Voxtral Small is an enhancement of Mistral Small 3, 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.
Voxtral builds upon Mistral Small 3 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, Mistral Small 3.1
Average word error rate (WER) over the FLEURS, Mozilla Common Voice and Multilingual LibriSpeech benchmarks:
The model can be used with the following frameworks;
vllm (recommended): See here
Notes:
temperature=0.2andtop_p=0.95for chat completion (e.g. Audio Understanding) andtemperature=0.0for transcription- Multiple audios per message and multiple user turns with audio are supported
- Function calling is supported
- System prompts are not yet supported
We recommend using this model with vLLM.
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.
To check:
python -c "import mistral_common; print(mistral_common.__version__)"
You can test that your vLLM setup works as expected by cloning the vLLM repo:
git clone https://github.com/vllm-project/vllm && cd vllm
and then running:
python examples/offline_inference/audio_language.py --num-audios 2 --model-type voxtral
We recommend that you use Voxtral-Small-24B-2507 in a server/client setting.
- Spin up a server:
vllm serve mistralai/Voxtral-Small-24B-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral --tensor-parallel-size 2 --tool-call-parser mistral --enable-auto-tool-choice
Note: Running Voxtral-Small-24B-2507 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
- To ping the client you can use a simple Python snippet. See the following examples.
Leverage the audio capabilities of Voxtral-Small-24B-2507 to chat.
Make sure that your client has mistral-common with audio installed:
pip install --upgrade mistral_common\[audio\]
Python snippet
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://<your-server-host>: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? Answer in French.")
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 model could give the following answer:
# ```L'orateur le plus inspirant est le président.
# Il est plus inspirant parce qu'il parle de ses expériences personnelles
# et de son optimisme pour l'avenir du pays.
# Il est différent de l'autre orateur car il ne parle pas de la météo,
# mais plutôt de ses interactions avec les gens et de son rôle en tant que président.```
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)
Voxtral-Small-24B-2507 has powerful transcription capabilities!
Make sure that your client has mistral-common with audio installed:
pip install --upgrade mistral_common\[audio\]
Python snippet
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://<your-server-host>: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)
Voxtral has some experimental function calling support. You can try as shown below.
Make sure that your client has mistral-common with audio installed:
pip install --upgrade mistral_common\[audio\]
Python snippet
from mistral_common.protocol.instruct.messages import AudioChunk, UserMessage, TextChunk
from mistral_common.protocol.transcription.request import TranscriptionRequest
from mistral_common.protocol.instruct.tool_calls import Function, Tool
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://<your-server-host>:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
tool = Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the user's location.",
},
},
"required": ["location", "format"],
},
)
)
tools = [tool.to_openai()]
weather_like = hf_hub_download("patrickvonplaten/audio_samples", "fn_calling.wav", repo_type="dataset")
def file_to_chunk(file: str) -> AudioChunk:
audio = Audio.from_file(file, strict=False)
return AudioChunk.from_audio(audio)
audio_chunk = file_to_chunk(weather_like)
print(30 * "=" + "Transcription" + 30 * "=")
req = TranscriptionRequest(model=model, audio=audio_chunk.input_audio, language="en", temperature=0.0).to_openai(exclude=("top_p", "seed"))
response = client.audio.transcriptions.create(**req)
print(response.text) # How is the weather in Madrid at the moment?
print("\n")
print(30 * "=" + "Function calling" + 30 * "=")
audio_chunk = file_to_chunk(weather_like)
user_msg = UserMessage(content=[audio_chunk]).to_openai()
response = client.chat.completions.create(
model=model,
messages=[user_msg],
temperature=0.2,
top_p=0.95,
tools=[tool.to_openai()]
)
print(30 * "=" + "BOT 1" + 30 * "=")
print(response.choices[0].message.tool_calls)
print("\n\n")
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