The problem aiRis solves

We are developing an Android-based VoiceBot that leverages LLMs and faster-whisper speech recognition to deliver real-time, emotionally intelligent, context-aware conversations in Indian English, providing personalized, seamless human-like interactions.

Challenges we ran into

Fine-Tuning LLaMA LLM for Conversational Accuracy: Adapting LLaMA to provide relevant, context-aware responses required extensive tuning to minimize hallucinations and ensure accuracy.

Managing Latency for Real-Time Interactions: Optimizing response times in the LLaMA model and minimizing delays was critical for a smooth, natural conversation flow.

Implementing High-Accuracy TTS: We chose Pyttsx3 for Text-to-Speech as it offered better accuracy and clearer pronunciation, enhancing the overall user experience.

Ensuring Context Awareness: Achieving consistent context retention in multi-turn conversations was a challenge, requiring careful management of conversation history.

Emotion Understanding for Personalized Responses: Analyzing user sentiment and adapting responses accordingly added complexity, but it was essential for creating an empathetic, engaging experience.

Building Personalized Conversations: Tailoring responses to the user's emotional tone and conversational style to create a sense of personalization presented unique challenges in natural language processing.