feat: combined install/configure tui screens into one menu, removed extraneous wizard screens

3 files changed, 4 insertions, 4 deletions

diff --git a/app/docs/backend-audiocpp.md b/app/docs/backend-audiocpp.md
index c098d57..c0c2360 100644
--- a/app/docs/backend-audiocpp.md
+++ b/app/docs/backend-audiocpp.md
@@ -2,7 +2,7 @@
 
 `--backend audiocpp` talks to `audiocpp_server` from [audio.cpp](https://github.com/0xShug0/audio.cpp), which hosts numerous TTS model families.
 
-The easiest way is the TUI: run `python audiobook.py`, choose **Configure backends… → Install Backend → audio.cpp**, and it clones `audio.cpp` into `app/audio.cpp` (or reuses an existing checkout), builds `audiocpp_server`, lets you pick model families/packages from an expandable checkbox tree (reading the checkout's `model_specs/`), transcribes `.wav` voices with `whisper`, writes `server.json` into the checkout, syncs `app/converter/config.py`, and prints the launch command (the hub can also start the server for you via the **Start/Stop Backend Servers** menu or automatically when converting). The clone, build, transcription and model downloads all run inside the TUI — each shows a status (and, where the tool can measure it, a progress bar), and can be cancelled — instead of dropping to console output. Run it directly with `python -m backends.audiocpp` (from `app/`) (flags like `--wavs`, `--families`, `--build-backend`, `--clone` skip the corresponding screens for scripting). The TUI runs in the managed `app/envs/tts` venv, which includes `whisper` via `requirements.txt`; for a manual setup, make sure `whisper` (or `faster_whisper`) is installed in the environment you run the wizard from. The Qwen3-TTS model tree also offers hosting the VoiceDesign package as a `vdes` entry.
+The easiest way is the TUI: run `python audiobook.py`, choose **Configure backends… → Install Backend → audio.cpp**, and it clones `audio.cpp` into `app/audio.cpp` (or reuses an existing checkout), builds `audiocpp_server`, lets you pick model families/packages from an expandable checkbox tree (reading the checkout's `model_specs/`), transcribes `.wav` voices with `whisper`, writes `server.json` into the checkout, syncs `app/converter/config.py`, and prints the launch command (the hub can also start the server for you via the **Start/Stop Backend Servers** menu or automatically when converting). The setup asks exactly two screens: first the model tree, then one combined options form (like **Generate audiobooks**) for everything else — the inference backend and whether to build it now, the voice-clone `.wav` directory and how to transcribe it, automatic model download, the default-model sync, and deleting models dropped on a re-run; rows that do not apply to your selection are hidden. The server always binds `127.0.0.1` on the port configured in `AUDIOCPP_API_URL` (edit it in **Settings**), so neither is ever asked. The clone, build, transcription and model downloads all run inside the TUI — each shows a status (and, where the tool can measure it, a progress bar), and can be cancelled — instead of dropping to console output. Run it directly with `python -m backends.audiocpp` from `app/` — flags like `--wavs`, `--families`, `--build-backend`, `--clone` skip the corresponding parts for scripting. The TUI runs in the managed `app/envs/tts` venv, which installs faster-whisper when wheels exist for your platform (it is tagged optional in `requirements.txt`: on platforms without compatible builds the setup skips it and voice-clone transcription degrades to manual transcripts). For a manual setup, make sure `whisper` or `faster_whisper` is installed in the environment you run the wizard from. The Qwen3-TTS model tree also offers hosting the VoiceDesign package as a `vdes` entry.
 
 The hub's backend status table distinguishes how far audio.cpp is set up: `unavailable` (nothing present), `downloaded (not built)` (checkout cloned, `audiocpp_server` not built), `built (not configured)` (binary built, no `server.json`), `installed` (ready; or `installed (models missing)` when the config references undownloaded models), and `running` once its server answers. Whenever the checkout exists but `audiocpp_server` is missing, **Configure backends… → Build audio.cpp server** builds it from the TUI (the wizard offers the build during setup too), so a backend whose build you skipped is never stuck as "unavailable". On a fresh install the setup is one continuous flow: clone → configure → and then the build and the model downloads run **simultaneously** in a split view (half building, half downloading). The setup steps are therefore ordered build > configure > download, and **Build audio.cpp server** and **Download Missing Models (audio.cpp)** are never offered at the same time; **Build audio.cpp server** downloads any missing models alongside the build, and **Download Missing Models (audio.cpp)** remains only as a fallback for when a download fails or is interrupted.
 
@@ -30,7 +30,7 @@ python tools/model_manager_v2.py install qwen3_tts_1_7b_base_q8_0
 python tools/model_manager_v2.py install qwen3_tts_1_7b_customvoice_q8_0
 ```
 
-You can run `python tools/model_manager_v2.py list` to see all available models.
+You can run `python tools/model_manager_v2.py list` to see all available models. Inside the tool's setup the models are downloaded automatically from the TUI (models already on disk are reported and skipped); if you need to download them manually instead, decline the automatic download and it prints these commands for only the models that are still missing.
 
 ### Create server.json
 
diff --git a/app/docs/backend-faster.md b/app/docs/backend-faster.md
index 3036ca7..37d0f23 100644
--- a/app/docs/backend-faster.md
+++ b/app/docs/backend-faster.md
@@ -2,7 +2,7 @@
 
 `--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.
 
diff --git a/app/docs/backend-qwen.md b/app/docs/backend-qwen.md
index 800c7ae..d0e7c92 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`), configures the two ports and the built-in speaker in `app/converter/config.py`, and prints the launch commands. 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 managed venv (`app/envs/tts`) — that's all there is to it, the install asks no questions. The two demo ports live in `app/converter/config.py` (edit them in the hub's **Settings** screen), and you pick the built-in speaker per run on the **Generate audiobooks** screen (it defaults to `SPEAKER` in `app/converter/config.py`). 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 demo servers on another machine, set `QWEN_REMOTE_URL`/`CLONE_REMOTE_URL` in `app/converter/config.py` to their `host:port` (defaults `127.0.0.1:7860`/`:7861`) — the hub probes each and offers the matching `qwen-tts [remote]` mode — or pass `--api-url` on the CLI.