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Speech Command Recognizer\n\nThe 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\".\n\nIt uses the web browser's\n[WebAudio API](https://developer.mozilla.org/en-US/docs/Web/API/Web_Audio_API).\nIt is built on top of [TensorFlow.js](https://js.tensorflow.org) and can\nperform inference and transfer learning entirely in the browser, using\nWebGL GPU acceleration.\n\nThe underlying deep neural network has been trained using the\n[TensorFlow Speech Commands Dataset](https://www.tensorflow.org/tutorials/sequences/audio_recognition).\n\nFor more details on the data set, see:\n\nWarden, P. (2018) \"Speech commands: A dataset for limited-vocabulary\nspeech recognition\" https://arxiv.org/pdf/1804.03209.pdf\n\n## API Usage\n\nA speech command recognizer can be used in two ways:\n\n1. **Online streaming recognition**, during which the library automatically\n   opens an audio input channel using the browser's\n   [`getUserMedia`](https://developer.mozilla.org/en-US/docs/Web/API/MediaDevices/getUserMedia)\n   and\n   [WebAudio](https://developer.mozilla.org/en-US/docs/Web/API/Web_Audio_API)\n   APIs (requesting permission from user) and performs real-time recognition on\n   the audio input.\n2. **Offline recognition**, in which you provide a pre-constructed TensorFlow.js\n   [Tensor](https://js.tensorflow.org/api/latest/#tensor) object or a\n   `Float32Array` and the recognizer will return the recognition results.\n\n### Online streaming recognition\n\nTo use the speech-command recognizer, first create a recognizer instance,\nthen start the streaming recognition by calling its `listen()` method.\n\n```js\nconst 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 => {\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(() => recognizer.stopListening(), 10e3);\n```\n\n#### Vocabularies\n\nWhen calling `speechCommands.create()`, you can specify the vocabulary\nthe loaded model will be able to recognize. This is specified as the second,\noptional argument to `speechCommands.create()`. For example:\n\n```js\nconst recognizer = speechCommands.create('BROWSER_FFT', 'directional4w');\n```\n\nCurrently, the supported vocabularies are:\n - '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 '_background_noise_' and '_unknown_'.\n - 'directional4w': The four directional words: 'up', 'down', 'left', and\n   'right', in addition to '_background_noise_' and '_unknown_'.\n\n'18w' is the default vocabulary.\n\n#### Parameters for online streaming recognition\n\nAs the example above shows, you can specify optional parameters when calling\n`listen()`. The supported parameters are:\n\n* `overlapFactor`: Controls how often the recognizer performs prediction on\n  spectrograms. Must be >=0 and <1 (default: 0.5). For example,\n  if each spectrogram is 1000 ms long and `overlapFactor` is set to 0.25,\n  the prediction will happen every 250 ms.\n* `includeSpectrogram`: Let the callback function be invoked with the\n  spectrogram data included in the argument. Default: `false`.\n* `probabilityThreshold`: The callback function will be invoked if and only if\n  the maximum probability score of all the words is greater than this threshold.\n  Default: `0`.\n* `invokeCallbackOnNoiseAndUnknown`: Whether the callback function will be\n  invoked if the \"word\" with the maximum probability score is the \"unknown\"\n  or \"background noise\" token. Default: `false`.\n* `includeEmbedding`: Whether an internal activation from the underlying model\n  will be included in the callback argument, in addition to the probability\n  scores. Note: if this field is set as `true`, the value of\n  `invokeCallbackOnNoiseAndUnknown` will be overridden to `true` and the\n  value of `probabilityThreshold` will be overridden to `0`.\n\n### Offline recognition\n\nTo 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 `tf.Tensor` object. Note that the\nshape of the Tensor must match the expectation of the recognizer instance.\nE.g.,\n\n```js\nconst 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]);\n```\n\nNote that you must provide a spectrogram value to the `recognize()` call\nin order to perform the offline recognition. If `recognize()` is called\nwithout a first argument, it will perform one-shot online recognition\nby collecting a frame of audio via WebAudio.\n\n### Preloading model\n\nBy default, a recognizer object will load the underlying\ntf.Model via HTTP requests to a centralized location, when its\n`listen()` or `recognize()` 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 `ensureModelLoaded()` method of the\nrecognizer object. The `ensureModelLoaded()` 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.\n\n### Transfer learning\n\n**Transfer learning** 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.\n\nThe 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\n\n```js\nconst 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// trasnfer-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) => {\n      console.log(`Epoch ${epoch}: loss=${logs.loss}, accuracy=${logs.acc}`);\n    }\n  }\n});\n\n// After the transfer learning completes, you can start online streaming\n// recognition using the new model.\nawait transferRecognizer.listen(result => {\n  // - result.scores contains the scores for the new vocabulary, which\n  //   can be checked with:\n  const words = transferRecognizer.wordLabels();\n  // `result.scores` contains the scores for the new words, not the original\n  // words.\n  for (let i = 0; i < words; ++i) {\n    console.log(`score for word '${words[i]}' = ${result.scores[i]}`);\n  }\n}, {probabilityThreshold: 0.75});\n\n// Stop the recognition in 10 seconds.\nsetTimeout(() => transferRecognizer.stopListening(), 10e3);\n```\n\n### Serialize examples from a transfer recognizer.\n\nOnce examples has been collected with a transfer recognizer,\nyou can export the examples in serialized form with the `serielizedExamples()`\nmethod, e.g.,\n\n```js\nconst serialized = transferRecognizer.serializeExamples();\n```\n\n`serialized` is a binary `ArrayBuffer` amenable to storage and transmission.\nIt contains the spectrogram data of the examples, as well as metadata such\nas word labels.\n\nYou can also serialize the examples from a subset of the words in the\ntransfer recognizer's vocabulary, e.g.,\n\n```js\nconst serializedWithOnlyFoo = transferRecognizer.serializeExamples('foo');\n// Or\nconst serializedWithOnlyFooAndBar = transferRecognizer.serializeExamples(['foo', 'bar']);\n```\n\nThe serialized examples can later be loaded into another instance of\ntransfer recognizer with the `loadExamples()` method, e.g.,\n\n```js\nconst clearExisting = false;\nnewTransferRecognizer.loadExamples(serialized, clearExisting);\n```\n\nTheo `clearExisting` flag ensures that the examples that `newTransferRecognizer`\nalready holds are preserved. If `true`, the existing exampels will be cleared.\nIf `clearExisting` is not specified, it'll default to `false`.\n\n## Live demo\n\nA developer-oriented live demo is available at\n[this address](https://storage.googleapis.com/tfjs-speech-model-test/2019-01-03a/dist/index.html).\n\n## How to run the demo from source code\n\nThe demo/ folder contains a live demo of the speech-command recognizer.\nTo run it, do\n\n```sh\ncd speech-commands\nyarn\nyarn publish-local\ncd demo\nyarn\nyarn link-local\nyarn watch\n```\n","_attachments":{},"homepage":"https://github.com/tensorflow/tfjs-models#readme","bugs":{"url":"https://github.com/tensorflow/tfjs-models/issues"},"license":"Apache-2.0"}