Voxtral Realtime
📰 ArXiv cs.AI
Voxtral Realtime is a streaming automatic speech recognition model that achieves offline transcription quality at sub-second latency
Action Steps
- Train the model end-to-end for streaming using the Delayed Streams Modeling framework
- Introduce explicit alignment between audio and text streams to improve transcription accuracy
- Implement causal audio encoding to reduce latency
- Evaluate the model's performance on streaming audio data to ensure sub-second latency and high transcription quality
Who Needs to Know This
Speech recognition engineers and researchers on a team can benefit from Voxtral Realtime's ability to provide high-quality transcription in real-time, enabling applications such as live captioning and voice assistants
Key Insight
💡 Voxtral Realtime's end-to-end training and causal audio encoding enable high-quality, real-time speech recognition
Share This
💡 Voxtral Realtime achieves offline transcription quality at sub-second latency! #ASR #StreamingAI
Key Takeaways
Voxtral Realtime is a streaming automatic speech recognition model that achieves offline transcription quality at sub-second latency
Full Article
Title: Voxtral Realtime
Abstract:
arXiv:2602.11298v3 Announce Type: replace Abstract: We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adapt offline models through chunking or sliding windows, Voxtral Realtime is trained end-to-end for streaming, with explicit alignment between audio and text streams. Our architecture builds on the Delayed Streams Modeling framework, introducing a new causal audio encode
Abstract:
arXiv:2602.11298v3 Announce Type: replace Abstract: We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adapt offline models through chunking or sliding windows, Voxtral Realtime is trained end-to-end for streaming, with explicit alignment between audio and text streams. Our architecture builds on the Delayed Streams Modeling framework, introducing a new causal audio encode
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