Voice Assistant for Fast Food restaurants using DistilBERT
Shailaja Nilesh Uke, Sagar Shahari, Revati Shimpi, Renuka Pawar, Rushabh Rode · 2024
In a time of accelerating technology development, the fast-food business is constantly looking for new ways to improve client experiences. To improve and streamline the client order-taking procedure, this research study offers a revolutionary voice-activated fast food ordering helper. We show a strong and effective voice assistant system that is capable of accurately interpreting and responding to consumer queries by using the capabilities of DistilBERT which is a BERT NLP model. This pre-trained model serves as the basis for the subsequent fine-tuning procedure, which improves performance by utilising domain-specific data from fast-food restaurant menus. User's responses and menus are stored in MongoDB which can be forwarded to the cooking section. The user's responses can be used for constantly training the voice assistant to better understand user intent. The Google-Ttsand STT library converts Text to speech and speech to text respectively. After fine-tuning to DistilBERT the model gave an accuracy of 95%.