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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 -m backends.qwen`): the TUI pip-installs `qwen-tts` into its managed venv (`envs/tts`), configures the two ports and the built-in speaker in `converter/config.py`, and prints the launch commands. You can also start the server from the hub's **Server** 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
-conda activate audiobook
-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
-conda activate audiobook
-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 `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
-conda activate audiobook
-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
-conda activate audiobook
-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/)