Moonshine v2 is an open-source automatic speech recognition (ASR) engine released in February 2026 by Useful Sensors, a US-based company. In a nutshell, it can be described as "Whisper's lightweight sibling – an STT that runs in real-time on Raspberry Pi, wearables, and mobile devices." The existing OpenAI Whisper is accurate but heavy (even the tiny model is 39M+), making real-time processing difficult on edge devices. Moonshine v2 uses an "Ergodic Streaming Encoder" architecture with sliding-window self-attention, achieving 5x faster inference speed while maintaining Whisper-level accuracy with 27M parameters. It also minimizes memory and computational waste by removing zero-padding with RoPE (Rotary Position Embedding). In essence, "if Whisper is an SUV for cloud GPUs, then Moonshine is an electric bicycle for edge IoT." From the perspective of biomedical researchers, it enables (1) real-time local transcription of patient interviews and clinical trial meeting minutes without cloud transfer (HIPAA/GDPR protection), (2) automatic storage of voice memos such as "drug treatment in 3 minutes" with timestamps during live-cell imaging, and (3) the creation of a Raspberry Pi-based experimental notebook auxiliary device. The moonshine-voice SDK provides integrated STT + intent recognition + TTS, allowing the creation of voice agents that can perform tasks like "hands-free voice commands for experiments.

Ultra-lightweight speech recognition engine optimized for edge devices, offering 5x faster performance compared to Whisper.

On-device STT for Clinical Interviews and Medical Records: Deploy the Moonshine tiny (27M) model on a Mac mini M2 to transcribe patient interviews in real-time. Zero cloud transmission (HIPAA/GDPR compliant), with a 30-minute interview taking approximately 6 seconds to transcribe. By adding intent recognition rules such as "Patient ID" and "Symptom onset time" to the moonshine-voice SDK, structured EMR input can be automatically generated.