Voxtral Mini 4B Realtime: Streaming Speech Transcription
Voxtral Mini 4B Realtime 2602 is a multilingual, realtime speech-transcription model from Mistral AI that transcribes with a delay of under 500 milliseconds. It transcribes audio while it is still being spoken, which suits voice assistants, live subtitles, and meeting transcription.
On Vast, the model is served by vLLM's realtime API: a client opens a WebSocket session on /v1/realtime, streams audio in small chunks, and receives the transcript back as text deltas while the audio is still arriving. Recorded files can also be sent to the OpenAI-compatible /v1/audio/transcriptions endpoint.
Key Features
Natively Streaming
The model is built on a streaming architecture with a custom causal audio encoder, so it does not wait for a complete recording before it starts transcribing. Both the audio encoder and the language model use sliding-window attention, which lets a session run for long stretches of audio.
Configurable Delay
The transcription delay can be tuned to trade latency against accuracy, from 80 milliseconds up to 2.4 seconds. Mistral recommends 480 milliseconds as the balance between latency and accuracy.
Multilingual
The model supports 13 languages: Arabic, German, English, Spanish, French, Hindi, Italian, Dutch, Portuguese, Chinese, Japanese, Korean, and Russian.
Compact
At 4 billion parameters, the model is compact enough for realtime streaming.
Use Cases
- Private meeting transcription
- Live subtitles and captions
- Realtime voice assistants with speech understanding
- Transcribing live streams and calls as they happen
Architecture
Voxtral Mini 4B Realtime pairs a language model of about 3.4 billion parameters with an audio encoder of about 970 million parameters. The audio encoder was trained from scratch with causal attention, which is what makes streaming possible. One text token covers 80 milliseconds of audio.
Benchmarks
On the multilingual FLEURS benchmark, Mistral reports an average word error rate of 8.72 percent at a 480 millisecond delay, against 5.90 percent for its offline Voxtral Mini Transcribe 2.0. On long-form English, the gap is narrow: 3.17 percent on TEDLIUM against 2.86 percent offline, and 5.05 percent on Meanwhile against 4.08 percent. Full results are on the model card.
