denizsafak/abogen

Generate audiobooks from EPUBs, PDFs and text with synchronized captions.

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Summary Information

Updated 22 minutes ago
Added to GitGenius on September 11th, 2026
Created on April 24th, 2025
Open Issues & Pull Requests: 48 (+0)
GitHub issues: Enabled
Number of forks: 464
Total Stargazers: 5,984 (+0)
Total Subscribers: 31 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 5.3 hours
Mean response time: 7.5 days
90th percentile: 17.3 days
Tracked items: 115

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 82% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 3% of issues opened in the past year have been closed. Three people close 83% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 45
New in 7 days: 3
Closed in 7 days: 0
Avg open age: 217 days
Stale 30+ days: 39
Stale 90+ days: 25

Recent activity

Opened in 7 days: 3
Closed in 7 days: 0
Comments in 7 days: 1
Events in 7 days: 3

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Detailed Description

Abogen is a text-to-speech conversion tool that transforms ePub, PDF, text, markdown, and subtitle files into audiobooks with synchronized captions.

The tool addresses the need to quickly generate high-quality audio content from written material. It uses the Kokoro-82M text-to-speech model to produce natural-sounding narration and automatically generates matching subtitles. The approach handles multiple input formats and produces output suitable for various platforms including social media and traditional audiobook distribution.

Abogen suits creators who need rapid audiobook generation, content creators producing voiceovers for video platforms, and anyone converting existing written content to audio format. The tool is designed for speed, capable of generating approximately one minute of audio with synchronized subtitles in seconds. Installation varies by platform: Windows users can run an automated batch script that handles dependencies including CUDA without requiring a separate Python installation, while Mac and Linux users install via the uv package manager. The README does not compare this tool to alternatives.

The project shows active maintenance with responsiveness to user issues, including documented solutions for common problems like CUDA availability warnings and platform-specific errors. Contributors have added features such as AMD GPU support based on community feedback. The codebase includes platform-specific installation guidance and troubleshooting documentation that reflects real-world usage patterns across Windows, Mac, and Linux environments.