libsonare 1.2.1

Fast audio analysis library with C++ and Python bindings

libsonare

A dependency-free audio DSP toolkit for Python — librosa-compatible analysis plus broadcast-grade mastering, mixing, and editing.

Built on a C++ core with zero Python dependencies. Analysis defaults match librosa (validated against generated librosa reference values in CI), and mastering ships 77 named DSP processors implemented against published references (ITU-R BS.1770-4 true-peak limiting, Linkwitz-Riley crossovers, Vicanek matched-Z biquads, ADAA-antialiased saturation) — Apache-2.0, no model weights.

Installation

pip install libsonare

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

Quick Start

Audio is the recommended entry point. The top-level libsonare.detect_* / libsonare.analyze functions are thin wrappers around Audio for one-shot calls and are kept for convenience.

Load from a file

import libsonare

audio = libsonare.Audio.from_file("song.mp3")  # or "song.wav"
print(f"BPM: {audio.detect_bpm():.1f}")
print(f"Key: {audio.detect_key().root.name} {audio.detect_key().mode.name}")

# Advanced key options are opt-in; defaults preserve existing behavior.
key_with_options = audio.detect_key(
    use_hpss=True,
    loudness_weighted=True,
    high_pass_hz=80.0,
)

result = audio.analyze()  # BPM + key + time signature + beats
print(f"BPM: {result.bpm:.1f}  Key: {result.key.root.name} {result.key.mode.name}")

Room acoustics

Use detect_acoustic for blind RT60/EDT estimation from ordinary audio. Use analyze_impulse_response when you have a measured impulse response and need clarity metrics (c50, c80, d50). Blind mode returns nan for clarity metrics because they are not reliable without an impulse response.

import libsonare

audio = libsonare.Audio.from_file("recording.wav")
blind = audio.detect_acoustic(
    n_octave_bands=6,
    n_third_octave_subbands=24,
    min_decay_db=30.0,
    noise_floor_margin_db=10.0,
)
print(blind.rt60, blind.edt, blind.is_blind)

ir = libsonare.Audio.from_file("room_ir.wav")
params = ir.analyze_impulse_response()
print(params.rt60, params.c50, params.c80, params.d50)

Load from a numpy array / in-memory samples

from_buffer accepts any 1D float sequence. With numpy, use a mono float32 array (stereo must be downmixed first, e.g. samples.mean(axis=1)).

import numpy as np
import libsonare

sr = 22050
samples = np.asarray(my_mono_float32_signal, dtype=np.float32)

audio = libsonare.Audio.from_buffer(samples, sample_rate=sr)
bpm = audio.detect_bpm()

# Or call the function directly (equivalent shortcut)
bpm = libsonare.detect_bpm(samples, sample_rate=sr)

Mastering

The Python binding exposes the same mastering processors as the C, CLI, Node, and WASM APIs.

import libsonare

names = libsonare.mastering_processor_names()  # e.g. "dynamics.compressor"

compressed = libsonare.mastering_process(
    "dynamics.compressor",
    samples,
    sample_rate=sr,
    params={"thresholdDb": -24.0, "ratio": 1.5},
)

widened = libsonare.mastering_process_stereo(
    "stereo.imager",
    left,
    right,
    sample_rate=sr,
    params={"width": 1.1},
)

pair_names = libsonare.mastering_pair_processor_names()  # e.g. "match.abCrossfade"
crossfaded = libsonare.mastering_pair_process(
    "match.abCrossfade",
    source,
    reference,
    sample_rate=sr,
    params={"mix": 0.25},
)

loudness_json = libsonare.mastering_pair_analyze(
    "match.referenceLoudness",
    source,
    reference,
    sample_rate=sr,
)
mono_compat_json = libsonare.mastering_stereo_analyze(
    "stereo.monoCompatCheck",
    left,
    right,
    sample_rate=sr,
)

Mastering chain

mastering_chain runs the full configurable mastering pipeline (EQ, dynamics, saturation, repair, stereo, loudness, ...). The Python binding accepts flat dot-notation keys for the config dict — addressing any module parameter directly — and an optional on_progress(progress, stage) callback that is invoked after each stage (progress in [0, 1]).

import libsonare

result = libsonare.mastering_chain(
    samples,
    sample_rate=sr,
    config={
        "eq.tilt.tiltDb": 0.5,
        "dynamics.compressor.thresholdDb": -24.0,
        "dynamics.compressor.ratio": 1.5,
        "dynamics.transientShaper.attackGainDb": 2.0,
        "repair.declick.enabled": True,
        "loudness.targetLufs": -14.0,
        "loudness.ceilingDb": -1.0,
    },
    on_progress=lambda p, stage: print(f"[{p * 100:5.1f}%] {stage}"),
)

stereo_result = libsonare.mastering_chain_stereo(
    left, right, sample_rate=sr,
    config={"stereo.imager.width": 1.1, "loudness.targetLufs": -14.0},
)

MasteringChainResult exposes the rendered samples plus loudness telemetry (input_lufs, output_lufs, applied_gain_db, latency_samples).

Mastering presets

Named presets ship sensible defaults for common targets. master_audio applies a preset and lets you override any individual parameter using the same flat dot-notation keys as mastering_chain.

import libsonare

libsonare.mastering_preset_names()
# -> ['pop', 'edm', 'acoustic', 'hipHop', 'aiMusic', 'speech', 'streaming', 'youtube', 'broadcast', 'podcast', 'audiobook', 'cinema', 'jpop', 'ambient', 'lofi', 'classical', 'drumAndBass', 'techno', 'metal', 'trap', 'rnb', 'jazz', 'kpop', 'trance', 'gameOst']

result = libsonare.master_audio(
    samples,
    sample_rate=sr,
    preset="aiMusic",
    overrides={
        "loudness.targetLufs": -13.0,
        "dynamics.multibandComp.enabled": True,
    },
)

# Audio shortcut
audio = libsonare.Audio.from_file("song.wav")
pop_mastered = audio.master_audio("pop")

Mixing

import libsonare

libsonare.mixing_scene_preset_names()
# -> ['vocalReverbSend', ...]
scene_json = libsonare.mixing_scene_preset_json("vocalReverbSend")

offline = libsonare.mix_stereo(
    [(vocal_l, vocal_r), (music_l, music_r)],
    sample_rate=sr,
    input_trim_db=[3.0, 0.0],
    fader_db=[-3.0, -12.0],
    pan=[0.0, -0.2],
    width=[1.0, 0.9],
)

mixer = libsonare.Mixer.from_scene_json(scene_json, sample_rate=sr, block_size=512)
try:
    block_l, block_r = mixer.process_stereo(
        [vocal_block_l, return_block_l],
        [vocal_block_r, return_block_r],
    )
finally:
    mixer.close()

Streaming mastering chain

StreamingMasteringChain runs the same pipeline block-by-block for real-time or chunked workflows. The constructor takes the same flat dot-notation config as mastering_chain; non-streamable stages (repair.denoise, loudness) cause the constructor to raise, so omit those keys for streaming use. The object is also a context manager.

import libsonare

with libsonare.StreamingMasteringChain({
    "eq.tilt.tiltDb": 0.5,
    "dynamics.compressor.thresholdDb": -20.0,
    "dynamics.transientShaper.attackGainDb": 1.5,
}) as chain:
    chain.prepare(sample_rate=48000, max_block_size=512, num_channels=1)
    print(chain.stage_names())
    print(f"latency = {chain.latency_samples()} samples")

    out_block = chain.process_mono([0.0] * 512)
    # Stereo:
    # chain.prepare(48000, 512, 2)
    # out_l, out_r = chain.process_stereo(left_block, right_block)
    chain.reset()

Library version

import libsonare
libsonare.__version__   # e.g. "1.0.4"  (preferred — matches importlib.metadata)
libsonare.version()     # native library version string

Supported audio formats

Format	Default build	With FFmpeg support
WAV (PCM 16/24/32, float32)	yes	yes
MP3	yes	yes
M4A / AAC / FLAC / OGG / Opus / WMA / ...	no	yes

If Audio.from_file() is given an unsupported format you will see a clear error such as:

RuntimeError: Unsupported audio format: '.m4a'. Supported: WAV, MP3.
Rebuild with -DSONARE_WITH_FFMPEG=ON for M4A/AAC/FLAC/OGG,
or convert via: ffmpeg -i "song.m4a" output.wav

The PyPI wheels are pinned to SONARE_WITH_FFMPEG=OFF so the distributed wheel never silently links against the build host's FFmpeg. From-source builds default to AUTO detection via pkg-config: if FFmpeg dev libraries are present they are enabled, otherwise WAV/MP3 only.

To enable FFmpeg-backed decoding when building from source:

git clone https://github.com/libraz/libsonare
cd libsonare
SONARE_FFMPEG=1 bash bindings/python/build_wheel.sh   # require FFmpeg
# or
SONARE_FFMPEG=AUTO bash bindings/python/build_wheel.sh # detect via pkg-config
pip install bindings/python/dist/*.whl

This links against the system FFmpeg shared libraries (LGPL by default), so ensure they are installed (e.g. brew install ffmpeg, apt install libavformat-dev libavcodec-dev libavutil-dev libswresample-dev).

Function vs Audio method

Both APIs return the same results. Use whichever is more convenient:

# Functional (good for ad-hoc numpy work)
bpm = libsonare.detect_bpm(samples, sample_rate=22050)        # -> float
key = libsonare.detect_key(samples, sample_rate=22050)        # -> Key(root, mode, confidence)
key_hpss = libsonare.detect_key(
    samples,
    sample_rate=22050,
    n_fft=4096,
    hop_length=512,
    use_hpss=True,
    loudness_weighted=True,
    high_pass_hz=80.0,
)
result = libsonare.analyze(samples, sample_rate=22050)        # -> AnalysisResult

# Audio class (recommended for files, multiple operations on the same audio)
audio = libsonare.Audio.from_file("song.wav")
bpm = audio.detect_bpm()
key = audio.detect_key()
result = audio.analyze()

Audio caches the decoded samples and is the only way to load files, so it is the recommended entry point when you do more than a single computation on the same signal.

Input format expectations

API	dtype	shape	range
Audio.from_buffer(samples, sample_rate=...)	float32 (float64 also accepted)	1D mono	nominally [-1.0, 1.0]
Audio.from_memory(data)	bytes of an encoded WAV / MP3 / (FFmpeg) file	—	—
Audio.from_file(path)	path to an encoded audio file	—	—
libsonare.detect_bpm(samples, sample_rate=...) etc.	float32 (float64 also accepted)	1D mono	nominally [-1.0, 1.0]

Stereo input is not downmixed automatically when passed as samples — downmix yourself (e.g. samples.mean(axis=1, dtype=np.float32)). File loaders downmix to mono internally.

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

sonare mastering song.wav -o mastered.wav --target-lufs -14
sonare mastering-processor song.wav --processor dynamics.compressor --params thresholdDb=-24,ratio=1.5
sonare mastering-pair-processor source.wav --reference reference.wav --processor match.abCrossfade
sonare mastering-pair-analyze source.wav --reference reference.wav --analysis match.referenceLoudness
sonare mastering-stereo-analyze left.wav --reference right.wav --analysis stereo.monoCompatCheck

Features

• Detection: BPM (float), key (Key(root, mode, confidence)), beats (list[float] seconds), onsets (list[float] seconds)
• Analysis: Full music analysis (AnalysisResult with BPM, key, time signature, beat times)
• Effects: HPSS, pitch shift, time stretch, normalize, trim
• Features: STFT, mel spectrogram, MFCC, chroma, spectral features, pitch tracking (YIN / pYIN with voicing probabilities)
• Conversions: Hz / mel / MIDI / note, frames / time
• I/O: Load WAV / MP3 (and M4A/AAC/FLAC/OGG when built with FFmpeg), resample

librosa-compatible defaults

Parameter	Default
Sample rate	22050 Hz
n_fft	2048
hop_length	512
n_mels	128
fmin / fmax	0.0 / sr/2

Also available

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

License

Apache-2.0