Moonshine is an open-source voice toolkit that bundles transcription, text-to-speech, intent recognition, and conversational agent capabilities into a single library. It is designed for on-device execution across multiple platforms without requiring external API keys.
Project overview
The toolkit consolidates multiple real-time audio processing frameworks into a single library designed for low-latency streaming applications.
Project type
Audio & Speech
Use cases
Meeting Notes · Audio & Speech
Deployment
Refer to project documentation
License
License pending
Best for
Developers building real-time voice agents and interfaces who want a single library for on-device processing.
Applications requiring low-latency audio processing on constrained hardware, including Raspberry Pis, IoT devices, and microcontrollers.
Key capabilities
Captures live speech from microphone or streams and transcribes it into text with low latency.
Recognizes user-defined action phrases using semantic matching so natural language variations trigger callbacks.
Synthesizes speech from text in multiple languages and voices for playback or audio data retrieval.
Provides voice cloning capabilities for text to speech.
Identifies different speakers in an audio stream.
Provides DialogFlow and Dialog objects to build multi-step, branching conversational voice agents that can understand queries and respond appropriately.
Supports multiple languages for both speech to text and text to speech.
A single consistent API running on Python, iOS, Android, MacOS, Linux, Windows, Raspberry Pis, IoT devices, and microcontrollers.
Limitations and risks
Models for non-English languages are released under a non-commercial Moonshine Community License.
The library is in active development with features like binary size reduction and domain customization still on the roadmap.
Getting started
Developers can install the toolkit via pip using 'pip install moonshine-voice' and immediately test microphone transcription by running 'moonshine-voice mic --language en'.
Developers must acquire the required models, such as Moonshine STT models which may need to be downloaded manually or via script, before running inference.
Alternatives and comparisons
Provides a local-first application for audio/video transcription and structured note generation.
Provides a completely offline voice assistant on Mac using local models.
Always-on personal AI that processes captured audio data, transcribes conversations, and creates summaries.