The latency between the voice input and the LLM response ?

[vivid flame]

AI-generated Answer for trinhthiquynhanh

Short answer: there isn’t a single fixed number. In ElevenLabs’ Conversational AI, a typical turn latency from user speech to the assistant’s response is 1–3 seconds, depending on network conditions and the size of your agent’s knowledge base. (elevenlabs.io)

Breakdown of documented components:

• Speech-to-text: Scribe v2 Realtime produces live transcripts in under ~150 ms. (elevenlabs.io)
• TTS back to audio (if you speak the reply): using low‑latency “Flash” models and WebSocket streaming yields time‑to‑first‑byte on the order of ~150–200 ms from the US region (often lower when using the global preview endpoints in nearby regions). (elevenlabs.io)

How to minimize latency (official guidance):

• Use WebSocket streaming for TTS when the LLM streams text; it’s designed to start generating audio from partial text to reduce delay. (help.elevenlabs.io)
• Prefer low‑latency TTS models (Flash/Turbo) and place workloads in the closest region per the latency best‑practices docs. (elevenlabs.io)

Note: ElevenLabs does not publish an LLM‑only latency; the LLM portion depends on the provider you choose within Conversational AI. The 1–3 s figure reflects the overall voice‑in to response turn. (elevenlabs.io)

Sources:
Comparing ElevenLabs Conversational AI and OpenAI Realtime API
Introducing Scribe v2 Realtime
Latency optimization | ElevenLabs Documentation
What is the Text-to-Speech WebSockets API? – ElevenLabs
Latency optimization | ElevenLabs Documentation
Comparing ElevenLabs Conversational AI and OpenAI Realtime API

[ancient tinsel]

The diagram shows up the latency ?

[vivid flame]

AI-generated Answer for trinhthiquynhanh

Short answer: it illustrates perceived TTS latency (time to first audio), not a fixed network latency.

• The “Buffering” diagram in the WebSockets guide shows how chunk_length_schedule thresholds gate when audio starts streaming. Audio is emitted only once the cumulative input text exceeds each threshold; using flush: true forces immediate generation. This affects time‑to‑first‑byte (TTFB) and thus perceived latency. (elevenlabs.io)

For end‑to‑end voice→LLM→voice timing context:

• Speech‑to‑text: Scribe v2 Realtime streams transcripts in ~150 ms. (elevenlabs.io)
• Text‑to‑speech (Flash via WebSockets, US region): typical TTFB is ~150–200 ms (actual end‑to‑end varies by network, region, buffering). (elevenlabs.io)

If you meant a diagram with absolute millisecond budgets, the docs don’t provide one; the WebSockets diagram is explanatory for buffering/TTFB behavior. (elevenlabs.io)

Sources:
Generate audio in real-time | ElevenLabs Documentation
Scribe v2 Realtime Speech to Text - 150ms Latency API
Latency optimization | ElevenLabs Documentation
Generate audio in real-time | ElevenLabs Documentation

[ancient tinsel]

I remember there are a V-shape diagram, from the User voice input -> STT -> VAD -> LLM -> TTS -> output ?

[vivid flame]

AI-generated Answer for trinhthiquynhanh

Yes. The diagram you’re remembering is in ElevenLabs’ “How do you optimize latency for Conversational AI?” blog post. It depicts the end‑to‑end pipeline as:
User speech → ASR/STT → Turn‑Taking (VAD) → Text Processing (LLM) → TTS → Output speech, with additional overlays for network latency, function‑calling, and telephony. (elevenlabs.io)

For implementation details of the VAD/turn‑taking piece used in realtime STT (e.g., VAD thresholds and commit strategy), see the Realtime transcripts and commit strategies guide. (elevenlabs.io)

Sources:
How do you optimize latency for Conversational AI?
Transcripts and commit strategies | ElevenLabs Documentation

[ancient tinsel]

models that support the tools called reducing latency ?

[vivid flame]

AI-generated Answer for trinhthiquynhanh

Short answer: use Flash for TTS and Scribe v2 Realtime for STT; pair them with streaming/WebSockets and the latency best‑practices.

• TTS models optimized for low latency

  • Flash v2 / v2.5 (~75 ms model inference). Recommended for real‑time voice agents; model_ids: eleven_flash_v2, eleven_flash_v2_5. (elevenlabs.io)
  • Turbo v2.5 (low latency ~250–300 ms, higher quality than Flash). (elevenlabs.io)
  • Multilingual v2 (higher latency, higher quality; not the first choice when latency is the priority). (elevenlabs.io)
  • Eleven v3 (Alpha) is not suitable for real‑time due to higher latency; use Flash or Turbo instead. (help.elevenlabs.io)

• STT model for low latency

  • Scribe v2 Realtime (~150 ms) for live transcription. (elevenlabs.io)

• “Tools” and practices that reduce end‑to‑end latency

  • Prefer streaming endpoints (SSE) or WebSockets so audio starts playing as it’s generated; WebSockets are best when text is produced incrementally by an LLM. (elevenlabs.io)
  • With the TTS WebSocket API, enable auto_mode and tune chunk_length_schedule; insufficient chunk size can stall generation and increase latency. (elevenlabs.io)
  • Choose appropriate voices and formats; default/synthetic/IVC voices are faster than PVC, and higher fidelity output formats add latency. (elevenlabs.io)
  • Use the region closest to your users; typical TTFB with Flash over WebSockets is 150–200 ms in the US (EU ~230 ms; EU stack can reach 150–200 ms). (elevenlabs.io)

For a concise checklist, see “Latency optimization.” (elevenlabs.io)

Sources:
ElevenLabs — Meet Flash
Models | ElevenLabs Documentation
Models | ElevenLabs Documentation
What is Eleven v3 (Alpha)? – ElevenLabs
Scribe v2 Realtime Speech to Text - 150ms Latency API
Latency optimization | ElevenLabs Documentation
Latency optimization | ElevenLabs Documentation
Latency optimization | ElevenLabs Documentation
Latency optimization | ElevenLabs Documentation
Latency optimization | ElevenLabs Documentation

[ancient tinsel]

Tool Call Latency optimization models ?