Clubhouse-Podcast-Automation

An open-source tool to automate publishing Clubhouse recordings to Spotify and YouTube. Supports both local and cloud deployment from a single codebase.

Features

• Download Clubhouse recordings from dynamic links
• Extract audio track from video (MP4 to MP3)
• Transcribe audio using Gemini API (Google AI Studio)
• Generate descriptions for YouTube and Spotify
• Create video with waveform visualization using MoviePy
• Upload to YouTube with scheduled publishing
• Prepare files for Spotify manual upload

Deployment Options

Mode	Best For	Infrastructure	Status
Local	Individual creators, development	Your machine	Available
Cloud	Teams, automation, scheduled jobs	GCP Cloud Run Jobs	To be implemented

Quick Start (Local)

Prerequisites

• Python 3.10+
• ffmpeg (brew install ffmpeg on macOS)
• Gemini API key (Get one here)
• YouTube API credentials (Setup guide)

Installation

# Clone the repository
git clone https://github.com/WWStoryMode/Clubhouse-Podcast-Automation.git
cd Clubhouse-Podcast-Automation

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Copy configuration templates
cp config/config.example.yaml config/config.yaml
cp .env.example .env

# Edit .env with your API keys

Configuration

1. Edit .env with your API keys:

   GEMINI_API_KEY=your_gemini_api_key
   

2. Edit config/config.yaml with your settings

3. Add your template assets to templates/:

   • background.png - Video background image (1920x1080)
   • icon.png - Logo/icon overlay

Usage

# Full pipeline
python -m src.cli process --url "https://clubhouse.com/..." --title "Episode 1"

# Individual steps
python -m src.cli download --url "https://clubhouse.com/..."
python -m src.cli extract --input output/audio/raw.mp4
python -m src.cli transcribe --input output/audio/audio.mp3
python -m src.cli summarize --input output/transcripts/transcript.txt
python -m src.cli generate-video --input output/audio/audio.mp3 --title "Episode 1"
python -m src.cli upload-youtube --input output/videos/video.mp4

Cloud Deployment (GCP)

Status: To be implemented

Cloud deployment via GCP Cloud Run Jobs is planned for a future release. This will enable:

• Scheduled/automated processing
• Team collaboration
• Webhook triggers
• Cloud storage integration

Project Structure

Clubhouse-Podcast-Automation/
├── src/
│   ├── core/                 # Business logic
│   │   ├── pipeline.py       # Orchestration
│   │   ├── downloader.py     # Download from Clubhouse
│   │   ├── audio_extractor.py
│   │   ├── transcriber.py    # Gemini API
│   │   ├── summarizer.py     # Gemini API
│   │   ├── video_generator.py # MoviePy
│   │   └── uploader.py       # YouTube API
│   │
│   ├── adapters/             # Platform adapters
│   │   ├── storage/          # Local/Cloud storage
│   │   └── runtime/          # Local/Cloud execution
│   │
│   └── cli.py                # CLI entry point
│
├── config/                   # Configuration
├── templates/                # Video templates
└── output/                   # Generated files

Technology Stack

Component	Technology
Audio extraction	ffmpeg
Transcription	Gemini API
Summarization	Gemini API
Video generation	MoviePy + librosa
YouTube upload	YouTube Data API v3
Cloud deployment	GCP Cloud Run Jobs

Contributing

Contributions are welcome! Please read CONTRIBUTING.md for guidelines.

License

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