Core Analysis¶

Quantified comparison: README reports ~600 chars/sec on a Colab T4 (≈5 minutes for 160k chars) versus ~60 chars/sec on an M2 CPU (≈1 hour), implying roughly a 10–12x wall-clock speedup with GPU.

Technical Analysis¶

• Time cost: GPU inference benefits from parallel tensor ops; CPU is limited by vectorized/multi-threaded performance.
• Resource usage: GPU runs are constrained by VRAM (model + buffers); both CPU and GPU workflows need disk for intermediate WAV files, and IO can affect overall time.

Practical Recommendations¶

1. Short books/experiments: Use CPU if no GPU is available, but split into small chapters and expect longer processing time.
2. Long books/batches: Prefer CUDA-enabled GPUs (local or Colab/cloud) to significantly reduce turnaround time.
3. Resource management: Monitor VRAM and disk; delete intermediate WAVs after m4b creation or use larger temp storage.

Caveats¶

• VRAM limits & parallelism: If VRAM is low, reduce concurrency or process by chapter.
• Cost tradeoff: For occasional short conversions, CPU time may be acceptable; for frequent use, GPU rental/purchase is cost-effective.

Important Notice: Based on README numbers, GPU gives ~10x speed advantage—critical for long books or batch processing.

Summary: Use CUDA GPUs for timely full-book generation; otherwise accept longer CPU runtimes and manage disk/cleanup accordingly.

Core Analysis¶

Project Positioning: audiblez implements an end-to-end pipeline to parse .epub into chapter wav files, synthesize speech with Kokoro-82M via PyTorch, and package the output into a standard .m4b using ffmpeg.

Technical Features¶

• Complete pipeline: parsing -> text normalization (optionally using espeak-ng) -> PyTorch/Kokoro synthesis -> ffmpeg packaging to m4b.
• Performance options: CPU and GPU (--cuda) supported; README reports ~600 chars/sec on T4 and ~60 chars/sec on M2 CPU.

Usage Recommendations¶

1. Prerequisites: ensure EPUB is not DRM-protected, install ffmpeg and espeak-ng, and configure PyTorch/CUDA properly.
2. Pilot run: convert a single chapter first to tune voice, speed, and normalization.
3. Hardware: use a CUDA-enabled GPU for long books or faster turnaround.

Caveats¶

• Disk: intermediate chapter_x.wav files are large; plan disk space and cleanup.
• Content/format: complex layout (footnotes/tables) may require manual preprocessing or exclusion via --pick.

Important Notice: DRM-protected or corrupted EPUBs cannot be processed; ensure legal compliance.

Summary: Given proper dependencies, hardware, and light preprocessing, audiblez can reliably produce playable m4b audiobooks; GPU use yields major speed gains.

Core Analysis¶

User Pain Points: Key local usage issues for audiblez are environment dependencies, disk/memory resource management, cross-platform GUI dependencies, and parsing failures due to DRM or complex layout.

Technical Analysis¶

• Dependency issues: ffmpeg and espeak-ng are required; missing them blocks packaging or causes poor normalization.
• Resource management: Chapter wav files are large; long books can quickly exhaust disk space.
• Platform compatibility: wxPython/Pillow and PyTorch/CUDA versions may require extra configuration; Apple Silicon support is limited.

Practical Recommendations¶

1. Environment prep: Install ffmpeg/espeak-ng via OS package manager and use a Python venv (README recommends venv for Windows).
2. Pilot & tune: Convert one chapter first to validate voice, speed (-s), and text normalization.
3. Disk strategy: Set an output folder and delete intermediate WAV files after m4b creation, or use temp/external storage.
4. GUI install: Only install wxpython and pillow if you need the GUI; prefer CLI to reduce complexity.

Caveats¶

• DRM & legality: DRM or copyrighted books cannot be processed without permission.
• Performance expectations: No-GPU runs are slow; consider Colab or a local GPU if needed.

Important Notice: Following standard installation steps, running samples first, and planning disk usage will significantly improve local UX.

Summary: Most local issues stem from environment and resources; following README guidance and testing reduces failure rates.

Core Analysis¶

Issue: Complex layouts (footnotes, tables, annotations) and multilingual mixing challenge automated parsing and TTS, causing awkward pauses, mispronunciations, or context confusion that degrade listening quality.

Technical Analysis¶

• Parsing limits: EPUB is HTML—if the parser does not strip footnotes/annotations, TTS may read them as body text.
• Pronunciation/language detection: Kokoro supports multiple languages but not all cases; mixed-language passages need explicit segmentation to avoid wrong pronunciations.
• Normalization role: espeak-ng can assist with pronunciation hints and special character handling but is not a cure-all.

Practical Recommendations (preprocessing checklist)¶

1. Clean the EPUB: Use Calibre or scripts to remove frontmatter, indices, or move footnotes to the end.
2. Chapter/segment selection: Use --pick to read only selected chapters or split complex chapters into smaller segments.
3. Multilingual handling: Segment multilingual passages by language and synthesize them using matching voices, then merge.
4. Normalize text: Run a cleaning script to replace special symbols and standardize punctuation to reduce unnatural pauses.

Caveats¶

• Manual review: Important chapters benefit from human proofreading before synthesis, especially dialogues or technical sections.
• Extra time required: Fully automated perfect output is unlikely for intricate layouts.

Important Notice: Pre-cleaning and language-segmentation greatly improve output but cannot fully replace human intervention.

Summary: For complex layout or multilingual EPUBs, text-level cleaning and segmentation are essential; audiblez supplies helpful tools (e.g., --pick, espeak-ng) but human preprocessing yields the best listening quality.

Core Analysis¶

Project Positioning: The Kokoro-82M (82M params) + PyTorch stack is chosen to balance local deployability and natural-sounding speech, while enabling GPU acceleration for whole-book synthesis performance.

Technical Features¶

• Advantage 1 (lightweight & good quality): Kokoro-82M is compact for easier local runs and loading, and README highlights its natural sounding output.
• Advantage 2 (ecosystem & acceleration): PyTorch offers robust GPU/CUDA support, enabling significant speedups (README example: Colab/T4).
• Limitations: Smaller models may lag in emotional expressiveness or dramatic reading compared to larger models; coverage and naturalness can vary across languages/voices; native support on Apple Silicon is limited per project notes.

Usage Recommendations¶

1. Match goals: If your goal is local, privacy-preserving TTS without theatrical performance, Kokoro is appropriate.
2. Hardware planning: Use CUDA-enabled GPUs for long books or batch jobs; expect slower CPU-only conversion.
3. Quality validation: Test representative passages in target language/voice to confirm acceptability.

Caveats¶

• Emotive performance: Do not expect professional voice acting quality.
• Environment compatibility: Watch PyTorch/CUDA version compatibility; Windows users should prefer a venv.

Important Notice: The selection favors controllable, cost-effective local synthesis rather than ultimate fidelity or universal platform support.

Summary: Kokoro-82M + PyTorch is a pragmatic choice for privacy-focused, cost-efficient local audiobook generation, with trade-offs in expressiveness and platform universality.

Core Analysis¶

Fit: audiblez is well-suited for individuals, small publishers, and creators to quickly produce internal samples, demos, or non-commercial evaluation audiobooks. Its strengths are local operation, low recurring cost, and privacy control.

Tech & business evaluation points¶

• Pros: Directly outputs standard .m4b from EPUB, multi-language/multi-voice support, avoids cloud costs and privacy exposure.
• Limits: Kokoro has natural output but limited emotional/dramatic expressiveness; commercial use requires attention to copyright/DRM and possible licensing; Apple Silicon support is limited.

Alternatives (brief)¶

1. Cloud closed-source TTS (Amazon Polly, Google TTS): Better prosody/emotion and SLAs but ongoing cost and privacy tradeoffs.
2. Larger local models: Higher fidelity but require more compute and VRAM.
3. Professional voice actors: Best quality, highest cost—appropriate for commercial release.

Practical recommendations¶

1. Match use: Use audiblez for sample generation, internal review, and rapid iteration; for a final commercial release, evaluate upgrade to professional audio or paid TTS.
2. Compliance: Confirm copyright licenses before commercial use and document generation steps.
3. Hybrid strategy: Generate drafts with audiblez, then humanize key chapters with professional talent or cloud services to balance cost and quality.

Important Notice: audiblez is not a one-size-fits-all tool—its value is strongest for quick, local, low-cost generation; commercial release requires further QA and licensing checks.

Summary: Treat audiblez as a cost-effective prototyping/internal tool; for production-grade commercial audiobooks, consider augmenting with paid TTS or human narration.