moonshine-ai/moonshine
Moonshine
Moonshine is a complete on-device voice interface toolkit that provides speech recognition, text-to-speech synthesis, phonetic processing, speaker diarization, and intent recognition, all running locally on edge hardware without any cloud dependency. It executes quantized neural networks for speech and language tasks directly on the device, enabling fully offline conversational AI capabilities.
The toolkit distinguishes itself by orchestrating multi-turn spoken exchanges through a conversational flow manager that maintains context across interactions and manages branching dialog flows. It includes model weight quantization for reducing model size and improving inference speed on edge devices, multicore compute distribution for optimizing performance across CPU cores, and a streaming audio pipeline that processes audio in chunks with real-time transcription events. Speaker diarization distinguishes individual voices in multi-speaker audio streams, while semantic intent matching identifies user commands through embedding similarity.
Moonshine provides a conversational agent builder for defining multi-step dialog flows that understand user intent and respond with synthesized speech. It supports real-time live speech transcription from microphone or file input, concurrent audio stream processing, and grapheme-to-phoneme conversion for text-to-speech synthesis across multiple languages. The toolkit includes model asset downloading and caching, audio input quality debugging, internal API call logging, and transcription latency benchmarking for evaluating real-time performance.
Features
- On-Device Model Runtimes - Executes speech-to-text and text-to-speech inference entirely on the local device without requiring cloud connectivity or API keys.
- Intent Recognition - Matches spoken utterances against registered command phrases using semantic embedding similarity.
- On-Device Intent Recognizers - Identifies the user's intent from spoken commands directly on the device for building voice-driven interfaces.
- Semantic Intent Matchers - Matches spoken utterances against registered command phrases using semantic embedding similarity for intent recognition.
- Spoken Dialog Managers - Orchestrates multi-turn spoken exchanges between a user and an agent, maintaining context across interactions.
- Dialog Flow Builders - Defines multi-step, branching dialog flows that understand user intent and respond with synthesized speech.
- Concurrent Stream Transcriptions - Handles concurrent audio inputs through a single transcriber instance, avoiding redundant model resource allocation.
- Chunked Audio Transcribers - Feeds audio data in chunks to a transcriber and receives text output as speech is detected.
- Multi-Turn Flow Managers - Orchestrates multi-turn spoken exchanges by maintaining context across interactions and managing branching dialog flows.
- Edge AI Runtimes - Executing quantized neural networks for speech and language models on local hardware without cloud dependency.
- On-Device Inference - Executes quantized neural networks locally on edge hardware without cloud connectivity for speech and language tasks.
- On-Device Speech-to-Text SDKs - Transcribing spoken audio into text locally on edge hardware without requiring a cloud connection or internet access.
- Real-Time Speech Transcription - Captures audio from a microphone or file and converts speech to text in real time, emitting events as phrases are recognized.
- Speaker Diarization - Distinguishes individual voices in multi-speaker audio streams to enable per-speaker transcription and attribution.
- Voice-Driven Interfaces - A set of tools for building voice-driven applications with intent recognition, dialog management, and audio processing.
- On-Device Text-to-Speech Synthesizers - A local speech synthesis system that generates audible speech from text using neural networks.
- Speech-Based Dialog Builders - Building multi-step, branching dialog flows that understand user intent and respond with synthesized speech.
- Real-Time Transcription Pipelines - Processes audio in chunks through a real-time transcription pipeline that emits events for speech start, update, and completion.
- Voice Command Recognition - Detecting predefined action phrases from natural speech using semantic matching and triggering corresponding callbacks.
- Grapheme To Phoneme Conversion - Translates written text into IPA phonetic representations without performing full speech synthesis.
- Phonetic Text Processors - A tool that converts written text into IPA phonetic representations for speech synthesis and analysis.
- Speech Synthesis Generators - Generates audible speech from text using International Phonetic Alphabet representations and phonetic lexicons.
- Weight Quantization - Reduces model size and improves inference speed by quantizing neural network weights for edge deployment.
- Streaming Transcription Events - Notifies application code when a speech segment starts, updates, or completes, providing real-time transcript updates.
- Multi-Language Speech Generators - Generates spoken audio output from text input, supporting multiple languages and voices.
- Multicore CPU Distributors - Optimizes performance by distributing neural network inference workloads across multiple CPU cores on local hardware.
- AI & Machine Learning - Fast speech recognition model for edge devices.
