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.
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100% Local & Private
All training happens on your machine. Your voice data never leaves your device, ensuring complete privacy and data ownership.
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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.
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Few-Shot Learning
Train effective models with just 1-5 voice recordings. TTS engines generate diverse variations automatically.
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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] - Record - Speak your wake word 1-5 times into your microphone
- Augment - WakeBuilder generates thousands of variations using multiple TTS voices
- Train - A classifier learns to recognize your specific wake word
- Test - Validate your model with real-time testing
- Deploy - Export and use your model with WakeEngine
Quick Links¶
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Getting Started
Install WakeBuilder and start training your first wake word model.
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Configuration
Fine-tune training parameters, audio settings, and model architecture options.
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Technical Deep Dive
Understand the AST architecture, training pipeline, and data generation process.
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API Reference
Explore the FastAPI endpoints for programmatic access to training and testing.
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.
Making wake word technology accessible to everyone.