feat: manage venv for all backends

1 files changed, 6 insertions, 2 deletions

diff --git a/docs/backend-faster.md b/docs/backend-faster.md
index 40b10f7..c407a70 100644
--- a/docs/backend-faster.md
+++ b/docs/backend-faster.md
@@ -2,7 +2,11 @@
 
 `--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.
+The easiest way is to run `python audiobook.py` → **Set up a backend… → faster-qwen3-tts** (or `python -m backends.faster path/to/clone/wavs`): the TUI pip-installs `faster-qwen3-tts[demo]` into its managed venv (`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 **Server** 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.
+
+Install into your environment (the same one used for qwen-tts is fine):
 
 ```bash
 conda activate audiobook
@@ -19,7 +23,7 @@ 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.
+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
 {