local-ai-podcast-studio 0.1.0

Transform articles into natural podcast episodes using local TTS models

Local AI Podcast Studio

Transform articles, papers, and notes into natural-sounding podcast episodes—locally, privately, and without limits.

What is this?

Local AI Podcast Studio converts written content into engaging multi-voice podcast episodes using local text-to-speech models. Drag and drop a URL, PDF, or Markdown file, and get back a finished podcast with realistic dialogue, ad-libbed transitions, and chapter markers—all processed on your machine with zero cloud dependencies or subscription fees.

Features

• Local-first processing – No cloud APIs, no data upload, no usage limits
• Multi-voice conversations – Automatic dialogue generation with distinct speaker personalities
• Content extraction – Direct URL support plus PDF and Markdown file parsing
• Realistic audio – Distilled TTS models deliver natural-sounding speech with contextual pausing
• Smart structuring – Chapter markers, transitions, and pacing optimized for listening
• Privacy by design – All inference runs on your hardware
• One-time purchase – $29 perpetual license, no subscriptions

Quick Start

Installation

Clone the repository and install dependencies:

git clone https://github.com/yourusername/local-ai-podcast-studio.git
cd local-ai-podcast-studio
pip install -e .

Usage

Via CLI:

podcast-studio --input article.pdf --output podcast.mp3
podcast-studio --url https://example.com/article --voice clara,james

Via API:

from podcast_studio import PodcastStudio

studio = PodcastStudio()
podcast = studio.create(
    source="https://example.com/article",
    voices=["clara", "james"],
    output_path="podcast.mp3"
)

Via Desktop App:

1. Launch the application
2. Drag and drop a URL, PDF, or Markdown file
3. Select voices and customize settings
4. Click "Generate Podcast"
5. Download your MP3

Tech Stack

• Language: Python 3.10+
• TTS Engine: Local distilled models (Needle 26M or similar)
• Content Extraction: BeautifulSoup, PyPDF2, Markdown parser
• API Framework: FastAPI
• Desktop UI: PyQt6 (or Electron for cross-platform)
• Audio Processing: librosa, pydub
• Testing: pytest, unittest
• CI/CD: GitHub Actions

Architecture

podcast_studio/
├── extractor.py      # URL/PDF/Markdown parsing
├── scripter.py       # Dialogue generation & structuring
├── synthesizer.py    # TTS and audio composition
├── api.py           # REST API endpoints
├── cli.py           # Command-line interface
├── main.py          # Desktop application
└── config.py        # Configuration management

License

MIT