Nabrah plugin for LiveKit Agents

Support for Nabrah Speech-to-Text in LiveKit Agents, with client-side end-of-turn detection tuned for Arabic conversation.

Installation

pip install livekit-plugins-nabrah

Pre-requisites

You'll need an API key from Nabrah. It can be set as an environment variable: NABRAH_API_KEY.

Usage

Use Nabrah STT in an AgentSession:

from livekit.agents import AgentSession
from livekit.plugins import nabrah

session = AgentSession(
    stt=nabrah.STT(
        recognition_model="eot_nabrah",
        language="ar-SA",
        end_of_turn_confirm_delay_seconds=0.4,
    ),
)

Turn detection

recognition_model="eot_nabrah" emits the end-of-turn signal the plugin uses to close a turn. end_of_turn_confirm_delay_seconds is how long it waits after that signal before committing. Speaking again inside the window keeps the turn open. max_transcript_inactivity_seconds optionally commits after that many seconds without new transcript text. It is disabled by default because it measures recognizer inactivity, not acoustic silence, and a provider delay could otherwise split continuous speech.

Nabrah's unnamed model (recognition_model="") is more accurate but emits no end-of-turn signal. When using it, configure another turn detector or explicitly set max_transcript_inactivity_seconds as a fallback heuristic.

Word boosting

Bias recognition toward terms the model gets wrong:

stt = nabrah.STT(
    priority_words=["مستشفى الملك فيصل التخصصي", "رقم الهوية الوطنية"],
    priority_words_strength=0.5,
)

0.5 is the recommended strength. Higher values make boosted terms appear in places they were not said, so keep the list to terms your callers actually say and add one only after hearing it come out wrong. Multi-word phrases are boosted as a unit.

Loading terms from a file

For anything beyond a handful of terms, keep them in a JSON file so they can be edited without touching code. Create boosting.json next to your agent:

{
  "boost_threshold": 0.5,
  "words": [
    "مستشفى الملك فيصل التخصصي",
    "رقم الهوية الوطنية"
  ]
}

Load it at startup and pass it to the plugin:

import json
import pathlib

from livekit.agents import AgentSession
from livekit.plugins import nabrah

boosting = json.loads(
    pathlib.Path(__file__).with_name("boosting.json").read_text(encoding="utf-8")
)

session = AgentSession(
    stt=nabrah.STT(
        recognition_model="eot_nabrah",
        language="ar-SA",
        end_of_turn_confirm_delay_seconds=0.4,
        priority_words=boosting["words"],
        priority_words_strength=boosting["boost_threshold"],
    ),
)

Read the file with encoding="utf-8". Without it, Arabic terms fail to load on platforms that default to a different encoding.

Full example

A complete agent using Nabrah STT with word boosting loaded from a file:

import json
import pathlib

from dotenv import load_dotenv
from livekit import agents
from livekit.agents import (
    Agent,
    AgentServer,
    AgentSession,
    JobContext,
    TurnHandlingOptions,
    inference,
)
from livekit.plugins import nabrah

load_dotenv()

boosting = json.loads(
    pathlib.Path(__file__).with_name("boosting.json").read_text(encoding="utf-8")
)

server = AgentServer()


class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice assistant.")


@server.rtc_session()
async def entrypoint(ctx: JobContext) -> None:
    session = AgentSession(
        stt=nabrah.STT(
            recognition_model="eot_nabrah",
            language="ar-SA",
            end_of_turn_confirm_delay_seconds=0.4,
            max_transcript_inactivity_seconds=1.5,
            priority_words=boosting["words"],
            priority_words_strength=boosting["boost_threshold"],
        ),
        llm=inference.LLM("openai/gpt-4.1-mini"),
        tts=inference.TTS("cartesia/sonic-3"),
        turn_handling=TurnHandlingOptions(
            turn_detection="stt",
            endpointing={"min_delay": 0},
        ),
    )

    await session.start(agent=Assistant(), room=ctx.room)
    await ctx.connect()


if __name__ == "__main__":
    agents.cli.run_app(server)

Parameters