Moonshine

Fast and accurate automatic speech recognition model for edge devices.

CommonProductProductivityAutomatic Speech RecognitionEdge Computing

Moonshine is a suite of speech-to-text models optimized for resource-constrained devices, making it ideal for real-time, on-device applications such as live transcription and voice command recognition. It outperforms the OpenAI Whisper model of the same size in word error rate (WER) on test datasets used in the OpenASR leaderboard maintained by HuggingFace. Additionally, Moonshine's computational requirements vary with the length of the input audio, allowing for quicker processing of shorter audio compared to the Whisper model, which processes everything in 30-second chunks. Moonshine processes 10-second audio segments at a speed five times faster than Whisper while maintaining the same or better WER.

Real-time transcription: Suitable for live transcription and voice command recognition.
Optimized word error rate: Outperforms the Whisper model across multiple datasets.
Fast processing: Processes shorter input audio five times faster than Whisper.
Multi-platform support: Compatible with Torch
TensorFlow
and JAX backends.
Flexible deployment: Operates on resource-constrained edge devices.
Easy installation: Provides detailed installation guides and virtual environment setup.
Model selection: Offers two model choices: 'moonshine/tiny' and 'moonshine/base'.

\Moonshine is designed for users who need fast and accurate voice recognition on resource-constrained devices
such as developers
businesses
and individuals requiring real-time voice transcription services. It is particularly suited for scenarios where voice interaction is needed on mobile or IoT devices.\

Developers can leverage Moonshine to add real-time voice recognition features to mobile applications.
Companies can integrate Moonshine into customer service systems to provide voice-to-text services.
Individuals can use Moonshine to transcribe audio records of meetings or lectures.

1. Install uv for Python environment management.
2. Create and activate a virtual environment: use 'uv venv env_moonshine' and 'source env_moonshine/bin/activate'.
3. Install the Moonshine package
choosing an appropriate backend (Torch
TensorFlow

Visit

Moonshine Visit Over Time

Monthly Visits

488643166

Bounce Rate

37.28%

Page per Visit

5.7

Visit Duration

00:06:37

Moonshine Visit Trend

Moonshine Visit Geography

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Moonshine

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