PhaseFeed

A local podcast monitoring and transcription system that:

• Monitors RSS feeds for new podcast episodes
• Downloads new episodes automatically
• Transcribes audio using either OpenAI Whisper API or local mlx-whisper
• Summarizes content using either OpenAI GPT-4 or local LLMs via Ollama
• Stores metadata in SQLite
• Generates daily feed summaries

Installation

1. Ensure you have Python 3.x installed
2. Install ffmpeg (required for audio processing):

brew install ffmpeg

3. Install and set up Ollama (required only for local summarization):

# Install Ollama
brew install ollama

# Start Ollama server
ollama serve

# In a new terminal, pull the model
ollama pull qwen2.5:3b

# Verify the model is working
ollama run qwen2.5:3b "Hello, how are you?"

4. Set up Python environment:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

5. Configure environment variables (if using OpenAI):

cp .env.example .env
# Edit .env and add your OpenAI API key if using OpenAI services

Configuration

The application can be configured through config.py. Key settings include:

Storage Configuration

• AUDIO_STORAGE_PATH: Where to store downloaded podcasts (default: ~/Podcasts)
• TRANSCRIPT_STORAGE_PATH: Where to store transcripts (default: ~/Podcasts/Transcripts)

Feed Configuration

• PODCAST_FEEDS: List of RSS feed URLs to monitor
• MAX_EPISODES_PER_FEED: Maximum number of episodes to pull from each feed (default: 5)

Transcription Configuration

• TRANSCRIPTION_MODE: Choose between "local" or "openai"
  • "local": Uses mlx-whisper locally (free, no API key needed)
  • "openai": Uses OpenAI's Whisper API (requires API key)
• WHISPER_MODEL: Model to use for local transcription (default: "mlx-community/distil-whisper-large-v3")

Summarization Configuration

• SUMMARIZATION_MODE: Choose between "local" or "openai"
  • "local": Uses Ollama locally (free, no API key needed)
  • "openai": Uses OpenAI's GPT-4 (requires API key)
• OPENAI_SUMMARY_MODEL: Model to use for OpenAI summarization
• OLLAMA_MODEL: Model to use for local summarization (default: "qwen2.5:3b")
• OLLAMA_URL: URL for Ollama server (default: "http://localhost:11434")

Processing Configuration

• CHECK_INTERVAL_MINUTES: How often to check feeds (default: 60)
• RETAIN_DAYS: How many days of history to keep (default: 30)

Usage

1. Configure your settings in config.py
2. If using local summarization, ensure Ollama is running:

# Start Ollama in a separate terminal if not already running
ollama serve

3. Run the services:

Background Processing Service

To run the background service that monitors feeds and processes episodes:

python main.py

The background service will:

• Check configured RSS feeds
• Download new episodes
• Generate transcripts (using chosen transcription method)
• Create summaries (using chosen summarization method)
• Save all metadata to a local SQLite database

Audio files are stored in ~/Podcasts/ by default Transcripts are stored in ~/Podcasts/Transcripts/

Web Server

To access the content through a web interface:

python web_server.py

Then open your browser to http://localhost:8000

Observability with OpenLIT (optional)

This project uses OpenLIT to track observability metrics. To enable, set the OTEL_EXPORTER_OTLP_ENDPOINT in your .env file or set the environment variable in your shell.

export OTEL_EXPORTER_OTLP_ENDPOINT=http://127.0.0.1:4318

Clone the OpenLIT repo:

git clone [email protected]:openlit/openlit.git

Start Docker Compose:

docker compose up -d