rss2podcast

<img src="resources/ui.jpg" width=600px></img>

A locally hosted, AI generated podcast from an rss feed.

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<img src="resources/rss.png" width=80px height=80px></img> <img src="resources/ollama.png" width=80px height=80px></img> <img src="resources/coqui.png" width=80px height=80px></img> <img src="resources/kokoro.jpg" width=80px height=80px></img>

Features

• RSS feed parsing and article extraction
• Article summarization using Ollama
• Text-to-speech conversion using multiple engines:
  • Kokoro TTS (Recommended)
  • MLX Audio TTS
  • Coqui TTS
• Podcast generation with customizable settings
• Web interface for configuration and control

Requirements

• Go 1.21 or later
• Ollama (for article summarization)
• One of the following TTS engines:
  • Kokoro TTS (recommended)
  • MLX Audio TTS
  • Coqui TTS

Installation

1. Clone the repository:

git clone https://github.com/intothevoid/rss2podcast.git
cd rss2podcast

2. Install dependencies:

go mod download

3. Configure the application by editing config.yaml or using the web interface.

Configuration

The application can be configured using the web interface or by editing the config.yaml file. The following settings are available:

RSS Settings

• url: The RSS feed URL to parse
• max_articles: Maximum number of articles to process
• filters: List of filters to apply to articles

Ollama Settings

• end_point: The Ollama API endpoint
• model: The Ollama model to use for summarization

Podcast Settings

• subject: The podcast subject
• podcaster: The podcaster name

TTS Settings

• engine: The TTS engine to use ("kokoro", "mlx", or "coqui")
• kokoro: Kokoro TTS settings
  • url: The Kokoro TTS API endpoint
  • voice: The voice to use
  • speed: The speech speed (0.25 to 4.0)
  • format: The audio format (mp3, opus, flac, wav, pcm)
• mlx: MLX Audio TTS settings
  • url: The MLX Audio TTS API endpoint
  • voice: The voice to use
  • speed: The speech speed (0.5 to 2.0)
  • format: The audio format (mp3, wav)
• coqui: Coqui TTS settings
  • url: The Coqui TTS API endpoint

Usage

1. Start the application:

go run cmd/rss2podcast/main.go

2. Access the web interface at http://localhost:8080

3. Configure the application using the web interface or edit config.yaml

4. The application will:

   • Parse the RSS feed
   • Extract and summarize articles
   • Convert the summary to audio using the selected TTS engine
   • Generate a podcast file

TTS Engines

Kokoro TTS (Recommended)

Kokoro TTS offers OpenAI-compatible speech synthesis with support for multiple voices and formats. It provides excellent quality with low latency.

MLX Audio TTS

MLX Audio TTS is a powerful text-to-speech engine that provides high-quality speech synthesis with support for multiple voices and formats. It offers additional features like direct audio playback and output folder management.

Coqui TTS

Coqui TTS provides high-quality speech synthesis with support for multiple voices and formats.

License

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

Acknowledgments

• Ollama for the LLM API
• Kokoro TTS for the TTS engine
• MLX Audio TTS for the TTS engine
• Coqui TTS for the TTS engine

How it works

The application reads an rss feed, extracts the articles and summarises them.

RSS + Ollama + TTS = Podcast

RSS

The application reads an rss feed and extracts the articles. Each of these articles are then processed by scraping the article content.

Ollama

The application uses a locally hosted version of Ollama. The Ollama API is used to summarise the article content. Default model used is mistral:7b

TTS

The summarised article content is then converted into an audio podcast using the Coqui TTS API.

Dependencies

This project requires the following dependencies to be installed on your system.

Ollama

You can install the Ollama server by following the instructions on the official website.

Ollama needs to be running on your local machine for the application to work. The application is configured to use the default Ollama server URL http://localhost:11434/api/generate. This can be changed via the config.yaml file.

ffmpeg

ffmpeg is a command-line tool for handling multimedia files. It is used to convert the generated audio files to the MP3 format.

macOS

You can use Homebrew to install ffmpeg on macOS:

brew install ffmpeg

Windows

1. Download the ffmpeg build for Windows from the official website.
2. Extract the downloaded ZIP file.
3. Add the bin directory from the extracted folder to your system's PATH.

Linux

The installation command depends on your Linux distribution.

Ubuntu/Debian

sudo apt update
sudo apt install ffmpeg

Kokoro TTS (Recommended)

Kokoro TTS is a text-to-speech synthesis system that uses deep learning to create human-like speech from text. You can install the Kokoro TTS server by following the instructions on the official website.

Docker:

Create a docker-compose.yml file and add the following:

services:
kokoro-fastapi-cpu:
    ports:
        - 8880:8880
    image: ghcr.io/remsky/kokoro-fastapi-cpu:latest # or v0.2.3 for last stable version

Start the server by running the following command:

docker compose up -d

This will start the Kokoro TTS server on port 8880. The server provides a REST API for text-to-speech conversion.

Coqui TTS

Coqui TTS is a text-to-speech synthesis system that uses deep learning to create human-like speech from text. You can install the Coqui TTS server by following the instructions on the official website.

Docker

Start the container by using the following command:

docker run -d -p 5002:5002 --platform linux/amd64 --entrypoint /usr/local/bin/tts-server ghcr.io/coqui-ai/tts-cpu --model_name tts_models/en/ljspeech/vits

MLX Audio TTS

MLX Audio TTS is a text-to-speech synthesis system that uses deep learning to create human-like speech from text. You can install the MLX Audio TTS server by following the instructions on the official website.

Docker

As of this writing, MLX Audio TTS needs to be run locally as Docker does not allow GPU access on Apple Silicon.

# Install the package
pip install mlx-audio

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
source venv/bin/activate

# Install the dependencies
pip install -r requirements.txt

# Run the server
mlx_audio.server

rss2podcast will automatically request the MLX Audio TTS server to generate the audio file.

Testing

To run the tests, use the following command:

go test ./...