ChatPaper.aiChatPaper

ボクストラル・リアルタイム

Voxtral Realtime

February 11, 2026
著者: Alexander H. Liu, Andy Ehrenberg, Andy Lo, Chen-Yo Sun, Guillaume Lample, Jean-Malo Delignon, Khyathi Raghavi Chandu, Patrick von Platen, Pavankumar Reddy Muddireddy, Rohin Arora, Sanchit Gandhi, Sandeep Subramanian, Soham Ghosh, Srijan Mishra, Abhinav Rastogi, Alan Jeffares, Albert Jiang, Alexandre Sablayrolles, Amélie Héliou, Andrew Bai, Angele Lenglemetz, Anmol Agarwal, Anton Eliseev, Antonia Calvi, Arjun Majumdar, Baptiste Bout, Baptiste Rozière, Baudouin De Monicault, Benjamin Tibi, Clémence Lanfranchi, Connor Chen, Corentin Barreau, Corentin Sautier, Cyprien Courtot, Darius Dabert, Diego de las Casas, Elliot Chane-Sane, Enguerrand Paquin, Faruk Ahmed, Federico Baldassarre, Gabrielle Berrada, Gaëtan Ecrepont, Gauthier Guinet, Genevieve Hayes, Georgii Novikov, Giada Pistilli, Guillaume Martin, Gunjan Dhanuka, Gunshi Gupta, Han Zhou, Indraneel Mukherjee, Irene Zhang, Jaeyoung Kim, Jan Ludziejewski, Jason Rute, Joachim Studnia, John Harvill, Jonas Amar, Josselin Somerville Roberts, Julien Tauran, Karmesh Yadav, Kartik Khandelwal, Kush Jain, Laurence Aitchison, Léonard Blier, Lingxiao Zhao, Louis Martin, Lucile Saulnier, Luyu Gao, Maarten Buyl, Manan Sharma, Margaret Jennings, Marie Pellat, Mark Prins, Mathieu Poirée, Mathilde Guillaumin, Matthieu Dinot, Matthieu Futeral, Maxime Darrin, Maximilian Augustin, Mert Unsal, Mia Chiquier, Nathan Grinsztajn, Neha Gupta, Olivier Bousquet, Olivier Duchenne, Patricia Wang, Paul Jacob, Paul Wambergue, Paula Kurylowicz, Philomène Chagniot, Pierre Stock, Piotr Miłoś, Prateek Gupta, Pravesh Agrawal, Quentin Torroba, Ram Ramrakhya, Rishi Shah, Romain Sauvestre, Roman Soletskyi, Rosalie Millner, Sagar Vaze, Samuel Humeau, Siddharth Gandhi, Sumukh Aithal, Szymon Antoniak, Teven Le Scao, Théo Cachet, Theo Simon Sorg, Thibaut Lavril, Thomas Chabal, Thomas Foubert, Thomas Robert, Thomas Wang, Tim Lawson, Tom Bewley, Tom Edwards, Tyler Wang, Valeriia Nemychnikova, Van Phung, Vedant Nanda, Victor Jouault, Virgile Richard, Vladislav Bataev, Wassim Bouaziz, Wen-Ding Li, William Marshall, Xinghui Li, Xingran Guo, Xinyu Yang, Yannic Neuhaus, Yihan Wang, Zaccharie Ramzi, Zhenlin Xu
cs.AI

要旨

本論文では、サブ秒レベルの遅延でオフライン書き起こし精度に匹敵する、ネイティブストリーミング方式の自動音声認識モデル「Voxtral Realtime」を提案する。オフラインモデルをチャンク分割やスライディングウィンドウで適応させる手法とは異なり、Voxtral Realtimeは音声とテキストストリーム間の明示的なアライメントを伴う、エンドツーエンドのストリーミング向けに訓練されている。本アーキテクチャはDelayed Streams Modelingフレームワークを基盤とし、改良された遅延条件付けのために新たな因果的音声エンコーダとAda RMS-Normを導入する。大規模データセット(13言語)を用いた事前学習を実施し、480msの遅延条件下において、広く普及しているオフライン書き起こしシステムWhisperと同等の性能を達成した。本モデルの重みはApache 2.0ライセンスの下で公開する。
English
We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adapt offline models through chunking or sliding windows, Voxtral Realtime is trained end-to-end for streaming, with explicit alignment between audio and text streams. Our architecture builds on the Delayed Streams Modeling framework, introducing a new causal audio encoder and Ada RMS-Norm for improved delay conditioning. We scale pretraining to a large-scale dataset spanning 13 languages. At a delay of 480ms, Voxtral Realtime achieves performance on par with Whisper, the most widely deployed offline transcription system. We release the model weights under the Apache 2.0 license.
PDF50February 14, 2026