EffeTune for Python

Documentation: effetune.frieve.com/dsp/

Source and issues: Frieve-A/effetune

effetune.__version__ comes from installed wheel metadata. An unpacked source tree without distribution metadata reports 0+source.

EffeTune is a deterministic audio-effects library backed by the same host-neutral C++20 DSP core used by the EffeTune application. Version 0.4.0 provides 83 semantic effect classes, ordered serial chains, stateful block processing, semantic presets, bounded impulse-response bundles, and a small audio-file CLI.

Install and process

pip install effetune

import numpy as np
import effetune as et

frames = 512
phase = np.arange(frames, dtype=np.float32)
mono = (0.5 * np.sin(2 * np.pi * phase / 97)).astype(np.float32)
audio = np.ascontiguousarray(np.stack((mono, mono)))
chain = et.Chain([et.Volume(volume=-6)])
output = chain.process(audio, sample_rate=48_000)
print(output.shape, float(np.max(np.abs(output))))

Generated effect constructors and create_effect() accept Python snake_case keywords:

shift = et.PitchShifter(pitch_shift=3)
same_shift = et.create_effect("PitchShifter", pitch_shift=3)

Chain JSON and scheduled event parameter objects use semantic catalog names, such as pitchShift. CamelCase semantic names are not constructor aliases.

Audio arrays are C-contiguous planar float32 with shape (channels, frames). Offline calls return a new array and start from fresh DSP state. No resampling is performed.

SoundFile returns (frames, channels). Convert decoded files explicitly:

import numpy as np
import soundfile as sf

decoded, sample_rate = sf.read("input.wav", dtype="float32", always_2d=True)
audio = np.ascontiguousarray(decoded.T, dtype=np.float32)
output = chain.process(audio, sample_rate=sample_rate)
sf.write("output.wav", output.T, sample_rate, subtype="FLOAT")

For persistent filter history and tails:

with chain.stream(48_000, channels=2, block_size=512, seed=42) as stream:
    first = stream.process(block_a)
    second = stream.process(block_b)
    stream.reset()

block_size must be from 1 through 16384 and controls the largest native processing window; process() may receive a longer array and partitions it internally. Parameter events use frame offsets relative to that process() input. They must be ordered, identify an enabled effect with an explicit id, and provide one or more semantic parameter updates:

output = stream.process(audio, events=[
    {"frame": 0, "effectId": "voice", "parameters": {"threshold": -24}},
    {"frame": 0, "effectId": "voice", "parameters": {"ratio": 6}},
])

Each event is merged with the effect's current parameters. Frame zero applies before the first sample. Multiple events at one frame keep their supplied order, so later updates see earlier updates. The final frame is not an event position. reset() restores the initial parameters, state, and seed. Events cannot change parameters that require convolution assets to be staged again. Open a new stream after changing IRReverb.channelMode, latency, or convolutionRate; FIRCrossover.bandCount, latencyMode, or filterDelaySamples; or latencyMode / filterDelaySamples on FiveBandFIRPEQ, GroupDelayEQ, or RoomEQ. close() is idempotent. Processing or resetting a closed stream raises StateError.

Presets and bundles

Chain.from_preset() accepts only canonical Chain v1:

{
  "version": 1,
  "chain": [
    {
      "id": "voice",
      "type": "Compressor",
      "enabled": true,
      "channel": "all",
      "parameters": {"threshold": -18, "ratio": 4}
    }
  ]
}

Application pipeline and plugins presets are deliberately separate. Use Chain.from_legacy_preset() or import_legacy_preset() to convert an app preset whose effects form one ordered serial path. Branched and multi-bus routing is rejected because flattening it would change the acoustic result. Move or copy the desired effects into one serial path in the app and export it again, or reproduce the branching in the host around separate Chains. Unsupported channels, effects, partial short-key arrays, and unknown fields are reported rather than silently dropped.

LevelMeter, Oscilloscope, SpectrumAnalyzer, Spectrogram, and StereoMeter provide opt-in decoded telemetry. Pass on_telemetry to Chain.process() or Chain.stream(), or manage a streaming subscription:

with chain.stream(48_000, channels=2) as stream:
    unsubscribe = stream.subscribe(lambda frame: print(frame.kind))
    output = stream.process(audio)
    print(stream.dropped_telemetry_frames)
    unsubscribe()

The first subscriber enables observations and the last unsubscribe disables them. Delivered tuples are caller-owned semantic values. Raw DSP telemetry is not a public API.

FIRCrossover, FiveBandFIRPEQ, GroupDelayEQ, IRReverb, and RoomEQ require an impulseResponse reference and an asset resolver. The four FIR filter effects use prepared coefficient impulses at the processing sample rate. Resolvers return AssetData containing finite, C-contiguous planar float32 samples, an integer sample rate, and an explicit or unambiguous topology. The runtime rejects missing, malformed, hash-mismatched, ambiguous, and oversized assets. It does not decode or resample an IR.

Bundle.load(path) reads either a JSON manifest or a directory containing bundle.json. Bundle.pack(destination, chain, assets) writes a deterministic Bundle v1 directory from Chain v1 and caller-supplied AssetData. Its asset entries use the canonical ETA1 payload: a 32-byte header, optional 12-byte matrix path records, then planar little-endian float32 samples. Referenced payloads are restricted to the bundle directory and verified against manifest metadata, exact length, SHA-256, header, path records, finite samples, and the native 32 MiB footprint limit before use:

bundle = et.Bundle.load("room-bundle")
chain = et.Chain.from_preset(
    bundle.chain_document,
    asset_resolver=bundle.resolver,
)

The CLI exposes the same writer for decoded IR audio:

effetune bundle pack room-chain.json room-bundle --asset room-ir=room-ir.wav
effetune render input.wav convolved.wav --preset room-bundle --subtype FLOAT

--preset room-bundle/bundle.json is equivalent. For WAV output, omitting --subtype keeps SoundFile's PCM_16 default; --subtype FLOAT preserves 32-bit floating-point samples. render prints a one-line warning to standard error when the default output subtype reduces the input's precision, which passing --subtype explicitly silences, and when the rendered peak exceeds full scale and is clipped by an integer PCM output. Both warnings leave the exit code at 0.

EFFECT_METADATA is the public machine-readable semantic catalog for all 83 root effect classes. It contains channel choices, parameters, required assets, telemetry, and latency declarations without private native implementation details. Stream.latency_samples reports aggregate runtime latency and matches JavaScript ChainStream.latencySamples for the same chain and sample rate. Chain.latency_samples(sample_rate, ...) reports the same aggregate without opening a stream, which aligns offline process() output.

CLI

effetune render input.wav output.wav --preset mastering.json
effetune render input.wav output.wav --preset room-bundle --subtype FLOAT
effetune render input.wav output.flac --chain "[{\"type\":\"Volume\",\"parameters\":{\"volume\":-3}}]"
effetune chain validate mastering.json
effetune preset inspect mastering.json
effetune bundle pack room-chain.json room-bundle --asset room-ir=room-ir.wav

Audio decoding and encoding are delegated to SoundFile. The CLI does not invoke ffmpeg, resample, measure loudness, or change the input sample rate. --preset accepts a Chain file, a Bundle directory, or its bundle.json.

Supported Python wheels

The package supports CPython 3.10 and newer. Nanobind's stable ABI starts at CPython 3.12, so 3.10 and 3.11 wheels are version-specific. Release jobs build the 3.12 wheel with CMake 3.26 or newer:

python -m build -Ccmake.define.ET_PYTHON_STABLE_ABI=ON -Cwheel.py-api=cp312

That cp312-abi3 wheel covers supported newer CPython versions. This is a private static-link extension; the repository's wasm32 C ABI is not exposed as a native public ABI. Linux x86-64, Windows AMD64, macOS Intel, and macOS Apple Silicon wheels are built and clean-install tested independently.

Official Python releases are wheels only. An sdist built from this subproject would omit DSP sources located above the Python package directory, so source builds are supported only from a complete EffeTune repository checkout.