标签：#elevenlabs

ElevenLabs now offers a Dubbing Translation API designed to turn multilingual video and audio localization into a single automated workflow. The service accepts a video, audio file, or source URL, lets developers choose one or more target languages, and coordinates transcription, translation, voice generation, speaker identity preservation, and timing alignment before returning dubbed assets. The central change is not simply another voice feature. ElevenLabs is presenting the pipeline as an integrated API operation rather than a set of services developers must orchestrate independently. Its Dubbing Translation API product page says users can dub and translate video in 90+ languages in one call , with translation, voice cloning, and timing synchronization handled server-side. For localization teams, that approach could reduce the application logic required to move from an original asset to versions for multiple language audiences. It does not remove the need for teams to assess translation quality, brand requirements, and appropriate use of cloned voices, but it consolidates the underlying production stages into one API surface. What the unified dubbing workflow does ElevenLabs' dubbing documentation describes an end-to-end sequence comprising transcription, translation, voice generation, and video synchronization. The Dubbing Translation API places those stages behind a single request flow, with the aim of retaining the original speaker's identity and the timing of the source material in the resulting dubbed output. That matters because dubbing is more than text translation. A usable localized video or audio asset needs speech that fits the surrounding media, while the voice and delivery should remain coherent for the intended audience. ElevenLabs describes its synchronization capability in terms of preserving timing and tone, positioning the API for workflows where the final media asset, rather than a translated script alone, is the required output. The documented

ElevenLabs has introduced Dubbing v2 , a rearchitected AI dubbing model built to retain a speaker's emotion, delivery, and timing while translating content across more than 90 languages. The update expands the company’s localization proposition beyond a basic language replacement: its documented controls cover dialect-specific accents, multi-speaker material, and background audio management for more complex video and audio scenes. According to ElevenLabs’ Dubbing v2 announcement , the model is designed for creators, marketers, studios, and broadcasters that need to localize video at production scale. It is integrated with ElevenCreative for one-click video localization and ElevenProductions, the company’s professional localization service. The central goal is preserving the original performance rather than simply generating translated speech. That matters for material in which pacing, vocal emphasis, and emotional delivery are part of the message, including marketing campaigns, creator videos, and professionally produced programming. Dubbing v2 is intended to synchronize translated dialogue with the source speaker’s timing and delivery across its supported languages. What Dubbing v2 changes for localization workflows The most practical additions are the controls documented for ElevenLabs' dubbing workflow. They give teams more ways to shape a dub around the source material and the intended audience, particularly when a project includes regional language variation or a mix of dialogue and sound. Localization need Dubbing v2 capability Documented control or workflow Regional language variation Dialect-specific accent selection target_accent , marked experimental Scenes with several voices Multi-speaker dubbing num_speakers Music, effects, or ambient sound Background audio management foreground_audio_file , background_audio_file , and drop_background_audio End-to-end video localization Integrated production workflows ElevenCreative and ElevenProductions For Spanish-lan

ElevenLabs has expanded ElevenAgents into a broader enterprise automation platform built around the full lifecycle of an AI agent: defining workflow-driven behavior, testing changes, deploying across customer channels, and improving performance in production. The most recent addition, ElevenAgents Spotlight , monitors voice and chat conversations in real time and provides recommendations intended to help teams improve agent outcomes. In its official ElevenAgents Spotlight announcement , ElevenLabs describes Spotlight as an observation and improvement layer for production agents . It is designed to review every conversation, organize interactions by topic, evaluate quality against criteria written in plain language, and surface context-aware suggestions. Combined with Procedures, Experiments, and Versioning, the release set gives enterprises a more structured way to operate AI agents beyond initial deployment. Spotlight adds continuous production feedback Spotlight is aimed at a common operational problem with customer-facing AI agents: a system can appear ready in testing but encounter unexpected patterns, failure modes, or shifts in customer sentiment after it reaches live traffic. Rather than requiring teams to manually sample conversations, Spotlight analyzes production voice and chat interactions as they occur. The platform can automatically group conversations by topic and track metrics including success rate and sentiment . Teams can define evaluations in plain language to score the quality of interactions against their own standards. That matters because a useful agent evaluation may depend on a company's specific process, such as whether the agent followed an escalation policy or collected required information, rather than a single generic measure of response quality. Spotlight also includes anomaly detection for changes in key metrics. Its integration support for observability tools such as Datadog, Grafana, and OpenTelemetry means the agent data can fit in

This is a submission for Weekend Challenge: Passion Edition What I Built The theme was passion, and mine has always been the sky and everything beyond it. Day or night, there's a specific kind of awe in remembering that the sky isn't a backdrop. It's real, it's happening right now, and every point of light is an actual place. Night is simply when you can see the most of it. I wanted to put that feeling into a browser tab. Zenith takes your location, cinematically lowers you from orbit down onto your exact spot on Earth, and becomes a first-person view of your real sky, one you can drag to look around. Every star is where it actually is. The Sun, the Moon, and the visible planets are computed for your latitude, longitude, and this exact minute, and placed where they truly are. It isn't a fixed picture either: the whole sky rotates slowly in real time, so stars rise and set while you watch. Tap any object and you travel to it. The camera flies out through the real starfield, the object grows from a point into a detailed close-up, and a short, grounded briefing appears telling you what you're actually looking at, from where you're standing, right now. A warm voice reads it to you. Stay a while and Zenith reminds you that there are people over your head: it shows how many humans are in space this moment, by name, and draws the real International Space Station crossing your sky whenever it's above your horizon. Not information about space. The quiet, enormous wonder of looking up and knowing, for a moment, exactly what you're looking at. Demo Live: https://zenith-rgerjeki.vercel.app A short walkthrough: the descent to your location, dragging the real sky, and flying to a planet for an AI briefing read aloud in a warm voice. Code rgerjeki / Zenith Zenith The sky above you, right now. I've always been drawn to the sky, and everything beyond it. Zenith is a first-person view of yours : it takes your location, lowers you onto your exact spot on Earth, and gives you the real

In the hyper-competitive landscape of modern B2B outbound sales, speed-to-lead and outreach capacity are the ultimate drivers of pipeline volume . Yet, traditional Sales Development Representative (SDR) teams face a exhausting bottleneck: reaches and qualifications are limited by human bandwidth . A typical outbound SDR spends up to 80% of their day dialing numbers, navigating IVR phone trees, hitting voicemail, and dealing with incorrect contact records. When an inbound lead submits a form requesting a product demo, the average company takes 42 minutes to respond. By that time, prospect engagement has cooled by over 400%. To shatter this operational limit, modern revenue operations (RevOps) teams are transitioning from rigid auto-dialers and static voice bots to autonomous voice AI sales agents . By pairing the hyper-realistic conversational engine of ElevenLabs with the visual orchestration power of n8n , you can deploy a scalable, context-aware calling agent that handles inbound qualification and outbound follow-up calls in real-time. This technical blueprint provides an end-to-end guide to designing, securing, and deploying a production-grade Voice AI Sales Agent using ElevenLabs Conversational AI and n8n . We will cover how to manage conversation state, execute live database tool calls, secure webhook communication, route calls dynamically, and configure infrastructure to achieve sub-second response latency . The Architecture of an Enterprise Voice Agent Building a conversational voice agent requires a multi-layered system that operates in near real-time. When a human speaks over a telephony network, their voice must be digitized, transcribed, processed by a large language model (LLM), synthesized back into audio, and sent back down the line—all within a fraction of a second. To ensure stability, scalability, and absolute separation of concerns, our architecture decouples the telephony and voice generation layer from the logic and database integration layer . [

ElevenLabs' new model will let users regenerate a section of a song without affecting the rest of the track.