---
language:
- en
- fr
- de
- es
- it
- pt
- nl
- hi
license: apache-2.0
library_name: vllm
inference: false
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our Privacy Policy.
pipeline_tag: audio-text-to-text
---
# Voxtral Mini 1.0 (3B) - 2507
Voxtral Mini is an enhancement of [Ministral 3B](https://mistral.ai/news/ministraux), incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding.
Learn more about Voxtral in our blog post [here](https://mistral.ai/news/voxtral).
## Key Features
Voxtral builds upon Ministral-3B with powerful audio understanding capabilities.
- **Dedicated transcription mode**: Voxtral can operate in a pure speech transcription mode to maximize performance. By default, Voxtral automatically predicts the source audio language and transcribes the text accordingly
- **Long-form context**: With a 32k token context length, Voxtral handles audios up to 30 minutes for transcription, or 40 minutes for understanding
- **Built-in Q&A and summarization**: Supports asking questions directly through audio. Analyze audio and generate structured summaries without the need for separate ASR and language models
- **Natively multilingual**: Automatic language detection and state-of-the-art performance in the world’s most widely used languages (English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian)
- **Function-calling straight from voice**: Enables direct triggering of backend functions, workflows, or API calls based on spoken user intents
- **Highly capable at text**: Retains the text understanding capabilities of its language model backbone, Ministral-3B
## Benchmark Results
### Audio
Average word error rate (WER) over the FLEURS, Mozilla Common Voice and Multilingual LibriSpeech benchmarks:
### Text
## Usage
The model can be used with the following frameworks;
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
**Notes**:
- `temperature=0.2` and `top_p=0.95` for chat completion (*e.g. Audio Understanding*) and `temperature=0.0` for transcription
- Multiple audios per message and multiple user turns with audio are supported
- System prompts are not yet supported
### vLLM (recommended)
We recommend using this model with [vLLM](https://github.com/vllm-project/vllm).
#### Installation
Make sure to install vllm from "main", we recommend using `uv`:
```
uv pip install -U "vllm[audio]" --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly
```
Doing so should automatically install [`mistral_common >= 1.8.1`](https://github.com/mistralai/mistral-common/releases/tag/v1.8.1).
To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```
#### Offline
You can test that your vLLM setup works as expected by cloning the vLLM repo:
```sh
git clone https://github.com/vllm-project/vllm && cd vllm
```
and then running:
```sh
python examples/offline_inference/audio_language.py --num-audios 2 --model-type voxtral
```
#### Serve
We recommend that you use Voxtral-Small-24B-2507 in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Voxtral-Mini-3B-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral
```
**Note:** Running Voxtral-Mini-3B-2507 on GPU requires ~9.5 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet. See the following examples.
### Audio Instruct
Leverage the audio capabilities of Voxtral-Mini-3B-2507 to chat.
Make sure that your client has `mistral-common` with audio installed:
```sh
pip install --upgrade mistral_common\[audio\]
```
Python snippet
```py
from mistral_common.protocol.instruct.messages import TextChunk, AudioChunk, UserMessage, AssistantMessage, RawAudio
from mistral_common.audio import Audio
from huggingface_hub import hf_hub_download
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset")
bcn_file = hf_hub_download("patrickvonplaten/audio_samples", "bcn_weather.mp3", repo_type="dataset")
def file_to_chunk(file: str) -> AudioChunk:
audio = Audio.from_file(file, strict=False)
return AudioChunk.from_audio(audio)
text_chunk = TextChunk(text="Which speaker is more inspiring? Why? How are they different from each other?")
user_msg = UserMessage(content=[file_to_chunk(obama_file), file_to_chunk(bcn_file), text_chunk]).to_openai()
print(30 * "=" + "USER 1" + 30 * "=")
print(text_chunk.text)
print("\n\n")
response = client.chat.completions.create(
model=model,
messages=[user_msg],
temperature=0.2,
top_p=0.95,
)
content = response.choices[0].message.content
print(30 * "=" + "BOT 1" + 30 * "=")
print(content)
print("\n\n")
# The speaker who is more inspiring is the one who delivered the farewell address, as they express
# gratitude, optimism, and a strong commitment to the nation and its citizens. They emphasize the importance of
# self-government and active citizenship, encouraging everyone to participate in the democratic process. In contrast,
# the other speaker provides a factual update on the weather in Barcelona, which is less inspiring as it
# lacks the emotional and motivational content of the farewell address.
# **Differences:**
# - The farewell address speaker focuses on the values and responsibilities of citizenship, encouraging active participation in democracy.
# - The weather update speaker provides factual information about the temperature in Barcelona, without any emotional or motivational content.
messages = [
user_msg,
AssistantMessage(content=content).to_openai(),
UserMessage(content="Ok, now please summarize the content of the first audio.").to_openai()
]
print(30 * "=" + "USER 2" + 30 * "=")
print(messages[-1]["content"])
print("\n\n")
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=0.2,
top_p=0.95,
)
content = response.choices[0].message.content
print(30 * "=" + "BOT 2" + 30 * "=")
print(content)
```
#### Transcription
Voxtral-Mini-3B-2507 has powerful transcription capabilities!
Make sure that your client has `mistral-common` with audio installed:
```sh
pip install --upgrade mistral_common\[audio\]
```
Python snippet
```python
from mistral_common.protocol.transcription.request import TranscriptionRequest
from mistral_common.protocol.instruct.messages import RawAudio
from mistral_common.audio import Audio
from huggingface_hub import hf_hub_download
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset")
audio = Audio.from_file(obama_file, strict=False)
audio = RawAudio.from_audio(audio)
req = TranscriptionRequest(model=model, audio=audio, language="en", temperature=0.0).to_openai(exclude=("top_p", "seed"))
response = client.audio.transcriptions.create(**req)
print(response)
```