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<!DOCTYPE html> <html lang="en" id="facebook" class="no_js"> <head><meta charset="utf-8" /><meta name="referrer" content="default" id="meta_referrer" /><script nonce="J8fTmz4L">function envFlush(a){function b(b){for(var c in a)b[c]=a[c]}window.requireLazy?window.requireLazy(["Env"],b):(window.Env=window.Env||{},b(window.Env))}envFlush({"useTrustedTypes":true,"isTrustedTypesReportOnly":false,"ajaxpipe_token":"AXhX59utXvgfg_U790Y","stack_trace_limit":30,"timesliceBufferSize":5000,"show_invariant_decoder":false,"compat_iframe_token":"AUVqxdLM0fgTJgKo_KUHrEtl0Vc","isCQuick":false,"brsid":"7440565360692635894"});</script><script nonce="J8fTmz4L">(function(a){function b(a){var b;a=Object.getOwnPropertyDescriptor(Document.prototype,a);if(a===void 0||a.configurable!==!0)return null;b=(b=a.set)==null?void 0:b.bind(document);a=(a=a.get)==null?void 0:a.bind(document);return b===void 0||a===void 0?null:{get:a,set:b}}function c(c){var d=b("cookie");if(d==null)return;var e=!1;c.I_AM_CORE_COOKIE_INFRASTRUCTURE_AND_NEED_TO_ACCESS_COOKIES=function(){if(e)throw new Error("CookieStore already initialized");e=!0;return d};Object.defineProperty(document,"cookie",{get:function(){throw new Error()},set:function(a){throw new Error()}})}function d(){var a=b("domain");if(a==null)return;Object.defineProperty(document,"domain",{get:function(){var b=a.get();return b==null?null:b},set:function(a){throw new Error()}})}c(a);d()})(this);</script><script nonce="J8fTmz4L">window.openDatabase&&(window.openDatabase=function(){throw new Error()});</script><script nonce="J8fTmz4L">_btldr={};</script><script nonce="J8fTmz4L">function parentIsNotHeadNorBody(a){return a.parentElement!==document.body&&a.parentElement!==document.head}function isTagSupported(a){return a.nodeName==="SCRIPT"||a.nodeName==="LINK"&&((a=getNodeDataSet(a))==null?void 0:a.asyncCss)}function getNodeDataSet(a){return!(a.dataset instanceof window.DOMStringMap)?null:a.dataset}function addLoadEventListeners(a){var b;try{if(a.nodeType!==Node.ELEMENT_NODE)return}catch(a){return}if(parentIsNotHeadNorBody(a)||!isTagSupported(a))return;var c=(b=getNodeDataSet(a))==null?void 0:b.bootloaderHash;if(c!=null&&c!==""){var d=null,e=function(){window._btldr[c]=1,d==null?void 0:d()};d=function(){a.removeEventListener("load",e),a.removeEventListener("error",e)};a.addEventListener("load",e);a.addEventListener("error",e)}}(function(){Array.from(document.querySelectorAll('script,link[data-async-css="1"]')).forEach(function(a){return addLoadEventListeners(a)});var a=new MutationObserver(function(a,b){a.forEach(function(a){a.type==="childList"&&Array.from(a.addedNodes).forEach(function(a){addLoadEventListeners(a)})})});a.observe(document.getElementsByTagName("html")[0],{attributes:!1,childList:!0,subtree:!0})})();</script><style nonce="J8fTmz4L"></style><script nonce="J8fTmz4L">__DEV__=0;</script><noscript><meta http-equiv="refresh" content="0; URL=/blog/muavic-audio-visual-speech-translation-benchmark/?_fb_noscript=1" /></noscript><title id="pageTitle">MuAViC: The first audio-video speech translation benchmark</title><meta name="bingbot" content="noarchive" /><meta name="description" content="Introducing MuAViC — A new benchmark for audio-visual learning for robust speech translation. We used it to train model to translate speech in noisy,..." /><link rel="canonical" href="https://ai.meta.com/blog/muavic-audio-visual-speech-translation-benchmark/" /><meta name="viewport" content="width=device-width, initial-scale=1" /><meta name="title" content="MuAViC: The first audio-video speech translation benchmark" /><meta name="og:title" content="MuAViC: The first audio-video speech translation benchmark" /><meta name="description" content="Introducing MuAViC — A new benchmark for audio-visual learning for robust speech translation. We used it to train model to translate speech in noisy, challenging settings, achieving results outperforming other leading translation models." /><meta name="og:description" content="Introducing MuAViC — A new benchmark for audio-visual learning for robust speech translation. We used it to train model to translate speech in noisy, challenging settings, achieving results outperforming other leading translation models." /><meta property="og:image" content="https://scontent-hkg4-1.xx.fbcdn.net/v/t39.2365-6/334793505_583125787173687_542838236294006040_n.png?_nc_cat=100&ccb=1-7&_nc_sid=e280be&_nc_ohc=XFk2-Xww34kQ7kNvgEFlHx4&_nc_zt=14&_nc_ht=scontent-hkg4-1.xx&_nc_gid=Ayv7xMdTrLE4ezT5UBaReqb&oh=00_AYCxrrh7ZF0lnQNpGMtbHv85N53_dFRdG0Z2c-q5qkbYYA&oe=675C8DC4" /><meta property="twitter:description" content="Introducing MuAViC — A new benchmark for audio-visual learning for robust speech translation. We used it to train model to translate speech in noisy, challenging settings, achieving results outperforming other leading translation models." /><meta name="og:url" content="https://ai.meta.com/blog/muavic-audio-visual-speech-translation-benchmark/" /><meta name="url" content="https://ai.meta.com/blog/muavic-audio-visual-speech-translation-benchmark/" /><meta property="twitter:title" content="MuAViC: The first audio-video speech translation benchmark" /><meta property="twitter:image" content="https://ai.meta.com/static-resource/default-meta-image/" /><meta name="twitter:card" content="summary" /><link rel="icon" href="https://static.xx.fbcdn.net/rsrc.php/v3/y4/r/WUJbsVI4ruF.png" /><link type="text/css" rel="stylesheet" href="https://static.xx.fbcdn.net/rsrc.php/v4/yv/l/1,cross/KaxZ1Da-YEc.css" data-bootloader-hash="sjogf/J" crossorigin="anonymous" /> <link type="text/css" rel="stylesheet" href="https://static.xx.fbcdn.net/rsrc.php/v4/y_/l/1,cross/i1du4dXRvDt.css" 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sound of traffic, music, other people speaking – makes it more difficult to understand what other people are saying. Humans often use information from our other senses, especially vision, to help us communicate (as pointed out by Harry McGurk and John MacDonald in their 1976 study “<a href="https://en.wikipedia.org/wiki/McGurk_effect" target="_blank" data-lnfb-mode="ie">Hearing Lips and Seeing Voices</a>”). For example, if you're speaking to a friend at a loud concert, you will likely focus on their face to supplement what you can hear. </p><p class="_8w6f _8w61 _8w6h"> AI researchers have recently built systems (such as Meta AI’s publicly available <a href="https://ai.facebook.com/blog/ai-that-understands-speech-by-looking-as-well-as-hearing/" target="_blank">AV-HuBERT</a> and <a href="https://arxiv.org/abs/2212.06246" target="_blank" data-lnfb-mode="ie">RAVen</a> models) that use visual information to improve performance for English speech recognition tasks. Now, Meta AI is releasing <a href="https://arxiv.org/abs/2303.00628" target="_blank" data-lnfb-mode="ie">MuAViC (Multilingual Audio-Visual Corpus),</a> the first benchmark that makes it possible to use audio-visual learning for highly accurate speech translation. We’ve used MuAViC to train our AV-HuBERT model to translate speech in noisy, challenging settings, where it outperforms other leading translation models. </p><p class="_8w6f _8w61 _8w6h"><div class="_8zkc" id="u_0_7_iR"><div class="_1c_u _8zkd" id="u_0_8_e3"><div class="_53j5 _37u6 _9kkd" style="padding-bottom:56.25%;" tabindex="0" id="u_0_9_SL"><video muted="1" height="360" width="640" preload="metadata" style="" class="_ox1 _21y0" data-video-width="640" data-video-height="360" id="u_0_a_gN"></video><div class="_4ubd _170l _27db _3htz" id="u_0_b_b4"></div><div class="_m54 _1jto _3htz" id="u_0_c_ED"><img class="_1445 _2sy9 img" src="https://static.xx.fbcdn.net/rsrc.php/v3/y4/r/-PAXP-deijE.gif" alt="" style="background-image: url('https\3a //scontent-hkg1-2.xx.fbcdn.net/v/t15.5256-10/334906210_142986888396949_8554318088610587755_n.jpg?stp\3d dst-jpg_s640x640_tt6\26 _nc_cat\3d 103\26 ccb\3d 1-7\26 _nc_sid\3d d919f7\26 _nc_ohc\3d bZYA95sluTwQ7kNvgHPTitw\26 _nc_zt\3d 23\26 _nc_ht\3d scontent-hkg1-2.xx\26 _nc_gid\3d Ayv7xMdTrLE4ezT5UBaReqb\26 oh\3d 00_AYCdzTljszKXVYy9l95gS94eS5FAsoK9TJX_M1BOdjFMCQ\26 oe\3d 6747FE53');" id="u_0_d_MS" /></div><div class="_18xu _9omd _3htz"><div class="_5l7x _360f _167h hidden_elem" id="u_0_1_0v"></div><input aria-label="播放影片" class="_530-" type="button" id="u_0_0_R9" /></div><div class="_4ctp _1jto _3htz"><div class="_4ctq"><div class="_4ctr" id="u_0_e_Dk">發生錯誤</div><div id="u_0_f_Nd">播放此影片時發生問題。若要觀看影片，請升級網頁瀏覽器。</div><div class="_5hdy"><a class="_4cts" href="/unsupportedbrowser" id="u_0_g_qJ">了解詳情</a></div></div></div></div><div class="_2i84"><div id="u_0_h_2P"></div></div></div></div></p><div class="_7dik _7f2d"><div class="_7dil _9whr"></div><p class="_8w6f _8w6g"><div>In this example, the AV-HuBERT model's transcription contains one error ("Either" instead of "Hi there") but still achieves much greater accuracy than the other model.</div></p></div><p class="_8w6f _8w61 _8w6h"> With <a href="https://ai.facebook.com/blog/teaching-ai-to-translate-100s-of-spoken-and-written-languages-in-real-time/" target="_blank">No Language Left Behind</a> and <a href="https://ai.facebook.com/blog/ai-translation-hokkien/" target="_blank">Universal Speech Translator</a>, Meta has focused on speech translation research because it has incredible potential to break down communication barriers and bring people together. We’re excited to see how others in the research community use MuAViC to create translation systems that work well in real-world conditions. </p><h2 class="_8w6a _8w6c _8w61">Creating MuAViC</h2><p class="_8w6f _8w61 _8w6h"> Because of the scarcity of suitable training data, extending audio-visual understanding to speech translation was previously unexplored. Collecting and processing audio-video data typically requires more resources than collecting audio data alone. </p><p class="_8w6f _8w61 _8w6h"><a href="https://arxiv.org/abs/2303.00628" target="_blank" data-lnfb-mode="ie">MuAViC</a> is the first benchmark for audio-video speech translation and the largest multilingual benchmark for audio-video speech recognition. It contains roughly 1,200 hours of transcribed data spanning 9 languages. </p><p class="_8w6f _8w61 _8w6h"> For English talks, we reuse audio-visual data from the LRS3 dataset and align it with a machine translation corpus using a text-matching algorithm. Matched examples are then paired with the corresponding target sentences in the machine translation corpus for translation labels. We apply exact text matching for development set and test set examples to ensure the best accuracy. For training set examples without a match, we acquire pseudo-translation labels from a machine translation model. </p><p class="_8w6f _8w61 _8w6h"> For non-English talks, we reuse the audio-only data, transcriptions, and text translations collected in the speech translation dataset. To add the visual modality, we acquire video tracks of the original recordings and align processed video data with the audio data to create audio-visual data. Although all the audio data is transcribed, only a subset of it is translated. We acquire pseudo-translation labels using the same machine translation model as earlier. </p><h2 class="_8w6a _8w6c _8w61">Training end-to-end models</h2><p class="_8w6f _8w61 _8w6h"> We used Meta’s AV-HuBERT architecture to create end-to-end audio-video speech recognition and audio-video speech translation models. Given an aligned pair of audio-video data, our model is able to process both modalities and fuse their representations into a unified space that can be used for either speech recognition or translation tasks. And if either modality is missing, AV-HuBERT can still process the available input modality (but with less efficiency). </p><p class="_8w6f _8w61 _8w6h"><div class="_8zkc" id="u_0_i_yc"><div class="_1c_u _8zkd" id="u_0_j_7+"><div class="_53j5 _37u6 _9kkd" style="padding-bottom:56.25%;" tabindex="0" id="u_0_k_dD"><video muted="1" height="360" width="640" preload="metadata" style="" class="_ox1 _21y0" data-video-width="640" data-video-height="360" id="u_0_l_3T"></video><div class="_4ubd _170l _27db _3htz" id="u_0_m_z9"></div><div class="_m54 _1jto _3htz" id="u_0_n_J4"><img class="_1445 _2sy9 img" src="https://static.xx.fbcdn.net/rsrc.php/v3/y4/r/-PAXP-deijE.gif" alt="" style="background-image: url('https\3a //scontent-hkg1-1.xx.fbcdn.net/v/t15.5256-10/334979346_150142890948596_1342769049357495047_n.jpg?stp\3d dst-jpg_s640x640\26 _nc_cat\3d 105\26 ccb\3d 1-7\26 _nc_sid\3d d919f7\26 _nc_ohc\3d KRIYv6dQTjEQ7kNvgHR0erL\26 _nc_zt\3d 23\26 _nc_ht\3d scontent-hkg1-1.xx\26 _nc_gid\3d Ayv7xMdTrLE4ezT5UBaReqb\26 oh\3d 00_AYBRoFml-KfxlzQ8Xz9EqTVlYJZfqNA4Ahff3o2FNO2wFg\26 oe\3d 6747F638');" id="u_0_o_eX" /></div><div class="_18xu _9omd _3htz"><div class="_5l7x _360f _167h hidden_elem" id="u_0_3_ad"></div><input aria-label="播放影片" class="_530-" type="button" id="u_0_2_QY" /></div><div class="_4ctp _1jto _3htz"><div class="_4ctq"><div class="_4ctr" id="u_0_p_92">發生錯誤</div><div id="u_0_q_Yb">播放此影片時發生問題。若要觀看影片，請升級網頁瀏覽器。</div><div class="_5hdy"><a class="_4cts" href="/unsupportedbrowser" id="u_0_r_kP">了解詳情</a></div></div></div></div><div class="_2i84"><div id="u_0_s_ve"></div></div></div></div></p><div class="_7dik _7f2d"><div class="_7dil _9whr"></div><p class="_8w6f _8w6g"><div>In this video, the model must deal with background music (rather than background noise, as in the first video above).</div></p></div><p class="_8w6f _8w61 _8w6h"> Our model’s most noteworthy feature is its robustness to noise. If the audio modality is distorted because of noise or any other factor, the model will rely more on the visual modality to perform the task properly. We tested our models against a state-of-the-art model for speech recognition and X-En speech translation tasks in environments both with and without noise. </p><p class="_8w6f _8w61 _8w6h"><img class="_8zlc _7f2d img" src="https://scontent-hkg4-1.xx.fbcdn.net/v/t39.2365-6/334768851_151406987465301_3864540730167445048_n.png?_nc_cat=106&ccb=1-7&_nc_sid=e280be&_nc_ohc=upb5T3bvc50Q7kNvgGEHMHr&_nc_zt=14&_nc_ht=scontent-hkg4-1.xx&_nc_gid=Ayv7xMdTrLE4ezT5UBaReqb&oh=00_AYBRfHhjYBgORBv1H6zBoBaCUOeuObT6XXMxhxusnQ86tw&oe=675C5B72" alt="" /><div class="_7dik _7f2d"><div class="_7dil _9whr"></div><p class="_8w6f _8w6g"><div>This chart compares model performance on speech recognition tasks spanning nine different languages. Meta's AV-HuBERT model doesn't degrade significantly in noisy environments, while the current state-of-the-art model does.</div></p></div></p><p class="_8w6f _8w61 _8w6h"><img class="_8zlc _7f2d img" src="https://scontent-hkg4-1.xx.fbcdn.net/v/t39.2365-6/334783963_222407923496558_7677917660748681202_n.jpg?_nc_cat=100&ccb=1-7&_nc_sid=e280be&_nc_ohc=P7Z_DLHeWI8Q7kNvgEh7n-T&_nc_zt=14&_nc_ht=scontent-hkg4-1.xx&_nc_gid=Ayv7xMdTrLE4ezT5UBaReqb&oh=00_AYAfawLrWMif0tFPNHX4z69V4ULa7T6yhtzJeZ47I2WJBg&oe=675C5998" alt="" /><div class="_7dik _7f2d"><div class="_7dil _9whr"></div><p class="_8w6f _8w6g"><div>Similarly, the performance of Meta’s AV-HuBERT model does not significantly degrade compared with that of the state-of-the-art model, using the X-En speech translation task spanning six different languages.</div></p></div></p><h2 class="_8w6a _8w6c _8w61">Toward robust speech translation</h2><p class="_8w6f _8w61 _8w6h"> MuAVic enables researchers to build robust speech recognition and translation systems for different languages. We’ve released <a href="https://github.com/facebookresearch/muavic" target="_blank" data-lnfb-mode="ie">the corpus</a> as well as our audio-visual speech recognition and translation models covering nine different languages. We hope this will help the community build even better, more robust speech models. We are excited about the future of powerful robust models. </p><p class="_8w6f _8w61 _8w6h"><h2 class="_8w6a _8w6c _8w61">Read the full papers:</h2></p><ul class="_7fd2 _7fd3 _7f2d"><li><p class="_8w6f _8w61 _8w6h"><a href="https://arxiv.org/abs/2303.00628" target="_blank" data-lnfb-mode="ie">MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation</a></p></li><li><p class="_8w6f _8w61 _8w6h"><a href="https://github.com/facebookresearch/muavic" target="_blank" data-lnfb-mode="ie">MuAViC GitHub Repository</a></p></li></ul><p class="_8w6f _8w61 _8w6h"><i>We’d like to acknowledge the contributions of Vedanju Goswami, Wei-Ning Hsu, Bowen Shi to the work discussed in this blog post. </i></p></div><div class="_8znc"><div class="_8zne"><h2 class="_8w6a _8w6c _8w61">Written By</h2></div><div class="_8znd"><div class="_8znf" id="u_0_t_pB"><div class="_8xm7" data-ms="{"creative":"card","creative_detail":"card","create_type":"card","create_type_detail":"card"}"><div class="_8wpt"><div class="_8y3f"><div class="_8zmo"><div class="_8y3g"><div class="_a56-"><h4 class="_8w6a _8w6e _8w61">Mohamed Anwar</h4></div></div><p class="_8w6f _8xm4 _8w61 _8w6g">Meta AI Resident</p></div></div></div></div></div><div class="_8znf" id="u_0_u_Gt"><div class="_8xm7" data-ms="{"creative":"card","creative_detail":"card","create_type":"card","create_type_detail":"card"}"><div class="_8wpt"><div class="_8y3f"><div class="_8zmo"><div class="_8y3g"><div class="_a56-"><h4 class="_8w6a _8w6e _8w61">Jeff Wang</h4></div></div><p class="_8w6f _8xm4 _8w61 _8w6g">Product Manager</p></div></div></div></div></div><div class="_8znf" id="u_0_v_9b"><div class="_8xm7" data-ms="{"creative":"card","creative_detail":"card","create_type":"card","create_type_detail":"card"}"><div class="_8wpt"><div class="_8y3f"><div class="_8zmo"><div class="_8y3g"><div class="_a56-"><h4 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