Voxtral Mini 4B Realtime 2602 is a multilingual, realtime speech-transcription model and among the first open-source solutions to achieve accuracy comparable to offline systems with a delay of <500ms. It supports 13 languages and outperforms existing open-source baselines across a range of tasks, making it ideal for applications like voice assistants and live subtitling.
Built with a natively streaming architecture and a custom causal audio encoder - it allows configurable transcription delays (240ms to 2.4s), enabling users to balance latency and accuracy based on their needs. At a 480ms delay, it matches the performance of leading offline open-source transcription models, as well as realtime APIs.
As a 4B-parameter model, is optimized for on-device deployment, requiring minimal hardware resources. It runs in realtime with on devices minimal hardware with throughput exceeding 12.5 tokens/second.
This model is released in BF16 under the Apache-2 license, ensuring flexibility for both research and commercial use.
We recommend deploying with the following best practices:
Always set the temperature to 0.0
A single text-token is worth 80ms. Hence, make sure to set your --max-model-len accordingly. To live-record a 1h meeting, you need to set --max-model-len >= 3600 / 0.8 = 45000. In theory, you should be able to record with no limit; in practice, pre-allocations of RoPE parameters among other things limits --max-model-len. For the best user experience, we recommend to simply instantiate vLLM with the default parameters which will automatically set a maximum model length of 131072 (~ca. 3h).
We strongly recommend using websockets to set up audio streaming sessions. For more info on how to do so, check Usage.
We recommend using a delay of 480ms as we found it to be the sweet spot of performance and low latency. If, however, you want to adapt the delay, you can change the "transcription_delay_ms": 480 parameter in the tekken.json file to any multiple of 80ms between 80 and 1200, as well as 2400 as a standalone value.
We compare Voxtral Mini 4B Realtime to similar models - both offline models and realtime. Voxtral Mini 4B Realtime is competitive to leading offline models and shows significant gains over existing open-source realtime solutions.
Tip. We've worked hand-in-hand with the vLLM team to have production-grade support for Voxtral Mini 4B Realtime 2602 with vLLM. Special thanks goes out to Joshua Deng, Yu Luo, Chen Zhang, Nick Hill, Nicolò Lucchesi, Roger Wang, and Cyrus Leung for the amazing work and help on building a production-ready audio streaming and realtime system in vLLM.
Warning. Due to its novel architecture, Voxtral Realtime is currently only support in vLLM. We very much welcome community contributions to add the architecture to Transformers and Llama.cpp.
We've worked hand-in-hand with the vLLM team to have production-grade support for Voxtral Mini 4B Realtime 2602 with vLLM. vLLM's new Realtime API is perfectly suited to run audio streaming sessions with the model.
You can set --max-num-batched-tokens to balance throughput and latency, higher means higher throughput but higher latency.
You can reduce the default --max-model-len to allocate less memory for the pre-computed RoPE frequencies, if you are certain that you won't have to transcribe for more than X hours. By default the model uses a --max-model-len of 131072 (> 3h).
Warning. Running Voxtral-Realtime on-device with ExecuTorch is not throughly tested and hence there might be some sharp edges. If you encounter any problems, please file a bug report directly on ExecuTorch's GitHub
ExecuTorch enables you to deploy Voxtral-Realtime locally—either on-device or on your laptop.
You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.
Files
9 files · 17.7 GBWeights 8.86 GB (safetensors). Sizes and sha256 from the signed manifest.
transformersSpeech recognition with the transformers pipeline.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="mistralai/Voxtral-Mini-4B-Realtime-2602", revision="2769294da9567371363522aac9bbcfdd19447add")
print(asr("audio.wav")["text"])
mistralai <huggingface.co/mistralai>Newsgroups: alt.freeweights.mistralSubject: 
