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It directly indexes speech without relying on a text representation. This acoustic-only approach boosts accuracy by removing out-of-vocabulary limitation. All processing is done via WebAssembly and Workers in a separate thread.\n\n## Compatibility\n\n- Chrome / Edge\n- Firefox\n- Safari\n\nThis library requires several modern browser features: WebAssembly, Web Workers, and promises. Internet Explorer will _not_ work.\n\n## AccessKey\n\nOctopus requires a valid Picovoice `AccessKey` at initialization. `AccessKey` acts as your credentials when using Octopus SDKs.\nYou can get your `AccessKey` for free. Make sure to keep your `AccessKey` secret.\nSignup or Login to [Picovoice Console](https://console.picovoice.ai/) to get your `AccessKey`.\n\n## Packages\n\nThe Octopus SDK for Web is split into separate worker and factory packages; import each as required.\n\n### Workers \n\nFor typical cases, use the worker package. The worker package creates complete `OctopusWorker` instances that can be immediately used.\n\n* [@picovoice/octopus-web-en-worker](https://www.npmjs.com/package/@picovoice/octopus-web-en-worker)\n\n### Factories\n\nFactory packages allow you to create instances of `Octopus` directly. Useful for building your own custom Worker/Worklet, or some other bespoke purpose.\n\n* [@picovoice/octopus-web-en-factory](https://www.npmjs.com/package/@picovoice/octopus-web-en-factory)\n\n## Installation & Usage\n\n### Worker\n\nTo obtain an `OctopusWorker`, we can use the static `create` factory method from the OctopusWorkerFactory. Here is a complete example that:\n\n1. Obtains an `OctopusWorker` from the `OctopusWorkerFactory`\n2. Passes a standard audio stream to be indexed and stores the result in `octopusMetadata` object. The audio should be in the voice recognition standard format (16-bit 16kHz linear PCM, single-channel)\n3. Searches a phrase and receives the occurrences time if there are any matches with their probabilities\n\n\nE.g.:\n\n```console\nyarn add @picovoice/octopus-web-en-worker\n```\n\n```javascript\nimport { OctopusWebEnWorker } from \"@picovoice/octopus-web-en-worker\";\n\n// The metadata object to save the result of indexing for later searches\nlet octopusMetadata = undefined;\n\nfunction octopusIndexCallback(metadata) {\n  octopusMetadata = metadata;\n}\n\nfunction octopusSearchCallback(matches) {\n  console.log(`Search results (${matches.length}):`);\n  console.log(\n    `Start: ${match.startSec}s -> End: ${match.endSec}s (Probability: ${match.probability})`\n  );\n}\n\nasync function startOctopus() {\n  // Create an Octopus Worker\n  // Note: you receive a Worker object, _not_ an individual Octopus instance\n  const accessKey = ... // .. AccessKey string provided by Picovoice Console (https://console.picovoice.ai/)\n  const OctopusWorker = await OctopusWorkerFactory.create(\n    accessKey,\n    octopusIndexCallback,\n    octopusSearchCallback\n  );\n}\n\nstartOctopus();\n\n// Send Octopus the audio signal\nconst audioSignal = new Int16Array(/* Provide data with correct format*/);\nOctopusWorker.postMessage({\n  command: \"index\",\n  input: audioSignal,\n});\n\n...\n\nconst searchText = ...;\nOctopusWorker.postMessage({\n  command: \"search\",\n  metadata: octopusMetadata,\n  searchPhrase: searchText,\n});\n\n...\n\n// Finished with Octopus? Release the worker.\nif (done) {\n  OctopusWorker.sendMessage({ command: \"release\" });\n}\n\n```\n**Important Note**: Because the workers are all-in-one packages that run an entire machine learning inference model in WebAssembly, they are approximately 9MB in size. While this is tiny for a speech recognition model, it's large for web delivery. Because of this, you likely will want to use dynamic `import()` instead of static `import {}` to reduce your app's starting bundle size. See e.g. https://webpack.js.org/guides/code-splitting/ for more information.\n\n### Factory\n\nIf you wish to build your own worker, or perhaps not use workers at all, use the factory packages. This will let you instantiate Octopus engine instances directly.\n\nE.g.:\n\n```javascript\nimport { Octopus } from \"@picovoice/octopus-web-en-factory\";\n\nasync function startOctopus() {\n  const accessKey = \"\"; // .. AccessKey string provided by Picovoice Console (https://console.picovoice.ai/)\n  const handle = await Octopus.create(accessKey);\n}\n\nstartOctopus();\n\n...\n\n// Send Octopus the audio signal\nconst audioSignal = new Int16Array(/* Provide data with correct format*/);\nlet octopusMetadata = await handle.index(audioSignal);\n\n...\n\nconst searchText = \"\";\nlet octopusMatches = await handle.search(octopusMetadata, searchText);\nconsole.log(`Search results (${octopusMatches.length}):`);\nconsole.log(\n  `Start: ${octopusMatches.startSec}s -> End: ${octopusMatches.endSec}s (Probability: ${octopusMatches.probability})`\n);\n\n```\n**Important Note**: Because the workers are all-in-one packages that run an entire machine learning inference model in WebAssembly, they are approximately 9MB in size. While this is tiny for a speech recognition model, it's large for web delivery. Because of this, you likely will want to use dynamic `import()` instead of static `import {}` to reduce your app's starting bundle size. See e.g. https://webpack.js.org/guides/code-splitting/ for more information.\n\n## Build from source (IIFE + ESM outputs)\n\nThis library uses Rollup and TypeScript along with Babel and other popular rollup plugins. There are two outputs: an IIFE version intended for script tags / CDN usage, and a JavaScript module version intended for use with modern JavaScript/TypeScript development (e.g. Angular, Create React App, Webpack).\n\n```console\nyarn\nyarn build\n```\n\nThe output will appear in the ./dist/ folder.\n\nFor example usage refer to the [web demo](/demo/web/)\n","readmeFilename":"README.md"}