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# Backend Option 2: Qwen3-TTS
The easiest way is to run `python audiobook.py` → **Set up a backend… → qwen-tts** (or `python app/backends/qwen.py`): the TUI pip-installs `qwen-tts` into its managed venv (`app/envs/tts`), configures the two ports and the built-in speaker in `app/converter/config.py`, and prints the launch commands. You can also start the server from the hub's **Start/Stop Backend Servers** menu, or let a conversion start it automatically.
If you prefer to install the backend yourself (in your own environment, not the managed venv), the manual steps are below. Either way the hub detects a running server by its port, so a manually-installed backend works once its server is up.
Install qwen-tts with pip into your environment:
```bash
python -m venv audiobook
source audiobook/bin/activate
pip install -U qwen-tts
```
Run the backend with `qwen-tts-demo`. Add `--no-flash-attn` if FlashAttention isn't installed (see below). Note that the Base model and CustomVoice model run on different ports.
## Voice clone
```bash
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-Base --ip 127.0.0.1 --port 7861 [--no-flash-attn]
```
Then in another terminal:
```bash
python audiobook.py --backend qwen --clone 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 `--transcription "What the .wav says"` or skip transcription with `--no-transcription`.
## Custom voice (i.e. built-in voice)
```bash
source audiobook/bin/activate
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --ip 127.0.0.1 --port 7860 [--no-flash-attn]
```
```bash
python audiobook.py --backend qwen
```
Change the voice settings in `app/converter/config.py`.
## Optional: FlashAttention for qwen-tts-demo server
FlashAttention provides a *small* speed boost on the `qwen` backend. It is **not** relevant with other backends, and switching to either of those will provide a bigger speed boost.
`qwen-tts-demo` server tries to use FlashAttention 2 by default and requires `--no-flash-attn` without it. You have two options to install FlashAttention in your python environment:
1. Build from source (takes absolutely forever). If you run out of memory, lower MAX_JOBS until you don't.
```bash
source audiobook/bin/activate
pip install ninja packaging psutil
MAX_JOBS=4 pip install --no-build-isolation flash-attn
```
2. pip install a prebuilt wheel matching your torch / CUDA / Python / CXX11-ABI combination:
```bash
source audiobook/bin/activate
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/)
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