A vocal pitch correction web application (like Autotune): https://alexcrist.github.io/autotone/

This project works using CREPE's pitch detection model and Stephan Bernsee's approach to Fourier-transform based pitch shifting:

• CREPE: A Convolutional REpresentation for Pitch Estimation -- pre-trained model (ICASSP 2018)
• Pitch Shifting Using The Fourier Transform by Stephan Bernsee

To perform vocal pitch correction, the input audio goes through two stages: pitch detection and pitch shifting.

To detect pitches, one of CREPE's pre-trained models is run using TensorFlowJS. To pitch shift, WebAssembly is used to run a C library that performs Fourier-based pitch shifting according to Stephan Bernsee's blog post above. Additionally, the audio processing algorithms are run in the background on web workers to prevent the browser from becoming unresponsive.

• Node v14.18.2
• NPM v6.14.15
• Emscripten v3.1.29

1. Clone this repo
2. Init the crepe submodule with git submodule init
3. Build the C pitch-shifting library to WebAssembly by entering the tuner directory and running make
4. Install the web dependencies by entering the react directory and running npm install
5. Start the development server by entering the react directory and running npm start
6. View the app at http://localhost:8080

Deploy the app to GitHub Pages using scripts/deploy.sh

• TensorFlow.js
• WebAssembly (Emscripten)
• Web audio API
• Web workers
• React
• Webpack

• Display a message if browser mic access is not granted
• Display a warning if sample rate is not a multiple of 16000
• Allow user to set pitch detection over-sampling factor
  • This will involve moving audio window/buffer building logic out of BufferNode/Processor and into Autotoner
• Add export functionality
• Add more types of scales
• Allow user to pitch correct according to a separate target audio or MIDI input