Ross ROSS = Recommend OSS · open-source software intelligence for agents

moonshine-ai/moonshine

Very low latency speech to text, intent recognition, and text to speech, for building voice agents and interfaces observed · 2026-08-28

github.com/moonshine-ai/moonshine · homepage · C++ · NOASSERTION (other) observed · 2026-08-28

Health v2 · maintenance only

89/100

  • Activity 99
  • Release rhythm 99
  • Longevity 49

Flags: no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 5.5
  • age_days: 698
  • days_rel: 9
  • days_push: 9
  • n_releases_24m: 23

Full methodology

Adoption not part of the score

10944 stars · 598 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Moonshine Voice is an open-source on-device AI toolkit providing very low latency speech-to-text, intent recognition, and text-to-speech for building real-time voice agents and interfaces. It ships models from 1MB up to Whisper-Large-V3-beating accuracy, with a unified API across Python, JavaScript/WASM, iOS, Android, macOS, Linux, Windows, and Raspberry Pi.

Use cases

  • transcribe live microphone audio to text with low latency
  • build a real-time voice agent that responds while the user is still talking
  • run speech-to-text entirely on-device without API keys or cloud calls
  • convert text to natural speech in the browser or on mobile
  • clone a custom voice from a few seconds of recorded speech
  • add intent recognition to a spoken voice interface
  • build a dictation or meeting-notes app with speaker-aware transcripts

When to choose

  • you need low-latency streaming transcription for live voice agents
  • privacy matters and audio must never leave the device
  • you need one speech stack across many platforms including embedded and WASM
  • you want tiny models for resource-constrained devices like Raspberry Pi
  • you want MIT-licensed speech models without per-call cloud costs

When to avoid

  • you need batch offline transcription of large audio archives where latency is irrelevant
  • you need the widest language coverage, since streaming models focus on English and a few languages
  • you require commercial use of the legacy non-streaming non-English models, which carry a non-commercial license
  • you need heavy-duty NLU beyond intent recognition in a full conversational AI platform

Facets

library · maturity active

speech-recognition tts nlp machine-learning sdk speech-processing artificial-intelligence cross-platform python cpp wasm windows browser cross-platform voice-agents on-device low-latency streaming-asr voice-cloning intent-recognition dictation natural-language-processing ios android macos linux

3 sources

Member repositories

RepositoryRoleHealth v2
moonshine-ai/moonshinemain89

For agents

markdown · JSON · MCP: product_card(name="moonshine-ai/moonshine")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem