, arm64 (Apple Silicon)

Python 3.11, 3.12, and 3.13 are supported.

pip install ultrastar-score

For MP3 support:

pip install ultrastar-score[mp3]

From source (development)

See Development below for build requirements and setup instructions.

Usage

Command Line

# Basic scoring (Easy difficulty)
uscore song.txt vocals.wav

# Different difficulty levels
uscore song.txt vocals.ogg --difficulty medium
uscore song.txt vocals.ogg --difficulty hard

# Custom tolerance (1.5 semitones)
uscore song.txt vocals.wav --tolerance 1.5

# JSON output for automation
uscore song.txt vocals.wav --json

# Detailed breakdowns
uscore song.txt vocals.wav --per-line
uscore song.txt vocals.wav --per-note

Python API

from ultrastar_score import score_song, parse_ultrastar, Difficulty

song = parse_ultrastar("song.txt")
result = score_song(song, "vocals.wav", difficulty=Difficulty.MEDIUM)

print(f"Score: {result.total:.0f}/10000 ({result.percentage:.1f}%)")
print(f"Rating: {result.rating}")
print(f"Beats hit: {result.notes_hit}/{result.notes_total}")

Scoring Model

Follows the USDX scoring model:

Component	Max Points	Description
Notes	9000	Per-beat scoring, golden notes worth 2x
Line Bonus	1000	Awarded proportionally per line
Total	10000

Note Types

Type	Symbol	Score Factor	Pitch Required
Normal	:	1x	Yes
Golden	*	2x	Yes
Freestyle	F	0	No
Rap	R	1x	No
Rap Golden	G	2x	No

Octave Folding

Like USDX, detected pitch is octave-folded to within 6 semitones of the expected note before comparison. This means octave errors don't count as misses.

Supported Formats

• Audio: WAV, OGG, FLAC (native), MP3 (with [mp3] extra)
• Song files: UltraStar TXT (legacy and v1.2.0 format)
• Encodings: UTF-8, UTF-8-BOM, CP1252, Latin-1

Development

Building from source requires a C++ compiler and CMake (≥3.18):

Platform	Compiler	Install
Windows	MSVC Build Tools	winget install Microsoft.VisualStudio.2022.BuildTools --override "--quiet --add Microsoft.VisualStudio.Workload.VCTools --includeRecommended"
Linux	GCC/Clang	sudo apt install build-essential cmake (Debian/Ubuntu)
macOS	Clang (Xcode)	xcode-select --install

CMake (≥3.18) is also required. On Windows, install via winget install Kitware.CMake. On Linux/macOS it's typically included or available via your package manager.

git clone https://github.com/MrDix/ultrastar-score
cd ultrastar-score
pip install -e ".[dev]"
pytest

The C++ extension is built automatically by pip install. If you encounter build issues, run pip install -e ".[dev]" -v for verbose build output.

Algorithm Details

Pitch Detection (ptAKF)

The pitch detection uses the combined AKF/AMDF method (Kobayashi & Shimamura, 2001):

f(τ) = AKF(τ) / (AMDF(τ) + 1)

This is a compiled C++ extension — there is no Python fallback. This is intentional: ultrastar-score is designed as an independent validation tool for UltraStar song generators. Using a completely different pitch detection algorithm (Vocaluxe ptAKF) from the generator (e.g., UltraSinger's SwiftF0) ensures that systematic pitch detection errors are caught rather than masked.

Key parameters:

• Window: 2048 samples with Hamming window
• Hop: 1024 samples (~23ms at 44.1kHz)
• Range: C2 (65.4 Hz) to G#6 (~1661 Hz)
• Fine-tuning: ±1/3 semitone sub-resolution
• Validation: AKF at detected lag must be ≥33% of signal energy
• Smoothing: Median filter over 3 consecutive frames

Based on Vocaluxe's C++ implementation (GPL v3).

Publishing to PyPI (Maintainers)

One-Time Setup: OIDC Trusted Publishing

The release workflow uses PyPI's Trusted Publishing (OIDC) so no API tokens are needed.

1. Go to https://pypi.org and log in (or create an account).
2. Navigate to https://pypi.org/manage/account/publishing/.
3. Under "Add a new pending publisher", fill in:
   • PyPI project name: ultrastar-score
   • Owner: MrDix
   • Repository: ultrastar-score
   • Workflow name: release.yml
   • Environment name: pypi
4. Click "Add".

This tells PyPI to trust tokens issued by GitHub Actions for this specific repository and workflow.

Creating a Release

1. Update the version in pyproject.toml.
2. Commit and push to main.
3. Create a GitHub release with a tag matching the version (e.g., v0.2.0).
4. The CI automatically builds wheels for all platforms (Linux x86_64/aarch64, Windows x86_64/ARM64, macOS x86_64/arm64) and publishes to PyPI.
5. Users can then install with pip install ultrastar-score.

Workflow Requirements

The publish job in .github/workflows/release.yml must have these two settings for OIDC to work:

environment: pypi          # must match the environment name on PyPI
permissions:
  id-token: write          # allows the job to request an OIDC token

Both are already configured in the current workflow.

License

GPL-3.0-or-later (due to Vocaluxe ptAKF code)