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Cross-platform audio capture, playback, and voice activity detection

Project description

Decibri

Cross-platform audio capture, playback, and processing for Python.

PyPI version Python versions License

Decibri is a native Python package that delivers microphone capture, speaker output, local voice activity detection, device enumeration, and sample format conversion. It is written in Rust (via PyO3 / abi3) and ships pre-built wheels for Linux, macOS Apple Silicon, and Windows.

Install

Recommended with uv:

uv pip install decibri

Or with pip:

pip install decibri

Quickstart

Capture audio

import decibri

with decibri.Microphone(sample_rate=16000, channels=1) as mic:
    for chunk in mic:
        print(f"Got {len(chunk)} bytes")
        break  # exit after first chunk for demo

Record one second to a WAV file

import decibri

decibri.record_to_file("output.wav", duration_seconds=1.0, sample_rate=16000)

Capture with Silero VAD

import decibri

with decibri.Microphone(sample_rate=16000, vad="silero") as mic:
    for chunk in mic:
        print(f"Got {len(chunk)} bytes; VAD score {mic.vad_score}; speaking={mic.is_speaking}")
        break  # exit after first chunk for demo

Async capture

import asyncio
import decibri

async def main():
    async with await decibri.AsyncMicrophone.open(sample_rate=16000, vad="silero") as mic:
        async for chunk in mic:
            print(f"Got {len(chunk)} bytes; VAD score {mic.vad_score}")
            break  # exit after first chunk for demo

asyncio.run(main())

Speaker output

import decibri

with decibri.Speaker(sample_rate=24000, channels=1) as spk:
    audio_bytes = b"\x00\x00" * 24000  # 1 second of silence at 24kHz int16
    spk.write(audio_bytes)  # int16 PCM
    spk.drain()

Public API

Core classes

  • Microphone: synchronous audio capture
  • Speaker: synchronous audio output
  • AsyncMicrophone: async-await audio capture
  • AsyncSpeaker: async-await audio output

Module-level functions

  • decibri.input_devices(): enumerate available input devices
  • decibri.output_devices(): enumerate available output devices
  • decibri.version(): version + audio backend info
  • decibri.record_to_file(path, duration_seconds, ...): record N seconds to a WAV file
  • decibri.async_record_to_file(path, duration_seconds, ...): async equivalent

Value types

MicrophoneInfo, SpeakerInfo, VersionInfo, Chunk.

Exceptions

The full hierarchy lives at decibri.exceptions. Top-level catch-targets surfaced at the package root:

  • DecibriError: base of the hierarchy
  • DeviceError: input / output device problems
  • OrtError: ONNX Runtime issues
  • OrtPathError: ORT dylib path resolution issues
  • ForkAfterOrtInit: Linux fork-after-ORT-init detection

Voice Activity Detection

Decibri ships with two VAD modes: a lightweight RMS energy threshold (opt-in via vad="energy") and a Silero ONNX model (~2.3 MB, bundled in the wheel; no API keys required).

# Energy mode (lightweight, no model)
mic = decibri.Microphone(vad="energy", vad_threshold=0.01)

# Silero mode (ML-based, more accurate in noisy environments)
mic = decibri.Microphone(vad="silero", vad_threshold=0.5)

Use mic.vad_score (a value in [0, 1]) to gate downstream processing. mic.is_speaking returns the boolean above-threshold view.

Compatibility

Python Platforms
3.10, 3.11, 3.12, 3.13, 3.14 Linux x64, Linux ARM64, macOS Apple Silicon, Windows x64

Bundled assets

The wheel includes:

  • Silero VAD ONNX model (~2.3 MB): no downloads or API keys required for vad="silero".
  • ONNX Runtime dylib (~15-20 MB platform-specific): no system dependency on pip install onnxruntime.

First ORT load on vad="silero" initialization is ~100 to 500 ms (amortized across subsequent calls).

Async usage

For Silero VAD in async code, use the open() factory to dispatch the synchronous ORT init off the event loop:

async with await decibri.AsyncMicrophone.open(vad="silero") as mic:
    async for chunk in mic:
        ...

Synchronous constructors (AsyncMicrophone(...)) remain supported and unchanged; open() is the recommended pattern when ORT load cost matters.

Multiprocessing

Linux multiprocessing with Silero VAD requires set_start_method('spawn'). Calling fork() after ORT initialization raises ForkAfterOrtInit:

import multiprocessing as mp

mp.set_start_method('spawn')  # required on Linux for Silero

See the ecosystem guides under bindings/python/docs/ecosystem/ (jupyter, docker, multiprocessing) for environment-specific details.

Documentation

License

Apache-2.0. See LICENSE for details.

Copyright (c) 2026 Decibri.

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