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Releases: itsdevcoffee/mojo-audio

v0.2.0 - macOS Support 🍎

04 Feb 22:57
@itsdevcoffee itsdevcoffee
v0.2.0
82ab14e

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v0.2.0 - macOS Support 🍎 Latest
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🎉 macOS Support Arrives!

mojo-audio now runs natively on macOS with blazing performance on Apple Silicon!

✨ What's New

Platform Support:

  • ✅ macOS Apple Silicon (M1/M2/M3/M4)
  • ✅ macOS Intel (x86_64)
  • ✅ Linux x86_64

Performance (Apple Silicon M-series):

  • 30s audio: 8.34ms (3,598x realtime)
  • 10s audio: 2.81ms (3,562x realtime)
  • 1s audio: 0.40ms (2,530x realtime)

vs Python librosa:

  • 1.75x faster on 30s audio
  • 4.5x faster on 10s audio
  • 1.6x faster on 1s audio

🔧 Technical Improvements

  • Fixed Mojo 0.26.2+ API compatibility
  • Switched to OpenBLAS for cross-platform BLAS support
  • Inlined FFI types for reliable shared library builds
  • All tests passing on macOS and Linux

📦 Installation

macOS:

curl -L https://github.com/itsdevcoffee/mojo-audio/releases/download/v0.2.0/mojo-audio-v0.2.0-macos-arm64.tar.gz | tar xz
sudo cp libmojo_audio.dylib /usr/local/lib/
sudo cp mojo_audio.h /usr/local/include/

Build from source:

pixi install
pixi run build-ffi-optimized

See README.md for detailed build instructions.

Assets 4
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mojo-audio v0.1.0 - First Public Release

22 Jan 09:57
@itsdevcoffee itsdevcoffee
v0.1.0
87cef42

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mojo-audio v0.1.0 - First Public Release

mojo-audio v0.1.0 - Release Notes

Release Date: January 22, 2026

High-performance mel spectrogram preprocessing library in Mojo that beats Python's librosa by 1.5-3.6x on short/medium audio.

🎉 What's New

First Public Release

Complete Whisper-compatible audio preprocessing pipeline built from scratch in Mojo, achieving production-ready performance and beating Python's librosa at all audio durations.

Performance Highlights

Duration mojo-audio librosa Result
1 second 1.1 ms 4.0 ms 3.6x faster
10 seconds 7.5 ms 15.3 ms 2.0x faster
30 seconds 27.4 ms 30.4 ms 1.1x faster

Key advantages:

  • 1.5-3.6x faster on short/medium audio (most common use case)
  • Far more consistent (5-10% variance vs librosa's 22-39%)
  • ~1100x realtime throughput on 30s audio
  • Zero dependencies, pure Mojo implementation

📦 Release Assets

Pre-built Binaries (FFI)

mojo-audio-v0.1.0-linux-x86_64.tar.gz (15KB)

Pre-built shared library for Linux x86_64:

  • libmojo_audio.so - Optimized shared library (26KB, -O3)
  • mojo_audio.h - C header file
  • INSTALL.md - Installation and usage guide

Use from:

  • ✅ C/C++
  • ✅ Rust
  • ✅ Python (ctypes/cffi)
  • ✅ Go (cgo)
  • ✅ Any language with C FFI support

mojo-audio-ffi-examples.tar.gz (3.1KB)

FFI usage examples:

  • demo_c.c - Complete C example
  • demo_rust.rs - Complete Rust example
  • Makefile - Build system for examples

Source Code

  • Source code (zip) - Auto-generated by GitHub
  • Source code (tar.gz) - Auto-generated by GitHub

✨ Features

Core Audio Processing

  • Mel Spectrogram Pipeline - Complete Whisper-compatible preprocessing
  • Window Functions - Hann and Hamming with SIMD optimization
  • FFT Operations - Radix-2/4 iterative FFT + true RFFT for real signals
  • STFT - Parallelized short-time Fourier transform across CPU cores
  • Mel Filterbank - Sparse-optimized triangular filters (80 or 128 bands)
  • Normalization - Multiple modes (Whisper, min-max, z-score, raw)

FFI Support

  • Zero-overhead C API - Same performance as native Mojo
  • Type-safe interface - Proper error handling and memory management
  • Comprehensive examples - C, Rust, Python usage samples
  • Easy installation - Pre-built binaries for Linux x86_64

Testing & Benchmarking

  • 17 test cases - All passing
  • Robust methodology - Deterministic signals, warmup, outlier exclusion
  • Comparison scripts - Back-to-back mojo-audio vs librosa
  • Web UI - Interactive benchmark visualization

🚀 Quick Start

Native Mojo

git clone https://github.com/itsdevcoffee/mojo-audio.git
cd mojo-audio
pixi install
pixi run demo-mel

FFI (C/Rust/Python)

# Download and install pre-built library
wget https://github.com/itsdevcoffee/mojo-audio/releases/download/v0.1.0/mojo-audio-v0.1.0-linux-x86_64.tar.gz
tar xzf mojo-audio-v0.1.0-linux-x86_64.tar.gz
cd linux-x86_64
sudo cp libmojo_audio.so /usr/local/lib/
sudo cp mojo_audio.h /usr/local/include/
sudo ldconfig

# Test it
wget https://github.com/itsdevcoffee/mojo-audio/releases/download/v0.1.0/mojo-audio-ffi-examples.tar.gz
tar xzf mojo-audio-ffi-examples.tar.gz
gcc demo_c.c -lmojo_audio -lm -o demo
./demo

🔧 Requirements

Native Mojo

  • Mojo 0.26.1 or later
  • pixi package manager
  • Linux (primary platform)

FFI (Pre-built Binaries)

  • Linux x86_64
  • glibc 2.31 or later
  • No Mojo installation required!

🎯 Whisper Compatibility

Fully compatible with OpenAI Whisper models:

  • ✅ Sample rate: 16kHz
  • ✅ FFT size: 400
  • ✅ Hop length: 160 (10ms frames)
  • ✅ Mel bands: 80 (v2) or 128 (v3)
  • ✅ Output shape: (n_mels, ~3000) for 30s
  • ✅ Normalization: Whisper-compatible mode

📚 Documentation

🏆 Achievements

  • 🥇 Beats librosa at all durations (1.1-3.6x faster)
  • 🚀 17-68x speedup from naive to optimized implementation
  • ⚡ ~1100x realtime throughput on 30s audio
  • 📊 Far more consistent than librosa (5-10% vs 22-39% variance)
  • ✅ 100% from scratch in Mojo - no black-box dependencies
  • 🎓 Complete learning resource with educational examples

🔬 Technical Highlights

9 Major Optimizations

  1. Iterative FFT (3.0x) - Cache-friendly Cooley-Tukey
  2. Twiddle Precompute (1.7x) - Pre-computed rotation factors
  3. Sparse Mel Filterbank (1.24x) - Skip zero-weight bins
  4. Twiddle Caching (2.0x) - Reuse across frames
  5. Float32 Precision (1.07x) - 2x SIMD width
  6. True RFFT (1.43x) - Exploits real signal symmetry
  7. Parallelization (1.3-1.7x) - Multi-core frame processing
  8. Radix-4 FFT (1.1-1.2x) - Optimized butterflies
  9. Compiler Optimization (1.2-1.5x) - -O3 flag

Total: 17-68x faster than naive implementation!

🐛 Known Issues

None reported. This is the initial stable release.

🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Areas where we'd love help:

  • Additional FFI language bindings (Go, Julia, Zig, Swift)
  • ARM platform support and optimization
  • Additional audio features (MFCC, CQT, etc.)
  • Documentation improvements

📝 License

MIT License - See LICENSE

🔗 Links

📊 Benchmark Methodology

All benchmarks use improved methodology:

  • Signal: Deterministic chirp (20Hz-8000Hz, reproducible)
  • Warmup: 5 runs (JIT stabilization)
  • Iterations: 20 (statistical significance)
  • Analysis: Outlier exclusion, mean ± std dev
  • Comparison: Back-to-back mojo-audio vs librosa

Run benchmarks yourself:

pixi run bench-compare        # Full comparison
pixi run bench-stable 5       # Stable (5 runs, median)

Built with Mojo 🔥 | Faster than Python | Production-Ready

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