---
id: openwakeword
version: "0.6.0"
license: unclear
license_treatment: permissive
maintenance: aging
---

# openwakeword — An open-source audio wake word (or phrase) detection framework with a focus on performance and simplicity

License: permissive · Maintenance: aging · Downloads: 276.6K/mo

## What it is and what it does

openWakeWord is an open-source framework for detecting wake words and phrases in audio streams. It provides pre-trained models for common trigger words that work in real-world noisy environments, and can be extended with custom models trained on synthetic speech. The library processes audio in 80 ms frames and returns confidence scores between 0 and 1, making it suitable for voice-activated applications like smart assistants, IoT devices, and web-based voice interfaces.

The package depends on onnxruntime and tflite-runtime for inference, plus scipy and scikit-learn for audio processing. It includes optional Speex noise suppression for Linux systems and a built-in Silero voice activity detection model to reduce false positives. The framework is designed to be lightweight enough for resource-constrained devices while remaining simple to integrate into Python applications.

Use it for:

- Build a voice-activated smart home controller that listens for specific phrases to trigger actions.
- Add wake-word detection to a web application using the included streaming audio examples to process microphone input.
- Train a custom wake-word model for a specific application using the provided Google Colab notebook and synthetic speech generation.
- Reduce false positives in a voice interface by enabling voice activity detection (VAD) alongside wake-word scoring.
- Deploy multiple wake-word models simultaneously on edge devices for multi-trigger voice control.
- Batch-process large audio file collections to detect wake words using the bulk_predict utility with multiprocessing.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

openWakeWord detects wake words and phrases in audio streams using pre-trained neural network models, enabling voice-activated applications to listen for specific spoken triggers in real-time.

Yes, if you need open-source wake-word detection with pre-trained models and low install friction. The permissive Apache 2.0 license and active repository are strengths. However, the aging maintenance status and lack of recent updates warrant caution if you require ongoing support or compatibility with the latest Python ecosystem. Suitable for prototyping and production use in voice-activated applications where you can tolerate a slower release cadence.

## Install

pip install openwakeword
uv add openwakeword
poetry add openwakeword

## Installing openwakeword

Before you install: Low install friction with a pure-Python wheel and six runtime dependencies. Maintenance is aging—last release was 2024-02-11, though the repository remains active and unarchived with 2662 stars.

License in practice: Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions.

Quickstart:

pip install openwakeword

import openwakeword
from openwakeword.model import Model

openwakeword.utils.download_models()
model = Model()
prediction = model.predict(frame)

Requires 16-bit 16kHz PCM audio input; on Windows, only onnxruntime inference is supported (tflite-runtime not available).

Verify before relying:

- Whether the aging maintenance status will affect long-term compatibility with current Python and dependency versions.
- Real-world false-positive and false-reject rates for the included pre-trained models in production environments.
- Performance characteristics on specific hardware beyond the mentioned Raspberry Pi 3 example.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 276.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

wake word detection, voice activation audio, speech trigger recognition, audio wake phrase detection, real-time voice listening, wakeword model inference, audio stream voice activation, voice-activation, audio-processing, edge-ml

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