Switchboard for voice AI
Open source tools and examples for voice AI developers.
Switchboard for Voice AI uses a hybrid on-device + cloud architecture to help you get the best of both worlds. On-device processing with hand-off to cloud only when necessary.
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Reduce costs
Process audio on-device to minimize expensive API calls and bandwidth usage.
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Improve latency
Local processing eliminates network round-trips for near-instant voice interactions.
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Enhance privacy
Keep sensitive audio data on-device and send only processed text to the cloud.
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Enable offline
Build voice AI features that work without an internet connection.
Open source repositories
Production-ready examples and reusable components to accelerate your voice AI development
EdgeSpeech
React Native
On device speech recognition (ASR / STT) and text-to-speech (TTS) so that you can cut costs and latency while simplifying cloud infra. You only send text to the LLM so don't have to worry about webRTC, sockets, or scaling audio in the cloud.
STT (local) • LLM (cloud) • TTS (local)
EdgeAudio
Swift, Kotlin
On-device preprocessing for speech to speech models (aka S2S or audio models). On device voice activity detection (VAD), echo cancellation, and other audio preprocessing runs locally before connecting to cloud-based speech model (such as OpenAI Realtime API) to optimize performance.
VAD • Echo Cancellation • Specific Speaker Recognition • OpenAI Realtime API
EdgeWhisper
iOS, Android, macOS, Windows, Linux
Run OpenAI's Whisper speech recognition (ASR) model entirely on-device for maximum privacy and offline functionality across mobile and desktop platforms (iOS, Android, mac, Windows, Linux).
Whisper (local ASR)
EdgeAgent
React Native
Run a full STT-LLM-TTS pipeline locally. The STT and LLM components each have optional hand-off (or fallback) to cloud alternatives.
STT (local) • LLM (local) • TTS (local)
EmbeddedVoice
Linux (& custom upon request)
Optimized voice AI components for resource-constrained IoT and embedded systems, including smart speakers, wearables, and edge devices.
Embedded • IoT • Edge Computing
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Additional resources
Voice AI resource hub
Guides, tutorials, and best practices for building production voice AI applications.
On-device speech recognition
Learn how companies are implementing local speech-to-text to reduce costs and improve privacy.
Building voice AI agents
Explore real-world implementations of conversational AI agents with voice interfaces.