Moonshine AI
Optimized for edge hardware, this open-source tool provides fast, private on-device speech recognition for real-time transcription, command and speaker identification, lower word-error rates than Whisper models, and multi-language support over varied platforms.
Features
Properties
- Lightweight
Features
- Ad-free
- No registration required
- Python-based
- Speech Recognition
- Speech to text
- AI-Powered
- Voice Commands
Moonshine AI News & Activities
Recent News
Recent activities
Jotr added Moonshine AI as alternative to Jotr
POX added Moonshine AI as alternative to WhatDidYouSay
POX added Moonshine AI as alternative to Wave Dictation
thehung21 added Moonshine AI as alternative to Voice2Sub
DanielFormind added Moonshine AI as alternative to Live Subtitles
Danilo_Venom added Moonshine AI as alternative to SnailText
Danilo_Venom added Moonshine AI as alternative to Super Voice Mode
zhiii0x added Moonshine AI as alternative to SRTWhisper
POX added Moonshine AI as alternative to Snaply
maciej-klunder added Moonshine AI as alternative to SaidVault
Moonshine AI information
Developed by
Useful Sensors Licensing
Open Source and Free product.Written in
Alternatives
105 alternatives listedSupported Languages
- English
AlternativeTo Categories
AI Tools & Services, Audio & MusicGitHub repository
- 7,839 Stars
- 405 Forks
- 21 Open Issues
- Updated
What is Moonshine AI?
Moonshine AI offers optimized speech-to-text models for efficient automatic speech recognition (ASR) on devices with limited resources. It is suitable for real-time applications like live transcription and voice command recognition, achieving lower word-error rates than comparable Whisper models from OpenAI. Unlike Whisper models that process audio in 30-second segments, Moonshine AI's processing times are proportional to the audio length, resulting in faster processing for shorter inputs.
Moonshine Voice is an open-source AI toolkit for developers to create real-time voice applications. It operates on-device for fast, private operations without the need for an account or API keys. The framework is designed for live streaming applications, providing low latency responses and higher accuracy than Whisper Large V3. It supports integration across various platforms, including Python, iOS, Android, MacOS, Linux, Windows, Raspberry Pis, IoT devices, and wearables. It offers solutions for transcription, speaker identification, and command recognition, supporting multiple languages including English, Spanish, Mandarin, Japanese, Korean, Vietnamese, Ukrainian, and Arabic.