3 files changed, 7 insertions, 7 deletions
diff --git a/app/docs/backend-audiocpp.md b/app/docs/backend-audiocpp.md
index 41da554..47115ef 100644
--- a/app/docs/backend-audiocpp.md
+++ b/app/docs/backend-audiocpp.md
@@ -2,9 +2,9 @@
 
 `--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 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 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.
+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.
 
 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.
 
@@ -98,8 +98,8 @@ python audiobook.py --backend audiocpp --model <id> --voice narrator \
     --option emotion=neutral --option speed=1.1
 ```
 
-In the hub's **Generate audiobooks** form the Model picker shows each entry's voice capability (`speaker` / `clone` / `design`). The Voice field is labelled **Built-in voice** on CustomVoice entries (listing the model's speakers) and **Voice to clone** everywhere else (listing the server's preset/voice_dir entries). Instructions are shown for every entry: required for `vdes` design models, an optional style/delivery instruction elsewhere — and on families without built-in speakers that read instructions, a description alone can define the voice, so leaving Voice empty is fine there. A Request options field accepts the same `KEY=VALUE` items as `--option`, and appears only for model families whose audio.cpp checkout spec declares request options (e.g. Neutts, Outetts, F5-TTS — not Qwen3-TTS or Higgs Audio). Editing Instructions or Request options shows a short dim hint with an example (for Instructions: `"Speak in a calm, soothing, and happy tone."`). Language is a static picker over the languages of audio.cpp's WebUI menus (it shows the same "Check model documentation for supported languages." hint while editing) and overrides the global setting for this run only.
+In the hub's **Generate Audiobooks** form the Model picker shows each entry's voice capability (`speaker` / `clone` / `design`). The Voice field is labelled **Built-in voice** on CustomVoice entries (listing the model's speakers) and **Voice to clone** everywhere else (listing the server's preset/voice_dir entries). Instructions are shown for every entry: required for `vdes` design models, an optional style/delivery instruction elsewhere — and on families without built-in speakers that read instructions, a description alone can define the voice, so leaving Voice empty is fine there. A Request options field accepts the same `KEY=VALUE` items as `--option`, and appears only for model families whose audio.cpp checkout spec declares request options (e.g. Neutts, Outetts, F5-TTS — not Qwen3-TTS or Higgs Audio). Editing Instructions or Request options shows a short dim hint with an example (for Instructions: `"Speak in a calm, soothing, and happy tone."`). Language is a static picker over the languages of audio.cpp's WebUI menus (it shows the same "Check model documentation for supported languages." hint while editing) and overrides the global setting for this run only.
 
-The hub also works with an audio.cpp server that runs somewhere else (another checkout, another machine): set `AUDIOCPP_REMOTE_URL` in `app/converter/config.py` (or the TUI **Settings** → "audio.cpp remote URL") to its `host:port`. The hub probes that URL and, when it answers, offers an `audio.cpp [remote]` entry in **Generate audiobooks…** whose models and voices are queried live (`GET /v1/models` and `GET /v1/audio/voices`) — alongside the managed `audio.cpp` entry, which keeps reading the local `server.json`. The remote URL defaults to `127.0.0.1:8080`, so a server started outside this tool on the local port is found automatically. On the CLI, pass `--api-url http://host:port` (and `--model`/`--voice` matching that server's config).
+The hub also works with an audio.cpp server that runs somewhere else (another checkout, another machine): set `AUDIOCPP_REMOTE_URL` in `app/converter/config.py` (or the TUI **Settings** → "audio.cpp remote URL") to its `host:port`. The hub probes that URL and, when it answers, offers an `audio.cpp [remote]` entry in **Generate Audiobooks…** whose models and voices are queried live (`GET /v1/models` and `GET /v1/audio/voices`) — alongside the managed `audio.cpp` entry, which keeps reading the local `server.json`. The remote URL defaults to `127.0.0.1:8080`, so a server started outside this tool on the local port is found automatically. On the CLI, pass `--api-url http://host:port` (and `--model`/`--voice` matching that server's config).
 
 Before converting, `audiobook.py` asks the server to unload all currently loaded models (`POST /v1/tasks/unload_all_models`) so models left resident by earlier runs free their memory (e.g. VRAM on GPU backends) and only the selected entry loads. A server without that endpoint, or one busy unloading, only produces a warning. This behavior is controlled by the **Settings** → "Unload models" option (or `AUDIOCPP_UNLOAD_MODELS` in `app/converter/config.py`), which defaults to **Yes**; set it to **No** to keep other models resident across runs.
diff --git a/app/docs/backend-faster.md b/app/docs/backend-faster.md
index 9aa297e..f5b728f 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 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.
+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.
 
diff --git a/app/docs/backend-qwen.md b/app/docs/backend-qwen.md
index 8325d95..b1e1773 100644
--- a/app/docs/backend-qwen.md
+++ b/app/docs/backend-qwen.md
@@ -1,10 +1,10 @@
 # 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 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.
+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.
 
-Model weights download automatically from HuggingFace into the standard cache (`~/.cache/huggingface/hub`) the first time a server for each model starts — there is nothing else to install per model. To pre-fetch or remove a single model's weights without starting its server, open **Configure backends… → Configure qwen-tts**: each of Base / CustomVoice / VoiceDesign gets an Install (a streamed, resumable download — canceling one just means it resumes later) or Uninstall action, with a server hosting that model stopped first. Uninstalling the whole backend deletes all three of those directories along with the pip package; only they are ever touched — anything else in your HuggingFace cache is left alone.
+Model weights download automatically from HuggingFace into the standard cache (`~/.cache/huggingface/hub`) the first time a server for each model starts — there is nothing else to install per model. To pre-fetch or remove a single model's weights without starting its server, open **Configure Backends… → Configure qwen-tts**: each of Base / CustomVoice / VoiceDesign gets an Install (a streamed, resumable download — canceling one just means it resumes later) or Uninstall action, with a server hosting that model stopped first. Uninstalling the whole backend deletes all three of those directories along with the pip package; only they are ever touched — anything else in your HuggingFace cache is left alone.
 
 Install qwen-tts with pip into your environment: