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34 Pith papers citing it
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We present Voxtral Mini and Voxtral Small, two multimodal audio chat models. Voxtral is trained to comprehend both spoken audio and text documents, achieving state-of-the-art performance across a diverse range of audio benchmarks, while preserving strong text capabilities. Voxtral Small outperforms a number of closed-source models, while being small enough to run locally. A 32K context window enables the model to handle audio files up to 40 minutes in duration and long multi-turn conversations. We also contribute three benchmarks for evaluating speech understanding models on knowledge and trivia. Both Voxtral models are released under Apache 2.0 license.

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Benchmarking Speech-to-Speech Translation Models

cs.CL · 2026-06-02 · unverdicted · novelty 6.0

COMPASS is a new reproducible benchmarking framework for S2ST that deploys 46 metrics on 1248 configurations, shows single-metric rankings mislead, reduces to 10 metrics per direction, and finds domain-specific metrics better match human judgments than standalone MOS predictors.

MURMUR: An Efficient Inference System for Long-Form ASR

cs.LG · 2026-05-31 · conditional · novelty 6.0

Murmur matches single-pass long-context ASR accuracy on AMI-IHM while cutting latency 4.2x by tuning chunk size and using intra-chunk attention sparsity via KV eviction.

Voxtral Realtime

cs.AI · 2026-02-11 · unverdicted · novelty 6.0

Voxtral Realtime is an end-to-end trained streaming ASR model that achieves Whisper-level transcription quality at 480ms delay after scaling pretraining across 13 languages.

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