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Deepgram is a powerful Speech-to-Text (STT) platform that provides fast, accurate, and customizable transcription services. It’s designed for real-time and batch audio processing, with support for features like word-level timestamps, speaker diarization, and multilingual transcription. The Deepgram plugin in the Stream Python AI SDK enables real-time transcription of voice input, making it ideal for voice agents, call analysis, meeting transcriptions, and more.

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Installation

Install the Stream Deepgram plugin with 
uv add vision-agents[deepgram]

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Example

Check out our Deepgram 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 Deepgram plugin for Stream exists in the form of the STT class: 
from vision_agents.plugins import deepgram

stt = deepgram.STT()
To initialise without passing in the API key, make sure the DEEPGRAM_API_KEY is available as an environment variable. You can do this either by defining it in a .env file or exporting it directly in your terminal.

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Parameters

These are the parameters available in the DeepgramSTT plugin for you to customise:
NameTypeDefaultDescription
api_keystr or NoneNoneYour Deepgram API key. If not provided, the plugin will use the DEEPGRAM_API_KEY environment variable.
optionsLiveOptions or NoneNoneOptional Deepgram configuration options, such as tier, model, or features like punctuation or diarization.
sample_rateint48000The sample rate (in Hz) of the audio stream being transcribed.
languagestr"en-US"Language code for transcription.
keep_alive_intervalfloat3.0Interval (in seconds) for sending keep-alive messages to maintain the WebSocket connection.
interim_resultsboolTrueWhether to receive partial_transcript events when speaking.

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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 Deepgram STT
        await stt.process_audio(pcm, user)

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Events

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Transcript Event

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

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Partial Transcript Event

The partial transcript event is fired in real time as Deepgram generates intermediate (partial) transcriptions: 
@stt.on("partial_transcript")
async def on_partial_transcript(text: str, user: any, metadata: dict):
    # Process partial 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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