Why transcripts beat pasting video links
NotebookLM can ingest a YouTube link directly, but one link at a time, and only the video's own transcript. For a whole channel — a course, a podcast archive, a technical back catalog — you want the transcripts as files, uploaded in bulk, so a single notebook can answer across hundreds of hours of material at once: "when did she change her position on X?", "list every tool he has recommended," "summarize the argument across the series."
The workflow
1. On scripthaul.com, paste the channel or playlist link.
2. Choose TXT as the file type with timestamps Off — clean prose is what NotebookLM digests best; timestamp markers on every line just add noise to its retrieval. (If you want to jump back to moments in the video afterwards, run the job again with timestamps on — the second pass is instant from cache.)
3. Download the ZIP and unzip it.
4. In NotebookLM, create a notebook and add the .txt files as sources. Files upload in batches; each becomes a citable source named by date and title, so answers point at the specific video they came from.
Working with NotebookLM's source limits
As of mid-2026, a NotebookLM notebook accepts up to 50 sources on the free plan (more on paid tiers), each up to roughly 500,000 words — check Google's current limits, as they change. For channels bigger than the source cap, two honest strategies: curate the 50 most relevant videos (the manifest's date-and-title listing makes triage fast), or split the channel across several notebooks by era or topic. A typical 15-minute video runs ~2,000 words, so a single source slot can hold dozens of concatenated transcripts if you merge files — the trade-off is coarser citations.
Quality notes
Auto-generated captions carry YouTube's recognition errors and no punctuation; creator-written captions read like edited text. The ZIP's manifest.csv records which kind each video used (the caption_kind column — manual vs asr), so you know how much to trust a quoted line before citing it anywhere serious.
Related: download a whole channel, JSON for RAG pipelines (when you outgrow NotebookLM), or the channel downloader tool.