libsonare 1.0.2

Fast audio analysis library — librosa-like API, tens of times faster (C++ with Python bindings)

libsonare

Fast audio analysis library for Python -- librosa-like API, tens of times faster.

Built on a C++ core with zero Python dependencies.

Installation

pip install libsonare

Supported platforms: Linux (x86_64, aarch64), macOS (Apple Silicon).

Quick Start

import libsonare

# From raw samples
bpm = libsonare.detect_bpm(samples, sample_rate=22050)
key = libsonare.detect_key(samples, sample_rate=22050)
print(f"{key.root.name} {key.mode.name}")  # e.g. "C MAJOR"

# Full analysis
result = libsonare.analyze(samples, sample_rate=22050)
print(f"BPM: {result.bpm}, Key: {result.key}")

# Audio class (load files directly)
audio = libsonare.Audio.from_file("song.mp3")
print(f"BPM: {audio.detect_bpm()}")
print(f"Key: {audio.detect_key()}")
print(f"Mel: {audio.mel_spectrogram().n_mels}x{audio.mel_spectrogram().n_frames}")

CLI

sonare analyze song.mp3
# > Estimated BPM : 161.00 BPM  (conf 75.0%)
# > Estimated Key : C major  (conf 100.0%)

sonare bpm song.mp3 --json       # {"bpm": 161.0}
sonare key song.mp3              # Key: C major (confidence: 100.0%)
sonare spectral song.mp3         # Spectral features table
sonare pitch song.mp3            # Pitch tracking (pYIN)
sonare mel song.mp3              # Mel spectrogram shape
sonare chroma song.mp3           # Chromagram with visualization

Features

• Detection: BPM, key, beats, onsets
• Analysis: Full music analysis (BPM + key + time signature + beats)
• Effects: HPSS, pitch shift, time stretch, normalize, trim
• Features: STFT, mel spectrogram, MFCC, chroma, spectral features, pitch tracking (YIN/pYIN)
• Conversions: Hz/mel/MIDI/note, frames/time
• I/O: Load WAV/MP3 files, resample

librosa Compatibility

Default parameters match librosa:

• Sample rate: 22050 Hz
• n_fft: 2048
• hop_length: 512
• n_mels: 128

Also available

npm install @libraz/libsonare  # JavaScript/TypeScript (WASM)

License

Apache-2.0