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</div>
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</h1>
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<div class="SVELTE_HYDRATER contents" data-props="{"tagObjs":[{"id":"automatic-speech-recognition","label":"Automatic Speech Recognition","subType":"audio","type":"pipeline_tag"},{"id":"nemo","label":"NeMo","type":"library"},{"id":"pytorch","label":"PyTorch","type":"library"},{"id":"dataset:librispeech_asr","label":"librispeech_asr","type":"dataset","disabled":false},{"id":"en","label":"en","type":"language"},{"id":"arxiv:2104.01721","label":"arxiv:2104.01721","type":"arxiv"},{"id":"speech","label":"speech","type":"other"},{"id":"audio","label":"audio","type":"other"},{"id":"CTC","label":"CTC","type":"other"},{"id":"Citrinet","label":"Citrinet","type":"other"},{"id":"Transformer","label":"Transformer","type":"other"},{"id":"NeMo","label":"NeMo","type":"other"},{"id":"hf-asr-leaderboard","label":"hf-asr-leaderboard","type":"other"},{"id":"model-index","label":"Eval Results","type":"other"},{"id":"license:cc-by-4.0","label":"cc-by-4.0","type":"license"}]}" data-target="ModelHeaderTags"><div class="flex flex-wrap mb-3 md:mb-4"><a class="tag tag-white" href="/models?pipeline_tag=automatic-speech-recognition"><div class="tag-ico tag-ico-yellow"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 18 18"><path fill-rule="evenodd" clip-rule="evenodd" d="M8.38893 3.42133C7.9778 3.14662 7.49446 3 7 3C6.33696 3 5.70108 3.26339 5.23223 3.73223C4.76339 4.20107 4.5 4.83696 4.5 5.5C4.5 5.99445 4.64662 6.4778 4.92133 6.88893C5.19603 7.30005 5.58648 7.62048 6.04329 7.8097C6.50011 7.99892 7.00278 8.04843 7.48773 7.95196C7.97268 7.8555 8.41814 7.6174 8.76777 7.26777C9.1174 6.91814 9.3555 6.47268 9.45197 5.98773C9.54843 5.50277 9.49892 5.00011 9.3097 4.54329C9.12048 4.08648 8.80005 3.69603 8.38893 3.42133ZM5.05551 2.58986C5.63108 2.20527 6.30777 2 7 2C7.92826 2 8.8185 2.36875 9.47488 3.02513C10.1313 3.6815 10.5 4.57174 10.5 5.5C10.5 6.19223 10.2947 6.86892 9.91015 7.4445C9.52556 8.02007 8.97894 8.46867 8.33939 8.73358C7.69985 8.99849 6.99612 9.0678 6.31719 8.93275C5.63825 8.7977 5.01461 8.46436 4.52513 7.97487C4.03564 7.48539 3.7023 6.86175 3.56725 6.18282C3.4322 5.50388 3.50152 4.80015 3.76642 4.16061C4.03133 3.52107 4.47993 2.97444 5.05551 2.58986ZM14.85 9.6425L15.7075 10.5C15.8005 10.5927 15.8743 10.7029 15.9245 10.8242C15.9747 10.9456 16.0004 11.0757 16 11.207V16H2V13.5C2.00106 12.5721 2.37015 11.6824 3.0263 11.0263C3.68244 10.3701 4.57207 10.0011 5.5 10H8.5C9.42793 10.0011 10.3176 10.3701 10.9737 11.0263C11.6299 11.6824 11.9989 12.5721 12 13.5V15H15V11.207L14.143 10.35C13.9426 10.4476 13.7229 10.4989 13.5 10.5C13.2033 10.5 12.9133 10.412 12.6666 10.2472C12.42 10.0824 12.2277 9.84811 12.1142 9.57403C12.0006 9.29994 11.9709 8.99834 12.0288 8.70737C12.0867 8.41639 12.2296 8.14912 12.4393 7.93934C12.6491 7.72956 12.9164 7.5867 13.2074 7.52882C13.4983 7.47094 13.7999 7.50065 14.074 7.61418C14.3481 7.72771 14.5824 7.91997 14.7472 8.16665C14.912 8.41332 15 8.70333 15 9C14.9988 9.22271 14.9475 9.44229 14.85 9.6425ZM3.73311 11.7331C3.26444 12.2018 3.00079 12.8372 3 13.5V15H11V13.5C10.9992 12.8372 10.7356 12.2018 10.2669 11.7331C9.79822 11.2644 9.1628 11.0008 8.5 11H5.5C4.8372 11.0008 4.20178 11.2644 3.73311 11.7331Z" fill="currentColor"></path></svg></div>
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<span>Automatic Speech Recognition</span>
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</a><a class="tag tag-white" href="/models?library=nemo"><svg class="text-black inline-block ml-2 text-sm" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg" width="1em" height="1em"><path d="m5.9698 5.86543v-.95636c.09285-.00661.18663-.01156.28219-.01456a5.75152 5.75152 0 0 1 4.33173 2.24749s-1.8534 2.57431-3.84063 2.57431a2.40975 2.40975 0 0 1 -.77329-.12364v-2.9c1.01832.123 1.223.57279 1.83539 1.59325l1.36157-1.148a3.60517 3.60517 0 0 0 -2.66934-1.30361 4.93745 4.93745 0 0 0 -.52762.03112m0-3.15922v1.42853c.09389-.00742.1879-.0134.28219-.0168 3.63754-.12254 6.0073 2.98317 6.0073 2.98317s-2.722 3.31-5.55774 3.31a4.18488 4.18488 0 0 1 -.73175-.06444v.883a4.81728 4.81728 0 0 0 .60938.03947c2.639 0 4.54736-1.34759 6.39542-2.94267.30618.24532 1.56062.8421 1.8186 1.1037-1.75722 1.47088-5.852 2.65644-8.17346 2.65644-.22369 0-.43886-.01352-.64994-.03376v1.241h10.0302v-10.58764zm0 6.88646v.754a4.26109 4.26109 0 0 1 -3.11821-2.97239 5.27645 5.27645 0 0 1 3.11821-1.50885v.8272l-.0038-.0004a2.34214 2.34214 0 0 0 -1.81935.83163 3.25091 3.25091 0 0 0 1.82315 2.06881m-4.33507-2.32834a6.045 6.045 0 0 1 4.33507-2.35526v-.77433c-3.19927.25677-5.9698 2.96637-5.9698 2.96637s1.56908 4.53638 5.9698 4.95171v-.82318c-3.22936-.4063-4.33507-3.96531-4.33507-3.96531z" fill="#76b900"></path></svg>
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<span>NeMo</span>
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</a><a class="tag tag-white" href="/models?library=pytorch"><svg class="text-black inline-block ml-2 text-sm" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
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<span>PyTorch</span>
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</a><div class="relative inline-block mr-1 mb-1 md:mr-1.5 md:mb-1.5">
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<button class=" " type="button">
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<a class="tag mr-0 mb-0 md:mr-0 md:mb-0 tag-indigo" href="/models?dataset=dataset:librispeech_asr"><svg class="flex-none ml-2 -mr-1 opacity-40" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 25 25"><ellipse cx="12.5" cy="5" fill="currentColor" fill-opacity="0.25" rx="7.5" ry="2"></ellipse><path d="M12.5 15C16.6421 15 20 14.1046 20 13V20C20 21.1046 16.6421 22 12.5 22C8.35786 22 5 21.1046 5 20V13C5 14.1046 8.35786 15 12.5 15Z" fill="currentColor" opacity="0.5"></path><path d="M12.5 7C16.6421 7 20 6.10457 20 5V11.5C20 12.6046 16.6421 13.5 12.5 13.5C8.35786 13.5 5 12.6046 5 11.5V5C5 6.10457 8.35786 7 12.5 7Z" fill="currentColor" opacity="0.5"></path><path d="M5.23628 12C5.08204 12.1598 5 12.8273 5 13C5 14.1046 8.35786 15 12.5 15C16.6421 15 20 14.1046 20 13C20 12.8273 19.918 12.1598 19.7637 12C18.9311 12.8626 15.9947 13.5 12.5 13.5C9.0053 13.5 6.06886 12.8626 5.23628 12Z" fill="currentColor"></path></svg>
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<span>librispeech_asr</span>
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</a>
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</button>
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</div><a class="tag tag-green" href="/models?language=en"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="ml-2 text-green-600/80" preserveAspectRatio="xMidYMid meet" width="1em" height="1em" viewBox="0 0 10 10"><path fill-rule="evenodd" clip-rule="evenodd" d="M0.625 5C0.625 6.16032 1.08594 7.27312 1.90641 8.09359C2.72688 8.91406 3.83968 9.375 5 9.375C6.16032 9.375 7.27312 8.91406 8.09359 8.09359C8.91406 7.27312 9.375 6.16032 9.375 5C9.375 3.83968 8.91406 2.72688 8.09359 1.90641C7.27312 1.08594 6.16032 0.625 5 0.625C3.83968 0.625 2.72688 1.08594 1.90641 1.90641C1.08594 2.72688 0.625 3.83968 0.625 5ZM7.64365 7.48027C7.61734 7.50832 7.59054 7.53598 7.56326 7.56326C7.13828 7.98824 6.61864 8.2968 6.0539 8.46842C6.29802 8.11949 6.49498 7.64804 6.63475 7.09483C7.00845 7.18834 7.35014 7.3187 7.64365 7.48027ZM8.10076 6.87776C8.37677 6.42196 8.55005 5.90894 8.60556 5.37499H6.86808C6.85542 5.71597 6.82551 6.04557 6.77971 6.35841C7.25309 6.47355 7.68808 6.6414 8.062 6.85549C8.07497 6.86283 8.08789 6.87025 8.10076 6.87776ZM6.03795 6.22536C6.07708 5.95737 6.1044 5.67232 6.11705 5.37499H3.88295C3.89666 5.69742 3.92764 6.00542 3.9722 6.29287C4.37075 6.21726 4.79213 6.17749 5.224 6.17749C5.50054 6.17749 5.77294 6.19376 6.03795 6.22536ZM4.1261 7.02673C4.34894 7.84835 4.68681 8.375 5 8.375C5.32122 8.375 5.66839 7.82101 5.8908 6.963C5.67389 6.93928 5.45082 6.92699 5.224 6.92699C4.84316 6.92699 4.47332 6.96176 4.1261 7.02673ZM3.39783 7.21853C3.53498 7.71842 3.72038 8.14579 3.9461 8.46842C3.42141 8.30898 2.93566 8.03132 2.52857 7.65192C2.77253 7.48017 3.06711 7.33382 3.39783 7.21853ZM3.23916 6.48077C3.18263 6.13193 3.14625 5.76074 3.13192 5.37499H1.39444C1.4585 5.99112 1.67936 6.57938 2.03393 7.08403C2.3706 6.83531 2.78055 6.63162 3.23916 6.48077ZM1.39444 4.62499H3.13192C3.14615 4.24204 3.18211 3.87344 3.23794 3.52681C2.77814 3.37545 2.36731 3.17096 2.03024 2.92123C1.67783 3.42469 1.45828 4.011 1.39444 4.62499ZM2.5237 2.35262C2.76812 2.52552 3.06373 2.67281 3.39584 2.78875C3.53318 2.28573 3.71928 1.85578 3.9461 1.53158C3.41932 1.69166 2.93178 1.97089 2.5237 2.35262ZM3.97101 3.71489C3.92709 4.00012 3.89654 4.30547 3.88295 4.62499H6.11705C6.10453 4.33057 6.07761 4.04818 6.03909 3.78248C5.77372 3.81417 5.50093 3.83049 5.224 3.83049C4.79169 3.83049 4.3699 3.79065 3.97101 3.71489ZM5.8928 3.04476C5.67527 3.06863 5.45151 3.08099 5.224 3.08099C4.84241 3.08099 4.47186 3.04609 4.12405 2.98086C4.34686 2.1549 4.68584 1.625 5 1.625C5.32218 1.625 5.67048 2.18233 5.8928 3.04476ZM6.78083 3.6493C6.826 3.95984 6.85552 4.28682 6.86808 4.62499H8.60556C8.55029 4.09337 8.37827 3.58251 8.10436 3.1282C8.0903 3.1364 8.07618 3.14449 8.062 3.15249C7.68838 3.36641 7.25378 3.53417 6.78083 3.6493ZM7.64858 2.52499C7.35446 2.68754 7.0117 2.81868 6.63664 2.91268C6.49676 2.35623 6.29913 1.88209 6.0539 1.53158C6.61864 1.7032 7.13828 2.01176 7.56326 2.43674C7.59224 2.46572 7.62068 2.49514 7.64858 2.52499Z" fill="currentColor"></path></svg>
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<span>English</span>
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</a><div class="relative inline-block mr-1 mb-1 md:mr-1.5 md:mb-1.5">
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<button class=" " type="button">
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<a class="tag mr-0 mb-0 md:mr-0 md:mb-0 tag-purple" href="/models?other=arxiv:2104.01721">
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<span>arxiv:2104.01721</span>
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</a>
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</button>
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</div><a class="tag tag-purple" href="/models?other=speech">
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<span>speech</span>
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</a><a class="tag tag-purple" href="/models?other=audio">
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<span>audio</span>
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</a><a class="tag tag-purple" href="/models?other=CTC">
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<span>CTC</span>
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</a><a class="tag tag-purple" href="/models?other=Citrinet">
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<span>Citrinet</span>
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</a><a class="tag tag-purple" href="/models?other=Transformer">
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<span>Transformer</span>
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</a><a class="tag tag-purple" href="/models?other=NeMo">
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<span>NeMo</span>
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</a><a class="tag tag-purple" href="/models?other=hf-asr-leaderboard">
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<span>hf-asr-leaderboard</span>
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</a><a class="tag tag-purple" href="/models?other=model-index"><svg class="ml-2 text-orange-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M30 30h-8V4h8z" fill="currentColor"></path><path d="M20 30h-8V12h8z" fill="currentColor"></path><path d="M10 30H2V18h8z" fill="currentColor"></path></svg>
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<span>Eval Results</span>
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</a><a class="tag tag-white rounded-full" href="/models?license=license:cc-by-4.0"><svg class="ml-2 text-xs text-gray-900" width="1em" height="1em" viewBox="0 0 10 10" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1.46009 5.0945V6.88125C1.46009 7.25201 1.75937 7.55129 2.13012 7.55129C2.50087 7.55129 2.80016 7.25201 2.80016 6.88125V5.0945C2.80016 4.72375 2.50087 4.42446 2.13012 4.42446C1.75937 4.42446 1.46009 4.72375 1.46009 5.0945ZM4.14022 5.0945V6.88125C4.14022 7.25201 4.4395 7.55129 4.81026 7.55129C5.18101 7.55129 5.48029 7.25201 5.48029 6.88125V5.0945C5.48029 4.72375 5.18101 4.42446 4.81026 4.42446C4.4395 4.42446 4.14022 4.72375 4.14022 5.0945ZM1.23674 9.78473H8.38377C8.75452 9.78473 9.0538 9.48545 9.0538 9.1147C9.0538 8.74395 8.75452 8.44466 8.38377 8.44466H1.23674C0.865993 8.44466 0.566711 8.74395 0.566711 9.1147C0.566711 9.48545 0.865993 9.78473 1.23674 9.78473ZM6.82036 5.0945V6.88125C6.82036 7.25201 7.11964 7.55129 7.49039 7.55129C7.86114 7.55129 8.16042 7.25201 8.16042 6.88125V5.0945C8.16042 4.72375 7.86114 4.42446 7.49039 4.42446C7.11964 4.42446 6.82036 4.72375 6.82036 5.0945ZM4.39484 0.623142L0.865993 2.48137C0.682851 2.57517 0.566711 2.76725 0.566711 2.97273C0.566711 3.28094 0.816857 3.53109 1.12507 3.53109H8.49991C8.80365 3.53109 9.0538 3.28094 9.0538 2.97273C9.0538 2.76725 8.93766 2.57517 8.75452 2.48137L5.22568 0.623142C4.9666 0.484669 4.65391 0.484669 4.39484 0.623142V0.623142Z" fill="currentColor"></path></svg>
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<span class="text-gray-400 !pr-0 -mr-1">License: </span>
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<span>cc-by-4.0</span>
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</a></div></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-attr">language:</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">en</span>
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<span class="hljs-attr">library_name:</span> <span class="hljs-string">nemo</span>
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<span class="hljs-attr">datasets:</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">librispeech_asr</span>
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<span class="hljs-attr">thumbnail:</span> <span class="hljs-literal">null</span>
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<span class="hljs-attr">tags:</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">automatic-speech-recognition</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">speech</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">audio</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">CTC</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">Citrinet</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">Transformer</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">pytorch</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">NeMo</span>
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<span class="hljs-bullet">-</span> <span class="hljs-string">hf-asr-leaderboard</span>
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<span class="hljs-attr">license:</span> <span class="hljs-string">cc-by-4.0</span>
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<span class="hljs-attr">widget:</span>
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<span class="hljs-bullet">-</span> <span class="hljs-attr">example_title:</span> <span class="hljs-string">Librispeech</span> <span class="hljs-string">sample</span> <span class="hljs-number">1</span>
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<span class="hljs-attr">src:</span> <span class="hljs-string">https://cdn-media.huggingface.co/speech_samples/sample1.flac</span>
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<span class="hljs-bullet">-</span> <span class="hljs-attr">example_title:</span> <span class="hljs-string">Librispeech</span> <span class="hljs-string">sample</span> <span class="hljs-number">2</span>
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<span class="hljs-attr">src:</span> <span class="hljs-string">https://cdn-media.huggingface.co/speech_samples/sample2.flac</span>
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<span class="hljs-attr">model-index:</span>
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<span class="hljs-bullet">-</span> <span class="hljs-attr">name:</span> <span class="hljs-string">stt_en_citrinet_1024_ls</span>
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<span class="hljs-bullet">-</span> <span class="hljs-attr">task:</span>
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<span class="hljs-attr">name:</span> <span class="hljs-string">Automatic</span> <span class="hljs-string">Speech</span> <span class="hljs-string">Recognition</span>
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<span class="hljs-attr">type:</span> <span class="hljs-string">automatic-speech-recognition</span>
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<span class="hljs-attr">dataset:</span>
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<span class="hljs-attr">name:</span> <span class="hljs-string">LibriSpeech</span> <span class="hljs-string">(clean)</span>
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<span class="hljs-attr">type:</span> <span class="hljs-string">librispeech_asr</span>
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<span class="hljs-attr">config:</span> <span class="hljs-string">clean</span>
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<span class="hljs-attr">split:</span> <span class="hljs-string">test</span>
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<span class="hljs-attr">args:</span>
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<span class="hljs-attr">language:</span> <span class="hljs-string">en</span>
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<span class="hljs-attr">metrics:</span>
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<span class="hljs-bullet">-</span> <span class="hljs-attr">name:</span> <span class="hljs-string">Test</span> <span class="hljs-string">WER</span>
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<span class="hljs-attr">type:</span> <span class="hljs-string">wer</span>
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<span class="hljs-attr">value:</span> <span class="hljs-number">2.5</span>
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<span class="hljs-bullet">-</span> <span class="hljs-attr">task:</span>
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<span class="hljs-attr">type:</span> <span class="hljs-string">Automatic</span> <span class="hljs-string">Speech</span> <span class="hljs-string">Recognition</span>
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<span class="hljs-attr">name:</span> <span class="hljs-string">automatic-speech-recognition</span>
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<span class="hljs-attr">dataset:</span>
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<span class="hljs-attr">name:</span> <span class="hljs-string">LibriSpeech</span> <span class="hljs-string">(other)</span>
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<span class="hljs-attr">type:</span> <span class="hljs-string">librispeech_asr</span>
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<span class="hljs-attr">config:</span> <span class="hljs-string">other</span>
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<span class="hljs-attr">split:</span> <span class="hljs-string">test</span>
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<span class="hljs-attr">args:</span>
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<span class="hljs-attr">language:</span> <span class="hljs-string">en</span>
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<span class="hljs-attr">metrics:</span>
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<span class="hljs-bullet">-</span> <span class="hljs-attr">name:</span> <span class="hljs-string">Test</span> <span class="hljs-string">WER</span>
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<span class="hljs-attr">type:</span> <span class="hljs-string">wer</span>
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<span class="hljs-attr">value:</span> <span class="hljs-number">6.3</span>
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<!-- HTML_TAG_END --></pre></div>
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<!-- HTML_TAG_START --><h1 class="relative group flex items-center">
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<a rel="noopener nofollow" href="#nvidia-citrinet-ctc-1924-librispeech-en-us" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="nvidia-citrinet-ctc-1924-librispeech-en-us">
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<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
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</a>
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<span>
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NVIDIA Citrinet CTC 1924 Librispeech (en-US)
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</span>
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</h1>
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<p>| <a rel="noopener nofollow" href="#model-architecture"><img alt="Model architecture" src="https://img.shields.io/badge/Model_Arch-Citrinet--CTC-lightgrey#model-badge"></a>
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| <a rel="noopener nofollow" href="#model-architecture"><img alt="Model size" src="https://img.shields.io/badge/Params-140M-lightgrey#model-badge"></a>
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| <a rel="noopener nofollow" href="#datasets"><img alt="Language" src="https://img.shields.io/badge/Language-en--US-lightgrey#model-badge"></a>
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| <a rel="noopener nofollow" href="#deployment-with-nvidia-riva"><img alt="Riva Compatible" src="https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge"></a> |</p>
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<p>This model transcribes speech in lower case English alphabet along with spaces and apostrophes.
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It is an "large" versions of Citrinet-CTC (around 140M parameters) model.<br>See the <a rel="noopener nofollow" href="#model-architecture">model architecture</a> section and <a rel="noopener nofollow" href="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet">NeMo documentation</a> for complete architecture details.
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It is also compatible with NVIDIA Riva for <a rel="noopener nofollow" href="#deployment-with-nvidia-riva">production-grade server deployments</a>. </p>
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<h2 class="relative group flex items-center">
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<a rel="noopener nofollow" href="#nvidia-nemo-training" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="nvidia-nemo-training">
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<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
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</a>
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NVIDIA NeMo: Training
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</span>
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</h2>
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<p>To train, fine-tune or play with the model you will need to install <a rel="noopener nofollow" href="https://github.com/NVIDIA/NeMo">NVIDIA NeMo</a>. We recommend you install it after you've installed latest Pytorch version.</p>
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<pre><code>pip install nemo_toolkit['all']
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</code></pre>
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<h2 class="relative group flex items-center">
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<a rel="noopener nofollow" href="#how-to-use-this-model" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="how-to-use-this-model">
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<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
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</a>
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<span>
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How to Use this Model
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</span>
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</h2>
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<p>The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.</p>
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<h3 class="relative group flex items-center">
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<a rel="noopener nofollow" href="#automatically-instantiate-the-model" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="automatically-instantiate-the-model">
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<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
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</a>
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<span>
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Automatically instantiate the model
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</span>
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</h3>
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<pre><code class="language-python"><span class="hljs-keyword">import</span> nemo.collections.asr <span class="hljs-keyword">as</span> nemo_asr
|
| 359 |
-
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(<span class="hljs-string">"nvidia/stt_en_citrinet_1024_ls"</span>)
|
| 360 |
-
</code></pre>
|
| 361 |
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<h3 class="relative group flex items-center">
|
| 362 |
-
<a rel="noopener nofollow" href="#transcribing-using-python" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="transcribing-using-python">
|
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<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
|
| 364 |
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</a>
|
| 365 |
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<span>
|
| 366 |
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Transcribing using Python
|
| 367 |
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</span>
|
| 368 |
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</h3>
|
| 369 |
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<p>First, let's get a sample</p>
|
| 370 |
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<pre><code>wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
|
| 371 |
-
</code></pre>
|
| 372 |
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<p>Then simply do:</p>
|
| 373 |
-
<pre><code>asr_model.transcribe(['2086-149220-0033.wav'])
|
| 374 |
-
</code></pre>
|
| 375 |
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<h3 class="relative group flex items-center">
|
| 376 |
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<a rel="noopener nofollow" href="#transcribing-many-audio-files" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="transcribing-many-audio-files">
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<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
|
| 378 |
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</a>
|
| 379 |
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<span>
|
| 380 |
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Transcribing many audio files
|
| 381 |
-
</span>
|
| 382 |
-
</h3>
|
| 383 |
-
<pre><code class="language-shell">python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
|
| 384 |
pretrained_name="nvidia/stt_en_citrinet_1024_ls"
|
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audio_dir="
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-
|
| 441 |
-
|
| 442 |
-
|
| 443 |
-
</h2>
|
| 444 |
-
<p>The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.</p>
|
| 445 |
-
<div class="max-w-full overflow-auto">
|
| 446 |
-
<table>
|
| 447 |
-
<thead><tr>
|
| 448 |
-
<th>Version</th>
|
| 449 |
-
<th>Tokenizer</th>
|
| 450 |
-
<th>Vocabulary Size</th>
|
| 451 |
-
<th>LS test-other</th>
|
| 452 |
-
<th>LS test-clean</th>
|
| 453 |
-
</tr>
|
| 454 |
-
|
| 455 |
-
</thead><tbody><tr>
|
| 456 |
-
<td>1.0.0</td>
|
| 457 |
-
<td>SentencePiece Unigram [2]</td>
|
| 458 |
-
<td>256</td>
|
| 459 |
-
<td>6.3</td>
|
| 460 |
-
<td>2.5</td>
|
| 461 |
-
</tr>
|
| 462 |
-
</tbody>
|
| 463 |
-
</table>
|
| 464 |
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</div>
|
| 465 |
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<h2 class="relative group flex items-center">
|
| 466 |
-
<a rel="noopener nofollow" href="#limitations" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="limitations">
|
| 467 |
-
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
|
| 468 |
-
</a>
|
| 469 |
-
<span>
|
| 470 |
-
Limitations
|
| 471 |
-
</span>
|
| 472 |
-
</h2>
|
| 473 |
-
<p>Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.</p>
|
| 474 |
-
<h2 class="relative group flex items-center">
|
| 475 |
-
<a rel="noopener nofollow" href="#deployment-with-nvidia-riva" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="deployment-with-nvidia-riva">
|
| 476 |
-
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
|
| 477 |
-
</a>
|
| 478 |
-
<span>
|
| 479 |
-
Deployment with NVIDIA Riva
|
| 480 |
-
</span>
|
| 481 |
-
</h2>
|
| 482 |
-
<p>For the best real-time accuracy, latency, and throughput, deploy the model with <a rel="noopener nofollow" href="https://developer.nvidia.com/riva">NVIDIA Riva</a>, an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded.
|
| 483 |
-
Additionally, Riva provides: </p>
|
| 484 |
-
<ul>
|
| 485 |
-
<li>World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours </li>
|
| 486 |
-
<li>Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization </li>
|
| 487 |
-
<li>Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support</li>
|
| 488 |
-
</ul>
|
| 489 |
-
<p>Check out <a rel="noopener nofollow" href="https://developer.nvidia.com/riva#demos">Riva live demo</a>.</p>
|
| 490 |
-
<h2 class="relative group flex items-center">
|
| 491 |
-
<a rel="noopener nofollow" href="#references" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="references">
|
| 492 |
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<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
|
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</a>
|
| 494 |
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<span>
|
| 495 |
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References
|
| 496 |
-
</span>
|
| 497 |
-
</h2>
|
| 498 |
-
<p>[1] <a rel="noopener nofollow" href="https://arxiv.org/abs/2104.01721"> Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition</a>
|
| 499 |
-
[2] <a rel="noopener nofollow" href="https://github.com/google/sentencepiece">Google Sentencepiece Tokenizer</a>
|
| 500 |
-
[3] <a rel="noopener nofollow" href="https://github.com/NVIDIA/NeMo">NVIDIA NeMo Toolkit</a></p>
|
| 501 |
-
<h2 class="relative group flex items-center">
|
| 502 |
-
<a rel="noopener nofollow" href="#licence" class="block pr-1.5 text-lg with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" id="licence">
|
| 503 |
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<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
|
| 504 |
-
</a>
|
| 505 |
-
<span>
|
| 506 |
-
Licence
|
| 507 |
-
</span>
|
| 508 |
-
</h2>
|
| 509 |
-
<p>License to use this model is covered by the <a rel="noopener nofollow" href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a>. By downloading the public and release version of the model, you accept the terms and conditions of the <a rel="noopener nofollow" href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a> license.</p>
|
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|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
library_name: nemo
|
| 5 |
+
datasets:
|
| 6 |
+
- librispeech_asr
|
| 7 |
+
thumbnail: null
|
| 8 |
+
tags:
|
| 9 |
+
- automatic-speech-recognition
|
| 10 |
+
- speech
|
| 11 |
+
- audio
|
| 12 |
+
- CTC
|
| 13 |
+
- Citrinet
|
| 14 |
+
- Transformer
|
| 15 |
+
- pytorch
|
| 16 |
+
- NeMo
|
| 17 |
+
- hf-asr-leaderboard
|
| 18 |
+
license: cc-by-4.0
|
| 19 |
+
widget:
|
| 20 |
+
- example_title: Librispeech sample 1
|
| 21 |
+
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
|
| 22 |
+
- example_title: Librispeech sample 2
|
| 23 |
+
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
|
| 24 |
+
model-index:
|
| 25 |
+
- name: stt_en_citrinet_1024_ls
|
| 26 |
+
results:
|
| 27 |
+
- task:
|
| 28 |
+
name: Automatic Speech Recognition
|
| 29 |
+
type: automatic-speech-recognition
|
| 30 |
+
dataset:
|
| 31 |
+
name: LibriSpeech (clean)
|
| 32 |
+
type: librispeech_asr
|
| 33 |
+
config: clean
|
| 34 |
+
split: test
|
| 35 |
+
args:
|
| 36 |
+
language: en
|
| 37 |
+
metrics:
|
| 38 |
+
- name: Test WER
|
| 39 |
+
type: wer
|
| 40 |
+
value: 2.5
|
| 41 |
+
- task:
|
| 42 |
+
type: Automatic Speech Recognition
|
| 43 |
+
name: automatic-speech-recognition
|
| 44 |
+
dataset:
|
| 45 |
+
name: LibriSpeech (other)
|
| 46 |
+
type: librispeech_asr
|
| 47 |
+
config: other
|
| 48 |
+
split: test
|
| 49 |
+
args:
|
| 50 |
+
language: en
|
| 51 |
+
metrics:
|
| 52 |
+
- name: Test WER
|
| 53 |
+
type: wer
|
| 54 |
+
value: 6.3
|
| 55 |
+
---
|
| 56 |
+
|
| 57 |
+
# NVIDIA Citrinet CTC 1924 Librispeech (en-US)
|
| 58 |
+
|
| 59 |
+
<style>
|
| 60 |
+
img {
|
| 61 |
+
display: inline;
|
| 62 |
+
}
|
| 63 |
+
</style>
|
| 64 |
+
|
| 65 |
+
| [](#model-architecture)
|
| 66 |
+
| [](#model-architecture)
|
| 67 |
+
| [](#datasets)
|
| 68 |
+
| [](#deployment-with-nvidia-riva) |
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
This model transcribes speech in lower case English alphabet along with spaces and apostrophes.
|
| 72 |
+
It is an "large" versions of Citrinet-CTC (around 140M parameters) model.
|
| 73 |
+
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet) for complete architecture details.
|
| 74 |
+
It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva).
|
| 75 |
+
|
| 76 |
+
## NVIDIA NeMo: Training
|
| 77 |
+
|
| 78 |
+
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
|
| 79 |
+
```
|
| 80 |
+
pip install nemo_toolkit['all']
|
| 81 |
+
```
|
| 82 |
+
|
| 83 |
+
## How to Use this Model
|
| 84 |
+
|
| 85 |
+
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
|
| 86 |
+
|
| 87 |
+
### Automatically instantiate the model
|
| 88 |
+
|
| 89 |
+
```python
|
| 90 |
+
import nemo.collections.asr as nemo_asr
|
| 91 |
+
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("nvidia/stt_en_citrinet_1024_ls")
|
| 92 |
+
```
|
| 93 |
+
|
| 94 |
+
### Transcribing using Python
|
| 95 |
+
First, let's get a sample
|
| 96 |
+
```
|
| 97 |
+
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
|
| 98 |
+
```
|
| 99 |
+
Then simply do:
|
| 100 |
+
```
|
| 101 |
+
asr_model.transcribe(['2086-149220-0033.wav'])
|
| 102 |
+
```
|
| 103 |
+
|
| 104 |
+
### Transcribing many audio files
|
| 105 |
+
|
| 106 |
+
```shell
|
| 107 |
+
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
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|
| 108 |
pretrained_name="nvidia/stt_en_citrinet_1024_ls"
|
| 109 |
+
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
|
| 110 |
+
```
|
| 111 |
+
|
| 112 |
+
### Input
|
| 113 |
+
|
| 114 |
+
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
|
| 115 |
+
|
| 116 |
+
### Output
|
| 117 |
+
|
| 118 |
+
This model provides transcribed speech as a string for a given audio sample.
|
| 119 |
+
|
| 120 |
+
## Model Architecture
|
| 121 |
+
|
| 122 |
+
Citrinet-CTC model is an autoregressive variant of Citrinet model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer Loss. You may find more info on the detail of this model here: [Citrinet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).
|
| 123 |
+
|
| 124 |
+
## Training
|
| 125 |
+
|
| 126 |
+
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citrinet/citrinet_1024.yaml) (Note: Change the `model.model_defaults.filters` to match the model size).
|
| 127 |
+
|
| 128 |
+
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
|
| 129 |
+
|
| 130 |
+
### Datasets
|
| 131 |
+
|
| 132 |
+
All the models in this collection are trained on a just the Librispeech Dataset:
|
| 133 |
+
|
| 134 |
+
- Librispeech 960 hours of English speech
|
| 135 |
+
|
| 136 |
+
|
| 137 |
+
## Performance
|
| 138 |
+
|
| 139 |
+
The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
|
| 140 |
+
|
| 141 |
+
| Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean |
|
| 142 |
+
|---------|---------------------------|-----------------|---------------|---------------|
|
| 143 |
+
| 1.0.0 | SentencePiece Unigram [2] | 256 | 6.3 | 2.5 |
|
| 144 |
+
|
| 145 |
+
## Limitations
|
| 146 |
+
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
|
| 147 |
+
|
| 148 |
+
## Deployment with NVIDIA Riva
|
| 149 |
+
|
| 150 |
+
For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded.
|
| 151 |
+
Additionally, Riva provides:
|
| 152 |
+
|
| 153 |
+
* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
|
| 154 |
+
* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
|
| 155 |
+
* Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support
|
| 156 |
+
|
| 157 |
+
Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
|
| 158 |
+
|
| 159 |
+
## References
|
| 160 |
+
[1] [ Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721)
|
| 161 |
+
[2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
|
| 162 |
+
[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
|
| 163 |
+
|
| 164 |
+
## Licence
|
| 165 |
+
|
| 166 |
+
License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
|
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