livekit-plugins-speechmatics 1.5.19rc1

Agent Framework plugin for Speechmatics

Speechmatics STT plugin for LiveKit Agents

Support for Speechmatics STT.

See https://docs.livekit.io/agents/integrations/stt/speechmatics/ for more information.

Installation

pip install livekit-plugins-speechmatics

Diarization

Speechmatics STT engine can be configured to emit information about individual speakers in a conversation. This needs to be enabled using enable_diarization=True. The text output of the transcription can be configured to include this information using the macros speaker_id and text, as shown in the examples below.

• <{speaker_id}>{text}</{speaker_id}> -> <S1>Hello</S1>
• [Speaker {speaker_id}] {text} -> [Speaker S1] Hello

You should adjust your system instructions to inform the LLM of this format for speaker identification.

Turn detection modes

The turn_detection_mode parameter controls how end-of-turn is detected:

• EXTERNAL (default) — Speechmatics does not endpoint on its own; turn boundaries are driven by an external VAD or by calling finalize(). If no vad is passed, Silero is auto-loaded (requires livekit-plugins-silero). Pass vad=None to opt out and drive finalize() yourself.
• ADAPTIVE — Speechmatics controls end of turn using its own VAD and the pace of speech.
• SMART_TURN — Speechmatics ML-based endpointing.
• FIXED — Endpoints after a fixed silence duration set by end_of_utterance_silence_trigger.

Usage (LiveKit Turn Detection)

The default EXTERNAL mode pairs naturally with LiveKit's turn detector. The format for the output text needs to be adjusted to not include any extra content at the end of the utterance. Using [Speaker S1] ... as the speaker_active_format should work well. You may need to adjust your system instructions to inform the LLM of this format for speaker identification. You must also include the listener for when the VAD has detected the end of speech.

The end_of_utterance_silence_trigger parameter controls the amount of silence before the end of turn detection is triggered. The default is 0.5 seconds.

Usage:

from livekit.agents import AgentSession
from livekit.plugins.turn_detector.multilingual import MultilingualModel
from livekit.plugins import speechmatics, silero

agent = AgentSession(
    stt=speechmatics.STT(
        end_of_utterance_silence_trigger=0.2,
        speaker_active_format="[Speaker {speaker_id}] {text}",
        speaker_passive_format="[Speaker {speaker_id} *PASSIVE*] {text}",
    ),
    vad=silero.VAD.load(),
    turn_detection=MultilingualModel(),
    min_endpointing_delay=0.3,
    max_endpointing_delay=5.0,
    ...
)

Usage (Speechmatics end of utterance detection and speaker ID)

To delegate end-of-turn detection to Speechmatics, set turn_detection_mode=TurnDetectionMode.ADAPTIVE (or SMART_TURN / FIXED) and pair it with turn_detection="stt" on the AgentSession.

from livekit.agents import AgentSession
from livekit.plugins import speechmatics

agent = AgentSession(
    stt=speechmatics.STT(
        turn_detection_mode=speechmatics.TurnDetectionMode.ADAPTIVE,
        speaker_active_format="[Speaker {speaker_id}] {text}",
        speaker_passive_format="[Speaker {speaker_id} *PASSIVE*] {text}",
        additional_vocab=[
            speechmatics.AdditionalVocabEntry(
                content="LiveKit",
                sounds_like=["live kit"],
            ),
        ],
    ),
    turn_detection="stt",
    ...
)

Pre-requisites

You'll need to specify a Speechmatics API Key. It can be set as environment variable SPEECHMATICS_API_KEY or .env.local file.