Mozilla Document-to-Podcast vs Sprep
Mozilla Document-to-Podcast
github.comSprep
sprep.chSide by side
| Figure | Mozilla Document-to-Podcast | Sprep |
|---|---|---|
| Price from | — | $36/mo |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 0 | 4 |
| Spec score · AI Podcast Generators | 7.4 (#15) | 7.6 (#10) |
| Purpose | Document-to-Podcast is a Mozilla.ai Blueprint for turning input documents into podcasts featuring two speakers. | Sprep turns uploaded documents such as PDFs, presentations, and articles into editable AI-generated podcasts. |
| Workflow | The pipeline extracts and cleans document text, generates a conversational script with a language model, then creates audio with text-to-speech. | Users can review, edit, and approve the generated script before audio is rendered. |
Plans
In their own words
Mozilla Document-to-Podcast
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.