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# Backend Option 2: Qwen3-TTS
The easiest way is to run `python audiobook.py` → **Configure Backends… → Install Backend → qwen-tts** (or `python app/backends/qwen.py`): the TUI pip-installs `qwen-tts` into its own managed venv (`app/envs/qwen`, separate from the app's venv and from the faster backend's — the two TTS stacks ship conflicting versions of a shared `qwen_tts` module) — that's all there is to it, the install asks no questions. The demo port lives in `app/converter/config.py` (edit it in the hub's **Settings** screen). The qwen backend runs **one model at a time** on that single port: pick Base, CustomVoice or VoiceDesign per run on the **Generate Audiobooks** screen (the choice is remembered in `QWEN_MODEL` and re-used by the next autostart; switching models while a managed server is up restarts it with the newly-selected model). 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 (its `GET /info` names which of the three demos answers), so a manually-installed backend works once its server is up. To use a demo server on another machine, set `QWEN_REMOTE_URL` in `app/converter/config.py` to its `host:port` (default `127.0.0.1:7860`) — the hub probes it and offers the matching `qwen-tts [remote]` mode limited to the model that server hosts — or pass `--api-url` on the CLI.
Model weights download automatically from HuggingFace into the standard cache (`~/.cache/huggingface/hub`) the first time a server for each model starts — there is nothing else to install per model. To pre-fetch or remove a single model's weights without starting its server, open **Configure Backends… → Configure qwen-tts**: each of Base / CustomVoice / VoiceDesign gets an Install (a streamed, resumable download — canceling one just means it resumes later) or Uninstall action, with a server hosting that model stopped first. Uninstalling the whole backend deletes all three of those directories along with the pip package; only they are ever touched — anything else in your HuggingFace cache is left alone.
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 <model>`. Add `--no-flash-attn` if FlashAttention isn't installed (see below).
## Voice design
```bash
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --ip 127.0.0.1 --port 7860 [--no-flash-attn]
```
Then:
```bash
python audiobook.py --backend qwen \
--instructions "A warm adult female narrator with a British accent"
```
The narrator is tagged "designed" in output file names.
## Voice clone
```bash
qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-Base --ip 127.0.0.1 --port 7860 [--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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