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Synaptics/moonshine-tiny-bf16-torq

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Moonshine Tiny

Model Overview

Moonshine is a high-efficiency automatic speech recognition (ASR) model designed specifically for real-time speech recognition. Unlike Whisper, which processes audio in fixed 30-second chunks, Moonshine uses a variable-length architecture that only computes the actual duration of the speech received.

Useful Sensors developed Moonshine and released the English model as open-source. There are 2 models of different sizes and capabilities - base and tiny. The tiny version utilizes 27M parameters.

Model Features

  • Model Type: Automatic Speech Recognition
  • Input: Raw waveform (1D array of floats) 16kHz mono audio up to 30 seconds
  • Output: Sequence of token IDs (integers)
  • Quantization: None

Recommended Platforms

  • Synaptics Astra™ SL2600-Series with Torq
  • Synaptics Astra™ SL1600-Series with Synap

Metrics

Platform Model / Stage Environment Inference Time Infer / s
SL2610 Moonshine Tiny Decoder Torq v2.0.0 15.6 64.1
SL2610 Moonshine Tiny Encoder (5 sec audio) Torq v2.0.0 156.1 6.4

Deployment

Compiled Models

Torq compiled model files are provided in this repository. To recompile the models, see the Torq Documentation.

Source Models

The source model files are available at Synaptics/Moonshine.

Usage Tutorials / Example Apps

Synaptics AI Developer Zone Tutorials

Example App GitHub Repositories

License

Both the source model and the compiled model for on-device deployment are licensed under MIT License.

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