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I built a simple voice assistant in 70 lines of Python code. It uses: • LiveKit - The voice agent • AssemblyAI - To turn your voice into text • OpenAI - The brain of the agent, and to turn text into audio There's something really cool about this:

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Santiago

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34,632 просмотров • 11 месяцев назад •via X (Twitter)

Наука и технологииОбразование
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Add a face to your voice agent. LiveAvatar by HeyGen is now supported in LiveKit Agents. Add a realtime human avatar to your agent without rebuilding the conversation loop. Your LiveKit agent still owns the room, turn-taking, model orchestration, and voice pipeline. LiveAvatar renders the synchronized face and video stream. Useful for product demos, onboarding, tutoring, and support agents that need a visual layer.
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Add a face to your voice agent. LiveAvatar by HeyGen is now supported in LiveKit Agents. Add a realtime human avatar to your agent without rebuilding the conversation loop. Your LiveKit agent still owns the room, turn-taking, model orchestration, and voice pipeline. LiveAvatar renders the synchronized face and video stream. Useful for product demos, onboarding, tutoring, and support agents that need a visual layer.

LiveKit

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Here is the fastest way to turn text into a voice. Less than 100ms to start streaming. This is an absolute must for anyone who wants to build a voice agent that doesn't sound robotic (latency kills the perception of reality). Here is how easy this is:
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Here is the fastest way to turn text into a voice. Less than 100ms to start streaming. This is an absolute must for anyone who wants to build a voice agent that doesn't sound robotic (latency kills the perception of reality). Here is how easy this is:

Santiago

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We shipped Agent Console, a realtime debugging surface for voice agents. Talk to your agent and see the entire pipeline live, from audio and latency to tool calls, transcripts, and participant state. Available now in the LiveKit Cloud dashboard.
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We shipped Agent Console, a realtime debugging surface for voice agents. Talk to your agent and see the entire pipeline live, from audio and latency to tool calls, transcripts, and participant state. Available now in the LiveKit Cloud dashboard.

LiveKit

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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 to a whole new level! It simplifies systems that used to require separate TTS (text-to-speech) and STT (speech-to-text) processes for both sides of the conversation. Now, it’s much simpler! Create your own agent here What will you create with this?
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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 to a whole new level! It simplifies systems that used to require separate TTS (text-to-speech) and STT (speech-to-text) processes for both sides of the conversation. Now, it’s much simpler! Create your own agent here What will you create with this?

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27,722 просмотров • 1 год назад

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:
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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:

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87,484 просмотров • 1 год назад

I really enjoyed doing these voice impressions, and honestly, I'd love to one day turn the dream of becoming a voice actor into reality!
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I really enjoyed doing these voice impressions, and honestly, I'd love to one day turn the dream of becoming a voice actor into reality!

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We shipped the tutorial for Agents UI. In 5 minutes you'll have a fully wired voice agent frontend with audio visualizers, media controls, and session management built directly into your codebase. Watch it, build it, own it. shadcn inside™.
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We shipped the tutorial for Agents UI. In 5 minutes you'll have a fully wired voice agent frontend with audio visualizers, media controls, and session management built directly into your codebase. Watch it, build it, own it. shadcn inside™.

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New course: Add voice to your AI agents and applications, built with Vocal Bridge (disclosure: an AI Fund portfolio company) and taught by its CEO Ashwyn Sharma. Voice applications historically required making a hard tradeoff: using fast voice-to-voice models that sacrifice reliability, or accurate speech-to-text pipelines that add latency. This course teaches you how to build voice agents that are both reliable and fast. You'll build three types of voice-enabled applications: a voice-interactive game where voice commands and mouse clicks work together over a single channel, an agent that gains a voice in about 10 lines of code without touching its prompts or tools, and an agent that places outbound phone calls using a make_phone_call function. Skills you'll gain: - Add a voice layer to an existing agent without rewriting your prompts, RAG pipeline, or tools - Give an agent the ability to place outbound calls and stream transcripts back live - Set up voice evaluation to score calls, catch regressions, and improve quality before deployment Join and add voice to your agents without overhauling your architecture:

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Introducing Line by Cartesia: the modern voice agent development platform. Line was built to be code-first, because best-in-class products are built in code. ▶️ Watch us build an advanced voice agent with background reasoning in just minutes.

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Some examples: - Text to Speech: Read aloud content or create audiobooks. - Speech to Text: Transcribe audio and video into text. - Voice Designer: Create custom AI voices. - Conversational AI: Build dynamic voice agents and make outbound calls.

ElevenLabs

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Maven Voice is our enterprise-grade AI voice agent built for the chaotic, noisy and unpredictable situations we encounter everyday. Thank you to OpenAI, Phonic, , and for helping make this possible.

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