Have you ever tried to study a long interview on YouTube and lost the thread? Or needed to quote a 90-minute video and the scrub bar turned into guesswork?
Cloud services solve part of that, but they charge per minute, ship your audio to a third party, and rarely fit a workflow that already runs local Whisper, Ollama, and agents on your own machine.
down-youtube (YouTube Transcriber) targets that gap: a Windows and Linux desktop app that turns a URL or local file into a searchable, exportable transcript and, optionally, a RAG-queryable library.
- Repo: https://github.com/elzobrito/down-youtube
- License: MIT
- Stack: Python, yt-dlp, FFmpeg, whisper.cpp, SQLite, Tkinter; optional Ollama and rag-sqlite
The problem it solves
People who work with technical video or interviews hit five frictions:
- Content stays trapped in the player. No full-text search, no “already used” flag, no clean SRT/PDF export without hacks.
- Long interviews break Whisper. Small models hallucinate at the end: repeated phrases for minutes.
- Playlists become manual labor. One video at a time, copy URL, download, convert, repeat.
- Media quality is opaque. yt-dlp “best” is not always the best stream; archive audio and ASR audio are different needs.
- Privacy and cost. Shipping hours of audio to a commercial API does not scale for people who already have GPU and disk at home.
down-youtube collapses the pipeline:
URL (video or playlist) or local file
→ expand playlist if needed
→ download / convert
→ transcribe (chunk if audio is long)
→ library + export + chat + long-term memoryMain features
Input and download
- Single video or playlist URLs (flat expansion via yt-dlp).
- Local audio/video files without YouTube.
- Cookies for restricted sessions / bot checks.
- Streaming pipeline (download + convert in parallel) or traditional mode.
- Best video quality option (
bv*+ba, richer clients, MKV merge when needed). - Best audio quality option (
ba/b, keep HQ m4a/opus if “keep audio”) and always 16 kHz mono WAV for Whisper.
Local transcription
- Runs whisper.cpp (CPU or CUDA GPU, depending on your build).
- Audio longer than 60 minutes is split into 30-minute chunks, transcribed, then merged with correct timestamps (fewer end-of-file loops).
- Duplicate detection by audio hash.
- Stage progress, cancel, and NERD metrics panel.
Library and export
- Full-text search, filters, preview.
- Mark transcripts as used / unused.
- Export TXT, SRT, VTT, DOCX, PDF.
- History and failed-item retry.
- URL queue (
.txtlists, retries).
Chat and long-term memory (optional)
- Ollama chat over a transcript.
- Projection into rag-sqlite (
youtube_rag.sqlite+ corpus): ask the whole library or the current video. - SQLite backup via Online Backup API, integrity check, and hash.
Why people should use it
| Audience | Why |
|---|---|
| Researchers / students | Lectures and interviews become searchable, citable text. |
| Creators | Local SRT/VTT without SaaS queues. |
| Devs / AI agents | SQLite library + RAG with a stable JSON contract (rag-sqlite). |
| Privacy-minded users | Audio and models stay on the machine. |
| Anyone with long interviews | 30-minute chunking reduces classic Whisper end loops. |
It does not pretend to be studio-grade diarization (who said what). It is a personal production harness: download, transcribe, organize, and ask again over what you already processed.
One-liner
git clone https://github.com/elzobrito/down-youtube.git
cd down-youtube
python -m venv .venv && source .venv/bin/activate # Windows: .venv\Scripts\Activate.ps1
pip install -r requirements.txt
python main.py "https://www.youtube.com/watch?v=..."System deps: FFmpeg and whisper-cli (whisper.cpp) configured in Settings. Ollama only if you want chat/RAG with local embeddings.
What it does not fake
- Perfect English ASR forced into Portuguese: the bottleneck becomes the Whisper model and language, not the download button.
- One-click speaker labels for interviews: needs a diarization pipeline.
- “Best quality” media does not replace a larger ASR model if you want frontier-grade captions.
Fit with the rest of the stack
down-youtube pairs with tools I already ship:
- rag-sqlite for local library memory.
- ESAA to govern app changes with claim/complete workflows in-repo.
Same theme throughout: local artifacts, clear contracts, less cloud magic.
Closing
If your problem is “I have hours of YouTube and zero trustworthy text on disk,” down-youtube fits. It does not sell an API; it closes the loop download → Whisper → library → export/RAG, with extra care for long audio and honest media quality knobs.
Open source, MIT, issues and PRs welcome: