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# 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**.
Install into the **same `audiobook` conda environment** used for qwen-tts.
```bash
conda activate audiobook
pip install -U qwen-tts
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 `tools/make_faster_voices_json.py` helper (see below).
The pip package does not include the server script, so clone the repository:
```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). Optionally run `python ./tools/make_faster_voices_json.py path/to/clone/wavs` to automatically create a `voices.json` using whisper to automatically transcribe the test audio.
```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]
```
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