526 lines
16 KiB
Markdown
526 lines
16 KiB
Markdown
---
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library_name: mistral-common
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language:
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- en
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- fr
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- de
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- es
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- it
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- pt
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- nl
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- hi
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license: apache-2.0
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inference: false
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extra_gated_description: >-
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If you want to learn more about how we process your personal data, please read
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our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
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tags:
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- vllm
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---
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# Voxtral Mini 1.0 (3B) - 2507
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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.
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Learn more about Voxtral in our blog post [here](https://mistral.ai/news/voxtral) and our [research paper](https://arxiv.org/abs/2507.13264).
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## Key Features
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Voxtral builds upon Ministral-3B with powerful audio understanding capabilities.
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- **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
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- **Long-form context**: With a 32k token context length, Voxtral handles audios up to 30 minutes for transcription, or 40 minutes for understanding
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- **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
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- **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)
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- **Function-calling straight from voice**: Enables direct triggering of backend functions, workflows, or API calls based on spoken user intents
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- **Highly capable at text**: Retains the text understanding capabilities of its language model backbone, Ministral-3B
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## Benchmark Results
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### Audio
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Average word error rate (WER) over the FLEURS, Mozilla Common Voice and Multilingual LibriSpeech benchmarks:
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### Text
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## Usage
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The model can be used with the following frameworks;
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- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
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- [`Transformers` 🤗](https://github.com/huggingface/transformers): See [here](#transformers-🤗)
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**Notes**:
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- `temperature=0.2` and `top_p=0.95` for chat completion (*e.g. Audio Understanding*) and `temperature=0.0` for transcription
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- Multiple audios per message and multiple user turns with audio are supported
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- System prompts are not yet supported
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### vLLM (recommended)
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We recommend using this model with [vLLM](https://github.com/vllm-project/vllm).
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#### Installation
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Make sure to install vllm >= 0.10.0, we recommend using `uv`:
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```
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uv pip install -U "vllm[audio]" --system
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```
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Doing so should automatically install [`mistral_common >= 1.8.1`](https://github.com/mistralai/mistral-common/releases/tag/v1.8.1).
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To check:
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```
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python -c "import mistral_common; print(mistral_common.__version__)"
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```
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#### Offline
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You can test that your vLLM setup works as expected by cloning the vLLM repo:
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```sh
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git clone https://github.com/vllm-project/vllm && cd vllm
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```
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and then running:
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```sh
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python examples/offline_inference/audio_language.py --num-audios 2 --model-type voxtral
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```
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#### Serve
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We recommend that you use Voxtral-Small-24B-2507 in a server/client setting.
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1. Spin up a server:
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```
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vllm serve mistralai/Voxtral-Mini-3B-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral
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```
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**Note:** Running Voxtral-Mini-3B-2507 on GPU requires ~9.5 GB of GPU RAM in bf16 or fp16.
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2. To ping the client you can use a simple Python snippet. See the following examples.
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### Audio Instruct
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Leverage the audio capabilities of Voxtral-Mini-3B-2507 to chat.
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Make sure that your client has `mistral-common` with audio installed:
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```sh
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pip install --upgrade mistral_common\[audio\]
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```
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<details>
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<summary>Python snippet</summary>
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```py
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from mistral_common.protocol.instruct.messages import TextChunk, AudioChunk, UserMessage, AssistantMessage, RawAudio
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from mistral_common.audio import Audio
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from huggingface_hub import hf_hub_download
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from openai import OpenAI
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# Modify OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://<your-server-host>:8000/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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models = client.models.list()
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model = models.data[0].id
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obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset")
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bcn_file = hf_hub_download("patrickvonplaten/audio_samples", "bcn_weather.mp3", repo_type="dataset")
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def file_to_chunk(file: str) -> AudioChunk:
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audio = Audio.from_file(file, strict=False)
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return AudioChunk.from_audio(audio)
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text_chunk = TextChunk(text="Which speaker is more inspiring? Why? How are they different from each other?")
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user_msg = UserMessage(content=[file_to_chunk(obama_file), file_to_chunk(bcn_file), text_chunk]).to_openai()
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print(30 * "=" + "USER 1" + 30 * "=")
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print(text_chunk.text)
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print("\n\n")
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response = client.chat.completions.create(
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model=model,
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messages=[user_msg],
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temperature=0.2,
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top_p=0.95,
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)
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content = response.choices[0].message.content
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print(30 * "=" + "BOT 1" + 30 * "=")
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print(content)
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print("\n\n")
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# The speaker who is more inspiring is the one who delivered the farewell address, as they express
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# gratitude, optimism, and a strong commitment to the nation and its citizens. They emphasize the importance of
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# self-government and active citizenship, encouraging everyone to participate in the democratic process. In contrast,
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# the other speaker provides a factual update on the weather in Barcelona, which is less inspiring as it
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# lacks the emotional and motivational content of the farewell address.
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# **Differences:**
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# - The farewell address speaker focuses on the values and responsibilities of citizenship, encouraging active participation in democracy.
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# - The weather update speaker provides factual information about the temperature in Barcelona, without any emotional or motivational content.
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messages = [
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user_msg,
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AssistantMessage(content=content).to_openai(),
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UserMessage(content="Ok, now please summarize the content of the first audio.").to_openai()
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]
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print(30 * "=" + "USER 2" + 30 * "=")
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print(messages[-1]["content"])
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print("\n\n")
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response = client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=0.2,
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top_p=0.95,
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)
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content = response.choices[0].message.content
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print(30 * "=" + "BOT 2" + 30 * "=")
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print(content)
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```
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</details>
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#### Transcription
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Voxtral-Mini-3B-2507 has powerful transcription capabilities!
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Make sure that your client has `mistral-common` with audio installed:
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```sh
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pip install --upgrade mistral_common\[audio\]
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```
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<details>
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<summary>Python snippet</summary>
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```python
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from mistral_common.protocol.transcription.request import TranscriptionRequest
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from mistral_common.protocol.instruct.messages import RawAudio
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from mistral_common.audio import Audio
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from huggingface_hub import hf_hub_download
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from openai import OpenAI
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# Modify OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://<your-server-host>:8000/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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models = client.models.list()
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model = models.data[0].id
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obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset")
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audio = Audio.from_file(obama_file, strict=False)
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audio = RawAudio.from_audio(audio)
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req = TranscriptionRequest(model=model, audio=audio, language="en", temperature=0.0).to_openai(exclude=("top_p", "seed"))
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response = client.audio.transcriptions.create(**req)
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print(response)
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```
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</details>
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### Transformers 🤗
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Starting with `transformers >= 4.54.0` and above, you can run Voxtral natively!
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Install Transformers:
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```bash
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pip install -U transformers
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```
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Make sure to have `mistral-common >= 1.8.1` installed with audio dependencies:
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```bash
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pip install --upgrade "mistral-common[audio]"
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```
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#### Audio Instruct
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<details>
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<summary>➡️ multi-audio + text instruction</summary>
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```python
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from transformers import VoxtralForConditionalGeneration, AutoProcessor
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import torch
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device = "cuda"
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repo_id = "mistralai/Voxtral-Mini-3B-2507"
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processor = AutoProcessor.from_pretrained(repo_id)
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model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
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conversation = [
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{
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"role": "user",
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"content": [
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/mary_had_lamb.mp3",
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},
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
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},
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{"type": "text", "text": "What sport and what nursery rhyme are referenced?"},
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],
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}
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]
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inputs = processor.apply_chat_template(conversation)
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inputs = inputs.to(device, dtype=torch.bfloat16)
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outputs = model.generate(**inputs, max_new_tokens=500)
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decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("\nGenerated response:")
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print("=" * 80)
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print(decoded_outputs[0])
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print("=" * 80)
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```
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</details>
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<details>
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<summary>➡️ multi-turn</summary>
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```python
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from transformers import VoxtralForConditionalGeneration, AutoProcessor
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import torch
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device = "cuda"
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repo_id = "mistralai/Voxtral-Mini-3B-2507"
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processor = AutoProcessor.from_pretrained(repo_id)
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model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
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conversation = [
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{
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"role": "user",
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"content": [
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
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},
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
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},
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{"type": "text", "text": "Describe briefly what you can hear."},
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],
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},
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{
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"role": "assistant",
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"content": "The audio begins with the speaker delivering a farewell address in Chicago, reflecting on his eight years as president and expressing gratitude to the American people. The audio then transitions to a weather report, stating that it was 35 degrees in Barcelona the previous day, but the temperature would drop to minus 20 degrees the following day.",
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},
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{
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"role": "user",
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"content": [
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
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},
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{"type": "text", "text": "Ok, now compare this new audio with the previous one."},
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],
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},
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]
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inputs = processor.apply_chat_template(conversation)
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inputs = inputs.to(device, dtype=torch.bfloat16)
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outputs = model.generate(**inputs, max_new_tokens=500)
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decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("\nGenerated response:")
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print("=" * 80)
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print(decoded_outputs[0])
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print("=" * 80)
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```
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</details>
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<details>
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<summary>➡️ text only</summary>
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```python
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from transformers import VoxtralForConditionalGeneration, AutoProcessor
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import torch
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device = "cuda"
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repo_id = "mistralai/Voxtral-Mini-3B-2507"
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processor = AutoProcessor.from_pretrained(repo_id)
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model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
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conversation = [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "Why should AI models be open-sourced?",
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},
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],
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}
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]
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inputs = processor.apply_chat_template(conversation)
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inputs = inputs.to(device, dtype=torch.bfloat16)
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outputs = model.generate(**inputs, max_new_tokens=500)
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decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("\nGenerated response:")
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print("=" * 80)
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print(decoded_outputs[0])
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print("=" * 80)
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```
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</details>
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<details>
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<summary>➡️ audio only</summary>
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```python
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from transformers import VoxtralForConditionalGeneration, AutoProcessor
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import torch
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device = "cuda"
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repo_id = "mistralai/Voxtral-Mini-3B-2507"
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processor = AutoProcessor.from_pretrained(repo_id)
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model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
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conversation = [
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{
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"role": "user",
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"content": [
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
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},
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],
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}
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]
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inputs = processor.apply_chat_template(conversation)
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inputs = inputs.to(device, dtype=torch.bfloat16)
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outputs = model.generate(**inputs, max_new_tokens=500)
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decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("\nGenerated response:")
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print("=" * 80)
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print(decoded_outputs[0])
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print("=" * 80)
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```
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</details>
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<details>
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<summary>➡️ batched inference</summary>
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```python
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from transformers import VoxtralForConditionalGeneration, AutoProcessor
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import torch
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device = "cuda"
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repo_id = "mistralai/Voxtral-Mini-3B-2507"
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processor = AutoProcessor.from_pretrained(repo_id)
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model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
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conversations = [
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[
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{
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"role": "user",
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"content": [
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
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},
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
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},
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{
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"type": "text",
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"text": "Who's speaking in the speach and what city's weather is being discussed?",
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},
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],
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}
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],
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[
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{
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"role": "user",
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"content": [
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{
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"type": "audio",
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"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
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},
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{"type": "text", "text": "What can you tell me about this audio?"},
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],
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}
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],
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]
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inputs = processor.apply_chat_template(conversations)
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inputs = inputs.to(device, dtype=torch.bfloat16)
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outputs = model.generate(**inputs, max_new_tokens=500)
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decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("\nGenerated responses:")
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print("=" * 80)
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for decoded_output in decoded_outputs:
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print(decoded_output)
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print("=" * 80)
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```
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</details>
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#### Transcription
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<details>
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<summary>➡️ transcribe</summary>
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```python
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from transformers import VoxtralForConditionalGeneration, AutoProcessor
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import torch
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device = "cuda"
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repo_id = "mistralai/Voxtral-Mini-3B-2507"
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processor = AutoProcessor.from_pretrained(repo_id)
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model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
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inputs = processor.apply_transcription_request(language="en", audio="https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3", model_id=repo_id)
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inputs = inputs.to(device, dtype=torch.bfloat16)
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outputs = model.generate(**inputs, max_new_tokens=500)
|
||
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
|
||
|
||
print("\nGenerated responses:")
|
||
print("=" * 80)
|
||
for decoded_output in decoded_outputs:
|
||
print(decoded_output)
|
||
print("=" * 80)
|
||
```
|
||
</details> |