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| author | historia <historiavg@proton.me> | 2026-08-24 03:41:57 -0400 |
|---|---|---|
| committer | historia <historiavg@proton.me> | 2026-08-24 03:41:57 -0400 |
| commit | 73b466fbc054e80b50e318b49643aee8a03c784b (patch) | |
| tree | df3d78efa84956bcf47586af736479f319a80abe /app/docs | |
| parent | 471798cf5e967b2d1bceb02d12a47fe9ad1cbed1 (diff) | |
| download | tts-audiobook-generator-73b466fbc054e80b50e318b49643aee8a03c784b.tar.gz | |
feat: settings for ports in TUI
Diffstat (limited to 'app/docs')
| -rw-r--r-- | app/docs/backend-audiocpp.md | 2 | ||||
| -rw-r--r-- | app/docs/backend-faster.md | 2 | ||||
| -rw-r--r-- | app/docs/backend-qwen.md | 2 |
3 files changed, 3 insertions, 3 deletions
diff --git a/app/docs/backend-audiocpp.md b/app/docs/backend-audiocpp.md index 271c9b6..5001598 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 **Set up a 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 **Server** menu or automatically when converting). Run it directly with `python app/backends/audiocpp.py` (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 **Set up a 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). Run it directly with `python app/backends/audiocpp.py` (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. 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. diff --git a/app/docs/backend-faster.md b/app/docs/backend-faster.md index 4d193b7..1f22d99 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` → **Set up a 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 **Server** menu, or let a conversion start it automatically. +The easiest way is to run `python audiobook.py` → **Set up a 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. 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. diff --git a/app/docs/backend-qwen.md b/app/docs/backend-qwen.md index af5f63e..0db3214 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` → **Set up a 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 **Server** menu, or let a conversion start it automatically. +The easiest way is to run `python audiobook.py` → **Set up a 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. 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. |
