LiveKit docs › Models › STT › Speechmatics --- # Speechmatics STT plugin guide > How to use the Speechmatics STT plugin for LiveKit Agents. Available in: - [ ] Node.js - [x] Python ## Overview This plugin allows you to use [Speechmatics](https://www.speechmatics.com/) as an STT provider for your voice agents. ### Installation Install the plugin from PyPI: ```shell uv add "livekit-agents[speechmatics]~=1.5" ``` ### Authentication The Speechmatics plugin requires an [API key](https://docs.speechmatics.com/introduction/authentication). Set `SPEECHMATICS_API_KEY` in your `.env` file. ### Usage Use Speechmatics STT in an `AgentSession` or as a standalone transcription service. For example, you can use this STT in the [Voice AI quickstart](https://docs.livekit.io/agents/start/voice-ai.md). ```python from livekit.plugins import speechmatics session = AgentSession( stt = speechmatics.STT(), # ... llm, tts, etc. ) ``` ### Speaker diarization You can enable [speaker diarization](https://docs.speechmatics.com/features/diarization#speaker-diarization) to identify individual speakers and their speech. When enabled, the transcription output can include this information through the `speaker_id` and `text` attributes. See the following for example configurations and outputs: - `<{speaker_id}>{text}`: `Hello`. - `[Speaker {speaker_id}] {text}`: `[Speaker S1] Hello`. ```python stt = speechmatics.STT( enable_diarization=True, speaker_active_format="<{speaker_id}>{text}", ) ``` Use the `MultiSpeakerAdapter` to detect the primary speaker and format the transcripts by speaker. See the [Speaker diarization and primary speaker detection](https://docs.livekit.io/agents/models/stt.md#speaker-diarization) section for more details. ### Parameters This section describes the key parameters for the Speechmatics STT plugin. See the [plugin reference](https://docs.livekit.io/reference/python/livekit/plugins/speechmatics/index.html.md#livekit.plugins.speechmatics.STT) for a complete list of all available parameters. - **`operating_point`** _(string)_ (optional) - Default: `enhanced`: Operating point to use for the transcription. This parameter balances accuracy, speed, and resource usage. To learn more, see [Operating points](https://docs.speechmatics.com/speech-to-text/#operating-points). - **`language`** _(LanguageCode)_ (optional) - Default: `en`: [Language code](https://docs.livekit.io/agents/models/stt.md#language-codes) for the input audio. All languages are global, meaning that regardless of which language you select, the system can recognize different dialects and accents. For the full list, see [Supported Languages](https://docs.speechmatics.com/introduction/supported-languages). - **`enable_partials`** _(bool)_ (optional) - Default: `true`: Enable partial transcripts. Partial transcripts allow you to receive preliminary transcriptions and update as more context is available until the higher-accuracy [final transcript](https://docs.speechmatics.com/rt-api-ref#addtranscript) is returned. Partials are returned faster but without any post-processing such as formatting. When enabled, the STT service emits `INTERIM_TRANSCRIPT` events. - **`enable_diarization`** _(bool)_ (optional) - Default: `false`: Enable speaker diarization. When enabled, spoken words are attributed to unique speakers. You can use the `speaker_sensitivity` parameter to adjust the sensitivity of diarization. To learn more, see [Diarization](https://docs.speechmatics.com/speech-to-text/features/diarization). - **`max_delay`** _(number)_ (optional) - Default: `1.0`: The maximum delay in seconds between the end of a spoken word and returning the final transcript results. Lower values can have an impact on accuracy. - **`end_of_utterance_silence_trigger`** _(float)_ (optional): The duration of silence in seconds that triggers end of utterance. The delay is used to wait for any further transcribed words before emitting `FINAL_TRANSCRIPT` events. - **`turn_detection_mode`** _(TurnDetectionMode)_ (optional) - Default: `TurnDetectionMode.ADAPTIVE`: Controls how the STT engine detects the end of speech turns. Valid values are: - `TurnDetectionMode.ADAPTIVE`: Uses simple voice activity detection (VAD) to determine end of speech. - `TurnDetectionMode.SMART_TURN`: Uses the plugin's built-in ML-based smart turn detection for more accurate endpointing. - `TurnDetectionMode.FIXED`: Uses a fixed silence duration as determined by the `end_of_utterance_silence_trigger` parameter. - `TurnDetectionMode.EXTERNAL`: Turn boundaries are controlled manually, for example via an external VAD or the `finalize()` method. To use LiveKit's [end of turn detector model](https://docs.livekit.io/agents/logic/turns.md#turn-detector-model), set this parameter to `TurnDetectionMode.EXTERNAL`. - **`speaker_active_format`** _(string)_ (optional): Formatter for speaker identification in transcription output. The following attributes are available: - `{speaker_id}`: The ID of the speaker. - `{text}`: The text spoken by the speaker. By default, if speaker diarization is enabled and this parameter is not set, the transcription output is _not_ formatted for speaker identification. The system instructions for the language model might need to include any necessary instructions to handle the formatting. To learn more, see [Speaker diarization](#speaker-diarization). - **`diarization_sensitivity`** _(float)_ (optional) - Default: `0.5`: Sensitivity of speaker detection. Valid values are between `0` and `1`. Higher values increase sensitivity and can help when two or more speakers have similar voices. To learn more, see [Speaker sensitivity](https://docs.speechmatics.com/speech-to-text/features/diarization#speaker-sensitivity). The `enable_diarization` parameter must be set to `True` for this parameter to take effect. - **`prefer_current_speaker`** _(bool)_ (optional) - Default: `false`: When speaker diarization is enabled and this is set to `True`, it reduces the likelihood of switching between similar sounding speakers. To learn more, see [Prefer current speaker](https://docs.speechmatics.com/speech-to-text/features/diarization#prefer-current-speaker). ## Additional resources The following resources provide more information about using Speechmatics with LiveKit Agents. - **[Python package](https://pypi.org/project/livekit-plugins-speechmatics/)**: The `livekit-plugins-speechmatics` package on PyPI. - **[Plugin reference](https://docs.livekit.io/reference/python/livekit/plugins/speechmatics/index.html.md#livekit.plugins.speechmatics.STT)**: Reference for the Speechmatics STT plugin. - **[GitHub repo](https://github.com/livekit/agents/tree/main/livekit-plugins/livekit-plugins-speechmatics)**: View the source or contribute to the LiveKit Speechmatics STT plugin. - **[Speechmatics docs](https://docs.speechmatics.com/introduction/)**: Speechmatics STT docs. - **[Voice AI quickstart](https://docs.livekit.io/agents/start/voice-ai.md)**: Get started with LiveKit Agents and Speechmatics STT. --- This document was rendered at 2026-06-07T11:35:48.745Z. For the latest version of this document, see [https://docs.livekit.io/agents/models/stt/speechmatics.md](https://docs.livekit.io/agents/models/stt/speechmatics.md). To explore all LiveKit documentation, see [llms.txt](https://docs.livekit.io/llms.txt).