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+ <div class="SVELTE_HYDRATER contents" data-props="{&quot;commitLast&quot;:{&quot;date&quot;:&quot;2022-07-15T21:33:44.000Z&quot;,&quot;subject&quot;:&quot;Update README.md&quot;,&quot;authors&quot;:[{&quot;_id&quot;:&quot;6254f8e5d21e4cc386b881ad&quot;,&quot;avatar&quot;:&quot;https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1649899774659-6254f8e5d21e4cc386b881ad.jpeg?w=200&amp;h=200&amp;f=face&quot;,&quot;isHf&quot;:false,&quot;user&quot;:&quot;smajumdar94&quot;}],&quot;commit&quot;:{&quot;id&quot;:&quot;2ed90e3b83bd85d3a7adebb1d91816926fe33fc9&quot;,&quot;parentIds&quot;:[&quot;6c41f3c2f923b340e726abf6ce4223ed98c6257e&quot;]},&quot;title&quot;:&quot;Update README.md&quot;},&quot;repo&quot;:{&quot;name&quot;:&quot;nvidia/stt_en_citrinet_1024_ls&quot;,&quot;type&quot;:&quot;model&quot;}}" data-target="LastCommit"><div class="border border-b-0 dark:border-gray-800 px-3 py-2 flex items-baseline rounded-t-lg bg-gradient-to-t from-gray-100-to-white"><img class="w-4 h-4 rounded-full mt-0.5 mr-2.5 self-center" alt="smajumdar94's picture" src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1649899774659-6254f8e5d21e4cc386b881ad.jpeg?w=200&amp;h=200&amp;f=face">
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+ <a class="capitalize rounded-md px-1.5 " href="/nvidia/stt_en_citrinet_1024_ls/blob/main/README.md?code=true">code</a></div>
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+ blame
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+ contribute
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+ </a><a class="flex items-center hover:underline my-1 mr-4 " href="/nvidia/stt_en_citrinet_1024_ls/delete/main/README.md"><svg class="mr-1.5" 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="M12 12h2v12h-2z" fill="currentColor"></path><path d="M18 12h2v12h-2z" fill="currentColor"></path><path d="M4 6v2h2v20a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2V8h2V6zm4 22V8h16v20z" fill="currentColor"></path><path d="M12 2h8v2h-8z" fill="currentColor"></path></svg>
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+ delete
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+ </a>
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+ <div class="text-gray-400 flex items-center mr-4"><svg class="text-gray-300 text-sm mr-1.5 -translate-y-px" width="1em" height="1em" viewBox="0 0 22 28" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M15.3634 10.3639C15.8486 10.8491 15.8486 11.6357 15.3634 12.1209L10.9292 16.5551C10.6058 16.8785 10.0814 16.8785 9.7579 16.5551L7.03051 13.8277C6.54532 13.3425 6.54532 12.5558 7.03051 12.0707C7.51569 11.5855 8.30234 11.5855 8.78752 12.0707L9.7579 13.041C10.0814 13.3645 10.6058 13.3645 10.9292 13.041L13.6064 10.3639C14.0916 9.8787 14.8782 9.8787 15.3634 10.3639Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M10.6666 27.12C4.93329 25.28 0 19.2267 0 12.7867V6.52001C0 5.40001 0.693334 4.41334 1.73333 4.01334L9.73333 1.01334C10.3333 0.786673 11 0.786673 11.6 1.02667L19.6 4.02667C20.1083 4.21658 20.5465 4.55701 20.8562 5.00252C21.1659 5.44803 21.3324 5.97742 21.3333 6.52001V12.7867C21.3333 19.24 16.4 25.28 10.6666 27.12Z" fill="currentColor" fill-opacity="0.22"></path><path d="M10.0845 1.94967L10.0867 1.94881C10.4587 1.8083 10.8666 1.81036 11.2286 1.95515L11.2387 1.95919L11.2489 1.963L19.2489 4.963L19.25 4.96342C19.5677 5.08211 19.8416 5.29488 20.0351 5.57333C20.2285 5.85151 20.3326 6.18203 20.3333 6.52082C20.3333 6.52113 20.3333 6.52144 20.3333 6.52176L20.3333 12.7867C20.3333 18.6535 15.8922 24.2319 10.6666 26.0652C5.44153 24.2316 1 18.6409 1 12.7867V6.52001C1 5.82357 1.42893 5.20343 2.08883 4.94803L10.0845 1.94967Z" stroke="currentColor" stroke-opacity="0.30" stroke-width="2"></path></svg>
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+
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+ No virus
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+ </div>
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+
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+ <div class="dark:text-gray-300 sm:ml-auto">6.6 kB</div></div>
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+
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+ <div class="border border-t-0 rounded-b-lg dark:bg-gray-925 dark:border-gray-800 leading-tight"><div class="py-4 px-4 sm:px-6 prose hf-sanitized hf-sanitized-ePMI-0Dy6tnIb6JbaZ2vM"><div class="min-w-full max-h-[300px] transition-all overflow-auto border-b mb-8 -mx-6 -mt-4 px-6 pt-4 pb-5 font-mono text-xs not-prose bg-gradient-to-t from-gray-50 dark:from-gray-900 dark:to-gray-950"><div class="border px-2 py-1 rounded-lg inline-block font-mono text-xs leading-none mb-2">metadata</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-attr">results:</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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+
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+
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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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+ <span>
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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>
355
+ Automatically instantiate the model
356
+ </span>
357
+ </h3>
358
+ <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
+ <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">
363
+ <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
+ </a>
365
+ <span>
366
+ Transcribing using Python
367
+ </span>
368
+ </h3>
369
+ <p>First, let's get a sample</p>
370
+ <pre><code>wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
371
+ </code></pre>
372
+ <p>Then simply do:</p>
373
+ <pre><code>asr_model.transcribe(['2086-149220-0033.wav'])
374
+ </code></pre>
375
+ <h3 class="relative group flex items-center">
376
+ <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">
377
+ <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
+ </a>
379
+ <span>
380
+ 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"
385
+ audio_dir="&lt;DIRECTORY CONTAINING AUDIO FILES&gt;"
386
+ </code></pre>
387
+ <h3 class="relative group flex items-center">
388
+ <a rel="noopener nofollow" href="#input" 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="input">
389
+ <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>
390
+ </a>
391
+ <span>
392
+ Input
393
+ </span>
394
+ </h3>
395
+ <p>This model accepts 16000 KHz Mono-channel Audio (wav files) as input.</p>
396
+ <h3 class="relative group flex items-center">
397
+ <a rel="noopener nofollow" href="#output" 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="output">
398
+ <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>
399
+ </a>
400
+ <span>
401
+ Output
402
+ </span>
403
+ </h3>
404
+ <p>This model provides transcribed speech as a string for a given audio sample.</p>
405
+ <h2 class="relative group flex items-center">
406
+ <a rel="noopener nofollow" href="#model-architecture" 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="model-architecture">
407
+ <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>
408
+ </a>
409
+ <span>
410
+ Model Architecture
411
+ </span>
412
+ </h2>
413
+ <p>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: <a rel="noopener nofollow" href="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html">Citrinet Model</a>. </p>
414
+ <h2 class="relative group flex items-center">
415
+ <a rel="noopener nofollow" href="#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="training">
416
+ <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>
417
+ </a>
418
+ <span>
419
+ Training
420
+ </span>
421
+ </h2>
422
+ <p>The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this <a rel="noopener nofollow" href="https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py">example script</a> and this <a rel="noopener nofollow" href="https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citrinet/citrinet_1024.yaml">base config</a> (Note: Change the <code>model.model_defaults.filters</code> to match the model size).</p>
423
+ <p>The tokenizers for these models were built using the text transcripts of the train set with this <a rel="noopener nofollow" href="https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py">script</a>.</p>
424
+ <h3 class="relative group flex items-center">
425
+ <a rel="noopener nofollow" href="#datasets" 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="datasets">
426
+ <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>
427
+ </a>
428
+ <span>
429
+ Datasets
430
+ </span>
431
+ </h3>
432
+ <p>All the models in this collection are trained on a just the Librispeech Dataset:</p>
433
+ <ul>
434
+ <li>Librispeech 960 hours of English speech</li>
435
+ </ul>
436
+ <h2 class="relative group flex items-center">
437
+ <a rel="noopener nofollow" href="#performance" 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="performance">
438
+ <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>
439
+ </a>
440
+ <span>
441
+ Performance
442
+ </span>
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
+ </div>
465
+ <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
+ <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>
493
+ </a>
494
+ <span>
495
+ 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
+ <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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