Audify vs Podcastfy

Audify is a free, open-source application that turns PDF, DOC and DOCX files into editable two-speaker podcast scripts and downloadable MP3 episodes. It extracts document text and can use OCR when a scanned PDF yields little text. Users review and revise speaker turns, wording and flow, choose host and guest voices, and select conversational, educational or professional tones before generating audio. Ollama is the primary script-generation route, with OpenAI as a fallback; an OpenAI API key is required for text-to-speech, while Ollama is optional. The TTS service creates speech for dialogue turns, combines it into MP3, and supports playback and download. Audify uses FastAPI microservices with a React and Vite frontend and is orchestrated for local Docker Compose deployment. Its API handles uploads, generation, job status and downloads. Docker Engine 24.x or later and Docker Compose Plugin v2 or later are required. Uploads are limited to 10 MB, and jobs reset after container restarts unless storage is added.

Podcastfy is a free, open-source Python package that turns multimodal source material into multilingual audio conversations with generative AI. It accepts websites, PDFs, images, YouTube videos, raw text, transcript files and user-provided topics; topic generation can use grounded real-time web search. Users can set the podcast format, style, voices, language and structure, then generate short episodes of 2–5 minutes or longform episodes of 30 minutes or more. Transcript generation supports more than 100 language-model options, including OpenAI, Anthropic and Google models. Listed speech engines include OpenAI, Google, ElevenLabs and Microsoft Edge. Local language models are supported for transcript generation, offering the project’s stated privacy and control benefits. Usage is available through a Python package, command-line interface and beta FastAPI service for URLs, with MP3 audio export. API-key needs depend on selected models; a documented local-model and Edge TTS setup requires none. Python 3.11 or higher and ffmpeg are required. The project uses the Apache 2.0 license.