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WakeBuilder Documentation

🎙️ Create Custom Wake Words Locally

No cloud. No API credits. No subscriptions. No ML expertise required.


What is WakeBuilder?

WakeBuilder is a comprehensive training platform that enables you to create custom wake word detection models entirely on your local machine. Whether you want to build a personal voice assistant that responds to "Phoenix," create a smart home automation triggered by "Hey Computer," or develop a unique voice interface for your application, WakeBuilder makes it possible—without sending your voice data to the cloud.

  • 🔒 100% Local & Private


    All training happens on your machine. Your voice data never leaves your device, ensuring complete privacy and data ownership.

  • Accessible Voice Training


    Perfect for users with unique speech patterns or pronunciation differences due to health conditions—train a model that recognizes your voice specifically.

  • ✨ Few-Shot Learning


    Train effective models with just 1-5 voice recordings. TTS engines generate diverse variations automatically.

  • 🎨 Sophisticated Augmentation


    Automatic generation of thousands of training variations using multiple TTS engines and audio effects.


How It Works

WakeBuilder uses a powerful transfer learning approach based on the Audio Spectrogram Transformer (AST):

graph LR
    A[Your Voice<br/>1-5 Recordings] --> B[Data Augmentation<br/>+ TTS Synthesis]
    B --> C[Thousands of<br/>Training Samples]
    C --> D[AST Feature<br/>Extractor]
    D --> E[Trained<br/>Classifier]
    E --> F[Your Custom<br/>Wake Word Model]
  1. Record - Speak your wake word 1-5 times into your microphone
  2. Augment - WakeBuilder generates thousands of variations using multiple TTS voices
  3. Train - A classifier learns to recognize your specific wake word
  4. Test - Validate your model with real-time testing
  5. Deploy - Export and use your model with WakeEngine

  • Getting Started


    Install WakeBuilder and start training your first wake word model.

    Quick Start

  • Configuration


    Fine-tune training parameters, audio settings, and model architecture options.

    Configuration Guide

  • Technical Deep Dive


    Understand the AST architecture, training pipeline, and data generation process.

    Technical Guide

  • API Reference


    Explore the FastAPI endpoints for programmatic access to training and testing.

    API Documentation


Key Features

🎯 Transfer Learning with AST

WakeBuilder uses the pre-trained MIT/ast-finetuned-speech-commands-v2 model as a frozen feature extractor. This 87-million parameter model already understands speech patterns from training on diverse audio datasets. You only train a small classifier head (~230K parameters), enabling effective learning from minimal examples.

🔊 Multi-TTS Data Augmentation

Generate diverse training data using multiple Text-to-Speech engines:

TTS Provider Voices Characteristics
Piper TTS ~87 High-quality offline voices
Edge TTS ~302 Microsoft neural voices
Kokoro TTS ~40+ American & British accents
Coqui TTS ~10 Multi-speaker models

🛡️ Hard Negative Generation

WakeBuilder automatically generates phonetically similar words (like "jarv" for "jarvis") to train your model to reject near-matches, dramatically reducing false positives.

♿ Accessibility-Focused

WakeBuilder is especially valuable for users with unique speech patterns or pronunciation differences:

  • Speech disabilities - Train a model tuned to your specific voice
  • Accents - Create models that understand your natural pronunciation
  • Health conditions - Works with 1-5 recordings of however you speak

🌐 Beautiful Web Interface

A modern, intuitive web interface guides you through the entire process:

  • Model Dashboard - View, test, and manage all your wake word models
  • Training Wizard - Step-by-step guidance for creating new models
  • Real-time Testing - Test models with live audio from your microphone
  • Training Metrics - Monitor progress with live charts and statistics

System Requirements

GPU Strongly Recommended

While WakeBuilder can run on CPU, training without a GPU can be extremely slow (potentially hours). A CUDA-compatible GPU is strongly recommended for practical training times.

Resource Minimum Recommended
RAM 8GB free 16GB+ free
GPU VRAM 6GB NVIDIA 8GB+ NVIDIA
Storage 10GB free 20GB+ free
CPU Multi-core 8+ cores
Python 3.12+ 3.12+

Training Time Expectations

Training time varies significantly based on your hardware:

Hardware Approximate Training Time
High-end GPU (RTX 3080+) 30 minutes - 1.5 hours
Mid-range GPU (RTX 3060) 1 - 2 hours
Entry GPU (GTX 1650) 2 - 4 hours
CPU only 4+ hours (not recommended)

Why Local Training Matters

  • No API credits - Train unlimited models at no cost
  • No cloud dependency - Works completely offline (except Edge TTS)
  • Complete privacy - Your voice never leaves your machine
  • Full ownership - You own your trained models completely

License

WakeBuilder is released under the Apache License 2.0, allowing both personal and commercial use. See the full license for details.


Made with ❤️ by Sami RAJICHI
Making wake word technology accessible to everyone.