1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
|
# 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 `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 (`python audiobook.py` → **Set up a backend… → faster-qwen3-tts**, or `python -m backends.faster path/to/clone/wavs`) pip-installs the package, clones the repo, transcribes the `.wav` files with `whisper`, and writes `voices.json` for you.
```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]
```
|
