Moonshine AI icon
Moonshine AI icon

Moonshine AI

 9 likes

Moonshine is a family of speech-to-text models optimized for fast and accurate automatic speech recognition (ASR) on resource-constrained devices. It is well-suited to real-time, on-device applications like live transcription and voice command recognition.

Moonshine AI screenshot 1

License model

  • FreeOpen Source

Application type

Country of Origin

  • US flagUnited States

Platforms

  • Python
  • Self-Hosted
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0news articles

Features

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Properties

  1.  Lightweight

Features

  1.  No registration required
  2.  Ad-free
  3.  Speech to text
  4.  AI-Powered
  5.  Voice Commands
  6.  Speech Recognition
  7.  Python-based

 Tags

  • ai-model
  • live-transcription
  • ai-transcription

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Moonshine AI information

  • Developed by

    US flagUseful Sensors
  • Licensing

    Open Source (MIT) and Free product.
  • Written in

  • Alternatives

    64 alternatives listed
  • Supported Languages

    • English

AlternativeTo Categories

Audio & MusicAI Tools & Services

GitHub repository

  •  2,755 Stars
  •  144 Forks
  •  21 Open Issues
  •   Updated May 12, 2025 
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Moonshine AI was added to AlternativeTo by Paul on Oct 27, 2024 and this page was last updated Oct 27, 2024.
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What is Moonshine AI?

Moonshine is a family of speech-to-text models optimized for fast and accurate automatic speech recognition (ASR) on resource-constrained devices. It is well-suited to real-time, on-device applications like live transcription and voice command recognition. Moonshine obtains word-error rates (WER) better than similarly-sized Whisper models from OpenAI on the datasets used in the OpenASR leaderboard maintained by HuggingFace.

Moonshine's compute requirements scale with the length of input audio. This means that shorter input audio is processed faster, unlike existing Whisper models that process everything as 30-second chunks. To give you an idea of the benefits: Moonshine processes 10-second audio segments 5x faster than Whisper while maintaining the same (or better!) WER.