# Backend Option 3: faster-qwen-tts

`--backend faster` talks to the OpenAI-compatible server from [faster-qwen3-tts](https://github.com/andimarafioti/faster-qwen3-tts), which uses CUDA graph capture for roughly 5-10x faster inference with the same models. **It requires an NVIDIA GPU**.

The easiest way is to run `python audiobook.py` → **Configure Backends… → Install Backend → faster-qwen3-tts** (or `python app/backends/faster.py path/to/clone/wavs`): the TUI pip-installs `faster-qwen3-tts[demo]` into its own managed venv (`app/envs/faster`, separate from the app's venv and from the qwen backend's — both TTS stacks ship conflicting versions of a shared `qwen_tts` module; the faster wheel pulls its own `qwen-tts-hf` build of it automatically), clones the repo, transcribes the `.wav` files with whisper (faster-whisper, installed when wheels exist for your platform — otherwise you type the transcripts), and writes `voices.json` for you — all on one options screen (voices directory, language, whisper model, and what to re-transcribe on a modify run). The server port is not asked: it lives in `FASTER_API_URL` (edit it in **Settings**). 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. To use a server on another machine, set `FASTER_REMOTE_URL` in `app/converter/config.py` to its `host:port` (default `127.0.0.1:8000`) — the hub probes it and offers a `faster-qwen3-tts [remote]` entry — or pass `--api-url` on the CLI.

Install into your environment (this backend does **not** need the `qwen-tts`
pip package — the wheel pulls the compatible `qwen-tts-hf` build of the
`qwen_tts` library automatically, so keep it out of any venv that also has
upstream `qwen-tts` installed):

```bash
python -m venv audiobook-faster
source audiobook-faster/bin/activate
pip install "faster-qwen3-tts[demo]"
```

**This backend always uses voice cloning**. The reference voice and language are configured on the **server**, not through the converter. The server does not transcribe reference audio itself, so do it manually or use the `backends.faster` setup wizard (see below).

The pip package does not include the server script, so clone the repository (the `backends.faster` wizard does this for you into `./faster-qwen3-tts`):

```bash
git clone https://github.com/andimarafioti/faster-qwen3-tts
cd faster-qwen3-tts
```

Create a `voices.json` mapping names to reference configurations (.wav to clone, transcript, language). The TUI setup writes this for you; manually it looks like:

```json
{
    "default": {"ref_audio": "voice1.wav", "ref_text": "Transcript of voice 1.", "language": "English"},
    "obama": {"ref_audio": "voice2.wav", "ref_text": "Transcript of voice 2.", "language": "English"}
}
```

Run the server

```bash
python examples/openai_server.py --voices voices.json --port 8000
```

Then from another terminal, run audiobook.py with `--backend faster`

```bash
python audiobook.py --backend faster --voice NAME
```

`--voice` is required: NAME must be a key in the server's `voices.json`
(the server silently falls back to its first configured voice if it is not).