MinusPod
Self-hosted podcast server that uses AI to automatically remove ads from episodes before you hit play.
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MinusPod is a self-hosted server that automatically removes advertisements from podcast episodes before you ever hit play. It integrates seamlessly with your existing podcast apps by serving modified RSS feeds and processed audio files.
The system transcribes episodes using OpenAI Whisper (local via faster-whisper or remote API), then uses a large language model to detect and cut ad segments. A built-in verification pass re-transcribes the processed audio to catch any missed ads, while audio enforcement uses volume and transition signals to validate detections programmatically.
A standout feature is pattern learning: as you correct detections, MinusPod builds cross-episode ad patterns that promote from podcast-level to network-level and even global scope, so repeat sponsors get caught without repeatedly querying the LLM. It supports any OpenAI-compatible provider including Claude, Ollama, and OpenRouter.
Processing runs on-demand when you play an episode or automatically when new episodes appear. Once processed, the output is stored on disk and served directly on subsequent plays. The web interface includes an ad editor for manual corrections and fine-tuning.
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Frequently Asked Questions
Which podcast apps work with MinusPod?
MinusPod serves re-cut RSS feeds per podcast, so any app that supports a custom RSS URL works — Apple Podcasts, Pocket Casts, Overcast, Spotify, and the rest just point at the MinusPod feed. It also emits Podcasting 2.0 transcripts, chapters, value-for-value tags, and AI-content disclosure, and uses Podping so your app refreshes within seconds of a new episode, not on a polling schedule.
Can I run MinusPod without an NVIDIA GPU?
Yes. The default docker-compose assumes an NVIDIA GPU for local Whisper, but MinusPod ships a multi-arch CPU image (docker-compose.cpu.yml) that runs natively on amd64 and arm64. You can also offload Whisper entirely to a remote API — Groq, OpenAI Whisper, whisper.cpp, or OpenVINO on an Intel GPU — so a small home server with no discrete GPU is enough.
Do I have to pay for an LLM API, or can I run everything locally?
Both work. You can point MinusPod at Anthropic, OpenRouter, or any OpenAI-compatible endpoint, or run fully local inference with Ollama. Per-stage settings (model, temperature, token limits, prompts) are editable, and the LLM Benchmark Report in the repo lists per-model F1, latency, and cost so you can pick a tradeoff that matches your hardware and budget.
How does the cross-episode ad pattern learning actually work?
When you correct or confirm an ad detection in the web editor, MinusPod extracts a pattern from that span and promotes it up the scope chain: podcast to network to global. Repeat sponsors then get caught automatically on new episodes without re-querying the LLM. There is also an opt-in community pattern sync, with changes submitted back via PR, so popular sponsors across many feeds can be blocked from day one.
How are my LLM provider keys and backups protected?
Provider keys are encrypted with AES-256-GCM behind a MINUSPOD_MASTER_PASSPHRASE that you set on first boot — without it, keys sit in the database as plaintext. Scheduled backups are also encrypted and tied to the same passphrase, so restoring requires the original secret. The web UI is gated by a single shared password with login lockout, and the app is designed to run behind a reverse proxy or a Cloudflare tunnel for remote access.
Repository details
Updated 8/14/2026, 6:00:27 PM
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