---
library_name: vllm
language:
- en
- fr
- es
- de
- ru
- zh
- ja
- it
- pt
- nl
- ar
- hi
- ko
license: apache-2.0
inference: false
base_model:
- mistralai/Ministral-3-3B-Base-2512
extra_gated_description: >-
  If you want to learn more about how we process your personal data, please read
  our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
pipeline_tag: automatic-speech-recognition
tags:
- mistral-common
---

# Voxtral Mini 4B Realtime 2602

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.

For more details, see our:
- [Blog post](https://mistral.ai/news/voxtral-transcribe-2)
- [Demo](https://huggingface.co/spaces/mistralai/Voxtral-Mini-Realtime)
- [Technical report](https://arxiv.org/abs/2602.11298)
- [vLLM's blog on streaming input](https://blog.vllm.ai/2026/01/31/streaming-realtime.html)


## Key Features
Voxtral Mini 4B Realtime consists of two main architectural components:
- **≈3.4B Language Model**
- **≈970M Audio Encoder**
- The audio encoder was trained from scratch with causal attention enabling streaming capability
- Both the audio encoder as well as the LLM backbone use sliding window attention allowing for "infinite" streaming
- For more details, refer to the [technical report](https://arxiv.org/abs/2602.11298)

![Voxtral-Realtime Architecture](https://raw.githubusercontent.com/sanchit-gandhi/notebooks/refs/heads/main/voxtral-realtime.jpeg)

The Voxtral Mini 4B Realtime model offers the following capabilities:
- **High-Quality Transcription**: Transcribe audio to text with confidence.
- **Multilingual**: Supports dozens of languages, making it perfect for multilingual transcription tasks.
- **Real-Time**: Fast streaming ASR model, enabling real-time transcription use cases.
- **Configurable Transcription Delays**: Customize the transcription delay to balance quality and latency, from 80ms to 2.4s.

### Use Cases
**Real-Time Transcription Purposes:**
- Private meeting transcriptions
- Live subtitle creation
- Real-time assistants with speech understanding
- And more

Bringing real-time transcription capabilities to all.

### Recommended Settings

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](#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](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602/blob/main/params.json) file to any multiple of 80ms between 80 and 1200, as well as 2400 as a standalone value.

## Benchmark Results

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.

### Fleurs

| Model                                   | Delay       | AVG     | Arabic | German | English | Spanish | French | Hindi  | Italian | Dutch | Portuguese | Chinese | Japanese | Korean | Russian |
|-----------------------------------------|-------------|---------|--------|--------|---------|---------|--------|--------|---------|-------|------------|---------|----------|--------|---------|            
| Voxtral Mini Transcribe 2.0             | Offline     | 5.90%   | 13.54% | 3.54%  | 3.32%   | 2.63%   | 4.32%  | 10.33% | 2.17%   | 4.78% | 3.56%      | 7.30%   | 4.14%    | 12.29% | 4.75%   | 
| **Voxtral Mini 4B Realtime 2602**       | 480 ms      | 8.72%   | 22.53% | 6.19%  | 4.90%   | 3.31%   | 6.42%  | 12.88% | 3.27%   | 7.07% | 5.03%      | 10.45%  | 9.59%    | 15.74% | 6.02%   |
|                                         |             |         |        |        |         |         |        |        |         |       |            |         |          |        |         |
|                                         | 160 ms      | 12.60%  | 24.33% | 9.50%  | 6.46%   | 5.34%   | 9.75%  | 15.28% | 5.59%   | 11.39%| 10.01%     | 17.67%  | 19.17%   | 19.81% | 9.53%   |
|                                         | 240 ms      | 10.80%  | 23.95% | 8.15%  | 5.91%   | 4.59%   | 8.00%  | 14.26% | 4.41%   | 9.23% | 7.51%      | 13.84%  | 15.17%   | 17.56% | 7.87%   |
|                                         | 960 ms      | 7.70%   | 20.32% | 4.87%  | 4.34%   | 2.98%   | 5.68%  | 11.82% | 2.46%   | 6.76% | 4.57%      | 8.99%   | 6.80%    | 14.90% | 5.56%   |
|                                         | 2400 ms     | 6.73%   | 14.71% | 4.15%  | 4.05%   | 2.71%   | 5.23%  | 10.73% | 2.37%   | 5.91% | 3.93%      | 8.48%   | 5.50%    | 14.30% | 5.41%   |

### Long-form English

| Model                              | Delay  | Meanwhile (<10m) | E-21 (<10m) | E-22 (<10m) | TEDLIUM (<20m) |
| ---------------------------------- | ------ | ---------------- | ----------- | ----------- | -------------- |
| Voxtral Mini Transcribe 2.0        | Offline| 4.08%            | 9.81%       | 11.69%      | 2.86%          |
| **Voxtral Mini 4B Realtime 2602**  | 480ms  | 5.05%            | 10.23%      | 12.30%      | 3.17%          |


### Short-form English

| Model                              | Delay  | CHiME-4 | GigaSpeech 2k Subset | AMI IHM | SwitchBoard | CHiME-4 SP | GISpeech 2k Subset |
| ---------------------------------- | ------ | ------- | -------------------- | ------- | ----------- | ---------- | ------------------ | 
| Voxtral Mini Transcribe 2.0        | Offline | 10.39%  | 6.81%               | 14.43%  | 11.54%      | 10.42% | 1.74% |
| **Voxtral Mini 4B Realtime 2602**  | 480ms  | 10.50%  | 7.35%                | 15.05%  | 11.65%      | 12.41% | 1.73% |

## Usage

The model can also be deployed with the following libraries:
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
- *Community Contributions*: See [here](#community-contributions-untested)

### vLLM (recommended)

> [!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](https://github.com/joshuadeng), [Yu Luo](https://github.com/ErickLuo90), [Chen Zhang](https://github.com/heheda12345), [Nick Hill](https://github.com/njhill), [Nicolò Lucchesi](https://github.com/NickLucche), [Roger Wang](https://github.com/ywang96), and [Cyrus Leung](https://github.com/DarkLight1337)
> 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](https://github.com/huggingface/transformers) and [Llama.cpp](https://github.com/ggml-org/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](https://github.com/vllm-project/vllm)'s [new Realtime API](https://docs.vllm.ai/en/latest/serving/openai_compatible_server/?h=realtime#realtime-api) is perfectly suited to 
run audio streaming sessions with the model.

#### Installation

Make sure to install [vllm](https://github.com/vllm-project/vllm) from the nightly pypi package. 
See [here](https://docs.vllm.ai/en/latest/getting_started/installation/) for a full installation guide.

```
uv pip install -U vllm \
    --torch-backend=auto \
    --extra-index-url https://wheels.vllm.ai/nightly # add variant subdirectory here if needed
```

Doing so should automatically install [`mistral_common >= 1.9.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.9.0).

To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```

You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/nightly/images/sha256-6ae33f5001ab9d32346ce2c82c660fe57021c4f0c162ed0c60b843319829b810).

Make sure to also install all required audio processing libraries:

```
uv pip install soxr librosa soundfile
```

Also we recommend using Transformers v5 as v4 can clutter the terminal with unnecessary warnings (see [here](https://github.com/vllm-project/vllm/issues/34642))

```
uv pip install --upgrade transformers
```

#### Serve

Due to size and the BF16 format of the weights - `Voxtral-Mini-4B-Realtime-2602` can run on a single GPU with >= 16GB memory.

The model can be launched in both "eager" mode:

```bash
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{"cudagraph_mode": "PIECEWISE"}'
```

Additional flags:
* 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).

#### Client

After serving `vllm`, you should see that the model is compatible with `vllm's` new realtime endpoint:
```
...
(APIServer pid=3246965) INFO 02-03 17:04:43 [launcher.py:58] Route: /v1/realtime, Endpoint: realtime_endpoint
...
```

We have added two simple example files that allow you to:
- [Stream audio files](https://docs.vllm.ai/en/latest/examples/online_serving/openai_realtime_client/?h=realtime#openai-realtime-client)
- [Simple gradio live transcription demo](https://docs.vllm.ai/en/latest/examples/online_serving/openai_realtime_microphone_client/#openai-realtime-microphone-client)

[![Screenshot 2026-02-03 at 18.30.08](https://cdn-uploads.huggingface.co/production/uploads/5dfcb1aada6d0311fd3d5448/STM6r9lsL8_NRmS3bcZ9x.png)](https://huggingface.co/spaces/mistralai/Voxtral-Mini-Realtime)

**To try out a demo, click [here](https://huggingface.co/spaces/mistralai/Voxtral-Mini-Realtime)**

### Transformers

Starting with `transformers >= 5.2.0`, you can run Voxtral Realtime natively in Transformers!

For more details, refer to the [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/voxtral_realtime).

#### Installation

Install Transformers:

```bash
pip install --upgrade transformers
```

Make sure to have `mistral-common` installed with audio dependencies:

```bash
pip install --upgrade "mistral-common[audio]"
```

#### Usage

```python
from transformers import VoxtralRealtimeForConditionalGeneration, AutoProcessor
from mistral_common.tokens.tokenizers.audio import Audio
from huggingface_hub import hf_hub_download

repo_id = "mistralai/Voxtral-Mini-4B-Realtime-2602"

processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralRealtimeForConditionalGeneration.from_pretrained(repo_id, device_map="auto")

repo_id = "patrickvonplaten/audio_samples"
audio_file = hf_hub_download(repo_id=repo_id, filename="bcn_weather.mp3", repo_type="dataset")

audio = Audio.from_file(audio_file, strict=False)
audio.resample(processor.feature_extractor.sampling_rate)

inputs = processor(audio.audio_array, return_tensors="pt")
inputs = inputs.to(model.device, dtype=model.dtype)

outputs = model.generate(**inputs)
decoded_outputs = processor.batch_decode(outputs, skip_special_tokens=True)

print(decoded_outputs[0])
```

### Community Contributions (Untested)

Voxtral Realtime integrations in:
- [Executorch](https://github.com/pytorch/executorch/tree/main/examples/models/voxtral_realtime) - thanks [Mergen Nachin](https://github.com/mergennachin)
- [Pure C](https://github.com/antirez/voxtral.c) - thanks [Salvatore Sanfilippo](https://github.com/antirez)
- [mlx-audio framework](https://github.com/Blaizzy/mlx-audio) - thanks [Shreyas Karnik](https://github.com/shreyaskarnik)
- [MLX](https://github.com/awni/voxmlx) - thanks [Awni Hannun](https://github.com/awni)
- [Rust](https://github.com/TrevorS/voxtral-mini-realtime-rs) - thanks [TrevorS](https://github.com/TrevorS)

## License

This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt).

*You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.*