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# Backend Option 1: audio.cpp
`--backend audiocpp` talks to `audiocpp_server` from [audio.cpp](https://github.com/0xShug0/audio.cpp), which hosts numerous TTS model families.
The easiest way is the TUI: run `python audiobook.py`, choose **Set up a backend… → audio.cpp**, and it clones `audio.cpp` into `app/audio.cpp` (or reuses an existing checkout), builds `audiocpp_server`, lets you pick model families/packages from an expandable checkbox tree (reading the checkout's `model_specs/`), transcribes `.wav` voices with `whisper`, writes `server.json` into the checkout, syncs `app/converter/config.py`, and prints the launch command (the hub can also start the server for you via the **Start/Stop Backend Servers** menu or automatically when converting). Run it directly with `python app/backends/audiocpp.py` (flags like `--wavs`, `--families`, `--build-backend`, `--clone` skip the corresponding screens for scripting). The TUI runs in the managed `app/envs/tts` venv, which includes `whisper` via `requirements.txt`; for a manual setup, make sure `whisper` (or `faster_whisper`) is installed in the environment you run the wizard from. The Qwen3-TTS model tree also offers hosting the VoiceDesign package as a `vdes` entry.
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.
### Download and build audiocpp_server
Download and build `audiocpp_server` for your platform and backend `(cuda, vulkan, hip, cpu)`. Check [audio.cpp's readme](https://github.com/0xShug0/audio.cpp) for details. I'm using one of the helper scripts:
```bash
git clone https://github.com/0xShug0/audio.cpp
cd audio.cpp
scripts/build_linux.sh --backend cuda --target audiocpp_server
```
### Install models
Download model packages with the python model manager script from the audio.cpp checkout. Each installs to `./models`. Here are two examples, Higgs Audio and Qwen3-TTS:
```bash
python tools/model_manager_v2.py install higgs_audio_tts_4b_q8_0
python tools/model_manager_v2.py install qwen3_tts_1_7b_base_q8_0
python tools/model_manager_v2.py install qwen3_tts_1_7b_customvoice_q8_0
```
You can run `python tools/model_manager_v2.py list` to see all available models.
### Create server.json
Create a `server.json` config file. One server can host multiple models and multiple cloned voices. The `id:` fields are the model names you will set for `tts-audiobook-generator` with `--model`.
```json
{
"host": "127.0.0.1",
"port": 8080,
"backend": "cuda",
"lazy_load": true,
"voice_dir": "/path/to/clone/wavs",
"models": [
{
"id": "higgs",
"family": "higgs_audio_tts",
"path": "models/Higgs-Audio-v3-TTS-4B-GGUF",
"task": "tts",
"mode": "offline"
},
{
"id": "qwen",
"family": "qwen3_tts",
"path": "models/Qwen3-TTS-12Hz-1.7B-CustomVoice-GGUF",
"task": "tts",
"mode": "offline"
},
{
"id": "qwen-clone",
"family": "qwen3_tts",
"path": "models/Qwen3-TTS-12Hz-1.7B-Base-GGUF",
"task": "tts",
"mode": "offline"
}
]
}
```
### Run audio.cpp and the audiobook script
Run the server with this config file. The `audiocpp_server` path will be slightly different depending on your platform and build options:
```bash
./build/linux-cuda-release/bin/audiocpp_server --config server.json
```
In a different terminal, run `audiobook.py`. Pick the TTS `--model` and `--voice` from server.json:
```bash
# Higgs Audio (clone-only)
python audiobook.py --backend audiocpp --model higgs --voice narrator
# Qwen3-TTS built-in speaker
python audiobook.py --backend audiocpp --model qwen
# Qwen3-TTS voice cloning
python audiobook.py --backend audiocpp --model qwen-clone --voice narrator
# Qwen-TTS voice design
python audiobook.py --backend audiocpp --model qwen-design \
--instructions "A warm adult female narrator with a British accent"
```
The hub also works with an audio.cpp server that runs somewhere else (another checkout, another machine) as long as it answers on the configured port: when there is no local `server.json`, the convert menus query the running server directly (`GET /v1/models` and `GET /v1/audio/voices`) instead of reading one. On the CLI, pass `--model`/`--voice` matching that server's config.
Before converting, `audiobook.py` asks the server to unload all currently loaded models (`POST /v1/tasks/unload_all_models`) so models left resident by earlier runs free their memory (e.g. VRAM on GPU backends) and only the selected entry loads. A server without that endpoint, or one busy unloading, only produces a warning.
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