Voxtral Realtime
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 encoder and Ada RMS-Norm for improved delay conditioning. We scale pretraining to a large-scale dataset spanning 13 languages. At a delay of 480ms, Voxtral Realtime achieves performance on par with Whisper, the most widely deployed offline transcription s...
| Authors | Mistral-AI, :, Alexander H. Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 February 2026 |
| Last updated at source | 6 April 2026 |
Models and datasets referencing this paper
- mistralai/Voxtral-Mini-4B-Realtime-2602Model
- handy-computer/Voxtral-Mini-4B-Realtime-2602-ggufModel
- onnx-community/Voxtral-Mini-4B-Realtime-2602-ONNXModel
- Bercraft/Voxtral-Mini-4B-Realtime-2602-GGUFModel
- RedHatAI/Voxtral-Mini-4B-Realtime-2602Model
- ldov/Voxtral-Mini-4B-Realtime-2602-ggufModel
- praiselab-picuslab/Voxtral-Mini-4B-Realtime-2602-GGUFModel
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