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recognition based on a WASM build of the Vosk library","homepage":"https://github.com/ccoreilly/vosk-browser#readme","keywords":["kaldi","vosk","wasm","asr","stt"],"repository":{"type":"git","url":"git://github.com/ccoreilly/vosk-browser.git"},"author":{"name":"Ciaran O'Reilly","email":"ciaran@oreilly.cat"},"bugs":{"url":"https://github.com/ccoreilly/vosk-browser/issues"},"license":"Apache-2.0","readme":"# Vosk-Browser\nA somewhat opinionated speech recognition library for the browser using a WebAssembly build of [Vosk](https://github.com/alphacep/vosk-api)\n\n## Live Demo\n\nCheckout the demo running in-browser speech recognition of microphone input or audio files in 13 languages.\n\n[Vosk-Browser Live Demo](https://ccoreilly.github.io/vosk-browser/)\n\n## Installation\n\nYou can install vosk-browser as a module:\n\n```\n$ npm i vosk-browser\n```\n\nYou can also use a CDN like jsdelivr to add the library to your page, which will be accessible via the global variable `Vosk`:\n\n```\n<script type=\"application/javascript\" src=\"https://cdn.jsdelivr.net/npm/vosk-browser@0.0.3/dist/vosk.js\"></script>\n```\n\n## Basic example\n\nOne of the simplest examples that assumes `vosk-browser` is loaded via a `script` tag. It loads the model named `model.tar.gz`located in the same path as the script and starts listening to the microphone. Recognition results are logged to the console.\n\n```typescript\nasync function init() {\n    const model = await Vosk.createModel('model.tar.gz');\n\n    const recognizer = new model.KaldiRecognizer();\n    recognizer.on(\"result\", (message) => {\n        console.log(`Result: ${message.result.text}`);\n    });\n    recognizer.on(\"partialresult\", (message) => {\n        console.log(`Partial result: ${message.result.partial}`);\n    });\n    \n    const mediaStream = await navigator.mediaDevices.getUserMedia({\n        video: false,\n        audio: {\n            echoCancellation: true,\n            noiseSuppression: true,\n            channelCount: 1,\n            sampleRate: 16000\n        },\n    });\n    \n    const audioContext = new AudioContext();\n    const recognizerNode = audioContext.createScriptProcessor(4096, 1, 1)\n    recognizerNode.onaudioprocess = (event) => {\n        try {\n            recognizer.acceptWaveform(event.inputBuffer)\n        } catch (error) {\n            console.error('acceptWaveform failed', error)\n        }\n    }\n    const source = audioContext.createMediaStreamSource(mediaStream);\n    source.connect(recognizerNode);\n}\n\nwindow.onload = init;\n```\n## Model format\n\nThe library loads models as gzipped tar archives of a model folder following the model structure expected by Vosk.\n\n- model/am/final.mdl - acoustic model\n- model/conf/mfcc.conf - mfcc config file. Make sure you take mfcc_hires.conf version if you are using hires model (most external ones)\n- model/conf/model.conf - provide default decoding beams and silence phones. you have to create this file yourself, it is not present in kaldi model\n- model/ivector/final.dubm - take ivector files from ivector extractor (optional folder if the model is trained with ivectors)\n- model/ivector/final.ie\n- model/ivector/final.mat\n- model/ivector/splice.conf\n- model/ivector/global_cmvn.stats\n- model/ivector/online_cmvn.conf\n- model/graph/phones/word_boundary.int - from the graph\n- model/graph/HCLG.fst - this is the decoding graph, if you are not using lookahead\n- model/graph/HCLr.fst - use Gr.fst and HCLr.fst instead of one big HCLG.fst if you want to run rescoring\n- model/graph/Gr.fst\n- model/graph/phones.txt - from the graph\n- model/graph/words.txt - from the graph\n- model/rescore/G.carpa - carpa rescoring is optional but helpful in big models. Usually located inside data/lang_test_rescore\n- model/rescore/G.fst - also optional if you want to use rescoring\n\n# Reference\n\n## Model\n\n```typescript\nnew Model(modelUrl: string): Model\n```\n\nThe Model constructor. Creates a new Model that will spawn a new Web Worker in the background. `modelUrl` can be a relative or absolute URL but be aware that CORS also applies to Workers. As Model initialization can take a while depending on the model size, you should either wait for the `load` event or use the `createModel` function.\n\n### Model#ready\n\n```typescript\nready: boolean;\n```\n\nA property that specifies if the model is loaded and ready.\n\n### Model#KaldiRecognizer\n\n```typescript\nnew model.KaldiRecognizer(): KaldiRecognizer\n```\n\nCreates a new KaldiRecognizer. Multiple recognizers can be created from the same model\n\n### Model#setLogLevel\n\n```typescript\nsetLogLevel(level: number): void\n```\n\nSets the log level:\n-2: Error\n-1: Warning\n0: Info\n1: Verbose\n2: More verbose\n3: Debug\n\n### Model#terminate\n\n```typescript\nterminate(): void\n```\n\nWill terminate the Web Worker as well as free all allocated memory. Make sure to call this method when you are done\nusing a model in order to avoid memory leaks. The method will also remove all existing KaldiRecognizer instances.\n\n### Events\n\nA Model will also emit some useful events:\n\n```typescript\n.on('load', function(message: {\n    event: \"load\";\n    result: boolean;\n}) {\n    // If the model load was successful or not\n})\n\n.on('error', function(message: {\n    event: \"error\";\n    error: string;\n}) {\n    // An error occured\n})\n\n```\n\n## KaldiRecognizer\n\n### KaldiRecognizer#id\n\n```typescript\nid: string;\n```\n\nA unique ID for this KaldiRecognizer instance, passed in every event as `recognizerId`\n\n### KaldiRecognizer#acceptWaveform\n\n```typescript\nacceptWaveform(buffer: AudioBuffer): void;\n```\n\nPreprocesses AudioBuffers and transfers them to the Web Worker for inference.\n\n### KaldiRecognizer#setWords\n\n```typescript\nsetWords(words: boolean): void;\n```\n\nReturn word timestamps in the recognition message\n\n### KaldiRecognizer#remove\n\n```typescript\nremove(): void\n```\n\nWill remove a recognizer from the model and free all allocated memory. Make sure to call this method when you are done using a recognizer in order to avoid memory leaks.\n\n### Events\n\nIn order to obtain recognition results you should listen to the events of the KaldiRecognizer:\n\n```js\n.on('result', function(message: {\n    event: \"result\";\n    recognizerId: string;\n    result: {\n        result: Array<{\n            conf: number;\n            start: number;\n            end: number;\n            word: string;\n        }>;\n        text: string;\n    };\n}) {\n    // A final recognition result\n})\n\n.on('partialresult', function(message: {\n    event: \"partialresult\";\n    recognizerId: string;\n    result: {\n        partial: string;\n    };\n}) {\n    // A partial recognition result\n})\n\n```","readmeFilename":"README.md"}