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`--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 managed venv (`app/envs/tts`), clones the repo, transcribes the `.wav` files with `whisper`, and writes `voices.json` for you. You can also start the server from the hub's **Start/Stop Backend Servers** menu, or let a conversion start it automatically.
+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 managed venv (`app/envs/tts`), 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.