feat: separate venvs for qwen-tts and faster, manage (un)installs

2 files changed, 8 insertions, 6 deletions

diff --git a/app/docs/backend-faster.md b/app/docs/backend-faster.md
index 37d0f23..9aa297e 100644
--- a/app/docs/backend-faster.md
+++ b/app/docs/backend-faster.md
@@ -2,16 +2,18 @@
 
 `--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 (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.
+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 (the same one used for qwen-tts is fine):
+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
-source audiobook/bin/activate
-pip install -U qwen-tts
+python -m venv audiobook-faster
+source audiobook-faster/bin/activate
 pip install "faster-qwen3-tts[demo]"
 ```
 
diff --git a/app/docs/backend-qwen.md b/app/docs/backend-qwen.md
index 3fd55ca..8325d95 100644
--- a/app/docs/backend-qwen.md
+++ b/app/docs/backend-qwen.md
@@ -1,6 +1,6 @@
 # Backend Option 2: Qwen3-TTS
 
-The easiest way is to run `python audiobook.py` → **Configure backends… → Install Backend → qwen-tts** (or `python app/backends/qwen.py`): the TUI pip-installs `qwen-tts` into its managed venv (`app/envs/tts`) — that's all there is to it, the install asks no questions. The demo port lives in `app/converter/config.py` (edit it in the hub's **Settings** screen). The qwen backend runs **one model at a time** on that single port: pick Base, CustomVoice or VoiceDesign per run on the **Generate audiobooks** screen (the choice is remembered in `QWEN_MODEL` and re-used by the next autostart; switching models while a managed server is up restarts it with the newly-selected model). 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 → qwen-tts** (or `python app/backends/qwen.py`): the TUI pip-installs `qwen-tts` into its own managed venv (`app/envs/qwen`, separate from the app's venv and from the faster backend's — the two TTS stacks ship conflicting versions of a shared `qwen_tts` module) — that's all there is to it, the install asks no questions. The demo port lives in `app/converter/config.py` (edit it in the hub's **Settings** screen). The qwen backend runs **one model at a time** on that single port: pick Base, CustomVoice or VoiceDesign per run on the **Generate audiobooks** screen (the choice is remembered in `QWEN_MODEL` and re-used by the next autostart; switching models while a managed server is up restarts it with the newly-selected model). 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 (its `GET /info` names which of the three demos answers), so a manually-installed backend works once its server is up. To use a demo server on another machine, set `QWEN_REMOTE_URL` in `app/converter/config.py` to its `host:port` (default `127.0.0.1:7860`) — the hub probes it and offers the matching `qwen-tts [remote]` mode limited to the model that server hosts — or pass `--api-url` on the CLI.