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Add LibriVox transcripts

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  license: cc0-1.0
 
 
 
 
 
 
 
 
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  license: cc0-1.0
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+ language:
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+ - en
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+ task_categories:
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+ - automatic-speech-recognition
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+ - text-generation
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+ pretty_name: LibriVox public-domain chapter transcripts (9 chapters)
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+ size_categories:
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+ - n<1K
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  ---
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+
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+ # LibriVox public-domain chapter transcripts
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+
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+ Machine-made transcripts with segment timestamps for 9 short LibriVox chapters (about 80 minutes of audio, about 14,100 words).
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+
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+ ## Files
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+ - `librivox_transcripts.jsonl`: one row per chapter, with `segments` as a list of `{start, end, text}` (seconds).
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+ - `librivox_transcripts.csv`: same rows; `segments_json` holds the segments as a JSON string.
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+
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+ Columns: `title`, `author`, `chapter`, `source_url` (direct archive.org audio file), `archive_item` (archive.org page), `duration_seconds`, `language`, `word_count`, `transcript`, `segments`.
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+
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+ ## Source
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+ Audio: LibriVox volunteer recordings hosted on archive.org. LibriVox recordings are dedicated to the public domain, and the books read are public domain. Items:
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+ - https://archive.org/details/adventures_pinocchio_librivox (chapters 1-4)
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+ - https://archive.org/details/art_of_war_librivox (parts 1-2 and 3-4)
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+ - https://archive.org/details/alice_in_wonderland_librivox (chapter 1)
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+ - https://archive.org/details/secret_garden_librivox (chapter 1)
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+ - https://archive.org/details/peter_pan_0707_librivox (chapter 1)
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+
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+ ## License
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+ Audio: public domain (LibriVox). Transcripts and timestamps in this dataset: CC0 1.0.
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+
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+ ## Method
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+ 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.
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+
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+ 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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