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Muse (Project ARS)

We got second prize in the assembly AI hackathon: https://www.assemblyai.com/blog/top-speech-ai-projects-and-winners-at-2024-assemblyai-hackathon/

Problem Statement:

In today's fast-paced world, understanding and improving emotional well-being has become a critical necessity. However, many individuals struggle to find effective and accessible ways to track their emotions, manage stress, and foster mindfulness. A lack of personalized insights and actionable recommendations often hinders their journey toward emotional resilience and mental well-being.

Solution:

Muse - Emotional Well-Being Audio Journaling Application Our application empowers users to take control of their emotional health by enabling them to record audio journals and track their daily emotions. With the integration of advanced technologies, we aim to provide a seamless, insightful, and personalized experience for emotional well-being management.

Project Workflow:

Step 1: User records/uploads audio journal for the day

Step 2: Transcribe the audio to text, send to lemur LLM and find the mood of the user

Step 3: Provide recommendations and tasks to make the user feel better and keep track of the mood over a course of time

Key Features:

  • Audio Journaling: Users can record their thoughts and emotions through voice, making it a convenient and therapeutic way to document their mental state.

  • Emotion Tracking: Keep a record of daily emotional patterns and progress over time.

  • Personalized Insights and Recommendations: Using AssemblyAI's advanced transcription and Lemur LLM capabilities, the application provides tailored suggestions to help users:

  • Manage stress effectively

  • Enhance mindfulness practices

  • Build emotional resilience

Technology Stack:

  • AssemblyAI Audio-to-Text Transcriber: Converts users' audio journals into text for analysis.

  • Lemur LLM by AssemblyAI: Delivers personalized recommendations based on the transcribed content.

  • Machine Learning Integration: Continuously trains the model to align with users' needs, improving interaction quality and adapting recommendations over time.

Screen.Recording.2024-12-06.at.2.23.24.PM.copy.mp4

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