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# Ticket: Evaluate and Select Wake-Word Engine ## Ticket Information - **ID**: TICKET-005 - **Title**: Evaluate and Select Wake-Word Engine - **Type**: Research - **Priority**: High - **Status**: Backlog - **Track**: Voice I/O - **Milestone**: Milestone 1 - Survey & Architecture - **Created**: 2024-01-XX ## Description Evaluate wake-word detection options and select one: - Compare openWakeWord and Porcupine for: - Hardware compatibility (Linux box/Pi/NUC) - Licensing requirements - Ability to train custom "Hey Atlas" wake-word - Performance and resource usage - False positive/negative characteristics ## Acceptance Criteria - [ ] Comparison matrix of wake-word options - [ ] Selected engine documented with rationale - [ ] Hardware requirements documented - [ ] Licensing considerations documented - [ ] Decision recorded in architecture docs ## Technical Details Options to evaluate: - openWakeWord (open source, trainable) - Porcupine (Picovoice, commercial) - Other open-source alternatives Considerations: - Custom wake-word training capability - Resource usage on target hardware - Latency requirements - Integration complexity ## Dependencies - TICKET-004 (architecture) - helpful but not required - Hardware availability for testing ## Related Files - `docs/WAKE_WORD_EVALUATION.md` (to be created) - `ARCHITECTURE.md` Reviewed-on: #3
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Wake-Word Engine Evaluation
This document outlines the evaluation of wake-word engines for the Atlas project, as described in TICKET-005.
Comparison Matrix
| Feature | openWakeWord | Porcupine (Picovoice) |
|---|---|---|
| Licensing | Apache 2.0 (Free for commercial use) | Commercial license required for most use cases, with a limited free tier. |
| Custom Wake-Word | Yes, supports training custom wake-words. | Yes, via the Picovoice Console, but limited in the free tier. |
| Hardware Compatibility | Runs on Linux, Raspberry Pi, etc. Models might be large for MCUs. | Wide platform support, including constrained hardware and microcontrollers. |
| Performance/Resource Usage | Good performance, can run on a single core of a Raspberry Pi 3. | Highly optimized for low-resource environments. |
| Accuracy | Good accuracy, but some users report mixed results. | Generally considered very accurate and reliable. |
| Language Support | Primarily English. | Supports multiple languages. |
Recommendation
Based on the comparison, openWakeWord is the recommended wake-word engine for the Atlas project.
Rationale:
- Licensing: The Apache 2.0 license allows for free commercial use, which is a significant advantage for the project.
- Custom Wake-Word: The ability to train a custom "Hey Atlas" wake-word is a key requirement, and openWakeWord provides this capability without the restrictions of a commercial license.
- Hardware: The target hardware (Linux box/Pi/NUC) is more than capable of running openWakeWord.
- Performance: While Porcupine may have a slight edge in performance on very constrained devices, openWakeWord's performance is sufficient for our needs.
The main risk with openWakeWord is the potential for lower accuracy compared to a commercial solution like Porcupine. However, given the open-source nature of the project, we can fine-tune the model and contribute improvements if needed. This aligns well with the project's overall philosophy.