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license: cc0-1.0
language:
- en
task_categories:
- automatic-speech-recognition
- text-generation
pretty_name: LibriVox public-domain chapter transcripts (9 chapters)
size_categories:
- n<1K
---
# LibriVox public-domain chapter transcripts
Machine-made transcripts with segment timestamps for 9 short LibriVox chapters (about 80 minutes of audio, about 14,100 words).
## Files
- `librivox_transcripts.jsonl`: one row per chapter, with `segments` as a list of `{start, end, text}` (seconds).
- `librivox_transcripts.csv`: same rows; `segments_json` holds the segments as a JSON string.
Columns: `title`, `author`, `chapter`, `source_url` (direct archive.org audio file), `archive_item` (archive.org page), `duration_seconds`, `language`, `word_count`, `transcript`, `segments`.
## Source
Audio: LibriVox volunteer recordings hosted on archive.org. LibriVox recordings are dedicated to the public domain, and the books read are public domain. Items:
- https://archive.org/details/adventures_pinocchio_librivox (chapters 1-4)
- https://archive.org/details/art_of_war_librivox (parts 1-2 and 3-4)
- https://archive.org/details/alice_in_wonderland_librivox (chapter 1)
- https://archive.org/details/secret_garden_librivox (chapter 1)
- https://archive.org/details/peter_pan_0707_librivox (chapter 1)
## License
Audio: public domain (LibriVox). Transcripts and timestamps in this dataset: CC0 1.0.
## Method
Each file was fetched from archive.org and transcribed with the Apify actor [audio-transcriber](https://apify.com/pennydata/audio-transcriber) (language `en`, segments on). The text is raw machine output: it was not proofread, so expect errors in names, punctuation and rare words, and compare it with the printed book before using it as ground truth.
**See also** (more public data samples from the same publisher): [greenhouse-jobs-snapshot-2026-10](https://huggingface.co/datasets/pennydata/greenhouse-jobs-snapshot-2026-10), [top-websites-tech-stack-2026-10](https://huggingface.co/datasets/pennydata/top-websites-tech-stack-2026-10), [hn-whoishiring-2026-10](https://huggingface.co/datasets/pennydata/hn-whoishiring-2026-10), [polymarket-midterms-2026-odds](https://huggingface.co/datasets/pennydata/polymarket-midterms-2026-odds), [polymarket-midterms-2026](https://huggingface.co/datasets/pennydata/polymarket-midterms-2026), [hpr-podcast-transcripts](https://huggingface.co/datasets/pennydata/hpr-podcast-transcripts), [top-site-social-preview-images](https://huggingface.co/datasets/pennydata/top-site-social-preview-images), [top-sites-tech-stack](https://huggingface.co/datasets/pennydata/top-sites-tech-stack), [apify-store-catalog-2026-10](https://huggingface.co/datasets/pennydata/apify-store-catalog-2026-10). Collection: https://huggingface.co/collections/pennydata/scraped-web-data-2026-10-6addb145d728d5f0435ca4c3
I built the actor and earn from its runs. Transcripts were generated with AI (speech recognition) and the dataset was assembled with AI assistance (Claude).
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