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A note to new users: Alexandria has recently seen a sudden surge of attention and new users. As a small project, I may not be able to respond to every issue promptly. Before opening an issue, please read this README and the Wiki thoroughly — most common questions are already answered there. Thank you for your patience!
Transform any book or novel into a fully-voiced audiobook using AI-powered script annotation and text-to-speech. Features a built-in Qwen3-TTS engine with batch processing and a browser-based editor for fine-tuning every line before final export.
Local & Cloud LLM Support - Use any OpenAI-compatible API (LM Studio, Ollama, OpenAI, etc.)
Automatic Script Annotation - LLM parses text into JSON with speakers, dialogue, and TTS instruct directions
LLM Script Review - Optional second LLM pass that fixes common annotation errors: strips attribution tags from dialogue, splits misattributed narration/dialogue, merges over-split narrator entries, and validates instruct fields
Persona Generation - LLM analyzes the script to create voice descriptions for each character, then generates reference audio via VoiceDesign and assigns clone voices automatically — one click to go from script to fully-voiced cast
Speaker Aliases - Map multiple speaker names to the same voice (e.g. "YOUNG ELENA" → "ELENA") so variants share a single voice configuration
Smart Chunking - Groups consecutive lines by speaker (up to 500 chars) for natural flow
Context Preservation - Passes character roster and last 3 script entries between chunks for name and style continuity
Voice Generation
Built-in TTS Engine - Qwen3-TTS runs locally with no external server required
External Server Mode - Optionally connect to a remote Qwen3-TTS Gradio server
Multi-Language Support - English, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish, or Auto-detect
ALGORITHMICALLY RELATED
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Each TTS model uses ~3.4 GB; remaining VRAM determines batch size
CPU mode available on all platforms but significantly slower
RAM: 16 GB recommended (8 GB minimum)
Disk: ~20 GB (8 GB venv/PyTorch, ~7 GB for model weights, working space for audio)
GPU Compatibility
GPU
OS
Status
Driver Requirement
Notes
NVIDIA
Windows
Full support
Driver 550+ (CUDA 12.8)
Flash attention included for faster encoding
NVIDIA
Linux
Full support
Driver 550+ (CUDA 12.8)
Flash attention + triton included
AMD
Linux
Full support
ROCm 6.3+
ROCm optimizations applied automatically
AMD
Windows
CPU only
N/A
GPU acceleration is not supported — the app runs in CPU mode. For GPU acceleration with AMD, use Linux
Apple Silicon
macOS
CPU only
N/A
MPS acceleration is not currently supported. Functional but slow
Intel
macOS
CPU only
N/A
Note: No external TTS server is required. Alexandria includes a built-in Qwen3-TTS engine that loads models directly. Model weights are downloaded automatically on first use (~3.5 GB per model variant).
Documentation: For in-depth guidance on voice types, LoRA training, batch generation, and more, see the Wiki.
Or manually: in Pinokio, click Download and paste https://github.com/Finrandojin/alexandria-audiobook
Click Install to set up dependencies
Click Start to launch the web interface
Option B: Google Colab (No Install Required)
No GPU or wrong OS? Run Alexandria on a free T4 GPU in your browser:
Requires a free ngrok account for the web UI tunnel. See the notebook for full instructions.
Option C: Docker (NVIDIA GPU)
For integration into automated pipelines or server deployments:
git clone https://github.com/Finrandojin/alexandria-audiobook.git
cd alexandria-audiobook
docker compose up --build
Requires Docker with the NVIDIA Container Toolkit. The web UI is available at http://localhost:4200. TTS models download on first use and are cached in a Docker volume. User data (uploads, voice configs, trained LoRA adapters, audio output) persists via bind mounts to the project directory.
First Launch — What to Expect
If this is your first time running Alexandria, read this before anything else.
1. You Need an LLM Server Running First
Alexandria does not include an LLM — it connects to one over an API. Before generating a script, you must have one of these running:
The interface is split into a 5-step core pipeline (green tabs, numbered) and advanced tools (blue tabs, unnumbered). You only need the core pipeline to produce an audiobook.
Core Pipeline
Step 1 — Setup
Configure your LLM connection and TTS engine. At minimum you need:
LLM Base URL: http://localhost:1234/v1 (LM Studio) or http://localhost:11434/v1 (Ollama)
LLM API Key: Your API key (use local for local ser
