Deepgram STT

Overview

Deepgram speech-to-text is available in LiveKit Agents through LiveKit Inference and the Deepgram plugin. With LiveKit Inference, your agent runs on LiveKit's infrastructure to minimize latency. No separate provider API key is required, and usage and rate limits are managed through LiveKit Cloud. Use the plugin instead if you want to manage your own billing and rate limits. Pricing for LiveKit Inference is available on the pricing page.

LiveKit Inference

Use LiveKit Inference to access Deepgram STT without a separate Deepgram API key.

Usage

To use Deepgram, use the STT class from the inference module:

from livekit.agents import AgentSession, inference

session = AgentSession(
    stt=inference.STT(
        model="deepgram/flux-general",
        language="en"
    ),
    # ... llm, tts, vad, turn_handling, etc.
)

import { AgentSession, inference } from '@livekit/agents';

session = new AgentSession({
    stt: new inference.STT({
        model: "deepgram/flux-general",
        language: "en"
    }),
    // ... llm, tts, vad, turnHandling, etc.
});

Parameters

Model parameters

Pass the following parameters inside extra_kwargs (Python) or modelOptions (Node.js). Supported fields depend on the selected model.

Multilingual transcription

Deepgram Nova-3 and Nova-2 models support multilingual transcription. In this mode, the model automatically detects the language of each segment of speech and can accurately transcribe multiple languages in the same audio stream.

Multilingual transcription is billed at a different rate than monolingual transcription. Refer to the pricing page for more information.

To enable multilingual transcription on supported models, set the language to multi.

String descriptors

As a shortcut, you can also pass a model ID string directly to the stt argument in your AgentSession:

from livekit.agents import AgentSession

session = AgentSession(
    stt="deepgram/flux-general:en",
    # ... llm, tts, vad, turn_handling, etc.
)

import { AgentSession } from '@livekit/agents';

session = new AgentSession({
    stt: "deepgram/flux-general:en",
    // ... llm, tts, vad, turnHandling, etc.
});

Colocation of model and agent

LiveKit Inference includes an integrated deployment of Deepgram models in Mumbai, India, delivering significantly lower latency for voice agents serving users in India and surrounding regions. By reducing the round-trip to external API endpoints, this regional deployment with co-located STT and agent improves response times, resulting in more responsive and natural-feeling conversations.

Automatic routing

LiveKit Inference automatically routes requests to the regional deployment when your configuration matches one of the supported models and languages below. No code changes or configuration are required. For other configurations, requests are routed to Deepgram's API.

Supported configurations

For example, to use Hindi transcription with Nova-3:

from livekit.agents import AgentSession

session = AgentSession(
    stt="deepgram/nova-3-general:hi",
    # ... llm, tts, etc.
)

import { AgentSession } from '@livekit/agents';

session = new AgentSession({
    stt: "deepgram/nova-3-general:hi",
    // ... llm, tts, etc.
});

Turn detection

Deepgram Flux includes a custom phrase endpointing model that uses both acoustic and semantic cues. To use this model for turn detection, set turn_detection="stt" in the turn handling options. You should also provide a VAD plugin for responsive interruption handling.

session = AgentSession(
    turn_handling=TurnHandlingOptions(
        turn_detection="stt",
    ),
    stt=inference.STT(
        model="deepgram/flux-general",
        language="en"
    ),
    vad=silero.VAD.load(),  # Recommended for responsive interruption handling
    # ... llm, tts, etc.
)

Plugin

LiveKit's plugin support for Deepgram lets you connect directly to Deepgram's API with your own API key.

Installation

Install the plugin from PyPI or npm:

uv add "livekit-agents[deepgram]~=1.4"

pnpm add @livekit/agents-plugin-deepgram@1.x

Authentication

The Deepgram plugin requires a Deepgram API key.

Set DEEPGRAM_API_KEY in your .env file.

Nova-3 and other models

Use the STT class for Nova-3 and other Deepgram models. It connects to Deepgram's /listen/v1 websocket API for realtime streaming STT.

Usage

from livekit.plugins import deepgram

session = AgentSession(
   stt=deepgram.STT(
      model="nova-3",
      language="en",
   ),
   # ... llm, tts, etc.
)

import * as deepgram from '@livekit/agents-plugin-deepgram';

const session = new voice.AgentSession({
    stt: new deepgram.STT({
        model: "nova-3",
        language: "en",
    }),
    // ... llm, tts, etc.
});

Parameter reference

This section describes the key parameters for the Deepgram STT plugin. See the plugin reference for a complete list of all available parameters.

Speaker diarization

You can enable speaker diarization so the STT assigns a speaker identifier to each word or segment. When enabled, transcript events include a speaker_id, and the STT reports capabilities.diarization = True.

With diarization enabled, you can wrap the Deepgram STT with MultiSpeakerAdapter for primary speaker detection and transcript formatting.

Enable speaker diarization by setting enable_diarization=True in the STT constructor:

stt = deepgram.STT(
   model="nova-3",
   language="en",
   enable_diarization=True,
)

Deepgram Flux

Use the STTv2 class for the Flux model. It connects to Deepgram's /listen/v2 websocket API, which is designed for turn-based conversational audio. Currently, the only available model is Flux in English.

Usage

Use STTv2 in an AgentSession or as a standalone transcription service. For example, you can use this STT in the Voice AI quickstart.

from livekit.plugins import deepgram

session = AgentSession(
   stt=deepgram.STTv2(
      model="flux-general-en",
      eager_eot_threshold=0.4,
   ),
   # ... llm, tts, etc.
)

import * as deepgram from '@livekit/agents-plugin-deepgram';

const session = new voice.AgentSession({
    stt: new deepgram.STTv2({
        model: "flux-general-en",
        eagerEotThreshold: 0.4,
    }),
    // ... llm, tts, etc.
});

Parameter reference

STTv2 exposes parameters specific to Deepgram's v2 API.

For the full list of STTv2 parameters, see the plugin reference in Additional resources.

Turn detection

Deepgram Flux includes a custom phrase endpointing model that uses both acoustic and semantic cues. To use this model for turn detection, set turn_detection="stt" in the turn handling options. You should also provide a VAD plugin for responsive interruption handling.

session = AgentSession(
    turn_handling=TurnHandlingOptions(
        turn_detection="stt",
    ),
    stt=deepgram.STTv2(
        model="flux-general-en",
        eager_eot_threshold=0.4,
    ),
    vad=silero.VAD.load(),  # Recommended for responsive interruption handling
    # ... llm, tts, etc.
)

const session = new voice.AgentSession({
    stt: new deepgram.STTv2({
        model: "flux-general-en",
        eagerEotThreshold: 0.4,
    }),
    vad: await silero.VAD.load(),  // Recommended for responsive interruption handling
    turnHandling: {
        turnDetection: "stt",
    },
    // ... llm, tts, etc.
});

Additional resources

The following resources provide more information about using Deepgram with LiveKit Agents.

Python package

ReferenceGitHubPyPI

Node.js plugin

ReferenceGitHubNPM

Deepgram docs

Deepgram's full docs site.

Voice AI quickstart

Get started with LiveKit Agents and Deepgram.

Deepgram TTS

Guide to the Deepgram TTS plugin with LiveKit Agents.