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Moonshine is a high-performance Speech-to-Text (STT) engine optimized for local and edge deployments. Designed to work efficiently on low-resource environments like browsers and embedded devices, Moonshine is ideal for real-time, offline voice applications where speed, privacy, and low latency are critical. The Moonshine plugin in the Vision Agents SDK allows you to integrate STT services to your application.

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Installation

Install the Stream Moonshine plugin with 
uv add vision-agents[moonshine]
You’ll also need to install the Moonshine ONNX version from GitHub: 
uv add "useful-moonshine-onnx@git+https://github.com/usefulsensors/moonshine.git#subdirectory=moonshine-onnx"

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Example

Check out our Moonshine example to see a practical implementation of the plugin and get inspiration for your own projects, or read on for some key details.

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Initialisation

The Moonshine plugin for Stream exists in the form of the STT class: 
from vision_agents.plugins import moonshine

stt = moonshine.STT()
We recommend using the Moonshine plugin combined with a VAD plugin like Silero to avoid excessive local processing.

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Parameters

These are the parameters available in the MoonshineSTT plugin for you to customise:
NameTypeDefaultDescription
model_namestr"moonshine/base"The Moonshine model to use. Supported options are "moonshine/tiny" and "moonshine/base".
sample_rateint16000The sample rate (in Hz) of the audio input. Must match Moonshine’s expected rate.
languagestr"en-US"Language code for transcription. Currently, only "en-US" is supported.
min_audio_length_msint100Minimum length (in milliseconds) of audio required before processing.
target_dbfsfloat-26.0Target RMS loudness level (in dBFS) for audio normalization before transcription.

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Functionality

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Process Audio

Once you join the call, you can listen to the connection for audio events. You can then pass along the audio events for the STT class to process: 
from getstream.video import rtc

async with rtc.join(call, bot_user_id) as connection:

    @connection.on("audio")
    async def on_audio(pcm: PcmData, user):
        # Process audio through Moonshine STT
        await stt.process_audio(pcm, user)

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Events

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Transcript Event

The transcript event is triggered when a final transcript is available from Moonshine: 
@stt.on("transcript")
async def on_transcript(text: str, user: any, metadata: dict):
    # Process transcript event here

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Error Event

If an error occurs, an error event is fired: 
@stt.on("error")
async def on_stt_error(error):
    # Process error event here

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Close

You can close the STT connection with the close() method: 
stt.close()
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