Speech to Text — Most Accurate Speech to Text Model

What

ElevenLabs Speech to Text is an automatic speech recognition product built around two models: Scribe v2 for recorded audio and video, and Scribe v2 Realtime for live transcription. It is designed for teams and developers that need to turn speech into text for captions, subtitles, transcripts, editing, meetings, calls, and real-time voice applications.

The product appears positioned as an API-first and enterprise-capable transcription offering, while also supporting direct use in ElevenLabs Studio and ElevenLabs Agents. Its core workflow covers both batch transcription of uploaded media files and low-latency streaming transcription for live interactions, with an emphasis on multilingual coverage, speaker awareness, and operational controls for production use.

Features

• Realtime transcription with sub-150 ms latency: Scribe v2 Realtime converts live speech to text quickly enough for agents, meetings, and other applications that need immediate language understanding.
• Batch transcription for recorded media: Scribe v2 can process uploaded audio and video files, including formats such as MP4, MOV, MP3, and WAV, to produce editable transcripts, captions, and subtitles.
• Support for 90+ languages: The models are designed for multilingual transcription across a wide range of accents, dialects, and recording conditions, which helps teams work across global content and conversations.
• Voice Activity Detection: The realtime model automatically detects when speech starts and stops, improving segmentation for live processing workflows.
• Keyterm prompting: Users can provide up to 100 words or sentences to guide transcription toward context-specific terminology and improve accuracy on important terms.
• Speaker, entity, and sound-event detection: Scribe v2 can label speakers, calculate entity timestamps, and tag non-speech events such as laughter or footsteps to create richer transcripts.

Helpful Tips

• Match the model to the workflow: Use Scribe v2 Realtime for live conversational systems and Scribe v2 for post-production, archived media, and transcript editing.
• Validate language performance for your mix: The page provides different quality bands by language, so multilingual buyers should test priority languages rather than assuming uniform accuracy.
• Use keyterm prompting for domain vocabulary: Industry terms, names, product references, and specialized phrases are likely good candidates when transcription precision matters.
• Check deployment and data-handling needs early: The page references encrypted processing, zero-retention options, EU data residency, and on-premise configurations, which may matter for regulated or security-sensitive environments.
• Confirm collaboration needs outside core transcription: The page mentions Studio and team permissions, but buyers should verify the exact editing, workflow, and governance features needed for their operating model.

OpenClaw Skills

Within the OpenClaw ecosystem, this product could likely serve as a speech ingestion layer for agents and workflow automations. Likely use cases include skills that transcribe calls in real time, extract entities and action items from meetings, convert media libraries into searchable knowledge, or trigger downstream automations when specific phrases or events appear in transcripts. The API orientation makes it suitable for agent pipelines that need text output as a structured intermediate step.

Combined with OpenClaw agents, this could shift work for support, operations, media, and research teams from manual listening toward continuous analysis and response. For example, a likely workflow could use Scribe v2 Realtime to power live call understanding, then route the transcript into OpenClaw skills for summarization, ticket creation, compliance review, or CRM note generation. Native OpenClaw integration is not stated on the page, so these should be treated as plausible implementation patterns rather than confirmed built-in connections.