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---
title: Voice-to-Summary AI Notepad
emoji: 🎙️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.1.1
app_file: app.py
pinned: false
license: mit
Upload audio or record with your microphone to get transcriptions and concise summaries.
Set the API_URL environment variable in your Space settings if you want this frontend
to call a remote FastAPI backend for transcription/summarization. If `API_URL` is empty,
the app will use a local dummy summarizer for testing.
Check the Spaces docs: https://huggingface.co/docs/hub/spaces
# Voice-to-Summary AI Notepad Backend
A production-ready FastAPI backend for audio transcription and AI summarization. Upload audio files, get transcriptions, and receive intelligent summaries.
## Features
- **Audio Transcription**: Support for MP3, WAV, and raw audio files
- **AI Summarization**: Intelligent text summarization using OpenAI GPT or Hugging Face models
- **Modular Architecture**: Easy to extend and customize
- **Production Ready**: Async FastAPI with proper error handling
- **Flexible Model Support**: Switch between OpenAI API and local models via environment variables
## Project Structure
```
/voice-summary-api/
├── app/
│ ├── main.py # FastAPI entry point
│ ├── routes/
│ │ ├── transcribe.py # POST /transcribe - accepts audio file
│ │ └── summarize.py # POST /summarize - accepts transcript
│ ├── services/
│ │ ├── whisper_service.py # Handles transcription (local or API)
│ │ └── llm_service.py # Handles LLM summarization
│ ├── schemas/
│ │ └── summary.py # Request/response Pydantic schemas
│ └── utils/
│ └── audio_utils.py # Validate and preprocess audio
├── requirements.txt
├── README.md
└── .env.example
```
## Installation
1. **Clone the repository**
```bash
git clone https://github.com/webcodelabb/Voice-to-Summary-AI-Notepad.git
cd Voice-to-Summary-AI-Notepad
```
2. **Create virtual environment**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Set up environment variables**
```bash
cp .env.example .env
# Edit .env with your API keys and configuration
```
5. **Run the server**
```bash
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
```
## Configuration
Create a `.env` file with the following variables:
```env
# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4
# Whisper Configuration
WHISPER_MODEL=base # Options: tiny, base, small, medium, large
USE_OPENAI_WHISPER=false # Set to true to use OpenAI Whisper API instead of local
# LLM Configuration
LLM_PROVIDER=openai # Options: openai, huggingface
HUGGINGFACE_MODEL=facebook/bart-large-cnn
# Server Configuration
HOST=0.0.0.0
PORT=8000
DEBUG=true
```
## API Endpoints
### 1. Transcribe Audio
**POST** `/transcribe`
Upload an audio file to get its transcription.
**Request:**
- Content-Type: `multipart/form-data`
- Body: Audio file (MP3, WAV, or raw audio)
**Response:**
```json
{
"transcript": "This is the transcribed text from the audio file.",
"confidence": 0.95,
"language": "en"
}
```
**Example:**
```bash
curl -X POST "http://localhost:8000/transcribe" \
-H "Content-Type: multipart/form-data" \
-F "file=@audio.mp3"
```
### 2. Summarize Text
**POST** `/summarize`
Send transcribed text to get an AI-generated summary.
**Request:**
```json
{
"text": "Your transcribed text here...",
"max_length": 150,
"style": "bullet_points"
}
```
**Response:**
```json
{
"summary": "• Key point 1\n• Key point 2\n• Action items",
"word_count": 45,
"original_length": 250
}
```
**Example:**
```bash
curl -X POST "http://localhost:8000/summarize" \
-H "Content-Type: application/json" \
-d '{
"text": "Your transcribed text here...",
"max_length": 150,
"style": "bullet_points"
}'
```
## Complete Workflow
1. **Upload audio file** → Get transcription
2. **Send transcription** → Get AI summary
3. **Use summary** → Integrate with your frontend
## 🧠 Model Options
### Transcription Models
- **Local Whisper**: Free, runs offline, good quality
- **OpenAI Whisper API**: Higher accuracy, requires API key
### Summarization Models
- **OpenAI GPT-4**: Best quality, requires API key
- **Hugging Face BART**: Free, runs locally, good quality
## Authentication
This backend is designed to be easily extended with authentication. The `/auth/` module is left as a placeholder for future JWT or OAuth implementation.
## 🚀 Production Deployment
1. **Environment Setup**
```bash
export OPENAI_API_KEY=your_key
export WHISPER_MODEL=base
export LLM_PROVIDER=openai
```
2. **Run with Gunicorn**
```bash
gunicorn app.main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000
```
3. **Docker Deployment**
```bash
docker build -t voice-summary-api .
docker run -p 8000:8000 voice-summary-api
```
## 🧪 Testing
Test the API endpoints:
```bash
# Test transcription
curl -X POST "http://localhost:8000/transcribe" \
-H "Content-Type: multipart/form-data" \
-F "file=@test_audio.mp3"
# Test summarization
curl -X POST "http://localhost:8000/summarize" \
-H "Content-Type: application/json" \
-d '{"text": "This is a test transcript for summarization.", "max_length": 50}'
```
## 🔧 Customization
### Adding New Models
1. Extend `WhisperService` in `app/services/whisper_service.py`
2. Extend `LLMService` in `app/services/llm_service.py`
3. Add new environment variables for configuration
### Adding Authentication
1. Create `app/auth/` directory
2. Implement JWT or OAuth middleware
3. Add authentication decorators to routes
### Frontend Integration
This backend is designed to work with any frontend:
- React/Vue/Svelte web apps
- Mobile apps (React Native, Flutter)
- Desktop applications
## 📝 License
MIT License - feel free to use this as a starting point for your projects!
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request
---Built with ❤️ for developers who want to build amazing voice-to-text applications! Fixing coauthor format