Jellypod vs Mozilla Document-to-Podcast

Jellypod uses AI to turn ideas, documents and scripts into podcasts, videos, shorts and voiceovers. Its podcast agent can research a topic or source, draft scripts for multiple hosts, generate audio and let users edit lines with changed audio regenerated. For video, it creates narrated illustrations in 16 visual styles and vertical shorts of 30 seconds or one minute with animated visuals and synchronized captions. Users can choose from more than 3,300 voices across 121 languages, design characters and clone their own voice. Each podcast show includes a hosted website and RSS feed for distribution to Spotify, Apple Podcasts and YouTube. Developers have a REST API and OpenAPI spec, plus an MCP server for AI assistants; API access is listed for Creator and higher plans. Generation uses 30 credits per audio minute, 7 per image and 20 per video clip, while rendering, publishing and downloading are free. Jellypod says users own created content and that it does not train AI models on it. Paid plans start at $25/mo billed annually; a free plan is not available, though a trial is listed.

Mozilla Document-to-Podcast is an open-source Mozilla.ai blueprint that turns documents into two-speaker podcasts. Its workflow extracts and cleans text, uses a language model to create a conversational script, then generates speech audio. The CLI accepts PDF, HTML, TXT, DOCX and Markdown files, and saves podcast.txt and podcast.wav in the selected output folder. Users can customize prompts, speakers, voice profiles and model settings, and select text models that load with llama.cpp; Kokoro-82M is supported out of the box for text-to-speech. The project offers a command-line interface and a Streamlit demo app, with installation through PyPI or an editable clone. It is designed for local processing without external API calls or GPU access. The stated requirements include Windows, macOS or Linux, Python 3.10 or later (3.12 or later for Apple M chips), at least 8 GB RAM and 20 GB disk space. When cleaned text exceeds the model's context-based character limit, the CLI may use only part of the input.