Multimodal Browser AI with Transformers.js for Images and Speech
Most browser AI tutorials cover text because it is a natural starting point, but the applications people actually want to build are rarely text-only. In reality, we live in a world where images, speech, and other forms of data are just as important as text. This is where multimodal browser AI comes in – the ability to develop AI applications that can process and understand multiple forms of data, including images and speech.
In this article, we’ll explore how to build a multimodal browser AI using Transformers.js, a popular JavaScript library for natural language processing (NLP) and computer vision tasks. We’ll cover the basics of Transformers.js, how to use it for image and speech processing, and provide examples of how to integrate these capabilities into a single application.
Introduction to Transformers.js
Transformers.js is a JavaScript library that provides a wide range of pre-trained models for NLP and computer vision tasks. The library is built on top of the popular TensorFlow.js library and provides a simple, easy-to-use API for loading and using pre-trained models in the browser.
Transformers.js supports a wide range of models, including BERT, RoBERTa, and ViT, among others. These models can be used for a variety of tasks, including text classification, sentiment analysis, image classification, and object detection.
Multimodal Processing with Transformers.js
To build a multimodal browser AI, we need to be able to process and understand multiple forms of data, including images and speech. Transformers.js provides several models that can be used for image and speech processing, including:
- ViT (Vision Transformer): A pre-trained model for image classification and object detection tasks.
- Wav2Vec 2.0: A pre-trained model for speech recognition and audio classification tasks.
These models can be used together to build a multimodal browser AI that can process and understand both images and speech.
Image Processing with Transformers.js
To use Transformers.js for image processing, we need to load a pre-trained model and pass the image data to the model for processing. Here’s an example of how to use the ViT model for image classification:
<script>
import { loadModel } from "@tensorflowjs/tfjs";
import { VitModel } from "@tensorflowjs/tfjs-models/vit";
// Load the ViT model
const model = await loadModel('https://storage.googleapis.com/tfjs-models/tfjs/vit/model.json');
// Load the image
const img = document.getElementById('image');
// Pre-process the image
const tensor = tf.browser.fromPixels(img);
const resizedTensor = tf.image.resizeBilinear(tensor, [224, 224]);
const normalizedTensor = resizedTensor.toFloat().div(255);
// Run the model on the image
const predictions = await model.predict(normalizedTensor);
// Get the top prediction
const topPrediction = predictions.argMax(-1);
// Log the result
console.log(topPrediction);
</script>
This code loads the ViT model, loads an image from the page, pre-processes the image, runs the model on the image, and logs the top prediction.
Speech Processing with Transformers.js
To use Transformers.js for speech processing, we need to load a pre-trained model and pass the audio data to the model for processing. Here’s an example of how to use the Wav2Vec 2.0 model for speech recognition:
<script>
import { loadModel } from "@tensorflowjs/tfjs";
import { Wav2Vec2Model } from "@tensorflowjs/tfjs-models/wav2vec2";
// Load the Wav2Vec 2.0 model
const model = await loadModel('https://storage.googleapis.com/tfjs-models/tfjs/wav2vec2/model.json');
// Get the audio context
const audioContext = new AudioContext();
// Create a media stream source
const source = audioContext.createMediaStreamSource(stream);
// Create a gain node
const gainNode = audioContext.createGain();
// Connect the source to the gain node
source.connect(gainNode);
// Connect the gain node to the destination
gainNode.connect(audioContext.destination);
// Pre-process the audio
const audioTensor = tf.tensor3d(stream.getAudioData(), [stream.duration, 16000, 1]);
// Run the model on the audio
const predictions = await model.predict(audioTensor);
// Get the top prediction
const topPrediction = predictions.argMax(-1);
// Log the result
console.log(topPrediction);
</script>
This code loads the Wav2Vec 2.0 model, gets the audio context, creates a media stream source, pre-processes the audio, runs the model on the audio, and logs the top prediction.
Integrating Image and Speech Processing
To build a multimodal browser AI, we need to integrate the image and speech processing capabilities. Here’s an example of how to use both models together:
<script>
import { loadModel } from "@tensorflowjs/tfjs";
import { VitModel } from "@tensorflowjs/tfjs-models/vit";
import { Wav2Vec2Model } from "@tensorflowjs/tfjs-models/wav2vec2";
// Load the ViT model
const vitModel = await loadModel('https://storage.googleapis.com/tfjs-models/tfjs/vit/model.json');
// Load the Wav2Vec 2.0 model
const wav2Vec2Model = await loadModel('https://storage.googleapis.com/tfjs-models/tfjs/wav2vec2/model.json');
// Load the image
const img = document.getElementById('image');
// Pre-process the image
const tensor = tf.browser.fromPixels(img);
const resizedTensor = tf.image.resizeBilinear(tensor, [224, 224]);
const normalizedTensor = resizedTensor.toFloat().div(255);
// Run the ViT model on the image
const imagePredictions = await vitModel.predict(normalizedTensor);
// Get the audio context
const audioContext = new AudioContext();
// Create a media stream source
const source = audioContext.createMediaStreamSource(stream);
// Create a gain node
const gainNode = audioContext.createGain();
// Connect the source to the gain node
source.connect(gainNode);
// Connect the gain node to the destination
gainNode.connect(audioContext.destination);
// Pre-process the audio
const audioTensor = tf.tensor3d(stream.getAudioData(), [stream.duration, 16000, 1]);
// Run the Wav2Vec 2.0 model on the audio
const audioPredictions = await wav2Vec2Model.predict(audioTensor);
// Get the top predictions
const topImagePrediction = imagePredictions.argMax(-1);
const topAudioPrediction = audioPredictions.argMax(-1);
// Log the results
console.log(topImagePrediction);
console.log(topAudioPrediction);
</script>
This code loads both models, loads the image and audio, pre-processes the data, runs the models, and logs the top predictions.
Conclusion
In this article, we’ve explored how to build a multimodal browser AI using Transformers.js. We’ve covered the basics of Transformers.js, how to use it for image and speech processing, and provided examples of how to integrate these capabilities into a single application. By using both image and speech processing, we can build more powerful and robust AI applications that can understand and process multiple forms of data.
Multimodal browser AI has a wide range of applications, including:
- Virtual assistants: Multimodal browser AI can be used to build virtual assistants that can understand and respond to voice commands and visual input.
- Image and speech recognition: Multimodal browser AI can be used to build applications that can recognize and classify images and audio.
- Healthcare: Multimodal browser AI can be used to build applications that can analyze medical images and audio data to diagnose and treat diseases.
By using Transformers.js and the examples provided in this article, you can start building your own multimodal browser AI applications today.
