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tfjs-models/speech-commands at master · tensorflow/tfjs-models · GitHub

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flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;discussions&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;discussions_link_product_navbar&quot;}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Discussions</div> Collaborate outside of code </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;code_search&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;code_search_link_product_navbar&quot;}" href="https://github.com/features/code-search"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;all_features&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;all_features_link_product_navbar&quot;}" href="https://github.com/features"> All features </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;documentation&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;documentation_link_product_navbar&quot;}" href="https://docs.github.com"> Documentation <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 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2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;blog&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;blog_link_product_navbar&quot;}" href="https://github.blog"> Blog <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 pb-lg-3 mb-3 mb-lg-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-company-size-heading">By company size</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-company-size-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;enterprises&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;enterprises_link_solutions_navbar&quot;}" href="https://github.com/enterprise"> Enterprises </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;small_and_medium_teams&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;small_and_medium_teams_link_solutions_navbar&quot;}" href="https://github.com/team"> Small and medium teams </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;startups&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;startups_link_solutions_navbar&quot;}" href="https://github.com/enterprise/startups"> Startups </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-use-case-heading">By use case</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-use-case-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;devsecops&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;devsecops_link_solutions_navbar&quot;}" href="/solutions/use-case/devsecops"> DevSecOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;devops&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;devops_link_solutions_navbar&quot;}" href="/solutions/use-case/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;ci_cd&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;ci_cd_link_solutions_navbar&quot;}" href="/solutions/use-case/ci-cd"> CI/CD </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;view_all_use_cases&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;view_all_use_cases_link_solutions_navbar&quot;}" href="/solutions/use-case"> View all use cases </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-industry-heading">By industry</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-industry-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;healthcare&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;healthcare_link_solutions_navbar&quot;}" href="/solutions/industry/healthcare"> Healthcare </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;financial_services&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;financial_services_link_solutions_navbar&quot;}" href="/solutions/industry/financial-services"> Financial services </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;manufacturing&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;manufacturing_link_solutions_navbar&quot;}" href="/solutions/industry/manufacturing"> Manufacturing </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;government&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;government_link_solutions_navbar&quot;}" href="/solutions/industry/government"> Government </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;view_all_industries&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;view_all_industries_link_solutions_navbar&quot;}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Resources <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul class="list-style-none f5" aria-labelledby="resources-topics-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;ai&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;ai_link_resources_navbar&quot;}" href="/resources/articles/ai"> AI </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;devops&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;devops_link_resources_navbar&quot;}" href="/resources/articles/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;security&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;security_link_resources_navbar&quot;}" href="/resources/articles/security"> Security </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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data-target="react-app.embeddedData">{"payload":{"allShortcutsEnabled":false,"path":"speech-commands","repo":{"id":127968704,"defaultBranch":"master","name":"tfjs-models","ownerLogin":"tensorflow","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2018-04-03T21:04:35.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/15658638?v=4","public":true,"private":false,"isOrgOwned":true},"currentUser":null,"refInfo":{"name":"master","listCacheKey":"v0:1732384074.0","canEdit":false,"refType":"branch","currentOid":"18a73420e555a3c1948209ebfa807c7086c7ff0c"},"tree":{"items":[{"name":".vscode","path":"speech-commands/.vscode","contentType":"directory"},{"name":"demo","path":"speech-commands/demo","contentType":"directory"},{"name":"src","path":"speech-commands/src","contentType":"directory"},{"name":"training","path":"speech-commands/training","contentType":"directory"},{"name":".gitignore","path":"speech-commands/.gitignore","contentType":"file"},{"name":".npmignore","path":"speech-commands/.npmignore","contentType":"file"},{"name":"README.md","path":"speech-commands/README.md","contentType":"file"},{"name":"cloudbuild.yml","path":"speech-commands/cloudbuild.yml","contentType":"file"},{"name":"package.json","path":"speech-commands/package.json","contentType":"file"},{"name":"rollup.config.js","path":"speech-commands/rollup.config.js","contentType":"file"},{"name":"run_tests.ts","path":"speech-commands/run_tests.ts","contentType":"file"},{"name":"tsconfig.json","path":"speech-commands/tsconfig.json","contentType":"file"},{"name":"tsconfig.test.json","path":"speech-commands/tsconfig.test.json","contentType":"file"},{"name":"tslint.json","path":"speech-commands/tslint.json","contentType":"file"},{"name":"yarn.lock","path":"speech-commands/yarn.lock","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":{"displayName":"README.md","richText":"\u003carticle class=\"markdown-body entry-content container-lg\" itemprop=\"text\"\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSpeech Command Recognizer\u003c/h1\u003e\u003ca id=\"user-content-speech-command-recognizer\" class=\"anchor\" aria-label=\"Permalink: Speech Command Recognizer\" href=\"#speech-command-recognizer\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe Speech Command Recognizer is a JavaScript module that enables\nrecognition of spoken commands comprised of simple isolated English\nwords from a small vocabulary. The default vocabulary includes the following\nwords: the ten digits from \"zero\" to \"nine\", \"up\", \"down\", \"left\", \"right\",\n\"go\", \"stop\", \"yes\", \"no\", as well as the additional categories of\n\"unknown word\" and \"background noise\".\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIt uses the web browser's\n\u003ca href=\"https://developer.mozilla.org/en-US/docs/Web/API/Web_Audio_API\" rel=\"nofollow\"\u003eWebAudio API\u003c/a\u003e.\nIt is built on top of \u003ca href=\"https://js.tensorflow.org\" rel=\"nofollow\"\u003eTensorFlow.js\u003c/a\u003e and can\nperform inference and transfer learning entirely in the browser, using\nWebGL GPU acceleration.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe underlying deep neural network has been trained using the\n\u003ca href=\"https://www.tensorflow.org/datasets/catalog/speech_commands\" rel=\"nofollow\"\u003eTensorFlow Speech Commands Dataset\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor more details on the data set, see:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWarden, P. (2018) \"Speech commands: A dataset for limited-vocabulary\nspeech recognition\" \u003ca href=\"https://arxiv.org/pdf/1804.03209.pdf\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/1804.03209.pdf\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAPI Usage\u003c/h2\u003e\u003ca id=\"user-content-api-usage\" class=\"anchor\" aria-label=\"Permalink: API Usage\" href=\"#api-usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA speech command recognizer can be used in two ways:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eOnline streaming recognition\u003c/strong\u003e, during which the library automatically\nopens an audio input channel using the browser's\n\u003ca href=\"https://developer.mozilla.org/en-US/docs/Web/API/MediaDevices/getUserMedia\" rel=\"nofollow\"\u003e\u003ccode\u003egetUserMedia\u003c/code\u003e\u003c/a\u003e\nand\n\u003ca href=\"https://developer.mozilla.org/en-US/docs/Web/API/Web_Audio_API\" rel=\"nofollow\"\u003eWebAudio\u003c/a\u003e\nAPIs (requesting permission from user) and performs real-time recognition on\nthe audio input.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOffline recognition\u003c/strong\u003e, in which you provide a pre-constructed TensorFlow.js\n\u003ca href=\"https://js.tensorflow.org/api/latest/#tensor\" rel=\"nofollow\"\u003eTensor\u003c/a\u003e object or a\n\u003ccode\u003eFloat32Array\u003c/code\u003e and the recognizer will return the recognition results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOnline streaming recognition\u003c/h3\u003e\u003ca id=\"user-content-online-streaming-recognition\" class=\"anchor\" aria-label=\"Permalink: Online streaming recognition\" href=\"#online-streaming-recognition\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo use the speech-command recognizer, first create a recognizer instance,\nthen start the streaming recognition by calling its \u003ccode\u003elisten()\u003c/code\u003e method.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"const tf = require('@tensorflow/tfjs');\nconst speechCommands = require('@tensorflow-models/speech-commands');\n\n// When calling `create()`, you must provide the type of the audio input.\n// The two available options are `BROWSER_FFT` and `SOFT_FFT`.\n// - BROWSER_FFT uses the browser's native Fourier transform.\n// - SOFT_FFT uses JavaScript implementations of Fourier transform\n// (not implemented yet).\nconst recognizer = speechCommands.create('BROWSER_FFT');\n\n// Make sure that the underlying model and metadata are loaded via HTTPS\n// requests.\nawait recognizer.ensureModelLoaded();\n\n// See the array of words that the recognizer is trained to recognize.\nconsole.log(recognizer.wordLabels());\n\n// `listen()` takes two arguments:\n// 1. A callback function that is invoked anytime a word is recognized.\n// 2. A configuration object with adjustable fields such a\n// - includeSpectrogram\n// - probabilityThreshold\n// - includeEmbedding\nrecognizer.listen(result =\u0026gt; {\n // - result.scores contains the probability scores that correspond to\n // recognizer.wordLabels().\n // - result.spectrogram contains the spectrogram of the recognized word.\n}, {\n includeSpectrogram: true,\n probabilityThreshold: 0.75\n});\n\n// Stop the recognition in 10 seconds.\nsetTimeout(() =\u0026gt; recognizer.stopListening(), 10e3);\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erequire\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'@tensorflow/tfjs'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espeechCommands\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erequire\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'@tensorflow-models/speech-commands'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// When calling `create()`, you must provide the type of the audio input.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// The two available options are `BROWSER_FFT` and `SOFT_FFT`.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - BROWSER_FFT uses the browser's native Fourier transform.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - SOFT_FFT uses JavaScript implementations of Fourier transform\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// (not implemented yet).\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espeechCommands\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecreate\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'BROWSER_FFT'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// Make sure that the underlying model and metadata are loaded via HTTPS\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// requests.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eensureModelLoaded\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// See the array of words that the recognizer is trained to recognize.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003econsole\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ewordLabels\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// `listen()` takes two arguments:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// 1. A callback function that is invoked anytime a word is recognized.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// 2. A configuration object with adjustable fields such a\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - includeSpectrogram\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - probabilityThreshold\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - includeEmbedding\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elisten\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eresult\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// - result.scores contains the probability scores that correspond to\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// recognizer.wordLabels().\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// - result.spectrogram contains the spectrogram of the recognized word.\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eincludeSpectrogram\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eprobabilityThreshold\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.75\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// Stop the recognition in 10 seconds.\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003esetTimeout\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003estopListening\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10e3\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVocabularies\u003c/h4\u003e\u003ca id=\"user-content-vocabularies\" class=\"anchor\" aria-label=\"Permalink: Vocabularies\" href=\"#vocabularies\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhen calling \u003ccode\u003espeechCommands.create()\u003c/code\u003e, you can specify the vocabulary\nthe loaded model will be able to recognize. This is specified as the second,\noptional argument to \u003ccode\u003espeechCommands.create()\u003c/code\u003e. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"const recognizer = speechCommands.create('BROWSER_FFT', 'directional4w');\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espeechCommands\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecreate\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'BROWSER_FFT'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'directional4w'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCurrently, the supported vocabularies are:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e'18w' (default): The 20 item vocaulbary, consisting of:\n'zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven',\n'eight', 'nine', 'up', 'down', 'left', 'right', 'go', 'stop',\n'yes', and 'no', in addition to '\u003cem\u003ebackground_noise\u003c/em\u003e' and '\u003cem\u003eunknown\u003c/em\u003e'.\u003c/li\u003e\n\u003cli\u003e'directional4w': The four directional words: 'up', 'down', 'left', and\n'right', in addition to '\u003cem\u003ebackground_noise\u003c/em\u003e' and '\u003cem\u003eunknown\u003c/em\u003e'.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e'18w' is the default vocabulary.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eParameters for online streaming recognition\u003c/h4\u003e\u003ca id=\"user-content-parameters-for-online-streaming-recognition\" class=\"anchor\" aria-label=\"Permalink: Parameters for online streaming recognition\" href=\"#parameters-for-online-streaming-recognition\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAs the example above shows, you can specify optional parameters when calling\n\u003ccode\u003elisten()\u003c/code\u003e. The supported parameters are:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003eoverlapFactor\u003c/code\u003e: Controls how often the recognizer performs prediction on\nspectrograms. Must be \u0026gt;=0 and \u0026lt;1 (default: 0.5). For example,\nif each spectrogram is 1000 ms long and \u003ccode\u003eoverlapFactor\u003c/code\u003e is set to 0.25,\nthe prediction will happen every 250 ms.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eincludeSpectrogram\u003c/code\u003e: Let the callback function be invoked with the\nspectrogram data included in the argument. Default: \u003ccode\u003efalse\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eprobabilityThreshold\u003c/code\u003e: The callback function will be invoked if and only if\nthe maximum probability score of all the words is greater than this threshold.\nDefault: \u003ccode\u003e0\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003einvokeCallbackOnNoiseAndUnknown\u003c/code\u003e: Whether the callback function will be\ninvoked if the \"word\" with the maximum probability score is the \"unknown\"\nor \"background noise\" token. Default: \u003ccode\u003efalse\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eincludeEmbedding\u003c/code\u003e: Whether an internal activation from the underlying model\nwill be included in the callback argument, in addition to the probability\nscores. Note: if this field is set as \u003ccode\u003etrue\u003c/code\u003e, the value of\n\u003ccode\u003einvokeCallbackOnNoiseAndUnknown\u003c/code\u003e will be overridden to \u003ccode\u003etrue\u003c/code\u003e and the\nvalue of \u003ccode\u003eprobabilityThreshold\u003c/code\u003e will be overridden to \u003ccode\u003e0\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOffline recognition\u003c/h3\u003e\u003ca id=\"user-content-offline-recognition\" class=\"anchor\" aria-label=\"Permalink: Offline recognition\" href=\"#offline-recognition\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo perform offline recognition, you need to have obtained the spectrogram\nof an audio snippet through a certain means, e.g., by loading the data\nfrom a .wav file or synthesizing the spectrogram programmatically.\nAssuming you have the spectrogram stored in an Array of numbers or\na Float32Array, you can create a \u003ccode\u003etf.Tensor\u003c/code\u003e object. Note that the\nshape of the Tensor must match the expectation of the recognizer instance.\nE.g.,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"const tf = require('@tensorflow/tfjs');\nconst speechCommands = require('@tensorflow-models/speech-commands');\n\nconst recognizer = speechCommands.create('BROWSER_FFT');\n\n// Inspect the input shape of the recognizer's underlying tf.Model.\nconsole.log(recognizer.modelInputShape());\n// You will get something like [null, 43, 232, 1].\n// - The first dimension (null) is an undetermined batch dimension.\n// - The second dimension (e.g., 43) is the number of audio frames.\n// - The third dimension (e.g., 232) is the number of frequency data points in\n// every frame (i.e., column) of the spectrogram\n// - The last dimension (e.g., 1) is fixed at 1. This follows the convention of\n// convolutional neural networks in TensorFlow.js and Keras.\n\n// Inspect the sampling frequency and FFT size:\nconsole.log(recognizer.params().sampleRateHz);\nconsole.log(recognizer.params().fftSize);\n\n\nconst x = tf.tensor4d(\n mySpectrogramData, [1].concat(recognizer.modelInputShape().slice(1)));\nconst output = await recognizer.recognize(x);\n// output has the same format as `result` in the online streaming example\n// above: the `scores` field contains the probabilities of the words.\n\ntf.dispose([x, output]);\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erequire\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'@tensorflow/tfjs'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espeechCommands\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erequire\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'@tensorflow-models/speech-commands'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espeechCommands\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecreate\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'BROWSER_FFT'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// Inspect the input shape of the recognizer's underlying tf.Model.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003econsole\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003emodelInputShape\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// You will get something like [null, 43, 232, 1].\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - The first dimension (null) is an undetermined batch dimension.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - The second dimension (e.g., 43) is the number of audio frames.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - The third dimension (e.g., 232) is the number of frequency data points in\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// every frame (i.e., column) of the spectrogram\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// - The last dimension (e.g., 1) is fixed at 1. This follows the convention of\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// convolutional neural networks in TensorFlow.js and Keras.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// Inspect the sampling frequency and FFT size:\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003econsole\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eparams\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003esampleRateHz\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003econsole\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eparams\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003efftSize\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\n\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003etensor4d\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003emySpectrogramData\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003econcat\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003emodelInputShape\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eslice\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003erecognize\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// output has the same format as `result` in the online streaming example\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// above: the `scores` field contains the probabilities of the words.\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003edispose\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote that you must provide a spectrogram value to the \u003ccode\u003erecognize()\u003c/code\u003e call\nin order to perform the offline recognition. If \u003ccode\u003erecognize()\u003c/code\u003e is called\nwithout a first argument, it will perform one-shot online recognition\nby collecting a frame of audio via WebAudio.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePreloading model\u003c/h3\u003e\u003ca id=\"user-content-preloading-model\" class=\"anchor\" aria-label=\"Permalink: Preloading model\" href=\"#preloading-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBy default, a recognizer object will load the underlying\ntf.Model via HTTP requests to a centralized location, when its\n\u003ccode\u003elisten()\u003c/code\u003e or \u003ccode\u003erecognize()\u003c/code\u003e method is called the first time.\nYou can pre-load the model to reduce the latency of the first calls\nto these methods. To do that, use the \u003ccode\u003eensureModelLoaded()\u003c/code\u003e method of the\nrecognizer object. The \u003ccode\u003eensureModelLoaded()\u003c/code\u003e method also \"warms up\" model after\nthe model is loaded. \"Warm up\" means running a few dummy examples through the\nmodel for inference to make sure that the necessary states are set up, so that\nsubsequent inferences can be fast.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTransfer learning\u003c/h3\u003e\u003ca id=\"user-content-transfer-learning\" class=\"anchor\" aria-label=\"Permalink: Transfer learning\" href=\"#transfer-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTransfer learning\u003c/strong\u003e is the process of taking a model trained\npreviously on a dataset (say dataset A) and applying it on a\ndifferent dataset (say dataset B).\nTo achieve transfer learning, the model needs to be slightly modified and\nre-trained on dataset B. However, thanks to the training on\nthe original dataset (A), the training on the new dataset (B) takes much less\ntime and computational resource, in addition to requiring a much smaller amount of\ndata than the original training data. The modification process involves removing the\ntop (output) dense layer of the original model and keeping the \"base\" of the\nmodel. Due to its previous training, the base can be used as a good feature\nextractor for any data similar to the original training data.\nThe removed dense layer is replaced with a new dense layer configured\nspecifically for the new dataset.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe speech-command model is a model suitable for transfer learning on\npreviously unseen spoken words. The original model has been trained on a relatively\nlarge dataset (~50k examples from 20 classes). It can be used for transfer learning on\nwords different from the original vocabulary. We provide an API to perform\nthis type of transfer learning. The steps are listed in the example\ncode snippet below\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"const baseRecognizer = speechCommands.create('BROWSER_FFT');\nawait baseRecognizer.ensureModelLoaded();\n\n// Each instance of speech-command recognizer supports multiple\n// transfer-learning models, each of which can be trained for a different\n// new vocabulary.\n// Therefore we give a name to the transfer-learning model we are about to\n// train ('colors' in this case).\nconst transferRecognizer = baseRecognizer.createTransfer('colors');\n\n// Call `collectExample()` to collect a number of audio examples\n// via WebAudio.\nawait transferRecognizer.collectExample('red');\nawait transferRecognizer.collectExample('green');\nawait transferRecognizer.collectExample('blue');\nawait transferRecognizer.collectExample('red');\n// Don't forget to collect some background-noise examples, so that the\n// transfer-learned model will be able to detect moments of silence.\nawait transferRecognizer.collectExample('_background_noise_');\nawait transferRecognizer.collectExample('green');\nawait transferRecognizer.collectExample('blue');\nawait transferRecognizer.collectExample('_background_noise_');\n// ... You would typically want to put `collectExample`\n// in the callback of a UI button to allow the user to collect\n// any desired number of examples in random order.\n\n// You can check the counts of examples for different words that have been\n// collect for this transfer-learning model.\nconsole.log(transferRecognizer.countExamples());\n// e.g., {'red': 2, 'green': 2', 'blue': 2, '_background_noise': 2};\n\n// Start training of the transfer-learning model.\n// You can specify `epochs` (number of training epochs) and `callback`\n// (the Model.fit callback to use during training), among other configuration\n// fields.\nawait transferRecognizer.train({\n epochs: 25,\n callback: {\n onEpochEnd: async (epoch, logs) =\u0026gt; {\n console.log(`Epoch ${epoch}: loss=${logs.loss}, accuracy=${logs.acc}`);\n }\n }\n});\n\n// After the transfer learning completes, you can start online streaming\n// recognition using the new model.\nawait transferRecognizer.listen(result =\u0026gt; {\n // - result.scores contains the scores for the new vocabulary, which\n // can be checked with:\n const words = transferRecognizer.wordLabels();\n // `result.scores` contains the scores for the new words, not the original\n // words.\n for (let i = 0; i \u0026lt; words.length; ++i) {\n console.log(`score for word '${words[i]}' = ${result.scores[i]}`);\n }\n}, {probabilityThreshold: 0.75});\n\n// Stop the recognition in 10 seconds.\nsetTimeout(() =\u0026gt; transferRecognizer.stopListening(), 10e3);\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebaseRecognizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espeechCommands\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecreate\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'BROWSER_FFT'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebaseRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eensureModelLoaded\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// Each instance of speech-command recognizer supports multiple\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// transfer-learning models, each of which can be trained for a different\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// new vocabulary.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// Therefore we give a name to the transfer-learning model we are about to\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// train ('colors' in this case).\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebaseRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecreateTransfer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'colors'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// Call `collectExample()` to collect a number of audio examples\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// via WebAudio.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecollectExample\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'red'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecollectExample\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'green'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecollectExample\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'blue'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecollectExample\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'red'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// Don't forget to collect some background-noise examples, so that the\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// transfer-learned model will be able to detect moments of silence.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecollectExample\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'_background_noise_'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecollectExample\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'green'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecollectExample\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'blue'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecollectExample\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'_background_noise_'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// ... You would typically want to put `collectExample`\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// in the callback of a UI button to allow the user to collect\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// any desired number of examples in random order.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// You can check the counts of examples for different words that have been\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// collect for this transfer-learning model.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003econsole\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ecountExamples\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// e.g., {'red': 2, 'green': 2', 'blue': 2, '_background_noise': 2};\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// Start training of the transfer-learning model.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// You can specify `epochs` (number of training epochs) and `callback`\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// (the Model.fit callback to use during training), among other configuration\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// fields.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003etrain\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eepochs\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003ecallback\u003c/span\u003e: \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-en\"\u003eonEpochEnd\u003c/span\u003e: \u003cspan class=\"pl-k\"\u003easync\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eepoch\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogs\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-smi\"\u003econsole\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e`Epoch \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-kos\"\u003e${\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eepoch\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/span\u003e: loss=\u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-kos\"\u003e${\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elogs\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eloss\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/span\u003e, accuracy=\u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-kos\"\u003e${\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elogs\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eacc\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/span\u003e`\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// After the transfer learning completes, you can start online streaming\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// recognition using the new model.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elisten\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eresult\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// - result.scores contains the scores for the new vocabulary, which\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// can be checked with:\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ewords\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ewordLabels\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// `result.scores` contains the scores for the new words, not the original\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// words.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ewords\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003elength\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e++\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-smi\"\u003econsole\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e`score for word '\u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-kos\"\u003e${\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ewords\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/span\u003e' = \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-kos\"\u003e${\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eresult\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003escores\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/span\u003e`\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eprobabilityThreshold\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.75\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// Stop the recognition in 10 seconds.\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003esetTimeout\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003estopListening\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10e3\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSerialize examples from a transfer recognizer.\u003c/h3\u003e\u003ca id=\"user-content-serialize-examples-from-a-transfer-recognizer\" class=\"anchor\" aria-label=\"Permalink: Serialize examples from a transfer recognizer.\" href=\"#serialize-examples-from-a-transfer-recognizer\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOnce examples has been collected with a transfer recognizer,\nyou can export the examples in serialized form with the \u003ccode\u003eserielizedExamples()\u003c/code\u003e\nmethod, e.g.,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"const serialized = transferRecognizer.serializeExamples();\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eserialized\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eserializeExamples\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eserialized\u003c/code\u003e is a binary \u003ccode\u003eArrayBuffer\u003c/code\u003e amenable to storage and transmission.\nIt contains the spectrogram data of the examples, as well as metadata such\nas word labels.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can also serialize the examples from a subset of the words in the\ntransfer recognizer's vocabulary, e.g.,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"const serializedWithOnlyFoo = transferRecognizer.serializeExamples('foo');\n// Or\nconst serializedWithOnlyFooAndBar = transferRecognizer.serializeExamples(['foo', 'bar']);\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eserializedWithOnlyFoo\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eserializeExamples\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'foo'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// Or\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eserializedWithOnlyFooAndBar\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eserializeExamples\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'foo'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'bar'\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe serialized examples can later be loaded into another instance of\ntransfer recognizer with the \u003ccode\u003eloadExamples()\u003c/code\u003e method, e.g.,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"const clearExisting = false;\nnewTransferRecognizer.loadExamples(serialized, clearExisting);\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eclearExisting\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enewTransferRecognizer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eloadExamples\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eserialized\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eclearExisting\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTheo \u003ccode\u003eclearExisting\u003c/code\u003e flag ensures that the examples that \u003ccode\u003enewTransferRecognizer\u003c/code\u003e\nalready holds are preserved. If \u003ccode\u003etrue\u003c/code\u003e, the existing exampels will be cleared.\nIf \u003ccode\u003eclearExisting\u003c/code\u003e is not specified, it'll default to \u003ccode\u003efalse\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLive demo\u003c/h2\u003e\u003ca id=\"user-content-live-demo\" class=\"anchor\" aria-label=\"Permalink: Live demo\" href=\"#live-demo\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA developer-oriented live demo is available at\n\u003ca href=\"https://storage.googleapis.com/tfjs-speech-model-test/2019-01-03a/dist/index.html\" rel=\"nofollow\"\u003ethis address\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to run the demo from source code\u003c/h2\u003e\u003ca id=\"user-content-how-to-run-the-demo-from-source-code\" class=\"anchor\" aria-label=\"Permalink: How to run the demo from source code\" href=\"#how-to-run-the-demo-from-source-code\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe demo/ folder contains a live demo of the speech-command recognizer.\nTo run it, do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd speech-commands\nyarn\nyarn publish-local\ncd demo\nyarn\nyarn link-local\nyarn watch\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e speech-commands\nyarn\nyarn publish-local\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e demo\nyarn\nyarn link-local\nyarn watch\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Speech Command Recognizer","anchor":"speech-command-recognizer","htmlText":"Speech Command Recognizer"},{"level":2,"text":"API Usage","anchor":"api-usage","htmlText":"API Usage"},{"level":3,"text":"Online streaming recognition","anchor":"online-streaming-recognition","htmlText":"Online streaming recognition"},{"level":4,"text":"Vocabularies","anchor":"vocabularies","htmlText":"Vocabularies"},{"level":4,"text":"Parameters for online streaming recognition","anchor":"parameters-for-online-streaming-recognition","htmlText":"Parameters for online streaming recognition"},{"level":3,"text":"Offline recognition","anchor":"offline-recognition","htmlText":"Offline recognition"},{"level":3,"text":"Preloading model","anchor":"preloading-model","htmlText":"Preloading model"},{"level":3,"text":"Transfer learning","anchor":"transfer-learning","htmlText":"Transfer learning"},{"level":3,"text":"Serialize examples from a transfer recognizer.","anchor":"serialize-examples-from-a-transfer-recognizer","htmlText":"Serialize examples from a transfer recognizer."},{"level":2,"text":"Live demo","anchor":"live-demo","htmlText":"Live demo"},{"level":2,"text":"How to run the demo from source code","anchor":"how-to-run-the-demo-from-source-code","htmlText":"How to run the demo from source code"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ftensorflow%2Ftfjs-models%2Ftree%2Fmaster%2Fspeech-commands"}},"totalCount":15,"showBranchInfobar":false},"fileTree":{"":{"items":[{"name":".tslint","path":".tslint","contentType":"directory"},{"name":".vscode","path":".vscode","contentType":"directory"},{"name":"blazeface","path":"blazeface","contentType":"directory"},{"name":"body-pix","path":"body-pix","contentType":"directory"},{"name":"body-segmentation","path":"body-segmentation","contentType":"directory"},{"name":"coco-ssd","path":"coco-ssd","contentType":"directory"},{"name":"deeplab","path":"deeplab","contentType":"directory"},{"name":"depth-estimation","path":"depth-estimation","contentType":"directory"},{"name":"face-detection","path":"face-detection","contentType":"directory"},{"name":"face-landmarks-detection","path":"face-landmarks-detection","contentType":"directory"},{"name":"gpt2","path":"gpt2","contentType":"directory"},{"name":"hand-pose-detection","path":"hand-pose-detection","contentType":"directory"},{"name":"handpose","path":"handpose","contentType":"directory"},{"name":"knn-classifier","path":"knn-classifier","contentType":"directory"},{"name":"mobilenet","path":"mobilenet","contentType":"directory"},{"name":"model-playground","path":"model-playground","contentType":"directory"},{"name":"pose-detection","path":"pose-detection","contentType":"directory"},{"name":"posenet","path":"posenet","contentType":"directory"},{"name":"qna","path":"qna","contentType":"directory"},{"name":"scripts","path":"scripts","contentType":"directory"},{"name":"shared","path":"shared","contentType":"directory"},{"name":"speech-commands","path":"speech-commands","contentType":"directory"},{"name":"tasks","path":"tasks","contentType":"directory"},{"name":"tools","path":"tools","contentType":"directory"},{"name":"toxicity","path":"toxicity","contentType":"directory"},{"name":"universal-sentence-encoder","path":"universal-sentence-encoder","contentType":"directory"},{"name":".gitignore","path":".gitignore","contentType":"file"},{"name":"CONTRIBUTING.md","path":"CONTRIBUTING.md","contentType":"file"},{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"cloudbuild.yml","path":"cloudbuild.yml","contentType":"file"},{"name":"package.json","path":"package.json","contentType":"file"},{"name":"presubmit.ts","path":"presubmit.ts","contentType":"file"},{"name":"run_python_tests.sh","path":"run_python_tests.sh","contentType":"file"},{"name":"test_util.ts","path":"test_util.ts","contentType":"file"},{"name":"tsconfig.json","path":"tsconfig.json","contentType":"file"},{"name":"tsconfig.test.json","path":"tsconfig.test.json","contentType":"file"},{"name":"tslint.json","path":"tslint.json","contentType":"file"},{"name":"update_yarn_lock.sh","path":"update_yarn_lock.sh","contentType":"file"},{"name":"workspace.code-workspace","path":"workspace.code-workspace","contentType":"file"},{"name":"yarn.lock","path":"yarn.lock","contentType":"file"}],"totalCount":41}},"fileTreeProcessingTime":4.29501,"foldersToFetch":[],"treeExpanded":true,"symbolsExpanded":false,"csrf_tokens":{"/tensorflow/tfjs-models/branches":{"post":"I-5M4HYaeoiqToieCpEMgz75NgzBUM5SZdoYUNuQsTcWSvgof4a5zvx7LZlF_qB_BUoNVDlurNKfF4bZKXmSJA"},"/tensorflow/tfjs-models/branches/fetch_and_merge/master":{"post":"HgLguEWSKZmSvEER0cBcgrJJVCXIHHNhB5aiW_Pbc34kL9hP8q2b9vEd2aewyjetB_G2WEKDFoboGgnWc-vg2g"},"/tensorflow/tfjs-models/branches/fetch_and_merge/master?discard_changes=true":{"post":"mpuw2kOBWeD73FJZshX_p5i9pjN6zsoKDroki5opdOegtogt9L7rj5h9yu_TH5SILQVETvBRr-3hNo8GGhnnQw"}}},"title":"tfjs-models/speech-commands at master · tensorflow/tfjs-models","appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}</script> <div data-target="react-app.reactRoot"></div> </react-app> </turbo-frame> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex flex-items-center flex-shrink-0 mx-2"> <a aria-label="Homepage" title="GitHub" 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data-analytics-event="{&quot;location&quot;:&quot;footer&quot;,&quot;action&quot;:&quot;dont_share_info&quot;,&quot;context&quot;:&quot;subfooter&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;dont_share_info_link_subfooter_footer&quot;}" > Do not share my personal information </button> </cookie-consent-link> </li> </ul> </nav> </div> </footer> <ghcc-consent id="ghcc" class="position-fixed bottom-0 left-0" style="z-index: 999999" data-initial-cookie-consent-allowed="" data-cookie-consent-required="false"></ghcc-consent> <div id="ajax-error-message" class="ajax-error-message flash flash-error" hidden> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>

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