- This application depicts an offline speech to text engine that consists of a limited set of vocabulary
- The speech recognition is done by a trained model.
- I used tensorflowjs to achieve this
- In particular, I followed this tutorial for the speech recognition implementation
- Upon implementation of the speech recognition, the offline capabilities are enabled by deploying the web app as a PWA
- All required static resources are cached
- I used a known caching strategy to achieve this.
- Clone repo
- Change logo if need be
- npm install serve OR npm install http-server
- This is to serve static files from a given directory
- Going forward, I have plans to do either of the following:
- Extrapolate from this initial implementation and train my own model with a larger set of vocabulary
- I will use either my own data or data acquired from the Common Voice Project
- Extrapolate from this implementation and train my own model using data acquired from Common Voice Project with hopes of full English speech recognition
- Extrapolate from this initial implementation and train my own model with a larger set of vocabulary
- My knowledge in both ML and PWA deployment is beginner at best. Hence, the project is based heavily on a variety of resources.
- Implementation has not been thoroughly vetted and is meant to serve as a proof-of-concept
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