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Speech recognition has been missing half the conversation. Soniox STT v6 is the world’s first STT model that knows what your voice agent is saying. Introducing agent text input. Developers can stream the agent’s outgoing text into STT as it is sent to TTS, giving recognition context from both...

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Soniox

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18,188 Aufrufe • vor 3 Tagen •via X (Twitter)

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17 Kommentare

Profilbild von Zara Tech & Tools
Zara Tech & Toolsvor 2 Tagen

Smart context boost for voice recognition

Profilbild von Aina Ai | Tools & Updates
Aina Ai | Tools & Updatesvor 2 Tagen

This is a game changer finally STT that hears the full conversation not just half of it

Profilbild von Hitesh Ganjoo
Hitesh Ganjoovor 2 Tagen

Used it, loved it. Already better than v5 with agent text.

Profilbild von Aaliya
Aaliyavor 2 Tagen

like it giving the STT model both sides of the conversation is a smart fix for missed context.

Profilbild von Alex Ai | Tools & Updates
Alex Ai | Tools & Updatesvor 2 Tagen

STT that knows what your agent said changes everything

Profilbild von Leon Ai | tech & tools
Leon Ai | tech & toolsvor 2 Tagen

Finally context aware speech recognition done right

Profilbild von Hana Ai - Marketing Network
Hana Ai - Marketing Networkvor 2 Tagen

Both sides context finally makes sense

Profilbild von Define Ai
Define Aivor 2 Tagen

Using both sides of conversation makes short answers finally land

Profilbild von Alice The Ai Expert
Alice The Ai Expertvor 2 Tagen

Finally STT that actually listens to both sides of the conversation is a game-changer for voice agents.

Profilbild von darkzodchi
darkzodchivor 2 Tagen

you guys be shipping with some insane speed, mad

Profilbild von bodila
bodilavor 2 Tagen

60 languages, that's really impressive... Thanks, team top launch and product!

Profilbild von Vadim
Vadimvor 2 Tagen

It’s cool, but not first. Assembly had it already for a few months. Saying as a big fun of Soniox STT

Profilbild von XXXTENTACIOFF
XXXTENTACIOFFvor 3 Tagen

Good stuff

Profilbild von Sharon Riley
Sharon Rileyvor 2 Tagen

This could make voice agents smarter.

Profilbild von Jeddi in Japan 🇯🇵
Jeddi in Japan 🇯🇵vor 2 Tagen

im going to try this version for a small side project

Profilbild von AURORA Ai
AURORA Aivor 2 Tagen

Finally, speech recognition that understands the full conversation, not just isolated words. This could make voice agents dramatically smarter, more accurate, and natural! 🚀

Profilbild von Veyra | AI & Tech
Veyra | AI & Techvor 2 Tagen

Using agent output as recognition context is brilliant

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Introducing Soniox STT v6, the world’s first speech recognition model that knows what your voice agent is saying and automatically filters out the agent’s own voice. Two connected innovations make this possible: → Agent text input gives recognition context from both sides of the conversation, improving its understanding of user responses. → Agent voice echo cancellation filters out the agent’s playback while continuing to listen to the user, even when they interrupt. The result: fewer false interruptions, fewer clarification loops, and more reliable conversations. V6 also delivers significantly higher accuracy across 60+ languages for voice typing, meeting transcription, multilingual recognition, and real-time translation, with stronger speaker diarization, language identification, endpoint detection, and context customization. One unified speech model. All 60+ languages. The full feature set, with languages freely mixed. Available now for real-time streaming and asynchronous transcription. Explore what’s new in Soniox STT v6:
1:04

Introducing Soniox STT v6, the world’s first speech recognition model that knows what your voice agent is saying and automatically filters out the agent’s own voice. Two connected innovations make this possible: → Agent text input gives recognition context from both sides of the conversation, improving its understanding of user responses. → Agent voice echo cancellation filters out the agent’s playback while continuing to listen to the user, even when they interrupt. The result: fewer false interruptions, fewer clarification loops, and more reliable conversations. V6 also delivers significantly higher accuracy across 60+ languages for voice typing, meeting transcription, multilingual recognition, and real-time translation, with stronger speaker diarization, language identification, endpoint detection, and context customization. One unified speech model. All 60+ languages. The full feature set, with languages freely mixed. Available now for real-time streaming and asynchronous transcription. Explore what’s new in Soniox STT v6:

Soniox

481,145 Aufrufe • vor 4 Tagen

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?

Melvin Vivas

27,722 Aufrufe • vor 1 Jahr

Speech-to-speech no longer needs speech-to-text! Until now, our stack was VAD -> STT -> LLM -> TTS. Now it can send audio directly to multimodal LLMs: VAD → MLLM → TTS No STT. The model understands your voice. Now go build better voice agents!
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Speech-to-speech no longer needs speech-to-text! Until now, our stack was VAD -> STT -> LLM -> TTS. Now it can send audio directly to multimodal LLMs: VAD → MLLM → TTS No STT. The model understands your voice. Now go build better voice agents!

Andi Marafioti

122,929 Aufrufe • vor 2 Monaten

Introducing SeamlessM4T, the first all-in-one, multilingual multimodal translation model. This single model can perform tasks across speech-to-text, speech-to-speech, text-to-text translation & speech recognition for up to 100 languages depending on the task. Details ⬇️
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Introducing SeamlessM4T, the first all-in-one, multilingual multimodal translation model. This single model can perform tasks across speech-to-text, speech-to-speech, text-to-text translation & speech recognition for up to 100 languages depending on the task. Details ⬇️

AI at Meta

592,910 Aufrufe • vor 3 Jahren

Introducing Fish Audio Realtime STT (preview): speech-to-text that understands the conversation, not just the words. It knows when to answer, when to keep talking, and when to stop. Ranked #1 of 20 on Cekura’s STT benchmark (2.21% overall word error rate). Built for voice agents. Try it now 👇
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Introducing Fish Audio Realtime STT (preview): speech-to-text that understands the conversation, not just the words. It knows when to answer, when to keep talking, and when to stop. Ranked #1 of 20 on Cekura’s STT benchmark (2.21% overall word error rate). Built for voice agents. Try it now 👇

Fish Audio

102,194 Aufrufe • vor 11 Tagen

I've been building voice agents for the last 6mo and I think the chat-supervisor pattern is a game changer. Stitched model (STT-LLM-TTS) is slow, but realtime audio models aren't (yet) as smart as text. This has the best of both worlds. Here's how it works:
4:41

I've been building voice agents for the last 6mo and I think the chat-supervisor pattern is a game changer. Stitched model (STT-LLM-TTS) is slow, but realtime audio models aren't (yet) as smart as text. This has the best of both worlds. Here's how it works:

Noah MacCallum

73,682 Aufrufe • vor 1 Jahr

Another example of the multiple TTS parallel pipelines pattern. Here's a voice AI agent that speaks both English and Arabic, using a specific model/voice for each language. These are PlayAI voices. The STT, TTS, and LLM inference is all running on GroqInc. (The LLM is LLama 4 Maverick.)
1:26

Another example of the multiple TTS parallel pipelines pattern. Here's a voice AI agent that speaks both English and Arabic, using a specific model/voice for each language. These are PlayAI voices. The STT, TTS, and LLM inference is all running on GroqInc. (The LLM is LLama 4 Maverick.)

kwindla

13,538 Aufrufe • vor 1 Jahr

Introducing Realtime TTS-2, a new generation of voice model built for realtime conversation. It is the first voice model that hears the conversation, takes natural-language voice direction, holds one voice identity across over 100 languages, and speaks like a person who is paying attention. The result is voice AI that feels as good as it sounds. Try it out: Learn More:
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Introducing Realtime TTS-2, a new generation of voice model built for realtime conversation. It is the first voice model that hears the conversation, takes natural-language voice direction, holds one voice identity across over 100 languages, and speaks like a person who is paying attention. The result is voice AI that feels as good as it sounds. Try it out: Learn More:

Inworld AI

327,778 Aufrufe • vor 6 Monaten

Big moment for Voice AI. A typical voice-agent pipeline has three model stages. Speech-to-text converts the caller’s audio into text. The LLM processes that text and generates a response. Text-to-speech converts the response back into audio. For speech accuracy, information can be lost at two boundaries. - STT must preserve what the caller said on the way in. - TTS must preserve what the LLM generated on the way out. If a caller says “RX-B17,” the transcription model could hear “RX-D17” before the LLM receives it. Even if the LLM generates “RX-B17” correctly, the speech model could still make the final audio sound like “RX-D17.” The LLM’s reasoning accuracy is a separate problem. Both speech layers must preserve the information entering and leaving it. This becomes important with medication names, dosages, appointment times, tracking numbers, and alphanumeric codes. Deepgram built Flux TTS for the speech layer. In an evaluation containing drug names, tracking numbers, dollar amounts, technical strings, and alphanumerics, Flux recorded a 3.4% word error rate. I tested the complete voice loop through a pet-hospital conversation about my dog Biscuit’s prescription refill. The agent had to understand and repeat the medication, dosage, prescription number, and a collection time that changed during the call. The test worked well. The agent retained the changing details, the voice stayed natural throughout, and the exchange felt like a call I would be comfortable having with an actual pet hospital. Build with Flux TTS: For every $1 spent on Flux TTS, Deepgram will match $1 in credits, up to $500. My recording is below, and I worked with the Deepgram team to put this post together.
1:09

Big moment for Voice AI. A typical voice-agent pipeline has three model stages. Speech-to-text converts the caller’s audio into text. The LLM processes that text and generates a response. Text-to-speech converts the response back into audio. For speech accuracy, information can be lost at two boundaries. - STT must preserve what the caller said on the way in. - TTS must preserve what the LLM generated on the way out. If a caller says “RX-B17,” the transcription model could hear “RX-D17” before the LLM receives it. Even if the LLM generates “RX-B17” correctly, the speech model could still make the final audio sound like “RX-D17.” The LLM’s reasoning accuracy is a separate problem. Both speech layers must preserve the information entering and leaving it. This becomes important with medication names, dosages, appointment times, tracking numbers, and alphanumeric codes. Deepgram built Flux TTS for the speech layer. In an evaluation containing drug names, tracking numbers, dollar amounts, technical strings, and alphanumerics, Flux recorded a 3.4% word error rate. I tested the complete voice loop through a pet-hospital conversation about my dog Biscuit’s prescription refill. The agent had to understand and repeat the medication, dosage, prescription number, and a collection time that changed during the call. The test worked well. The agent retained the changing details, the voice stayed natural throughout, and the exchange felt like a call I would be comfortable having with an actual pet hospital. Build with Flux TTS: For every $1 spent on Flux TTS, Deepgram will match $1 in credits, up to $500. My recording is below, and I worked with the Deepgram team to put this post together.

Akshay 🚀

33,399 Aufrufe • vor 9 Tagen

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:
6:55

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:

Santiago

34,718 Aufrufe • vor 1 Jahr

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
0:22

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,180 Aufrufe • vor 5 Monaten

Introducing Soniox TTS v2, our most powerful text-to-speech model yet. Soniox TTS v2 brings extraordinary voice quality, expressive control through audio tags, exceptional precision, high-fidelity voice cloning, more than 60 languages, natural language mixing, and low-latency streaming together in one model. Built from the ground up with a new model architecture, audio codec, and highly optimized inference engine, Soniox TTS v2 delivers frontier TTS at just $0.70 per generated hour. Available globally today as tts-rt-v2. Try it out: Turn the sound on and hear what’s possible. 🔊
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Introducing Soniox TTS v2, our most powerful text-to-speech model yet. Soniox TTS v2 brings extraordinary voice quality, expressive control through audio tags, exceptional precision, high-fidelity voice cloning, more than 60 languages, natural language mixing, and low-latency streaming together in one model. Built from the ground up with a new model architecture, audio codec, and highly optimized inference engine, Soniox TTS v2 delivers frontier TTS at just $0.70 per generated hour. Available globally today as tts-rt-v2. Try it out: Turn the sound on and hear what’s possible. 🔊

Soniox

670,209 Aufrufe • vor 2 Monaten

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
2:57

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

201,127 Aufrufe • vor 2 Jahren

Today we’re launching our first homegrown AI model: an open source turn detection model for building voice agents. Instead of relying solely on voice activity detection (VAD), which only considers when a user is speaking, our model also considers what has and is being said in the context of a conversation and predicts when a user is finished expressing their thoughts before the agent responds. Conversations with AI voice agents using this new model flow much more naturally without constant interruptions from the AI— check it out (more videos, details, and code in the thread):
2:36

Today we’re launching our first homegrown AI model: an open source turn detection model for building voice agents. Instead of relying solely on voice activity detection (VAD), which only considers when a user is speaking, our model also considers what has and is being said in the context of a conversation and predicts when a user is finished expressing their thoughts before the agent responds. Conversations with AI voice agents using this new model flow much more naturally without constant interruptions from the AI— check it out (more videos, details, and code in the thread):

LiveKit

127,014 Aufrufe • vor 1 Jahr

Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.
4:10

Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.

AK

290,729 Aufrufe • vor 3 Jahren

How to build the world's fastest voice AI bot: - Self-host speech-to-text, LLM inference, and text-to-speech all together in the same container/cluster. - Route audio over the internet using WebRTC and edge networking. - Configure timings for voice activity detection, phrase endpointing, and other parts of the pipeline to optimize for latency. (There are trade-offs to doing this!) Here's a LLama 3 voice bot that has voice-to-voice response times of ~500ms. We used Deepgram's STT and TTS for this bot, and everything is hosted on cerebriumai's serverless GPU infrastructure.
4:18

How to build the world's fastest voice AI bot: - Self-host speech-to-text, LLM inference, and text-to-speech all together in the same container/cluster. - Route audio over the internet using WebRTC and edge networking. - Configure timings for voice activity detection, phrase endpointing, and other parts of the pipeline to optimize for latency. (There are trade-offs to doing this!) Here's a LLama 3 voice bot that has voice-to-voice response times of ~500ms. We used Deepgram's STT and TTS for this bot, and everything is hosted on cerebriumai's serverless GPU infrastructure.

kwindla

282,890 Aufrufe • vor 2 Jahren

Voice agents are awkward, and everyone notices: You ask a question. The agent thinks. You wait. And wait... Nobody wants this. I'd rather talk to a person. If your model's response time is over 300ms, you won't make it. Unfortunately, most text-to-speech models can't get anywhere close to that. I want you to take a look at the latest model released by : TTS-1.5. I built a simple voice agent using the model so you can see it in action and test it on your computer. You'll find the repository link below. The latency numbers of this model are wild: • Max model → under 250ms • Mini model → under 130ms That's 4x faster than prior generations and faster than human response times!
5:09

Voice agents are awkward, and everyone notices: You ask a question. The agent thinks. You wait. And wait... Nobody wants this. I'd rather talk to a person. If your model's response time is over 300ms, you won't make it. Unfortunately, most text-to-speech models can't get anywhere close to that. I want you to take a look at the latest model released by : TTS-1.5. I built a simple voice agent using the model so you can see it in action and test it on your computer. You'll find the repository link below. The latency numbers of this model are wild: • Max model → under 250ms • Mini model → under 130ms That's 4x faster than prior generations and faster than human response times!

Santiago

69,467 Aufrufe • vor 9 Monaten

Today we released our Voice Agent API, the world’s only enterprise-ready, real-time, and cost-effective Conversational AI API. If you're building voice agents and you’re tired of the demo day stitching: STT → LLM → Orchestration → TTS → Hoping it works We killed it with...
1:20

Today we released our Voice Agent API, the world’s only enterprise-ready, real-time, and cost-effective Conversational AI API. If you're building voice agents and you’re tired of the demo day stitching: STT → LLM → Orchestration → TTS → Hoping it works We killed it with...

Deepgram

11,229 Aufrufe • vor 1 Jahr

xAI STT is live. You can now run a complete cascaded voice agent pipeline on xAI (STT + Grok + TTS) through LiveKit Inference with one API key, giving you more control, full visibility, and easy component swaps.
5:03

xAI STT is live. You can now run a complete cascaded voice agent pipeline on xAI (STT + Grok + TTS) through LiveKit Inference with one API key, giving you more control, full visibility, and easy component swaps.

LiveKit

10,544 Aufrufe • vor 6 Monaten