where the most latency occurs in the voicepipeline

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does latency differ in livekit inference and livekit plugin?

Thanks — you’re asking whether latency differs between LiveKit Inference and the LiveKit Plugin. Short answer: yes. Why Inference is usually lower / more predictable • Co-location with models and routing on a private backbone reduces network hops and variability. • Provisioned capacity avoids public-provider queuing and throttling. • LiveKit handles routing so you don’t need separate provider keys or public API calls that add delays. Why Plugins can be higher / less predictable • Plugins call external provider endpoints you manage, so network distance, public internet congestion, and provider-side queuing can add latency. • Variation depends on the STT/LLM/TTS providers and regions you use. If you want, tell me: • Are you using Inference or Plugins right now? • Which STT/LLM/TTS providers and regions/models are in your pipeline? I’ll suggest specific tests and optimizations based on your setup. Sources: Deepgram STT | LiveKit Documentation | Introducing LiveKit Inference: A unified model interface for voice AI | Liv... | Understand and Improve Agent Latency | LiveKit | Voice Agent Architecture: STT, LLM, and TTS Pipelines Explained | LiveKit, with interactive elements

how do I know if my llm , stt and tts is streaming.

You're asking how to confirm whether your LLM, STT, and TTS are running in streaming mode. Short answer: check for incremental outputs (partials/tokens/audio) and the pipeline metrics/config that show token/audio time-to-first values. Below are quick, actionable checks for each component and two diagnostic steps you can run now. STT (speech-to-text) • Indicator: you receive interim/partial transcripts while the user is still speaking. • Metric: first partial arrives ~100–200 ms in streaming STT. • Config check: your STT client/provider is a streaming-enabled endpoint (e.g., Deepgram Nova, AssemblyAI Universal Streaming) and not set to batch or file transcription mode. LLM • Indicator: the model emits tokens incrementally (you observe token-by-token output) rather than returning one final text block. • Metric:

llm_node_ttft

(time-to-first-token) is low (hundreds of ms) when streaming is active. • Config check: you’re calling a streaming API or using a driver that supports streaming responses (and not waiting for a full response before forwarding to TTS). TTS • Indicator: audio playback begins before the full response text is available (audio arrives in chunks). • Metric: time-to-first-audio (TTFA) is ~100–300 ms for streaming TTS. • Config check: your TTS provider/API supports streaming synthesis and you’re using the streaming method (e.g.,

tts.stream()

/ chunked audio output), not batch synthesis to a full file. Two quick diagnostics to run now 1. Trigger a short test turn and watch behavior: ◦ If you see interim STT events, then incremental LLM tokens, and audio starts before the final text, your pipeline is streaming end-to-end. 1. Inspect metrics/logs: ◦ Check

llm_node_ttft

, `tts_node_ttfb`/TTFA, and STT partial-event timestamps. Low TTFT/TTFA and early STT partials indicate streaming. If you tell me which providers/models you’re using (Inference vs Plugins, and specific STT/LLM/TTS models), I’ll give exact config entries and the best commands/log locations to confirm streaming. Sources: Sequential Pipeline Architecture for Voice Agents | LiveKit | AssemblyAI STT | LiveKit Documentation | Build a multilingual voice agent that automatically switches languages | Li... | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit | Text-to-speech (TTS) models overview | LiveKit Documentation, with interactive elements

I am using plugin, using deepgram and elevenlabs

Thanks — since you’re using Plugins with Deepgram (STT) and ElevenLabs (TTS), here’s how to confirm streaming and what to check. STT — Deepgram • What to watch: you should receive interim/partial transcripts while speaking (not only a final transcript). • Config: ensure you’re using Deepgram’s streaming endpoint (WebSocket or streaming websocket/gRPC) and not a batch/file transcription call. • Logs/metrics: check timestamps of partial transcript events — first partial should appear ~100–200 ms after speech starts for streaming. • If you see only a single final transcript after silence, Deepgram is running in batch mode. LLM (plugin-managed) • What to watch: token-by-token output forwarded from your LLM plugin to downstream TTS. • Metrics: look at

llm_node_ttft

— a low TTFT (few hundred ms) indicates streaming. • If your plugin waits for the full text response before emitting to TTS, the LLM is not streaming. TTS — ElevenLabs • What to watch: audio playback should begin before the full response text is received (audio arrives in chunks). • Config: use ElevenLabs streaming/real-time synthesis API (not full-file synthesis endpoint). • Metrics: time-to-first-audio (TTFA) around 100–300 ms suggests streaming. • If ElevenLabs only returns a completed audio file after the text is done, it’s batch mode. End-to-end check 1. Run a short test turn and observe sequence: ◦ Interim STT events → incremental LLM tokens → audio playback begins early. ◦ If this happens, your pipeline is streaming end-to-end. 1. Inspect timestamps/metrics: ◦

stt

partial timestamp,

llm_node_ttft

, and `tts_node_ttfb`/TTFA. Low values and overlap confirm streaming. 1. If any stage blocks, identify which one by seeing where overlap stops: ◦ No STT partials → STT not streaming. ◦ LLM TTFT high or waits for full text → LLM not streaming. ◦ Audio only after full text → TTS not streaming. If you want, share: • the Deepgram client method you’re calling (WebSocket vs REST) and • whether you’re calling ElevenLabs’ real-time/streaming API or the standard file synth endpoint. I’ll give the exact config snippets and log locations to switch to streaming. , with interactive elements