ORA (Audio Journal)

Transform your voice into meaningful insights with AI-powered transcription and emotion analysis

Features

• Audio Recording & Upload - Seamlessly record or upload audio files
• AI Transcription - Powered by OpenAI Whisper for accurate speech-to-text
• Emotion Analysis - Detect emotions in your journal entries using advanced NLP
• Journal Management - Organize entries into multiple journals
• Top Journals - Track your most active journals
• PDF Export - Export your thoughts as beautifully formatted PDFs
• Secure Authentication - Protected user sessions with Better Auth
• Cloud Storage - Reliable audio storage with AWS S3
• Privacy Controls - Private entries and journals by default

Architecture

The application follows a microservices architecture with clear separation of concerns:

• Frontend: Next.js with TypeScript for the user interface
• Backend API: Next.js API routes handling business logic
• ML Service: FastAPI service for audio processing and analysis
• Database: PostgreSQL with Prisma ORM and Prisma Accelerate
• Storage: AWS S3 for audio file storage
• Authentication: Better Auth for secure user management

Getting Started

Prerequisites

• Node.js 18+ and Yarn
• Python 3.8+
• PostgreSQL database (or Prisma Accelerate)
• AWS S3 bucket

Installation

1. Clone the Repository

git clone https://github.com/iaadi4/Ora.git
cd Ora

2. Frontend Setup

# Install dependencies
yarn install

# Set up environment variables
.env

Environment Variables:

# Authentication (Better Auth)
BETTER_AUTH_SECRET="your-secret-key"
BETTER_AUTH_URL="http://localhost:3000"

# Database (Prisma Accelerate)
DATABASE_URL="prisma+postgres://accelerate.prisma-data.net/?api_key=your-api-key"

# AWS S3
AWS_ACCESS_KEY_ID="your-access-key"
AWS_SECRET_ACCESS_KEY="your-secret-key"
AWS_BUCKET_NAME="your-bucket-name"
BUCKET_REGION="eu-north-1"

3. Database Setup

# Generate Prisma client
yarn prisma generate

# Run migrations (if using direct database connection)
yarn prisma migrate dev

4. Whisper Server Setup

# Navigate to whisper-server directory
cd whisper-server

# Install dependencies
pip install -r requirements.txt

# Activate virtual environment
source .venv/bin/activate

# Start the service
uvicorn main:app --host 0.0.0.0 --port 8000

5. Start the Application

# Start Next.js development server
yarn dev

The application will be available at http://localhost:3000

Project Structure

audio-journal/
├── app/
│   ├── (pages)/
│   ├── api/
│   │   ├── auth/[...all]/
│   │   ├── export-pdf/
│   │   ├── journals/
│   │   ├── records/
│   │   └── transcribe/
│   ├── generated/
│   │   └── prisma/           # Generated Prisma client
│   ├── globals.css
│   ├── layout.tsx
│   └── page.tsx
├── components/
├── helper/
├── lib/
├── prisma/
│   └── schema.prisma
├── public/
├── whisper-server/
│   ├── __pycache__/
│   ├── .venv/
│   ├── main.py
│   └── requirements.txt
└── [configuration files]

AI Models

Speech-to-Text

• Model: OpenAI Whisper (tiny.en, you can change it)
• Features: Fast, english transcription
• Performance: Optimized for real-time processing

Emotion Analysis

• Model: j-hartmann/emotion-english-distilroberta-base
• Emotions: Joy, Sadness, Anger, Fear, Surprise, Disgust, Neutral
• Output: Confidence scores for each detected emotion

📊 Database Schema

model User {
  id            String    @id
  name          String
  email         String    @unique
  emailVerified Boolean
  image         String?
  createdAt     DateTime
  updatedAt     DateTime
  sessions      Session[]
  accounts      Account[]
  Entry         Entry[]
  Journal       Journal[]
}

model Session {
  id        String   @id
  expiresAt DateTime
  token     String   @unique
  createdAt DateTime
  updatedAt DateTime
  ipAddress String?
  userAgent String?
  userId    String
  user      User     @relation(fields: [userId], references: [id], onDelete: Cascade)
}

model Entry {
  id        String   @id @default(cuid())
  userId    String
  user      User     @relation(fields: [userId], references: [id])
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
  
  audioUrl   String
  transcript String?
  sentiment  Json?
  duration   Float?
  language   String?
  isPrivate  Boolean @default(true)
  
  Journal   Journal @relation(fields: [journalId], references: [id])
  journalId String
  
  @@index([userId, createdAt])
}

model Journal {
  id        String   @id @default(cuid())
  userId    String
  user      User     @relation(fields: [userId], references: [id])
  title     String
  content   String?
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
  isPrivate Boolean  @default(true)
  entries   Entry[]
}

🚀 Deployment

Frontend (Vercel)

yarn build
vercel --prod

Environment Variables for Production

Make sure to set these in your deployment platform:

• BETTER_AUTH_SECRET
• BETTER_AUTH_URL (your production URL)
• DATABASE_URL (Prisma Accelerate connection string)
• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY
• AWS_BUCKET_NAME
• BUCKET_REGION

ML Service (Docker)

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Package Management

This project uses Yarn as the package manager. Common commands:

# Install dependencies
yarn install

# Add a package
yarn add package-name

# Add a dev dependency
yarn add -D package-name

# Start development server
yarn dev

# Build for production
yarn build

# Run Prisma commands
yarn prisma generate
yarn prisma studio
yarn prisma migrate dev

Database Management

Prisma Accelerate

This project uses Prisma Accelerate for improved database performance and connection pooling. The Prisma client is generated to app/generated/prisma/ directory.

Useful Commands

# Generate client
yarn prisma generate

# View database in browser
yarn prisma studio

# Reset database (development)
yarn prisma migrate reset

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Screenshots

---

Made with ❤️ for capturing and understanding your thoughts

⭐ Star this repo if you found it helpful!