Building a speech-to-text application is an exciting way to explore the potential of modern web technologies, especially when combined with AI-powered models. In this blog, I will walk you through the steps of building a speech-to-text app using React and Transformers.js, leveraging the Whisper and Moonshine models. My demo is available at https://kitt.tools/ai/speech-to-text for you to try out.
Step 1: Setting Up React and Dependencies
To begin, you will need a React project. If you don't have one already, you can easily set up a new React app by running:
npx create-react-app speech-to-text
cd speech-to-text
Once your React environment is set up, you'll need to integrate the necessary dependencies, including Transformers.js and the models Whisper and Moonshine. Transformers.js allows you to run machine learning models directly in the browser, making it perfect for building lightweight, client-side AI applications.
Whisper, an automatic speech recognition (ASR) model from OpenAI, is known for its high accuracy in transcribing speech to text. Moonshine, a tool that enhances the quality of audio input, provides noise reduction and better handling of challenging audio environments, improving Whisper's transcription results.
Install the required dependencies with pnpm:
pnpm add @huggingface/transformers
Step 2: Integrating Whisper for Speech Recognition
The core of our speech-to-text functionality lies in Whisper. With its pre-trained model, Whisper can recognize speech in various languages and transcribe it into text. To use Whisper with Transformers.js, you'll integrate it into a React component that handles audio input.
Create an AudioRecorder component where users can start recording their speech. Use the navigator.mediaDevices.getUserMedia() API to capture audio from the user's microphone.
Once the audio is captured, send it to the Whisper model for transcription. Transformers.js makes it easy to interact with Whisper's model, requiring just a few lines of code. After processing, the transcribed text is displayed in your React app.
Step 3: Enhancing Audio with Moonshine
To ensure the best transcription accuracy, you can use Moonshine to enhance the raw audio input. Moonshine helps reduce background noise, making the speech clearer, which in turn improves the accuracy of Whisper's transcriptions.
In your app, you can apply Moonshine to the audio stream before passing it to Whisper. This enhancement is particularly useful in noisy environments, ensuring that Whisper can focus on the speech while filtering out unwanted sounds. Moonshine works seamlessly with Transformers.js, providing an easy integration.
Step 4: Implementing the Speech-to-Text Flow
Now that Whisper and Moonshine are integrated, build the main flow for your application:
- Capture audio: Use the microphone to capture user speech.
- Enhance audio: Apply Moonshine to the captured audio for noise reduction.
- Transcribe speech: Pass the processed audio to Whisper for transcription.
- Display transcription: Show the transcribed text in real-time on your React app.
React's state management will help you update the UI dynamically as the transcription process progresses.
Step 5: Testing and Deployment
After implementing the speech-to-text flow, thoroughly test your app with various types of audio inputs. Experiment with different environments to evaluate how well Moonshine handles background noise and how accurate Whisper’s transcription is. You can check out my live demo at this link.
Conclusion
Building a speech-to-text app with React and Transformers.js is an exciting way to combine cutting-edge AI with modern web technologies. By using Whisper for speech recognition and Moonshine for audio enhancement, you can create a powerful, client-side solution that transcribes speech to text in real-time, directly in the browser.
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