1548 lines
462 KiB
HTML
1548 lines
462 KiB
HTML
<!DOCTYPE html>
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<!-- Created by pdf2htmlEX (https://github.com/pdf2htmlEX/pdf2htmlEX) -->
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<html xmlns="http://www.w3.org/1999/xhtml">
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<head>
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<meta charset="utf-8"/>
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<meta name="generator" content="pdf2htmlEX"/>
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<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"/>
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<style type="text/css">
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/*!
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* Base CSS for pdf2htmlEX
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* Copyright 2012,2013 Lu Wang <coolwanglu@gmail.com>
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* https://github.com/pdf2htmlEX/pdf2htmlEX/blob/master/share/LICENSE
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*/#sidebar{position:absolute;top:0;left:0;bottom:0;width:250px;padding:0;margin:0;overflow:auto}#page-container{position:absolute;top:0;left:0;margin:0;padding:0;border:0}@media screen{#sidebar.opened+#page-container{left:250px}#page-container{bottom:0;right:0;overflow:auto}.loading-indicator{display:none}.loading-indicator.active{display:block;position:absolute;width:64px;height:64px;top:50%;left:50%;margin-top:-32px;margin-left:-32px}.loading-indicator img{position:absolute;top:0;left:0;bottom:0;right:0}}@media print{@page{margin:0}html{margin:0}body{margin:0;-webkit-print-color-adjust:exact}#sidebar{display:none}#page-container{width:auto;height:auto;overflow:visible;background-color:transparent}.d{display:none}}.pf{position:relative;background-color:white;overflow:hidden;margin:0;border:0}.pc{position:absolute;border:0;padding:0;margin:0;top:0;left:0;width:100%;height:100%;overflow:hidden;display:block;transform-origin:0 0;-ms-transform-origin:0 0;-webkit-transform-origin:0 0}.pc.opened{display:block}.bf{position:absolute;border:0;margin:0;top:0;bottom:0;width:100%;height:100%;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;user-select:none}.bi{position:absolute;border:0;margin:0;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;user-select:none}@media print{.pf{margin:0;box-shadow:none;page-break-after:always;page-break-inside:avoid}@-moz-document url-prefix(){.pf{overflow:visible;border:1px solid #fff}.pc{overflow:visible}}}.c{position:absolute;border:0;padding:0;margin:0;overflow:hidden;display:block}.t{position:absolute;white-space:pre;font-size:1px;transform-origin:0 100%;-ms-transform-origin:0 100%;-webkit-transform-origin:0 100%;unicode-bidi:bidi-override;-moz-font-feature-settings:"liga" 0}.t:after{content:''}.t:before{content:'';display:inline-block}.t span{position:relative;unicode-bidi:bidi-override}._{display:inline-block;color:transparent;z-index:-1}::selection{background:rgba(127,255,255,0.4)}::-moz-selection{background:rgba(127,255,255,0.4)}.pi{display:none}.d{position:absolute;transform-origin:0 100%;-ms-transform-origin:0 100%;-webkit-transform-origin:0 100%}.it{border:0;background-color:rgba(255,255,255,0.0)}.ir:hover{cursor:pointer}</style>
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<style type="text/css">
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/*!
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* Fancy styles for pdf2htmlEX
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* Copyright 2012,2013 Lu Wang <coolwanglu@gmail.com>
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* https://github.com/pdf2htmlEX/pdf2htmlEX/blob/master/share/LICENSE
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*/@keyframes fadein{from{opacity:0}to{opacity:1}}@-webkit-keyframes fadein{from{opacity:0}to{opacity:1}}@keyframes swing{0{transform:rotate(0)}10%{transform:rotate(0)}90%{transform:rotate(720deg)}100%{transform:rotate(720deg)}}@-webkit-keyframes swing{0{-webkit-transform:rotate(0)}10%{-webkit-transform:rotate(0)}90%{-webkit-transform:rotate(720deg)}100%{-webkit-transform:rotate(720deg)}}@media screen{#sidebar{background-color:#2f3236;background-image:url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI0IiBoZWlnaHQ9IjQiPgo8cmVjdCB3aWR0aD0iNCIgaGVpZ2h0PSI0IiBmaWxsPSIjNDAzYzNmIj48L3JlY3Q+CjxwYXRoIGQ9Ik0wIDBMNCA0Wk00IDBMMCA0WiIgc3Ryb2tlLXdpZHRoPSIxIiBzdHJva2U9IiMxZTI5MmQiPjwvcGF0aD4KPC9zdmc+")}#outline{font-family:Georgia,Times,"Times New Roman",serif;font-size:13px;margin:2em 1em}#outline ul{padding:0}#outline li{list-style-type:none;margin:1em 0}#outline li>ul{margin-left:1em}#outline a,#outline a:visited,#outline a:hover,#outline a:active{line-height:1.2;color:#e8e8e8;text-overflow:ellipsis;white-space:nowrap;text-decoration:none;display:block;overflow:hidden;outline:0}#outline a:hover{color:#0cf}#page-container{background-color:#9e9e9e;background-image:url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI1IiBoZWlnaHQ9IjUiPgo8cmVjdCB3aWR0aD0iNSIgaGVpZ2h0PSI1IiBmaWxsPSIjOWU5ZTllIj48L3JlY3Q+CjxwYXRoIGQ9Ik0wIDVMNSAwWk02IDRMNCA2Wk0tMSAxTDEgLTFaIiBzdHJva2U9IiM4ODgiIHN0cm9rZS13aWR0aD0iMSI+PC9wYXRoPgo8L3N2Zz4=");-webkit-transition:left 500ms;transition:left 500ms}.pf{margin:13px auto;box-shadow:1px 1px 3px 1px #333;border-collapse:separate}.pc.opened{-webkit-animation:fadein 100ms;animation:fadein 100ms}.loading-indicator.active{-webkit-animation:swing 1.5s ease-in-out .01s infinite alternate none;animation:swing 1.5s ease-in-out .01s infinite alternate none}.checked{background:no-repeat url(data:image/png;base64,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)}}</style>
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<style type="text/css">
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.ff0{font-family:sans-serif;visibility:hidden;}
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@font-face{font-family:ff1;src:url('data:application/font-woff;base64,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')format("woff");}.ff2{font-family:ff2;line-height:0.956000;font-style:normal;font-weight:normal;visibility:visible;}
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</style>
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<script>
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/*
|
|
Copyright 2012 Mozilla Foundation
|
|
Copyright 2013 Lu Wang <coolwanglu@gmail.com>
|
|
Apachine License Version 2.0
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*/
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(function(){function b(a,b,e,f){var c=(a.className||"").split(/\s+/g);""===c[0]&&c.shift();var d=c.indexOf(b);0>d&&e&&c.push(b);0<=d&&f&&c.splice(d,1);a.className=c.join(" ");return 0<=d}if(!("classList"in document.createElement("div"))){var e={add:function(a){b(this.element,a,!0,!1)},contains:function(a){return b(this.element,a,!1,!1)},remove:function(a){b(this.element,a,!1,!0)},toggle:function(a){b(this.element,a,!0,!0)}};Object.defineProperty(HTMLElement.prototype,"classList",{get:function(){if(this._classList)return this._classList;
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var a=Object.create(e,{element:{value:this,writable:!1,enumerable:!0}});Object.defineProperty(this,"_classList",{value:a,writable:!1,enumerable:!1});return a},enumerable:!0})}})();
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</script>
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<script>
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(function(){/*
|
|
pdf2htmlEX.js: Core UI functions for pdf2htmlEX
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|
Copyright 2012,2013 Lu Wang <coolwanglu@gmail.com> and other contributors
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|
https://github.com/pdf2htmlEX/pdf2htmlEX/blob/master/share/LICENSE
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*/
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|
var pdf2htmlEX=window.pdf2htmlEX=window.pdf2htmlEX||{},CSS_CLASS_NAMES={page_frame:"pf",page_content_box:"pc",page_data:"pi",background_image:"bi",link:"l",input_radio:"ir",__dummy__:"no comma"},DEFAULT_CONFIG={container_id:"page-container",sidebar_id:"sidebar",outline_id:"outline",loading_indicator_cls:"loading-indicator",preload_pages:3,render_timeout:100,scale_step:0.9,key_handler:!0,hashchange_handler:!0,view_history_handler:!0,__dummy__:"no comma"},EPS=1E-6;
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function invert(a){var b=a[0]*a[3]-a[1]*a[2];return[a[3]/b,-a[1]/b,-a[2]/b,a[0]/b,(a[2]*a[5]-a[3]*a[4])/b,(a[1]*a[4]-a[0]*a[5])/b]}function transform(a,b){return[a[0]*b[0]+a[2]*b[1]+a[4],a[1]*b[0]+a[3]*b[1]+a[5]]}function get_page_number(a){return parseInt(a.getAttribute("data-page-no"),16)}function disable_dragstart(a){for(var b=0,c=a.length;b<c;++b)a[b].addEventListener("dragstart",function(){return!1},!1)}
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function clone_and_extend_objs(a){for(var b={},c=0,e=arguments.length;c<e;++c){var h=arguments[c],d;for(d in h)h.hasOwnProperty(d)&&(b[d]=h[d])}return b}
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function Page(a){if(a){this.shown=this.loaded=!1;this.page=a;this.num=get_page_number(a);this.original_height=a.clientHeight;this.original_width=a.clientWidth;var b=a.getElementsByClassName(CSS_CLASS_NAMES.page_content_box)[0];b&&(this.content_box=b,this.original_scale=this.cur_scale=this.original_height/b.clientHeight,this.page_data=JSON.parse(a.getElementsByClassName(CSS_CLASS_NAMES.page_data)[0].getAttribute("data-data")),this.ctm=this.page_data.ctm,this.ictm=invert(this.ctm),this.loaded=!0)}}
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"px"},view_position:function(){var a=this.page,b=a.parentNode;return[b.scrollLeft-a.offsetLeft-a.clientLeft,b.scrollTop-a.offsetTop-a.clientTop]},height:function(){return this.page.clientHeight},width:function(){return this.page.clientWidth}};function Viewer(a){this.config=clone_and_extend_objs(DEFAULT_CONFIG,0<arguments.length?a:{});this.pages_loading=[];this.init_before_loading_content();var b=this;document.addEventListener("DOMContentLoaded",function(){b.init_after_loading_content()},!1)}
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Viewer.prototype={scale:1,cur_page_idx:0,first_page_idx:0,init_before_loading_content:function(){this.pre_hide_pages()},initialize_radio_button:function(){for(var a=document.getElementsByClassName(CSS_CLASS_NAMES.input_radio),b=0;b<a.length;b++)a[b].addEventListener("click",function(){this.classList.toggle("checked")})},init_after_loading_content:function(){this.sidebar=document.getElementById(this.config.sidebar_id);this.outline=document.getElementById(this.config.outline_id);this.container=document.getElementById(this.config.container_id);
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this.loading_indicator=document.getElementsByClassName(this.config.loading_indicator_cls)[0];for(var a=!0,b=this.outline.childNodes,c=0,e=b.length;c<e;++c)if("ul"===b[c].nodeName.toLowerCase()){a=!1;break}a||this.sidebar.classList.add("opened");this.find_pages();if(0!=this.pages.length){disable_dragstart(document.getElementsByClassName(CSS_CLASS_NAMES.background_image));this.config.key_handler&&this.register_key_handler();var h=this;this.config.hashchange_handler&&window.addEventListener("hashchange",
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function(a){h.navigate_to_dest(document.location.hash.substring(1))},!1);this.config.view_history_handler&&window.addEventListener("popstate",function(a){a.state&&h.navigate_to_dest(a.state)},!1);this.container.addEventListener("scroll",function(){h.update_page_idx();h.schedule_render(!0)},!1);[this.outline].concat(Array.from(this.container.querySelectorAll("a.l"))).forEach(function(a){a.addEventListener("click",h.link_handler.bind(h),!1)});this.initialize_radio_button();this.render()}},find_pages:function(){for(var a=
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[],b={},c=this.container.childNodes,e=0,h=c.length;e<h;++e){var d=c[e];d.nodeType===Node.ELEMENT_NODE&&d.classList.contains(CSS_CLASS_NAMES.page_frame)&&(d=new Page(d),a.push(d),b[d.num]=a.length-1)}this.pages=a;this.page_map=b},load_page:function(a,b,c){var e=this.pages;if(!(a>=e.length||(e=e[a],e.loaded||this.pages_loading[a]))){var e=e.page,h=e.getAttribute("data-page-url");if(h){this.pages_loading[a]=!0;var d=e.getElementsByClassName(this.config.loading_indicator_cls)[0];"undefined"===typeof d&&
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(d=this.loading_indicator.cloneNode(!0),d.classList.add("active"),e.appendChild(d));var f=this,g=new XMLHttpRequest;g.open("GET",h,!0);g.onload=function(){if(200===g.status||0===g.status){var b=document.createElement("div");b.innerHTML=g.responseText;for(var d=null,b=b.childNodes,e=0,h=b.length;e<h;++e){var p=b[e];if(p.nodeType===Node.ELEMENT_NODE&&p.classList.contains(CSS_CLASS_NAMES.page_frame)){d=p;break}}b=f.pages[a];f.container.replaceChild(d,b.page);b=new Page(d);f.pages[a]=b;b.hide();b.rescale(f.scale);
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disable_dragstart(d.getElementsByClassName(CSS_CLASS_NAMES.background_image));f.schedule_render(!1);c&&c(b)}delete f.pages_loading[a]};g.send(null)}void 0===b&&(b=this.config.preload_pages);0<--b&&(f=this,setTimeout(function(){f.load_page(a+1,b)},0))}},pre_hide_pages:function(){var a="@media screen{."+CSS_CLASS_NAMES.page_content_box+"{display:none;}}",b=document.createElement("style");b.styleSheet?b.styleSheet.cssText=a:b.appendChild(document.createTextNode(a));document.head.appendChild(b)},render:function(){for(var a=
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k=0;d<b;++d){var f=a[d].page,l=f.offsetTop+f.clientTop,f=f.clientHeight;if(l>c)break;f=(Math.min(c,l+f)-Math.max(e,l))/f;if(d===h&&Math.abs(f-1)<=EPS){g=h;break}f>k&&(k=f,g=d)}this.cur_page_idx=g}},schedule_render:function(a){if(void 0!==this.render_timer){if(!a)return;clearTimeout(this.render_timer)}var b=this;this.render_timer=setTimeout(function(){delete b.render_timer;b.render()},this.config.render_timeout)},register_key_handler:function(){var a=this;window.addEventListener("DOMMouseScroll",function(b){if(b.ctrlKey){b.preventDefault();
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1):a.container.scrollTop-=a.container.clientHeight;c=!0;break;case 34:h?a.scroll_to(a.cur_page_idx+1):a.container.scrollTop+=a.container.clientHeight;c=!0;break;case 35:a.container.scrollTop=a.container.scrollHeight;c=!0;break;case 36:a.container.scrollTop=0,c=!0}c&&b.preventDefault()},!1)},rescale:function(a,b,c){var e=this.scale;this.scale=a=0===a?1:b?e*a:a;c||(c=[0,0]);b=this.container;c[0]+=b.scrollLeft;c[1]+=b.scrollTop;for(var h=this.pages,d=h.length,f=this.first_page_idx;f<d;++f){var g=h[f].page;
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"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">Trax v2 Research and Architecture Analysis: A</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">Deep Dive into Performance, Speaker Diarization,</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">and Advanced Features</div><div class="t m0 x1 h3 y4 ff1 fs1 fc0 sc0 ls0 ws0">The Next Frontier of Accuracy: Multi-Pass Processing and</div><div class="t m0 x1 h3 y5 ff1 fs1 fc0 sc0 ls0 ws0">Domain-Specific Enhancement</div><div class="t m0 x1 h4 y6 ff2 fs2 fc0 sc0 ls0 ws0">The pursuit of transcription accuracy beyond the 95% baseline achieved by Whisper distil-large-v3 is</div><div class="t m0 x1 h4 y7 ff2 fs2 fc0 sc0 ls0 ws0">a primary driver for Trax v2. The research indicates that this can be accomplished through two</div><div class="t m0 x1 h4 y8 ff2 fs2 fc0 sc0 ls0 ws0">distinct yet complementary strategies: multi-pass processing to refine transcriptions iteratively, and</div><div class="t m0 x1 h4 y9 ff2 fs2 fc0 sc0 ls0 ws0">domain-specific enhancement to tailor the model's understanding to specialized content. These</div><div class="t m0 x1 h4 ya ff2 fs2 fc0 sc0 ls0 ws0">approaches move beyond a single-pass inference model, embracing a more sophisticated pipeline</div><div class="t m0 x1 h4 yb ff2 fs2 fc0 sc0 ls0 ws0">architecture where outputs from one stage serve as inputs or context for subsequent stages, leading</div><div class="t m0 x1 h4 yc ff2 fs2 fc0 sc0 ls0 ws0">to significant quality improvements.</div><div class="t m0 x1 h4 yd ff2 fs2 fc0 sc0 ls0 ws0">Multi-pass processing represents a paradigm shift in ASR systems, designed to bridge the gap</div><div class="t m0 x1 h4 ye ff2 fs2 fc0 sc0 ls0 ws0">between real-time responsiveness and offline-quality accuracy. This strategy involves chaining</div><div class="t m0 x1 h4 yf ff2 fs2 fc0 sc0 ls0 ws0">multiple models together, often with different strengths, to progressively improve the final transcript.</div><div class="t m0 x1 h4 y10 ff2 fs2 fc0 sc0 ls0 ws0">One of the most compelling examples is the two-pass end-to-end speech recognition system</div><div class="t m0 x1 h4 y11 ff2 fs2 fc0 sc0 ls0 ws0">developed by Google researchers <span class="_ _0"> </span>. This architecture pairs a fast, streaming Recognizer Neural</div><div class="t m0 x1 h4 y12 ff2 fs2 fc0 sc0 ls0 ws0">Transducer (RNN-T) model with a slower, non-streaming Listen, Attend and Spell (LAS) model that</div><div class="t m0 x1 h4 y13 ff2 fs2 fc0 sc0 ls0 ws0">shares an encoder network <span class="_ _1"> </span>. The RNN-T acts as a first pass, providing a preliminary hypothesis</div><div class="t m0 x1 h4 y14 ff2 fs2 fc0 sc0 ls0 ws0">quickly, while the LAS model performs a deeper rescoring of the top-K hypotheses from the first</div><div class="t m0 x1 h4 y15 ff2 fs2 fc0 sc0 ls0 ws0">pass, leveraging its attention-based mechanism to capture longer-range dependencies <span class="_ _0"> </span>. This</div><div class="t m0 x1 h4 y16 ff2 fs2 fc0 sc0 ls0 ws0">approach has been shown to achieve a 17%-22% relative Word Error Rate (WER) reduction</div><div class="t m0 x1 h4 y17 ff2 fs2 fc0 sc0 ls0 ws0">compared to the RNN-T alone, effectively closing the quality gap with traditional non-streaming</div><div class="t m0 x1 h4 y18 ff2 fs2 fc0 sc0 ls0 ws0">models, all while keeping the latency increase under 200ms—a trade-off that is highly favorable for</div><div class="t m0 x1 h4 y19 ff2 fs2 fc0 sc0 ls0 ws0">many applications <span class="_ _2"> </span>. Further innovation comes from cascaded systems that optimize for</div><div class="t m0 x1 h4 y1a ff2 fs2 fc0 sc0 ls0 ws0">efficiency; one study demonstrated reducing the frame rate of the second pass by half resulted in a</div><div class="t m0 x1 h4 y1b ff2 fs2 fc0 sc0 ls0 ws0">20% reduction in Real-Time Factor (RTF) and 13% power savings without impacting final accuracy </div><div class="t m0 x2 h4 y1c ff2 fs2 fc0 sc0 ls0 ws0">.</div><div class="t m0 x1 h4 y1d ff2 fs2 fc0 sc0 ls0 ws0">Another powerful technique within the multi-pass framework is iterative refinement, which leverages</div><div class="t m0 x1 h4 y1e ff2 fs2 fc0 sc0 ls0 ws0">the output of a transcription model to directly improve its own performance. Research shows that</div><div class="t m0 x1 h4 y1f ff2 fs2 fc0 sc0 ls0 ws0">self-supervised speech models like HuBERT become better at representing linguistic features with</div><div class="t m0 x1 h4 y20 ff2 fs2 fc0 sc0 ls0 ws0">each training iteration, improving their correlation with canonical phoneme and word identities while</div><div class="t m0 x1 h4 y21 ff2 fs2 fc0 sc0 ls0 ws0">de-correlating from speaker identity <span class="_ _0"> </span>. This suggests that using a model's own pseudo-labels to</div><div class="t m0 x1 h4 y22 ff2 fs2 fc0 sc0 ls0 ws0">create new training data for subsequent iterations enhances its core capabilities. An even more</div><div class="t m0 x1 h4 y23 ff2 fs2 fc0 sc0 ls0 ws0">advanced concept is mutual enhancement, where ASR and Voice Conversion (VC) models are</div><div class="t m0 x1 h4 y24 ff2 fs2 fc0 sc0 ls0 ws0">trained in a loop, with the ASR model generating text to train the VC model, and the VC model</div><div class="t m0 x3 h5 y25 ff1 fs3 fc1 sc0 ls0 ws0">8</div><div class="t m0 x4 h5 y26 ff1 fs3 fc1 sc0 ls0 ws0">4<span class="_ _3"> </span>8</div><div class="t m0 x5 h5 y27 ff1 fs3 fc1 sc0 ls0 ws0">8</div><div class="t m0 x6 h5 y28 ff1 fs3 fc1 sc0 ls0 ws0">2<span class="_ _3"> </span>4<span class="_ _3"> </span>8</div><div class="t m0 x7 h5 y29 ff1 fs3 fc1 sc0 ls0 ws0">22</div><div class="t m0 x8 h5 y2a ff1 fs3 fc1 sc0 ls0 ws0">23</div><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,706.44,null]'><div class="d m1" style="border-style:none;position:absolute;left:220.575000px;bottom:385.496000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfb" data-dest-detail='[11,"XYZ",77.25,177.126,null]'><div class="d m1" style="border-style:none;position:absolute;left:190.817000px;bottom:354.272000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,706.44,null]'><div class="d m1" style="border-style:none;position:absolute;left:200.501000px;bottom:354.272000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,706.44,null]'><div class="d m1" style="border-style:none;position:absolute;left:459.690000px;bottom:323.047000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfb" data-dest-detail='[11,"XYZ",77.25,256.326,null]'><div class="d m1" style="border-style:none;position:absolute;left:150.055000px;bottom:260.598000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfb" data-dest-detail='[11,"XYZ",77.25,177.126,null]'><div class="d m1" style="border-style:none;position:absolute;left:159.739000px;bottom:260.598000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,706.44,null]'><div class="d m1" style="border-style:none;position:absolute;left:169.423000px;bottom:260.598000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,135.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:63.460500px;bottom:213.761000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,95.6398,null]'><div class="d m1" style="border-style:none;position:absolute;left:232.573000px;bottom:123.699000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x1 h4 y2b ff2 fs2 fc0 sc0 ls0 ws0">generating synthesized audio to augment the ASR training set <span class="_ _0"> </span>. While complex, this approach</div><div class="t m0 x1 h4 y2c ff2 fs2 fc0 sc0 ls0 ws0">demonstrates how models can learn from each other without requiring massive annotated datasets,</div><div class="t m0 x1 h4 y2d ff2 fs2 fc0 sc0 ls0 ws0">pointing towards a future where Trax could continuously improve its own transcription engine.</div><div class="t m0 x1 h4 y2e ff2 fs2 fc0 sc0 ls0 ws0">Domain-specific enhancement addresses the challenge of maintaining high accuracy across diverse</div><div class="t m0 x1 h4 y2f ff2 fs2 fc0 sc0 ls0 ws0">content types, such as technical lectures, medical consultations, or noisy conference calls. The</div><div class="t m0 x1 h4 y30 ff2 fs2 fc0 sc0 ls0 ws0">industry standard for this is full-scale fine-tuning on domain-specific data, but this is computationally</div><div class="t m0 x1 h4 y31 ff2 fs2 fc0 sc0 ls0 ws0">expensive and risks catastrophic forgetting of general knowledge <span class="_ _0"> </span>. Fortunately, the field of</div><div class="t m0 x1 h4 y32 ff2 fs2 fc0 sc0 ls0 ws0">Parameter-Efficient Fine-Tuning (PEFT) offers elegant solutions. Low-Rank Adaptation (LoRA) is a</div><div class="t m0 x1 h4 y33 ff2 fs2 fc0 sc0 ls0 ws0">standout method that freezes the vast majority of the pre-trained model's weights and injects small,</div><div class="t m0 x1 h4 y34 ff2 fs2 fc0 sc0 ls0 ws0">trainable "rank decomposition" matrices into the transformer layers <span class="_ _1"> </span>. This drastically reduces</div><div class="t m0 x1 h4 y35 ff2 fs2 fc0 sc0 ls0 ws0">memory requirements and training time while achieving near-full fine-tuning performance. For</div><div class="t m0 x1 h4 y36 ff2 fs2 fc0 sc0 ls0 ws0">instance, LoRA was used to adapt Whisper for domain adaptation with less than 0.1% of the total</div><div class="t m0 x1 h4 y37 ff2 fs2 fc0 sc0 ls0 ws0">model parameters, resulting in WER reductions of over 3 points <span class="_ _0"> </span>. Other PEFT methods include</div><div class="t m0 x1 h4 y38 ff2 fs2 fc0 sc0 ls0 ws0">prompt tuning, which learns a small, trainable "prompt" vector to steer the model's behavior, and</div><div class="t m0 x1 h4 y39 ff2 fs2 fc0 sc0 ls0 ws0">speech prefix tuning (SPT), which appends fixed-length vectors to input features and has been</div><div class="t m0 x1 h4 y3a ff2 fs2 fc0 sc0 ls0 ws0">shown to outperform LoRA on certain tasks <span class="_ _1"> </span>. A particularly innovative approach involves text-</div><div class="t m0 x1 h4 y3b ff2 fs2 fc0 sc0 ls0 ws0">only fine-tuning, where only the Language Model (LLM) component of a Speech LLM is adapted</div><div class="t m0 x1 h4 y3c ff2 fs2 fc0 sc0 ls0 ws0">using unpaired target-domain text, preserving the original speech encoder's integrity and avoiding</div><div class="t m0 x1 h4 y3d ff2 fs2 fc0 sc0 ls0 ws0">performance degradation on general domains <span class="_ _0"> </span>. This allows Trax to build a highly accurate,</div><div class="t m0 x1 h4 y3e ff2 fs2 fc0 sc0 ls0 ws0">specialized model for a niche domain like financial reporting or legal proceedings without bloating</div><div class="t m0 x1 h4 y3f ff2 fs2 fc0 sc0 ls0 ws0">the overall system.</div><div class="t m0 x9 h4 y40 ff1 fs2 fc2 sc0 ls0 ws0">Feature<span class="_ _4"> </span>Multi-Pass Processing</div><div class="t m0 xa h4 y41 ff1 fs2 fc2 sc0 ls0 ws0">Domain-Specific Enhancement</div><div class="t m0 xa h4 y42 ff1 fs2 fc2 sc0 ls0 ws0">(PEFT)</div><div class="t m0 x9 h4 y43 ff1 fs2 fc0 sc0 ls0 ws0">Core Principle</div><div class="t m0 xb h4 y44 ff2 fs2 fc0 sc0 ls0 ws0">Chaining multiple models (e.g., fast-first,</div><div class="t m0 xb h4 y45 ff2 fs2 fc0 sc0 ls0 ws0">slow-second) to refine results iteratively <span class="_ _0"> </span>.</div><div class="t m0 xa h4 y46 ff2 fs2 fc0 sc0 ls0 ws0">Modifying a small fraction of a</div><div class="t m0 xa h4 y47 ff2 fs2 fc0 sc0 ls0 ws0">pre-trained model's weights to</div><div class="t m0 xa h4 y48 ff2 fs2 fc0 sc0 ls0 ws0">adapt it to a specific domain <span class="_ _0"> </span>.</div><div class="t m0 x9 h4 y49 ff1 fs2 fc0 sc0 ls0 ws0">Primary Benefit</div><div class="t m0 xb h4 y4a ff2 fs2 fc0 sc0 ls0 ws0">Achieves near-offline accuracy with low</div><div class="t m0 xb h4 y4b ff2 fs2 fc0 sc0 ls0 ws0">latency <span class="_ _0"> </span>. Enables high accuracy in</div><div class="t m0 xb h4 y4c ff2 fs2 fc0 sc0 ls0 ws0">specialized contexts <span class="_ _0"> </span>.</div><div class="t m0 x9 h4 y4d ff1 fs2 fc0 sc0 ls0 ws0">Key Techniques</div><div class="t m0 xb h4 y4e ff2 fs2 fc0 sc0 ls0 ws0">Shared-encoder architectures <span class="_ _0"> </span>, N-best</div><div class="t m0 xb h4 y4d ff2 fs2 fc0 sc0 ls0 ws0">rescoring <span class="_ _0"> </span>, adaptive beam search <span class="_ _0"> </span>,</div><div class="t m0 xb h4 y4f ff2 fs2 fc0 sc0 ls0 ws0">cascaded systems <span class="_ _0"> </span>.</div><div class="t m0 xa h4 y50 ff2 fs2 fc0 sc0 ls0 ws0">Low-Rank Adaptation (LoRA) </div><div class="t m0 xc h4 y51 ff2 fs2 fc0 sc0 ls0 ws0">, Prompt Tuning <span class="_ _0"> </span>, Text-</div><div class="t m0 xa h4 y52 ff2 fs2 fc0 sc0 ls0 ws0">Only Fine-Tuning <span class="_ _0"> </span>, Adapter</div><div class="t m0 xa h4 y53 ff2 fs2 fc0 sc0 ls0 ws0">Tuning <span class="_ _0"> </span>.</div><div class="t m0 x9 h4 y54 ff1 fs2 fc0 sc0 ls0 ws0">Performance</div><div class="t m0 x9 h4 y55 ff1 fs2 fc0 sc0 ls0 ws0">Impact</div><div class="t m0 xb h4 y56 ff2 fs2 fc0 sc0 ls0 ws0">17-22% relative WER reduction vs. single-</div><div class="t m0 xb h4 y57 ff2 fs2 fc0 sc0 ls0 ws0">pass models <span class="_ _0"> </span>. 3-11 absolute point WER</div><div class="t m0 xb h4 y58 ff2 fs2 fc0 sc0 ls0 ws0">reduction <span class="_ _1"> </span>.</div><div class="t m0 x9 h4 y59 ff1 fs2 fc0 sc0 ls0 ws0">Implementation</div><div class="t m0 x9 h4 y5a ff1 fs2 fc0 sc0 ls0 ws0">Cost</div><div class="t m0 xb h4 y5b ff2 fs2 fc0 sc0 ls0 ws0">Increased complexity in pipeline</div><div class="t m0 xb h4 y5c ff2 fs2 fc0 sc0 ls0 ws0">architecture. Requires managing multiple</div><div class="t m0 xb h4 y5d ff2 fs2 fc0 sc0 ls0 ws0">models.</div><div class="t m0 xa h4 y5e ff2 fs2 fc0 sc0 ls0 ws0">Low computational cost for</div><div class="t m0 xa h4 y5f ff2 fs2 fc0 sc0 ls0 ws0">adaptation. Minimal storage</div><div class="t m0 xa h4 y60 ff2 fs2 fc0 sc0 ls0 ws0">overhead for adapter modules.</div><div class="t m0 xd h5 y61 ff1 fs3 fc1 sc0 ls0 ws0">1</div><div class="t m0 xe h5 y62 ff1 fs3 fc1 sc0 ls0 ws0">32</div><div class="t m0 xf h5 y63 ff1 fs3 fc1 sc0 ls0 ws0">31<span class="_ _5"> </span>33</div><div class="t m0 x10 h5 y64 ff1 fs3 fc1 sc0 ls0 ws0">28</div><div class="t m0 x11 h5 y65 ff1 fs3 fc1 sc0 ls0 ws0">34<span class="_ _5"> </span>57</div><div class="t m0 x12 h5 y66 ff1 fs3 fc1 sc0 ls0 ws0">29</div><div class="t m0 x13 h5 y67 ff1 fs3 fc1 sc0 ls0 ws0">8</div><div class="t m0 x14 h5 y68 ff1 fs3 fc1 sc0 ls0 ws0">31</div><div class="t m0 x15 h5 y69 ff1 fs3 fc1 sc0 ls0 ws0">4</div><div class="t m0 x16 h5 y6a ff1 fs3 fc1 sc0 ls0 ws0">51</div><div class="t m0 x17 h5 y6b ff1 fs3 fc1 sc0 ls0 ws0">8</div><div class="t m0 x18 h5 y6c ff1 fs3 fc1 sc0 ls0 ws0">8<span class="_ _6"> </span>4</div><div class="t m0 x19 h5 y6d ff1 fs3 fc1 sc0 ls0 ws0">22</div><div class="t m0 x1a h5 y6e ff1 fs3 fc1 sc0 ls0 ws0">36<span class="_ _7"> </span>41</div><div class="t m0 x1b h5 y6f ff1 fs3 fc1 sc0 ls0 ws0">29</div><div class="t m0 x1c h5 y70 ff1 fs3 fc1 sc0 ls0 ws0">30</div><div class="t m0 x1d h5 y71 ff1 fs3 fc1 sc0 ls0 ws0">2</div><div class="t m0 x18 h5 y72 ff1 fs3 fc1 sc0 ls0 ws0">28<span class="_ _5"> </span>29</div><a class="l" href="#pfb" data-dest-detail='[11,"XYZ",77.25,295.926,null]'><div class="d m1" style="border-style:none;position:absolute;left:351.566000px;bottom:778.259000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",77.25,452.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:366.050000px;bottom:672.585000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",77.25,491.64,null]'><div class="d m1" 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KD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAD8mNwIAgL5LKaoqDlXsdjEexziPcR5ZZjBARESWUjIFAID+2h/iZRlvb98en01jUhoPoPoAAPps/TFWm+/eWhZxd2tIMHR+1wcA0Fe7/XvJ93nBZmtOoPoAAOihlOJ11bxsuTYqUH0AAPTQ4VjzWz4A1QcAcDnVB6D6AAAut/oObVd++M20QPUBANA3eevrLs+mpgWqDwCAvhm3rj5X7QPVBwDAJVcfoPoAAOjhx7hRTK+bl5WFUYHqAwCgn64mzTt+sxtzgqHLUkqmAADQX8t1bLY1x8siZjcx8iU/qD7VBwDQd8djfNrH/hC7fYzzKMZRFs7tBFQfAADAANjyBwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAOrkRgAMVlXFahPb3dcjZRFXkyiL5vuuP8ZmG29vX/4cjeJqEte/GCoAcHaylJIpAAO02cZyXX/TpIzbm8iy+luPx/j9z6+990+jUdzfRu77NABA9QGcbfJ9VhZxd1tzvKri1z8aHvzhPkZOnwcAzoYPJsDgVFVD8kXEbv+vMz//9rJsfvzXlRkDAKoP4HRWm1bLXpc1KXg4Nt9xt4/9wZgBANUHcCK1m3httEm+H10JAKD6AM5F+x28g70+AED1AfRO7f/tBABQfQAXohi3XeniDQCA6gM4mTYXYf+Pd2zfhwAAqg/gJ7uatFo2va6pvjYX4huNVB8AoPoATqcsYlI2rBnn9XF4f9v8+G3WAACoPoD/0Wz6XviN87j7TrnlecymDY/sR30AwFnJUkqmAAzTdldzKfbpdfMpoFUVL8tvL8pXFjG7aXUKKACA6gMAAODn8KU0AACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAA4K/27q43bTsM4/Bjx8alJJA2077/t5umKguBttQv+L+DVWmTEfC6JGB0XWc1tirdB4hfMTWg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAeF2FCQAAnqibWK6j7x8dLIv4MI+Li30X9n0s11E3T49fz2Na2RU4jiylZAUAgAfLVWzqZ1/d029NG7fLZy+sJvFxYV3gCNzhCQDww5fNvuT7pwm32x3H+35f8kVE3cTqs4EB1QcAcDzb7aAwu1vtOPjX/aCk7DozA6oPAOBI9n/L96DtduRiOyznvn4zM6D6AACOpGl/8cJu++p/BYDqAwD4v/79f28O1HYvfyaA6gMAeOkPRr/6ySjLjAeoPgCAk1eVr36hp/YBqg8A4Gim737xwv2Pbv9ZWZoZUH0AAEdSTaIsDp82mz49kmUxvxzwwSvfcS2A6gMAeDsf5gdOKIvdgTebHi7Gm4WBgSPIUkpWAAB40HVxex99v+OlaRWLq2f/75aU4n69+6F/eR43iygK6wKqDwDgNHz+Gk37/VkOeR5VGe+nMRnwq7ymja+bqNvv3VhNYlLG5XuLAqoPAACAV+B3fQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAMAuhQkAABhuU0ddx6b+/sdpFVUV08owcLqylJIVAAA4aLuNu1W03Y6XqklcX0XuNjJQfQAAjFTfx5+3+07I8/j9Y2SZqeDk+AcZAAAOW64PZ+H92k6g+gAAGKGmjbo5fNrDj/0A1QcAwJjs/C0foPoAADiX6mttAKoPAIDz1W1tAKoPAIDzNSltAKoPAIDzVao+UH0AAJyxd5NBT2D3lHZQfQAAjFKWxfXV4dOGnAOoPgAATlE1iavZvhOuZlFN7ASnKEspWQEAgCG6Lm7vo+8fHczzuFlEUZgHVB8AAABvzh2eAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAHCeChMAvKCU4n4dm/rRwTyPm0UU3nEBgGPIUkpWAHgRTRt3q+j73a/OpjG/NBIA8Nbc4QnwMvo+bpfPJl9EfNk8/Q4QAED1AYzGcj3gnJWdAADVBzBC223UjRkAANUHcKaazgYAgOoDOF9tO/TMPz5ZCwBQfQCjezMd/G46rawFAKg+gLEpBz+L73puLQBA9QGMTXFhAwBA9QGcr4uLQbdu5t50AQDVBzBSi6vDUXezsBMAoPoAxinL4rfrZ3/gl+fxcRFFYScA4M0/paSUrADwgr5sYvMt2u5H702ruJpFltkGAFB9AAAAvCh3eAIAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPva8JVUAAAVjSURBVAAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAIBTUJgAAADYabuNpou2jb6PsoyyiElplfHJUkpWAAAAfpZS3K9jUz89nudxs4jCl0eqDwAAGK++j0930ffPnjC/jNnUTqPhd30AAMAjy/W+5IuI1WcjqT4AAGCcNnXUzaDTUH0AAMD41MNybrkyleoDAABGyJd4qg8AAADVBwAAgOoDAABOzbSygeoDAADOVzWs+sSh6gMAAEZpWkVZHD7tem4q1QcAAIzTh0NFN5saaUyylJIVAACAn/V9LNe7H9d+PXd7p+oDAADOQt1E20XTRttFVUZVRVVG7n5B1QcAAMDp0OkAAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAPiv/gYnuPA2CwjA8wAAAABJRU5ErkJggg=="/><div class="t m0 x9 h4 y73 ff1 fs2 fc2 sc0 ls0 ws0">Feature<span class="_ _4"> </span>Multi-Pass Processing</div><div class="t m0 xa h4 y74 ff1 fs2 fc2 sc0 ls0 ws0">Domain-Specific Enhancement</div><div class="t m0 xa h4 y75 ff1 fs2 fc2 sc0 ls0 ws0">(PEFT)</div><div class="t m0 x9 h4 y76 ff1 fs2 fc0 sc0 ls0 ws0">Use Case for</div><div class="t m0 x9 h4 y77 ff1 fs2 fc0 sc0 ls0 ws0">Trax</div><div class="t m0 xb h4 y78 ff2 fs2 fc0 sc0 ls0 ws0">Ideal for creating a "quality" processing</div><div class="t m0 xb h4 y79 ff2 fs2 fc0 sc0 ls0 ws0">path that users can select for critical</div><div class="t m0 xb h4 y7a ff2 fs2 fc0 sc0 ls0 ws0">transcripts. Enables creation of lightweight,</div><div class="t m0 xb h4 y7b ff2 fs2 fc0 sc0 ls0 ws0">specialized "modules" for different</div><div class="t m0 xb h4 y7c ff2 fs2 fc0 sc0 ls0 ws0">verticals.</div><div class="t m0 x1 h4 y7d ff2 fs2 fc0 sc0 ls0 ws0">In summary, the path to 99.5%+ accuracy for Trax v2 is not through a single architectural leap but</div><div class="t m0 x1 h4 y7e ff2 fs2 fc0 sc0 ls0 ws0">through a layered, intelligent approach. By implementing a multi-pass processing pipeline, Trax can</div><div class="t m0 x1 h4 y7f ff2 fs2 fc0 sc0 ls0 ws0">offer superior accuracy as a selectable feature. By integrating PEFT techniques like LoRA, Trax can</div><div class="t m0 x1 h4 y80 ff2 fs2 fc0 sc0 ls0 ws0">provide deep domain specialization without sacrificing its core generality or performance, positioning</div><div class="t m0 x1 h4 y81 ff2 fs2 fc0 sc0 ls0 ws0">itself as a versatile and powerful tool for a wide range of professional use cases.</div><div class="t m0 x1 h3 y82 ff1 fs1 fc0 sc0 ls0 ws0">Mastering Conversational Audio: State-of-the-Art Speaker</div><div class="t m0 x1 h3 y83 ff1 fs1 fc0 sc0 ls0 ws0">Diarization and Voice Profiling</div><div class="t m0 x1 h4 y84 ff2 fs2 fc0 sc0 ls0 ws0">Speaker diarization—the process of identifying <span class="ff3">who</span> spoke when in a conversation—is a critical</div><div class="t m0 x1 h4 y85 ff2 fs2 fc0 sc0 ls0 ws0">feature for any modern transcription platform, transforming a monolithic transcript into an</div><div class="t m0 x1 h4 y86 ff2 fs2 fc0 sc0 ls0 ws0">actionable document. For Trax v2, achieving robust and accurate speaker diarization is paramount,</div><div class="t m0 x1 h4 y87 ff2 fs2 fc0 sc0 ls0 ws0">especially given the user's interest in handling conversations. The current state of the art offers a</div><div class="t m0 x1 h4 y88 ff2 fs2 fc0 sc0 ls0 ws0">spectrum of solutions, from established open-source frameworks to cutting-edge deep learning</div><div class="t m0 x1 h4 y89 ff2 fs2 fc0 sc0 ls0 ws0">models, each with distinct trade-offs in accuracy, latency, and resource consumption.</div><div class="t m0 x1 h4 y8a ff2 fs2 fc0 sc0 ls0 ws0">The most effective speaker diarization systems today are typically modular, combining several</div><div class="t m0 x1 h4 y8b ff2 fs2 fc0 sc0 ls0 ws0">components: a Voice Activity Detector (VAD) to segment the audio into speech turns, an</div><div class="t m0 x1 h4 y8c ff2 fs2 fc0 sc0 ls0 ws0">embedding extractor to generate a compact speaker representation ("d-vector" or "x-vector") for</div><div class="t m0 x1 h4 y8d ff2 fs2 fc0 sc0 ls0 ws0">each turn, and a clustering algorithm to group these embeddings by speaker <span class="_ _0"> </span>. Frameworks like</div><div class="t m0 x1 h4 y8e ff2 fs2 fc0 sc0 ls0 ws0">Pyannote.audio have become a de facto standard in this space, offering a well-engineered</div><div class="t m0 x1 h4 y8f ff2 fs2 fc0 sc0 ls0 ws0">implementation of this pipeline <span class="_ _1"> </span>. However, recent advancements in end-to-end (E2E) neural</div><div class="t m0 x1 h4 y90 ff2 fs2 fc0 sc0 ls0 ws0">speaker diarization promise to simplify this process. Models like EEND (End-to-End Neural</div><div class="t m0 x1 h4 y91 ff2 fs2 fc0 sc0 ls0 ws0">Speaker Diarization) replace the separate clustering step with a single neural network that predicts</div><div class="t m0 x1 h4 y92 ff2 fs2 fc0 sc0 ls0 ws0">the number of speakers and assigns a label to each frame of the input <span class="_ _1"> </span>. While promising, E2E</div><div class="t m0 x1 h4 y93 ff2 fs2 fc0 sc0 ls0 ws0">models often face challenges with latency and require fixed speaker limits, making them less suitable</div><div class="t m0 x1 h4 y94 ff2 fs2 fc0 sc0 ls0 ws0">for real-time applications or scenarios with an unknown number of participants <span class="_ _0"> </span>.</div><div class="t m0 x1 h4 y95 ff2 fs2 fc0 sc0 ls0 ws0">Comparative studies provide crucial insights into the performance of these systems. In a direct</div><div class="t m0 x1 h4 y96 ff2 fs2 fc0 sc0 ls0 ws0">comparison on a hobbyist-grade project, Taishin Maeda evaluated Pyannote.audio against NVIDIA</div><div class="t m0 x1 h4 y97 ff2 fs2 fc0 sc0 ls0 ws0">NeMo on two different audio files <span class="_ _0"> </span>. On a 5-minute, two-speaker file, NeMo achieved a lower</div><div class="t m0 x1 h4 y98 ff2 fs2 fc0 sc0 ls0 ws0">Diarization Error Rate (DER) of 16.1% compared to Pyannote's 25.2%, albeit at the cost of double</div><div class="t m0 x1 h4 y99 ff2 fs2 fc0 sc0 ls0 ws0">the execution time (63.9s vs 31.3s). On a more challenging 9-minute, nine-speaker file, Pyannote</div><div class="t m0 x1 h4 y9a ff2 fs2 fc0 sc0 ls0 ws0">performed slightly better with a DER of 8.3% versus NeMo's 9.7% (with pre-identified speakers) <span class="_ _0"> </span>.</div><div class="t m0 x1 h4 y9b ff2 fs2 fc0 sc0 ls0 ws0">Another study benchmarked various systems on the Voxconverse dataset and found that DIART,</div><div class="t m0 x1 h6 y9c ff2 fs2 fc0 sc0 ls0 ws0">based on <span class="ff4">pyannote/segmentation</span> and <span class="ff4">pyannote/embedding</span>, had the lowest latency at</div><div class="t m0 x1e h5 y9d ff1 fs3 fc1 sc0 ls0 ws0">9</div><div class="t m0 x1f h5 y9e ff1 fs3 fc1 sc0 ls0 ws0">10<span class="_ _5"> </span>12</div><div class="t m0 x20 h5 y9f ff1 fs3 fc1 sc0 ls0 ws0">7<span class="_ _8"> </span>13</div><div class="t m0 x21 h5 ya0 ff1 fs3 fc1 sc0 ls0 ws0">13</div><div class="t m0 x22 h5 ya1 ff1 fs3 fc1 sc0 ls0 ws0">12</div><div class="t m0 x23 h5 ya2 ff1 fs3 fc1 sc0 ls0 ws0">12</div><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,666.84,null]'><div class="d m1" style="border-style:none;position:absolute;left:417.176000px;bottom:323.435000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,627.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:211.922000px;bottom:292.210000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,548.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:221.606000px;bottom:292.210000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,746.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:388.041000px;bottom:245.373000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,491.64,null]'><div class="d m1" style="border-style:none;position:absolute;left:397.725000px;bottom:245.373000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,491.64,null]'><div class="d m1" style="border-style:none;position:absolute;left:435.111000px;bottom:214.149000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,548.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:226.189000px;bottom:155.312000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,548.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:517.480000px;bottom:108.475000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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ED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAgP0qE8B7k1LcPcTD+q/PlGWczuLsF9sAAByhIqVkBXg/2k3c3MUw7HioLOPyPCr/FwQAcFxc4QnvSN/H19vdyRcRwxCfr599FAAA1Qccui+3+8/c3NkJAED1ARlat6Oex2s30ffWAgBQfUBuum70ya21AABUH5Cbzejqa1trAQCoPiA3W2/TAgCg+oAj1tSjTzbWAgBQfUB21Te65eqJtQAAVB+Qm5NplCP+xdeVG7UDAKg+IENFEcv5/mO/npsKAED1AXlqprFcPP/toIzL5ajnAwEAyEiRUrICvCvDEF9vo/vxVuyzJs7nURTmAQBQfQAAAOTDtVwAAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAPD/qEwAAFlb3cemi67/9mEzjdlJzJr9f3DdRtvGuv32YV3FtI75aRSFUQGOSpFSsgIA5Kjv48ttDMOOh5ppLOdRPnNNT0pxvYp2s+OhsozlPJqpdQFUHwDw1sn3+fqlA2UZV5e7H/rv9V/PDe7020VUrgcCOBZe1wcAWfpyu+fAMMTqfsfnH9Z7km/MXw6A6gMA/kXrdveFnX8PvCfHnkvBvxcjAKoPAHgzbTv25JOn9fqt8QBUHwBw+NXX/cPq23tt53f/+WxmANUHALyR8VdgPjnpTdwAVB8AkIExt+P7U13/+OHod+ZcLswMoPoAgDfypOVeMP0x86rJ64clAKoPAHhlI5OsrmLyY+ZNJu7ADqD6AIDD//ldjroC82LXmeV8/x9cnNkYQPUBAG9q1sTp7KUDv54/faLvezFeLqMs//nfDEBeiuTNvAAgW30fX26fvlFnM43l/KWui4iU4noV7eZpEC7nLgEFUH0AAADkwxWeAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9fG6+r43Ar4q8JUMhymlZAR8T7PnMalM8CY/Sz5+/GgHAADemw8fPhhB9b0LRVFExNXVlSn4X58+ffJVga9kOISvYT+m8T3tX/33xc/nCk8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAA/NHe3TQljgVgGL0XYkJEGWbvKv//N5GN6ynaLyRibi+6SrscGlnEwG3PWaZZvRUhD4k2qD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAMDXiCklK4wspdS2rR0AAPhumqYxwvgKE5wgtWN0xvN/q9XKWYEzGc7hHPYxjfe0L/35Ynye8AQAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAICeFCQAAYFibbbh7CH3/fmQyCcvrUJW2QfUBAEDO+j6s78O223P8vx+hrsJyYSTG5glPAAAYzN7ke7PZhvWdkVB9AACQp832UPK9vQZUHwAAZOnI+3jCD9UHAADiEFQfAAAAqg8AAED1AQAAoPoAAIDD19bHXVz7L/tQfQAAkKXl9VEvqytTofoAACBDVfl50c1rO6H6AAAgW8vFofCb12FxZSTGVpgAAAAGDr8u3D+Gl937wYsiLK5CeWEeVB8AAOSvKkNVmoFz4QlPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAGBoMaVkhZGllNq2tQMAAN9N0zRGGF9hghOkdowhhJubG1Pwu9vbW2cFzmQ4h3PYxzTe077054vxecITAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAICcFCYAAIBh7V7jZjvpXqZvR8qL18vZ69Q9F1QfAABkLaXw9Dx97qYfjncv0+5lejnbzco+RjsxKt82AADAYB42e5LvzdNz8fQ8tRKqDwAAstS9xN+f6tzrQBOC6gMAgLP2sDnq96dee1Oh+gAAIEMpHfUbe+v70laoPgAAAFQfAAAAqg8AAED1AQAAI7mqd0ZA9QEAQH4uZ0flXFX6I56oPgAAyNCs7KeTT4ruopB8qD4AAMhTjGEx3x0Iv4ui93gnqg8AAHK+vJ6Ef652ex/1vJztri93ExfgjK4wAQAADCjGUFd9XXWm4Ez4qgEAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAF7B2QEAAAa0SURBVABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAGFpMKVlhZCmltm3tAADAd9M0jRHG517fKVI7RiMAAADjKExwKr7n4IPVauWswJkM53AO+5jGe9qX/nwxPvf6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAp1GYAAAAhvW4CXcPHw8uF6GubIPqAwCAnPV9WN+Hbbfnn9Z3YVOGfxchRjsxKk94AgDAYP6UfL9su/Dj3kioPgAAyNNmeyj53l4Dqg8AALK0vjs2DkH1AQDAd49DUH0AAACoPgAAANUHAACA6gMAAA5eWx93cb1cmArVBwAAGVpeH/WyujIVqg8AADJUlZ8X3by2E6oPAACytVwcCr95HRZXRmJshQkAAGDg8OvC4yZsu/eDVRkW81C4+kb1AQDAX6AqQ1WagXPhCU8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAPxZTClZYWQppbZt7QAAwHfTNI0Rxude3ylSO0YjAAAAIwWIe30AAAB/Mff6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAADAqf0EYGeB7CoAOwEAAAAASUVORK5CYII="/><div class="t m0 x1 h4 y2b ff2 fs2 fc0 sc0 ls0 ws0">just 0.057 seconds per chunk on a CPU, whereas another E2E model, UIS-RNN-SML, became</div><div class="t m0 x1 h4 y2c ff2 fs2 fc0 sc0 ls0 ws0">impractically slow on long recordings <span class="_ _0"> </span>. These findings suggest that for a project like Trax, which</div><div class="t m0 x1 h4 y2d ff2 fs2 fc0 sc0 ls0 ws0">values both accuracy and performance, a hybrid approach might be optimal: using a highly efficient,</div><div class="t m0 x1 h4 ya3 ff2 fs2 fc0 sc0 ls0 ws0">lightweight system like DIART or a custom-built module for initial, real-time processing, and</div><div class="t m0 x1 h4 ya4 ff2 fs2 fc0 sc0 ls0 ws0">reserving heavier, more accurate models like Pyannote for post-processing or user-selected</div><div class="t m0 x1 h4 ya5 ff2 fs2 fc0 sc0 ls0 ws0">"enhanced" analysis modes.</div><div class="t m0 x1 h4 ya6 ff2 fs2 fc0 sc0 ls0 ws0">Latency is a critical factor, especially for real-time applications. Most modern systems operate with</div><div class="t m0 x1 h4 ya7 ff2 fs2 fc0 sc0 ls0 ws0">some degree of look-ahead, analyzing a few hundred milliseconds of future audio to make more</div><div class="t m0 x1 h4 ya8 ff2 fs2 fc0 sc0 ls0 ws0">confident decisions about speaker changes and endpointing. Bilal Rahou et al. proposed a causal</div><div class="t m0 x1 h4 ya9 ff2 fs2 fc0 sc0 ls0 ws0">segmentation model that uses a multi-latency look-ahead during training, allowing it to dynamically</div><div class="t m0 x1 h4 yaa ff2 fs2 fc0 sc0 ls0 ws0">adjust its latency to balance performance with speed, nearly matching offline model accuracy with</div><div class="t m0 x1 h4 yab ff2 fs2 fc0 sc0 ls0 ws0">just 500ms of look-ahead <span class="_ _0"> </span>. In contrast, AssemblyAI's diarization model is currently limited to</div><div class="t m0 x1 h4 yac ff2 fs2 fc0 sc0 ls0 ws0">asynchronous transcription, not real-time streams, highlighting the technical hurdles involved <span class="_ _0"> </span>. For</div><div class="t m0 x1 h4 yad ff2 fs2 fc0 sc0 ls0 ws0">Trax v2, a configurable latency setting would be a powerful feature, allowing users to trade a slight</div><div class="t m0 x1 h4 yae ff2 fs2 fc0 sc0 ls0 ws0">delay for significantly improved accuracy in detecting overlapping speech and speaker turns.</div><div class="t m0 x1 h4 yaf ff2 fs2 fc0 sc0 ls0 ws0">Beyond simple diarization, voice profiling and privacy-preserving methods represent the next</div><div class="t m0 x1 h4 yb0 ff2 fs2 fc0 sc0 ls0 ws0">frontier. Speaker identification, the task of labeling who a speaker is, can be enhanced by adapting</div><div class="t m0 x1 h4 yb1 ff2 fs2 fc0 sc0 ls0 ws0">models like ECAPA-TDNN with speaker embeddings, which has been shown to improve DER on</div><div class="t m0 x1 h4 yb2 ff2 fs2 fc0 sc0 ls0 ws0">children's speech data <span class="_ _0"> </span>. For privacy-sensitive applications, techniques like zero-party</div><div class="t m0 x1 h4 yb3 ff2 fs2 fc0 sc0 ls0 ws0">authentication, where no actual voice samples are stored, become essential. Furthermore, a novel and</div><div class="t m0 x1 h4 yb4 ff2 fs2 fc0 sc0 ls0 ws0">highly effective technique involves using a Large Language Model (LLM) as a post-processing step to</div><div class="t m0 x1 h4 yb5 ff2 fs2 fc0 sc0 ls0 ws0">correct diarization errors. Researchers fine-tuned a Mistral 7b model on the Fisher corpus to analyze</div><div class="t m0 x1 h4 yb6 ff2 fs2 fc0 sc0 ls0 ws0">transcripts from various ASR systems and correct speaker labels, demonstrating an ASR-agnostic</div><div class="t m0 x1 h4 yb7 ff2 fs2 fc0 sc0 ls0 ws0">correction capability that significantly improved accuracy <span class="_ _1"> </span>. This opens up a fascinating possibility</div><div class="t m0 x1 h4 yb8 ff2 fs2 fc0 sc0 ls0 ws0">for Trax v2: after producing a raw transcript with speaker labels, it could run the text through a</div><div class="t m0 x1 h4 yb9 ff2 fs2 fc0 sc0 ls0 ws0">specialized LLM-based diarization-corrector to produce a polished, expertly labeled version. This</div><div class="t m0 x1 h4 yba ff2 fs2 fc0 sc0 ls0 ws0">approach decouples the core transcription task from the complex, context-dependent task of speaker</div><div class="t m0 x1 h4 ybb ff2 fs2 fc0 sc0 ls0 ws0">attribution, potentially leading to higher overall accuracy and greater flexibility.</div><div class="t m0 x9 h4 ybc ff1 fs2 fc2 sc0 ls0 ws0">System /</div><div class="t m0 x9 h4 ybd ff1 fs2 fc2 sc0 ls0 ws0">Technique</div><div class="t m0 x24 h4 ybe ff1 fs2 fc2 sc0 ls0 ws0">Key Strengths<span class="_ _9"> </span>Key Weaknesses<span class="_ _a"> </span>Latency Profile<span class="_ _b"> </span>Source(s)</div><div class="t m0 x9 h4 ybf ff1 fs2 fc0 sc0 ls0 ws0">Pyannote.audio</div><div class="t m0 x24 h4 yc0 ff2 fs2 fc0 sc0 ls0 ws0">High accuracy, mature</div><div class="t m0 x24 h4 ybf ff2 fs2 fc0 sc0 ls0 ws0">ecosystem, extensive</div><div class="t m0 x24 h4 yc1 ff2 fs2 fc0 sc0 ls0 ws0">documentation.</div><div class="t m0 x25 h4 yc2 ff2 fs2 fc0 sc0 ls0 ws0">Slower than some</div><div class="t m0 x25 h4 yc3 ff2 fs2 fc0 sc0 ls0 ws0">alternatives, especially</div><div class="t m0 x25 h4 yc4 ff2 fs2 fc0 sc0 ls0 ws0">on long audio.</div><div class="t m0 x26 h4 yc5 ff2 fs2 fc0 sc0 ls0 ws0">~31s for 5min</div><div class="t m0 x26 h4 yc6 ff2 fs2 fc0 sc0 ls0 ws0">audio on RTX</div><div class="t m0 x26 h4 yc7 ff2 fs2 fc0 sc0 ls0 ws0">3090.</div><div class="t m0 x9 h4 yc8 ff1 fs2 fc0 sc0 ls0 ws0">NVIDIA</div><div class="t m0 x9 h4 yc9 ff1 fs2 fc0 sc0 ls0 ws0">NeMo</div><div class="t m0 x24 h4 yca ff2 fs2 fc0 sc0 ls0 ws0">Lower DER than</div><div class="t m0 x24 h4 ycb ff2 fs2 fc0 sc0 ls0 ws0">Pyannote on short,</div><div class="t m0 x24 h4 ycc ff2 fs2 fc0 sc0 ls0 ws0">clean audio.</div><div class="t m0 x25 h4 ycd ff2 fs2 fc0 sc0 ls0 ws0">Slower execution time,</div><div class="t m0 x25 h4 yce ff2 fs2 fc0 sc0 ls0 ws0">requires more GPU</div><div class="t m0 x25 h4 ycf ff2 fs2 fc0 sc0 ls0 ws0">memory.</div><div class="t m0 x26 h4 yd0 ff2 fs2 fc0 sc0 ls0 ws0">~64s for 5min</div><div class="t m0 x26 h4 yd1 ff2 fs2 fc0 sc0 ls0 ws0">audio on RTX</div><div class="t m0 x26 h4 yd2 ff2 fs2 fc0 sc0 ls0 ws0">3090.</div><div class="t m0 x9 h4 yd3 ff1 fs2 fc0 sc0 ls0 ws0">DIART</div><div class="t m0 x9 h7 yd4 ff1 fs2 fc0 sc0 ls0 ws0">(<span class="ff5">pyannote</span>)</div><div class="t m0 x24 h4 yd5 ff2 fs2 fc0 sc0 ls0 ws0">Extremely low latency</div><div class="t m0 x24 h4 yd6 ff2 fs2 fc0 sc0 ls0 ws0">(~57ms/chunk on</div><div class="t m0 x24 h4 yd7 ff2 fs2 fc0 sc0 ls0 ws0">CPU), scalable.</div><div class="t m0 x25 h4 yd8 ff2 fs2 fc0 sc0 ls0 ws0">May be less accurate</div><div class="t m0 x25 h4 yd9 ff2 fs2 fc0 sc0 ls0 ws0">than fully optimized</div><div class="t m0 x25 h4 yda ff2 fs2 fc0 sc0 ls0 ws0">models on very</div><div class="t m0 x25 h4 ydb ff2 fs2 fc0 sc0 ls0 ws0">challenging data.</div><div class="t m0 x26 h4 ydc ff2 fs2 fc0 sc0 ls0 ws0">Very low</div><div class="t m0 x26 h4 ydd ff2 fs2 fc0 sc0 ls0 ws0">latency (0.057s</div><div class="t m0 x26 h4 yde ff2 fs2 fc0 sc0 ls0 ws0">per chunk).</div><div class="t m0 x27 h5 ydf ff1 fs3 fc1 sc0 ls0 ws0">10</div><div class="t m0 x28 h5 ye0 ff1 fs3 fc1 sc0 ls0 ws0">16</div><div class="t m0 x29 h5 y64 ff1 fs3 fc1 sc0 ls0 ws0">18</div><div class="t m0 x2a h5 ye1 ff1 fs3 fc1 sc0 ls0 ws0">19</div><div class="t m0 x2b h5 ye2 ff1 fs3 fc1 sc0 ls0 ws0">20<span class="_ _5"> </span>50</div><div class="t m0 x2c h5 ye3 ff1 fs3 fc1 sc0 ls0 ws0">12</div><div class="t m0 x2c h5 ye4 ff1 fs3 fc1 sc0 ls0 ws0">12</div><div class="t m0 x2c h5 ye5 ff1 fs3 fc1 sc0 ls0 ws0">10</div><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,627.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:239.662000px;bottom:762.646000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,372.84,null]'><div class="d m1" style="border-style:none;position:absolute;left:183.715000px;bottom:594.523000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,293.64,null]'><div class="d m1" style="border-style:none;position:absolute;left:499.019000px;bottom:578.911000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,254.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:167.574000px;bottom:473.237000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,214.44,null]'><div class="d m1" style="border-style:none;position:absolute;left:330.543000px;bottom:395.176000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfe" data-dest-detail='[14,"XYZ",77.25,305.64,null]'><div class="d m1" style="border-style:none;position:absolute;left:340.227000px;bottom:395.176000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,548.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:484.349000px;bottom:238.777000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,548.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:484.349000px;bottom:179.190000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,627.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:484.349000px;bottom:111.797000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x9 h4 ye6 ff1 fs2 fc2 sc0 ls0 ws0">System /</div><div class="t m0 x9 h4 ye7 ff1 fs2 fc2 sc0 ls0 ws0">Technique</div><div class="t m0 x24 h4 ye8 ff1 fs2 fc2 sc0 ls0 ws0">Key Strengths<span class="_ _9"> </span>Key Weaknesses<span class="_ _a"> </span>Latency Profile<span class="_ _b"> </span>Source(s)</div><div class="t m0 x9 h4 y76 ff1 fs2 fc0 sc0 ls0 ws0">UIS-RNN-SML</div><div class="t m0 x24 h4 y78 ff2 fs2 fc0 sc0 ls0 ws0">High accuracy, but</div><div class="t m0 x24 h4 ye9 ff2 fs2 fc0 sc0 ls0 ws0">latency increases</div><div class="t m0 x24 h4 yea ff2 fs2 fc0 sc0 ls0 ws0">dramatically with</div><div class="t m0 x24 h4 yeb ff2 fs2 fc0 sc0 ls0 ws0">audio length.</div><div class="t m0 x25 h4 yec ff2 fs2 fc0 sc0 ls0 ws0">Becomes impractical</div><div class="t m0 x25 h4 yed ff2 fs2 fc0 sc0 ls0 ws0">for long recordings</div><div class="t m0 x25 h4 yee ff2 fs2 fc0 sc0 ls0 ws0">(>9s for 9min audio).</div><div class="t m0 x26 h4 yef ff2 fs2 fc0 sc0 ls0 ws0">High latency</div><div class="t m0 x26 h4 yf0 ff2 fs2 fc0 sc0 ls0 ws0">that scales with</div><div class="t m0 x26 h4 yf1 ff2 fs2 fc0 sc0 ls0 ws0">audio duration.</div><div class="t m0 x9 h4 yf2 ff1 fs2 fc0 sc0 ls0 ws0">LLM Post-</div><div class="t m0 x9 h4 yf3 ff1 fs2 fc0 sc0 ls0 ws0">Processing</div><div class="t m0 x24 h4 yf4 ff2 fs2 fc0 sc0 ls0 ws0">ASR-agnostic, can fix</div><div class="t m0 x24 h4 yf5 ff2 fs2 fc0 sc0 ls0 ws0">contextual errors,</div><div class="t m0 x24 h4 yf6 ff2 fs2 fc0 sc0 ls0 ws0">improves accuracy.</div><div class="t m0 x25 h4 yf7 ff2 fs2 fc0 sc0 ls0 ws0">Adds computational</div><div class="t m0 x25 h4 yf8 ff2 fs2 fc0 sc0 ls0 ws0">overhead, requires a</div><div class="t m0 x25 h4 yf9 ff2 fs2 fc0 sc0 ls0 ws0">fine-tuned LLM.</div><div class="t m0 x26 h4 yfa ff2 fs2 fc0 sc0 ls0 ws0">Not specified,</div><div class="t m0 x26 h4 yfb ff2 fs2 fc0 sc0 ls0 ws0">but adds to</div><div class="t m0 x26 h4 yfc ff2 fs2 fc0 sc0 ls0 ws0">overall</div><div class="t m0 x26 h4 yfd ff2 fs2 fc0 sc0 ls0 ws0">processing</div><div class="t m0 x26 h4 yfe ff2 fs2 fc0 sc0 ls0 ws0">time.</div><div class="t m0 x9 h4 yff ff1 fs2 fc0 sc0 ls0 ws0">SelfVC</div><div class="t m0 x9 h4 y100 ff1 fs2 fc0 sc0 ls0 ws0">Framework</div><div class="t m0 x24 h4 y101 ff2 fs2 fc0 sc0 ls0 ws0">No explicit speaker</div><div class="t m0 x24 h4 y102 ff2 fs2 fc0 sc0 ls0 ws0">labels needed, works</div><div class="t m0 x24 h4 y103 ff2 fs2 fc0 sc0 ls0 ws0">on unlabeled data.</div><div class="t m0 x25 h4 y104 ff2 fs2 fc0 sc0 ls0 ws0">Focuses on voice</div><div class="t m0 x25 h4 y105 ff2 fs2 fc0 sc0 ls0 ws0">conversion, not</div><div class="t m0 x25 h4 y106 ff2 fs2 fc0 sc0 ls0 ws0">diarization.</div><div class="t m0 x26 h4 y105 ff2 fs2 fc0 sc0 ls0 ws0">Not applicable.</div><div class="t m0 x1 h4 y107 ff2 fs2 fc0 sc0 ls0 ws0">Ultimately, the choice of speaker diarization technology for Trax v2 depends on the desired user</div><div class="t m0 x1 h4 y108 ff2 fs2 fc0 sc0 ls0 ws0">experience. A pure hobby project might prioritize a quick and easy integration of a library like</div><div class="t m0 x1 h4 y109 ff2 fs2 fc0 sc0 ls0 ws0">Pyannote.audio. A more ambitious v2 could implement a dual-pathway architecture: a fast, low-</div><div class="t m0 x1 h4 y10a ff2 fs2 fc0 sc0 ls0 ws0">latency pathway for real-time transcription and a slower, more accurate pathway for post-processing</div><div class="t m0 x1 h4 y10b ff2 fs2 fc0 sc0 ls0 ws0">that employs advanced techniques like E2E models or LLM-based correction. This would give users</div><div class="t m0 x1 h4 y10c ff2 fs2 fc0 sc0 ls0 ws0">control over the trade-off between immediacy and precision, delivering a truly state-of-the-art</div><div class="t m0 x1 h4 y10d ff2 fs2 fc0 sc0 ls0 ws0">conversational transcription experience.</div><div class="t m0 x1 h3 y10e ff1 fs1 fc0 sc0 ls0 ws0">Architectural Evolution: From Iterative Refinement to Scalable</div><div class="t m0 x1 h3 y10f ff1 fs1 fc0 sc0 ls0 ws0">Cloud-Native Systems</div><div class="t m0 x1 h4 y110 ff2 fs2 fc0 sc0 ls0 ws0">To realize the ambitions of Trax v2—achieving 99.5%+ accuracy, supporting thousands of</div><div class="t m0 x1 h4 y111 ff2 fs2 fc0 sc0 ls0 ws0">concurrent users, and enabling advanced features like multi-pass processing and domain-specific</div><div class="t m0 x1 h4 y112 ff2 fs2 fc0 sc0 ls0 ws0">models—the underlying architecture must evolve significantly from the current production-ready,</div><div class="t m0 x1 h4 y113 ff2 fs2 fc0 sc0 ls0 ws0">protocol-based design. The existing architecture, centered on a batch processor with parallel workers,</div><div class="t m0 x1 h4 y114 ff2 fs2 fc0 sc0 ls0 ws0">excels at deterministic, sequential tasks. However, the new requirements demand a more dynamic,</div><div class="t m0 x1 h4 y115 ff2 fs2 fc0 sc0 ls0 ws0">distributed, and service-oriented structure. This evolution will involve decomposing the monolith</div><div class="t m0 x1 h4 y116 ff2 fs2 fc0 sc0 ls0 ws0">into microservices, adopting a message-driven communication pattern, and embracing cloud-native</div><div class="t m0 x1 h4 y117 ff2 fs2 fc0 sc0 ls0 ws0">principles for scalability and resilience.</div><div class="t m0 x1 h4 y118 ff2 fs2 fc0 sc0 ls0 ws0">The most fundamental architectural change required is the transition from a synchronous, blocking</div><div class="t m0 x1 h4 y119 ff2 fs2 fc0 sc0 ls0 ws0">batch processor to an asynchronous, event-driven workflow. The current system processes files in a</div><div class="t m0 x1 h4 y11a ff2 fs2 fc0 sc0 ls0 ws0">tight loop, which is simple but inefficient for the complex pipelines envisioned for v2. An event-</div><div class="t m0 x1 h4 y11b ff2 fs2 fc0 sc0 ls0 ws0">driven architecture (EDA) is far better suited. In this model, the process begins when a user uploads</div><div class="t m0 x1 h6 y11c ff2 fs2 fc0 sc0 ls0 ws0">a file. The system creates a <span class="ff4">TranscriptionJob</span> event containing metadata (file ID, source</div><div class="t m0 x1 h4 y11d ff2 fs2 fc0 sc0 ls0 ws0">language, requested enhancements) and publishes it to a message broker like Apache Kafka or</div><div class="t m0 x1 h4 y11e ff2 fs2 fc0 sc0 ls0 ws0">RabbitMQ. This immediately returns control to the user, fulfilling the low-latency requirement for</div><div class="t m0 x2c h5 y11f ff1 fs3 fc1 sc0 ls0 ws0">10</div><div class="t m0 x2c h5 y120 ff1 fs3 fc1 sc0 ls0 ws0">20<span class="_ _5"> </span>50</div><div class="t m0 x2c h5 y121 ff1 fs3 fc1 sc0 ls0 ws0">24<span class="_ _5"> </span>26</div><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,627.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:484.349000px;bottom:704.116000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,214.44,null]'><div class="d m1" style="border-style:none;position:absolute;left:484.349000px;bottom:621.110000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfe" data-dest-detail='[14,"XYZ",77.25,305.64,null]'><div class="d m1" style="border-style:none;position:absolute;left:494.033000px;bottom:621.110000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",77.25,785.64,null]'><div class="d m1" style="border-style:none;position:absolute;left:484.349000px;bottom:545.911000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",77.25,706.44,null]'><div class="d m1" style="border-style:none;position:absolute;left:494.033000px;bottom:545.911000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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AAArg8AAADXBwAA4PoAAABwfQAAALg+AAAAXB8AAACuDwAAANcHAACA6wMAAHB9AAAAuD4AAABcHwAAAK4PAAAA1wcAAIDrAwAAwPUBAAC4PgAAAFwfAAAArg8AAADXBwAAgOsDAADA9QEAALg+CQAAAFwfAAAArg8AAADXBwAAgOsDAADA9QEAAOD6AAAAXB8AAACuDwAAANcHAACA6wMAAMD1AQAA4PoAAABwfQAAAK4PAAAA1wcAAIDrAwAAwPUBAADg+gAAAHB9AAAAuD4AAADXBwAAgOsDAADA9QEAAOD6AAAAcH0AAAC4PgAAAFwfAACA6wMAAMD1AQAA4PoAAABwfQAAALg+AAAAXB8AAIDrAwAAwPUBAADg+gAAAHB9AAAAuD4AAABcHwAAAK4PAADA9QEAAOD6AAAAcH0AAAC4PgAAAFwfAAAArg8AAADXBwAA4PoAAABwfQAAALg+AAAAXB8AAACuDwAAANcHAACA6wMAAHB9AAAAuD4AAABcHwAAAK4PAAAA1wcAAIDrAwAAwPUBAAC4PgAAAFwfAAAArg8AAADXBwAAgOsDAADA9QEAALg+AAAAXB8AAACuDwAAANcHAACA6wMAAMD1AQAA4PoAAABcHwAAAK4PAAAA1wcAAIDrAwAAwPUBAADg+gAAAHB9AAAArg8AAADXBwAAgOsDAADA9QEAAOD6AAAAcH0AAAC4PgAAANcHAACA6wMAAMD1AQAA4PoAAABwfQAAALg+AAAAXB8AAIDrAwAAwPUBAADg+gAAAHB9AAAAuD4AAABcHwAAgOsDAADA9QEAAOD6AAAAcH0AAAC4PgAAAFwfAAAArg8AAMD1AQAA4PoAAABwfQAAALg+AAAAXB8AAACuDwAAANcHAADg+gAAAHB9AAAAuD4AAABcHwAAAK4PAAAA1wcAAIDrAwAAcH0AAAC4PgAAAFwfAAAArg8AAADXBwAAgOsDAABwfRIAAAC4PgAAAFwfAAAArg8AAADXBwAAgOsDAADA9QEAALg+AAAAXB8AAACuDwAAANcHAACA6wMAAMD1AQAA4PoAAABcHwAAAK4PAAAA1wcAAIDrAwAAwPUBAADg+gAAAHB9AAAArg8AAADXBwAAgOsDAADA9QEAAOD6AAAAcH0AAAC4PgAAANcHAACA6wMAAMD1AQAA4PoAAABwfQAAALg+AAAA1wcAAIDrAwAAwPUBAADg+gAAAHB9AAAAuD4AAABcHwAAgOsDAADA9QEAAOD6AAAAcH0AAAC4PgAAAFwfAAAArg8AAMD1AQAA4PoAAABwfQAAALg+AAAAXB8AAACuDwAAANcHAADg+gAAAHB9AAAAuD4AAABcHwAAAK4PAAAA1wcAAIDrAwAAcH0AAAC4PgAAAFwfAAAArg8AAADXBwAAgOsDAABwfQAAALg+AAAAXB8AAACuDwAAANcHAACA6wMAAMD1AQAAuD4AAABcHwAAAK4PAAAA1wcAAIDrAwAAwPUBAADg+gAAAFwfAAAArg8AAADXBwAAgOsDAADA9QEAAOD6AAAAcH0AAACuDwAAANcHAACA6wMAAMD1AQAA4PoAAABwfQAAALg+AAAA1wcAAIDrAwAAwPUBAADg+gAAAHB9AAAAuD4AAADXBwAAgOsDAADA9QEAAOD6AAAAcH0AAAC4PgAAAFwfAACA6wMAAMD1AQAA4PoAAABwfQAAALg+AAAAXB8AAACuDwAAwPUBAADg+gAAAHB9AAAAuD4AAABcHwAAAK4PAAAA1wcAAOD6AAAAcH0AAAC4PgAAAFwfAAAArg8AAADXBwAA4PokAAAAcH0AAAC4PgAAAFwfAAAArg8AAADXBwAAgOsDAABwfQAAALg+AAAAXB8AAACuDwAAANcHAACA6wMAAMD1AQAAuD4AAABcHwAAAK4PAAAA1wcAAIDrAwAAwPUBAADg+gAAAFwfAAAArg8AAADXBwAAgOsDAADA9QEAAOD6AAAAcH0AAACuDwAAANcHAACA6wMAAMD1AQAA4PoAAABwfQAAAK4PAAAA1wcAAIDrAwAAwPUBAADg+gAAAHB9AAAAuD4AAADXBwAAgOsDAADA9QEAAOD6AAAAcH0AAAC4PgAAAFwfAACA6wMAAMD1AQAA4PoAAABwfQAAALg+AAAAXB8AAACuDwAAwPUBAADg+gAAAHB9AAAAuD4AAABcHwAAAK4PAAAA1wcAAOD6AAAAcH0AAAC4PgAAAFwfAAAArg8AAADXBwAA4PoAAABwfQAAALg+AAAAXB8AAACuDwAAANcHAACA6wMAAHB9AAAAuD4AAABcHwAAAK4PAAAA1wcAAIDrAwAAwPUBAAC4PgAAAFwfAAAArg8AAADXBwAAgOsDAADA9QEAAOD6AAAAXB8AAACuDwAAANcHAACA6wMAAMD1AQAA4PoAAABwfQAAAK4PAAAA1wcAAIDrAwAAwPUBAADg+gAAAHB9AAAArg8AAADXBwAAgOsDAADA9QEAAOD6AAAAcH0AAAC4PgAAANcHAACA6wMAAMD1AQAA4PoAAABwfQAAALg+AAAAXB8AAIDrAwAAwPUBAADg+gAAAHB9AAAAuD4AAABcHwAAAK4PAADA9QEAAOD6AAAAcH0AAAC4PgAAAFwfAAAArg8AAMD1SQAAAOD6AAAAcH0AAAC4PgAAAFwfAAAArg8AAADXBwAA4PoAAABwfQAAALg+AAAAXB8AAACuDwAAANcHAACA6wMAAHB9AAAAuD4AAABcHwAAAK4PAAAA1wcAAIDrAwAAwPVB27uDlQSiAArDR0dnMErNKOj9n65FJFrImNxpE0UQ6rJr37eb8a7O7mdGBgAAVB8AAAAXYGICAACo165P32fXf152bdpprq8Mw7fRMAxWAACA6pSS9Tb9/pefxuPcLTLxiAfVBwAA9Xp6TinHDjys0jR2wv/6AACgQpvXE8mX5GVjJ1QfAABUqJS87U4fez+YCtUHAAAVknOoPgAAUH1JsvaSJ6oPAACqczi7+r6+6IDqAwAAqtF15558vLcWqg8AAGrT+hAfqg8AAC5Y02Qq/FB9AABwwW7np88s53ZC9QEAQJ2aJqvFsQOzLrPOTiTJaBgGKwAAQI1KyXqbfv/j5nic5U261jyoPgAAgH/AG54AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVB64BtfsAAAC4SURBVAAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAwN/zAZuvSErezxiLAAAAAElFTkSuQmCC"/><div class="t m0 x1 h4 y2b ff2 fs2 fc0 sc0 ls0 ws0">initiating a job. Multiple, independent worker services then subscribe to this topic. One worker might</div><div class="t m0 x1 h4 y2c ff2 fs2 fc0 sc0 ls0 ws0">handle the initial audio preprocessing, another the primary transcription, a third the speaker</div><div class="t m0 x1 h4 y2d ff2 fs2 fc0 sc0 ls0 ws0">diarization, and so on. Each service performs its task and, upon completion, publishes a new event</div><div class="t m0 x1 h6 ya3 ff2 fs2 fc0 sc0 ls0 ws0">(e.g., <span class="ff4">PrimaryTranscriptionComplete</span>) with its output, triggering the next service in the</div><div class="t m0 x1 h4 y122 ff2 fs2 fc0 sc0 ls0 ws0">chain. This decouples the processing stages, allowing them to scale independently and fail without</div><div class="t m0 x1 h4 y123 ff2 fs2 fc0 sc0 ls0 ws0">bringing down the entire system. It also naturally enables the multi-pass and multi-model processing</div><div class="t m0 x1 h4 y124 ff2 fs2 fc0 sc0 ls0 ws0">flows discussed previously, where the output of one model becomes the input for another.</div><div class="t m0 x1 h4 ya7 ff2 fs2 fc0 sc0 ls0 ws0">This EDA forms the basis for a microservice architecture. Instead of a single, large application, Trax</div><div class="t m0 x1 h4 y125 ff2 fs2 fc0 sc0 ls0 ws0">v2 would consist of a collection of small, focused services: 1. <span class="ff1">API Gateway</span>: The single entry point</div><div class="t m0 x1 h4 y126 ff2 fs2 fc0 sc0 ls0 ws0">for all client requests. It authenticates users, routes requests to the appropriate backend service, and</div><div class="t m0 x1 h4 y127 ff2 fs2 fc0 sc0 ls0 ws0">aggregates responses. 2. <span class="ff1">Transcription Service</span>: Manages the lifecycle of transcription jobs, interacting</div><div class="t m0 x1 h4 y128 ff2 fs2 fc0 sc0 ls0 ws0">with the message broker to trigger and coordinate workflows. 3. <span class="ff1">Worker Services</span>: Specialized</div><div class="t m0 x1 h6 y129 ff2 fs2 fc0 sc0 ls0 ws0">services for different processing tasks (e.g., <span class="ff4">WhisperWorker</span>, <span class="ff4">DeepSeekEnhancer</span>, </div><div class="t m0 x1 h6 y12a ff4 fs2 fc0 sc0 ls0 ws0">DiarizationWorker<span class="ff2">). These can be scaled independently based on their computational</span></div><div class="t m0 x1 h4 y12b ff2 fs2 fc0 sc0 ls0 ws0">intensity. 4. <span class="ff1">Model Management Service</span>: Handles the loading, caching, and versioning of machine</div><div class="t m0 x1 h4 y12c ff2 fs2 fc0 sc0 ls0 ws0">learning models. This is crucial for efficiently swapping in different PEFT-adapted models for</div><div class="t m0 x1 h4 y12d ff2 fs2 fc0 sc0 ls0 ws0">various domains. 5. <span class="ff1">Storage Service</span>: Manages access to the PostgreSQL database and object storage</div><div class="t m0 x1 h4 y12e ff2 fs2 fc0 sc0 ls0 ws0">for audio files and processed transcripts. 6. <span class="ff1">Metrics & Logging Service</span>: Collects telemetry data to</div><div class="t m0 x1 h4 y12f ff2 fs2 fc0 sc0 ls0 ws0">monitor system health, performance, and error rates.</div><div class="t m0 x1 h4 y130 ff2 fs2 fc0 sc0 ls0 ws0">This modular design offers immense benefits. It allows teams to develop and deploy services</div><div class="t m0 x1 h4 y131 ff2 fs2 fc0 sc0 ls0 ws0">independently, facilitates experimentation with new models, and improves maintainability. For</div><div class="t m0 x1 h4 y132 ff2 fs2 fc0 sc0 ls0 ws0">example, if a new, more accurate diarization model is released, only the Diarization Worker needs to</div><div class="t m0 x1 h4 y133 ff2 fs2 fc0 sc0 ls0 ws0">be updated and redeployed, without touching the rest of the system.</div><div class="t m0 x1 h4 y134 ff2 fs2 fc0 sc0 ls0 ws0">Scalability is a key success metric, targeting 1000+ concurrent transcriptions. This is best achieved</div><div class="t m0 x1 h4 y135 ff2 fs2 fc0 sc0 ls0 ws0">through containerization and orchestration. Docker should be used to package each service, ensuring</div><div class="t m0 x1 h4 y136 ff2 fs2 fc0 sc0 ls0 ws0">consistency across development and production environments. Kubernetes would then serve as the</div><div class="t m0 x1 h4 y137 ff2 fs2 fc0 sc0 ls0 ws0">orchestrator, managing the deployment, scaling, and operation of these containers. Kubernetes'</div><div class="t m0 x1 h4 y138 ff2 fs2 fc0 sc0 ls0 ws0">Horizontal Pod Autoscaler (HPA) can automatically increase the number of replicas for a service like</div><div class="t m0 x1 h6 y139 ff2 fs2 fc0 sc0 ls0 ws0">the <span class="ff4">WhisperWorker</span> when CPU utilization or the length of the message queue exceeds a</div><div class="t m0 x1 h4 y13a ff2 fs2 fc0 sc0 ls0 ws0">threshold, and decrease them when load is low. This ensures resources are used efficiently. To meet</div><div class="t m0 x1 h4 y13b ff2 fs2 fc0 sc0 ls0 ws0">the <1GB memory per worker target, careful selection of container base images and optimization of</div><div class="t m0 x1 h6 y13c ff2 fs2 fc0 sc0 ls0 ws0">the Python environment (e.g., using <span class="ff4">uv</span> as planned) is critical. Additionally, using smaller, more</div><div class="t m0 x1 h4 y13d ff2 fs2 fc0 sc0 ls0 ws0">efficient models, such as quantized versions of Whisper, can further reduce memory footprint <span class="_ _0"> </span>.</div><div class="t m0 x1 h4 y13e ff2 fs2 fc0 sc0 ls0 ws0">Finally, the architecture must incorporate mechanisms for cost optimization and reliability. Caching</div><div class="t m0 x1 h4 y13f ff2 fs2 fc0 sc0 ls0 ws0">is a powerful tool. Transcripts of frequently used content or common phrases can be cached in a</div><div class="t m0 x1 h4 y140 ff2 fs2 fc0 sc0 ls0 ws0">system like Redis to avoid redundant processing and reduce API costs. Intelligent caching of</div><div class="t m0 x1 h4 y141 ff2 fs2 fc0 sc0 ls0 ws0">intermediate results from multi-pass processing can also yield significant performance gains. For</div><div class="t m0 x1 h4 y142 ff2 fs2 fc0 sc0 ls0 ws0">reliability, the system must be designed for failure. This includes implementing idempotency keys for</div><div class="t m0 x1 h4 y143 ff2 fs2 fc0 sc0 ls0 ws0">API requests to prevent duplicate processing, using dead-letter queues in the message broker to</div><div class="t m0 x1 h4 y144 ff2 fs2 fc0 sc0 ls0 ws0">handle failed messages for later inspection, and ensuring all services are stateless so they can be</div><div class="t m0 x1 h4 y145 ff2 fs2 fc0 sc0 ls0 ws0">restarted or replaced without losing data. With a cloud-native architecture built on these principles,</div><div class="t m0 x1 h4 y146 ff2 fs2 fc0 sc0 ls0 ws0">Trax v2 can confidently scale to meet demanding workloads while remaining performant, cost-</div><div class="t m0 x1 h4 y147 ff2 fs2 fc0 sc0 ls0 ws0">effective, and resilient.</div><div class="t m0 x2d h5 y148 ff1 fs3 fc1 sc0 ls0 ws0">17</div><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,333.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:502.191000px;bottom:242.665000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x1 h3 y149 ff1 fs1 fc0 sc0 ls0 ws0">Optimizing for Scale and Speed: Strategies for Concurrent</div><div class="t m0 x1 h3 y14a ff1 fs1 fc0 sc0 ls0 ws0">Transcription and Resource Efficiency</div><div class="t m0 x1 h4 y14b ff2 fs2 fc0 sc0 ls0 ws0">Achieving the ambitious targets of 1000+ concurrent transcriptions and <$0.005 per transcript</div><div class="t m0 x1 h4 y14c ff2 fs2 fc0 sc0 ls0 ws0">requires a multi-faceted approach to optimization, focusing on workload distribution, resource</div><div class="t m0 x1 h4 y14d ff2 fs2 fc0 sc0 ls0 ws0">management, and computational efficiency. The foundation of this effort lies in moving beyond the</div><div class="t m0 x1 h4 y14e ff2 fs2 fc0 sc0 ls0 ws0">current single-machine, multi-worker setup to a distributed, cloud-native architecture capable of</div><div class="t m0 x1 h4 y14f ff2 fs2 fc0 sc0 ls0 ws0">horizontal scaling. This involves leveraging containerization, message queues, and efficient model</div><div class="t m0 x1 h4 y150 ff2 fs2 fc0 sc0 ls0 ws0">deployment strategies to maximize throughput and minimize operational costs.</div><div class="t m0 x1 h4 y151 ff2 fs2 fc0 sc0 ls0 ws0">The first step toward high concurrency is to eliminate bottlenecks in the processing pipeline. As</div><div class="t m0 x1 h4 y152 ff2 fs2 fc0 sc0 ls0 ws0">previously discussed, transitioning to an event-driven architecture with a message broker is central.</div><div class="t m0 x1 h4 y153 ff2 fs2 fc0 sc0 ls0 ws0">This decouples the frontend from the backend processing, allowing the system to accept thousands</div><div class="t m0 x1 h4 y154 ff2 fs2 fc0 sc0 ls0 ws0">of new transcription jobs instantly without being blocked by the processing capacity. The message</div><div class="t m0 x1 h4 y155 ff2 fs2 fc0 sc0 ls0 ws0">broker acts as a buffer, smoothing out spikes in demand. The workers that consume from this queue</div><div class="t m0 x1 h4 y156 ff2 fs2 fc0 sc0 ls0 ws0">can then be deployed as a scalable Kubernetes deployment. When the volume of jobs increases,</div><div class="t m0 x1 h4 y157 ff2 fs2 fc0 sc0 ls0 ws0">Kubernetes can automatically spin up more worker pods to consume messages from the queue in</div><div class="t m0 x1 h4 y158 ff2 fs2 fc0 sc0 ls0 ws0">parallel, distributing the load across multiple machines or cores. This horizontal scaling is the most</div><div class="t m0 x1 h4 y159 ff2 fs2 fc0 sc0 ls0 ws0">direct way to handle thousands of concurrent users.</div><div class="t m0 x1 h4 y15a ff2 fs2 fc0 sc0 ls0 ws0">Efficient resource management within each worker is equally critical. The goal is to keep the memory</div><div class="t m0 x1 h4 y15b ff2 fs2 fc0 sc0 ls0 ws0">usage below 1GB per worker. This can be achieved through several techniques. First, selecting leaner</div><div class="t m0 x1 h6 y15c ff2 fs2 fc0 sc0 ls0 ws0">base images for Docker containers (e.g., <span class="ff4">python:slim</span> instead of a full OS image) and carefully</div><div class="t m0 x1 h4 y15d ff2 fs2 fc0 sc0 ls0 ws0">managing dependencies is important. Second, and most critically, is the use of Post-Training</div><div class="t m0 x1 h4 y15e ff2 fs2 fc0 sc0 ls0 ws0">Quantization (PTQ). PTQ is a technique that converts the floating-point weights of a trained model</div><div class="t m0 x1 h4 y15f ff2 fs2 fc0 sc0 ls0 ws0">into lower-bitwidth integers (e.g., 8-bit or 4-bit) without retraining, significantly reducing the model's</div><div class="t m0 x1 h4 y160 ff2 fs2 fc0 sc0 ls0 ws0">memory footprint and accelerating computation. Research has shown that w8-a8 quantization (8-bit</div><div class="t m0 x1 h4 y161 ff2 fs2 fc0 sc0 ls0 ws0">weights, 8-bit activations) generally preserves accuracy, while w4-a8 can cause significant degradation</div><div class="t m0 x1 h4 y162 ff2 fs2 fc0 sc0 ls0 ws0">in smaller models but is surprisingly robust in larger ones like Whisper Small <span class="_ _0"> </span>. Methods like GPTQ</div><div class="t m0 x1 h4 y163 ff2 fs2 fc0 sc0 ls0 ws0">and SpQR have demonstrated strong robustness across configurations <span class="_ _0"> </span>. By applying PTQ, Trax</div><div class="t m0 x1 h4 y164 ff2 fs2 fc0 sc0 ls0 ws0">can deploy multiple instances of the Whisper model on a single GPU, drastically increasing batch</div><div class="t m0 x1 h4 y165 ff2 fs2 fc0 sc0 ls0 ws0">processing capacity and reducing the overall hardware cost per transcription.</div><div class="t m0 x1 h4 y166 ff2 fs2 fc0 sc0 ls0 ws0">Further computational efficiency can be gained by optimizing the processing logic itself. For multi-</div><div class="t m0 x1 h4 y167 ff2 fs2 fc0 sc0 ls0 ws0">pass systems, as explored in the accuracy section, one study successfully reduced the frame rate of</div><div class="t m0 x1 h4 y168 ff2 fs2 fc0 sc0 ls0 ws0">the second pass by 50% without affecting final accuracy, leading to a 20% reduction in Real-Time</div><div class="t m0 x1 h4 y169 ff2 fs2 fc0 sc0 ls0 ws0">Factor (RTF) and 13% power savings <span class="_ _0"> </span>. This principle can be applied to Trax's v2 processing</div><div class="t m0 x1 h4 y16a ff2 fs2 fc0 sc0 ls0 ws0">pipeline, where the initial, faster pass can be executed with a higher frame rate, and a more</div><div class="t m0 x1 h4 y16b ff2 fs2 fc0 sc0 ls0 ws0">computationally intensive second pass can run at a lower frame rate on a subset of the audio. This</div><div class="t m0 x1 h4 y16c ff2 fs2 fc0 sc0 ls0 ws0">targeted optimization focuses compute resources where they are most needed, improving overall</div><div class="t m0 x1 h4 y16d ff2 fs2 fc0 sc0 ls0 ws0">efficiency.</div><div class="t m0 x1 h4 y16e ff2 fs2 fc0 sc0 ls0 ws0">Cost optimization is intrinsically linked to resource efficiency. The target of <$0.005 per transcript is</div><div class="t m0 x1 h4 y16f ff2 fs2 fc0 sc0 ls0 ws0">aggressive and will only be met by minimizing every component of the cost equation: compute,</div><div class="t m0 x1 h4 y170 ff2 fs2 fc0 sc0 ls0 ws0">storage, and data transfer. Beyond model quantization and efficient pipelines, strategic use of spot</div><div class="t m0 x1 h4 y171 ff2 fs2 fc0 sc0 ls0 ws0">instances or preemptible VMs in the cloud can dramatically reduce compute costs, provided the</div><div class="t m0 x2e h5 y172 ff1 fs3 fc1 sc0 ls0 ws0">17</div><div class="t m0 x2f h5 y173 ff1 fs3 fc1 sc0 ls0 ws0">17</div><div class="t m0 x30 h5 y174 ff1 fs3 fc1 sc0 ls0 ws0">22</div><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,333.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:420.890000px;bottom:333.400000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,333.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:393.419000px;bottom:317.788000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,135.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:241.214000px;bottom:212.114000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x1 h4 y2b ff2 fs2 fc0 sc0 ls0 ws0">system is designed to gracefully handle interruptions. Storage costs can be managed by using tiered</div><div class="t m0 x1 h4 y2c ff2 fs2 fc0 sc0 ls0 ws0">storage, where raw audio files are stored in cheaper archival storage and only moved to high-</div><div class="t m0 x1 h4 y2d ff2 fs2 fc0 sc0 ls0 ws0">performance storage when actively being processed. Data transfer costs, particularly if using a cloud</div><div class="t m0 x1 h4 ya3 ff2 fs2 fc0 sc0 ls0 ws0">provider, can be minimized by running the API gateway, message broker, and worker services within</div><div class="t m0 x1 h4 ya4 ff2 fs2 fc0 sc0 ls0 ws0">the same availability zone or region. Finally, intelligent caching, as mentioned earlier, can reduce the</div><div class="t m0 x1 h4 ya5 ff2 fs2 fc0 sc0 ls0 ws0">need for reprocessing identical content, saving both time and compute cycles.</div><div class="t m0 x1 h4 ya6 ff2 fs2 fc0 sc0 ls0 ws0">The following table summarizes key optimization strategies and their potential impact:</div><div class="t m0 x9 h4 y175 ff1 fs2 fc2 sc0 ls0 ws0">Optimization</div><div class="t m0 x9 h4 y176 ff1 fs2 fc2 sc0 ls0 ws0">Strategy</div><div class="t m0 x31 h4 y177 ff1 fs2 fc2 sc0 ls0 ws0">Description<span class="_ _c"> </span>Potential Impact<span class="_ _d"> </span>Source(s)</div><div class="t m0 x9 h4 y178 ff1 fs2 fc0 sc0 ls0 ws0">Event-Driven</div><div class="t m0 x9 h4 y179 ff1 fs2 fc0 sc0 ls0 ws0">Architecture</div><div class="t m0 x31 h4 y17a ff2 fs2 fc0 sc0 ls0 ws0">Use a message broker (e.g.,</div><div class="t m0 x31 h4 y17b ff2 fs2 fc0 sc0 ls0 ws0">Kafka) to decouple job</div><div class="t m0 x31 h4 y17c ff2 fs2 fc0 sc0 ls0 ws0">submission from</div><div class="t m0 x31 h4 y17d ff2 fs2 fc0 sc0 ls0 ws0">processing.</div><div class="t m0 x32 h4 y17e ff2 fs2 fc0 sc0 ls0 ws0">Enables thousands of</div><div class="t m0 x32 h4 y17f ff2 fs2 fc0 sc0 ls0 ws0">concurrent job submissions</div><div class="t m0 x32 h4 y180 ff2 fs2 fc0 sc0 ls0 ws0">and independent scaling of</div><div class="t m0 x32 h4 y181 ff2 fs2 fc0 sc0 ls0 ws0">worker services.</div><div class="t m0 x33 h4 y182 ff2 fs2 fc0 sc0 ls0 ws0">Analytical</div><div class="t m0 x33 h4 y183 ff2 fs2 fc0 sc0 ls0 ws0">Reasoning</div><div class="t m0 x9 h4 y184 ff1 fs2 fc0 sc0 ls0 ws0">Horizontal Scaling</div><div class="t m0 x31 h4 y185 ff2 fs2 fc0 sc0 ls0 ws0">Deploy worker services as</div><div class="t m0 x31 h4 y186 ff2 fs2 fc0 sc0 ls0 ws0">scalable Kubernetes</div><div class="t m0 x31 h4 y187 ff2 fs2 fc0 sc0 ls0 ws0">deployments.</div><div class="t m0 x32 h4 y188 ff2 fs2 fc0 sc0 ls0 ws0">Directly supports handling</div><div class="t m0 x32 h4 y189 ff2 fs2 fc0 sc0 ls0 ws0">thousands of concurrent</div><div class="t m0 x32 h4 y18a ff2 fs2 fc0 sc0 ls0 ws0">transcription tasks.</div><div class="t m0 x9 h4 y18b ff1 fs2 fc0 sc0 ls0 ws0">Post-Training</div><div class="t m0 x9 h4 y18c ff1 fs2 fc0 sc0 ls0 ws0">Quantization</div><div class="t m0 x9 h4 y18d ff1 fs2 fc0 sc0 ls0 ws0">(PTQ)</div><div class="t m0 x31 h4 y18e ff2 fs2 fc0 sc0 ls0 ws0">Reduce model size by</div><div class="t m0 x31 h4 y18f ff2 fs2 fc0 sc0 ls0 ws0">converting weights to</div><div class="t m0 x31 h4 y190 ff2 fs2 fc0 sc0 ls0 ws0">lower-bitwidth integers</div><div class="t m0 x31 h4 y191 ff2 fs2 fc0 sc0 ls0 ws0">(e.g., w4-a8).</div><div class="t m0 x32 h4 y192 ff2 fs2 fc0 sc0 ls0 ws0">Reduces memory usage,</div><div class="t m0 x32 h4 y193 ff2 fs2 fc0 sc0 ls0 ws0">accelerates inference, and</div><div class="t m0 x32 h4 y194 ff2 fs2 fc0 sc0 ls0 ws0">increases batch size, lowering</div><div class="t m0 x32 h4 y195 ff2 fs2 fc0 sc0 ls0 ws0">cost per transcript.</div><div class="t m0 x9 h4 y196 ff1 fs2 fc0 sc0 ls0 ws0">Efficient Multi-</div><div class="t m0 x9 h4 y197 ff1 fs2 fc0 sc0 ls0 ws0">Pass Pipelines</div><div class="t m0 x31 h4 y198 ff2 fs2 fc0 sc0 ls0 ws0">Reduce computational load</div><div class="t m0 x31 h4 y199 ff2 fs2 fc0 sc0 ls0 ws0">in later passes (e.g., by</div><div class="t m0 x31 h4 y19a ff2 fs2 fc0 sc0 ls0 ws0">lowering frame rate).</div><div class="t m0 x32 h4 y19b ff2 fs2 fc0 sc0 ls0 ws0">Decreases overall latency and</div><div class="t m0 x32 h4 y19c ff2 fs2 fc0 sc0 ls0 ws0">computational cost without</div><div class="t m0 x32 h4 y19d ff2 fs2 fc0 sc0 ls0 ws0">sacrificing accuracy.</div><div class="t m0 x9 h4 y19e ff1 fs2 fc0 sc0 ls0 ws0">Cloud-Native Cost</div><div class="t m0 x9 h4 y19f ff1 fs2 fc0 sc0 ls0 ws0">Management</div><div class="t m0 x31 h4 y1a0 ff2 fs2 fc0 sc0 ls0 ws0">Utilize spot/preemptible</div><div class="t m0 x31 h4 y1a1 ff2 fs2 fc0 sc0 ls0 ws0">instances, regional</div><div class="t m0 x31 h4 y1a2 ff2 fs2 fc0 sc0 ls0 ws0">deployments, and tiered</div><div class="t m0 x31 h4 y1a3 ff2 fs2 fc0 sc0 ls0 ws0">storage.</div><div class="t m0 x32 h4 y1a4 ff2 fs2 fc0 sc0 ls0 ws0">Drastically reduces compute</div><div class="t m0 x32 h4 y1a5 ff2 fs2 fc0 sc0 ls0 ws0">and data transfer costs,</div><div class="t m0 x32 h4 y1a6 ff2 fs2 fc0 sc0 ls0 ws0">meeting aggressive pricing</div><div class="t m0 x32 h4 y1a7 ff2 fs2 fc0 sc0 ls0 ws0">targets.</div><div class="t m0 x9 h4 y1a8 ff1 fs2 fc0 sc0 ls0 ws0">Parameter-</div><div class="t m0 x9 h4 y1a9 ff1 fs2 fc0 sc0 ls0 ws0">Efficient Fine-</div><div class="t m0 x9 h4 y1aa ff1 fs2 fc0 sc0 ls0 ws0">Tuning (PEFT)</div><div class="t m0 x31 h4 y1ab ff2 fs2 fc0 sc0 ls0 ws0">Use LoRA or similar</div><div class="t m0 x31 h4 y1ac ff2 fs2 fc0 sc0 ls0 ws0">methods for domain</div><div class="t m0 x31 h4 y1ad ff2 fs2 fc0 sc0 ls0 ws0">adaptation.</div><div class="t m0 x32 h4 y1ae ff2 fs2 fc0 sc0 ls0 ws0">Avoids deploying large, full-</div><div class="t m0 x32 h4 y1af ff2 fs2 fc0 sc0 ls0 ws0">scale fine-tuned models, saving</div><div class="t m0 x32 h4 y1b0 ff2 fs2 fc0 sc0 ls0 ws0">memory and storage.</div><div class="t m0 x1 h4 y1b1 ff2 fs2 fc0 sc0 ls0 ws0">By systematically applying these strategies, Trax v2 can build a highly performant, scalable, and cost-</div><div class="t m0 x1 h4 y1b2 ff2 fs2 fc0 sc0 ls0 ws0">effective platform. The architectural shift to a distributed, event-driven system provides the necessary</div><div class="t m0 x1 h4 y1b3 ff2 fs2 fc0 sc0 ls0 ws0">foundation for concurrency. Within that system, optimizations in model quantization, pipeline</div><div class="t m0 x1 h4 y1b4 ff2 fs2 fc0 sc0 ls0 ws0">design, and cloud resource management will ensure that the performance and cost targets are not just</div><div class="t m0 x1 h4 y1b5 ff2 fs2 fc0 sc0 ls0 ws0">met, but exceeded.</div><div class="t m0 x34 h5 y1b6 ff1 fs3 fc1 sc0 ls0 ws0">22</div><div class="t m0 x34 h5 y1b7 ff1 fs3 fc1 sc0 ls0 ws0">17</div><div class="t m0 x34 h5 y1b8 ff1 fs3 fc1 sc0 ls0 ws0">22</div><div class="t m0 x35 h5 y1b9 ff1 fs3 fc1 sc0 ls0 ws0">5</div><div class="t m0 x34 h5 y1ba ff1 fs3 fc1 sc0 ls0 ws0">28<span class="_ _5"> </span>31</div><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,135.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:469.593000px;bottom:503.436000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,333.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:469.593000px;bottom:436.043000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfc" data-dest-detail='[12,"XYZ",77.25,135.24,null]'><div class="d m1" style="border-style:none;position:absolute;left:469.593000px;bottom:368.650000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfb" data-dest-detail='[11,"XYZ",77.25,137.526,null]'><div class="d m1" style="border-style:none;position:absolute;left:469.593000px;bottom:301.257000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",77.25,610.44,null]'><div class="d m1" style="border-style:none;position:absolute;left:469.593000px;bottom:233.864000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",77.25,491.64,null]'><div class="d m1" style="border-style:none;position:absolute;left:479.277000px;bottom:233.864000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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ABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAACA0jUmAIAvHI/xsol0+PTi0Mf6IarKPAAUoMo5WwEATtruYvN++q26jqdVdK2RALh17vAEgNPG/dnki4hpit+vcTzaCQDVBwAFyjle3y4fe9mYCgDVBwAF+ruPabp87J/n/QBA9QFAGcbRBgCoPgBYcPUlGwCg+gBgub75hgRA9QHAgvlLBgBUHwAsWav6AFB9ALBgQx/1jC/JvjMVAKoPAMr0Y335zOODnQBQfQBQpqaJ5/XZK351HT+fZl0PBIDrqnLOVgCAc3KOt21sd596726I+++2AUD1AQAAcG1uTAEAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKg+AAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAABUHwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAED1AQAAoPoAAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACg+gAAAFB9AAAAqD4AAABUHwAAAKoPAAAA1QcAAKD6AAAAUH0AAACoPgAAAFQfAAAAqg8AAADVBwAAgOoDAABQfQAAAKg+AAAAVB8AAACqDwAAANUHAACA6gMAAED1AQAAqD4AAABUHwAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFQfAAAAqg8AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACqDwAAANUHAACA6gMAAED1AQAAoPoAAABQfQAAAKoPAAAA1QcAAIDqAwAAQPUBAACg+gAAAFB9AAAAqD4AAADVBwAAgOoDAABA9QEAAKD6AAAAUH0AAACoPgDgo/06FgAAAAAY5G89jD1lEQBYHwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAADWBwAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAID1AQAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAID1AQAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAID1AQAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAID1AQAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAAWB8AAID1AQAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAA1gcAAID1AQAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAA1gcAAID1AQAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAA1gcAAID1AQAAYH0AAABYHwAAANYHAACA9QEAAGB9AAAA1gcAAID1AQAAYH0AAABYHwAAANYHAACA9QEAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAACA9QEAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAACA9QEAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAACA9QEAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAACA9QEAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAANYHAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAABYHwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAYH0AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAPhe5NcAAAEeSURBVAAA1gcAAID1AQAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAAAA1gcAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAACA9QEAAGB9AAAAWB8AAADWBwAAgPUBAABgfQAAAFgfAACA9QEAAGB9AAAAWB8AAACfAMkAjBBNw2SBAAAAAElFTkSuQmCC"/><div class="t m0 x1 h3 y149 ff1 fs1 fc0 sc0 ls0 ws0">The User Experience Imperative: Designing a Modern Interface</div><div class="t m0 x1 h3 y14a ff1 fs1 fc0 sc0 ls0 ws0">and Workflow</div><div class="t m0 x1 h4 y14b ff2 fs2 fc0 sc0 ls0 ws0">While backend performance and feature set are crucial, the ultimate success of Trax v2 hinges on a</div><div class="t m0 x1 h4 y14c ff2 fs2 fc0 sc0 ls0 ws0">seamless and intuitive user experience. For a hobby project aimed at becoming a serious tool, the</div><div class="t m0 x1 h4 y14d ff2 fs2 fc0 sc0 ls0 ws0">user interface must evolve from a functional but dated Command Line Interface (CLI) to a modern,</div><div class="t m0 x1 h4 y14e ff2 fs2 fc0 sc0 ls0 ws0">web-based platform that simplifies complex workflows and provides powerful, accessible tools for</div><div class="t m0 x1 h4 y14f ff2 fs2 fc0 sc0 ls0 ws0">reviewing and editing transcripts. The focus should be on streamlining the path from audio upload to</div><div class="t m0 x1 h4 y150 ff2 fs2 fc0 sc0 ls0 ws0">final, usable text, catering to the needs of researchers, journalists, and other professionals who rely</div><div class="t m0 x1 h4 y1bb ff2 fs2 fc0 sc0 ls0 ws0">on accurate transcription.</div><div class="t m0 x1 h4 y1bc ff2 fs2 fc0 sc0 ls0 ws0">The immediate priority is the development of a comprehensive web interface. This interface should</div><div class="t m0 x1 h4 y153 ff2 fs2 fc0 sc0 ls0 ws0">be built using a modern front-end framework like React or Vue.js, ensuring a responsive and mobile-</div><div class="t m0 x1 h4 y1bd ff2 fs2 fc0 sc0 ls0 ws0">friendly design <span class="_ _0"> </span>. The core workflow should be clear and logical. Upon visiting the site, a user</div><div class="t m0 x1 h4 y1be ff2 fs2 fc0 sc0 ls0 ws0">should see a prominent "Upload Audio" button. After uploading a file, the system should present a</div><div class="t m0 x1 h4 y1bf ff2 fs2 fc0 sc0 ls0 ws0">clean dashboard displaying the status of the transcription job. Once the job is complete, the interface</div><div class="t m0 x1 h4 y1c0 ff2 fs2 fc0 sc0 ls0 ws0">should display the transcript in a readable format. Crucially, this is not just a static display. The</div><div class="t m0 x1 h4 y1c1 ff2 fs2 fc0 sc0 ls0 ws0">transcript should be interactive, allowing users to click on any word to hear the corresponding audio</div><div class="t m0 x1 h4 y1c2 ff2 fs2 fc0 sc0 ls0 ws0">snippet, a feature that greatly aids verification and correction.</div><div class="t m0 x1 h4 y1c3 ff2 fs2 fc0 sc0 ls0 ws0">One of the most valuable additions to enhance the user experience is real-time collaboration. While</div><div class="t m0 x1 h4 y1c4 ff2 fs2 fc0 sc0 ls0 ws0">the user indicated no critical integrations, the ability for multiple users to review, edit, and comment</div><div class="t m0 x1 h4 y1c5 ff2 fs2 fc0 sc0 ls0 ws0">on a single transcript simultaneously is a powerful productivity tool. This feature, however, presents a</div><div class="t m0 x1 h4 y1c6 ff2 fs2 fc0 sc0 ls0 ws0">significant engineering challenge, especially regarding performance. To support this, Trax v2 must be</div><div class="t m0 x1 h4 y1c7 ff2 fs2 fc0 sc0 ls0 ws0">architected with real-time capabilities from the ground up. This likely involves using WebSockets or a</div><div class="t m0 x1 h4 y1c8 ff2 fs2 fc0 sc0 ls0 ws0">similar persistent connection technology to facilitate low-latency updates. The system must be</div><div class="t m0 x1 h4 y1c9 ff2 fs2 fc0 sc0 ls0 ws0">designed to handle concurrent edits efficiently, perhaps using Operational Transformation (OT) or</div><div class="t m0 x1 h4 y1ca ff2 fs2 fc0 sc0 ls0 ws0">Conflict-Free Replicated Data Types (CRDTs) to merge changes from multiple users without</div><div class="t m0 x1 h4 y1cb ff2 fs2 fc0 sc0 ls0 ws0">conflict. The target of <500ms latency for updates is achievable but will require a highly optimized</div><div class="t m0 x1 h4 y1cc ff2 fs2 fc0 sc0 ls0 ws0">backend and a well-designed front-end architecture <span class="_ _0"> </span>.</div><div class="t m0 x1 h4 y1cd ff2 fs2 fc0 sc0 ls0 ws0">The interface should also provide advanced export options and a flexible workflow. Users should be</div><div class="t m0 x1 h4 y1ce ff2 fs2 fc0 sc0 ls0 ws0">able to download transcripts in a variety of formats, including SRT for subtitles, DOCX for editable</div><div class="t m0 x1 h4 y1cf ff2 fs2 fc0 sc0 ls0 ws0">documents, and JSON for programmatic access. Integration with popular note-taking platforms like</div><div class="t m0 x1 h4 y1d0 ff2 fs2 fc0 sc0 ls0 ws0">Obsidian or Notion, though not a "critical" partner, would be a significant value-add and could be</div><div class="t m0 x1 h4 y1d1 ff2 fs2 fc0 sc0 ls0 ws0">implemented via a browser extension or bookmarklet that allows users to send highlighted text</div><div class="t m0 x1 h4 y1d2 ff2 fs2 fc0 sc0 ls0 ws0">directly to their preferred tool. The workflow should be streamlined, minimizing clicks and cognitive</div><div class="t m0 x1 h4 y1d3 ff2 fs2 fc0 sc0 ls0 ws0">load. For example, after correcting an error, the user should be able to immediately request a new</div><div class="t m0 x1 h4 y1d4 ff2 fs2 fc0 sc0 ls0 ws0">translation of a specific sentence or paragraph without having to re-run the entire transcription job.</div><div class="t m0 x1 h4 y1d5 ff2 fs2 fc0 sc0 ls0 ws0">Finally, the design must be tailored to different user types. A researcher may need to tag specific</div><div class="t m0 x1 h4 y1d6 ff2 fs2 fc0 sc0 ls0 ws0">sections of the transcript with metadata, while a journalist may prioritize quick searching and</div><div class="t m0 x1 h4 y1d7 ff2 fs2 fc0 sc0 ls0 ws0">quoting. The interface should be adaptable, perhaps through user profiles or customizable</div><div class="t m0 x1 h4 y1d8 ff2 fs2 fc0 sc0 ls0 ws0">dashboards, to accommodate these varying needs. The goal is to create an interface that feels both</div><div class="t m0 x1 h4 y1d9 ff2 fs2 fc0 sc0 ls0 ws0">powerful and intuitive, empowering users to leverage the advanced capabilities of the Trax engine</div><div class="t m0 x1 h4 y1da ff2 fs2 fc0 sc0 ls0 ws0">without being overwhelmed by its complexity. By investing in a modern, collaborative, and user-</div><div class="t m0 x36 h5 y1db ff1 fs3 fc1 sc0 ls0 ws0">25</div><div class="t m0 x37 h5 y173 ff1 fs3 fc1 sc0 ls0 ws0">3</div><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",77.25,746.04,null]'><div class="d m1" style="border-style:none;position:absolute;left:134.577000px;bottom:563.973000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfb" data-dest-detail='[11,"XYZ",77.25,216.726,null]'><div class="d m1" style="border-style:none;position:absolute;left:304.074000px;bottom:317.788000px;width:7.263000px;height:7.263000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x1 h4 y2b ff2 fs2 fc0 sc0 ls0 ws0">centric web interface, Trax v2 can transform from a powerful engine into an indispensable tool for</div><div class="t m0 x1 h4 y2c ff2 fs2 fc0 sc0 ls0 ws0">anyone working with spoken language.</div><div class="t m0 x1 h3 y1dc ff1 fs1 fc0 sc0 ls0 ws0">Synthesizing the Future: A Roadmap for Trax v2 Implementation</div><div class="t m0 x1 h3 y1dd ff1 fs1 fc0 sc0 ls0 ws0">and Success</div><div class="t m0 x1 h4 y1de ff2 fs2 fc0 sc0 ls0 ws0">The journey from Trax v1.0.0 to a next-generation transcription platform is an exciting opportunity</div><div class="t m0 x1 h4 y1df ff2 fs2 fc0 sc0 ls0 ws0">to build a system that is not only faster and more accurate but also architecturally robust and user-</div><div class="t m0 x1 h4 y1e0 ff2 fs2 fc0 sc0 ls0 ws0">centric. The research clearly indicates that the path forward involves a deliberate and phased</div><div class="t m0 x1 h4 y1e1 ff2 fs2 fc0 sc0 ls0 ws0">implementation, starting with foundational architectural upgrades before layering on advanced</div><div class="t m0 x1 h4 y1e2 ff2 fs2 fc0 sc0 ls0 ws0">features. This synthesis outlines a practical roadmap to guide the development of Trax v2, ensuring</div><div class="t m0 x1 h4 y1e3 ff2 fs2 fc0 sc0 ls0 ws0">that the project remains focused, manageable, and aligned with the user's goals of performance and</div><div class="t m0 x1 h4 y1e4 ff2 fs2 fc0 sc0 ls0 ws0">ambition.</div><div class="t m0 x1 h4 y1e5 ff1 fs2 fc0 sc0 ls0 ws0">Phase 1: Foundation and Core Pipeline (4-6 Weeks)</div><div class="t m0 x1 h4 y1e6 ff2 fs2 fc0 sc0 ls0 ws0">The initial phase must establish the groundwork for all future enhancements. The primary objective</div><div class="t m0 x1 h4 y1e7 ff2 fs2 fc0 sc0 ls0 ws0">is to overhaul the current architecture. 1. <span class="ff1">Architectural Decomposition:</span> Begin by breaking down the</div><div class="t m0 x1 h4 y1e8 ff2 fs2 fc0 sc0 ls0 ws0">monolithic CLI and batch processor into a suite of microservices. Develop the core services: an API</div><div class="t m0 x1 h4 y1e9 ff2 fs2 fc0 sc0 ls0 ws0">Gateway, a Transcription Service, and a set of generic Worker Services. Integrate a message broker</div><div class="t m0 x1 h4 y1ea ff2 fs2 fc0 sc0 ls0 ws0">(e.g., Kafka) to enable the event-driven workflow. 2. <span class="ff1">Implement Multi-Pass Engine:</span> Build the core</div><div class="t m0 x1 h4 y1eb ff2 fs2 fc0 sc0 ls0 ws0">engine for multi-pass processing. This involves designing a workflow definition language or</div><div class="t m0 x1 h4 y1ec ff2 fs2 fc0 sc0 ls0 ws0">configuration system that allows for the chaining of different models (e.g., a fast Whisper variant</div><div class="t m0 x1 h4 y1ed ff2 fs2 fc0 sc0 ls0 ws0">followed by a DeepSeek enhancement pass). This phase should focus on the orchestration logic</div><div class="t m0 x1 h4 y1ee ff2 fs2 fc0 sc0 ls0 ws0">rather than building entirely new models. 3. <span class="ff1">Establish Baseline Accuracy:</span> Implement the current</div><div class="t m0 x1 h4 y1ef ff2 fs2 fc0 sc0 ls0 ws0">best-in-class single-pass accuracy pipeline using the existing Whisper and DeepSeek models. This</div><div class="t m0 x1 h4 y1f0 ff2 fs2 fc0 sc0 ls0 ws0">serves as a stable baseline against which the improvements from Phase 2 can be measured.</div><div class="t m0 x1 h4 y1f1 ff2 fs2 fc0 sc0 ls0 ws0">Document performance metrics (e.g., 95%+ WER on test sets).</div><div class="t m0 x1 h4 y1f2 ff1 fs2 fc0 sc0 ls0 ws0">Phase 2: Advanced Features and Domain Adaptation (6-8 Weeks)</div><div class="t m0 x1 h4 y1f3 ff2 fs2 fc0 sc0 ls0 ws0">With the new architecture in place, this phase focuses on adding the advanced features that</div><div class="t m0 x1 h4 y1f4 ff2 fs2 fc0 sc0 ls0 ws0">differentiate Trax v2. 1. <span class="ff1">Integrate Speaker Diarization:</span> Implement a robust speaker diarization</div><div class="t m0 x1 h4 y1f5 ff2 fs2 fc0 sc0 ls0 ws0">system. A pragmatic approach would be to integrate a lightweight, efficient library like DIART for</div><div class="t m0 x1 h4 y1f6 ff2 fs2 fc0 sc0 ls0 ws0">real-time processing and pair it with a more accurate model like Pyannote.audio for post-processing.</div><div class="t m0 x1 h4 y1f7 ff2 fs2 fc0 sc0 ls0 ws0">Add user-facing controls to toggle diarization on/off and select the level of detail. 2. <span class="ff1">Deploy</span></div><div class="t m0 x1 h4 y1f8 ff1 fs2 fc0 sc0 ls0 ws0">Parameter-Efficient Fine-Tuning (PEFT):<span class="ff2"> Implement a system for applying PEFT methods like</span></div><div class="t m0 x1 h4 y1f9 ff2 fs2 fc0 sc0 ls0 ws0">LoRA. This involves creating a Model Management Service that can handle the storage and loading</div><div class="t m0 x1 h4 y1fa ff2 fs2 fc0 sc0 ls0 ws0">of adapter modules. Develop a user interface or API endpoint for selecting a domain-specific model</div><div class="t m0 x1 h4 y1fb ff2 fs2 fc0 sc0 ls0 ws0">for a particular job. 3. <span class="ff1">Develop Confidence Scoring:</span> Implement a confidence scoring mechanism.</div><div class="t m0 x1 h4 y1fc ff2 fs2 fc0 sc0 ls0 ws0">Given the mixed results in the literature, a practical approach would be to extract available scores</div><div class="t m0 x1 h4 y1fd ff2 fs2 fc0 sc0 ls0 ws0">from the underlying models and provide them as a supplementary tool, clearly documenting their</div><div class="t m0 x1 h4 y1fe ff2 fs2 fc0 sc0 ls0 ws0">limitations. Do not treat them as a reliable error detection mechanism.</div><div class="t m0 x1 h4 y1ff ff1 fs2 fc0 sc0 ls0 ws0">Phase 3: Scalability and Optimization (4-6 Weeks)</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x1 h4 y2b ff2 fs2 fc0 sc0 ls0 ws0">This phase is dedicated to ensuring the platform can handle high loads and remain cost-effective. 1. </div><div class="t m0 x1 h4 y2c ff1 fs2 fc0 sc0 ls0 ws0">Containerize the Application:<span class="ff2"> Package all microservices into Docker containers. This ensures</span></div><div class="t m0 x1 h4 y200 ff2 fs2 fc0 sc0 ls0 ws0">portability and lays the groundwork for deployment. 2. <span class="ff1">Orchestrate with Kubernetes:</span> Deploy the</div><div class="t m0 x1 h4 y201 ff2 fs2 fc0 sc0 ls0 ws0">application on a Kubernetes cluster. Configure Horizontal Pod Autoscalers (HPA) to automatically</div><div class="t m0 x1 h4 y122 ff2 fs2 fc0 sc0 ls0 ws0">scale the worker services based on message queue depth or CPU load. 3. <span class="ff1">Optimize for Performance</span></div><div class="t m0 x1 h4 y123 ff1 fs2 fc0 sc0 ls0 ws0">and Cost:<span class="ff2"> Apply Post-Training Quantization (PTQ) to the core Whisper model to reduce its memory</span></div><div class="t m0 x1 h4 y202 ff2 fs2 fc0 sc0 ls0 ws0">footprint and accelerate inference. Benchmark the system to verify that the <1GB memory per</div><div class="t m0 x1 h4 y203 ff2 fs2 fc0 sc0 ls0 ws0">worker and <$0.005 per transcript targets are met. Optimize the multi-pass pipeline for</div><div class="t m0 x1 h4 y204 ff2 fs2 fc0 sc0 ls0 ws0">computational efficiency.</div><div class="t m0 x1 h4 y126 ff1 fs2 fc0 sc0 ls0 ws0">Phase 4: User Interface and Polishing (2-4 Weeks)</div><div class="t m0 x1 h4 y205 ff2 fs2 fc0 sc0 ls0 ws0">The final phase brings the platform to life for the user. 1. <span class="ff1">Build the Web Interface:</span> Develop a</div><div class="t m0 x1 h4 y206 ff2 fs2 fc0 sc0 ls0 ws0">modern, responsive web interface using a framework like React or Vue.js. This interface should</div><div class="t m0 x1 h4 y207 ff2 fs2 fc0 sc0 ls0 ws0">manage the entire workflow: uploading files, monitoring job status, viewing and editing transcripts,</div><div class="t m0 x1 h4 y208 ff2 fs2 fc0 sc0 ls0 ws0">and downloading results. 2. <span class="ff1">Implement Real-Time Collaboration:</span> Develop the back-end and front-</div><div class="t m0 x1 h4 y209 ff2 fs2 fc0 sc0 ls0 ws0">end logic for real-time collaboration. Start with basic functionality (e.g., shared cursor, simultaneous</div><div class="t m0 x1 h4 y20a ff2 fs2 fc0 sc0 ls0 ws0">highlighting) and iterate based on usability testing. 3. <span class="ff1">Final Testing and Documentation:</span> Conduct</div><div class="t m0 x1 h4 y20b ff2 fs2 fc0 sc0 ls0 ws0">comprehensive testing, including performance benchmarks against the v1.0.0 baseline. Generate</div><div class="t m0 x1 h4 y20c ff2 fs2 fc0 sc0 ls0 ws0">detailed documentation for developers and end-users.</div><div class="t m0 x1 h4 y20d ff2 fs2 fc0 sc0 ls0 ws0">In conclusion, the development of Trax v2 is a feasible and highly rewarding endeavor. By following</div><div class="t m0 x1 h4 y20e ff2 fs2 fc0 sc0 ls0 ws0">this structured roadmap, the project can systematically address the key challenges of architecture,</div><div class="t m0 x1 h4 y20f ff2 fs2 fc0 sc0 ls0 ws0">accuracy, scalability, and user experience. The most critical decision is the architectural pivot to a</div><div class="t m0 x1 h4 y210 ff2 fs2 fc0 sc0 ls0 ws0">distributed, event-driven system. This choice will unlock the ability to implement multi-pass</div><div class="t m0 x1 h4 y211 ff2 fs2 fc0 sc0 ls0 ws0">processing, PEFT, and high concurrency, transforming Trax from a competent tool into a powerful</div><div class="t m0 x1 h4 y212 ff2 fs2 fc0 sc0 ls0 ws0">and scalable platform for the future of speech recognition.</div><div class="t m0 x1 h8 y213 ff1 fs4 fc2 sc0 ls0 ws0">Reference</div><div class="t m0 x38 h4 y214 ff2 fs2 fc0 sc0 ls0 ws0">Iteratively Improving Speech Recognition and Voice Conversion <span class="fc3">https://arxiv.org/abs/</span></div><div class="t m0 x38 h4 y215 ff2 fs2 fc3 sc0 ls0 ws0">2305.15055</div><div class="t m0 x38 h4 y216 ff2 fs2 fc0 sc0 ls0 ws0">Two-Pass End-to-End Speech Recognition - Google Research <span class="fc3">https://research.google/pubs/</span></div><div class="t m0 x38 h4 y217 ff2 fs2 fc3 sc0 ls0 ws0">two-pass-end-to-end-speech-recognition/</div><div class="t m0 x38 h4 y218 ff2 fs2 fc0 sc0 ls0 ws0">Two-pass endpoint detection for speech recognition - arXiv <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y219 ff2 fs2 fc3 sc0 ls0 ws0">2401.08916v1</div><div class="t m0 x38 h4 y21a ff2 fs2 fc0 sc0 ls0 ws0">[1908.10992] Two-Pass End-to-End Speech Recognition - ar5iv - arXiv <span class="fc3">https://</span></div><div class="t m0 x38 h4 y21b ff2 fs2 fc3 sc0 ls0 ws0">ar5iv.labs.arxiv.org/html/1908.10992</div><div class="t m0 x38 h4 y21c ff2 fs2 fc0 sc0 ls0 ws0">Align-Refine: Non-autoregressive speech recognition via iterative ... <span class="fc3">https://</span></div><div class="t m0 x38 h4 y21d ff2 fs2 fc3 sc0 ls0 ws0">www.amazon.science/publications/align-refine-non-autoregressive-speech-recognition-via-</div><div class="t m0 x38 h4 y21e ff2 fs2 fc3 sc0 ls0 ws0">iterative-realignment</div><div class="t m0 x39 h4 y214 ff2 fs2 fc0 sc0 ls0 ws0">1. </div><div class="t m0 x39 h4 y216 ff2 fs2 fc0 sc0 ls0 ws0">2. </div><div class="t m0 x39 h4 y218 ff2 fs2 fc0 sc0 ls0 ws0">3. </div><div class="t m0 x39 h4 y21a ff2 fs2 fc0 sc0 ls0 ws0">4. </div><div class="t m0 x39 h4 y21c ff2 fs2 fc0 sc0 ls0 ws0">5. </div><a class="l" href="https://arxiv.org/abs/2305.15055"><div class="d m1" style="border-style:none;position:absolute;left:381.301000px;bottom:279.126000px;width:106.478000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/abs/2305.15055"><div class="d m1" style="border-style:none;position:absolute;left:381.301000px;bottom:279.126000px;width:106.478000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/abs/2305.15055"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:262.326000px;width:53.279000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/abs/2305.15055"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:262.326000px;width:53.279000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://research.google/pubs/two-pass-end-to-end-speech-recognition/"><div class="d m1" style="border-style:none;position:absolute;left:370.614000px;bottom:239.526000px;width:144.492000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://research.google/pubs/two-pass-end-to-end-speech-recognition/"><div class="d m1" style="border-style:none;position:absolute;left:370.614000px;bottom:239.526000px;width:144.492000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://research.google/pubs/two-pass-end-to-end-speech-recognition/"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:222.726000px;width:194.266000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://research.google/pubs/two-pass-end-to-end-speech-recognition/"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:222.726000px;width:194.266000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/html/2401.08916v1"><div class="d m1" style="border-style:none;position:absolute;left:358.841000px;bottom:199.926000px;width:112.730000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/html/2401.08916v1"><div class="d m1" style="border-style:none;position:absolute;left:358.841000px;bottom:199.926000px;width:112.730000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/html/2401.08916v1"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:183.126000px;width:64.535000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/html/2401.08916v1"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:183.126000px;width:64.535000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://ar5iv.labs.arxiv.org/html/1908.10992"><div class="d m1" style="border-style:none;position:absolute;left:412.144000px;bottom:160.326000px;width:38.253000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://ar5iv.labs.arxiv.org/html/1908.10992"><div class="d m1" style="border-style:none;position:absolute;left:412.144000px;bottom:160.326000px;width:38.253000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://ar5iv.labs.arxiv.org/html/1908.10992"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:143.526000px;width:174.000000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://ar5iv.labs.arxiv.org/html/1908.10992"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:143.526000px;width:174.000000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://www.amazon.science/publications/align-refine-non-autoregressive-speech-recognition-via-iterative-realignment"><div class="d m1" style="border-style:none;position:absolute;left:393.747000px;bottom:120.726000px;width:38.253000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://www.amazon.science/publications/align-refine-non-autoregressive-speech-recognition-via-iterative-realignment"><div class="d m1" style="border-style:none;position:absolute;left:393.747000px;bottom:120.726000px;width:38.253000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://www.amazon.science/publications/align-refine-non-autoregressive-speech-recognition-via-iterative-realignment"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:103.926000px;width:424.120000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://www.amazon.science/publications/align-refine-non-autoregressive-speech-recognition-via-iterative-realignment"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:103.926000px;width:424.120000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://www.amazon.science/publications/align-refine-non-autoregressive-speech-recognition-via-iterative-realignment"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:87.126000px;width:95.500000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://www.amazon.science/publications/align-refine-non-autoregressive-speech-recognition-via-iterative-realignment"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:87.126000px;width:95.500000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x38 h4 y21f ff2 fs2 fc0 sc0 ls0 ws0">Two-Pass Endpoint Detection for Speech Recognition - IEEE Xplore <span class="fc3">https://</span></div><div class="t m0 x38 h4 y220 ff2 fs2 fc3 sc0 ls0 ws0">ieeexplore.ieee.org/document/10389743/</div><div class="t m0 x38 h4 y221 ff2 fs2 fc0 sc0 ls0 ws0">[PDF] End-to-End Neural Speaker Diarization with an Iterative Refinement ... <span class="fc3">https://www.isca-</span></div><div class="t m0 x38 h4 y222 ff2 fs2 fc3 sc0 ls0 ws0">archive.org/interspeech_2022/rybicka22_interspeech.pdf</div><div class="t m0 x38 h4 y223 ff2 fs2 fc0 sc0 ls0 ws0">Two-Pass End-to-End Speech Recognition - ResearchGate <span class="fc3">https://www.researchgate.net/</span></div><div class="t m0 x38 h4 y224 ff2 fs2 fc3 sc0 ls0 ws0">publication/335830044_Two-Pass_End-to-End_Speech_Recognition</div><div class="t m0 x38 h4 y225 ff2 fs2 fc0 sc0 ls0 ws0">An enhanced deep learning approach for speaker diarization using ... <span class="fc3">https://www.nature.com/</span></div><div class="t m0 x38 h4 y226 ff2 fs2 fc3 sc0 ls0 ws0">articles/s41598-025-09385-1</div><div class="t m0 x38 h4 y227 ff2 fs2 fc0 sc0 ls0 ws0">Systematic Evaluation of Online Speaker Diarization Systems ... - arXiv <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y228 ff2 fs2 fc3 sc0 ls0 ws0">2407.04293v1</div><div class="t m0 x38 h4 y229 ff2 fs2 fc0 sc0 ls0 ws0">[Literature Review] Two-pass Endpoint Detection for Speech ... <span class="fc3">https://www.themoonlight.io/</span></div><div class="t m0 x38 h4 y22a ff2 fs2 fc3 sc0 ls0 ws0">en/review/two-pass-endpoint-detection-for-speech-recognition</div><div class="t m0 x38 h4 y22b ff2 fs2 fc0 sc0 ls0 ws0">Pyannote.audio vs Nvidia Nemo, and Post-Processing Approach ... <span class="fc3">https://docs.voice-ping.com/</span></div><div class="t m0 x38 h4 y22c ff2 fs2 fc3 sc0 ls0 ws0">voiceping-corporation-company-profile/apr-2024-speaker-diarization-performance-evaluation-</div><div class="t m0 x38 h4 y22d ff2 fs2 fc3 sc0 ls0 ws0">pyannoteaudio-vs-nvidia-nemo-and-post-processing-approach-using-openais-gpt-4-turbo-1</div><div class="t m0 x38 h4 y22e ff2 fs2 fc0 sc0 ls0 ws0">A Review of Common Online Speaker Diarization Methods - arXiv <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y22f ff2 fs2 fc3 sc0 ls0 ws0">2406.14464v1</div><div class="t m0 x38 h4 y230 ff2 fs2 fc0 sc0 ls0 ws0">Exploring the trade-off between speed and accuracy in real-time ... <span class="fc3">https://</span></div><div class="t m0 x38 h4 y231 ff2 fs2 fc3 sc0 ls0 ws0">blog.speechmatics.com/latency_accuracy</div><div class="t m0 x38 h4 y232 ff2 fs2 fc0 sc0 ls0 ws0">[PDF] Latency and Quality Trade-offs for Simultaneous Speech-to-Speech ... <span class="fc3">https://www.isca-</span></div><div class="t m0 x38 h4 y233 ff2 fs2 fc3 sc0 ls0 ws0">archive.org/interspeech_2023/dugan23_interspeech.pdf</div><div class="t m0 x38 h4 y234 ff2 fs2 fc0 sc0 ls0 ws0">[PDF] Multi-latency look-ahead for streaming speaker segmentation <span class="fc3">https://www.isca-</span></div><div class="t m0 x38 h4 y235 ff2 fs2 fc3 sc0 ls0 ws0">archive.org/interspeech_2024/rahou24_interspeech.pdf</div><div class="t m0 x38 h4 y236 ff2 fs2 fc0 sc0 ls0 ws0">Edge-ASR: Towards Low-Bit Quantization of Automatic Speech ... <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y237 ff2 fs2 fc3 sc0 ls0 ws0">2507.07877v2</div><div class="t m0 x38 h4 y238 ff2 fs2 fc0 sc0 ls0 ws0">What is speaker diarization and how does it work? 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class="t m0 x38 h4 y21f ff2 fs2 fc0 sc0 ls0 ws0">SelfVC: Voice Conversion With Iterative Refinement using Self ... <span class="fc3">https://research.nvidia.com/</span></div><div class="t m0 x38 h4 y220 ff2 fs2 fc3 sc0 ls0 ws0">labs/conv-ai/publications/2024/2024-selfvc/</div><div class="t m0 x38 h4 y221 ff2 fs2 fc0 sc0 ls0 ws0">[PDF] Comparative Analysis of Personalized Voice Activity Detection ... <span class="fc3">https://www.isca-</span></div><div class="t m0 x38 h4 y222 ff2 fs2 fc3 sc0 ls0 ws0">archive.org/interspeech_2024/buddi24_interspeech.pdf</div><div class="t m0 x38 h4 y223 ff2 fs2 fc0 sc0 ls0 ws0">(PDF) SelfVC: Voice Conversion With Iterative Refinement using ... <span class="fc3">https://</span></div><div class="t m0 x38 h4 y224 ff2 fs2 fc3 sc0 ls0 ws0">www.researchgate.net/publication/</div><div class="t m0 x38 h4 y245 ff2 fs2 fc3 sc0 ls0 ws0">381121265_SelfVC_Voice_Conversion_With_Iterative_Refinement_using_Self_Transformations</div><div class="t m0 x38 h4 y226 ff2 fs2 fc0 sc0 ls0 ws0">Thinking aloud.. LoRA & Prompt Tuning - DeepLearning.AI <span class="fc3">https://</span></div><div class="t m0 x38 h4 y246 ff2 fs2 fc3 sc0 ls0 ws0">community.deeplearning.ai/t/thinking-aloud-lora-prompt-tuning/465150</div><div class="t m0 x38 h4 y228 ff2 fs2 fc0 sc0 ls0 ws0">A Domain Adaptation Framework for Speech Recognition Systems ... <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y247 ff2 fs2 fc3 sc0 ls0 ws0">2501.12501v1</div><div class="t m0 x38 h4 y22a ff2 fs2 fc0 sc0 ls0 ws0">Low-Resource Domain Adaptation for Speech LLMs via Text-Only ... <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y248 ff2 fs2 fc3 sc0 ls0 ws0">2506.05671v1</div><div class="t m0 x38 h4 y22c ff2 fs2 fc0 sc0 ls0 ws0">A Comparison of Parameter-Efficient ASR Domain Adaptation ... <span class="fc3">https://ieeexplore.ieee.org/</span></div><div class="t m0 x38 h4 y22d ff2 fs2 fc3 sc0 ls0 ws0">document/10445894/</div><div class="t m0 x38 h4 y22e ff2 fs2 fc0 sc0 ls0 ws0">[PDF] Smarter Fine-Tuning: How LoRA Enhances Large Language Models <span class="fc3">https://hal.science/</span></div><div class="t m0 x38 h4 y22f ff2 fs2 fc3 sc0 ls0 ws0">hal-04983079/document</div><div class="t m0 x38 h4 y230 ff2 fs2 fc0 sc0 ls0 ws0">Fine-tuning ASR Models: Boosting Accuracy and Adaptability <span class="fc3">https://lamarr-institute.org/blog/</span></div><div class="t m0 x38 h4 y231 ff2 fs2 fc3 sc0 ls0 ws0">fine-tuning-asr-models/</div><div class="t m0 x38 h4 y232 ff2 fs2 fc0 sc0 ls0 ws0">Fine-Tuning Transformers Efficiently: A Survey on LoRA and Its Impact <span class="fc3">https://</span></div><div class="t m0 x38 h4 y233 ff2 fs2 fc3 sc0 ls0 ws0">www.preprints.org/manuscript/202502.1637/v1</div><div class="t m0 x38 h4 y234 ff2 fs2 fc0 sc0 ls0 ws0">Parameter-efficient adaptation with multi-channel adversarial ... <span class="fc3">https://asmp-</span></div><div class="t m0 x38 h4 y235 ff2 fs2 fc3 sc0 ls0 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sc0 ls0 ws0">Machine Learning Confidence Scores — All You Need to Know as a ... <span class="fc3">https://medium.com/</span></div><div class="t m0 x38 h4 y24c ff2 fs2 fc3 sc0 ls0 ws0">voice-tech-global/machine-learning-confidence-scores-all-you-need-to-know-as-a-conversation-</div><div class="t m0 x38 h4 y24d ff2 fs2 fc3 sc0 ls0 ws0">designer-8babd39caae7</div><div class="t m0 x38 h4 y24e ff2 fs2 fc0 sc0 ls0 ws0">How to Use Confidence Scores in Machine Learning Models - Mindee <span class="fc3">https://</span></div><div class="t m0 x38 h4 y24f ff2 fs2 fc3 sc0 ls0 ws0">www.mindee.com/blog/how-use-confidence-scores-ml-models</div><div class="t m0 x38 h4 y250 ff2 fs2 fc0 sc0 ls0 ws0">Evaluating ASR Confidence Scores for Automated Error Detection in ... <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y251 ff2 fs2 fc3 sc0 ls0 ws0">2503.15124v1</div><div class="t m0 x3a h4 y21f ff2 fs2 fc0 sc0 ls0 ws0">24. </div><div class="t m0 x3a h4 y221 ff2 fs2 fc0 sc0 ls0 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class="t m0 x38 h4 y21f ff2 fs2 fc0 sc0 ls0 ws0">Using transcription confidence scores to improve slot filling in ... - AWS <span class="fc3">https://</span></div><div class="t m0 x38 h4 y220 ff2 fs2 fc3 sc0 ls0 ws0">aws.amazon.com/blogs/machine-learning/using-transcription-confidence-scores-to-improve-</div><div class="t m0 x38 h4 y252 ff2 fs2 fc3 sc0 ls0 ws0">slot-filling-in-amazon-lex/</div><div class="t m0 x38 h4 y222 ff2 fs2 fc0 sc0 ls0 ws0">[PDF] Prompt-tuning in ASR systems for efficient domain-adaptation <span class="fc3">https://</span></div><div class="t m0 x38 h4 y253 ff2 fs2 fc3 sc0 ls0 ws0">assets.amazon.science/cf/6f/65b75c8544fabc2e2adab334140c/prompt-tuning-in-asr-systems-</div><div class="t m0 x38 h4 y254 ff2 fs2 fc3 sc0 ls0 ws0">for-efficient-domain-adaptation.pdf</div><div class="t m0 x38 h4 y245 ff2 fs2 fc0 sc0 ls0 ws0">Modular Domain Adaptation for Conformer-Based Streaming ASR <span class="fc3">https://</span></div><div class="t m0 x38 h4 y255 ff2 fs2 fc3 sc0 ls0 ws0">www.researchgate.net/publication/373248113_Modular_Domain_Adaptation_for_Conformer-</div><div class="t m0 x38 h4 y256 ff2 fs2 fc3 sc0 ls0 ws0">Based_Streaming_ASR</div><div class="t m0 x38 h4 y257 ff2 fs2 fc0 sc0 ls0 ws0">[PDF] Improving Speech Recognition with Prompt-based Contextualized ... <span class="fc3">https://www.isca-</span></div><div class="t m0 x38 h4 y258 ff2 fs2 fc3 sc0 ls0 ws0">archive.org/interspeech_2024/manhtienanh24_interspeech.pdf</div><div class="t m0 x38 h4 y259 ff2 fs2 fc0 sc0 ls0 ws0">Prompting Large Language Models for Zero-Shot Domain ... <span class="fc3">https://www.researchgate.net/</span></div><div class="t m0 x38 h4 y25a ff2 fs2 fc3 sc0 ls0 ws0">publication/377538976_Prompting_Large_Language_Models_for_Zero-</div><div class="t m0 x38 h4 y25b ff2 fs2 fc3 sc0 ls0 ws0">Shot_Domain_Adaptation_in_Speech_Recognition</div><div class="t m0 x38 h4 y25c ff2 fs2 fc0 sc0 ls0 ws0">What is the significance of confidence scores in speech recognition? <span class="fc3">https://zilliz.com/ai-faq/</span></div><div class="t m0 x38 h4 y25d ff2 fs2 fc3 sc0 ls0 ws0">what-is-the-significance-of-confidence-scores-in-speech-recognition</div><div class="t m0 x38 h4 y25e ff2 fs2 fc0 sc0 ls0 ws0">What is the significance of confidence scores in speech recognition? <span class="fc3">https://milvus.io/ai-quick-</span></div><div class="t m0 x38 h4 y25f ff2 fs2 fc3 sc0 ls0 ws0">reference/what-is-the-significance-of-confidence-scores-in-speech-recognition</div><div class="t m0 x38 h4 y260 ff2 fs2 fc0 sc0 ls0 ws0">What do confidence scores mean in speech recognition? <span class="fc3">https://stackoverflow.com/questions/</span></div><div class="t m0 x38 h4 y261 ff2 fs2 fc3 sc0 ls0 ws0">61331681/what-do-confidence-scores-mean-in-speech-recognition</div><div class="t m0 x38 h4 y262 ff2 fs2 fc0 sc0 ls0 ws0">[PDF] Using Automatically Created Confidence Measures - LSEG <span class="fc3">https://www.lseg.com/</span></div><div class="t m0 x38 h4 y263 ff2 fs2 fc3 sc0 ls0 ws0">content/dam/data-analytics/en_us/documents/white-papers/lseg-itg-automatic-transcript-</div><div class="t m0 x38 h4 y264 ff2 fs2 fc3 sc0 ls0 ws0">research-paper.pdf</div><div class="t m0 x38 h4 y265 ff2 fs2 fc0 sc0 ls0 ws0">Ensuring Transcription Accuracy: Techniques and Best Practices <span class="fc3">https://waywithwords.net/</span></div><div class="t m0 x38 h4 y266 ff2 fs2 fc3 sc0 ls0 ws0">resource/transcription-accuracy-best-practices/</div><div class="t m0 x38 h4 y267 ff2 fs2 fc0 sc0 ls0 ws0">LLM-based speaker diarization correction: A generalizable approach <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y268 ff2 fs2 fc3 sc0 ls0 ws0">2406.04927v3</div><div class="t m0 x38 h4 y269 ff2 fs2 fc0 sc0 ls0 ws0">How accurate is speech-to-text in 2025? - AssemblyAI <span class="fc3">https://www.assemblyai.com/blog/how-</span></div><div class="t m0 x38 h4 y26a ff2 fs2 fc3 sc0 ls0 ws0">accurate-speech-to-text</div><div class="t m0 x38 h4 y26b ff2 fs2 fc0 sc0 ls0 ws0">Survey of End-to-End Multi-Speaker Automatic Speech Recognition ... <span class="fc3">https://arxiv.org/html/</span></div><div class="t m0 x38 h4 y26c ff2 fs2 fc3 sc0 ls0 ws0">2505.10975v1</div><div class="t m0 x38 h4 y24d ff2 fs2 fc0 sc0 ls0 ws0">Speech-to-Text APIs: Key Players and Innovations in 2024 - Krisp <span class="fc3">https://krisp.ai/blog/speech-</span></div><div class="t m0 x38 h4 y26d ff2 fs2 fc3 sc0 ls0 ws0">to-text-apis-key-players-and-innovations-in-2024/</div><div class="t m0 x38 h4 y24f ff2 fs2 fc0 sc0 ls0 ws0">Moving beyond word error rate to evaluate automatic speech ... <span class="fc3">https://www.sciencedirect.com/</span></div><div class="t m0 x38 h4 y26e ff2 fs2 fc3 sc0 ls0 ws0">science/article/pii/S0165178125003385</div><div class="t m0 x38 h4 y26f ff2 fs2 fc0 sc0 ls0 ws0">Top Real-Time Speech-to-Text Tools in 2024 - Galileo AI <span class="fc3">https://galileo.ai/blog/best-real-time-</span></div><div class="t m0 x38 h4 y270 ff2 fs2 fc3 sc0 ls0 ws0">speech-to-text-tools</div><div class="t m0 x3a h4 y21f ff2 fs2 fc0 sc0 ls0 ws0">40. </div><div class="t m0 x3a h4 y222 ff2 fs2 fc0 sc0 ls0 ws0">41. </div><div class="t m0 x3a h4 y245 ff2 fs2 fc0 sc0 ls0 ws0">42. </div><div class="t m0 x3a h4 y257 ff2 fs2 fc0 sc0 ls0 ws0">43. </div><div class="t m0 x3a h4 y259 ff2 fs2 fc0 sc0 ls0 ws0">44. </div><div class="t m0 x3a h4 y25c ff2 fs2 fc0 sc0 ls0 ws0">45. </div><div class="t m0 x3a h4 y25e ff2 fs2 fc0 sc0 ls0 ws0">46. </div><div class="t m0 x3a h4 y260 ff2 fs2 fc0 sc0 ls0 ws0">47. </div><div class="t m0 x3a h4 y262 ff2 fs2 fc0 sc0 ls0 ws0">48. </div><div class="t m0 x3a h4 y265 ff2 fs2 fc0 sc0 ls0 ws0">49. </div><div class="t m0 x3a h4 y267 ff2 fs2 fc0 sc0 ls0 ws0">50. </div><div class="t m0 x3a h4 y269 ff2 fs2 fc0 sc0 ls0 ws0">51. </div><div class="t m0 x3a h4 y26b ff2 fs2 fc0 sc0 ls0 ws0">52. </div><div class="t m0 x3a h4 y24d ff2 fs2 fc0 sc0 ls0 ws0">53. </div><div class="t m0 x3a h4 y24f ff2 fs2 fc0 sc0 ls0 ws0">54. 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style="border-style:none;position:absolute;left:77.250000px;bottom:752.040000px;width:436.466000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://aws.amazon.com/blogs/machine-learning/using-transcription-confidence-scores-to-improve-slot-filling-in-amazon-lex/"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:752.040000px;width:436.466000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://aws.amazon.com/blogs/machine-learning/using-transcription-confidence-scores-to-improve-slot-filling-in-amazon-lex/"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:735.240000px;width:122.604000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://aws.amazon.com/blogs/machine-learning/using-transcription-confidence-scores-to-improve-slot-filling-in-amazon-lex/"><div class="d 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class="l" href="https://arxiv.org/html/2505.10975v1"><div class="d m1" style="border-style:none;position:absolute;left:410.857000px;bottom:209.640000px;width:112.730000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/html/2505.10975v1"><div class="d m1" style="border-style:none;position:absolute;left:410.857000px;bottom:209.640000px;width:112.730000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/html/2505.10975v1"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:192.840000px;width:64.535000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/html/2505.10975v1"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:192.840000px;width:64.535000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a 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class="t m0 x38 h4 y21f ff2 fs2 fc0 sc0 ls0 ws0">Method and system for correcting speech-to-text auto-transcription ... <span class="fc3">https://</span></div><div class="t m0 x38 h4 y220 ff2 fs2 fc3 sc0 ls0 ws0">patents.google.com/patent/US20200160866A1/en</div><div class="t m0 x38 h4 y221 ff2 fs2 fc0 sc0 ls0 ws0">Prompt-tuning in ASR systems for efficient domain-adaptation - arXiv <span class="fc3">https://arxiv.org/abs/</span></div><div class="t m0 x38 h4 y222 ff2 fs2 fc3 sc0 ls0 ws0">2110.06502</div><div class="t m0 x3a h4 y21f ff2 fs2 fc0 sc0 ls0 ws0">56. </div><div class="t m0 x3a h4 y221 ff2 fs2 fc0 sc0 ls0 ws0">57. </div><a class="l" href="https://patents.google.com/patent/US20200160866A1/en"><div class="d m1" style="border-style:none;position:absolute;left:404.870000px;bottom:768.840000px;width:38.253000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://patents.google.com/patent/US20200160866A1/en"><div class="d m1" style="border-style:none;position:absolute;left:404.870000px;bottom:768.840000px;width:38.253000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://patents.google.com/patent/US20200160866A1/en"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:752.040000px;width:238.019000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://patents.google.com/patent/US20200160866A1/en"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:752.040000px;width:238.019000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/abs/2110.06502"><div class="d m1" style="border-style:none;position:absolute;left:408.468000px;bottom:729.240000px;width:106.478000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/abs/2110.06502"><div class="d m1" style="border-style:none;position:absolute;left:408.468000px;bottom:729.240000px;width:106.478000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/abs/2110.06502"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:712.440000px;width:53.279000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://arxiv.org/abs/2110.06502"><div class="d m1" style="border-style:none;position:absolute;left:77.250000px;bottom:712.440000px;width:53.279000px;height:16.800000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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