1 files changed, 41 insertions, 0 deletions
diff --git a/docs/backend-faster.md b/docs/backend-faster.md
new file mode 100644
index 0000000..83614c7
--- /dev/null
+++ b/docs/backend-faster.md
@@ -0,0 +1,41 @@
+# Backend Option 3: faster-qwen-tts
+
+`--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.
+
+```bash
+conda activate audiobook
+pip install -U qwen-tts
+pip install "faster-qwen3-tts[demo]"
+```
+
+**This backend always uses voice cloning**. The reference voice and language are configured on the **server**, not through the converter. The server does not transcribe reference audio itself, so do it manually or use the `tools/make_faster_voices_json.py` helper (see below).
+
+The pip package does not include the server script, so clone the repository:
+
+```bash
+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). Optionally run `python ./tools/make_faster_voices_json.py path/to/clone/wavs` to automatically create a `voices.json` using whisper to automatically transcribe the test audio.
+
+```json
+{
+    "default": {"ref_audio": "voice1.wav", "ref_text": "Transcript of voice 1.", "language": "English"},
+    "obama": {"ref_audio": "voice2.wav", "ref_text": "Transcript of voice 2.", "language": "English"}
+}
+```
+
+Run the server
+
+```bash
+python examples/openai_server.py --voices voices.json --port 8000
+```
+
+Then from another terminal, run audiobook.py with `--backend faster`
+
+```bash
+python audiobook.py --backend faster [--voice NAME]
+```