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# Qwen3 Audiobook Converter
Convert TXT, PDF, and EPUB files into audiobooks using the Qwen3-TTS voice model. This builds upon [WhiskeyCoder/Qwen3-Audiobook-Converter](https://github.com/WhiskeyCoder/Qwen3-Audiobook-Converter) adding more output files, transcription/speed options, better text cleanup, and clearer instructions. It also expects the qwen-tts server to be on different ports per model, so two server processes can run at once.
## Overview
The converter sends text extracted from your books to a locally running Qwen3-TTS server and assembles the returned audio into a single audiobook file.
- Input: `.txt`, `.pdf`, or `.epub`
- Output: `.mp3`, `.m4b`, `.ogg`, or `.flac`
- Output a single mp3 or one per chapter
- Two voice modes:
- Custom voice: pre-built speakers
- Voice clone: clone a voice from a `.wav` reference audio file
## Prerequisites
- Python 3.12
- ffmpeg
- Enough VRAM to run the 1.7B model (~6GB)
## Installation
Install ffmpeg and conda, e.g.
```bash
sudo pacman -S conda ffmpeg #Arch Linux
sudo apt-get install ffmpeg #Debian, conda must be installed separately
```
### Install Qwen3-TTS (Server)
```bash
conda create -n qwen3-tts python=3.12 -y
conda activate qwen3-tts
pip install -U qwen-tts
```
### Install the conversion script
```bash
git clone https://git.historia.vg/git/qwen3-audiobook-converter
cd qwen3-audiobook-converter
pip install -r requirements.txt
```
## Running the Qwen-TTS server
The converter script talks to a Qwen3-TTS Gradio server that is run using `qwen-tts-demo`. Add `--no-flash-attn` if FlashAttention isn't installed (see below). The script expects the custom voice model and base model to be on different ports depending on which you're using:
### Custom voice
```bash
conda activate qwen3-tts
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --ip 127.0.0.1 --port 7860
```
### Voice clone
```bash
conda activate qwen3-tts
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-Base --ip 127.0.0.1 --port 7861
```
## Converting books
Put your book files (epub, etc.) in the `input/` folder. Then run the script. The output goes to `output/`.
### Custom voice
```bash
python audiobook_converter.py
```
Edit `converter/config.py` to change which built-in voice is used.
```
CUSTOM_VOICE_SPEAKER = "Vivian" # Serena, Vivian, Uncle_Fu, Aiden, Ono_Anna, Sohee, Eric, Dylan
CUSTOM_VOICE_LANGUAGE = "English"
CUSTOM_VOICE_INSTRUCT = "Speak naturally and clearly, as if reading a dramatic book to an adult audience."
```
### Voice clone
```bash
python audiobook_converter.py --voice-clone --voice-sample path/to/reference.wav
```
The reference `.wav` should be ~10-15 seconds (3 second minimum, 60 second maximum; ~15 seconds is ideal). Longer is **not** better.
Whisper (`faster_whisper` or `whisper`) is used automatically to transcribe the reference audio; without a Whisper backend it falls back to x-vector-only cloning. Override with `--voice-sample-text "..."` or skip transcription with `--no-transcription`.
### Options
| Flag | Description |
| ----------------------------- | ----------------------------------------------------------------------------------------------------- |
| `--speed <n>` | Playback speed, pitch-preserving (`1.0` = normal). A normal-speed copy is also output. |
| `--format {mp3,m4b,ogg,flac}` | Output format (default `mp3`). `m4b` uses AAC audio and has built-in chapters. |
| `--single-file` | Not m4b: Merge all chapters into a single file (default: one file per chapter). |
| `--voice-sample-text "..."` | Override whisper auto-transcription with your own manual reference audio transcript. Not required. |
| `--no-transcription` | Skip auto-transcription of the reference audio. Usually worse, but can give a different voice affect. |
## Running tests
```bash
python -m unittest discover -s tests -t .
```
## FlashAttention (optional)
The server tries to use FlashAttention 2 by default, but `--no-flash-attn` works without it. On supported GPUs FlashAttention can give a modest speedup.
1. Build from source (takes absolutely forever). If you run out of memory, lower MAX_JOBS until you don't.
```bash
conda activate qwen3-tts
pip install ninja packaging psutil
MAX_JOBS=4 pip install --no-build-isolation flash-attn
```
2. Or pip install a prebuilt wheel matching your torch / CUDA / Python / CXX11-ABI combination:
```bash
python -c "import torch; print(torch.__version__, torch.version.cuda, torch._C._GLIBCXX_USE_CXX11_ABI)"
```
Official wheels: https://github.com/Dao-AILab/flash-attention/releases (pick `cp312` + matching `cuX` + `torchX.Y` + `cxx11abiTRUE/FALSE`).
Third-party wheels: https://mjunya.com/flash-attention-prebuild-wheels/ (hosted at https://github.com/mjun0812/flash-attention-prebuild-wheels).
## License
MIT
## Credits
- [Qwen3-Audiobook-Converter](https://github.com/WhiskeyCoder/Qwen3-Audiobook-Converter) by WhiskeyCoder.
- [Qwen3-TTS](https://github.com/QwenLM/Qwen3-TTS) voice model.
|
