Batch EPUB-to-Audiobook Production Pipeline via Neural TTS
Pain Points (Public)
Content creators and publishers with multi-title catalogs waste hours manually copying chapters into web-based TTS dashboards and struggle to track character quota burn rates across high-fidelity models like ElevenLabs.
Suggested Approach (Public)
An automated batch pipeline that parses EPUB structures, segments text at natural sentence boundaries within ElevenLabs Multilingual v2 payload limits, orchestrates API calls with credit monitoring, and stitches generated audio into tagged chapter-level MP3 or M4B files.
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Opportunity assessment PRO
Development brief PRO
- Acknowledge that initial tasks (e.g., converting 10 EPUB titles totaling 17,961 KB using ElevenLabs 'Brian' voice) are one-off freelance deliverables, and enter initially as a turnkey conversion service before attempting software automation.
- Build a Python-based EPUB parsing engine to extract raw chapter text while stripping incompatible formatting, footnotes, and metadata across multi-chapter EPUB files (handling files up to 18 MB).
Competitor evidence PRO
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Public Demand Evidence · 2 task(s)
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