Lark API Readme

What is it?

• Lark API is a speech assessment REST API built using NextJS in Typescript.
• It provides accuracy scores, speech to text transcription, and the projected IELTS pronunciation band.
• It allows English learning apps and websites to assess and provide real-time feedback on the users’ pronunciation.

How does it work?

The Machine Learning part:

• Lark utilizes the Wav2Vec2 model from Meta for analyzing the speech sample.
• It converts the speech to it’s phonetic transcription (S2P) using zero-shot cross-lingual recognition.
• After recognizing the phonetics of the speech, it compares it with the ideal pronunciation of the transcribed speech using the Jaro-Winkler string similarity algorithm.

The Backend API part:

• The API is written completely in NextJS using next-pages routing.
• I have used next-auth for user authentication via GitHub and maintaining/persisting sessions.
• I used Redis for rate-limiting the API based on the IP of the call.

The Frontend part:

• The Frontend is written using NextJS in Typescript.
• I opted for TailwindCSS as the CSS framework for this project.
• For the tables and icons, Material UI has been used.

The Database part:

• I used Prisma ORM on top of a PlanetScale database which is a serverless MySQL DB.
• Here is the UML Diagram for the database:

ML Models used

• facebook/wav2vec2-xlsr-53-espeak-cv-ft
• facebook/wav2vec2-base-960h

References

• [2109.11680] Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (arxiv.org)
• luozhouyang/python-string-similarity (github.com)
• Hugging Face JS libraries