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*This model was released on 2025-07-15 and added to Hugging Face Transformers on 2025-07-18 and contributed by [eustlb](https://huggingface.co/eustlb).*

# Voxtral

[Voxtral](https://huggingface.co/papers/2507.13264) are multimodal audio–text chat models designed to process both spoken audio and written language. They achieve state-of-the-art results on diverse audio benchmarks while maintaining strong text performance. Voxtral Small, despite its compact size and ability to run locally, surpasses several closed-source systems. Both models support a 32K context window—allowing analysis of up to 40-minute audio segments and long conversations—and are released under the Apache 2.0 license along with three new benchmarks for evaluating speech understanding in knowledge and trivia tasks.

<hfoptions id="usage">
<hfoption id="">

```py
import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor

processor = AutoProcessor.from_pretrained( "mistralai/Voxtral-Mini-3B-2507")
model = VoxtralForConditionalGeneration.from_pretrained( "mistralai/Voxtral-Mini-3B-2507", dtype="auto")

inputs = processor.apply_transcription_request(language="en", audio="https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3", model_id=mistralai/Voxtral-Mini-3B-2507)

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)
```

</hfoption>
</hfoptions>

## VoxtralConfig

[[autodoc]] VoxtralConfig

## VoxtralEncoderConfig

[[autodoc]] VoxtralEncoderConfig

## VoxtralProcessor

[[autodoc]] VoxtralProcessor

## VoxtralEncoder

[[autodoc]] VoxtralEncoder
    - forward

## VoxtralForConditionalGeneration

[[autodoc]] VoxtralForConditionalGeneration
    - forward
