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I built a service desk agent using ElevenLabs’ new Conversational AI Agents feature. Watch the video to see how responsive it is! Previously, I used ElevenLabs for cloning my voice and used my generated voice for narrations in some of my youtube videos. This feature takes elevenlabs' voice AI...

27,722 次观看 • 1 年前 •via X (Twitter)

10 条评论

Houdini 的头像
Houdini1 年前

@elevenlabsio Does it support RAG for context purposes? Like uploading documents/pdf as source of info.

Melvin Vivas 的头像
Melvin Vivas1 年前

@elevenlabsio it has but i haven't tried it yet

tj 的头像
tj1 年前

@elevenlabsio It’s expensive checkout Vapi

David Fergus 的头像
David Fergus1 年前

@elevenlabsio I've tried using the API link with Agent ID in a Web app, using and @pico, and none of them are showing the chat widget to talk with. Nightmare. Conversational AI, Customer Support Agent.. Any ideas?

Jack C Crawford 的头像
Jack C Crawford1 年前

@elevenlabsio C o o l

Shamshudein 的头像
Shamshudein1 年前

@elevenlabsio What about pricing?

Melvin Vivas 的头像
Melvin Vivas1 年前

@elevenlabsio it's usage based per minute. 500 credits per minute

Jon Wentel 的头像
Jon Wentel1 年前

@elevenlabsio George is the best voice for nonfiction books (PDF -> audiobook).

Sidhant Kabra 的头像
Sidhant Kabra1 年前

@elevenlabsio That's incredible! Building a service desk agent using ElevenLabs' Conversational AI Agents is a great example of the power of AI. To ensure the accuracy and reliability of your AI agents, consider using Vocera ( Vocera specializes in refining voice AI.

Munsif 的头像
Munsif1 年前

@elevenlabsio Did you get the chance to create an event using Google calendar API with this ?

相关视频

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:

Andrew Ng

87,484 次观看 • 1 年前