santinic

santinic/audiblez

Python MIT Media Single maintainer risk

Generate audiobooks from e-books

7.9k stars
682 forks
slow
GitHub +34 / week

7.9k

Stars

682

Forks

67

Open issues

11

Contributors

AI Analysis

Audiblez converts EPUB e-books into M4B audiobooks using Kokoro, a lightweight 82M-parameter text-to-speech model. It serves readers and accessibility users who want to convert their e-book libraries to audio format, with support for 9 languages, GPU acceleration, and a graphical interface. It is not a general-purpose TTS tool but rather a specialized application for e-book-to-audiobook conversion.

Media Application Discovery value: 5/10
Documentation 7/10
Activity 6/10
Community 8/10
Code quality 5/10

Inferred from signals mentioned in the README (tests, CI, type safety) — not a review of the actual code.

Overall score 7/10

AI's overall editorial judgment — not an average of the bars above, can weigh other factors too.

text-to-speech epub-conversion kokoro-tts audiobook-generation multilingual-tts
MIT licensed Actively maintained Niche/specialized use case Popular Beginner friendly Production ready
Deep Analysis · Based on README and public signals
3w ago

Audiblez converts EPUB e-books to M4B audiobooks using Kokoro's local TTS model

Audiblez is a Python CLI and GUI tool that converts EPUB files into M4B audiobook files using the Kokoro-82M text-to-speech model, running entirely locally. It targets privacy-conscious readers, self-hosters, and anyone with a large personal e-book library who wants audiobook equivalents without paying per-minute cloud TTS fees. With ~7,800 stars gained in roughly 17 months, it has found a clear audience among technically capable book enthusiasts. The GUI addition (v4) broadens access beyond CLI users.

Origin

Created in January 2025, shortly after Kokoro-82M was released on HuggingFace. The project quickly iterated from a basic CLI script to a GUI-enabled, CUDA-accelerated, multilingual tool across four major versions in under a year.

Growth

Growth was driven almost entirely by the timing of Kokoro-82M's release — a compact, Apache-licensed, surprisingly natural-sounding TTS model that removed the main barrier to local audiobook generation. Audiblez was among the first polished tools to expose Kokoro for this use case. Ongoing star velocity (~222 per week as of evaluation date) suggests it still benefits from word-of-mouth and periodic press coverage.

In production

Adoption not formally verified in enterprise or production contexts. Community signals (stars, forks at 669, pip package availability) indicate broad personal/hobbyist use. Mentions of Colab T4 usage suggest the tool is used in cloud notebook environments as well as local machines.

Code analysis
Architecture

Appears to follow a thin pipeline design: EPUB parsing → text extraction → Kokoro TTS inference (chapter-by-chapter WAV output) → FFmpeg M4B assembly. Likely a single-process flow with optional CUDA acceleration via PyTorch. The GUI appears to be a wxPython wrapper around the same core logic. Architecture likely does not include a daemon or server component.

Tests

Two CI workflows are visible (pip-install and git-clone-and-run), suggesting basic smoke/integration tests exist. Unit test depth is not documented in README; coverage level is unknown.

Maintenance

Last push was 2026-02-27, approximately 4 months before the evaluation date. For a project of this scope, a 4-month gap without a push is notable but not alarming — it may indicate feature stability rather than abandonment. Active CI badges and multiple version increments suggest the project was actively maintained through early 2026.

Honest verdict

ADOPT IF: you want a free, fully local, no-API-key solution to convert personal EPUB files to M4B audiobooks and have a capable GPU or patience for CPU processing. AVOID IF: you need commercial-grade voice quality, robust handling of complex formatting (tables, footnotes, mixed scripts), or guaranteed long-term support. MONITOR IF: you are evaluating it for a library or reading app integration — maintenance cadence needs to resume before committing to a dependency.

Independent dimensions

Mainstream potential

5/10

Technical importance

6/10

Adoption evidence

4/10

Risks
  • Dependency on Kokoro-82M model availability and its upstream maintenance on HuggingFace; a model deprecation would require significant rework.
  • 4-month gap since last commit (as of evaluation date) raises mild continuity concern for a tool with active user expectations.
  • CPU conversion speed (~60 chars/sec on M2) makes converting long books impractical without GPU, limiting accessibility for average users.
  • EPUB parsing quality varies widely; complex e-books with heavy HTML, images, or unusual structure may produce poor or garbled audio output.
  • espeak-ng and ffmpeg as system-level dependencies add friction on Windows and create potential versioning issues across platforms.
Prediction

Likely to remain a popular entry point for local e-book TTS conversion as long as Kokoro remains competitive in quality. May plateau if ebook2audiobook continues adding polish and GUI features. A return to active development or community fork could sustain it.

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Languages

Python
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Information

Language
Python
License
MIT
Last updated
4mo ago
Created
18mo ago
Analyzed with
anthropic/claude-haiku-4-5

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vs. alternatives
DrewThomasson/ebook2audiobook

More stars (19K) and broader TTS model support (not limited to Kokoro). More complex setup. Audiblez is simpler to install and has a native GUI, making it more approachable for non-technical users.

hexgrad/kokoro

Kokoro is the underlying TTS engine audiblez depends on. Kokoro itself is a JavaScript repo for the model; audiblez is the Python application layer for the book-conversion use case.

kovidgoyal/calibre

Calibre is a full e-book management suite with its own TTS plugin ecosystem. Audiblez is narrowly focused on high-quality neural TTS conversion and produces standard M4B files; it does not compete on library management.

souzatharsis/podcastfy

Podcastfy targets podcast-style content generation from web/text sources, not e-book-to-audiobook pipelines. Overlapping in TTS usage but distinct in purpose and audience.