To help you get the best out of the Speech-to-Text (STT) experience using OpenAI Whisper with manual turn detection in the LiveKit Python SDK, here are some friendly tips: • Enable Manual Turn Detection: You can set

turn_detection="manual"

in the

AgentSession

constructor. Then, remember to explicitly commit the turn using

session.commit_user_turn()

once speech ends. Also, the

transcript_timeout

parameter is handy to wait for STT results. • Configure OpenAI Whisper: Using the

whisper-1

model usually gives better accuracy. If you want, you can provide a

prompt

to help guide the transcription, especially if you’re working with domain-specific vocabulary or accents. • Optimize Audio Input: It’s a good idea to disable audio input initially and enable it only during active speech, like with push-to-talk. This helps minimize background noise and improves overall performance. • Ignore VAD Parameters: Since you’re using manual turn detection, VAD or endpointing settings (like

threshold

and

silence_duration_ms

) won’t have an effect because automatic detection is turned off. These settings give you full control over the transcription process, making your STT experience more reliable and responsive. If you have any questions or need more help, feel free to reach out—I’m here for you! Sources: Turn detection and interruptions | LiveKit Docs | livekit-plugins/livekit-plugins-assemblyai/livekit/plugins/assemblyai/stt.p...